Nikunj Goel

Successful implementation of evolutionary programs to rescue climatically threatened species requires identification of adaptive variation. Although many genotype–environment association methods have been successful in identifying adaptive variation, current approaches can be improved in two important aspects. First, most existing methods do not account for genotype uncertainty in widely available low-coverage whole-genome sequencing data. Researchers often restrict analysis to loci for which genotypes can be inferred reliably or call the most probable genotype, allowing the use of genotype-based methods. However, discarding data and false genotype calls increase the uncertainty in estimates of genetic variation and can introduce systematic biases. Second, most methods use phenomenological approaches, such as logistic regression, to partition estimated variation into adaptive and nonadaptive components. Consequently, current approaches may fail to account for evolutionary processes, such as migration-selection balance. Structured migration between climatically disparate locations can produce deviations from a smooth S-shaped response curve, which can be difficult to accommodate using generalized linear models. To overcome these challenges, we developed a method that accounts for genotype uncertainty in sequencing data and propagates this uncertainty to inform the parameters of an evolutionary model. A key feature of this model is that it describes mechanistically how genetic variation arises from joint interactions between local adaptation, structured migration, mutation, and drift. Our synthetic simulation tests reveal that accounting for genotype uncertainty and structured migration substantially reduces false negatives. We also applied our approach to analyze data on North American rosy-finches (3.7 million SNPs), a high-alpine, climatically threatened clade of bird species.

Accelerated warming at high elevations is having a disproportionate impact on alpine species. While assessments of climate vulnerability require quantifying the ecological and evolutionary components of adaptive capacity, such assessments are rare, especially in alpine systems. We leverage recent advances in population and landscape genomics to assess how variation in spatial heterogeneity and population connectivity across alpine systems influences adaptive capacity, using the North American Rosy-Finch species complex as a model system. In doing so, we clarify taxonomic relationships across the complex and identify one new ESU, the Sierra Nevada Rosy-Finch, based on its combined ecological and evolutionary distinctiveness. We then illustrate how combining genomic analyses with ecological data can improve estimates of adaptive capacity, sensitivity, and exposure and ultimately clarify climate vulnerability. Overall, our integrative analyses revealed that more isolated lineages, such as the Sierra Nevada Rosy-Finch, have lower adaptive capacity and face disproportionately high risks from climate change. This work highlights how conservation strategies that account for the multidimensional aspects of adaptive capacity can improve estimates of climate vulnerability.

Successful implementation of evolutionary management programs to rescue climatically threatened species requires identification of adaptive genetic variation. Although current genotype-environment association methods have been successful in identifying adaptive variation, they can be improved in two important aspects. First, most existing methods do not account for genotype uncertainty in widely available low-coverage whole-genome sequencing data. Researchers often restrict analysis to loci for which genotypes can be inferred reliably or call the most probable genotype, allowing the use of traditional genotype-based methods, such as BayeScEnv and Bayenv. However, discarding data and false genotype calls increases the uncertainty in estimates of genetic variation and introduces systematic biases. Second, most methods use phenomenological approaches, such as logistic regression, to partition estimated genetic variation into adaptive and non-adaptive components. Consequently, current approaches may inadvertently fail to account for evolutionary processes, such as migration-selection balance. Structured migration between climatically disparate locations can produce deviations from a smooth S-shape response curve, which can be difficult to accommodate using generalized linear regression models. To overcome these challenges, we developed a method that accounts for genotype uncertainty in sequencing data and propagates this uncertainty to inform the parameters of a model of evolution. A key feature of this evolutionary model is that it mechanistically describes how genetic variation arises from joint interactions between local adaptation, structured migration, mutation, and drift. We apply our approach to analyze multiple synthetic datasets and a real dataset of North American rosy-finches (3.7 million SNPs), a high-alpine, climatically threatened clade of bird species.

