Biology subjects

Zavaleta, E.

Publications and source records attributed to Zavaleta, E..

3 recordsLinked to original sources

bioRxiv2026

Co-limitation by stable, dynamic and directional habitat features shapes climate vulnerability in an alpine specialist

Alpine ecosystems are among the most climate sensitive on Earth, yet logistical challenges and detection biases often impede robust assessment of alpine dependent species. We investigated habitat associations and density patterns of the Sierra Nevada subspecies of the Gray-crowned Rosy-Finch (Leucosticte tephrocotis dawsoni), an alpine obligate and regional endemic, over five breeding seasons from 2018 to 2022 using hierarchical distance sampling and mark-recapture distance sampling to explicitly account for imperfect detection and spatial heterogeneity. Density estimates tracked annual snowpack variation, ranging from 4.77 individuals/km{superscript 2} in a low snow year to 12.08 individuals/km{superscript 2} in a high snow year. Abundance was highest near persistent snow patches that provide foraging habitat and near cliffs that provide nesting substrate, and declined sharply above approximately 10% woody cover, with densities approaching zero beyond approximately 25%, indicating a steep ecological threshold. In contrast, the proportion of surveyed blocks with detections remained relatively stable across years. Together, these patterns indicate a three timescale co-limitation framework in which breeding habitat is shaped by static features (cliffs), dynamic annual drivers (snowpack), and longer-term directional change (woody encroachment). By linking population density to climate sensitive habitat features, this study provides a high-resolution abundance-based baseline for long term monitoring and offers a framework for evaluating climate vulnerability in alpine and other resource-limited systems. Open Research StatementData necessary to replicate the analyses and results presented in this manuscript will be archived in the Dryad Digital Repository upon acceptance, with no embargo on the material. The R code associated with this manuscript is not novel. All analyses use publicly available packages and functions without modification, including mrds (v2.3.0), unmarked (v1.2.5), tidyverse (v2.0.0), MuMIn (v1.47.5), and standard ggplot2 visualization tools. All code is properly cited within the manuscript and publicly available through CRAN. Complete analysis scripts will be archived in Dryad alongside the data upon acceptance to facilitate full reproducibility of results.

ecology↗

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↗