DOI: 10.5061/dryad.905qftts8
title: After the ‘Black Summer’ fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type

description: Climate change and human activities have disrupted historical fire
 regimes, leading to complex and far-reaching impacts on global ecosystems.
 Despite extensive research in fire ecology, studies exploring vertebrate
 responses to megafires, and to nuanced fire characteristics, remain
 limited. We collected camera trap data 3–27 months following Australia’s
 2019–20 ‘Black Summer’ megafires from 30 burnt sites and 10 unburnt sites.
 Our data included 14 animal species/groups, encompassing mammalian
 predators, small and medium-sized mammals, large herbivores, and birds. We
 used generalised additive mixed models to assess the influence of
 time-since-the-fires, burn status, fire severity, proportional area burnt,
 and vegetation type on species' activity. Models that included fire
 variables were well-supported for all species. The proportional cover of
 low-moderate or high-extreme severity fire had substantial support for
 five species, particularly herbivores, which generally showed a preference
 for burnt sites but at differing fire severities. The proportional area
 burnt, disregarding severity, was well supported for four species. At
 highly burned sites, fox activity peaked shortly after the fires, while
 small to medium-sized mammal activity increased more gradually. Vegetation
 type strongly influenced the response of four species to fire; in
 particular, wet forest birds preferred unburnt areas. Policy implications.
 We document variable short- to medium-term responses of a range of species
 to fire which could help guide management interventions. We demonstrate
 that animal species’ responses to fire are diverse and better captured
 using broader landscape-scale fire variables. We found that species were
 strongly influenced by proportional area burnt, fire severity, and
 vegetation type. Introduced foxes were attracted to recently burnt areas,
 so timely predator control may benefit vulnerable prey species. Wet forest
 species were sensitive to fires and could benefit from preservation and
 restoration of these habitats. Some species exploited low-moderate
 severity burnt areas, while others preferred high-severity burns. This
 suggests that species will face diverse challenges and opportunities in
 future extreme fire events. We emphasise the importance of using
 multi-faceted approaches to account for the complex responses of
 co-occurring species to fire events.
description: # After the ‘Black Summer’ fires: faunal responses to megafire depend on
 fire severity, proportional area burnt, and vegetation type
 [https://doi.org/10.5061/dryad.905qftts8](https://doi.org/10.5061/dryad.905qftts8) This dataset encompasses independent species observations collected 3–27 months following 2019–20 Australian 'Black Summer' megafires. The data were collected using motion-sensing cameras across 40 sites, of which 30 were directly affected by the fires and 10 were unburnt control sites. Independent observations were defined as images of the same species captured on the same camera, separated by at least 60 minutes. The dataset includes an array of vertebrates, including mammalian predators, small to medium-sized mammals, large herbivores, and various avian species. We used generalised additive mixed models (GAMMs) to analyse the dataset. We specifically looked at the impact of different predictor variables on species responses to the fires, including time since the fires, burn status, fire severity, the proportional area burnt, and vegetation type. ## Description of the data and file structure The dataset is made up of two main files: one is the species record table which includes all of the independent observations, and the other is the camera variables file that includes camera locations, operation times, and variables. Description of camera variables columns: * Station: camera site name. * Session: 24 one-month survey periods, based on the number of months the cameras were operating post-fire. Each month was based off the StartDate and EndDate columns. * Elevation: elevation at camera station in meters. * MonthsPF: time since the fires in months. * Problem1\_from and Problem1\_to: start and end dates, respectively, of any periods during which the camera was not operating due to malfunction, within each monitoring Session, mainly due to malfunction. Used to account for trap effort. Blank cells within these columns suggest camera was operating normally. * BurnStatus: whether a site was burnt or not within the immediate vicinity of the camera. ## Sharing/Access information We also used data that is not in this dataset from the following sources: * Department of Planning, Industry and Environment, 2022. NSW State Vegetation Type Map. [https://datasets.seed.nsw.gov.au/dataset/nsw-state-vegetation-type-map](https://datasets.seed.nsw.gov.au/dataset/nsw-state-vegetation-type-map) * Department of Planning, Industry and Environment, 2020. Fire Extent and Severity Mapping. [https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severity-mapping-fesm](https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severity-mapping-fesm) ## Code/Software We used QGIS to map the camera sites and overlayed them with the fire severity and vegetation type layers. We used QGIS to create the buffers and exported the proportion burnt, fire severity, and vegetation type data as CSV files for analysis—these values are included in the camera variables file. Data clean up and analysis were completed using R v.4.3.1. * We processed the camera image metadata and generated the species record table using the 'camtrapR' package. Jürgen Niedballa, Rahel Sollmann, Alexandre Courtiol, Andreas Wilting (2016). camtrapR: an R package for efficient camera trap data management. Methods in Ecology and Evolution 7(12), 1457-1462. * GAMM analysis was completed using the 'mgcv' package. Wood, S.N. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC.