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Camera trapping technology is a non-invasive and growing tool for collecting wildlife management data for a wide variety of applications. Camera trapping technology is particularly useful for detecting rare, elusive, and threatened/endangered animals. In the northwestern Texas Panhandle, one such species of concern is the swift fox (Vulpes velox) which the Texas Parks and Wildlife Department considers a Species of Greatest Conservation Need in the state. I used 30 camera traps on the Rita Blanca National Grassland (RBNG) to conduct a community-level study to assess mammal usage of the grassland. I documented 24 species of mammals across the property and a total of 4.5 million images were collected from August 2019 to January 2022. I compared mammal community metrics across the 30 camera locations. I did not detect statistically significant differences between the different camera sites for the standard community metrics of total mammal abundance or indices of species richness, diversity, and evenness. Habitat variables (n=12) were digitized from aerial photography at each camera location and processed through fragmentation statistics. Elevation contour lines were calculated for each camera location using LIDAR aerial photography to assess whether elevation changes had any effect on mammal activity. To assess whether environmental variables had an effect on the mammal community, a permutational multivariate analysis of variance (PERMANOVA) was used to look at the relationships between the environmental variables (independent variables) and the activity indexes’ for each camera as the dependent variables. I used a PERMANOVA for analysis due to the constraints of a traditional multivariate analysis of variance (MANOVA). The small mammal, meso-mammal, and large mammal communities were analyzed separately. The three models only contained species that were detected at a majority of the camera locations and were considered native animals. The activity abundance of the coyote species was used as an independent variable in the small mammal and meso-mammal models. Three environmental variables were statistically significant in the small mammal model: coyote activity, prairie dog town habitat, and standard deviation of elevation. Four environmental variables were statistically significant in the meso-mammal model: coyote activity, agriculture habitat (ha), Shannon patch habitat diversity, and distance to center pivot. For the large mammal model, number of patches was statistically significant. The 24 species of mammal I documented all use habitats differently. The relationships I observed between wildlife species and agriculture habitats give insight into the remaining value and use of these fragmented and disturbed areas to some mammal species. However, species like the swift fox exhibited a negative association with agriculture habitat and coyote activity index. The influence of anthropogenic areas on wildlife should be analyzed in more detail to assess the impact they have on native species. Longer, finer-scale monitoring is needed to better understand how the mammal community responds to environmental changes and interspecies interactions. My study demonstrates how habitat composition influences mammal presence in a grassland ecosystem.



Agriculture, Forestry and Wildlife


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