An official website of the United States government
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.
more »
« less
This content will become publicly available on October 1, 2026
Identifying genomic adaptation to local climate using a mechanistic evolutionary model
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
- PAR ID:
- 10654171
- Publisher / Repository:
- John Wiley & Sons Ltd
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 16
- Issue:
- 10
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 2448 to 2460
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Summary Local adaptation to climate is common in plant species and has been studied in a range of contexts, from improving crop yields to predicting population maladaptation to future conditions. The genomic era has brought new tools to study this process, which was historically explored through common garden experiments.In this study, we combine genomic methods and common gardens to investigate local adaptation in red spruce and identify environmental gradients and loci involved in climate adaptation. We first use climate transfer functions to estimate the impact of climate change on seedling performance in three common gardens. We then explore the use of multivariate gene–environment association methods to identify genes underlying climate adaptation, with particular attention to the implications of conducting genome scans with and without correction for neutral population structure.This integrative approach uncovered phenotypic evidence of local adaptation to climate and identified a set of putatively adaptive genes, some of which are involved in three main adaptive pathways found in other temperate and boreal coniferous species: drought tolerance, cold hardiness, and phenology. These putatively adaptive genes segregated into two ‘modules’ associated with different environmental gradients.This study nicely exemplifies the multivariate dimension of adaptation to climate in trees.more » « less
-
Abstract Stomata play a critical role in regulating plant responses to climate. Where sister species differ in stomatal traits, interspecific gene flow can influence the evolutionary trajectory of trait variation, with consequences to adaptation.Leveraging six latitudinally-distributed transects spanning the natural hybrid zone betweenPopulus trichocarpa–P. balsamifera, we used whole genome resequencing and replicate common garden experiments to test the role that interspecific gene flow and selection play to stomatal trait evolution.While species-specific differences in the distribution of stomata persist betweenP. balsamiferaandP. trichocarpa, hybrids on average resembledP. trichocarpa. Admixture mapping identified several candidate genes associated with stomatal trait variation in hybrids includingTWIST, a homolog ofSPEECHLESSinArabidopsis, that initiates stomatal development via asymmetric cell divisions. Geographic clines revealed candidate genes deviating from genome-wide average patterns of introgression, suggesting restricted gene flow and the maintenance of adaptive differences. Climate associations, particularly with precipitation, indicated selection shapes local ancestry at candidate genes across transects.These results highlight the role of climate in shaping stomatal trait evolution inPopulusand demonstrate how interspecific gene flow creates novel genetic combinations that may enhance adaptive potential in changing environments.more » « less
-
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.more » « less
-
Summary In coevolving species, parasites locally adapt to host populations as hosts locally adapt to resist parasites. Parasites often outpace host local adaptation since they have rapid life cycles, but host diversity, the strength of selection, and external environmental influence can result in complex outcomes.To better understand local adaptation in host–parasite systems, we examined locally adapted switchgrass (Panicum virgatum), and its leaf rust pathogen (Puccinia novopanici) across a latitudinal range in North America. We grew switchgrass genotypes in 10 replicated multiyear common gardens, measuring rust severity from natural infection in a ‘host reciprocal transplant’ framework for testing local adaptation. We conducted genome‐wide association mapping to identify genetic loci associated with rust severity.Genetically differentiated rust populations were locally adapted to northern and southern switchgrass, despite host local adaptation to environmental conditions in the same regions. Rust resistance was polygenic, and distinct loci were associated with rust severity in the north and south. We narrowed a previously identified large‐effect quantitative trait locus for rust severity to a candidate YELLOW STRIPE‐LIKE gene and linked numerous other loci to defense‐related genes.Overall, our results suggest that both hosts and parasites can be simultaneously locally adapted, especially when parasites impose less selection than other environmental factors.more » « less
