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dc.contributor.authorThieler, E. Robert  Concept link
dc.contributor.authorZeigler, Sara L.  Concept link
dc.contributor.authorWinslow, Luke A.  Concept link
dc.contributor.authorHines, Megan K.  Concept link
dc.contributor.authorRead, Jordan S.  Concept link
dc.contributor.authorWalker, Jordan I.  Concept link
dc.date.accessioned2016-12-20T19:33:34Z
dc.date.available2016-12-20T19:33:34Z
dc.date.issued2016-11-09
dc.identifier.citationPLoS ONE 11 (2016): e0164979en_US
dc.identifier.urihttps://hdl.handle.net/1912/8605
dc.descriptionThis is an open access article, free of all copyright. The definitive version was published in PLoS ONE 11 (2016): e0164979, doi: 10.1371/journal.pone.0164979.en_US
dc.description.abstractUnderstanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.en_US
dc.description.sponsorshipThis work was supported by the North Atlantic Landscape Conservation Cooperative through the U.S. Department of the Interior Hurricane Sandy recovery program under the Disaster Relief Appropriations Act of 2013, and the U.S. Geological Survey Coastal and Marine Geology Program.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.hasparthttps://doi.org/10.5066/F70V89X3
dc.relation.urihttps://doi.org/10.1371/journal.pone.0164979
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.titleSmartphone-based distributed data collection enables rapid assessment of shorebird habitat suitabilityen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0164979


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal