SimulationLevelAfterFilteringOutMAF

It is the exact same file as SimulationLevel.txt but with the data computed only on the SNPs whose Minor Allele Frequency (MAF) is greater than 0.05

SNPLevel.txt

The file contains the details for all SNPs (one SNP per line) for all generations of the default treatment. The columns are PatchSize (number of individuals per patch), migrationRate (explicit), isThereSelection (presence / absence of BGS), patch0AlleleFrequency (allele frequency in patch 0), patch1AlleleFrequency (allele frequency in patch 1), SimulationID (identifier for the simulation), JostD (explicit), Fst (Weir and Cockerham estimator of Fst), Gst (Nei's estimator of Fst), nbPatches (number of patches), meanAlleleFrequency (mean allele frequency among both patches), meanAlleleFrequencyAfterFilteringOutAlleleFrequencyLowerThan5Percent (explicit), varianceInAlleleFrequencyAmongPatches (variance in allele frequency among both patches), Treatment (explicit), B_theoreticalIndexOfBGS (Index of BGS selection called B, see Hudson and Kaplan 2015), GenerationIn2Nunit (generation sampled)

SimulationLevel

The data are a summary of all SNP for each simulation (all treatments and generations). It contains the number of migration rate, population size, presence/absence of selection, SNPs, JostD, Fst, Fst_averageOfRatios (meaning explained in the paper), Tajima's D, Dxy (Dxy average over all sites), Dxy_SNP (Dxy averaged over all plymorphic sites), Hs (within population genetic diversity) and Ht (total genetic diversity) both averaged over all sites, not only the polymorphic ones (as made clear from the long column name; to obtain the average of all SNP just multiply the the number of SNPs and divide by the number of sites in the focal region), and Hudson and Kaplan (1998) B statistic

Fdist2

Contains the False Positive Rate (FPR) for each set of Fdist2 runs for each treatment for each sampled generation

ComparisonZC

This is the data used in Appendix A to compare the working of SimBit with previous work by Zeng and Corcoran (2015)

SoftwareComparisons

This file contains the results of simulations used in appendix A, comparing the working of the softwares SimBit, Nemo and SLiM

fastsimcoal2 : demographic inference under complex evolutionary scenarios

Motivation: fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework. Results: Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes. Availability and implementation: fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples

Data from: Evolution of invasiveness by genetic accommodation

Invasion success of species introduced to novel environments may be facilitated by adaptive evolution and by phenotypic plasticity. Here we investigate the independent and joint contribution of both mechanisms as drivers of invasiveness in the perennial sunflower Helianthus tuberosus. We show that invasive genotypes have multiple origins, and that invasive spread was facilitated by the repeated evolution of extreme values in a single trait, clonality. In line with genetic accommodation theory, we establish that this evolutionary transition occurred by refining a preexisting plastic response of clonality to water availability. Further, we demonstrate that under the non-drought conditions typically experienced by this plant in its introduced range, invasive spread is mediated by hybrid vigor and/or two major additive-effect loci, and that these mechanisms are complementary. Thus, in H. tuberosus, evolution of invasiveness was facilitated by phenotypic plasticity, and involved the use of multiple genetic solutions to achieve the same invasiveness trait