Body size and life history shape the historical biogeography of tetrapods.
Journal
Nature ecology & evolution
ISSN: 2397-334X
Titre abrégé: Nat Ecol Evol
Pays: England
ID NLM: 101698577
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
17
01
2023
accepted:
04
07
2023
medline:
8
9
2023
pubmed:
22
8
2023
entrez:
21
8
2023
Statut:
ppublish
Résumé
Dispersal across biogeographic barriers is a key process determining global patterns of biodiversity as it allows lineages to colonize and diversify in new realms. Here we demonstrate that past biogeographic dispersal events often depended on species' traits, by analysing 7,009 tetrapod species in 56 clades. Biogeographic models incorporating body size or life history accrued more statistical support than trait-independent models in 91% of clades. In these clades, dispersal rates increased by 28-32% for lineages with traits favouring successful biogeographic dispersal. Differences between clades in the effect magnitude of life history on dispersal rates are linked to the strength and type of biogeographic barriers and intra-clade trait variability. In many cases, large body sizes and fast life histories facilitate dispersal success. However, species with small bodies and/or slow life histories, or those with average traits, have an advantage in a minority of clades. Body size-dispersal relationships were related to a clade's average body size and life history strategy. These results provide important new insight into how traits have shaped the historical biogeography of tetrapod lineages and may impact present-day and future biogeographic dispersal.
Identifiants
pubmed: 37604875
doi: 10.1038/s41559-023-02150-5
pii: 10.1038/s41559-023-02150-5
pmc: PMC10482685
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1467-1479Informations de copyright
© 2023. The Author(s).
Références
Bond, M. et al. Eocene primates of South America and the African origins of New World monkeys. Nature 520, 538–541 (2015).
pubmed: 25652825
doi: 10.1038/nature14120
Lynch Alfaro, J. The monkeying of the Americas: primate biogeography in the neotropics. Annu. Rev. Anthropol. 46, 317–336 (2017).
doi: 10.1146/annurev-anthro-102116-041510
Tolley, K. A. et al. Large-scale phylogeny of chameleons suggests African origins and Eocene diversification. Proc. Biol. Sci. 280, 20130184 (2013).
pubmed: 23536596
pmcid: 3619509
Tolley, K. A. & Herrel, A. The Biology of Chameleons (Univ. California Press, 2013).
doi: 10.1525/9780520957381
Simpson, G. G. Mammals and land bridges. J. Wash. Acad. Sci. 30, 137–163 (1940).
Whitmee, S. & Orme, C. D. L. Predicting dispersal distance in mammals: a trait-based approach. J. Anim. Ecol. 82, 211–221 (2013).
pubmed: 22924343
doi: 10.1111/j.1365-2656.2012.02030.x
Stevens, V. M. et al. A comparative analysis of dispersal syndromes in terrestrial and semi‐terrestrial animals. Ecol. Lett. 17, 1039–1052 (2014).
pubmed: 24915998
doi: 10.1111/ele.12303
Andrews, R. M. & Pough, F. H. Metabolism of squamate reptiles: allometric and ecological relationships. Physiol. Zool. 58, 214–231 (1985).
doi: 10.1086/physzool.58.2.30158569
Blom, M. P. K. et al. Habitat preference modulates transoceanic dispersal in a terrestrial vertebrate. Proc. Biol. Soc. 286, 1–10 (2019).
Nicolaï, M. P. J. & Matzke, N. J. Trait-based range expansion aided in the global radiation of Crocodylidae. Glob. Ecol. Biogeogr. 28, 1244–1258 (2019).
doi: 10.1111/geb.12929
Weil, S. S. et al. Chameleon biogeographic dispersal is associated with extreme life history strategies. Ecography 2022, e06323 (2022).
doi: 10.1111/ecog.06323
Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992).
Caswell, H. et al. Demography and dispersal: life table response experiments for invasion speed. Ecology 84, 1968–1978 (2003).
doi: 10.1890/02-0100
Blackburn, T. M. et al. The influence of numbers on invasion success. Mol. Ecol. 24, 1942–1953 (2015).
pubmed: 25641210
doi: 10.1111/mec.13075
Reynolds, J. D. in: Macroecology: Concepts and Consequences (eds Blackburn, T. M. & Gaston, K. J.) 195–217 (Blackwell Publishing, 2003).
