Pre lim in ary Eco s ys te m
Sim u latio n Mo d e ls fo r th e
Bird ’s H e ad Se as cap e , Pap u a
Cam e ro n Ain s w o rth , D ivya Varke y an d To n y J Pitch e r
Decem ber 20 0 6
F ISHERIES E COSYSTEMS R ESTORATION R ESEARCH
F ISHERIES CENTRE U NIVERSITY OF BRITISH COLUMBIA
PRELIMINARY ECOSYSTEM SIMULATION MODELS
FOR THE BIRD’S HEAD SEASCAPE, PAPUA
Cameron Ainsworth, Divya Varkey and Tony Pitcher
December 2006
ABSTRACT
Preliminary Ecopath with Ecosim models are described for the marine ecosystem of the Raja
Ampat (RA) archipelago in Papua province, eastern Indonesia.
The models are based on
literature and output data emerging from the Birds Head Seascape Ecosystem Based
Management (BHS EBM) project, a joint Packard-funded initiative between TNC, CI, WWF and
UBC. A new diet allocation algorithm is developed for use in tropical ecosystems, based on
predator gape and prey body size. The algorithm predicts feeding relationships in order to make
better use of FishBase diet information. Time series of catch, effort and catch-per-unit-effort are
developed from governmental fisheries statistics assembled in the field. A historic model,
representing 1990 AD is developed based on this time series information, and a 16-year dynamic
Ecosim simulation is fitted to agree with time series. The model incorporates four mediation
functions to capture key non-trophic interactions important in reef ecosystems. A primary
production anomaly is developed that would help explain the difference between observed and
predicted biomass dynamics from 1990 to 2006. The anomaly shows a non-significant negative
correlation with sea surface temperature. An equilibrium analysis and various challenges to
Ecosim are used to test the behaviour of the model. Policy optimizations are conducted to sketch
the potential trade-off frontier between economic and ecological harvest benefits available in
RA. Ecospace maps are designed for RA and sub-area models of Kofiau Island and Dampier
Strait. Some comments are made regarding future developments in the UBC spatial modelling
component of the BHS EBM project. For example, a graphical output routine, Ecolocator, is
under development to help visualize Ecospace results and provide relevant management advice
for local areas.
A Narrative Technical Report from the Fisheries Ecosystem Restoration Research group
in the Fisheries Centre at UBC
274 pages © Fisheries Centre, University of British Columbia, 2006
Suggested citation:
Ainsworth, C., Varkey, D. and Pitcher, T.J. 2006. Preliminary ecosystem simulation models for
the Bird’s Head Seascape, Papua. Mid-term narrative technical report. Birds Head Seascape
Ecosystem-Based Management Project.
University of British Columbia Fisheries Centre.
December, 2006, 274 pp. [Contact: c.ainsworth@fisheries.ubc.ca].
2
TABLE OF CONTENTS
Abstract ............................................................................................................................................................. 1
Table of contents............................................................................................................................................... 3
List of Figures ................................................................................................................................................... 5
List of Tables..................................................................................................................................................... 6
List of Equations............................................................................................................................................... 7
1. Introduction .................................................................................................................................................. 8
1.1 Ecopath with Ecosim ...............................................................................................................................8
1.2 Raja Ampat Islands................................................................................................................................10
1.3 Project synthesis ....................................................................................................................................12
1.4 First field trip .........................................................................................................................................14
1.5 Ecopath parameterization ......................................................................................................................16
1.5.1 Raja Ampat model..........................................................................................................................16
1.5.2 Kofiau model..................................................................................................................................17
1.5.3 Dampier Strait model .....................................................................................................................20
2. Methods ....................................................................................................................................................... 21
2.1 Ecopath ..................................................................................................................................................21
2.2 Ecosim ...................................................................................................................................................22
2.2.1 Predator-prey vulnerabilities ..........................................................................................................23
2.2.2 Mediation factors ...........................................................................................................................24
2.3 Ecospace ................................................................................................................................................27
2.4 Ecopath parameterization ......................................................................................................................27
2.4.1 Functional group designations........................................................................................................27
2.4.2 Fish groups .....................................................................................................................................28
2.4.3 Bioeroders ......................................................................................................................................32
2.5 Basic parameterization...........................................................................................................................33
2.5.1 Growth parameters .........................................................................................................................34
2.5.2 Estimating consumption rate (Q/B)................................................................................................37
2.5.3 Estimating natural mortality (M) for fish .......................................................................................39
2.5.4 Daily ration.....................................................................................................................................40
2.5.5 Ingestion rate in deposit feeders.....................................................................................................41
2.5.6 Estimating P/B of invertebrates......................................................................................................41
2.5.7 Group maturity parameters.............................................................................................................42
2.5.8 Biomass density estimates..............................................................................................................42
2.5.9 Diet algorithm ................................................................................................................................43
2.5.10 Fisheries .......................................................................................................................................51
2.5.11 Functional group descriptions ......................................................................................................60
2.6 The 1990 Raja Ampat model ............................................................................................................... 113
3
2.6.1 Group biomasses .......................................................................................................................... 113
2.6.2 Fisheries ....................................................................................................................................... 114
2.6.3 Fitting to time series..................................................................................................................... 114
2.6.4 Equilibrium analysis..................................................................................................................... 117
2.6.5 Challenges to Ecosim ................................................................................................................... 119
2.7 Ecospace parameterization................................................................................................................... 120
2.7.1 Raja Ampat 2006 Ecospace model............................................................................................... 123
2.7.2 Kofiau Island model ..................................................................................................................... 127
2.7.3 Dampier Strait model ................................................................................................................... 129
2.8 Fishing policy optimizations................................................................................................................ 131
3. Results........................................................................................................................................................ 133
3.1 Time series fitting ................................................................................................................................ 133
3.1.1 Predicted climate anomaly ........................................................................................................... 134
3.2 Equilibrium analysis ............................................................................................................................ 136
3.3 Challenges to Ecosim........................................................................................................................... 138
3.4 Fishing policy optimizations................................................................................................................ 140
4. Discussion .................................................................................................................................................. 145
4.1 Fitting the model.................................................................................................................................. 145
4.2 Fishing policy optimizations................................................................................................................ 146
4.3 Fisherman interview forms .................................................................................................................. 147
4.4 Stomach content analysis..................................................................................................................... 147
4.5 Ecolocator ............................................................................................................................................ 148
5. Conclusions ............................................................................................................................................... 151
6. References ................................................................................................................................................. 153
Appendix A - EwE parameterization.......................................................................................................... 173
Appendix A.1 - Species level data............................................................................................................. 173
Appendix A.2 - Fish family data................................................................................................................ 209
Appendix A.3 - Ecopath parameters: 2006 RA model .............................................................................. 214
Appendix A.4 - Ecopath parameters: 1990 RA model .............................................................................. 242
Appendix A.5 - Ecosim parameters: 1990-2006 RA model ...................................................................... 244
Appendix A.6 - Time series data ............................................................................................................... 244
Appendix B - EwE results ............................................................................................................................ 247
Appendix B.1 - Ecopath results ................................................................................................................. 247
Appendix B.2 - Ecosim results .................................................................................................................. 248
Appendix C - Supplemental forms .............................................................................................................. 256
Appendix C.1 - Fishermen interview forms............................................................................................... 256
Appendix C.2 - Stomach sampling protocol.............................................................................................. 258
4
LIST OF FIGURES
Figure 1.1 - Area represented by Raja Ampat (RA) model.
18
Figure 1.2 - Area described by Kofiau Island model.
19
Figure 1.3 - Area described by Dampier Strait model.
20
Figure 2.1 - Fish maximum length (LMAX) distribution.
31
Figure 2.2 - Flow chart showing W∞ parameterization method.
36
Figure 2.3 - Flow chart showing Q/B parameterization method.
39
Figure 2.4 - Flow chart showing M parameterization method.
40
Figure 2.5 - Flow chart showing diet allocation algorithm.
46
Figure 2.6 - Ecospace habitat map of RA.
124
Figure 2.7 - Spatial primary production (P/B) for Raja Ampat.
126
Figure 2.8 - Ecospace habitat map of Kofiau Island.
128
Figure 2.9 - An enclosed lagoon on Taudore Island.
129
Figure 2.10 - Dona Carmalita seamount.
129
Figure 2.11 - Ecospace habitat map of Dampier Strait.
130
Figure 2.12 - Myalibit Bay entrance.
131
Figure 3.1 - Raja Ampat ecosystem indicators (1990-2006).
134
Figure 3.2 - Primary production anomaly.
135
Figure 3.3 - Coefficient of variation (CV) of RA functional group biomass (1990-2006).
136
Figure 3.4. Group biomass change following extreme fishing policies (2006-2022).
138
Figure 3.5 - Tradeoff between NPV and B/P.
140
Figure 3.6 - Tradeoff between catch and biodiversity.
141
Figure 3.7 - Equilibrium catch and biodiversity levels for optimal fishing plans.
142
Figure 3.8 - Principle components analysis showing policy search response surface.
143
Figure 3.9 - Characterization of optimal fleet-effort patterns.
144
Figure 4.1 - User interface of Ecolocator.
149
Figure A.6.1 - Estimated fisheries catch in Raja Ampat.
245
Figure A.6.2 - Estimated catch per unit effort in Raja Ampat.
246
Figure B.1.1 - Food web diagram.
247
Figure B.2.1 - Equilibrium analysis of commercial groups.
248
Figure B.2.2 - Predicted and observed biomass time series 1990-2006 for Raja Ampat.
250
Figure B.2.3 - Predicted and observed catch time series 1990-2006 for Raja Ampat.
252
Figure B.2.4 - Challenges to RA Ecosim model (2006-2022) .
254
5
LIST OF TABLES
Table 2.1 - Diet algorithm supporting parameters.
44
Table 2.2 - Fishbase prey items assigned to EwE functional groups.
47
Table 2.3 - Fishing gear types included in the Raja Ampat model.
52
Table 2.4 - Functional group catch distribution by gear type.
56
Table 2.5 - Gear effort assignments.
57
Table 2.6 - Biomass and production rates used in EwE to represent reef-building corals.
100
Table 2.7 - Habitats occupied by functional groups in three 2006 Ecospace models.
121
Table 2.8 - Designated fishing activity in Ecospace habitat types.
122
Table 3.1 - Fishery indicators for major commercial groups in the 2006 RA model.
137
Table 3.2 - Group depletion risk following extreme fishing scenarios.
139
Table A.1.1 - Fish species represented in the RA EwE models.
173
Table A.2.1 - Fish families in Raja Ampat model.
209
Table A.3.1 - Functional groups for 2006 Raja Ampat model.
214
Table A.3.2 - Basic parameters for 2006 Raja Ampat model.
217
Table A.3.3 - Multi-stanza life history information for 2006 Raja Ampat model
219
Table A.3.4 - Ecopath landings matrix for 2006 Raja Ampat model.
220
Table A.3.5 - Ecopath price matrix for the 2006 Raja Ampat model.
223
Table A.3.6 - RA model trophic linkages: diet composition and flow parameters.
224
Table A.4.1 - 1990 RA model parameters.
242
Table A.5.1 - Feeding rate parameters.
244
6
LIST OF EQUATIONS
Equation 1.1 - Ecopath mass-balance.
21
Equation 1.2 - Ecopath consumption rate.
21
Equation 1.3 - Ecosim biomass dynamics.
22
Equation 2.1 - Length-weight relationship.
35
Equation 2.2 - WMAX.
35
Equation 2.3 - LMAX.
35
Equation 2.4 - FL to TL conversion (forked tail).
37
Equation 2.5 - FL to TL conversion (emarginated tail).
37
Equation 2.6 - SL to TL conversion.
37
Equation 2.7 - Q/B empirical formula (feeding mode).
38
Equation 2.8 - Q/B empirical formula (aspect ratio).
38
Equation 2.9 - M empirical formula.
40
Equation 2.10 - Marine mammal ration.
40
Equation 2.11 - Bird ration.
41
Equation 2.12 - Ingestion rate for deposit feeders.
41
Equation 2.13 - P/B empirical formula (invertebrates).
41
Equation 2.14 - Domed consumption rate.
49
Equation 2.15 - Policy search objective function.
132
7
1. INTRODUCTION
This report presents the methodology used to create Ecopath with Ecosim (EwE) and Ecospace
ecosystem models of the Raja Ampat Islands in Papua, Indonesia, and provides a preliminary
analysis of ecosystem functioning and resource potential of coral reefs. The models created here
are based on scientific data emerging from the research project “Towards Ecosystem-Based
Management in the Bird’s Head Functional Seascape of Papua, Indonesia”, being conducted
jointly by The Nature Conservancy (TNC), Conservation International (CI), World Wildlife
Fund (WWF), and the University of British Columbia (UBC).
Ecosystem models are being developed for the RA region at various spatial scales. These
temporal and spatial dynamic models capture biotic and abiotic interactions in the ecosystem.
By accurately representing ecological processes on coral reefs, they will help us to improve our
understanding of reef ecosystem behaviour. The models can be used to assist ecosystem-based
marine policy; with them we can design sustainable fishing strategies that maximize economic
benefits while protecting coral reef communities. Fishing policies developed with these tools
can be made robust against various future climate scenarios, and the risk and uncertainty
surrounding harvest recommendations can be evaluated and quantified. Importantly, the spatial
models are able to forecast the effects and benefits of spatial management schemes, such as the
application of marine protected areas (MPAs). The models are built within a flexible framework
that can be continually modified and improved as new data becomes available. The work
presented here should provide a starting point for further study of ecosystem-based management
(EBM) strategies helpful to the management of the Bird’s Head Functional Seascape (BHS).
1.1 Ecopath with Ecosim
We have used the family of modelling tools, Ecopath with Ecosim (EwE) and Ecospace to
represent the food web of Raja Ampat and simulate trophic interactions of interest to fisheries
and conservation. Invented by Polovina (1984) and advanced by Christensen and Pauly (1992,
1993), Walters et al. (1997, 1998) and Christensen and Walters (2004a) among others, EwE is a
mass-balance trophic simulator that acts as a thermodynamic accounting system. Summarizing
all ecosystem components into a small number of functional groups (i.e., species aggregated by
8
trophic similarity), the box model describes the flux of matter and energy in and out of each
group, and can represent human influence through fishery removals and other ways. There are
now dozens of published articles that use EwE to describe ecosystems, test hypotheses and
demonstrate innovative applications useful for EBM (see review in Christensen and Walters,
2005). EwE has been used in actual fisheries management, but to a limited extent. Reviews and
criticisms of the EwE approach are provided by Fulton et al. (2003), Christensen and Walters
(2004a), and Plagányi and Butterworth (2004).
An EwE model is presented here for the marine ecosystem of Raja Ampat (RA) as it appeared in
2006 AD. The model utilizes BHS EBM project information and data from literature sources.
New methodologies are developed to make the best use of Fishbase (FB) data. For example, a
new diet allocation algorithm determines likely prey items based on predator gape-size and
processes FB diet data to the level of functional groups.
An Ecopath model of RA representing the system in 1990 AD is created based on the 2006
model. Relative functional group biomass and catch is estimated for these years based on
Indonesian governmental statistics, and ecosystem dynamics are tuned to agree with the historic
trends from the years 1990-2006.
The data fitting process attempts to capture ecosystem
responses to fishing and climate that occurred over the last 16 years. The dynamic Ecosim
model utilizes advanced features such as mediation functions, which capture critical animal
behaviours and allow us to represent important non-trophic relationships present in the coral reef
environment. A primary production time series anomaly is determined that may explain the
discrepancy between the observed and predicted catch and biomass trends. The anomaly is
compared to various environmental indices.
Insight gained can potentially improve our
understanding of regional climate and its affect on marine production.
A comprehensive review of model behaviour is performed using the equilibrium analysis facility
in Ecosim. This routine describes the exploitation status of commercial functional groups,
allowing us to judge the accuracy of the baseline model condition against our knowledge of the
ecology and fishing history of the region. The analysis generates catch and biomass curves
9
equivalent to those used by classical fisheries methods. Diagnostic challenges are presented to
the model to test its performance - extreme combinations of fishing practices that reveal the
behaviour and stability of the model. By presenting these early diagnostic outputs in this report,
we hope to the draw attention of local experts and enlist their help to establishing realistic model
behaviours.
Basic policy optimization is conducted using the tuned Ecosim model, and the
socioeconomic/ecological tradeoff frontier is mapped to reveal the sustainable production
potential of the ecosystem.
This application should demonstrate the power of policy
optimizations in EwE, although the specific values and estimates of resource potential will
continue to change as our understanding of the RA ecosystem improves.
Initial efforts to produce spatially explicit models are described here. Habitat maps, based on
data collected in the BHS EBM project, provide a foundation for the Ecospace models of RA and
two smaller-scale models: Kofiau Island and Dampier Strait. Basic parameterization of the
Ecospace models is described.
Finally, we discuss our goals and the current direction of the spatial modelling component for the
BHS-EBM project. We introduce Ecolocator, a new graphical output routine for Ecospace that
is being developed as part of this study to improve fine-scale visualization of coral reef
dynamics.
1.2 Raja Ampat Islands
The RA archipelago extends over 45,000 km2 and consists of approximately 610 islands
including the ‘four kings’, Batanta, Misool, Salawati and Waigeo (COREMAP, 2005). Erdmann
and Pet (2002) provide a summary of the major oceanographic features occurring in the Raja
Ampat archipelago. The area encompasses a variety of marine habitats, including some of the
most biodiverse coral reef areas on Earth (Donnelly et al., 2003; McKenna et al., 2002a). It is
estimated that RA possesses over 75 percent of the world’s known coral species (Halim and
Mous, 2006).
10
Fisheries by native peoples of Papua have likely persisted for centuries; although there is
evidence that long-term ‘chronic’ exploitation of coral reefs had an early impact on reef health in
many places throughout the world (Pandolfi et al., 2003). However, record keeping typically
begins long after the major depletion of reef resources occurs (Bellwood et al., 2004). This is the
case in Indonesia and many other countries that manage coral reef resources. The gradual or
early declines may therefore go unnoticed thanks to the shifting-baseline syndrome (Pauly, 1995)
in which each generation of scientists and resource users accept a lower standard of abundance
as normal. It is therefore difficult to estimate the loss of potential productivity that has occurred,
especially since there are few pristine areas remaining with which to form a baseline comparison.
The use of ecosystem models does allow us to predict virgin biomass levels for critical species,
but accurate predictions depend on the quality of the models, and the models can only be vetted
against time-series catch and abundance data. Unfortunately, quantitative data is limited in RA,
and much of the knowledge we have about ecosystem changes comes in the form of local
ecological knowledge (LEK) from scientists and inhabitants.
Currently, the main marine commodities in the RA archipelago include skipjack tuna
(Katsuwonus
pelamis),
yellowfin
tuna
(Thunnus
albacares)
and
Spanish
mackerel
(Scomberomorus commerson), but significant artisanal fisheries also exist for reef-associated fish
and invertebrates. Indonesia is known to have suffered a rapid depletion in recent decades of
near-shore fish stocks and coral reef animals, especially sharks, tunas and reef-associated fish
(Tomascik et al., 1997). The pressures on the reef systems in Eastern Indonesia can only be
expected to increase as the human population grows. Overfishing has reduced the average life
span of some marine species (Myers and Worm, 2003). Consequently, marine ecosystems may
be increasingly unstable and responsive to environmental fluctuations (Hughes et al., 2005). The
effect is likely to be pronounced in coral reef environments, where large and influential predators
and herbivores are targeted (Hughes et al., 2003). Increased system volatility could potentially
be a long-lasting effect if the constant antagonism of fisheries asserts an evolutionary pressure
towards early maturation and high turnover rates in exploited species.
11
Challenges to management of coral reefs now centre on the serious issues of overexploitation
(Pandolfi et al., 2003), land-based pollution (McCullock et al., 2003), disease outbreaks
(Kaczmarsky et al., 2005) and outbreaks of coralivores such as the crown of thorns starfish
(Acanthaster planci) - a source of mass mortality in corals (Chesher, 1969). Loss of coral cover
from these stressors has far reaching impacts throughout the food web, and may result in a longterm loss of fish biodiversity (Wilson et al., 2006).
1.3 Project synthesis
The BHS EBM project contains 17 major scientific components.
The wide diversity of
information resulting from these projects can readily be incorporated into various aspects of
Ecopath, Ecosim and Ecospace; and novel methodologies are under development that will allow
us to use, for the first time in the EwE, the sort of highly resolved biogeographic information
emerging from BHS EBM studies. Generally, data collected for the RA region will help to make
the EwE models, presented here in a preliminary and generic form, more relevant to the local
context.
The resulting suite of EBM tools should be able to test specific ecological and
socioeconomic hypotheses relevant to the management of coral reef ecosystems in RA.
Importantly, the unique opportunity provided by this project, to collate and integrate data
resulting from multidisciplinary studies, will strengthen our understanding of coral reef ecology,
improve EBM tools, and increase scientific dividends resulting from the BHS EBM project.
TNC reef health monitoring ongoing at Kofiau, Boo and Misool Islands among other sites (see
Mous and Muljadi, 2005) is intended to provide coral cover and biomass data for important
species of herbivorous fish and large piscivorous fish. Results from this analysis were not
available in time for this report; the final reef monitoring report for Kofiau Island is expected in
December 2006 (P. Mous. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia. Personal
communication). The biomass estimates we are currently using for herbivorous fish and large
piscivorous fish species are based on reef transect data from sites near Weigeo Island
(COREMAP 2005). Unless the updated biomass information is very different from current
estimates, integrating this new data should be straight forward, and should not require extensive
reworking of the models. It will, however, allow us to produce more accurate site-specific
12
versions of the model representing the various field sites. Depending on the precision and extent
of the data, it may also help up in developing scenarios for the novel EwE sub-routine now under
development, Ecolocator (contact: C. Ainsworth, UBC Fisheries Centre.
2202 Main Mall,
Vancouver BC. Canada).
The CI seascape connectivity analysis may provide us with an independent check of Ecospace
dispersal parameters and advection patterns. In EwE, dispersion represents the tendency of
populations to shift or expand their occupied range. It is not necessarily related to swimming
ability or speed of movement, but more closely reflects the fidelity of individuals to their natal
habitats, or the ability of planktonic propagules to travel and settle new areas. Populations that
display genetic homogeneity across the study area may therefore be assumed to have higher rates
of dispersion, while heterogeneous populations may reveal the action of isolating biogeographic
effects. At the time of this report, connectivity data is forthcoming.
Interviews conducted in the Seascape reproduction study, which identifies and monitors
spawning aggregation sites (SPAGS), may provide critical habitat data for Ecospace that will
allow us to accurately represent source-sink dynamics of major commercial fish populations.
This information may influence our expectations of the ecological and economic merit of spatial
management schemes (see Sanchirico et al., 2006). Adding to this output, oceanographic data
resulting from the SPAG vial release program may help us to track advection currents and
predict areas of larval settlement. This may prove to be an important factor, both in determining
sustainable exploitation levels, and in the siting of marine protected areas, as mortality during
settlement may be a bottleneck for some reef fish species (Doherty et al., 2005; Hughes et al.,
2005). At the time of this report, the SPAGS studies had failed to confirm the existence of any
large spawning aggregations sites on Kofiau Island; but further studies are planned for SE
Misool.
The WWF Seascape migration and dispersal analysis for turtles is expected to provide habitat
and movement information for Ecospace (contact: L. Pet-Soede. Jl. Raya Puputan No. 488,
Renon Denpasar, Bali, Indonesia). The TNC fish stomach content analysis study will provide
13
valuable diet information directly usable by Ecopath, and should supplement (or obsolete) the
current diet allocation algorithm used to parameterize the EwE food web (contact: P. Mous,
TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia). The CI socioeconomic study will provide
cost and price information essential to the socioeconomic optimization facilities in Ecosim
(contact: A. Dohar, CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia). The historical ecology
study can provide us with more accurate model baselines with which to parameterize fisheries
indicators in Ecosim (contact: S. Heymans, UBC Fisheries Centre. 2002 Main Mall, Vancouver,
Canada). The TNC marine resource utilization survey has generated aerial photographs of RA,
providing useful habitat data and allowing us to estimate fishing effort distribution; this will be
useful for validating Ecospace (contact: P. Mous, TNC-CTC. Jl Pengembak 2, Sanur, Bali,
Indonesia). Analyses using MARXAN to assess the conservation potential of protected areas
will guide the Ecospace research and provide candidate closure scenarios for socioeconomic
evaluation (contact: M. Barmawi, TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia). Results
from the historical ecology study and aerial photography are currently being assessed for
integration into the models; results from other project components are forthcoming.
1.4 First field trip
The UBC synthesis model Post-Doctoral Fellow, this author, traveled to Papua and Bali in Feb.
20 – Apr. 19, 2006. The purpose of the trip was to meet key personnel involved with the BHS
EBM project, collect data from Indonesian repositories, gather preliminary information and
literature which had been assembled by project partners, and collect early data resulting from the
project. The first week was spent in Bali liaising with researchers from TNC, CI and WWF.
Meetings were held with senior project staff from the partner organizations in which we planned
the strategic direction of the spatial modelling effort and discussed ways to incorporate project
information into the models. We agreed on the general outputs that are expected from the
modelling, and determined what outputs might assist the regional management of RA marine
fisheries. Contributing to those meetings were Peter Mous (TNC), Lida Pet-Soede (WWF), Jos
Pet, Muhammad Barmawi and Abdul Halim (TNC). I was briefed on the state of major research
projects, and collected preliminary GIS data that had been collated, and interview materials from
the perception monitoring study.
14
Traveling to Sorong, Papua provided the opportunity to discuss specific model requirements with
experts knowledgeable in the ecology and fisheries of RA. Functional group structure and fleet
design were discussed at length. By representing the most critical functional elements in the
ecosystem, we hoped to provide a suitable basis for the models that was capable of capturing
important processes. The basic structure of the model was designed so that it could provide
outputs that would be relevant to the management process and hold resonance with managers,
policy makers and the public.
The extensive field experience of TNC and CI scientists, divers and research staff was invaluable
to model design.
Particularly, their knowledge of coral reef animals and their habits,
biogeographic and oceanographic features laid the foundation for the Ecopath and Ecospace
models especially. Although many researchers contributed to the early design of the models,
Peter Mous, Andreas Muljadi and Obed Lense provided particularly valuable assistance in
designing the functional group structure and fisheries. We also acknowledge the contributions of
Chris Rotinsulu, Reinhard Poat, Anton Suebu, Adityo Setiawan and other researchers in TNC
and CI Sorong offices. Throughout this report, specific contributions are acknowledged as
personal communications.
In Sorong we visited the offices of the Sorong Regency Fisheries Office (Departemen Kelautan
dan Perikanan, DKP), the Raja Ampat Regency Fisheries Office, the Trade and Industry Office
(Departemen Perinustrian dan Perdagangan) and the Agricultural Quarantine Office (Badan
Karantina Pertanian). I also had the opportunity to talk with student researchers from the State
University of Papua (UNIPA), who were in the process of collecting information for the
socioeconomic evaluation study (CI).
A week spent in Deer Village on Kofiau Island allowed me to become familiar with the artisanal
fishing methods, to witness fishing operations and to interact with residents. Penny Goodwyn, a
student researcher from the University of Canberra provided valuable translation assistance. I
had the opportunity to snorkel and SCUBA dive, and we also released spawning aggregation
15
(SPAG) tracking vials at a suspected grouper aggregation site (Gebe Island) to study local
currents and larval settlement patterns. Some data was collected from the marine use monitoring
study.
Returning to Bali, researchers from UBC, TNC, CI participated in a modelling coordination
workshop, April 10-14 in Sanur. The model format was presented for review: structure, data
sources and preliminary parameters were vetted. The local knowledge and scientific experience
of Mark Erdmann helped set the direction of the UBC modelling study. Presentations by the
UBC modelling study and socioeconomic study, as well as TNC, CI and WWF staff helped to
coordinate team members.
1.5 Ecopath parameterization
1.5.1 Raja Ampat model
The Raja Ampat model describes the region from 129o 12' E and 0o 12' N to 131o 30' E and 2o 42'
S (Fig. 1.1). This large-scale model includes all the waters of Raja Ampat. The functional
groups represent reef-associated fish identified by McKenna et al., 2002b, as well as pelagic and
deepwater fish occurring in Eastern Indonesia. In order to be included in the model, a fish
species had to be listed both under the ‘Indonesia’ country code in FB (FB country code 360)
and the ‘Papua New Guinea’ code (FB country code 598). That information is found on the
“DemersPelag” (habitat) field of the “Species” table in the FB database.
16
1.5.2 Kofiau model
The Kofiau Island Ecospace model extends from 129o 14' E and 1o 5' S in the north-west corner
to 130o 1' E and 1o 20' S in the south east corner (Fig. 1.2). Kofiau was selected as a study area
for a small-scale model based on a number of advantages. Firstly, it is the most well developed
TNC RA field site in the BHS EBM project. The permanent field office in Deer Village is
staffed throughout the year, and there are many marine experts on site and in Sorong that have
extensive knowledge of its ecology and biogeography. Secondly, the process of data gathering is
furthest along at this location. At the time of this report, reef fish abundance counts have been
made in transect studies; however, the data is not yet available. Community interviews for the
resource use assessment are underway, the SPAG vial release program has so far only been
conducted at Kofiau, and MPA site selection using MARXAN is also furthest advanced for this
area (P. Mous, TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia). Thirdly, this site provides
an excellent example of the valuable reef habitat that is associated with RA, and is responsible
for the biodiversity and beauty of the coral triangle. For example, Wambong Bay on Kofiau
Island has the highest number of fish species ever recorded from a single site (208) (Allen,
2000).
17
Figure 1.1 - Area represented by Raja Ampat (RA) model. RA model is delimited at 129o
12' E and 0o 12' N at the northwest corner and 131o 30' E and 2o 42' S at the southeast corner.
From north to south, inset rectangles show areas described by Dampier Strait, Kofiau Island
and SE Misool Island models.
18
Figure 1.2 - Area described by Kofiau Island model. Kofiau Island model is delimited
at 129o 14' E and 1o 5' S at the northwest corner and 130o 1' E and 1o 20' S at the southeast
corner.
The small-scale model representing Kofiau Island is primarily a coral and reef-fish model that
has been expanded to include important pelagic elements. Reef fish species in the Kofiau Island
model are based on the 940 species identified by McKenna et al., (2002b) to species level. The
species list for the Kofiau model was expanded to include key pelagic species occurring around
Kofiau Island such as tunas (Scombridae), sardines and herrings (Clupeidae), wolf-herring
(Chirocentridae), anchovies (Engraulidae), flying fish (Exocoetidae) based on expert
communications (Andreas Muljadi, Obed Lense, Reinhart Poat, Adityo Setiawan. TNC-CTC. Jl
Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413; Chris Rotinsulu. CI.
Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. Personal communication). The pelagic
species list also includes species mentioned in Venema (1997). Individual parameters were set
for each of the 940 reef fish species in the model. Fish families were then divided into functional
groups. The fishing fleet in the model represents near shore and artisanal gear types; the foreign
fishing fleet, which operates in RA, is excluded.
19
1.5.3 Dampier Strait model
The Dampier Strait model extends from 130o 25' 12'' E and 0o 18' S at the northwest corner to
131o 21' 36'' E and 0o 50' S at the southeast corner. The model includes Waisai Bay in the
northwest and incorporates a large extent of the southern coast of Weigeo Island, including Gam
Island and Kabui Bay (Fig. 1.3). Mayalibit Bay, a shallow enclosed body of turbid water
occupying a south-central position of Weigeo Island, was excluded from the model as it likely
ecologically distinct from the deeper and faster flowing Dampier Strait (Mark Erdmann. CI. Jl.
Dr. Muwardi. 17 Renon Denpasar, Bali, Indonesia. Personal communication). The modelled
area is bounded by the convoluted shore of Batanta Island in the south. Dampier Strait is an
important and productive area in Raja Ampat that sustains a major artisanal fishery for anchovy
due to a region of strong upwelling.
Figure 1.3 - Area described by Dampier Strait model.
Dampier Strait model is
delimited at 130o 25' 12'' E and 0o 18' S at the northwest corner and 131o 21' 36'' E and 0o
50' S at the southeast corner.
20
2. METHODS
2.1 Ecopath
Ecopath (Polovina, 1984; Christensen and Pauly, 1992) operates under two main assumptions.
The first assumption is that biological production within a functional group equals the sum of
mortality caused by fisheries and predators, net migration, biomass accumulation and other
unexplained mortality. Eq. 1.1 expresses this relationship:
Bi ⋅ (P B )i = Yi + ∑ B j ⋅ (Q B ) j ⋅ DC ij + E i + BAi + Bi (P B )i ⋅ (1 − EE i )
n
j =1
Equation 1.1
Where Bi and Bj are biomasses of prey (i) and predator (j), respectively;
P/Bi is the production/biomass ratio;
Yi is the total fishery catch rate of group (i);
Q/Bj is the consumption/biomass ratio;
DCij is the fraction of prey (i) in the average diet of predator (j);
Ei is the net migration rate (emigration – immigration); and
BAi is the biomass accumulation rate for group (i).
EEi is the ecotrophic efficiency; the fraction of group mortality explained in the model;
The second assumption is that consumption within a group equals the sum of production,
respiration and unassimilated food, as in eq. 1.2.
B ⋅ (Q B ) = B ⋅ (P / B ) + (1 − GS ) ⋅ Q − (1 − TM ) ⋅ P + B(Q B ) ⋅ GS
Equation 1.2
Where GS is the proportion of food unassimilated; and TM is the trophic mode expressing the
degree of heterotrophy; 0 and 1 represent autotrophs and heterotrophs, respectively.
Intermediate values represent facultative consumers.
21
Ecopath uses a set of algorithms (Mackay, 1981) to simultaneously solve n linear equations of
the form in eq. 1.1, where n is the number of functional groups. Under the assumption of massbalance, Ecopath can estimate missing parameters. This allows modelers to select their inputs.
Ecopath uses the constraint of mass-balance to infer qualities of unsure ecosystem components
based on our knowledge of well-understood groups. It places piecemeal information on a
framework that allows us to analyze the compatibility of data, and it offers heuristic value by
providing scientists a forum to summarize what is known about the ecosystem and to identify
gaps in knowledge.
2.2 Ecosim
Ecosim (Walters et al., 1997) adds temporal dynamics. It accounts for the biomass flux between
groups using coupled differential equations derived from the first Ecopath master equation (eq.
1.1). The set of differential equations is solved using the Adams-Bashford integration method by
default. Biomass dynamics are described by eq. 1.3.
n
n
dBi
= g i ∑ f (B j , Bi ) − ∑ f (Bi , B j ) + I i − (M i + Fi + ei ) ⋅ Bi
dt
j =1
j =1
Equation 1.3
Where dBi/dt represents biomass growth rate of group (i) during the interval dt;
gi represents the net growth efficiency (production/consumption ratio);
Ii is the immigration rate;
Mi and Fi are natural and fishing mortality rates of group (i), respectively;
ei is emigration rate; and
ƒ(Bj,,Bi) is a function used to predict consumption rates of predator (j) on prey (i) according to
the assumptions of foraging arena theory (Walters and Juanes 1993; Walters and Korman, 1999;
Walters and Martell, 2004). It is modified by the predator-prey vulnerability parameter assigned
to the interaction.
Variable speed splitting enables Ecosim to simulate the trophic dynamics of both slow and fast
growing groups (e.g., whales/plankton), while multi-stanza pools (Christensen and Walters,
22
2004a) allow us to represent life histories and model ontogenetic dynamics. The multi-stanza
routine back-calculates juvenile cohort abundance based on the adult pool biomass and on life
stage mortality rates, employing a Deriso-Schnute delay difference model. For a complete
description of the multi-stanza routine see Walters et al. (2000).
2.2.1 Predator-prey vulnerabilities
The principle innovation in Ecosim considers risk-dependant growth by attributing a specific
vulnerability term for each predator-prey interaction. The vulnerability parameter is directly
related to the carrying capacity of the system. Each predator-prey trophic interaction is assigned
a vulnerability coefficient, from one to infinity. The figure is unitless and it describes the
maximum increase in predation mortality allowable on that feeding interaction. By assigning a
low value, we imply a donor driven density-dependant interaction. In foraging arena theory
(Walters and Juanes, 1993; Walters and Korman, 1999; Walters and Martell 2004), the prey can
remain hidden or defended during periods of high predator abundance. Predators are never
satiated and handling time or physiological constraints do not limit predation mortality
(Essington et al., 2000).
By assigning a high value, we imply a predator driven density-
independent interaction, in which predation mortality is proportional to the product of prey and
predator abundance (i.e., Lotka-Volterra). This implies a high flux rate for prey species in and
out of vulnerable biomass pools.
Strict bottom-up control in Ecosim may produce unrealistically smooth changes in prey and
predator biomass that fail to propagate through the food web (Christensen et al., 2004), and can
impart an unrealistic degree of resilience to the effects of fishing (Martell et al. 2002). Strict topdown control may cause rapid oscillations in biomass and unpredictable simulation behaviour
(Christensen et al., 2004; Mackinson, 2002) and will often produce a complex response surface
that is difficult to work with under policy optimizations (Cheung et al., 2002; Ainsworth, 2006).
In the absence of better information, many modelers assume mid-range vulnerabilities to temper
the dynamics (Okey and Wright, 2004).
23
The preferable parameterization method is to fit the model’s dynamic behaviour to time series of
catch or biomass by altering the vulnerabilities manually, or with the assistance of automated
routines in Ecosim (Christensen et al., 2004). Data fitting is done here using the available time
series that we collated from governmental fisheries statistics. Future revisions to this model will
incorporate time series abundance information recently collected in community interviews
(contact: C. Rotinsulu. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413), and catch and
effort data collected by the CI socioeconomic analysis (contact: A. Dohar, CI. Jl.Gunung
Arfak.45.Sorong, Papua, Indonesia).
2.2.2 Mediation factors
Ecosim offers the capability to represent non-trophic effects that have a strong influence on food
web dynamics. Using mediation functions, the vulnerability of a given prey to a given predator
can be affected according to the biomass density of a third mediating group. This can be used to
capture important behavioral aspects of populations and more accurately simulate ecosystem
functioning.
The most common types of mediation models applied in Ecosim include facilitation and
protection. An example of facilitation is seen when pelagic piscivores like tuna drive small
pelagics to surface waters, increasing their vulnerability to avian predators (e.g., Dill et al.,
2003). This is known as ‘competitor facilitation’ because birds compete with tuna for a common
prey type. Similarly, small pelagics may be corralled into tight aggregations as an anti-predator
defense in response to fish or marine mammal predation. This may increase their vulnerability to
other types of predators that attack dense prey schools, such as diving birds. Dayton (1973)
provides a different example of competition facilitation in which urchins, taking insecure
footholds to avoid predation by sea stars, are dislodged through wave action and made available
to anemone predators.
Strand (1988) provides another example concerning inter-specific
foraging associations; where nuclear-follower behaviour improves hunting success in certain reef
species. Protection effects occur when structure-forming species, such as reef-building corals,
provide shelter for reef dwelling fish or invertebrates. Elimination of the biotic structure by
24
grazing corallivorous fish or crown of thorns starfish for example may regulate the survival of
fish and invertebrate species taking refuge within the reefs.
Four mediation functions have been entered into the BHS-EBM models. The first function
describes a major facilitation effect, in which tunas (both ‘skipjack tuna’ and the ‘other tuna’
group) corral small pelagics and anchovy near to the surface, and make them more vulnerable to
predation by birds. The mediation function is entered so that the vulnerability of the prey groups
increases in linear proportion to the biomass of tuna, up to a maximum increase of 2X the
baseline vulnerability. The mediating groups, skipjack tuna and other tuna, contribute equally to
this effect. Prey groups subject to this mediation effect are adult and juvenile anchovy, and adult
and juvenile small pelagics.
The second mediation function represents a major protection effect, in which hermatypic (reefbuilding) scleractinian corals confer protection against predators to the following groups: small
and medium reef-associated fish, sub-adult groupers, sub-adult snappers, juvenile and sub-adult
Napoleon wrasse, juvenile coral trout and octopus.
The function is modeled so that the
vulnerability of the prey species changes in inverse linear proportion to coral biomass. All the
predators of these prey species are affected equally. The vulnerabilities of these small reef fish
are free to increase to a maximum of 2X the baseline value (during periods of low coral biomass)
and can decrease to near 1 (during periods of high coral biomass).
The third mediation function represents a minor protection effect, in which cleaner wrasse
improves the health of large reef-associated fish. This effect is applied to the adult stanzas for
groupers, snappers, large reef-associated fish, coral trout and Napoleon wrasse. The effect is
modelled so that vulnerability of the large reef fish to their predators changes in inverse linear
proportion to cleaner wrasse biomass. All the predators of these large reef fish species are
affected equally by this mediation effect. Vulnerabilities may increase to a maximum of 1.5
times the baseline value (during periods of low cleaner wrasse biomass), and they may decrease
to a minimum of 0.5 times the baseline value (during periods of high cleaner wrasse biomass).
25
By using the mediation functions we are making the assumption that cleaner wrasse improve the
health of large reef fish populations, and that this allows them to avoid predation.
The fourth mediation function represents a minor protection effect, in which mangroves and sea
grasses provide protection to juvenile groupers and snappers. The effect is weighted so that the
vulnerabilities of these juvenile reef fish species increases to all their predators as the biomass of
mangroves and sea grasses goes down; i.e., in inverse linear proportion. The vulnerabilities are
allowed to increase to 1.5X the baseline value, or decrease to 0.5X the baseline value. The
mediating groups, mangroves and sea grasses, do not affect the predator-prey interactions
equally; mangroves have a stronger affect than sea grasses on the order of 3:1.
We have chosen to use simple linear effects for all of the mediation functions pending review by
experts.
However, it may be difficult to differentiate the relative effects of behavioural
interactions with those of trophic cascades (Carpenter and Kitchell, 1993; Walters et al., 1997).
Even if animal behaviour and ecology is well understood, it may be difficult to prescribe
mediation functions based on empirical data, as there are currently significant limitations in the
mediation routine.
The increase in vulnerabilities is currently restricted to maximum of 2X the baseline value,
regardless of the biomass of the mediating group(s). This limitation was less of a concern when
the routine was originally integrated into Ecosim, but since the release of Ecopath V5.1 the
definition of the vulnerability parameter has changed. It is now set for each predator-prey
interaction from 1 to infinity (Christensen and Walters, 2004a). When vulnerabilities are low, as
in donor-driven interactions, a relative increase of 2X has a much larger affect than when
vulnerabilities are high. If the same mediation effect is applied to numerous trophic interactions,
it may be difficult to forecast the relative impact on each group.
A second limitation in the routine is that each predator-prey interaction can be governed by only
one mediation function. Therefore, we are currently forced to choose only the most influential
mediating effect for any given predator-prey interaction. We cannot, for example, model the
26
protection that coral reefs impart on a reef fish population, while simultaneously representing the
advantage conferred on them by cleaner wrasse. In the present models, cleaner wrasse are
assumed to be more important to the adult reef fish stanzas, while reef protection is assumed to
be more important to sub-adult or juvenile stanzas. This limitation will be resolved with the
upcoming release of EwE V6.0 in September 2007.
2.3 Ecospace
Ecospace (Walters et al. 1998) models the feeding interactions of functional groups in a spatially
explicit way. A simple grid represents the study area, and it is divided into a number of habitat
types. Each functional group is allocated to its appropriate habitat(s), where it must find enough
food to eat, grow and reproduce - while providing energy to its predators and to fisheries. Each
cell hosts its own Ecosim simulation and cells are linked through symmetrical biomass flux in
four directions; the rate of transfer is affected by habitat quality. Optimal and sub-optimal
habitat can be distinguished using various parameters such as the availability of food,
vulnerability to predation and immigration/emigration rate. By delimiting an area as a protected
zone, and by defining which gear types are allowed to fish there and when, we can explore the
effects of marine protected areas (MPAs) and test hypotheses regarding ecological function and
the effect of fisheries. Previous authors have used Ecospace in this capacity (e.g., Walters et al.,
1998; Beattie, 2001; Pitcher and Buchary, 2002a/b; Buchary et al., 2002; Pitcher et al., 2001;
Salomon et al., 2002; Sayer et al., 2005).
2.4 Ecopath parameterization
2.4.1 Functional group designations
Ninety-eight functional groups are used to represent the marine ecosystem of Raja Ampat.
These include mammals, birds, reptiles, fish, invertebrates, plants, zooplankton, phytoplankton,
and non-living groups such as fishery discards and organic detritus (Table A.3.1). The models
have been designed to serve at various spatial scales. Ideally, smaller area models, such as the
one representing Kofiau Island, would have a group structure especially suited to represent coral
reef organisms and their interactions, while the larger area RA model should consider pelagic
and deep-water species in more detail. However, to keep the various models comparable,
27
identical group structures are used. A compromise solution is therefore used that tends to
emphasize reef communities, while providing the basic level of functionality necessary to assist
management of pelagic and deep-water resources. .
High-order food web dynamics are carefully represented in the BHS EBM models in order to
provide reliable forecasts concerning the impacts of fisheries on coral reefs.
Important
predatory, herbivorous and commercial fish tend to be allotted into highly specialized functional
groups, while basal organisms are generally aggregated. At 98 functional groups these are
complex models, but we believe that this approach is necessary in order to provide sufficient
resolution to capture important processes occurring on coral reefs.
2.4.2 Fish groups
Because of the enormous amount of differentiation in life-history, morphology and feeding
guilds that appears within coral reef fish families, delineating functional groups by fish family or
clad is impractical and may be unwise.
Through evolutionary convergence, similar niche
specializations can be present in unrelated taxa; or, a single fish family may include multiple
functional niches. The specific group structure in a EwE model is largely subjective and should
be tailored to satisfy specific requirements of the investigation. Therefore, most of the functional
groups developed for the preliminary Raja Ampat ecosystem models are based on the functional
role that the fishes play in the ecosystem, with additional groups configured to allow the
representation of important commercial, social and ecological interests.
The important
specializations were determined based on the ecological literature available for coral reef
ecosystems (e.g. Bellwood et al., 2004) and through expert communication.
There are 1203 fish species represented in the RA model. The common and scientific name of
each species is presented in Table A.1.1 along with their assigned functional group. The fish
species are apportioned into 57 functional groups; of which 30 represent unique species or
species groups. The remaining functional groups correspond to various juvenile, sub-adult and
adult life history stages included in the model to represent ontogenetic feeding, mortality and
behaviour.
28
Fish functional groups may be designed to represent specific functional roles (e.g., grooming by
cleaner wrasse, algae mediation by herbivorous echinoids), to represent species of commercial
interest (e.g., skipjack tuna, groupers) or to cover the wide diversity of fishes in aggregated
species groups (e.g., large reef-associated fish). Fish have been allocated into functional groups
based also on body size (e.g., small, medium and large groups), feeding guild (e.g., planktivorous
and piscivorous) and habitat (e.g., pelagic, demersal, reef-associated). The rationale behind
functional group designation is provided in Table A.3.1.
Reef fish
Reef fish functional groups were established based on Bellwood et al. (2004) and Ayre and
Hughes (2004), and modified based on expert opinion (T. Pitcher, UBC Fisheries Centre. 2204
Main Mall. Vancouver, BC; P. Mous, TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia; A.
Muljadi, Reinhart Poat, Obed Lense TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru,
Sorong, Papua, Indonesia 98413). The groups were revised again following the TNC, CI, WWF,
UBC Modelling coordination workshop (Sanur, Bali, Indonesia April 10-14). McKenna et al.
(2002b) identified 940 species of coral reef fish present in Raja Ampat. Although all of these
species are associated with reefs to some degree, the species were subdivided into pelagic and
demersal based on the comment field in the FB Ecology table.
Where a single fish species could suitably fit into several aggregate functional groups, it was
usually assigned to the most taxonomically specific group.
For example, kawakawa tuna
(Euthynnus affinis) are large and piscivorous, and so could fit into the piscivorous ‘large pelagic’
functional group. Instead, kawakawa is slotted into the more exclusive ‘other tuna’ functional
group. Similarly, the group ‘large planktivorous fish’ includes planktivorous species that are
both reef-associated and pelagic, but these were kept apart from those larger aggregate groups to
highlight their uncommon feeding mode.
29
Planktivorous fish
Obligate and facultative planktivorous species are included in the planktivorous functional
groups. Where quantitative diet information is unavailable from the FishBase (FB) Diet table,
assigning fish to planktivorous functional groups may require a judgment call based on
qualitative information as contained in the FB Species, Fooditems and Ecology tables. For a
species to be included into a planktivorous functional group a prominent mention of planktivory
is required in diet remarks on the Species table. A comment such as ‘eats mainly zooplankton’ is
assumed to indicate planktivory. The Ecology table provides a simple diet classification in its
‘Mainfood’ and ‘Feeding type’ fields. Positive indicators for planktivory include the entry
‘zooplankton’ in the ‘Mainfood’ field, and ‘selective plankton feeding’ or ‘filtering plankton’ in
the ‘Feeding type’ field. The Fooditems table lists prey items in order of importance, and a
prominent mention of a planktivorous prey item is said to qualify the species for a planktivorous
EwE group. In addition, species may be designated as planktivorous without specific mention of
planktivory if their specified prey items are among the more common planktivorous taxa (e.g.,
copepods, euphasiids, ostracods) and if their diet does not contain a large portion of nonplanktonic components.
Subdividing habitat type and feeding guilds by fish size
Fish size was based on maximum length, converted to TL (see Section 2.5.1 - Length-length
conversions) since an LMAX could be found for 96.3% of species (FB; Allen, 2000). The size
distributions of ‘pelagic fish’, ‘reef-associated / demersal fish’ and ‘planktivorous fish’ are
presented in Fig. 2.1. The functional groups ‘pelagic fish’ and ‘reef-associated / demersal fish’
consist of 133 and 674 species respectively. The pelagic habitats categorized by FB include
‘pelagic’ and ‘benthopelagic’ zones, while the demersal habitat includes the ‘demersal’ zone.
Fish occurring in the ‘bathypelagic’ or ‘bathydemersal’ zones (i.e., occurring at depths > 200m)
are considered to be deep-water species.
30
Reef associated & demersal (n=688)
A)
Frequency
120
90
60
30
0
5
35
65
94
124
154
184
214
243
273
309
360
410
460
129
150
171
191
129
150
171
191
Lmax TL (cm)
Pelagic (n=133)
25
B)
Frequency
20
15
10
5
0
7
57
108
158
208
259
Lmax TL (cm)
Deepwater (n=57)
C)
Frequency
10
8
6
4
2
0
5
25
46
67
88
108
Lmax TL (cm)
Planktivorous (n=160)
D)
Frequency
20
15
10
5
0
5
25
46
67
88
108
Lmax TL (cm)
Figure 2.1 - Fish maximum length (LMAX) distribution. Histograms based on total body length
(TL). A-C) based on habitat type; D) based on feeding guild. Species are mutually inclusive.
31
These groups, aggregated by habitat type, were further divided into either 2 or 3 size categories
(e.g., small, medium and large).
The size category for each species was determined by
comparing their length against the length of other species occurring in their habitat. Fish species
that are present in the ‘planktivorous’ functional groups, for example, were divided into small,
medium or large size categories based on a comparison against other reef-associated fish (in the
case of reef-associated planktivores) or pelagic fish (in the case of pelagic planktivores).
They were not compared strictly to other planktivores, but to all sympatric species. This method
was preferred in order to maximize the number of species serving as a comparison. There are
eleven sharks in the Raja Ampat model; these are divided based on LMAX into ‘small sharks’
(five species < 200 cm) and ‘large sharks’ (six species > 200 cm).
2.4.3 Bioeroders
Bioerosion is the process whereby certain species of fish, invertebrates, plants, fungi and bacteria
cause mechanical and/or chemical erosion of calcareous skeletons of corals and other reef
organisms through feeding and burrowing behaviour. It is known to be a major structuring force
in coral reef ecosystems (Hutchings 2002). Bioeroders can be classified into browers, who
scrape or rasp the reef substrate feeding on epilithic algae or invertebrates, and grazers who
consume much more reef material in search of endolithic prey (Holt, 2003). Bioeroders can have
a positive impact on the reef community by oxygenating the reef substrate and removing dead
coral to facilitate settlement and growth of new individuals, but they can also initiate a cascading
destruction of the reef if chronic degradation weakens resistance to biological invasion or wave
action.
In the BHS EBM models, bioeroding fish are classified into three functional groups based on
Bellwood et al. (2004). Causing the least damage to reefs are the herbivorous ‘macro-algal
browers’, selected so based on diet information and qualitative remarks in FB. More damaging
are the ‘scraping grazers’, including members of Scaridae (parrotfish), Acanthuridae
(surgeonfish), Monacanthidae (filefish), and Tetraodontidae (puffers). The functional group
32
‘eroding grazers’ is reserved for the two most damaging species of parrotfish, which use their
specialized beak-like jaws and pharyngeal mill to process coral substrate: doubleheaded
parrotfish (Scarus microhinos) and green humphead parrotfish (Bolbometopon muricatum).
These species are thought to have a serious impact on reefs in RA (Adityo Setiawan. TNC-CTC.
Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413.
Personal
communication; Bellwood et al., 2004).
The crown-of-thorns starfish (Acanthaster planci) was given its own functional group to describe
the serious impact that these animals can have on reefs. As a dominant corallivore, periodic
outbreaks of the sea star can have long-lasting impacts on the health of the coral reef community.
Such outbreaks may be a direct or indirect consequence of human activities (Endean, 1969;
Randall, 1972) but empirical evidence is scarce (Pratchett, 2005). Removal of large fish species
(e.g., Lethrinidae, Napoleon wrasse Cheilinus undulatus) may also reduce the natural predation
mortality on A. planci populations permitting outbreaks, although Sweatman (1995) could not
confirm it empirically.
The activity of bioeroding species is captured through the diet matrix. They consume coral
groups, including hermatypic scleractinian corals, non-reef building corals and soft corals, as
well as calcareous algae.
By used of mediation functions in Ecosim (see Section 2.2.2 -
Mediation factors) a realistic impact of bioeroders can be modelled, where removal of the
substrate impacts the survival of juvenile fish by limiting their refuges and increasing predation
mortality.
2.5 Basic parameterization
The data needs of Ecopath can be summarized as follows. Four data points are required for each
functional group: biomass (in t·km-2), the ratio of production over biomass (P/B; in yr-1), the ratio
of consumption over biomass (Q/B; in yr-1), and ecotrophic efficiency (EE; unitless). Ecopath
also provides an input field representing the ratio of production over consumption (P/Q;
unitless), which users may alternatively use to infer either P/B or Q/B based on the other. Each
functional group requires 3 out of 4 of these input parameters and the remaining parameter is
33
estimated using the mass-balance relationship in eq. 1.1. A biomass accumulation rate may be
entered optionally; the default setting assumes a zero-rate instantaneous biomass change.
These
Ecopath data points are referred to collectively in this report as the basic parameters. For a more
thorough description of Ecopath data needs and parameter definitions please refer to Christensen
et al., (2004).
This section ‘Basic parameterization’ describes the general methodology used to assign fish
functional groups their basic parameters using FB information.
Section ‘Functional group
parameterization’ addresses each group specifically, reporting where literature values and other
special data sources were used to set the basic parameters. Most often, Q/B was set using the
empirical formulae of Pauly (1986); a few species were set using Palomares and Pauly (1998)
using tail aspect ratio as modified by Christensen et al. (2004). P/B was determined based on the
sum of the natural mortality rate (M), estimated using the empirical formula of Pauly (1980), and
some fishing mortality rate (F), which is an assumed fraction of M. As a guideline, heavily
exploited species were assumed to have an F approximately equal to M, while moderately
exploited species were assumed to have an F equal to M/2 or less.
2.5.1 Growth parameters
All growth parameters utilized from the FB PopGrowth table, including length at infinity (L∞),
weight at infinity (W∞) and the Von Bertalanffy growth constant (K) were selected among values
in the temperature range 28 ± 2oC. An average value was taken for each species for values
within this temperature range. When no growth parameters were available from within this
range, an average value of all available parameters, regardless of the temperature, was used for
the species. Some growth data is duplicated in other FB tables, for example W∞ occurs in the
PopGrowth table and the QB table. The growth constant K can occur in the FB PopGrowth table
or the QB table. In all cases, growth data was taken from the PopGrowth table preferentially,
then the ‘QB’ table, then the ‘Species’ table, as illustrated in Figs. 2.2 - 2.4.
34
Estimating weight at infinity (W∞)
The W∞ parameter is utilized by the Q/B regression formula presented below (eq. 2.7), and it is
also required by the multi-stanza routine. W∞ is the asymptotic fish body weight in grams. Fig.
2.2 illustrates the method used to establish W∞ for fish species. W∞ is taken directly from FB, if
it is available in the ‘aveWinf’ field of the PopGrowth table or the ‘Winf’ field of the QB table.
Where no value is available from FB, the parameter is calculated from the length-weight (L·W)
relationship (eq. 2.1), utilizing a and b growth parameters found respectively in the ‘a’ and ‘b’
fields of the FB PopGrowth table, and L∞. L∞ is taken preferentially from the ‘aveLinf (TL)’
field of the PopGrowth table.
L=a·Wb
Equation 2.1
If any of these L·W parameters are unavailable, then W∞ is instead estimated from the maximum
weight (WMAX), which occurs in the ‘Max weight’ field of the FB Species table, according to the
assumption shown in eq. 2.2.
WMAX = W∞ · 0.95
Equation 2.2
If WMAX is unavailable, then L∞ is estimated from LMAX, which can be found in the FB
PopGrowth table, according to eq. 2.3 as found in Pauly et al., (1993), and the L·W relationship
(eq. 2.1) is subsequently used to establish W∞. A decision flow tree is presented in Appendix
A.1 summarizing the data source used to calculate W∞.
LMAX = L∞ · 0.95
Equation 2.3
To maximize the number of species contributing data towards parameter values for aggregate
functional groups, average family values for LW parameters were calculated for some functional
groups if there were example values for at least five species per family.
35
Start
Is W ∞ available from
FB GROWTH table?
Y
N
Is W∞ available from
FB Q/B table?
W∞ found
Y
N
Are a, b and L∞
available?
Y
Use L• W
relationship
(convert length to TL)
N
Is Wmax
available?
Y
Assume
Wmax = 0.95• W
∞
N
Are a, b and Lmax
available?
Y
Assume
L
Lmax = 0.95• ∞
(Pauly et al., 1993)
N
Cannot estimate W∞
Figure 2.2 - Flow chart showing W∞ parameterization method. All growth
parameters are taken from areas occupying the temperature range 28 ± 2oC; where
values were unavailable from within this temperature range, an average value was
used for all available parameters regardless of temperature.
Length-length conversions
The empirical formula of Pauly (1980) for estimating M and the formula of Pauly (1986) for
estimating Q/B both require L∞ as measured in total length (TL). Entries for L∞ in FB (in both
Species and PopGrowth tables) are usually provided in TL. Where length measurement are
given in other formats by the original data sources (e.g. in fork length (FL) or standard length
(SL)), FB usually provides conversions to TL in the ‘TLinfinity’ field; no conversions are
provided for maximum lengths found in the ‘Species’ table. When required, conversions were
performed manually.
36
To convert FL to TL, the linear empirical relationships of Booth and Isted (1997) were used. For
fish with forked tails, the relationship employed is based on panga (Pterogymnus laniarus), as in
eq. 2.4:
FL = 0.901·TL – 0.6848
Equation 2.4
For fish with emarginated tails, the relationship is based on the lesser gurnard (Chelidonichthys
quekerri) as in eq. 2.5:
FL = 0.9454·TL + 3.6166
Equation 2.5
All pelagic, benthopelagic and bathypelagic fish were assumed to have forked tails, while all reef
fish, demersal and bathydemersal fish were assumed to have emarginated tails. Each FB species
is demarked into one of these six habitat classifications according to habitat data indicated in the
Habitat field of the FB Species table.
Where SL was provided, the conversion factor to TL was applied from Christensen and Pauly
(1992) as in eq. 2.6.
TL = 1.1757·SL – 0.1215
Equation 2.6
2.5.2 Estimating consumption rate (Q/B)
Q/B was taken preferentially from the literature or as estimated in FB. Estimates of Q/Bs from
FB sources were accepted if the data is based on a study of similar temperature to Raja Ampat
(28oC ± 2oC). For each fish species, the Q/B value was taken directly from FB, if available from
the ‘PopQB’ field of the ‘QB’ table. Otherwise, an empirical relationship was used to estimate
Q/B for each species. The empirical formula of Pauly (1986) based on feeding mode was
preferred (eq. 2.7), using W∞ as determined above.
37
Q/B = 10 6.37 · 0.0313 (1000 / T) · W∞ -0.168 · 1.38 Pf · 1.89 Hd
Equation 2.7
The mean annual temperature (T) is expressed as 1000 / (ToC + 273.1) where ToC is temperature
in degrees Celsius (assumed 28oC). The feeding mode parameter (Pf) is set equal to 1 for
predators and zooplankton feeders, and zero for other fish species as per Pauly (1986). The diet
composition parameter (Hd) is set to 1 for herbivores, and 0 for omnivores and carnivores.
Parameters Pf and Hd were set for each species based on qualitative feeding remarks located in
MainFood, Herbivory2 and FeedingType fields of the FB Ecology table, and in the general
comment field of the Species table.
If W∞ could not be determined, then the empirical formula of Palomares and Pauly (1998) was
used instead to estimate Q/B based on caudal fin aspect ratio (eq. 2.8). Here, aspect ratio (A) is
defined as (tail height / area)2; it is available from the AspectRatio field of the FB Swimming
table. Parameters h and the d refer to the types of food consumed (i.e., for herbivores h=1, d=0;
for carnivores h=0, d=0; for detritivores d=1, h=0 as defined by Palomares (1991) and reported
by Palomares and Pauly (1998)). These binary values were set for each species based on diet
information provided in the FB diet table or on comment fields (e.g., in the Species table).
