A method for selecting health index metrics in the absence of independent measures of ecological condition
Hallett, C.S., Valesini, F.J. and Clarke, K.R. (2012) A method for selecting health index metrics in the absence of independent measures of ecological condition. Ecological Indicators, 19 . pp. 240-252.
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We describe a novel, weight of evidence-based approach for selecting fish community metrics to assess estuarine health, and its application in selecting metrics for a multi-metric health index for the Swan Estuary, Western Australia. In the absence of reliable, independent measures of estuarine condition against which to test the sensitivity of candidate metrics, objective, multivariate statistical analyses and multi-model inference were employed to select metric subsets likely to be most sensitive to inter-annual changes in the health of this ecosystem. Novel pre-treatment techniques were first applied to down-weight the influence of highly erratic metrics and to minimise the effects of seasonal and spatial differences in sampling upon metric variability. A weight of evidence approach was then adopted to select those metrics which responded most consistently across multiple analyses of nearshore and offshore fish abundance data sets collected between 1976 and 2009. Sets of 11 and seven metrics were selected for assessing the health of the nearshore and offshore waters of the Swan Estuary, respectively. Selected metrics represented species composition and diversity, trophic structure, life history and habitat functions and, in the case of the nearshore index, a potential sentinel species. These metric sets are currently being used to construct a multi-metric health index for the Swan Estuary, which is the first such tool to be developed for assessing the health of estuaries in Australia. More broadly, while the methodology has in the present case been applied to the fish fauna of the Swan Estuary, it is generally applicable to any ecosystem and type of biotic community from which an ecosystem health index might be sensibly derived.
|Publication Type:||Journal Article|
|Murdoch Affiliation:||School of Biological Sciences and Biotechnology|
|Copyright:||2011 Elsevier Ltd|
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