The effects of past, present and future climate change on the

red algae Corallina officinalis within the North Atlantic

Amy Jackson

September 2016

A thesis submitted for the partial fulfillment of the requirements for the degree of Master of Science

at Imperial College London

Formatted in the journal style of the Journal of Phycology

Submitted for the MSc in Ecology, Evolution and Conservation

Abstract

Anthropogenically driven climate change is projected to influence a vast number of species. Of

particular concern to oceanic acidification and ocean warming are the red calcifying macroalgae,

Corallina officinalis. Due their vulnerability to climate change and ecological importance within marine

ecosystems, there is a need to develop genetic markers to assess the conservation status of this

species by identifying adaptation “hotspots” and potentially vulnerable populations to climatic changes

within the ocean. In this study, genetic markers were developed for populations of C. officinalis from

across the northeast Atlantic (Spain, UK and Iceland) to examine the genetic structure across a large

spatial scale. 12 SNPs were sequenced from the nuclear genome of 190 individuals, across 5 different

sites from the northeast Atlantic. Pairwise Fst revealed fine-scale structuring between Kent

populations of C. officinalis and Wembury point, which is most likely due to numerous physical and

oceanic barriers within the English Channel which is constricting admixture. In addition, Icelandic

populations were shown to be genetically similar to Spanish populations, despite their large

geographical distance, which could be attributed to a shipping-mediated colonisation event that

occurred post Last Glacial Maximum (LGM; ca. 21 KYA). DAPC analysis and heterozygosity within

populations uncover how C. officinalis’ phylogeographic history was shaped by the LGM. There is

supporting evidence for a glacial refugium for C. officinallis during the LGM with high heterozygosity

and genetic isolation within Wembury Point, supporting the putative Brittany/ English Channel

refugium. In contrast, there is evidence conflicting the general premise of an Iberian Peninsula, as

individuals from Spain were placed within the same genetic clusters as with the UK, indicating

admixture. Spanish populations, alternatively, might have undergone a southward recolonisation

sweep from the UK to northern Spain. In regards to future climatic changes, Iceland and Kent are of

particular concern due to their genetic isolation and low genetic diversity found within these

populations, which might contribute to reduced population viability. Conversely, UK and Spanish

populations exhibited high genetic diversity, allowing flexibility for these populations to adapt to

environmental variation.

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1 CONTENTS

1 Table of Contents………………………………………………………………………..……...……… i

Acknowledgements……………………………………………………...………..…………………… ii

2 Introduction ................................................................................................................................ 1

3 Methods and Materials............................................................................................................... 4

3.1 Algae samples and DNA extraction .................................................................................... 4

3.2 SNP Calling ........................................................................................................................ 5

3.3 Genotyping ......................................................................................................................... 6

3.4 Statisical analyses .............................................................................................................. 7

4 Results ...................................................................................................................................... 8

4.1 SNP discovery .................................................................................................................... 8

4.2 Population Structure and diversity ...................................................................................... 8

5 Discussion ............................................................................................................................... 12

5.1 Patterns of isolation by distance ....................................................................................... 12

5.2 Historical patterns of genetic diversity ............................................................................... 13

5.3 Climate change and conservation ..................................................................................... 16

5.4 Caveats and future research ............................................................................................. 17

6 Conclusion ............................................................................................................................... 17

7 References .............................................................................................................................. 18

8 Appendix ................................................................................................................................. 22

ii

Acknowledgements

I would like to thank my two supervisors Juliet Brodie and Chris Yesson for providing me with the

opportunity to undertake this project, as well as their guidance and support they have provided

throughout its duration. I also would like to thank Stephen Russell for the training and assistance his

provided throughout the molecular lab work.

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2 INTRODUCTION

Oceans cover 71% of the Earth’s surface and are host to a vast array of marine organisms,

which are among the most ecologically and socio-economically important components of this

planet (Costanza et al., 1997). They play a vital role in regulating the climate and nutrient

cycle, as well as supporting a tremendous wealth of biodiversity (Worm et al., 2006; Hoegh-

Guldberg & Bruno, 2010). Marine organisms are increasingly impacted by anthropogenic

climate change, predominately caused by the steady increase in carbon dioxide (CO2)

emissions which have risen by 40% since pre-industrial times (IPCC, 2013).There is

overwhelming evidence that rising sea surface temperatures (SSTs) and ocean acidification

(OA) are the two most significant products of climate change in driving the negative impacts

seen in marine ecosystems (Hughes, 2000; Doney et al., 2012; Portner et al., 2014; McCauley

et al., 2015). Global temperatures of the oceans have risen on average by 0.2 C per decade

over the past 30 years (Hansen et al., 2005). This is projected to result in shifts in species’

geographic ranges, local population extinctions and increased invasion of species (Hughes,

2000; Harley et al., 2012; Brodie et al., 2014). OA, the ongoing decrease in pH of the oceans,

will have a wide range of destructive consequences on many marine species. In particular,

calcifying organisms, those species that have calcium carbonate shells or skeletal structures,

are projected to adversely affected (Kroeker et al., 2010, 2013). The combination of OA and

rising SSTs poses a serious risk of degradation to marine ecosystems, along with population

decline and extinction (Harley et al., 2006). Therefore, it is imperative we are able to predict

and monitor these changes in order to fully understand the effect that climate change is having

on the world’s oceans, and eventually provide mitigation for those species that are most

vulnerable.

