DOI: 10.5061/dryad.5qfttdzgn
title: Widespread resilience of animal species, functional diversity, and predator-prey networks to an unprecedented gigafire

description: Climate change is altering fire regimes globally, leading to an increased
 incidence of large and severe wildfires, including gigafires
 (&gt;100,000 ha), that homogenise landscapes. Despite this, our
 understanding of how large, severe wildfires affect biodiversity at the
 landscape scale remains limited. We investigated the impact of a gigafire
 that occurred during the unprecedented 2019–20 Australian ‘Black Summer’
 on terrestrial fauna. We selected 24 study landscapes, each 3.142 km2 in
 size, that represented a gradient in the extent of high severity fire,
 unburnt vegetation, and the diversity of fire severity classes
 (‘pyrodiversity’). We used wildlife cameras to survey biodiversity across
 each landscape, and quantified species activity, community and functional
 diversity, and predator-prey network metrics. We used Bayesian mixed
 effects models to assess the influence of fire-induced landscape
 properties on these measures. Most native species showed resilience to the
 2019–20 wildfires, displaying few relationships with fire-induced
 properties of landscapes, including the extent of high severity fire,
 unburnt vegetation, or pyrodiversity. Community and functional diversity,
 and measures of predator-prey networks, were also largely unaffected by
 fire-induced landscape properties, although landscapes with a greater
 proportion of high-severity fire had higher abundance and richness of
 introduced animal species. Synthesis and applications. Despite prevailing
 narratives of widespread ecological destruction following the 2019-20
 wildfires, our findings suggest widespread resilience, potentially
 facilitated by evolutionary adaptations of animals to fire. Interventions
 aimed at helping such species recover may not be necessary and could
 instead focus on the subset of species that are vulnerable to severe fire.
 While mixed-severity fires are often advocated to promote biodiversity
 through pyrodiversity, our results suggest that such management efforts
 might not be necessary in our study region. Given that severe fire favours
 introduced animal species, invasive species management should focus on
 large, severely burnt areas.
description: <strong>Materials and Methods</strong>
 Study area We conducted our study across
 1801.1 km<sup>2</sup>, encompassing five protected areas in
 Victoria and New South Wales, Australia (Figure 1). The average annual
 rainfall for the protected areas is 703–886 mm, occurring mainly in winter
 and spring (Bureau of Meteorology 2023). Study areas are characterised by
 eucalypt forest with understories of shrubs and grasses. Between
 29<sup>th</sup> December 2019 and 18<sup>th</sup>
 February 2020, parts of the study area burnt during the Green Valley fire,
 which was part of a larger gigafire (Linley<em> et al.</em>
 2022) in which six fires merged and burnt 632,315 ha (Figure 1). While
 many <em>Eucalyptus</em> spp. woodlands are fire-adapted,
 fires have long lasting impacts nonetheless (Bradstock 2008).
 Landscape selection We selected 24 circular
 study landscapes, each 3.142 km<sup>2 </sup>in size (1 km
 diameter), stratified to capture variation in (i) the extent of unburnt
 vegetation within a landscape, and (ii) the extent of high and very high
 severity fire, and (iii) diversity in fire severity classes, as one
 measure of pyrodiversity (Figure 1). Fire severity maps from the
 Australian Google Earth Engine Burnt Area Map of the 2019–20 wildfires
 (Aus GEEBAM: 32 m resolution) (Department of Agriculture 2020) were used
 to map the extent of unburnt vegetation and fire severities classes within
 each landscape (see supporting information 1). High and very high severity
 (hereafter called high severity) areas were combined as the amount of
 habitat burnt at very high severity was limited and differences between
 the two classes were not always evident in the field. Landscapes did not
 overlap, were located away from farmland and forestry plantations, and
 were within a relatively narrow elevational (~400–1000 m) and average
 rainfall range (807–1150 mm/year). A total of 18 of the 24 landscapes were
 located within the boundaries of the 2019–20 wildfires, while the
 remainder were outside of the fire grounds (Figure 1). Landscapes within
