A landmark environmental law looks ahead.

In late December 1973, the United States enacted what some would come to call "the pitbull of environmental laws." In the 50 years since, the formidable regulatory teeth of the Endangered Species Act (ESA) have been credited with considerable successes, obliging agencies to draw upon the best available science to protect species and habitats. Yet human pressures continue to push the planet toward extinctions on a massive scale. With that prospect looming, and with scientific understanding ever changing, Science invited experts to discuss how the ESA has evolved and what its future might hold. -Brad Wible.

Evolutionary potential mitigates extinction risk under climate change in the endangered southwestern willow flycatcher.

The complexity of global anthropogenic change makes forecasting species responses and planning effective conservation actions challenging. Additionally, important components of a species' adaptive capacity, such as evolutionary potential, are often not included in quantitative risk assessments due to lack of data. While genomic proxies for evolutionary potential in at-risk species are increasingly available, they have not yet been included in extinction risk assessments at a species-wide scale. In this study, we used an individual-based, spatially explicit, dynamic eco-evolutionary simulation model to evaluate the extinction risk of an endangered desert songbird, the southwestern willow flycatcher (Empidonax traillii extimus), in response to climate change. Using data from long-term demographic and habitat studies in conjunction with genome-wide ecological genomics research, we parameterized simulations that include 418 sites across the breeding range, genomic data from 225 individuals, and climate change forecasts spanning 3 generalized circulation models and 3 emissions scenarios. We evaluated how evolutionary potential, and the lack of it, impacted population trajectories in response to climate change. We then investigated the compounding impact of drought and warming temperatures on extinction risk through the mechanism of increased nest failure. Finally, we evaluated how rapid action to reverse greenhouse gas emissions would influence population responses and species extinction risk. Our results illustrate the value of incorporating evolutionary, demographic, and dispersal processes in a spatially explicit framework to more comprehensively evaluate the extinction risk of threatened and endangered species and conservation actions to promote their recovery.

Genomics-informed delineation of conservation units in a desert amphibian.

Delineating conservation units (CUs, e.g., evolutionarily significant units, ESUs, and management units, MUs) is critical to the recovery of declining species because CUs inform both listing status and management actions. Genomic data have strengths and limitations in informing CU delineation and related management questions in natural systems. We illustrate the value of using genomic data in combination with landscape, dispersal and occupancy data to inform CU delineation in Nevada populations of the Great Basin Distinct Population Segment of the Columbia spotted frog (Rana luteiventris). R. luteiventris occupies naturally fragmented aquatic habitats in this xeric region, but beaver removal, climate change and other factors have put many of these populations at high risk of extirpation without management intervention. We addressed three objectives: (i) assessing support for ESUs within Nevada; (ii) evaluating and revising, if warranted, the current delineation of MUs; and (iii) evaluating genetic diversity, effective population size, adaptive differentiation and functional connectivity to inform ongoing management actions. We found little support for ESUs within Nevada but did identify potential revisions to MUs based on unique landscape drivers of connectivity that distinguish these desert populations from those in the northern portion of the species range. Effective sizes were uniformly small, with low genetic diversity and weak signatures of adaptive differentiation. Our findings suggest that management actions, including translocations and genetic rescue, might be warranted. Our study illustrates how a carefully planned genetic study, designed to address priority management goals that include CU delineation, can provide multiple insights to inform conservation action.

Habitat-linked genetic variation supports microgeographic adaptive divergence in an island-endemic bird species.

