phenetic dan cladistic

PhylogeneticsPhylogenetics The affinities of all the beings of the same class haveThe affinities of all the beings of the same class havesometimes been represented by sometimes been represented by a great treea great tree . . . As . . . Asbudsbuds give rise by growth to fresh buds, and these if give rise by growth to fresh buds, and these ifvigorous, vigorous, branchbranch out and overtop on all sides many a out and overtop on all sides many afeebler branch, so by generation I believe it has beenfeebler branch, so by generation I believe it has beenwith the great with the great Tree of LifeTree of Life, which fills with its dead, which fills with its deadand broken branches the crust of the earth, and coversand broken branches the crust of the earth, and coversthe surface with its ever branching and beautifulthe surface with its ever branching and beautifulramifications.ramifications.

Charles Darwin, 1859Charles Darwin, 1859

PhylogeneticsPhylogenetics

What exactly is What exactly is phylogeneticsphylogenetics and how is done? and how is done?

PhylogeneticsPhylogeneticsBuilding Building ““treestrees”” - showing how - showing how ““branchesbranches”” or or ““cladesclades”” are areconnected to each otherconnected to each other


But . . . We only have But . . . We only have extant speciesextant species - the - the ““leavesleaves””


- and so we must - and so we must estimateestimate how the how the ““branchesbranches”” once onceconnected the connected the ““leavesleaves””

??

Thus . . . Phylogenetics is the estimation of theThus . . . Phylogenetics is the estimation of the““treetree”” through through ““timetime”” knowing only the knowing only the ““leavesleaves””


However, the However, the ““leavesleaves”” are scattered over are scattered over ““spacespace””. Some. Someareas have related areas have related ““leavesleaves””, others have unrelated , others have unrelated ““leavesleaves””..Thus, phylogenetics is compounded by issues of both Thus, phylogenetics is compounded by issues of both ““timetime””and and ““spacespace””..

PhylogeneticsPhylogeneticsAdditionally, many related Additionally, many related ““leavesleaves”” divergediverge in in ““formform””,,while other unrelated while other unrelated ““leavesleaves”” convergeconverge in in ““formform””. Thus,. Thus,phylogenetics is compounded by issues of phylogenetics is compounded by issues of ““timetime”” and and““spacespace”” and and ““formform””..


PhylogeneticsPhylogeneticsIn natural and phylogenetic systems of classification, In natural and phylogenetic systems of classification, characterscharactersare selected are selected a posterioria posteriori for their value in correlating with other for their value in correlating with othercharacterscharacters to form hierarchical structure of groups to form hierarchical structure of groups

PhylogeneticsPhylogeneticsWhat characters are selected or even considered, has been veryWhat characters are selected or even considered, has been verysubjective. Consider Cronquist and Dalghren with mustard oilsubjective. Consider Cronquist and Dalghren with mustard oilfamilies . . .families . . .

PhylogeneticsPhylogeneticsDo you rely on morphological or chemical characters or both?Do you rely on morphological or chemical characters or both?

PhylogeneticsPhylogeneticsThese first phylogenetic classifications are These first phylogenetic classifications are ““phyleticphyletic”” - involve - involvea a subjective selection of characterssubjective selection of characters for classification for classification

PhylogeneticsPhylogeneticsTwo main groups of systematists became dissatisfied with theTwo main groups of systematists became dissatisfied with thephyletic approach and developed more phyletic approach and developed more objectiveobjective methods: methods:pheneticphenetic and and cladisticcladistic

PhylogeneticsPhylogeneticsWith the rise of molecular phylogenetics, additional approachesWith the rise of molecular phylogenetics, additional approachesare now invoked (ML, Bayesian) - far more of a continuum ofare now invoked (ML, Bayesian) - far more of a continuum ofmodels are now seenmodels are now seen

Phenetics vs. CladisticsPhenetics vs. Cladistics

R. Sokal

W. HennigPhenetics:Phenetics: ( (Principles ofPrinciples ofNumerical Taxonomy,Numerical Taxonomy,1963) by Peter Sneath1963) by Peter Sneathand Robert Sokaland Robert Sokal

Cladistics:Cladistics:((PhylogeneticPhylogeneticSystematics, Systematics, 1966) by1966) byWilli HennigWilli Hennig

. . . all out war in the. . . all out war in the1960s and 1970s!1960s and 1970s!