Oceanic acidification and rising sea temperatures are predicted to affect an infinite number of

marine species. Of particular concern are the calcified coralline Rhodophyta (red) macroalgae

(Brodie et al., 2014). Across the world’s oceans, Coralline algae represent a key calcifying

element of the marine benthos (Johansen, 1981). They play a significant ecological role as

structural components within coastal ecosystems and provide habitat and shelter for a high

diversity of associated organisms (Nelson, 2008, 2009). In addition, through CaCO3 production

and dissolution, coralline algae contribute to the CO2 fluxes within the ocean, acting as an

essential component within the carbon and carbonate nutrient cycles of coastal marine

systems (Martin & Gattuso, 2009). However, there is growing evidence that corallines are

particularly sensitive to climate change. Coralline algae are some of the most susceptible

marine organisms to OA due to their calcareous deposits, as exemplified by their absence in

naturally acidic water (Martin et al., 2008). OA is predicted to have two main impacts on

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calcifying algae: firstly, corrosion of their calcium carbonate skeletons (Nelson, 2009) and

secondly, increase the metabolic cost of calcification (Riebesell et al., 2000; Feely et al., 2004;

Nelson, 2009). These two effects can be seen to have noticeable consequences on the growth

rates by favouring reduced calcification and thus decreased tissue accretion (Fabry et al.,

2008; Kroeker et al., 2010; McCoy, 2013). In parallel with OA, rising SSTs are projected to

result in significant range shifts with extinction at the southern and colonisation of the northern

boundary (Parmesan & Yohe, 2003). Although there is an indication that populations can

confer resilience to fluctuating levels of CO2 and thermal variation (Egilsdottir et al., 2012;

Jueterbock et al., 2014), other algal species that benefit from high CO2 levels might out

compete C. officinalis populations (Kroeker et al., 2013). Brodie et al. (2014) has projected a

significant reduction in calcifying algae within the North Atlantic by 2100, which could have

dramatic consequences for local ecosystem functioning. Therefore, it is imperative to examine

how populations respond to climatic changes by assessing their susceptibility to these

changes, as well as the potential for physiological and evolutionary adaptation.

Corallina officinalis (Figure 1) has the potential to be an excellent model system to investigate

the impact of current and future climate change. This is due to the abundance of evidence to

their direct dependence on temperature and vulnerability to changes of the ocean’s chemical

composition, as well as representing coralline algae over a large distributional range within

the North Atlantic. As the persistence of a species is profoundly guided by its genetic

composition, genetic approaches are increasingly being used to monitor how environmental

Figure 1: Corallina officinalis (Corallinales, Rhodophyta) is a calcareous red seaweed

found in the lower and mid-littoral zones on rocky shores across the northeast Atlantic. It

is distributed primarily across the North Atlantic coast, from northern Norway to Morocco,

as well as in USA and parts of Japan, China and Australasia.

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changes are affecting species, in addition to reviewing their ability for evolutionary adaption.

Within a conservation context, this information could then be used to identify populations of C.

officinalis that are most threatened by climate change and accordingly be applied to develop

informative conservation management strategies.

The present day genetic structure of populations is often assessed in the context of past

climatic forces that occurred across the species geographical range (Hewitt, 2000). The

Pleistocene ice age, including the Last Glacial Maximum (LGM; ca. 18-21 KYA), experienced

the combinations of expanding ice sheets, reduced sea temperatures and a drastic drop in

sea level. All of which resulted in a profound impact on the population structure of many

terrestrial and marine organisms in across the northern Atlantic (Hewitt, 2000). The advancing

glacial ice sheets forced species into refugial areas, small isolated pockets of habitat where

populations can persist. These refugial areas are often characterised by high genetic diversity

within the refugia and high genetic isolation between refugia (Hewitt, 2000, 2004). After the

ice sheets receded, populations expanded their range through recolonisation. This process

often results in Hewitt’s ‘southern richness, northern purity’ (Hewitt, 2000), where reduced

genetic diversity within populations in seen with increase latitudes as southerly populations

recolonise the north. Signatures of refugias are often complicated by admixture events, which

give the appearance of candidate refugial areas. Conversely, sudden population declines due

to, for example, extreme environmental conditions, could result in present-day low genetic

diversity. By reconstructing phylogeographic history of a species over its distributional range,

it allows for the understanding of how the distribution of genetic variation has changed over

space and time in regards to past climatic events.