We investigated the potential mechanisms driving habitat-linked genetic divergence within a bird species endemic to a single 250-km2 island. The island scrub-jay (Aphelocoma insularis) exhibits microgeographic divergence in bill morphology across pine-oak ecotones on Santa Cruz Island, California (USA), similar to adaptive differences described in mainland congeners over much larger geographic scales. To test whether individuals exhibit genetic differentiation related to habitat type and divergence in bill length, we genotyped over 3000 single nucleotide polymorphisms in 123 adult island scrub-jay males from across Santa Cruz Island using restriction site-associated DNA sequencing. Neutral landscape genomic analyses revealed that genome-wide genetic differentiation was primarily related to geographic distance and differences in habitat composition. We also found 168 putatively adaptive loci associated with habitat type using multivariate redundancy analysis while controlling for spatial effects. Finally, two genome-wide association analyses revealed a polygenic basis to variation in bill length with multiple loci detected in or near genes known to affect bill morphology in other birds. Our findings support the hypothesis that divergent selection at microgeographic scales can cause adaptive divergence in the presence of ongoing gene flow.

Genomic signatures of thermal adaptation are associated with clinal shifts of life history in a broadly distributed frog.

Temperature is a critical driver of ectotherm life-history strategies, whereby a warmer environment is associated with increased growth, reduced longevity and accelerated senescence. Increasing evidence indicates that thermal adaptation may underlie such life-history shifts in wild populations. Single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) can help uncover the molecular mechanisms of temperature-driven variation in growth, longevity and senescence. However, our understanding of these mechanisms is still limited, which reduces our ability to predict the response of non-model ectotherms to global temperature change. In this study, we examined the potential role of thermal adaptation in clinal shifts of life-history traits (i.e. life span, senescence rate and recruitment) in the Columbia spotted frog Rana luteiventris along a broad temperature gradient in the western United States. We took advantage of extensive capture-recapture datasets of 20,033 marked individuals from eight populations surveyed annually for 14-18 years to examine how mean annual temperature and precipitation influenced demographic parameters (i.e. adult survival, life span, senescence rate, recruitment and population growth). After showing that temperature was the main climatic predictor influencing demography, we used RAD-seq data (50,829 SNPs and 6,599 putative CNVs) generated for 352 individuals from 31 breeding sites to identify the genomic signatures of thermal adaptation. Our results showed that temperature was negatively associated with annual adult survival and reproductive life span and positively associated with senescence rate. By contrast, recruitment increased with temperature, promoting the long-term viability of most populations. These temperature-dependent demographic changes were associated with strong genomic signatures of thermal adaptation. We identified 148 SNP candidates associated with temperature including three SNPs located within protein-coding genes regulating resistance to cold and hypoxia, immunity and reproduction in ranids. We also identified 39 CNV candidates (including within 38 transposable elements) for which normalized read depth was associated with temperature. Our study indicates that both SNPs and structural variants are associated with temperature and could eventually be found to play a functional role in clinal shifts in senescence rate and life-history strategies in R. luteiventris. These results highlight the potential role of different sources of molecular variation in the response of ectotherms to environmental temperature variation in the context of global warming.

MrIML: Multi-response interpretable machine learning to model genomic landscapes.

We introduce a new R package "MrIML" ("Mister iml"; Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.

Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field.

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.

Population structure in a continuously distributed coastal marine species, the harbor porpoise, based on microhaplotypes derived from poor-quality samples.

Harbor porpoise in the North Pacific are found in coastal waters from southern California to Japan, but population structure is poorly known outside of a few local areas. We used multiplexed amplicon sequencing of 292 loci and genotyped clusters of single nucleotide polymoirphisms as microhaplotypes (N = 271 samples) in addition to mitochondrial (mtDNA) sequence data (N = 413 samples) to examine the genetic structure from samples collected along the Pacific coast and inland waterways from California to southern British Columbia. We confirmed an overall pattern of strong isolation-by-distance, suggesting that individual dispersal is restricted. We also found evidence of regions where genetic differences are larger than expected based on geographical distance alone, implying current or historical barriers to gene flow. In particular, the southernmost population in California is genetically distinct (FST  = 0.02 [microhaplotypes]; 0.31 [mtDNA]), with both reduced genetic variability and high frequency of an otherwise rare mtDNA haplotype. At the northern end of our study range, we found significant genetic differentiation of samples from the Strait of Georgia, previously identified as a potential biogeographical boundary or secondary contact zone between harbor porpoise populations. Association of microhaplotypes with remotely sensed environmental variables indicated potential local adaptation, especially at the southern end of the species' range. These results inform conservation and management for this nearshore species, illustrate the value of genomic methods for detecting patterns of genetic structure within a continuously distributed marine species, and highlight the power of microhaplotype genotyping for detecting genetic structure in harbor porpoises despite reliance on poor-quality samples.

Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration.

Despite the importance of climate-adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron-rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype-phenotype-environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site-adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.

Modelling multilocus selection in an individual-based, spatially-explicit landscape genetics framework.

We implemented multilocus selection in a spatially-explicit, individual-based framework that enables multivariate environmental gradients to drive selection in many loci as a new module for the landscape genetics programs, CDPOP and CDMetaPOP. Our module simulates multilocus selection using a linear additive model, providing a flexible platform to evaluate a wide range of genotype-environment associations. Importantly, the module allows simulation of selection in any number of loci under the influence of any number of environmental variables. We validated the module with individual-based selection simulations under Wright-Fisher assumptions. We then evaluated results for simulations under a simple landscape selection model. Next, we simulated individual-based multilocus selection across a complex selection landscape with three loci linked to three different environmental variables. Finally, we demonstrated how the program can be used to simulate multilocus selection under varying selection strengths across different levels of gene flow in a landscape genetics framework. This new module provides a valuable addition to the study of landscape genetics, allowing for explicit evaluation of the contributions and interactions between gene flow and selection-driven processes across complex, multivariate environmental and landscape conditions.

Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections.

Local adaptations can determine the potential of populations to respond to environmental changes, yet adaptive genetic variation is commonly ignored in models forecasting species vulnerability and biogeographical shifts under future climate change. Here we integrate genomic and ecological modeling approaches to identify genetic adaptations associated with climate in two cryptic forest bats. We then incorporate this information directly into forecasts of range changes under future climate change and assessment of population persistence through the spread of climate-adaptive genetic variation (evolutionary rescue potential). Considering climate-adaptive potential reduced range loss projections, suggesting that failure to account for intraspecific variability can result in overestimation of future losses. On the other hand, range overlap between species was projected to increase, indicating that interspecific competition is likely to play an important role in limiting species' future ranges. We show that although evolutionary rescue is possible, it depends on a population's adaptive capacity and connectivity. Hence, we stress the importance of incorporating genomic data and landscape connectivity in climate change vulnerability assessments and conservation management.

Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation.

Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations for restoration, genetic rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, and in some cases may not be a worthwhile use of resources. Here, we offer an adaptive management framework to help conservation biologists and managers decide when genomics is likely to be effective in detecting local adaptation, and how to plan assessment and monitoring of adaptive variation to address conservation objectives. Studies of adaptive variation using genomic tools will inform conservation actions in many cases, including applications such as assisted gene flow and identifying conservation units. In others, assessing genetic diversity, inbreeding, and demographics using selectively neutral genetic markers may be most useful. And in some cases, local adaptation may be assessed more efficiently using alternative approaches such as common garden experiments. Here, we identify key considerations of genomics studies of locally adaptive variation, provide a road map for successful collaborations with genomics experts including key issues for study design and data analysis, and offer guidelines for interpreting and using results from genomic assessments to inform monitoring programs and conservation actions.

Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations.

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false-positive and high true-positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re-analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

Coherent synthesis of genomic associations with phenotypes and home environments.

