Structural Ecology @ MIT

Understanding the dynamics of ecological communities under changing conditions

Our research program

Ecological communities are formed by the collection of co-occurring and interacting populations (be they plants, mammals, insects, bacteria, viruses, etc.) in a given place (be it a human host or natural habitat) and time (be it a short or large time scale). The composition of these communities is responsible for key ecosystem services, such as soil formation, water purification, CO2 sequestration, and human health, among others. Importantly, one of the biggest problems that our planet is currently facing is an accelerated rate of ecosystem change. Hence, regulating the composition of ecological communities has tremendous potential for bio-conservation, bio-technologies, and bio-medicine.

However, the dynamics of ecological communities are context-dependent (biotic and abiotic conditions), we do not know the exact equations governing their dynamics, and currently we do not have the capacity to infer all the exact conditions changing over time. This implies that we need to develop model systems (theoretical and experimental) to establish system-level and causative knowledge that can be used to understand the changing behavior of complex natural communities.

The Structural Ecology Group works towards developing generalizable and tractable formalisms (parametric and nonparametric) in order to explain and predict changes in the composition of experimental and natural communities. These formalisms are based on the notion of structural stability (the set of conditions compatible with a given behavior) in order to derive a system-level and probabilistic understanding of ecological dynamics under changing environmental conditions. That is, the larger the set of conditions compatible with a given behavior, the higher the probability of observing such behavior. Our work provides the theoretical platforms to derive such probabilities as generalizable and tractable models systems, while integrating them and validating them with experimental and natural communities.