Scenario: Parallel “universe” lattices with some migration between “universes” in a zone of “universe” overlap.

A demographic model is a list of population models on the same grid, with a bit more information (time to switch for each). The way to build a demographic model is to:

The function add_to_demography does this copy-and-modify step; and demographic models can be modified directly by subsetting.

Building the demographic model

Initialization

First, let’s choose the parameters for the grid:

nrow <- 100               # width of grid
ncol <- 40                # height of grid
overlap.size <- 20        # with of the overlap (in the middle)
admix.rate <- 0.05
mig.rate <- 1

Now, set up the grid.

ga <- grid_array(nlayers=2,nrow=nrow,ncol=ncol,N=1,mig.rate=mig.rate,admix.rate=admix.rate) 
plot(ga)

Create the overlap

Then, we modify that first state, by zeroing out populations in each layer where we don’t want them. We do this by creating ‘masks’ for each and using modify_grid_layer, which multiplies N by the mask we pass in with dN:

mask.1 <- mask.2 <- matrix(1,nrow=nrow(ga),ncol=ncol(ga))
mask.1[row(mask.1) > nrow(ga)/2+overlap.size/2] <- 0
mask.2[row(mask.1) < nrow(ga)/2-overlap.size/2] <- 0
ga <- modify_grid_layer( ga, layer=1, dN=mask.1 )
ga <- modify_grid_layer( ga, layer=2, dN=mask.2 )
plot(ga)

Simulating with ms

Trees, in R

Since we’ve only got one, unchanging, demographic scenario, we don’t have to make a special demography object. We’ll use a convenience function to pick some random sampling locations, two each from 30 random locations.

sample.config <- sample_locations( ga, n=30, each=2 )
sample.cols <- rainbow(nrow(sample.config))
sample.config

##       row col layer n
##  [1,]  11   8     1 2
##  [2,]  54   8     1 2
##  [3,]  25  12     1 2
##  [4,]  78  15     1 2
##  [5,]  99  19     1 2
##  [6,]  17  24     1 2
##  [7,]  29  26     1 2
##  [8,]  50  32     1 2
##  [9,]  80  35     1 2
## [10,]  45  36     1 2
## [11,]  53  39     1 2
## [12,]  96   2     2 2
## [13,]  33   3     2 2
## [14,]  26  14     2 2
## [15,]  92  14     2 2
## [16,]  15  19     2 2
## [17,]  27  20     2 2
## [18,]  31  24     2 2
## [19,]  13  25     2 2
## [20,]  48  27     2 2
## [21,]  66  27     2 2
## [22,]  44  31     2 2
## [23,]  52  32     2 2
## [24,]  97  33     2 2
## [25,]  77  34     2 2
## [26,]  62  36     2 2
## [27,]  11  37     2 2
## [28,]  55  37     2 2
## [29,]  37  38     2 2
## [30,]  84  40     2 2

With this in hand, we can run ms. We’ll ask it to output trees, not sequence. For some reason, it gives an error if we keep it all in R: maybe the string of parameters is too long? (Using tofile=TRUE puts the parameters in a file, passed to ms via -f.)

ms.output <- run_ms( ga, nsamp=sample.config, trees=TRUE, nreps=3 )

## Outputting to  msarg_752142  .

tree.output <- trees_from_ms( file.path(ms.output,"msoutput.txt") )
layout(t(1:2))
for (tree in tree.output) {
    plot_sample_config( ga, sample.config )
    abline(v=(nrow(ga)+c(1,-1)*overlap.size)/2, lty=2, lwd=2, col='grey')
    ape::plot.phylo( tree, tip.color=sample.cols[findInterval(as.numeric(tree$tip.label),1+c(0,cumsum(sample.config[,4])))] )
    ape::axisPhylo(1)
}

Sequence, to a file

To get sequence, we could do as follows, this time outputting to a directory:

ms.output <- run_ms( ga, nsamp=sample.config, theta=0.01 )

## Outputting to  msarg_976869  .

cat(paste(scan(file.path(ms.output,"msoutput.txt"),what='char',sep='\n'),collapse='\n'))

