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Summary Genetic load can reduce fitness and hinder adaptation. While its genetic underpinnings are well established, the influence of environmental variation on genetic load is less well characterized, as is the relationship between genetic load and putatively adaptive genetic variation. This study examines the interplay among climate, species range dynamics, adaptive variation, and mutational load – a genomic measure of genetic load – inVitis arizonica, a wild grape native to the American Southwest.We estimated mutational load and identified climate‐associated adaptive genetic variants in 162 individuals across the species' range. Using a random forest model, we analyzed the relationship between mutational load, climate, and range shifts.Our findings linked mutational load to climatic variation, historical dispersion, and heterozygosity. Populations at the leading edge of range expansion harbored higher load and fewer putatively adaptive alleles associated with climate. Climate projections suggest thatV. arizonicawill expand its range by the end of the century, accompanied by a slight increase in mutational load at the population level.This study advances understanding of how environmental and geographic factors shape genetic load and adaptation, highlighting the need to integrate deleterious variation into broader models of species response to climate change.
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Life on the edge: A new toolbox for population‐level climate change vulnerability assessments
Abstract Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range.To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatusandA. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escaleraiandM. crypticus).Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers.
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- PAR ID:
- 10547845
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 11
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 2038-2058
- Size(s):
- p. 2038-2058
- Sponsoring Org:
- National Science Foundation
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