Title: Biological Assessment Methods: Controlling the Quality of Biological Data. Package 1 The variability of data used for assessing the biological condition of rivers
Author: M.T. Furse
Author: R.T. Clarke
Author: J.M. Winder
Author: K.L. Symes
Author: J.H. Blackburn
Author: N.J. Grieve
Author: R.J.M. Gunn
Document Type: Monograph
Abstract:
Macro-invertebrate sampling is widely used for assessing the biological condition of rivers.
Sampling programmes can be used to investigate pollution incidents or for routine monitoring
or surveillance at local, regional or at national levels. Commonly, lists of families captured during a standardised pond-net sample are used to calculate simple indices summarising the condition of a site. The most widely used indices are those comprising the Biological Monitoring Working Party (BMWP) score system. Each family present is assigned a score according to its perceived tolerance to organic pollution. The site index may be expressed either as the sum of the scores of the families present, the number of families present or the average score of those families present. This document forms part of a linked programme of research designed to improve the statistical reliability of aquatic macro-invertebrate assemblage data for assessing the ecological quality of running waters in England and Wales. The principal objective of the study, as set out by the NRA, is: To quantify and, wherever possible, control sources of variability in macroinvertebrate data for a range o f river types and biological quality bands in order to increase the value o f NRA data in water quality management.
Publisher: Environment Agency
Publication Date: 1995
Publication Place: Bristol
Subject Keywords: Macroinvertebrates; Sampling; Identification; BMWP; RIVPACS; Assessment; Ecological factors; Water quality; Error (measurement); Statistical dispersion; Statistics; Biological indicators
Geographic Keywords: England; Wales
Extent: 139
Permalink: http://www.environmentdata.org/archive/ealit:4291
Total file downloads: 333
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