Abundance, diversity, and structure of freshwater invertebrates and fish communities: An artificial neural network approach

Abstract Artificial neural networks (ANN) are models inspired by the structure and processes of biological cognition and learning. To illustrate the ecological applications of ANN, we present analyses of two complementary examples. ANN is first used to predict the diversity of macroinvertebrates, at the macrohabitat scale, in tributaries of a large river in New Zealand and, second, to predict the distribution and abundance of several fish species at the microhabitat scale in a French lake. The predictive abilities of the models were high, with correlation coefficients between observed and estimated values from 0.61 and 0.92. Moreover, the environmental variables found to be associated with invertebrate diversity and fish abundance were in accord with results of previous studies. The combination of ANN with a multivariate analysis offish community composition provided both for accurate prediction of fish assemblages and effective visualisation of their relationships with environmental variables. On the basis of these studies in different locations (New Zealand streams, French lake), involving various population and community attributes, we conclude that ANN is an appropriate tool for both prediction and explanation of ecological relationships at various spatial scales (microhabitat and macrohabitat), and for a range of aquatic ecosystems (lakes and rivers), organisms (invertebrates and fish), and ecological descriptors (abundance, Shannon diversity index, and community composition).