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Global elevational diversity and diversification of birds

Nature volume 555, pages 246–250 (2018)Cite this article

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Abstract

Mountain ranges harbour exceptionally high biodiversity, which is now under threat from rapid environmental change. However, despite decades of effort, the limited availability of data and analytical tools has prevented a robust and truly global characterization of elevational biodiversity gradients and their evolutionary origins1,2. This has hampered a general understanding of the processes involved in the assembly and maintenance of montane communities2,3,4. Here we show that a worldwide mid-elevation peak in bird richness is driven by wide-ranging species and disappears when we use a subsampling procedure that ensures even species representation in space and facilitates evolutionary interpretation. Instead, richness corrected for range size declines linearly with increasing elevation. We find that the more depauperate assemblages at higher elevations are characterized by higher rates of diversification across all mountain regions, rejecting the idea that lower recent diversification rates are the general cause of less diverse biota. Across all elevations, assemblages on mountains with high rates of past temperature change exhibit more rapid diversification, highlighting the importance of climatic fluctuations in driving the evolutionary dynamics of mountain biodiversity. While different geomorphological and climatic attributes of mountain regions have been pivotal in determining the remarkable richness gradients observed today, our results underscore the role of ongoing and often very recent diversification processes in maintaining the unique and highly adapted biodiversity of higher elevations.

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Figure 1: Elevational gradients of avian species richness across mountain systems.
Figure 2: Mountain-specific and worldwide patterns of elevational species richness gradients.
Figure 3: Hypotheses, predictions and results for explaining mountain level variation in species richness and diversification rates.
Figure 4: Effect of past rates of climate change on the interplay between diversification rates and species richness along elevation.

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References

  1. Jetz, W. & Fine, P. V. A. Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment. PLoS Biol. 10, e1001292 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. McCain, C. M. Global analysis of bird elevational diversity. Glob. Ecol. Biogeogr. 18, 346–360 (2009)

    Article  Google Scholar 

  3. Rahbek, C. The relationship among area, elevation, and regional species richness in neotropical birds. Am. Nat. 149, 875–902 (1997)

    Article  CAS  PubMed  Google Scholar 

  4. Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014)

    Article  Google Scholar 

  5. Price, T. D. et al. Niche filling slows the diversification of Himalayan songbirds. Nature 509, 222–225 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Terborgh, J. Distribution on environmental gradients: theory and a preliminary interpretation of distributional patterns in the avifauna of the Cordillera Vilcabamba, Peru. Ecology 52, 23–40 (1971)

    Article  Google Scholar 

  7. Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2016)

    Article  Google Scholar 

  9. Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Colwell, R. K. & Lees, D. C. The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000)

    Article  CAS  PubMed  Google Scholar 

  12. Kozak, K. H. & Wiens, J. J. Niche conservatism drives elevational diversity patterns in Appalachian salamanders. Am. Nat. 176, 40–54 (2010)

    Article  PubMed  Google Scholar 

  13. Kisel, Y., McInnes, L., Toomey, N. H. & Orme, C. D. L. How diversification rates and diversity limits combine to create large-scale species-area relationships. Phil. Trans. R. Soc. Lond. B 366, 2514–2525 (2011)

    Article  Google Scholar 

  14. Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003)

    Article  Google Scholar 

  15. Päckert, M. et al. Horizontal and elevational phylogeographic patterns of Himalayan and Southeast Asian forest passerines (Aves: Passeriformes). J. Biogeogr. 39, 556–573 (2012)

    Article  Google Scholar 

  16. Rosenblum, E. B. et al. Goldilocks meets Santa Rosalia: an ephemeral speciation model explains patterns of diversification across time scales. Evol. Biol. 39, 255–261 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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)

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73–78 (2011)

    Article  Google Scholar 

  19. Barker, F. K., Burns, K. J., Klicka, J., Lanyon, S. M. & Lovette, I. J. New insights into New World biogeography: an integrated view from the phylogeny of blackbirds, cardinals, sparrows, tanagers, warblers, and allies. Auk 132, 333–348 (2015)

    Article  Google Scholar 

  20. Smith, B. T. et al. The drivers of tropical speciation. Nature 515, 406–409 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  21. Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010)

    Article  PubMed  Google Scholar 

  22. Marshall, C. R. & Quental, T. B. The uncertain role of diversity dependence in species diversification and the need to incorporate time-varying carrying capacities. Phil. Trans. R. Soc. Lond. B 371, 20150217 (2016)

    Article  CAS  Google Scholar 

  23. Wright, D. H., Currie, D. J. & Maurer, B. A. in Species Diversity in Ecological Communities: Historical and Geographical Perspectives (Univ. Chicago Press, 1993)

