Abstract

In rangelands, monitoring spatial regime boundaries (i.e., boundaries between ecological states) could provide early warnings of state transitions, elucidate the spatial nature of state transitions, and quantify management outcomes. Here, we test the ability of established regime shift detection methods and traditional, local-scale rangeland monitoring data to identify spatial regime boundaries in a complex rangeland system. We collected plant community composition data via point-intercept sampling at 400 evenly-spaced locations along a 4000m transect. We then applied three statistical regime shift detection methods to identify spatial regimes and compared outcomes of each statistical method. Statistical detection of spatial regimes held up to traditional field monitoring practices. Spatial regime locations matched historic plant communities in the study site going back 130 years, but we also detected a localized wildfire-driven state transition: a relict ponderosa pine (Pinus ponderosa) spatial regime transitioned to a bur oak (Quercus macrocarpa) - annual grass regime. The spatial regimes monitoring approach capitalizes on the existence of spatial boundaries between states to track ecological states as they move, expand, contract, or disappear. This is an advancement over traditional time series approaches to regime shift/state transition detection which only detect state transitions if enough sites transition. Existing local-scale rangeland monitoring, used strategically, can complement current coarse, broad-scale applications of spatial regimes monitoring by detecting subtle, fine-scale boundaries that broad-scale monitoring cannot capture. (C) 2020 The Society for Range Management. Published by Elsevier Inc. All rights reserved.

Keywords

Alternative states; Plant community; Resilience; Regime shift; State transition

Published in

Rangeland Ecology and Management
2021, Volume: 74, number: 1, pages: 114-118

SLU Authors

• Angeler, David

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Ecology


Publication identifier

DOI: https://doi.org/10.1016/j.rama.2020.09.002

Permanent link to this page (URI)

https://res.slu.se/id/publ/108076