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AN INDEX TO DETECT EXTERNALLY-FORCED DYNAMIC REGIME SHIFTS IN ECOSYSTEMS
Citation:
Mayer*,A.L., A., C. W. Pawlowski**, AND H Cabezas*. AN INDEX TO DETECT EXTERNALLY-FORCED DYNAMIC REGIME SHIFTS IN ECOSYSTEMS. Presented at Int. Sym. Dynamic Systems Theory & its Applications to Biology & Environmental Systems, Shizuoka University, JAPAN, March 14 - 17, 2003.
Impact/Purpose:
To inform the public
Description:
The concept of dynamic regimes, and nonlinear shifts between regimes, has gained acceptance and importance in ecosystem research. Regimes in ecosystems are identified as states with characteristic species abundances and abiotic conditions. Ecosystems are maintained in particular regimes through self-regulating feedbacks within the system, and can be pushed between regimes by external perturbations. Shifts between these regimes are rapid and nonlinear with respect to external forces, and therefore tend to be viewed as unexpected and catastrophic. Distinguishing regimes and their boundaries in real ecosystems is complicated by several factors. Data collected from ecosystems are inherently noisy, due to random fluctuations and chaotic behavior of the variables, and the interaction between internal self-regulating variables and external forcing variables, which usually operate over longer time and larger spatial scales. Separating out interaction effects from fast and slow variables from data can be especially difficult. We have developed an index, based on Fisher Information, which can summarize the behavior of a high-dimensional system into a more easily interpreted measure. The index tracks the path of the system through space, and identifies time periods when the system state is changing nonlinearly. Here, we demonstrate Fisher Information behavior on a simple predator-prey system, uncomplicated by noise or overlapping forcings operating at different scales. We then apply Fisher Information to long-term data on 82 variables collected from Lake Mendota in Wisconsin, USA. The index identifies shifts in internal variables of the lake (abundance of different fish species, algal density, etc.) that are correlated with seasonal weather patterns and the multi-annual cycles of the El Nino-Southern Oscillation system.