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Can AusRivAS be adopted for monitoring and assessing Australian wetlands?

Davis, J. (1998) Can AusRivAS be adopted for monitoring and assessing Australian wetlands? In: Williams, W.D., (ed.) Wetlands in a dry land : understanding for management. Environment Australia, Canberra, Australia, pp. 275-279.

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Abstract

Current research in south-western Australia indicates that a predictive modelling technique used for the assessment of the biological quality of rivers in Australia (AusRivAS) and the United Kingdom (RIVPACS) may also be usefully applied to the monitoring and assessment of wetland ecosystems. The River In Vertebrate Prediction and Classification System (RIVPACS) was developed by the Institute of Freshwater Ecology to generate predictions of the macroinvertebrate fauna to be expected at a river site in the absence of disturbance by human impacts. Each prediction is based on a small number of environmental variables used to characterise the site. The fauna predicted can then be compared with the actual fauna present at the site. Differences between observed and expected values provide a measure of the biological quality of the site. A similar approach, AusRivAS, based on 1,500 Australian stream and river sites, has been implemented as part of the National River Health Program. This approach has value not only as a technique for assessing wetland quality as part of environmental monitoring programmes but also provides a means of assessing the effectiveness of wetland creation and restoration projects. Results from research in progress suggest that the AusRivAS methodology is applicable to wetlands, but the question remains as to whether the needs and the resources exist to adopt this approach on an Australia-wide basis.

Item Type: Book Chapter
Murdoch Affiliation(s): School of Biological and Environmental Sciences
Publisher: Environment Australia
Copyright: © Environment Australia and Land and Water Resources Research and Development Corporation 1998
URI: http://researchrepository.murdoch.edu.au/id/eprint/23907
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