For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

中文翻译：

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整合数据类型以估计西半球鸟类迁徙的空间格局

对于许多鸟类物种，空间迁徙模式在很大程度上仍未得到描述，尤其是在整个半球范围内。跟踪技术和高分辨率物种分布模型（即 eBird Status 和 Trends 产品）的最新进展提供了对候鸟运动的新见解，并为整合独立数据源来描述鸟类迁徙提供了一个有希望的机会。在这里，我们提出了一个三阶段建模框架，用于估计鸟类迁徙的空间模式。首先，我们整合跟踪和带重遇数据来量化迁徙连通性，定义为个体在繁殖区和非繁殖区之间迁徙的相对比例。接下来，我们使用估计的连通性比例以及 eBird 发生概率来生成概率最低成本路径 (LCP) 指数。在最后一步中，我们使用广义加性混合模型 (GAMM) 来评估 LCP 指数准确预测（即作为协变量）从跟踪和带重遇数据集导出的观察位置与伪缺失位置的能力迁徙时期，并创建一个完全集成的（即 eBird 发生、LCP 和跟踪/带重遇数据）空间预测指数，用于绘制特定物种的季节性迁徙图。为了说明这种方法，我们应用该框架来描述 12 种鸟类在繁殖前和繁殖后迁徙期间（即分别为春季和秋季）跨越西半球的季节性迁徙。我们发现，与单独出现 eBird 的模型相比，在 GAMM 中包含带有 eBird 出现的 LCP 指数通常可以提高准确预测观察到的迁徙位置的能力。使用三个性能指标，对于 24 个物种季节 GAMM 中的 22 个，eBird + LCP 模型证明了与仅 eBird 模型等效或更优的拟合。特别是，综合指数填补了跨水迁移物种和那些很少见到电子鸟的陆地迁移物种的空间空白，因此，电子鸟出现概率的预测能力较低（例如，南美洲的亚马逊雨林）。这种将基于个体的季节性运动数据与时间动态物种分布模型相结合的方法提供了一种整合多种数据类型以描述动物运动的大尺度空间模式的综合方法。这种方法的进一步发展和定制将继续推进关于整个年度周期和候鸟保护的知识。