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Despite considerable criticism in recent years, the use of local (SL) and regional species richness (SR) plots has a long tradition to test for community saturation. The traditional approach has been to compare linear and polynomial regression models of untransformed measures of SL and SR with a statistically significant linear or polynomial model indicating unsaturated and saturated communities, respectively. This approach has been the target of much controversy owing to statistical issues, the confounding effects of the arbitrary choice for the size of the local and regional area, and the difficulty in attributing ecological processes to the underlying SL SR pattern. The statistical issues and effects of scale stem from the lack of statistical independence and induced correlation between SL SR arising from the mathematical constraint, SL<SR. However, by removing this mathematical constraint by means of a logratio transformation, SL SR relationships can be calculated using ordinary linear regression and with a logical and definitive null-hypothesis based solely on the presence of a statistically significant slope, which provides a quantitative measure of curvature. Simulations of SL SR relationships with varying curvature and SL:SR ratio demonstrate that the logratio model can accurately measure curvature independent of the SL:SR ratio. Therefore, the tendency for studies with high local:regional area ratio to result in linear SL SR trends when analysed by traditional regression methods may be mitigated by reanalysis by the logratio model. By alleviating the effects of scale, the logratio model offers a more statistically sound assessment of the SL SR relationship, which in turn can serve as an effective tool to complement emerging process-based models.