Use of morphometric characters of a fish species to predict its location; a statistical approach

Precise taxonomic identification is the preliminary requirement in a study of an organ- ism/specimen. Correct identification however gives only the identity of the specimen. The value of the correctly identified specimen as a study material becomes low when the habitat/location of its collection is unknown. Knowing the exact place of collection, enables to gather information on the distribution of the organism, possible environmental conditions that the organisms encounter and to describe the variations found in morphological and genetic features of the organism. Present study therefore, aimed on to develop a statistical rule to predict place of collection (river which is unknown) of a given Puntius dorsalis (a freshwater fish species) specimen using its morphometric characters. Fifty-two individuals were collected from four major rivers (Mahaweli, Kelani, Kalu, Nil- wala) in Sri Lanka and 23 morphometric characters were measured from each specimen. Those individuals were categorized into 4 groups according to the river from which they were collected. Measured morphometric characters were used as independent variables of the model to predict unknown group membership (river) of a given Puntius dorsalis specimen. In the case of re-substitution, 82.7% of the Puntius dorsalis specimens were successfully classified or predicted with respect to the place of collection (river) using their posterior probabilities. The process had a hit ratio of 69.2% when generalized, as a valid tool to classify fresh Puntius dorsalis specimen of unknown group membership. It was also discovered that linear classification function could be used to predict unknown place of collection of a fish. The paper concludes with some suggestions to move into nonparametric approach like Classification and Regression Trees (CART) and Neural Networks.