NEW METHOD FOR PRACTICAL ASSESSMENT OF THE COMPLETENESS OF MACROBENTHOS SPECIES RICHNESS ESTIMATION AT DIFFERENT SAMPLING EFFORTS

Background. Species richness (SR) is recognized as the main parameter in calculating the structural biodiversity indices of any community. The relevance of SR evaluation is determined by the need to assess taxonomic knowledge of biota in the course of various ecological studies. The aim of this study is to perform a comparative prognostic assessment of the expected species richness (Sexp) of macrozoobenthos in 8 silty sand biotopes along the Crimean coast. Methods. This study involves the use of the data collected over the course of benthic surveys conducted in 1957-2009 (about 290 samples in total). The nonparametric extrapolation algorithms (estimators) from the groups Chao, jackknife and bootstrap have been applied for calculation of the parameters of region-specific model cumulative curves. Results. With application of various estimation algorithms, the pattern of randomized increment in the number of new species with the increase in the number of samples has been identified, as well as the realism of estimating the Sexp level relative to the number of observed species (Sobs) at different sampling efforts. The Chao-2 and JN-1 algorithms have been found to be the most feasible estimators for the expected species richness, as they provide the most realistic (20-38 %) exceedance of Sexp over Sobs at similar sampling effort. It is shown that the use of these estimators is effective when more than 20-25 samples are taken. A practical graphic calculation method for determining the conditionally complete species richness (or total observed species richness, Sobs-total) is proposed, which allows for optimization of the ratio between the number of samples and the completeness of the list of identified species. On the example of zoobenthos communities from the areas with different environmental conditions, it was calculated that, on average, at least 3-4 random samples should be taken to reveal 33 % of Sobs-total, and at least 6-7 samples should be analyzed to detect 50 % of it. Detection of 75 % and 90 % of the total number of species in a benthic community is possible with sampling more than 16-18 samples and 25 samples, respectively. Conclusion. The use of the proposed calculation method and the empirical relationships (number of species vs. number of samples) obtained on its basis is recommended for the enhancement of the completeness of benthos species richness assessment in insufficiently studied coastal aquatic areas of the Black Sea.