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A simple dynamic model explains the diversity of island birds worldwide

Nature volume 579, pages 92–96 (2020)Cite this article

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Abstract

Colonization, speciation and extinction are dynamic processes that influence global patterns of species richness1,2,3,4,5,6. Island biogeography theory predicts that the contribution of these processes to the accumulation of species diversity depends on the area and isolation of the island7,8. Notably, there has been no robust global test of this prediction for islands where speciation cannot be ignored9, because neither the appropriate data nor the analytical tools have been available. Here we address both deficiencies to reveal, for island birds, the empirical shape of the general relationships that determine how colonization, extinction and speciation rates co-vary with the area and isolation of islands. We compiled a global molecular phylogenetic dataset of birds on islands, based on the terrestrial avifaunas of 41 oceanic archipelagos worldwide (including 596 avian taxa), and applied a new analysis method to estimate the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation). Our model predicts—with high explanatory power—several global relationships. We found a decline in colonization with isolation, a decline in extinction with area and an increase in speciation with area and isolation. Combining the theoretical foundations of island biogeography7,8 with the temporal information contained in molecular phylogenies10 proves a powerful approach to reveal the fundamental relationships that govern variation in biodiversity across the planet.

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Fig. 1: Archipelago and island bird colonization time data.
Fig. 2: Estimated relationships between island area and isolation, and local island biogeography parameters.
Fig. 3: Goodness of fit of the preferred model (M19).
Fig. 4: Observed and predicted island diversity–area and island diversity–distance relationships.

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Data availability

New sequence data produced for this study have been deposited in GenBank with the accession codes: MH307408–MH307656. The following datasets have been deposited in Mendeley: DNA alignments (https://doi.org/10.17632/vf95364vx6.1), new phylogenetic trees produced for this study (https://doi.org/10.17632/p6hm5w8s3b.2), and DAISIE R objects (https://doi.org/10.17632/sy58zbv3s2.2). The 11 previously published trees are available upon request.

Code availability

The custom computer code used for this study is freely available in the DAISIE R package (https://github.com/rsetienne/DAISIE).

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Acknowledgements

We thank the skilled guides and field assistants who helped with sample collection in the field; the ornithologists and collection curators who were kind enough to reply to requests for material; T. von Rintelen, K. von Rintelen and C. Zorn for support or advice; A. Pigot for comments on the manuscript; N. Bunbury (Seychelles Islands Foundation), who organized sample loans of Aldabra island; J. van de Crommenacke, J. Groombridge and H. Jackson for providing samples or DNA sequences; J. A. Alcover, J. C. Rando, F. Sayol and S. Faurby for sharing data on extinct species; C. Baeta, M. Hammers, J. Hume, D. Shapiro, J. Varela and P. Cascão for permission to use photographs or illustrations; P. Hearty, R. Stern and M. Reagan for expertise on island geological ages; A. Cibois, J. McGuire, H. Lerner, P. Marki, B. Milá, G. Friis, J. Fuchs, J. P. Dumbacher and O. Carmi for providing phylogenetic data; P. Weigelt for map data. We thank the following for permission to obtain new samples or to access existing samples, and for logistic support (locations are given in brackets): A. Carvalho and the Department of the Environment (São Tomé and Príncipe); J. Obiang, N. Calvo and the Universidad Nacional de Guinea Ecuatorial for Bioko and Annobón samples (Equatorial Guinea); the Ministry of Environment, Energy and Climate Change of the Republic of Seychelles, the Seychelles Bureau of Standards, BirdLife Seychelles and Seychelles Islands Foundation (Seychelles); Centre National de Documentation et de Recherche Scientifique (Grande Comore & Anjouan), Action Comores, Direction de l’Agriculture et de la Foret (Mayotte) (Comoros); Ministere des Eaux et Forets (Madagascar) and the Madagascar Institute pour la Conservation des Ecosystemes Tropicaux (Madagascar); Mauritius National Parks and Conservation Service and Mauritius Wildlife Foundation (Mauritius); O. Hébert, W. Waheoneme, N. Clark, the Direction de L’Environment (South Province), Direction du Développement Economique (Loyalty Islands Province), and local chiefs and landowners (New Caledonia); Moroccan Environment Ministry (Morocco); Cape Verde Agriculture and Environment Ministry (Cape Verde); F. Njie and the Limbe Botanical and Zoological Garden (Cameroon); Station de Recherche de l’IRET at Ipassa-Makokou (Gabon); Fernanda Lages (ISCED-Huíla) (Angola); the regional governments of Andalucía and the Canary Islands (Spain); regional governments of Madeira and the Azores (Portugal). We thank the Department of Ornithology and Mammalogy of the California Academy of Sciences (L. Wilkinson and M. Flannery) for loaning Galápagos samples; the Natural History Museum at Tring (M. Adams) for loaning Comoros samples; the Stuttgart State Museum of Natural History for loaning stonechat samples from Madagascar. S. Block assisted with cluster analyses at the Museum für Naturkunde. The Center for Information Technology of the University of Groningen provided support and access to the Peregrine high-performance computing cluster. L.V. was funded by the German Science Foundation (DFG Research grant VA 1102/1-1), the Alexander von Humboldt Foundation, the Brandenburg Postdoc Prize 2015 and by a VIDI grant from the Netherlands Organisation for Scientific Research (NWO); R.S.E. was supported by a NWO VICI grant; M.M. was supported by the Portuguese Science and Technology Foundation (post-doctoral grant: SFRH/BPD/100614/2014); S.M.C. was supported by the National Geographic Society (CRE grant 9383-13); J.C.I. was supported by the Spanish Ministry of Science, Innovation and Universities (PGC2018-097575-B-I00) and by a GRUPIN research grant from the Regional Government of Asturias (IDI/2018/000151); and C.T. was supported by the ‘Laboratoire d’Excellence’ TULIP (ANR-10-LABX-41).

