Structure Et Biogéographie Des Communautés De Pico- Et De Nanoeucaryotes Pélagiques Autour De L’Archipel Du Svalbard

Structure et biogéographie des communautés de pico- et de nanoeucaryotes pélagiques autour de l’archipel du Svalbard

Mémoire

Vincent Carrier

Maîtrise en biologie Maître ès sciences (M.Sc.)

Québec, Canada

Structure et biogéographie des communautés de pico- et de nanoeucaryotes pélagiques autour de l’archipel du Svalbard

Mémoire

Vincent Carrier

Sous la direction de :

Connie Lovejoy, directrice de recherche Tove M. Gabrielsen, codirectrice de recherche

Résumé L’Arctique s’est réchauffé rapidement et il y a urgence d’anticiper les effets que cela pourrait avoir sur les protistes à la base de la chaîne alimentaire. Le phytoplancton de l’Océan Arctique inclut les pico- et nano-eucaryotes (0.45-10 µm diamètre de la cellule) et plusieurs de ceux-ci sont des écotypes retrouvés seulement dans l’Arctique alors que d’autres sont introduits des océans plus méridionaux. Alors que les océans tempérés pénètrent dans l’Arctique, il devient pertinent de savoir si ces communautés microbiennes pourraient être modifiées. L’archipel du Svalbard est une région idéale pour observer la biogéographie des communautés microbiennes sous l’influence de processus polaires et tempérés. Bien qu’ils soient géographiquement proches, les régions côtières entourant le Svalbard sont sujettes à des intrusions alternantes de masses d’eau de l’Arctique et de l’Atlantique en plus des conditions locales. Huit sites ont été échantillonnés en juillet 2013 pour identifier les protistes selon un gradient de profondeur et de masses d’eau autour de l’archipel. En plus des variables océanographiques standards, l’eau a été échantillonnée pour synthétiser des banques d’amplicons ciblant le 18S SSU ARNr et son gène pour ensuite être séquencées à haut débit. Cinq des sites d’étude avaient de faibles concentrations de chlorophylle avec des compositions de communauté post-efflorescence dominée par les dinoflagellés, ciliés, des alvéolés parasites putatifs, chlorophycées et prymnesiophytées. L’intrusion des masses d’eau et les conditions environnementales locales étaient corrélées avec la structure des communautés ; l’origine de la masse d’eau contribuant le plus à la distance phylogénétique des communautés microbiennes. Au sein de trois fjords, de fortes concentrations de chlorophylle sous- entendaient des activités d’efflorescence. Un fjord était dominé par Phaeocystis, un deuxième par un clade arctique identifié comme un Pelagophyceae et un troisième par un assemblage mixte. En général, un signal fort d’écotypes liés à l’Arctique prédominait autour du Svalbard.

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Abstract The Arctic is warming rapidly and there is an urgent need to anticipate the effect this will have on the microbial eukaryotes at the base of the food chain. Arctic Ocean phytoplankton include pico- and nano-eukaryotes (0.45-10 µm cell size), many of these are unique ecotypes found only in the Arctic, but others are advected in from lower latitude oceans. As temperate oceans waters penetrate further into the Arctic, knowledge of whether microbial communities could be displaced is needed. Svalbard is an ideal region to address questions on microbial communities under the influence of polar and temperate processes. Although geographically close, the fjords and offshore regions surrounding Svalbard are subjected to alternate intrusions of Atlantic and Arctic waters in addition to local conditions. Eight sites were surveyed in July 2013 with the aim of identifying microbial eukaryotes at a range of depths and water masses around Svalbard. In addition to standard oceanographic variables, seawater was collected for targeted amplicon libraries based on the 18S SSU rRNA gene and rRNA using high throughput amplicon sequencing. Five of the sites had low chlorophyll concentrations with typical post bloom summer communities; dinoflagellates, ciliates, putative alveolate parasites, chlorophytes and prymnesiophytes. Intrusive water masses and local environmental conditions correlated to community structure, with the origin of the water mass contributing most to the phylogenetic distance of the microbial communities. In three of the fjords, chlorophyll concentrations were high, consistent with a bloom. One fjord was dominated by Phaeocystis, a second by a putative Arctic clade of Pelagophyceae, and the third by mixed species. Overall, a strong signal of Arctic ecotypes prevailed around Svalbard.

Table des matières

Résumé iii

Abstract iv

Table des matières v

Liste des tableaux viii

Liste des figures xi

List des abbréviations xiv

Avant-Propos xviii

1. INTRODUCTION GÉNÉRALE 1

L’Océan Arctique et la biodiversité de ses microorganismes 1

Caractérisation des périodes d’efflorescence dans l’Arctique et autour de l’archipel du Svalbard 8 Phaeocystis pouchetii (Haptophyta: Prymnesiophyceae) 10 Pelagophyceae 10

L’hydrographie du Svalbard 12 Dynamise hydrographique des fjords 14 Systèmes hydrographiques ciblés 15

Biogéographie: barrières géographiques et conditions environnementales 16 Biogéographie: un exemple du protiste pélagique Micromonas pusilla (Chlorophyceae) 19

Relèvements taxonomiques des communautés microbiennes 20

Séquençages à haut débit (HTS) 21

Objectifs de ce projet de recherche 23

GENERAL INTRODUCTION 24

The Arctic Ocean and its microbial biodiversity 24

Characterization of blooms in the Arctic and around the archipelago of Svalbard 30 Phaeocystis pouchetii (Haptophyta: Prymnesiophyceae) 32 Pelagophyceae 33

Hydrography of Svalbard 34 Fjord hydrographic systems 36 Targeted local hydrographic systems 37

Biogeography: geographical barriers and environmental conditions 38 Importance of biogeography: example of the marine protist Micromonas pusilla (Chlorophyceae) 41

Taxonomic surveys of microbial communities 42

High throughput sequencing (HTS) 43

Objectives of the present project 45

2. PELAGIC COMMUNITIES OF NANO- AND PICOEUKARYOTES AROUND THE ARCHIPELAGO OF SVALBARD AND THEIR BIOGEOGRAPHY 46

Résumé 46

Abstract 47

Introduction 48

Methods 51 Experimental design and sampling locations 51 Physicochemical and biological analysis 52 Flow cytometry (FCM) analysis 52 DNA and RNA extraction and conversion to cDNA 53 Illumina library preparation 54 Bioinformatics 56 Data analysis 56

Results 61 Hydrography, nutrients and chlorophyll a 61 Flow cytometry results 64 Sequence quality filtering and analysis 65 Community diversity and composition 65 Biogeographic analyses 73

Discussion 77 Svalbard hydrographic dynamics 77 Pico- and nanoeukaryote community composition 78 Biogeographic analyses 82

Conclusion 85

3. BLOOMS OF PELGIC PICO- AND NANOEUKAYOTES AROUND THE ARCHIPELAGO OF SVALBARD 86

Abstract 87

Introduction 88

Methods 89 Experimental design and sampled sites 89 Physicochemical and biological analysis 90 Flow cytometry (FCM) analysis 91 DNA and RNA extraction and conversion to cDNA 91 Illumina library preparation 92 Bioinformatics 94 Phylogenetic analysis of Pelagophytes 95 Phylogenetic analysis of the Phaeocystales 95

Results 96 Hydrography, nutrients and chlorophyll a profiles 96 Community composition and structure 99 Phylogenetic placement of the Hornsund pelagophyte 100 Phylogeny of the bloom forming Phaeocystales in Storfjorden 103

Discussion 105 Microbial eukaryotes from Storfjorden and Erik Eriksen Strait 105 Arctic pelagophytes: a new occurrence of a pan-Arctic species 108

Conclusion 109

4. GENERAL CONCLUSIONS 111

BIBLIOGRAPHY 116

APPENDIX A. SUPPLEMENTAL MATERIALS FOR CHAPTER 2 136

APPENDIX B. SUPPLEMENTAL MATERIALS FOR CHAPTER 3 148

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Liste des tableaux Table 1.1. Intervalles de température (T) et de salinité (S) utilisés pour déterminer l’origine des masses d’eau identifiées dans ce mémoire déterminées par Cottier et coll. (2005). Les abbréviations des sites d’échantillonnage sont présentées entre parenthèses...... 13

Table 1.2. Ranges of temperature (T) and salinity (S) used to determine origin of the water masses identified in this study as defined by Cottier et al. (2005). Water mass abbreviations are given in brackets...... 35

Table 2.1. Geogaphical positions and sampling day. At each sampling site, seawater sampling started at noon local time. Sampling site abbreviations are given in brackets...... 51

Table 2.2. PCR primers used in this study and the orientation of the primer (O): forward (F) or reverse (R). Short28SF and Short28SR were used to assess the success of DNA and RNA extractions. The combination of the specific primers E572F/ E1009R acting on the V4 region of 18S nrDNA was used for amplicons library preparation...... 54

Table 2.3: Randomization analyses of null models for the OTU table based on the checkerboard score (C-score) of the OTU abundances calculated from the rRNA gene libraries (99 permutations). Z is the standard score for a confidence level (ρ = 0.05). Libraries from all stations and depths are included, except the 150 m depth and the 240 m depth communities in the Hinlopen Strait...... 74

Table 2.4: Statistical results from Mantel tests based on 999 permutations between distance matrices of the community composition of all samples (Hinlopen 150 and 240 m were discarded) and the environmental factors, the water mass origins and the distance (latitude, longitude and depth). Biotic data matrices are based either on Bray- Curtis or UniFrac dissimilarity of the relative abundance of the major taxonomic groups. Significant values are in bold (α = 0.05). The correlation between the environment and the distance matrices is based on the Euclidean distance...... 74

Table 3.1. Geogaphical positions and the sampling day. At each sampling site, seawater sampling started c. noon local time. Sampling site abbreviations are given in brackets...... 90

Table 3.2. PCR primers used in this study and the orientation of the primer (O): forward (F) or reverse (R). The combination of the specific primers E572F/E1009R acting on the V4 region of 18S nrDNA was used for amplicons library preparation. 92

Table 3.3: Genetic distances within and between clades based on pairwise distances calculations established in Figure 3.5...... 103

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Table A.1. Number of reads per rRNA gene based library and rRNA based library after quality filtering and demultiplexing. The sampling sites were Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRAM) and Adventfjorden (ISA)...... 138

Table A.2. OTUs clustered at 98% similarity (rRNA gene and rRNA libraries respectively) where numbers in red represent samples where biodiversity is underrepresented and not taken account into biogeographic analysis, but used for phylogeny. Deep samples correspond to 370 m in Erik Erikssen Strait, 240 m in Hinlopen Strait and 490 m in Fram Strait. The depths at the ISA sampling site are 5, 15, 25 and 60 m. Samples that were not successfully amplified are indicated as not available (NA)...... 139

Table A.3. Physical and biological variables from each station and depth sampled around Svalbard in July 2013. Nutrient concentrations (NO3/NO2, PO4 and SiO2), water mass indentification follows Cottier et al. (2005) see text for abbreviations, temperature, salinity, chlorophyll a >10.0 µm (Lchl) and total chlorophyll a (Tchl). Maximum depth at each station is indicated in brackets...... 140

Table A.4. Relative abundance of reads assigned to the major abundant taxomic groups of Ciliophora, Dinophyceae (Dinos), Marine Alveolates (MALVs), Phaeocystaceae (Phaeo), Prymnesiophyceae (Prymn), Picozoa, Cercozoa, Chlorophyceae (Chloro), Marine Stramenopiles groups (MASTs) and Pelagophyceae (Pelago; taxa with >1% of the total rRNA gene-based community reads) across the sampling sites of Hinlopen (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait, (FRAM) and Adventfjoden (ISA) communities. The depth of the sample is indicated after the sampling site abbreviation...... 142

Table A.5. Relative abundance of reads assigned to the most abundant genera of ciliates Laboea, Novistrombidium and Parastrombidinopsis across the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRAM) and Adventfjoden (ISA) communities. Depth of sample in metres (m) indicated after the sample abbreviation...... 143

Table A.6. Relative abundance of reads assigned to the most abundant taxonomic groups of dinoflagellates Gymnodinium beii (Gb), Gyrodinium fusiforme (Gf), Gyrodinium helveticum/rubrum group (Gh/r), Gyrodinium gutrula (Gg), Karenia/Prorocentrum group (K/P), Karlodinium (K), Katodinium (Ka) and Nematodinium (N) across the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait, (FRAM) and Adventfjoden (ISA) communities. Depth of sample in metres (m) indicated after the sample abbreviation...... 144

Table A.7. Relative abundance of reads assigned to the major abundant taxonomic groups (taxa covering >1% of the total rRNA-based community sequences) across the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait, (FRAM) and Adventfjoden (ISA) communities. Depth of sample in metres (m) indicated after the sample abbreviation...... 145

Table A.8. Relative abundance of reads assigned to the most abundant dinoflagellates taxa: Gyrodinium fusiforme (Gf), Karenia/Prorocentrum group (K/P), Katodinium (Ka) and Nematodinium (Ne) across the Hinlopen Strait(HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait, (FRAM) and Adventfjoden (ISA) communities. Depth of sample in metres (m) indicated after the sample abbreviation...... 146

Table A.9. Absolute and relative (%) contribution to the overall dissimilarity of the community composition and structure of each abundant taxonomic group between the rRNA gene and RNA based libraries for surface (0-35 m) layers and for the deeper (60-75 m) layers based on SIMPER analysis...... 147

Table B.1. Pelagophyte 18S rRNA sequences used for phylogenetic analysis: GenBank accession numbers, taxonomic identification and their original reference...... 148

Table B.2. Phaeocystis 18S rRNA sequences used for phylogenetic analysis: GenBank accession numbers of the, taxonomic identification and oarignal reference...... 149

Table B.3. Number of sequences per rRNA gene based library and rRNA based library after quality filtering and demultiplexing. HOR, STO and EES are the respective sampling site IDs of Hornsund, Storfjorden and the Erik Eriksen Strait. 151 Table B.4. Relative abundance of reads assigned to the major abundant taxonomic groups Ciliophora, Dinophyceae (Dinos), Marine Alveolates (MALVs), Phaeocystaceae (Phaeo), Picozoa and Pelagophyceae (Pelago; taxa covering >1% of the total rRNA gene-based community) across the Hornsund (HOR), Storfjorden (STO) and Erik Eriksen Strait (EES) communities. Depth of the sample is given after the sampling site abbreviation...... 152

Table B.5. Relative abundance of reads assigned to the major taxonomic groups (taxa covering >1% of the total rRNA (R) and rRNA gene (D)-based community) across the blooming Hornsund (HOR), Storfjorden (STO) and Erik Eriksen Strait (EES) communities. Depth of the sample is given after the sampling site abbreviation. .... 153

Liste des figures Figure 1.1. Projection orthographique du pôle Nord (60-90°N). À noter la position de l’archipel du Svalbard à la marge de la mer de Barents...... 1

Figure 1.2. Principaux courants marins autour de Svalbard et à l’intérieur de la mer de Barents. Notez que le courant ouest du Spitsberg (WSC) origine des eaux plus chaudes et salées du Gulf Stream (flèches rouges; AW) et le courant est du Spitsberg (ESC) des eaux plus froides et fraîches de l’Océan Arctique (flèches bleues; Hop et coll. 2002). La dominance des eaux Atlantique vs Arctique dans les fjords du Svalbard dépend fortement des facteurs barotropiques et topographiques (Table 1.1; Cottier et coll. 2005)...... 12

Figure 1.3. North Pole orthographic projection (60-90°N). Note the position of the archipelago of Svalbard on the margin of the Barents Sea...... 24

Figure 1.4. Currents system around Svalbard and inside the Barents Sea. Note that the West Spitsbergen Current originates from the warm and saline Gulf Stream (red arrows; Atlantic Water) and that the Sørkapp Current from colder and fresher Arctic Waters (blue arrows; Hop et al. 2002). The domination of Atlantic or Arctic waters in Svalbard fjords strongly depends on barotropic and topographic factors (Table 1.2.; Cottier et al. 2005)...... 34

Figure 2.1. (Left) Geographical position of Svalbard and (right) of each sampling site: Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRA) and Adventfjorden (ISA)...... 51

Figure 2.2. Non-metric multidimensional scaling based on Bray-Curtis dissimilarity index of the relative abundance of major taxonomic groups of the rRNA gene samples, note that the deep layers from Hinlopen Strait clustering far apart. Therefore, the community composition and structure of these depths are described but are not considered in the further biogeographic analyses...... 58

Figure 2.3. Salinity and temperature profiles by depth in the upper 200 m of the five sampling sites. Values of salinity and sea temperature at the sampled depths are listed in Table A.3...... 61

Figure 2.4. Total chlorophyll a biomass (upper light green bar), chlorophyll a biomass of cells >10 µm (lower dark green bar). Nutrients (Silicate - yellow, Phosphate - blue, Nitrite+Nitrate - red) indicated by dashed lines for all sampled depths...... 62

Figure 2.5. Log transformed abundance of cells ml-1 of picoeukaryotes, nanoeukaryotes, cryptophytes and cyanobacteria of Synechococcus at the different sampling depths from flow cytometry...... 64

Figure 2.6. Abundant taxonomic groups and their relative abundances of associated sequences per rRNA gene based library based on Table A.4. Fjords sampled were the

Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRA) and Adventfjorden (ISA). Non-abundant and unidentified sequences were combined in the “Other” category...... 66

Figure 2.7. Abundant ciliate genera representing at least 1% relative abundance in each rRNA gene based library based on Table A.5. Sampling sites were the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), the Fram Strait (FRAM) and Adventfjorden (ISA). Rare and unidentified taxa were combined in the “Other” category...... 67

Figure 2.8. Abundant dinoflagellate genera with at least 1% relative abundances of associated sequences in the rRNA gene based libraries based on Table A.6. Sampling sites were the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), the Fram Strait (FRAM) and Adventfjorden (ISA). Rare and unidentified dinoflagellates were combined in the “Other” category...... 68

Figure 2.9. Abundant taxonomic groups and their relative abundances of associated sequences per rRNA gene based library based on Table A.7. Stations abbreviations are as in Figure 2.6. Rare and unidentified taxa are combined in the “Other” category...... 71

Figure 2.10. Abundant dinoflagellate taxonomic groups and their relative abundances of associated sequences per rRNA gene based library based on Table A.8. Stations abbreviations are as in Figure 2.6. Rare unidentified dinoflagellates are combined in “Other” category...... 72

Figure 2.11. Relative contribution to the overall dissimilarity of the community composition and structure of each abundant taxonomic group based on Table A.9 between the rRNA gene and the RNA based libraries for surface (0-35 m) layers and for deeper (>60 m) layers based on SIMPER analysis...... 73

Figure 2.12. CCA (999 permutation) of the abundant taxa ( >1% of total sequences) with selected environmental factors. Sampling sites are represented by colours (see Figure 2.2) and numbers are depths. Blue dots are taxa and are assigned to MALV I (MALV_1), Picozoa (Pico), Gyrodinium gutrula (G_gut), Katodinium spp. (Kato), Gyrodinium fusiforme (G_fusi), Nematodinium spp. (Nemato), Gyrodinium helveticum/rubrum (G_helrub), Gymnodinium beii (G_beii), Mamiellophyceae (Mam), Parastrombidinopsis (Parastrom), Novistrombidinium (Novastrom), Prymnesiophyceae (Prymn), Phaeocystaceae (Phaeo) and Pelagophyceae (Pelago). The environmental factors tested in this ordination are: temperature (T), salinity (S), Silicate (Si), Phosphate (P), Nitrate+nitrite (N), total chlorophyll a biomass (TC) and >10 µm cell size chlorophyll a biomass proportion to TC (STC). Axis 1 explains 36.32% (ρ = 0.09) of the variability among the abundant taxa within the samples while Axis 2 explains 34.48% (ρ = 0.001)...... 76

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Figure 3.1. (A) Geographical position of Svalbard and (B) of each site with high chlorophyll a values indicating a bloom: Hornsund (HOR), Storfjorden (STO) and Erik Eriksen Strait (EES)...... 90

Figure 3.2. Water column profiles (upper 200 m) of salinity and sea temperature along by depth for the sampling sites Hornsund, Storfjorden and Erik Eriksen Strait. Values for each sample are given in Table A.3...... 97

Figure 3.3. On the left, total chlorophyll a concentration (upper light green bar), chlorophyll a concentration of >10 µm size cells (lower dark green bar) and molar concentrations of nutrients (dashed lines for Silicate, yellow; Phosphate, blue; Nitrite+Nitrate, red) at Hornsund, Storfjorden and Erik Eriksen Strait. On the right side are cell ml-1 log transformed concentrations of picoeukaryotes, nanoeukaryotes, cryptophytes and the cyanobacteria Synechococcus at the same sites...... 98

Figure 3.4. Relative abundance of the major taxonomic groups in the high chlorophyll a samples (bloom strata) from the rRNA gene (DNA) and rRNA (RNA) based libraries based on Table B.4. Groups with low abundance and unidentified sequences are combined in the “Other” category...... 100

Figure 3.5. Rooted Pelagophyceae phylogenetic tree using maximum likelihood from an alignment of 25 sequences (Table B.1). Pseudopedinella sp. (CCMP 3052) was used as an outgroup. The pelagophyte sequences from this study are OTUs 734 and 1445 and were placed at nodes using RAxML...... 102

Figure 3.6. Rooted Phaeocystaceae phylogenetic tree from sequences listed in Table B.2 using maximum likelihood from an alignment of 12 sequences. Only 5 of 12 described species of the genus Phaeocystis had 18S SSU rRNA reference sequence were available on NCBI and are represented: Phaeocystis antarctica, Phaeocystis pouchetii, Phaeocystis globusa, Phaeocystis cordata and Phaeocystis jahnii. The Chrysochromulina parva (CCMP 29) is used as an ourgroup. Phaeocystis sequences retrieved in this study are identified as OTUs 2, 109 and 2003 and were placed using RAxML...... 104

Figure A.1. Multiple rarefactions curves based on number of observed OTUs (Y axis) compared to the sequences per sample (X axis) for the rRNA gene-based libraries.136

Figure A.2. Multiple rarefactions curves based on number of observed OTUs (Y axis) compared to the sequences per sample (X axis) for the rRNA based libraries...... 137

Figure B.1. Multiple rarefactions curves based on the number of observed OTUs (Y axis) compared to the number of sequences per sample (X axis) for the rRNA gene- based libraries ...... 150

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List des abbréviations 18S – eukaryotic small ribosomal subunit ADN – Acide désoxyribonucléique ADNc – Acide désoxyribonucléique complémentaire APP – Annual primary productivity ARN – Acide ribonucléique ArW – Arctic water mass BLASTn – Basic Local Alignment Search Tool, nucleotide search BOC – Bockfjorden, Norway Bp – base pair CCA – Canonical Correlation Analysis CCMP – Culture Collection of Marine Phytoplankton (from National Centre of Marine Algae and Microbiota, Bigelow, Maine, USA) cDNA – complementary deoxyribonucleic acid CRNSG – Conseil de Recherche en sciences naturelles et en génie du Canada DNA/ADN – deoxyribonucleic acid EES – Erik Eriksen Strait, Norway EPA – Evolutionary Placement Algorithm ESC – East Spitsbergen Current FCM - Flowcytometry FRAM – Fram Strait, Norway GPP – Group of dinoflagellates (gymnodinoids, peridinoids, prorocentroids) HAB – Harmful algal bloom HIN – Hinlopen Strait, Norway HOR – Hornsund, Norway HTS – High Throughput Sequencing IBIS – Institut de biologie intégrative des systèmes, Université Laval ISA – Adventfjorden, Norway

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IW – Intermediate water mass KG – Kongsfjorden, Norway LCM – Local conditions matrix LW – Local water mass MALINA – Joint French and Canadian project carried out in 2009 MALVs – Diverse uncultivated marine alveolates thought to be mostly parasitic MASTs – Diverse clades of uncultivated heterotrophic marine stramenopiles MEGA – Molecular Evolutionary Genetics Analysis (software) NCBI – National Cetnre for Biotechnology Information NIVA – Norsk Institutt for Vanforksning (Norwegian institute on water research)

NO2 – Nitrite

NO3 – Nitrate OTU – Operational taxonomic unit PAR – Photosynthetically available radiation (irradiance) PAST – Paleontological Statistics (software) PCR – Polymerase Chain Reaction

PO4 – Phosphate PPA – Productivité Primaire Annuelle QIIME – Quantitative Insights Into Microbial Ecology (software) RaxML – Randomized axelerated Maximum Likelihood RCC – Roscoff Culture Collection RNA/ARN – ribonucleic acid rRNA – ribosomal ribonucleic acid RT-PCR – Reverse transcription Polymerase Chain Reaction SC – Sørkapp Current SILVA – Ribosomal RNA databases SIMPER – Similarity Percentages analyses

SiO2 - Silicate SPRI – Solid Phase Reversible Immobilization SSU – Small subunit (of rRNA) STO – Storfjorden, Norway SW – Surface water TAW – Transformed Atlantic water mass UNIS – University Centre in Svalbard, Norway V4 – variable region on the 18S rRNA gene WCW – Winter cooled water mass WIJ – Wijdefjorden WML – Winter mixed layer WMM – water masses matrix WSC – Western Spistsbergen Current

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Ce mémoire est dédié à mes parents Julie et Michel. C’est grâce à vous si je suis rendu ici aujourd’hui. Merci de m’avoir permis de découvrir le monde.

Master’s project in cooperation with the University Centre in Svalbard (Norway)

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Avant-Propos Ce mémoire écrit en langue anglaise est composé de quatre chapitres incluant des figures et tableaux ainsi que des annexes. Le premier chapitre est une introduction générale sur la biologie microbienne de l’Arctique et leur biogéographie, le système hydrographique autour de l’archipel du Svalbard ainsi que les récentes technologies moléculaires récemment développées utilisées dans ce projet. Ces connaissances de base sont par la suite suivies par les objectifs de ce projet de recherche. Car ce mémoire sera disponible à l’Université Laval et au Centre Universitaire du Svalbard une version française précède une version anglaise de l’introduction générale. Le deuxième et le troisième chapitre constituent le projet de recherche et ont été rédigés sous forme d’articles scientifiques dont je suis l’auteur principal.

Des articles scientifiques découleront de ces deuxième et troisième chapitres où Dre. Connie Lovejoy, Dre Tove M. Gabrielsen, Dre Anna Vader et M.Sc. Miriam Marquardt seront également coauteures. Le dernier chapitre synthétise les conclusions du deuxième et troisième chapitre.

Ce mémoire a été corrigé par le Dre Connie Lovejoy - ma directrice de recherche au sein de l’Université Laval - ainsi que par le Dre Tove M. Gabrielsen - ma codirectrice au sein du Centre Universitaire du Svalbard (UNIS) en Norvège. Je leur voue toute ma gratitude et ma reconnaissance pour leur support, leur aide et les nombreuses opportunités qui m’ont été offertes dans le cadre de ce projet et dans la vie. Je les remercie de m’avoir si bien encadré au cours de ce beau projet qui m’a fait découvrir l’Arctique, son océanographie et ses communautés microbiennes.

Cette maîtrise a été réalisée dans le cadre du projet MicroFun (RiS : 6167, Svalbard). Ce projet basé à l’UNIS a pour but d’étudier la biodiversité ainsi que les fonctions des eucaryotes microbiens marins et terrestres dans l’archipel du Svalbard. L’échantillonnage s’est déroulé durant l’été 2013 à bord d’un navire et la majorité des travaux en laboratoire se sont déroulés sur Svalbard. J’aimerais remercier particulièrement : M.Sc. Miriam Marquardt – pour sa patience et ses instructions précieuses durant l’échantillonnage et dans le laboratoire de biologie moléculaire -, Dre Anna Vader –également pour ses connaissances et son aide lors des

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manipulations avec l’ARN et la synthèse d’ADN complémentaire- ainsi que M.Sc. Sunil Mundra - pour ses nombreux conseils dans les protocoles de biologie moléculaire et le traitement des séquences. Je remercie également M.Sc. Ida Kessel Nordgård pour les analyses des échantillons au cytomètre de flux.

Les analyses génétiques ont été réalisées à l’Institut de Biologie Intégrative des Systèmes (IBIS) à l’Université Laval. Pour cette période, j’aimerais remercier tout le laboratoire de Dre. Connie Lovejoy pour leur support dans mes analyses. Du traitement de séquences jusqu’aux analyses statistiques, leur appui a été inestimable. Ces travaux ont été présentés à trois évènements différents : la journée étudiante de l’IBIS (Québec, août 2014) ainsi qu’aux Assemblées Générales de Québec-Océan (Rivière-du-Loup, novembre 2014; Québec, novembre 2015). Ils seront également présentés au Symposium de l’International Society for Microbial Ecology qui se déroulera à Montréal en août 2016.

Ce projet a été réalisé grâce au financement et au support reçus par ConocoPhillips, le Lundin Northern Area Program, le Forum Scientifique du Svalbard, le département de biologie arctique de l’UNIS, le Conseil de Recherche en Sciences Naturelles et Génie du Canada (CRSNG), le groupe de recherche Québec- Océan, le réseau de centres d’excellence du Canada ArcticNet et l’IBIS.

