# Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Québec, Canada) Genetic structure of sugar kelp

Marie Treillefort, Sabrina Le Cam, Myriam Valero, Stéphane Mauger, Paolo Ruggeri, Flora Salvo, Isabelle Gendron-Lemieux, Tamara Provencher, Rénald Belley, France Dufresne

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marie Treillefort, Sabrina Le Cam, Myriam Valero, Stéphane Mauger, Paolo Ruggeri, et al.. Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Québec, Canada) Genetic structure of sugar kelp. Journal of Phycology, In press, 10.1111/jpy.70141. hal-05494465

HAL Id: hal-05494465

https://hal.science/hal-05494465v1

Submitted on 4 Mar 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Québec, Canada)

Marie Treillefort<fnref n="1" /> | Sabrina Le Cam<fnref n="1" /> | Myriam Valero<fnref n="2" />,<fnref n="3" /> | Stéphane Mauger<fnref n="2" /> |

Paolo Ruggeri<fnref n="3" /> | Flora Salvo<fnref n="4" /> | Isabelle Gendron-Lemieux<fnref n="4" />,<fnref n="5" /> | Tamara Provencher<fnref n="4" /> |

Rénald Belley<fnref n="6" /> | France Dufresne<fnref n="1" /> ID

**Correspondence**

France Dufresne, Département de Biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada.

Email: france_dufresne@uqar.ca

**Present address**

Stéphane Mauger, CNRS, UMR7266, Littoral Environnement et Sociétés, La Rochelle Université, La Rochelle, France

Paolo Ruggeri, Xelect Ltd, St Andrews, UK

**Funding information**

Mitacs, Grant/Award Number: IT28986; Aquaculture Collaborative Research and Development Program; Centre National de la Recherche Scientifique; Institut France-Québec

Editor: M. Edwards

**Abstract**

The sugar kelp, *Saccharina latissima*, is cultivated at low scale in Quebec, Canada, and current practice involves seeding meiospores or gametophyte stocks onto spools carrying twine and transferring these to a seaweed farm site. As the stocks can originate from locations spanning several hundreds of kilometers from the farm sites, such practices could involve genetic contamination and disrupt local adaptations. Assessing genetic structure can inform us of the potential risks associated with these practices. Here, we characterized the genetic diversity and structure of *S. latissima* from locations in the St. Lawrence Estuary and Gulf at both microsatellite loci (308 sporophytes at 22 loci in 16 sites) and genomic markers (228 sporophytes at 6578 single nucleotide polymorphisms, or SNPs, in 13 sites). Several populations had low heterozygosity values and significant *F*<sub>IS</sub> values at microsatellite loci. No genetic structure was observed among populations

**Abbreviations:** AMOVA, analysis of molecular variance; ANOVA, analysis of variance; AR, allelic richness; BON, Bonaventure; BONDC, sporophytes from Bonaventure on growing lines; CAD, Cap Desrosiers; CAO, Cap-aux-Os; CASCA, Cascapedia; COL, Colbourne; COR, Cormorandière; dNTP, deoxyribonucleotide; EST, expressed-sequence-tag; *F*<sub>IS</sub>, inbreeding coefficient; GRA, Grande-Rivière; He, expected heterozygosity; Ho, observed heterozygosity; IV, Ile verte; LUD, Baie St-Ludger; MgCl2, magnesium chloride; NEW, in the Baie-des-Chaleurs: Newport; NOR, Sept-Iles; PAS, Paspébiac; PCA, principal component analysis; PCR, polymerase chain reaction; Purmer algaculture site: PUR, in the Gulf along the North Shore: Ile Grosse Boule; ROO, around the Iles-de-la-Madeleine: Rochers-aux-Oiseaux; SAM, along the Gaspé Peninsula: Sainte-Anne-des-Monts; sNMF, sparse non-negative matrix factorization; SNP, single nucleotide polymorphisms; TAD, Tadoussac.

Marie Treillefort and Sabrina Le Cam are equally contributed.

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2026 The Author(s). *Journal of Phycology* published by Wiley Periodicals LLC on behalf of Phycological Society of America.

<footnote n="1">¹Département de Biologie, Université du Québec à Rimouski, Rimouski, Quebec, Canada</footnote>

<footnote n="2">²CNRS, PUCCH, UACH, UMI 3614, Evolutionary Biology and Evolution of Algae, Station Biologique de Roscoff, Sorbonne Université, Roscoff, France</footnote>

<footnote n="3">³CNRS, UMR 7144, Dispersal Speciation and Evolution of Marine Species, Laboratory Adaptation et Diversité en Milieu Marin (AD2M), Station Biologique de Roscoff, Sorbonne Université, Roscoff, France</footnote>

<footnote n="4">⁴Merinov, Gaspé, Quebec, Canada</footnote>

<footnote n="5">⁵École des pêches et de L'aquaculture du Québec, Cégep de la Gaspésie et des Îles, Grande-Rivière, Quebec, Canada</footnote>

<footnote n="6">⁶Fisheries and Oceans Canada, Maurice Lamontagne Institute, Mont-Joli, Quebec, Canada</footnote>
with microsatellite loci, but strong genetic structuring was observed with the genomic data. Population structure followed a geographic pattern and was congruent with major currents. Individuals from the wild population in the vicinity of the farm site were genetically distinct from the sporophytes on the growing lines that belong to a genetically distinct group. There was no significant genetic differentiation between wild individuals living in proximity to the farm site and another wild population of the same area. Hence, aquaculture practices have not resulted in changes in the genetic composition of the wild population on a large scale. Our results are important to guide future conservation efforts and the seaweed farming industry.

KEYWORDS

DartSeq, genomic markers, microsatellite markers, population genetics, *Saccharina latissima*, St. Lawrence Estuary and Gulf

INTRODUCTION

Kelp farming has long been carried out in coastal Asia and is expanding in Europe, South America, and North America (Augyte et al., 2017; Bjerregaard et al., 2016; Breton et al., 2018; Campbell et al., 2019; Goecke et al., 2020; Grebe et al., 2019; Hwang et al., 2019; Kim et al., 2019; Tamigneaux & Johnson, 2016). Kelp is usually harvested when mature in the wild and then grown on lines and harvested for use in many industries (pharmaceutical and biomedical products, cosmetics, feed supplements, or biofuels; Bartsch et al., 2008; Forbord et al., 2012; Jahan et al., 2017; Marinho et al., 2015). *Saccharina latissima* is currently one of the main cultivated species in Europe and North America (Breton et al., 2018; Tamigneaux & Johnson, 2016). The increased popularity of this species for seaweed farming has raised consideration of developing best practices, including farming without perturbing wild populations.

In the St. Lawrence Gulf, *Saccharina latissima* has been cultivated experimentally and at low scale since 2006 (Tamigneaux & Johnson, 2016). This cultivation involves collecting mature sori from wild individuals and seeding young sporophytes on farming lines until sufficient growth is reached to harvest. Seedlings are then transferred onto lines at sites that can be located several hundreds of kilometers away from their original location. Even though sporophytes are usually harvested before developing fertile meiospores, there is a risk of genetic contamination if sporophytes are detached from the culture lines and disperse into nearby wild populations. The risk of genetic contamination depends on the extent of genetic differentiation between the population on the cultivation lines and the wild populations near the algaculture sites. Addressing the genetic diversity and genetic structure of this important founding species in the St. Lawrence Estuary and Gulf is an important step toward efficient management.

