Data from: Genomic vulnerability of a sentinel mammal under climate change
Data files
Feb 14, 2025 version files 2.64 MB
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indv_info_update.txt
16.47 KB
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Rcode_SchmidtRussello_update.r
48.75 KB
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README.md
3.26 KB
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site_info.txt
1.55 KB
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VCF.zip
2.57 MB
Abstract
Climate change poses a significant threat to biodiversity, particularly in alpine ecosystems where species have already undergone elevational range shifts. Genomics can be used to estimate the adaptive potential of species, as well as the shift in adaptive genomic composition necessary for populations to adjust to climate change (e.g. genomic offset). Here, we investigated patterns of climate-mediated adaptive genetic variation and predicted the degree of genomic offset under multiple climate change scenarios for a sentinel alpine mammal, the American pika (Ochotona princeps). We collected genome-wide data (29,709 SNPs) from 363 individuals spanning the entire range in western North America and employed genotype-environment association analyses to identify 938 robust outlier SNPs, several of which were linked to genes previously associated with high elevation and hypoxia responses in various pika species (Ochotonidae). Adaptive genomic variation was most strongly influenced by mean warmest month temperature, followed by precipitation of the coldest quarter. Spatial patterns of genomic offset were heterogeneous, significantly predicted by levels of adaptive genetic variation, elevation and latitude. Sites within the Northern Rocky Mountains exhibited the highest genomic offset under projected climate change despite possessing high levels of adaptive genetic variation. As such, while our study provides an example of how genomic data can be used to explore the potential consequences of climate change, it further highlights the need for careful consideration of genomic offset values within their proper ecological context.
GENERAL INFORMATION
- Title of Dataset: Data from: Genomic vulnerability of a sentinel mammal under climate change
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Author Information:
Name: Dr Danielle Schmidt Institution: The University of British Columbia, Kelowna, British Columbia, Canada Email: dschmid2@uw.edu Name: Dr Michael Russello Institution: The University of British Columbia, Kelowna, British Columbia, Canada Email: michael.russello@ubc.ca
- Date of data collection: 2014-2020
- Geographic location of data collection: Western North America (USA and Canada; see metadata for specific location information)
- Funding sources that supported the collection of the data: Natural Sciences and Engineering Research Council of Canada
- Citation: Schmidt, Danielle; Russello, Michael (2025), Data from: Genomic vulnerability of a sentinel mammal under climate change, Dryad, Dataset,
#DRYAD FILES INCLUDE:
#VCFs: these files are formatted to have one row per sample, with one genotype per column. Each column represents a single-nucleotide polymorphisms (SNP)
#363_29709.vcf
This files contains the 29,709 SNPs used for outlier detection in this study, filtered from Schmidt et al. (2024). Dataset includes SNPs genotyped across 363 American pika individuals sampled across the entire range distribution (see indv_info_update.txt.txt)
#363_924.recode.vcf
This file contains the robust outlier dataset of 924 SNPs that were significantly correlated with composite climate variables that were generated for outlier detection in this study.
#363_924_shuffled.vcf
This file contins the shuffled dataset of 924 robust outlier SNPs used to test that the outlier dataset gradient forest model was not driven by differences in environmnet across sampling sites. This file was generated using custom bash scripting to randomize the genotypes for each SNP across individuals.
#SAMPLE INFORMATION
#indv_info_update.txt
This file contains individual metadata used in R analyses to download climate data for the genotype-environment association analyses (GEAs) [i.e. outlier detection]
Latitude (in decimal degrees)
Longitude (in decimal degrees)
Elevation (m)
Lin= lineage of American pikas of a given sample based on Schmidt et al. (2024)
CR= Cascade Range
CRM= Central Rocky Mountains
CU= Central Utah
SN= Sierra Nevada
NRM= Northern Rocky Mountains
SRM= Southern Rocky Mountains
site= sampling site number of a given sample
#site_info.txt
This file contains site metadata used in the R analyses to download climate data for the gradient forest modeling, produce Principal Coordinates of Neighbor Matrices (PCNMs), and to plot figures
ID1= the sampling site
ID2= numerical site number
Latitude (in decimal degrees)
Longitude (in decimal degrees)
Elevation (m)
#CODE
#Rcode_SchmidtRussello.R
This file contains R scripts used for data analysis:
A. Downloading and extracting WorldClim2 climate data for individuals and sites
B. Generating composite cliamte variables
C. Downloading and extracting topographical data from a digital elevation model
D. Latent factor mixed modeling (LFMM)
E. Redundancy analysis (RDA)
F. Identifying robust outliers
G. Generating PCNMs
H. Gradient forest modeling
I. Plotting genomic offset maps
VCF: folder of VCF files of the full dataset used for outlier detection (363_29709.vcf), the robust outlier dataset of 924 SNPs (363_924.recode.vcf), and the shuffled dataset of 924 robust outlier SNPs (363_924_shuffled.vcf)
indv_info_update.txt: Text file of individual metadata used in R analyses
site_info.txt: Text file of site metadata used in the R analyses
Rcode_SchmidtRussello: R code used for data analysis