Population structure in the red calcifying alga

Corallina officinalis in the North Atlantic: implications in a time of global climate change

William Henry Glynn SN: 60732

Supervisors: Professor Juliet Brodie and Dr Chris Yesson

Thesis for Project 1, MRes Biodiversity, Evolution and

Conservation; 27/04/2018

Contents

Abstract 2 Introduction 2 Material and Methods 7 1. Sample collection and preparation 7 1.1. Contemporary material 7 1.2. Herbarium material 8 1.3. Sample preparation and DNA extraction 8 2. Genotyping 9 2.1. Kompetitive Allele Specific PCR genotyping 9 2.2. Initial screening of results 9 2.3. DNA barcoding for species identification 12 3. Statistical analyses 13 3.1. Tests for Hardy-Weinberg equilibrium and linkage disequilibrium 13 3.2. Genetic differentiation and isolation by distance 13 3.3. Discriminant analysis of principal components (DAPC) 13 3.4. Haplotype network analysis 14

3.5. DNA barcoding for species identification 14 Results 14 1. Genetic differentiation and isolation by distance 14 2. Discriminant analysis of principle components (DAPC) 16 3. Haplotype network analysis 18 4. DNA barcoding for species identification 19 Discussion 20 1. Contemporary genetic differentiation 21

2. Connectivity and gene flow 22 3. Adaptive capacity 23 4. Conclusions and further research 25 Acknowledgements 26 References 27 Appendices 33

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Abstract

The intertidal zone consists of a variety of habitats providing shelter and resource for a wide array of wildlife including commercial species. Red coralline algae are an important component of these habitats as ecosystem engineers, primary producers and links in the carbon and carbonate cycles. Global climate change leading to increased sea surface temperatures and oceanic acidification threatens the survival of coralline algae through complex interactions affecting the production of their calcite skeletons. Much research has been undertaken investigating the effects of this on small scales, but very little is known about the potential impacts on population or species scales. On these scales genetic differentiation, connectivity between populations, and available genetic diversity enabling adaptation needs to be considered. For the first time, this paper uses population genetic analyses on single nucleotide polymorphisms identified in the red calcifying alga Corallina officinalis across the North Atlantic to determine how these factors may influence this species under a changing climate. C. officinalis showed significant population structuring over fine and large scales and demonstrated strong isolation by distance. Discriminant analysis of principal components identified 7 genetic clusters which broadly corresponded to geographical regions and highlighted that Icelandic and Spanish populations were the most isolated. However gene flow was observed between most populations. The highest levels of diversity were observed around the British Isles particularly around the Irish Sea. This indicates that the British Isles may be a goldilocks zone: a hotspot for adaptation to climate change, and highlights the importance of protecting the intertidal zone to facilitate the spread of adaptive genes across the wider population.

Introduction

The intertidal zone is a highly productive environment which is of great importance ecologically and economically. As well as providing habitat for numerous commercial shellfish species (e.g. Coscia et al., 2013), it also offers refuge for juvenile pelagic species important for many fisheries (Dugan & Davis, 1993). Furthermore the dynamic environmental, and complex structural conditions provide a wealth of habitats and ecological niches which has driven the evolution of an equally complex and diverse

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array of species (Ray, 1991). The intertidal zone is therefore deserving of focussed ecological research, and conservation of this zone is a high priority. However the intertidal zone is subject to numerous pressures threatening to degrade and disrupt the important ecological services it provides. The main threats originate from human activities in the coastal zone and include habitat loss (Dugan et al., 2008), exploitation of natural resources (Beck et al., 2015), the introduction and spread of invasive species and pollution (Crowe et al., 2000) all of which diminish the functionality of the ecosystem. Global environmental changes are already being seen to impact on the health of intertidal systems and are likely to exacerbate the effects of other threats (Brodie et al., 2014).

Human activities have led to an increase of anthropogenic greenhouse gas (GHG) emissions of 181.5% between 1971 and 2010, and the rate of these emissions increased by 2.2% yr-1 between 2000-2010 (IPCC, 2014). This has caused substantial changes to the marine environment including temperature increases in the uppermost 75 m of the ocean of around 0.11oC per decade between 1971-2010 (IPCC, 2014) and an increase in acidification of 26% since the end of the 19th century (a decrease in pH of 0.1; Orr et al., 2005; Rhein et al., 2013). Increasing sea surface temperatures (SST) and oceanic acidification (OA) are predicted to have consequences for the distribution of numerous marine organisms which are adapted to their own particular thermal and chemical niches (Brodie et al., 2014; Helmuth et al., 2006; Poloczanska et al., 2013).

Those which deposit calcium carbonate (CaCO3) shells or skeletal structures are likely to be amongst the worst affected (Feely et al., 2004; Williamson et al., 2014).

Thirty percent of anthropogenic carbon dioxide (CO2) is absorbed by the ocean (Le

Quéré et al., 2009; Sitch et al., 2013) where it reacts with H20 to form carbonic acid + 3- (H2CO3). This dissociates to form hydrogen ions (H ) and bicarbonate ions (HCO ) increasing the concentration of H+, and therefore the acidity of the oceans. Surplus hydrogen ions leads to a decreased saturation state of CaCO3 owing to increased 2- conversion of carbonate ions (CO3 ) to bicarbonate (H2CO3) (Jokiel, 2011). This 2- removes the availability of CO3 for the precipitation of CaCO3, resulting in increased bioenergetic costs of organic CaCO3 production (Waldbusser et al., 2016) and potentially the dissolution of organic structures (e.g. calcite skeletal elements of

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calcifying organisms). Models of CO2 input and the effects of increased acidification suggest that by the end of this century parts of the ocean particularly in cool, deep and high latitude regions will become undersaturated with carbonates, leading to net dissolution of calcites (Feely et al., 2004; Andersson et al., 2008; Basso, 2012).

In the intertidal zone coralline red algae (Rhodophyta), seaweeds with calcite skeletons, are an important component of the ecosystem. The production of calcite facilitates the growth and development of intertidal reefs through the stabilisation of loose substrate, providing opportunities for other organisms to settle and grow. Coralline algae produce a high-magnesium form of calcite (HMC), wherein magnesium is partially substituted for the calcium component (Basso, 2012). HMC is more susceptible to dissolution under acidification, and as such coralline algae have been identified as at high risk from OA (Andersson et al., 2008; Basso, 2012; Brodie et al., 2014).

To date there has been much research focussed on the effect of OA on calcite production in calcified algae which have demonstrated a complex response. Many report decreased production of calcite structures (for example Hofmann et al., 2012; Jokiel et al., 2008; Kuffner et al., 2008), reduction in overall cover of coralline algae (Kuffner et al., 2008; Johnson & Carpenter, 2012) and differing strategies for maintaining calcite production under decreased pH (McCoy & Ragazzola, 2014). Increased acidification has also been seen to affect settlement success in coralline algae (Fabricius et al., 2015). Although the literature agrees on the overall negative impacts to calcite production with increased OA, there have also been studies which have recognised increased calcite production at moderate levels of OA (Johnson & Carpenter, 2012; Ries et al., 2009; Noisette et al., 2013), demonstrating that the impacts are affected by other factors. Temperature influences the algal response to increased OA, with higher temperatures stimulating increased growth regardless of OA (Legrand et al., 2017; Martin & Gattuso, 2009), although Diaz-Pulido et al. (2012) found that warmer temperatures exacerbated the effects of increased OA on the dissolution of calcite skeletons. However, species found in rock pools are exposed to local and frequent changes in temperature and pH due to tidal movements and evaporation, greater than those predicted under climate change. Rock pool species

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are therefore adapted to high variability in pH and temperature and may succumb less readily to future climate change (Williamson et al., 2017).

