Evaluation of Green Microalgae Biodiversity in the Alpine Ecosystem Adeline Stewart

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Evaluation of green microalgae biodiversity in the alpine ecosystem Adeline Stewart

To cite this version:

Adeline Stewart. Evaluation of green microalgae biodiversity in the alpine ecosystem. Vegetal Biology. Université Grenoble Alpes [2020-..], 2021. English. NNT : 2021GRALV012. tel-03261557

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Présentée par Adeline STEWART

Thèse dirigée par Eric MARECHAL, DR1, CNRS, et codirigée par Eric COISSAC, MCF, UGA et par Jean-Gabriel VALAY, PR, UGA préparée au sein du Laboratoire d’Ecologie Alpine et au Laboratoire de Physiologie Cellulaire et Végétale avec le soutien de l’unité mixte de service Lautaret dans l'École Doctorale de Chimie Sciences du Vivant

Evaluation de la biodiversité des microalgues vertes dans l'écosystème alpin

Thèse soutenue publiquement le 3 Mars 2021, devant le jury composé de :

Madame, Christelle BRETON Professeur, Université Grenoble Alpes, Présidente du jury Madame, Stéphanie MANEL Professeur, EPHE, Rapportrice Madame, Yonghua LI-BEISSON Directeur de recherches, CEA Cadarache, Rapportrice Monsieur, Stéphane RAVANEL Directeur de recherches, INRAE, LPCV, Examinateur Madame, Nathalie SIMON Maître de Conférences, Sorbonne Université, Examinatrice Madame, Christiane GALLET Professeur, Université Savoie Mont-Blanc, Examinatrice

Acknowledgements

There are many people who have made this possible, and I can not thank them enough. First and foremost, I want to thank my amazing thesis jury: the thesis reviewers, Yonghua Li- Beisson and Stéphanie Manel, and the jury members, Christelle Breton, Natalie Simon, Stéphane Ravanel and Christiane Gallet. Thank you for accepting to review this work and for accepting to be present at my PhD defense, physically or virtually, especially in these complicated times. I look forward to our exchange; it will be an honor to discuss this project with you.

Second, I want to thank the three most important people for this project, as they created it, my three PhD directors: Eric Maréchal, Eric Coissac and Jean-Gabriel Valay. You have taken me to a completely new level in science. You greatly encouraged me to participate in many scientific activities outside of experiments, which I am very grateful for, as it exposed me to so much I never thought I would love. Beyond science, all three of you have taught me very valuable life lessons which have made me that much stronger. Thank you for trusting me with this amazing project. I want to give an extra thanks to Eric Maréchal, who has given so much to this project, he was the leader it needed, the one admittedly everyone relied on, and who had a hand in every single part of this project and my scientific education during my thesis.

I also want to thank the IDEX Glyco@Alps, who financed this project. Beyond that, Glyco@Alps meant much more to me, it was like a family, it made me feel surrounded and supported. I am grateful for the so many wonderful opportunities that a PhD student rarely gets anywhere else, and the chance to get to know scientists from all different backgrounds and places. I especially want to thank Ferielle, Anne and Christelle, who are at the heart of Glyco@Alps. I also want to thank all the other PhD students involved, especially Marie, Rubal, François and Juliette; your friendship has meant more to me than you know, you were there during difficult times, especially in the beginning, and you got me through, thank you so much.

A warm thank you to Isabelle Domaizon and Frederic Beisson, for participating in both of my thesis committees (CSI) and for all the help and advice they have given me. I want to thank Isabelle especially, for the lake DNA samples and a very interesting collaboration on that study.

I have also had help from the ISTerre platform for snow composition analysis with the wonderful Delphine Tisserand and Sarah Bureau. Thank you so much for this collaboration, I learned so much with you! I also had the pleasure of working with the MeteoFrance Centre d'Études de la Neige with Marie Dumont during sampling. I want to thank her for her advice and time, and for coming with us sampling snow algae, it was an enriching experience and collaboration.

I want to thank everyone at LECA for making my time at the lab vividly memorable. The LECA is full of interesting personalities, discovered during long conversations in the break room and during runs on the trails at lunch. I thank Marc, Irene, Amélie, Céline, Fred, Monica, 2

Julien, Marius, Sylvie and Clément for their precious help and sympathy. Thank you so much to Delphine Rioux and Christian Miquel for their help on métabar experiments! I am grateful to my intern Auria Kallend, it was amazing to work on metabarcoding data with someone so efficient and that has her own ideas despite being still at the beginning of her science career. Her work and the help of Aurélie lead to Chapter 7 happening, so thanks to the both of you! Aurélie Bonin is one of my ultimate favorite people from LECA. I am so grateful for the help you gave me, I won’t forget it! I am also very grateful to François Pompanon, our lab director, for his enthusiasm and much needed help when I came to talk about projects and other matters. I have great respect for him as a labhead, a PI and a teacher. I also want to thank Stephane Reynaud, whose support was very deeply appreciated. Good luck being the new lab director! I also thank Florence, Agnès, Delphine and the rest of the admin staff who have come and gone, you make everything we do possible, thank you so much for everything! At LECA, I found some remarkable friends: Marie Usal, Joaquim Germain, Laure Denoyelle and Charlotte Her. I don't think I would've made it without you guys, you were and still are, my biggest sources of comfort and help at work and outside. You guys are my friends for life now, so I hope you are not tired of shameless Harry Potter references and non-stop nagging to go for runs with me.

