Climate, topography, or fuels? Top-down versus bottom-up controls on fire refugia across diverse landscapes of British Columbia

Citation Kuntzemann, C. E., E. Whitman, D. Lewis, and D. Stralberg. (In Review). Climate, topography, or fuels? Top-down versus bottom-up controls on fire refugia across diverse landscapes of British Columbia. Ecosphere. Data description This data consists of 5 GEOTIFF rasters covering the extent of British Columbia, Canada, as well as 2 CSV files. All rasters have a 90 m resolution, datums of D North American (1983), and a latitude of origin of 45. Projections are Albers Conic Equal Area (predictions) and NAD (1983) BC Environment Albers (FRUs; EPSG: 3005). The final fire sample consists of all final points (truncated at 25K randomly selected points per FRU) and variables used in analyses, extracted at a 30 m scale in the NAD (1983) BC Environment Albers projection. Data included: Rasters of predicted fire refugia probabilities under wetter (2016), drier (2017), and average climate conditions, as well as those based on static terrain features (topography), for the province of British Columbia, Canada. - Fire_Refugia_2016.tif - Fire_Refugia_2017.tif - Fire_Refugia_Average.tif - Fire_Refugia_Topography.tif 2. Fire regime unit (FRU) boundaries used in analyses, as well as a lookup table with each FRU’s associated biogeoclimatic ecosystem classification (BEC) zones and natural disturbance types (NDT). - FRUs.tif - FRU_BEC_Lookup.csv 3. Final sample used in analyses. - Fire_Sample.csv A publicly available web application, created through the Google Earth Engine App program, can be found at: https://ee-cekfirerefugia.projects.earthengine.app/view/predicted-fire-refugia-probability-across-british-columbia This app includes visualizations of each of the predictive maps, as well as a map detailing the various fire regime units (FRUs) and their associated regions throughout the study area. Abstract Surviving pockets of vegetation within fire perimeters, termed fire refugia, are an important component of ecological recovery following disturbance. Understanding the relative influence of the drivers of fire refugia throughout diverse landscapes and climate conditions can help identify areas that are conducive to their formation. We investigated the role of various top-down (climate) and bottom-up (fuels, physical setting) controls on fire refugia creation throughout twenty-one unique fire regime units in the forests of British Columbia, Canada, over a 20-year (2000-2019) period. Boosted regression tree models were used to determine the relative influence of each of these controls and their associated variables on fire refugia, as well as to create predictive maps of fire refugia probabilities over a range of interannual climate conditions. We found that the bottom-up controls, particularly variables relating to physical setting, generally held the greatest influence on fire refugia creation, though those relating to fuels were of higher importance in the boreal and central interior regions. These bottom-up controls, however, can be overwhelmed by extreme climate conditions, which have variable effects on refugia depending on the region. There was an overall positive correspondence between locations of persistent (long-term) fire refugia and mapped old-growth, suggesting that strong, static terrain features may shelter some forests over the course of multiple fire events, allowing for the development of old-growth stands. We concluded that, while strong topographic features confer the strongest measure of protection in some regions of the province, there are many areas in which fuel mitigation tactics (e.g., fuel thinning, prescribed burns) may be particularly useful for protecting areas of high human or ecological value in the face of increasingly extreme climate conditions. Methods Summary We fit a series of boosted regression tree models (Elith et al. 2008) to determine the relative importance of top-down and bottom-up controls on fire refugia probability for each of 21 fire regime units (FRU, Erni et al. 2020) in British Columbia. Fires were sampled via randomly generated points representing 1% of fire pixels (30-m resolution). We extracted point and landscape variables (Appendix S1: Table S1) at each sample point. Landscape variables were extracted using square-shaped moving windows of 300 m or 1200 m on a side. All processing and extraction of the covariates was conducted in Google Earth Engine (Gorelick et al. 2017). Fire sampling and model development was conducted using R version 4.4.1 (R Core Team 2024). Final models were used to create predictive maps of fire refugia probability in each FRU under a range of climatic conditions. References Elith J, Leathwick JR, Hastie T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77:802–813. Erni S, Wang X, Taylor S, Boulanger Y, Swystun T, Flannigan M, Parisien M-A. 2020. Developing a two-level fire regime zonation system for Canada. Canadian Journal of Forest Research:259–273. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available from https://www.R-project.org/.

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