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Exclusion principle

In the 1930's, G. F. Gause corroborated the competitive exclusion principle showing the impossible systems to be realized:

(i) Two species competing for the same limited resource cannot coexist.

(ii) Natural selection will favor the species having the larger growth rate.

Stability principle

In the 1970's, R. M. May formalized the stability principle showing the possible systems but cannot be realized:

(i) Living systems have a complexity threshold beyond which they will lose their capacity to return to their current state (stability)

(ii) Stable living systems will tend to be either small or characterized by weak interactions

Feasibility principle

Over the last 10 years, we have formalized and corroborated the feasibility principle showing the possibilities for a system to be realized:

(i) Every possible biological solution (e.g., a community of interacting populations) is feasible for a given collection of environmental challenges.

(ii) Self-organization leads to solutions that are feasible for the largest set of local environmental challenges. 

[Notice that this is different from convergent evolution, which postulates that for every environmental challenge there is a finite number of feasible solutions (the possibility space of solutions).]

We have formalized and corroborated the feasibility principle in ecology explaining the limits in the diversity of biotic communities in nature (see below). We are now looking to understand the feasible bioenergetic limits shaping the development, evolvability, and adaptability of complex living systems under changing environments—the limits of life itself.

Key things we now know that we did not know before:

• The association of the structure of ecological communities with their persistence, feasibility, and dynamical stability completely depends on the parameter values (biotic and abiotic contexts) used in dynamical models (acting on ecological communities) (Saavedra et al NatComm  ; Song et al Ecology ; Cenci et al E&E).

  • Changes in parameter values are inherently linked to environmental variations. Because it is virtually impossible to know all these environmental variations, structural stability can allow us to measure (from a probabilistic point of view) the range of conditions (parameter values) compatible with the persistence of ecological communities (Saavedra et al EcoMon ; Song et al JTB ; Cenci et al PRE).

  • Under highly changing environments, ecological communities tend to exhibit structures associated with higher structural stability (Saavedra et al JAE ; Song et al JAE ; Cenci et al JTB).

  • The assembly and disassembly of ecological communities do not follow a random pattern, but they are linked with the internal constraints and the structural stability of the community (Saavedra et al Ecology ; Song et al PRSB ; Song et al ELE).

  • Community dynamics, structure, and local structural stability can be non-parametrically reconstructed/estimated from time-series data (Cenci et al RSIF ; Cenci et al ME&E ; Cenci et al NatE&E).

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

• Ecological systems are formed by the collection of interacting species (known as ecological community) 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, carbon sequestration, and human health, among others. Hence, the structure, composition, and function of ecological systems has tremendous implications for our planet and human well-being.

• However, ecological systems are in continuous change, reacting and adapting to environmental variations, in what appears, a strongly context-dependent manner.  Importantly, the challenge in identifying regularities governing changes in the species composition of ecological systems resides on the fact that no two systems 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 systems evolve.

• Our research is centered on understanding the possibility and probability (feasibility) of observing the emergence and transformation of an ecological system under environmental uncertainty (e.g., climate change). Our works is based on a  complex systems perspective (e.g., simple rules can give rise to complex phenomena) rooted on the notion of structural stability (the capacity of a system to display a particular behavior despite perturbations to its dynamics) and uses tools as varied as: population dynamics, statistical mechanics, information theory, metabolic scaling theory, homology, matrix theory, network theory, geometry, causal inference, and empirical dynamic modeling. While our work is theoretical, we work closely with experimental and field ecologists to corroborate hypotheses and theoretical predictions.