reems

In reems: Estimating Effective Migration Surfaces from Single Nucleotide Polymorphism Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

library(reems)

This vignette will walk you through the procedure of estimating effective migration surfaces from single-nucleotide polymorphism data (SNPs). It is designed to fit within the spatial population genetics ecosystem and to be compatible in its input format with other R packages such as conStruct.

The best introduction to the EEMS method itself is the original article by Petkova et al. , a preprint of which is also available on bioRxiv (https://www.biorxiv.org/content/10.1101/011809v2.full). Both the preprint version of the original article and the documentation of the original command-line utility are also available on their GitHub repository and contain many high-resolution examples of what the EEMS outplot plots look like.

At the beginning of this process, we assume you have an allele frequency matrix freqs and a coordinate matrix coords in your workspace. This package includes example objects ex.freqs and ex.coords.

Estimating Effective Migration Surfaces

Standard usage

The simplest way to run an EEMS analysis is by using the eems() function of this package. It can be invoked with only minimal arguments:

eems(
  freqs = freqs,
  coords = coords,
  mcmcpath = "/full/path/to/directory/prefix"
)   

This runs an EEMS analysis for a set of diploid individuals on a single MCMC chain for 2000000 iterations, and with a default area outline derived from the coordinates themselves by adding a buffer around them. It also assumes a default number of demes 6 times the number of individuals. The results will be stored in an output directory full/path/to/file/prefix-chain1. The return value of the function is the output directory, so it is easy to retrieve.

Those defaults are designed to ensure an adequate resolution of the analysis, but they may not be suitable for your exact application context. In that case, you can set those parameters manually by specifying additional arguments. The most useful ones are illustrated below, where the MCMC chain has been shortened by an order of magnitude for a more exploratory analysis on haploid samples. It also shows that the seed can be set manually to ensure reproducibility.

eems(
  seed = 1024,  
  freqs = freqs,
  coords = coords,
  mcmcpath = "/full/path/to/directory/prefix",
  diploid = FALSE,
  numMCMCIter = 100000,
  numBurnIter = 50000,
  numThinIter = 999
)   

Usually, it is advisable to run more than one MCMC chain and combine the results. eems() allows you to do so either sequentially or in parallel. This example will run 5 chains in parallel with default settings, although it will not use more workers than there are cores available. Note that when run in parallel, the chains will run silently and console output is redirected to a log file which you can find among the other output files in the output directory.

eems(
  freqs = freqs,
  coords = coords,
  mcmcpath = "full/path/to/directory/prefix",
  nChains = 5,
  parallel = TRUE
)   

The output will then be in 5 separate directories, called /full/path/to/directory/prefix-chain1 to full/path/to/directory/prefix-chain5.

Specifying a more complex habitat

Sometimes, one wants to use a more complex habitat which cannot be described as everything within a single outline. In those cases, we can use the function grid.make() to create a grid that has both boundary points and holes. This grid can then be passed to eems() using the optional parameters demes and edges. The following code loads an example outline and places two holes inside it before running an EEMS analysis with the input.

data_path <- system.file("extdata", package = "reems")
input <- file.path(data_path, "barrier-schemeX-nIndiv300-nSites3000")

outer <- read.table(paste0(input, ".outer"))

hole1 <- data.frame(V1 = c(2., 5., 5., 2., 2.), V2 = c(2., 2., 5., 5., 2.))
hole2 <- data.frame(V1 = c(6.5, 10., 8., 6.5), V2 = c(2.5, 5., 5., 2.5))

# Create the new grid  with holes
grid <- grid.make(outer = outer, holes = list(hole1, hole2), nDemes = 300)

eems(
  freqs = freqs,
  coords = coords,
  mcmcpath = "full/path/to/directory/prefix",
  demes = grid$demes,
  edges = grid$edges,
  nChains = 5,
  parallel = TRUE
)   

Hyperparameters

Various additional parameters can be set with additional arguments to eems(), documentation of which can be found in the function documentation for eems(). This includes the various hyperparameters available for tuning.

