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Application of Bayesian Belief Networks for the prediction of macroinvertebrate taxa in rivers

Published online by Cambridge University Press:  15 February 2009

V. Adriaenssens
Affiliation:
Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University , Jozef Plateaustraat 22, B-9000 Gent, Belgium.
P. L.M. Goethals
Affiliation:
Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University , Jozef Plateaustraat 22, B-9000 Gent, Belgium.
J. Charles
Affiliation:
Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University , Jozef Plateaustraat 22, B-9000 Gent, Belgium.
N. De Pauw
Affiliation:
Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University , Jozef Plateaustraat 22, B-9000 Gent, Belgium.
Corresponding
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Abstract

Integrated ecological models are of great potential as predictive tools in decision support of river management. Such models need to be transparent and consistent with the existing expert knowledge, and give the river manager adequate information regarding their inherent uncertainty. One way to fulfil these needs is through the use of Bayesian Belief Networks (BBNs). Such networks represent cause-and-effect assumptions between system variables in a graphical structure. To establish the potential of Bayesian Belief Networks in river management, a small-scale study was performed with the aim of assessing the success of prediction of macroinvertebrate taxa in rivers by means of a selected number of environmental variables. Gammaridae and Asellidae were chosen because of their high relative abundances in small and large brooks in contrast to other macroinvertebrate taxa. Based on one-layered BBN networks, the predictive capacity of the models was assessed by means of the number of Correctly Classified Instances (CCI) and Cohen’s Kappa (K). The performance of these models was moderate to good for presence/absence classifications but showed a low to moderate performance when predicting abundance classes. When extending the former BBN network to a two-layered one, enhancing the number of links and variables, no obvious improvement in model performance was detected. The results indicate that thoughtful input variable selection as well as sensitivity analysis will improve the models for practical use in river restoration management.

Type
Research Article
Copyright
© Université Paul Sabatier, 2004

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