1. Phylogenetic comparative trait and community analyses

2. Questions Discussions: – Robbie: posting paper and questions for this week – Vania & Samoa: will be picking a paper to post for week after spring break Reschedule Monday’s class? – 9:30-10:45 Wed in Benton 240 Any questions?

3. Ferns Gymnosperms Angiosperms

4. Part 1: Evolutionary trees What is systematics? What are phylogenies? Why are phylogenies useful? Background information

5. What is systematics? Systematics is the study of the diversity of organisms and the relationships among these organisms

6. Ways to examine relationships Evolutionary systematics: Based on similarity as determined by expert (Mayr, Simpson) Phenetics: Based on overall similarity (Rolf, Sokal, Sneath) Cladistics: Based on shared derived characters (synapomorphies; Hennig)

7. Ways to examine relationships Cladistics: Based on synapomorphies – Maximum Parsimony: Form the shortest possible tree (based on minimum steps) – Maximum Likelihood: Based on probability of change in character state and then calculate likelihood that a tree would lead to data (useful for molecular data) – Bayesian Inference: Based on the likelihood that the data would lead to the tree based on prior probabilities assigned using Bayes Theorem

8. Part 1: Evolutionary trees What is systematics? What are phylogenies? Why are phylogenies useful? Background information

9. What are phylogenies? Phylogenies are our hypotheses of evolutionary relationships among groups (taxa or taxon for singular) Graphically represented by trees When based on shared derived characters = cladogram a node 1 bc node 2 ch. 3 ch. 2 ch. 1

10. Part 1: Evolutionary trees What is systematics? What are phylogenies? Why are phylogenies useful? Background information

11. Why are phylogenies useful? Useful for studying – Evolutionary relationships – Evolution of characters: Correlated (PICs vs. sister pairs), Signal, Partition variation, Ancestral state, Simulations – Types (Brownian vs. OU) and rates of evolution (Homogenous vs. heterogeneous) – Group ages (fossils, biogeography) – Diversity/Diversification: Speciation vs. Extinction? – Biogeographic history – Community phylogenetics – Phyloclimatic modeling and conservation Assist in – Identification – Classification

12. Part 1: Evolutionary trees What is systematics? What are phylogenies? Why are phylogenies useful? Background information

13. Trees Characters Groups Other

14. Trees Tips: Living taxa Nodes: Common ancestor Branches: Can represent time since divergence Root: Common ancestor to all species in study a node 1 bc node 2 branch root tips

15. Trees Sister group: Closest relative to a taxon – c and d are sister – b = sister to c,d – a = sister to b,c,d ad b c

16. Trees Our goal is to make bifurcating trees But a polytomy is when we are unable to resolve which are the sister taxa (hard vs. soft) ad b c

17. Trees Phylogenetic trees can be rotated around their nodes and not change the relationships abc d bca d

18. Trees Toplogy: shape Branch lengths: differentiation (e.g., 1 = punctuated, speciational) or time = ultrametric

19. Characters Characters: Attribute (e.g., morphological, genetic) – Eye color – Production of flowers – Position 33 in gene X Character state: Value of that character – Blue, green, hazel, brown – Yes, No – A, T, G, C

20. Picking Characters Variable Heritable Comparable (homologous) Independent

21. Characters Homology: A character is homologous in > 2 taxa if found or derived from their common ancestor 1 or 1’ 11 homologous

22. Homology Homology is determined by: – Similar position or structures – Similar during development – Similar genetically – Evolutionary character series (transformational homology) from ancestor to descendents

23. Characters Homoplasy: A character is homoplasious in > 2 taxa if the common ancestor did not have this character 0 11 analogous

24. Homoplasy Due to – Convergent evolution: Similar character states in unrelated taxa – Reversals: A derived character state returns to the ancestral state