I want to thank everyone at LPCV. I will start with Melissa Conte, Alberto Amato and Etienne Deragon, who always got on board with my ideas and are probably the nicest people one can ever meet. You are the real MVPs of this project, experimentally. Thank you so much to Juliette Jouhet, our fearless team leader, a wonderful person; Big thanks to Dimitrios Petroutsos for help with NPQ experiments and for your kindness, encouragements and advice for my science career! Thank you to Giovanni Finazzi and for all the photosynthesis and light- stress related experiments; Thanks to Sassia, Valérie, Juliette Salvaing, Denis, Fabrice, Claude, Catherine, Mathilde for all your wonderful help on various things, thanks to Véronique for the FACS experiments, and Marcel Kuntz for the HPLC pigment experiments. And thank you to everyone else at PCV! You are all incredible people, you are a big part of the reason PCV is one of the best labs, and I will miss you all greatly. I want to give huge thanks to the PCV staff Tiffany, Sophie and Alexandre, you already know this but you are amazing, nothing would get done without you! I have made some wonderful friends here, too. Stéphanie, Nolwenn & Greg especially, but also Sebastien, Cecile, Natacha, Chrispi, Benoît… you were always there when I needed it the most and I won’t ever forget that.

I want to thank the SAJF –now Jardin du Lautaret- team for all the help with the sampling campaign, especially Pascal, and with help organizing events. I also want to thank Rolland for trusting me to give plant biology classes to his students, it was a very rewarding experience!

I had the amazing opportunity to go to the Flora Course at the Roscoff Marine Station, and I want to thank all the teachers and the other students there. It was an unforgettable adventure that I cherish and will never forget. I also want to thank the ‘R in Grenoble’ team, for moments of pure R coding joy, so much support and super interesting R sessions. On the same note, I want to thank Dr. Marc de Boissieu, the organizing team of the Scientific Game Jam and its participants. It was my favorite event, I met so many wonderful people there, it was a true highlight of my thesis.

I also want to thank all the teaching and admin staff at Université Grenoble Alpes for an incredible, rewarding experience. I especially want to thank Dr. Corinne Mercier, a true source of inspiration. She introduced me to parasitology and Toxoplasma gondii, my first love in science, which lead me to study lipids and plants. Thank you so, so much for everything you do! In the same line, I want to thank my previous supervisors during my Master's because they were also incredibly inspiring and contributed to my career so much. Dr. Yoshiki Yamaryo- Botté for her contagious love of plant lipids and her hours-long presentation preparation sessions, Dr. Cyrille Botté for trusting me, training me, providing a really wonderful working environment, and for the most helpful presentation and writing advice. I can't even begin to explain how much everything you two did means to me and I miss the lab so much! I also want to thank David from the apicolipid team, for the best conversations and for introducing me to my favorite podcast, This Week in Virology. Massive thanks to Svetlana Artemova and Anne-Sophie Silvent from CHUGA and TIMC, for all the support and for the incredible learning experience you’ve given me. I will never forget that all of you have contributed to building the scientist that I am today, and I am forever grateful to you.

On a more personal note, I want to thank my other IAB and non-lab friends Margot, Laurine, Mél, Emma, Clém, Jean-Pierre, Christophe ‘JM’ and ‘les Jeans’, Mathieu, Sinan, Jerôme, Razak, Benoît & Emna, Benoît ‘le paladin de Dlul’ & Caro, Alicia, and Marine. I also want to mention my forever best friend Marie, and her family, my second family, Bernadette, Pierre, and everyone in the Carron family. I thank my family (my sister Ludivine, my parents, Christian, Elisa, Annabelle, Timo, little Alexandra, my grandparents and my wonderful family in law) for helping me proofread my thesis (and enjoying the science more than I thought you would!), and/or for the huge support throughout this adventure, and in life in general. I especially thank my mom who contributed the most to my education as I was a homeschooled kid. Last but not least, I want to thank my husband, Dr. Mashal Ahmed. Mash, you’ve been there through it all. You are the most wonderful human I’ve ever met, my biggest inspiration, my whole world.