Author information

Authors and Affiliations

  1. Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany

    Luis Valente & Tina Aschenbach

  2. Naturalis Biodiversity Center, Leiden, The Netherlands

    Luis Valente

  3. Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands

    Luis Valente & Rampal S. Etienne

  4. Unit of Evolutionary Biology/Systematic Zoology, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Luis Valente, Katja Havenstein & Ralph Tiedemann

  5. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

    Albert B. Phillimore

  6. Museu de História Natural e da Ciência da Universidade do Porto, Porto, Portugal

    Martim Melo

  7. Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO), InBio, Laboratório Associado, Universidade do Porto, Vairão, Portugal

    Martim Melo

  8. FitzPatrick Institute, DST-NRF Centre of Excellence, University of Cape Town, Cape Town, South Africa

    Martim Melo

  9. Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d’Histoire Naturelle, CNRS, Sorbonne Université, EPHE, UA, Paris, France

    Ben H. Warren

  10. Edward Grey Institute, Department of Zoology, University of Oxford, Oxford, UK

    Sonya M. Clegg

  11. Environmental Futures Research Institute, Griffith University, Brisbane, Queensland, Australia

    Sonya M. Clegg

  12. Research Unit of Biodiversity (UO-CSIC-PA), Oviedo University, Mieres, Spain

    Juan Carlos Illera

  13. Unité Mixte de Recherche 5174, CNRS-IRD-Paul Sabatier University, Toulouse, France

    Christophe Thébaud

Authors
  1. Luis Valente
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  2. Albert B. Phillimore
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Contributions

L.V., A.B.P. and R.S.E. designed the study, developed the analytical framework and performed statistical analyses. L.V. compiled the data, conducted most of the analyses and wrote the first draft. R.S.E. developed the likelihood method. A.B.P. and R.S.E. contributed substantially to the writing. M.M., B.H.W., S.M.C., J.C.I. and C.T. provided expertise on island birds, collected bird tissue samples, and provided molecular and/or phylogenetic data. K.H. and J.C.I. performed laboratory work. R.T. contributed to molecular analyses. T.A. performed analyses. All authors commented on the draft.

