# Seascape genetic study on Laminaria digitata underscores the critical role of sampling schemes

Louise Fouqueau, L Reynes, F Tempera, T Bajjouk, A Blanfuné, C Chevalier, M Laurans, S Mauger, M Sourisseau, J Assis, et al.

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

## To cite this version:

Louise Fouqueau, L Reynes, F Tempera, T Bajjouk, A Blanfuné, et al. "Seascape genetic study on Laminaria digitata underscores the critical role of sampling schemes." Marine Ecology Progress Series, 2024. hal-04624490

HAL Id: hal-04624490

https://hal.science/hal-04624490

Submitted on 25 Jun 2024

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

Distributed under a Creative Commons Attribution 4.0 International License
# Seascape genetic study on Laminaria digitata underscores the critical role of sampling schemes

Louise Fouqueau, L Reynes, F Tempera, T Bajjouk, A Blanfuné, C Chevalier, Martial Laurans, S Mauger, M Sourisseau, J Assis, et al.

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

## To cite this version:

Louise Fouqueau, L Reynes, F Tempera, T Bajjouk, A Blanfuné, et al.. Seascape genetic study on Laminaria digitata underscores the critical role of sampling schemes. Marine Ecology Progress Series, 2024, 740, pp.23-42. 10.3354/meps14640. hal-04624490

HAL Id: hal-04624490

https://hal.science/hal-04624490v1

Submitted on 25 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished 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 Attribution 4.0 International License
1 Seascape genetic study on *Laminaria digitata* underscores the critical role of sampling schemes

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3 Fouqueau L.¹,∞,*, Reynes L.¹, Tempera F.², Bajjouk T.², Blanfuné A.³, Chevalier C.³, Laurans M.⁴,

4 Mauger S.¹, Sourisseau M.², Assis J.⁵,⁶, Lévêque L.⁷, Valero M.¹

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6 ¹IRL 3614, CNRS, Sorbonne Université, Pontificia Universidad Católica de Chile, Universidad

7 Austral de Chile, Station Biologique de Roscoff, 29688 Roscoff, France; ²Dynamic of Coastal

8 Ecosystems, DYNECO, Ifremer, Technopôle Brest Iroise, Plouzané 29280, France; ³Aix-

9 Marseille Université, Université de Toulon, CNRS, IRD, MIO, 13288 Marseille, France; ⁴LBH-

10 Halgo-DECOD, Ifremer, Centre de Bretagne, 29280 Plouzané, France; ⁵CCMAR, University of

11 the Algarve, 8005-139 Faro, Portugal; ⁶Faculty of Bioscience and Aquaculture, Nord Universitet,

12 1490 Bodø, Norway; ⁷FR2424, CNRS, Sorbonne Université, Station Biologique de Roscoff,

13 29688 Roscoff, France.

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15 ∞ Present address: Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria.

16 * Corresponding author: louise.fouqueau@ist.ac.at.

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18 SHORT TITLE: Seascape genetic study on *Laminaria digitata*

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20
1 Seascape genetic study on *Laminaria digitata* underscores the critical role of sampling schemes

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3 Fouqueau L.¹,∞,*, Reynes L.¹, Tempera F.², Bajjouk T.², Blanfuné A.³, Chevalier C.³, Laurans M.⁴,

4 Mauger S.¹, Sourisseau M.², Assis J.⁵,⁶, Lévêque L.⁷, Valero M.¹

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6 ¹IRL 3614, CNRS, Sorbonne Université, Pontificia Universidad Católica de Chile, Universidad

7 Austral de Chile, Station Biologique de Roscoff, 29688 Roscoff, France; ²Dynamic of Coastal

8 Ecosystems, DYNECO, Ifremer, Technopôle Brest Iroise, Plouzané 29280, France; ³Aix-

9 Marseille Université, Université de Toulon, CNRS, IRD, MIO, 13288 Marseille, France; ⁴LBH-

10 Halgo-DECOD, Ifremer, Centre de Bretagne, 29280 Plouzané, France; ⁵CCMAR, University of

11 the Algarve, 8005-139 Faro, Portugal; ⁶Faculty of Bioscience and Aquaculture, Nord Universitet,

12 1490 Bodø, Norway; ⁷FR2424, CNRS, Sorbonne Université, Station Biologique de Roscoff,

13 29688 Roscoff, France.

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15 ∞ Present address: Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria.

16 * Corresponding author: louise.fouqueau@ist.ac.at.

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18 SHORT TITLE: Seascape genetic study on *Laminaria digitata*

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ABSTRACT:

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Understanding connectivity patterns in endangered species living in fragmented habitats is fundamental to improve management and conservation actions. Such improvements can be particularly pressing at trailing edges where populations are facing the greatest challenges of climate change, and appear all the more crucial if the species is commercially harvested. Seascape genetics have been increasingly used to meet these needs. In this study, we examined connectivity patterns among 32 populations located at the southern range limit of the oarweed kelp *Laminaria digitata*. Our populations were sampled in a roughly continuous manner, with the distance between neighboring populations ranging from a few km to about a few hundreds. By genotyping 11 microsatellite markers, our aim was to (1) refine analyses of population structure, (2) test whether on-shelf islands are genetically more differentiated compared to mainland populations, (3) evaluate the relative importance of various abiotic conditions in shaping the genetic structure and (4) evaluate if the relative importance of each environmental factor varied according to sampling schemes. Our analyses revealed a positive relation between connectivity links and genetic diversity: populations with high levels of connectivity were genetically enriched while isolated populations showed signs of genetic erosion. The genetically impoverished populations corresponded to the southernmost populations as well as populations along the northern coast of Brittany (Locquirec, Saint-Malo Bay) and the northernmost population in Pas-de-Calais. By performing db-RDA on various sampling schemes, geographic distance appeared as the dominant factor influencing connectivity between populations separated by great distances, while hydrodynamic processes were the main factor when analyzing continuously distributed populations.

**KEYWORDS:** connectivity, seascape genetics, *Laminaria digitata*, microsatellite markers, sampling scheme
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ABSTRACT:

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Understanding connectivity patterns in endangered species living in fragmented habitats is fundamental to improve management and conservation actions. Such improvements can be particularly pressing at trailing edges where populations are facing the greatest challenges of climate change, and appear all the more crucial if the species is commercially harvested. Seascape genetics have been increasingly used to meet these needs. In this study, we examined connectivity patterns among 32 populations located at the southern range limit of the oarweed kelp *Laminaria digitata*. Our populations were sampled in a roughly continuous manner, with the distance between neighboring populations ranging from a few km to about a few hundreds. By genotyping 11 microsatellite markers, our aim was to (1) refine analyses of population structure, (2) test whether on-shelf islands are genetically more differentiated compared to mainland populations, (3) evaluate the relative importance of various abiotic conditions in shaping the genetic structure and (4) evaluate if the relative importance of each environmental factor varied according to sampling schemes. Our analyses revealed a positive relation between connectivity links and genetic diversity: populations with high levels of connectivity were genetically enriched while isolated populations showed signs of genetic erosion. The genetically impoverished populations corresponded to the southernmost populations as well as populations along the northern coast of Brittany (Locquirec, Saint-Malo Bay) and the northernmost population in Pas-de-Calais. By performing db-RDA on various sampling schemes, geographic distance appeared as the dominant factor influencing connectivity between populations separated by great distances, while hydrodynamic processes were the main factor when analyzing continuously distributed populations.

**KEYWORDS:** connectivity, seascape genetics, *Laminaria digitata*, microsatellite markers, sampling scheme
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# 1. INTRODUCTION

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Understanding connectivity patterns in endangered species living in fragmented habitats is fundamental to assess their vulnerability to environmental changes and to define conservation measures at appropriate scales (Manel et al. 2019, Benestan et al. 2023). Such knowledge is particularly relevant at the trailing edge of species distribution, where the numerous effects of global warming can interfere with the strong genetic and demographic drifts (Nadeau & Urban 2019). Investigating connectivity can additionally pinpoint populations that are isolated from gene flow. Yet genetic isolation can, in some cases, impede adaptation and ultimately lead to extinction (Lynch & Lande 1993). A deeper understanding of population connectivity can therefore advance conservation action aiming to preserve species adaptive potential via measures that prevent the loss of genetic diversity.

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Most marine organisms disperse through planktonic propagules (e.g., spores, eggs, larvae) which are deemed unable to counter hydrodynamic forces (Alberto et al. 2010). The influence of oceanographic processes on population genetic structure has therefore been repeatedly proven in different marine taxa by means of biophysical models (e.g., Gilg & Hilbish 2003, Treml et al. 2008, Mitarai et al. 2009 and more recently by Xuereb et al. 2018, Benestan et al. 2021, Reynes et al. 2021). For instance, hydrodynamic processes have been associated with larval retention and asymmetrical gene flow (Gaylord & Gaines 2000), both of which inevitably affect metapopulation dynamics. White et al. (2010) have additionally demonstrated that ocean currents enable exchanges between coastal islands - defined as the ones located on the continental shelf - and mainland populations. Although coastal islands have limited evolutionary and ecological interests compared to more remote oceanic islands (Dawson 2016), understanding their connectivity patterns could still have significant implications for conservation. Yet, to this date, a formal comparison of the connectivity between on-shelf island and coastal populations is still lacking for marine species in comparison to terrestrial ones (Bell 2008). Although oceanic currents are fundamental in shaping the genetic structure of marine species, their effects on their own are insufficient to explain entire connectivity patterns (Hu et al. 2020). Species' life history traits and larval behavior appear to

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# 1. INTRODUCTION

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Understanding connectivity patterns in endangered species living in fragmented habitats is fundamental to assess their vulnerability to environmental changes and to define conservation measures at appropriate scales (Manel et al. 2019, Benestan et al. 2023). Such knowledge is particularly relevant at the trailing edge of species distribution, where the numerous effects of global warming can interfere with the strong genetic and demographic drifts (Nadeau & Urban 2019). Investigating connectivity can additionally pinpoint populations that are isolated from gene flow. Yet genetic isolation can, in some cases, impede adaptation and ultimately lead to extinction (Lynch & Lande 1993). A deeper understanding of population connectivity can therefore advance conservation action aiming to preserve species adaptive potential via measures that prevent the loss of genetic diversity.

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Most marine organisms disperse through planktonic propagules (e.g., spores, eggs, larvae) which are deemed unable to counter hydrodynamic forces (Alberto et al. 2010). The influence of oceanographic processes on population genetic structure has therefore been repeatedly proven in different marine taxa by means of biophysical models (e.g., Gilg & Hilbish 2003, Treml et al. 2008, Mitarai et al. 2009 and more recently by Xuereb et al. 2018, Benestan et al. 2021, Reynes et al. 2021). For instance, hydrodynamic processes have been associated with larval retention and asymmetrical gene flow (Gaylord & Gaines 2000), both of which inevitably affect metapopulation dynamics. White et al. (2010) have additionally demonstrated that ocean currents enable exchanges between coastal islands - defined as the ones located on the continental shelf - and mainland populations. Although coastal islands have limited evolutionary and ecological interests compared to more remote oceanic islands (Dawson 2016), understanding their connectivity patterns could still have significant implications for conservation. Yet, to this date, a formal comparison of the connectivity between on-shelf island and coastal populations is still lacking for marine species in comparison to terrestrial ones (Bell 2008). Although oceanic currents are fundamental in shaping the genetic structure of marine species, their effects on their own are insufficient to explain entire connectivity patterns (Hu et al. 2020). Species' life history traits and larval behavior appear to
influence dispersal kernels to a certain extent (Sanford & Kelly 2011), as illustrated by the
differences in phylogeographic breaks among marine taxa, albeit historical patterns and/or habitat
availability being also at play (Teske et al. 2011, 2017, Martins et al. 2022). When considering
heterogeneous environments, local adaptation can constrain gene flow (or the rate of effective
migration, Aeschbacher et al. 2017) even in the absence of physical barriers to dispersal, a
phenomenon that has been termed “isolation by adaptation” (Nosil et al. 2009, Schoville et al.
2012). In such scenarios, contrasted patterns of genetic structure can be observed at neutral and
putatively selected loci (see Grummer et al. 2019 for review and Sandoval-Castillo et al. 2018,
Van Oppen & Coleman 2022).

Seascape genetics aims to investigate which and how marine environmental features
influence the spatial distribution of genetic variation at neutral or putatively adaptive loci (Xuereb
et al. 2019, Boussarie et al. 2022). Among the tested features, water depth (e.g., Engel et al. 2004,
Hickey et al. 2009, Krueger-Hadfield et al. 2013), habitat discontinuity (e.g., Alberto et al. 2010,
2011, D’Aloia et al. 2014, Durrant et al. 2018), sea surface temperature (“SST”, e.g., Johansson
et al. 2015, Benestan et al. 2016, Guzinski et al. 2020) and salinity (e.g., Gaggiotti et al. 2009,
Sjöqvist et al. 2015) have been shown to explain, separately or jointly, a large part of the variation
in genetic structure. However, most of these studies have relied on linear regressions and Mantel
tests to show the influence of environmental factors (see Benestan et al. 2016 for references),
although these statistical frameworks suffer from high false positive rates (Graves et al. 2013).
This problem has been significantly reduced by the use of variance partitioning methods
(Capblancq & Forester 2021), such as distance-based redundancy analysis (db-RDA; Legendre &
Anderson 1999). In addition to the statistical advances, the improvement of hydrodynamic models
and the developments of Moran’s and asymmetric eigenvector maps (Dray et al. 2006, Blanchet
et al. 2008b) allowed a better apportioning of the contribution of geographic distance and
oceanographic features (e.g., Benestan et al. 2016, 2021, Xuereb et al. 2018, Reynes et al. 2021).
Yet some of the oceanographic processes have very localized effects (Legendre & Demers 1984),
and other abiotic factors influencing genetic structure (e.g., SST) can similarly vary at a fine
influence dispersal kernels to a certain extent (Sanford & Kelly 2011), as illustrated by the
differences in phylogeographic breaks among marine taxa, albeit historical patterns and/or habitat
availability being also at play (Teske et al. 2011, 2017, Martins et al. 2022). When considering
heterogeneous environments, local adaptation can constrain gene flow (or the rate of effective
migration, Aeschbacher et al. 2017) even in the absence of physical barriers to dispersal, a
phenomenon that has been termed “isolation by adaptation” (Nosil et al. 2009, Schoville et al.
2012). In such scenarios, contrasted patterns of genetic structure can be observed at neutral and
putatively selected loci (see Grummer et al. 2019 for review and Sandoval-Castillo et al. 2018,
Van Oppen & Coleman 2022).

Seascape genetics aims to investigate which and how marine environmental features
influence the spatial distribution of genetic variation at neutral or putatively adaptive loci (Xuereb
et al. 2019, Boussarie et al. 2022). Among the tested features, water depth (e.g., Engel et al. 2004,
Hickey et al. 2009, Krueger-Hadfield et al. 2013), habitat discontinuity (e.g., Alberto et al. 2010,
2011, D’Aloia et al. 2014, Durrant et al. 2018), sea surface temperature (“SST”, e.g., Johansson
et al. 2015, Benestan et al. 2016, Guzinski et al. 2020) and salinity (e.g., Gaggiotti et al. 2009,
Sjöqvist et al. 2015) have been shown to explain, separately or jointly, a large part of the variation
in genetic structure. However, most of these studies have relied on linear regressions and Mantel
tests to show the influence of environmental factors (see Benestan et al. 2016 for references),
although these statistical frameworks suffer from high false positive rates (Graves et al. 2013).
This problem has been significantly reduced by the use of variance partitioning methods
(Capblancq & Forester 2021), such as distance-based redundancy analysis (db-RDA; Legendre &
Anderson 1999). In addition to the statistical advances, the improvement of hydrodynamic models
and the developments of Moran’s and asymmetric eigenvector maps (Dray et al. 2006, Blanchet
et al. 2008b) allowed a better apportioning of the contribution of geographic distance and
oceanographic features (e.g., Benestan et al. 2016, 2021, Xuereb et al. 2018, Reynes et al. 2021).
Yet some of the oceanographic processes have very localized effects (Legendre & Demers 1984),
and other abiotic factors influencing genetic structure (e.g., SST) can similarly vary at a fine
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geographical scale (see for example Gallon et al. 2014). Their relative influence on genetic structure is therefore expected to vary according to geographical extents (Jombart et al. 2009, Dalongeville et al. 2018), which underlines how sampling design can itself influence the conclusions.

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Kelp forests are a dominant feature along many temperate to boreal rocky shores and play a foundation role for numerous species by providing them substrate, shelter or food (Teagle et al. 2017, Jayathilake & Costello 2020, Coleman & Veenhof 2021). The decline they have shown in some North-East Atlantic biogeographic regions has justified their recent inclusion in the OSPAR list of threatened and declining habitats (de Bettignies et al. 2021). SST is considered as the major abiotic factor shaping kelp species' distribution as denoted by the cold-water niches they tend to associate with (Lüning 1990, Bartsch et al. 2008). Over the past decade, various studies have shown the harmful consequences of rising SST on kelp forests, notably as a result of ever more frequent and intense marine heatwaves, which especially affect warm-edge populations (Fernández 2011, Starko et al. 2019, Arafeh-Dalmau et al. 2019, Filbee-Dexter et al. 2020, but see Klingbeil et al. 2022). In this context, clarifying connectivity in the warm distribution edge might be particularly valuable to gauge the resilience of marginal populations. For instance, detecting effective dispersal from the range center towards the margin could indicate the possibility of an evolutionary rescue. Yet, an evolutionary rescue will be possible only if marginal and core populations are adapted to similar environmental conditions (Bridle et al. 2009, DuBois et al. 2022).

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*Laminaria digitata* (Hudson) J.V. Lamouroux is a boreal kelp species with an amphi-Atlantic distribution. Its geographic range in the northeast Atlantic extends from temperate southern Brittany (47°N, France) to the arctic Spitsbergen archipelago (79°N, Norway) (Kain 1979, Lüning 1990, Araújo et al. 2016). Along the shorelines, *L. digitata* generally occurs within a narrow band spanning the lower intertidal and upper subtidal zones (Robuchon et al. 2014). This species thus presents an interesting case to test the effect of coastal oceanographic currents on genetic structure, especially along the rugged coast of Brittany, where hydrodynamics is highly complex due to
geographical scale (see for example Gallon et al. 2014). Their relative influence on genetic structure is therefore expected to vary according to geographical extents (Jombart et al. 2009, Dalongeville et al. 2018), which underlines how sampling design can itself influence the conclusions.

Kelp forests are a dominant feature along many temperate to boreal rocky shores and play a foundation role for numerous species by providing them substrate, shelter or food (Teagle et al. 2017, Jayathilake & Costello 2020, Coleman & Veenhof 2021). The decline they have shown in some North-East Atlantic biogeographic regions has justified their recent inclusion in the OSPAR list of threatened and declining habitats (de Bettignies et al. 2021). SST is considered as the major abiotic factor shaping kelp species' distribution as denoted by the cold-water niches they tend to associate with (Lüning 1990, Bartsch et al. 2008). Over the past decade, various studies have shown the harmful consequences of rising SST on kelp forests, notably as a result of ever more frequent and intense marine heatwaves, which especially affect warm-edge populations (Fernández 2011, Starko et al. 2019, Arafeh-Dalmau et al. 2019, Filbee-Dexter et al. 2020, but see Klingbeil et al. 2022). In this context, clarifying connectivity in the warm distribution edge might be particularly valuable to gauge the resilience of marginal populations. For instance, detecting effective dispersal from the range center towards the margin could indicate the possibility of an evolutionary rescue. Yet, an evolutionary rescue will be possible only if marginal and core populations are adapted to similar environmental conditions (Bridle et al. 2009, DuBois et al. 2022).

