Data from: Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework - Landguth, Erin; Forester, Brenna R.; Eckert, Andrew J.; Shirk, Andrew J.; Menon, Mitra; Whipple, Amy; Day, Casey C.; Cushman, Samuel A. (Dryad, 2019)
We implemented multilocus selection in a spatially-explicit,
individual-based framework that enables multivariate environmental
gradients to drive selection in many loci as a new module for the
landscape genetics programs, CDPOP and CDMetaPOP. Our module simulates
multilocus selection using a linear additive model, providing a flexible
platform to evaluate a wide range of genotype-environment associations.
Importantly, the module allows simulation of selection in any number of
loci under the influence of any number of environmental variables. We
validated the module with individual-based selection simulations under
Wright-Fisher assumptions (Figure 3 and data provided here). We
then evaluated results for simulations under a simple landscape selection
model (Figure 4 and data provided here). Next, we simulated
individual-based multilocus selection across a complex selection landscape
with three loci linked to three different environmental variables (Figure
5 and data provided here). Finally, we demonstrated how the program can be
used to simulate multilocus selection under varying selection strengths
across different levels of gene flow in a landscape genetics framework
(Figure 6 and data provided here). This new module provides a valuable
addition to the study of landscape genetics, allowing for explicit
evaluation of the contributions and interactions between gene flow and
selection-driven processes across complex, multivariate environmental and
landscape conditions.
Simulated datasets are provided for Figures 3-6 of the
manuscript.  Figure 3: The simulations for the single
(A) and double (B) diallelic locus selection models. For the single
diallelic locus selection model, we used F = X 1 ([b 111 A 111 +
b 112 A 112 ]), and set the average effects, b111 = 10 and b112 = -10. For
the double diallelic locus selection model, we used F = X 1 ([b
111 A 111 + b 112 A 112 ]+[ b 121 A 121 + b 122 A 122 ]), and set set b111
= 10, b112 = -10, b121 = 10, and b122 = -10. X1 was a uniform spatial
selection surface with all values of 1. Each simulated dataset contains 50
replicates. For both the single and double diallelic locus
selection model simulations, the Wright-Fisher model was assumed (i.e.,
random mating, sexual reproduction with both female and male with
replacement, offspring randomly disperse until a constant population is
reached that has an equal sex ratio, no mutation, and non-overlapping
generations) with one exception: each mated pair produced 2 offspring to
ensure a constant population size. We simulated individual genetic
exchange across 100 non-overlapping generations among 1000 randomly
spatially located individuals in a 1024 x 1024 gridded landscape for each
selection model. All simulated populations contained an additional 50
diallelic neutral loci. Figure 4: Three simulation datasets of a
single spatially-variable selection landscape and single diallelic locus.
These datasets were produced with the same simulation parameters as in
Figure 3's datasets, but replacing the uniform spatial seelction
surface with a spatially-variable seelction surface. Using a 1024
x 1024 gridded raster, we created a categorical landscape that included an
upper triangle with values of 1, a lower triangle with values of -1, and
diagonal cells with a value of 0. The three simulated datasets varied how
the genotypes are initialized by starting the simulations with (i) only
AA, (ii) only aa, and (iii) random assignment. 50 replicates are
included in each dataset. Figure 5: Simulation dataset of a complex
landscape and three loci. These data considered three environmental
variables that affect fitness as shown in Figure 1 of the manuscrip, with
three loci and two alleles per locus operating in the selection process.
The first environmental variable was the previously described categorical
landscape used for datasets in Figure 4.. The second environmental
variable was a gradient landscape with continuous values ranging from 1 to
-1 from the North-South. The third environmental variable
represented a fragmented landscape with equal proportion of values for 1
(e.g., favored habitat) and -1 (e.g., non-favored habitat) created in the
program QRULE. For simplicity, we set b111 = 10 and b112 = -10
for the first locus and environmental variable, X1, b221 = 10 and b222 =
-10 for the second locus and environmental variable, X2, and b331 = 10 and
b332 = -10 for the third locus and environmental variable, X3, where X1,
X2, and X3 are the diagonal, gradient, and habitat variables shown in
Figure 1, respectively. The remaining betas were set to 0. Unlike the
previous panmictic movement simulations, we restricted movement of the
individuals in these simulations to follow an inverse-square probability
function constrained to a 25% maximum threshold of the entire landscape.
We initialized all genotypes randomly at the start of the simulations.
3 replicates are included. Figure 6: Multilocus selection
simulations under three selection strengths (strong, moderate, and weak)
and three dispersal scenarios (5%, 10%, and 15% of the landscape). These
simulations include 1000 loci with two alleles per locus: 100
loci under selection in response to a single environmental variable and
900 neutral loci. We used a gradient landscape with continuous values
ranging from 1 to -1 from the North-South. We set the first l = 1, 2, …,
20 loci effect sizes to b1l1 = 0.15 and b1l2 = -0.15, the following l =
21, 22, …, 50 loci effect sizes to b1l1 = 0.10 and b1l2 = -0.10, and the
last l = 51, 52, …, 100 loci effect sizes to b1l1 = 0.05 and b1l2 = -0.05
(reflecting “strong” [n=20], “moderate” [n=30], and “weak” [n=50]
selection, respectively). We increased the population size to 5000 for
these simulations within the same previous 1024 x1024 simulation
landscape. We restricted movement of the individuals in these final
simulations to follow an inverse-square probability function constrained
to 5%, 10%, and 15% maximum threshold of the entire landscape. We
initialized all genotypes randomly at the start of the simulations and ran
the simulations for 200 generations, using the first 100 generations as a
‘burn-in’ period where no selection was operating.
CDPOP individual files are included in folders (e.g., batchrun0mcrun0),
and labeled grid0.csv,..., grid{final time}.csv. Within each
grid{time}.csv file, includes many field identifies (X,Y, age, sex, and
genotypes, etc.). For more information on the format of these files,
please refer to the CDPOP usermanual found at
github.com/ComputationalEcologyLab/CDPOP/docs.
Keywords: Natural Selection and Contemporary Evolution; CDPOP; computer simulations; CDMetaPOP; fitness surfaces