Plant community andtraits assembly

Alain FrancINRA, UMR BioGeCo, FranceDEB workshop, Amsterdam,

January 2008

How can order emerge from noise?

How can order emerge from noise?

By which miracle can mathematical modelling

be relevant forbiological diversity?

Evolutionary convergence

Ilex aquifoliumAquifoliaceaeAquifoliales

Quercus ilexFagaceaeFagales

A series of hypothesis

1 - A plant is an assamblage of traits

2 - This assemblage is non random

3 - But the outcome of an evolutionary process

4 - Under selection pressure due to biotic intercations

5 - It is possible to study it through evolutionary biology models

Mecanism 1:

There exist variability of the trait between units

Mecanism 3:

There exist transmission of the trait by units

Mécanism 2:

There exist selection of units which contribute to the next generation

Lewontin R.C., 1970. Annu. Rev. Ecol. Syst. 1: 1-18

Evolution by selection (Lewontin, 1970)

Ann. Rev. Ecol. Syst.

Euphorbiaceae and Cactaceae

CaryophyllalesMalpighiales

Weight of history …

… and localadaptation !

Convergence in architecturefor trees

Selection for trait assembly?

Lewontin programme for trait assembly variationselectioninheritance

Some basic ideas

Law (1999) : Constant exchange between regional pool and local assemblages

Ricklefs (2004) : Selection within local assemblages

Model’s hypothesisA community is described by the abundances of species building it

Local community is in relation wit a regional pool

Introductions from pool occur with regular time step (say, 1 y)

Between introductions, abundances are driven by L.-V. model

Emphasis on weight of competition :

Hence

Pool and local assemblage

Pool Local assemblage(community)

Long distance dispersalselected randomlyat regular time step

Outcome

Expulsion (failure)Digestion (success)Digestion with

extinctions

Pool and local assemblage

Pool Local assemblage(community)

Long distance dispersalselected randomlyat regular time step

Outcome

Expulsion (failure)Digestion (success)Digestion with

extinctions

Invasion

Local L.-V.

Extinction

Questions adressed

Influence of the structure of matrix A

on community assembly

Parameters of the programme

Uniform law

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Time

Siz

e

A mess, as in Lawton’s paper

0 20 40 60 80 100

10

15

20

25

Time

Nb

r sp

eci

es

Macroscopic regularitie, as in Lawton’s paper

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Time

Siz

e

Gaussian lawImproving?

0 20 40 60 80 100

67

89

10

Time

Nb

r sp

eci

es

Plants as trait assemblages

A competition matrix has bee computed, wih the hypothesis that- Interacting plants are trait assemblages- competition coefficient ij is calculated knowing the traits in each plant

Each trait is binaryPhenotypes are labelled 0 or 1There exist four interacting types: (0,0) ; (0,1) ; (1,0) ; (1,1)

Fitness for plant i when interacting with plant j (simply) is the average of fitness for each trait

Programme : simple (R)

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Time

Siz

e

Trait assemblage

0 20 40 60 80 100

10

15

20

Time

Nb

r sp

eci

es

Perspectives : analogies

Perspectives : analogies

Quick translation into genetic algorithms

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Here: + biotic intercations (which is true …)

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Here: + biotic intercations (which is true …)

Assemblage : assemblage of traits modelled as a genomeexample: example : 011001101

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Here: + biotic intercations (which is true …)

Assemblage : assemblage of traits modelled as a genomeexample: example : 011001101

Fitness = f(génome genome environnement)

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Here: + biotic intercations (which is true …)

Assemblage : assemblage of traits modelled as a genomeexample: example : 011001101

Fitness = f(génome genome environnement)

Very close to a model of co-evolution

Perspectives : analogies

Quick translation into genetic algorithms

Classical: Fitness = f(genome environnement)

Here: + biotic intercations (which is true …)

Assemblage : assemblage of traits modelled as a genomeexample: example : 011001101

Fitness = f(génome genome environnement)

Very close to a model of co-evolution

Towards community assembly as evolution of genomes assembly