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. 稳定氢同位素（Stable hydrogen isotope）示踪动物移动的方法应用广泛，但往往仅能得到低分辨率的溯源结果。引入丰度、分布或移动模式的先验知识可缓解这一局限，但多数物种缺乏此类相关数据。本研究展示了如何借助公民科学家报告的观测数据，可靠估算物种分布并约束δD来源归属结果。
2. 我们构建了贝叶斯框架（Bayesian framework），基于由公民科学家观测数据构建的物种分布模型（Species Distribution Models, SDM），对迁徙动物的起源地同位素估算结果进行修正。为阐释该方法的应用效果，我们分析了弗吉尼亚秧鸡（*Rallus limicola*）的迁徙连通性——该物种是北美地区一种隐秘且种群呈下降趋势的迁徙猎鸟。
3. 公民科学家的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来源归属结果基于此类物种分布模型进行约束后，可得到分辨率相当高的溯源结果。
4. 越冬于墨西哥湾沿岸（Gulf Coast）5个站点的弗吉尼亚秧鸡，多数在前一个夏季活动于五大湖（Great Lakes）周边区域，但仍有相当比例的个体起源于切萨皮克湾流域（Chesapeake Bay watershed）或北达科他州的草原坑洼区域（Prairie Pothole region）。与之相反，来自五大湖某站点的迁徙弗吉尼亚秧鸡，多数大概率在前一个冬季活动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. 综合与应用：本分析显示，弗吉尼亚秧鸡的迁徙连通性并未完全契合用于管理迁徙鸟类的行政飞行航线（administrative flyways）。本案例表明，随着可用于构建物种分布模型的公民科学数据日益丰富，我们的框架可为众多动物（包括隐蔽物种（cryptic species））提供高精度的迁徙连通性估算结果。季节性栖息地间关联的实证证据，将助力开展高效的栖息地管理、狩猎配额制定与种群监测，同时也能明确关键的研究空白。