Tree Building

1. Tree Building • What is a tree ? • Cladograms • Trees • Scenario • How to build a tree ? • Observations • First Principles • Assumptions • Methods

2. What is a tree ? • Cladograms and Trees • Both are graphs in mathematical terms: • A graph is a collection of nodes (vertices) and lines / branches (edges) connecting the nodes. • A cladogram/tree for our purposes is allowed at most one edge between any two vertices.

3. Cladogram & Trees - 1 • The degree of a node is the number of branches that contain that node. • A node of degree 1 is called a leaf (or terminal node) • All nodes that are not leaves are called internal.

4. A tree is elementary if no node has degree 2 (‘net-work’ in cladistic jargon). A root is a distinguished node with degree 2, locating the ‘start’ of the tree. Cladograms& Trees - 2

5. Cladograms& Trees - 3 • An unrooted tree is binary if every node has degree 1 or 3. • A rooted tree is binary if it has a root of degree 2 and every other node has degree 1 or 3.

6. A B C Cladograms& Trees - 4 Label Leaf Branch ? Node Root • Labeled rooted binary tree

7. Cladograms & Trees - 5 • What’s the difference? • Cladogram • Cladogenesis: • branching events as indicated by character state changes • Tree • + Anagenesis: • amount and duration of change + inference of ancestor-descendant relationships

8. A Cladogram is a: • Statement about the distribution of (shared) character states. • Branching diagram depicting nested sets of synapomorphies resulting in a summary statement of sister-group relations among taxa.

9. Z Y X abcdE aBCdE aBCde BC Syn-apomorphy E convergence ad Sym-plesiomorphy Nested sets of Synapomorphies • Detection of relationships by distribution of character-states in species X, Y, and Z. aBCde abcde

10. Relationship, and Kind of Groups • Relationship criterion: • Recency of common ancestry • “A species X is more closely related to another species Y than it is to another species Z if, and only if, it has at least one stem species in common with species Y that is not a stem species of Z” • (Hennig, 1966, p.74) • X and Y are sistergroups.

11. A Phylogenetic Tree is a: • Branching diagram where: • the nodes represent real or hypothetical ancestors, • the branching represents speciation, and • the branches represent descent with modification.

12. A B C Cladograms & Trees - 6 • Cladogram = set of trees Every picture tells a story ? =

13. B A B C C = A ? ? B A B C B C C ? A B C ? A ? C B C A A A B ? Cladogram = Set of Trees

14. The Cladistic Party Line ... • “There is simply no possible way to distinguish ancestors from extinct lineages.” • “… if something is in fact an ancestor, there are no data that can refute the hypothesis that it is an extinct lineage and not an ancestor.” • (Mark Siddal, 09/01/96, sci.bio.systematics).

15. … and its Counterpart • “…’A is the ancestor of B’ is a perfectly valid hypothesis, and one that is easily falsified. All it would take to falsify it is to find an autapomorphy in A that is not found in B.” • (Ron DeBry, 18/01/96, sci.bio.systematics)

16. Observations Character-state distri-butions over taxa (data matrix), or derivation thereof (distance matrix) First principles Assumptions Process Model Data Type and Quality How to build a cladogram

17. First Principles • Evolution (descent with modification) occurs. • Evolution results predominantly in a hierarchical scheme of relationships among the entities involved. • ...?

18. Assumptions - 1 • “ The fact that parsimony methods are known to fail in reconstructing phylogeny when there are unequal rates of evolution, and fail in a systematic way (e.g., put long branches together when they really should each go with one of the short branches) suggest […] that certain conditions of the process of evolution have to be met in order for the method to be useful […]. If a method is only useful when certain conditions of the evolutionary process are met, I would think that these conditions might as well be thought of as assumptions.” • (Andrew J. Roger, 08/01/96, sci.bio.systematics)

19. Process • “I have a pretty good idea of how evolution works, thus I can check how my data fit these ideas.” • “Given the phylogeny, what is the probability to find the data as I did ?” • Model = Statistical Framework • Maximum likelihood

20. Assumptions - 2 • “The philosophical part that deserves more explanation is how you get from whatever general principles you invoke (‘parsimony’) to the specific numerical method used.” • “Compatibility methods represent discarding a character because it has some sign of conflict with others. If there are two kinds of characters, really horribly noisy and pretty clean, that is a sensible thing to do. If there are instead two kinds, pretty clean and a little noisy, it is not. So I do not see how the principle of parsimony decides in advance which of these situations we are facing.” • (Joe Felsenstein, 14/12/95, sci.bio.systematics)

21. Data • “My data will tell me what the optimal set of branching events is and from there I will try to grasp what actually could have happened.” • Parsimony • Group / Component Compatibility • Character Compatibility

22. Observations • Molecular data • DNA sequences: • nuclear, mitochondrial, ribosomal • DNA-DNA hybridization • Restriction-site and -fragment • Allelic isozymes • Morphological data • Anatomical data • Chemical data

23. Black Boxes ? Phylogenetic Trees Principles - 1 Observations Assumptions Assumptions Optimality Methods Cladogram(s)