Genotype-environment Association Research Articles

Genetic basis of local adaptation in the cold-tolerant mangrove Kandelia obovata.

Understanding the genetic basis of local adaption is crucial in the context of global climate change. Mangroves, as salt-tolerant trees and shrubs in the intertidal zone of tropical and subtropical coastlines, are particularly vulnerable to climate change. Kandelia obovata, the most cold-tolerant mangrove species, has undergone ecological speciation from its cold-intolerant counterpart, Kandelia candel, with geographic separation by the South China Sea. In this study, we conducted whole-genome re-sequencing of K. obovata populations along China's southeast coast, to elucidate the genetic basis responsible for mangrove local adaptation to climate. Our analysis revealed a strong population structure among the three K. obovata populations, with complex demographic histories involving population expansion, bottleneck, and gene flow. Genome-wide scans unveiled pronounced patterns of selective sweeps in highly differentiated regions among pairwise populations, with stronger signatures observed in the northern populations compared to the southern population. Additionally, significant genotype-environment associations for temperature-related variables were identified, while no associations were detected for precipitation. A set of 39 high-confidence candidate genes underlying local adaptation of K. obovata were identified, which are distinct from genes under selection detected by comparison between K. obovata and its cold-intolerant relative K. candel. These results significantly contribute to our understanding of the genetic underpinnings of local adaptation in K. obovata and provide valuable insights into the evolutionary processes shaping the genetic diversity of mangrove populations in response to climate change.

Genetic diversity, disease resistance, and environmental adaptation of Arachis duranensis L.: New insights from landscape genomics.

The genetic diversity that exists in natural populations of Arachis duranensis, the wild diploid donor of the A subgenome of cultivated tetraploid peanut, has the potential to improve crop adaptability, resilience to major pests and diseases, and drought tolerance. Despite its potential value for peanut improvement, limited research has been focused on the association between allelic variation, environmental factors, and response to early (ELS) and late leaf spot (LLS) diseases. The present study implemented a landscape genomics approach to gain a better understanding of the genetic variability of A. duranensis represented in the ex-situ peanut germplasm collection maintained at the U.S. Department of Agriculture, which spans the entire geographic range of the species in its center of origin in South America. A set of 2810 single nucleotide polymorphism (SNP) markers allowed a high-resolution genome-wide characterization of natural populations. The analysis of population structure showed a complex pattern of genetic diversity with five putative groups. The incorporation of bioclimatic variables for genotype-environment associations, using the latent factor mixed model (LFMM2) method, provided insights into the genomic signatures of environmental adaptation, and led to the identification of SNP loci whose allele frequencies were correlated with elevation, temperature, and precipitation-related variables (q < 0.05). The LFMM2 analysis for ELS and LLS detected candidate SNPs and genomic regions on chromosomes A02, A03, A04, A06, and A08. These findings highlight the importance of the application of landscape genomics in ex situ collections of peanut and other crop wild relatives to effectively identify favorable alleles and germplasm for incorporation into breeding programs. We report new sources of A. duranensis germplasm harboring adaptive allelic variation, which have the potential to be utilized in introgression breeding for a single or multiple environmental factors, as well as for resistance to leaf spot diseases.

Highly structured populations of copepods at risk to deep-sea mining: Integration of genomic data with demogenetic and biophysical modelling.

Copepoda is the most abundant taxon in deep-sea hydrothermal vents, where hard substrate is available. Despite the increasing interest in seafloor massive sulphides exploitation, there have been no population genomic studies conducted on vent meiofauna, which are known to contribute over 50% to metazoan biodiversity at vents. To bridge this knowledge gap, restriction-site-associated DNA sequencing, specifically 2b-RADseq, was used to retrieve thousands of genome-wide single-nucleotide polymorphisms (SNPs) from abundant populations of the vent-obligate copepod Stygiopontius lauensis from the Lau Basin. SNPs were used to investigate population structure, demographic histories and genotype-environment associations at a basin scale. Genetic analyses also helped to evaluate the suitability of tailored larval dispersal models and the parameterization of life-history traits that better fit the population patterns observed in the genomic dataset for the target organism. Highly structured populations were observed on both spatial and temporal scales, with divergence of populations between the north, mid, and south of the basin estimated to have occurred after the creation of the major transform fault dividing the Australian and the Niuafo'ou tectonic plate (350 kya), with relatively recent secondary contact events (<20 kya). Larval dispersal models were able to predict the high levels of structure and the highly asymmetric northward low-level gene flow observed in the genomic data. These results differ from most studies conducted on megafauna in the region, elucidating the need to incorporate smaller size when considering site prospecting for deep-sea exploitation of seafloor massive sulphides, and the creation of area-based management tools to protect areas at risk of local extinction, should mining occur.

