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Quantifying uncertainty in species discovery with approximate Bayesian computation (ABC): single samples and recent radiations Mike Hickerson University of California, Berkeley Chris Meyer Museum of Vertebrate Zoology Craig Moritz Outline

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Presentation Transcript
slide1

Quantifying uncertainty in species discovery

with approximate Bayesian computation

(ABC):

single samples

and

recent radiations

Mike Hickerson University of California, Berkeley

Chris Meyer Museum of Vertebrate Zoology

Craig Moritz

slide2

Outline

Introduction - Species Discovery

Potential problems - Simulations

Potential problems - Empirical data

Potential statistical solutions

slide4

Match new specimen’s DNA “barcode” to voucher

specimens with barcodes in database

slide6

Proposed genetic thresholds for discovery

Comparing sample to closest sister taxon in reference database

1.Hebert’s 10X rule

between species divergence must be

> 10 times the average within species divergence

2. Reciprocal Monophyly

slide7

Noisy Problem

Species Tree

Gene Tree

Usually a

“near miss”

Species A

Species B

Species C

4 Sampled Individuals

Species C

slide8

Doubly Noisy Problem

(mtDNA

Barcode locus)

Genetic

Threshold

Equal?

Species

Delimitation

Criteria

Moving

Target

(Mental

Construct?)

slide9

Doubly Noisy Problem

Not sensitive

enough

(mtDNA

Barcode locus)

Under-Discovery

Genetic

Threshold

too sensitive

Over-Discovery

Equal?

Species

Delimitation

Criteria

Moving

Target

slide10

Joint Simulation

Exploration

DNA-barcode gene

(mtDNA, CO1 690 bp)

Simple BDM Model of

Reproductive isolation:

(Bateson-Dobzhansky-Muller)

Coalescent model

Problematic parameter space?

Potential statistical solutions?

slide11

BDM Model

(Bateson-Dobzhansky-Muller)

Genotype

A , b

OK

a , b

A, B

a, B

Bad

Neutral and divergent selection (Gavrilets 2004)

Speciation events - Poisson process

slide12

BDM loci

Barcode locus

(mtDNA)

Divergence Time (generations)

Island/Continent (peripatric)

slide14

Reciprocal monophyly

Threshold

Hickerson et al. 2006 (in press; Systematic Biology)

slide16

Coyne and Orr 1997

10X

Not Species

slide19

Coyne and Orr 1997

Presgraves 2002

Mendelson 2003

Bolnick and Near 2005

Zigler et al. 2005

Sasa et al. 1998

slide20

Move beyond “Yes/No” answers:

Nielsen and Metz 2005

Bayesian posterior probabilities w/ ABC

-answers with quantified uncertainty

-very fast (< 30 seconds per query)

-flexible (parameter threshold, model and prior

changes according to taxonomic group)

= moderate support for new

species

Migration

Isolation time

slide21

Prior, parameter threshold and operative model is

adjustable as appropriate for particular taxonomic

group

?

Mymarommatid wasps

(10 rare living fossil species)

African Cichlids

(recent radiation)

slide22

Ongoing Work

Extension of msBayes software pipeline

Determining appropriate priors, thresholds and models

Testing:

Simulated data -Yule model

(stochastic speciation/extinction)

Empirical data - Chris Meyer (marine taxa)

slide23

Simulated data -Yule model

Speciation and extinction follows a random birth/death

process

Time

Speciation

Extinction

slide24

Test = what % of sisters and orphans are detected as

new species “discoveries?

Test Data

1.Closest Divergence times - Sister’s and Orphans

2. Population sizes - Gamma distributed 50K-2.5M

3. Single specimens from “new” species

3,5,10,20, and 40 specimens from reference species

Orphan

Sister-pair

slide25

Yule model

Empirical Data (Cowries)

100 lineages

per clade

135 lineages

Is it a new species?

Function of Posterior

Probability of divergence

Time and gene flow

Discovery?

Reference

Species

slide26

msBayes Software pipeline

SIMULATE

1,000,000

\ draws

from

model

Flexible

Pre-simulated

prior

ABC

observed

data

Accept

0.2%

Posterior

probability

surface

~< 1 minute

slide27

Approximate Bayesian

Computation (ABC)

Posterior

Prior

slide28

Parameter threshold?

Posterior

Prior

slide29

M1= yes, new species

M2= no, same old species

f (M1given Data)

f (M2given Data)

Bayes Factor =

prior (M1)

prior (M2)

A way to compare evidence for these 2 discrete models

slide32

Very Near Future

1. Better priors

Species divergence time AND intra-species coalescence

2. Incorporate Migration

  • Hierarchical Model

New species status

Hyper-Parameter

Yes

No

Hyper-Prior

Prior(T,N)

Prior(N, T=0)

slide33

ACKNOWLEDGEMENTS

Discussion

C. Moritz

C. Meyer

T. Mendelson

K. Zigler

N. Rosenberg

J. Degnan

Coauthors

C. Meyer

C. Moritz

cpu resources

J. McGuire

Museum of Vertebrate Zoology

Funding

NSF

DIMACS