Understanding postglacial recolonisation and refugias have important implications for how C.

officinalis will respond to future climate changes for two reasons. Firstly, refugias could provide

information on past migration and range shifts that arose in response to past climatic changes,

as well as barriers or corridors to gene flow. For instance, during the LGM for European

populations of the red seaweed Chondrus crispus, recolonisation after deglaciation primarily

occurred from the Brittany/ English Channel refugia (Provan et al., 2013). From there,

populations recolonised the northern UK via routes of suitable habitat and climatic conditions

along the coast. Conversely, the Iberian Peninsula, a putative glacial refugia in the North

Atlantic (Coyer et al., 2003; Hoarau et al., 2007, Provan et al., 2013), failed to contribute to

recolonisation due to thermal barriers resulting in the isolation of populations (Coyer et al.,

2003; Hoarau et al., 2007).

Secondly, glacial refugias can help describe the distribution of genetic diversity across the

species distributional range. Refugias are often reservoirs for unique genetic variation, with

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the potential of being adaptation “hotspots” that will enable populations to be more resistant

to environmental variation. Whilst OA is predicted to have negative impacts on the calcifying

coralline algae, there is suggestion that populations which experience natural pH variability

within their local environment may confer increased resilience (Egilsdottir et al., 2012; Kelly &

Hofmann, 2013). Similar acclimatisation is seen in the Iberian Peninsula on a number of

seaweed species to thermal variation (Brawley et al., 2004; Jueterbock et al., 2014). The

Iberian Peninsula is an area where seaweeds are at their maximum thermal tolerance. High

temperatures combined with the low dispersal potential often found in seaweeds (Coyer et al.,

2003), has conferred a level of local thermal adaptation within Fucus serratus (Hampe & Petit,

2005). The preservation of these hotspots where tolerant genotypes prevails is intrinsic for the

conservation of the species as it allows populations to adaptively evolve and phenotypic

plasticity in light of future climatic changes (Pauls et al., 2013).

In this study, we developed the first population genetic markers for a calcifying macroalgal

species to investigate the population connectivity and genetic diversity of Corallina officinalis

over the Northeast Atlantic. A set of 12 single nucleotide polymorphisms (SNPs) from existing

C. officinalis nuclear sequences were developed and sequenced from populations across a

lateral distance, where oceanic conditions vary. By reconstructing their postglacial

recolonisation history and potential refugias, it would help facilitate predictions of future OA

and warming impacts on these ecosystems, as well as identify vulnerable populations or those

with local adaptation.

3 METHODS AND MATERIALS

3.1 ALGAE SAMPLES AND DNA EXTRACTION

Samples of Corallina officinalis were collected by hand from five localities within the North

Atlantic: three sites within the UK (Wembury Point, Comb Martin and Kent), one in Spain and

another from Iceland (Figure 2, Table 1; See Appendix, Table S1). These samples were either

pressed and dried, or stored within silica gel. Thirty-eight individuals were taken from each of

these 5 regions, totalling 190 samples.

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DNA was extracted from approximately 0.5 cm2 of fresh, silica gel preserved or herbarium

material using a modified CTAB microextraction protocol (Robba et al., 2006). Due to issues

over the correct identification of C. officinalis samples, DNA barcodes were created using

mitochondrial cytochrome c oxidase subunit I (cox1) (5’ -TCAACAAATCATAAAGATATTGG-

3’) (Robba et al., 2006) and psbA (5’ -GTTATGCATGAACGTAATGCTC- 3’) (Sang et al.,

1997) primers from 3 extracted sample, to confirm the identity of the samples used for SNP

discovery. Samples were suspended in 50 l of Tris buffer and nanodroped to determine the

concentration of DNA.

3.2 SNP CALLING

Existing shotgun sequence reads of Corallina officinalis from five populations (Table 1),

generated using a miseq platform, were quality assessed using FastQC (v 0.9; Babraham

Bioinformatics, Cambridge UK). Sequence reads of each individual from the different

populations were filtered based on quality; sequences were trimmed between 66-236 bps to

remove those reads with three or more consecutive bases with an average Phred-like quality

score of less than 13. Sequences with a quality score below 20 as inferred by FastQC were

removed and those with more than 100 bp in length after trimming were retained. The

mitochondrial and plastic genomes of C. officinalis were removed from the remaining

sequence reads, leaving just nuclear DNA sequences.

Table 1: Summary of the populations of C. officinalis that were sampled across the North

Atlantic. N, number of samples sequenced.

Region Population Latitude Longitude N

UK

Combe Martin CM 51° 13' 0.2856'' N 4° 1' 32.5812'' W 38

Kent KE 51° 17' 20.9904'' N 1° 22' 46.0956'' E 38

Wembury Point WP 50° 18' 44.8632'' N 4° 4' 50.6424'' W 38

Iceland

þorlákshöfn IC 63° 50' 54.5856'' N 21° 21' 41.5512'' W 38

Spain

Comillas SP 43° 23' 31.2216'' N 4° 17' 27.6684'' W 38

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Due to the lack of a nuclear reference

genome that is available, the SISRA (Site

Identification from Short Read Sequence)

package was used to identify potential

loci containing variable sites from the

sequence reads (Schwartz et al., 2015).

The raw shotgun sequence reads of the

C. officinalis nuclear genome were

assembled to produce a “composite

genome”. This is then used as a reference

to align the sequencing reads to the

genome. SISRA then identifies sites

across the genome for informative

variable regions.

