Artificial neural networks are increasingly being used by ecosystem, behavioural and evolutionary ecologists. A particularly popular model is the three-layer, feedforward network, trained with the back-propagation algorithm (e.g. Arak & Enquist, 1993; Ghirlanda & Enquist, 1998; Spitz & Lek, 1999; Manel et al., 1999; Holmgren & Getz, 2000; Kamo et al., 2002, Beauchard et al., 2003). The utility of this design (especially if, as is common, the output layer consists of a single node) is that for a given set of input data, the network can be trained to make decisions, and this decision apparatus can subsequently be applied to inputs that are novel to the network. For example, an ecosystem ecologist with a finite set of ecological, biochemical and bird-occurrence data for a river environment can train a network to produce a predictive tool that will determine the likelihood of bird occurrence through sampling of the environment (Manel et al., 1999). Or in behavioural and evolutionary ecology, a network can be trained to distinguish between a ‘resident animal’ signal and ‘background’ signals, and subsequently used to determine how stimulating a mutant animal signal is, and hence, how signals can evolve to exploit receiver training (Kamo et al., 2002). Reasons for the popularity of the back-propagation training method (Rumelhart et al., 1986) include its computational efficiency, robustness and flexibility with regard to network architecture (Haykin, 1999).