Many studies have examined the genetic structure of the sugar kelp, *Saccharina latissima*, using different

types of molecular markers reflecting different time scales (Breton et al., 2018; Guzinski et al., 2016; Nielsen et al., 2016; Neiva et al., 2018). Deep biogeographic history has been revealed with mitochondrial DNA (Neiva et al., 2018). Three phylogroups corresponding to (1) NE (British Columbia) Pacific, Greenland, and Hudson Bay; (2) NE Atlantic; and (3) NW Atlantic were characterized (Neiva et al., 2018). Colonization from the NE Pacific into the Arctic and the NE Atlantic is thought to have occurred 5.5 million years ago (Luttikhuizen et al., 2018). Pleistocene glaciations have compressed NE Atlantic populations to the south and isolated NE and NW Atlantic populations (Neiva et al., 2018). At the scale of the eastern Atlantic region, northern (Norway, Sweden, Denmark) and southern (France) populations formed distinct clusters at 25 microsatellite loci, with an average $F_{ST}$ of 0.36 (Guzinski et al., 2016). This strong differentiation between northern and southern Europe was refined recently based on sampling from of 24 sites, which confirmed the occurrence of two refugia, one in Southern Europe and the other at high latitude further north than northern Norway (Jaugeon et al., 2025). Strong genetic structure has been detected at genomic markers among sites from Scotland to Portugal (Guzinski et al., 2020). Within-population genetic diversity was lowest for the southern populations (Spain and Portugal) and the isolated island population on Helgoland, German Bight, and highest in Spitsbergen for both single nucleotide polymorphism (SNP) and expressed-sequence-tag (EST)-derived microsatellites (Guzinski et al., 2020). This pattern was in agreement with the hypothesis of a high-latitude refugia that has persisted through glaciation cycles (Jaugeon et al., 2025). A survey of 21 sites along the Norwegian coastline using nine microsatellite loci showed a relatively uniform genetic structure along 1000 km (Ribeiro et al., 2022). By contrast, fine-scale genetic structure and low within-population genetic diversity have been observed using microsatellite markers along the Maine region, United States (Breton et al., 2018).
Despite its importance as a foundation species and for the seaweed farming industry, baseline levels of genetic and genomic diversity have never been recorded in *Saccharina latissima* from the Estuary and Gulf of St. Lawrence. A recent study of *Zostera marina* revealed significant levels of genetic structuring in the Estuary and Gulf of St. Lawrence (Treillefort, 2023). By contrast, the few studies that have examined genetic structure in animals that disperse more actively have noted very weak structuring (Ferchaud et al., 2022). Populations of northern shrimp, *Pandalus borealis*, from the Estuary did not differ genetically from those from the northern Gulf (Bourret et al., 2024). Genetic differentiation was also very low among larger sampling regions: Estuary and northern St. Lawrence Gulf, Scotian Shelf, Newfoundland and Labrador, and the Flemish Cap at neutral SNP (Bourret et al., 2024).

This study aimed to characterize genetic diversity and assess the genetic structure of wild populations of *Saccharina latissima* from the St. Lawrence seaway using microsatellite and genomic markers. As this species does not disperse actively, we expected currents to drive much of the genetic structure at neutral SNP (Jaugeon et al., 2025). We expected that sites located on the North Shore would be differentiated from other sites because of the presence of the Anticosti gyre (Figure 1). The Gaspé Current outflows from the St. Lawrence River, which moves around the Gaspé Peninsula and along the southern shore of the Gulf of St. Lawrence (Savenkoff et al., 1996). Therefore, sites in the Estuary and the Gaspé coast may form another genetic cluster. Finally, sites from the southern Gaspé coast and the southern Gulf should be genetically distinct due to the downward movement of the Gaspé Current. As individuals of *S. latissima* may dislodge from lines during their cultivation, there may be genetic contamination by individuals grown from the Baie-des-Chaleurs in the North Shore population. Assuming that sugar kelps from these two areas are genetically distinct, genetic contamination would result in sporophytes from areas near the culture sites being more genetically similar to those from the Baie-des-Chaleurs sites than to populations from their own region. More subtle genetic contamination may be revealed through higher observed heterozygosity levels at the farm site. Information on this key species can help create appropriate recommendations for managing aquaculture and preserving biodiversity in the St. Lawrence Gulf and Estuary.

# MATERIALS AND METHODS

## Microsatellite markers

### Sampling

Adult sporophytes of *Saccharina latissima* (n=480) were sampled throughout the Estuary and Gulf of St.

Lawrence in 2018 and 2019. Thirty individuals were collected at each site in the Estuary: Tadoussac (TAD), Ile verte (IV), Baie St-Ludger (LUD); in the Gulf along the North Shore: Ile Grosse Boule (Purmer algaculture site: PUR), Sept-Iles (NOR); along the Gaspé Peninsula: Sainte-Anne-des-Monts (SAM), Cap Desrosiers (CAD), Cap-aux-Os (CAO), Grande-Rivière (GRA); in the Baie-des-Chaleurs: Newport (NEW), Colbourne (COL), Paspébiac (PAS), sporophytes from Bonaventure on growing lines (BONDC), Bonaventure (BON), and Cascapedia (CASCA); and around the Iles-de-la-Madeleine: Rochers-aux-Oiseaux (ROO) and Cormorandière (COR; Figure 1; Table 1). Two small pieces of tissue (1 cm²) were cut from the basal blades of individual sporophytes and placed in silica gels until DNA extraction. The site PUR was located in the vicinity of the growing lines and BONDC consisted of sporophytes on the growing lines.

## DNA extractions

Total genomic DNA was extracted from 5 to 10 mg of dry tissue using the Nucleospin 96 plant kit (Macherey-Nagel, Germany). The extractions were performed according to the manufacturer's instructions, except that samples were left in the lysis buffer at room temperature for 1 h rather than being heated to 65°C for 30 min. The extracted DNA was eluted in 200 µL of the supplied elution buffer and one additional wash with buffer PW1 and one additional wash with buffer PW2 to more effectively remove polymerase chain reaction (PCR) inhibitors and polysaccharides.

## Microsatellite loci amplification

Overall, 44 microsatellite loci for *Saccharina latissima* were amplified. Twelve of these loci were previously described in Paulino et al. (2016), and 32 Expressed Sequenced Tag (EST)-derived microsatellite loci were selected from the study of Guzinsky. For the multiplexes with the 12 loci characterized by Paulino et al. (2016), PCR mixes were prepared following their instructions except for deoxyribonucleotides (dNTPs, which were adjusted to 150 µM) and the Taq polymerase (adjusted to 0.5 U). For the multiplexe from the ESTs, amplifications were carried out in 20-µL reaction volumes with each reaction comprising 2 µL of DNA template diluted 1:50 (to help reduce PCR inhibitors), 2 mM of MgCl₂, 1×GoTaq® Flexi PCR buffer, 150 µM of dNTP, 400 nM of forward primer, 400 nM of reverse primer and 0.5 U of GoTaq® Flexi (Promega Corporation, Madison, Wisconsin, United States). All PCR amplifications were performed using a T100TM Thermal Cycler (Bio-Rad Laboratories, Inc., Hercules, California, United States),
<figure><img src="image_3.png" /><figcaption>FIGURE 1 (a) Map of the sampling sites for *Saccharina latissima* in the Estuary and Gulf of St. Lawrence, Quebec, Canada. The source population site (BON) is represented with a circle, and the industry farm (PUR) with a star. Pie charts correspond to the relative proportions of the seven putative ancestral populations inferred by Bayesian clustering of the genetic diversity at SNP markers. (b) Graphical representation of ancestry estimates for 249 individuals, each represented by a vertical line with K segments colored proportionally to their belonging to a genetic cluster ($k=7$). The percent ancestry is plotted on the y-axis.</figcaption></figure>

and parameters used were described in Paulino et al. (2016) and Guzinski et al. (2016) for the EST multiplexes. Next, 2µL of PCR product was added to 10µL of loading buffer made up of 0.5µL of the SM594 size standard (Mauger et al., 2012) and 9.5µL of Hi-Di formamide, denatured at 95°C for 3min, and run in an ABI 3130 XL capillary sequencer (Applied Biosystems, United States). Genotypes were scored manually in

Genemapper version 4.0 (Applied Biosystems) and verified by two readers.

