A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features

dc.contributor.author Gieder, Katherina D.
dc.contributor.author Karpanty, Sarah M.
dc.contributor.author Fraser, James D.
dc.contributor.author Catlin, Daniel H.
dc.contributor.author Gutierrez, Benjamin T.
dc.contributor.author Plant, Nathaniel G.
dc.contributor.author Turecek, Aaron M.
dc.contributor.author Thieler, E. Robert
dc.date.accessioned 2015-04-17T19:48:20Z
dc.date.available 2015-04-17T19:48:20Z
dc.date.issued 2014-01-31
dc.description © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005. en_US
dc.description.abstract Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input. en_US
dc.description.sponsorship Funding for the research presented in this paper was provided by the North Atlantic Landscape Conservation Cooperative and the U.S. Geological Survey. en_US
dc.format.mimetype application/pdf
dc.identifier.citation Ecological Modelling 276 (2014): 38–50 en_US
dc.identifier.doi 10.1016/j.ecolmodel.2014.01.005
dc.identifier.uri https://hdl.handle.net/1912/7233
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.relation.uri https://doi.org/10.1016/j.ecolmodel.2014.01.005
dc.rights Attribution-NonCommercial-NoDerivs 3.0 Unported *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject Bayesian network en_US
dc.subject Development en_US
dc.subject Habitat en_US
dc.subject Piping plover en_US
dc.subject Sea-level rise en_US
dc.subject Shorebird en_US
dc.title A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features en_US
dc.type Article en_US
dspace.entity.type Publication
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