Landscape Ecology

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

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

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

Abstract

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.

Keywords

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

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Robert A. Long
    • 1
    • 4
  • 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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