Uses of phylogenies Yves Desdevises Université Pierre et Marie Curie (Paris 6) Observatoire Océanologique de Banyuls France [email protected] http://desdevises.free.fr

References • Felsenstein J. 1985. Phylogeny and the comparative method. American Naturalist 125: 1-15.

• Harvey P. & Pagel M. 1991. The comparative method in evolutionary biology. Oxford University Press.

• Brooks D. & McLennan D. 1991. Phylogeny, ecology, and

behaviour. A research program in comparative biology. The University of Chicago Press.

• Harvey P. et al. 1996. New uses for new phylogenies. Oxford University Press.

• Brooks D. & McLennan D. 2003. The nature of diversity. An evolutionary voyage of discovery. The University of Chicago Press.

• Reconstruction of phylogenies for something else than taxonomy/systematic

• Trees: from all character types but molecular data can generate branch lengths

• Most methods require robust and well resolved trees: limitation in many cases

Phylogenies • Uses • Discover the origin of organisms • Understand their evolutionary relationships

• Classification

• But also... • Inference on ancestral character state • Study of correlated evolution • Study of adaptation • Molecular evolution • Diversification rates and key innovations • Estimation of dates of divergence • Biogeography and phylogeography • Cospeciation • etc.

Adaptation • Adaptation = trait, or set of traits, increasing

fitness, then leading to individuals to produce more descendants than those without this trait

• Trait which origin is associated to an increase of

functional efficiency favoured by natural selection

• Produce of natural selection • Difficult to reveal: must be tested

• Act on design, and/or reproduction • Must be investigated in an evolutionary context • Adaptation “appears” in a lineage • ≠ inherited from an ancestor

• Example: adaptation = hydrodynamic shape

• In the cetacean clade, hydrodynamic shape is

inherited, and is not an adaptation sensu stricto in each species

Adaptation/Acclimatization/ Phenotypic plasticity evolution via natural selection, in a • Adaptation: species, many generations. Modification of the genotype. Often not reversible

physiological, biochemical, • Acclimatization: anatomical change, in an individual, due to the

exposure to a new environment. Often reversible

plasticity: ability of a genotype to • Phenotypic produce different phenotypes related to an

environmental change, in an individual. Plasticity can be adaptive.

Acclimatization

Phenotypic plasticity

Adaptation

Time

A component of evolution • Evolution ≠ adaptation • Adaptation (selection) • Structure (physics) • Phylogenetic inertia (traits from ancestor) • Constraints (everything is not possible) • Organism = trade-offs

• Gradual (Darwin) or punctuational changes • Many transitional steps • Regulatory genes: small genetic modification = big phenotypic change

• Combination of several elements: different

structures, genes, organisms (endosymbiosis)

Adaptation ≠ perfection • Time lag between adaptation and environmental change: “imperfections”, maladaptation

• Constraints • Genetic: e.g. persistence of deleterious

homozygous if heterozygous have a superior fitness

• Development: development influences the possibility of variations

• Historical • Adaptation arises on existing state: the possible solution may not be optimal

• Do not infer abusive adapative interpretations!! • “Spandrelism”

Study of adaptation • Theoretical approach: models, predictions • Observational approach: in the field • Experimental approach: alteration of potentially adaptive structure

• Comparative approach: correlation between structures or structure/function in several lineages (usually species), taking phylogeny into account. Comparative method/analysis.

Comparative analysis • Study of character correlated evolution • Search for adaptations via a cross-species correlative approach

• Link: trait/trait or trait/environment • Size / longevity • Metabolism / temperature • Colour / gregarity

• Some key dates • 1985: Felsenstein • 1991: Harvey & Pagel; Brooks & McLennan • Closely related species tend to be similar, and are not independent observations (pseudoreplication)

• Phylogenetic constraints (morphology,

physiology, genetic, development, ...): inertia

• Phylogeny = confounding variable

Example Species

Altitude

TSizeaille

A B C D E F G H I J

5 6 8 12 14 24 25 28 29 30

2 6 6 7 12 13 14 14 16 17

Size

Hypothesis: size is an adaptation to altitude

Altitude correlation: adaptation?