Je tiens à remercier tous les membres des laboratoires de Dre Connie Lovejoy et Dre Tove M. Gabrielsen pour leur aide, leurs conseils et leur amitié. J’exprime spécialement toute ma reconnaissance envers M. Deo Florence L. Onda pour son aide et support tout au long du projet, sa patience et surtout pour son incroyable support moral. Je tiens également à remercier les autres membres de mon comité d’évaluation, Dr Jean-Éric Tremblay et Dr Maurice Levasseur, pour leur disponibilité et leurs précieux conseils pour mon projet.

Je suis reconnaissant également envers la communauté du Svalbard de l’UNIS pour avoir contribué à mon épanouissement dans ce milieu reculé de l’Arctique.

Finalement, je remercie de tout cœur ma famille, particulièrement mes parents Julie et Michel, ma sœur Julie-Anne et mes grands-mères Léa et Madeleine pour leur support inestimable.

xix

1. Introduction générale

L’Océan Arctique et la biodiversité de ses microorganismes

L’Océan Arctique couvre une superficie de plus de 14 millions km2 et augmente jusqu’à 18,8 millions km2 lorsqu’on inclut les mers de Béring et d’Okhotsk. Plus de 50% de la superficie totale est située sur des plateaux continentaux tels que le plateau de la mer de Barents, une des régions les plus biologiquement productives du globe (Jakobsson 2002).

Figure 1.1. Projection orthographique du pôle Nord (60-90°N). À noter la position de l’archipel du Svalbard à la marge de la mer de Barents.

La région à l’étude dans ce mémoire est située autour de l’archipel du Svalbard, à la conjonction de la mer de Barents, le détroit de Fram et l’Océan Arctique (Figure 1.1).

Les communautés microbiennes dans l’Océan Arctique rencontrent plusieurs défis où souvent seulement les espèces particulièrement adaptées aux conditions persistent à l’année. La luminosité est un facteur limitant majeur, car la disponibilité en luminosité propre pour la photosynthèse (PAR) varie considérablement avec les saisons. Même au sein de l’Océan Arctique, les régimes de luminosité durant la saison de noirceur varient du sud au nord à travers le crépuscule civil, la nuit polaire civile et la nuit polaire nautique. Traditionnellement, les scientifiques supposaient que les longues périodes de noirceur s’étirant sur plusieurs mois dans l’Arctique inhiberaient l’activité biologique. Toutefois, plusieurs études se déroulant durant l’hiver ont rapporté du broutage par le zooplancton ainsi que la persistance d’organismes photosynthétiques (Scherr et coll. 2003, Berge et coll. 2009). Les études portant sur les communautés microbiennes et utilisant les marqueurs moléculaires ciblant la SSU, 18S de l’ADN et l’ARN, provenant d’échantillons pélagiques récoltés dans la mer de Beaufort et dans le nord du Svalbard indiquaient que Micromonas Pusilla, un organisme autotrophe avec un écotype lié à l’Arctique, est actif durant la période hivernale (Lovejoy et coll. 2007, Vader et coll. 2014). Récemment, McKie-Krisberg et Sanders (2014) démontraient chez l’écotype arctique de Micromonas un comportement alimentaire de phagotrophie comme une autre option lorsque les conditions deviennent défavorables à l’activité photosynthétique. La luminosité et les concentrations de nutriments, deux paramètres très variables dans l’Arctique, sont des facteurs qui pourraient influencer le taux d’ingestion de ce mixotrophe.

Les communautés microbiennes photosynthétiques dans la majorité des océans sont dominées par les picocyanobactéries, telles que Prochlorococcus et Synechococcus, qui sont responsables de 8,5% et 16,7% de la productivité primaire dans les océans respectivement (Flombaum et coll. 2013). La température et le PAR influencent grandement la biogéographie de ces deux genres. Ces cyanobactéries

tendent à décroître en abondance aux latitudes >40° et les Prochlorococcus sont rarement observés dans les océans ayant une température sous 8°C (Johnson et coll. 2006, Huang et coll. 2012, Flombaum et coll. 2013). Les Synechococcus croient sous un large spectre de conditions et dominent les communautés de picocyanobactéries aux latitudes tempérées incluant le long des côtes norvégiennes (Zwirglmaier et coll. 2008, Cottrel & Kirchman 2009). Ces auteurs rapportaient également la présence de clades de Synechococcus typiques dans les estuaires des mers de Béring et de Chukchi et suggéraient que les picocyanobactéries pourraient être autochtones à l’Océan Arctique alors que d’autres contre-argumentent que la plupart des Synechococcus observés dans les eaux pélagiques de l’Arctique sont allochtones ou origine des apports d’eau douce terrestres ou de l’Océan Pacifique (Vincent et coll. 2000, Waleron et coll. 2007, Huang et coll. 2012). Malgré tout, les Synechococcus tolérant le froid sont rares avec des abondances variant entre 0 et 103 cellules ml-1 dans les océans Arctique et de l’Antarctique (Bouman et coll. 2006, Waleron et coll. 2007). Bien qu’ils peuvent contribuer jusqu’à 25% de l’activité phytoplanctonique dans le fjord arctique de Kongsfjorden, comparativement aux latitudes plus méridionales, les cyanobactéries ont peu d’impact sur la chaîne alimentaire dans l’Océan Arctique (Piquet et coll. 2014). En contraste, les picophytoplancton eucaryotes peuvent contribuer jusqu’à 90% de la productivité primaire annuelle dans l’Arctique (Gradinger & Lenz 1995, Jardillier et coll. 2010, Grob et coll. 2011).

Les diatomées pélagiques, qui sont en général plus grosses que les eucaryotes microbiens sujet dans cette étude, contribuent jusqu’à 40% des 45 à 50 milliards tonnes métriques de carbone organique produites chaque année dans les milieux marins du monde et supportent les chaînes alimentaires des régions côtières (Nelson et coll. 1997). Elles dominent la biomasse des communautés d’eucaryotes dans les eaux mélangées et riches en nutriments particulièrement au printemps et au début de l’été lorsque les nutriments sont abondants (Boyd et coll. 2004, Sarthou et coll. 2005). Les larges diatomées sont observées en grandes concentrations au nord du Cercle Arctique avec une prévalence des espèces Thalassiosira et de Chaetoceros (Grøntved et coll. 1938, Von Quillfeldt 2000, Lovejoy et coll. 2002). Dans le Bassin Canadien, la réduction du volume de la banquise et l’augmentation de l’apport d’eau

douce approfondissent l’halocline. Les eaux stratifiées provoquent ainsi des eaux de surface pauvres en nitrate (Li et coll. 2009). Le rafraîchissement qui approfondit la nutricline à l’extérieur de la zone euphotique mène à la dominance de pico- et nanoeucaryotes. De plus petites cellules sont plus avantagées pour survivre lorsqu’il y a carence de nutriments et/ou de lumière dû à leur ratio surface :volume plus avantageux dans une masse d’eau pauvre en nutriments (Li et coll. 2009, McLaughlin & Carmack 2010). Dans le nord-ouest de l’Atlantique où le rafraîchissement de la surface est dû à la fonte de la banquise et aux précipitations, on observe des modifications dans la trajectoire des eaux profondes du nord-ouest de l’Alantique (NADW) dues à la forte stratification, ce qui peut influencer la structure des communautés microbiennes (Greene & Pershing 2007, Greene et coll. 2008). De plus, la modification de la stratification peut réduire le transport d’eau plus chaude, salée et riche en nitrate de l’Atlantique vers l’Océan Arctique à travers le détroit de Fram (Jones et coll. 1998).

Les pico- et nanoeucaryotes photosynthétiques (définis ici comme 0,45 à 10 µm) sont abondants et constituent une grande proportion de la biomasse durant et à l’extérieur des périodes de floraison dans la mer du Nord (Brandsma et coll. 2013) et dans l’Océan Arctique et ses mers environnantes (Li et coll. 2009, Martin et coll. 2010, Min Joo et coll. 2011, Balzano et coll. 2012). Bien qu’ils contribuent pour moins de 20% de la chlorophylle a totale au sommet de productivité durant la floraison printanière, Hodal & Kristiansen (2008) attribuaient tout de même 46% de la productivité primaire totale produite durant la floraison aux cellules < 10 µm. Les scientifiques supposaient traditionnellement que les plus petites cellules (c.-à-d. < 10 µm), qui atteignent des concentrations de 2,6 à 10,2 x 103 cellules ml-1 dans le nord de la mer de Barents, contribuaient peu au transfert d’énergie vers le benthos dû à leur faible taux de sédimentation (Michael & Silver 1988, Not et coll. 2005). Toutefois, Richardson & Jackson (2007) ont démontré que l’exportation de carbone de pico-organismes (0,2-2,0 µm) peut équivaloir à leur contribution relative à la productivité primaire totale nette et ainsi jouer un rôle équivalant aux plus gros phytoplanctons. L’exportation vers les profondeurs de petites cellules a des impacts sur les échanges benthiques et pélagiques ainsi que dans les cycles biogéochimiques.

En plus du PAR, les régimes de mélange d’eau, les masses d’eau, la stratification, l’apport d’eau douce et la formation et la fonte saisonnière de la banquise influencent les régimes océanographiques physiques à travers l’Arctique (Yamamoto-Kawai et coll. 2009, McLaughlin & Carmack 2010, Arrigo et coll. 2011, Piquet et coll. 2014). Par exemple, des températures plus froides et une forte stratification dues au rafraîchissement des eaux de surface superposant les eaux plus denses de l’Atlantique et du Pacifique influencent le succès relatif de différentes espèces dans la mer de Beaufort et dans le Bassin Canadien (Lovejoy et coll. 2007, Li et coll. 2009, Monier et coll. 2014). Ceci suggère que généralement, la diversité, la distribution et la structure des communautés microbiennes d’eucaryotes dans les mers Arctique sont fortement associées avec les conditions océanographiques prévalentes. Les études suggèrent également que dans l’Arctique les communautés microbiennes pourraient être vulnérables et pourraient être affectées par les changements induits par le climat et apporter des conséquences pour les niveaux supérieurs des chaînes trophiques (Terrado et coll. 2012).

Les taxons régulièrement identifiés des communautés microbiennes dans l’Océan Arctique et les mers environnantes incluent des groupes pélagiques similaires à ceux trouvés dans les autres océans: haptophytes, chlorophytes, dinoflagellés, ciliés, alvéolés marins non cultivés (MALVs), straménopiles autotrophiques et marins hétérotrophiques (MASTs). La biomasse des protistes hétérotrophiques est plus grande que celle du phytoplancton pour la majeure partie de l’année avec par exemple une contribution minimale de 20 à 38% de la biomasse totale durant la floraison dans Konsfjorden (Hodal et coll. 2012). Ci-dessous, une attention spéciale est attribuée aux groupes taxonomiques soulignés dans les résultats de ce mémoire.

Les haptophytes sont abondants autour de Svalbard durant les périodes de floraisons hivernales, particulièrement l’espèce Phaeocystis pouchetii (Not et coll. 2005, Piquet et coll. 2014). Les chlorophytes, particulièrement M. pusilla, constituent la majorité du picophytoplancton (cellules < 3 µm) dans l’Arctique. Un écotype arctique et panarctique remplace les cyanobactéries comme picoautotrophes dominant dans l’Océan Arctique (Lovejoy et coll. 2007). Une plus grande description de

l’écologie de P. pouchetii et de M. pusilla sont présentées respectivement dans les sections 1.1.2.1 et 1.1.2.2 de ce mémoire.

Les principaux groupes d’alvéolés présents dans l’Arctique sont les ciliés, les dinoflagellés et les alvéolés marins non cultivés et potentiellement parasites MALVs (Terrado et al. 2009). Les ciliés et les dinoflagellés sont considérés comme des éléments clés dans le transfert de la productivité primaire aux niveaux supérieurs de la chaîne alimentaire dans les eaux septentrionales alors qu’ils sont les proies des Calanus zooplanctoniques (Levinsen et coll. 2000, Turner et coll. 2001, Sherr & Sherr 2007). En plus des facteurs abiotiques tels que la température, les communautés de ciliés et de dinoflagellés hétérotrophes sont fortement influencées par la disponibilité de nourriture et le nombre réduit de copépodes dû à la diapause de ceux- ci à la fin du printemps et durant l’été (Levinsen & Nielsen 2002, Scherr et coll. 2003). Alors que la majorité des ciliés sont hétérotrophes et s’alimentent sur les algues et les bactéries, les comportements alimentaires des dinoflagellés sont diversifiés et incluent l’autotrophie, l’hétérotrophie et la mixotrophie (Graham & Wilcox 2000). Les ciliés sont communément observés au-dessus du cercle polaire dans les mers de Beaufort (Terrado et coll. 2009, Comeau et coll. 2011) et de Barents (Amdt et coll. 2005). Terrado et coll. (2009), en utilisant des outils de biologie moléculaire, ont observé que la majorité des ciliés étaient assignés au genre Strombidium. Lovejoy et coll. (2006) ont quant à eux identifié deux groupes de Strombidium spp. constitués d’écotypes restreints à l’Arctique. En plus de Strombdium spp., Jensen & Hansen (2000) ont noté une codominance de Strobilidium spp. dans la mer de Barents au printemps.

Plusieurs espèces de dinoflagellés sont mixotrophes et incluent des genres comme Karenia et Prorocentrum qui peuvent être associés à des efflorescences d’algues nuisibles (Richardson et coll. 2006, Jeong et coll. 2010). Des études microscopiques ont observé les genres Karenia et Prorocentrum dans l’Arctique canadien et russe (Poulin et coll. 2011). D’autres dinoflagellés mixotrophes incluant Gymnodinium ont été antérieurement observés dans Kongsfjorden et semblent être dominants avec Gyrodinium, étant principalement hétérotrophes (Levinsen & Nielsen

2002, Luo et coll. 2011). Parmi Gyrodoinium, Gyrodinium helveticum et Gyrodinimum rubrum forment un groupe génétiquement rapproché. Traditionnellement, G. helveticum est principalement retrouvé en eau douce alors que G. rubrum est observé en milieu marin (Takano & Horiguchi 2004, Lovejoy et coll. 2006). Dans la Baie de Franklin, la structure des communautés de fin d’été et d’automne a démontré une dominance de séquences associée à G. rubrum (Terrado et coll. 2009).

Les genres Katodinium et Nematodinium sont également des dinoflagellés hétérotrophes. Katodinium a été observé dans le Bassin Canadien ainsi que dans Hornsund et Kongsfjorden (Scherr et coll. 2003, Wiktor & Wojciechowska 2005). Nematodinium a été observé dans Kongsfjorden, toutefois seulement en septembre et en décembre alors que Katodinium et les autres dinoflagellés sont observés durant toute l’année (Seuthe et coll. 2011).

Les MALV I et MALV II sont discriminés par la phylogénie de leur gène de SSU 18S d’ARNr et ont été originalement découverts en utilisant des techniques génétiques et sans culture (Lopez-Garcia et coll. 2001, Moon-Van der Staay et coll. 2001, Guillou et coll. 2008). Ces groupes incluent des endoparasites d’organismes marins comme les crustacés, les dinoflagellés, les poissons et les bivalves (Stentiford & Shields 2005, Chambouvet et coll. 2008, Skovgaard et coll. 2009, Miller et coll. 2012, Noguchi et coll. 2013). Les MALV II sont associés principalement aux parasites de copépodes dans les Syndiniales. Toutefois, seulement quelques espèces ont été identifiées dans ces groupes. Les MALV I et II sont régulièrement observés dans l’Arctique (Lovejoy et coll. 2006, Bachy et coll. 2011, Lovejoy et coll. 2011) incluant autour de Svalbard dans Isfjorden et Billefjorden (Sørensen et coll. 2012, Thompson 2014).

Finalement, les straménopiles marins non cultivés (MAST) principalement hétérotrophes ont 12 groupes phylogénétiquement différents. MAST-1, -3, -4 et -7 sont régulièrement observés dans les océans et les milieux côtiers. Lovejoy et coll. (2006) notaient que 45 des 236 séquences de protistes récoltés dans l’Océan Arctique

et les mers adjacentes appartenaient aux MAST-1, -3 et -7. MAST-4 est généralement absent dans cette région (Massana et coll. 2006). Parmi ceux-ci, MAST-7 est retrouvé en forte abondance dans l’Atlantique Nord et en Antarctique. De plus, des séquences de 18S de la SSU ARNr presque identiques ont été retrouvées dans ces deux sites distancés (Massana et coll. 2004).

Caractérisation des périodes d’efflorescence dans l’Arctique et autour de l’archipel du Svalbard

Les efflorescences de phytoplancton dans les eaux pélagiques de l’Arctique sont des composantes majeures de la productivité primaire annuelle (PPA) dans l’Arctique (Leu et coll. 2006, Hegseth et coll. 2008). Par exemple, durant l’efflorescence printanière de 2002, 1,85 g C m-3 par jour étaient produits dans Kongsfjorden (Hodal et coll. 2012). Toutefois, la PPA varie énormément d’une année à l’autre et l’efflorescence printanière de 2002 dans Kongsfjorden était jusqu’à 8,75 fois plus élevée que les observations passées dans le même fjord (Hop et coll. 2012). Après la longue période de noirceur hivernale, la lumière redevient graduellement disponible alors que la durée du jour s'allonge et la couverture de la banquise rétrécit. Dans l’Arctique norvégien, les efflorescences ont lieu dès avril le long des côtes norvégiennes jusqu’en août/septembre au nord de la mer de Barents (Hegseth et coll. 1995, Falk-Petersen et coll. 2000). Lorsque la luminosité et les nutriments sont en abondance, les grosses cellules telles que les diatomées tendent à dominer la biomasse. Alors que la luminosité est généralement considérée comme le principal élément déclencheur des efflorescences, la progression de l’efflorescence est déterminée par d’autres facteurs. Hodal et coll. (2012) et d’autres scientifiques ont observé une succession d’espèces durant l’efflorescence commençant avec les diatomées Fragilariopsis spp., Chaetoceros spp. et Thalassiosira spp (Tverberg & Hegseth 2013). Les diatomées réduisant les concentrations de nitrate et de silicate dans les eaux de surface, de plus petits autotrophes avantagés par leur taille tel que l’haptophyte Phaeocystis leur succède.

Les masses d’eau ont également une influence sur la composition des communautés microbiennes au cœur des efflorescences alors que les efflorescences

de diatomées autour de l’archipel du Svalbard semblent associées à des eaux influencées par l’Océan Arctique (ArW) et les eucaryotes plus petits par l’Océan Atlantique (AW; von Quillfeldt 2000, Leu et coll. 2006, Iversen & Seuthe 2011). Par exemple l’intrusion d’AW dans Kongsfjorden pourrait empêcher la convection de la colonne d’eau en hiver qui suspend à nouveau les diatomées néritiques en dormance (Backhaus et coll. 1999). La dormance est un mécanisme de protection utilisé par les diatomées dans des conditions environnementales défavorables par exemple celles lors des carences en lumière. Ce concept où les masses d’eau pourraient influencer l’identité de l’organisme dominant l’efflorescence a été également observé en 2007 dans Kongsfjorden. Une intrusion continue d’AW empêchant potentiellement la résuspension des diatomées a causé une efflorescence de Phaeocystis pouchetii (Hegseth et coll. 2008).

Alors que les efflorescences dans l’Arctique prennent place principalement au printemps, de fortes productivités primaires peuvent également se produire durant l’été sous certaines conditions (Garneau et coll. 2007). De longues périodes productives peuvent être induites par une luminosité continue et un renouvellement actif de nutriments comme observées dans la Polynie North Water (Lovejoy et coll. 2002) ou dans Kongsfjorden. Dans ce fjord, l'efflorescence de chlorophylle a était forte d’avril jusqu’à août en 2007 et 2008 (Wallace et coll. 2010).

Également, il peut y avoir des observations de plusieurs efflorescences durant la saison estivale comme au Cap Bathurst et dans plusieurs régions de l’Arctique (Arrigo & Van Dijken 2004). Les efflorescences durant l’été peuvent être influencées entre autres par la stratification causée par les apports d’eau douce, le mélange de la strate d’eau de surface causée par les vents ainsi qu’un retard dans la formation de la banquise (Ardyna et al. 2014). En plus des conditions environnementales, les relations interspécifiques peuvent également jouer un rôle comme dans l’occurrence d’une seconde efflorescence dans les eaux subarctiques au nord de l’Irlande (Gislason & Asthhorsson 1998). Dans ce cas, la faible abondance d’herbivores aurait contribué à maintenir une efflorescence de productivité primaire.

Phaeocystis pouchetii (Haptophyta: Prymnesiophyceae)

Phaeocystis est un prymnesiophyte qui forme des efflorescences en cellules individuelles ou en colonies. Degerlund & Eilertsen (2010) ont compilé des études depuis 1922 sur cette espèce de phytoplancton responsable d’efflorescences en Norvège de Vestfjord jusqu’au nord-ouest du Spitsberg (68°-82°N). Phaeocystis pouchetii était l’espèce la plus régulièrement observée (34% des observations) durant les efflorescences de mars à mai. L’espèce a également été observée dans Isfjorden, Hornsund ainsi que les détroits de Fram et d’Erik Eriksen et à plus faibles concentrations dans le détroit d’Hinlopen (Søreide et coll. 2008, Sørensen et coll. 2012). Autour de l’archipel, sa fréquence d’observation est semblable aux diatomées Thalassiossira gravida/Thalassiossira antarctica var. borealis (32%). L’haptophyte est généralement abondant dans les hautes latitudes lorsqu’il y a intrusion d’AW dans les fjords limitant l’activité des diatomées ou en phase post-efflorescence où P. pouchetii succède aux diatomées (Degerlund & Eilertsen 2010, Hegseth & Tverberg 2013). L’initiation des efflorescences de Phaeocystis demeure nébuleuse dans l’Arctique et Hegseth & Tverberg (2013) soulignent deux possibles explications dans la région septentrionale de la mer de Barents. Ils suggèrent en premier lieu que Phaeocystis pourrait voir un état hivernal au cours de son cycle de vie lié au benthos. En 2007, l’efflorescence de Phaeocystis pourrait avoir été retardée par l’absence de convection dans Kongsfjorden empêchant la forme benthique de l’haptophyte de remonter dans la zone euphotique. La deuxième explication pourrait être liée aux interactions spécifiques où les cellules de l’haptophyte requièrent entre autres des frustules de diatomées pour former des colonies (Eilersten 1989, Nejtsgaard et coll. 2006). Toutefois, ces explications demeurent spéculatives et l’initiation des efflorescences de Phaeocystis requiert plus d’attention.

Pelagophyceae

Les Pelagophyceae sont communs dans les océans (Popels et coll. 2003) et inclus des espèces responsables des marées brunes telles qu’Aureaumbra lagunesis dans le Golfe du Mexique (DeYoe et coll. 1997). Les causes des marées brunes varient selon l’espèce. Aureococcus anophageffens est probablement l’espèce la

mieux étudiée avec des efflorescences observées en Amérique du Nord, en Afrique et en Asie (Garry et coll. 1998, Lomas et coll. 2001, Koch et coll. 2013). La croissance des marées brunes semble être favorisée dans des milieux faibles en nutriments, particulièrement en azote inorganique, et en luminosité démontrant ainsi un type non conventionnel d’efflorescence. L’azote et le phosphore sont généralement deux éléments clés dans la prolifération du phytoplancton, y compris lors des efflorescences d’algues nuisibles (Anderson et coll. 2002). Les liens interspécifiques peuvent également être pris en compte où une efflorescence d’Aureoumbra lagunensis observée au Texas a été attribuée à la soudaine absence de compétition et de prédation causée par une hypersalinisation de l’environnement alors que ce Pelagophyceae peut croître dans un milieu dont la salinité atteint 90 (Liu & Buskey 2000). Cette espèce modifie sa couche extracellulaire faite de substance polymérique à la salinité de son environnement (DeYoe & Suttle 1994).

Plus près de la région à l’étude dans ce mémoire, des Pelagophyceae ont été collectés dans l’Atlantique Nord au nord de l’Irlande alors que les concentrations en nitrate étaient faibles. Ces Pelagophyceae étaient génétiquement très similaires à Ankylochrysis lutea, une espèce au sein des Sarcinochrysidales. Ce pélagophyte est également l’espèce en culture la plus similaire (98% de similarité) au Pelagophyceae en efflorescence échantillonné dans le cadre de ce mémoire. Durant l’expédition MALINA dans la mer de Beaufort, Balzano et coll. (2012) a isolé et cultivé un pélagophyte similaire à Ankylochrysis lutea et d’autres Pelagophyceae ont également été observés dans l’Arctique en utilisant des outils métagénomiques (Lovejoy et coll. 2006). Jusqu’à ce jour, des marées brunes de Pelagophyceae n’ont pas encore été observées au meilleur de nos connaissances. Toutefois, Vader et coll. (données non publiées) ont observé une présence abondante d’un pélagophyte inconnu dans Billefjorden (Svalbard) à la mi-juin 2011. La séquence de ce pélagophyte a été ajoutée dans les analyses phylogénétiques de ce mémoire.

L’hydrographie du Svalbard

L’archipel du Svalbard est un système hydrographique très complexe causé entre autres par sa proximité au front polaire séparant deux masses d’eau importantes: les eaux de l’Atlantique (AW) du courant chaud du Gulf Stream et de l’Océan Arctique (ArW; Figure 1.1).

Table 1.1. Intervalles de température (T) et de salinité (S) utilisés pour déterminer l’origine des masses d’eau identifiées dans ce mémoire déterminées par Cottier et coll. (2005). Les abbréviations des sites d’échantillonnage sont présentées entre parenthèses.

Origine de la masse d’eau T (ᵒC) S Eau de l’Atlantique (AW) >3 >34,9 Eau de l’Atlantique transformée (TAW) 1;3 34,7;34.,9 Eau refroidie durant l’hiver (WCW) <-0.5 >34.7 Eau locale (LW) <1 - Eau de surface (SW) >1 28,0;34,0 Eau de l’Arctique (ArW) <1 34,2;34,8

Deux principaux courants sont présents; le courant ouest du Spitsberg (WSC) qui origine de l’Atlantique et qui est plus chaude et salée que l’eau du courant est du Spitsberg (ESC) qui origine de l’Océan Arctique descendant le long du versant est de Svalbard (Wiktor & Wojciechowska 2005). Dans la documentation le courant est du Spitsberg est également référée à courant du cap sud (Piwoscz et coll. 2002, Majewski et coll. 2009). Le WSC circule le long du versant ouest du Spitsberg et se dirige au-dessus de l’archipel. L’ESC descend de l’Océan Arctique et contourne le Cap Sud du Spitsberg apportant une masse d’eau plus fraîche et froide et s’interpose entre le WSC et l’archipel.

Autour du Spitsberg, l’eau de surface provient principalement du ruissellement terrestre ainsi que de l’eau de fonte des glaciers et de la banquise. Le mélange d’AW du Gulf Stream avec l’ArW de l’ESC forme l’eau de l’Atlantique transformée (TAW) et devient de l’eau intermédiaire lorsque celle-ci est mélangée à nouveau avec TAW ou AW. Ainsi, autant les organismes de l’AW et que de l’ArW sont advectés dans les fjords occidentaux du Svalbard et la magnitude des intrusions des masses d’eau varie selon les saisons et les années (Hop et coll. 2006, Willis et coll. 2006). Les déséquilibres barotropiques dans le WSC contraints géostrophiquement dus à l’absence de front de densité entre WSC et ESC, induisent des échanges importants entre les masses d’eau (Saloranta & Svendsen 2001). L’eau locale (LW) quant à elle est associée à l’eau refroidie par des processus de convection

dans les fjords durant l’automne et l’hiver (Svendsen et coll. 2002). LW peut également être formée lorsque de l’eau plus chaude flotte près d’un glacier et coule lorsqu’elle est refroidie. LW est généralement retrouvé au-dessus de l’AW, mais peut également se retrouver en dessous lorsque l’eau entrant dans le fjord est TAW. Finalement, l’eau refroidie durant l’hiver (WCW) résulte de l’approfondissement de l’eau froide suite au refroidissement de l’hiver et la formation de la banquise.

L’hydrographie sur les versants est et sud de l’archipel du Svalbard est moins étudiée. L’eau froide de l’Océan Arctique est introduite le long de la côte est du Svalbard entre Nordaustlanded et Franz Josef Land. Ceci forme l’ESC circulant vers le sud du Spitsberg et aussi dans le détroit de Hinlopen. Contournant Edgøya, ESC s’introduit dans Storfjorden avant de contourner le Cap Sud. Ainsi, les détroits de Hinlopen et d’Erik Eriksen sont fort probablement influencés surtout par ArW (Hop et coll. 2002). Toutefois, la partie septentrionale du détroit d’Hinlopen est plus influencée par WSC entrant dans le détroit à partir du nord (Daase & Eiane 2007). Hornsund dû à sa position méridionale est le fjord sur la côte ouest du Spitsberg le plus influencé par ESC (Figure 1.2; Wiktor & Wojciechowska 2005). Toutefois, des observations dans ce fjord ont également révélé à maintes reprises la présence d’AW, suggérant une variation saisonnière et annuelle de la masse d’eau entrant dans le fjord (Piwosz et coll. 2002).

Dynamise hydrographique des fjords

L’advection horizontale dans les systèmes de fjords est fortement dépendante de l’apport d’eau douce provenant du ruissellement et de la fonte. Elle est également affectée par l’effet de Coriolis influençant la circulation océanique au sein du fjord et cette influence peut être calculée à partir du radius de déformation de Rossby (Cottier et coll. 2010). Le radius de Rossby détermine la magnitude où la circulation des eaux de surface est déviée par la rotation de la Terre. Les effets de rotations dans un fjord sont régulièrement observés dû à la haute latitude et à une largeur moyenne du fjord plus grande (Cottier et coll. 2005, Skogseth et coll. 2005, Skardhamar & Svendsen 2010). Le système hydrographique influencé par la rotation de la Terre devient ainsi plus complexe à étudier.

