The Australian Google Earth Engine Burnt Area Map (AUS GEEBAM) is a rapid, national approach to fire severity mapping. It has been developed rapidly to support the immediate needs of the Department of Climate Change, Energy, the Environment and Water (DAWE) in:a) quantifying the potential impacts of the 2019/20 bushfires on wildlife, plants and ecological communities, andb) identifying appropriate response and recovery actions.AUS GEEBAM Fire Severity uses Sentinel 2A satellite imagery from before and after fire to estimate the severity of burn within each 40m grid cell. Fire severity is defined as a metric of the loss or change in organic matter caused by fire.The extent of the 2019/2020 fires was derived from the National Indicative Aggregated Fire Extent Dataset (NIAFED). NIAFED was sourced from the national Emergency Management Spatial Information Network Australia (EMSINA) data service, which is the official fire extent currently used by the Commonwealth and adds supplementary data from other sources to form a cumulative national view of fire extent.AUS GEEBAM relies on a vegetation index (Relativised Normalized Burnt Ratio, RNBR) that is calculated for burnt areas and adjacent unburnt areas, before and after the fire season. The result is a map of four fire severity classes that represent how severely vegetation was burnt during the 2019/2020 fires.To determine a reference unburnt condition, the NIAFED extent was buffered by 2km. For each NVIS broad vegetation type, in each IBRA bioregion a reference unburnt RNBR class was determined. That value was available to calculate a standardised offset or a reference unburnt value.Each IBRA bioregion was systematically assessed to correct for obvious errors. For example, the Very High severity class could be adjusted down by one RNBR Value for a fire where its extent extended into an area of lower severity. Conversely, there were areas of shrublands that had clearly burnt at Very High severity where all of the biomass is likely to have been consumed but low pre-fire biomass had given it a lower RNBR Value.Each pixel of AUS GEEBAM contains the raw RNBR Value, the RNBR Class and the GEEBAM Value. This allows an end user to observe which values have been adjusted during the calibration away from the default global RNBR Value and allows for some transparency in the process.GEEBAMValueGEEBAM ClassDescription1No dataNo data indicates areas outside NIAFED or NVIS categories that do not represent native vegetation (e.g. cleared land, water)2UnburntLittle or no change observed between pre-fire and post-fire imagery.3Low and ModerateSome change or moderate change detected when compared to reference unburnt areas outside the NIAFED extent.4HighVegetation is mostly scorched.5Very highVegetation is clearly consumed.Known Issues:The dataset has a number of known issues, both in its conceptual design and in the quality of its inputs. These are outlined below and should be taken into account when interpreting the data and developing any derived analyses.The list of known issues below is not comprehensive, it is anticipated that further issues will be identified, and the Department welcomes feedback on this. We will seek as far as possible to continuously improve the dataset in future versions.AUS GEEBAM classes are not based on field data and no confidence interval or report on accuracy has been provided.The number of severity classes has been reduced by combining low and moderate severity fires. Single index thresholds are known to feature poor delineation of low fire severity classes.AUS GEEBAM classes are calibrated systematically for each bioregion using visual interpretation of Sentinel 2 false colour composites. The limitations associated with the NIAFED are carried through to this dataset. Users are advised to refer to the NIAFED documentation to better understand limitations.This continental dataset includes large burnt areas, particularly in northern Australia, which can be considered part of the natural landscape dynamics. For the intended purpose of informing on the potential impact of fire on the environmental, some interpretation and filtering may be required. The NIAFED dataset used as the extent layer for AUS GEEBAM Fire Severity is current as of 24 February 2020. More recent versions were available at the time of creation, however, these would have introduced burnt areas from a second fire season in Northern Australia where fire patterns differ greatly to that of southern Australia.NOTE: Report methodology and supporting material is available on request to geospatial@dcceew.gov.auTo download this data go to AUS GEEBAM download file

Climate change is altering fire regimes globally, leading to an increased incidence of large and severe wildfires, including gigafires (>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. Methods Materials and Methods Study area We conducted our study across 1801.1 km2, 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 29th December 2019 and 18th February 2020, parts of the study area burnt during the Green Valley fire, which was part of a larger gigafire (Linley et al. 2022) in which six fires merged and burnt 632,315 ha (Figure 1). While many Eucalyptus spp. woodlands are fire-adapted, fires have long lasting impacts nonetheless (Bradstock 2008). Landscape selection We selected 24 circular study landscapes, each 3.142 km2 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 >100 m apart and >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 et al. 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 et al. 2020). We treated detections as independent events when more than 30 minutes separated detections of the same species at each camera (Cunningham et al. 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 et al. 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 et al. 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 et al. 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 et al. 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 omnivore 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 et al. 2024), which can provide insight into trophic networks (Lu et al. 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