*Laminaria digitata* (Hudson) J.V. Lamouroux is a boreal kelp species with an amphi-Atlantic distribution. Its geographic range in the northeast Atlantic extends from temperate southern Brittany (47°N, France) to the arctic Spitsbergen archipelago (79°N, Norway) (Kain 1979, Lüning 1990, Araújo et al. 2016). Along the shorelines, *L. digitata* generally occurs within a narrow band spanning the lower intertidal and upper subtidal zones (Robuchon et al. 2014). This species thus presents an interesting case to test the effect of coastal oceanographic currents on genetic structure, especially along the rugged coast of Brittany, where hydrodynamics is highly complex due to
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numerous mesoscale features (e.g., fronts, upwellings, river plumes, low salinity lenses) and
macrotidal ranges (Salomon & Breton 1993, Ayata et al. 2010, Nicolle et al. 2017). This species
has been historically harvested in Brittany, where an adaptation of the ongoing exploitation is
currently under discussion (Garineaud 2021). Additionally, annual mean SST varies at a fine scale
across Brittany, with the western and north-western sectors being cooler and currently less affected
by climate change than Southern and North-Eastern Brittany (Gallon et al. 2014). As suggested by
Liesner et al. (2020), this mosaic of SST conditions might have driven the distinct thermal
adaptations observed between populations from northern and southern Brittany. Finally, by using
a hierarchical sampling scheme, previous analyses of population structure at the scale of Brittany
have revealed significant genetic differentiation at both small (< 1 km) and large scales (> 10-50
km) relative to the region (Billot et al. 2003, Robuchon et al. 2014).

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In this study, we extended the geographical sampling of previous microsatellite analyses
(Billot et al. 2003, Valero et al. 2011, Couceiro et al. 2013, Robuchon et al. 2014) by using a
dataset built from 32 populations ranging from the southern range limit of *L. digitata* to the
northernmost population found on the French coast, situated in the Strait of Dover. Using this
dataset, we explored the respective and combined effects of sampling year, hydrodynamic
processes, habitat discontinuity, spatial distance and SST on the genetic structure. We specifically
aimed to (1) refine the previous analyses of population structure for this species and identify
isolated populations, (2) test whether on-shelf island populations are genetically more
differentiated compared to mainland populations and thus show signs of genetic erosion, (3)
evaluate the relative importance of each environmental factor in shaping the genetic structure using
a db-RDA and finally (4) evaluate if their relative importance varies according to the sampling
scheme.

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## 2. MATERIAL AND METHODS

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### 2.1. Study area

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Brittany is the northwestern-most region of mainland France (NE Atlantic, Figure 1). Its 2,860 km-length coastline encompasses a major biogeographical transition zone between cold- and
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numerous mesoscale features (e.g., fronts, upwellings, river plumes, low salinity lenses) and
macrotidal ranges (Salomon & Breton 1993, Ayata et al. 2010, Nicolle et al. 2017). This species
has been historically harvested in Brittany, where an adaptation of the ongoing exploitation is
currently under discussion (Garineaud 2021). Additionally, annual mean SST varies at a fine scale
across Brittany, with the western and north-western sectors being cooler and currently less affected
by climate change than Southern and North-Eastern Brittany (Gallon et al. 2014). As suggested by
Liesner et al. (2020), this mosaic of SST conditions might have driven the distinct thermal
adaptations observed between populations from northern and southern Brittany. Finally, by using
a hierarchical sampling scheme, previous analyses of population structure at the scale of Brittany
have revealed significant genetic differentiation at both small (< 1 km) and large scales (> 10-50
km) relative to the region (Billot et al. 2003, Robuchon et al. 2014).

In this study, we extended the geographical sampling of previous microsatellite analyses
(Billot et al. 2003, Valero et al. 2011, Couceiro et al. 2013, Robuchon et al. 2014) by using a
dataset built from 32 populations ranging from the southern range limit of *L. digitata* to the
northernmost population found on the French coast, situated in the Strait of Dover. Using this
dataset, we explored the respective and combined effects of sampling year, hydrodynamic
processes, habitat discontinuity, spatial distance and SST on the genetic structure. We specifically
aimed to (1) refine the previous analyses of population structure for this species and identify
isolated populations, (2) test whether on-shelf island populations are genetically more
differentiated compared to mainland populations and thus show signs of genetic erosion, (3)
evaluate the relative importance of each environmental factor in shaping the genetic structure using
a db-RDA and finally (4) evaluate if their relative importance varies according to the sampling
scheme.

## 2. MATERIAL AND METHODS

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### 2.1. Study area

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Brittany is the northwestern-most region of mainland France (NE Atlantic, Figure 1). Its 2,860 km-length coastline encompasses a major biogeographical transition zone between cold- and
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warm-temperate waters (Spalding et al. 2007). The northern shores, which border the English Channel, are characterized by well-mixed waters produced by a macrotidal regime that intensifies eastwards. Contrastingly, waters in southern Brittany are seasonally to permanently stratified and show larger temperature fluctuations throughout the year (Gallon et al. 2014, Blauw et al. 2019).

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Brittany's western-most sector is subject to both the macrotidal regime and the full impact of Atlantic storms, which collectively maintain well-mixed conditions throughout the year and curb seasonal temperature fluctuations (Gallon et al. 2014). Various boreal-affinity species benefit from this nutrient-rich and thermally-buffered environment, including various kelp species that form some of Europe's most important marine forests along Brittany's rocky shores.

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## 2.2. Samples

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Populations of *Laminaria digitata* were sampled between 2003 and 2015 in 32 sites ranging from the southern limit of its NE Atlantic distribution (47°N) up to the Strait of Dover, at the entrance of the North Sea (50°N, Table 1, Figure 1). The population at the Strait of Dover (S32) is known to be geographically isolated, with the closest population along the French coast being in Étretat, at *circa* 200 km to the southwest (see Figure 2C of Araújo et al. 2016) and for which we did not have any sample. Among our populations, 10 were located around islands while 22 were on the mainland coast (Table 1, Figure 1). In our study, we have defined *island* populations as those situated on the continental shelf (i.e., above 200 m depth, Dawson 2016). All of them were part of the mainland during the Last Glacial Maximum when the ocean level was approximately 120 m lower than present (Figure 1, Frenzel et al. 1992, Lambeck 1997). Some of these populations were sampled and genotyped for previous studies as indicated in Table 1. At each site, blade tissue was collected from 30 to 50 randomly selected sporophytes. Tissue samples were then wiped, cleaned from epiphytes and stored in silica-gel crystals until DNA extraction.

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## 2.3. Microsatellite genotyping

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### 2.3.1. DNA extraction

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DNA was extracted from 8–12 mg of dried tissue using the NucleoSpin 96 Plant II kit (Macherey-Nagel GmbH & Co. KG) following manufacturer's instructions. The lysis, microsatellite
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warm-temperate waters (Spalding et al. 2007). The northern shores, which border the English Channel, are characterized by well-mixed waters produced by a macrotidal regime that intensifies eastwards. Contrastingly, waters in southern Brittany are seasonally to permanently stratified and show larger temperature fluctuations throughout the year (Gallon et al. 2014, Blauw et al. 2019).

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Brittany's western-most sector is subject to both the macrotidal regime and the full impact of Atlantic storms, which collectively maintain well-mixed conditions throughout the year and curb seasonal temperature fluctuations (Gallon et al. 2014). Various boreal-affinity species benefit from this nutrient-rich and thermally-buffered environment, including various kelp species that form some of Europe's most important marine forests along Brittany's rocky shores.

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## 2.2. Samples

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Populations of *Laminaria digitata* were sampled between 2003 and 2015 in 32 sites ranging from the southern limit of its NE Atlantic distribution (47°N) up to the Strait of Dover, at the entrance of the North Sea (50°N, Table 1, Figure 1). The population at the Strait of Dover (S32) is known to be geographically isolated, with the closest population along the French coast being in Étretat, at *circa* 200 km to the southwest (see Figure 2C of Araújo et al. 2016) and for which we did not have any sample. Among our populations, 10 were located around islands while 22 were on the mainland coast (Table 1, Figure 1). In our study, we have defined *island* populations as those situated on the continental shelf (i.e., above 200 m depth, Dawson 2016). All of them were part of the mainland during the Last Glacial Maximum when the ocean level was approximately 120 m lower than present (Figure 1, Frenzel et al. 1992, Lambeck 1997). Some of these populations were sampled and genotyped for previous studies as indicated in Table 1. At each site, blade tissue was collected from 30 to 50 randomly selected sporophytes. Tissue samples were then wiped, cleaned from epiphytes and stored in silica-gel crystals until DNA extraction.

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## 2.3. Microsatellite genotyping

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### 2.3.1. DNA extraction

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DNA was extracted from 8–12 mg of dried tissue using the NucleoSpin 96 Plant II kit (Macherey-Nagel GmbH & Co. KG) following manufacturer's instructions. The lysis, microsatellite
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amplification and scoring were performed for 12 polymorphic loci following Robuchon et al. (2014). Multiplex PCRs were modified using 5X GoTaq Flexi colorless reaction buffer (Promega Corp., Madison, USA) instead of 1X and performed using a T100™ Thermal Cycler (Bio-Rad Laboratories Inc.).

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### 2.3.2. Microsatellite amplification, scoring

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Among the markers used for this study, six were developed for *L. digitata* (Ld148, Ld158, Ld167, Ld371, Ld531, and Ld704; Billot et al. 1998) and five for *L. ochroleuca* (Lo4-24, Lo454-17, Lo454-23, Lo454-24, and Lo454-28; Coelho et al. 2014). Alleles were sized using the SM594 size standard (Mauger et al. 2012) and scored manually using GeneMapper 4.0 (Applied Biosystems). Individuals for which more than one locus did not amplify were removed from the dataset.

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### 2.4. Preliminary analyses

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Prior to genetic analyses, a Principal Component Analysis (PCA) was run using the “adegenet” package (Jombart 2008) in R (R Core Team 2017) to check for the presence of other species. The identity of the individuals was additionally confirmed based on allelic size at markers Ld148, Ld158, Ld704, Lo4-24 and Lo454-23 following Mauger et al. (2021). In order to test the independence of each marker, genotypic linkage disequilibrium was calculated for all pairs of markers within and across populations using Genepop 4.7.5 (Rousset 2008). We used 1,000 dememorization, 100 batches and 1,000 iterations per batch for the Markov chain parameters. The presence of null alleles was tested using MICROCHECKER (Van Oosterhout et al. 2004) and genotypes were corrected according to the Van Oosterhout method. Difference between the uncorrected and corrected *F*<sub>ST</sub> matrices (pairwise estimates of genetic differentiation, see section below) was checked using a Mantel test available in the “vegan” package (Oksanen et al. 2013). Given the non-independence of the two datasets (corrected and uncorrected), differences in genetic diversity parameters were checked using a Wilcoxon test available in the “stats” R package.

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### 2.5. Population structure

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Population structure was first assessed with two multivariate methods: a Principal Coordinate Analysis (PCoA) available in the “ape” package (Paradis & Schliep 2019) and STRUCTURE
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amplification and scoring were performed for 12 polymorphic loci following Robuchon et al. (2014). Multiplex PCRs were modified using 5X GoTaq Flexi colorless reaction buffer (Promega Corp., Madison, USA) instead of 1X and performed using a T100™ Thermal Cycler (Bio-Rad Laboratories Inc.).

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### 2.3.2. Microsatellite amplification, scoring

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Among the markers used for this study, six were developed for *L. digitata* (Ld148, Ld158, Ld167, Ld371, Ld531, and Ld704; Billot et al. 1998) and five for *L. ochroleuca* (Lo4-24, Lo454-17, Lo454-23, Lo454-24, and Lo454-28; Coelho et al. 2014). Alleles were sized using the SM594 size standard (Mauger et al. 2012) and scored manually using GeneMapper 4.0 (Applied Biosystems). Individuals for which more than one locus did not amplify were removed from the dataset.

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### 2.4. Preliminary analyses

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Prior to genetic analyses, a Principal Component Analysis (PCA) was run using the “adegenet” package (Jombart 2008) in R (R Core Team 2017) to check for the presence of other species. The identity of the individuals was additionally confirmed based on allelic size at markers Ld148, Ld158, Ld704, Lo4-24 and Lo454-23 following Mauger et al. (2021). In order to test the independence of each marker, genotypic linkage disequilibrium was calculated for all pairs of markers within and across populations using Genepop 4.7.5 (Rousset 2008). We used 1,000 dememorization, 100 batches and 1,000 iterations per batch for the Markov chain parameters. The presence of null alleles was tested using MICROCHECKER (Van Oosterhout et al. 2004) and genotypes were corrected according to the Van Oosterhout method. Difference between the uncorrected and corrected *F*<sub>ST</sub> matrices (pairwise estimates of genetic differentiation, see section below) was checked using a Mantel test available in the “vegan” package (Oksanen et al. 2013). Given the non-independence of the two datasets (corrected and uncorrected), differences in genetic diversity parameters were checked using a Wilcoxon test available in the “stats” R package.

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Population structure was first assessed with two multivariate methods: a Principal Coordinate Analysis (PCoA) available in the “ape” package (Paradis & Schliep 2019) and STRUCTURE

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210 v2.3.4 (Pritchard et al. 2000). The PCoA was run as it represents the first step of the distance-based redundancy analysis (db-RDA). STRUCTURE was run with the admixture model without prior population information and 10 independent replicate runs were performed from K = 1 to 20 rather than 32 to reduce computation time. For each replicate, the burn-in was set to 100,000 and the Markov chain Monte Carlo iterations to 500,000 following guidance from Gilbert et al. (2012). The most likely value of K was determined using both Evanno ΔK (Evanno et al. 2005) and log Pr(X|K) methods (Pritchard & Wen 2003) in structureHarvester (Earl & VonHoldt 2012). Based on the recommendations of Janes et al. (2017), the number of genetic clusters was assessed by comparing these two methods. Finally, CLUMPAK (Kopelman et al. 2015) was used to summarize and visualize STRUCTURE outputs. Population structure was additionally investigated with pairwise estimates of FST (Weir & Cockerham 1984) and confidence intervals were obtained with 95% confidence level and 1,000 resampling procedure using the “hierfstat” package (Goudet 2005). A Wilcoxon test was performed to test the null hypothesis that the average FST associated with divergent (i.e., populations associated with the highest FST values) and the rest of the populations did not significantly differ. The same test was used to test the effect of the location (i.e., island or mainland) on FST values, this test was performed on the continuous dataset (S1 to S24), as no islands were sampled from S25 to S32 (Figure 1 and Table 1).

227 2.6. Genetic diversity

228 Single and multilocus estimates of genetic diversity were calculated for each population as the expected heterozygosity (*He*, *sensu* Nei 1978), observed heterozygosity (*Ho*) and the mean number of private alleles (Pa) using GenAlEx 6.5 (Peakall & Smouse 2006). In addition, standardized allelic richness (*Ar*) was computed using the rarefaction method in “hierfstat”. Departure from Hardy Weinberg equilibrium (*FIS*) was calculated according to the method of Weir & Cockerham (1984) in GENETIX 4.02 (Belkhir et al. 2004) as well as the 1,000 permutations computed to test for their statistical significance. Wilcoxon tests were used to assess the effect of the type (i.e., divergent or not) and the location (island or mainland) on each estimator of genetic diversity and *FIS*. As previously stated, the effect of the location was performed on the continuous dataset.
210 v2.3.4 (Pritchard et al. 2000). The PCoA was run as it represents the first step of the distance-based
211 redundancy analysis (db-RDA). STRUCTURE was run with the admixture model without prior
212 population information and 10 independent replicate runs were performed from K = 1 to 20 rather
213 than 32 to reduce computation time. For each replicate, the burn-in was set to 100,000 and the
214 Markov chain Monte Carlo iterations to 500,000 following guidance from Gilbert et al. (2012).
215 The most likely value of K was determined using both Evanno ΔK (Evanno et al. 2005) and log
216 Pr(X|K) methods (Pritchard & Wen 2003) in structureHarvester (Earl & VonHoldt 2012). Based
217 on the recommendations of Janes et al. (2017), the number of genetic clusters was assessed by
218 comparing these two methods. Finally, CLUMPAK (Kopelman et al. 2015) was used to summarize
219 and visualize STRUCTURE outputs. Population structure was additionally investigated with
220 pairwise estimates of FST (Weir & Cockerham 1984) and confidence intervals were obtained with
221 95% confidence level and 1,000 resampling procedure using the “hierfstat” package (Goudet
222 2005). A Wilcoxon test was performed to test the null hypothesis that the average FST associated
223 with divergent (i.e., populations associated with the highest FST values) and the rest of the
224 populations did not significantly differ. The same test was used to test the effect of the location
225 (i.e., island or mainland) on FST values, this test was performed on the continuous dataset (S1 to
226 S24), as no islands were sampled from S25 to S32 (Figure 1 and Table 1).
227

2.6. Genetic diversity

228 Single and multilocus estimates of genetic diversity were calculated for each population as the
229 expected heterozygosity (*He*, *sensu* Nei 1978), observed heterozygosity (*Ho*) and the mean number
230 of private alleles (Pa) using GenAlEx 6.5 (Peakall & Smouse 2006). In addition, standardized
231 allelic richness (*Ar*) was computed using the rarefaction method in "hierfstat". Departure from
232 Hardy Weinberg equilibrium (*FIS*) was calculated according to the method of Weir & Cockerham
233 (1984) in GENETIX 4.02 (Belkhir et al. 2004) as well as the 1,000 permutations computed to test
234 for their statistical significance. Wilcoxon tests were used to assess the effect of the type (i.e.,
235 divergent or not) and the location (island or mainland) on each estimator of genetic diversity and
236 FIS. As previously stated, the effect of the location was performed on the continuous dataset.
237 2.7. Connectivity model

238 2.7.1. Hydrodynamic modeling

239 Flow fields were provided by a 3D regional configuration (MANGA2500) of the MARS3D
240 hydrodynamic model (Petton et al. 2023). The model domain ranges from 41°N to 55°N in latitude,
241 and 18°W to 9°30'E in longitude, encompassing all studied populations from southern Brittany to
242 the Strait of Dover. The domain is resolved horizontally by a regular 2.5 km grid and vertically by
243 40 sigma levels. The model was forced by meteorological conditions obtained from the ARPEGE
244 and AROME models (Yessad 2015) for the nested zooms with spatial and temporal resolutions of
245 2.5 km and 1 h, respectively. At the boundaries of the global 3D domain, tidal forcing was provided
246 by a larger 2D model covering the northwest Atlantic, driven by 14 tidal constituents (Lyard et al.
247 2006).

248 2.7.2. Lagrangian Modeling

249 The Lagrangian trajectory of the particles (meiospores or fertile parts of the thallus) was simulated
250 using ICHTHYOP v.3.3.6 (Lett et al. 2008), which employed the hourly 3D flow fields as input
251 data. Particles were released within circles with a radius of 2 km and were allowed to drift for 15
252 days to account for dispersal by fertile thallus and by meiospores while reducing the duration of
253 the simulations. The computational time step of the Lagrangian model was set to 300 s and the
254 position of the particle was recorded every 20 min. For each simulation, 5,000 particles were
255 released every two hours. This configuration was applied for each location, repeated for eight days
256 per month from June to October (corresponding to *L. digitata*'s highest peak of fertility, Bartsch
257 et al. 2008), using the hydrodynamic outputs of years 2014 and 2015. The eight days were chosen
258 by randomly selecting two days per week in order to account for tidal variation occurring over a
259 month while reducing computational time. A total of 960 dispersal events were hence simulated
260 over two years for each site. Pairwise oceanographic connectivity ($P_{ij}$) was estimated following
261 Reynes et al. (2021) and is defined as: $P_{ij} = N_{i-j}/N_i$, with $N_i$ corresponding to the total number of
262 released particles from site i and $N_{i-j}$ the total number of released particles from i arriving at j within
263 15 days of dispersal. $P_{ij}$ was then averaged over the full period of the simulation to obtain the mean
237 2.7. Connectivity model

238 2.7.1. Hydrodynamic modeling

239 Flow fields were provided by a 3D regional configuration (MANGA2500) of the MARS3D
240 hydrodynamic model (Petton et al. 2023). The model domain ranges from 41°N to 55°N in latitude,
241 and 18°W to 9°30'E in longitude, encompassing all studied populations from southern Brittany to
242 the Strait of Dover. The domain is resolved horizontally by a regular 2.5 km grid and vertically by
243 40 sigma levels. The model was forced by meteorological conditions obtained from the ARPEGE
244 and AROME models (Yessad 2015) for the nested zooms with spatial and temporal resolutions of
245 2.5 km and 1 h, respectively. At the boundaries of the global 3D domain, tidal forcing was provided
246 by a larger 2D model covering the northwest Atlantic, driven by 14 tidal constituents (Lyard et al.
247 2006).

