# Population genetic diversity and structure of wild and hatchery-raised populations of European abalone *Haliotis tuberculata* : guidelines for future restocking and stock-enhancement programs

Ronan Le Gall, Pierre Chauvaud, Amélia Viricel, Sabine Roussel, Eric Pante, Pierre Boudry, Grégory Charrier

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► To cite this version:

Ronan Le Gall, Pierre Chauvaud, Amélia Viricel, Sabine Roussel, Eric Pante, et al.. Population genetic diversity and structure of wild and hatchery-raised populations of European abalone *Haliotis tuberculata* : guidelines for future restocking and stock-enhancement programs. Canadian Journal of Fisheries and Aquatic Sciences, 2025, 82, pp.1-16. 10.1139/cjfas-2024-0369. hal-05375461

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https://hal.science/hal-05375461v1

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1 Population genetic diversity and structure of wild and hatchery-raised populations of European

2 abalone *Haliotis tuberculata* tuberculata: guidelines for future restocking and stock-

3 enhancement programs

4

5 Ronan Le Gall¹, Pierre Chauvaud¹, Amélia Viricel¹, Sabine Roussel¹, Eric Pante¹, Pierre Boudry²,

6 Grégory Charrier¹

7 ¹Univ Brest, CNRS, IRD, Ifremer, UMR 6539, LEMAR, Plouzané, France

8 ²Ifremer, Département Ressources Biologiques et Environnement, Plouzané, France

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10 Email info & ORCID Numbers:

11 Ronan Le Gall (ronan.legall@univ-brest.fr; 0000-0001-8676-9969)

12 Pierre Chauvaud (pierre.chauvaud@hotmail.fr; 0000-0001-6075-809X)

13 Amélia Viricel (amelia.viricel-pante@univ-brest.fr; 0000-0002-2789-6869)

14 Sabine Roussel (sabine.roussel@univ-brest.fr; 0000-0001-9275-0818)

15 Pierre Boudry (pierre.boudry@ifremer.fr; 0000-0002-5150-2276)

16 Eric Pante (eric.pante@univ-brest.fr; 0000-0001-7680-2112)

17 Grégory Charrier (gregory.charrier@univ-brest.fr; 0000-0002-8730-9267)

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19 Corresponding authors: Ronan Le Gall, Grégory Charrier

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23 Populations of *Haliotis tuberculata tuberculata* declined sharply over the last two decades, mainly
due to pathogenic bacteria, *Vibrio Harveyi*. In this context, restocking or stock-enhancement
24 operations based on hatchery juveniles might be relevant to restore populations and ensure
25 sustainable fisheries. Maintaining the genetic diversity of wild populations and hatchery
26 individuals is a primary concern in supplementation programs. Here, the genetic diversity of 14
27 hatchery samples and ten wild populations was assessed using 158 nuclear SNPs. Genetic diversity
28 was comparable between wild and hatchery samples, and even slightly higher in the hatchery.

29 However, high genetic differentiation and small effective population sizes suggested strong
30 genetic drift in the hatchery. Pooling hatchery samples decreased differentiation levels with wild
31 samples, suggesting that released juveniles should be composed of several cohorts and/or
32 generations to limit the genetic heterogeneity between seed and wild populations. Moreover,
33 reduced connectivity was detected between northwestern and southeastern populations,
34 suggesting that restocking broodstock should be chosen depending on the locality where it would
35 be released. Overall, this study provides useful guidelines for future restocking programs.
36

37 **Keywords:**

38 abalone, population structure, populations genetics, molluscs, genomics, *Haliotis tuberculata*,
39 Iroise Sea, SNPs, genetic diversity, restocking, stock enhancement, hatchery.

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# Introduction

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The worldwide demand for abalone (*Haliotis* spp.) has increased sharply over the last decades (Hamasaki & Kitada, 2008), leading to higher fishing pressure in many countries (Cook, 2014) and the development of aquaculture (Cook, 2023). Stock collapses have been reported for the twenty commercial species of abalone (Hamasaki & Kitada, 2008), with a global reduction from 20,000 tonnes in 1970 to 6,500 tonnes in 2015 of abalone fisheries (Cook, 2016). To restore depleted stocks and ensure sustainable fisheries, several restocking or stock-enhancement programs have been initiated, based on the seeding of hatchery abalone (Hamasaki & Kitada, 2008; Searcy-Bernal et al., 2013). While the objective of restocking is to re-establish collapsed populations, stock-enhancement aims at increasing the supply of juveniles in wild populations by enhancing the spawning biomass, in order to maximise both fishing harvests and recruitment (Bell et al., 2008). The success of such programs requires substantial knowledge on the seeded individuals (Huchette et al., 2005), including their physiological and behavioural responses to natural stressors (Lorenzen et al., 2012; Roussel et al., 2019), their ecological requirements (Lessard & Campbell, 2007), and their genetic characteristics in comparison to indigenous counterparts (Huchette et al., 2005; Le Vay et al., 2007).

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In particular, restocking and stock-enhancement programs aim at increasing the abundance of populations without adversely influencing wild populations (Grant et al., 2017; Rhode et al., 2017). Therefore, before seeding hatchery-reared individuals in the wild, the spatial genetic structure of wild populations should be estimated, and the genetic variability of hatchery-reared cohorts should be compared with those of the wild populations located in the vicinity of the seeding sites (Blankenship & Leber, 1995; Laikre et al., 2010; Bester-Van der Merwe et al., 2011). Ideally, the broodstock and reproduction protocol used in the hatchery should be chosen in order to produce cohorts that display (1) high level of genetic variability and (2) limited genetic
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differentiation with wild local populations (Rhode et al., 2012; Grant et al., 2017). Meeting these
two goals is generally challenging for hatchery programs.

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Captive breeding generally consists in producing many offspring from few genitors.
Therefore, most captive cohorts display a small effective population size (Ne), leading to an
erosion of their genetic diversity through genetic drift. This is particularly true for shellfish species
that are characterised by high fecundity and large variance in reproductive success (Hedgecock &
Sly, 1990; Boudry et al. 2002; Gutierrez-Gonzalez & Perez-Enriquez, 2005; Gaffney, 2006). As a
consequence, lower genetic diversity is commonly reported in cohorts produced in hatchery
compared to wild populations, as shown in other abalone species *Haliotis midae* and *H. rubra*
(Evans et al. 2004) or the European flat oyster *Ostrea edulis* (Lallias et al., 2010; Alves Monteiro et
al., 2022). Such a lower genetic diversity of hatchery seed may have a negative impact on wild
populations, since genetic diversity can correlate with adaptation and survival potential even in
marine invertebrates (e.g. Gamfeldt et al., 2005; Gamfeldt & Källström, 2007). In some cases,
specific hatchery practices may limit the erosion of the genetic diversity in hatchery-raised
cohorts. For instance, using a large number of breeders may avoid a reduction of the genetic
diversity, as reported in *H. fulgens* (Gutierrez-Gonzalez & Perez-Enriquez, 2005). Alternatively, the
use of a wild broodstock renewed each year may explain the relatively high levels of genetic
diversity found in hatchery-raised cohorts of the great scallop *Pecten maximus* (Morvezen et al.,
2016).

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Genetic drift may also lead to significant genetic differentiation between hatchery and wild
groups. For instance, changes in allele frequencies were observed between farmed and wild
populations of *H. midae* and *H. rubra* (Evans et al. 2004). Similarly, in *P. maximus*, hatchery-raised
cohorts released in the Bay of Brest were found to be genetically differentiated from the wild local
population, even though the broodstock used in the hatchery came from this wild population
(Morvezen et al. 2016). As a consequence, the release of hatchery-raised juveniles in the wild may

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alter the genetic diversity of wild populations, thus inducing a potential risk of reducing their
adaptation to local environmental pressures (Price, 1984; Bell et al., 2008; Laikre et al., 2010;
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93 Grant et al., 2017). Moreover, the release in the wild of individuals born and raised in hatchery
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95 may introduce traits adapted to captive life and domestication, that could also impact the genetic
background and adaptive potential of wild populations (Mercer et al., 2007; Vandersteen et al.,
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The European abalone, *Haliotis tuberculata*, is distributed along the coasts of the NE
Atlantic, from northern Morocco to northwestern France (Normandy; Clavier & Chardy, 1989).

