Systematic error and the contours of a theory of macroevolution

Systematic Error and the Contours of a Theory of Macroevolution

!Liliana M. DávalosAssociate Professor, Department of Ecology & EvolutionSUNY, Stony Brook!Field Museum of Natural History17 December 2014

Our research mission

Biological diversityDiversification Human

impact

Two kinds of questions

Biological diversity

Diversification, speciation decrease Habitat lossincrease

Sampling error vs. systematic errorFelsenstein 1978 Syst Zool

Thinking about errors

• Let’s say we want to answer a question:• In a finite

population, what is the frequency of an allele?

Sampling vs. systematic

How to answer this question

• We go out, get samples, genotype different individuals

• Then we count the alleles

• What is the main source of error?


This is sampling error

• We want to get a better estimate of the allele frequency• => Sample more

• We could sample the entire population• => Best possible

estimate of allele frequency


0.1 substitutions/site

Mycobacterium bovis BCG str. Pasteur 1173P2M. tuberculosis H37RaM. bovis BCG str. Tokyo 172M. bovis AF212297M. tuberculosis CDC1551M. tuberculosis F11M. tuberculosis KZN 1435M. tuberculosis H37Rv

M. avium subsp. paratuberculosis K10M. avium 104

M. vanbaalenii PYR1M. sp. Spyr1

M. smegmatis str. MC2 155M. sp. KMSM. sp. MCSM. sp JLS

Mycobacterium sp. *Nocardia farcinica IFM 10152

Gordonia bronchialis DSM 43247Rhodococcus opacus B4

R. equi ATCC 33707R. equi 103S

Segniliparus rotundus DSM 44985Bifidobacterium longum NCC2705 B. longum DJO10A B. longum subsp. infantis 157FB. longum subsp. longum JCM 1217B. longum subsp. longum BBMN68 B. longum subsp. infantis ATCC 55813B. longum subsp. longum JDM301 B. longum subsp. infantis ATCC 15697B. breve DSM 20213

B. dentium Bd1B. dentium ATCC 27679

B. adolescentis ATCC 15703 B. bifidum PRL2010B. bifidum S17Bifidobacterium sp. *

Corynebacterium matruchotii ATCC 14266C. efficiens YS314

C. genitalium ATCC 33030 Sca01C. glucuronolyticum ATCC 51866

C. urealyticum DSM 7109Arthrobacter sp. FB24

A. chlorophenolicus A6Kocuria rhizophila DC2201

Micrococcus luteus NCTC 2665Clavibacter michiganensis subsp. michiganensis NCP

C. michiganensis subsp. sepedonicus Cellulomonas flavigena DSM 20109

Kineococcus radiotolerans SRS30216Nakamurella multipartita DSM 44233

Saccharopolyspora erythraea NRRL 2338 Geodermatophilus obscurus DSM 43160

Amycolatopsis mediterranei U32Intrasporangium calvum DSM 43043

Kytococcus sedentarius DSM 20547Nocardioides sp. JS614

Streptomyces avermitilis MA4680S. scabiei 87 22

S. coelicolor A3 2Catenulispora acidiphila DSM 44928

Thermobifida fusca YXThermobispora bispora DSM 43833

Thermomonospora curvata DSM 43183Streptosporangium roseum DSM 43021

Micromonospora aurantiaca ATCC 27029M. sp. L5 Salinispora tropica CNB440

Salinispora arenicola CNS205Acidothermus cellulolyticus 11B

Rhodococcus jostii RHA1Mycobacterium gilvum PYRGCK

Frankia alni ACN14a

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pathogenic Mycobacterium complex(avium-bovis-tuberculosis)

non-pathogenic Mycobacterium smegmatis complex

Phylogenetics

• Testing relatedness• All of comparative

biology• Historical

biogeography• Evolutionary aspects

of community ecology• Diagnostics and

similar applications

Corthals et al. 2012 PLoS One


Now let’s ask a different question

• We want to find out how these 3000 microbial lineages relate to one another

• We get their genomes, map out each of the single-copy genes, estimate a phylogeny

Lang, Darling, Eisen 2013 PLoS One


But our results don’t make sense

• Is it sampling error?• Can we sample

more than the whole genome?

• We discover the model of gene evolution we are using was wrong• What kind of error is

this?



This is systematic error

• Even sampling whole genomes won’t fix the problem• Having more data

can make the problem worse!