248 2.7.2. Lagrangian Modeling

249 The Lagrangian trajectory of the particles (meiospores or fertile parts of the thallus) was simulated
250 using ICHTHYOP v.3.3.6 (Lett et al. 2008), which employed the hourly 3D flow fields as input
251 data. Particles were released within circles with a radius of 2 km and were allowed to drift for 15
252 days to account for dispersal by fertile thallus and by meiospores while reducing the duration of
253 the simulations. The computational time step of the Lagrangian model was set to 300 s and the
254 position of the particle was recorded every 20 min. For each simulation, 5,000 particles were
255 released every two hours. This configuration was applied for each location, repeated for eight days
256 per month from June to October (corresponding to *L. digitata*'s highest peak of fertility, Bartsch
257 et al. 2008), using the hydrodynamic outputs of years 2014 and 2015. The eight days were chosen
258 by randomly selecting two days per week in order to account for tidal variation occurring over a
259 month while reducing computational time. A total of 960 dispersal events were hence simulated
260 over two years for each site. Pairwise oceanographic connectivity ($P_{ij}$) was estimated following
261 Reynes et al. (2021) and is defined as: $P_{ij} = N_{i-j}/N_i$, with $N_i$ corresponding to the total number of
262 released particles from site i and $N_{i-j}$ the total number of released particles from i arriving at j within
263 15 days of dispersal. $P_{ij}$ was then averaged over the full period of the simulation to obtain the mean
264

connectivity matrix. The level of self-recruitment ($P_{ii}$) was estimated with the same method as $P_{ij}$ but using a threshold of 48 h to distinguish emitted particles (from the release area) from those returning to the release area after 48 h. A Wilcoxon test was performed to assess the effect of the type (i.e., divergent or not) or the pair of populations (continuous dataset) on the rate of self-recruitment.

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## 2.8. Geographic distance and habitat discontinuity

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The bathymetric contour was extracted from a 0.001°-resolution digital terrain model (or DTM, data.shom.fr) using ArcGIS 10. In order to standardize the calculation of pairwise distances between coastal populations around the peninsula, distances were calculated following the 5 m isobath. However, when at least one of the populations was situated on an island, the straight-line distance across the sea was considered. A 0.009°-resolution raster layer representing the distribution of rocky seabed in the English Channel and around Brittany was prepared from the best available substrate information available at Ifremer in May 2019. Information regarding the spatial distribution of bedrock comes from the combination of several sources: (1) maps of coastal habitats produced as part of the Rebent project, with the scales varying from 1/2,000 to 1/10,000; (2) maps of existing habitat produced as part of the MESH project (Bajjouk 2005), with the scales varying from 1/50,000 to 1/10,000 and (3) from the local extraction of the information on the presence of rock from topo-bathymetric Lidar data (DTM of 5 m resolution) (Gorman et al. 2013, Bajjouk et al. 2015). This layer was subsequently used to compute the continuity of rocky substrata between pairs of sites. Only the rock extents situated above 5 m depth (corresponding to 5 m below the lowest astronomical tide, Figure 1) were considered in this calculation given that *L. digitata* is considered unable to colonize areas below this depth (Robuchon et al. 2014). The proportion of the geographic distance unoccupied by rocky substrata was considered for subsequent analyses.

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## 2.9. Temperature data

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Sea-surface temperature (SST) was generated using E.U. Copernicus Marine Service Information, with 0.05°x0.05° resolution (product with DOI 10.48670/moi-00169 and see Merchant et al. 2019 and Good et al. 2020 for product details). To account for the fact that populations are still trying
connectivity matrix. The level of self-recruitment ($P_{ii}$) was estimated with the same method as $P_{ij}$ but using a threshold of 48 h to distinguish emitted particles (from the release area) from those returning to the release area after 48 h. A Wilcoxon test was performed to assess the effect of the type (i.e., divergent or not) or the pair of populations (continuous dataset) on the rate of self-recruitment.

## 2.8. Geographic distance and habitat discontinuity

The bathymetric contour was extracted from a 0.001°-resolution digital terrain model (or DTM, data.shom.fr) using ArcGIS 10. In order to standardize the calculation of pairwise distances between coastal populations around the peninsula, distances were calculated following the 5 m isobath. However, when at least one of the populations was situated on an island, the straight-line distance across the sea was considered. A 0.009°-resolution raster layer representing the distribution of rocky seabed in the English Channel and around Brittany was prepared from the best available substrate information available at Ifremer in May 2019. Information regarding the spatial distribution of bedrock comes from the combination of several sources: (1) maps of coastal habitats produced as part of the Rebent project, with the scales varying from 1/2,000 to 1/10,000; (2) maps of existing habitat produced as part of the MESH project (Bajjouk 2005), with the scales varying from 1/50,000 to 1/10,000 and (3) from the local extraction of the information on the presence of rock from topo-bathymetric Lidar data (DTM of 5 m resolution) (Gorman et al. 2013, Bajjouk et al. 2015). This layer was subsequently used to compute the continuity of rocky substrata between pairs of sites. Only the rock extents situated above 5 m depth (corresponding to 5 m below the lowest astronomical tide, Figure 1) were considered in this calculation given that *L. digitata* is considered unable to colonize areas below this depth (Robuchon et al. 2014). The proportion of the geographic distance unoccupied by rocky substrata was considered for subsequent analyses.

## 2.9. Temperature data

Sea-surface temperature (SST) was generated using E.U. Copernicus Marine Service Information, with 0.05°x0.05° resolution (product with DOI 10.48670/moi-00169 and see Merchant et al. 2019 and Good et al. 2020 for product details). To account for the fact that populations are still trying
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to catch up the effect of past temperature variations, we considered SST data obtained for a period of 10 years preceding each sampling year (i.e., from 1995 to 2004 for a population sampled in 2005). While the annual mean and range of annual SST were averaged across the period, the minimum (resp. maximum) was chosen as being the minimum (resp. maximum) SST observed across these 10 years as we can imagine that extreme thermal episodes can represent a strong selection.

## 2.10. Linking environmental variables to genetic structure

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Global (resp. partial) db-RDA (Legendre & Anderson 1999) were conducted to investigate the joint (resp. individual) effect of geographic distance, ocean currents, sampling years, SST (minimum, maximum, mean and range) and habitat discontinuity on the total explained genetic variation. The first two axes of the PCoA performed on the *F*<sub>ST</sub> matrix were used as the response variables representing the core of the genetic structure in the db-RDA. The analyses were repeated on three datasets to assess *if* and *how* the contribution of each predictor varied according to the sampling scheme. The *overall* dataset corresponded to the whole dataset (i.e., 32 populations), the *regional* dataset accounted for the 29 populations from Brittany (S1 to S29) and the *continuous* dataset solely included the populations that were continuously sampled along the coast (S1 to S24, Figure 1).

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Sampling years were taken into account to assess whether variation in sampling year across samples influenced the contemporary genetic differentiation. The environmental explanatory variables accounting for geographic distance and ocean currents were respectively transformed into distance-based Moran's eigenvector maps (dbMEM, Dray et al. 2006) and Asymmetric Eigenvector Maps (AEM, Blanchet et al. 2008b). dbMEM eigenvectors were computed from the matrix of geographic distance with the "adespatial" package using the default settings for truncations, while only retaining positive eigenvalues as we did not expect any negative autocorrelation between sampling positions (Dray et al. 2006). For the construction of AEMs, the oceanographic connectivity matrix was transformed into a nodes-to-edges matrix and the edges were weighted by the probability of dispersal before calculating AEM eigenvectors with the
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to catch up the effect of past temperature variations, we considered SST data obtained for a period of 10 years preceding each sampling year (i.e., from 1995 to 2004 for a population sampled in 2005). While the annual mean and range of annual SST were averaged across the period, the minimum (resp. maximum) was chosen as being the minimum (resp. maximum) SST observed across these 10 years as we can imagine that extreme thermal episodes can represent a strong selection.

## 2.10. Linking environmental variables to genetic structure

Global (resp. partial) db-RDA (Legendre & Anderson 1999) were conducted to investigate the joint (resp. individual) effect of geographic distance, ocean currents, sampling years, SST (minimum, maximum, mean and range) and habitat discontinuity on the total explained genetic variation. The first two axes of the PCoA performed on the *F*<sub>ST</sub> matrix were used as the response variables representing the core of the genetic structure in the db-RDA. The analyses were repeated on three datasets to assess *if* and *how* the contribution of each predictor varied according to the sampling scheme. The *overall* dataset corresponded to the whole dataset (i.e., 32 populations), the *regional* dataset accounted for the 29 populations from Brittany (S1 to S29) and the *continuous* dataset solely included the populations that were continuously sampled along the coast (S1 to S24, Figure 1).

Sampling years were taken into account to assess whether variation in sampling year across samples influenced the contemporary genetic differentiation. The environmental explanatory variables accounting for geographic distance and ocean currents were respectively transformed into distance-based Moran's eigenvector maps (dbMEM, Dray et al. 2006) and Asymmetric Eigenvector Maps (AEM, Blanchet et al. 2008b). dbMEM eigenvectors were computed from the matrix of geographic distance with the “adespatial” package using the default settings for truncations, while only retaining positive eigenvalues as we did not expect any negative autocorrelation between sampling positions (Dray et al. 2006). For the construction of AEMs, the oceanographic connectivity matrix was transformed into a nodes-to-edges matrix and the edges were weighted by the probability of dispersal before calculating AEM eigenvectors with the
“adespatial” package. Calculations of dbMEM and AEM eigenvectors were iterated for each dataset following the aforementioned method. It is worth noting that the first eigenvectors in both dbMEMs and AEMs (e.g., AEM1, dbMEM1) are associated with broad-scale patterns while the highest (e.g., dbMEM5, AEM25) highlight finer patterns. To test for correlation between dbMEMs and SST variables, a Spearman correlation was measured using the “stats” package.

Prior to each global db-RDA, an elastic net regularized regression was performed using the “glmnet” package (Friedman et al. 2010) with the elastic net mixing parameter α fixed at 0.08 in order to decrease the number of explanatory variables (Ogutu et al. 2012). We considered all independent predictors with Variance Inflation Factor (VIF) below 10 to avoid multicollinearity effects using the vif_func function available on walterxie/ComMA. The remaining variables were then selected via a stepwise forward procedure using the ordiR2step function from the “vegan” package. This function selects variables to build the *optimal* model, defined as the model maximizing the adjusted coefficient of determination (R²<sub>adj</sub>), while minimizing the *p*-value (Blanchet et al. 2008a). For partial db-RDAs, the variables were directly selected using the ordiR2step function as the number of explanatory variables were already lower than the sample size. Finally, ANOVAs (1,000 permutations) were performed to assess the significance of each model (global and partial db-RDA), axes and retained variables.

## 3. RESULTS

### 3.1. Preliminary analyses

The PCA ran on the whole dataset allowed to identify 12 *Laminaria hyperborea* individuals (Figure S1, Table S1), a sister species with overlapping range distribution (Robuchon et al. 2014). These individuals were sampled in S6 (2 ind.), S9 (1 ind.), S21 (3 ind.), S22 (3 ind.), S25 (1 ind.), S28 (1 ind.) and S30 (1 ind.) and were removed from our dataset, giving a total of 896 individuals of *L. digitata*. No significant genotypic linkage disequilibrium was obtained, neither within nor across populations (Table S2). Null alleles were present at locus Ld158 in five populations (S1, S2, S7, S10 and S19, Table S3) and several homozygous genotypes were corrected by adding an allele which was initially absent from the whole dataset (allelic size 246, Table S4). The same
“adespatial” package. Calculations of dbMEM and AEM eigenvectors were iterated for each dataset following the aforementioned method. It is worth noting that the first eigenvectors in both dbMEMs and AEMs (e.g., AEM1, dbMEM1) are associated with broad-scale patterns while the highest (e.g., dbMEM5, AEM25) highlight finer patterns. To test for correlation between dbMEMs and SST variables, a Spearman correlation was measured using the “stats” package.

Prior to each global db-RDA, an elastic net regularized regression was performed using the “glmnet” package (Friedman et al. 2010) with the elastic net mixing parameter α fixed at 0.08 in order to decrease the number of explanatory variables (Ogutu et al. 2012). We considered all independent predictors with Variance Inflation Factor (VIF) below 10 to avoid multicollinearity effects using the vif_func function available on walterxie/ComMA. The remaining variables were then selected via a stepwise forward procedure using the ordiR2step function from the “vegan” package. This function selects variables to build the optimal model, defined as the model maximizing the adjusted coefficient of determination (R²<sub>adj</sub>), while minimizing the p-value (Blanchet et al. 2008a). For partial db-RDAs, the variables were directly selected using the ordiR2step function as the number of explanatory variables were already lower than the sample size. Finally, ANOVAs (1,000 permutations) were performed to assess the significance of each model (global and partial db-RDA), axes and retained variables.

## 3. RESULTS

### 3.1. Preliminary analyses

The PCA ran on the whole dataset allowed to identify 12 *Laminaria hyperborea* individuals (Figure S1, Table S1), a sister species with overlapping range distribution (Robuchon et al. 2014). These individuals were sampled in S6 (2 ind.), S9 (1 ind.), S21 (3 ind.), S22 (3 ind.), S25 (1 ind.), S28 (1 ind.) and S30 (1 ind.) and were removed from our dataset, giving a total of 896 individuals of *L. digitata*. No significant genotypic linkage disequilibrium was obtained, neither within nor across populations (Table S2). Null alleles were present at locus Ld158 in five populations (S1, S2, S7, S10 and S19, Table S3) and several homozygous genotypes were corrected by adding an allele which was initially absent from the whole dataset (allelic size 246, Table S4). The same
analyses were rerun on the corrected dataset (detection of MLG, *F*<sub>ST</sub>, parameters of genetic diversity, db-RDA etc.), which brought us to the decision to keep our original dataset given: (1) the high correlation between genetic diversity parameters (whichever the parameter, *p*-value of the Wilcoxon tests were always higher than 0.6) and *F*<sub>ST</sub> matrices (Tables S5 and S6, Mantel statistic based on Pearson's product-moment correlation *r* = 0.9993), (2) the acquisition of the same db-RDA results, (3) the correlation between the number of alleles and the number of genotypes with missing data (*p*-value = 0.462) suggesting a negligible presence of null alleles and finally (4) the impossibility to rerun multilocus analyses such as STRUCTURE or linkage disequilibrium analyses due to the fact that MICROCHECKER modifies the original sample numbers.

## 3.2. Population Structure

According to both ΔK and ln Pr(X|K) methods, K = 2 appeared as the optimal number of genetic clusters (Figure S2). The barplot for K = 2 illustrated a general North-South differentiation, with southern Brittany (S1-S4, referred to as “SB” hereafter) and Saint-Malo Bay (S26-S29, referred to as “SMB” hereafter) being particularly well separated (Figure 2A). ΔK method suggested K = 3 to be the next optimal value, followed by K = 12, while the ln Pr(X|K) method first indicated K = 12 and then K = 3 (Figure S2). The barplot for K = 3 separated SB in another cluster (Figure 2B), and the one for K = 12 additionally separated southwest Brittany (S5-S9), S21 (Locquirec), S25 (Roche-Douvres), SMB, Normandy (S30-S31) and S32 (Wissant), while western and northern populations (S10-S20, S22-S24) remained highly admixed (Figure 2C). From these results, eight major genetic groups can be defined: SB, southwest Brittany (S5-S9), S21, populations from western and northern Brittany (S10-S20, S22-S24), S25, SMB, Normandy and S32 (Figure 3). Among these genetic groups, populations from SB, S21, SMB and S32 were associated with significantly higher mean *F*<sub>ST</sub> values (Table 2, and Table S5 for pairwise *F*<sub>ST</sub> values and their significance; *p*-value < 0.0001) and will be referred to as *divergent* populations from here onwards. The high mean *F*<sub>ST</sub> obtained for S32 can be interpreted from allele frequencies spectra, in particular at loci Ld148, Ld371, Lo454-17 and Lo454-28 (Figure S3). However, allelic spectra were less informative for the remaining divergent populations. STRUCTURE results suggested that island
analyses were rerun on the corrected dataset (detection of MLG, *F*<sub>ST</sub>, parameters of genetic diversity, db-RDA etc.), which brought us to the decision to keep our original dataset given: (1) the high correlation between genetic diversity parameters (whichever the parameter, *p*-value of the Wilcoxon tests were always higher than 0.6) and *F*<sub>ST</sub> matrices (Tables S5 and S6, Mantel statistic based on Pearson's product-moment correlation *r* = 0.9993), (2) the acquisition of the same db-RDA results, (3) the correlation between the number of alleles and the number of genotypes with missing data (*p*-value = 0.462) suggesting a negligible presence of null alleles and finally (4) the impossibility to rerun multilocus analyses such as STRUCTURE or linkage disequilibrium analyses due to the fact that MICROCHECKER modifies the original sample numbers.

## 3.2. Population Structure

According to both ΔK and ln Pr(X|K) methods, K = 2 appeared as the optimal number of genetic clusters (Figure S2). The barplot for K = 2 illustrated a general North-South differentiation, with southern Brittany (S1-S4, referred to as “SB” hereafter) and Saint-Malo Bay (S26-S29, referred to as “SMB” hereafter) being particularly well separated (Figure 2A). ΔK method suggested K = 3 to be the next optimal value, followed by K = 12, while the ln Pr(X|K) method first indicated K = 12 and then K = 3 (Figure S2). The barplot for K = 3 separated SB in another cluster (Figure 2B), and the one for K = 12 additionally separated southwest Brittany (S5-S9), S21 (Locquirec), S25 (Roche-Douvres), SMB, Normandy (S30-S31) and S32 (Wissant), while western and northern populations (S10-S20, S22-S24) remained highly admixed (Figure 2C). From these results, eight major genetic groups can be defined: SB, southwest Brittany (S5-S9), S21, populations from western and northern Brittany (S10-S20, S22-S24), S25, SMB, Normandy and S32 (Figure 3). Among these genetic groups, populations from SB, S21, SMB and S32 were associated with significantly higher mean *F*<sub>ST</sub> values (Table 2, and Table S5 for pairwise *F*<sub>ST</sub> values and their significance; *p*-value < 0.0001) and will be referred to as *divergent* populations from here onwards. The high mean *F*<sub>ST</sub> obtained for S32 can be interpreted from allele frequencies spectra, in particular at loci Ld148, Ld371, Lo454-17 and Lo454-28 (Figure S3). However, allelic spectra were less informative for the remaining divergent populations. STRUCTURE results suggested that island
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populations are generally well admixed with coastline populations with the exception of S25 (Figure 2C). This result is supported by the fact that the average values of *F*<sub>ST</sub> did not significantly differ between coastline and island populations (*p*-value = 0.379).

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### 3.3. Genetic diversity

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Estimates of genetic diversity averaged over the 11 markers are provided in Table 3. The lowest value was always associated to S32 and the highest to populations at the western tip (S8-S14). Variation in genetic diversity across populations was the highest for *Ar*, with values ranging from 2.542 (S32) to 6.151 (S12, Figure 3). Values of *F*<sub>IS</sub> showed a significant deficit of heterozygotes for several populations in the raw dataset. The correction for null alleles did not significantly change the values (*F*<sub>IS</sub> = -0.04; 0.02; 0.016; 0.068 and 0.067 after correction for respectively S1, S2, S7, S10 and S19. Wilcoxon test, *p*-value = 0.421 when only accounting for the five populations) as well as the significance of the departure from Hardy-Weinberg (HW) equilibrium, except for S7 for which the *F*<sub>IS</sub> was not significantly different from 0 after correction. One multilocus genotype shared by two individuals was detected in S4 and S28 but was not correlated with any significant deviation from HW equilibrium (Table 3).

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The 10 divergent populations were associated with the lowest level of genetic diversity (*p*-value < 0.001 for *He*, *Ho* and *Ar*, see Figure 3) although the same tendency was not observed for *Pa* (*p*-value: 0.283). These populations also appeared to have significantly lower *F*<sub>IS</sub> values compared to the rest of the populations (*p*-value = 0.023). In contrast, while genetic diversity estimates were always higher in coastal than island populations, differences were not significant based on Wilcoxon tests (*p*-value for *He*: 0.347, *Ho*: 0.263, *Ar*: 0.174 and *Pa*: 0.165). Similarly, higher *F*<sub>IS</sub> values were observed in islands but the difference was, again, not significant (*p*-value: 0.905).