La stratification dans un fjord est également importante, car elle influence le renouvellement des nutriments dans la zone euphotique, la dynamique des efflorescences ainsi que la composition des communautés microbiennes (Basedow et coll. 2004, Willis et coll. 2006). Le ruissellement terrestre ainsi que l’eau de fonte sont des facteurs majeurs qui déterminent la stratification à travers l’apport d’eau douce et réduisent la profondeur de la zone euphotique par l’apport de particules minérales (Svendsen et coll. 2002).

Les vents influencent également les eaux de surface dans les fjords. La strate de Ekman, ou la strate d’eau de surface influencée par le vent a une profondeur variant de 15 à 40 m de profondeur pour une vitesse de vent de 4 ms-1 à 8 m s-1 dans Storfjorden par exemple (Skogseth et coll. 2006). De plus, les vents catabatiques, plus persistants, forts et froids que les vents de mi-latitude, peuvent également contrôler l’export d’eau de surface hors du fjord et limiter l’advection de masse d’eau externe (Svendsen et coll. 2002).

Systèmes hydrographiques ciblés

Cette introduction à l’hydrographie locale du Svalbard sera orientée vers les fjords échantillonnés dans ce mémoire : Hornsund, autour de Edgøya dans Storfjorden, Wijdefjorden, Bockfjorden et Isfjorden-Adventfjorden.

Hornsund: Hornsund est le fjord le plus méridional du Svalbard et très influencé par les apports d’eau douce provenant des glaciers. Plus de 10% de l’eau dans Hornsund proviendrait de la fonte de ces derniers (Welawski et coll. 1991). Ce fjord est également caractérisé par un seuil topographique et à tour de rôle les masses d’eaux d’AW et d’ArW sont observées dans le fjord (Hald & Korsun 1997, Piwosz et coll. 2009).

Storfjorden: Storfjorden est une large baie semi-fermée avec un seuil topographique (120 m) au 77e parallèle. Il est également bordé par un haut-fond, Storfjordenbanken, au sud-est. La proximité du front polaire le long la pente de Storfjordrenna, au sud, induit des changements rapides des masses d’eau advectées

dans la baie (Loeng 1991, Schauer 1995). Skogseth et coll. (2008) observait que AW s’introduit régulièrement, généralement entre 30 et 90 m de profondeur dans la colonne d’eau sous SW.

Wijdefjorden and Bockfjorden: Étant le plus long fjord du Svalbard, Wijdefjorden est également caractérisé d’un bassin profond (profondeur maximale de 246 m) isolé par un seuil et influencé par l’alternance de TAW et d’ArW variant selon l’activité de WSC (Dale et coll. 2006, Walczowski & Piechura 2006). En contraste, Bockfjorden, un bras de Wijdefjorden, est plus fréquemment visité par TAW et est très influencé par la fonte des glaciers, particulièrement le large glacier Karlsbreen (Sapota et coll. 2009).

Isfjorden-Adventfjorden: Isfjorden est le plus large fjord sur la côte ouest du Spitsberg et possède plusieurs bras. Le site d’échantillonnage est situé dans Adventfjorden, un bras d’Isfjorden sur son côté sud. Adventfjorden est influencé régulièrement par AW et TAW, mais également par le ruissellement terrestre des rivières Longyearelva et Adventelva. Le site d’échantillonnage est également situé près des habitations de Longyearbyen.

Biogéographie: barrières géographiques et conditions environnementales

Que les protistes soient globalement distribués ou s’il y a endémisme est encore débattu (Bano et coll. 2004, Finlay & Fench 2004, Lachance 2004, Medlin 2007). En superposant les conditions environnementales aux contraintes géographiques, Martiny et coll. (2006) a suggéré une structure de 4 hypothèses alternatives pour déterminer les facteurs influençant la biogéographie des microorganismes. La première, l’hypothèse nulle, décrit qu’il y a absence de schéma biogéographique; les communautés sont distribuées et structurées aléatoirement. La deuxième hypothèse suggère que les communautés sont principalement influencées par les conditions environnementales locales présentes ou récentes telles que la salinité, la température, la qualité et la disponibilité de PAR, etc. Ainsi la différence est moindre entre deux communautés géographiquement éloignées, mais sous des

conditions environnementales similaires que l’opposé. D’un autre point de vue, la troisième hypothèse stipule que l’influence de barrières géographiques limite la dispersion des espèces. La similarité entre deux sites dépend des évènements de dispersion tels que l’advection de masses d’eau hébergeant des communautés microbiennes endémiques dans un fjord. Finalement, la quatrième hypothèse propose la combinaison des deux précédentes où les barrières géographiques et les conditions environnementales locales expliquent la composition et la structure des communautés microbiennes.

Toutefois, l’échelle spatiale de l’étude a une forte influence sur les résultats. Les effets des barrières géographiques sont négligeables sur la composition biotique des communautés sur une courte distance; par exemple au sein de la colonne d’eau. Toutefois, une échelle spatiale intermédiaire (10-3000km), semblable à ce projet de mémoire permet de détecter l’influence relative des barrières géographiques et des conditions environnementales sur la composition et la structure des communautés microbiennes (Martiny et coll. 2006).

L’influence des conditions environnementales locales sur la composition des communautés réfère l’image de plusieurs habitats dans une seule province où «everything is everywhere, but the environment selects». Cette hypothèse suppose que les microbes marins sont distribués et transportés de manière homogène sur Terre et la plupart des espèces sont présentes dans un même environnement, mais dans un état latent et trop peu pour être observées (Bass Becking 1934). Les pico et nanoeucaryotes sont toutefois transportés passivement dans le milieu marin où les vents, les courants et les vagues contribuent à leur dispersion. Wit & Bouvier (2006) ont suggéré que la petite taille combinée avec le potentiel de distribution rapide des bactéries leur permettait d’être distribuées partout sur le globe. La composition biotique sur un site dépend ainsi seulement des conditions environnementales lorsque les organismes arrivent sur les lieux. Rodriguez-Martinez et coll. (2013), par exemple, ont démontré que la diversité d’un groupe de straménopiles marins (MAST- 4) est principalement influencée par la température et que la composition et la

structure des communautés de MAST-4 étaient plus similaires entre des sites avec une température semblable, mais plus distancée, que l’inverse.

En contraste, Galand et coll. (2010) ont observé les schémas de distribution biogéographique des bactéries et des archaea dans les profondeurs marines et ont remis en question le concept où les microbes seraient globalement distribués uniformément en l’absence de gradients de température, de salinité, PAR ou de disponibilité en nutriments (Bouman et coll. 2006). Ainsi, d’autres mécanismes régulariseraient la biogéographie des microorganismes. La biogéographie vicariante vise à trouver un processus commun et simultané qui explique la distribution de microorganismes non nécessairement liés génétiquement (Myers & Giller 1988). Les différences de structure et de distribution entre plusieurs communautés microbiennes d’aujourd’hui, même sous les mêmes conditions environnementales contemporaines, démontrent l’impact qu’a eu la distance ou l’influence de l’isolation de différentes populations d’espèces par des barrières physiques (c.-à-d. masses terrestres, courants, masses d’eau) sur la dispersion des microorganismes (Martiny et coll. 2006). Dans l’Océan Arctique canadien, les communautés au sein d’une colonne d’eau stratifiée et influencée par plusieurs masses d’eau présentent une plus grande diversité microbienne. La similitude par exemple entre les eaux de surface de l’Arctique et de profondeur de l’Atlantique était de moins de 20%, et ce à un même site (Hamilton et coll. 2008).

Toutefois, Hamilton et coll. (2008) démontraient que les communautés étaient également différentiées selon la salinité et la luminosité disponible pour la photosynthèse. La forte stratification de la salinité dans l’Océan Arctique ainsi que la diminution de la luminosité avec la profondeur expliquent probablement les distinctions dans la composition des communautés au-dessus et au sein de la strate de chlorophylle maximum sous la surface. Ainsi, dans cette étude, les analyses démontrent qu’autant les masses d’eau que les conditions environnementales présentes influencent la biogéographie des microorganismes dans l’Océan Arctique.

Biogéographie: un exemple du protiste pélagique Micromonas pusilla (Chlorophyceae)

L’Océan Arctique héberge plusieurs organismes endémiques. Micromonas pusilla (1-2 µm) est un organisme globalement distribué et est un taxon important au sein des communautés de picoeucaryotes à l’année entre autres dans l’ouest de la Manche (Not et coll. 2004) et abondant le long des côtes norvégiennes (Throndsen & Kristiansen 1991), la mer Méditerranée (Zingone et coll. 1999) et le Golfe du Mexique (Hernadez-Becerril et coll. 2012). Les conditions environnementales uniques et très variables dans la région de l’Arctique ont des répercussions significatives sur la phylogénie de cet organisme à travers la distinction d’écotypes. Les premières analyses phylogénétiques menées par Foulon et coll. (2008) ont démontré que trois différents clades de M. pusilla varient en abondance dans les environnements tropicaux, tempérés et en hautes latitudes. Par exemple, leur clade A était limité aux mers de Barents et de Norvège et semblait lié à l’apport d’eau de l’Atlantique. À noter que ces trois différents clades ne sont pas les mêmes établis par Slapĕta et coll. (2006) qui a identifié cinq différents clades. Une culture d’un Micromonas pusilla abondant dans l’Arctique a présenté une température favorable de croissance entre 0 et 6°C et nulle à 12,5°C (Lovejoy et coll. 2007, Luo et coll. 2009, Balzano et coll. 2012). Ainsi, les différents écotypes, originalement créés par des barrières géographiques passées, défient l’idée de «everything is everywhere, but the environment selects» (Baas Becking 1934). Cette variation dans la distribution des communautés pouvant être causée par de simples changements physicochimiques et/ou des modifications des courants peut avoir un grand impact sur les transferts dans la chaîne alimentaire et les cycles biogéochimiques (Sørensen et coll. 2012, Brandsma et coll. 2013). Dans la mer de Beaufort, Lovejoy & Potvin (2011) ont identifié un autre phylotype (> 99% similarité) traditionnellement associé à des latitudes plus tempérées. Celui-ci est le clade C de M. pusilla (Slapĕta et coll. 2006) et est seulement présent dans la strate mélangée d’eau de surface sur le plateau continental où l’eau était plus chaude et fraîche. Le Micromonas arctique continuait toutefois de dominer en dessous dans la masse d’eau mélangée hivernale, plus froide et une disponibilité en luminosité plus faible. Ce schéma était associé à une récente

intrusion d’eau plus chaude du Pacifique le long du plateau de Beaufort. Ces intrusions peuvent éventuellement changer l’environnement local et favoriser l’établissement de phylotypes non arctiques.

Relèvements taxonomiques des communautés microbiennes

L’utilisation de méthodes conventionnelles liées à la culture pour étudier les microorganismes a grandement sous-estimé la diversité telle démontrée pour les bactéries où seulement <5% de leur diversité était établie par ces méthodes (Amann et coll. 1995). Les récents séquençages à haut débit (HTS) utilisant la technologie du 454 dans l’Océan Arctique ont démontré la coexistence d’espèces de bactéries abondances avec une faible diversité et de rares espèces, mais très diversifiées (Galand et coll. 2010). Alors que celle-ci et d’autres études ont porté sur les bactéries, les connaissances sur la biosphère rare des eucaryotes demeurent limitées. Toutefois, de faibles abondances relatives d’une grande diversité de groupes taxonomiques ont été répertoriées dans les systèmes tempérés (Countway et coll. 2005, Howe et coll. 2009, Dasilva et coll. 2014) et dans l’Arctique (Comeau et coll. 2011).

L’introduction de méthodes basées sur l’ADN, incluant l’amplification de l’ADN couplée avec le clonage et le séquençage (Handelsmann et coll. 1998) ainsi que le HTS (Sogin et coll. 2006), a permis d’accroître la détection de la diversité dans des échantillons environnementaux. Toutefois, l’ADN est une molécule relativement stable et persiste dans l’environnement comme un brin libre d’ADN ou dans des cellules mortes ou inactives. Sa détection peut ainsi mener à des interprétations et des conclusions incorrectes puisque les analyses basées sur l’ADN ne fournissent pas nécessairement un signal de viabilité ou d’activité métabolique des cellules dans l’environnement (Lorenz & Wackernagel 1987). Par exemple, Behnke et coll. (2006) ont identifié sous des gradients d’oxygène ou de sulfite d’hydrogène des organismes qui n’auraient pu survivre. La conclusion de cette étude était qu’une grande partie des signaux d’ADN détectés dans les strates d’eau anoxiques provenait de la sédimentation des cellules de la surface.

Pour identifier les espèces actives au sein d’une communauté, l’ARN, très instable, peut être utilisé avec toutefois des précautions dans les manipulations (Blazewicz et coll. 2013). Pour former des amplicons, l’ARN subit une transcription inverse suivie d’une réaction en chaîne par polymérase (RT-PCR) pour ainsi obtenir de l’ADNc, plus stable. Pour l’identification taxonomique à l’aide d’une méthode de HTS, les régions variables de la sous-unité (SSU) de l’ARNr dans les ribosomes sont amplifiées. Les ribosomes sont responsables de la synthèse des protéines dans les cellules métaboliquement actives et vivantes (Wagner 1994, Millner et coll. 2001). Pour faciliter la lecture de ce chapitre, le gène codant pour la SSU ARNr se réfèrera au gène d’ARNr et le produit de l’ARNr sera référé comme l’ARNr.

Stoeck et coll. (2007) ont comparé les banques de clones obtenues à partir de l’ARNr et du gène d’ARNr d’eucaryotes d’un même échantillon dans un fjord danois. Ils ont observé que seulement 27% des phylotypes étaient partagés entre les deux banques et 48% étaient exclusifs à la banque du gène d’ARNr, tous dominés par des séquences d’alvéolés et de straménopiles. Le fait que plusieurs des phylotypes retrouvés exclusivement dans les banques de gène d’ARNr peut être expliqué par leur faible signal d’activité chez quelques eucaryotes. Les auteurs suggéraient que les dinoflagellés de la mer Baltique ont une activité limitée dans le fjord danois. En contraste, les prasinophytes étaient seulement détectés dans les banques d’ARNr. Stoeck et coll. (2007) l’expliquaient par le fait que les petites algues vertes contiennent quelques copies du gène d’ARNr, mais produisent un très grand nombre de ribosomes lorsqu’elles sont actives. Ainsi, le gène d’ARNr mènerait à une faible détection de plusieurs groupes due au plus faible nombre de copies du gène d’ARNr par cellule. De ce fait, l’utilisation de méthodes basées sur l’ARNr et du gène de l’ARNr pour caractériser les communautés microbiennes peuvent révéler de nouvelles propriétés non observables en utilisant seulement une des deux ou les méthodes conventionnelles de culture.

Séquençages à haut débit (HTS)

Les récentes avancées dans la technologie du séquençage ont facilité l’accès à l’utilisation de gènes ciblés et amplifiés (séquençage d’amplicons) ou du génome

total de l’ADN à partir d’échantillons environnementaux (séquençage shotgun). Le séquençage d’amplicons ciblés, particulièrement de la SSU ARNr, génère des informations sur la composition taxonomique et la structure phylogénétique des communautés microbiennes. Les séquences sont ainsi alignées et regroupées à un certain niveau de similarité prédéfinie et ces groupes sont identifiés comme des unités taxonomiques opérationnelles (OTUs) pour de plus amples analyses (Zarraonaindia et coll. 2013). Cette approche procure la capacité d’analyser plusieurs centaines d’échantillons dans une seule manipulation en utilisant le «multiplexing» où les séquences sont étiquetées pour identifier l’échantillon. Les analyses de l’ADN génomique d’une communauté entière d’organismes non cultivés, incluant plusieurs approches différentes, sont collectivement associées à la métagénomique (Handelsman 2004, Gilbert & Dupont 2011).

Depuis les 10 dernières années, les deux plus populaires plateformes de séquençage HTS sont la technologie de pyroséquençage 454 (Roche) et le système Illumina (www.454.com, www.illumina.com). Le pyroséquençage est basé sur les réactions en chaîne de polymérase en émulsion et produit environ un million de séquences d’une longueur de 500 à 800 bp. Les plateformes Illumina MiSEQ utilisant la technique de séquençage à deux extrémités peut produire de 2 à 3 milliards de séquences d’une longueur de 400 à 450 bp. Cette méthode consiste à extraire deux segments de la séquences ciblée de l’ADN ou de l’ADNc à l’aide d’une amorce sens et une amorce anti-sens avant de les superposer.

Un grand nombre de séquences par manipulation (séquençage en profondeur) permet l’observation d’un plus grand nombre d’espèces, ou d’OTUs, dans un échantillon lorsque le 16S et le 18S de l’ARNr sont utilisées respectivement pour les bactéries et les eucaryotes (Edgcomb et coll. 2011, Zarraonaindia et coll. 2013). Pour la métagénomique, Luo et coll. (2012) ont démontré que les assemblages de contigs obtenues des plateformes d’Illumina et de 454/Roche sont similaires à 90% d’un même échantillon environnemental. Toutefois, la plateforme Illumina permet de séquencer des contigs plus long et à un coût plus faible. De plus, alors que les homopolymères peuvent influencer les résultats de la plateforme 454/Roche (jusqu’à

14% moins de séquences après les filtres de qualité), la plateforme Illumina est moins biaisée et a un taux d’erreurs de séquençage dû aux homopolymères de 1%.

Objectifs de ce projet de recherche

Les objectifs de ce projet de maîtrise étaient (i) d’entreprendre la première étude des communautés de pico et nanoeucaroytes (0.45-10 µm) autour de l’archipel du Svalbard en utilisant le HTS, (ii) de décrire la composition et la structure des communautés en utilisant des banques d’amplicons formées avec l’ARNr et le gène d’ARNr et (iii) de déterminer l’influence relative de l’hydrographie et des conditions environnementales sur la biogéographie des communautés microbiennes. Durant l’échantillonnage, nous avons observé que les trois stations à l’étude les plus méridionales et orientales (Hornsund, Storjfjorden et le détroit d’Erik Eriksen) étaient en efflorescence; incluant une efflorescence d’un pélagophyte. Les valeurs de chlorophylle a étaient également relativement élevées dans le détroit de Fram et dans Adventfjorden, mais à une moindre mesure. Le deuxième chapitre de ce mémoire focalisera sur les schémas biogéographiques des communautés ne proliférant pas alors que le troisième focalisera sur la composition des communautés en efflorescence (Hornsund, Storfjorden et le détroit d’Erik Eriksen) ainsi que la phylogénie des espèces proliférant.

General Introduction

The Arctic Ocean and its microbial biodiversity

The Arctic Ocean covers an area of at least 14 million km2 and up to 18.8 million km2 including the Bering Sea and the Sea of Okhotsk. More than 50% of the total area is covered by shallow continental shelves such as the Barents Sea shelf, one of the most biologically productive regions of the globe (Jakobsson 2002).

Figure 1.3. North Pole orthographic projection (60-90°N). Note the position of the archipelago of Svalbard on the margin of the Barents Sea.

The area investigated for this memoire is located around Svalbard, at the conjunction of the Barents Sea, Fram Strait and the Arctic Ocean (Figure 1.3).

Microbial communities in the Arctic Ocean encounter challenging conditions where often only specifically adapted species, persist throughout the year. Light is a major limiting factor since photosynthetically active radiation (PAR) varies considerably with season. Even within the Arctic Ocean, light regimes during the dark season vary following a latitudinal gradient along civil twilight, civil polar night and nautical polar night. Traditionally, researchers assumed that long periods of darkness lasting for several months in the Arctic would inhibit biological activities. However, several studies carried out in winter reported active zooplankton grazing and the persistence of photosynthetic organisms (Sherr et al. 2003; Berge et al. 2009). Microbial community surveys targeting 18S rRNA genes and 18S rRNA molecular markers from seawater samples in the Beaufort Sea and northern Svalbard indicate that Micromonas pusilla, an autotrophic organism with an Arctic ecotype is active, during winter (Lovejoy et al. 2007; Vader et al. 2014). Recently, McKie-Krisberg and Sanders (2014) demonstrated phagotrophic feeding behavior by the arctic ecotype Micromonas, implying heterotrophy as an alternative during unfavorable conditions for photosynthetic activity. Light and nutrient concentrations, two highly variable parameters in the Arctic are factors that could influence ingestion rates of this mixotroph.

Photosynthetic microbial communities in most oceans are dominated by picocyanobacteria such as Prochlorococcus and Synechococcus, which are responsible for 8.5% and 16.7% of the oceans’ net primary production, respectively (Flombaum et al. 2013). Temperature and PAR strongly influence the biogeography of the two genera. These cyanobacteria tend to decrease in abundance at latitudes > 40° and Prochlorococcus is rarely observed in oceans with temperatures below 8°C (Johnson et al. 2006, Huang et al. 2012, Flombaum et al. 2013). Synechococcus grows over a wider range of conditions and dominates the picocyanobacterial communities in temperate latitudes including the Norwegian coastline (Zwirglmaier et al. 2008, Cottrel and Kirchman 2009). Researchers also reported clades of typically

estuarine Synechococcus from the Bering and Chukchi Seas and suggested picocynaobacteria could be autochtonous to the Arctic Ocean while others argue most Synechococcus collected from Arctic marine waters are allochtonous or inputs from terrestrial runoffs and inflow from the Pacific Ocean (Vincent et al. 2000; Waleron et al. 2007, Huang et al. 2012). Nevertheless, cold tolerant marine Synechococcus are rare, with abundances reported from 0-103 cells ml-1 in the Arctic and Southern Oceans (Bouman et al. 2006, Waleron et al. 2007). Although they were reported to contribute up to 25% of the phytoplankton communities inside the arctic fjord Kongsfjorden, compared to the lower latitudes, cyanobacteria have little impact on the food web in the Arctic Ocean (Piquet et al. 2014). In contrast, eukaryotic picophytoplankton can contribute up to 90% of primary productivity in the Arctic (Gradinger et Lenz 1995, Grob et al. 2011).

Marine diatoms, which are larger microbial eukaryotes, are estimated to contribute up to 40% of the 45 to 50 billion metric tons of organic carbon produced each year in world marine environments and support higher food webs of coastal regions (Nelson et al. 1997). They dominate the biomass of eukaryotic communities in well-mixed nutrient-rich waters especially in spring and early summer where half saturation constants of nutrients for diatom growth are reached (Boyd et al. 2004, Sarthou et al. 2005). Large diatoms occur at high concentrations during blooms north of the Arctic Circle with prevalence of Thalassiosira and Chaetoceros species (Grøntved et al. 1938, Von Quillfeldt 2000, Lovejoy et al. 2002). In the Canada Basin, the decrease of ice cover and increase of freshwater budget have deepened the halocline. The stratified waters result then in nitrate poor surface waters (Li et al. 2009). The freshening that pushes the nutricline deeper, away from the euphotic zone, leads to the dominance of pico- and nano-eukaryotes. Smaller cells are better able to access nutrients and light because of their surface to volume advantage in the nutrient poor mixed layer (Li et al. 2009; McLaughlin & Carmack 2010). In the Northwest Atlantic where freshening of the sea surface is due to sea ice melt and precipitation, there have been modification in the circulation patterns of the North Atlantic Deep Water by intense stratification, which may affect the microbial community structure (Greene and Pershing 2007; Greene et al. 2008). In addition, modification of the

stratification may reduce the ocean’s meridional overturning and decrease the transport of warm, highly-saline and nitrate-rich Atlantic water (AW) towards the Arctic through Fram Strait (Jones et al. 1998).

Photosynthetic pico- and nanoeukaryotes (defined here as 0.45-10µm) are abundant and account a high proportion of biomass during and outside of bloom periods in the North Sea (Brandsma et al. 2013) and the Arctic Ocean and surrounding seas (Li et al 2009, Martin et al 2010, Min Joo et. al 2011, Balzano et al. 2012). Despite accounting for less than 20% of the total chlorophyll a at the peak of the spring bloom, Hodal & Kristiansen (2008) still attributed 46% of the total primary production to cells <10 µm throughout the entire period.

Investigators traditionally assumed that smaller cells (i.e. < 10 µm), which in the Northern Barents Sea have concentration of 2.6 to 10.2 x 103 cells ml-1, would contribute little to the transfer of energy to the benthos because of low sinking rates (Michaels & Silver 1988, Not et al. 2005). However, Richardson & Jackson (2007) demonstrated that the carbon export of pico-size organisms (0.2-2.0 µm) can match their relative contribution to total net primary production, thus, on par with larger phytoplankton cells. The export of small cells to deep water has implications for both pelagic and benthic coupling and biogeochemical cycles.

In addition to photosynthetically available radiation (PAR), mixing regimes, water masses, stratification, relative freshwater input and seasonal loss and buildup of ice influence physical oceanographic regimes across the Arctic (Yamamoto-Kawai et al. 2009; McLaughlin & Carmack 2010; Arrigo et al. 2011, Piquet et al. 2014). For example, colder temperatures and strong stratification due to freshening of the surface waters overlying Pacific and Atlantic water masses with greater density, influences the relative success of different species in the Beaufort Sea and Canada Basin (Lovejoy et al. 2007, Li et al. 2009, Monier et al. 2014). This suggests that generally, diversity, distribution and community structure of microbial eukaryotic communities in Arctic seas are strongly associated with the prevailing oceanographic conditions. Studies also suggest that in the Arctic microbial communities may be vulnerable and

could be affected by climate-induced changes in the Arctic with implications to the higher trophic levels (Terrado et al. 2013).

Taxa regularly identified from microbial eukaryote communities in the Arctic Ocean and surrounding seas include similar marine groups that are found in other oceans: haptophytes, chlorophytes, dinoflagellates, ciliates, uncultivated marine alveolates (MALVs), photosynthetic stramenopiles and heterotrophic marine stramenopiles (MASTs). However, heterotrophic protist biomass is greater than that of phytoplankton over much of the year, with minimal contribution of 20-38% during the bloom period in Kongsfjorden (Hodal et al. 2012). Below, attention is given to taxonomic groups highlighted in the results of this memoire.

Haptophyta are reported to be abundant around Svalbard during bloom periods in early spring, especially the species Phaeocystis pouchetii, (Not et al. 2005, Piquet et al. 2014). Chlorophyta, especially M. pusilla, account for the majority of picophytoplankton (cells with a diameter of < 3 µm) in the Arctic. An arctic ecotype, which is pan-Arctic, replaces cyanobacteria as dominant picoautotrophs in the Arctic Ocean (Lovejoy et al. 2007). A description of the ecology of P. pouchetii and M. pusilla can be found respectively in sections 1.2.1 and 1.3.1 of this memoire.

Major alveolate groups present in the Arctic are: ciliates, dinoflagellates and the mostly likely parasitic Marine Alveolates MALVs (Terrado et al. 2009). Ciliates and dinoflagellates are considered major components in the transfer of primary productivity to higher food webs in northern waters as they are grazed by Calanus zooplankton (Levinsen et al. 2000, Turner et al. 2001, Sherr and Sherr 2007). In addition to abiotic factors such as temperature, communities of heterotrophic ciliates and dinoflagellates are strongly influenced by the availability of food and the abondance of copepods in diapause during late spring and summer (Levinsen & Nielsen 2002, Sherr et al. 2003). While a majority of ciliates species are heterotroph and feed on algae and bacteria, dinoflagellate nutritional behaviors are diverse including autotrophy, heterotrophy and mixotrophy (Graham & Wilcox 2000). The subphyla of Ciliophora, Intramacronucleata, is widespread and commonly reported

above the polar circle in the Beaufort Sea (Terrado et al. 2009, Comeau et al. 2011) and Barents Sea (Amdt et al. 2005). Terrado et al. (2009) reported that the majority of ciliates from molecular data in Franklin Bay were Strombidium. Lovejoy et al. (2006) reported 2 environmental clusters of Strombidium spp. from Arctic waters consistent with the presence of arctic ecotypes. In addition to Strombidium spp., Jensen & Hansen (2000) report a co-dominance of Strobilidium spp. in Barents Sea during spring.

Many species of dinoflagellates are mixotrophic and include genera such as Karenia and Prorocentrum (Richardson et al. 2006, Jeong et al. 2010), which may be associated with harmful algal blooms. Microscopy studies have reported Karenia and Prorocentrum from the in the Canadian and Russian Arctic (Poulin et al. 2011). Other mixotrophic dinoflagellates including Gymnomidium have been previously observed in Kongsfjorden (Lou et al. 2011) and tend to be dominant in Arctic waters along with Gyrodinium spp., most of which are heterotrophic (Levinsen & Nielsen 2002). Within the Gyrodinium genera, Gyrodinium helveticum and G. rubrum form a single clade. This clade includes likely transitions from marine to freshwater as G. helveticum was originally described from freshwater and the G. rubrum from marine environments (Takano & Horiguchi 2004, Lovejoy et al. 2006). In Franklin Bay, late summer and autumn community structures are dominated by sequences with best matches to G. rubrum (Terrado et al. 2009).