Corresponding author

Correspondence to Luis Valente.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Thomas Matthews, Kostas Triantis, Jason Weir and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Variation of cladogenesis with isolation and area.

Contour plot showing how the local rate of cladogenesis varies with area and Dm assuming the maximum-likelihood global hyperparameters of the M19 model (equations describing the relationships are provided in Supplementary Table 1). Numbers correspond to the archipelago numbers from Fig. 1 and show the local cladogenesis rates for each of the archipelagos in our dataset. Area is shown as a log scale.

Extended Data Fig. 2 Bootstrap precision estimates of the parameters of the M19 model.

Parametric bootstrap analysis fitting the M19 model to 1,000 global datasets simulated with maximum-likelihood parameters of the M19 model. Plots are frequency histograms of estimated parameters. Black lines show the median estimated values across all simulations and the blue lines the simulated values. Dashed lines show 2.5–97.5 percentiles. Parameters are explained in Supplementary Table 1. Bootstrap parameter estimates for the M14 model are shown in Extended Data Table 5.

Extended Data Fig. 3 Randomization analysis of the M19 model.

Distribution of global hyperparameters estimated from each of 1,000 datasets with the same phylogenetic data as our main global dataset but randomly reshuffling archipelago area and isolation among the 41 archipelagos in the dataset. Grey histograms show DAISIE maximum-likelihood parameter estimates for the M19 model. Red arrows show the estimated parameter from the real data. In most cases, the hyperparameters describing the exponent of the power models (x, α, β and d0) are estimated as zero in the reshuffled datasets, which is not the case in the real data (red). Parameters are explained in Supplementary Table 1.

Extended Data Fig. 4 Goodness of fit of the preferred model (M19).

Plots show the observed total number of species, cladogenetic species and colonizations versus those simulated by the model. Median and 95% percentiles are shown for 1,000 simulations of each archipelago. Selected archipelagos mentioned in the main text or well-known archipelagos for which one or more of the diversity metrics are under- or overestimated are highlighted in colour. Dashed line is y = x. See also Fig. 3.

Extended Data Fig. 5 Ratio of pseudo-R2 observed over pseudo-R2 simulated.

Estimates were based on 10,000 datasets simulated using the M19 model. A ratio centred on 1 would indicate that the model explains the observed data as well as it is able to explain the average dataset simulated under the maximum-likelihood parameters.

Extended Data Fig. 6 Sensitivity to colonization and branching times.

a, Maximum-likelihood parameter estimates of the M19 model (preferred model) for datasets differing in colonization and branching times. D6 represents 100 datasets, therefore, the 2.5 and 97.5 percentiles are shown. Parameter symbols are described in Supplementary Table 1. b, Estimated relationships between island area and isolation and local island biogeography parameters for each dataset. Under the M19 model, cladogenesis rate increases with both area and isolation, and thus plots for more (far, 5,000 km) and less (near, 50 km) isolated islands are shown.

Extended Data Table 1 Archipelago characteristics and references for island geological ages
Full size table
Extended Data Table 2 Primer sequences used in this study
Full size table
Extended Data Table 3 The 80 alignments used in the phylogenetic analyses
Full size table
Extended Data Table 4 Previously published dated trees used
Full size table
Extended Data Table 5 Bootstrap of M14 and M19 models
Full size table

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1-7.

Reporting Summary

Supplementary Data 1

| Island birds database Includes the database of taxa of our focal group found on each of the 41 archipelagos.

Supplementary Data 2

| Full archipelago data Includes physical data, species richness and sampling (from the focal group of taxa) for the 41 archipelagos included in this study.

Supplementary Data 3

| Sensitivity to archipelago selection and isolation metrics Results of the sensitivity analyses excluding single islands, using different isolation metrics and with Mascarenes fused as a single archipelago.

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Valente, L., Phillimore, A.B., Melo, M. et al. A simple dynamic model explains the diversity of island birds worldwide. Nature 579, 92–96 (2020). https://doi.org/10.1038/s41586-020-2022-5

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