Problem Species

Altitude

Size

A B C D E F G H I J

5 6 8 12 14 24 25 28 29 30

2 6 6 7 12 13 14 14 16 17

Size

• Phylogeny makes data non independent

Altitude

Solution

Size (corrected)

• Control phylogenetic constraints

Altitude (corrected)

no correlation Comparative analysis

Comparative analysis • Several methods exist to take phylogeny into account in analyses

• Most known: independent contrasts (Felsenstein, 1985)

• Trait variation partitionning: Environment

Phylogeny

?

100 % of trait variation Westoby et al. (1995)

When to use it?

• Look for causality: hypothesis • Does X influence Y? • Not in a predictive framework • What is the value of Y for a given X? • Not with any kind of character: assume a link

with phylogeny, the character must be potentially transmittable

Quantitative characters • Many methods • Also for semi-quantitative variables • Qualitative variables may be used via recoding

Phylogenetic association matrices

• Assess phylogenetic dependence (structure) of data

• Two basic types • Variance-covariance matrices • Distance matrices

Variance-covariance matrix Species

1

3 4

Species

2

S2 S1,2 S1,3

0

0

S2 S2,3

0

0

0

0

S2

S2 S4,5 S2

5

Patristic distance matrix Species

1

Species

2 3 4

D5,1

0 0

D3,2

D5,3

0 0

0

5

Models of character evolution

• Two basic models • Brownian motion (BM): variance is a linear function of (proportional to) time = branch lengths on tree

• Brownian motion

Time

.

Trait (x)

• Addition of constraints (adaptive, ...) and estimation of corresponding parameters: different relationships between branch lengths and character variances (e.g. Ornstein-Uhlenbeck model: OU)

Divergence

genetic drift (BM) BM + stabilizing selection

Time

Methods

• Methods based on evolutionary models • Independent contrasts (FIC; Felsenstein 1985) (CAIC, COMPARE, PDAP, ...)

• Phylogenetic generalized least squares (PGLS; Martins 1994) (COMPARE)

• Phylogenetic mixed model (PMM; Lynch et al. 2004) (COMPARE)

• Methods with statistical bases • Autoregressive method (ARM; Cheverud et al. 1985) (AUTOPHY)

• Phylogenetic eigenvector regression (PVR; Diniz-Filho et al. 1998)

• Different assumptions • Results may be different • Methods may be difficult to compare • Interesting approach: use several methods and compare results

Independent contrasts FIC; PIC

• Most used method • Based on a variance-covariance matrix • Evolutionary model = Brownian motion: genetic drift, some selective regimes

• Time = branch length = variance • Supposes well known phylogeny • Ideally for quantitative variables but a variable can be qualitative

• Correlation among traits

Time

.

ρ = 0.9 ρ = 0.0 Traits (x, y)

Estimation of ancestral character values (weighted means)

8 22 9 24

X Y

Contrasts

7 20

X Y 2 4

9 24

2

10 25

8 11

3

14 30 12

42,5

6 10

40 37,5

Y

32,5 30

20 40

27,5 25 22,5

10 9 IC Y

17 35

35

11

8 7 6 5 4

20

3

17,5

2 6

8

10

12

14 X

16

18

20

22

1

2

3

4

5 IC X

6

7

• Contrasts must be standardised: divided by their standard deviation (√variance)

8

9

• Contrasts can be used in various kinds of analyses • Regressions (through the origin, because contrasts computation eliminate the constant term)

• Multivariate analyses • Powerful because few parameters to estimate • Model testing: contrasts vs SD • Slope = 0 if BM • Else: transformation of branch lengths or different model (PGLS, ...)