Q/B = 7.964 · 0.204 log W∞ + 1.965 T + 0.083A + 0.532h + 0.398d
Equation 2.8
The decision tree in Fig. 2.3 demonstrates the parameterization method for the consumption rate
(Q/B) of fish species. Q/B was set individually for all species and then averaged to obtain
functional group parameters reported in Table A.3.2.
38
Start
Q/B found
Is Q/B available from
FB Q/B table?
Empirical formula based on
aspect ratio
Y
(Palomares and Pauly, 1998)
Empirical formula based
feeding mode
N
(Pauly, 1990)
Is W ∞
available?
Y
Is hd and pf
available?
Y
N
N
Are a, b and L ∞
available?
Y
Use L• W relationship
to estimate L ∞
Is hd and
aspect ratio
available?
Y
N
N
Cannot estimate Q/B
(use family parameter if available)
Figure 2.3 - Flow chart showing Q/B parameterization method. Feeding mode (pf) and
diet composition (hd) parameters for empirical formulae were obtained from FB Ecology
table (Herbivory and FeedingType fields, respectively), or set according to qualitative
description of feeding habits in FB Species table.
2.5.3 Estimating natural mortality (M) for fish
Natural mortality (M) is used to represent the P/B rate for species that are unexploited; for
species with an annual catch, P/B is estimated as the sum of M and fishing mortality (F). Fig.
2.4 shows the decision tree used to parameterize M for fish species. Where available, the M was
taken directly from literature sources or from data tables in FB. Where an estimate could not be
found, the regression equation of Pauly (1980) was used to determine M (eq. 2.9), which requires
growth information: the Von Bertalanffy growth constant (K) and the asymptotic length at
infinity (L∞). These values were obtained for most species from FB PopGrowth table. When L∞
was unavailable, the maximum specimen length observed LMAX was substituted, assuming that
L∞ = 0.95·LMAX.
39
M = K 0.65 · L∞ -0.279 · T 0.463
Equation 2.9
Start
Is M available from
FB Q/B table?
Y
M found
N
Is K available from
FB GROWTH table?
Y
Is L∞ available?
Y
Empirical formula
(Pauly, 1980)
N
N
Is K available from
FB Q/B table?
Y
Are a, b and W∞
available?
Y
Use L• W relationship
to estimate L ∞
N
N
Cannot estimate M
L∞ may be estimated as 0.95• LMAX
Figure 2.4 - Flow chart showing M parameterization method.
2.5.4 Daily ration
Marine mammals
The empirical equation for daily ration of marine mammals, modified from Innes et al. (1987) in
Trites and Heise (1996), is used for estimating the consumption per unit of biomass (Q/B) as in
eq. 2.10.
R = 0.1 · W 0.8
Equation 2.10
W is body weight in kg and R is the daily ration in kg·d-1.
40
Birds
The empirical equation for daily ration for birds given by Nilsson and Nilsson (1976) in Wada
(1996), is used for estimating the consumption per unit of biomass (Q/B) as in eq. 2.11,
log R = -0.293 + 0.85 · log W
Equation 2.11
W is the body weight in grams and R the ration in grams per day.
2.5.5 Ingestion rate in deposit feeders
An empirical model for the ingestion rate of aquatic deposit feeders and detritivores was used in
the calculation of Q/B for sea cucumbers, as in eq. 2.12.
C = -0.381 · W0.742
Equation 2.12
Consumption (C) is in mg·d-1 and dry weight (W) is in mg.
2.5.6 Estimating P/B of invertebrates
The P/B ratio for benthic invertebrate functional groups was obtained by an empirical model
established by Brey (1995); it is presented in eq. 2.13.
log P/B = 1.672 + 0.993 · log (1/AMAX) - 0.0335 · log (MMAX) - 300.447 · 1/(T + 273)
Equation 2.13
AMAX is the maximum age in years, MMAX is the maximum individual body mass in grams dry
mass (gDM) and T is the bottom water temperature in degrees Celsius.
41
2.5.7 Group maturity parameters
The multi-stanza routine in Ecopath (Christensen et al., 2004) requires the following growth and
maturity information: the Von Bertalanffy growth constant K, recruitment power, relative
biomass accumulation rate, the weight at maturity (WMAT) the weight at infinity (W∞) and the
age at maturity. W∞ was compiled at the species level for each fish functional group according
to the methodology described in the above section. K was taken as a direct average of FB entries
in the PopGrowth table. To calculate WMAT from FB length at maturity (LMAT) data, a lengthweight relationship was employed as in eq. 2.1. For all multi-stanza groups, the adult stage is
considered to be reproductive, and so the WMAT represents the average body weight at the
transition (i.e., knife edge entry to the reproductive cohort is assumed).
For species that had multiple data values, the maturation parameters are taken as an average,
regardless of geographic origin of the data points. Where a range of values is provided from a
single publication, an average value was accepted. Maturity data is utilized for 148 reefassociated species and 122 pelagic/deepwater species.
2.5.8 Biomass density estimates
Biomass density estimates are based directly on COREMAP (2005) reef transect data for 26 reefassociated fish functional groups out of 48 in the models. Biomass estimates could be made for
17 additional reef-associated fish groups based on the subjective abundance rankings provided
by McKenna et al. (2002b) (e.g., common, rare). Biomass weighting factors were assigned to
each abundance ranking offered by McKenna et al. (2002b).
The weighting factors were
determined by comparing McKenna et al. (2002b) abundance rankings against biomass densities
of known species estimated from COREMAP (2005). The biomasses of unknown species are
extrapolated based on the weighting factors.
COREMAP abundance counts are based on reef resource inventory and line intercept transects
(see Appendices 3 and 6 in COREMAP, 2005). The abundance data is converted into biomass
by multiplying fish numbers by an average species weight. The average weight is calculated at
the species level using an age-structured model.
The model uses a Ricker recruitment
42
relationship and Von Bertalanffy growth function, and employs species-specific A and B lengthweight parameters from FB, length at infinity (L∞) and growth constant (K). We assume a
simple mortality schedule for each species based on whether the groups are heavily exploited,
lightly exploited or unexploited. Species-level biomass estimates were compiled into the current
functional groups (Table A.3.1), to provide biomass estimates for the groups. The biomass
density estimates determined from COREMAP (2005) transects represents fish biomass on reef
areas. Therefore, to calculate an average biomass density for the whole of RA, including
offshore and deeper areas, the COREMAP (2005) biomass density was reduced to 1.75 % of the
original reef area value. This ratio represents the reef area to marine area ratio for all of
Indonesia used by Spalding et al. (2001).
2.5.9 Diet algorithm
Quantitative diet information was obtained from the FB Diet table for 255 out of 1196 species in
the Raja Ampat model. 26% of the reef fish and demersal fish species had available diet
information, while 17% of the pelagic and deep water fish species had data. Of the 30 fish
groups present in the model, 23 had data on at least one representative species. Availability of
diet information is summarized in Table 2.1.
Categories of prey items listed in the FB Diet table are imprecise (e.g., ‘bony fish’, ‘benthic
invertebrates’) and there are formatting and spelling variations.
The FB data is therefore
standardized. FB prey items are sorted into their corresponding EwE functional groups, either in
equal proportions for non-fish prey items, or in specific proportions for fish prey items calculated
using a diet allocation algorithm. The algorithm determines likely prey species for each predator
based on habitat co-occupation and gape size / body depth limitations, determines the fractional
contribution of each prey species according to a size-based vulnerability function, and aggregates
the values to produce a predator-prey diet matrix at the functional group level suitable for EwE.
43
Table 2.1 - Diet algorithm supporting parameters. Habitats assigned to EwE fish functional groups for prey
item allocation algorithm. FishBase (FB); Raja Ampat (RA).
Diet algorithm
Fish functional group
Number of
species in RA
model
Species with FB
diet data
% spp. with diet
data
Habitat
Feeding
mode
Groupers
46
8
17%
reef-associated
swallows
Snappers
32
16
50%
reef-associated
swallows
Napoleon wrasse
1
0
0%
reef-associated
swallows
Skipjack tuna
1
1
100%
pelagic
swallows
Other tuna
10
9
90%
pelagic
swallows
Mackerel
9
3
33%
pelagic
swallows
Billfish
5
2
40%
pelagic
swallows
Coral trout
6
0
0%
reef-associated
swallows
Large sharks
6
6
100%
pelagic
bites
Small sharks
5
3
60%
pelagic
bites
Whale shark
1
1
100%
pelagic
swallows
Manta ray
1
1
100%
reef-associated
bites
Rays
8
2
25%
reef-associated
bites
Butterflyfish
57
29
51%
reef-associated
swallows
Cleaner wrasse
3
3
100%
reef-associated
swallows
Large pelagic
26
8
31%
pelagic
swallows
Medium pelagic
9
1
11%
pelagic
swallows
Small pelagic
75
0
0%
pelagic
swallows
Large reef associated
212
80
38%
reef-associated
swallows
Medium reef associated
175
38
22%
reef-associated
swallows
Small reef associated
206
17
8%
reef-associated
swallows
Large demersal
10
2
20%
reef-associated
swallows
Small demersal
11
0
0%
reef-associated
swallows
Large planktivore
52
15
29%
either
swallows
Small planktivore
62
13
21%
either
swallows
Anchovy
17
1
6%
either
swallows
Deepwater fish
58
4
7%
either
swallows
Macro-algal browsing
3
0
0%
reef-associated
swallows
Eroding grazers
1
0
0%
reef-associated
swallows
Scraping grazers
82
24
29%
reef-associated
swallows
Detritivore fish
7
0
0%
reef-associated
swallows
44
The algorithm used to allocate prey fish species to predators is presented in Fig. 2.5. For each
predator, the algorithm assigns appropriate functional groups to each prey item category as listed
in FB; the group assignments are presented in Table 2.2. Ontogenetic FB diet records for
predator fish are characterized into either adult or juvenile entries based on the data field
‘SampleStage’ in the FB Diet table. The FB entries ‘larvae’ and ‘recruits/juv.’ are assumed to
refer to juvenile fish, while the entries ‘juv/adults’ and ‘adults’ are assumed to refer to adult fish.
These data categories are used to parameterize adult and juvenile stanzas respectively for
corresponding EwE predator fish functional groups. In some cases, adult and juvenile diet
records were combined to provide an overall diet composition for certain Ecopath functional
groups that are not differentiated into life history stages.
The diet allocation algorithm first eliminates potential prey species from the predator’s diet if
they do not occur in the same habitat as the predator. Predator habitat was determined at the
functional group level as listed in Table 2.1. Predator habitat classification is divided into two
categories, reef-associated / demersal and pelagic; it is based on the ‘habitat’ field of the FB
Species table. Species categorized in FB as pelagic, benthopelagic or bathypelagic are assumed
to occupy a ‘pelagic’ habitat, while reef-associated and demersal fish are assumed to occupy a
‘reef-associated/demersal’ habitat. Prey habitat types are similarly simplified from FB habitat
entries, but at the level of species.
A minimum and maximize prey size is then determined for each predator based on mouth gape
size. These may be important parameters governing population dynamics (Claessen et al.,
2002). In aquatic systems, the lower limit to the consumption relationship may be set by the
encounter rate, the predator’s ability to visually locate prey (Lundvall et al., 1999) or to retain
the prey after capture (Persson, 1987). The maximum limit may be determined by mouth gape
size (Hoyle and Keast, 1988, Scharf et al., 2000), or by changes in capture and handling
efficiency (Christensen, 1996), changes in prey fish behaviour, prey visibility/camouflage
(Lundvall et al., 1999), nutritional content, toxicity and other factors. Both the minimum and
maximum prey sizes may also be constrained by the precepts of optimal foraging (Emlen 1966;
45
FB diet item
for predator
For each FB diet item
Categorize ontogenetic diet records into ‘juvenile’ or
‘adult’ stanzas based on FishBase description
Average out multiple diet records for same
species / life history stages
(case data from any world area is used)
Allocate appropriate EwE functional groups to each
FB prey item (e.g., ‘bony fish’)1
For each prey group
N
Is prey group
a fish?
Y
Identify potential prey species in prey group from
Raja Ampat species list2
Determine predator functional group based on
FishBase species code2
For each prey species
Does prey species
co-occupy habitat with
the predator group?3
(pelagic or demersal/reef)
N
Species excluded from
predator diet
Y
N
Does predator
group swallow
prey whole?3
Y
The percent contribution that each FB diet item makes
towards a predator’s diet is divided uniformly across all
suitable prey functional groups1
Calculate
prey body
size
Calculating predator gape
Is there a familyspecific gape-length
relationship available
for this predator’s
family?4
Y
Use family relationship to
calculate maximum gape size
based on Lmax
N
Is predator family
mainly
piscivorous?5
Y
Use Labridae gapelength relationship to
calculate gape size
Calculating prey body size
N
Is prey species eellike or elongated?5
Family is mainly
planktivorous
Use Mullidae gapelength relationship
to calculate gape
size
Is prey species
fusiform or no
data?5
Y
Max. dimension
1/8 TL
Predator gape size
Calculate
prey body
size
N
N
Y
Max. dimension
1/4 TL
Prey species is deep
or flattened.
Max. dimension
1/2 TL
Max. prey dimension
Is predator gape
larger than prey
max. dimension?
N
Species excluded from
predator diet
Y
Prey species contributes to the diet
composition of the predator
according to a quadratic relationship
Figure 2.5 - Flow chart showing diet allocation algorithm. 1.) Table 2.2. 2.) Table A.1.1. 3.) Table 2.1.
4.) Karpouzi and Stergiou (2003). 5.) Table A.2.1.
46
Table 2.2 - Fishbase prey items assigned to EwE functional groups. Relevant EwE functional groups are
assigned to each prey item listed for Raja Ampat fish species in the FB Diet table. Groups 1-98 refer to Ecopath
functional groups listed in Table A.3.1; Groups 99, 100 and 101 are diet import, juvenile fish and unidentified items
respectively. Juvenile fish items were distributed evenly across juvenile prey fish groups, unidentified items were
omitted from predator diets.
FB prey item
bony fish
squids/cuttlefish
n.a./other benth. crustaceans
n.a./other mollusks
benthic algae/weeds
n.a./other plank. Crustaceans
mysids
stomatopods
n.a./other plank. crustaceans
bivalves
crabs
isopods
shrimps/prawns
n.a./other plank. Invertebrates
n.a./other plank. invertebrates
plank. copepods
n.a./other annelids
ascidians
euphausiids
gastropods
amphipods
benth. copepods
jellyfish/hydroids
sea birds
n.a./other mammals
octopi
sea stars/britte stars
n.a./other cephalopods
carcasses
n.a./other phytoplankton
47
Relevant EwE groups
10,11,12,13,14,15,16,17,18,19,
20,21,22,33,34,35,36,37,38,39,
40,41,42,43,44,45,46,47,48,49,
50,51,52,53,54,55,56,57,58,59,
60,61,62,63,64,65,66
75
74,78,79,80,82,86,87,88
75,76,82,84,86,87,88
94,95
74,90,91,92
91,92
90
74,90,91,92
84
79,80
86,87,88
74
74,75,89,90,91,92
74,75,89,90,91,93
90,92
86,87,88
85
90
82,86,87,88
86,87,88,90,91,92
87,88
89
5
1,2,3
76
87
75,76
97,98
93
FB prey item
insects
toads/frogs
fish eggs/larvae
n.a./others
n.a./other finfish
Relevant EwE groups
99
99
100
101
10,11,12,13,14,15,16,17,18,19,
20,21,22,25,26,27,28,30,31,32,
33,34,35,36,37,38,39,40,41,42,
43,44,45,46,47,48,49,50,51,52,
53,54,55,56,57,58,59,60,61,62,
63,64,65,66
n.a./other benth. Invertebrates
72,74,76,77,78,79,80,81,82,83,
84,85,86,87,88
87,88
86,87,90,91,92
6,7,8,9,99
93
72,74
74,76,77,78,79,80,81,82,83,84,
85,86,87,88
87
86,87,88
74,78,79,80,82,86,87,88
78
83
87
97,98
91,92
83
67,68,69
67,68,69,70
85
90,92
93
polychaetes
ostracods
n.a./other reptiles
diatoms
n.a./other benth. invertebrates
n.a./other echinoderms
chitons
non-annelids
n.a./other benth. Crustaceans
lobsters
sea urchins
sea stars/brittle stars
debris
cladocerans
sea cucumbers
hard corals
n.a./other polyps
sponges
dinoflagellates
blue-green algae
Schoener 1971). However, within this ‘predation window’, prey species are vulnerable.
Simple rules were used here to establish the predation window and the consumption rate of fish
predators on fish prey. Predator functional groups that swallow their prey whole are assumed to
be constrained through gape size limitations with respect to the size of prey they can consume.
All of the predator fish functional groups are assumed to swallow prey whole except for the
functional groups ‘large sharks’, ‘small sharks’, ‘Manta ray’ and ‘rays’ and their corresponding
juvenile groups (Table 2.1). These groups feed by biting or tearing pieces off their prey, and so
are assumed able to feed on larger fish than a gape-restricted species of similar size.
To determine the maximum gape size of swallowing predator species, family-specific gape-body
length relationships were utilized from Karpouzi and Stergiou (2003) for Synodontidae,
Scorpaenidae, Serranidae, Carangidae, Mullidae, Labridae and Scaridae. Calculating the gape
size requires an estimate of body length standardized into TL (see Section 2.5.1 - Length-length
conversions). For other predator families, the maximum gape size was determined by assuming
a similar gape-body length ratio as Labridae, in the case of mainly piscivorous predator families,
or Mullidae, in the case of mainly planktivorous predator families (see Table A.2.1). Each
predator family was designated as being mainly piscivorous or planktivorous based on the
predominant feeding mode seen in member species. The proportion of species within each
family exhibiting piscivory is reported in Table A.2.1; this figure applies specifically to species
present in RA. Member species are considered to be planktivorous under the same criteria used
for assigning fish into planktivorous EwE functional groups (see Section 2.4.2 - Planktivorous
fish). Briefly, the main food items must be planktonic as reported either quantitatively in the FB
Diet table, or qualitatively as reported in the Ecology, Fooditems or Species tables.
The smallest body dimension of the prey species, i.e., the dimension limiting consumption by a
potential predator, is determined in a separate calculation. The body morphology is assessed for
each fish family based on representative members that have morphological information in FB.
For ‘eel-like’ or ‘elongated’ fish familes, the smallest body dimension is assumed to be 12.5% of
the maximum body length (LMAX in TL, taken at the species level). For ‘fusiform’ fish or fish
48
with no data, the smallest body dimension is assumed to be equal to 25% of LMAX. For ‘deep
bodied’ or ‘flattened’ fish, the smalled body dimension is assumed to equal 50% of LMAX. Fish
family body morphologies used by the algorithm are reported in Table A.2.1.
We assume that the predator-prey consumption rate follows a domed relationship that is
dependant on the relative sizes of the species.
The quadratic model used to predict the
consumption is initialized so that the consumption rate is zero at the minimum and maximum
prey sizes available to the predator, and it is highest in the mid-range. Eq. 2.14 shows the dome
shaped quadratic function passing through (0,0) and (0,1).
Qij = −4 ⋅ x 2 + 4 x
Equation 2.14
Q equals the relative consumption of predator (j) on prey (i), and x equals the smallest body
dimension of prey species (i), divided by the gape size of predator (j).
A dome-shaped vulnerability function may be an appropriate model to describe prey mortality as
a function of predator length (Lundvall et al., 1999; Claessen et al., 2002). Nevertheless, the
relationship can be confused by the presence of refugia (Lundvall et al., 1999), which may be an
important factor on coral reefs with high substrate complexity. The lower limit to the predation
window may have an especially influential impact on population dynamics through the effects of
cannibalism (Claessen et al., 2002; Persson et al., 2000).
An alternative to the quadratic
consumption rate equation may be to use a right-skewed relationship such as a beta distribution,
so that a wide range of smaller prey sizes are accessible, but predation mortality falls quickly as
prey size approaches the predator gape-size limit. This may be appropriate if the minimum prey
size consumed by the predator does not tend to increase as fast as the maximum gape size, (e.g.,
Scharf et al., 2000). Another prospective improvement to the algorithm may be to implement a
monotonically increasing consumption rate function for smaller predators, or to employ a
dynamic predation mortality function, whose peak shifts right with larger prey sizes (Lundvall et
al., 1999).
49
The diet algorithm in place also assumes that the availability of prey species is affected by prey
abundance. ‘Abundant’ prey species identified by McKenna et al. (2002b) are assumed to incur
130% of the baseline predation mortality rate; ‘common’ prey incur 120%, ‘moderately
common’ prey incur 110% mortality, ‘occasional’ prey suffer 90% mortality and ‘rare’ prey
suffer 80% mortality. All other species are assumed to incur baseline predation rates (100%) and
the prey-consumption ratios are normalized for each predator group so that the fractional sum of
prey species equals 1. The consumption rate of a given predator on a given prey is therefore
affected by both the relative sizes of the species, and the relative abundance of the prey species.
Another key assumption required by this algorithm is worth discussing. The assumption made is
that, for both predator and prey, LMAX can serve as an adequate proxy for LAVE, the average fish
length in the population. However, if predator populations have been reduced significantly from
virgin levels, or if their age structure is shifted towards smaller fish by the influence of fisheries
or other factors, then the algorithm will overestimate the range of prey sizes available to the
predator. Conversely, if the prey population is reduced in size or average length, then the range
of prey sizes available to predators will be underestimated. If predator and prey populations are
reduced in size or skewed from their virgin age-structure by a proportionately equal amount, then
the LMAX: LAVE proxy may hold true. However, the further depressed the populations are, the
more inaccurately will the algorithm predict the likely size range of prey consumed, since gape
size and prey body length change non-linearly with length. Additional sampling work could help
describe the current population age structure for critical species and address this potential source
of error.
The output of the diet allocation algorithm has been modified during the process of balancing
and tuning the model to time series data. The diet matrix used in the 2000 RA model is
presented in Table A.3.6.
50
2.5.10 Fisheries
Gear types
The preliminary gear types included in the RA model were selected based on discussions with
local fisheries experts and on Indonesian fishery records and publications (Departemen
Pertanian. Jakarta; Subani and Barus, 1989; Andreas Muljadi, Obed Lense, Reinhart Poat, Arif
Pratomo. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia
98413. Personal communication). For the RA model, the gear structure is shown in Table 2.3.
The gear types include spear fishing, reef gleening, shore gillnets, driftnets, permanent and
portable traps, spear diving (for fish and invertebrates), diving specifically for live fish, diving
with use of cyanide and surface-supplied air, blast fishing using dynamite, trolling, purse seining,
pole and line, set lines, lift nets, the foreign fleet and shrimp trawl. Three diving gear types are
used to represent distinct fishing methods, markets, and commodity prices received for product.
Blast fishing using explosives is known to occur throughout the archipelago, although BHSEMB aerial surveys have not detected any (M. Barmawi. Unpublished manuscript. TNC-CTC.
Jl Pengembak 2, Sanur, Bali, Indonesia).
The foreign fleet consists mainly of powered
Philippino tuna vessels operating in deeper areas in the north of RA (A. Muljadi. TNC-CTC. Jl
Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413.
Personal
communication). The shrimp trawl fishery is located in the Arafura Sea, and only a small
fraction of that area is considered by the RA models.
51
Table 2.3 - Fishing gear types included in the Raja Ampat model. Source: (Andrease Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru,
Sorong, Papua, Indonesia 98413. Personal communication). 1.) Reef fish catch includes mainly fusiliers, rabbitfish, parrotfish and jacks. 2.) Small pelagic
Bubu
Diving spear and gleen
Molo / Menyelam
Diving live fish
Molo / Menyelam
Diving air supply
(cyanide)
Molo / Menyelam
Blast fishing
Bom
Trolling with FAD
Pancing tonda
Purse seine with FAD
Rumpon
Pole and line with FAD
Rumpon
Set lines
Rawai
Lift net
Bagan Apung
Illegal foreign fleet
Hard shell
inverts.3
Portable trap
Octopus
Sero
Cucumbers
Permanent trap
Squid
Jaring hanyut
Small
pelagics2
Driftnets
Sharks
Jaring insang
Demersals
Shore gillnet
Mackerel
Balobe / Meting
Reef fish1
Reef gleaning
Napoleon
wrasse
Aco / panah
Snappers
Spear fishing / harpoon
Groupers
Indonesian name
Other tuna
Gear type
Skipjack
tuna
catch includes mainly anchovy and sardine. 3.) Hard shell invertebrate catch includes mainly shellfish and snails.
Catch time series
Fishery statistics were collected from several agency offices in Sorong: the Sorong Regency
Fisheries Office (Departemen Kelautan dan Perikanan, DKP), the Raja Ampat Regency Fisheries
Office and the Trade and Industry Office (Departemen Perinustrian dan Perdagangan). The data
were collated into catch and effort time series, and converted into standard units for use in
Ecosim. Catch and effort data from 1990 to 1999 are contained in the Sorong Regency Fisheries
Office statistics as well as commodity prices; catch and effort data from 2000 to 2004 are
contained in the Trade and Industry Office statistics. The Raja Ampat Regency Fisheries Office
had additional fisheries export data for 2005. Export data was also acquired from the Sorong
Quarantine Service for 2002 and 2004-2006. However, the data were not used because we could
not reconcile those export figures with other information from the principal data agencies
mentioned above. However, information from the Quarantine Service is largely concerned with
the activities of specific fishing companies, and so there may be potential for further
socioeconomic analysis if the ambiguity can be resolved. For the purposes of this preliminary
report, the data series assembled from DKP and the Trade and Industry Office statistics seem to
form a continuous and coherent time series of catch and effort. Trade and Industry Office data
was received in hard copy, as was 2005 data from the RA Regency statistics office (DKP). Data
from the Sorong Regency office (DKP) was received in electronic format as was data from the
Quarantine office (Pos Karantina Ikan Sorong). For some species catch data was taken directly
from other literature sources (e.g, Venhema et al., 1997). The collated time series catch data is
presented in time series in Figs. A.3.1 and A.3.2.
Interpreting catch statistics
Assumptions must be made in order to translate imprecise and incomplete fishery statistics into
useable series for the EwE models. In some cases, data from Indonesian governmental sources
may contain statistical inaccuracies (Dudley and Harris, 1987) due to the complexity of catch
reporting in tropical reef-based fisheries, and common resource limitations in the fisheries
bureau. The problems reduce the challenge to guesswork in some cases of estimating catch for
an area like RA.
The species names recorded in the catch statistics varied slightly from year to year. Pelagic fish
that were consistently included are anchovy, scad, trevally, sardines, mackerels, Spanish
mackerels and tuna. Anchovy catch was allotted entirely to the adult anchovy EwE group.
Based on expert communications, the most important scad in terms of biomass and harvest value
in RA is the oxyeye scad (Selar boops) (Obed Lense, TNC-CTC. Jl Gunung Merapi No. 38,
Kampung Baru, Sorong, Papua, Indonesia 98413), which occurs in the large planktivore group.
All of the scad catch was therefore allotted to this group. There are nine trevally species in the
RA model, and they all occur in the large reef associated group; the adult stanza therefore
received 100% of the trevally catch.
Sardine catch was apportioned to the adult small
planktivore group. Mackerels and Spanish mackerel catch was attributed to the adult Mackerel
functional group. Tuna catch was divided between the adult skipjack tuna group (92%) and the
adult other tuna group (8%) in the same proportion as landings observed throughout Indonesia
(Venema, 1997). The ‘other’ component was divided evenly among small, medium and large
adult pelagic groups.
Demersal species reported in the catch statistics are: Leiognathids, threadfin bream, croakers,
hairtails, Polynemus spp., catfishes, Emperor bream, groupers, snappers and others.
Leiognathidae catch was allotted completely to the adult large reef associated functional group.
However, no Leiognathidae (ponyfish) appear in the Raja Ampat species list provided by
McKenna et al., (2002b). According to IFDG (2001), these are a common catch in the Arafura
Sea, indicating that the Sorong DKP statistics include landings from the Arafura Sea. Since the
RA model only includes a sliver of the Arafura Sea, the landing density could well be
overestimated for our geographic scope.
After having allocated DKP and Trade and Industry Office catch statistics into their most
relevant groups, there was a quantity left over representing ‘other’ unidentified species. This
quantity was divided between the functional groups that lacked explicit catch estimates, in the
proportion suggested by the total number of species in each group. We therefore assumed that
the catch of each species was equal, and that functional groups possessing many species, such as
butterflyfish and the aggregate reef-associated groups, should receive a larger relative fraction of
the undetermined catch component. The catch for ‘other’ reef associated fish was divided
54
between butterflyfish, macro-algal browsing fish, eroding grazers, detritivorous fish and the
aggregate groups: large, medium and small reef-associated fish.
There is a large amount of frozen catch recorded in the Trade and Industry Office statistics for
the years 2000-2002. On average, the total frozen quantity is 13% of the total recorded catch.
However, the frozen product is likely bycatch from shrimp trawl fisheries operating in the
Arafura Sea (C. Rotinsulu. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. Personal
communication). As this is outside of the study area, this amount was not included in the model.
Splitting catch between functional groups
Total catch was first determined for each functional group according to the methodology
described above. Catches were then divided into juvenile, sub-adult and adult stanzas using
ratios described in Section 2.5.11 - Functional group descriptions. Generally, juveniles are
assumed to comprise 10% of the total fisheries catch for all reef associated and demersal groups,
the remaining 90% is allotted to the adult and subadult stanzas.
For each age class and
functional group, the total calculated catch was divided among the 17 gear types in the model
according to ratios presented in Table 2.4. Each functional group was slotted into one of six
categories that define the principle gear types used to pursue it. Catches for each gear type
category are divided among EwE fisheries in a unique proportion. Interactions marked as
bycatch in Table 2.4 were assumed to catch half as much as directed landings. The final EwE
landings matrix, including catch and bycatch, is presented in Table A.3.4.
We include bycatch in the catch matrix, rather than the discard matrix, because we assume that it
is always sold. Discards are set as a small fraction of the total estimated catch for each EwE
fishery. Blast fishing is assumed to discard a quantity of hermatypic scleractinian corals equal to
1% of their catch weight; trolling with FAD is assumed to discard a weight of birds equal to 1%
of their catch; set lines are assumed to discard 1% of their catch weight in birds, green turtles and
oceanic turtles combined; shrimp trawl is assumed to discard 50% of its catch weight in small
demersals, deepwater fish, epifaunal detritivorous and carnivorous invertebrates.
55
Table 2.4 - Functional group catch distribution by gear type. Each functional group is assigned into one of six
gear type categories (SPEL, DIVING, etc.). Catch for each group is distributed among 17 EwE fisheries according
to unique ratios for each category. A.) Catch ratios used for each gear type category; tuna catch ratio is based on
Indonesian trends (Venema, 1997). B.) Functional groups pursued by fisheries; D = Directed catch; B = Bycatch.
Bycatch is assumed to catch half as much as directed catch. SKIP indicates all EwE fisheries catch an equal
proportion.
B.)
Gear type
DIVING
DIVING
DIVING
DIVING
DIVING
DIVING
DIVING
DIVING
DIVING
TUNA
TUNA
TUNA
SKIP
DEM
DEM
SKIP
SKIP
SKIP
SKIP
DEM
DEM
DEM
DEM
DEM
PEL
PEL
PEL
PEL
SPEL
SPEL
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
PEL
PEL
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
DEM
SKIP
SKIP
SKIP
SKIP
INVERT
INVERT
INVERT
INVERT
INVERT
INVERT
INVERT
INVERT
SKIP
INVERT
INVERT
#
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
68
73
74
75
76
77
78
79
80
82
83
84
85
86
87
Group Name
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juvenile coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Ad. rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef associated
Juv. large reef associated
Ad. medium reef associated
Juv. medium reef associated
Ad. small reef associated
Juv. small reef asociated
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Hermatypic corals
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn.inverts.
20%
19%
25%
19%
20%
15%
19%
15%
19%
15%
10%
20%
D
D
B
D
D
B
D
D
B
D
B
D
B
D
B
Shrimp trawl
Foreign fleet
Lift net
Set line
25%
10%
25%
D
D
D
D
D
D
D
D
D
D
D
D
B
D
B
8%
38%
D
D
B
D
D
B
D
D
B
12%
13%
D
D
B
D
D
D
D
D
B
Pole and line
with FAD
5%
5%
1%
5%
29%
D
D
B
D
D
B
Purse seine with
FAD
25%
Trolling with
FAD
25%
Blast fishing
Diving spear
and glean
Portable trap
Permanent trap
15%
10%
Diving air
supply cyanide
20%
10%
60%
Diving live fish
5%
19%
19%
Driftnet
Gear name
Small pelagic gears
Demersal gear & diving
Invertebrate gears
Demersal gears
Pelagic gears
Tuna gears
Shore gillnet
Gear type
SPEL
DIVING
INVERT
DEM
PEL
TUNA
Reef gleaning
A.)
Spear and
harpoon
EwE Fisheries
D
D
D
D
D
D
D
D
D
D
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
D
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
D
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
D
D
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
D
D
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
D
B
D
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
D
B
B
B
B
B
B
B
B
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
56
A small discarding of crocodiles was added to account for incidental capture or hunting; this
corresponds to about 20 animals per year from RA at 200 kg per animal.
Effort time series
DKP statistics included a limited fishing effort series for the years 1994-1999. In order to
produce a continuous effort trend suitable for Ecosim analysis, we have extrapolated fishing
effort back to the year 1990 using linear regression. Similarly, effort was estimated for the years
1999-2006 by assuming a constant annual rate of increase. The average rate of increase is based
on data for all available years; however, we limit the maximum effort increase at 5% per year in
the absence of better information.
Gear-effort
categories
identified
in
the
statistics are as follows: hand line (HL), gill net
(GN), lift bag net (LB), lift bag net in raft
(LBR), troll (TR), trammel net (TN), pole and
Table 2.5 - Gear effort assignments. See text for
explanation of effort series.
EwE gear type
Relevant DKP effort categories
Spear and harpoon
POP
Reef gleaning
POP
are: non-motorized (NM), wooden outboard
Shore gillnet
GN
(WO), wooden inboard (WI) and inboard
Driftnet
GN+TN
motorboats (IM). In order to produce a relative
Permanent trap
TW
Portable trap
FT
Diving spear
NM+WO
we assigned each EwE gear type to one or
Diving live fish
WI+WO
more appropriate gear-effort categories as
Diving cyanide
POP
Blast fishing
POP
line (PL), bottom long line (BL), tidal weir
(TW) and fish trap (FT). Boat-effort categories
effort series for each EwE fishing gear type,
listed in the statistics.
The assignments are
Trolling
TR
provided in Table 2.5. The effort of each EwE
Purse seine
WI+WO
gear type is assumed to follow these categories.
Pole and line
PL
Where EwE gear type effort follows more than
Set line
BL
Lift net
LB+LBR
Foreign fleet
IM
Shrimp trawl
WI+IM
one DKP effort category the effort series used
by EwE represents the average of the relevant
DKP categories. For certain gear types, the
57
effort increase from 1990 to 2006 was assumed to follow the population increase in Papua. The
average annual population increase is recorded as 3.22% per year by Badan Pusat Statistik (BPS)
Provinsi Papua for the years 1990-2000 (BPS, 2006).
This assumption was used for the
following artisanal fisheries: spear and harpoon, reef gleaning, diving with cyanide and blast
fishing.
To produce an effort series at the level of functional groups requires some basic assumptions
concerning the relative contribution made by each EwE gear type. The fishing effort exerted on
a particular group is assumed to equal the weighted average of recorded efforts for all gear type
that are catching it. Each gear type contributes to the weighted average in a proportion equal to
the relative amount of catch claimed by that gear type.
In order to determine a CPUE trend for functional groups, both for use in parameterizing
biomass values of the 1990 model and in fitting temporal dynamics, we simply divide the catch
of each functional group estimated from DKP statistics (Section 2.5.10 - Catch time series) by
the calculated effort series for each biomass pool. The resulting trends are provided in Fig.
A.6.2.
Prices
For commercial functional groups in the model, an export price is determined from Trade and
Industry Office statistics; these represent average ex-vessel prices for the years 2000-2004.
Export prices are determined for groupers, Napoleon wrasse, octopus. Prices for export product
are determined for a further 32 reef-associated functional groups based on generic price listings
in the Trade and Industry Office statistics for ‘mixed fish’. The price of all these groups is
assumed to equal by unit weight. Commodity prices for domestic sale were determined based on
1993-1994 information from the DKP (Sorong Regency Office). The value of products were
divided into local prices (i.e., vended in Sorong market) and prices received at island markets,
which are typically lower. These were averaged to produce an overall domestic price. Domestic
prices were calculated for groupers, snappers, tuna, shrimp, shark fins, sea cucumbers, mollusks,
squid, lobster and crabs. The prices of aggregate groups (i.e., large, medium and small reefassociated / demersal / planktivorous groups and others) are set based on the generic ‘mixed fish’
58
price entry. Prices in Venema (1997) were applied to tunas (export), crabs, jellyfish, seaweed
and corals. Since we had catch estimates for both export and local consumption, prices were set
for each commercial group as a realistic weighted average of export and domestic prices. Where
catch data was lacking, the price of groups were assumed to be an average of export and
domestic prices. Juvenile fish always received the local prices, as we assume that they were
unsuitable for export. The price of small pelagics was modelled after anchovy. Small pelagics
are assumed to be sold locally, as no export price was found. Market prices in the model are
presented in (Table A.3.5).
Unreported catch
Most of the catch information available to us originated in Sorong or nearby cities, but fisheries
catches occurring in smaller villages, especially those off the mainland, are subject to little or no
observation (C. Rotinsulu. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. Personal
communication). Therefore, the catch statistics presented in Fig. A.6.1 probably represent only a
fraction of total fisheries catch, considering the disperse and artisanal nature of reef fisheries, and
the minimal reporting infrastructure. Preliminary figures for unreported catch quantities have
therefore been entered as placeholders into the model to allow a more accurate representation of
energy flow in the system. As major reef predators are likely harvested in unreported fisheries,
the trophic implications of the missing catch could be major. Artisanal and unreported catch
estimates are now being developed by the CI socioeconomic analysis component of the BHS
EBM project (contact: A. Dohar, CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia) and the
UBC development options study (contact: R. Sumaila, UBC Fisheries Centre. 2202 Main Mall,
Vancouver BC. Canada).
59
2.5.11 Functional group descriptions
Mysticetae
The species of the cetacean suborder mysticetae occurring in RA were short-listed based on
Kahn (2001) and Kreb and Budiono (2005). The estimated proportions of global abundance for
the species found in FAO Area 71 were obtained from Kaschner (2004). However, the estimates
from her model are not meant to be applied to small geographic areas such as RA, and the
uncertainties involved are relatively high (K. Kaschner, Forschungs-und Technologiezentrum
Westküste, Hafentörn, 25761 Büsum, Germany, personal communication). These were not used
for the biomass estimates. Instead, the EE of the group was fixed at 0.025 and Ecopath was
allowed to estimate the biomass as 0.033 t·km-2.
The P/B is calculated as the average of r/2 (Schmitz and Lavigne, 1984), where r is the intrinsic
rate of growth, for Sei whale, Minke whale and Fin whale. P/B is estimated to be 0.0583 yr-1.
The r/2 method was also used as a measure for mammal P/B in Guénette (2005).
The average body weight of 6 baleen whale species is taken from Trites and Pauly (1998)
(Balanoptera musculus, Balanoptera borealis, Balanoptera edeni, Balanoptera acutorostrata,
Balanoptera physalis and Megaptera novaeangliae). To calculate Q/B, the feeding ration of is
determined using the relationship in Innes et al. (1987) as modified by Trites and Heise (1996);
Q/B is then averaged among species to obtain a value of 4.850 yr-1.
Piscivorous and deep-diving odontocetae
The species of odontocetae visiting the area were short-listed based on Kahn (2001) and Kreb
and Budiono (2005). The estimated proportion of the global abundance of the species that can be
found in FAO Area 71 were obtained from Kaschner (2004) but the uncertainties involved were
relatively high (K. Kaschner, Forschungs-und Technologiezentrum Westküste, Hafentörn, 25761
Büsum, Germany, personal communication), these were not used for the biomass estimates. The
EE of the group was fixed at 0.0025 and Ecopath was allowed to estimate the biomasses for
piscivorous and deep-diving odontocetae as 0.052 and 0.091 t·km-2, respectively.
60
The P/B for piscivorous odontocetae is calculated as the average of r/2 (Schmitz and Lavigne,
1984), where r is the intrinsic rate of growth, for Stenella longirostris, Tursiops truncates,
Stenella attenuate to be 0.0325 yr-1. The P/B for deep-diving odontocetae is calculated as the
average of r/2 for Physeter macrocephalus and Ziphius cavirostris (after Guénette, 2005) to be
0.02 yr-1.
The average weight of 13 piscivorous odontocetae species are taken from Trites and Pauly
(1998) and Noren and Williams (2000) (long nosed spinner dolphin, Stenella longirostris;
bottlenose dolphin, Tursiops truncates; pan-tropical spotted dolphin, Stenella attenuate; Fraser's
dolphin, Lagenodelphis hosei; Risso's dolphin, Grampus griseus; common dolphin, Delphinus
spp.; rough toothed dolphin, Steno bredanensis; Indo-Pacific humpbacked dolphin, Sousa
chinensis; Irrawady dolphin, Orcella brevirostris; melon headed whale, Peponocephala electra;
Pygmy killer whale, Feresa attenuate; dwarf sperm whale, Kogia simus; pygmy/dwarf sperm
whale, Kogia spp.,). That source also provides the body weights of 5 deep-diving odontocetae
species (sperm whale, Physeter macrocephalus; false killer whale, Pseudorca crassidens;
Cuvier's beaked whale, Ziphius cavirostris; short finned pilot whale, Globicephala macrorhynus
and orca, Orca orca). For both piscivorous and deep-diving odontocetae Q/B is based on the
feeding ration determined using the relation given by Innes et al. (1987) as modified by Trites
and Heise (1996); individual Q/B is averaged among species to obtain a Q/B value of 14.476 yr-1
and 8.531 yr-1 for piscivorous and deep-diving odontocetae, respectively. However, these values
produce a very low P/Q value ~0.001, and so consumption rates were ultimately reduced for both
groups, so that the EE value matched the one employed for Mysticetae. The resulting Q/B
values are 6.1 yr-1 and 3.6 yr-1, for piscivorous and deep-diving odontocetae, respectively.
Dugongs
The biomass of dugongs was calculated based on a population estimate in Torres Strait by Marsh
et al. (1997) (i.e., 24225 individuals in 30561 km-2 survey area). The mean size of an individual
is assumed to be 400kg based on the weight range reported to be between 250kg and 600kg by
Blanshard (2001). The biomass is thus estimated to be 0.317 t·km-2. This estimate is scaled to
the shelf area in RA, assuming that these animals occur on the shelf, to obtain the final biomass
61
estimate 0.054 t·km-2 used in the model. The maximum rate of increase in dugong population is
approximately 5% per annum (Marsh et al., 1997). The P/B is calculated to be equal to r/2 =
0.025 yr-1, where r is the intrinsic rate of growth.
The mean size of individual dugongs was reported to be between 250 kg and 600 kg. An
average value of 400 kg is used to calculate the ration based on the empirical relation given by
Innes et al. (1987). The Q/B was calculated to be 11.012 yr-1. Another estimate by Goto et al.
(2004) places consumption of captive dugongs at 14% of their body weight, before maturity, and
7% after maturity. This leads to Q/B estimates of 51.1 yr-1 and 25.6 yr-1 for dugong before and
after maturity. The values are considered to be too high in relation to the estimated production
rate, and so the lower alternative is used.
Birds
The biomass for the birds in the RA model is estimated to be 0.366 t·km-2. The value based on
the biomass of 11 species (black-naped tern, Sterna sumatrana; brown noddy, Anous stolidus;
bridled tern, Sterna anaethetus; crested tern, Sterna bergii; brown booby, Sula leucogaster; redfooted booby, Sula sula; great frigatebird, Fregata minor; white-tailed tropicbird, Phaethon
lepturus; red-tailed tropicbird, Phaethon rubricauda; sooty tern, Sterna fuscata; masked booby,
Sula dactylatra) from the Banda sea (Karpouzi, 2005). The extent of Banda Sea was obtained
from (Britannica, 2006). The estimated value is high compared to Opitz (1993), who used a
biomass density for seabirds of 0.015 t·km-2 for a Carribean reef.
P/B for Leach’s storm petrel (Oceanodroma leucorhoa) 0.381 yr-1 is used as P/B for the group in
the model based on Russel (1999). This value is low compared to the production rate for birds in
French Frigate Shoals by Polovina (1984) 5.4 yr-1; the same value was used for Carribean coral
reefs by Opitz (1993) and also Vidal and Basurto (2003) for Bahía de la Ascensión.
The Q/B was determined by first calculating the ration using the empirical formula given by
Nilsson and Nilsson (1976) win Wada (1996), and then averaging the values for 11 species (i.e.,
the same species that were used to calculate biomass). A weighted average was used based on
62
relative biomass of each species to obtain the group Q/B, which is equal to 63.95 yr-1. This high
value is comparable to Polovina’s (1984) estimate for Hawaiian reefs of 80 yr-1.
Reef-associated, Green and Oceanic turtles
The turtles are grouped into three functional groups based on their habitat and feeding habits:
Reef associated (hawksbill turtle, Eretmochelys imbricate; loggerhead turtle, Caretta caretta);
green turtle (Chelonia mydas) and oceanic turtles (leatherback turtle, Dermochelys coriacae;
olive ridley, Lepidochelys olivacae; flatback turtle, Natator depressus).
The total biomass of turtles is approximately 0.02 t·km-2 (Alias, 2003), this was scaled in a ratio
(1:2:2) for reef associated, green turtles and oceanic turtles. Mast and Hutchinson (2005)
estimated the leatherback population to be about 650 nesting females in the BHS. Studies of sea
turtle nesting site at Jamursba Medi Beach in Raja Ampat estimated 2983 Leatherback nests, 171
green turtle nests, 13 Hawksbill nests and 77 Olive Ridley nests (Putrawidjaja, 1997). These
estimates could be used for partitioning the biomass estimate into the three functional groups,
however at present, the ratio was maintained at (1:2:2) until better estimates becomes available
from additional sites and nesting seasons. Biomass values are therefore 0.004, 0.008 and 0.008
t·km-2 for reef associated, green and oceanic turtles, respectively. The latter two groups were
overfished in the initial model from the effects of set line discarding, and so a biomass
accumulation rate was allowed of -0.02 yr-1.
The survival of loggerhead turtle was estimated as 0.8613 yr-1 by Chaloupka and Limpus (2002).
The P/B is calculated using the relation (M = -ln S) to be 0.1493 yr-1. The survival estimate of
green turtle, 0.984 yr-1 is obtained from Mortimer et al., (2000) and P/B is calculated, using the
same method, as 0.053 yr-1. Opitz (1993) used a higher production rate for marine turtles on
Caribbean reefs, 0.2 yr-1. The P/B estimate for green turtles is used for oceanic turtles in the
absence of better estimates. Survivorship estimates were obtained for adult female turtles. The
values were not used in the calculation of P/B, but they are informative about the proportion of
hatchlings that reach the adult stage: 0.93 yr-1 for flatback (Parameter and Limpus, 1995); 0.61
63
yr-1 for green turtle (Bjorndal, 1980,); 0.43 yr-1 for Kemp’s ridley (Marquez et al., 1982b); 0.48
yr-1 for olive ridley (Marquez et al., 1982a) and 0.81 yr-1 for loggerhead (Frazer, 1983).
A Q/B value of 3.5 yr-1 was used for all the turtle groups; the value taken from a trophic model
for the coastal ecosystem of the West Coast of Penisular Malaysia (Alias, 2003).
Crocodiles
The biomass of crocodiles is estimated to be 5.75E-3 t·km-2 based on population estimate of 55
animals (Kushlan 1980) and individual weight of 230 kg (Pritchard, 1978) in Florida Bay; the
area is assumed to be about 2200 km-2 (Healy, 1996). However, the value is uncertain. Due to
diet matrix conflicts, Ecopath was ultimately allowed to estimate crocodile biomass as 1.33E-3
t·km-2. The estimate of P/B (0.408 yr-1) is based on Davis and Odgen (1994); the estimate of
Q/B (6.5 yr-1) is based on estimates for American crocodile, Crocodylus acutus, from Day et al.
(1990).
In the RA Ecospace model, crocodiles are restricted to shallow water habitat (<10 m). This
habitat type implicitly represents marine and brackish environments. Crocodiles are limited to
these areas using dispersal parameters that are strictly prohibitive to movement. In the small
scale models for Kofiau and SW Misool estuaries are entered as an explicit habitat type;
crocodiles are restricted to these regions.
There is no directed catch entered in Ecopath for crocodiles, but there is a small amount of
discarding, 0.0001 t·km-2. This is about 4.5 tonnes for all of RA, or about 20 animals per year at
200 kg per animal. Although we expect very little crocodile catch from the study area, it is
known to occur. A large male specimen was killed by villagers on Kofiau Island in February of
2006 (C. Ainsworth, UBC Fisheries Centre. 2202 Main Mall. Vancouver, BC. Canada Personal
observation). For safety reasons, the villagers attempt to kill every crocodile they encounter,
according to their accounts. We therefore entered this as a discard in Ecopath, so no monetary
catch value will be recorded.
64
Groupers
Groupers are divided into three functional groups representing life history stages: adult, subadult
and juveniles. These groups incorporate information from 46 species and 16 genera of family
Serranidae (Table A.1.1). Grouper biomass is calculated from COREMAP (2005) abundance
counts, adjusted for the relative reef area in RA using the reef area to marine area ratio for all of
Indonesia (Spalding et al., 2001). Abundance counts are converted to biomass using an average
individual weight obtained from an age-structured model (see Section 2.5.8 - Biomass density
estimates). Biomass density is estimated to be 0.257 t·km-2. This amount is split by Ecopath
among the three life history stages according to the mortality schedule in Table A.3.3.
Ontogenetic parameters used by the multi-stanza routine represent species-level averages for RA
species determined with FB maturity data. Biomass accumulation rate is set at -2% per year.
The COREMAP (2005) abundance counts suggested a high biomass density in sites near Weigeo
Island, 0.256 t·km-2. This value has been scaled to represent the average biomass density in RA,
according to the relative marine area to reef area ratio in Spalding et al. (2001). The adult, subadult and juvenile stanzas receive 72%, 22% and 6% of the biomass respectively by employing
the multi-stanza parameter estimates (Table A.3.3). When similarly scaled for reef area in RA,
Wolanski’s (2001) estimate of “Large groupers” biomass is 0.035 t·km-2; Kongchai et al., (2003)
estimated only 0.0025 t·km-2 for the Gulf of Thailand. Allen et al., (2005) provided grouper
densities for East Andaman Sea of approximately 0.032 t·km-2 (this value was converted to
weight using length-weight parameters for RA serranids).
The P/B of adult groupers was set at 0.225 yr-1 after 5 RA species of genus Epinephelus
(Grandcourt, 2005). Subadult and juvenile groupers was set at 0.4 and 1.2 yr-1, respectively to
provide a realistic age distribution as quantified by Ecopath’s multi-stanza routine. Opitz (1993)
used a production value for large groupers of 0.37 yr-1.
The Q/B of adult groupers was
determined to be 9.086 yr-1 using the empirical regression of Pauly (1986) based on the average
of 41 grouper species out of 46. Q/B of subadult and juvenile groups is estimated by Ecopath as
13.224 and 26.908 yr-1, respectively.
65
Groupers are pursued by all three diving gear types in the model, as well as blast fishing, spear
and harpoon and permanent traps. Cyanide fishing, which supplies premium live fish to the
Hong Kong market, has also resulted in the loss of valuable reef-associated species like
Napoleon wrasse (Cheilinus undulatus) and giant grouper (Epinephelus lanceolatus) due to
overexploitation (Erdmann and Pet-Soede, 1996; Mous et al., 2000). Total catch was estimated
for this group based on DKP statistics as 0.022 t·km-2, or approximately 990 tonnes annually for
all of Raja Ampat. 50% of the total grouper catch was allotted to the adult functional group;
40% was attributed to sub-adults and 10% to juveniles. Adult groupers were lightly exploited in
the initial RA model, and catch was increased to represent the impact of unreported catches
occurring in this group. Assuming that grouper catches reported in Sorong constitute 40% of the
entire RA catch provides the following fishery indicators from Ecosim’s equilibrium analysis:
FMSY estimate of 0.21 yr-1, F2006 of 0.094 yr-1 and MSY of 0.027 t·km-2. This MSY value is an
average for all of RA, but when corrected to represent only the reef area this value equates to
roughly 1.35 t·km-2. This amount compares well with the ‘typical’ grouper MSY estimate
offered by Jennings and Polunin (1995) of 1 t·km-2. We assume that there is no discarding of this
valuable species group.
Snappers
Snappers are divided into three functional groups representing life history stages: adult, subadult
and juveniles. These groups incorporate information from 32 species and 9 genera of family
Lutjanidae (Table A.1.1).
The biomass for snappers was determined from COREMAP (2005) abundance counts.
Abundance counts are converted to biomass using an average individual weight obtained from an
age-structured model (see Section 2.5.8 - Biomass density estimates).
Biomass density is
estimated to be 0.152 t·km-2. This amount is split among the three life history stages by Ecopath
according to the mortality schedule in Table A.3.3, with adults, sub-adults and juveniles stanzas
receiving 53%, 27% and 20% respectively. Ontogenetic parameters used by the multi-stanza
routine represent species-level averages for RA species determined with FB maturity data.
Biomass accumulation rate is set at -10% per year.
66
The P/B rate of adult snappers is set at 0.4 yr-1. This represents the average M of 17 species of
family Lutjanidae from independent sampling studies, 0.3 yr-1 (Marcano, et al, 1996; Burton,
2001; Burton, 2002; Newman et al., 1996; Newman, 2002; Newman et al., 2000; Kamukuru et
al., 2005, Wilde and Sawynok 2004), but the value has been increased by one third to account
for fishing mortality. This value is not too different from the one used to represent snappers in
EwE models by Vidal and Basurto (2003) and Arreguín-Sánchez et al. (1993); their value is 0.49
yr-1. They did not use age stanzas, and so their value implicitly includes younger age classes and
should be higher. The sub-adult production rate was set higher relative to adults at 1.1 yr-1, while
the juvenile production rate was set at 1.47 yr-1. These production rates reflect the M estimate
for 18 RA snapper species based on the empirical equation of Pauly (1980); but the values have
been increased by 50% and 100% respectively to represent additional predation mortality
incurred by the immature stanzas (as well as any fishing mortality). These rates generate a
realistic age-biomass distribution under the species-specific growth and mortality values
obtained from FB, in which the majority of biomass is concentrated in the adult and sub-adult
stanzas. The consumption rate of adult snappers, 7.105 yr-1 is based on the empirical equation of
Pauly (1986); this uses species-specific information for 29 species of RA snappers out of 32, and
represents an average species value. It is slightly higher than the consumption rate used to model
snappers in the Mexican Caribbean, 5.6 yr-1 by Vidal and Basurto (2003).
Snapper catch is estimated from DKP and Trade and Industry Office statistics as 0.031 t·km-2.
This represents average catches between 2000-2005. 45% of the total snapper catch was allotted
to the adult functional group; 45% was attributed to sub-adults and 10% to juveniles. This catch
quantity includes an estimate of unreported artisanal catch equal to 50% of the reported value.
Snappers are represented in the RA model as fully exploited, with an F2006 of 0.15 yr-1, which is
close to the FMSY (0.21 yr-1). MSY is predicted to be 8.4 kg·km-2 for RA, or about 0.479 t·km-2
on coral reefs.
67
Napoleon wrasse
This functional group represents only Napoleon wrasse (Cheilinus undulatus), which is a
conspicuously large growing reef fish species in family Labridae. It is also commonly referred to
as humphead wrasse or double-headed Maori wrasse among other names (Allen, 2000). The
functional group is divided into adult, subadult and juvenile stanzas.
A biomass value for this species could not be calculated based on the reef transects in
COREMAP (2005) because Cheilinus is only reported to the genus level (four other Cheilnus are
also present in the model in the medium and large reef associated groups). However, Donaldson
and Sadovy (2001) suggested that Napoleon wrasse is uncommon wherever it occurs, and
Russell (2004) suggested a typical density of 10 fish per hectare in reef environments and a
maximum density of 20 fish per hectare. Since there is a heavy fishery on Napoleon wrasse in
RA, we assume that the standing biomass should fall toward the lower end of that possible range.
10 fish per hectare equates to 2 t·km-2 on reefs; and when corrected for reef area a possible
overall biomass density in RA is determined as 0.035 t·km-2. This amount was split into adult
(33%), subadult (57%) and juvenile groups (10%) using the mortality schedule in Table A.3.3.
The P/B of adult Napoleon wrasse is set at 0.5 yr-1. It is based on the M regression formula of
Pauly (1980), but the M value (0.25 yr-1) was then doubled to estimate P/B and account for
fishing mortality. A similar P/B value was used for sub-adults, but juveniles were set higher at
1.2 yr-1 to represent additional predation mortality suffered by the immature stanzas. Sampling
data for C. undulatus suggests that the natural mortality rate may be lower, 0.11 yr-1 (Eckert,
1987). However, the contribution of fishing mortality to total mortality is in question, and we
have therefore made a precautionary assumption that F is at least equal to M. Q/B rate for adults
is set at 8.9 yr-1, and the rates for immature stanzas were calculated according to the mortality
schedule in place. A consumption rate could not be found for C. undulatus, and so this value
was designed to represent a slightly lower consumption rate than of groupers. This is appropriate
since Napoleon wrasse is among the largest reef-associated fish species, therefore consuming
less per unit body mass.
68
This species is subject to a live reef food fishery supplying high value export product (Mous et
al., 2000). The export of Napoleon wrasse is regulated by CITES Appendix II, of which
Indonesia is a signatory. The fishery in RA is conducted primarily by surface air supplied divers
who may use cyanide to stun the fish, and it is also pursued by reef bombing operations (Andreas
Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia
98413. Personal communication). Catch of Napoleon wrasse is estimated from DKP and Trade
and Industry Office statistics. It represents an average of the years 2000-2005. The value was
doubled from the official sources to represent unreported artisanal catch. However, the total
catch estimate remains small at only 2.07 kg·km-2. It is divided between age stanzas: 45% was
attributed to the adult functional group, 45% to sub-adults and 10% to juveniles. With this small
amount of catch, Ecosim predicts that F2006 equals 0.085 yr-1, which is short of FMSY (0.23 yr-1).
MSY in RA is predicted to be 1.8 kg·km-2 for adults, and approximately 5.3 kg·km-2 for adults
and sub-adults together. This equates to 0.302 t·km-2 on coral reefs.
Skipjack tuna
This group represents only Skipjack tuna (Katsuwonus pelamis). They were allotted their own
functional group because they are heavily exploited in eastern Indonesia and constitute a major
commercial resource. The species also exhibits faster growth and mortality rates than other
major tuna stocks in the area, (e.g., yellowfin and bigeye tuna: Thunnus albacares and T.
obesus), which are incorporated in the other tuna functional group.
Between the years 1998-2001, biomass of the western and central Pacific Ocean stock was
thought to be at the highest levels in 30 years thanks to an upward shift in recruitment rates
occurring during the mid-1980s (Langley et al., 2003), and El Ninõ events in the 1990s may
have benefited Skipjack tuna recruitment as well (SCTB, 2004). Skipjack biomass is now
thought to lie above the level that produces MSY (BMSY) (SCTB, 2004). Our RA model predicts
that the values are close. We have allowed Ecopath to estimate Skipjack tuna biomass as 0.699
t·km-2, while BMSY is predicted to be 0.765 t·km-2 by the equilibrium analysis. There is no
biomass accumulation entered.
69
A range of values are reported in the literature for skipjack tuna mortality rates, a summary is
provided by Wild and Hampton (1994). Those authors cite Bayliff (1977) who suggests an
upper limit, 6.48 yr-1, while the inter-American Tropical Tuna Commission assumes a lower
mortality for management, between 1.39 and 2.30 yr-1 (IATTC, 1989).
We assumed an
intermediate value for total mortality, 2 yr-1, which is applied as the P/B value for skipjack. This
estimate is similar to one derived from Pauly’s (1980) empirical M formula. When applied, the
estimate of M, 0.99 yr-1, can be doubled to represent a fully exploited stock, where M=F. The
resulting P/B is 1.99 yr-1.
Skipjack tuna received a high Q/B value of 32.57 yr-1 from Pauly (1989), and we do expect a
high consumption rate for fishes with high-performance physiology like tunas and billfish due to
elevated metabolism rates that facilitate their pelagic-hunter niche (Magnuson, 1969; Brill,
1996). However, Pauly’s (1989) value is very high compared to our aggregate group for large
pelagics (5.644 yr-1), indicating that skipjack are voracious predators. The stock evaluated by
Pauly’s (1989) in fact represents a Pacific stock at a lower temperature (24 oC) than Raja Ampat
(28 oC), and so the consumption rate in RA may be higher still. However, we have chosen to use
a lower value, 6.64 yr-1, so that production over consumption (P/Q) ratio approximate equals 0.3.
This is a rule-of-thumb applicable to a fast growing pelagic species. As a highly migratory
pelagic species, we have assumed a large amount of diet import in the models (85%) and we
have applied a low EE (0.42). This represents the high rate of mortality caused by fisheries and
predation elsewhere in the Pacific, external to the model.
The stock of western and central Pacific Ocean skipjack tuna is thought to be exploited at a
modest level relative to its biological potential (Langley et al., 2003). We have calculated a
catch value of 0.347 t·km-2 based on DKP and Trade and Industry Office catch statistics - this
represents an average of the years 2000-2005. The catch record is relatively well documented
for skipjack tuna, and so we assume zero unreported catch. We also assume zero discards for
this group. The equilibrium analysis provides the following fishery indicators: F2006 = 0.548 yr-1,
FMSY = 0.479 yr-1, MSY = 0.366 t·km-2, predicted MSY for RA is about 16,400 tonnes. The
stock is assumed for management purposes to be contiguous throughout the eastern and central
Pacific (Wild and Hampton, 1994).
