Introduction

We have already introduced the Colony class that holds colony-specific information and caste individuals. However, when working with honeybees, we usually do not work with a single colony, but with apiaries or even whole populations of colonies. To cater for this, SIMplyBee provides a MultiColony class. It behaves as a list of Colony objects but with additional functionality - you can apply function directly to the MultiColony objects. A MultiColony can represent different apiaries or sub-populations in terms of either age of the queens or geographical location of the apiaries etc. This vignette demonstrates creating and working with MultiColony objects. First, we again load the package.

library(package = "SIMplyBee")

Initial settings

We first initiate our simulation with founders genomes, simulation parameters, base population of virgin queens and a drone congregation area (DCA).

# Create 20 founder genomes
founderGenomes <- quickHaplo(nInd = 30, nChr = 1, segSites = 100)
# Set up new global simulation parameters
SP <- SimParamBee$new(founderGenomes)
# Create a base population of 20 virgin queens
basePop <- createVirginQueens(founderGenomes)
# Create a DCA from the drones of the first 10 queens
DCA <- createDrones(basePop[1:10], nInd = 100)

Creating a MultiColony object

We create a MultiColony object with createMultiColony() function. Let’s say you want to create a MultiColony object that represents a single apiary. The first option is to initialise an empty MultiColony object that represents an empty apiary without any colonies and individuals within them.

# Create an empty apiary
emptyApiary <- createMultiColony()
emptyApiary
#> An object of class "MultiColony" 
#> Number of colonies: 0 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0

Let’s inspect the printout of the MultiColony object. This tells how many colonies are within, how many of them are empty and contain no individuals, how many are NULL objects, how many have experienced a split, swarm, supersedure, or a collapse (you can read more about these events in the Colony events vignette), and how many of them are productive, meaning that we can collect a production phenotype from them such as honey yield.

The second option is again to create an empty MultiColony object that represents an empty apiary without any individuals within, but with a defined number of colony slots.

# Create an empty apiary with 10 colony slots
emptyApiary1 <- createMultiColony(n = 10)
emptyApiary1
#> An object of class "MultiColony" 
#> Number of colonies: 10 
#> Are empty: 10 
#> Are NULL: 10 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0

The third option is to create a MultiColony object with a population of either virgin or mated queens. For this, we first have to initialise the simulation with founder genomes and creating a base population of virgin queens. We will use 10 virgin queens to produce drones and create a DCA - we will take these from the initial settings above.

We will now create an apiary with 10 virgin colonies with the createMultiColony() function by providing the second set of 10 virgin queens as the input parameter. Let’s call this apiary apiary1 and say that it is positioned at the location (1,1).

# Create an apiary with the remaining virgin queens
apiary1 <- createMultiColony(x = basePop[11:20])
# Set the location of the apiary
apiary1 <- setLocation(apiary1, c(1,1))

Let’s now use functions isQueenPresent() and isVirginQueensPresent() to confirm all the colonies are virgin.

# Check whether all the colonies are virgin
isQueenPresent(apiary1)
#>     1     2     3     4     5     6     7     8     9    10 
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
isVirginQueensPresent(apiary1)
#>    1    2    3    4    5    6    7    8    9   10 
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

MultiColony operations

Once we have a non-empty MultiColony object, we can do basic operations on it. First, we can select some colonies by either specifying their IDs, desired number or percentage of randomly selected colonies.

# Get the IDs of the colonies
getId(apiary1)
#>  [1]  1  2  3  4  5  6  7  8  9 10
# Select colonies according to IDs
selectColonies(apiary1, ID = c(1,2))
#> An object of class "MultiColony" 
#> Number of colonies: 2 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0
# Randomly select a given percentage of colonies
selectColonies(apiary1, p = 0.1)
#> Randomly selecting colonies: 1
#> An object of class "MultiColony" 
#> Number of colonies: 1 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0

Second, we can pull some colonies from the MultiColony object. This means, that the pulled colonies are removed from the original object. The function pullColonies() therefore returns two object - the pulled colonies and the remnant colonies.