•• Phenetics uses Phenetics uses ““overalloverallsimilaritysimilarity”” - all characters- all charactersused! (used! (““distancedistance”” approaches) approaches)


•• Phenetics uses Phenetics uses ““overalloverallsimilaritysimilarity”” - all characters- all charactersused! (used! (““distancedistance”” approaches) approaches)

•• Graphical representation is Graphical representation iscalled a called a phenogram,phenogram,dendrogram, networkdendrogram, network


•• Cladistics uses onlyCladistics uses onlyphylogenetically informativephylogenetically informativecharacterscharacters•• Derived state is shared by Derived state is shared by2 but not all taxa - 2 but not all taxa - ““sharedsharedderived character statesderived character states””

only informative

cladogramcladogram


cladogramcladogram phenogramphenogram=/

PheneticsPheneticsData MatrixData Matrix

1. 1. MagnoliaMagnolia

2. 2. NymphaeaNymphaea

3. 3. RosaRosa

4. 4. PrimulaPrimula

5. 5. GentianaGentiana

6. 6. AsterAster

taxataxa

apoc

arpy

apoc

arpy

sym

peta

lysy

mpe

taly

epip

etal

yep

ipet

aly

trees

trees

epig

yny

epig

yny

beet

le p

oll.

beet

le p

oll.

tepa

lste

pals

hete

rosty

lyhe

tero

styly

bica

rpel

late

bica

rpel

late

vess

els

vess

els

characterscharacters

++ ++ -- ++ -- --++ -- ++--

-- ++ -- ++ -- ---- -- ++--

++ ++ -- -- -- ---- -- ----

++ -- ++ -- -- ++-- -- --++

++ -- ++ -- ++ ---- -- --++

++ -- ++ -- ++ ---- ++ --++

statesstates



3. 3. RosaRosa



6. 6. AsterAster

++ ++ -- ++ -- --++ -- ++--

-- ++ -- ++ -- ---- -- ++--

++ ++ -- -- -- ---- -- ----

++ -- ++ -- -- ++-- -- --++

++ -- ++ -- ++ ---- -- --++

++ -- ++ -- ++ ---- ++ --++

PheneticsPheneticsData MatrixData Matrix

•• convert data matrix into convert data matrix intopair-wise matrix based onpair-wise matrix based onoverall similarityoverall similarity

UPGMA cluster analysisUPGMA cluster analysis

PheneticsPhenetics

•• convert data matrix into convert data matrix intopair-wise matrix based onpair-wise matrix based onoverall similarityoverall similarity


•• identify most similar pair of identify most similar pair oftaxa and cluster themtaxa and cluster them

Gen

tiana

Gen

tiana

Aste

rAs

ter

90%90%

PheneticsPhenetics

•• reduce overall similarity reduce overall similaritymatrix by clustering togethermatrix by clustering togetherGentianaGentiana and and AsterAster and andrecalculate similarity valuesrecalculate similarity values



Nym

phae

aN

ymph

aea

Mag

nolia

Mag

nolia

80%80%

PheneticsPhenetics

•• reduce overall similarity reduce overall similaritymatrix by clustering togethermatrix by clustering togetherMagnoliaMagnolia and and NymphaeaNymphaea and andrecalculate similarity valuesrecalculate similarity values



Gen

tiana

Gen

tiana

Aste

rAs

ter

75%75%

Prim

ula

Prim

ula

PheneticsPhenetics

•• cluster together cluster together Gentiana,Gentiana,Aster,Aster, and and PrimulaPrimula and andrecalculate valuesrecalculate values



Mag

nolia

Mag

nolia

Nym

phae

aN

ymph

aea

70%70%

Rosa

Rosa

PheneticsPhenetics

•• cluster together cluster together Magnolia,Magnolia,Nymphaea,Nymphaea, and and RosaRosa and andrecalculate valuesrecalculate values


•• cluster the two remaining cluster the two remaininglarger groups at 42.5% tolarger groups at 42.5% tomake final make final phenogramphenogram

PheneticsPhenetics•• many different methods based on similarity or differences many different methods based on similarity or differences(including multiple components, ordination, etc.)(including multiple components, ordination, etc.)

