Abstract
This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the ‘PaD‘ (‘Partial Derivatives‘) method consists of a calculation of the partial derivatives of the output in relation to the input variables; (ii) the ‘Weights‘method is a computation using the connection weights of the backpropagation Artificial Neural Networks; (iii) the ‘Perturb‘method analyses the effect of a perturbation of the input variables on the output variable; (iv) the ‘Profile‘ method is a successive variation of one input variable while the others are kept constant at a fixed set of values. The dataset consisted of 179 samples, collected over a three-year period in the Zwalm river basin in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Gammarus pulex were used in this study. The different contribution methods gave similar results concerning the order of importance of the input variables. Moreover, the stability of the methods was confirmed by gradually removing variables. Only in a limited number of cases a shift in the relative importance of the remaining input variables could be observed. Nevertheless, differences in sensitivity and stability of the methods were detected, probably as a result of the different calculation procedures. In this respect, the ‘PaD‘method made a more severe discrimination between minor and major contributing environmental variables in comparison to the ‘Weights‘, ‘Profile‘ and ‘Perturb‘ methods. From an ecological point of view, the input variables ‘Ammonium‘ and to a smaller extent ‘COD‘, were selected by these methods as dominant river characteristics for the prediction of the abundance of Gammarus pulex in this study area.
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Adriaenssens, V., Goethals, P. L. M. and De Pauw, N.: in press, ‘Fuzzy knowledge-based models for prediction of Asellus aquaticus, Asellus meridianus and Gammarus pulex in watercourses’, Ecol. Model.
Baran, P., Lek, S., Delacoste, M. and Belaud, A.: 1996, ‘Stochastic models that predict trout population density or biomass on a mesohabitat scale’, Hydrobiologia 337, 1–9.
Beauchard, O., Gagneur, J. and Brosse, S.: 2003, ‘Macroinvertebrate richness patterns in North African streams’, J. Biogeogr. 30, 1821–1833.
Bayerisches Landesamt für Wasserwirtschaft: 1996, Ökologische typisierung der aquatischen Makrofauna’, Inforamtionsberichte des Bayerischen Landesamtes für Wasserwirtschaft, Heft 4/96.
Bournaud, M. and Cogerino, L.: 1986, ‘Les microhabitats aquatiques des rives d'un grand cours d'eau: Approche faunistique’, Ann. Limnol. 23(3), 285–294.
Brehm, J. and Meijering, M.P.D.: 1990, Flieβgewässerkunde. Einfürhrung in de Limnolie der Quellen, Bäche und Flüsse. Biologische Arbeitsbücher, Verlag, Heidelberg, Wiesbaden.
Brosse, S., Lek, S. and Townsend, C. R.: 2001, ‘Abundance, diversity, and structure of freshwater invertebrates and fish communities: An artificial neural network approach’, N. Z. J. Marine Freshw. Res. 35, 135–145.
Brosse, S., Arbuckle, C. J. and Townsend, C. R.: 2003, ‘Habitat scale and biodiversity: Influence of catchment, stream reach and bedform scales on local invertebrate diversity’, Biodivers. Conserv. 12, 2057–2075.
Davis, W. S. and Simon, T. P.: 1996, Biological Assessment and Criteria Tools for Water Resource Planning and Decision Making, Lewis Publisher, Boca Raton, FL.
Dedecker, A. P., Goethals, P. L. M. and De Pauw, N.: 2002, ‘Comparison of artificial neural network (ANN) model development methods for prediction of macroinvertebrate communities in the Zwalm river basin in Flanders, Belgium’, Sci. World J. 2, 96–104.
Dedecker, A., Goethals, P. L. M., Gabriels, W. and De Pauw, N.: 2004, ‘Optimisation of artificial neural network (ANN) model design for prediction of macroinvertebrate communities in the Zwalm river basin (Flanders, Belgium)’, Ecol. Model. 174(1–2), 161–173.