Cáceres, C. E. Temporal variation, dormancy and coexistence: a field test of the storage effect. Proc. Natl Acad. Sci. USA 94, 9171–9175 (1997).
pubmed: 11038565
pmcid: 23092
doi: 10.1073/pnas.94.17.9171
Sæther, B. E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).
doi: 10.1890/0012-9658(2000)081[0642:ALHVAC]2.0.CO;2
Jeppsson, T. & Forslund, P. Can life history predict the effect of demographic stochasticity on extinction risk? Am. Nat. 179, 706–720 (2012).
pubmed: 22617260
doi: 10.1086/665696
Hackel, J. & Sanmartín, I. Modelling the tempo and mode of lineage dispersal. Trends Ecol. Evol. 36, 1102–1112 (2021).
pubmed: 34462154
doi: 10.1016/j.tree.2021.07.007
Sukumaran, J. & Knowles, L. L. Trait-dependent biogeography: (re)integrating biology into probabilistic historical biogeographical models. Trends Ecol. Evol. 33, 390–398 (2018).
pubmed: 29685579
doi: 10.1016/j.tree.2018.03.010
Estrada, A. et al. Usefulness of species traits in predicting range shifts. Trends Ecol. Evol. 31, 190–203 (2016).
pubmed: 26776962
doi: 10.1016/j.tree.2015.12.014
Tolkoff, M. R. et al. Phylogenetic factor analysis. Syst. Biol. 67, 384–399 (2018).
pubmed: 28950376
doi: 10.1093/sysbio/syx066
Hassler, G. W. et al. Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis. Methods Ecol. Evol. 13, 2181–2197 (2022).
pubmed: 36908682
pmcid: 9997680
doi: 10.1111/2041-210X.13920
Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).
pubmed: 23258408
doi: 10.1126/science.1228282
Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions. J. Biogeogr. 37, 2029–2053 (2010).
doi: 10.1111/j.1365-2699.2010.02375.x
Klaus, K. V. & Matzke, N. J. Statistical comparison of trait-dependent biogeographical models indicates that Podocarpaceae dispersal is influenced by both seed cone traits and geo-graphical distance. Syst. Biol. 69, 61–75 (2020).
pubmed: 31099388
doi: 10.1093/sysbio/syz034
Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).
pubmed: 25601943
doi: 10.1093/sysbio/syu131
Caetano, D. S., O’Meara, B. C. & Beaulieu, J. M. Hidden state models improve state‐dependent diversification approaches, including biogeographical models. Evolution 72, 2308–2324 (2018).
pubmed: 30226270
doi: 10.1111/evo.13602
Atkins, J. L., Perry, G. L. & Dennis, T. E. Effects of mis-alignment between dispersal traits and landscape structure on dispersal success in fragmented landscapes. R. Soc. Open Sci. 6, 181702 (2019).
pubmed: 30800399
pmcid: 6366165
doi: 10.1098/rsos.181702
Cote, J. et al. Dispersal syndromes in challenging environments: a cross-species experiment. Ecol. Lett. 25, 2675–2687 (2022).
pubmed: 36223413
pmcid: 9828387
doi: 10.1111/ele.14124
Bloomfield, N. J. et al. A comparison of network and clustering methods to detect biogeographical regions. Ecography 41, 1–10 (2018).
doi: 10.1111/ecog.02596
Jenkins, D. G. et al. Does size matter for dispersal distance? Glob. Ecol. Biogeogr. 16, 415–425 (2007).
doi: 10.1111/j.1466-8238.2007.00312.x
Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).
pubmed: 23809102
doi: 10.1111/ele.12136
Ozgo, M. et al. Dispersal of land snails by sea storms. J. Molluscan Stud. 82, 341–343 (2016).
doi: 10.1093/mollus/eyv060
Lindo, Z. Transoceanic dispersal of terrestrial species by debris rafting. Ecography 43, 1364–1372 (2020).
doi: 10.1111/ecog.05155
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).
pubmed: 16701373
doi: 10.1016/j.tree.2005.02.004
Zamora‐Camacho, F. J., Reguera, S. & Moreno‐Rueda, G. Bergmann’s Rule rules body size in an ectotherm: heat conservation in a lizard along a 2200‐metre elevational gradient. J. Evol. Biol. 27, 2820–2828 (2014).