Résumé de la thèse en Français

Les algues regroupent des organismes photosynthétiques divers, procaryotes et eucaryotes, peuplant la quasi-totalité des milieux, des eaux douces et océans, aux sols, surfaces rocheuses, neiges etc. Parmi celles-ci, les algues vertes constituent un groupe particulièrement répandu et divers dans les écosystèmes aéro-terrestres. Les algues sont des producteurs primaires à la base des réseaux trophiques et peuvent jouer un rôle pionnier dans la conquête de milieux. L’impact du changement climatique sur les milieux de montagne est marqué par le raccourcissement de la saison froide, la baisse des niveaux des lacs, la fonte des glaciers, et de nombreux bouleversements environnementaux. Il est attendu que l’augmentation du CO2 atmosphérique ait un impact positif sur la prolifération de microalgues, que l’on peut de ce fait considérer comme des marqueurs du changement climatique. Puisque les microalgues accélèrent la fonte des glaciers, elles sont aussi des acteurs des changements environnementaux engagés. Notre compréhension de l’évolution des écosystèmes alpins est néanmoins limitée par notre manque de connaissance de la microflore photosynthétique. La biodiversité des Chlorophyta a été examinée par des analyses de metabarcoding. Les Chlorophyceae étant la classe la plus importante et diverse, nos études se sont focalisées sur ce groupe. Dans un premier temps, deux nouveaux marqueurs des Chlorophyta et des Chlorophyceae, Chlo01 et Chlo02, ont donc été conçus et validés sur une sélection d’algues marines de la Roscoff Culture Collection. Ces marqueurs ont été exploités pour analyser des échantillons de sols collectés sur des gradients altitudinaux, au niveau de cinq sites différents des Alpes Françaises, de 1000 à 3000 m. Notre analyse montre une faible présence d’ADN de Chlorophyta dans le sol, celui-ci étant plutôt dominé par les Trebouxiophyceae. Plusieurs facteurs environnementaux, dont l’altitude était le plus important, affectent la distribution des algues au niveau de l’espèce et du genre. Nous avons ensuite évalué la biodiversité des algues vertes dans les lacs alpins. Les lacs présentent une biodiversité riche, avec une plus grande proportion de Chlorophyceae et de Trebouxiophyceae que d’Ulvophyceaea. Nous avons ensuite fait le même type d’analyse sur un site de « neige rouge » à 2500 m d’altitude. Ce site présentait quasi-exclusivement des Chlorophyceae tandis que le sol environnant était dominé par les Trebouxiophyceae. Le genre Sanguina était abondant dans les échantillons de neige, confirmant nos analyses microscopiques montrant la présence de formes enkystées pigmentées. Le sol contenait très peu d’ADN de Sanguina, suggérant que ce genre ne s’y divise pas activement. Enfin, des échantillons d’algues collectées de 2017 à 2019 dans des neiges rouges dans la région du col du Lautaret ont été mises en culture au laboratoire puis isolées et axénisées. Elles présentent une diversité génétique inattendue, mais aussi une diversité morphologique et physiologique. L’algue de neige la plus connue, Chlamydomonas nivalis, n’a pas été détectée dans les échantillons collectés, mais une lignée de la collection de l’Université du Texas a été incluse dans notre étude à titre de référence. Nous avons ainsi établi une collection originale de 15 lignées isolées de la neige, formant le cœur d’une collection nouvelle pour la recherche et l’éducation, dont trois lignées ont déjà été soumises à un séquençage génomique pour de futures études.

List of abbreviations

2,4D: dichlorophenoxyacetic acid

AF(G)P: antifreeze (glyco)protein

ARA: arachidonic acid

Asl: above sea level

ATP: adenosine triphosphate

Ax: antheraxanthin

BC: black carbon

BrC: brown carbon

BER: base excision repair

BOLD: barcode of life database

CARRTEL: centre alpin de recherche sur les réseaux trophiques et ecosystèmes limniques

CB: carbon black

CC: col de cerces

CCCryo: culture collection of cryophilic algae Fraunhofer IZI-BB

Chl: chlorophyll

COI/COX1: cytochrome c oxidase subunit I

CPos: positive control

CR: col des rochilles

DAPI: 4',6-diamidino-2-phenylindole

DGCC: diacylglycerylcarboxylhydroxymethylcholine

DGTA: 1,2-diacylglyceryl-3-O-2’(hydroxymethyl)-(N,N,N-trimethyl)-beta alanine

DGTS: diacylglyceryl-O-(N,N,N-trimethyl)-homoserine

DM: dry matter

DMSO: dimehtylsulfoxide

DNA: desoxyribonucleic acid

DSB: double strand break

DSBR: double strand break repair

DW: dry weight

EB: Epibrassinolide eDNA: environmental DNA

EMBL: the european molecular biology laboratory

EPA: eicosapentaenoic acid

ER: endoplasmic reticulum

FA: fatty acid

FAME: FA methyl esters

FECA: first eukaryotic common ancestor

HAB: harmful algal blooms

DGDG: digalactosyldiacylglycerol

GC-FID: gas chromatography coupled with flame ionization detection

GSH: glutathione

HGT: horizontal gene transfer

HPLC: high performance liquid chromatography

HL: high light

HR: homologous recombination

HTS: high throughput sequencing

IAA: indole-3-acetic acid

ICP-AES: inductively coupled plasma - atomic emission spectroscopy

ITS: internal transcribed spacer

LabEx ITEM: Laboratoire d’excellence Innovation & Territoires de Montagne

LCC: lautaret culture collection

LECA: laboratoire d’ecologie alpine

LHCII: light-harvesting complex

LGT: lateral gene transfer

LL: low light

LPCV: laboratoire de physiologie cellulaire et végétale

LOI: loss on ignition

LUCA: last unicellular common ancestor

MAAs: mycosporine-like amino acids 7

MATH: ménage-à-trois hypothesis

MatK: maturase K

MeOH: methanol

MGDG: monogalactosyldiacylglycerol

MMR: mismatch repair

MOTU: molecular operational taxonomic unit

NADPH: nicotinamide adenine dinucleotide phosphate

NER: nucleotide excision repair

NHEJ: non-homologous end joining

NPQ: non-photochemical quenching

NR: nile red

NS: not significant

OC: organic carbon

OD: optical density

OM: organic matter

ORCHAMP: observatoire spatio-temporel de la biodiversité et du fonctionnement des socioécosystèmes de montagne

