This dataset details the proportion of the geographic range of 26,062 Australian plant species burnt in the 2019-2020 megafire; threatened listing status on state and Commonwealth threatened species legislation; species endemic status in each state/territory according to the Australian Plant Census; and risk ranking for exposure to high fire frequency (short intervals between fires) and cumulative impacts of fire (populations dominated by immature individuals). Further details are provided in the users should consult and cite the associated paper:

Gallagher, R.V., Allen, S., MacKenzie, B.D.E., Yates, C.D., Gosper C.R, Keith, D.A., 29 Merow, C., White, M., Wenk, E., Maitner, B.S., He, K., Adams, V.M. & Auld, T.D. (2021) High fire frequency and the impact of the 2019-2020 megafires on Australian plant diversity. Diversity & Distributions.

Usage Notes Species names were listed as accepted in the Australian Plant Census as of July 2020. Range data was sourced from three lines of evidence: (1) cleaned occurrence data (latitude-longitude point locations) associated with digitised herbarium specimens accessed from the Australasian Virtual Herbarium (https://avh.ala.org.au/) via the Atlas of Living Australia Application Programming Interface (https://api.ala.org.au/) in July 2020; (2) range mapping built from Poisson Point Process models, range bagging and area of occurrence (AOO) calculations; and (3) maps for Species of National Environmental Significance (SNES) for species listed on the Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) available from the SPRAT database (http://www.environment.gov.au/cgi-bin/sprat/public/sprat.pl). Details of the building of range models are available in the paper associated with this dataset.

The spatial extent of the 2019-2020 fires was quantified using the National Indicative Aggregated Fire Extent Dataset (NIAFED; https://data.gov.au/dataset/ds-environment-9ACDCB09-0364-4FE8-9459-2A56C792C743/details?q=). Geographic ranges were intersected with the NIAFED dataset and proportion of burnt range is reported in the columns: "Proportion of range map burnt", "Proportion of SNES range map burnt (EPBC Act species only) ", and "Proportion of point locations burnt".

Exposure to high fire frequency and the cumulative fire risk rankings were created by intersecting ranges with fire history data for the last 5 years (non-woody species), 15 years (woody species) and 50 years (rainforest trees) and trait data on fire response. Species level data on growth form and fire response traits (resprouter, obligate seeder) were sourced from the AusTraits database (https://www.biorxiv.org/content/10.1101/2021.01.04.425314v1).

The annual spatial extent of fires between September-March between 1969-2018 was quantified by combining data from remote sensing and state agency fire history databases. Remotely sensed data on fire extent in each season between 2003 and 2016 was accessed from the Global Fire Atlas https://www.globalfiredata.org/fireatlas.html and – using the same methods – fire extent data was created for the 2017 and 2018 seasons using imagery from the MODIS product (MCD64A1). Alternate data on annual fire history were accessed under license from environment agency databases in three Australian states – New South Wales (NSW National Parks and Wildlife Service Fire History – Wildfire and Prescribed Burns dataset https://data.nsw.gov.au/data/dataset/1f694774-49d5-47b8-8dd0-77ca8376eb04), Western Australia (Western Australian Department of Biodiversity, Conservation and Attractions Fire History dataset (1969-2020)), and Victoria (Victorian Department of Environment, Land, Water and Planning Fire History dataset). Methods for assigning species ranks are provided in Gallagher (2020) https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf

All correspondence about the dataset should be directed to rachael.gallagher@mq.edu.au. Additional data about fire impacts and threat interactions, as well as code for anlayses, are also available.

