Classification of communities

1. And now, for something completely different.... Classification of communities

2. Association analysis • 4 major approaches: • 1) Tabular methods • 2) Cluster analysis • 3) Association analysis • 4) Ordination methods

3. Cluster analysis • Expresses similarity of stands graphically (2 D) • Similarity? Coefficient of Community (CC) • CC: how close composition is b/w samples

4. Cluster analysis • 2 major indices: • Jaccard’s Index • Sorensen’s Index • Presence or cover (“weighted by cover”) • Values: from 100 (same) to 0 (no sp. in common)

5. Cluster analysis • Generate dendrogram (tree diagram showing similarities) • Y axis = “resolving power:” 40

6. Cluster analysis • Decide level similarity: • CC = 10 (threshold III): 2 associations • CC = 20 (threshold II): 7 associations • CC = 30 (threshold I): 15 associations • Result: associations

7. Association analysis • 4 major approaches: • 1) Tabular methods • 2) Cluster analysis • 3) Association analysis • 4) Ordination methods

8. Association analysis • Uses differential spp. (influence on other spp.) • How document? Use contingency table analysis

9. Association analysis • Contingency table analysis • Calculate chi-square for every pair sp. (all samples) Old Lab Exercise

10. Association analysis • Matrix chi-square values • Species • Species A B C D A X 57 12 23 B 57 X 17 3 C 12 17 X 1 D 23 3 1 X Total 92 77 30 27

11. Association analysis Species • Species A B C D A X 57 12 23 B 57 X 17 3 C 12 17 X 1 D 23 3 1 X Total 92 77 30 27 • Select sp. w/ highest chi-square total • Split stands into 2 groups: w/ A & w/o A

12. Association analysis • Repeat on each split group • 1 sp. A present • Other lacks sp. A • Continue subdivide until total chi-square < some number

13. Association analysis • Ex, 70 quadrats in salt marsh • Association analysis: use until total chi-square = 7

14. Association analysis • Dendrogram (70 quadrats)

15. Association analysis • 8 groups: presence/absence sp. • +32, +38 = group 1 • +32, -38 = group 2 • Etc. Group = association

16. Association analysis • Summary: Associations defined

17. Classification of communities • 4 major approaches: • 1) Tabular methods • 2) Cluster analysis • 3) Association analysis • 4) Ordination methods

18. Ordination Methods Plant Ecology Theatre! Not that kind of ordination…

19. Plant Ecology Theatre Reg: Trouble at t’mill Lady M: Oh no! What sort of trouble? Reg: One on’t cross beams gone owt askew on’t treddle. Lady M: Pardon? Reg: One on’t cross beams gone owt askew on’t treddle. Lady M: I don’t understand what you’re saying. Reg: One of the cross beams has gone out of skew on the treadle. Lady M: What on earth does that mean? Reg: *I* don’t know! Mr. Wentworth just told me to come in here and say that there was trouble at the mill, that’s all. I didn’t expect a kind of Spanish Inquisition! http://www.youtube.com/watch?v=vt0Y39eMvpI Nobody expects the Spanish Inquisition....

20. Ordination Methods • Data graphical • “ordination space”

21. Ordination Methods • Associations based on dominants • Correlate vars. w/ axes: 4 methods

22. Ordination Methods (4) • 1) Principal Components Analysis (PCA) • (called components) • Axes

23. Ordination Methods • Ex, PCA output: 60-stand by 30-species matrix • Can plot spp. (LITU=Liriodendron tulipifera, QURU=Quercus rubra) • PCA-1 correlates w/ stand age (open circles=young stands) Open=young stands

24. Ordination Methods (4) • 2) Detrended Correspondence Analysis (DCA): DCA PCA

25. Ordination Methods (4) • 3) Nonmetric Multidimensional Scaling (NMDS): • Vectors: correlations w/ env. vars. (show direction) K=soil potassium BA=basal area DEN=tree density NO3=soil nitrate Open=young stands

26. Ordination Methods (4) • 4) Canonical Correspondence Analysis (CCA): • Vectors: relate vars. (length = strength influence of var.) CEC=soil cation exchange capacity SAND=amt sand in soil OM=soil organic matter BA=basal area DEN=tree density NO3=soil nitrate Open=young stands

27. Ordination Methods • Dominant

28. Remote sensing

29. Remote sensing • Reflectance info:

30. Remote sensing • Can measure: • protein, lignin, Mapping conifers killed by bark beetles in Canada

31. Remote sensing • Involves ground truthing:

32. Vegetation mapping • Vegetation:

33. Vegetation mapping • National Vegetation Classification System (U.S.) • Hierarchical classification (Division largest, Association smallest units) • Physiognomy important: • Floristics important: USA! USA!

34. Vegetation mapping • Ex: Populus deltoides/Salix woodland

35. Vegetation mapping • Use GIS (geographic information system)

36. Vegetation mapping • What map? • 1) Actual vegetation Ground truthing needed to verify patterns

37. Vegetation mapping • 2) pre-human vegetation: aka Potential vegetation

38. Vegetation mapping • Early explorers • Ex, William Bartram • SE US early 1700s