Dedecker, A. P., Goethals, P. L. M., D'heygere, T., Gevrey, M., Lek, S. and De Pauw, N.: submitted, ‘Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models’, Environ. Model. Assess.
De Pauw, N. and Hawkes, H. A.: 1993, ‘Biological Monitoring of River Water Quality’, in: W. J. Walley and S. Judd (eds), River Water Quality Monitoring and Control, Aston University, Birmingham, pp. 87–111.
De Pauw, N., Lambert, V., Van Kenhove, A. and bij De Vaate, A.: 1994, ‘Performance of two artificial substrate samplers for macroinvertebrates in biological monitoring of large and deep rivers and canals in Belgium and the Netherlands’, Environ. Monit. Assess. 30, 25–47.
De Pauw, N. and Vanhooren, G.: 1983, ‘Method for biological assessment of watercourses in Belgium’, Hydrobiologia 100, 153–168.
De Pauw, N. and Vannevel, R.: 1991, Macroinvertebrates and Water Quality, Stichting Leefmilieu. Dossier No 11, Antwerp, p. 316 (in Dutch).
D'heygere, T., Goethals, P. L. M. and De Pauw, N.: 2003, ‘Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates’, Ecol. Model. 160, 291–300.
D'heygere, T., Goethals, P. L. M. and De Pauw, N.: in press, ‘Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks’, Ecol. Model.
Dimopoulos, Y., Bourret, P. and Lek, S.: 1995, ‘Use of some sensitivity criteria for choosing networks with good generalization ability’, Neural Process. Lett. 2, 1–4.
Dimopoulos, I., Chronopoulos, J., Chronopoulou Sereli, A. and Lek, S.: 1999, ‘Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece)’, Ecol. Model. 120, 157–165.
EU: 2000, Directive of the European Parliament and of the council 2000/60/EC Establishing a Framework for Community Action in the Field of Water Policy, European Union. The European Parliament. The Council. PE-CONS 3639/1/00 REV 1 EN.
Fontoura, A. P. and De Pauw, N.: 1994, ‘Microhabitat preference of stream macrobenthos and its significance in water quality assessment’, Verh. Int. Verein. Limnol. 25, 1936–1940.
Gabriels, W., Dedecker, A., Goethals, P. L. M., Lek, S. and De Pauw, N.: in press, ‘Analysing and predicting the effect of river pollution on macrobenthos communities in Flanders (Belgium) using a stepwise input variable selection procedure in combination with artificial neural networks’, Aquat. Ecol.
Garson, G. D.: 1991, ‘Interpreting neural-network connection weights’, Artif. Intell. Expert 6, 47–51.
Gevrey, M., Dimopoulos, I. and Lek, S.: 2003, ‘Review and comparison of methods to study the contribution of variables in artificial neural network models’, Ecol. Model. 160, 249–264.
Goethals, P. L. M. and De Pauw, N.: 2001, ‘Development of a concept for integrated ecological river assessment in Flanders, Belgium’, J. Limnol. 60(1), 7–16.
Guégan, J. F., Lek, S. and Oberdorff, T.: 1998, ‘Energy availability and habitat heterogeneity predict global riverine fish diversity’, Nature 391, 382–384.
Goh, A. T. C.: 1995, ‘Back-propagation neural networks for modelling complex systems’, Artif. Intell. Eng. 9, 143–151.
Hawkes, H. A.: 1979, ‘Invertebrates as Indicators of River Water Quality’, in A. James and L. Evinson (eds), Biological Indicators of Water Quality, Wiley, New York.
Hawkes, H. A. and Davies, L. J.: 1971, ‘Some Effects of Organic Enrichment on Benthic Invertebrate Communities in Stream Riffles’, in: E. Duffey and A. Watt (eds), The Scientific Management of Animal and Plant Communities for Conservation, Blackwell, Oxford, pp. 271–299.