25. Characters Apomorphy: Derived character Pleisiomorphy: Ancestral character abc ch. 2 ch. 1

26. Characters Synapomorphy: Shared derived character Autapomorphy: Uniquely derived character Symplesiomorphy: Shared ancestral character chs. 2, 3 = Synapomorphies chs. 5, 6 = Autapomorphies ch. 1 = Symplesiomorphy ch. 4 = False synapomorphy a node 1 bc node 2 ch. 3 ch. 2 ch. 1 ch. 6 ch. 4 ch. 5 ch. 4 1,4,5 1,2,3,41,2,3,6

27. Monophyletic groups Monophyletic groups: Contain the common ancestor and all of its descendents What are the monophyletic groups? ad b c –c,d –b,c,d –a,b,c,d

28. Other groups (not recognized) Paraphyletic groups: Contain the common ancestor and some of its descendents ad b c ch. 1 Based on sympleisiomorphic character

29. Other groups (not recognized) Polyphyletic groups: Descendants with 2 or more ancestral sources ad b c Based on false synapomorphy e ch. 4

30. Getting trees From the literature, Phylomatic, Genbank, collect data yourself (may need name scrubbing tools: Phylomatic, TaxonScrubber) – Methods for assembly: Supertree, Supermatrix, Megatree, Zip them together – Getting the topology vs. getting branch lengths? – Discord among trees based on different characters? Gene trees vs. species trees

31. Storing trees Newick: ((b:1, c:1), a:1):1; Nexus (output of Paup) Pagel Distance matrix abc abc a033 b302 c320

32. Part 2: Hypothesis Testing Using Evolutionary Trees

33. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogeography – Evolutionary dating – Phylogenetic community structure – Coevolution/Cospeciation – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal http://treetapper.org/http://treetapper.org/, http://cran.r-project.org/web/views/Phylogenetics.htmlhttp://cran.r-project.org/web/views/Phylogenetics.html

34. When do we need to use phylogenies? Is it always necessary in ecological questions? – Yes, taxa are not independent points so we must “correct for” phylogeny – Sometimes, it is interesting to “incorporate” phylogenetic hypotheses to see how they influence our analyses – No, evolutionary questions can be asked by incorporating phylogenies but each species represents a separate successful event and should be analyzed with that in mind

35. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogenetic community structure – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal

36. Phylogenetic Community Structure Webb (2000) tested the alternate hypotheses that co-occurring species are (1) more or (2) less closely related than a random assembly of species He examined the phylogenetic structure in 28 plots in 150 ha of Bornean forest

38. Phylogenetic Community Structure He found species were more closely related than a random distribution

39. Phylogenetic Community Structure Recent development of metrics: NRI, NTI, PSV, PSC Do you use abundance or presence/absence? What regional pool do you compare to? What null models should you use?

40. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogenetic community structure – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal

41. Mapping Characters Once we have a known phylogeny, we can map on characters of interest to test hypotheses The phylogeny must be built on characters independent of those of interest

42. Types of Characters If we have a character that appears in a number of taxa, we may – Test the alternate hypotheses that it is (1) analogous or (2) homologous – Test hypotheses as to which state is ancestral and derived We can map the character onto the phylogeny to test these hypotheses

43. Homologous vs. Analogous Characters

44. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogenetic community structure – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal

45. Correlated Change Comparative biologists often try to test hypotheses about the relationships between two or more characters by taking measurements across many species – Seed size and seedling size – Body mass and surface area – Fruit size and branch size Fruit size Branch size

46. Correlated Change We might want to ask whether the correlation between traits is due to repeated coordinated evolutionary divergences We might expect closely related species to resemble one another

47. Correlated Change If our phylogeny looked something like this Then all of the change is really the result of one evolutionary event Branch size Fruit size

48. Correlated Change To incorporate phylogeny into comparative analyses, looking for correlated change, we can use – Sister pairs analyses – Felsenstein’s Independent Contrasts – Grafen’s Phylogenetic regression (ML and Bayesian approaches too) – Pagel’s Discrete and Multistate (Change in character state)

49. 0 1 2 3 trees & lianas shrubs Sign test: 32 of 45 are negative (p < 0.01) Strychnos Hamelia Miconia

