knitr::opts_chunk$set( collapse = TRUE, comment = "#>", include = TRUE )
We have already introduced the Colony class that holds colony-specific
information and caste individuals. However, when working with honeybees, we
usually do not work with a single colony, but with apiaries or even whole
populations of colonies. To cater for this, SIMplyBee provides a MultiColony
class. It behaves as a list of Colony
objects but with additional
functionality - you can apply function directly to the MultiColony
objects. A
MultiColony
can represent different apiaries or sub-populations in terms of
either age of the queens or geographical location of the apiaries etc. This
vignette demonstrates creating and working with MultiColony
objects. First, we
again load the package.
library(package = "SIMplyBee")
We first initiate our simulation with founders genomes, simulation parameters, base population of virgin queens and a drone congregation area (DCA).
# Create 20 founder genomes founderGenomes <- quickHaplo(nInd = 30, nChr = 1, segSites = 100) # Set up new global simulation parameters SP <- SimParamBee$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
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
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) isVirginQueensPresent(apiary1)
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) # Select colonies according to IDs selectColonies(apiary1, ID = c(1,2)) # Randomly select a given percentage of colonies selectColonies(apiary1, p = 0.1)
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)
Third, we can also remove some colonies from the MultiColony
object with
removeColonies()
function.
removeColonies(apiary1, ID = 13)
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.
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)
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).
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) isVirginQueensPresent(apiary2) # Create a DCA from all the drones in apiary DCA <- createDCA(apiary1) # Check how big is the DCA DCA # 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.
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