1
Supplementary Material
2
Materials and methods
3
Descriptive statistics
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The ‘Check raw data option’ in Genalex 6.5 (Peakall and Smouse, 2006; Peakall and Smouse, 2012), allowed
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calculating the PCR amplification success (in percentage) for each locus and sampling location (Table S1).
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Mean number of alleles (Na), observed (Ho) and unbiased expected heterozygosity (uHe) were calculated with
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GenAlEx 6.5 (Table S2). FSTAT 2.9.3.2 (Goudet, 2002), was used to calculate Allelic richness (Ar), using the
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rarefaction method (El Mousadik and Petit, 1996)(Table S2). Deviations from Hardy–Weinberg equilibrium (HWE)
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were tested (Table S2; null hypothesis H1 = heterozygote deficiency), as implemented in GENEPOP 4.2 (Rousset,
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2008). The inbreeding coefficient FIS (Weir and Cockerham, 1984)(Table S2) was computed with GENODIVE v2.0b23
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(Meirmans and Van Tienderen, 2004).
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To correct for multiple comparisons, a false discovery rate FDR correction (Benjamini and Hochberg, 1995) was
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performed using SGoF+ (Carvajal-Rodriguez and de Uña-Alvarez, 2011), whenever necessary.
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Clustering analyses
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Three Bayesian Markov Chain Monte Carlo programs with different algorithms and assumptions were used to infer
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population structure of the studied populations: STRUCTURE v2.3.4 (Falush et al., 2003; Falush et al., 2007; Hubisz et
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al., 2009; Pritchard et al., 2000), InStruct (Gao et al., 2007) , and BAPS v6 (Corander and Marttinen, 2006; Corander et
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al., 2006; Corander et al., 2008). In all approaches, individuals are assigned probabilistically to one subpopulation or
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jointly to two or more subpopulations if their genotypes indicate that they are admixed.
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The software STRUCTURE v.2.3.4 was used because of its ability in providing a simultaneous description of clines and
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clusters by making use of multilocus genotypes (François and Durand, 2010). In STRUCTURE, underlying population
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structure was investigated using directly the admixture model, with correlated alleles frequencies between clusters and
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LOCPRIOR option (Hubisz et al., 2009). Ten different runs from K=1 to K= 7 of 100000 burn-in followed by 500000
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iterations were computed for each K value. To determine the most appropriate value of K, two statistics were
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considered: the maximum value of LnPr(X|K), and the ΔK statistic developed by (Evanno et al., 2005)(Figure S1). The
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R package CorrSieve1.6-8 (Campana et al., 2011) was used to calculate the ΔK statistic.
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Bottleneck, population growth, and recent migration rates
1
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BOTTLENECK 1.2.02 (Piry et al., 1999) was used to test for recent demographic changes using two-phase model
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(TPM) with 70% of the stepwise mutation model (SMM) (Piry et al., 1999) and the Wilcoxon’s signed rank-test (Table
30
S7). In order to detect signatures of population growth, the intralocus variance k test (Reich and Goldstein, 1998) and
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the interlocus g test (Reich et al., 1999) were performed using the Kgtests Excel Macro (Bilgin, 2007). It computes the
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significance of the k statistic using the one-tailed binomial distribution. Significance levels for the interlocus g test are
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available in Table 1 in (Reich et al., 1999). The statistics g is interpreted as an indication of expansion when it has an
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unusually low value; for seven loci (our study), g values lower than 0.14 (20<n >40) or 0.015 (n<20) will indicate a
35
population expansion (Table S7).
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The BayesAss v3 program (BA3) was used to estimate recent migration rates (m) between populations (groups of
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populations) using a non-equilibrium Bayesian method through Markov chain Monte Carlo techniques (Wilson and
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Rannala, 2003)(Table S8).
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To generate correct results in BA3 the strategy proposed by Rannala (2011) was followed. The mixing parameters were
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adjusted (dM=0.5; dA= 0.60; dF= 0.6) to have an acceptance rate between 20% and 40%, with 1*106 iterations, 1*105
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iterations as burn-in, and 10 independent runs (starting with different random number seeds). In order to assess for
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significance, migration rates were averaged over the 10 independent runs, and compared to average migration rates of
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10 randomly permuted data sets (generated in GENODIVE v2.0b23). Estimated migration rates were considered
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significant when the 95% confidence interval (CI) did not overlap with the 95% CI of the randomly permuted data
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(Andras et al., 2013). The BayesAss analysis was realized pooling together CF3 and CF4, considering them indistinct
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(see results: Bayesian clustering, and F ST and DEST estimates of differentiation).
