DOI: 10.5061/dryad.jsxksn0jd
title: Repeated fuel treatments fall short of fire-adapted regeneration objectives in a Sierra Nevada mixed conifer forest, USA

description: Fire exclusion over the last two centuries has driven a significant fire
 deficit in the forests of western North America, leading to widespread
 changes in the composition and structure of these historically
 fire-adapted ecosystems. Fuel treatments have been increasingly applied
 over the last few decades to mitigate fire hazard, yet it is unclear if
 these fuels-focused treatments restore the fire-adapted conditions and
 species that will allow forests to persist into the future. A vital
 pre-requisite of restoring fire-adaptedness is ongoing establishment of
 fire-tolerant tree species, and both the type and reoccurrence of fuel
 treatments are likely to strongly influence stand trajectories. Here, we
 leveraged a long-term study of repeated fuel treatments in a Sierra Nevada
 mixed-conifer forest to examine the regeneration response of six native
 tree species to repeated application of common fuel treatments: prescribed
 fire, mechanical, mechanical plus fire, and untreated controls. Our
 objectives were to 1) quantify differences in forest structure and
 composition following repeated application of alternative fuel treatments
 that may influence the establishment environment and then 2) identify the
 stand structure and climate conditions influencing seedling dynamics. We
 found that both treatment type and intensity are highly influential in
 shifting forests toward more fire-adapted conditions and determining
 species-specific regeneration dynamics. Specifically, the conifer species
 tracked here increased in either colonization or persistence potential
 following repeated applications of fire, indicating fire may be most
 effective for restoring regeneration conditions broadly across species.
 Fire alone, however, was not enough to promote fire-adapted composition,
 with concurrent mechanical treatments creating more favorable conditions
 for promoting colonization and increasing abundances of fire-tolerant
 ponderosa pine. Yet, even with repeated fuel treatment application,
 establishment of fire-intolerant species far exceeded that of
 fire-tolerant species over this 20-year study period. Moreover, increasing
 growing season water stress negatively impacted seedling dynamics across
 all species regardless of treatment type and intensity, an important
 consideration for ongoing management under heightened climatic stress.
 While repeated treatments are waypoints in restoring fire-adapted
 conditions, more intense treatments via gap-creation or hotter prescribed
 fires targeting removal of fire-intolerant species will be necessary to
 sustain recruitment of fire-tolerant species.
description: Seedling data were collected in .004 hectare and
 1m<sup>2 </sup>plots. Overstory tree data (used for
 calculating basal area) were collected in 0.04 hectare plots. Weather data
 was collected from an on-site weather station. Data were processed using
 the R statistical software. 
description: # Fire & Fire Surrogate Seedling Study Code and data for Nagelson et.
 al. 2024 All data (.rdata files) and code are available to replicate the
 analyses and generate figures and tables. All intermediary data products
 are also provided (e.g. JAGS outputs), which means that one can jump
 straight to figure and table generation by using
 "FFSseedling_Figures_and_Tables.Rmd". # Scripts
 **FFSseedling_Figures_and_Tables.Rmd** - R markdown document used to
 generate all tables and figures in manuscript. This is the only script
 necessary if the user is only interested in reproducing figures/tables or
 the statistical results defined under research objective 1. ## Bayesian
 models and post-processing scripts **postprocessing_functions.R** -
 post-processing code for abundance and occupancy models.
 **abundancemod.R** - pre-processing and run abundance model. **occmod.R**
 - pre-processing and run occupancy model. **FFS_all_scripts.R** - model
 scripts written in JAGS syntax. These are called by abundancemod.R and
 occmod.R. **dens.mod.txt** - JAGS script for abundance model generated by
 FFS_all_scripts.R and called in abundancemod.R **occ.mod.txt** - JAGS
 script for occupancy model generated by FFS_all_scripts.R and called in
 occmod.R ## Pre-packaged data (outputs from scripts listed above)
 **results_and_functions.rdata** - packaged dataframes and functions
 created by postprocessing_functions.R. This includes outputs from JAGS
 models. * `dens.fp.df()` and `occ.fp.df()` are functions to extract draws
 from the posterior distribution for the density models and occupancy
 models. Used to create `dens.mod.results` and `occ.mod.results`. *
 `dens.mod.results` and `occ.mod.results` are dataframes containing
 summaries of the posterior distribution for the density and occupancy
 models. They have the following columns: term (predictor variable in
 model), coeff.type (scaled or unscaled), data.type (continuous,
 categorical, or time), species (two-letter species code), estimate (mean
 of posterior), median (median of posterior), conf.low (lower bound of
 credible interval), conf.high (upper bound of credible interval), overlap0
 (boolean for whether the credible interval overlaps zero), model (model
 type, occupancy or density). * `curves.fun()` is a function to plot random
 draws from the posterior distribution across the range of a chosen
 predictor. This is the main function used to create figures 3 and 4. *
 `occ.curves.y` and `occ.curves.median` are the output from `curves.fun()`.
 `occ.curves.y` is the complete set of random draws from the posterior
 distribution. `occ.curves.median` is just the median of the posterior.
 **FFS_seedling_data.rdata** - pre-processed data for use in
 FFSseedling_Figures_and_Tables.Rmd. Datasets: * `climate` = climate data
 for the length of the study, with columns: sample.year (year of sampled
 seedling data for which the climate variable applies), value (climate
 value, either precipitation climatic water deficit in millimeters), season
 (cool or warm), variable (cwd, climatic water deficit, or ppt,
 precipitation) * `light` = total transmitted radiation data with columns:
 year, comp (stand identifier), plot (plot identifier within compartment),
 TTR (total transmitted radiation, %). * `ba.all` = plot-level basal area
 with one record for each plot x timestep x species. Columns: PlotID (plot
 identifier), treatment (treatment type), source (data carpenter name),
 timestep, species (two-letter code), species.ba (species-level basal area
 in square meters/hectare), total.ba (total basal area in square
 meters/hectare).  * `adapt.table.raw` = dataframe used to generate table 1
 * `occ.model.data` = hierarchical list of dataframes and vectors. Useful
 in some cases for figure generation but can generally be ignored if using
 the .Rmd file. # Raw data files ## **abundance\_data.csv** * PlotID =
 unique plot identifier * comp = stand identifier * treatment = treatment
 type * trt = numeric treatment type * timestep = timestep identifier
 relative to initial treatment implementation * species = two-letter code
 for species. BO=black oak, DF = douglas-fir, IC=incense-cedar,
 PP=ponderosa pine, SP=sugar pine, WF = white fir * plotsize = size of
 sampled plot area in hectares * seedlings.count = count of seedlings *
 seedlings.ha = seedlings per hectare * timestep.num = numeric timestep
 identifier * year = year of sampling * timesince = years elapsed since the
 most recent treatment * trtnum = number of treatment applications *
 species.ba = conspecific basal area in square meters per hectare *
 total.ba = total basal area in square meters per hectare * cool.cwd = cool
 season climatic water deficit in millimeters * warm.cwd = warm season
 climatic water deficit in millimeters * cool.ppt = cool season
 precipitation in millimeters ## **occupancy\_data.csv** * ID = unique plot
 identifier, with added specificity for clustered subplots (e.g. 40-0002-1,
 40-0002-2, etc.) * occupancy = presence (1) or absence (0) of seedlings at
 that plot * type = plot type, either CSP (clustered subplot) or FI (full
 inventory) * plotsize = plot size in square meters * all other column
 names are defined the same as in abundance_data.csv