Data from: Animal behavior, cost-based corridor models, and real corridors

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datacite.RelatedIdentifierhttp://www.springer.com/life+sciences/ecology/journal/10980
datacite.RelatedIdentifier.relatedIdentifierTypeURL
datacite.RelatedIdentifier.relationTypeIsSupplementTo
dc.contributor.authorLaPoint, Scott
dc.contributor.authorGallery, Paul
dc.contributor.authorWikelski, Martin
dc.contributor.authorKays, Roland
dc.date.accessioned2013-07-02T09:06:09Z
dc.date.available2013-07-02T09:06:09Z
dc.date.issued2013-07-02
dc.date.submitted2013
dc.description.abstractCorridors are popular conservation tools because they are thought to allow animals to safely move between habitat fragments, thereby maintaining landscape connectivity. Nonetheless, few studies show that mammals actually use corridors as predicted. Further, the assumptions underlying corridor models are rarely validated with field data. We categorized corridor use as a behavior, to identify animal-defined corridors, using movement data from fishers (Martes pennanti) tracked near Albany, New York, USA. We then used least-cost path analysis and circuit theory to predict fisher corridors and validated the performance of all three corridor models with data from camera traps. Six of eight fishers tracked used corridors to connect the forest patches that constitute their home ranges, however the locations of these corridors were not well predicted by the two cost-based models, which together identified only 5 of the 23 used corridors. Further, camera trap data suggest the cost-based corridor models performed poorly, often detecting fewer fishers and mammals than nearby habitat cores, whereas camera traps within animal-defined corridors recorded more passes made by fishers, carnivores, and all other non-target mammal groups. Our results suggest that (1) fishers use corridors to connect disjunct habitat fragments, (2) animal movement data can be used to identify corridors at local scales, (3) camera traps are useful tools for testing corridor model predictions, and (4) that corridor models can be improved by incorporating animal behavior data. Given the conservation importance and monetary costs of corridors, improving and validating corridor model predictions is vital.
dc.identifier.doidoi:10.5441/001/1.2tp2j43g
dc.identifier.urihttps://datarepository.movebank.org/handle/10255/move.328
dc.language.isoeng
dc.relationLandscape Ecology
dc.relation.haspartdoi:10.5441/001/1.2tp2j43g/1
dc.relation.haspartdoi:10.5441/001/1.2tp2j43g/2
dc.relation.isreferencedbydoi:10.1007/s10980-013-9910-0
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectanimal movement
dc.subjectcarnivore
dc.subjectcircuit theory
dc.subjectconnectivity
dc.subjectconservation
dc.subjectfisher
dc.subjectleast-cost path
dc.subjectMartes pennanti
dc.titleData from: Animal behavior, cost-based corridor models, and real corridors
dc.typeArticle
dspace.entity.typeData package
mdr.animal.count8
mdr.citation.BibTex
@misc{001/1_2tp2j43g,
  title = {Data from: Animal behavior, cost-based corridor models, and real corridors},
  author = {LaPoint, S and Gallery, P and Wikelski, M and Kays, R},
  year = {2013},
  URL = {http://dx.doi.org/10.5441/001/1.2tp2j43g},
  doi = {doi:10.5441/001/1.2tp2j43g},
  publisher = {Movebank data repository}
}
mdr.citation.CSE
LaPoint S, Gallery P, Wikelski M, Kays R. 2013. Data from: Animal behavior, cost-based corridor models, and real corridors. Movebank Data Repository. https://doi.org/10.5441/001/1.2tp2j43g
mdr.citation.RIS
TY  - DATA
ID  - doi:10.5441/001/1.2tp2j43g
T1  - Data from: Animal behavior, cost-based corridor models, and real corridors
AU  - LaPoint, Scott
AU  - Gallery, Paul
AU  - Wikelski, Martin
AU  - Kays, Roland
Y1  - 2013/07/02
KW  - animal movement
KW  - carnivore
KW  - circuit theory
KW  - connectivity
KW  - conservation
KW  - fisher
KW  - least-cost path
KW  - Martes pennanti
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.2tp2j43g
DO  - doi:10.5441/001/1.2tp2j43g
ER  -
mdr.journal.titleLandscape Ecology
mdr.location.count47347
mdr.study.id6925808
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sensor.nameGPS
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