# Pull one colony - returns a list with $remnant and $pulled nodes
pullColonies(apiary1, n = 1)
#> Randomly pulling colonies: 1
#> $pulled
#> An object of class "MultiColony" 
#> Number of colonies: 1 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0 
#> 
#> $remnant
#> An object of class "MultiColony" 
#> Number of colonies: 9 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0

Third, we can also remove some colonies from the MultiColony object with removeColonies() function.

removeColonies(apiary1, ID = 13)
#> Warning in removeColonies(apiary1, ID = 13): ID parameter contains come invalid
#> IDs!
#> An object of class "MultiColony" 
#> Number of colonies: 10 
#> Are empty: 0 
#> Are NULL: 0 
#> Have split: 0 
#> Have swarmed: 0 
#> Have superseded: 0 
#> Have collapsed: 0 
#> Are productive: 0

These three functions can also select, pull, and remove colonies based on some values (phenotypes, genetic values …). You can read more about that in the Quantitative genetics vignette.

Crossing a MultiColony

Next, we will cross all the virgin queens in the apiary with the cross() function to groups of drones that we collected from the DCA with the pullDroneGroupsFromDCA() function. We have to collect at least as many groups of drones as we have colonies in our MultiColony.

# Pull 10 groups of drones from the DCA
droneGroups <- pullDroneGroupsFromDCA(DCA, n = 10, nDrones = nFathersPoisson)
# Cross all virgin queens in the apiary to the selected drones
apiary1 <- cross(apiary1, drones = droneGroups, checkCross = "warning")
# Check whether the queens are present (and hence mated)
isQueenPresent(apiary1)
#>    1    2    3    4    5    6    7    8    9   10 
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Once we have mated queens in the apiary, we can apply all the event functions directly to the MultiColony object: buildUp(), downsize(), swarm(), split(), supersede(), collapse() but also all the functions that either add, replace, or remove individuals from the castes. Let’s say we want to build-up all the colonies in our apiary.

# Build-up all the colonies in the apiary1
apiary1 <- buildUp(apiary1, nWorkers = 1000, nDrones = 100)

Furthermore, we can use the pullColonies() or selectColonies() to subset the colonies that will for example swarm, collapse, or supersede (presented in the Colony events vignette), or the ones that we decided to split (check out the Colony events vignette).

Working with multiple MultiColony objects

Let’s now initiate another MultiColony named as apiary2 that is placed at location (2,2). Here, we define different MultiColony object according to the location of the apiary, but the objects could also be defined according to the age of the queens (such as age0, age1…). apiary2 contains only virgin queens and we want to mate them to a DCA made of drones from apiary1.

# Initiate apiary2 at the location (2,2)
apiary2 <- createMultiColony(basePop[21:30])
apiary2 <- setLocation(apiary2, c(2,2))

Since some time has passed, we want to first replace the drones in apiary1 with new drones. We can do that with replaceDrones() function.

apiary1 <- replaceDrones(apiary1)

Now that we have a new set of drones, we can create a DCA with the function createDCA() and mate virgin queens in apiary2 to the DCA.

# Check whether all colonies in apiary2 are virgin
isQueenPresent(apiary2)
#>    11    12    13    14    15    16    17    18    19    20 
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
isVirginQueensPresent(apiary2)
#>   11   12   13   14   15   16   17   18   19   20 
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# Create a DCA from all the drones in apiary
DCA <- createDCA(apiary1)
# Check how big is the DCA
DCA
#> An object of class "Pop" 
#> Ploidy: 2 
#> Individuals: 1000 
#> Chromosomes: 1 
#> Loci: 100 
#> Traits: 0
# Sample drones groups from the DCA
droneGroups <- pullDroneGroupsFromDCA(DCA, 
                                      n = nColonies(apiary2), 
                                      nDrones = nFathersPoisson)
# Cross virgin queens in apiary2 to selected drones
apiary2 <- cross(apiary2, drones = droneGroups, checkCross = "warning")

To learn more about the nFathersPoisson() function and other similar functions, read the Sampliong functions vignette.