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### 3.4. Habitat discontinuity

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The proportion of habitat discontinuity shown in Table S7 suggested a patchy pattern with several populations appearing particularly isolated. As illustrated by Figure 1, island populations appeared naturally isolated by the absence of continuous favorable substrate (i.e., rocky substrata above 5

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populations are generally well admixed with coastline populations with the exception of S25 (Figure 2C). This result is supported by the fact that the average values of $F_{ST}$ did not significantly differ between coastline and island populations ($p$-value = 0.379).

### 3.3. Genetic diversity

Estimates of genetic diversity averaged over the 11 markers are provided in Table 3. The lowest value was always associated to S32 and the highest to populations at the western tip (S8-S14). Variation in genetic diversity across populations was the highest for *Ar*, with values ranging from 2.542 (S32) to 6.151 (S12, Figure 3). Values of $F_{IS}$ showed a significant deficit of heterozygotes for several populations in the raw dataset. The correction for null alleles did not significantly change the values ($F_{IS}$ = -0.04; 0.02; 0.016; 0.068 and 0.067 after correction for respectively S1, S2, S7, S10 and S19. Wilcoxon test, $p$-value = 0.421 when only accounting for the five populations) as well as the significance of the departure from Hardy-Weinberg (HW) equilibrium, except for S7 for which the $F_{IS}$ was not significantly different from 0 after correction. One multilocus genotype shared by two individuals was detected in S4 and S28 but was not correlated with any significant deviation from HW equilibrium (Table 3).

The 10 divergent populations were associated with the lowest level of genetic diversity ($p$-value < 0.001 for *He*, *Ho* and *Ar*, see Figure 3) although the same tendency was not observed for $\overline{Pa}$ ($p$-value: 0.283). These populations also appeared to have significantly lower $F_{IS}$ values compared to the rest of the populations ($p$-value = 0.023). In contrast, while genetic diversity estimates were always higher in coastal than island populations, differences were not significant based on Wilcoxon tests ($p$-value for *He*: 0.347, *Ho*: 0.263, *Ar*: 0.174 and $\overline{Pa}$: 0.165). Similarly, higher $F_{IS}$ values were observed in islands but the difference was, again, not significant ($p$-value: 0.905).

### 3.4. Habitat discontinuity

The proportion of habitat discontinuity shown in Table S7 suggested a patchy pattern with several populations appearing particularly isolated. As illustrated by Figure 1, island populations appeared naturally isolated by the absence of continuous favorable substrate (i.e., rocky substrata above 5
m depth) connecting them to the nearest mainland or island populations thus revealing high fragmentation. At the same time, the expanse of rocky habitat appeared continuous along northern Brittany (S15-S29) as shown by the small proportion of geographic distance which was unoccupied by rocky substrata. The increase in habitat discontinuity between southern (< S11) and northern populations (> S11) was mainly driven by the topography of the Rade of Brest and its lack of rocky substrata at the 5 m isobath, and even above, as suggested by our dataset on rocky substrata.

### 3.5. Oceanographic connectivity

Connectivity probabilities resulting from Lagrangian simulations are shown in Figure 4 and Table S8. In most cases, the highest probabilities of connectivity were associated with neighboring populations. Contrastingly, groups of populations such as SB, SMB and Normandy appeared highly connected to each other, but isolated from other populations (Figure 4). The probability of connectivity of S32 with any other population was null, highlighting its strong isolation. In the case of S21, while a particle released from this population attained neighboring populations, this was not true for the other way around, as suggested by the low probabilities that particles released from neighboring populations recruited in S21. The difference between divergent and non-divergent populations was noticeable when comparing the number of connectivity links, defined as the mean number of populations with which the probability of connectivity is different from 0 (Table S8). Indeed, spores released from divergent populations reached on average 5.2 populations compared to 7.9 for non-divergent populations. Similarly, spores recruiting in divergent populations originated on average from 4.9 populations, compared to 9.1 for non-divergent ones. The difference in connectivity was less pronounced between island and coastal populations: spores released from islands reached on average 8.5 populations, compared to 6.6 for coastal populations; and spores recruiting in islands originated from an average of 9.2 populations, compared to 6.5 for coastal populations.

In terms of directionality, dispersal appeared to have a north-western direction in the south of the Peninsula (S1-S7) and a north-eastern direction when considering north-western and northern
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m depth) connecting them to the nearest mainland or island populations thus revealing high fragmentation. At the same time, the expanse of rocky habitat appeared continuous along northern Brittany (S15-S29) as shown by the small proportion of geographic distance which was unoccupied by rocky substrata. The increase in habitat discontinuity between southern (< S11) and northern populations (> S11) was mainly driven by the topography of the Rade of Brest and its lack of rocky substrata at the 5 m isobath, and even above, as suggested by our dataset on rocky substrata.

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### 3.5. Oceanographic connectivity

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Connectivity probabilities resulting from Lagrangian simulations are shown in Figure 4 and Table S8. In most cases, the highest probabilities of connectivity were associated with neighboring populations. Contrastingly, groups of populations such as SB, SMB and Normandy appeared highly connected to each other, but isolated from other populations (Figure 4). The probability of connectivity of S32 with any other population was null, highlighting its strong isolation. In the case of S21, while a particle released from this population attained neighboring populations, this was not true for the other way around, as suggested by the low probabilities that particles released from neighboring populations recruited in S21. The difference between divergent and non-divergent populations was noticeable when comparing the number of connectivity links, defined as the mean number of populations with which the probability of connectivity is different from 0 (Table S8). Indeed, spores released from divergent populations reached on average 5.2 populations compared to 7.9 for non-divergent populations. Similarly, spores recruiting in divergent populations originated on average from 4.9 populations, compared to 9.1 for non-divergent ones. The difference in connectivity was less pronounced between island and coastal populations: spores released from islands reached on average 8.5 populations, compared to 6.6 for coastal populations; and spores recruiting in islands originated from an average of 9.2 populations, compared to 6.5 for coastal populations.

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In terms of directionality, dispersal appeared to have a north-western direction in the south of the Peninsula (S1-S7) and a north-eastern direction when considering north-western and northern
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populations (S10-S25, Figure 4). For instance, a spore released from S10 reached S11 to S14 with
a higher probability than in the opposite direction (Figure 4, Table S8). The pattern became less
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directionality analyses less relevant.

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Finally, self-recruitment probability varied from 0.005 (S8) to 0.476 (S25, Table S8). The
probability did not significantly differ between island and coastline populations (0.211 and 0.189,
respectively, *p*-value = 0.077), nor between divergent and non-divergent populations (0.255 and
0.169, respectively, *p*-value = 0.64).

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3.6. Hierarchical effects of environmental factors on genetic structure

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Whichever the geographical scale, habitat discontinuity and sampling years were not retained in
the global nor partial db-RDA (Table 4). Therefore, for the sake of parsimony, these two variables
will not be mentioned in the following results.

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3.6.1. Overall dataset

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The model and the two first axes associated with the global db-RDA were significant (*p*-value <
0.001) with an adjusted coefficient of determination (R²<sub>adj</sub>) of 0.604 (Table 4). Five variables were
selected by the ordiR2step function which were related to geographic distance (dbMEM1, 3 and
5) and oceanographic connectivity (AEM1 and 3, Table 4, Figure 5B). The first axis, accounting
for 71.6% of the variance, showed a clear differentiation between southern and northern Brittany
which appeared to be driven by geographic distance (dbMEM1, Figure 5B) and by oceanographic
processes to a lesser extent (AEM1 and 3, Figure 5B and S4). The second axis, accounting for
18.1% of the variance and mostly driven by geographic distance (dbMEM3), isolated S32 whilst
populations from Normandy remained poorly differentiated (Figure 5B). When partitioning the
respective effects of oceanographic connectivity, geographical distance and SST using partial db-
RDA, each predictor appeared significant. Geographic distance explained the greatest amount of
variance with R²<sub>adj</sub> = 0.475, against 0.165 for SST (minimum and mean) and 0.115 for
oceanographic connectivity (Table 4).

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populations (S10-S25, Figure 4). For instance, a spore released from S10 reached S11 to S14 with
a higher probability than in the opposite direction (Figure 4, Table S8). The pattern became less
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428 clear from SMB. However, as previously stated, these populations tended to be highly connected
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430 within each genetic group (SMB, Normandy) but not with populations beyond them, making
directionality analyses less relevant.

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Finally, self-recruitment probability varied from 0.005 (S8) to 0.476 (S25, Table S8). The
probability did not significantly differ between island and coastline populations (0.211 and 0.189,
respectively, *p*-value = 0.077), nor between divergent and non-divergent populations (0.255 and
0.169, respectively, *p*-value = 0.64).

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3.6. Hierarchical effects of environmental factors on genetic structure

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Whichever the geographical scale, habitat discontinuity and sampling years were not retained in
the global nor partial db-RDA (Table 4). Therefore, for the sake of parsimony, these two variables
will not be mentioned in the following results.

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3.6.1. Overall dataset

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The model and the two first axes associated with the global db-RDA were significant (*p*-value <
0.001) with an adjusted coefficient of determination (R²<sub>adj</sub>) of 0.604 (Table 4). Five variables were
selected by the ordiR2step function which were related to geographic distance (dbMEM1, 3 and
5) and oceanographic connectivity (AEM1 and 3, Table 4, Figure 5B). The first axis, accounting
for 71.6% of the variance, showed a clear differentiation between southern and northern Brittany
which appeared to be driven by geographic distance (dbMEM1, Figure 5B) and by oceanographic
processes to a lesser extent (AEM1 and 3, Figure 5B and S4). The second axis, accounting for
18.1% of the variance and mostly driven by geographic distance (dbMEM3), isolated S32 whilst
populations from Normandy remained poorly differentiated (Figure 5B). When partitioning the
respective effects of oceanographic connectivity, geographical distance and SST using partial db-
RDA, each predictor appeared significant. Geographic distance explained the greatest amount of
variance with R²<sub>adj</sub> = 0.475, against 0.165 for SST (minimum and mean) and 0.115 for
oceanographic connectivity (Table 4).

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### 3.6.2. Regional dataset

To investigate the relative contribution of each environmental factor at a regional scale, populations S30, S31 and S32 were excluded because of their high geographic isolation (ca. 600 km from the rest of the populations). The model and the two first axes associated with the global db-RDA were again significant ($p$-value < 0.001) with $R^2_{adj} = 0.614$ (Table 4). Six variables were retained by the ordiR2step selection process: two accounted for geographic distance (dbMEM2 and 3, Table 4) and four for oceanographic connectivity (AEM1, 2, 23 and 25, Table 4). The first axis (84.9% of the variance) was driven by geographic distance (dbMEM2, Figure 5C), while oceanographic processes appeared important for finer genetic structure (Figure S4). The first axis again clearly separated populations according to their latitude and SMB appeared isolated due to geographic distance. The second axis (10.3%) was driven by AEM25 and isolated S21, suggesting the role of oceanographic currents on its genetic isolation (Figure 5C). When partitioning the respective effects of oceanographic connectivity, geographical distance and SST, each predictor appeared significant. With this dataset, oceanographic connectivity explained a slightly higher amount of variance compared to geographic distance ($R^2_{adj} = 0.549$ and 0.485, respectively) while $R^2_{adj} = 0.221$ for SST (mean, Table 4).

### 3.6.3. Continuous dataset

The third analysis accounting for the 24 continuously sampled populations (S1-S24) was performed by additionally excluding SMB and S25. At this scale, the highest distance between two neighboring populations is reduced to ca. 160 km (between S11 and S12 when following coastline) without any major gap in connectivity (Figure 4). The model and the three first axes were significant ($p$-value < 0.001 for the model and the first axis, $p$-value < 0.05 for the remaining two axes) with $R^2_{adj} = 0.777$ (Table 4). Eight variables remained after the two selection processes: seven associated to oceanographic currents (AEM1, 2, 3, 5, 10, 16 and 19) and one to geographic distance (dbMEM3, Table 4, Figure 5D). This first axis was mostly explained by AEM1, denoting a gradual north-wise differentiation (Figure 5D), whilst higher order AEMs mostly revealed the strong density of connectivity links amongst northern (S10-S25) or southern populations (S1-S7,
### 3.6.2. Regional dataset

To investigate the relative contribution of each environmental factor at a regional scale, populations S30, S31 and S32 were excluded because of their high geographic isolation (ca. 600 km from the rest of the populations). The model and the two first axes associated with the global db-RDA were again significant ($p$-value < 0.001) with $R^2_{adj} = 0.614$ (Table 4). Six variables were retained by the ordiR2step selection process: two accounted for geographic distance (dbMEM2 and 3, Table 4) and four for oceanographic connectivity (AEM1, 2, 23 and 25, Table 4). The first axis (84.9% of the variance) was driven by geographic distance (dbMEM2, Figure 5C), while oceanographic processes appeared important for finer genetic structure (Figure S4). The first axis again clearly separated populations according to their latitude and SMB appeared isolated due to geographic distance. The second axis (10.3%) was driven by AEM25 and isolated S21, suggesting the role of oceanographic currents on its genetic isolation (Figure 5C). When partitioning the respective effects of oceanographic connectivity, geographical distance and SST, each predictor appeared significant. With this dataset, oceanographic connectivity explained a slightly higher amount of variance compared to geographic distance ($R^2_{adj} = 0.549$ and 0.485, respectively) while $R^2_{adj} = 0.221$ for SST (mean, Table 4).

### 3.6.3. Continuous dataset

The third analysis accounting for the 24 continuously sampled populations (S1-S24) was performed by additionally excluding SMB and S25. At this scale, the highest distance between two neighboring populations is reduced to ca. 160 km (between S11 and S12 when following coastline) without any major gap in connectivity (Figure 4). The model and the three first axes were significant ($p$-value < 0.001 for the model and the first axis, $p$-value < 0.05 for the remaining two axes) with $R^2_{adj} = 0.777$ (Table 4). Eight variables remained after the two selection processes: seven associated to oceanographic currents (AEM1, 2, 3, 5, 10, 16 and 19) and one to geographic distance (dbMEM3, Table 4, Figure 5D). This first axis was mostly explained by AEM1, denoting a gradual north-wise differentiation (Figure 5D), whilst higher order AEMs mostly revealed the strong density of connectivity links amongst northern (S10-S25) or southern populations (S1-S7,
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Figure S4). The second axis was mostly driven by AEM5, which again isolated S21. With this dataset, oceanographic connectivity explained a greater amount of variance than geographic distance ($R^2_{adj}$ = 0.794 and 0.513, respectively) denoting the fact that genetic differentiation between populations was widely explained by oceanographic processes, while SST explained 21.0% of the variance (maximum SST).

Finally, whichever the dataset, SST was never retained in global db-RDAs, which is most likely due to the correlation between some of the SST-related variables and dbMEMs observed whichever the datasets (Table S9).

## 4. DISCUSSION

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Our study builds on previous microsatellite research (Billot et al. 2003, Valero et al. 2011, Couceiro et al. 2013, Robuchon et al. 2014) to refine our understanding of *Laminaria digitata*'s population structure in the southern edge of its East Atlantic distribution. We specifically aimed at deepening our knowledge of connectivity patterns among the studied populations which comprised 22 coastline and 10 on-shelf island populations. To do this, we combined inference and analyses of genetic structure, genetic diversity and dispersal probabilities resulting from Lagrangian simulations. This enabled us to identify divergent populations, defined as those associated with high genetic differentiation and poor diversity, surrounded by highly connected populations associated with high genetic diversity. This pattern is consistent with the ecological and demographic status of *L. digitata* forest which has been described as highly contrasted in the studied area, ranging from a "no declined reported" status in north-western Brittany to a "local decline" status in South Brittany and Saint-Malo Bay (de Bettignies et al. 2021). We additionally compared connectivity patterns between coastal and island populations and found that island populations are generally well connected to other islands or coastal populations. A distance-based redundancy analysis (db-RDA) was applied to disentangle the contribution of various environmental factors which are thought to influence connectivity and thus genetic structure, namely: habitat discontinuity, geographical distance, hydrodynamic processes, sea surface temperature (SST) and sampling year. This analysis was repeated for three datasets corresponding
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Figure S4). The second axis was mostly driven by AEM5, which again isolated S21. With this dataset, oceanographic connectivity explained a greater amount of variance than geographic distance ($R^2_{adj}$ = 0.794 and 0.513, respectively) denoting the fact that genetic differentiation between populations was widely explained by oceanographic processes, while SST explained 21.0% of the variance (maximum SST).

Finally, whichever the dataset, SST was never retained in global db-RDAs, which is most likely due to the correlation between some of the SST-related variables and dbMEMs observed whichever the datasets (Table S9).

## 4. DISCUSSION

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Our study builds on previous microsatellite research (Billot et al. 2003, Valero et al. 2011, Couceiro et al. 2013, Robuchon et al. 2014) to refine our understanding of *Laminaria digitata*'s population structure in the southern edge of its East Atlantic distribution. We specifically aimed at deepening our knowledge of connectivity patterns among the studied populations which comprised 22 coastline and 10 on-shelf island populations. To do this, we combined inference and analyses of genetic structure, genetic diversity and dispersal probabilities resulting from Lagrangian simulations. This enabled us to identify divergent populations, defined as those associated with high genetic differentiation and poor diversity, surrounded by highly connected populations associated with high genetic diversity. This pattern is consistent with the ecological and demographic status of *L. digitata* forest which has been described as highly contrasted in the studied area, ranging from a "no declined reported" status in north-western Brittany to a "local decline" status in South Brittany and Saint-Malo Bay (de Bettignies et al. 2021). We additionally compared connectivity patterns between coastal and island populations and found that island populations are generally well connected to other islands or coastal populations. A distance-based redundancy analysis (db-RDA) was applied to disentangle the contribution of various environmental factors which are thought to influence connectivity and thus genetic structure, namely: habitat discontinuity, geographical distance, hydrodynamic processes, sea surface temperature (SST) and sampling year. This analysis was repeated for three datasets corresponding
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to varying sampling schemes, as we expect the presence of rocky substrata, hydrodynamic features
and SST to vary at relatively small spatial scales. Overall, our analyses have shown that the amount
of variation explained by seascape features increased as the sampling gap between sampled
populations was reduced. Our study therefore shows how the conclusions heavily depend on the
presence and extent of sampling gaps. Given that seascape genetics tools are nowadays being more
and more used, we call for greater attention in the conclusions that are drawn.

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Analyses of population structure have shown a discrepancy between southern and northern
Brittany, which can be related to oceanic currents according to the db-RDA run on the dataset with
continuously distributed populations. This discrepancy is in line with the fact that this region
corresponds to an oceanographic boundary: while the main ocean current in the southern coast of
Brittany corresponds to the shelf residual current which has a northwest-ward direction, northern
Brittany is dominated by the English Channel residual circulation which has a northeast-ward
direction (Pingree & Le Cann 1989). This genetic divergence was also reported for other species
(Roman & Palumbi 2004, Jolly et al. 2006, Nunes et al. 2021) and could also correspond to the
limit between the northern European Sea and the Lusitanian biogeographical provinces which was
pointed out to explain the biogeographic breaks observed in various marine taxa (Spalding et al.
2007). The directionality observed in the Lagrangian simulations is consistent with the direction
of these two major currents, albeit the variation in directionality due to tides and winds (Ayata et
al. 2010).

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Previous results on *L. digitata* have shown significant population differentiation from 1 to
10 km (Billot et al. 2003, Valero et al. 2011, Robuchon et al. 2014), a fine-scale genetic structure
supported by the commonly reported short dispersal distance in kelps (Dayton 1985, Norton 1992).
Contrastingly, our results suggest that genetic differentiation was not significant between several
pairs of populations separated by more than 50 km, particularly for populations located in western
and north-western Brittany. Low genetic differentiation at large geographic distances was
particularly clear for island populations which were separated by more than 100 km from the coast
and despite the absence of continuous favorable substrate (i.e., rocky substrata above 5 m depth)
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to varying sampling schemes, as we expect the presence of rocky substrata, hydrodynamic features and SST to vary at relatively small spatial scales. Overall, our analyses have shown that the amount of variation explained by seascape features increased as the sampling gap between sampled populations was reduced. Our study therefore shows how the conclusions heavily depend on the presence and extent of sampling gaps. Given that seascape genetics tools are nowadays being more and more used, we call for greater attention in the conclusions that are drawn.