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Three subspecies have been described for this species: *H. tuberculata lamellosa* (Mediterranean
Sea; Geiger et Owen, 2012), *H. tuberculata coccinea* (northern Morocco to southern Cantabrian
Sea, Azores and Canary Island; Roussel et Van Wormhoudt, 2017) and the subspecies of interest in
the present study, *H. tuberculata tuberculata* (Clavier, 1987). This last subspecies is located in the
coastal waters of Brittany and Normandy in the northwest (NW) of France. Several populations of
this subspecies have declined over the last twenty years, mainly due to the virulence of the
pathogenic bacteria *Vibrio harveyi* (Nicolas et al., 2002; Travers et al., 2009). To restore collapsed
populations and sustain local fishing activities, restocking and stock-enhancement programs are
presently under consideration for this subspecies along the coasts of Brittany and Normandy,
based on hatchery produced juveniles. Levels of spatial genetic differentiation of *H. t. tuberculata*
populations remain poorly known along the coasts of Brittany and Normandy. Only two studies
have explored the population genetic structure of *H. tuberculata*, which mostly focused on the
degree of differentiation among its three subspecies across the entire distribution range of the
species (Van Wormhoudt et al., 2009; Roussel & Van Wormhoudt, 2017). Even though signals of
significant population structure were reported among *H. t. tuberculata* samples from Brittany and
Normandy, no clear geographical pattern emerged (Roussel & Van Wormhoudt, 2017).

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In this context, the present study aimed at assessing the distribution of genetic variability among wild and hatchery samples of *H. t. tuberculata*, to assess the effect of hatchery breeding on the diversity of hatchery-raised cohorts, and to explore the spatial distribution of the genetic variability among wild populations. Hatchery samples were collected in two successive generations produced in the only commercial farm producing *H. t. tuberculata*. On the other hand, wild populations were sampled at ten sites along the coasts of Brittany and Normandy (NW France), covering the majority of the distribution range of the subspecies. All samples were analysed with 158 nuclear Single Nucleotide Polymorphism (SNP) markers previously developed by Harney et al. (2018). This approach will help provide guidelines for future restocking and stock-enhancement programs, and thus support the sustainability of commercial fisheries targeting local abalone populations.

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## 127 Materials and methods

### 128 Sample collection

### 129 Hatchery cohorts

In total, 467 hatchery *H. t. tuberculata* abalone were obtained in 2018 and 2019 from France Haliotis (Plouguerneau, Brittany), which is the only commercial hatchery still producing *H. t. tuberculata*. Before 2014, the reproduction on the farm consisted in breeding a wild male (or female) with a female (or male) grown on the farm with no specific selection strategy (generations G1 to G3). From 2014, a directional selection program was applied where the largest 5 % (95th percentile) 3-year-old males and 4-year-old females were selected and used as broodstock each year (G4 to G6) (Supplementary Material A). The wild broodstock used initially by the farm were sampled locally, 30 kilometres around the hatchery. Each breeder contributed only once to the reproduction.
The broodstock samples corresponded to 3-year-old males and 4-year-old females hatched in 2014 to 2016 respectively (Table 1; Supplementary Material A), and belonged to the hatchery generation G4 (Table 1; Supplementary Material A). Broodstock individuals sampled in 2018 were sexually differentiated (i.e. males and females), while the gender was not determined for those collected in 2019. One-year-old offspring juvenile samples resulted from the 2017 and 2018 breeding events (generation G5).

## Wild populations

A total of 429 wild individuals were sampled by scuba-diving over ten sites located across most of the distribution range of the subspecies *H. t. tuberculata*. Sampling was conducted from March to June 2019 along the coasts of Brittany and Normandy (NW France) (Figure 1, Table 2). For each site, specimens were collected within a 50 metres radius of an anchored vessel during a single dive (N = 10-30). Multiple size classes were sampled (i.e. from 20 to 115 mm) to avoid sampling a single cohort each time. For the Aber-Wrac'h, Molène and Bay of Brest sites, sampling was replicated three to four times in order to evaluate the robustness of the genetic signal. At each of these three sites, replicated sampling points were separated by one to five kilometres (Table 2).

For each individual, a mantle epipodium was sampled from the periphery of the foot, for DNA extraction. Wild individuals were systematically released to their original site after tissue collection. Samples were transferred to 1.5 mL microtubes filled with 70 % ethanol and stored at 4°C.

## Selection of SNP markers and laboratory genetic analysis

DNA extractions were performed by LGC Genomics (Ostendstraße 25, 12459 Berlin, Germany) according to their standard protocol (tissue lysis with Proteinase K, subsequent binding,
163 washing and elution steps achieved using proprietary buffers). Individual samples were genotyped
164 with 192 SNPs markers.

165 These markers were chosen among the 500 SNPs developed by Harney et al. (2018) based
166 on the transcriptome of larvae and adults from two wild populations (sampled in Molène and
167 Saint-Malo, both represented in the sampling design of the present study; Harney et al. 2016).
168 First, we included the subset of 123 SNPs selected by Harney, et al. (2018) for conducting
169 parentage assignment in the hatchery stock of France Haliotis. These SNPs were selected by the
170 previous study based on the following criteria: SNPs were at least 100 bp apart, 50 bp from the
171 ends of the transcriptome contigs, had a Minor Allele Frequency MAF > 0.1 and were
172 unambiguously genotyped in ≥ 90% of the samples. From the remaining markers, we included 69
173 additional SNPs with a MAF ranging between 0.38 and 0.50, to reach a total of 192 SNPs. Stringent
174 MAF filters were applied as it is preferable for delineation populations or assessing breeding
175 strategies (Linck & Battey, 2019; Hemstrom et al., 2024).

176 High throughput SNP genotyping was conducted by LGC Genomics using Kompetitive Allele
177 Specific PCR (KASP) assays, allowing bi-allelic SNP scoring. Of the 192 SNP initially analysed by LGC
178 Genomics, 158 were unambiguously genotyped and used for subsequent data analysis.

180 **Data analysis**

181 First, deviations from the Hardy-Weinberg Equilibrium (HWE), linkage disequilibrium and
182 outlier analyses were performed for each of the 158 SNPs within the wild population samples to
183 select only neutral and independent markers for further analyses. Deviations from HWE were
184 evaluated by estimating the Wright's *F*<sub>IS</sub> statistics according to Weir and Cockerham (1984), and
185 were tested using the R package *DiveRsity* v. 1.9.90 (Keenan et al., 2013) with *Monte-Carlo*
186 replicates set à 10,000 to conduct pseudo-exact HWE tests (Engels, 2009). Loci deviating from
187 HWE in at least eight population samples out of fifteen were removed from the dataset. Linkage 188 disequilibrium was calculated among loci using the R package genepop v. 1.2.2 (Rousset, 2008), 189 and the significance was tested with 1,000 iterations. Corrections for multiple testing were applied 190 with p ajust in R package Stats (R Core Team, 2022) using the false discovery rate method (FDR; 191 Benjamini & Hochberg, 1995); pairs of markers that still displayed a p-value<0.05 after FDR 192 correction were considered in linkage disequilibrium. Outlier loci that departed from neutral 193 expectations were assessed with three different methods: pcadapt (Privé et al., 2020), OutFlank 194 (Whitlock & Lotterhos, 2015) and BayeScan (Foll & Gaggiotti, 2008). The R package pcadapt v. 195 4.3.3 uses a Principal Component Analysis (PCA) for the detection of outliers; results from this 196 analysis were then corrected for multiple testing the FDR method. The R package OutFlank v. 0.2 197 uses a likelihood approach on a trimmed distribution of FST values to infer the distribution of FST 198 for neutral markers. Finally, BayeScan v. 2.1 detects outliers using differences in allele frequencies 199 among specified groups. SNPs with P-value<0.05 in any of these three methods were considered 200 "Outliers" if they were detected by at least two of the three methods. Outlier identity was finally 201 searched with a NCBI blastn search (Sayers et al., 2022).