• Improvements to take polytomies into account • Do not consider data variance! • “Remove phylogeny” (without explicitly quantifying it) in correlation among traits

• Paradox to study adaptation because supposes selection (≠ BM)

MacroCAIC: study of taxonomical diversification

• Derived from CAIC: independent contrasts • Uses the variable “number of taxa per clade” in a comparative analysis

• values at the nodes are not means (like in classical IC) but sums of values for their descendants

5

3

2

• Measures if the value at a node is correlated to the number of species descending from it

• Can assess diversification rate (if branch lengths = time)

Phylogenetic generalized least square regression PGLS

• Generalization of FIC to other models • Consider phylogenetic structure of the error term

(non independent observations, heteroscedasticity) via variance-covariance matrix

• Estimation of a constraint parameter (adaptation, stabilizing selection...) adding to BM

• Consider data variances • The number of parameters to estimate reduces power

• Allows to reconstruct ancestral character states (like IC)

Phylogenetic eigenvector regression PVR

• Statistical basis (no explicit model) • Transforms distance matrice in principal coordinates (PCs)

• Problem: account only for a part of the phylogeny • The first PCs are the tip of the tree

• Simple solution: patristic distance matrix (genetic distances) between species

1

1

n

1

n

n

Problem: requires to express other variables (”environment”) also in distance matrices

• Transformation of phylogenetic distances in principal coordinates ACGTTCGGA ACTGTCGGA AGTGTCCGA

010010100 010110110 001110110

( )

Distance matrix Raw or patristic distances

1

n

Obtention of maximum n-1 independent variables (principal coordinates) representing phylogenetic distances

1

n

1

n

Principal coordinate analysis

1 1

n

n-1 (max)

• Variance splitting is function of the phylogeny size and structure

• PCs selection: Broken stick, Backward elimination, ...

• Efficient method with few taxa • Explicitely quantifies the fraction of trait variation linked to phylogeny

• Y = βX + ε ; T = P + S

• Versatility of distance matrices: trees, reticulograms, raw distances, ...

• Allows variation partitionning and quantification of “phylogenetic niche conservatism”

• Software • R 4.0 (Mac), DistPCoA (Mac, PC) for PCoA • Permute! (Mac) for tests

Phylogenetically structured environmental variation

• “Phylogenetic” fraction(inertia) • “Non historical” fraction, specific (adaptation, ...) • We can quantify the common fraction (phylogenetic niche conservatism) Environment

Phylogeny

100 % of trait variation

?

Variation partitionning

• Example: effect of temperature (X ) and humidity • 1 Effect of 2 variables X1 and X2 on a variable Y (X2) on growth (Y)

• Temperature and humidity each have an influence on growth

• Temperature and humidity are correlated

Variation explained by X1 = R21 = a+b 2 Variation explained by X2 = R 2 = b+c

a

b

c

d

100 % of Y variation Variation explained by X1 and X2 = R21,2 With a+b+c+d = 100 %

= a+b+c Unexplained variation = d

a, b, c, et d are computed via subtraction

Application to a phylogenetic context

• Y = variable we want to explain: “trait” potentially controlled (at least in part) by phylogeny

X1 = “environment”: regression (e.g. linear)

• X = “phylogeny”: we have to express it • 2

Method

• Phylogenetic effect: regression of the studied trait Y 2 on principal coordinates (P): R

P

• Environmental effect: regression of Y on the 2 variable(s) E: R

E

• Phylogeny and environment: multiple regression of 2 Y on variables P and E: R

P+E

Phylogeny = R2P (= a+b)

a

Environment = R2E (= b+c)

b

c

d

100 % of Y variation 2 Phylogeny and environment = R

P+E

(= a+b+c)

b = phylogenetica!y structured environmental variation Desdevises et al. (2003)

Discrete characters • Binary characters • 0/1 (presence/absence, 2 states, ...) • Characters with multiple states, ordered or not • 0/1/2/3 • Question: for 2 characters • Is the presence of a character state associated to (leads to) the presence (the appearance) of a given state in the other character?

2 Χ test on transitions

• Independent species

• P < 0,005 • Transitions • P = 0,99

Concentrated Change Test

• Maddison (1990) • Are changes in character B located at the same places in the tree than those for character A ?

• Generation of the null distribution (no

association) via random permutations of data (MacClade)

• Do not take branch lengths into account • Depends on branch number

Pairwise comparisons

• Maddison (2000) • Comparison of pairs of taxa with no phylogenetic connexion (Mesquite)

• Algorithm to find the optimal number of taxon pairing, with various options concerning the contrasts between character states

• Pairwise comparison: flight/no flight • Separated pairs: independent comparisons

• Pair choice: contrasting one character or the other, or both, or none (maximum number of pairs).

