SPECIATION OF THE TINTINNID GENUS CYMATOCYLIS BY MORPHOMETRIC ANALYSIS OF THE LORICAE

Samples of the tintinnid genus Cymatocylis were collected at an oceanic site near South Georgia in January 1990. The shapes and sizes of loricae observed included most forms previously reported by other authors and were representative of the entire genius. Measurements were taken from the loricae of over 700 specimens and 201 photomicrographs were obtained, from which further detailed measurements were taken. Univariate frequency histograms and bivariate scatter plots of the morphometric measurements were compared with multivariate techniques including: hierarchical nearest neighbour cluster analysis, linear discriminant analysis and canonical analysis with resubstitution on the model to 95% confidence intervals. Fourier transforms of digitised images of the photomicrographs were utilised as functions of the overall shape of the organisms, and input to both the linear discriminant function and canonical function with resubstitution on the model to 99% confidence intervals for comparison with results obtained from the manual morphometric measurements. Linear discriminant analysis showed 5 clear taxonomic classes corresponding to the original descriptions of C. calyciformis, C. convallaria, C. vanhoffeni, C. parva and C. drygalskii. Resubstitution onto the canonical models gave correct classification for the manual morphometric data and 100% correct classification for the Fourier transform data. These results showed that a clearer discrimination was obtained by utilising a multivariate 'description' of the overall shape. The cluster analysis showed that absolute size was not necessary for the identification. The univariate and bivariate approaches demonstrated some discernible separation, but with considerable overlap between species, especially C. vanhoffeni and C. drygalskii. These statistical methods were used to demonstrate that clear discrimination can be obtained from morphometric data and should allow for the development of automated taxonomic classification.