202 After filtering, a final dataset of 144 putatively-neutral loci was used to conduct population 203 genetic analyses. Genetic diversity was assessed within population samples by calculating allelic 204 richness (Ar), and observed (Ho) and expected under HWE (He) heterozygosities over all loci using 205 DiveRsity. Moreover, multi-locus FIS was estimated within each population in DiveRsity to detect 206 departures from HWE, and its significance was estimated with 10,000 bootstrap replicates. 207 Normality of the data was tested with Shapiro-Wilks test of the R package rstatix v. 0.7.2 208 (Kassambara, 2023) and homoscedasticity with Levene's test of the R package car v. 3.1-2 (Fox & 209 Weisberg, 2019). Both were not respected in all samples, and therefore, a Wilcoxon test was 210 performed with the R package rstatix v. 0.7.2 to compare averages of genetic diversity statistics 211 between wild populations and hatchery cohorts.
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Effective population size (Ne) was estimated using the linkage disequilibrium (LD) method of Waples & Do (2010), which is considered more powerful and accurate than other methods such as heterozygosity excess method and molecular co-ancestry (Gilbert & Whitlock, 2015; Jones et al., 2016; Waples, 2016). It predicts that non-random associations of alleles are stronger in smaller populations, which is reflected in greater statistical LD over time. Ne estimates were calculated with NeESTIMATOR v.2.1 (Do et al., 2014), assuming random mating and set $P_{crit}$ (minimum rare allele frequency) at 0.02, as recommended in most cases (Waples & Do, 2010).

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Levels of population differentiation were calculated using the R package hierfstat v. 0.5-11 (Goudet, 2005). Pairwise population genetic differentiation was estimated using Wright's $F_{ST}$ statistics, based on Weir & Cockerham's equations (1984). The statistical significance of $F_{ST}$ estimates was tested with 1,000 permutations of individuals among populations, using bootstraps calculated with hierfstat. Pairwise $F_{ST}$ were calculated for both SNP datasets including and excluding outliers. To explore the population structure among sampled sites, genetic admixture among wild populations was estimated with the R package tess3r v. 1.1.0 (Caye, et al., 2016), with and without using geographic coordinates as a prior (Lambda = 1 with geography, Lambda = 0 without the geography component). Furthermore, K = 1 to K = 8 were calculated for both analyses, and the chosen K was determined by 'Cross-validation score.' In addition, Genetic differentiation between wild and hatchery samples was also analysed with an analysis of molecular variance (AMOVA) performed in Arlequin (version 3.5.2.2; Excoffier & Lischer, 2010), and the significance was tested using 1,000 permutations.

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Finally, Isolation by distance (IBD) was tested with a Mantel's test in Genetix v. 4.05 (Belkhir, 2004). The correlation between linearized pairwise $F_{ST}$ estimates ($F_{ST}/(1-F_{ST})$) and geographical distances was evaluated between all wild population pairs with a Z-statistics, and its significance was tested with 5,000 permutations. A second correlation was tested using linearized pairwise $F_{ST}$ estimates against logarithm of geographic distances based on Rousset (1997). Least-

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cost geographical distances were estimated using the R package marmap v. 1.0.10 (Pante & Simon-Bouhet, 2013). Analyses using R packages were run using R v. 4.2.1 (R Core Team, 2022). R scripts used in the analyses are presented in supplementary materials (Supplementary Material B).

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## 241 Results

### 242 Selection of SNP markers

Prior to obtaining the final dataset, nine SNPs showing evidence of significant deviations from HWE in at least eight populations (Haltu_081, 105, 150, 155, 258, 317, 324, 361 and 512) were removed from the dataset. Two loci were also found to be linked in every population (Haltu_174 and 370). Therefore, Haltu_174 was removed based on its higher percentage of missing values. Bayescan, pcadapt and Outflank collectively detected 51 SNPs as potential outliers, but only four SNPs were identified by at least two methods (Table 3). These SNPs were then removed for subsequent analyses. A NCBI blastn search on these four outliers retrieved *Haliotis rubra* sequences: Haltu_091 was identified as related to trifunctional enzyme subunit alpha (94% of ID), Haltu_201 related to intra-flagellar transport protein 52 homolog (83.75%), Haltu_205 related to exportin-2-like (76.64%), and Haltu_215 related to probable ATP-dependent RNA helicase DDX31 (93.88%). After applying all these filtering steps, the final dataset consisted of 144 putatively neutral loci.

### 255 Genetic diversity

Allelic richness (Ar; Table 4) ranged from 1.840 to 1.916 among all population samples, with largely overlapping, yet statistically different distributions between wild (1.867-1.913) and hatchery (1.840-1.916) groups (Table 5). Observed heterozygosity ranged from 0.292 to 0.368, and expected heterozygosity ranged from 0.324 to 0.370. Ho was lower than He in all wild samples, while they were similar among hatchery samples. Like Ar, levels of heterozygosity were
comparable between wild population samples (Ho = 0.292-0.354, He = 0.337-0.370) and hatchery samples (Ho = 0.311-0.368, He = 0.324-0.369), even though Ho was significantly lower in the wild, compared to the hatchery (Table 5). Higher and positive FIS values in wild samples reflected a deficit in heterozygotes in this group (0.009≤ FIS ≤ 0.155; Tables 4-5), however, only one of those estimates was significant (BOB-3). Three out of fourteen hatchery samples displayed heterozygote deficiencies (FIS = 0.010-0.102), and one being at equilibrium (G5-18-3). Moreover, AMOVA results displayed most of the variance was within the individuals (>97%), and secondly among groups (i.e. Wild and Hatchery; >2%), with the fixation index between groups (FCT) being significant (Table 6). Ar, He, Ho and FIS values were also calculated for the dataset containing 144 SNPs and the 4 outliers, resulting in similar patterns as without outliers (Supplementary Material C). Ne estimates were very large for the wild samples, with the CI 95% including the infinite in all samples (Table 4). On the other hand, Ne estimates were small for all hatchery samples, ranging from 3.3 to 43.9, with finite CI 95% ranging from 1.9 to 81.9. Pooling hatchery samples increased Ne estimates only marginally (pooled G4s, Ne = 65; pooled G5s, Ne = 53.8; all cohorts Ne = 66.8), with CI 95% ranging from 46.5 to 73.8.

276 Population genetic structure

Pairwise FST showed a high differentiation of hatchery samples from wild ones (FST = 0.0145-0.0725) and from each other (FST = 0.057-0.0873), all these comparisons being significant (Figure 2, Supplementary Material D). In addition, a northeast (NE)/southwest (SW) differentiation pattern was detected among wild samples by pairwise FST comparisons (FST = 0.0047-0.0322 between NE and SW samples). Conversely, a lack of genetic differentiation was found within each of the NE (FST = 0.0003-0.0129) and SW (FST = 0-0.0066) groups. Levels of genetic differentiation between hatchery and wild samples were much higher than those found between wild NE and SW samples (Figure 3; Supplementary Material D). However, pooling all hatchery samples together sharply decreased the magnitude of the genetic differentiation between hatchery and wild
samples ($F_{ST}$ = 0.0165-0.0275; Figure 3). Levels of genetic differentiation between wild samples and the pooled hatchery ones were in the same range as those found among wild populations.