  24. Stephens, P. R. & Wiens, J. J. Explaining species richness from continents to communities: the time-for-speciation effect in emydid turtles. Am. Nat. 161, 112–128 (2003)

    Article  PubMed  Google Scholar 

  25. Fine, P. V. A. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015)

    Article  Google Scholar 

  26. Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967)

    Article  Google Scholar 

  27. Cadena, C. D. et al. Latitude, elevational climatic zonation and speciation in New World vertebrates. Proc. R. Soc. Lond. B 279, 194–201 (2012)

    Article  Google Scholar 

  28. Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Wallis, G. P., Waters, J. M., Upton, P. & Craw, D. Transverse alpine speciation driven by glaciation. Trends Ecol. Evol. 31, 916–926 (2016)

    Article  PubMed  Google Scholar 

  30. Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Weir, J. T., Bermingham, E. & Schluter, D. The Great American Biotic Interchange in birds. Proc. Natl Acad. Sci. USA 106, 21737–21742 (2009)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  32. Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Royle, J. A. & Dorazio, R. Parameter-expanded data augmentation for Bayesian analysis of capture–recapture models. J. Ornithol. 152, 521–537 (2012)

    Article  Google Scholar 

  34. Kéry, M. & Royle, J. A. Applied Hierarchical Models in Ecology (Academic, 2015)

  35. Plummer, M. JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling (2003)

  36. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015)

  37. Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012)

  38. Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014)

    Article  ADS  Google Scholar 

  39. Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007)

    Article  ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  40. Bodenhofer, U., Kothmeier, A. & Palme, J. apcluster: Affinity Propagation Clustering (2013)

  41. Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. (eds.). Handbook of the Birds of the World Alive (Lynx Edicions, 2015)

  43. Cleveland, W. S., Grosse, E. & Shyu, W. M. in Statistical Models in S (eds. Chambers, J. M. & Hastie, T. J. ) (Wadsworth & Brooks/Cole, 1992)

  44. Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012)

    Article  PubMed  Google Scholar 

  45. Walther, B. A. & Martin, J.-L. Species richness estimation of bird communities: how to control for sampling effort? Ibis 143, 413–419 (2001)

    Article  Google Scholar 

  46. Martins, T. G., Simpson, D., Lindgren, F. & Rue, H. Bayesian computing with INLA: New features. Comput. Stat. Data Anal. 67, 68–83 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  47. Hijmans, R. J. & van Etten, J. raster: Geographic Analysis and Modeling with raster. Data (Kbh.) (2012)

  48. Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the Geospatial Data Abstraction Library (2015)

  49. Plummer, M. & Stukalov, A. rjags: Bayesian Graphical Models using MCMC (2013)

  50. Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  51. Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol. 71, 319–392 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  52. Huelsenbeck, J. P., Rannala, B. & Masly, J. P. Accommodating phylogenetic uncertainty in evolutionary studies. Science 288, 2349–2350 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  53. Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004)

    Article  Google Scholar 

  55. New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002)

    Article  Google Scholar 

  56. Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015)

    Article  ADS  CAS  PubMed  Google Scholar 

  57. Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. & Hu, A. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311, 1751–1753 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  58. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    Article  ADS  Google Scholar 

  59. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005)

    Article  Google Scholar 

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Acknowledgements

We thank R. E. Ricklefs, P. V. A. Fine, T. Price, C. D. Cadena, J. Beck, E. Spriggs, N. Upham and R. Freckleton for manuscript comments; members of the Future Earth Global Mountain Biodiversity Assessment, including C. Körner, E. Spehn, M. Fischer, and D. Payne for feedback; B. Klempay, A. Houston and A. Ranipeta for help collecting the elevational data; and the ‘Monitoring Häufige Brutvögel (MBH)’ project of the Vogelwarte Sempach (M. Kery) for sharing data on the Swiss breeding bird survey from 2007. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. This material is based upon work supported by NSF grants DGE-1122492, DEB-1441737, and DBI-1262600.

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, 06520, Connecticut, USA

    Ignacio Quintero & Walter Jetz

  2. Department of Life Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, Berkshire, UK

    Walter Jetz

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  1. Ignacio Quintero
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Contributions

I.Q. and W.J. designed the research and wrote the manuscript. I.Q. conducted the analyses.

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Correspondence to Ignacio Quintero.

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Reviewer Information Nature thanks A. Antonelli 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 Figure 1 Elevational biases in species detection in Switzerland.