Putative SNPs were chosen based on a

number of control criteria: 50 base pairs

either side of the variable loci, good

sequence coverage (5 – 6 coverage) and

the SNP is an adequate distance from

other potential SNPs to avoid linkage

disequilibrium. The list of putative SNPs

was filtered once more to remove low

quality markers. Firstly, we ensured that only one SNP was located in the middle of the read,

with at least 50 nucleotides either side, to ensure adequate flanking region for assay

development. Secondly, only SNPs that contained variability across all five populations were

chosen. This allowed us to choose higher quality SNPs with greater representation across the

individuals. A total of 12 SNPs were selected to be used to genotype 190 C. officinalis

samples.

3.3 GENOTYPING

The 190 samples were eluted in 10mM Tris buffer and placed into two 96 well plates with two

control wells, at concentrations of over 5ng l-1 at 40 l volumes (DNA integrity was checked

with nanodrops). The plates were then heat sealed, stored on dry ice and outsourced to LGC

Genomics for a PCR-based KASP™ (Kompetitve Allele Specific PCR) genotyping assay.

Results were provided by LGC in a bi-allelic scoring for each of the twelve SNPs.

Figure 2: Map of the sample sites for C.

officinalis across the northeast Atlantic.

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3.4 STATISTICAL ANALYSES

To identify the most informative and highest quality SNPs, we calculated Hardy-Weinberg

Equilibrium for each SNP and then analysed for linkage disequilibrium (LD) using Bonferroni

correction on p-values to see if any SNPs were correlated, with the statistical programme ‘R’

with the package ‘genetics’ v. 2.12. 1 (Warnes, 2013). The LD showed significant linkage

between six of the eleven SNPs (See results and Appendix, Table S2), however, the exclusion

of linked markers resulted in a data set that did not have sufficient power to provide reliable

results, therefore, all these markers were included within the analyses. The sequenced SNP

data was further filtered in two ways: firstly, extreme heterozygosity deficiency was found in

SNP12 (0%) and was excluded from analyses on the assumption that it was a defect marker

(See results, Table 2). Secondly, there was numerous missing data due to the failure to call

markers for certain individual samples and so only those individuals where data for more than

ten SNPs were available, were included within the analyses.

Summary statistics of genetic diversity were calculated within populations including Nei’s

genetic diversity (HE), observed heterozygosity (HO), an inbreeding coefficient (FIS) and a

fixation index (FST).

The genetic structure between populations was assessed using a pairwise FST (Φ; Weir &

Cockerham, 1984), based on a 10,000 permutations. A mantel isolation by distance (IBD) test

against minimum marine distances (as measured in Google Earth 5.1, See Appendix, Table

S3) was performed in R using the ‘hierfstat’ (Goudet, 2015), ‘adegenet’ and ‘geosphere’

(Hijmans, 2016) R packages. The results were plotted in a regression scatter graph. The

statistical significance of the genetic and geographic association was assessed with a Monte-

Carlo test, based on 10500 replicates.

The Discriminant Analyses of Principle Component (DAPC) was performed in R using the

‘adegenet’ package (Jombary, 2008) (function dapc) within ‘R’. Group priors were unknown

so the number of clusters was assessed using the find.cluster function, which runs successive

K-means clustering with increasing number of clusters (k). Through Bayesian information

criterion (BIC) analysis, it was determined that 4 clusters (k = 4) would provide the most

accurate summary of the data, and 10 PCs were retained. A structure analysis was also

performed using the results from the find.cluster function.

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4 RESULTS

4.1 SNP DISCOVERY

All SNPs were in Hardy-Weinberg equilibrium (p < 0.05; Table 2), with only significant

heterozygote deficiency in SNP12. Linkage disequilibrium was found to be present for six of

the eleven SNPs (p < 0.05; See Appendix, table S2), but were included within the analyses.

After filtering, the resulting data excluded 49 samples, with 141 individuals across all 5

populations with eleven SNPs being analysed.

4.2 POPULATION STRUCTURE AND DIVERSITY

Nei’s diversity index varied across the sampled populations of North Atlantic (Table 3). The

highest heterozygosity was found in Spanish populations (Ho = 0.4), whereas Kent exhibited

the lowest diversity (Ho = 0.24).

Table 2: Candidate SNPs found within Corallina officinalis nuclear genome. HO, observed

heterozygosity; HE, expected heterozygosity.

SNP ID Variation Ho He

SNP1 A/T 0.129032 0.234256

SNP2 G/A 0.22 0.498

SNP3 C/T 0.07895 0.122922

SNP4 A/T 0.426573 0.499902

SNP5 C/T 0.366667 0.469578

SNP6 G/C 0.228571 0.312

SNP7 A/C 0.32593 0.410864

SNP8 T/A 0.298343 0.488874

SNP9 G/A 0.29878 0.400933

SNP10 C/T 0.923611 0.499397

SNP11 A/T 0.2 0.497355

SNP12 G/T 0 0.475104

Table 3: summary of the genetic diversity within each C. officinalis population and a total

form all populations sampled. N, number of individuals; HO (Nei’s diversity), observed

heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient; FST, fixation index.

