<p>Plant Systematics May 5th, 2005 :</p><p>Overview of Malvaceae(From Tuesday)</p><p>Do folks know how to do a t-test?</p><p>Keying update and advice - read the species descriptions</p><p>Heterostyly (primulaceae from last week)</p><p>Pop quiz</p><p>Hope to outline chapters very briefly in advance (prior tues or prior thurs)</p><p>************ Lecture start*********</p><p>Overview of the systematics and the book (where we are going, how it all ties together)</p><p>Keying - read species description</p><p>Family familiarity</p><p>Evolutionary relationships (uncovering them) Phylogenetics</p><p>Plant classification - taxonomy Evolutionary processes</p><p>Green plant evolution</p><p>Classification goals and characteristics vs phylogenetic analysis</p><p>Why do we need to classify things?</p><p>1 Common vocabulary to aid communication</p><p>2 Stable</p><p>3 Informative or predictive</p><p>4 Anything else we've forgotten?</p><p>Brief history of classification goals - </p><p>How would you classify plants and animals before Darwin?</p><p>God's creatures catalogued (Macroscopic morphology primary)</p><p>Similar things grouped for ease of study</p><p>Evolution is implied by similarity (microscopic, development, chemistry, proteins) Classification MUST adhere to evolutionary relationships - strict monophyly</p><p>Let's get explicit about evolutionary hypotheses - not characters but DNA, </p><p> genetic environment</p><p>Umm. Perhaps classification could be decoupled somewhat from evolutionary </p><p> relationships - </p><p> usefulness of classification is primary to classification; </p><p> phylogeny is primary for evolutionary relationships</p><p>Da Book</p><p>Ch 2 Methods and philosophy of uncovering evolutionary relationships (phylogenetics) and creating a classification system - the heart of the matter, the multiple ways.</p><p>Ch 3 The history of thinking about classification- how things got to be such a mess now Ch 4 Morphological traits and methods used to classify (sans pollination)</p><p>Ch 5 Genetic methods used to model evolutionary relationships</p><p>Ch 6 Evolution + pollination - speciation, change and how diversity is created - the good stuff. Ch 7 Green plant evolution- how did plants evolve into angiosperms and what makes them so special. - More good stuff Ch 2 brief outline</p><p>That which has transpired</p><p>What is phylogeny (13-- 15) basic concepts and terms</p><p> lineages</p><p> characters</p><p> character states</p><p> derived characters or character states (apomorphies) - NEW!</p><p> ancestral character states (pleisomorphies) - OLD!</p><p> shared derived character states (synapomorphies) Creating trees of evolutionary relationships (16 -30)</p><p> characters and states</p><p> determining similarity of state among taxa</p><p> good characters (non-overlapping states, heritable, more than one </p><p> distinguishable state, consistent within taxa)</p><p>*evolutionary relationship networks ( path from one species to the next via </p><p> character state changes) - topology</p><p>*creating evolutionary relationship Trees (rooting networks)</p><p>Outgroups (assumed! )</p><p>*Mono/para/polyphyly homology (common ancestor)</p><p> determined by creating phylogeny</p><p> e.g. limb -> arm-fin homoplaisy (not common ancestor)</p><p> parallelism, reversal</p><p> e.g. bees wing xxx bird wing Onward!</p><p>Choosing the best tree - </p><p> three ways although book focuses on parsimony</p><p>1 *Parsimony (Fig 2-10, p 22)</p><p> fewest character state changes</p><p> parallelism- number of times it evolved</p><p> reversal- number of times it got lost 2 Distance</p><p> p distance, Jukes-Cantor, Kimura 2-parameter</p><p>P-distance - proportional difference</p><p> n= total number of nucleotides compared</p><p> d= number that are different</p><p> p = d/n</p><p>A A A A G T C A T C</p><p>A A A A C T C A T C 3 Maximum likelihood (probability of events)</p><p> takes into account the complex nature of