SimulationLevelThe 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.SimulationLevelAfterFilteringOutMAFIt 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.Fdist2Contains the False Positive Rate (FPR) for each set of Fdist2 runs for each treatment for each sampled generation.ComparisonZCThis is the data used in Appendix A to compare the working of SimBit with previous work by Zeng and Corcoran (2015).SoftwareComparisonsThis file contains the results of simulations used in appendix A, comparing the working of the softwares SimBit, Nemo and SLiM.SNPLevel.txtThe 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). Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using recombination maps from humans and sticklebacks, to investigate how variation in the intensity of background selection affects FST and other statistics of population differentiation in sexual, outcrossing species. We show that, in populations connected by gene flow, Weir & Cockerham's (1984) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST, however, dXY is negatively correlated with background selection. Moreover, background selection does not greatly affect the false positive rate in FST outlier studies in populations connected by gene flow. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.

AbstractAlthough there is no known general explanation as to why sexual populations resist asexual invasion, previous work has shown that sexuals can outcompete asexuals in structured populations. However, it is currently unknown whether costly sex can be maintained with the weak structure that is commonly observed in nature. We investigate the conditions under which obligate sexuals resist asexual invasion in structured populations subject to recurrent mutation. We determine the level of population structure needed to disfavour asexuals, as calculated using the average Fst between all pairs of demes. We show that the critical Fst needed to maintain sex decreases as the population size increases, and approaches modest levels as observed in many natural populations. Sex is maintained with lower Fst if there are both advantageous and deleterious mutation, if mutation rates are sufficiently high, and if deleterious mutants have intermediate selective strengths, which maximises the effect of Muller's Ratchet. Additionally, the critical Fst needed to maintain sex is lower when there are a large number of subpopulations. Lower Fst values are needed to maintain sex when demes vary substantially in their pairwise distances (e.g., when arrayed along one dimension), although this effect is often modest, especially if some long-distance dispersal is present., Usage notesSimulation files as used in paperREADME.TXT FOR SIMULATION FILES: These files are the source code for the one-dimensional and two-dimensional population simulations used in the Hartfield et al 2012 paper "The maintenance of obligate sex in finite, structured populations subject to recurrent beneficial and deleterious mutation". Simulations are written in C and need to be compiled prior to execution. See blurb at start of each program for description and how to execute. Please also note that simulations use routines that are part of the GNU Scientific Library (GSL). Since GSL is distributed under the GNU General Public License(http://www.gnu.org/copyleft/gpl.html), you must download it separately from these files. Note that we have also uploaded a Mathematica file, outlining the study into obtaining confidence intervals, as described in the manuscript. Comments should be sent to Matthew Hartfield (matthew.hartfield@ird.fr)Hartfield_etal_2012_sims.zip

