Systematics the branch of biology that infers phylogeny, classification, (taxonomy), and patterns of macroevolution (i.e., major evolutionary change, e.g., morphological, coevolutionary, biogeographic, etc.) in a phylogenetic context

phylogeny - genealogy, literally the relationships of organisms, often presented in the form of a "family tree" with axes of time and divergence although biologists accept as fact that organisms have ancestors, phylogenies cannot be known except for very rapidly evolving organisms (e.g., bacteria) phylogenies are hypotheses, they must be inferred or estimated

Linnaean classification - system of “binomial nomenclature” (i.e., genus and species) and hierarchical classification, with increasingly inclusive taxa of higher rank a system of classification should be one from which we can recover information about phylogeny a hierarchical system is one that is similar to the branching of a tree, with higher ranks closer to the trunk and lower ranks closer to the tips of the branches

taxonomic category = rank = level, e.g., the genus rank is above the species taxon – a proper noun, an entity, like your own name traditional taxonomic ranks Kingdom, Phylum, Class, Order, Family, Genus, Species common prefixes higher: Super-, Supra-, lower: Sub-, Infra-, Parv- cohort – a term used to describe a taxon of no predefined rank

Phylogenies are expressed graphically as “trees” a tree has a root, nodes, and branches which may be internodes or tips, ending at terminal taxa or “operational taxonomic units” (OTUs) deeper nodes in a tree can represent higher taxonomic ranks of increasing inclusiveness sister taxa - two most closely related taxa sister taxon - the next most related taxon to another outgroup - a taxon unrelated to some group of interest an unrooted tree differs from a rooted tree because it lacks a root, i.e., the outgroup is not distinguished from the ingroup (it may be unknown)

Tree

OTU’s

Outgroup

Nodes

Internodes

(= Internal Branches)

Internodes

(= Internal Branches)

Internodes

(= Internal Branches)

Terminals

(= tips)

Sisters

Family

Class

Phylum

Rooted Tree (rooted by outgroup)

1

1

Unrooted Tree

Rooted Tree Unrooted Tree (rooted by outgroup)

2

2

Rooted Tree Unrooted Tree (rooted by outgroup)

3

3

Rooted Tree Unrooted Tree (rooted by outgroup)

4 4

Rooted to Plant

Rooted to Worm

Rooted to Bullfrog

Rooted to Platypus

Rooted to Horse

The same tree

Still the same tree

cladogenesis - the splitting of a lineage into two or more daughter lineages anagenesis - some measure of change of a lineage through time without cladogenesis

higher taxa are meant to describe monophyletic groups monophyly - the property of a group being descended from a common ancestor and including all descendants of that ancestor; in other words, all the taxa that branch from a single node in a tree monophyletic taxa are each others’ nearest relatives clade - a monophyletic group grade - a group of organisms that are similar in that they represent a ‘stage’ of evolution, although this can be because of the retention of primitive characters

monophyly – the property of group that includes all descendants of a common ancestor polyphyly - the property of being unrelated by descent (i.e., winged organisms are polyphyletic) paraphyly - the property of being descended from a common ancestor but not including all evolutionary derivatives of that ancestor (i.e., reptiles are a paraphyletic group)

Monophyly – the property of an

inclusive group of organisms of

shared common ancestry

a b c d e

a b c d e

a b c d e

Polyphyly – the property of being

unrelated by descent

Paraphyly – the property of a group

of organisms of shared common

ancestry that does not include all of

the evolutionary derivatives of that

common ancestor

Monophyletic groups are the only ones

intended to be classified taxonomically

a b c d e

a b c d e Paraphyletic groups are undesirable

in classification because those

organisms most closely related

(i.e., a and b) are not grouped together

-most likely to have been based on superficially

conspicuous traits, therefore many examples discovered

with the application of molecular data to large samples

“Apes” are a paraphyletic group

guenons gibbons orang gorilla chimps human

Neotropical toucans Neotropical barbets African barbets Asian barbets

Ramphastidae Capitonidae Lybiidae Megalaimidae

There are plenty of examples of paraphyletic groups among birds

e.g., “barbets”