Therefore, fishery catches elsewhere will affect the
70
abundance of animals occurring in RA. This limits our ability to predict stock dynamics for this
group (see Martell, 2004 for a discussion on modelling migratory species in EwE).
Other tuna
Other tuna includes 10 species of scombrids: wahoo (Acanthocybium solandri), bullet tuna
(Auxis rochei rochei), frigate tuna (A. thazard thazard), Kawakawa (Euthynnus affnis), dogtooth
tuna (Gymnosarda unicolor), albacore tuna (Thunnus alalunga), yellowfin tuna (T. albacares),
bigeye tuna (T. obesus), Pacific bluefin tuna (T. orientalis) and longtail tuna (T. tonggol).
The current biomass of bigeye tuna is thought to lie above the MSY level (SCTB, 2004). The
biomass of albacore in the south Pacific may be (as of 2004) at approximately 60% of B0, while
the biomass of yellowfin in the western central Pacific Ocean may be 65-80% of B0 (SCTB,
2004). Our biomass estimate of 0.604 t·km-2 was calculated by Ecopath by assuming a low EE
of 0.4 for this migratory group. That biomass is approximately 88% of the B0 predicted by the
equilibrium analysis1. A biomass accumulation rate of -5% per year is included.
The production rate P/B (1.408 yr-1) is set according to Pauly’s (1980) empirical formula for M,
which is calculated at the species level and doubled to represent the contribution of F. P/B
values were averaged for 8 species to provide an estimate for this group. The value compares
well with M estimates for T. albacares and T. obesus obtained from (Hampton, 2000), which
average out to 1.2 yr-1, once doubled to account for fishing mortality. The Q/B value for other
tuna was estimated using species-specific parameters based on 9 RA species and applying the
empirical equation of Pauly (1986). The original estimate 5.587 yr-1 was reduced to 4.693 yr-1,
so that P/Q equals 0.3.
In RA, the fishery for tuna is primarily conducted by the pole and line fleet. Catch of other tuna
is represented from DKP and Trade and Industry Office statistics. It was estimated to be very
1
The equilibrium analysis presented in Appendix B shows a lower B0 for ‘other tuna’, 0.472 t·km-2, because it does
not consider trophic interactions. These increase the potential surplus production.
71
low from government statistics, 0.0263 t·km-2 - this represents an average of the years 20002005. We increased this amount by 80% to account for unreported catch and represent a fully
exploited stock. We did not include any additional discards. This results in the following fishery
indicators: F2006 = 0.746 yr-1, FMSY = 0.746 yr-1, MSY = 0.058 t·km-2. Predicted MSY for the
whole of RA is therefore predicted to be about 2,610 tonnes.
Mackerel
The Mackerel group contains 9 species of scombrids identified in McKenna et al. (2002b) or
reported as being present in the area by FB records. The species included are Double-lined
mackerel (Grammatorcynus bilineatus), Short mackerel (Rastrelliger brachysoma), Island
mackerel (R. faughni), Indian mackerel (R. kanagurta), Blue mackerel (Scomber australasicus),
Narrow-barred Spanish mackerel (Scomberomorus commerson), Australian spotted mackerel (S.
munroi), Queensland school mackerel (S. queenslandicus) and Broadbarred king mackerel (S.
semifasciatus).
Biomass of mackerel, 0.086 t·km-2, is based on an estimate obtained from the relative abundance
rankings of McKenna et al., (2002b) for RA. The species-level abundance rankings were
converted to absolute biomass by applying weighting factors. Weighting factors were calculated
based on common species found in both the McKenna et al. (2002b) species list and the
COREMAP (2005) biomass transects (see Section 2.5.8 - Biomass density estimates). No
biomass accumulation rate is entered for this group.
The P/B rate for mackerels was set according to the empirical formula for M of Pauly (1980),
based on 9 mackerel species and using species-specific growth parameters available from FB.
The M morality rate was doubled to represent the contribution of F, so that P/B is set at 2.913 yr1
. Species-level P/B values were averaged to provide an estimate for this group. Independent
mortality estimates from sampling could only be found for one RA mackerel species,
Scomberomorus commerson, at 0.59 yr-1 (McIlwain, 2005). This is a low value, even when
increased to account for fishing mortality, and it was not used for the group average. Buchary
(1999) used a higher P/B rate for Rastrelliger spp., 4.248 yr-1. Q/B was set at 9.712 yr-1 so that
the gross efficiency (P/Q) ratio equals 0.3.
The Q/B formula of Pauly (1986) suggested a
72
slightly lower rate, 8.593 yr-1, based on 10 mackerel species. Buchary (1999) maintained a
similar P/Q ratio (3.3) as in the present model.
Catch of mackerels was estimated based on DKP and Trade and Industry Office statistics (0.064
t·km-2); this represent average RA catches between the years 2000-2005. We assume there is
zero unreported catch in this group. Under these assumptions, the equilibrium analysis suggests
that the group is now fully exploited: F2006 (0.746 yr-1) lies very close to FMSY (0.746 yr-1), while
current catches are slightly above MSY (0.058 t·km-2).
Billfish
The billfish functional group includes highly migratory sailfish and billfish species: Indo-Pacific
sailfish (Istiophorus platypterus), black marlin (Makaira indica), Indo-Pacific blue marlin
(Makaira mazara), shortbill spearfish (Tetrapturus angustirostris), striped marlin (Tetrapturus
audax) and swordfish (Xiphias gladius).
The biomass of billfish was estimated by Ecopath as 0.825 t·km-2 based on an assumed EE of
0.2. This low EE value was used to represent a highly migratory species, where a large fraction
of natural mortality (80%) occurs outside the modelled system. A significant diet import term
(approx. 88% of diet) was also included to represent feeding that occurs outside of RA.
The P/B rate of billfish (0.956 yr-1) was set at the species level according to the empirical M
formula of Pauly (1980), which was doubled to represent the contribution of F. This value
represents the average of 4 billfish species. Q/B was set so that the P/Q ratio is equal to 0.3.
This assumption results in a Q/B value of 3.187 yr-1, which is similar to the estimate derived
from the consumption rate formula of Pauly (1986), 3.256 yr-1 for 5 species of RA billfish.
There was no data available on billfish landings in the governmental fisheries statistics, and so
we assume a small catch for billfish occurring in RA from trolling operations, including
recreational fisheries. A catch of 0.05 t·km-2 in the RA model (~5% of standing biomass)
corresponds to an F2006 of 0.06 yr-1, or about 40% of FMSY (0.148 yr-1) representing a lightly
73
exploited stock. MSY is estimated by the equilibrium analysis to be approximately 0.068 t·km-2,
equivalent to almost 3,100 tonnes for RA. Billfish biomass is depleted in the present-day RA
model to approximately 75% of the pristine level (B0).
Coral trout
This functional group encompasses six species that are commonly referred to as coral trout: coral
hind (Cephalopholis miniata), leopard coralgrouper (Plectropomus leopardus), blacksaddeled
coralgrouper (P. laevis), spotted coralgrouper (P. maculates), highfin coralgrouper (P.
oligocanthus) and squaretail coralgrouper (P. areolatus).
Coral trout biomass is based on reef transects conducted on Weigeo Island (COREMAP, 2005).
It is calculated to be 0.040 t·km-2, with about 93% of the biomass occurring in the adult group
and the remainder in the juvenile group as determined by the multi-stanza routine using mortality
parameters in Table A.3.3. A biomass accumulation rate of -0.07 yr-1 was entered to adjust the
surplus production potential so that current (2006) fishing mortality lies close to FMSY,
representing a fully exploited stock.
The P/B rate of coral trout is set at 0.35 yr-1 for adults and 0.7 yr-1 for juveniles. The adult value
is based on P. leopardus (ages 6-8) and P. laevis; it is an average of natural mortalities from
sampling (Russ et al., 1998), and it has been increased by 50% to account for fishing mortality.
The juvenile production rate is based on a high value for total mortality (Z) found in the
literature for P. maculatus (Ferrira and Russ, 1992), but it has been increased by 25% to account
for additional predation mortality incurred by juvenile stanzas. The M predicted by Pauly’s
(1980) formula is 0.5 yr-1 for two RA coral trout species, which falls between the values used for
our life history stanzas. Similarly, Gribble (2001) used 0.35 yr-1 for coral trout on the Great
Barrier Reef, which lies between the adult and juvenile estimates. The parameters in use
generate a realistic age-biomass distribution under assumed maturity parameters. Coral trout
Q/B was estimated from Pauly’s (1986) empirical formula as 6.1 yr-1 and is based on 6 species.
This amount was ultimately decreased to 3.3 yr-1 for the adult group during balancing in order to
more accurately reflect the consumption rates of physiologically comparable groups, such as
74
large reef associated fish. The consumption rate for the juvenile stanza was estimated by the
multi-stanza routine to be 8.393 yr-1 based on the adult rate and the given mortality schedule.
Catch of coral trout was estimated directly from DKP and Trade and Industry Office statistics at
about 1.8 kg·km-2, which falls just below MSY indicating a fully exploited stock. If there are
significant sources of unreported catch for coral trout, then this functional group may actually be
overexploited. The catch of coral trout is based on the ‘other’ reef fish catch category listed in
DKP and Trade and Industry Office statistics. That quantity was divided among reef-associated
functional groups whose catch was not quantified explicitly by other catch statistic categories.
The ‘other’ catch was divided between reef associated groups according to their relative number
of species, assuming that the more specious groups contribute a greater fraction to the
undetermined catch. In the preliminary models we assumed zero discarding. Equilibrium
statistics are as follows: F2006 = 0.045 yr-1, FMSY = 0.092 yr-1, MSY = 1.9 kg·km-2, or about 85.5
tonnes total catch for RA. This is equivalent to 0.108 t·km-2 on coral reefs.
Large and small sharks
Large sharks include the grey reef shark (Charcharhinus amblyrhynchos), pondicherry shark (C.
hemiodon), blacktip reef shark (C. melanopterus), blue shark (Prionace glauca), whitetip reef
shark (Triaenodon obesus) and tawny nurse shark (Nebrius ferrugineus). Small sharks include
the
graceful
shark
(Carcharhinus
amblyrhynchoides),
Australian
sharpnose
shark
(Rhizoprionodon taylori), smallfin gulper shark (Centrophorus moluccensis), Indonesian
speckled carpetshark (Hemiscyllium freycineti) and tasseled wobbegong (Eucrossorhinus
dasypogon).
The biomass of large sharks is estimated to be approximately 0.115 t·km-2 in the RA model based
on the subjective species-level abundance ratings provided by McKenna et al. (2002b); the
biomass of small sharks is estimated to be 0.057 t·km-2.
Biomass weighting factors were
assigned to each qualitative abundance rating offered by McKenna et al. (2002b) (e.g., rare,
occasional, common) based on quantitative values provided by COREMAP (2005) for certain
species that were common to both lists. A biomass density is extrapolated for species missing
75
from the COREMAP list according to the subjective biomass rating in McKenna et al. (2002b),
and the biomass of the functional groups large and small sharks are calculated as the sum of the
biomasses of constituent species. Group biomass is divided between the adult and juvenile
stanzas for large sharks (56% and 44%, respectively) and small sharks (14% and 86%,
respectively) according to the mortality schedule in Table A.3.3.
An EE of 0.5 was entered into adult large sharks to represent a migratory group that moves (and
dies) outside the model boundaries. With biomass, production rate, consumption rate and EE
entered as input parameters, Ecopath was able to estimate the biomass accumulation rate to be
9% yr-1. Small sharks were assumed to have more restricted ranges, and their EE is estimated by
Ecopath to reflect a more sedentary nature (>0.95). Since the major depletion of large sharks
probably occurred before 1980 in RA, the group is not of major trophodynamic consequence in
the present-day model. Nevertheless, these top predators have the potential to fulfill a keystone
functional role, and so it is important that their dynamics are accurately represented in the model.
Forecasting scenarios will also require an accurate account in order to represent conservation
interests. By design, the biomass of large sharks in the 2006 RA model represents approximately
10% of the virgin biomass (B0), as determined using the equilibrium routine. This should
provide a realistic scope for growth in restoration studies.
The P/B of adult large sharks was estimated based on Pauly (1980). His empirical formula
predicts an M equal to 0.64 yr-1, which we can increase by 50% to represent fishing mortality
(0.967 yr-1). This value, based on 2 species, was used in the initial model, but it was subsequently
increased to 1.1 yr-1 during the process of balancing. Juvenile sharks were set slightly higher, 1.3
yr-1. Polovina (1984) used a lower value for his ‘reef sharks’ group in the French Frigate Shoals,
0.18 yr-1 and Opitz (1993) used 0.24 yr-1 for her ‘large sharks/rays’ category in the Caribbean.
However, our higher P/B value is appropriate for a heavily exploited stock. Consumption rate
for the adult stanza was set at 3.6 yr-1 in order to initialize the gross efficiency P/Q ratio at 0.3.
The juvenile Q/B was estimated based on the mortality schedule in Table A.3.3. The production
and consumption rates of small sharks were set relatively higher than large sharks, at 1.2 yr-1 and
4 yr-1, respectively for adult stanzas; juvenile small shark consumption rates were estimated by
the multi-stanza routine.
76
Elasmobranchs are an important marine resource for many artisanal fishers in RA. In fact,
Indonesia has among the highest landings of chondrichthyans in the world (Stevens et al., 2000),
yet the significant catch goes largely unregulated. Although the gross catch of sharks is typically
small compared to other oceanic resources such as teleosts (FAO 2005), fishing can have a major
impact on the species group considering the slow growth rates of large sharks, in particular, and
the low fecundity of viviparous species. Trolling fisheries in RA pursue large sharks for their
high value fins, and these animals are also likely to be taken as bycatch in pelagic fishing
operations, although no records were found. Catch of large sharks is estimated from DKP and
Trade and Industry Office statistics to be 0.028 t·km-2, of which 90% is assumed to originate
from the adult stanza. 78% of the catch is exported, according DKP statistics. This value
represents an average of the years 2000-2005. There is also a minor bycatch of large sharks
entered for the trolling fleet (0.001 t·km-2). The catch statistics available from the government
bureaus referred to ‘shark fins’. We assumed that this catch was entirely attributable to the large
sharks functional group, and we back-calculated the total amount of shark biomass required to
provide that quantity of shark fins. Under the assumed conversion ratio, 1 tonne of fins equates
to roughly 24.1 tonnes of sharks. This rough estimate is based on a remark made for Gulf of
Mexico fisheries (P. Ortiz, National Oceanic and Atmospheric Administration, cited in Raloff,
2002).
Despite the positive biomass accumulation rate estimated by Ecopath in 2006, the large shark
group stands as overexploited in the model, with F2006 (0.97 yr-1) well in excess of FMSY (0.48 yr1
). The MSY of large sharks is estimated to be only 0.01 t·km-2, or 470 tonnes for the whole area
of RA. It is less than half of the current estimated catch. Only a miniscule catch was estimated
for small sharks from governmental fisheries statistics, although this value is highly uncertain. A
revised catch figure was therefore entered for small sharks that would represent a lightly
exploited stock, where F2006 is approximately equal to 0.25 FMSY. The catch of small sharks is
set at 6.24 kg·km-2 in the preliminary RA model. We assume that small shark species constitute
50% of the domestic catch, while exported catch consists entirely of large sharks.
77
Whale shark
This group represents the planktivorous whale shark, Rhincodon typus. Little is known about the
abundance of this animal in RA or the health of the stock. However, sightings recorded by an
ecotourism company operating in the Andaman Sea out of Phuket, Thailand suggests that whale
shark abundance may have dropped by as much as 96% between 1998 and 2001 (Theberge and
Dearden, 2006), although there are a variety of possible explanations. Biomass was estimated by
Ecopath to be 3.2 kg·km-2, providing an estimate of 143 tonnes of whale sharks in RA. This
suggests that there could be very few of these animals in the study area, especially considering
that their maximum size may be as large as 36 tonnes per animal (Ritter, 2000), although WMAX
is frequently cited as less than 20 tonnes per animal.
The P/B rate for whale shark was entered in very approximately as 0.068 yr-1, based on the
empirical relationship for M offered by Pauly (1980), and assuming zero fishing mortality. Q/B
was set at 0.228 yr-1 to establish a P/Q ratio of 0.3. The value used for Q/B is preliminary. It
could be low considering the empirical formula of Pauly (1986) provides a much higher Q/B
estimate, 2.022 yr-1.
We have applied a very low EE of 0.025, but it is not clearly known the extent to which these
animals migrate (Wilson et al., 2005; Colman, 1997). Similarly, an 80% diet import term was
applied to represent the potentially wide-ranging habits of these individuals. There is a fishery
operating for these animals throughout the world in countries such as Indonesia, India,
Philippines, Pakistan, Iraq and other places (Theberge and Dearden, 2006; Colman, 1997 and
references therein). Catches in Philippines are thought to have declined in recent years, although
the cause is unsure (Colman, 1997). We have entered in a zero catch rate for whale shark and
zero discards, pending better information.
Manta ray and Rays
The manta ray group includes the giant manta (Manta birostris). The ray group, which is
divided into adult and juvenile stanzas, represents 7 species of families Dasyatididae, Mobulidae
and Myliobatidae: the bluespotted stingray (Dasyatis kuhlii), Bluespotted ribbontail ray
(Taeniura lymma), Chilean devil ray (Mobula tarapacana), spotted eagle ray (Aetobatus
78
narinari), painted maskray (Dasyatis leylandi), blackspotted whipray (Himantura toshi) and
pygmy devilray (Mobula eregoodootenkee).
The biomass of manta rays is estimated by Ecopath to be low, only 3.166 kg·km-2. This equates
to about 142 tonnes in all of Raja Ampat. The biomass of rays (0.177 t·km-2) was estimated
based on subjective species-level abundance rankings offered by McKenna et al. (2002b).
Weighting factors were determined for each abundance ranking based on the estimated absolute
biomass of certain species in common to both the McKenna et al. (2002b) species list and
COREMAP (2005). COREMAP (2005) abundance counts were converted to biomass density
using an average species weight obtained from an age-structured model (see Section 2.5.8 Biomass density estimates).
The P/B for manta rays is set slightly lower than that of rays, at 0.6 yr-1. The P/B for rays is set
at 0.96 yr-1 in order to establish a realistic age distribution as quantified by the multi-stanza
routine. The multi-stanza routine utilizes species-specific growth and maturity parameters from
FB. In the Java Sea, Buchary (1999) estimated a production rate for demersal rays of 1.3 yr-1,
which compares sufficiently well with our P/B estimate. The Q/B rate for manta rays is set at 2
yr-1 to provide a gross efficiency (P/Q) ratio of 0.3. The Q/B rate for rays was estimated based
on the empirical formula Pauly (1986) to be 3.817 yr-1 (based on 5 species), but this was later
decreased to 2.416 yr-1 to reduce the P/Q ratio.
For comparison, Buchary (1999) used a
-1
consumption rate for demersal rays of 8.2 yr , and Opitz (1993) used a value of 4.9 yr-1 for her
‘large sharks/rays’ group.
We assume zero catch of manta rays. Ray catch is set at 0.021 t·km-2 based on the ‘other’
demersal catch category listed in DKP statistics. That quantity was divided among demersal
functional groups whose catch was not quantified in a more precise catch category; those are
rays and large demersals. The ‘other’ catch was divided between rays and large demersal groups
according to their relative number of species in each group, assuming that the more specious
large demersal group contributed a greater fraction of the undetermined catch.
79
Butterflyfish
The butterflyfish functional group consists of 57 member species of family Chaetodontidae.
Fourteen genera are represented, but almost half the species in this group belong to genus
Chaetodon, This functional group was designed to capture the unique ability of butterflyfish to
predate on sea anemones, although some species may also predate on coral polyps (anthozoids),
invertebrates and plant material (Cox, 1994).
Biomass density was calculated for RA to be 0.325 t·km-2 based on 44 species counted in the reef
resource inventory of COREMAP (2005). To convert the coral reef biomass density to an
average value for all of RA, we applied a correction factor based on the marine area to reef area
ratio of Indonesia presented in Spalding et al. (2001).
Abundance counts are converted to
biomass using an average individual weight obtained from an age-structured model (see Section
2.5.8 - Biomass density estimates).
This biomass was divided between adults (79%) and
juveniles (21%) by implementing the multi-stanza mortality schedule in Table A.3.3.
The P/B rate for butterflyfish is set at 1.0 yr-1 after a natural mortality estimate for Centropyge
bicolor (Aldenhoven, 1986). We determined a higher alternate value, 2.14 yr-1, based on Pauly’s
(1980) M formula for two RA species. However, the higher value is not used because it leads to
an left-skewed age-biomass distribution under species-specific growth and mortality rates
available from FB. The production rate of juveniles was set at 1.6 yr-1. A relatively high rate
was required to resolve issues with over-predation of juveniles in the diet matrix. The Q/B of
adult butterflyfish was estimated to be 11.282 yr-1 based on FB information for 49 RA species of
butterflyfish. However, this rate was ultimately reduced to 6.720 yr-1 to reduce the P/Q ratio and
allow the Q/B estimate to lie closer to the value for physiologically similar groups, such as
medium reef associated fish.
Although there is no explicit mention of butterflyfish in governmental fishery statistics, it is
likely that fishery on this group is minor compared to their substantial biomass. These species
are typically solitary, or occur in pairs, and do not tend to form large shoaling aggregations
suitable for targeted fisheries. This group is underexploited in the model. Total catch for this
group is estimated to be 0.017 t·km-2, of which 90% is directed at the adult stanza. The
80
following fishery indicators are estimated by the equilibrium analysis: F2006 = 0.06 yr-1; FMSY =
0.553 yr-1; MSY = 0.079 t·km-2, or about 3550 tonnes for RA.
Cleaner wrasse
The cleaner wrasse functional group includes 3 labrids: the tubelip wrasse (Labrichthys
unilineatus), the bicolor cleaner wrasse (Labroides bicolor) and the Bluestreak cleaner wrasse
(Labroides dimidiatus). Cleaner wrasse was given its own functional group to represent the
cleaning mutualism effect seen between members of these species and larger reef fish that solicit
their services. The removal of ectoparasites, dead skin and other refuse is assumed to improve
the health of reef fish. This is represented in the BHS EBM models through mediation effects;
adult groupers and snappers benefit from high biomass density of cleaner wrasse (see Section
2.2.2 - Mediation factors).
The biomass of cleaner wrasse in the RA model, 0.009 t·km-2, is based on COREMAP (2005)
reef resource inventory transects. The value is adjusted for the relative reef area in RA using the
reef area to marine area ratio for all of Indonesia (Spalding et al., 2001). Abundance counts are
converted to biomass using an average individual weight obtained from an age-structured model
(see Section 2.5.8 - Biomass density estimates).
Production rate of cleaner wrasse (3.779 yr-1) is set after the small reef associated fish group, as
there was insufficient data available for cleaner wrasse. The only independent sampling-based
mortality figure located for this group refers to L. dimidiatus (Eckert, 1987). The adult M is
estimated as 0.11 yr-1 and the juvenile M is 0.5 yr-1. These values are not used because they are
too low compared to the Q/B estimate for this group. The Q/B calculation used the empirical
formula of Pauly (1986). The figure, 13.097 yr-1, is based on L. unilineatus and L. dimidiatus.
A miniscule catch for cleaner wrasse is incorporated, 0.819 kg·km-2. The figure is based on the
‘other’ reef fish catch category listed in DKP and Trade and Industry Office statistics. That
quantity was divided among reef-associated functional groups whose catch was not quantified
explicitly by other catch statistic categories.
81
The ‘other’ catch was divided between reef
associated groups according to their relative number of species in each group, assuming that the
more specious groups contribute a greater fraction to the undetermined catch. This data-poor
group would benefit from further investigation.
Large pelagic fish
The large pelagic fish group consists of mainly piscivorous fish. It is divided into adult and
juvenile stanzas. It is diverse and includes 25 species in the following familes: Belonidae,
Bregmacerotidae, Chirocentridae, Coryphaenidae, Elopidae, Exocoetidae, Gonostomatidae,
Hemiramphidae,
Leiognathidae,
Molidae,
Myctophidae,
Nettastomatidae,
Polynemidae,
Pristigasteridae, Salmonidae, Scombridae, Sphyraenidae, Stomiidae and Tetragonuridae.
The biomass of large pelagic fish (0.086 t·km-2) was estimated based on the subjective specieslevel abundance rankings offered by McKenna et al., (2002b).
Weighting factors were
determined for each abundance ranking based on the estimated absolute biomass of certain
species in common to both the McKenna et al. (2002b) species list and COREMAP (2005).
COREMAP (2005) abundance counts were converted to biomass density using an average
species weight obtained from an age-structured model (see Section 2.5.8 - Biomass density
estimates). The biomass of this group was divided between the adult (63%) and juvenile (37%)
stanzas according to mortality schedule in Table A.3.3.
The P/B rate of adult large pelagic fish is set at 0.8 yr-1, slightly lower than tuna groups or
smaller pelagic fish groups. This rate was modified during balancing from the original value
calculated with Pauly’s (1980) empirical formula for M, 1.079 yr-1, which was increased by 50%
to account for F. The higher P/B rate, which is based on 9 species out of 26, is instead retained
for the juvenile group. Q/B is set at 2.667 yr-1 to maintain a gross efficiency P/Q ratio of 0.3.
The empirical Q/B formula of Pauly (1986) predicted 5.644 yr-1. For comparison, Buchary
(1999) used the following values to represent large pelagic predators in the Java Sea: P/B = 1.2
yr-1 and Q/B = 8.65 yr-1.
Catch of large pelagic fish are calculated based on the miscellaneous or generic catch categories
listed in DKP statistics for domestic landings (e.g., other pelagics), and Trade and Industry
82
Office statistics for exported landings (mixed, frozen and smoked fish). These miscellaneous
categories were divided among 8 aggregate groups in the model (all size classes of pelagic, reefassociated and demersal groups). Total catch for large pelagics is estimated to be about 0.035
t·km-2, or about 186 tonnes for RA. 90% of the catch was assumed to originate from the adult
stanza, and 10% from the juvenile stanza. The equilibrium analysis suggests that this group is
fully exploited; current fishing mortality lies very close to FMSY.
The following fishery
indicators are estimated: F2006 = 0.575 yr-1; FMSY = 0.575 yr-1; MSY = 0.023 t·km-2, or about
1030 tonnes for RA.
Medium pelagic fish
The medium pelagic fish group contains adult and juvenile stanzas for yellowtail barracuda
(Sphyraena flavicauda), herring scad (Alepes vari), leaping bonito (Cybiosarda elegans),
Hawaiian lady fish (Elops hawaiensis), slender suckerfish (Phtheirichthys lineatus), long tom
(Strongylura krefftii), spottail needlefish (S. strongylura), largescale archerfish (Toxotes
chatareus) and silvermouth trevally (Ulua aurochs).
The biomass of medium pelagic fish (0.029 t·km-2) is determined in the same way as large
pelagic fish. It is based on the subjective species-level abundance rankings offered by McKenna
et al., (2002b), where an absolute biomass value is calculated based on representative species
found in COREMAP (2005) species transects (see Section 2.5.8 - Biomass density estimates).
The biomass of this group was divided between the adult (40%) and juvenile (60%) stanzas
according to mortality schedule in Table A.3.3.
The P/B rate of adult medium pelagic fish is set at 1.0 yr-1, and the rate for juveniles is set at 1.5
yr-1. These values produce a reasonable age distribution in the multi-stanza routine for an
exploited group under species-specific FB growth and mortality parameters, and they are in line
with respect to physiologically similar groups. Q/B for the adult group was set at 5.0 yr-1; the
estimate was revised downward during balancing from the initial estimate based on Pauly’s
(1986) empirical equation, 7.729 yr-1. The P/Q ratio is 2.
83
Catches for medium pelagic fish were estimated from DKP statistics for domestic landings based
on the ‘other’ pelagic fish miscellaneous category, and from Trade and Industry Office statistics
for exported landings based on a fraction of the miscellaneous catch categories (frozen, mixed
and smoked). These miscellaneous categories were divided among 8 aggregate groups in the
model (all size classes of pelagic, reef-associated and demersal groups). However, the catch of
adult medium pelagic fish was ultimately reduced during balancing to 6.912 kg·km-2, which is
25% of the initial estimate. Without this amendment, the fishing mortality of the group was
predicted to be more than 10 times the predation mortality, which is unrealistic. The juvenile
catch estimate remains unaltered. Therefore, the overall catch for this group is represented in the
RA model as 9.984 kg·km-2 for both stanzas combined, with approximately 2/3 of that amount
attributed to the adult group. The following fishery indicators are estimated: F2006 = 1.383 yr-1;
FMSY = 1.276 yr-1; MSY = 0.023 t·km-2, or about 1030 tonnes for RA.
Small pelagic fish
The small pelagic fish are divided into adult and juvenile stanzas. This group contains 75
species and 47 genera in the following families: Atherinidae, Bregmacerotidae, Carangidae,
Centrolophidae,
Champsodontidae, Clupeidae, Dentatherinidae, Exocoetidae, Gobiidae,
Hemiramphidae, Lactariidae, Leiognathidae, Melanotaeniidae, Microstomatidae, Myctophidae,
Nomeidae, Pristigasteridae, Pseudomugilidae, Scombridae, Scopelosauridae, Sternoptychidae,
Stomiidae and Terapontidae.
The biomass of small pelagic fish (0.178 t·km-2) is determined in the same way as large and
medium pelagic fish. It is based on the subjective species-level abundance rankings offered by
McKenna et al., (2002b), where an absolute biomass value is calculated based on representative
species found in COREMAP (2005) species transects (see Section 2.5.8 - Biomass density
estimates). The biomass of this group was divided between the adult (40%) and juvenile (60%)
stanzas according to mortality schedule in Table A.3.3.
P/B for small pelagic fish was estimated to be 3.99 yr-1 based on an empirical formula for M of
Pauly (1980); the value was increased by 50% to account for additional fishing mortality. This is
high compared to previously used values, and so we considered it an upper estimate of total
84
mortality or production and it was applied to the juvenile stanza. Rates used for similar groups
in coral reef models are 1.1 yr-1 (small pelagics; Polovina, 1984) and 1.8 yr-1 (small schooling
fish; Opitz, 1993). The adult stanza P/B was instead set at 2.0 yr-1. Q/B was estimated using the
empirical formula of Pauly (1986) as 18.462 yr-1 based on 8 species, but this rate was reduced by
about 30% during balancing to 13.267 yr-1.
Catches for small pelagic fish were estimated from DKP and Trade and Industry Office statistics
and represents an average of the years 2000-2005. Miscellaneous catch categories (e.g., mixed
fish, other pelagics) were divided among 8 aggregate groups in the model (all size classes of
pelagic, reef-associated and demersal groups). Total catch for small pelagics is 0.038 t·km-2, or
about 1690 tonnes for RA. 90% of the catch was assumed to originate from the adult stanzas,
and 10% from the juvenile stanzas. The following fishery indicators are estimated: F2006 = 0.824
yr-1; FMSY = 1.154 yr-1; MSY = 0.042 t·km-2, or about 1900 tonnes for RA.
Large reef-associated fish
Large reef-associated fish are divided into adult and juvenile stanzas. This is the most specious
group in the RA model. It represents 213 species (54 families and 111 genera) not elsewhere
included in functional groups. Since it is a large aggregate group, many life histories and feeding
modes are implicitly represented.
Estimates vary greatly in the literature as to the biomass of large reef-associated fish on coral
reefs. Reef transect results from Weigeo Island in COREMAP (2005) lead to an estimate of
11.640 t·km-2; this value has been compiled at the species level and corrected for reef area to
represent all of RA. We divided biomass between the adult (55%) and juvenile stanzas (45%)
according to mortalities in Table A.3.3.
Although we have entered this value into the
preliminary RA model, it is worth noting that Weigeo may be less exploited than other areas in
RA. This value may therefore represent an upper estimate of large reef fish biomass. It is high
compared to the biomass value of large reef fish 3.5 t·km-2 for Caribbean reefs (Opitz, 1993;
corrected for reef area ratio), 3.0 t·km-2 for NW Philippines reefs (based on a compilation of
functional group data from Aliño et al., 1993) or 0.5 t·km-2 for the Gulf of Thailand (estimated
85
from Khongchai et al., 2003). However, the COREMAP value is in line with the biomass
density used by Polovina (1984) for a large area surrounding French Frigate Shoals, 23 t·km-2;
especially since his value could be reduced somewhat for accurate comparison vis. species
composition and relative reef area coverage. Project outputs are expected to provide a better
estimate of large reef-associated fish biomass. A negative biomass accumulation rate of -0.15 yr1
was entered to reproduce the observed rate of decline seen in time series abundance estimates.
P/B of large reef associated fish is set preliminarily as 0.4 yr-1 for adults and 0.6 yr-1 for
juveniles. The M formula of Pauly (1980) suggested a high natural mortality rate for RA large
reef associated species of 1.29 yr-1, to which we can likely add a sizable amount of fishing
mortality. An alternate M estimate for this group, 1.31 yr-1, can be based on four member
species of families Mullidae, Labridae and Siganidae (calculated from Macpherson et al., 2000;
Eckert, 1987; Pajuelo et al., 1997; Ozbilgin et al., 2004; Kaunda-Arara et al., 2003). However,
these high values perturbed the age structure; they lead to a left-skewed distribution and were not
used. Q/B of large reef associated fish (4.0 yr-1) was reduced significantly during balancing from
an initial estimate based on the formula of Pauly (1986) of over 8.9 yr-1. Further investigation is
required to parameterize this influential functional group.
Large reef associated catch estimated from DKP statistics was about 0.069 t·km-2. This amount
was based on a compilation of catch statistic categories listed in governmental records including
trevallies, breams, catfish and the ‘other’ unidentified reef fish category. The latter category was
divided between this EwE functional group and other reef-associated groups not explicitly
mentioned by catch statistics, in a proportion equal to the relative number of species in each
group. The large reef associated group, having many species, garnered a large fraction (55%) of
this undermined catch component. However, compared with the adult large reef-associated fish
biomass estimate from COREMAP (2005) of 6.368 t·km-2, the fishery did not appear to be a
major source of mortality. The calculated catch value is low even compared to Venema (1997),
whose catch estimate for ‘coral fish’ in an adjacent area can be converted to 0.509 t·km-2.
However, it is likely that there is a large amount of unreported catch also occurring in this group.
The statistics recorded by the DKP and the Trade and Industry Office refer to fish landed in
Sorong. However, this group is targeted throughout the archipelago by commercial and artisanal
86
fisheries. Catches may go unreported even when landed in port (M. Bailey, UBC Fisheries
Centre. 2202 Main Mall, Vancouver, Canada. Personal communication). Until we have a more
formal estimate of unreported artisanal catch occurring in this group, we will make the
precautionary assumption that 10% of the catch is recorded by government statistics. Total
landings for this group are therefore 0.690 t·km-2, which is applied to the adult stanza (80%) and
juvenile stanza (20%). For the adult large reef-associated group this yields a current fishing
mortality approximately equal to 1/3 of FMSY, representing a lightly exploited stock. F2006 is
determined to be 0.081 yr-1, FMSY is 0.178 and MSY is 0.343 t·km-2, or about 15,400 tonnes
annually from RA.
Medium reef-associated fish
The medium reef-associated fish group includes 176 species, with 26 fish families represented
and 79 genera. The majority of species belong to three families: wrasses (Labridae), damselfish
(Pomacentridae) and cardinalfish (Apogonidae). The biomass of medium reef-associated fish is
estimated based on COREMAP (2005) reef transect abundance counts to be about 5.2 t·km-2.
About 55% of biomass is concentrated in the adult stanza and the remainder is in the juvenile
stanza according to the multi-stanza mortality schedule (Table A.3.3). The biomass estimate for
the RA model, determined on transect sites near Weigeo Island, has been scaled to represent the
relative marine to reef area ratio in Raja Ampat after Spalding et al. (2001).
P/B of medium reef-associated fish (adults: 0.8 yr-1; juveniles: 1.4 yr-1) is set arbitrarily to a
reasonable value, as there is not enough species-level information to apply an empirical formula.
Independent natural mortality estimates were located for three species in this group, Selaroides
leptolepis, Stethojulis strigiventer and Istigobius decoratus (Torres et al., 2004; Eckert, 1987;
Kritzer, 2001). These values average out to 4.64 yr-1, but this M value is high compared to the
other reef fish groups in the model, and it is probably not representative of medium reefassociated fish. The Q/B value was set at 5.0 yr-1 for adult medium reef associated fish to
maintain an intermediate value with respect to the small and large reef associated groups.
87
The catch of medium reef-associated fish, as estimated from DKP and the Trade and Industry
Office statistics, was very low: only 0.027 t·km-2. The estimate barely constitutes 2% of the
standing stock biomass, and the resulting F is only 2% of M. We have assumed, as with the
large reef-associated group, that there is a substantial amount of unreported catch. Catch for
adult medium reef-associated fish was increased in the RA model to 0.3 t·km-2, such that F ≈ 1/4
M. This represents a lightly exploited stock. The assumption implies that the annual artisanal
and unreported catch (and discards) could be as much as 10 times the reported landings.
However, the methodology used to estimate the catch of this group, based on an assigned
fraction of miscellaneous catch categories in governmental statistics, is very approximate.
Therefore, the ‘reported’ catch figure is also highly uncertain. Juvenile catches were increased in
the same proportion as the adults with respect to the initial fishery estimate. The following
fishery indicators are estimated for this group: F2006 = 0.123 yr-1, FMSY = 0.4 yr-1 and MSY =
0.824 t·km-2, or about 37,000 tonnes for RA annually.
Small reef-associated fish
The small reef-associated fish group is divided into adult and juvenile stanzas. 206 fish species
are included in 22 families and 92 genera. About 1/3 of the species in this group are gobies
(Gobiidae); the other major familes are cardinalfish (Apogonidae), damselfish (Pomacentridae)
and wrasses (Labridae).
The biomass of this group is estimated from COREMAP (2005) abundance transects as 0.394
t·km-2. We converted the abundance counts into biomass using an average weight for each
species, calculated with an age-structured model (see Section 2.5.8 - Biomass density estimates).
This value has been adjusted to represent average biomass density in RA using reef area to
marine area ratio for all of Indonesia (Spalding et al., 2001). The biomass value was split into
adult (66%) and juvenile stanzas (34%) according to the multi-stanza mortality schedule (Table
A.3.3). This functional group is energetically less important to the system than the medium or
large aggregate reef fish groups due to the size-based criteria used in assigning fish species to
groups.
88
P/B of adult small reef associated fish is based on data for 4 member species (1 cardinalfish and
3 damselfish). We applied the empirical relationship for M described by Pauly (1980) to
estimate a production rate of 3.779 yr-1. We did not explicitly incorporate fishing mortality. For
comparison, Aliño et al. (1993) used 14.02 yr-1 for gobies, 3.88 yr-1 for cardinal fish and 3.3 yr-1
for damselfish in modelling a reef flat in the Philippines. Independent sampling estimates a
production rate of 5.95 - 6.37 yr-1 for one species of blenny present in RA (Salarias patzneri,
Wilson, 2004); rightfully, this small species should fall higher than the group average. Q/B for
the adult stanza is set at 15.0 yr-1. It was reduced slightly from the estimate based on Pauly’s
(1986) formula, 18.3 yr-1 (based on 77 species out of 206) in order to keep the P/Q ratio (0.2)
similar to other reef-associated groups.
As with large and medium reef associated fish, the catch figure derived from DKP and Trade and
Industry Office statistics was low, 0.019 t·km-2. This number was not used, and a higher catch
figure was added to Ecosim (0.165 t·km-2), so that F ≈ 1/4 M. This represents a lightly exploited
stock. Fishery values are as follows: F2006 = 0.579 yr-1, FMSY = 2.422 yr-1 and MSY = 0.371 t·km2
, or about 16,700 tonnes for RA annually.
Large and small demersal fish
The large demersal group includes the following species: Japanese rubyfish (Erythrocles
schlegelii), whipfin silverbiddy (Gerres filamentosus), gobies (Amblyeleotris arcupinna, Trimma
griffthsi, T. halonevum), freshwater moray (Gymnothorax polyuranodon), barredfin moray (G.
zonipectus), spotted armoured gurnard (satyrichthys rieffeli), Japanese flathead (Inegocia
japonica) and Jarbua terapon (Terapon jarbua). The small demersal group includes: Ocellated
waspfish (Apistus carinatus), cardinal fish (Apogon fleurieu, A. ocellicaudus), spotwing flying
gurnard (Dactyloptena macracantha), blue speckled prawn goby (Cryptocentrus octofasciatus),
wrasse (Choerodon zosterophorus), black-edged sweeper (Pempheris mangula), tuberculated
flathead (Sorsogona tuberculata), freshwater demoiselle (Neopomacentrus taeniurus), insular
shelf beauty (Symphysanodon typus) and threadfin blenny (Enneapterygius philippinus).
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The total biomass for large demersal fish, 0.415 t·km-2, is based on subjective species-level
abundance rankings provided by McKenna et al., 2004. The quantity is divided between adult
(48%) and juvenile stanzas (52%) based on the mortality schedule in Table A.3.3. A weighing
factor was assigned to each abundance ranking based on species common to both the McKenna
et al. (2004) list and COREMAP (2005). The biomass for this group represents a sum of
member species’ values; it is corrected for area to represent a RA average, using marine to reef
area ratios in Spalding et al. (2001). The biomass compares well to the estimate of Venema
(1997), who quoted a biomass density in 1995 equivalent to 0.6 t·km-2, and whose estimate
included other specific taxa incorporated here into other functional groups. Biomass of small
demersal fish (0.327 t·km-2) is calculated in the same way as large demersal fish. It is similarly
divided into adult (59%) and juvenile (41%) stanzas.
Pauly’s (1980) equation was used initially to determine natural mortality for large demersals as
1.69 yr-1, based on a tigerfish and a silverbiddy. This value is high compared to the one used by
Buchary (1999) for modelling large demersal predators (0.92 yr-1).
It also provided an
unrealistic age distribution in the multi-stanza routine and so was not used. A lower value is
substituted for adult large demersals (0.6 yr-1). Buchary’s (1999) value was assumed to represent
an upper limit for this group, and it was applied to the juvenile stanza. Similarly, a value of 2 yr1
is set for small demersals, and Buchary’s (1999) value for small demersal predators is applied
to the juvenile stanza (2.56 yr-1). Large consumption rates were estimated using the Q/B
relationship of Pauly (1986): for large demersals, 8.42 yr-1 and for small demersals, 18.5 yr-1.
These figures are uncertain and they are only based on 2 and 1 species, respectively. Buchary
used lower values, 6.13 yr-1 and 12.84 yr-1 for large and small demersals, yet any of these
produce unrealistic P/Q ratios.
Lower consumption rates are therefore in place: for large
demersals (3.1 yr-1) and for small demersals (8.6 yr-1), providing P/Q ratios ≈ 0.2.
We estimate that there is a catch of approximately 0.029 t·km-2 for large demersal fish. This is
based on DKP statistics, and it represents an average of the years 2000-2004. The figure
combines recorded catch for croakers, threadfins and miscellaneous groups such as ‘other
demersal fish’. The figure has also been increased by 50% to represent unreported artisanal
catch and scaled in proportion to relative reef area (from marine area ratios in Spalding et al.,
90
2001) to provide an average RA estimate. The fishery catch offered by Venema (1997) for an
adjacent area in Eastern Indonesia can be re-stated as 0.297 t·km-2, when corrected for relative
reef area in RA; it is an order of magnitude higher than the present estimate, and in fact higher
than the standing biomass of the large demersal group. However, this catch value considers
species which have been placed into other functional groups in the RA models. 90% of the large
demersal catch is assumed to originate from the adult stanza; the remainder is considered
juvenile catch. Small demersal catch is determined in the same way, as 0.032 t·km-2. These
catch values result in the following fishery indicators: for large demersals F2006 = 0.679 yr-1,
FMSY = 0.561 yr-1 and MSY = 0.040 t·km-2, or about 670 tonnes for RA annually; for small
demersals, F2006 = 0.210 yr-1, FMSY = 2.868 yr-1 and MSY = 0.247 t·km-2, or about 11,100 tonnes
for RA.
Large demersals are therefore represented as being overexploited, while small
demersals are underexploited.
Large and small planktivore fish
The large planktivore fish group contains 52 species (19 families and 31 genera); wellrepresented are planktivorous species of fusiliers, trevally, jacks, scads and soldierfish. The
small planktivore group contains 62 species (17 families and 39 genera); almost half of the
species in this group are in family Pomacentridae (mainly damselfish and demoiselles), with
some species of cardinalfish, blennies and gobies as well. These groups are divided into adult
and juveniles stanzas.
The planktivorous functional groups were created to represent an
important trophic link on coral reefs, through which energy passes from planktonic secondary
producers to the benthic reef fish community. Obligate and facultative planktivorous species are
included in the planktivorous functional groups. For a species to be included into a planktivorous
functional group a prominent mention of planktivory is required in diet remarks on the FB
Species, Ecology, or FoodItems table (see Section 2.4.2 - Planktivorous fish).
Abundance data available from reef transects at Weigeo Island lead to a very high biomass
density for large planktivorous for the RA model, 9.56 t·km-2. Under the current mortality
scheme (Table A.3.3) this would amount to more than 5 t·km-2 of adult fish in RA (about 290
t·km-2 on reefs). Although this group contains some abundant species, such as the red-bellied
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fusilier (Caesio cuning) (COREMAP, 2005) and the oxeye scad (Selar boops) (Obed Lense,
TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413), we
considered this value to be too high for an RA average. The adult biomass was therefore set
arbitrarily to 1.0 t·km-2, and the juvenile stanza biomass was calculated by Ecopath as 0.89 t·km2
. This may be a critical group in the trophic functioning of the RA ecosystem, and we hope that
project outputs will allow us to improve this parameter.
P/B rate of adult and juvenile large planktivorous fish is determined based on the M relationship
described by Pauly (1980). The value represents an average for 17 RA species. The calculated
value, 2.0 yr-1, was applied to the juvenile stanza, while the adult stanza received a lower value,
1.5 yr-1. These figures provided a suitable age-biomass distribution. The small planktivore
group uses a P/B rate of 2.0 yr-1 for adult and juvenile stanzas. Pauly’s (1980) M formula had
been used to predict a P/B rate for small planktivorous fish of over 6.0 yr-1 but when applied as a
mortality rate, this high value produces a left-skewed age-biomass distribution. The empirical
equation of Pauly (1986) predicted a Q/B consumption rate of over 20 yr-1 for small
planktivorous fish. This value was reduced substantially, so that P/Q equals 0.33. Q/B of the
adult stanza was similarly set to produce a P/Q ratio of 0.3.
Catch was estimated for large planktivores at a very low quantity from DKP and Trade and
Industry Office statistics, less than 0.019 t·km-2. However, this estimate is based on highly
aggregated statistics and may be missing a large amount of unreported catch. As this amount did
not have any noticeable influence on the functional group in preliminary fishery simulations, the
figure was discarded in favour of a larger value, 0.33 t·km-2, so that F ≈ 0.4M. This quantity
elicits a more reasonable response from the large planktivore group under a realistic variety of
fishing pressures. Small planktivore catch is set at 0.014 t·km-2. For both large and small
groups, 90% of the catch is assumed to originate from the adult stanza; the remainder from the
juvenile stanza. The following fishery indicators are determined: for large planktivores F2006 =
0.3 yr-1, FMSY = 0.7 yr-1 and MSY = 0.478 t·km-2, or about 11,100 tonnes for RA; for small
planktivores, F2006 = 0.031 yr-1, FMSY = 0.6 yr-1 and MSY = 0.223 t·km-2, or about 10,000 tonnes
for RA. Large and small planktivores are therefore underexploited in the RA model.
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Anchovy
The Anchovy group contains 17 Engraulids of genera Stolephorus, Thryssa, Setipinna and
Thryssa; Stolephorus is the dominant genus by biomass in shallow habitats surrounding the Raja
Ampat islands; especially important is S. indicus (Mark Erdmann. CI. Jl. Dr. Muwardi. 17
Renon Denpasar, Bali, Indonesia; Chris Rotinsulu. CI. Jl Arfak No. 45. Sorong, Papua,
Indonesia 98413. Personal communication). This species supports large artisanal fisheries
throughout the RA archipelago. A major artisanal fishery is located on southern Weigeo Island
in Kabui Bay and surrounding areas. Villagers export the anchovies for bait to northern Weigeo
pelagic fisheries, or dry them for local consumption. The large anchovy population is thought to
be supported by a productive upwelling area in central Dampier Strait (M. Erdmann. CI. Jl. Dr.
Muwardi. 17 Renon Denpasar, Bali, Indonesia. Personal communication).
Wolanski (2001) used an anchovy biomass of 3.122 t·km-2 for an inter-reef / lagoon Great
Barrier Reef model. As our study area contains a greater proportion of deep areas, the coastal
anchovy species will have a lower average biomass density. We have elected to use a smaller
arbitrary value, pending better information. Adult anchovy biomass is set at 1.5 t·km-2 and
juvenile biomass is estimated by the multi-stanza routine, providing a total anchovy biomass of
3.737 t·km-2. Fishermen in Waisai indicated that there has been a recent reduction in the
available biomass of anchovies (M. Bailey, UBC Fisheries Centre. 2202 Main Mall, Vancouver,
Canada. Personal communication). A negative biomass accumulation rate of -0.2 yr-1 was
entered to represent this and set baseline surplus stock production so that FMSY is approximately
2-3 times greater than the current fishing mortality (Fig. B.2.1).
The P/B ratio entered for anchovy is 3.37 yr-1, based on M of S. indicus (Torres et al., 2004).
Pauly’s (1980) equation predicts a similar value, M = 3.27 yr-1, averaged for eight RA anchovy
species present in the model, while an average of 5 world engraulids from independent sampling
studies yields M = 5 yr-1 (Torres et al., 2004). These values could be increased somewhat to
represent fishing mortality. However, a greater mortality value than the one used results in an
unrealistic left-skewed age-biomass distribution under the estimated maturity parameters.
Moreover, other authors have used even lower production rate values for anchovy, such as
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Heymans et al., 2004 (1.2 yr-1 for Benguela upwelling) and Ainsworth et al., 2001 (1.15 yr-1 for
Bay of Biscay). Q/B rate for anchovy (14.625 yr-1) is estimated from 9 RA engraulids using the
regression relationship of Pauly (1986).
A rough estimation of anchovy catch based on reported catch rates from Waisai fishermen by
Bailey et al. (unpublished manuscript) suggests an unreported catch rate of 0.401 t·km-2 for all
Weigeo fisheries. Increasing that value by 20% to consider other area in RA, and adding the
official fraction of reported catch (0.028 t·km-2), leads us to an overall catch estimate of 0.509
t·km-2 for RA, which we apply entirely to the adult stanza. Here we have assumed that Weigeo
fisheries constitute the large majority of RA anchovy catch, as is the consensus among fieldbased researchers. This provides a similar estimate to an independent catch calculation based on
data in Venema (1997). They reported a catch rate in 1993 of 70 thousand tonnes of small
pelagics from Area III.3 (Ceram, Maluku and Tomini), or 1.829 t·km-2. Assuming that anchovies
comprise 30% of this catch we may expect 0.549 t·km-2, which is close to our current estimate.
The following fishery indicators are estimated for anchovy: F2006 = 0.391 yr-1, FMSY = 1.218 yr-1
and MSY = 0.887 t·km-2, or almost 40,000 tonnes for RA.
Deep-water fish
Deep-water fish is a large aggregate group representing 58 species that occur at depths greater
than 200 m, in the bathyal zone or deeper. Bathypelagic and bathydemersal species were
included in this group based on their habitats listed in the FB Species table ‘Habitat’ field, and on
the depth range reported in the ‘DepthRangeDeep’ field. This group summarizes data for 26 fish
families, but almost half the species in this group belong to families Myctophidae (lanternfishes)
and Stomiidae (dragonfishes).
No species of deep-water fish were identified in the available surveys for RA. However, the
rough estimate for adult stanza biomass, 0.6 t·km-2, provides an appropriate EE value (~0.9).
With juvenile biomass estimated by the multi-stanza routine, overall biomass for deep-water fish
is set at 1.394 yr-1.
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The production rate used is 1.13 yr-1, and it is based on Myctophum asperum (Palomares and
Pauly, 1998). This value is significantly lower than the natural mortality estimate made using
Pauly’s (1980) M formula, 3.94 yr-1 based on six RA deepwater species. The higher value is not
used because it results in a left-skewed age-biomass distribution under the assumed maturity
parameters. An alternate estimate for Myctophids is 0.91 yr-1, which is based on 5 world species
(Palomares and Pauly, 1998). Q/B (3.667 yr-1) is set relative to the production rate, so that the
gross efficiency P/Q ratio equals 0.3.
The catch estimate for deep-water fish is based on the entry for hairtails in DKP statistics, as
there are 4 species of family Trichiuridae in the models and they all occur in this group. The
figure represents an average of the years 2000-2004. We assume zero unreported catch and zero
discards. The catch estimate (9.2 kg·km-2) is divided between adult (90%) and juvenile (10%)
stanzas. Ecosim estimates the following fishery indices: F2006 = 0.034 yr-1, FMSY = 0.450 yr-1 and
MSY = 0.115 t·km-2, or about 5100 tonnes for RA.
Macro-algal browsing
Herbivorous fish in the RA models are divided into three functional groups according to how
severely they impact the substrate. Most damaging are the eroding grazers, followed by scraping
grazers and then macro-algal browsing fish. Macro-algal browsing fish represent an important
functional link in coral reef ecosystems. Together with sea urchins, they regulate the biomass of
algae, and may help coral recruits to settle by keeping the substrate exposed (Mous and Muljadi,
2005).
Ecosim’s accurate predictions may depend on the correct parameterization of this
important keystone group.
The group represents three herbivorous species: Piaractus
brachypomus, Nematalosa erebi and Valamugil buchanani.
Local abundance data is expected in late 2006 for some herbivorous species on reefs from
Kofiau Island transects, but it was not available for this report. No species in this group were
identified by the available RA surveys, and so a preliminary biomass value of 0.25 t·km-2 was
entered. It is a relatively low value for this selective group of 3 species. With juvenile biomass
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estimated by the multi-stanza routine, total biomass of macro-algal browsing fish is estimated to
be 0.75 t·km-2.
P/B was set at 1.339 yr-1 using the M formula of Pauly (1980) based on V. buchanani and P.
brachypomus. Juvenile P/B is set at 1.4 yr-1. Q/B was estimated for all species as 13.76 yr-1
using the empirical formula of Pauly (1986).
A small catch was entered in for macro algal browsing fish of about 8 kg·km-2. This is a small
fraction of the large reef associated group catch estimated from the DKP and Trade and Industry
Office statistics. The amount is proportional to the relative number of species occurring in each
group. This group is underexploited in the model. The fishery indicators for this group are: F2006
= 3E-3 yr-1, FMSY = 0.25 yr-1 and MSY = 0.033 t·km-2, or about 1500 tonnes for RA.
Eroding grazers
This group consists of green humphead parrotfish (B. muricatum) and the doubleheaded
parrotfish (S. microhinos).
The biomass estimate for eroding grazers is derived from
COREMAP (2005) reef transects on Weigeo Island, 0.783 t·km-2. The value has been scaled
down based on the relative reef area in RA using ratios in Spalding et al. (2001). That amount is
divided between the adult (67%) and juvenile stanzas (33%) according to the mortality schedule
in Table A.3.3. The abundance data refers to Bolbometropon spp. but we assume the majority of
biomass is accounted for by the member species of this group.
The production rate of eroding grazers is based on B. muricatum.
The value, 0.435 yr-1
represents M from Pauly’s (1980) equation, but it has been increased by 50% to account for
fishing mortality. Juvenile P/B is set at 1.0 yr-1. We estimated the Q/B of B. muricatum as 4.319
yr-1 using the formula of Pauly (1986), but subsequently we accepted a lower value of 1.45 yr-1
so that P/Q equals 0.3.
A small amount of catch was entered in the base model representing a fraction of the estimated
large reef associated fish catch. The proportion is set based on the relative number of species in
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each group. The fishery indicators for this group are: F2006 = 3E-3 yr-1, FMSY = 0.25 yr-1 and
MSY = 0.056 t·km-2, or about 2500 tonnes for RA.
Scraping grazers
Scraping grazers include 82 species of parrotfish (family Scaridae), surgeonfish and unicornfish
(Acanthuridae) and filefile (Monacanthidae). Scraping grazers were given their own functional
group in the RA models to represent the important role that these animals play on coral reefs
(Bellwood et al., 2004). The biomass estimate for eroding grazers is derived from COREMAP
(2005) reef transects on Weigeo Island, 2.004 t·km-2. The value has been scaled down based on
the relative reef area in RA. It is divided between adult (17%) and juvenile stanzas (83%)
according to the mortality schedule (Table A.3.3).
P/B is based on Pauly’s (1980) M relationship, and averages 18 fish in the scraping grazers
group. The value, 2.339 yr-1 has been increased by 50% to account for additional fishing
mortality. Juvenile P/B is set at 3.0 yr-1. The Q/B value is was estimated from the equation of
Pauly (1986) as 12.74 yr-1 based on 50 species.
There is a small amount of catch entered for scraping grazers, 0.025 t·km-2. This represents a
fraction of the large reef associated catch proportional to the relative number of species in each
group. Scraping grazers are underexploited in the model. The fishery indicators for this group
are: F2006 = 0.094 yr-1, FMSY = 0.7 yr-1 and MSY = 0.092 t·km-2, or about 4100 tonnes for RA.
Detritivore fish
Seven detritivorous fish species were categorized into this group. To qualify, a prominent
mention of detritivory is required for the predator species in the FB ‘Diet’ table, or in the
‘Species’ table comment field. Although many species consume detritus incidentally, or as a
minor diet component, only species that rely on detritus as a main food source were included in
these functional groups. Detritivory is based on the FB ‘Species’ table ‘comment’ field and
‘Ecology’ table ‘Herbivory2’ field.
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The detritivorous fish group contributes relatively little to the overall reef fish biomass in the RA
model, as it has been noted that invertebrates, not detritivorous reef fish, are primarily
responsible for energy cycling in the ecosystem (A. Muljadi. TNC-CTC. Jl Gunung Merapi No.
38, Kampung Baru, Sorong, Papua, Indonesia 98413. Personal communication). Reef transects
confirm a low biomass of these species, 0.016 t·km-2 (COREMAP, 2005). This value has been
scaled to represent the average of RA.
The P/B rate of detritivorous fish is set at 2.339 yr-1; it is set equal to the value used for scraping
grazers. No addition mortality information could be found for these species. A Q/B value was
estimated using Pauly’s (1986) empirical equation, 11.86 yr-1, but this was reduced to 8.33 yr-1
so that P/Q would lie closer to 0.3.
A small catch is entered for this group in the base model as a fraction of the large reef associated
group catch. The proportion represents the relative number of species in each group. The group
is lightly exploited in the model; F is about 6% of M.
Azooxanthellate corals
Azooxanthellate corals are consumers. We have allowed Ecopath to estimate their biomass in
the system based on the assumption that EE ≈ 0.95. Biomass is estimated to be 0.6 t·km-2. A
production rate of 1.44 yr-1 was used because it is two-thirds of the value used for reef-building
corals, and a Q/B rate of 3.6 provides a P/Q ratio of 0.4.
Hermatypic scleractinian corals
These are the reef-building scleractinian corals. Hermatypic scleractinian corals are modelled as
facultative consumers because they predate on zooplankton, yet also have endosymbiotic
zooxanthellae, autotrophic dinoflagellates that provide photosynthetic products to the coral.
There have been a wide range of parameters applied to coral functional groups in previous EwE
studies (Table 2.6). We use a biomass value calculated from Crossland et al. (1991), who
suggested a global coral reef biomass density in the range of 10-100 gC·m-2. Their mean
reported value was used, 30 gC·m-2. This amount was converted to wet weight using a carbon to
98
carbohydrate conversion factor and a dry to wet weight conversion factor from Atkinson et al.
(1984). This calculation gives a biomass estimate of about 50 t·km-2 on reefs. Corrected for the
relative reef area in RA using the marine to reef area ratio for Indonesia reported by Spalding et
al. (2001) gives an overall biomass value for the study area of 0.875 t·km-2. We hope to replace
this approximate value with a better estimate from RA coral cover estimates.
The production rate of reef building corals was calculated from Crossland et al. (1991). They
estimated a daily turnover rate of reef biomass on the order of 0.003 day-1. This equates to 1.095
yr-1. The Q/B was set at 3.6 yr-1, giving a high P/Q ratio of 0.6. This is appropriate for a
facultative consumer.
The loss rate of hard corals from destructive fishing methods and other stressors is not well
known, but one estimate from Bolinao, Philippines supposes that 0.4% of the live coral cover is
lost each year (McManus et al., 1997); and cyanide fishing is also known to have caused damage
to reefs in Raja Ampat. The damage arises from the toxin’s direct contact on coral polyps, and
also from the action of divers breaking coral away to retrieve the stunned fish. The loss of coral
can cause major changes in the reef ecosystem, and it has been associated with a decline in fish
biodiversity Wilson et al. (2006).
A range of possible values for coral loss were identified for Indonesia: a conservative 0.050.06% per year estimate to 0.5-0.7% per year (Mous et al., 2000); the authors note that this is a
small possible rate of loss compared to coral re-growth rates. We therefore enter a biomass
accumulation rate into this group of -0.5% per year for the 2006 RA model, which has a minimal
impact on dynamics.
99
Table 2.6 - Biomass and production rates used previously in EwE to represent reef-building corals.
