DOI: 10.5061/dryad.bzkh189gb
title: Forest restoration and fuels reduction work: Different pathways for achieving success in the Sierra Nevada

description: Fire suppression and past selective logging of large trees have
 fundamentally changed frequent-fire adapted forests in California. The
 culmination of these changes produced forests that are vulnerable to
 catastrophic change by wildfire, drought, and bark beetles, with climate
 change exacerbating this vulnerability. Management options available to
 address this problem include mechanical treatments (Mech), prescribed fire
 (Fire), or combinations of these treatments (Mech + Fire). We quantify
 changes in forest structure and composition, fuel accumulation, modeled
 fire behavior, inter-tree competition, and economics from a 20-year forest
 restoration study in the northern Sierra Nevada. All three active
 treatments (Fire, Mech, Mech + Fire) produced forest conditions that were
 much more resistant to wildfire than the untreated control. The treatments
 that included prescribed fire (Fire, Mech + Fire) produced the lowest
 surface and duff fuel loads and the lowest modeled fire hazards. Mech
 produced low fire hazards beginning 7-years after the initial treatment
 and Mech + Fire had lower tree growth than controls. The only treatment
 that produced inter-tree competition similar to historical California
 mixed-conifer forests was Mech + Fire, indicating that stands under this
 treatment would likely be more resilient to enhanced forest stressors.
 While Fire reduced modeled fire hazard and reintroduced a fundamental
 ecosystem process, it was done at a net cost to the landowner. Using Mech
 that included mastication and commercial thinning resulted in positive
 revenues and was also relatively strong as an investment in reducing
 modeled fire hazard. The Mech + Fire treatment represents a compromise
 between the desire to sustain financial feasibility and the desire to
 reintroduce fire. One key component to long-term forest conservation will
 be continued treatments to maintain or improve the conditions from forest
 restoration. Many Indigenous people speak of ‘active stewardship’ as one
 of the key principles in land management and this aligns well with the
 need for increased restoration in western US forests. If we do not use the
 knowledge from 20+ years of forest research and the much longer tradition
 of Indigenous cultural practices and knowledge, frequent-fire forests will
 continue to be degraded and lost.
description: # Forest restoration and fuels reduction work: Different pathways for
 achieving success in the Sierra Nevada
 [https://doi.org/10.5061/dryad.bzkh189gb](https://doi.org/10.5061/dryad.bzkh189gb) Inventory data and code needed to produce figures, tables, and statistical results for the paper. Raw data have been aggregated to the resolution used for the various analyses. ## Description of the data and file structure analysis.RMD (and analysis.html) is/are a (knit) Rmarkdown script which performs the statistical analyses featured in the paper and produces figures and summary tables. Data for each component analysis are included as .csv files. Each .csv file is named for the corresponding data object in the analysis script. Included data files and columns are: * cc\_data.csv * plot\_id: Character string identifying a physical inventory plot * year: Integer year of observation * inv\_type: Character code identifying the scheme for sampling. Only type "N" ("normal") plots are included. * canopy: Integer giving the canopy cover in percent observed. * comp: Compartment (experimental treatment unit) code. Plots are nested within compartments. * treatment: Treatment regime for the compartment containing the observation. Options are "control", "mech", "burn", and "mechburn" * canopy\_perc: Canopy cover expressed as a proportion, with the range 0-1. * canopy\_trans: Canopy cover transformed so as to map from the range 0 &lt;= cover &lt;= 1 to the range 0 &lt; cover &lt; 1. See analysis.rmd for details. * duff\_data.csv * treatment: See above description * comp: See above description * timestep: The timestep for the observation (e.g. "Pretreatment" for pretreatment observations in 2001, "post\_18" for observations 18 years after the installation of treatments in 2002) * plot\_id: See above description * duff\_mgha: Observed duff load in megagrams per hectare * log\_duff\_mgha: Observed duff load transformed by adding the minimum nonzero value to all observations and then log-transforming * growth\_and\_removals.csv * treatment: See above description * Change: The type of change in live basal area; either "Growth", "Mortality", or "Harvest" * delta: The magnitude of the change in basal area (in square meters per hectare) from 2003 to 2020 in each treatment type. * delta\_ba\_m2hayr: The magnitude of the change in basal area from 2003 to 2020 in square meters per hectare per year in each treatment type * large\_tree\_stats.csv * treatment: See above description * comp: see above description * plot\_id: see above description * timestep: see above description * tph: The stems per hectare (stems greater than 76.2 cm DBH) observed on the plot at the timestep * count: The total number of stems greater than 76.2 cm DBH on the plot at the timestep * Treatment: See above description * Timestep: See above description * net\_ba\_data.csv * treatment: see above description * comp: see above description * post\_1: The observed live basal area (square meters per hectare) in 2003 (one year post treatment) on the plot * post\_18: The observed live basal area (square meters per hectare) in 2020 (18 years post-treatment) on the plot * delta\_ba\_m2ha: The change in live basal area from 2003 to 2020 in square meters per hectare * delta\_ba\_m2hayr: The change in live basal area from 2003 to 2020 in square meters per hectare per year * ptorch\_data.csv * comp: see above description * plot\_id: see above description * treatment: see above description * timestep: see above description * ptorch: The PTorch value (from 0 to 1) estimated by FVS for the plot * pmort: The PMort value (from 0 to 100) estimated by FVS for the plot * ptorch\_transformed: The Ptorch value estimated by FVS and transformed so that the bounds of the interval 0 &lt; x &lt; 1 are closed, rather than open. See methods and script for details. * sdi\_data.csv * plot\_id: see above description * inv\_year: see above description * timestep: see above description * treatment: see above description * sdi\_metric: The stand density index (trees per hectare) as calculated in NOrth et al. 2021. * max\_sdi: The theoretical maximum stand density index for mesic mixed conifer forests as given in Long and Shaw 2012 * rel\_sdi: The relative stand density index as described in the methods (sdi\_metrid / max\_sdi) * sdi\_zone: The rel\_sdi binned into categories * comp: see above description * sltc\_data.csv * treatment: see above description * comp: see above description * plot\_id: see above description * carbon\_mgha: Live aboveground tree carbon in megagrams per hectare * pmort: FVS-predicted PMort * stable\_ltc\_mgha: Stavle live tree carbon in megagrams per hectare * surface\_fuels\_data.csv * treatment: see above description * comp: see above description * timestep: see above description * plot\_id: see above description * surface\_mgha: Observed megagrams per hectare of surface fuels (litter, fine woody debris, and coarse woody debris) * log\_surface\_mgha: The observed megagrams per hectare of surface fuels transformed by adding the minimum non-zero value to all observations and then log-transforming ## Sharing/Access information Data was derived from the following sources: * Raw inventory datasheets compiled by Blodgett Forest and UC Berkeley field crews. ## Code/Software Output of sessionInfo(): ``` R version 4.1.1 (2021-08-10) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19045) Matrix products: default locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] DHARMa_0.4.3 glmmTMB_1.1.3 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 [9] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0 here_1.0.1 loaded via a namespace (and not attached): [1] tidyselect_1.2.0 xfun_0.31 TMB_1.7.21 splines_4.1.1 lattice_0.20-44 colorspace_2.0-2 [7] vctrs_0.6.3 generics_0.1.0 htmltools_0.5.5 yaml_2.2.2 utf8_1.2.2 rlang_1.1.1 [13] pillar_1.9.0 nloptr_1.2.2.2 glue_1.6.1 withr_2.5.0 bit64_4.0.5 foreach_1.5.1 [19] emmeans_1.8.6 lifecycle_1.0.3 munsell_0.5.0 gtable_0.3.0 mvtnorm_1.1-2 codetools_0.2-18 [25] coda_0.19-4 evaluate_0.14 knitr_1.37 tzdb_0.1.2 fastmap_1.1.0 parallel_4.1.1 [31] fansi_0.5.0 Rcpp_1.0.8 xtable_1.8-4 scales_1.2.1 vroom_1.6.3 bit_4.0.4 [37] lme4_1.1-27.1 hms_1.1.3 digest_0.6.29 stringi_1.7.6 numDeriv_2016.8-1.1 grid_4.1.1 [43] rprojroot_2.0.2 cli_3.6.1 tools_4.1.1 magrittr_2.0.3 crayon_1.4.1 pkgconfig_2.0.3 [49] MASS_7.3-60 Matrix_1.3-4 estimability_1.4.1 timechange_0.2.0 iterators_1.0.13 minqa_1.2.4 [55] rmarkdown_2.14 rstudioapi_0.14 R6_2.5.1 boot_1.3-28 nlme_3.1-153 compiler_4.1.1 ```