Performance comparison among multivariate and data mining approaches to model presence/absence of Austropotamobius pallipes complex in Piedmont (North Western Italy)

Abstract

Freshwater inhabitants in Piedmont (Italy) have been deeply disadvantaged by environmental changes caused by human disturbance. Hence there are engendered species that need human intervention of an entirely different kind - better management through the development of innovative practical tools. The most ecologically important of the river-dwelling invertebrates is a threatened species, the native white-clawed crayfish Austropotamobius pallipes. This is the species that we focused on in our effort to contribute to species conservation. Specifically we contrasted three different techniques of managing data relating to the presence/absence of this species: logistic regression, decision-tree models and artificial neural networks (ANN). Logistic regression and decision tree models (unpruned and pruned) performed worse than ANN. In this case, tree-pruning techniques did not make these models significantly more reliable, but did make the trees less complex and therefore did make the models clearer. ANN performed the best. Therefore we have judged them to be the most effective techniques.