Search

Excellence, professional development, and educational leadership – all of these terms can be seen as unstable, dereferentialised, or empty signifiers - as their meaning (or the work they do) is not fixed. However, in spite of this, they have become ‘part and parcel’ of educational leadership in the Enlightenment institutions of the ‘not so’ modern universities and schools, which are in ruins. These terms are products of what Foucault (1977) termed regimes of truth, and they have become focused on meeting the perceived needs of the neoliberal marketplace. In this paper I address the regime of truth that is entitled: – Educational Leadership. Some have called this turn the ‘Learning Paradigm’ or the ‘Learnification’ of higher education. In order to reveal how this move is made possible, I have drawn upon the work of Hargreaves (1998) and Cary (2004) to investigate this epistemological construction with an increasing sense of urgency. Indeed, as a cis-gendered white woman and leader in learning and teaching I have turned back to the poststructural feminist theoretical understandings that informed my earliest work to theorise what ‘messy leadership’ might look like in this space, as a strategic move to work within/against these external reductive forces. We need to interrupt totalizing and exclusivist regimes of truth and I believe Messy Leadership has the potential to contribute to this. This helps move the discussion into the current context which I suggest is a major ‘legitimation crisis’. Sadly, this historic moment has revealed not only how unstable the notion of educational leadership is, but also how the current moves at work to stabilize and constrain leadership can be seen as a marketplace response. Finally, I suggest it is time to address the elephant in the room - if educational leadership is unstable and in crisis, how might we make use of a Messy Leadership to interrupt specific regimes of truth? As Manalansan (2014) reminds us, ““mess is seen not as aberrant but rather as constitutive of social realities and systems” (p. 99). By bringing this lens to our leadership work we can reveal the technologies of power at play, interrupt exclusive and reductionist understandings and create new spaces in leadership. We need to ensure previously erased stories and subjects are made visible and celebrated. This grounded approach to understand leadership ‘from below’, to listen carefully and constructively and to reorienting our stance as leaders has the potential to produce significant shifts in what it means to lead, by interrupting the masculinist dominant subjectivities of educational leadership.

The McArthur grassland and forest fire danger indices, widely used in Australia, predict six fire danger classes from ‘Low-Moderate’ to ‘Catastrophic.’ These classes were linked to the rate of fire spread and difficulty of suppression. However, the lack of rate of fire spread data, especially for elevated fire danger classes, has hindered improvement of the McArthur methodology or an alternate approach. We explored the relationship between fire danger classes and burned areas (derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument) within six climate zones during the 2000–2016 Australian fire seasons. A negative binomial linear regression model was used to explore this relationship. The fire danger classes demonstrated a corresponding increase in burned area from ‘Low-Moderate’ to ‘Very High’ classes in Australia’s inland regions. The elevated fire danger classes did not contribute to this trend. In coastal regions, the satellite-derived burned area showed no relationship between fire danger classes and satellite-derived burned area. We used accumulated burned area from the daily MODIS product, which could be subjected to lagged detection as observed in the Kilmore East fire. Thus, the satellite-derived total burned area may not be a suitable metric for informing the McArthur fire danger classes across Australia.

Analyses of the effects of topography, weather, land management, and fuel on fire severity are increasingly common, and generally apply fire severity indices derived from satellite optical remote sensing. However, these indices are commonly interpreted with insufficient appreciation for their limitations and may be inappropriately invoked as representing physical fire effects and fire behaviour. For a large wildfire in southeast Australia, we investigated three considerations for inferring robust insights from fire severity analyses – the potentially confounding influences of pre-fire vegetation height and tall vegetation cover, and the choice of fire severity response variable. Using nonparametric regression, we found that different fire severity indices gave rise to substantially different modelled relationships with commonly invoked environmental predictors, as is consistent with dissimilarities in index design. Further, pre-fire vegetation height was a strong control of fire severity, with equivalent importance to weather. Importantly, strong covariation between vegetation height and environmental predictors suggests that modelled fire severity effects are strongly influenced by variation in vertical distance between flames and vegetation, and this confounds fire behaviour insights. To enable more robust and mechanistic insights into the determinants of fire severity, we recommend greater consideration of the meaning and limitations of optical remote sensing indices.

