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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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Abstract
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.
Keywords
Rivers, Sensitivity analysis, Predictive modelling, Macroinvertebrates, Environmental impact, Biological assessment, Multiple regression, MACROINVERTEBRATE COMMUNITIES, BENTHIC MACROINVERTEBRATES, ENVIRONMENTAL VARIABLES, GENETIC ALGORITHMS, WATER-QUALITY, PREDICTION, BELGIUM, WATERCOURSES, GAMMARUS, ECOLOGY

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Chicago
Mouton, Ans, Andy P Dedecker, Sovan Lek, and Peter Goethals. 2010. “Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models.” Environmental Modeling & Assessment 15 (1): 65–79.
APA
Mouton, A., Dedecker, A. P., Lek, S., & Goethals, P. (2010). Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models. ENVIRONMENTAL MODELING & ASSESSMENT, 15(1), 65–79.
Vancouver
1.
Mouton A, Dedecker AP, Lek S, Goethals P. Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models. ENVIRONMENTAL MODELING & ASSESSMENT. 2010;15(1):65–79.
MLA
Mouton, Ans, Andy P Dedecker, Sovan Lek, et al. “Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models.” ENVIRONMENTAL MODELING & ASSESSMENT 15.1 (2010): 65–79. Print.
@article{666590,
  abstract     = {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.},
  author       = {Mouton, Ans and Dedecker, Andy P and Lek, Sovan and Goethals, Peter},
  issn         = {1420-2026},
  journal      = {ENVIRONMENTAL MODELING \& ASSESSMENT},
  language     = {eng},
  number       = {1},
  pages        = {65--79},
  title        = {Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models},
  url          = {http://dx.doi.org/10.1007/s10666-009-9192-8},
  volume       = {15},
  year         = {2010},
}

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