Population N HO HE FIS FST

CM 28 0.363592 0.370075 0.115177 -0.12315

IC 36 0.292904 0.256465 -0.27679 0.221646

KE 23 0.241725 0.252465 -0.29702 0.233786

SP 27 0.399058 0.372187 0.120198 -0.12956

WP 27 0.316208 0.374182 0.124888 -0.13562

Total 141 0.327451 0.329497

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Pairwise FST (ΦST) were found to be all significant (p < 0.05) with values ranging between

0.068 to 0.35. Iceland was the most isolated population, showing the largest pairwise values

between all populations; Kent and Iceland were the most genetically isolated (ΦST = 0.35, 2400

km), followed by Combe

Martin and Iceland (ΦST =

0.26, 1850 km). The UK

populations were found to be

the most genetically similar;

Combe Martin and Kent (ΦST

= 0.068, 770 km), followed

by Combe Martin and

Wembury Point (ΦST =

0.073, 330 km).

The IBD indicates that there

is significant isolation

between the populations,

demonstrating a positive

correlation between

geographic distance and

genetic distance (Figure 3; p

< 0.05). This finding was

supported by the Monte-

Carlo test (p < 0.05).

The sampled individuals

from 5 populations were

placed into 4 genetic clusters

Table 4: Pairwise FST (ΦST) between North Atlantic populations of C. officinalis

calculated from eleven SNPs.

CM IC KE SP WP

CM 0

IC 0.262955 0

KE 0.067823 0.351922 0

SP 0.142455 0.165025 0.245035 0

WP 0.073285 0.240834 0.175763 0.113339 0

Figure 3: Isolation-by-distance analyses for C. officinalis

population in North Atlantic. The graph showed the

geographic distance (km) against genetic distance (Fst/1-

Fst) based on eleven SNPs.

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(k=4) (Figure 5, also see

Appendix, Table S3). The

STRUCTURE analysis shows

individuals from Kent were

placed only within a single

cluster (cluster 4), while Combe

Martin and Wembury Point are

split between several (Figure

4). There were some

genetically unique individuals

found: individuals placed in

cluster 3 were only found within

Icelandic populations and

cluster 1 was predominantly

individuals from the Spanish

population, with one individual

in Wembury Point.

The DAPC scatterplot shows

clearly that Iceland is

genetically isolated from the

other 4 populations, deriving

from just cluster 3 (Figure 6).

Similarly, individuals from

Spain forms their own unique cluster with cluster 1, but more skewed towards UK populations

Figure 4: STRUCTURE plot representing the probability of membership coefficient to each

hypothetic population for 141 individuals to genetic cluster (K = 4) based on 11 SNPs. Colours

indicate percentage contribution of individuals to assigned clusters (y axis), individuals

represented by each line (x axis).

Figure 5: Pie charts representing the genetic cluster

composition (k = 4) of the 5 populations sampled. Pie

graphs indicate the number value of individual from each

population assigned to the cluster. Colours indicate the

assigned cluster(s).

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on the axis compared to Iceland. The UK population are localised within either cluster 2 or 4:

cluster 4 is mostly comprised of Combe Martin and Kent, whilst cluster 2 is composed of

individuals from Combe Martin and Wembury Point. Wembury Point is seen to be the most

diverse population, placed between 3 genetic clusters, whereas, Iceland, Kent and Spain are

the most genetically isolated of the 5 populations.

Figure 6: DAPC plot for 141 individual Corallina officinalis when K = 4. The colour and

number of cluster represents four different genetics clusters found that best summaries the

populations (See Appendix, Table S3). Each point represents an individual sample with

different symbols for each population. Discriminant analyses eigenvalues at bottom left.

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5 DISCUSSION

5.1 PATTERNS OF ISOLATION-BY-DISTANCE

Both large- and small-scale population differentiation was detected in the sampled Corallina

officinalis populations, as reflected in significant pairwise FST values for all population

comparisons and supported by the DAPC analysis. The largest isolation was found between

the Iceland population and the other four sampled populations of C. officinalis (ΦST = 0.17 –

0.35, 1800-2400 km). As with many species of seaweed, life-history traits often constrain its

ability to disperse across large distances, which is intrinsic to patterns of isolation-by-distance

(IBD). The propagules of most red macroalgae tend to settle near the parent plant, as algae

spore and gamete are immotile (Norton, 1992). Therefore, dispersal is most likely determined

by oceanic currents. However, the directional movement of the west-to-east North Atlantic

current, which flows north along northern Europe, fails to connect Iceland to UK and Spanish

populations. Therefore, the limited dispersal ability of C. officinalis, combined with large

geographic distance, resulted in a lack of admixture between sites and causing genetic

isolation between Iceland and the UK and Spain.

