Detecting spatial regimes in ecosystems.

Area of Science:

• Ecology
• Information Theory
• Spatial Analysis

Background:

• Early warning indicator research traditionally focuses on temporal changes, with limited application to spatial regime detection.
• Conventional ecoregion maps often rely on static ecological potential, failing to capture dynamic changes from climate and land use.
• Existing multivariate analyses lack explicit spatial information on community shifts.

Purpose of the Study:

• To apply Fisher information, an information theory method, for identifying ecological spatial boundaries.
• To compare Fisher information's effectiveness against traditional early warning indicators and ecoregion mapping methods.
• To assess the utility of animal community data in defining spatial regimes.

Main Methods:

• Utilized Fisher information on terrestrial (U.S. Breeding Bird Survey) and aquatic (marine zooplankton) animal data.
• Compared Fisher information results with traditional early warning indicators, conventional ecoregion maps, and multivariate analyses (nMDS, cluster analysis).

Main Results:

• Successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information.
• Fisher information provided explicit spatial insights into community change, outperforming other multivariate approaches.
• Identified ecological boundaries based on animal communities, offering a dynamic alternative to static ecoregion maps.

Conclusions:

• Fisher information is a robust method for detecting spatial ecological boundaries and transitions.
• Defining spatial regimes using animal communities provides a more accurate reflection of ecological reality, especially during periods of rapid change.
• This approach offers a valuable tool for understanding and managing ecosystems in the face of environmental change.