Detecting spatial regimes in ecosystems

Authors

Shana M. Sundstrom, University of Nebraska-LincolnFollow
Tarsha Eason, U.S. Environmental Protection Agency
R. John Nelson, University of Victoria
David G. Angeler, Swedish University of Agricultural SciencesFollow
Chris Barichievy, Zoological Society of London
Ahjond S. Garmestani, U.S. Environmental Protection AgencyFollow
Nicholas A.J. Graham, Lancaster UniversityFollow
Dean Granholm, U.S. Fish & Wildlife Service
Lance Gunderson, Emory University
Melinda Knutson, Region 3 U.S. Fish & Wildlife Service
Kirsty L. Nash, University of Tasmania
Trisha L. Spanbauer, U.S. Environmental Protection AgencyFollow
Craig A. Stow, National Oceanographic and Atmospheric Administration Great Lakes Environmental Research LaboratoryFollow
Craig R. Allen, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2017

Citation

Ecology Letters (2017) 20: 19–32

Comments

Copyright © 2016 John Wiley & Sons Ltd/CNRS

Abstract

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.