Genetics of Norwegian Kelp Forests

Genetics of Norwegian kelp forests

Microsatellites reveal the genetic diversity, differentiation, and structure of two foundation kelp species in Norway

Ann M. Evankow

MSc Thesis Centre for Ecology and Evolutionary Synthesis Department of Biosciences

University of Oslo

June 2015

© Ann M. Evankow 2015 Genetics of Norwegian kelp forests: Microsatellites reveal the genetic diversity, differentiation, and structure of two foundation kelp species in Norway Ann M. Evankow http://www.duo.uio.no/ Print: University Print Centre, University of Oslo

Preface

Two years is gone already. And I can honesty say that I am happy that my world has revolved around kelp for the majority of that time. Thank you, Claudia, for thinking up this amazing project. Without you, I wouldn’t be submitting a thesis about kelp genetics today. And thank you for introducing me to the University of Oslo. I felt welcomed by all of the members of the shark and kelp meetings. Thank you, Hartvig & Janne, for agreeing with Claudia and collecting kelp samples. This project would have never started without you, or finished. Thank you to all the other NIVA folks you have made this project possible and helped me along the way. Thank you, Anne, for accepting me as your student, even though my ideas were far from polyploidy and plants. You have been there for me this entire time and I could not have hoped for a more caring, patient, and optimistic supervisor. Thank you, Stein, for agreeing to supervise me, even though I didn’t end up working with seagrasses. There is always more time, yes? And kelps are wonderful, even though I spent most of the time in a DNA lab, instead of out in the field. Thank you, Marit, for helping me extract and amplify kelp DNA for the first time. I may have given up early on without your support. Thank you, Mikkel, for sharing your PCR supplies and answering my questions in the lab, often at strange hours and about unusual things. And Ryan, for letting me rant about my problems. Thank you, Robin, Sandy and, Eli, for helping me attempt to create a RADseq library of kelp. Next time. I would also like to thank Tove, Anne, Jim, and all the other amazing people I met at UNIS who inspired me and kept me believing that this project could happen. Jim, I am especially grateful for your extra assistance in the lab and comments on this paper. I hope I can visit Appledore this summer or soon! At this point, I need to thank the other masters students (and PhDers). Without you I could not have been my cheery self. You’ve made this entire project possible through coffee breaks, Bunnpris runs, late nights, early computer lab Fridays and millions of other very special moments. No really. I needed you these past two years and I’m sad that it is over so quickly. Lastly, and firstly, thank you Emil for believing in me, every moment of everyday. And… I would like to thank my family. Although I am far from home, I feel your support day in and day out. And with that, I bid you, ha det bra!

III

Annie with Laminaria hyperborea in Bergen, Norway. Photo credit: Hildur Magnúsdóttir

Table of Contents

Abstract ...... 1 Introduction ...... 3 Loss of kelp forests in Norway ...... 3 Exploring gene flow with genetics ...... 4 Study questions ...... 6 Materials & Methods ...... 7 Study system and species ...... 7 Sample collection and preservation ...... 10 DNA extraction ...... 11 Microsatellite analyses ...... 11 Data analyses ...... 13 Results ...... 17 Microsatellite selection ...... 17 Assumptions of genetic analyses ...... 18 Genetic diversity ...... 21 Site differentiation ...... 25 Genetic structure ...... 29 Discussion ...... 35 The Skagerrak: one region, two patterns of diversity ...... 35 The North: an oasis of unique genetic diversity ...... 37 Implications for management ...... 38 Future investigations ...... 39 Conclusions ...... 40 Acknowledgements ...... 42 References ...... 43 Appendix ...... 49

Abstract

Various stressors such as higher temperatures, grazing by sea urchins, and anthropogenic effects may threaten kelp forests around the world. Conservation efforts can benefit from an understanding of current and historic patterns of gene flow and population connectivity of kelp. However, relatively little is known about these patterns, especially with regards to the northern edge of the distribution of kelp species in the North Atlantic Ocean. Knowledge of kelp population genetic diversity and structure can provide crucial information about the resilience and recolonization potential for already threatened populations. This study investigated the genetic diversity, differentiation, and structure of the two foundation kelp species in Norway, Saccharina latissima and Laminaria hyperborea. Nearly 500 individuals were genotyped from 16 different sites along the Norwegian coast using microsatellite loci that cross-amplified from other species. Roughly half of the samples per species amplified and were score-able for three polymorphic markers for S. latissima and 11 for L. hyperborea. Significant genetic structure, differentiation, and variation in genetic diversity were found among sites for both species. There were at least two distinct clusters of S. latissima and four of L. hyperborea. Genetic patterns corresponding to isolation by distance were significant for both species, except within the Skagerrak region. Genetic diversity of L. hyperborea was low in the Skagerrak region and significantly increased with higher latitudes along the Norwegian coast. Genetic diversity of S. latissima was significantly different between sites, but did not vary significantly between larger regions. Overall, this study established molecular tools for future investigations and provided the first glimpse into population genetic patterns of S. latissima and L. hyperborea in Norway.

Introduction

Kelps are foundation species for highly productive marine coastal ecosystems. These large brown seaweeds create three-dimensional forest-like habitats for multitudes of species, including juvenile fish important for fisheries (Norderhaug et al. 2005, Christie et al. 2009). As primary producers, kelp species contribute significant amounts of energy to coastal marine food webs in the form of particulate organic matter (Fredriksen 2003, Norderhaug et al. 2003). Some species, such as sea urchins, are capable of digesting kelp directly and preferentially feed on kelp biomass (Vadas 1977).

In addition to their ecological worth, kelps are valuable economic commodities. Kelp species are cultivated and harvested for their alginates and accumulation of rare elements (Vásquez 2009, Kerrison et al. 2015). In China, Saccharina species have been cultivated for over a century (Zhang et al. 2015). In Europe, kelp species are grown in aquaculture and harvested along the coasts of Ireland, France, and Norway for production of many consumer goods (Draget et al. 2005, Kerrison et al. 2015).

Various stressors such as higher temperatures, grazing by sea urchins, and anthropogenic effects threaten kelp species around the world (Steneck et al. 2002, 2004, Wernberg 2011, 2013, Raybaud et al. 2013, Fagerli et al. 2014). The ranges of kelp are determined by temperature (Lüning 1984). Warm temperatures above 20°C reduce the efficiency of photosystem II (Sogn Andersen et al. 2013a), impairing kelp’s source of energy. As a result, kelps are forced into deeper or more exposed, cooler water to avoid the heat (Moy & Christie 2012). Depth distribution of kelp is additionally limited by light. Increasing water turbidity from pollution, algae blooms, and sediments reduces light penetration into the water column, preventing kelp from living below certain depths (Sogn Andersen et al. 2011, Harley 2012). Loss of predators can also lead to spikes in grazer populations, resulting in abnormally large sea urchin populations (Sivertsen 1997, 2006, Fagerli et al. 2014). All together, kelps are being squeezed into smaller coastal zones by multiple pressures (Leblanc et al. 2011, Harley 2012).

Loss of kelp forests in Norway

In recent years, large areas of kelp forest have disappeared from northern and southern Norway. In the North, an abnormally large sea urchin population in the 1970s grazed down vast areas of the kelp forests from 63˚N latitude to 71˚N at the northern tip of Norway, resulting in 2000 km2 of barren ground (Silvertsen 1997, Norderhaug & Christie 2009). Few species can live in sea urchin barrens, resulting in substantial loss of biodiversity in the barrens compared to kelp forest (Christie et al. 2009). Since 1990, this barren zone has shrunk as kelp species have re-established areas at the southern-most edge of the zone (Røv et al. 1990). Higher temperatures have lowered sea urchin recruitment, reducing their population size and giving kelp an opportunity to recolonize parts of the barren areas up to 65˚N and beyond (Norderhaug & Christie 2009, Fagerli et al. 2013, Rinde et al. 2014). The fast-

3 growing kelp Saccharina latissima (L.) Lane, Mayes, Druehl & Saunders recolonizes the barrens in the first year after year after a drop in urchin population size, followed in later years by the slower-growing, long-lived kelp Laminaria hyperborea (Gunnerus) Foslie (Leinas & Christie 1996).

Along the southern coast, warmer temperatures in conjunction with increased shading by epibionts and decreased water transparency have led to substantial kelp forest loss (Moy & Christie 2012, Sogn Andersen et al. 2011, 2013b). Saccharina latissima has declined by 51% to 80% in the Skagerrak region (Bekkby & Moy 2011, Moy & Christie 2012). Most of the decline has occurred in sheltered areas and at shallow depths due to higher temperatures above the optimum of 15˚C (tom Dieck 1989). Pollution in the water column limits light availability, preventing S. latissima from surviving at lower depths. Once the kelp species disappear, filamentous algae and sediment become dominant and may inhibit recolonization of kelps as shown by seaweed species in several regions (Schiel et al. 2006, Gorman & Connell 2009, Sogn Andersen et al. 2011, 2013b, Dieman et al. 2012).

Exploring gene flow with genetics

These changes in kelp forest distribution come at a time when we are just beginning to understand the population genetic dynamics of kelp forests (Valero et al. 2011). Under increasing population pressures, marine species will need to adapt to changing environments, migrate or ultimately disappear. Integrated “seascape genetics” that combines traditional research with genetic tools is the next step in studying these developments (Selkoe et al. 2008). This approach uses baseline genetic data of populations to understand the evolutionary processes that shape stressed populations and make informed management decisions, such as focusing scarce funding on protecting areas with high genetic diversity (Couceiro et al. 2012) or threatened populations with unique diversity (Petit et al. 1998, McDonald-Madden et al. 2008).

In conservation, understanding the characteristics of different populations within a species is crucial to effectively manage groups that could be at risk of local extinction (Manier & Arnold 2006). Populations are defined as groups of individuals that are potential mating partners (Hartl & Clark 2007). In practice, distinct populations can be inferred from the distribution of genetic variation within a species. The genetic variation is measured by allele frequencies, the number of different forms of a gene, within and between groups of individuals. In addition to the population structure of a species, the distribution of alleles provides an indirect assessment of population connectivity and gene flow between distinct groups (Höglund 2009). Species with high gene flow between groups will show relatively little genetic structure and species with low gene flow between groups will show stronger patterns of genetic structure.

Microsatellites are excellent genetic markers to assess allele frequencies in population-level genetic studies. These molecular markers are short tandem repeats of DNA that mutate rapidly and are often highly variable within a species (Avise 2004, Gruar & Li 1999). A

4 microsatellite region or locus consists of one to six base pair sequences that are repeated four to 50 times (Selkoe & Toonen 2006). They are co-dominant markers and therefore can be used to investigate genetic diversity based on levels of heterozygosity within and between populations (Höglund 2009). Moreover, microsatellites can be used with a wide quality range of samples, including DNA from brown algae that is notoriously difficult to extract and amplify (Snirc et al. 2010). However, microsatellites are usually species- or genus-specific and need to be developed independently for new study organisms or cross-amplified from related species, which has had with varying levels of success in the past (Selkoe & Toonen 2006).

Several studies have investigated population genetic patterns of brown seaweeds along the Norwegian coastline (Hoarau et al. 2007a, Olsen et al. 2010, Coyer et al. 2011). However, very little is known about population genetics of kelp in this area. Previous studies investigating kelp population genetics from other parts of the world provide testable hypotheses about the expected genetic patterns of kelp species in Norway. Studies suggest the potential for gene flow between kelp populations is determined by morphology (Valero et al. 2011), ocean currents (Billot et al. 2003, Tellier et al. 2009), distance (Alberto et al. 2010, Robuchon et al. 2014) and occasional floating rafts (Fraser et al. 2010, Neiva et al. 2012). In general, studies of marine coastal ecosystems predict correlations of isolation by distance, with increasing genetic differentiation between sites as distance between sites increases (Wright 1943, Rousset 1997, Guo 2012). However this trend is not always supported by real systems because of occasional long-range dispersal and the overall stochastic nature of coastal marine currents (Siegal et al. 2008, White et al. 2010). Moreover, in the northern hemisphere, diversity is expected to be highest at low latitudes as a result of glacial refugia in southern regions (Hewitt 1993, 2000, 2004, Maneiro et al. 2011, Neiva et al. 2012). The northern, leading edge populations are expected to have less genetic diversity (Hampe & Petit 2005). Some studies suggest this latitudinal trend will vary for warm- and cold-adapted species (Bennet & Provan 2008, Provan & Bennet 2008, Stewart et al. 2010), with potential northern refugia. To this end, several studies with terrestrial (Jaramillo-Correa et al. 2004) and marine species (Coyer et al. 2011, Olsen et al. 2013) support a hypothesis that a glacial refugium existed in Andøya, Norway.

Study questions

The main goal of this thesis is to define genetically distinct populations and explore patterns of genetic diversity within two foundation kelp species in Norway, Saccharina latissima and Laminaria hyperborea.

The first aim is to find suitable molecular markers through cross-amplification of microsatellites developed for other kelp species in the Laminaria and Saccharina genera. Specifically, I will…

1) Develop new polymorphic markers for S. latissima 2) Develop additional polymorphic markers for L. hyperborea that will complement the existing markers tested by Robuchon et al. (2014)

The second aim is to use the microsatellite markers to investigate patterns of genetic diversity, differentiation and structure with individual, site- and regional-level analyses in both species. Specifically, I will…

1) Investigate genetic diversity across sites and regions using three measures: i) allelic richness, ii) expected heterozygosity and iii) observed heterozygosity. I will test the null hypotheses:

a. H0: Genetic diversity does not vary between sites b. H0: Genetic diversity does not vary between regions c. H0: Genetic diversity decreases as latitude increases 2) Determine the level of genetic differentiation between individuals, sites and regions using i) analysis of molecular variance (AMOVA), ii) exact G tests between each site pair and iii) isolation by distance. I will test the null hypotheses:

a. H0: There is no significant genetic differentiation between sites and regions b. H0: There is no genetic differentiation between sites within regions c. H0: Genetic distance does not increase with geographic distance 3) Identify patterns of genetic structure based on individuals and sites using i) STRUCTURE analysis of individuals into distinct genetic clusters, ii) factorial correspondence analysis, and iii) neighbor-joining trees based on pairwise genetic distances between sites. I will test the null hypothesis:

a. H0: There is one genetic cluster and no significant population structure

Finally, I will compare and contrast the genetic patterns of the two species and relate these findings to existing literature about kelp populations in Norway.

Materials & Methods

Study system and species

Kelps are large, brown seaweeds in the order Laminariales (Phaeophyceae). These species live around in the world, along high nutrient, cold-water coastlines (Steneck et al. 2002, Bolton 2010). They first evolved in the western Pacific and migrated into the Atlantic when the Bering Strait opened 3.5 million years ago (Adey et al. 2008). The two species in this study, Saccharina latissima and Laminaria hyperborea, were formerly in the same genus before molecular studies supported the establishment of a separate Saccharina genus (Yoon et al. 2001, Lane et al. 2006).

Figure 1: Kelp life cycle of Laminaria hyperborea, described in the text below. Figure adapted from Rinde et al. (1998). Reproduced with permission from the author.