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Analyses of population structure have shown a discrepancy between southern and northern Brittany, which can be related to oceanic currents according to the db-RDA run on the dataset with continuously distributed populations. This discrepancy is in line with the fact that this region corresponds to an oceanographic boundary: while the main ocean current in the southern coast of Brittany corresponds to the shelf residual current which has a northwest-ward direction, northern Brittany is dominated by the English Channel residual circulation which has a northeast-ward direction (Pingree & Le Cann 1989). This genetic divergence was also reported for other species (Roman & Palumbi 2004, Jolly et al. 2006, Nunes et al. 2021) and could also correspond to the limit between the northern European Sea and the Lusitanian biogeographical provinces which was pointed out to explain the biogeographic breaks observed in various marine taxa (Spalding et al. 2007). The directionality observed in the Lagrangian simulations is consistent with the direction of these two major currents, albeit the variation in directionality due to tides and winds (Ayata et al. 2010).

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Previous results on *L. digitata* have shown significant population differentiation from 1 to 10 km (Billot et al. 2003, Valero et al. 2011, Robuchon et al. 2014), a fine-scale genetic structure supported by the commonly reported short dispersal distance in kelps (Dayton 1985, Norton 1992). Contrastingly, our results suggest that genetic differentiation was not significant between several pairs of populations separated by more than 50 km, particularly for populations located in western and north-western Brittany. Low genetic differentiation at large geographic distances was particularly clear for island populations which were separated by more than 100 km from the coast and despite the absence of continuous favorable substrate (i.e., rocky substrata above 5 m depth)
534

connecting them to the nearest mainland or island populations. In fact, the number of connectivity
links (defined as the mean number of populations with which the probability of connectivity is non-
null), as well as values of genetic differentiation and of genetic diversity revealed that islands are
generally well connected. Nonetheless, this conclusion might be affected by species' means of
dispersal and should not be taken as a general rule. In addition, we have only considered on-shelf
coastal populations which were connected to the mainland at the Last Glacial Maximum when the
sea level was 100 to 120 m below present (Frenzel et al. 1992, Lambeck 1997). These islands
therefore share the same evolutionary history as populations found along the coastline, and this
historical pattern is expected to influence the observed low genetic differentiation of island
populations.

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541

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In contrast to the low genetic differentiation observed for island populations, our analyses
have revealed genetically divergent populations, associated with high genetic differentiation and
significantly lower values of genetic diversity. Amongst these divergent populations, the ones from
southern Brittany and Saint-Malo Bay appeared to be particularly differentiated. Results from
Lagrangian simulations have shown that populations within these two genetic clusters are highly
connected but largely unrelated to other populations. Nonetheless, results from db-RDA revealed
that Saint-Malo Bay was genetically isolated due to geographic distance rather than by the
occurrence of tidal gyres, as previously suggested by Billot et al. (2003) and Robuchon et al.
(2014). However, geographic distance might appear as the causal factor for the divergence of
Saint-Malo Bay due to gaps in the sampling. Indeed, by repeating the db-RDA on three datasets,
we have demonstrated how sampling schemes can greatly influence the conclusions that can be
drawn. In particular, we have shown that as the extent of the sampling gaps decreased, the relative
importance of ocean currents (mainly in relation to geographic distance) increased. Therefore, our
dataset does not allow us to properly infer the origin of the genetic isolation of Saint-Malo Bay.

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In addition to South Brittany and Saint-Malo Bay, two other divergent populations were
identified: Locquirec, which also appeared highly differentiated from surrounding populations at
small spatial scale (< 20 km), and the geographically isolated population of Wissant. All of these

559

560
connecting them to the nearest mainland or island populations. In fact, the number of connectivity links (defined as the mean number of populations with which the probability of connectivity is non null), as well as values of genetic differentiation and of genetic diversity revealed that islands are generally well connected. Nonetheless, this conclusion might be affected by species' means of dispersal and should not be taken as a general rule. In addition, we have only considered on-shelf coastal populations which were connected to the mainland at the Last Glacial Maximum when the sea level was 100 to 120 m below present (Frenzel et al. 1992, Lambeck 1997). These islands therefore share the same evolutionary history as populations found along the coastline, and this historical pattern is expected to influence the observed low genetic differentiation of island populations.

In contrast to the low genetic differentiation observed for island populations, our analyses have revealed genetically divergent populations, associated with high genetic differentiation and significantly lower values of genetic diversity. Amongst these divergent populations, the ones from southern Brittany and Saint-Malo Bay appeared to be particularly differentiated. Results from Lagrangian simulations have shown that populations within these two genetic clusters are highly connected but largely unrelated to other populations. Nonetheless, results from db-RDA revealed that Saint-Malo Bay was genetically isolated due to geographic distance rather than by the occurrence of tidal gyres, as previously suggested by Billot et al. (2003) and Robuchon et al. (2014). However, geographic distance might appear as the causal factor for the divergence of Saint-Malo Bay due to gaps in the sampling. Indeed, by repeating the db-RDA on three datasets, we have demonstrated how sampling schemes can greatly influence the conclusions that can be drawn. In particular, we have shown that as the extent of the sampling gaps decreased, the relative importance of ocean currents (mainly in relation to geographic distance) increased. Therefore, our dataset does not allow us to properly infer the origin of the genetic isolation of Saint-Malo Bay.

In addition to South Brittany and Saint-Malo Bay, two other divergent populations were identified: Locquirec, which also appeared highly differentiated from surrounding populations at small spatial scale (< 20 km), and the geographically isolated population of Wissant. All of these
divergent populations (populations from South Brittany, Saint-Malo Bay, Locquirec and Wissant)
were associated with lower genetic diversity, with Wissant being particularly impoverished.
Unlike what has been previously suggested by Billot et al. (2003), the results of the db-RDA as
well as the ones from Lagrangian simulations suggest that Locquirec is isolated due to oceanic
currents. This result is supported by the description of eddies at the entrance of Lannion Bay and
a slow residual current within the Bay (Beslier et al. 1980, Garreau 1993). This confirms that ocean
currents not only affect long-distance dispersal (e.g., between islands and coastline) but also have
localized effects as reported for *L. digitata* in Strangford Narrows (Brennan et al. 2014) and for
other species of seaweed characterized by low dispersal abilities (Buonomo et al. 2017, Reynes et
al. 2021). Nonetheless, habitat appeared highly discontinuous between Locquirec and its
surrounding populations (> 90% of the total distance is devoid of rocky substrata above 5 m depth),
which could intensify the genetic isolation of this population. As opposed to Locquirec, geographic
distance appears as the main cause for the isolation of Wissant. In this case, a finer sampling
scheme along the French coast might not change the results given the absence of nearby
populations (Araújo et al. 2016). The low genetic diversity measured on neutral markers reflects a
low effective population size and thus higher strength of genetic drift (compared to selection),
which ultimately leads to an increase in the probability of going extinct. Careful attention and
appropriate conservation measures should therefore be taken in order to prevent this end.

The stepwise variable selection procedure applied prior to each global db-RDA has never
selected variables associated with sampling year, SST or habitat discontinuity. Although
parameters associated to SST were significant in partial db-RDA, habitat discontinuity was never
significant despite the findings on other kelp species (Alberto et al. 2010, 2011, Selkoe et al. 2010,
Johansson et al. 2015). This discrepancy could stem from the fact that our rock layer was built by
combining various data with different precisions, or from the fact that the calculation of habitat
discontinuity was constrained by the 5 m bathymetric contour. Another argument is that Alberto
et al. (2010, 2011) and Selkoe et al. (2010) have measured habitat continuity by looking at kelp
coverage rather than proportion of rocky substrata *per se*. Yet, evidence from other kelp species
divergent populations (populations from South Brittany, Saint-Malo Bay, Locquirec and Wissant)
were associated with lower genetic diversity, with Wissant being particularly impoverished.
Unlike what has been previously suggested by Billot et al. (2003), the results of the db-RDA as
well as the ones from Lagrangian simulations suggest that Locquirec is isolated due to oceanic
currents. This result is supported by the description of eddies at the entrance of Lannion Bay and
a slow residual current within the Bay (Beslier et al. 1980, Garreau 1993). This confirms that ocean
currents not only affect long-distance dispersal (e.g., between islands and coastline) but also have
localized effects as reported for *L. digitata* in Strangford Narrows (Brennan et al. 2014) and for
other species of seaweed characterized by low dispersal abilities (Buonomo et al. 2017, Reynes et
al. 2021). Nonetheless, habitat appeared highly discontinuous between Locquirec and its
surrounding populations (> 90% of the total distance is devoid of rocky substrata above 5 m depth),
which could intensify the genetic isolation of this population. As opposed to Locquirec, geographic
distance appears as the main cause for the isolation of Wissant. In this case, a finer sampling
scheme along the French coast might not change the results given the absence of nearby
populations (Araújo et al. 2016). The low genetic diversity measured on neutral markers reflects a
low effective population size and thus higher strength of genetic drift (compared to selection),
which ultimately leads to an increase in the probability of going extinct. Careful attention and
appropriate conservation measures should therefore be taken in order to prevent this end.

The stepwise variable selection procedure applied prior to each global db-RDA has never
selected variables associated with sampling year, SST or habitat discontinuity. Although
parameters associated to SST were significant in partial db-RDA, habitat discontinuity was never
significant despite the findings on other kelp species (Alberto et al. 2010, 2011, Selkoe et al. 2010,
Johansson et al. 2015). This discrepancy could stem from the fact that our rock layer was built by
combining various data with different precisions, or from the fact that the calculation of habitat
discontinuity was constrained by the 5 m bathymetric contour. Another argument is that Alberto
et al. (2010, 2011) and Selkoe et al. (2010) have measured habitat continuity by looking at kelp
coverage rather than proportion of rocky substrata *per se*. Yet, evidence from other kelp species
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suggests that sporophyte recruitment largely depends on meiospore density ensuring sperm-egg
encountering (Reed 1990). By considering kelp coverage rather than rocky substrata, Alberto et
al. (2010, 2011) and Selkoe et al. (2010) might have incorporated the effect of meiospores dilution
into habitat discontinuity, which was not our case. Nonetheless, the use of other methods (e.g.,
resistance matrix, Wilcox et al. 2023) could have led to other conclusions.

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The fact that SST variables were never retained in global db-RDA could stem from the limitation to the use of dbMEMs which may underestimate the importance of other environmental variables that show some correlations with one of these Moran's eigenvector decomposition (Dalongeville et al. 2018). To overcome this limitation, one could consider hourly temperature data (e.g., from *in situ* dataloggers) which, in addition to its relevance for intertidal species, could decrease the correlation with dbMEMs. Despite these results, partial db-RDA accounting for SST were significant whichever the datasets, therefore pointing out its relevance in explaining genetic structure. This could be particularly true for this kelp species as sporulation has been shown to be severely impacted at temperatures above 17°C (Bartsch et al. 2013), yet SST in southern Brittany reaches 21°C in summer (Gallon et al. 2014). Although mechanisms linked to sporulation could be adapted to higher temperature, Oppliger et al. (2014) have shown that the mean number of released meiospores is significantly lower in Quiberon (corresponding to S4) compared to a population in northern Brittany. Yet a decreased amount of released meiospores should lead to a decreased connectivity and thus higher genetic differentiation. This underlines the fact that SST could have various consequences on life history traits in *L. digitata* which should be apparent on data obtained using neutral markers through the phenomenon of isolation by adaptation (Nosil et al. 2009, Schoville et al. 2012). Similarly, the sharp genetic break observed between southwestern populations (S5-S9) and southern Brittany could have also been attributed to the difference in SST between these two regions.

Conclusion
suggests that sporophyte recruitment largely depends on meiospore density ensuring sperm-egg
encountering (Reed 1990). By considering kelp coverage rather than rocky substrata, Alberto et
al. (2010, 2011) and Selkoe et al. (2010) might have incorporated the effect of meiospores dilution
into habitat discontinuity, which was not our case. Nonetheless, the use of other methods (e.g.,
resistance matrix, Wilcox et al. 2023) could have led to other conclusions.

The fact that SST variables were never retained in global db-RDA could stem from the
limitation to the use of dbMEMs which may underestimate the importance of other environmental
variables that show some correlations with one of these Moran's eigenvector decomposition
(Dalongeville et al. 2018). To overcome this limitation, one could consider hourly temperature
data (e.g., from in situ dataloggers) which, in addition to its relevance for intertidal species, could
decrease the correlation with dbMEMs. Despite these results, partial db-RDA accounting for SST
were significant whichever the datasets, therefore pointing out its relevance in explaining genetic
structure. This could be particularly true for this kelp species as sporulation has been shown to be
severely impacted at temperatures above 17°C (Bartsch et al. 2013), yet SST in southern Brittany
reaches 21°C in summer (Gallon et al. 2014). Although mechanisms linked to sporulation could
be adapted to higher temperature, Oppliger et al. (2014) have shown that the mean number of
released meiospores is significantly lower in Quiberon (corresponding to S4) compared to a
population in northern Brittany. Yet a decreased amount of released meiospores should lead to a
decreased connectivity and thus higher genetic differentiation. This underlines the fact that SST
could have various consequences on life history traits in L. digitata which should be apparent on
data obtained using neutral markers through the phenomenon of isolation by adaptation (Nosil et
al. 2009, Schoville et al. 2012). Similarly, the sharp genetic break observed between southwestern
populations (S5-S9) and southern Brittany could have also been attributed to the difference in SST
between these two regions.

Conclusion
Our study revealed a mosaic of connectivity patterns with well-connected populations surrounded by populations that appeared less connected and less diverse, with the population of Wissant being particularly impoverished. This pattern is consistent with the ecological and demographic status of *Laminaria digitata* forest which has been described as highly contrasted in the studied area.

Various db-RDA were conducted to deepen our understanding of the observed genetic variation.

Unlike previous studies carried out on *L. digitata* as well as on other kelp species, our results indicated a limited effect of habitat fragmentation compared to ocean currents and geographic distance. Moreover, the relative effect of these two latter environmental variables varied according to sampling schemes: while geographic distance was particularly relevant in our overall dataset, which included sampling gaps of more than 600 km, oceanographic processes appeared as the main drivers at finer spatial resolution.

625
Our study revealed a mosaic of connectivity patterns with well-connected populations surrounded by populations that appeared less connected and less diverse, with the population of Wissant being particularly impoverished. This pattern is consistent with the ecological and demographic status of *Laminaria digitata* forest which has been described as highly contrasted in the studied area.

Various db-RDA were conducted to deepen our understanding of the observed genetic variation.

Unlike previous studies carried out on *L. digitata* as well as on other kelp species, our results indicated a limited effect of habitat fragmentation compared to ocean currents and geographic distance. Moreover, the relative effect of these two latter environmental variables varied according to sampling schemes: while geographic distance was particularly relevant in our overall dataset, which included sampling gaps of more than 600 km, oceanographic processes appeared as the main drivers at finer spatial resolution.

625
626 Acknowledgements

627 We thank Eric Thiébaut, Thierry Comtet, Jean-François Arnaud and Ophélie Ronce for helpful
discussions and comments. The OSU Pythéas Institute (Marseille) is thanked for the use of the
628 high-performance computing (HPC) cluster. We are also grateful to the Biogenouest genomics
629 core facility (Genomer Plateforme génomique at the Biological Station of Roscoff in particular
630 Gwenn Tanguy) for their technical support. We additionally warmly thank several phycologists,
631 the Marine Observation Department and the diving team of the Biological Station of Roscoff for
632 sampling, and François Gevaert for his valuable insights regarding the rare occurrence of *L.
digitata* in the strait of Dover. LF was funded by the EU project MARFOR Biodiversa/004/2015,
633 Region Bretagne (ARED 2017 REEALG) and the NOMIS-ISTA Fellowship Program. JA is
634 funded by Portuguese national funds through projects UIDB/04326/2020, UIDP/04326/2020,
635 LA/P/0101/2020, PTDC/BIA-CBI/6515/2020 and the Individual Call to Scientific Employment
636 Stimulus 2022.00861.CEECIND.
637
638
639

640
641
642
643

**Competing interests**

The authors declare no competing interests.
626 Acknowledgements

627 We thank Eric Thiébaut, Thierry Comtet, Jean-François Arnaud and Ophélie Ronce for helpful
discussions and comments. The OSU Pythéas Institute (Marseille) is thanked for the use of the
628 high-performance computing (HPC) cluster. We are also grateful to the Biogenouest genomics
629 core facility (Genomer Plateforme génomique at the Biological Station of Roscoff in particular
630 Gwenn Tanguy) for their technical support. We additionally warmly thank several phycologists,
631 the Marine Observation Department and the diving team of the Biological Station of Roscoff for
632 sampling, and François Gevaert for his valuable insights regarding the rare occurrence of *L.
digitata* in the strait of Dover. LF was funded by the EU project MARFOR Biodiversa/004/2015,
633 Region Bretagne (ARED 2017 REEALG) and the NOMIS-ISTA Fellowship Program. JA is
634 funded by Portuguese national funds through projects UIDB/04326/2020, UIDP/04326/2020,
635 LA/P/0101/2020, PTDC/BIA-CBI/6515/2020 and the Individual Call to Scientific Employment
636 Stimulus 2022.00861.CEECIND.
637
638
639

639

640 Competing interests

641 The authors declare no competing interests.

642

643
645 Aeschbacher S, Selby JP, Willis JH, Coop G (2017) Population-genomic inference of the strength
646 and timing of selection against gene flow. Proc Natl Acad Sci 114:7061–7066.

647 Alberto F, Raimondi PT, Reed DC, Coelho NC, Leblois R, Whitmer A, Serrão EA (2010) Habitat
648 continuity and geographic distance predict population genetic differentiation in giant kelp.
649 Ecology 91:49–56.

650 Alberto F, Raimondi PT, Reed DC, Watson JR, Siegel DA, Mitarai S, Coelho N, Serrão EA
651 (2011) Isolation by oceanographic distance explains genetic structure for *Macrocystis*
652 *pyrifera* in the Santa Barbara Channel. Mol Ecol 20:2543–2554.

653 Arafeh-Dalmau N, Montaño-Moctezuma G, Martínez JA, Beas-Luna R, Schoeman DS, Torres-
654 Moye G (2019) Extreme marine heatwaves alter kelp forest community near its
655 equatorward distribution limit. Front Mar Sci 6.

656 Araújo RM, Assis J, Aguillar R, Airoldi L, Bárbara I, Bartsch I, Bekkby T, Christie H, Davoult
657 D, Derrien-Courtel S, Fernandez C, Fredriksen S, Gevaert F, Gundersen H, Le Gal A,
658 Lévêque L, Mieszkowska N, Norderhaug KM, Oliveira P, Puente A, Rico JM, Rinde E,
659 Schubert H, Strain EM, Valero M, Viard F, Sousa-Pinto I (2016) Status, trends and drivers
660 of kelp forests in Europe: An expert assessment. Biodivers Conserv 25:1319–1348.

661 Ayata SD, Lazure P, Thiébaut É (2010) How does the connectivity between populations mediate
662 range limits of marine invertebrates? A case study of larval dispersal between the Bay of
663 Biscay and the English Channel (North-East Atlantic). Prog Oceanogr 87:18–36.

664 Bajjouk T (2005) Traitement et structuration des données en zones côtières. Projet MESH.
665 Contrat Ifremer-Capgemini N° 04/2 210 934. 29p.

666 Bajjouk T, Rochette S, Laurans M, Ehrhold A, Hamdi A, Le Nilot P (2015) Multi-approach
667 mapping to help spatial planning and management of the kelp species *Laminaria digitata*
668 and *Laminaria hyperborea*: Case study of the Molène Archipelago, Brittany. J Sea Res
669 100:2–21.

670 Bartsch I, Vogt J, Pehlke C, Hanelt D (2013) Prevailing sea surface temperatures inhibit summer
671 reproduction of the kelp *Laminaria digitata* at Helgoland (North Sea). J Phycol 49:1061–
672 1073.

