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

• Published in: Ecological Applications
• Main Authors: Buhnerkempe, Michael G., Webb, Colleen T., Merton, Andrew A., Buhnerkempe, John E., Givens, Geof H., Miller, Ryan S., Hoeting, Jennifer A.
• Other Authors: National Institutes of Health
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2016
• Subjects: Ecology; Branta canadensis; Canada Goose
• Online Access: http://dx.doi.org/10.1890/15-0934; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F15-0934; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/15-0934

Abstract 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.