Abstract
Myriad environmental and biological traits have been investigated for their roles in influencing the rate of molecular evolution across various taxonomic groups. However, most studies have focused on a single trait, while controlling for additional factors in an informal way, generally by excluding taxa. This study utilized a dataset of cytochrome c oxidase subunit I (COI) barcode sequences from over 7000 ray-finned fish species to test the effects of 27 traits on molecular evolutionary rates. Environmental traits such as temperature were considered, as were traits associated with effective population size including body size and age at maturity. It was hypothesized that these traits would demonstrate significant correlations with substitution rate in a multivariable analysis due to their associations with mutation and fixation rates, respectively. A bioinformatics pipeline was developed to assemble and analyze sequence data retrieved from the Barcode of Life Data System (BOLD) and trait data obtained from FishBase. For use in phylogenetic regression analyses, a maximum likelihood tree was constructed from the COI sequence data using a multi-gene backbone constraint tree covering 71% of the species. A variable selection method that included both single- and multivariable analyses was used to identify traits that contribute to rate heterogeneity estimated from different codon positions. Our analyses revealed that molecular rates associated most significantly with latitude, body size, and habitat type. Overall, this study presents a novel and systematic approach for integrative data assembly and variable selection methodology in a phylogenetic framework.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Code Availability
The source code used for this research is available at https://github.com/jmay29/phylo.
Data Availability
All sequence and trait data used are available as supplementary material for this article.
References
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2
April J, Mayden RL, Hanner RH, Bernatchez L (2011) Genetic calibration of species diversity among North America’s freshwater fishes. Proc Natl Acad Sci USA 108:10602–10607. https://doi.org/10.1073/pnas.1016437108
April J, Hanner RH, Mayden RL, Bernatchez L (2013) Metabolic rate and climatic fluctuations shape continental wide pattern of genetic divergence and biodiversity in fishes. PLoS ONE 8:e70296. https://doi.org/10.1371/journal.pone.0070296
Athey TBT (2013) Assessing errors in DNA barcode sequence records. Master’s thesis, University of Guelph
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Betancur-R R, Wiley EO, Arratia G, Acero A, Bailly N, Miya M, Lecointre G, Ortí G (2017) Phylogenetic classification of bony fishes. BMC Evol Biol 17:162. https://doi.org/10.1186/s12862-017-0958-3
Blanco MA, Sherman PW (2005) Maximum longevities of chemically protected and non-protected fishes, reptiles, and amphibians support evolutionary hypotheses of aging. Mech Ageing Dev 126:794–803. https://doi.org/10.1016/j.mad.2005.02.006
Blueweiss L, Fox H, Kudzma V, Nakashima D, Peters R, Sams S (1978) Relationships between body size and some life history parameters. Oecologia 37:257–272
Boettiger C, Lang DT, Wainwright P (2012) rfishbase: Exploring, manipulating and visualizing FishBase data from R. J Fish Biol 81:2030–2039. https://doi.org/10.1111/j.1095-8649.2012.03464.x
Bromham L (2011) The genome as a life-history character: why rate of molecular evolution varies between mammal species. Philos Trans R Soc Lond B 366:2503–2513. https://doi.org/10.1098/rstb.2011.0014
Bromham L, Hua X, Lanfear R, Cowman PF (2015) Exploring the relationships between mutation rates, life history, genome size, environment, and species richness in flowering plants. Am Nat 185:507–524. https://doi.org/10.1086/680052
Chamberlain S (2017) rgbif: interface to the global ‘biodiversity’ information facility API. R package version 0.9.9