?eems

Regarding those, in the documentation of the EEMS command-line utility, available at https://github.com/dipetkov/eems/blob/master/Documentation/EEMS-doc.pdf, Petkova writes the following:

---

Choosing values for the proposal variances is something of a dark art– the goal is to choose the parameters so that proposals are accepted about 20% 30% of the time. In practice, it seems to be sufficient to choose the variances so that proposals are not accepted too rarely (less than 10% of the time) or too often (more than 40% of the time).

Suppose that we run EEMS (say, with the default values for all proposal distributions) and at the end EEMS reports the following information about the frequency of accepting proposals of different types.

1 (2050/2593) = 79.06% for type "qTileRate"
## change the rate of one tile in the diversity tessellation
2 (1414/2478) = 57.06% for type "qTileMove"
## move the seed of one tile in the diversity tessellation
3 (1047/2494) = 41.98% for type "qBirthDeath"
## add/remove one tile in the diversity tessellation
4 (20625/47530) = 43.39% for type "mTileRate"
## change the rate of one tile in the migration tessellation
5 (15486/50098) = 30.91% for type "mMeanRate"
## change the overall log10 migration rate
6 (22352/47532) = 47.03% for type "mTileMove"
## move the seed of one tile in the migration tessellation
7 (26305/47275) = 55.64% for type "mBirthDeath"
## add/remove one tile in the migration tessellation

It seems that qTileRate and qTileMove are accepted too often. So it might be a good idea to increase qEffctProposalS2 and qSeedsProposalS2. [If the proposal variance is higher, then bigger changes/moves will be proposed and hence the updates will be accepted less often.]

Finally, the parameter qVoronoiPr specifies how often to propose updating the diversity Voronoi tessellation. Its default value is 0.05, which means that about 5% of the time the proposals are about the diversity tessellation and about 95% of the time the proposals are about the migration tessellation.

---

Plotting the output of an EEMS analysis

The output of the EEMS analysis performed with eems() can be plotted using eems.plots(), which produces several figures:

• mrates01 is the effective migration surface. This contour plot visualizes the estimated effective migration rates $m$, on the log10 scale after mean centering.

• mrates02 shows posterior probabilities $P(m > 0 | \texttt{diffs})$ and $P(m < 0 | \texttt{diffs})$ for each location in the habitat. Since migration rates are visualized on the log10 scale after mean centering, 0 corresponds to the overall mean migration rate. This contour plot emphasizes regions with effective migration that is significantly higher/lower than the overall average.

• qrates01 plots the effective diversity surface. This contour plot visualizes the estimated effective diversity rates $q$, on the log10 scale after mean centering.

• qrates02 shows the posterior probabilities $P(q > 0 | \texttt{diffs})$ and $P(q < 0 | \texttt{diffs})$. Similar to mrates02 but applied to the effective diversity rates.
• rdist01 is a scatter plot of the observed vs the fitted between-deme component of genetic dissimilarity, where one point represents a pair of sampled demes.
• rdist02 is a scatter plot of the observed vs the fitted between-deme component of genetic dissimilarity, where one point represents a sampled deme.
• rdist03 is a scatter plot of observed genetic dissimilarities between demes vs observed geographic distances between demes.
• pilogl01 is the posterior probability trace plot.

The mrates and qrates figures visualize (properties of) the effective migration and diversity rates across the habitat. The other figures can help to check that the MCMC sampler has converged (the trace plot pilogl) and that the EEMS model fits the data well (the scatter plots of genetic dissimilarities rdist).

The function eems.plots() will work given the results from a single EEMS run (one directory in mcmcpath) but it is better to run EEMS several times, randomly initializing the MCMC chain each time.

