Hindawi Publishing Corporation
Journal of Marine Biology
Volume 2011, Article ID 460173, 13 pages
doi:10.1155/2011/460173
Research Article
Defining Boundaries for Ecosystem-Based Management:
A Multispecies Case Study of Marine Connectivity across the
Hawaiian Archipelago
Robert J. Toonen,1 Kimberly R. Andrews,1, 2 Iliana B. Baums,3 Christopher E.
Bird,1 Gregory T. Concepcion,1, 2 Toby S. Daly-Engel,1, 2 Jeff A. Eble,1, 2 Anuschka Faucci,4
Michelle R. Gaither,1, 2 Matthew Iacchei,1, 2 Jonathan B. Puritz,1, 2 Jennifer K. Schultz,1
Derek J. Skillings,1, 2 Molly A. Timmers,5 and Brian W. Bowen1
1 Hawai‘i
Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa,
P.O. Box 1346 Kāne‘ohe, HI 96744, USA
2 Department of Zoology, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
3 Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
4 Department of Biology, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
5 Joint Institute for Marine and Atmospheric Research, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
Correspondence should be addressed to Robert J. Toonen, toonen@hawaii.edu
Received 16 July 2010; Revised 8 October 2010; Accepted 4 November 2010
Academic Editor: Benjamin S. Halpern
Copyright © 2011 Robert J. Toonen et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Determining the geographic scale at which to apply ecosystem-based management (EBM) has proven to be an obstacle for many
marine conservation programs. Generalizations based on geographic proximity, taxonomy, or life history characteristics provide
little predictive power in determining overall patterns of connectivity, and therefore offer little in terms of delineating boundaries
for marine spatial management areas. Here, we provide a case study of 27 taxonomically and ecologically diverse species (including
reef fishes, marine mammals, gastropods, echinoderms, cnidarians, crustaceans, and an elasmobranch) that reveal four concordant
barriers to dispersal within the Hawaiian Archipelago which are not detected in single-species exemplar studies. We contend that
this multispecies approach to determine concordant patterns of connectivity is an objective and logical way in which to define
the minimum number of management units and that EBM in the Hawaiian Archipelago requires at least five spatially managed
regions.
1. Introduction
Global catches of commercially fished species have declined
by up to 90% under classic single-species fisheries models
[1–3]. The high-profile failures of fisheries managed for
maximum sustainable yield has led to widespread interest
in a shift toward ecosystem-based management (EBM) of
marine resources (reviewed by [4]). EBM can be broadly
defined as an integrated approach that considers the entire
ecosystem, including linkages and the cumulative impacts
of all human activities within and as part of the system. As
such, EBM is explicitly place-based and adaptive in nature,
and therefore particularly attractive for management. In
recognition of the need for explicit boundaries in ecosystembased management, Spalding et al. [5] divided the oceans
into 232 ecoregions. However, marine ecosystems are highly
complex, with many linkages and feedbacks that occur across
multiple scales of space and time in ways that have proven
difficult to predict [4]. Existing approaches to EBM in
marine systems include spatial control of human activities
through the use of marine protected areas (MPAs) and/or
ocean zoning, changes in governance, monitoring and
evaluation via ecosystem indicators derived from multiple
disciplines (e.g., oceanography, ecology, economics, political
2
science, and sociology), risk assessment, and precautionary
adaptive management [6]. Successful spatial management
requires a complex system of zones, each of which seeks
to match resource exploitation with biological productivity,
local population levels, and socioeconomic payoffs [7].
Delineation of the appropriate spatial scales for management
zones within a specific management network requires a
detailed understanding of dispersal pathways and population
connectivity (reviewed by [8–10]). Despite the central role of
dispersal and connectivity in sustaining marine populations,
our understanding of these processes is still largely underdeveloped, and “a strong commitment to understanding
patterns of connectivity in marine populations will clearly
be necessary to guide the practical design of networks of
marine reserves” [10, p.113]. In effect, managers cannot
practice EBM if they do not know the boundaries of the
corresponding ecosystems.
Understanding connectivity in the sea is complicated
by the fact that most marine organisms have a biphasic
life cycle with benthic or sedentary adults and dispersing
eggs and/or larvae, which may be pelagic for as little as
a few minutes to more than a year. Following the pelagic
phase, larvae settle onto a patch of suitable habitat, where
they may remain throughout their lives, and in cases of
sessile organisms such as corals, the act of settlement
includes permanent attachment to a single site. Thus, longdistance dispersal is accomplished almost exclusively during
the pelagic larval phase, which can potentially span large
expanses of open ocean [11–15]. On the other hand, species
which lack a pelagic larval phase, such as marine mammals
and elasmobranchs, have the potential to range widely
throughout the oceans and face few obvious barriers to
dispersal. Despite the potential for long-distance movement
in most marine species, the geographic limits of such
dispersal remain uncertain, because it is virtually impossible
to track microscopic juveniles during the pelagic phase
(reviewed by [16]), making indirect methods of quantifying
larval dispersal particularly attractive (reviewed by [8, 17–
19]). Intuitive expectations that larval dispersal is a function
of pelagic larval duration (PLD) are not supported by recent
meta-analyses ([20–25]). Despite considerable research, the
scale of larval dispersal and the boundaries for EBM remain
nebulous due to the complex interaction of larval biology,
oceanographic regimes, habitat quality and distribution, and
the variability of each through time [26].
Delineating management units is further complicated by
the fact that single-species studies of genetic connectivity
are often contradictory. Analyses of connectivity frequently
focus on single-species exemplars which are then extrapolated to the level of the community, but the utility of
exemplars in such cases is limited; even among closely related
species with similar ecology, life histories, and geographic
ranges, the corresponding patterns of connectivity can be
very different [27, 28]. In other cases, animals with highly
divergent biology can have surprisingly similar patterns of
connectivity [26]. Such variability among species appears to
be the rule rather than the exception, and has led to a call
for multispecies comparisons of connectivity across trophic
levels to broadly define the boundaries for management, and
Journal of Marine Biology
to determine shared avenues of exchange among ecosystems.