Clarke A, Johnston NM (1999) Scaling of metabolic rate with body mass and temperature in teleost fish. J Anim Ecol 68:893–905. https://doi.org/10.1046/j.1365-2656.1999.00337.x
Cowman PF (2014) Historical factors that have shaped the evolution of tropical reef fishes: a review of phylogenies, biogeography, and remaining questions. Front Genet 5:394. https://doi.org/10.3389/fgene.2014.00394
Davies TJ, Savolainen V, Chase MW, Moat J, Barraclough TG (2004) Environmental energy and evolutionary rates in flowering plants. Proc R Soc Lond Ser B 271:2195–2200. https://doi.org/10.1098/rspb.2004.2849
Dececchi TA, Mabee PM, Blackburn DC (2016) Data sources for trait databases: comparing the phenomic content of monographs and evolutionary matrices. PLoS ONE 11:e0155680. https://doi.org/10.1371/journal.pone.0155680
Dunham JB, Vinyard GL (1997) Relationships between body mass, population density, and the self-thinning rule in stream-living salmonids. Can J Fish Aquat Sci 54:1025–1030. https://doi.org/10.1139/f97-012
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 19:1792–1797. https://doi.org/10.1093/nar/gkh340
Floyd R, Abebe E, Papert A, Blaxter M (2002) Molecular barcodes for soil nematode identification. Mol Ecol 11:839–850. https://doi.org/10.1046/j.1365-294X.2002.01485.x
Freckleton RP, Harvey PH, Pagel M (2002) Phylogenetic analysis and comparative data: a test and review of evidence. Am Nat 160:712–726. https://doi.org/10.1086/343873
Fritz SA, Purvis A (2010) Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic strength in binary traits. Conserv Biol 24:1042–1051. https://doi.org/10.1111/j.1523-1739.2010.01455.x
Froese R, Pauly D (eds) (2019) FishBase. World Wide Web electronic publication. www.fishbase.org
Fujisawa T, Vogler AP, Barraclough TG (2015) Ecology has contrasting effects on genetic variation within species versus rates of molecular evolution across species in water beetles. Proc R Soc B Biol Sci 282:1–9. https://doi.org/10.1098/rspb.2014.2476
GBIF: The Global Biodiversity Information Facility (2019) What is GBIF? https://www.gbif.org/what-is-gbif
Gillman LN, Keeling DJ, Ross HA, Wright SD (2009) Latitude, elevation and the tempo of molecular evolution in mammals. Proc R Soc B 276:3353–3359. https://doi.org/10.1098/rspb.2009.0674
Gillooly JF, Allen AP, West GB, Brown JH (2005) The rate of DNA evolution: effects of body size and temperature on the molecular clock. Proc Natl Acad Sci USA 102:140–145. https://doi.org/10.1073/pnas.0407735101
Goolsby EW, Bruggeman J, Ané C (2017) Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol Evol 8:22–27. https://doi.org/10.1111/2041-210X.12612
Grafen A (1989) The phylogenetic regression. Philos Trans R Soc Lond B 326:119–154. https://doi.org/10.1098/rstb.1989.0106
Häder DP, Kumar HD, Smith RC, Worrest RC (2007) Effects of solar radiation on aquatic ecosystems and interactions with climate change. Photochem Photobiol Sci 6:267–285. https://doi.org/10.1039/C0PP90036B
Hebert PDN, Remigio EA, Colbourne JK, Taylor DJ, Wilson CC (2002) Accelerated molecular evolution in halophilic crustaceans. Evolution 56:09–926. https://doi.org/10.1111/j.0014-3820.2002.tb01404.x
Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proc R Soc B 270:313–321. https://doi.org/10.1098/rspb.2002.2218
Hua X, Cowman P, Warren D, Bromham L (2015) Longevity is linked to mitochondrial mutation rates in rockfish: a test using Poisson regression. Mol Biol Evol 32:2633–2645. https://doi.org/10.1093/molbev/msv137
Jarić I, Gačić Z (2012) Relationship between the longevity and the age at maturity in long-lived fish: Rikhter/Efanov’s and Hoenig’s methods. Fish Res 129–130:61–63. https://doi.org/10.1016/j.fishres.2012.06.010
Jones GP, Planes S, Thorrold SR (2005) Coral reef fish larvae settle close to home. Curr Biol 15:131401318. https://doi.org/10.1016/j.cub.2005.06.061
Kalinka AT (2019) Multiple sequence alignment with MUSCLE. R package version 3.28.0
Lanave C, Preparata G, Saccone C, Serio G (1984) A new method for calculating evolutionary substitution rates. J Mol Evol 20:86–93. https://doi.org/10.1007/BF02101990
