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

Citation

Cao, Ke et al. (2021), Undersampling correction methods to control γ-dependence for comparing β-diversity between regions, Dryad, Dataset, https://doi.org/10.5061/dryad.zgmsbcc80

Abstract

Measures of β-diversity are known to be highly constrained by the variation in γ-diversity across regions (i.e., γ-dependence), making it challenging to infer underlying ecological processes. Undersampling correction methods have attempted to estimate the actual β-diversity in order to minimize the effects of γ-dependence arising from the problem of incomplete sampling. However, no study has systematically tested their effectiveness in removing γ-dependence, and examined how well undersampling-corrected β-metrics reflect true β-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 γ-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 γ-dependence. In particular, the undersampling-corrected β-Shannon diversity index was most independent on γ-diversity and was the most reflective of the true β-diversity pattern along the ecological gradient. Moreover, the null model-corrected Jaccard-Chao index removed γ-dependence more effectively than either approach alone. Our validation of undersampling correction methods as effective tools for accommodating γ-dependence greatly facilitates the comparison of β-diversity across regions.

Methods

The data were generated using the simulation code in the dataset.

Usage Notes

Please cite the paper if anyone use this dataset or R code.

Funding

The Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDA19050500

National Natural Science Foundation of China, Award: NSFC 31770478

Villum Fonden, Award: 16549

The Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDA19050500