How to determine best diversity ordering method for a community data set?

Abstract

Background:We conducted a study about how to determine best diversity ordering method for a community data set. Using 12 hypothetical and one ecological datasets, we tested the performances of 20 diversity ordering (divo) methods based on four criteria. Number of intersections (ints) amongst the diversity curves was taken the most important criterion into account. We defined the other criteria considering whether parametric values of a divo method contains SHD (species richness, Shannon entropy and Simpson index), potentially uSHD (unbiased values of SHD), and potentially true species diversity, tSHD (bias corrected values as effective number of SHD). All the criteria were collected into an equation called the relative selection value, rVd i .Results: According to the rVd i values of hypothetical community data sets, the best performances in seven community data sets were provided by N α . This was followed by intrinsic diversity related methods with five community data sets. For ecological data set, the best results were obtained from the methods, (i,M i ), (log i,k i ) and N α , with the rVd i values of 6.883, 6.881 and 6.859, respectively. Conclusion:Findings suggest that the characteristics of community data sets play important role in defining the best diversity ordering method. This tells us that diversity is certainly a multifaceted phenomenon for a single community but perhaps it is a single phenomenon for a group of communities.

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“…There are many indices speci cally used in determining alpha diversity (Whittaker, 1972). Shannon Wiener and Simpson's D are two indices commonly used in de ning alpha diversity (Magurran, 1988;Gorelick, 2006;Özkan et al, 2022). Diversity analyses were successfully conducted using the Python programming language.…”