Crossing

2024-09-19

Introduction

Crossing the queen is the most complicated action in SIMplyBee, since mating of honeybees is versatile. Virgin queens can be mated in a controlled manner, either using artificial insemination or a mating station, or uncontrolled in an open mating setting. In the latter case, the mating depends on the distance between the virgin queens and the drones or drone producing colonies.

In this vignette, we first explain five ways of mating virgin queens and at the end we describe two special cases of mating virgin queens on a mating station and mating multiple virgin queens in different ways.

Crossing decision tree

Crossing decision tree

To facilitate the understanding of the crossing function and choosing the correct option for your need, we created a diagram with a decision tree following the questions:

  1. How many virgin queens/colonies are you mating?
  1. If you are mating multiple virgin queens/colonies, do you want to:
  1. When mating according to a cross plan, you can either:
  1. When mating according to an internally created cross plan, you can either:

Start by loading the package:

library(package = "SIMplyBee")
library(package = "ggplot2")

First, we create a founder population and some virgin queen and virgin colonies for crossing. We will also create four MultiColony objects that will represent colonies from four different beekeepers.

# Simulate 40 founder genomes
founderGenomes <- quickHaplo(nInd = 60, nChr = 1, segSites = 100)
# Set global population paramaters
SP <- SimParamBee$new(founderGenomes)
# Create a base population of 40 virgin queens
basePop <- createVirginQueens(founderGenomes)

# Prepare populations with a single virgin queen
virginQueen1 <- basePop[1]
# Prepare populations with multiple virgin queens
virginQueens1 <- basePop[2:4]
# Prepare virgin MultiColony objects
beekeeper1 <- createMultiColony(basePop[6:11])
beekeeper2 <- createMultiColony(basePop[12:16])
beekeeper3 <- createMultiColony(basePop[17:21])
beekeeper4 <- createMultiColony(basePop[22:31])
beekeeper5 <- createMultiColony(basePop[32:40])
beekeeper6 <- createMultiColony(basePop[41:50]) 
beekeeper7 <- createMultiColony(basePop[51:60])

We will now create a groups of drones from the remaining queens with 1,000 drones per queen that will represent a drone congregation area (DCA).

# Create a DCA from the remaining virgin queens
DCA <- createDrones(basePop[42:50], nInd = 1000)

Option 1: Cross a single virgin queen/colony

We start by crossing a single virgin queen that can be provided either in a Pop, Colony, or MultiColony objects. In this case, you just need to provide a Pop of drones she will mate with. Note that no sampling is applied to the drones! Hence, if you provide a Pop with 100 drones, she will be mated to 100 drones!

# Samples some drone for mating from the DCA
drones <- selectInd(DCA, nInd = 10, use = "rand")
# Mate the virgin queen with the drones
queen1 <- cross(x = virginQueen1, drones = drones, checkCross = "warning")
# Check the number of fathers for the queen
nFathers(queen1)
#> [1] 10

The above code shows an example for mating the virgin queen in a Pop object, but exact same holds for Colony and MultiColony objects with a single queen.

Option2: Cross with drone packages (pre-selecting drone populations)

We continue by crossing our virgin females to drone packages, that is a pre-selected populations of drones. We recommend this option when mating a larger number of queens, although it could be used for mating a single virgin queen/colony as well, and when you want more control over the drone selection. We pre-select the groups by pulling a desired number of drone packages from a DCA with the function pullDroneGroupsFromDCA(). This function requires you to specify a group of drones (DCA), how many groups you want to pull from the DCA (n), and how many drones per group you want (nDrones). For nDrones, you can either specify an integer or a sampling function, which results in a different number of drones in each of the pulled groups (you can read more about this in the Sampling functions vignette). These sampling functions are particularly useful in crossing simulations:

If these functions do not satisfy your needs, you can specify your own sampling function(s).

We can pull the drone groups out separately for each crossing or pull them out all at once.

# Pre-select drone (father) populations from a DCA
droneGroups <- pullDroneGroupsFromDCA(DCA, n = 20, nDrones = nFathersTruncPoisson)

There is a different number of drones in each group because we used a sampling function:

sapply(droneGroups, FUN = nInd)
#>  [1] 17 17 21 13 17 13 15 17 15 13 17 17 23 10 13 14 10 16 11 12

Now, we can cross our virgin queens to drone groups.