Results from N-mixture occupancy model showcasing the differences in species richness between the observed naive species richness, and the modelled species richness. a, Naive and estimated species richness at each of the 267 locations along elevation (n = 41,652 species presence/absence observations at different times). Closed grey points correspond to the observed richness while open blue points and orange bars correspond to the posterior average and 95% CI, respectively, of the estimated richness. To reduce figure cluttering, the estimated values are to the right of the corresponding elevation. b, Differences between naive and estimated richness relative to the observed species richness of the locality every 250 m (n = 267 localities). Effectively, this is the estimated proportion of species that are present but were not detected during the survey. As corroborated in the model parameters (Supplementary Information), the probability of detection of species decreases with higher elevations. Boxplot centres corresponds to the median, the upper and lower limit of the boxes correspond to the interquartile range, whiskers represent maximum or minimum points that do not exceed 1.5× the interquartile distance, and outliers correspond to points that exceed this distance. From left to right the n for each boxplot is: 3, 8, 33, 24, 15, 20, 11, 12, 7, 11, 15, 13, 11, 4, 6, 6, 19, 13, 12, 3, 8, 6, 4, 1, 2.

Extended Data Figure 2 Global mountain systems and sampling locations.

a, The final 46 distinct global mountain systems used in this study. b, Map of the sampling locations used in this study across the globe. Each sampling location is at least 1° apart in longitude and latitude and 500 m apart in elevation from every other sampling unit. This sampling allows for denser sampling in slopes and thus takes advantage of the added resolution brought by the species’ elevational ranges. c, Map of the subset of sampling locations that lie within the mountain systems in this study. Continental coastlines from Natural Earth.

Extended Data Figure 3 Sampling unit.

Graphical representation of a sampling unit used in this study. We tailored our sampling to the resolution of our data: range maps validated to approximately 1° latitude/longitude and 500 m elevation. The yellow coloured area in the map inset corresponds to the orange coloured area in the 3D plot and represents one such sampling unit at a random location in the Colombian Andes. Continental coastlines from Natural Earth.

Extended Data Figure 4 Comparisons between field work and range map species inventories.

Data and results from the multilevel regression between richness estimates from map ranges and field surveys. a, Each panel corresponds to an elevational transect in which species richness inventories were conducted. Red points correspond to the accrued richness count in the field and green points correspond to the richness inferred from expert range maps and elevational ranges at the same geographical coordinates. Each fieldwork locality varies markedly in sampling effort, focal group and elevational extent, among others; for specific information on each transect see Supplementary Table 2. b, Comparison of richness estimates: each line corresponds to the association between richness estimates from a field survey and expert range maps. The colour warmth corresponds to the natural logarithm of sampling effort. The dashed line corresponds to the 1:1 line. c, Plot of the estimated slope for each survey versus range map richness association against the natural logarithm of sampling effort.

Extended Data Figure 5 Comparison between tip DR and BAMM tip rates.

a, Average species tip DR versus BAMM tip speciation (left) and diversification rates (right). Continuous line corresponds to an ordinary linear regression; dashed line corresponds to the 1:1 line (n = 9,993 extant species). b, Average assemblage tip DR versus BAMM tip speciation (left) and diversification (right) rates. Continuous line corresponds to an ordinary linear regression; dashed line corresponds to the 1:1 line. See Supplementary Information for further information. c–e, Comparison between multilevel models of average assemblage tip DR (c), BAMM tip diversification (d) and speciation rates (e) along elevation (equation (4) in Methods). In each regression, the black line corresponds to the mean while the grey dashed lines correspond to the 95% credible interval (CI). Higher colour intensity corresponds to higher density of points. f, Comparison between the intercept and the linear and quadratic coefficients (posterior average and 95% CI) from the three regressions in c–e. For b–f, n = 8,410 assemblages.

Extended Data Figure 6 Comparison between tip DR and clade-level diversification metrics.

Comparison between average tip DR, estimates of speciation and diversification given a birth–death model, and average BAMM tip diversification and speciation rates for clades defined through different nested spatial boundaries and different trees. ‘Tree’ refers to the species group present in the tree when estimating the rates, while ‘clade’ refers to the species group for which rates are being estimated. For instance, if the clade is ‘Andes’ and the tree is ‘South America’, then we used a tree with every non-South American species pruned out, and estimated the rates among the species present in the Andes only. Global corresponds to all bird species, South America to all South American species, Andes to all species present in our mountain delineation of the Cordillera de los Andes, and alpha Andes to all species intersecting with a random point within the Cordillera de los Andes. Similarly, Australia corresponds to all Australian species, Great Dividing Range to all species present in our delineation of the Great Dividing Range and alpha Great Dividing Range to all species intersecting with a random point within this mountain system. Boxplot centres show to the median, the upper and lower limit of the boxes correspond to the interquartile range, whiskers represent maximum or minimum points that do not exceed 1.5× the interquartile distance, and outliers correspond to points that exceed this distance. Each boxplot consists of: for tip DR, n = 100 tip DR harmonic means for 100 trees; for BAMM diversification and speciation rates, n = harmonic averages across the species for 50 trees; for birth–death diversification and speciation rates, n = 10,000 samples from the posterior.

