Data from: Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

1. Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution, or movement patterns can ameliorate this limitation but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. 2. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. 3. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. 4. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. 5. Synthesis and applications. In this analysis Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas, and population monitoring and also highlight critical knowledge gaps.1. 用于追踪动物迁移的稳定氢同位素（δD）方法虽应用广泛，但通常仅能得到分辨率较低的种群归属结果。引入丰度、分布或迁移模式的先验知识可改善这一局限，但多数物种缺乏此类数据。本研究展示了如何利用公民科学（citizen science）上报的观测数据，构建可靠的物种分布估算结果，并约束δD归属分析。 2. 我们开发了贝叶斯框架（Bayesian framework），基于由公民科学观测数据构建的物种分布模型，对迁徙动物起源的同位素估算结果进行优化。为阐释该方法，我们分析了北美隐秘性且种群数量呈下降趋势的迁徙猎鸟——弗吉尼亚秧鸡（Rallus limicola）的迁徙连通性。 3. 公民科学观测数据既可用于估算采样偏差，也可用于构建经偏差校正的物种分布模型。将δD归属分析基于此类物种分布模型进行约束后，可得到分辨率相当的归属结果。 4. 在墨西哥湾沿岸5个站点越冬的弗吉尼亚秧鸡中，多数个体前一个夏季的活动区域位于五大湖附近，不过仍有相当比例的个体起源于切萨皮克湾流域或北达科他州的草原坑洼湿地带。与之相反，来自五大湖某站点的迁徙弗吉尼亚秧鸡中，多数个体前一个冬季的越冬地位于德克萨斯州与路易斯安那州之间的墨西哥湾沿岸区域。 5. 总结与应用：本研究的分析显示，弗吉尼亚秧鸡的迁徙连通性并未完全匹配用于迁徙鸟类管理的行政划定候鸟迁徙通道。本案例表明，随着用于构建物种分布模型的公民科学数据日益丰富，我们的框架可为包括隐秘性物种在内的众多动物提供高精度的迁徙连通性估算结果。季节性栖息地关联的实证证据，将有助于实现有效的栖息地管理、狩猎配额制定与种群监测，同时也能明确亟待填补的关键认知空白。