The genera Katodinium and Nematodinium are also heterotrophic dinoflagellates. Katodinium was reported in the Canadian Basin and in both Hornsund and Kongsfjorden (Sherr et al. 2003, Wiktor & Wojciechowska 2005). Nematodinium was also reported in Kongsfjorden, however only in September and December while Katodinium and other dinoflagellates were observed all year (Seuthe et al. 2011).

MALV I and MALV II are separated by their 18S rRNA gene phylogeny and were originally discovered using culture-independent techniques (Lopez-Garcia et al. 2001, Moon-Van der Staay et al. 2001, Guillou et al. 2008). It is now known that both

groups include endoparasites of marine organisms such as crustaceans, dinoflagellates, fish and bivalves (Stentiford & Shields 2005, Chambouvet et al. 2008, Skovgaard et al. 2009, Miller et al. 2012, Noguchi et al. 2013). MALV II are associated with the copepod parasites in the Syndiniales. However, only a few species have been identified within those groups. Both MALV I and MALV II are common in the Arctic (Lovejoy et al. 2006, Bachy et al. 2011, Lovejoy & Potvin. 2011) including around Svalbard in Isfjorden and in Billefjorden (Sørensen et al. 2012, Thompson 2014).

Finally, the uncultured marine stramenopiles (MAST) that are thought to be mostly heterotrophic, have been placed into 12 phylogenetically distinct clades. MAST-1, -3,-4 and -7 are regularly reported from open ocean and coastal marine environments. Lovejoy et al. (2006) reported 45 out of 236 protist sequences from clone libraries from the Arctic Ocean and surrounding seas belong to MAST, mainly within MAST-1. -3 and -7, with MAST 4 usually absent (Massana et al 2006). Among these, MAST-7 is found in significant numbers in several marine planktonic sites including North Atlantic and Antarctic. Moreover, nearly identical 18S rRNA sequences were found from both distant sampling sites (Masssana et al. 2004).

Characterization of blooms in the Arctic and around the archipelago of Svalbard

Blooms of phytoplankton are conspicuous components of the Arctic marine waters. Blooms are major contributors to the annual primary productivity in the Arctic (Leu et al. 2006, Hegseth et al. 2008). For example, during the spring bloom of 2002, 1.85 g C m-3 per day was reported in Kongsfjorden (Hodal et al. 2012). The total annual primary productivity (APP) varies and the spring bloom of 2002 in Kongsfjorden was 0.15-8.75 times greater than earlier estimates of APP (Hop et al. 2002). After an extended period of darkness during winter, light gradually becomes available as day length increases and ice cover is reduced. In the Norwegian Arctic, blooms occur from April along northern Norwegian coastlines to August/September along northern boundaries of Barents Sea (Hegseth et al .1995, Falk-Petersen et al. 2000). When both light and nutrients are in abundance, larger cells such as diatoms

tend to dominate the biomass. While light is generally accepted as the main trigger the progression of the bloom is determined by other factors and high concentrations of chlorophyll a below the euphotic zone are common during the post-bloom period (Kristiansen unpublished) and may be a result of phytoplankton sinking. Blooms of diatoms deplete silicate and nitrate in surface waters. Hodal et al. (2012) and other authors observed species succession over the bloom beginning with the diatoms Fragilariopsis spp., Chaetoceros spp. and Thalassiosira spp. followed by smaller cells such as the haptophyte Phaeocystis pouchetii.

However, water masses may have an influence on the composition of the bloom where diatoms bloom in presence of Arctic Water while smaller eukaryotes bloom in Atlantic influenced waters (von Quillfeldt 2000, Leu et al. 2006, Iversen & Seuthe 2011). Inflow of Atlantic Water may prevent winter convection, which would suspend resting stages of neritic diatoms in the water column (Backhaus et al. 2003). Forming resting cells is a mechanism to protect the diatoms from inhospitable environmental conditions, including low light regimes. The concept that water masses can determine species dominance was supported in 2007 where a continuous inflow of Atlantic waters resulted in a bloom of P. pouchetii in Kongsfjorden (Hegseth et al. 2008). When Kongsfjorden is not impacted by Atlantic waters, bloom timing may be more synchronized by the influence of meteorology and events that affect the stratification (Hodal et al. 2012).

Although the spring bloom tends to be predominant in the Arctic, high productivity may also occur throughout summer under some conditions (Garneau et al. 2007). Long-lasting production can be induced by continuous irradiance and advected nutrient supplies as in the North Water Polynya (Lovejoy et al. 2002) or in Kongfjorden (Wallace et al. 2010). In Kongfjorden, chlorophyll fluorescence increased from April to August in both 2007 and 2008 (Wallace et al. 2010).

Another explanation for high summer productivity may be multiple blooms as reported in Cape Bathurst and by remote sensing over Arctic regions (Arrigo & Van Dijken 2004). Those authors suggest that the intensity of the late summer bloom may

have been influenced by the increased stratification from freshwater input and the vertical mixing induced strong winds and delayed freez-up (Ardyna et al. 2014). In addition, a second bloom was also observed in August in subarctic waters north of Ireland (Gislason & Astthorsson 1998). However, while Cape Bathurst is a polynya where winds mix upper layers to resuspend nutrients, the second bloom in the north of Ireland was thought to be induced by the decrease of predators. In temperate regions, summer blooms are often associated with harmful algae. Among these are dinoflagellate blooms as mentioned above, but also blooms of pelagophytes (DeYoe et al. 1997), raphidophytes, some diatoms and Phaeocystis (Ryan et al 2014).

Phaeocystis pouchetii (Haptophyta: Prymnesiophyceae)

Phaeocystis pouchetii is a prymnesiophyte which forms bloom as single cells or more often as colonies. Degerlund & Eilertsen (2010) compiled studies from 1922 onwards on the species of phytoplankton responsible for blooms in Norway from Vestfjord to North-West Spitsbergen (68°-82°N). P. pouchetii was the most frequently reported species (34% of all data analyzed) during spring blooms from March to May. P. pouchetii was also observed in Isfjorden, Hornsund, Fram Strait and Erik Eriksen Strait and at lower concentrations in the Hinlopenx Strait (Søreide et al. 2008, Sørensen et al. 2012). It has a similar occurrence to the diatom Thalassiossira gravida/Thalassiossira antarctica var. borealis (32%). The abundance of P. pouchetii has been positively correlated to northern latitudes, delayed spring blooms due to late intrusions of Atlantic water postponing mixing and to seasonality where P. pouchetii dominates late-bloom periods (Degerlund & Eilertsen 2010, Hegseth & Tverberg 2013). Phaeocystis blooms initialization remains unclear in the Arctic and Hegseth & Tverberg (2013) outline two possible explanations in the area of northern Barents Sea. They suggest firstly that Phaeocystis could have a winter stage in its life cycle, linked to a benthic stage. In 2007 in absence of convection in the fjord the Phaeocystis bloom in Kongsfjorden was delayed. The second explanation is linked to species interactions where single cells may require diatom frustules to form colonies (Eilersten 1989, Nejstgaard et al. 2006). However, these

ideas are speculative and the initialization of Phaeocystis blooms remains unclear and needs further investigation.

Pelagophyceae

Pelagophyceae are common in the open oceans (Popels et al. 2003) but also include species responsible for brown tides such as Aureaumbra lagunesis in the Gulf of Mexico (DeYoe et al. 1997). Causes of brown tides may differ among species. A. anophageffens is probably the best-studied species, with blooms reported from North America, Africa and Asia (Garry et al. 1998, Lomas et al. 2001, Koch et al. 2013). It has been reported that an increase in inorganic nitrogen resulted in lowering the brown algal tide population and growth rate in Narragansett Bay and beside New York (Keller & Rice 1989, Kana et al. 2004). Occurrence of A. anophageffens blooms in nutrient-poor waters and under low light conditions demonstrates an unconventional type of bloom development, which is in contrast to sudden inputs of limiting-nutrients such as nitrogen and phosphorous that usually enhance phytoplankton proliferation including other harmful algae blooms (Anderson et al. 2002).

A bloom of Aureoumbra lagunensis was observed in Texas and was attributed to the sudden absence of competition and predation following a hypersaline event (Liu & Buskey 2000). Aureoumbra lagunensis will grow over a wide range of salinities, from 10 to 90. This species also produces a layer of extracellular polymeric substance proportional to salinity of the environment (DeYoe & Suttle 1994).

Pelagophyceae sampled in North Atlantic above Ireland were negatively correlated to nitrate concentration. These pelagophytes were closely related to Ankylochrysis lutea, a species within the Sarcinochrysidales, which is also the closest Pelagophyceae reference sequence in this project. During the MALINA cruise in Beaufort Sea, Balzano et al. 2012 isolated and cultured A. lutea and other Pelagophyceae have been reported from environmental gene surveys (Lovejoy et al. 2006). To date Arctic blooms of Pelagophyceae brown tides have not been reported to the best of our knowledge. However, Vader et al. (unpublished data) observed a

bloom of an unknown pelagophyte in Billefjorden (Svalbard) in mid-June 2011. The pelagophyte from Billefjorden is included in the phylogenetic analysis of this Master’s project.

Hydrography of Svalbard

The Svalbard archipelago is a highly complex hydrographic system due to its proximity to the Polar Front, which separates two main oceanic water masses: Atlantic Water (AW) from the warm Gulf Stream and Arctic water (ArW) from the Arctic Ocean (Figure 1.3).

Water mass origin T (ᵒC) S Atlantic Water (AW) >3 >34.9 Transformed Atlantic Water (TAW) 1,3 34.7,34.9 Winter Cooled Water (WCW) <-0.5 >34.7 Local Water (LW) <1 - Surface water (SW) >1 28.0,34.0 Arctic Water (ArW) <1 34.2,34.8

Two main currents are active; the West Spitsbergen current (WSC) originates from AW and has higher temperature and salinity than the Sørkapp Current (SC) originating from ArW coming down along eastern Svalbard and Barents Sea (Wiktor & Wojciechowska 2005). The SC is also sometimes referred to as the Eastern Spitsbergen Current ESC or the South Cape Current (Majewski et al. 2009, Piwosz et al. 2009). The WSC goes along the western side of Spitsbergen and heads northeast above the archipelago. The SC coming from the Arctic Ocean on the eastern side of Svalbard turns around near Sørkapp and brings cold and less saline water on the western side of Spitsbergen, between the land and the WSC.

On the Western side of Spitsbergen, surface water (SW) is mainly composed of waters from river runoff, glacial melting and melted sea ice. Mixing of AW from the Gulf Stream and ArW from SC form Transformed Atlantic Water (TAW) and becomes Intermediate Water (IW) when mixed with TAW or AW in the fjords. Thus, organisms of both AW and ArW are advected into western fjords and the magnitude of transport of each water mass varies seasonally and annually (Willis et al. 2006; Hop et al. 2006). Barotropic instabilities in the geostrophically constrained WSC, due to absence of a density front between WSC and SC, induces important onshore exchange (Saloranta & Svendsen 2001). Local Water (LW) corresponds to surface cooling of SW and IW by convectional processes inside fjords during autumn/winter (Svendsen et al. 2002). LW can also be formed when warmer water floats close to glacier and sinks down when cooled. LW is mostly found above AW, but can also be

in deeper layers as TAW enters fjords as an intermediate layer. In the outer part of Kongsfjorden, salinity of LW was reported to be similar to TAW, while lower inside the basin due to meltwater input. Finally, Winter Cooled Water is the result of sinking of dense cold water after winter cooling and sea ice formation. Due to its southern position, Hornsund is the most influenced fjord by the SC on the western side of Spitsbergen (Figure 1.2; Wiktor & Wojciechowska 2005). However, the fjord has also been reported to be mainly influenced by warm AW (Piwosz et al. 2002), thus suggesting annual or seasonal variability of water mass dominance.

Hydrography on the eastern and northern sides of Svalbard is less studied. Cold arctic water from the Arctic Ocean is introduced on the eastern coast of Svalbard between Nordaustlandet and Franz Josef Land. This forms the East Spitsbergen Current ESC (not shown on Figure 1.2) going down eastern Svalbard and also into the Hinlopen Strait. Surrounding Edgøya, ESC flow into Storfjorden before becoming the SC. Thus, EES and the southern part of the Hinlopen Strait is expected to be influenced mainly by ArW (Hop et al. 2002) while the northern part of the Hinlopen Strait can be strongly influenced by the WSC entering from the north (Daase & Eiane 2007).

Fjord hydrographic systems

Horizontal advection in fjord systems is strongly dependent on the freshwater released by runoff and ice melt, which then further increases the stratification and by the influence of the Coriolis effect on the circulation within the fjord as determined by the internal Rossby radius of deformation (Cottier et al. 2010). The Rossby radius determines the influence of earth’s rotations on a cross-stream variation where surface layers entering the fjord are deflected. Effects of rotation on fjord hydrographic systems are regularly reported in the Arctic (Cottier et al. 2005, Skogseth et al. 2005, Skardhamar & Svendsen 2010). Fjords identified to be influenced by earth’s movements are called “broad” and could hardly be studied as a two dimensional fjord.

Stratification within the fjord is particularly relevant as it influences nutrient supply to the euphotic zone, bloom dynamics and community species composition (Basedow et al. 2004, Willis et al. 2006). Runoff from inland sources such as glacial melt are major processes that determine fjord stratification through the influx of freshwater and import of high quantity of mineral particles reducing the thickness of the euphotic layer (Svendsen et al. 2002).

Winds also influence surface layers of fjords. The Ekman layer, or the surface layer induced by wind stress, was reported to vary from 15 m to 40 m depth for wind speeds of 4 m s-1 to 8 m s-1 in Storfjoden (Skogseth et al. 2008). In addition, katabatic winds, which are more persistent, stronger and colder than mid-latitude winds, are a dominant factor as they can control the outflow of SW (Svendsen et al. 2002).

Targeted local hydrographic systems

This introduction to local hydrography of Svalbard will be oriented to the sampling regions in this study: Hornsund, around Edgøya inside Storfjorden, Wijdefjorden, Bockfjorden, and Adventfjorden. Most regions around Svalbard are heavily influenced by fjord systems such as ISA, HOR, STO, WIJ and BOC. EES, HIN and KG are located in more open areas, KG being the only station not located on the continental shelf, but on the slope between the shelf and Fram Strait Basin. Below are importants key points for local hydrography in particular areas:

Hornsund: Hornsund is the Svalbard southernmost fjord and is highly influenced by runoffs. Above 10% of the water in Hornsund may originate from glacial melt (Weslawski et al. 1991). Hornsund is characterized by a sill. Both warm and saline WSC and fresher ESC can enter the fjord (Hald & Korsun 1997, Piwosz et al. 2009).

Storfjorden: Storfjorden is a large semi-enclosed silled bay (120m) at 77°N and is enclosed by a shallow bank, Storfjordenbanken, in the southeast. The proximity of the Polar Front along the Storfjordrenna slope induces rapid changes in origin of the water masses advected in Storjforden (Loeng 1991, Schauer 1995).

Skogseth et al. (2008) reported AW inflows in Storfjorden from 30m to 90m depth, below surface layer. Wind direction also influences the origin of advected water where eastern winds result in mixing above 40m in shallow waters is induced by combination of wind stress and tidal current (Skogseth et al. 2008).

Wijdefjorden and Bockfjorden: As the longest fjord in Svalbard, Wijdefjorden has a deep basin (deepest point at 246m) isolated by a sill and is influenced by the mixing of Arctic and Atlantic Water, depending on the activity extent of the WSC (Dale et al. 2006, Walczowski & Piechura 2006). In contrast, Bockfjorden, an arm of Wijdefjorden, is dominated by the WSC. and is strongly influenced runoffs from glaciers, especially the large Karlsbreen glacier (Sapota et al. 2009).

Adventfjorden: Isfjorden is the largest western Spitsbergen fjord and has several smaller sidearm fjords. The sampling site of the current project is located in Adventfjorden, a sidearm fjord on the southern side of Isfjorden. Adventfjorden is influenced by the warm and saline Atlantic water but also strongly affected by river runoffs through Longyearelva and Adventelva rivers (Dobrzyn et al. 2005). The sampling site is also located next to the largest inhabited city of Svalbard, Longyearbyen.

Biogeography: geographical barriers and environmental conditions

Whether protists are globally distributed or there is endemism is a subject of discussion (Finlay & Fenchel 2004, Medlin 2007). By overlaying environmental conditions and geographic location on community clustering, Martiny et al. (2006) suggested a framework of 4 alternative hypotheses to determine the factors influencing biogeography of microorganisms. The first is associated with the null hypothesis that there is an absence in biogeographic patterns; species are randomly distributed and structured. The second hypothesis proposes that the biotic composition is mainly explained by the present or recent environmental conditions such as salinity, temperature, quality and availability of light, etc. Thus, variability is less between two sites further apart with similar environmental conditions than

between two sites with different conditions but separated by a shorter distance. In contrast, the third hypothesis focuses on the influence of geographic barriers limiting dispersal of species to a community. The similarity in biotic composition between two sites depends on historical dispersal events, for example, advection of water masses enclosing endemic microbial communities inside a fjord. Fourth, the combination of both historical dispersal events and contemporary variable environmental factors could explain the present distribution and structure of microbial communities.

Spatial scales can have a strong influence on results of biogeographic analyses. Effect of geographic barriers was negligible on biotic composition over small distances such as within the water column. In another perspective, intermediate distances (10-3000 km), similar to the design of this research project, are suitable for detecting the relative influence by both geographic barriers and contemporary environmental conditions on the biogeography of microbes (Martiny et al. 2006).

Influence of contemporary environmental factors on community composition refers to different habitat types within a province where “everything is everywhere, but the environment selects”. This hypothesis assumes that individual marine microbes are distributed and transported homogenously on earth and most of species are present in a given environment, but at a latent stage and too few so hidden from observation (Baas Becking 1934). As mentioned earlier, picoeukaryotes and nanoeukaryotes are passively transported in a marine habitat where waves, currents and winds contribute to dispersion. Wit & Bouvier (2006) suggested that small size combined with fast dispersal potential of bacteria enable these organisms to be distributed everywhere on the globe. Biotic composition within a place is the result of environmental conditions when organisms arrived in a new location. Rodriguez- Martinez et al. (2013) for example showed that the diversity of a particular group of marine stramenopiles (MAST-4) is mainly influenced by temperature and that similar composition patterns were similar in distant sampling sites with similar temperatures than closer stations with varied temperature.

In contrast, Galand et al. (2010) observed deep sea biogeographic distribution patterns of Bacteria and Archaea, enhancing doubt that microbes are distributed uniformly in the absence of gradients of temperature, salinity, PAR or nutrient availability (Bouman et al. 2006). Therefore, another mechanism may regulate biogeography of microorganisms. Vicariance biogeography is concerned with discovering a common and simultaneous process that has led to actual distribution of unrelated taxa (Myers & Giller 1988). Distance effect or the influence of isolation of different populations of species by physical barriers (i.e. land mass, currents, water mass) with the same actual environmental conditions, reveals influence of past events rather than present-day physicochemical conditions on present communities’ structure and distribution (Martiny et al. 2006). In the Canadian Arctic Ocean, changes in community composition have been observed in depths where there are layers originating from distinct water masses with an abrupt halocline, thus less mixing, while regions meeting fronts of different water masses showed highest diversity, probably due to physical mixing. Low assemblage similarity (<20%) between biotic composition of Arctic surface water and Atlantic deep water illustrates progressive separation of microbial habitat between water masses (Hamilton et al. 2008). Vicariance biogeography is also supported by geographic isolations affecting genetic diversity of microorganisms where non-random distribution is found in samples separated by only few kilometers (Whitaker et al. 2003).

However, Hamilton et al. (2008) showed that assemblages are also differentiated by salinity and light available for photosynthesis. The Arctic Ocean being salinity-stratified and irradiance diminishing with depth probably explains distinct biotic composition of picoeukaryotes above and within the subsurface chlorophyll maximum layer. Thereby, in the study from Hamilton et al. (2008), biogeographic patterns of picoeukaryotes showed both influence of contemporary environmental variables and origin of the host water mass.

Importance of biogeography: example of the marine protist Micromonas pusilla (Chlorophyceae)

The Arctic Ocean hosts several endemic organisms. Micromonas pusilla (1-2 µm) is a globally distributed organism and remains a major taxon in the picoeukaryotic communities all year round in the Western English Channel (Not et al. 2004) and an abundant taxon along the Norwegian coast (Throndsen & Kristiansen 1991), the Mediterranean Sea (Zingone et al. 1999) and the Gulf of Mexico (Hernadez-Becerril et al. 2012). The unique and highly variable environmental conditions in the Arctic region have significant repercussions on the phylogeny of this organism through niche selection of ecotypes. First, phylogenetic analyses led Foulon et al. (2008) suggesting three different clades of M. pusilla varying in relative abundance in tropical, temperate and high latitudes environments. As an example, their clade A was more limited to the Norwegian and Barents Seas and seemed linked to Atlantic water inflow (Foulon et al. 2008). Notice here the three different clades aren’t the same as established by Slapĕta et al. (2006) where they retrieved 5 different clades. An Arctic isolate of the M. pusilla is abundant throughout the Arctic optimally growing in culture at 0 to 4°C but not at 12.5°C (Lovejoy et al. 2007, Luo et al. 2009, Balzano et al. 2012). In comparison, the different ecotypes, originally enhanced by historical geographical barriers, challenge the idea of “everything is everywhere but the environment selects” (Bass Becking 1934). That variation in community distribution could be enhanced even by minor physicochemical events and/or current physical processes could drive fluxes through food chain and biogeochemical cycles (Sørensen et al. 2012, Brandsma et al. 2013). In the Beaufort Sea, Lovejoy & Potvin (2011) found one other phylotype (>99% similarity) that is thought to be more associated with warmer latitude. This phylotype is in the widespread clade C of Micromonas (Slapĕta et al. 2006) and was present only in the surface mixed layer (SML) on the shelf which is warmer and fresher. At the same station. The Arctic Micromonas continued to dominate in the deeper winter mixed layer (WML) where it is colder and light levels are low. The pattern was associated with a recent intrusion of warmer Pacific waters along the Beaufort shelf. Such

intrusions may hint at possible changes in long term conditions that could favor the establishment of non-Arctic phylotypes.

Taxonomic surveys of microbial communities

The use of conventional culture-dependent methods to study microorganisms strongly underestimate the true diversity, as shown for bacteria where <5% of the biodiversity was detected (Amann et al. 1995). Early high throughput sequencing (HTS) surveys using 454 technology in the Arctic Ocean indicated the coexistence of abundant species with low diversity and rare species, which were highly diverse (Galand et al. 2009). While that study and others have been carried out on bacteria, the extent of a rare biosphere is unclear in eukaryotic communities. But low abundance of rare taxa has been reported from temperate systems (Countway et al. 2005) and the Arctic (Comeau et al. 2011, Dasilva et al. 2013).

The introduction of DNA-based methods, including both DNA amplification coupled with cloning and sequencing (Handelsmann et al. 1998) and HTS (Sogin et al. 2006), enabled increased detection of the diversity in environmental samples. However, DNA is a relatively stable molecule and persists in the environment as free DNA or inside of inactive or dead cells, the detection of which may lead to incorrect conclusions and understanding since DNA-based analysis does not necessarily signal viability and metabolic activity of the cells (Lorenz & Wackernagel 1987). For example, Behnke et al. (2006) identified aerobic taxa that should not be able to sustain in hypoxic zones. The conclusion of that study was that many of the DNA- based signatures detected in deep anoxic layers would have originated from cells sinking out from the surface.

To identify active species within the community, RNA can be used as template with some caveats (Blazewicz et al. 2013). Once RNA has been amplified by reverse transcriptase polymerase chain reaction (RT-PCR) to obtain cDNA it is more stable and can be used to target rRNA or transcripts of specific amplicons. For HTS, variable regions of small-subunit (SSU) rRNA in ribosomes is amplified. Ribosomes are responsible for protein synthesis in actively metabolizing and viable

cells with some exception (Wagner 1994, Millner et al. 2001). For convenience, throughout the remainder of this chapter the gene coding for SSU rRNA will be referred to as rDNA and the product from RNA will be referred as rRNA.

Stoeck et al. (2007) compared rRNA and rDNA derived clone libraries of eukaryotes from the same sample in the Danish fjord and found only 27% of phylotypes were shared by both libraries with 48% exclusively in the rDNA library, all dominated by alveolates and stramenopile sequences. The many phylotypes found exclusively in the rDNA libraries could be explained by the low signal of activity from some eukaryotes. The authors suggested that dinoflagellates from Baltic Sea had limited activity in the Danish fjord. In contrast, the prasinophytes were only detected from rRNA. Stoeck et al. (2007) suggest that small green algae contain few copies of the rRNA genes but produce high numbers of ribosomes when active. Thus, rDNA would lead to poor detection of some groups due to small number of rRNA genes per cell while giving signal in RNA-based libraries due to higher numbers of ribosomes. Overall, the use of both RNA and DNA-based methods to investigate microbial communities can reveal properties of the community that may not be attained using one template or conventional methods.

High throughput sequencing (HTS)

Recent advances in sequencing technology have made it affordable to use amplified target-genes (amplicon sequencing) or total genomic DNA from environmental samples (shotgun sequencing). Targeted amplicon sequencing of the SSU rRNA in particular, generates information on the taxonomic composition and phylogenetic structure of microbial communities. Reads are then aligned and clustered at a predefined level of similarity and these are then used to delineate operational taxonomic units (OTUs) for downstream comparisons (Zarraonaindia et al. 2013). This approach provides capacity to analyze up to a few hundred samples in one run using multiplexing where sequences are tagged to identify the sample. The analysis of genomic DNA from a whole community of uncultured organisms,which may use different approaches is collectively called metagenomics (Handelsman 2004; Gilbert and Dupont 2011).

Over the last 10 years, the two most common HTS sequencing platforms are the 454 pyrosequencing technology (Roche) and the Illumina system (www.454.com, www.illumina.com). Pyrosequencing is based on emulsion-PCR and produces c.a million reads of 500-800 bp length while 2 to 3 billion reads of 400 to 450 bp length can be produced by Illumina MiSEQ platforms using paired ends sequencing technique. The pair-end method is a longer fragment amplified using two primers, one forward and one reverse. The two sequences are thereafter joined by identifying common overlapping bps.

Higher number of reads per run (deeper sequencing) results in the recovery of more species or OTUs in a sample when hyper-variable regions of the 16S rRNA for bacteria and the18S rRNA for eukaryotes are studied (Edgcomb et al. 2011). For metagenomics Luo et al. (2012) showed that Illumina and 545/Roche platforms agreed on 90% of the assembled contigs from the same environmental sample from Lake Lanier, Atlanta. The Illumina platform yielded longer and more accurate contigs and at a cost of sequencing around 25% of 454/Roche. While homopolymers influence results of 454/Roche (14% fewer complete reads than Illumina after quality filtering), less pronounced biases and high sequence coverage are advantages using Illumina with 1% homopolymer-associated sequencing errors. However, to convert intensities values to bases, the Illumina base caller uses a model-based approach with run-specific parameters.

Objectives of the present project

The objectives of this Master’s project were (i) to carry out the first survey of the pico- and nanoeukaryote (0.45-10 µm) communities around Svalbard using HTS, (ii) to describe the community composition and structure using DNA and RNA converted to cDNA based libraries and (iii) to determine if hydrography and environmental factors shape microbial communities. During the sampling mission we noted that the three southeastern sampling sites (Hornsund, Storfjorden and Erik Eriksen Strait) were in bloom states; including an unsual bloom of pelagophytes in Hornsund. Chlorophyll a biomass concentration values were also relatively high in Fram Strait and in Adventfjorden, but to a lesser extent. Therefore, the second chapter focuses on the biogeographic patterns of communities in non-bloom state while the third chapter focuses on the community composition of the bloom stations (Hornsund, Storfjorden and Erik Eriksen Strait) and the phylogeny of the bloom species.

2. Pelagic communities of nano- and picoeukaryotes around the archipelago of Svalbard and their biogeography

Résumé L’Arctique se réchauffe rapidement, avec une menace que les espèces de l’Atlantique pénètrent et persistent au nord des normes historiques. En plus des conditions locales telles que l’apport de fonte glaciaire et les courants influencés par la topographie, l’influence relative d’eaux de l’Atlantique et de l’Arctique varie à travers le temps et l’espace créant de multiples environnements pour les petits eucaryotes (0.45-10 µm) dominant généralement le plancton estival autour de l’archipel du Svalbard. Pour examiner les facteurs formant la composition et la structure des communautés de petits eucaryotes, nous avons effectué un relevé océanographique autour de l’archipel du Svalbard du 1er au 10e juillet 2013. Les communautés ont été identifiées à l’aide du séquençage haut débit d’amplicons synthétisés à partir de la région V4 de la SSU 18S de l’ARNr et de son gène. Les communautés microbiennes différaient à l’échelle spatiale et en fonction de la profondeur. Divers sous-groupes de dinoflagellés et de ciliés étaient très abondants et les Chlorophyceae étaient la classe de phototrophes la plus abondante. Nous avons par la suite appliqué une approche d’analyses biogéographiques pour comparer la contribution relative des facteurs environnementaux locaux et l’origine des masses d’eau sur la composition et la structure des communautés. Généralement, pour les larges groupes taxonomiques, la composition et la structure des communautés étaient influencées autant par les facteurs environnementaux locaux et l’origine des masses d’eau. Toutefois, les lignées au sein de ces groupes taxonomiques étaient plus associées à l’origine de la masse d’eau. Ainsi nous suggérons que les changements de circulation océanographique dans l’Océan Arctique pourrait influencer la distribution de clades adaptés à l’Arctique, avec le clade arctique de Micromonas (Chlorophyceae) probablement le plus affecté.

