| Abstract: | Abstract. Predictive mapping of vegetation using models linking vegetation units to mapped environmental variables has been advocated for remote areas. In this study, three different types of model were employed (within a GIS) to produce vegetation maps of the Hamersley Ranges region of Western Australia. The models were: (1) decision trees; (2) statistical models; and (3) heuristic/conceptual models. Maps were produced for three different levels of a floristic classification, i.e. 16 communities in two community groups with eight subgroups. All models satisfactorily established relationships between the vegetation units and available predictor variables, except where the number of sites of a particular unit was small. The different models often made similar predictions, especially for more widespread vegetation units. Map accuracy (as determined by field testing of maps) improved with increasing level of abstraction, with plant community maps ca. 50 % correct, subgroup maps ca. 60 % correct and group maps 90 % correct. Map inaccuracies were due to several factors, including low sample numbers producing unrepresentative models, poor resolution of and errors in available maps of predictor variables, and available predictor variables not being able to differentiate between certain vegetation units, particularly at the plant community level. Of these factors, poor resolution of maps was seen as the most critical. One type of model could not be recommended over another; however the choice of model will be largely dependent on the nature of the data set and the type of map coverage required. |