Due to logistical difficulties in completing such comparisons
in marine habitat, few such studies exist (e.g., [29–31]).
The linear nature of the Hawaiian Archipelago (Figure 1)
provides an excellent forum for resolving shared barriers to
gene flow across species and trophic levels, with the goal of
developing a geographic framework for EBM.
The Hawaiian archipelago stretches more than 2600 km
in length and consists of two regions: the Main Hawaiian
Islands (MHI) which are high volcanic islands with a heavy
human presence and the Northwestern Hawaiian Islands
(NWHI) which are a string of tiny islands, atolls, shoals, and
banks that are essentially uninhabited. Due to their isolation,
the roughly 4,500 square miles of coral reefs in the NWHI
are among the healthiest and most extensive remaining in the
world [32] with abundant large apex predators, a high proportion of endemic species [33, 34], and few human impacts
compared to the MHI [18, 35]. In contrast, coral reefs in the
MHI are under considerable anthropogenic pressure from
the 1.29 million residents (with over 900,000 of those living
on the island of O‘ahu) and the more than 7 million tourists
that visit the state annually. Coral reefs in many of the
urban areas and popular tourist destinations have sustained
significant impacts, and many show ongoing declines [35–
37]. The primary impacts to coral reefs in the MHI are local
and anthropogenic, including coastal development, landbased sources of pollution, overfishing, recreational overuse,
and alien species. In contrast, the primary stressors in the
NWHI are global in nature, including climate change, ocean
acidification, and marine debris [18, 35–37].
On June 15, 2006, the President of the United States
signed a proclamation creating the Papahānaumokuākea
Marine National Monument (PMNM), encompassing the
entire NWHI, at the time the world’s largest marine
protected area (MPA). The monument designation affords
the NWHI the greatest possible marine environmental
protection under United States law. The PMNM spans nearly
140,000 square miles and is home to more than 7,000
currently described species including fishes, invertebrates,
algae, marine mammals, and birds although many biologists
believe that this is a gross underestimate of the true
biodiversity in the region [38]. While the full extent of
PMNM biodiversity is unknown, about 25% of the known
species are found nowhere else on Earth [39–42]. In 2010, the
PMNM was inscribed to the UNESCO World Heritage List,
the first U.S. site to be designated in over 15 years. The remote
PMNM and surrounding waters became the first primarily
marine site to be named in the United States, and the first
primarily marine location in the world to be designated as a
mixed site for both its outstanding natural and cultural value.
Our research group has embarked on a genetic survey
of approximately 60 species of reef-associated fishes, gastropods, crustaceans, echinoderms, cnidarians, and marine
mammals, designed to address the issue of population
connectivity across the Northwestern and Main Hawaiian
Islands and linkages of the Hawaiian Archipelago to other
locations throughout the Central Pacific. This effort seeks to
inform ecosystem-based management of the PMNM and to
evaluate the potential for spillover from the protected area of
Journal of Marine Biology
3
Pap
a
hān
a
um
oku
āke
aM
arin
N
eN
atio
nal
Pearl and Hermes Reef
Mo
num
(Holoikauaua)
ent
Laysan Island (Kauō)
Gardner
Lisianski
Pinnacles
Necker
(Papa‘āpoho)
Maro Reef
(Pūhāhonu) Islands
(Nalukākala)
(Mokumanamana) Nihoa
(Moku Manu)
NH
French
RC
Kaua‘i
Frigate
Ni
ihau
‘
Shoals
Moloka‘i
Northwestern
(Mokupāpapa)
O‘ahu
Maui
Hawaiian Islands
Lāna‘i
30
Kure Atoll (Kānemiloha‘i)
Midway Atoll (Pihemanu)
SCC
25
20
Hawai‘i
Johnston Atoll
Main
Hawaiian Islands
HLCC
Pacific Ocean
180
175
170
165
160
155
Figure 1: Map of the Hawaiian Archipelago with major currents denoted: the North Hawaiian Ridge Current (NHRC), the Hawaiian
Lee Countercurrent (HLCC), and the Subtropical Countercurrent (SCC). The lines around the two regions of the archipelago highlight
the islands, atolls, and banks protected within the Papahānaumokuākea Marine National Monument in the Northwestern Hawaiian
Islands (NWHI) and the inhabited high islands of Main Hawaiian Islands (MHI) with each of the 15 primary target areas for collection
labeled. For purposes of this analysis, the islands of Lāna‘i, Maui & Moloka‘i are treated as a single site within the Maui Nui complex
of the MHI. Listed from northwest to southeast, these are: Kure Atoll (Kānemiloha‘i), Midway Atoll (Pihemanu), Pearl and Hermes Reef
(Holoikauaua), Lisianski (Papa‘āpoho), Laysan Island (Kauō), Maro Reef (Nalukākala), Gardner Pinnacles (Pūhāhonu), French Frigate
Shoals (Mokupāpapa), Necker Island (Mokumanamana), Nihoa (Moku Manu), Ni‘ihau, Kaua‘i, O‘ahu, Maui Nui, and Hawai‘i.
the NWHI to the heavily populated and exploited MHI. Here
we take a molecular genetic approach to infer patterns and
magnitude of connectivity in a suite of taxonomically diverse
reef-associated species and present preliminary results from
27 species, a subset of the 60 or so target species being
collected to understand connectivity across the Hawaiian
Archipelago and aid in defining the spatial scale over which
EBM should be considered. Although EBM is explicitly
place-based, and superficially the definition of an ecosystem
seems straightforward, the resolution of geographic boundaries is confounded by obscure biological and oceanographic
processes in most marine locations that complicates direct
application to management (reviewed by [43]). In managing
reefs in the Hawaiian Archipelago, what exactly constitutes a
coral reef ecosystem? Is it a reef complex, an island or atoll,
an arbitrary geographic distance, a series of adjacent islands
and atolls, or the entire Archipelago that is the appropriate
geographic scale for management? This work seeks to resolve
and quantify the direction and magnitude of exchange
among reef habitats across a broad taxonomic spectrum, and
to use this information to define objective boundaries, as a
necessary prerequisite for the implementation of EBM.