Area
Biomass
(t·km-2)
P/B
(·y-1)
Original group name
Source
Central Java
17.48
0.1
"Living bottom
structure"
Nurhakim, 2003
Mexican Caribbean
1-30
0.7-1.8
"Sessile animal
feeders"
Arias-González, 1998
Java Sea
20
0.1
"Living bottom
structure"
Buchary, 1999
Carribbean
1000
0.8
"Sessile animals"
Opitz, 1993
Bolinao, Philippines
200
0.1
"Sessile invertebrate
consumers"
Aliño et al., 1993
Hong Kong
0.399
1.09
"Corals"
Buchary, 1999
Tiahura, Moorea Island,
French Polynesia
19.74
1.92
"Corals"
Arias-González, 1997
New Caledonia
1.47
1.47
"Corals/zooxanthellae"
Bozec et al., 2004
French Frigate Shoals
289
3
"Heterotrophic
benthos"
Polovina, 1984
Great Barrier Reef
0.04
Sorokin, 1981
World averages
1.095
Crossland et al., 1991
Non reef-building scleractinian corals and soft corals
Like the hermatypic sceractinian corals, the non-reef building ahermatypic scleractinian corals
and the soft corals are both facultative consumers containing symbiotic zooxanthellae. Until we
can find or produce more accurate information for RA, we have allowed Ecopath to estimate the
biomass of these groups based on the assumption that EE ≈ 0.95. For both groups, this produces
a biomass value very close to 0.6 yr-1. The production rate of non-reef building scleractinian
corals is set arbitrarily at 1.4 yr-1, slightly lower than azooxanthellate corals.
Penaeid shrimps and Shrimps and prawns
Biomass of penaeid shrimp and shrimps and prawns is set as 2.0 t·km-2 respectively pending
better information. The P/B value used for penaeid shrimps in the RA model is 3.824 yr-1. This
100
value was calculated as the average of the P/B value calculated for 4 species. The P/B for
Penaeus duorarum and Metapenaeus monoceros was calculated using Brey’s (1995) equation.
The maximum age and maximum weight for Penaeus duorarum is 1.167 yr and 38.05 g (Bielsa
et al, 1983). Maximum age for Metapenaeus monoceros was estimated by Srivatsa (1953) as
1.59 yr. The maximum weight is calculated based on length-based relationship of Abdurahiman
et al. (2004). P/B values of 5.245 yr-1 and 3.83 yr-1 were used for the average based on
Trachypenaeus fulvus and Parapenaeus longipes (Pauly et al., 1984). For comparison, Buchary
(1999) used a P/B ratio for penaeid shrimps of 5 yr-1, which agrees, but Pauly et al. (1993)
calculated a higher ratio for shrimps, equivalent to 18.2 yr-1.
The Q/B for penaeid shrimps is taken as the average value for Penaeus longistylus and Penaeus
esculentus from an Ecopath model of Great Barrier Reef (Gribble, 2003), 37.9 yr-1. Buchary
(1999) had used a Q/B ratio of 28.95 yr-1 for her adult penaeid shrimp group. The value for other
prawns from Gribble (2003) was used as the Q/B for shrimps and prawns in the RA model, 20
yr-1.
The Q/B estimate of Schwamborn and Criales (2000) for juvenile pink shrimp
(Farfantepenaeus duorarum) might also be applicable to this group at 48.976 yr-1. The value is
likely too high for our use as it refers to juvenile animals. Pauly et al. (1993) suggested a Q/B
ratio for shrimps of 28.94 yr-1, which is more in line with our estimate.
Squid and Octopus
The P/B value used for squid in the RA model is 4.348 yr-1. This value was calculated as the
average of the P/B value calculated for 7 species using Brey’s (1995) equation. Maximum age
and weights for Photololigo chinensis, Photololigo edulis, Sepioteuthis australis and Sepioteuthis
lessoniana is obtained from (BRS, 1999). Maximum ages for Loligo duvauceli and Loligo
chinensis is from Jackson (2004) and maximum weights were obtained from Kongprom et al.
(2003). Maximum age for Sepia officinalis is from Zielinski and Portner (2000) and maximum
weight is from FAO (2006). The production rate used by Buchary (1999) for cephalopods in the
Java Sea is slightly lower at 3.1 yr-1; the rate used by Optiz (1993) for a Caribbean reef was also
3.1 yr-1. Our value is intermediate though compared to the P/B rate for squid calculated by Pauly
et al. (1993) for a Philippines reef, which equates to 10.66 yr-1. The P/B ratio used for octopus in
101
the RA model is 2.327 yr-1. This value was calculated using Brey’s (1995) equation based on the
maximum age of Octopus cyanea (Cascorbi, 2004) and maximum weight obtained from FAO
(2006). Pauly et al. (1993) used a higher value for octopus, 4.49 yr-1.
The Q/B rate used by Buchary (1999) for cephalopods was 20.318 yr-1; the Q/B rate used by
Opitz was 11.7 yr-1. The Q/B value used in the RA model is intermediate, at 14.792 yr-1. This
value was calculated as a weighted average according to the biomasses of three species:
Sepioteuthis lessoniana, Sepia officinalis and Sepiola affinis. Q/B is based on food intake studies
of these species by Wells (1996). Q/B for octopus was also calculated from the same source to
be 13.24 yr-1; this value was the average for 5 species: Eledone moschata, E. cirrhosa, Octopus
cyanea, O. dofleini, O. maya and O. vulgaris. An alternative estimate of Q/B is 10.95 yr-1 based
on Sepioteuthis lessoniana from food intake studies (Rodhouse and Nigmatullin, 1996). This
value was not used in the calculation of the group Q/B estimate. Pauly et al. (1993) used the
following Q/B values for the Bolinao Reef Ecosystem in the Philippines: squids, 16.64 yr-1,
octopus 7.3 yr-1.
Sea cucumbers
Sea cucumber biomass was estimated for the RA model based on reef transect results provided in
COREMAP (2005). The average number of individuals on the Weigeo Island reef top was 2.3
individuals per 40 m2. We converted this density to weight by assuming an individual animal
weight of 965 g, as calculated from Desumont (2003) based on 20 sea cucumber species
occurring in Papua New Guinea. Total biomass density for sea cucumbers in RA then is 0.971
t·km-2, when corrected for reef area using ratios in Spalding et al., (2001). This amount equates
to 55.5 t·km-2 biomass density on coral reefs. For comparison, the biomass of sea cucumbers
was identified by Aliño et al. (1993) as 35.77 t·km-2 on coral reefs. However, Trobe-Bateman et
al., (2004) provided a much higher estimate for sea cucumber biomass in Papua New Guinea.
Using the same assumptions regarding average individual weight, their biomass density on reefs
computes to 221.95 t·km-2, which is about four times higher than the RA model estimates
currently in place.
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The P/B rate used for sea cucumbers, 0.74 yr-1, represents an average value for Actinopyga
echinites and Holothuria scabra (Shelley, 1985). Aliño et al. (1993) suggested a higher value
for sea cucumbers, 4.45 yr-1, and a rate from Pauly et al. (1993) is equivalent to 2.66 yr-1.
The Q/B value used in the RA model for sea cucumbers is 8.248 yr-1. The value is an average of
the individual consumption to biomass ratios calculated for 20 species using an empirical model
by Cammen (1980). The average weight of sea cucumber species was obtained from species
identification cards issued by Papua New Guinea National Fisheries Authority. The average
weight was converted to dry weight using a factor 0.11 (Brey, 2006). For comparison, Pauly et
al. (1993) used a lower value, 3.58 yr-1.
Lobsters
Lobster biomass was estimated for the RA model to be 0.219 t·km-2 based on reef transect
results provided in COREMAP (2005). The average number of individuals in reef top transects is
0.5 individuals. Reef top transect area is 40 m-2. This gives an average density of 0.0125
individuals·m-2. This density was converted to weight using the average individual weight of 1
kg. Biomass density on reefs was corrected for reef area based on Spalding et al. (2001).
The P/B value used for squid in the RA model is 0.446 yr-1. This value was calculated using
Brey’s (1995) equation as the average P/B value for 4 species. Maximum age and weights for
Panulirus ornatus, Thenus spp, Jasus verreauxi and Panulirus cygnus were obtained from (BRS,
1999). The Q/B value of 15.207 yr-1 was calculated from a consumption estimate of 0.1151
gC·m-2·y-1 and a biomass estimate 0.076 gC·m-2 obtained from Florida Bay (Jorgensen et al.,
1991).
Large and small crabs
In the Java Sea, Buchary (1999) estimated the biomass of crustaceans to be 0.86 t·km-2. This
value is equally distributed among the three crustacean groups (lobsters, large crabs and small
crabs) to obtain the biomass estimate of 0.286 t·km-2 for each. The P/B for large crabs is
calculated to be 1.24 yr-1. The value is the average of P/B values for 3 species (Portunus
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pelagicus, Ranina ranina and Scylla serrata) determined using Brey’s (1995) equation. The
maximum weight and age for the species are from BRS (1999). The P/B for small crabs was
calculated as 2.610 yr-1. The value is the average of P/B values for 8 species (Uca rapax, Uca
maracoani, Uca cumulanta, Uca vocator, Eurytium limosum, Emerita analoga, Pachygrapsus
gracilis and Ucides cordatus) from Brey (2006).
The Q/B values of 14.55 yr-1 and 20.21 yr-1 are calculated for large and small crabs respectively
from consumption and biomass estimates in Jorgensen et al. (1991); data is from Florida Bay:
The annual catch made on large and small crabs represents a very rough approximation (2.76
kg·km-2 each). It was determined by splitting evenly the quantity of ‘other’ catch reported in
DKP and Trade and Industry Office between several groups which were not explicitly recorded
in other catch categories. The statistics represent catch in the years 2000-2005. We are awaiting
improved estimates for catch.
Crown of thorns starfish
This functional group contains only the crown-of-thorns starfish (Acanthaster planci), which is a
highly influential keystone predator species on coral reefs (Pearson, 1981; Moran, 1986).
Biomass for crown of thorns starfish is taken from COREMAP (2005). They estimated 0.5
individuals on average per 40m2 of reef top. Assuming that each animal weighs 1 kg provides a
biomass estimate on reefs of 12.5 t·km-2. Scaling this by the marine area to reef area ratio of
Spalding et al., (2001) provides an estimate for RA of 0.218 t·km-2. Evidence of coral damage
from crown-of-thorns starfish in Raja Ampat was weak according to an earlier survey. McKenna
et al. (2002a) observed coral damage in only 6.7% of the sites surveyed in RA.
The P/B value used in the RA model is 0.463 yr-1. This value was calculated using Brey’s (1995)
equation. The maximum age used was 8 years (Zann, et al., 1990) and maximum weight was
calculated using a diameter to weight equation (Birkeland and Lucas, 1990). The maximum size
was 60 cm from Moran (1990).
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The Q/B value is based on consumption by juveniles and adults (Jangoux, 1982); the value is
weighted according to the relative percentages of juveniles (23.5%) and adults (76.5%) in the
population (Engelhardt et al., 2000) and the average body weight of juveniles and adults (Bass
and Miller, 2006).
Giant triton
The giant triton biomass was assumed to be 1% of the bivalve biomass estimated for the RA
model and was fixed at 0.05 t·km-2.
The P/B value calculated for epifaunal detrivorous
invertebrates was also used for giant triton in the RA model. This value is equal to 1.224 yr-1. A
P/Q ratio of 0.3 was assumed as this is a slow growing species; the Q/B ratio was thus calculated
as 4.08 yr-1. It has been suggested that removal of predators such as the giant triton (Charonia
tritonis) may play a role in the periodic crown-of-thorn outbreaks that threaten coral reefs,
although the evidence is not conclusive (Sweatman, 1995).
Herbivorous echinoids
Herbivorous urchins probably have a more influential affect on algal cover in the coral reef
environment than do herbivorous fishes such as parrot fishes (Scaridae) and surgeon fishes
(Acanthuridae) (Levinton, 1982).
The biomass for herbivorous echinoids is based on
COREMAP (2005) estimate of 3.3 sea urchins per reef-top transects for sites near Weigeo
Island. Assuming an average weight of 0.5 kg per animal, and correcting for reef area in RA
based on the marine area to reef area ratio for all of Indonesia (Spalding et al., 2001), provides a
herbivorous echinoids biomass estimate for RA model of 0.722 t·km-2. The biomass on coral
reefs alone is then approximately 41.25 t·km-2, which compares favorably with the value used by
Aliño et al., (1993). They assumed a sea urchin biomass on Philippine reefs of 35.77 t·km-2
based on unpublished data cited therein. However, for a Caribbean reef, Opitz (1993) assumed a
very high biomass value for Echinoderms of 600 t·km-2.
The P/B for the RA model is 0.541 yr-1. A histogram of the average test diameter of 20 sea
urchin species was plotted and 5 cm was found to be the most frequent value for test diameter.
The test diameter was converted to weight using the relationship W= 0.247*D-2.66 (Russo, 1977).
105
The maximum age estimate is 8 years for the species Brissopsis lyrifera (Hollertz, 2002) is used
in Brey’s (1995) equation to obtain the P/B value. Our production rate is lower than the one
used by Pauly et al. (1993) for sea urchins, which is equivalent to 2.34 yr-1.
The Q/B value for the model (9.423 yr-1) was calculated as the average for 2 species (Tripneustes
gratilla and Salmacis sphaeroides) based on feeding ecology of tropical sea urchins (Klumpp et
al, 1993). A consumption rate cited in Pauly et al. (1993) is 3.58 yr-1.
A small catch for sea urchins was entered into the RA model, 2.76E-3 t·km-2. It is based on the
‘other invertebrate’ category identified in DKP fisheries statistics. That unidentified amount was
divided between 5 invertebrate groups that did not have more precise catch information
available. The statistics represent average catch in the years 2000-2005.
Bivalves
Bivalve biomass is estimated for the RA model based on reef transect results for giant clam
provided in COREMAP (2005). The average number of individuals in reef top transects is 2.1
individuals.
Reef top transect area is 40 m2.
This gives an average density of 0.053
individuals·m-2. This density was converted to weight using the average individual weight of 2
kg to obtain a total biomass estimate of 1.8377 t·km-2. Assuming that giant clam contributed to a
fifth of the biomass of bivalves, bivalve biomass was estimated to be 9.189 t·km-2. Biomass
density on reefs has been corrected for the relative reef area in RA based on Spalding et al.
(2001).
The P/B for bivalves in the model is 2.514 yr-1. The value is calculated as the average of 31
warm water species from Brey (2006). The Q/B value of 5.618 yr-1 is calculated for bivalves
from consumption and biomass estimates from the same source.
A catch is calculated for RA as 5.89E-3 t·km-2. It is based on entries in DKP fisheries statistics
for pearl oyster, unidentified mollusks, clam and abalone. It is an average of the years 20002004.
106
Sessile filter feeders
The biomass of the group, 4.58 t·km-2, is based on estimates of sponge biomass from fore-reef,
lagoon and back-reef environments on Davies Reef (Wilkinson and Evans, 1989). The P/B value
of 1.48 yr-1 for sessile filter feeders was borrowed from a Mexican coral reef model (AlverezHernandez, 2003). The Q/B value, 5.258 yr-1, was calculated from consumption estimates of
epireefal sponges from Kötter (2002). A small catch was entered for sessile filter feeders, 0.001
t·km-2.
Epifaunal detritivorous invertebrates
The biomass of the epifaunal detritivorous invertebrate group is based on starfish biomass
calculated from COREMAP (2005) reef transect densities.
That document suggests 3.2
2
individuals per 40 m of reef area. Assuming an average weight of 1 kg per animal, and
multiplying the total estimate by five to account for unsampled taxa in this functional group, a
biomass estimate is determined for RA as 7.001 t·km-2 (this density has been corrected for reef
area in RA based on ratios in Spalding et al., 2001). This amount was split between epifaunal
detritivorous and carnivorous invertebrate groups in the ratio of 1 to 5. Biomass of epifaunal
detritivorous invertebrates is therefore estimated for RA to be 1.4 t·km-2. The total biomass of
epifaunal invertebrates calculated here (7.001 t·km-2) equates 400 t·km-2 on reefs. This agrees
well with the reef biomass density used by Optiz (1993) for miscellaneous mollusks/worms on a
Caribbean reef system, 430 t·km-2.
The P/B was calculated as the average of the P/B of grazing, suspension feeding, deposit feeding
and scavenger gastropods (13 yr-1) and echinoderm species (16 yr-1) from (Brey, 2006).
Detritivores ingest about 0.01 to 0.4 times their body weight daily (Lopez and Levington, 1987).
For the calculation of Q/B, the average was arbitrarily chosen, 0.05, for all the detritivorous
invertebrates. This gave a Q/B equal to 18.25 yr-1. The consumption rate cited in AlverezHernandez (2003) is 15 yr-1.
A small catch for epifaunal detritivorous invertebrates (3.08E-3 t·km-2) is entered into the RA
model. It is based on the ‘other invertebrate’ category identified in DKP fisheries statistics. That
107
unidentified amount was divided between 5 invertebrate groups that did not have more precise
catch information available. The catch used in the RA model also includes figures listed for
mancadu, a gastropod. The statistics represent average catch in the years 2000-2005.
Epifaunal carnivorous invertebrates
As mentioned in the group description above, total epifaunal biomass was estimated to be 7.001
t·km-2 based on starfish abundance counts from COREMAP (2005).
The starfish biomass
estimates were inflated by five times to represent other taxa, and this amount was split between
epifaunal detritivorous and carnivorous invertebrate groups in the ratio of 1 to 5 (see above entry
for more information). Biomass of epifaunal carnivorous invertebrates is therefore estimated to
be 5.833 t·km-2, although that amount was subsequently reduced in balancing the model to 5.6
t·km-2.
The P/B ratio for the predatory echinoderm Asterias forbesi (2.64 yr-1) was used (Robertson,
1979). The Q/B value of 10.52 yr-1 was calculated for predatory gastropods and echinoderms
from consumption and biomass estimates in Jorgensen et al. (1991) from Florida Bay.
A small catch for epifaunal carnivorous invertebrates (3.6E-3 t·km-2) is entered into the RA
model. It is based on the ‘other invertebrate’ category identified in DKP fisheries statistics. That
unidentified amount was divided between 5 invertebrate groups that did not have more precise
catch information available. Catch for this group also includes the figures listed for snails. The
statistics represent average catch in the years 2000-2005.
Infaunal invertebrates
The infaunal biomass was estimated for a coral reef lagoon to be 3.181 gC·m-2. This value was
converted to wet weight using the factor 0.116 (Brey, 2006) to obtain the biomass estimate
27.422 t·km-2 for the RA model. The P/B ratio for large and small macrophagus polychaetes,
microphagus polychaetes, crustaceans, bivalves, gastropods, and other infauna (Riddle et al.,
1990) was weighted by relative biomass and averaged to obtain a P/B equal to 4.014 yr-1. The
Q/B was estimated to be 19.267 yr-1 from consumption estimates of infauna from shallow and
deep zones (Riddle et al., 1990).
108
Jellyfish and hydroids
The biomass estimate from Buchary (1999) for the Java sea model 0.1 t·km-2 was used for the
model. An alternate estimate equal to 0.222 t·km-2 was found for Florida Bay (Uye and
Shimauchi, 2005). The P/B ratio 10.230 yr-1 (Venier, 1997) was used for the RA model. The Q/B
value (25.463 yr-1) is based on consumption estimates of Aurelia aurita in the inland sea of Japan
(Uye and Shimauchi, 2005). For comparison, Buchary (1999) used a lower P/B ratio for jellyfish
from the Java Sea of 5.011 yr-1 but her Q/B value was very similar, 25.050 yr-1.
Carnivorous zooplankton
Aliño et al., (1993) used a zooplankton biomass estimate for a Philippines reef system of 2.87
t·km-2, while Buchary (1999) used 0.310 t·km-2. The value used in this model, 1.0 t·km-2 is
intermediate. The P/B value 63.875 yr-1 was based on Borgne (1982) daily P/B estimate in the
range 15 to 20% daily for carnivorous zooplankton species. The mid value of the range was used
to calculate the P/B value.
The Q/B value used by Aliño et al., (1993) was 133.33 yr-1, although they allowed Ecopath to
estimate this figure.
The Q/B value used by Buchary (1999) was similar at 135.05 yr-1.
Although the consumption rate used in the RA model for carnivorous zooplankton (196.28 yr-1)
was finally estimated by Ecopath assuming an EE of 0.95, estimates of Q/B were located for
chaetognaths, mysids, planktonic amphipods and ichtyoplankton. Q/B for chaetognaths is
calculated as 53.851 (Saito and Kiorobe, 2001), mysids consumption is 26.456 yr-1 based on a
laboratory experiment (Chipps and Bennett, 2002), planktonic amphipod consumption is 23.884
yr-1 based on ingestion rate experiments (Ikeda and Shiga, 1999) and ichthyoplankton
consumption is 178.487 yr-1 based on an empirical relationship (Houde and Schekter, 1980).
Another similar estimate was obtained for Sagitta elegans Q/B equal to 65.6746 was calculated
based on consumption estimate by Terazaki (1996); this assumes a mean size of 7.5 to 10 mm
and weight of 41 µgC per individual (Saito and Kiorobe, 2001).
109
Large and Small herbivorous zooplankton
The RA model uses the biomass estimate from Buchary (1999) for the Java sea of 0.56 t·km-2 for
large herbivorous zooplankton and 2.43 t·km-2 for small herbivorous zooplankton. An alternate
estimate was made as 6.645 t·km-2 for all the zooplankton groups. This represents the average of
zooplankton biomass for the latitude (0.5N to 2.5N) and longitude (129.5E to 130.5E) (O'Brien,
2005). This estimate agrees with the one used in the RA model when the figure is divided among
the EwE zooplankton groups.
The P/B value for large herbivorous zooplankton 29.2 yr-1 is based on Borgne’s (1982) daily P/B
estimate in the range 6 to 10% of body weight daily for herbivorous zooplankton species. The
mid value of the range was used to calculate the P/B value. The P/B value used for the model is
32.0 yr-1; it was increased slightly to lay closer to the carnivorous zooplankton rate, as the
carnivorous group would consist of larger species. An alternate estimate was made as 101.284 yr1
as the average P/B for Daphnia galeata and Bosmina longirostris (McCauley et al., 1996),
although these high rates are likely inappropriate as a group average. The value for herbivorous
zooplankton used by Pauly and Christensen (1996) is 27 yr-1, close to our estimate.
The P/B value for small herbivorous zooplankton 91.25 yr-1 is based on Borgne’s (1982) P/B
estimate in the range 22 to 28% daily for small herbivorous zooplankton species. The mid value
of the range was used to calculate the P/B value. Another estimate was calculated based on the
average P/B for small herbivorous plankton from 4 sites (Princess Charlotte Bay,
CairnseInnisfail, North West Cape shelf and NWC shelf break) in Great Barrier Reef. That
value is 54.743 yr-1 (McKinnon et al., 2005). A conversion ratio of 0.29 is used to convert
zooplankton weight in mgC to wet weight (Hansen et al., 2004).
The Q/B for large zooplankton is calculated based on ingestion rates estimated by McCauley et
al., (1996). The adult weight of Daphnia galeata and Bosmina longirostris is determined using a
size-weight table for Daphnia (Gorokhova and Kyle, 2002); it was assumed that Bosmina has the
same relation of body size to body weight. The Q/B for small herbivorous zooplankton is
calculated based on consumption by copepods estimated by Borgne et al. (1989) to be 265.813
110
yr-1. Another estimate calculated using an empirical relation for ingestion rate for copepods
(Huntley, 1988) was too high 2232.537 yr-1 and was abandoned.
Phytoplankton
A biomass estimate of 26.1 t·km-2 was calculated from the average of phytoplankton standing
biomass during upwelling (3.7 gC·m-2) and downwelling (2.1 gC·m-2) from the Banda Sea
(Tomascik et al., 1997). The P/B value of 109.118 yr-1 was calculated by dividing the average
PP estimate 2848 g·m-2·yr-1 (GoMor SAI, Italy 2006) for the cells used in the RA model by the
biomass estimate. Production rates used for phytoplankton in other reef Ecopath models follow:
Caribbean: 160 yr-1 (Arias-Gonzalez, 1998), 70 yr-1 (Opitz, 1993); Java Sea: 135 yr-1 (Buchary,
1999). Ours is therefore an intermediate value.
Macro-algae
The biomass estimate for macro-algae is 39.389 t·km-2 based on algal biomass on Carribean coral
reefs (Odum and Odum, 1995). The biomass estimate was obtained as 2250.4762 t·km-2. This
was scaled according to coral reef area (using ratios in Spalding et al., 2001) to obtain the value
39.389 yr-1.
The P/B rate for macro-algae was set in the RA model as 10.225 yr-1; it is based on benthic algal
production in coral reefs reported by Russ and McCook (1999). The same value is cited in
Wolanski (2001) for ‘benthic autotroph’ production rates. By comparison, the P/B rate used for
benthic autotrophs in Caribbean reef systems by Opitz (1993) and by Arias-Gonzalez (1998) was
13.25 yr-1. The value used by Buchary (1999) for the Java Sea was 11.885 yr-1 for the group
‘benthic producers’; and the value used by Aliño et al., (1993) in the Philippines for ‘seaweeds’
was 15.34 yr-1. These estimates all agree closely.
Sea grass
The biomass was calculated based on the biomass estimates of 9 sea grass species (Enhalus
acoroides, Cymodocea rotundata, Cymodocea serrulata, Halophia ovalis, Halodule pinifolia,
Halodule uninervis, Syringodium isoetifolium, Thalassia hemprichii and Thalassodendron
111
ciliatum) from Flores Sea (Tomascik et al., 1997). The biomass was calculated to be 3180.952
t·km-2, this value was scaled according to the potential sea grass area in the model to obtain the
value 20.157 t·km-2. This amount is similar to the value used by Aliño et al., (1993), who based
their value on Fortes (1990) and estimated 702 g·ww·m-2, which converts to about 14 t·km-2
when corrected for the area of coral reefs in RA. Other estimates of sea grass biomass when
scaled to seagrass area are 3.680 t·km-2 (DeIongh et al., 1995) and 6.97 t·km-2 (Erftemeijer.
1994). P/B is calculated to be 13.758 yr-1 based on leaf biomass and production of Thallassia
hemprichii (Erftemeijer. 1994).
Mangroves
The average litter production was estimated to be 7.7 t·dw·10000 m-2. (Tomascik et al., 1997).
The litter biomass can be calculated by dividing the production estimate by mangrove P/B.
Alternatively, the primary production from mangroves was given by Tomascik et al. (1997) as
25.936 kgC·d-1·10000 m-2. Biomass was calculated using this production estimate, however the
values obtained in both cases were very high (>30097 t·km-2), so were not used for the model.
Instead the EE of the mangroves was fixed at 0.02 to account for terrestrial mortality and
Ecopath was allowed to calculate the biomass as 19.136 t·km-2.
The P/B was calculated from the leaf litter production over total mangrove biomass (includes the
roots, trunk, branches and leaves) for the 2 species Rhizophora mucronata and Ceriops tagal.
The average P/B is calculated to be 0.066 yr-1 based on biomass and production estimates from
Slim et al (1996). Alternatively, P/B could be calculated based on net primary production of
Rhizophora apiculata as 0.170 yr-1 (Christensen, 1978).
Mangroves thrive in close proximity to the shoreline reefs in Mayalibit Passage in Waigeo, there
is an excellent mix of mangroves and sheltered reefs in Wayag Islands (Tomascik et al., 1997).
However, Indonesia’s mangrove forests face a variety of threats. They are harvested for timber
and they are also being removed for land reclamation and to make habitat for fish ponds (Priyono
and Sumiono, 1997). Loss of mangroves hurts the shrimp fishery (Martosubroto and Naamin,
1977) and may impact the survival of juvenile fish that congregate to forage and avoid predation
(Laegdsgaard and Johnson, 2001).
These behaviours are incorporated in the EwE models
112
through use of mediation functions (Section 2.5.2 - Mediation factors). Faunce and Serafy
(2006) provide a comprehensive review of field studies that consider mangroves as fish habitat.
Fishery discards and detritus
The standing biomass of fishery discards is set at 20 t·km-2. Detritus biomass is set at 100 t·km-2.
2.6 The 1990 Raja Ampat model
2.6.1 Group biomasses
A 1990 RA Ecopath model is designed based on the 2006 RA model. Biomasses for the 1990
model are shown in Table A.4.1, along with the rationale used to parameterize biomass and catch
of each functional group. Groups for which no relative biomass estimates are available are
assumed to have a similar biomass in 1990 as in 2006; this mainly applies to non-commercial
and non-fish groups (i.e., listed as “No change” in Table A.4.1). The biomasses of some
commercial groups are set in the 1990 model according to the relative abundance change
suggested by catch per unit effort (CPUE) series (“CPUE” in Table A.4.1). The CPUE series did
not suggest significant biomass declines for groupers or snappers, despite the fact that they have
been heavily fished since 1990. We assume that the decline in these species has been masked by
the CPUE series due to their reproductive biology. Because they congregate in spawning
aggregations, and because fishers target those aggregations, the biomass density available to
fisheries may remain constant over a wide range of population sizes. For a discussion on the
dangers of using CPUE data as a proxy for abundance see Beverton and Holt (1957), Gulland
(1974) or Hilborn and Walters (1992). Grouper and snapper biomass was therefore assumed to
have decreased by 50% since 1990 (“Custom” in Table A.4.1). In general, the CPUE data
suggested a higher abundance of commercial fish predators in 1990 relative to 2006. This was
evident in balancing the 1990 model because many of the invertebrate prey groups appeared
overexploited by fish predators. We therefore allowed Ecopath to estimate the biomass of
several basal invertebrate and planktonic groups, by assuming an EE of 0.99 (“Ecopath” in Table
A.4.1).
113
For multi-stanza groups, the total biomass of all age classes combined is assumed to have
increased or decreased since 1990 in direct proportion to CPUE, or according to the custom rules
used. Group biomass is divided into age stanzas according to the mortality schedule, which is
inherited from the 2006 model for all functional groups except groupers, snappers, coral trout,
napoleon wrasse and large reef associated fish. Recent fisheries have developed for these groups
that might have shifted their population age structures, and so we assume that the 2006
ecosystem contains a greater proportion of immature individuals relative to 1990. The total
mortality of adult stanzas is reduced in the 1990 model by 20% for these groups. Large sharks
are also heavily exploited in RA, but we assume that they have a similar age distribution today as
they did in 1990, since it is likely that most of the depletion of this group occurred prior to 1990.
2.6.2 Fisheries
Fishery catches for 1990 were set for commercial groups directly from the trends suggested by
the recorded landings in DKP and/or Trade and Industry Office statistics (see Section 2.5.10 Interpreting catch statistics) (i.e., listed as “Time series” in Table A.4.1). For some groups, the
time series data does not extend as far back as 1990, and so the values used to initialize the 1990
Ecopath model typically record the earliest catch figure available.
Where the time series
landings record is uncertain, we have made critical assumptions regarding the annual quantities
of group catch over the last 16 years. A relatively new and major fishery has developed for
Napoleon Wrasse, for example. As the time series data was inadequate, we assumed that the
catch in 1990 was equivalent to 10% of the current amount (listed as “10%” in Table A.4.1).
Other heavily exploited species also had inadequate time series catch information. Most often,
we made the assumption that the catch in 1990 was equal to 50% of the current landings; this is
listed as “50%” in Table A.4.1. Non-commercial groups are generally assigned “no catch” in
Table 1990 model parameters.
2.6.3 Fitting to time series
The 1990 RA model is driven forward 16 years using an independent series of fishing effort.
Refinements were made to the model structure to improve the data fit with respect to the
observed catch and CPUE time series. Coarse corrections were made to basic parameters to
correct the model’s dynamic behaviour (see functional group descriptions).
The proper
114
adjustment of P/B ratios for multi-stanza groups is especially critical to Ecosim’s performance.
The diet matrix and the vulnerability matrix were modified, as well as Ecosim’s feeding
parameters. 1990 vulnerabilities are presented in Table A.3.6; feeding parameters are in Table
A.5.1.
Trophic flow parameters
The vulnerability matrix for the 1990 RA model was parameterized manually and by using the
automated vulnerability search routine available in Ecosim (Christensen and Walters, 2004a).
The search routine uses an iterative procedure to first identify predator-prey interactions critical
to model functioning. With a least-squares criterion, it optimizes those key vulnerabilities in
order to recreate observed time series of catch, biomass or other input data. The optimization
was performed first on a large number of interactions. Then, additional searches were used
throughout the balancing process on a fewer number of groups that are highly influential in the
system. The most influential predator groups in the 1990 model tend to be mackerel, large and
medium reef associated fish, skipjack and other tuna.
Biomass accumulation rates were used widely to manipulate the initial mortality to production
ratios of functional groups, and achieve realistic biomass change as suggested by CPUE data.
Generally, commercial groups are made to follow their observed pattern of biomass change
through the impacts of fisheries; we coerced other groups to follow time series data by
redistributing predation mortality throughout the diet matrix and reshaping the predation
mortality trends using vulnerability adjustments. The assumption that we have made is therefore
that the decline seen in commercial groups is attributable primarily to fishing, while changes to
non-commercial groups are due to trophodynamics.
In many cases with commercial groups, the catch recorded in governmental statistics is not
sufficient to cause the decrease in group biomass suggested by CPUE data. The time series catch
estimates used for fitting were therefore increased over the original DKP and Trade and Industry
Office statistics to acknowledge the impact of unreported catches. For each year between 1990
and 2006, the reported catch was increased by a fixed percentage. The relative proportion is
115
based on the estimates of unreported catch used in the 2006 and 1990 models (see Section 2.5.11
- Functional group descriptions). The catch has therefore been scaled so that the time series
forms a continuous trend passing through the start and end point model values. The CPUE trend,
which serves as a proxy for biomass, is entered into Ecosim as a relative trend. The suspected
decline in grouper and snapper biomass over the last 16 years is not reflected in the CPUE trends
for biological reasons discussed earlier.
These groups were therefore omitted from the
vulnerability search criterion; the automated routine did not attempt to fit these groups to data.
Primary production anomaly
We introduce a primary production anomaly trend using Ecosim’s data fitting technique. Ecosim
generated a climate anomaly trend for the years 1990-2006 that would minimize the residuals
between observed and predicted catch and CPUE. The P/B anomaly is applied only to the most
variable group, phytoplankton, and it is designed to reduce the sum of squares with regard to all
ecosystem components. Five spline points are introduced to smooth the production trend.
We rescaled and reentered the production modifier so that the predicted annual phytoplankton
biomass variability from simulations matched the observed variability, as determined by SeaWifs
primary production data (SAU, 2006). The annual coefficient of variation (CV) is estimated to
be 4.7% from the satellite data. That is an average for all the cells listed in the database for RA,
and it represents the average variability of each 5 year period between 1990-2005. We used the
average 5-year variability so that random environmental fluctuations would be the main cause of
interannual biomass change, and not directional biomass reductions caused by fisheries. The CV
is based on data from the years 1998-2002. The amplitude of the primary production forcing
pattern was reduced by 42% to generate the required CV.
By adjusting the primary production anomaly trend in this way, discrepancy between predicted
and observed catch and relative biomass for the ecosystem is made slightly worse (sum of
squares is increased by 1.7%). However, Ainsworth (2006) demonstrated that this method can
lead to realistic population variability at higher trophic levels. Accurately representing the
variability of production trends throughout the system can greatly improve the output of more
advanced analyses in Ecosim.
For example, a Monte Carlo technique can be used in an
116
ecosystem-based population viability analysis to estimate the extinction risk for commercial
species associated with various fishing policies (Ainsworth, 2006; Pitcher et al., 2005). This
technique is applied for RA in Section 3.4 - Challenges to Ecosim.
2.6.4 Equilibrium analysis
The equilibrium analysis routine in Ecosim helps us examine functional group dynamics under
varying degrees of fishing pressure. This routine is an invaluable diagnostic tool and it can be
used to answer fundamental questions regarding the production potential of stocks and their
resilience to fishing. The routine sketches the surplus yield curve and the biomass equilibrium
curve2 (Equilibrium routine: Christensen et al. 2004a). Increasing fishing mortality stepwise
from zero to several times the baseline model value, the automated routine calculates the
equilibrium biomass of a subject functional group under a long-term fishing scheme (1000
years).
At the left-most extent, the biomass equilibrium curves tell us what biomass level the group
assumes under zero fishing mortality (i.e., virgin biomass or B0). The catch equilibrium curves
are essentially single-species surplus production curves; the maximum height of the curve shows
maximum sustainable yield (MSY) of the stock and the fishing mortality at which that occurs,
the FMSY. It is useful to compare the current fishing mortality, for example in 2006 (F2006), with
the FMSY. In a properly parameterized model, the baseline fishing mortality of underexploited
groups should be less than FMSY and greater than FMSY for overexploited stocks.
How a
functional group behaves under dynamic simulation will be greatly influenced by the initial
relative level of exploitation represented in the basic Ecopath model. The predictions made by
this routine can be compared to estimates derived from single-species tools, and presented to
fisheries experts in Indonesia for the purposes of validation.
The equilibrium routine offers several settings, including one that holds the biomass of other
functional groups in the model at their (static) baseline conditions. By selecting this option,
2
Hilborn and Walters (1992) discuss the theory and applications of equilibrium stock assessment models.
117
Ecosim is reduced to a single-species model, and higher order trophic interactions are removed
from consideration. Alternatively, the user can permit the usual predator-prey dynamics to occur
that Ecosim is designed to simulate. In this use, the equilibrium analysis will consider the multispecies context and provide, in principle, a more accurate representation of ecosystem response
to fishing that is suitable for EBM. By excluding these interactions, the analysis serves as a
validation tool, by which we can compare Ecosim’s predictions with ‘classical’ single-species
analysis models.
Where possible, the equilibrium analysis is performed for subject groups while holding the
biomass constant for other functional groups to facilitate comparison with single-species models.
However, where multi-stanza groups were employed, it was sometimes necessary to perform the
equilibrium analysis manually in Ecosim to circumvent a current limitation in the equilibrium
analysis routine.
The equilibrium analysis routine increments fishing mortality only for the subject functional
group being tested (V. Christensen. UBC Fisheries Centre. 2202 Main Mall, Vancouver Canada.
Personal communication). If the test is run on an adult stanza, then the routine holds fisheries
constant for the juvenile and/or sub-adult stanzas. If there is normally a high fishing rate on the
juvenile or sub-adult stanzas, then the equilibrium analysis can be misleading. It will suggest a
high estimate of sustainable adult fishing mortality because the adult pool is being fed by
constant recruitment from the immature stanzas. In reality, an increasing amount of catch on
adults will usually be accompanied by increased catch on immature groups, either intentionally
or incidentally. This increase is currently missed by the equilibrium routine. This has always
been a potential source of error, where split-pools were used to describe age stanzas, but the
problem was amplified with the addition of the multi-stanza routine. Now, large amounts of
fishing effort may typically be modelled on sub-adult groups, yet the equilibrium analysis routine
will continue to assume knife edge entry to the fishery effectively, and increment fishing effort
only on the adult stanza. To provide a more realistic view of surplus stock production for these
groups, it is necessary to perform the equilibrium analysis manually, increasing fishing mortality
on juvenile and sub-adult groups as well as the adults. This was done here on key groups, and
118
these runs are indicated by asterisks in Fig. B.2.1. Trophic interactions must necessarily be
included in these runs.
2.6.5 Challenges to Ecosim
The 2006 RA Ecosim model is subjected to various challenges to test its behaviour and stability.
By applying extreme fishing scenarios we can see how the model performs when fishery and
functional group parameters vary far from their initialization values. Species interactions, which
may appear nominal under baseline conditions, can compound in unexpected ways to cause
oscillations or chaotic model behaviour. If a region of instability exists in the fleet-effort
responses of the model, normal fishing forecasts that apply conservative or realistic fishing
strategies may or may not be affected. However, if a combination of fishing efforts exists that
can drive the model to instability, the policy search routine will typically become useless as it
then only locates unstable solutions that offer impossibly large benefits.
We use three fishing strategies to challenge the model: no fishing, baseline fishing and
increasing fishing. For the ‘no fishing’ scenario, the fishing mortality of each gear type is
reduced to zero for all simulation years and target groups (including directed and bycatch
mortality). Under this fishing test, we expect depleted commercial functional groups to rebound,
and the prey of these groups should see a corresponding decrease. The baseline fishing scenario
is provided for comparison; it represents the current (2006) fishing mortalities applied into the
future. The ‘increasing fishing’ scenario assumes an annual increase in fishing mortalities of
3.2% across all gear types. This rate corresponds to the recent increase in the human population
in eastern Indonesia (BPS, 2006). We expect to see the biomass of exploited functional groups
decline and a corresponding increase in the biomass of their prey. All Ecosim simulations are
for 16 years, from 2006-2022.
As an additional test of the model’s performance, we have used 50 Monte Carlo simulations to
vary Ecopath’s biomass parameters for commercial groups. For this sensitivity analysis, groups
are allowed to vary +/- 20% from their Ecopath biomass values, the Monte Carlo draws from a
uniform distribution. The following commercial groups are varied: adult and sub-adult groupers,
119
adult and sub-adult snappers, adult and sub-adult Napoleon wrasse, skipjack tuna, other tuna,
mackerel, billfish, and the adult stanzas of coral trout, large sharks, large, medium and small
pelagic, large, medium and small reef-associated, large and small demersals, large and small
planktivores, and anchovy. The Monte Carlo routine allows us to test the sensitivity of initial
biomass parameters, establish a range of error for predictions, and determine the depletion risk
for functional groups in a population viability analysis.
2.7 Ecospace parameterization
An Ecospace model is designed for RA and presented here. Ecospace maps are also presented
for high resolution models of Kofiau Island and Dampier Strait, as well as habitat classifications
(Table 2.7) and fishery locations (Table 2.8). All of the Ecospace work is preliminary thus far;
dynamics have not been analyzed and we present these here only for expert evaluation. Highresolution models for Kofiau Island, Dampier Strait and SE Misool Island will be integrated into
Ecospace in 2007, following the development and refinement of reef-based Ecopath models,
where biomass, catch and other parameters are adjusted to represent the smaller study areas
using site-specific data.
The RA Ecospace model utilizes GIS information assembled by the BHS EBM project, as well
as oceanographic and biological data from the literature to represent the study area in a 2
dimensional grid matrix of spatial habitat cells. Standard Ecopath and Ecosim parameters are
inherited from the 2006 RA model described in this report.
120
Table 2.7 - Habitats occupied by functional groups in three 2006 Ecospace models. RA, Kofiau Island and
Dampier Strait.
Area occupied
Mangroves
Enclosed lagoon
Reef
Bathypelagic (> 1000m)
Mesopelagic (200-1000m)
200m
20m
All
Area occupied
Mangrovev
Dampier St.
Estuary
Enclosed lagoon
Reef
Deep (> 200m)
200m
20m
All
Kofiau
Area occupied
Reef
Deep (> 200m)
200m
20m
10m
All
Raja Ampat
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Dugongs
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Whale shark
Manta ray
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
0.90
0.96
0.90
0.08
1.00
1.00
1.00
1.00
0.02
0.04
0.04
0.01
0.04
0.04
0.01
0.04
0.04
0.01
0.99
0.99
0.99
0.90
0.04
0.01
0.90
0.90
0.90
0.90
0.90
1.00
1.00
1.00
0.04
0.01
0.01
1.00
1.00
1.00
1.00
1.00
1.00
0.04
0.01
0.04
0.01
0.04
0.93
0.93
0.84
0.16
1.00
1.00
1.00
1.00
0.02
0.06
0.06
0.04
0.06
0.06
0.04
0.06
0.06
0.04
0.99
0.99
0.99
0.93
0.06
0.04
0.97
0.97
0.99
0.99
0.97
0.99
0.99
0.99
0.06
0.07
0.06
0.95
0.96
0.95
0.96
0.95
0.96
0.97
0.16
0.99
0.16
0.99
0.92
0.92
0.22
0.44
1.00
1.00
1.00
1.00
0.05
0.07
0.07
0.02
0.07
0.07
0.02
0.07
0.07
0.02
0.99
0.99
0.99
0.92
0.07
0.02
0.58
0.58
0.99
0.99
0.58
0.99
0.99
0.99
0.07
0.08
0.07
0.97
0.98
0.97
0.98
0.97
0.98
0.94
0.44
0.77
0.44
0.77
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
0.01
0.39
0.39
0.39
0.39
0.04
0.01
0.04
0.01
0.41
0.41
0.59
0.90
0.04
0.01
0.16
0.93
0.13
0.95
0.13
0.97
0.16
0.99
0.16
1.00
1.00
0.84
0.95
0.99
0.16
0.44
0.92
0.42
0.97
0.42
0.72
0.44
0.77
0.44
0.56
0.56
0.56
0.97
0.99
0.44
121
Table 2.7 - (cont.)
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Mangroves
Fishery discards
Detritus
0.04
0.01
0.04
0.01
0.10
0.10
0.01
0.10
0.10
0.10
0.99
0.96
0.96
0.90
1.00
1.00
1.00
1.00
1.00
0.04
0.10
1.00
0.41
0.39
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
0.41
0.08
0.02
1.00
1.00
0.99
0.16
0.99
0.16
0.99
0.15
0.04
0.15
0.15
0.15
0.99
0.99
0.99
0.93
0.99
1.00
1.00
1.00
1.00
0.06
0.06
1.00
0.99
0.99
1.00
1.00
0.96
0.99
1.00
1.00
1.00
1.00
0.99
0.01
0.01
1.00
1.00
Area occupied
Mangroves
Enclosed lagoon
Reef
Bathypelagic (> 1000m)
Mesopelagic (200-1000m)
200m
20m
All
Area occupied
Mangrovev
Dampier St.
Estuary
Enclosed lagoon
Reef
Deep (> 200m)
200m
20m
All
Kofiau
Area occupied
Reef
Deep (> 200m)
200m
20m
10m
All
Raja Ampat
0.77
0.44
0.77
0.44
0.77
0.43
0.02
0.43
0.43
0.43
0.99
0.99
0.99
0.92
0.77
1.00
1.00
1.00
1.00
0.43
0.43
1.00
0.99
0.99
1.00
1.00
0.98
0.99
1.00
1.00
1.00
1.00
0.77
0.01
0.01
1.00
1.00
Table 2.8 - Designated fishing activity in Ecospace habitat types.
200m
Deep (> 200m)
Reef
Enclosed lagoon
Estuary
Mangrovev
All
20m
200m
Mesopelagic (200-1000m)
Bathypelagic (> 1000m)
Reef
Enclosed lagoon
Mangroves
Dampier St.
20m
Reef
Deep (> 200m)
200m
20m
Kofiau
All
Spear and harpoon
Reef gleaning
Shore gillnet
Driftnet
Permanent trap
Portable trap
Diving spear
Diving live fish
Diving cyanide
Blast fishing
Trolling
Purse seine
Pole and line
Set line
Lift net
Foreign fleet
Shrimp trawl
10m
All
Raja Ampat
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
122
2.7.1 Raja Ampat 2006 Ecospace model
The RA Ecospace map is 100 x 120 cells and describes an area about 256 km east to west, and
321 km north to south; each cell represents an area 2.56 x 2.57 km, or approximately 6.57 km2 at
mid-latitudes. The north-westernmost coordinate lies at 129o 12’ E, 0o 12’N, and the southeasternmost coordinate lies at 130o 30’ E, 2o 42’S. Five aquatic habitat types are used in the RA
Ecospace model. The habitats are based partly on bathymetry, with 10, 20 and 200 m contours
represented as light green, orange and dark green cell colours in Fig. 2.6. A deep-water habitat
type describes cells greater than 200 m in depth (blue), and a reef habitat type shows the
locations of submerged reefs (red). Bathymetric information was obtained from Indonesian
nautical charts collected by the BHS-EBM project in GIS files (contact: M. Barmawi, TNCCTC. Jl Pengembak 2, Sanur, Bali, Indonesia).
123
Figure 2.6 - Ecospace habitat map of RA. Land cells are black, and five habitat types
summarize major oceanographic zones (light green: 10m isobath; orange: 20m isobath;
dark green: 200m isobath; blue: deep water (> 200m); red: reef areas). Map dimensions
are 125 x 100 cells. Cell dimensions are approximately 2.56 x 2.57 km.
Primary production spatial forcing pattern
A primary production spatial forcing pattern is entered for the RA model using data from the Sea
Around Us ecology database (SAU, 2006); the information is retrieved automatically by an
Ecospace sub-routine (contact: V. Christensen, UBC Fisheries Centre.
2202 Main Mall,
Vancouver, Canada). The primary production has been estimated using a model by Platt and
Satyendranath (1999) that integrates PP by depth based on chlorophyll pigment concentrations
124
and photosynthetically active radiation (Lai, 2004; Hoepffner et al., 1999). Ocean colour is
provided by the SeaWiFS database at a spatial resolution of approximately 6 minutes, or 11 km
(http://oceancolor.gsfc.nasa.gov/SeaWiFS/).
There is an average primary production of about 315.4 gC·m-2·yr-1 in the study area. The
chlorophyll data showed an area of high productivity (>1000 gC·m-2·yr-1) in the northern extent
of the Arafura Sea, in the southeast region of the study area. However, there is a large amount of
river input to the sea along the mainland coast of Papua, and remote sensing techniques based on
ocean colour may become confused as the concentration of optically absorbing particles
increases. Suspended material, dissolved organic matter, and bottom reflectance can influence
the data. Although terrigenous nutrient loading will legitimately increase primary production in
that area, we caution that the colour signal north of the Arafura Sea may also be biased by the
suspension of sediments which affects ocean reflectance. Similarly, the area of high production
indicated to the west of Salawati Island may be biased by turbidity due to suspended sediments
from waves, and upwelling produced in the Sagewin Strait to the NE (M. Erdmann. CI. Jl. Dr.
Muwardi. 17 Renon Denpasar, Bali, Indonesia. Personal communication). We have applied the
primary production data directly as obtained from SAU database, but we offer these caveats.
Further work can test the sensitivity of the Ecospace analyses to our assumptions concerning the
distribution of primary production.
There were no elevated levels of primary production reported by SAU (2006) data for the central
portion of Dampier Strait, despite the presence of a strong and productive region of upwelling
that supports a large anchovy fishery (Mark Erdmann. CI. Jl. Dr. Muwardi. 17 Renon Denpasar,
Bali, Indonesia. Personal communication). To capture this production in the Ecospace model, a
region of high productivity was entered in manually. The most productive region of central
Dampier Strait was set arbitrarily at 250% of the reported production rate; the area of high
production tapers off to 30% above the reported rate to the east and west of the central upwelling
zone. On average, cells in this zone are in the 90th percentile of production rates among map
cells. The maximum production rate in this zone is 723 gC·m-2·yr-1, and the region contributes
1.5% to the total RA primary production. This modification to SAU (2006) data is visible in Fig.
2.7 as a yellow-green area in central Dampier Strait. Note that this modification does not change
125
the overall primary productivity of the Ecopath model.
Ecospace scales the relative cell
production rates so that their average equals the phytoplankton P/B defined in the basic
parameter set (Table A.3.2).
Figure 2.7 - Spatial primary production (P/B) for Raja Ampat.
High production (red)
corresponds to production values > 1000 gC·m-2·yr-1; green areas represent medium production
~400-600 gC·m-2·yr-1; blue areas are low production ~200-400 gC·m-2·yr-1; white areas are
oligotrophic <200 gC·m-2·yr-1. Resolution: 6 minutes. Modified from: Sea Around Us database
(SAU, 2006).
126
2.7.2 Kofiau Island model
The Kofiau Island model is represented by 150 cells east to west, and 50 cells north to south
(Fig. 2.8). The modelled area is approximately 27.8 km by 87.6 km; each cell covers an area 555
m by 580 m, incorporating about 0.32 km2 of sea area per cell.
The north-westernmost
coordinate lies at 129o 14’ 20’’ E, 1o 5’S, and the south-easternmost coordinate lies at 130o 1’
20’’E, 1o 20’S. Seven habitat types are used for the Kofiau Island Ecospace model. Three are
based on bathymetric data from GIS collections: 20 m isobath (orange habitat), 200 m isobath
(green habitat) and deep water (> 200m) (dark blue habitat). Reef areas (red), mangrove areas
(teal) and estuaries (light blue) were identified by the marine use survey; these are all
incorporated as dedicated habitat types.
The model relies on GIS data collected by the BHS EBM project (M. Barmawi, TNC-CTC. Jl
Pengembak 2, Sanur, Bali, Indonesia. Unpublished data), and on habitat information reported in
COREMAP (2005). We include ecologically significant areas identified by expert knowledge
and by community interviews in the BHS EBM resource use assessment study. The habitats
consider an enclosed lagoon area (Fig. 2.9) in the Boo Island group west of Kofiau Island. This
area will function as an enclosed lagoon in Ecospace by having little biomass exchange with
outside areas due to land cell proximity. By assigning a dedicated habitat type we can further
quarantine this area from the rest of the system, removing or limiting the trophic influence of
oceanic and high sea species, like billfish, large sharks or whales. A seamount area (Dona
Carmalita) (Fig. 2.10) was identified by the marine use survey; it is included in Ecospace as a
collection of reef and shallow water habitats.
127
Figure 2.8 - Ecospace habitat map of Kofiau Island. Land cells are black, and seven habitat types are used to
describe the marine area (orange: 20m isobath; dark green: 200m isobath; blue: deep water (> 200m); red: reef areas;
light blue: enclosed lagoon; purple: estuary; teal: mangroves). Map dimensions are 50 x 150 cells. Cell dimensions
are approximately 0.56 x 0.58 km, or about 0.32 km2.
A simple advection pattern surrounding the Kofiau Island group has been entered based on
results from the SPAG vial release program. 286 out of 1000 vials have been recovered. They
were found in disperse places, but all east of the release point. The majority of vials (255) were
recovered south and east of Kofiau, near Wejim Is. (north of Misool). (C. Rotinsulu. CI. Jl
Arfak No. 45. Sorong, Papua, Indonesia 98413. Personal communication). We therefore
applied a southeast advection current. Ocean currents must be monitored in RA throughout the
year, however, so that we can account for seasonal variations. Vials were also found to the east
on Salawati and Batanta Islands, and to the north on Weigeo Island indicating complex current
systems. When the Ecospace models are more advanced, we will try to adjust the dispersal rates
of juvenile groups to allow settlement throughout RA.
There are also tidal flows north to south throughout the year passing between the Kofiau Island
group and the Boo Island group in a deep central area. Tidal mixing may sustain a population of
small pelagics providing a significant feeding area for sea birds (A. Muljadi, TNC-CTC. Jl
Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413). We will model that
interaction when Ecospace is more fully developed.
128
2.7.3 Dampier Strait model
The Dampier Strait model uses 105 cells east to west, and 60 cells north to south (Fig. 2.11).
The area covered by the model is
approximately 139 km by 80 km.
Each cell is 1.33 km by 1.33 km, or
about 1.76 km2. Seven habitat types
were used to represent Dampier
Strait. Two habitat types are based
on bathymetry from assembled GIS
data: 20 m isobath (orange habitat),
200 m isobath (green habitat) (M.
Barmawi, TNC-CTC. Jl Pengembak
2,
Sanur,
Bali,
Indonesia.
Unpublished data). Although we did
Figure 2.9 - An enclosed lagoon on Taudore Island. Location
not have accurate depth contour
is west of Kofiau Island, photographed during aerial surveys.
information for areas greater than
Photo credit: Erdi Lazuardi.
200 m, we have assumed that the
interior of Dampier Strait consists of
a deeper area between 500-1000 m
(dark blue).
Mangrove areas (teal)
and reef areas (red) are based on
recent BHS EBM project outputs
(Firman and Azhar, 2006). The
enclosed Mayalibit bay (light blue)
receives its own habitat type to
distinguish the sheltered, shallow bay
from the deeper oceanic areas of
Dampier Strait.
A narrow channel
connects Mayalibit bay to Dampier
Figure 2.10 - Dona Carmalita seamount. Location is south of
Strait (Fig. 2.12).
the Boo Island group (west of Kofiau Island) photographed
during aerial surveys. Photo credit: Andreas Muljadi.
129
Figure 2.11 - Ecospace habitat map of Dampier Strait. Weigeo Island borders on the north,
Batana Island is in the south. Land cells are black, and seven habitat types are used to describe
the marine area (orange: 20m isobath; dark green: 200m isobath; blue: deep water (> 200m); red:
reef areas; light blue: enclosed lagoon; purple: estuary; teal: mangroves). Map dimensions are
50 x 150 cells. Cell dimensions are approximately 0.56 x 0.58 km, or about 0.32 km2.
130
2.8 Fishing policy optimizations
The policy search routine in Ecosim
(Christensen and Walters, 2004b)
iteratively varies the fleet effort and
reruns harvest simulations until it
finds the optimal combination of
fishing mortalities that maximizes
harvest benefits. The routine can be
used to identify fishing patterns that
increase
ecological
economic,
social
and
benefits
from
the
ecosystem by use of a multi-
Figure 2.12 - Myalibit Bay entrance. A narrow channel connects
criterion objective function. We use
Dampier Strait to the shallow enclosed area of Myalibit bay. Photo
this routine here to explore the
credit: Andreas Muljadi.
sustainable production potential of
the RA ecosystem, and quantify the ecological impacts of various optimal fishing policies. We
use randomly selected F per gear type for initialization, and employ the Fletcher-Powell
conjugate gradient optimization method (Fletcher and Powell, 1963). For each gear type in the
model, a single optimal fishing mortality is calculated and applied to each year in the simulation
to find the best equilibrium-level solution in a 16 year forecast, from 2006-2022.
The objective function used here considers two criteria, the economic value produced from the
ecosystem, and the ecological health of the system measured using a proxy for functional group
longevity (B/P). This is the default ecological objective in the policy search routine and it is
inspired by Odum’s (1969) description of mature ecosystems. Economic benefits are assessed
according to their net present value (NPV). NPV is a term used in cost-benefit analysis to
summarize the expected future flow of benefits into a single value, which can be compared
across investment alternatives. Intergenerational discounting (Sumaila, 2001; 2004; Sumaila and
Walters, 2005) is used by default in Ecosim, where the standard discount rate (δ) is 4% and the
131
rate for future generations (δfg) is 10%. This is a precautionary economic criterion because it
assures that resources will be preserved for future generations (Sumaila, 2001).
A range of optimal policies are generated that incrementally vary the relative weightings of the
economic and ecological criteria. The weightings are represented by WECON and WECOL in eq.
2.15; OBJ is the objective function to be maximized by the search.
OBJ = WECON · Σ NPVij + WECOL · Σ B/Pit
Equation 2.15
The summed terms evaluate socio-economic and ecological benefits of the harvest plan across
functional group (i), gear type (j) and simulation time step (t).
This application of the policy search routine will allow us to sketch the trade-off frontier between
profit and ecosystem health, and calculate the marginal costs and benefits of conservation.
132
3. RESULTS
3.1 Time series fitting
Fits to biomass and catch data for functional groups are presented in Figs. B.2.2 and B.2.3,
respectively. Regarding biomass predictions, there is acceptable agreement with data for most
functional groups. Groupers and snappers show a poor fit, but CPUE data is a poor proxy for the
biomass of these groups due to their aggregation behaviour.
Because they congregate in
spawning aggregations, and because fishers target those aggregations, the biomass density
available to fisheries may remain constant over a wide range of population sizes.
The
populations of groupers and snappers have likely declined throughout RA, and Ecosim predicts
this case under the effort assumptions in place (Section 2.5.10 - Effort time series).
One other discrepancy in the time series fitting from 1990 to 2006 is that there is little or no
decrease seen in the biomass of large, medium and small reef associated fish, despite a reported
decline in the CPUE during that period. These groups will be the subject of further tuning after
we have processed abundance data from fisherman interviews (see discussion), and improved
our understanding of the relative biomass change in the RA ecosystem.
For many commercial functional groups, the catch required to cause the biomass decline
suggested by CPUE is substantially larger than the catch estimated from government records
(Fig. B.2.3); the difference may serve as a first estimate of unreported catch for the study area.
Biodiversity is predicted to have declined from 1990-2006 according to Fig. 3.1.
The
biodiversity statistic in use, Q75, is a variant on Kempton’s Q Index (Kempton and Taylor, 1976)
that has been designed for use with ecosystem box models3.
3
Kempton’s Q index represents the inter-quartile slope of the cumulative species log-abundance curve; it evaluates
both species evenness and species richness. Larger values indicate a more biodiverse system. For a discussion on
the use of Kempton’s Q Index in ecosystem models, see Ainsworth and Pitcher (2006); for implementation of the
Q75 index in EwE see Christensen et al. (2004).
133
12
3.30
analysis suggests that the average
11
3.28
trophic level may have seen a slight
downward trend for most of the last
16 years. However, the pattern has
been variable, and the total overall
Biodiversity (Q75)
in RA is reported in Fig. 3.1. The
10
3.26
9
3.24
8
decline from 1990 to 2006 remains
7
3.22
small, from 0 to 0.07 TL.
6
3.20
For
comparison, Essington et al. (2006)
suggested that a mean decline in the
trophic level of catch of 0.15
constitutes evidence of ecologically
1990
1994
1998
2002
TL of catch
The average trophic level of the catch
2006
Year
Figure 3.1 - Raja Ampat ecosystem indicators (1990-2006).
Biodiversity trajectory predicted for 1990-2006 (shaded area).
significant ‘fishing down the food
Biodiversity is measured using Q75, a variant of Kempton’s Q
web’ (Pauly et al., 1998).
index. Trophic level of catch (broken line) may have decreased,
In the
model, the trophic level of the catch
indicating ‘fishing down the food web’.
remains somewhat constant because
of an expanding fishery on high trophic level predator fish groups throughout the length of the
simulation, and decreased catches of anchovy because of a population decline (Fig. B.2.3).
3.2 Predicted climate anomaly
The optimal primary production anomaly trend determined by Ecosim (broken line; Fig. 3.2)
suggests a higher than average rate of production for early years in the simulation, 1990-1995, as
high as 12% above the mean value. The trend shows a lower relative rate of production in recent
years, 2002-2006, approximately 15% below average. Applying the P/B forcing pattern to
phytoplankton reduces ecosystem residuals by 5%. When we scale the primary production
anomaly trend to reproduce the observed phytoplankton variability, ecosystem residuals are
reduced only 3.7% versus the baseline simulation. Future work may confirm whether the
predicted biomass variability of higher trophic level groups has been improved, although data is
limiting.
134
The estimated anomaly shows a weak non-significant negative correlation with the Southern
Oscillation Index (SOI) (Spearmans rank correlation ρ = -0.05; ρ0.05(2),16 = 0.503) (AGBM, 2006)
and a stronger but non-significant negative correlation with sea surface temperature (SST) (ρ = 0.31) (IRI, 2006). The west coast of Papua has the highest primary production variability in
Indonesia (Susanto et al., 2006). Therefore, the outputs of the primary production analysis could
potentially affect our choice of sustainable fishing policies in RA.
Fig. 3.3 shows the coefficient of variation for functional group biomass in RA. The dynamics
from 1990-2006 are conservative without the primary production forcing pattern in place. Using
the optimal forcing pattern determined by Ecosim results in the greatest reduction in residuals
versus observed data, and also causes a high degree of ecosystem volatility. By rescaling the
primary production trend to match phytoplankton observations, the variability of functional
groups is reduced in the model.
0.15
0.10
PP anomaly
0.05
0.00
-0.05
-0.10
-0.15
-0.20
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year
Figure 3.2 - Primary production anomaly.
Anomaly is predicted to reduce
discrepancy between observed and predicted catch and relative biomass; 5 spline points
are used. Broken line indicates optimal forcing pattern predicted by Ecosim; shaded
area shows pattern rescaled to match observed phytoplankton variability.
135
3.3 Equilibrium analysis
Table B.2.1 presents the equilibrium analysis results. Key fishery indicators for commercial
functional groups are summarized in Table 3.1. Where catch on juveniles is significant, the
equilibrium analysis was performed manually.
Trophic level
4+
3-4
2-3
1-2
0
0.2
0.4
0.6
0.8
Coefficient of variation
Figure 3.3 - Coefficient of variation (CV) of RA functional group
biomass (1990-2006). CV is sorted by trophic level. Black bars indicate
no primary production (PP) forcing, grey bars show optimal PP forcing
pattern, white bars show rescaled pattern that improves phytoplankton
variability.
136
Table 3.1 - Fishery indicators for major commercial groups in the 2006 RA model. Values are
determined by the equilibrium analysis. Asterix indicates that the equilibrium values were determined
manually and fishing mortality was incremented for all life history stanzas (see text).
#
MSY
FMSY
F2006
(t•km-2)
(yr-1)
(yr-1)
F2006/FMSY
2006 catch
Group
(t•km-2)
10
Ad. groupers
0.027
0.207
0.094
0.454
0.017
13
Ad. snappers
0.008
0.210
0.155
0.735
0.014
16
Ad. Napoleon wrasse*
0.002
0.228
0.085
0.372
0.001
17
Sub. Napoleon wrasse*
0.003
0.244
0.048
0.197
0.001
19
Skipjack tuna
0.366
0.479
0.548
1.144
0.348
20
Other tuna
0.058
0.251
0.097
0.385
0.047
21
Mackerel
0.058
0.746
0.746
1.000
0.064
22
Billfish
0.068
0.147
0.061
0.417
0.050
23
Ad. coral trout
0.002
0.092
0.045
0.484
0.002
25
Ad. large sharks
0.010
0.476
0.971
2.041
0.025
33
Ad. butterflyfish
0.079
0.553
0.060
0.109
0.016
36
Ad. large pelagic
0.023
0.575
0.575
1.000
0.031
38
Ad. medium pelagic
0.008
1.276
1.383
1.084
0.014
40
Ad. small pelagic
0.042
1.154
0.825
0.714
0.034
42
Ad. large reef assoc.
0.343
0.178
0.081
0.455
0.577
44
Ad. medium reef assoc.*
0.824
0.400
0.123
0.307
0.438
46
Ad. small reef assoc.
0.371
2.422
0.142
0.059
0.019
48
Ad. large demersal
0.040
0.561
0.679
1.210
0.024
50
Ad. small demersal
0.247
2.868
0.211
0.074
0.028
52
Ad. large planktivore*
0.478
0.700
0.300
0.429
0.339
53
Juv. large planktivore*
0.452
0.700
0.034
0.048
0.034
54
Ad. small planktivore*
0.130
0.600
0.031
0.052
0.013
55
Juv. small planktivore*
0.223
0.600
0.002
0.004
0.001
56
Ad. anchovy
0.887
1.218
0.391
0.321
0.509
58
Ad. deepwater fish*
0.115
0.450
0.034
0.075
0.022
59
Juv. deepwater fish*
0.158
0.300
0.016
0.055
0.014
60
Ad. macro algal browsing*
0.033
0.250
0.003
0.013
0.001
61
Juv. macro algal browsing*
0.034
0.300
0.000
0.001
0.000
62
Ad. eroding grazers*
0.056
0.250
0.003
0.013
0.000
63
Juv. eroding grazers*
0.036
0.250
0.000
0.001
0.000
64
Ad. scraping grazers*
0.092
0.700
0.094
0.134
0.022
65
Juv. scraping grazers*
0.495
0.800
0.002
0.002
0.002
137
3.4 Challenges to Ecosim
Fig. 3.4 shows synoptic results of the
Reef-associated fish
Fish TL 2-3
challenges to Ecosim. It summarizes the
105%
105%
Biomass
functional groups (including specific and
Biomass
biomass change in reef associated fish
100%
100%
aggregated reef groups), pelagic fish,
95%
95%
predator fish (TL > 3), forage fish (TL 2-
0F
3), invertebrates and mammals. The ‘no
1F
0F
F+
Pelagic fish
F+
Invertebrates
fishing’ scenario (0F) completes the
100.4%
simulation with the highest standing
Biomass
200%
Biomass
biomass for exploited species groups such
1F
100%
100.0%
99.6%
as reef fish, pelagic fish and high trophic
level fish.
scenario
0%
The ‘increasing fishing’
(F+)
ends
with
99.2%
0F
depressed
1F
0F
F+
Fish TL 3+
biomasses for these exploited groups.
1F
F+
Mammals
140%
105%
120%
Biomass
and 3 are the prey for piscivores, and their
Biomass
Forage fish, with trophic levels between 2
100%
100%
biomasses do see an appropriate decrease
once predators recover.
Likewise, the
80%
95%
0F
1F
F+
0F
1F
F+
invertebrate biomasses are highest in the
Figure 3.4. Group biomass change following extreme
‘increased
when
fishing policies (2006-2022). No fishing (0F), baseline
predators have been removed from the
fishing (1F) and increasing fishing (F+). Mean biomass
fishing’
scenario,
system, and lowest in the ‘no fishing’
values are shown that result from a Monte Carlo that varies
input biomass for commercial fish. Simulations are from
scenario, when predators are allowed to
2006-2022; biomass is relative to baseline endstate.
recover.
Fig. B.2.4 shows biomass predictions for functional groups under the three fishing scenario
challenges. The error bars show the variation in biomass trajectories predicted by the Monte
Carlo analysis, when Ecopath biomass parameters are allowed to vary for key groups by +/20%. The error bars represent 1 standard deviation around the mean; the mean is indicated by an
open circle. All groups see an appropriate decline in biomass relative to baseline levels for the
138
‘increased fishing’ scenario; all groups see an increase relative to baseline biomass for the ‘no
fishing’ scenario. Only the absolute biomass values of coral trout and large reef-associated fish
appear unsatisfactory; they should recover under relieved fishing pressure. Biomass dynamics
will be revisited for these groups.
Table 3.2 shows the depletion risk of functional groups associated with the three fishing
scenarios. The ‘no fishing’ scenario has the fewest number of depletions, and the least severe
depletions. Under baseline fishing conditions, where current 2006 fishing mortalities are carried
on until 2022, adult snappers drop below 30% of their current biomass value in 6% of trials.
Also threatened are coral trout and large sharks, which each decline to 40% of their current
biomass under baseline conditions in 6% of simulations. Under increasing fishing mortality, the
depletions are more severe. Snappers are prone to collapse to 15% of their current biomass value
in 80% of trials, while mackerel, coral trout, sharks and large pelagics all a serious depletion risk.
Table 3.2 - Group depletion risk following extreme fishing scenarios. No fishing (0F), baseline
fishing (1F) and increasing fishing (F+). Depletion risk shows the percentage of times that each
functional group declined to a given level of biomass during Monte Carlo simulations (n = 50). Biomass
depletion is stated relative to baseline model biomass. Adult (ad.); sub-adult (sub.); juvenile (juv.).
End-state biomass (2022) vs. baseline model (2006)
15%
20%
30%
40%
50%
0F
1F
Ad. coral trout
16%
Juv. coral trout
12%
Juv. large sharks
44%
Ad. snappers
6%
100%
100%
Sub. snappers
20%
62%
Juv. snappers
28%
64%
Ad. coral trout
6%
50%
Juv. coral trout
38%
Ad. large sharks
F+
Ad. snappers
18%
100%
100%
100%
100%
Sub. snappers
8%
64%
92%
98%
Juv. snappers
12%
72%
94%
96%
2%
54%
94%
10%
76%
Mackerel
Ad. coral trout
80%
6%
Juv. coral trout
139
46%
Ad. large sharks
8%
32%
Ad. large pelagic
52%
100%
Juv. large pelagic
8%
56%
Large reef associated fish perform poorly in most simulations; this group requires further tuning.
3.5 Fishing policy optimizations
Fig. 3.5 shows the relationship between the expected NPV from the optimal harvest policy, and
ecosystem maturity B/P. 156 optimal policies have been computed based on a random F vector
starting. More valuable harvest plans tend to result in a lower ecosystem maturity score due to a
depletion of slow-growing animals such as large predators. The current ecosystem state is
reported in Fig. 3.5 for comparison. The current RA fisheries appear sub-optimal; neither
economic nor ecological benefits are being realized to their full potential.
A convex relationship appears
between
the
suggesting
that
two
criteria,
a
win-win
2.5
harvest policy may exist that will
2.0
return
while
ecosystem.
could
preserving
the
However, results
change
with
model
improvements, and no such winwin policy exists with respect to
total catch and biodiversity. Fig.
3.6 re-expresses the benefits of
the optimal scenarios in these
NPV opt /NPV base
generate the greatest economic
1.5
1.0
0.5
0.0
0.99
1
1.01
1.02
1.03
1.04
1.05
B/P opt / B/P base
terms, and indicates a linear
relationship. The correct fishing
Figure 3.5 - Tradeoff between NPV and B/P. Points are determined
policy to employ remains a
through policy optimizations. Open circle show current RA fishery
matter of social priority.
(sub-optimal), black points show the trade-off frontier (optimal).
140
8
Catch (t·km
-2
)
6
4
2
0
8
9
10
11
Biodiversity (Q75)
Figure 3.6 - Tradeoff between catch and biodiversity. Points
determined through policy optimizations. Open circle show current RA
fishery, black points show the trade-off frontier. Linear best fit shown.
Results suggest that under an optimal fishing policy, the RA ecosystem could sustainably deliver
more catch that it currently does. The high degree of biodiversity estimated for the 2006 RA
ecosystem can be expected to decrease if any of the optimal policies presented here are applied.
In order to preserve biodiversity explicitly, the policy search routine needs to be updated to
include Q75 as an objective function4. This is on the horizon.
Fig. 3.7 re-expresses the inherent trade off in RA between catch and biodiversity. From left to
right, the optimal fishing plans put a heavier relative weighting on economic returns and a lower
weighting on ecological benefits. Any optimal fishing plan that considers these two objectives
will fall somewhere on this scale.
4
Previous versions of the policy search routine (e.g., in Ainsworth, 2006; Ainsworth and Pitcher, in press) have had
this modification in place, but they can not function with multi-stanza models.
141
Fig. 3.8 analyzes the fishing strategies
8
11
6
10
4
9
2
8
0
7
resulting from the policy search routine.
policy search. The F vector represents
the optimal equilibrium-level fishing
mortalities applicable to each of the 17
Catch (t·km
the optimal F vectors developed by the
-2
)
to summarize the similarities between
Biodiversity (Q75)
It uses a principle components analysis
gear types in the RA model. Each point
represents a unique combination of
optimal fishing mortalities. The relative
position of any two points in the X-Y
Figure 3.7 - Equilibrium catch and biodiversity levels for
optimal fishing plans.
The X-axis shows 156 policy
plane indicates the similarity of the
optimizations conducted from random F starting points. From
fishing solutions; the Z value is indicated
left to right, the relative contribution of the economic
by grayscale, where lighter values
criterion increases versus the ecological criterion. The best
indicate
catch (grey area) and biodiversity (line) achieved by the
greater
equilibrium
harvest
benefits (catch on left; biodiversity on
policy search is shown.
right).
A pattern emerges among the fleet-effort solutions; they can be grouped into three broad
categories. On the right side of the graphs, solutions cluster that tend to generate high catch at
the expensive of biodiversity. These solutions were found by applying a high weighting on the
economic harvest criterion. They tend to concentrate and increase fishing effort in the spear and
harpoon gear type (Fig. 3.9).
The cluster in the center of the plots (vertex) tends to preserve
biodiversity but generates less catch; optimal fishing effort is low overall, only a slight increase
in spear and harpoon effort is permissible. The cluster on the left has located a compromise
solution, using shore gillnet as the principle fishing apparatus. All solutions tend to increase
fishing effort of shrimp trawl. Note that habitat impacts of fishing gear are not considered in the
model.
142
Total catch
Biodiversity (Q75)
Figure 3.8 - Principle components analysis showing policy search response surface. These plots show the
similarity between optimal fleet-effort vectors (i.e., one F per gear type, n = 17) determined by the policy search
routine. Points located close to each other use similar fishing strategies; points distant from each other use
dissimilar strategies. The resulting equilibrium-level catch and biodiversity from the optimal plans are shown in
grayscale, where lighter colours indicate higher catch (left) and higher biodiversity (right). Darker colours show low
values. The fishing strategies employed by the policy search routine can be roughly categorized into 3 clusters
(rectangles). The right-most cluster achieves high catch at the expense of biodiversity, the centre cluster (vertex)
preserves biodiversity but generates less catch, and the left-most cluster represents a compromise solution.
143
Left
F OPT /F 2006
16
12
8
4
0
Vertex
F OPT /F 2006
16
12
8
4
0
Right
F OPT /F 2006
16
12
8
4
Shrimp trawl
Foreign fleet
Lift net
Set line
Pole and line
Purse seine
Trolling
Blast fishing
Diving cyanide
Diving live fish
Diving spear
Portable trap
Permanent trap
Driftnet
Shore gillnet
Reef gleaning
Spear and harpoon
0
Figure 3.9 - Characterization of optimal fleet-effort patterns. Three clusters of solutions are identified from
PCA (left, vertex, right), the bars show the average F for each cluster as a fraction of baseline (2006) F. Broken line
indicates the baseline fishing mortality per gear type. All fishing strategies tend to increase shrimp trawl.
144
4. DISCUSSION
4.1 Fitting the model
In this report, we have used our best guess vulnerability matrix for the 2006 model because it
produced reasonable group behaviour under the equilibrium analysis and under challenges to the
model outlined in this report. Ideally, we would like to extend the fitted vulnerabilities of the
1990 model to the 2006 model after being corrected for differences in the predation mortalities
between those two time periods. This approach assumes stationarity in the density-dependant
foraging tactics of species; however the vulnerability values must still be scaled properly to be
relevant to the present day model.
If predation mortality was higher in the past, then the vulnerability parameter, which represents
the maximum increase in predation mortality as compared to model baseline, should be
proportionately reduced for a given prey (C. Walters, University of British Columbia. 2202
Main Mall, Vancouver, BC. Personal communication). For each trophic interaction, the product
of the vulnerability rate and the predation mortality rate is conserved between time periods. It
was demonstrated that this method is more reliable for parameterizing adjacent time periods than
alternative assumptions, such as global vulnerabilities or scaling by trophic level (Ainsworth,
2006).
When better time series information becomes available, we will repeat the fitting
procedure presented here. We should then have enough confidence in the fitted vulnerability
parameters to warrant replacing them in the 2006 matrix.
The CPUE proxy for relative biomass is generally flawed. Although fitting to these series does
set the model’s behaviour to within satisfactory limits for a first draft of the model, better time
series information will soon be available as we continue to process fisherman interview
information that was recently compiled by TNC field staff (contact: C. Rotinsulu. CI. Jl Arfak
No. 45. Sorong, Papua, Indonesia 98413) (also see Section 4.3 – Fishermen interview forms).
145
4.2 Fishing policy optimizations
The policy search routine in Ecosim is used here for a very basic analysis, a comparison of the
trade-offs between economic harvest benefits (measured using NPV) and ecological harvest
benefits (based on ecosystem maturity). A clear relationship emerges among the optimal fleeteffort vectors developed by the policy search routine.
Policies designed to maximize the
economic value of the fishery tend to increase spear and harpoon effort (right cluster in Fig. 3.8),
while policies designed to maintain the ecology tend to reduce overall effort on most fleets from
the baseline situation (vertex cluster in Fig. 3.8). There exists a third, moderate, policy option
where the shore gillnet fleet is the principle fishing method employed (left cluster in Fig. 3.8).
This solution achieves an effective compromise between economics and ecology.
There exists a continuum of optimal fishing solutions connecting the left cluster with the vertex,
and the right cluster with the vertex. Interestingly, the left and right clusters appear mutually
exclusive.
That is, no optimizations utilized both spear/harpoon and shore gillnet
simultaneously, perhaps indicating that these gear types conflict with each other
trophodynamically. Indeed, the mixed trophic impacts analysis (network analysis) confirms that
they do compete with each other, although it does not necessarily follow that the use of these two
gear types must be mutually exclusive. We will have to explore this preliminary finding and
comment in later reports.
Almost all solutions, regardless of the harvest criterion in place, increased the shrimp trawl
fishery over baseline exploitation levels. Only in 3 out of 156 optimizations did the shrimp trawl
fishery appear reduced from the baseline levels. Penaeid shrimp, being largely underexploited in
the model, can evidently support higher sustainable harvests in RA. However, the majority of
the Penaeid shrimp fishery in BHS occurs to the southeast of our study region in the Arafura Sea
(DF, 2001); the optimal fishing rates should not be implicitly extended to that area without
further analysis.
146
4.3 Fisherman interview forms
In a series of community interviews conducted by TNC in various RA villages, we presented a
species list to local fishermen, who were asked to comment on the relative abundance change of
these animals during their lifetimes. The English version of those interview forms is provided in
Appendix C.1 - Fishermen interview form. For each decade from 1970 to present, the fishermen
indicated whether the populations of these commercial species were increasing or decreasing.
We intend to use a fuzzy logic approach to convert the qualitative statements into relative
biomass abundance trends.
As we come to understand more fully the changes in the ecosystem over the last 30 or more
years, we will be able to generate models of earlier time periods. Having several models that
represent various snap shots in time will help us improve biomass dynamics; trends can be
maneuvered to coincide with these point estimates. We will be able to evaluate major ecosystem
changes over the scale of decades, and we will be able to hone the trophic flow parameters,
improving forecasts into the future. At the time of this report, the interview results had just
become available; data processing continues.
4.4 Stomach content analysis
To improve the diet matrix, and to validate the diet allocation algorithm used in this paper, we
are now in the process of collecting and analyzing stomach content data from RA as a
component of the BHS EBM project (field work contact: C. Rotinsulu. CI. Jl Arfak No. 45.
Sorong, Papua, Indonesia 98413). Specimens of commercial reef associated and pelagic fish
groups are being purchased from fishers and market. Stomach dissections are being performed
by UNIPA student researchers in the laboratory. 134 stomachs have so far been analyzed from
11 reef fish families. We expect to complete the laboratory work in January; the information
will be analyzed and processed into Ecopath functional groups for comparison against the fitted
diet matrix. In addition to the stomach content data, information on the predator is being
collected, such as body length and gape size.
This will help improve the diet allocation
algorithm. The protocol for the stomach content analysis study is presented in Appendix C.2 Stomach sampling protocol.
147
4.5 Ecolocator
We are in the process of developing a new routine for use with Ecospace that will allow us to
incorporate high-spatial resolution data into the model, and provide localized EBM advice for the
study area. The new tool, called Ecolocator, consists of a statistical algorithm that allocates
species biomass distribution within Ecospace cells or within Ecospace output regions based on
habitat suitability and the ecology of organisms. The Visual C++ .net routine is being designed
as a stand-alone application. However, we hope to incorporate the routine into the upcoming
release of EwE V6 .net (due Sept, 2007). This possibility was discussed with the EwE V6
development team at a EwE programming workshop in August, 2006. Ecolocator will be made
available to other researchers following this project.
The habitat map interface is on the right side of Ecolocator’s main form (Fig. 4.1). The map is
fixed at 100 x 100 cells; this area can represent a single Ecospace cell, or a collection of
Ecospace cells summarized as an output region. The land area is sketched onto the map interface
in black. To make this task easier, the user can import an image file as a background.
For each functional group, Ecolocator requires a habitat map and a cross-sectional estimate of
the biomass distribution throughout the habitat. The habitat area occupied by each functional
group is entered on the map in pink, and yellow nodes are distributed inside the habitat area. The
nodes will usually represent the centre of the biomass distribution for the functional group, while
the habitat cells will indicate the occupied area under the baseline biomass density. The habitat
map and nodes cells can be copied between similar functional groups.
148
Baseline biomass
Node
Boundary
Figure 4.1 - User interface of Ecolocator. Ecolocator is a new routine under development for use with Ecospace.
Ecolocator computes the likely biomass distribution of functional groups at small spatial scales based on a userdefined habitat map, which may represent single or multiple Ecospace map cells, and a group distribution pattern
entered on a sketchpad. On the habitat map, black cells indicate land areas; pink cells indicate the occupied habitat
area under baseline biomass densities. As the biomass changes in simulations, the area occupied may contract or
expand. The example shows Worop Bay, on south-east Kofiau Island.
As biomass changes dynamically in the Ecospace simulation, the area occupied by each
functional group will change according to the biomass distribution entered. The biomass in any
given cell depends on the cell’s relative distance to a node or habitat boundary (i.e., the interface
between habitat and non-habitat cells). The relationship is set using a sketchpad interface on the
left side of Ecolocator’s main form (Fig. 4.1). The X-axis represents the cell’s relative distance
to a node and boundary cell. Dragging the vertical green line left or right can set the scale of the
X-axis; the position of this green line represents the baseline biomass value.
The Y-axis
represents the relative biomass density in the cell. The scale of the Y-axis can be adjusted by
149
dragging the horizontal green line up or down; the green line indicates the baseline biomass
density. Both X and Y-axes are linear. The absolute biomass of cells will be scaled so that the
sum equals the total area biomass predicted by Ecospace for that grid cell or output region. The
biomass distribution can be drawn into the sketchpad (red), or selected from a list of suggested
shapes representing various ecological guilds.
A typical case representing the distribution of a piscivorous coral reef fish may use the habitat
cells to represent the area of coral reef cover. The fish biomass may be concentrated in the
center of a reef patch by placing nodes in the centre of the habitat areas and by sketching a
biomass distribution like the one in Fig. 4.1, which is highest at the node and decreases towards
the habitat boundary. A planktivorous fish group may increase in biomass towards the edge of
the reef, where they situate for feeding, and so would use an increasing function from the node to
the boundary.
A CSV file produced by Ecospace is inputted to drive Ecolocator. The CSV file contains time
series biomass information. If the biomass of a functional group increases in Ecospace, the
number of cells occupied by the functional group will increase in Ecolocator. By entering a
uniform biomass distribution on the sketchpad, the functional group would be evenly distributed
in every cell regardless of the habitat area in place. Applying a knife-edge biomass distribution
that falls off after the boundary will ensure that the group never spills out over the assigned
habitat cells if biomass increases in the Ecospace simulation. This condition may be used to
model the distribution of reef-building corals, for example.
Ecolocator does not contain a dynamic population model; it only distributes the biomass
predicted by Ecospace. However, it would be possible to expand the routine to predict areas of
feeding interactions based on the relative abundance of predator and prey, and the partial diet
contribution in the EwE diet matrix. Foraging arena rules could further modify the interaction
rates. This would require the input of the EwE vulnerability matrix.
Development of this routine continues. Important areas selected by the BHS EBM project will
be analyzed using the tool, and future publications will test its biomass predictions against site150
specific information from RA.
We intend to apply this tool to evaluate species biomass
distributions for well-studied areas around Kofiau, SE Misool or Weigeo Islands. The analysis
will consider a spatial region in the scale of 100s to 1000s of metres.
5. CONCLUSIONS
The preliminary EwE models presented in this report will continue to be modified and improved
over the coming months. Functional group dynamics will be revisited once the UBC spatial
modelling group has completed its analysis of the biomass trend information recently obtained
from LEK interviews. The Synthesis Post-Doctoral Fellow, Cameron Ainsworth, and PostGraduate researcher, Divya Varkey, will present the models to local marine experts during the
second field visit to Indonesia, which will be from January 17 to March 5, 2007. The main
purpose of this trip will be to validate the model structure and functioning through expert
opinion. We will especially be interested in feedback concerning the Ecospace models of Kofiau
Island, SE Misool and Dampier Strait from TNC, CI and WWF scientists, and from field site
coordinators.
In addition, we will collect BHS EBM project information that has recently become available.
Animal migration patterns will be entered into the Ecospace for turtles and cetaceans. Biomass
information resulting from transect studies will form the basis of the fine-scale models. Results
from the CI socioeconomic study will be used to strengthen the price and cost fields of Ecosim
and Ecospace, and allow more detailed economic evaluation using the policy search routine and
Ecospace. By applying the findings of the MPA zoning exploration work currently being
conducted with MARXAN (contact: M. Barmawi, TNC-CTC. Jl Pengembak 2, Sanur, Bali,
Indonesia), we will be able to model the proposed closure areas and evaluate the tropho-dynamic
and socioeconomic consequences of site protection.
It is hoped that during the field visit we can establish finalized models of the areas presented in
this report, so that we may then proceed with development and analysis using Ecolocator. The
deadline for project information contributing to the Kofiau Island model has been set for the end
151
of January 2007. The deadline for project information contributing to the Dampier Strait and
SW Misool models has been set for the end of March 2007.
A later meeting is planned between UBC researchers and TNC, CI and WWF staff, which will
take place during a third field visit, July 2007. The purpose of that meeting will be to present the
outcomes of the spatial modelling study, accept any final recommendations or changes to the
models, and arrange a publication schedule for co-authored contributions to be completed by the
end of the UBC spatial modelling component, in December 2007. We also hope to discuss the
goals and outputs of the spatial modelling component to ensure that this study assists the
development of EBM policies and aids the Regency, Provincial and Federal marine policy
makers in Indonesia.
As this is a mid-term technical report, we are interested to receive any comments or suggestions
with regard to the EwE models, or the planned analyses and contributions to ecosystem based
management of RA and the BHS. Please direct comments or questions to the lead author.
152
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APPENDIX A - EWE PARAMETERIZATION
Appendix A.1 - Species level data
Table A.1.1 - Fish species represented in the RA EwE models.
Functional Group
FB
species
code
Family
Scientific name
Common name
No.
Spp.
Groupers
6441
Serranidae
Aethaloperca rogaa
Redmouth grouper
46
4922
Serranidae
Anyperodon leucogrammicus
Slender grouper
6396
Serranidae
Cephalopholis argus
Peacock hind
6444
Serranidae
Cephalopholis boenack
Chocolate hind
6445
Serranidae
Cephalopholis cyanostigma
Bluespotted hind
6448
Serranidae
Cephalopholis leopardus
Leopard hind
6449
Serranidae
Cephalopholis microprion
Freckled hind
6453
Serranidae
Cephalopholis sexmaculata
Sixblotch hind
6454
Serranidae
Cephalopholis sonnerati
Tomato hind
6455
Serranidae
Cephalopholis spiloparaea
Strawberry hind
6456
Serranidae
Cephalopholis urodeta
Darkfin hind
6457
Serranidae
Cromileptes altivelis
Humpback grouper
6603
Serranidae
Diploprion bifasciatum
Barred soapfish
5367
Serranidae
Epinephelus areolatus
Areolate grouper
7331
Serranidae
Epinephelus bilobatus
Twinspot grouper
6440
Serranidae
Epinephelus caruleopunctatus
Whitespotted grouper
6465
Serranidae
Epinephelus coioides
Orange-spotted grouper
6466
Serranidae
Epinephelus corallicola
Coral grouper
5348
Serranidae
Epinephelus fasciatus
Blacktip grouper
4460
Serranidae
Epinephelus fuscoguttatus
Brown-marbled grouper
6468
Serranidae
Epinephelus lanceolatus
Giant grouper
6661
Serranidae
Epinephelus macrospilos
Snubnose grouper
5350
Serranidae
Epinephelus maculatus
Highfin grouper
4923
Serranidae
Epinephelus merra
Honeycomb grouper
6472
Serranidae
Epinephelus ongus
White-streaked grouper
6473
Serranidae
Epinephelus polyphekadion
Camouflage grouper
5837
Serranidae
Epinephelus spilotoceps
Foursaddle grouper
5525
Serranidae
Epinephelus tukula
Potato grouper
6477
Serranidae
Gracila albimarginata
Masked grouper
4925
Serranidae
Grammistes sexlineatus
Sixline soapfish
173
Table A.1.1 - (cont.)
Functional Group
Snappers
FB
species
code
Family
Scientific name
Common name
7315
Serranidae
Grammistops ocellatus
Ocellate soapfish
7318
Serranidae
Liopropoma susumi
Meteor perch
7453
Serranidae
Luzonichthys waitei
Waite's splitfin
12727
Serranidae
Pogonoperca punctata
7454
Serranidae
Pseudanthias dispar
Peach fairy basslet
10632
Serranidae
Pseudanthias fasciatus
One-stripe anthias
6567
Serranidae
Pseudanthias huchtii
Red-cheeked fairy basslet
8124
Serranidae
Pseudanthias hypselosoma
Stocky anthias
7458
Serranidae
Pseudanthias luzonensis
Yellowlined anthias
6569
Serranidae
Pseudanthias pleurotaenia
Square-spot fairy basslet
6571
Serranidae
Pseudanthias randalli
Randall's fairy basslet
6568
Serranidae
Pseudanthias squamipinnis
Sea goldie
6502
Serranidae
Pseudanthias tuka
Yellowstriped fairy basslet
7320
Serranidae
Pseudogramma polyacanthum
Honeycomb podge
6478
Serranidae
Variola albimarginata
White-edged lyretail
5354
Serranidae
Variola louti
Yellow-edged lyretail
1385
Lutjanidae
Etelis coruscans
Flame snapper
1394
Lutjanidae
Lipocheilus carnolabrum
Tang's snapper
1407
Lutjanidae
Lutjanus argentimaculatus
Mangrove red snapper
1410
Lutjanidae
Lutjanus biguttatus
Two-spot banded snapper
1417
Lutjanidae
Lutjanus bohar
Two-spot red snapper
1418
Lutjanidae
Lutjanus boutton
Moluccan snapper
1424
Lutjanidae
Lutjanus carponotatus
Spanish flag snapper
1428
Lutjanidae
Lutjanus decussatus
Checkered snapper
793
Lutjanidae
Lutjanus ehrenburgi
Blackspot snapper
261
Lutjanidae
Lutjanus fulviflamma
Dory snapper
262
Lutjanidae
Lutjanus fulvus
Blacktail snapper
265
Lutjanidae
Lutjanus gibbus
Humpback red snapper
264
Lutjanidae
Lutjanus johnii
John's snapper
156
Lutjanidae
Lutjanus kasmira
Common bluestripe snapper
157
Lutjanidae
Lutjanus lemniscatus
Yellowstreaked snapper
159
Lutjanidae
Lutjanus lutjanus
Bigeye snapper
166
Lutjanidae
Lutjanus monostigma
Onespot snapper
No.
Spp.
32
174
Table A.1.1 - (cont.)
FB
species
code
Family
Scientific name
Common name
172
Lutjanidae
Lutjanus quinquelineatus
Five-lined snapper
173
Lutjanidae
Lutjanus rivulatus
Blubberlip snapper
176
Lutjanidae
Lutjanus russelli
Russell's snapper
179
Lutjanidae
Lutjanus semicinctus
Black-banded snapper
184
Lutjanidae
Lutjanus vitta
Brownstripe red snapper
186
Lutjanidae
Macolor macularis
Midnight snapper
187
Lutjanidae
Macolor niger
Black and white snapper
192
Lutjanidae
Paracaesio sordidus
Dirty ordure snapper
8430
Lutjanidae
Pinjalo lewisi randall,
Slender pinjalo
200
Lutjanidae
Pristipomoides auricilla
Goldflag jobfish
201
Lutjanidae
Pristipomoides filamentosus
Crimson jobfish
209
Lutjanidae
Pristipomoides sieboldii
Lavender jobfish
211
Lutjanidae
Pristipomoides zonatus
Oblique-banded snapper
214
Lutjanidae
Symphorichthys spilurus
Sailfin snapper
215
Lutjanidae
Symphorus nematophorus
Chinamanfish
Napoleon wrasse
5604
Labridae
Cheilinus undulatus
Napoleon / Humphead wrasse
1
Skipjack tuna
107
Scombridae
Katsuwonus pelamis
Skipjack tuna
1
Other tuna
89
Scombridae
Acanthocybium solandri
Wahoo
10
93
Scombridae
Auxis rochei rochei
Bullet tuna
94
Scombridae
Auxis thazard thazard
Frigate tuna
96
Scombridae
Euthynnus affinis
Kawakawa
106
Scombridae
Gymnosarda unicolor
Dogtooth tuna
142
Scombridae
Thunnus alalunga
Albacore
143
Scombridae
Thunnus albacares
Yellowfin tuna
146
Scombridae
Thunnus obesus
Bigeye tuna
14290
Scombridae
Thunnus orientalis
Pacific bluefin tuna
148
Scombridae
Thunnus tonggol
Longtail tuna
104
Scombridae
Grammatorcynus bilineatus
Double-lined mackerel
109
Scombridae
Rastrelliger brachysoma
Short mackerel
110
Scombridae
Rastrelliger faughni
Island mackerel
111
Scombridae
Rastrelliger kanagurta
Indian mackerel
Functional Group
Mackerel
175
No.
Spp.
9
Table A.1.1 - (cont.)
Functional Group
Billfish
Coral trout
Large sharks
Small sharks
Whale shark
FB
species
code
Family
Scientific name
Common name
116
Scombridae
Scomber australasicus
Blue mackerel
121
Scombridae
Scomberomorus commerson
Narrow-barred Spanish
mackerel
129
Scombridae
Scomberomorus munroi
Australian spotted mackerel
133
Scombridae
Scomberomorus queenslandicus
Queensland school mackerel
135
Scombridae
Scomberomorus semifasciatus
Broadbarred king mackerel
77
Istiophoridae
Istiophorus platypterus
Indo-Pacific sailfish
217
Istiophoridae
Makaira indica
Black marlin
218
Istiophoridae
Makaira mazara
Indo-Pacific blue marlin
3915
Istiophoridae
Tetrapturus angustirostris
Shortbill spearfish
223
Istiophoridae
Tetrapturus audax
Striped marlin
226
Xiphiidae
Xiphias gladius
Swordfish
6450
Serranidae
Cephalopholis miniata
Coral hind
6082
Serranidae
Plectropomus areolatus
Squaretail coralgrouper
7372
Serranidae
Plectropomus laevis
Blacksaddled coralgrouper
4826
Serranidae
Plectropomus leopardus
Leopard coralgrouper
4886
Serranidae
Plectropomus maculatus
Spotted coralgrouper
7319
Serranidae
Plectropomus oligocanthus
Highfin coralgrouper
861
Carcharhinidae
Carcharhinus amblyrhynchos
Grey reef shark
871
Carcharhinidae
Carcharhinus hemiodon
Pondicherry shark
877
Carcharhinidae
Carcharhinus melanopterus
Blacktip reef shark
898
Carcharhinidae
Prionace glauca
Blue shark
907
Carcharhinidae
Triaenodon obesus
Whitetip reef shark
5895
Ginglymostomatidae
Nebrius ferrugineus
Tawny nurse shark
860
Carcharhinidae
Carcharhinus amblyrhynchoides
Graceful shark
904
Carcharhinidae
Rhizoprionodon taylori
Australian sharpnose shark
651
Centrophoridae
Centrophorus moluccensis
Smallfin gulper shark
5904
Hemiscylliidae
Hemiscyllium freycineti
Indonesian speckled
carpetshark
756
Orectolobidae
Eucrossorhinus dasypogon
Tasselled wobbegong
2081
Rhincodontidae
Rhincodon typus
Whale shark
No.
Spp.
6
6
6
5
1
176
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
No.
Spp.
Manta ray
2061
Mobulidae
Manta birostris
Giant manta
1
Rays
15390
Dasyatidae
Dasyatis leylandi
Painted maskray
7
15487
Dasyatidae
Himantura toshi
Black-spotted whipray
4508
Dasyatididae
Dasyatis kuhlii
Bluespotted stingray
5399
Dasyatididae
Taeniura lymma
Bluespotted ribbontail ray
13194
Mobulidae
Mobula tarapacana
Chilean devil ray
1250
Myliobatidae
Aetobatus narinari
Spotted eagle ray
25622
Myliobatidae
Mobula eregoodootenkee
Pygmy devilray
6525
Chaetodontidae
Apolemichthys trimaculatus
Threespot angelfish
5454
Chaetodontidae
Centropyge bicolor
Bicolor angelfish
5458
Chaetodontidae
Centropyge bispinosus
Twospined angelfish
5664
Chaetodontidae
Centropyge flavicauda
Whitetail angelfish
6647
Chaetodontidae
Centropyge nox
Midnight angelfish
6548
Chaetodontidae
Centropyge tibicen
Keyhole angelfish
5447
Chaetodontidae
Centropyge vroliki
Pearlscale angelfish
6515
Chaetodontidae
Chaetodon adiergastos
Philippine butterflyfish
5557
Chaetodontidae
Chaetodon auriga
Threadfin butterflyfish
5558
Chaetodontidae
Chaetodon baronessa
Eastern triangular butterflyfish
5559
Chaetodontidae
Chaetodon bennetti
Bluelashed butterflyfish
5561
Chaetodontidae
Chaetodon citrinellus
Speckled butterflyfish
5562
Chaetodontidae
Chaetodon ephippium
Saddle butterflyfish
5446
Chaetodontidae
Chaetodon kleinii
Sunburst butterflyfish
5564
Chaetodontidae
Chaetodon lineolatus
Lined butterflyfish
5565
Chaetodontidae
Chaetodon lunula
Raccoon butterflyfish
14300
Chaetodontidae
Chaetodon lunulatus
Oval butterflyfish
5566
Chaetodontidae
Chaetodon melannotus
Blackback butterflyfish
5568
Chaetodontidae
Chaetodon meyeri
Scrawled butterflyfish
5569
Chaetodontidae
Chaetodon ocellicaudus
Spot-tail butterflyfish
5570
Chaetodontidae
Chaetodon octofasciatus
Eightband butterflyfish
6550
Chaetodontidae
Chaetodon ornatissimus
Ornate butterflyfish
5472
Chaetodontidae
Chaetodon oxycephalus
Spot-nape butterflyfish
Butterflyfish
177
57
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
5571
Chaetodontidae
Chaetodon punctatofasciatus
Spotband butterflyfish
5573
Chaetodontidae
Chaetodon rafflesi
Latticed butterflyfish
6634
Chaetodontidae
Chaetodon selene
Yellow-dotted butterflyfish
5575
Chaetodontidae
Chaetodon semeion
Dotted butterflyfish
5576
Chaetodontidae
Chaetodon speculum
Mirror butterflyfish
5578
Chaetodontidae
Chaetodon trifascialis
Chevron butterflyfish
5580
Chaetodontidae
Chaetodon ulietensis
Pacific double-saddle
butterflyfish
5581
Chaetodontidae
Chaetodon unimaculatus
Teardrop butterflyfish
5582
Chaetodontidae
Chaetodon vagabundus
Vagabond butterflyfish
6508
Chaetodontidae
Chaetodon xanthurus
Pearlscale butterflyfish
10472
Chaetodontidae
Chaetodontoplus dimidatus
Black-velvet angelfish
5660
Chaetodontidae
Chaetodontoplus mesoleucus
Vermiculated angelfish
5483
Chaetodontidae
Chelmon rostratus
Copperband butterflyfish
5583
Chaetodontidae
Coradion chrysozonus
Goldengirdled coralfish
5584
Chaetodontidae
Forcipiger flavissimus
Longnose butterflyfish
5585
Chaetodontidae
Forcipiger longirostris
Longnose butterflyfish
6612
Chaetodontidae
Genicanthus lamarck
Blackstriped angelfish
8710
Chaetodontidae
Genicanthus melanospilos
Spotbreast angelfish
5586
Chaetodontidae
Hemitaurichthys polylepis
Pyramid butterflyfish
5588
Chaetodontidae
Heniochus acuminatus
Pennant coralfish
5589
Chaetodontidae
Heniochus chrysostomus
Threeband pennantfish
7769
Chaetodontidae
Heniochus diphreutes
False moorish idol
5590
Chaetodontidae
Heniochus monoceros
Masked bannerfish
5591
Chaetodontidae
Heniochus singularius
Singular bannerfish
5592
Chaetodontidae
Heniochus varius
Horned bannerfish
5666
Chaetodontidae
Paracentropyge multifasciatus
Barred angelfish
7887
Chaetodontidae
Parachaetodon ocellatus
Sixspine butterflyfish
7902
Chaetodontidae
Pomacanthus annularis
Bluering angelfish
6504
Chaetodontidae
Pomacanthus imperator
Emperor angelfish
5661
Chaetodontidae
Pomacanthus navarchus
Bluegirdled angelfish
5663
Chaetodontidae
Pomacanthus semicirculatus
Semicircle angelfish
6564
Chaetodontidae
Pomacanthus sexstriatus
Sixbar angelfish
5662
Chaetodontidae
Pomacanthus xanthometopon
Yellowface angelfish
6572
Chaetodontidae
Pygoplites diacanthus
Royal angelfish
No.
Spp.
178
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
No.
Spp.
Cleaner wrasse
5109
Labridae
Labrichthys unilineatus
Tubelip wrasse
3
5650
Labridae
Labroides bicolor
Bicolor cleaner wrasse
5459
Labridae
Labroides dimidiatus
Bluestreak cleaner wrasse
14497
Belonidae
Strongylura urvillii
24904
Bregmacerotidae
Bregmaceros rarisquamosus
Big-eye unicorn-cod
1452
Chirocentridae
Chirocentrus nudus
Whitefin wolf-herring
7
Coryphaenidae
Coryphaena equiselis
Pompano dolphinfish
5512
Elopidae
Elops machnata
Tenpounder
60182
Exocoetidae
Cheilopogon antoncichi
10333
Gonostomatidae
Manducus greyae
16884
Hemiramphidae
Oxyporhamphus convexus
Halfbeak
10344
Leiognathidae
Leiognathus rapsoni
Rapson's ponyfish
1732
Molidae
Mola mola
Ocean sunfish
15974
Myctophidae
Bolinichthys pyrsobolus
10267
Myctophidae
Diaphus signatus
24278
Nettastomatidae
Saurenchelys stylura
340
Polynemidae
Eleutheronema tetradactylum
27617
Pristigasteridae
Ilisha lunula
238
Salmonidae
Salmo trutta trutta
Sea trout
114
Scombridae
Sarda orientalis
Striped bonito
1235
Sphyraenidae
Sphyraena barracuda
Great barracuda
4827
Sphyraenidae
Sphyraena jello
Pickhandle barracuda
5736
Sphyraenidae
Sphyraena novaehollandiae
Australian barracuda
7939
Sphyraenidae
Sphyraena qenie
Blackfin barracuda
10324
Stomiidae
Astronesthes chrysophekadion
24527
Stomiidae
Bathophilus abarbatus
27411
Stomiidae
Bathophilus kingi
26385
Tetragonuridae
Tetragonurus pacificus
Pacific squaretail
8817
Belonidae
Strongylura krefftii
Long tom
1316
Belonidae
Strongylura strongylura
Spottail needlefish
1891
Carangidae
Alepes vari
Herring scad
1931
Carangidae
Ulua aurochs
Silvermouth trevally
Large pelagic
Medium pelagic
179
25
Fourfinger threadfin
9
Table A.1.1 - (cont.)
Functional Group
Small pelagic
FB
species
code
Family
Scientific name
Common name
3544
Echeneidae
Phtheirichthys lineatus
Slender suckerfish
6415
Elopidae
Elops hawaiensis
Hawaiian ladyfish
95
Scombridae
Cybiosarda elegans
Leaping bonito
7937
Sphyraenidae
Sphyraena flavicauda
Yellowtail barracuda
8079
Toxotidae
Toxotes chatareus
Largescale archerfish
14937
Atherinidae
Atherinomorus balabacensis
Balabac Island silverside
15461
Atherinidae
Hypoatherina tropicalis
Whitley's silverside
24833
Bregmacerotidae
Bregmaceros japonicus
Japanese codlet
8422
Bregmacerotidae
Bregmaceros nectabanus
Smallscale codlet
1890
Carangidae
Alepes melanoptera
Blackfin scad
60570
Centrolophidae
Psenopsis humerosa
Blackspot butterfish
10357
Champsodontidae
Champsodon nudivittis
1620
Clupeidae
Anodontostoma selangkat
Indonesian gizzard shad
1564
Clupeidae
Clupeoides venulosus
West Irian river sprat
1488
Clupeidae
Herklotsichthys castelnaui
Castelnau's herring
1490
Clupeidae
Herklotsichthys gotoi
Goto's herring
1496
Clupeidae
Herklotsichthys lippa
Australian spotted herring
1611
Clupeidae
Nematalosa come
Western Pacific gizzard shad
1652
Clupeidae
Opisthopterus tardoore
Tardoore
1504
Clupeidae
Sardinella brachysoma
Deepbody sardinella
1506
Clupeidae
Sardinella fijiense
Fiji sardinella
1507
Clupeidae
Sardinella fimbriata
Fringescale sardinella
1513
Clupeidae
Sardinella melanura
Blacktip sardinella
1459
Clupeidae
Spratelloides lewisi
Lewis' round herring
7186
Dentatherinidae
Dentatherina merceri
Mercer's tusked silverside
15316
Exocoetidae
Cheilopogon abei
15319
Exocoetidae
Cheilopogon arcticeps
White-finned flyingfish
7509
Exocoetidae
Cheilopogon atrisignis
Glider flyingfish
7696
Exocoetidae
Cheilopogon nigricans
African flyingfish
23049
Exocoetidae
Cheilopogon unicolor
13690
Exocoetidae
Cypselurus angusticeps
Narrowhead flyingfish
7726
Exocoetidae
Cypselurus naresii
Pharao flyingfish
5123
Exocoetidae
Exocoetus monocirrhus
Barbel flyingfish
13727
Exocoetidae
Fodiator rostratus
No.
Spp.
75
180
Table A.1.1 - (cont.)
Functional Group
181
FB
species
code
Family
Scientific name
Common name
13715
Exocoetidae
Hirundichthys albimaculatus
Whitespot flyingfish
1034
Exocoetidae
Hirundichthys oxycephalus
Bony flyingfish
1037
Exocoetidae
Parexocoetus brachypterus
Sailfin flyingfish
4904
Exocoetidae
Parexocoetus mento
African sailfin flyingfish
15382
Exocoetidae
Prognichthys brevipinnis
Shortfin flyingfish
5124
Exocoetidae
Prognichthys sealei
Sailor flyingfish
23694
Gobiidae
Pandaka lidwilli
16808
Hemiramphidae
Arrhamphus sclerolepis sclerolepis
Northern snubnose garfish
16811
Hemiramphidae
Hemiramphus robustus
Three-by-two garfish
16817
Hemiramphidae
Hyporhamphus neglectissimus
Black-tipped garfish
8262
Hemiramphidae
Hyporhamphus quoyi
Quoy's garfish
4666
Hemiramphidae
Rhynchorhamphus georgii
Long billed half beak
17111
Hemiramphidae
Zenarchopterus caudovittatus
Long-jawed river garfish
17043
Hemiramphidae
Zenarchopterus dunckeri
Duncker's river garfish
13014
Hemiramphidae
Zenarchopterus kampeni
Sepik River halfbeak
25579
Hemiramphidae
Zenarchopterus novaeguineae
Fly River garfish
17114
Hemiramphidae
Zenarchopterus rasori
363
Lactariidae
Lactarius lactarius
7966
Leiognathidae
Secutor indicius
25595
Melanotaeniidae
Chilatherina axelrodi
Axelrod's rainbowfish
25602
Melanotaeniidae
Chilatherina bulolo
Bulolo rainbowfish
25605
Melanotaeniidae
Chilatherina campsi
Highlands rainbowfish
25608
Melanotaeniidae
Chilatherina crassispinosa
Silver rainbowfish
25610
Melanotaeniidae
Chilatherina fasciata
Barred rainbowfish
25612
Melanotaeniidae
Chilatherina lorentzii
Lorentz's rainbowfish
9116
Microstomatidae
Xenophthalmichthys danae
7405
Myctophidae
Bolinichthys distofax
7428
Myctophidae
Centrobranchus andreae
16748
Myctophidae
Diaphus malayanus
16665
Myctophidae
Hygophum macrochir
Large-finned lanternfish
7412
Myctophidae
Lampadena urophaos urophaos
Sunbeam lampfish
10299
Nomeidae
Psenes arafurensis
Banded driftfish
1641
Pristigasteridae
Pellona ditchela
Indian pellona
10525
Pseudomugilidae
Pseudomugil connieae
Popondetta blue-eye
False trevally
Andre's lanternfish
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
Large reef
associated
FB
species
code
Family
Scientific name
Common name
10531
Pseudomugilidae
Pseudomugil majusculus
Cape blue-eye
25661
Pseudomugilidae
Pseudomugil novaeguineae
New Guinea blue-eye
25662
Pseudomugilidae
Pseudomugil paludicola
Swamp blue-eye
128
Scombridae
Scomberomorus multiradiatus
Papuan seerfish
22972
Scopelosauridae
Scopelosaurus hoedti
7388
Sternoptychidae
Danaphos oculatus
51179
Stomiidae
Astronesthes quasiindicus
27412
Stomiidae
Eustomias perplexus
27415
Stomiidae
Leptostomias leptobolus
11791
Stomiidae
Photonectes mirabilis
11796
Stomiidae
Photonectes parvimanus
59890
Terapontidae
Mesopristes iravi
5951
Acanthuridae
Zebrasoma scopas
Twotone tang
1266
Acanthuridae
Zebrasoma veliferum
Sailfin tang
5781
Apogonidae
Cheilodipterus macrodon
Large toothed cardinalfish
1309
Aulostomidae
Aulostomus chinensis
Chinese trumpetfish
6025
Balistidae
Balistapus undulatus
Orange-lined triggerfish
2300
Balistidae
Balistoides conspicillum
Clown triggerfish
6026
Balistidae
Balistoides viridescens
Titan triggerfish
4278
Balistidae
Canthidermis maculatus
Spotted oceanic triggerfish
6027
Balistidae
Pseudobalistes flavimarginatus
Yellowmargin triggerfish
4466
Balistidae
Pseudobalistes fuscus
Yellow-spotted triggerfish
5839
Balistidae
Rhinecanthus aculeatus
Blackbar triggerfish
5840
Balistidae
Rhinecanthus rectangulus
Wedge-tail triggerfish
6028
Balistidae
Rhinecanthus verrucosus
Blackbelly triggerfish
6029
Balistidae
Sufflamen bursa
Boomerang triggerfish
5842
Balistidae
Sufflamen chrysoptera
Halfmoon triggerfish
1312
Balistidae
Sufflamen fraenatus
Masked triggerfish
10747
Batrachoididae
Batrachomeous tripsinosus
Three-spined frogfish
10748
Batrachoididae
Halophryne diemensis
Banded frogfish
1314
Belonidae
Platybelone platyura
Keeled needlefish
977
Belonidae
Tylosurus crocodilus
Hound needlefish
61221
Blenniidae
Salarias sibogae
No.
Spp.
Bottlelights
213
182
Table A.1.1 - (cont.)
Functional Group
183
FB
species
code
Family
Scientific name
Common name
7641
Bothidae
Bothus mancus
Flowery flounder
1321
Bothidae
Bothus pantherinus
Leopard flounder
918
Caesionidae
Caesio caerulaurea
Blue and gold fusilier
49419
Callionymidae
Callionymus pleurostictus
1923
Carangidae
Carangoides bajad
Orangespotted trevally
1921
Carangidae
Carangoides ferdau
Blue trevally
1926
Carangidae
Carangoides fulvoguttatus
Yellowspotted trevally
1910
Carangidae
Carangoides plagiotaenia
Barcheek trevally
1895
Carangidae
Caranx ignobilis
Giant trevally
1906
Carangidae
Caranx melampygus
Bluefin trevally
6360
Carangidae
Caranx papuensis
Brassy trevally
1917
Carangidae
Caranx sexfasciatus
Bigeye trevally
1940
Carangidae
Decapterus kurroides
Redtail scad
4464
Carangidae
Gnathanodon speciosus
Golden trevally
1951
Carangidae
Scomberoides lysan
Doublespotted queenfish
1963
Carangidae
Trachinotus blochii
Snubnose pompano
8212
Centropomidae
Psammoperca waigiensis
Waigieu seaperch
5831
Cirrhitidae
Cirrhitus pinnulatus
Stocky hawkfish
23013
Congridae
Gorgasia maculata
Whitespotted garden eel
12619
Congridae
Heteroconger haasi
Spotted garden-eel
4485
Dactylopteridae
Dactyloptena orientalis
Oriental flying gurnard
1022
Diodontidae
Diodon hystrix
Spot-fin porcupinefish
6552
Diodontidae
Diodon liturosus
Black-blotched porcupinefish
2467
Echeneidae
Echeneis naucrates
Live sharksucker
10547
Ephippidae
Platax batavianus
Humpback batfish
14307
Ephippidae
Platax boersi
Golden spadefish
5737
Ephippidae
Platax orbicularis
Orbicular batfish
5739
Ephippidae
Platax teira
Tiera batfish
5444
Fistulariidae
Fistularia commersoni
Bluespotted cornetfish
5996
Gerreidae
Gerres oyena
Common silver-biddy
27553
Gobiidae
Amblygobius esakiae
Snoutspot goby
11618
Gobiidae
Pleurosicya elongata
Cling goby
59437
Gobiidae
Pleurosicya labiata
8454
Gobiidae
Priolepis fallacincta
61008
Gobiidae
Trimma anaima
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
7224
Gobiidae
Valenciennea helsdingenii
Twostripe goby
4465
Haemulidae
Diagramma pictum
Painted sweetlips
6364
Haemulidae
Plectorhinchus chaetodontoides
Harlequin sweetlips
56810
Haemulidae
Plectorhinchus chrysotaenia
Yellow-striped sweetlips
6366
Haemulidae
Plectorhinchus gibbosus
Harry hotlips
50052
Haemulidae
Plectorhinchus lessoni
6940
Haemulidae
Plectorhinchus lineatus
Yellowbanded sweetlips
6368
Haemulidae
Plectorhinchus obscurus
Giant sweetlips
8316
Haemulidae
Plectorhinchus polytaenia
Ribboned sweetlips
60418
Haemulidae
Plectorhinchus unicolor
25706
Haemulidae
Plectorhinchus vittatus
Indian Ocean oriental sweetlips
5404
Hemiramphidae
Hemirhamphus far
Blackbarred halfbeak
17041
Hemiramphidae
Zenarchopterus buffonis
Buffon's river-garfish
26201
Holocentridae
Myripristis botche
Blacktip soldierfish
6582
Holocentridae
Neoniphon opercularis
Blackfin squirrelfish
4911
Holocentridae
Neoniphon sammara
Sammara squirrelfish
4907
Holocentridae
Sargocentron caudimaculatum
Silverspot squirrelfish
5406
Holocentridae
Sargocentron cornutum
Threespot squirrelfish
5345
Holocentridae
Sargocentron melanospilos
Blackblotch squirrelfish
6625
Holocentridae
Sargocentron rubrum
Redcoat
6507
Holocentridae
Sargocentron spiniferum
Sabre squirrelfish
4908
Holocentridae
Sargocentron tiere
Blue lined squirrelfish
4909
Holocentridae
Sargocentron violaceum
Violet squirrelfish
5804
Kyphosidae
Kyphosus bigibbus
Grey sea chub
5805
Kyphosidae
Kyphosus cinerascens
Blue seachub
5806
Kyphosidae
Kyphosus vaigiensis
Brassy chub
4888
Labridae
Anampses caeruleopunctatus
Bluespotted wrasse
4891
Labridae
Anampses geographicus
Geographic wrasse
4890
Labridae
Anampses neoguinaicus
New Guinea wrasse
5497
Labridae
Bodianus anthioides
Lyretail hogfish
5498
Labridae
Bodianus axillaris
Axilspot hogfish
6580
Labridae
Bodianus bilunulatus
Tarry hogfish
5500
Labridae
Bodianus diana
Diana's hogfish
5501
Labridae
Bodianus mesothorax
Splitlevel hogfish
No.
Spp.
184
Table A.1.1 - (cont.)
Functional Group
185
FB
species
code
Family
Scientific name
Common name
5598
Labridae
Cheilinus chlorurus
Floral wrasse
5600
Labridae
Cheilinus fasciatus
Redbreast wrasse
5603
Labridae
Cheilinus trilobatus
Tripletail wrasse
5623
Labridae
Cheilio inermis
Cigar wrasse
5502
Labridae
Choerodon anchorago
Orange-dotted tuskfish
6433
Labridae
Choerodon schoenleinii
Blackspot tuskfish
5625
Labridae
Coris gaimardi
Yellowtail coris
5606
Labridae
Epibulus insidiator
Slingjaw wrasse
5626
Labridae
Gomphosus varius
Bird wrasse
12663
Labridae
Halichoeres hortulanus
Checkerboard wrasse
4856
Labridae
Halichoeres melasmopomus
Cheekspot wrasse
5635
Labridae
Hemigymnus fasciatus
Barred thicklip
5636
Labridae
Hemigymnus melapterus
Blackeye thicklip
5638
Labridae
Hologymnosus. doliatus
Pastel ringwrasse
5610
Labridae
Novaculichthys taeniourus
Rockmover wrasse
5597
Labridae
Oxycheilinus celebicus
Celebes wrasse
5599
Labridae
Oxycheilinus diagrammus
Cheeklined wrasse
5605
Labridae
Oxycheilinus unifasciatus
Ringtail maori wrasse
5594
Labridae
Pseudodax moluccanus
Chiseltooth wrasse
5645
Labridae
Thalassoma lunare
Moon wrasse
5647
Labridae
Thalassoma purpureum
Surge wrasse
5613
Labridae
Xyrichtys pavo
Peacock wrasse
1832
Lethrinidae
Gnathodentex aurolineatus
Striped large-eye bream
1834
Lethrinidae
Gymnocranius grandoculus
Blue-lined large-eye bream
1854
Lethrinidae
Lethrinus atkinsoni
Pacific yellowtail emperor
1862
Lethrinidae
Lethrinus erythracanthus
Orange-spotted emperor
1842
Lethrinidae
Lethrinus erythropterus
Longfin emperor
1851
Lethrinidae
Lethrinus harak
Thumbprint emperor
1857
Lethrinidae
Lethrinus laticaudis
Grass emperor
1863
Lethrinidae
Lethrinus lentjan
Pink ear emperor
1847
Lethrinidae
Lethrinus obsoletus
Orange-striped emperor
1864
Lethrinidae
Lethrinus olivaceous
Longface emperor
1866
Lethrinidae
Lethrinus ornatus
Ornate emperor
1849
Lethrinidae
Lethrinus semicinctus
Black blotch emperor
1852
Lethrinidae
Lethrinus xanthocheilus
Yellowlip emperor
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
1869
Lethrinidae
Monotaxis grandoculis
Humpnose big-eye bream
5795
Malacanthidae
Malacanthus brevirostris
Quakerfish
5796
Malacanthidae
Malacanthus latovittatus
Blue blanquillo
59225
Microdesmidae
Aioliops megastigma
12883
Moringuidae
Moringua ferruginea
Rusty spaghetti eel
8051
Moringuidae
Moringua microchir
Lesser thrush eel
5653
Mugilidae
Crenimugil crenilabis
Fringelip mullet
5656
Mugilidae
Liza vaigiensis
Squaretail mullet
5983
Mullidae
Mulloidichthys flavolineatus
Yellowstripe goatfish
5986
Mullidae
Parupeneus barberinoides
Bicolor goatfish
5987
Mullidae
Parupeneus barberinus
Dash-and-dot goatfish
60947
Mullidae
Parupeneus bifasciatus
5990
Mullidae
Parupeneus cyclostomus
Goldsaddle goatfish
5991
Mullidae
Parupeneus heptacanthus
Cinnabar goatfish
5992
Mullidae
Parupeneus indicus
Indian goatfish
5993
Mullidae
Parupeneus multifasciatus
Manybar goatfish
5994
Mullidae
Parupeneus pleurostigma
Sidespot goatfish
5443
Mullidae
Upeneus tragula
Freckled goatfish
5388
Muraenidae
Echidna nebulosa
Snowflake moray
6494
Muraenidae
Gymnothorax enigmaticus
Enigmatic moray
6495
Muraenidae
Gymnothorax fimbriatus
Fimbriated moray
5392
Muraenidae
Gymnothorax flavimarginatus
Yellow-edged moray
6380
Muraenidae
Gymnothorax javanicus
Giant moray
7284
Muraenidae
Gymnothorax melatremus
Dwarf moray
6395
Muraenidae
Gymnothorax pictus
Peppered moray
8594
Muraenidae
Rhinomuraena quaesita
Ribbon moray
10217
Muraenidae
Uropterygius micropterus
Tidepool snake moray
5868
Nemipteridae
Pentapodus emeryii
Double whiptail
5873
Nemipteridae
Pentapodus trivittatus
Three-striped whiptail
5890
Nemipteridae
Scolopsis affinis
Peters' monocle bream
5885
Nemipteridae
Scolopsis bilineatus
Two-lined monocle bream
5877
Nemipteridae
Scolopsis lineatus
Striped monocle bream
5878
Nemipteridae
Scolopsis margaritifer
Pearly monocle bream
5879
Nemipteridae
Scolopsis monogramma
Monogrammed monocle bream
5880
Nemipteridae
Scolopsis temporalis
Bald-spot monocle bream
No.
