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A general model of continuous character evolution

by Carl Boettiger
Nature Precedings (2011)

Cite this document (BETA)

Available from Carl Boettiger's profile on Mendeley.
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A general model of continuous character evolution

A general model for continuous character
evolution
Carl Boettiger
UC Davis
June 20, 2011
Carl Boettiger, UC Davis Release of Constraint 1/37
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A Key Innovation in Jaw Morphology?
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A long time ago. . .
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This fish
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Got a good idea
Westneat et. al. (2005)
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and did this:
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and did this:
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Parrotfish with the intramandibular joint:
have over 6 times greater disparity
in their jaw opening lever ratio
have over 3 times greater disparity
in their closing lever ratio
comparable variation in protrusion
and they are a younger clade
relative to other parrotfish
(corrected for body mass)
Price et. al. (2010)
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Parrotfish with the intramandibular joint:
have over 6 times greater disparity
in their jaw opening lever ratio
have over 3 times greater disparity
in their closing lever ratio
comparable variation in protrusion
and they are a younger clade
relative to other parrotfish
(corrected for body mass)
Price et. al. (2010)
Carl Boettiger, UC Davis Release of Constraint 7/37
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A Key Innovation* in Jaw Morphology?
* With respect to the resulting diversity of morphology,
rather than species diversity
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One group remains under
selective constraint
One with innovation can
diversify morphology
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This is a nice question for comparative
phylogenetic models. . .
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We don’t have that model.
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I have that model
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Models for Continuous Character
Evolution
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A Phylogeny of Phylogenetic ModelsBM Brownie ouch OU
1985
1997
1999
2006
2004
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Something is missing
Brownie:
Differing rates of diversification ouch:
Differing selective optima
Where’s the link? Can it be both?
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Some comparative physiology: Brownie
dX︸︷︷︸
trait change
= σ︸︷︷︸
rate
× dBt︸ ︷︷ ︸
Brownian
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Some comparative physiology: Brownie
dX︸︷︷︸
trait change
= σ︸︷︷︸
rate
× dBt︸ ︷︷ ︸
Brownian
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Some comparative physiology: ouch
dX = α( θ︸︷︷︸
optimum
−X)dt+ σdBt
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How about:
dX = α︸︷︷︸
selection
(θ −X)dt+ σdBt
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How about:
dX = α︸︷︷︸
selection
(θ −X)dt+ σdBt
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Combine both:
dX = α( θ︸︷︷︸
optimum
−X)dt+ σ︸︷︷︸
divers. rate
dBt
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Combine both:
dX = α( θ︸︷︷︸
optimum
−X)dt+ σ︸︷︷︸
divers. rate
dBt
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Many other combinations:
dX = α︸︷︷︸
selection
( θ︸︷︷︸
optimum
−X)dt+ σdBt
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One model to rule them all?
dX = α︸︷︷︸
selection
( θ︸︷︷︸
optimum
−X)dt+ σ︸︷︷︸
divers. rate
dBt
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One model to rule them all?
dX = α︸︷︷︸
selection
( θ︸︷︷︸
optimum
−X)dt+ σ︸︷︷︸
divers. rate
dBt
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A dangerous path
Ridges so flat and long you may
be stuck forever
Rugged likelihood surfaces such that
common ML algorithms (Nelder-Mead,
L-BFGS-B), will fail
AIC, familiar guide to model choice,
is slimy and treacherous
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A dangerous path
Ridges so flat and long you may
be stuck forever
Rugged likelihood surfaces such that
common ML algorithms (Nelder-Mead,
L-BFGS-B), will fail
AIC, familiar guide to model choice,
is slimy and treacherous
Carl Boettiger, UC Davis Release of Constraint 23/37
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A dangerous path
Ridges so flat and long you may
be stuck forever
Rugged likelihood surfaces such that
common ML algorithms (Nelder-Mead,
L-BFGS-B), will fail
