To evaluate trap success among camera types and across species as well as assess habitat selection by target carnivore species, we established 16 infrared-triggered camera stations across a 26.9-km2 study area located on primarily Jefferson National Forest land in Virginia. We monitored camera stations for 72 days (August to October 2005) for a total of 891 trap nights (TN) of effort. Overall trap success for all animals combined was 40.74 captures per 100 TN. Procyon lotor (raccoon) had the highest predator trap success (2.81/100 TN), followed by: Ursus americanus (black bear, 1.91/100 TN); Lynx rufus (bobcat, 1.46/100 TN); Canis latrans (coyote, 1.01/100 TN); and Urocyon cinereoargenteus (gray fox, 0.56/100 TN). Odocoileus virginianus (white-tailed deer) had the highest overall trap success (21.32/100 TN), followed by Sciurus carolinensis (gray squirrel, 6.17/100 TN). Passive camera units, especially DeerCam, had higher trap success than active camera units, and digital camera units (Reconyx) out-performed film cameras. We extracted percent cover of habitat features (% coniferous, % deciduous, % water, % agricultural) from a geographic information system (GIS) using circular buffers around each trap site and compared carnivore-present sites to carnivore-absent sites. We compared carnivore trap success to the distance to the main access road and to trap success of prey species, primarily deer and gray squirrel. We also compared each carnivore's trap success to that of the other carnivore species to determine if carnivore presence or activity levels influenced other carnivores. Black bear, coyote, and raccoon tended to avoid areas with a high percentage of coniferous forest, and only bobcat showed significant avoidance of coniferous forest. Bobcat trap success increased with distance to the main road, and coyote trap success was positively (but weakly) related to gray squirrel trap success. Human foot traffic did not affect carnivore trap success. This study elucidates differences among camera trap systems, and highlights the potential to monitor carnivore species simultaneously and in combination with a GIS to predict occurrence across a landscape.
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