Evaluator | Optimality criteria* |
---|---|
Non-geometric evaluator | |
Morisita’s index of niche overlap (Horn 1966) | This index represents a measure of group overlap for a particular clustering solution. High proportional occurrence of species within a single group causes to niche overlap is decreased. It means minimal niche overlap indicates optimal solutions, so we used 1- Morisita instead of Morisita in the analysis. |
ISAMIC-indicator species analysis to minimize intermediate constancy (Robert 2010) | This index is a measure of consistent presence or absence of species in groups and it is bounded 0-1. The higher values the better. |
ISA (number of significant indicators) (Aho et al. 2008) | High ISA values indicate high fidelity and abundance of species within clusters. P-values for ISA-values calculated with Monte-Carlo procedures. |
Geometric evaluators | |
C-index (Hubert and Levin 1976) | This index shows the ratio of within to between group distances. This index is confined to interval 0–1 and minimum C-index scores is its optimality. So 1- (C-index) was considered instead of C-index in the analysis. |
PARTANA ratio (Robert 2005) | High PARTANA value implies Low within group dissimilarity and high dissimilarity of relevés within groups to relevés outside of groups. So higher PARTANA values were considered as optimal clustering solutions. |
Point biserial correlation (PBC) (Brogden, 1949) | The higher PBC values were considered as optimal clustering solutions |
ASW-average silhouette width (Rousseeuw 1987) | High ASW indicates samples within clusters are compositionally similar, and dissimilar to nearest neighbor samples outside clusters. This index is confined to interval 0–1 and a high value indicates optimal solutions. |
ANOSIM-analysis of similarities(Clarke 1993) | This index uses the rank of dissimilarity values in between and within groups. This index is confined to interval − 1, 0, + 1 and a high value indicates optimal solutions. Value 0 indicating completely random grouping. |