community phylogeography of a carnivorous plant and its ... · the pale pitcher plant sarracenia...

Community phylogeography of a carnivorous plant and its

arthropod and microbe symbionts: New methods of data collection

enable an expansion of phylogeographic investigations.

Bryan CarstensDepartment of Biological Science

The Pale Pitcher plant Sarracenia alata

• carnivorous

• inhabits pine savannahs and bogs

• long lived, clonal perennial with a patchy distribution

• extrafloral nectaries, down-pointing hairs, modified leaves, digestive fluids

Gulf of Mexico

Community phylogeography of Sarracenia alata and its symbionts

No previous genetic investigations into S. alata, apart from its

inclusion in two phylogenetic studies (Bayer et al 1996; Oard

1997)

environmental differentiation

(mainly E-W)

Mississippi River discontinuity

(Soltis et al. 2007)


The Pale Pitcher plant Sarracenia alata

• Mississippi River / Atchafalaya Swamp are the dominant geographic barriers

Gulf of Mexico


0

5

10

15

20

25

30

35

40

45

Presetlement 1900 1986

Percentage

Gulf Coast Habitat Loss

Longleaf Pine Wetlands

red

raw

n fro

m N

oss

1989

Pine Savannah habitat is severely reduced and fragmented . . .


Sarracenia alata - data from 86 plants across five populations

• rps16-trnK region of the cpDNA

• 8 MSATs developed in lab (Koopman et al. 2009)


Population structure

• STRUCTURE (Pritchard et al. 2000;

Evanno et al. 2005)

k = 2 partitions (East—West)

• STRUCTURAMA

k = 4 partitions, largely corresponding

to sampled populations (AS + T)

Koopman & Carstens 2010


Selection - cpDNA (Tajima’s D)

Tajima 1989

Selection - MSATs (BOTTLENECK)Cornuet & Luikart 1996

Selection - MSATs (Lewontin KrakhauerTest)

Lewontin & Krakhauer 1973

-0.1067; P > 0.10



There is no compelling evidence for adaptive differentiation . . .


M + q (LAMARC)Kuhner 2006

M + q + g (LAMARC)Kuhner 2006

M + q (MIGRATE-N)Beerli & Felsenstein 2001


Estimates of migration are non-zero and dependent on model assumptions . . .



Estimates of migration are non-zero and dependent on model assumptions . . .


Goal: Identify the factors that contribute to evolution of S. alata.

Is local adaptation important?

Is there evidence of gene flow?

Is there evidence of population expansion?


How do we identify these factors?

Summarize genetic variation with statistics

• FST, Tajima’s D, Fu & Li’s F

Estimate parameters using some model

• Nm with Wright’s Island model

• genealogies using a phylogenetic model

• migration rates with a coalescent-model

Use these statistics or estimates to understand or infer the

evolutionary history that produced the genetic variation.

How do we analyze genetic data in evolutionary biology?


Summaries and estimates are formally generated, but

interpreted by researchers in a qualitative manner.

• over-interpretation – more detailed historical scenarios are

proposed than the data support (Knowles & Maddison 2002)

• confirmation bias – novel information is interpreted in a

manner consistent with preconceived ideas (Nickerson

1998)

The most egregious examples are found in phylogeography . .

.



M + q + t (IMa)

Hey & Nielsen 2007

M + q (LAMARC)Kuhner 2006

M + q + g (LAMARC)Kuhner 2006

M + q (MIGRATE-N)Beerli & Felsenstein 2001

How do we move past this qualitative approach to data analysis?

Hypothesis testing?

Prob (data|null model is true) is calculated, but because genetic

data are not independent and identically distributed, simulations

are used to construct the test distribution.

We reject or fail to reject the hypothesis.

Knowles & Carstens 2007


How do we move past this qualitative approach to data analysis?

Prob (data|null model is true) is calculated, but because genetic

data are not independent and identically distributed, simulations

are used to construct the test distribution.