Abstract The Arctic is increasingly warming, with a threat of Atlantic species penetrating and persisting further north than historic norms. In addition to local conditions such as glacial melt input and topographically defined currents, the relative influence of Atlantic and Arctic water current varies across space and time creating multiple environments for the small eukaryotes (0.45-10 µm) that dominate much of the summer plankton around Svalbard. To examine factors that shape the composition and the structure of the small eukaryote communities, we carried out an oceanographic survey around the archipelago from 5 to 10 July 2013. The communities themselves were identified using high throughput amplicon sequencing targeting the V4 region of the 18S rRNA gene and rRNA. The microbial communities differed on both spatial and depth scales. Diverse dinoflagellates and ciliates were the most abundant taxonomic groups with Chlorophyceae the most common Class of phototrophs. We then applied a microbial biogeography analysis approach to compare the relative contribution of local environmental factors and the origin of the water masses to the community structure and composition. Generally, for the broad taxonomic groups, the composition and the structure of the communities was influenced by local environmental conditions and the origin of the water mass. However, lineages within these taxonomic groups were more associated with the origin of the water mass. From this we suggest that changes in the Arctic Ocean oceanographic circulation could influence the distribution of arctic adapted clades, with the Arctic clade of Micromonas (Chlorophyceae) possibly be affected.

Introduction

Microbial communities above the Arctic Circle have been studied over the past few decades. The earlier studies performed with cultures and microscopy revealed high diversity of diatoms (Hegseth 1989, Gilstad & Sakshaug 1990, Levasseur et al. 1994, von Quillfeldt 1997, von Quillfeldt et al. 2003) and dinoflagellates (Bursa 1963, Okolodkov & Dodge 1996, Sherr et al. 1997, Okolodkov 1998). An overview of the whole community was not possible due to inherent limitations of these techniques as a large proportion of organisms are not easily cultured and smaller microbial eukaryotes are difficult to identify morphologically. Application of molecular biological tools such as polymerase chain reaction (PCR) amplification, cloning and sequencing opened up a new era in the identification of small eukaryotes (Handelsman et al. 1998, Vaulot et al. 2008). More recently high throughput sequencing (HTS) targeting taxonomically informative regions of the 18S rRNA has led to an explosion in microbial biodiversity studies in many environments (Sogin et al. 2006, Huber et al. 2007, Roesch et al. 2007).

Salinity stratification is a defining feature of the Arctic Ocean and surrounding seas; freshwater from rivers, sea ice and glacial melt enforce the halocline, limiting mixing between nutrient rich deeper oceanic waters from the Pacific and Atlantic Oceans with the Arctic surface layer. The net effect is limited nutrients in the euphotic zone over much of the Arctic, except in regions where coastal processes, upwelling or polynya formation dominate physical oceanic processes (Carmack & McLaughlin 2011). While colder sea temperatures limit growth of cyanobacteria such as Synechococcus, nutrient scarcity restricts diatoms and favors smaller cells categorized as picoeukaryotes and nanoeukaryotes with surface to volume ratios that favour nutrient uptake (Bouman et al. 2006, Lovejoy et al. 2007, Li et al. 2009). In this project, 0.45 to 10µm cell size range was used targeting picoeukaryotes and nanoflagellates. Evidence of small cells dominating Arctic microbial primary producers includes the ubiquity of the prasinophyte Micromonas (Lovejoy et al. 2007, Comeau et al. 2011, Balzano et al. 2012, Vader et al. 2014) over much of the year and across the Arctic. The seasonally abundant prymnesiophyte Phaeocystis,

which can occur either as small single cells or in the form of colonies can dominate spring blooms (Degerlund & Eilertsen 2010, Lasternas & Agusti 2010, Saiz et al. 2013).

In addition to strictly photosynthetic microbial eukaryotes, heterotrophic and mixotrophic species can represent a large proportion of biomass in northern ecosystems and they can regulate phytoplankton biomass (Verity et al. 2002). Heterotrophic protist biomass is greater than that of phytoplankton over much of the year in Kongsfjorden for example, with minimal contribution of 20-38% during the bloom period (Hodal et al. 2012). Among these mixotrophs and heterotrophs, dinoflagellates, ciliates, uncultured marine stramenopiles (MAST) and uncultured alveolates (MALV) are always reported in arctic microbial communities (Lovejoy et al. 2006, Hamilton et al. 2008, Luo et al. 2009, Comeau et al. 2011). However, information on the spatial variability of these groups is poorly known (Hamilton et al. 2008, Lovejoy & Potvin 2011). In particular, most studies on smaller eukaryotes in the Svalbard area have been restricted to the Fram Strait and the western fjords Kongsfjorden, Hornsund and Isfjorden (Luo et al. 2009, Piquet et al. 2010, Sørensen et al. 2012, Kilias et al. 2014, Piquet et al. 2014, Nöthig et al. 2015, Piwosz et al. 2015).

Svalbard is an archipelago situated at the conjunction of the Arctic Ocean, the Barents Shelf and the Fram Strait where both warm and saline Atlantic water and fresher Arctic water regularly influence the hydrography (Svendsen et al. 2002, Cottier et al. 2005). Knowledge on the composition and structure of microbial eukaryote communities in other regions apart from the western fjords of the archipelago is scarce. This leads to the first objective of this study; to provide the first survey of the pico- and nanoeukaryote communities around Svalbard using HTS of amplicons from the rRNA gene and the rRNA. In addition to microbial community monitoring, HTS is of value to test the influence of contemporary environmental factors and historical dispersal events on current community structure and distribution of small eukaryotes. Martiny et al. (2006) proposed four major hypotheses on microbial biogeography. In the Arctic, Hamilton et al. (2008) tested these using

patterns from 18S rRNA gene clones separated by denaturing gradient gel electrophoresis. The first hypothesis tested was that microbes are randomly distributed among sampling site. If the first hypothesis would be refuted, the second hypothesis tested the claim that microbes are distributed according to biogeographical barriers such as water masses, basins, etc. Another alternative to this hypothesis to be tested mentioned that the microbes are everywhere, but the environment selects (Baas Becking 1934). Therefore, assemblages of microbial communities would be influenced strictly by the local conditions. Finally, the fourth hypothesis suggested that both biogeographical barriers and local conditions would have an impact in the similarity of biotic assemblages.A greater understanding of these processes would also provide insight into the possible responses of microbial eukaryotic communities to modification in ocean dynamics brought on by the changing climate (Martiny et al. 2006, Hamilton et al. 2008).

The second objective of this study was to explore questions of biogeography of ecologically relevant groups in a changing Arctic Ocean by identifying distribution patterns in their local biogeography.

Methods

Experimental design and sampling locations

Sites around Svalbard north of Norway were selected to represent a large variety in physical, chemical and biological conditions (Figure 2.1; Table 2.1 & A.3). Samples were collected around 12:00 GMT+2 between July 5th and July 10th 2013 onboard MV Stålbas. Seawater was collected using a 10-L Niskin bottle (Denmark A/S, Denmark). Between 4 and 6 depths predetermined at each sampling location were sampled (Table A.3).

Figure 2.1. (Left) Geographical position of Svalbard and (right) of each sampling site: Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRA) and Adventfjorden (ISA).

Table 2.1. Geogaphical positions and sampling day. At each sampling site, seawater sampling started at noon local time. Sampling site abbreviations are given in brackets.

Sampling Site Sampling day Latitude (°) Longitude (°) Hinlopen Strait (HIN) 6 July 2013 79.6846 18.3254 Wijdefjorden (WIJ) 7 July 2013 79.3187 15.7594 Bockfjorden (BOC) 8 July 2013 79.4965 13.4192 Fram Strait (FRA) 9 July 2013 79.1379 7.9416 Adventfjorden (ISA) 10 July 2013 78.2523 15.4893

Physicochemical and biological analysis

Samples for chlorophyll a concentrations, as a measure of phytoplankton biomass, were size fractionated with 3 replicates filtered directly onto GF/F filters (Whatman®, USA) and 3 replicates filtered onto 10 µm mesh size polycarbonate filters (Whatman®, USA) where 400 ml of seawater was filtered for each replicate for each sample. Filters were wrapped in aluminium foil and stored at -80°C until chlorophyll a was extracted in methanol between 20 and 24 hours (Holm-Hansen & Riemann 1978). Chlorophyll a was measured using a 10-AU-005-CE fluorometer (Turner Designs, USA).

Samples for nutrients, nitrate NO3 + nitrite NO2, phosphate PO4 and silicate SiO2, were collected directly from the Niskin bottle with 100 ml of seawater per sample collected in acid-washed and sample rinsed bottles and stored at -20°C until analysis. Samples were analyzed at Norsk Institutt for Vannforskning (NIVA; Norway) using international ISO standards NS-EN ISO 4745:1991 for Nitrate+nitrite, NS-EN ISO 16264:2004 for silicate and NS ISO 4724 for phosphate.

Hydrographic profiles of the water column of each sampling site were conducted using a SD200 (SAIV A/S, Norway) conductivity temperature depth (CTD) profiler on the downcast. Data were transferred to computer using the software SD200W (SAIV A/S, Norway). Theta/S diagrams were visualized in Ocean Data View 4 (Alfred Wegener Institute, Germany) to identify and assign water masses following Cottier et al. (2005; Table 1.2).

Flow cytometry (FCM) analysis

Duplicate samples of 1,8 ml of seawater were fixed with 36 µl of 50% EM-grade glutaraldehyde and stored in liquid nitrogen before being transferred to a -80°C freezer (Marie et al. 1999). FCM samples were analyzed at the University of Bergen (Norway) on a FACS Calibur (Beckton Dickinson, USA). Chlorophyll-containing picoeukaryotes, nanoeukaryotes, cryptophytes and Synechococcus cells were separated. Quantification for the phytoplankton and bacteria were done according to Marie et al. (1999) and heterotrophic organisms as Zubkov et al. (2007).

Discrimination between bacteria and protists was performed using dot-plots from the side-scatter signals against fluorescence on FlowJo software (Tree Star Software, United States; Nordgård 2014).

DNA and RNA extraction and conversion to cDNA

Approximately 4 liters of seawater were collected from the Niskin bottle into containers. Water was then poured through a 10.0 µm mesh net (KC Denmark, Denmark) before being filtrated onto a 0.45 µm polycarbonate filter (Millipore, Germany) using a vacuum pump. Filters were cut into two and each half was stored in separate cryotubes, for eventual analysis of rRNA gene and for RNA converted to cDNA. Tubes with filters for RNA extraction were added 600 µl of RNAqueous Lysis and Binding Buffer (Ambion®, United States) was added to each tube. All filters were stored at -80°C until extraction.

DNA and RNA were extracted from the filters using the commercial kit DNeasy Plant Mini kit (Qiagen, Germany) and the RNAqueous® Kit (Ambion®, United States) respectively, with few modifications to the original protocol as follows. Each sample was beaten twice with 300 mg of 200 µm molecular biology grade zirconium beads (OPS diagnostics, United States) with additional buffer using a MM301 Mixer Mill (Retsch, Germany) to break the cells. rRNA gene samples were beaten at 30 Hertz for 1 minute while RNA samples were beaten at 22 Hertz for 1 minute.

Contamination and extraction success were checked by gel electrophoresis following a Polymerase Chain Reaction (PCR) with the primers forward Short28SF and reverse Short28SR (Vestheim & Jarman 2009). rRNA gene extraction was successful for all stations.

For each RNA sample, elimination of rRNA gene was performed using Turbo DNA- freeTM kit (Ambion®, United States). cDNA was synthetized using a two-steps PCR approach with the RETROscript® Kit (Ambion®, United States). Both kits were used according to the manufacturers’ protocol without modification.

Illumina library preparation

RNA and rRNA gene based amplicon libraries for Illumina sequencing were prepared using a protocol developed at the University Centre in Svalbard and presented in Mundra et al. (2015). The V4 region of 18S rRNA was amplified using PCR with 5’ phosphorylated E572F (forward) and E1009R (reverse) primers (Comeau et al. 2011; Table 2.2). 1X DreamTaq Buffer, 0.2 µM of each primer, 1 µm dNTPs and 0,5 U of Dreamtaq enzyme were prepared for each sample. Two µL of template of rRNA gene or cDNA were used and Mili-Q water was added for 25 µl reaction. For DNA based samples, the PCR program was executed on a GeneAmp®PCR System 9700 (Applied Biosystems, United States) included an initial denaturing step at 95°C for 120s, 28 amplification cycles at 95°C for 30s, annealing at 53°C for 30s, and extension at 72°C for 60s with a final extension at 72°C for 30mins. For cDNA based samples, the PCR program executed 15 amplification cycles.

Primer O Sequence Reference Short28SF F GTGTAACAACTCACCTGCCG Vestheim & Jarman 2008 Short28SR R GCTACTACCACCAAGATCTG Vestheim & Jarman 2008 E572F F CYGCGGTAATTCCAGTCTC Comeau et al. 2011 E1009R R AYGGTATCTRATCRTCTTYG Comeau et al. 2011

PCR products were purified using the solid-phase reversible immobilization (SPRI) method with 1 volume of 1% SPRI solution. In this step, DNA is attached to the beads.

Adaptors were ligated to DNA of SPRI cleaned PCR products with 0.75 M pre- hybridized adapter, 1X T4 DNA ligase buffer and 0,2 U of T4 DNA ligase. Solutions

were adjusted to 25 µl with Milli-Q H2O and incubated at 20.0°C in the thermocycler for 20 minutes.

The adaptor-ligated amplicons were purified a second time using SPRI. They were mixed with 1 volume of 20% PEG+ 2.5M NaCl. After the cleaning, 19.5 µl of Tris and 0.5 µl of USER enzyme were added. Each sample was incubated at 37.0°C for 15 minutes before being held at 65.0°C for 5 minutes. In this step, DNA is released from the beads and only the supernatant is kept for further processing.

A pre-quantitative PCR (qPCR) was performed for 12 cycles for a final enrichment step to the amplicons libraries. 1X Q5 Buffer, 0.5 µm of each Illumina adaptors PE PCR Primer 1.0 and PE PCR Primer 2.0, 0.332 µm of dNTPs, 0.01 U Q5 polymerase were mixed with 10 µl of rRNA gene template and MiliQ-water for a 25 µl total volume. All samples were quantified on an agarose gel and adjusted to the same concentration of DNA. If needed extracts were precipitated to increase rRNA gene concentration. 0.1 volume of 3 M pH 5.2 sodium acetate was added in addition to cold 96% ethanol. Samples were stored at -20.0°C overnight before being centrifuged for 30 minutes at maximum speed at 10°C. Once the supernatant was discarded, pellets were washed with 70% ethanol before being suspended in Tris pH 8. Final rRNA gene concentration was 22.7 µg µL-1.

The rRNA gene amplicon libraries for samples FRAM 150 m, FRAM 470 m and ISA 5 m were not successful, other problems were encountered with the RNA amplicon libraries. These includes poor RNA extraction, cDNA synthesis or insufficient cDNA amplified. Because of this, several samples were not available for further analysis. Samples used in this study are presented in table A.2.

The amplicon libraries were sequenced at Laval University Institut de biologie intégrative et des systèmes (IBIS, Canada). Samples were cleaned with magnetic beads to remove remaining primers and/or primer dimers. All sequencing was carried out using MiSeq Illumina platform (Illumina, United States).

Bioinformatics

A customized bioinformatics pipeline using the open-source software Quantitative Insights Into Microbial Ecology (QIIME) was performed for paired-end Illumina read analysis (Caporaso et al. 2010). Forward and reverse reads were joined with a 100 bp overlap with a percentage of 40. Sequences were demultiplexed and quality filter threshold was established at 29 with a maximum consecutive number of bad quality bases of 5 and a maximum of 2 N allowed in a sequence to be retained. All singletons, a read clustering alone among all samples, were discarded and chimeras were identified and removed using USEARCH61 (Edgar 2010). Thereafter, OTUs were clustered using UCLUST with a similarity level of 0.98 (Edgar 2010). Sequences were aligned using PyNast (Carporaso et al. 2010) and assigned to taxon using mothur (Schloss et al. 2009) following a modified SILVA 18S rRNA reference database (Comeau et al. 2011, Monier et al. 2013) using PyNast (Carporaso et al. 2010). Fungi and Metazoan taxa were removed. The phylogenic tree was built using FastTree implemented in QIIME (Price et al. 2009).

Quality filtering and taxonomic assignment pipelines adapted in this project have been previously used based on the same-curated 18S sequence database (Comeau et al. 2011, Monier et al. 2012, Monier et al. 2014). Further verification of the identity of the most abundant OTUs against the NCBI GenBank were consistent with mothur taxonomic assignments. BLASTn results performed on OTU reads assigned to Karenia/Prorocentrum group had the same hit scores (% similarity) for Azidinium spp., Prorocentrum spp. and Karenia spp., suggesting a lack of differention of the 18S rRNA V4 region within this group.

Data analysis

First, the assessment of the diversity coverage was done through rarefaction curves on the number of OTUs in relation to the number of reads considered per sample (Figure A.1).

Non-metric multidimensional scaling using Bray Curtis dissimilarity index (Figure 2.1) demonstrated the deep water samples (>100 m) in the Hinlopen Strait to cluster

apart. Therefore, they were not considered for biogeographic analyses. The dissimilarity between the Hinlopen Strait deeper samples and the others, could have been influenced by the drifting of the boat during the sampling. Upper water column samples from the different fjords, in contrast, showed high levels of similarity and were retained for the biogeographic analyses.

With an aim to detect major differences among the fjords, taxonomic Classes or Phyla, depending on the group that were relatively abundant (≥1% of the total assemblage) in either or both RNA and rRNA gene based assemblages were used for further analyses. Ciliates and dinoflagellates were further subdivided and assigned to the genus level where possible, as a means of determining likely trophic role. For example, we expected the mixotrophic ciliate Laboea would be influenced by different environmentral variables compared to the heterotrophic ciliate Novistrombidinum. The trophic role of most other microbial eukaryotes are more readily infered from their Class or Phylum e.g. Chlorophyceae and Haptophyceae were considered to have mostly photosynthetic and mixotrophic representatives, while MALV I are thought to be predominantly parisitoids (Guillou et al. 2008).

Samples with both rRNA (RNA) and rRNA gene (DNA) based libraries were compared using the similarity percentage (SIMPER) test implemented in R with the package Vegan (Warton et al. 2012). Samples were grouped according to four categories: RNA-surface (RS), RNA-bottom (RB), DNA-surface (DS) and DNA- bottom depths. Notice, the 75 m deep sample was near the bottom of Wijdefjorden and Bockfjorden but at an intermediate depth in the Fram Strait and Hinlopen Strait. For simplification, the depths 35 m and deeper will be considered “deeper”.

Biogeographical patterns were inferred from rRNA gene based libraries as rRNA in ribosomes is more variable since it is related to the growth rate or protein production within the cell (Stoeck et al. 2007). A first biotic matrix was constructed from the abundance of OTUs. To address the hypothesis of whether distribution was random, randomization of null models based on checkerboard score (Stone & Roberts 1990) of OTUs were permuted 99 times on the biotic matrix using the packages Vegan and bipartite on R (Gotelli & Entsminger 2003, Miklos & Podani 2004).

To evaluate the relative influence of water masses, environmental and spatial factors, Mantel tests (Mantel 1967) were used with the package ade4 on R. The identification of water masses used in the matrix was based on water mass characteristics described by Cottier et al. (2005) and evaluated using the Jaccard dissimilarity index. Environmental factors (temperature, salinity, phosphate, nitraite+nitrate, silicate, total chlorophyll a biomass (TCB) and the proportion of chlorophyll α biomass from cells ≥10.0 µm) were log (x+1) transformed, except temperature which was log(x+2) transformed. The measured environmental factors are commonly considered to affect microbial community structure and composition (Chapter 1). Thereafter, a dissimilarity matrix based on the Bray-Curtis index was constructed. The spatial matrix was a geodesic 3-dimensional matrix derived from the latitude, longitude and depth of the samples. These matrices were tested against two biotic dissimilarity matrices based on the Bray Curtis index (rely on the relative abundance of abundant taxa) and on the unweighted UniFrac index, which rely on the phylogenetic distance between samples, with 999 permutations. Distance matrices based on the Bray-Curtis index were built using the Vegan package in R while the unweighted UniFrac

distance based matrix was constructed in QIIME. The OTU table was jackknifed to avoid the influence of sequences/sample (Lozupone et al. 2011).

Canonical Correspondence Analysis (CCA) was used to explain the variability of the taxonomic composition from the same selected environmental factors as in the Mantel tests. CCA was permuted 999 times in PAST (Legendre & Legendre 1998). Spearman’s rho correlation was performed using packages Hmisc and Corrplot in R to determine significance (p ≤ 0.05) of negatively or positively linked occurrences between environmental factors and the abundant taxa (Hollander & Wolfe 1973).

Results

Hydrography, nutrients and chlorophyll a

The hydrographic systems around Svalbard varied (Figures 2.3 & 2.4). The identification of the water masses and the environmental parameters measured of each sample are given in Table A.3. The Hinlopen Strait (HIN), separating Spitsbergen and Nordaustlandet, lacked a clear thermocline but was salinity stratified below 10 m. The salinity of the upper mixed layer was 33.3 compared to a salinity of ca. 34.9 below 50 m. The sea temperature was 2°C at 5 m with a cooling to 1.6°C at 15m. Below 50 m temperatures were relatively constant at ca. 2°C. The HIN halocline delimited SW in the upper part and TAW in the lower part. The nutricline was below 75 m with highest concentrations deeper (150 and 370 m depths). The Chlorophyll a biomass was relatively low with the highest values of 0.52 µg L-1 measured at 50 m, which was the base of the halocline.

Figure 2.3. Salinity and temperature profiles by depth in the upper 200 m of the five sampling sites. Values of salinity and sea temperature at the sampled depths are listed in Table A.3.

The sampling sites Wijdefjorden (WIJ) and Bockfjorden (BOC) are geographically close (Figure 2.1). The surface salinity in WIJ was 33 but rapidly increased and the

bottom of the halocline was at 25 m. The halocline in BOC was more gradual with salinity continuing to increase below 50 m. For comparison, the salinity at 15 m depth in WIJ was 34.3 while the same depth in BOC was fresher at 33.7.

The sea temperature gradually decreased with depth (3.7 to 0.7°C) in WIJ. BOC was warmer on the surface with 4.6°C at 5 m and gradually cooling to 2°C at 25 m. Nitrate+nitrite and phosphate concentrations increased gradually with depth for both fjords with a dip in concentrations at 35 m depth in BOC. However, silicate concentrations were greater near the surface in both fjords. The silicate concentration decreased from 3.03 to 1.65 µmol l-1 down to 15 m in WIJ. In BOC, it decreased from 4.13 to 1.72 µmol l-1, than increased at 35 m depth. In both fjords the total chlorophyll a concentration was relatively high at 5 m depth (1.17µg l-1 in BOC with a contribution of 7.7% by cells larger than 10 µm).

To compare with the fjords, we also sampled FRAM, which is at the same latitude as Kongsfjorden. The maximum depth at this site was >1000 m but we sampled only to 480 m due to logistic constraints. The hydrographic profile indicated the presence of AW without being mixed with ArW with fairly uniform salinity of c. 35.1 below 50 m. Salinity at 5m depth was 34.95 and gradually increased in the upper layer. The sea temperature profile decreased gradually from 6.26°C at 5 m depth to 2.32°C at 470 m. Nutrient concentrations increased between 15 and 150 m with up to 10 fold higher concentrations at depth compared to near the surface. Nutrient concentrations below 150 m were among the highest detected during this study with nitrate+nitrite, phosphate and silicate reaching 10.71, 0.26 and 4.85 µmol l-1 respectively.

Finally, the sampling site located at the mouth of Adventfjorden (ISA) was characterized by a fresher salinity profile with salinity increasing from 5 to 70 m. The temperature decreased from 6.3°C at 5 m compared to 1.54°C at 60 m. There was no obvious nitricline, silicate values were higher with concentrations of 2.37 µmol l-1 at 5m and 2.52 µmol l-1 at 15 m depths, but the low concentrations of nitrate+nitrite and phosphate increased little with depth ranging from 0.07 to 0.43 µmol l-1 for nitrate+nitrite and from 0.06 to 0.09 µmol L-1 for phosphate.

Figure 2.5. Log transformed abundance of cells ml-1 of picoeukaryotes, nanoeukaryotes, cryptophytes and cyanobacteria of Synechococcus at the different sampling depths from flow cytometry.

Flow cytometry results

Synechocccus was detected at the majority of the sampling sites (Figure 2.5). The concentrations of Synechococcus were greater in the upper 25 m and decreased with depth. The highest concentration of Synechococcus was recorded at 75 m in HIN and BOC and at 5 m depth in ISA. In general, picoeukaryote concentrations were high compared to nanoeukaryotes especially closer to the surface.

Sequence quality filtering and analysis

A total of 6,630,492 paired-end sequences were successfully generated after merging the forward and reverse reads. Number of sequences per rRNA gene and rRNA based library after quality filtering and demultiplexing are shown in Table A.1. Further sequence screening by de novo comparison and against the curated database identified 14,531 chimeras which were then removed. Out of the remaining sequences, 1960 and 931 OTUs at 98% similarity were separately identified from rRNA genes and RNA libraries, respectively, while 848 OTUs (43%) were found in both libraries. Including all libraries, 2120 OTUs were found. Rarefaction curves that were used to visually select libraries covering most of the diversity were included as an addition to the mémoire (Figure A.1).

Community diversity and composition

DNA based communities

Throughout the different DNA based libraries except HIN 150 m, dinoflagellates were the dominant taxonomic group, represented by 20 to 80% of the sequences per sample (Figure 2.6, Table A.4). Other major taxonomic groups include ciliates, Phaeocystaceae, Chlorophyceae, MALVs and Pelagophyceae. Ciliates and dinoflagellates demonstrated high biodiversity at the abundant genus/species level. Therefore, deeper taxonomic assignment was performed among those taxonomic groups (Figures 2.7 & 2.8, Table A.5 & A.6).

Ciliates

Ciliates were ubiquitous, representing from one to 18% of the total number of sequences per sample. Higher relative abundances of ciliates were observed in the northern most fjords in this study: WIJ and BOC while lower relative abundances were found in communities below 150m in HIN in addition to communities analyzed from FRAM. Deeper taxonomic assignment revealed dominance by different genera (Figure 2.7, Table A.5). Presence of the ciliate genus Laboea was mainly restricted to the surface samples of HIN, WIJ, BOC and ISA and were almost absent from the surface layer of FRAM. Laboea was particularly dominant in WIJ and BOC.

Sequence reads assigned to Novistrombidium were particularly abundant in WIJ and to a lesser extent in HIN. The ciliate genus Parastrombidinopsis was present in BOC and in the deeper samples of ISA.

Figure 2.6. Abundant taxonomic groups and their relative abundances of associated sequences per rRNA gene based library based on Table A.4. Fjords sampled were the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait (FRA) and Adventfjorden (ISA). Non-abundant and unidentified sequences were combined in the “Other” category.

Dinoflagellates

In general, dinoflagellates dominated the DNA based libraries. The highest relative abundance of the dinoflagellates among the total community was at 15m depth in FRAM while the lowest relative abundance was at 150 m depth in HIN where, 81 and 20% of the total number of sequences were assigned to dinoflagellates respectively (Figure 2.6, Table A.4). The dinoflagellates group “Other” represented, throughout the different microbial communities, relative abundances varying from 8 to 28%. Among this group, 70 to 95% of the sequences were unidentified dinoflagellates GPP (gymnodinoids, peridinoids and prorocentroids). A BLASTn analysis on the most common unidentified dinoflagellate OTUs showed similarity with the Amphidomataceae Azadinium spp. from the North Atlantic (Nezan et al. 2012), a Karlodinium sp. from China (Genebank: JN986577.1) and Peridiniaceae from the Gulf of Mexico (Rocke et al. 2013). Other unidentified dinoflagellates showed high similarity with Gymnodiniaceae reported from the English Channel (Marie et al. 2010), Ross Sea (Lie et al. 2014) and Columbia River (Kahn et al. 2014).

Among the dinoflagellates, several taxonomic groups were identified to putative species: Gymnodinium beii, Gyrodinium fusiforme, Gyrodinium gutrula; to a species complex, Gyrodinium group (G. helveticum and G. rubrum); or genus Karlodinium, Katodinium and Nematdodinium; or to the taxonomically uncertain Karenia/ Prorocentrum group (Figure 2.8, Table A.6).

Gyrodinium fusiforme and Gyrodinium helveticum/rubrum related reads were the most abundantly identified taxonomic groups among dinoflagellates and were ubiquitous in the samples. Reads associated with the Gyrodinium helveticum/rubrum group were particularly abundant in the deepest community of HIN and in the surface communities of HIN and FRAM. They represented 53% of the total sequence reads in the library of the 240 m depth in HIN and 25 to 30% in the libraries of the surface layer of HIN and FRAM. The highest relative abundances of G. fusiforme were observed in HIN and at 35 m deep in WIJ, reaching up to 32% of the total number of sequences per sample. At a lesser extent, communities from FRAM were also characterized by a strong presence of G. fusiforme.