Extended Data Figure 7 Correlation matrix between the different mountain system level predictors used.

Pearson’s correlation coefficient for mountain level covariates (n = 46).

Extended Data Figure 8 Mountain system level predictors for non-subsampled data.

Global multilevel models with mountain system as random effect and mountain-level predictors for non-subsampled assemblages. We used total mountain area, total elevational range, average NPP, average temperature, mountain system age, average latitude, average temperature seasonality, average surrounding band temperature, regional species richness and past rates of temperature change. Each line corresponds to a mountain system; lines are coloured such that the redder end of the spectrum corresponds to higher covariate values while bluer colours correspond to lower values. The inset plots show the effect of each covariate on the intercept (γ0), the linear (γ1) and the quadratic coefficient (γ2; posterior average and 95% CI); blue coloured effects correspond to coefficients where the 95% CI does not overlap with 0 (that is, Pr(effect is not 0) > 0.975). Vertical dashed black line corresponds to the mean of the x-axis values (that is, corresponds to ‘0’ when the x axis is standardized). For interpretation of these results see Supplementary Information. Multilevel models assessing the effects of mountain characteristics on assemblage richness along elevation (a), assemblage tip DR along elevation (b) and the relationship of assemblage richness with tip DR (c). For all regressions, n = 8,410 assemblages.

Extended Data Figure 9 Mountain system level predictors for subsampled data.

Global multilevel models with mountain system and sampling locality as random effect and mountain-level predictors. Figure description as in Extended Data Fig. 8. For all regressions, n = 42,526 subsampled assemblages.

Extended Data Figure 10 Sensitivity of multilevel model results to resolution of species elevational range data.

a, b, Raw assemblage richness along elevation contrasting the use of 500 m (a) and 300 m (b) resolution in elevation sampling. The black solid line corresponds to the expectation of species richness along elevation, while the grey dashed lines correspond to the 95% CI. Higher colour intensity corresponds to higher density of points. c, Parameter quantiles (0.025, 0.5 & 0.975) for the intercept, slope and quadratic coefficient of the regressions shown in a, b. d–f, The same relationships as a–c but for tip DR as response. For 500 m, n = 8,410 assemblages; for 300 m, n = 14,218 assemblages.

Supplementary information

Life Sciences Reporting Summary (PDF 70 kb)

Supplementary Information

This file contains Supplementary Text, Supplementary references and Supplementary Figures 1-46. (PDF 13102 kb)

Supplementary Table 1

Elevational ranges for each species for each mountain range they inhabit. The fields are: Species, following the taxonomy of (Jetz et al. 2012); Country, specifies, for Andean species only (i.e., Mountain ID 404), which country the elevational range corresponds to; Mountain ID, specifies the mountain system the elevational range corresponds to (Mountain ID can be matched with the name in Table S3); Minimum Elevation and Maximum Elevation, corresponds to the elevational range used in this study; Source and Notes, species-specific references and, sometimes, specific decisions for certain elevational ranges. (XLSX 1281 kb)

Supplementary Table 2

Field surveys along elevation we used to compare and validate the assemblage richness data (Extended Data Fig. 3). The fields are: ID, the ID of each independent elevational transect; N_sites, number of localities where species richness was assessed; min_ele and max_ele, the elevational extent of the survey; Effort_months, an approximate measure of time spent in the filed during the survey (in months); Bird Group, the focal subset of bird species surveyed, ALL = all species, F = Forest species, B = breeding species only; Locality; Country; Reference; Notes, transect specific notes for some of the surveys. (XLSX 49 kb)

Supplementary Table 3

Compilation of mountain ranges uplift times from the literature. The fields are: Continent; Mountain ID, specifies the mountain system; Mountain Range, specifies the mountain range the Mountain ID corresponds to; Uplift Ages, the first pulse of uplift (Start) and the most recent one (Last) and other significant ones in between (Other significant); Notes, specific notes related to the information compiles; Reference, numeric correspondence with the articles below where the information was obtained. (XLSX 27 kb)

Supplementary Table 4

Specific Global Climate Models and Modeling groups used in this study. (XLSX 9 kb)

Supplementary Table 5

Cross tables summarizing parameter changes from the sensitivity analyses. (XLSX 22 kb)

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Quintero, I., Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018). https://doi.org/10.1038/nature25794

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