The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO=0.65) was higher in the recipient population than predicted had no augmentation occurred (HO=0.58) but less than what was predicted by our model (HO=0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

How useful are genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

Genotype-environment associations reveal genes potentially linked to avian malaria infection in populations of an endemic island bird.

Patterns of pathogen prevalence are, at least partially, the result of coevolutionary host-pathogen interactions. Thus, exploring the distribution of host genetic variation in relation to infection by a pathogen within and across populations can provide important insights into mechanisms of host defence and adaptation. Here, we use a landscape genomics approach (Bayenv) in conjunction with genome-wide data (ddRADseq) to test for associations between avian malaria (Plasmodium) prevalence and host genetic variation across 13 populations of the island endemic Berthelot's pipit (Anthus berthelotii). Considerable and consistent spatial heterogeneity in malaria prevalence was observed among populations over a period of 15 years. The prevalence of malaria infection was also strongly positively correlated with pox (Avipoxvirus) prevalence. Multiple host loci showed significant associations with malaria prevalence after controlling for genome-wide neutral genetic structure. These sites were located near to or within genes linked to metabolism, stress response, transcriptional regulation, complement activity and the inflammatory response, many previously implicated in vertebrate responses to malarial infection. Our findings identify diverse genes - not just limited to the immune system - that may be involved in host protection against malaria and suggest that spatially variable pathogen pressure may be an important evolutionary driver of genetic divergence among wild animal populations, such as Berthelot's pipit. Furthermore, our data indicate that spatio-temporal variation in multiple different pathogens (e.g. malaria and pox in this case) may have to be studied together to develop a more holistic understanding of host pathogen-mediated evolution.

Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize.

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

Genomic signatures of climate adaptation in bank voles.

Evidence for divergent selection and adaptive variation across the landscape can provide insight into a species' ability to adapt to different environments. However, despite recent advances in genomics, it remains difficult to detect the footprints of climate-mediated selection in natural populations. Here, we analysed ddRAD sequencing data (21,892 SNPs) in conjunction with geographic climate variation to search for signatures of adaptive differentiation in twelve populations of the bank vole (Clethrionomys glareolus) distributed across Europe. To identify the loci subject to selection associated with climate variation, we applied multiple genotype-environment association methods, two univariate and one multivariate, and controlled for the effect of population structure. In total, we identified 213 candidate loci for adaptation, 74 of which were located within genes. In particular, we identified signatures of selection in candidate genes with functions related to lipid metabolism and the immune system. Using the results of redundancy analysis, we demonstrated that population history and climate have joint effects on the genetic variation in the pan-European metapopulation. Furthermore, by examining only candidate loci, we found that annual mean temperature is an important factor shaping adaptive genetic variation in the bank vole. By combining landscape genomic approaches, our study sheds light on genome-wide adaptive differentiation and the spatial distribution of variants underlying adaptive variation influenced by local climate in bank voles.

Seascapes Shaped the Local Adaptation and Population Structure of South China Coast Yellowfin Seabream (Acanthopagrus latus).

Understanding the genetic composition and regional adaptation of marine species under environmental heterogeneity and fishing pressure is crucial for responsible management. In order to understand the genetic diversity and adaptability of yellowfin seabream (Acanthopagrus latus) along southern China coast, this study was conducted a seascape genome analysis on yellowfin seabream from the ecologically diverse coast, spanning over 1600 km. A total of 92 yellowfin seabream individuals from 15 sites were performed whole-genome resequencing, and 4,383,564 high-quality single nucleotide polymorphisms (SNPs) were called. By conducting a genotype-environment association analysis, 29,951 adaptive and 4,328,299 neutral SNPs were identified. The yellowfin seabream exhibited two distinct population structures, despite high gene flow between sites. The seascape genome analysis revealed that genetic structure was influenced by a variety of factors including salinity gradients, habitat distance, and ocean currents. The frequency of allelic variation at the candidate loci changed with the salinity gradient. Annotation of these loci revealed that most of the genes are associated with osmoregulation, such as kcnab2a, kcnk5a, and slc47a1. These genes are significantly enriched in pathways associated with ion transport including G protein-coupled receptor activity, transmembrane signaling receptor activity, and transporter activity. Overall, our findings provide insights into how seascape heterogeneity affects adaptive evolution, while providing important information for regional management in yellowfin seabream populations.

