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Using a complete inventory method to analyze the species distribution of a rainforest is a daunting task due to the extensive area to be sampled. The answer for this problem is to find the most suitable sampling plot size, which realistically represents the entire species population distribution. In this study, optimal plot size, which contains the highest number of species cover per unit area for the Sinharaja rainforest was determined using multivariate methods, namely, cluster analysis, correspondence analysis, non-metric multidimensional scaling and procrustean analysis. Plot sizes selected for the study were 5 × 5, 5 × 10, 10 × 10, 10 × 20, 20 × 20 and 20 × 40 m2. It was revealed that the average linkage method is the most robust hierarchical clustering method according to the cophenetic correlation coefficient. The cluster solution was verified graphically using the four ordination techniques, correspondence analysis (CA), detrended correspondence analysis (DCA), principal coordinate analysis (PCoA) and non-metric multidimentional scaling (NMS), and the solutions were then compared using procrustean analysis. According to the procrustean rotations CA and NMS pair had the highest correlation, and procrustes sum of squares of pairwise rotations showed that NMS has the lowest sum of squares with CA. Since the ordination plots drawn by using NMS show clear differences among all 25 sites, it suggests that the NMS method is the most robust ordination method for forest data analyses. By assuming that the time for sampling work is constant for six plot sizes, it was noted that the 20 × 40 m2 plot contains the highest species cover per unit area. Mantel test indicates that the species of 20 × 40 m2 plots are highly correlated with 20 × 20 m2 plots, and the result was verified using the average linkage method and NMS ordination. These results suggest that 20 × 40 m2 is the most suitable rectangular shaped plot, and 20 × 20 m2 is the most suitable square shaped plot, which contain the highest number of species cover per unit area compared to the other selected plot sizes. J.Natn.Sci.Foundation Sri Lanka 2015 43 (1):91-98

Application of clustering and ordination methods to determine the optimal plot size for vegetation sampling of a Sri Lankan dipterocarp forest