The proximal drivers of large fires: A Pyrogeographic study

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

Comparison of adeno-associated virus pseudotype 1, 2, and 8 vectors administered by intramuscular injection in the treatment of murine phenylketonuria

Phenylketonuria (PKU) is caused by hepatic phenylalanine hydroxylase (PAH) deficiency and is associated with systemic accumulation of phenylalanine (Phe). Previously we demonstrated correction of murine PKU after intravenous injection of a recombinant type 2 adeno-associated viral vector pseudotyped with type 8 capsid (rAAV2/8), which successfully directed hepatic transduction and Pah gene expression. Here, we report that liver PAH activity and phenylalanine clearance were also restored in PAH-deficient mice after simple intramuscular injection of either AAV2 pseudotype 1 (rAAV2/1) or rAAV2/8 vectors. Serotype 2 AAV vector (rAAV2/2) was also investigated, but long-term phenylalanine clearance has been observed only for pseudotypes 1 and 8. Therapeutic correction was shown in both male and female mice, albeit more effectively in males, in which correction lasted for the entire period of the experiment (>1 year). Although phenylalanine levels began to rise in female mice at about 8-10 months after rAAV2/8 injection they remained only mildly hyperphenylalaninemic thereafter and subsequent supplementation with synthetic tetrahydrobiopterin resulted in a transient decrease in blood phenylalanine. Alternatively, subsequent administration of a second vector with a different AAV pseudotype to avoid immunity against the previously administrated vector was also successful for long-term treatment of female PKU mice. Overall, this relatively less invasive gene transfer approach completes our previous studies and allows comparison of complementary strategies in the development of efficient PKU gene therapy protocols

Mapping live fuel moisture content and flammability for continental Australia using optical remote sensing

We present the first continental-scale methodology for estimating Live Fuel Moisture Content (FMC) and flammability in Australia using satellite observations. 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 363 observations at 33 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r 2 =0.57, RMSE=40%) but without site-specific calibration. Logistic regression models were developed to predict 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, obtaining performance metrics of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction

Unburnt habitat patches are critical for survival and in situ population recovery in a small mammal after fire

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

Interactions between climate change, fire regimes and biodiversity in Australia: A preliminary assessment

An assessment of the potential impacts of climate change on fire regimes in Australia, and the consequences of these changed fire regimes for Australia's biodiversity was commissioned by the Australian Government to help increase our understanding of the complex interactions between climate change, fire regimes and biodiversity for future fire management. The report synthesises understanding of the drivers of fire regimes in Australia, identifies changes to fire regimes projected from climate change and identifies the broad implications of these changes for biodiversity. 'Fire regime' is defined as the history of fire events at a point in the landscape. The report finds that fire weather will become more severe in many regions, particularly southern Australia, and that the interactions between biodiversity and fire regimes are complex. It develops a national framework to assess the likely impacts of climate change on fire regimes and biodiversity for different bioregions, using a case study approach. Climate change may affect fire regimes across the Australian landscape through changes to temperature, rainfall, humidity, wind, and the amount of carbon dioxide in the atmosphere. • Modeled climate projections show that much of southern Australia may become warmer and drier. This modeling suggests that, by 2020, extreme fire danger days in south-eastern Australia may occur 5 to 65 per cent more often than at present. • For example, modeling of climate change impacts on the fire regimes of Australian Capital Territory (ACT) landscapes predicts that a 2oC increase in mean annual temperature would increase fire intensity by 25%, increase the area burnt, and halve the mean interval between fires in the ACT. • Climate change is expected to have greater effects on fire regimes in regions where fire weather factors like temperature and wind strength determine fire occurrence and fire intensity. These are regions such as the temperate forests of the south-east and south-west of Australia. Climate change is expected to have less effect on fire regimes in places where fuel levels or ignition sources determine fire occurrence and intensity, such as northern tropical savannas. • Managing fire regimes to reduce risk to property, people and biodiversity under climate change will be increasingly challenging

Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather

The purpose of this study was to compare the sensitivity of modelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer & wetter, and warmer & drier) and weather (year-to-year variability) was determined for four existing landscape-fire-succession models (EMBYR, FIRESCAPE, LANDSUM and SEM-LAND) and a new model implemented in the LAMOS modelling shell (LAMOS(DS)). Sensitivity was measured as the variance in area burned explained by each of the four factors, and all of the interactions amongst them, in a standard generalised linear modelling analysis. Modelled area burned was most sensitive to climate and variation in weather, with four models sensitive to each of these factors and three models sensitive to their interaction. Models generally exhibited a trend of increasing area burned from observed, through warmer and wetter, to warmer and drier climates with a 23-fold increase in area burned, on average, from the observed to the warmer, drier climate. Area burned was sensitive to terrain for FIRESCAPE and fuel pattern for EMBYR. These results demonstrate that the models are generally more sensitive to variation in climate and weather as compared with terrain complexity and fuel pattern, although the sensitivity to these latter factors in a small number of models demonstrates the importance of representing key processes. The models that represented fire ignition and spread in a relatively complex fashion were more sensitive to changes in all four factors because they explicitly simulate the processes that link these factors to area burned