Species abundance distributions should underpin ordinal cover-abundance transformations

The cover and abundance of individual plant species have been recorded on ordinal scales for millions of plots world-wide. Many ecological questions can be addressed using these data. However ordinal cover data may need to be transformed to a quantitative form (0 to 100%), especially when scrutinising summed cover of multiple species. Traditional approaches to transforming ordinal data often assume that data are symmetrically distributed. However, skewed abundance patterns are ubiquitous in plant community ecology. A failure to account for this skew will bias plant cover estimates, especially when cover of multiple species are summed. The questions this paper addresses are (i) how can we estimate transformation values for ordinal data that accounts for the underlying right-skewed distribution of plant cover; (ii) do different plant groups require different transformations and (iii) how do our transformations compare to other commonly used transformations within the context of exploring the aggregate properties of vegetation? Using a continuous cover dataset, each occurrence record was mapped to its commensurate ordinal value, in this case, the ubiquitous Braun-Blanquet cover-abundance (BBCA) scale. We fitted a Bayesian hierarchical beta regression to estimate the predicted mean (PM) cover of each of six plant growth forms within different ordinal classes. We illustrate our method using a case study of 2 809 plots containing 95 812 occurrence records with visual estimates of cover for 3 967 species. We compare the model derived estimates to other commonly used transformations. Our model found that PM estimates differed by growth form and that previous methods overestimated cover, especially of smaller growth forms such as forbs and grasses. Our approach reduced the cumulative compounding of errors when transformed cover data were used to explore the aggregate properties of vegetation and was robust when validated against an independent dataset. By accounting for the right-skewed distribution of cover data, our alternate approach for estimating transformation values can be extended to other ordinal scales. A more robust approach to transforming floristic data and aggregating cover estimates can strengthen ecological analyses to support biodiversity conservation and management.