Mon, Aug 02, 2021:On Demand
Background/Question/Methods
Many species depend on specific features on the landscape to persist and as such, characterizing the vegetation available in an area can be essential for managing these specialist species. One such habitat specialist is the ocelot (Leopardus pardalis), a medium-sized wild felid keenly adapted to dense thornshrub vegetation. Availability of this habitat type in its native South Texas has plummeted due to agriculture and urbanization, leading to population declines of ocelots and an eventual listing as federally endangered. As such, quantifying the characteristics of vegetation most suitable for ocelots has become an essential aspect to conservation of the species. Light detection and ranging (LiDAR) differs from other remote sensing techniques in its ability to penetrate through the canopy surface and describe the inner structure of the vegetation. Using LiDAR, we quantified the overall canopy height (m) and the percentage cover at 1 m increments. We captured 8 ocelots on the East Foundation’s El Sauz Ranch in Southern Texas from 2017 – 2020 and fitted individuals with global positioning system (GPS) collars that recorded locations every 30 minutes. We used a step selection function to determine selection of canopy height and cover by ocelots. We examined 4 spatial scales to examine how selection varied from 1.5m scale up to 30m scale and evaluated the same set of candidate models at all 4 scales.
Results/Conclusions We found strong selection for greater canopy near the ground (1m and 2m heights) at both the 30m and 10.5m scales, suggesting at the broader resolution, ocelots seek areas with dense cover at or near ground level. By combining accurate, fine-scale measurements derived from LiDAR data with high-frequency GPS locations, a more detailed understanding of habitat selection can be obtained. With a greater understanding of habitat use patterns, conservation strategies can be tailored to target the specific characteristics of vegetation that ocelots require by preserving existing patches and reforesting previous habitat. Further, results can be used to identify key areas that may provide habitat for reintroduction and translocation, thereby sustaining populations of ocelots in additional portions of their historic range.
Results/Conclusions We found strong selection for greater canopy near the ground (1m and 2m heights) at both the 30m and 10.5m scales, suggesting at the broader resolution, ocelots seek areas with dense cover at or near ground level. By combining accurate, fine-scale measurements derived from LiDAR data with high-frequency GPS locations, a more detailed understanding of habitat selection can be obtained. With a greater understanding of habitat use patterns, conservation strategies can be tailored to target the specific characteristics of vegetation that ocelots require by preserving existing patches and reforesting previous habitat. Further, results can be used to identify key areas that may provide habitat for reintroduction and translocation, thereby sustaining populations of ocelots in additional portions of their historic range.