Local adaptation is often studied via (i) multiple common garden experiments comparing performance of genotypes in different environments and (ii) sequencing genotypes from multiple locations and characterizing geographic patterns in allele frequency. Both approaches aim to characterize the same pattern (local adaptation), yet the complementary information from each has not yet been coherently integrated. Here, we develop a genome-wide association model of genotype interactions with continuous environmental gradients (G × E), that is reaction norms. We present an approach to impute relative fitness, allowing us to coherently synthesize evidence from common garden and genome-environment associations. Our approach identifies loci exhibiting environmental clines where alleles are associated with higher fitness in home environments. Simulations show our approach can increase power to detect loci causing local adaptation. In a case study on Arabidopsis thaliana, most identified SNPs exhibited home allele advantage and fitness trade-offs along climate gradients, suggesting selective gradients can maintain allelic clines. SNPs exhibiting G × E associations with fitness were enriched in genic regions, putative partial selective sweeps and associations with an adaptive phenotype (flowering time plasticity). We discuss extensions for situations where only adaptive phenotypes other than fitness are available. Many types of data may point towards the loci underlying G × E and local adaptation; coherent models of diverse data provide a principled basis for synthesis.

Spatial detection of outlier loci with Moran eigenvector maps.

The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.

Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.

The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

Integrating environmental, molecular, and morphological data to unravel an ice-age radiation of arctic-alpine Campanula in western North America.

Many arctic-alpine plant genera have undergone speciation during the Quaternary. The bases for these radiations have been ascribed to geographic isolation, abiotic and biotic differences between populations, and/or hybridization and polyploidization. The Cordilleran Campanula L. (Campanulaceae Juss.), a monophyletic clade of mostly endemic arctic-alpine taxa from western North America, experienced a recent and rapid radiation. We set out to unravel the factors that likely influenced speciation in this group. To do so, we integrated environmental, genetic, and morphological datasets, tested biogeographic hypotheses, and analyzed the potential consequences of the various factors on the evolutionary history of the clade. We created paleodistribution models to identify potential Pleistocene refugia for the clade and estimated niche space for individual taxa using geographic and climatic data. Using 11 nuclear loci, we reconstructed a species tree and tested biogeographic hypotheses derived from the paleodistribution models. Finally, we tested 28 morphological characters, including floral, vegetative, and seed characteristics, for their capacity to differentiate taxa. Our results show that the combined effect of Quaternary climatic variation, isolation among differing environments in the mountains in western North America, and biotic factors influencing floral morphology contributed to speciation in this group during the mid-Pleistocene. Furthermore, our biogeographic analyses uncovered asynchronous consequences of interglacial and glacial periods for the timing of refugial isolation within the southern and northwestern mountains, respectively. These findings have broad implications for understanding the processes promoting speciation in arctic-alpine plants and the rise of numerous endemic taxa across the region.

Integrating landscape genomics and spatially explicit approaches to detect loci under selection in clinal populations.

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual-based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under "weak" selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.

Deep genetic divergence between disjunct Refugia in the Arctic-Alpine King's Crown, Rhodiola integrifolia (Crassulaceae).

Despite the strength of climatic variability at high latitudes and upper elevations, we still do not fully understand how plants in North America that are distributed between Arctic and alpine areas responded to the environmental changes of the Quaternary. To address this question, we set out to resolve the evolutionary history of the King's Crown, Rhodiola integrifolia using multi-locus population genetic and phylogenetic analyses in combination with ecological niche modeling. Our population genetic analyses of multiple anonymous nuclear loci revealed two major clades within R. integrifolia that diverged from each other ~ 700 kya: one occurring in Beringia to the north (including members of subspecies leedyi and part of subspecies integrifolia), and the other restricted to the Southern Rocky Mountain refugium in the south (including individuals of subspecies neomexicana and part of subspecies integrifolia). Ecological niche models corroborate our hypothesized locations of refugial areas inferred from our phylogeographic analyses and revealed some environmental differences between the regions inhabited by its two subclades. Our study underscores the role of geographic isolation in promoting genetic divergence and the evolution of endemic subspecies in R. integrifolia. Furthermore, our phylogenetic analyses of the plastid spacer region trnL-F demonstrate that among the native North American species, R. integrifolia and R. rhodantha are more closely related to one another than either is to R. rosea. An understanding of these historic processes lies at the heart of making informed management decisions regarding this and other Arctic-alpine species of concern in this increasingly threatened biome.