Advancing spatial & remote sensing methods for wildlife & landscape modelingSDMs · Land Cover Land Change · LiDAR · Landscape Structure
Species distribution models, habitat suitability analyses, and landscape forecasting underpin many conservation planning decisions. Their reliability depends on data and analytical choices that are rarely tested, including which remote sensing inputs to use, whether habitat is better represented by binary thresholds or continuous gradients, and how vegetation structure measured at different resolutions relates to resource selection. When these methodological assumptions go unexamined, ecological inferences can be misinterpreted and downstream management or conservation actions may be affected.
A major aspect of my research focuses on refining how remotely sensed data, species occurrence data, and telemetry data can be used to examine ecologically meaningful questions. I am particularly interested in how different remote sensing data types and landscape-structure metrics converge or diverge in explaining ecological patterns, and in advancing species distribution modeling methods.
In past work, I and collaborators have: (1) developed new approaches for incorporating species-specific telemetry data into ecological niche models; (2) forecasted climate envelopes using range-wide occurrence data to identify climatic drivers in core and peripheral populations; (3) integrated LiDAR-derived canopy metrics and camera data to examine population density and potential wildlife crossing use; and (4) used the gradient concept of landscape structure and the theory of slack to examine habitat suitability.
Texas DOT · CKWRI · East Foundation · USFWS
