DOI: 10.5061/dryad.jsxksn0m4
title: Data from: Genomic vulnerability of a sentinel mammal under climate change

description: 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.
description: 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 
description: GENERAL INFORMATION 1. Title of Dataset: Data from: Genomic vulnerability
 of a sentinel mammal under climate change 2. 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 ``` 3. Date of data
 collection: 2014-2020 4. Geographic location of data collection: Western
 North America (USA and Canada; see metadata for specific location
 information) 5. Funding sources that supported the collection of the data:
 Natural Sciences and Engineering Research Council of Canada 6. 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