DOI: 10.5061/dryad.63xsj3vb9
title: Data and code from: Big trees burning: Divergent wildfire effects on large trees in open- vs. closed-canopy forests

description: Wildfire activity has accelerated with climate change, sparking concerns
 about uncharacteristic impacts on mature and old-growth forests containing
 large trees. Recent assessments have documented fire-induced losses of
 large-tree habitats in the US Pacific Northwest, but key uncertainties
 remain regarding contemporary vs. historical fire effects in different
 forest composition types, specific impacts on large trees within closed
 versus open canopies, and the role of fuel reduction treatments. Focusing
 on the 2021 Schneider Springs Fire, which encompassed 43,000 ha in the
 eastern Cascade Range of Washington, this study addresses three
 interrelated questions: (1) Are burn severity distributions consistent
 with historical fire regimes in dry, moist, and cold forest types? (2) How
 does burn severity vary among forest structure classes, particularly large
 trees with open vs. closed canopies? (3) What is the influence of fuel
 reduction treatments on burn severity inside and outside of treated areas
 and among structure classes? Within each forest type, burn severity
 proportions were similar to historical estimates, with lower overall
 severity in dry forests than in moist and cold forests. However, across
 all forest types combined, high-severity fire affected 30% (4,500 ha) of
 large-tree locations with tree diameters &gt;50 cm. In each forest
 type, burn severity was lower in locations with large-open structure
 (&lt;50% canopy cover) than in locations with large-closed structure
 (&gt;50% canopy cover). Burn severity was also lower inside than
 outside treated sites in all structure classes, and untreated large-closed
 forests tended to burn at lower severity closer to treatments. These
 results highlight the susceptibility of dense, late-successional forests
 to contemporary fires, even in events with widespread potentially
 beneficial effects consistent with historical fire regimes. These results
 also underscore the effectiveness of treatments that shift large-closed to
 large-open structures and suggest that treatments may help mitigate fire
 effects in adjacent large-closed forests. Long-term monitoring and
 adaptive management will be essential for conserving critical wildlife
 habitats and fostering ecosystem resilience to climate change, wildfires,
 and other disturbances.
description: # Big trees burning: Divergent wildfire effects on large trees in open-
 vs. closed-canopy forests
 [https://doi.org/10.5061/dryad.63xsj3vb9](https://doi.org/10.5061/dryad.63xsj3vb9) Dataset author: Caden P. Chamberlain Contact: [caden.chamberlain@colostate.edu](mailto:cc274@uw.edu) Manuscript author: Garrett Meigs Contact: [gmeigs@gmail.com](mailto:gmeigs@gmail.com) Creation date: June 16, 2025 This dataset supports the analyses and results presented in "Big trees burning: Divergent wildfire effects on large trees in open- vs. closed-canopy forests". All primary analyses were conducted using R (version 4.3.1). Datasets that were produced by other means are included with this download, and a description of their creation process is included below. ## Description of the data and file structure The parent directory folder (data_code_Meigs_et_al_v3_20250709.zip) contains all scripts, data, and files to run the primary and supplemental analyses. Users are encouraged to open and run the analyses using the R Project file ('ss_part2_analyses.Rproj'). In the parent directory, the 'data' folder contains all primary and derived datasets used in the analyses, the 'scripts' folder contains three scripts for running different aspects of the analysis, and the 'tables' folder contains tabular summaries used to produce tables in the main manuscript. We also include a blank 'figures' folder, which is where final figures are exported after running the analyses. Belo, we describe the contents of each folder in the parent directory. We refer the user to the main manuscript and supplemental material for additional details. All original data sources are cited in-text below, and a full citation is provided at the end of the document in the References Cited section. ### Files and variables \scripts\ '01_hrv_psize.R': R script to run the first phase of analysis focused on evaluating burn severity distributions among dry, moist, and cold forest types in the context of historical fire regimes (Research Question 1). '02_type_class_gam.R': R script to run the second phase of analysis focused on comparing burn severity among forest structure classes (Research Question 2). '03_trt_effects.R': R script to run