## Genomic markers

A total of 282 adult sporophytes of *Saccharina latissima* selected from 13 locations were sent to
<table><caption>TABLE 1 Sampling site and their GPS coordinates of *Saccharina latissima* individuals in the St. Lawrence Estuary and Gulf.</caption><thead><tr><td>Site</td><td>Latitude</td><td>Longitude</td></tr></thead><tbody><tr><td>Tadoussac (TAD)</td><td>48.133119</td><td>-69.705842</td></tr><tr><td>Ile verte (IV)</td><td>48.034772</td><td>-69.340821</td></tr><tr><td>Baie St-Ludger (LUD)</td><td>49.161609</td><td>-68.173798</td></tr><tr><td>Ste-Anne-des-Monts (SAM)</td><td>49.130887</td><td>-66.500031</td></tr><tr><td>Pointe-Noire (NOR)</td><td>50.163282</td><td>-66.467896</td></tr><tr><td>Ile Grosse Boule (PUR)</td><td>50.151276</td><td>-66.325630</td></tr><tr><td>Cap-des-Rosiers (CAD)</td><td>48.859972</td><td>-64.190300</td></tr><tr><td>Cap-aux-Os (CAO)</td><td>48.840380</td><td>-64.331696</td></tr><tr><td>Grande-Rivière (GRA)</td><td>48.392669</td><td>-64.501934</td></tr><tr><td>Newport (NEW)</td><td>48.263541</td><td>-64.735703</td></tr><tr><td>Colborne (COL)</td><td>48.181379</td><td>-64.895210</td></tr><tr><td>Paspébiac (PAS)</td><td>48.017946</td><td>-65.265587</td></tr><tr><td>Bonaventure (BONDC)</td><td>48.031149</td><td>-65.479357</td></tr><tr><td>Bonaventure(BON)</td><td>48.041124</td><td>-65.491133</td></tr><tr><td>Cascapédia (CASCA)</td><td>48.175107</td><td>-65.918175</td></tr><tr><td>Cormorandière (COR)</td><td>47.496607</td><td>-61.723938</td></tr><tr><td>Rocher-aux-Oiseaux (ROO)</td><td>47.806514</td><td>-61.232300</td></tr></tbody></table>

Note: BON is the source site and PUR is the farm site.

Diversity Arrays Technology (DART) at the University of Canberra (Australia). Three sites were in the St. Lawrence Estuary (TAD, IV, and LUD), two were along the North Shore (PUR and NOR), three were along the Gaspé Peninsula (SAM, CAD, and CAO), three were in the Baie-des-Chaleurs (NEW, PAS, and BON), and two were around the Iles-de-la-Madeleine (ROO and COR). Libraries were constructed according to Kilian et al. (2012). DARTseq is a proprietary genome complexity reduction-based sequencing technology that differs from other methods by its ability to select the predominantly active—low copy sequence— areas in a genome, which are the ones containing the most useful information. At the same time, DARTseq masks the lesser value, repetitive sequences. It does this through the application of a combination of restriction enzymes to fragment DNA samples in a highly reproducible manner. DARTseq complexity reduction method was used through digestion of genomic DNA with two restriction enzymes (PstI and MseI) and ligation of barcode adapters followed by PCR amplification of adapter-ligated fragments. Libraries were sequenced using single-read sequencing runs for 77 bases by Hiseq2500. In the primary pipeline the fastq files were first processed to filter away poor quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence (minimum Phred pass score = 30 and minimum pass percentage = 75). Approximately 2,500,000 sequences per barcode/ sample were identified and used in marker calling.

Finally, identical sequences were collapsed into “fastqcoll files.” The fastqcoll files were “groomed” using DART PL’s proprietary algorithm, which corrects low-quality bases from singleton tag into a correct base using collapsed tags with multiple members as a template. The “groomed” fastqcoll files were used in the secondary pipeline for DART PL’s proprietary SNP and SilicoDART (presence/absence of restriction fragments in representation) calling algorithms (DARTsoft14). Thirty replicates were produced to assess the reproducibility of the sequences and average reproducibility was 99%. Markers were aligned to the *S. japonica* (SJ6l.1) genome.

## Data analysis

### Microsatellite markers

The presence of sequencing errors like stuttering bands, allelic dropouts, and null alleles was verified with the MICRO-CHECKER software version 2.2.3 (Van Oosterhout et al., 2004). Monomorphic loci, loci with more than 30% of missing data, and loci with missing data for all individuals for a site were removed from the database. All individuals with more than three missing data points per loci were also removed from the database. After selection, 22 loci were retained for 308 individuals. All locations were kept for analyses except CAO, since it contained too much missing data. The number of individuals per site ranged from nine to 28. Allelic richness was estimated in FSTAT, version 2.9.4 (Goudet, 2005a).

### Genomic markers

Initially, we received 68,971 single-row SNP markers from DART Pty Ltd. These DART loci were then filtered using the dartR library (Gruber et al., 2018) in Rstudio (version 4.2; Rstudio, 2015) to obtain only informative loci. Four filters were used. We removed (1) all the monomorphic loci (27,525 remaining SNPs in 282 individuals), (2) the loci with more than 0.5% of missing data (20,131 remaining SNPs in 282 individuals), (3) the duplicate loci (18,623 remaining SNPs in 282 individuals), and (4) the individuals with more than 20% of missing data (33 individuals). After recalculating the loci call rate, an additional 11,771 loci were removed, resulting in a total of 6582 SNPs remaining in 249 individuals. The number of individuals ranged from nine to 30, except for at TAD, which had only two individuals and was not considered for diversity indices estimations. Genetic diversity indices and population differentiation analyses were estimated only for sampling sites with a minimum sample size of nine individuals.
## Both marker types

Genetic diversity indices were estimated using the hierfstat package (Goudet, 2005b). Inbreeding coefficients ($F_{IS}$) were calculated together with these indices, and the significance was calculated with 1000 iteration bootstraps. Genetic differentiation was estimated with the hierfstat package (Gruber et al., 2018) using fixation index $F_{ST}$ (Weir & Cockerham, 1984), and its statistical significance was tested by performing 1000 bootstraps.