# Pop-class with multiple queens
queens1 <- cross(x = virginQueens1, drones = droneGroups[1:3], checkCross = "warning")
nFathers(queens1)
#> [1] 17 17 21
# MultiColony-class
beekeeper1 <- cross(x = beekeeper1, drones = droneGroups[4:9], checkCross = "warning")
nFathers(beekeeper1)
#>  1  2  3  4  5  6 
#> 13 17 13 15 17 15

Option 3: Cross according to a provided cross plan

When mating a large number of queens, creating drone populations can be computationally demanding! A better option is to cross according to a cross plan that includes all the females and the males that they will mate with, and males as a single object. The cross plan is a list whose names are the IDs of either the virgin queens or the colonies you want to cross (but not both in the same cross plan!), and the content for each queen is either the IDs of drone or drone producing colonies she will mate with (again, use either only drone or only drone producing colony’s IDs in one cross plan!). When providing drone IDs, you also have to provide the males through the drones argument as a single Pop class that represents the DCA with all the drones. The DCA can represent a DCA of randomly assembled drones in the vicinity, a ‘global’ DCA of all drones available at a time, or a DCA at a mating station. If you are providing drone producing colonies’ IDs, you need to provide the males through the droneColonies argument as a single MultiColony class. Mating with drone producing colonies might be computationally more efficient since only the drones needed for mating are created on the spot.

You can use the cross plan either to gain more control over mating and create the cross plan in a manual manner, or to simply reduce the computational burden when mating a large number of virgin queens. For that, you can either use the function createCrossPlan(), if you want to inspect the cross plan before deployment, or simply let the cross() function to create the cross plan internally (see options 5 and 6). The latter is just a shortcut since it calls the createCrossPlan() function from within the cross(). Both option hence allow to create the cross plan by randomly sampling available drones or drone producing queens, or sample them according to their geographical proximity to the virgin colony. Do note that to perform spatial sampling, the females and males have to be provided and MultiColony objects with a set location. Another situation where you would want to use the cross plan is when you want.

NOTE: You can create one cross plan for a number of virgin queens or colonies and then mate each one separately. Let’s say you have 100 colonies from two beekeepers. You can create a cross plan for all of them in one go, and then mate colonies from each beekeeper separately by providing the same cross plan. Do note again, that one cross plan can either contain only virgin queen or only virgin colonies’ IDs, no mix and and match!

Let us now create a random cross plan with drone IDs to mate the virgin colonies from one of the beekeepers.

crossPlan1 <- createCrossPlan(x = beekeeper2,
                              drones = DCA,
                              nDrones = nFathersPoisson)
# Inspect the cross plan
crossPlan1
#> $`7`
#>  [1] "7847" "4000" "2130" "481"  "4054" "5267" "3811" "1980" "3833" "7920"
#> [11] "6255" "7649" "8789"
#> 
#> $`8`
#>  [1] "3625" "7296" "8221" "925"  "8568" "4391" "384"  "2269" "172"  "8000"
#> [11] "8326"
#> 
#> $`9`
#>  [1] "952"  "3256" "4384" "1224" "8026" "1271" "3703" "6850" "3759" "2031"
#> [11] "1242"
#> 
#> $`10`
#>  [1] "4942" "2734" "756"  "7345" "7100" "5265" "6386" "2685" "5924" "2321"
#> [11] "5022" "2456" "1804"
#> 
#> $`11`
#>  [1] "4972" "4824" "6875" "4276" "3382" "185"  "8399" "3815" "7280" "8514"
#> [11] "3523" "5879"
sapply(crossPlan1, length)
#>  7  8  9 10 11 
#> 13 11 11 13 12

We see that the cross plan is a list, with names being colony IDs and elements of the list being the ID of drones that each virgin colony will mate with. Now, we can cross the colonies of each beekeeper by providing the crossPlan1 to the cross() function and the created DCA through the drones argument.

# Cross the colonies of the beekeeper 2
beekeeper2 <- cross(x = beekeeper2, drones = DCA, crossPlan = crossPlan1, checkCross = "warning")
nFathers(beekeeper2)
#>  7  8  9 10 11 
#> 13 11 11 13 12

Now, let us now create a spatial cross plan, this time by sampling drone producing colonies that will provide drones to mate with another one of our virgin apiaries. We will mate colonies of beekeeper 3. In this examples, colonies from beekeeper 1 and beekeeper 2 will serve as drone producing colonies. Although we are not sampling drones directly, you still need to provide nDrones argument to determine the number of sampled drone producing colonies.

For spatial mating, we need to set the location to our apiaries. Here, we are reading the locations in from a file with the coordinates of all colonies, then selecting the colonies of a specific beekeeper, creating a list of coordinate pairs (with the Map function). We could also set locations randomly.