2. Methods
2.1. Sample Collection, DNA Extraction, and Amplification.
Tissue samples for DNA analyses were collected from
approximately 60 species at as many of the 16 primary
islands and atolls as possible in the Hawaiian Archipelago,
including the remote and tightly regulated NWHI (Figure 1).
It is important to note that sampling remote areas of the
Pacific is difficult and expensive and requires extensive
permitting and voyage planning compared to collections
on the mainland; permitted collections are limited to a
maximum of 50 individuals per species at each site, and
there are only 1 or 2 days per location, during which the
researchers are at the mercy of the weather as to whether
or not they can even launch dive boats. Thus, we do not
have complete coverage for all species, but in addition to
the two species available in the published literature (e.g.,
[44]), we have currently analyzed 25 additional species (total
27, Tables 1 and 2) from many of the islands and atolls
across the Hawaiian Archipelago (Figure 1). Details for the
sampling protocols, tissue preservation, DNA extraction, and
amplification can be found in Iacchei and Toonen [45] and
Skillings and Toonen [46]. Briefly, tissue biopsy samples were
taken in the field and stored in either 20% dimethyl sulfoxide
salt-saturated buffer [47] or >70% ethanol. DNA was
extracted using either a commercially available extraction kit
(e.g., Qiagen DNeasy), the chloroform extraction protocol
described in Concepcion et al. [48] or a modified saltingout protocol [49]. Following extraction, DNA was stored at
−20◦ C. Most studies were conducted with direct sequencing
of a mtDNA fragment using the polymerase chain reaction
4
(PCR) as outlined in references from Table 1. In general, a
segment of approximately 600–800 base pairs of the mtDNA
cytochrome b (Cytb) was amplified from most of the fishes,
and cytochrome oxidase subunit I (COI) was amplified from
the majority of invertebrate species, but some used other
mitochondrial or nuclear sequence regions or microsatellite
markers (see Table 1 for details). PCR recipes and cycling
conditions for individual species are provided in the publications cited in Table 1 and upon request from the authors.
PCR products to be sequenced were treated with 1.5 units
of Exonuclease I and 1.0 units of Fast Alkaline Phosphatase
(ExoFAP, Fermentas) per 15 µL PCR products at 37◦ C for 60
minutes, followed by deactivation at 80◦ C for 10 minutes.
DNA sequencing was performed with fluorescently–labeled
dideoxy terminators on an ABI 3130XL Genetic Analyzer
(Applied Biosystems) at the Hawai‘i Institute of Marine
Biology EPSCoR Sequencing Facility. All specimens were
initially sequenced in one direction and unique genotypes
were confirmed by sequencing in the opposite direction.
For analysis of microsatellite loci, amplification products
were visualized on an ABI 3130XL Genetic Analyzer using
GS500LZ size standards, and analyzed using Genemapper
4.0 (Applied Biosystems).
2.2. Genetic Analyses. For each species, details of the analyses
are provided in the studies cited in Table 1, or upon request
from the authors. In brief, overall genetic variation was
partitioned among sites as pairwise ΦST using the best fit
model of sequence evolution, as determined by Modeltest
3.7 [62], that could be implemented by Arlequin 3.11
[63]. For most of the studies, FST was standardized for
within population levels of heterozygosity [64, 65], and
calculations of Dest [66] were done manually using formula
macros in Microsoft Excel ([67] in review). The number
and location of shared genetic breaks among species across
the Archipelago are unchanged whether corrected or uncorrected F- statistics or Dest was used because the relative differences between these values are all highly correlated with our
data set (data not shown). Because any set level of divergence
selected is ultimately arbitrary, we use a significant pairwise
FST among populations sampled on either side of the channel
of interest as our metric of divergence. Significance of
pairwise values was determined by permutation testing in
Arlequin, with False Discovery Rate (FDR) correction for
multiple tests [68] unless otherwise specified in the original
publication (Table 1). Significant pairwise differences among
adjacent islands, were overlaid visually on a map of the
Hawaiian Archipelago (Figure 1) species-by-species. The
number of significant pairwise differences among locations
was summed across all 27 species and those that exceed
random expectations (see below) are depicted in Figure 2.
2.3. Statistical Testing of Shared Genetic Barriers. Because
not all species are collected in all locations, we looked only
at the channels between adjacent islands where samples of
that species were available on both sides so that a test for
pairwise population differentiation was possible at that site.
We initially excluded any sites for which there were fewer
than 20 individuals from each location on adjacent sides of
Journal of Marine Biology
the channel being tested, but found that the presence and
location of barriers was unaffected in these analyses with
any sample size greater than 5 individuals per site (data not
shown). Thus, in the interest of including as much data as
possible in this comparison, we include all sites for which
the sample size was 5 or more (Table 2). We observed a
total of 73 significant pairwise differences among the 178
possible pairwise tests for these species (Tables 1 and 2).
The distribution of these pairwise differences was tested
using a χ 2 test with 13 degrees of freedom (14 between
island channels); we calculate the expected number of the
73 pairwise differences that would occur, weighted by sample
size between each island, at random within each channel. The
validity of a shared genetic break at any given location was
also tested using a χ 2 to determine if the number of observed
significant pairwise differences across the species sampled at
that location differed significantly from the null expectation
that all detected breaks were distributed equally among the
14 interisland channels.
