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BackgroundWidespread invasion by non-native plants has resulted in substantial change in fire-fuel characteristics and fire-behaviour in many of the world's ecosystems, with a subsequent increase in the risk of fire damage to human life, property and the environment. Models used by fire management agencies to assess fire risk are dependent on accurate assessments of fuel characteristics but there is little evidence that they have been modified to reflect landscape-scale invasions. There is also a paucity of information documenting other changes in fire management activities that have occurred to mitigate changed fire regimes. This represents an important limitation in information for both fire and weed risk management.Methodology/Principal FindingsWe undertook an aerial survey to estimate changes to landscape fuel loads in northern Australia resulting from invasion by Andropogon gayanus (gamba grass). Fuel load within the most densely invaded area had increased from 6 to 10 t ha−1 in the past two decades. Assessment of the effect of calculating the Grassland Fire Danger Index (GFDI) for the 2008 and 2009 fire seasons demonstrated that an increase from 6 to 10 t ha−1 resulted in an increase from five to 38 days with fire risk in the ‘severe’ category in 2008 and from 11 to 67 days in 2009. The season of severe fire weather increased by six weeks. Our assessment of the effect of increased fuel load on fire management practices showed that fire management costs in the region have increased markedly (∼9 times) in the past decade due primarily to A. gayanus invasion.Conclusions/SignificanceThis study demonstrated the high economic cost of mitigating fire impacts of an invasive grass. This study demonstrates the need to quantify direct and indirect invasion costs to assess the risk of further invasion and to appropriately fund fire and weed management strategies.

One contribution of 16 to a theme issue 'Measuring the difference made by protected areas: methods, applications and implications for policy and practice'.

Summary1. The understanding of large-scale patterns in expanding populations of alien invasive plants can be used to infer the environmental limiting factors, habitat heterogeneity and, ultimately, the range expansion potential of invasive plants. 2. We used multivariate analysis and a novel quantile regression technique accounting for spatial autocorrelation to compare and contrast factors influencing the abundance and distribution of the African grass Andropogon gayanus (gamba grass) at two large-scale invasion sites in the tropical savanna region of Australia. We collected data using aerial and ground surveys and tested for limiting factors using three landscape-scale indices related to soil quality, soil moisture and invasion history. 3. In one site, gamba grass was principally colonising drainage lines and riparian areas. Occupation of these areas was limited in proportion to the distance from the original gamba grass source. In the second site, gamba grass abundance was independent of distance from the original source and was well established in all vegetation communities, although abundance was also limited in higher elevation sites away from drainage lines. 4. Comparisons between these sites with different patterns of invasion enabled the estimation of both the invasion pathways and range expansion potential of gamba grass. Our results indicated that gamba grass spreads from riparian communities to invade upland sites and has the potential to invade 70% of north Australia's upland savanna communities. 5. Aerial surveys comprehensively assessed patterns over a larger area than ground surveys and were much more economical. 6. Synthesis and applications. Large-scale surveys across multiple sites are critical to understanding the dynamics of recent alien species invasions where little is known about the pattern and potential range of spread. The application of quantile regression and aerial surveys shows promise as aerial surveys are efficient at capturing a large amount of data. The novel quantile regression technique we demonstrate here can account for both spatial autocorrelation and noisy ecological data from aerial surveys while returning robust results. We were thus able to demonstrate widespread colonisation of creek lines by gamba grass and recommend that management focuses on detection and eradication along drainage lines in addition to the present focus on transport corridors.

Invasive plants are recognised as a major threat to biodiversity conservation worldwide. Despite this recognition, our understanding of the mechanisms controlling the invasion process and its impact on flora and fauna is often poor. We examined the impact of an invasive aquatic grass species, para grass (Urochloa mutica), on seasonally inundated wetlands in tropical northern Australia. Flora and avifauna were surveyed at sites invaded by para grass and in native vegetation. Spatial information systems were used to design surveys and determine environmental correlates of para grass distribution and so predict the potential future spread of para grass and infer impacts in the absence of control. Where para grass was present the median number of plant taxa was ~75% lower. Few birds showed preference for habitats invaded by para grass, and most birds were associated with areas of native vegetation or other habitats with little or no para grass. The study identified several wetland habitats that are at greater risk of invasion, based on the apparent habitat preferences of para grass. The degradation or loss of some of these 'at-risk' habitats, including Oryza meriodionalis grasslands that play an integral role in the wetland food chain, has important ramifications for the levels of biodiversity supported by the wetlands.

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Summary1. Invasive weeds are a major cause of biodiversity loss and economic damage world-wide. There is often a limited understanding of the biology of emerging invasive species, but delay in action may result in escalating costs of control, reduced economic returns from management actions and decreased feasibility of management. Therefore, spread models that inform and facilitate on-ground control of invasions are needed. 2. We developed a spatially explicit, individual-based spread model that can be applied to both data-poor and data-rich situations to model future spread and inform effective management of the invasion. The model is developed using a minimum of two mapped distributions for the target species at different times, together with habitat suitability variables and basic population data. We present a novel method for internally calibrating the reproduction and dispersal distance parameters. We use a sensitivity analysis to identify variables that should be prioritized in future research to increase robustness of model predictions. 3. We apply the model to two case studies, gamba grass and para grass, to provide management advice on emerging weed priorities in northern Australia. For both species, we find that the current extent of invasion in our study regions is expected to double in the next 10 years in the absence of management actions. The predicted future distribution identifies priority areas for eradication, control and containment to reduce the predicted increase in infestation. 4. The model was built for managers and policymakers in northern Australia working on species where expert knowledge and environmental data are often lacking, but is flexible and can be easily adapted for other situations, for example where good data are available. The model provides predicted probability of occurrence over a user-specified, typically short-term, time horizon. This output can be used to direct surveillance and management actions to areas that have the highest likelihood of rapid invasion and spread. Directing efforts to these areas provides the greatest likelihood of management success and maximizes the return on investment in management response.

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