DOI: 10.5061/dryad.m0cfxpp12
title: Data: The effects of weather variability on patterns of genetic diversity in Tasmanian bettongs

description: While the effects of climate (long-term, prevailing weather) on species
 abundance, range, and genetic diversity are widely studied, short-term,
 localised variations in atmospheric conditions (i.e., weather) can also
 rapidly alter species’ geographic ranges and population sizes, but little
 is known about how they affect genetic diversity. We investigated the
 relationship between weather and range-wide genetic diversity in a
 marsupial, Bettongia gaimardi, using dynamic species distribution models
 (SDMs). Genetic diversity was lower in parts of the range where
 weather-based SDM predicted high variability in probability of B. gaimardi
 occurrence during 1950–2009. This is likely an effect of lower population
 sizes and extinction-recolonisation cycles in places with highly variable
 weather. Spatial variation in genetic diversity was also better predicted
 by mean probabilities of B. gaimardi occurrence from weather- than
 climate-based SDMs. Our results illustrate the importance of weather in
 driving population dynamics and species distributions on decadal
 time-scales and thereby affecting genetic diversity. Modelling the links
 between changing weather patterns, species distributions and genetic
 diversity will allow researchers to better forecast biological impacts of
 climate change.
description: The genetic data are represented by a genepop file of 5057
 filtered SNP loci from 188 <em>Bettongia
 gaimardi</em> individuals, and a csv file containing metadata for
 each individual sampled. Ear biopsies were collected from 188 live-trapped
 or road-killed <em>B. gaimardi </em>individuals at 17 sites
 across Tasmania in 2006-2007 and 2015-2017. Individuals were genotyped
 using a reduced representation sequencing technique,
 DArTseq<sup>TM</sup>, and the resulting SNPs were filtered as
 described in the Supplementary Materials S1 section of the associated
 paper. The
 climate and weather model datasets represent rasters of probability of
 <em>B. gaimardi</em> occurrence based on climate models, and
 mean and standard deviation of probability of occurrence based on weather
 models. As described in the associated paper, we used Maxent to create
 models of probability of occurrence based on climate and weather, using
 1043 occurrence records spanning the years 1961 – 2009, and 8 long-term
 climatic variables (climate model) or 14 short-term weather variables
 (weather model). Climate and weather probability of occurrence were then
 projected for <em>B. gaimardi </em>in each grid cell across
 Tasmania. A single projection was created for the climate model. For
 weather, we projected probability of occurrence at monthly intervals from
 1950 – 2009, and then calculated the mean and standard deviation of
 probability in each grid cell across the whole time period. Weather and
 climate suitability models were developed following the methodology of
 Bateman et al. (2012)
description: The SNP alleles in the genepop file are represented
 as ‘03’ and ‘04’. Missing values are represented by ‘0000’. In the
 metadata file, samples (and populations) are listed in the same order as
 in the genepop file, and missing data is represented by 0.
 The No Data value for the raster files is
 -3.39999995214e+38
description: # Data: The effects of weather variability on patterns of genetic
 diversity in Tasmanian bettongs
 [https://doi.org/10.5061/dryad.m0cfxpp12](https://doi.org/10.5061/dryad.m0cfxpp12) ## Description of the data and file structure Data were collected to assess the relationship between past weather suitability at a site for a native marsupial, Bettongia gaimardi, and the population genetic diversity at that site. ### Files and variables #### File: Weather\_probability\_mean.tif **Description:** raster of mean weather probability. The No Data value for the raster files is -3.39999995214e+38 #### File: Weather\_probability\_sd.tif **Description:** raster of standard deviation weather probability. The No Data value for the raster files is -3.39999995214e+38 #### File: Climate\_probability.tif **Description:**  raster of mean climate probability. The No Data value for the raster files is -3.39999995214e+38 #### File: SNP\_dataset\_Proftetal.gen **Description:** SNP dataset representing 5057 filtered SNPs from 188 individuals. Genepop format. The SNP alleles in the genepop file are represented as ‘03’ and ‘04’. Missing values are represented by ‘0000’. #### File: Proft\_et\_al\_mtDNA\_haplotypes.arp **Description:** mitochondrial DNA dataset from 188 individuals. Arlequin format. #### File: Genetic\_sample\_metadata\_final.csv **Description:** Metadata that accompanies the genetically sampled individuals. Samples (and populations) are listed in the same order as in the genepop file, and missing data is represented by NA. Variables are Sample_name (denoting an individual animal), Population (denoting the geographic location representing the population), Sex (individual sex), Eastings_individual and Northings_individual (exact sampling location of the individual), and Eastings_pop_centroid and Northings_pop_centroid (centroid of samples representing a population). ## Code/software Text editors can be used to access the data files, with the exception of the raster files, for which the raster package in R can be used or a regular photo viewer.