Finer scale structuring was detected between Kent and Wembury Point (UK) (ΦST = 0.18),

despite their short geographic distance (< 400 km) and connection via the Channel current. A

similar study on the red algal Chondrus crispus also identified potential IBD of 500 km

(Krueger-Hadfield et al., 2011). However, no such isolation is seen between Wembury Point

and Combe Martin, despite having a similar over-water distance (ΦST = 0.07, 340 km). This is

reflected within the DABC plot, where the UK populations have been split into two clusters,

with Combe Martin being the shared population. The south-east coastline of England could

act as a potential barrier to admixture between Kent and Wembury Point populations of C.

officinalis. Movement between the English Channel and North Sea is constrained by the

narrowest part of the English Channel, the Straits of Dover (~20 miles). Furthermore, local

oceanic conditions probably also play an important role in the observed patterns (Gibbard,

2007). For example, currents at the Straits of Dover are slowed down due to formation of

counter-currents and rocky areas (Prandle, 1993). A combination of these factors could

sufficiently reduce gene flow between Kent and Wembury Point to result in an increased risk

of inbreeding and incidence of clonal reproduction, thus leading to the development of high

genetic isolation.

Recently, it has been suggested that historical forces have played a greater role in shaping

patterns of genetic diversity across the species’ range. Life-history traits and IBD models are

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often too simplified an explanation. IBD is frequently obscured by signatures of historical

climate changes, for example the LGM, which have been shown to be primarily responsible

for shaping the range-wide genetic diversity of macroalgae across the North Atlantic (Hampe

& Petit, 2005; Provan, 2005; Olsen et al., 2010; Provan & Maggs, 2012). Furthermore, given

the varying oscillation of currents and the heterogeneity of sea temperatures and chemical

composition in the North Atlantic, IBD models do not fully resolve the patterns of isolation

between populations (Hutchison & Templeton, 1999).

5.2 HISTORICAL PATTERNS OF GENETIC DIVERSITY

The sampled C. officinalis populations over the North Atlantic appears to illustrate potential

postglacial climatic shifts after the LGM, which peaked 21,000 years ago, resulting from

migrations and expansions. Hewitt’s pattern of a poleward decrease of genetic diversity

between populations is exhibited within this red macroalgae (Hewitt, 2004). The most southern

population in Spain exhibits the highest level of heterozygosity (HO = 0.4), whereas the most

northerly population, Iceland, holds the lowest (HO = 0.29). Therefore, we can suggest that

after deglaciation, C. officinalis spread from low to high latitudes with repeated recolonisation

and founder effects, resulting in the erosion of genetic diversity with increase latitudes.

High heterozygosity was detected within UK populations, located in the British Channel, and

evidence for high isolation from Spanish and Icelandic populations. These genetic signature

support the suggestion of a Brittany/ English Channel refugium, as seen in numerous intertidal

marine species, for example, rocky intertidal invertebrates (Maggs et al., 2008), as well as for

the seaweeds F. serratus (Coyer et al., 2003; Hoarau et al., 2007), Palmaria palmate (Provan

et al., 2005) and Ascophyllum nodosum (Olsen et al., 2010). Until sea levels began to rise

13,000 years ago, deep valleys within the English Channel such as the Hurd Deep would have

likely remained as sea. Such pockets of water had the potential to support C. officinalis

populations as paleoclimate studies suggest that it provided adequate environmental

conditions to support such life (Smith & Hamilton, 1970; Lericolais et al., 2003). The observed

distribution of genotypes, coupled with the concordance of similar patterns across several

seaweed species, highlights the possible existence of a Brittany refugia. However, it is unclear

whether the diversity was due to a putative glacial refugium or from an admixture from a

different recolonisation events. An alternative explanation is that the high diversity is a result

of a post-LGM admixture event between the UK and other regions. This is possible because

individuals from UK populations and Spain were placed within the same genetic clusters.

Conversely, moderate pairwise FST and suggested isolation from DAPC plot between these

populations suggest that they are isolates. To gain a clearer resolution on the existence of a

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Brittany refugia for C. officinalis, a number of samples need to be taken from the English

Channel, e.g France.

Similarly, an Icelandic refugia has been proposed from numerous genetic studies, for example,

the isopod Idotea balthica (Wares & Cunningham, 2001) and the seaweed Palamria (Maggs

et al., 2008) is consistent with a refugium in Iceland. However, no evidence of an Icelandic

refugium for C. officinalis was found within the sampled populations due to the low genetic

diversity and little isolation with Spanish individuals. The particularly low genetic diversity

found in Iceland is characteristic for high latitudinal populations, particular those near the rear-

edge of the species distributional range (Hewitt, 2004). But in addition, more recent climatic

forces could have shaped Iceland’s diversity. Southern Iceland often experienced cycles of

temperature and salinity variation, which could result in repeated population declines

(Astthorsson, 2007). The salinity was particularly low during repeated periods of the 20th

century, resembling the “Great Salinity Anomalies” in the northern North Atlantic (Belkin,

2004). Furthermore, the Iceland shelf is placed within a rapid warming cluster, for example,

the SST in Breidafjördur (Iceland) increased by 2 °C in 1993–2003 (Jonasson et al., 2007;

Belkin, 2009). These extreme environmental events could be sufficient enough to result in

local bottlenecks effects, contributing to the observed low genetic diversity.