A principal component analysis (PCA) was performed to allow a visual interpretation of the genetic structure of the data using the DartR package version 2.9.7 (Gruber et al., 2018). The analyses of isolation by distance were tested using the in-water non-Euclidean paths as the distance between sites. These data were retrieved using the marmap package version 1.0.10 (Pante & Simon-Bouhet, 2013). Individual ancestry coefficients for both microsatellites and the SNP data were estimated using the sparse non-negative matrix factorization (sNMF) method (Frichot et al., 2014) as implemented in the R package LEA (Frichot & François, 2015). The sNMF method is robust against deviations from the Hardy-Weinberg equilibrium and does not rely on any a priori population genetic assumptions (Frichot et al., 2014). This method offers comparable performance to Structure and ADMIXTURE while providing significantly faster run times for genomic datasets. The number of putative clusters ($K$) ranged between 1 and the number of sampling sites (depending on the marker), and for each $K$, 100 repetitions with 99 maximum iterations were carried out. Ten percent of the genotypes were masked at each run to compute the cross-entropy criterion. The $K$ with the smallest cross-entropy value and/or from which this parameter stabilized was selected. Analyses of molecular variance (AMOVA) were performed to examine the distribution of genetic variation within and among populations using the poppr R package (Kamvar et al., 2015). This analysis, analogous to an analysis of variance (ANOVA) analysis, partitions genetic diversity at multiple hierarchical levels, such as within individuals, among individuals within populations, and among populations themselves, and allows for the identification of genetic population structure. Group discrimination was based on PCA results on SNP markers—Group 1: IV, LUD, SAM, and CAD; Group 2: NOR and PUR; Group 3: NEW, BON, PAS, and CAO; and Group 4: Iles-de-la-Madeleine (ROO and COR)—and applied to both microsatellite and SNP data.

# RESULTS

## Genetic diversity

Genetic diversity indices calculated with microsatellite markers were relatively low, with expected heterozygosity ($He$) values ranging from 0.249 to 0.339 and a mean

number of alleles of 2.8 on average per site (Table 2, Figure 2a). Moreover, more than half of the sites had positive and significant $F_{IS}$ values. Only five sites were in Hardy-Weinberg equilibrium; the others showed heterozygote deficiency. Genetic diversity indices using SNP indices were 10× lower, with $He$ ranging from 0.0656 (IV) to 0.083 (PUR; Table 2, Figure 2b). Populations from the Estuary and Gaspé Peninsula (CAD, SAM, IV, LUD) had lower heterozygosity than the rest of the sampling sites. Seven sites had positive $F_{IS}$ values; five of them also showed positive values at microsatellite markers. Sites from the Estuary had lower heterozygosity values than those from the Gulf at SNP but not at microsatellite markers. The farm site (PUR) had lower allelic richness and heterozygosity than the wild site in the vicinity (NOR).

## Genetic structure

The matrix of pairwise $F_{ST}$ (Figure 3a) values revealed low genetic differentiation among the 16 locations using microsatellite markers. Significant $F_{ST}$ values were observed between the Iles-de-la-Madeleine location (ROO) and most of the other sites and between the Sept-Iles site and many locations. *Saccharina latissima* from Bonaventure (BON) used for algal culture showed small but significant genetic differentiation with individuals collected in NOR (located 1 km from the cultivation line on the North Shore). All pairwise $F_{ST}$ obtained from SNPs were significant, with the highest values between the Iles-de-la-Madeleine and the Estuary (from 0.18 to 0.12) and the lowest values among sites within the Baie-des-Chaleurs (from 0.01 to 0.08; Figure 3b). Individuals from PUR (aquaculture site) showed little genetic differentiation from NOR individuals located 1 km from that site and were genetically differentiated from the Baie-des-Chaleurs site (BON), where meiospores were collected from 2014 to 2018. Mantel tests revealed no significant isolation by distance at microsatellites (Figure 4a) but a significant relationship at SNPs (Figure 4b). The PCAs from microsatellite markers revealed the presence of a single group composed of all 16 sites (Figure 5a). By contrast, the PCA with SNP markers revealed significant structure, with Estuary (TAD, IV, LUD) and Gaspé Peninsula sites (SAM and CAD) forming the first group and North Shore sites (NOR and PUR) on the first axis (6.93% of total genetic variance) forming a second group. The Baie-des-Chaleurs sites (BON, PAS, and NEW), CAO, and the Iles-de-la-Madeleine sites (ROO and COR) separated along the second principal component (4.71%; Figure 5b) and formed the third group.

Population structure analyses determined that one ancestral population was the most likely scenario with microsatellite markers, with the minimum
<table><caption>TABLE 2 Genetic diversity indices for microsatellite and SNP methods: Sample size (n), expected heterozygosity (He), mean allelic richness (AR), inbreeding coefficient values (FIS).</caption><thead><tr><th rowspan="2">Site</th><th colspan="4">Microsatellites</th><th colspan="3">SNPs</th></tr><tr><th>n</th><th>He</th><th>AR</th><th>F<sub>IS</sub></th><th>n</th><th>He</th><th>F<sub>IS</sub></th></tr></thead><tbody><tr><td>TAD</td><td>14</td><td>0.330</td><td>1.68</td><td>-0.106</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IV</td><td>15</td><td>0.249</td><td>1.51</td><td><strong>0.236*</strong></td><td>9</td><td>0.0656</td><td><strong>0.0396*</strong></td></tr><tr><td>LUD</td><td>9</td><td>0.336</td><td>1.66</td><td>0.188</td><td>9</td><td>0.0671</td><td><strong>0.1040*</strong></td></tr><tr><td>SAM</td><td>22</td><td>0.321</td><td>1.66</td><td><strong>0.131*</strong></td><td>20</td><td>0.0719</td><td><strong>0.0200*</strong></td></tr><tr><td>NOR</td><td>22</td><td>0.339</td><td>1.71</td><td><strong>0.118*</strong></td><td>29</td><td>0.0890</td><td><strong>0.0256*</strong></td></tr><tr><td>PUR</td><td>20</td><td>0.306</td><td>1.64</td><td><strong>0.088*</strong></td><td>23</td><td>0.0833</td><td>-0.0063</td></tr><tr><td>CAD</td><td>19</td><td>0.332</td><td>1.69</td><td><strong>0.077*</strong></td><td>12</td><td>0.0701</td><td><strong>0.0158*</strong></td></tr><tr><td>CAO</td><td>-</td><td>-</td><td>-</td><td>-</td><td>21</td><td>0.0795</td><td>-0.0076</td></tr><tr><td>GRA</td><td>15</td><td>0.317</td><td>1.66</td><td>0.045</td><td>-</td><td>-</td><td>-</td></tr><tr><td>NEW</td><td>21</td><td>0.308</td><td>1.66</td><td><strong>0.156*</strong></td><td>30</td><td>0.0764</td><td>0.005</td></tr><tr><td>COL</td><td>23</td><td>0.280</td><td>1.58</td><td>0.038</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PAS</td><td>23</td><td>0.274</td><td>1.57</td><td><strong>0.150*</strong></td><td>11</td><td>0.081</td><td>-0.0141</td></tr><tr><td>BONDC</td><td>13</td><td>0.281</td><td>1.6</td><td><strong>0.381*</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>BON</td><td>23</td><td>0.270</td><td>1.58</td><td>0.047</td><td>24</td><td>0.0804</td><td>0.0102</td></tr><tr><td>CASCA</td><td>22</td><td>0.260</td><td>1.53</td><td><strong>0.105*</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>COR</td><td>21</td><td>0.273</td><td>1.56</td><td><strong>0.155*</strong></td><td>13</td><td>0.0809</td><td><strong>0.0611*</strong></td></tr><tr><td>ROO</td><td>26</td><td>0.277</td><td>1.58</td><td>-0.060</td><td>25</td><td>0.0803</td><td><strong>0.0368*</strong></td></tr></tbody></table>

Note: Values in bold with an asterisk are significant. BON is the source site and PUR is the farm site.