Spectral indices derived from satellite optical remote sensing data have typically been used for fire severity estimation, although other remote sensing systems such as Light Detection and Ranging (LiDAR) are increasingly applied. Despite a multitude of remotely sensed fire severity estimation methods, comparisons of method performance are few. Insights into the merits and limitations of remotely sensed fire severity methods help develop appropriate spatial tools for the management of fire-affected areas. We evaluated the performance of seven passive (optical) and active (LiDAR) remotely sensed fire severity estimation methods in classifying and explaining variation in a field-estimated modified Composite Burn Index (MCBI) for a recent large wildfire in south-eastern Australia. Our evaluation included three commonly applied indices; the differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR) and Relative Burn Ratio (RBR). We compared these NBR indices against two recently proposed fire severity estimation methods that have not previously been evaluated with CBI field data–the Vegetation Structure Perpendicular Index (VSPI) spectral index and the LiDAR point cloud-derived Profile Area Change (PAC), along with experimental relativized forms of these indices (RVSPI and RPAC, respectively). The RVSPI (κ = 0.47) demonstrated similar overall classification accuracy (N classes = 4) to the PAC (κ = 0.48), however both indices had lower classification accuracy than the dNBR (κ = 0.59), RdNBR (κ = 0.59) and RBR (κ = 0.61). The VSPI and PAC were unable to accurately represent non-structural changes caused by lower severity fire. Application of these optical and LiDAR indices should consider their discussed limitations in relation to the objectives of their application.

The current historic COVID-19 Pandemic moment has thrown into sharp relief the need for flexible and rigorous higher education that meets upskilling and reskilling needs of global workforces. Discussions of micro-credentialing predate the Pandemic but have received increased focus as potentially assisting in addressing perceived skills gaps. However, not all commentators have been complimentary about the possibilities inherent in micro-credentialing. In this paper we discuss Ralston (Postdigital Science and Education 3:83–101, 2021) criticism of the “microcredentialing craze” as provocation to consider how equitable, thoughtful and just educative aims may be met. We address Ralston’s argument that micro-credentials present an educative “moral hazard” by arguing that micro-credentialing will allow universities to respond quickly to changing worker educational needs rather than only offering full degrees that may not be economically viable or personally desirable for individuals. Rather, we suggest, the potential of micro-credentials lies in their pathways and potential to enhance lifelong learning and suggest that micro-credentials do not stand outside of the pedagogical ethical imperative that learning experiences should be positive and inclusive.

The explosive hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), a major component of munitions, is used extensively on military training ranges. As a result, widespread RDX pollution in groundwater and aquifers in the United States is now well documented. RDX is toxic, but its removal from training ranges is logistically challenging, lacking cost-effective and sustainable solutions. Previously, we have shown that thale cress (Arabidopsis thaliana) engineered to express two genes, xplA and xplB, encoding RDX-degrading enzymes from the soil bacterium Rhodococcus rhodochrous 11Y can break down this xenobiotic in laboratory studies. Here, we report the results of a 3-year field trial of XplA/XplB-expressing switchgrass (Panicum virgatum) conducted on three locations in a military site. Our data suggest that XplA/XplB switchgrass has in situ efficacy, with potential utility for detoxifying RDX on live-fire training ranges, munitions dumps and minefields. Switchgrass engineered to express two enzymes that degrade the explosive hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) detoxifies military sites contaminated with the munition when grown on these plots in a 3-year field trial.