Context: Sound taxonomy is the cornerstone of biodiversity conservation. Without a fundamental understanding of species delimitations, as well as their distributions and ecological requirements, our ability to conserve them is drastically impeded. Cryptic species – two or more distinct species currently classified as a single species – present a significant challenge to biodiversity conservation. How do we assess the conservation status and address potential drivers of extinction if we are unaware of a species’ existence? Here, we present a case where the reclassification of a species formerly considered widespread and secure – the sugar glider (Petaurus breviceps) – has dramatically increased our understanding of the potential impacts of the catastrophic 2019–20 Australian megafires to this species. Methods: We modelled and mapped the distribution of the former and reclassified sugar glider (Petaurus breviceps). We then compared the proportional overlap of fire severity classes between the former and reclassified distribution, and intersect habitat suitability and fire severity to help identify areas of important habitat following the 2019–20 fires. Key Results: Taxonomic revision means that the distribution of this iconic species appears to have been reduced to 8% of its formerly accepted range. Whereas the 2019–20 Australian megafires overlapped with 8% of the formerly accepted range, they overlapped with 33% of the proposed range of the redefined Petaurus breviceps. Conclusions: Our study serves as a sombre example of the substantial risk of underestimating impacts of mega-disturbance on cryptic species, and hence the urgent need for cataloguing Earth’s biodiversity in the age of megafire.,Methods Occurrence data Occurrence records of P. breviceps were collected from the Atlas of Living Australia (https://www.ala.org.au) and were subject to a filtering process. Because sugar gliders were introduced to Tasmania (Campbell et al. 2018), we excluded all Tasmanian records. We then removed dubious records by clipping all records to either the former P. breviceps range (based on IUCN maps; IUCN 2020) or the proposed reclassified P. breviceps range (Cremona et al. 2021) to create two sets of occurrence data (i.e., one each for the former and reclassified P. breviceps). It is worth noting, however, that the reclassified distribution of P. breviceps proposed by Cremona et al. (2021) is an estimate based on genetic and morphological data. Although evidence currently suggests that the Great Dividing Range acts as the western edge of the distribution of P. breviceps (Cremona et al. 2021), we cannot be certain of this. However, for the purposes of this study we have assumed it to be so. In both datasets, records were removed if: (i) they were missing date information or were collected before the year 2000; or (ii) they had high locational uncertainty (e.g., vague or inaccurate locations). Records within any 1 × 1 km grid cell were collapsed into a single record. The final filtered data base consisted of 7777 presence records within the formerly considered geographic range, and 5089 within the reclassified range (see Figure S1). Geographic range estimation We mapped the extent of occurrence (EOO) of the former and reclassified P. breviceps using the occurrence datasets. Extent of occurrence is defined as the area enclosed by the shortest possible boundary containing all sites in which a species is known to be present (IUCN 2021). We calculated EOO as α‐hulls (a generalisation of convex polygons that allow for breaks in species ranges), using the ‘alphahull’ package in R version 3.6.2 (R Core Team 2021), specifying a α value of two (IUCN 2021). We regarded EOO as preferable to area of occupancy (AOO) because maps of the latter showed clear spatial bias indicated by high densities of records surrounding major capital cities. Species distribution modelling Using the maxent algorithm, we developed species distribution models (SDMs) based on the two occurrence datasets outlined above (Phillips et al. 2006). We selected SDM environmental layers based on their likely importance to P. breviceps habitat suitability. All environmental layers were resampled to 1 × 1 km resolution prior to being included in models. A set of 10,000 background points were included within the SDM to compare densities in environmental values occupied by P. breviceps with those of the surrounding unoccupied environment. We addressed sample bias within the study area with a ‘target group’ background sampling approach (Phillips et al. 2009) (see Figure S1). We defined the target group as arboreal mammal species occurring within the study area, including P. breviceps. Sampling intensity for target group species was mapped by converting species presence records of the target group to a kernel density map using the kde2d function of the ‘MASS’ package (Venables and Ripley 2002) set with the default kernel bandwidth. Model performance was measured as area under the curve (AUC) of the receiver operating characteristic (ROC) plot, and the contribution of environmental variables to the response variable was measured as permutation importance (Phillips 2005). Fire overlap We overlapped the former and reclassified P. breviceps EOO with 2019–20 bushfire severity maps from the Google Earth Engine Burnt Area Mapping (GEEBAM; DIPE 2020). GEEBAM classifies the cells within the fire boundary as one of five fire severity classes: no data (cleared land, water etc.); unburnt (unburnt and lightly burnt); low and moderately burnt (some or moderate change post-fire); high severity (vegetation mostly scorched); and very high severity (vegetation clearly consumed). When calculating fire overlap, we considered only fires occurring within the Department of Agriculture, Water and Environment’s (2020) ‘preliminary area for environmental analysis’ (following Legge et al. 2020). This area encompasses bioregions that were deemed to have experienced anomalously substantial fire activity during the 2019–20 bushfire season. Overlap measures were calculated using QGIS version 3.14.1 (QGIS Development Team 2021). We created a fire severity × habitat quality matrix to help identify the spatial intersection between fire severity and habitat quality for the reclassified P. breviceps. First, we classified the continuous output of relative habitat quality derived from the SDM into four discrete classes: low quality (relative likelihood of occurrence 0–0.25); low–medium quality (relative likelihood of occurrence 0.25–0.50); medium–high quality (relative likelihood of occurrence 0.5–0.75); and high quality (relative likelihood of occurrence 0.75–1). We then combined the reclassified SDM with the GEEBAM fire severity layer to derive a layer with 16 unique combinations of all combinations of habitat quality and fire severity and mapped this across the range of P. breviceps.,