Phylogeographic hypothesis testing is

flawed because the biological realism of

any historical model is difficult to assess.


Assumptions

•accuracy of qi, other values

•adequacy of sampling strategy

• timing of population model

• topology of population model

•adequacy of summary statistics

Prob (data | null model is true)

Knowles & Carstens 2007


Is Hypothesis-testing the best way to move beyond

qualitative data analysis?

• rejecting an unrealistic hypothesis does not help us understand

anything about the demographic history

• it may promote false confidence regarding our understanding of

the system

• we are not able to differentiate among hypotheses that can not

be rejected


In order to identify the historical forces that generate biodiversity,

we must understand the historical demography of the species.

• We can not replicate evolutionary history.

• We do not have experimental controls.

Evolutionary genetics is a historical discipline . . .

. . . that uses statistical tools developed for experimental

research.


How should we proceed?

1. Propose a set of possible hypotheses, where each hypothesis

represents a plausible historical scenario (Chamberlin 1890).

2. Calculate the probability of each hypothesis given the data.

3. Rank the hypotheses; evaluate the relative support for each.


• Information theory is a statistical framework developed for

quantifying the loss of information that occurs when a model is

used to describe reality (K-L distance; Kullback & Leibler 1951).

• Akaike (1973) linked K-L distance and maximum likelihood.

• Following Chamberlin, we can calculate Prob (Hj | data) for j

hypotheses and rank them using AIC.

An information theoretic approach to phylogeography is

statistically rigorous (like hypothesis testing) but more broadly

applicable because it does not depend on the adequacy of model

assumptions.


• IMa Hey & Nielsen 2007

• approach described by Carstens et al 2009

Information theoretic approach to evolutionary genetics

• little support in the data for demographic models with migration



• parameter estimates are interdependent



• parameter estimates are interdependent

• parameter estimates weighted by model probabilities (wi)

• compare to estimates from normal IMa run


12.695 2.79 4.303 0.003 0.0002 0.371

Phylogeography as an exercise in in

demographic model selection . . .

• assess the statistical fit of a wide range of demographic

models to the data

• rank these models using information theory

• estimate parameters using model averaging


IMa model is not flexible; but simulation-based approaches are

• Approximate Bayesian Computation

• Approximate Likelihoods

. . . but we need better data than Koopman & Carstens 2010

Second-generation sequencing (Roche 454 Titanium)


• number of samples / species

• number of loci

• number of species (comparative studies)


Molecular Ecology Resources (2008)

Second-generation sequencing methods, such as Roche 454 ,

enable expansion along each of the phylogeographic sampling

axes:

Second-generation sequencing for phylogeography

(Margaret Koopman & John McCormack)

• restriction digest / reduced representation libraries

• amplify parallel portions of the genome in multiple

individuals

• size selection / gel extraction

• PCR used to add individual-identifying barcodes and linkers

• sequence using ROCHE 454 (Titanium Chemistry)

• bioinformatics processing (Sarah Hird)

Koopman et al. (wet lab); Hird et al. (software) in review


• 8-10 individuals from each of 10 fragmented populations

• 2 trial runs on 1/8th of a sequencing plate

• 1 full sequencing plate

• > 1,000,000 sequencing reads averaging > 350 bp

• 522 variable and 674 non-variable loci

• Average length 378 bp; ~4.6 variable sites per locus

• ~450 kb of data!

• ~0.016% of the S. alata genome


Second-generation sequencing in S. alata

If we treat phylogeography as an exercise in demographic model

selection, we need to assess the statistical fit of a wide range

(100s) of demographic models to the data:

relative posterior probabilities

(Approximate Bayesian Computation)

model likelihoods

(Approximate Likelihoods)


Approximate Bayesian Computation

• Compute the joint posterior distribution of any number of

models | data by simulation of prior distribution under a set

of models {Mi}, rejection filtering, and calculation of the

contribution of each model to the posterior distribution.