Types of Trees phenogram - tree reflects similarity stratophenogram - tree reflects similarity against measured time cladogram - independent of time, simply a rooted tree derived from character data phylogram - branch lengths proportional to the number of character state changes that occur along each lineage consensus tree - combining information from different trees into one summary tree strict consensus - makes polytomies of all nodes for which there are differences majority rule consensus - shows percentage of trees supporting the most frequently obtained tree

stratophenogram

Equal branches – cladograms unrooted

Unequal branches – phylograms unrooted

A B C D E

individual nodes do not convey information about branching order they define all the descendants of a common ancestor, i.e., a monophyletic group or clade

(ABCD)

(ABC)

(AB)

A B C D E 75% 100% 100%

similarly, individual nodes of majority rule consensus trees do not convey information about branching order they convey what percentage greater than 50% of trees recover a particular node, i.e., group all the same OTUs as descendants of that ancestral node

A B C D E

A B C D E

A B C D E

A C B D E

A B C D E

strict consensus trees collapse branching conflicts into polytomies they do not contradict any of the trees from which they are computed, but they convey less specific information

A B C D E

A B C D E

A B C D E

A C B D E

character - any trait, e.g., morphological, developmental, behavioral, molecular (i.e., relating to DNA), biogeographic, etc. character state - a variation of a character

Examples Character – color Character states – red, white, blue Character – number of eyes Character state – one, two, three, eight Character – a nucleotide position in a DNA sequence Character state – adenine, cytosine, guanine, thymine Character – gene Character state – allele

How do we go about inferring phylogeny? usually, in the same way that organisms are classified (that is circular) historically, on the basis of similarity - largely justifiable since organisms that are similar are usually related but primitive characteristics can be misleading

examples paraphyly in reptiles (anapsids turtles, synapsids mammals, diapsids birds) paraphyly in apes humans paraphyly in barbets toucans

Definitions of character homology homology - the property of any traits, genotypic or phenotypic, that are shared by two or more biological entities (taxa, individuals) by virtue of inheritance (i.e., descent) from a common ancestor, whether or not similar in function analogy - similarity in function without homology, convergent homoplasy - convergence, reversal, parallelism, i.e., any character state shared for any reason other than inheritance from a common ancestor

Definitions of character polarity apomorphy – a derived character state synapomorphy – a shared derived character state that is specific to a clade; it unites members of that clade autapomorphy – a derived character state unique to only one taxon plesiomorphy – a primitive character state that is not specific to a clade because it also exists in outgroups symplesiomorphy – a shared primitive character state

both plesiomorphies and apomorphies are homologous characters; homoplasies are not a single trait can be both plesiomorphic and apomorphic in different contexts e.g., the possession of four legs is a synapomorphy of tetrapod vertebrates, but it is a symplesiomorphy of mammals

A B C D E A B C D E

A B C D E A B C D E

autapomorphy of B

plesiomorphy of C in clade (ABC)

assume (ABC) is a monophyletic clade

synapomorphy of clade (ABC)

symplesiomorphy of clade (ABC)

Assessments of Character Homology and Character State Polarity a priori – “before hand” – an educated guess based on detailed similarity, development, outgroup comparison, etc. a posteriori – “after the fact” - homology evaluated in the context of formal phylogenetic analysis many putatively homologous characters are "mapped" onto a tree, many are found to be mutually inconsistent with other characters that favor a different branching pattern of a tree Example OTUs characters

species A 1, 1, 1 species B 1, 1, 2 species C 2, 2, 1

Importance of a posteriori Homology Assessment homoplasy is rampant paraphyletic groups based on symplesiomorphies are common

Methods of Phylogenetic Inference

phenetic cladistic or parsimony maximum likelihood Bayesian multispecies coalescent

phenetic - similarity or distance data, quantitative, not qualitative (includes stratophenetics) "distance" - a measure of similarity (or dissimilarity) is treated as a measure of relatedness a pairwise matrix of distances is "fit" to a tree using an algorithm, e.g., UPGMA (unweighted pair group method) or NJ (neighbor joining)

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

example of phenetic phylogeny reconstruction

then construct a Distance Matrix

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Count number of character state differences between each pair of taxa

