Project Grant 2610451

The National Science Foundation Division of Mathematical Sciences awarded Middlebury College $330,740 on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop Bayesian statistical models for ecological data analysis.

The project develops three complementary Bayesian hierarchical models that estimate hidden ecological processes—including species interactions, animal movement, and population growth—from imperfect and incomplete observational data while rigorously quantifying uncertainty. The first model addresses zero-inflated, non-negative community abundance data and accounts for species interactions through a joint change-point approach. The second model characterizes demographic processes through longitudinal physiological measurements using a stochastic growth framework. The third model, described partially in the source, represents a third complementary modeling approach. These models integrate ecological knowledge with statistical inference methods related to machine learning and artificial intelligence to produce biologically meaningful insights and more reliable predictions supporting conservation, environmental management, and regulatory decision-making. The project also provides research opportunities to undergraduate students.

Performance runs through August 31, 2029, at Middlebury College in Middlebury, Vermont. This is a Research in Undergraduate Institutions (RUI) award, indicating the research integrates undergraduate education within the framework of a primarily undergraduate institution.