Pairwise $F_{ST}$ values were also calculated for the dataset containing 144 SNPs and the four outliers, resulting in similar differentiation patterns as without outliers (Supplementary Material E)

The NE/SW structure among wild populations was also detected with rtess3 (Figure 4).

When geography was considered as a prior in rtess3, substructure was visible within the NE region, despite its smaller geographical extent in comparison to the SW region (200 vs. 270 kilometres of coastline from SQP to CHER, and from LOC to GUI, respectively). Moreover, rtess3 revealed a weak substructure between the eastern (CHE, BLA, CHA) and western (SQP and SMA) parts of the NE region (also visible with low and non-significant pairwise $F_{ST}$ values; Figure 2).

ANOVA results determined that 97.16% of genetic variation was observed among wild individuals, while variation among subgroups (2.11%), population within groups (0.14%) and individuals within populations (0.59%) accounted for only 2.84% of the variation all together.

Mantel tests conducted to explore the dependence of genetic distances on geographical distances were not applied on the whole set of wild population samples, given the detected NE/SW genetic structure pattern (see Discussion section and Meirmans (2012)). No IBD was detected among samples from the SW region (n=78, Z = -2.94, p-value = 0.431; Figure 5A). In contrast, wild samples from the NE region displayed an IBD pattern (n=15, Z = 11.61, p-value = 0.043; Figure 5B), but this was mainly driven by two samples corresponding to the two farthest sites of the grouping (CHE/SQP and CHE/SMA).

## Discussion

Based on a set of 158 SNP markers, the present study assessed levels of genetic diversity and structure among wild and hatchery samples. This study was able to (1) explore effects of the
hatchery breeding process on the genetic variability of hatchery-raised cohorts, and (2) evaluate
the population genetic structure among ten sampling sites spanning over most of the distribution
range of the subspecies. All together, this information will provide relevant guidelines to develop
future restocking or stock-enhancement programs to ensure the long-term sustainability of *H. t.*
*tuberculata* fisheries.

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316 *Retention of the genetic diversity in hatchery cohorts*

Levels of genetic diversity in wild and hatchery samples were largely overlapping. Genetic diversity seemed relatively stable over hatchery-raised generations. Several other studies conducted on various abalone species obtained similar results. For instance, heterozygosity values appeared to be comparable between wild broodstock and hatchery offspring in *H. fulgens*, even though the reproduction in hatchery had been conducted with a very small broodstock that included only 16 to 22 adults (Gutierrez-Gonzalez & Perez-Enriquez, 2005). In the same way, equivalent levels of allelic diversity and heterozygosity were found between wild and F₁ hatchery individuals of *H. midae* (Rhode et al., 2012).

In contrast, *Ne* estimates were much smaller in hatchery samples than in wild ones, which
appeared to be very large. Small *Ne* values are a proxy of strong genetic drift (Hare et al., 2011)
and, as a consequence, are generally associated with low genetic diversity estimates (Allendoff,
1986; Gaffney, 1992; Hedgecock et al. 1992). This generality is not supported in the present study,
and this might be explained by four possible hypotheses. First, heterozygosity is relatively
insensitive to genetic drift occurring over a limited number of generations (Hedgecock & Sly,
1990), which could explain high heterozygosity estimates in spite of small *Ne*. Second, effects of
genetic drift on allelic diversity are a negative function of allele frequencies: rare alleles are more
prone to be lost than common ones in response to genetic drift. Therefore, given the fact that SNP
markers used in the study were chosen based on a MAF > 0.38, these markers might display only
few rare alleles, thus being less sensitive to the effects of genetic drift. Third, SNP markers used in
the study have been previously chosen to be polymorphic in the hatchery in order to perform
parentage assignment in the hatchery (Harney et al. 2018), thus inducing a possible ascertainment
bias. Such bias may potentially cause departures of population genetic statistics from neutral
expectations (Lachance & Tishkoff, 2013; Malomane et al., 2018), and may lead to an
overestimation of genetic diversity in hatchery samples. Fourth, the breeding process applied in
the hatchery is based on the principle of crossing males and females from different generations (3-
years old males crossed with 4-years old females; Supplementary Material A). However, genetic
drift in the hatchery induces a strong variation in allele frequencies among males and females of
different generations, as shown with pairwise FST estimates. As a consequence, crossing males and
females with contrasted allele frequencies is expected to increase heterozygosity in the offspring.
This last hypothesis is likely to explain the maintenance of relatively high levels of genetic diversity
in hatchery-produced cohorts of abalone, even after five generations spent in the farm. However,
it is important to note that such a breeding process does not preclude the progressive loss of the
rarest alleles over generations. As stated by Gaffney (2006), a loss of allelic diversity is a necessary
consequence of selective breeding and should not be a source of alert, and inbreeding should be
the main concern. Inbreeding is already taken into account in the breeding process implemented
by France Haliotis hatchery, and no sign of inbreeding was detected with IS results.

Interestingly, genetic diversity and Ne estimates slightly increased when hatchery samples
were grouped by generation or over the whole farm. Diversity estimates in pooled hatchery
generations or in the whole hatchery cohort were even a bit higher than those reported in wild
populations. This result points to the fact that implanting a mix of different hatchery produced
cohorts/generations, rather than a single one, might be beneficial to maintain the genetic diversity
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in restocking or stock-enhancement programs. Such a conclusion is in line with previous
observations reported in an oyster restoration program (Hornick & Plough, 2019).

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Genetic differentiation of hatchery cohorts

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Hatchery samples were consistently highly differentiated from wild population samples. A similar pattern has been reported between farmed and wild populations in other abalone species, i.e. *H. midae* in South Africa and *H. rubra* in Australia (Evans et al., 2004; Rhode et al., 2012), as well as the great scallop in the Bay of Brest (Morvezen et al., 2016). Such a strong genetic divergence of hatchery-raised cohorts is a common phenomenon that is attributable to small *Ne* in hatcheries (e.g. Evans et al., 2004; Lallias et al., 2010; Rhode et al., 2012; Morvezen et al., 2016) as well as substantial variance in reproductive success (e.g. Boudry et al., 2002; Morvezen et al., 2013), that trigger large and stochastic variations of allele frequencies in cultured cohorts due to genetic drift (Laikre et al., 2010).

363

364

365

366

367

368

369

370

371

However, the genetic differentiation with wild populations was largely decreased when
hatchery samples were grouped by generation or gathered in a single pool for the whole farm.
This suggests that seed used in restocking or stock-enhancement programs should not result from
a single spawning batch, but should rather be composed of several cohorts and/or generations. In
addition to ensuring high genetic diversity levels in implanted abalone (see above), this approach
is likely to decrease the overall genetic differentiation between hatchery-raised individuals and
their wild counterparts, thus reducing the risk of altering the genetic diversity of wild populations
(Hornick & Plough, 2019). Moreover, using juveniles produced with a wild broodstock instead of
using offspring descending from several generations raised in the hatchery, is preferable to further
decrease the level of genetic heterogeneity between hatchery-raised seed and wild populations.

372

373

374

375

376

377

378

379

380

381
383 The spatial distribution of the genetic variability among the ten wild samples spread over
384 most of the distribution range of *H. t. tuberculata* revealed hierarchical population structure
385 patterns. The most significant genetic break was found between NE (from Cherbourg to Saint-
386 Quay-Portrieux) and SW (from Guissény to Locmariaquer) sites. A small substructure was also
387 detected in the NE region within the Normano-Breton Gulf. These results contrast with previous
388 studies that did not detect such NE-SW structuring patterns (Roussel & Van Wormhoudt, 2017). In
389 the later study, only the southernmost population sample (Merrien, slightly south of
390 Locmariaquer) was significantly differentiated from the rest of the samples distributed along the
391 coasts of Brittany and Normandy. This weak population structure reported by Roussel & Van
392 Wormhoudt (2017) was only supported by a mitochondrial marker (Cytochrome Oxidase I) and a
393 single nuclear gene (sperm lysine gene), but not by microsatellite loci.