pubmed: 25387908
doi: 10.1111/jeb.12546
Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
pubmed: 12511946
doi: 10.1038/nature01286
Crisp, M. D., Trewick, S. A. & Cook, L. G. Hypothesis testing in biogeography. Trends Ecol. Evol. 26, 66–72 (2011).
pubmed: 21146898
doi: 10.1016/j.tree.2010.11.005
Herrera‐Alsina, L. et al. The missing link in biogeographic reconstruction: accounting for lineage extinction rewrites history. J. Biogeogr. 49, 1941–1951 (2022).
doi: 10.1111/jbi.14489
Gallien, L. et al. Does the legacy of historical biogeography shape current invasiveness in pines? N. Phytol. 209, 1096–1105 (2016).
doi: 10.1111/nph.13700
Gallien, L. et al. Global predictors of alien plant establishment success: combining niche and trait proxies. Proc. Biol. Soc. 286, 20182477 (2019).
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).
pubmed: 14712274
doi: 10.1038/nature02121
Bánki, O. et al. Catalogue of life checklist (annual checklist 2021). Catalogue of Life https://www.catalogueoflife.org (2021).
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).
pubmed: 29581588
doi: 10.1038/s41559-018-0515-5
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).
doi: 10.1016/j.biocon.2016.03.039
Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).
pubmed: 31800571
pmcid: 6892540
doi: 10.1371/journal.pbio.3000494
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
pubmed: 23123857
doi: 10.1038/nature11631
Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).
pubmed: 26444237
doi: 10.1038/nature15697
Cooney, C. R. et al. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature 542, 344–347 (2017).
pubmed: 28146475
pmcid: 5321581
doi: 10.1038/nature21074
Dufour, P. et al. Reconstructing the geographic and climatic origins of long‐distance bird migrations. J. Biogeogr. 47, 155–166 (2020).
doi: 10.1111/jbi.13700
Oaks, J. R. A time‐calibrated species tree of Crocodylia reveals a recent radiation of the true crocodiles. Evolution 65, 3285–3297 (2011).
pubmed: 22023592
doi: 10.1111/j.1558-5646.2011.01373.x
Colston, T. J., Kulkarni, P., Jetz, W. & Pyron, R. A. Phylogenetic and spatial distribution of evolutionary diversification, isolation, and threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol. Biol. 20, 1–16 (2020).
doi: 10.1186/s12862-020-01642-3
The IUCN Red List of threatened species. IUCN www.iucnredlist.org (2019).
Bird species distribution maps of the world. BirdLife International http://datazone.birdlife.org/species/requestdis (2019).
Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R. F1000Research 2, 191 (2013).
pubmed: 24555091
pmcid: 3901538
doi: 10.12688/f1000research.2-191.v1
Chamberlain, S. et al. taxize: Taxonomic Information from around the Web. R Package Version 0.9.98 (R Project, 2020).
Davis Rabosky, A. R. et al. Coral snakes predict the evolution of mimicry across New World snakes. Nat. Commun. 7, 1–9 (2016).
doi: 10.1038/ncomms11484
AmphibiaWeb. Univ. California https://amphibiaweb.org (2016).
Uetz P., & Hosek, J. (ed.). The Reptile Database http://www.reptile-database.org (2016).
The BirdLife checklist of the birds of the world: version 8. BirdLife International http://datazone.birdlife.org/home (2015).
Mammal Species of the World. A Taxonomic and Geographic Reference 3rd edn (eds Wilson, D. E. & Reeder, D. M.) (Johns Hopkins Univ. Press, 2005).
Chamberlain, S. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. Preprint at PeerJ PrePrints https://doi.org/10.7287/peerj.preprints.3304v1 (2017).
Zizka, A. et al. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).
doi: 10.1111/2041-210X.13152
Allen, W. L., Street, S. E. & Capellini, I. Fast life history traits promote invasion success in amphibians and reptiles. Ecol. Lett. 20, 222–230 (2017).
pubmed: 28052550
pmcid: 6849728
doi: 10.1111/ele.12728
Cooney, C. R. & Thomas, G. H. Heterogeneous relationships between rates of speciation and body size evolution across vertebrate clades. Nat. Ecol. Evol. 5, 101–110 (2021).
