DOI: 10.25338/b8ch2t
title: Areas of low natural regeneration potential post-fire in shrublands of southern California (selected years between 2008 and 2020)

description: Identifying locations where shrubland vegetation will not recover
 naturally post-fire is a challenge given the vast areas that are regularly
 burned in southern California. When shrublands are within the historic
 fire return interval, e.g., 55 years for low-elevation shrubland (Keeley
 and Safford 2016, Van de Water and Safford 2011), biomass accumulates and
 shrub cover recovers after 10–14 years (Black et al. 1987, Bohlman et al.
 2018). However, in many parts of southern California, the fire return
 interval has decreased, often in conjunction with an increase in
 non-native plant species, drought, and nitrogen deposition (Pratt et al.
 2014, Allen et al. 2018, Syphard et al. 2019, Safford et al. 2022). Under
 these conditions, post-fire biomass recovery can be impeded and, in some
 cases, may result in type conversion from native shrubland to non-native
 grassland (Syphard et al. 2019). We developed a repeatable method to
 identify areas of low regeneration potential in southern California using
 fire history data (FRAP 2021), using two rules guided by the published
 literature (Zedler et al. 1983, Haidinger and Keeley 1993, Keeley and
 Brennan 2012, Syphard et al. 2019, Underwood et al. 2021, Underwood and
 Safford 2021). First, we set the threshold of the ‘number of fires in the
 last 40 years’ to three or more fires, and second, we set the ‘time since
 last fire’ to a threshold of &lt;10 years.  We identified
 pixels that met these criteria as having low natural regeneration
 potential post-fire and, as a consequence, these areas could represent
 candidate areas for post-fire restoration in shrublands.   The
 rasters of low natural regeneration potential are a key input into the
 online web mapping tool SoCal EcoServe, developed for US Department of
 Agriculture Forest Service resource managers to calculate the long-term
 impacts of wildfire on carbon lost. The tool is available at
 https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in
 Underwood et al. (2022). The resulting rasters of low natural regeneration
 potential provide a basis for integrating additional factors that might
 affect the post-fire recovery of shrubands, including climatic water
 deficit or nitrogen deposition.
description: We obtained the historical wildfire perimeter database from the
 California Department of Forestry and Fire Protection (FRAP 2021). We then
 generated from this database a set of binary rasters indicating the
 occurrence of fire in a pixel for a given year across the southern
 California study area. The years for this input data stack range from 1967
 to 2020. We have included this input data stack in this collection in the
 file L4fires.zip. We implemented a script to calculate
 the low natural regeneration potential rasters in the Julia language using
 the Rasters library (https://github.com/rafaqz/Rasters.jl), opting to use Julia for performance reasons. This script uses a collection of binary rasters, one for each year, that indicate whether a pixel was burned in a given year, across the entire study region. The code is structured as follows. It has two functions (last40yrs and last10yrs) that construct year-by-year raster stacks of all fires in the past 40 and 10 years. A subsequent function (firecount2) uses a reduce operator to create a single raster containing the total number of fires over a given period. A function entitled simpleprep then checks the criterion for inclusion as having low regeneration: have there been 3 or more fires in the past 40 years or 1 or more fires in the past 10 years?  Finally, a function simpleprepraster calculates a low natural regeneration potential raster for an input year and outputs it as a geotiff file. The main loop of the script iterates over the list of years of interest,  calling simpleprepraster as it does so. This script is provided here and is named L4fires.jl. <strong>References</strong>     <strong>       </strong> Allen, E. B., K. Williams, J. L. Beyers, M. Phillips, S. Ma, and C. M. D’Antonio. 