A highly contiguous genome assembly for the Yellow Warbler (Setophaga petechia).

The Yellow Warbler (Setophaga petechia) is a small songbird in the wood-warbler family (Parulidae) that exhibits phenotypic and ecological differences across a widespread distribution and is important to California's riparian habitat conservation. Here, we present a high-quality de novo genome assembly of a vouchered female Yellow Warbler from southern California. Using HiFi long-read and Omni-C proximity sequencing technologies, we generated a 1.22 Gb assembly including 687 scaffolds with a contig N50 of 6.80 Mb, scaffold N50 of 21.18 Mb, and a BUSCO completeness score of 96.0%. This highly contiguous genome assembly provides an essential resource for understanding the history of gene flow, divergence, and local adaptation in Yellow Warblers and can inform conservation management of this charismatic bird species.

Genomic analyses reveal poaching hotspots and illegal trade in pangolins from Africa to Asia.

The white-bellied pangolin (Phataginus tricuspis) is the world's most trafficked mammal and is at risk of extinction. Reducing the illegal wildlife trade requires an understanding of its origins. Using a genomic approach for tracing confiscations and analyzing 111 samples collected from known geographic localities in Africa and 643 seized scales from Asia between 2012 and 2018, we found that poaching pressures shifted over time from West to Central Africa. Recently, Cameroon's southern border has emerged as a site of intense poaching. Using data from seizures representing nearly 1 million African pangolins, we identified Nigeria as one important hub for trafficking, where scales are amassed and transshipped to markets in Asia. This origin-to-destination approach offers new opportunities to disrupt the illegal wildlife trade and to guide anti-trafficking measures.

Genetic identification of avian samples recovered from solar energy installations.

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots"-known to be of avian origin but not readily assignable to species via morphology-may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

Chromosome-length genome assemblies and cytogenomic analyses of pangolins reveal remarkable chromosome counts and plasticity.

We report the first chromosome-length genome assemblies for three species in the mammalian order Pholidota: the white-bellied, Chinese, and Sunda pangolins. Surprisingly, we observe extraordinary karyotypic plasticity within this order and, in female white-bellied pangolins, the largest number of chromosomes reported in a Laurasiatherian mammal: 2n = 114. We perform the first karyotype analysis of an African pangolin and report a Y-autosome fusion in white-bellied pangolins, resulting in 2n = 113 for males. We employ a novel strategy to confirm the fusion and identify the autosome involved by finding the pseudoautosomal region (PAR) in the female genome assembly and analyzing the 3D contact frequency between PAR sequences and the rest of the genome in male and female white-bellied pangolins. Analyses of genetic variability show that white-bellied pangolins have intermediate levels of genome-wide heterozygosity relative to Chinese and Sunda pangolins, consistent with two moderate declines of historical effective population size. Our results reveal a remarkable feature of pangolin genome biology and highlight the need for further studies of these unique and endangered mammals.

Strong habitat-specific phenotypic plasticity but no genome-wide differentiation across a rainforest gradient in an African butterfly.

Habitat-specific thermal responses are well documented in various organisms and likely determine the vulnerability of populations to climate change. However, the underlying roles of genetics and plasticity that shape such habitat-specific patterns are rarely investigated together. Here we examined the thermal plasticity of the butterfly Bicyclus dorothea originating from rainforest and ecotone habitats in Cameroon under common garden conditions. We also sampled wild-caught butterflies from forest and ecotone sites and used RADseq to explore genome-wide population differentiation. We found differences in the level of phenotypic plasticity across habitats. Specifically, ecotone populations exhibited greater sensitivity in wing eyespot features with variable development temperatures relative to rainforest populations. Known adaptive roles of wing eyespots in Bicyclus species suggest that this morphological plasticity is likely under divergent selection across environmental gradients. However, we found no distinct population structure of genome-wide variation between habitats, suggesting high level of ongoing gene flow between habitats is homogenizing most parts of the genome.

