Assessing vegetation recovery from energy development using a dynamic reference approach. Issue 2 (17th February 2022)

• Record Type: Journal Article
• Authors: Monroe, Adrian P.; Nauman, Travis W.; Aldridge, Cameron L.; O'Donnell, Michael S.; Duniway, Michael C.; Cade, Brian S.; Manier, Daniel J.; Anderson, Patrick J.
• Is Part Of: Ecology and evolution. Volume 12:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-17
• Subjects: Artemisia -- quantile regression -- resilience -- sagebrush -- soil properties -- weather; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.8508
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush ( Artemisia spp.), where communities may require decades to recover from disturbance. We demonstrated application of a dynamic reference approach to studying sagebrush recovery using three decades of sagebrush cover estimates from remote sensing (1985–2018). We modelled recovery on former oil and gas well pads ( n = 1200) across southwestern Wyoming, USA, relative to paired references identified by the Disturbance Automated Reference Toolset. We also used quantile regression to account for unmodelled heterogeneity in recovery, and projected recovery from similar disturbance across the landscape. Responses to weather and site‐level factors often differed among quantiles, and sagebrush recovery on former well pads increased more when paired reference sites had greater sagebrush cover. Little (<5%) of the landscape was projected to recover within 100 years for low to mid quantiles, and recovery often occurred at higher elevations with cool and moist annual conditions. Conversely, 48%–78% of the landscape recovered quickly (within 25 years) for high quantiles of sagebrush cover. Our study demonstrates advantages of using dynamic reference sites when studying vegetation recovery, as well as how additional inferences obtained from quantile regression can inform management. Abstract : Using remote sensing and ecologically relevant reference locations, we applied a dynamic reference approach to studying sagebrush cover following energy development. Quantile regression indicated little of the landscape would recover under poor to median restoration conditions (low to mid quantiles), but also identified areas unlikely to recovery even under optimistic restoration conditions (high quantiles).