Biology subjects

Hooten, M. B.

Publications and source records attributed to Hooten, M. B..

4 recordsLinked to original sources

bioRxiv2026

Loss of Adaptive Capacity Drives Climate Vulnerability Across Taxonomic Scales in an Alpine Specialist Species Complex

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.

evolutionary biology↗

bioRxiv2026

Identifying adaptive variation in spatially structured populations using low-coverage whole-genome sequencing data

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 non-adaptive 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-shape 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.

genomics↗

bioRxiv2024

Identifying genomic adaptation to local climate using a mechanistic evolutionary model

O_LIIdentifying 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. C_LIO_LIWe 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 centered at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms. C_LIO_LIAnalysis 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. C_LIO_LIMoreover, 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. C_LI

genomics↗

bioRxiv2018

Predatory Behavior is Primary Predictor of Movement of Wildland-Urban Cougars

While many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars (Puma concolor), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), but RSFs do not adequately account for the habitat available to an individual at a given time and may mask conflict or avoidance behavior. Contemporary approaches to incorporate landscape availability into the assessment of habitat preference include spatio-temporal point process models, step-selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement. We used the CTMC framework to model speed and directionality of movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates. The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model. We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas.

ecology↗