Other abundant taxonomic groups

The Phaeocystaceae, Chlorophyceae, Pelagophyceae and MALVs related sequences were also relatively abundant in the rRNA gene based libraries (Figure 2.6, Table A.4). The Phaeocystaceae were recovered from nearly all samples although their highest relative abundance reached only 6% at 15 m depth in WIJ. Surface samples of HIN and WIJ in addition to the deepest samples of WIJ, BOC and ISA hosted also sequences related to Phaeocystaceae with relative abundances varying between 2 to 4% of the total number of sequences per sample.

Most of the sequences grouped in Chlorophyceae were associated with the Mamiellophyceae Micromonas pusilla culture (CCMP 2099), which represents a pan- arctic ecotype. Though Micromonas were present in most of the samples, their relative abundances were higher in surface layers of WIJ and BOC where they accounted for up to 25% of the total reads of the community (Figure 2.6). In both stations, their relative abundances decreased with depth.

The Pelagophyceae related reads were associated to a potentially new phylotype of Pelagophyceae retrieved previously in Billefjorden (Svalbard) but also in the Beaufort Sea (unpublished). Its presence in this study was strictly limited to the ISA samples, with the highest relative abundance at 15m depth with up to 22% of the total community reads (Table A.4). Below 15m, Pelagophyceae remained an abundant taxonomic group with relative abundances of c. 8 %.

Finally, the MALVs were ubiquitous with the highest relative abundance at 150m depth in HIN. Excluding the 240 m depth sample of HIN, the relative abundance of the MALVs increased with depth at all stations.

RNA based communities

Ciliates and dinoflagellates were a major component of all rRNA-based communities, contributing up to 10% of the total relative abundance (Figure 2.9, Table A.7). Similar to the rRNA gene based communities; the “Other” fraction accounted for a high proportion of the rRNA based communities. Using the overall abundant taxa

selection set at 1% of all reads from all samples missed taxa that were abundant in a few samples and sites.

Among the dinoflagellates, several also matched the DNA identified abundant taxonomic groups: Gyrodinium fusiforme, Karenia/Prorocentrum associated species, Katodinium spp. and Nematodinium spp. but not Gyrodinium helveticum/rubrum (Figure 2.6). In addition, similar to the rRNA gene based libraries, unidentified dinoflagellates retrieved from the rRNA-based libraries were the major contributors (up to 60%) to the “Other” group. Some OTUs were shared with the rRNA gene based communities, in particular gymnodinoids and Azadinium spp. The Gyrodinium fusiforme was the relatively most abundant dinoflagellate taxonomic group of the rRNA-based libraries (Figure 2.10; Table A.8).

In addition to the ciliates and the dinoflagellates, the Prymnesiaceae, the Phaeocystaceae, the Chlorophyceae, and the Pelagophyceae were also relatively abundant. The Prymnesiaceae were mostly found in deeper Bockfjorden and Adventfjorden within rRNA gene based communities, but had a higher contribution to the total abundance in the rRNA based communities. Prymnesiaceae were also present in the Fram Strait (15 m), at the surface of the Hinlopen Strait and in Wijdefjorden. Contributions varied between 1 and 15% with highest contributions in Adventfjorden and at 75 m in Wijdefjorden (up to 5%). Chlorophyceae also followed similar patterns, being primarily present in surface waters of fjord systems (Figure 2.9). The relative contribution of Phaeocystaceae to the total community, especially in the surface of Hinlopen Strait, Wijdefjorden and the Fram Strait, reached up to 9%. They were also observed in deeper depths of Adventfjorden. However, Phaeocystaceae were rare or not found in the Bockfjorden samples.

DNA vs RNA based libraries: surface vs bottom layers

The taxa “Other” contributed to the dissimilarity between rRNA gene and RNA based libraries for both surface and deeper layers. However, the category was associated with several phylogenetically separate taxa, which were rare or unclassified. Therefore, the taxa group “Other” was not be taken into account in the remaining analyses of RNAvsDNA based libraries comparisons and biogeography.

Using SIMPER, the overall variation between DNA and RNA based communities at the surface was mainly explained by the Gyrodinium helveticum/rubrum group (dinoflagellates), the ciliates and the chlorophytes. Those taxa contributed up to 48% of the total dissimilarity among the two types of libraries (Figure 2.11, Table A.9).

To a lesser extent, MALV I, Prymnesiophyceae, Nematodinium spp., Gyrodinium fusiforme and the Pelagophyceae were also contributors.

In the deeper waters, the ciliates, the dinoflagellate Gyrodinium helveticum/rubrum, the MALV I, the Prymnesiophycae and the Pelagophyceae were the major contributors accounting together for up to 68% of the overall dissimilarity.

Biogeographic analyses

Assessment of the different hypotheses

The main hypothesis of randomizing null models is that the community composition and structure is based on a null model of a random distribution of OTUs among the sampling sites. To assess the hypothesis, 99 communities were randomly assembled from the OTU table. As it is a two-tailed test, statistic C-score toward lower values (< 0.1979) and upper values (> 0.1986) are evidence against the null hypothesis based on a p-value threshold of 0.05. Therefore, with a statistic value of 0.1986 (ρ = 0.03), the hypothesis of a random distribution of the OTUs among the different sampling sites was rejected (Table 2.3).

C-score Z Mean 2.5% 50% 97.5% ρ 0.1986 1.8801 0.19803 0.1976 0.1980 0.1986 0.03

Environment Water masses Distance Biotic data Bray-Curtis 0.234 (ρ =0.03) 0.268 (ρ =0.01) 0.247 (ρ =0.02) UniFrac 0.288 (=0.05) 0.429 (ρ = 0.001) 0.303 (ρ =0.01) Environment 0.138 (ρ =0.15)

The remaining biogeographic hypotheses were tested. Statistically significant positive correlations were found between the community composition, the origin of the water masses and the spatial distance either based on the unweighted UniFrac or Bray- Curtis dissimilarity indexes (Table 2.4). Significant values indicate communities to be similar within the same environmental characteristics, water mass and region. Based on the Bray-Curtis index analysis, the very similar statistics values highlight an equal impact by environmental factors, water masses and distances on the community composition and structure among the abundant taxa. However, higher values between the dissimilarity matrices of the biotic data based on unweighted UniFrac and the water mass origins highlight a stronger importance of water masses in the phylogenetic diversity than environmental factors or the spatial distance.

Correlation within the abundant taxa and with abiotic factors

Environmental factors with a significant impact on community composition and structure within the rRNA gene based libraries were identified. The resulting CCA showed that close to 71% of the biotic variability explained by the selected environmental factors. Among the environmental factors, Axis 1 was more associated with temperature, salinity, total biomass of chlorophyll a (TC) and the proportion of chlorophyll a cells >10µm (STC) to the total biomass while TC and STC were the dominant contributors to Axis 2 (Figure 2.11). According to the Spearman’s rho test, temperature had a significant positive correlation with Gymnodinium beii and Gyrodinium helveticum/rubrum group while being negative for the dinoflagellates Gyrodinium gutrula and the ciliate Novistrombidinium. The Chlorophyceae, the Pelagophyceae and the ciliate Laboea were negatively correlated with salinity in the opposite sense of MALV I, which were positively associated with salinity. The total biomass of chlorophyll a had a positive influence on Gymnodinium beii and on the ciliate Nematodinium and negative association with the ciliate Parastrombidinium, the dinoflagellate Gyrodinium gutrula and the Picozoa. Finally, a stronger proportion of cells >10µm in the chlorophyll a biomass negatively affected the ciliate Laboea, the Chlorophyceae and the Cryptophytes while being positively associated with the MALV I. Among the other environmental factors, nitrite+nitrate and >10.0 µm size contribution to chlorophyll a was positively associated with Cercozoa and the Pelagophyceae.

Discussion

Svalbard hydrographic dynamics

The sampling locations were selected to retrieve the pico- and nanoeukaryote communites across a variety of local environmental conditions and dominant water masses. While the Fram Strait was clearly dominated by AW throughout the water column, the other sampling sites demonstrated typical fjord water mass layering consisting of three layers (surface, intermediate and deep water layers) for the silled Wijdfejorden and for the Hinlopen Strait and two layers (surface and intermediate layers) in Bockfjorden and Adventfjorden where there was an absence of topographical barriers (Farmer & Freeland 1983). The fresher surface layer identified at all sites except in the Fram Strait overlayed TAW probably resulting from the mix of AW and ArW (Svendsen et al. 2002). The colder and more dense deeper layer found in Wijdefjorden and in the Hinlopen Strait was presumably formed locally from sea ice during winter and retained due to the sill in Wijdefjorden and due to topographic constraints in the Hinlopen Strait.

The wide range of temperatures and salinities found in the surface water (SW) presumably resulted from the variability of freshwater discharges, sea ice melting and surface warming at the different sites. In a Greenland fjord, glacial melt was found to create surface layers of high silicate and low salinity values (Azetsu-Scott & Syvitski 1999). In this study, Bockfjorden, Wijdefjorden and Adventfjorden surface waters were similar and silicate concentrations reached higher values compared to in the Fram or Hinlopen Straits (Figure 2.4). Though the surface layer in the Hinlopen Strait also had low salinity compared to the fjords, it was characterized by lower silicate values (1.40 µmol l-1 in Hinlopen Strait vs. 4.56 µmol l-1 in Bockfjorden). Lower silicate values may have been induced by either less input of glacial melt or the stage of the current bloom.

The depth of the halocline varied; from c. 50 m in the fresher water column in Adventfjorden to c. 15 m in the more saline Wijdefjorden (Figure 2.3, Table A.3). In Adventfjorden, salinity and temperature profiles demonstrated strong stratification

primarily caused by freshwater input from the glacial melt rivers Adventelva and Longyearelva (Keck et al. 1999).

The intermediate layer in the western Svalbard fjords was reported to be generally derived from the WSC (Cottier et al. 2005, Nilsen et al. 2008). The intermediate layer in all sampling sites except the Fram Strait was characterized as TAW (Table A.3). Intrusions of TAW were observed in Bockfjorden below 25 m, in Adventfjorden below 50 m and in Wijdefjorden with typical sea temperature of c. 2°C and salinity above 34,7.

Finally, the deepest layer in silled fjords or in the deep basins around Svalbard are reported to be formed from winter cooled Atlantic Water through sea ice formation (Nilsen et al. 2008). The deepest water samples of Wijdefjorden and the Hinlopen Strait were characterized by colder (c. 0°C) water layers but too saline to be considered as ArW (>34.8). With a strongly temperature stratified water column below 50 m, the hydrographic profiles (Figure 2.5) suggests that the sampling site was located behind the sill in Wijdeforden, although not in the deepest part of the basin where it can reach 246 m. In the Hinlopen Strait, few deep basins are recorded on nautical charts and the deep water layer result from the presence of a basin at our sampling location. However, studies on oceanographic currents are limited in this region and influence of the marine topography on the water masses layering in the Hinlopen Strait should be taken with caution.

Conclusions on the composition of each layer are speculative as temperature and salinity profiles alone are challenging to determine the source of water, though using silicate concentrations as Azetsu-Scott & Syvitski (1999) did permit the assessment of the relative influence of glacial melt on the freshwater input origin in the surface layer.

Pico- and nanoeukaryote community composition

The combination of rRNA gene and rRNA based amplicons should provide insights on the active versus latent/dead organisms (Stoeck et al. 2007). Only 48% of the total

OTUs were shared between the two set of libraries. Several OTUs among the dinoflagellates for instance, were undetected in the rRNA based libraries, though they represented up to 54% of the total abundance in a sample from the rRNA gene based libraries (Figures 2.6 & 2.9, Tables A.4 & A.7). We conclude therefore that these taxa were not active at the time of the sampling. To simplify the discussion, we will focus mostly on the most abundant groups common in both type of libraries, with some comparative context.

Ciliates

The ciliates were represented by taxa with different trophic potential, including mixotrophy and heterotrophy. The most abundant ciliates identified in this project were Laboea spp., Novistrombidinum spp. and Parastrombidinopsis spp. (Figure 2.7). Laboea, which is considered mixotrophic (e.g. Johnson 2011) was found in the surface layers of Bockfjorden, Wijdefjorden and Adventfjorden. Their relative abundance reached up to 11% of the rRNA and rRNA gene based communities (Tables A.5). The biomass of Laboea strobili was previously reported during April to reach 291 mg C m-2 and in July 130 mg C m-2 in Kongsfjorden (Seuthe et al. 2011). Putt et al (1990) reported that up to 22% of the photosynthetic activity of a coastal community was carried out by Laboea. To the best of our knowledge, the impact of the Laboea spp. as primary producers has not been assessed around Svalbard and should be investigated further.

Other ciliate taxa were also abundant in the microbial communities such as the heterotrophic oligotrich Novistrombidium. Traditionally, the genus Novistrombidium is assumed to occur only in the northern hemisphere, excluding the Arctic Ocean, although its presence was first noticed in Franklin Bay in 2007 and its abundance has increased since then (Agatha 2011, Comeau et al. 2011). Recent observations of this taxon in this project around Svalbard and in the Beaufort Sea (Monier et al. 2015) may suggest a movement of this genus from mid latitudes to the Arctic including the Norwegian Arctic. In other oceans, the abundance of this oligotrichous ciliate was positively correlated to the abundance of Synnechococcus and other small phytoplankton (Agatha et al. 2011). The heterotrophic ciliates along the east coast of

Greenland accounted for 15 to 30% of the primary producer removal in a fjord where primary producers were dominated by < 10 µm phytoplankton (Montagnes et al. 2010). Among the rRNA libraries in this project, low relative abundances of the oligotrichs were found in absence of the cyanobacteria, for example at 75 m depth in Wijdefjorden. Presence of cyanobacteria in the fjord systems may provide a favorable environment for the Novistrombidium and further investigations on the distribution of these ciliates are required, as they may be strongly influenced by the oceanographic changes around Svalbard.

Dinoflagellates

Dinoflagellates were the most abundant taxonomic group in most of the samples. Deeper taxonomic assignation revealed a high diversity of different dinoflagellate genera and species among the samples. Gyrodinium helveticum/rubrum from surface samples accounted for a high proportion of the dinoflagellate reads (Figure 2.8). In addition to Gymnodinium béii and Gyrodinium gutrula, the Gyrodinium helveticum/rubrum group was only abundant in rRNA gene amplicon libraries, suggesting they were inactive around Svalbard at the time of sampling. G. beii is referred as a basionym on Algaebase as Pelagodinium bei (Guiry et al. 2015) and was detected from culture methods in 2011 in outer Oslofjorden for the first time in the region (Fodor 2014). It is considered as a dinoflagellate symbiont of the planktonic Foraminifera Orbulina universa, which occurs in mid latitudes of the North Atlantic only in stratified waters warmer than 16°C (Gast & Caron 1996). Therefore, detection of Gymnodinium béii only in Fram Strait and in Adventfjorden could be explained by non-living organisms brought by the Atlantic current, or cryptic biodiversity within a species complex.

Gyrodinium gutrula and the Gyrodinium helveticum/rubrum group were previously detected in marine samples of Kongsfjorden in early summer (Piquet et al. 2010). Piquet et al. (2010) suggested that a decrease in salinity in the surface layer may favor Gyrodinium gutrula; however, low salinity values were not associated with relative abundance among rRNA gene and rRNA based libraries from surface samples (Figures 2.6 & 2.9). Piquet et al. (2010) found higher abundances close to the glaciers

and we suspect upwelling processes from glacial melt to have resuspended cysts in the water column and thus producing the detected signal (Horne 1985). The Gyrodinium helveticum/rubrum group was also a major taxonomic group among the communities sampled, contributing up to 54% of the assemblage in the deepest community in the Hinlopen Strait (Table A.6). Similar observations were previously reported in the early spring north of Svalbard where a phylotype associated to the heterotrophic Gyrodinium rubrum was frequently observed at all depths in rRNA gene libraries (Bachy et al. 2011). It constituted the major taxon also during summer and autumn in the protist communities in the Beaufort Sea (Terrado et al. 2009).

Although., reads associated with Gyrodinium gutrula as Gyrodinium helveticum/rubrum and Gymnodinium béii were mostly from the rRNA gene libraries and not the rRNA libraries and therefore suggesting that these taxa were inactive in Svalbard fjords, further studies at different times of the year may reveal their biological role in Svalbard fjord systems.

Gyrodinium fusiforme, dinoflagellate sequences associated with a poorly delineated Karenia/Prorocentrum group, Katodinium spp. and Nematodinium spp. were found in both rRNA genes and rRNA amplicon libraries. Microscopy surveys have reported Gyrodinium fusiforme collected in the area close to the geographical North Pole (Bachy et al. 2011). High biomass of G. fusiforme was recorded in Kongsfjorden in April during the spring bloom (Seuthe et al. 2011). In our project, the relative abundance of G. fusiforme was also greater for both rRNA gene and rRNA based communities at the surface, especially in the Hinlopen Strait and Bockfjorden (24% and 19% respectively of sequence reads in the rRNA library reads).

Reads that assigned to the Karenia/Prorocentrum group should be taken with caution. OTUs among this taxa group were verified using BLASTn and equal similarities were found with Karenia spp. Prorocentrum spp., Azidinium spp. and Gymnodinium spp. These dinoflagellates have a very conserved V4 region of the SSU rRNA 18S and complexify the assignment to a reference database. Therefore, more accurate phylogeny assignment such as the evolutionary placement algorithm and additional

targeted genes such as in the mitochondrial cytochrome c oxidase may strengthen the confidence in the taxonomic assignment of dinoflagellates at the genus/species level (Lin et al. 2009).

Other abundant taxonomic groups

Among the other abundant taxonomic groups were Chlorophyceae which were especially abundant in the rRNA gene and rRNA communities at the surface layers of Bockfjorden and Wijdefjorden. They represented up to 27% of the total community in the rRNA libraries (Table A.4). BLASTn results of the most abundant OTUs showed ≥ 99% similarity with the arctic clade Micromonas CCMP2099. This Micromonas is likely a major primary producer across the Arctic Ocean (Lovejoy et al. 2006) and one of the most abundant phototrophic organisms in Norwegian Arctic Seas (Not et al. 2005). Temporal studies in Billefjorden showed higher abundance of Micromonas during post bloom and late summer, especially between June and August 2011 (Christensen 2012), and elevated Micromonas concentrations may be a summer phenomenon in Svalbard fjords and other Arctic regions (Lovejoy et al. 2007, Sørensen et al. 2012). In our study, the high relative Micromonas abundance and the low nutrients concentrations at the surface of Wijdefjorden and Bockfjorden suggested these fjords may be in post-bloom stage. The low relative abundance of the Micromonas arctic clade in other sampling sites may be due to the timing of sampling.

The potential bloom forming Phaeocystaceae were putatively active in all depths, though to a lesser extent in surface samples of Bockfjorden (Fgure 2.6). In contrast, Pelagophyceae were only detected in the Adventfjorden samples and mostly in the rRNA community from 15m (Figure 2.6). These two groups were the causative taxa for blooms in three other fjords sampled in a parallel study (Chapter 3).

Biogeographic analyses

Martiny et al. (2006) proposed four major hypotheses on microbial biogeography that where tested in this investigation. In our study of dispersed fjords, there was low similarity (<20%) between samples of different water masses suggesting non-uniform

distribution of microbes in the upper Arctic Ocean. In this project, we used null model analysis to analyze how communities would be distributed under stochastic factors (Gotelli & Graves 1996). Results significantly demonstrated the limited influence of randomization in the assemblage composition and structure of pico- and nanoeukayote communities (Table 2.3). These results were supported by the different clusters of biotic assemblages according to water layers in a fjord (Figure 2.4).

The relative abundances of the abundant taxonomic groups among the samples were significantly and similarly explained by the WMM and LCM (Table 2.4). Therefore, we hypothesized, as in the North Water, that the distribution of pico- and nanoeukaryotes was influenced at the same magnitude by the local environmental conditions and the origin of the water masses (biogeographical barriers) and the fourth hypothesis suggested by Martiny et al. (2006) was retained (Hamilton et al. 2008). The fourth hypothesis was also upheld when WMM and LCM were tested against the biotic matrix constructed from the unweighted UniFrac phylogenetic distance. It captured the total amount of evolution, presumably reflecting possible adaptation to a given environment (Lozupone & Knight 2005). Although both LCM and WMM were significantly correlated to the biotic matrix, the correlation was higher with the origin of the water masses (Table 2.4). In this project, we further demonstrated that although the relative abundance of the taxa group may be similarly influenced by LCM and WMM, the phylogenetic distances between the samples were conserved within the same water mass (Table 2.4). The UniFrac phylogenetic technique taking into account the amount of evolutionary divergence between taxa between the water mass may explain the different results between both community matrices (Lazopone et al. 2006). The results suggest therefore that the biogeography of particular lineages of the abundant taxa groups are more correlated to the water masses than to the local environmental conditions.

Ordination analyses were used to estimate influences of pre-selected environmental factors on the assemblage structure of the communities (Figure 2.11). We showed that the Axis 1 (36.32%) was explained by salinity and temperature. As suggested by Hamilton et al. (2008), salinity probably indicated a depth-related gradient

considering the strong stratification observed at the sampling locations. The concentration of Chlorophyll a and the relative contribution of cells >10 µm were also major contributors to Axis 1. The stronger ordinations between Axis 1 and Pelagophyceae, Mamiellophyceae (Chlorophyceae) and the ciliate Laboea were most likely explained by their photosynthetic activity. The presence of the dinoflagellate Nematodinium may indicate grazing preference on the smaller picophytoplankton. In the opposite sense, most of the other taxa, such as the dinoflagellates Gyrodinium fusiforme and the ciliates Parastrombidinopsis and Novistrombidium, could be associated with a preference to graze on larger cells. However, those results are preliminary and further investigation would be required to establish predator-prey relations.

Conclusion

This project was the first survey on the large-scale spatial variability of the microbial communities (0.45 to 10 µm size cells) in Svalbard waters using the recent high throughput sequencing technologies. The use of these molecular tools provided details on the community structure of assemblages not possible using optical microscopy or cultures. Among the major taxonomic groups were ciliates, dinoflagellates, chlorophytes, phaeocystales and pelagophytes.

Measured environmental factors were highly variable and the water masses were diverse among the sampling locations. Most of the sampling sites were under the influence of Atlantic originated waters, TAW or AW. Except in the Fram Strait, the water column profiles at the different sampling sites showed the overlaying of distinct water masses: a fresher and warmer surface layer, an advected intermediate layer and occasionally a deep layer retained by a sill or in a basin. The different water layers also presented different characteristics including the nutrient concentrations and the phototrophic biomass. However, the absence of sampling sites influenced by ArW may have attenuated the impact assessment of the origin of the water mass on the characteristics of the assemblage.

Four hypotheses on the biogeography of the microorganisms established by Martiny et al. (2006) and used in previous microbial biogeography studies were tested. Results demonstrated that both origin of the water mass and local environmental condtions shape the microbial community structure and composition.

Finally, this project brought additional data from the use of recent developed molecular biology and phylogenetic tools to the numerous studies of the microbial communities in Isfjorden and in the Fram Strait. Even more, it brought an updated picture of the microbial communities in fjords that have been unregularly studied such as Wijdfjorden, Bockfjorden and the Hinlopen Strait. The diversity among the different sampling sites will hopefully motivate new investigations to undertake surveys outside the commonly studied fjords.

3. Blooms of pelgic pico- and nanoeukayotes around the archipelago of Svalbard Le système hydrographique autour de l’archipel du Svalbard est complexe dû aux courants d’eau de l’Arctique et de l’Atlantique présents ainsi qu’aux conditions environnementales très variables. L’hétérogénéité des conditions environnementales dans les différents fjords et détroits autour du Svalbard contribue probablement à la diversité des principaux groupes formant les efflorescences avec les haptophytes, les chlorophytes ou les diatomées observés de différents fjords à différentes années. Lors d’un relèvement océanographique, en juillet 2013, de fortes concentrations de chlorophylle suggéraient des efflorescences récentes ou actives de phytoplancton au sein de trois différents systèmes hydrographiques autour de l’archipel du Svalbard. Le phytoplancton dominant responsable des efflorescences a été identifié en utilisant le séquençage haut débit d’amplicons ciblant la région V4 de la 18S de l’ARNr et de son gène des eucaryotes microbiens (0.45 to 10 µm). Les principales espèces de phytoplanctons au sein des communautés ont été identifiées en utilisant l’Evolutionary Placement Algorithm qui facilite l’identification à partir de courtes séquences. Dans Storfjorden, le principal élément de l’efflorescence a été assigné à Phaeocystis pouchetii avec plus de 73% de la totalité des séquences de la communauté. Pour le détroit d’Erik Eriksen, aucune espèce n’était dominante et les cellules > 10 µm ont probablement contribué à l’efflorescence. Dans Hornsund, près de 66% des séquences associées au gène d’ARNr et 86% à l’ARNr étaient associées à un potentiellement nouveau phylotype de Pelagophyceae. Que ce pelagohpyte ait récemment été introduit par les eaux de l’Atlantique ou préalablement non détecté est à ce jour inconnu, mais l’identification de nouveaux et écologiquement importants phylotypes dans les eaux du Svalbard pourrait augmenter avec les changements des conditions climatiques.

Abstract

The hydrographic system of the archipelago of Svablard is complex with Atlantic and Arctic water currents and the local environmental conditions are highly variable. The heterogeneity of the environmental conditions in the different fjords and straits around Svalbard may contribute to the diversity of the major groups that form blooms, with haptophytes, chlorophytes or diatoms reported from different fjords in different years. During an oceanographic survey, in July 2013, high chlorophyll a concentrations suggested recent or ongoing phytoplankton blooms were occurring in three different hydrographic systems around Svalbard. The dominant phytoplankton responsible for the blooms was identified using amplicon tag sequencing targeting the V4 region of the 18S rRNA gene and rRNA of microbial eukaryotes in the 0.45 to 10 µm size class. The main phytoplankton species within the communities were identified using Evolutionary Placement Algorithm that facilitates identification from short reads. In Storfjorden, the main bloom driver was assigned to Phaeocystis pouchetii with up to 73% of the total community reads. For the Erik Eriksen Strait no single species was dominant and cells > 10 µm may have contributed to the bloom. In Hornsund, a potentially novel phylotype in Pelagophyceae reached up to 66% of the rRNA gene reads and 86% of the rRNA reads. Whether this blooming pelagophyte has been recently introduced due to increased Atlantic water inflow to the western Svalbard fjords or previously overlooked is currently unknown, but the finding of novel, ecologically important phylotypes in Svalbard waters may be expected to increase due to changing climatic conditions.

Introduction Bloom periods, especially during spring, account for much of the annual primary productivity in the highly seasonal Arctic. In the Barents Sea, cells <10µm made up to around 46% of the total carbon production over the spring bloom period in May 2005 (Sakshaug et al. 2004, Hodal & Kristiansen 2008). Aside from being the main source of energy and carbon, blooms also represent key events for the Arctic food chain, as the long chain omega-3 fatty acids produced exclusively by marine algae are important for the growth and development of higher trophic organisms (Ackman 1989). Co-occurrence of Arctic and Atlantic waters in addition to other oceanic processes (e.g. upwelling, mixing, and ice cover) and external factors such as inland freshwater input create an annually and spatially variable hydrographic system around Svalbard (Gloersen et al. 1993, Vinje 1997, Hop et al. 2002, Sørensen et al. 2012). For example, inflow of Atlantic water influenced by the North Atlantic Oscillation in Kongsfjorden affects the phenology and species composition of major phytoplankton assemblages. The nanoeukaryote Phaeocystis pouchetii tends to dominate when there is weaker water column mixing caused by delay or absence of Atlantic water inflow (Hegseth & Tverberg 2013). In periods of intense mixing due to intrusion of the denser AW, diatoms are favored because of resuspension of their spores deposited in deeper layers during winter. On a spatial scale, western fjords are often characterized by the presence of eddies associated with the warm and saline West Spitsbergen Current (WSC) originating from the Gulf Stream Atlantic Water. In contrast, eastern fjords are under stronger influence of the colder and fresher Arctic water mass from the East Spitsbergen Current (Saloranta & Svendsen 2001). The colder sea temperature delays the break-up of sea ice, thus postponing bloom periods in eastern Svalbard compared to the western fjords (Søreide et al. 2008). Other highly variable factors such as the Rossby radius that proportionally increases with the width of the fjord and latitude, and/or input of freshwater from glacial melt or river runoff create heterogeneous spatial hydrographic systems. Therefore, microbial communities in fjords around Svalbard, although close to each other, differ in their structure and composition throughout most of the year related to the differences in hydrographic conditions in the fjords. Most of the knowledge and insights on blooms around the

Svalbard archipelago are derived from studies in the western fjords such as Kongsfjorden (Hop et al. 2002, Leu et al. 2006, Hodal et al. 2012, Hegseth & Tverberg 2013) and Isfjorden (Dobrzyn et al. 2009, Sørensen et al. 2012). In addition, those studies focused essentially only on the spring period and techniques that were limited to microscopy and cultures. Søreide et al. (2008) reported blooms in eastern Svalbard (Erik Eriksen Strait) that occurred in August for both 2008 and 2009, a month after the sea ice breakup. However, few studies have been carried out on blooms in the eastern region. Here, phytoplankton communities of three high chlorophyll a sites were sampled and were investigated using high throughput amplicon tag sequencing on the MiSeq (Illumina) platform. Our first objective was to characterize the overall pico- and nanoeukaryote community composition during the bloom in the less studied areas of Hornsund and Storfjorden, and the Erik Eriksen Strait. Subsequently, we identified the major taxonomic groups that contributed to the blooms through phylogenetic placements of the short sequences.

