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

• Published in: Freshwater biology Vol. 45; no. 3; pp. 343 - 369
• Main Authors: Davies, Nerida M., Norris, Richard H., Thoms, Martin C.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Science Ltd 01.11.2000; Blackwell Science; Wiley Subscription Services, Inc
• Subjects: Animal, plant and microbial ecology; Applied ecology; assessment; AUSRIVAS; Biological and medical sciences; catchment scale; Conservation, protection and management of environment and wildlife; Environmental degradation: ecosystems survey and restoration; Freshwater; Fundamental and applied biological sciences. Psychology; macroinvertebrate habitat; predictive model; river condition; Australia; Oceania; Australia, New South Wales, Murrumbidgee R; Environmental factor; Perturbation; Multivariate analysis; Forecast model; Empirical model; Murrumbidgee River; Freshwater environment; Characteristics; Animal community; Environment impact; Water quality; Stream; Habitat; Environmental monitoring; Invertebrata; Anthropogenic factor; Environment quality
• ISSN: 0046-5070; 1365-2427
• DOI: 10.1111/j.1365-2427.2000.00625.x

1. Knowledge of what a habitat should be like, in the absence of the effects of human activities, is fundamental to local stream habitat assessment. It has been suggested that stream habitats are influenced by large‐scale catchment features. This study aimed to identify these relationships so that local‐scale habitat features could be predicted from larger‐scale characteristics. 
2. Fifty‐one reference sites from the Upper Murrumbidgee River catchment, south‐eastern Australia, were classified on the basis of the local features of their stream habitat. Large‐scale variables, namely catchment area, stream length, relief ratio, alkalinity, percentage of volcanic rocks, percentage of metasediments, dominant geology and dominant soil type, provided sufficient 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 site in the absence of the effects of human activities. These were compared with 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 impacted. A list of habitat features that are expected at a site can provide targets 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 Assessment System (AUSRIVAS) predictive model, it was possible to identify whether habitat degradation or water quality degradation was the cause of biological impairment. Such assessment may make it possible to identify rehabilitation goals relevant to the biota.