sim.BipartiteEvol: Simulation of the BipartiteEvol model

In RPANDA: Phylogenetic ANalyses of DiversificAtion

Simulation of the BipartiteEvol model

Description

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

Usage

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

Value

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

Author(s)

O. Maliet

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

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)
 }
 

RPANDA documentation built on Oct. 24, 2022, 5:06 p.m.