Fire drives animal population dynamics across many ecosystems. Yet, we still lack an understanding of how most species recover from fire and the effects of fire severity and patchiness on recovery processes. This information is crucial for fire‐mediated biodiversity conservation, particularly as fire regimes change globally. We conducted an experiment to test whether post‐fire recovery is driven by in situ survival or recolonisation, and to determine whether this varies with fires of increasing percentage area burnt (burn cover) and severity. We used the pale field rat Rattus tunneyi as a model, because it represents the extinction process for a suite of mammal species suffering population collapse across Australia's northern savannas. Our treatments spanned a gradient from patchy, low severity fires (simulating early dry season management burns) to thorough, high severity fires (simulating wildfires). We performed capture–mark–recapture, vegetation and aerial surveys before, 6 weeks after and 1 year after fire. Six weeks after fire, pale field rats were only captured in unburnt patches of vegetation, and capture rates were proportional to the amount of unburnt habitat. One year later, both vegetation and pale field rat populations recovered across all sites. However, population recovery after low severity fires was likely achieved through in situ survival and reproduction in unburnt micro‐refuges, compared to recolonisation driving recovery after high severity fires. Synthesis and applications. Pale field rat persistence is strongly dependent on the retention of unburnt habitat patches within fire‐affected areas. Management strategies that increase micro‐refugia within burnt areas may facilitate pale field rat population recovery. Globally, building recovery mechanisms into fire management will be vital for supporting the long‐term persistence of fire‐affected species.

Forests exhibit thresholds in disturbance intervals that influence sustainability of production and natural values including sawlog production, species existence and habitat attributes. Fire is a key disturbance agent in temperate forests and frequency of fire is increasing, threatening sustainability of these forest values. We used mechanistically diverse, theoretical fire interval distributions for mountain ash forest in Victoria, Australia, in the recent past and future to estimate the probability of realising: (i) minimum sawlog harvesting rotation time; (ii) canopy species maturation; and (iii) adequate habitat hollows for fauna. The likelihood of realising fire intervals exceeding these key stand age thresholds diminishes markedly for the future fire regime compared with the recent past. For example, we estimate that only one in five future fire intervals will be sufficiently long (~80 years) to grow sawlogs in this forest type, and that the probability of forests developing adequate habitat hollows (~180 years) could be as low as 0.03 (3% of fire intervals). Therefore, there is a need to rethink where sawlogs can be sourced sustainably, such as from fast-growing plantations that can be harvested and then regrown rapidly, and to reserve large areas of existing 80-year-old forest from timber harvesting.

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models—random forest, support vector machine, and multilayer perceptron—as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron (R2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 × 106 kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models—random forest, support vector machine, and multilayer perceptron—as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron ( R 2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 × 106 kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.

Fire behaviour research has largely focused on dry ecosystems that burn frequently, with far less attention on wetter forests. Yet, the impacts of fire in wet forests can be high and therefore understanding the drivers of fire in these systems is vital. We sought to identify and rank by importance the factors plausibly driving flammability in wet eucalypt forests, and describe relationships between them. In doing so, we formulated a set of research priorities. Conceptual models of forest flammability in wet eucalypt forests were elicited from 21 fire experts using a combination of elicitation techniques. Forest flammability was defined using fire occurrence and fireline intensity as measures of ignitability and heat release rate, respectively. There were shared and divergent opinions about the drivers of flammability in wet eucalypt forests. Widely agreed factors were drought, dead fine fuel moisture content, weather and topography. These factors all influence the availability of biomass to burn, albeit their effects and interactions on various dimensions of flammability are poorly understood. Differences between the models related to lesser understood factors (e.g. live and coarse fuel moisture, plant traits, heatwaves) and the links between factors. By documenting alternative conceptual models, we made shared and divergent opinions explicit about flammability in wet forests. We identified four priority research areas: (1) quantifying drought and fuel moisture thresholds for fire occurrence and intensity, (2) modelling microclimate in dense vegetation and rugged terrain, (3) determining the attributes of live vegetation that influence forest flammability, (4) evaluating fire management strategies.

