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

Measures of {beta}-diversity are known to be highly constrained by the variation in {gamma}-diversity across regions (i.e., {gamma}-dependence), making it challenging to infer underlying ecological processes. Undersampling correction methods have attempted to estimate the actual {beta}-diversity in order to minimize the effects of {gamma}-dependence arising from the problem of incomplete sampling. However, no study has systematically tested their effectiveness in removing {gamma}-dependence, and examined how well undersampling-corrected {beta}-metrics reflect true {beta}-diversity patterns that respond to ecological gradients. Here, we conduct these tests by comparing two undersampling correction methods with the widely used individual-based null model approach, using both empirical data and simulated communities along a known ecological gradient across a wide range of {gamma}-diversity and sample sizes. We found that undersampling correction methods using diversity accumulation curves were generally more effective than the null model approach in removing {gamma}-dependence. In particular, the undersampling-corrected {beta}-Shannon diversity index was most independent on {gamma}-diversity and was the most reflective of the true {beta}-diversity pattern along the ecological gradient. Moreover, the null model-corrected Jaccard-Chao index removed {gamma}-dependence more effectively than either approach alone. Our validation of undersampling correction methods as effective tools for accommodating {gamma}-dependence greatly facilitates the comparison of {beta}-diversity across regions.