pubmed: 33106601
doi: 10.1038/s41559-020-01321-y
Trakimas, G., Whittaker, R. J. & Borregaard, M. K. Do biological traits drive geographical patterns in European amphibians? Glob. Ecol. Biogeogr. 25, 1228–1238 (2016).
doi: 10.1111/geb.12479
Pincheira‐Donoso, D., Meiri, S., Jara, M., Olalla‐Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42, 1682–1690 (2019).
doi: 10.1111/ecog.04644
Meiri, S. et al. The global diversity and distribution of lizard clutch sizes. Glob. Ecol. Biogeogr. 29, 1515–1530 (2020).
doi: 10.1111/geb.13124
Schwarz, R. & Meiri, S. The fast‐slow life‐history continuum in insular lizards: a comparison between species with invariant and variable clutch sizes. J. Biogeogr. 44, 2808–2815 (2017).
doi: 10.1111/jbi.13067
Feldman, A. et al. The geography of snake reproductive mode: a global analysis of the evolution of snake viviparity. Glob. Ecol. Biogeogr. 24, 1433–1442 (2015).
doi: 10.1111/geb.12374
Myhrvold, N. P. et al. An amniote life‐history database to perform comparative analyses with birds, mammals, and reptiles: ecological archives E096‐269. Ecology 96, 3109–3109 (2015).
doi: 10.1890/15-0846R.1
Stark, G., Tamar, K., Itescu, Y., Feldman, A. & Meiri, S. Cold and isolated ectotherms: drivers of reptilian longevity. Biol. J. Linn. Soc. 125, 730–740 (2018).
doi: 10.1093/biolinnean/bly153
Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals: ecological archives E090‐184. Ecology 90, 2648–2648 (2009).
doi: 10.1890/08-1494.1
Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626 (2018).
pubmed: 29989146
doi: 10.1002/ecy.2443
Tacutu, R. et al. Human ageing genomic resources: new and updated databases. Nucleic Acids Res. 46, D1083–D1090 (2018).
pubmed: 29121237
doi: 10.1093/nar/gkx1042
Ernest, S. M. Life history characteristics of placental nonvolant mammals: ecological archives E084‐093. Ecology 84, 3402–3402 (2003).
doi: 10.1890/02-9002
Fisher, D. O., Owens, I. P. & Johnson, C. N. The ecological basis of life history variation in marsupials. Ecology 82, 3531–3540 (2001).
doi: 10.1890/0012-9658(2001)082[3531:TEBOLH]2.0.CO;2
Tsuboi, M. et al. Breakdown of brain–body allometry and the encephalization of birds and mammals. Nat. Ecol. Evol. 2, 1492–1500 (2018).
pubmed: 30104752
doi: 10.1038/s41559-018-0632-1
Williams, S. E. et al. Distributions, life-history specialization, and phylogeny of the rain forest vertebrates in the Austalian wet tropics. Ecology 91, 2493 (2010).
doi: 10.1890/09-1069.1
Smith, F. A. et al. Body mass of late Quaternary mammals. Ecology 84, 3403 (2003).
doi: 10.1890/02-9003
Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M. & Rondinini, C. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology 102, e03344 (2021).
pubmed: 33742448
doi: 10.1002/ecy.3344
Capellini, I., Baker, J., Allen, W. L., Street, S. E. & Venditti, C. The role of life history traits in mammalian invasion success. Ecol. Lett. 18, 1099–1107 (2015).
pubmed: 26293900
pmcid: 4989474
doi: 10.1111/ele.12493
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 1–9 (2020).
doi: 10.1038/s41467-020-16313-6
Botero, C. A., Dor, R., McCain, C. M. & Safran, R. J. Environmental harshness is positively correlated with intraspecific divergence in mammals and birds. Mol. Ecol. 23, 259–268 (2014).
pubmed: 24283535
doi: 10.1111/mec.12572
O’Gorman, E. J. & Hone, D. W. Body size distribution of the dinosaurs. PLoS ONE 7, e51925 (2012).
pubmed: 23284818
pmcid: 3526529
doi: 10.1371/journal.pone.0051925
Garnett, S. T. et al. Biological, ecological, conservation and legal information for all species and subspecies of Australian bird. Sci. Data 2, 1–6 (2015).
doi: 10.1038/sdata.2015.61
Burgio, K. R. et al. Phylogenetic supertree and functional trait database for all extant parrots. Data Brief. 24, 103882 (2019).