Litter moisture content plays a critical role in fire danger rating systems and forest ecosystems. Soil moisture content has been proposed to affect litter moisture due to vapour flux and capillary rise from the soil. However, few models consider soil water content when predicting litter moisture, and to our knowledge, no model includes capillary fluxes. In this study, we represented soil moisture coupling in a physics-based litter moisture prediction model by describing the vapour and capillary fluxes from the soil to litter. We aimed to evaluate if litter moisture predictions can be improved by incorporating the hydrological process at the soil-litter interface and explored the possible role of soil moisture in litter moisture simulations. Three model versions were compared against observations at a dry and wet experimental site in Australia: the original physics-based model, a model version extended with soil vapour flux only, and a version that incorporates both soil vapour flux and capillary rise. The simulation results suggest that soil moisture considerably influences litter moisture through soil vapour flux and capillary rise, which can lead to more than 10% of oven-dried weight, particularly under wet soil conditions. The corresponding model showed the best performance in comparison with field observations. The contribution of upward soil moisture fluxes was small after long dry and warm periods but noticeable during more moist periods. Further research is needed to evaluate the revised model for a broader range of weather, soil and litter conditions.

Fuel moisture content (FMC) of fine surface litter fuel plays a decisive role in determining fire ignition and spread. Understanding physical processes that determine litter moisture dynamics is necessary to accurately predict litter FMC. It is known that vapour exchange, precipitation and latent heat transfer are dominant factors governing changes in litter FMC. However, limited research has been undertaken on physical processes at the soil-litter interface, despite the presumed importance of soil moisture in hydrating litter. In this study, we investigated the influence of soil moisture on litter FMC in factorial controlled field experiments at a dry and a wet site in Canberra, Australia. Each site had two treatments: with and without contact between soil and litter. Litter FMC, temperature and relative humidity were automatically and continuously measured in both surface and subsurface litter with a combination of fuel moisture, temperature and relative humidity sensors. The two treatments showed a different influence of soil moisture on litter FMC at the dry and wet site. There was limited moisture transport from soil to litter at the dry site, almost certainly dominated by vapour flux. Here, the influence of soil moisture on litter FMC is through its influence on local humidity in the litter layer. However, at the wet site capillary flow occurred in addition to vapour flux from the soil. The capillary flow responded to the matric potential gradient between soil and litter. The measurements confirmed that subsurface litter was more strongly coupled to the soil than the surface litter, which was more strongly coupled with the atmosphere. The improved understanding of physical processes governing water transport from soil to litter should help predict litter FMC more accurately, especially under wet soil conditions. Further research is needed to incorporate soil-litter interactions in litter FMC simulations.

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable’s scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities.

Fire is one of the predominant drivers of the structural and functional dynamics of forest ecosystems. In recent years, novel fire regimes have posed a major challenge to the management of pyrodiverse forests. While previous research efforts have focused on quantifying the impacts of fire on above-ground forest biodiversity, how microbial communities respond to fire is less understood, despite their functional significance. Here, we describe the effects of time since fire, fire frequency and their interaction on soil and leaf litter fungal and bacterial communities from the pyrodiverse, Eucalyptus pilularis forests of south-eastern Australia. Using structural equation models, we also elucidate how fire can influence these communities both directly and indirectly through biotic–abiotic interactions. Our results demonstrate that fire is a key driver of litter and soil bacterial and fungal communities, with effects most pronounced for soil fungal communities. Notably, recently burnt forest hosted lower abundances of symbiotic ectomycorrhizal fungi and Acidobacteria in the soil, and basidiomycetous fungi and Actinobacteriota in the litter. Compared with low fire frequencies, high fire frequency increased soil fungal plant pathogens, but reduced Actinobacteriota. The majority of fire effects on microbial communities were mediated by fire-induced changes in litter and soil abiotic properties. For instance, recent and more frequent fire was associated with reduced soil sulphur, which led to an increase in soil fungal plant pathogens and saprotrophic fungi in these sites. Pathogenic fungi also increased in recently burnt forests that had a low fire frequency, mediated by a decline in litter carbon and an increase in soil pH in these sites. Synthesis. Our findings indicate that predicted increases in the frequency of fire may select for specific microbial communities directly and indirectly through ecological interactions, which may have functional implications for plants (increase in pathogens, decrease in symbionts), decomposition rates (declines in Actinobacteriota and Acidobacteriota) and carbon storage (decrease in ectomycorrhizal fungi). In the face of predicted shifts in wildfire regimes, which may exacerbate fire-induced changes in microbial communities, adaptive fire management and monitoring is required to address the potential functional implications of fire-altered microbial communities. Published version This work was funded by the Australian Research Council, Parks Australia and the Australian Department of Defence through ARC Linkage Project LP170100152.