3. Results
Although each species differs in the particular pattern
of population structure and the inferred magnitude of
larval exchange among sites, some consistent genetic breaks
are apparent among these divergent species (Figure 2). In
particular, the data indicate four strong barriers to gene flow
in the channels between: (1) the Big Island of Hawai‘i and
Maui, (2) the islands of O‘ahu and Kaua‘i, (3) the MHI
and NWHI, and (4) the far NW end and the rest of the
NWHI chain around Pearl and Hermes Reef (Figure 2). The
presence or absence and the strength of a given barrier vary
among species (see references in Table 1). Likewise, there are
some significant barriers that appear for only one or a few
species, but do not appear in the majority of study organisms
(e.g., Laysan Island for the sea cucumber, Holothuria atra, see
Skillings et al. [56], or Gardner Pinnacles for the endemic
grouper Epinephelus (=Hyporthodus) quernus, see Rivera et
al. [51]).
Despite the vast differences in natural history among
taxa, more than 50% of the species surveyed to date share
the same four concordant barriers to gene flow across the
Hawaiian Archipelago (Figure 2). Notably 8 of 19 species also
show a break between O‘ahu and Maui Nui, but this partition
is not significantly different from random expectations
(χ 2 = 0.17, df = 1, p > 0.05). Essentially, roughly 50%
of the sampled species must share a genetic discontinuity
in order to deviate from the random expectation of 5.2
significant differences in each channel (χ 2 = 4.4, df = 1,
p < 0.05). Other than the four significant breaks depicted
in Figure 2, and the nonsignificant split between O‘ahu
and Maui Nui, no other inter-island channel constitutes a
barrier for more than 4 of the sampled species. Thus, with
the caveat that additional sampling may yet demonstrate
a fifth significant barrier between O‘ahu and Maui Nui,
there are currently four significant shared barriers to gene
flow that divide the Hawaiian Archipelago into a minimum
of five distinct ecoregions with limited exchange. In stark
contrast to those locations, other inter-island channels have
Journal of Marine Biology
5
Pap
a
Kure Atoll (Kānemiloha‘i)
Midway Atoll
(Pihemanu)
10/18
hān
a
umo
Pearl and Hermes Reef
(Holoikauaua)
kuā
kea
Mar
in
Laysan Island
(Kauō)
Maro Reef
Lisianski
(Nalukākala)
(Papa‘āpoho)
Northwestern
Hawaiian Islands
e Na
tion
al M
onu
men
t
Gardner
Pinnacles
(Pūhāhonu)
French Frigate Shoals
(Mokupāpapa)
Nihoa
Necker
Island (Moku Manu)
(Mokumanamana)
14/20
25
Kaua‘i
12/21
O‘ahu
Ni‘ihau
Lāna‘i
Moloka‘i
Maui
20
16/24
Hawai‘i
Figure 2: Map of the Hawaiian Archipelago with significant consensus genetic breaks among the 27 taxa listed in Table 1 overlaid as blue bars
between islands. In each bar, the number of species that show evidence for restricted gene flow across the barrier is listed in the numerator,
and the total number of species for which we have data across that geographic area is listed in the denominator. The total number of sites
included for each species is variable because not all species have been collected or analyzed at each site. The dotted line between Maui Nui
and O‘ahu highlights the location of the barrier that is shared by 8 of the surveyed species but is not significantly different than random
expectations. The images include some of the species included in these analyses (left to right): Panulirus penicillatus, Panulirus marginatus,
Holothuria atra, Dendropoma rhyssoconcha, Monachus schauinslandi, Porites lobata, Acanthaster planci, Calcinus hazletti, Lutjanus kasmira,
and Cellana sandwicensis (photo credits to Derek Smith, Joe O’Malley, and the authors).
significantly fewer barriers than expected by chance (e.g., the
region between French Frigate Shoals and Pearl & Hermes
Atoll in the NWHI, χ 2 = 3.85, df = 1, p = 0.05). This
overall pattern of high connectivity among some locations
and shared genetic barriers in others across the archipelago
is significantly nonrandom (χ 2 = 56.18, df = 13, p < 0.0001).
Distance alone is a poor predictor of the locations of
these barriers to dispersal. The distance between areas that
are isolated can be quite small (such as the ‘Alenuihāhā
Channel between Hawai‘i and Maui, ∼45 km) whereas much
larger distances between atolls in the NWHI generally show
no consistent barriers to dispersal (for example Gardner
Pinnacles is ∼180 km northwest of French Frigate Shoals).
Likewise, more of the significant barriers to dispersal are
found in the geographically smaller (600 km) MHI with the
significant absence of barriers occurring in the geographically larger (2000 km) NWHI. Because adjacent sites can
be highly differentiated whereas more distant sites are not,
relatively few species (7/27) show a significant signal of
isolation-by-distance across the Hawaiian archipelago (see
Table 2 for highlighted exceptions).
4. Discussion
These data are striking in that more than half of the
species surveyed show significant concordant barriers to
gene flow concentrated in the four highlighted regions of
the Archipelago (Figure 2). Given the broad differences in
taxonomy, life history, and ecology of the species surveyed,
including limpets, sea cucumbers, vermetid tube snails, reef
fishes, monk seals, and spinner dolphins (Table 1), there is no
a priori reason to expect that patterns of connectivity would
be shared among the majority of the species. However, the
four shared barriers to dispersal highlighted here indicate
that these species are responding to common factors that
limit dispersal and delineate independent units in terms
of connectivity over management-relevant time scales. We
hypothesize that the dominant factors are likely abiotic as
opposed to biotic, given the diversity of species with radically
divergent life histories that share the pattern of isolation.