• Only constraint: phylogenetically separate • The more pairs, the more statistical power, but choice must be relevant

• e.g. contrast pairs for the independent character,

and see what happens for the response character

• Several choices

• No model • No information on direction of change • No need to infer ancestral states: but few pairs

• Low power if few pairs

ML tests

• Test via ML: estimation of transition rates from a character test to another (BayesTraits (includes Discrete))

• LRT: is likelihood different when changes are constrained to be independent?

• Take branch lengths into account • Inference of ancestral states

Inference of ancestral character states • Character mapping on tree • Discrete or continuous characters • With or without model • Discrete characters: Markov chain (MC) • Quantitative characters: Brownian motion (BM), OU, ...

Discrete characters • Parcimony (MacClade, Mesquite) • Maximum likelihood (generates confidence intervals; BayesTraits, Mesquite)

• Bayesian inference (BayesTraits, Mesquite, MrBayes, SIMMAP)

Example • Determination of character polarity: ancestral or derived

• Environmental attribute • Example: pollination in flowers

bees → birds

• Adaptation test • Character evolution

bees → birds

Support hypothesis of adaptation

bees → birds

Adaptation unsupported

• Parsimony • Simple • No explicit model

• No

uncertainty

• Do not

consider branch lengths

• Example: feeding in echinoderm larvae

• Maximum likelihood • Explicit model (e.g. MC or BM) • Estimation of transition rates from a state to another: π

• Assess uncertainty in reconstruction:

probability of observed from model and tree

• Importance of model (fixed values of

parameters, estimated or not) and tree characteristics (topology and branch lengths)

• Same example

• Bayesian inference • Needs prior probability on transition rates π between states (hypothesis + MCMC)

• Can consider uncertainty on tree • Also for continuous characters

MP

ML

BI

• Simulation of character evolution

Continuous characters • Linear parimony: minimise quantity of changes

(absolute or squared (= BM)) between descendants and ancestors (MacClade, Mesquite)

• Generalized linear model: includes a model • More general (includes previous approaches)

Datation

• How to date speciation events (nodes)? • Test of evolutionary hypotheses

• Strict molecular clock

No clock

Clock

Test via LRT

• Need of a calibration: known date (fossil,

geological event, ...). Ideally several dates

• If available, use mutation rate • Need branch lengths to infer other nodes, which implies linking divergence to time

• Fossils: difficult to locate on branches, only minimum ages

• Differences fossil/molecules:

“stem” (fossils)/”crown” (molecules)

• If molecular clock (= constant evolutionary rate for a gene or a protein) and (at least) one calibration point, it is easy

• But... in general substitution rate varies with time or lineage: no global molecular clock

• Problems with a global clock

Datation error Evolutionary rate of gene Y different from X Gene Z: highly variable rate

Solutions

• Remove sites/genes/lineages responsible for rate variation

• OK if variation is rare • Need a previous relative rate test A • e.g. d = d then d = d - d = 0 • Idem with d = d • d follows a normal distribution under AO

BO

AC

AO

BO

BC

some models of DNA substitution: standard statistical tables

• Low power

O

B

C

• Methods incorporating rate variations (relaxed clocks) • Local molecular clocks: different according to tree regions (few different rates)

• Relaxed clocks (many different rates) Local clock Global clock

No clock

Local clocks

• Method based on quartets (Qdate) • Subgroups of 4 taxa split in 2 monophyletic groups with known divergence dates

• Based on character evolutionary model (ML) • Take into account variation in substitution rate (maximum 2 rates)

• Generates confidence intervals for dates

(precision depends on sequence lengths)

• Methods based on full trees (PAML, r8s, BEAST) • Rates assigned to different tree parts, with tests, or external knowledge

• Good if many fossils with accurate dates • Different models for estimation

• If model: very important here

Relaxed clocks

• Rate smoothing • Many different rates • Need of a relationship to link rates

• Gradual rate change: each rate partly depends on the precedent (autocorrelation)