Populus cathayana genome and population resequencing provide insights into its evolution and adaptation.

Populus cathayana Rehder, an indigenous poplar species of ecological and economic importance, is widely distributed in a high-elevation range from southwest to northeast China. Further development of this species as a sustainable poplar resource has been hindered by a lack of genome information the at the population level. Here, we produced a chromosome-level genome assembly of P. cathayana, covering 406.55Mb (scaffold N50 = 20.86Mb) and consisting of 19 chromosomes, with 35 977 protein-coding genes. Subsequently, we made a genomic variation atlas of 438 wild individuals covering 36 representative geographic areas of P. cathayana, which were divided into four geographic groups. It was inferred that the Northwest China regions served as the genetic diversity centers and a population bottleneck happened during the history of P. cathayana. By genotype-environment association analysis, 947 environment-association loci were significantly associated with temperature, solar radiation, precipitation, and altitude variables. We identified local adaptation genes involved in DNA repair and UV radiation response, among which UVR8, HY5, and CUL4 had key roles in high-altitude adaptation of P. cathayana. Predictions of adaptive potential under future climate conditions showed that P. cathayana populations in areas with drastic climate change were anticipated to have greater maladaptation risk. These results provide comprehensive insights for understanding wild poplar evolution and optimizing adaptive potential in molecular breeding.

Projection of current and future distribution of adaptive genetic units in an alpine ungulate.

Climate projections predict major changes in alpine environments by the end of the 21st century. To avoid climate-induced maladaptation and extinction, many animal populations will either need to move to more suitable habitats or adapt in situ to novel conditions. Since populations of a species exhibit genetic variation related to local adaptation, it is important to incorporate this variation into predictive models to help assess the ability of the species to survive climate change. Here, we evaluate how the adaptive genetic variation of a mountain ungulate-the Northern chamois (Rupicapra rupicapra)-could be impacted by future global warming. Based on genotype-environment association analyses of 429 chamois using a ddRAD sequencing approach, we identified genetic variation associated with climatic gradients across the European Alps. We then delineated adaptive genetic units and projected the optimal distribution of these adaptive groups in the future. Our results suggest the presence of local adaptation to climate in Northern chamois with similar genetic adaptive responses in geographically distant but climatically similar populations. Furthermore, our results predict that future climatic changes will modify the Northern chamois adaptive landscape considerably, with various degrees of maladaptation risk.

Genetic and environmental drivers of migratory behavior in western burrowing owls and implications for conservation and management.

Migration is driven by a combination of environmental and genetic factors, but many questions remain about those drivers. Potential interactions between genetic and environmental variants associated with different migratory phenotypes are rarely the focus of study. We pair low coverage whole genome resequencing with a de novo genome assembly to examine population structure, inbreeding, and the environmental factors associated with genetic differentiation between migratory and resident breeding phenotypes in a species of conservation concern, the western burrowing owl (Athene cunicularia hypugaea). Our analyses reveal a dichotomy in gene flow depending on whether the population is resident or migratory, with the former being genetically structured and the latter exhibiting no signs of structure. Among resident populations, we observed significantly higher genetic differentiation, significant isolation-by-distance, and significantly elevated inbreeding. Among migratory breeding groups, on the other hand, we observed lower genetic differentiation, no isolation-by-distance, and substantially lower inbreeding. Using genotype-environment association analysis, we find significant evidence for relationships between migratory phenotypes (i.e., migrant versus resident) and environmental variation associated with cold temperatures during the winter and barren, open habitats. In the regions of the genome most differentiated between migrants and residents, we find significant enrichment for genes associated with the metabolism of fats. This may be linked to the increased pressure on migrants to process and store fats more efficiently in preparation for and during migration. Our results provide a significant contribution toward understanding the evolution of migratory behavior and vital insight into ongoing conservation and management efforts for the western burrowing owl.

A window into local adaptation.