673 Bartsch I, Wiencke C, Bischof K, Buchholz CM, Buck BH, Eggert A, Feuerpfeil P, Hanelt D,
674 Jacobsen S, Karez R, Karsten U, Molis M, Roleda MY, Schubert H, Schumann R, Valentin
675 K, Weinberger F, Wiese J (2008) The genus *Laminaria* sensu lato: Recent insights and
676 developments. Eur J Phycol 43:1–86.

677 Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous
678 Windows pour la génétique des populations. Laboratoire Génome, Populations,
679 Interactions, CNRS UMR 5000, Université de Montpellier II, Montpellier.

680 Bell PR (2008) Connectivity between island Marine Protected Areas and the mainland. Biol
681 Conserv 141:2807–2820.

682 Benestan L, Loiseau N, Guérin P, Pérez-Ruzafa A, Forcada A, Arcas E, Lenfant P, Mallol S,
683 Goñi R, Velez L, Mouillot D, Puebla O, Manel S (2023) Contrasting influence of seascape,
684 space and marine reserves on genomic variation in multiple species. Ecography 2023.

685 Benestan L, Quinn BK, Maaroufi H, Laporte M, Clark FK, Greenwood SJ, Rochette R,
686 Bernatchez L (2016) Seascape genomics provides evidence for thermal adaptation and
687 current-mediated population structure in American lobster (*Homarus americanus*). Mol
688 Ecol 25:5073–5092.

689 Benestan LM, Rougemont Q, Senay C, Normandeau E, Parent E, Rideout R, Bernatchez L,
690 Lambert Y, Audet C, Parent GJ (2021) Population genomics and history of speciation
691 reveal fishery management gaps in two related redfish species (*Sebastes mentella* and
692 *Sebastes fasciatus*). Evol Appl 14:588–606.

693 Beslier A, Birrien JL, Cabioch L, Larsonneur C, Borgne L (1980) La pollution des Baies de
694 Morlaix et de Lannion par les hydrocarbures de l' "Amoco Cadiz": Répartition sur les fonds
695 et évolution. Helgoländer Meeresunters 33:209–224.

696 de Bettignies T, Hébert C, Assis J, Bartsch I, Bekkby T, Christie H, Dahl K, Derrien-Courtel S,
645 Aeschbacher S, Selby JP, Willis JH, Coop G (2017) Population-genomic inference of the strength
646 and timing of selection against gene flow. Proc Natl Acad Sci 114:7061–7066.

647 Alberto F, Raimondi PT, Reed DC, Coelho NC, Leblois R, Whitmer A, Serrão EA (2010) Habitat
648 continuity and geographic distance predict population genetic differentiation in giant kelp.
649 Ecology 91:49–56.

650 Alberto F, Raimondi PT, Reed DC, Watson JR, Siegel DA, Mitarai S, Coelho N, Serrão EA
651 (2011) Isolation by oceanographic distance explains genetic structure for *Macrocystis*
652 *pyrifera* in the Santa Barbara Channel. Mol Ecol 20:2543–2554.

653 Arafeh-Dalmau N, Montaño-Moctezuma G, Martínez JA, Beas-Luna R, Schoeman DS, Torres-
654 Moye G (2019) Extreme marine heatwaves alter kelp forest community near its
655 equatorward distribution limit. Front Mar Sci 6.

656 Araújo RM, Assis J, Aguillar R, Airoldi L, Bárbara I, Bartsch I, Bekkby T, Christie H, Davoult
657 D, Derrien-Courtel S, Fernandez C, Fredriksen S, Gevaert F, Gundersen H, Le Gal A,
658 Lévêque L, Mieszkowska N, Norderhaug KM, Oliveira P, Puente A, Rico JM, Rinde E,
659 Schubert H, Strain EM, Valero M, Viard F, Sousa-Pinto I (2016) Status, trends and drivers
660 of kelp forests in Europe: An expert assessment. Biodivers Conserv 25:1319–1348.

661 Ayata SD, Lazure P, Thiébaut É (2010) How does the connectivity between populations mediate
662 range limits of marine invertebrates? A case study of larval dispersal between the Bay of
663 Biscay and the English Channel (North-East Atlantic). Prog Oceanogr 87:18–36.

664 Bajjouk T (2005) Traitement et structuration des données en zones côtières. Projet MESH.
665 Contrat Ifremer-Capgemini N° 04/2 210 934. 29p.

666 Bajjouk T, Rochette S, Laurans M, Ehrhold A, Hamdi A, Le Nilot P (2015) Multi-approach
667 mapping to help spatial planning and management of the kelp species *Laminaria digitata*
668 and *Laminaria hyperborea*: Case study of the Molène Archipelago, Brittany. J Sea Res
669 100:2–21.

670 Bartsch I, Vogt J, Pehlke C, Hanelt D (2013) Prevailing sea surface temperatures inhibit summer
671 reproduction of the kelp *Laminaria digitata* at Helgoland (North Sea). J Phycol 49:1061–
672 1073.

673 Bartsch I, Wiencke C, Bischof K, Buchholz CM, Buck BH, Eggert A, Feuerpfeil P, Hanelt D,
674 Jacobsen S, Karez R, Karsten U, Molis M, Roleda MY, Schubert H, Schumann R, Valentin
675 K, Weinberger F, Wiese J (2008) The genus *Laminaria* sensu lato: Recent insights and
676 developments. Eur J Phycol 43:1–86.

677 Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous
678 Windows pour la génétique des populations. Laboratoire Génome, Populations,
679 Interactions, CNRS UMR 5000, Université de Montpellier II, Montpellier.

680 Bell PR (2008) Connectivity between island Marine Protected Areas and the mainland. Biol
681 Conserv 141:2807–2820.

682 Benestan L, Loiseau N, Guérin P, Pérez-Ruzafa A, Forcada A, Arcas E, Lenfant P, Mallol S,
683 Goñi R, Velez L, Mouillot D, Puebla O, Manel S (2023) Contrasting influence of seascape,
684 space and marine reserves on genomic variation in multiple species. Ecography 2023.

685 Benestan L, Quinn BK, Maaroufi H, Laporte M, Clark FK, Greenwood SJ, Rochette R,
686 Bernatchez L (2016) Seascape genomics provides evidence for thermal adaptation and
687 current-mediated population structure in American lobster (*Homarus americanus*). Mol
688 Ecol 25:5073–5092.

689 Benestan LM, Rougemont Q, Senay C, Normandeau E, Parent E, Rideout R, Bernatchez L,
690 Lambert Y, Audet C, Parent GJ (2021) Population genomics and history of speciation
691 reveal fishery management gaps in two related redfish species (*Sebastes mentella* and
692 *Sebastes fasciatus*). Evol Appl 14:588–606.

693 Beslier A, Birrien JL, Cabioch L, Larsonneur C, Borgne L (1980) La pollution des Baies de
694 Morlaix et de Lannion par les hydrocarbures de l' "Amoco Cadiz": Répartition sur les fonds
695 et évolution. Helgoländer Meeresunters 33:209–224.

696 de Bettignies T, Hébert C, Assis J, Bartsch I, Bekkby T, Christie H, Dahl K, Derrien-Courtel S,
697 Edwards H, Filbee-Dexter K, Franco J, Gillham K, Harrald M, Hennicke J, Hernández S,
698 Le Gall L, Martinez B, Mieszkowska N, Moore P, Moy F, Mueller M, Norderhaug KM,
699 Parry M, Ramsay K, Robuchon M, Russel T, Serrão E, Smale D, Steen H, Street M,
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722 Bridle JR, Gavaz S, Kennington WJ (2009) Testing limits to adaptation along altitudinal
723 gradients in rainforest Drosophila. Proc R Soc B Biol Sci 276:1507–1515.

724 Buonomo R, Assis J, Fernandes F, Engelen AH, Airoldi L, Serrão EA (2017) Habitat continuity
725 and stepping-stone oceanographic distances explain population genetic connectivity of the
726 brown alga *Cystoseira amentacea*. Mol Ecol 26:766–780.

727 Capblancq T, Forester BR (2021) Redundancy analysis: A Swiss Army Knife for landscape
728 genomics. Methods Ecol Evol 12:2298–2309.

729 Cavanaugh KC, Reed DC, Bell TW, Castorani MCN, Beas-Luna R (2019) Spatial variability in
730 the resistance and resilience of giant kelp in Southern and Baja California to a multiyear
731 heatwave. Front Mar Sci 6.

732 Coelho NC, Serrão EA, Alberto F (2014) Characterization of fifteen microsatellite markers for
733 the kelp Laminaria ochroleuca and cross species amplification within the genus. Conserv
734 Genet Resour 6:949–950.

735 Coleman MA, Minne AJP, Vranken S, Wernberg T (2020) Genetic tropicalisation following a
736 marine heatwave. Sci Rep 10:1–11.

737 Coleman MA, Veenhof RJ (2021) Reproductive versatility of kelps in changing oceans. J Phycol
738 57:708–710.

739 Couceiro L, Robuchon M, Destombe C, Valero M (2013) Management and conservation of the
740 kelp species *Laminaria digitata*: Using genetic tools to explore the potential exporting role
741 of the MPA “Parc naturel marin d’Iroise.” Aquat Living Resour 26:197–205.

742 D’Aloia CC, Bogdanowicz SM, Harrison RG, Buston PM (2014) Seascape continuity plays an
743 important role in determining patterns of spatial genetic structure in a coral reef fish. Mol
744 Ecol 23:2902–2913.

745 Dalongeville A, Andrello M, Mouillot D, Lobreaux S, Fortin M-J, Lasram F, Belmaker J, Rocklin
746 D, Manel S (2018) Geographic isolation and larval dispersal shape seascape genetic
747 patterns differently according to spatial scale. Evol Appl 11:1437–1447.

748 Dawson MN (2016) Island and island-like marine environments. Glob Ecol Biogeogr 25:831–
749 846.
697 Edwards H, Filbee-Dexter K, Franco J, Gillham K, Harrald M, Hennicke J, Hernández S,
698 Le Gall L, Martinez B, Mieszkowska N, Moore P, Moy F, Mueller M, Norderhaug KM,
699 Parry M, Ramsay K, Robuchon M, Russel T, Serrão E, Smale D, Steen H, Street M,
700 Tempera F, Valero M, Werner T, La Rivière M (2021) Case Report for kelp forests habitat.
701 OSPAR 787/2021.

702 Billot C, Engel CR, Rousvoal S, Kloareg B, Valero M (2003) Current patterns, habitat
703 discontinuities and population genetic structure: The case of the kelp *Laminaria digitata* in
704 the English Channel. Mar Ecol Prog Ser 253:111-121.

705 Billot C, Rousvoal S, Estoup A, Epplen JT, Saumitou-Laprade P, Valero M, Kloareg B (1998)
706 Isolation and characterization of microsatellite markers in the nuclear genome of the brown
707 alga *Laminaria digitata* (Phaeophyceae). Mol Ecol 7:1778-1780.

708 Blanchet FG, Legendre P, Borcard D (2008a) Forward selection of explanatory variables.
709 Ecology 89:2623-2632.

710 Blanchet FG, Legendre P, Borcard D (2008b) Modelling directional spatial processes in
711 ecological data. Ecol Model 215:325-336.

712 Blauw A, Eleveld M, Prins T, Zijl F, Groenenboom J (2019) Coherence in assessment framework
713 of chlorophyll a and nutrients as part of the EU project ‘Joint monitoring programme of the
714 eutrophication of the North Sea with satellite data’.

715 Boussarie G, Momigliano P, Robbins WD, Bonnin L, Cornu J, Fauvelot C, Kiszka JJ, Manel S,
716 Mouillot D, Vigliola L (2022) Identifying barriers to gene flow and hierarchical
717 conservation units from seascape genomics: a modelling framework applied to a marine
718 predator. Ecography 2022.

719 Brennan G, Kregting L, Beatty GE, Cole C, Elsäßer B, Savidge G, Provan J (2014)
720 Understanding macroalgal dispersal in a complex hydrodynamic environment: A combined
721 population genetic and physical modelling approach. J R Soc Interface 11:1-12.

722 Bridle JR, Gavaz S, Kennington WJ (2009) Testing limits to adaptation along altitudinal
723 gradients in rainforest Drosophila. Proc R Soc B Biol Sci 276:1507-1515.

724 Buonomo R, Assis J, Fernandes F, Engelen AH, Airoldi L, Serrão EA (2017) Habitat continuity
725 and stepping-stone oceanographic distances explain population genetic connectivity of the
726 brown alga *Cystoseira amentacea*. Mol Ecol 26:766-780.

727 Capblancq T, Forester BR (2021) Redundancy analysis: A Swiss Army Knife for landscape
728 genomics. Methods Ecol Evol 12:2298-2309.

729 Cavanaugh KC, Reed DC, Bell TW, Castorani MCN, Beas-Luna R (2019) Spatial variability in
730 the resistance and resilience of giant kelp in Southern and Baja California to a multiyear
731 heatwave. Front Mar Sci 6.

732 Coelho NC, Serrão EA, Alberto F (2014) Characterization of fifteen microsatellite markers for
733 the kelp Laminaria ochroleuca and cross species amplification within the genus. Conserv
734 Genet Resour 6:949-950.

735 Coleman MA, Minne AJP, Vranken S, Wernberg T (2020) Genetic tropicalisation following a
736 marine heatwave. Sci Rep 10:1-11.

737 Coleman MA, Veenhof RJ (2021) Reproductive versatility of kelps in changing oceans. J Phycol
738 57:708-710.

739 Couceiro L, Robuchon M, Destombe C, Valero M (2013) Management and conservation of the
740 kelp species *Laminaria digitata*: Using genetic tools to explore the potential exporting role
741 of the MPA "Parc naturel marin d'Iroise." Aquat Living Resour 26:197-205.

742 D'Aloia CC, Bogdanowicz SM, Harrison RG, Buston PM (2014) Seascape continuity plays an
743 important role in determining patterns of spatial genetic structure in a coral reef fish. Mol
744 Ecol 23:2902-2913.

745 Dalongeville A, Andrello M, Mouillot D, Lobreaux S, Fortin M-J, Lasram F, Belmaker J, Rocklin
746 D, Manel S (2018) Geographic isolation and larval dispersal shape seascape genetic
747 patterns differently according to spatial scale. Evol Appl 11:1437-1447.

748 Dawson MN (2016) Island and island-like marine environments. Glob Ecol Biogeogr 25:831-
749 846.
750 Dayton PK (1985) Ecology of kelp communities. Annu Rev Ecol Syst Vol 16:215-245.

751 Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model 196:483-493.

752 DuBois K, Pollard KN, Kauffman BJ, Williams SL, Stachowicz JJ (2022) Local adaptation in a marine foundation species: Implications for resilience to future global change. Glob Change Biol 28:2596-2610.

753 Durrant H, Barrett N, Edgar G, Coleman M, Burridge C (2018) Seascape habitat patchiness and hydrodynamics explain genetic structuring of kelp populations. Mar Ecol Prog Ser 587:81-92.

754 Earl DA, VonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359-361.

755 Engel CR, Destombe C, Valero M (2004) Mating system and gene flow in the red seaweed *Gracilaria gracilis*: Effect of haploid-diploid life history and intertidal rocky shore landscape on fine-scale genetic structure. Heredity 92:289-298.

756 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611-2620.

757 Fernández C (2011) The retreat of large brown seaweeds on the north coast of Spain: the case of *Saccorhiza polyschides*. Eur J Phycol 46:352-360.

758 Filbee-Dexter K, Feehan C, Scheibling R (2016) Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Mar Ecol Prog Ser 543:141-152.

759 Filbee-Dexter K, Wernberg T, Grace SP, Thormar J, Fredriksen S, Narvaez CN, Feehan CJ, Norderhaug KM (2020) Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci Rep 10:1-11.

760 Frenzel B, Beug H-J, Brunnacker K, Busche D, Frankenberg P, Fritz P, Geyh MA, Hagedorn H, Hövermann J, Kessler A, Königswald W v., Krumsiek K, Lauer W, Mensching H, Moser H, Münnich K-O, Sonntag Chr, Vinken R (1992) Climates during the Last Glacial Maximum. In: *Atlas of paleoclimates and paleoenvironments of the Northern Hemisphere*. Frenzel B, Pécsi M, Velichko AA (eds) Geographical Research Institute, Hungarian Academy of Sciences, Budapest; Gustav Fischer Verlag, Stuttgart; Jena; New York

761 Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1-20.

762 Gaggiotti OE, Bekkevold D, Jørgensen HBH, Foll M, Carvalho GR, Andre C, Ruzzante DE (2009) Disentangling the effects of evolutionary, demographic and environmental factors influencing genetic structure of natural populations: Atlantic herring as a case study. Evolution 63:2939-2951.

763 Gallon RK, Robuchon M, Leroy B, Le Gall L, Valero M, Feunteun E (2014) Twenty years of observed and predicted changes in subtidal red seaweed assemblages along a biogeographical transition zone: Inferring potential causes from environmental data. J Biogeogr 41:2293-2306.

764 Garineaud C (2021) Appréhender et s'adapter. Rev D'ethnoécologie:0-25.

765 Garreau P (1993) Hydrodynamics of the North Brittany coast: a synoptic study. Oceanol Acta 16:469-477.

766 Gaylord B, Gaines SD (2000) Temperature or transport? Range limits in marine species mediated solely by flow. Am Nat 155:769-789.

767 Gilbert KJ, Andrew RL, Bock DG, Franklin MT, Kane NC, Moore JS, Moyers BT, Renaut S, Rennison DJ, Veen T, Vines TH (2012) Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol Ecol 21:4925-4930.

768 Gilg MR, Hilbish TJ (2003) The geography of marine larval dispersal: coupling genetics with fine-scale physical oceanography. Ecology 84:2989-2998.

769 Good S, Fiedler E, Mao C, Martin MJ, Maycock A, Reid R, Roberts-Jones J, Searle T, Waters J, While J, Worsfold M (2020) The current configuration of the OSTIA system for
750 Dayton PK (1985) Ecology of kelp communities. Annu Rev Ecol Syst Vol 16:215-245.

751 Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model 196:483-493.

752 DuBois K, Pollard KN, Kauffman BJ, Williams SL, Stachowicz JJ (2022) Local adaptation in a marine foundation species: Implications for resilience to future global change. Glob Change Biol 28:2596-2610.

753 Durrant H, Barrett N, Edgar G, Coleman M, Burridge C (2018) Seascape habitat patchiness and hydrodynamics explain genetic structuring of kelp populations. Mar Ecol Prog Ser 587:81-92.

754 Earl DA, VonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359-361.

755 Engel CR, Destombe C, Valero M (2004) Mating system and gene flow in the red seaweed *Gracilaria gracilis*: Effect of haploid-diploid life history and intertidal rocky shore landscape on fine-scale genetic structure. Heredity 92:289-298.

756 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611-2620.

757 Fernández C (2011) The retreat of large brown seaweeds on the north coast of Spain: the case of *Saccorhiza polyschides*. Eur J Phycol 46:352-360.

758 Filbee-Dexter K, Feehan C, Scheibling R (2016) Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Mar Ecol Prog Ser 543:141-152.

759 Filbee-Dexter K, Wernberg T, Grace SP, Thormar J, Fredriksen S, Narvaez CN, Feehan CJ, Norderhaug KM (2020) Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci Rep 10:1-11.

760 Frenzel B, Beug H-J, Brunnacker K, Busche D, Frankenberg P, Fritz P, Geyh MA, Hagedorn H, Hövermann J, Kessler A, Königswald W v., Krumsiek K, Lauer W, Mensching H, Moser H, Münnich K-O, Sonntag Chr, Vinken R (1992) Climates during the Last Glacial Maximum. In: *Atlas of paleoclimates and paleoenvironments of the Northern Hemisphere*. Frenzel B, Pécsi M, Velichko AA (eds) Geographical Research Institute, Hungarian Academy of Sciences, Budapest; Gustav Fischer Verlag, Stuttgart; Jena; New York

761 Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1-20.

762 Gaggiotti OE, Bekkevold D, Jørgensen HBH, Foll M, Carvalho GR, Andre C, Ruzzante DE (2009) Disentangling the effects of evolutionary, demographic and environmental factors influencing genetic structure of natural populations: Atlantic herring as a case study. Evolution 63:2939-2951.

