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Heteroskedasticity as a leading indicator of desertification in spatially explicit data
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
2015 (English)In: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 5, no 11, 2185-2192 p.Article in journal (Refereed) Published
Abstract [en]

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.

Place, publisher, year, edition, pages
2015. Vol. 5, no 11, 2185-2192 p.
Keyword [en]
Critical transition, desertification, early warning indicator, heteroskedasticity, regime shift, resilience, spatial autocorrelation, spatial pattern
National Category
Ecology
Identifiers
URN: urn:nbn:se:umu:diva-106497DOI: 10.1002/ece3.1510ISI: 000355731300006PubMedID: 26078855OAI: oai:DiVA.org:umu-106497DiVA: diva2:842082
Available from: 2015-07-16 Created: 2015-07-14 Last updated: 2017-12-04Bibliographically approved

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Seekell, David A.

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  • apa
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Language
  • de-DE
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  • en-US
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  • nn-NO
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  • Other locale
More languages
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  • asciidoc
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