•• in lab you will be using in lab you will be using Neighbor-joiningNeighbor-joining using a using acomputer program PAUPcomputer program PAUP



3. 3. RosaRosa



6. 6. AsterAster

apoc

arpy

apoc

arpy

sym

peta

lysy

mpe

taly

epip

etal

yep

ipet

aly

trees

trees

epig

yny

epig

yny

beet

le p

oll.

beet

le p

oll.

tepa

lste

pals

hete

rosty

lyhe

tero

styly

bica

rpel

late

bica

rpel

late

vess

els

vess

els

++ ++ -- ++ -- --++ -- ++--

-- ++ -- ++ -- ---- -- ++--

++ ++ -- -- -- ---- -- ----

++ -- ++ -- -- ++-- -- --++

++ -- ++ -- ++ ---- -- --++

++ -- ++ -- ++ ---- ++ --++

CladisticsCladisticsHow do you analyze this same data based on How do you analyze this same data based on cladisticscladistics - - ““sharedsharedderived character statesderived character states””??

CladisticsCladisticsIssue #1Issue #1 - ordering or polarizing character states - ordering or polarizing character states

CladisticsCladisticsIssue #1 - ordering or polarizing character statesIssue #1 - ordering or polarizing character states

plesiomorph - primitive stateplesiomorph - primitive state apomorph - derived stateapomorph - derived state

- use outgroups- use outgroups

CladisticsCladisticsIssue #1 - ordering or polarizing character statesIssue #1 - ordering or polarizing character states

Blue flowers = synapomorph - shared derived stateBlue flowers = synapomorph - shared derived state

sister groupsister group



3. 3. RosaRosa



6. 6. AsterAster

apoc

arpy

apoc

arpy

sym

peta

lysy

mpe

taly

epip

etal

yep

ipet

aly

trees

trees

epig

yny

epig

yny

beet

le p

oll.

beet

le p

oll.

tepa

lste

pals

hete

rosty

lyhe

tero

styly

bica

rpel

late

bica

rpel

late

vess

els

vess

els

++ ++ -- ++ -- --++ -- ++--

-- ++ -- ++ -- ---- -- ++--

++ ++ -- -- -- ---- -- ----

++ -- ++ -- -- ++-- -- --++

++ -- ++ -- ++ ---- -- --++

++ -- ++ -- ++ ---- ++ --++

CladisticsCladisticsAdd in Add in AmborellaAmborella as sister outgroup to rest of angiosperms as sister outgroup to rest of angiosperms



3. 3. RosaRosa



6. 6. AsterAster

apoc

arpy

apoc

arpy

sym

peta

lysy

mpe

taly

epip

etal

yep

ipet

aly

trees

trees

epig

yny

epig

yny

beet

le p

oll.

beet

le p

oll.

tepa

lste

pals

hete

rosty

lyhe

tero

styly

bica

rpel

late

bica

rpel

late

vess

els

vess

els

++ ++ -- ++ -- --++ -- ++--

-- ++ -- ++ -- ---- -- ++--

++ ++ -- -- -- ---- -- ----

++ -- ++ -- -- ++-- -- --++

++ -- ++ -- ++ ---- -- --++

++ -- ++ -- ++ ---- ++ --++

CladisticsCladistics

-- ++ -- ++ -- --++ -- ++-- Amborella Amborella

Convert dataConvert datamatrix to matrix to ““00””& & ““11””



3. 3. RosaRosa



6. 6. AsterAster

Apo

carp

y -

Apo

carp

y -

sync

arpy

sync

arpy

Poly

peta

ly -

Poly

peta

ly -

sym

peta

lysy

mpe

taly

Free

stam

ens -

Free

stam

ens -

epip

etal

yep

ipet

aly

Tree

s -Tr

ees -

herb

she

rbs

Hyp

ogyn

y -

Hyp

ogyn

y -

epig

yny

epig

yny

beet

le p

oll.