763 Gallon RK, Robuchon M, Leroy B, Le Gall L, Valero M, Feunteun E (2014) Twenty years of observed and predicted changes in subtidal red seaweed assemblages along a biogeographical transition zone: Inferring potential causes from environmental data. J Biogeogr 41:2293-2306.

764 Garineaud C (2021) Appréhender et s'adapter. Rev D'ethnoécologie:0-25.

765 Garreau P (1993) Hydrodynamics of the North Brittany coast: a synoptic study. Oceanol Acta 16:469-477.

766 Gaylord B, Gaines SD (2000) Temperature or transport? Range limits in marine species mediated solely by flow. Am Nat 155:769-789.

767 Gilbert KJ, Andrew RL, Bock DG, Franklin MT, Kane NC, Moore JS, Moyers BT, Renaut S, Rennison DJ, Veen T, Vines TH (2012) Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol Ecol 21:4925-4930.

768 Gilg MR, Hilbish TJ (2003) The geography of marine larval dispersal: coupling genetics with fine-scale physical oceanography. Ecology 84:2989-2998.

769 Good S, Fiedler E, Mao C, Martin MJ, Maycock A, Reid R, Roberts-Jones J, Searle T, Waters J, While J, Worsfold M (2020) The current configuration of the OSTIA system for
803

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operational production of foundation sea surface temperature and ice concentration analyses. Remote Sens 12:720.

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Hu ZM, Zhong KL, Weinberger F, Duan DL, Draisma SGA, Serrão EA (2020) Linking ecology to genetics to better understand adaptation and evolution: A review in marine macrophytes. Front Mar Sci 7:1–12.

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Jayathilake DRM, Costello MJ (2020) A modelled global distribution of the kelp biome. Biol Conserv 252:1–10.

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806

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808

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810

Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol Ecol 22:3888–3903.

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812

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Guzinski J, Ruggeri P, Ballenghien M, Mauger S, Jacquemin B, Jollivet C, Coudret J, Jaugeon L, Destombe C, Valero M (2020) Seascape genomics of the sugar kelp *Saccharina latissima* along the North Eastern Atlantic latitudinal gradient. Genes 11:1503.

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818

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Hickey AJR, Lavery SD, Hannan DA, Baker CS, Clements KD (2009) New Zealand triplefin fishes (family Tripterygiidae): contrasting population structure and mtDNA diversity within a marine species flock. Mol Ecol 18:680–696.

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Hu ZM, Zhong KL, Weinberger F, Duan DL, Draisma SGA, Serrão EA (2020) Linking ecology to genetics to better understand adaptation and evolution: A review in marine macrophytes. Front Mar Sci 7:1–12.

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Lagrangian modeling shed light on dispersal events in the Mediterranean endemic *Ericaria*

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982 White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, Toonen RJ (2010) Ocean currents help explain population genetic structure. Proc R Soc B Biol Sci 277:1685-1694.

984 Wilcox MA, Jeffery NW, DiBacco C, Bradbury IR, Lowen B, Wang Z, Beiko RG, Stanley RRE (2023) Integrating seascape resistances and gene flow to produce area-based metrics of functional connectivity for marine conservation planning. Landsc Ecol 38:2189-2205.

987 Xuereb A, Benestan L, Normandeau É, Daigle RM, Curtis JMR, Bernatchez L, Fortin M-J (2018) Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (*Parastichopus californicus*). Mol Ecol 27:2347-2364.

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995 Yessad K (2015) Basics about ARPEGE/IFS, ALADIN and AROME in the cycle 42 of ARPEGE/IFS. Research report. Météo-France/CNRM/GMAP/ALGO.

997

998
962 Biogeogr 44:217-229.

963 Teske PR, Von Der Heyden S, McQuaid CD, Barker NP (2011) A review of marine phylogeography in southern Africa. South Afr J Sci 107:43-53.

965 Treml EA, Halpin PN, Urban DL, Pratson LF (2008) Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landsc Ecol 23:19-36.

967 Valero M, Destombe C, Mauger S, Ribout C, Engel CR, Daguin-Thiebaut C, Tellier F (2011) Using genetic tools for sustainable management of kelps: A literature review and the example of *Laminaria digitata*. Cah Biol Mar 52:467-483.

970 Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535-538.

973 Van Oppen MJH, Coleman MA (2022) Advancing the protection of marine life through genomics. PLOS Biol 20:e3001801.

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982 White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, Toonen RJ (2010) Ocean currents help explain population genetic structure. Proc R Soc B Biol Sci 277:1685-1694.

984 Wilcox MA, Jeffery NW, DiBacco C, Bradbury IR, Lowen B, Wang Z, Beiko RG, Stanley RRE (2023) Integrating seascape resistances and gene flow to produce area-based metrics of functional connectivity for marine conservation planning. Landsc Ecol 38:2189-2205.

987 Xuereb A, Benestan L, Normandeau É, Daigle RM, Curtis JMR, Bernatchez L, Fortin M-J (2018) Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (*Parastichopus californicus*). Mol Ecol 27:2347-2364.

991 Xuereb A, D'Aloia CC, Daigle RM, Andrello M, Dalongeville A, Manel S, Mouillot D, Guichard F, Côté IM, Curtis JMR, Bernatchez L, Fortin M-J (2019) Marine Conservation and Marine Protected Areas. In: *Population Genomics: Marine Organisms*. Oleksiak MF, Rajora OP (eds) Springer, Cham., p 423-446

995 Yessad K (2015) Basics about ARPEGE/IFS, ALADIN and AROME in the cycle 42 of ARPEGE/IFS. Research report. Météo-France/CNRM/GMAP/ALGO.

997

998
<table><caption>**Table 1.** Information on the sampled populations: population codes as indicated in Figure 1; geographic locations (Latitude, Longitude); locality; year of sampling; position relative to the coastline; number of genotyped individuals and source of the data.</caption><thead><tr><th>N°</th><th>Lat.</th><th>Long.</th><th>Locality</th><th>Year</th><th>Position</th><th>Nb.</th><th>Source</th></tr></thead><tbody><tr><td>S1</td><td>47.339</td><td>-2.892</td><td>Hoedic</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S2</td><td>47.394</td><td>-2.958</td><td>Houat</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S3</td><td>47.328</td><td>-3.124</td><td>Belle-île</td><td>2006</td><td>Island</td><td>30</td><td>This study</td></tr><tr><td>S4</td><td>47.470</td><td>-3.091</td><td>Quiberon</td><td>2008</td><td>Coastline</td><td>28</td><td>This study</td></tr><tr><td>S5</td><td>47.762</td><td>-3.549</td><td>Lorient</td><td>2006</td><td>Coastline</td><td>24</td><td>Valero et al. (2011)</td></tr><tr><td>S6</td><td>47.791</td><td>-3.761</td><td>Pont Aven</td><td>2006</td><td>Coastline</td><td>28</td><td>Valero et al. (2011)</td></tr><tr><td>S7</td><td>47.700</td><td>-3.983</td><td>Glénan</td><td>2008</td><td>Island</td><td>30</td><td>This study</td></tr><tr><td>S8</td><td>47.786</td><td>-4.152</td><td>Loctudy</td><td>2008</td><td>Coastline</td><td>20</td><td>This study</td></tr><tr><td>S9</td><td>47.799</td><td>-4.383</td><td>Penmarc'h</td><td>2006</td><td>Coastline</td><td>29</td><td>Valero et al. (2011)</td></tr><tr><td>S10</td><td>48.032</td><td>-4.835</td><td>Ile de Sein</td><td>2009</td><td>Island</td><td>24</td><td>This study</td></tr><tr><td>S11</td><td>48.284</td><td>-4.601</td><td>Crozon</td><td>2011</td><td>Coastline</td><td>30</td><td>This study</td></tr><tr><td>S12</td><td>48.326</td><td>-4.764</td><td>Le Conquet</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S13</td><td>48.381</td><td>-4.919</td><td>Le Conquet</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S14</td><td>48.519</td><td>-4.779</td><td>Porspoder</td><td>2003</td><td>Coastline</td><td>25</td><td>Robuchon et al. (2014)</td></tr><tr><td>S15</td><td>48.673</td><td>-4.216</td><td>Plouescat</td><td>2005</td><td>Coastline</td><td>30</td><td>Valero et al. (2011)</td></tr><tr><td>S16</td><td>48.700</td><td>-4.152</td><td>Téven-Meur</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S17</td><td>48.711</td><td>-4.060</td><td>Ile de Sieck</td><td>2005</td><td>Coastline</td><td>30</td><td>Valero et al. (2011)</td></tr><tr><td>S18</td><td>48.725</td><td>-3.919</td><td>Roscoff</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S19</td><td>48.721</td><td>-3.803</td><td>Plougasnou</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S20</td><td>48.775</td><td>-3.777</td><td>Plateau de la Méloine</td><td>2015</td><td>Island</td><td>26</td><td>This study</td></tr><tr><td>S21</td><td>48.687</td><td>-3.613</td><td>Locquirec</td><td>2006</td><td>Coastline</td><td>27</td><td>Valero et al. (2011)</td></tr><tr><td>S22</td><td>48.871</td><td>-3.643</td><td>Triagoz</td><td>2012</td><td>Island</td><td>27</td><td>This study</td></tr><tr><td>S23</td><td>48.878</td><td>-3.513</td><td>Trégastel</td><td>2006</td><td>Island</td><td>27</td><td>This study</td></tr><tr><td>S24</td><td>48.881</td><td>-3.099</td><td>Tréguier</td><td>2006</td><td>Coastline</td><td>20</td><td>This study</td></tr><tr><td>S25</td><td>49.118</td><td>-2.821</td><td>Roche Douvres</td><td>2012</td><td>Island</td><td>26</td><td>This study</td></tr></tbody></table>
<table><caption>**Table 1.** Information on the sampled populations: population codes as indicated in Figure 1; geographic locations (Latitude, Longitude); locality; year of sampling; position relative to the coastline; number of genotyped individuals and source of the data.</caption><thead><tr><th>N°</th><th>Lat.</th><th>Long.</th><th>Locality</th><th>Year</th><th>Position</th><th>Nb.</th><th>Source</th></tr></thead><tbody><tr><td>S1</td><td>47.339</td><td>-2.892</td><td>Hoedic</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S2</td><td>47.394</td><td>-2.958</td><td>Houat</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S3</td><td>47.328</td><td>-3.124</td><td>Belle-île</td><td>2006</td><td>Island</td><td>30</td><td>This study</td></tr><tr><td>S4</td><td>47.470</td><td>-3.091</td><td>Quiberon</td><td>2008</td><td>Coastline</td><td>28</td><td>This study</td></tr><tr><td>S5</td><td>47.762</td><td>-3.549</td><td>Lorient</td><td>2006</td><td>Coastline</td><td>24</td><td>Valero et al. (2011)</td></tr><tr><td>S6</td><td>47.791</td><td>-3.761</td><td>Pont Aven</td><td>2006</td><td>Coastline</td><td>28</td><td>Valero et al. (2011)</td></tr><tr><td>S7</td><td>47.700</td><td>-3.983</td><td>Glénan</td><td>2008</td><td>Island</td><td>30</td><td>This study</td></tr><tr><td>S8</td><td>47.786</td><td>-4.152</td><td>Loctudy</td><td>2008</td><td>Coastline</td><td>20</td><td>This study</td></tr><tr><td>S9</td><td>47.799</td><td>-4.383</td><td>Penmarc'h</td><td>2006</td><td>Coastline</td><td>29</td><td>Valero et al. (2011)</td></tr><tr><td>S10</td><td>48.032</td><td>-4.835</td><td>Ile de Sein</td><td>2009</td><td>Island</td><td>24</td><td>This study</td></tr><tr><td>S11</td><td>48.284</td><td>-4.601</td><td>Crozon</td><td>2011</td><td>Coastline</td><td>30</td><td>This study</td></tr><tr><td>S12</td><td>48.326</td><td>-4.764</td><td>Le Conquet</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S13</td><td>48.381</td><td>-4.919</td><td>Le Conquet</td><td>2011</td><td>Island</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S14</td><td>48.519</td><td>-4.779</td><td>Porspoder</td><td>2003</td><td>Coastline</td><td>25</td><td>Robuchon et al. (2014)</td></tr><tr><td>S15</td><td>48.673</td><td>-4.216</td><td>Plouescat</td><td>2005</td><td>Coastline</td><td>30</td><td>Valero et al. (2011)</td></tr><tr><td>S16</td><td>48.700</td><td>-4.152</td><td>Téven-Meur</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S17</td><td>48.711</td><td>-4.060</td><td>Ile de Sieck</td><td>2005</td><td>Coastline</td><td>30</td><td>Valero et al. (2011)</td></tr><tr><td>S18</td><td>48.725</td><td>-3.919</td><td>Roscoff</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S19</td><td>48.721</td><td>-3.803</td><td>Plougasnou</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S20</td><td>48.775</td><td>-3.777</td><td>Plateau de la Méloine</td><td>2015</td><td>Island</td><td>26</td><td>This study</td></tr><tr><td>S21</td><td>48.687</td><td>-3.613</td><td>Locquirec</td><td>2006</td><td>Coastline</td><td>27</td><td>Valero et al. (2011)</td></tr><tr><td>S22</td><td>48.871</td><td>-3.643</td><td>Triagoz</td><td>2012</td><td>Island</td><td>27</td><td>This study</td></tr><tr><td>S23</td><td>48.878</td><td>-3.513</td><td>Trégastel</td><td>2006</td><td>Island</td><td>27</td><td>This study</td></tr><tr><td>S24</td><td>48.881</td><td>-3.099</td><td>Tréguier</td><td>2006</td><td>Coastline</td><td>20</td><td>This study</td></tr><tr><td>S25</td><td>49.118</td><td>-2.821</td><td>Roche Douvres</td><td>2012</td><td>Island</td><td>26</td><td>This study</td></tr></tbody></table>
<table><tr><td>S26</td><td>48.688</td><td>-2.325</td><td>Plévenon</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S27</td><td>48.651</td><td>-2.118</td><td>Saint-Lunaire</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S28</td><td>48.694</td><td>-1.984</td><td>Saint-Malo</td><td>2011</td><td>Coastline</td><td>29</td><td>Robuchon et al. (2014)</td></tr><tr><td>S29</td><td>48.697</td><td>-1.919</td><td>Saint-Malo</td><td>2006</td><td>Coastline</td><td>27</td><td>This study</td></tr><tr><td>S30</td><td>49.729</td><td>-1.919</td><td>Omonville</td><td>2006</td><td>Coastline</td><td>29</td><td>This study</td></tr><tr><td>S31</td><td>49.653</td><td>-1.234</td><td>Barfleur</td><td>2006</td><td>Coastline</td><td>30</td><td>This study</td></tr><tr><td>S32</td><td>50.912</td><td>1.677</td><td>Wissant</td><td>2006</td><td>Coastline</td><td>30</td><td>This study</td></tr></table>

1003

1004
<table><tr><td>S26</td><td>48.688</td><td>-2.325</td><td>Plévenon</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S27</td><td>48.651</td><td>-2.118</td><td>Saint-Lunaire</td><td>2011</td><td>Coastline</td><td>30</td><td>Robuchon et al. (2014)</td></tr><tr><td>S28</td><td>48.694</td><td>-1.984</td><td>Saint-Malo</td><td>2011</td><td>Coastline</td><td>29</td><td>Robuchon et al. (2014)</td></tr><tr><td>S29</td><td>48.697</td><td>-1.919</td><td>Saint-Malo</td><td>2006</td><td>Coastline</td><td>27</td><td>This study</td></tr><tr><td>S30</td><td>49.729</td><td>-1.919</td><td>Omonville</td><td>2006</td><td>Coastline</td><td>29</td><td>This study</td></tr><tr><td>S31</td><td>49.653</td><td>-1.234</td><td>Barfleur</td><td>2006</td><td>Coastline</td><td>30</td><td>This study</td></tr><tr><td>S32</td><td>50.912</td><td>1.677</td><td>Wissant</td><td>2006</td><td>Coastline</td><td>30</td><td>This study</td></tr></table>

