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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.

Alburnus alburnus alborella is a fish species native to northern Italy. It has suffered a very sharp decrease in population over the last 20 years due to human impact. Therefore, it was selected for reintroduction projects. In this research project, support vector machines (SVM) were tested as possible tools for building reliable models of presence/absence of the species. A system of 198 sites located along the rivers of Piedmont in North-Western Italy was investigated. At each site, 19 physical-chemical and environmental variables were measured. We verified that performances did not improve after feature selection but, instead, they slightly decreased (from Correctly Classified Instances [CCI] = 84.34 and Cohen's k [ k ] = 0.69 to CCI = 82.81 and k = 0.66). However, feature selection is crucial in identifying the relevant features for the presence/absence of the species. We then compared SVMs performances with decision trees (DTs) and artificial neural networks (ANNs) built using the same dataset. SVMs outperformed DTs (CCI = 81.39 and k = 0.63) but not ANNs (CCI = 83.03 and k = 0.66), showing that SVMs and ANNs are the best performing models, proving that their application in freshwater management is more promising than traditional and other machine-learning techniques.