Spp.
186
Table A.1.1 - (cont.)
Functional Group
187
FB
species
code
Family
Scientific name
Common name
5883
Nemipteridae
Scolopsis vosmeri
Whitecheek monocle bream
7473
Ophichthidae
Leiuranus semicinctus
Saddled snake eel
8053
Ophichthidae
Myrichthys colubrinus
Harlequin snake eel
2650
Ophichthidae
Myrichthys maculosus
Tiger snake eel
756
Orectolobidae
Orectolobus dasypogon
Tasselled wobbegong
6555
Ostraciidae
Ostracion cubicus
Yellow boxfish
6556
Ostraciidae
Ostracion meleagris
Whitespotted boxfish
7892
Pentacerotidae
Histiopterus typus
Sailfin armourhead
4433
Pholidichthyidae
Pholidichthys leucotaenia
Convict blenny
6561
Pinguipedidae
Parapercis clathrata
Latticed sandperch
6562
Pinguipedidae
Parapercis cylindrica
Cylindrical sandperch
7866
Pinguipedidae
Parapercis hexophthalma
Speckled sandperch
6674
Pinguipedidae
Parapercis tetracantha
Reticulated sandperch
12668
Pinguipedidae
Parapercis xanthozona
Yellowbar sandperch
15197
Platycephalidae
Cociella punctata
Spotted flathead
12826
Platycephalidae
Cymbacephalus beauforti
Crocodile fish
12902
Platycephalidae
Thysanophrys chiltoni
Longsnout flathead
4706
Plotosidae
Plotosus lineatus
Striped eel catfish
5687
Pomacentridae
Abudefduf septemfasciatus
Banded sergeant
5689
Pomacentridae
Abudefduf sordidus
Blackspot sergeant
5730
Pomacentridae
Pomacentrus vaiuli
Ocellate damselfish
5791
Priacanthidae
Priacanthus hamrur
Moontail bullseye
14273
Pseudochromidae
Pseudoplesiops annae
27610
Ptereleotridae
Parioglossus philippinus
Philippine dartfish
4698
Scatophagidae
Scatophagus argus
Spotted scat
5828
Scorpaenidae
Dendrochirus zebra
Zebra turkeyfish
5195
Scorpaenidae
Pterois volitans
Red lionfish
5822
Scorpaenidae
Scorpaenopsis oxycephala
Tassled scorpionfish
4614
Siganidae
Siganus argenteus
Streamlined spinefoot
4456
Siganidae
Siganus canaliculatus
White-spotted spinefoot
4611
Siganidae
Siganus corallinus
Blue-spotted spinefoot
4588
Siganidae
Siganus guttatus
Orange-spotted spinefoot
4618
Siganidae
Siganus javus
Streaked spinefoot
4625
Siganidae
Siganus lineatus
Golden-lined spinefoot
4617
Siganidae
Siganus puellus
Masked spinefoot
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
Med. reef
associated
FB
species
code
Family
Scientific name
Common name
4620
Siganidae
Siganus punctatissimus
Peppered spinefoot
4621
Siganidae
Siganus punctatus
Goldspotted spinefoot
4457
Siganidae
Siganus spinus
Little spinefoot
4624
Siganidae
Siganus virgatus
Barhead spinefoot
4629
Siganidae
Siganus vulpinus
Foxface
5826
Synanceiidae
Inimicus didactylus
Bearded ghoul
6389
Synanceiidae
Synanceja horrida
Estuarine stonefish
5825
Synanceiidae
Synanceja verrucosa
Stonefish
5960
Syngnathidae
Corythoichthys haematopterus
Messmate pipefish
53790
Syngnathidae
Hippocampus bargibanti
Pygmy seahorse
5955
Syngnathidae
Hippocampus kuda
Spotted seahorse
5980
Syngnathidae
Syngnathoides biaculeatus
Alligator pipefish
4534
Synodontidae
Saurida gracilis
Gracile lizardfish
12620
Synodontidae
Synodus dermatogenys
Sand lizardfish
5398
Synodontidae
Synodus variegatus
Variegated lizardfish
4829
Terapontidae
Terapon theraps
Largescaled therapon
8229
Toxotidae
Toxotes jaculatrix
Banded archerfish
5950
Zanclidae
Zanclus cornutus
Moorish idol
5402
Antennariidae
Antennarius coccineus
Scarlet frogfish
7294
Antennariidae
Antennarius dorhensis
New Guinean frogfish
3089
Antennariidae
Histrio histrio
Sargassumfish
5766
Apogonidae
Apogon angustatus
Broadstriped cardinalfish
4837
Apogonidae
Apogon aureus
Ring-tailed cardinalfish
13003
Apogonidae
Apogon chrysotaenia
5769
Apogonidae
Apogon compressus
Ochre-striped cardinalfish
5756
Apogonidae
Apogon exostigma
Narrowstripe cardinalfish
4838
Apogonidae
Apogon fleurieu
Cardinalfish
5758
Apogonidae
Apogon kallopterus
Iridescent cardinalfish
23455
Apogonidae
Apogon rhodopterus
Redfin cardinalfish
5767
Apogonidae
Apogon taeniophorus
Reef-flat cardinalfish
12906
Apogonidae
Apogon talboti
Flame cardinalfish
5748
Apogonidae
Apogon trimaculatus
Three-spot cardinalfish
5775
Apogonidae
Archamia biguttata
Twinspot cardinalfish
No.
Spp.
176
188
Table A.1.1 - (cont.)
Functional Group
189
FB
species
code
Family
Scientific name
12876
Apogonidae
Cheilodipterus alleni
5780
Apogonidae
Cheilodipterus artus
Wolf cardinalfish
5482
Apogonidae
Cheilodipterus quinquelineatus
Five-lined cardinalfish
5782
Apogonidae
Cheilodipterus singapurensis
Truncate cardinalfish
5743
Apogonidae
Fowleria punctulata
Spotcheek cardinalfish
4362
Apogonidae
Pseudamia gelatinosa
Gelatinous cardinalfish
1307
Atherinidae
Hypoatherina temminckii
Samoan silverside
6066
Blenniidae
Aspidontus taeniatus
False cleanerfish
4387
Blenniidae
Cirripectes castaneus
Chestnut eyelash-blenny
4398
Blenniidae
Cirripectes polyzona
4402
Blenniidae
Cirripectes stigmaticus
Red-streaked blenny
6033
Blenniidae
Ecsenius bicolor
Bicolor blenny
7664
Blenniidae
Ecsenius namiyei
Black comb-tooth
6043
Blenniidae
Entomacrodus striatus
Reef margin blenny
6049
Blenniidae
Istiblennius edentulus
Rippled rockskipper
6050
Blenniidae
Istiblennius lineatus
Lined rockskipper
6069
Blenniidae
Meiacanthus grammistes
Striped poison-fang blenny
6073
Blenniidae
Petroscirtes breviceps
Striped poison-fang blenny
mimic
6071
Blenniidae
Plagiotremus rhinorhynchus
Bluestriped fangblenny
6072
Blenniidae
Plagiotremus tapeinosoma
Piano fangblenny
6058
Blenniidae
Salarias fasciatus
Jewelled blenny
59272
Blenniidae
Salarias ramosus
Starry blenny
22515
Blenniidae
Salarias segmentatus
Segmented blenny
388
Carangidae
Selaroides leptolepis
Yellowstripe scad
6510
Centriscidae
Centriscus scutatus
Grooved razor-fish
6604
Cirrhitidae
Cirrhitichthys aprinus
Spotted hawkfish
5833
Cirrhitidae
Oxycirrhitus typus
Longnose hawkfish
5835
Cirrhitidae
Paracirrhites arcatus
Arc-eye hawkfish
5952
Cirrhitidae
Paracirrhites forsteri
Blackside hawkfish
23581
Gobiidae
Acentrogobius janthinopterus
7230
Gobiidae
Amblyeleotris fontanesii
Giant prawn-goby
11614
Gobiidae
Amblyeleotris gymnocephala
Masked shrimpgoby
55505
Gobiidae
Amblygobius bynoensis
Byno goby
7198
Gobiidae
Amblygobius phalaena
Banded goby
Common name
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
12679
Gobiidae
Cryptocentrus fasciatus
Y-bar shrimp goby
370
Gobiidae
Exyrias bellisimus
Mud reef-goby
377
Gobiidae
Exyrias puntang
Puntang goby
4328
Gobiidae
Istigobius decoratus
Decorated goby
4322
Gobiidae
Istigobius ornatus
Ornate goby
7480
Gobiidae
Periophthalmus argentilineatus
Barred mudskipper
7482
Gobiidae
Periophthalmus kalolo
Common mudskipper
7226
Gobiidae
Valenciennea muralis
Mural goby
7246
Gobiidae
Valenciennea puellaris
Maiden goby
7227
Gobiidae
Valenciennea sexguttata
Sixspot goby
6575
Gobiidae
Valenciennea strigata
Blueband goby
4699
Holocentridae
Sargocentron diadema
Crown squirrelfish
5622
Labridae
Anampses melanurus
White-spotted wrasse
4889
Labridae
Anampses meleagrides
Spotted wrasse
4893
Labridae
Anampses twistii
Yellowbreasted wrasse
5602
Labridae
Cheilinus oxycephalus
Snooty wrasse
25688
Labridae
Coris batuensis
Batu coris
5107
Labridae
Coris pictoides
Blackstripe coris
5608
Labridae
Cymolutes torquatus
Finescale razorfish
4857
Labridae
Halichoeres argus
Argus wrasse
5627
Labridae
Halichoeres biocellatus
Red-lined wrasse
4859
Labridae
Halichoeres chloropterus
Pastel-green wrasse
4855
Labridae
Halichoeres chrysus
Canary wrasse
5628
Labridae
Halichoeres hartzfeldi
Hartzfeld's wrasse
56811
Labridae
Halichoeres leucurus
Greyhead wrasse
5630
Labridae
Halichoeres margaritaceus
Pink-belly wrasse
5631
Labridae
Halichoeres marginatus
Dusky wrasse
6929
Labridae
Halichoeres melanochir
4858
Labridae
Halichoeres melanurus
Tail-spot wrasse
6614
Labridae
Halichoeres miniatus
Circle-cheek wrasse
6663
Labridae
Halichoeres nebulosus
Nebulous wrasse
58179
Labridae
Halichoeres nigrescens
Bubblefin wrasse
56813
Labridae
Halichoeres papilionaceus
4861
Labridae
Halichoeres podostigma
Axil spot wrasse
4862
Labridae
Halichoeres prosopeion
Twotone wrasse
No.
Spp.
190
Table A.1.1 - (cont.)
Functional Group
191
FB
species
code
Family
Scientific name
Common name
5632
Labridae
Halichoeres richmondi
Richmond's wrasse
5633
Labridae
Halichoeres scapularis
Zigzag wrasse
11622
Labridae
Halichoeres solorensis
Green wrasse
5651
Labridae
Labroides pectoralis
Blackspot cleaner wrasse
4984
Labridae
Macropharyngodon meleagris
Blackspotted wrasse
4985
Labridae
Macropharyngodon negrosensis
Yellowspotted wrasse
5609
Labridae
Novaculichthys macrolepidotus
Seagrass wrasse
5596
Labridae
Oxycheilinus bimaculatus
Two-spot wrasse
5601
Labridae
Oxycheilinus orientalis
Oriental maori wrasse
6924
Labridae
Pseudocoris philippina
Philippine wrasse
52464
Labridae
Pteragogus enneacanthus
Cockerel wrasse
6633
Labridae
Stethojulis interrupta
Cutribbon wrasse
5641
Labridae
Stethojulis strigiventer
Three-ribbon wrasse
6622
Labridae
Stethojulis trilineata
Three-lined rainbowfish
5644
Labridae
Thalassoma jansenii
Jansen's wrasse
5648
Labridae
Thalassoma quinquevittatum
Fivestripe wrasse
1850
Lethrinidae
Lethrinus variegatus
Slender emperor
5792
Malacanthidae
Hoplolatilus cuniculus
Dusky tilefish
15342
Malacanthidae
Hoplolatilus purpureus
Purple sand tilefish
5794
Malacanthidae
Hoplolatilus starcki
Stark's tilefish
6496
Muraenidae
Gymnothorax fuscomaculatus
Brown spotted moray
5876
Nemipteridae
Scolopsis ciliatus
Saw-jawed monocle bream
5881
Nemipteridae
Scolopsis trilineatus
Three-lined monocle bream
5882
Nemipteridae
Scolopsis xenochrous
Oblique-barred monocle bream
6577
Ostraciidae
Ostracion solorensis
Reticulate boxfish
6670
Pinguipedidae
Parapercis millepunctata
Black dotted sand perch
14983
Plesiopidae
Plesiops corallicola
Bluegill longfin
6517
Pomacentridae
Abudefduf bengalensis
Bengal sergeant
5685
Pomacentridae
Abudefduf lorenzi
Black-tail sergeant
5686
Pomacentridae
Abudefduf notatus
Yellowtail sergeant
6655
Pomacentridae
Acanthochromis polyacantha
Spiny chromis
5477
Pomacentridae
Amblyglyphidodon curacao
Staghorn damselfish
5691
Pomacentridae
Amblyglyphidodon leucogaster
Yellowbelly damselfish
5692
Pomacentridae
Amblyglyphidodon ternatensis
Ternate damsel
4551
Pomacentridae
Amphiprion chrysopterus
Orangefin anemonefish
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
5448
Pomacentridae
Amphiprion clarkii
Yellowtail clownfish
4654
Pomacentridae
Amphiprion melanopus
Fire clownfish
6509
Pomacentridae
Amphiprion ocellaris
Clown anemonefish
8086
Pomacentridae
Amphiprion polymnus
Saddleback clownfish
6523
Pomacentridae
Amphiprion sandaracinos
Yellow clownfish
6551
Pomacentridae
Chromis analis
Yellow chromis
5450
Pomacentridae
Chromis atripectoralis
Black-axil chromis
11858
Pomacentridae
Chromis cinerascens
5680
Pomacentridae
Chromis weberi
Weber's chromis
5693
Pomacentridae
Chrysiptera biocellata
Twinspot damselfish
5112
Pomacentridae
Dascyllus trimaculatus
Threespot dascyllus
6553
Pomacentridae
Dischistodus chrysopoecilus
Lagoon damsel
9982
Pomacentridae
Dischistodus fasciatus
Banded damsel
5703
Pomacentridae
Dischistodus melanotus
Black-vent damsel
6608
Pomacentridae
Dischistodus prosopotaenia
Honey-head damsel
5487
Pomacentridae
Hemiglyphidodon plagiometopon
Lagoon damselfish
12457
Pomacentridae
Neoglyphidodon crossi
Cross' damsel
5707
Pomacentridae
Neoglyphidodon melas
Bowtie damselfish
5708
Pomacentridae
Neoglyphidodon nigroris
Black-and-gold chromis
11988
Pomacentridae
Neoglyphidodon oxyodon
Bluestreak damselfish
12459
Pomacentridae
Neopomacentrus filamentosus
Brown demoiselle
5709
Pomacentridae
Plectroglyphidodon dickii
Blackbar devil
5713
Pomacentridae
Plectroglyphidodon leucozonus
Singlebar devil
5723
Pomacentridae
Pomacentrus grammorhynchus
Bluespot damsel
12493
Pomacentridae
Pomacentrus littoralis
Smoky damsel
10280
Pomacentridae
Pomacentrus taeniometopon
Brackish damsel
4340
Pomacentridae
Stegastes albifasciatus
Whitebar gregory
4347
Pomacentridae
Stegastes fasciolatus
Pacific gregory
4351
Pomacentridae
Stegastes lividus
Blunt snout gregory
4352
Pomacentridae
Stegastes nigricans
Dusky farmerfish
4353
Pomacentridae
Stegastes obreptus
Western gregory
12662
Pseudochromidae
Labracinus cyclophthalmus
Fire-tail devil
12654
Pseudochromidae
Pseudochromis bitaeniatus
Double-striped dottyback
24444
Pseudochromidae
Pseudochromis perspicillatus
Southeast Asian blackstripe
dottyback
No.
Spp.
192
Table A.1.1 - (cont.)
Functional Group
Small reef
associated
193
FB
species
code
Family
Scientific name
Common name
12714
Pseudochromidae
Pseudochromis splendens
Splendid dottyback
4377
Ptereleotridae
Ptereleotris hanae
Blue hana goby
4381
Ptereleotridae
Ptereleotris microlepis
Blue gudgeon
4384
Ptereleotridae
Ptereleotris zebra
Chinese zebra goby
4914
Scorpaenidae
Pterois antennata
Broadbarred firefish
5819
Scorpaenidae
Scorpaenodes guamensis
Guam scorpionfish
4915
Scorpaenidae
Scorpaenodes parvipinnis
Lowfin scorpionfish
5820
Scorpaenidae
Scorpaenopsis macrochir
Flasher scorpionfish
22544
Soleidae
Soleichthys heterorhinos
5959
Syngnathidae
Corythoichthys flavofasciatus
Network pipefish
5961
Syngnathidae
Corythoichthys intestinalis
Scribbled pipefish
5963
Syngnathidae
Corythoichthys ocellatus
Ocellated pipefish
5965
Syngnathidae
Corythoichthys schultzi
Schultz's pipefish
5972
Syngnathidae
Doryrhamphus dactyliophorus
Ringed pipefish
5970
Syngnathidae
Doryrhamphus janssi
Janss' pipefish
5974
Syngnathidae
Halicampus dunckeri
Duncker's pipefish
5975
Syngnathidae
Halicampus mataafae
Samoan pipefish
8119
Synodontidae
Saurida nebulosa
Clouded lizardfish
7943
Synodontidae
Synodus jaculum
Lighthouse lizardfish
10706
Synodontidae
Synodus rubromarmoratus
Redmarbled lizardfish
25701
Tetrarogidae
Ablabys macracanthus
waspfish
23560
Trichonotidae
Trichonotus elegans
Long-rayed sand-diver
12670
Trichonotidae
Trichonotus setiger
Spotted sand-diver
9230
Acanthoclinidae
Belonepterygium fasciolatum
14043
Antennariidae
Histiophryne cryptacanthus
Cryptic anglerfish
5763
Apogonidae
Apogon bandanensis
Bigeye cardinalfish
25109
Apogonidae
Apogon cavitiensis
10342
Apogonidae
Apogon ceramensis
Ceram cardinalfish
58153
Apogonidae
Apogon chrysopomus
Spotted-gill cardinalfish
12992
Apogonidae
Apogon crassiceps
Transparent cardinalfish
5759
Apogonidae
Apogon dispar
Redspot cardinalfish
5753
Apogonidae
Apogon doryssa
Longspine cardinalfish
5757
Apogonidae
Apogon fraenatus
Bridled cardinalfish
No.
Spp.
206
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
5771
Apogonidae
Apogon fragilis
Fragile cardinalfish
59777
Apogonidae
Apogon fuscus
5760
Apogonidae
Apogon hartzfeldi
Hartzfeld's cardinalfish
8588
Apogonidae
Apogon hoeveni
Frostfin cardinalfish
5773
Apogonidae
Apogon leptacanthus
Threadfin cardinalfish
25370
Apogonidae
Apogon melanoproctus
Blackvent cardinalfish
11619
Apogonidae
Apogon multilineatus
Many-lined cardinalfish
25037
Apogonidae
Apogon nanus
25049
Apogonidae
Apogon neotes
4836
Apogonidae
Apogon nigrofasciatus
Blackstripe cardinalfish
8590
Apogonidae
Apogon notatus
Spotnape cardinalfish
5768
Apogonidae
Apogon novemfasciatus
Sevenstriped cardinalfish
23452
Apogonidae
Apogon parvulus
5774
Apogonidae
Apogon perlitus
Pearly cardinalfish
6230
Apogonidae
Apogon sealei
Seale's cardinalfish
27037
Apogonidae
Apogon selas
Meteor cardinalfish
12747
Apogonidae
Apogon thermalis
Half-barred cardinal
12658
Apogonidae
Apogon timorensis
Timor cardinalfish
59184
Apogonidae
Apogon wassinki
5740
Apogonidae
Apogonichthys ocellatus
Ocellated cardinalfish
5776
Apogonidae
Archamia fucata
Orangelined cardinalfish
59188
Apogonidae
Archamia macropterus
Dusky-tailed cardinalfish
5777
Apogonidae
Archamia zosterophora
Blackbelted cardinalfish
14876
Apogonidae
Cercamia eremia
Glassy cardinalfish
12878
Apogonidae
Cheilodipterus nigrotaeniatus
8010
Apogonidae
Fowleria aurita
Crosseyed cardinalfish
5787
Apogonidae
Gymnapogon urospilotus
B-spot cardinalfish
4363
Apogonidae
Pseudamia hayashi
Hayashi's cardinalfish
5747
Apogonidae
Rhabdamia gracilis
Luminous cardinalfish
5778
Apogonidae
Sphaeramia nematoptera
Pajama cardinalfish
17462
Blenniidae
Atrosalarias fuscus
4389
Blenniidae
Cirripectes filamentosus
Filamentous blenny
4399
Blenniidae
Cirripectes quagga
Squiggly blenny
7560
Blenniidae
Crossosalarias macrospilus
Triplespot blenny
12633
Blenniidae
Ecsenius bandanus
Banda comb-tooth
No.
Spp.
194
Table A.1.1 - (cont.)
Functional Group
195
FB
species
code
Family
Scientific name
Common name
12692
Blenniidae
Ecsenius bathi
Bath's comb-tooth
7661
Blenniidae
Ecsenius lividinalis
7663
Blenniidae
Ecsenius stigmatura
12640
Blenniidae
Ecsenius trilineatus
Three-lined blenny
6036
Blenniidae
Ecsenius yaeyamensis
Yaeyama blenny
46625
Blenniidae
Meiacanthus crinitus
59273
Blenniidae
Salarias patzneri
Patzner's blenny
7299
Bythitidae
Brosmophyciops pautzkei
Slimy cuskeel
17464
Callionymidae
Anaora tentaculata
Tentacled dragonet
17468
Callionymidae
Callionymus ennactis
Mangrove dragonet
12643
Callionymidae
Synchiropus morrisoni
Morrison's dragonet
7981
Callionymidae
Synchiropus ocellatus
Ocellated dragonet
12644
Callionymidae
Synchiropus splendidus
Mandarinfish
7873
Caracanthidae
Caracanthus maculatus
Spotted coral croucher
10744
Carapidae
Onuxodon margaritiferae
Bivalve pearlfish
5445
Cirrhitidae
Cirrhitichthys falco
Dwarf hawkfish
5830
Cirrhitidae
Cirrhitichthys oxycephalus
Coral hawkfish
12891
Gobiesocidae
Diademichthys lineatus
Urchin clingfish
7494
Gobiesocidae
Discotrema crinophila
Crinoid clingfish
7229
Gobiidae
Amblyeleotris fasciata
Red-banded prawn-goby
6671
Gobiidae
Amblyeleotris guttata
Spotted prawn-goby
47046
Gobiidae
Amblyeleotris latifasciata
7231
Gobiidae
Amblyeleotris periophthalma
Periophthalma prawn-goby
7195
Gobiidae
Amblyeleotris steinitzi
Steinitz' prawn-goby
7196
Gobiidae
Amblyeleotris wheeleri
Gorgeous prawn-goby
46531
Gobiidae
Amblyeleotris yanoi
Flagtail shrimpgoby
56800
Gobiidae
Amblygobius buanensis
Buan goby
7197
Gobiidae
Amblygobius decussatus
Orange-striped goby
7243
Gobiidae
Amblygobius nocturnus
Nocturn goby
5478
Gobiidae
Amblygobius rainfordi
Old glory
58617
Gobiidae
Asterropteryx bipunctatus
Orange-spotted goby
7200
Gobiidae
Asterropteryx semipunctatus
Starry goby
7202
Gobiidae
Bathygobius cocosensis
Cocos frill-goby
11801
Gobiidae
Bathygobius cyclopterus
Spotted frillgoby
7203
Gobiidae
Bryaninops amplus
Large whip goby
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
52430
Gobiidae
Bryaninops loki
Loki whip-goby
7205
Gobiidae
Bryaninops natans
Redeye goby
7251
Gobiidae
Bryaninops yongei
Whip coral goby
26684
Gobiidae
Callogobius inframaculatus
7206
Gobiidae
Callogobius maculipinnis
Ostrich goby
17056
Gobiidae
Coryphopterus duospilus
Barenape goby
7215
Gobiidae
Coryphopterus neophytus
Common fusegoby
7513
Gobiidae
Coryphopterus signipinnis
Signalfin goby
7233
Gobiidae
Cryptocentroides insignis
Insignia prawn-goby
7208
Gobiidae
Cryptocentrus cinctus
Yellow prawn-goby
25799
Gobiidae
Cryptocentrus leptocephalus
Pink-speckled shrimpgoby
13767
Gobiidae
Cryptocentrus leucostictus
Saddled prawn-goby
7209
Gobiidae
Cryptocentrus strigilliceps
Target shrimp goby
7237
Gobiidae
Ctenogobiops aurocingulus
Gold-streaked prawn-goby
7238
Gobiidae
Ctenogobiops feroculus
Sandy prawn-goby
7210
Gobiidae
Ctenogobiops pomastictus
Gold-specked prawn-goby
7259
Gobiidae
Eviota albolineata
Spotted fringefin goby
7213
Gobiidae
Eviota bifasciata
Twostripe pygmy goby
25452
Gobiidae
Eviota guttata
Spotted pygmy goby
7214
Gobiidae
Eviota nigriventris
Blackbelly goby
7269
Gobiidae
Eviota pellucida
Pellucida pygmy goby
7270
Gobiidae
Eviota prasina
Green bubble goby
7271
Gobiidae
Eviota prasites
Prasites pygmy goby
7275
Gobiidae
Eviota sebreei
Sebree's pygmy goby
7635
Gobiidae
Eviota sparsa
Speckled pygmy goby
23595
Gobiidae
Gnatholepis anjerensis
9950
Gobiidae
Gnatholepis cauerensis
Eyebar goby
7217
Gobiidae
Gobiodon okinawae
Okinawa goby
59869
Gobiidae
Gobiodon unicolor
4324
Gobiidae
Istigobius rigilius
23719
Gobiidae
Luposicya lupus
1246
Gobiidae
Macrodontogobius wilburi
Largetooth goby
7240
Gobiidae
Mahidolia mystacina
Flagfin prawn goby
7218
Gobiidae
Oplopomus oplopomus
Spinecheek goby
61315
Gobiidae
Phyllogobius platycephalops
Slender spongegoby
No.
Spp.
Rigilius goby
196
Table A.1.1 - (cont.)
Functional Group
197
FB
species
code
Family
Scientific name
Common name
23079
Gobiidae
Pleurosicya mossambica
Toothy goby
7245
Gobiidae
Signigobius biocellatus
Twinspot goby
4311
Gobiidae
Stonogobiops xanthorhinica
Yellownose prawn-goby
59924
Gobiidae
Sueviota atronasus
23644
Gobiidae
Tomiyamichthys oni
Monster shrimpgoby
28081
Gobiidae
Trimma benjamini
Redface dwarfgoby
25541
Gobiidae
Trimma emeryi
Emery's goby
26320
Gobiidae
Trimma macrophthalma
Flame goby
7222
Gobiidae
Trimma okinawae
Okinawa rubble goby
58619
Gobiidae
Trimma rubromaculata
7223
Gobiidae
Trimma striata
Stripehead goby
12752
Gobiidae
Trimma taylori
Yellow cave goby
12754
Gobiidae
Trimma tevegae
Blue-striped cave goby
12607
Gobiidae
Valenciennea bella
12613
Gobiidae
Valenciennea randalli
Greenband goby
23645
Gobiidae
Vanderhorstia lanceolata
Lanceolate shrimpgoby
5499
Labridae
Bodianus bimaculatus
Twospot hogfish
54179
Labridae
Cirrhilabrus condei
46517
Labridae
Cirrhilabrus flavidorsalis
59640
Labridae
Cirrhilabrus tonozukai
5108
Labridae
Diproctacanthus xanthurus
Yellowtail tubelip
56789
Labridae
Halichoeres pallidus
Pale wrasse
4863
Labridae
Labropsis alleni
Allen's tubelip
27014
Labridae
Parachelinus cyaneus
5615
Labridae
Pseudocheilinops ataenia
Pelvic-spot wrasse
5616
Labridae
Pseudocheilinus evanidus
Striated wrasse
5617
Labridae
Pseudocheilinus hexataenia
Pyjama wrasse
57494
Labridae
Pseudojuloides kaleidos
5620
Labridae
Pteragogus cryptus
Cryptic wrasse
4869
Labridae
Wetmorella albofasciata
Whitebanded sharpnose wrasse
7587
Microdesmidae
Gunnelichthys pleurotaenia
Onestripe wormfish
4606
Pegasidae
Eurypegasus draconis
Short dragonfish
8005
Plesiopidae
Plesiops coeruleolineatus
Crimsontip longfin
54180
Pomacentridae
Amblyglyphidodon batunai
9721
Pomacentridae
Amblypomacentrus breviceps
Yellowfin fairy wrasse
Black-banded demoiselle
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
2024
Pomacentridae
Amphiprion perideraion
Pink anemonefish
5684
Pomacentridae
Cheiloprion labiatus
Big-lip damsel
5670
Pomacentridae
Chromis alpha
Yellow-speckled chromis
5671
Pomacentridae
Chromis amboinensis
Ambon chromis
4982
Pomacentridae
Chromis atripes
Dark-fin chromis
5672
Pomacentridae
Chromis caudalis
Blue-axil chromis
5673
Pomacentridae
Chromis delta
Deep reef chromis
4983
Pomacentridae
Chromis elerae
Twinspot chromis
5128
Pomacentridae
Chromis lineata
Lined chromis
5676
Pomacentridae
Chromis retrofasciata
Black-bar chromis
5677
Pomacentridae
Chromis ternatensis
Ternate chromis
12521
Pomacentridae
Chrysiptera bleekeri
Bleeker's damsel
56326
Pomacentridae
Chrysiptera brownriggii
Surge demoiselle
5695
Pomacentridae
Chrysiptera cyanea
Sapphire devil
12448
Pomacentridae
Chrysiptera parasema
Goldtail demoiselle
5699
Pomacentridae
Chrysiptera rex
King demoiselle
12450
Pomacentridae
Chrysiptera springeri
Springer's demoiselle
5702
Pomacentridae
Chrysiptera unimaculata
Onespot demoiselle
5113
Pomacentridae
Dascyllus reticulatus
Reticulate dascyllus
10227
Pomacentridae
Neopomacentrus azysron
Yellow-tail demoiselle
12458
Pomacentridae
Neopomacentrus bankieri
Chinese demoiselle
8209
Pomacentridae
Neopomacentrus cyanomos
Regal demoiselle
5712
Pomacentridae
Plectroglyphidodon lacrymatus
Whitespotted devil
12476
Pomacentridae
Pomacentrus adelus
Obscure damsel
12483
Pomacentridae
Pomacentrus auriventris
Goldbelly damsel
5719
Pomacentridae
Pomacentrus burroughi
Burrough's damsel
5721
Pomacentridae
Pomacentrus chrysurus
Whitetail damsel
12488
Pomacentridae
Pomacentrus cuneatus
Wedgespot damsel
10277
Pomacentridae
Pomacentrus opisthostigma
Brown damsel
5727
Pomacentridae
Pomacentrus philippinus
Philippine damsel
5728
Pomacentridae
Pomacentrus reidi
Reid's damsel
5729
Pomacentridae
Pomacentrus simsiang
Blueback damsel
10279
Pomacentridae
Pomacentrus smithi
Smith's damsel
8277
Pomacentridae
Pomacentrus tripunctatus
Threespot damsel
14271
Pseudochromidae
Amsichthys knighti
No.
Spp.
198
Table A.1.1 - (cont.)
Functional Group
Large demersal
Small demersal
199
FB
species
code
Family
Scientific name
Common name
12645
Pseudochromidae
Cypho purpurescens
Oblique-lined dottyback
12656
Pseudochromidae
Lubbockichthys multisquamatus
Fine-scaled dottyback
14279
Pseudochromidae
Pseudochromis cyanotaenia
Surge dottyback
46486
Pseudochromidae
Pseudochromis elongatus
6627
Pseudochromidae
Pseudochromis fuscus
Brown dottyback
7323
Pseudochromidae
Pseudochromis marshallensis
Marshall Is. dottyback
7461
Pseudochromidae
Pseudochromis porphyreus
Magenta dottyback
14274
Pseudochromidae
Pseudochromis tapienosoma
Blackmargin dottyback
17480
Ptereleotridae
Parioglossus formosus
Beautiful hover goby
5815
Scorpaenidae
Scorpaenodes hirsutus
Hairy scorpionfish
5811
Scorpaenidae
Sebastapistes cyanostigma
Yellowspotted scorpionfish
5814
Scorpaenidae
Sebastapistes strongia
Barchin scorpionfish
5824
Scorpaenidae
Taenianotus triacanthus
Leaf scorpionfish
5958
Syngnathidae
Choeroichthys brachysoma
Short-bodied pipefish
7742
Syngnathidae
Phoxocampus belcheri
Rock pipefish
7745
Syngnathidae
Phoxocampus tetrophthalmus
7192
Syngnathidae
Siokunichthys nigrolineatus
51555
Tripterygiidae
Enneapterygius rubricauda
Redtail triplefin
47048
Tripterygiidae
Enneapterygius ziegleri
Ziegler's triplefin
47204
Tripterygiidae
Ucla xenogrammus
Largemouth triplefin
13766
Xenisthmidae
Xenisthmus polyzonatus
Bullseye wriggler
7693
Emmelichthyidae
Erythrocles schlegelii
Japanese rubyfish
4463
Gerreidae
Gerres filamentosus
Whipfin silverbiddy
59331
Gobiidae
Amblyeleotris arcupinna
59344
Gobiidae
Trimma griffthsi
61010
Gobiidae
Trimma halonevum
17227
Muraenidae
Gymnothorax polyuranodon
Freshwater moray
5397
Muraenidae
Gymnothorax zonipectus
Barredfin moray
8291
Peristediidae
Satyrichthys rieffeli
Spotted armoured-gurnard
10335
Platycephalidae
Inegocia japonica
Japanese flathead
4458
Terapontidae
Terapon jarbua
Jarbua terapon
6383
Apistidae
Apistus carinatus
Ocellated waspfish
4838
Apogonidae
Apogon fleurieu
Cardinalfish
No.
Spp.
10
11
Table A.1.1 - (cont.)
Functional Group
Large planktivore
FB
species
code
Family
Scientific name
25034
Apogonidae
Apogon ocellicaudus
8239
Dactylopteridae
Dactyloptena macracantha
Spotwing flying gurnard
7235
Gobiidae
Cryptocentrus octofasciatus
Blue-speckled prawn goby
51738
Labridae
Choerodon zosterophorus
25449
Pempheridae
Pempheris mangula
Black-edged sweeper
10310
Platycephalidae
Sorsogona tuberculata
Tuberculated flathead
5705
Pomacentridae
Neopomacentrus taeniurus
Freshwater demoiselle
10580
Serranidae
Symphysanodon typus
Insular shelf beauty
49509
Tripterygiidae
Enneapterygius philippinus
6017
Acanthuridae
Paracanthurus hepatus
Palette surgeonfish
1303
Atherinidae
Atherinomorus lacunosus
Hardyhead silverside
1311
Balistidae
Odonus niger
Redtoothed triggerfish
919
Caesionidae
Caesio cuning
Redbelly yellowtail fusilier
920
Caesionidae
Caesio lunaris
Lunar fusilier
923
Caesionidae
Caesio teres
Yellow and blueback fusilier
933
Caesionidae
Pterocaesio digramma
Double-lined fusilier
935
Caesionidae
Pterocaesio marri
Marr's fusilier
936
Caesionidae
Pterocaesio pisang
Banana fusilier
938
Caesionidae
Pterocaesio tessellata
One-stripe fusilier
939
Caesionidae
Pterocaesio tile
Dark-banded fusilier
993
Carangidae
Decapterus macarellus
Mackerel scad
412
Carangidae
Elegatis bipinnulatus
Rainbow runner
1954
Carangidae
Selar boops
Oxeye scad
387
Carangidae
Selar crumenophthalmus
Bigeye scad
1619
Clupeidae
Anodontostoma chacunda
Chacunda gizzard shad
1494
Clupeidae
Herklotsichthys quadrimaculatus
Bluestripe herring
1595
Clupeidae
Hilsa kelee
Kelee shad
1613
Clupeidae
Nematalosa flyensis
Fly river gizzard shad
1617
Clupeidae
Nematalosa papuensis
Strickland river gizzard shad
6
Coryphaenidae
Coryphaena hippurus
Common dolphinfish
5738
Ephippidae
Platax pinnatus
Dusky batfish
7695
Exocoetidae
Cheilopogon cyanopterus
Margined flyingfish
1028
Exocoetidae
Cheilopogon furcatus
Spotfin flyingfish
15346
Exocoetidae
Cheilopogon intermedius
15358
Exocoetidae
Cheilopogon spilopterus
No.
Spp.
Common name
52
Manyspotted flyingfish
200
Table A.1.1 - (cont.)
Functional Group
Small planktivore
201
FB
species
code
Family
Scientific name
15362
Exocoetidae
Cypselurus hexazona
15365
Exocoetidae
Cypselurus oligolepis
Largescale flyingfish
5159
Exocoetidae
Cypselurus opisthopus
Black-finned flyingfish
5122
Exocoetidae
Cypselurus poecilopterus
Yellow-wing flyingfish
1032
Exocoetidae
Exocoetus volitans
Tropical two-wing flyingfish
1036
Exocoetidae
Hirundichthys speculiger
Mirrorwing flyingfish
3156
Hemiramphidae
Euleptorhamphus viridis
Ribbon halfbeak
12895
Hemiramphidae
Hyporhamphus. dussumieri
Dussumier's halfbeak
12112
Hemiramphidae
Oxyporhamphus micropterus
micropterus
Bigwing halfbeak
6506
Holocentridae
Myripristis adusta
Shadowfin soldierfish
4910
Holocentridae
Myripristis berndti
Blotcheye soldierfish
7305
Holocentridae
Myripristis hexagona
Doubletooth soldierfish
7306
Holocentridae
Myripristis kuntee
Shoulderbar soldierfish
5408
Holocentridae
Myripristis murdjan
Pinecone soldierfish
7308
Holocentridae
Myripristis pralinia
Scarlet soldierfish
7309
Holocentridae
Myripristis violacea
Lattice soldierfish
6505
Holocentridae
Myripristis vittata
Whitetip soldierfish
11620
Labridae
Pseudocoris heteroptera
Torpedo wrasse
5643
Labridae
Thalassoma hardwicke
Sixbar wrasse
84
Lutjanidae
Aprion virescens
Green jobfish
7536
Osteoglossidae
Scleropages jardinii
Australian bonytongue
10350
Pempheridae
Pempheris vanicolensis
Vanikoro sweeper
6630
Pomacentridae
Abudefduf vaigiensis
Indo-Pacific sergeant
1633
Pristigasteridae
Ilisha melastoma
Indian ilisha
7463
Pseudochromidae
Pseudoplesiops typus
Hidden basslet
4544
Sillaginidae
Sillago sihama
Silver sillago
4600
Apogonidae
Apogon cyanosoma
Yellowstriped cardinalfish
5746
Apogonidae
Rhabdamia cypselurus
Swallowtail cardinalfish
4926
Apogonidae
Sphaeramia orbicularis
Orbiculate cardinalfish
15462
Atherinidae
Hypoatherina valenciennei
Sumatran silverside
6067
Blenniidae
Meiacanthus atrodorsalis
Forktail blenny
6068
Blenniidae
Meiacanthus ditrema
One-striped poison-fang blenny
8421
Bregmacerotidae
Bregmaceros mcclellandii
Spotted codlet
Common name
No.
Spp.
62
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
928
Caesionidae
Dipterygonatus balteatus
Mottled fusilier
929
Caesionidae
Gymnocaesio gymnoptera
Slender fusilier
6503
Centriscidae
Aeoliscus strigatus
Razorfish
7764
Cirrhitidae
Cyprinocirrhites polyactis
Swallowtail hawkfish
1563
Clupeidae
Clupeoides papuensis
Papuan river sprat
1522
Clupeidae
Escualosa thoracata
White sardine
1457
Clupeidae
Spratelloides delicatulus
Delicate round herring
1458
Clupeidae
Spratelloides gracilis
Silver-stripe round herring
1459
Clupeidae
Spratelloides lewisi
Lewis' round herring
58618
Gobiidae
Asterropteryx striatus
7282
Gobiidae
Bryaninops tigris
Black coral goby
22495
Gobiidae
Trimma naudei
Naude's rubble goby
5106
Labridae
Cirrhilabrus cyanopleura
Blueside wrasse
5614
Labridae
Cirrhilabrus exquisitus
Exquisite wrasse
10576
Labridae
Leptojulis cyanopleura
Shoulder-spot wrasse
4844
Labridae
Parachelinus filamentosus
Filamentous wrasse
5639
Labridae
Pseudocoris yamashiroi
Redspot wrasse
5640
Labridae
Stethojulis bandanensis
Red shoulder wrasse
5642
Labridae
Thalassoma amblycephalum
Bluntheaded wrasse
5803
Pempheridae
Parapriacanthus ransonneti
Pigmy sweeper
7872
Pinguipedidae
Parapercis schauinslandi
Redspotted sandperch
5688
Pomacentridae
Abudefduf sexfasciatus
Scissortail sergeant
5690
Pomacentridae
Amblyglyphidodon aureus
Golden damselfish
5674
Pomacentridae
Chromis lepidolepis
Scaly chromis
5675
Pomacentridae
Chromis margaritifer
Bicolor chromis
12431
Pomacentridae
Chromis scotochiloptera
Philippines chromis
5679
Pomacentridae
Chromis viridis
Blue green damselfish
5681
Pomacentridae
Chromis xanthochira
Yellow-axil chromis
5682
Pomacentridae
Chromis xanthura
Paletail chromis
6919
Pomacentridae
Chrysiptera hemicyanea
Azure demoiselle
5698
Pomacentridae
Chrysiptera oxycephala
Blue-spot demoiselle
5486
Pomacentridae
Chrysiptera rollandi
Rolland's demoiselle
5700
Pomacentridae
Chrysiptera talboti
Talbot's demoiselle
5110
Pomacentridae
Dascyllus aruanus
Whitetail dascyllus
5111
Pomacentridae
Dascyllus melanurus
Blacktail humbug
No.
Spp.
202
Table A.1.1 - (cont.)
Functional Group
Anchovy
203
FB
species
code
Family
Scientific name
Common name
5683
Pomacentridae
Lepidozygus tapeinosoma
Fusilier damselfish
6922
Pomacentridae
Neoglyphidodon thoracotaeniatus
Barhead damsel
12462
Pomacentridae
Neopomacentrus nemurus
Coral demoiselle
5715
Pomacentridae
Pomacentrus amboinensis
Ambon damsel
5717
Pomacentridae
Pomacentrus bankanensis
Speckled damselfish
5718
Pomacentridae
Pomacentrus brachialis
Charcoal damsel
5720
Pomacentridae
Pomacentrus coelestis
Neon damselfish
6620
Pomacentridae
Pomacentrus lepidogenys
Scaly damsel
5724
Pomacentridae
Pomacentrus moluccensis
Lemon damsel
5716
Pomacentridae
Pomacentrus nagasakiensis
Nagasaki damsel
5725
Pomacentridae
Pomacentrus nigromanus
Goldback damsel
6621
Pomacentridae
Pomacentrus nigromarginatus
Blackmargined damsel
5726
Pomacentridae
Pomacentrus pavo
Sapphire damsel
6632
Pomacentridae
Premnas biaculeatus
Spinecheek anemonefish
15169
Pseudomugilidae
Pseudomugil inconspicuus
Inconspicuous blue-eye
6629
Ptereleotridae
Nemateleotris magnifica
Fire goby
4375
Ptereleotridae
Ptereleotris evides
Blackfin dartfish
4378
Ptereleotridae
Ptereleotris heteroptera
Blacktail goby
23333
Serranidae
Holanthias borbonius
Checked swallowtail
8514
Tripterygiidae
Helcogramma striata
Tropical striped triplefin
605
Engraulidae
Papuengraulis micropinna
Littlefin anchovy
611
Engraulidae
Setipinna taty
Scaly hairfin anchovy
612
Engraulidae
Setipinna tenuifilis
Common hairfin anchovy
561
Engraulidae
Stolephorus andhraensis
Andhra anchovy
1690
Engraulidae
Stolephorus brachycephalus
Broadhead anchovy
564
Engraulidae
Stolephorus carpentariae
Gulf of Carpenteria anchovy
566
Engraulidae
Stolephorus commersonnii
Commerson's anchovy
569
Engraulidae
Stolephorus indicus
Indian anchovy
578
Engraulidae
Stolephorus waitei
Spotty-face anchovy
581
Engraulidae
Thryssa aestuaria
Estuarine thryssa
583
Engraulidae
Thryssa brevicauda
Short-tail thryssa
587
Engraulidae
Thryssa encrasicholoides
False baelama anchovy
589
Engraulidae
Thryssa hamiltonii
Hamilton's thryssa
594
Engraulidae
Thryssa mystax
Moustached thryssa
No.
Spp.
17
Table A.1.1 - (cont.)
Functional Group
Deepwater fish
FB
species
code
Family
Scientific name
Common name
597
Engraulidae
Thryssa rastrosa
Fly river thryssa
598
Engraulidae
Thryssa scratchleyi
New Guinea thryssa
599
Engraulidae
Thryssa setirostris
Longjaw thryssa
10338
Acropomatidae
Synagrops philippinensis
5064
Alepocephalidae
Xenodermichthys copei
Bluntsnout smooth-head
2308
Anoplogastridae
Anoplogaster cornuta
Common fangtooth
1984
Carangidae
Uraspis uraspis
Whitetongue jack
10358
Champsodontidae
Champsodon guentheri
Günther's sabre-gills
9061
Congridae
Bathyuroconger vicinus
Large-toothed conger
1041
Gempylidae
Gempylus serpens
Snake mackerel
3907
Gempylidae
Nealotus tripes
Black snake mackerel
7573
Gempylidae
Nesiarchus nasutus
Black gemfish
8486
Gempylidae
Rexea bengalensis
Bengal escolar
7698
Gempylidae
Thyrsitoides marleyi
Black snoek
27376
Gibberichthyidae
Gibberichthys pumilus
Gibberfish
7383
Gonostomatidae
Gonostoma elongatum
Elongated bristlemouth fish
10285
Holocentridae
Ostichthys kaianus
Deepwater soldier
1870
Lethrinidae
Wattsia mossambica
Mozambique large-eye bream
7516
Lophiidae
Lophiodes mutilus
Smooth angler
16854
Melamphaidae
Melamphaes danae
Bigscale
15709
Melamphaidae
Poromitra oscitans
Yawning
10284
Melamphaidae
Scopelogadus mizolepis mizolepis
8302
Monacanthidae
Thamnaconus tessellatus
11687
Moridae
Physiculus roseus
7423
Myctophidae
Benthosema fibulatum
Spinycheek lanternfish
10238
Myctophidae
Benthosema pterotum
Skinnycheek lanternfish
6589
Myctophidae
Benthosema suborbitale
Smallfin lanternfish
10329
Myctophidae
Diaphus coeruleus
Blue lantern fish
10264
Myctophidae
Diaphus effulgens
Headlight fish
7437
Myctophidae
Diaphus fragilis
Fragile lantern fish
10265
Myctophidae
Diaphus garmani
10266
Myctophidae
Diaphus lucidus
10174
Myctophidae
Diaphus splendidus
7411
Myctophidae
Lampadena luminosa
No.
Spp.
58
204
Table A.1.1 - (cont.)
Functional Group
Macro-algal
browsing
Eroding grazers
205
FB
species
code
Family
Scientific name
Common name
4488
Myctophidae
Myctophum asperum
Prickly lanternfish
10699
Myctophidae
Myctophum brachygnathum
Short-jawed lanternfish
7441
Myctophidae
Nannobrachium nigrum
Black lantern fish
5099
Nemichthyidae
Avocettina infans
Avocet snipe-eel
2660
Nemichthyidae
Nemichthys scolopaceus
Slender snipe eel
5049
Nomeidae
Cubiceps pauciradiatus
Longfin fathead
24766
Oneirodidae
Oneirodes sabex
56351
Ophidiidae
Mastigopterus imperator
10170
Paralepididae
Lestidium atlanticum
Atlantic barracudina
10441
Peristediidae
Peristedion liorhynchus
Armoured gurnard
10538
Peristediidae
Peristedion moluccense
Black-finned armoured-gurnard
27620
Sternoptychidae
Polyipnus unispinus
10325
Stomiidae
Astronesthes cyanea
10211
Stomiidae
Astronesthes indicus
1786
Stomiidae
Chauliodus sloani
10214
Stomiidae
Echiostoma barbatum
10326
Stomiidae
Eustomias bifilis
56549
Stomiidae
Eustomias monoclonus
10157
Stomiidae
Malacosteus niger
Stoplight loosejaw
7395
Stomiidae
Melanostomias valdiviae
Valdivia black dragon fish
10327
Stomiidae
Photonectes albipennis
10261
Stomiidae
Stomias nebulosus
Alcock's boafish
3263
Trachipteridae
Desmodema polystictum
Polka-dot ribbonfish
8546
Trichiuridae
Benthodesmus macrophthalmus
Bigeye frostfish
8547
Trichiuridae
Benthodesmus neglectus
Neglected frostfish
8563
Trichiuridae
Benthodesmus tenuis
Slender frostfish
8566
Trichiuridae
Benthodesmus vityazi
Vityaz' frostfish
5808
Characidae
Piaractus brachypomus
Pirapitinga
1612
Clupeidae
Nematalosa erebi
Australian river gizzard shad
4817
Mugilidae
Valamugil buchanani
Bluetail mullet
5537
Scaridae
Bolbometopon muricatum
Green humphead parrotfish
60479
Scaridae
Scarus microhinos
No.
Spp.
Sloane's viperfish
3
2
Table A.1.1 - (cont.)
Functional Group
FB
species
code
Family
Scientific name
Common name
No.
Spp.
Scraping grazers
4307
Acanthuridae
Acanthurus bariene
Black-spot surgeonfish
82
4750
Acanthuridae
Acanthurus blochi
Ringtail surgeonfish
4745
Acanthuridae
Acanthurus fowleri
Fowler's surgeonfish
4741
Acanthuridae
Acanthurus leucocheilus
Palelipped surgeonfish
1258
Acanthuridae
Acanthurus lineatus
Lined surgeonfish
4746
Acanthuridae
Acanthurus maculiceps
White-freckled surgeonfish
1255
Acanthuridae
Acanthurus mata
Elongate surgeonfish
6011
Acanthuridae
Acanthurus nigricans
Whitecheek surgeonfish
4747
Acanthuridae
Acanthurus nigricaudus
Epaulette surgeonfish
4739
Acanthuridae
Acanthurus nigrofuscus
Brown surgeonfish
4744
Acanthuridae
Acanthurus olivaceus
Orangespot surgeonfish
4742
Acanthuridae
Acanthurus pyroferus
Chocolate surgeonfish
4734
Acanthuridae
Acanthurus thompsoni
Thompson's surgeonfish
1260
Acanthuridae
Acanthurus triostegus
Convict surgeonfish
1261
Acanthuridae
Acanthurus xanthopterus
Yellowfin surgeonfish
6012
Acanthuridae
Ctenochaetus binotatus
Twospot surgeonfish
1262
Acanthuridae
Ctenochaetus striatus
Striated surgeonfish
6015
Acanthuridae
Ctenochaetus strigosus
Spotted surgeonfish
6016
Acanthuridae
Ctenochaetus tominiensis
Tomini surgeonfish
6019
Acanthuridae
Naso annulatus
Whitemargin unicornfish
6020
Acanthuridae
Naso brachycentron
Humpback unicornfish
6021
Acanthuridae
Naso brevirostris
Spotted unicornfish
27318
Acanthuridae
Naso caeruleacauda
1263
Acanthuridae
Naso hexacanthus
Sleek unicornfish
1264
Acanthuridae
Naso lituratus
Orangespine unicornfish
6022
Acanthuridae
Naso lopezi
Elongate unicornfish
6933
Acanthuridae
Naso minor
Slender unicorn
6932
Acanthuridae
Naso thynnoides
Oneknife unicornfish
1265
Acanthuridae
Naso unicornis
Bluespine unicornfish
6024
Acanthuridae
Naso vlamingii
Bignose unicornfish
7849
Monacanthidae
Acreichthys tomentosus
Bristle-tail file-fish
4275
Monacanthidae
Aluterus scriptus
Scrawled filefish
6672
Monacanthidae
Amanses scopas
Broom filefish
5836
Monacanthidae
Cantherines dumerilii
Whitespotted filefish
206
Table A.1.1 - (cont.)
Functional Group
207
FB
species
code
Family
Scientific name
Common name
7842
Monacanthidae
Cantherines fronticinctus
Spectacled filefish
6635
Monacanthidae
Cantherines pardalis
Honeycomb filefish
6559
Monacanthidae
Oxymonacanthus longirostris
Harlequin filefish
6560
Monacanthidae
Paraluteres prionurus
Blacksaddle filefish
7977
Monacanthidae
Paramonacanthus japonicus
Hairfinned leatherjacket
4368
Monacanthidae
Pervagor janthinosoma
Blackbar filefish
4370
Monacanthidae
Pervagor melanocephalus
Redtail filefish
4371
Monacanthidae
Pervagor nigrolineatus
Blacklined filefish
10598
Monacanthidae
Pseudomonacanthus macrurus
Strap-weed file-fish
4355
Scaridae
Calotomus carolinus
Carolines parrotfish
5538
Scaridae
Cetoscarus bicolor
Bicolour parrotfish
4976
Scaridae
Chlorurus bleekeri
Bleeker's parrotfish
5542
Scaridae
Chlorurus bowersi
Bower's parrotfish
4978
Scaridae
Chlorurus japanensis
Palecheek parrotfish
60479
Scaridae
Chlorurus microrhinos
5556
Scaridae
Chlorurus sordidus
Daisy parrotfish
5539
Scaridae
Hipposcarus longiceps
Pacific longnose parrotfish
4360
Scaridae
Leptoscarus vaigiensis
Marbled parrotfish
5543
Scaridae
Scarus chameleon
Chameleon parrotfish
4973
Scaridae
Scarus dimidiatus
Yellowbarred parrotfish
4968
Scaridae
Scarus flavipectoralis
Yellowfin parrotfish
5545
Scaridae
Scarus forsteni
Forsten's parrotfish
5546
Scaridae
Scarus frenatus
Bridled parrotfish
5548
Scaridae
Scarus ghobban
Blue-barred parrotfish
4970
Scaridae
Scarus globiceps
Globehead parrotfish
12707
Scaridae
Scarus hypselopterus
Yellow-tail parrotfish
5550
Scaridae
Scarus niger
Dusky parrotfish
5551
Scaridae
Scarus oviceps
Dark capped parrotfish
4971
Scaridae
Scarus prasiognathos
Singapore parrotfish
5553
Scaridae
Scarus psittacus
Common parrotfish
5554
Scaridae
Scarus quoyi
Quoy's parrotfish
4969
Scaridae
Scarus rivulatus
Rivulated parrotfish
5555
Scaridae
Scarus rubroviolaceus
Ember parrotfish
4975
Scaridae
Scarus schlegeli
Yellowband parrotfish
4974
Scaridae
Scarus spinus
Greensnout parrotfish
No.
Spp.
Table A.1.1 - (cont.)
Functional Group
Detritivore fish
FB
species
code
Family
Scientific name
Common name
6438
Scaridae
Scarus tricolor
Tricolour parrotfish
13051
Tetraodontidae
Arothron caeruleopunctatus
Blue-spotted puffer
5425
Tetraodontidae
Arothron hispidus
White-spotted puffer
7187
Tetraodontidae
Arothron manilensis
Narrow-lined puffer
7857
Tetraodontidae
Arothron mappa
Map puffer
6400
Tetraodontidae
Arothron nigropunctatus
Blackspotted puffer
6526
Tetraodontidae
Arothron stellatus
Starry toadfish
7840
Tetraodontidae
Canthigaster amboinensis
Spider-eye puffer
6541
Tetraodontidae
Canthigaster bennetti
Bennett's sharpnose puffer
6542
Tetraodontidae
Canthigaster compressa
Compressed toby
6543
Tetraodontidae
Canthigaster janthinoptera
Honeycomb toby
55072
Tetraodontidae
Canthigaster papua
Papuan toby
6544
Tetraodontidae
Canthigaster valentini
Valentinni's sharpnose puffer
5838
Balistidae
Melichthys vidua
Pinktail triggerfish
6047
Blenniidae
Blenniella chrysospilos
Red-spotted blenny
5807
Monodactylidae
Monodactylus argenteus
Silver moony
15762
Mugilidae
Rhinomugil nasutus
Shark mullet
5659
Mugilidae
Valamugil seheli
Bluespot mullet
5704
Pomacentridae
Dischistodus perspicillatus
White damsel
23487
Terapontidae
Mesopristes cancellatus
Tapiroid grunter
No.
Spp.
7
208
Appendix A.2 - Fish family data
Table A.2.1 - Fish families in Raja Ampat model.
Fish families present in Raja Ampat (RA) model; number of species in RA models. Morphological and feeding characteristics
utilized by the diet allocation algorithm are presented. Body morphology of families is determined based on representative
species for which FB has morphological information. ‘Main feeding mode’ indicates the majority (>50%) feeding mode of
member species. Piscivory and planktivory are determined respectively at the species level using characteristics listed in the
MainFood and FeedingType fields of the FB Ecology table and the comments field of the FB Species table. Total length
(TL); piscivorous (pisc.).
Diet algorithm
Body morphology
Average
length
(TL; cm)
Main feeding
mode
% pisc.
spp.
Fish family
Common family name
# spp. in
RA
model
Orectolobidae
Wobbeongs
1
Elongated
125.0
Piscivore
100%
Hemiscylliidae
Carpetsharks
1
Elongated
46.0
Piscivore
100%
Ginglymostomatidae
Nurse sharks
1
Elongated
320.0
Piscivore
100%
Carcharhinidae
Requiem sharks
7
Elongated
211.0
Piscivore
86%
Dasyatididae
Rays
2
Flattened
70.0
Piscivore
100%
Myliobatidae
Eagle and manta rays
2
Flattened
880.0
Piscivore
50%
Mobulidae
Manta rays
2
Flattened
0.0
Piscivore
100%
Moringuidae
Eels
2
Eel-like
0.0
Piscivore
100%
Muraenidae
Morays
12
Eel-like
86.5
Piscivore
92%
Ophichthidae
Snake eels
3
Eel-like
87.7
Piscivore
100%
Congridae
Conger and garden eels
3
Eel-like
66.0
Planktivore
33%
Clupeidae
Herrings, shads, sardines
22
Fusiform compressed
18.9
Piscivore
57%
Plotosidae
Catfish
1
No data
32.0
Piscivore
100%
Synodontidae
Lizardfish
6
Elongated circular
26.8
Piscivore
83%
Carapidae
Pearlfish
1
Eel-like compressed
10.0
Planktivore
0%
Bythitidae
Cuskeels
1
Elongated compressed
7.0
Piscivore
100%
Batrachoididae
Toadfish
2
Deep oval
28.0
Piscivore
100%
Antennariidae
Frogfish
4
Deep compressed
14.0
Piscivore
100%
Gobiesocidae
Clingfish
2
Elongated circular
5.4
Piscivore
100%
Atherinidae
Silversides
5
Elongated compressed
13.7
Piscivore
80%
Belonidae
Needlefishes
5
Eel-like
82.4
Piscivore
100%
Hemiramphidae
Halfbeaks
17
Elongated oval
26.3
Piscivore
88%
Holocentridae
Squirrelfishes, soldierfishes
20
Deep compressed
32.7
Piscivore
100%
209
Table A.2.1 - (cont.)
Diet algorithm
Body morphology
Average
length
(TL; cm)
Main feeding
mode
% pisc.
spp.
