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Detecting spatial regimes in ecosystems
Citation:
Sundstrom, S., T. Eason, J. Nelson, D. Angeler, C. Barichievy, A. Garmestani, N. Graham, D. Granholm, L. Gunderson, M. Knutson, K. Nash, T. Spanbauer, C. Stow, AND C. Allen. Detecting spatial regimes in ecosystems. ECOLOGY LETTERS. John Wiley & Sons, Inc, Hoboken, NJ, 20(1):19-32, (2017). https://doi.org/10.1111/ele.12709
Impact/Purpose:
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 (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect 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. Designed to serve as a spatial framework for the research, assessment, and monitoring of ecosystems and ecosystem components, ecoregions denote areas of similarity in the mosaic of biotic, abiotic, terrestrial, and aquatic ecosystem components with humans being considered as part of the biota. These regions are critical for structuring and implementing ecosystem management strategies across Federal agencies, state agencies, and nongovernmental organizations that are responsible for different types of resources within the same geographic areas.
Description:
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 (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect 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.
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DOI: Detecting spatial regimes in ecosystems
Detecting Spatial Regimes in Ecosystems
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