phylogeny and uncertainty in analyses of life span
TRANSCRIPT
Phylogeny and uncertainty in analyses of life span
Owen R. Jones* and Fernando ColcheroMax Planck Institute for Demographic Research, Rostock*[email protected], website: owenjon.es
7th June 2012, EvoDemo Workshop, MPIDR, Germany
Phot
o: b
ram
blej
ungl
e/fli
ckr
de Magalhaes & Costa 2009 J. Evol. Biol.
Robinson 2005 BTO Research Report 407
Fulmar (Fulmarus glacialis)
Grey partridge (Perdix perdix)
Sample size
Max
. obs
erve
d life
span
0 20 40 60 80 100
0
5
10
15
20
25
30
Data issues: sample size
• Maximum observed life span increases with sample size
• Species with small sample sizes are problematic
Birth/hatching Death
Data issues: truncation/censoring
DeathBirth/hatching
Data issues: truncation/censoring
Truncation
Death
Censoring
DeathBirth/hatching
Truncation
Data issues: truncation/censoring
Trait evolution
‣ Closely related species tend to share similar trait values by inheritance (phylogenetic signal)
‣ Traits can also be similar due to similar life style (convergent evolution)
Trait evolution
Correlation can be due to the influence of the trait in question, or an inherited characteristic.
• To develop and test a statistical modelling framework that accounts for these data issues while controlling for phylogeny
Aim
• British Trust for Ornithology has carried out mark-capture-recovery since 1933
• Maximum recorded life span for >200 species• Clutch size, number of broods, body mass
The data set
Robinson 2005 BTO Research Report 407
Bird illustrations: RSPB
Cuckoo (Cuculus canorus)
Phylogeny: Thomas, GH 2008 Proc. R. Soc. B
Bird illustrations: RSPB
Bird illustrations: RSPB
Bird illustrations: RSPB
Bird illustrations: RSPB
Pagel’s Lambda ~ 0.73
Phylogenetic signal measures the amount that phylogeny influences trait (0 - 1).
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
Ordinary least squares regression
R2 = 0.27R2 = 0.26
Ordinary least squares regression
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
R2 = 0.27 to <0.01R2 = 0.26 to <0.01
Phylogenetic correction
Independent contrastsAssumes Lambda = 1
Independent contrasts
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
R2 = 0.27 to <0.01R2 = 0.26 to <0.01
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
Optimised PGLS
R2 = 0.27 to 0.07R2 = 0.26 to 0.06
Phylogenetic correction
Assumes Lambda = 1 Lambda = 0.73
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
R2 = 0.27 to <0.01R2 = 0.26 to <0.01
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
R2 = 0.27 to 0.07R2 = 0.26 to 0.06
Phylogenetic correction
Can we improve the fit by accounting for data problems?
Independent contrasts Optimised PGLSAssumes Lambda = 1 Lambda = 0.73
Process model
X YPredictor Observed Response
State-space model
Phylogeny
Process model
True Response
Y*
X YPredictor Observed Response
•Sample size•Censoring•Truncation
Data model
State-space model
Phylogeny
Process model
True Response
Y*
X YPredictor Observed Response
•Sample size•Censoring•Truncation
Data model
Maximise likelihood of both
Phylogeny
• MCMC framework• Simultaneously estimates:
• Coefficients of process model• Phylogenetic signal• True response• Error in process model• Error in data model• -> Degree of censoring,
truncation and sample size effects.
State-space model
R2 = 0.07 to 0.12R2 = 0.06 to 0.10
State-space regression models
Weight (g)
Life
spa
n (y
rs)
5 50 500 5000
2
5
10
20
50
Effort (clutch size * broods)
1 2 5 10 20
BTO data underestimates lifespan for many species
Effort
% d
iffer
ence
in li
fe s
pan
0 5 10 15 20
020
040
060
080
010
00
BTO data underestimates lifespan for many species
Effort
% d
iffer
ence
in li
fe s
pan
0 5 10 15 20
020
040
060
080
010
00
Conclusions
• Life history patterns are moderated by phylogeny
• Method of correction is fundamentally important
• Data issues can be solved
• Further analyses are in the pipeline!
ComPADRe ComADRe DATLife BiDDaBaMaDDaBa
MPIDR CNRS
Projection matrices
Life tables
Age structures
Recapture histories
Integral projection models
Life spans
MPIDR Germany - Dr. Fernando Colchero, Dr. Dalia Conde Ovando, Dr. Alex Scheuerlein, Dr. Roberto Salguero-Gómez, Julia Barthold, Dr. Annette Baudisch, Prof. James W. VaupelCNRS, France - Profs. Jean-Dominique Lebreton, Jean-Michel GaillardBritish Trust for Ornithology, Max Planck Society
Acknowledgements
PHYLOGENETIC SIGNAL AS A NUISANCE
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• Apparently strong relationships can be misleading.
• Driven by few independent events.
• Effectively overestimating degrees of freedom - that’s why it is sometimes called ‘phylogenetic pseudocorrelation’.
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• Apparently weak relationships can be misleading.
• Within clade effects can be strong.
PHYLOGENETIC SIGNAL AS A NUISANCE
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Future work• Model tempo and mode of evolution of
life span and reproductive effort.
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Constrained to an optimum Random walk Niche separation