Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size.

Area of Science:

• Ecology
• Biodiversity Science
• Statistical Ecology

Background:

• Comparing species richness across ecological communities is fundamental to biodiversity research.
• Traditional methods using equal sample sizes can introduce bias due to varying community diversity.
• Existing rarefaction and extrapolation techniques may not accurately represent true richness differences.

Purpose of the Study:

• To develop and validate an integrated sampling, rarefaction, and extrapolation methodology for comparing species richness.
• To establish a new standard for comparison based on equal sample completeness (coverage) rather than equal sample size.
• To reduce bias in richness comparisons and optimize sampling effort.

Main Methods:

• Developed a novel analytical method for seamless coverage-based rarefaction and extrapolation.
• Integrated this method with an adaptive coverage-based stopping rule for sampling.
• Compared the performance of the new method against traditional size-based methods using hypothetical and real datasets.

Main Results:

• The coverage-based method yields significantly less biased comparisons of species richness between communities.
• The proposed methodology requires less total sampling effort to achieve reliable comparisons.
• When integrated with a stopping rule, samples can be compared directly, eliminating the need for post-hoc rarefaction.

Conclusions:

• Coverage-based rarefaction and extrapolation offer a more accurate and efficient approach to comparing species richness.
• This method improves the ranking of communities by their true richness, even without an adaptive stopping rule.
• The findings have broad implications for ecological monitoring and biodiversity assessments.