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Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

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Abstract

The effect of environmental conditions on river macrobenthic communities was studied using a dataset consisting of 343 sediment samples from unnavigable watercourses in Flanders, Belgium. Artificial neural network models were used to analyse the relation among river characteristics and macrobenthic communities. The dataset included presence or absence of macroinvertebrate taxa and 12 physicochemical and hydromorphological variables for each sampling site. The abiotic variables served as input for the artificial neural networks to predict the macrobenthic community. The effects of the input variables on model performance were assessed in order to identify the most diagnostic river characteristics for macrobenthic community composition. This was done by consecutively eliminating the least important variables and, when beneficial for model performance, adding previously removed ones again. This stepwise input variable selection procedure was tested not only on a model predicting the entire macrobenthic community, but also on three models, each predicting an individual taxon. Additionally, during each step of the stepwise leave-one-out procedure, a sensitivity analysis was performed to determine the response of the predicted macroinvertebrate taxa to the input variables applied. This research illustrated that a combination of input variable selection with sensitivity analyses can contribute to the development of reliable and ecologically relevant ANN models. The river characteristics predicting presence or absence of the benthic macroinvertebrates best were the Julian day, conductivity, and dissolved oxygen content. These conditions reflect the importance of discharges of untreated wastewater that occurred during the period of investigation in nearly all Flemish rivers.

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Abbreviations

ANN:

Artificial neural network

BBI:

Belgian Biotic Index

BSI:

Biotic Sediment Index

CCI:

Number of correctly classified instances

RIVPACS:

River In Vertebrate Prediction And Classification System

RMSE:

Root mean square error

SLOO:

Stepwise leave-one-out

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Acknowledgements

The authors wish to thank ir. Tom D’heygere and Lic. Steven Heylen (Research Unit Aquatic Ecology, Ghent University), Lieven Detemmerman and ir. Ward De Cooman (Flemish Environment Agency), and AMINAL, Division Water, Brussels for providing useful data on the sediment samples in unnavigable watercourses in Flanders, Belgium, and two anonymous reviewers for valuable remarks. Andy Dedecker is a recipient of a grant of the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT). The international cooperation within this research was financed by the scientific exchange program Tournesol (project T2003.01). The applied neural network sensitivity analysis toolbox was developed within the European PAEQANN project (EVK1-CT1999-00026).

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Authors and Affiliations

  1. Department of Applied Ecology and Environmental Biology, Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, 9000, Ghent, Belgium

    Wim Gabriels, Peter L. M. Goethals, Andy P. Dedecker & Niels De Pauw

  2. LADYBIO UMR 5172, CNRS-University Paul Sabatier, 118, route de Narbonne, 31062, Toulouse cedex, France

    Sovan Lek

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  1. Wim Gabriels
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  2. Peter L. M. Goethals
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  3. Andy P. Dedecker
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  4. Sovan Lek
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  5. Niels De Pauw
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Correspondence to Peter L. M. Goethals.

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Gabriels, W., Goethals, P.L.M., Dedecker, A.P. et al. Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks. Aquat Ecol 41, 427–441 (2007). https://doi.org/10.1007/s10452-007-9081-7

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