4.1. Discordance between Genetic and Oceanographic Predictions. The most obvious candidates for such physical
6
Journal of Marine Biology
Table 1: Species of marine organisms, total sample size, total number of sites, genetic marker(s) used, and study citation for each of the
organisms surveyed for population genetic structure across the Hawaiian Archipelago to date. Not all samples were included in subsequent
analysis, therefore, the actual sample sizes by site for each species in this analysis are provided in Table 2. Abbreviations for genetic markers
used are: SSR = microsatellites; NIS = nuclear intron sequence data; Cytb = cytochrome b; COI = cytochrome oxidase subunit I; COII =
cytochrome oxidase subunit II; CR = control region.
Species name
Sample size
Number of sites
Marker
Reference
301
219
10
7
SSR, CR
CR
Rivera et al. (see [50, 51])
Ramon et al. [44].
(3) Dascylus albisella
(4) Ctenochaetus strigosus
102
499
7
15
CR
Cytb
Ramon et al. [44].
Eble et al. [52].
(5) Zebrasoma flavescens
(6) Acanthurus nigrofuscus
(7) Lutjanus kasmira
528
305
385
15
11
9
Cytb
Cytb
Cytb, NIS
Eble et al. [52, 53].
Eble et al. [52].
Gaither et al. [54].
112
6
CR
Daly-Engel et al. [55].
(9) Cellana exarata
(10) Cellana sandwicensis
(11) Cellana talcosa
150
109
105
7
6
5
COI
COI
COI
Bird et al. [28].
Bird et al. [28].
Bird et al. [28].
(12) Dendropoma gregaria
(13) Dendropoma platypus
176
143
15
15
COI
COI
Faucci et al. (unpubl. data)
Faucci et al. (unpubl. data)
94
73
11
13
COI
COI
Faucci et al. (unpubl. data)
Faucci et al. (unpubl. data)
(16) Calcinus haigae
(17) Calcinus hazletti
(18) Calcinus seurati
146
179
161
5
12
4
COI
COI
COI
Baums et al. (unpubl. data)
Baums et al. (unpubl. data)
Baums et al. (unpubl. data)
(19) Panulirus marginatus
(20) Panulirus penicillatus
449
227
14
9
COII
COI
Iacchei et al. (unpubl. data)
Iacchei et al. (unpubl. data)
Echinoderms:
(21) Holothuria atra
(22) Holothuria whitmaei
399
427
15
10
COI
COI
Skillings et al. [56]
Skillings et al. (unpubl. data)
(23) Acanthaster planci
Scleractinian:
338
11
CR
Timmers et al. [57]
(24) Montipora capitata
(25) Porites lobata
Marine Mammals:
551
443
13
11
SSR
SSR
Concepcion et al. (unpubl. data)
Polato et al. [58]
2409
386
8
8
SSR
SSR, CR
Schultz et al. [59, 60]
Andrews et al. [61].
Fishes:
(1) Epinephelus (=Hyporthodus) quernus
(2) Stegastes fasciolatus
(8) Squalus mitsukurii
Gastropods:
(14) Dendropoma rhyssoconcha
(15) Serpulorbis variabilis
Crustaceans:
(26) Monachus schauinslandi
(27) Stenella longirostris
barriers to gene flow are geographic distance and oceanic
currents. For most species there are enigmatic restrictions
to dispersal that appear to have little to do with geographic
distance. Many of the studies listed in Table 1 provide
cases of divergence among proximate sites in the face of
lower divergence among more distant sites elsewhere in the
archipelago. Regardless, the overall dataset indicates that
much of the NWHI is well connected despite greater average
distances among the sites whereas the MHI show greater
structure on average despite geographic proximity. Although
some species do show isolation-by-distance, there appears
to be a substantial taxonomic effect because three of the
seven cases are sister species of Cellana limpets, and two of
the remaining four are scleractinian corals (Table 2). While
we cannot rule out the role of distance in limiting dispersal
within the Hawaiian Archipelago, the impact of distance on
the probability of dispersal does not appear to be a simple
linear effect for the majority of species surveyed to date. This
discord is not particularly surprising given the complexity
of oceanographic current patterns. Recent analyses of larval
dispersal in the Southern California Bight showed that
probability of exchange among sites was uncorrelated with
Species Name
Hyporthodus (=Epinephelus)
quernus
Hawai‘i
(=Big Island)
Maui
Nui
O‘ahu
36
30
9
30
49
Kaua‘i Ni‘ihau
Nihoa
44
Necker
French
Gardner Maro
Island Frigate Shoals Pinnacles Reef
30
30
Stegastes fasciolatus
27
42
Dascylus albisella
10
8
Ctenochaetus strigosus
102
100
40
28
29
37
Zebrasoma flavescens
146
122
35
42
20
40
Acanthurus nigrofuscus
33
65
39
26
39
33
Lutjanus kasmira
101
39
50
36
∗
Cellana exarata
41
18
21
∗
Cellana sandwicensis
42
21
20
∗
Cellana talcosa
∗
43
24
Dendropoma gregaria
53
25
39
20
Dendropoma platypus
16
20
40
21
Dendropoma rhyssoconcha
29
24
25
Serpulorbis variabilis
13
25
15
Calcinus haigae
51
21
Calcinus hazletti
21
31
Calcinus seurati
9
30
78
19
48
Panulirus marginatus
11
5
Holothuria atra
30
Holothuria whitmaei
26
Acanthaster planci
106
81
25
43
20
42
7
14
31
33
32
38
26
29
33
35
28
6
9
40
9
29
28
40
23
36
18
8
5
5
5
5
5
5
5
5
21
47
34
56
47
24
30
5
5
33
24
30
47
51
25
23
16
18
7
91
6
5
5
5
5
5
5
33
22
11
56
33
53
53
5
36
47
18
30
46
28
12
37
35
23
26
22
57
24
59
27
40
21
29
23
47
34
48
50
33
44
43
51
44
21
22
260
134
222
47
119
51
8
7
54
40
5
13
Porites lobata
59
15
28
44
Montipora capitata
79
24
25
49
7
∗
Stenella longirostris
27
8
∗
∗
29
6
12
46
Monachus schauinslandi
46
38
Panulirus penicillatus
Squalus mitsukurii
Pearl &
Laysan
Midway Kure
Lisianski Hermes
Island
Atoll
Atoll
Reef
Journal of Marine Biology
Table 2: Sample size per location (refer to Figure 1 for Hawaiian names of sites) for each of the 27 species included in the combined multispecies analysis of population genetic structure
across the Hawaiian Archipelago to date. Locations with fewer than 5 individuals were excluded from all analyses and are not included here (see text). Species with ∗ show a significant
isolation-by-distance signature across the archipelago.
