DOI: 10.5061/dryad.t4b8gtj5x
title: Dataset for: Paleoenvironmental models for Australia and the impact of aridification on blindsnake diversification

description: Aim: Shifts in diversification rates of Australian flora and fauna have
 been associated with aridification, but the relationship between
 diversification rates and aridity has never been quantified. We employed
 multiple approaches to accurately reconstruct paleoenvironments of
 Australia for the first time. We used this information, and
 phylogenetic-based analyses, to explore how changes in temperature and
 increasing aridity during the Neogene influenced the diversification of
 the Australian blindsnakes. We tested whether diversification rates differ
 between arid-adapted and mesic-adapted lineages. Taxon: Typhlopidae,
 Anilios blindsnakes Location: Australia Materials and Methods: We
 estimated the historical biogeography of blindsnakes using BioGeoBEARS. We
 synthesised multiple approaches to reconstruct paleotemperature and
 paleoaridity of Australia during the Neogene. We fit several birth-death
 models and estimate diversification rates under paleoenvironmental
 conditions using RPANDA. We further compare diversification rates between
 arid-adapted lineages versus mesic-adapted lineages using ClaDS and
 GeoHiSSE. Results: Ancestral area estimation indicated Australian
 blindsnakes have tropical grassland origins. We found that
 Australia-specific regional paleotemperature and paleoaridity provided a
 better explanation for diversification rate variation than global
 paleotemperature. Specifically, our best-fitting model indicated that
 speciation rates of blindsnakes decreased with increasing aridity. We
 found no difference in diversification rates between arid- and
 mesic-adapted lineages. Main conclusions: Soon after dispersing to
 Australia, the common ancestors of Australian blindsnakes diversified
 rapidly in mesic habitats during the early Miocene. However, as the
 continent became increasingly arid, diversification rates decreased. We
 found that shifts in the environment led to the emergence of two major
 clades: one remaining in primarily mesic habitats and the other adapting
 to the expanding arid biome. Our results emphasise the importance of both
 arid and tropical biomes as sources and sinks of diversification.
description: Check the README.md for details about how to structure your
 analyses folder. All scripts can be run using
 open-source software. R is required to run
 R scripts (.R). Julia is required to run Julia
 scripts (.jl).
description: # Data set for "Paleoenvironmental models for Australia and the
 impact of aridification on blindsnake diversification" --- These data
 sets were used to perform analyses included in the research paper
 "Paleoenvironmental models for Australia and the impact of
 aridification on blindsnake diversification." Main aims for the
 project: 1. Estimate the historical biogeography of Australian blindsnakes
 using 'BioGeoBEARS.' 2. Fit birth-death models and estimate
 diversification rates under different paleoenvironmental conditions using
 'RPANDA.' 3. Compare diversification rates between arid-adapted
 and mesic-adapted lineages using 'ClaDS' and 'hisse.'
 ## Data and file structure **/2021_ALA_blindsnake_occurence_data/** --
 this folder should be replaced with the
 2021_ALA_blindsnake_occurence_data.zip on Zenodo
 ([https://doi.org/10.5281/zenodo.7571419](https://doi.org/10.5281/zenodo.7571419)) * Data from ALA can be downloaded at [https://doi.org/10.26197/ala.d92678b1-ad2d-437b-9457-9f52737ba003](https://doi.org/10.26197/ala.d92678b1-ad2d-437b-9457-9f52737ba003) **/bears_txt/** -- this folder contains .txt files necessary for fitting biogeographical models in' BioGeoBEARS' * geofile.txt - geographic range for each species * biome_distance.txt - modify distance for +x analysis **/intermediate_data/bears/** -- files in this folder are generated from code * blindsnake_b.tre - phylogeny for fitting BioGeoBEARS models. **/intermediate_data/diversification_analyses/** -- file for 'RPANDA' analyses * blindsnake.trees - contains two versions of *Anilios* trees. **/intermediate_data/geohisse/** * 2022_species_list_arid_nonarid_widespread.csv - includes information about which species are in the phylogeny to calculate fraction. * arid_nonarid_both_states.csv - list of lineages and their geographic state. 0 = widespread, 1 = arid, and 2 = mesic. Designation of geographic states were based on distribution data and literature review. * arid_nonarid_both_states.txt - .txt version of the previous file **/paleo_env** -- this folder contains the reconstructed paleoenvironment data from three data sources as described in the paper. * Australia_climate_data_45Mya_Scotese.csv - reconstructed from Scotese & Wright, (2018) * Australia_climate_data_45Mya_Straume.csv - reconstructed from Straume et al., (2020) * Australia_climate_data_45Mya_Valdes.csv - reconstructed from Valdes et al., (2021) * Australia_climate_data_70Mya_Scotese.csv - reconstructed from Scotese & Wright, (2018) * Australia_climate_data_65Mya_Straume.csv - reconstructed from Straume et al., (2021) * climate_data_headers.txt - expalanation of climate data headers **/tree/** -- this folder contains the subset of phylogenies for *Anilios*. * anilios_newick_b.tre - tree with *A. splendidus* * anilios_newick_st.tre - tree with *A. splendidus* * subset_anilios_newick_b.tre - tree without *A. splendidus* * subset_anilios_newick_st.tre - tree without *A. splendidus* ## Other information Zenodo repository for "Data set: Australia's hidden radiation - phylogenomic analysis reveals rapid Miocene radiation of blindsnakes" * *Anilios* phylogeny can be found at [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7155340.svg)](https://doi.org/10.5281/zenodo.7155340) ## Code/Software All scripts can be run using open source software. * R is required to run R scripts (.R). * Julia is required to run Julia scripts (.jl). **/Code** * 00_*.R - scripts are used for preparing data for analyses * 01_fit_*.R - scripts were used to fit various RPANDA models. Note difference in initial lambda parameters for some models. * 01_env_data_plots.R - script to plot different paleoenvironmental data * 02_diversification_plots_b.R - script for plotting results * 02_diversification_plots_b.R - script for plotting results * 02_table_fit_env_results_b.R - script to summarise model fit * 03_BioGeoBEARS_analyses_parallel.R - script for fitting multiple BioGeoBEARS models * 04_BioGeoBEARS_results_bsm.R - Conduct Biogeographic Stochastic Mapping for the best fitting model * 04_BioGeoBEARS_results_plots.R - plotting Biogeographic Stochastic Mapping from the best fitting model * 04_BioGeoBEARS_results_tables.R - Biogeographic Stochastic Mapping result table from the best fitting model * 05_GeoHiSSE_fit.R - fit GeoSSE and GeoHiSSE using 'hisse' package. * 06_ClaDS2.jl - Julia script to estimate branch-specific rate under ClaDS * 06_ClaDS_plot_tips.R - plot results from ClaDS ## Contact Should you have questions about these analysis scripts, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github ([https://github.com/stiatragul](https://github.com/stiatragul))