1003

1004
<table><caption>**Table 2.** *F*<sub>ST</sub> values averaged across all pairwise comparisons (F&#773;<sub>ST</sub>) for the samples or the group of samples mentioned in the first column. When the first column indicates a group of samples (which correspond to a genetic cluster, see Figure 2), the *F*<sub>ST</sub> was additionally averaged across the populations belonging to that group. The populations or groups of populations from the southernmost populations (S1-S4); Locquirec (S21); populations from Saint-Malo Bay (S26-S29) and Dover Strait (S32) appeared to be particularly differentiated, as shown by higher F&#773;<sub>ST</sub> compared to the rest of the populations (last line, “Others”). Minimum and maximum F&#773;<sub>ST</sub> values are also indicated. The full pairwise *F*<sub>ST</sub> matrix can be found in Table S5.</caption><thead><tr><th>Samples or group of samples</th><th>F&#773;<sub>ST</sub></th><th>F<sub>ST</sub> min</th><th>F<sub>ST</sub> max</th></tr></thead><tbody><tr><td>S1-S4</td><td>0.183</td><td>0.024</td><td>0.395</td></tr><tr><td>S21</td><td>0.168</td><td>0.107</td><td>0.317</td></tr><tr><td>S26-S29</td><td>0.144</td><td>0.019</td><td>0.395</td></tr><tr><td>S32</td><td>0.227</td><td>0.159</td><td>0.356</td></tr><tr><td>Others</td><td>0.08</td><td>0.01</td><td>0.23</td></tr></tbody></table>
<table><caption>**Table 2.** *F*<sub>ST</sub> values averaged across all pairwise comparisons (F&#773;<sub>ST</sub>) for the samples or the group of samples mentioned in the first column. When the first column indicates a group of samples (which correspond to a genetic cluster, see Figure 2), the *F*<sub>ST</sub> was additionally averaged across the populations belonging to that group. The populations or groups of populations from the southernmost populations (S1-S4); Locquirec (S21); populations from Saint-Malo Bay (S26-S29) and Dover Strait (S32) appeared to be particularly differentiated, as shown by higher F&#773;<sub>ST</sub> compared to the rest of the populations (last line, “Others”). Minimum and maximum F&#773;<sub>ST</sub> values are also indicated. The full pairwise *F*<sub>ST</sub> matrix can be found in Table S5.</caption><thead><tr><th>Samples or group of samples</th><th>F&#773;<sub>ST</sub></th><th>F<sub>ST</sub> min</th><th>F<sub>ST</sub> max</th></tr></thead><tbody><tr><td>S1-S4</td><td>0.183</td><td>0.024</td><td>0.395</td></tr><tr><td>S21</td><td>0.168</td><td>0.107</td><td>0.317</td></tr><tr><td>S26-S29</td><td>0.144</td><td>0.019</td><td>0.395</td></tr><tr><td>S32</td><td>0.227</td><td>0.159</td><td>0.356</td></tr><tr><td>Others</td><td>0.08</td><td>0.01</td><td>0.23</td></tr></tbody></table>
<table><caption>**Table 3.** Estimates of genetic diversity associated to each population using *Ho*: observed heterozygosity, *He*: expected heterozygosity, *Ar*: allelic richness (calculated with minimum 19 diploid individuals), *Pa*: mean private alleles and deviation from Hardy-Weinberg Equilibrium estimated using *F*<sub>IS</sub>, *\* when the deviation is significant (p-value < 0.001, based on 1,000 permutations using GENETIX).</caption><thead><tr><th>N°</th><th>Position</th><th>Ho</th><th>He</th><th>Ar</th><th>Pa</th><th>F<sub>IS</sub></th></tr></thead><tbody><tr><td>S1</td><td>Island</td><td>0.552 ± 0.065</td><td>0.541 ± 0.055</td><td>3.724 ± 0.406</td><td>0 ± 0</td><td>-0.002</td></tr><tr><td>S2</td><td>Island</td><td>0.49 ± 0.051</td><td>0.51 ± 0.051</td><td>3.543 ± 0.304</td><td>0 ± 0</td><td>0.057</td></tr><tr><td>S3</td><td>Island</td><td>0.43 ± 0.068</td><td>0.425 ± 0.067</td><td>3.595 ± 0.31</td><td>0.091 ± 0.091</td><td>0.003</td></tr><tr><td>S4</td><td>Coastline</td><td>0.432 ± 0.072</td><td>0.426 ± 0.063</td><td>3.733 ± 0.452</td><td>0.091 ± 0.091</td><td>0.003</td></tr><tr><td>S5</td><td>Coastline</td><td>0.547 ± 0.066</td><td>0.555 ± 0.053</td><td>5.05 ± 0.684</td><td>0 ± 0</td><td>0.035</td></tr><tr><td>S6</td><td>Coastline</td><td>0.551 ± 0.066</td><td>0.539 ± 0.05</td><td>4.67 ± 0.584</td><td>0.091 ± 0.091</td><td>-0.004</td></tr><tr><td>S7</td><td>Island</td><td>0.539 ± 0.062</td><td>0.565 ± 0.051</td><td>5.22 ± 0.68</td><td>0.091 ± 0.091</td><td>0.063 *</td></tr><tr><td>S8</td><td>Coastline</td><td>0.586 ± 0.052</td><td>0.579 ± 0.05</td><td>5.554 ± 1.014</td><td>0.182 ± 0.182</td><td>0.013</td></tr><tr><td>S9</td><td>Coastline</td><td>0.513 ± 0.055</td><td>0.549 ± 0.05</td><td>5.443 ± 0.769</td><td>0.182 ± 0.182</td><td>0.083 *</td></tr><tr><td>S10</td><td>Island</td><td>0.561 ± 0.054</td><td>0.596 ± 0.05</td><td>5.913 ± 0.854</td><td>0 ± 0</td><td>0.08 *</td></tr><tr><td>S11</td><td>Coastline</td><td>0.555 ± 0.067</td><td>0.62 ± 0.065</td><td>5.551 ± 0.995</td><td>0.091 ± 0.091</td><td>0.122 *</td></tr><tr><td>S12</td><td>Coastline</td><td>0.561 ± 0.051</td><td>0.609 ± 0.055</td><td>6.151 ± 0.87</td><td>0.455 ± 0.207</td><td>0.097 *</td></tr><tr><td>S13</td><td>Island</td><td>0.552 ± 0.06</td><td>0.576 ± 0.062</td><td>5.613 ± 0.859</td><td>0 ± 0</td><td>0.06 *</td></tr><tr><td>S14</td><td>Coastline</td><td>0.585 ± 0.063</td><td>0.582 ± 0.054</td><td>6.004 ± 0.812</td><td>0.182 ± 0.122</td><td>0.014</td></tr><tr><td>S15</td><td>Coastline</td><td>0.617 ± 0.063</td><td>0.584 ± 0.059</td><td>5.355 ± 0.989</td><td>0 ± 0</td><td>-0.038</td></tr><tr><td>S16</td><td>Coastline</td><td>0.526 ± 0.06</td><td>0.555 ± 0.062</td><td>5.404 ± 0.898</td><td>0 ± 0</td><td>0.07 *</td></tr><tr><td>S17</td><td>Coastline</td><td>0.561 ± 0.073</td><td>0.543 ± 0.07</td><td>5.476 ± 0.99</td><td>0.091 ± 0.091</td><td>-0.015</td></tr><tr><td>S18</td><td>Coastline</td><td>0.495 ± 0.073</td><td>0.508 ± 0.078</td><td>4.814 ± 0.809</td><td>0.091 ± 0.091</td><td>0.043</td></tr><tr><td>S19</td><td>Coastline</td><td>0.445 ± 0.081</td><td>0.488 ± 0.074</td><td>4.451 ± 0.699</td><td>0.091 ± 0.091</td><td>0.103 *</td></tr><tr><td>S20</td><td>Island</td><td>0.418 ± 0.077</td><td>0.426 ± 0.076</td><td>3.942 ± 0.734</td><td>0.091 ± 0.091</td><td>0.04</td></tr><tr><td>S21</td><td>Coastline</td><td>0.387 ± 0.072</td><td>0.376 ± 0.066</td><td>3.158 ± 0.456</td><td>0 ± 0</td><td>-0.01</td></tr><tr><td>S22</td><td>Island</td><td>0.461 ± 0.087</td><td>0.471 ± 0.09</td><td>4.482 ± 1.077</td><td>0 ± 0</td><td>0.04</td></tr></tbody></table>
<table><caption>**Table 3.** Estimates of genetic diversity associated to each population using *Ho*: observed heterozygosity, *He*: expected heterozygosity, *Ar*: allelic richness (calculated with minimum 19 diploid individuals), *Pa*: mean private alleles and deviation from Hardy-Weinberg Equilibrium estimated using *F*<sub>IS</sub>, *\* when the deviation is significant (p-value < 0.001, based on 1,000 permutations using GENETIX).</caption><thead><tr><th>N°</th><th>Position</th><th>Ho</th><th>He</th><th>Ar</th><th>Pa</th><th>F<sub>IS</sub></th></tr></thead><tbody><tr><td>S1</td><td>Island</td><td>0.552 ± 0.065</td><td>0.541 ± 0.055</td><td>3.724 ± 0.406</td><td>0 ± 0</td><td>-0.002</td></tr><tr><td>S2</td><td>Island</td><td>0.49 ± 0.051</td><td>0.51 ± 0.051</td><td>3.543 ± 0.304</td><td>0 ± 0</td><td>0.057</td></tr><tr><td>S3</td><td>Island</td><td>0.43 ± 0.068</td><td>0.425 ± 0.067</td><td>3.595 ± 0.31</td><td>0.091 ± 0.091</td><td>0.003</td></tr><tr><td>S4</td><td>Coastline</td><td>0.432 ± 0.072</td><td>0.426 ± 0.063</td><td>3.733 ± 0.452</td><td>0.091 ± 0.091</td><td>0.003</td></tr><tr><td>S5</td><td>Coastline</td><td>0.547 ± 0.066</td><td>0.555 ± 0.053</td><td>5.05 ± 0.684</td><td>0 ± 0</td><td>0.035</td></tr><tr><td>S6</td><td>Coastline</td><td>0.551 ± 0.066</td><td>0.539 ± 0.05</td><td>4.67 ± 0.584</td><td>0.091 ± 0.091</td><td>-0.004</td></tr><tr><td>S7</td><td>Island</td><td>0.539 ± 0.062</td><td>0.565 ± 0.051</td><td>5.22 ± 0.68</td><td>0.091 ± 0.091</td><td>0.063 *</td></tr><tr><td>S8</td><td>Coastline</td><td>0.586 ± 0.052</td><td>0.579 ± 0.05</td><td>5.554 ± 1.014</td><td>0.182 ± 0.182</td><td>0.013</td></tr><tr><td>S9</td><td>Coastline</td><td>0.513 ± 0.055</td><td>0.549 ± 0.05</td><td>5.443 ± 0.769</td><td>0.182 ± 0.182</td><td>0.083 *</td></tr><tr><td>S10</td><td>Island</td><td>0.561 ± 0.054</td><td>0.596 ± 0.05</td><td>5.913 ± 0.854</td><td>0 ± 0</td><td>0.08 *</td></tr><tr><td>S11</td><td>Coastline</td><td>0.555 ± 0.067</td><td>0.62 ± 0.065</td><td>5.551 ± 0.995</td><td>0.091 ± 0.091</td><td>0.122 *</td></tr><tr><td>S12</td><td>Coastline</td><td>0.561 ± 0.051</td><td>0.609 ± 0.055</td><td>6.151 ± 0.87</td><td>0.455 ± 0.207</td><td>0.097 *</td></tr><tr><td>S13</td><td>Island</td><td>0.552 ± 0.06</td><td>0.576 ± 0.062</td><td>5.613 ± 0.859</td><td>0 ± 0</td><td>0.06 *</td></tr><tr><td>S14</td><td>Coastline</td><td>0.585 ± 0.063</td><td>0.582 ± 0.054</td><td>6.004 ± 0.812</td><td>0.182 ± 0.122</td><td>0.014</td></tr><tr><td>S15</td><td>Coastline</td><td>0.617 ± 0.063</td><td>0.584 ± 0.059</td><td>5.355 ± 0.989</td><td>0 ± 0</td><td>-0.038</td></tr><tr><td>S16</td><td>Coastline</td><td>0.526 ± 0.06</td><td>0.555 ± 0.062</td><td>5.404 ± 0.898</td><td>0 ± 0</td><td>0.07 *</td></tr><tr><td>S17</td><td>Coastline</td><td>0.561 ± 0.073</td><td>0.543 ± 0.07</td><td>5.476 ± 0.99</td><td>0.091 ± 0.091</td><td>-0.015</td></tr><tr><td>S18</td><td>Coastline</td><td>0.495 ± 0.073</td><td>0.508 ± 0.078</td><td>4.814 ± 0.809</td><td>0.091 ± 0.091</td><td>0.043</td></tr><tr><td>S19</td><td>Coastline</td><td>0.445 ± 0.081</td><td>0.488 ± 0.074</td><td>4.451 ± 0.699</td><td>0.091 ± 0.091</td><td>0.103 *</td></tr><tr><td>S20</td><td>Island</td><td>0.418 ± 0.077</td><td>0.426 ± 0.076</td><td>3.942 ± 0.734</td><td>0.091 ± 0.091</td><td>0.04</td></tr><tr><td>S21</td><td>Coastline</td><td>0.387 ± 0.072</td><td>0.376 ± 0.066</td><td>3.158 ± 0.456</td><td>0 ± 0</td><td>-0.01</td></tr><tr><td>S22</td><td>Island</td><td>0.461 ± 0.087</td><td>0.471 ± 0.09</td><td>4.482 ± 1.077</td><td>0 ± 0</td><td>0.04</td></tr></tbody></table>
<table><tr><td>S23</td><td>Island</td><td>0.465 ± 0.095</td><td>0.452 ± 0.09</td><td>4.483 ± 0.927</td><td>0.273 ± 0.141</td><td>-0.01</td></tr><tr><td>S24</td><td>Coastline</td><td>0.496 ± 0.086</td><td>0.486 ± 0.078</td><td>4.508 ± 0.832</td><td>0.182 ± 0.122</td><td>0.006</td></tr><tr><td>S25</td><td>Island</td><td>0.457 ± 0.08</td><td>0.485 ± 0.078</td><td>3.762 ± 0.626</td><td>0 ± 0</td><td>0.078 *</td></tr><tr><td>S26</td><td>Coastline</td><td>0.33 ± 0.083</td><td>0.347 ± 0.082</td><td>3.361 ± 0.679</td><td>0.091 ± 0.091</td><td>0.064</td></tr><tr><td>S27</td><td>Coastline</td><td>0.45 ± 0.08</td><td>0.403 ± 0.07</td><td>2.951 ± 0.532</td><td>0 ± 0</td><td>-0.1 *</td></tr><tr><td>S28</td><td>Coastline</td><td>0.426 ± 0.078</td><td>0.382 ± 0.062</td><td>3.22 ± 0.431</td><td>0.091 ± 0.091</td><td>-0.098 *</td></tr><tr><td>S29</td><td>Coastline</td><td>0.424 ± 0.078</td><td>0.423 ± 0.068</td><td>3.271 ± 0.58</td><td>0.091 ± 0.091</td><td>0.017</td></tr><tr><td>S30</td><td>Coastline</td><td>0.448 ± 0.086</td><td>0.454 ± 0.076</td><td>4.361 ± 0.846</td><td>0 ± 0</td><td>0.03</td></tr><tr><td>S31</td><td>Coastline</td><td>0.487 ± 0.091</td><td>0.477 ± 0.078</td><td>4.414 ± 0.812</td><td>0 ± 0</td><td>-0.003</td></tr><tr><td>S32</td><td>Coastline</td><td>0.308 ± 0.083</td><td>0.29 ± 0.073</td><td>2.542 ± 0.407</td><td>0 ± 0</td><td>-0.046</td></tr></table>
<table><tr><td>S23</td><td>Island</td><td>0.465 ± 0.095</td><td>0.452 ± 0.09</td><td>4.483 ± 0.927</td><td>0.273 ± 0.141</td><td>-0.01</td></tr><tr><td>S24</td><td>Coastline</td><td>0.496 ± 0.086</td><td>0.486 ± 0.078</td><td>4.508 ± 0.832</td><td>0.182 ± 0.122</td><td>0.006</td></tr><tr><td>S25</td><td>Island</td><td>0.457 ± 0.08</td><td>0.485 ± 0.078</td><td>3.762 ± 0.626</td><td>0 ± 0</td><td>0.078 *</td></tr><tr><td>S26</td><td>Coastline</td><td>0.33 ± 0.083</td><td>0.347 ± 0.082</td><td>3.361 ± 0.679</td><td>0.091 ± 0.091</td><td>0.064</td></tr><tr><td>S27</td><td>Coastline</td><td>0.45 ± 0.08</td><td>0.403 ± 0.07</td><td>2.951 ± 0.532</td><td>0 ± 0</td><td>-0.1 *</td></tr><tr><td>S28</td><td>Coastline</td><td>0.426 ± 0.078</td><td>0.382 ± 0.062</td><td>3.22 ± 0.431</td><td>0.091 ± 0.091</td><td>-0.098 *</td></tr><tr><td>S29</td><td>Coastline</td><td>0.424 ± 0.078</td><td>0.423 ± 0.068</td><td>3.271 ± 0.58</td><td>0.091 ± 0.091</td><td>0.017</td></tr><tr><td>S30</td><td>Coastline</td><td>0.448 ± 0.086</td><td>0.454 ± 0.076</td><td>4.361 ± 0.846</td><td>0 ± 0</td><td>0.03</td></tr><tr><td>S31</td><td>Coastline</td><td>0.487 ± 0.091</td><td>0.477 ± 0.078</td><td>4.414 ± 0.812</td><td>0 ± 0</td><td>-0.003</td></tr><tr><td>S32</td><td>Coastline</td><td>0.308 ± 0.083</td><td>0.29 ± 0.073</td><td>2.542 ± 0.407</td><td>0 ± 0</td><td>-0.046</td></tr></table>
<table><caption>**Table 4.** Results of the partial and global db-RDA for each dataset: overall (32 populations), regional (29 populations) and continuous datasets (24 populations). Environmental predictors selected by the stepwise forward selection (ordiR2step) are included in the db-RDA framework, the predictors highlighted in bold are significant at *p* < 0.001 according to the ANOVA. The adjusted coefficient of determination (R²<sub>adj</sub>) and the *p*-value of the model are reported. Habitat continuity and sampling year were not selected as a significant predictor in the global nor partial db-RDA and are therefore not represented in this table.</caption><thead><tr><th>Overall</th><th>Oceanographic Connectivity</th><th>Geographic Position</th><th>Sea Surface Temperature</th><th>p-value</th><th>R<sup>2</sup><sub>adj</sub></th></tr></thead><tbody><tr><td>Global db-RDA</td><td>AEM1, AEM3, AEM26, AEM2</td><td>dbMEM1, dbMEM3, dbMEM5</td><td></td><td>0.001</td><td>0.604</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM7, AEM6, AEM26</td><td></td><td></td><td>0.012</td><td>0.115</td></tr><tr><td></td><td>dbMEM1, dbMEM3, dbMEM5</td><td></td><td>0.001</td><td>0.475</td></tr><tr><td></td><td></td><td>Mean, Min</td><td>0.002</td><td>0.165</td></tr><tr><th colspan="6">Regional</th></tr><tr><td>Global db-RDA</td><td>AEM23, AEM2, AEM1, AEM25</td><td>dbMEM2, dbMEM3</td><td></td><td>0.001</td><td>0.614</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM2, AEM1, AEM23, AEM25, AEM7, AEM6, AEM9</td><td></td><td></td><td>0.001</td><td>0.549</td></tr><tr><td></td><td>dbMEM2, dbMEM3, dbMEM1</td><td></td><td>0.001</td><td>0.485</td></tr><tr><td></td><td></td><td>Mean, Min, Max, Range</td><td>0.001</td><td>0.221</td></tr><tr><th colspan="6">Continuous</th></tr><tr><td>Global db-RDA</td><td>AEM1, AEM2, AEM16, AEM10, AEM5, AEM19, AEM3</td><td>dbMEM3</td><td></td><td>0.001</td><td>0.748</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM1, AEM2, AEM16, AEM10, AEM5, AEM19, AEM3, AEM20, AEM9, AEM7</td><td></td><td></td><td>0.001</td><td>0.794</td></tr><tr><td></td><td>dbMEM2, dbMEM3, dbMEM1</td><td></td><td>0.001</td><td>0.513</td></tr><tr><td></td><td></td><td>Max</td><td>0.003</td><td>0.210</td></tr></tbody></table>

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<table><caption>**Table 4.** Results of the partial and global db-RDA for each dataset: overall (32 populations), regional (29 populations) and continuous datasets (24 populations). Environmental predictors selected by the stepwise forward selection (ordiR2step) are included in the db-RDA framework, the predictors highlighted in bold are significant at *p* < 0.001 according to the ANOVA. The adjusted coefficient of determination (R²<sub>adj</sub>) and the *p*-value of the model are reported. Habitat continuity and sampling year were not selected as a significant predictor in the global nor partial db-RDA and are therefore not represented in this table.</caption><thead><tr><th>Overall</th><th>Oceanographic Connectivity</th><th>Geographic Position</th><th>Sea Surface Temperature</th><th>p-value</th><th>R<sup>2</sup><sub>adj</sub></th></tr></thead><tbody><tr><td>Global db-RDA</td><td>AEM1, AEM3, AEM26, AEM2</td><td>dbMEM1, dbMEM3, dbMEM5</td><td></td><td>0.001</td><td>0.604</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM7, AEM6, AEM26</td><td></td><td></td><td>0.012</td><td>0.115</td></tr><tr><td></td><td>dbMEM1, dbMEM3, dbMEM5</td><td></td><td>0.001</td><td>0.475</td></tr><tr><td></td><td></td><td>Mean, Min</td><td>0.002</td><td>0.165</td></tr><tr><th colspan="6">Regional</th></tr><tr><td>Global db-RDA</td><td>AEM23, AEM2, AEM1, AEM25</td><td>dbMEM2, dbMEM3</td><td></td><td>0.001</td><td>0.614</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM2, AEM1, AEM23, AEM25, AEM7, AEM6, AEM9</td><td></td><td></td><td>0.001</td><td>0.549</td></tr><tr><td></td><td>dbMEM2, dbMEM3, dbMEM1</td><td></td><td>0.001</td><td>0.485</td></tr><tr><td></td><td></td><td>Mean, Min, Max, Range</td><td>0.001</td><td>0.221</td></tr><tr><th colspan="6">Continuous</th></tr><tr><td>Global db-RDA</td><td>AEM1, AEM2, AEM16, AEM10, AEM5, AEM19, AEM3</td><td>dbMEM3</td><td></td><td>0.001</td><td>0.748</td></tr><tr><td rowspan="3">Partial db-RDA</td><td>AEM1, AEM2, AEM16, AEM10, AEM5, AEM19, AEM3, AEM20, AEM9, AEM7</td><td></td><td></td><td>0.001</td><td>0.794</td></tr><tr><td></td><td>dbMEM2, dbMEM3, dbMEM1</td><td></td><td>0.001</td><td>0.513</td></tr><tr><td></td><td></td><td>Max</td><td>0.003</td><td>0.210</td></tr></tbody></table>

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

**Figure 1.** Geographic position of the sampled populations, the Northernmost site (S32) is indicated in the inset. Population numbers correspond to the ones indicated in Table 1 and the area illustrated in green corresponds to rocky substrata above 5m depth obtained from IFREMER in May 2019. The information on the spatial distribution of bedrock comes from the combination of several sources as specified in the Material and Method section. The dotted line corresponds to the 120m isobath, which could correspond to the coastline at the Last Glacial Maximum. The inset illustrates the general circulation in the Bay of Biscay and in the English Channel, drawn according to Ayata et al. (2010).

<figure><img src="image_4.png" /></figure>

**Figure 2.** STRUCTURE barplot obtained for K = 2 (A), K = 3 (B) and K = 12 (C) which appeared the best number of clusters according to both ΔK and log Pr(X|K) methods. Each vertical bar corresponds to one individual and the colors represent the individual's estimated proportion of ancestry in each of the K clusters. The numbers below correspond to the population code as indicated in Table and Figure 1.