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Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

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This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.

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Acknowledgements

The first author is a grant holder of the Special Research Fund (BOF) of Ghent University, while the second author is a grant holder of the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT). Co-operation between Ghent University and CNRS-University Paul Sabatier was promoted via the Flemish Government (Administration of Higher Education and Scientific Research) in the context of the TOURNESOL 2003 programme (project T-2003.01). The authors also wish to thank two anonymous reviewers for their valuable comments on the manuscript.

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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

    Ans M. Mouton, Andy P. Dedecker & Peter L. M. Goethals

  2. Flemish Environment Agency, Zandvoordestraat 375, 8400, Ostend, Belgium

    Andy P. Dedecker

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

    Sovan Lek

  4. Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, 9000, Ghent, Belgium

    Ans M. Mouton

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  1. Ans M. Mouton
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Correspondence to Ans M. Mouton.

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Mouton, A.M., Dedecker, A.P., Lek, S. et al. Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models. Environ Model Assess 15, 65–79 (2010). https://doi.org/10.1007/s10666-009-9192-8

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