32
45
766
33
656
310
7
8
geographic distance, but strongly correlated with a derived
“oceanographic distance” including realistic annual water
movement patterns across many years [26, 69].
In Hawai‘i, however, the patterns of genetic differentiation do not generally match predictions for larval dispersal
based on water movement information from either a twodimensional Eulerian advection-diffusion model [70, 71] or
a Lagrangian particle-tracking model [72, 73]. One of the
primary predictions of both simulation models is that the
average distance of larval dispersal is short, roughly on the
order of 50–150 km, and that the Main Hawaiian Islands
(MHI) ought to be consistently connected and well mixed
whereas the NWHI ought to show a number of isolated
populations [70, 71]. For a PLD of less than about 45
days, the larval dispersal simulations predict a majority of
local recruitment of larvae to their natal island/atoll or the
adjacent ones (see [51]). In stark contrast to the primary
prediction of the available larval dispersal simulation models
(a well-mixed MHI and comparatively patchy NWHI), the
consensus finding across 27 taxa to date is the opposite:
the MHI show far more population structure than any
equivalent geographical scale within the NWHI, and the
primary dispersal barrier predicted by Eulerian simulation
models is located in the region of the NWHI in which there
is a significant paucity of population structure among surveyed species (Figure 2). Possible reasons for oceanographic
simulations failing to predict the structure observed in the
empirical genetic data are many (reviewed by [22, 74, 75]),
but given the number and diversity of taxa across which the
pattern holds, the genetic inference of isolation between the
four regions highlighted in Figure 2 is robust.
4.2. Multispecies Approaches to Measuring Connectivity. All
connectivity studies face practical limitations in terms of
the number of specimens, sample sites, and taxonomic
scope of study, which is why the vast majority of studies
to date have focused on one or a few exemplar species
to draw generalizations. Exemplar species are an attractive
compromise to guide conservation and management efforts
given the imposing logistic and resources challenges of
conducting connectivity studies on all species of management relevance. However, the utility of exemplar species
depends on whether they represent the community as a
whole. Unfortunately, in most cases where this assumption
has been tested explicitly, the patterns of dispersal and
genetic structure differ significantly and unpredictably even
among closely related species with similar life histories (e.g.,
[28, 52, 54, 76–78]). Despite the perceived potential for
long-distance dispersal and broad mixing in the ocean,
many taxa show unique archipelagic diversity (e.g., [79])
and even finer scale population structure than expected
(e.g., [31, 80]). Regardless of whether we compare within
taxonomic groups or between them, some of the species we
have surveyed (e.g., Myripristis berndti, Centropyge loricula,
Lutjanus kasmira, Acanthaster planci, and Calcinus spp.)
appear to live up to their expected potential for dispersal and
show no significant population structure across the Central
Pacific (see [54, 57, 81–84]). In contrast, other species that
appear capable of extensive dispersal (Epinephelus quernus,
Journal of Marine Biology
Ctenochaetus strigosus, Stenella longirostris, and Zebrasoma
flavescens) show significant population differentiation within
the Hawaiian Archipelago [50, 52, 53, 61, 85] and islandby-island or in some cases even site-by-site differences in
population structure (e.g., [28, 44] Faucci et al. unpubl.
data). Despite the potential for wide dispersal, Christie
et al. [85] use individual parentage analyses to document
self-recruitment in the Yellow Tang (Zebrasoma flavescens)
and illustrate that at least some larvae recruit to the same
region of the Kona coastline from which they were originally
spawned.
Such variability among species greatly complicates efforts
to generalize management implications from single-species
studies and severely restricts the utility of exemplar species
for decision making in conservation and management. While
there is a consistent push to move beyond single-species
management plans and implement EBM at a national and
international level (e.g., [86, 87]), the exact geographic
scale at which EBM should be applied is seldom obvious,
and the accumulating data indicate that studies of marine
connectivity cannot be generalized easily for this purpose. It
is clearly impractical to study every species individually, and
even if we could, how would the connectivity matrix from
all those species be combined into a single coherent data set
to guide EBM? For example, the multispecies conservation
plan for U.S. federal lands states: “conservation objectives
will not be achieved with a single reserve or a single population. Rather, local populations widely scattered across the
landscape, but connected by movement, will be necessary.
Few of these populations will be large enough to avoid
problems faced by small populations, such as extirpation due
to stochastic factors and inbreeding depression. Connectivity
maintenance is therefore one of the most critical aspects of
multispecies conservation. Connectivity, however, is notoriously difficult to directly measure” [88, p.64]. A variety of
landscape genetic approaches to identifying cryptic barriers
to connectivity have been proposed (e.g., [69, 89, 90]), but
with few exceptions (e.g., [26]) such work has also been
conducted on single-species. The push to implement EBM
highlights an explicit need for multi-species comparisons of
connectivity across all trophic levels to define the boundaries
for management and resolve shared avenues of exchange
among ecosystems.