394 The NE-SW population genetic structure reported in the present study might be driven by
395 a strong barrier to larval dispersal induced by the Ushant front. This seasonal tidal front occurs
396 from May to October and can reduce population connectivity in local marine bentho-pelagic
397 invertebrates by preventing larval dispersal (Ayata et al., 2010). The possible influence of the
398 Ushant front on the larval connectivity of *H. t. tuberculata* is supported by the fact that the
399 spawning period of this species extends mostly from June to September (Clavier, 1987; Mgaya &
400 Mercer, 1994; Le Pennec et al., 2003), and thus coincides with the period when the Ushant front is
401 well established. Another possible barrier to gene flow could be due to the peculiar environmental
402 conditions of the Normano-Breton Gulf, that is characterised by several residual gyres around the
403 Channel Islands (Garreau, 1993; Trigui, 2009). In addition, temperature is also higher in summer
404 and lower in winter in the Normano-Breton Gulf compared to western Brittany. All together, these
405 particular features of the Normano-Breton Gulf might create a potential barrier to gene flow
406 between Guissény and Saint-Quay-Portrieux. Interestingly, a significant IBD pattern was also
highlighted within the Normano-Breton Gulf, but not outside (see further details below). A further
exploration of the two proposed hypothetical barriers to larval dispersal that may restrict gene
flow between NE and SW populations of *H. t. tuberculata* would require additional sampling
between Guissény and Saint-Quay-Portrieux, to better determine the precise location of the
barrier(s) to gene flow. In addition, a bio-physical modelling approach would be very relevant to
assess patterns of larval dispersal among *H. t. tuberculata* distributed along the coasts of Brittany
and Normandy.

Despite the short pelagic larval stage and the limited dispersal potential of adults in *H. t. tuberculata*, a lack of population genetic structure was observed at a small spatial scale in the SW region. In particular, the lack of IBD pattern in the SW group suggests that geographic distances do not restrict gene flow among populations in the SW region of Brittany and Normandy, in spite of the supposed limited potential for dispersal of abalone. Congruently, a high genetic connectivity has also been reported among populations of other abalone species, such as *H. rufescens* in California (Gruenthal et al., 2007) or *H. rubra* in Australia (Miller et al., 2016) across distances of 1300 to 1400 kilometres. In contrast, a significant IBD pattern was observed within the NE region. This is in line with the observation of reduced gene flow at a very small spatial scale (i.e. 100 metres to 10 kilometres) in *Haliotis rubra* in Tasmania, that suggested a restriction of larval dispersal to less than 100 metres in this species (Temby et al., 2007). Such a phenomenon might be driven by the peculiar environmental conditions encountered in the Norman-Breton Gulf, as described above. However, the hypothesis of restricted gene flow at small spatial scales in the Normano-Breton Gulf should be considered with caution, and needs to be further explored. In particular, the Mantel test has been under some criticism for its statistical power (Legendre & Fortin, 2010; Diniz-Filho et al., 2013; Guillot & Rousset, 2013), and its use for testing IBD may be confounded by other processes that would shape spatial variations of genetic diversity among sites (Meirmans, 2012). Subtle structuring patterns at a small spatial scale could be explored by (1)
applying a population genomics approach (using a RADseq approach as explained above) to screen
a larger set of markers, and (2) testing the effect of small-scale environmental heterogeneity, as
performed by Miller et al. (2019) for *H. rubra* in south-eastern Australia. Based on 5239 SNPs and a
hierarchical sampling design, Miller et al. (2019) detected subtle genetic structure at local (i.e. 10s
to 100s of metres) and regional (i.e. 100s of kilometres) scales in association with environmental
heterogeneity (i.e. currents, temperature, wave energy, geology), although a lack of population
genetic differentiation was found at a global scale within its distributional area.

439

440 Guidelines for future restocking/stock enhancement programs and perspectives

The present study provides relevant information to guide future restocking and sock
enhancement programs that are under development to ensure the sustainability of wild exploited
populations of *H. t. tuberculata* along the coasts of Brittany and Normandy. First, the significant
genetic differentiation observed between northern and southern populations in the studied area
should be carefully taken into consideration for future restocking or stock-enhancement
programs. In other words, the broodstock used to produce juveniles for restocking or stock-
enhancement programs should originate from the same region as where juveniles are expected to
be released in order to avoid altering the genetic variability of wild populations (Laikre et al., 2010;
Van den Bergh & Roodt-Wilding, 2010; Bester-Van der Merwe et al., 2011).

Second, diversity estimates suggested that the breeding protocol used here (i.e. crossing
20 males and 20 females of different generations) avoided a detectable erosion of the genetic
variability across generations. Therefore, the production of juveniles with a high degree of genetic
diversity might be compatible with restocking or stock-enhancement perspectives, as advocated
by Grant et al. (2017). However, small *Ne* estimates and large genetic differentiation of hatchery
samples underlined substantial genetic drift in hatchery cohorts. Moreover possible biases in
456 genetic diversity estimates pointed to a need for further investigation of the genetic diversity in
457 hatchery-raised abalone cohorts. In particular, genetic variability levels should be further
458 confirmed with a larger set of unlinked, neutral markers. A methodology belonging to the family of
459 the RADseq (Restriction Site-Associated DNA sequencing) approaches (Baird et al., 2008; Andrews
460 et al., 2016) would be appropriate to analyse several thousands of SNPs randomly distributed in
461 the genome in order to compare the genetic diversity between wild populations and hatchery
462 cohorts. In addition, these markers would help to detect signatures of potential selective
463 pressures related to domestication processes in the hatchery. Even though the breeding practices
464 applied here seem appropriate to ensure a high level of genetic diversity in hatchery-raised
465 cohorts, the strong genetic differentiation and contrasted Ne estimates reported between
466 hatchery and wild samples suggest that seed released in wild populations must be composed of
467 several cohorts and/or generations in order to limit the genetic heterogeneity between hatchery-
468 raised individuals and wild populations. In addition, pooling several cohorts and/or generations
469 might limit a potential reduction of genetic diversity in the hatchery due to genetic drift.

470 Third, using a wild broodstock should limit the risk of genetic drift and domestication of
471 produced juveniles in the hatchery. Domestication processes generally induce a lower fitness of
472 farmed organisms after their release in the wild, notably because of intentional or unintentional
473 selective forces occurring in the hatchery (Christie et al., 2014). Such a lower fitness of seed
474 released in the wild may in turn reduce the adaptive potential of seeded populations to
475 environmental variability. As demonstrated in salmonids, a reduction of the fitness of hatchery-
476 raised individuals in the wild can happen very rapidly, even after a single generation in hatchery
477 (Araki et al., 2007; Christie et al., 2012, 2014). Therefore, using wild broodstock to produce seed
478 appears as the best strategy to maintain the adaptive potential of wild seeded populations, and
479 thus ensure their long-term sustainability.
Overall, all the precautions listed above should strongly reduce the risk of altering the genetic diversity of wild populations, and thus maintain their adaptive potential to environmental variability. However, the fitness of hatchery-raised individuals should be investigated in the wild, in order to evaluate the possible impact of released seed on the adaptive potential of wild populations. This issue must be addressed to develop appropriate management measures that would ensure the long-term sustainability of abalone populations exploited along the coasts of Brittany and Normandy.
487 Acknowledgements

488 We thank all people who kindly provided biological samples: Erwan Amice, Isabelle Bihannic,

489 Thierry Le Bec and Emilie Grossteffan (Institut Universitaire Européen de la Mer), Olivier Basuyaux

490 (Synergie Mer et Littoral), Françoise Calvez, Christian Aillet and Philippe Orveillon (professional

491 fisher.wo.men), Sylvain Huchette et Guillaume Colin from France Haliotis's farm. We thank the

492 SYSAAF team, Romain Morvezen, Florian Enez and Pierrick Haffray, for their help and support in

493 conducting the genotyping process. We thank Yann Dorant for his help and support with the

494 bioinformatic analyses. Finally, we thank the editor and two anonymous reviewers for

495 constructively commenting on the first draft for this manuscript.