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Pairwise Differences

Distance Matrix ass bat cat dog ass - 1 bat cat dog

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Pairwise Differences

Distance Matrix ass bat cat dog ass - 1 3 bat cat dog

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Pairwise Differences

Distance Matrix ass bat cat dog ass - 1 3 2 bat cat dog

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Pairwise Differences

Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 cat dog

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Pairwise Differences

Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat dog

taxa characters 1 2 3 4 5

ass A T A T T

bat A C A T T

cat A C G T C

dog A T G T C

Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat - 1 dog -

Pairwise Differences

PHENETIC APPROACH: Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat - 1 dog - Next, fit distances to trees

PHENETIC APPROACH: Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat - 1 dog - but these distance aren't perfectly metric (additive) ass and bat are sisters, but ass is closer to dog than it is to cat, and bat is closer to cat than it is to dog ass bat cat dog

1 0 1 0 1 0

ass bat cat dog

0 1 0 1 0 1

there isn't any way to fit the original distances together on a dichotomously bifurcating tree, so they are averaged

PHENETIC APPROACH: Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat - 1 dog - Unweighted Pair Group Method with Arithmetic Mean (UPGMA) fit distances to trees, beginning with closest pairs Join nodes using average distance between all OTUs being joined (ass, bat, cat, dog) = (2+2+3+3)/4 = 2.5

ass bat cat dog

½ ½ ½ ½

ass bat cat dog

½ ½ ½ ½ ¾ ¾

fractional nucleotide changes are impossible, but distances are usually calculated for larger numbers of characters

even though A and D differ only by 2 characters, parsimony will show that they share a homoplasy that is undetected by the phenetic approach there are other more sophisticated phenetic methods to fit distances to trees (e.g., least squares) or that attempt to ‘correct’ distances for undetected homoplasy

Distance Matrix ass bat cat dog ass - 1 3 2 bat - 2 3 cat - 1 dog -

The cause of the distance discrepancy

phenetic methods must be used if the data collected are inherently quantitative rather than qualitative (i.e., continuously variable like percentages, e.g., DNAxDNA hybridization: DNA of chimps and humans is 98% identical, chimps and gorillas 95% identical, etc.) Advantages of phenetic methods • they do not require much computation so they are

very fast Disadvantages of phenetic methods • one and only one tree is produced – it does not

produce a ‘next best’ tree to which the result can be compared

• more likely to be misled by homoplasy to incorrect

tree than other methods

Non-phenetic methods of phylogeny reconstruction unlike phenetic methods, cladistic, maximum likelihood, and Bayesian methods utilize qualitative character data and employ an optimality criterion optimality criterion - a method for evaluating competing hypotheses of phylogeny; a predefined metric of how to evaluate what is ‘best’

cladistic or parsimony analysis - employs an optimality criterion known as parsimony parsimony - "stingy", the tree that invokes the fewest number of character state changes in cladistic analysis, synapomorphies are the only kind of characters that are useful as evidence of monophyly homoplasies, autapomorphies, and symplesiomorphies are considered “uninformative” - this distinguishes parsimony from phenetic and all other methods, which use all characters unlike phenetic methods, parsimony can often distinguish homoplasy from homology, and apomorphy from plesiomorphy

Steps in cladistic analysis 1) define all topologically discrete unrooted trees 2) map characters one at a time onto each tree

this is in contrast to the phenetic approach, which compares differences in pairs of OTUs

- phenetic comparison by OTUs - cladistic comparison by characters

3) choose the optimal tree using the criterion of parsimony 4) root the optimal tree using an unambiguous outgroup

Number of topologically distinct unrooted trees is defined by number of OTUs OTUs trees 3 1 4 3 5 15 6 105 7 945 8 10,395 9 135,135 10 2,027,025 (you get the point) there are more topologically distinct unrooted trees for 32 OTUs than the estimated total number of atoms in the universe (or so I’ve read) therefore, various heuristic methods must be relied upon to find optimal trees when considering more than ~12 OTUs rather than evaluating all possible trees

example of cladistic phylogeny reconstruction OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

step 1: define all the topologically distinct trees

There are three topologically distinct unrooted trees for 4 OTUs

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

1) 2) 3)

Ass Cat

Bat Dog

A D

B C

B C

A D

B D

A C

A C

B D

1)

Note: all the unrooted trees below are identical to number 1) !