How organisms adapt to their environment is not only a central topic of evolutionary biology but also a pressing question in the light of recent global change. Unravelling the genetic basis of these local adaptations can help to predict the response of a population to an increase in temperature or the more frequent occurrence of droughts. A popular approach to study the genes that drive local adaptation is the analysis of genotype-environment associations (GEA), testing the correlation of genomic features (typically single-nucleotide polymorphisms, SNPs) and environmental conditions. In this issue of Molecular Ecology Resources, Booker et al. (Molecular Ecology Resources, 2023) present a new approach to GEA, introducing genomic window analysis. They combine the information of neighbouring SNPs instead of analysing each SNP independently, therefore gaining power for detecting genomic signals of environmental adaptation. Using simulations of local adaptation to a heterogeneous environment as well as previously published real data from a natural population of lodgepole pine, they prove the superiority of their method over several established GEA approaches, especially in the case of small sample sizes. Leveraging the information present in closely linked genomic sites, Booker et al. (Molecular Ecology Resources, 2023) take genotype-environment association studies to the next level.

Genomic Signatures of Local Adaptation under High Gene Flow in Lumpfish-Implications for Broodstock Provenance Sourcing and Larval Production.

Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for "cleaner fish" to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype-environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our 'global' sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.

Evolutionary history and seascape genomics of Harbour porpoises (Phocoena phocoena) across environmental gradients in the North Atlantic and adjacent waters.

The Harbour porpoise (Phocoena phocoena) is a highly mobile cetacean species primarily occurring in coastal and shelf waters across the Northern hemisphere. It inhabits heterogeneous seascapes broadly varying in salinity and temperature. Here, we produced 74 whole genomes at intermediate coverage to study Harbour porpoise's evolutionary history and investigate the role of local adaptation in the diversification into subspecies and populations. We identified ~6 million high quality SNPs sampled at eight localities across the North Atlantic and adjacent waters, which we used for population structure, demographic and genotype-environment association analyses. Our results suggest a genetic differentiation between three subspecies (P.p. relicta, P.p. phocoena and P.p. meridionalis), and three distinct populations within P.p. phocoena: Atlantic, Belt Sea and Proper Baltic Sea. Effective population size and Tajima's D suggest population contraction in Black Sea and Iberian porpoises, but expansion in the P.p. phocoena populations. Phylogenetic trees indicate post-glacial colonization from a southern refugium. Genotype-environment association analysis identified salinity as major driver in genomic variation and we identified candidate genes putatively underlying adaptation to different salinity. Our study highlights the value of whole genome resequencing to unravel subtle population structure in highly mobile species, shows how strong environmental gradients and local adaptation may lead to population differentiation, and how neutral and adaptive markers can give different perspectives on population subdivision. The results have great conservation implications as we found inbreeding and low genetic diversity in the endangered Black Sea subspecies and identified the critically endangered Proper Baltic Sea porpoises as a separate population.

Adaptive phenotypic and genomic divergence in the common chaffinch (Fringilla coelebs) following niche expansion within a small oceanic island.

According to models of ecological speciation, adaptation to adjacent, contrasting habitat types can lead to population divergence given strong enough environment-driven selection to counteract the homogenizing effect of gene flow. We tested this hypothesis in the common chaffinch (Fringilla coelebs) on the small island of La Palma, Canary Islands, where it occupies two markedly different habitats. Isotopic (δ13 C, δ15 N) analysis of feathers indicated that birds in the two habitats differed in ecosystem and/or diet, and analysis of phenotypic traits revealed significant differences in morphology and plumage colouration that are consistent with ecomorphological and ecogeographical predictions respectively. A genome-wide survey of single-nucleotide polymorphism revealed marked neutral structure that was consistent with geography and isolation by distance, suggesting low dispersal. In contrast, loci putatively under selection identified through genome-wide association and genotype-environment association analyses, revealed amarked adaptive divergence between birds in both habitats. Loci associated with phenotypic and environmental differences among habitats were distributed across the genome, as expected for polygenic traits involved in local adaptation. Our results suggest a strong role for habitat-driven local adaptation in population divergence in the chaffinches of La Palma, a process that appears to be facilitated by a strong reduction in effective dispersal distances despite the birds' high dispersal capacity.