## ms 60 1 -t 0.01 -f msarg_976869/msarg.txt 
## 11265 54137 46168
## //
## segsites: 301
## positions: 0.0013 0.0048 0.0081 0.0098 0.0161 0.0205 0.0215 0.0221 0.0256 0.0264 0.0318 0.0319 0.0333 0.0410 0.0413 0.0415 0.0428 0.0436 0.0457 0.0488 0.0495 0.0512 0.0532 0.0534 0.0564 0.0577 0.0678 0.0682 0.0805 0.0844 0.0890 0.0903 0.0939 0.0962 0.0968 0.0972 0.0990 0.1006 0.1072 0.1113 0.1188 0.1198 0.1219 0.1243 0.1292 0.1310 0.1333 0.1410 0.1426 0.1449 0.1483 0.1484 0.1518 0.1527 0.1552 0.1628 0.1674 0.1711 0.1742 0.1766 0.1793 0.1843 0.1852 0.1865 0.1870 0.1874 0.1956 0.2006 0.2009 0.2021 0.2062 0.2191 0.2318 0.2321 0.2343 0.2353 0.2377 0.2480 0.2492 0.2502 0.2540 0.2590 0.2670 0.2726 0.2743 0.2800 0.2820 0.2843 0.2844 0.2905 0.2905 0.2946 0.2966 0.3023 0.3026 0.3028 0.3032 0.3128 0.3256 0.3269 0.3283 0.3335 0.3355 0.3365 0.3371 0.3386 0.3440 0.3441 0.3446 0.3457 0.3465 0.3472 0.3472 0.3579 0.3582 0.3585 0.3636 0.3706 0.3755 0.3900 0.3923 0.3952 0.3962 0.3973 0.4002 0.4003 0.4108 0.4149 0.4217 0.4264 0.4295 0.4296 0.4301 0.4328 0.4488 0.4499 0.4507 0.4525 0.4527 0.4601 0.4643 0.4648 0.4660 0.4770 0.4840 0.4851 0.4852 0.4858 0.4876 0.4899 0.4905 0.4911 0.4923 0.4937 0.5052 0.5131 0.5133 0.5181 0.5203 0.5220 0.5260 0.5269 0.5316 0.5402 0.5439 0.5442 0.5510 0.5694 0.5700 0.5761 0.5767 0.5768 0.5783 0.5822 0.5864 0.5883 0.5923 0.5983 0.5990 0.5998 0.6003 0.6056 0.6098 0.6101 0.6150 0.6154 0.6181 0.6252 0.6287 0.6325 0.6402 0.6432 0.6437 0.6445 0.6481 0.6485 0.6500 0.6549 0.6641 0.6692 0.6746 0.6949 0.6953 0.6998 0.7054 0.7065 0.7103 0.7166 0.7190 0.7190 0.7200 0.7237 0.7272 0.7324 0.7386 0.7420 0.7423 0.7512 0.7567 0.7671 0.7680 0.7695 0.7710 0.7728 0.7740 0.7759 0.7775 0.7866 0.7885 0.7928 0.7945 0.7977 0.7982 0.8067 0.8088 0.8099 0.8187 0.8189 0.8218 0.8270 0.8287 0.8337 0.8365 0.8470 0.8490 0.8498 0.8504 0.8550 0.8584 0.8637 0.8667 0.8671 0.8680 0.8687 0.8748 0.8790 0.8871 0.8873 0.8944 0.8978 0.9009 0.9021 0.9027 0.9033 0.9055 0.9055 0.9073 0.9162 0.9188 0.9195 0.9233 0.9311 0.9344 0.9346 0.9378 0.9385 0.9398 0.9399 0.9407 0.9449 0.9475 0.9501 0.9504 0.9532 0.9543 0.9561 0.9615 0.9620 0.9625 0.9666 0.9751 0.9759 0.9764 0.9771 0.9780 0.9806 0.9853 0.9885 0.9934 0.9986 0.9993 
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Simulating parallel universes with ms