Fish family
Common family name
# spp. in
RA
model
Pegasidae
Dragonfish
1
Flattened
10.0
Planktivore
0%
Aulostomidae
Trumpetfish
1
Eel-like compressed
80.0
Planktivore
0%
Fistulariidae
Cornetfish
1
Eel-like flattened
0.0
Planktivore
0%
Centriscidae
Razorfish
2
Elongated compressed
15.0
Piscivore
100%
Syngnathidae
Pipefish/seahorses
16
Eel-like
15.6
Piscivore
94%
Scorpaenidae
Scorpionfish
11
Fusiform normal
17.9
Piscivore
55%
Tetrarogidae
Waspfish
1
No data
20.0
Planktivore
0%
Synanceiidae
Stonefish/ghouls
3
Fusiform normal
44.0
Piscivore
67%
Caracanthidae
Crouchers
1
No data
5.0
Planktivore
0%
Dactylopteridae
Flying gurnards
2
Elongated circular
35.0
Piscivore
100%
Platycephalidae
Flatheads
5
Elongated
29.8
Piscivore
100%
Centropomidae
Seaperch
1
Elongated compressed
47.0
Piscivore
100%
Serranidae
Sea basses
54
Fusiform compressed
52.9
Piscivore
94%
Pseudochromidae
Dottybacks
15
Fusiform normal
12.4
Piscivore
100%
Plesiopidae
Longfins
2
Fusiform normal
13.0
Piscivore
100%
Acanthoclinidae
Spiny basslets
1
Elongated oval
5.0
Piscivore
100%
Cirrhitidae
Hawkfish
8
Fusiform normal
16.2
Piscivore
100%
Terapontidae
Grunters or tigerperches
4
Fusiform oval
31.0
Piscivore
100%
Priacanthidae
Bullseyes
1
Deep compressed
45.0
Planktivore
0%
Apogonidae
Cardinalfishes
61
Fusiform compressed
10.1
Piscivore
84%
Sillaginidae
Sillagos/smelts/whitings
1
Elongated circular
35.2
Piscivore
100%
Malacanthidae
Tilefish
5
Elongated
25.6
Piscivore
100%
Echeneidae
Remoras
2
Eel-like
93.0
Piscivore
100%
Carangidae
Jacks and pomanos
21
Fusiform normal
81.8
Piscivore
90%
Lutjanidae
Snappers
33
Fusiform normal
62.4
Piscivore
94%
Caesionidae
Fusilier
11
Fusiform compressed
31.6
Piscivore
100%
Gerreidae
Silverbiddy
2
Eel-like
32.5
Piscivore
100%
Haemulidae
Sweetlips
10
Deep compressed
72.4
Piscivore
60%
Lethrinidae
Emperors or scavengers
16
Fusiform oval
55.2
Piscivore
94%
Nemipteridae
Whiptails/breams/false snappers
12
Fusiform oval
25.8
Piscivore
58%
Mullidae
Goatfish
10
Fusiform oval
39.7
Planktivore
20%
Pempheridae
Sweepers
3
Deep normal
15.3
Piscivore
100%
210
Table A.2.1 - (cont.)
Diet algorithm
Body morphology
Average
length
(TL; cm)
Main feeding
mode
% pisc.
spp.
Fish family
Common family name
# spp. in
RA
model
Toxotidae
Archerfishes
2
Deep normal
38.5
Piscivore
100%
Kyphosidae
Chubs
3
Fusiform normal
65.0
Piscivore
100%
Monodactylidae
Moonyfishes or fingerfishes
1
Deep compressed
25.0
Piscivore
50%
Chaetodontidae
Butterflyfish/angelfish
57
Deep compressed
21.3
Piscivore
96%
Mugilidae
Mullets
5
Elongated oval
70.8
Piscivore
100%
Pomacentridae
Damselfish/demoiselles/sergeants
109
Deep oval
11.3
Piscivore
83%
Labridae
Parrotfish/rainbowfish/wrasses
97
Deep compressed
25.9
Piscivore
82%
Scaridae
Parrotfish
27
Elongated compressed
50.6
Piscivore
93%
Trichonotidae
Sanddivers
2
Elongated compressed
21.5
Piscivore
50%
Pinguipedidae
Sandperch
7
Elongated oval
23.0
Piscivore
71%
Pholidichthyidae
Convict blennies
1
Elongated compressed
34.0
Piscivore
100%
Tripterygiidae
Threadfin blennies
5
Fusiform oval
4.3
Piscivore
60%
Blenniidae
Blennies
32
Elongated oval
10.3
Piscivore
100%
Callionymidae
Dragonets/scotter blennies
6
Elongated circular
7.0
Piscivore
100%
Gobiidae
Gobies
97
Elongated
7.3
Piscivore
94%
Microdesmidae
Wormfish
2
Fusiform normal
9.0
Piscivore
100%
Ptereleotridae
Dart gobies
8
Elongated
11.0
Piscivore
100%
Xenisthmidae
Wrigglers
1
No Data
3.0
Piscivore
100%
Ephippidae
Batfish
5
Deep
51.0
Piscivore
100%
Scatophagidae
Scats
1
Deep
38.0
Piscivore
100%
Siganidae
Spinefoots
12
Deep compressed
35.5
Piscivore
100%
Zanclidae
Moorish idol
1
Deep compressed
23.0
Piscivore
100%
Acanthuridae
Surgeonfish/unicornfish/tangs
33
Deep compressed
41.6
Piscivore
79%
Sphyraenidae
Barracudas
5
Elongated
136.0
Piscivore
100%
Scombridae
Tuna-like
23
Fusiform oval
140.3
Piscivore
92%
Bothidae
Flounders
2
Deep
40.5
Piscivore
100%
Soleidae
Soles
1
Deep compressed
15.0
Piscivore
100%
Balistidae
Triggerfish
14
Deep oval
41.9
Piscivore
93%
Monacanthidae
Filefishes
14
Deep
25.1
Piscivore
71%
Ostraciidae
Boxfish
3
Deep
27.0
Piscivore
67%
Tetraodontidae
Puffers
12
Deep circular
39.5
Piscivore
75%
Diodontidae
Porcupine fish
2
Deep
78.0
Piscivore
100%
211
Table A.2.1 - (cont.)
Diet algorithm
Body morphology
Average
length
(TL; cm)
Main feeding
mode
% pisc.
spp.
Fish family
Common family name
# spp. in
RA
model
Coryphaenidae
Dolphinfishes
2
Elongated compressed
168.5
Piscivore
100%
Dasyatidae
Stingrays
2
Flattened
25.0
Piscivore
100%
Engraulidae
Anchovies
17
Elongated compressed
16.3
Piscivore
76%
Exocoetidae
Flyingfishes
26
Elongated
26.8
Piscivore
81%
Istiophoridae
Billfishes
5
Eel-like
384.2
Piscivore
100%
Nemichthyidae
Snipe eels
2
Eel-like
102.3
Piscivore
50%
Nomeidae
Driftfishes
2
Deep
22.5
Piscivore
50%
Polynemidae
Threadfins
1
Elongated compressed
200.0
Piscivore
100%
Rhincodontidae
Whaleshark
1
Elongated
2000.0
Planktivore
0%
Salmonidae
Salmonids
1
Fusiform oval
164.5
Piscivore
100%
Stomiidae
Dragonfishes
18
Elongated compressed
22.9
Piscivore
100%
Xiphiidae
Swordfish
1
Elongated
505.8
Piscivore
100%
Acropomatidae
Lanternbellies
1
Elongated compressed
13.0
Piscivore
100%
Alepocephalidae
Fangtooths
1
Elongated
23.7
Piscivore
100%
Apistidae
Waspfishes
1
Fusiform normal
20.0
Piscivore
100%
Bregmacerotidae
Codlets
4
Elongated
9.8
Piscivore
50%
Centrolophidae
Medusafishes
1
Deep compressed
23.0
Piscivore
100%
Centrophoridae
Gulpersharks
1
Elongated
100.0
Piscivore
100%
Champsodontidae
Benttooths and gapers
1
Elongated
12.8
Piscivore
100%
Characidae
Characins
1
Deep compressed
88.0
Piscivore
100%
Chirocentridae
Wolf herring
1
Elongated compressed
117.5
Piscivore
100%
Dentatherinidae
Tusked silversides
1
No data
5.8
Piscivore
100%
Elopidae
Tenpounders
2
Elongated
107.0
Piscivore
100%
Emmelichthyidae
Rovers
1
Elongated
72.0
Planktivore
0%
Gempylidae
Snake mackerels
5
Eel-like
105.0
Piscivore
100%
Gibberichthyidae
Gibberfishes
1
Fusiform normal
12.0
Piscivore
100%
Gonostomatidae
Bristlemouths
2
Elongated
27.5
Piscivore
100%
Lactariidae
False trevallies
1
Deep compressed
40.0
Piscivore
100%
Leiognathidae
Slimys, slipmouths
2
Deep compressed
10.2
Piscivore
100%
Lophiidae
Goosefish
1
Deep
45.0
Piscivore
100%
Melamphaidae
Bigscale fishes or ridgeheads
3
Fusiform
7.6
Piscivore
67%
Melanotaeniidae
Rainbowfishes, blueeyes
6
No data
10.4
Piscivore
100%
212
Table A.2.1 - (cont.)
Diet algorithm
Body morphology
Average
length
(TL; cm)
Main feeding
mode
% pisc.
spp.
Fish family
Common family name
# spp. in
RA
model
Microstomatidae
Deep sea smelts
1
Elongated
11.6
Piscivore
100%
Molidae
Ocean sunfishes
1
Deep compressed
333.0
Piscivore
100%
Moridae
Morid cods
1
Elongated
0.0
Piscivore
100%
Myctophidae
Lanternfish
21
Fusiform
11.1
Piscivore
86%
Nettastomatidae
Duck-bill eels
1
Eel-like
0.0
Piscivore
100%
Oneirodidae
Dreamers
1
Deep
14.1
Piscivore
100%
Ophidiidae
Cusk-eels
1
Elongated compressed
62.8
Piscivore
100%
Osteoglossidae
Arowanas
1
Elongated
105.7
Planktivore
0%
Paralepididae
Barracudinas
1
Eel-like
25.0
Piscivore
100%
Pentacerotidae
Armorheads
1
Deep
42.0
Piscivore
100%
Peristediidae
Armored searobins / gurnards
3
Elongated
41.7
Piscivore
100%
Pristigasteridae
Pristigasterids
3
Fusiform
19.3
Piscivore
67%
Pseudomugilidae
Blue-eyes
5
Fusiform
4.4
Piscivore
80%
Scopelosauridae
Waryfishes
1
No data
16.3
Piscivore
100%
Sternoptychidae
Hatchetfishes
2
Fusiform normal
4.9
Piscivore
100%
Tetragonuridae
Squaretails
1
No data
0.0
Piscivore
100%
Trachipteridae
Ribbonfishes
1
Elongated
110.0
Piscivore
100%
Trichiuridae
Cutlassfishes
4
Eel-like compressed
111.6
Piscivore
100%
Anoplogastridae
Fangtooths
1
Deep compressed
17.7
Piscivore
100%
Champsodontidae
Benttooths and gapers
1
Elongated
12.8
Piscivore
100%
213
Appendix A.3 - Ecopath parameters: 2006 RA model
Table A.3.1 - Functional groups for 2006 Raja Ampat model.
Description
No.
Group name
Rationale
Mammals / birds / reptiles
1
Mysticetae
Conservation interest
2
Piscivorous odontocetae
Conservation interest
3
Deepdiving odontocetae
Conservation interest
4
Dugongs
Conservation interest
5
Birds
Conservation interest
6
Reef associated turtles
Conservation interest
7
Green turtles
Conservation interest
8
Oceanic turtles
Conservation interest
9
Crocodiles
Conservation interest
10
Adult groupers
Commercial
11
Subadult groupers
Commercial
12
Juvenile groupers
Immature life history stanza
13
Adult snappers
Commercial
14
Subadult snappers
Commercial
15
Juvenile snappers
Immature life history stanza
16
Adult Napoleon wrasse
Commercial
17
Subadult Napoleon wrasse
Commercial
18
Juvenile Napoleon wrasse
Immature life history stanza
19
Skipjack tuna
Commercial
20
Other tuna
Commercial
21
Mackerel
Commercial
22
Billfish
Commercial
23
Adult coral trout
Commercial
24
Juvenile coral trout
Immature life history stanza
25
Adult large sharks
Commercial/conservation interest
26
Juvenile large sharks
Immature life history stanza
27
Adult small sharks
Commercial/conservation interest
28
Juvenile small sharks
Immature life history stanza
29
Whale shark
Conservation interest
30
Manta ray
Conservation interest
31
Adult rays
Conservation interest
32
Juvenile rays
Immature life history stanza
Highly commercial fish
Predator fish
214
Table A.3.1 - (cont.)
Description
Herbivorous fish
215
No.
Group name
Rationale
33
Adult butterflyfish
Keystone species
34
Juvenile butterflyfish
Immature life history stanza
35
Cleaner wrasse
Non-trophic functional role
36
Adult large pelagic
Aggregate group
37
Juvenile large pelagic
Immature life history stanza
38
Adult medium pelagic
Aggregate group
39
Juvenile medium pelagic
Immature life history stanza
40
Adult small pelagic
Aggregate group
41
Juvenile small pelagic
Immature life history stanza
42
Adult large reef associated
Aggregate group
43
Juvenile large reef associated
Immature life history stanza
44
Adult medium reef associated
Aggregate group
45
Juvenile medium reef associated
Immature life history stanza
46
Adult small reef associated
Aggregate group
47
Juvenile small reef associated
Immature life history stanza
48
Adult large demersal
Aggregate group
49
Juvenile large demersal
Immature life history stanza
50
Adult small demersal
Aggregate group
51
Juvenile small demersal
Immature life history stanza
52
Adult large planktivore
Aggregate group
53
Juvenile large planktivore
Immature life history stanza
54
Adult small planktivore
Aggregate group
55
Juvenile small planktivore
Immature life history stanza
56
Adult anchovy
Subsistence use
57
Juvenile anchovy
Immature life history stanza
58
Adult deepwater fish
Aggregate group
59
Juvenile deepwater fish
Immature life history stanza
60
Adult macro algal browsing
Keystone species
61
Juvenile macro algal browsing
Immature life history stanza
62
Adult eroding grazers
Non-trophic functional role
63
Juvenile eroding grazers
Immature life history stanza
64
Adult scraping grazers
Non-trophic functional role
65
Juvenile scraping grazers
Immature life history stanza
66
Detritivore fish
Energy cycling
Table A.3.1 - (cont.)
Description
No.
Group name
Rationale
Structure forming benthos
67
Azooxanthellate corals
Non-trophic role/conservation interest
68
Hermatypic scleractinian corals
Non-trophic role/conservation interest
69
Non reef building scleractinian corals
Conservation interest
70
Soft corals
Non-trophic functional role
71
Calcareous algae
Non-trophic functional role
72
Anemones
Non-trophic functional role
73
Penaeid shrimps
Commercial
74
Shrimps and prawns
Commercial
75
Squid
Commercial
76
Octopus
Commercial
77
Sea cucumbers
Commercial
78
Lobsters
Commercial
79
Large crabs
Commercial
80
Small crabs
Commercial
81
Crown of thorns
Keystone species
82
Giant triton
Keystone species
83
Herbivorous echinoids
Keystone species
84
Bivalves
Commercial
85
Sessile filter feeders
Aggregate group
86
Epifaunal detritivorous invertebrates
Energy cycling/aggregate group
87
Epifaunal carnivorous invertebrates
Aggregate group
88
Infaunal invertebrates
Aggregate group
89
Jellyfish and hydroids
Secondary production
90
Carnivorous zooplankton
Secondary production
91
Large herbivorous zooplankton
Secondary production
92
Small herbivorous zooplankton
Secondary production
93
Phytoplankton
Basal group
94
Macro algae
Basal group
95
Sea grass
Non-trophic role/basal group
96
Mangroves
Non-trophic role/conservation interest
97
Fishery discards
Energy cycling
98
Detritus
Energy cycling
Other invertebrates
Nekton
Primary producers
Detritus
216
Table A.3.2 - Basic parameters for 2006 Raja Ampat model.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
217
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Dugongs
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Whale shark
Manta ray
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Biomass
(t·km-2)
0.033
0.052
0.091
0.054
0.366
0.043
0.082
0.087
0.001
0.184
0.057
0.016
0.081
0.042
0.030
0.011
0.020
0.004
0.693
0.541
0.086
0.825
0.033
0.007
0.061
0.053
0.041
0.017
0.003
0.003
0.177
0.068
0.243
0.081
0.009
0.054
0.032
0.011
0.017
0.071
0.108
7.128
4.512
2.853
2.355
0.259
0.135
0.127
0.135
0.192
P/B (yr-1)
0.055
0.035
0.020
0.025
0.381
0.143
0.053
0.050
0.408
0.225
0.400
1.200
0.400
1.100
1.447
0.500
0.500
1.200
2.000
1.408
2.913
0.956
0.350
0.700
1.100
1.300
1.200
2.432
0.068
0.600
0.960
1.200
1.004
1.600
3.779
0.800
1.079
1.000
1.500
2.000
3.980
0.400
0.600
0.800
1.400
3.000
4.000
0.600
0.920
2.000
P/B based on
n spp.
6
13
5
1
11
10
18
1
1
8
9
4
2
2
1
-
2
9
-
11
45
10
4
2
-
Q/B (yr-1)
4.850
6.105
3.599
11.012
63.949
3.500
3.500
3.500
6.500
9.086
13.110
26.675
7.105
11.085
21.377
8.900
12.845
29.599
6.644
4.693
9.712
3.187
3.303
7.476
3.600
6.451
4.000
7.321
0.228
2.000
2.416
5.227
6.720
10.906
13.097
2.667
4.544
5.000
7.860
13.266
25.284
4.000
5.696
5.000
8.114
15.000
30.345
3.100
5.140
8.600
Q/B based on
EE
n spp.
6
0.02
13
0.02
5
0.02
1
0
11
0.02
0.06
0.27
0.27
0.46
41
0.95
0.72
0.85
29
0.82
0.91
0.86
0.96
0.86
0.89
1
0.42
9
0.4
10
0.84
5
0.2
6
0.64
0.40
5
0.5
0.45
3
0.20
0.93
1
0.02
0.02
5
0.96
0.90
49
0.78
0.85
0.76
12
0.96
0.99
5
0.93
0.83
8
0.61
0.43
147
0.77
0.88
88
0.95
0.87
77
0.86
0.96
4
0.60
0.94
1
0.968
Table A.3.2 – (cont.)
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Mangroves
Fishery discards
Detritus
Biomass
(t·km-2)
0.135
1.000
0.887
0.414
0.614
1.500
2.237
0.600
0.794
0.250
0.500
0.526
0.256
0.348
1.656
0.016
0.600
0.875
0.600
0.600
0.100
0.500
2.000
2.000
0.237
1.000
0.971
0.219
0.286
0.286
0.219
0.050
0.722
9.189
4.580
1.400
5.600
27.422
0.100
1.000
0.560
2.430
26.100
39.389
20.157
19.136
20.000
100.000
P/B (yr-1)
2.568
1.500
2.000
2.000
2.000
3.370
3.370
1.100
1.000
1.339
1.400
0.435
1.000
2.339
3.000
2.339
1.440
2.160
1.398
0.917
0.475
0.050
3.824
2.228
4.348
2.327
0.740
0.446
0.953
2.610
0.920
1.224
0.541
2.514
1.480
1.178
2.640
4.014
10.230
63.875
31.000
91.250
109.119
10.225
13.758
0.066
-
P/B based on
Q/B (yr-1)
n spp.
15.718
17
4.500
7.511
6
6.000
7.373
8
14.625
26.706
6
3.667
5.316
2
13.760
18.888
1
1.451
2.200
18
12.740
22.729
8.333
3.600
3.600
2.330
1.913
0.069
4
37.900
3
20.000
7
14.792
1
13.240
2
8.248
4
15.207
3
14.558
10
20.208
1
9.423
4.080
21
9.423
31
5.617
5.268
29
18.250
1
10.521
19.267
26.462
195.815
2
256.773
4 sites
265.810
8 sites
1
1
-
Q/B based on
n spp.
38
54
9
17
3
1
50
4
3
6
20
2
1
2
EE
0.95
0.69
0.92
0.40
0.78
0.58
0.21
0.86
0.96
0.33
0.40
0.83
0.89
0.64
0.37
0.92
0.94
0.97
0.96
0.89
0.95
0.97
0.76
0.91
0.95
0.86
0.98
0.99
0.95
0.90
0.96
0.93
0.94
0.95
0.98
0.99
0.97
0.89
0.91
0.95
0.99
0.95
0.32
0.38
0.82
0.02
1.00
0.14
218
Table A.3.3 - Multi-stanza life history information for 2006 Raja Ampat model
Total mortality (Z); consumption over biomass (Q/B); Von Bertalanffy growth constant (K); weight at maturity
(WMAT); weight at infinity (W∞).
Group
Juv. groupers
Sub. groupers
Ad. groupers
Juv. snappers
Sub. snappers
Ad. snappers
Juv. Napoleon wrasse
Sub. Napoleon wrasse
Ad. Napoleon wrasse
Juv. coral trout
Ad. coral trout
Juv. large sharks
Ad. large sharks
Juv. small sharks
Ad. small sharks
Juv. rays
Ad. rays
Juv. butterflyfish
Ad. butterflyfish
Juv. large pelagic
Ad. large pelagic
Juv. medium pelagic
Ad. medium pelagic
Juv. small pelagic
Ad. small pelagic
Juv. large reef assoc.
Ad. large reef assoc.
Juv. medium reef assoc.
Ad. medium reef assoc.
Juv. small reef assoc.
Ad. small reef assoc.
Juv. large demersal
Ad. large demersal
Juv. small demersal
Ad. small demersal
Juv. large planktivore
Ad. large planktivore
Juv. small planktivore
Ad. small planktivore
Juv. anchovy
Ad. anchovy
Juv. deepwater fish
Ad. deepwater fish
Juv. macro algal browsing
Ad. macro algal browsing
Juv. eroding grazers
Ad. eroding grazers
Juv. scraping grazers
Ad. scraping grazers
219
Age, start
(months)
0
24
56
0
24
48
0
24
72
0
48
0
24
0
12
0
24
0
12
0
24
0
24
0
12
0
48
0
24
0
8
0
36
0
12
0
15
0
10
0
12
0
24
0
20
0
24
0
18
Biomass
(t·km-2)
0.016
0.057
0.184
0.030
0.042
0.081
0.004
0.020
0.011
0.007
0.033
0.053
0.061
0.017
0.041
0.068
0.177
0.081
0.243
0.032
0.054
0.017
0.011
0.108
0.071
4.512
7.128
2.355
2.853
0.135
0.259
0.135
0.127
0.135
0.192
0.887
1.000
0.614
0.414
2.237
1.500
0.794
0.600
0.500
0.250
0.256
0.526
1.656
0.348
Z (yr-1)
1.2
0.4
0.225
1.447
1.1
0.4
1.2
0.5
0.5
0.7
0.35
1.3
1.1
2.432
1.2
1.2
0.96
1.6
1.004
1.079
0.8
1.5
1
3.980
2
0.6
0.4
1.4
0.8
4
3
0.92
0.6
2.568
2
2
1.5
2
2
3.37
3.37
1
1.1
1.4
1.339
1
0.435
3
2.339
Q/B (yr-1)
26.675
13.110
9.086
21.377
11.085
7.105
29.599
12.845
8.9
7.476
3.303
6.451
3.6
7.321
4
5.227
2.416
10.906
6.72
4.544
2.667
7.860
5
25.284
13.266
5.696
4
8.114
5
30.345
15
5.140
3.1
15.718
8.6
7.511
4.5
7.373
6
26.706
14.625
5.316
3.667
18.888
13.76
2.200
1.451
22.729
12.740
Growth
constant
(K)
0.32
0.29
0.25
0.17
0.51
1.18
0.25
1.50
0.62
0.93
1.24
0.58
0.83
1.08
0.50
1
1.11
4.56
0.94
1
1.59
1
1.03
-
K based on
n spp.
5
10
1
1
2
1
0
1
5
0
5
16
5
1
1
0
8
2
2
3
1
0
6
-
WMAT/W?
0.12
0.27
0.09
0.10
0.38
0.38
0.44
0.42
0.08
0.18
0.28
0.13
0.13
0.09
0.12
0.09
0.22
0.16
0.25
0.17
0.02
0.21
0.21
-
WMAT/W?
based on n
spp.
16
22
1
2
7
2
2
2
7
0
6
42
14
6
2
0
13
5
2
11
1
0
6
-
Table A.3.4 - Ecopath landings matrix for 2006 Raja Ampat model.
Ecopath landings for 2006 Raja Ampat model, including targeted catch and bycatch sold at port. Values are in t·km-2.
6.5E-04
6.5E-04
3.5E-03
3.5E-03
Shrimp trawl
3.2E-04
1.7E-03
Foreign fleet
3.2E-04
Ad. snappers
Lift net
Juv. groupers
Set line
8.7E-03
Pole and line
Blast fishing
0.017
9.7E-04
Purse seine
Diving cyanide
1.9E-03
4.9E-04
Trolling
Diving live fish
9.6E-04
2.4E-03
Portable trap
4.8E-03
2.4E-03
Permanent trap
4.8E-03
9.7E-04
Driftnet
1.9E-03
9.7E-04
Shore gillnet
1.9E-03
4.9E-04
Reef gleaning
9.6E-04
Sub. groupers
harpoon
Ad. groupers
Group Name
Spear and
Diving spear
EwE Fisheries
Total
1.9E-03
3.5E-03
1.7E-03
0.014
Sub. snappers
1.7E-03
3.5E-03
3.5E-03
3.5E-03
1.7E-03
0.014
Juv. snappers
3.8E-04
7.7E-04
7.7E-04
7.7E-04
3.8E-04
3.1E-03
Ad. Nap. wrasse
4.2E-04
4.2E-04
8.5E-05
9.3E-04
Sub. Nap. wrasse
4.2E-04
4.2E-04
8.5E-05
9.3E-04
Juv. Nap. wrasse
2.1E-04
2.1E-04
Skipjack tuna
0.102
0.026
0.131
0.043
0.046
0.348
Other tuna
0.012
6.1E-03
0.014
7.4E-03
7.6E-03
0.047
Mackerel
0.021
5.5E-03
0.028
9.7E-03
0.064
Billfish
0.050
Ad. coral trout
3.1E-04
3.1E-04
3.1E-04
3.1E-04
3.1E-04
8.2E-05
Juv. coral trout
3.1E-05
3.1E-05
3.1E-05
3.1E-05
3.1E-05
8.2E-06
0.050
1.6E-03
1.6E-04
Ad. large sharks
0.025
0.025
Juv. large sharks
2.8E-03
2.8E-03
Ad. small sharks
5.6E-03
5.6E-03
6.2E-04
6.2E-04
Juv. small sharks
Adult rays
4.8E-03
Juv. rays
4.8E-03
4.8E-03
4.8E-03
0.019
4.8E-04
4.8E-04
4.8E-04
4.8E-04
Ad. butterflyfish
3.0E-03
3.0E-03
3.0E-03
3.0E-03
3.0E-03
7.8E-04
0.016
1.9E-03
Juv. butterflyfish
3.0E-04
3.0E-04
3.0E-04
3.0E-04
3.0E-04
7.8E-05
1.6E-03
Cleaner wrasse
1.9E-04
1.9E-04
1.9E-04
1.9E-04
5.1E-05
8.2E-04
Ad. large pelagic
7.8E-03
6.2E-03
4.7E-03
4.7E-03
7.8E-03
0.031
Table A.3.4 - (cont.)
Shrimp trawl
Foreign fleet
Lift net
Set line
Pole and line
Purse seine
Trolling
Blast fishing
Diving cyanide
Diving live fish
Diving spear
Portable trap
Permanent trap
Driftnet
Shore gillnet
Reef gleaning
harpoon
Group Name
Spear and
EwE Fisheries
Total
Juv. large pelagic
1.0E-03
8.3E-04
6.2E-04
6.2E-04
1.0E-03
4.1E-03
Ad. medium pelagic
1.7E-03
1.4E-03
1.0E-03
1.0E-03
1.7E-03
6.9E-03
Juv. medium pelagic
7.7E-04
6.1E-04
4.6E-04
4.6E-04
Ad. small pelagic
6.8E-03
6.8E-03
5.1E-03
5.1E-03
Juv. small pelagic
Ad. large reef assoc.
0.110
7.7E-04
3.1E-03
1.7E-03
8.5E-03
0.034
9.4E-04
7.5E-04
7.5E-04
5.6E-04
5.6E-04
1.9E-04
0.110
0.110
0.110
0.110
0.029
3.8E-03
0.577
Juv. large reef assoc.
0.021
0.021
0.021
0.021
0.021
5.6E-03
0.112
Ad. medium reef assoc.
0.067
0.067
0.067
0.067
0.067
0.018
0.350
Juv. medium reef assoc.
6.7E-03
6.7E-03
6.7E-03
6.7E-03
6.7E-03
1.8E-03
0.035
Ad. small reef assoc.
0.029
0.029
0.029
0.029
0.029
7.5E-03
0.150
Juv. small reef assoc.
2.9E-03
2.9E-03
2.9E-03
Ad. large demersal
7.3E-03
2.9E-03
2.9E-03
7.5E-04
0.015
7.3E-03
7.3E-03
1.9E-03
0.024
Juv. large demersal
1.5E-03
1.5E-03
1.5E-03
3.8E-04
4.8E-03
Ad. small demersal
8.7E-03
8.7E-03
8.7E-03
2.3E-03
0.028
Juv. small demersal
9.7E-04
Ad. large planktivore
0.057
0.057
0.057
9.7E-04
9.7E-04
2.6E-04
3.2E-03
0.057
0.057
0.015
0.300
Juv. large planktivore
5.7E-03
5.7E-03
5.7E-03
5.7E-03
5.7E-03
1.5E-03
0.030
Ad. small planktivore
2.4E-03
2.4E-03
2.4E-03
2.4E-03
2.4E-03
6.4E-04
0.013
Juv. small planktivore
2.7E-04
7.1E-05
2.7E-04
2.7E-04
2.7E-04
2.7E-04
Ad. anchovy
0.114
0.091
0.069
0.069
0.166
1.4E-03
0.509
Juv. anchovy
0.013
0.010
7.6E-03
7.6E-03
0.013
0.051
Ad. deepwater fish
2.1E-03
2.1E-03
2.1E-03
2.1E-03
8.3E-03
Juv. deepwater fish
2.3E-04
2.3E-04
2.3E-04
2.3E-04
9.2E-04
Ad. macro algal brows
2.0E-04
2.0E-04
2.0E-04
2.0E-04
2.6E-05
8.2E-04
Juv. macro algal brows
1.9E-05
1.9E-05
1.9E-05
1.9E-05
5.1E-06
8.2E-05
Ad. eroding grazers
6.6E-05
6.6E-05
6.6E-05
6.6E-05
8.7E-06
2.7E-04
221
Table A.3.4 - (cont.)
Shrimp trawl
Foreign fleet
Lift net
Set line
Pole and line
Purse seine
Trolling
Blast fishing
Diving cyanide
Diving live fish
Diving spear
Portable trap
Permanent trap
Driftnet
Shore gillnet
Reef gleaning
harpoon
Group Name
Spear and
EwE Fisheries
Total
Juv. eroding grazers
6.4E-06
6.4E-06
6.4E-06
6.4E-06
1.7E-06
2.7E-05
Ad. scraping grazers
5.4E-03
5.4E-03
5.4E-03
5.4E-03
7.1E-04
0.022
Juv. scraping grazers
5.3E-04
5.3E-04
5.3E-04
5.3E-04
1.4E-04
2.2E-03
Detritivore fish
4.6E-04
4.6E-04
4.6E-04
4.6E-04
6.1E-05
1.9E-03
Hermatypic corals
1.0E-03
1.0E-03
Penaeid shrimps
Shrimps and prawns
Squid
0.145
0.145
0.017
0.017
6.3E-03
6.3E-03
Octopus
2.3E-06
7.4E-06
2.5E-06
1.2E-07
1.2E-05
Sea cucumbers
1.2E-03
3.9E-03
1.3E-03
6.5E-05
6.5E-03
Lobsters
0.033
0.011
5.5E-04
0.044
Large crabs
2.0E-03
6.8E-04
3.4E-05
2.8E-03
Small crabs
2.0E-03
6.8E-04
3.4E-05
2.8E-03
Giant triton
2.6E-03
8.5E-04
4.3E-05
3.5E-03
Herbivorous echinoids
2.0E-03
6.8E-04
3.4E-05
Bivalves
5.9E-03
Sessile filter feeders
1.0E-03
Epifaunal det. inverts.
2.3E-03
Epifaunal carn. inverts
Sum
1.0E-03
7.6E-04
2.7E-03
0.330
0.057
2.8E-03
5.9E-03
3.8E-05
8.9E-04
0.472
0.437
0.437
0.426
0.027
3.1E-03
4.4E-05
8.1E-03
8.1E-03
0.096
3.6E-03
0.185
0.038
0.173
0.088
0.206
0.063
0.162
3.213
222
Table A.3.5 - Ecopath price matrix for the 2006 Raja Ampat model.
Source: DKP and Trade and Industry Office. Values are in thousands of Rupiah per kg.
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Adult rays
Ad. butterflyfish
Juv. butterflyfish
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
223
2.89
2.89
2.89
2.25
2.25
2.25
18.57
2.89
2.89
31.13
31.13
2.25
2.25
2.25
2.25
18.57
2.89
68.40
68.40
68.40
68.40
21.46
21.46
21.46
21.46
21.46
21.46
31.13
31.13
2.25
2.90
2.90
2.90
2.90
3.47
2.90
3.47
2.90
Shrimp trawl
Foreign fleet
Lift net
Set line
Pole and line
18.57
2.89
2.89
31.13
31.13
2.25
12.17
12.17
2.89
18.57
2.89
2.89
3.33
0.90
3.16
10.17
2.90
2.90
Purse seine
Trolling
Blast fishing
Diving cyanide
Diving live fish
Diving spear
Portable trap
Permanent trap
Driftnet
Shore gillnet
Reef gleaning
Group Name
Spear and harpoon
EwE Fisheries
3.33
0.90
3.16
3.33
0.90
3.16
3.33
0.90
3.47
2.90
4.44
2.51
3.28
3.28
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
3.28
2.90
2.90
2.90
2.90
2.90
2.90
1.34
1.34
2.90
2.90
2.90
2.90
2.90
2.90
3.28
2.90
2.90
2.90
2.90
2.90
2.90
1.34
1.34
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
5.19
5.19
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
2.90
5.19
5.19
2.90
2.90
2.90
2.90
2.90
2.90
2.90
4.93
3.47
2.90
3.03
2.90
3.04
2.90
1.34
1.34
2.95
2.90
3.17
2.90
3.22
2.90
3.04
2.90
3.11
2.90
2.90
2.90
2.90
2.90
5.19
5.19
3.47
2.90
3.47
2.90
3.47
2.90
3.47
4.93
3.47
2.90
3.03
2.90
3.04
2.90
3.47
2.90
1.34
1.34
2.95
2.90
3.17
2.90
3.22
2.90
3.04
2.90
3.11
2.90
2.90
2.90
2.90
2.90
2.95
2.90
3.17
2.90
3.22
2.90
3.04
2.90
3.11
2.90
2.90
2.90
2.90
2.90
5.19
5.19
3.47
2.90
3.47
2.90
3.47
2.90
3.47
3.03
2.90
3.04
2.90
1.34
1.34
5.19
5.19
32.76
7.16
25.72
6.50
30.42
6.50
30.42
18.95
4.05
4.05
5.00
6.08
5.75
1.15
1.15
1.15
67.99
65.85
20.35
4.05
4.05
67.99
65.85
20.35
4.05
4.05
6.08
6.08
1.15
1.15
1.15
1.15
Table A.3.6 - RA model trophic linkages: diet composition and flow parameters.
Diet column shows the percentage of each prey in the diet of the predator (2000 RA model); vulnerabilities
refer to the fitted 1990 matrix, which was extended to the 2000 model.
Predator
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Dugongs
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Prey
Juv. medium pelagic
Juv. small pelagic
Squid
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Skipjack tuna
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large demersal
Ad. small demersal
Juv. large planktivore
Juv. small planktivore
Ad. deepwater fish
Squid
Ad. large pelagic
Juv. large pelagic
Juv. large demersal
Juv. deepwater fish
Squid
Octopus
Epifaunal det. inverts.
Epifaunal carn. inverts
Sea grass
Mackerel
Juv. small pelagic
Juv. small planktivore
Juv. anchovy
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Fishery discards
Penaeid shrimps
Shrimps and prawns
Sea cucumbers
Large crabs
Small crabs
Herbivorous echinoids
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Macro algae
Sea grass
Sea cucumbers
Large crabs
Small crabs
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Juv. large pelagic
Juv. small pelagic
Juv. large planktivore
Juv. small planktivore
Penaeid shrimps
Lobsters
Large crabs
Diet %
1.65
4.53
13.40
20.00
40.21
20.21
2.38
0.70
1.50
0.10
1.00
2.00
20.00
0.50
10.00
23.37
14.77
10.00
13.68
0.50
0.50
3.00
9.70
25.58
9.70
15.24
29.09
100.00
0.27
1.42
5.40
45.00
5.65
4.64
13.31
4.00
4.43
4.43
11.08
0.23
0.23
3.50
60.58
4.43
5.54
5.54
8.00
1.02
1.02
38.84
51.11
13.07
0.62
0.68
26.14
13.07
13.07
13.07
20.29
6.06
1.21
1.21
1.21
5.49
12.11
12.11
12.11
12.11
8.02
3.35
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Predator
Ad. groupers
Sub. groupers
Prey
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Diet %
0.18
0.01
0.10
0.01
0.05
2.00
0.30
0.20
9.88
11.90
10.52
8.08
1.42
0.04
0.54
3.00
2.39
0.58
2.90
2.90
2.90
0.11
0.08
7.92
0.28
5.62
4.55
0.14
0.14
0.28
0.27
1.15
0.80
0.14
3.80
4.53
3.96
6.33
0.11
0.05
0.20
0.01
0.10
2.00
0.20
0.24
3.50
11.53
7.22
17.90
0.63
5.00
0.02
1.32
3.30
1.16
1.32
5.30
2.90
3.29
0.09
0.02
4.84
0.13
6.40
2.52
0.17
0.17
0.11
0.12
Vulnerability
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
2.00
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
7.62
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.01
5.2E+21
5.2E+21
2.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
224
Table A.3.6 - (cont.)
Predator
Juv. groupers
Ad. snappers
225
Prey
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Juv. coral trout
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Diet %
0.38
0.33
0.67
3.17
6.51
2.07
5.00
0.03
0.10
0.10
0.05
0.10
1.80
0.10
0.42
3.45
10.62
2.10
8.56
1.05
10.00
0.02
1.85
4.25
0.86
2.12
10.35
1.33
6.37
0.02
4.47
0.13
1.06
1.33
4.70
0.10
0.10
2.34
0.26
4.01
5.55
5.30
5.00
0.24
0.05
0.10
0.50
0.20
0.07
0.05
0.05
0.27
0.41
0.09
0.05
0.09
2.88
1.00
0.20
0.02
0.16
0.05
0.57
7.68
8.31
2.43
1.72
2.08
0.07
0.09
Vulnerability
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
Predator
Sub. snappers
Prey
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Juv. Napoleon wrasse
Other tuna
Mackerel
Juv. coral trout
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Diet %
0.07
0.55
0.07
3.42
0.07
3.75
0.11
0.82
3.43
3.56
3.56
0.05
0.05
0.02
0.01
6.17
0.07
0.46
2.60
2.60
5.08
0.12
0.05
0.30
0.39
9.97
0.04
1.08
0.05
1.56
7.96
2.77
3.25
2.74
0.27
2.22
0.58
0.55
0.02
0.10
0.10
0.50
0.09
0.07
0.04
0.17
0.02
0.16
1.34
0.40
0.09
< 0.01
0.01
0.10
1.09
4.53
4.27
< 0.01
0.96
1.61
15.63
0.03
0.09
1.17
0.09
3.52
0.09
1.98
0.94
1.00
Vulnerability
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.03
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
Table A.3.6 - (cont.)
Predator
Juv. snappers
Ad. Napoleon wrasse
Prey
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Large crabs
Small crabs
Carn. zooplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Juv. coral trout
Diet %
1.00
3.19
3.19
0.02
0.05
< 0.01
0.02
4.34
0.09
0.25
7.19
3.19
6.22
0.14
0.07
0.21
0.18
0.55
0.05
0.52
0.04
1.84
9.73
3.02
6.81
3.35
0.34
2.72
0.67
0.67
0.02
0.05
0.05
0.42
1.23
0.30
0.07
3.54
3.54
0.10
0.50
0.78
5.82
0.02
0.31
0.92
1.03
0.10
0.10
1.08
0.92
0.03
0.01
2.87
0.13
4.62
5.70
0.11
0.47
65.16
0.13
0.50
0.13
1.00
1.56
0.13
0.64
0.64
0.13
0.13
Vulnerability
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
3.4E+09
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
1.00
5.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Predator
Sub. Napoleon wrasse
Prey
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Juv. large demersal
Ad. small demersal
Juv. small demersal
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Diet %
0.13
0.77
1.15
0.64
0.77
0.77
0.03
2.64
0.13
0.28
0.13
6.40
0.13
3.19
0.13
6.40
0.13
3.00
1.28
1.28
2.56
0.10
0.13
0.03
0.64
0.13
0.52
2.56
2.56
2.56
0.86
0.34
2.59
8.17
2.60
12.81
5.12
7.04
2.56
2.56
2.56
6.65
0.14
0.10
0.09
0.40
0.96
0.09
0.41
0.68
0.09
0.02
0.09
0.81
0.63
0.09
0.58
0.02
3.22
0.09
0.09
10.30
0.09
0.09
4.50
0.09
0.68
0.68
0.27
0.05
226
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Table A.3.6 - (cont.)
Predator
Juv. Napoleon wrasse
Skipjack tuna
227
Prey
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Ad. butterflyfish
Juv. butterflyfish
Ad. medium reef assoc.
Ad. small reef assoc.
Ad. small demersal
Ad. small planktivore
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Detritus
Skipjack tuna
Other tuna
Mackerel
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Diet %
0.03
0.02
0.14
0.09
0.11
2.72
2.72
5.43
0.38
0.57
5.43
2.85
1.83
13.58
8.15
7.47
5.43
5.43
5.43
6.89
0.01
0.37
0.37
0.37
0.37
0.37
0.31
0.31
< 0.01
1.34
0.61
0.42
0.31
0.73
0.12
0.02
0.10
0.10
1.22
2.44
6.09
0.69
0.36
6.09
4.96
1.66
7.64
6.09
6.60
9.74
9.74
9.74
9.76
2.44
2.42
6.11
0.69
1.53
0.20
0.01
0.02
< 0.01
< 0.01
0.02
0.38
0.09
3.16
0.12
1.00
10.00
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
7.0E+08
1.00
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
1.02
Predator
Other tuna
Mackerel
Prey
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Other tuna
Juv. large sharks
Juv. small sharks
Juv. rays
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Sea grass
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Other tuna
Mackerel
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Diet %
0.10
2.41
1.00
1.48
0.15
0.06
0.24
0.02
< 0.01
0.01
< 0.01
0.24
0.30
0.32
0.32
0.18
0.18
0.24
0.12
< 0.01
0.05
0.01
< 0.01
0.01
0.02
0.10
< 0.01
0.10
0.01
3.71
0.07
1.00
10.00
0.01
0.45
0.09
< 0.01
0.28
0.05
0.05
0.07
< 0.01
< 0.01
< 0.01
0.01
< 0.01
< 0.01
< 0.01
0.07
< 0.01
0.13
0.23
0.26
< 0.01
8.64
3.51
0.44
< 0.01
< 0.01
< 0.01
0.01
< 0.01
0.03
< 0.01
0.01
0.57
0.89
< 0.01
0.40
0.01
Vulnerability
1.01
7.0E+08
1.01
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
7.0E+08
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.0E+05
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Table A.3.6 - (cont.)
Predator
Billfish
Ad. coral trout
Prey
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. large pelagic
Ad. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Diet %
0.14
0.13
0.05
0.29
0.72
1.09
0.29
1.72
< 0.01
0.17
0.20
< 0.01
0.06
0.03
0.29
4.09
10.00
7.59
0.03
0.17
0.03
< 0.01
0.11
0.02
0.08
0.11
4.26
2.49
< 0.01
0.29
< 0.01
0.03
0.01
0.20
0.12
0.10
2.00
10.00
0.10
0.02
1.09
< 0.01
0.12
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.02
0.02
0.11
0.20
0.68
2.93
0.11
0.11
3.83
2.80
0.18
20.28
18.93
0.70
2.28
10.82
0.36
0.57
3.27
2.26
0.45
3.27
Vulnerability
1.00
1.00
1.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
4.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.01
5.2E+21
5.2E+21
1.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Predator
Juv. coral trout
Ad. large sharks
Prey
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Whale shark
Manta ray
Adult rays
Ad. butterflyfish
Juv. butterflyfish
Diet %
3.04
3.04
0.11
0.04
11.72
1.76
1.01
1.01
2.30
0.16
0.03
0.09
0.19
0.45
0.45
0.45
0.01
0.15
0.15
0.30
0.15
15.00
1.09
0.15
33.21
< 0.01
7.30
2.02
< 0.01
2.00
3.54
5.12
0.74
13.29
1.33
5.87
0.15
6.53
0.06
0.12
0.12
0.73
0.87
0.02
0.02
0.02
< 0.01
0.11
0.11
0.11
0.11
0.02
0.09
0.07
0.30
0.02
0.05
0.02
0.23
0.16
0.09
0.01
0.10
< 0.01
0.10
< 0.01
0.02
< 0.01
0.41
0.37
228
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.01
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Table A.3.6 - (cont.)
Predator
Juv. large sharks
229
Prey
Cleaner wrasse
Ad. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Sea grass
Fishery discards
Detritus
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Ad. small sharks
Juv. small sharks
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Diet %
0.01
0.03
0.01
0.03
3.08
5.47
< 0.01
0.22
0.05
0.05
0.76
0.32
0.07
0.46
0.90
0.46
< 0.01
< 0.01
1.37
0.08
< 0.01
0.14
2.85
0.39
< 0.01
0.04
< 0.01
0.01
< 0.01
0.10
< 0.01
0.12
< 0.01
0.23
0.23
0.23
< 0.01
0.16
< 0.01
< 0.01
0.16
0.16
0.01
0.03
< 0.01
< 0.01
< 0.01
0.04
< 0.01
< 0.01
0.04
0.05
0.02
0.50
0.02
0.06
6.97
< 0.01
2.00
0.31
< 0.01
< 0.01
0.06
0.10
1.97
0.04
0.81
0.06
0.94
0.02
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Predator
Ad. small sharks
Juv. small sharks
Prey
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Skipjack tuna
Other tuna
Mackerel
Billfish
Juv. large sharks
Ad. large pelagic
Ad. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. large sharks
Ad. small sharks
Juv. butterflyfish
Ad. large pelagic
Ad. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Shrimps and prawns
Diet %
0.25
0.02
1.00
< 0.01
< 0.01
1.98
0.02
0.33
0.33
0.08
0.06
< 0.01
< 0.01
< 0.01
< 0.01
0.06
0.08
0.08
0.08
1.79
0.02
0.02
0.24
2.36
1.79
1.07
2.00
0.24
0.01
0.86
0.51
0.21
0.05
2.57
0.35
4.63
0.38
2.22
0.11
0.14
0.51
0.80
0.08
0.02
0.05
0.10
0.24
0.24
0.24
0.21
1.17
0.31
0.87
0.06
0.31
0.25
0.50
< 0.01
0.01
0.13
4.69
0.04
6.27
0.07
1.77
0.25
2.75
< 0.01
0.13
0.02
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Table A.3.6 - (cont.)
Predator
Whale shark
Manta ray
Adult rays
Prey
Large crabs
Small crabs
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Skipjack tuna
Other tuna
Mackerel
Ad. medium pelagic
Ad. small pelagic
Ad. small planktivore
Ad. anchovy
Ad. deepwater fish
Juv. deepwater fish
Shrimps and prawns
Squid
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Ad. medium pelagic
Ad. small pelagic
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Penaeid shrimps
Shrimps and prawns
Squid
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Diet %
< 0.01
0.12
0.02
0.02
0.02
0.10
1.51
0.89
0.01
0.44
1.69
0.10
0.76
0.30
0.30
5.52
1.09
1.33
0.65
1.33
4.03
0.01
0.51
0.25
1.59
2.00
< 0.01
2.24
1.25
2.24
2.49
2.49
1.18
2.49
1.25
< 0.01
0.02
0.02
0.02
0.02
0.02
< 0.01
0.12
0.14
< 0.01
0.02
< 0.01
< 0.01
0.02
0.02
< 0.01
0.02
0.02
0.02
< 0.01
< 0.01
0.08
< 0.01
1.59
3.15
0.01
< 0.01
0.07
0.09
0.08
0.08
6.30
2.38
2.38
2.38
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
Predator
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Prey
Carn. zooplankton
Ad. small reef assoc.
Ad. small demersal
Penaeid shrimps
Shrimps and prawns
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Herbivorous echinoids
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Juv. medium reef assoc.
Diet %
0.91
0.27
0.64
0.50
0.51
1.92
1.28
0.10
0.08
0.13
0.11
0.82
1.28
4.64
3.85
3.85
0.06
0.06
< 0.01
< 0.01
0.04
0.06
0.06
1.00
0.01
0.06
0.06
0.06
0.02
0.14
0.01
0.06
0.50
0.13
0.50
0.50
0.50
0.87
0.10
0.50
0.56
0.07
0.01
0.02
0.40
0.05
0.04
0.10
0.84
12.42
0.87
8.00
18.00
0.50
10.64
1.04
9.85
9.64
15.00
2.96
0.01
3.67
0.01
0.01
< 0.01
< 0.01
0.01
0.03
0.01
3.70
230
Vulnerability
5.2E+21
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
10.70
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
Table A.3.6 - (cont.)
Predator
Cleaner wrasse
Ad. large pelagic
231
Prey
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Ad. groupers
Sub. groupers
Ad. butterflyfish
Juv. butterflyfish
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Diet %
0.21
0.01
0.01
3.02
< 0.01
0.01
< 0.01
2.15
0.32
0.02
0.32
0.32
0.01
0.12
< 0.01
0.57
0.02
< 0.01
< 0.01
< 0.01
0.02
< 0.01
0.01
0.57
0.84
0.10
11.47
23.96
0.15
2.53
1.96
1.50
25.00
14.18
6.80
< 0.01
0.15
< 0.01
< 0.01
0.36
0.36
< 0.01
3.17
5.00
0.10
0.29
0.29
2.12
0.87
0.29
0.51
0.51
0.12
0.05
14.56
4.44
11.78
10.56
22.23
22.23
< 0.01
0.05
< 0.01
0.12
< 0.01
< 0.01
0.24
6.71
2.04
1.32
Vulnerability
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
1.1E+12
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
Predator
Juv. large pelagic
Prey
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Octopus
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Sea grass
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Ad. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Diet %
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.01
2.28
0.06
0.05
0.27
0.20
< 0.01
1.03
< 0.01
0.60
9.59
< 0.01
0.65
2.00
1.56
0.01
0.24
0.30
< 0.01
0.24
4.56
5.02
7.31
0.60
7.31
0.17
0.01
0.20
0.10
12.36
0.24
1.32
0.03
< 0.01
0.02
< 0.01
0.05
0.48
0.60
0.60
1.44
0.72
1.32
< 0.01
< 0.01
< 0.01
0.01
< 0.01
0.06
0.11
2.28
1.48
0.91
0.11
2.00
0.06
0.04
< 0.01
0.47
0.40
0.23
21.70
< 0.01
1.64
Vulnerability
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
22.90
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Table A.3.6 - (cont.)
Predator
Ad. medium pelagic
Juv. medium pelagic
Prey
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Other tuna
Mackerel
Billfish
Juv. butterflyfish
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Other tuna
Mackerel
Juv. large sharks
Juv. small sharks
Juv. rays
Juv. butterflyfish
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Diet %
0.49
3.41
0.01
0.57
0.23
0.28
0.60
2.73
8.18
44.32
0.11
0.57
0.05
< 0.01
0.09
2.50
0.05
0.01
0.12
0.02
< 0.01
< 0.01
0.03
2.37
0.07
0.07
1.60
2.09
1.58
< 0.01
0.33
0.09
0.42
< 0.01
0.08
3.48
19.63
0.13
0.62
11.77
14.65
39.12
0.52
1.70
1.05
0.02
0.14
0.26
0.70
0.17
0.39
0.39
0.66
0.22
0.22
0.22
0.22
0.01
0.31
0.01
0.09
< 0.01
0.03
1.20
0.08
0.11
0.22
0.14
0.22
11.88
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.49
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Predator
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Prey
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Juv. large sharks
Juv. small sharks
Juv. rays
Juv. large pelagic
Juv. medium pelagic
Juv. small pelagic
Juv. large planktivore
Juv. small planktivore
Juv. anchovy
Juv. deepwater fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Juv. large sharks
Juv. small sharks
Juv. rays
Cleaner wrasse
Juv. large pelagic
Juv. medium pelagic
Juv. small pelagic
Juv. large planktivore
Juv. small planktivore
Juv. anchovy
Juv. deepwater fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Diet %
0.32
0.27
1.03
0.04
0.22
0.07
0.01
0.01
< 0.01
0.11
0.22
0.22
1.30
27.00
27.00
16.20
10.80
0.04
0.04
0.04
0.02
0.01
< 0.01
0.04
0.02
< 0.01
0.04
0.05
0.06
0.03
< 0.01
< 0.01
< 0.01
0.10
0.03
0.51
0.05
49.17
1.00
48.71
0.20
0.31
0.01
0.07
0.03
0.04
0.06
0.31
0.17
5.12
0.61
0.04
< 0.01
0.02
0.01
0.01
< 0.01
0.08
0.07
0.20
0.16
10.23
9.16
20.87
52.21
< 0.01
< 0.01
< 0.01
< 0.01
0.01
232
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
Table A.3.6 - (cont.)
Predator
233
Prey
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Diet %
0.01
< 0.01
0.01
< 0.01
0.05
0.05
0.05
0.05
0.02
< 0.01
0.01
< 0.01
< 0.01
0.30
0.02
0.10
< 0.01
< 0.01
< 0.01
0.01
< 0.01
< 0.01
4.10
1.00
4.00
3.79
0.60
0.20
0.05
0.01
0.10
0.18
1.60
0.20
0.10
0.11
1.00
0.25
0.20
0.10
0.08
0.05
< 0.01
0.06
0.20
0.32
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.01
3.00
0.35
1.37
0.45
0.02
0.20
0.20
0.35
< 0.01
0.15
2.30
4.00
0.50
8.48
20.88
0.06
1.37
2.00
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
1.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Predator
Juv. large reef assoc.
Prey
Small herb. zooplankton
Macro algae
Sea grass
Fishery discards
Detritus
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Juv. coral trout
Juv. small sharks
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Diet %
8.18
9.47
9.10
< 0.01
8.52
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.02
< 0.01
< 0.01
0.02
< 0.01
< 0.01
0.02
1.00
< 0.01
2.00
0.01
0.10
< 0.01
0.01
0.20
0.20
< 0.01
1.75
< 0.01
0.30
0.10
0.10
< 0.01
0.10
< 0.01
0.10
< 0.01
< 0.01
< 0.01
0.38
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.10
0.50
0.31
0.08
0.01
< 0.01
0.08
0.01
< 0.01
0.01
2.31
0.82
0.20
6.24
30.02
0.09
13.53
7.24
12.71
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Table A.3.6 - (cont.)
Predator
Ad. medium reef assoc.
Prey
Phytoplankton
Macro algae
Sea grass
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Juv. coral trout
Ad. large sharks
Ad. small sharks
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Diet %
0.83
9.50
8.97
< 0.01
0.01
0.01
0.02
0.02
0.08
< 0.01
< 0.01
< 0.01
0.01
0.01
0.01
0.01
< 0.01
< 0.01
0.01
0.01
0.15
0.20
0.05
0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.12
0.35
1.29
2.00
1.00
0.10
0.01
0.10
0.10
0.06
0.38
0.15
0.35
1.00
0.06
0.01
0.10
< 0.01
0.23
< 0.01
< 0.01
0.70
0.35
0.02
0.50
0.03
0.61
0.69
0.01
5.00
0.50
0.35
0.23
0.01
0.23
1.15
0.19
< 0.01
0.20
3.99
8.07
1.11
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Predator
Juv. medium reef assoc.
Ad. small reef assoc.
Prey
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Ad. groupers
Sub. groupers
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Juv. large demersal
Ad. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Ad. deepwater fish
Juv. macro algal browsing
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Diet %
14.34
11.40
0.50
9.49
2.70
4.88
2.00
8.59
9.40
0.06
4.53
< 0.01
< 0.01
< 0.01
0.01
< 0.01
< 0.01
0.01
0.01
0.20
1.17
1.20
0.10
0.10
0.05
< 0.01
< 0.01
0.28
0.02
0.20
0.02
< 0.01
0.10
0.57
< 0.01
0.11
< 0.01
0.17
0.34
0.01
0.09
< 0.01
0.46
0.11
< 0.01
0.01
0.04
< 0.01
< 0.01
0.20
0.46
0.57
0.10
1.89
5.22
0.39
5.61
1.94
3.72
0.05
33.58
35.77
0.06
5.00
0.06
0.20
< 0.01
< 0.01
0.34
0.10
234
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.00
1.00
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
1.0E+05
6.06
6.06
6.06
6.06
2.00
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
6.06
1.70
1.70
1.70
1.70
1.70
1.70
Table A.3.6 - (cont.)
Predator
Juv. small reef assoc.
235
Prey
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Juv. large demersal
Ad. small demersal
Juv. small demersal
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Diet %
0.34
0.03
0.65
0.34
0.34
0.34
0.34
0.34
< 0.01
1.57
2.01
0.07
1.62
0.02
0.10
2.57
0.11
0.03
0.03
0.10
0.18
0.17
0.03
0.05
0.02
0.56
2.68
1.41
12.06
9.95
0.09
4.62
2.75
8.78
7.97
16.79
17.85
0.03
2.34
< 0.01
< 0.01
< 0.01
0.01
0.01
0.33
< 0.01
0.04
< 0.01
0.02
0.01
0.01
0.11
2.56
0.04
0.48
2.00
1.00
< 0.01
0.10
< 0.01
0.01
0.06
< 0.01
0.06
0.11
0.09
0.01
< 0.01
< 0.01
0.01
Vulnerability
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
1.70
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
Predator
Ad. large demersal
Juv. large demersal
Prey
Giant triton
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Sea grass
Fishery discards
Detritus
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Lobsters
Large crabs
Small crabs
Giant triton
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Ad. groupers
Sub. groupers
Ad. butterflyfish
Juv. butterflyfish
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Diet %
< 0.01
0.70
0.16
1.91
17.06
< 0.01
9.40
5.00
7.63
21.69
14.34
0.01
15.01
0.02
0.10
0.39
0.39
1.06
1.06
0.09
2.70
2.12
< 0.01
1.81
1.00
0.08
0.87
0.87
1.56
0.39
0.87
0.77
0.77
0.01
1.89
0.16
73.70
0.04
0.32
1.01
0.04
0.39
0.39
5.15
< 0.01
0.01
0.05
0.05
0.61
3.10
< 0.01
1.00
0.23
1.76
< 0.01
0.40
0.47
0.33
0.81
0.47
0.61
1.08
0.55
0.05
1.37
0.04
0.01
0.05
< 0.01
0.27
Vulnerability
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
8.7E+05
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
25.70
1.0E+05
25.70
25.70
25.70
25.70
25.70
25.70
25.70
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
Table A.3.6 - (cont.)
Predator
Ad. small demersal
Juv. small demersal
Prey
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Juv. large demersal
Ad. small demersal
Juv. small demersal
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Diet %
1.07
13.97
13.97
15.71
15.71
15.75
0.07
0.37
0.03
0.01
0.43
0.20
0.72
2.16
< 0.01
2.32
0.79
0.40
< 0.01
1.00
2.00
2.00
3.72
1.44
1.19
3.00
17.06
0.72
2.30
2.50
2.00
0.10
0.43
0.12
7.34
1.52
2.45
7.34
0.11
0.10
2.20
0.53
< 0.01
2.21
1.45
1.45
1.45
4.85
14.79
5.13
< 0.01
0.02
< 0.01
< 0.01
0.40
0.10
1.98
0.13
6.65
1.20
0.20
0.30
0.02
0.20
2.76
0.31
1.00
9.65
2.26
1.83
Vulnerability
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
8.6E+02
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
1.00
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
47.30
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Predator
Ad. large planktivore
Prey
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Juv. coral trout
Juv. large sharks
Juv. small sharks
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Diet %
2.43
0.10
< 0.01
1.07
0.04
0.97
0.15
1.52
1.59
0.04
0.04
0.73
0.16
< 0.01
1.00
3.85
3.05
1.00
12.00
17.93
23.31
< 0.01
0.01
0.01
< 0.01
0.01
0.01
0.01
0.08
0.91
0.69
0.34
< 0.01
< 0.01
0.02
0.02
0.40
0.10
0.03
0.03
0.02
< 0.01
< 0.01
0.05
< 0.01
0.10
2.29
0.20
< 0.01
0.34
0.50
< 0.01
0.01
0.11
0.01
0.50
< 0.01
0.47
< 0.01
0.34
0.02
1.37
0.10
0.01
0.01
< 0.01
< 0.01
1.00
0.01
0.03
236
Vulnerability
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
Table A.3.6 - (cont.)
