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Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from abase data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (aver- age air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables. [ABSTRACT FROM AUTHOR]

Invasive weeds represent one of the greatest threats to ecosystem integrity worldwide, with climate change predicted to allow expansion of weed ranges in coming decades. One of Australia's worst weeds is lantana (Lantana camara) which, given the potential for climatic change, is of increasing concern to those managing the mountainous regions in the country's southeast. In order to identify potential additional threats lantana may pose for Australia's valued biodiversity, this research develops a habitat suitability model for lantana in a portion of the Greater Blue Mountains World Heritage Area under current and simulated warmer conditions. Minimum temperature was found to be the most important predictor correlated with potential lantana establishment, explaining over 88% of the variation in lantana presence predicted by the model. Currently, 8% of the study area was found to be suitable for lantana, with this figure reaching 94% after a simulated 2°C rise in temperature anticipated by 2050. The sharp increase in suitable habitat highlights the importance of keeping the weed's range restricted in the study area. The strong link between temperature and predicted lantana establishment confirms prior research and further stresses the threat this weed poses to the area's biodiversity values as the climate warms. In addition, the model identified low-lying riparian areas as potential incursion pathways for the weed to travel further inland. Given the weed's invasiveness, potential for adverse impacts, and high capacity for dispersal, these pathways should not be overlooked when monitoring potential invasion of mountainous regions by lantana and other tropical weeds. [ABSTRACT FROM AUTHOR]