Interestingly, despite the large geographical distance, Icelandic populations of C. officinalis is

more genetically similar to Spanish populations (ΦST = 0.165) than to the UK (ΦST = 0.24 –

0.35). Stronger factors other than IBD must be shaping these genetic distributions. Iceland

could harbour remnants of a post-LGM admixture event involving European populations.

There is a possibility that C. officinalis was introduced onto Iceland from Europe after the

melting of the Icecaps through shipping-mediate dispersal from Europe, as seen in other

seaweed genetic studies (Coyer et al., 2006; Brawley et al., 2009). For example, it has been

proposed that the source the Icelandic populations of the brown macroalgae, F. serratus,

originated from Norway in the late 1800s (Coyer et al., 2006). Similar results were found in

Brawley’s study (2009) which associated the introductions of F. serratus to Iceland with

emigration and trade from Europe between 1773 – 1861 (Brawley et al., 2009). If C. officinalis

was introduced to Iceland by European trade ships, it would conflict with the general premise

that this red algal is endemic to Iceland.

Alternatively, the genetic similarity between Spain and Icelandic populations could be a

reflection of widespread old populations that predate the LGM that would have shared similar

haplotypes. Similar patterns were observed in the macroalgae Fucus distichus, where multiple

colonisation events occurred from the older North Pacific populations to the North Atlantic

between the opening of the Bering Strait and before the onset of the LGM (Coyer et al., 2011).

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In order to fully resolve these issues, ideally, more samples from populations across Europe

(e.g Norway, proposed sources of introduction; Coyer et al., 2006) need to be included within

the analyses in order to identify the source(s) of Icelandic populations.

Spanish populations show 37% higher heterozygosity with Icelandic populations and 25% with

the Wembury Point population, and clearly placed within their own genetic cluster as inferred

by the DABC plot. There are two hypotheses that may explain our observation in Spanish

populations. Firstly, during the LGM, the Iberian Peninsula could have been a glacial refugia

for C. officinalis, as there is a concordant amount of evidence from many coastal marine

species supporting its existence (Coyer et al., 2003; Hoarau et al., 2007; Maggs et al., 2008).

Although the typical characteristic signature for a refugium is a high proportion of private

haplotypes, the lack of unique genotype seen within Spain could be attributed to repeated

population declines and expansion, often experienced by marine species in northern Spain

due to thermal barriers and extreme weather conditions along the Spanish coastline (Hoarau

et al., 2007). This pattern was observed within the seaweed F. serratus, where extreme

summers caused mass mortality across its Spanish distribution (Hoarau et al., 2007). North

Spain already represents C. officinalis’ most southerly distribution, and so high thermal

variation could result in genetic bottlenecks brought about by repeated extinctions and

recolonisation.

Alternatively, Spanish populations of coralline algae could have undergone a southward

recolonisation sweep from the UK to northern Spain. Genotypes found within UK and Spanish

populations were distributed between these two regions, indicating admixture. Furthermore,

the pairwise FST value were relatively low between Wembury Point and Spain (0.11) for such

a large geographical distance (2000 km). It seems possible that the opening up of vast area

of new habitat following postglacial sea level rise resulted in marine recolonisation into

northern Spain. For many species of macroalgae, Brittany served as a source of colonisers

after the LGM, for example, F. serratus (Maggs et al., 2008). The heterozygosity within

Spanish populations (HO = 0.36) amplifies this; although high diversity is a characteristic of

refugia, it can also indicate rapid repeated colonisation from other regions, e.g Wembury Point,

UK. The resulting admixture of immigrating populations could result in a secondary increase

in diversity (Comps et al., 2001). Despite the majority of cases from coastal marine organisms

suggested the former hypothesis of an Iberian refugia, more samples and populations are

needed to fully resolve the picture of C. officinalis’ recolonisation patterns after the LGM.

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5.3 CLIMATE CHANGE AND CONSERVATION

Kent populations of C. officinalis have demonstrated potential vulnerability to climate change

due to their isolation with other sampled North Atlantic populations and comparatively low

heterozygosity due to barriers to admixture. Inbreeding poses a concern and with encroaching

environmental stress. Therefore, these populations are potentially vulnerable to population

decline and might merit special attention in regards to conservation in light of climate change.

In contrast, the high diversity seen in Brittany populations of C. officinalis means they might

be able to serve as a source of colonisers for C. officinalis, as seen in the seaweed F. serratus

after deglaciation of the LGM (Maggs et al., 2008), Wembury Point and Combe Martin

populations may be able to shift their distribution by tracking suitable habitats and

environments conditions during future climate change. In addition, a study on Brittany

populations of Corallina elongate showed seasonal fluctuations of pCO2 resulted in more

robust populations against these variations (Egilsdottir et al., 2012), Therefore, UK populations

of C. officinalis could result in being adaption hotspots during OA.