Evidence for ecotone speciation across an African rainforest-savanna gradient.

Accelerating climate change and habitat loss make it imperative that plans to conserve biodiversity consider species' ability to adapt to changing environments. However, in biomes where biodiversity is highest, the evolutionary mechanisms responsible for generating adaptative variation and, ultimately, new species are frequently poorly understood. African rainforests represent one such biome, as decadal debates continue concerning the mechanisms generating African rainforest biodiversity. These debates hinge on the relative importance of geographic isolation versus divergent natural selection across environmental gradients. Hindering progress is a lack of robust tests of these competing hypotheses. Because African rainforests are severely at-risk due to climate change and other anthropogenic activities, addressing this long-standing debate is critical for making informed conservation decisions. We use demographic inference and allele frequency-environment relationships to investigate mechanisms of diversification in an African rainforest skink, Trachylepis affinis, a species inhabiting the gradient between rainforest and rainforest-savanna mosaic (ecotone). We provide compelling evidence of ecotone speciation, in which gene flow has all but ceased between rainforest and ecotone populations, at a level consistent with infrequent hybridization between sister species. Parallel patterns of genomic, morphological, and physiological divergence across this environmental gradient and pronounced allele frequency-environment correlation indicate speciation is mostly probably driven by ecological divergence, supporting a central role for divergent natural selection. Our results provide strong evidence for the importance of ecological gradients in African rainforest speciation and inform conservation strategies that preserve the processes that produce and maintain biodiversity.

Genotype-environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence.

Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide-ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype-environment associations in the hermit thrush (Catharus guttatus), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype-environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature-associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature-related barriers to gene flow and/or temperature-driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype-environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population-level sensitivity to climate change and other stressors.

A non-parametric Hawkes model of the spread of Ebola in west Africa.

Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.

Landscape Genomics to Enable Conservation Actions: The California Conservation Genomics Project.

The California Conservation Genomics Project (CCGP) is a unique, critically important step forward in the use of comprehensive landscape genetic data to modernize natural resource management at a regional scale. We describe the CCGP, including all aspects of project administration, data collection, current progress, and future challenges. The CCGP will generate, analyze, and curate a single high-quality reference genome and 100-150 resequenced genomes for each of 153 species projects (representing 235 individual species) that span the ecological and phylogenetic breadth of California's marine, freshwater, and terrestrial ecosystems. The resulting portfolio of roughly 20 000 resequenced genomes will be analyzed with identical informatic and landscape genomic pipelines, providing a comprehensive overview of hotspots of within-species genomic diversity, potential and realized corridors connecting these hotspots, regions of reduced diversity requiring genetic rescue, and the distribution of variation critical for rapid climate adaptation. After 2 years of concerted effort, full funding ($12M USD) has been secured, species identified, and funds distributed to 68 laboratories and 114 investigators drawn from all 10 University of California campuses. The remaining phases of the CCGP include completion of data collection and analyses, and delivery of the resulting genomic data and inferences to state and federal regulatory agencies to help stabilize species declines. The aspirational goals of the CCGP are to identify geographic regions that are critical to long-term preservation of California biodiversity, prioritize those regions based on defensible genomic criteria, and provide foundational knowledge that informs management strategies at both the individual species and ecosystem levels.

Evolutionary and ecological drivers of local adaptation and speciation in a North American avian species complex.