# Read locations from a file
locations = read.csv("Colony_locations.csv")
# Set location to apiaries
beekeeper1_locations <- locations[locations$ColonyID %in% getId(beekeeper1),]
beekeeper1 <-  setLocation(beekeeper1, 
                           location = Map(c, beekeeper1_locations$X, beekeeper1_locations$Y))

beekeeper2_locations <- locations[locations$ColonyID %in% getId(beekeeper2),]
beekeeper2 <-  setLocation(beekeeper2, 
                           location = Map(c, beekeeper2_locations$X, beekeeper2_locations$Y))

beekeeper3_locations <- locations[locations$ColonyID %in% getId(beekeeper3),]
beekeeper3 <-  setLocation(beekeeper3, 
                           location = Map(c, beekeeper3_locations$X, beekeeper3_locations$Y))

We can plot the location of colonies to see how they arrange in space. Again, we want to mate the virgin queens of beekeeper 3 with drone produced by colonies of beekeepers 1 and 2.

locationsDF <- data.frame(Location = getLocation(c(beekeeper1, beekeeper2, beekeeper3), collapse = TRUE),
                          Beekeeper = c(rep("Beekeeper1", nColonies(beekeeper1)),
                                        rep("Beekeeper2", nColonies(beekeeper2)),
                                        rep("Beekeeper3", nColonies(beekeeper3))))

ggplot(data = locationsDF, aes(x = Location.1, y = Location.2, colour = Beekeeper)) + 
  geom_point()

Next, we create a spatial cross plan for mating our colonies. We set the radius to however far we believe our drone can fly according to the scale of locations coordinates. All the drones within that area then have the same probability of mating with the queen, which follows the believe that drones aggregate in a DCA.

crossPlan2 <- createCrossPlan(x = beekeeper3,
                              droneColonies = c(beekeeper1, beekeeper2),
                              spatial = TRUE,
                              radius = 3,
                              nDrones = 13)
# Inspect the cross plan
crossPlan2
#> $`12`
#> [1] "10" "7"  "5"  "3" 
#> 
#> $`13`
#> [1] "5" "4"
#> 
#> $`14`
#> [1] "5"  "10" "4" 
#> 
#> $`15`
#> [1] "9" "4" "3" "6" "2" "8"
#> 
#> $`16`
#> [1] "3"  "1"  "11" "10"
sapply(crossPlan2, length)
#> 12 13 14 15 16 
#>  4  2  3  6  4

We see that there is a different number of drone producing queen for each virgin queen in the specified radius . It could also happen, there are no drone producing colonies in that radius. In that case, you can either increase the radius, if you think that is sensible, or some virgin queens will remain unmated. Once we have the spatial cross plan, we can mate our colonies.

beekeeper3 <- cross(x = beekeeper3, 
                    crossPlan = crossPlan2,
                    droneColonies = c(beekeeper1, beekeeper2),
                    nDrones = 13,
                    checkCross = "warning")
# Inspect the number of fathers
nFathers(beekeeper3)
#> 12 13 14 15 16 
#> 13 13 13 13 13

As you see, we have duplicated quite some information in running first a function that creates a cross plan and then performing the crossing. If you are not really keen on inspecting the cross plan before mating, we suggest you use the options 4 and 5 (see below).

You may also want to provide an existing cross plan if you want to create more tailored cross plans and cross virgin queens with different methods (i.e. you want to mate one queen in the apiary in an artificial insemination, some of them would be open mated etc.). For that, see the section “Special cases” at the end of this vignette.

Options 4 and 5: Cross according to an internally created cross plan

The procedure behind this option is exactly the same as described in option 4 with using createCrossPlan() function to create either a random and spatial cross plan and then crossing according to the plan. The difference is that in this option, the two steps are combined within the cross() function.

