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The drivers of tropical speciation

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Abstract

Since the recognition that allopatric speciation can be induced by large-scale reconfigurations of the landscape that isolate formerly continuous populations, such as the separation of continents by plate tectonics, the uplift of mountains or the formation of large rivers, landscape change has been viewed as a primary driver of biological diversification. This process is referred to in biogeography as vicariance1. In the most species-rich region of the world, the Neotropics, the sundering of populations associated with the Andean uplift is ascribed this principal role in speciation2,3,4,5. An alternative model posits that rather than being directly linked to landscape change, allopatric speciation is initiated to a greater extent by dispersal events, with the principal drivers of speciation being organism-specific abilities to persist and disperse in the landscape6,7. Landscape change is not a necessity for speciation in this model8. Here we show that spatial and temporal patterns of genetic differentiation in Neotropical birds are highly discordant across lineages and are not reconcilable with a model linking speciation solely to landscape change. Instead, the strongest predictors of speciation are the amount of time a lineage has persisted in the landscape and the ability of birds to move through the landscape matrix. These results, augmented by the observation that most species-level diversity originated after episodes of major Andean uplift in the Neogene period, suggest that dispersal and differentiation on a matrix previously shaped by large-scale landscape events was a major driver of avian speciation in lowland Neotropical rainforests.

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Figure 1: Sampling within the landscape matrix.
Figure 2: Gene tree composed of 27 lineages of Neotropical birds, with species at tips inferred using a Bayesian coalescent model.
Figure 3: Asynchronous divergence times across barriers and the influence of lineage-specific traits on species diversity.

Accession codes

Primary accessions

GenBank/EMBL/DDBJ

Data deposits

Mitochondrial sequences generated for this study were deposited at GenBank under accession numbers KM079656KM081611. This work was conducted under Louisiana State University Institutional Animal Care and Use Committee Protocol 09-001.

Change history

  • 19 November 2014

    Minor changes were made to the main text, Fig. 2 and Extended Data Fig. 1.

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Acknowledgements

We thank the collectors, preparators, collection managers and curators of vouchered tissue samples who made this study possible. We thank the following people and institutions for providing samples: D. Dittmann, F. Sheldon (LSUMZ), N. Rice (ANSP), M. Robbins (KU), D. Willard, S. Hackett (FMNH), G. Graves, J. Dean (USNM), J. Cracraft, P. Sweet, T. Trombone (AMNH), S. Birks, J. Klicka (UWBM), K. Bostwick, I. Lovette (CUMV), B. Hernández-Baños, A. Navarro (MZFC), D. López (IAvH-BT), F. G. Stiles (ICN), M. Lentino (COP), F. Raposo, C. Miyaki (LGEMA, USP) and Museo de Historia Natural de la Universidad de los Andes. This study was supported by NSF awards to R.T.B. (DEB-0841729), M.J.H. (DEB 1253710; DEB 1343578) and CUNY HPCC (CNS-0855217), the Coypu Foundation, Brazilian Research Council (Conselho Nacional de Desenvolvimento Científico e Tecnológico) (grant numbers: 574008-2008-0; 490131/2009-3; 310593/2009-3; 574008/2008-0; 563236/2010-8 and 471342/ 2011-4) and FAPESPA awards (ICAAF 023/2011) to A.A., and support from CDCH and INPMA to J.P.-E. We thank G. Thomas, N. Gutiérrez-Pinto, N. Reid, G. Bravo, J. Miranda, G. Seeholzer, C. Salisbury, C. Cooney, R. Bryson Jr, B. Riddle, N. Takebayashi, B. Winger, V. Chua and J. Weckstein for their assistance, comments and feedback. We thank Lynx Edicions and E. Badia for granting us permission to reuse bird plates from the Handbook of Birds of the World in Fig. 2.

Author information

Authors and Affiliations

Authors

Contributions

B.T.S. performed ecological niche modelling and conducted all statistical analyses except for hABC analyses, which were performed and interpreted by M.J.H. and X.X. J.E.M., A.M.C., A.A., C.D.C., J.P.-E., C.W.B., E.P.D., J.P. and S.F. assisted with sampling and mitochondrial data collection. B.C.F., M.G.H., T.C.G. and B.T.S. collected ultraconserved element multi-locus sequence capture data. R.T.B. conceived the study. R.T.B., C.D.C., A.A., J.P.-E., B.T.S. and J.E.M. designed the study. B.T.S. and R.T.B. wrote the paper with help from M.J.H., M.G.H., C.D.C., J.E.M., A.M.C., A.A., J.P.-E., B.C.F. and T.C.G.

Corresponding author

Correspondence to Robb T. Brumfield.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Areas of endemism for lowland rainforest birds in Central and South America.

A full description of the geographical limits of each area is available in the Supplementary Information.

Extended Data Figure 2 hABC output showing estimates of mean and dispersion indices of population divergence times and times of co-divergence pulses inferred from mitochondrial DNA.

The left panels illustrate the approximate joint posterior estimates of , the dispersion index of τ and , the mean of τ across n population pairs, where τi is the divergence time of the ith of n population-pairs and is scaled in coalescent time units of 4 generations where is the mean effective population size averaged across population-pairs. The right panels depict the posterior distributions of the relative times of the co-divergence pulses across barriers, scaled by coalescent units. The shading intensity of each distribution is conditional on the posterior probability of ψ, the associated number of different pulses of co-divergence across each barrier. Sample sizes for each barrier: Andes, n = 29; Isthmus of Panama, n = 14; Amazon River, n = 14; Negro River, n = 17; Madeira River, n = 14.

Extended Data Figure 3 hABC output showing estimates of mean and dispersion indices of population divergence times across the Andes inferred from ultraconserved elements (UCEs).

Left panel illustrates the approximate joint posterior estimates of , the dispersion index of τ and , the mean of τ across n population-pairs, where τi is the divergence time of the ith of n population-pairs and is scaled in coalescent time units of 4 generations where is the mean effective population size averaged across population-pairs. The right panel depicts the posterior distribution of the relative times of the co-divergence pulses across the Andes (n = 5) scaled by coalescent units.

Extended Data Figure 4 Bar plot showing the number of estimated species using a Bayesian general mixed Yule-coalescent (bGMYC) model from complete and randomly pruned data sets.

The coloured columns for each lineage correspond to the percentage (0–60%) of individuals randomly pruned from each data set.

Supplementary information

Supplementary Information

This file contains Supplementary Text, Supplementary References, Supplementary Tables 1-16 and Supplementary Figures 1-28. (PDF 3940 kb)

Supplementary Data

This file contains Supplementary Table 17. (XLSX 589 kb)

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Smith, B., McCormack, J., Cuervo, A. et al. The drivers of tropical speciation. Nature 515, 406–409 (2014). https://doi.org/10.1038/nature13687

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