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dc.contributorVirginia Tech. Department of Fish and Wildlife Conservationen_US
dc.contributor.authorHuang, Jianen_US
dc.contributor.authorFrimpong, Emmanuel A.en_US
dc.contributor.editorBritton, Roberten_US
dc.date.accessioned2016-02-16T08:03:32Z
dc.date.available2016-02-16T08:03:32Z
dc.date.issued2015-06-15
dc.identifier.citationHuang J, Frimpong EA (2015) Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes. PLoS ONE 10(6): e0129995. doi:10.1371/journal.pone.0129995en_US
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10919/64832
dc.description.abstractUnderstanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species' rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs.en_US
dc.description.sponsorshipU.S. Geological Survey‏. Aquatic Gap Analysis Programen_US
dc.description.sponsorshipRWO 156en_US
dc.description.sponsorshipVirginia Tech. Open Access Subvention Funden_US
dc.format.extent19 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectFreshwater fishen_US
dc.subjectHabitatsen_US
dc.subjectSpatial autocorrelationen_US
dc.subjectDecision treesen_US
dc.subjectLand useen_US
dc.subjectInvasive speciesen_US
dc.subjectRiversen_US
dc.subjectSnakesen_US
dc.titleUsing Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishesen_US
dc.typeArticle - Refereeden_US
dc.rights.holderHuang, Jianen_US
dc.rights.holderFrimpong, Emmanuel A.en_US
dc.identifier.urlhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129995
dc.date.accessed2016-02-12
dc.title.serialPLOS Oneen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0129995
dc.identifier.volume10
dc.identifier.issue6
dc.type.dcmitypeTexten_US


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License: Attribution 4.0 International