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.