Thu, Aug 18, 2022: 4:30 PM-4:45 PM
520C
Background/Question/MethodsCamera traps have been increasingly used over the past decade to document mammalian diversity across a variety of habitat types. However, camera traps have generally been placed at ground-level and thus have the potential to miss detecting the purely or semi-arboreal species. More recently, to account for this problem, arboreal camera traps have been deployed in many places to allow for the detection of the full mammalian community. Previous studies using arboreal camera traps have detected higher mammalian species richness than ground camera traps alone and have resulted in detections of new species not previously known to inhabit a particular protected area. In this presentation, we will provide an overview of the use of arboreal camera trapping in mammalian studies as well as a specific case study using a combination of ground and arboreal camera traps to document mammalian species diversity across forest fragments outside of Nyungwe National Park, Rwanda. We combined camera data with single-season occupancy models to estimate species richness and understand factors driving species richness across fragments.
Results/ConclusionsOverall, we found that 27 mammal species were detected on the cameras, with 18 species detected on ground cameras, 22 species detected on mid-canopy arboreal cameras, and 17 species detected on high-canopy arboreal cameras. Of these, 9 species were only detected on arboreal cameras. Estimated species richness per fragment ranged from 14 to 21 mammalian species, with distance from the fragment to the main protected forest as the best predictor variable of species richness. However, if we focus on species by order, we found that primate species richness was best predicted by the area of the forest fragment and the land use type around the fragment. For carnivores, we found the best predictor of species richness was the ratio of area to perimeter for the fragment. These findings highlight the high mammalian species richness found across the forest fragments surrounding the national park and provide rationale for protecting these forest fragments with the national park. In addition, we also show that different forest fragment characteristics can be important for different species groups.
Results/ConclusionsOverall, we found that 27 mammal species were detected on the cameras, with 18 species detected on ground cameras, 22 species detected on mid-canopy arboreal cameras, and 17 species detected on high-canopy arboreal cameras. Of these, 9 species were only detected on arboreal cameras. Estimated species richness per fragment ranged from 14 to 21 mammalian species, with distance from the fragment to the main protected forest as the best predictor variable of species richness. However, if we focus on species by order, we found that primate species richness was best predicted by the area of the forest fragment and the land use type around the fragment. For carnivores, we found the best predictor of species richness was the ratio of area to perimeter for the fragment. These findings highlight the high mammalian species richness found across the forest fragments surrounding the national park and provide rationale for protecting these forest fragments with the national park. In addition, we also show that different forest fragment characteristics can be important for different species groups.