so many different kinds of mistakes
TRANSCRIPT
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So many different kinds of mistakes
Or why systematic error is the 21st century’s sampling error!Liliana M. DávalosAssistant Professor, Department of Ecology & EvolutionSUNY, Stony Brook!Grand Valley State University10 April 2014
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My lab’s research mission
Biological diversityDiversification Human
impact
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Two kinds of questions
Biological diversity
Diversification, speciation decrease Habitat lossincrease
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So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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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
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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?
Sampling vs. systematic
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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
Sampling vs. systematic
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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
Sampling vs. systematic
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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?
Lang, Darling, Eisen 2013 PLoS One
Sampling vs. systematic
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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
Lang, Darling, Eisen 2013 PLoS One
Sampling vs. systematic
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So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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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
100
10084
9642
10063
63
65
55
84
10074
51
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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...Dávalos 2012 PLoS One
How phenotypes evolve
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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
How phenotypes evolve
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• We use morphological characters
• How good are the models of evolution for morphological characters?• Characteristics of
the data• Compare to models
molecular evolution
Fossils without genomes
Dávalos & Russell 2012 Ecol Evol
How phenotypes evolve
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Species CharactersThese are morphological characters
• They look like this —>• Discontinuous
between species• Factors, not
numbers• Difficult to model
How phenotypes evolve
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The organisms in question
New World Leaf-nosed bats and relatives
How phenotypes evolve
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Baker et al. 2003 Occas Pap Mus TTU Dávalos, Cirranello et al. 2012 Biol Rev
Wetterer et al. 2000 B Am Mus Nat Hist
How phenotypes evolve
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The trouble with morphological characters
• At first, only model was parsimony
• Neutral Jukes-Cantor 1969 model implemented 2001• Current model has
gamma variation across characters
• Applying this model does not solve conflict
Dávalos, Cirranello et al. 2012 Biol Rev
How phenotypes evolve
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If the Jukes-Cantor model yields conflicting answer, could the model be inadequate given these data?
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Homoplasy I: inconsistency!
q
pp
Felsenstein 1978 Syst Biol
How phenotypes evolve
consistent
Non consistent
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Homoplasy II: ecological convergence
• Can bring together unrelated ecologically similar lineages• This example: mt
cytochrome b gene of nectar-feeding bats
• Association adaptive molecular evolution and supporting wrong node Dávalos, Cirranello et al. 2012 Biol Rev
How phenotypes evolve
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Homoplasy III: correlated evolution
• Expected in protein-coding genes
• Models in use for codons, aminoacids, ribosomal RNA secondary structure
Dávalos & Perkins 2008 Genomics
How phenotypes evolve
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Might these 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.
How phenotypes evolve
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Dávalos, Cirranello et al. 2012 Biol Rev
Inconsistency!
How phenotypes evolve
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Figure 12
Dávalos et al. In Press Syst Biol Dávalos, Cirranello et al. 2012 Biol Rev
Convergent evolution!
How phenotypes evolve
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Correlated evolution!
How phenotypes evolve
Dissimilarity between characters ->
‘
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Models incur systematic error
• Morphology = phenotype• Neutrality and
independence wrong for models• Not neutral• Not independent
Skelly et al. 2013 Genome Res
How phenotypes evolve
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How does morphology evolve?
• Ordering: each character state gives rise to a finite range of states
• There are limits to states because of• Development• Natural selection
Dávalos, Cirranello et al. 2012 Biol Rev
How phenotypes evolve
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Modeling selection in morphology
• Brownian motion vs. Ornstein-Uhlenbeck models
• Continuous phenotypic traits
• Might selection explain homoplasy in morphological data?
How phenotypes evolve
Butler & King 2004 Am Nat
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A BB C D
nectarivorous
other
OU2a
frugivorous (figs)
other
OU2b
frugivorous (figs)
other
nectarivorous
OU3
frugivorous (figs)
other
nectarivorous
strictly frugivorous (figs, Short-faced bats)
OU4
Figure 5
Ardops
Ariteus
Carollia
Diphylla
MimonTonatia
Sturnira
Ametrida
Centurio
PygodermaSphaeronycteris
Stenoderma
Lonchophylla
Chrotopterus
DesmodusDiaemus
Lampronycteris
Lophostoma
Macrotus
Micronycteris
Phylloderma
Phyllostomus
Rhinophylla
Trachops
Vampyrum
Artibeus
Chiroderma
EctophyllaEnchisthenes
Mesophylla
Platyrrhinus
Uroderma
Vampyressa
Vampyrodes
Metavampyressa
LonchophyllaPlatalina
Anoura
Choeroniscus
Choeronycteris
Hylonycteris
Erophylla
Glossophaga
LeptonycterisMonophyllus
PhyllonycterisBrachyphylla
Dumont ... Dávalos 2014 Evolution
Engineering model of performance
How phenotypes evolve
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0
100
200
300
400
500
0.0 0.4 0.8 1.2MA
count
dietfigs
figs only
nectar
other
• Performance related to diet• Low mechanical
advantage in nectar-feeding bats• Convergence on
this phenotype• Analyzing function and
integrating selection better than ignoring
Three performance peaks
How phenotypes evolve
Mechanical advantage
Freq
uenc
y
Dumont ... Dávalos 2014 Evolution
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Morphology...
AminoacidsCodons
How phenotypes evolve
Neutral genotype
Model complexity
How phenotypes evolve
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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• Reduce data
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So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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My lab’s research mission
Biological diversityDiversification Human
impact
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Why do rainforests decline? Three hypotheses
Hamburger! (or steak)Kaimowitz et al. 2004 CIFOR
CocaDávalos et al. 2011 Environ
Sci Technol
Land tenure and propertyHecht 1993 BioScience
Why lose forests?
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Predictions
Hamburger! (or steak)Kaimowitz et al. 2004 CIFOR
CocaDávalos et al. 2011 Environ
Sci Technol
Land tenure and propertyHecht 1993 BioScience
Why lose forests?
+ demand beef + beef, + cattle + cattle, + pasture + pasture, - forest
+ demand cocaine + cocaine, + coca + coca, - forest
+ demand land + pasture, + cattle + cattle, - forest
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The real drivers of habitat loss
Forest, coca nothing Eradicationdecrease
Urbanization &
Development
Dávalos et al. 2014 Biol Cons
becomes
Pasture &
Cowsisproperty
Why lose forests?
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These systematic errors are scary
• Models inform policy• Real decisions are
made based on these inadequate models
• Models influence what data we collect• If we focus on cattle
and the problem is palm, we are missing the real story
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Shifting to the present
• 20th century challenge• Collecting enough data• i.e., sampling
• Still relevant in many cases
• New challenges• Formulating models • “Big” data• Correlated data• Otherwise biased data
Fjeldsa et al. 2005 Ambio
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•Funding•NSF–DEB, CIDER–SBU
•Speciation & diversification: A. Cirranello, A. Russell, N. Simmons, P. Velazco
•Functional evolution: E. Dumont, S. Rossiter, E. Teeling
•Conservation & policy: D. Armenteras, A. Bejarano, A. Corthals, L. Correa, J. Holmes, N. Rodriguez, C. Romero
•Dávalos Lab•Phylogenetics: R. Dahan, S. DelSerra, A. Goldberg, O. Warsi, L. Yohe, X. Zhang
• Land use: P. Connell, M. Hall, E. Simola, G. Tudda, Y. Shah
Thanks!