DOI: 10.5061/dryad.ncjsxkt2b
title: Evaluating short-term effects of fuel treatments on fuel loading in western US dry conifer forests: a meta-analysis

description: Fuel treatments are crucial for reducing wildfire hazard, especially as
 severe wildfires increase across western United States (US) dry conifer
 forests. While many studies have documented the effectiveness of fuel
 treatments in reducing future wildfire severity, few have synthesized data
 to predict post-treatment fuel loads for major classes of fuels. We
 conducted a Bayesian meta-analysis using 1,932 observations from 65
 published papers in western US dry conifer forests to (1) evaluate the
 short-term effects of different fuel treatments on fuel loading and
 overstory structure and (2) characterize patterns of post-treatment fuel
 loading and overstory structure across multiple fuel components.
 Treatments included thinning followed by prescribed burning (THIN+BURN),
 thinning only (THIN), and first-entry prescribed fire only (BURN). Our
 results show that treatments effectively reduce fuel loads, but outcomes
 vary based on treatment type, forest type, and initial stand conditions.
 THIN+BURN treatments were most effective in reducing overstory fuel loads
 while preventing surface fuel buildup, whereas BURN treatments were the
 most effective at reducing surface fuel loads, even after a single entry.
 Our findings underscore the importance of treatment type and pre-treatment
 stand conditions in influencing fuel reduction outcomes. Fuel treatments,
 especially in landscapes with heavy fuel loads, offer a valuable tool for
 moderating wildfire severity, reducing fire risk, and promoting forest
 restoration. Our synthesis of post-treatment fuel loads provides important
 insights for assessing forest vulnerability, improving fire behavior model
 estimates, and informing wildfire management strategies in a changing
 climate.
description: # Evaluating short-term effects of fuel treatments on fuel loading in
 western US dry conifer forests: a meta-analysis We collected data from 65
 publications and 95 distinct studies that quantify the change in fuel
 loading after a variety of fuel treatments across western dry conifer
 forests in the United States. ``` 1) 'ROM_effect_size.csv' ```
 Dataset used for the meta-analysis that includes effect size calculation
 (section II), calculated as the log response ratio (lnRR), and raw fuel
 data (section III). More than one lnRR was calculated for one study when
 there were multiple treatment types, forest types, and/or fuel variables
 reported. The dataset includes all fuels data from publications that met
 the search criteria, including data that was not used in the final
 analysis. Fields are as follows: * uniqueID: A unique ID for each distinct
 study. * fuel_variable: Fuel variable calculated * forest_type: Unique
 forest type * treatmentB: Broad categorizations of fuel treament that
 include: *BURN_RX* for prescribed fire only, *BURN_MW* for managed
 wildfire only, *THIN* for thinning only, *MAST* for mastication only,
 *THIN+BURN* for thinning followed by prescribed burning, *MAST+BURN* for
 mastication followed by prescribed burning, *THIN+MAST* for thinning
 followed by mastication, *FULL* for a combination of thinning, mastication
 or raking fuels, and prescribed fire. * forest_type_plus_region: Broad
 forest type category that incorporates ecoregion (ECO_NAME) * TSLTRT: Time
 between treatment and post-treatment survey (years) * trt_fuel_load: Mean
 post-treatment fuel load * trt_n: Experiemental replicates of
 post-treatment fuel load within an individual study * trt_SD: Standard
 deviation of post-treatment fuel load within an individual study * trt_SE:
 Standard error of post-treatment fuel load within an individual study *
 trt_units: Unit of fuel load * type: A category to represent how untreated
 fuel loads were measured: *PRETREATMENT* for studies that used values
 measure prior to treatment in plots that were ultimately treated or
 *CONTROL* for studies that measured fuels in control plots at the same
 time point (post-treatment) that the treated plots were measured. To
 represent untreated fuel loads in this analysis, we used values measured
 prior to treatment when available and values measured at control plots
 otherwise. * TSLSURVEY: Time between pre-treatment fuel measurement and
 treatment (years) * control_fuel_load: Mean untreated fuel load *
 control_n: Experimental replicates of untreated fuel load within an
 individual study * control_SD: Standard deviation of untreated fuel load
 within an individual study * control_SE: Standard error of untreated fuel
 load within an individual study * control_units: Units of fuel load *
 percent_change: Percentage change due to fuel treatment, calculated as:
 100*(exp(lnRR)-1) * Last_name: Author(s) of publication *
 Publication_year: Year of publication * ECO_NAME: Ecoregion for each study
 location, extracted using the 'Terrestrial Ecoregions of the
 World' dataset in R (Dinerstein et al. 2018). * yi: The calculated
 log response ratio (lnRR), which is defined as ln(xA/xB) where xA is the
 post-treatment fuel load mean and xB is the untreated fuel load mean. *
 vi: Sampling error variance * weight: Weighting value calculated using
 experimental replications, calculated as:
 (trt_n*control_n)/(trt_n+control_n)