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File S1: Additional information of methods and parameters used for statistical analyses.
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SUPPLEMENTARY INFORMATION
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Detection and characterization of outlier SNPs
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The first analysis approach followed a method implemented in the software Arlequin 3.5
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(Excoffier & Lischer 2010), based on coalescent simulations to obtain a null distribution of FST
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or FCT across loci, depending on the assumed demographic model (i.e. finite island or
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hierarchical island models, respectively; Excoffier et al. 2009), as a function of
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heterozygosity. Loci showing higher or lower differentiation with respect to the simulated
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confidence intervals are identified as candidates for divergent or balancing selection
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(Beaumont & Nichols 1996). Excoffier and colleagues (2009) showed that the hierarchical
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island model reduces the excess of false positives that arises when samples belonging to a
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structured population are analyzed under a finite island model. Taking into account the
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genetic differentiation between Atlantic and Mediterranean populations demonstrated in
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previous studies, large-scale outlier detection analysis was carried out under the assumption
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of a hierarchical island model, grouping samples according to the basin of origin. The finite
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island model was used to analyze the subset of samples within each basin, with 50,000
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simulations were run and 100 demes per group were assumed, simulating 10 groups under
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the hierarchical model assumption.
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The second approach, based on the Bayesian method and implemented in the software
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Bayescan 2.0 (Foll & Gaggiotti 2008), tests for departure from neutrality by evaluating the
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weight of the locus-specific contribution with respect to the population-specific effect. The
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posterior probability for a locus being under selection is calculated according to two
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alternative models, neutrality and under natural selection. This approach appeared to be
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robust under complex demographic scenarios (Foll & Gaggiotti 2008), and was found to have
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lower type I (false positive) and type II (false negatives) error rates for divergent selection
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compared to other outlier detection methods (Narum & Hess 2011). All analyses were based
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on 20 pilot runs, each consisting of 5,000 iterations, followed by 100,000 iterations with a
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burn-in of 50,000 iterations. The prior odds for the neutral model was set to 10, as
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suggested for the identification of candidate loci with a few hundred markers. Posterior
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Odds (PO), indicating the increased likelihood of the model including selection compared to
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the neutral model, were interpreted according to the Jeffreys' scale of evidence for Bayes
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Factors (BF) (Jeffreys 1961). Following this method, a log10PO between 1 and 1.5 denotes
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strong evidence for selection, between 1.5 and 2 it can be considered as very strong
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evidence, while values higher than 2 indicate a decisive signal.
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Correlation analysis between genetic and environmental variation
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The method first estimates a null model based on neutral markers describing how allele
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frequencies co-vary across populations, and subsequently it tests whether the correlation
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observed between allele frequencies at specific markers of interest and an environmental
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variable is higher than expected under a null model. In this way, the underlying population
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structure is taken into account, and the probability of obtaining false positive results due to
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shared population history or gene flow is limited. The software provides the Bayes Factor
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(BF) as a measure in support to the model including a significant correlation. Putatively
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neutral, unlinked SNPs were used to calculate the covariance matrix, and then for each
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candidate SNP the BF was calculated in relation to selected environmental parameters,
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following the multiple spatial scale approach adopted in outlier analysis. Independent runs
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were carried out to ensure that results were not sensitive to stochastic errors. Results were
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evaluated comparing the distribution of BF values across putatively neutral and outlier loci
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and according to the Jeffreys' scale of evidence for BF (Jeffreys 1961).
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Annual mean values of seawater surface temperature (SST, °C) and salinity (S, psu) were
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retrieved
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(www.nodc.noaa.gov/OC5/SELECT/dbsearch.html), referring to the geographic coordinates
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as close as possible to actual sampling locations for which data were available (Table S1).
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NODC statistics on SST and S parameters are based on long-term observations (50 years,
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from 1955 to 2006), and records vary across a quarter-degree latitude-longitude grid
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(Antonov et al. 2010; Locarnini et al. 2010). To assess the robustness of correlation statistics
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the annual mean values of temperature and salinity at – 100 m depth were also tested (data
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not shown).
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Population genetic structure: neutral vs outlier divergence
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Structure uses a Bayesian algorithm to infer the number of distinct K clusters of individuals,
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based on their multilocus genotypes, assuming HWE and Linkage Equilibrium. A posterior
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probability for each inferred K is calculated, allowing the estimate of the most likely number
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of clusters. The algorithm was run assuming an admixture model and correlated allele
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frequencies among populations, as well as providing sampling information as a prior, in
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order to improve accuracy in detecting population structure (Hubisz et al. 2009). For each
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analysis 6 iterations were used per K value, a burn-in period length of 100,000, and 500,000
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MCMC repetitions. To identify the most likely number of clusters accounting for the
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observed genetic structure the optimal K was selected according to two criteria: the
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evaluation of the log probability of the data and the Evanno method (Evanno et al. 2005). A
from
the
National
Oceanographic
3
Data
Center
(NODC)
database
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final long run was performed (10 iterations, 500,000 burn-in, 750,000 MCMC repetitions)
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based on the most probable values of K. Results from multiple runs at the optimal K were
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combined using the CLUMPP software v.1.1.2 (Jakobsson & Rosenberg 2007), and the
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graphical output was obtained by Distruct (Rosenberg 2004).
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To corroborate the genetic structure inferred from Bayesian clustering, the DAPC approach,
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a multivariate method that does not rely on specific population genetic models, was also
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used. According to this method, genetic data are first transformed using Principal
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Component Analysis (PCA) into components explaining most of the genetic variation. These
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components are then used to perform a linear Discriminant Analysis (DA), which provides
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variables describing genetic groups, minimizing the genetic variance within populations,
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while maximizing among-population variation.
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References
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Antonov JI, Seidov D, Boyer TP, et al. (2010) World Ocean Atlas 2009, Volume 2: Salinity. U.S.
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Government Printing Office, Washington, D.C.
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Beaumont MA, Nichols RA (1996) Evaluating Loci for Use in the Genetic Analysis of
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Population Structure. Proceedings of the Royal Society of London. Series B: Biological
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Sciences, 263, 1619-1626.
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Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured
population. Heredity, 103, 285-298.
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Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform
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population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10,
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564-567.
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Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using
the software structure: a simulation study. Molecular Ecology, 14, 2611-2620.
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Foll M, Gaggiotti O (2008) A Genome-Scan Method to Identify Selected Loci Appropriate for
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Both Dominant and Codominant Markers: A Bayesian Perspective. Genetics, 180, 977-
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993.
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Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure
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with the assistance of sample group information. Molecular Ecology Resources, 9, 1322-
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1332.
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Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program
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for dealing with label switching and multimodality in analysis of population structure.
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Bioinformatics, 23, 1801-1806.
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Jeffreys H (1961) Theory of probability Clarendon Press, Oxford.
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Locarnini RA, Mishonov AV, Antonov JI, et al. (2010) World Ocean Atlas 2009, Volume 1:
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Temperature. U.S. Government Printing Office, Washington.
Narum SR, Hess JE (2011) Comparison of FST outlier tests for SNP loci under selection.
Molecular Ecology Resources, 11, 184-194.
Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure.
Molecular Ecology Notes, 4, 137-138.
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