Different mathematical relationships (models) can link rates: penalty for important changes (penalty function)

• Non parametric rate smoothing, (r8s) • Considers fixed branch lengths (then uses

observed number of substitutions) to infer rates

• Penalized likelihood (r8s) • Uses a model that may change the number of substitutions for a given branch lenght)

• Bayesian approach (BEAST, Multidivtime) • Use prior parameter estimation and MCMC to yield posterior and confidence intervals

• For all approaches, hypothesis of gradual change

Cophylogeny

• Study of the common history of two associated phylogenetic trees

areas. At the molecular level, each gene has a phylogenetic Host-parasite history that is intimately connectedassociations with, but not necessarily identical to, the history of the organisms in which the Parasites Hosts Parasites 5,6 gene resides . Processes such as gene duplication, lineage sorting and horizontal transfer can produce complex gene Hosts trees that differ from organismal trees3,7,8. Associations

(a)

(b)

Organism Gene

(c)

Host Parasite

Area Organism

Time

1998 Fig. 1. Historical associations among genes, organisms and areas:Page (a)&aCharleston gene tree

• Cospeciation; coevolution; cophylogeny; parallel cladogenesis; cocladogenesis ; cophylogenetic descent ; cophylogenetic maps ...

• Here: macroevolutionary context • How to reconstruct the common evolutionary history of two clades, for example host and parasites

• Some key dates • 1981: Brooks (see Klassen 1992) • 1994: Page; Hafner et al.

Theoretical prerequisites

• Well known and fully resolved trees • Exhaustive sampling • Monophyletic groups • Not so easy

Four coevolutionary events

Cospeciation

Transfer

Duplication

Sorting

Tests

• Global congruence • Individual events • Host-parasite links • Importance of the null hypothesis • Cospeciation (e.g. Johnson et al. 2001) • Random associations (Legendre et al. 2002)

Methods

• Brooks parsimony analysis (BPA; Brooks 1981) • Reconciled trees (Component, TreeMap 1 et 2; Page 1993, 1994; Charleston 1998)

• Generalised parsimony (TreeFitter; Ronquist 1995)

• Probabilistic methods: ML, Bayes (Huelsenbeck et al. 1997, 2000)

• Homogeneity test (Johnson et al. 2001) • Congruence test (ParaFit; Legendre et al. 2002)

• Most methods work well if • Widespread cospeciation • ≈ 1 host / 1 parasite • Small phylogenies • Else: computationally intensive, optimal solution not guaranteed

• Different methods: different results

Reconciled trees TreeMap (Page 1994)

• Goal: fit parasites tree onto host tree by adequately mixing the 4 types of events

• Criterion: maximise cospeciations (TM 1) • Test against a random distribution • Can take branch lengths into account

• TreeMap 1: problems • Transfers added a posteriori • Limited optimality criterion (can generate many optimal solutions)

• Difficulty with widespread parasites

• TreeMap 2 (Charleston & Page, 2002) • Introduces event costs • Optimisation of global cost • Many modifiable parameters • Tests: • Global cost • Cherry Picking Test: influence of individual associations

• Assume that parasite’s ancestor is on host’s ancestor

• Each host must have at least a parasite(!!) • Needs fully resolved trees • TreeMap cana generate files for BPA and TreeFitter

Example talpoides

wardi 17

13 bottae

minor thomomyus

bursarius hispidus

15

actuosi 10 ewingi

14

18 cavator 12 11

underwoodi

chapini 16 panamensis 12 setzeri

10 9

cherriei 8

14 13

cherriei 11

heterodus

costaricensis

Phylogenies: COI Pocket Gophers

Chewing Lice

TreeMap 1 talpoides wardi thomomyus bottae minor actuosi bursaris ewingi

(a)

