Identifying Habitat Linkages for American Black Bears in North Carolina, USA
Identifieur interne : 000B49 ( Istex/Corpus ); précédent : 000B48; suivant : 000B50Identifying Habitat Linkages for American Black Bears in North Carolina, USA
Auteurs : Jason L. Kindall ; Frank T. Van ManenSource :
- The Journal of Wildlife Management [ 0022-541X ] ; 2007-04.
English descriptors
- KwdEn :
Abstract
ABSTRACT Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data‐based method for identifying local‐ and regional‐scale habitat linkages for American black bears (Ursus americanus) on the Albemarle‐Pamlico Peninsula of North Carolina, USA. We used weights‐of‐evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2‐km2 scale: forest cohesion, forest diversity, and forest‐agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10‐fold model validation. Forest cohesion and forest‐agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.
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DOI: 10.2193/2005-709
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We present a data‐based method for identifying local‐ and regional‐scale habitat linkages for American black bears (Ursus americanus) on the Albemarle‐Pamlico Peninsula of North Carolina, USA. We used weights‐of‐evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2‐km2 scale: forest cohesion, forest diversity, and forest‐agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10‐fold model validation. Forest cohesion and forest‐agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>JASON L. KINDALL</name><affiliations><json:string>Department of Forestry, Wildlife and Fisheries, University of Tennessee, 274 Ellington Plant Sciences Building, Knoxville, TN 37996, USA</json:string></affiliations></json:item><json:item><name>FRANK T. 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We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. 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We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. 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countryCode="US"><unparsedAffiliation>Department of Forestry, Wildlife and Fisheries, University of Tennessee, 274 Ellington Plant Sciences Building, Knoxville, TN 37996, USA</unparsedAffiliation></affiliation><affiliation xml:id="a2" countryCode="US"><unparsedAffiliation>United States Geological Survey, Southern Appalachian Research Branch, 274 Ellington Plant Sciences Building, University of Tennessee, Knoxville, TN 37996, USA</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en"><keyword xml:id="k1">American black bear</keyword><keyword xml:id="k2">habitat analysis</keyword><keyword xml:id="k3">habitat linkages</keyword><keyword xml:id="k4">least‐cost‐path</keyword><keyword xml:id="k5">North Carolina</keyword><keyword xml:id="k6">transportation infrastructure</keyword><keyword xml:id="k7"><i>Ursus americanus</i></keyword><keyword xml:id="k8">weights‐of‐evidence</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><p><b>ABSTRACT</b> Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data‐based method for identifying local‐ and regional‐scale habitat linkages for American black bears (<i>Ursus americanus</i>) on the Albemarle‐Pamlico Peninsula of North Carolina, USA. We used weights‐of‐evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (<i>n</i> = 35 bears). The model included 3 variables measured at a 0.2‐km<sup>2</sup> scale: forest cohesion, forest diversity, and forest‐agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10‐fold model validation. Forest cohesion and forest‐agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Identifying Habitat Linkages for American Black Bears in North Carolina, USA</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Identifying Habitat Linkages for American Black Bears in North Carolina, USA</title></titleInfo><name type="personal"><namePart type="given">JASON L.</namePart><namePart type="family">KINDALL</namePart><affiliation>Department of Forestry, Wildlife and Fisheries, University of Tennessee, 274 Ellington Plant Sciences Building, Knoxville, TN 37996, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">FRANK T.</namePart><namePart type="family">VAN MANEN</namePart><affiliation>United States Geological Survey, Southern Appalachian Research Branch, 274 Ellington Plant Sciences Building, University of Tennessee, Knoxville, TN 37996, USA</affiliation><affiliation>E-mail: vanmanen@utk.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><place><placeTerm type="text">Oxford, UK</placeTerm></place><dateIssued encoding="w3cdtf">2007-04</dateIssued><copyrightDate encoding="w3cdtf">2007</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="references">36</extent></physicalDescription><abstract>ABSTRACT Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data‐based method for identifying local‐ and regional‐scale habitat linkages for American black bears (Ursus americanus) on the Albemarle‐Pamlico Peninsula of North Carolina, USA. We used weights‐of‐evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2‐km2 scale: forest cohesion, forest diversity, and forest‐agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10‐fold model validation. Forest cohesion and forest‐agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4‐lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.</abstract><subject lang="en"><genre>keywords</genre><topic>American black bear</topic><topic>habitat analysis</topic><topic>habitat linkages</topic><topic>least‐cost‐path</topic><topic>North Carolina</topic><topic>transportation infrastructure</topic><topic>Ursus americanus</topic><topic>weights‐of‐evidence</topic></subject><relatedItem type="host"><titleInfo><title>The Journal of Wildlife Management</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">0022-541X</identifier><identifier type="eISSN">1937-2817</identifier><identifier type="DOI">10.1002/(ISSN)1937-2817</identifier><identifier type="PublisherID">JWMG</identifier><part><date>2007</date><detail type="volume"><caption>vol.</caption><number>71</number></detail><detail type="issue"><caption>no.</caption><number>2</number></detail><extent unit="pages"><start>487</start><end>495</end><total>9</total></extent></part></relatedItem><identifier type="istex">2F02E303754EE26906A31D6F621F2AEDB73A3D75</identifier><identifier type="DOI">10.2193/2005-709</identifier><identifier type="ArticleID">JWMG1478</identifier><accessCondition type="use and reproduction" contentType="copyright">2007 The Wildlife Society</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Ltd</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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