Data: The effects of weather variability on patterns of genetic diversity in Tasmanian bettongs
Data files
Aug 26, 2024 version files 10.36 MB
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Climate_probability.tif
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Genetic_sample_metadata_final.csv
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Proft_et_al_mtDNA_haplotypes.arp
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README.md
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SNP_dataset_Proftetal.gen
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Weather_probability_mean.tif
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Weather_probability_sd.tif
Abstract
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.
README: Data: The effects of weather variability on patterns of genetic diversity in Tasmanian bettongs
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.
Methods
The genetic data are represented by a genepop file of 5057 filtered SNP loci from 188 Bettongia gaimardi individuals, and a csv file containing metadata for each individual sampled. Ear biopsies were collected from 188 live-trapped or road-killed B. gaimardi individuals at 17 sites across Tasmania in 2006-2007 and 2015-2017. Individuals were genotyped using a reduced representation sequencing technique, DArTseqTM, 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 B. gaimardi 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 B. gaimardi 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)
Usage notes
- 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