Plant community and traits assembly Alain Franc INRA

Plant community and traits assembly Alain Franc INRA, UMR Bio. Ge. Co, France DEB workshop, Amsterdam, January 2008

How can order emerge from noise?

How can order emerge from noise? By which miracle can mathematical modelling be relevant for biological diversity?

Evolutionary convergence Ilex aquifolium Aquifoliaceae Aquifoliales Quercus ilex Fagaceae Fagales

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

Evolution by selection (Lewontin, 1970) Mecanism 1: There exist variability of the trait between units Mécanism 2: There exist selection of units which contribute to the next generation Mecanism 3: There exist transmission of the trait by units Lewontin R. C. , 1970. Annu. Rev. Ecol. Syst. 1: 1 -18

Ann. Rev. Ecol. Syst.

Euphorbiaceae and Cactaceae

Caryophyllales Malpighiales

Weight of history … … and local adaptation !

Convergence in architecture for trees

Selection for trait assembly? Lewontin programme for trait assembly variation selection inheritance

Some basic ideas Law (1999) : Constant exchange between regional pool and local assemblages Ricklefs (2004) : Selection within local assemblages

Model’s hypothesis üA community is described by the abundances of species building it üLocal community is in relation wit a regional pool üIntroductions from pool occur with regular time step (say, 1 y) üBetween introductions, abundances are driven by L. -V. model üEmphasis on weight of competition : üHence

Pool and local assemblage Pool Local assemblage (community) Expulsion (failure) Digestion (success) Digestion with extinctions Outcome Long distance dispersal selected randomly at regular time step

Pool and local assemblage Pool Local assemblage (community) Expulsion (failure) Digestion (success) Digestion with extinctions Outcome Long distance dispersal selected randomly at regular time step Extinction Invasion Local L. -V.

Questions adressed Influence of the structure of matrix A on community assembly

Parameters of the programme

A mess, as in Lawton’s paper Uniform law

Macroscopic regularitie, as in Lawton’s paper

Improving? Gaussian law

Plants as trait assemblages A competition matrix has bee computed, wih the hypothesis that - Interacting plants are trait assemblages - competition coefficient aij is calculated knowing the traits in each plant Each trait is binary Phenotypes are labelled 0 or 1 There 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)

Trait assemblage

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 genome example: example : 01101

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 genome example: example : 01101 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 genome example: example : 01101 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 genome example: example : 01101 Fitness = f(génome genome environnement) Very close to a model of co-evolution Towards community assembly as evolution of genomes assembly