Landscape Ecology

, Volume 26, Issue 3, pp 327–340 | Cite as

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

  • Robert A. LongEmail author
  • Therese M. Donovan
  • Paula MacKay
  • William J. Zielinski
  • Jeffrey S. Buzas
Research Article


Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers (Martes pennanti), and bobcats (Lynx rufus) throughout the rugged Vermont landscape. We analyzed these data using occupancy modeling that explicitly incorporated detectability as well as habitat and landscape variables. For black bears, percentage of forested land within 5 km of survey sites was an important positive predictor of occupancy, and percentage of human developed land within 5 km was a negative predictor. Although the relationship was less clear for bobcats, occupancy appeared positively related to the percentage of both mixed forest and forested wetland habitat within 1 km of survey sites. The relationship between specific covariates and fisher occupancy was unclear, with no specific habitat or landscape variables directly related to occupancy. For all species, we used model averaging to predict occurrence across the study area. Receiver operating characteristic (ROC) analyses of our black bear and fisher models suggested that occupancy modeling efforts with data from noninvasive surveys could be useful for carnivore conservation and management, as they provide insights into habitat use at the regional and landscape scale without requiring capture or direct observation of study species.


Black bear Bobcat Detectability Detection dog Distribution Fisher Lynx rufus Martes pennanti Ursus americanus Vermont 



We thank B. Davenport, D. MacKenzie, B. Mitchell, D. Paetkau, A. Royle, S. Wasser, S. Weigley, J. Weldon, and various field personnel and scat detection dogs for their contributions to this project. The Vermont Cooperative Fish and Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey, the Vermont Fish and Wildlife Department, the University of Vermont, and the Wildlife Management Institute. Funding for this project was provided by the Vermont Department of Fish and Wildlife, the Northeastern States’ Research Cooperative, the Jon C. and Katherine L. Harvey Charitable Foundation, the Southern Lake Champlain Valley Office of the Nature Conservancy, Sweet Water Trust, the USDA Forest Service, and the U.S. Geological Survey. Finally, we thank Robert L. Schooley and two reviewers for their valuable comments on this manuscript. Mention of services used in this research does not confer endorsement by the U.S. federal government.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Robert A. Long
    • 1
    • 4
    Email author
  • Therese M. Donovan
    • 2
  • Paula MacKay
    • 3
    • 4
  • William J. Zielinski
    • 5
  • Jeffrey S. Buzas
    • 6
  1. 1.Vermont Cooperative Fish and Wildlife Research UnitUniversity of VermontBurlingtonUSA
  2. 2.U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research UnitUniversity of VermontBurlingtonUSA
  3. 3.University of VermontBurlingtonUSA
  4. 4.Western Transportation InstituteMontana State UniversityEllensburgUSA
  5. 5.USDA Forest ServicePacific Southwest Research StationArcataUSA
  6. 6.Department of Mathematics and StatisticsUniversity of VermontBurlingtonUSA

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