DOI: 10.25338/b8w63r
title: Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action

description: 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.
description: The provided datasets contain randomly selected
 locations (using the sp package in R) that were identified using a
 combination of:   1) Areas that burned in one wildfire between
 1985-2005, according to the Monitoring Trends Survey Dataset
   2) Areas that either received
 (treated_sagebrush_locations) or did not receive
 (untreated_sagebrush_locations) post-fire seeding treatments for at least
 one Artemisia species on BLM across the Great Basin (USA), according to
 the Land Treatment Digital Library   For each location, using the raster package in R, we
 extracted or calculated sagebrush cover across the period before and after
 fires (as described in the manuscript) using the RCMAP product, elevation,
 soil percent clay and sand (using a product aggregating the USDA-NCSS
 SSURGO and STATSGO datasets), heatload, the U.S. Environmental Protection
 Agency’s Level III ecoregion, fire size, and distance to a major road from
 the U.S. Census Bureau’s TIGER database. We calculated various seasonal
 mean temperatures and mean total precipitation values for the period for
 between 1984-2014 for each location, using the gridMet modeled
 meteorological dataset. GridMet contains daily, high-spatial resolution
 (4-km) climate estimates for the contiguous U.S. from 1979 to present.
 Specific variable names are described below, and full citations for each
 described data product can be found in the associated
 manuscript.   The "source data" file contains the data
 used to generate the figures found in our associated manuscript. See https://github.com/absimler/nonrandom-seedings for details about figure development. 
description: This dataset is an initial set of locations identified using the
 criteria described above. However, these data were subsetted to a smaller
 set of 20,000 randomly selected locations that did not contain missing
 values for any of the variables required in the subsequent analysis. The
 full procedure for this subsetting is contained in the associated Github
 repository (https://github.com/absimler/nonrandom-seedings).