Predator
Juv. large planktivore
Ad. small planktivore
237
Prey
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. large sharks
Juv. small sharks
Juv. rays
Juv. butterflyfish
Cleaner wrasse
Juv. large pelagic
Juv. medium pelagic
Juv. small pelagic
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Mackerel
Diet %
8.00
1.50
0.01
< 0.01
0.05
0.43
0.05
< 0.01
0.12
< 0.01
0.69
3.15
0.56
4.11
3.77
2.50
23.08
12.36
18.34
3.50
3.09
3.36
0.02
0.91
< 0.01
< 0.01
< 0.01
< 0.01
0.03
0.03
0.01
0.01
< 0.01
< 0.01
0.01
0.09
1.26
< 0.01
1.61
0.12
0.01
0.09
4.00
0.01
2.00
1.00
0.50
0.50
0.13
< 0.01
0.86
1.06
< 0.01
< 0.01
< 0.01
< 0.01
< 0.01
3.66
0.48
1.18
10.59
0.38
28.00
13.33
29.00
0.02
0.01
< 0.01
< 0.01
< 0.01
Vulnerability
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
2.00
8.95
8.95
8.95
0.00
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
8.95
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
1.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.00
2.00
2.00
2.00
Predator
Juv. small planktivore
Prey
Juv. coral trout
Juv. large sharks
Juv. small sharks
Juv. rays
Juv. butterflyfish
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Sea grass
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. large sharks
Juv. small sharks
Juv. rays
Juv. butterflyfish
Juv. large pelagic
Juv. medium pelagic
Juv. small pelagic
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Juv. anchovy
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Diet %
0.01
0.42
0.30
0.15
0.21
0.18
< 0.01
0.05
< 0.01
0.08
0.21
0.02
0.09
0.21
0.21
< 0.01
0.32
0.21
< 0.01
0.42
< 0.01
0.42
0.21
< 0.01
0.76
0.02
0.21
0.11
0.02
0.02
0.05
0.05
0.11
0.02
< 0.01
0.01
0.02
1.07
0.90
6.66
6.66
0.09
28.68
0.90
28.75
10.56
10.56
< 0.01
< 0.01
< 0.01
< 0.01
0.11
0.11
< 0.01
0.05
0.03
0.01
0.02
0.10
< 0.01
0.02
0.01
0.10
0.07
0.03
0.11
0.11
0.10
< 0.01
0.10
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
Table A.3.6 - (cont.)
Predator
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Prey
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Shrimps and prawns
Lobsters
Large crabs
Small crabs
Giant triton
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Penaeid shrimps
Shrimps and prawns
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Shrimps and prawns
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Ad. groupers
Sub. groupers
Ad. snappers
Sub. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. small sharks
Juv. small sharks
Adult rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Diet %
0.05
< 0.01
0.03
0.05
0.09
0.02
< 0.01
0.02
< 0.01
1.45
0.34
0.37
5.85
11.53
0.61
14.65
15.81
28.03
20.00
2.11
0.20
0.80
40.00
3.59
35.23
18.07
0.01
< 0.01
1.24
0.90
10.00
87.84
< 0.01
0.02
0.02
0.11
0.02
0.02
2.83
2.83
3.06
2.83
0.07
0.30
2.60
0.01
0.30
0.40
0.03
0.11
< 0.01
0.20
0.33
2.01
2.38
0.52
1.00
0.71
1.00
0.01
2.00
0.20
0.45
1.02
1.00
2.53
0.39
1.00
2.00
0.37
Vulnerability
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
1.04
2.5E+07
2.5E+07
2.5E+07
2.5E+07
2.5E+07
2.5E+07
2.5E+07
2.0E+02
2.0E+02
2.0E+02
2.0E+02
2.0E+02
2.0E+02
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.01
5.2E+21
2.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.01
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
1.01
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
Predator
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Prey
Juv. macro algal browsing
Ad. eroding grazers
Ad. scraping grazers
Detritivore fish
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Juv. small reef assoc.
Juv. small planktivore
Juv. anchovy
Juv. deepwater fish
Juv. macro algal browsing
Shrimps and prawns
Squid
Lobsters
Large crabs
Small crabs
Giant triton
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Macro algae
Juv. butterflyfish
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Ad. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Ad. small demersal
Ad. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Ad. small pelagic
Ad. small reef assoc.
Juv. small reef assoc.
Ad. small demersal
Juv. small demersal
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Diet %
1.00
< 0.01
0.26
0.11
5.72
2.78
0.10
0.09
3.08
< 0.01
3.13
2.94
11.32
15.80
11.00
8.00
0.10
0.10
0.50
1.00
0.05
0.14
0.06
< 0.01
< 0.01
0.30
< 0.01
0.48
0.14
9.97
12.42
0.01
24.00
15.00
30.00
3.46
0.22
0.02
0.14
< 0.01
0.42
0.54
0.54
< 0.01
0.57
0.36
0.02
1.08
0.54
1.38
2.05
1.08
0.54
0.87
2.16
1.08
5.40
5.40
5.40
5.45
53.96
10.79
< 0.01
0.01
0.19
0.01
0.01
< 0.01
0.25
< 0.01
238
Vulnerability
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
2.00
5.2E+21
5.2E+21
5.2E+21
5.2E+21
5.2E+21
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
5.0E+02
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Table A.3.6 - (cont.)
Predator
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
239
Prey
Juv. anchovy
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Mangroves
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Mangroves
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. coral trout
Juv. rays
Juv. butterflyfish
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Diet %
0.13
0.10
0.08
< 0.01
3.19
1.71
6.10
17.01
53.51
17.10
0.59
15.46
9.51
15.24
17.80
1.10
1.76
2.20
0.55
35.28
1.10
18.49
11.10
18.49
18.49
0.92
2.95
3.70
1.83
24.04
3.00
0.10
2.38
0.55
0.11
0.10
0.55
0.33
0.11
0.11
0.02
0.02
0.09
0.23
0.11
1.07
0.22
0.22
0.55
0.55
0.26
0.66
0.32
0.66
4.77
39.59
32.74
0.05
10.56
< 0.01
< 0.01
< 0.01
< 0.01
0.01
0.01
0.34
0.03
0.07
0.05
0.11
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Predator
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Anemonies
Prey
Juv. large planktivore
Juv. small planktivore
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Fishery discards
Detritus
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Fishery discards
Detritus
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Juv. large reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Diet %
0.34
0.55
0.10
< 0.01
0.34
0.11
< 0.01
0.06
0.11
0.16
0.06
0.22
0.01
0.01
0.01
0.02
< 0.01
< 0.01
< 0.01
0.28
1.47
0.07
0.15
0.56
< 0.01
6.40
4.16
7.06
7.23
29.76
24.45
0.05
15.60
1.30
2.60
0.98
0.36
0.34
4.33
1.43
1.30
1.47
1.78
1.78
0.05
82.28
9.50
5.13
24.39
48.78
12.20
9.50
5.13
24.39
48.78
12.20
9.50
5.13
24.39
48.78
12.20
9.50
5.13
24.39
48.78
12.20
6.42
0.56
2.66
3.07
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Table A.3.6 - (cont.)
Predator
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Prey
Small crabs
Epifaunal det. inverts.
Epifaunal carn. inverts
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Penaeid shrimps
Shrimps and prawns
Large crabs
Small crabs
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Macro algae
Sea grass
Detritus
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Sea grass
Detritus
Juv. medium pelagic
Juv. small pelagic
Juv. large reef assoc.
Juv. small planktivore
Juv. anchovy
Penaeid shrimps
Shrimps and prawns
Squid
Carn. zooplankton
Large herb. zooplankton
Detritus
Juv. large reef assoc.
Juv. deepwater fish
Juv. macro algal browsing
Juv. scraping grazers
Detritivore fish
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Carn. zooplankton
Detritus
Macro algae
Detritus
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Penaeid shrimps
Shrimps and prawns
Diet %
0.61
3.22
6.42
19.26
19.26
12.84
12.84
12.84
1.84
1.00
0.10
0.05
6.66
0.40
0.05
1.79
33.19
0.92
10.39
4.17
39.44
0.80
0.10
0.14
0.18
21.18
21.18
56.42
0.02
0.40
8.10
1.00
5.00
12.15
1.31
0.50
35.01
32.39
4.12
1.48
0.30
0.28
0.85
0.02
15.00
0.35
1.37
8.00
0.05
0.02
< 0.01
0.10
0.20
30.71
6.40
0.50
13.57
13.48
2.25
5.08
40.00
60.00
1.45
18.86
3.00
1.24
37.73
37.72
26.41
1.24
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
2.00
Predator
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Prey
Small crabs
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Detritus
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Sea grass
Detritus
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Calcareous algae
Crown of thorns
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Infaunal inverts.
Macro algae
Sea grass
Small herb. zooplankton
Phytoplankton
Detritus
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Detritus
Infaunal inverts.
Macro algae
Sea grass
Detritus
Juv. large reef assoc.
Juv. small demersal
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Juv. scraping grazers
Detritivore fish
Hermatypic corals
Non reef building corals
Shrimps and prawns
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Detritus
Juv. large demersal
Juv. small demersal
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Juv. scraping grazers
Penaeid shrimps
Shrimps and prawns
Diet %
0.10
39.62
0.69
0.25
5.28
13.21
13.21
0.82
0.38
49.40
6.17
37.05
6.17
10.00
80.92
7.55
1.53
8.40
9.72
30.70
30.70
20.47
0.04
55.53
44.43
20.00
50.90
29.10
4.58
1.99
20.29
59.23
13.91
0.09
14.83
14.83
70.25
0.10
0.05
0.10
0.10
< 0.01
0.30
0.33
0.80
< 0.01
< 0.01
0.16
0.08
0.13
0.13
< 0.01
< 0.01
< 0.01
0.06
8.34
1.38
0.20
0.35
75.52
11.84
< 0.01
0.01
< 0.01
< 0.01
< 0.01
< 0.01
0.11
0.01
< 0.01
240
Vulnerability
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2.00
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
Table A.3.6 - (cont.)
Predator
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
241
Prey
Sea cucumbers
Bivalves
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Macro algae
Sea grass
Detritus
Juv. groupers
Juv. snappers
Juv. Napoleon wrasse
Juv. butterflyfish
Juv. large pelagic
Juv. medium pelagic
Juv. small pelagic
Juv. large reef assoc.
Juv. medium reef assoc.
Juv. small reef assoc.
Juv. large demersal
Juv. small demersal
Juv. large planktivore
Juv. small planktivore
Juv. anchovy
Juv. deepwater fish
Juv. macro algal browsing
Juv. eroding grazers
Juv. scraping grazers
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Phytoplankton
Diet %
0.03
0.60
< 0.01
0.01
0.10
16.31
33.64
49.16
0.01
0.01
0.01
0.10
0.04
0.07
0.46
0.11
< 0.01
0.06
0.10
0.33
0.55
0.10
3.74
1.18
0.10
0.04
0.22
1.80
22.05
24.70
22.10
22.10
15.00
2.50
82.50
100.00
100.00
Vulnerability
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
6.1E+06
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
5.2E+21
1.50
5.2E+21
7.14
1.00
Appendix A.4 - Ecopath parameters: 1990 RA model
Table A.4.1 - 1990 RA model parameters.
Biomass in t·km-2. CPUE change is CPUE2006/CPUE1990. For multistanza groups, the biomass assumption
refers to the combined biomass of all life history stanzas. Group biomasses estimated by Ecopath assume
EE = 0.99.
# Functional group
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Dugongs
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Whale shark
Manta ray
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
2006
biomass
0.033
0.052
0.091
0.054
0.366
0.043
0.082
0.087
0.001
0.184
0.057
0.016
0.081
0.042
0.030
0.011
0.020
0.004
0.693
0.541
0.086
0.825
0.033
0.007
0.061
0.053
0.041
0.017
0.003
0.003
0.177
0.068
0.243
0.081
0.009
0.054
0.032
0.011
0.017
0.071
0.108
7.128
4.512
2.853
2.355
0.259
0.135
0.127
0.135
CPUE
change
1.16
1.16
1.16
0.81
0.81
0.81
0.22
0.21
0.21
0.87
1.09
1.09
-
0.64
0.53
0.88
1.08
0.18
0.11
0.32
-
Biomass
assumption
1990
biomass
No change
No change
No change
No change
No change
No change
No change
No change
No change
Custom
Custom
Custom
Custom
Custom
Custom
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
No change
No change
No change
CPUE
CPUE
CPUE
CPUE
No change
No change
No change
No change
No change
No change
No change
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
0.033
0.052
0.091
0.054
0.366
0.043
0.082
0.087
0.001
0.435
0.062
0.016
0.164
0.079
0.061
0.060
0.084
0.016
3.188
2.517
0.098
0.825
0.036
0.005
0.020
0.084
0.007
0.046
0.003
0.003
0.185
0.060
0.249
0.076
0.009
0.091
0.043
0.031
0.021
0.104
0.102
6.778
4.043
13.892
14.426
1.313
2.137
0.238
0.589
2006 catch Catch assumption 1990 catch
0
0
0
0
0
0
0
0
0
0.017
0.009
0.002
0.014
0.014
0.003
9.3E-04
9.3E-04
2.1E-04
0.348
0.047
0.064
0.050
0.002
1.6E-04
0.025
0.003
0.006
6.2E-04
0
0
0.019
0.002
0.016
0.002
8.2E-04
0.031
0.004
0.007
0.003
0.034
0.004
0.577
0.112
0.350
0.035
0.150
0.015
0.024
0.005
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
Time series
Time series
Time series
Time series
Time series
Time series
10%
10%
10%
Time series
Time series
Time series
50%
50%
50%
Time series
as 2006
Time series
50%
No catch
No catch
50%
50%
50%
50%
50%
Time series
50%
Time series
50%
Time series
50%
Time series
50%
Time series
50%
Time series
50%
Time series
50%
0
0
0
0
0
0
0
0
0
0.007
0.003
7.7E-04
0.004
0.004
9.6E-04
9.3E-05
9.3E-05
2.1E-05
0.335
0.045
0.022
0.025
8.2E-04
8.2E-05
0.017
0.003
0.004
3.1E-04
0
0
0.010
9.5E-04
0.008
7.8E-04
4.1E-04
0.009
0.002
0.003
0.002
0.008
0.002
0.093
0.056
0.332
0.018
0.173
0.008
0.010
0.002
242
Table A.4.1 - (cont.)
# Functional group
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
243
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Phytoplankton
Macro algae
Sea grass
Mangroves
Fishery discards
Detritus
2006
biomass
0.192
0.135
1.000
0.887
0.414
0.614
1.500
2.237
0.600
0.794
0.250
0.500
0.526
0.256
0.348
1.656
0.016
0.600
0.875
0.600
0.600
0.100
0.500
2.000
2.000
0.237
1.000
0.971
0.219
0.255
0.255
0.219
0.050
0.722
9.189
4.580
1.400
5.600
27.422
0.100
1.000
0.560
2.430
26.100
39.389
20.157
19.136
20.000
100.000
CPUE
change
0.20
0.71
0.30
0.48
1.40
1.40
1.03
0.92
0.85
1.14
0.89
0.89
1.54
1.49
Biomass
assumption1
CPUE
CPUE
CPUE
No change
No change
No change
CPUE
CPUE
CPUE
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
CPUE
No change
No change
No change
CPUE
No change
No change
Ecopath
No change
No change
Ecopath
Ecopath
No change
No change
No change
No change
No change
No change
No change
1990
biomass
0.977
0.688
1.496
1.168
0.300
0.728
4.518
7.840
0.675
0.719
0.164
0.585
0.525
0.255
0.239
1.137
0.016
0.600
0.875
0.600
0.600
0.100
0.500
1.426
1.426
0.231
1.086
1.138
0.192
0.286
0.286
0.219
0.050
0.722
5.973
4.580
1.400
4.861
27.422
0.220
1.548
1.086
2.430
26.100
39.389
20.157
6.147
20.000
100.000
2006 catch Catch assumption 1990 catch
0.028
0.003
0.300
0.030
0.013
0.001
0.509
0.051
0.008
9.2E-04
8.2E-04
8.2E-05
2.7E-04
2.7E-05
0.022
0.002
0.002
0
1.0E-03
0
0
0
0
0.145
0.017
0.006
1.2E-05
0.006
0.044
0.003
0.003
0
0.003
0.003
0.006
1.0E-03
0.003
0.004
0
0
0
0
0
0
0
0
0
0
0
Time series
50%
Time series
50%
50%
50%
Time series
50%
Time series
50%
50%
50%
50%
50%
50%
50%
50%
No catch
as 2006
No catch
No catch
No catch
No catch
Time series
Time series
Time series
Time series
Time series
Time series
Time series
Time series
no catch
50%
50%
Time series
50%
50%
Time series
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
No catch
0.019
0.002
0.115
0.015
0.006
7.1E-04
0.442
0.025
0.004
4.6E-04
4.1E-04
4.1E-05
1.4E-04
1.4E-05
0.011
1.1E-03
9.6E-04
0
1.0E-03
0
0
0
0
0.064
0.007
0.005
0.000
0.004
0.012
0.002
0.002
0
0.002
1.4E-03
0.003
5.0E-04
0.002
0.002
0
0
0
0
0
0
0
0
0
0
0
Appendix A.5 - Ecosim parameters: 1990-2006 RA model
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.5
0.5
0.5
0.5
0.5
0.5
0
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.1
0.1
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0
0.5
0.05
0.5
0.5
Group
Juv. small reef assoc.
Ad. large demersal
Juv. large demersal
Ad. small demersal
Juv. small demersal
Ad. large planktivore
Juv. large planktivore
Ad. small planktivore
Juv. small planktivore
Ad. anchovy
Juv. anchovy
Ad. deepwater fish
Juv. deepwater fish
Ad. macro algal browsing
Juv. macro algal browsing
Ad. eroding grazers
Juv. eroding grazers
Ad. scraping grazers
Juv. scraping grazers
Detritivore fish
Azooxanthellate corals
Hermatypic corals
Non reef building corals
Soft corals
Calcareous algae
Anemonies
Penaeid shrimps
Shrimps and prawns
Squid
Octopus
Sea cucumbers
Lobsters
Large crabs
Small crabs
Crown of thorns
Giant triton
Herbivorous echinoids
Bivalves
Sessile filter feeders
Epifaunal det. inverts.
Epifaunal carn. inverts
Infaunal inverts.
Jellyfish and hydroids
Carn. zooplankton
Large herb. zooplankton
Small herb. zooplankton
Feeding time
adjustment
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Max relative
feeding time
Feeding time
adjustment
Group
Mysticetae
Pisc. odontocetae
Deep. odontocetae
Dugongs
Birds
Reef assoc. turtles
Green turtles
Oceanic turtles
Crocodiles
Ad. groupers
Sub. groupers
Juv. groupers
Ad. snappers
Sub. snappers
Juv. snappers
Ad. Napoleon wrasse
Sub. Napoleon wrasse
Juv. Napoleon wrasse
Skipjack tuna
Other tuna
Mackerel
Billfish
Ad. coral trout
Juv. coral trout
Ad. large sharks
Juv. large sharks
Ad. small sharks
Juv. small sharks
Whale shark
Manta ray
Adult rays
Juv. rays
Ad. butterflyfish
Juv. butterflyfish
Cleaner wrasse
Ad. large pelagic
Juv. large pelagic
Ad. medium pelagic
Juv. medium pelagic
Ad. small pelagic
Juv. small pelagic
Ad. large reef assoc.
Juv. large reef assoc.
Ad. medium reef assoc.
Juv. medium reef assoc.
Ad. small reef assoc.
Max relative
feeding time
Table A.5.1 - Feeding rate parameters.
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
244
Appendix A.6 - Time series data
Figure A.6.1 - Estimated fisheries catch in Raja Ampat.
Source: DKP and Trade and Industry Office. Life history stanzas and body size categories may be aggregated.
A.) Catch
Snappers
10
20
1995
2000
0
1990
2005
1995
Sharks
2000
2000
2000
0
1990
2005
1995
2000
2005
Year
Demersal fish
Large planktivores
400
300
800
1995
2000
0
1990
2005
40
20
1995
Year
2000
0
1990
2005
1995
2000
0
1990
2005
1995
2000
Squid
250
2005
Year
Year
Shrimp
15
200
100
Year
Deepwater fish
1000
1995
Year
400
0
1990
Anchovy
0
1990
2005
kg• km-2
kg• km-2
40
2005
2000
60
60
Year
Octopus
8
0.020
6
0.015
150
kg• km-2
100
5
200
2000
2005
1995
2000
0
1990
2005
1995
Year
Year
Sea cucumber
Lobsters
10
2000
0
1990
2005
kg• km-2
kg• km-2
4
20
2000
Year
245
2005
0
1990
4
1995
2000
Year
2005
0
1990
1995
2000
2005
Year
Epifaunal invertebrates
10
8
8
6
6
4
4
2
2
2
1995
kg• km-2
6
40
0.000
1990
2005
Bivalves
8
8
2000
Year
Crabs
60
6
1995
Year
-2
1995
0
1990
0.010
0.005
2
50
0
1990
4
kg• km
400
10
kg• km-2
kg• km-2
600
kg• km-2
200
800
0
1990
1995
40
20
1200
20
1995
400
Reef-associated fish
10
0
1990
60
1600
80
20
600
Year
Pelagic fish
kg• km-2
kg• km-2
0
1990
2005
100
30
80
200
Year
40
kg• km-2
10
5
Year
kg• km-2
15
10
0
1990
800
20
kg• km-2
20
Mackerel
kg• km-2
30
Tuna
kg• km-2
30
Napoleon wrasse
25
kg• km-2
40
kg• km-2
kg• km-2
Groupers
40
2
1995
2000
Year
2005
0
1990
1995
2000
Year
2005
0
1990
1995
2000
Year
2005
Figure A.6.2 - Estimated catch per unit effort in Raja Ampat.
Source: DKP and Trade and Industry Office. Life history stanzas and body size categories may be aggregated.
B.) Catch per unit effort
1995
Year
4
3
2
1
0
1990
1995
2000
kg• km-2 • unit effort -1
kg• km-2 • unit effort -1
50
5
40
30
20
10
0
1990
2005
1995
Year
2000
2005
kg• km-2 • unit effort -1
2
5
0
1990
1995
2000
1
0
1990
2005
1995
Sea cucumber
10
1995
2000
1
2000
2005
0
1990
4
2
2000
Year
2005
2005
1995
2
1
0
1990
2005
50
2000
2005
2000
2.5
0.5
1995
2000
2005
Year
2.0
1.5
1.0
0.5
0.0
1990
1995
4
0.6
0.3
1995
2000
Year
2005
3
2
1
0
1990
1995
2000
2000
Year
Bivalves
0.9
2005
Octopus
1.0
0.0
1990
1995
Year
1.5
1995
3
Squid
100
0.0
1990
2000
4
Year
1.2
1995
10
Crabs
6
0
1990
20
Year
8
2000
Year
30
2005
150
2005
kg• km-2 • unit effort -1
kg• km-2 • unit effort -1
2
Year
20
0
1990
10
1995
30
Lobsters
3
1995
5
Year
Year
0
1990
2000
0
1990
2005
40
40
200
-2
10
2
50
Shrimp
3
2000
4
Year
Year
-1
kg• km • unit effort
15
1995
6
Large planktivores
0
1990
4
20
2005
8
Large demersal fish
Deepwater fish
25
2000
10
Reef-associated fish
Year
Anchovy
1995
0
1990
Year
Pelagic fish
6
kg• km-2 • unit effort -1
2005
Year
Sharks
kg• km-2 • unit effort -1
2000
100
-1
2005
0
1990
200
-2
0
1990
12
300
kg• km • unit effort
2000
1
1
kg• km-2 • unit effort -1
1995
2
2
kg• km-2 • unit effort -1
0
1990
3
Mackerel
400
kg• km-2 • unit effort -1
-2
1
4
kg• km-2 • unit effort -1
kg• km-2 • unit effort -1
kg• km • unit effort
-1
-1
2
-2
kg• km • unit effort
3
Tuna
3
5
4
kg• km-2 • unit effort -1
Napoleon wrasse
kg• km-2 • unit effort -1
Snappers
kg• km-2 • unit effort -1
Groupers
2005
Year
246
2005
APPENDIX B - EWE RESULTS
Appendix B.1 - Ecopath results
Figure B.1.1 - Food web diagram.
Trophic flows in the Raja Ampat marine ecosystem. Y-axis indicates functional group trophic level (TL);
apex predators appear at the top, basal species are at the bottom. Boxes show model functional groups
(simplified); box size is scaled logarithmically to represent relative group biomass. Lines show diet matrix
connectances of 20% or greater. Coloured lines indicate direction of trophic flow (blue lines: predator is
higher trophic level; red lines: predator is lower trophic level).
247
Appendix B.2 - Ecosim results
Figure B.2.1 - Equilibrium analysis of commercial groups.
X-axis shows fishing mortality (F), curved line shows surplus yield; vertical solid line shows FMSY; broken vertical line
shows fishing mortality in 2006 (F2006) (i.e., model baseline); open circles show equilibrium biomass. Adult stanzas shown
for multi-stanza groups unless otherwise specified. Asterix indicates equilibriums were determined manually; F was
incremented for all life history stanzas.
Snappers
0.2
-2
0.08
0.02
0.0
0.004
0.1
0.000
0.2
0.0
0.5
-1
0
4
5
0.15
0.10
0.02
0.01
0.00
0.02
0.00
0
1
2
3
-1
F (yr )
4
)
-2
)
-2
0.06
0.04
0.01
0.02
0.05
0.00
0.00
0.3
0.6
0.9
0.00
0
1.2
0.5
1
1.5
-1
F (yr )
F (yr )
Large reef associated
Medium reef associated*
5.00
4.00
4.0
0.8
-2
0.3
-2
)
)
0.4
Catch (t·km
)
0.06
0.04
0.000
-2
)
0.20
0.04
1.0
0.08
0.03
0.005
0.08
0.02
-1
-2
Catch (t·km
0.010
0.002
Biomass (t·km
-2
0.015
0.10
0.03
)
)
0.25
0.0
-2
0.04
0.3
0.30
0.06
2.0
Biomass (t·km
0.05
0.020
)
0.025
0.004
F (yr )
0.35
0.00
Small pelagic
-2
0.006
3
Large pelagic
-1
0.008
-1
1.5
Butterflyfish
F (yr )
Medium pelagic
2
1.0
0.2
F (yr )
0.02
0.00
F (yr )
0.1
F (yr )
-2
-2
)
0.04
0.02
0.00
0
-1
0.08
-2
)
0.06
0.008
0.01
0.E+00
0.0
1.8
3.00
0.2
2.00
0.1
1.00
0.0
0.00
0
0.1
0.2
0.3
0.4
0.5
0.6
)
5.E-04
1
1.2
0.10
0.08
Catch (t·km
0.02
Catch (t·km
1.E-03
0
0.6
-1
0.012
-2
0.03
0
0
Biomass (t·km
0.05
Biomass (t·km
)
0.6
Large sharks
-2
Catch (t·km
0.5
)
Coral trout
)
0.4
-2
F (yr )
C atch (t·km
0.3
-1
2.E-03
0.2
0.00
Catch (t·km
0.2
0.00
)
0.1
0.00
F (yr )
0.04
0.4
3.0
-2
0
1
-1
2.E-03
0.6
0.04
0.02
Catch (t·km
0.5
0.8
0.04
0.00
0.00
0
Biomass (t·km
-2
-2
Catch (t·km
0.2
)
-2
-2
)
0.02
0.12
1.0
0.06
)
)
0.16
0.04
0.30
0.000
0.08
0.20
0.06
)
0.4
Billfish
-2
0.04
0.4
)
0.6
0.3
-2
0.06
Biomass (t·km
0.60
0.100
Catch (t·km
0.90
0.2
-1
Mackerel**
)
0.200
Biomass (t·km
)
Catch (t·km
-2
1.20
0.1
F (yr )
1.50
0.300
0.00
0
F (yr )
Other tuna
1.80
0.E+00
0.4
-1
-1
0.400
0.3
Biomass (t·km
0.5
0.1
F (yr )
Skipjack tuna
Biomass (t·km
Catch (t·km
-2
0.000
0
)
0.25
0.01
0.E+00
Catch (t·km
0
-1
F (yr )
0.01
1.E-03
Biomass (t·km
0.00
0.02
0.6
2.0
0.4
1.0
0.2
0.0
0.0
0.0
-1
F (yr )
0.5
1.0
-1
F (yr )
* Equilibrium was determined manually; fishing mortality was incremented for all life history stanzas.
** F2006 lies close to FMSY and may not be visible.
248
-2
0.000
0.8
0.005
0.02
2.E-03
Biomass (t·km
0.02
-2
0.6
0.002
0.010
Biomass (t·km
0.4
0.04
1.E-03
3.E-03
)
0.2
0.004
)
-2
-2
0.0
0
0.06
0.015
Biomass (t·km
0.00
0.03
0.08
0.006
Biomass (t·km
)
0.1
C atch (t·km
0.01
0.03
4.E-03
0.020
)
-2
0.2
Biomass (t·km
Catch (t·km
-2
0.02
2.E-03
Catch (t·km
0.3
0.10
)
0.12
0.008
Subadult Napoleon wrasse*
-2
0.010
)
0.4
)
0.03
Adult Napoleon wrasse*
Biomass (t·km
Groupers
Figure B.2.1 - (cont.)
0.00
0.3
0.6
0.9
0.3
0.50
0.6
0.08
0.4
0.04
0.2
0.00
0.00
0.0
0.75
0.25
0.50
-2
C atch (t·km
0.1
0.2
0.4
0.1
0.01
0.00
0.0
0.0
0.6
0.2
0.04
Adult scraping grazers*
0.10
0.3
0.1
0.00
0.0
0.0
0.2
0.4
-1
F (yr )
0.6
0.01
0.06
0.04
0.1
0.02
0.00
0.0
0.0
0.2
0.4
-1
F (yr )
0.6
0.00
0.0
0.0
0.4
0.8
1.2
-1
F (yr )
* Equilibrium was determined manually; fishing mortality was incremented for all life history stanzas.
249
1.2
0.50
1.0
0.40
0.8
0.30
0.6
0.20
0.4
0.10
0.2
)
)
-2
)
-2
0.2
Catch (t·km
0.1
Catch (t·km
0.02
0.60
)
)
-2
0.2
-2
)
0.3
0.08
0.03
Biomass (t·km
0.2
0.02
Catch (t·km
-2
0.3
Biomass (t·km
Catch (t·km
-2
)
)
0.4
0.04
Juvenile scraping grazers*
-2
0.5
0.6
F (yr )
Biomass (t·km
0.06
0.4
-1
-1
Juvenile eroding grazers*
)
0.2
0.02
F (yr )
Adult eroding grazers*
-2
)
0.3
0.03
0.0
0.0
-2
)
0.04
)
)
0.02
0.00
F (yr )
F (yr )
0.2
0.01
0.0
0.75
3
Juvenile macro algal browsing*
0.3
0.03
-1
2
-1
-2
-2
-2
)
)
-2
0.8
0.12
-1
1
F (yr )
Adult macro algal browsing*
C atch (t·km
0.2
Catch (t·km
0.04
0.0
0
1.0
0.16
Biomass (t·km
)
0.4
0.0
1.2
0.04
0.8
0.6
0.9
F (yr )
Juvenile deepwater fish*
0.08
0.6
0.6
-1
F (yr )
0.12
-2
0.00
0.0
1.2
0.4
0.2
0.10
-1
Adult deepwater fish*
)
0.20
1.2
0.6
Biomass (t·km
0.00
0.0
Biomass (t·km
)
0.30
0.05
1.8
0.8
)
)
0.10
0.10
0.00
1.5
0.40
0.03
1.0
0.60
0.50
0.20
1.5
Anchovy
0.70
0.15
0.06
1.0
-1
-2
-2
-2
)
0.30
-1
0.5
F (yr )
0.20
)
)
-2
0.0
1.0
0.40
0.09
F (yr )
0.0
0.0
)
0.0
0.0
4
)
0.3
3
0.25
Catch (t·km
0.2
Biomass (t·km
)
-2
2
Juvenile small planktivores*
Biomass (t·km
0.6
0.1
-2
1
F (yr )
0.50
Catch (t·km
Catch (t·km
-2
)
0.9
0.3
0.4
0.00
0
Adult small planktivores*
Biomass (t·km
0.4
Catch (t·km
0.00
0.8
0.2
-1
0.12
0.25
0.05
1.5
1.2
0.00
0.00
0.05
F (yr )
0.5
0.5
0.10
0.4
-2
1.0
0.10
1.2
Catch (t·km
0.5
0.15
-1
Juvenile large planktivores*
0.0
-2
0.00
0.0
0.20
0.15
Biomass (t·km
0.00
5
0.20
1.6
0.6
-2
4
0.25
Catch (t·km
3
-1
F (yr )
)
2
Biomass (t·km
1
0.30
0.25
-2
0.00
0
0.05
0.01
0.30
)
0.02
0.05
0.0
-2
0.10
Catch (t·km
0.10
Biomass (t·km
0.15
-2
)
0.2
0.03
Catch (t·km
0.20
0.1
0.15
-2
Biomass (t·km
)
0.25
-2
Catch (t·km
)
0.3
0.04
0.00
0.0
0.0
0.4
0.8
-1
F (yr )
1.2
-2
0.20
0.05
0.30
-2
0.35
Adult large planktivores*
Biomass (t·km
0.4
Small demersal
Biomass (t·km
Large demersal
Biomass (t·km
Small reef associated
Figure B.2.2 - Predicted and observed biomass time series 1990-2006 for Raja Ampat.
Time series fits are based on DKP and Trade and Industry Office catch per unit effort (CPUE). Black lines indicate biomass
predictions, open circles represent CPUE scaled to minimize residuals versus predicted biomass.
2
0
1990
2005
1995
Year
1995
0.8
4.0
0.2
2000
2005
)
-2
2.0
1.0
0.0
1990
1995
2000
Year
2005
-2
-2
Biomass (t·km
)
-2
1995
2000
2005
Small reef associated
4
)
-2
Biomass (t·km
3
15
10
3
2
1
5
1995
2000
0
1990
2005
1995
2000
2.0
1.0
1995
0
1990
2005
1995
2000
Year
2005
2000
2005
Year
Year
3.0
0.0
1990
0.02
Year
20
Adult large planktivore
Biomass (t·km
)
-2
0.4
Year
6
Adult anchovy
4.0
3.0
0.04
0.00
1990
2005
Medium reef associated
Year
Adult small demersal
2000
0.06
25
9
0
1990
2005
Adult large demersal
0.6
1995
30
12
Year
Biomass (t·km
)
2000
2005
0.08
Year
-2
0.06
0.00
1990
2005
2005
)
2000
0.12
Biomass (t·km
0.02
2000
Large reef associated
)
)
0.04
1995
1995
0.01
0.00
1990
15
-2
Biomass (t·km
)
-2
Biomass (t·km
0.06
0.0
1990
0.03
0.02
Year
0.18
Year
-2
2005
Adult small pelagic
0.08
1995
0.06
Year
Adult medium pelagic
0.00
1990
2000
2000
0.10
0.03
0.09
0.00
1990
1995
Adult large pelagic
)
1
0.0
1990
Adult large sharks
-2
0.5
0.1
Year
Biomass (t·km
3
0.2
0.0
1990
2005
Biomass (t·km
1.0
Biomass (t·km
1.5
4
2000
)
-2
)
2.0
1995
0.3
Year
0.12
-2
Biomass (t·km
-2
Biomass (t·km
2.5
0.00
1990
2005
Mackerel
)
5
)
6
3.0
2000
Year
Other tuna
3.5
2000
1995
-2
Skipjack tuna
1995
0.00
1990
2005
Year
Year
Biomass (t·km
2000
0.06
Adult large deepwater
5
2.5
4
2.0
)
1995
0.4
-2
0.00
1990
2005
Biomass (t·km
2000
0.06
0.12
-2
1995
0.02
0.12
Biomass (t·km
0.0
1990
0.04
0.5
Biomass (t·km
0.1
0.06
Biom ass (t·km
0.2
Biomass (t·km
-2
Biomass (t·km
Biomass (t·km
0.3
Napoleon wrasse
0.18
)
0.08
0.18
)
0.4
Sub-adult snappers
-2
0.10
)
0.5
Adult snappers
)
Sub-adult groupers
-2
)
Adult groupers
3
2
1
0
1990
1995
2000
Year
2005
1.5
1.0
0.5
0.0
1990
1995
2000
Year
250
2005
Figure B.2.2 - (cont.)
A.) Biomass
Shrimps and prawns
0.6
1.5
1.0
0.5
0.5
0.0
1990
0.0
1990
2005
1995
2005
0.05
2000
Year
251
2005
-2
Biomass (t·km
0.10
0.3
0.2
0.1
0.0
1990
)
-2
)
2000
2005
1995
2000
Year
1995
2005
2005
1.2
0.9
0.6
8
4
2
1995
2000
Year
2005
6
4
2
0
1990
1995
2000
Year
0.0
1990
1995
2000
Year
Epifaunal carnivorous inv.
6
0
1990
2000
Year
)
0.4
-2
Biomass (t·km
0.15
0.4
0.0
1990
8
)
)
)
-2
0.20
1995
1995
Bivalves
0.5
0.25
0.8
Year
Large crabs
0.30
Biomass (t·km
2000
0.0
1990
Year
Lobsters
1.5
0.3
Year
0.00
1990
0.2
-2
2000
0.4
Biomass (t·km
1995
Biomass (t·km
1.0
2.0
-2
-2
)
Biomass (t·km
1.5
1.8
1.2
-2
-2
Biomass (t·km
2.0
Sea cucumbers
Biomass (t·km
2.5
Octopus
)
3.0
2.5
)
3.0
Squid
Biomass (t·km
Penaeid shrimps
2005
2005
Figure B.2.3 - Predicted and observed catch time series 1990-2006 for Raja Ampat.
Time series fits are based on DKP and Trade and Industry Office landings. Black lines indicate catch predictions including
unreported catch, open circles represent absolute reported landings. Models are driven by an independant effort series.
Adult groupers
Sub-adult groupers
Adult snappers
0.02
Sub-adult snappers
0.020
0.08
0.015
0.06
Napoleon wrasse
5.E-04
0.08
)
-2
Catch (t·km
-2
2000
0.E+00
1990
2005
Adult large sharks
0.008
)
)
0.03
0.02
0.01
0.00
1990
2005
2000
)
0.03
0.02
0.01
1995
2000
0.00
1990
2005
1995
Adult large demersal
Adult small demersal
2.0
0.08
0.3
0.4
0.4
0.04
0.02
0.2
1995
2000
0.1
1995
2000
Year
2005
1995
2000
Year
2005
0.0
1990
1995
2000
Year
1995
2005
2000
2005
Year
Adult anchovy
1.0
1.0
0.8
0.8
0.6
0.4
0.0
1990
0.6
0.4
0.2
0.2
0.00
1990
1
0
1990
2005
-2
0.2
2005
2
Adult large planktivore
Catch (t·km
-2
Catch (t·km
Catch (t·km
0.8
3
)
)
0.06
-2
)
)
-2
1.2
0.6
Year
Small reef associated
1.6
4
0.0
1990
2005
2000
Medium reef associated
0.8
Year
Year
Year
2000
1995
Year
-2
-2
0.003
0.000
1990
2005
Catch (t·km
0.006
0.002
0.000
1990
2005
Large reef associated
0.04
0.009
2000
)
Adult small pelagic
)
)
0.01
1995
0.004
Year
0.05
-2
Catch (t·km
-2
0.02
0.00
1990
2005
0.006
-2
Adult medium pelagic
0.012
0.03
2000
Year
Year
0.04
1995
1995
Catch (t·km
2000
2005
Adult small sharks
0.03
0.06
2000
Year
)
1995
1995
-2
0.00
1990
2005
Adult large pelagic
)
1995
Catch (t·km
2000
Year
0.0
1990
1.E-04
0.04
1995
2.E-04
-2
-2
Catch (t·km
0.2
0.12
3.E-04
Year
0.09
)
0.3
0.02
0.00
1990
2005
Mackerel
-2
Catch (t·km
-2
Catch (t·km
0.4
2000
)
0.16
)
0.20
0.5
0.1
Catch (t·km
1995
0.04
Year
Other tuna
0.6
0.00
1990
0.000
1990
2005
Year
Skipjack tuna
Catch (t·km
2000
4.E-04
Catch (t·km
1995
Year
0.0
1990
)
)
-2
0.000
1990
2005
-2
2000
0.005
Catch (t·km
1995
0.004
0.010
Catch (t·km
0.00
1990
0.008
Catch (t·km
0.01
Catch (t·km
-2
Catch (t·km
Catch (t·km
-2
)
)
0.012
1995
2000
Year
2005
0.0
1990
1995
2000
Year
252
2005
Figure B.2.3 - (cont.)
0.04
0.00
1990
1995
0.00
1990
2005
1995
2000
0.E+00
1990
2005
Small crabs
1995
2000
Year
2005
0.003
0.002
0.004
0.003
0.002
0.001
0.001
0.000
1990
0.000
1990
1995
2000
Year
2005
-2
-2
0.00
1990
)
0.006
)
0.04
0.02
2005
Bivalves
0.007
0.004
2000
Year
0.005
0.03
1995
Year
0.006
0.000
1990
253
2000
0.005
0.01
Year
5.E-06
0.006
0.002
2005
1.E-05
0.05
Catch (t·km
Catch (t·km
0.004
2000
1995
Large crabs
-2
-2
0.006
0.01
Year
)
)
0.008
1995
0.000
1990
2005
Lobsters
0.010
Catch (t·km
2000
Year
Year
Sea cucumber
2.E-05
0.005
Catch (t·km
2005
0.02
)
2000
0.010
-2
1995
0.015
Catch (t·km
0.00
1990
-2
)
-2
0.08
2.E-05
0.03
Catch (t·km
0.04
Catch (t·km
0.08
0.12
Octopus
)
0.020
Squid
)
0.025
0.16
-2
Catch (t·km
Catch (t·km
0.20
)
0.12
-2
)
0.16
Shrimp and prawns
-2
Penaeid shrimp
Catch (t·km
Adult deepwater fish
0.005
0.004
0.003
0.002
0.001
1995
2000
Year
2005
0.000
1990
1995
2000
Year
2005
Figure B.2.4 - Challenges to RA Ecosim model (2006-2022).
Three fishing scenarios are used to challenge Ecosim. The ‘increased fishing’ scenario increments fishing
mortality on all exploited groups by 3.2% per year; the ‘baseline fishing’ scenario extends current (2006)
fishing mortalities forward; the ‘no fishing’ scenario reduces F to zero for all groups. We expect exploited
functional groups to increase relative to baseline when fishing is reduced, and decrease when fishing is
increased. Error bars show the error range predicted by a Monte Carlo analysis that varies initial biomass
parameters in Ecopath for commercial groups +/- 20%. Error bars show 1 SD around the mean (white circle).
Black line shows the baseline model run (i.e., applying the described group biomass values).
Increasing fishing
Baseline fishing
0.4
0.4
0.2
0.1
2010
2014
2018
0.2
0.1
0
2006
0
2006
0.3
Biomass (t•km
Biomass (t•km
0.3
-2
-2
)
)
)
0.4
-2
Biomass (t•km
Grouper
2022
Biomass (t•km
0.06
0.04
0.02
0.00
2014
2018
2022
2010
2010
2014
2018
2010
)
-2
0.005
2018
2022
0.015
0.010
0.005
0.000
2010
2014
2018
2022
2006
2010
2014
Year
3
)
)
2.5
-2
0.6
Biomass (t•km
-2
2014
0.020
0.8
Biomass (t•km
)
-2
Biomass (t•km
2006
Year
0.2
2022
0.02
Year
0.010
2006
0.4
2018
0.04
2022
0.015
2022
0.6
0.4
0.2
2
1.5
1
0.5
0
0
2010
2014
2018
2006
2022
0
2010
2014
2018
2022
2006
0.8
0.4
0.2
0
2014
Year
2018
2022
)
-2
0.6
0.4
0.2
0
2006
2018
2022
0.8
Biomass (t•km
Biomass (t•km
-2
)
0.6
2014
Year
0.8
2010
2010
Year
Year
2006
2018
0.000
0.8
2006
2014
Biomass (t•km
)
-2
Biomass (t•km
)
-2
Biomass (t•km
0.005
2022
0.00
2006
0.020
0.010
2018
0.06
Year
0.015
2014
-2
-2
Biomass (t•km
0.02
)
)
)
-2
Biomass (t•km
0.04
2010
2010
Year
0.08
0.06
Year
)
2006
0.10
2006
-2
2022
0.08
0.000
Biomass (t•km
2018
0.10
0.020
Other tuna
2014
0.08
Year
Skipjack
0.1
0.10
2006
wrasse
0.2
Year
0.00
Napoleon
0.3
0
2010
Year
Snapper
No fishing
2010
2014
Year
2018
2022
0.6
0.4
0.2
0.0
2006
2010
2014
Year
2018
2022
254
Figure B.2.4 (cont.)
0.02
2010
2014
Year
2018
)
0.02
2010
0.02
0.01
2010
2014
2018
)
0.02
0.01
2010
2014
2018
2022
)
-2
Biomass (t•km
)
-2
)
-2
Biomass (t•km
Biomass (t•km
2010
0.02
0.00
2006
2010
0.00
2006
2022
2018
2022
2018
2022
2010
0.04
0.02
2018
)
-2
0.06
0.04
0.02
0.00
2006
2022
Biomass (t•km
)
-2
0.06
2014
Year
2018
2
0
2022
2006
2010
2014
8
)
-2
6
4
2
2006
0.02
Year
6
4
2
0
0
2018
0.04
2022
Biomass (t•km
)
-2
Biomass (t•km
4
0.06
0.00
2010
8
6
2014
Year
0.08
Biomass (t•km
)
-2
Biomass (t•km
)
-2
2018
0.10
2014
Year
2022
Year
8
Biomass (t•km
2014
0.08
2010
2018
0.02
0.10
2006
2014
0.04
0.08
2014
2010
0.06
0.10
2010
2022
Year
0.04
Year
255
0.00
2006
2022
0.08
0.00
2006
associated
2018
0.06
Year
Large reef-
2014
Year
0.10
0.02
2018
0.01
0.08
0.04
2014
Year
0.02
0.10
0.06
2010
0.03
0.08
2006
pelagic
0.04
0.10
0.00
Large
0.08
0.00
2006
2022
0.03
0.00
2006
2022
0.12
0.04
Year
sharks
2018
-2
-2
)
0.03
0.00
Large
2014
Year
0.04
Biomass (t•km
)
-2
Biomass (t•km
0.04
0.00
2006
2022
0.04
2006
0.06
Biomass (t•km
2006
Biomass (t•km
0.04
0.00
Coral trout
0.16
-2
0.06
No fishing
-2
0.08
)
0.10
0.08
Biomass (t•km
Biomass (t•km
Mackerel
Baseline fishing
0.10
-2
)
Increasing fishing
2010
2014
Year
2018
2022
2006
2010
2014
Year
2018
2022
APPENDIX C - SUPPLEMENTAL FORMS
Appendix C.1 - Fishermen interview form
Questionnaire for fishers in support of EwE model.
BHS EBM project.
Contact:
Cameron H. Ainsworth
Fisheries Centre University of British Columbia
+1 604 822 1639
c.ainsworth@fisheries.ubc.ca
Required fisher demographic information
Main species fished:
Main gear types used:
Migratory/seasonal fisherman? :
Number of years experience in Raja Ampat:
Questions for fishermen (please answer on table below)
•
•
Has the relative abundance increased or decreased during their career?
Note: the interviewer may also ask, has it become easier or harder to catch these animals.
When did they first notice an increase or decrease in abundance (what decade?)
Has there been a large increase in the price for this fish (i.e., has a recent
market developed, for example for the live fish trade?). If yes, please
•
indicate the approximate year.
•
no.
Has there been a major depletion or extirpation of these species? Yes or
Have fish become smaller? Yes or no. If they are much smaller, please
circle.
256
The interviewer should fill out this table.
Abundance change?
(-/0/+)
REEF FISH
Groupers (Serranidae/Anthiinae)
Snappers (Lutjanidae)
Rabbitfishes (Siganidae)
Surgeonfishes (Acanthuridae)
Groupers / Sea bass (Serranidae)
Trevallies (Carangidae)
Fusiliers (Caesionidae)
Sweetlips / Grunts (Haemulidae)
Emperors (Lethrinidae)
Breams (Nemipteridae)
Goatfishes (Mullidae)
Batfishes (Ephippidae)
Soldierfishes (Holocentridae)
Wrasses (Labridae)
Parrotfishes (Scaridae)
Triggerfishes (Balistidae)
Angelfishes (Pomacanthidae)
Damselfishes (Pomacentridae)
Hawkfishes (Cirrhitidae)
Filefish (Monacanthdae)
Scorpionfish (Scorpaenidae)
Trumpetfish (Aulostomidae)
Pufferfish (Tetrodontidae)
Boxfish (Ostraciidae)
Cardinalfish (Apogonidae)
Butterflyfish (Chaetodontidae)
OTHER FISH
Large sharks
Wobbegongs (Orectolobidae)
Rays
Eels
Tunas (Scombridae)
Spanish mackerel (Scombridae)
Anchovy (Engraulidae)
INVERTEBRATES
Octopus
Squids
Sea urchins
Sea cucumbers
Penead Shrimp
OTHER
Turtles
Birds
Dolphins
Whales
Crocodiles
Dugongs
257
1970
1980
1990
Major price
Major
increase? depletion?
2000
(what year)
(Y/N)
Are fish
smaller?
(Y/N)
Appendix C.2 - Stomach sampling protocol
Fish stomach sampling protocol for BHS EBM project
CTC Training protocol
Version 1.1, June 2006
Cameron Ainsworth
The Nature Conservancy
and
University of British Columbia
Fisheries Centre
Fish stomach sampling protocol for BHS EBM project
CTC training protocol
258
Version 1.1
June 2006
Suggested citation:
Ainsworth, C. 2006. Fish stomach sampling protocol for BHS EBM project. Version 1.1
(June 2006). Publication from the Nature Conservancy Coral Triangle Center, Sanur,
Bali, Indonesia. 13 p.
Contact: c.ainsworth@fisheries.ubc.ca
Fisheries Centre
University of British Columbia
2202 Main Mall,
Vancouver, BC, Canada
Phone: +1 604 822 1639, fax: +1 604 822 8934
259
Table of contents
Table of contents ....................................................................................................................................... 260
1. Summary ................................................................................................................................................. 261
2. Objectives and audience..................................................................................................................... 261
Part 1 – Specimen collection .................................................................................................................. 262
3. Materials and methods ........................................................................................................................ 262
3.1 Materials ............................................................................................................................................ 262
3.2 Protocol.............................................................................................................................................. 262
3.2.1 Specimen collection ................................................................................................................. 263
3.2.2 Measuring fish length and weight .......................................................................................... 263
3.2.3 Gape size and maturity stage................................................................................................. 264
3.2.4 Stomach removal...................................................................................................................... 264
3.2.5 Preserving stomachs .............................................................................................................. 265
3.3.1 Other notes ................................................................................................................................... 265
Part 2 – Stomach content analysis ....................................................................................................... 266
4. Materials and methods ........................................................................................................................ 266
4.1 Materials ............................................................................................................................................ 266
4.2 Protocol.............................................................................................................................................. 266
4.2.1 Stomach dissection .................................................................................................................. 267
4.2.2 Weighing stomach contents................................................................................................... 267
4.3.1 Other notes .................................................................................................................................... 268
4. Version history ...................................................................................................................................... 268
5. Guidelines for adjusting this protocol............................................................................................. 268
Annex A Specimen collection form ..................................................................................................... 269
Annex B Laboratory analysis form ...................................................................................................... 270
Annex C Laboratory analysis form (EXAMPLE) ............................................................................... 271
260
1. Summary
This protocol is divided into two parts. Part one ‘specimen collection’ is to be conducted
in the field by TNC researchers during the resource use assessment survey. It consists of
obtaining fish stomachs from fishers or from markets, preserving them in numbered jars
using formalin, and recording information about the fish being sampled (e.g., type of fish,
size). Part two ‘stomach content analysis’ is to be conducted in the laboratory by UNIPA
researchers. The preserved stomachs will be dissected. The stomach contents will be
identified, sorted and weighed.
The purpose of this stomach sampling study is to
determine the diet (in percent composition) of the common commercial fish species
captured in Raja Ampat.
2. Objectives and audience
The objective of this training protocol is to train CTC staff (especially resource use
assessment staff and training facilitators) in fish stomach sampling techniques to be used
in the Birds Head Seascape Ecosystem Based Management (BHS EBM) project. The
purpose of sampling is to identify and quantify the prey items of commercial fish, to the
species or family level. The stomach content data collected using this procedure will
help to parameterize a food web computer model for the marine ecosystem of the Raja
Ampat islands. All fish species caught in Raja Ampat are of interest.
261
Part 1 – Specimen collection
Specimen collection should be conducted in the field by TNC during the resource use
assessment survey.
3. Materials and methods
3.1 Materials
Materials required for this specimen collection protocol include:
Several printouts of the form in Annex A
Ruler
Scalpel or sharp knife
Dissecting scissors
Dissecting tray
Approximately 20 numbered jars, glass or plastic
Formalin solution for preserving whole fish stomachs
3.2 Protocol
General procedure is as follows:
•
Purchase fish from market or fishers, or pay fishers nominal fee to remove
•
stomach.
•
and the area of capture.
Record pertinent information on the predator being studied, the gear type used,
Measure the total or fork length, weight, gape size and maturity stage of the fish
specimen.
262
•
Remove the stomach and preserve it in formalin for later study.
3.2.1 Specimen collection
Purchase fish directly from fishers or from the fish market. Alternatively, pay fishers a
fee to remove the stomach. The fees may vary depending on sizes, numbers and the
location of where fish are collected. A rough estimation suggests that Rp. 25,000 per one
big fish in the village and Rp. 30,000 per one big fish at the public fish market in Sorong
are appropriate fees. Record pertinent information on Annex A sheet, such as common
species name or fish family (e.g., grouper, butterfly fish, trigger fish); also indicate adult
or juvenile stage if known. Record gear type used to catch the fish, the type of bait used,
and area where the fish was caught (i.e., place name if possible, and also habitat type:
such as reef, sea mount, estuary, open water or deep water).
3.2.2 Measuring fish length and weight
Using a ruler, measure the total length or fork length of the fish specimen. Total length is
a measure from the tip of the mouth with the jaws closed to the tip of the tail, with the tail
fin lobes compressed to give the maximum possible length. Fork length is a measure
from the tip of the mouth with the jaws closed to the central part of the tail fin (Fig. 1).
Fork length should be used for reef species with long tail fins.
Specify which
measurement you are taking by recording TL or FL on the sheet in Annex A.
263
Fork length
Total length
Figure 1. Fork length and total length for fish specimen.
3.2.3 Gape size and maturity stage
To determine the gape size (mouth size), measure the length of the premaxilla bone
(upper jaw bone) from tip (foremost point) to the far end of the hinge (Fig. 2) for teleost
fish. Gape size should be measured to the nearest millimeter, or to the nearest eighth of
an inch. Record the maturity stage of the fish (juvenile or adult) if known. Do not
measure gape size for non-teleost fish such as sharks or rays, or for eels.
3.2.4 Stomach removal
Cut the fish open using scissors or a scalpel, from the anus to the bottom of the jaw.
Remove the stomach by cutting the esophagus near the anterior end; gently pull to
disconnect it from the intestines. The stomach is a U-shaped balloon-like organ; it will
vary in appearance with species and contents.
264
Premaxilla bone
Gape size
Figure 2. Gape size of fish specimen. Measure the premaxilla
bone from tip of the nose to the end of the hinge.
3.2.5 Preserving stomachs
Place the fish stomach in a numbered jar and record the jar number on the form in Annex
A. Stomachs from the same species or family can be combined into the same specimen
jar, but it is important to classify each commercial fish to the lowest taxonomic category
possible. A variety of jar sizes may be required depending on the types of fish being
sampled. Jars of 200-500 ml capacity should accommodate most reef fish stomachs, but
1000 ml jars or larger should be available for larger fish specimens such as billfish. Fill
the jar with formalin solution and pierce the stomach to allow the preservative to enter.
To increase contact between the preservative and the stomach contents, gently manipulate
the stomach by hand. Seal jar for later laboratory analysis.
3.3.1 Other notes
In cases where the available catch is large, fish should be selected randomly from among
species and size classes listed in the Annex. No more than 20 stomachs need to be
sampled for any particular species or maturity stage. For example, no more than 20 adult
groupers, or 20 juvenile snappers should be sampled. A variety of fish is important; try
to sample each commercial species available.
265
Part 2 – Stomach content analysis
To be conducted in the laboratory by UNIPA researchers.
4. Materials and methods
4.1 Materials
Materials required for stomach content analysis include:
•
•
Specimen jars containing preserved stomachs
•
Scalpel or a sharp knife
•
Dissecting tray
•
Paper towel
•
Several print outs of the form in Annex B
•
Dissecting scissors
•
Tweezers or forceps
•
Electronic or mechanical scale
Disposable plastic trays labeled by prey groups in Annex B.
4.2 Protocol
General procedure is as follows:
•
•
Remove stomach from the specimen jar.
•
Label plastic trays by prey group categories listed in Annex B.
•
Weigh whole stomach.
•
Dissect stomachs and blot the contents dry using paper towel.
Sort contents into appropriate plastic trays.
266
•
Weigh contents of each tray and record result in Annex B form.
4.2.1 Stomach dissection
Remove the stomach from the specimen jar and weigh it whole. Record the data in the
Annex B form. Cut open the stomach using scissors or a scalpel and remove all of the
contents onto a dissecting tray. Sort through the stomach contents and identify each prey
item to the lowest taxonomic category possible (i.e., species or family level). Enter the
description in Column A. Do not consider material found in the intestines, as it will be
too digested to accurately identify. Please note: for the purposes of the ecosystem model,
it is important that we identify every type of prey present in the stomach.
4.2.2 Weighing stomach contents
Blot the stomach contents using a paper towel to remove excess preservative. Record the
empty weight of each plastic tray first (Column B), and then sort the stomach contents
into the appropriately labeled trays. Weigh each specimen tray and subtract the weight of
the empty tray to determine the wet weight of the stomach contents. Report results in
Column C. The objective will be to determine a percent diet composition for each
commercial fish.
If any prey items cannot be identified, place them into the jar labeled ‘unidentified fish’,
or into the jar labeled ‘unidentified other’ if it cannot be determined whether the prey
item is a fish. Several commercial fish may be sampled before weighing the specimen
trays provided that they are in the same taxonomic category as reported on the Annex A
form (e.g., they are all juvenile groupers). In this case, also record the total number of
stomachs sampled on the form in Annex B.
267
4.3.1 Other notes
•
The numerical quantity of prey items is not important, only the identity of
•
prey items and the wet weight.
•
possible after capture to avoid decomposition of stomach contents.
Dissection and sorting of stomach contents should be done as soon as
Annex C provides a completed example of the form in Annex B.
4. Version history
•
•
Version 1.0: Draft version (March 2006)
Version 1.1: Includes revisions from April 10-14 TNC/CI/WWF/UBC Bali coordination
meeting (June 2006)
Planned improvements
- Peer review
- Translation to Bahasa Indonesian
5. Guidelines for adjusting this protocol
CTC field staff and training facilitators send proposed changes to the CTC Technical
Manager. Such proposed changes and improvements will be entered in the version
history under ‘Planned improvements’.
Proposed improvements may only be
implemented in the field if they do not affect the agreed-upon protocol (i.e., only ‘addons’ may be implemented). Once final agreement has been reached, the Technical
Manager adds proposed changes to the protocol.
268
Annex A - Specimen collection form
#
Specimen
Length (mm)
(TL or FL)
Gape size
(mm)
Area caught
Habitat type
Gear type used
Bait
Jar #
1
Juvenile grouper
75 (TL)
8
Teluk Alyui Waigeo
Shallow reef
Jaring insang (gillnet)
none
1
2
Adult parrotfish
650
66
NE Waigeo
Lagoon
Sero (trap)
none
2
3
Adult skipjack
980
17
Selat Dampir
Ocean
Rumpon (handline)
anchovy
3
-
Annex B - Laboratory analysis form
Specimen jar #:
Predator sampled:
Whole stomach weight:
Number of stomachs
Column A
Plastic
Prey categories
tray #
Inverts.
Idenity of prey
or family)
(species
Column B
Column C
Weight of plastic tray (g)
Weight of prey items (g)
1 Shrimp / prawn
2 Squid / cuttlefish
3 Jellyfish
4 Worms
5 Octopus
6 Sponges / tunicates
7 Bivalves
8 Snails
9 Starfish / sea cucumbers
10 Small crab
(< 5 cm)
11 Large crab
(> 5 cm)
Fish
12 Small pelagic fish
(e.g. sardine) (< 10 cm)
13 Large pelagic fish
(e.g.mackerel, tuna) (> 10 cm)
14 Small reef fish
(e.g. wrasse) (<10cm)
15 Large reef fish (e.g. grouper)
16 Small demersal fish
17 Large demersal fish
18 Unidentified fish
270
Annex C - Laboratory analysis form (EXAMPLE)
Specimen jar #: 1
Predator sampled: Juvenile grouper
Whole stomach weight: 102 g
Number of stomachs 1
Column A
Plastic
Prey categories
tray #
Inverts.
Idenity of prey
or family)
(species
Column B
Column C
Weight of plastic tray (g)
Weight of prey items (g)
1 Shrimp / prawn
2 .1 g
2 Squid / cuttlefish
2 .4 g
3 Jellyfish
2 .1 g
4 Worms
Po ly ch a e t e s
Tu b e w o r m s
2g
5 Octopus
2 .1 g
6 Sponges / tunicates
2 .2 g
7 Bivalves
2 .1 g
8 Snails
lim p e t
w h e lk
9 Starfish / sea cucumbers
10 Small crab
2 .2 g
20 g
2 2 .2 g
2 .1 g
u n id e n t .
2 .4 g
15.5 g
(< 5 cm)
11 Large crab
2 .1 g
(> 5 cm)
Fish
12 Small pelagic fish
2 .1 g
(e.g. sardine) (< 10 cm)
13 Large pelagic fish
2 .1 g
(e.g.mackerel, tuna) (> 10 cm)
14 Small reef fish
(e.g. wrasse) (<10cm)
271
fu s ilie r
u n id e n t .
2 .1 g
15 Large reef fish (e.g. grouper)
2 .1 g
16 Small demersal fish
2 .1 g
17 Large demersal fish
2 .2 g
18 Unidentified fish
2 .3 g
18 .4 g
Cameron Ainsworth