Featured PUBlications
Related news
This group aims to be forward-thinking in the development and incorporation of new and cutting-edge methods and models to maximize the information gained from ecological data over large spatial extents.

Our projects and team members are advancing our capabilities in describing ecological and evolutionary processes from spatiotemporal species distribution models to individual niche dynamics. Many of our approaches are based around connecting datasets from disparate, multi-modal sources by leveraging advances in statistics and machine learning that allow us to gain deeper insights on complex ecological processes.

Guiding Questions


Ongoing Projects

Borrowing strength

Project Title

Borrowing predictive strength for undersampled Species

Biodiversity data, specifically data on species’ locations and abundances, are critical to parameterize species distribution models (SDMs) that are used to inform conservation analyses. Despite rapid growth in data, many species continue to lack sufficient data to characterize their geographic distribution because they occur in remote, undersampled areas, are difficult to detect or are otherwise underreported. The under-representation of these data-deficient species has the potential to constrain and bias inference and decision-making. Here, we conceptualize the potential for data-deficient species to borrow predictive strength from those more thoroughly sampled.

Invasive species

Phylo SDMs

deep SDMs