Similarly, the large pH variation experienced by Icelandic populations throughout the 20th

century could also confer resilience within those local populations (Belkin, 2004; Egilsdottir et

al., 2012). However, these adaptive processes rely on sufficient genetic variation upon which

natural selection can act upon. In addition, future oceans are projected to experience more

extreme pCO2 scenarios, and therefore, it can not be excluded that these populations will

negatively impacted by OA.

Spanish populations of C. officinalis hold higher heterozygosity than the other sampled

populations across the North Atlantic, which is coherent with other marine species situated

within this distributional range (Coyer et al., 2003; Hoarau et al., 2007; Maggs et al., 2008;

Provan et al., 2013). Although there is an indication of admixture with the UK, Spanish

populations are presently isolated. The preservation of this population is essential due to the

high diversity found within this region. However, due to the extreme higher temperatures found

within the Iberian Peninsula, Martínez et al. (2012) has projected that brown algae Himanthalia

elongate will experience higher rates in range reduction than to the rest of the northern

Atlantic. Their loss may have disproportional consequences on the permanence of the species

as there is an indication they could hold adaptive genotypes. It was found that Spanish

population of the F. serratus were more resilient to heat stress than Brittany populations,

indicating local thermal adaptation (Jueterbock et al., 2014). This acclimatisation to warmer

temperatures might mitigate against the predicted retreat of the seaweed’s distribution

boundaries. However, this resilience has been linked to high expression of ATP-dependent

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hsp genes, causing higher metabolic costs at the expense of growth and reproduction. These

fitness costs, in conjunction with their limited dispersal ability, can ultimately inhibit

responsiveness to further stress, such as ocean acidification.

5.4 CAVEATS AND FUTURE RESEARCH

Often in admixture models, genetic markers are assumed to independent and without

correlation. This deficiency within the model and inclusion of linked SNPs within the analyses

could provide a deceiving picture of genetic connectivity between C. officinalis populations,

suggesting more genetic isolation than what is true. In addition, the low number of SNPs

(eleven) used in this study may have underestimated the level of genetic diversity seen within

populations. Provan’s study (2012) comparing the genetic diversity of C. crispus from

microsatellite data and SNPs, showed high levels of disparity between the two types of marker

class. It was suggested that this observation was because of the low number of SNPs used

within the study (six). By adding more markers, and thus increase the data size, it would

provide a better resolution on the distribution of genetic diversity and isolation between and

within populations across the distributional range of C. officinalis.

6 CONCLUSION

Rising carbon dioxide emissions are causing rates of global warming and ocean acidification

that will severely influence marine organisms across the world’s oceans (Hoegh-Guldberg &

Bruno, 2010). By understanding the interplay of short- and long-term natural and

anthropogenic processes that shape populations genetics, inferences can then be made on

the vulnerability of populations to climatic changes and provide predictions of the ability and

rate of populations to evolve. These findings from C. officinalis suggest that Iceland and Kent

populations merit special attention as these populations have demonstrated to be particularly

isolated with low genetic diversity, making it vulnerable to population declines in light of climate

change. In contrast, there is indication that populations from Spain and the UK could harbour

genotypes that are more resilient to environmental variation. These adaptation hotspots will

be vital for the preservation of the species in the future. However, more sampling from different

regions across C. officinalis’ distributional range are needed in order to fully resolve

recolonisation patterns and potential refugias from the LGM. Therefore, this would provide

better indicator populations for future change occurring through climate change.

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8 APPENDIX

Table S3

Table S3: Geographic distance (km) between populations of C. officinalis

CM IC KE SP WP

CM 0

IC 1856 0

KE 776 2440 0

SP 1840 2270 1420.81 0

WP 330 1990 427.6 797.96 0

Table S1

Table S1: summary of the collected samples of C. officinalis from different sample sites.

Site Date(s) of collection Collected by Sample type

Combe Martin, UK 09.09.12 Chris Williamson Dried

Wembury Point, UK 18.06.12 Chris Williamson Dried

19.06.12

Kent, UK 06.05.16 Juliet Brodie Silica

Comillas, Spain 07.05.16 Cesar Peterio Dried

Þorlákshöfn, Iceland 05.01.14 Chris Williamson Dried

13.04.14

Table S2

Table S2: Linkage disequilibrium (LD) between eleven SNPs with 5% Bonferroni correction on p-values.

FALSE, linkage between SNPs was not detected (p < 0.05).

SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 SNP9 SNP10 SNP11

SNP1 FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE

SNP2 FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE

SNP3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE

SNP4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE

SNP5 FALSE TRUE FALSE TRUE FALSE FALSE

SNP6 FALSE TRUE TRUE FALSE TRUE

SNP7 FALSE FALSE FALSE FALSE

SNP8 TRUE FALSE FALSE

SNP9 FALSE FALSE

SNP10 FALSE

SNP11

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Table S4

Table S4: The number of individual’s contributing to each population to assigned each cluster (K=4)

CM IC KE SP WP

1 0 36 0 0 0

2 0 0 0 27 1

3 19 0 23 0 1

4 9 0 0 0 25

CM, Combe Martin; IC, Iceland; KE, Kent; SP, Spain; WP, Wembury Point.