Biodiversity is defined as the presence of all species of plants, animals and other living things at a given place and time. These species share their environment and interact in complex ways. Scientists know that biodiversity is critical for providing many ecological services necessary for the planet’s health. Examples of these services include nutrient cycling, water purification, and soil formation. Thus, understanding and predicting changes in biodiversity (called "bioforecasting") is important for human wellbeing. However, because of larger and faster environmental changes, knowing whether a species in a given place will persist has become one of the biggest ecological challenges that scientists face. The main problem is that it’s virtually impossible to know how, where, when and which environmental conditions will change. The changes are obvious only in hindsight. This project will create a new way to solve the problem. The goal is to estimate the chance that a species will persist in a given place and how its presence (or absence) can affect the chance of other species persisting there, too. This project will also help launch the careers of a scientist and the students he will mentor as they together craft a new theory.
The main difficulty in understanding and predicting species persistence resides in knowing the exact equations governing the dynamics of ecological communities and the high uncertainty regarding initial conditions, parameter values, intrinsic randomness, and more importantly, how the changing external conditions will affect all of these dynamics. While bioforecasting is already a well-established endeavor in ecological research, current bioforecasting approaches demand extensive amounts of data and their generalization often lacks experimental validation. Thus, it has been emphasized that radically new frameworks are needed. Development of such frameworks involve large risks and many challenges. This project will break new ground by integrating concepts from the ensemble theory of statistical mechanics with the mathematical concepts of structural stability, applied to population dynamics to tackle current limitations in bioforecasting. Specifically, instead of aiming to study the future behavior of a system by inferring the main conditions acting upon it, this project will provide a testable methodology to estimate and interpret the probability of a future behavior based on the fraction of possible conditions compatible with the observability of such behavior. The investigator will develop model-driven and data-driven approaches to estimate such probabilities and validate them with empirical data already compiled in the literature.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