- be

etle

pol

l. - o

ther

othe

rpo

ll.po

ll.Te

pals

-Te

pals

-se

pals

+ pe

als

sepa

ls +

peal

s

Hom

osty

ly -

Hom

osty

ly -

hete

rost

yly

hete

rost

yly

Var

ious

car

pels

-V

ario

us c

arpe

ls -

bica

rpel

late

bica

rpel

late

No

vess

els -

No

vess

els -

vess

els

vess

els

11 00 00 00 00 0000 00 0000

00 00 00 00 00 0011 00 0000

11 00 00 11 00 0011 00 1100

11 11 11 11 00 1111 00 1111

11 11 11 11 11 0011 00 1111

11 11 11 11 11 0011 11 1111


00 00 00 00 00 0000 00 0000 Amborella Amborella

Plesiomorph Plesiomorph 00

Apomorph Apomorph 11



3. 3. RosaRosa



6. 6. AsterAster

Apo

carp

y -

Apo

carp

y -

sync

arpy

sync

arpy

Poly

peta

ly -

Poly

peta

ly -

sym

peta

lysy

mpe

taly

Free

stam

ens -

Free

stam

ens -

epip

etal

yep

ipet

aly

Tree

s -Tr

ees -

herb

she

rbs

Hyp

ogyn

y -

Hyp

ogyn

y -

epig

yny

epig

yny

beet

le p

oll.

- be

etle

pol

l. - o

ther

othe

rpo

ll.po

ll.Te

pals

-Te

pals

-se

pals

+ pe

als

sepa

ls +

peal

s

Hom

osty

ly -

Hom

osty

ly -

hete

rost

yly

hete

rost

yly

Var

ious

car

pels

-V

ario

us c

arpe

ls -

bica

rpel

late

bica

rpel

late

No

vess

els -

No

vess

els -

vess

els

vess

els

11 00 00 00 00 0000 00 0000

00 00 00 00 00 0011 00 0000

11 00 00 11 00 0011 00 1100

11 11 11 11 00 1111 00 1111

11 11 11 11 11 0011 00 1111

11 11 11 11 11 0011 11 1111



Note: 2Note: 2uniformativeuniformativecharacterscharacters

CladisticsCladisticsIssue #2Issue #2 - how do you select the - how do you select the ““bestbest”” tree? tree?

•• estimation procedure from estimation procedure from vast number of possiblevast number of possible““treestrees””

•• this study with 7 taxa - there are 945 possible tree this study with 7 taxa - there are 945 possible treetopologiestopologies

•• examining all trees examining all trees is possible here, but with largeris possible here, but with largernumbers of taxa (as the 14 taxa used in lab this week) anumbers of taxa (as the 14 taxa used in lab this week) aheuristic heuristic approach is requiredapproach is required

Big NumbersBig Numbers

•• cars worldwide cars worldwide –– 6.0 X 106.0 X 1088

•• cell phones worldwide cell phones worldwide –– 5.0 X 105.0 X 1099

•• neurons/brain OR stars/Milky Way ORneurons/brain OR stars/Milky Way ORgalaxies/universe galaxies/universe –– 1.0 X 101.0 X 101111

•• stars/universe stars/universe –– 1.0 X 101.0 X 102222

•• atoms in the universe atoms in the universe –– 1.0 X 101.0 X 1078-7978-79


••for a study with 50 taxa - there are for a study with 50 taxa - there are 3 X 10 3 X 10 7474 possiblepossibletrees or approaching number of atoms in universe (10 trees or approaching number of atoms in universe (10 7979)!)!

•• landmark paper in 1993 for angiosperms had 499 taxa - landmark paper in 1993 for angiosperms had 499 taxa -astronomical number of possible trees! astronomical number of possible trees! >> 10 >> 10 10001000

•• for a study of the for a study of the Tree of Life - 10 Tree of Life - 10 70,000,00070,000,000

•• estimation procedure from estimation procedure from vast number of possiblevast number of possible““treestrees””

•• this study with 7 taxa - there are 945 possible tree this study with 7 taxa - there are 945 possible treetopologies topologies —— how about 50 taxa? how about 50 taxa?


•• the the ““bestbest”” tree is dependent on assumption of antree is dependent on assumption of anoptimality criterion: e.g., optimality criterion: e.g., likelihoodlikelihood, , parsimonyparsimony

•• cladistics (morphology) often uses cladistics (morphology) often uses parsimony parsimony - based on- based on““OckhamOckham’’s Razors Razor””

William of Ockham William of Ockham –– EntiaEntia non non suntsuntmultiplicandamultiplicanda praeterpraeter necessitatemnecessitatem oror““Entities should not be multipliedEntities should not be multipliedunnecessarilyunnecessarily””


•• in the context of evolution, in the context of evolution, maximum parsimony maximum parsimony ==choosing the tree that requires the choosing the tree that requires the fewest number offewest number ofevolutionary changesevolutionary changes (apomorphies) (apomorphies)

•• choose the tree with the choose the tree with the least amountleast amountof homoplasyof homoplasy - convergences or - convergences orreversals or character conflictreversals or character conflict

•• choose the choose the shortestshortest, simplest, most, simplest, mostefficient treeefficient tree


•• Elliot Sober (UW Philosophy Dept): Elliot Sober (UW Philosophy Dept):Reconstructing the Past: Parsimony,Reconstructing the Past: Parsimony,Evolution, and InferenceEvolution, and Inference (1988) (1988)

•• Francis Crick: " Francis Crick: "While Ockham'sWhile Ockham'srazor is a useful tool in the physicalrazor is a useful tool in the physicalsciences, sciences, it can be a very dangerousit can be a very dangerousimplement in biologyimplement in biology. It is thus very. It is thus veryrash to use simplicity and elegancerash to use simplicity and eleganceas a guide in biological research.as a guide in biological research.""


•• 36 of the around 36 of the around 370 370 phylogeneticphylogenetic software programs software programs available!available!

•• many can be used on about 50 free web servers (including many can be used on about 50 free web servers (includingsupercomputers or supercomputers or teratera-grids)-grids)

http://evolution.genetics.washington.edu/phylip/software.htmlhttp://evolution.genetics.washington.edu/phylip/software.html



3. 3. RosaRosa



6. 6. AsterAster

Apo

carp

y -

Apo

carp

y -

sync

arpy

sync

arpy

Poly

peta

ly -

Poly

peta

ly -

sym

peta

lysy

mpe

taly

Free

stam

ens -

Free

stam

ens -

epip

etal

yep

ipet

aly

Tree

s -Tr

ees -

herb

she

rbs

Hyp

ogyn

y -

Hyp

ogyn

y -

epig

yny

epig

yny

beet

le p

oll.

- be

etle

pol

l. - o

ther

othe

rpo

ll.po

ll.Te

pals

-Te

pals

-se

pals

+ pe

als

sepa

ls +

peal

s

Hom

osty

ly -

Hom

osty

ly -

hete

rost

yly

hete

rost

yly

Var

ious

car

pels

-V

ario

us c

arpe

ls -

bica

rpel

late

bica

rpel

late

No

vess

els -

No

vess

els -

vess

els

vess

els


Amborella Amborella

•• use use maximum parsimony maximum parsimony to findto findthe the ““bestbest”” of 945 possible trees of 945 possible trees



3. 3. RosaRosa



6. 6. AsterAster

Apo

carp

y -

Apo

carp

y -

sync

arpy

sync

arpy

Poly

peta

ly -

Poly

peta

ly -

sym

peta

lysy

mpe

taly

Free

stam

ens -

Free

stam

ens -

epip

etal

yep

ipet

aly

Tree

s -Tr

ees -

herb

she

rbs

Hyp

ogyn

y -

Hyp

ogyn

y -

epig

yny

epig

yny

beet

le p

oll.

- be

etle

pol

l. - o

ther

othe

rpo

ll.po

ll.Te

pals

-Te

pals

-se

pals

+ pe

als

sepa

ls +

peal

s

Hom

osty

ly -

Hom

osty

ly -

hete

rost

yly

hete

rost

yly

Var

ious

car

pels

-V

ario

us c

arpe

ls -

bica

rpel

late

bica

rpel

late

No

vess

els -

No

vess

els -

vess

els

vess

els

11 00 00 00 00 0000 00 0000

00 00 00 00 00 0011 00 0000

11 00 00 11 00 0011 00 1100

11 11 11 11 00 1111 00 1111

11 11 11 11 11 0011 00 1111

11 11 11 11 11 0011 11 1111



2 characters2 charactersconflict!conflict!