Laminaria hyperborea and S. latissima have a heteromorphic, diplohaplontic life cycle with macroscopic sporophyte and microscopic gametophyte generations (Figure 1). Reproduction begins with the release of haploid male and female spores from sori produced by the diploid sporophyte. The spores travel with flagella and water currents (Fredriksen et al. 1995). The zoospores germinate into microscopic male and female gametophytes. Sexual reproduction occurs when the male releases a spermatozoid that successfully finds and fertilizes a female egg. A new sporophyte grows from the zygote where the female attached to a hard substrate, such as a rock. Mating is possible between individuals of some kelp species that originate in different places, as long as the male and female spores end up in the same place, within 1mm apart (Reed 1990). L. hyperborea lives in the eastern North Atlantic from 40°N to 71°N (Kain 1967). Saccharina latissima overlaps with L. hyperborea, but extends further to the north to Spitsbergen and Greenland, and into the western Atlantic and northern Pacific oceans. Lethal summer temperatures above 20°C limit both species from expanding south (van den Hoek 1982), while cold temperatures and the timing of sporogenesis limit L. hyperborea from expanding North (Breeman 1988, Sjøtun & Schoschina 2002). Both species are depth limited, due to the lack of light below 30 meters (Lüning 1990). Laminaria hyperborea prefers exposed areas, while S. latissima is found more often in sheltered areas (Sjøtun et al. 1993).

Collector JKG JKG HCC JKG JKG JKG AME HCC HCC HCC HCC HCC AME AME Collector JKG HCC JKG JKG JKG AME HCC HCC HCC HCC

4 Date Collected Jun. 201 Jun. 2014 2014 Apr. Jun. 2014 Jun. 2014 Jun. 2014 Jul.2014 Jun. 2014 2014Sep. 2014 Aug. 2014 Aug. 2012 Aug. 2014 Oct. 2014 Oct. Date Collected Jun. 2013 2014 Apr. Jun. 2014 Jun. 2014 Jun. 2014 Jul.2014 2014 Apr. 2012 May. 2012 May. 2012 Aug.

6 6 9 5 9 7 9 18 23 20 14 13 10 13 2# 1# 4# 16 23 16 16 17 1# 3# (JKG), Hartvig C. Christie (HCC), (HCC), Christie C. Hartvig (JKG),

markers. N Data N Data k

7 9 7 6 9 24 24 24 24 17 20 24 31 19 16 23 12 24 27 24 21 20 20 24 N Total N Total

anneKim Gitmar J

: Long. Long. 9.80854 9.75373 9.60771 8.52282 8.06637 5.71851 5.22189 6.53257 9.80854 9.60988 8.94430 8.53720 5.71851 5.03798 6.50928 12.13096 25.24863 25.46456 29.37542 14.20133 15.41303 12.13097 11.54940 29.37542

luded in analyses with seven with analyses in luded

Code, Location, Latitude (Lat.), Longitude (Long.) Sample Size of of Size Sample (Long.) Longitude (Lat.), Latitude Location, Code,

Lat. Lat. 58.99358 59.02330 58.88209 58.25454 58.13230 59.05966 60.26975 62.79949 65.69011 70.42583 70.38939 70.68840 78.06583 78.25372 58.99358 58.87283 58.51320 58.27320 59.05966 60.16200 62.81124 65.69011 65.67637 70.68840

. Region, Site

Agder

- Spitsbergen

nnmark Agder - Agder Agder - -

d Laminariahyperborea istiansand, Vest istiansand, øy, Møre & Romsdal & Møre øy, and inc only was *Bar_3 analyses. data all in used

lmen, Stavanger, Rogaland Stavanger, lmen, Location Arøya,Store Helgero, Vestfold Telemark Langesund, Risøyodden, Telemark Jomfruland, Øytangen, Aust Grimstad, Homborøy, Kr Korsvikfjord, Rogaland Stavanger, Rossholmen, Hordaland Bergen, Raunefjorden, Sand Gåsøya, Nordland Vega, Igerøy, Sandholmen,Porsangerfjord, Finnmark Leirpollen,Porsangerfjord, Fi Finnmark Berlevåg, Kongsfjord, Spitsbergen Barentsburg, Grønfjorden, Longyearbyen, Adventfjorden, Location Arøya,Store Helgero, Vestfold Saltsteinbåen,Jomfruland, Telemark Tromøy N.,Arendal Aust , Prestholmen,Grimstad, Aust Rossho Hordaland Bergen, Viksøyna, Romsdal Møre & Finnøy, Kvaløya, Nordlan Vega, Igerøy, NordlandVega, Ivarsbraken, Finnmark Berlevåg, Kongsfjord,

samples werenot

# Saccharina latissima Saccharina

ka_3

Site Code Ska_1 Ska_2 Ska_3 Ska_5 Ska_6 Nor_1 Nor_2 Nwg_1 Nwg_2 Bar_1 Bar_2 Bar_3# Gre_1 Gre_2# Site Code Ska_1 S Ska_4 Ska_5 Nor_1 Nor_2 Nwg_1 Nwg_2# Nwg_3# Bar_3*

sitesfor

Study

ak ak r rak r er ge Table 1: Table sites(N Total), Sample sizewith successfulamplification (NData), Date of collection andCollector (AME). Evankow M.Ann Saccharinalatissima Region Skag Sea North Sea Norwegian Sea Barents GreenlandSea Laminariahyperborea Region Ska Sea North Sea Norwegian Sea Barents five than fewer with #Sites

Figure 2: Map of the sampling sites used in this study. Red triangles represent Saccharina latissima and green circles represent Laminaria hyperborea. Sites are labeled with the corresponding number and region shown in Table 1. Map was created using ArcGIS.

Figure 3: Photos of the two study species as kelp forests (left) and individuals (right) in Norway, Laminaria hyperborea (top) and Saccharina latissima (bottom). Letters identify different morphological features, B: blade, S: stipe, H: holdfast, and M: meristematic region. Photo sources: Camilla Fagerli, Maia Røst Kile, Janne Kim Gitmark, Norwegian Institute for Water Research (NIVA).

Sample collection and preservation

A total of 274 Saccharina latissima individuals were collected from 14 sites and 215 Laminaria hyperborea individuals from 10 sites between 2012 and 2014 (Table 1, Figure 2). All individuals were in the sporophytic, diploid phase of their life cycle. Divers from the Norwegian Institute for Water Research (NIVA) collected the majority of samples by clipping a section from the youngest tissue in the meristematic region of the blade (Figure 3) from individuals along an approximate 20 m zig-zag path between 6 and 10 m depth. In addition, I collected samples from several sites by wading, snorkeling and trawling. Samples of S. latissima were collected from Spitsbergen (Gre_1) while wading along a rocky beach at 1 m depth and from Hordaland (Nor_2) while snorkeling along a 20 m stretch of rocky beach at 2 m depth. Trawled samples of S. latissima were obtained from Spitsbergen (Gre_2) between 17 and 5 m depth and L. hyperborea from Hordaland (Nor_2) at 6 to 9 m depth. All samples collected in 2012 were stored in ethanol at room temperature, and samples collected in the following years were stored in silica gel at room temperature.

DNA extraction

Genomic DNA was extracted from 2 to 10 mg of dried tissue with the cetyltrimethyl ammonium bromide (CTAB) protocol developed for plants (Murray & Thompson 1980), with modifications for brown algae (Hoarau et al. 2007b, Coyer et al. 2009), and eluted into 100 µl AE buffer (QIAGEN, Hilden, Germany). In addition, samples used in the initial testing of microsatellites were extracted with the DNeasy Plant Mini Kit (QIAGEN) with modifications from Snirc et al. (2010). The samples preserved in ethanol were freeze-dried prior to extraction. To test the quality of extracted DNA, a selection of samples from each extraction plate was loaded onto a 1% TAE agarose gel stained with GelRed (VWR, Radnor, Pennsylvania, USA). These samples were also tested in standard PCR reactions with ITS1 primers shown to amplify kelp DNA (Lane et al. 2006). Samples were diluted 10x with Milli-Q water before PCR amplification.

Microsatellite analyses

A total of 68 microsatellite markers developed for Laminaria and Saccharina species (Billot et al. 1998, Wang et al. 2011, Liu et al. 2012, Coelho et al. 2014, Zhang et al. 2014) were tested for cross-amplification potential with at least two individuals of S. latissima and L. hyperborea (Appendix: Tables A1-A4). Successful cross-amplification was evaluated on a gel and markers with clear bands were run on the ABI sequencer, described below. Robuchon et al. (2014) demonstrated that eight of these markers cross-amplify and are polymorphic in L. hyperborea along the coast of France (Appendix, Table A3).

Primers were ordered from Integrated DNA Technologies (Coralville, Iowa, USA) with an additional sequence of complementary DNA on the forward primers to anneal during PCR to fluorescent M13 tails (Schuelke 2000) to maximize testing efficiency and reduce costs. M13 tails (5´-TGTAAAACGACGGCCAGT-3´) were fluorescently labeled with PET (red), NED (yellow) or VIC (green) from Life Technologies (Thermo Fisher Scientific Inc., Waltham, MA, USA) standard dye set or with 6-FAM (blue) from Integrated DNA Technologies.

The markers were amplified using a Mastercycler nexus (Eppendorf, Hamburg, Germany) in 5 µl reactions, including 2.5 µl 2x Multiplex Master Mix (QIAGEN) with HotStarTaq DNA polymerase, 0.08 µl forward primer (5 µM) with fluorescently labeled M13 tail, 0.33 µl reverse primer (5 µM), 0.33 µl fluorescent-labeled M13 tail (5 µM), 0.75 µl Milli-Q water and 1 µl 10x diluted template DNA. The PCR conditions included an initial denaturation step at 95°C for 15 min and two rounds of cycles: 30 cycles of denaturation at 94°C for 30 sec, annealing at 50 or 55°C for 45 sec, and extension at 72°C for 45 sec, followed by seven cycles of denaturation at 95°C for 30 sec, annealing at 53°C for 45 sec, and extension at 73°C for 45 sec. The cycles were followed by an extension at 72°C for 20 minutes and a 10°C hold.

Fragments were separated by capillary electrophoresis using an ABI-3130 sequencer (Applied Biosystems, Waltham, Massachusetts, USA) at the Natural History Museum, University of Oslo. The PCR products were pooled according to Table 2 and 1 µl was mixed with 10.5 µl of HiDi formamide (Life Technologies, Carlsbad, California, USA) and 0.5 µL of GeneScan 500 LIZ (Applied Biosystems) size standard. Peaks were scored manually using GENEMAPPER 4.0 (Applied Biosystems).

Table 2: Final selection of three markers for Saccharina latissima and 11 markers for Laminaria hyperborea. Table includes: marker name, annealing temperature (Ta), group of markers pooled on the ABI sequencer, fluorescent dye used with M13 tail, final lengths detected, number (#) of alleles detected in the final data sets and markers developed as expressed sequence tag (EST), yes (Y) or no (N). All 11 L. hyperborea markers were used for a data set including three of the four geographical regions. Markers labeled with ‘*’ were used in the second data set including all regions. These markers were also used in a previous study with by Robuchon et al. (2014). Fragment lengths are corrected for the 18 bp of the M13 sequence and A overhang (total of -19 base pairs) Schuelke (2000).

Saccharina latissima Pooled Fluorescent Allele size # of Marker T (°C) Developed by: EST a on ABI Dye range alleles CS12 50 A FAM 215 - 277 17 Wang et al. 2011 Y CS13 50 A VIC 225 - 312 18 Wang et al. 2011 Y SSR261 50 A NED or PET 198 - 212 8 Zhang et al. 2014 N

Laminaria hyperborea Pooled Fluorescent Allele size # of Marker T (°C) Developed by: EST a on ABI Dye range alleles CS20 50 A PET 255 - 262 3 Wang et al. 2011 Y CS34 50 A VIC 229 - 231 3 Wang et al. 2011 Y LD3 50 A NED 259 - 262 2 Liu et al. 2012 Y LD6 50 A FAM 460 - 521 8 Liu et al. 2012 Y Ld-148* 55 B PET 204 - 244 6 Billot et al. 1998 N Ld-158* 55 B NED 203 - 226 3 Billot et al. 1998 N Ld-167* 55 B VIC 131 - 164 7 Billot et al. 1998 N LOL-24* 55 B FAM 160 - 171 4 Coelho et al. 2014 N LOL-15* 55 C FAM 184 - 225 5 Coelho et al. 2014 N LOL-23* 55 C PET 268 - 299 7 Coelho et al. 2014 N LOL-28* 55 C NED 199 - 274 9 Coelho et al. 2014 N

Markers names in original papers: Ld2/148, Ld2/158, Ld2/167, LolVVIV-15, LolVVIV-23, LolVVIV-24, LolVVIV-28

Data analyses

Assumptions of genetic analyses

Microsatellites should fulfill basic assumptions before they can be used for genetic common population genetic analyses. The assumptions of Hardy-Weinberg Equilibrium (HWE), linkage disequilibrium and neutrality of markers were evaluated before testing the main hypotheses of this thesis. The programs CONVERT (version 1.31, Glaubitz 2004) and PGDSpider (version 2.0.8.2, Lischer & Excoffier 2012) were used to create various input files.

Based on initial inspection of the data with a principal coordinates analysis (PCoA) in GenAlEx 6.5 (Peakall & Smouse 2006, 2012) and a STRUCTURE 2.3.3 (Pritchard et al. 2000) analysis, several outliers were detected. Eight samples were removed from the L. hyperborea data set that were potentially another species, as well as one sample that may be a hybrid (See Outlier section in Appendix, Figure A1, A2).

Significant deviations from HWE and linkage disequilibrium were tested within sites and markers using GENEPOP 4.3 (Rousset 2008). GENEPOP estimates exact P-values with a Markov chain algorithm (Guo & Thompson, 1992). The Markov chain parameters were 10000 dememorizations, 1000 batches with 10000 iterations per batch. Sequential Bonferroni were applied to avoid Type I errors that are associated with multiple testing (Rice 1989).

The programs BAYESCAN and LOSITAN were used to test the neutrality of markers. BAYESCAN 2.1 (Foll & Gaggiotti 2008) is based on a hierarchical Bayesian method (Beaumont & Balding 2004), which uses a reversible-jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the posterior probability (>0.50 is significant) that each marker is a candidate for selection. The program was used with 20 pilot runs, 50000 burn in, thinning interval of 10, and 100000 total iterations. Positive alpha values indicate candidates for diversifying selection and negative alpha values indicate candidates for balancing or purifying selection.

The program LOSITAN (Beaumont & Nichols 1996, Antao et al. 2008) was also used to detect candidate markers for selection. LOSITAN is based on an FST outlier approach, which uses coalescent simulations to generate a null distribution of FST values. The program was set to calculate a neutral mean FST and force mean FST and run using four CPU cores, 50000 simulations, and a sampling size of 24. Markers with unusually high FST values are candidates for directional selection and markers with unusually low FST values are candidates for stabilizing selection (Glover et al. 2012).

Genetic diversity

Three common measures of genetic diversity including allelic richness, expected heterozygosity and observed heterozygosity were used to compare genetic diversity across sites and regions. The program HP-Rare (v. June-6-2006, Kalinowski 2005) was used to calculate the observed number of alleles (NO) and sample size corrected (rarefaction method) allelic richness (NA), and GenAlEx 6.5 (Peakall & Smouse 2006, 2012) was used to calculate observed heterozygosity (HO) and unbiased expected heterozygosity (HE). Analysis of variance (ANOVA) and student t-tests were used to test the null hypotheses that genetic diversity does not vary between sites or regions and that genetic diversity decreases as latitude increases. Assumptions of normality and equality of variances were tested prior to running ANOVA and t-tests.