Due to resource constraints as well as the logistical
difficulties in completing such multispecies comparisons,
only a few such studies exist. The few explicit multispecies
connectivity studies that have been conducted to date
(e.g., [29–31]) all face the limitation that there is no
generally accepted method by which to analyze the aggregate
connectivity data. Thus, like the study presented here, the
primary method of analyzing shared genetic breaks is by
counting the number or proportion of species that share
a genetic discontinuity among locations. For example, a
survey of 50 coastal marine species along the west coast
of North America concluded that a greater proportion of
species show significant genetic differentiation between the
central (40% of species between Monterey, CA and Cape
Blanco, OR) and northern sites (33% of species between
Cape Blanco and Sitka, AK) than between the southern sites
Journal of Marine Biology
9
(15% of species between Monterey and Santa Barbara,
CA; [30]). Likewise, a survey of 9 species of fish and
10 species of invertebrates in Indonesia defines partitions
where more than two or three species share a phylogenetic
break [31]. We have employed a similar approach with
counting up shared genetic discontinuities in the data set,
but elected to test whether these shared breaks deviate
significantly from random. In our study, a surprisingly
high number of species need to share a break to deviate
significantly from random: even where 8 of the 19 species
show differentiation between O‘ahu and Maui Nui, that
result was non-significant. The overall pattern of genetic
divergence among sites within the Hawaiian Archipelago is
highly non-random, with the central region of the NWHI
having significantly fewer genetic breaks, and four individual
channels emerge as having significantly more species sharing
a break than expected at random (Figure 2).
There are substantial caveats to comparing FST and ΦST
values directly among studies and marker classes (reviewed
by [91–93]). Further, several recent publications have
pointed out that the maximum attainable FST is inversely
proportional to the mean within-population heterozygosity
[64, 65], and therefore does not accurately measure population differentiation [66]. Thus, for highly polymorphic
genetic markers, such as microsatellite loci, the maximum
attainable FST is reduced far below one [64]. Contrary to the
intuition that more polymorphic loci will reveal finer population structure, FST values are actually constrained to be
lower as allelic diversity gets higher [67]. This limitation has
led some to advocate the use of “true genetic differentiation”
(Dest ) as the primary or only means of comparison (e.g.,
[66]). While an attractive alternative in theory, there is as
yet no means of significance testing for Dest , and researchers
have to pick an arbitrary value at which to determine a
genetic break before comparisons can be made; however,
in the absence of statistics any cutoff value selected can
be arbitrary and problematic [94]. For example, Kelly and
Palumbi [30] chose ΦST = 0.10 as the delineation between
strong (ΦST = 0.11 − 0.60) and moderate (ΦST = 0.02 − 0.10)
population structure. While there is nothing wrong with
this delineation, one could have also chosen ΦST = 0.05 or
ΦST = 0.15 with equal justification, and there is no consistent
and defensible level of population structure that determines
the cut-off at which management decisions ought to be
made [60]. Most published estimates of population structure
remain uncorrected for marker variation and heterozygosity;
thus, a value of 0.10 in one species may be on a completely
different scale than in the next species if they have different
levels of mean within population heterozygosity [64, 66, 67].
For this reason we use statistical significance as our cutoff and draw no inferences regarding the magnitude of the
barriers beyond the number of species that share them. A
method by which the boundaries of an ecosystem can be
defined with multi-species data sets, and linkages between
ecosystems can be quantified, is a logical prerequisite for
successful implementation of EBM in the sea.
is that the Hawaiian Archipelago is not a single, well-mixed
community, but rather there are at least four significant
multi-species barriers to dispersal along the length of the
island chain. Additional sampling or more sophisticated
statistical analyses may reveal additional barriers, but we
report four strong concordant breaks here. As outlined
above, some species cross these barriers, others do not, and
the patterns of connectivity can, and do, vary dramatically
among individual species (see refs. in Table 1). Regardless, a
strong and consistent pattern emerges from the multispecies
comparison in which the majority of 27 taxonomically
diverse species share four significant concordant genetic
breaks across the archipelago. It is noteworthy that the
variability among individual species studies of connectivity
published to date certainly does not lend itself to an
expectation of such strong concordant patterns. Despite the
suite of taxonomic, ecological, and biological differences
that might lead us to expect highly divergent patterns
among these diverse taxa, some unknown barriers appear to
consistently limit dispersal in a majority of the 27 species
surveyed to date. These results illustrate that while a single
species is rarely representative of the average connectivity,
concordant patterns can emerge when many species are
examined simultaneously. Insofar as this is a general result,
it would mandate that a broad suite of reef species across
multiple taxonomic groups and ecological niches ought
to be surveyed to resolve general trends and to provide
connectivity information pertinent to management of any
large marine management area such as the PMNM.
The two primary caveats to this finding are that: (1)
the basis for these shared genetic restrictions is poorly
understood and discovering the location of these barriers is
only the first step, and (2) it is an overly simplistic statistical
model to show significant deviations from random pairwise
differences across species as a measure of the strength of
dispersal barriers. Nonetheless, such summing is the primary
means of comparison available at this time, and this is the
only multispecies study that employs even this simplistic
statistical approach. In terms of the first caveat, it will be
valuable to determine the ecological and oceanographic
factors driving regional, island, or site specific genetic
structure; this will likely be important for ecosystem-based
management of both the Main and Northwestern Hawaiian
Islands, and may provide general characteristics to predict
ecosystem-level partitions among coral reefs elsewhere.
Discovering the existence and location of these barriers leads
to questions about the underlying cause for so many species
sharing these concordant patterns, and what maintains those
barriers to dispersal among taxa as diverse as limpets and
dolphins. In terms of the second caveat, as outlined above,
we need to develop a quantitative method for multispecies
studies of connectivity among many locations. Ultimately, it
would be ideal to bring the multispecies data sets together in
a single analysis to determine both the relative strength and
statistical confidence in each of the detected barriers, but no
such method exists currently.