the third phase of analysis focused on assessing the influence of fuel reduction treatments on burn severity within and adjacent to treated areas (Research Question 3). \data\categorical\dap_classes\ 'ewa_sc_2019.tif': raster file representing the four forest structure classes derived using digital aerial photogrammetry and aerial lidar (see manuscript for full details); 1 = small trees (&lt;10" QMD of the top 25th percentile trees), 2 = medium trees (10-20" QMD top 25th percentile), 3 = large open (&gt;20" QMD top 25th & &lt; 50% canopy cover), 4 = large closed (&gt;20" QMD top 25th & &gt; 50% canopy cover) (WA DNR 2024) \data\categorical\dnr_pvt\ 'veg_type_dnr.tif': raster file representing the Henderson update to the ILAP potential vegetation types (PVT) that were classified into dry, moist, and cold forest types (see zMaster_rascodes_Veg_PVT_20YP.csv and Appendix S1: Table 1) (WA DNR 2024; Halofsky et al. 2014; Region 6 PVT 2023) \data\categorical\nso\ 'NSO_Habitat_Lidar_wMask.tif': raster file representing the three northern spotted owl (NSO) habitat classes used in the supplemental material analyses; 1 = High Quality Habitat, 2 = Moderate Quality Habitat, 3 = Low Quality Habitat (Halofsky et al. 2024) \data\categorical\ogsi\ 'ogsi_cls_2020_clip_ss_reclass.tif': raster file representing the three old-growth site index (OGSI) classes used in the supplemental material analyses; 0 = Other, 1 = OGSI-80 (including 80, 120, and 160), 2 = OGSI-200 (Ohmann et al. 2012) \data\categorical\progression\ 'burn_progression_julian_20230310': raster file representing burn progression of the Schneider Springs fire. This layer was primarily used to develop our point sampling grid, though it was also used for producing fire weather layers (described below). This layer was produced using the United States Department of Agriculture Forest Service National Infrared Operations (NIROPS) Unit data. (NIROPS 2022) \data\csvs\ ‘predictor_variables_20221108’: table used to link variable names to figure names when creating figures. This table includes all variables evaluated in the Chamberlain et al. 2024 publication, though only three of these variables were used in the analyses for this publication: Data_Category = data category, File_Name = raw file name, Figure_Name = figure name; name_w_units = figure name with metric units. ‘treatment_names’: table used to link treatment codes to figure names when creating figures; trt_comb = character treatment code; trt_code = numeric treatment code; figure_nm = figure name before consolidating treatments; color = color for figure; new_code = new numeric code after consolidating treatments; new_nm = figure name 'zMaster_rascodes_Veg_PVT_20YP': table used 1) to classify potential vegetation types into dry, moist, and cold forest types and 2) to quantify historical severity proportions by forest type, with historical fire severity values originating from Haugo et al. (2019); RasCode = numeric code representing the ILAP potential vegetation type (PVT) simple name, SimpleName = ILAP PVT name, PVG_simple = dry, moist, or cold classification used in these analyses, PVGCode = numeric code for PVG_simple, Low-5th, Low-Mean, and Low-95th = the 5th pecentile, mean, and 95th percentile of historical low severity for ach forest type, as quantified in Haugo et al. (2019), Mod-5th, Mod-Mean, and Mod-95th = the 5th pecentile, mean, and 95th percentile of historical moderate severity for ach forest type, as quantified in Haugo et al. (2019), High-5th, High-Mean, and High-95th = the 5th pecentile, mean, and 95th percentile of historical moderate severity for ach forest type, as quantified in Haugo et al. (2019) (WA DNR 2024; Halofsky et al. 2014; Region 6 PVT 2023) \data\predictors\ ‘Annual_Deficit_V2_1981_2010.tif’: Mean climatic water deficit across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) ‘SS_minrh.tif’: GRIDMET daily relative humidity (%) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) ‘tpi_10res_8010win.tif’: topographic position index derived from USGS 10-m digital elevation model using a 8010-m window (USGS 2022; Evans and Murphy 2021) \data\severity\ ‘2021_SchneiderSprings_rdnbr_w_offset_DATESADJUSTED.tif’: relativized differenced normalized burn ratio at 30-m spatial resolution (Parks et al. 2018; Parks et al. 2021) \data\study_area\ ‘ss_perimeter_w_hole.shp’: shapefile of Schneider Springs Fire perimeter from Monitoring Trends in Burn Severity datasets (MTBS 2022) \data\treatments\ ‘manuscript_treatments_20240321.shp’: shapefile of Schneider Springs Fire treatments; only treatments analyzed in final analyses after treatments were excluded that did not meet certain criteria (see details in Chamberlain et al. (2024)) (treatment