Supporting data for increasing fire activity reinforces shrub conversion in Southwestern US forests
Cite this dataset
Hurteau, Matthew et al. (2023). Supporting data for increasing fire activity reinforces shrub conversion in Southwestern US forests [Dataset]. Dryad. https://doi.org/10.5061/dryad.qrfj6q5b6
Fire-exclusion in historically frequent-fire forests of the southwestern United States has altered forest structure and increased the probability of high-severity fire. Warmer and drier conditions, coupled with dispersal distance limitations are limiting tree seedling establishment and survival following high-severity fire. Post-fire conversion to non-forest vegetation can be reinforced by subsequent fire events. We sought to determine the influence of fire probability on post-fire vegetation development in a severely burned landscape in New Mexico, USA. We used LANDIS-II to simulate three fire probability scenarios—contemporary mean fire return interval (CMFRI), and 1.5 times and 2 times CMFRI—with contemporary climate. As fire probability increased, the mean size of the largest fires and the mean landscape fire severity increased. These changes in fire characteristics resulted in a net decrease in total above ground biomass and photosynthetic capacity on the landscape. Additionally, the distribution of individual species biomass shifted, with early successional species, especially those that resprout after fire, increasing as a fraction of total biomass with increasing fire occurrence. Continued increases in fire frequency are likely to favor resprouting species and result in a loss of forest biomass and ecosystem productivity in this southwestern forest landscape.
These data are outputs of a set of simulations using the LANDIS-II (v6.2) model with the PnET Succession extension (v.2.1.1) and the Dynamica Fuels and Fire System (v2.1)
extension. We used current climate data from Daymet and atmospheric CO2 data from the Mauna Loa observatory. We ran the model with three fire ignition probability scenarios; these are labelled 'Low or LO', 'Medium or MED', and 'High or HI'. Our study objective was to examine differences in post-fire vegetation recovery under
variable fire regimes. Our simulation area was the footprint of the 2011 Las Conchas fire in New Mexico, USA.
We ran 30 replicate simluations for each scenario; the length of each was 50 years. We summarized most data by calculating the mean and standard deviation of the 30 replicates. Mean and standard deviation for fire severity data were calculated for all years/replicates together. We present the data sets (raw and final) and code for creating figures in Keyser et al.
The parameter files for the PnET succession and Dynamic Fire and Fuels extensions are also included.
The data is organized as follows:
The directory includes folders for 1) Fire Size and Severity, 2) Resprouter Probability,
3) Vegetation Distribution, and 4) Photosynthesis. The directory also includes a folder with the parameter files for the PnET succession and Dynamic Fire and Fuels extensions.
Within each directory is R code for creating figures and the data used to create each figure.
1) Area Burned and Fire Size Distributions:
'CAB_allScen.csv' contains summarized data for cumulative area burned for all
scenarios. (Figure 1)
'bigFireLO2.csv' contains a subset of the summary log records for the Low fire
scenario. The subset is the top 25% of fires by size. The field
TotalSitesBurned is the number of 1ha pixels that burned in the
fire event. The logAB field is the log of area burned.
'bigFireMED2.csv' contains the same as above, but for the Medium fire scenario.
'bigFireHI2.csv' contains the same as above, but for the High fire scenario.
'bigFireALL2.csv' contains all records in the above three files. This file is used
to create the combination figure. The individual fires are
require for statistical analyses. (Figure 2)
'FireSizeDistributionsAndAreaBurned.html' contains the code for creating figures.
1) Fire Severity:
'muFSevAllScen2_df.csv' contains the mean fire severity data for all scenarios, years,
and replicates. (Figure 5)
'FireSeverity.html' contains the code for creating figures.
2) Resprouter Probability
LOfire, MEDfire, HIfire Each of these contains the year 50 values for aboveground
biomass (AGB) for each species in *.csv format for each of
30 replicates. At the top of each directory is a *.csv with
the mean number of fire counts for all replicates at each
'sproutsProbAll2_df.csv' contains final data for the probability that the fraction of
total aboveground biomass in a site composed of
resprouting is greater than or equal to 50%, at year
50 for all scenarios. (Figure 3)
'ResprouterProbability.html contains R markdown code for calculating the probability that
the site biomass will be greater than 50% resprouters and
code for creating the figures.
3) Vegetation Distribution:
'sppAGByr0.50fireCntLMH_hiLow.csv' contains data for the fration of mean aboveground
biomass of resprouting species and obligate seeders by
number of times sites burned at years 0 and 50. All
scenarios are combined in one data frame. Number of
fire occurrences are binned. (Supplementary Figure)
'sppAGByr0.50fireCntLMHdiffs.csv' contains the difference in aboveground biomass between
years 0 and 50 (0minus50) for all species grouped by
by fire count bin, for all scenarios. (Suppl. Figure)
'fuelCntBYfireCnt_df.csv' contains data for fractional coverage of fuel types
by fire count bin, for all scenarios. (Figure 4)
'VegetationDistributionPlots.html' contains code for making manuscript and supplementary
LOfire, MEDfire, HIfire Each of these contains the mean annual photosynthesis
records for each species in *.csv format
Example file name:
'abieconc_PSNmuDataFrame.csv' Each file prefix is the 8 letter genus species code
with the same suffix.
'CumulativePhotosynthesis.html' contains the code to read in each species annual
photosynthesis output, calculate annual and cumulative
sums, calculate the difference between scenarios, and
plot Figure 6 from the manuscript.
Joint Fire Science Program, Award: 16-1-05-8
National Institute of Food and Agriculture
National Institute of Food and Agriculture, Award: grant no. 2017-67004-26486/project accession no. 1012226