• MS (Hudson 2002) used to simulate under coalescent

models

• PERL script to generate prior distributions for models

(12,500,000 draws)

• MSBAYES (Hickerson et al. 2007) to perform rejection step



• ABC has be used to compare a small number of models.


Fagundes et al. 2007



Our goal is an even parameterization of potential model space.


Parameterization of {Mi} for S. alata

• divergence model, samples partitioned E-W

• used only 50 loci with best representation across individuals

• vectors of summary statistics were calculated from each

quartile of the data (π)

• parameterization of the models as follows:

(Α=1=2, Α 1=2, 1 Α=2, 2 Α=1, Α 1 2)

migration (0, M1=2, M1≠2, M1, M2)

population expansion (0, 1 2, 1=2, 1, 2)

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

Relative Posterior Probability of 125 Models

Rela

tive P

oste

rior

Pro

babili

tyInformation theoretic approach to evolutionary genetics

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

Relative Posterior Probability of 125 Models

Rela

tive P

oste

rior

Pro

babili

tyInformation theoretic approach to evolutionary genetics

ABC does not differentiate large numbers of models; experimentation with

multiple combinations of summary statistics did not improve the resolution.


Approximate likelihoods

. . . approach in development with Brian O’Meara

• simulate genealogies under {Mi} that match those used in

ABC

• calculate the proportion of times that a gene tree from the

empirical data is found in the set of simulated genealogies

• this proportion approximates Probability (GT | Mi)

• compute AIC values and information theoretic metrics such

as model probabilities

• gene trees have long been used in phylogeography . . .

wi


0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

Exhaustive Model Selection

The three models with the highest AIC score exclude migration

and allow population size change . . .

GT matching approximates the P(GT | Model)

Expansion of Approximately Likelihood approach to 10

populations

• > 10,000 models for this scenario

• include ‘species’ delimitation, parameter estimation using

model averaging, heuristic exploration of parameter space?

In a single – species investigation, 2nd generation sequencing

data and demographic model selection allows us to identify the

forces that influence the population genetic structure of the

species . . .

. . . but benefit of NGS data is felt with multi-species

comparisons.

Community Genetics of Sarracenia alata and its symbionts


Before our work, nothing was known regarding the microbial fauna in Sarracenia alata . .

.


In Sarracenia purpurea, microbes dominant

the pitcher fluid, and play an important role

in prey digestion (Plummer & Jackson

1963; Harvey & Miller 1996) including

mineral-ization of the majority of nutrients

that plants derive from prey (Butler et al.

2008)


In the plant, Eastern - Western populations are geographically

and genetically isolated and do not exchange migrants.

• Mississippi River predates Sarracenia

• tiny seeds (~2mm)

• no ornamentation (flight, eliasomes)

• short seed dispersal (~5cm)

• absent from flood plains

Did the entire community move simultaneously?

• Seeds dispersed by floods with idiosyncratic colonization by

symbiontic species; predicts many divergence events

• Community divergence following Atchafalaya Embayment

(~10,000 ybp), or community pattern of Mississippi River

discontinuity; predicts a single divergence event


Metagenomic Community Phylogeography

• genomic DNA extracted from pitcher fluid

5 populations, 7 pitchers from four time points

• PCR amplification of COI, 12s rRNA using barcoding

primers

• Roche 454 sequencing to isolate unique haplotypes

• BLAST search to determine sequence identity


Ph

oto

s: R

. K

itko

Habrotrocha rosa Sarraceniopusgibsoni

>30 eukaryotic lineages identified from

Eastern & Western populations

(yeasts, algae, dipterans, mites, rotifers, ants)

MSBAYES: Hierarchical ABC used to test for simultaneous

divergence

Hic

kers

on e

t al. 2

006,

2007


yeasts, algae, dipterans, mites, rotifers, ants, S. alata (eukaryotes)

Simultaneous Divergence?

Number of Divergence events

Poste

rior

Pro

babili

ty


Eukaryotes appear to diverge across Mississippi River in idiosyncratic manner .

. .


Roche 454 sequencing of bacterial DNA extracted from pitcher

fluid and surrounding environment

383,660 16s rRNA bacterial sequences from 73 S. alata pitchers


Bacterial communities in the pitcher fluid are distinct from those in the soil . . .


Bacterial diversity peaks in July and bacterial communities become

more similar as the season progresses . . .


Koopman & Carstens in review

Phylogenetic community structure

analysis using UniFrac (Lozupone &

Knight 2005) produces a rooted

summary of the dominant phylogenetic

pattern exhibited by the community.

15 possible rooted 4-taxon topologies

Long odds that this happened by

chance

(p = 0.0044)

?

Enterobacteria dominate the pitcher fluid communities . . .

. . . Enterobacteria are commonly isolated from animal

guts.

all sequencesubiquitous

idiosyncraticTaxonomy (ubiquitous)


Koopman & Carstens in review

Phylogenetic Community Structure Analysis

(Webb et al. 2002; Cavender-Bares et al. 2009)

H0: bacterial assemblages in each pitcher are evenly distributed throughout the

phylogeny, indicating assembly as a result of neutral processes (Hubbell

2001).

Ha: phylogenies can be overly-clustered

(habitat filtering)

Ha: phylogenies over-dispersed

(competitive species interactions)

(Cavender-B

are

s e

t al. 2

009)




• 15 pitchers (3 from each of 5 sites)

• 102 community structure tests using PICANTE (Kembel et al 2010)

• analyses conducted at family level

• data from 8 most diverse families used




• 95 of 102 tests were clustered, 53 were significant at P = 0.05

• 7 of 102 were over dispersed (none significant)

• (Enterobacteriaceae) 13/15 pitchers were clustered, 12 significant at P =

0.05.

• If ecological function is correlated with phylogeny, the standard interpretation

is that there are significant ecological differences conducive to the

colonization and growth of different groups of bacteria among all pitchers

(Fine et al. 2006).




Do habitat differences produce correspondingly similar differences

in the pitcher-fluid environment?

Clustering could result from the colonization of pitchers by non-

random packages of bacteria . . .

via symbiontic arthropods?

via prey items?


Ants comprise the majority of Sarracenia prey items (Ellison

& Gotelli 2009) and constitute most of S. alata’s prey (~ 80%).

• collected pitcher fluid from 10 plants in the Abita Springs

population, extracted Bacterial DNA

• collected all ants within 1 m2 of sampled plants

• bacterial DNA was isolated from the digestive tracts of the ants

and extracted

• bacterial community fingerprints of both pitcher fluid and ant

guts were generated using ARISA


H0: no significant difference between the bacterial

Communities in the ant guts and pitcher fluid

Analysis of Similarity

R statistics from ANOSIM analyses for the ARISA data partitioned

by

by fluid(total) versus ant(total) (R value 0.217, P = 0.03)

by individual plot (fluidi, anti) (R value 0.018, P = 0.291)


There are no significant differences between the

bacterial communities of pitcher fluid and ant guts.

Ant microbiomes have evolved to facilitate the digestion of

arthropod prey (Holldöbler & Wilson 2000).

Do carnivorous plants co-opt the digestive microbiomes

of their dominant prey items?


• LSU Faculty Research Program

• NSF EPSCoR Pfund

• Louisiana Board Of Regents Research Competitiveness Subprogram

• Chancellors' Future Leaders in Research

• Howard Hughes Medical Institute

• NSF (DEB-0918212)

• NSF (DEB-0956069)

Acknowledgements

Sarah Hird

Noah Reid

John McVay

Tara Pelletier

Lowell Urbatch, Gary King, Brent Christner

Danielle

Fuselier

Hannah

Fullerton

Joey

Charboneau

Dan Ence

Jen Carstens

Margaret Koopman

Yi-Hsin Erica Tsai

Amanda Zellmer

community phylogeography of a carnivorous plant and its ... · the pale pitcher plant sarracenia...

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