1. Phylogeny and uncertainty in
analyses of life span
Photo: bramblejungle/flickr
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

3. Grey partridge (Perdix perdix)
Fulmar (Fulmarus glacialis)

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

5. Data issues: truncation/censoring
Birth/hatching Death

6. Data issues: truncation/censoring
Birth/hatching Death
Truncation

7. Data issues: truncation/censoring
Birth/hatching Death
Truncation
Censoring

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)

10. Trait evolution
Correlation can be due to the influence of
the trait in question, or an inherited
characteristic.

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

13. Bird illustrations: RSPB

14. Cuckoo (Cuculus canorus)

15. Phylogeny:Thomas, GH 2008 Proc. R. Soc. B

16. Bird illustrations: RSPB

17. Bird illustrations: RSPB

18. Bird illustrations: RSPB

19. Bird illustrations: RSPB

20. Phylogenetic signal measures the amount that
phylogeny influences trait (0 - 1).
Pagel’s Lambda ~ 0.73

21. Ordinary least squares regression
50
20
Life span (yrs)
10
5
2
5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods)

22. Ordinary least squares regression
50
20
Life span (yrs)
10
5
2
5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods)
R2 = 0.26 R2 = 0.27

23. Phylogenetic correction
Independent contrasts
Assumes Lambda = 1
50
20
Life span (yrs)
10
5
2
5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods)
R2 = 0.26 to <0.01 R2 = 0.27 to <0.01

24. Phylogenetic correction
Independent contrasts Optimised PGLS
Assumes Lambda = 1 Lambda = 0.73
50 50
20 20
Life span (yrs)
Life span (yrs)
10 10
5 5
2 2
5 50 500 5000 1 2 5 10 20 5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods) Weight (g) Effort (clutch size * broods)
R2 = 0.26 to <0.01 R2 = 0.27 to <0.01 R2 = 0.26 to 0.06 R2 = 0.27 to 0.07

25. Phylogenetic correction
Independent contrasts Optimised PGLS
Assumes Lambda = 1 Lambda = 0.73
50 50
20 20
Life span (yrs)
Life span (yrs)
10 10
5 5
2 2
5 50 500 5000 1 2 5 10 20 5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods) Weight (g) Effort (clutch size * broods)
R2 = 0.26 to <0.01 R2 = 0.27 to <0.01 R2 = 0.26 to 0.06 R2 = 0.27 to 0.07
Can we improve the fit by accounting
for data problems?

26. State-space model
Process model
Predictor Observed Response
X Y
Phylogeny

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.

29. State-space regression
models
50
20
Life span (yrs)
10
5
2
5 50 500 5000 1 2 5 10 20
Weight (g) Effort (clutch size * broods)
R2 = 0.06 to 0.10 R2 = 0.07 to 0.12

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!

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

36. PHYLOGENETIC SIGNAL AS A
NUISANCE
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• Apparently strong relationships
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can be misleading.
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Driven by few independent
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Effectively overestimating
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degrees of freedom - that’s why
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it is sometimes called ●
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37. PHYLOGENETIC SIGNAL AS A
NUISANCE
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38. PHYLOGENETIC SIGNAL AS A
NUISANCE
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can be misleading.
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Within clade effects can be
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strong.
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39. PHYLOGENETIC SIGNAL AS A
NUISANCE
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40. Future work
• Model tempo and mode of evolution of
life span and reproductive effort.
Constrained to an optimum Random walk Niche separation
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Trait value
Trait value
Trait value
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