Journal
ECOLOGICAL APPLICATIONS
Volume 26, Issue 3, Pages 740-751Publisher
WILEY
DOI: 10.1890/15-0934
Keywords
Anas acuta; Anas carolinensis; Anas platyrhnchos; Branta canadensis; community detection; consolidation factor; flyways; migration; network; waterfowl
Categories
Funding
- Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directorate, Department of Homeland Security
- Fogarty International Center, National Institutes of Health
- USDA [07-7100-0228-CA9, 09-7100-0305-CA, 11-9208-0269-CA, 09-9208-0235-CA]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1107046] Funding Source: National Science Foundation
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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 yr. 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 characterized 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.
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