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Mammalian resilience to megafire in western U.S. woodland savannas

Citation

Calhoun, Kendall et al. (2022), Mammalian resilience to megafire in western U.S. woodland savannas, Dryad, Dataset, https://doi.org/10.6078/D1W70R

Abstract

Increasingly frequent megafires, wildfires that exceed the size and severity of historical fires, are dramatically altering landscapes and critical habitats across the world. Across the western U.S., megafires have become an almost annual occurrence, but the implications of these fires for the conservation of native wildlife remain relatively unknown. Woodland savannas are among the world’s most biodiverse ecosystems and provide important food and structural resources to a variety of wildlife, but they are potentially threatened by megafires. Understanding the resistance and resilience in wildlife assemblages following these extreme perturbations can help inform future management interventions that limit biodiversity loss due to megafire. We assessed the resilience of a woodland mammal community to the short-term impacts of megafire. Specifically, we utilized a 5-year camera trap data set (2016-2020) from the Hopland Research and Extension Center to examine the impacts of the 2018 Mendocino Complex fire, California’s largest recorded wildfire at the time, on the distributions of 12 observed mammal species. We used single-species occupancy models to quantify the effect of megafire on species’ space use and a multi-species occupancy model for robust estimates of fire’s impacts on species diversity across space and time. Megafire had a strong, negative effect on mammalian occupancy and activity directly following wildfire, but most species showed high resiliency and returned to were resilient and returned to activity and occupancy levels comparable to unburned sites by the end of the study period. Following fire, species richness was highest in burned areas that retained some canopy cover. Change in habitat use following wildfire varied by species: several species temporarily reduced their use of severely burned areas, while others became more active in those areas. Fire management that prevents large-scale canopy loss is critical to providing refugia for vulnerable species immediately following fire in oak woodlands, and likely other mixed-forest landscapes.

Methods

We conducted our study at the 5,300-acre U.C. Hopland Research and Extension Center (HREC) in Mendocino County, northern California (39°00′ N, 123°04’ W). The HREC ecosystem is composed of a diverse range of habitat types including grassland, oak woodland, and shrubland (chaparral). HREC is situated at an intersection of wildlands and ranchlands; it provides habitat for a diverse group of wildlife and serves as pastoral land for people and livestock. The region is characterized by a Mediterranean climate, with mild seasons and rains in the winter.

On July 27, 2018, the 2018 River Fire, part of the much larger 2018 Mendocino Complex fire, burned over 3,400 acres of the 5,300-acre Hopland Research and Extension Center. At the time, the Mendocino Complex Fire was the largest fire in California’s recorded history, burning 459,123 acres. The scale and severity of this fire contrasted the historical fire regime in this region which is characterized by frequent, cooler fires in woodlands and infrequent, more severe burns in shrubland habitats (Syphard and Keeley, 2020).

We established a grid of 36 motion-sensor trail cameras (Reconyx Hyperfire HC600), and, for this study, extracted photos taken from March 2016 to December 2020. We placed cameras at the centroid of hexagonal grid cells, where each camera was positioned 750 m apart from its six neighbors. At each grid cell center, we placed a camera at the most suitable location (e.g., game trails) within 50 m of the centroid to maximize the detection probability of species 1m above the ground. We programmed cameras to take 3 photos per trigger. Of the 36 total cameras, 25 were within the fire perimeter of the 2018 River Fire. Seven of these cameras were not operational following the fire and were replaced when conditions were safe to do so in August 2018. For this reason, and due to a natural increase in biodiversity detected in the fall months due to concurrent acorn masting, we restrict our sampling window for analyses to October 1st - November 30th for each year.

The species in all collected images were classified by two independent observers who were members of the Brashares Lab at the University of California - Berkeley. We created species record tables for each year from these cataloged images using the ‘camtrapR’ package in R (Niedballa et al., 2016; Team, R. C, 2020). To create independent detections for analyses, we aggregated images of the same species and site using a 15-minute quiet period.

For this study, we modeled occupancy for the 12 mammal species with 10 or more independent detections across the entire study period: black bear (Ursus americanus), bobcat (Lynx rufus), coyote (Canis latrans), black-tailed deer (Odocoileus hemionus columbianus), gray fox (Urocyon cinereoargenteus), western gray squirrel (Sciurus griseus), California ground squirrel (Otospermophilus beecheyi), black-tailed jackrabbit (Lepus californicus), mountain lion (Puma concolor), wild boar (Sus scrofa), raccoon (Procyon lotor), and striped skunk (Mephitis mephitis).

Usage Notes

This is a repository containing the data described in the Methods section of Mammalian resilience to megafire in western U.S. woodland savannas by Calhoun et al.

Data are divided across the following files.

- Twelve detection histories of the form "[species]_detection_history.csv"
- One metadata file, "stacked_metadata.csv"
- Three auxilliary files, "species_groups.csv", "RAI_detection_table.csv", and
"hopland_mammals.csv"

All files are .csv files and can be opened with Microsoft Excel or any equivalent, as well as any programming language.

These 16 files are described in detail in the accompanying README, a .md file, which can be viewed with any plaintext reader.

Funding

California Department of Fish and Wildlife, Award: P1680002