sim.BipartiteEvol function

Simulation of the BipartiteEvol model

Simulateof the BipartiteEvol model from Maliet et al. (2020)

sim.BipartiteEvol(nx, ny = nx, NG, dSpace = Inf, D = 1, muP,
muH, alphaP = 0, alphaH = 0, iniP = 0, iniH = 0, nP = 1, nH = 1, 
rP = 1, rH = 1, effect = 1, verbose = 100, thin = 1, P = NULL, H = NULL)

Arguments

• nx: Size of the grid (the grid has size nx * ny)
• ny: Size of the grid (default to nx, the grid has size nx * ny)
• NG: Number of time step the model is run
• dSpace: Size of the dispersal kernel (default to Inf, meaning there are no restrictions on dispersion)
• D: Dimention of the trait space (default to 3)
• muP: Mutation probability at reproduction for the individuals of clade P
• muH: Mutation probability at reproduction for the individuals of clade H
• alphaP: alpha parameter for clade P (1/alpha is the niche width)
• alphaH: alpha parameter for clade H (1/alpha is the niche width)
• iniP: Initial trait value for the individuals in clade P
• iniH: Initial trait value for the individuals in clade P
• nP: Number of individuals of clade P killed at each time step
• nH: Number of individuals of clade H killed at each time step
• rP: r parameter for clade P (r is the ratio between the fitness maximum and minimum)
• rH: r parameter for clade H (r is the ratio between the fitness maximum and minimum)
• effect: Standard deviation of the trait mutation kernel
• verbose: The simulation
• thin: The number of iterations between two recording of the state of the model (default to 1)
• P: Optionnal, used to continue one precedent run: traits of the individuals of clade P at the end of the precedent run
• H: Optionnal, used to continue one precedent run: traits of the individuals of clade H at the end of the precedent run

Returns

a list with - Pgenealogy: The genalogy of clade P

• Hgenealogy: The genalogy of clade H

• xP: The trait values at each time step for clade P

• xH: The trait values at each time step for cladeH

• P: The trait values at present for clade P

• H: The trait values at present for clade P

• Pmut: The number of new mutations at each time step for clade P

• Hmut: The number of new mutations at each time step for clade H

• iniP: The initial trait values for the individuals of clade P used in the simulation

• iniH: The initial trait values for the individuals of clade H used in the simulation

• thin.factor: The thin value used in the simulation

References

Maliet, O., Loeuille, N. and Morlon, H. (2020), An individual-based model for the eco-evolutionary emergence of bipartite interaction networks. Ecol Lett. doi:10.1111/ele.13592

Author(s)

O. Maliet

Examples

# run the model
set.seed(1)

if(test){
mod = sim.BipartiteEvol(nx = 8,ny = 4,NG = 500,
                        D = 3, muP = 0.1 , muH = 0.1,
                        alphaP = 0.12,alphaH = 0.12,
                        rP = 10, rH = 10,
                        verbose = 100, thin = 5)

#build the genealogies
gen = make_gen.BipartiteEvol(mod)
plot(gen$H)

#compute the phylogenies
phy1 = define_species.BipartiteEvol(gen,threshold=1)

#plot the result
plot_div.BipartiteEvol(gen,phy1, 1)

#build the network
net = build_network.BipartiteEvol(gen, phy1)

trait.id = 1
plot_net.BipartiteEvol(gen,phy1,trait.id, net,mod, nx = 10, spatial = FALSE)

## add time steps to a former run
seed=as.integer(10)
set.seed(seed)

mod = sim.BipartiteEvol(nx = 8,ny = 4,NG = 500,
                        D = 3, muP = 0.1 , muH = 0.1,
                        alphaP = 0.12,alphaH = 0.12,
                        rP = 10, rH = 10,
                        verbose = 100, thin = 5,
                        P=mod$P,H=mod$H)  # former ru output

# update the genealogy
gen = make_gen.BipartiteEvol(mod,
                             treeP=gen$P, treeH=gen$H)

# update the phylogenies...
phy1 = define_species.BipartiteEvol(gen,threshold=1)

#... and the network
net = build_network.BipartiteEvol(gen, phy1)

trait.id = 1
plot_net.BipartiteEvol(gen,phy1,trait.id, net,mod, nx = 10, spatial = FALSE)
 }