Data acquisitionOccurrence data for bee species were downloaded from ALA60 using ALA4R version 1.8.064 in R version 3.6.265.Floral visitation data were obtained from ALA60, Museums Victoria, the Western Australian Museum66,67, and publications (Tables S1 and S2). Floral visitation records were checked for errors and synonymies using the Australian Plant Name Index68. Life-history traits for bee species were sourced, in most cases, from the most recent taxonomic descriptions, or other publications (Tables S1 and S2). A one-hectare resolution Major Vegetation Subgroup (MVS) map was sourced from Geoscience Australia’s National Mapping Division (NMD)61. Fire frequency data from 1988 to 2016 were downloaded from the Department of Environment and Energy (DEE)69, 2019–20 wildfire occurrence data (National Indicative Aggregated Fire Extent Dataset — NIAFED — version 20200623) were sourced from the Department of Agriculture, Water and the Environment (DAWE)36, and 2019–20 wildfire intensity data (Google Earth Engine Burnt Area Map — GEEBAM) were sourced from the Department of Planning, Industry and Environment (DPIE)62. All raster data sources were matched in resolution to the one-hectare MVS map. These GIS data sources may vary in spatial uncertainty or resolution and their caveats can be found at their respective locations online.Data filtering and analysesOccurrence data from ALA were filtered to include only reliable (“preserved specimens”, “machine observations” — e.g., malaise traps, — and data from published datasets) and “present” (compared to “absent”) records. Records without geographic locations or that did not align with base maps were excluded from GIS analyses. Species were then filtered for minimum sample size (n = 30) and minimum number of unique localities (n = 5). However, if there were 15 or more unique localities and a sample size of less than 30, the species was included.The MVS map was reprojected to a world geodetic system (WGS 1984, EPSG:4326) and clipped to the 2019–20 wildfire map in QGIS version 3.1270. The NIAFED and GEEBAM maps were aligned and matched to the resolution of the MVS map using the package raster version 3.0-1271 in R version 3.6.265. Major vegetation subgroups61, 2019–20 wildfire status36, and fire frequency69 were extracted for each ALA record using raster. The proportion of each MVS burnt was calculated by clipping MVS maps with the 2019–20 burn map in ArcMap Version 10.6.172. All map files used in our analyses are available at (html location to be confirmed upon acceptance) for use with our R script.We complemented species distributional data (ALA60 point data) with spatial information on their associated habitat (MVS61), to avoid reliance on the limited data for some species. To determine the potential distribution of each species we buffered the latitudinal and longitudinal extents of the raster datasets (MVS, fire frequency, NIAFED, and GEEBAM) by 20% in each direction. For geographically-restricted species with latitudinal or longitudinal ranges less than one degree (~111 km), we buffered their extent by one degree in each direction along that axis or axes. These values were chosen as conservative estimates of species distributional extents, but we recognize that this treatment may over-inflate the distribution of some species with highly-localized ranges. These data are broken into four files:Map_data — hosts all of the map files used in the analysesBee-plant_point_data — hosts the ALA download data, combined bee dataset, and the life history and plant data spreadsheetWard_comparison_data — hosts some of the data used for the Ward co-analysis using our methodAll_other_R_data — hosts many of the runfiles from our main analysis

Aim: Catastrophic events such as south-eastern Australia's 2019/20 megafires are predicted to increase in frequency and severity under climate change. Rapid, well-informed conservation prioritisation will become increasingly crucial for minimising biodiversity losses resulting from megafires. However, such assessments are susceptible to bias, because the quality of monitoring data underpinning knowledge of species' distributions is highly variable and they fail to account for differences in life-history traits such as aggregative breeding. We aimed to assess how impact estimates of the 2019/20 megafires on the critically endangered regent honeyeater Anthochaera phrygia varied according to the quality of available input data and assessment methodology.

Innovation: Using Google Earth Engine Burnt Area Mapping, we estimated the impact of the megafires on the regent honeyeater using six monitoring datasets that differ in quality and temporal span. These datasets are representative of the variable quality of monitoring data available for assessing fire impact on 326 other threatened species; most are poorly monitored and few have standardised, species-specific monitoring programs. We found that assessments based on Area of Occupancy (AOO), Extent of Occurrence (EOO) and public sightings underestimated the fire impact relative to recent, targeted monitoring datasets; a MaxEnt model, sightings from a national monitoring program and nest locations since 2015. Using an impact threshold of 30% of habitat burned, regent honeyeaters would not meet this criteria using estimates derived from EOO, AOO or public sightings, but would exceed the cut-off based on estimates derived from the targeted monitoring data that account for population density.

Main conclusions: To ensure that conservation prioritisation has the greatest capacity to minimise biodiversity losses, we highlight the need to improve targeted, threatened species monitoring. We demonstrate the importance of using recent, standardised monitoring data to estimate accurately the impact of major ecological disturbances, particularly for declining, nomadic species undergoing range contractions.