Artificial Neural Networks Enable Discrimination Of Taxa Using Morphological Characters: An Example With A Problematic Small Mammal, The Water Vole (Genus Arvicola) (Rodentia, Cricetidae)

The aim of this study is to assess the reliability of different artificial neural networks as tools for identifying taxonomic or ecotypic subdivisions in European water voles (genus Arvicola). Self-organizing maps show that taxa at subspecies or species level are organized along morphological gradients correlated with ecotypes (from extreme forms of fossorial Arvicola terrestris to strictly aquatic Arvicola sapidus). An automatic discrimination process based on a multilayer feed-forward network, working with 17 French and one Spanish reference populations, proved able to correctly identify species in test samples from France, but sometimes misidentified specimens from other countries. Three morpho-ecotypes were identified using a self-organizing map with a single-species dataset (Arvicola terrestris only). However, there were no clues as to how these morpho-ecotypes might correlate with the known phylogenetics of the species. Our model can identify species on the basis of a limited range of cranial measurements which are available from vole skull fragments commonly found in owl pellets. This can have very practical risk-assessment applications as it allows easier identification of the more fossorial Arvicola terrestris populations which are a threat to crops.