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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:
- 10579473
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 11
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 2038 to 2058
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Identifying genomic adaptation is key to understanding species' evolutionary responses to environmental changes. However, current methods to identify adaptive variation have two major limitations. First, when estimating genetic variation, most methods do not account for observational uncertainty in genetic data because of finite sampling and missing genotypes. Second, many current methods use phenomenological models to partition genetic variation into adaptive and non‐adaptive components.We address these limitations by developing a hierarchical Bayesian model that explicitly accounts for observational uncertainty and underlying evolutionary processes. The first layer of the hierarchy is the data model that captures observational uncertainty by probabilistically linking RAD sequence data to genetic variation. The second layer is a process model that represents how evolutionary forces, such as local adaptation, mutation, migration and drift, maintain genetic variation. The third layer is the parameter model, which incorporates our knowledge about biological processes. For example, because most loci in the genome are expected to be neutral, the environmental sensitivity coefficients are assigned a regularized prior centred at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms.Analysis of simulated RAD‐seq data shows that our statistical model can reliably infer adaptive genetic variation. To show the real‐world applicability of our method, we re‐analysed RAD‐Seq data (~105 k SNPs) from Willow Flycatchers (Empidonax traillii) in the United States. We found 30 genes close to 47 loci that showed a statistically significant association with temperature seasonality. Gene ontology suggests that several of these genes play a crucial role in egg mineralization, feather development and the ability to withstand extreme temperatures.Moreover, the data and process models can be modified to accommodate a wide range of genetic datasets (e.g. pool and low coverage genome sequencing) and demographic histories (e.g. range shifts) to study climatic adaptation in a wide range of natural systems.more » « less
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Summary Eukaryotic genomes harbor many forms of variation, including nucleotide diversity and structural polymorphisms, which experience natural selection and contribute to genome evolution and biodiversity. However, harnessing this variation for agriculture hinges on our ability to detect, quantify, catalog, and utilize genetic diversity.Here, we explore seven complete genomes of the emerging biofuel crop pennycress (Thlaspi arvense) drawn from across the species’s current genetic diversity to catalogue variation in genome structure and content.Across this new pangenome resource, we find contrasting evolutionary modes in different genomic regions. Gene-poor, repeat-rich pericentromeric regions experience frequent rearrangements, including repeated centromere repositioning. In contrast, conserved gene-dense chromosome arms maintain large-scale synteny across accessions, even in fast-evolving immune genes where microsynteny breaks down across species but the macrosynteny of gene cluster positioning is maintained.Our findings highlight that multiple elements of the genome experience dynamic evolution that conserves functional content on the chromosome scale but allows rearrangement and presence-absence variation on a local scale. This diversity is invisible to classical reference-based approaches and highlights the strength and utility of pangenomic resources. These results provide a valuable case study of rapid genomic structural evolution within a species and powerful resources for crop development in an emerging biofuel crop.more » « less
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ABSTRACT Restoring connectivity via assisted migration is a useful but currently underused approach for maintaining genetic diversity and preventing extirpations of threatened species. The use of assisted migration as a conservation strategy may be limited by the difficulty of balancing the benefits of reconnecting populations (including reduced inbreeding depression and increased adaptive capacity) with the perceived risk of outbreeding depression, which requires comprehensive knowledge of the landscape of adaptive, neutral, deleterious, and structural variation across a species' range. Using a combination of reduced‐representation and whole‐genome sequencing, we characterized genomic diversity and differentiation for the Arkansas Darter (Etheostoma cragini) across its range in the Midwestern US. We found strong population structure and large differences in genetic diversity and effective population sizes across drainages. The strength of genetic isolation by river distance differed among drainages, with landscape type surrounding streams and impoundments also contributing to genetic isolation. Despite low effective population sizes in some populations, there was surprisingly little evidence for recent inbreeding (based on the absence of long runs of homozygosity) or for elevated levels of deleterious variation in smaller populations. Considering neutral, adaptive, deleterious, and structural variation allowed us to identify several potential recipient populations that may benefit from translocations and potential donor sites throughout the range. Planning translocation strategies intended for restored connectivity and possible genetic rescue at earlier stages in species decline will likely increase the probability of retaining genetic diversity and population persistence over the long term while minimizing risks associated with translocation.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.14429
