Undersampling correction methods to control γ-dependence for comparing β-diversity between regions

Measures of β-diversity are known to be biased by differences in γ-diversity (i.e., γ-dependence), making it challenging to compare β-diversity across regions. Undersampling corrections have been designed to reduce effects of γ-dependence on β-diversity arising from the problem of incomplete sampling. However, no study has systematically tested the effectiveness of these corrections or examined how well they reflect β-diversity patterns across ecological gradients. Here, we conduct these tests by comparing two undersampling corrections with the widely used individual-based null model approach, using both simulated communities along an ecological gradient and empirical data across a wide range of γ-diversity and sample sizes. We found that undersampling corrections using diversity accumulation curves were more effective than the null-model approaches in removing γ-dependence. In particular, the corrected β-Shannon diversity index was least dependent on γ-diversity, and was the most reflective of the β-diversity pattern along a simulated ecological gradient. Moreover, a corrected Jaccard-Chao index applied to null model results removed γ-dependence more effectively than either the correction alone or the null model alone. Undersampling corrections are effective tools for removing γ-dependence bias, thus facilitating comparisons of β-diversity across regions.