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Phylogeny and uncertainty in analyses of life span
1. Phylogeny and uncertainty in
analyses of life span
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Owen R. Jones* and Fernando Colchero
Max Planck Institute for Demographic Research, Rostock
*jones@demogr.mpg.de, website: owenjon.es
7th June 2012, EvoDemo Workshop, MPIDR, Germany
2. de Magalhaes & Costa 2009 J. Evol. Biol.
Robinson 2005 BTO Research Report 407
4. Data issues: sample size
30
25
Max. observed lifespan
• Maximum observed life
20
span increases with
sample size 15
• Species with small sample 10
sizes are problematic 5
0
0 20 40 60 80 100
Sample size
9. 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)
11. Aim
• To develop and test a statistical modelling
framework that accounts for these data
issues while controlling for phylogeny
12. The data set
• 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
Robinson 2005 BTO Research Report 407
27. State-space model
Process model
Predictor Observed Response
X Y
Phylogeny
Data model
•Sample size
•Censoring True Response
•Truncation Y*
28. State-space model
Maximise likelihood of both
Process model
• MCMC framework
Predictor Observed Response
• Simultaneously estimates:
X Y • Coefficients of process model
Phylogeny • Phylogenetic signal
• True response
Data model • Error in process model
• Error in data model
•Sample size • -> Degree of censoring,
•Censoring True Response truncation and sample size
•Truncation Y* effects.
30. BTO data underestimates lifespan for
many species
1000
800
% difference in life span
600
400
200
0
0 5 10 15 20
Effort
31. BTO data underestimates lifespan for
many species
1000
800
% difference in life span
600
400
200
0
0 5 10 15 20
Effort
32. 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!
33.
34. ComPADRe ComADRe DATLife MaDDaBa BiDDaBa
MPIDR CNRS
Life spans
Life tables Recapture histories
Projection matrices
Integral projection models
Age structures
35. Acknowledgements
MPIDR Germany - Dr. Fernando Colchero, Dr. Dalia Conde Ovando, Dr. Alex Scheuerlein,
Dr. Roberto Salguero-Gómez, Julia Barthold, Dr. Annette Baudisch, Prof. James W. Vaupel
CNRS, France - Profs. Jean-Dominique Lebreton, Jean-Michel Gaillard
British Trust for Ornithology, Max Planck Society
40. Future work
• Model tempo and mode of evolution of
life span and reproductive effort.
Constrained to an optimum Random walk Niche separation
15
15
15
10
10
10
5
5
5
Trait value
Trait value
Trait value
0
0
0
−5
−5
−5
−10
−10
−10
−15
−15
0 10 20 30 40 0 10 20 30 40 −15 0 10 20 30 40
Time Time Time