OTU: operational taxonomic unit

PAR: photosynthetically active radiation

PBS: phosphate buffer saline

PC: phosphatidylcholine

PCA: principal component analysis

PCR: polymerase chain reaction

PE: phosphatidylethanolamine

PERMANOVA: permutational multivariate analyses of variance

PFA: paraformaldehyde

PG: phosphatidylglycerol

PI: phosphatidylinositol ppbC: part per billion carbon ppm: part per million

8 pptv: part per billion volume

PS: phosphatidylserine

PSII: photosystem II

PUFA: polyunsaturated fatty acids

RbcL: large subunit of the ribulose 1,5-bisphosphate carboxylase/Oxydase

RCC: roscoff culture collection

RNA: ribonucleic acid rRNA: ribosomal RNA

ROS: reactive oxygen species

SOD: superoxide dismutase sp: unidentified single species spp: multiple unidentified species

SQDG: sulfoquinovosyldiacylglycerol

SSB: single strand break

SSBR: single strand break repair

TAG: triacylglycerol

TAP: tris acetate phosphate medium

TP: tris phosphate minimum medium

TSAR: telonemids, stramenopiles, alveolates, and Rhizaria

TufA: synthesis elongation factor EF-Tu

UN: United Nations

UPA: universal plastid amplicon

UTEX: culture collection of algae at the university of texas at austin

UV: ultra-violet

V-cycle: xanthophyll cycle

VLCPUFA: very long chain PUFA

Vx: violaxanthin

Zx: zeaxanthin

Table of contents

Acknowledgements...... 2 Résumé de la thèse en Français ...... 5 List of abbreviations ...... 6 General introduction ...... 13 Goal of the project ...... 15 PART 1. INTRODUCTION ...... 17 Chapter 1. Evolution and diversity of algae ...... 18 1.1 Origins and diversity of microalgae ...... 18 1.2 Microalgae diversity ...... 21 1.2.1 Cyanobacteria ...... 22 Chapter 2. The alpine environment ...... 36 2.1 The alpine biome ...... 36 2.2 The French Alps geography and topology ...... 37 2.3 Climate and environmental conditions in the French Alps ...... 37 2.4 Climate change and anthropogenic activity impact on the Alps ...... 47 Chapter 3. Alpine microalgae biodiversity ...... 49 3.1 Microalgae diversity in alpine lakes and rivers ...... 49 3.2 Aero-terrestrial microalgae diversity of cold environments ...... 51 3.3 Microalgae diversity in glaciers, cryoconites and moraines ...... 52 3.4 Snow microalgae of ice and snow blooms ...... 53 Chapter 4. Microalgae acclimation and adaptation mechanisms ...... 59 4.1 Oxidative stress: a multi-stress response ...... 59 4.2 Acclimation and adaptation to cold temperatures ...... 63 4.3. Acclimation and adaptation to high-irradiance and UV light ...... 67 4.4. Photochilling ...... 72 4.5 Resistance to dehydration and desiccation ...... 72 4.6 Resistance to nutrient starvation ...... 74 4.7. Metal and heavy metal stress ...... 76

4.8 CO2 flows ...... 76 4.9 Non-lichen algae-fungi/bacteria interactions ...... 77 4.10 Biotic stresses ...... 77 4.11 Concluding remarks ...... 77 10

Chapter 5. Methods for assessing green microalgae biodiversity ...... 79 5.1 Definition and importance of biodiversity ...... 79 5.2. Biodiversity measurements ...... 81 5.3 Experimental tools for biodiversity studies ...... 84 5.4 Operational taxonomic units (OTU) ...... 89 5.5 Environmental microalgae DNA ...... 89 5.6 Currently used DNA markers for algae ...... 90 5.7 Concluding remarks ...... 96 PART 2. RESULTS ...... 97 Chapter 6 ...... 98 6.1 Preamble ...... 98 6.2 Article 1 ...... 99 Keywords: Green alga; Chlorophyta; metabarcoding; mountain environment; soil; biodiversity; high elevation ...... 100 Abstract ...... 101 Introduction...... 102 Material and Methods ...... 105 Soil sampling ...... 105 DNA Metabarcoding markers ...... 106 Read filtering and processing ...... 108 Concluding remarks ...... 125 References...... 127 Chapter 7 ...... 141 7.1. Preamble ...... 141 7.2 Article 2 ...... 143 Abstract ...... 145 Introduction ...... 145 Material and Methods ...... 147 Results ...... 151 Discussion ...... 159 References ...... 161 Author Contributions ...... 164 Funding ...... 164 Acknowledgements ...... 165