AIC, familiar guide to model choice,
is slimy and treacherous
Carl Boettiger, UC Davis Release of Constraint 23/37
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A dangerous path
Ridges so flat and long you may
be stuck forever
Rugged likelihood surfaces such that
common ML algorithms (Nelder-Mead,
L-BFGS-B), will fail
AIC, familiar guide to model choice,
is slimy and treacherous
Carl Boettiger, UC Davis Release of Constraint 23/37
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Likelihood ridges
α ∝ σ2 ridge-line
small or deeply branching
phylogenies
Closely nested regime paintings
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Likelihood ridges
α ∝ σ2 ridge-line
small or deeply branching
phylogenies
Closely nested regime paintings
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Likelihood ridges
α ∝ σ2 ridge-line
small or deeply branching
phylogenies
Closely nested regime paintings
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Survival Strategies
on rugged likelihood surfaces
Sub-models
Simulated annealing (if max-likelihood)
MCMC→ posterior distributions (in
Bayesian mode)
PMC for power estimates & model choice*
(“A simulation analysis in a box” for your study)
* Boettiger et al. 2011 in review
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Survival Strategies
on rugged likelihood surfaces
Sub-models
Simulated annealing (if max-likelihood)
MCMC→ posterior distributions (in
Bayesian mode)
PMC for power estimates & model choice*
(“A simulation analysis in a box” for your study)
* Boettiger et al. 2011 in review
Carl Boettiger, UC Davis Release of Constraint 25/37
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Survival Strategies
on rugged likelihood surfaces
Sub-models
Simulated annealing (if max-likelihood)
MCMC→ posterior distributions (in
Bayesian mode)
PMC for power estimates & model choice*
(“A simulation analysis in a box” for your study)
* Boettiger et al. 2011 in review
Carl Boettiger, UC Davis Release of Constraint 25/37
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Survival Strategies
on rugged likelihood surfaces
Sub-models
Simulated annealing (if max-likelihood)
MCMC→ posterior distributions (in
Bayesian mode)
PMC for power estimates & model choice*
(“A simulation analysis in a box” for your study)
* Boettiger et al. 2011 in review
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Returning to our opening example. . .
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Can a key innovation release selective constraint?
Differing trait diversification
rates, or
Differing strengths of stabilizing
selection?
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Differing Trait Diversification Rate Model
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So which is the better model?
Differing Diversification
Log Likelihood: -94
Parameters: 4
Differing Selection Log
Likelihood: -92
Parameters: 4
Not enough power for this comparison in this trait.
. . . must try harder. . .
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So which is the better model?
Differing Diversification
Log Likelihood: -94
Parameters: 4
Differing Selection Log
Likelihood: -92
Parameters: 4
Not enough power for this comparison in this trait.
. . . must try harder. . .
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A slightly simpler model comparison
Differing trait diversification
rates, or
Differing strengths of stabilizing
selection?
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Comparison
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If we prefer to be Bayesian: MCMC
These are posteriors, not likelihood bootstraps. Requires
priors, etc.
But, looks pretty much the same.
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Conclusions
1 A new phylogenetic method
2 Complex models are dangerous with inadequate power
3 Have methods to quantify power and uncertainty.
4 Hobbit-sized swords: Smaller sub-models for smaller data
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Conclusions
1 A new phylogenetic method
2 Complex models are dangerous with inadequate power
3 Have methods to quantify power and uncertainty.
4 Hobbit-sized swords: Smaller sub-models for smaller data
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Acknowledgements
Peter Wainwright
Wainwright Lab
Graham Coop
Peter Ralph
Funding:
Download the development version now:
https://github.com/cboettig/wrightscape
Slides and more our on new Lab Blog:
http://wainwrightlab.wordpress.com
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An Addendum on Reproducible Research
Slides on http://wainwrightlab.wordpress.com
See meta-data, script, version of code, data, recent
changes. . .
Carl Boettiger, UC Davis Release of Constraint 37/37

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