Fire and herbivores alter vegetation structure and function. Future fire activity is predicted to increase, and quantifying changes in vegetation communities arising from post-fire herbivory is needed to better manage natural environments. We investigated the effects of post-fire herbivory on understory plant communities in a coastal eucalypt forest in southeastern Australia. We quantified herbivore activity, understory plant diversity, and dominant plant morphology following a wildfire in 2017 using two sizes of exclosures. Statistical analysis incorporated the effect of exclusion treatments, time since fire, and the effect of a previous prescribed burn. Exclusion treatments altered herbivore activity, but time since fire did not. Herbivory reduced plant species richness, diversity, and evenness and promoted the dominance of the most abundant plants within the understory. Increasing time since fire reduced community diversity and evenness and influenced morphological changes to the dominant understory plant species, increasing size and dead material while decreasing abundance. We found the legacy effects of a previous prescribed burn had no effect on herbivores or vegetation within our study. Foraging by large herbivores resulted in a depauperate vegetation community. As post-fire herbivory can alter vegetation communities, we postulate that management burning practices may exacerbate herbivore impacts. Future fire management strategies to minimize herbivore-mediated alterations to understory vegetation could include aggregating management burns into larger fire sizes or linking fire management with herbivore management. Restricting herbivore access following fire (planned or otherwise) can encourage a more diverse and species-rich understory plant community. Future research should aim to determine how vegetation change from post-fire herbivory contributes to future fire risk.

Summary Forested environments are subject to large and high intensity unplanned fire events, owing to, among other factors, the high quantity and complex structure of fuel in these environments. Compiling accurate and spatially comprehensive fuel information is necessary to inform various aspects of land management in forested environments. Remote sensing may offer distinct advantages for this in comparison to traditional site-based approaches. We conducted a literature review of the past 10 years of research in the remote sensing of fire fuel in forested environments, with a focus on emerging methods of fuel estimation, and the fuel attributes estimated. We position our review of remote sensing research in relation to the fuel attributes that influence fire behaviour, as suggested by contemporary physics-based fire behaviour knowledge, and a summary of fuel inputs to widely applied forest fire behaviour models. We find a disconnect between recent remote sensing research and fuel characterisations relevant to contemporary fire behaviour knowledge. Specifically, we find a tendency in remote sensing research towards estimation of forest overstorey fuel attributes, and a relative lack of research that estimates more obscured, though highly relevant, fuel components such as understorey, surface, and bark fuel. We also find a tendency for recent remote sensing research to conceptualise fire fuel by existing fire behaviour models, with particular emphasis on matching pre-existing fuel model classifications. A case is made for remotely sensed forest fuel estimation grounded in current knowledge of fire behaviour processes and the fuel attributes known to influence these processes. Shortcomings in remote sensing of key forest fuel attributes are partly due to inherent limitations of current technologies, and we discuss recent and expected advancements in remote sensing research and technology that may drive significant future advancement in forest fuel estimation. Further, we suggest that recognition of interactions between fuel attributes and measurable biophysical forest properties can assist in addressing present limitations in remote sensing of key forest fuel attributes. Such process-based methods would be more spatially and temporally applicable, encourage new techniques for estimating fuel attributes using remote sensing data, and may encourage the development of fire behaviour and risk prediction systems that are more suited to remote sensing.