Supplementary tables and figures ...... 166 Chapter 8. Isolation and preliminary study of axenic strains of snow algae collected in the Lautaret region and creation of a reference culture collection ...... 178 Introduction ...... 178 Methods ...... 180 Results ...... 187 Discussion and perspectives ...... 207 References ...... 211 PART 3. CONCLUSION AND DISCUSSION ...... 214 Conclusion and perspectives ...... 215 Designing and confirming two new green microalgae markers ...... 215 Exploring green algae biodiversity in soils, lakes and snow in the French Alps revealed a complex and multifactorial spatiotemporal distribution ...... 217 Establishing a new snow algae culture collection ...... 218 Why a new culture collection? ...... 218 Identification of the strains ...... 219 Genome sequencing ...... 219 Algae-bacteria-fungi mutualistic interactions and the question of specific holobionts ...... 221 Annexes ...... 223 Bibliography ...... 232

General introduction

Algae populate all known environments from aquatic habitats, such as rivers, lakes and oceans, to all kinds of aeroterrestrial habitats, like soil, tree bark, animal hair, and even dry surfaces such as rocks, walls and roofs, artificial surfaces in urban areas, etc., or extreme environments such as volcanoes, acidic hot springs or snowpack and ice (Häubner et al., 2006; Cardol et al., 2008; Halman et al., 2016; Dittami et al., 2017; Maréchal, 2019). Algae play a major role in the corresponding ecosystems as primary producers (Chapman, 2013) and are at the base of trophic networks (Falkowski et al., 1998; Jonasdottir et al., 2019). They act as primary and secondary colonizers (Hu and Liu, 2003), enabling other microorganisms and ultimately vascular plants to settle, transform and stabilize soils in open areas.

For all these reasons, they are suspected to play a critical role in mountains, currently subjected to major and rapid environmental transformations due to climate change. In particular, algae are presumed to be involved in the early conquest of areas opened by retreating glaciers. Algae interact closely with bacteria and fungi, even outside of the well- known symbiosis they form in lichen, developing inter-kingdom connectivity especially in inhospitable environments like snow and ice (Krug et al., 2020). Microalgae present a large diversity genetically, morphologically and physiologically, which enables them to adapt to a vast variety of environments.

Algae in the broadest sense of the term include both prokaryotic and eukaryotic forms. Photosynthetic prokaryotes considered as microalgae are cyanobacteria (Barsanti et al., 2014). Eukaryotic algae can be multicellular as well as unicellular, and their size spans from 0.2 µm (picoplankton) to several meters (seaweed). While marine algae have been explored a great deal, aeroterrestrial species are still relatively unknown. In the soil, algae have mostly been studied in biological soil crusts (Rippin, 2018). Because of the importance of algae in ecosystems, knowledge on their biodiversity, distribution, population and community dynamics as well as their biological cycles and physiology seem critical to address the impact of global warming, especially since the increase in temperature has been evidenced to strongly affect algae distribution (Lima et al., 2007). Alpine environments are particularly vulnerable to global warming, which influences them disproportionally. They also possess endemic species, which will be extinct if their environment disappears (Dirnböck et al., 2011).

The opening section of this thesis is devoted to introducing the different concepts initially needed for our studies. The first bibliographic chapter addresses the diversity of algae. The eukaryotic algae are divided into two major groups: the Archaeplastida and the secondary endosymbionts. The Archaeplastida comprises three lineages which are the green, red and glaucophyte algae. They derive from a single event of endosymbiosis during which a eukaryotic cell engulfed a cyanobacteria (for review, see Maréchal, 2018). The second group is composed of the secondary endosymbionts, comprising a large number of branches, such as the stramenopiles, which have undergone a second endosymbiosis event during which an Archaeplastida was engulfed by another eukaryotic cell (Burki et al., 2020). This thesis focuses on green algae, which are prominent in aeroterrestrial habitats, and in particular the Chlorophyta lineage. 13

The second chapter focuses on general features of alpine environments. The term ‘Alps’ can be used as a generic name for mountain ranges worldwide, including Asia and North- America, that represent the highest-altitude terrestrial habitats, and that are part of the cryosphere, i.e. they contain some of the coldest environments in our planet. We focused specifically on the French Alps, which are part of the European Alps, a mountain range that spans from France to Slovenia. The alpine environment is characterized by a sharp altitude gradient correlating with decreasing temperatures and increasing exposition to light and ultraviolet radiation (UV). This chapter highlights general topographic, climatic and ecosystemic features of the French Alps. It also presents how climate change affects this environment.

The third chapter introduces the previous inventories of algae biodiversity done in the Alps.

The fourth chapter focuses on known mechanisms of adaptation of green microalgae to the many stresses that are characteristic of alpine environments. As this PhD project sought to understand how snow algae from the French Alps were able to live and/or survive, we present some known responses of green algae to abiotic stresses and their acclimation or adaptation to cold, low nutrient concentration, and high luminosity and UV exposure.

A fifth and final chapter from this bibliographic section introduces methods to study green microalgae biodiversity with a focus on DNA-based approaches. An important part of the project is aimed at evaluating the biodiversity of green microalgae, specifically Chlorophyta. Barcoding and metabarcoding methods are described, as well as DNA markers used in green algae studies.