Tropical savannas are characterised by high primary productivity and high fire frequency, such that much of the carbon captured by vegetation is rapidly returned to the atmosphere. Hence, there have been suggestions that management-driven reductions in savanna fire frequency and/or severity could significantly reduce greenhouse gas emissions and sequester carbon in tree biomass. However, a key knowledge gap is the extent to which savanna tree biomass will respond to modest shifts in fire regimes due to plausible, large-scale management interventions. Here, we: (1) characterise relationships between the frequency and severity of fires and key demographic rates of savanna trees, based on long-term observations in vegetation monitoring plots across northern Australia; (2) use these relationships to develop a process-explicit demographic model describing the effects of fire on savanna tree populations; and (3) use the demographic model to address the question: to what extent is it feasible, through the strategic application of prescribed burning, to increase tree biomass in Australian tropical savannas? Our long-term tree monitoring dataset included observations of 12,344 tagged trees in 236 plots, monitored for between 3 and 24 years. Analysis of this dataset showed that frequent high-severity fires significantly reduced savanna tree recruitment, survival and growth. Our demographic model suggested that: (1) despite the negative effects of frequent high-severity fires on demographic rates, savanna tree biomass appears to be suppressed by only a relatively small amount by contemporary fire regimes, characterised by a mix of low- to high-severity fires; and (2) plausible, management-driven reductions in the frequency of high-severity fires are likely to lead to increases in tree biomass of about 11.0 t DM ha–1 (95% confidence interval: -1.2–20.8) over a century. Accounting for this increase in carbon storage could generate significant carbon credits, worth on average three times those generated annually by current greenhouse gas (methane and nitrous oxide) abatement projects, and has the potential to significantly increase the economic viability of fire/carbon projects, thereby promoting ecologically sustainable management of tropical savannas in Australia and elsewhere. This growing industry has the potential to bring much-needed economic activity to savanna landscapes, without compromising important natural and cultural values.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

1. Global change is threatening ecosystems and biodiversity worldwide, creating a pressing need to understand how climate and disturbance regimes interact and influence the persistence of species. We quantify how three ecosystem drivers– rainfall, fire and herbivory – influence vital rates in the perennial resprouting graminoid, Triodia scariosa, a foundation species of semi-arid Australia.
2. We used an 11 year data set from a fire and herbivore exclosure experiment, to model flowering, post-fire recruitment and the post-fire survival of seedlings and resprouting plants. Regression modelling quantified the effect of rainfall, inter-fire interval, fire type (wildfire or prescribed fire), grazing by herbivores (native and feral) and an interaction between fire type and herbivory on T. scariosa populations.
3. Rainfall, fire and herbivory had significant effects on post-fire recruitment and the survival of seedlings and resprouting plants, including strong interactions between these drivers. Herbivory following wildfire had a minor effect, but in years of below-average rainfall herbivory following prescribed fire had a large effect, reducing the survival of seedlings and resprouting plants by 20% and over 50% respectively, relative to post-fire survival under average rainfall conditions.
4. Variation in rainfall underpinned significant variation in post-fire resprouting and seedling survival, thus we postulate rainfall primarily drives the dynamics of T. scariosa populations.
5. Synthesis. This study highlights the importance of modelling interactions between key ecosystem drivers when predicting how changes in global climate and disturbance regimes influence plant vital rates. Relatively small changes to disturbance regimes can substantially alter population processes, even in perennial resprouting species. This work suggests that conservation of foundation species, such as T. scariosa, will benefit if fire management decisions are better integrated with inter-annual weather forecasts and herbivore management.

Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire-adapted plants in fire-prone regions such as Mediterranean-type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire-killed shrub Banksia hookeriana dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long-distance dispersal mechanisms of seeds, such as post-fire wind and bird-mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process-based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire-prone ecosystems. Here we extend a recent mechanistic, process-based, spatially implicit population model for the well-studied fire-killed and serotinous shrub species B. hookeriana to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988–2002) and current (2003–2017) climatic conditions, including (i) effects of different spatiotemporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions, and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of B. hookeriana. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter-fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long-term persistence (defined as > 400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, i.e., improved habitat quality in some large habitat patches (≥ seven) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source-sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio-temporal fire regime for B. hookeriana, and likely as well for many other fire-killed species.

This report was prepared by Geoscience Australia for the Bushfire CRC. It is intended that this report be used as part of the background material for the reports prepared for the Royal Commission into the Victorian Bushfires 2009. This report contains a demographic analysis of some of the areas directly affected by the bushfires. The areas included in this report (with alternative fire names in brackets) are: Churchill (Churchill - Jeeralang) Bunyip (Bunyip SP - Bunyip Ridge Trk) Bendigo (Mainden Gully/Eaglehawk - Bracewell St) Kilmore (Kilmore East - Murrindindi Complex South) Murrindindi/Yea (Kilmore East - Murrindindi Complex North) Beechworth Horsham Narre Warren

The Metropolitan Fire and Emergency Services Board (MFB) provides a world- class fire and rescue service. Covering more than 1,000 square kilometres, we protect more than two million residents, …Show full descriptionThe Metropolitan Fire and Emergency Services Board (MFB) provides a world- class fire and rescue service. Covering more than 1,000 square kilometres, we protect more than two million residents, workers and visitors to Melbourne, as well as billions of dollars of assets and infrastructure - 24 hours a day, 365 days a year. MFB firefighters are the first to respond to specific medical emergencies under the Emergency Medical Response First Responder Program (EMR) - a first in Australia. The MFB consistently reports some of the fastest emergency response times and achieves the highest percentage of fire containment to room of origin among all other Australian fire services.