Throughout the speciation process, genomic divergence can be differentially impacted by selective pressures, as well as gene flow and genetic drift. Disentangling the effects of these evolutionary mechanisms remains challenging, especially for nonmodel organisms. Accounting for complex evolutionary histories and contemporary population structure often requires sufficient sample sizes, for which the expense of full genomes remains prohibitive. Here, we demonstrate the utility of partial-genome sequence data for range-wide samples to shed light into the divergence process of two closely related ducks, the Mexican duck (Anas diazi) and mallard (A. platyrhynchos). We determine the role of selective and neutral processes during speciation of Mexican ducks by integrating evolutionary and demographic modelling with genotype-environment and genotype-phenotype association testing. First, evolutionary models and demographic analyses support the hypothesis that Mexican ducks originally diverged ~300,000 years ago in climate refugia arising during a glacial period in southwest North America, and that subsequent environmental selective pressures played a key role in divergence. Mexican ducks then showed cyclical demographic patterns that probably reflected repeated range expansions and contractions, along with bouts of gene flow with mallards during glacial cycles. Finally, we provide evidence that sexual selection acted on several phenotypic traits as a co-evolutionary process, facilitating the development of reproductive barriers that initially arose due to strong ecological selection. More broadly, this work reveals that the genomic and phenotypic patterns observed across species complexes are the result of myriad factors that contribute in dynamic ways to the evolutionary trajectories of a lineage.

Point Process Models for the Spread of Coccidioidomycosis in California.

Coccidioidomycosis is an infectious disease of humans and other mammals that has seen a recent increase in occurrence in the southwestern United States, particularly in California. A rise in cases and risk to public health can serve as the impetus to apply newly developed methods that can quickly and accurately predict future caseloads. The recursive and Hawkes point process models with various triggering functions were fit to the data and their goodness of fit evaluated and compared. Although the point process models were largely similar in their fit to the data, the recursive point process model offered a slightly superior fit. We explored forecasting the spread of coccidioidomycosis in California from December 2002 to December 2017 using this recursive model, and we separated the training and testing portions of the data and achieved a root mean squared error of just 3.62 cases/week.

Genomic vulnerability and socio-economic threats under climate change in an African rainforest bird.

Preserving biodiversity under rapidly changing climate conditions is challenging. One approach for estimating impacts and their magnitude is to model current relationships between genomic and environmental data and then to forecast those relationships under future climate scenarios. In this way, understanding future genomic and environmental relationships can help guide management decisions, such as where to establish new protected areas where populations might be buffered from high temperatures or major changes in rainfall. However, climate warming is only one of many anthropogenic threats one must consider in rapidly developing parts of the world. In Central Africa, deforestation, mining, and infrastructure development are accelerating population declines of rainforest species. Here we investigate multiple anthropogenic threats in a Central African rainforest songbird, the little greenbul (Andropadus virens). We examine current climate and genomic variation in order to explore the association between genome and environment under future climate conditions. Specifically, we estimate Genomic Vulnerability, defined as the mismatch between current and predicted future genomic variation based on genotype-environment relationships modeled across contemporary populations. We do so while considering other anthropogenic impacts. We find that coastal and central Cameroon populations will require the greatest shifts in adaptive genomic variation, because both climate and land use in these areas are predicted to change dramatically. In contrast, in the more northern forest-savanna ecotones, genomic shifts required to keep pace with climate will be more moderate, and other anthropogenic impacts are expected to be comparatively low in magnitude. While an analysis of diverse taxa will be necessary for making comprehensive conservation decisions, the species-specific results presented illustrate how evolutionary genomics and other anthropogenic threats may be mapped and used to inform mitigation efforts. To this end, we present an integrated conceptual model demonstrating how the approach for a single species can be expanded to many taxonomically diverse species.

Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California.

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.

Precipitation and vegetation shape patterns of genomic and craniometric variation in the central African rodent Praomys misonnei.

Predicting species' capacity to respond to climate change is an essential first step in developing effective conservation strategies. However, conservation prioritization schemes rarely take evolutionary potential into account. Ecotones provide important opportunities for diversifying selection and may thus constitute reservoirs of standing variation, increasing the capacity for future adaptation. Here, we map patterns of environmentally associated genomic and craniometric variation in the central African rodent Praomys misonnei to identify areas with the greatest turnover in genomic composition. We also project patterns of environmentally associated genomic variation under future climate change scenarios to determine where populations may be under the greatest pressure to adapt. While precipitation gradients influence both genomic and craniometric variation, vegetation structure is also an important determinant of craniometric variation. Areas of elevated environmentally associated genomic and craniometric variation overlap with zones of rapid ecological transition underlining their importance as reservoirs of evolutionary potential. We also find that populations in the Sanaga river basin, central Cameroon and coastal Gabon are likely to be under the greatest pressure from climate change. Lastly, we make specific conservation recommendations on how to protect zones of high evolutionary potential and identify areas where populations may be the most susceptible to climate change.