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Heterozygosities (observed and expected) estimated from (a) microsatellite markers and (b) SNP and presented for each sampling site.</figcaption></figure>

cross-entropy recorded at K = 1. When using the SNP markers, the most probable number of ancestral populations was seven, after which the cross-entropy criterion reached a plateau (Figure 1). The estuarian sites (IV, TAD, and LUD) all shared a common major ancestral cluster, the Gaspesian sites (SAM and CAD) shared another one, and the sites from Iles-de-la-Madeleine (ROO and COR) formed a third cluster (Figure 1). The CAO site, separated from CAD on the Gaspé peninsula by narrow land, formed a group

with NEW. The sites located deeper in the Baie-des-Chaleurs (PAS and BON) formed another cluster. The sites from the north shore (NOR and PUR) were separated into distinct clusters.

F analyses explained only 0.71% of the variation at microsatellite loci—this percentage was not significant—whereas the variance explained by the four groups at SNP markers accounted for more than 7% of the total variation and was significant ($F_{SR}=0.045$, $p=0.01$; Table 3).
<figure><img src="image_5.png" /></figure>

FIGURE 3 Matrix of pairwise $F_{ST}$ estimated from (a) microsatellite and (b) SNP markers. Values in bold represent significant values. Significance was tested using bootstrapping (N = 10,000) and significant values are presented in bold. Pairwise comparisons most relevant are PUR-BON, NOR-BON, and PUR-NOR.

<figure><img src="image_6.png" /></figure>

FIGURE 4 Mantel test plot performed using (a) microsatellites, and (b) SNP markers displaying the correlation between genetic and coastal distances among sugar kelp populations. Correlation coefficient *r* and *p*-value are shown.

DISCUSSION

This study examined patterns of genetic diversity and structure in wild populations of *Saccharina latissima* from the St. Lawrence Estuary and Gulf. Results showed low genetic diversity in *S. latissima* from the St. Lawrence seaway, with half of the sites showing high *F*<sub>IS</sub> indices and a low number of effective alleles per locus. Observed low levels of genetic diversity may be due to bottlenecks following postglacial colonization in the St. Lawrence River. A similar study in Maine, United States, observed higher levels of allelic richness (3.6 and 4.2) with only six microsatellite markers and no inbreeding (Breton et al., 2018). A worldwide survey using 12 microsatellite loci reported that the Eastern Atlantic (Europe) populations were more genetically diversified than the Western Atlantic (US and Canadian Coasts) and the Pacific populations (Neiva et al., 2018). Heterozygosity levels were lower in NW Atlantic *S. latissima* populations, and several sites had high inbreeding levels (Neiva et al., 2018). Three of our sites were sampled in this Neiva

et al. (2018) study (NOR, NEW, and BON) and had an average allelic richness of 3.9, which was similar to other populations from the NW Atlantic but higher than our values (around 3). In contrast to our present results comparing the same sites, Neiva et al. (2018) did not detect any inbreeding at our three locations but did at several other NW sites including the Canadian Maritimes. Allelic richness was twice as high in NE Atlantic populations compared to our results for the same set of microsatellite loci (Guzinski et al., 2016). Higher levels of genetic diversity in the NE than in the NW populations have also been observed in *Laminaria digitata* (Neiva et al., 2020). Guzinski et al. (2020) observed higher levels of genetic diversity at SNPs (from ddradseq) in the northernmost localities of Norway and Scotland (Ho of 0.05) than in more southern populations located in Portugal (Ho of 0.030). Among studies, comparisons of genetic diversity indices are more difficult with SNP than with microsatellites, since the type of sequencing can differ as can the sequencing coverage as well as the various filtrations on the datasets.
<figure><img src="image_7.png" /><figcaption>FIGURE 5 Principal Component Analysis (PCA) based on (a) microsatellite and (b) SNP markers from individuals of *Saccharina latissima* (n=228) collected in the Estuary and Gulf of St. Lawrence. Each point represents an individual, and each color corresponds to one site. TAD=Tadoussac, IV=Ile verte, LUD=Baie de St-Ludger, PUR=Purmer algaculture site, NOR=Sept-Iles, SAM=Sainte-Anne-des-Monts, CAD=Cap Desrosiers, CAO=Cap-aux-Os, GRA=Grande-Rivière, NEW=Newport (NEW), COL=Colbourne, PAS=Paspébiac, BONDC= growing lines in Bonaventure, BON=Bonaventure, CASCA=Cascapedia, ROO=Rochers-aux-Oiseaux, COR=Cormorandière. The percentage of variation explained by both axes is shown.</figcaption></figure>

<table><caption>TABLE 3 Analysis of molecular variance (AMOVA) using four groups based on group discrimination revealed by PCA on SNP markers: Group 1: IV, LUD, SAM, and CAD; Group 2: NOR and PUR; Group 3: NEW, BON, PAS, and CAO; and Group 4: Iles-de-la-madeleine (ROO and COR).</caption><thead><tr><th>Markers</th><th>Source of variation</th><th>df</th><th>Sum of squares</th><th>Variance components</th><th>Percentage of variation</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="5">Microsatellites</td><td>Among groups</td><td>3</td><td>66.638</td><td>22.21</td><td>0.71</td><td>0.16</td></tr><tr><td>Among populations within groups</td><td>12</td><td>184.75</td><td>15.39</td><td>4.54</td><td><strong>0.01</strong></td></tr><tr><td>Among individuals within populations</td><td>292</td><td>1687.15</td><td>5.78</td><td>7.98</td><td><strong>0.01</strong></td></tr><tr><td>Within individuals</td><td>308</td><td>1503.01</td><td>4.88</td><td>86.76</td><td><strong>0.01</strong></td></tr><tr><td>Total</td><td>615</td><td>1625.60</td><td>5.60</td><td>100</td><td></td></tr><tr><td rowspan="5">SNPs</td><td>Among groups</td><td>3</td><td>18,731.33</td><td>6243.7762</td><td>7.30</td><td><strong>0.01</strong></td></tr><tr><td>Among populations within groups</td><td>8</td><td>13,182.29</td><td>1647.7862</td><td>6.04</td><td><strong>0.01</strong></td></tr><tr><td>Among individuals within populations</td><td>214</td><td>10,3574.25</td><td>483.9918</td><td>1.87</td><td>0.07</td></tr><tr><td>Within individuals</td><td>226</td><td>104,755.91</td><td>463.5217</td><td>84.77</td><td><strong>0.01</strong></td></tr><tr><td>Total</td><td>451</td><td>240,243.77</td><td>532.6913</td><td>100</td><td></td></tr></tbody></table>

Note: BON is the source site and PUR is the farm site. Significant *p* values less than 0.05 are indicated in bold.

