Genetic identification of avian samples recovered from solar energy installations.

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots"-known to be of avian origin but not readily assignable to species via morphology-may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla).

Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low-coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine-scale population structure in a widespread songbird, the American Redstart (Setophaga ruticilla), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population-specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate-Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.

Genomic approaches to mitigating genetic diversity loss in declining populations.

The accelerating pace of global biodiversity loss is exacerbated by habitat fragmentation and subsequent inbreeding in small populations. To address this problem, conservation practitioners often turn to assisted breeding programmes with the aim of enhancing genetic diversity in declining populations. Although genomic information is infrequently included in these efforts, it has the potential to significantly enhance the success of such programmes. In this study, we showcase the value of genomic approaches for increasing genetic diversity in assisted breeding efforts, specifically focusing on a highly inbred population of Western burrowing owls. To maximize genetic diversity in the resulting offspring, we begin by creating an optimal pairing decision tree based on sex, kinship and patterns of homozygosity across the genome. To evaluate the effectiveness of our strategy, we compare genetic diversity, brood size and nestling success rates between optimized and non-optimized pairs. Additionally, we leverage recently discovered correlations between telomere length and fitness across species to investigate whether genomic optimization could have long-term fitness benefits. Our results indicate that pairing individuals with contrasting patterns of homozygosity across the genome is an effective way to increase genetic diversity in offspring. Although short-term field-based metrics of success did not differ significantly between optimized and non-optimized pairs, offspring from optimized pairs had significantly longer telomeres, suggesting that genetic optimization can help reduce the risk of inbreeding depression. These findings underscore the importance of genomic tools for informing efforts to preserve the adaptive potential of small, inbred populations at risk of further decline.