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Supporting Information for online publication
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Materials and Methods
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Genetic dataset
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To obtain the genetic dataset we followed the genotyping methods described in Arora et al. (2010)
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and also detailed below.
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Genotyping: The PCR amplifications were carried out as multiplex reactions in an 8 μL volume with
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the following: 1 μL DNA, 4 μL Multiplex Master Mix (QIAGEN), 0.8 μL primer mix, and 2.2 μL water.
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The conditions were: initial denaturation at 95°C for 15 minutes, followed by 40 cycles of 94°C for
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30s, 58°C for 90s, 72°C for 1 min, and a final extension at 60°C for 30 mins.
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Capillary electrophoresis was conducted on the 3730xl DNA Analyzer (Applied Biosystems), and
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products were analysed with GeneMapper v4.0 (Applied Biosystems).
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We used Arlequin 3.11 to calculate deviation from Hardy Weinberg equilibrium (HWE) and GenePop
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4.0 (Raymond & Rousset 1995; Rousset 2008) to assess linkage disequilibrium (LD). In order to check
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for allelic dropout and null alleles, we used ML-NullFreq (Kalinowski & Taper 2006)
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Haplotyping: Sequences from the HVRI of the mtDNA were amplified using the primers DLF (5’-CCT
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GCC CCT GTA GTA CAA ATA AGT A-3’) and D5 (Warren et al. 2001). PCR amplifications were carried
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out in a 20 μL reaction volume containing the following: 0.25 μM of each primer, 0.2 mM dNTPs, 1 x
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PCR Buffer (Qiagen), 2μ l Bovine Serum Albumin (BSA), 0.5 units HotStarTaq DNA Polymerase
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(Qiagen) and 1 μL template DNA. The PCR conditions were an initial denaturation at 95°C for 15
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minutes, followed by 45 cycles of 94°C for 40s, 52°C for 30s, 72°C for 30s, and final extension at 72°C
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for 10 mins. The cycle sequencing conditions were initial denaturation at 95°C for 45 seconds,
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followed by 30 cycles of 95°C for 30s, 52°C for 20s, and final extension at 60°C for 2 mins. All raw
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data were viewed and edited in Sequencing Analysis 5.2 (Applied Biosystems), and sequences were
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subsequently aligned in Bioedit 7.0.9.0 (Hall 1999) with ClustalW (Thompson et al. 1994).
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Statistical descriptors to detect sex-biased dispersal
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We also assessed sex-biased dispersal using the statistical descriptors proposed by Goudet et al.
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(2002): i) FIS, tests for heterozygote deficiency, and is expected to be positive for the dispersing sex as
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they should be composed of residents as well as non residents, resulting in a Wahlund effect and
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therefore a heterozygote deficit; ii) HO, the observed heterozygosity, which provides information on
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inbreeding; iii) HE, expected heterozygosity or gene diversity; iv) mean of the corrected assignment
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index (mAIc), calculates the probability of a multilocus genotype in a population and is expected to
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be higher for the philopatric sex; and v) variance of the corrected assignment index (vAIc), computes
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the spread from the mean for the assignment indices and is expected to be higher for the dispersing
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sex (Goudet et al. 2002). We estimated FIS, HO and HE using GENETIX (Belkhir et al. 1996-2004;
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available at http://kimura.univ-montp2.fr/genetix/). The deviation of FIS from Hardy Weinberg
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Equilibrium was tested through 1000 permutations. Estimates of mAIc and vAIc were calculated with
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FSTAT 2.9.3.2 (Goudet 1995), and significance levels were obtained through 1000 randomizations
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and a two-tailed t-test.
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Marker informativeness and estimator performance
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Recent analyses have shown that different relatedness estimators vary in their precision depending
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on number of markers, levels of polymorphism, allele frequency distributions, and population
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composition (Van de Casteele et al. 2001; Csillery et al. 2006; Wang 2006). Furthermore, the
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available estimators also perform differently depending on the true relatedness being assessed
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(Csillery et al. 2006; Wang 2006).
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We assessed the information content of the markers and simulated their overall power for
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relatedness estimation using KinInfor v1.0 (Wang 2006). First, we examined two measures of
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information content: i) Ir, the informativeness of relatedness as a continuous measure of identity by
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descent, and ii) IR, the informativeness of discrete relationship categories. Our specifications were
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parent-offspring (PO) and unrelated (U) dyads as the primary and null hypothetical relationships,
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respectively, a Dirichlet distribution of (1, 1, 1) that takes into account uncertainty in the distribution
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of relationships, and a significance level of 0.05. Next, we conducted iterative simulations of 100,000
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dyads on KinInfor to quantify the power of the highest ranking marker, the second-highest ranking
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markers, and so on, until we had assessed the six markers used in the identification analyses and a
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few more (to a total of ten markers). For these simulations we evaluated the discrimination power of
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half-sib (HS) versus U dyads, and PO versus U dyads.
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In order to choose suitable relatedness estimators we used KinInfor to compute the multilocus
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reciprocal of the mean squared deviations (RMSD) of relatedness estimates, which measures marker
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information according to estimator. The moment estimators of Wang (2002), Lynch & Li (Lynch 1988;
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Li et al. 1993), Lynch & Ritland (1999), Ritland (Ritland 1996), and Queller & Goodnight (1989) were
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assessed.
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Results
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Statistical descriptors to detect sex-biased dispersal
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As shown in Table S2, we found a signficantly positive FIS for the males, indicative of a Wahlund effect
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and as expected for the dispersing sex, which comprises both residents and immigrants. The FIS for
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females was not statistically significant, but the higher expected heterozygosity (HE) compared to the
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observed heterozygosity (HO) might result from the pooling together of philopatric females from
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different mtDNA lineages, or from the sampling bias correction applied to HE. Also in line with a
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model of male-biased dispersal, the mean corrected assignment index (mAIc) for the males was
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significantly lower than that of females. However, the higher variance in the corrected assignment
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index (vAIc) for males was not statistically significant. Thus, these results alone are not conclusive.
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Marker informativeness and estimator performance
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We assessed the informativeness of each marker in discriminating relatedness categories and
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estimating relatedness. Based on the IR ranking, we further conducted iterative simulations of dyads
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of different relationship categories to analyze the power discrimination of different marker sets. As
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illustrated in Figure S1, our results show that the set of 7-8 markers with highest IR are very powerful
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in discriminating parent-offspring (PO) dyads from unrelated pairs (U).
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In terms of estimator performance, our results indicate that the multilocus reciprocal of the mean
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squared deviations (RMSD) is highest for the Lynch & Li (LL) and Wang (W) estimators, pointing to
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their higher precision, compared to the Lynch & Ritland (LR), Queller & Goodnight (QG) and Ritland
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(R) estimators, in estimating relatedness. The RMSD per marker for each estimator is provided in
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Table S4.
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