Methods

Experimental design and sampled sites

The sampling sites around Svalbard north of Norway were selected to represent a large variety in physical, chemical and biological characteristics (Figure 3.1; Table 3.1 & A.3). Samples were collected around 12:00 GMT+2 between July 2nd and July 4th 2013 onboard MV Stålbas. Seawater was collected using a 10-L Niskin bottle (KC Denmark A/S, Denmark). The 4 to 6 depths sampled were predetermined at each sampling location and are shown in Table A.3.

A B

Figure 3.1. (A) Geographical position of Svalbard and (B) of each site with high chlorophyll a values indicating a bloom: Hornsund (HOR), Storfjorden (STO) and Erik Eriksen Strait (EES).

Table 3.1. Geogaphical positions and the sampling day. At each sampling site, seawater sampling started c. noon local time. Sampling site abbreviations are given in brackets.

Sampling site Sampling day Latitude (°) Longitude (°) Hornsund (HOR) 2 July 2013 76.9768 15.7454 Storfjorden (STO) 4 July 2013 77.3242 19.9568 Erik Eriksen Strait (EES) 5 July 2013 79.1268 26.2333

Physicochemical and biological analysis

Samples for chlorophyll a concentration, as an estimate of phytoplankton biomass, were size fractionated with 3 replicates filtered directly onto GF/F filters (Whatman®, USA) and 3 replicates filtered onto 10 µm polycarbonate filters (Whatman®, USA) where 400 ml of seawater was filtrated for each replicate for each sample. Filters were wrapped in aluminium foil and stored at -80.0°C until chlorophyll a was extracted in methanol between 20 and 24 hours (Holm-Hansen and Riemann 1978). Chlorophyll a was measured using a 10-AU-005-CE fluorometer (Turner Designs, USA).

Samples for nutrients, nitrate NO3 + nitrite NO2, phosphate PO4 and silicate SiO2, were collected directly form the Niskin bottle with 100 ml of seawater per sample collected in acid-washed and sample rinsed bottles and stored at -20°C until analysis.

Samples were analyzed at Norsk Institutt for Vannforskning (NIVA; Norway) using international ISO standards NS-EN ISO 4745:1991 for Nitrate+nitrite, NS-EN ISO 16264:2004 for silicate and NS ISO 4724 for phosphate.

Hydrographic profiles of the water column of each sampling site were taken using a SD200 (SAIV A/S, Norway) conductivity temperature depth (CTD) profiler on the downcast. Data were transferred to computer using the software SD200W (SAIV A/S, Norway). Theta/S diagrams were visualized in Ocean Data View 4 (Alfred Wegener Institute, Germany) to identify and assign water masses following Cottier et al. (2005; Table 1.2).

Flow cytometry (FCM) analysis

Duplicate samples of 1,8 ml of seawater were fixed with 36 µl of 5% EM-grade glutaraldehyde and stored in liquid nitrogen before being transferred in -80°C freezer (Marie et al. 1999). FCM samples were analyzed at the University of Bergen (Norway) on a FACS Calibur (Beckton Dickinson, USA). Chlorophyll-containing picoeukaryotes, nanoeukaryotes other than cryptophytes, cryptophytes and Synechococcus cells were separated. Quantification for the phytoplankton and bacteria were done according to Marie et al. (1999) and heterotrophic organisms as Zubkov et al. (2007). Discrimination between bacteria and protists was performed using dot-plots from the side-scatter signals against fluorescence on FlowJo software (Tree Star Software, United States; Nordgård 2014).

DNA and RNA extraction and conversion to cDNA

Approximately 4 L of seawater were collected from the Niskin bottle into containers. Water was then poured through a 10 µm mesh before being filtrated onto 0.45 µm polycarbonate filters (Millipore, Germany) using a vacuum pump. Filters were cut into two and each half was stored in separate cryotubes, for eventual analysis of the rRNA gene and for RNA converted to cDNA to detect rRNA. For RNA tubes 600 µl of RNAqueous Lysis and Binding Buffer (Ambion®, United States) was added to each tube. All filters were stored at -80°C until extraction.

Nucleic acids, DNA and RNA, were extracted from the filters using the commercial kit DNeasy Plant Mini kit (Qiagen, Germany) and the RNAqueous® Kit (Ambion®, United States) respectively, with minor modifications as follows. Each sample was beaten twice with 300 mg of 200 µm molecular biology grade zirconium beads (OPS diagnostics, United States) with additional buffer using a MM301 Mixer Mill (Retsch, Germany) to break the cells. rRNA gene samples were beaten at 30 Hertz for 1 minute while RNA samples were beaten at 22 Hertz for 1 minute.

Contamination and extraction success were checked by gel electrophoresis following a Polymerase Chain Reaction (PCR) with the primers forward Short28SF and reverse Short28SR (Vestheim & Jarman 2008). DNA extraction was successful for all stations.

For each RNA sample, elimination of DNA was performed using Turbo DNA-freeTM kit (Ambion®, United States). cDNA was synthetized using a two-steps PCR approach with the RETROscript® Kit (Ambion®, United States). Both kits were used according to the manufacturers’ protocol without modification.

Illumina library preparation

The rRNA and rRNA gene amplicon libraries for Illumina sequencing were prepared using an adapted protocol developed by Mundra et al. (2015) at the University Centre in Svalbard (Norway). First, the V4 region of 18S rDNA was amplified using PCR with the 5’ phosphorylated E572F (forward) and E1009R (reverse) primers. 1X

DreamTaq Buffer, 0.2 µM of each primer, 1 µM dNTPs and 0,5U of DreamTaq enzyme were prepared for each sample. Two µl of template of DNA or cDNA were used and Milli-Q water for 25 µl reaction. For DNA based samples, the PCR program was executed on a GeneAmp®PCR System 9700 (Applied Biosystems, United States) included an initial denaturing step at 95°C for 120s, 28 amplification cycles at 95°C for 30s, annealing at 53°C for 30s, and extension at 72°C for 60s with a final extension at 72°C for 30 minutes. For cDNA based samples, the PCR program executed 15 amplification cycles.

Secondly, PCR products were purified using solid-phase reversible immobilization (SPRI) method with 1 volume of 1% SPRI solution. In this step, DNA is attached to the beads.

Third, adaptors were ligated to DNA of SPRI cleaned PCR products with 0.75 M pre- hybridized adapter, 1X T4 DNA ligase buffer and 0,2 U of T4 DNA ligase. Solutions were adjusted to 25 µl with Milli-Q H2O and incubated at 20.0°C in the thermocycler for 20 minutes.

Fourth, solutions were purified a second time using SPRI. They were mixed with 1 volume of 20% PEG+ 2.5M NaCl. After the cleaning, 19.5 µl of Tris and 0.5 µl of USER enzyme were added. Each sample was incubated at 37.0°C for 15 minutes before being held at 65.0°C for 5 minutes. In this step, DNA is released from the beads and only the supernatant is kept for further processing.

A quantitative PCR (qPCR) was performed for 12 cycles for a final enrichment step to the amplicon libraries. 1X Q5 Buffer, 0.5 µM of each Illumina adaptors PE PCR Primer 1.0 and PE PCR Primer 2.0, 0.332 µM of dNTPs, 0.01 U Q5 polymerase were mixed with 10 µl of DNA template and Mili-Q water for a 25 µl total volume. A GelAnalyzer (GelAnalyzer, USA) was used to quantify amplicons. All samples were adjusted to the same concentration of DNA. If needed, extracts were precipitated to increase DNA concentration using 0.1 volume of 3M pH 5.2 sodium acetate in addition to cold 96% ethanol. Samples were stored at -20.0°C overnight before being centrifuged for 30 minutes at maximum speed at 10°C. Once the supernatant was

discarded, pellets were washed with 70% ethanol before being suspended in Tris pH 8. Final DNA concentration was 22.7 µg µl-1.

The rRNA gene amplicon libraries for samples FRA 150m, FRA 470 m and ISA 5m were not successful; other problems were encountered with the rRNA amplicon libraries. These included poor RNA extraction, cDNA synthesis or insufficient cDNA amplified and the affected samples were not available for further analysis. Samples used in this study are listed in Table B.1.

Amplicon libraries were sequenced at Laval University Institut de biologie intégrative et des systèmes (IBIS, Canada). Samples were cleaned with magnetic beads to remove remaining primers and/or primer dimers. All sequencing was carried out using MiSeq Illumina platform (Illumina, United States).

Bioinformatics

A customized bioinformatics pipeline using the open-source software Quantitative Insights Into Microbial Ecology (QIIME) was performed for paired-end Illumina read analysis. Forward and reverse reads were joined with a 100 bp overlap with a percentage of 40%. Sequences were de-multiplexed and quality filter threshold was established at 29, with a maximum consecutive number of bad quality bases of 5, and a maximum of 2 Ns allowed in a sequence to be retained. All singletons, a read clustering alone among all samples, were discarded and chimeras were identified and removed using USEARCH61 (Edgar 2010). Thereafter, OTUs were clustered using UCLUST with a similarity level of 0.98 (Edgar 2010). Sequences were aligned using PyNast (Carporaso et al. 2010) and assigned using mothur (Schloss et al. 2009) following a modified SILVA 18S rRNA reference database (Comeau et al. 2011, Monier et al. 2013) using PyNast (Carporaso et al. 2010). Fungi and Metazoan reads were removed. The phylogenetic tree was built using FastTree implemented in QIIME (Price et al. 2009). Rarefaction curves of the 35m depth samples from Storfjorden and Erik Eriksen Strait and the 5m depth sample from Hornsund demonstrated sufficient coverage of the diversity (Figure B.1).

Phylogenetic analysis of Pelagophytes

The pelagophyte OTUs from Hornsund were investigated using Evolutionary Placement Algorithm (EPA; Stamakis et al. 2007). An alignment and tree was constructed using 58 nearly complete 18S rRNA sequences of cultured and uncultured pelagophytes retrieved from NCBI, SILVA and PR2 databases. Results from 58 reference sequences were similar to the 25 reference sequences retained in Figure 3.5. Reference sequences are listed in Table B.2. Current taxonomic standing of the cultured pelagophytes was verified in AlgaeBase.org (Guiry et al. 2015).

Sequences were aligned using MUSCLE implemented in Molecular Evolutionary Genetics Analysis (MEGA) 6 software (Edgar 2004). A non-pelagophyte sequence of Pseudopedinella sp. (Yang et al. 2012) was used for rooting. The phylogenetic tree was built using RaxML v1.9 (Stamakis et al. 2005) and viewed in Dendroscope v3.0 (Hudson and Scomavacca 2012) before being re-rooted with Pseudopedinella sp. Then, the pelagophyte OTUs from the present study were placed in the phylogenetic tree with bootstrapping using the option –f v in RaxML-EPA (Stamakis et al. 2005).

Four clusters, labelled as clades A, B, C and D, were defined after visually inspecting the dendrograms. Clade D was divided in two subclades (Da and Db). Genetic distances of the intra- and interclades were calculated using the software MEGA 6 (Kumar et al. 2008).

Phylogenetic analysis of the Phaeocystales

Similar to the pelagophytes, Phaeocystales OTUs in Storfjorden were assigned to species level using EPA (Stamakis et al. 2007). According to Algaebase.org, Phaeocystales is an order composed of a single family (Phaeocystaceae) which contain a single genus: Phaeocystis. Phaeocystis is composed of 12 species (Medlin 2007). In order to determine the species, 18S rRNA gene sequences of cultured and uncultured Phaeocystis were retrieved from the NCBI. Eight species have 18S rRNA sequences available, Phaeocystis antarctica, P. cordata, P. globosa, P. jahnii and P. pouchetii (Hariot) Lagerheim (P. pouchetii) from NCBI and SILVA databases. Reference sequences are listed in Table B.3.

Results Hydrography, nutrients and chlorophyll a profiles

Hydrographic systems around Svalbard differed (Figure 3.2). Sea temperature in Hornsund, the southernmost of the western Svalbard fjords, varies between 1 and 2°C with a thermohaline weaker than other fjord sampling sites. (Figure 3.2, Table A.3). Down to approximately 25 m deep the halocline strengths the stratification where upper layer is most likely freshened to 31.09 due to runoffs and glacial melt. The salinity of the lower layer was greater (34.79) at 25 m depth suggesting the presence of TAW below the surface layer. The high concentration of chlorophyll a (10.23 µg L-1) revealed a bloom at 5 m depth (Figure 3.3). Larger than 10 µm size cells contributed less than 5% of the total phototrophic biomass underlining the prevalence of 0.45-10 µm size primary producers.

Storfjorden was also strongly stratified with a thermocline, halocline and nitracline from 15 and 35 m. Water temperature and salinity above the stratification were typical of SW with 3.74°C and 34.14 respectively at 5m. Below, the water mass characteristics were associated to the TAW with a high salinity (34.72 at 35 m depth) and sea temperature varied between 1 and 2°C. While nutrients were depleted at the surface, their concentrations increased up to 4 fold at the deepest layer. High chlorophyll a concentrations (7.87 µg l-1) suggested a bloom in progress at 35 m depth. The >10 µm size relative contribution to the phototrophic biomass was 22%, higher than in Hornsund (Figure 3.3).

The easternmost sampling site, in the Erik Eriksen Strait, had a pronounced thermocline at 20-25 m depth and weaker thermal stratification between 75 and 100 m. A halocline was absent and salinity gradually increased from 34.23 to 34.71 with depth. The surface layer (SW) with a sea temperature of 2.03°C was clearly separated from an intermediate layer located between 25 and 75 m that was associated to ArW identified by its colder and fresher water (-1.63°C at the 35 m depth). Below, sea temperatures increased to -0.82°C. This increase may have been due to either a cooled TAW or a TAW mixed with ArW, forming an IW. The nitracline was located between 35 m and 150 m where nutrient concentrations in the deepest layer were up to 12 times the concentrations in the surface layer.

Figure 3.3. On the left, total chlorophyll α concentration (upper light green bar), chlorophyll a concentration of >10 µm size cells (lower dark green bar) and molar concentrations of nutrients (dashed lines for Silicate, yellow; Phosphate, blue; Nitrite+Nitrate, red) at Hornsund, Storfjorden and Erik Eriksen Strait. On the right side are cell ml-1 log transformed concentrations of picoeukaryotes, nanoeukaryotes, cryptophytes and the cyanobacteria Synechococcus at the same sites.

Finally, FCM analyses demonstrated higher concentrations of picoeukaryote cells at the surface. Nanoeukaryote cell concentrations were lower in the surface with the greatest concentrations at 35 m. The concentrations below 75 m depth were lower. In contrast to the communities in Storfjorden and in Erik Eriksen Strait, Synechococcus concentrations in Hornsund were high. At the two other sites, cynanobacteria concentrations were negligible except at 75 m in Erik Eriksen Strait and at 15 m in Storfjorden (Figure 3.3).

Community composition and structure

DNA based communities

The rRNA gene based community composition and structure differed in the three fjords and at the different depths. Where chlorophyll a concentrations were low, the most abundant reads belonged to dinoflagellates. The relative abundances of dinoflagellate reads per library ranged from 60% at 5 m in Storfjorden to 97 % at 15 m depth in Hornsund (Figure 3.4). Reads associated to the MALVs were particularly abundant in the deeper samples of Erik Eriksen Strait while Picozoa were observed throughout most of the water column in Storfjorden and in Erik Eriksen Strait (Figure 3.4, Table B.4).

In Hornsund, the surface (5 m depth) was dominated by an unidentified pelagophyte with a relative read abundance of 66% of total reads (Figure 3.4). At 15 m depth, the relative abundance of the pelagophyte was reduced to 3%. Dinoflagellates, especially reads assigned to Gyrodinium fusiforme, Gyrodinium helveticum and Gyrodinium rubrum accounted for the highest proportion of reads (not shown).

In Storfjorden, the surface water community consisted mostly of Phaeocystaceae (17% of total reads) and dinoflagellates (61%). Along with the higher chlorophyll a concentrations, the relative abundance of reads associated with Phaeocystaceae accounted for up to 73% and 66% of total reads at 35 and 75 m depths, respectively.

Microbial communities from Erik Eriksen Strait, the easternmost study site, were dominated principally by Gyrodinium and a strong representation of MALV I throughout the water column. In addition, a single abundant prymnesiales OTU retrieved with higher relative abundances in the rRNA based library of Erik Eriksen Strait at 5 and 15 m depths was identified as the harmful algae bloom (HAB) Chrysochromulina leadbeateri using BLASTn (100% similarity). Although this species was present in the surface sample, its contribution to the bloom at the 35 m depth was not established..

RNA based communities

The rRNA based communities were taxonomically different in the three bloom stations. The 5m depth bloom in Hornsund was predominantly Pelagophyceae (86%). Below this surface depth were mainly ciliates (17%), Phaeocystaceae (9%) and Pelagophyceae (7%). In Storfjorden, the bloom community at 35 m depth was mostly composed of Phaocystaceae (95%). In the Erik Eriksen Strait, the two upper communities with lower chlorophyll a values were dominated by ciliates and Prymnesiaceae (not shown). In addition, Chrysophyceae, Chlorophyceae Mamiellophyceae and MASTs were also abundant taxonomic groups. At the 35m depth, where the chl a concentrations suggested a bloom, the relative abundance of reads associated with Phaeocystaceae increased to 18% (Figure 3.4).

Phylogenetic placement of the Hornsund pelagophyte

The evolutionary tree constructed from reference sequences showed the two main divisions of Pelagophyceae; the Pelagomonadaceae and the Sarcinochryisidaceae.

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The Sacinochysidacea were further divided into 3 clades, including one with two subclades (Figure 3.5). Clade D was composed of reference sequences from the Arctic Ocean and the northern seas, Ankylochrysis lutea (RCC 286, Reeb et al. 2009) and a culture (CCMP2097) isolated from the Northern Baffin Bay (Terrado et al. 2012).

Within Clade D we detected high intraclade genetic distance separating into subclades Da and Db. The subclade Da was composed of uncultured pelagophytes with a higher similarity to Ankylochrysis lutea (Figure 3.5). The subclade Db grouped our OTUs 1445 and 734 retrieved in this project, an unnamed pelagophyte found in a bloom in Billefjorden (Svalbard) in 2011, the CCMP2097 culture and uncultured sequences from the Gulf of Finland and the Beaufort Sea.

The subclade Db showed a relatively small intraclade genetic distance (0.012). Nevertheless, the interclade genetic distances with the other clades were higher than the intraclade genetic distance. These results suggested the existence of a novel phylotype most likely endemic and pan-distributed in the Arctic (subclade Db).

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Table 3.3: Genetic distances within and between clades based on pairwise distances calculations established in Figure 3.5.

Clade Within Between B C Da Db A 0.017 0.043 0.046 0.054 0.025 B 0.03 0.044 0.070 0.046 C 0.009 0.063 0.042 Da 0.034 0.051 Db 0.012

Phylogeny of the bloom forming Phaeocystales in Storfjorden

To identify the Phaeocystis sp. that was present during the bloom in Storfjorden and in the Erik Eriksen Strait, sequences for P. jahnii, P. cordata, P. globose, P. pouchetii and P. antarctica were retrieved and a reference tree was constructed. Reads of the OTUs found in this study were more closely related to Phaeocystis pouchetii and an uncultured haptophyte sequences. The uncultured haptophyte and Phaeocystis reads were collected from Raunefjorden (Svalbard), Amundsen Gulf (Canada) and Beaufort Sea (Canada).

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Discussion

Microbial eukaryotes from Storfjorden and Erik Eriksen Strait

Hornsund, Storfjorden and Erik Eriksen Strait, had very different blooms. While high chlorphyll a concentrations indicating a bloom were detected in the 35 m samples in Storfjorden and in Erik Eriksen Strait, the bloom in Hornsund was found near the surface at 5m depth (Figure 3.3). We note that this is a single timepoint and sampling constraints prevented assessment of the temporal variations at the three sites. Blooms around Svalbard vary both inter and intra-annually, with variable succession of dominant phytoplankton (Eriksen 2010, Hodal et al. 2012, Rousseau et al. 2000, Wassmann et al. 1999). The blooms detected in Storfjorden, in Erik Eriksen Strait and in Horsund are discussed below.

Storfjorden

Phaeocystis pouchetii was identified through EPA in Storfjorden at the 35m depth (Figure 3.6), and reads assigned to Phaeocystaceae dominated both the rRNA gene and rRNA based libraries at this depth from Storfjorden (Figure 3.4). Phaeocystis pouchettii has also been identified from blooms in most of the regions around the archipelago of Svalbard including Hornsund, Billefjorden, Kongsfjorden, Erik Eriksen Strait and Storfjorden (Wiktor & Wojciechowska 2005), and contributed up to 50% of the total community during previous blooms in Storfjorden (Norrbin et al. 2009). Nevertheless, conditions and mechanisms in bloom dynamics of Phaeocystis remain unclear (Degerlund & Eilertsen 2010). Hodal et al. (2012) argued that the dominance of P. pouchetii during a bloom in Kongsfjorden could be associated with watermass intrusions affecting microbial community structure in different ways described below.

In Kongsfjorden, late intrusion of deep AW in 2002 inhibited the re-suspension of diatom resting spores to the water column and allowed Phaeocystis to become dominant (Hegseth et al. 2008). The water column in Storfjorden was mainly influenced by TAW (Figure 3.2). Therefore, timing in the water mass turnover during

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winter and spring may have had an impact on the prevailing organisms in the spring bloom also in Storfjorden.

Nutrient concentrations could also influsence the relative abundance of P. pourchetii. In this study, the water column of Storfjorden was strongly stratified (Figure 3.3), with nutrients concentrations up to four fold greater at the 35 m depth and below compared to surface samples (Figure 3.3). Diatoms would be limited by the low nutrients at 5m and 15 m in Storfjorden, where irradiance conditions were favorable. High latitude Phaeocystis increases its photosynthetic efficiency at low light intensities and would be able to access higher nutrients concentrations at depth giving it a competitive advantage (Palmisano et al. 1986, Eilertsen et al. 1989, Hegarty & Villareal 1998).

The single date and profile obtained in this study still provide some insight into the microbial community structures in Storfjorden. Nutrient concentrations were likely higher earlier in the season in the euphotic zone, and the community we sampled may be the result of a succession of species across the different bloom stages. During earlier stages, diatoms may have dominated and Phaeocystis took over when the nutrients became scarce as is often the case in the Barents Sea and around Svalbard (Wassmann et al. 1999, Rouseeau et al. 2000, Smith et al. 2003, Degerlund & Eilertsen 2010, Hegseth & Tverberg 2013). However, Phaeocystis can dominate also the early stages of the bloom under certain circumstances in Kongsfjorden and is thus not only found in late bloom scenarios (Hegseth & Tverberg 2013).

The July 2013 bloom in Storfjorden was similar to previous blooms around Svalbard and the Norwegian coastline. The different physical characteristics of Storfjorden such as the presence of polynyas and a bank, suggest additional research in this region of Svalbard would increase the understanding of mechanisms influencing Phaeocystis blooms.

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Erik Eriksen Strait

Similar to Storfjorden, a bloom was detected at 35m depth (Figure 3.3) in the Erik Eriksen Strait. However, the fractionated chlorophyll a data indicated that cells >10 µm accounted for over half of the total chlorophyll a biomass (Table A.3). By comparison, the >10µm size cells fraction represented 22% and 4% of the total chlorophyll a in the Storfjorden and Hornsund blooms respectively (Figure 3.3). However, the proportion of photosynthetic cells with a size >10µm may be greater as Booth & Horner (1994) demonstrated that large cells without rigid walls or needle- shaped frustules could pass through the filters.

As most of the eastern fjord hydrographic systems, little is known about microbial communities in the Erik Eriksen Strait. Diatoms dominated the upper 50 m of the water column in presence of cold and fresh ArW (Eriksen 2010) in May 2006, north of our study site. However Phaeocystis dominated a bloom and late-bloom stages in the study area at the end of July 2003 (Søreide et al. 2008).

In this study, haptophytes accounted for less than 1% rRNA genes in Erik Eriksen Strait (Figure 17, Table B.4). The bloom was very different from other communities throughout the water column and the 35m depth community was dominated by a Gyrodinium with over >50% of the reads. In contrast, in the rRNA based library, the relative abundance of the haptophyte reads was up to 29%: 18% associated with Phaeocystales and 11% to the other prymnesiales (Figure 3.4, Table B.4), while the relative abundance of Gyrodinium reads was 8% (not shown).

The most abundant OTUs associated with the Phaeocystales were identified as Phaeocystis pouchetii. This species was also recovered at the 35m depth in Storfjorden, and may have contributed to the bloom. Phaeocystis forms colonies under for example high organic nitrogen conditions (Sandersen et al. 2008) and may have been retained by the 10 µm filter and contributed to the large cell fraction, explaining the high concentration of larger size phototrophic cells biomass (Schoemann et al. 2005). However, colonies were not observed through an optical microscope in a phytoplankton net sample preserved in a mixture of lugols and

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glutaraldehyde (Tove M. Gabrielsen personal observation). As this study focused mainly on cell sizes between 0.45 and 10 µm, we were not able to positively indentify a particular species responsible for the bloom in the size range >10 µm. Optical microscopy of a sample from the 35 m in Erik Eriksen Strait also failed to show abundant diatoms nor abundant large dinoflagellates observed.

Previous studies the Erik Eriksen Strait were mainly focused on zooplankton or on the ratios of Phaeocystis to diatoms (Søreide et al. 2008, Eriksen 2010). While rRNA gene based libraries revealed low relative abundance of Phaeocystis, the use of rRNA based libraries demonstrated an active population of Phaeocystis at 35 m depth. However Phaeocystis relative abundance of rRNA sequences was much less than in Storfjorden where it reached up to 95% (Figure 3.4). The difficulty in identifyin a single major contributor to this bloom occurring in Erik Eriksen Strait highlights the need for further research on the development and scenescense of blooms.

Arctic pelagophytes: a new occurrence of a pan-Arctic species

To the best of our knowledge, this is the first study report a bloom of Pelagophyceae in the Arctic. Previous studies have reported it to be sporadically distributed in the Beaufort Sea (Suzuki et al. 2002, Balzano et al. 2012). Around the archipelago of Svalbard, in 2011, an unknown pelagophyte was reported from Billefjorden (Vader et al. unpublished). During a sampling cruise in 2011, the OTUs associated with the unknown pelagophyte retrived from Hornsund and Billefjorden was also identified in samples in Adventfjorden. Balzano et al. (2012) suggested the possibility of a new Pelagophyceae lineage as some OTUs clustered apart from the available reference sequences.

The evolutionary placement algorithm (EPA) allows the identification of short reads in a phylogenetic tree constructed using longer and/or full-length reference sequences (Berger et al. 2011). Two OTUs (OTUs 734 and 1445) clustered with the unknown pelagophyte sampled in Billefjorden in 2011 suggesting the occurrence of the same organism (Figure 3.5). Among the reference sequences were a culture of Pelagophyceae sp. CCMP2097 (Beaufort Sea, Canada), an uncultured stramenopile

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(Gulf of Finland, Finland) and an uncultured pelagophyte (Beaufort Sea, Canada). The clade 4a, a cluster of the closest similar sequences to the clade 4b, were also mostly originating from Nordic regions: from the Beaufort Sea (JF794050, JN934690) and from Svalbard (EU050972). The only identified organism in the clade 4a is associated to Ankylochrysis lutea (FJ973363).

The subclade Db had high values of inter-clade genetic distance with the other clades, even Da, and a low value of intra-clade genetic distance (Table 3.3). These estimates indicates that the ecotype/phylotype adapted exclusively to the Arctic Ocean and the Nordic seas, suggested by Balazano et al. (2012), could represent an entire clade.

The strong halocline separating the surface layer from the bottom layers, the low salinity (31) and the relatively high silicate value of 4 µmol L-1 in surface waters suggest a strong influence from the melting glaciers (Table A.3). While we do not have any measure of PAR the glacier melt may have reduced the light levels in the water column. The layer below was characteristic of TAW (Figure 3.3) and stratification was reflected by the microbial community, where the pelagophytes were restricted to 5m depth (Figure 3.4). Although Pelagophyceae includes species responsible for brown tides that have been reported from North America, Africa and Asia (Koch et al. 2013). The results from the EPA suggest a lineage of Pelagophyceae in the Arctic is not closely related. Further investigations are required to understand the causes and the potential impacts of pelagophyte blooms in the Arctic. This pelegophyte may have prolifereated under favorable conditions caused by the climate changes in this and other fjords around Svalbard (Figure 2.9; Marquardt et al. 2016, Vader et al. unpublished).

Conclusion

The first objective of the study was to characterize the overall pico- and nanoeukaryote communities associated with high chlorophyll concentations suggesting a bloom in the less studied areas of Hornsund, Storfjorden, and the Erik Eriksen Strait. The composition and the structure of the different blooms differed as did the environmental characteristics.

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The bloom that occurred in Hornsund was dominated by a pelagophyte phylotype with the most similar reference sequences available collected from the Arctic Ocean or northern seas; suggesting an endemic pelagophyte with a pan-Arctic distribution. Its occurrence near the surface in a low saline marine environment influenced by both the intrusion of TAW and the glacial melt suggest unusual drivers of its blooming conditions.