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Supplementary figures
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Figure S1 CorrSieve output of the STRUCTURE results for the Corallium rubrum data set. The graph shows the mean
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estimated Ln probabilities of data (black), and ΔK values (orange) for each K.
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3
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Supplementary tables
Table S1 PCR amplification success (in percentage) for each locus and sampling location calculated with the ‘Check raw data option’ in Genalex 6.5. In bold the values <75%.
(A) PCR Amplification success (%) on the whole dataset: 12 loci and 122 colonies
Sample/locus
COR9
COR46
COR48
COR58
Mic13
Mic20
93.75
87.50
100.00
87.50
100.00
93.75
AL1
95.00
90.00
100.00
100.00
CF2
65.00
60.00
96.30
100.00
96.30
100.00
100.00
CF3
62.96
96.43
96.43
100.00
100.00
100.00
CF4
53.57
87.10
87.10
77.42
90.32
100.00
100.00
PCo5
92.62
91.80
88.52
100.00
99.18
Overall
71.31
(B) PCR Amplification rate after the quality check: 7 loci and 115 colonies
Sample/locus
COR46
COR48
Mic13
Mic20
Mic24
Mic26
93.33
100.00
100.00
100.00
100.00
86.67
AL1
95.00
90.00
100.00
100.00
100.00
100.00
CF2
96.30
100.00
100.00
100.00
100.00
96.30
CF3
100.00
100.00
100.00
100.00
92.00
84.00
CF4
92.86
82.14
100.00
100.00
96.43
85.71
Pco5
95.65
93.91
100.00
100.00
97.39
90.43
overall
Mic22
87.50
65.00
22.22
46.43
0.00
37.70
Mic23
81.25
85.00
92.59
100.00
70.97
86.07
Mic24
100.00
100.00
100.00
89.29
93.55
95.90
Mic25
100.00
95.00
96.30
92.86
64.52
87.80
Mic26
87.50
100.00
96.30
78.57
87.10
89.34
Mic27
100.00
100.00
96.30
96.43
100.00
98.36
Mic27
100.00
100.00
96.30
100.00
100.00
99.13
4
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Table S2 Descriptive statistics for the samples (total number of genotypes) and loci analyzed: locus name (total
60
number of alleles): Na = mean number of alleles; Ar = allelic richness calculated with the rarefaction method; Ho =
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observed heterozygosity; uHe = unbiased expected heterozygosity; FIS = inbreeding fixation index; Nu = frequencies of
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null alleles; PHWE = probability for the global Hardy Weinberg test when H1= heterozygote deficit. Bold underlined
63
values indicate significant values after FDR correction.
Sample/Locus
AL1(15)
CF2(20)
CF3(27)
CF4(25)
PCo5(28)
Overall (115)
Na
Ar
Ho
uHe
Fis
Nu
PHWE
Na
Ar
Ho
uHe
Fis
Nu
PHWE
Na
Ar
Ho
uHe
Fis
Nu
PHWE
Na
Ar
Ho
uHe
Fis
Nu
PHWE
Na
Ar
Ho
uHe
Fis
Nu
PHWE
Na
Ar
Ho
uHe
Fis
Nu
PHWE
COR46(16)
8.00
7.92
0.43
0.87
0.52
0.22
0.00
7.00
6.54
0.21
0.77
0.72
0.31
0.00
5.00
4.97
0.35
0.78
0.56
0.25
0.00
8.00
6.59
0.40
0.80
0.50
0.22
0.00
10.00
8.26
0.65
0.83
0.20
0.08
0.04
7.60
9.40
0.41
0.81
0.45
0.22
0.00
COR48(20)
11.00
10.82
0.27
0.93
0.72
0.33
0.00
7.00
6.30
0.33
0.64
0.49
0.19
0.00
7.00
5.05
0.11
0.64
0.83
0.32
0.00
9.00
7.50
0.44
0.77
0.44
0.19
0.00
12.00
9.87
0.22
0.86
0.75
0.34
0.00
9.20
10.27
0.27
0.77
0.63
0.27
0.00
Mic13(6)
4.00
3.87
0.07
0.61
0.89
0.33
0.00
3.00
2.99
0.35
0.44
0.22
0.08
0.13
3.00
2.74
0.22
0.50
0.56
0.17
0.02
3.00
2.52
0.12
0.36
0.67
0.19
0.00
2.00
1.46
0.04
0.04
0.00
0.00
1.00
3.00
3.16
0.16
0.39
0.57
