Evaluating short-term effects of fuel treatments on fuel loading in western US dry conifer forests: a meta-analysis
Data files
Jun 26, 2025 version files 435.33 KB
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README.md
3.41 KB
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ROM_effect_size.csv
431.92 KB
Abstract
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.
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)
- Grupenhoff, Ashley R.; Young, Derek J.N.; Barbato, Michele; Latimer, Andrew M. (2025). Evaluating short-term effects of fuel treatments on fuel loading in western US dry conifer forests: A meta-analysis. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2025.122808