Smartphone Technologies and Bayesian Networks to Assess Shorebird Habitat Selection

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dc.contributor.authorZeigler, Sara L.en
dc.contributor.authorThieler, E. Roberten
dc.contributor.authorGutierrez, Benjamin T.en
dc.contributor.authorPlant, Nathaniel G.en
dc.contributor.authorHines, Meganen
dc.contributor.authorFraser, James D.en
dc.contributor.authorCatlin, Daniel H.en
dc.contributor.authorKarpanty, Sarah M.en
dc.contributor.departmentFish and Wildlife Conservationen
dc.date.accessioned2019-09-17T19:20:05Zen
dc.date.available2019-09-17T19:20:05Zen
dc.date.issued2017-12en
dc.description.abstractUnderstanding patterns of habitat selection across a species' geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers (Charadrius melodus) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species' breeding range within 1-2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, iPlover, to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications. (c) 2017 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.en
dc.description.notesWe thank our federal and private collaborators who supervised, participated in, and coordinated field-testing and data collection (listed in Table S1), particularly H. Abouelezz and T. Pearl for providing spatial data for the Breezy Point Unit of the Gateway National Recreation Area. A. Milliken and A. Hecht of the North Atlantic Landscape Conservation Cooperative and U.S. Fish and Wildlife Service, respectively, provided funding and motivation for this research. S. Haefner of the U.S. Geological Survey (USGS) provided code snippets that were used in an alpha version of the iPhone application. We thank N. Booth, M. Schneider, and M. Wernimont of the USGS Office of Water Information and T. Kern and H. Schweizer of the USGS Fort Collins Science Center for their support throughout the development of iPlover. K. Weber of the USGS Woods Hole Coastal and Marine Science Center provided analyses of dune characteristics for the Rockaway Peninsula used in our Geographic Information System analyses. Finally, we appreciate comments on earlier versions of this manuscript made by A. Hecht, N. Ganju, the editorial staff at the Wildlife Society Bulletin, Associate Editor Donahgy Cannon, and 4 anonymous reviewers. This work was supported by the U.S. Department of the Interior Hurricane Sandy recovery program under the Disaster Relief Appropriations Act of 2013, the U.S. Geological Survey Coastal and Marine Geology Program, the U.S. Fish and Wildlife Service, and the North Atlantic Landscape Conservation Cooperative. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.en
dc.description.sponsorshipNorth Atlantic Landscape Conservation Cooperative; U.S. Fish and Wildlife Service; U.S. Department of the Interior Hurricane Sandy recovery program under the Disaster Relief Appropriations Act; U.S. Geological Survey Coastal and Marine Geology Programen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/wsb.820en
dc.identifier.issn1938-5463en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/93741en
dc.identifier.volume41en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAtlantic coasten
dc.subjectbarrier islandsen
dc.subjectBayesian networken
dc.subjectCharadrius melodusen
dc.subjectcoastal geomorphologyen
dc.subjecthabitat availabilityen
dc.subjectiPloveren
dc.subjectnesting habitaten
dc.subjectpiping ploversen
dc.titleSmartphone Technologies and Bayesian Networks to Assess Shorebird Habitat Selectionen
dc.title.serialWildlife Society Bulletinen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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