In addition, regional genetic diversity of L. hyperborea in Norway was compared to the existing genetic data from France, reported by Robuchon et al. (2014). The data was downloaded from the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.pv28c). The same seven markers were compared in both data sets and corrected for sample size.

Genetic differentiation

Genetic differentiation was assessed using analysis of molecular variance (AMOVA), exact G tests and isolation-by-distance (IBD) correlations.

AMOVA (Excoffier et al. 1992, Michalakis & Excoffir 1996) was assesed in GenAlEx 6.5 (Peakall & Smouse 2006, 2012) to test the null hypotheses that there is no significant genetic differentiation between different sites and regions and between sites within regions. The program determines hierarchical partitioning of genetic variation within individuals, among individuals, among sites and among regions. These observations are statistically tested with random permutations.

The program GENEPOP 4.3 (Rousset 2008) was used to assess genetic differentiation for all pairs of sites with exact G tests to further test the null hypothesis that there is no significant genetic differentiation between sites. GENEPOP estimates exact P-values for G tests with a Markov chain algorithm (Guo & Thompson, 1992). The Markov chain parameters were 10000 dememorizations, 1000 batches with 10000 iterations per batch.

To test the null hypothesis that genetic distance does not increase with geographic distance,

FST values were first calculated in GENETIX 4.05.2 (Belkhir et al. 2004) as a proxy used to infer genetic distances (Rousset 1997). Two matrices were created using these FST values

(Weir & Cockerham, 1984) with FST/(1-FST) and geographic distance between sites as input for the IBD analysis (Wright 1943, Slatkin 1993). Geographic distances were measured in Google Earth in kilometers using the shortest distance by sea between sites. Mantel tests with 1000 replicates were used (Manly, 1994) in the IBD web service v.3.23 (Jensen et al. 2005) to test for significance.

Genetic structure

Genetic structure was assessed with STRUCTURE, neighbor-joining trees, and factorial correspondence analysis. These analyses were used to test the null hypothesis that there is one genetic cluster and no significant population structure.

To investigate the number of populations that best fit the data, the Bayesian framework of STRUCTURE 2.3.3 (Pritchard et al. 2000) was used on Lifeportal at the University of Oslo. The number of populations or clusters (K) were tested from K = 1 to K= 10 with 10 iterations, a burn-in period of 500000, and a Markov chain Monte Carlo (MCMC) of 3000000. The

STRUCTURE data was analyzed using STRUCTURE HARVESTER (Earl & vonHoldt, 2012) and CLUMPAK (Kopelman et al. 2015). The most likely value of K was determined using the mean estimated probability of the data plot (L(K)(mean+-SD)) and the DeltaK plot. Delta K is an ad hoc statistic (Evanno et al. 2005) based on the rate of change of P (X/K) between different values of K.

To create a neighbor-joining tree, FST and FIS values were calculated in GENETIX 4.05.2 (Belkhir et al. 2004) and imported into the PHYLIP software package (Felsenstein, 1994). Within PHYLIP, Cavalli-Sforza and Edwards chord distance (Cavalli-Sforza & Edwards, 1967) were used in SEQBOOT for bootstrap resampling, GENEDIST for computing genetic distances, NEIGHBOR for constructing the trees, and CONSENS for constructing the consensus tree with 1000 replicates.

The program GENETIX 4.05.2 was used to analyze the microsatellite loci as active elements in a factorial correspondence analysis (FCA). FCA is a multivariate analysis that projects all individuals in a space defined by the individual components (Benzécri 1973). With genetic data, the components of individuals are determined by their allele scores as nonparametric data and number of copies of the allele (heterozygous or homozygous).

Results

Microsatellite selection

Saccharina latissima

Using S. latissima DNA template, 47 of the 68 markers (69%) developed for other species successfully amplified with visible bands on an agarose gel (Tables A1-A4 in Appendix). Of these, 34 markers (70%) displayed multiple bands on the gels. Twenty markers that appeared to have one band were further tested using capillary electrophoresis on an ABI sequencer. Of these, four constituted polymorphic loci (CS12, CS13, SSR261, SSR278) and three constituted well-supported monomorphic loci (CS07, CS11, SSR052) with over 100 individual samples. Four more markers were monomorphic but were tested only on small sample sizes and require additional testing (LOL-17, SSR032, SSR155, SSR176). Although polymorphic, SSR278 was excluded from further analysis due to amplification problems.

Of 275 total S. latissima individuals, 203 (74%) amplified successfully for at least one of the three polymorphic markers (CS12, CS13, SSR261). Of these, 149 individuals with 100% amplification success were used in subsequent data analysis. See Table 1 for specific numbers of sample size per site (N data) used for the data analysis.

Laminaria hyperborea

Using L. hyperborea DNA template, 45 markers of 68 markers (63%) successfully amplified with visible bands on an agarose gel (Tables A1-A4 in Appendix). Of these, 18 (42%) displayed multiple bands on the gels. Of 24 markers that were tested further with fragment analysis, 12 displayed polymorphic loci, including eight markers that were reported as population genetic markers for L. hyperborea by Robuchon et al. (2014). I discovered four additional polymorphic markers (CS20, CS34, LD3, LD6). Four markers (CS27, CS47, LD4, LD9) were monomorphic when tested in more than 10 individuals. An additional four markers were monomorphic but tested only on small sample sizes and require further investigation (CS29, SSR094, SSR163, SSR165). LOL-17 was excluded from further analysis due to scoring problems with multiple bands.

Of 215 total L. hyperborea individuals, 196 (79%) amplified successfully for at least one of 11 polymorphic markers. Of these, 102 individuals with at least nine of 11 loci (81%) were used in a data set including nine sites. In order to include the northernmost site in the Barents Sea (Bar_3), a second data set was constructed using 104 individuals with at least five of seven loci (71%). See Table 1 for specific numbers of sample size per site (N data) used for the data analysis.

Assumptions of genetic analyses

The assumptions of Hardy-Weinberg Equilibrium (HWE), linkage disequilibrium and neutrality of markers were evaluated before testing the main hypotheses of this thesis.

Saccharina latissima

The final S. latissima data set included 149 individuals from 14 sites amplified with three microsatellite markers, here-after referred to as loci. For site-level analysis, two sites (Bar_3 & Gre_2) were removed due to low sample sizes, for a total of 146 individuals from 12 sites. There was no missing data in this data set.

Overall, the majority of sites were in HWE. Before corrections, six of 36 site and locus pairwise comparisons deviated significantly from HWE. Only one site and locus pair (Bar_2 & SSR261) deviated significantly also after sequential Bonferroni corrections (Appendix, Table A5). None of the loci were significant candidates of selection using LOSITAN (P > 0.05) or BAYESCAN (prob. < 0.50). However, one loci pair, CS13 & CS12, significantly deviated from linkage disequilibrium. Of 36 loci pairs (three loci pairs in 12 sites), nine significantly deviated from linkage equilibrium. The loci pair CS13 and CS12 remained out of linkage equilibrium at four sites after sequential Bonferroni corrections. Both loci were included in the following analyses due to the already low marker count.

Laminaria hyperborea

There were two final L. hyperborea data sets, one with more markers (loci) and the second with more sites. The first data set included all of the 11 microsatellite markers and 93 individuals from nine sites. For site-level analysis, I removed three sites with low sample sizes for a total of 87 individuals from six sites. The second data set included seven microsatellite markers and the northernmost site, Bar_3, for a total of 104 individuals from 10 sites. For site-level analysis, three sites were removed with low samples sizes for a total of 96 individuals from seven sites. Missing data did not exceed 28% per individual and 18% for markers in both data sets.

Overall, the majority of sites were in HWE, with the exception of two site and loci pairs. In total, 29 of 66 site and locus pairwise comparisons were monomorphic and could not be tested for HWE. Of the remaining 37 comparisons, six pairs significantly deviated from HWE, with one pair (Ld167 & Nwg_1) that remained significant after sequential Bonferroni corrections. The same site significantly deviated from HWE after corrections also in the second data set, in addition to Ld148 & Bar_3 (Appendix, Table A6).

In both data sets, there was no significant departures from linkage equilibrium between loci pairs after sequential Bonferroni corrections (P > 0.05). In total, 10 of 147 loci pairs significantly deviated from linkage equilibrium before corrections.

BAYESCAN and LOSITAN identified several markers as candidates for selection. BAYESCAN identified two markers as candidates for diversifying selection in the first data set: LOL24 (prob = 0.989, alpha = 1.413) and LOL15 (prob = 0.647, alpha = 0.780). Analysis of the second data set also discovered LOL24 as a candidate for diversifying selection (prob = 0.965, alpha = 1.213). LOSITAN identified five different markers as candidates for selection from the first data set using the Stepwise mutation model (SMM), four candidates for balancing selection: Ld148 (FST = 0.0126, P = 0.00035), LOL28 (FST = 0.139, P = 0.0152), CS20 (FST = -0.01723, P = 0.000), LD6 (FST = 0.115, P = 0.00449) and one candidate for positive selection: LD3 (FST = 0.226 , P = 0.999). LOSITAN with the infinite alleles model (IAM) also identified Ld148, CS20 and LD6 as candidates for balancing selection. Analysis with both models and the second data set also found that Ld148 is a candidate for balancing selection. Although BAYESCAN and LOSITAN identified these candidate markers for selection, the programs did not identify the same makers. (See Appendix: Candidate Loci for selection, Figure A3).

6.0 * * 5.0

4.0

3.0 *

2.0 AllelicRichness 1.0

0.0

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

ExpectedHeterozygosity 0.1 0.0

1.0

0.8

0.6

0.4

0.2

ObservedHeterozygosity 0.0

Figure 4: Average allelic richness (NA), expected heterozygosity, (HE) and observed heterozygosity (HO) of 12 Saccharina latissima sites based on three microsatellite markers. See Table 1 for site information. * represents the sites that caused a significant effect of site on allelic richness in an ANOVA test (P = 0.008). ANOVA tests were not significant for expected and observed heterozygosity (P > 0.05). Mean ± 1SE.

Genetic diversity

Three common measures of genetic diversity including allelic richness, expected heterozygosity and observed heterozygosity were used to compare genetic diversity across sites and regions. Analysis of variance (ANOVA) and student t-tests were used to test the null hypotheses that genetic diversity does not vary between sites or regions and that genetic diversity decreases as latitude increases.

Saccharina latissima

The expected and observed heterozygosity results accepted the two null hypotheses that genetic diversity does not vary between sites or regions (Figure 4). This was supported by allelic richness at the regional level, but the hypothesis was rejected at site level. The results reject the third null hypothesis that genetic diversity will decrease with increasing latitude. The data corresponding to these tests are presented in the Appendix in Table A5 and Table A7.

The number of alleles ranged from eight to 18 per locus (Table 2). The allelic richness per site (corrected for five individuals) ranged from 2.12 to 4.77 (Appendix, Table A5). An ANOVA test showed significant effect of site on allelic richness (df = 11, F = 3.222, P = 0.008). Tukey’s test showed that one site (Nor_2) had significantly lower allelic richness and two sites had significantly higher allelic richness (Nwg_2 & Gre_1) (Figure 4). When one of these sites was removed from the analysis (Nor_2), there was no longer a significant effect of site on allelic richness. ANOVA tests showed that there were no significant effects of site on expected heterozygosity (HE) and observed heterozygosity (HO) (Figure 4, P > 0.05).

The allelic richness per region (corrected for 10 individuals) ranged from 4.08 to 6.00 (Appendix, Table A5). An ANOVA test showed there was no significant differences between regions in allelic richness, expected heterozygosity (HE), or observed heterozygosity (HO) (P > 0.05, Appendix Table A7).

Laminaria hyperborea

All three measures of allelic richness and expected and observed heterozygosity suggested genetic diversity significantly varies between sites and regions, rejecting the null hypotheses that there are no differences. These results also reject the third null hypothesis that genetic diversity decreases with an increase in latitude. When the French regions from Robuchon et al. (2014) were included, the third null hypothesis that genetic diversity decreases with an increase in latitude was no longer rejected (Figure 6). The data corresponding to these tests are presented in the Appendix in Table A6 and Table A8.

The number of alleles ranged from two to nine per locus (Table 2). The allelic richness per site (corrected for seven individuals) ranged from 1.58 to 2.78 in the first data set with more markers and 1.44 to 2.96 in the second data set with more sites (Appendix, Table A6). ANOVA tests found significant effects of site in both the first dataset (df = 5, F = 2.6334, P = 0.0368) and second dataset (df = 6, F = 4.12, P = 0.00243). At the regional level (Figure 4), the allelic richness (corrected for 12 individuals) ranged from 1.84 to 3.14 in the first dataset and 1.61 to 2.96 in the second dataset. T-tests showed that the Skagerrak region (Ska) had significantly lower allelic richness than the Norwegian Sea (Nwg) in the first data set (df = 20, t = 2.79, P = 0.00564) and than all three regions in the second data set (Nor (df = 10, t = 2.4117, P = 0.0183), Nwg (df = 11, t = 3.370, P = 0.00313) & Bar (df = 12, t = 3.471, P = 0.00231)), without significant differences between the other three regions (P > 0.05).

ANOVA tests showed that expected and observed heterozygosity varied significantly with sites in both datasets (P < 0.05). At the regional level (Figure 5), t-tests showed that the average expected heterozygosity in the Skagerrak region was significantly less than the other regions in both data sets (P < 0.05) and there were not significant differences between the other regions (P > 0.05). The observed heterozygosity showed a different trend. T-tests showed that the observed heterozygosity in the Skagerrak region was significantly lower than in the North Sea and Norwegian Sea (P < 0.05), however there was no detectable difference between the Skagerrak region and Barents Sea (P > 0.05). Additional results are presented in the appendix (Table A8).

3.5

3.0 2.5 * 2.0

1.5 1.0 AllelicRichness 0.5 0.0 SKA NOR NWG BAR

1.0

0.8

0.6

0.4 * 0.2

ExpectedHeterozygosity 0.0 SKA NOR NWG BAR

1.0

0.8

0.6 b b 0.4 a, b 0.2 a ObservedHeterozygosity 0.0 SKA NOR NWG BAR

Figure 5: Average allelic richness (NA), expected heterozygosity, (HE) and observed heterozygosity (HO) of four Laminaria hyperborea regions based on seven microsatellite markers. See Table 1 for site information. * represents a region that is significantly different the other regions based on t-tests (P < 0.05). Letters (a & b) represent which regions are significantly different in the observed heterozygosity, regions with shared letters are not significantly different (P > 0.05). Mean ± 1SE.

5.0 4.5 4.0 3.5 3.0 2.5 2.0

AllelicRichness 1.5 1.0 0.5 0.0 SBR IRS MBY SML SKA NOR NWG BAR

Figure 6: Average allelic richness of eight Laminaria hyperborea regions based on seven microsatellite markers. Red indicates regions in France from data collected by Robuchon et al. (2014). Blue are Norwegian regions. Regions are listed in order of increasing latitude, shown on the map. See Table 1 for site information in Norway. French regions correspond to Southern Brittany (SBR), Iroise Sea (IRS), Morlaix Bay (MoB), St. Malo Bay (SML). Regions were compared using the same seven microsatellite markers corrected for sample size. (mean ± 1SE).