There is also evidence to suggest that fleshy algal species will respond positively to OA (Johnson et al., 2014; Kroeker et al., 2013) and there are predictions that sea grasses will also do well (Brodie et al., 2014). This could have significant impacts on benthic community composition through interspecific competition. Mesocosm experiments have shown that algal communities may shift towards becoming dominated by fleshy species (Kroeker et al., 2012; Kuffner et al., 2008). Changes at the algal community level are likely to impact the survival of other species which depend on coralline turfs and other calcified algae for food and protection.

Despite the wealth of research into the effects of climate change on coralline algae at the individual and community scale, little research has been undertaken into the potential effects on the population or species scale. The ability of species to track these environmental changes will depend on their dispersal abilities and also on their capacity to evolve tolerance to novel conditions (Harley et al., 2006; Ragazzola et al., 2013). In addition, impacts on entire species are likely to be observed much later than the effects happening at a population level (Razgour et al., 2017). It is therefore important to develop our understanding of the factors which impede or promote dispersal, and how genetic variation in populations may contribute to their adaptive capacity, in order to make predictions and develop strategies to mitigate for future impacts on marine populations (Dawson et al., 2011; Valero et al., 2001).

Advances in high throughput sequencing and the discovery of highly variable genetic markers have encouraged the growth of population genetics. Population genetic studies make use of these highly variable DNA sequences to examine patterns of inheritance over several generations (as opposed to multi-generational or evolutionary timescales). The results of these techniques can be obscured when the DNA regions are under selection, or influenced by other biological factors (for example non-random mating). But they can be used to infer population structuring and connectivity at a population level. Furthermore they can be used to identify ‘hot spots’ of genetic diversity which may provide refugia for the species under future climatic conditions. These hot spots may also be more likely to have or to produce phenotypes beneficial

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in changing environmental conditions, and so serve as centres for adaptive capacity. These factors combined allow us to anticipate the risks to populations through the loss of genetic diversity, the potential for populations to expand and diversify, and for scientists and policy makers to prioritise conservation measures.

There have been relatively few population genetic studies of red algae, and many of these have made use of DNA sequence regions (e.g. Bouza et al., 2006; Wang et al., 2008) or microsatellite markers (e.g. Krueger-Hadfield et al., 2011; Song et al., 2013). Single nucleotide polymorphisms (SNPs) which are single base pair variations at a particular locus in the genome are now widely used for population genetic studies. They show codominant inheritance and there are potentially 1000s available throughout the genome allowing for a detailed investigation of population structuring. SNPs have been widely used for many species, but their application to studies of algae has been limited. A study by Provan et al. (2013) made use of six SNPs in the red alga Chondrus crispus, and demonstrated their worth in identifying population structuring.

Recently a set of 11 SNP markers were published for C. officinalis (Yesson et al., 2018) and their use has highlighted geographic isolation of populations across the north east Atlantic, and hotspots of diversity and population structuring on the south coast of the United Kingdom (Jackson, 2016; Vale, 2017; Yesson et al., 2018). These patterns of connectivity and diversity merit further consideration and may be resolved by sampling from a greater number of geographically separated sites.

Corallina officinalis is a geniculate coralline alga found in temperate waters throughout the littoral zone in the North Atlantic (Brodie et al., 2013). It is part of a complex of morphologically very similar species which can be difficult to differentiate in the field (Hind et al., 2014). Recent phylogenetic studies of the Corallineae tribe identified widespread misidentification of museum specimens, and recognised 20 different clades within the genus Corallina which likely correspond to species yet to be formally described (Williamson et al., 2015).

As an important component of intertidal habitats it is essential to assess the threats to coralline algae, and C. officinalis is a good candidate model for several reasons. Taxonomic stability is an important factor in selecting models as indicators of environmental change (Manoylov, 2014; Pauls et al., 2013), and the recent advances

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in establishing phylogenetic relationships within the Corallinoideae (Brodie et al., 2016, 2013; Hind & Saunders, 2013; Walker et al., 2009; Robba et al., 2018) along with the confirmation of the C. officinalis clade (Williamson et al., 2015) provides this. The cosmopolitan distribution of the species, spanning a wide range of habitats and climatic conditions allows for in situ comparison of population dynamics and plasticity to differing environmental conditions (Ragazzola et al., 2013; Brodie et al., 2014). As an important component of the carbon and carbonate cycles, the use of C. officinalis also opens up the possibility of gaining insights into wider effects on geochemical and abiotic factors (Andersson et al., 2008; Basso, 2012). There is also a wealth of literature on the susceptibility of corallines to OA and temperature fluctuations (e.g. Hofmann et al., 2012; Martin & Gattuso, 2009; Noisette et al., 2013) enabling forecasting of how future changes may affect the species.

In this report I will build on the work of Jackson (2016), Vale (2017) and Yesson et al. (2018) to demonstrate how the use SNPs obtained from specimens of C. officinalis from around the north east Atlantic can be used to investigate the population genetic structure of this species. Furthermore historic samples obtained from the algal herbarium of the Natural History Museum (London) have been included to increase the sampling range. This paper focuses on three areas pertinent to understanding the response of species to environmental changes: current genetic differentiation, gene flow and adaptive capacity. These are discussed in the context of global environmental change in order to gain a deeper understanding of the potential impact of global changes on this important component of temperate intertidal habitats.

Material and Methods

1. Sample collection and preparation 1.1. Contemporary material

Samples of Corallina officinalis were collected from around the north east Atlantic between 2012-2018 (Table 2 and Figures 1 & 1a). Where possible three subpopulations comprising ~17 individuals were sampled at each location, with two sets of material taken for each: one stored in silica beads for molecular analysis, and - 7 -

the other pressed and dried for storage in the algal herbarium of the Natural History Museum (BM). For this study 8 new populations were sampled (Table 2).

1.2. Herbarium material

C. officinalis samples from the BM algal herbarium collections were identified spanning a range of dates and locations from which material could be taken without damaging the integrity of the specimen (n=106; Table S1 and Figures 1 and 1a).

1 1a HF IC BA SG HE

SI SL FC NE

SP SK AN KE CM BL WP BR AZ JY

Figure 1. Map showing the locations of contemporary ( ) and successful herbarium (HB; ) samples across the north Atlantic. Figure 1a. Sample locations of contemporary samples ( ) across the British Isles. Refer to site codes in Table 1.

1.3. Sample preparation and DNA extraction

Approximately 0.5 cm2 of dry material was removed from each sample and given a unique identifier. Samples were disrupted using 3 mm tungsten carbide beads for contemporary samples and two 5 mm glass beads for historic samples in the TissueLyser II on two 1 minute cycles at 30 Hz, before adding 300 µl of RLT Buffer. Historic samples were left in RLT Buffer to allow for an extended lysis period, whereas the contemporary material was left for 30 minutes. DNA extraction was carried out using the BioSprint 96 following the procedure laid out in the BioSprint DNA Plant Handbook (QIAGEN, 2016). Following completion of the BioSprint protocol the DNA

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was suspended in 200 µl AE Buffer. In order to achieve the required concentration of 50 ng µl-1, samples which were slightly below this value were evaporated in a vacuum for between 30 minutes and 1 hour, and samples above this value were diluted in AE buffer.

DNA was successfully extracted from all samples, although following preparation, 23 contemporary samples (Table 2) and 52 of the BM samples (Table S1) failed to produce sufficient yields for genotyping.

