Prediction and assessment of local stream habitat features using large-scale catchment characteristics

1. Knowledge of what a habitat should be like, in the absence of the effect s of human activities, is fundamental to local stream habitat assessment. I t has been suggested that stream habitats are influenced by large-scale cat chment features. This study aimed to identify these relationships so that l ocal-scale habitat features could be predicted from larger-scale characteri stics. 2. Fifty-one reference sites from the Upper Murrumbidgee River catchment, s outh-eastern Australia, were classified on the basis of the local features of their stream habitat. Large-scale variables, namely catchment area, stre am length, relief ratio, alkalinity, percentage of volcanic rocks, percenta ge of metasediments, dominant geology and dominant soil type, provided suff icient information for classifying 69% of reference sites into appropriate reference site groups. 3. A model created using these large-scale catchment variables was able to predict the local habitat features that were expected (E) to occur at a sit e in the absence of the effects of human activities. These were compared wi th observed (O) local habitat features to provide an observed-to-expected ( O/E) ratio, an assessment score of the habitat at a site. The departure of this ratio from 1 enables identification of those sites that may be impacte d. A list of habitat features that are expected at a site can provide targe ts for habitat restoration or enhancement. 4. For impacted sites, when habitat assessment from the habitat predictive model was compared with biological assessment from the Australian River Ass essment System (AUSRIVAS) predictive model, it was possible to identify whe ther habitat degradation or water quality degradation was the cause of biol ogical impairment. Such assessment may make it possible to identify rehabil itation goals relevant to the biota.