Recurrent bottlenecks following Pleistocene glaciation may have affected the genetic diversity of sugar kelp in the St. Lawrence. During this period, 25,000–18,000 years ago, the ice sheet covered North America, from the Arctic to approximately New York, and the St-Lawrence was wholly sealed in ice (Maggs

et al., 2008). After this period, many species, such as *Saccharina latissima*, recolonized ice-free areas. It has been assumed that populations of the NW Atlantic were composed of individuals that migrated across the Atlantic Ocean from Europe following the last glaciation (Lee, 1973; Luttikhuizen et al., 2018; Taylor, 1957).
Neiva et al. (2018) suggested that NW and NE lineages evolved in the Atlantic and split into two groups prior to the late glacial maximum. The consequences of glaciations were harsher in the NW Atlantic due to the paucity of rocky shores, resulting in lower genetic diversity in members of this phylogroup. Few studies have examined recolonization patterns in species from the St. Lawrence. The anadromous rainbow smelt (*Osmerus mordax*) recolonized the St. Lawrence Estuary from two refugia (Bernatchez, 1997). The Mysid *Neomysis americana* includes two different lineages, one that would have colonized the St. Lawrence via a southern refuge around Georges Bank with a migration route via the Champlain Sea, and another one with an origin east of the Grand Banks, Newfoundland (Cortial et al., 2019). Few studies have compared genetic diversity among the Estuary and the Gulf of St. Lawrence in species that do not actively disperse. Our results showed low levels of genetic diversity, suggesting recolonization from a small number of individuals and a single refugium. Additional studies examining mitochondrial genomes would be needed for confirmation. Environmental factors may also affect genetic diversity levels. Populations from the more brackish environment (lower Estuary) were less genetically diverse than those from the Gulf at genomic markers. A similar result was observed for *Zostera marina* populations from the same area and sequenced with the same technique (Dartseq; Treillefort, 2023). Nielsen et al. (2016) observed that brackish populations of *S. latissima* in Denmark were less genetically diverse than marine populations and likely more vulnerable to climate change due to their genetic isolation.

A very weak genetic structure was revealed for the 22 microsatellite markers in comparison with SNPs. The use of genomic markers allowed an increased resolution and the detection of restriction to gene flow at major barriers in the St. Lawrence Seaway. Spatial analysis of genetic distance using SNPs supported the influence of isolation by distance (IBD) on population differentiation across the whole region. The dataset also supported the presence of important hierarchical structuring. The Gaspé Current flows along the south shore of the Estuary and along the Gaspé Peninsula, thus explaining the first and second cluster comprising individuals from the lower Estuary (IV, TAD, and LUD) and the Gaspé Peninsula (SAM and CAD). The isolation of North Shore populations (NOR and the algal culture site, PUR) can be explained by the presence of a gyre near Anticosti Island (Archambault et al., 2017). These two sites were observed to be distinct from one another despite the low $F_{ST}$ values between them. Additional samples at sites located to the east of this location would help in the understanding of the genetic divergence between these sites. The populations (BON and PAS) that are located to the west of the Baie-des-Chaleurs clustered together, whereas

NEW and CAO, occupying the eastern part of that area, formed a distinct cluster. The Gaspé Current flowing from the Gaspé Peninsula to the Iles-de-la-Madeleine (Savenkoff et al., 1996) likely prevents mixing between these sites and the sites from the Gaspé Peninsula. The Iles-de-la-Madeleine (ROO and COR) are further isolated due to distances from the rest of the sites. Oceanographic processes appear to be more important drivers than geographic distance for finer spatial resolution in populations of *Laminaria digitata* (Fouqueau et al., 2024). In Maine, *Saccharina latissima* showed genetic differentiation within a few kilometers: Four groups were detected with microsatellites along only 210 km of shoreline (Breton et al., 2018). In the eastern United States, the Gulf of Maine and southern New England populations were separated by Cape Cod, a peninsula that appears to be a biogeographic barrier to the gene flow in this area (Mao et al., 2020). In comparison, the Gaspesian Peninsula could be a barrier to the gene flow separating the Estuary, the North Shore, and the Gaspesian coast from the Baie-des-Chaleurs.

The use of locally sourced seeding material has been advocated to reduce the risk of genetic pollution and depression from introduced non-local strains (Yarish et al., 2017). Low levels of introgression have been reported in naturalized populations of *Undaria pinnatifida* from nearby farm sites in France (Grulois et al., 2011). A recent study comparing 24 wild and three farmed *Saccharina latissima* populations at 21 microsatellite loci revealed no significant genetic differentiation between them, reflecting the current cultivation practices of using local meiospores in Brittany, France (Jaugeon et al., 2025). In China, few signs of gene flow from farm to wild have been reported in *U. pinnatifida* (Li et al., 2020). Macroalgae farming has been in operation at very small scales since 2006 in the St. Lawrence. Our study revealed that the wild population of the Baie-des-Chaleurs (BON), where the broodstock is collected, is genetically distinct from the wild population of Sept-Iles. The North Shore population where the aquaculture has recently been in operation (PUR) did not differ genetically from the wild population (NOR) located 1 km away and did not have higher levels of genetic diversity as would have been expected if there was admixture. This suggests an absence of genetic contamination from the seed broodstock. As sea farming has not been practiced intensively, our results are not surprising. As genetic diversity appears limited in our populations and genetic structure is high, it would be important to take this into consideration for selection programs and if farming activities intensify. The introduction of genetic diversity through the crossing of divergent populations of *S. latissima* has been demonstrated to result in heterosis, leading to increased yield (Cohen et al., 2025). The recent report of sporeless sporophyte production could alleviate the problem of genetic contamination and enable the
production of desirable traits in cultivated *S. latissima* (Vissers et al., 2024). However, such a strategy raises the issue of farmers' dependence on seed producers. To promote more independence from seed producers, it will be important to maintain/explore alternative options such as local strains adapted to their specific populations.

## AUTHOR CONTRIBUTIONS

**Marie Treillefort:** Formal analysis (equal); methodology (equal); writing – original draft (equal). **Sabrina Le Cam:** Formal analysis (equal); software (lead); writing – original draft (equal); writing – review and editing (equal). **Myriam Valero:** Funding acquisition (equal); supervision (supporting); writing – review and editing (equal). **Stéphane Mauger:** Methodology (equal); supervision (supporting); writing – review and editing (supporting).

**Paolo Ruggeri:** Software (equal); supervision (supporting); writing – review and editing (supporting). **Flora Salvo:** Funding acquisition (equal); supervision (equal); writing – review and editing (equal).

**Isabelle Gendron-Lemieux:** Funding acquisition (supporting); writing – review and editing (supporting). **Tamara Provencher:** Funding acquisition (supporting); project administration (supporting); writing – review and editing (supporting).

**Rénald Belley:** Funding acquisition (supporting); writing – review and editing (supporting). **France Dufresne:** Conceptualization (equal); formal analysis; funding acquisition; investigation (equal); methodology; project administration; supervision (lead); writing – original draft; writing – review and editing.

## ACKNOWLEDGMENTS

This study was supported by an aquaculture Collaborative Research and Development Program (ACRDP) to Tamara Provencher, Rénald Belley, and France Dufresne, and core funding from the CNRS and Sorbonne Université to the IRL EBEA 3614. Marie Treillefort and Sabrina Le Cam received internships with Merinov, funded by Mitacs acceleration grants. We acknowledge traveling grants from Institut-France-Québec for a stay in the laboratory of Myriam Valero in Roscoff. We thank the various people and industrial partners who helped with sampling sugar kelp. We are also grateful to the Biogenouest genomics core facility (Genomer Plateforme génomique) at the Station Biologique de Roscoff for their technical support.