496 Competing interests statement

497 The domesticated abalone originated from France Haliotis's farm.

498 Author contribution statement

499 RLG performed data analysis and wrote the manuscript. PC performed the sampling and the first

500 analyses and wrote the first draft. AV and EP performed data analysis and participated in

501 manuscript writing. GC designed the study, performed data analysis and participated in

502 manuscript writing. SR designed the study, and participated in manuscript writing.

503 Community involvement statement

504 There was no indigenous community involvement in this study.

505 Funding statement

506 This work was funded by the European Union (FEAMP program 26, "Innovation in Fisheries",

507 "Ourmel" project) and the ANR ("ORMER"; ANR-23-CE32-0005). The doctoral research of Ronan Le

508 Gall and Pierre Chauvaud was funded by the Region Bretagne (50 %) and the University of Brest

509 (50 %).
510 Data availability statement

511 R scripts used in this study are available as Supplementary Material B. All SNP genotyping primers

512 and genotypes are available at:

513 Le Gall Ronan, Chauvaud Pierre, Viricel Amélia, Roussel Sabine, Boudry Pierre, Pante Eric, Charrier

514 Grégory (2025). SNP dataset for Haliotis tuberculata tuberculata: 158 markers for population

515 genetics of wild and hatchery-raised European abalone. SEANOE.

516 https://doi.org/10.17882/106440

517
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764
767 **Table 1:** Hatchery cohorts samples of *Haliotis tuberculata tuberculata* from France Haliotis analysed in the study: generation in hatchery, sampling year, year of birth, sex (if determined), life stage, spawning batch, sample code and sample size. Samples are ordered based on their hatchery generation and year of birth.

768

769

770

<table><thead><tr><th>Generation</th><th>Sampling year</th><th>Year of birth</th><th>Sex</th><th>Life stage</th><th>Spawning batch</th><th>Sample code</th><th>Sample size</th></tr></thead><tbody><tr><td>4</td><td>2018</td><td>2014</td><td>Females</td><td>Broodstock</td><td>1</td><td>G4-18F1</td><td>20</td></tr><tr><td>4</td><td>2018</td><td>2014</td><td>Females</td><td>Broodstock</td><td>2</td><td>G4-18F2</td><td>18</td></tr><tr><td>4</td><td>2018</td><td>2014</td><td>Females</td><td>Broodstock</td><td>3</td><td>G4-18F3</td><td>20</td></tr><tr><td>4</td><td>2018</td><td>2015</td><td>Males</td><td>Broodstock</td><td>1</td><td>G4-18M1</td><td>18</td></tr><tr><td>4</td><td>2018</td><td>2015</td><td>Males</td><td>Broodstock</td><td>2</td><td>G4-18M2</td><td>17</td></tr><tr><td>4</td><td>2018</td><td>2015</td><td>Males</td><td>Broodstock</td><td>3</td><td>G4-18M3</td><td>18</td></tr><tr><td>4</td><td>2019</td><td>2015-2016</td><td>Undetermined</td><td>Broodstock</td><td>1</td><td>G4-19-1</td><td>36</td></tr><tr><td>4</td><td>2019</td><td>2015-2016</td><td>Undetermined</td><td>Broodstock</td><td>2</td><td>G4-19-2</td><td>32</td></tr><tr><td>4</td><td>2019</td><td>2015-2016</td><td>Undetermined</td><td>Broodstock</td><td>3</td><td>G4-19-3</td><td>38</td></tr><tr><td>5</td><td>2018</td><td>2017</td><td>Undetermined</td><td>Offspring</td><td>1</td><td>G5-17-1</td><td>50</td></tr><tr><td>5</td><td>2018</td><td>2017</td><td>Undetermined</td><td>Offspring</td><td>2</td><td>G5-17-2</td><td>50</td></tr><tr><td>5</td><td>2019</td><td>2018</td><td>Undetermined</td><td>Offspring</td><td>1</td><td>G5-18-1</td><td>50</td></tr><tr><td>5</td><td>2019</td><td>2018</td><td>Undetermined</td><td>Offspring</td><td>2</td><td>G5-18-2</td><td>50</td></tr><tr><td>5</td><td>2019</td><td>2018</td><td>Undetermined</td><td>Offspring</td><td>3</td><td>G5-18-3</td><td>50</td></tr></tbody></table>

771

772
**Table 2:** Wild population samples of *Haliotis tuberculata* tuberculata analysed in the study: geographic region, location, sampling code, GPS coordinates and sample size. Populations are ordered from the southeastern population to the northeastern population, following Brittany and Normandy coastline.

<table><thead><tr><th>Geographic region</th><th>Location</th><th>Sample code</th><th>GPS coordinates</th><th>Sample size</th></tr></thead><tbody><tr><td rowspan="15">Brittany</td><td rowspan="3">Locmariaquer</td><td>LOC</td><td>47°32'22.4"N / 2°59'06.4"W</td><td>30</td></tr><tr><td>BOB-1</td><td>48°18'12.2"N / 4°32'07.3"W</td><td>27</td></tr><tr><td>BOB-2</td><td>48°19'25.8"N / 4°27'49.4"W</td><td>15</td></tr><tr><td rowspan="4">Bay of Brest</td><td>BOB-3</td><td>48°37'01.4"N / 4°36'53.8"W</td><td>30</td></tr><tr><td>MOL-1</td><td>48°23'15.3"N / 4°57'24.2"W</td><td>27</td></tr><tr><td>MOL-2</td><td>48°22'23.8"N / 4°56'48.7"W</td><td>30</td></tr><tr><td>MOL-3</td><td>48°23'10.9"N / 4°55'58.5"W</td><td>10</td></tr><tr><td rowspan="3">Molène</td><td>MOL-4</td><td>48°23'44.0"N / 4°53'47.3"W</td><td>30</td></tr><tr><td>ABW-1</td><td>48°36'38.6"N / 4°35'25.2"W</td><td>30</td></tr><tr><td>ABW-2</td><td>48°37'00.0"N / 4°36'50.7"W</td><td>15</td></tr><tr><td rowspan="2">Aber-Wrac'h</td><td>ABW-3</td><td>48°38'20.4"N / 4°33'49.7"W</td><td>25</td></tr><tr><td>GUI</td><td>48°39'55.3"N / 4°27'26.6"W</td><td>28</td></tr><tr><td>Saint-Quay-Portrieux</td><td>SQP</td><td>48°39'07.1"N / 2°47'09.9"W</td><td>30</td></tr><tr><td>Saint-Malo</td><td>SMA</td><td>48°40'36.2"N / 2°04'42.4"W</td><td>30</td></tr><tr><td rowspan="3">Normandy</td><td>Chausey</td><td>CHA</td><td>48°54'08.4"N / 1°50'57.0"W</td><td>16</td></tr><tr><td>Blainville-Sur-Mer</td><td>BLA</td><td>49°03'54.3"N / 1°37'27.7"W</td><td>26</td></tr><tr><td>Cherbourg</td><td>CHE</td><td>49°39'53.7"N / 1°32'19.0"W</td><td>30</td></tr></tbody></table>

