Research

Understanding the drivers of species distributions, forest resources, 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 the Statistical Ecology and Forest Science Lab, we account for these complexities to provide a more complete understanding of macroscale ecological processes and inform effective monitoring and conservation approaches across spatial scales.

Forest management and production increasingly require estimates of forest variables in small domains. Classical design-based approaches are limited in their ability to provide unbiased estimates at small spatial scales using national forest inventory data. We develop Bayesian model-based approaches for improved estimation of forest parameters across user-defined small areas.

Effective forest and wildlife management requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers, foresters, wildlife professionals, and conservation practitioners. A key pillar of the lab’s 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 LiDAR, acoustic recording units, camera traps, and other remote sensing methods can provide massive amounts of data to inform forest and wildlife management, yet analyzing these data presents novel computational complexities. We develop quantitative approaches to leverage these complex data to inform a variety of forest and wildlife management objectives.

The Statistical Ecology and Forest Science Lab works closely with colleagues across the world to apply our modeling developments to different management, conservation, and ecological questions.