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Intermediate fire severity diversity promotes richness of forest carnivores in California

Cite this dataset

Furnas, Brett; Goldstein, Benjamin; Figura, Peter (2021). Intermediate fire severity diversity promotes richness of forest carnivores in California [Dataset]. Dryad.


Aim: Fire can strongly influence ecosystem function, and human activities are disrupting fire activity at the global scale. Ecological theory and a growing body of literature suggest that a mixed severity fire regime promotes biodiversity in western North America. Some researchers advocate the use of pyrodiversity (i.e., heterogeneity in aspects of the fire regime such as time since fire or severity) as a conservation index to be maximized. Others caution against this approach arguing that the index oversimplifies fire-biodiversity interactions across trophic, spatial, and temporal scales. We evaluated the effects of several landscape-scale pyrodiversity indices, and their severity and time-since-fire components, on species richness of forest carnivores.

Location: Northern California, United States

Methods: We gathered data on fire history and mammal occurrence from camera trap surveys at 1,451 sites across Northern California public and private forestlands during 2009–2018. We used this data to model the effects of fire severity diversity, and its components (i.e., low, moderate, and high severity wildfires), on carnivore richness at short (10 years) and longer (25 years) timeframes. We repeated the modeling using a measure of time-since-fire diversity and its components (<10 yrs, 10–20 yrs, 20–30 ys, 30–40 yrs, 40–100 yrs.). We used Bayesian multispecies occupancy modeling to correct for imperfect measurement of species richness.

Results: We found that carnivore richness was highest at locations with intermediate fire severity diversity (0.46, 90%CI: 0.40–0.52) calculated using Simpson’s Measure of Evenness (range: 0–1) for the 10-yr timeframe, and the results were almost identical yet less precise for the longer timeframe. When we separated fire severity diversity into its components, we found that carnivore richness was highest at locations where 17% (90%CI: 4–20) of the landscape had experienced low severity burns over the past decade. In contrast, we found no association between time-since-fire diversity and carnivore richness, however, an intermediate amount of one of the components (e.g, the total amount of fire in the past 10 years) was positively associated with carnivore richness. Our results are consistent with a mixed severity fire regime wherein there is a greater extent of low severity than high severity fire.

Main Conclusions: Overall our results suggest that carnivores would benefit from landscapes managed for greater, but not maximal, fire severity diversity. Our results also suggest that prescribed, low severity burns may provide ecological services to wildlife not otherwise provided by silviculture in a managed forest landscape.


For 2009 through 2018 we conducted wildlife surveys using camera traps at a total of 1,451 sites across the study area. Our annual survey season began in early August and continued through late November or early December. The duration of surveys was 2–5 weeks, and both camera traps within a hexagon were always surveyed concurrently. We reviewed photos from camera surveys to identify all mammal detections to species. We created a detection history for each site that indicated whether (“1”) or not (“0”) a species was observed for each 24-hour survey day up to 30 days. Average survey duration was 18.7 days.

We used the Monitoring Trends in Burn Severity (MTBS) spatial database of fire severity of all wildfires >500 ha in the USA since 1984. To measure fire severity diversity, we computed Simpson’s Measure of Evenness (SME) of the four burn category proportions from MTBS (none, low, moderate, high) at both the 10-year and 25-year timeframes. We repeated a similar analytical process for calculating covariates pertaining to time since fire, although the data available allowed us to inspect a longer timeframe than we used for fire severity.

Data were processed in R and are available in a .Rdata file.

See the accompanying manuscript for a full description of methodology.

Usage notes

The .Rdata file provided comprises eight R objects:

  1. covars_site, a data frame containing site-level covariates for sampling locations. The column "site" is a unique ID for each site
  2. rands.out, a data frame containing site-level covariates for randomly selected, non-sampled sites used in calculating the typical conditions over the landscape
  3. spec_trasnl, a two-column data frame translating species codes into common names
  4. standards, a matrix giving the mean and standard deviation of the covariate data columns for use in back-transforming scaled covariates
  5. yday, a matrix giving the Julian date of each each site-sampling event
  6. lag, a numeric array giving the site-species-event specific covariate for time since last detection
  7. project, a binomial vector indicating which of two project protocols was used for each site
  8. y, the binomial array of detection/nondetection data. The value of y[i, j, k] indicates whether species k was detected on visit j to site i, with a 0 representing nondetection, a 1 representing detection, and a NA indicating that site i was sampled fewer than j times.

These data can be imported into an R session via the command load([filepath]).