A genoscape-network model for conservation prioritization in a migratory bird.

Migratory animals are declining worldwide and coordinated conservation efforts are needed to reverse current trends. We devised a novel genoscape-network model that combines genetic analyses with species distribution modeling and demographic data to overcome challenges with conceptualizing alternative risk factors in migratory species across their full annual cycle. We applied our method to the long distance, Neotropical migratory bird, Wilson's Warbler (Cardellina pusilla). Despite a lack of data from some wintering locations, we demonstrated how the results can be used to help prioritize conservation of breeding and wintering areas. For example, we showed that when genetic, demographic, and network modeling results were considered together it became clear that conservation recommendations will differ depending on whether the goal is to preserve unique genetic lineages or the largest number of birds per unit area. More specifically, if preservation of genetic lineages is the goal, then limited resources should be focused on preserving habitat in the California Sierra, Basin Rockies, or Coastal California, where the 3 most vulnerable genetic lineages breed, or in western Mexico, where 2 of the 3 most vulnerable lineages overwinter. Alternatively, if preservation of the largest number of individuals per unit area is the goal, then limited conservation dollars should be placed in the Pacific Northwest or Central America, where densities are estimated to be the highest. Overall, our results demonstrated the utility of adopting a genetically based network model for integrating multiple types of data across vast geographic scales and better inform conservation decision-making for migratory animals.

Un Modelo de Redes de Panorama Poblacional para la Priorización de la Conservación de un Ave Migratoria Resumen Los animales migratorios están pasando por una declinación mundial y se requieren esfuerzos coordinados de conservación para revertir las tendencias actuales. Diseñamos un modelo novedoso de redes de panorama poblacional que combina el análisis genético con el modelado de la distribución de especies y los datos demográficos para sobreponerse a los obstáculos con la conceptualización de los factores alternativos de riesgo en las especies migratorias durante su ciclo anual completo. Aplicamos nuestro método al chipe de corona negra (Cardellina pusilla), un ave migratoria neotropical que recorre largas distancias. A pesar de la falta de datos de algunas localidades de invernación, mostramos cómo pueden usarse los resultados para ayudar a priorizar la conservación de las áreas de reproducción y de invernación. Por ejemplo, mostramos que cuando se consideraron en conjunto los resultados del modelado genético, demográfico y de redes queda claro que las recomendaciones de conservación diferirán dependiendo de si el objetivo es preservar linajes genéticos únicos o el mayor número de aves por unidad de área. Más específicamente, si el objetivo es la conservación de los linajes genéticos, entonces los recursos limitados deberían enfocarse en preservar el hábitat en la Sierra de California, la Cuenca de las Rocallosas, la costa de California (lugares en donde se reproducen los tres linajes genéticos más vulnerables) o en el oeste de México (en donde dos de los tres linajes más vulnerables pasan el invierno). Alternativamente, si el objetivo es la conservación del mayor número de individuos por unidad de área, entonces el financiamiento limitado debería aplicarse en el noroeste del Pacífico o en América Central, en donde se estima que las densidades poblacionales son las más altas. En general, nuestros resultados demostraron la utilidad de adoptar un modelo de redes basadas en la genética para la integración de datos a lo largo de escalas geográficas amplias y para informar de mejor manera la toma de decisiones de conservación para los animales migratorios.

Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models.

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.

Natural re-colonization and admixture of wolves (Canis lupus) in the US Pacific Northwest: challenges for the protection and management of rare and endangered taxa.

