The drivers of tropical speciation

Journal name:
Nature
Volume:
515,
Pages:
406–409
Date published:
DOI:
doi:10.1038/nature13687
Received
Accepted
Published online
Corrected online

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.

At a glance

Figures

  1. Sampling within the landscape matrix.
    Figure 1: Sampling within the landscape matrix.

    Sampling points of the 27 bird lineages (circles) and prominent dispersal barriers within the landscape matrix, including the Andes (and associated arid habitats in the Caribbean lowlands of South America), the Isthmus of Panama and three major rivers in the Amazon Basin (Amazon, Negro and Madeira Rivers).

  2. Gene tree composed of 27 lineages of Neotropical birds, with species at tips inferred using a Bayesian coalescent model.
    Figure 2: Gene tree composed of 27 lineages of Neotropical birds, with species at tips inferred using a Bayesian coalescent model.

    An exemplar taxon for each lineage is illustrated30. Yellow bars correspond to the 95% highest posterior density for divergence times of each species. The Quaternary (2.6 Myr ago–present) and the Neogene (23–2.6 Myr ago) periods are shaded in grey and light blue, respectively. Mean stem ages for 25 of the lineages occurred within the Neogene and for two lineages within the Quaternary. Outgroups for each lineage are not included in the depicted phylogeny.

  3. Asynchronous divergence times across barriers and the influence of lineage-specific traits on species diversity.
    Figure 3: Asynchronous divergence times across barriers and the influence of lineage-specific traits on species diversity.

    a, The variation in divergence times across barriers cannot be attributed to ecologically mediated vicariance. There was no significant association between dispersal ability and divergence times across the Andes and the Isthmus of Panama. Only part of the variance in divergence times across rivers was attributable to dispersal ability. Divergence levels across Amazonian rivers were generally shallower in canopy birds, but understorey birds diverged multiple times across each river. Circles represent mean estimates and bars represent the 95% highest posterior density. Colour coding of the points corresponds to the foraging stratum of each lineage: understorey, orange; canopy, green. Vertical hashed lines at 2.58 million years represent the transition between the Neogene (to the right of line) and Quaternary (to the left of line). b, Within-lineage species diversity increases with lineage (stem) age. Solid lines represent the fit of the data to a model using phylogenetic generalized least-squares analyses. Black points and line correspond to mean stem ages, and the purple points and lines correspond to the high and low values of the stem age 95% highest posterior density. c, Box plot illustrating that species diversity is significantly higher in the understorey lineages than in forest canopy lineages. The box plot shows the first, second and third quartiles, the lines are the 95% confidence intervals and the circles represent outliers. Significant associations in panels a, b and c are supported by phylogenetic generalized least-squares analyses shown in Table 1 and Supplementary Tables 9–15. Statistical tests were performed independently on each data set except for divergences across rivers; all rivers were combined into a single analysis.

  4. Areas of endemism for lowland rainforest birds in Central and South America.
    Extended Data Fig. 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.

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

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

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

Accession codes

Change history

Corrected online 19 November 2014
Minor changes were made to the main text, Fig. 2 and Extended Data Fig. 1.

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Author information

  1. These authors contributed equally to this work.

    • Brian Tilston Smith &
    • Robb T. Brumfield

Affiliations

  1. Museum of Natural Science, Louisiana State University, Baton Rouge, Louisiana 70803, USA

    • Brian Tilston Smith,
    • John E. McCormack,
    • Andrés M. Cuervo,
    • Curtis W. Burney,
    • Michael G. Harvey,
    • Elizabeth P. Derryberry,
    • Jesse Prejean,
    • Samantha Fields &
    • Robb T. Brumfield
  2. Department of Ornithology, American Museum of Natural History, New York, New York 10024, USA

    • Brian Tilston Smith
  3. Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803, USA

    • Andrés M. Cuervo,
    • Curtis W. Burney,
    • Michael G. Harvey,
    • Jesse Prejean,
    • Samantha Fields &
    • Robb T. Brumfield
  4. Biology Department, City College of New York, New York, New York 10031, USA

    • Michael. J. Hickerson &
    • Xiaoou Xie
  5. Division of Invertebrate Zoology, American Museum of Natural History, New York, New York 10024, USA

    • Michael. J. Hickerson
  6. Coordenação de Zoologia, Museu Paraense Emílio Goeldi, Caixa Postal 399, CEP 66040-170, Belém, Brazil

    • Alexandre Aleixo
  7. Laboratorio de Biología Evolutiva de Vertebrados, Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia

    • Carlos Daniel Cadena
  8. Instituto de Zoología y Ecología Tropical, Universidad Central de Venezuela, Av. Los Ilustres, Los Chaguaramos, Apartado Postal 47058, Caracas 1041-A, Venezuela

    • Jorge Pérez-Emán
  9. Colección Ornitológica Phelps, Apartado 2009, Caracas 1010-A, Venezuela

    • Jorge Pérez-Emán
  10. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California 90095, USA

    • Brant C. Faircloth
  11. Department of Environmental Health Science, University of Georgia, Athens, Georgia 30602, USA

    • Travis C. Glenn
  12. Present addresses: Moore Laboratory of Zoology, Occidental College, 1600 Campus Road, Los Angeles, California 90041, USA (J.E.M.); Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana 70118, USA (A.M.C. & E.P.D.); Department of Biology, 2355 Faculty Drive, Suite 2P483, United States Air Force Academy, Colorado 80840, USA (C.W.B.); Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana 70803, USA (B.C.F.).

    • John E. McCormack,
    • Andrés M. Cuervo,
    • Curtis W. Burney,
    • Brant C. Faircloth &
    • Elizabeth P. Derryberry

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

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.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Areas of endemism for lowland rainforest birds in Central and South America. (399 KB)

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

  2. 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. (277 KB)

    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.

  3. 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). (100 KB)

    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.

  4. 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. (295 KB)

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

Supplementary information

PDF files

  1. Supplementary Information (6.3 MB)

    This file contains Supplementary Text, Supplementary References, Supplementary Tables 1-16 and Supplementary Figures 1-28.

Excel files

  1. Supplementary Data (589 KB)

    This file contains Supplementary Table 17.

Additional data