Data from: Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

Stable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using from point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data oven have sampling bias. To avoid this potential bias, an approach using SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of the species range. We then used the SDMs to refine isotopic assignments of breeding areas of autumn-migrating and wintering Sora (Porzana carolina), Virginia Rails (Rallus limicola), and Yellow Rails (Coturnicops noveboracensis) based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi Flyways. Yellow Rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Quebec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia Rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.稳定同位素技术已被广泛应用于多种鸟类的迁徙连通性估算。当结合物种分布模型（Species Distribution Models，SDMs）时，此类估算的精度往往能得到显著提升——此类模型可基于物种出现记录对估算结果进行校准。物种分布模型可依托多种来源的点位定位数据构建，其中包括通常由公民科学家收集的大规模监测数据集。然而，物种分布模型存在一个潜在局限：这类数据源往往存在采样偏差问题。为规避该偏差，本研究采用了基于湿地鸟类监测项目数据的物种分布模型构建方案：该监测数据由公民科学家及其他参与者按照专门设计的标准化流程收集，旨在最大化在能代表物种分布范围的点位上对目标物种的检出效率。随后，我们利用构建完成的物种分布模型，优化了对从美国境内明尼苏达州向南至路易斯安那州与南卡罗来纳州的多个捕获点位收集的羽毛样本的同位素归属判定，以明确秋季迁徙及越冬个体的索拉秧鸡（Porzana carolina）、弗吉尼亚秧鸡（Rallus limicola）与黄秧鸡（Coturnicops noveboracensis）的繁殖区域。研究结果显示，索拉秧鸡的繁殖区域涵盖美国西部大部分区域与加拿大草原省份，包含太平洋迁徙通道、中部迁徙通道及密西西比迁徙通道的部分区段。黄秧鸡的繁殖区域分布于曼尼托巴省北部及安大略省沿哈得孙湾与詹姆斯湾的广阔地带，同时覆盖魁北克省、明尼苏达州、威斯康星州与密歇根州的部分区域，涉及密西西比与大西洋迁徙通道的部分区域。弗吉尼亚秧鸡的繁殖区域则来自多个离散斑块，包括科罗拉多州、新墨西哥州的部分区域，加利福尼亚州中央谷地，以及位于太平洋与中部迁徙通道内的萨斯喀彻温省南部与曼尼托巴省区域。本研究证实，来自规范化公民科学监测项目的大规模数据集，在优化鸟类迁徙连通性的同位素归属判定方面具备极高应用价值，该成果最终可为更具科学性的管理决策与保护行动提供有力支撑。