2. Genotyping 2.1. Kompetitive Allele Specific PCR genotyping

Samples were eluted into 96-well plates at volumes of 50 µl for contemporary material (n=273) and 30 µl for historic material (n=52) due to lower volumes following evaporation. These were then delivered to LGC Genomics (https://www.lgcgroup.com/services/genotyping) for Kompetitive Allele Specific PCR genotyping (Semagn et al., 2014) of 10 SNPs (Table 1). All SNPs performed well, other than Coff10 which failed to amplify samples into clusters, as such this SNP has been excluded from further analysis.

2.2. Initial screening of results

Results were returned in the form of a bi-allelic scoring matrix which was viewed in LGCs SNP viewing software which assigns samples to clusters for each SNP. Where this failed, samples were manually assigned to the most appropriate cluster where possible, or discounted if not. These results were then combined with the results from previous studies (Tables 2 and 3; Jackson, 2016; Vale, 2017; Yesson et al., 2018).

Populations were screened to identify samples for which 8 or more SNPs were recovered to ensure that the data was robust enough for analysis (Table 3). Samples with less than 8 SNPs were removed from the analysis. Populations represented by 30 or more remaining samples were considered to be core populations; sites with fewer samples (HF, JY and SG) were retained for further analyses to enable a comparison (Table 3).

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Name Sequence Variation HO HE P-value Coff1 TATTGGAATTTAAAA[A/T]TTGTGACTCTGAAA A/T 0.077 0.112 5.73e-13 Coff2 ACCAAGGGCCCTGCT[G/A]CCGCCGACAATGCG G/A 0.109 0.223 < 2.2e-16 Coff3 GACAGTGTATAGGAG[C/T]TGTGCCGTATTGAA C/T 0.056 0.083 1.24e-11 Coff4 CAATTGACAGACTAA[A/T]GTACAAATCTAACG A/T 0.195 0.240 1.23e-06 Coff5 TGTGTAAGGTGATGA[C/T]CATCGTCGTCGAAC C/T 0.110 0.160 2.34e-14 Coff6 GCTGGCTACAAGACC[G/C]AGACAAAACAACGC G/C 0.129 0.201 < 2.2e-16 Coff8 ATGAAGAACGAAGTA[T/A]GTCACTATGCGTCT T/A 0.161 0.249 < 2.2e-16 Coff9 TTGGATTAAGATAGA[G/A]TTTTTATTTATTTA G/A 0.174 0.237 3.20e-12 Coff10 TTATTCTATGGAATG[C/T]CAAGGGCAATATCA C/T N/A N/A N/A Coff11 AAGAGACACCGAAAC[A/T]TTCAAATTCGGAAG A/T 0.160 0.242 < 2.2e-16 Table 1. SNP markers used in the present study. Coff7 was excluded as it had previously failed to amplify samples into clusters (Vale, 2017); Coff10 failed to amplify into clusters during this study so is also excluded from analyses. Hardy-Weinberg statistics: HO/HE observed/expected heterozygosity and associated P-value.

Site n Site n AN 34 JY 9 AZ 0† KE 31 BA 50 SG 17 BL 50 SI 0† BR 34 SK 39 CB/WH 44 SL 47 CM 42 SP 45 FC 37 WP 41 HE 47 HF 14 HB 24 IC 44 HUK 17 Table 3. Number of samples from each population for which data on 8 or more SNPs was recovered. CB and WH have been combined into a north eastern UK population as they are geographically close. †= populations which were not included in further analyses.

Table 2 (on next page). Summary of locations sampled for the contemporary dataset of C. officinalis; * sampled for this report; N = the number of samples collected at each site, ΣN = 785; figures in brackets are the number of samples genotyped for this study Σ = 273.

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Coordinates Date Location Code Site N (Lat, Long) Site 1 53.406475, -4.517825 10/01/2017 Anglesey, Wales AN Site 2 53.406602, -4.528673 50 Site 3 53.405182, -4.559862 Site 1 37.70811, -25.50875 29/07/2017 Azores* AZ 11 (11) Site 2 37.74211, -25.65075 Site 1 57.756721, -3.906876 08/01/2018 Baltinore, Scotland* BA Site 2 57.758901, -3.901935 50 (50) Site 3 57.761972, -3.897277 Site 1 50.688305, -1.069063 Bembridge, Isle of 25/05/2017 BL Site 2 50.687853, -1.068749 50 Wight Site 3 50.688155, -1.068393 Site 1 50.799239, -0.038340 24/05/2017 Brighton, U.K. BR Site 2 50.801225, -0.048572 49 Site 3 50.801850, -0.056961 Site 1 54.25292, -0.3658333 27/08/2017 Cayton Bay, U.K.* NE Site 2 54.24094, -0.3484167 50 (42) Site 3 54.304106, -0.407667 08/09/2012 Combe Martin, U.K. CM Site 1 51.21675, -4.025717 38 Site 1 55.839626, -4.891948 Firth of Clyde, 08/01/2017 FC Site 2 55.836984, -4.891692 50 Scotland Site 3 55.827237, -4.889727 Site 1 56.455242, -6.262189 18/09/2016 Hebrides, Scotland HE Site 2 56.447656, -6.262432 50 Site 3 56.466924, -6.235313 13/11/2016 Hafrsfjord, Norway* HF Site 1 58.96222, 5.601944 17 (15) 05/01/2014 þorlákshöfn, Iceland IC Site 1 63.8485, -21.36154 38 Site 1 49.160403, -2.070304 Jersey, Channel 14/01/2018 JY Site 2 49.163547, -2.058067 51 (47) Islands* Site 3 49.160679, -2.043783 06/05/2012 Thanet, Kent, U.K. KE Site 1 51.28916, 1.379471 38 25/10/2017 Sandgerdi, Iceland* SG Site 1 64.046254, -22.715201 17 (17) 2017 Taorminoa, Sicily* SI Site 1 37.850681,-15.300516 1 (1) Site 1 53.582710, -6.117631 02/06/2017 Skerries, Ireland* SK Site 2 53.586215, -6.103418 49 (43) Site 3 53.586789, -6.134009 Site 1 54.219434,-9.096202 31/12/2017 Sligo, Ireland* SL Site 2 54.217225,-9.095711 50 (47) Site 3 54.225677,-9.097123 07/05/2016 Comillas, Spain SP Site 1 43.39201, -4.291019 38 Site 1 54.508889, -0.672704 15/05/2017 Whitby, U.K. NE Site 2 54.490724, -0.608681 50 Site 3 54.488254, -0.589468 18/06/2012 Wembury Point, U.K. WP Site 1 50.31246, -4.080734 38

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2.3. DNA barcoding for species identification

During the initial screening process a large number of samples failed to amplify using the SNP primers, notably from the Azores, Jersey, the north east U.K. and Sicily (Table 3). In order to identify whether sampling error was the cause, sequence data for six randomly selected samples (Table 4) was generated based on the cyctochrome c oxidase I (COI; Robba et al., 2006), ribulose-bisphosphate carboxylase (rbcL; Freshwater & Rueness, 1994) and the PSII reaction centre protein D1 (psbA; Hind & Saunders, 2013) genes.

Region Primers JY_4 JY_5 JY_28 JY_40 SI_1 AZ_11 psbA psbA F+R y y y y y y GazF1-GazR1 n y y y y y COI RWCOF1-RWCOR1 n y y y n y F57-R753 y y y y y y rbcL RWCWF1-RWCWR1 y n y y y y Table 4. Samples and primers used for sequencing for species identification. 'y' indicates successful amplification and 'n' unsuccessful amplification.

The COI region was amplified using the primers GazF1 and GazR1 (Saunders, 2005), and RWCOF1 and RWCOR1 (Williamson et al., 2015). The primers F57 and R753 (Freshwater & Rueness, 1994), and RWCWF1 and RWCWR1 (Williamson et al., 2015) were used to amplify the rbcL region. Finally, amplification of the psbA gene region was achieved using the primers psbAF1 and psbAF2 (Saunders & Moore, 2013).