Identifying genomic adaptation is key to understanding species' evolutionary responses to environmental changes. However, current methods to identify adaptive variation have two major limitations. First, when estimating genetic variation, most methods do not account for observational uncertainty in genetic data because of finite sampling and missing genotypes. Second, many current methods use phenomenological models to partition genetic variation into adaptive and non‐adaptive components. We address these limitations by developing a hierarchical Bayesian model that explicitly accounts for observational uncertainty and underlying evolutionary processes. The first layer of the hierarchy is the data model that captures observational uncertainty by probabilistically linking RAD sequence data to genetic variation. The second layer is a process model that represents how evolutionary forces, such as local adaptation, mutation, migration and drift, maintain genetic variation. The third layer is the parameter model, which incorporates our knowledge about biological processes. For example, because most loci in the genome are expected to be neutral, the environmental sensitivity coefficients are assigned a regularized prior centred at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms. Analysis of simulated RAD‐seq data shows that our statistical model can reliably infer adaptive genetic variation. To show the real‐world applicability of our method, we re‐analysed RAD‐Seq data (~105 k SNPs) from Willow Flycatchers (Empidonax traillii) in the United States. We found 30 genes close to 47 loci that showed a statistically significant association with temperature seasonality. Gene ontology suggests that several of these genes play a crucial role in egg mineralization, feather development and the ability to withstand extreme temperatures. Moreover, the data and process models can be modified to accommodate a wide range of genetic datasets (e.g. pool and low coverage genome sequencing) and demographic histories (e.g. range shifts) to study climatic adaptation in a wide range of natural systems.

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life‐history dynamics with heterogeneity in human‐mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as the time and place of species introduction and life‐history characteristics, due to spatial biases in the presence‐only records and lack of informative demographic data. To address these gaps, we first formulate an age‐structured metapopulation model that uses a probability matrix to emulate human‐mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter‐patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence‐only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel ( Dreissena polymorpha ) expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of 5 years between the time of introduction and first detection in late 1986. Furthermore, using our statistical model, we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find that survival, fecundity, and dispersal during early life (1–2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.

Empirical work suggests that spatial sorting, a mechanism of evolutionary change fueled by spatial assortment of phenotypes, may lead to phenotypic shifts on ecological timescales. However, we currently lack theoretical tools to measure the strength of spatial sorting, as we do to measure the strength of natural selection. To address this gap, we present a quantitative genetics model and identify an evolutionary parameter in the model to measure the strength of spatial sorting. This parameter, the standardized sorting gradient, is structurally akin to the standardized selection gradient, commonly used to measure the strength of natural selection. To show the utility of our approach, we analyzed wing-morphology data of soapberry bugs (Jadera haematoloma) recolonizing flooded habitats extirpated by a hurricane. We found that the estimated strength of spatial sorting ranked in the top ten percentile of standardized selection gradients documented in the scientific literature. Our results underscore that, like natural selection, spatial sorting, too, can yield rapid evolution after extreme events.

Identifying genomic adaptation is key to understanding species' evolutionary responses to climate change. However, current methods to identify adaptive variation have two major limitations. First, when estimating genetic variation, most methods do not account for observational uncertainty in genetic data because of finite sampling and missing genotypes. Second, current methods use phenomenological models to partition genetic variation into adaptive and non-adaptive components. These phenomenological models are not mechanistic models of evolution and, therefore, do not faithfully capture the demographic history of the species. We address these limitations by developing a hierarchical Bayesian model that explicitly accounts for both the observational uncertainty and underlying evolutionary processes. The first layer of the hierarchy is the data model that captures observational uncertainty by probabilistically linking RAD-sequence data to genetic variation. The second layer is a process model that provides a mechanistic explanation of how evolutionary forces, such as local adaptation, mutation, migration, and drift, maintain genetic variation. The third layer is the parameter model, which incorporates our knowledge about biological processes. For example, because most loci in the genome are expected to be neutral, the environmental sensitivity coefficients are assigned a regularized prior centered at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms. Analysis of simulated RAD-seq data shows that our statistical model can reliably infer adaptive genetic variation. To show the real-world applicability of our method, we re-analyzed RAD-seq data (~105k SNPs) from Willow Flycatchers (Empidonax traillii) in the USA. We found 30 genes close to loci that showed a statistically significant association with temperature seasonality. Gene ontology suggests that several of these genes play a crucial role in egg mineralization, feather development, and the ability to withstand extreme temperatures. Moreover, biogeographers can easily modify the data and process models to accommodate a wide range of genetic datasets (e.g., pool and low coverage genome sequencing) and demographic histories (e.g., range shifts), allowing them to construct statistical models specific to their study system.

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invader’s life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel ( Dreissena polymorpha ) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.