Site differentiation

Genetic differentiation between sampling sites was assessed using analysis of molecular variance (AMOVA), exact G tests and isolation-by-distance (IBD) correlations. AMOVA and G tests were used to test the null hypotheses that there is no significant genetic differentiation between different sites and regions and between sites within regions. Mantel tests were used to test the null hypothesis that genetic distance does not increase with geographic distance

Saccharina latissima

The G tests, analysis of molecular variance (AMOVA) and isolation by distance (IBD) correlations indicate that there is significant differentiation between sites and regions, rejecting the null hypotheses that there is no differentiation. The G tests and AMOVA support differentiation between sites within regions, rejecting the null hypothesis that there is no genetic differentiation between sites within regions. Further, the IBD correlations support increasing genetic differentiation between sites with increasing distance, rejecting the null hypothesis that genetic distance does not increase with geographic distance.

The G tests determined that all sites except for seven site pairs were not significantly different after sequential Bonferroni corrections. The significant pairwise comparisons are presented in Table 3.

The AMOVA revealed 72% of genetic variation exists within sites, 7% among sites and 21% among regions (Figure 7). FRT = 0.213 (P = 0.001) for variation among regions divided by the total variation, FSR = 0.082 (P = 0.001) for variation among sites divided by variation within sites and among sites, FST 0.277 (P = 0.001) for variation among sites and regions divided by total variation.

Mantel tests showed that there was a significant overall relationship between genetic and geographic distance for S. latissima with a pairwise comparison of 12 sites (Figure 8) (Z = 33112, r = 0.642, r2 = 0.412, P < 0.0010). The trend was still significant when the only site that is not on the Norwegian mainland (Gre_1) was removed (Z = 26181.27, r = 0.758, r2 = 0.574, P = 0.002). Isolation by distance was also significant with only sites along the Norwegian West coast (Nor, Nwg and Bar, (Z = 5741.11, r= 0.683, r2=0.467, P = 0.008). However, mantal tests showed IBD was not significant between sites within the Skagerrak region (Z = 91.4, r = 0.222, r2 = 0.0491, P = 0.204).

Among Regions 21% Within Among Sites Sites 72% 7%

Figure 7: Analysis of molecular variance (AMOVA) of Saccharina latissima from 12 sites with three markers. The pie chart shows the distribution of genetic variation at different levels: within sites, among sites and among regions.

Table 3: Matrix of pairwise genetic distances measured by FST values (lower half) and geographic distance in km (upper half) between 12 sites of Saccharina latissima based on three microsatellite markers. Significant genetic differentiation with pairwise exact G tests are indicated with bold FST values.

Ska_1 Ska_2 Ska_3 Ska_5 Ska_6 Nor_1 Nor_2 Nwg_1 Nwg_2 Bar_1 Bar_2 Gre_1 Ska_1 - 5 18 114 157 415 587 965 1404 2436 2438 2726 Ska_2 0.0108 - 20 116 159 417 589 967 1406 2438 2438 2728 Ska_3 0.0701 0.1147 - 96 139 397 569 947 1386 2418 2418 2708 Ska_5 0.0522 0.1398 0.1706 - 43 301 473 851 1290 2322 2322 2612 Ska_6 0.0125 0.1231 0.0858 0.0268 - 258 430 808 1247 2279 2279 2569 Nor_1 0.0258 0.0801 0.0750 0.0524 0.0236 - 172 550 989 2021 2021 2311 Nor_2 0.3033 0.3794 0.1321 0.3491 0.2917 0.2510 - 378 817 1849 1849 2139 Nwg_1 0.2586 0.2377 0.1396 0.3567 0.2877 0.2388 0.1309 - 439 1471 1471 1761 Nwg_2 0.1973 0.1911 0.0791 0.2870 0.2186 0.1971 0.1181 0.0284 - 1032 1032 1406 Bar_1 0.4040 0.4029 0.3917 0.4915 0.4372 0.4182 0.3880 0.1539 0.1028 - 9 1025 Bar_2 0.3527 0.3189 0.2971 0.4383 0.3861 0.3553 0.3009 0.0864 0.0277 0.0018 - 1025 Gre_1 0.2652 0.2161 0.1786 0.3699 0.3063 0.2700 0.2438 0.0559 0.0251 0.1094 0.0564 -

Figure 8: Isolation by distance correlation of log transformed pairwise genetic distances (FST/(1-FST) and geographic distances (km) between 12 sites of Saccharina latissima based on allele frequency data from three microsatellite markers.

Laminaria hyperborea

The G tests determined that all sites, except for sites within the Skagerrak region, were not significantly different after sequential Bonferroni corrections for both datasets. Results for the second data set are shown in Table 4.

The AMOVA for the first dataset revealed 73% of variation exists among within sites, 2% among sites and 25% among regions (Figure 9). FRT = 0.246 (P = 0.001) for variation among regions divided by the total variation, FSR = 0.033 (P = 0.003) for variation among sites divided by variation within sites and among sites, FST 0.271 (P = 0.001) for variation among sites and regions divided by total variation. In the second dataset, 70% of variation exists within sites, 3% among sites and 27% among regions (Figure 9). FRT = 0.273 (P = 0.001) for variation among regions divided by the total variation, FSR = 0.039 (P = 0.005) for variation among sites divided by variation within sites and among sites, FST 0.301 (P = 0.001) for variation among sites and regions divided by total variation.

Mantel tests showed a significant relationship between genetic distance and geographic distance for the pairwise comparison of six and seven L. hyperborea sites with the first and second dataset respectively (Figure 10) (Dataset 1: Z = 3028, r = 0.942, r2 = 0.888, P = 0.004; Dataset 2: variables log-transformed, Z = -40.0, r = 0.805, r2 = 0.649, P = 0.010). Mantel tests showed that IBD was not significant within the Skagerrak region (Z = 91.4, r = 0.222, r2 = 0.0491, P = 0.204), or along the West coast (Nor, Nwg & Bar regions) (Z = 5741, r = 0.683, r2=0.467, P = 0.008).

Among Among Regions Regions 25% 27%

Among Within Sites Within Sites 2% Sites Among 73% 70% Sites 3% First data set Second data set

Figure 9: Analysis of molecular variance of Laminaria hyperborea from six sites with 11 microsatellite markers (left) and seven sites with seven microsatellite markers (right). The pie charts show the distribution of genetic variation at different levels: within sites, among sites and among regions.

Table 4: Matrix of pairwise genetic distances measured FST values (lower half) and geographic distance in km (upper half) between seven sites of Laminaria hyperborea based on seven microsatellite markers (second data set). Significant pairwise exact G tests are indicated with bold FST values.

Ska_3 Ska_4 Ska_5 Nor_1 Nor_2 Nwg_1 Bar_3

Ska_3 - 63 96 397 569 947 2630 Ska_4 -0.0178 - 38 339 511 889 2572 Ska_5 -0.0067 -0.0209 - 301 473 851 2534 Nor_1 0.2431 0.2801 0.1622 - 172 550 2233 Nor_2 0.3062 0.3513 0.2350 0.0916 - 378 2061 Nwg_1 0.4937 0.5339 0.4295 0.3246 0.1777 - 1683

Bar_3 0.4384 0.5057 0.3363 0.1889 0.2289 0.3140 -

Figure 10: Isolation by distance correlation of log transformed pairwise genetic distances (FST/(1-FST) and geographic distances (km) between seven sites of Laminaria hyperborea based on allele frequency data from seven microsatellite markers.

Genetic structure

STRUCTURE analysis, neighbor-joining trees, and factorial correspondence analysis were used to investigate genetic structure. These analyses tested the null hypothesis that there is one genetic cluster and no significant population structure.

Saccharina latissima

The STRUCTURE, neighbor-joining tree, and factorial correspondence analyses suggest that there is significant genetic structure with more than one genetic cluster, rejecting the null hypothesis that there is no structure.

The STRUCTURE analysis showed a strong genetic split between the southern and northern sites (Figure 11). The split occurred in the middle of the North Sea region, with the Nor_1 site clustering together with the Skagerrak region, shown by the red group, and the Nor_2 site clustered together with the northern regions, shown by the blue group in Figure 11. The neighbor-joining tree (Figure 12) and factorial correspondence analysis (Figure 13) support the split between the southern and northern clusters. DeltaK indicated K = 2 as the most likely number of clusters for the STRUCTURE analysis (Appendix, Figure A3). A few individuals did not fit and were assigned to the other cluster from sites Ska_3, Nor_1, Nor_2 and Nwg_2. With four genetic clusters, the northern sites were separated into two genetic groups, the first including Nor_2, Nwg_1 and Nwg_2, which also clustered with the southern Ska_3 site, and second with the sites from the Barents Sea and Greenland Sea regions. This pattern of four genetic clusters was also supported by the factorial correspondence analysis (Figure 13). The site Ska_3 branched close to the northern group in the neighbor-joining tree, supporting the clustering shown by the STRUCTURE analysis. Further, the Greenland Sea region branched between the Barents Sea and Norwegian Sea region in Figure 12. This is supported by the relative placement of the Barents Sea individuals in the factorial correspondence analysis, shown by blue circles in Figure 13.

K = 2

K = 4

Figure 11: STRUCTURE analysis based on three microsatellite loci of Saccharina latissima from 14 sampling sites in Norway. Each horizontal bar represents an individual. The number of colors represents the number of distinct genetic clusters (K). The sample sites are identified along the bottom of the figure corresponding to Table 1. DeltaK predicted two clusters as best fit for the data and L(K) predicted up to seven clusters (Appendix, Figure A3).

Gre 2 Bar 2

Gre 1 Bar 1

93 74 71 Nwg 1 74 Bar 3

63 Nwg 2 74

Nor 2 96

Nor 1 71 Ska 3

67 Ska 4

100

Ska 2 Ska 5

Ska 1

Figure 12: Neighbor-joining unrooted tree based on three microsatellite loci of Saccharina latissima. The branches illustrate potential genetic relationships among 14 sites. Color coding corresponds to the two clusters identified with K = 2 in the STRUCTURE analysis (Figure). Shapes represent the five regions: square - Skagerrak (Ska), star - North Sea (Nor), triangle - Norwegian Sea (Nwg), diamond - Barents Sea (Bar) and circle - Greenland Sea (Gre). Bootstrap values are from 10000 resamplings.

100

-50

-100

-150

-200

Axis 2 (22.74 %) -250

-300

-350

-400 -5,000 0 5,000 10,000 15,000 Axis 1 (58.10 %)

Figure 13: Factorial correspondence analysis of Saccharina latissima individuals from five regions based on three microsatellite markers. Color coding corresponds to the the two clusters identified with K = 2 in the STRUCTURE analysis (Figure 11). Shapes represent the five regions: square - Skagerrak (Ska), star - North Sea (Nor), triangle - Norwegian Sea (Nwg), diamond - Barents Sea (Bar) and circle - Greenland Sea (Gre). Symbols are also used to represent regions in the neighbor-joining tree (Figure 12). Together, axis 1 and axis 2 explain 80.84% of the variation in the data set.

Laminaria hyperborea

The STRUCTURE, neighbor-joining tree, and factorial correspondence analyses suggest that there is significant genetic structure with three or four distinct genetic clusters, rejecting the null hypothesis that there is no structure.

In the STRUCTURE analysis not including the Barents Sea site (Figure 14) DeltaK indicated K = 3 as the most likely number of clusters (Appendix A3), with each of the three clusters largely corresponding to the Skagerrak, the North Sea and the Norwegian Sea regions respectively. With K = 2, the North Sea sites were clearly admixed between the red cluster characterizing the Skagerrak sites and the blue cluster characterizing the North Sea sites. Nor_1 had a higher allocation to the Skagerrak cluster and Nor_2 had a higher allocation to the North Sea cluster. This intermediate position of the North Sea was supported by the neighbor-joining tree (Figure 16) and the factorial correspondence analysis (Figure 17), in which the North Sea is between the Skagerrak and Norwegian regions.

When the Barents Sea site was added to the STRUCTURE analysis, it clustered with the purple North Sea cluster (Results not shown). The neighbor-joining tree supported this trend, with the Barents Sea branching in between the North Sea and Norwegian Sea regions (Figure 16). The factorial correspondence analysis showed the Barents Sea region is clearly separate from the other three regions (Figure 17).

The Skagerrak region, shown in red, is tightly clustered together in the factorial correspondence analysis (Figure 17). In comparison, the other three regions are more spread out and take up roughly the same size clusters. This trend supports the genetic diversity results, with reduced genetic diversity in the Skagerrak region, compared to the other three regions (Figure 5).

Figure 14: STRUCTURE analysis based on 11 microsatellite loci of Lamininaria hyperborea from eight sampling sites in Norway. Each horizontal bar represents an individual. The number of colors represents the number of clusters (K). The number of colors represents the number of distinct genetic clusters (K). DeltaK and L(K) predicted three clusters as best fit for the data Appendix, Figure A3).

Figure 15: STRUCTURE analysis based on seven microsatellite loci of Lamininaria hyperborea from nine sites in Norway. Each horizontal bar represents an individual. The number of colors represents the number of clusters (K). The number of colors represents the number of distinct genetic clusters (K). L(K) and DeltaK calculated by STRUCTURE HARESTOR predict the best K fit for the data. Here, L(K) could be three or four and Delta K is three. L(K) predicted four clusters as best fit for the data (Appendix, Figure A3).

Nwg 1

Nwg 2

Bar 3 74*

70* Nor 2 62 Nor 1

99**

70* Ska 1

61 Ska 4

Ska 5

Ska 3

Figure 16: Neighbor-joining unrooted tree of Laminaria hyperborea based on seven microsatellite loci and corresponding location of each region in Norway. The branches illustrate potential genetic relationships among nine sites. Colors coding corresponds to the four clusters identified with K = 4 in the STRUCTURE analysis (Figure 15). Bootstrap values are from 1000 resamplings. * split is supported by ≥70% permutations, ** split is supported by ≥95% permuations.

100 80 60 40 20 0 -20 -40 -60 -80

Axis 2 (28.32 %) -100 -120 -140 -160 -180 -5,000 0 5,000 10,000 15,000 Axis 1 (47.60 %)

Figure 17: Factorial correspondence analysis of Laminaria hyperborea individuals from nine sites based on seven markers. Colors coding corresponds to the four clusters identified with K =4 in the STRUCTURE analysis (Figure 15). Regions: red –Skagerrak (Ska), purple - North Sea (Nor), blue - Norwegian Sea (Nwg), and yellow – Barents Sea (Bar). Symbols are also used to represent regions in in the neighbor-joining tree (Figure 16). Together, axis 1 and axis 2 explain 75.92% of the variation in the data set.

Discussion

Surveys of genetic diversity provide a platform for monitoring and understanding evolutionary processes of marine species (Selkoe et al. 2008). This study provides an initial survey of two foundation kelp species in Norway, Saccharina latissima and Laminaria hyperborea. All null hypotheses were rejected, except for one- the hypothesis that the regional diversity of Saccharina latissima does not vary among regions. Overall, the results suggest that there is significant genetic structure, differentiation, and varying genetic diversity of S. latissima and L. hyperborea along the Norwegian coast.