Each PCR tube contained 2.5 µl NH4 reaction buffer, 1.5 µl 50 mM MgCl2, 0.5 µl Taq

polymerase (all from BIOTAQ DNA polymerase kit, Bioline, UK), 17 µl dH2O, 0.5 µl deoxynucleotide triphosphate stock, 1 µl each of 10 mM forward and reverse primers and 1 µl of DNA template material. All PCR reactions followed a standard protocol of one cycle at 94oC for 2 minutes; 5 cycles at 94oC for 30 s, followed by 30 s annealing at 45oC, and extension at 72oC for 1 minute; 35 cycles of 94oC for 30 s, 46.5oC annealing for 30s and extension at 72oC for 1 minute; and a final extension for 7 minutes at 72oC (following Saunders & Moore, 2013). PCR runs were carried out using a Techne Thermal Cycler (Bibby Scientific, UK). Sequences were generated using the 3730 XL Applied Biosystems Capillary Sequencer.

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3. Statistical analyses

All statistical analyses were carried out in R Studio v1.1.383 (R Studio Team, 2016).

3.1. Tests for Hardy-Weinberg equilibrium and linkage disequilibrium

The contemporary and historic datasets were combined and each SNP tested for Hardy-Weinberg Equilibrium using the HWE.test function of the R package ‘genetics’ (Warnes & Lesich, 2013). Pairwise linkage disequilibrium was examined using the LD function also from the ‘genetics’ package, p-values were subjected to Bonferroni correction to assess significance.

3.2. Genetic differentiation and isolation by distance

Pair-wise FST values (Weir & Cockerham, 1984) were calculated for all contemporary data using the pairwise.fst function in the package ‘hierfstat’ (Goudet, 2005; Goudet & Jombart, 2015). Bootstrap values were then calculated over 10,000 cycles using the bootstrap.ppfst to assess their significance. Geographic distances between populations across water were measured in QGIS (Table 6), before being incorporated into a Mantel test using the mantel.randtest function in ‘ade4’ (Dray & Dufour, 2007) in order to assess patterns of genetic isolation by distance.

3.3. Discriminant analysis of principal components (DAPC)

The full contemporary dataset was combined with the BM material from across the species’ range (HB; Table A1) and analysis was carried out using ‘adegenet’ (Jombart, 2008). The find.clusters function was used to find the number of genetic clusters (K), by referring to the Bayesian information criterion to optimise K by assessing successive K-means (Jombart et al., 2010). The “diffNgroup” automatic clustering calculation was used at 1e6 iterations with n.start=1e4 and the number of principle components was selected based on optimising the amount of variance explained (to 95%). K-selection failed to converge during analysis, however the results over successive tests were consistent and therefore the outcomes were accepted.

DAPC (Jombart et al., 2010) was carried out using the dapc function and selection of principle components to retain for analysis was automatically carried out using the optim.a.score function. The assignment of samples to genetic clusters was visualised using the compoplot function.

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3.4. Haplotype network analysis

In order to further assess levels of gene flow between populations a haplotype network for the combined contemporary and historic (HB) samples was generated using the haploNet function of ‘pegas’ (Paradis, 2010). Samples were grouped into broad geographic regions based on the results of the DAPC to enable assessment of regional patterns, and SNP sequences were converted into the IUPAC format in order to be compatible with the haploNet function.

3.5. DNA barcoding for species identification

Sequences were edited in BioEdit v7.0.5.3 (Hall, 1999), and aligned using the ClustalW application. Sequences were then compared to others recorded in GenBank using the Basic Local Assignment Search Tool (BLAST; Altschul et al., 1990).

Results

For this study 666 samples of C. officinalis from 14 complete populations, 3 partial populations and several historic locations from across the north east Atlantic were genotyped for 9 SNPs (Tables 1 and 3). All SNPs showed significant deviation from Hardy-Weinberg equilibrium (see Table 2), with a high occurrence of homozygotes being evident for each SNP. Out of 45 pairwise combinations of SNPs, 15 showed significant evidence of linkage (Table 5).

1. Genetic differentiation and isolation by distance

The majority of pairwise FST values were highly significant (Table 6), with the greatest difference being observed between the Kent and Icelandic populations. The lowest genetic differentiation was seen between Skerries and Coombe Martin. Kent showed the highest levels of differentiation amongst populations from the British Isles, and Coombe Martin the lowest. Genetic differentiation is lower overall around the Irish Sea and northern populations. A significant pattern of isolation by distance was observed (r = 0.69, p = 0.0001; Figure 2).

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Average Region Norway Northern British Isles Ireland and Irish Sea Eng. Channel (W) Eng. Channel (E) Spain Iceland Fst Pop HF NE BA HE FC SL SK AN CM WP JY BL BR KE SP IC SG 0.157 HF Ψ 0.096 0.130 0.053 0.086 0.107 0.091 0.103 0.077 0.157 0.244 0.189 0.130 0.112 0.137 0.456 0.344 0.155 NE 634 Ψ 0.071 0.088 0.155 0.085 0.035 0.131 0.058 0.186 0.165 0.118 0.047 0.105 0.278 0.484 0.373 0.159 BA 572 507 Ψ 0.027 0.126 0.035 0.079 0.095 0.089 0.164 0.128 0.186 0.145 0.239 0.243 0.441 0.348 0.158 HE 901 919 524 Ψ 0.082 0.036 0.100 0.107 0.104 0.180 0.180 0.223 0.158 0.216 0.180 0.438 0.358 0.186 FC 1131 1132 739 232 Ψ 0.113 0.111 0.163 0.087 0.202 0.210 0.226 0.220 0.255 0.130 0.441 0.369 0.182 SL 1175 1181 800 309 389 Ψ 0.086 0.096 0.096 0.209 0.225 0.210 0.157 0.228 0.253 0.530 0.442 0.132 SK 1241 1244 869 348 269 485 Ψ 0.083 0.011 0.158 0.059 0.046 0.053 0.095 0.280 0.466 0.352 0.160 AN 1260 1271 892 363 282 500 111 Ψ 0.044 0.065 0.158 0.095 0.129 0.160 0.253 0.492 0.382 0.118 CM 1556 1173 1193 635 580 796 326 303 Ψ 0.082 0.092 0.036 0.060 0.099 0.214 0.427 0.317 0.197 WP 1329 839 1346 863 790 869 529 513 307 Ψ 0.191 0.151 0.189 0.239 0.213 0.431 0.336 0.224 JY 1282 806 1279 1000 938 1020 697 668 466 191 Ψ 0.105 0.177 0.274 0.393 0.548 0.437 0.194 BL 1121 640 1108 1072 1003 1106 737 737 518 225 186 Ψ 0.086 0.099 0.375 0.533 0.429 0.179 BR 1050 546 1048 1137 1066 1168 816 805 601 291 246 77 Ψ 0.058 0.330 0.523 0.401 0.224 KE 896 404 890 1262 1204 1294 941 935 719 423 363 198 128 Ψ 0.363 0.575 0.469 0.265 SP 2050 1552 2028 1460 1404 1387 1142 1135 934 778 793 938 1004 1105 Ψ 0.322 0.281 0.446 IC 1546 1641 1259 1172 1387 1277 1498 1512 1804 2001 2145 2230 2123 2019 2523 Ψ 0.030 0.354 SG 1627 1734 1348 1250 1450 1348 1579 1597 1869 2062 2213 2275 2246 2123 2585 100 Ψ

Table 6. Upper triangle - pairwise genetic distances between populations (FST values), all significant p<0.05 apart from those shaded grey. Lower triangle - pairwise geographic distances over water between populations (km). Colour scale indicates distance - red (greatest distance) to green (shortest distance). Populations grouped by geographic region, see Table 1 for site codes. Highlights and corresponding value - greatest FST, lowest FST, greatest geographic distance, lowest geographic distance.