pubmed: 31193185
pmcid: 6520560
doi: 10.1016/j.dib.2019.103882
Storchová, L. & Hořák, D. Life‐history characteristics of European birds. Glob. Ecol. Biogeogr. 27, 400–406 (2018).
doi: 10.1111/geb.12709
Tobias, J. A. & Pigot, A. L. Integrating behaviour and ecology into global biodiversity conservation strategies. Philos. Trans. R. Soc. B 374, 20190012 (2019).
doi: 10.1098/rstb.2019.0012
Rotenberry, J. T. & Balasubramaniam, P. Estimating egg mass–body mass relationships in birds. Auk 137, ukaa019 (2020).
doi: 10.1093/auk/ukaa019
DATLife. The demography across the Tree of Life database. Max-Planck Institute for Demographic Research https://www.demogr.mpg.de/en/publications_databases_6118/online_databases_6676/ (2021).
Matzke, N. J. Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 242–248 (2013).
doi: 10.21425/F55419694
Lennon, J. J. et al. The geographical structure of British bird distributions: diversity, spatial turnover and scale. J. Anim. Ecol. 70, 966–979 (2001).
doi: 10.1046/j.0021-8790.2001.00563.x
Simpson, G. G. Mammals and the nature of continents. Am. J. Sci. 241, 1–31 (1943).
doi: 10.2475/ajs.241.1.1
Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812 (2012).
doi: 10.1111/j.2041-210X.2012.00224.x
Müller, R. D. et al. GPlates: building a virtual earth through deep time. Geochem. Geophys. Geosyst. 19, 2243–2261 (2018).
doi: 10.1029/2018GC007584
Matthews, K. J. et al. Global plate boundary evolution and kinematics since the late Paleozoic. Glob. Planet. Change 146, 226–250 (2016).
doi: 10.1016/j.gloplacha.2016.10.002
Hassler, G. et al. Inferring phenotypic trait evolution on large trees with many incomplete measurements. J. Am. Stat. Assoc. 117, 678–692 (2022).
pubmed: 36060555
doi: 10.1080/01621459.2020.1799812
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
pubmed: 29942656
pmcid: 6007674
doi: 10.1093/ve/vey016
Dupin, J. et al. Bayesian estimation of the global biogeographic history of the Solanaceae. J. Biogeogr. 44, 887–899 (2016).
doi: 10.1111/jbi.12898
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn (Sage, 2019).
Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R Package Version 1.8.2 (R Project, 2022).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. Fourth Edition (Springer, 2002).
doi: 10.1007/978-0-387-21706-2
Croissant, Y. Estimation of random utility models in R: the mlogit package. J. Stat. Softw. 95, 1–41 (2020).
doi: 10.18637/jss.v095.i11
Revell, L. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
doi: 10.1111/j.2041-210X.2011.00169.x
Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).
doi: 10.1111/2041-210X.12628
Raffard, A. et al. Dispersal syndromes can link intraspecific trait variability and meta-ecosystem functioning. Trends Ecol. Evol. 37, 322–331 (2022).
pubmed: 34952726
doi: 10.1016/j.tree.2021.12.001
Stevens, V. M., Pavoine, S. & Baguette, M. Variation within and between closely related species uncovers high intra-specific variability in dispersal. PLoS One 5, e11123 (2010).
pubmed: 20559551
pmcid: 2886073
doi: 10.1371/journal.pone.0011123
Burnham, K. & Anderson, D. Model Selection and Multimodel Inference (Springer, 2004).
Caplat, P. et al. Looking beyond the mountain: dispersal barriers in a changing world. Front. Ecol. Environ. 14, 261–268 (2016).
doi: 10.1002/fee.1280
Ali, J. R. & Vences, M. Mammals and long‐distance over‐water colonization: the case for rafting dispersal; the case against phantom causeways. J. Biogeogr. 46, 2632–2636 (2019).
doi: 10.1111/jbi.13659
Graham, C. H., Storch, D. & Machac, A. Phylogenetic scale in ecology and evolution. Glob. Ecol. Biogeogr. 27, 175–187 (2018).
doi: 10.1111/geb.12686
Ronquist, F. & Sanmartín, I. Phylogenetic methods in biogeography. Annu. Rev. Ecol. Evol. Syst. 42, 441–464 (2011).
doi: 10.1146/annurev-ecolsys-102209-144710