D A

C B

C B

D A

C A

D B

D B

C A

step 2: map each character one by one onto each unrooted tree OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

1) 2) 3)

the minimum number of character state changes that can be explained by an unrooted tree is one less than the number of character states the actual number of changes observed depends on the topology of the tree

OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

character 1

1) 2) 3)

0 changes 0 changes 0 changes

OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

character 2

1) 2) 3)

2 changes 2 changes 1 change

OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Bat

Cat Dog

Ass Bat

Dog Cat

Ass Cat

Bat Dog

character 3

1) 2) 3)

1 change 2 changes 2 changes

OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

character 4

1) 2) 3)

0 changes 0 changes 0 changes

OTUs characters 1 2 3 4 5

Ass A T A T T

Bat A C A T T

Cat A C G T C

Dog A T G T C

Ass Bat

Cat Dog

Ass Bat

Dog Cat

Ass Cat

Bat Dog

character 5

1) 2) 3)

1 change 2 changes 2 changes

Ass Cat

Bat Dog

Ass Bat

Cat Dog

Ass Bat

Dog Cat

Tree 1) Tree 2) Tree 3 Character 1: 0 steps 0 steps 0 steps Character 2: 2 steps 2 steps 1 step Character 3: 1 step 2 steps 2 steps Character 4: 0 steps 0 steps 0 steps Character 5: 1 step 2 steps 2 steps Sum: 4 steps* 6 steps 5 steps *most parsimonious

step 3: sum character state changes for each tree

1) 2) 3)

Ass Cat

Bat Dog

step 4: root optimal tree by outgroup

Dog Cat Ass Bat

Question: What is most closely related to bat?

what if all character state changes are not equally probable? transition substitution: purine purine (A G) or pyrimidine pyrimidine (C T) transversions substitution: purine pyrimidine (A or G C or T)

if transition substitutions occur at twice the rate of transversions then is it appropriate to count them equally? enter the realm of substitution modeling…

Maximum Likelihood - employs an optimality criterion of maximum likelihood a computationally intensive method that calculates the likelihood of terminal taxa exhibiting the character states they do on a given tree, given one of numerous models for the probability of character state changes along branches and among nucleotide sites (likelihood ~ chance that something would have come to pass the way it already did under specified conditions) (probability – chance that something will happen in the future) of all possible trees evaluated, the one calculated to have the highest likelihood score is chosen as optimal a great method if the model chosen is accurate

Bayesian – employs an optimality criterion of Bayesian posterior probability, based on Bayesian statistics similar to maximum likelihood in using substitution models but faster and more readily capable of handling independent data partitions with multiple substitution models simultaneously

Bayesian analysis is computationally faster than ML because:

1) it relies on the specification of priors (previously “known” information) that are refined based on Bayesian statistics 2) it uses a Monte Carlo Markov Chain model to explore Bayesian posterior probabilities in tree space (the “universe” of possible trees, given that it is not necessarily tractable to really calculate them all) 3) the MCMC is used to refine the posterior probability density as new priors and converge on the ‘best’ tree For more a thorough explanation of the Bayesian approach (although not specifically in a phylogenetic context), see:

https://www.quantstart.com/articles/Bayesian-Statistics-A-Beginners-Guide

Multispecies Coalescent - finds a species tree that best combines a large collection of independently inferred gene trees that may differ from one another The best species tree is not necessarily the majority consensus of gene trees – or even any of the gene trees! The method is designed to minimize the impact of a phenomenon known as incomplete lineage sorting, in which some ancestral polymorphisms are inherited by one daughter species but not another, and thus may produce incongruence among gene phylogenies and to species phylogeny Many of the concepts relating to genetic drift, coalescent theory, lineage sorting, and effective population size that are necessary to understand the multispecies coalescent will not be covered until much later in this course

Distinct evolutionary histories of species and their genes

modified from Ebersberger et al. Mol Biol Evol 2007

Polymorphism arises before speciation

Multispecies Coalescent - a few watered down details

• Gene trees can be inferred by any method, but maximum likelihood is used most often

• Each gene tree is decomposed into its component rooted trees of OTU triplets

• The branch length of the internode in each rooted triplet is calculated in coalescent units from the primary data

• A likelihood score is calculated for that branch length based on the known probabilities of those of both congruent and incongruent gene trees

𝑃 = 13 −𝑡

2N𝑒

• The probabilities of both congruent and incongruent gene trees with a true species tree are calculated using the Pamilo and Nei 1988 model:

where t = time in generations or coalescent units

Ne = effective population size

• A pseudo-likelihood score is calculated by summing the likelihoods

• The optimality criterion is the tree with the maximum pseudolikelihood

Confidence in phylogenetic estimates

the one thing that is guaranteed in phylogenetic analysis is that it will produce a tree phylogenetic reconstructions can differ because

• they are dependent on the data they use • varying assumptions of techniques • varying models and parameters chosen to describe

character state transformations and priors since there is only one historical truth, how do we evaluate contradictory phylogenetic reconstructions?

