Prediction of macroinvertebrate diversity of freshwater bodies by adaptive learning algorithms

Abstract

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 Guegan 2000, Recknagel 2003).