Using neural networks to predict spatial structure in ecological systems

We describe an approach in which a neural network (NN) can be trained on sets of driving variables (inputs) and output variables relating to spatial structure. The trained NN provides a predictive tool for defining spatial structure in a specified ecological system. We demonstrate the approach using a modified version of the Crawley and May [J. Theor. Biol. 125 (1987) 475] model that describes simplified annual/perennial plant interactions in a disturbed system. The model is implemented within a cellular automaton with randomised start conditions and is run for periods of up to 50 time steps (50 years). Neural networks are trained using a large set of modelled situations, and the ability of the network to predict plant spatial distributions is then measured. Different image analysis methods are applied to the plant array, and the ability of each method to provide an accurate description of the end-state of the modelled system is investigated. Reconstruction of the plant array from these image analysis measurements is carried out using a stochastic error minimisation method. The partial derivatives method is applied to the trained neural network in order to determine which variables input to the model most strongly influence the eventual plant population distribution. In the example presented, it is found that a relatively simple boundary:area ratio measurement provides a rapid and effective method of describing the spatial structure of the plant community, while the variables that are most influential on the system’s end-state are those describing annual fecundity and perennial mortality rates.