data provided by Washington Department of Natural Resources) ‘global_treatments_w_wx_20221011.shp’: shapefile of all Schneider Springs Fire treatments (treatment data provided by Washington Department of Natural Resources) \figures\ This is a blank folder where figures are exported after running the full analyses \tables\ 'ar_by_for_str_sevcls': tabular summaries of area (ha) by forest type, forest structure class, and severity class; type = forest type, class = forest structure class, sev_class = burn severity class, tot_ar_ha = total area (ha) 'ar_for_ptyp_psz': tabular summaries of area (ha) by forest type, severity patch type, and severity patch size; type = forest type, ptype = severity patch type, psize = patch size class, tot_ar_ha = total area (ha) 'medsev_by_cls_for': tabular summaries of median severity by forest type and forest structure class; class = forest structure class, type = forest type, med_sev = median severity ### References Cited Abatzoglou, John T. 2013. “Development of gridded surface meteorological data for ecological applications and modelling.” International Journal of Climatology 33 (1): 121–31. [https://doi.org/10.1002/joc.3413](https://doi.org/10.1002/joc.3413). Cansler, C. Alina, Van R. Kane, Paul F. Hessburg, Jonathan T. Kane, Sean M. A. Jeronimo, James A. Lutz, Nicholas A. Povak, Derek J. Churchill, and Andrew J. Larson. 2022. “Previous wildfires and management treatments moderate subsequent fire severity.” Forest Ecology and Management 504 (January): 119764. [https://doi.org/10.1016/j.foreco.2021.119764](https://doi.org/10.1016/j.foreco.2021.119764). Chamberlain, Caden P.; Meigs, Garrett W.; Churchill, Derek J. et al. (2024). Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity. Ecosphere. [https://doi.org/10.1002/ecs2.70073](https://doi.org/10.1002/ecs2.70073) Evans, Jeffrey S., Murphy, Melanie A. 2021. “spatialEco.” R package version 1.3-6, [https://github.com/jeffreyevans/spatialEco](https://github.com/jeffreyevans/spatialEco) Halofsky, J. E., M. K. Creutzburg, and M. A. Hemstrom, eds. 2014. Integrating social, economic, and ecological values across large landscapes. Gen. Tech. Rep. PNW-GTR-896. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 206 p. Halofsky, J. S., D. C. Donato, P. H. Singleton, D. J. Churchill, G. W. Meigs, W. L. Gaines, J. T. Kane, V. R. Kane, D. Munzing, and P. F. Hessburg. 2024. Reconciling species conservation and ecosystem resilience: Northern spotted owl habitat sustainability in a fire-dependent forest landscape. Forest Ecology and Management 567:122072. Haugo, R. D., B. S. Kellogg, C. A. Cansler, C. A. Kolden, K. B. Kemp, J. C. Robertson, K. L. Metlen, N. M. Vaillant, and C. M. Restaino. 2019. The missing fire: quantifying human 850 exclusion of wildfire in Pacific Northwest forests, USA. Ecosphere 10:e02702. MTBS. 2022. Monitoring Trends in Burn Severity, Fire Occurrence Dataset. [https://www.mtbs.gov/direct-download](https://www.mtbs.gov/direct-download) NIROPS. 2022. United States Department of Agriculture Forest Service National Infrared Operations (NIROPS) Unit. [https://fsapps.nwcg.gov/nirops/](https://fsapps.nwcg.gov/nirops/) Ohmann, J. L., M. J. Gregory, H. M. Roberts, W. B. Cohen, R. E. Kennedy, and Z. Yang. 2012. Mapping change of older forest with nearest-neighbor imputation and Landsat time-series. Forest Ecology and Management 272:13-25. Parks, S. A., L. M. Holsinger, M. A. Voss, R. Loehman, and N. P. Robinson. 2018. “Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential.” Remote Sensing 10 (6): 879. [https://doi.org/10.3390/rs10060879](https://doi.org/10.3390/rs10060879). Parks, S. A., L. M. Holsinger, M. A. Voss, R. A. Loehman, and N. P. Robinson. 2021. “Correction: Parks et al. mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential. Remote Sens. 2018, 10, 879.” Remote Sensing 13 (22): 4580. [https://doi.org/10.3390/rs13224580](https://doi.org/10.3390/rs13224580). Region 6 PVT. 2023. United States Department of Agriculture Region 6 Potential Vegetation Type. [https://oregonexplorer.info/node/38886?topic=23616&ptopic=98](https://oregonexplorer.info/node/38886?topic=23616&ptopic=98) USGS. 2022. United States Geological Survey 1/3rd Arc Second Digital Elevation Model. [https://www.usgs.gov/tools/national-map-viewer](https://www.usgs.gov/tools/national-map-viewer). WA DNR. 2024. 20-Year Forest Health Strategic Plan: Eastern Washington - Monitoring Report 2024. Washington State Department of Natural Resources. Olympia, WA. Available online: [https://www.dnr.wa.gov/sites/default/files/publications/rp_forest_health_monitoring_report_2024.pdf](https://www.dnr.wa.gov/sites/default/files/publications/rp_forest_health_monitoring_report_2024.pdf).