"feel good" confidence vs statistical confidence non-rigorous methods of assessing confidence

• congruence between independent datasets • congruence between different methods of analysis on

the same data • Phenetic - residuals from least squares fitting of

distances to branches • Maximum likelihood and Bayesian - difference in

likelihoods or Bayesian posteriors* • Parsimony –

• heteroskedasticity (skewness) of tree length distribution

• length to next shortest tree* • Decay (Bremer) Index – minimum number of steps

longer a tree must be to “lose” a specific clade • Consistency Index (next page)

* Statistical tests for significance of difference between optimal and suboptimal trees can be applied to any method that produces both

Consistency Index (CI) for one character - the minimum number of times a character could undergo state changes if a tree was optimized for that character alone (i.e., number of observed character states minus one) divided by the actual number of state changes on the tree in question Ensemble CI (i.e., CI of a tree) - average of all CI's (of parsimony informative characters only) Consistency Index is always a value between 0.0 and 1.0 Homoplasy Index = 1.0-CI

CI = 1.0

CI = 0.5

Statistical methods – the jackknife resampling without replacement reiterative analysis omitting one different taxon each time a majority rule consensus tree shows the percentage of trees supporting each node

Statistical methods – the bootstrap

resampling with replacement reiterative analysis of a data sets equal in size to the original but generated by randomly sampling the original data (thus, some data will be sampled repeatedly, others not at all) a majority rule consensus tree shows the percentage of trees supporting each node the bootstrap has been ‘calibrated’ to statistical confidence intervals

what is a statistical confidence interval? it is measured by an alpha or "P" value scientists hold that a P value equal to or less than 0.05 is significant this means that you make a hypothesis and test it and look up P values on a statistical table

type 1 statistical error Incorrect rejection of a true null hypothesis at P < 0.01 you would incorrectly reject your null hypothesis 1% of the time at P < 0.05 you would incorrectly reject your null hypothesis 5% of the time – good enough for scientists type 2 statistical error Failure to reject a false null hypothesis At P < 0.05 you would accept a hypothesis that was in fact false 5% of the time

a bootstrap score of 95% has a value of approximately P<0.05 Bootstrap scores are annotated individually to each node of a majority rule consensus tree Very loosely stated, this means that we would do the bootstrap analysis and get this result (i.e., node, clade) and it would in fact be incorrect 5% of the time the bootstrap usually underestimates confidence interval, but the relationship depends on the number of taxa and the topology of the tree

Does a bootstrap score of 100% mean that a relationship is certain? No way! outcomes are dependent on input data, phylogenetic method, models and parameters of character state substitution, etc. a node on a tree with a bootstrap score of 96% is better supported than another node on the same tree with a bootstrap score of 95% bootstrap scores are not directly comparable between trees, analyses, or data sets

in Bayesian analysis, confidence intervals are expressed as Bayesian Posteriors instead of bootstrap scores Bayesian Posteriors are not equivalent to bootstrap scores Bayesian Posteriors are more “generous” and “flattering” so they are commonly presented in publications

Phylogenetic analyses may be “statistically inconsistent” under certain conditions “statistically inconsistent” means that increasing the size of the data set provides ever-stronger statistical support for an incorrect result

One cause of statistical inconsistency is “long branch attraction” (LBA), i.e., when there are very short internal branches (internodes) and both long and short terminal branches

Susceptibility to LBA: phenetic >> parsimony > Bayesian >? Maximum Likelihood

Incomplete Lineage Sorting is potentially another cause of statistical inconsistency when multiple loci with different histories (different gene trees) are concatenated and analyzed collectively the multispecies coalescent was developed specifically to be immune from this source of inconsistency

Methods to compare trees Kishino-Hasegawa Test (KH) test – Shimodaira and Hasegawa (SH) test –