50. Correlated Change To incorporate phylogeny into comparative analyses, looking for correlated change, we can use – Sister pairs analyses – Felsenstein’s Independent Contrasts (Brownian) – Grafen’s Phylogenetic regression (Other models) ML and Bayesian approaches too – Pagel’s Discrete and Multistate (Change in character state)

51. Independent Contrasts

52. BCD A E 5 15 10 5 5 G F Ch 1 20 102 4 Ch 2 10 40100 120 Red = Branch Lengths X = Character Values, V = Branch Length Values

53. Contrasts values: Ck = Xi – Xj Vi + Vj Ancestral Values: Xk = Vj Xi + Vi Xj Vi + Vj Branch Length: V’k = Vk + Vi Vj Correction Vi + Vj Independent Contrasts X = Character Values, V = Branch Length Values

54. Independent Contrasts BCD A E 5 15 10 5 5 G F Red = Branch Lengths X = Character Values, V = Branch Length Values Ch 1 20 102 4 Ch 2 10 40100 120

55. Independent Contrasts C E1 = 4 - 2 = 2 = 0.63 5 + 5 10 C E2 = 120 - 100 = 20 = 6.32 5 + 5 10 X E1 = 5 * 4 + 5 * 2 = 10 + 20 = 3 5 + 5 10 X E2 =5 * 120 + 5 * 100 =600 + 500=110 5 + 5 10 V’ E = 10 + 5 * 5 = 10 + 25 = 12.5 5 + 5 10 CD E 10 5 5 Ch 1 2 4 Ch 2 100 120 X = Character Values, V = Branch Length Values

56. Independent Contrasts C F1 = 3 - 10 = -7 = -1.5 10 + 12.5 22.5 C F2 = 110 - 40 = 70 = 14.8 10 + 12.5 22.5 X F1 =10 * 3 +12.5 * 10=30 +125 =6.9 10 + 12.5 22.5 X F2 =10*110+12.5 *40=1100 +500=71.1 10 + 12.5 22.5 V’ F =15 + 10 * 12.5 =15 + 125 =20.6 10 + 12.5 22.5 B E 15 10 12.5 F Ch 1 103 Ch 2 40110 X = Character Values, V = Branch Length Values

57. Independent Contrasts C G1 = 6.9 - 20 = -13.1 = -2.6 5 + 20.6 25.6 C G2 = 71.1 - 10 = 61.1 = 12.1 5 + 20.6 25.6 X G1 =5*6.9+20.6*20=34.5+411=17.4 5 + 20.6 25.6 X G2 =5*71.1+20.6 *10=355.5 +206=22 5 + 20.6 25.6 A 5 20.6 G F Ch 1 20 6.9 Ch 2 10 71.1 X = Character Values, V = Branch Length Values

58. Independent Contrasts Note: these should be fit through the origin

59. Independent Contrasts BCD A E 5 15 10 5 5 G F E F G Ch 1 20 102 4 Ch 2 10 40100 120

60. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogenetic community structure – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal

61. Dependent Change We find that two characters show correlated change We might hypothesize that change in one character is dependent on the state of a second character This can be tested easily on discrete characters – Seed size and disperser size

62. Dependent Change

63. Part 2: Hypothesis testing What sort of hypotheses can we test? – Phylogenetic community structure – Mapping characters Types of characters Correlated Change Dependent Change Phylogenetic Signal

64. We may want to test the alternate hypotheses that (1) the evolutionary history or (2) the recent ecological pressures most strongly influence species’ characters We can examine the amount of “phylogenetic signal” (whether two closely related species are more similar than two random species) for a character

65. Phylogenetic Signal Y Strong correlation with phylogeny Weak correlation with phylogeny

66. Phylogenetic Signal Ackerly: Based on PICs (randomizing across the tree) Pagel’s lambda Blomberg’s K: K 1 (clustered) Mantel tests: distance based

67. Partitioning variation Previously done with Taxonomic Hierarchical ANOVA (e.g., the Family, Genus, Species levels) – This assumes that Families are equivalent units But instead the % variation in a trait can be calculated for each node and compared across the tree