Species abundance distributions should underpin ordinal cover‐abundance transformations

Abstract

Questions: The cover and abundance of individual plant species have been recorded on ordinal scales for millions of plots world-wide. Ordinal cover data often need to be transformed to a quantitative form (0%-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. The questions this paper addresses are (a) how can we estimate transformation values for ordinal data that account for the underlying right-skewed distribution of plant cover; (b) do different plant groups require different transformations; and (c) how do our transformations compare to other commonly used transformations within the context of exploring the aggregate properties of vegetation? Location: Global. Methods:We assigned Braun-Blanquet cover-abundance ordinal values to continuous cover observations. We fitted a Bayesian hierarchical beta regression to estimate the predicted mean (PM) cover of each of six plant growth forms within six ordinal classes. We illustrate our method using a case study (2,809 plots containing 95,812 observations), compare the model-derived estimates to other commonly used transformations and validate our model using an independent dataset (2,227 plots containing 51,497 observations) accessed through the VegBank database. Results: 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 and was robust when validated against an independent dataset. Conclusions: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. |

Different sets of traits explain abundance and distribution patterns of European plants at different spatial scales

“…However, local cover is in general low at most sites and high at only a few sites across a species' distribution range (Murphy et al, 2006). In contrast to "everywhere sparse" species, these "somewhere abundant" species are reflected in right-skewed species abundance distributions, a common pattern in plant community ecology (McNellie et al, 2019). This skewness in local abundance might be caused by the distribution of optimal ecological conditions, and thus, might be causally linked to functional traits.…”

Optimal transformation of species cover for vegetation classification

“…Currently, these conversions are internationally most common due to the widespread use of Turboveg for storing vegetation-plot data. For lower classes of the Braun-Blanquet scale, this conversion provides slightly higher values than most of the published conversion tables (Tüxen & Ellenberg, 1937;Braun-Blanquet, 1964;van der Maarel, 1979van der Maarel, , 2007McNellie et al, 2019), meaning that species with low cover values will have a slightly higher importance in the classification than in the case of other conversions. It is important to note that the differences between various conversions are usually smaller than the error of visual cover estimation (Sykes et al, 1983;Kennedy & Addison, 1987;Lepš & Hadincová, 1992;Klimeš, 2003) or phenological changes in species cover over the growing season (Vymazalová et al, 2014).…”

“…Vegetation classification is often based on unsupervised numerical classification of vegetation-plot records, also called relevés in the phytosociological tradition (Whittaker, 1978;Peet & Roberts, 2013;De Cáceres et al, 2015). Species quantities in these plots are measured in different ways (Wilson, 2011), but most commonly they are visually estimated in percentages or by using cover-abundance scales that can be transformed into percentage covers, e.g., the Braun-Blanquet, Daubenmire, Domin or Hult-Sernander scale (van der Maarel, 1979;McNellie et al, 2019;Pätsch et al, 2019).…”

Modeling biodiversity benchmarks in variable environments

“…We previously used a beta regression model to estimate quantitative cover from BBCA data, using a subset (2,809 plots) of the 6,789 true cover estimates (McNellie et al. ), and transformed all BBCA data to quantitative estimates accordingly. Cover estimates of trees were based on crown cover, foliage cover, or projective foliage cover (Walker and Hopkins ).…”