## ORCID

France Dufresne https://orcid.org/0000-0002-2481-4864

## REFERENCES

Archambault, P., Schloss, I. R., Grant, C., & Plante, S. (2017). *Les Hydrocarbures dans le Golfe du Saint-Laurent - Enjeux sociaux, économiques et environnementaux* (p. 324). Notre Golfe.

Augyte, S., Yarish, C., Redmond, S., & Kim, J. K. (2017). Cultivation of a morphologically distinct strain of the sugar kelp, *Saccharina latissima* forma angustissima, from coastal Maine, USA, with implications for ecosystem services. *Journal of Applied Phycology*, *29*(4), 1967-1976.

Bartsch, I., Wiencke, C., Bischof, K., Buchholz, C. M., Buck, B. H., Eggert, A., & Karez, R. (2008). The genus *Laminaria sensu lato*: Recent insights and developments. *European Journal of Phycology*, *43*(1), 1-86.

Bernatchez, L. (1997). Mitochondrial DNA analysis confirms the existence of two glacial races of rainbow smelt *Osmerus mordax* and their reproductive isolation in the St Lawrence River estuary (Quebec, Canada). *Molecular Ecology*, *6*(1), 73-83.

Bjerregaard, R., Valderrama, D., Radulovich, R., Diana, J., Capron, M., Mckinnie, C. A., Cedric, M., Hopkins, K., Yarish, C., & Goudey, C. (2016). *Seaweed aquaculture for food security, income generation and environmental health in tropical developing countries* (Report No. 107147). World Bank Group, Washington, DC.

Bourret, A., Leung, C., Puncher, G. N., Le Corre, N., Deslauriers, D., Skanes, K., Bourdages, H., Cassista-Da Ros, M., Walkusz, W., Jeffery, N. W., Stanley, R. R. E., & Parent, G. J. (2024). Diving into broad-scale and high-resolution population genomics to decipher drivers of structure and climatic vulnerability in a marine invertebrate. *Molecular Ecology*, *15*, e17448. https://doi.org/10.1111/mec.17448

Breton, T. S., Nettleton, J. C., O'Connell, B., & Bertocci, M. (2018). Fine-scale population genetic structure of sugar kelp, *Saccharina latissima* (*Laminariales, Phaeophyceae*), in eastern Maine, USA. *Phycologia*, *57*(1), 32-40.

Campbell, I., Macleod, A., Sahlmann, C., Neves, L., Funderud, J., Överland, M., & Stanley, M. (2019). The environmental risks associated with the development of seaweed farming in Europe prioritizing key knowledge gaps. *Frontiers in Marine Science*, *6*, 107.

Cohen, J., Twijnstra, R., Schiller, J., Montecinos Arismendi, G., Reus, B., Soetaert, K., & Timmermans, K. (2025). Global fertility and heterosis in sugar kelp populations: A next step in sugar kelp breeding. *Journal of Applied Phycology*, *37*, 1213-1226. https://doi.org/10.1007/s10811-025-03447-7

Cortial, G., Woodland, R., Lasley-Rasher, R., & Winkler, G. (2019). Phylogeography of *Neomysis americana* (Crustacea, Mysida), focusing on the St. Lawrence system. *Journal of Plankton Research*, *41*(5), 723-739.

Ferchaud, A. L., Normandeau, E., Babin, C., Præbel, K., Hedeholm, R., Audet, C., Morgan, J., Treble, M., Walkusz, W., Sirois, P., & Bernatchez, L. (2022). A cold-water fish striving in a warming ocean: Insights from whole-genome sequencing of the Greenland halibut in the Northwest Atlantic. *Frontiers in Marine Science*, *9*, 992504.

Forbord, S., Skjermo, J., Arff, J., Handå, A., Reitan, K. I., Bjerregaard, R., & Lüning, K. (2012). Development of *Saccharina latissima* (*Phaeophyceae*) kelp hatcheries with year-round production of zoospores and juvenile sporophytes on culture ropes for kelp aquaculture. *Journal of Applied Phycology*, *24*(3), 393-399.

Fouqueau, L., Reynes, L., Tempera, F., Bajjouk, T., Blanfuné, A., Chevalier, C., Laurans, M., Mauger, S., Sourisseau, M., Assis, J., Lévêque, L., & Valero, M. (2024). Seascape genetic study on *Laminaria digitata* underscores the critical role of sampling schemes. *Marine Ecology Progress Series*, *740*, 23-42.

Frichot, E., & François, O. (2015). LEA: An R package for landscape and ecological association studies. *Methods in Ecology and Evolution*, *6*, 925-929. https://doi.org/10.1111/2041-210X.12382

Frichot, E., Mathieu, F., Théo, T., Bouchard, G., & François, O. (2014). Fast and efficient estimation of individual ancestry coefficients. *Genetics*, *196*, 973-983.
Goecke, F., Klemetsdal, G., & Ergon, Å. (2020). Cultivar development of kelp for commercial cultivation - past lessons and future prospects. *Frontiers in Marine Science*, **7**, 110.

Goudet, J. (2005a). FSTAT (version 2.9.4): A program to estimate and test population genetics parameters.

Goudet, J. (2005b). Hierstat, a package for R to compute and test hierarchical F-statistics. *Molecular Ecology Notes*, **5**, 184–186.

Grebe, G. S., Byron, C. J., St. Gelais, A., Kotowicz, D. M., & Olson, T. K. (2019). An ecosystem approach to kelp aquaculture in the Americas and Europe. *Aquaculture Reports*, **15**, 100215.

Gruber, B., Unmack, P. J., Berry, O. F., & Georges, A. (2018). dartr: An r package to facilitate analysis of SNP data generated from reduced representation genome sequencing. *Molecular Ecology Resources*, **18**, 691–699. https://doi.org/10.1111/1755-0998.12745

Grulois, D., Lévêque, L., & Viard, F. (2011). Mosaic genetic structure and sustainable establishment of the invasive kelp *Undaria pinnatifida* within a bay (Bay of St-Malo, Brittany). *Cahier Biologique Marine*, **52**, 485–498.

Guzinski, J., Mauger, S., Cock, J. M., & Valero, M. (2016). Characterization of newly developed expressed sequence tag-derived microsatellite markers revealed low genetic diversity within and low connectivity between European *Saccharina latissima* populations. *Journal of Applied Phycology*, **28**(5), 3057–3070.

Guzinski, J., Ruggeri, P., Ballenghien, M., Mauger, S., Jacquemin, B., Jollivet, C., & Valero, M. (2020). Seascape genomics of the sugar kelp *Saccharina latissima* along the north eastern Atlantic latitudinal gradient. *Genes*, **11**(12), 1503.

Hwang, E. K., Yotsukura, N., Pang, S. J., Su, L., & Shan, T. F. (2019). Seaweed breeding programs and progress in eastern Asian countries. *Phycologia*, **58**(5), 484–495.

Jahan, A., Ahmad, I. Z., Fatima, N., Ansari, V. A., & Akhtar, J. (2017). Algal bioactive compounds in the cosmeceutical industry: A review. *Phycologia*, **56**(4), 410–422.