The second section of this thesis is devoted to results obtained during the project concerning the biodiversity of green microalgae in the French Alps as well as first lines of investigations on physiological responses to different stresses simulating the alpine environment. The sixth chapter thus presents the biodiversity of green microalgae along altitudinal gradients in the soil, at various locations, from 1,000 to 3,000 meters above sea level. The seventh chapter analyzes the biodiversity of green microalgae in lakes at different altitudes, in soil and in a snow algae bloom. The eighth chapter showcases physiological responses of snow algae, including the presentation of the new Lautaret culture collection, a library of fifteen snow algae collected before and during this PhD project. Each of these chapters includes a dedicated discussion of presented results.

Finally, a conclusion is given, with an outline of some possible perspectives for future works.

Goal of the project

This interdisciplinary project aimed at evaluating the biodiversity of green microalgae in the French Alps and provide preliminary functional analyses to comprehend how some phenotypic traits might enable these organisms to adapt to high altitude conditions, particularly in the snow and under high UV exposure.

The study was divided into two axes.

The first axis aimed at detecting and identifying green microalgae in soil, lakes and snow in representative locations. It drew on bioinformatics and ecology methods and concentrated on the Chlorophyta lineage of the green algae. The Alps are composed of aeroterrestrial and fresh water ecosystems. Algae in these environments are either free-living, or grow in crusts, such as biological soil crusts, or in symbiosis with fungi and bacteria as lichen. In fresh water environments, algae are present in lakes, rivers, snow and ice. The least-known algae are the free-living soil algae. The LECA laboratory hosts the Orchamp Consortium, an observatory of soil ecosystems in the Alps, which studies the relationship between abiotic factors, such as pH and altitude, and the distribution of vascular plants. The Orchamp project sampled soil along 24 altitude gradients (as of 2019), of which the five sampled in 2016 were chosen in our study. We also used DNA sampled from lakes at different altitudes obtained by the CARRTEL laboratory in Thônon-les-Bains (provided with the help of Isabelle Domaizon). Lastly, snow samples were collected in the region of Col du Lautaret, with the logistic assistance of Jardin du Lautaret (under the supervision of Jean-Gabriel Valay), at around ~2,500 m above sea level (asl). Both were sampled to compare communities of algae in blooms (red snow) and outside of blooms (white snow). Snow was collected at different depths to evaluate how proximity to the surface, and therefore to the light, may influence the distribution of species. We chose to study the biodiversity using metabarcoding, a DNA-based method, which is more comprehensive and reliable than microscopy identification. Two new metabarcoding markers were designed, as existing markers seemed ill adapted for a multi-environment, non-marine Chlorophyta study.

The second axis aimed at cultivating and purifying collected strains, with the help of the jardin du Lautaret and the network refuges sentinelles (a research program supported by Labex ITEM, Zone Atelier Alpes and parc national des Ecrins), in red snows located at 2,000-3,000 m altitude. Snow algae are adapted to the cold, high light exposition and low nutrient concentration, but it is not entirely clear how they adapt to these conditions, and if they all adapt in the same way. Their potential conversion into pigmented cysts is still a puzzling and unresolved question. Four sites were selected for sampling over three consecutive years from 2017 to 2019. Samples containing pigmented algal cysts were cultured in the laboratory in order to isolate and axenize species that dwell in red snow. Additionally, the reference snow alga Chlamydomonas nivalis was also obtained from the University of Texas culture collection (UTEX 2824). Recent information was provided to us by Thomas Leya (Postdam, Germany) who addressed the taxonomic characterization of the UTEX 2824 reference, and indicates that this strain is rather C. typhlos, also found in the vicinity of snowpack. Selected strains were then analyzed and preliminary physiologic and genomic studies performed. 15

PART 1. INTRODUCTION

Chapter 1. Evolution and diversity of algae

1.1 Origins and diversity of microalgae 1.1.1 Endosymbiotic origins of plastids The first living organisms, who may have emerged >3.5 Billion years ago, led to the formation of a cell prototype called the last unicellular common ancestor (LUCA), a prokaryotic cell at the root of Archaea and Bacteria radiation (Weiss et al., 2016). The first photosynthetic organism was a prokaryote related to cyanobacteria (Mereschkowsky, 1910). The emergence of the eukaryotic cell is a hotly debated subject. The prototype of eukaryotes, called the first eukaryotic common ancestor (FECA), may have preceded the acquisition of the mitochondrion via a primary endosymbiosis with an ancestral bacterium, often described as related to current -proteobacteria, or it may have appeared concomitantly with mitochondrion emergence (Doolittle, 1998; Imachi et al., 2020) (Figure 1.1).

First eukaryotic common ancestor (FECA) Unknown 1 -proteobacterium

2 phagosome

Acquisition of the The outer membrane mitochondrion of the mitochondria envelope 3 IS NOT the relict of the phagosome

Unknown Last eukaryotic cyanobacteria common ancestor (LECA)

4 YES NO phagosome

5 The outer membrane of the chloroplast envelope IS NOT the relict of the phagosome Acquisition of the chloroplast

Algae and plants = YES ARCHAEPLASTIDA NO

Figure 1.1 From phagocytosis to primary endosymbiosis. In this scheme from Maréchal et al., 2018, the first eukaryotic common ancestor (1) is shown containing an endomembrane system (in blue). The last eukaryotic common ancestor (2) appears when an unknown -proteobacterium is engulfed within the cell, giving rise to the mitochondrion. The phagosome is not conserved (3). The primary chloroplast derives from the engulfment of an unknown cyanobacterium (4). Again, the phagosome is not conserved (5). The two membranes limiting the mitochondrion and the chloroplast are therefore supposed to derive mainly from the outermost membranes of the  -proteobacterium and the cyanobacterium, respectively. This scenario is not sufficient to explain why some mitochondria and chloroplast proteins are encoded by genes unrelated with -proteobacteria and cyanobacteria, respectively. The input of components from other prokaryotic partners, subsequently lost, has therefore be hypothesized.

