Biology subjects

Ruegg, K. C.

Publications and source records attributed to Ruegg, K. C..

8 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↗

bioRxiv2025

Summer rainfall drives adaptation with gene flow in a widespread butterfly

Understanding how environmental variation interacts with gene flow to shape population genomic patterns is a central goal in evolutionary biology. We investigated how geographic and environmental differences impact genomic variation in the clouded sulfur butterfly (Colias philodice eriphyle) by conducting whole-genome resequencing across replicated transects consisting of paired high- and low-elevation sites on both sides of a major mountain range. Despite sampling across steep environmental gradients, we found no evidence of discrete population structure, indicating high connectivity across the region. Nonetheless, significant isolation by distance - strongest in eastern populations - revealed that geographic distance still imposes limits on gene flow, and genetic diversity was also elevated in the east. Genotype-environment association analyses identified more than 16,000 loci associated with elevation, precipitation, and solar radiation. Our redundancy analysis identified precipitation as the strongest predictor of adaptive genomic differentiation, and candidate genes included those linked to melanization and thermoregulation (e.g., TH and yellow). These results demonstrate that even in a largely panmictic population, environmental variation can maintain regional-scale signals of local adaptation. Because insects are declining globally and remain underrepresented in genomic monitoring, conducting whole-genome analyses in a widespread species provides valuable context for assessing how insects today persist across such diverse landscapes and their potential for withstanding future environmental change.

evolutionary biology↗

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↗

bioRxiv2022

Genetic and ecological drivers of molt in a migratory bird

The ability of animals to sync the timing and location of molting (the replacement of hair, skin, exoskeletons or feathers) with peaks in resource availability has important implications for their ecology and evolution. In migratory birds, the timing and location of pre-migratory feather molting, a period when feathers are shed and replaced with newer, more aerodynamic feathers, can vary within and between species. While hypotheses to explain the evolution of intraspecific variation in the timing and location of molt have been proposed, little is known about the genetic basis of this trait or the specific environmental drivers that may result in natural selection for distinct molting phenotypes. Here we take advantage of intraspecific variation in the timing and location of molt in the iconic songbird, the Painted Bunting (Passerina ciris) to investigate the genetic and ecological drivers of distinct molting phenotypes. Specifically, we use genome-wide genetic sequencing in combination with stable isotope analysis to determine population genetic structure and molting phenotype across thirteen breeding sites. We then use genome-wide association analysis (GWAS) to identify a suite of genes associated with molting and pair this with gene-environment association analysis (GEA) to investigate potential environmental drivers of genetic variation in this trait. Associations between genetic variation in molt-linked genes and the environment are further tested via targeted SNP genotyping in 25 additional breeding populations across the range. Together, our integrative analysis suggests that molting is in part regulated by genes linked to feather development and structure (GLI2 and CSPG4) and that genetic variation in these genes is associated with seasonal variation in precipitation and aridity. Overall, this work provides important insights into the genetic basis and potential selective forces behind phenotypic variation in what is arguably one of the most important fitness-linked traits in a migratory bird.

ecology↗

bioRxiv2021

LI-Seq: A Cost-Effective, Low Input DNA method for Whole Genome Library Preparation

O_LISamples from species of high conservation concern are often low in total genomic DNA. Whole Genome Sequencing (WGS) can provide many insights that can be used to aid in species conservation, but current methods for working with low quality and low input samples can be cost prohibitive for population level genomic analyses. Thus, there is an urgent need for a cost-effective method of preparing WGS libraries from low input DNA samples. C_LIO_LITo bridge the gap between sampling techniques commonly used in conservation genetics that yield low quality and low input DNA and the powerful tool of WGS, we developed LI-Seq, a more efficient method that successfully produces libraries from low quality DNA with as low input as 0.48 ng of DNA, with an average final library size of 300-500 base pairs. C_LIO_LISequencing results suggest no difference in sequencing quality or coverage between low quality, low input and high quality, high input starting material using our protocol. We conclude that our new method will facilitate high-throughput WGS on low quality, low input samples, thus expanding the power of genomic tools beyond traditional high quality samples. C_LI

genetics↗

bioRxiv2020

The genomic landscape of divergence across the speciation continuum in island-colonising silvereyes (Zosterops lateralis)

A goal of the genomic era is to infer the evolutionary dynamics at play during the process of speciation by analysing the genomic landscape of divergence. However, empirical assessments of genomic landscapes under varying evolutionary scenarios are few, limiting the ability to achieve this goal. Here we combine RAD-sequencing and individual-based simulations to evaluate the genomic landscape in the silvereye (Zosterops lateralis). Using comparisons matched for divergence timeframe and gene flow context, we document how genomic patterns accumulate along the speciation continuum. In contrast to previous predictions, our results provide limited support for the idea that divergence accumulates around loci under divergent selection or that genomic islands widen with time. While a small number of genomic islands were found in populations diverging with and without gene flow, in few cases were SNPs putatively under selection tightly associated with genomic islands. Furthermore, we modelled the transition from localised to genome-wide levels of divergence using individual-based simulations that considered only neutral processes. Our results challenge the ubiquity of existing verbal models that explain the accumulation of genomic differences across the speciation continuum and instead support the idea that divergence both within and outside of genomic islands is important during the speciation process. DATA ACCESSION NUMBERSResequencing data from this study have been submitted to the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov) under accession number PRJNA489169.

evolutionary biology↗

bioRxiv2016

Identifying Migrant Origins Using Genetics, Isotopes, and Habitat Suitability

O_LIIdentifying migratory connections across the annual cycle is important for studies of migrant ecology, evolution, and conservation. While recent studies have demonstrated the utility of high-resolution SNP-based genetic markers for identifying population-specific migratory patterns, the accuracy of this approach relative to other intrinsic tagging techniques has not yet been assessed.\nC_LIO_LIHere, using a straightforward application of Bayes' Rule, we develop a method for combining inferences from high-resolution genetic markers, stable isotopes, and habitat suitability models, to spatially infer the breeding origin of migrants captured anywhere along their migratory pathway. Using leave-one-out cross validation, we compare the accuracy of this combined approach with the accuracy attained using each source of data independently.\nC_LIO_LIOur results indicate that when each method is considered in isolation, the accuracy of genetic assignments far exceeded that of assignments based on stable isotopes or habitat suitability models. However, our joint assignment method consistently resulted in small, but informative increases in accuracy and did help to correct misassignments based on genetic data alone. We demonstrate the utility of the combined method by identifying previously undetectable patterns in the timing of migration in a North American migratory songbird, the Wilson's warbler.\nC_LIO_LIOverall, our results support the idea that while genetic data provides the most accurate method for tracking animals using intrinsic markers when each method is considered independently, there is value in combining all three methods. The resulting methods are provided as part of a new computationally-efficient R-package, GIAIH, allowing broad application of our statistical framework to other migratory animal systems.\nC_LI

ecology↗