Sampling Amazonian forest with clusters to measure tree species diversity: dissociating the effect of cluster size and sample size

Abstract A mature forest remnant in the Amazon was sampled with 22 0.4-ha clusters, which means a sampling effort of 8.8 ha. From this complete sample, nine subdatasets reduced in cluster size and other nine reduced in sample size were created, totaling thus 18 subdatasets. The aim of this study was to dissociate the effects of sample size and cluster size on Hill’s numbers. A cluster consisted of a sample unit composed by four crosswise sub-units of 1,000 m² (20m×50m) each. The 18 subdatasets were aggregated into nine pairs equivalent in sampled area (SA), which decreased from 8.8 ha (complete sample) to successive reductions from 4.4 ha (first pair of subdataset) to 0.8 ha (nineth pair). Hill’s numbers were calculated for every subdataset and then sample-based rarefaction curves were constructed to generate diversity profiles. As a result, species richness was abruptly underestimated in approx. 40% for the first reduction in sample size (SA = 4.4 ha). Significant underestimation of species richness occurred when the SA is below 2.4 ha, and accuracy of the other diversity profiles was severely compromised below 1.6 ha. We concluded that species richness is more sensitive to reductions in SA than Shannon diversity and Pielou’s evenness indices. For a same sampling effort, the choice in installing more small units demonstrated to better capturate the diversity profiles than installing less large units, especially when estimating species richness. Although the patterns of under or overestimates of the other diversity profiles were not exactly clear, the accuracy is substantially hampered when the SA is less than 1.6 ha.