In the course of evolution, the engulfed -proteobacterium and cyanobacterium remained inside the cytosol of their host. Genes were laterally transferred to the host nucleus, whereas the genome of the engulfed prokaryote reduced its content to such an extent that it could not survive as a free organism anymore (Figure 1.2).

Genes of bacterial origin Sister group to acquired by horizontal gene Asgard Archaea Bacteria-to-Archaea transfer (HGT) 1 HGT Gene of archael origin Gene of a-proteobacterial origin Gene of cyanobacterial origin

Bacteria-to- First eukaryotic -proteobacterium Bacteria-to-pre-LECA common ancestor HGT HGT (FECA) 2 Unknown -proteobacterium Organelle-to-nucleus LGT

Last eukaryotic common ancestor (LECA)

Bacteria-to-cyanobacteria HGT

Unknown Bacteria-to-eukaryote cyanobacteria HGT

Organelle-to-organelle Organelle-to-nucleus LGT 3 LGT 4

First photosynthetic eukaryote (primary endosymbionts)

Figure 1.2 Importance of horizontal gene transfers in Eukarya evolution and in primary endosymbioses. In this scheme, the first eukaryotic common ancestor (1) contains genes originating from Archaea and unique Eukarya origin (blue circles). Some bacterial genes (blue square) could be incorporated via bacteria-to-archaea horizontal gene transfer (HGT). The acquisition of the mitochondrion could involve bacteria-to--proteobacterium HGT (2), explaining the presence of genes who do not carry an -proteobacterial signature in mitochondrion (brown square). This endosymbiotic event was followed by the escape of some of the mitochondrial genes to the nucleus by a specific HGT, called here lateral gene transfer (LGT). Likewise, the acquisition of the chloroplast could involve bacteria-to-cyanobacteria HGT (3), explaining the presence of genes who do not carry a cyanobacterial signature in chloroplasts (green square). Primary endosymbiosis of the chloroplast was followed by the escape of some genes to the nucleus by LGT. Some LGT between both organelles could then have occurred (4).

Oxygenic photosynthesis is a process by which solar light energy is used to couple the - + splitting of H2O into O2, electrons (e ) and protons (H ), with the production of energetically rich molecules of ATP and reducing power in the form of NADPH/H+, subsequently used to

reduce atmospheric CO2 into organic molecules, starting with glucose (C6H12O6). The conservation of chlorophyll in photosynthetic antennae suggests that the first oxygenic photosynthesizer had chlorophyll-based photosynthesis. It required an oxidant with a high redox potential and the ability to store molecules formed in the reaction center, two 19 evolutionary advantages only found in oxygenic photosynthesizing organisms (Blankenship and Hartman, 1998). Anoxygenic photosynthesis uses other electron donors than water, namely hydrogen sulfide (H2S), or organic substrates, and is presently exhibited by proteobacteria (purple bacteria), green sulfur bacteria, green filamentous bacteria, and the Gram-positive Heliobacteria (Hanada, 2016). These photosynthetic bacteria are spread over several bacterial phyla but are not classified as algae (Raymond et al., 2002).

In the strictest term, algae do not comprise cyanobacteria, however, for the purposes of the current work, we consider cyanobacteria as prokaryotic algae. Photosynthesis releases O2 into the atmosphere, and the successful spreading of cyanobacteria provoked the so-called ‘Great Oxidation’ event in the Precambrian Earth, an event during which the levels of oxygen rose dramatically, making aerobic metabolism possible (Sánchez‐Baracaldo and Cardona, 2020). Eukaryotes appeared around 100 million years later, around 2.4 billion years ago (De Duve, 2007, Raymond et al., 2002, Bekker et al., 2004). During a single endosymbiosis event dating back to ~1.8 billion years (Tirichine et al., 2011), a cyanobacterium was engulfed by an ancestral eukaryote. This primary endosymbiosis event is considered unique, at the origin of the chloroplast found in all photosynthetic eukaryotes (Bhattacharya et al., 2004), with the notable exception of Paulinellidae, who contain a photosynthetic organelle called the chromatophore, acquired much more recently, about 60 Mya (Marin et al., 2005; Maréchal, 2020).

Chloroplast-containing eukaryotes became the ancestors of eukaryotic algae and land plants that constitute the super group Archaeplastida (Selim et al., 2020). The ability to photosynthesize makes organisms in these phyla the foundation of all ecosystems and the basis of all food chains. They produce our oxygen, are a source of several vitamins, tetrapyrroles, essential fatty acids, isoprenoids, etc, which are necessary for secondary producers in the food network. Photosynthetic organisms also play critical roles in redox processes in various biogeochemical cycles, including those of carbon, nitrogen and sulfur.

