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Multivariate analysis of community structure data

Multivariate analysis of community structure data. Colin Bates UBC Bamfield Marine Sciences Centre. Goals. To understand the ideas behind multivariate community structure analysis. To understand how to perform these analyses in PRIMER.

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Multivariate analysis of community structure data

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  1. Multivariate analysis of community structure data Colin Bates UBC Bamfield Marine Sciences Centre

  2. Goals • To understand the ideas behind multivariate community structure analysis. • To understand how to perform these analyses in PRIMER. • To be prepared to analyse and interpret your class data later today.

  3. What are multivariate statistics? Statistics that allow us to look at how multiple variables change together

  4. What are multivariate statistics? Statistics that allow us to look at how multiple variables change together: EG: How do 50 species in a community react to an environmental perturbation?

  5. What are multivariate statistics? • Statistics that allow us to look at how multiple variables change together: • EG: How do 50 species in a community react to an environmental perturbation? • 50 ANOVAs?

  6. What are multivariate statistics? • Statistics that allow us to look at how multiple variables change together: • EG: How do 50 species in a community react to an environmental perturbation? • 50 ANOVAs? No… • Multivariate stats allow us to “condense” information for simplicity

  7. When might I use this type of analysis? • For a multi-species community, you may wish to: • pull order from complex systems • visualize these patterns • comparisons over time and space • test hypotheses

  8. The vehicle:

  9. Example: Seaweed Communities at Cape Beale • Is flora different at two close sites, each exposed to different wave intensity?

  10. Data collection:

  11. 2. Data Analysis Step 1: Entering your data into PRIMER

  12. How to analyze this type of data? 1. Diversity indices

  13. How to analyze this type of data? 1. Diversity indices Yet, most diversity indices do not consider species identity…

  14. How to analyze this type of data? 1. Diversity indices Yet, most diversity indices do not consider species identity… Multivariate community structure analyses

  15. b b a a a a b b b b a a c c c c c c Analysis flow samples species sample similarities ordination How? are sites different?

  16. b a a b b a c c c Analysis flow samples species sample similarities ordination Calculate Bray – Curtis Similarity  gives a triangular similarity matrix

  17. within within between

  18. b b a a a a b b b b a a c c c c c c Analysis flow samples species sample similarities ordination How? are sites different?

  19. b a a b b a c c c ordination Visualizing similarities Ordination “maps” similarity relationships between samples

  20. nMDS ordination example

  21. nMDS ordination example Distance between points reflects relative similarity!

  22. Nonmetric multidimensional scaling (nMDS) “the future of ordination is in nonmetric multidimensional scaling” – McCune & Grace, 2002 Nonmetric: no axes Multidimensional: represents relationships between multiple variables in two or three dimensions Scaling: the ratio between reality and representation

  23. How does nMDS work? nMDS uses the RANK ORDER of similarity relationships between samples: A1 is closer to A2 than it is to A3

  24. How does nMDS work? Then, nMDS tries to place points in 2 (or 3) dimensional space to represent this ranked order: A3 A1 is closer to A2 than it is to A3 A1 A2

  25. A2 A1 A3 How does nMDS work? Then, nMDS tries to place points in 2 (or 3) dimensional space to represent this ranked order: A1 is closer to A2 than it is to A3

  26. How accurate is the nMDS map? - Sometimes the nMDS can’t represent all relationship accurately - this is reflected by a high STRESS value

  27. How accurate is the nMDS map? - Sometimes the nMDS can’t represent all relationship accurately - this is reflected by a high STRESS value If Stress Value = 0.0 : perfect map 0.1 : decent map 0.2 : ok map 0.3 : don’t bother . . . . . . . . . . . . similarity in sim. matrix . . . . . . . . distance on nMDS

  28. b a a b b a c c c Main points about ordination! • Ordination is a way to visualize how similar your samples are • - nMDS tries to represent visually the rank order within the underlying similarity matrix • all that matters is the relative distance between points. • stress value allows you to estimate ‘quality’ of the nMDS’ sample similarities ordination

  29. Obviously distinct groups

  30. Less obvious! Are they really different?

  31. b b a a a a b b b b a a c c c c c c Analysis flow samples species sample similarities ordination are sites different?

  32. b b a a a a b b b b a a c c c c c c Analysis flow samples species sample similarities ordination How? are sites different?

  33. Are groups different? Analysis of Similarities – a statistical approach exposed sheltered

  34. Are groups different? Analysis of Similarities – a statistical approach Ho = sites the same Ha = sites are different exposed sheltered

  35. If Ho (sites the same) = true Similarity within = Similarity between

  36. If Ha (sites different) = true Similarity within > Similarity between

  37. (rbetween - rwithin ) R = standardizing factor Are groups different? Analysis of Similarities – a statistical approach

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