DOI: 10.5061/dryad.ttdz08m7d
title: Leveraging wildfire to augment forest management and amplify forest resilience

description: Successive catastrophic wildfire seasons in western North America have
 escalated the urgency around reducing fire risk to communities and
 ecosystems. In historically frequent-fire forests, fuel buildup as a
 result of fire exclusion is contributing to increased fire severity, but
 the probability of high severity fire can be reduced by active forest
 management that reduces fuels, prompting federal and state agencies have
 committed significant resources to increase the pace and scale of fuel
 reduction treatments. However, wildfires also have the potential to act as
 “treatments” in areas that burn at lower severity, but even catastrophic
 fires with large areas of high severity can still have substantial area of
 lower severity fire that may be improving forest conditions. We quantified
 active management and wildfire severity across yellow pine and mixed
 conifer forests (YPMC) in the Sierra Nevada of California over a 22-year
 period (2001-2022). We did not detect clear increases in the area treated
 through time but the area of beneficial wildfire (low to moderate
 severity) increased substantially, exceeding active treatment area in 9 of
 22 years. Overall, beneficial wildfire treated ~20% more area than all
 treatments combined, and nearly seven times more area than fire-related
 treatments alone. We then used disturbance history to evaluate resistance
 to high severity wildfire and forest loss across the YPMC range. Of the
 2.3 million ha that were YPMC forests in 2001, 19% lost mature forests due
 to high severity fire by 2022, nearly half of all YPMC area burned. Most
 of the landscape (47%) remains at risk of high severity fire because it
 had no restorative disturbances, but 33% of the study area has some level
 of resistance to high severity wildfire. In these areas, resistance will
 need to be enhanced and maintained over time via active management or
 managed wildfire. These treatment needs will likely outpace capacity even
 under optimistic implementation scenarios. Given limited resources for
 implementing active management and the likelihood of a more fiery future,
 incorporating beneficial wildfire into landscape-level treatment planning
 has the potential to amplify active management treatments, expanding the
 forest area that is resistant to high severity wildfire.
description: As detailed in the manuscript, we acquried publicly available
 data from a variety of sources and created fire severity maps for
 2018-2022 via Google Earth Engine.
description: # Leveraging wildfire to augment forest management and amplify forest
 resilience Dataset DOI: [10.5061/dryad.ttdz08m7d](10.5061/dryad.ttdz08m7d)
 ## Description of the data and file structure This file describes the data
 and code available for the publication: "Leveraging wildfire to
 augment forest management and amplify forest resilience," abstract
 below. Note that the workflow can be found in the README_Workflow.txt.
 Successive catastrophic wildfire seasons in western North America have
 escalated the urgency around reducing fire risk to communities and
 ecosystems. In historically frequent-fire forests, fuel buildup as a
 result of fire exclusion is contributing to increased fire severity. The
 probability of high severity fire can be reduced by active forest
 management that reduces fuels, prompting federal and state agencies to
 commit significant resources to increase the pace and scale of fuel
 reduction treatments. However, lower-severity areas of wildfires also have
 the potential to act as “treatments,” and even catastrophic fires with
 large areas of high severity can still have substantial area of lower
 severity fire that may be improving forest conditions locally. We
 quantified active management and wildfire severity across yellow pine and
 mixed conifer forests (YPMC) in the Sierra Nevada of California over a
 22-year period (2001-2022). We did not detect increases in the area
 treated through time but the area of beneficial wildfire (low to moderate
 severity) increased substantially, exceeding active treatment area in 8 of
 22 years. Overall, beneficial wildfire treated ~17% more area than all
 treatments combined, and roughly four times more area than fire-related
 treatments alone. We then used disturbance history to evaluate resistance
 to high severity wildfire and forest loss across the YPMC range. Of the
 2.3 million ha YPMC of forests in 2001, 20% lost mature forests due to
 high severity fire by 2022, which is nearly half of all YPMC area burned.
