SAMPLING STRATEGIES FOR THE ASSESSMENT OF TREE SPECIES-DIVERSITY

This paper aims at proposing efficient vegetation sampling strategies. It describes how the estimation of species richness and diversity of moist evergreen forest is affected by (1) sampling design (simple rand om sampling, random cluster sampling, systematic cluster sampling, str atified cluster sampling); (2) choice of species richness estimators ( number of observed species vs, non-parametric estimators) and (3) choi ce of diversity index (Simpson vs. Shannon). Two sites are studied: a 28-ha area situated in the Western Ghats of India and a 25-ha area loc ated at Pasoh in Peninsular Malaysia. The results show that: (1) whate ver the sampling strategy, estimates of species richness depend on sam ple size in these very diverse forest ecosystems which contain many ra re species; (2) Simpson's diversity index reaches a stable value at lo w sample sizes while Shannon's index is affected more by the addition of rare species with increasing sample size; (3) cluster sampling stra tegies provide a good compromise between cost and statistical efficien cy; (4) 300-400 sample trees grouped in small clusters (10-50 individu als) are enough to obtain unbiased and precise estimates of Simpson's index; (5) the local topography of the Western Ghats has a major influ ence on forest composition, the steep slopes being richer and more div erse than the ridges and gentle slopes; (6) stratified duster sampling is thus an interesting alternative to systematic cluster sampling.