Question: We asked whether ordinal cover scales cause biases in biodiversity indices derived from vegetation plots and, if so, whether a different back-translation of ordinal categories could improve the situation. Methods: We took three empirical vegetation-plot datasets from different regions and habitat types with species cover estimated in percent. We applied three ordinal cover scales (13-step Londo, 7-step Braun-Blanquet and 5-step Hult-Sernander-Du Rietz) and back-transformed the resulting categories to percent (mid-point of the respective cover class). For each plot, we then calculated three diversity metrics (Shannon diversity, Shannon evenness, Simpson diversity) before and after applying the ordinal scales and using arithmetic and geometric means for back-translation. Results: The Hult-Sernander-Du Rietz scale led to strongly increased values for the three diversity metrics when arithmetic mid-points were applied and to strongly decreased values when geometric mid-points were applied. Likewise, for the Braun-Blanquet and Londo scales the diversity indices had a positive bias in the case of arithmetic means and negative in the case of geometric means, but the differences were much smaller and not always significant. The ranking of communities by their biodiversity metrics was severely distorted for any combination of ordinal scale, biodiversity metric and transformation, but most strongly for the Hult-Sernander-Du Rietz scale. Conclusions: Our study suggests that in many cases the use of ordinal scales biases diversity metrics systematically. Since biodiversity metrics are commonly used to compare communities, our finding that the ranking of communities changed considerably when an ordinal scale was applied raises concerns about commonly applied practices. It suggests that in such studies percent cover estimates should be used. For the use of legacy data with ordinal scales we did not find a clear prevalence of arithmetic or geometric back-translation and thus recommend searching for alternative approaches.