In Storfjorden and in the Erik Eriksen Strait, the haptophyte Phaeocystis pouchetii was present and active, especially in Storfjorden. In Erik Eriksen Strait, the low relative abundance of the Phaeocystis in the rRNA gene libraries in addition to a high contribution of cells larger than 10 µm to the estimated phototrophic biomass suggest more limited contribution to the observed bloom at 35 m depth. Given that diatoms were are also reported to bloom in the region, the Erik Eriksen Strait might be an useful location to variability of the dominance of Phaeocystis and diatoms.

Traditionally, either Phaeocystis and/or diatoms are thought to be the main bloom formers around Svalbard. Blooms of Pelagophyceae have often had negative impacts to the marine ecosystem in other areas of the world and therefore further attention should be given to the identification of this novel phylotype, its distribution and the conditions that will promote proliferation of the species.

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4. General Conclusions

Most previous studies of the microbial eukaryote communities around Svalbard have focused on just three fjord systems, Kongsfjorden and Isfjorden, with an emphasis on temporal variability. This research project was the first to examine the protist community composition using high throughput amplicon sequencing in more remote regions, such as along the eastern coast of Svalbard. The first scientific chapter provides a window into the biogeography of the microbial eukaryotes around the archipelago. The in-depth analysis suggested that local conditions, perhaps driven by dispersal within water masses, played a significant role in the occurrence of particular taxa. The project provides a baseline for future studies and valuable insight into how such studies could be achieved. The second chapter was based on the serendipity of detecting unusual blooms in three of the remote fjords, suggesting that the Phaeocystis – diatom dichotomy of spring blooms does not apply uniformly to all fjords or years. The timing and extent of these blooms requires further investigation if the Svalbard fjord system is to be understood for the purposes of predicting future changes and modeling food webs.

Design of the project: The sampling was performed over a 10 days expedition and of a combined terrestrial and marine project. The aim was to do a survey of microbial eukaryotes around Svalbard. The original cruise plan included 9 pre-determined marine sampling sites, one of these, at South Cape, was cancelled due to weather conditions. The sites were selected to enable parallel terrestrial investigations, which led to a number of logistic constraints for the marine operations. Nevertheless, these 8 sampling sites provided a non-trivial increase in the number of fjords sampled and increased the general knowledge of the microbial composition of poorly studied fjords of Svalbard. However, several regions in particular on the northern and eastern coasts were not possible to access within the available time. Thus, there was only limited sampling in water of Arctic origin and the lack of samples from these key regions means that a comprehensive overview of the microbial community composition and distribution in the complex hydrographic system of Svalbard is not complete. Although time and financial constraints would most likely prevent a single

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expedition covering all poorly studied areas in the near future, it should be a priority. Even if these other fjords were sampled separately such an effort would contribute to the baseline established here, provided future expeditions use similar molecular tools and approaches.

A positive outcome of this study was that some fjords showed similar microbial characteristics, such as Bockfjorden and Wijdefjorden. These similarities suggest that researchers around Svalbard could work in collaboration; selecting a representative fjord for monitoring and eventually build a network of key sampling sites to monitor the impact of climate change on the microbial communities around the archipelago.

The sampling was executed over a short time span and chlorophyll a concentrations showed that that some sites were at different bloom stages. High chlorophyll a values in the southeast of the sampling region in Hornsund, Storfjorden and Erik Eriksen Strait led to the third chapter. The aim was to characterize the different blooms. The use of high throughput sequencing and careful use and curation of a reference data base provided insight into taxonomy of the bloom species.

First, Hornsund hosted a potentially new arctic Pelagophyceae, the 18S rRNA reads enabled us to show how this pelagophyte was also previously found in the Canadian and Norwegian Arctic. This pan-arctic pelagophyte had not been recorded as a bloom species, and was consistent with the notion that bloom species maintain a presence within a rare biosphere and bloom when conditions for growth are optimal. Further attention should be then given to this organism as several representatives of the Pelagophyceae are responsible of harmful algal brown tide blooms in temperate and subtropical seas. Changing conditions around Svalbard in seawater temperatures and the sea ice extent induced by climate change may favor proliferation of pelagophytes in the near future.

Second, while Phaeocystis pouchetii was identified as the phytoplankton responsible for the bloom in Storfjorden, the identification of the bloom species in Erik Eriksen Strait remains uncertain. The size (0.45-10 µm) used for 18S rRNA sequencing may have been smaller than the dominant taxa, with chlorophyll concentrations higher in

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the >10 µm fraction. Although the purpose of this study was to monitor the 0.45 – 10 µm size cells, sequencing of DNA and RNA extracted from the 10 µm size filters could be useful for testing whether diatoms, Phaeocystis colonies or a dinoflagellate for example were abundant in Erik Eriksen Strait.

Third, the limited number of samples from high chlorophyll a regions meant that correlative studies between the bloom taxa and environmental parameters was not statistically robust. However, results suggested that blooms around the archipelago differed and further attention should be given to the environmental conditions associated with blooms in different fjords.

Finally, a complete understanding of phytoplankton blooms requires time series data. Fjords around Svalbard have been regularly reported to host a succession of phytoplankton from late winter to early summer. The unexpected occurrence of a pelagophyte bloom in Svalbard underlines the pertinence of monitoring potential bloom species in fjords other than Isfjorden and Kongsfjorden. The execution of such a plan requires collaboration with existing international groups based around Svalbard. For example, it could be useful to work with Polish researchers from their station in Hornsund to investigate if the pelagophyte bloom is an annual occurrence.

Fieldwork operations: Several issues were faced throughout the fieldwork operations. First, the depths were pre-selected (5, 15, 35, 75 and/or 150 m, bottom) for all sampling sites. The sites differed, and the fixed depths failed to capture some ecologically relevant physicochemical characteristics such as the depth of the nutricline, the halocline and the subsurface chlorophyll a maximum, which varied among the stations. Future investigations would be better if sample depths could be chosen on the downcast of the CTD to precisely target ecologically relevant depths, such as the halocline.

Such depth selection on the fly was not possible from the opportunity sampling vessel MV Stålbas. This vessel was not equipped with a CTD rosette system and in the absence of a geostationary system could not maintain position over the prolonged sample collection times. A single Niskin bottle was available for water collection and

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a separate wire cast was needed for each depth sampled. On average, even when sampling a relatively few shallow depths, it took over an hour to sample the five predefined depths. During this time the ship could drift and given the size of the fjords, the topography and the winds acting on water column structure within a fjord, the depth distribution of microbial communities was probably approximate. The lack of sampling integration also meant that the samples and physical profiles did not necessarily match. However, we feel confident that we were able to generalize the communities within the individual fjords despite potential bias from the adapted equipment.

The CCA explained approximately 60% of the variability of the microbial community composition, which is high for ecological studies. However, additional control of community structure by variables that were not measured may have increased the confidence levels in the CCA. For example, the PAR light sensor was unreliable over the duration of the cruise and the data could not be used. The timing of the sea ice brake out was also missing.

Molecular biology tools: A major contribution of this research was to carry out the first survey of microbial communities (0.45 to 10 µm) using the recently optimized molecular biology tools around Svalbard. The use of tag sequencing from reads amplified and sequenced on the Illumina platform yielded the relative abundance of the different taxonomic groups across the communities. These methods were used to identify organisms that are not easily cultured or are too small to be identified through optical microscopy. The use of rRNA and the rRNA gene as targets revealed different aspects of the communities. The rRNA sequences originated most likely from active organisms. For instance, several taxonomic groups of dinoflagellates, while being abundant in rRNA gene libraries, were almost absent from the rRNA libraries. Presence/absence in the rRNA libraries of OTUs detected in the rRNA gene libraries may give insights into the relative abundance of persistent vs the active cells for further investigations.

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Dinoflagellates, ciliates and chlorophytes were the major abundant taxonomic groups. However, some taxa, such as the dinoflagellates, may have been overestimated as they possess larger genomes than most of the eukaryotes. Larger genomes appear to be correlated with rRNA gene copy number, this problematic issue could eventually be resolved using a combination of microscopy and other molecular biology tools such as qPCR.

Spatial analyses: The taxa within communities were not randomly distributed across the sampling sites, and the results demonstrated that the relative abundance of the taxonomic groups was related to the origin of the water mass, but also to contemporary environmental factors. The phylogenetic information in the amplicons enabled the estimation of phylogenetic distances between the communities. In this perspective, the phylogenetic distances of the samples were more correlated to the origin of the water mass than the environmental conditions (Chapter 2).

However, the interpretation of the data requires caution. Although Mantel tests have been widely used to study the influence of factors on biotic composition, these tests should be limited to the analyses of the dissimilarities between sampling sites (Pierre Legendre personal communication). Therefore, the spatial variability of the microbial community around Svalbard remains uncertain.

Overall, we found that the fjords around Svalbard are influenced not only by either Arctic or Atlantic water masses, but in addition, by local factors, which vary among sites and depths. The importance of regularly monitoring the microbial communities around Svalbard is paramount since only by local sampling can the effects of global warming induced changes in the Arctic be assessed. As microbial eukaryotes play significant roles in the Arctic ecosystems, further studies should be focused on them.

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Appendix A. Supplemental Materials for Chapter 2

Figure A.1. Multiple rarefactions curves based on number of observed OTUs (Y axis) compared to the sequences per sample (X axis) for the rRNA gene-based libraries.

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Figure A.2. Multiple rarefactions curves based on number of observed OTUs (Y axis) compared to the sequences per sample (X axis) for the rRNA based libraries.

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rRNA gene based libraries rRNA based libraries Depth (m) HIN WIJ BOC FRAM ISA1 HIN WIJ BOC FRAM ISA1 5 20777 93362 41775 82648 1 86076 33600 18326 NA 342 15 7063 28289 74820 110464 11660 3173 15307 74820 9283 9516 35 119580 30145 78542 102804 22383 176 NA NA NA 17933 75 NA 20489 65441 NA 101750 NA 21650 NA NA 11006 150 13289 NA NA 0 NA 741 NA NA NA NA deep NA NA 46 NA 58 NA NA 2957 NA 1Sample depths at ISA differed slightly and were 5, 15, 25 and 60.

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Depth Hornsund Storfjorden Erik Eriksen Hinlopen Wijdefjorden Bockfjorden Fram Strait ISA (m) Strait Strait 5 385,128 193,NA 395, 501 354,499 623,613 403,283 643,NA NA,NA 15 104,NA NA,NA 490, 242 237,257 419, 347 474, 512 610, 368 280, 323 35 246, NA 198, NA 213, 350 481, NA 353, NA 574, NA 797, NA 453, 435 75 394, NA 377, 410 595, NA 765, 423 150 136, NA 906, NA 295, NA NA, NA Deep 370, NA 322, NA NA, 321

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Table A.3. Physical and biological variables from each station and depth sampled around Svalbard in July 2013. Nutrient concentrations (NO3+NO2, PO4 and SiO2), water mass indentification follows Cottier et al. (2005) see text for abbreviations, temperature, salinity, chlorophyll a >10.0 µm (Lchl) and total chlorophyll a (Tchl). Maximum depth at each station is indicated in brackets.

Date Sampling Site Depth PO4 NO2+NO3 SiO2 Water Temperature Salinity Lchl Tchl (max. depth) (m) (µmol/L) Mass (°C) (psu) (µg/L) 2 July Hornsund 5 0.084 0.86 3.73 SW 2,60 31,09 0,4192 10,2333 (192 m) 15 0.11 1.57 1.18 IW 1,79 34,25 0,1608 0,4375 35 0.17 3.86 1.62 TAW 2,20 34,73 0,2391 0,5608 75 0.25 7.85 3.32 0.2391 0.3025 150 0.27 8.57 4.33 LW 0,93 34,79 0,0346 0,0981

4 July Storfjord 5 0.04 0 0.52 SW 3.74 34.14 0.0545 0.2440 (85 m) 15 0.03 0.14 0.45 SW 3.56 34.15 0.2783 0.9208 35 0.21 6.50 1.97 TAW 1.56 34.72 1.7466 7.8667 75 0.24 7.50 2.58 TAW 1.53 34.90 0.6644 NA

5 July Erik Erikssen 5 0.04 0.14 0 SW 2,03 34.23 0.0255 0.1721 Strait 15 0.04 0 0 ArW 0,74 34.24 0.0422 0.2052 (240 m) 35 0.07 0.86 0.88 ArW -1,63 34.39 6.7833 11.317 75 0.15 4.43 1.73 0.1881 0.3330 150 0.26 10.0 4.43 ArW -0,08 34.70 0.0545 0.0736 240 0.20 5.78 3.43 ArW -0,82 34.71 0.0404 0.0561

6 July Hinlopen Strait 5 0.06 0.36 1.40 SW 1.56 33.31 0.0333 0.2501 (378 m) 15 0.08 0.21 1.82 SW 1.02 33.94 0.0633 0.2567 35 0.15 1.78 1.92 TAW 1.62 34.50 0.1282 0.5192 75 0.14 1.78 1.25 TAW 1.92 34.75 0.3217 0.8342 150 0.21 4.71 2.35 TAW 1.98 34.94 0.2900 0.5200 370 0.23 5.78 3.20 LW 0.06 34.88 0.0898 0.3292

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7 July Wijdefjord 5 0.07 0.21 3.03 SW 3.71 33.49 0.0621 0.7608 (115 m) 15 0.07 0.29 1.65 IW 2.31 34.25 0.0825 0.7983 35 0.08 0.50 1.3 IW 1.89 34.59 0.0513 0.2522 75 0.09 0.86 1.38 LW 0.71 34.85 0.0356 0.1648

8 July Bockfjord 5 0.08 0.07 4.13 SW 4.56 32.54 0.0946 1.1650 (82 m) 15 0.08 0.29 3.42 SW 2.45 33.74 0.0558 0.5792 35 0.06 0.14 1.72 IW 1.73 34.40 0.0277 0.1510 75 0.13 0.43 2.15 IW 2.16 34.73 0.0349 0.1336

9 July Fram Strait 5 0.09 1.78 1.25 AW 6.26 34,95 0,2013 1,1533 (>1000 m) 15 0.09 1.57 1.18 AW 6.27 34,94 0,1956 1,0817 35 0.17 4.85 2.72 AW 5.81 35,05 0,4017 3,8667 75 0.22 7.78 3.75 150 0.26 10.70 4.78 AW 4.34 35,14 0,0677 NA 470 0.26 10.70 4.85 AW 2.32 35,11 NA 0,1881

10 July Adventdfjorden 5 0.06 0.14 2.37 SW 6.30 31.47 0.2134 1.2117 (88 m) 15 0.09 0.07 2.52 SW 5.07 33.10 0.4842 2.8667 25 0.08 0.07 1.52 SW 3.98 33.72 0.1109 0.9933 60 0.09 0.43 1.13 IW 1.54 34.38 0.0607 0.2263

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Sample Ciliates Dinos MALVs Phaeo Prymn Picozoa Cercozoa Chloro MASTs Pelago Other HIN 5 m 0.062 0.735 0.031 0.053 0.007 0.021 0.039 0.027 0.004 0.002 0.017 HIN 35 m 0.045 0.561 0.186 0.007 0.003 0.002 0.145 0.024 0.004 0.000 0.024 HIN 150 m 0.018 0.203 0.506 0.003 0.008 0.011 0.140 0.050 0.022 0.000 0.038 HIN 240 m 0.016 0.777 0.042 0.008 0.008 0.015 0.074 0.006 0.003 0.000 0.050 WIJ 5 m 0.099 0.376 0.025 0.032 0.012 0.042 0.056 0.272 0.016 0.001 0.068 WIJ 15 m 0.170 0.500 0.042 0.058 0.011 0.041 0.018 0.076 0.013 0.000 0.070 WIJ 35 m 0.072 0.624 0.171 0.004 0.003 0.003 0.081 0.023 0.003 0.000 0.016 WIJ 75 m 0.138 0.356 0.166 0.031 0.027 0.050 0.090 0.061 0.023 0.000 0.060 BOC 5 m 0.142 0.520 0.014 0.002 0.004 0.012 0.012 0.272 0.001 0.000 0.021 BOC 15 m 0.091 0.595 0.042 0.013 0.026 0.048 0.015 0.112 0.003 0.000 0.054 BOC 35 m 0.062 0.726 0.045 0.007 0.022 0.075 0.009 0.010 0.015 0.000 0.028 BOC 75 m 0.069 0.529 0.067 0.024 0.072 0.098 0.044 0.016 0.030 0.000 0.051 FRAM 5 m 0.031 0.772 0.037 0.023 0.012 0.006 0.015 0.020 0.018 0.000 0.067 FRAM 15 m 0.019 0.807 0.039 0.030 0.019 0.005 0.008 0.018 0.009 0.000 0.046 FRAM 35 m 0.028 0.563 0.128 0.035 0.011 0.011 0.009 0.035 0.008 0.000 0.173 ISA 15 m 0.035 0.604 0.024 0.004 0.008 0.009 0.035 0.024 0.011 0.225 0.020 ISA 25 m 0.027 0.696 0.039 0.007 0.017 0.047 0.019 0.013 0.021 0.081 0.033 ISA 60 m 0.060 0.520 0.044 0.037 0.023 0.084 0.019 0.036 0.037 0.080 0.059

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Sampling location + depth Laboea Novistrombidium Parastrombidinopsis Other HIN 5 m 0.010 0.022 0.001 0.030 HIN 35 m 0.000 0.034 0.004 0.007 HIN 150 m 0.000 0.010 0.005 0.003 HIN 240 m 0.000 0.006 0.006 0.004 WI J5 m 0.013 0.041 0.005 0.040 WIJ 15 m 0.109 0.012 0.007 0.042 WIJ 35 m 0.000 0.067 0.003 0.002 WIJ 75 m 0.000 0.104 0.021 0.013 BOC 5 m 0.053 0.030 0.007 0.052 BOC 15 m 0.033 0.009 0.021 0.028 BOC 35 m 0.001 0.005 0.042 0.014 BOC 75 m 0.000 0.021 0.034 0.014 FRAM 5 m 0.002 0.008 0.002 0.019 FRAM 15 m 0.001 0.004 0.002 0.011 FRAM 35 m 0.000 0.012 0.004 0.011 ISA 15 m 0.018 0.003 0.002 0.011 ISA 25 m 0.001 0.003 0.014 0.009 ISA 60 m 0.003 0.022 0.020 0.016

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Sample Gb Gf Gh/r Gg K/P K Ka N Other HIN 5 m 0.004 0.150 0.300 0.015 0.005 0.002 0.009 0.088 0.163 HIN 35 m 0.002 0.274 0.026 0.003 0.002 0.003 0.021 0.074 0.156 HIN 150 m 0.000 0.055 0.030 0.009 0.003 0.004 0.017 0.003 0.084 HIN 240 m 0.000 0.124 0.538 0.011 0.005 0.001 0.012 0.002 0.082 WI J5 m 0.008 0.032 0.068 0.006 0.013 0.003 0.018 0.058 0.171 WIJ 15 m 0.007 0.026 0.082 0.006 0.018 0.014 0.019 0.060 0.269 WIJ 35 m 0.007 0.319 0.034 0.017 0.003 0.007 0.060 0.031 0.146 WIJ 75 m 0.001 0.047 0.036 0.009 0.014 0.012 0.025 0.007 0.204 BOC 5 m 0.005 0.099 0.172 0.012 0.007 0.002 0.019 0.056 0.148 BOC 15 m 0.013 0.053 0.143 0.009 0.021 0.005 0.015 0.071 0.265 BOC 35 m 0.001 0.074 0.061 0.018 0.038 0.035 0.018 0.031 0.451 BOC 75 m 0.000 0.058 0.097 0.049 0.017 0.009 0.021 0.015 0.262 FRAM 5 m 0.084 0.163 0.266 0.002 0.004 0.000 0.013 0.121 0.117 FRAM 15 m 0.095 0.171 0.287 0.003 0.004 0.000 0.007 0.116 0.124 FRAM 35 m 0.051 0.113 0.139 0.002 0.010 0.005 0.010 0.040 0.192 ISA 15 m 0.049 0.059 0.182 0.007 0.015 0.001 0.021 0.098 0.172 ISA 25 m 0.045 0.041 0.183 0.016 0.017 0.003 0.043 0.102 0.246 ISA 60 m 0.009 0.027 0.098 0.014 0.017 0.013 0.022 0.032 0.287

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Table A.7. Relative abundance of reads assigned to the major abundant taxonomic groups (taxa covering >1% of the total rRNA- based community sequences) across the Hinlopen Strait (HIN), Wijdefjorden (WIJ), Bockfjorden (BOC), Fram Strait, (FRAM) and Adventfjoden (ISA) communities. Depth of sample in metres (m) indicated after the sample abbreviation.

HIN HIN WIJ WIJ WIJ BOC BOC FRAM ISA ISA ISA Taxa 5 m 15 m 5 m 15 m 75 m 5 m 15 m 15 m 15 m 25 m 60 m Ciliophora 0.225 0.168 0.241 0.286 0.119 0.299 0.277 0.223 0.191 0.166 0.203 Dinophyceae 0.459 0.295 0.255 0.243 0.412 0.373 0.330 0.306 0.335 0.415 0.375 MALVs 0.018 0.036 0.025 0.028 0.046 0.006 0.015 0.028 0.014 0.036 0.028 Choanoflagellida 0.002 0.037 0.007 0.010 0.062 0.000 0.002 0.006 0.007 0.014 0.020 Cryptophyta 0.015 0.008 0.040 0.047 0.001 0.013 0.032 0.028 0.009 0.009 0.004 Phaeocystaceae 0.076 0.094 0.050 0.087 0.043 0.001 0.017 0.079 0.007 0.028 0.050 Prymnesiophyceae 0.027 0.025 0.035 0.057 0.101 0.009 0.051 0.081 0.055 0.149 0.081 Picozoa 0.020 0.015 0.016 0.018 0.054 0.002 0.026 0.006 0.007 0.034 0.035 Cercozoa 0.009 0.064 0.008 0.008 0.017 0.002 0.004 0.010 0.067 0.028 0.015 Chlorophyceae 0.073 0.026 0.234 0.139 0.011 0.268 0.171 0.043 0.052 0.017 0.049 Chrysophyceae 0.018 0.053 0.006 0.004 0.004 0.012 0.017 0.005 0.011 0.011 0.018 Diatoms 0.015 0.016 0.016 0.010 0.003 0.003 0.023 0.038 0.007 0.011 0.009 MASTs 0.013 0.037 0.014 0.014 0.034 0.000 0.006 0.033 0.037 0.029 0.035 Pelagophyceae 0.005 0.067 0.001 0.000 0.001 0.000 0.001 0.002 0.186 0.021 0.017 Other 0.025 0.058 0.052 0.047 0.091 0.011 0.030 0.113 0.015 0.031 0.061

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Taxa Gf K/P Ka Ne Other HIN 5 m 0.240 0.009 0.005 0.016 0.188 WIJ 5 m 0.055 0.012 0.009 0.015 0.164 WIJ 15 m 0.046 0.012 0.005 0.008 0.173 WIJ 75 m 0.039 0.036 0.005 0.001 0.332 BOC 5 m 0.190 0.005 0.012 0.017 0.149 BOC 15 m 0.116 0.013 0.006 0.015 0.180 FRAM 15 m 0.061 0.013 0.008 0.029 0.195 ISA 15 m 0.061 0.016 0.017 0.032 0.209 ISA 25 m 0.041 0.031 0.024 0.021 0.298 ISA 60 m 0.038 0.024 0.015 0.008 0.290

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Surface Deep Taxonomic group Contribution % Contribution % Ciliophora 0.083 20.9 0.039 10.8 Dinophyceae 0.141 35.5 0.045 12.5 Chlorophytes 0.057 14.4 0.014 3.8 Pelagophyceae 0.029 7.2 0.022 6.1 Prymnesiales 0.024 6.1 0.037 10.1 Phaeocystales 0.018 4.6 0.007 1.9 Cercozoa 0.011 2.8 0.021 5.9 Picozoa 0.010 2.5 0.135 37.1 Cryptophytes 0.009 2.3 0.001 0.2 MALVs 0.007 1.9 0.038 10.5 MASTs 0.007 1.7 0.004 1.1

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Appendix B. Supplemental Materials for Chapter 3

Table B.1. Pelagophyte 18S rRNA sequences used for phylogenetic analysis: GenBank accession numbers, taxonomic identification and their original reference.

Accession number Taxonomic identification EF455763.1 Pelagomonas calceolata HM474466 Uncultured pelagophytes clone LC009879.1 Pelagomnas calceolata JQ781966.1 Uncultured stramenopile clone U14386.1 Pelagococcus subviridis U40927.1 Coccoid pelagophytes CCMP 1395 JF698769.1 Uncultured Aureococcus clone JQ420078.1 Aureococcus anophagefferens Q1-4 HQ710575.1 Chrysoreinhardia giraudii KF422611.1 Chrysoreinhardia sp. CCMP 2950 KP178600.1 Andersenia australica voucher CS-1115 KP178601.1 Andersenia nodulosa voucher CS-1113 HQ710573.1 Aureoumbra lagunensis CCMP 1507 KC581797.1 Uncultured Aureuoumbra clone KF899841.1 Pelagophyceae sp. CCMP 2135 KF422612.1 Chrysocystis fragilis CCMP 3189 KF422616.1 Sarcinochrysis sp. CCMP 770 FJ973363.1 Ankylochrysis lutea RCC 286 JF794050.1 Pelagophyceae sp. RCC 2040 EU050972.1 Uncultured eukaryote clone JN934690.1 Pelagophyceae sp. RCC 2505 EU247837.1 Pelagophyceae sp. CCMP 2097 FN690682.1 Uncultured stramenopile JF698781.1 Uncultured Pelagophyte clone HQ710562.1 Pseudopedinella sp. HSY-2011

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Table B.2. Phaeocystis 18S rRNA sequences used for phylogenetic analysis: GenBank accession numbers of the, taxonomic identification and oarignal reference.

Accession Taxonomic identification number AF163148.1 Phaeocystis jahnii KC488455.1 Uncultured haptophyte clone AF163147.1 Phaeocystis cordata EU127475.1 Phaeocystis globosa GQ118982.1 Phaeocystis globosa W07.009.01 AF182114.1 Phaeocystis pouchetii P360 AJ278036.1 Phaeocystis pouchetii P361 HM561167.1 Uncultured haptophyte clone CFL133R10 JF698991.1 Uncultured Phaeocystis sp. X77481.1 Phaeocystis antarctica SK23 X77477.1 Phaeocystis antarctica CCMP 1374 AM491019.2 Chrysochromulina parva CCMP 291

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Figure B.1. Multiple rarefactions curves based on the number of observed OTUs (Y axis) compared to the number of sequences per sample (X axis) for the rRNA gene-based libraries

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rRNA gene based libraries rRNA based libraries Depth (m) HOR STO EES HOR STO EES 5 78005 5085 41875 7195 NA 109774 15 2109 NA 68205 NA NA 6228 35 9526 9198 14499 NA 45687 NA 75 NA 42551 NA NA NA NA 150 2641 NA 75877 NA NA NA deep NA NA 48626 NA NA NA

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Table B.4. Relative abundance of reads assigned to the major abundant taxonomic groups Ciliophora, Dinophyceae (Dinos), Marine Alveolates (MALVs), Phaeocystaceae (Phaeo), Picozoa and Pelagophyceae (Pelago; taxa covering >1% of the total rRNA gene-based community) across the Hornsund (HOR), Storfjorden (STO) and Erik Eriksen Strait (EES) communities. Depth of the sample is given after the sampling site abbreviation.

Taxa Ciliates Dinos MALVs Phaeo Picozoa Pelago Other HOR 5 m 0.041 0.271 0.004 0.003 0.002 0.665 0.015 HOR 15 m 0.008 0.969 0.004 0.001 0.000 0.012 0.006 HOR 35 m 0.062 0.846 0.018 0.025 0.002 0.030 0.016 HOR 150 m 0.003 0.937 0.041 0.003 0.002 0.000 0.014 STO 5 m 0.025 0.606 0.016 0.168 0.097 0.001 0.087 STO 35 m 0.041 0.157 0.002 0.732 0.021 0.000 0.047 STO 75 m 0.061 0.185 0.018 0.665 0.019 0.000 0.051 EES 5 m 0.026 0.876 0.034 0.001 0.004 0.001 0.060 EES 15 m 0.078 0.717 0.051 0.003 0.012 0.003 0.136 EES 35 m 0.013 0.923 0.004 0.005 0.000 0.016 0.039 EES 150 m 0.034 0.717 0.122 0.002 0.011 0.001 0.113 EES 370 m 0.134 0.453 0.196 0.001 0.026 0.001 0.189

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HOR 5 m STO 35 m EES 35 m Taxonomic group D R D R D R Ciliophora 0.041 0.034 0.041 0.006 0.013 0.179 Dinophyceae 0.271 0.074 0.157 0.026 0.923 0.371 MALVs 0.004 0.000 0.002 0.000 0.004 0.000 Cryptophytes 0.000 0.002 0.000 0.000 0.000 0.041 Phaeocystaceae 0.003 0.005 0.732 0.948 0.005 0.178 Picozoa 0.002 0.000 0.021 0.000 0.000 0.000 Prymnesiophyceae 0.000 0.001 0.000 0.000 0.000 0.106 Pelagophyceae 0.665 0.860 0.000 0.000 0.016 0.001 Other 0.015 0.024 0.047 0.019 0.039 0.124

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