Understanding the drivers of species distributions and biodiversity at macroscales is complicated by a variety of ecological and observational complexities, such as spatial autocorrelation, nonstationarity in species-environment relationships, and species interactions. In my work, I account for these complexities to provide a more complete understanding of macroscale biodiversity and inform effective monitoring and conservation approaches across spatial scales.

Effective wildlife management and conservation requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers and conservation practitioners. A key pillar of my research is developing computationally-efficient and accessible software to understand the ecological and anthropogenic drivers of species distributions, population dynamics, and biodiversity patterns.

Autonomous monitoring systems such as acoustic recording units, camera traps, and remote sensing methods (e.g., LiDAR) can provide massive amounts of data to inform biodiversity conservation, yet analyzing these data presents novel computational complexities. In my work, I develop quantitative approaches to leverage these complex data to inform a variety of wildlife and natural resource management objectives.