So many different kinds of mistakes

So many different kinds of mistakes
Or why systematic error is the 21st century’s sampling error
!
Liliana M. Dávalos
Assistant Professor, Department of Ecology & Evolution
SUNY, Stony Brook
!
Grand Valley State University
10 April 2014

My lab’s research mission
Diversification Biological
Human
diversity impact

Two kinds of questions
Biological
diversity
Diversification,
speciation increase decrease Habitat loss

So many kinds of mistakes
• Sampling error vs. systematic error
• In phylogenetics
• How phenotypes evolve
• In environmental change
• Why we are losing forests?

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

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

Phylogenetics
Mycobacterium bovis BCG str. Pasteur 1173P2
M. tuberculosis H37Ra
M. bovis BCG str. Tokyo 172
M. bovis AF212297
M. tuberculosis CDC1551
pathogenic M. tuberculosis F11
(avium-M. tuberculosis KZN 1435
M. tuberculosis H37Rv
non-pathogenic Mycobacterium smegmatis M. avium subsp. paratuberculosis K10
M. avium 104
M. vanbaalenii PYR1
M. sp. Spyr1
M. smegmatis str. MC2 155
M. sp. KMS
M. sp. MCS
M. sp JLS
Mycobacterium sp. *
Nocardia farcinica IFM 10152
Gordonia bronchialis DSM 43247
Rhodococcus opacus B4
R. equi ATCC 33707
R. equi 103S
Segniliparus rotundus DSM 44985
Bifidobacterium longum NCC2705
B. longum DJO10A
B. longum subsp. infantis 157F
B. longum subsp. longum JCM 1217
B. longum subsp. longum BBMN68
B. longum subsp. infantis ATCC 55813
B. longum subsp. longum JDM301
B. longum subsp. infantis ATCC 15697
B. breve DSM 20213
B. dentium Bd1
B. dentium ATCC B. adolescentis ATCC B. bifidum PRL2010
100
100
84
96
42
100
63
63
65
55
51
70
84
74 100
98
92
99
74
100
100
100
75
99
100
20
88
• 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

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

Fossils without
genomes
• We use morphological
characters
• How good are the
models of evolution for
morphological
characters?
• Characteristics of
the data
• Compare to models
molecular evolution
Dávalos & Russell 2012 Ecol Evol

Species Characters
These are morphological
characters
• They look like this —>
• Discontinuous
between species
• Factors, not
numbers
• Difficult to model

The organisms in
question
New World Leaf-nosed bats and
relatives

p
i
i
p p
p
p p D
D
p
p D

pi

D p D
i
p p D
p D

p D
i

i p D
p D
p
p D i

pi

i Di

DD

D
D Di
pi
p D
p p D
p D

Di

p D i D

p D i D

p D D

p

Di
D D
D i
D i
D i i

Dp i
p

p
p p
p i
D p

p
D
Di

p D
p
p

D
D i

•
•

Ma
r M

r M aM

r M Ma

r M aM

rM M
r
a
a

ra

M Ma
ra
M
M
r
r M a

r
r r

ra

M r M
a
r r M
a

a r M
r M
r
r M

r M
r r M c
r r M rc M

r

Ma

r
r

r
r
Mr a
r
M
M r
M a
a
M a

M
Ma
r
M

r M

•
Baker et al. 2003 Occas Pap Mus TTU
Dávalos, Cirranello et al. 2012 Biol Rev
Wetterer et al. 2000 B Am Mus Nat Hist

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

If the Jukes-Cantor model yields conflicting answer,
could the model be inadequate given these data?

Non
consistent
q
p
p p
q
Homoplasy I: inconsistency!
Felsenstein 1978 Syst Biol
consistent

Homoplasy II:
ecological convergence
• Can bring together
unrelated ecologically
similar lineages
• This example: mt
c

cytochrome b gene
of nectar-feeding
bats
• Association adaptive
molecular evolution
and supporting wrong
node Dávalos, Cirranello et al. 2012 Biol Rev
P e

Homoplasy III:
correlated evolution
• Expected in protein-coding
genes
• Models in use for
codons, aminoacids,
ribosomal RNA
secondary structure
Dávalos Perkins 2008 Genomics

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.

Inconsistency!

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●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
c
A

e
r P P P

Dávalos et al. In Press Syst Biol
Convergent
evolution!

Correlated evolution!
Dissimilarity between characters -
‘

Models incur
systematic error
• Morphology =
phenotype
• Neutrality and
independence wrong
for models
• Not neutral
• Not independent
Skelly et al. 2013 Genome Res

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

Modeling selection in
morphology
• Brownian motion vs.
Ornstein-Uhlenbeck
models
• Continuous
phenotypic traits
• Might selection explain
homoplasy in
morphological data?
Butler King 2004 Am Nat

OU2b
OU3
nectarivorous
nectarivorous
strictly frugivorous (figs, Short-faced bats)
A B C D
OU2a
nectarivorous
other
frugivorous (figs)
other
frugivorous (figs)
other
frugivorous (figs)
other
OU4
Macrotus
Desmodus
Diaemus
Diphylla
Micronycteris
Lampronycteris
Carollia
Sturnira
Mesophylla
Vampyressa
Platyrrhinus
Vampyrodes
Chiroderma
Metavampyressa
Uroderma
Ardops
Ariteus
Figure Stenoderma
Ametrida
Centurio
Pygoderma
Sphaeronycteris
Artibeus
Ectophylla
Enchisthenes
Rhinophylla
Lonchophylla
LPolantcahlinoaphylla
Choeroniscus
Choeronycteris
Hylonycteris
Anoura
Glossophaga
Leptonycteris
Monophyllus
Erophylla
Phyllonycteris
Brachyphylla
Chrotopterus
Vampyrum
Lophostoma
Phyllostomus
Phylloderma
Mimon
Tonatia
Trachops
Dumont ... Dávalos 2014 Evolution
Engineering model of
performance

500
400
300
200
100
0
0.0 0.4 0.8 1.2
MA
count
diet
figs
figs only
nectar
other
Three performance
peaks
• Performance related to
diet
• Low mechanical
advantage in nectar-feeding
bats
• Convergence on
this phenotype
• Analyzing function and
integrating selection
better than ignoring
Mechanical advantage
Frequency
Dumont ... Dávalos 2014 Evolution

Morphology
...
Aminoacids
Codons
Neutral
genotype
Model complexity

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