Let us mate the colonies of the remaining beekeepers 4 and 5 with colonies of beekeepers 1 to 3 as drone producing colonies. Let’s first set the locations of the beekeepers 4 and 5 colonies.

beekeeper4_locations <- locations[locations$ColonyID %in% getId(beekeeper4),]
beekeeper4 <-  setLocation(beekeeper4, 
                           location = Map(c, beekeeper4_locations$X, beekeeper4_locations$Y))

beekeeper5_locations <- locations[locations$ColonyID %in% getId(beekeeper5),]
beekeeper5 <-  setLocation(beekeeper5, 
                           location = Map(c, beekeeper5_locations$X, beekeeper5_locations$Y))

Now, we will mate the colonies of beekeeper 4 in a random manner and colonies of beekeeper 5 in a spatial manner, all within the cross() function. For this, you have to pass a keyword “create” to the crossPlan argument in the cross().

beekeeper4 <- cross(x = beekeeper4,
                    droneColonies = c(beekeeper1, beekeeper2, beekeeper3),
                    crossPlan = "create",
                    spatial = FALSE,
                    nDrones = 12,
                    checkCross = "warning")
#> [1] "Cross plan, drone colonies"
nFathers(beekeeper4)
#> 17 18 19 20 21 22 23 24 25 26 
#> 12 12 12 12 12 12 12 12 12 12

beekeeper5 <- cross(x = beekeeper5,
                    droneColonies = c(beekeeper1, beekeeper2, beekeeper3),
                    crossPlan = "create",
                    spatial = TRUE,
                    radius = 3,
                    nDrones = 12,
                    checkCross = "warning")
nFathers(beekeeper5)
#> 27 28 29 30 31 32 33 34 35 
#> 12 12 12 12 12 12 12 12 12

Special cases

Cross virgin queens at a mating station

Mating virgin queens at a mating station is no different than mating them at an open DCA - the difference is in the DCA itself. In the case of open mating, the DCA consists of drones from multiple queens, all of which are usually unknown. In the case of a mating station, the DCA consists of drones coming from a sister group of drone producing queens (DPQ), the queen of which is known. This allows us to track the pedigree on the paternal side.

To simulate this, you can either: * create a group of sister queens and provide them to the cross() function through the droneColonies argument; * create a mating stations DCA and then either pull drone packages from it, create a random plan from it, or provide it directly to the cross() function through the drones argument.

Here, we will demonstrate one of the options. We will create a mating station DCA using createMatingStationDCA() function, which takes a single “sire” colony (queen of the DPQs). From the “sire” colony, the function first produces a desired number of sister DPQs, and next produces a desired number of drones per DPQ. The produced drones represent the mating station’s DCA.

# Create a DCA at a mating station from colony1
(matingStationDCA <- createMatingStationDCA(beekeeper1[[3]], nDPQs = 20, nDronePerDPQ = 1000))
#> An object of class "Pop" 
#> Ploidy: 2 
#> Individuals: 20000 
#> Chromosomes: 1 
#> Loci: 100 
#> Traits: 0

We see that the output of the function is a single population of 20,000 drones that represents the DCA. As mentioned, once you have the DCA, you can use it in different manners. Here, we will feed it directly to the cross() function and assume random sampling of drones. Spatial sampling does not make sense here, since all the drones and the virgin colonies are at the same location - the mating station.

# Mate only an beekeeper
beekeeper6 <- cross(beekeeper6,
                    drones = matingStationDCA,
                    spatial = FALSE,
                    crossPlan = "create",
                    nDrones = 15,
                    checkCross = "warning")
nFathers(beekeeper6)
#> 36 37 38 39 40 41 42 43 44 45 
#> 15 15 15 15 15 15 15 15 15 15

Cross colonies with varying methods

It could happen that you have some virgin colonies in an apiary and you want to inseminate one of the virgin queens artificially with a single drone, take three of them to a mating station, and mate the rest of them openly at a local DCA. Since the cross plan is a named list, you can concatenate multiple cross plans into one. Let’s mate the multicolony of beekeeper 4 in such a manner.

# Create a single drone for single drone insemination
singleDrone = createDrones(beekeeper1[[1]], nInd = 1)
# Create a cross plan for crossing some of the colonies in an open DCA, 
# some with single drone, and some on a mating station
crossPlanBeekeeper7 <- c(
  createCrossPlan(x = beekeeper7[1], 
                  drones = singleDrone, 
                  nDrones = 1),
  createCrossPlan(x = beekeeper7[2:6], 
                  drones = DCA, 
                  nDrones = nFathersTruncPoisson),
  createCrossPlan(x = beekeeper7[7:10], 
                  drones = matingStationDCA, 
                  nDrones = nFathersTruncPoisson)
  )

beekeeper7 <- cross(x = beekeeper7, 
                    crossPlan = crossPlanBeekeeper7, 
                    drones = c(singleDrone, DCA, matingStationDCA),
                    checkCross = "warning")
nFathers(beekeeper7)
#> 46 47 48 49 50 51 52 53 54 55 
#>  1 11 19 11 17 18 14 15 20  8