Date post: | 28-Jan-2018 |
Category: |
Science |
Upload: | liliana-davalos |
View: | 147 times |
Download: | 2 times |
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
Two kinds of questions
Biological diversity
Diversification, speciation decrease Habitat lossincrease
So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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?
Sampling vs. systematic
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
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
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
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
So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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
70
98
9299
74
100100
10075
99
100
78
4378
100
49
20
100
9992
32
10092
50
26
5618
14
6
37
32
11
66100
51
5
463878
15
100
100
10077
99
84
88
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
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
• 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
Species CharactersThese are morphological characters
• They look like this —>• Discontinuous
between species• Factors, not
numbers• Difficult to model
How phenotypes evolve
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 minuta
Micronycteris hirsuta
Micronycteris megalotis
Rhinophylla
Carollia
Sturnira
Enchisthenes hartii
Artibeus concolor
Artibeus jamaicensis
Artibeus cinereus
Uroderma
Platyrrhinus
Vampyrodes
Chiroderma
Vampyressa bidens
Vampyressa nymphaea
Vampyressa pusilla
Ectophylla
Mesophylla
Ametrida
Centurio
Sphaeronycteris
Pygoderma
Phyllops
Stenoderma
Ariteus
Ardops
≥ 75%
< 50%
≥ 50 & < 75%
MP bootstrap
Macrotus
Lampronycteris
Micronycteris minuta
Micronycteris schmidtorum
Micronycteris hirsuta
Microncyteris megalotis
Diphylla
Diaemus
Desmodus
Lonchorhina
Macrophyllum
Trachops
Chrotopterus
Vampyrum
Lophostoma
Tonatia
Phylloderma
Phyllostomus
Mimon
Anoura
Hylonycteris
Choeroniscus
Musonycteris
Choeronycteris
Erophylla
Brachyphylla
Monophyllus
Glossophaga
Leptonycteris
Lonchophylla
Lionycteris
Carollia
Trinycteris
Glyphonycteris daviesi
Glyphonycteris sylvestris
Rhinophylla
Sturnira
Mesophylla
Vampyressa
Platyrrhinus
Vampyrodes
Uroderma
Vampyressa bidens
Vampyressa brocki
Chiroderma
Enchisthenes
Ectophylla
Artibeus
Dermanura
Ariteus
Ardops
Stenoderma
Centurio
Pygoderma
Ametrida
Sphaeronycteris
< 0.97≥ 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 Hist
How phenotypes evolve
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
If the Jukes-Cantor model yields conflicting answer, could the model be inadequate given these data?
q
p
Homoplasy I: inconsistency!
q
pp
Felsenstein 1978 Syst Biol
How phenotypes evolve
consistent
Non consistent
A B
Background selection
Pe
rce
nt
co
do
ns o
f C
YT
B in
ea
ch
co
do
n t
yp
e
0
20
40
60
Background selection Selection shift
Significantly support
Significantly rejectRejectSupport
Type of codon
Amino acid position in alignment
Su
pp
ort
fo
r n
ecta
r-fe
ed
ing
cla
de
-3
-2
-1
0
1
2
3
100 200 300 400 500
Significant supportor rejection
Selection shiftSelection shift inGlossophaginae
Type of codon
CYTB COX1
Figure 12
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
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
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
Dental characters ●
●
MandibularMaxillary
● CanineIncisors
MolarsPremolars
● Significant
A−2
0
2
Supp
ort f
or n
ecta
r−fe
edin
g cl
ade
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
A B
Background selection
Pe
rce
nt
co
do
ns o
f C
YT
B in
ea
ch
co
do
n t
yp
e
0
20
40
60
Background selection Selection shift
Significantly support
Significantly rejectRejectSupport
Type of codon
Amino acid position in alignment
Su
pp
ort
fo
r n
ecta
r-fe
ed
ing
cla
de
-3
-2
-1
0
1
2
3
100 200 300 400 500
Significant supportor rejection
Selection shiftSelection shift inGlossophaginae
Type of codon
CYTB COX1
Figure 12
Dávalos et al. In Press Syst Biol Dávalos, Cirranello et al. 2012 Biol Rev
Convergent evolution!
How phenotypes evolve
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
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
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
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
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
Morphology...
AminoacidsCodons
How phenotypes evolve
Neutral genotype
Model complexity
How phenotypes evolve
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
So many kinds of mistakes
• Sampling error vs. systematic error• In phylogenetics• How phenotypes evolve
• In environmental change• Why we are losing forests?
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?
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
The real drivers of habitat loss
Forest, coca nothing Eradicationdecrease
Urbanization &
Development
Dávalos et al. 2014 Biol Cons
becomes
Pasture &
Cowsisproperty
Why lose forests?
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
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
•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!