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Coff1 Coff2 Coff3 Coff4 Coff5 Coff6 Coff8 Coff9 Coff11 Coff1 Ψ TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE Coff2 Ψ TRUE FALSE TRUE TRUE TRUE TRUE TRUE Coff3 Ψ FALSE FALSE FALSE FALSE FALSE TRUE Coff4 Ψ FALSE FALSE FALSE TRUE FALSE Coff5 Ψ FALSE FALSE FALSE TRUE Coff6 Ψ FALSE FALSE TRUE Coff8 Ψ TRUE TRUE Coff9 Ψ FALSE Coff11 Ψ Table 5. Results of the test for linkage disequilibrium; TRUE = significant linkage detected (Bonferroni corrected p<0.05).

Figure 2. Scatter plot of genetic distance (F ) by geographic distance. Result of ST Mantel test for correlation: r = 0.69, p = 0.0001.

2. Discriminant analysis of principle components (DAPC)

The DAPC function utilised 5 principal components and 2 discriminant functions, accounting for 54.3% of conserved variance. The cluster analysis selected 7 distinct genetic clusters which, despite a large amount of cross-over in the British Isles, show broadly geographic specificity (Figures 3 and 7 and Table A2). All of the Icelandic specimens, other than an individual in Sandgerdi and 2 historic samples grouped together into cluster 1 with the U.S.A., Faroes and historic Norwegian samples. The

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individual sample from Sandgerdi grouped with cluster 6 which is largely occupied by samples from the south coast of England, and the two historic Icelandic samples grouped with the northern British Isles. The Spanish population showed a greater diversity of genotypes than Iceland, however the majority of samples (41/45) grouped into a single cluster (4). Notably one sample from Spain exhibited a genotype conforming to the Icelandic cluster. One historic French sample and the Portuguese

Figure 3. Scatter plot of the ordination of principle components identified during DAPC. Ellipses are centred around each of the genetic clusters. Axis 1: 39.6%, Axis 2: 14.7% conserved variance. A=Anglesey, B=Baltinore, C=Bembridge Ledges, D=Brighton, E=Coombe Martin, F=Firth of Clyde, G=Hebrides, H=Hafrsfjord, I=þorlákshöfn, J=Jersey, K=Kent, L=North East U.K., M=Sandgerdi, N=Skerries, O=Sligo, P=Spain, Q=Wembury Point, R=Faroes, S=France, T=Germany, U=Iceland, V=Norway, W=Portugal, X=Sweden, Y=U.S.A.

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sample grouped with the Spanish population, and the other French and German samples with the British Isles. In the British Isles there was a weak distinction between the combined Irish Sea and northern populations (clusters 2 and 7) and the southerly populations (5 and 6). Assignment probabilities were generally high (79%-99%; Figure 4 and Table A2) other than for clusters 3 and 5 (61% and 54% respectively.

Figure 4. Assignment probabilities for the clustering of all C. officinalis samples.

3. Haplotype network analysis

Analysis of haplotypes amongst C. officinalis found 47 distinct haplotypes of which 73% were shared by 2 or more populations and 27% were ‘private’ (Figure 5). 40% of all samples belonged to haplotype I (Table A3), which consisted entirely of samples from the British Isles. Iceland and the combined British Isles showed 5 ‘private’ haplotypes each, whilst the northern British Isles showed the greatest haplotype diversity (n=21), followed by Iceland (n=16).

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Iceland Scandinavia North British Isles South East U.K. South West U.K. Spain U.S.A.

Figure 5. Haplotype network for broad geographic regions identified based on the results of the DAPC.

4. DNA barcoding for species identification

Two of the samples (JY_28 and AZ_11) failed to return sequences for COI using the primer pair GazF1 and GazR1. The majority (21/26) of regions sequenced were returned as Corallina caespitosa, although with varying query cover (94%-100%) and ident matches (95%-100%) which merits further investigation. The samples which returned values at the lower end tended to have chromatograms with low and indistinct peaks. The Azores sample showed a high match to Corallina pilulifera for the psbA region, however all other successful matches were with C. caespitosa. JY_40 was identified with Ceramium secundatum for the Gaz pair of primers which is likely due to contamination. Part of the rbcL region (primers F57 and R753) of the Sicilian sample showed the greatest match to Clade 6 of the concatenated Corallina phylogeny presented in Williamson et al. (2015), however it also returned as C. caespitosa for all other regions.

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Discussion

This study has uncovered complex population structuring in an important ecosystem engineer of the temperate intertidal zone, Corallina officinalis. For the first time in a red alga, SNPs have been used on a continental scale to assess the capacity for this species to respond to challenges faced under progressive climate change. This has wide ranging implications for future research and the management of C. officinalis, which faces significant barriers to movement and is in places completely isolated. The results are discussed here in the context of contemporary genetic differentiation between populations, connectivity and gene flow, and adaptive capacity.

The observed deviation from Hardy-Weinberg equilibrium (HWE) and evidence of significant linkage between SNPs has the potential to influence the interpretation of these results (Waples & Allendorf, 2015). The high preponderance of homozygotes indicates that inbreeding may be occurring, which is unsurprising given that the species in question is non-motile and has limited dispersal abilities (Krueger-Hadfield et al., 2011). Propagules of red algae are short lived and are not flagellated, relying on passive modes of dispersal such as currents (Engel et al., 2004). Propagules are more likely to successfully settle and grow in suitable habitats in close proximity to the parent plants (Norton, 1992) promoting biparental inbreeding.

Little research has been undertaken investigating the ploidy levels in C. officinalis, which may be polyploid (Kapraun & Freshwater, 2012). In a polyploid species the methods used here would sample a subset of available alleles since the calling process only accounts for 3 possible genotypes (Gidskehaug et al., 2011; Semagn et al., 2014). This may also account for a high occurrence of homozygotes. Further research would be necessary to determine which of these factors is in play and the impacts of this on the downstream analyses of this data (Waples & Allendorf, 2015).

Significant linkage disequilibrium was observed between SNPs (Table 5), however all of the markers were utilised for two reasons. Firstly, it was necessary to retain all of the markers for analysis in order to maintain a statistically meaningful dataset.

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Secondly, during a previous study using a smaller subset of this data, Yesson et al. (2018) conducted a comparison of the results obtained using the full dataset contrasted with just unlinked loci. This demonstrated that the results from each were comparable, and as such retaining the linked SNPs is unlikely to affect these results.

Contemporary genetic differentiation

Almost all pairwise comparisons yielded significant FST values (Table 6) indicating population structuring across large and fine scales consistent with other algal studies (Krueger-Hadfield et al., 2011). These findings also clearly demonstrate isolation by distance (IBD; Figure 2) indicative of a passive mode of dispersal whereby individuals are more likely to interbreed with geographically proximal populations. Similar patterns of IBD have been seen in Geldium canariense (Bouza et al., 2006) and Cystoseira amentacea (Buonomo et al., 2017) and are attributable to migration following a ‘stepping stone’ model (Valero et al., 2001).

The greatest genetic distances, supported by assignment to distinct clusters in the DAPC, were seen in the Icelandic and Spanish populations (Table 6 and Figure 3). These represent the northern and southern range of the species, where population structuring may be determined by their position on the leading or trailing edge of a range change. As the range shifts northwards Spanish populations become increasingly isolated, whereas in Iceland range expansion by a small founder population could lead to isolation by colonisation (Nadeau et al., 2016).

