Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development

R. Harrigan, Jasmine Rajbhandary, C. Bossu, Peter Sanzenbacher, Thomas Dietsch, Cristian Gruppi, Todd E. Katzner, Thomas B. Smith, Kristen C. Ruegg
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Abstract

With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called ‘genoscapes’) for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.
利用高分辨率遗传标记追踪候鸟在种群层面受可再生能源开发影响的情况
随着零碳能源满足能源需求比例的提高,人们更加关注评估和减轻可再生能源开发对敏感生态系统和野生动物的影响,其中鸟类尤其令人关注。研究人员面临的一个挑战是如何估算可再生能源开发对候鸟个体繁殖种群的影响,这部分是由于缺乏合适的工具。为了帮助解决这个问题,我们利用新开发的高分辨率基因标记方法,快速识别从可再生能源设施中回收的尸体的原产繁殖种群,并将其与已知受能源开发影响的五种候鸟的跨地理空间遗传变异图(称为 "基因景观")相结合,以评估候鸟种群水平的影响程度。我们证明,收集到的大多数鸟类遗骸都来自特定物种的最大种群。相反,那些来自较小的、正在减少的种群的鸟类遗骸在检测的鸟类总数中所占比例较小。结果表明,应用这种基因标记方法可以成功地确定可再生能源开发的种群暴露水平,并可能成为为未来与可再生能源项目相关的选址和缓解活动提供信息的有力工具。
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