Systems Ecology @ MIT

Understanding the observable (and unobservable) behavior of ecological communities from a systems perspective

Our research program

Systems ecology has been established as a holistic approach to understand the emergent context-dependent behavior of ecological communities using formal procedures of systems thinking, synthesis, and mathematical modeling.

Ecological communities are formed by the collection of interacting species (be they plants, mammals, insects, bacteria, viruses, etc.) in a given place (be it a human host or natural habitat) and time, exhibiting complex emergent behavior. Each local community forms a feedback process with the physical and chemical environment and regulates different ecosystem services and functions, such as soil formation, water purification, CO2 sequestration, and human health, among others. Hence, understanding the species composition of ecological communities and its transformations has tremendous potential for bio-conservation, bio-technologies, and bio-medicine.

However, the species composition of ecological communities is in continuous change, reacting and adapting to environmental variations (e.g., pH, temperature, humidity, species richness), in what appears, a strongly context-dependent manner. Importantly, the challenge in identifying regularities governing changes in the species composition of ecological communities resides on the fact that no two communities are the same, assembled under the same initial conditions, subject to identical environmental contexts, nor we have the technical capacity to infer all the exact factors forming the environmental contexts under which ecological communities evolve.

Our research is centered on explaining and predicting the observable (and unobservable) emergent behavior (e.g., species composition/biodiversity) of ecological communities under environmental uncertainty (context-dependency). For this purpose, our works is based on a probabilistic systems analysis rooted on the notion of structural stability and uses tools as varied as: population dynamics, probability, statistical mechanics, information theory, homology, matrix theory, network theory, geometry, causal inference, and empirical dynamic modeling.

In particular, we propose that the observable (and unobservable) behavior of ecological communities under unknown conditions can be systematically understood by investigating how possible is for a system to display a particular behavior despite perturbations to its dynamics---what is known as structural stability. Formally, we develop tractable model-driven and scalable data-driven methodologies to estimate the fraction of environmental conditions (i.e., a set of variables tied to specific values defining the dynamics of a system) compatible with a particular behavior of an ecological community. Following this general systems framework, we investigate transformation rules predicting when changes in the behavior of ecological communities are likely to happen, transformation principles dictating how changes in the behavior are likely to happen, and transformation theories explaining why changes in the behavior are likely to happen. While our work is theoretical, we work closely with experimental and field ecologists to corroborate hypotheses and theoretical predictions on the observability of ecological communities.