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Vonhof M, Russell AL.2015. Genetic approaches to understanding the population-level impact of wind energy development on migratory bats: a case study of the eastern red bat (Lasiurus borealis)PeerJ PrePrints3:e788v1https://doi.org/10.7287/peerj.preprints.788v1
Documented fatalities of bats at wind turbines have raised serious concerns about the future impacts of increased wind power development on populations of migratory bat species. However, for most bat species we have no knowledge of the size of populations and their demographic trends, the degree of structuring into discrete subpopulations, and whether different subpopulations use spatially segregated migratory routes. Here, we utilize genetic data from eastern red bats (Lasiurus borealis), one of the species most highly affected by wind power development in North America, to (1) evaluate patterns of population structure across the landscape, (2) estimate effective population size (Ne), and (3) assess signals of growth or decline in population size. Using a large dataset of both nuclear and mitochondrial DNA variation, we demonstrate that this species forms a single, panmictic population across their range with no evidence for the historical use of divergent migratory pathways by any portion of the population. Further, using coalescent estimates we estimate that the effective size of this population is in the hundreds of thousands to millions of individuals. Our results showing high population connectivity and gene flow among populations of eastern red bats provides valuable context for understanding levels and patterns of mortality, and indicate that monitoring and management of eastern red bats must integrate information across the range of this species.
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Tukey boxplot of ancestral Ne from msvar analyses
Estimates are given on the log10 scale. Datasets A and B represent different subsamples of the full dataset from each respective year.
Collection information for samples included in our study
Samples included in dataset 1 are listed first, sorted by population (in the same order as Table 1), followed by samples included in datasets 2 and 3 sorted by state and county. Note that samples included in dataset 1 may have also been included in dataset 2 if they were collected in 2002. Genbank accession numbers are provided for all unique sequences for each marker.