COS 175-3 - Detecting spatial regimes in ecosystems

Friday, August 11, 2017: 8:40 AM
C120-121, Oregon Convention Center
Shana M. Sundstrom1, Tarsha Eason2, R. John Nelson3, David Angeler4, Chris Barichievy5, Ahjond Garmestani6, Nicholas A.J. Graham7, Dean Granholm8, Lance Gunderson9, Melinda Knutson10, Kirsty Nash11, Trisha Spanbauer6, Craig Stow12 and Craig R. Allen13, (1)School of Natural Resources, University of Nebraska, Vancouver, WA, (2)U.S. Environmental Protection Agency, (3)University of Victoria, (4)Swedish University of Agricultural Sciences, (5)Zoological Society of London, (6)US Environmental Protection Agency, Cincinnati, OH, (7)Lancaster University, (8)U.S. Fish & Wildlife Service, (9)Department of Environmental Sciences, Emory University, Atlanta, GA, (10)Region 3 U.S. Fish & Wildlife Service, (11)University of Tasmania, (12)Great Lakes Environmental Research Laboratory, National Oceanic and Atmospheric Administration, Ann Arbor, MI, (13)Nebraska Cooperative Fish and Wildlife Research Unit, University of Nebraska, Lincoln, NE

We live on a rapidly changing planet, where non-stationarity is the rule rather than the exception. Rapid global change necessitates that we find ways to detect boundaries separating ecosystem or ecoregion types (spatial regimes), in part so that we can track changes in boundaries over time. Boundary detection has never been a trivial task, as remotely-sensed data is limited in its ability to distinguish physically similar but floristically different vegetation and is often outdated, and collecting field data is expensive and time-intensive. The use of ecoregion maps (such as those of Bailey and Omernik) to guide expectations for determining spatial regimes is problematic given the rate and extent of land use change and climate change impacts on plant and animal communities, because ecoregion maps are based on potential plant communities given classifications of bedrock, soil, altitude, temperature, and moisture. We tested an information-theory method called Fisher information for its ability to detect spatial regimes in both aquatic and terrestrial systems using zooplankton and avian community data, and we compared the results of Fisher information to the expectations of traditional ecoregion maps, as well as commonly used multivariate analyses like nMDS and cluster analysis.


We found that Fisher information, using a limited amount of animal community data, successfully detected spatial regimes that roughly coincided with the expectations of ecoregion maps but also differed in ways that suggest that defining spatial regimes based on animal communities may better reflect ecological reality than traditional ecoregion maps. Furthermore, Fisher information provided explicit spatial information about community change that is absent from multivariate techniques, suggesting that it may be valuable for tracking community change over time and space in a rapidly changing world. We suggest that approaches such as Fisher Information may advance our understanding of spatial boundaries in ecological systems, and enhance our ability to delineate and manage regimes.