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Debate concerning the environmental impact of human arrival in Australia has continued for more than a century. Here we review the evidence for human impact and the mechanisms by which humans may have affected the environment of tropical Australia. We limit our review to tropical Australia because, over three decades ago, it was proposed that the imposition of an anthropogenic fire regime upon human occupation of the Australian continent may have resulted in profound changes in regional vegetation and climate across this region. We conclude that ecological processes and vegetation-fire-climate-human feedbacks do exist that could have driven a significant shift in boundary conditions and ecosystem state at the sub-continental scale through the sustained imposition of an anthropogenic fire regime over tens of millennia. These potential feedbacks operate through the inhibition of forest expansion both directly, by targeted burning at established forest edges and newly irrupted forest patches, and indirectly, through lengthening of the dry season because of changes to the timing of burning. However, the impact of any such anthropogenic forcing may have been entirely overshadowed by the effects of natural climate change and variability, as well as the generally low nutrient status of Australian soils. A robust assessment of the degree to which the environment of tropical Australia at the large scale has been modified from its natural' state because of human occupation will require new, coordinated collaborations between indigenous traditional landowners, archaeologists, anthropologists, geochronologists, geoscientists, ecologists, climatologists and modellers.

Using climate-based DHF prediction as warning system A three-month in advance warning system for upcoming DHF epidemics is developed. The system uses a simple predictive regression model based on past and present DHF cases, climate and meteorological observations as inputs to predict future DHF cases. Using Peirce score as a measure of prediction skill, the model only successfully predicts a moderately-severe epidemic at lead times of up to 6 months. Another model uses a discriminant method is also developed. This model gives much higher skill score and longer lead times than that of the regression model. The economic benefit of using the discriminant model's forecast to protect a family from an epidemic is also demonstrated. It is shown that families who are implementing such a prediction into their decision making process gain more benefit than those with un-informed decisions.

A simple climate-based regression model for DHF prediction A statistical model for predicting monthly Dengue Hemorrhagic Fever (DHF) cases from the city of Makassar is developed and tested. The model uses past and present DHF cases, climate and meteorological observations as inputs. These inputs are selected using a stepwise regression method to predict future DHF cases. The model is tested independently and its skill assessed using two skill measures. Using the selected variables as inputs, the model is capable of predicting a moderately-severe epidemic at lead times of up to six months. The most important input variable in the prediction is the present number of DHF cases followed by the relative humidity three to four months previously. A prediction 1???6 months in advance is sufficient to initiate various activities to combat DHF epidemic. The model is suitable for warning and easily becomes an operational tool due to its simplicity in data requirement and computational effort.