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ABSTRACT Genomic projections of (mal)adaptation under future climate change, known as genomic offset, faces limited application due to challenges in validating model predictions. Individuals inhabiting regions with high genomic offset are expected to experience increased levels of physiological stress as a result of climate change, but documenting such stress can be challenging in systems where experimental manipulations are not possible. One increasingly common method for documenting physiological costs associated with stress in individuals is to measure the relative length of telomeres—the repetitive regions on the caps of chromosomes that are known to shorten at faster rates in more adverse conditions. Here we combine models of genomic offsets with measures of telomere shortening in a migratory bird, the yellow warbler (Setophaga petechia), and find a strong correlation between genomic offset, telomere length and population decline. While further research is needed to fully understand these links, our results support the idea that birds in regions where climate change is happening faster are experiencing more stress and that such negative effects may help explain the observed population declines. 

Abstract Low‐coverage whole‐genome sequencing (WGS) is increasingly used for the study of evolution and ecology in both model and non‐model organisms; however, effective application of low‐coverage WGS data requires the implementation of probabilistic frameworks to account for the uncertainties in genotype likelihoods.Here, we present a probabilistic framework for using genotype likelihoods for standard population assignment applications. Additionally, we derive the Fisher information for allele frequency from genotype likelihoods and use that to describe a novel metric, theeffective sample size, which figures heavily in assignment accuracy. We make these developments available for application through WGSassign, an open‐source software package that is computationally efficient for working with whole‐genome data.Using simulated and empirical data sets, we demonstrate the behaviour of our assignment method across a range of population structures, sample sizes and read depths. Through these results, we show that WGSassign can provide highly accurate assignment, even for samples with low average read depths (<0.01X) and among weakly differentiated populations.Our simulation results highlight the importance of equalizing the effective sample sizes among source populations in order to achieve accurate population assignment with low‐coverage WGS data. We further provide study design recommendations for population assignment studies and discuss the broad utility of effective sample size for studies using low‐coverage WGS data.