You are here:
FISHER INFORMATION AND DYNAMIC REGIME CHANGES IN ECOLOGICAL SYTEMS
Citation:
Mayer**, A, C. W. Pawlowski**, AND H C. Cabezas*. FISHER INFORMATION AND DYNAMIC REGIME CHANGES IN ECOLOGICAL SYTEMS. S. E. Jorgensen (ed.), ECOLOGICAL MODELLING. Elsevier Science, New York, NY, 195(1-2):72-82, (2006).
Description:
Ecosystems often exhibit transitions between dynamic regimes (or steady states), such as the conversion of oligotrophic to eutrophic conditions and associated aquatic ecological communities, due to natural (or increasingly) anthropogenic disturbances. As ecosystems experience perturbations of varying regularity and intensity, they may either remain within the state space neighborhood of the current regime, of "flip" into the neighborhood of a regime with different characteristics. An increasingly integral aspect of many ecological, economic, and social decisions is their impact on the sustainability of particular dynamic regimes of ecosystems. Sustainability entails a human preference for one particular regime versus another, and the persistence of that regime with regard to human and natural perturbations exacted on the system
Information theory has significantly advanced our ability to quantify the organizational complexity inherent in systems despite imperfect observations or "signals" from the source system. Fisher Information is one of several measures developed under the theme of estimation theory. Fisher Information can be described in three ways: as a measure of the degree to which a parameter (or state of a system) can be estimated; as a measure of the relative amount of information that exists between different states of a system; and as a measure of the disorder or chaos of a system. Fisher Information may be a useful measure to identify the degree to which a system is at risk of "flipping" into a different dynamic regime.
We developed a Fisher Information index for dynamic systems in a periodic steady state and applied it to a simple, two species Lotka-Volterra predator-prey model. Changes in the carrying capacity (size) of the system resulted in different stable steady states establishing themselves, each with a characteristic Fisher Information. By repeatedly calculating Fisher Information over time, transitions or "flips" between steady states were identified with changes in Fisher Information. We then examined data collected from four ecological systems (of increasingly large spatial and temporal scale) that have demonstrated regime transitions: the Bering Strait/ Pacific Ocean food web; the western Africa savanna; the Florida (USA) pine-oak system; and the global climate system. These datasets are noisy and reflect several to many cycles that are out of phase, which complicates the identification of both dynamic regimes and transitions. If transition phases between regimes can be detected early enough, human activity suspected of contributing to regime changes can be altered (or continued if the resultant steady state is desirable, such as in ecosystem restoration efforts).