777
778

779

780

<table><caption>**Table 3:** List of outliers detected by at least two methods, out of three possible (P-adjust, OutFlank and BayeScan). For each outlier, the table indicates the locus name (SNP ID), which method detected this locus as an outlier, the sequence, the blast output based on the most similar sequence obtained with NCBI blastn and the percentage of BLAST similarity (Perc. ID).</caption><thead><tr><td>SNP ID</td><td>P-adjust</td><td>OutFlank</td><td>BayeScan</td><td>BLASTn output</td><td>Perc. ID</td></tr></thead><tbody><tr><td>Haltu_091</td><td>Detected</td><td>Detected</td><td>Detected</td><td>Haliotis rubra trifunctional enzyme subunit alpha, mitochondrial-like (LOC124279900)</td><td>94%</td></tr><tr><td>Haltu_205</td><td>Detected</td><td>Detected</td><td>Not Detected</td><td>Haliotis rubra exportin-2-like (LOC124275443)</td><td>77%</td></tr><tr><td>Haltu_201</td><td>Detected</td><td>Not Detected</td><td>Detected</td><td>Haliotis rubra intraflagellar transport protein 52 homolog (LOC124275330)</td><td>84%</td></tr><tr><td>Haltu_215</td><td>Detected</td><td>Not Detected</td><td>Detected</td><td>PREDICTED: Haliotis rubra probable ATP-dependent RNA helicase DDX31 (LOC124261899)</td><td>94%</td></tr></tbody></table>
**Table 4:** Genetic diversity estimates of wild populations and hatchery cohorts of abalone based on 144 SNPs: sample size (n), allelic richness (Ar), observed heterozygosity (Ho), expected heterozygosity (He), departure from HWE ($F_{IS}$), effective population size estimates (Ne) and its 95% confidence intervals (CI 95%). The significance of $F_{IS}$ estimates was tested using 10 000 bootstrap replicates (* p < 0.05.). Mean values were calculated for both wild and hatchery samples with standard error. In addition to each hatchery samples, the genetic diversity within the hatchery was evaluated by grouping hatchery samples by generation (G4 pooled and G5 pooled) or for the whole farm (Farm-tot). Wild populations are ordered from the southeastern population to the northeastern population along Brittany and Normandy coastline. Hatchery samples are ordered based on their hatchery generation and year of birth.

<table><thead><tr><th>Population</th><th>n</th><th>Ar</th><th>Ho</th><th>He</th><th>FIS</th><th>Ne (Pcrit = 0.02)</th><th>JackKnife CI 95%</th></tr></thead><tbody><tr><td colspan="8" style="text-align: center; font-weight: bold;">Wild population samples</td></tr><tr><td>LOC</td><td>30</td><td>1.913</td><td>0.350</td><td>0.370</td><td>0.061</td><td>∞</td><td>125.5 - ∞</td></tr><tr><td>BOB-1</td><td>30</td><td>1.900</td><td>0.328</td><td>0.359</td><td>0.083</td><td>159.8</td><td>54.7 - ∞</td></tr><tr><td>BOB-2</td><td>15</td><td>1.889</td><td>0.326</td><td>0.349</td><td>0.054</td><td>664.8</td><td>36.5 - ∞</td></tr><tr><td>BOB-3</td><td>27</td><td>1.875</td><td>0.292</td><td>0.350</td><td><strong>0.155*</strong></td><td>∞</td><td>60.7 - ∞</td></tr><tr><td>MOL-1</td><td>30</td><td>1.907</td><td>0.345</td><td>0.363</td><td>0.051</td><td>547.1</td><td>118.0 - ∞</td></tr><tr><td>MOL-2</td><td>10</td><td>1.897</td><td>0.353</td><td>0.360</td><td>0.025</td><td>735.9</td><td>152.4 - ∞</td></tr><tr><td>MOL-3</td><td>30</td><td>1.867</td><td>0.309</td><td>0.337</td><td>0.088</td><td>∞</td><td>106.2 - ∞</td></tr><tr><td>MOL-4</td><td>27</td><td>1.905</td><td>0.341</td><td>0.364</td><td>0.070</td><td>∞</td><td>122.1 - ∞</td></tr><tr><td>ABW-1</td><td>25</td><td>1.897</td><td>0.351</td><td>0.364</td><td>0.028</td><td>178.9</td><td>73.4 - ∞</td></tr><tr><td>ABW-2</td><td>15</td><td>1.905</td><td>0.345</td><td>0.351</td><td>0.022</td><td>196.9</td><td>66.6 - ∞</td></tr><tr><td>ABW-3</td><td>30</td><td>1.907</td><td>0.354</td><td>0.358</td><td>0.009</td><td>911.1</td><td>74.6 - ∞</td></tr><tr><td>GUI</td><td>28</td><td>1.894</td><td>0.342</td><td>0.352</td><td>0.023</td><td>1636.1</td><td>73.0 - ∞</td></tr><tr><td>SQP</td><td>30</td><td>1.892</td><td>0.318</td><td>0.342</td><td>0.075</td><td><strong>10743.7</strong></td><td>94.3 - ∞</td></tr><tr><td>SMA</td><td>30</td><td>1.894</td><td>0.333</td><td>0.347</td><td>0.040</td><td>∞</td><td>122.8 - ∞</td></tr><tr><td>CHA</td><td>16</td><td>1.896</td><td>0.330</td><td>0.347</td><td>0.041</td><td>208.2</td><td>40.8 - ∞</td></tr><tr><td>BLA</td><td>26</td><td>1.894</td><td>0.329</td><td>0.356</td><td>0.086</td><td>678.5</td><td>40.6 - ∞</td></tr><tr><td>CHE</td><td>30</td><td>1.904</td><td>0.341</td><td>0.359</td><td>0.060</td><td>706.3</td><td>116.3 - ∞</td></tr><tr><td>Mean per sample</td><td>25.2 (±6.7)</td><td>1.896 (±0.011)</td><td>0.334 (±0.017)</td><td>0.355 (±0.009)</td><td>0.057 (±0.035)</td><td>-</td><td>-</td></tr></tbody></table>
<table><thead><tr><th colspan="8">Hatchery samples</th></tr></thead><tbody><tr><td>G4-18F1</td><td>20</td><td>1.874</td><td>0.349</td><td>0.327</td><td>-0.053</td><td>20.1</td><td>14.9 - 27.6</td></tr><tr><td>G4-18F2</td><td>18</td><td>1.876</td><td>0.348</td><td>0.339</td><td>-0.019</td><td>22.7</td><td>13.0 - 52.3</td></tr><tr><td>G4-18F3</td><td>20</td><td>1.899</td><td>0.351</td><td>0.348</td><td>-0.014</td><td>12.2</td><td>5.7 - 33.4</td></tr><tr><td>G4-18M1</td><td>18</td><td>1.840</td><td>0.367</td><td>0.324</td><td>-0.114</td><td>25.0</td><td>19.3 - 33.1</td></tr><tr><td>G4-18M2</td><td>17</td><td>1.861</td><td>0.344</td><td>0.325</td><td>-0.058</td><td>20.5</td><td>10.5 - 63.2</td></tr><tr><td>G4-18M3</td><td>18</td><td>1.873</td><td>0.335</td><td>0.335</td><td>-0.009</td><td>3.3</td><td>1.9 - 11.4</td></tr><tr><td>G4-19-1</td><td>36</td><td>1.909</td><td>0.368</td><td>0.362</td><td>-0.012</td><td>18.1</td><td>13.0 - 25.9</td></tr><tr><td>G4-19-2</td><td>32</td><td>1.866</td><td>0.362</td><td>0.334</td><td>-0.068</td><td>12.5</td><td>8.0 - 19.7</td></tr><tr><td>G4-19-3</td><td>38</td><td>1.916</td><td>0.365</td><td>0.369</td><td>0.010</td><td>43.9</td><td>28.1 - 81.9</td></tr><tr><td>G5-17-1</td><td>50</td><td>1.870</td><td>0.311</td><td>0.346</td><td>0.102</td><td>20.8</td><td>15.6 - 28.6</td></tr><tr><td>G5-17-2</td><td>50</td><td>1.876</td><td>0.350</td><td>0.340</td><td>-0.026</td><td>14.5</td><td>10.8 - 19.4</td></tr><tr><td>G5-18-1</td><td>50</td><td>1.873</td><td>0.338</td><td>0.336</td><td>0.011</td><td>30.3</td><td>25.9 - 35.8</td></tr><tr><td>G5-18-2</td><td>50</td><td>1.895</td><td>0.349</td><td>0.349</td><td>-0.002</td><td>12.1</td><td>6.9 - 23.7</td></tr><tr><td>G5-18-3</td><td>50</td><td>1.900</td><td>0.352</td><td>0.351</td><td>0.000</td><td>4.4</td><td>2.7 - 7.6</td></tr><tr><td>Mean per sample</td><td>33.4 (±14.5)</td><td>1.888 (±0.021)</td><td>0.349 (±0.015)</td><td>0.342 (±0.013)</td><td>-0.018 (±0.036)</td><td>-</td><td>-</td></tr><tr><td>G4 pooled</td><td>217</td><td>1.916</td><td>0.357</td><td>0.364</td><td>0.027</td><td>65.0</td><td>57.4 - 73.8</td></tr><tr><td>G5 pooled</td><td>250</td><td>1.915</td><td>0.341</td><td>0.360</td><td>0.063</td><td>53.8</td><td>46.5 - 62.4</td></tr><tr><td>Farm-Tot</td><td>467</td><td>1.916</td><td>0.348</td><td>0.363</td><td>0.055</td><td>66.8</td><td>61.3 - 72.8</td></tr></tbody></table>