Lanfear R, Thomas JA, Welch JJ, Brey T, Bromham L (2007) Metabolic rate does not calibrate the molecular clock. Proc Natl Acad Sci USA 104:15388–15393. https://doi.org/10.1073/pnas.0703359104
Lanfear R, Welch JJ, Bromham L (2010) Watching the clock: Studying variation in rates of molecular evolution between species. Trends Ecol Evol 25:495–503. https://doi.org/10.1016/Zj.tree.2010.06.007
Lewis HM, Law R, McKane AJ (2008) Abundance-body size relationships: the roles of metabolism and population dynamics. J Anim Ecol 77:1056–1062. https://doi.org/10.1111/j.1365-2656.2008.01405.x
Li WH, Tanimura M (1987) The molecular clock runs more slowly in man than in apes and monkeys. Nature 326:93–96. https://doi.org/10.1038/326093a0
Lourenço JM, Glémin S, Chiari Y, Galtier N (2013) The determinants of the molecular substitution process in turtles. J Evol Biol 26:38–50. https://doi.org/10.1111/jeb.12031
Maddison WP, FitzJohn RG (2015) The unresolved challenge to phylogenetic correlation tests for categorical characters. Syst Biol 64:127–136. https://doi.org/10.1093/sysbio/syu070
Martin AP, Palumbi SR (1993) Body size, metabolic rate, generation time, and the molecular clock. Proc Natl Acad Sci USA 90:4087–4091. https://doi.org/10.1073/pnas.90.9.4087
Nabholz B, Glemin S, Galtier N (2008) Strong variations of mitochondrial mutation rate across mammals—the longevity hypothesis. Mol Biol Evol 25:120–130. https://doi.org/10.1093/molbev/msm248
Nabholz B, Lanfear R, Fuchs J (2016) Body mass-corrected molecular rate for bird mitochondrial DNA. Mol Ecol 25:4438–4449. https://doi.org/10.1111/mec.13780
Ohta T (1993) An examination of the generation-time effect on molecular evolution. Proc Natl Acad Sci USA 90:10676–10680. https://doi.org/10.1073/pnas.90.22.10676
Orme CDL, Freckleton R, Thomas G, Petzoldt T, Fritz S, Isaac N, Pearse W (2013) caper: comparative analyses of phylogenetic and evolution in R. R package version 0.5.2
Orton MG, May JA, Ly W, Lee DJ, Adamowicz SJ (2019) Is molecular evolution faster in the tropics? Heredity 122:513–524. https://doi.org/10.1038/s41437-018-0141-7
Pagel M (1999) Inferring the historical patterns of biological evolution. Nature 401:877–884. https://doi.org/10.1038/44766
Paradis E (2013) Molecular dating of phylogenies by likelihood methods: a comparison of models and a new information criterion. Mol Phylogenet Evol 67:436–444. https://doi.org/10.1016/j.ympev.2013.02.008
Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290. https://doi.org/10.1093/bioinformatics/btg412
Qui F, Kitchen A, Burleigh JG, Miyamoto MM (2014) Scombroid fishes provide novel insights into the trait/rate associations of molecular evolution. J Mol Evol 78:338–348. https://doi.org/10.1007/s00239-014-9621-4
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Rabosky DL, Chang J, Title PO, Cowman PF, Sallan L, Friedman M, Kaschner K et al (2018) An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559:392–395. https://doi.org/10.1038/s41586-018-0273-1
Ratnasingham S, Hebert PDN (2007) BOLD: the barcode of life data system (www.barcodinglife.org). Mol Ecol Notes 7:355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x
Ratnasingham S, Hebert PDN (2013) A DNA-based registry for all animal species: the Barcode Index Number (BIN) System. PLoS ONE 8:e66213. https://doi.org/10.1371/journal.pone.0066213
Revell LJ (2012) Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol 3:217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x
Rohde K (1992) Latitudinal gradients in species-diversity—the search for the primary cause. Oikos 65:514–527. https://doi.org/10.2307/3545569
Sanderson MJ (2002) Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach. Mol Biol Evol 19:101–109. https://doi.org/10.1093/oxfordjournals.molbeva.003974
Santos JC (2012) Fast molecular evolution associated with high active metabolic rates in poison frogs. Mol Biol Evol 29:2001–2018. https://doi.org/10.1093/molbev/mss069
