Prediction of macroinvertebrate diversity of freshwater bodies by adaptive learning algorithms

The natural distribution of organisms is determined primarily by their environmental requirements (Huntley 1999). Thus, understanding community patterns is important to manage target ecosystems. Especially in aquatic ecosystems, communities of benthic macroinvertebrates are important to monitor changes of the target system. Benthic macroinvertebrates constitute a heterogeneous assemblage of animal phyla and consequently it is probable that some members will respond to stresses placed upon them (Hynes 1960, Hawkes 1979). Many are sedentary, which assists in detecting the precise location of pollutant sources, and some have relatively long life histories. They provide both a facility for examining temporal changes and integrating the effects of prolonged exposure to intermittent discharges or variable concentrations of pollutants (Hellawell 1986). Therefore, it is promising to characterize the changes occurring in communities to assess target ecosystems exposed to environmental disturbances. Species richness is an integrative descriptor of the community (Lenat 1988), as it is influenced by a large number of natural environmental factors as well as anthropogenic disturbances (Cummins 1979, Rosenberg and Resh 1993). The disturbances of environmental factors may lead to spatial discontinuities of predictable gradients and losses of taxa (Ward and Stanford 1979). Species richness is known to be sensitive to environment changes in stream ecosystems (Resh and Jackson 1993), and is used as a biological indicator of disturbance. As with species richness, diversity indices decrease under increasing disturbance and stress on the ecosystem. The Shannon-Weaver diversity index (Shannon and Weaver 1949) is commonly used to describe the diversity of a particular community. The index is a function of both the number of species in a sample and the distribution of individuals among those species (Klemm et al. 1990). The diversity index is often used as an ecological indicator for the assessments of ecosystems (Bahls et al. 1992). Development of methods for patterning spatial and/or temporal changes in communities has currently become an important issue in ecosystem management. The River Invertebrate Prediction And Classification System (RIVPACS) was developed to assess water quality. The RIVPACS and its derivates belong to the first integrated ecological assessment analysis techniques (Wright et al. 1993b, Norris 1995). The models are based on a stepwise progression of multivariate and univariate analyses (Barbour et al. 1999). With nonlinear and complex ecological data, however, nonlinear analysing methods should be preferred (Blayo and Demartines 1991). An artificial neural network is a versatile tool for dealing with problems to extract information out of complex, nonlinear data, and could be effectively applicable to classification and association (Lek and Guégan 2000, Recknagel 2003). In ecological modelling, artificial neural networks are more and more used for data organization and classifying groups (Chon et al. 1996, Park et al. 2001a), patterning complex relationships between variables (Lek et al. 1996a, Scardi 2000), and predicting population development (Tan and Smeins 1996, Stankovski et al. 1998). Most of these models used two popular artificial neural networks: a multiplayer perceptron using backpropagation algorithm (Rumelhart et al. 1986b) and a Kohonen's Self-Organizing Map (Kohonen 1982). In the study of the benthic macroinvertebrates, in particular, a SOM has been used for patterning communities (Chon et al. 1996, 2000a, Park et al. 2001a, 2003a), for water quality assessments (Walley et al. 2000, Aguilera et al. 2001), and for prediction of population and communities (Céréghino et al. 2001, Obach et al. 2001). In addition, the MLP has been applied to the prediction of community parameters and species composition (Chon et al. 2001, Park et al. 2001a, 2003a), and bioassessment of water quality (Schleiter et al. 1999). The networks are mainly used to predict target values or to classify input vectors in a model. It is not easy to conduct both classification and prediction in such networks at the same time. However, patterning and predicting could effectively be carried out in a network. One example is a counterpropagation network (Hecht-Nielsen 1987), which consists of unsupervised and supervised learning algorithms. It classifies input vectors and predicts output values. This study aims to apply a counterpropagation network for patterning and for predicting the ecological data consisting of benthic macroinvertebrate communities and environmental variables. It could be a useful tool in managing aquatic ecosystems according to the EU Water Framework Directive (European Parliament 2000).