One main finding is the level of genetic diversity varied between sites, however, diversity did not decrease with increasing latitude along the Norwegian coast (Figures 4 & 5), as expected by range expansion following glaciation (Hewitt 2004, Hampe & Petit 2005, Guo 2012). This trend changed for L. hyperborea when samples from the coast of France were included from Robuchon et al. (2014), with the greatest genetic diversity in the lowest latitudes (Figure 6). However, the northernmost sites of L. hyperborea still had relatively high levels of genetic diversity, including unique alleles. In comparison, there were no detectable differences in genetic diversity of S. latissima between different regions along the Norwegian coast. The distribution of genetic variation was consistent with long-lived species (Hamrick & Godt 1996), with the majority of genetic variation within sites and among regions, opposed to among sites (Figures 7 & 9). Further, there was genetic differentiation between sites, with greater differentiation between sites from different regions (Figures 8 & 10), following the isolation by distance model (Wright 1943, Rousset 1997). Genetic structure corresponded to regions (Figures 11 & 15), with some exceptions that are discussed later.

The Skagerrak: one region, two patterns of diversity

Laminaria hyperborea displayed remarkably low genetic diversity in the Skagerrak region for all three metrics (allelic richness, expected and observed heterozygosity, Figure 5). In addition, the Skagerrak sites had a high prevalence of monomorphic markers (Table A7), and the genetic structure analyses showed strong support for a split between the Skagerrak region with the other regions (Figure 15 & 16), suggesting that the Skagerrak region is isolated (Höglund 2009). This is similar to what was found by Robuchon et al. (2014) with L. hyperborea in one disconnected region in France that had lower genetic diversity than the other west coast regions.

In comparison, Saccharina latissima had relatively high diversity in the Skagerrak region (Figure 4). This finding is substantial, given the large decline in kelp forests in this region (Moy & Christie 2012). Sogn Andersen (2013b) suggested that the interbreeding potential for S. latissima could be high in the Skagerrak region, based on the ability of zoospores to survive in currents for several days (Kain 1975) and reports of synchronized life cycles in kelp forests from different areas along the coast. Fredriksen et al. (1995) found that L. hyperborea can disperse at least 200 meters. The longer range dispersal hypothesis is supported in this study

35 by the structure analyses, in which the southern site in the North Sea region clustered with the Skagerrak sites, implying the potential for connectivity along the southern coastline of Norway (Figure 11). Additionally, some individuals from one site in the Skagerrak region clustered with the northern regions, indicating that the site is highly genetically differentiated from other sites in the Skagerrak region. This differentiation could be explained by gene flow from unsampled sites in other areas of the Skagerrak region, such as the Swedish and Danish coastlines.

There are several reasons why L. hyperborea may have low diversity in the Skagerrak region. First, L. hyperborea may be more fragmented than S. latissima with lower densities at sites (Norderhaug et al. 2011) and fewer nearby populations as inputs of genetic material. Laminaria hyperborea may also be stressed. A study has found that L. hyperborea grows more slowly in sheltered areas than exposed areas (Sjøtun et al. 1993), which delays spore production (Kain 1975), reducing reproductive fitness. Increased wave exposure has a positive effect on L. hyperborea growth by reducing the boundary layer of water surrounding the kelp, thereby increasing the circulation of available nutrients (Wheeler 1988). The largest L. hyperborea are in western Norway, in exposed regions, where there is enough light and nutrients and an ideal temperature range to allow maximum growth (Rinde & Sjøtun 2005). In the Skagerrak region, kelp are exposed to higher temperatures, driven to shallower depths by limited light penetration into the water column (Sogn Andersen 2013c), and disadvantaged by day length during the growing season in the Spring compared to northern populations (Sjøtun & Schoschina 2002). As a result, L. hyperborea may produce fewer spores, thereby limiting interbreeding, which would otherwise increase diversity.

In a comparison of two sister kelp species, Robuchon et al. (2014) found that L. digitata populations were more genetically differentiated than those of L. hyperborea, most likely due to the smaller size of their distribution band in the intertidal zone. Laminaria digitata grows in a narrow band from the surface to 1 m depth along the Brittany coastline, whereas L. hyperborea grows in a wider band from 1 m to 30 m depth. Although L. hyperborea and S. latissima grow in thinner bands in the Skagerrak than the North Sea region (Norderhaug et al. 2011), there was less differentiation between sites within the Skagerrak region. This may be a result of high gene flow within the Skagerrak region, as could be expected by the circular currents within the region and potential synchrony of life cycles. However, L. hyperborea may instead have reduced site differentiation as a result of overall low levels of genetic diversity. It is challenging to detect gene flow in monomorphic populations.

The low diversity of L. hyperborea in the Skagerrak region suggests that this population may have limited fitness and resilience in a changing environment. However, there is potentially high connectivity of kelp within the Skagerrak region, which implies management should focus on keeping healthy ecosystems intact. There is already extensive coastal monitoring in the Skagerrak region (Norderhaug et al. 2011). This study supports the continued monitoring of kelp populations, especially L. hyperborea combined with increased efforts to improve water quality and reduce sedimentation, which have been shown to reduce seaweed recruitment (Schiel et al. 2006, Dieman et al. 2012).

The North: an oasis of unique genetic diversity

For both species genetic diversity was highest in the Northern regions of their distributions. For S. latissima, the highest level of diversity was found in in the Greenland Sea, off the coast of Spitsbergen (Figure 4). For L. hyperborea, the Norwegian Sea and Barents Sea regions had highest diversity (Figure 5), including several unique alleles and fewer monomorphic markers (Table A8) than the southern regions.

These observations contradict the leading edge hypothesis, which predicts lower genetic diversity at northern latitudes following recolonization of species from glacial refugia in southern regions (Hewitt 2004, Hampe & Petit 2005, Guo 2012). However, cold-adapted species may have survived in glacial refugia north of the presumed glacial refugia for warm- adapted species (Provan & Bennett 2008). There was likely a glacial refugium near the French coast, as a result of the high genetic diversity of L. hyperborea (Robuchon et al. 2014) and other seaweed species (Neiva et al. 2012). However, that does not exclude the possibility for additional refugia for kelp species in northern regions.

The high and unique genetic diversity of L. hyperborea in the northernmost sites can be explained by several hypotheses. This study cannot directly differentiate between these possible explanations, but it provides the foundation for more focused studies testing these and related hypotheses.

First, the genetic diversity may be remnant of a glacial refugium off the coast of Andøya, Norway. Andøya is located between the Norwegian Sea sites and the Barents Sea sites sampled in this study. High levels of diversity were observed in both of these regions. Several studies have found evidence of terrestrial (Jaramillo-Correa et al. 2004) and marine refugia at Andøya (Coyer et al 2011, Olsen et al. 2013). Current kelp populations have largely disappeared from this region as a result of sea urchin grazing, however some populations exist in exposed regions (Rinde et al. 2014). This hypothesis may in part also be supported by the fact that the Barents Sea sites are placed between the North Sea and Norwegian Sea sites in the neighbor-joining tree (Figure 16), implying that a stepping stone model of isolation by distance is not enough on its own to explain the patterns of genetic diversity and differentiation.

Another, and perhaps complementary hypothesis is the unique genetic diversity in the North can result from the fact that Laminaria in this region has been recognized as a separate subspecies, L. hyperborea spp. cucullata (Kain 1969). This subspecies grows in sheltered areas, and in the process of differentiation, unique genetic diversity is expected. Potential adaptation to the stresses of low temperatures and ice may have led to genetic differentiation over time.

A third hypothesis explaining the high and unique genetic diversity in the northern regions of L. hyperborea is genetic input from multiple, genetically distinct source populations. In practice, this may constitute input from multiple populations that were not included in this study or no longer exist as a result of the sea urchin grazing (Silvertsen 1997, Norderhaug & Christie 2009). For example, Coyer et al. (2011) found high levels of genetic diversity in a population of the brown seaweed, Fucus distichus, in an area affected by an oil spill. They suggested that the high diversity may have been a result of multiple founder populations recolonizing the area following the oil spill.

Implications for management

Management of kelp resources is already of high concern in Norway, which has extensive monitoring programs and rehabilitation projects (Norderhaug et al. 2011). This initial investigation of kelp diversity and structure in Norway provides valuable additional information. Some management implications have already been discussed with specific regard to the Skagerrak region. These goals also apply to the Norwegian coast as a whole. The best approach is to incorporate genetics into existing monitoring and management strategies (Selkoe et al. 2008), testing direct questions related to management concerns.

The results from this study support previous research suggesting that kelp can recolonize areas after population decline, provided that the environmental conditions are suitable for growth and reproduction (Leinas & Christie 1996, Sogn Andersen 2013b). Therefore, the best practices are to maintain coastal monitoring programs and assess potential sources of environmental pollution, especially sources that lead to reduced water clarity and sedimentation. In the North, efforts to keep sea urchin populations in check, such as protecting potential predators, will help kelp re-establish sea urchin barrens (Fagerli et al. 2014).

Additional protection should be considered for marginalized regions and regions with especially high diversity. These factors can influence the placement of Marine Protected Areas, in order to ensure the highest overall diversity in the population as a whole (Couceiro et al. 2012) For Norway, this means focusing on the potentially isolated L. hyperborea populations in the Skagerrak and protecting the unique diversity in the North. There are already many marine protected areas in Norway with varying levels of conservation, including several in the Skagerrak and the northern limits.

Potential transplant projects should consider the genetic component of the populations involved. Using transplant kelp from a site within the same genetic cluster will maintain the level of differentiation compared to other regions (McDonald-Madden et al. 2008). This has the potential to preserve useful adaptations within the population. On the contrary, bringing transplants from other regions may provide higher genetic diversity and building blocks for adaptation in isolated populations with low levels of diversity. Thus, project leaders should investigate and consider the impact of transplanting kelp on the genetic structure of the populations.

Future investigations

Future studies of S. latissima and L. hyperborea along the Norwegian coast should incorporate additional molecular markers. It should be possible to amplify additional polymorphic microsatellite markers for both species, specifically for S. latissima, with additional optimization of PCR, which time limitations prohibited in this study. Further, sequence data from mitochondrial, chloroplast, and nuclear gene regions can provide additional insight into population genetic structure and diversity. Different DNA regions have potentially unique evolutionary histories and may show new and perhaps complementary information to microsatellite data (Olsen et al. 2010, Coyer et al. 2011).

The next natural step is whole genome sequencing and restriction associated site DNA sequencing (RADseq), which provides thousands of single nucleotide polymorphisms (SNPs) to compare populations. The first kelp genome of Saccharina japonica was published in April (Ye et al. 2015). Another recent study provided the first RADseq library of kelp, also in S. japonica (Zhang et al. 2015). These techniques are revolutionizing genetic research, opening up opportunities for many new study questions.

As for potential future studies, I propose to investigate patterns of genetic diversity and structure within a site in Norway by comparing individuals by i) depth, ii) age, and iii) morphology. This can be investigated for single species or multiple sympatric species. Sogn Andersen (2013c) found that the optimum range for S. latissima with regard to light and temperature is between 15 and 10 m depth in the Skagerrak region. I would expect to find the highest genetic diversity in the middle part of the intertidal distribution, with lower genetic diversity towards the upper and lower limits of the distribution. Additionally, some generations may dominate a site for many years (Leinas & Christie 1996). This would add an additional time signature to the data and provide information about recruitment. Further, patterns in morphological differences may correspond to genetic differentiation, such as in subspecies.

Population connectivity can be investigated directly with zoospores. Zoospores can be collected from sites along the coast and tested for site of origin with assignment tests. This would provide high clarity information about dispersal potential.

At a larger scale, I propose to investigate patterns of genetic diversity and structure over the entire distribution of L. hyperborea and S. latissima. The kelp forests along the Norwegian coast are one part of a larger picture and more sites from other regions of the Atlantic would provide valuable information about overarching phylogeographic patterns.

Conclusions

Although the sample size and number of markers are limited, this study provides a glimpse into the population dynamics of two foundation kelp species in Norway. Kelps in Norway are largely structured by oceanic regions with increasing genetic differentiation with distance. Saccharina latissima has relatively high genetic diversity despite population decline in southern and northern Norway. Laminaria hyperborea has low genetic diversity in the Skagerrak region that may have resulted from isolation and environmental stress. There is unique genetic diversity in the northern regions, despite population decline from sea urchin grazing in these areas. Overall, this study established molecular tools for future investigations and provided the first glimpse into population genetic patterns of S. latissima and L. hyperborea in Norway.

Acknowledgements

Funding for this study came from the Nansen Fund (Nansen-fondet), Systematics Research Fund (SRF), Norwegian Institute for Water Research (NIVA) and the University of Oslo. Samples were collected by NIVA researchers, Hartvig Christie and Janne Gitmark. Additional samples were collected during courses with resources provided by the ForBio Research School in Biosystematics at the University of Bergen (UiB) and the University Centre in Svalbard (UNIS). Photos of kelp were provided by Camilla Fagerli, Maia Kile, and Janne Gitmark (NIVA). The kelp life cycle figure was provided by Eli Rinde. Data analysis assistance was provided by Jens Thaulow and Robin Cristofari.

References

Adey WH, Lindstrom SC, Hommersand MH. 2008. The biogeographic origin of Arctic seaweeds: a thermogeographic view. Journal of Phycology 44: 1384-1394. Alberto F, Raimondi PT, Reed DC, Coelho NC, Leblois R, Whitmer A, Serrão EA. 2010. Habitat continuity and geographic distance predict population genetic differentiation in giant kelp. Ecology 91(1): 49-56. Antao T, Lopes A, Lopes RJ, Beja-Pereira A, Luikart G. 2008. LOSITAN: A workbench to detect molecular adaptation based on Fst-outlier method. BMC Bioinformatics 2008 (9): 323. Avise JC. 2004. Molecular Markers, Natural History, and Evolution. Sinauer Associates, Inc.; 2nd edition Beaumont MA, Nichols RA. 1996. Evaluating loci for use in the genetic analysis of population structure. Proceedings of the Royal Society of London B 263: 1619-1626. Beaumont MA, Balding DJ. 2004. Identifying adaptive genetic divergence among populations from genome scans. Molecular Ecology 13: 969-980. Bekkby T, Moy F. 2011. Developing spatial models of sugar kelp (Saccharina latissima) potential distribution under natural conditions and areas of its disappearance in Skagerrak. Estuarine, Coastal and Shelf Science 95(4): 477-483. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. 1996-2004. GENETIX 4.05, logiciel sous WindowsTM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, Université de Montpellier II, Montpellier, France. Bennet KD, Provan J. 2008. What do we mean by ‘refugia’? Quaternary Science Reviews 27: 2449-2455. Benzécri, JP. 1973. L'Analyse des Données: T. 2, I' Analyse des correspondances. Paris: Dunod. Billot C, Rousvoal S, Estoup A, Epplen JT, Soumitou-Laprade P, Valero M, Kloareg B. 1998. Isolation and characterization of microsatellite markers in the nuclear genome of the brown alga Laminaria digitata (Phaeophyceae). Molecular Ecology 7(12): 1778-1780. Billot C, Engel C, Rousvoal S, Kloareg B, Valero M. 2003. Current patterns, habitat discontinuities and population genetic structure: the case of the kelp Laminaria digitata in the English Channel. Marine Ecology Progress Series 253: 111-121. Bolton JJ. 2010. The biogeography of kelps (Laminariales, Phaeophyceae): a global analysis with new insights from recent advances in molecular phylogenetics. Helgoland Marine Research, 64, 263-279. Breeman AM. 1988. Relative importance of temperature and other factors in determining geographic boundaries of seaweeds: experimental and phonological evidence. Hegoländer Meersuntersuchngen 42: 199-241. Cavalli-Sforza L, Edwards AWF. 1967. Phylogenetic analysis: models and estimation procedures. American Journal of Human Genetics 19: 233-257. Christie H, Norderhaug KM, Fredriksen S. 2009. Macrophytes as habitat for fauna. Marine Ecology Progress Series. 396: 221-233. Coelho NC, Serrão, Alberto F. 2014. Characterization of fifteen microsatellite markers for the kelp Laminaria ochroleuca and cross species amplification within the genus. 2014 6(4): 949-950. Couceiro L, Robuchon M, Destombe C, Valero M. 2012. Management and conservation of the kelp species Laminaria digitata: using genetic tools to explore potential exporting role of MPA “Parc naturel marin d’Iroise”. Aquatic Living Resources 26(2): 197-205. Coyer JA, Hoarau G, Beszerti B, Pearson G, Olsen JL. 2009. Expressed sequence tag derived polymorphic SSR markers for Fucus serratus and amplification in other species of Fucus. Molecular Ecology Resources 9: 168-170. Coyer JA. Hoarau G, Schaik JV, Luijckx P, Olsen JL. 2011. Trans-Pacific and trans-Arctic pathways of the intertidal macroalga Fucus distichus L. reveal multiple glacial refugia and colonizations from the North Pacific to the North Atlantic. Journal of Biogeography 38: 756-771. Dieman M, Iken K, Konar B. 2012. Susceptibility of Nereocystis luetkeana (Laminariales, Ochrophyta) and Eularia fistulosa (Laminariales, Ochrophyta) spores to sedimentation. Algae 27(2): 115-123. Draget KI, Smidsrød O, Skjåk-Bræk G. 2005. Alginates from algae. Biopolymers Online. Earl DA, vonHoldt BM. 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4(2): 359-361. Evanno G, Regnaut S, Goudet J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14(8): 2611-2620. Excoffier L, Smouse PE, Quattro JM. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction sties. Genetics 131: 479- 491.