Schliep KP (2011) phangorn: phylogenetic analysis in R. Bioinformatics 27:592–593
Smith AB, Lafay B, Christen R (1992) Comparative variation of morphological and molecular evolution through geologic time: 28S ribosomal RNA versus morphology in echinoids. Philos Trans R Soc Lond B 338:365–382. https://doi.org/10.1098/rstb.1992.0155
Speakman JR (2005) Body size, energy metabolism and lifespan. J Exp Biol 208:1717–1730. https://doi.org/10.1242/jeb.01556
Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. https://doi.org/10.1093/bioinformatics/btu033
Strohm JHT, Gwiazdowski RA, Hanner R (2015) Fast fish face fewer mitochondrial mutations: patterns of dN/dS across fish mitogenomes. Gene 572:27–34. https://doi.org/10.1016/j.gene.2015.06.074
Tamura K, Nei M (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10:512–526. https://doi.org/10.1093/oxfordjournals.molbev.a040023
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: molecular evolutionary genetics analysis (MEGA) software version 6.0. Mol Biol Evol 30:2725–2729. https://doi.org/10.1093/molbev/mst197
Thomas JA, Welch JJ, Lanfear R, Bromham L (2010) A generation time effect on the rate of molecular evolution in invertebrates. Mol Biol Evol 27:1173–1180. https://doi.org/10.1093/molbev/msq009
Uyeda JC, Zenil-Ferguson R, Pennell MW (2018) Rethinking phylogenetic comparative methods. Syst Biol 67:1091–1109. https://doi.org/10.1093/sysbio/syy031
Welch JJ, Bininda-Emonds ORP, Bromham L (2008) Correlates of substitution rate variation in mammalian protein-coding sequences. BMC Evol Biol 8:53. https://doi.org/10.1186/1471-2148-8-53
Woolfit M (2009) Effective population size and the rate and pattern of nucleotide substitutions. Biol Lett 5:417–420. https://doi.org/10.1098/rsbl.2009.0155
Wright SD, Gillman LN, Ross HA, Keeling DJ (2010) Energy and the tempo of evolution in amphibians. Global Ecol Biogeogr 19:733–740. https://doi.org/10.1111/j.1466-8238.2010.00549.x
Wright SD, Ross HA, Keeling DK, McBride P, Gillman LN (2011) Thermal energy and the rate of genetic evolution in marine fishes. Evol Ecol 25:525–530. https://doi.org/10.1007/s10682-010-9416-z
Xia X (2018) DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol Biol Evol 35:1550–1552. https://doi.org/10.1093/molbev/msy073
Zagarese HE, Williamson CE (2001) The implications of solar UV radiation exposure for fish and fisheries. Fish Fish 2:250–260. https://doi.org/10.1046/j.1467-2960.2001.00048.x
Ziegler AD, Leffel DJ, Kunala S, Sharma HW, Gailani M, Simon JA, Halperin AJ et al (1993) Mutation hotspots due to sunlight in the p53 gene of nonmelanoma skin cancers. Proc Natl Acad Sci USA 90:4216–4220. https://doi.org/10.1073/pnas.90.9.4216
Acknowledgements
We thank Dr. Robert Hanner for his advice regarding the processing and analyzing of fish barcode sequence data. We would like to thank the many contributors of sequence and trait data to BOLD and FishBase, respectively. Finally, we would like to thank three anonymous reviewers for their invaluable insight and suggestions for improving this manuscript.
Funding
This work was supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) (2016–06199 to S.J.A and 400095 to Z.F) and by a grant in Bioinformatics and Computational Biology (15401) from the Government of Canada through Genome Canada and Ontario Genomics (to S.J.A., Z.F., et al.). Additionally, this study represents a contribution to the “Food from Thought” research program led by the University of Guelph and supported through the Canada First Research Excellence Fund.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Handling Editor: Joana Projecto-Garcia.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
May, J.A., Feng, Z., Orton, M.G. et al. The Effects of Ecological Traits on the Rate of Molecular Evolution in Ray-Finned Fishes: A Multivariable Approach. J Mol Evol 88, 689–702 (2020). https://doi.org/10.1007/s00239-020-09967-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00239-020-09967-9