Hoang, H., Recknagel, F., Marshall, J. and Choy, S.: 2001, ‘Predictive modelling of macroinvertebrate assemblages for stream habitat assessments in Queensland (Australia)’, Ecol. Model. 146, 195–206.
IBN: 1984, (in Dutch and French) Norme Belge T 92–402. Biological Water Quality: Determination of the Biotic Index Based on Aquatic Macroinvertebrates, Institut Belge de Normalisation.
Karr, J. R. and Chu, E. W.: 1997, ‘Biological monitoring: Essential foundations for ecological risk assessment’, Hum. Ecol. Risk Assess. 3, 933–1004.
Klemm, D. J., Blocksom, K. A., Thoeny, W. T., Fulk, F. A., Herlihy, A. T., Kaufmann, P. R. and Cormier, S. M.: 2002, ‘Methods development and use of macroinvertebrates as indicators of ecological conditions for streams in the mid-atlantic highlands region’, Environ. Monit. Assess. 78, 169–212.
Klemm, D. J., Lewis, P. A., Fulk, F. and Lazorchak, J. M.: 1990, Macroinvertebrate Field and Laboratory Methods for Evaluating the Biological Integrity of Surface Waters, EPA.600/4-90/030, Environmental Monitoring Systems Laboratory, Office of Modeling, Monitoring Systems, and Quality Assurance, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH.
Lek, S., Belaud, A., Dimopoulos, I., Lauga, J. and Moreau, J.: 1995, ‘Improved estimation, using neural networks, of the food consumption of fish populations’, Marine Freshw. Res. 46, 1229–1236.
Lek, S., Belaud, A., Baran, P., Dimopoulos, I. and Delacoste, M.: 1996a, ‘Role of some environmental variables in trout abundance models using neural networks’, Aquat. Living Resour. 9, 23–29.
Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. and Aulagnier, S.: 1996b, ‘Application of neural networks to modelling nonlinear relationships in ecology’, Ecol. Model. 90, 39–52.
Lek, S. and Guégan, J. F.: 1999, ‘Artificial neural networks as a tool in ecological modelling, an introduction’, Ecol. Model. 120, 65–73.
MacNeil, C., Dick, J. T. A., Bigsby, E., Elwood, R. W., Montgomery, W. I., Gibbins, C. N. and Kelly, D.W.: 2002, ‘The validity of the Gammarus:Asellus ratio as an index of organic pollution: Abiotic and biotic influences’, Water Res. 36(2), 75–84.
Marshall, J., Hoang, H., Choy, S. and Recknagel, F.: 2002, ‘Relationships between habitat properties and the occurrence of macroinvertebrates in Queensland streams (Australia) discovered by a sensitivity analysis with artificial neural networks’, Verh. Int. Verein. Limnol. 28, 1415–1419.
Mastrorillo, S., Lek, S., Dauba, F. and Belaud, A.: 1997, ‘The use of artificial neural networks to predict the presence of small-bodied fish in a river’, Freshw. Biol. 38, 237–246.
Meyer, J. L.: 1997, ‘Stream health: Incorporating the human dimension to advance stream ecology’, J. N. Am. Benthol. Soc. 16, 439–447.
Olden, J. D. and Jackson, D. A.: 2002, ‘Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks’, Ecol. Model. 154, 135–150.
Park, Y. S., Céréghino, R., Compin, A. and Lek, S.: 2003a, ‘Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters’, Ecol. Model. 160, 265–280.
Park, Y. S., Kwak, I. S., Chon, T. S., Kim, J. K. and Jorgensen, S. E.: 2001, ‘Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams’, Ecol. Model. 146, 143–157.
Park, Y. S., Verdonschot, P. F. M., Chon, T. S. and Lek, S.: 2003b, ‘Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network' Water Res. 37, 1749–1758.
Rabeni, C. F. and Minshall, G. W.: 1977, ‘Factors affecting microdistribution of stream benthic insects’, Oikos 29, 33–43.
