Heteroskedasticity as a leading indicator of desertification in spatially explicit data

Ecology and Evolution

David A. Seekell¹ 

[1] Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia

关键词: Critical transition;    desertification;    early warning indicator;    heteroskedasticity;    regime shift;    resilience;    spatial autocorrelation;    spatial pattern;   

DOI  :  10.1002/ece3.1510

来源: Wiley

【 摘 要 】

Abstract

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.

【 授权许可】

CC BY   
© 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.