Abstract:
Precise taxonomic identification is the preliminary requirement in a study of an
organism/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
place/habitat of its collection is unknown. Knowledge on place of collection exactly, 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 etc.
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, Nilwala)
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.
The assumptions for predictive discriminant analysis were satisfied, and 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. Also four Fisher’s linear classification functions can be used to predict group
membership easily. The paper concludes with some suggestions to move into nonparametric
approach like classification and regression trees (CART) and Neural Networks.