Within the British Isles, low differentiation in and around the Irish Sea reflects high levels of connectivity, similar to patterns observed in the common cockle (Cerastoderma edule) in the Irish sea, which also utilises passive dispersal (Coscia et al., 2013). On the south coast of England a complex signature of differentiation was detected. South western populations are more similar to Irish Sea populations than they are to each other; and south eastern populations show a greater affinity to each other and to the Irish Sea than to those in the south west. This is weakly supported by the DAPC (Figure 3) and is reflective of the complex hydrographic features of the English Channel influencing patterns of gene flow.

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Connectivity and gene flow

Hydrographic features such as currents and tidal movements have a strong impact on the dispersal of algal propagules (Engel et al., 2004; Buonomo et al., 2017). Migration is facilitated by water movement, and is dependent on the availability of suitable habitat (Krueger-Hadfield et al., 2013).

The English Channel is formed of an east and a west basin separated between the Cherbourg Peninsula and the Isle of Wight. Differing hydrodynamic features on either side of this divide impede the mixing of water between the basins and may account for the disruption in gene flow between eastern and western populations seen here (Krueger-Hadfield et al., 2011). Hu et al. (2010) found similar patterns in the red alga Chondrus crispus, and further related this to expansion from a glacial refugia in the Hurd Deep following the flooding of the English Channel (Provan & Bennett, 2008).

The broad south-east to west and north to south groupings identified here also reflect known biogeographic regions around the British Isles (Figure 6; Connor et al., 2004). The boundary between the ‘Irish Sea’ and ‘Minches and western Scotland’ regions is a transitional zone across which environmental gradients change slowly. The stronger affinity between Irish Sea and Scottish populations indicates that transitional zone is not a barrier to gene flow, which may be facilitated by currents flowing through the North Channel. However, the coastal waters of the ‘Irish sea’ and the oceanic waters of the ‘Western

Figure 6. Marine biogeographic zones around the British Isles. Refer to Table 1 for site codes. North Sea (N) North Sea (S) E. Eng. Channel W. Channel & Celtic Sea Irish Sea Minches & W. Scot. Scot. Conti. Shelf Irish Atlantic

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Channel and Celtic Sea’ experience limited mixing, creating a barrier between northern and southern populations. Coupled with strong currents heading out of the Bristol Channel this accounts for higher levels of gene flow heading north to south.

The North Atlantic Drift passes between Scotland and the Faroe Isles creating an effective barrier to gene flow to the north of the British Isles (Figure 7). This was apparent in the DAPC which clustered all historic samples from the Faroe Islands with Iceland (Figure 3). The Faroes and Iceland are connected by the East Icelandic Current, which flows west to east along the Iceland-Faroes ridge. To the west of Iceland the East Greenland Current flows towards Northern America, and the sample from the north western Atlantic (eastern America) also fell within the Icelandic cluster. This raises the possibility of a distinct north western Atlantic population of C. officinalis.

The appearance of Icelandic and Spanish genotypes throughout the British Isles, and particularly an individual Icelandic genotype in the Spanish population demonstrates that longer distance migrations do occur. This is an important mechanism for maintaining diversity and may occur as the result of rare rafting events, for example in the ballasts of ships (Valero et al., 2001; Buonomo et al., 2017).This has been reported in other species with a high degree of fragmentation between populations, and was attributed to strong current movements transporting propagules between populations (Coleman & Kelaher, 2009). This seems unlikely to be the case with C. officinalis however, as prevailing currents in the north-east Atlantic form barriers to gene flow from Iceland.

Adaptive capacity

Understanding contemporary patterns of genetic diversity relies on an understanding of the historical context in which species developed and radiated. Higher levels of diversity are expected to be observed in areas which formed part of historic refugia, with lower diversity in areas which were subsequently colonised. Populations of C. officinalis in the Irish Sea and Scotland showed the highest levels of genetic diversity with the rest of the British Isles showing similarly high levels, particularly in comparison to Iceland and Spain (Figure 7). These high levels of diversity correspond with refugia reported for marine benthic organisms for the last glacial maximum, 21 thousand years ago (Figure 7; Maggs et al., 2008; Provan & Bennett, 2008). This is supported by the

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Figure 7. Spatial representation of the results of the DAPC, site codes in Table 1. Dashed arrows represent pertinent ocean currents; shaded ellipses indicate the approximate location of marine refugia during the last glacial maximum. Genetic Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7

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haplotype network (Figure 5) which demonstrates that the greatest diversity of haplotypes and the highest proportion of ‘private’ haplotypes occurs within the British Isles. The British Isles samples also form the main central nodes of the network with most other haplotypes radiating from them which is a further indication that the British Isles was part of the refugium for this species.

Figure 5 also indicates that there are shared haplotypes between most regions indicating gene flow on a wide scale. However it is necessary to be cautious of inferences of diversity and gene flow from the haplotype network. It was necessary to convert the SNP data into the IUPAC format for analysis, and by doing so ambiguity was introduced into the data which results in greater network complexity (Joly et al., 2007). Furthermore, the British Isles are disproportionately represented in the data, which inflates their importance as the central node of the network.

Conclusions and further research

This study has built upon previous research into the population genetic structure of C. officinalis (Jackson, 2016; Vale, 2017; Yesson et al., 2018) and further demonstrates high levels of genetic differentiation across the north eastern Atlantic. The British Isles is a reservoir of genetic diversity for C. officinalis, which may provide these populations with the adaptive capacity necessary to evolve in response to future climate change. Conversely, the low levels of diversity and apparent isolation observed in Spain and Iceland indicate that these populations are more at risk from a rapidly changing environment, as their ability to adapt to novel environments over relatively short timescales will be limited. Connectivity between populations is essential to enable the spread of climate adaptive genotypes which may bolster other regions. However given this species’ restricted dispersal abilities, the rate of spread of these genotypes will constrain the ability of C. officinalis to adapt through migration.

Expanding the geographic coverage of this study across the Faroes, Northern Isles and the north west Atlantic, as well as around the French coast will allow for the assessment of other connections to Iceland and Spain. This would also provide an insight into cross Atlantic migration, resolution of diversity in Iceland, and potentially identify genotypes adapted to warmer conditions in Europe which may become a vital source of adaptive variation. Furthermore the development of additional SNPs would

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not only improve the resolution of this study, enabling the assessment of population structure on a finer scale, but also may allow for the identification of gene regions experiencing climatic selection. Incorporating models of current dynamics and ecological niche modelling would also drive a better understanding of the mechanisms affecting gene flow in C. officinalis.

It is also necessary to examine the ecological consequences of a changing algal flora in a natural context. A large number of specimens in this study failed to amplify for SNP genotyping, and DNA barcoding revealed that most were likely to be C. caespitosa. C. caespitosa appears to be shifting its range northwards, following on the heels of C. officinalis (J. Brodie pers. comm), potentially heralding a shift in the community composition of the temperate intertidal zone. Such changes need to be explored to appreciate the downstream effects on the biodiversity in these areas including competition dynamics which could exacerbate the impacts of a changing environment.

This is the first time that research on such a large scale has been used to develop our understanding of how the population structure of a red alga will influence its future in a time of global climate change. It has highlighted the value of population genetic studies to assess risk to populations, and demonstrated the importance of protecting continuous stretches of coastal habitat to secure the future of this invaluable source of biodiversity.

Acknowledgments

I would like to thank Professor Juliet Brodie and Dr Chris Yesson for introducing me to the world of coralline algae and for their support in undertaking this project. I would also like to thank Steve Russel, Jo Wilbraham and Rob Mrowicki for their help and training, and finally everyone who has collected algal material for this project over the years.