life and assigns probabilities to </p><p> different evolutionary events. </p><p> creates the tree with the highest probability of happening</p><p> complex characters are less likely to occur twice than simple characters</p><p> may be easier to lose complex characters than gain them Compromising among several "best" trees. (Fig 2.11 p24)</p><p>Strict consensus - </p><p>Branch present on tree A AND B </p><p>Semi strict - </p><p>Branch present on tree A OR B but no incompatibilities</p><p>Majority rule -</p><p>Branch present on 50% or more trees - who says politics doesn't affect </p><p> science?</p><p>WE Stopped HERE ON THE 5th</p><p>Dealing with the real world - models Model assumptions</p><p>*Different character states evolve differently (ordering) (2.13 p26)</p><p>Fitch parsimony - unordered</p><p>Wagner parsimony - ordered</p><p> increases required steps in some cases</p><p>*Different characters evolve differently </p><p> complex characters are less likely to evolve (all else equal) </p><p>Parsimony - weighting</p><p>ML - different probabilities</p><p>*Reversals and parallelism </p><p> it can be harder to evolve (parallelism) than to lose (reversal) a </p><p> character state. - Dollo parsimony - forget this name weighting and probability should be based on biology or underlying knowledge "unweighted" is still an assumption Belief in the tree</p><p>The best tree (fewest steps, min dist, max likelihood)</p><p>The following for parsimony only (but some ideas may apply more broadly)</p><p>How good are the others - assessing homoplasy</p><p> autapomorphy </p><p> unique derived character - un shared and uninformative </p><p> adding these doesn't help but makes it look like you are </p><p> assessing more characters (fig 2.25 p38)</p><p>*consistency index (length = number of steps) (fig 2.10 matrix)</p><p>Min Length = number of character states in the group- need at </p><p> least 1 change to obtain the character</p><p>Actual length - reversals and parallelisms add to number of steps</p><p>Maximum length = every change to new state is unique. This </p><p> equals the number of new character states for each </p><p> taxon. CI = min/actual - 1.0 is the best 0 the worst</p><p>RI = (max-actual)/(max-min)= (9-5)/(9-4)=0.8 (fig 2.10)</p><p>Parts of trees may be good even if the tree has problems</p><p> for a branch, </p><p># changes compared to the *CI of these changes</p><p> decay rate and number of boot straps (fig 2.17 p 30)</p><p>*decay(d) - </p><p> in a tree with more steps, a branch may be gone, how many</p><p> steps are needed. A d value of 6 means that even with a </p><p> tree that has 6 more steps, the branch is still there</p><p>Bootstrap (%) (fig 2.16, 1.10)</p><p> random sample of characters to be used in analysis</p><p> character state stays the same for each taxon use different character sets *Belief in trees in general</p><p>Branched tree or net (hybridization and introgression)</p><p> species can re-merge after differentiation</p><p>OK so what do we do with these trees ? Classification of taxa (31-39)</p><p>*Placing the character traits on trees</p><p>*Ambiguities (2.18, 2.19</p><p> equally parsimonious reconstructions - placement random (2.19a)</p><p> outgroup can determine placement (2.19b)</p><p> placement depends on the outgroup used (2.20a,b)</p><p> unknown character states don't help - </p><p>*Constructing a classification</p><p>Goals (repeat from above)</p><p>Rant about cladistics</p><p>Named groups are monophyletic</p><p>Not all monophyletic groups are named</p><p>Ranks are arbitrary (not much to say unless you don't intuitively see this) Abandon Linean system (incl genera)</p><p>*Reality check - other ways of thinking about classification</p><p>*Cladistics</p><p>*Phenetics (2.24)</p><p>*Evolutionary Taxonomy (2.25)</p><p>*Practical implications of not adhering strictly to monophyly (for our studies)</p><p>Use malvaceae and Sapindales/maple as examples - put up the cladograms</p>