Combining mechanism and maximum entropy in a dynamic hybrid theory of disturbance macroecology

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

This research will advance the capacity of science to forecast the behavior of ecosystems undergoing change as a consequence of either natural or human disturbance. With the development of quantitative predictive theory and models, ecologists have already made considerable progress understanding the structure and properties of undisturbed ecosystems. But numerous observations indicate that this understanding fails when ecosystems are changing rapidly. The project will build upon ideas that have proven successful in statistical physics and the relatively new field of information theory. The expected output is a quantitative, predictive and thus testable, theory of ecosystem structure that will be applicable to both undisturbed ecosystems and to those that are changing rapidly in response to disturbance. Because our livelihoods are entangled with the health of our ecosystems in numerous ways, both insofar as our activities can alter nature and nature can enrich our lives, this research will contribute to the betterment of humankind as well as the advancement of basic science. This project will support a postdoctoral research fellow and provide research training experiences for graduate students. The objective of this research is to advance and test a dynamic theory that can predict patterns in the abundance, distribution, and energetics of individuals and species in ecosystems. The theory should apply to ecosystems that are either static or changing rapidly in response to disturbance. To achieve that goal, insights from statistical physics and information theory will be combined with explicit mechanisms governing organismal and population growth, as well as migration, diversification, and extinction. Stochastic mechanistic theory will predict the time-dependent probability distributions of a small set of governing state variables that play a role similar to that of pressure, volume, and temperature in thermodynamics. These state variables will then provide the constraints used in maximizing information entropy to yield predicted probability distributions for the detailed patterns displayed by individuals and species. The expected outcome is a general theory of both static and dynamic ecology. Ecological census data from the tropics to the tundra, from plants to animals, across a wide range of spatial scales, and under a variety of disturbances, will be used to test the predictions of the new theory. 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.