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Appendices

Table A1. Samples collected from the Herbarium of the Natural History Museum, London from which DNA was extracted. TRUE refers to samples from which sufficient DNA was collected for use in this study (n=54).

Coordinates Year Location Country HG Ref [DNA] (Lat, Long) 1896 Newport, Rhode Island USA 41.451240, -71.328500 HB_1 TRUE 1895 Revere, Massachusetts USA 42.404309, -70.987920 HB_2 TRUE 1908 Harpswell Sounds, Maine USA 43.784117, -69.969004 HB_3 1956 Fishermans praia, Ericeira Portugal 38.963980, -9.419047 HB_4 TRUE 1905 Oporto Portugal 41.147785, -8.677614 HB_5 TRUE 1902 Hirstshals Hole, Hirtshals Denmark 57.564702, 9.971149 HB_6 TRUE 1965 Svinoy Island Faroe Is 62.104768, -6.974942 HB_7 TRUE 1980 Strundur, Raktangi, Skalafjørdur Faroe Is 62.108650, -6.756084 HB_8 TRUE 1980 Grotta Nes, Reykjavic Iceland 64.147910, -21.831571 HB_10 TRUE 2006 Kalfavik Iceland 64.004975, -22.549917 HB_11 TRUE 1888 Oporto Portugal 41.147785, -8.677614 HB_12 1980 Raktangi, Skalafjørdur Faroe Is 62.108650, -6.756084 HB_13 1980 Raktangi, Skalafjørdur Faroe Is 62.108650, -6.756084 HB_14 TRUE 1980 Kirkubour Faroe Is 61.952301, -6.796939 HB_15 TRUE 1896 Mölen, Insula Østerø Faroe Is 62.307997, -7.083057 HB_16 1980 Strundur, Raktangi, Skalafjørdur Faroe Is 62.108650, -6.756084 HB_17 1991 Tønneberg Banke Denmark 57.459025, 11.261214 HB_18 1910 Brahuslan, Fjallbacka Sweden 58.600459, 11.279437 HB_19 TRUE 1955 Bud Norway 62.905385, 6.907053 HB_20 TRUE 1877 Sander, Heligoland Germany 54.182882, 7.883451 HB_21 1999 West Watt, Heligoland Germany 54.182882, 7.883451 HB_22 TRUE 2007 Dalatangi Iceland 65.270626, -13.573948 HB_23 TRUE 2005 Suour-Bar Iceland 64.980073, -23.226141 HB_24 TRUE 2005 Seljavik S Iceland 64.568252, -23.116357 HB_25 TRUE 2007 Vattarnes, Reyoarfjoro Iceland 65.615422, -22.677147 HB_26 TRUE 2005 Seljavik N Iceland 64.568252, -23.116357 HB_27 TRUE 2005 Kalfatjarnarkirkja Iceland 64.017742, -22.299014 HB_28 TRUE 2005 Krossavik fjara Iceland 64.932675, -23.850745 HB_29 TRUE 2005 Hjallkarseyri (1-2m) Iceland 65.746299, -23.682086 HB_30 2007 Selavogur litt Iceland 63.814405, -22.591672 HB_31 2006 Grettislaug (4-7m) Iceland 65.882160, -19.735280 HB_32 2006 Hrisey Laugatangi Iceland 65.996986, -18.389509 HB_33 TRUE 2005 Kjarlaksstaoir Iceland 65.52806, -21.30611 HB_34 TRUE 2005 Osabotnar (0-1m) Iceland 63.93861, -22.67833 HB_35 TRUE 1810 Golfe de Gascogne France 45.739235, -2.189415 HB_36 1969 Kerroyal Point, Morbihan France 47.548557, -2.958764 HB_37 TRUE 1971 Plage Sirene, Le Boulonnais France 50.871889, 1.581278 HB_38 TRUE 1972 West side of bay, Etretat France 49.709194, 0.201742 HB_39 1971 Cap Gris Nez, Le Boulonnais France 50.871889, 1.581278 HB_40 TRUE

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1971 Pourville France 49.918299, 1.031176 HB_41 TRUE 1862 Arromanche France 49.340742, -0.617027 HB_42 1860 Dezmazieres France 51.065030, 2.351927 HB_43 1868 Calvados France 49.312805, -0.315706 HB_44 1934 Robin Hood Bay, Yorkshire UK 54.434264, -0.530760 HUK_1 1904 Dover, Kent UK 51.121551, 1.316865 HUK_2 1890 Deal, Kent UK 51.223116, 1.405417 HUK_3 1972 Seasalter, Kent UK 51.347892, 0.998769 HUK_4 TRUE 1968 Whitstable, Kent UK 51.347892, 0.998769 HUK_5 TRUE 1856 Ventnor Cove, Isle of Wight UK 50.588731, -1.223195 HUK_6 1947 Culver Cliffs, Isle of Wight UK 50.665677, -1.108677 HUK_7 TRUE 1953 Church Reefs, Wembury, Devon UK 50.316625, -4.083881 HUK_8 TRUE 1889 Hele, Ilfracombe, Devon UK 51.211659, -4.097294 HUK_9 TRUE 1967 Rottingdean, Sussex UK 50.799541, -0.043836 HUK_10 1850 Brighton, Sussex UK 50.818151, -0.138776 HUK_11 1970 Newhaven, Sussex UK 50.780735, 0.044498 HUK_12 TRUE 1850 Kemp Town, Brighton, Sussex UK 50.816248, -0.124352 HUK_13 TRUE 1972 Oldstairs Bay, Kingsdown, Kent UK 51.180143, 1.405312 HUK_14 TRUE 1883 Walmer, Kent UK 51.204741, 1.403779 HUK_15 1876 Margate, Kent UK 51.394232, 1.384846 HUK_16 1967 St Margarets, Kent UK 51.150130, 1.385497 HUK_17 1890 Deal, Kent UK 51.223116, 1.405417 HUK_18 1892 Deal, Kent UK 51.223116, 1.405417 HUK_19 1968 Isle of Thanet, Kent UK 51.392731, 1.380693 HUK_20 TRUE 1907 Sandygate, Kent UK 51.072412, 1.139883 HUK_21 1969 Fan Bay, Dover, Kent UK 51.133758, 1.359651 HUK_22 TRUE 1968 Isle of Thanet, Kent UK 51.392731, 1.380693 HUK_23 TRUE 1907 Kingsgate, Kent UK 51.389735, 1.437953 HUK_24 1967 Isle of Thanet, Kent UK 51.392731, 1.380693 HUK_25 TRUE 1893 Dover, Kent UK 51.121551, 1.316865 HUK_26 1853 Kingsgate, Kent UK 51.389735, 1.437953 HUK_27 TRUE 1969 St Margarets Bay, Kent UK 51.150130, 1.385497 HUK_28 1966 Margate, Kent UK 51.392731, 1.380693 HUK_29 TRUE 1969 Isle of Thanet, Kent UK 51.392731, 1.380693 HUK_30 1970 Isle of Thanet, Kent UK 51.392731, 1.380693 HUK_31 TRUE 1968 Copt Rocks, Kent UK 51.083294, 1.199385 HUK_32 1968 Shakespeare Cliff, Dover, Kent UK 51.108607, 1.288563 HUK_33 TRUE 1970 Copt Point, Folkestone, Kent UK 51.083294, 1.199385 HUK_34 TRUE 1969 Mill Point, Sandgate, Kent UK 51.072639, 1.141856 HUK_35 TRUE 1968 Copt Rocks, Kent UK 51.083294, 1.199385 HUK_36 1967 St Margarets, Kent UK 51.150130, 1.385497 HUK_37 TRUE St Margarets Bay Ness Point, 1971 Kent UK 51.150130, 1.385497 HUK_38 TRUE Fairlie-Portinerofs, North 1892 Ayrshire UK 55.757067, -4.861660 HUK_39 TRUE 1874 Skerries, County Dublin Ireland 53.583390, -6.107724 HUK_40 1975 Braich-Lwyd, Anglesey UK 53.177214, -4.487013 HUK_41 TRUE 1975 Rhosneigr, Anglesey UK 53.230028, -4.523570 HUK_42 TRUE 1947 Bembridge, Isle of Wight UK 50.688155, -1.068393 HUK_43 34