Fagerli CW, Norderhaug KM, Christie HC. 2013. Lack of sea urchin settlement may explain kelp forest recovery in overgrazed areas in Norway. Marine Ecology Progress Series 488: 119-132. Fagerli CW, Norderhaug KM, Christie H, Pedersen MF, Fredriksen F. 2014. Predators of the destructive sea urchin grazer Stronglyocentrotus droebachiensis on the Norwegian coast. Marine Ecology Progress Series 502: 207-218. Fraser CI, Thiel M, Spencer HG, Waters JM. 2010. Contemporary habitat discontinuity and historic glacial ice drive genetic divergence in Chilean kelp. BMC Evolutionary Biology 10: 203. Fredriksen S, Sjøtun K, Lein TE, Rueness J. 1995. Spore dispersal in Laminaria hyperborea (Laminariales, Phaeophyceae). Sarsia 80: 47-53. Fredriksen S. 2003. Food web studies in a Norwegian kelp forest based on stable isotope (d13C d15and N) analysis. Marine Ecology Progress Series 260: 71-81. Felsenstein, J. 1994. PHYLIP (Phylogeny inference package). Department of Genetics, University of Washington, Seattle, WA. http://evolution.genetics.washington.edu/phylip.html Foll M and Gaggiotti OE. 2008. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180: 977-993. Glaubitz JC. 2004. CONVERT: a user friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Molecular Ecology Notes 4: 309-310. Glover KA, Quintela M, Wennevik V, Besnier F, Sørvik AGE, Skaala Ø. 2012. Three decades of farmed escapees in the wild: a spatio-temporal analysis of Atlantic Salmon population genetic structure throughout Norway. PloS ONE 7(8): e43129. Gorman D, Connell SD. 2009. Recovering subtidal forests in human-dominated landscapes. Journal of Applied Ecology 46(6): 1258-1265. Gruar D, Li WH. 1999. Fundamentals of Molecular Evolution. Sinauer Associates, Inc.; 2 sub edition. Guo SW & Thompson EA. 1992. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: 361-372. Guo Q. 2012. Incorporating latitudinal and central-marginal trends in assessing genetic variation across species ranges. Molecular Ecology 21: 5396-5403. Hampe A, Petit R. 2005. Conserving biodiversity under climate change: the rear edge matters. Ecology Letters 8: 461-467. Hamrick J, Godt M. 1996. Effects of the life history traits on genetic diversity in plant species. Philosophical Transactions of the Royal Society of London Series B 351: 1291-1298. Harley CDG, Anderson KM, Demes KW, Jorve JP, Kordas RL, Coyle TA, Graham MH. 2012. Effects of climate change on global seaweed communities. Journal of Phycology 48(5): 1064-1078 Hartl DL, Clark AG. 2007. Principles of Population Genetics. Sinauer Associates, Inc. 4th edition. Hewitt GM. 1993. Postglacial distribution and species substructure: lessons from pollen, insects and hybrid zones. In: Evolutionary Patterns and Processes (eds Lees DR, Edwards D). Linnean Society Symposium Series 14, Academic Press, London pp. 97-123 Hewitt GM. 2000. The genetic legacy of the ice ages. Nature 405: 907-913. Hewitt GM. 2004. Genetic consequences of climatic changes in the Quaternary. Philosophical Transactions of the Royal Society of London B 359: 183-195. Hoarau G, Coyer JA, Veldsink JH, Stam WT, Olsen JL. 2007a. Glacial refugia and recolonization patterns in the brown seaweed Fucus serratus. Molecular Ecology 16: 3606-3616. Hoarau G, Coyer JA, Stam WT, Olsen JL. 2007b. A fast and inexpensive DNA extraction/purification method for brown macroalgae. Molecular Ecology Notes 7: 191-193. Höglund J. 2009. Evoluationary Conservation Genetics. Oxford University Press Inc., New York Jaramillo-Correa JP, Beaulieu J, Bousquet J. 2004. Variation in mitochondrial DNA reveals multiple distant glacial refugia in black spruce (Picea mariana), a transcontinental North American conifer. Molecular Ecology 13: 2753-2747. Jensen JL, Bohonak AJ, Kelley ST. 2005. Isolation by distance, web service. BMC Genetics 6: 13. v.3.23 http://ibdws.sdsu.edu/ Kain JM. 1967. Populations of Laminaria hyperborea at various latitudes. Hegoländer Meersuntersuchngen 15: 489-499. Kain JM, Jones NS. 1969. The biology of Laminaria hyperborea. V. Comparison with early stages of competitors. Journal of Marine Biological Association of the United Kingdom 49: 455-473. Kain JM. 1975. The biology of Laminaria hyperborea. VII. Reproduction of the sporophyte. Journal of Marine Biology Association of the U.K. 55:567-582. Kalinowski ST. 2005. HP-Rare: A computer program for preforming rarefaction on measures of allelic diversity. Molecular Ecology Notes 5: 187-189.

Kerrison PD, Stanley MS, Kelly M, MacLeod A, Black KD, Hughes AD. 2015. Optimising the settlement and hatchery culture of Saccharina latissima (Phaeophyta) by manipulation of growth medium and substrate surface condition. Journal of Applied Phycology DOI 10.1007/s10811-01500621-6. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. 2015. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Accepted to Molecular Ecology Resources. Lane CH, Mayes C, Druehl LD, Saunders GW. 2006. A multi-gene molecular investigation of the kelp (Laminariales, Phaeophyceae) supports substantial taxonomic re-organization. Journal of Phycology 42: 493-512. Leblanc C, Schaal G, Cosse A, Destombe C, Valero M, Riera P, Potin P. 2011. Trophic and biotic interactions in Laminaria digitata beds: which factors could influence the persistance of marie kelp forests in northern Brittany? Cahiers de Biologie Marine, 52: 415-427. Leinas HP, Christie H. 1996. Effects of removing sea urchins (Stronglyocentrotus droebachiensis): stability of the barren state and succession of kelp forest recovery in the east Atlantic. Ocealogia 105: 524-536. Lessios HA. 2005. Diadema antillarum populations in Panama twenty years following mass mortality. Coral Reefs 24: 125-127. Lischer HEL and Excoffier L. 2012. PGDSpider: An automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28: 298-299. Liu F, Wang F, Duan D. 2012. EST-SSR markers derived from Laminaria digitata and its transferable application in Saccharina japonica. Journal of Applied Phycology 24: 501-505. Lüning K. 1984. Temperature tolerances and biogeography of seaweeds: The marine algae flora of Helgoland (North Sea) as an example. Hegoländer Meersuntersuchngen 38(2): 305-317. Manier MK, Arnold SJ. 2006. Ecological correlates of population genetic structure: a comparative approach using a vertebrate metacommunity. Proceedings of the Royal Society B 273: 3001-3009. Maneiro I, Couceiro L, Bárbara I, Cremades J, Ruiz JM, Barreiro R. 2011. Low genetic variation and isolation of northern peripheral populations of a red seaweed (Grateloupia lanceola). Aquatic Conservation: Marine and Freshwater Ecosystems. 21: 590-600. Manly BFJ. 1994. Multivariate statistical methods: a primer 2nd edition. New York, Chapman and Hall. McDonald-Madden E, Baxter PWJ, Possingham HP. 2008. Subpopulation Triage: How to allocate conservation effort among populations. Conservation Biology 22(3): 656-665. Michalakis Y, Excoffier L. 1996. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 142: 1061-1064. Moy FE, Chrisite HC. 2012. Large-scale shift from sugar kelp (Saccharina latissima) to ephemeral algae along the south and west coast of Norway. Marine Biology Research 8(4): 357-269. Murray MG, Thompson WF. 1980. Rapid isolation of high molecular weight plant DNA. Nucleic acids research 8(19): 4321-4326. Neiva J, Pearson GA, Valero M, Serrão EA. 2012. Drifting fronds and drifting alleles: range dynamics, local dispersal and habitat isolation shape the population structure of the estuarine seaweed Fucus ceranoides. Journal of Biogeography 39(6): 1167-1178. Norderhaug KM, Fredriksen S, Nygaard K. 2003. Trophic importance of Laminaria hyperborea to kelp forest consumers and the importance of bacterial degradation to food quality. Marine Ecology Progress Series 255: 135-144. Norderhaug KM, Christie H, Fosså JH, Fredriksen S. 2005. Fish-macrofauna interactions in a kelp (Laminaria hyperborea) forest. Journal of the Marine Biological Association of the United Kingdom. 85: 1297- 1286. Norderhaug KM, Christie HC. 2009. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Marine Biology Resources 5: 515-528. Norderhaug KM, Nautsvoll L, Ledang AB, Bjerkeng B, Gitmark JK. 2011. Sugar kelp monitoring in the coastal regions of Norway. Report for 2009 and 2010. Norwegian Institute for Water Research, NIVA Olsen JL, Zechman FW, Hoarau G, Coyer JA, Stam WT, Valero M, Åberg P. 2010. The phylogeographic architecture of the fucoid seaweed Ascophyllum nodosum: an intertidal ‘marine tree’ and survivor of more than one glacial-interglacial cycle. Journal of Biogeography 37: 842-856. Olsen JL, Coyer JA, Stam WT, Moy FE, Christie H, Jørgensen NM. 2013. Eelgrass Zostera marina populations in northern Norwegian fjords are genetically isolated and diverse. Marine Ecology Progress Series 486: 121-132. Peakall R, Smouse PE. 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288-295. Peakall R, Smouse PE. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28: 2537-2539.

Petit RJ, Mousadik AE, Pons O. 1998. Identifying populations for conservation on the basis of genetic markers. Conservation Biology 12(4): 844-855. Pritchard JK, Stephens M, Donnelly PJ. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945-959. Provan J, Bennett KD. 2008. Phylogeographic insights into cryptic glacial refugia. Trends in Ecology and Evolution 23: 564-571. Raybaud V, Beaugrand G, Goberville E, Delebecq G, Destombe C, Valero M, Davoult D, Morin P. 2013. Decline in kelp in West Europe and climate. PLos One I8, e66044. Reed DC. 1990. The effects of variable settlement and early competition on patterns of kelp recruitment. Ecology 71: 776-787. Rice WR. 1989. Analyzing tables of statistical tests. Evolution 43: 223-225. Rinde E, Bjørge A, Eggereide A, Tufteland G. 1998. Kystøkologi den ressursrike norskekysten Universitetsforlaget, Oslo. Rinde E, Sjøtun K. 2005. Demographic variation in the kelp Laminaria hyperborea along a latitudinal gradient. Marine Biology 146: 1051-1062. Rinde, E, Christie H, Fagerli CW, Bekkby T, Gundersen H, Norderhaug K, Hiermann DØ. 2014. The influence of physical factors on kelp and sea urchin distribution in previously and still grazed areas in the NE Atlantic. Public Library of Science ONE 9(6): 1-15. Robuchon M, Le Gall L, Mauger S, Valero M. 2014. Contrasting genetic diversity patterns in two sister kelp species co-distributed along the coast of Brittany, France. Molecular Ecology 23: 2669-2685. Robuchon M, Le Gall L, Mauger S, Valero M. 2014. Data from: Contrasting genetic diversity patterns in two sister kelp species co-distributed along the coast of Brittany, France. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.pv28c Rousset F. 1997. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145: 1219-1228. Rousset F. 2008. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Molecular Ecology Resources 8: 103-106. Røv N, Christie H, Fredriksen S, Leinaas HP, Lorentsen SH. 1990. Biologiske forundersøkelser I forbindelse med planer om taretråling I Sør-Trønelag, NINA Oppdragsmelding 52 Scheulke M. 2000. An economic method for the fluorescent labeling of PCR fragements. Nature biotechnology 18(2): 223-234. Schiel DR, Wood SA, Dunmore RA, Taylor DI. 2006. Sediment on rocky intertidal reefs: Effects of early post- settlement stages of habitat-forming seaweeds. Journal of Experimental Marine Biology and Ecology 331(2): 158-172. Selkoe KA, Toonen RJ. 2006. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters 9: 615-629. Selkoe KA, Henzler CM, Gaines SD. 2008. Seascape genetics and the spatial ecology of marine populations. Fish and Fisheries 9: 363-377. Siegal DA, Mitarai S, Costello CJ, Gaines SD, Kendall BE, Warner RR. 2008. The stochastic nature of larval connectivity among nearshore marine populations. Proceedings of the National Academy of Sciences of the United States of America 105: 8974-8979. Silvertsen K. 1997. Geographic and environmental factors affecting the distribution of kelp beds and barren grounds and changes in biota associated with kelp reduction sites along the Norwegian coast. Canadian Journal of Fisheries and Aquatic Sciences 54: 2872-2887. Silvertsen K. 2006. Overgrazing of kelp beds along the coast of Norway. Journal of Applied Phycology 18: 599- 610. Sjøtun K, Fredriksen S, Lein TE, Rueness J, Sivertsen K. 1993. Population studies of Laminaria hyperborea from its northern range of distribution in Norway. Hydrobiologia 260: 215-221. Sjøtun K, Schoschina EV. 2002. Gametophytic development of Laminaria spp. (Laminariales, Phaeophyta) at low temperature. Phycologia 41: 147-152. Slatkin M. 1993. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47: 264-279. Snirc A, Silberfeld T, Bonnet J, Tillier A, Tuffet S, Sun JS. 2010. Optimization of DNA extraction from brown algae (Phaeophyceae) based on a commercial kit. Journal of Phycology 46: doi: 10.1111/j.1529- 8817.2010.00817.x. Sogn Andersen G, Steen H, Christie H, Fredriksen S, Moy FE. 2011. Seasonal patterns of sporophyte growth, fertility, fouling, and mortality of Saccharina latissima in Skagerrak, Norway: Implications for Forest Recovery. Journal of Marine Biology 1-8, doi:10.1155/2011/690375. Sogn Andersen G, Pedersen MF, Nielsen S. 2013a. Temperature acclimation and heat tolerance of photosynthesis in Norwegian Saccharina latissima (Laminariales, Phaeophyceae). Phycological Society of America 49: 689-700. 46

