sim.metapopgen.monoecious.multilocus: Main function to simulate metapopulation genetics (monoecious...

In MarcoAndrello/MetaPopGen_0.0.8: Simulation of metapopulation genetics

Description

Simulates the metapopulation genetics. The function will calculate automatically from its arguments the number of gamete types l, the number of genotypes m, the number of demes n and the number of age-classes z. It is therefore important that all the arguments have the correct dimensions and size. See also the help of sim.metapopgen.monocious for more information.

Usage

sim.metapopgen.dioecious(input.type, demographic.data,
                           N1, sigma_M, sigma_F,
                           phi_M, phi_F, List_gene, mu, r=0.5, 
                           delta, recr.dd="settlers",
                           kappa0, T_max,save.res=F,
                           save.res.T=seq(1,T_max), verbose=F)

Arguments

input.type	Character vector defining whether the input data are giving as arrays ("array") or as a data frame ("data.frame")
demographic.data	If the input is given as a data.frame (input.type = "data.frame"), a data frame giving 1) survival probabilities and fecundities for each genotype, deme, age-class, sex and time and 2) number of individuals for each genotype, deme, sex and age-class at the beginning of the simulation
N1	Number of individuals at the beginning of the simulation. 3-D array giving the number of individuals of each genotype in each deme and each age-class at the beginning of the simulation. The first dimension is the genotype, the second dimension is the deme and the third dimension is the age-class. To be used when input.type = "array"
sigma_M	Survival probabilities for males. 4-D array of length c(m,n,z,T_max). sigma[j,i,x,t] is the survival probability for individuals of genotype j, age x in deme i at time t. Alternatively, sigma_M can be a 3-D array of length c(m,n,z): this will be interpreted as male survival probabilities constant in time. All the elements of sigma are bounded between [0,1]. To be used when input.type = "array".
sigma_F	Survival probabilities for females. The same as sigma_M.
phi_F	Mean female fecundity. 4-D array of length c(m,n,z,T_max). phi_F[j,i,x,t] is the female fecundity for individuals of genotype j, age x in deme i at time t. Alternatively, phi_F can be a 3-D array of length c(m,n,z): this will be interpreted as female fecundities constant in time. The female fecundity is the per capita number of female gametes (eggs for animals, embrio sacs for plants). All the elements of phi_F must be natural numbers. To be used when input.type = "array". Variation in fecundity among individuals follows a Poisson distribution.
phi_M	Mean male fecundity. 4-D array of length c(m,n,z,T_max). phi_M[j,i,x,t] is the male fecundity for individuals of genotype j, age x in deme i at time t. Alternatively, phi_M can be a 3-D array of length c(m,n,z): this will be interpreted as male fecundities constant in time. The male fecundity is the per capita number of male gametes (sperms for animals, pollen grains for plants). All the elements of phi_F must be natural numbers. To be used when input.type = "array". Variation in fecundity among individuals follows a Poisson distribution.
List_gene	Vector giving the number of alleles at each locus.
mu	Vector giving the mutation rate at each locus. Mutation follows a K-allele mutation model where the probability that an allele x mutates to another allele y is mu/(l-1), where l is the number of alleles at that locus
r	Recombination probability in simulations with two loci. r can take values between 0 and 0.5, with r = 0.5 (default) for two unlinked loci and 0 < r < 0.5 for linked loci. With more than two loci, loci are considered unlinked and this argument is not used.
delta	Dispersal probabilities. Square matrix of size n*n. delta[i,j] is the dispersal probability from deme j to deme i. Alternatively, delta can be a 2-D array of length c(n,n): this will be interpreted as dispersal constant in time.
recr.dd	Defines how settler survival probabilities depend on density. If "settlers" (default), settler survival probabilities depends on the number of settlers, through the function settler.survival. If "adults", settler survival probabilities depends on the number of adults (all individuals already present in the deme): only up to kappa0[i,t] (see next argument) individuals are allowed in a deme.
kappa0	2-D array of length c(n,T_max). Carrying capacity used to calculate the density-dependent settler survival probabilities. If the argument recr.dd = "settlers", kappa0[i,t] is the settler carrying capacity for deme i at time t (maximum number of settlers allowed to recruit), and is used in the function settler.survival. If recr.dd = "adults", kappa0[i,t] is the maximum number of individuals allowed for deme i at time t. kappa0 can also be a 1-D array of length n: this will be interpreted as a carrying capacity constant in time.
T_max	Number of years of simulations.
save.res	Boolean. If FALSE (default), the result of the simulation is stored in memory and returned to the user. If TRUE, the function does not return anything, but save the result in different files on disk. See "value" below.
save.res.T	Vector. If save.res=TRUE, this argument is used to define the time steps for which the state of the metapopulation will be saved on disk. It is used to save disk space in the case of long simulations with many demes or genotypes. It deafults to seq(1,T_max), that is, the results are saved every time step.
verbose	If TRUE, the function will print much information, mainly used for debugging purposes. Default to FALSE.

Value

If save.res is FALSE: a named list with one element, N a 4-D array (ex: N[k,i,x,t]) of length c(m,n,z,tmax) giving the number individuals of genotype k, age x in deme i at time t.
If save.res is TRUE: the function does not return anything. Results are saved in files on disk. The function will create a folder named with the date and time of your simulation and create one RData file per year of simulation (but see argument save.res.T for defining the time steps for which the results are saved). Each file will contain the variable N, 3-D arrays (N[k,i,x]) of length c(m,n,z) giving the number of individuals of genotype k, age x in deme i at that time.

Examples

# Example for a population with 10 demes, 2 age-classes and 5 time-steps.
# Two independent loci 
data("MultiMonoecious")
N<-sim.metapopgen.monoecious.multilocus(input.type="array",N1=N1,sigma=sigma,phi_F=phi_F,phi_M=phi_M,List_gene=List_gene,mu=mu,r=r,delta=delta,kappa0=kappa0,T_max=5) 

# This will save results on disk:
sim.metapopgen.monoecious.multilocus(input.type="array",N1=N1,sigma=sigma,phi_F=phi_F,phi_M=phi_M,List_gene=List_gene,List_mu=List_mu,r=r,delta=delta,kappa0=kappa0,T_max=5,save.res=T) 


# This will use a data frame input type
data("DataFrameMonociousMulti")
sim.metapopgen.monoecious.multilocus(input.type="data.frame",demographic.data=demographic.data, List_gene=List_gene, List_mu=List_mu,r=r,delta=delta, kappa0=kappa0,T_max=5)

MarcoAndrello/MetaPopGen_0.0.8 documentation built on May 25, 2019, 12:23 p.m.