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.
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)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 Have superseded: 0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: 0Let’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: 0The 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.
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: 0Second, 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: 0Third, 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: 0These 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.
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 TRUEOnce 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).
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.
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.