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Adaptive plasticity in thermal tolerance traits may buffer organisms against changing temperatures, making such responses of particular interest in the face of global climate change. Although population variation is integral to the evolvability of this trait, many studies inferring proxies of physiological vulnerability from thermal tolerance traits extrapolate data from one or a few populations to represent the species. Estimates of physiological vulnerability can be further complicated by methodological effects associated with experimental design. We evaluated how populations varied in their acclimation capacity (i.e., the magnitude of plasticity) for critical thermal maximum (CTmax) in two species of tailed frogs (Ascaphidae), cold‐stream specialists. We used the estimates of acclimation capacity to infer physiological vulnerability to future warming. We performed CTmax experiments on tadpoles from 14 populations using a fully factorial experimental design of two holding temperatures (8 and 15°C) and two experimental starting temperatures (8 and 15°C). This design allowed us to investigate the acute effects of transferring organisms from one holding temperature to a different experimental starting temperature, as well as fully acclimated responses by using the same holding and starting temperature. We found that most populations exhibited beneficial acclimation, where CTmax was higher in tadpoles held at a warmer temperature, but populations varied markedly in the magnitude of the response and the inferred physiological vulnerability to future warming. We also found that the response of transferring organisms to different starting temperatures varied substantially among populations, although accounting for acute effects did not greatly alter estimates of physiological vulnerability at the species level or for most populations. These results underscore the importance of sampling widely among populations when inferring physiological vulnerability, as population variation in acclimation capacity and thermal sensitivity may be critical when assessing vulnerability to future warming.

 

1. Critical thermal limits represent an important component of an organism's capacity to cope with future temperature changes. Understanding the drivers of variation in these traits may uncover patterns in physiological vulnerability to climate change. Local temperature extremes have emerged as a major driver of thermal limits, although their effects can be mediated by the exploitation of fine‐scale spatial variation in temperature through behavioural thermoregulation.

2. Here, we investigated thermal limits along elevation gradients within and between two cold‐water frog species (Ascaphusspp.), one with a coastal distribution (A. truei) and the other with a continental range (A. montanus). We quantified thermal limits for over 700 tadpoles, representing multiple populations from each species. We combined local temporal and fine‐scale spatial temperature data to quantify local thermal landscapes (i.e., thermalscapes), including the opportunity for behavioural thermoregulation.

3. Lower thermal limits for either species could not be reached experimentally without the water freezing, suggesting that cold tolerance is <0.3°C. By contrast, upper thermal limits varied among populations, but this variation only reflected local temperature extremes inA. montanus, perhaps as a consequence of the greater variation in stream temperatures across its range. Lastly, we found minimal fine‐scale spatial variability in temperature, suggesting limited opportunity for behavioural thermoregulation and thus increased vulnerability to warming for all populations.

4. By quantifying local thermalscapes, we uncovered different trends in the relative vulnerability of populations across elevation for each species. InA. truei, physiological vulnerability decreased with elevation, whereas inA. montanus, all populations were equally physiologically vulnerable. These results highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability of species, and in turn impact their vulnerability to future warming.

 

Abstract 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. 

We investigated the potential mechanisms driving habitat‐linked genetic divergence within a bird species endemic to a single 250‐km²island. 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.

 

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.

 

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.

 

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 CordilleranCampanulaL. (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.

 

New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.