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Classification

Classification. Similarity measures. Each ordination or classification method is based (explicitely or implicitely) on some similarity measure. (Two possible formulations of ordination problem). Similarities (dissimilarities, resemblance functions) based on qualitative/quantitative data.

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Classification

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  1. Classification

  2. Similarity measures Each ordination or classification method is based (explicitely or implicitely) on some similarity measure (Two possible formulations of ordination problem)

  3. Similarities (dissimilarities, resemblance functions) based on qualitative/quantitative data Other indices used for sample similarity and for species similarity Similarity of two samples has a meaning by itself: similarity of two species has meaning only in relation to the data set. Species set is „fixed“, samples are random selection from a population

  4. Sample similarity based on qualitative data Sörensen Jacquard d - number of species absent from both samples (usually not used)

  5. Species similarity based on presence absence d - number of quadrats without both species - absolutely necessary

  6. Quantitative data Transformation is an algebraic function Xij’=f(Xij) which is applied independently of the other values. Standardization is done either with respect to the values of other species in the sample (standardization by samples) or with respect to the values of the species in other samples (standardization by species). Centering means the subtraction of a mean so that the resulting variable (species) or sample has a mean of zero. Standardization usually means division of each value by the sample (species) norm or by the total of all the values in a sample (species).

  7. Euclidean distance For ED, standardize by sample norm, not by total The samples with t contain values standardized by the total, those with n samples standardized by sample norm. For samples standardized by total, ED12 = 1.41 (√2), whereas ED34=0.82, whereas for samples standardized by sample norm, ED12=ED34=1.41

  8. Percentual similarity (quantitative Sörensen)

  9. Similarity of species based on quantitative data Correlation coefficients (ordinary, rank)

  10. Similarity of samples vs. similarity of communities

  11. Normalized expected shared species = • expected number of shared species in two subsamples taken randomly from the second sample. 2 2

  12. Similarity matrices - directly used in Multidimensional scaling (both metric and non-metric) Mantel test

  13. Classification

  14. Hierarchical agglomerative (cluster analysis)

  15. Subjective decissions in the objective procedure

  16. Single linkage and complete linkage

  17. Single linkage - > chaining

  18. Order does not play a role

  19. TWINSPAN (Two Way INdicator SPecies ANalysis) Pseudospecies

  20. 01 is more similar to 1 than 00

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