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    Rights statement: This is the peer reviewed version of the following article: Sundstrom, S. M., Eason, T., Nelson, R. J., Angeler, D. G., Barichievy, C., Garmestani, A. S., Graham, N. A.J., Granholm, D., Gunderson, L., Knutson, M., Nash, K. L., Spanbauer, T., Stow, C. A. and Allen, C. R. (2017), Detecting spatial regimes in ecosystems. Ecol Lett, 20: 19–32. doi:10.1111/ele.12709 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/ele.12709/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Detecting spatial regimes in ecosystems

Research output: Contribution to journalJournal article

Published
  • Shana Sundstrom
  • Tarsha Eason
  • R. John Nelson
  • David G. Angeler
  • Chris Barichievy
  • Ahjond S. Garmestani
  • Nicholas Anthony James Graham
  • Dean Granholm
  • Lance H. Gunderson
  • Melinda Knutson
  • Kirsty L. Nash
  • Trisha L. Spanbauer
  • Craig A. Stow
  • Craig R. Allen
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<mark>Journal publication date</mark>01/2017
<mark>Journal</mark>Ecology Letters
Issue number1
Volume20
Number of pages14
Pages (from-to)19-32
Publication statusPublished
Early online date20/12/16
Original languageEnglish

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.

Bibliographic note

This is the peer reviewed version of the following article: Sundstrom, S. M., Eason, T., Nelson, R. J., Angeler, D. G., Barichievy, C., Garmestani, A. S., Graham, N. A.J., Granholm, D., Gunderson, L., Knutson, M., Nash, K. L., Spanbauer, T., Stow, C. A. and Allen, C. R. (2017), Detecting spatial regimes in ecosystems. Ecol Lett, 20: 19–32. doi:10.1111/ele.12709 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/ele.12709/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.