Supporting Information. File S1 contains detailed information on the following issue: “Testing the hypothesis of cryptic stock clustering in North East Atlantic minke whales”: including Material and Methods, and Results. This appendix also comprises eight figures (Fig. A–G) and thirteen tables (Table A–M). Figures. Fig. A1. Bayesian clustering of North East Atlantic minke whales genotyped at 8 microsatellites for the six sampled year classes. Inferred ancestry of individuals was calculated after averaging ten STRUCTURE runs with CLUMPP after Evanno's test. Fig. A2. Bayesian clustering of North East Atlantic minke whales genotyped at 10 microsatellites for the six sampled year classes. Inferred ancestry of individuals was calculated after averaging ten STRUCTURE runs with CLUMPP after Evanno's test. Fig. B. Bayesian clustering of North East Atlantic minke whale year class 2004 with outgroups: a) 95 individuals of the subspecies Pacific minke whale (B. a. scammoni); b) 93 individuals of the Antarctic minke whale (B. bonaerensis), and c) both former outgroups together. The number of clusters that best fitted the data was K = 2 after Evanno's [58] test in each case. This scenario was consistent across year classes. Fig. C. Bayesian clustering of North East Atlantic minke whale with outgroups in each year class. In the column to the left, the outgroup are 95 individuals of the subspecies Pacific minke whale (B. a. scammoni) whereas in the column to the right, the outgroup are 93 individuals of the Antarctic minke whale (B. bonaerensis). The number of clusters that best fitted the data was distinctively K = 2 after Evanno's [58] test in each case. Fig. D. Geographic distribution of individuals after different clustering methods: BAPS and STRUCTURE for microsatellites. Pie charts represent the percentage of individuals belonging to clusters 1 (dark grey) and 2 (light grey) per Management Area taking year class 2008 as an example (the full data for all the year classes is available in Table K in File S1). Fig. E. Bayesian clustering of individuals of ten of the simulated panmictic populations that showed K = 2 after Evanno's test. Inferred ancestry of individuals was calculated after averaging ten STRUCTURE runs with CLUMPP. Fig. F. Distribution of pairwise FST after 10000 random clustering of North Atlantic minke whale individuals per year class into two groups. Fig. G. Frequency distributions of the corrected assignment index (AIc) for 2156 females (light grey bars above axis) and 834 males (dark grey bars below axis). AIc values differed among sexes, males having on average negative values (−0.051) and higher variance (2.90) and females positive values (0.020) with lower variance (2.35). However, Mann–Whitney U-test proved sex-biased dispersal to be non-significant (P>0.5). Tables. Table A. Summary result of STRUCTURE without outgroups: a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table B. STRUCTURE without outgroups: Clustering of individuals per year class after Evanno's test (the two cases that showed the highest Evanno's ΔK at K = 3 are depicted in italics and analysed for K = 2 for comparison): Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA (FST and P-value) conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table C. Summary statistics after STRUCTURE clustering showing total number of alleles, number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). We show in italics the distribution of the individuals for K = 2 for the two year classes that showed the highest Evanno's ΔK at K = 3. a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table D. STRUCTURE with the Pacific minke whale subspecies (B. a. scammoni) as an outgroup. Clustering of individuals per year class and one randomly chosen simulated panmictic population after Evanno's test and CLUMPP averaging: Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA (FST and P-value) conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. Table E. STRUCTURE with Antarctic minke whale species (B. bonaerensis) as an outgroup. Clustering of individuals per year class and one randomly chosen simulated panmictic population after Evanno's test and CLUMPP averaging: Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA (FST and P-value) conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. Table F. STRUCTURE consensus clustering of individuals (i.e. agreement between Antarctic and Pacific outgroup clustering) into two groups per year class. Summary of the results of the AMOVA (FST and P-value) conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals at mtDNA. Statistically significant values are highlighted in boldface type. Table G. Summary statistics after STRUCTURE consensus clustering (i.e. consensus between Antarctic and Pacific outgroup clustering) showing total number of alleles, allelic richness (minimum sample size), number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). Table H. BAPS clustering of individuals genotyped with microsatellites into two groups per year class. Summary of the results of the AMOVA (FST and P-value) conducted with ARLEQUIN with 10000 permutations. Analyses were performed for the same sets of individuals at mtDNA. Statistically significant values were highlighted in boldface type. Table I. Summary statistics after BAPS clustering showing total number of alleles, allelic richness (minimium sample size), number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). Table J. GeneClass self-assignment: Percentage of individuals genotyped at microsatellites that were correctly assignment after clustering procedures. Table K. Number of individuals genotyped at microsatellites per Management Areas after clustering with BAPS and STRUCTURE (with and without outgroup). ND = No data. Table L. Matrix of numbers and percentage of coincident individuals when comparing the three clustering methods: BAPS, STRUCTURE without outgroup (STR), and STRUCTURE with outgroup (STR consensus). The percentage of coincident individuals was calculated by dividing the number of by the lowest number of individuals in the corresponding cluster. STRUCTURE analyses were performed with 8 microsatellites. Table M. STRUCTURE clustering of individuals in the 10 randomly selected simulated panmictic populations showing K = 2 after Evanno's test. Number of individuals per cluster and range of inferred membership to each of them (in brackets); number of non-assigned individuals (and % of the total). Summary of the results ofthe AMOVA (FST and P-value) conducted with Arlequin with 10000 permutations. Statistically significant values were highlighted in boldface type. (DOCX)

Predator-prey interactions have been suggested as drivers of diversity in different lineages, and the presence of anti-predator defences in some clades is linked to higher rates of diversification. Warning signals are some of the most widespread defenses in the animal world, and there is evidence of higher diversification rates in aposematic lineages. The mechanisms behind such species richness, however, are still unclear. Here, we test whether lineages that use aposematism as anti-predator defense exhibit higher levels of genetic differentiation between populations, leading to increased opportunities for divergence. We collated from the literature > 3,000 pairwise genetic differentiation values across more than 700 populations from over 60 amphibian species. We find evidence that, given the same geographic distance, populations of species of aposematic lineages exhibit greater genetic divergence relative to species that are not aposematic. Our results support a scenario where the us..., Data was collected from publications as described in the manuscript. It was processed using R but raw values can be found in the original publications used. , , # Anti-predator defenses are linked with high levels of genetic differentiation in frogs

Dataset from literature collection of genetic divergence values (Fst) and geographic distances. Geographic distances were calculated from location coordinates using the topodist package, meaning they are topographic distances.

Description of the data and file structure

Each row in the dataset corresponds to a comparison between two populations of the same species. Each row has information of the species used for comparisons, the geographic distance between populations, the marker used to estimate genetic differentiations and the genetic differentiation calculated as an Fst value. We also include information on the average sample size for each study.

Sharing/Access information

Data was derived from the sources listed in Ref-species file.

Additional file 1: Table S1: Sample information for each chicken (72 individuals) used in this study. Table S2. Chromosome-length and number of detected variants before and after filtering. Table S3. Mean number of ROHs longer than different classes (Kb).Table S4. Summary diversity results. Proportion of polymorphic SNPs (PN ), Observed heterozygosity (Ho), Expected heterozygosity (He), and average inbreeding coefficient (F) for each studied group. Table S5: Positively selected genes (top %5) identified between indigenous group and White-Leghorn identified by FST method. Table S6. Positively selected genes identified by top 1% highest log2 (θπ·Native-ecotypes/θπ·White-Leghorn). Table S7. Positively selected genes (top %5) identified by FST method between indigenous group and Arian. Table S8: Positively selected genes identified by top 1% highest log2 (θπ·Native-ecotypes/θπ·Arian). Table S9. Overrepresented GO categories among genes showing high Fst values between indigenous group and White-Leghorn. Table S10. Overrepresented GO categories among genes showing high log2 (θπ·Native-ecotypes/θπ·White-Leghorn). Table S11. Overrepresented GO categories among genes showing high Fst values (indigenous group versus Arian). Table S12. Overrepresented GO categories among genes showing high log2 (θπ·Native-ecotypes/θπ·Arian).