Fuel moisture content (FMC) of the litter is a critical factor determining fire ignition and spread and is an important input for most fire behaviour prediction models. Several models, ranging from empirical regression functions to physics-based models, have been developed to forecast litter FMC. Soil moisture below the litter layer has been shown to influence litter FMC, but few models explicitly consider its effect. This study aimed to evaluate how soil moisture content may affect litter FMC by coupling soil moisture as a boundary condition to the physics-based ‘Koba’ model, which simulates radiation, energy and moisture fluxes in the surface and subsurface litter layer. The coupled model was tested at five sites in Victoria, Australia, where litter FMC values were recorded continuously during 2014-2015 using calibrated fuel stick sensors. Two versions of the model were compared against the observations: an uncoupled model and a model version accounting for the vapour flux from soil to litter. The simulation results show that the influence of soil moisture depends on environmental conditions. Soil moisture appeared to have a minor influence on FMC when both soil and litter are dry, but a stronger influence when the soil is relatively wet. Correspondingly, the coupled model explained observed FMC better than the uncoupled model for the two wetter study sites. As expected, the subsurface litter in contact with the soil appeared more sensitive to soil moisture conditions than the surface litter. The influence of soil vapour flux on litter FMC can be considerable during the transition from wet to dry litter and soil conditions. This has implications for hazard reduction burning, which is typically planned to take advantage of transitional fuel moisture conditions. Further research is needed to understand the influence of the structure and thickness of litter on the importance of soil vapour flux.

In this chapter, we explicitly carry the notion of “thin” versus “thick” democracy across to more complex understandings of different approaches of citizenship: “thin citizenship” versus “thick citizenship”(Carr et al. 2012; Westheimer and Kahne 2004) especially in these times of metaphorical, theoretical and actual retreats from democratic practices and institutions globally. As we explore various challenging and hopeful realities at the currently problematic nexus of democracy and citizenship, we additionally layer in our conception of “lived citizenship” as an extension of “lived curriculum” (Cary and Pruyn, n.d.) and, especially, what this messy melange implies for the Australian Curriculum and daily pedagogical practices in the humanities in schools here and around the world. These arguments are interspersed with the tentative findings from our ongoing international study of central scholars also working in these spaces. However, we will include hints of the democratic nightmare that is the present, and the ongoing need to historicize the present to reveal that the social construction of difference and the political exclusion of those seen as “Other” is a continuing and constant challenge. We aim to address the current citizenship crisis in the Australian Curriculum as a representation of the retreat that is historic and ongoing.

Variations in global patterns of burning and fire regimes are relatively well measured, however, the degree of influence of the complex suite of biophysical and human drivers of fire remains controversial and incompletely understood. Such an understanding is required in order to support current fire management and to predict the future trajectory of global fire patterns in response to changes in these determinants. In this study we explore and compare the effects of four fundamental controls on fire, namely the production of biomass, its drying, the influence of weather on the spread of fire and sources of ignition. Our study area is southern Australia, where fire is currently limited by either fuel production or fuel dryness. As in most fire-prone environments, the majority of annual burned area is due to a relatively small number of large fires. We train and test an Artificial Neural Network’s ability to predict spatial patterns in the probability of large fires (>1,250 ha) in forests and grasslands as a function of proxies of the four major controls on fire activity. Fuel load is represented by predicted forested biomass and remotely sensed grass biomass, drying is represented by fraction of the time monthly potential evapotranspiration exceeds precipitation, weather is represented by the frequency of severe fire weather conditions and ignitions are represented by the average annual density of reported ignitions. The response of fire to these drivers is often non-linear. Our results suggest that fuel management will have limited capacity to alter future fire occurrence unless it yields landscape-scale changes in fuel amount, and that shifts between, rather than within, vegetation community types may be more important. We also find that increased frequency of severe fire weather could increase the likelihood of large fires in forests but decrease it in grasslands. These results have the potential to support long-term strategic planning and risk assessment by fire management agencies.