Hence, eems.plots() takes as input a vector of EEMS output directories. If the chains have been obtained from a single call of eems() with nChains greater than 1, or if only a single run is to be plotted, one can use the return value of eems(), which is precisely this vector of output directories. The other required arguments for eems.plots() are plotpath, which is the full path to a file prefix that will then be completed by the names of the plots given above, and longlat, a logical value that is set TRUE if the X coordinate is the longitude and the Y coordinate the latitude, and FALSE otherwise.

eems_results <- eems(
  freqs = freqs,
  coords = coords,
  mcmcpath = "/full/path/to/directory/prefix",
  nChains = 5,
  parallel = TRUE
)   

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/prefix",
  longlat = TRUE
)

Various optional parameters add additional plotting functionality, which we will illustrate with a series of examples.

We can add the original sampling locations on top of the contour plot.

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/with-sampling",
  longlat = TRUE,
  m.plot.xy = {
    points(coord, col = "purple")
  },
  q.plot.xy = {
    points(coord, col = "purple")
  }
)

We can flip the x and y axis, i.e., assume that the x coordinate is the latitude and the y coordinate is the longitude.

eems.plots(
  mcmcpath = eems_results,
  plotpath =  "/full/path/to/file/axes-flipped",
  longlat = FALSE
)

We can also control the output size and format. The code below generates all figures as PDF, with the mrates and qrates plots of height 12 inches and width 10 inches.

eems.plots(
  mcmcpath = eems_results,
  plotpath =  "/full/path/to/file/output-PDFs",
  longlat = TRUE,
  plot.height = 12,
  plot.width = 10
)

The following code will generate all figures as PNG, with the mrates and qrates plots of height 9 inches, width 8 inches and resolution 600 dots per inch.

eems.plots(
  mcmcpath = eems_results,
  plotpath =  "/full/path/to/file/output-PNGs",
  longlat = TRUE,
  plot.height = 9,
  plot.width = 8,
  res = 600,
  out.png = TRUE
)

We can set various colors and shapes for the outline, the grid and the demes.

eems.plots(
  mcmcpath = eems_results,
  plotpath =  "/full/path/to/file/demes-and-edges",
  longlat = TRUE,
  add.grid = TRUE,
  col.grid = "gray90",
  lwd.grid = 2,
  add.outline = TRUE,
  col.outline = "blue",
  lwd.outline = 5,
  add.demes = TRUE,
  col.demes = "red",
  pch.demes = 5,
  min.cex.demes = 0.5,
  max.cex.demes = 1.5
)

If we have the RColorBrewer package, we can also use a divergent Red to Blue color scheme instead of the default DarkOrange to Blue color scheme.

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/new-eems-colors",
  longlat = TRUE,
  eems.colors = RColorBrewer::Rbrewer.pal(11, "RdBu")
)

In a similar vein, we can specify the color scales for the migration and diversity rates.

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/fix-colscales",
  longlat = TRUE,
  add.outline = TRUE,
  col.outline = "gray",
  m.colscale = c(-3, 3),
  q.colscale = c(-0.3, +0.3)
)

Often, especially when dealing with larger spatial scales, it is helpful to produce contour plots that follow a certain cartographic projection, in this case the Mercator projection.

projection_none <- "+proj=longlat +datum=WGS84"
projection_mercator <- "+proj=merc +datum=WGS84"

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/cartographic-projections",
  longlat = TRUE,
  projection.in = projection_none,
  projection.out = projection_mercator
)

Using the additional packages rworldmap and rworldxtra, we can even add a high-resolution geographic map as a backdrop to our plots. Setting the corresponding option add.map = TRUE requires both those packages, and also requires that projection.in is specificed.

