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Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action

Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment involving post-fire seeding of declining sagebrush shrubs across semiarid areas of the western USA over two decades, we quantify drivers and consequences of selection biases in restoration using remotely sensed data. From following more than 1,500 wildfires, we find treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate unmeasured drivers of treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering sources of bias, restoration positively affected sagebrush recovery. Treatment effects varied with climate, indicating prioritization criteria for interventions. Our findings revise the perspective that post-fire sagebrush seedings have been broadly unsuccessful and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and the development of restoration theory.

Data Use
Creative Commons Zero v1.0 Universal (CC0 v1.0)
Recommended Citation
Simler-Williamson A, Germino M. 2022. Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action [Dataset]. Dryad.

US National Science Foundation and Idaho EPSCoR: OIA-1757324
US National Science Foundation: DBI-2010868
Southwest Climate Adaptation Center
Northwest and North Central Climate Adaptation Centers

Release Date
English (United States)
Allison B. Simler-Williamson and Matthew Germino
Contact Name
Allison Simler Williamson
Contact Email
Public Access Level
Data available on:: 
Friday, May 20, 2022