build_network.BipartiteEvol {RPANDA}

Build the interaction network in BipartiteEvol

Description

Build the phylogenies from the output of BipartiteEvol and the corresponding genealogies and phylogenies

Usage

build_network.BipartiteEvol( gen, spec)

Arguments

gen

The output of a run of make_gen.BipartiteEvol

spec

The output of a run of define_species.BipartiteEvol

Value

A matrix M where M[i,j] is the number of individuals from species i (from guild P) interacting with an individual from species j (from guild H)

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

See Also

sim.BipartiteEvol

Examples

# run the model
set.seed(1)


if(test){

mod = sim.BipartiteEvol(nx = 8,ny = 4,NG = 800,
                        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 = nx, spatial = FALSE)


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

mod = sim.BipartiteEvol(nx = 8,ny = 4,NG = 200,
                        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 run 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)

}