Transformation of cover-abundance values for appropriate numerical treatment –– Alternatives to the proposals by Podani
2007
van der Maarel, Eddy
Two alternatives are offered to Podani's proposals, based on the claim that Braun-Blanquet cover-abundance estimates cannot be properly analysed by conventional mul-tivariate methods.1. The ordinal transform scale, based on an extended Braun-Blanquet cover-abundance scale, comes close to a metric cover percentage scale after (1) the abundance values r (very few individuals), ++ (few ind.), 1 (abundant) and 2m (very abundant, cover < 5%%) are replaced by cover percentage estimates and (2) the higher Braun-Blanquet values, notably 4 and 5, with cover intervals 50-75%% and 75-100%%, respectively, are interpreted as estimates of considerably higher cover values than the usual visual projection on the ground (because of the position of stems and leaves in several layers). I propose the equation ln C == (OTV -2)1 a, where C == Cover%%, OTV is the 1 to 9 Ordinal Transfer Value and a is a factor weighting the cover values. With this equation cover values in a geometric series are achieved for the nine values in the extended Braun-Blanquet scale from 0.5 %% (OTV 1) to 140%% (OTV 9) for a == 1.415, and for a == 1.380 from 0.6 %% to 160%%.2. This makes use of an earlier developed ‘‘optimum-transformation’’ of cover-abundance values. For each species a frequency distribution of cover-abundance values is determined for a large data set, i.e. of dune slack vegetation. Tiny species have low values (OTVs 1-3) with high frequencies and hardly occur with higher OTV values; here all scores are considered ‘‘optimal’’. In dominant species OTVs 7 to 9 have the highest frequencies and only these values are considered optimal. Species with intermediate OTV ranges have optimum ranges with low-bound OTV == 2,3,4 and 5, respectively. No species were found in the dune slack data set with a frequency distribution justifying an optimum range with low-bound OTV == 6.For mathematically correct numerical treatments' optimum scores' can be converted to 1 and sub-optimal scores to 0 in order to approach a presence/absence situation.Both alternatives are suggested to be acceptable approximations to a metric basis for numerical analyses.
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