Species distribution models (SDMs) attempt to predict or statistically associate geographic record of a species with biospatial variables of interest over large spatial extents and are being increasingly utilized in wildlife management as remote sensing technology and our understanding of ecological distributional patterns advances. Most models use variables such as soil type, climatic patterns, topography, hydrology, vegetative communities, and other abiotic conditions to identify the predicted range of a species. However, species interactions and temporal influence have yet to be successfully quantified and included in SDMs. Expanding the focus from a single species to the assemblage distribution across time can increase our understanding of the community and improve our predictions for how changes in the environment might impact the ecosystem. Using avian occupancy data collected from 2014 through 2016 on the East Foundation’s San Antonio Viejo Ranch, we built baseline SDMs across relevant environmental variables and then improved the models by generating and incorporating biotic layers into the model.
Results/Conclusions
Consideration of biotic interactions significantly improved all models (Generalized Linear, Bioclim, Gaussian, Boosted Regression Tree, Random Forest, Support Vector Machine Learning, and Maxent). We created new coding language using ArcGIS, R, and ENVI and applied novel modeling techniques to traditional distributional data. With these new methods, we have built a more comprehensive approach to niche modeling for avian species. Conclusions drawn from these more robust models enable a better informed approach to community level management since they incorporate and predict a multi-species response to varying management regimes. The development of new modeling techniques is valuable to wildlife managers as the use of spatial statistics and landscape scale approaches become more heavily utilized to simulate changes in wildlife abundance or distribution in response to environmental variability and manipulation (e.g. cattle grazing, vegetation control, drought conditions).