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/geographic-map",
  longlat = TRUE,
  projection.in = projection_none,
  projection.out = projection_mercator,
  add.map = TRUE,
  col.map = "black",
  lwd.map = 5
)

We can explicitly add a specific map by passing the shape file.

map_world <- rworldmap::getMap()
map_africa <- map_world[which(map_world@data$continent == "Africa"), ]
eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/shapefile",
  longlat = TRUE,
  m.plot.xy = {
    plot(map_africa, col = NA, add = TRUE)
  },
  q.plot.xy = {
    plot(map_africa, col = NA, add = TRUE)
  }
)

Combining both, we can apply the Mercator projection and add the map of Africa. We must be careful to apply the same projection to the map as well.

map_africa <- sp::spTransform(map_africa,  sp::CRS(projection_mercator))
eems.plots(
  mcmcpath = eems_results,
  plotpath =  "/full/path/to/file/shapefile-projected",
  longlat = TRUE,
  projection.in = projection_none,
  projection.out = projection_mercator,
  m.plot.xy = {
    plot(map_africa, col = NA, add = TRUE)
  },
  q.plot.xy = {
    plot(map_africa, col = NA, add = TRUE)
  }
)

Similarly, we can add points, lines, labels, etc. Here is how to add a set of colored "labels" on top of the migration/diversity rates and the Africa map. Note that we also need to project any points we want to plot.

coords <- matrix(c(
  -10, 10,
  10, 10,
  30, 0,
  40, -10,
  30, -20
), ncol = 2, byrow = TRUE)
colors <- c("red", "green", "blue", "purple", "orange")
labels <- LETTERS[1:5]
coords_merc <- sp::spTransform(
  sp::SpatialPoints(coords, sp::CRS(projection_none)),
  sp::CRS(projection_mercator)
)

coords_merc <- coords_merc@coords

eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/labels-projected",
  longlat = TRUE,
  projection.in = projection_none,
  projection.out = projection_mercator,
  m.plot.xy = {
    text(coords_merc, col = colors, pch = labels, font = 2)
  },
  q.plot.xy = {
    text(coords_merc, col = colors, pch = labels, font = 2)
  }
)

Lastly, we can also use eems.plots to compute the migration and diversity rates at specific points. They will then be saved in an RData file together with the figures.

xy.coords <- matrix(c(
  13.70, 3.20,
  37.10, -7.20,
  36.10, -4.10,
  34.58, -5.67
), ncol = 2, byrow = TRUE)
eems.plots(
  mcmcpath = eems_results,
  plotpath = "/full/path/to/file/default",
  longlat = TRUE,
  xy.coords = xy.coords
)
load(paste0("/full/path/to/file/default", "-rdist.RData"))

The RData file contains xym.values and xyq.values. xym.values store the migration rate for each coordinate, on the log10 scale and after mean-centering. Similarly, xyq.values stores the diversity rate for each coordinate, on the log10 scale and after mean-centering.

An overview of all the parameters to eems.plots() can be found in its function documentation.

?eems.plots

Other functions

Besides the two main user-facing functions eems() and eems.plots(), which should suffice for the more common use cases, this package also provides some additional functions, which we will just describe very briefly here. More information about them can be found in their respective function documentation.

• eems.from.files() runs an EEMS analysis from input data available as tabular files on disk, and corresponds precisely to the original EEMS utility available at https://github.com/dipetkov/eems.

• eems.population.grid() plots the population grid, with all edges in the same color, to visualize the grid before estimating migration and diversity rates. This is sometimes useful for troubleshooting issues with grid connectivity.

• eems.voronoi.samples() and eems.posterior.draws() both take random samples from the posterior distribution of the migration and diversity rates and plot them to visualize the posterior variance (in a slightly different way).

• grid.make() constructs a regular triangular grid with circa nDemes vertices within a habitat outline. It can also be used to make a more complex habitat with holes.

• outer.buffered() computes the boundary polygon of a habitat encompassing a set of points, while keeping a specific buffer distance from the supplied points. This is used internally by eems() to create a default habitat, but can also be used directly to make the outer boundary of a more complex habitat.

reems documentation built on May 6, 2026, 1:07 a.m.