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

Area of Science:

• Ecology
• Biodiversity Science
• Quantitative Ecology

Background:

• Beta-diversity (β-diversity) measures are crucial for understanding community differences but are often biased by gamma-diversity (γ-diversity).
• This gamma-diversity dependence (γ-dependence) complicates comparisons of ecological communities across different regions or sampling efforts.
• Undersampling corrections aim to mitigate this bias caused by incomplete species sampling.

Purpose of the Study:

• To systematically evaluate the effectiveness of undersampling corrections in addressing γ-dependence in β-diversity.
• To compare these corrections against traditional null model approaches.
• To assess how well corrected β-diversity reflects ecological patterns across gradients.

Main Methods:

• Comparison of two undersampling correction methods with individual-based null models.
• Utilized simulated ecological communities along a gradient and empirical datasets.
• Analyzed data across varying γ-diversity levels and sample sizes.

Main Results:

• Undersampling corrections, particularly those using diversity accumulation curves, significantly outperformed null models in reducing γ-dependence.
• The corrected β-Shannon diversity index showed the least γ-dependence and best represented β-diversity patterns along the simulated gradient.
• Combining a corrected Jaccard-Chao index with null model results further enhanced the removal of γ-dependence.

Conclusions:

• Undersampling corrections are effective tools for mitigating γ-dependence bias in β-diversity analyses.
• These corrected measures facilitate more reliable comparisons of ecological communities across regions.
• The study highlights the importance of accounting for sampling completeness in biodiversity assessments.