Jaugeon, L., Destombe, C., Ruggeri, P., Mauger, S., Coudret, J., Cock, J. M., Potin, P., & Valero, M. (2025). The genetic structure of wild and cultivated *Saccharina latissima* populations across European coasts provides guidance for sustainable aquaculture, traceability, and conservation. *Journal of Applied Phycology*, **37**(5), 3135–3149.

Kamvar, Z. N., Brooks, J. C., & Grünwald, N. J. (2015). Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. *Frontiers in Genetics*, **6**, 208.

Kilian, A., Wenzl, P., Huttner, E., Carling, J., Xia, L., Blois, H., Caig, V., Heller-Uszynska, K., Jaccoud, D., Hopper, C., Aschenbrenner-Kilian, M., Evers, M., Peng, K., Cayla, C., Hok, P., & Uszynski, G. (2012). Diversity arrays technology: A generic genome profiling technology on open platforms. In F. Pompanon, & A. Bonin (Eds.), *Data Production and Analysis in Population Genomics: Methods and Protocols* (pp. 67–89). Springer.

Kim, J., Stekoll, M., & Yarish, C. (2019). Opportunities, challenges and future directions of open-water seaweed aquaculture in the United States. *Phycologia*, **58**(5), 446–461.

Lee, R. (1973). General ecology of the Canadian Arctic benthic marine algae. *Arctic*, **26**, 32–43.

Li, Q., Shan, T., Wang, X., Su, L., & Pang, S. (2020). Evaluation of the genetic relationship between the farmed populations on a typical kelp farm and the adjacent subtidal spontaneous population of *Undaria pinnatifida* (Phaeophyceae, Laminariales) in China. *Journal of Applied Phycology*, **32**, 653–659.

Luttikhuizen, P. C., van den Heuvel, F. H. M., Rebours, C., Witte, H. J., van Bleijswijk, J. D. L., & Timmermans, K. (2018). Strong population structure but no equilibrium yet: Genetic connectivity and phylogeography in the kelp *Saccharina latissima*

(Laminariales, Phaeophyta). *Ecology and Evolution*, **8**(8), 4265–4277.

Maggs, C. A., Castilho, R., Foltz, D., Henzler, C., Jolly, M. T., Kelly, J., & Väinölä, R. (2008). Evaluating signatures of glacial refugia for North Atlantic benthic marine taxa. *Ecology*, **89**(sp11), S108–S122.

Mao, X., Augyte, S., Huang, M., Hare, M. P., Bailey, D., Umanzor, S., & Lindell, S. (2020). Population genetics of sugar kelp throughout the NE United States using genome-wide markers. *Frontiers in Marine Science*, **7**, 694.

Marinho, G. S., Holdt, S. L., Birkeland, M. J., & Angelidaki, I. (2015). Commercial cultivation and bioremediation potential of sugar kelp, *Saccharina latissima*, in Danish waters. *Journal of Applied Phycology*, **27**(5), 1963–1973.

Mauger, S., Couceiro, L., & Valero, M. (2012). A simple and cost-effective method to synthesize an internal size standard amenable to use with a 5-dye system. *Prime Research on Biotechnology*, **2**, 40–46.

Neiva, J., Serrão, E. A., Paulino, C., Gouveia, L., Want, A., Tamigneaux, É., Ballenghien, M., Mauger, S., Fouqueau, L., Engel-Gautier, C., Destombe, C., & Valero, M. (2020). Genetic structure of amphi-Atlantic *Laminaria digitata* (Laminariales, Phaeophyceae) reveals a unique range-edge gene pool and suggests post-glacial colonization of the NW Atlantic. *European Journal of Phycology*, **54**, 1–12.

Neiva, J., Paulino, C., Nielsen, M. M., Krause-Jensen, D., Saunders, G. W., Assis, J., & Aires, T. (2018). Glacial vicariance drives phylogeographic diversification in the amphi-boreal kelp *Saccharina latissima*. *Scientific Reports*, **8**(1), 1112.

Nielsen, M. M., Paulino, C., Neiva, J., Krause-Jensen, D., Bruhn, A., & Serrão, E. A. (2016). Genetic diversity of *Saccharina latissima* (Phaeophyceae) along a salinity gradient in the North Sea-Baltic Sea transition zone. *Journal of Phycology*, **52**(4), 523–531. https://doi.org/10.1111/jpy.12428

Pante, E., & Simon-Bouhet, B. (2013). Marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. *PLoS ONE*, **8**, e73051.

Paulino, C., Neiva, J., Coelho, N. C., Aires, T., Marbà, N., Krause-Jensen, D., & Serrão, E. A. (2016). Characterization of 12 polymorphic microsatellite markers in the sugar kelp *Saccharina latissima*. *Journal of Applied Phycology*, **28**(5), 3071–3074.

Ribeiro, P. A., Næss, T., Dahle, G., Asplin, L., Meland, K., Fredriksen, S., & Sjøtun, K. (2022). Going with the flow – Population genetics of the kelp *Saccharina latissima* (Phaeophyceae, Laminariales). *Frontiers in Marine Science*, **9**, 876420.

RStudio Team. (2015). *RStudio: Integrated development for R* (version 4.2.2). RStudio Inc. https://www.r-project.org

Savenkoff, C., Vézina, A., & Gratton, Y. (1996). Les courants dans l'estuaire maritime du Saint-Laurent: Un casse-tête pour les biologistes? *Le Naturaliste Canadien*, **121**(1), 74–76.

Tamigneaux, É., & Johnson, L. (2016). Les macroalgues du Saint-Laurent: ne composante essentielle d'un écosystème marin unique et une ressource naturelle précieuse dans un contexte de changement global. *Le Naturaliste Canadien*, **140**(2), 62–73.

Taylor, W. R. (1957). *Marine algae of the NE coast of North America*. University of Michigan Press.

Treillefort, M. (2023). Structure et diversité génétique d'espèces fondatrices: un outil pour la gestion des zostères marines (Zostera marina) et des laminaires sucrées (*Saccharina latissima*) du St-Laurent [Master's thesis, Université du Québec à Rimouski]. https://semaphore.uqar.ca/id/eprint/2330/

Van Oosterhout, C., Hutchinson, W. F., Wills, D. P., & Shipley, P. (2004). MICRO-CHECKER: Software for identifying and
correcting genotyping errors in microsatellite data. *Molecular Ecology Notes*, **4**(3), 535–538.

Vissers, C., Lindell, S. R., Nuzhdin, S. V., Almada, A. A., & Timmermans, K. (2024). Using sporeless sporophytes as a next step towards upscaling offshore kelp cultivation. *Journal of Applied Phycology*, **36**, 313–320.

Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the analysis of population structure. *Evolution*, **38**, 1358.

Yarish, C., Kim, J. K., Lindell, S., & Kite-Powell, H. (2017). Developing an environmentally and economically sustainable sugar kelp aquaculture industry in southern New England: From seed to market. *EEB Articles*, **38**, 1–48.

**How to cite this article:** Treillefort, M., Le Cam, S., Valero, M., Mauger, S., Ruggeri, P., Salvo, F., Gendron-Lemieux, I., Provencher, T., Belley, R., & Dufresne, F. (2026). Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Québec, Canada). *Journal of Phycology*, **00**, 1–13. https://doi.org/10.1111/jpy.70141