 the fire grounds varied in the proportion of unburnt area (0.01%–62%),
 high severity (0.01%–91%), and the diversity of fire severity classes
 (0.06 H’–1.09 H’) (Figure 1). Within each of the 24 study landscapes, we
 deployed eight cameras (n = 192 cameras in total) (Figure 1). Area
 proportionate sampling was used to allocate cameras within each landscape
 according to the proportion of each burn severity (Figure 1b). Sites were
 located &gt;100 m apart and &gt;50 m from roads and
 tracks. Data collection and processing
 We surveyed terrestrial species using wildlife cameras (Reconyx
 HC600 Hyperfire, Reconyx Inc., USA and Swift Enduro, Outdoor Cameras,
 Australia). Cameras were deployed in October 2021, approximately 20 months
 after the 2019–20 wildfires, until October 2022 (358 days). We alternated
 deployment of the two camera trap types within each landscape. Both camera
 types were programmed to capture five images per burst, with one minute
 time delay between triggers, and camera sensitivity set to high. Cameras
 were mounted to a tree, 50 cm off the ground, facing downwards (20˚ ). A
 lure of tinned sardines, nailed to a stake 20 cm from the ground, was
 positioned 2 m in front of each camera. A cork tile was positioned
 underneath the stake and covered with a mix of tuna and linseed oil,
 sunflower seeds, and honey. Images were processed using Wildlife Insights
 (Ahumada<em> et al.</em> 2020). Animals were identified to
 species level or to the highest level of taxonomic resolution possible
 (typically genus-level). Identification was assisted by Wildlife Insights’
 artificial intelligence (AI), which automatically detects and identifies
 species in images (Ahumada<em> et al.</em> 2020). We treated
 detections as independent events when more than 30 minutes separated
 detections of the same species at each camera (Cunningham<em> et
 al.</em> 2019). The Charles Sturt University Animal Ethics Committee
 provided ethics approval for all fieldwork involving animals (A21031). The
 Department of Environment, Land, Water and Planning approved Research
 Authorisation (10009940) to conduct research at Victorian sites. Access
 agreements for research activities were approved by Parks Victoria to
 operate and research in Victorian national parks. An Aboriginal Cultural
 Heritage Protection Plan (MCT 2308) was approved to operate in Victorian
 sites to ensure that no registered Aboriginal Cultural Heritage sites were
 disturbed. No permit was required to conduct research with camera traps in
 New South Wales National Parks, and access was granted by NSW National
 Parks and Wildlife Service to operate in the New South Wales National
 Parks. A forest research permit (RES100103) was granted by the Forestry
 Corporation to operate in Woomargama State Forest.
 Response variables To measure the activity of
 individual species, we summed the number of independent events of each
 species across all cameras within each study landscape over the duration
 of the study. This index serves as a proxy for species abundance
 (Kenney<em> et al.</em> 2024). Individual species were
 modelled if recorded in at least five landscapes. We calculated species
 richness (i.e., count of species per landscape) and Shannon’s diversity H’
 (using the exponential of Shannon entropy) for all native and introduced
 species, and for native and introduced mammals. Second,
 we compiled species trait data and calculated the functional richness,
 dispersion, and evenness of native and introduced species within each
 landscape. Functional richness is the diversity of trait composition
 within an ecological community (Cooke<em> et al.</em> 2019),
 and measures the range of different traits present in a landscape (see
 supporting information 2). Functional dispersion refers to the average
 trait dissimilarity among species (Cooke<em> et al.</em>
 2019), and provides insight into how varied traits are within a landscape
 (see supporting information 2). Functional evenness is the degree of
 uniformity in the distribution of trait values among species within an
 ecological community (Mason<em> et al.</em> 2005), and
 assesses how evenly traits are spread across the landscape, providing
 insights into the balance of ecological functions within a community (see
 supporting information 2). To assess the impact of fire