CladisticsCladistics•• two trees are equally parsimonious two trees are equally parsimonious•• with with character conflictcharacter conflict, each is 11, each is 11steps long and not the expected 10steps long and not the expected 10

Consistency IndexConsistency Index = 0.91 = 0.91# changes minimally expected# changes minimally expected

# changes occurred on tree# changes occurred on tree

CladisticsCladistics•• tree 1 has tree 1 has vessels asvessels assynapomorphysynapomorphy for all taxa for all taxaexcept outgroup +except outgroup +NymphaeaNymphaea

⇒⇒ herbs herbs⇒⇒ vessels vessels

⇒⇒ trees trees

Tree 1

•• habithabit is homoplasious is homoplasious((““messymessy””) with an origin to) with an origin toherb and then reversal backherb and then reversal backto treeto tree

CladisticsCladistics•• tree 2 has tree 2 has herbs asherbs assynapomorphysynapomorphy for all taxa for all taxaexcept outgroup + except outgroup + MagnoliaMagnolia

⇒⇒ vessels vessels⇒⇒ herbs herbs

⇒⇒ no vessels no vessels

Tree 2

•• vesselsvessels is homoplasious is homoplasious((““messymessy””) with an origin to) with an origin tovessels and then reversalvessels and then reversalback to vessel-lessback to vessel-less

CladisticsCladistics•• a a consensus treeconsensus tree depicts the maximum information possible depicts the maximum information possiblefrom all most parsimonious treesfrom all most parsimonious trees

CladisticsCladisticsIssue #3Issue #3 - how do you construct a classification? - how do you construct a classification?

•• most most cladistscladists advocate monophyletic groupings only - stressing advocate monophyletic groupings only - stressingprimacy of primacy of descentdescent•• a different group, a different group, evolutionary taxonomistsevolutionary taxonomists, allow for, allow forparaphyletic grouping - stressing both paraphyletic grouping - stressing both descent and modificationdescent and modification


MilkweedsMilkweeds are a highly are a highlyderived lineage fromderived lineage fromwithin the dogbane familywithin the dogbane family- - ApocynaceaeApocynaceae

•• recognizing recognizingAsclepiadaceaeAsclepiadaceae makes the makes theApocynaceaeApocynaceae paraphyletic paraphyletic

•• some agree since some agree sinceAslepiadaceaeAslepiadaceae are so are sodivergentdivergent

Issue #3Issue #3 - how do you construct a classification? - how do you construct a classification?

CladisticsCladisticsIssue #3Issue #3 - how do you construct a classification? - how do you construct a classification?

peripheral isolatesperipheral isolates: new: newspecies forms at edge ofspecies forms at edge ofretained ancestral speciesretained ancestral species

LisianthiusLisianthius in central in centralPanamanian cloudPanamanian cloudforestsforests

Recognize Recognize paraphyleticparaphyleticspeciesspecies??

•• island or peripheral island or peripheralgeographic speciation is ageographic speciation is acommon model in plantscommon model in plants

•• ancestral species ancestral speciesbecomes paraphyletic, newbecomes paraphyletic, newspecies monophyleticspecies monophyletic

CladisticsCladisticsParaphyletic species - 3 options:Paraphyletic species - 3 options:

1.1. Recognize Recognize bothboth the derived (apo) species and the the derived (apo) species and theparaphyletic ancestral (plesio) species - paraphyletic ancestral (plesio) species - 2 species2 species


2.2. Recognize the derived (apo) species and Recognize the derived (apo) species and monophyleticmonophyleticunitsunits from the ancestral (plesio) species from the ancestral (plesio) species


3.3. Recognize only one monophyletic species Recognize only one monophyletic species

PhylogeneticPhylogenetic Analysis of Analysis ofAsteridsAsterids

1. Data set for 13 1. Data set for 13 asteridsasterids and one and one rosidrosid outgroupoutgroup

report should report should include data set (characters/states)include data set (characters/states)


22. Distance approach in PAUP . Distance approach in PAUP –– neighbor joining neighbor joining

report should report should include NJ treeinclude NJ tree

3. Parsimony approach in PAUP3. Parsimony approach in PAUP

report should report should include strict consensus tree (# trees)include strict consensus tree (# trees)

report optionally report optionally include include strict consensus strict consensus tree aftertree afterweighting charactersweighting characters

1. Data set for 13 1. Data set for 13 asteridsasterids and one and one rosidrosid outgroupoutgroup

report should report should include data set (characters/states)include data set (characters/states)


5. 5. PhylogeneticsPhylogenetics and classification and classification

report should report should include discussion of how include discussion of how asteridsasterids are areor should be classified based on YOUR dataor should be classified based on YOUR data

44. Mapping of characters onto DNA tree in . Mapping of characters onto DNA tree in MacCladeMacClade

report should report should include discussion of good vs. badinclude discussion of good vs. badcharacters (characters (homoplasyhomoplasy))

phenetic dan cladistic

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