Download slide presentation

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?
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
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
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
1.0
0.8
0.6
0.0
0.2
0.4
Size
0
200
400
600
Time
A mess, as in Lawton’s
paper
Uniform law
800
1000
25
20
15
10
Nbr species
0
20
40
60
Time
Macroscopic regularitie, as in Lawton’s paper
80
100
1.0
0.8
0.6
0.0
0.2
0.4
Size
0
200
400
600
Time
Improving?
Gaussian law
800
1000
0
20
40
60
Time
80
100
6
7
8
Nbr species
9
10
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)
1.0
0.8
0.6
0.0
0.2
0.4
Size
0
200
400
600
800
Time
Trait assemblage
1000
0
20
40
60
Time
80
100
10
15
Nbr species
20
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 : 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 genome
example: 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 genome
example: 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 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