4

hispidus chapini

hispidus chapini

cavator panamensis

cavator panamensis

chapini cavator panamensis

underwoodi setzeri

underwoodi setzeri

underwoodi setzeri

cherriei setzeri cherriei heterodus

cherriei setzeri cherriei heterodus

cherriei setzeri cherriei heterodus

costaricensis

14

3

(b)

costaricensis

talpoides wardi thomomyus bottae minor actuosi bursaris ewingi

talpoides wardi thomomyus bottae minor actuosi bursaris ewingi

hispidus chapini

hispidus

underwoodi setzeri

panamensis underwoodi setzeri

cherriei setzeri cherriei heterodus

cherriei setzeri cherriei heterodus

costaricensis

hispidus

(c)

(e)

costaricensis

costaricensis

14

talpoides wardi thomomyus bottae minor actuosi bursaris ewingi hispidus chapini

chapini cavator

cavator panamensis

(d)

talpoides wardi thomomyus bottae minor actuosi bursaris 5 ewingi

talpoides wardi thomomyus bottae minor actuosi bursaris ewingi

cavator panamensis underwoodi setzeri cherriei setzeri cherriei heterodus

(f)

costaricensis

TreeMap 2

• 6 optimal solutions (out of 9) 1 of 9 | Co = 8, Sw = 4 (total distance: 3.565), Du = 10, Lo = 20;oftotal 9 | Co cost = 8, = 14 Sw = 4 (total distance: 3.67), Du = 10, Lo = 0; total 3 of 9 cost | Co==14 12, Sw = 3 (total distance: 3.009), Du = 6, Lo = 1; total cost talpoides talpoides talpoides wardi wardi wardi thomomyus thomomyus thomomyus bottae bottae bottae minor minor minor actuosi actuosi actuosi bursarius bursarius bursarius ewingi ewingi ewingi hispidus chapini

hispidus chapini

hispidus chapini

cavator panamensis

cavator panamensis

cavator panamensis

underwoodi setzeri

underwoodi setzeri

underwoodi setzeri

cherriei cherriei

cherriei cherriei

cherriei cherriei

heterodus costaricensis

heterodus costaricensis

heterodus costaricensis

4 of 9 | Co = 12, Sw = 3 (total distance: 3.009), Du = 6, Lo = 61;oftotal 9 | Co cost = 12, = 10Sw = 2 (total distance: 1.9345), Du = 6, Lo = 3; 6 of total 9 | cost Co = =12, 11Sw = 2 (total distance: 1.9345), Du = 6, Lo = 3; total cos talpoides talpoides talpoides wardi wardi wardi thomomyus thomomyus thomomyus bottae bottae bottae minor minor minor actuosi actuosi actuosi bursarius bursarius bursarius ewingi ewingi ewingi hispidus chapini

hispidus chapini

hispidus chapini

cavator panamensis

cavator panamensis

cavator panamensis

underwoodi setzeri

underwoodi setzeri

underwoodi setzeri

cherriei cherriei

cherriei cherriei

cherriei cherriei

heterodus costaricensis

heterodus costaricensis

heterodus costaricensis

Cospeciation: Test

• Test against random distribution (randomised trees) of inferred number of cospeciations

• Confrontation with observed value Observed value

250

Fréquence

200

P < 0,05 The observed number of cospeciations is higher to random iterations

150

100

*

50

0 1

2

3

4

5

6

7

8

9

Nombre de cospéciations

10

11

12

TreeFitter (Ronquist 1995)

• Generalized parsimony • Can assign costs to 4 types of events (like TreeMap 2)

• Problem to define costs • Tests (randomisation) • Global congruence: minimisation of global reconstruction cost

• Events: significant contribution to global congruence

• Ronquist says BPA and TreeMap 1 can be found in TreeFitter with some given costs

• Needs fully resolved trees • No graphical output nor possibility to

individuality identify significant events

• Still experimental

ParaFit (Legendre, Desdevises & Bazin, 2002)

• Assess congruence between distance matrices

(potentially) computed from phylogenies of hosts and parasites, via host-parasite associations

• Statistical tests (via permutations) Global congruence between two trees/matrices • (H : random associations) 0

• Contribution of each individual association to this congruence (structuring effect)

