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Baseline requirements for assessment of mining impact using biological monitoring
Author(s) -
HUMPHREY C. L.,
FAITH D. P.,
DOSTINE P. L.
Publication year - 1995
Publication title -
australian journal of ecology
Language(s) - English
Resource type - Journals
eISSN - 1442-9993
pISSN - 0307-692X
DOI - 10.1111/j.1442-9993.1995.tb00529.x
Subject(s) - baseline (sea) , statistical power , univariate , replication (statistics) , computer science , environmental science , impact assessment , multivariate statistics , environmental resource management , statistics , mathematics , machine learning , biology , public administration , fishery , political science
Biological monitoring programmes for environmental protection should provide for both early detection of possible adverse effects, and assessment of the ecological significance of these effects. Monitoring techniques are required that include responses sensitive to the impact, that can be subjected to rigorous statistical analysis and for which statistical power is high. Such issues in baseline research of‘what and how to measure?’and‘for how long?’have been the focus of a programme being developed to monitor and assess effects of mining operations on the essentially pristine, freshwater ecosystems of the Alligator Rivers Region (ARR) in tropical northern Australia. Application of the BACIP (Before, After, Control, Impact, Paired differences) design, utilizing a form of temporal replication, to univariate (single species) and multivariate (community) data is described. The BACIP design incorporates data from single control and impact sites. We argue for modification of the design for particular studies conducted in streams, to incorporate additional independent control sites from adjacent catchments. Inferential power, by way of (i) more confidently attributing cause to an observed change and (ii) providing information about the ecological significance of the change, will be enhanced using a modified BACIP design. In highly valued environments such as the ARR, monitoring programmes require application of statistical tests with high power to guarantee that an impact no greater than a prescribed amount has gone undetected. A minimum number of baseline years using the BACIP approach would therefore be required in order to achieve some desired level of statistical power. We describe the results of power analyses conducted on 2–5 years (depending upon the technique) of baseline data from streams of the ARR and discuss the implications of these results for management.

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