Sogn Andersen G. 2013b. Patterns of Saccharina latissima recruitment. PLos ONE, 8(12): e81092, doi:10.1371/journal.pone.0081092 Sogn Andersen G. 2013c. Growth, survival and reproduction in the kelp Saccharina latissima: Seasonal patterns and the impact of epibionts. Ph. D. Thesis, University of Oslo Steneck RS, Graham MH, Bourque BJ, Corbett D, Erlandson JM, Estes JA, Tegner MJ. 2002. Kelp forest ecosystems: biodiversity, stability, resilience and future. Environmental Conservation 29: 436-459. Steneck RS, Vavrinec J, Leland AV. 2004. Accelerating trophic-level dysfunction in kelp forest ecosystems of the western North Atlantic. Ecosystems 7: 323-332 Stewart JR, Lister AM, Barnes I, Dalén L. 2010. Refugia revisited: individualistic responses of species in space and time. Proceedings of the Royal Socitey B: Biological Sciences 277: 661-671. Tellier F, Meynard AP, Correa JA, Faugeron S, Valero M. 2009. Phylogeographic analyses of the 30°S south- east Pacific biogeographic transition zone establish the occurrence of a sharp genetic discontinuity in the kelp Lessonia nigrescens: Vicariance or parapatry? Molecular Phylogenetics and Evolution 53: 679- 693. tom Dieck I. 1989. Vergleichende untersuchungen zur ökophysiologie und kreuzbarkeit innerhalb der digitalen sektion der gattung Laminaria Lamouroux (Phaeophyceae). Ph. D. Thesis University of Hambur, Germany Vadas RL. 1977. Preferential feeding: an optimization strategy in sea urchins. Ecological Monographs 47: 337- 371. Valero M, Destombe C, Mauger S, Ribout C, Engel C, Daguin-Theibaut C, Tellier F. 2011. Using genetic tools for sustainable management of kelps: a literature review and the example of Laminaria digitata. Cahiers de Biologie Marine 52: 467-483. van den Hoek C. 1982. The distribution of benthic algae in relation to the temperature regulation of their life histories. Biological Journal of the Linnean Society 18: 18-144. Vásquez J. 2009. Production, use and fate of Chilean brown seaweeds: re-sources for a sustainable fishery. In: Borowitzka M, Critchley A, Kraan S, Peters A, Sjøtun K, Notoya M. (eds.) Nineteenth International Seaweed Symposium. Springer Netherlands Wang G, Tan X, Shen J, Li J, Zhang L, Sun J, Wang B, Weng M, Liu T. 2011. Development of EST-SSR primers and their practicability test for Laminaria. Acta Oceanologica Sinica 30 (3): 112-117. Weir BS, Cockerham CC. 1984. Estimating F-statistics or the analysis of population structure. Evolution 38: 1358-1370. Wernberg T, Russell BD, Thomsen MS, Gurgel CFD, Bradshaw CJ, Poloczanska ES, Connell SD. 2011. Seaweed communities in retreat from ocean warming. Current Biology 21(21): 1828-1832. Wernberg T, Smale D, Tuya F, Thomsen MS, Langlois TJ, de Bettigneis T, Bennett S, Rousseaux CS. 2013. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change 3(1): 78-62. Wheeler WN. 1988. Algal productivity and hydrodynamics – a synthesis. Progress in Phycological Research 6: 23-58. White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, Toonen R. 2010. Ocean currents help explain population genetic structure. Proceedings of the Royal Society B: DOI: 10.1098/rspb.2009.2214 Wright S. 1943. Isolation by distance. Genetics 28: 114-138. Ye N, Zhang X, Miao M, Fan X, Zheng Y, Xu D, Wang J, Zhou L, Wang D, Gao T, Wang Y, Shi W, Ji P, Li D, Guan Z, Shao C, Zhuang Z, Gao Z, Qi J, Zhao F. 2015. Saccharina genomes provide novel insight into kelp biology. Nature Communications Doi: 10.1038/ncomms7986 Yoon HS, Lee JL, Boo SM, Bhattacharya D. 2001. Phylogeny of Alariaceae, Laminariaceae, and Lessoniaceae (Phaeophyceae) based on plastid-encoded RuBisCo spacer and nuclear-encoded ITS sequence comparisons. Molecular Phylogenetics Evolution 21: 231-243. Zhang J, Li W, Qu J, Wang X, Liu C, Liu T. 2014. Development and characterization of microsatellite markers from an enriched genomic library of Saccharina japonica. Journal of Applied Phycology 27: 479-487. Zhang N, Zhang L, Tao Y, Guo L, Sun J, Li X, Zhao N, Peng J, Li X, Zeng L, Chen J, Yang G. 2015. Construction of a high density SNP linkage map of kelp (Saccharina japonica) by sequencing Taq I site associated DNA and mapping of sex determining locus. BMC Genomics 16: 189-200.

Appendix

Table A1: Summary of PCR amplification and capillary electrophoresis results with two or more individuals of Saccharina latissima and Laminaria hyperborea for 23 Expressed Sequence Tag (EST) markers developed by Want et al. (2011). The markers were developed for Saccharina japonica, S. angustata, S. religiosa and Laminaria digitata. The table includes the marker name, amplification success based on gel electrophoresis (“+” amplified and “-“ no amplification detected), the detectable pattern (Polymorphic, Monomorphic, Multiple Bands (MB), Faint Band (FB)) and the allele size range from capillary electrophoresis with an ABI sequencer (T = tested, and NT = not tested). Alleles are not reported for markers that were tested (T) on the ABI and had multiple bands (MB), more than two alleles per individual. For monomorphic markers, “n” indicates the number of individuals tested.

Saccharina latissima Laminaria hyperborea Marker Amplified Pattern Allele size range Amplified Pattern Allele size range Wang et al. 2011 CS03 + MB - + FB NT CS04 + MB T - - - CS07 + Monomorphic 270 (n = 127) + FB NT CS09 + MB - - - - CS10 + MB - - - - CS11 + Monomorphic 292 (n = 137) - - - CS12 + Polymorphic 234 - 296 - - - CS13 + Polymorphic 244 - 331 - - - CS15 + MB T - - - CS16 + MB - + MB - CS20 + MB - + Polymorphic 272 - 298 CS27 + MB - + Monomorphic 274 (n =16) CS29 - - - + Monomorphic 287 (n = 2) CS33 + MB - + FB NT CS34 - - - + Polymorphic 243 - 250 CS38 + MB - - - - CS42 + MB - + MB - CS47 - - - + Monomorphic 232 (n = 20) CS53 + MB - + FB NT CS55 + MB T - - - CS60 ------CS61 + MB - - - - CS63 + FB NT - - -

Table A2: Summary of PCR amplification and capillary electrophoresis results with two or more individuals of Saccharina latissima and Laminaria hyperborea for 13 expressed sequence tag (EST) markers developed by Liu et al. (2012). The markers were developed for Saccharina japonica using template DNA from Laminaria digitata. The table includes the marker name, amplification success based on gel electrophoresis (“+” amplified and “-“ no amplification detected), the detectable pattern (Polymorphic, Monomorphic, Multiple Bands (MB), Faint Band (FB) and the allele size range from fragment analysis with an ABI sequencer (T = tested with fragment analysis and the pattern is confirmed, and NT = not tested). ). Alleles are not reported for markers that were tested (T) on the ABI and had multiple bands (MB), more than two alleles per individual. For monomorphic markers, “n” indicates the number of individuals tested.

Saccharina latissima Laminaria hyperborea Locus Amplified Pattern Allele size range Amplified Pattern Allele size range Liu et al 2012 LD1 + MB T - - - LD2 + MB - + FB NT LD3 - - - + Polymorphic 278 - 285 LD4 - - - + Monomorphic 198 (n = 14) LD5 + MB - + MB - LD6 + MB - + Polymorphic 471 - 540 LD7 + MB T + MB T LD8 + MB - + MB - LD9 + MB - + Monomorphic 294, 322 (n = 15) LD10 + MB - - - - LD11 + FB NT - - - LD12 + MB - + MB T LD13 + MB - + MB -

Table A3: Summary of PCR amplification and capillary electrophoresis results with 2 or more individuals of Saccharina latissima and Laminaria hyperborea for 4 microsatellite markers developed by Billot et al. (1998) and 5 microsatellite markers developed by Coelho et al. (2014). The first 4 markers were developed for Laminaria digitata. The last 5 markers were developed for Laminaria ochroleuca. The table includes the marker name, amplification success based on gel electrophoresis (“+” amplified and “-“ no amplification detected), the detectable pattern (Polymorphic, Monomorphic, Multiple Bands (MB), Faint Band (FB) and the allele size range from capillary electrophoresis with an ABI sequencer (T = tested with fragment analysis and the pattern is confirmed, and NT = not tested). Alleles are not reported for markers that were tested (T) on the ABI and had multiple bands (MB), more than two alleles per individual. For monomorphic markers, “n” indicates the number of individuals tested. * indicates the established markers for Laminaria hyperborea by Robuchon et al. (2014).

Saccharina latissima Laminaria hyperborea Locus Amplified Pattern Allele size range Amplified Pattern Allele size range Billot et al 1998 Ld1/124 - - - + MB - Ld2/148* - - - + Polymorphic 200 - 263 Ld2/158* - - - + Polymorphic 222 - 257 Ld2/167* - - - + Polymorphic 150 – 209 Coelho et al 2014

LolVVIV-15* - - - + Polymorphic 180 - 206 LolVVIV-17* + Monomorphic 379 (n = 2) + MB T LolVVIV-23* - - - + Polymorphic 203 - 331 LolVVIV-24* - - - + Polymorphic 291 - 248 LolVVIV-28* - - - + Polymorphic 218 - 293

Table A4: Summary of PCR amplification and capillary electrophoresis results with two or more individuals of Saccharina latissima and Laminaria hyperborea for 23 microsatellite markers developed by Zhang et al. (2014). The markers were developed for Saccharina japonica using template and tested with haploid gametophytes of S. japonica, S. ochotensis, S. angustata, S. religiosa, S. latissima, L. hyperborea and L. digitata. The table includes the marker name, amplification success based on gel electrophoresis (“+” amplified and “-“ no amplification detected), the detectable pattern (Polymorphic, Monomorphic, Multiple Bands (MB), Faint Band (FB)) and the allele size range from fragment analysis with an ABI sequencer (T = tested with capillary electrophoresis and the pattern is confirmed, and NT = not tested). Alleles are not reported for markers that were tested (T) on the ABI and had multiple bands (MB), more than two alleles per individual. For monomorphic markers, “n” indicates the number of individuals tested. * indicates trouble with amplification after initial testing.

Saccharina latissima Laminaria hyperborea Locus Amplified Pattern Allele size range Amplified Pattern Allele size range Zhang et al 2014 SSR002 + MB - + MB - SSR032 + Monomorphic 552 (n = 1) + MB - SSR035 + MB T - - - SSR038 ------SSR052 + Monomorphic 277 (n = 129) + FB NT SSR053 + MB - + MB - SSR094 + MB T + Monomorphic 364 (n = 2) SSR135 + MB T + MB T SSR144 ------SSR155 + Monomorphic 550 (n = 2) + Monomorphic 550, 589 (n = 3) SSR158 ------SSR163 + MB T + Monomorphic 289 (n = 1) SSR165 + MB - + Monomorphic 425, 478 (n = 2) SSR169 ------SSR176 + Monomorphic 288 (n =1) + MB - SSR187 + MB - + MB - SSR195 ------SSR218 + MB T + MB - SSR227 + Too Large NT - - - SSR229 ------SSR238 - MB - + MB - SSR261 + Polymorphic 217 - 231 - - - SSR278 + Polymorphic* 420 - 470 - - -

Appendix: Outliers

The principal coordinates analysis (PCoA, Figure A1) and the Structure plot (Figure A2) clearly show some outliers in the original Laminaria hyperborea dataset. These individuals are most likely not individuals of L. hyperborea. The size range of the outlier individuals matches the previously reported alleles for Laminaria digitata by Robuchon et al. (2014). One individual that is admixed in the Structure plot and between L. hyperborea and the possible L. digitata individuals in the PCoA is likely a hybrid of the two species. More testing is needed to say anything conclusively concerning the affinity of these samples. These outliers were removed from the main data analyses.

Ska_1 Ska_3 Ska_4 Ska_5 Nor_1 Nor_2

Coord. 2 (13.07% variation) Coord. 2 (13.07% variation) Nwg_1 Coord. 1 (47.21% variation) Nwg_2

Figure A1: The first two coordinates from a principal coordinates analysis (PCoA) of all Laminaria samples based on pairwise genetic distances between all samples. Each symbol represents an individual from one of the eight sites, labeled on the right side of the figure. The analysis was run in GENALEX. The PCoA is a multivariate technique that displays the major patterns in a dataset with multiple variables, in this case multiple molecular markers. The data exists in a multidimensional space. Each axis explains a certain percent of the variation in the data. Coordinate 1 (Coord. 1) and Coordinate 2 (Coord. 2), shown in the figure, explain in total 60.28% of the variation in the complete data set. The cluster of individuals in the lower left-hand corner are the potential outliers.

Figure A2: STRUCTURE analysis plot of Laminaria samples from eight sites from three regions using 11 markers. Colors represent four distinct clusters. The blue, green, and purple clusters follow regional patterns of site differentiation. The orange cluster shows the outliers. Delta (K) = 4 in STRUCTURE HARVESTOR (results not shown).

Appendix: Candidate loci for selection

BAYESCAN and LOSITAN identified candidate markers for selection with the Laminaria hyperborea samples. Principle coordinates analyses (PCoA) were used to assess if the candidate markers showed a different overall pattern than the non-candidate makers (Figure A3). Overall, the PCoAs have similar clustering of sites, with a higher degree of clustering when all markers are used, opposed to just the split data sets.