0.16
0.00
Mic20(8)
3.00
2.99
0.60
0.50
-0.22
0.00
0.90
4.00
3.88
0.80
0.66
-0.22
0.00
0.94
3.00
2.48
0.41
0.48
0.15
0.04
0.30
2.00
2.00
0.48
0.44
-0.08
0.00
0.81
6.00
4.39
0.50
0.67
0.26
0.09
0.02
3.60
4.06
0.56
0.55
0.02
0.03
0.30
Mic24(13)
6.00
5.45
0.27
0.36
0.27
0.10
0.03
4.00
3.93
0.55
0.56
0.02
0.07
0.02
5.00
3.90
0.41
0.38
-0.06
0.00
0.80
5.00
3.87
0.30
0.28
-0.09
0.00
1.00
3.00
2.60
0.15
0.18
0.16
0.00
0.18
4.60
5.62
0.34
0.35
0.01
0.03
0.04
Mic26(29)
16.00
16.00
0.62
0.96
0.37
0.16
0.00
7.00
6.65
0.45
0.81
0.45
0.22
0.00
14.00
9.83
0.46
0.78
0.42
0.17
0.00
13.00
10.26
0.57
0.85
0.34
0.12
0.01
16.00
12.12
0.67
0.91
0.27
0.11
0.00
13.20
13.59
0.55
0.86
0.29
0.16
0.00
Mic27(22)
17.00
15.97
0.93
0.96
0.03
0.00
0.44
12.00
10.12
0.40
0.86
0.54
0.23
0.00
13.00
11.20
0.62
0.92
0.33
0.16
0.00
15.00
12.27
0.76
0.92
0.17
0.08
0.01
16.00
12.62
0.89
0.93
0.04
0.03
0.02
14.60
12.85
0.72
0.92
0.21
0.10
0.00
5
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Table S3 Results for the Bottleneck software and the k-g tests. TPM excess = probability of Wilcoxon’s signed rank-
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test for heterozygosity excess using two-phase model (TPM); g = value of the interlocus g test, K = probability value
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associated with the intra-locus test. Significance levels for the interlocus g test are available in Table 1 in Reich et al
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(1999)
Samples
AL1
CF2
CF3
CF4
PCo5
TPM excess
(P-value)
0.148
0.531
0.406
0.766
0.973
g
0.503
1.528
0.617
1.084
1.037
K
(P-value)
0.467
0.203
0.467
0.467
0.467
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Table S4 Estimates of recent immigration derived from BayesAss v3. Means ± standard deviations (with associated
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95% confidence intervals) of the posterior distributions of migration rate (m) in the past one- to- three generations into
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each population. Immigrant source populations are given in rows (from), and receiving populations in columns (into).
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Numbers refers to the migration rates (off-diagonal) and self-recruitment non-migration rates (diagonal). In bold are
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indicated the pairwise estimates significantly different from zero.
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from/into
AL1
CF2
CF3+CF4
PCo5
AL1
0.931±0.031
(0.870;0.993)
0.013±0.013
(-0.012;0.038)
0.006±0.009
(-0.006;0.018)
0.010±0.016
(-0.009;0.029)
CF2
0.026±0.022
(-0.017;0.069)
0.929±0.038
(0.855;1.003)
0.010±0.009
(-0.008;0.028)
0.018±0.016
(-0.014;0.049)
CF3+CF4
0.019±0.019
(-0.017;0.056)
0.044±0.035
(-0.025;0.113)
0.975±0.013
(0.949;1.001)
0.029±0.022
(-0.014;0.072)
PCo5
0.023±0.020
(-0.017;0.063)
0.014±0.013
(-0.012;0.040)
0.009±0.008
(-0.007;0.024)
0.943±0.026
(0.892;0.994)
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90
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93
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96
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98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
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