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Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities


Braga, Pedro Henrique P.; Kembel, Steven; Peres-Neto, Pedro (2023), Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities, Dryad, Dataset,


Aim: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.

Location: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.

Time period: Last Glacial Maximum (~22,000 years ago) to the present.

Major taxa studied: Bats (Chiroptera)

Methods: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.

Results: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.

Main conclusions: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.


Details on the applications of this dataset are found within the Methods section of our study, accepted for publication in Global Ecology and Biogeography.

Usage notes

Code availability

All code necessary to reproduce the analyses, figures, and BAMM setup files are available within the Open Science Framework repository ( for this study.


All data manipulation and analyses were performed in R and RStudio (R Core Team, 2021; RStudio Team, 2021). Parallelized computations were done using snowfall and doSNOW (Knaus, 2015; Corporation & Weston, 2022). Geospatial manipulation was done using the sf, raster, terra, and exactextractr packages (Pebesma, 2018; Hijmans, 2021, 2022). Velocities of climate change and local spatial and long-term climatic gradients were calculated using the VoCC package (García Molinos et al., 2019). Phylogenetic manipulation and analyses were done using the packages ape, picante, PhyloMeasures and phangorn (Kembel et al., 2010; Schliep, 2011; Tsirogiannis & Sandel, 2016, 2017; Paradis & Schliep, 2019). Diversification rate estimation and posterior manipulation were done using BAMM 2.5.0 and the BAMMtools package (Rabosky et al., 2014). Effective sizes and diagnostics for MCMC chains were performed using the coda package (Plummer et al., 2006). Robust analyses of variance for repeated measurements were performed using the WRS2 package (Mair & Wilcox, 2020). Robust generalized linear models were performed with the robust package (Wang et al., 2022). Quantile regressions were performed using the quantreg package (Koenker, 2021). General data manipulation was done with the tidyverse package suite (Wickham et al., 2019). Figures were generated using the ggplot2 package (Wickham, 2016).


Analyses were run on servers with Intel Xeon E5-2630 v4 (40) @ 3.100GHz, 32 to 64 GB RAM, and running on Ubuntu 22.04.1 LTS.


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Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: CNPq-SwB-GDE 142493/2013-0

Natural Sciences and Engineering Research Council of Canada