Generation of trait surfaces
We used four fire regime traits to calculate pyrodiversity: 1) fire return interval (frequency), 2) burn severity, 3) burn season, and 4) patch size (Fig. 1b). These traits are commonly used to define fire regime groups, are important determinants of ecosystem process in fire-adapted systems (Agee 1996; van Wagtendonk et al. 2018), and follow the original characteristics of pyrodiversity defined by Martin and Sapsis (1992). We mapped each of the four fire regime traits across the western United States using fire perimeter data from the national Monitoring Trends in Burn Severity database (www.mtbs.gov). This database includes all moderate to large fires in the region between 1984 and 2018 (> 404 ha; Eidenshink et al. 2007). Burn intervals were calculated as the difference between burn years of overlapping fire perimeters, as well as the first and final year of the dataset. Burn season was determined by the ignition date and were transformed to cosine of radians to account for the cyclical nature of date (e.g. so that the last and first day of the year are consecutive). Burn severity was calculated for each fire using Landsat imagery (TM and OLI sensors) and Google Earth Engine following Parks et al. (2019). The Parks model uses a Random Forest Algorithm to estimate values of composite burn index (CBI) at a resolution of 30 m. Model validation shows severity estimates are most accurate in forest ecosystems of western North American (Parks et al. 2019). We calculated patch size by defining distinct patches in each burn year using the CBI categories of unchanged, low-, moderate- and high-severity as defined by Miller and Thode (2007).
When calculating contemporary fire regime traits, values are often averaged across a period of record or only the most recent fire event is used. For example, fire frequency could be quantified as the mean of inter-fire intervals since reliable records began or the time since the previous fire (Steel et al. 2015). Both options are sub-optimal if the phenomenon of interest (e.g., biodiversity) is sensitive to recent events but previous fires (the “invisible mosaic”) maintain some influence over landscape pattern and process (Brown & York 2017). We bridge these extremes by implementing a recency-weighted average when calculating pixel-wise trait values. Specifically, trait values from recent fires (or intervals) receive the greatest weight with the weight or importance of earlier events decaying with order. We rely on future applications of this pyrodiversity method to test and parameterize this importance decay rate for the ecosystem and processes of interest. Here we assigned a decay rate of 0.5, for which each prior value receives half the weight of the more recent. We chose to weight by fire order rather than time or interval length to avoid confounding between weighting and the fire frequency trait being measured. Trait rasters using the 0.5 decay rate can be found at https://figshare.com/articles/Pyrodiversity_westCONUS/12478832 and code is available at https://github.com/zacksteel/pyrodiversity for generating custom trait surfaces for future research. These data can be used to calculate pyrodiversity either across broad extents as demonstrated here or locally around biodiversity survey locations.