Detecting spatial regimes in ecosystems

• Type of Material: Article
• Authors: Shana M. Sundstrom, University of Nebraska Lincoln; Tarsha Eason, U.S. Environmental Protection Agency; R. John Nelson, University of Victoria; David G. Angeler, Swedish University of Agricultural Sciences; Chris Barichievy, Zoological Society of London; Ahjond S. Garmestani, U.S. Environmental Protection Agency; Nicholas A. J. Graham, Lancaster University; 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 Spanbauer, U.S. Environmental Protection Agency; Craig A. Stow, National Oceanographic and Atmospheric Administration Great Lakes Environmental Research Laboratory; Ann Arbor MI 48108 USA; Craig R. Allen, University of Nebraska Lincoln
• Language: English
• Date: 2017-01
• Publisher: Environmental Protection Agency
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 John Wiley & Sons Ltd/CNRS
• Final Published Version (URL): http://dx.doi.org/10.1111/ele.12709
• Title of Journal or Parent Work: Ecology Letters
• Volume: 20
• Issue: 1
• Start Page: 19
• End Page: 32
• Grant/Funding Information: This research arose from a workshop series, “Understanding and managing for resilience in the face of global change”, which was funded by the USGS John Powell Center for Synthesis and Analysis, and the USGS National Climate Change and Wildlife Center. Many thanks to Powell for supporting collaborative and interdisciplinary research efforts. Thanks to JC Nelson at the USGS Upper Midwest Environmental Sciences Center for creating Figure 1A. The Nebraska Cooperative Fish and Wildlife Research Unit is jointly supported by a cooperative agreement between the United States Geological Survey, the Nebraska Game and Parks Commission, the University of Nebraska−Lincoln, the United States Fish and Wildlife Service, and the Wildlife Management Institute.This research arose from a workshop series, “Understanding and managing for resilience in the face of global change”, which was funded by the USGS John Powell Center for Synthesis and Analysis, and the USGS National Climate Change and Wildlife Center. Many thanks to Powell for supporting collaborative and interdisciplinary research efforts. Thanks to JC Nelson at the USGS Upper Midwest Environmental Sciences Center for creating Figure 1A. The Nebraska Cooperative Fish and Wildlife Research Unit is jointly supported by a cooperative agreement between the United States Geological Survey, the Nebraska Game and Parks Commission, the University of Nebraska−Lincoln, the United States Fish and Wildlife Service, and the Wildlife Management Institute.
• Keywords: community change; regime shifts; boundary detection; spatial regimes; Fisher information; spatial resilience
• Research Categories: Biology, Oceanography; Agriculture, Fisheries and Aquaculture; Physical Oceanography; Biology, Ecology

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.