The paper proposes an integrated methodology for comparing species richness among communities based on samples of equal completeness, measured by sample coverage rather than equal size. Traditional methods, which standardize samples to equal size, can misrepresent the relationships between community richnesses because a given sample size may be insufficient to fully characterize a richer community. The authors derive a new analytic method for coverage-based rarefaction and extrapolation, which yields less biased comparisons of richness and requires less total sampling effort. They also present an adaptive coverage-based stopping rule that allows for direct comparison of samples without rarefaction, thus avoiding data loss. The method is demonstrated through hypothetical and real examples, showing its advantages in providing more meaningful and robust comparisons of community richness.The paper proposes an integrated methodology for comparing species richness among communities based on samples of equal completeness, measured by sample coverage rather than equal size. Traditional methods, which standardize samples to equal size, can misrepresent the relationships between community richnesses because a given sample size may be insufficient to fully characterize a richer community. The authors derive a new analytic method for coverage-based rarefaction and extrapolation, which yields less biased comparisons of richness and requires less total sampling effort. They also present an adaptive coverage-based stopping rule that allows for direct comparison of samples without rarefaction, thus avoiding data loss. The method is demonstrated through hypothetical and real examples, showing its advantages in providing more meaningful and robust comparisons of community richness.