 severity on predator-prey metrics, we calculated five network metrics:
 link density, throughflow, the number of interactions, connectance, and
 compartmentalisation (see supporting information 3) for each landscape
 using the <em>omnivore</em> package (Clément and Dominique
 2019). We compiled dietary information of identified species from
 published databases and literature, compiling predatory links between
 co-occurring predators and prey at each landscape (see supporting
 information 3, Table S2, Table S3). As diets were not quantified, we
 reconstructed predator-prey interactions based on publicly available diet
 data, linking local predator diets with prey they co-occurred with
 (O'Connor<em> et al.</em> 2024), which can provide
 insight into trophic networks (Lu<em> et al.</em>
 2023). Predictor variables Two
 predictor variables measured the extent of fire severity classes: the
 proportional extent of unburnt vegetation within the landscape and the
 extent of high severity classes. We quantified fire-severity-induced
 pyrodiversity across each landscape by calculating the Shannon diversity
 index (H’) based on the area of unburnt, low/moderate, and high fire
 severity classes. We also considered the longer-term fire history of each
 landscape by calculating Shannon’s diversity index of all historical fires
 (1903–2020, see supporting information 2) within each landscape. Finally,
 we selected four covariates to capture the environmental variation:
 distance to fire perimeter, normalised difference vegetation index (NDVI),
 elevation, and terrain ruggedness (see supporting information
 4). Data analysis We fit Bayesian
 mixed effects models using the package <em>brms</em> (Bürkner
 2017), which uses a Bayesian approach in <em>stan</em> to
 compile models to sample from posterior distributions (Stan Development
 Team 2022) in R ver. 4.2.1 (R Core Team 2024). In total, we fit eight
 models per response variable: three models to explore the extent of high
 fire severity, unburnt areas (areas that escaped fire within the fire scar
 and unburnt controls outside of the fire scar), and fire severity
 diversity, and three models to assess how this changed over the three time
 periods, as well as one model without a fire severity measurement, and one
 model without a fire severity measurement but still including the three
 periods. In these models, time was a three-level categorical predictor:
 period one (119 days, from October 2021 to February 2022), period two (119
 days, from February 2022 to June 2022), and period three (118 days, from
 June 2022 to October 2022). Common to all models were
 predictor variables including distance to fire perimeter, NDVI, elevation,
 and terrain ruggedness. <strong>Predictor variables were
 standardised by centring on the mean and scaling to unit
 variance.</strong><strong> </strong>All models included
 a random effect to account for the clustering of landscapes in three
 protected areas. Models that included ‘time’ also included ‘landscape’ as
 a random effect, and models that were over-dispersed included an
 observation-level random effect (Harrison 2014). Varying survey effort
 (due to occasional camera failure) was accounted for by including the log
 of deployment days per landscape as an offset. Species data and one
 predator-prey metric measure was analysed using Poisson distributed
 models, richness, diversity, and predator-prey metric measures were
 evaluated with Gaussian distributed models, and two binomial predator-prey
 metrics measures were assessed using Beta distributed models. We used
 weakly informative priors with a student’s t-distribution with four
 degrees of freedom, centred at 0, and a scale of 1, for both fixed and
 random effects, to aid with model convergence (Bürkner 2017). Model fit
 was assessed using Bayesian R<sup>2</sup> (Table S4). Models
 were ranked using WAIC, and a weighted average of coefficients was
 calculated through model averaging to assess the magnitude and direction
 of effect sizes. Model parameters were regarded as showing evidence of an
 effect when the 89% credible interval did not overlap zero (McElreath
 2020). References Ahumada JA,
 Fegraus E<em>, et al.</em> (2020) Wildlife Insights: A
 platform to maximize the potential of camera trap and other passive sensor
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 south-eastern Australia: disaster or template for diversity?