• As long as we don’t change the model, we will keep obtaining the wrong answer



Systematic error in molecular evolutionSullivan & Swofford 1997 J Mamm EvolD’Erchia et al. 1996 Nature

Systematic error = homoplasy that is not modeled

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Homoplasy I: inconsistency!

q

pp

Felsenstein 1978 Syst Biol

Phenotypic evolution

consistent

Non consistent

A B

Background selection

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Background selection Selection shift

Significantly support

Significantly rejectRejectSupport

Type of codon

Amino acid position in alignment

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100 200 300 400 500

Significant supportor rejection

Selection shiftSelection shift inGlossophaginae

Type of codon

CYTB COX1

Figure 12

Homoplasy II: ecological convergence

• Phenotype exposed to selection

• Selection from similar ecology produces similar phenotypes

Dávalos, Cirranello et al. 2012 Biol Rev


Homoplasy III: correlated evolution

• Expected in protein-coding genes

• Models in use for codons, aminoacids, ribosomal RNA secondary structure

Dávalos & Perkins 2008 Genomics


Example with favorite gene: mt cytb Agnarsson et al. 2011 PLoS

Currents ToL

Dávalos 2010 Island Bats Evolution, Ecology, and Conservation

Agnarsson et al. 2011 PLoS Currents ToL

Molecular evolution

© M. Tuttle© N. Simmons

One of these is evolving fast, very fast Dávalos, Gutiérrez & Velazco

unpublishedMolecular evolution

Speed cannot be the whole story

• High rates of change randomize shared changes

• Expect no resolution instead of actual conflict

• For conflict, must reflect other forces• E.g., base

composition Dávalos, Gutiérrez & Velazco unpublished

Molecular evolution

Dávalos, Gutiérrez & Velazco unpublished

Agnarsson et al. 2011 PLoS Currents ToL

Molecular evolution

© M. Tuttle© N. Simmons

MacrotusLampronycterisMicronycteris minutaMicronycteris schmidtorumMicronycteris hirsutaMicroncyteris megalotisDiphyllaDiaemusDesmodusLonchorhinaMacrophyllumTrachopsChrotopterusVampyrumLophostomaTonatiaPhyllodermaPhyllostomusMimon

AnouraHylonycterisChoeroniscusMusonycterisChoeronycteris

ErophyllaBrachyphyllaMonophyllusGlossophagaLeptonycteris

LonchophyllaLionycterisCarolliaTrinycterisGlyphonycteris daviesiGlyphonycteris sylvestrisRhinophyllaSturnira

MesophyllaVampyressaPlatyrrhinusVampyrodes

Uroderma

Vampyressa bidensVampyressa brocki

Chiroderma

EnchisthenesEctophyllaArtibeusDermanuraAriteusArdopsStenodermaCenturioPygodermaAmetridaSphaeronycteris

< 0.97��

BYS posterior probability

Dumont, Dávalos et al. 2012 P R Soc B

Molecular evolution

Baker et al. 2003 Occas Pap Mus TTU Dávalos, Cirranello et al. 2012 Biol Rev

A B


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Type of codon


Sup

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Type of codon

CYTB COX1

Figure 12

Turns out: ecological convergence!

• Brings together ecologically similar lineages• Mt cytochrome b

gene of nectar-feeding bats

• Link between molecular adaptation and support for node


Molecular evolution

The genome is littered with such genes Parker et al. 2013 Nature

© N. Simmons

Diphylla Diaemus Desmodus Brachyphylla Erophylla Phyllonycteris Platalina Lonchophylla Lionycteris Monophyllus Glossophaga Leptonycteris Anoura Hylonycteris Lichonycteris Scleronycteris Choeroniscus Musonycteris Choeronycteris Phylloderma Phyllostomus Macrophyllum Lonchorhina Mimon crenulatum Mimon bennettii Trachops Tonatia Chrotopterus Vampyrum Trinycteris Glyphonycteris Lampronycteris Macrotus Micronycteris minutaMicronycteris hirsutaMicronycteris megalotisRhinophylla Carollia Sturnira Enchisthenes hartiiArtibeus concolorArtibeus jamaicensisArtibeus cinereusUroderma Platyrrhinus Vampyrodes Chiroderma Vampyressa bidensVampyressa nymphaeaVampyressa pusillaEctophylla Mesophylla Ametrida Centurio Sphaeronycteris Pygoderma Phyllops Stenoderma Ariteus Ardops