Fuel Moisture Content (FMC) is one of the primary drivers affecting fuel flammability that lead to fires. Satellite observations well-grounded with field data over the highly climatologically and ecologically diverse Australian region served to estimate FMC and flammability for the first time at a continental-scale. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 360 observations at 32 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r = 0.58, RMSE = 40%) but without site-specific calibration. Logistic regression models were developed to generate a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest. The models obtained an “Area Under the Curve” calculated from the Receiver Operating Characteristic plot method of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction., Bushfire and Natural Hazards CRC

Removing vegetation close to houses is at the forefront of advice provided to home owners by fire management agencies. However, widespread clearing of trees and shrubs near houses impacts aesthetics, privacy, biodiversity, energy consumption and property values. Thus, stakeholders may oppose this practice. Regulators and property owners therefore require options for vegetation management that reduce risk to houses during wildfires without complete removal of trees and shrubs. Using data from 499 houses impacted by wildfires, we tested three hypotheses: (1) maintaining ‘green’ vegetation affords houses additional protection during wildfires; (2) risk posed by trees and shrubs near houses is reduced where they are arranged as many discrete patches; and (3) trees and shrubs retained in the upwind direction from which wildfires arrive represent greater risk to houses than trees and shrubs retained in the downwind direction. We found empirical support for each hypothesis. Increasing the mean Normalised Vegetation Difference Index (NDVI) (a measure of “greenness”) of vegetation near houses had the same effect on reducing house losses as removing some trees and shrubs. Trees and shrubs within 40 m of houses arranged as many discrete patches posed less risk than the same cover of trees and shrubs arranged as few discrete patches. Trees and shrubs retained downwind from houses represented less risk than trees and shrubs retained upwind. Our findings represent options for regulators or home owners seeking to balance risk posed by wildfires with benefits associated with retaining trees and shrubs near houses.

Understanding how landscape heterogeneity mediates the effects of fire on biodiversity is increasingly important under global changes in fire regimes. We used a simulation experiment to investigate how fire regimes interact with topography and weather to shape neutral and selection-driven genetic diversity under alternative dispersal scenarios, and to explore the conditions under which micro-refuges can maintain genetic diversity of populations exposed to recurrent fire. Spatial heterogeneity in simulated fire frequency occurred in topographically-complex landscapes, with fire refuges and fire-prone ‘hotspots’ apparent. Inter-annual weather variability reduced the effect of topography on fire patterns, with refuges less apparent under high weather variability. Neutral genetic diversity was correlated with long-term fire frequency under spatially heterogeneous fire regimes, being higher in fire refuges than fire-prone areas, except under high dispersal or low fire severity (low mortality). This generated different spatial genetic structures in fire-prone and fire-refuge components of the landscape, despite similar dispersal. In contrast, genetic diversity was only associated with time since the most recent fire in flat landscapes without predictable refuges and hotspots. Genetic effects of selection driven by fire-related conditions depended on selection pressure, migration distance and spatial heterogeneity in fire regimes. Allele frequencies at a locus conferring higher fitness under successional environmental conditions followed a pattern of ‘temporal adaptation’ to contemporary conditions under strong selection pressure and high migration. However, selected allele frequencies were correlated with spatial variation in long-term mean fire frequency (relating to environmental predictability) under weak dispersal, low selection pressure and strong spatial heterogeneity in fire regimes. This article is protected by copyright. All rights reserved.

Fire is a powerful ecological and evolutionary force. Animals that modify drivers of fire behaviour could therefore have far-reaching effects on ecosystems. Yet, with a few notable exceptions, effects of animals on fire have been often overlooked. We show how animals can affect fire behaviour by modifying the amount, structure, or condition of fuel or, more rarely, by altering other controls on fire such as wind speed or ignition patterns. Some effects are readily observed and quantified. Others are more subtle but could be considerable when accumulated over time, space, and animal taxa. A combination of manipulative experiments, landscape studies, and multiscale fire models will be necessary to understand the consequences of widespread changes in animal populations for landscape fire.