National Audubon Society, New York, New York, USA.
National Audubon Society, Washington, District of Columbia, USA.
Ecol Appl. 2022 Oct;32(7):e2679. doi: 10.1002/eap.2679. Epub 2022 Jul 6.
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.
对于许多鸟类物种来说，其空间迁徙模式在很大程度上仍未被描述，尤其是在跨半球的范围内。最近跟踪技术和高分辨率物种分布模型（即 eBird 状态和趋势产品）的进步为候鸟迁徙运动提供了新的见解，并为整合独立数据源以描述鸟类迁徙提供了有希望的机会。在这里，我们提出了一个三阶段的建模框架，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，以量化迁徙连接性，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，我们使用估计的连接性比例和 eBird 出现概率来生成概率最小成本路径 (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）来自跟踪和带重遇数据集的观测位置与迁徙期间的伪缺失位置的能力，并创建一个完全集成的（即 eBird 出现、LCP 和跟踪/带重遇数据）空间预测指数，以绘制特定物种的季节性迁徙。为了说明这种方法，我们将该框架应用于描述西半球 12 种鸟类在繁殖前和繁殖后迁徙期间（即春季和秋季）的季节性迁徙。我们发现，与仅使用 eBird 出现的模型相比，在 GAMM 中包含 LCP 指数通常可以提高准确预测观测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 模型相对于仅 eBird 模型表现出等效或更好的拟合。特别是，对于那些具有越洋迁徙或在没有 eBird 观测的内陆地区迁徙的物种，集成指数填补了空间空白，因为这些地区的 eBird 出现概率预测能力较低（例如，南美洲的亚马逊雨林）。这种将个体季节性运动数据与时间动态物种分布模型相结合的方法为整合多种数据类型以描述动物大规模空间运动模式提供了一种全面的方法。进一步开发和定制这种方法将继续推进关于候鸟完整年周期和保护的知识。