Admixture resulting from natural dispersal processes can potentially generate novel phenotypic variation that may facilitate persistence in changing environments or result in the loss of population-specific adaptations. Yet, under the US Endangered Species Act, policy is limited for management of individuals whose ancestry includes a protected taxon; therefore, they are generally not protected under the Act. This issue is exemplified by the recently re-established grey wolves of the Pacific Northwest states of Washington and Oregon, USA. This population was likely founded by two phenotypically and genetically distinct wolf ecotypes: Northern Rocky Mountain (NRM) forest and coastal rainforest. The latter is considered potentially threatened in southeast Alaska and thus the source of migrants may affect plans for their protection. To assess the genetic source of the re-established population, we sequenced a ~ 300 bp portion of the mitochondrial control region and ~ 5 Mbp of the nuclear genome. Genetic analysis revealed that the Washington wolves share ancestry with both wolf ecotypes, whereas the Oregon population shares ancestry with NRM forest wolves only. Using ecological niche modelling, we found that the Pacific Northwest states contain environments suitable for each ecotype, with wolf packs established in both environmental types. Continued migration from coastal rainforest and NRM forest source populations may increase the genetic diversity of the Pacific Northwest population. However, this admixed population challenges traditional management regimes given that admixture occurs between an adaptively distinct ecotype and a more abundant reintroduced interior form. Our results emphasize the need for a more precise US policy to address the general problem of admixture in the management of endangered species, subspecies, and distinct population segments.

Population Genomic Analysis of North American Eastern Wolves (Canis lycaon) Supports Their Conservation Priority Status.

The threatened eastern wolf is found predominantly in protected areas of central Ontario and has an evolutionary history obscured by interbreeding with coyotes and gray wolves, which challenges its conservation status and subsequent management. Here, we used a population genomics approach to uncover spatial patterns of variation in 281 canids in central Ontario and the Great Lakes region. This represents the first genome-wide single nucleotide polymorphism (SNP) dataset with substantial sample sizes of representative populations. Although they comprise their own genetic cluster, we found evidence of eastern wolf dispersal outside of the boundaries of protected areas, in that the frequency of eastern wolf genetic variation decreases with increasing distance from provincial parks. We detected eastern wolf alleles in admixed coyotes along the northeastern regions of Lake Huron and Lake Ontario. Our analyses confirm the unique genomic composition of eastern wolves, which are mostly restricted to small fragmented patches of protected habitat in central Ontario. We hope this work will encourage an innovative discussion regarding a plan for managed introgression, which could conserve eastern wolf genetic material in any genome regardless of their potential mosaic ancestry composition and the habitats that promote them.

Response to Comment on "Genomic signals of selection predict climate-driven population declines in a migratory bird".

Fitzpatrick et al discuss issues that they had with analyses and interpretation in our recent manuscript on genomic correlates of climate in yellow warblers. We provide evidence that our findings would not change with different analysis and maintain that our study represents a promising direction for integrating the potential for climate adaptation as one of many tools in conservation management.

Ecological genomics predicts climate vulnerability in an endangered southwestern songbird.

Few regions have been more severely impacted by climate change in the USA than the Desert Southwest. Here, we use ecological genomics to assess the potential for adaptation to rising global temperatures in a widespread songbird, the willow flycatcher (Empidonax traillii), and find the endangered desert southwestern subspecies (E. t. extimus) most vulnerable to future climate change. Highly significant correlations between present abundance and estimates of genomic vulnerability - the mismatch between current and predicted future genotype-environment relationships - indicate small, fragmented populations of the southwestern willow flycatcher will have to adapt most to keep pace with climate change. Links between climate-associated genotypes and genes important to thermal tolerance in birds provide a potential mechanism for adaptation to temperature extremes. Our results demonstrate that the incorporation of genotype-environment relationships into landscape-scale models of climate vulnerability can facilitate more precise predictions of climate impacts and help guide conservation in threatened and endangered groups.