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Plant community and traits assembly

Plant community and traits assembly. Alain Franc INRA, UMR BioGeCo, 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?.

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Plant community and traits assembly

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  1. Plant community andtraits assembly Alain Franc INRA, UMR BioGeCo, France DEB workshop, Amsterdam, January 2008

  2. How can order emerge from noise?

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

  4. Evolutionary convergence Ilex aquifolium Aquifoliaceae Aquifoliales Quercus ilex Fagaceae Fagales

  5. 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

  6. 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

  7. Ann. Rev. Ecol. Syst.

  8. Euphorbiaceae and Cactaceae

  9. Caryophyllales Malpighiales

  10. Weight of history … … and local adaptation !

  11. Convergence in architecture for trees

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

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

  14. Model’s hypothesis • A 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

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

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

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

  18. Parameters of the programme

  19. A mess, as in Lawton’s paper Uniform law

  20. Macroscopic regularitie, as in Lawton’s paper

  21. Improving? Gaussian law

  22. 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

  23. Programme : simple (R)

  24. Trait assemblage

  25. Perspectives : analogies

  26. Perspectives : analogies Quick translation into genetic algorithms

  27. Perspectives : analogies Quick translation into genetic algorithms Classical: Fitness = f(genome  environnement)

  28. Perspectives : analogies Quick translation into genetic algorithms Classical: Fitness = f(genome  environnement) Here: + biotic intercations (which is true …)

  29. 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 : 011001101

  30. 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 : 011001101 Fitness = f(génome  genome  environnement)

  31. 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 : 011001101 Fitness = f(génome  genome  environnement) Very close to a model of co-evolution

  32. 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 : 011001101 Fitness = f(génome  genome  environnement) Very close to a model of co-evolution Towards community assembly as evolution of genomes assembly

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