DOI: 10.5061/dryad.rjdfn2zgj
title: Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities

description: 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.
description: Details on the applications of this dataset are found
 within the Methods section of our study, accepted for publication in
 Global Ecology and Biogeography.
description: <strong>Code availability</strong>
 All code necessary to reproduce the analyses, figures, and BAMM
 setup files are available within the Open Science Framework repository
 (https://osf.io/amvp5/) for this study.
 <strong>Software</strong> 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 <em>et
 al</em>., 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 <em>et
 al</em>., 2014). Effective sizes
 and diagnostics for MCMC chains were performed using the
 coda package (Plummer <em>et
 al</em>., 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
 <em>et al</em>., 2019).
 Figures were generated using the ggplot2 package (Wickham, 2016).
 <strong>Hardware</strong> 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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