kelp species in Norway

(1)

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

(2)

II

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

(3)

III

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!

(4)

IV

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

(5)

V

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.

(8)

2

(9)

3

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 km²of 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-

(10)

4

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

(11)

5

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.

(12)

6

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.

(13)

7

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).

(14)

8

Table 1: Study sites for Saccharina latissima and Laminaria hyperborea. Region, Site Code, Location, Latitude (Lat.), Longitude (Long.) Sample Size of sites (N Total), Sample size with successful amplification (N Data), Date of collection and Collector:Janne Kim Gitmark (JKG), Hartvig C. Christie (HCC), Ann M. Evankow (AME). Saccharina latissima RegionSite CodeLocationLat.Long.N TotalN DataDate CollectedCollector SkagerrakSka_1Store Arøya, Helgero, Vestfold 58.99358 9.808542418Jun. 2014 JKG Ska_2Risøyodden, Langesund, Telemark 59.02330 9.753737 6 Jun. 2014 JKG Ska_3Øytangen, Jomfruland, Telemark 58.88209 9.60771246 Apr. 2014 HCC Ska_5Homborøy, Grimstad, Aust-Agder 58.25454 8.522822423Jun. 2014 JKG Ska_6Korsvikfjord, Kristiansand, Vest-Agder 58.13230 8.066372420Jun. 2014 JKG North SeaNor_1Rossholmen, Stavanger, Rogaland59.05966 5.718511714Jun. 2014 JKG Nor_2Raunefjorden, Bergen, Hordaland60.26975 5.22189209 Jul. 2014AME Norwegian SeaNwg_1Gåsøya, Sandøy, Møre & Romsdal 62.79949 6.532572413Jun. 2014 HCC Nwg_2Igerøy, Vega, Nordland65.69011 12.13096 315 Sep. 2014 HCC Barents SeaBar_1Sandholmen, Porsangerfjord, Finnmark 70.42583 25.24863 1910Aug. 2014 HCC Bar_2Leirpollen, Porsangerfjord, Finnmark 70.38939 25.46456 1613Aug. 2014 HCC Bar_3#Kongsfjord, Berlevåg, Finnmark 70.68840 29.37542 232#Aug. 2012 HCC Greenland SeaGre_1Grønfjorden, Barentsburg, Spitsbergen78.06583 14.20133 129 Oct. 2014AME Gre_2#Adventfjorden, Longyearbyen, Spitsbergen 78.25372 15.41303 9 1#Oct. 2014AME Laminaria hyperborea RegionSite CodeLocationLat.Long.N TotalN DataDate CollectedCollector SkagerrakSka_1#Store Arøya, Helgero, Vestfold 58.99358 9.808547 4#Jun. 2013 JKG Ska_3Saltsteinbåen, Jomfruland, Telemark 58.87283 9.609882416Apr. 2014 HCC Ska_4Tromøy N., Arendal , Aust-Agder 58.51320 8.94430277 Jun. 2014 JKG Ska_5Prestholmen, Grimstad, Aust-Agder 58.27320 8.537202423Jun. 2014 JKG North SeaNor_1Rossholmen, Stavanger, Rogaland 59.05966 5.718512116Jun. 2014 JKG Nor_2Viksøyna, Bergen, Hordaland 60.16200 5.037982016Jul. 2014AME Norwegian SeaNwg_1Kvaløya, Finnøy, Møre & Romsdal62.81124 6.509282017Apr. 2014 HCC Nwg_2# Igerøy, Vega, Nordland65.69011 12.13097 6 1#May. 2012 HCC Nwg_3# Ivarsbraken, Vega, Nordland65.67637 11.54940 9 3#May. 2012 HCC Barents SeaBar_3*Kongsfjord, Berlevåg, Finnmark 70.68840 29.37542 249 Aug. 2012 HCC #Sites with fewer than five samples were not used in all data analyses. *Bar_3 was only included in analyses with seven markers.

(15)

9

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.

(16)

10

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.

(17)

11

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.

(18)

12

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).

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

Saccharina latissima

Marker T_a (°C) Pooled

on ABI

Fluorescent Dye

Allele size range

# of

alleles Developed by: EST

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

Marker T_a (°C) Pooled

on ABI

Fluorescent Dye

Allele size range

# of

alleles Developed by: EST

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

(19)

13

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).

(20)

14 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.

(21)

15 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 STRUCTUREHARVESTER (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).

(22)

16

(23)

17

Results

Microsatellite selection

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.

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.

(24)

18

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.

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.

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.

(25)

19

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).

(26)

20

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.

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Allelic Richness

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Expected Heterozygosity

0.0 0.2 0.4 0.6 0.8 1.0

Observed Heterozygosity

* *

*

(27)

21

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.

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).

(28)

22 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).

(29)

23

Figure 5: Average allelic richness (N_A), expected heterozygosity, (H_E) 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.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

SKA NOR NWG BAR

Allelic Richness

0.0 0.2 0.4 0.6 0.8 1.0

SKA NOR NWG BAR

Expected Heterozygosity

0.0 0.2 0.4 0.6 0.8 1.0

SKA NOR NWG BAR

Observed Heterozygosity

*

b

a

b

a, b

kelp species in Norway

Genetics of Norwegian kelp forests

Microsatellites reveal the genetic diversity, differentiation, and structure of two foundation