2018. Chaparral restoration. In Underwood, E. C, H. D. Safford, N. A. Molinari, and J. E. Keeley (editors). Valuing Chaparral: Ecological, Socio-Economic, and Management Perspectives. Springer Series on Environmental Management. Springer, Cham, Switzerland. Black, C. H. 1987. Biomass, nitrogen, and phosphorus accumulation over a southern California fire cycle chronosequence. In Tenhunen, J.D., F. M. Catarino, O. L. Lange, and W. C. Oechel (Eds). Plant response to stress. NATO ASI Series G (vol 15): Ecological Sciences. Springer. Berlin/Heidelberg, Germany. Bohlman, G. N., E. C. Underwood, and H. D. Safford. 2018. Estimating biomass in California’s chaparral and coastal sage shrublands. Madroño 65(1): 28-46. FRAP [Fire and Resource Assessment Program]. 2021. California Department of Forestry and Fire Protection. Wildfire perimeters and prescribed burns geodatabase. https://frap.fire.ca.gov/frap-projects/fire-perimeters/ Haidinger, T. L. and J. E. Keeley. 1993. Role of high fire frequency in destruction of mixed chaparral. Madroño 40:141-147. Keeley, J. E., and T. J. Brennan. 2012. Fire-driven alien invasion in a fire-adapted ecosystem. Oecologia 169(4):1043-1052. doi: 10.1007/s00442-012-2253-8. Keeley, J. E. and H. D. Safford. 2016. Fire as an ecosystem process: Chapter 3. In Mooney, H. A., and E. S. Zavaleta (Eds). Ecosystems of California. University of California Press. Oakland, California, USA. Pratt, R. B., A. L. Jacobsen, A. R. Ramirez, A. M. Helms, C. A. Traugh, M. F. Tobin, M. S. Heffner, and S. D. Davis. 2014. Mortality of resprouting chaparral shrubs after a fire and during a record drought: Physiological mechanisms and demographic consequences. Global Change Biology 20: 893–907. Safford, H. D., Paulson, A. K., Steel, Z. L. et al. 2022. The 2020 Californai fire season: a year like no other, a return to the past, or a harbinger of the future? Global Ecology and Biogeography 00:1-21. DOI:10.1111/geb.13498 Syphard, A. D., T. J. Brennan, and J. E. Keeley. 2019. Extent and drivers of vegetation type conversion in southern California chaparral. Ecosphere 10: e02796 Underwood, E. C., A. D. Hollander, N. A. Molinari, L. Larios, and H. D. Safford. 2021.. Identifying priorities for post-fire restoration in California chaparral shrublands. Restoration Ecology. doi: 10.1111/rec.13513 Underwood, E. C. and H. D. Safford. 2021. Appendix 5: Postfire restoration prioritization tool for chaparral shrublands. Pages 183-185 in Meyer, M.D., J.W. Long, and H.D. Safford, editors. Postfire restoration framework for national forests in California. Gen. Tech. Rep. PSW-GTR-270. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. Albany, CA. 204p. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Van de Water, K. M. and H. D. Safford. 2011. A summary of fire frequency estimates for California vegetation before Euro-American settlement. Fire Ecology7: 26–58. Zedler, P. H., C. R. Gautier, and G. S. McMaster. 1983. Vegetation change in response to extreme events – the effect of a short interval between fires in California chaparral and coastal scrub. Ecology 64(4):809-818. doi: 10.2307/1937204.
description: The file archive here is named lowregenfiles.zip and is composed
 of three files: prepregenrasters.zip, which is the archive of the
 shrubland low regeneration rasters, L4firerasters.zip, which is the
 archive of the binary input rasters, and L4files.jl, which is the Julia
 script for processing the input rasters. The shrubland
 low regeneration raster layers are available for 9 years using the
 following naming structure: prepregen20XX.tif Where
 20XX is the year of the estimate and reflects that the fire perimeter data
 from that year. The years that are included in the data stack are 2008,
 2009, 2010, 2015, 2016, 2017, 2018, 2019 and 2020. The
 dimensions of the geotiff raster files is 20,632 rows by 15,603 columns at
 a 30-meter pixel resolution. The rasters are in a California Albers Equal
 Area projection (EPSG 3310). These rasters are encoded as bytes with a
 NoData value of 255. Intended users of this dataset include resource
 managers and researchers who are assessing post-fire shrubland recovery
 and restoration opportunities. This dataset is made available under a CC0
 license.