4.3. Connectivity in the Hawaiian Archipelago: Not 1 But at
Least 5 Distinct Regions. The primary finding of this work
4.4. Conclusions and Management Implications. This multispecies approach to understanding population connectivity
10
across the Hawaiian Archipelago reveals four previously
unrecognized barriers to dispersal that delineate five relatively isolated regions of the Hawaiian Archipelago. In
contrast to predictions based on either geographic distance
between islands (isolation by distance) or on larval dispersal model predictions using pelagic larval duration, there
are more barriers to dispersal within the Main Hawaiian
Islands (MHI, ∼600 km) than the Northwestern Hawaiian
Islands (NWHI, ∼2000 km). The underlying mechanism
of this isolation remains unknown, but the concordance
across 52% to 70% (depending on the barrier) of the 27
taxonomically and ecologically divergent species sampled
here demonstrates that the pattern is robust and likely
to derive from physical rather than biologically intrinsic
factors.
These data provide information pertinent to current
management issues facing the broader Pacific and efforts to
implement ecosystem-based management (EBM) in Hawai‘i.
In particular, these data directly address the controversy
about whether the NWHI is a series of isolated (and
therefore relatively fragile) island ecosystems, and whether
the Papaphānaumokuākea Marine National Monument provides spillover benefits to the highly exploited waters of the
MHI [35]. We find that the NWHI are far more connected
on average (and therefore comparatively robust) than the
MHI, but that connectivity between the MHI and NWHI is
limited. The results highlight that the Main Hawaiian Islands
are isolated in terms of resource management and will not
receive substantial subsidy from the Papahānaumokuākea
Marine National Monument; the MHI must stand alone
in management of marine resources. Furthermore, even
the comparatively small MHI are not a single panmictic
unit, and future management plans should incorporate
knowledge of the substantial isolation among multiple
regions within the MHI. For example, Bird et al. [28]
argue that each island should be considered a separate
management unit for the culturally important Hawaiian
limpets (‘opihi, genus Cellana). Likewise, the impact of
invasive species is felt globally and with 343 alien marine
species documented in Hawai‘i thus far [95], there is
considerable concern regarding the vulnerability of Hawaiian
reefs to invasion and the likely spread of aliens that are
already introduced. Our findings predict barriers through
which invasive species should have difficulty advancing, and
indeed recent studies of several species of invasive fishes and
invertebrates appear to corroborate those predictions (e.g.,
[96, 97].
This study is one of the few multispecies surveys of
marine connectivity to date and confirms that this approach
can illuminate general patterns pertinent to management
that do not emerge from single-species exemplar studies. The
manner in which policy makers delineate the boundaries for
ecosystem-based management remains a subject of considerable debate, but we argue this multispecies approach offers
a possible solution. Here, we resolve concordant patterns
of connectivity in an objective and quantitative manner to
define a minimum of five marine spatial management units
in the Hawaiian Archipelago.
Journal of Marine Biology
Acknowledgments
The authors thank the Papahānaumokuākea Marine
National Monument, US Fish and Wildlife Services, and
Hawai‘i Division of Aquatic Resources (DAR) for coordinating research activities and permitting, and the U.S.
National Oceanic and Atmospheric Administration (NOAA)
research vessel Hi‘ialakai and her crew for years of
outstanding service and support. Special thanks go to
J. Leong, S. Karl, S. Godwin, R. Kosaki and the members
of the ToBo Lab. We could not have completed this work
without the assistance of the UH Dive Safety Program,
U.S. National Marine Fisheries Service, the Pacific Island
Fisheries Science Center, National Marine Sanctuaries
Program, and Coral Reef Ecosystem Division, especially:
A. Tom, A.Wilhelm, H. Johnson, M. Pai, D. Carter, C.
Kane, C. Meyer, D. Smith, C. Kelley, D. Minton, P. Reath,
J. Zardus, D. Croswell, B. Holland, M. Stat, X. Pochon,
M. Rivera, E. Brown, M. Ramsay, J. Maragos, L. Eldredge,
H. Bollick, S. Coles, W. Walsh, B. Carmen, I. Williams,
A. Friedlander, J. Randall, S. Cotton, A. Montgomery, S.
Pooley, M. Seki, J. Zamzow, E. DeMartini, J. Polovina,
R. Humphreys, D. Kobayashi, F. Parrish, R. Moffitt, G.
DiNardo, J. O’Malley, R. Brainard, J. Kenyon, K. Schultz,
M. Duarte, H. Kawelo, E. Fielding, L. Sorenson, L. Basch,
A. Alexander, K. Selkoe, M. Craig, L. Rocha, Z. Forsman,
Z. Szabo, C. Musberger, D. White, K. Tenggardjaja, Y.
Papastamatiou, K. Gorospe, B. Wainwright, S. Daley, M.
Crepeau, A. Eggers, and the HIMB EPSCoR Core Genetics
Facility a sincere thanks to you all. We also appreciate
the feedback of the anonymous reviewers and B. Halpern
whose helpful comments greatly improved and clarified
the text. This work was funded in part by grants from
the National Science Foundation (DEB no. 99-75287,
OCE no. 04-54873, OCE no. 05-50294, OCE no. 0623678, OCE no. 09-29031), National Marine Sanctuaries
NWHICRER-HIMB partnership (MOA-2005-008/6882),
University of Hawai‘i Sea Grant College Program, National
Park Service PICRP, National Marine Fisheries Service,
Western Pacific Regional Fishery Management Council,
NOAA’s Coral Reef Conservation Program, the Hawai‘i
Coral Reef Initiative, NSF EPSCoR, EPA STAR Fellowship,
the Watson T. Yoshimoto Foundation, the Jessie D. Kay
Memorial Fellowship, UH Graduate Student Organization
Grants Program, PADI Foundation Research Grant, Charles
and Margaret Edmondson Research Fund, American
Malacological Society Student Award, Conchologists of
America Research Grant, Sigma Xi Grants-in-Aid, Society
for Integrative and Comparative Biology Student Award,
Western Society of Malacologists Student Award, and the
Ecology, Evolution, and Conservation Biology (EECB) NSF
GK-12 fellowships. This is HIMB contribution no. 1422 and
SOEST no. 8051.
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