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description: **Widespread Resilience of Animal Species, Functional Diversity, and
 Predator-Prey Networks to an Unprecedented Gigafire** This dataset
 encompasses comprehensive surveys of animal species across three
 wilderness areas affected by an unprecedented gigafire. It includes data
 on species richness, functional diversity, predator-prey interactions, and
 various environmental variables such as fire severity, vegetation types,
 and historical fire events. The data were collected to analyse the
 resilience of animal communities and ecosystem functions following
 large-scale fire disturbances. **Description of the Data and File
 Structure** **data_frame_landscapes_time_dryad**: This is the primary data
 frame used for analysis, containing observational data across different
 landscapes and time periods. **Variables in data_frame_landscapes_time**
 Below is a detailed description of the variables included in the
 data_frame_landscapes_time data frame: * **location**: Descriptor of which
 of the three wilderness areas the survey location was. * **Obs**:
 Observational level random effect label. * **landscape**: Identifier
 number for each landscape surveyed. * **total_deployment_days_period**:
 Number of deployment days for each camera, specific to each of the three
 periods. * **period**: The time period during which data was collected,
 divided into three distinct periods. * **total_cameras**: Total number of
 cameras deployed in each landscape. * **camera_enduro**: The proportion of
 the Enduro Swift camera type. * **camera_reconyx**: The proportion of the
 Reconyx camera type. **Fire Severity Information** * **no_data**: Fire
 severity information not available. * **unburnt**: Areas that were
 unburnt. * **low/moderate**: Areas with low to moderate fire severity. *
 **high**: Areas with high fire severity. * **very_high**: Areas with very
 high fire severity. * **high_and_very_high**: Combined data for areas with
 high and very high fire severity. * **control**: Control areas used for
 comparison in fire severity studies. * **shannon_fire_severity**:
 Diversity index of fire severity types in each landscape, representing
 pyrodiversity. **Environmental Variables** * **distance_to_perimetre**:
 Distance to the edge of the unburnt fire perimeter (in metres). *
 **elevation**: Average elevation of the landscape (in meters). *
 **rainfall**: Average annual rainfall of the landscape (in millimeters). *
 **ndvi**: Average Normalized Difference Vegetation Index of each site,
 representing vegetation health. * **terrain_ruggedness**: Calculation of
 the terrain ruggedness of each landscape. * **fire_count**: Number of
 historical fires in each landscape. **Vegetation Types** Indicators for
 specific vegetation types present in the landscape. Vegetation types taken
 from 'NVIS 6.0 Major Vegetation Subgroups' available from the
 Australian Government, Department of Climate Change, Energy, the
 Environment and Water. * **vegetation_4**: Eucalyptus open forests with a
 shrubby understorey * **vegetation_5**: Eucalyptus open forests with a
 grassy understorey * **vegetation_59**: Eucalyptus woodlands with ferns,
 herbs, sedges, rushes or wet tussock grassland * **vegetation_60**:
 Eucalyptus tall open forests and open forests with ferns, herbs, sedges,
 rushes or wet tussock grasses * **vegetation_32**: Other shrublands *
 **vegetation_8**: Eucalyptus woodlands with a shrubby understorey *
 **open_forest**: A summary of all forest vegetation type. * **woodlands**:
 A summary of all woodland vegetation type. **Historical Fire Data** *
 **1903, 1939, ..., 2020**: Proportion of each landscape burnt in
 historical fires, represented by the year of the fire. *
 **shannon_fire_size_diversity**: Diversity index of the amount of each
 landscape burnt from all historical fires. **Biodiversity Metrics** *
 **richness**: Species richness of all species recorded in a landscape. *
 **native mammal richness**: Richness of all native mammal species recorded
 in a landscape. * **introduced mammal richness**: Richness of all
 introduced mammal species recorded in a landscape. *
 **shannon_species_diversity**: Shannon diversity index of all species
 recorded in a landscape. * **shannon_native_mammal**: Shannon diversity
 index of all native mammal species. * **shannon_introduced_mammal**:
 Shannon diversity index of all introduced mammal species. **Functional
 Diversity Metrics** * **Fric_native_mammal**: Functional richness of
 native mammals. * **FDis_native_mammal**: Functional dispersion
 (diversity) of native mammals. * **FEve_native_mammal**: Functional
 evenness of native mammals. * **Fric_introduced_mammal**: Functional
 richness of introduced mammals. * **FDis_introduced_mammal**: Functional
 dispersion of introduced mammals. * **FEve_introduced_mammal**: Functional
 evenness of introduced mammals. **Predator-Prey Network Metrics** *
 **num_interactions**: Number of interactions observed in each landscape. *
 **connectance**: Connectance of the predator-prey network in each
 landscape. * **link_density**: Link density of the network in each
 landscape. * **compartmentalisation**: Compartmentalization of the
 network, indicating modularity. * **throughflow**: Throughflow metric for
 each landscape, representing energy flow in the network. **Species data**
 * Individual species data shows the number of 30-minute events at each
 landscape in each period. **Sharing/Access Information** The dataset can
 be accessed through the Dryad digital repository: *
 [https://doi.org/10.5061/dryad.5qfttdzgn](https://doi.org/10.5061/dryad.5qfttdzgn)