calcSelectionCriterion {SIMplyBee}

Calculate the selection criterion

Description

Level 0 function that calculates the selection criterion as the sum of workers (direct) and queen (maternal) effects of workers, as defined by Du et al. (2021). This can be seen as the expected value of virgin queens from the queen (as well as workers, but we would not be selecting workers).

Usage

calcSelectionCriterion(
  x,
  queenTrait = 1,
  queenTraitFUN = sum,
  workersTrait = 2,
  workersTraitFUN = sum,
  use = "gv"
)

Arguments

x

Colony-class or MultiColony-class

queenTrait

numeric (column position) or character (column name), trait that represents queen's effect on the colony value; if NULL then this contribution is 0

queenTraitFUN

function, that will be applied to the queen effect values of workers, default is sum (see examples), but note that the correct function will depend on how you will setup simulation!

workersTrait

numeric (column position) or character (column name), trait that represents workers' effect on the colony value; if NULL then this contribution is 0

workersTraitFUN

function, that will be applied to the workers effect values of workers, default is sum (see examples), but note that the correct function will depend on how you will setup simulation!

use

character, the measure to use for the calculation, being either "gv" (genetic value), "ebv" (estimated breeding value), or "pheno" (phenotypic value)

Value

integer when x is Colony-class and a named list when x is MultiColony-class, where names are colony IDs

References

Du, M., et al. (2021) Short-term effects of controlled mating and selection on the genetic variance of honeybee populations. Heredity 126, 733–747. doi:/10.1038/s41437-021-00411-2

See Also

calcInheritanceCriterion and calcPerformanceCriterion and as well as

Examples

founderGenomes <- quickHaplo(nInd = 8, nChr = 1, segSites = 100)
SP <- SimParamBee$new(founderGenomes)
meanA <- c(10, 10 / SP$nWorkers)
varA <- c(1, 1 / SP$nWorkers)
corA <- matrix(data = c( 1.0, -0.5,
                        -0.5,  1.0), nrow = 2, byrow = TRUE)
SP$addTraitA(nQtlPerChr = 100, mean = meanA, var = varA, corA = corA,
name = c("queenTrait", "workersTrait"))
varE <- c(3, 3 / SP$nWorkers)
corE <- matrix(data = c(1.0, 0.3,
                        0.3, 1.0), nrow = 2, byrow = TRUE)
SP$setVarE(varE = varE, corE = corE)
basePop <- createVirginQueens(founderGenomes)

drones <- createDrones(x = basePop[1], nInd = 1000)
droneGroups <- pullDroneGroupsFromDCA(drones, n = 10, nDrones = nFathersPoisson)

# Create a Colony and a MultiColony class
colony <- createColony(x = basePop[2])
colony <- cross(colony, drones = droneGroups[[1]])
colony <- buildUp(colony)

apiary <- createMultiColony(basePop[3:4], n = 2)
apiary <- cross(apiary, drones = droneGroups[c(2, 3)])
apiary <- buildUp(apiary)

calcSelectionCriterion(colony,
                       queenTrait = 1, queenTraitFUN = sum,
                       workersTrait = 2, workersTraitFUN = sum)
calcSelectionCriterion(apiary,
                       queenTrait = 1, queenTraitFUN = sum,
                       workersTrait = 2, workersTraitFUN = sum)

apiary[[2]] <- removeQueen(apiary[[2]])
calcSelectionCriterion(apiary, queenTrait = 1,
                       workersTrait = 2, workersTraitFUN = sum)