- both are maximum likelihood tests that can assign P-values on the significance of difference between optimal and suboptimal trees from the same analysis

Robinson-Foulds (RF) Distance –

RF = A+B where A = the number of nodes supported in one tree that are not supported in the second, and B = the number of nodes supported in the second tree that are not supported in the first

- does not take branch length or the data set into account

The “Molecular Clock” - Zuckerkandl and Pauling 1962

The notion that genes or genomes evolve at a constant rate Relative Rate Test - Sarich and Wilson (1976) modified from Margoliash 1963, first implemented in a microcomplement fixation study of primates - the difference in the distance between any two sister taxa to an outgroup can result only from a difference in the rate of evolution in the lineages of the sisters since the time they diverged from their common ancestor therefore, fossils are not required to document relative rates of evolution

“clock-like evolution” AC = BC where C is outgroup to A and B

Important things learned from (or corroborated by) systematics 1) homologous features are derived from common ancestors 2) homology - similarity in structure but not function is evidence for evolution; there is no other reason to make a whale's flipper from the same bones, muscles, nerves, and blood vessels as a bat's wing 3) homoplasy is common in evolution – convergence in function but not structure is evidence for evolution; there is no reason to construct bird’s and bat’s wings differently 4) phylogenetic analysis documents evolutionary trends, e.g., parallel trends such as reduction in number of digits in cursorial animals

Important things learned from systematics 5) rates of character evolution differ

mosaic evolution - evolution of different characters at different rates within individual lineages the concept of "living fossils" is erroneous - individual characters can be primitive but everything living is specialized in some way(s) to do what it does

Platypus is primitive as an egg laying mammal but specialized with respect to electrical sensitivity and poison glands

Important things learned from systematics 6) Major evolutionary innovations generally occur in many small gradual steps

ostensibly discrete characters in living organisms are generally found to be continuously variable characters if examined in sufficiently fine segments of time in the fossil record

Eunotosaurus

turtles have shoulders and hips inside their ribcage

Eunotosaurus proto-turtle Middle Permian (>250 Million years old)

birds have large stiff “pennaceous” feathers for flight

Sinosauropteryx close-up of proto-feathers

Important things learned from systematics 7) characteristics often owe their change in form to a change in function

Important things learned from systematics 8) most clades display evolutionary radiation https://www.bio.umass.edu/biology/research/gbi/evolution-of-new-world-leaf-nosed-bats

Important things learned from systematics 9) organisms can be classified into a hierarchical system of nomenclature because species evolve by divergence from common ancestors a historical process of branching and divergence will yield objects that can be hierarchically ordered, but few other processes will do so 10) Systematics provides the phylogenetic framework for comparative analyses of molecular evolution, e.g., nucleotide substitution (mutation) rates, natural selection at the nucleotide level, gene and regulatory region discovery, understanding the origins of emergent disease, and vaccine development

Types of data in phylogenetic analysis characters can be anything but molecular data are becoming the most commonly used in systematics Microcomplement fixation – phenetic; a measure of immunological distance, antibodies are made to one organism and then cross-reactivity to another organism is quantified by % red blood cell (RBC) lysis; poor reciprocity DNA X DNA hybridization - phenetic; single copy ds (double-stranded) DNA is isolated from 2 organisms, melted to ss (single-stranded) DNA and one radiolabeled, base mismatch of heteroduplex dsDNA (between species) is quantified by reduction in thermal stability compared to homoduplex ds DNA, dsDNA binds to hydroxylapatite (HAP), ssDNA does not

Types of data in phylogenetic analysis (cont’d)

protein electrophoresis - character or phenetic; allozymes, different alleles have different isoelectric points in pH electric fields, proteins stained

Restriction fragment length polymorphism (RFLP and related AFLP, RAPD, DNA fingerprinting, microsatellite, etc. technologies) - character or phenetic; presence or absence of restriction endonuclease sites measured as bands on electrophoretic gel

amino acid sequencing - character or phenetic; linear protein code, 20 states

nucleic acid sequencing - character or phenetic; linear DNA code, 4 states, various sequencing technologies of 2 main types: Sanger (dideoxy chain termination) and “Next Generation Sequencing” (NGS) Sequencing by Synthesis (SBS)