1948 Shanklin, Isle of Wight UK 50.633854, -1.168892 HUK_44 1948 Culver, Isle of Wight UK 50.665677, -1.108677 HUK_45 TRUE 1836 Ventnor Cove, Isle of Wight UK 50.588731, -1.223195 HUK_46 1937 Shanklin Ledge, Isle of Wight UK 50.633854, -1.168892 HUK_47 1947 Culver Cliffs, Isle of Wight UK 50.665677, -1.108677 HUK_48 Rubha Baile na H-Airde, Isle of 1966 Mull UK 56.432092, -6.139775 HUK_49 TRUE 1968 Calgary Bay, Isle of Mull UK 56.578797, -6.281586 HUK_50 1970 Balmeanach Farm, Isle of Mull UK 56.422694, -6.147715 HUK_51 1956 Coombe Martin, Devon UK 51.21675, -4.025717 HUK_52 1889 Hele, Ilfracombe, Devon UK 51.211659, -4.097294 HUK_53 1855 Ilfracombe, Devon UK 51.211659, -4.097294 HUK_54 1904 Wembury Bay, Devon UK 50.316625, -4.083881 HUK_55 1907 Lynmouth, Devon UK 51.231973, -3.830033 HUK_56 1903 Scarborough, Yorkshire UK 54.282559, -0.394769 HUK_57 1934 Robin Hood Bay, Yorkshire UK 54.434264, -0.530760 HUK_58 TRUE 1904 Scarborough, Yorkshire UK 54.282559, -0.394769 HUK_59 1937 Filey Brig, Yorkshire UK 54.214461, -0.259502 HUK_60 1855 Scarborough, Yorkshire UK 54.282559, -0.394769 HUK_61 1937 Filey Brig, Yorkshire UK 54.214461, -0.259502 HUK_62

Table A2. Breakdown of results from the DAPC, refer to Table 1 for contemporary site codes. Proportion of variance explained by the principle components: PC1: 0.396; PC2: 0.147; PC3: 0.113; PC4: 0.061; PC5: 0.020

Pop HF NE BA HE FC SL SK AN CM WP JY BL BR KE SP IC SG 1 1 1 1 44 16 2 1 3 16 7 3 11 1 10 3 8 1 2

3 4 5 6 11 1 3 10 7 4 6 2 6 9 15 4 3 1 1 7 14 2 2 6 11 2 2 41

Cluster 5 5 1 2 2 10 9 12 12 5 23 5 1 1 6 2 18 7 2 3 4 5 4 10 3 13 19 12 1 7 4 11 19 18 14 27 12 2 7 1 2 5 1 1 Pop ZIL ZFA ZUS ZNW ZUS ZFR ZPG ZGY Max. Assignment by cluster: ZFA Historic Faroes 1 10 4 1 1 1 0.9998 ZFR Historic France 2 2 0.9798 ZGY Historic Germany 3 1 0.6174 ZIL Historic Iceland 4 1 1 0.8958 ZNW Historic Norway

Cluster 5 0.5415 ZPG Historic Portugal 6 1 1 0.7931 ZSW Historic Sweden 7 0.9386 ZUS Historic US

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Table A3. Breakdown of results of the haplotype network, IC=Iceland, NB=Northern Britain, SC=Scandinavia, SE=South-east England, SW=South-west England, SP=Spain, and US=U.S.A.

Population Population Haplotype IC NB SC SE SW SP US Haplotype IC NB SC SE SW SP US I 128 31 83 XXV 11 II 38 28 14 XXVI 2 III 2 3 8 XXVII 2 IV 43 23 XXVIII 1 V 1 XXIX 19 12 VI 2 XXX 2 VII 13 XXXI 12 VIII 1 6 XXXII 2 IX 9 XXXIII 1 X 4 XXXIV 2 1 XI 2 XXXV 1 XII 4 XXXVI 1 2 XIII 1 XXXVII 1 XIV 7 XXXVIII 2 XV 2 XXXIX 1 XVI 8 XL 3 XVII 4 XLI 2 XVIII 1 XLII 1 XIX 3 XLIII 2 XX 8 XLIV 1 XXI 4 XLV 1 1 XXII 2 XLVI 1 XXIII 5 XLVII 1 XXIV 1

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Table A4. Results of the DNA barcoding for species identification. Includes the length each sequence was trimmed to during alignment, the query cover and ident metrics from BLAST, and the accession number of the sample they identified most with.

Query Sample Region Primer_F Primer_R Trimmed Species ID Cover Ident Acc Num JY_4 psbA PSBAF PSBAR 810 Corallina caespitosa 100% 99% BM 001158047 JY_5 psbA PSBAF PSBAR 867 Corallina caespitosa 98% 99% BM 001158047 JY_28 psbA PSBAF PSBAR 898 Corallina caespitosa 94% 99% BM 001158047 JY_40 psbA PSBAF PSBAR 887 Corallina caespitosa 95% 99% BM 001158047 SI psbA PSBAF PSBAR 887 Corallina caespitosa 95% 99% BM 001158048 AZ_11 psbA PSBAF PSBAR 894 Corallina pilulifera 100% 99% DQ787634 JY_5 COI GAZF1 GAZR1 609 Corallina caespitosa 100% 99% CP-1214 JY_28 COI GAZF1 GAZR1 n/a n/a n/a n/a n/a JY_40 COI GAZF1 GAZR1 599 Ceramium secundatum 100% 99% RMAR3092 SI COI GAZF1 GAZR1 621 Corallina caespitosa 100% 99% MD0001738 AZ_11 COI GAZF1 GAZR1 n/a n/a n/a n/a n/a JY_5 COI RWCOF1 RWCOR1 433 Corallina caespitosa 100% 100% BM000804354 JY_28 COI RWCOF1 RWCOR1 433 Corallina caespitosa 100% 100% BM000804521 JY_40 COI RWCOF1 RWCOR1 426 Corallina caespitosa 100% 99% BM000804521 AZ_11 COI RWCOF1 RWCOR1 430 Corallina caespitosa 100% 99% BM000804354 JY_4 rbcL F57 R753 595 Corallina caespitosa 100% 98% BM000806012 JY_5 rbcL F57 R753 603 Corallina caespitosa 100% 99% BM000806013 JY_28 rbcL F57 R753 609 Corallina caespitosa 100% 99% BM000806013 JY_40 rbcL F57 R753 472 Corallina caespitosa 100% 99% BM000806013 SI rbcL F57 R753 600 Corallina Clade6 100% 99% BM000806020 AZ_11 rbcL F57 R753 607 Corallina caespitosa 100% 100% BM000806441 JY_4 rbcL RWCWF1 RWCWR1 731 Corallina caespitosa 94% 95% BM001023961 JY_28 rbcL RWCWF1 RWCWR1 707 Corallina caespitosa 94% 100% BM001023961 JY_40 rbcL RWCWF1 RWCWR1 721 Corallina caespitosa 94% 99% BM001023961 SI rbcL RWCWF1 RWCWR1 714 Corallina caespitosa 94% 99% BM001023961 AZ_11 rbcL RWCWF1 RWCWR1 709 Corallina caespitosa 94% 99% BM001023961

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