Genetic and environmental drivers of migratory behavior in western burrowing owls and implications for conservation and management.

Migration is driven by a combination of environmental and genetic factors, but many questions remain about those drivers. Potential interactions between genetic and environmental variants associated with different migratory phenotypes are rarely the focus of study. We pair low coverage whole genome resequencing with a de novo genome assembly to examine population structure, inbreeding, and the environmental factors associated with genetic differentiation between migratory and resident breeding phenotypes in a species of conservation concern, the western burrowing owl (Athene cunicularia hypugaea). Our analyses reveal a dichotomy in gene flow depending on whether the population is resident or migratory, with the former being genetically structured and the latter exhibiting no signs of structure. Among resident populations, we observed significantly higher genetic differentiation, significant isolation-by-distance, and significantly elevated inbreeding. Among migratory breeding groups, on the other hand, we observed lower genetic differentiation, no isolation-by-distance, and substantially lower inbreeding. Using genotype-environment association analysis, we find significant evidence for relationships between migratory phenotypes (i.e., migrant versus resident) and environmental variation associated with cold temperatures during the winter and barren, open habitats. In the regions of the genome most differentiated between migrants and residents, we find significant enrichment for genes associated with the metabolism of fats. This may be linked to the increased pressure on migrants to process and store fats more efficiently in preparation for and during migration. Our results provide a significant contribution toward understanding the evolution of migratory behavior and vital insight into ongoing conservation and management efforts for the western burrowing owl.

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.

An analysis of spatial clustering and implications for wildlife management: a burrowing owl example.

Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl (Athene cunicularia). We assessed the ability of Ripley's K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during, and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short time frame. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley's K and GIS were effective in detecting owl nest clustering and show promise for future conservation uses.