Data from: Identification of migratory bird flyways in North America using community detection on biological networks

Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species —mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada goose (Branta canadensis) — in North America using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterize the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway identification in Canada geese exhibiting higher complexity. For mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals.

北美水禽种群的迁徙行为传统上被大致划分为四条南北向迁徙通道（flyway），且这些迁徙通道已成为水禽种群管理的核心框架，沿用至今已八十余年。然而，以往的迁徙通道划分方案难以适配当前鸟类移动数据进行更新，且无法针对特定地理区域在迁徙通道划定中的重要性开展评估。本研究基于鸟类环志与回收数据，针对北美四种水禽——绿头鸭（Anas platyrhnchos）、针尾鸭（Anas acuta）、美洲绿翅鸭（Anas carolinensis）以及加拿大黑雁（Branta canadensis）——构建了迁徙移动网络模型。随后，本研究通过群落检测算法（community detection algorithm）识别迁徙通道，并利用一种全新的指标——整合因子（consolidation factor）——量化小型地理区域在迁徙通道划定中的重要性。研究结果显示，绿头鸭、针尾鸭与美洲绿翅鸭均存在四条主要迁徙通道，而加拿大黑雁的迁徙通道识别结果则呈现出更高的复杂性。绿头鸭的迁徙通道随时间推移相对稳定，但整合因子分析结果表明，绿头鸭与美洲绿翅鸭的推定密西西比迁徙通道，实际上可能是不同迁徙通道间的高混合区域。本研究结果证实，网络分析方法可为迁徙通道划定提供一套稳健的研究方案：该方法对数据要求相对较低，可广泛适配各类研究场景，且未来可轻松结合新增的移动数据进行更新，以反映迁徙通道定义与管理目标的变化。