Multilocus environmental adaptation and population structure in the Cerrado gecko Gymnodactylus amarali (Sauria, Phyllodactylidae) from Serra da Mesa Hydroelectric Plant, Central Brazil

Contrasting environmental conditions across geographic space might promote divergent selection, making adaptation to local biotic and abiotic conditions necessary for populations to survive. In order to understand how populations adapt to different environmental conditions, studies of local adaptation have been largely used as an interface to address ecological and evolutionary questions. Here, we studied populations of Gymnodactylus amarali (Phyllodactylidae) isolated in rapidly created artificial islands. We combined a genotyping-by-sequence (GBS) survey and redundancy analyses (RDA) to investigate genotype–environment associations (GEA), while DAPC, Fst, and Admixture analyses were used to determine genetic structure. Our hypothesis is that G. amarali populations on the islands are going through a local adaptation process and consequently becoming genetically different from the populations on the mainland. Our results indicate that geographic and environmental differences are related to genetic variation, as we detected the presence of two or three distinct genetic lineages in Serra da Mesa, Minaçu, and Colinas do Sul. Fst analysis shows moderate isolation between Serra da Mesa and Minaçu (0.082). The RDA pointed out potential local genetic signal correlated with temperature and precipitation. We identified 230 candidate loci associated with the environment and at least two locally structured subpopulations (Serra da Mesa and Minaçu) show significant association with environmental variation.

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle.

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Warming appears as the main risk of non-adaptedness for western Mediterranean relict fir forests under expected climate change scenarios.

Circum-Mediterranean firs are considered among the most drought-sensitive species to climate change. Understanding the genetic basis of trees' adaptive capacity and intra-specific variability to drought avoidance is mandatory to define conservation measures, thus potentially preventing their extinction. We focus here on Abies pinsapo and Abies marocana, both relict tree species, endemic from south Spain and north Morocco, respectively. A total of 607 samples were collected from eight nuclei: six from Spanish fir and two from Moroccan fir. A genotyping by sequencing technique called double digestion restriction site-associated DNA sequencing (ddRAD-seq) was performed to obtain a genetic matrix based on single-nucleotide polymorphisms (SNPs). This matrix was utilized to study the genetic structure of A. pinsapo populations and to carry out selection signature studies. In order to understand how Spanish fir and Moroccan fir cope with climate change, genotype-environment associations (GEAs) were identified. Further, the vulnerability of these species to climate variations was estimated by the risk of non-adaptedness (RONA). The filtering of the de novo assembly of A. pinsapo provided 3,982 SNPs from 504 out of 509 trees sequenced. Principal component analysis (PCA) genetically separated Grazalema from the rest of the Spanish populations. However, FST values showed significant differences among the sampling points. We found 51 loci potentially under selection. Homolog sequences were found for some proteins related to abiotic stress response, such as dehydration-responsive element binding transcription factor, regulation of abscisic acid signaling, and methylation pathway. A total of 15 associations with 11 different loci were observed in the GEA studies, with the maximum temperature of the warmest month being the variable with the highest number of associated loci. This temperature sensitivity was also supported by the risk of non-adaptedness, which yielded a higher risk for both A. pinsapo and A. marocana under the high emission scenario (Representative Concentration Pathway (RCP) 8.5). This study sheds light on the response to climate change of these two endemic species.

Landscape genomics analysis provides insights into future climate change-driven risk in rhesus macaque

Climate change significantly affects the suitability of wildlife habitats. Thus, understanding how animals adapt ecologically and genetically to climate change is important for targeted species protection. Rhesus macaques (Macaca mulatta) are widely distributed and multi-climatically adapted primates. This study explored how rhesus macaques adapt to climate change by integrating ecological and genetic methods and applying species distribution models (SDMs) and a gradient forest (GF) model. The findings suggested that temperature seasonality primarily affects habitat suitability and indicated that climate change will have a dramatic impact on macaque populations in the future. We also applied genotype-environment association (GEA) analyses and selection signature analyses to identify genes associated with climate change and provide possible explanations for the adaptation of rhesus macaques to climatic environments. The population genomics analyses suggested that the Taihang population has the highest genomic vulnerability with inbreeding and low heterozygosity. Combined with the higher ecological vulnerability, additional conservation strategies are required for this population under higher risk of climate change. Our work measured the impact of climate change and enabled the identification of populations that exhibit high vulnerability to severe climate change. Such information is useful for selecting populations of rhesus macaques as subject of long-term monitoring or evolutionary rescue under future climate change.