Reice, S. R., Wissmar, R. C. and Naiman, R. J.: 1990, ‘Disturbance regimes, resilience, and recovery of animal communities and habitats in lotic ecosystems’, Environ. Manage. 14, 647–659.
Rech, V. H., Brown, A. V., Covich, A. P., Gurtz, M. E., Li, H. W., Minschall, G. W., Reice, S. R., Sheldon, A. L., Wallace, J. B. and Wissmar, R.: 1988, ‘The role of disturbance in stream ecology’, J. N. Am. Benthol. Soc. 7, 433–455.
Rosenberg, D. M. and Resh, V. H.: 1993, Freshwater Monitoring and Benthic Macroinvertebrates, Chapman & Hall, New York.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J.: 1986, ‘Learning representations by back-propagation errors’, Nature 323, 533–536.
Scardi, M. and Harding, L. W.: 1999, ‘Developing an empirical model of phytoplankton primary production: A neural network case study’, Ecol. Model. 120(2–3), 213–223.
Schleiter, I. M., Borchardt, D., Wagner, R., Dapper, T., Schmidt, K. D., Schmidt, H. H. and Werner, H.: 1999, ‘Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks’, Ecol. Model. 120(2–3), 271–286.
Schleiter, I. M., Obach, M., Borchardt, D. and Werner, H.: 2001, ‘Bioindication of chemical and hydromorphological habitat characteristics with benthic macro-invertebrates based on artificial neural networks’, Aquat. Ecol. 35, 147–158.
Steenbergen, H. A.: 1993, Macrofauna-atlas of North Holland: Distribution Maps and Responses to Environmental Factors of Aquatic Invertebrates, Haarlem, 651 pp.
Townsend, C. R., Scarsbrook, M. R. and Dolédec, S.: 1997, ‘Quantifying disturbance in streams: Alternative measures of disturbance in relation to macroinvertebrate species traits and species richness’, J. N. Am. Benthol. Soc. 16, 531–544.
Wagner, R., Dapper, T. and Schmidt, H. H.: 2000, ‘The influence of environmental variables on the abundance of aquatic insects: A comparison of ordination and artificial neural networks’, Hydrobiologia 422–423, 143–152.
Walley, W. J. and Fontama, V. N.: 1998, ‘Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain’, Water Res. 32(3), 613–622.
Wesenberg-Lund, C.: 1939 (reprint 1982), Biologie der Süsswassertiere. Wirbellose Tiere, Julius Springer, Wien, Cramer, Braunschweig, Koeltz, Koenigstein.
Whitehead, P. G., Howard, A. and Arulmani, C.: 1997, ‘Modelling algal growth and transport in rivers. A comparison of time series anlaysis, dynamic mass balance and neural network techniques’, Hydrobiologia 349, 39–46.
Whitehurst, I. T.: 1988, ‘Factors Affecting the Gammarus to Asellus Ratio in Unpolluted and Polluted Waters’, PhD Thesis, Brighton Polytechnic, Brighton, U.K.
Whitehurst, I. T. and Lindsey, B. I.: 1990, ‘Impact of organic enrichment on the benthic macroinvertebrate communities of a lowland river’, Water Res. 24(5), 625–630.
Witten, I. H. and Frank, E.: 2000, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, San Francisco, pp. 369.
Yao, J., Teng, N., Poh, H. L. and Tan, C. L.: 1998, ‘Forecasting and analysis of marketing data using neural networks’, J. Inform. Sci. Eng. 14, 843–862.
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Dedecker, A.P., Goethals, P.L.M., D'heygere, T. et al. Application Of Artificial Neural Network Models To Analyse The Relationships Between Gammarus pulex L. (Crustacea, Amphipoda) And River Characteristics. Environ Monit Assess 111, 223–241 (2005). https://doi.org/10.1007/s10661-005-8221-6
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DOI: https://doi.org/10.1007/s10661-005-8221-6