 Most of the landscape (47%) remains at risk of high severity fire because
 it had no restorative disturbances, but 33% of the study area has some
 level of resistance to high severity wildfire. In these areas, resistance
 will need to be enhanced and maintained over time via active management or
 managed wildfire, but these treatment needs will likely outpace capacity
 even under optimistic implementation scenarios. Given limited resources
 for implementing active management and the likelihood of a more fiery
 future, incorporating beneficial wildfire into landscape-level treatment
 planning has the potential to amplify the impact of active management
 treatments. ### Files and variables ### File:
 FTE\_Severity\_allyears\_revision\_28Jan2025.csv **Description:** Fire
 severity per year and landowner ##### Variables * Year: Year burned * FTE:
 Landowner type * Severity Category: Low, moderate, high, or unburned *
 Acres: area impacted in acres #### File:
 FTE\_Treatment\_allyears\_revision\_28Jan2025.csv
 **Description:** Treatment area by reclassified treatment types and
 landowner ##### Variables * Year: year treated * FTE: Landowner *
 Treatment: Reclassified treatment types * Commercial thin/Large hazard
 tree removal (note: in the code, this treatment type was combined with
 "mechanical fuels management" because the distinctions were not
 accurate enough, both represent forest and fuels management with heavy
 equipment or hand tools) * Mechanical fuels management (note: in the code,
 this treatment type was combined with "Commercial thin/Large hazard
 tree removal" because the distinctions were not accurate enough, both
 represent forest and fuels management with heavy equipment or hand tools)
 * Fire-related treatment: prescribed fire or pile burning treatments * No
 treatment: no treatment between 2001-2022 * Acres: area impacted in acres
 #### File: README\_Workflow\.txt **Description:** This file describes the
 overall workflow for the publication. #### File:
 ResistanceClassification\_Shive\_etal2025.R **Description:** R code for
 the resistance classification. #### File:
 SummaryTrends\_Shive\_etal2025\_Final.R **Description:** R code for
 summary data. #### File: SoS\_Code\_Final.ipynb **Description:** Python
 code for creating the csv's included here. #### File:
 Treatment\_data\_cleaning\_Shive\_etal2025.R **Description:** Python code
 for an intermediate data cleaning step. #### File:
 RefinedTreatmentData\_Shive\_etal2025.zip **Description:** Shapefile of
 treatment data that has been cleaned and reclassified to our selected
 treatment types. See methods section and Supplemental information for
 further information on data cleaning steps and the original sources for
 these data. #### File: Severity\_Shive\_etal2025.zip
 **Description:** Shapefile of severity data from the US Forest Service
 (2000-2017) and generated from Google Earth Engine (2018-2022) as
 described in the methods. In several years, there was some overlap at
 theedges of adjacent fires. To reduce these to one severity value per
 year, we dissolved these and retained the higher severity classification. 
 #### File: YPMC\_2001.zip **Description:** Shapefile of the yellow-pine
 mixed conifer forest area from a CALVEG classification from 2000 by the US
 Forest Service which was obtained directly from the agency. A table of
 forest types that were included can be found in Table 1. Additional
 information on CALVEG can be found
 here: [https://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192](https://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192).   ## Code/software R code (*.R) was generated in R 4.4.2. Python code (*.ipynb) was generated in Python version 3.12 using Jupyter Notebook 7.2, and uses these packages: os, pandas, arcpy (note that arcpy is not open source). See the README_Workflow.txt file for workflow information. ## Access information Some treatment data was obtained directly from the institution (University of California Berkeley Research Forests and Bureau of Land Management), and some severity data was generated via Google Earth Engine as described in Parks et al. 2018.  Data was derived from the following sources: * CAL FIRE, 2023a. CAL FIRE (California Department of Forestry and Fire Protection) Timber Harvesting Plans All WGS 1984 (2010-2023 [https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=0](https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=0) * CAL FIRE, 2023b. CAL FIRE (California Department of Forestry and Fire Protection) Historical Timber Harvesting Plans All WGS 1984 (1997-2009). [https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=1#overview](https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=1#overview) * CAL FIRE, 2023c. Historic Fire Perimeters 2022 Geodatabase: prescribed fire layer (rxburn22_1). [https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters. Accessed 6/1/2023](https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters.%20Accessed%206/1/2023). * NPS, 2024. National Park Service Complete Treatment Perimeters. [https://nifc.maps.arcgis.com/home/item.html?id=51f9750534c64b1d94b65d1fd2ff7d2f](https://nifc.maps.arcgis.com/home/item.html?id=51f9750534c64b1d94b65d1fd2ff7d2f) (accessed 4.15.24). * Parks, S.A., Holsinger, L.M., Voss, M.A., Loehman, R.A., Robinson, N.P., 2018. Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential. Remote Sens. 10, 879. [https://doi.org/10.3390/rs10060879](https://doi.org/10.3390/rs10060879) * U.S. Forest Service, 2023a. Hazard Fuels Treatments Database: S_USA.Activity_HazFuelTrt_PL.shp. [https://data.fs.usda.gov/geodata/edw/datasets.php](https://data.fs.usda.gov/geodata/edw/datasets.php) (accessed 1.1.23). U.S. Forest Service, 2023b. Timber Harvest Database: S_USA.Activity_TimberHarvest.shp. [https://data.fs.usda.gov/geodata/edw/datasets.php](https://data.fs.usda.gov/geodata/edw/datasets.php) (accessed 1.1.23). * U.S. Forest Service, 2018b. Vegetation burn severity using the Composite Burn Index, 1984-2017 (VegBurnSeverity.shp) (ESRI Shapefile). U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, McClellan, CA. [https://www.fs.usda.gov/detail/r5/landmanagement/gis/?cid=stelprdb5327833](https://www.fs.usda.gov/detail/r5/landmanagement/gis/?cid=stelprdb5327833)