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MP bootstrap

MacrotusLampronycterisMicronycteris minutaMicronycteris schmidtorumMicronycteris hirsutaMicroncyteris megalotisDiphyllaDiaemusDesmodusLonchorhinaMacrophyllumTrachopsChrotopterusVampyrumLophostomaTonatiaPhyllodermaPhyllostomusMimon

AnouraHylonycterisChoeroniscusMusonycterisChoeronycteris

ErophyllaBrachyphyllaMonophyllusGlossophagaLeptonycteris

LonchophyllaLionycterisCarolliaTrinycterisGlyphonycteris daviesiGlyphonycteris sylvestrisRhinophyllaSturnira

MesophyllaVampyressaPlatyrrhinusVampyrodes

Uroderma

Vampyressa bidensVampyressa brocki

Chiroderma

EnchisthenesEctophyllaArtibeusDermanuraAriteusArdopsStenodermaCenturioPygodermaAmetridaSphaeronycteris

< 0.97��

BYS posterior probability

Baker et al. 2003 Occas Pap Mus TTU Dávalos, Cirranello et al. 2012 Biol Rev

Wetterer et al. 2000 B Am Mus Nat HistSystematic error in phenotype

Dated trees more important than ever

• Dated trees need fossils

• Why use dated trees?• Trait evolution• History of

assemblages in time and space

• Key innovations

Dumont, Dávalos et al. 2012 P R Soc B


• We use morphological characters

• How do phenotypes evolve?• Characteristics of

the data• Compare to models

molecular evolution

Fossils without genomes

Dávalos & Russell 2012 Ecol Evol


Species CharactersThese are morphological characters

• They look like this —>• Discontinuous

between species• Factors, not

numbers• Difficult to model


© N. Simmons

© M. Tuttle

The trouble with morphological characters

• At first, only model was parsimony

• Neutral Jukes-Cantor 1969 model implemented 2001• Current model varies

rates across characters

• Applying this model does not solve conflict



Could conflict arise from systematic error?


Might systematic error affect morphological characters?

Reviewer 1:

I don't see the point. If the characters are good characters (meaning that they have some phylogenetic signal at some level), then there is nothing especially wrong with the fact that they are weighted a little more than other characters.



Inconsistency!


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CYTB COX1

Figure 12


Convergent evolution!


Dávalos, Velazco et al. 2014 Syst Biol

Correlated evolution!


Dissimilarity between characters ->

‘

Dávalos, Velazco et al. 2014 Syst Biol unpublished

Systematic error in phenotypes

• Morphology = phenotype• Neutrality and

independence wrong assumptions• Not neutral• Not independent

Skelly et al. 2013 Genome Res


The trouble with systematic error

• In sampling error mode• More is more• More characters• = thousands of

correlated phenotypes• This will fail, we have

systematic error• Improve model• Improve data• Understand data


The contours of a theory of macroevolution

Modern synthesis is about populations

• Features of populations become important• Population size• Migration

• Generations• Time

• Mutation rate

Dávalos & Russell 2014 J Mammal

Contours of macroevolution

From micro to macro: successful example

• Coalescent stochasticity• Genes may represent

different histories by chance

• No selective force, only population characteristics

• Solution• Account for pop size

(Ne), # generations (t), mutation rate (mu) Neigel & Avise 1986 Evolutionary

Processes and TheoryContours of macroevolution

Another example

• Phenotypic traits• Often expected to

be under selection• Solution• Extend quantitative

genetics• Compare Brownian

motion to Ornstein-Uhlenbeck process

• Model t, s, and optima Martins & Hansen 1997 Am Nat

Butler & King 2004 Am NatContours of macroevolution

Dávalos, Velazco et al. 2014 Syst Biol unpublishedContours of macroevolution

There is a limit to modeling these, though

The chasm genetics-phenotype

• Biochemistry defines units and processes of genetics

• Genetics defines units and processes of genotype evolution in population

• What is there for (morphological) phenotypes?

Contours of macroevolutionLöytynoja & Goldman 2008 Science

Units and process

• How to define units of morphological evolution?

• Can using functional and developmental processes help?• Aren’t these also

unknown?• Are such processes

generalizable?• I.e., the way Ne, T and

mu areContours of macroevolution

Rieppel & Kearney 2007 Biol Philos

Morphology...

AminoacidsCodons

The challenge ahead

Neutral genotype

Model complexity

Contours of macroevolution

Thank you for inviting me!