Similarity Bray-Curtis

In quality-control studies, Kelly (2001) used benthic diatom specimens to compare performance of a trained counter and an auditor. The Trophic Diatom Index counts for 58 UK river samples varied by as much as 30 per cent between counters, with 13 of the 58 counts having more than 10 per cent discrepancy. It seems in these circumstances that the errors are non-linear, with the most error-prone counts obtained from those samples with low degrees of species Bray-Curtis similarity (defined in Gauch, 1982 i.e. most variety within the sample). 

Fig. 16.2 A multidimensional scaling (MDS) plot in three dimensions for scent profiles of individuals from three spotted hyena clans (o Isiaka clan, N = 8 Pool clan, N = 14 A Mamba clan, N = 23). Similarities between samples were calculated with the Bray-Curtis coefficient. MDS Stress 0.08... 

Aquatic microcosms and mesocosms offer the ideal situation to investigate populations of species interacting in their natural environment (i.e., to study communities stressed in structured systems see Section 4.5.1). It is, however, only recently that these experiments have been analyzed at the community level (Van Wijngaarden et al. 1995 Sparks et al. 1999 van den Brink and Ter Braak 1999). Until 10 years ago, experiments were evaluated at the population level, largely ignoring species interactions and energy flows in the systems. The development of community-level endpoints offered the possibility to evaluate the experiments on a community level (i.e., they offered the opportunity to scale up the level of evaluation Kedwards et al. 1999). Summary community-level endpoints calculated from the results of these experiments are mostly structural ones measures of diversity (e.g. numbers of species, and the Shannon-Weaver diversity index) and similarity of the treated systems compared to the untreated controls (e.g., the principal response curves method, Bray-Curtis dissimilarity, or Stander s index see van den Brink and Ter Braak 1998 for a comparison). 

Bray-Curtis distance = Lance-Williams distance —> similarity/diversity (0 Table S7)... 

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