| Summary: | 2012 Fall. Includes bibliographical references. 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, recent desires to incorporate uncertainty regarding biological processes into an adaptive harvest management program have underscored the need to re-evaluate the traditional flyway concept and bring uncertainty in flyways themselves into management planning. Here, we use bird band and recovery data to develop 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. A community detection algorithm is then used to identify migratory flyways. Additionally, we compare flyway structure both across species and through time to determine broad applicability of the previous flyway concept. We also propose a novel metric, the consolidation factor, to describe a node's (i.e., small geographic area) importance in determining flyway structure. The community detection algorithm identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway structure of Canada geese exhibiting higher complexity. For mallards, flyway structure was relatively consistent through time. However, consolidation factors and cross-community mixing patterns revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between flyways. Additionally, interspersed throughout these major flyways were smaller mixing zones that point to added complexity and uncertainty in the four-flyway concept. Not only does the incorporation of this uncertainty due to mixing provide a potential alternative management strategy, but the network approach provides a robust, quantitative approach to flyway ...
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