Ska_3 Ska_4 Ska_6 Nor_1 Nor_2 Coord. 2 (9.82%) Nwg_1 Coord. 1 (25.50%)

A: All Markers

Ska_3 Ska_4 Ska_6 Nor_1

Coord. 2 (12.18%) Nor_2 Nwg_1 Coord. 1 (30.18%)

B: Candidates for selection

Ska_3 Ska_4 Ska_6 Nor_1

Coord. 2 (16.69%) Nor_2

Coord. 1 (29.59%) Nwg_1

C: Neutral Markers Figure A3: Three principal coordinates analyses (PCoA) of Laminaria hyperborea samples with different sets of markers. Letters identify the markers used in each analysis, A: all markers, B: candidate markers for selection (Ld148, LOL15, LOL28, LOL24, CS20, LD6), C: neutral markers (Ld158, Ld167, LOL23, CS34, LD3). The first two coordinates PCoA of all Laminaria hyperborea samples based on pairwise genetic distances between all samples. Each symbol represents an individual from one of the eight sites, labeled on the right side of the figure. The PCoA is a multivariate technique that displays the major patterns in a dataset with multiple variables, in this case multiple molecular markers. The data exists in a multidimensional space. Each axis explains a certain percent of the variation in the data. Coordinates cannot be directly compared between analyses. Instead, the overall patterns should be compared. 54

, )

8.00 locus 18.00 17.00 Total per Total

values ofvalues

7 NS NS NS 6.00 4.58 0.6 0.76 6.00 4.85 0.56 0.81 6.00 4.89 0.78 0.82 18.00 Gre_1 Greenland Sea Greenland

) and and )significant S S* NS E 6.00 3.57 0.39 0.52 5.00 3.77 0.39 0.60 5.00 4.07 0.31 0.75 16.00 Bar_2 including alleles per alleles includinglocus per

Barents Sea Barents NS NS NS 4.00 2.76 0.40 0.36 3.00 2.26 0.30 0.28 5.00 4.24 0.80 0.78 12.00 Bar_1 zygosity zygosity (H

NS NS NS 4.00 4.00 0.80 0.71 5.00 5.00 1.00 0.80 4.00 4.00 0.80 0.78 13.00 Nwg_2

* indicates significance after Bonferroni* sequentialafter significance indicates S

NS NS 6.00 4.42 0.46 0.76 6.00 4.31 0.69 0.71 2.00 2.00 0.15 0.52 14.00 Norwegian Sea Norwegian Nwg_1

NS NS NS 2.00 2.00 0.78 0.50 3.00 2.56 0.89 0.57 2.00 1.82 0.22 0.21 7.00 Nor_2 , unbiased expected hetero expected,unbiased ) O based on three microsatellite on microsatellite basedmarkers three

North Sea North NS NS NS 5.00 3.16 0.29 0.48 6.00 3.64 0.57 0.62 4.00 3.20 0.64 0.66 15.00 Nor_1

netic variation netic variation NS NS NS 4.00 3.16 0.55 0.52 4.00 2.96 0.55 0.46 6.00 3.86 0.65 0.71 14.00 Ska_6 ge

NS NS NS 4.00 3.10 0.39 0.52 6.00 3.28 0.44 0.50 5.00 2.84 0.44 0.43 15.00 Ska_5

, observed ,observed heterozygosity

)

S NS NS 1.00 (N 3.00 2.98 0.17 0.62 4.00 3.82 0.33 0.71 4.00 3.67 0.50 0.68

1 Ska_3 Skagerrak

Saccharina latissimaSaccharina

83 S S S 3.00 3.00 0.17 0.71 4.00 3.98 0.67 0.80 3.00 2. 0.17 0.53 10.00 Ska_2 . NS =applicable, =no not .NS not ND data. NA = significant,

NS NS NS 4.00 2.86 0.44 0.51 5.00 4.02 0.61 0.73 3.00 2.82 0.50 0.62 12.00 Ska_1

E E E O A O O A O O A O H H H N N H N N H N N H ummary statistics statistics ummaryof HWE HWE HWE

Region S Population

Weinberg Weinberg equilibriu - able A5: able Marker CS13 CS12 SSR261 site per Total T for sizeadjustedallelic samplerichness Hardy corrections.

Table A6: Summary statistics of Laminaria hyperborea genetic variation including alleles per locus (NO), allelic richness adjusted for sample size (NA), observed heterozygosity (HO), unbiased expected heterozygosity (HE) and significant values of Hardy-Weinberg equilibrium calculated in GENEPOP. S = significant, NS = not significant, NA = not applicable, ND = no data. * indicates significance after sequential Bonferroni corrections.

Region Skagerrak North Sea Norwegian Sea Barents Sea

Total alleles Population Ska_3 Ska_4 Ska_6 Nor_1 Nor_2 Nwg_1 Bar_3 Marker per locus

Ld148 NO 2.00 3.00 2.00 3.00 2.00 3.00 4.00 6.00 N 2.00 2.35 2.00 2.54 2.00 2.97 4.00 A H 0.55 0.45 0.57 0.31 0.44 0.50 0.00 O H 0.52 0.54 0.53 0.56 0.47 0.58 0.66 E HWE NS NS NS NS NS NS S*

Ld158 NO 1.00 2.00 1.00 1.00 1.00 2.00 2.00 3.00 N 1.00 1.30 1.00 1.00 1.00 1.97 2.00 A H Mono 0.04 Mono Mono Mono 0.24 0.00 O H Mono 0.04 Mono Mono Mono 0.30 0.53 E HWE - NS - - - NS S

Ld167 NO 2.00 4.00 3.00 4.00 3.00 4.00 4.00 7.00 N 1.78 2.66 3.00 3.53 3.00 3.35 3.87 A H 0.11 0.23 0.33 0.77 0.58 0.47 0.50 O H 0.11 0.29 0.44 0.56 0.67 0.63 0.59 E HWE NS S NS NS NS S* NS

LOL24 NO 1.00 1.00 1.00 3.00 3.00 2.00 2.00 7.00 N 1.00 1.00 1.00 2.70 2.99 1.50 2.00 A H Mono Mono Mono 0.40 0.36 0.07 0.00 O H Mono Mono Mono 0.47 0.65 0.07 0.44 E HWE - - - NS NS NA S

LOL15 NO 1.00 1.00 1.00 2.00 2.00 3.00 3.00 4.00 N 1.00 1.00 1.00 2.00 2.00 2.78 2.88 A H Mono Mono Mono 0.08 0.38 0.35 0.25 O H Mono Mono Mono 0.41 0.51 0.43 0.59 E HWE - - - S NS S NS

LOL28 NO 3.00 2.00 3.00 4.00 5.00 5.00 3.00 9.00 N 2.27 1.67 3.00 3.58 3.80 3.55 3.00 A H 0.09 0.13 0.14 0.58 0.27 0.50 0.33 O H 0.18 0.13 0.39 0.70 0.54 0.50 0.63 E HWE S NS NS NS S NS NS

LOL23 NO 1.00 1.00 1.00 2.00 3.00 4.00 3.00 5.00 N 1.00 1.00 1.00 2.00 2.99 3.29 3.00 A H Mono Mono Mono 0.33 0.56 0.59 0.25 O H Mono Mono Mono 0.51 0.64 0.54 0.58 E HWE - - - NS NS NS NS

CS20 NO 2.00 2.00 2.00 2.00 2.00 3.00 ND 3.00 N 1.96 1.52 2.00 1.58 1.44 1.82 - A H 0.27 0.09 0.14 0.08 0.06 0.12 - O H 0.25 0.09 0.14 0.08 0.06 0.12 - E HWE NS NS NS NS NS NS -

CS34 NO 1.00 2.00 1.00 1.00 1.00 2.00 ND 3.00 N 1.00 1.30 1.00 1.00 1.00 2.00 - A H Mono 0.04 Mono Mono Mono 0.60 - O H Mono 0.04 Mono Mono Mono 0.48 - E HWE - NS - - - NS -

LD3 NO 1.00 1.00 1.00 1.00 1.00 2.00 - 2.00 N 1.00 1.00 1.00 1.00 1.00 2.00 ND A H Mono Mono Mono Mono Mono 0.38 - O H Mono Mono Mono Mono Mono 0.39 - E HWE - - - - - NS -

LD6 NO 4.00 4.00 3.00 4.00 6.00 6.00 ND 8.00 N 3.40 2.64 3.00 3.70 5.80 5.36 - A H 0.60 0.50 0.43 0.90 0.77 0.73 - O H 0.54 0.55 0.60 0.73 0.86 0.78 - E HWE NS NS NS NS NS NS -

Total alleles per site 19.00 23.00 19.00 27.00 29.00 36.00 21.00

Table A7: Summary statistics of Saccharina latissima genetic variation per site and per region including number of individuals (N), allelic richness adjusted for sample size (NA), observed heterozygosity (HO), unbiased expected heterozygosity (HE) and private allelic richness (Pa), percent of monomorphic loci (%Fixed) and FIS values. (+ Standard Error)

Saccharina latissima

Region Site Code N NA HE HO Pa %Fixed FIS Skagerrak Ska_1 18 3.23 (+ 0.4) 0.619 (+ 0.061) 0.519 (+ 0.049) 0 0 0.136 (+ 0.010) Ska_2 6 3.27 (+ 0.4) 0.682 (+ 0.080) 0.333 (+ 0.167) 0.01 (+ 0.0) 0 0.499 (+ 0.118) Ska_3 6 3.49 (+ 0.3) 0.672 (+ 0.027) 0.333 (+ 0.096) 0.67 (+ 0.3) 0 0.466 (+ 0.085) Ska_5 23 3.07 (+ 0.1) 0.485 (+ 0.025) 0.420 (+ 0.014) 0.28 (+ 0.1) 0 0.107 (+ 0.041) Ska_6 20 3.33 (+ 0.3) 0.563 (+ 0.075) 0.583 (+ 0.033) 0.08 (+ 0.1) 0 -0.084 (+ 0.048) Ska Region 73 4.54 (+ 0.2) 0.600 (+ 0.030) 0.475 (+ 0.033) 0.86 (+ 0.3) 0 0.205 (+ 0.009) North Sea Nor_1 14 3.33 (+ 0.2) 0.587 (+ 0.055) 0.500 (+ 0.109) 0.51 (+ 0.2) 0 0.140 (+ 0.070) Nor_2 9 2.12 (+ 0.2) 0.427 (+ 0.111) 0.630 (+ 0.206) 0 0 -0.472 (+ 0.100) Nor Region 23 4.08 (+ 0.4) 0.610 (+ 0.048) 0.551 (+ 0.072) 0.72 (+ 0.3) 0 0.072 (+ 0.038) Norwegian Nwg_1 13 3.58 (+ 0.8) 0.664 (+ 0.075) 0.436 (+ 0.156) 0.49 (+ 0.3) 0 0.35 (+ 0.117) Sea Nwg_2 5 4.33 (+ 0.3) 0.763 (+ 0.027 0.867 (+ 0.067) 0.01 (+ 0.0) 0 -0.261 (+ 0.041) Nwg Region 18 5.51 (+ 0.9) 0.703 (+ 0.038) 0.556 (+ 0.128) 0.64 (+ 0.4) 0 0.200 (+ 0.052) Barents Sea Bar_1 10 3.09 (+ 0.6) 0.475 (+ 0.156) 0.500 (+ 0.153) 0.20 (+ 0.2) 0 -0.122 (+ 0.015) Bar_2 13 3.80 (+ 0.1) 0.622 (+ 0.066) 0.359 (+ 0.026) 0.81 (+ 0.4) 0 0.378 (+ 0.059) Bar_3 2 ------Bar Region 25 5.51 (+ 0.9) 0.567 (+ 0.048) 0.400 (+ 0.061) 0.96 (+ 0.5) 0 0.292 (+ 0.011) Greenland Gre_1 9 4.77 (+ 0.1) 0.797 (+ 0.020) 0.667 (+ 0.064) 1.28 (+ 0.4) 0 0.114 (+ 0.048) Sea Gre_2 1 ------Gre Region 1 4.79 (+ 0.2) 0.777 (+ 0.018) 0.667 (+ 0.033) 1.93 (+ 0.4) 0 0.095 (+ 0.019)

Table A8: Summary statistics of Laminaria genetic variation per site and per region including number of individuals (N), allelic richness adjusted for sample size (NA), observed heterozygosity (HO), unbiased expected heterozygosity (HE) and private allelic richness (Pa), percent of monomorphic loci (%Fixed) and FIS values. (+ Standard Error)

Laminaria hyperborea Site Code %Fixed %Fixed N NA (1) NA (2) H (1) H (2) H (1) H (2) P (1) P (2) F (1) F (2) E E O O a a (1) (2) IS IS Ska_1# 4 ------

1.58 1.44 0.145 0.115 0.147 0.107 0 0 -0.006 0.102 Ska_3 16 0.55 0.57 (± 0.2) (± 0.2) (± 0.063) (± 0.072) (± 0.068) (± 0.075) (± 0) (± 0) (± 0.024) (± 0.045)

1.59 1.57 0.148 0.138 0.135 0.122 0.12 0.14 0.035 0.06 Ska_4 7 0.36 0.43 (± 0.2) (± 0.3) (± 0.062) (± 0.075) (± 0.055) (± 0.064) (± 0) (± 0.1) (± 0.009) (± 0.018)

1.73 ( 1.71 0.191 0.222 0.147 0.178 0.14 0.22 0.153 0.124 Ska_5 23 0.55 0.43 ± 0.3) (± 0.4) (± 0.074) (± 0.087) (± 0.062) (± 0.081) (± 0.1) (± 0.2) (± 0.027) (± 0.05)

1.84 1.61 0.158 0.147 0.139 0.123 0.31 0.26 0.082 0.13 Skagerrak 50 0.27 0.29 (± 0.3) (± 0.3) (± 0.063) (± 0.073) (± 0.058) (± 0.069) (± 0.4) (± 0.1) (± 0.016) (± 0.029)

2.24 2.48 0.379 0.494 0.314 0.385 0.18 0.26 0.163 0.231 Nor_1 16 0.27 0.14 (± 0.3) (± 0.3) (± 0.091) (± 0.091) (± 0.097) (± 0.109) (± 0.1) (± 0.2) (± 0.032) (± 0.052)

2.46 2.54 0.399 0.491 0.311 0.42 0.09 0.1 0.173 0.115 Nor_2 16 0.27 0.14 (± 0.4) (± 0.4) (± 0.097) (± 0.087) (± 0.081) (± 0.089) (± 0.1) (± 0.1) (± 0.017) (± 0.035)

2.76 2.75 0.401 0.515 0.314 0.365 0.46 0.61 0.209 0.291 North Sea 32 0.27 0.14 (± 0.5) (± 0.4) (± 0.095) (± 0.091) (± 0.085) (± 0.079) (± 0.6) (± 0.2) (± 0.018) (± 0.023)

2.78 2.77 0.438 0.439 0.413 0.39 0.93 0.37 0.017 0.072 Nwg_1 17 0.00 0.00 (± 0.3) (± 0.3) (± 0.064) (± 0.072) (± 0.062) (± 0.067) (± 0.1) (± 0.1) (± 0.014) (± 0.019)

Nwg_2# 1 ------

Nwg_3# 3 ------

3.14 2.96 0.423 0.456 0.384 0.351 1.1 0.43 0.071 0.17 Norwegian Sea 21 0.00 0.00 (± 0.4) (± 0.3) (± 0.06) (± 0.077) (± 0.055) (± 0.054) (± 0.5) (± 0.1) (± 0.014) (± 0.023)

2.96 0.573 0.19 0.58 0.524 Bar_3* 9 - - - - - 0.00 - (± 0.3) (± 0.027) (± 0.074) (± 0.3) (± 0.054)

2.96 0.573 0.252 0.5 0.524 Barents Sea - - - - - 0.00 - (± 0.3) (± 0.027) (± 0.073) (± 0.2) (± 0.054)

Saccharina latissima

Laminaria hyperborea (Three regions, 11 markers)

Laminaria hyperborea (Four Regions, seven markers)

Figure A3: L(K) and DeltaK calculated by STRUCTURE HARESTOR, used to predict the best K fit for the STRUCTURE analyses. Top row: Saccharina latissima analysis, Middle row: Laminaria hyperborea analysis with three regions and 11 markers, Bottom row: Laminaria hyperborea analysis with four regions and seven markers.