**Table 5:** Results of the Shapiro-Wilks test, Levene's test and Wilcoxon test between wild populations and hatchery cohorts. The Wilcoxon test was conducted on values of Allelic richness (Ar), Observed heterozygosity (Ho), Expected heterozygosity (He) and Wright's *F*<sub>IS</sub> statistics. Only *n*, *v*-value, and *p*-values are presented in this table. Significant *p*-values are in bold.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">n</th><th>Shapiro Test</th><th colspan="2">Levene's Test</th><th colspan="2">Wilcoxon test</th></tr><tr><th>P-value</th><th>F value</th><th>P-value</th><th>W</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="2">Ar</td><td rowspan="2">Wild Hatchery</td><td>17</td><td>0.06</td><td rowspan="2">3.11</td><td rowspan="2">0.09</td><td rowspan="2"><strong>68.50</strong></td><td rowspan="2"><strong>0.05</strong></td></tr><tr><td>14</td><td>0.50</td></tr><tr><td rowspan="2">Ho</td><td rowspan="2">Wild Hatchery</td><td>17</td><td>0.09</td><td rowspan="2">0.39</td><td rowspan="2">0.54</td><td rowspan="2"><strong>179.00</strong></td><td rowspan="2"><strong>0.02</strong></td></tr><tr><td>14</td><td>0.10</td></tr><tr><td rowspan="2">He</td><td rowspan="2">Wild Hatchery</td><td>17</td><td>0.93</td><td rowspan="2">1.78</td><td rowspan="2">0.19</td><td rowspan="2"><strong>48.00</strong></td><td rowspan="2"><strong>&lt;0.01</strong></td></tr><tr><td>14</td><td>0.58</td></tr><tr><td rowspan="2">F<sub>IS</sub></td><td rowspan="2">Wild Hatchery</td><td>17</td><td>0.09</td><td rowspan="2">0.36</td><td rowspan="2">0.55</td><td rowspan="2"><strong>18.00</strong></td><td rowspan="2"><strong>&lt;0.01</strong></td></tr><tr><td>14</td><td>0.21</td></tr></tbody></table>

797

798
**Table 6:** AMOVA results with degree of freedom, sum of square values, variance components and the percentage of variations. The variation was calculated at four levels: Among groups, Among populations within groups, Among individuals within populations and Within individuals. Fixation indices are also indicated below the main table with fixation index between groups ($F_{CT}$), fixation index among populations within groups ($F_{SC}$), Wright's $F_{IS}$ statistics, and Overall fixation index ($F_{IT}$). Significant p-values are in bold.

<table><thead><tr><th>Source of Variation</th><th>d.f.</th><th>SS</th><th>Est. Var.</th><th>%</th></tr></thead><tbody><tr><td>Among groups</td><td>1</td><td>111.94</td><td>0.27</td><td>2.11%</td></tr><tr><td>Among populations within groups</td><td>15</td><td>201.83</td><td>0.02</td><td>0.14%</td></tr><tr><td>Among individuals within populations</td><td>412</td><td>5177.01</td><td>0.07</td><td>0.59%</td></tr><tr><td>Within individuals</td><td>429</td><td>5326.00</td><td>12.41</td><td>97.16%</td></tr><tr><td>Total</td><td>857</td><td>10816.77</td><td>12.78</td><td>100.00%</td></tr><tr><th>Fixation Indices</th><th>Value</th><th>P-value</th><th></th><th></th></tr><tr><td>F<sub>CT</sub></td><td><strong>0.02</strong></td><td><strong>&lt;0.01</strong></td><td></td><td></td></tr><tr><td>F<sub>SC</sub></td><td>0.00</td><td>0.11</td><td></td><td></td></tr><tr><td>F<sub>IS</sub></td><td>0.00</td><td>0.27</td><td></td><td></td></tr><tr><td>F<sub>IT</sub></td><td>0.03</td><td>0.08</td><td></td><td></td></tr></tbody></table>

805
806 **Figure captions**

807 **Figure 1:** Location of the wild samples of European abalone included in this study.

808

809 **Figure 2:** Heatmap of pairwise *F*<sub>ST</sub> estimates between wild population samples. The significance 810 was tested with 1000 permutations of individuals among populations (* F*<sub>ST</sub> estimates significantly 811 differentiated from null hypothesis, based on bootstraps). Wild populations are ordered from the 812 southeastern population to the northeastern population, following Brittany and Normandy 813 coastline.

814

815 **Figure 3:** Boxplots of Pairwise *F*<sub>ST</sub> values, subdivided as: wild northeastern (Wild NE) vs. wild 816 southwestern (Wild SW) samples, as a reference; wild NE or wild SW samples vs. the fourteen 817 hatchery samples (Hatchery samples); wild NE or wild SW samples vs. all hatchery samples pooled 818 together (Pooled hatchery).

819

820 **Figure 4:** Assignment results from Rtess3 with an ancestry matrix. Each individual was 821 proportionally assigned on a two group basis. (A) Rtess3 results without taking geographical 822 distances into account, (B) Rtess3 results taking into account geographical distances. Individuals 823 are ordered based on their population of origin, and populations are ordered from the 824 southeastern population to the northeastern population, following Brittany and Normandy 825 coastline.

826

827 **Figure 5:** Biplot of geographical (least-cost) and genetic (*F*<sub>ST</sub>/(1-*F*<sub>ST</sub>)) distances among wild 828 populations, with each point representing pairwise comparisons between two sites. Figure (A)
829 represents pairwise comparisons between populations from the "South" group only while (B)

830 represents only the "North" group.

831

<figure><img src="image_3.png" /></figure>

832

<figure><img src="image_4.png" /><figcaption>833 Figure 1</figcaption></figure>

834

<caption>835 Figure 2</caption>
<figure><img src="image_5.png" /></figure>

836

837 Figure 3
<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>

838

839

840
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 5</figcaption></figure>