The taxonomy of extant animal species is often based on biological information that is not normally preserved in fossils. Reducing discrepancies between taxonomy based only on hard tissues and that based on other biological information to a minimal level is crucial in studies focusing on species diversity that integrate extant and fossil material. In the present study, I address this issue using morphological analysis of the endemic Ogasawara Island land snails in the genus Mandarina. I first examine pairwise differences in shell morphology among 39 populations of 15 extant species that were discriminated by differences in their reproductive organs and their phylogenetic relationships. A classification model to assess whether the observed differences in fossil shell characters were inter- or intra-specific was developed by training an artificial neural network (ANN) with the patterns of differences in shell characters among the extant species. The average probability that the trained ANN misclassifies extant forms was 1.4%. The trained ANN was applied to discriminate morphological differences among the Pleistocene-Holocene fossil samples of Mandarina luhuana that occurred in Chichijima and Minamijima. As a result, three species were identified in the samples previously referred to M. luhuana. Mandarina pallasiana, previously treated as a synonym of M. luhuana, is separated, and one new species and one new subspecies are described.
|Number of pages||13|
|Publication status||Published - 2007 Dec 30|
- Land snails
- Neural network
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics