FourPopMonoecious: Example datasets for four-deme metapopulations.
In MarcoAndrello/MetaPopGen-2.0: Simulation of metapopulation genetics

Description Usage Format Details Examples

Datasets of survival, fecundity, mutation, dispersal and initial population size for a four-deme and hundred-deme metapopulations. Single-locus.

data(FourPopMonoecious)
data(FourPopDioecious)
data(FourPopMonoecious.selection)
data(HundredPop)

This dataset contains the objects N1_M, N1_F, sigma_M, sigma_F, phi_M, phi_F, mu, kappa0, delta and T_max.

N1_M and N1_F are a 3-D arrays of number of male and female individuals, respectively, 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, so N1[j,i,x] is the number of individuals of genotype j,in deme i, of age x at the beginning of the simulation. It is important to understand the order of genotypes; it will be easier to explain through some examples. If there are two alleles, say A1 and A2, there will be three genotypes; the order in which they are stored in N1 is as follows: A1A1, A1A2 and A2A2. If there are three alleles (A1, A2 and A3), the order is: A1A1, A1A2, A1A3, A2A2, A2A3, A3A3. With four alleles, the order is: A1A1, A1A2, A1A3, A1A4, A2A2, A2A3, A2A4, A3A3, A3A4, A4A4. So with four alleles N[6,1,3] gives the number of A2A4 individuals in deme 1 that are of age 3.

sigma_M, sigma_F, phi_M and phi_F are male survival probability, female survival probability, male fecundity and female fecundity, respectively. Female fecundity is a per capita number of female gametes (eggs for animals, embrio sacs for plants) and male fecundity is a per capita number of male gametes (sperms for animals, pollen grains for plants). All the elements of phi_F and phi_M must be natural numbers. All the elements of sigma must be rational numbers comprised between [0,1]. sigma, phi_F and phi_M are 4-D arrays and share the same structure: sigma[j,i,x,t] is is the survival probability for individuals of genotype j, age x in deme i at time t. The order of genotypes is of course the same as that in N1.

mu is is the mutation matrix. Square matrix of size l*l, with l = number of alleles. mu[i,j] is the probability that allele j mutates into allele i. The elements of the diagonal of mu are the probabilities that the allele does not mutate, i.e. it must be colSums(mu) = 1. It is assumed that the locus follows a K-allele mutation model (the number of alleles is fixed). This is appropriate for certain type of markers, such as single-nucleotide-polymorphisms (SNP).

delta is the dispersal matrix. Square matrix of size n*n, with n = number of demes. delta[i,j] is the dispersal probability from deme j to deme i. In case of asymmetric dispersal, this is not the same as dispersal from deme i to deme j. A ncesessary condition to check the correct orientation of delta is that no element of colSums(delta) exceeds 1.

kappa0 is the carrying capacity. See the function sim.metapopgen.dioecious for further details on this parameter.

T_max is the number of years of simulation.

The dependency of survival and fecundity on genotype means that one can model selection through its effect on the survival and/or fecundity of a given genotype. Setting the same values for all genotypes is equivalent to simulate a neutral locus. Setting genotype-specific values mimicks the effects of selection on survival and/or fecundity. The FourPop.selection dataset is the same as FourPop except that, in deme number 2, the first genotype (A1A1) has survival = 0 and the second, third and fourth genotypes (A1A2, A1A3 and A1A4) have survival = 0.18 (this corresponds to a selection coefficient s = 1 and dominance coefficient h = 0.7).

The FourPopMonoecious dataset has one locus with 4 alleles (therefore, 10 genotypes), 4 demes and 6 age-classes. The locus is fixed for a different allele in different demes. Survival and fecundities do not differ among demes, years or genotypes, but change with age. Survival increases from 0.4 at age 1 to 0.6 at ages 3-6. Fecundity is zero from age 1 to age 4, is moderate at age 5 and is maximal at age 6.

The FourPopDioecious dataset has one locus with 2 alleles (therefore, 3 genotypes), 4 demes and 6 age-classes. Survival and fecundities do not differ among demes, years or genotypes, but change with age. Survival probabilities differ by sex.

The HundredPopMonoecious dataset has one locus with 2 alleles (therefore, 3 genotypes), 100 demes and 5 age-classes. Initial demes are composed of 1000 individuals per age class, all of the same genotype. Survival and fecundities do not differ among demes, years or genotypes, but change with age. Survival increases from 0.4 at age 1 to 0.6 at ages 3-5. Fecundity is zero from age 1 to age 3, is moderate at age 4 and is maximal at age 5.

data(FourPopMonoecious)
# Shows the survival and fecundity functions
plot(sigma[1,1,,1],xlab="Age",ylab="Survival probability")
plot(phi_F[1,1,,1],xlab="Age",ylab="Female fecundity")

# Shows the genotypic composition of demes
par(xpd=NA)
colours <- rainbow(10)
barplot(N1[,,1],names.arg=c("1","2","3","4"),xlab="Deme",ylab="Number of age-1 individuals",col=colours)
lg.txt <- c("A1A1", "A1A2", "A1A3", "A1A4", "A2A2", "A2A3", "A2A4", "A3A3", "A3A4", "A4A4")
legend(-1,1500,lg.txt,fill=colours,ncol=5,bty="n")

# Shows the dispersal matrix
dim(delta)
require(lattice)
levelplot(delta[,,1])

# Shows the mutation matrix
dim(mu)
levelplot(mu)

# Run the simulation
N <- sim.metapopgen.monoecious(input.type="array",N1=N1, sigma=sigma, phi_F=phi_F, phi_M=phi_M, mu=mu, delta=delta, kappa0=kappa0, T_max=T_max)

# Shows the genotypic composition of demes at the end of the simulation
# Compare with that at the beginning!
colours <- rainbow(10)
barplot(N[,,1,dim(N)[4]],names.arg=c("1","2","3","4"),xlab="Deme",ylab="Number of age-1 individuals",col=colours)
lg.txt <- c("A1A1", "A1A2", "A1A3", "A1A4", "A2A2", "A2A3", "A2A4", "A3A3", "A3A4", "A4A4")
legend(-1,1500,lg.txt,fill=colours,ncol=5,bty="n")

# Simulation with selection against the first allele in the second deme
data(FourPopMonoecious.selection)
N <- sim.metapopgen.monoecious(input.type="array",N1=N1, sigma=sigma, phi_F=phi_F, phi_M=phi_M, mu=mu, delta=delta, kappa0=kappa0, T_max=T_max)
par(xpd=NA)
colours <- rainbow(10)
barplot(N[,,2,dim(N)[4]],names.arg=c("1","2","3","4"),xlab="Deme",ylab="Number of age-2 individuals",col=colours)
lg.txt <- c("A1A1", "A1A2", "A1A3", "A1A4", "A2A2", "A2A3", "A2A4", "A3A3", "A3A4", "A4A4")
legend(0.2,600,lg.txt,fill=colours,ncol=5,bty="n")

data(FourPopDioecious)
# Run the simulation
N <- sim.metapopgen.dioecious(input.type="array",N1_M = N1_M, N1_F=N1_F, sigma_M=sigma_M, sigma_F=sigma_F, phi_F=phi_F, phi_M=phi_M, mu=mu, delta=delta, kappa0=kappa0, T_max=T_max)