The Metropolitan Fire and Emergency Services Board (MFB) provides a world- class fire and rescue service.

CoveringA more thanA 1,000 square kilometres, we protect more than two million residents, workers and visitors to Melbourne, as well as billions of dollars of assets and infrastructure - 24 hours a day, 365 days a year.

MFB firefighters are the first to respond to specific medical emergencies under the Emergency Medical Response First Responder Program (EMR) - a first in Australia.

The MFB consistently reports some of the fastest emergency response times and achieves the highest percentage of fire containment to room of origin among all other Australian fire services.

Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well-documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long-term impacts and patterns of ecosystem recovery. Here, we investigated the effects of recent bushfires, land-cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (Synthemis eustalacta (Burmeister, 1839)), which inhabits forests that have recently undergone severe burning. We used a temporally dynamic species distribution modeling approach that incorporated 20 years of community-science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. We also compared this to an approach that combines multiple temporally static models that use annual data. We found that for both approaches, fire-specific variables had negligible importance for the models, while percent of tree and non-vegetative cover were the most important. We also found that the dynamic model outperformed the static ones when evaluated with cross-validation. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat but no patterns of habitat expansion, contraction, or shifting. These results highlight not only the efficacy of dynamic modeling to capture spatiotemporal variables, such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records. Methods Occurrence Records We acquired occurrence records of adult S. eustalacta from the Global Biodiversity Information Facility (GBIF, DOI: https://doi.org/10.15468/dl.c256kv). We first subsetted the raw data by selecting occurrence records identified as museum samples, curated research-grade community science observations, and published sightings from scientific surveys. We then filtered out records without coordinate information and those recorded outside the years 2001–2020. Finally, we restricted our analysis dataset to records from scientific institutions (Australian Museum, Queensland Museum, Naturalis Biodiversity Center, Murray Darling Basin Authority), community science websites (iNaturalist, Atlas of Living Australia), and governmental organizations (New South Wales Department of Planning, Industry, and Environment, South Australia Department for Environment and Water). In total, we acquired 483 occurrences (Table 1). Further occurrence filtering consisted of removing sightings with erroneous localities (specimens located at known institutions). Environmental Data We generated yearly sets of environmental predictor variables for modeling that included bioclimatic variables, as well as vegetation cover and seasonal burned area for the years 2001-2020. All analyses were conducted using the statistical programming language R v. 4.1.2 (Team, 2021), and all layers have a geographic coordinate system (i.e., degrees) with a WGS84 datum. We acquired monthly minimum and maximum temperature and precipitation rasters for Australia at 2.5 arcminutes resolution (approx. 5 km at the equator), produced by the Australia Bureau of Meteorology (Jones et al., 2009). From these rasters, we created a set of 19 bioclimatic variables representing means, variabilities, and extremes for temperature and precipitation using the dismo package (Hijmans et al., 2017). Due to known spatial artifacts, we omitted four of these variables that include temperature-precipitation interactions (bio08, bio09, bio18, bio19) from the analysis (Moo-Llanes et al., 2021). We also acquired remotely sensed variables from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) using the MODISstp package (Busetto & Ranghetti, 2016). Landscape variables describing fire and land cover have been shown to be important predictors of dragonfly range (Jolly et al., 2022). Increases in regional fire frequency lead to higher rates of river contamination via burned carbon and metal leaching (Kelly et al., 2020; Nasirian & Irvine, 2017; Nunes et al., 2018). Tree cover heavily affects other odonate species in terms of landscape patchiness (Dolný et al., 2014; Rith-Najarian, 1998; Suhonen et al., 2010; Suhonen et al., 2013). Loss of plant cover due to fires increases ambient temperature, which can drastically affect odonate survival (Castillo-Pérez et al., 2021). Based on this information and on knowledge of the species, we selected variables that we hypothesized were drivers of S. eustalacta distribution (Collins & Mcintyre, 2015; Theischinger & Hawking, 2006): vegetation continuous fields (VCF; defined as percent of pixel covered by each field) for percent tree cover, non-tree cover, and non-vegetated cover (percent tree cover subtracted from percent non-tree cover) (MOD44B, 250 m yearly resolution), annual evapotranspiration (MOD16A3, 500 m yearly resolution), Normalized Difference Vegetation Index (NDVI; MOD13A2, 1 km monthly resolution), and Burned Area data product (MDC64A1, 500 m monthly resolution). We calculated yearly averages of each MODIS variable to capture annual variability and resampled all variables to the coarsest resolution (2.5 arcminutes). Pixel values for Burned Area Product range from 0 (unburned) to 365 (366 for leap year), corresponding to the days of the year. From these data, we generated annual Burned Area layers by converting these values to binary (pixel values >1 = burned, 0 = unburned). Finally, we acquired categorical rasters representing Australia’s major vegetation groups from The Biodiversity and Climate Change Virtual Laboratory (BCCVL, https://bccvl.org.au/), and global terrestrial ecoregions from the World Wildlife Fund (https://www.worldwildlife.org). All raster analyses were conducted with the R package raster v3.5-29 (Hijmans et al., 2021). Species Distribution Modeling Before modeling, we processed our occurrences to account for sampling bias, delineated a study extent to sample background points, and omitted highly correlated environmental variables. We spatially thinned occurrences by 10 km to reduce the effects of sampling bias and artificial clustering (Veloz, 2009) using the spThin package (Aiello-Lammens et al., 2015), which resulted in more even sample sizes across years (n = 133 total; Table 1). Synthemis eustalacta is endemic to southern Australia and disperses roughly 500 m from streams in early adulthood, but upon reaching maturity, returns to its site of emergence (Theischinger & Hawking, 2006). We thus chose a study extent to include potentially unsampled areas yet exclude large areas outside the species’ dispersal limitations (Peterson & Soberón, 2012), defined as a minimum convex polygon around all localities (2001-2020) buffered by 1 degree (approx. 111 km at equator). Within this extent, we randomly sampled 50,000 background points for modeling and extracted their yearly environmental values. We used these values to calculate correlations between variables using the ‘vifcor’ and ‘vifstep’ functions in the usdm package v 1.1-18 (Naimi, 2017) and filtered out variables by year with correlation coefficients higher than 0.9 and a VIF threshold of 10. Finally, we retained variables for analysis which were kept among all yearly environmental backgrounds. To model the distribution of S. eustalacta, we used the presence-background algorithm Maxent v3.4.4 (Phillips et al., 2017), which remains one of the top-performing models for fitting SDMs with background data (Valavi et al., 2021). To automate model building and evaluation with different complexity settings and reporting of results, we used the R package ENMeval 2.0.0 (Kass et al., 2021). We constructed both dynamic models that incorporated data across years and static models that used year-specific data (Fig. 1). For the dynamic models, we extracted the environmental predictor values for each year from the occurrence and background points for that year and assembled them into a single training dataset. To construct a single background point dataset for the dynamic models, we extracted yearly environmental values for the same set of background points, then averaged these values across years per background point (we used the mode for categorical variables; Fig. 1). We evaluated models using random k-fold cross-validation, in which occurrences are randomly partitioned into a specified number of groups (i.e., “folds”), then models are sequentially trained on all groups but one (training data) and evaluated on the withheld group (validation data) (Hastie et al., 2009)—we used four folds (k = 4) for our evaluations. As random partitioning can result in spatial autocorrelation due to clustering within folds, spatial block cross-validation techniques are often prescribed to address this (Roberts et al., 2017). However, as our occurrences varied not only in space but also in time, and as we lacked enough records per time bin to additionally separate by temporal block, we chose to use simpler random partitioning for evaluation. In contrast to the single dynamic model, we also constructed one static model per year that used only occurrence and background environmental values for that year. For this approach, we did not make models for years with fewer than five associated occurrences (Phillips et al., 2017; Phillips & Dudík, 2008). For static models, we partitioned our data using the ‘leave-one-out’ strategy (referred to as “jackknife” in ENMeval), whereby one occurrence record is withheld from each model during cross-validation (k = n, or the number of occurrences). This cross-validation technique is most appropriate for small

Calculations of weighted model averaged coefficients (and standard errors) relative to season of fire, understory type, initial height, and the interaction between fire season and understory type, using all models an Akaike weight ≥ 0.10.