Cluster Analysis of Marine Nematodes

A small set of real data on marine nematode densities was analysed using 100 different combinations of methods of cluster analysis. The properties of 25 different similarity coefficients (sensu lato) were investigated in combination with four different clustering algorithms for three different measures of the nematode populations: species presence or absence, species abundance and species densities. The results of analysis by each combination of methods were compared directly with the individual species records. The best representation of the data was produced by the Bray-Curtis similarity coefficient and group average clustering algorithm. Species presences and absences were unsatisfactory in measuring nematode populations with all combinations of similarity coefficients and clustering algorithms. Percentage abundances of species produced generally good results with all combinations of methods but there were difficulties in interpreting results. Species densities were generally the best measure of nematode populations. Of the similarity coefficients, the Gower metric, euclidean distance squared and Bray-Curtis coefficients were generally the best but the former two were better with percentages and the latter with densities. The group average clustering algorithm was always the best. The results of other studies are considered in the light of the present results. The practical choices and problems of cluster analysis are discussed, including: the emphasis on rare and common species, the influence of the number of species and the distribution of individuals among species and the distortion of dendrograms by different clustering algorithms. Also discussed are more theoretical matters such as the statistical considerations of cluster analysis, the philosophy of cluster analysis and the relationship between species percentage abundances and densities.

Affiliations: 1: Department of Zoology, Australian National University, GPO Box 4, Canberra, ACT 2601, Australia