Host-parasite associations

Hosts

Parasites

B

A

C

Parasites tree

Host-parasite associations

Host tree

Princ. coordinates

Matrix A

Matrix B

Absence/presence of host-parasite associations (0/1 data)

Coordinates (col.) describing the parasite phylogenetic tree

Parasites

Parasites

Hosts

Matrix C Coordinates (rows) describing the host phylogenetic tree

Princ. Coordinates Host tree

Princ. coordinates

Hosts

Parasite tree princ. coordinates

Matrix D SSCP parameters to be estimated

Pocket gophers

T. talpoides T. bottae Z. trichopus P. bulleri O. hispidus O. underwoodi

T. barbarae T. minor G. trichopi G. nadleri G. chapini G. setzeri G. panamensis

O. cavator

G. cherriei

O. cherriei

G. costaricensis

O. heterodus

G. thomomyus

C. merriami

G. perotensis

C. castanops G. bursarius majus. G. bursarius halli

G. actuosi G. expansus G. geomydis G. oklahomensis

G. breviceps

G. ewingi

G. personatus

G. texanus

Chewing lice

• Drawbacks • Do not consider events • Do not propose scenarios • Advantages • Tests, et tested via simulations • Adapted to complex problems • Various numbes of hosts/parasite and parasites/ host

• Use matrices: no problem with polytomies, or multiple trees

Examples Gophers and lice

• Highly studied system • Many cospeciations • Transfer in host contact zones: cospeciation

explain by impossibility of colonisation, not by “extreme” adaptation to host

Monogeneans - Hosts

• A priori: many cospeciations • High specificity • Direct cycle

Sparidae and Lamellodiscus spp. S. cantharus

Hosts

B. boops S. salpa L. mormyrus D. sargus D. cervinus O. melanura D. puntazzo D. annularis D. vulgaris S. aurata P. acarne P. bogaraveo D. dentex P. pagrus P. erythrinus

L. furcosus L. coronatus L. elegans

Parasites

F. echeneis L. mormyri L. verberis L. drummondi L. virgula L. impervius L. parisi L. mirandus L. gracilis L. bidens L. hilii L. ergensi L. fraternus L. knoeppfleri L. ignoratus L. baeri L. erythrini

100 µm

S. cantharus

• TreeMap

B. boops L. elegans S. salpa L. knoeppfleri L. mormyrus

Frequencies

D. sargus L. ignoratus

Number of cospeciations inferred via TreeMap

350 300 250 200 150 100 50 0

*

1

P = 0.317

2 3 4 5 6 7 Number of cospeciations

8

mormyri L. furcosus D. parisi cervinus L. ignoratus L. coronatus verberis L. elegans O. ergensi melanura L. ignoratus coronatus D. puntazzo L. mirandus L. L. gracilis elegans D. annularis L. D. furcosus vulgaris L. ergensi ignoratus coronatus L. L. gracilis elegans L. impervius S. aurata L. ergensi ignoratus L. bidens fraternus L. elegans F. echeneis P. hilii acarne L. ignoratus L. L. ergensi virgula L. gracilis fraternus P. bogaraveo L. virgula L. drummondi D. dentex P. pagrus L. baeri P. erythrinus L. erythrini

• ParaFit • P global = 0,243 • 2 significant links S. cantharus

Hosts

P = 0.243

L. coronatus

B. boops

L. elegans

S. salpa

F. echeneis

L. mormyrus

L. mormyri

D. sargus

L. verberis L. drummondi

D. cervinus

L. virgula

O. melanura

L. impervius

D. puntazzo

L. parisi L. mirandus

D. annularis

L. gracilis

D. vulgaris

L. bidens

S. aurata

L. hilii

P. acarne

L. ergensi L. fraternus

P. bogaraveo D. dentex P. pagrus P. erythrinus

L. furcosus

L. knoeppfleri

P = 0.028 P = 0.018

L. ignoratus L. baeri L. erythrini

Parasites

• No cospeciation • Sympatric hosts (and parasites) • Other systems with geographically separated hosts (e.g. Polystomes/Amphibians): cospeciation

• Here, transfers not influenced by phylogeny but by hosts’ characteristics: association with other species, size