Figure 1. A) A simplified example of how pyrodiversity is calculated using the functional dispersion metric (FDis), adapted from Laliberté & Lengendre (2010). x represents the location of j unique fire histories (“species”) in multidimensional trait-space, c is the trait-space centroid of a landscape (“community”), zj is the trait distance of history j from c, and aj is the frequency (“abundance”) of history j within the landscape. FDis is calculated as the weighted mean distance from c. B) Fire trait surfaces used to calculate pyrodiversity for an example watershed. C) Conceptual model of the drivers of pyrodiversity. Solid lines represent direct effects and dashed lines represent mediated relationships.
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 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 was 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 most 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. 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. While differential trait weights can be applied when calculating FDis (Laliberté & Legendre 2010), here we weight the four pyrodiversity traits equally. We rely on future applications of this pyrodiversity method to test and parameterize the importance decay rate and relative trait weights for the ecosystem and processes of interest. 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.
Pyrodiversity Trait Covariance
While FDis accounts for redundancy among traits (Laliberté & Legendre 2010), understanding how fire traits covary is valuable for categorizing fire regime groups, as well as assessing the mechanisms by which variation in fire traits affects ecosystem pattern and process. We calculated correlations among the four pyrodiversity traits at the watershed scale. To test whether correlations varied with the amount of recorded fire history, we systematically filtered out less frequently burned watersheds with increasing higher thresholds of number of fires recorded. Specifically, correlations were made among traits for all study watersheds with minimum number of fires ranging from zero to fifteen.
Pyrodiversity drivers
We assessed the hypothesized ultimate drivers of climate, topography and human influence on pyrodiversity using a 1) pyrodiversity model and a 2) burn activity model. These models represent direct and indirect (burn activity-mediated) effects on pyrodiversity, respectively (Fig. 1c). We model direct effects on pyrodiversity as:
[Eq. 1]