Eukaryotic algae lineages that derive from the primary endosymbiosis event that formed the Archaeplastida include the red algae (Rhodophyta), the blue algae (Glaucophyta), the green algae (Chlorophyta, Charophyta, and the recently identified Prasinodermophyta lineage) (Li et al., 2020). These lineages are differentiated by their photosynthetic pigment composition. Red algae have chlorophyll a linked to phycobilline in their photosynthetic antennae, while Glaucophytes have chlorophyll a associated to phycocyanin and allophycocyanin, and green algae antennae contain chlorophyll a and b.

Other clades of photosynthetic eukaryotes derive from the primary red and green lineages, involving two or more events of endosymbiosis, indicated by additional biomembranes surrounding plastids. These events occurred in several instances, in many unrelated lineages of photosynthetic eukaryotes (Fussy and Obornik, 2018). These secondary endosymbiotic lineages are extremely diverse, and some species have even lost the ability to photosynthesize in the process (e.g. the Apicomplexan parasite responsible for malaria, Plasmodium falciparum, contains a plastid with 4 membranes called the apicoplast; Botté and Maréchal, 2014; Mcfadden and Yeh, 2017; Botté and Yamaryo-Botté, 2018).

Rhodophytes, Glaucophytes and secondary endosymbionts are found almost exclusively in marine and fresh water ecosystems, whereas green algae and land plants are mostly found in aero-terrestrial ecosystems where they diversified extensively. This specificity shows their plasticity and capacity to adapt well to very different ecosystems (Maréchal, 2018). 1.1.2 The constant evolution of algae classification and taxonomy Because of their plastid complexity, multiple origins and the absence of distinguishable features, algae, especially microalgae, are difficult to classify. Phylogenetic analysis is further made difficult due to horizontal gene transfers (HGT) (see Figure 1.2), an aspect of prokaryotic evolution. There is evidence that genetic components of the photosynthetic apparatus have been shared between prokaryotes in this way (Raymond et al., 2002). For cyanobacteria, the phylogeny is under constant debate and reworking. According to algaebase (Guiry and Guiry, 2020), cyanobacteria are considered a phylum within the Negibacteria (Gram-negative) super-kingdom and in the Glycobacteria infra-kingdom. According to 16S rRNA data, cyanobacteria also include taxa that secondarily lost the ability to photosynthesize. This suggests that cyanobacteria may be divided into three groups: the oxyphotobacteria (performing oxygenic photosynthesis), melainabacteria (found in aphotic environments and incapable of photosynthesis) and the sericytochromatia (ML635J-21 clade, previously classified under proteobacteria, also incapable of photosynthesis), see Figure 1.3A (Soo et al., 2017, Utami et al., 2018). It is unclear whether the latter two classes have lost the ability for photosynthesis, or if Oxyphotobacteria acquired it after they diverged.

Cyanobacteria are especially difficult to classify because they are represented in both the Botanical and the Bacteriological codes, which are based on different rules. There is currently no consensus on what defines a species for prokaryotes. Not all species of cyanobacteria described in the Botanical code are also found in the Bacteriological code. Additionally, ribosomal RNA sequencing is not considered sufficient for classification, and neither is morphology description because in vitro cultures introduce stress, which tend to uniform phenotypes (Palinska and Surosz, 2014). Algae used to be classified according to morphology (for instance the filamentous or flagellate morphotypes in the Ulotrichales), but they were synonymous rather than truly related. When ultrastructural data was obtained, flagella ultrastructure contradicted previous algae classifications, leading to rearrangements. Later, when sequencing was performed, algae classification evolved further, and keeps evolving as more genetic data is analyzed. It is common for algae species to be reattributed a new genus or even a new class (Fučíková and Lewis, 2012) and for species to not be universally accepted throughout databases (Liu et al., 2019). Recently, a third phylum of green algae was described (Li et al., 2020). 1.2 Microalgae diversity Microalgae are diverse genetically and morphologically (De Clerck et al. 2013). They have been able to adapt to almost any environment on earth, having been found both in the cold of the Arctic, and under the sweltering heat of deserts. They have adapted to living in environments of extreme pH, salinity, and light exposure (Manoylov, 2014). Algae range in

size from the micrometer range in picoplankton to multicellular algae that reach several meters in length (Figure 1.3). Microalgae range from 0.5 µm to a few micrometers.

Figure 1.3 Diversity of organization of photosynthetic eukaryotes and cyanobacteria.

1.2.1 Cyanobacteria

As detailed above, the cyanobacteria phylum, previously known as blue-green algae or cyanophytes, and sometimes-called oxyphotobacteria, is one of the most important groups of organisms, yet knowledge about their diversity, metabolism and evolution is still incomplete. Research has only begun to scratch the surface of their complex cell cycles (Hense and Beckmann, 2006). They get their name from the pigment they produce, phycocyanin, one of three accessory pigments to chlorophyll a, along with phycoerythrin and allophycocyanin (Whitton and Potts, 2012). They are Gram-negative prokaryotic organisms (negibacteria) from the bacteria domain, therefore lacking formal organelles, although photosynthetic membranes form intracellular membrane compartments.

Ancient cyanobacteria are considered at the origin of the chloroplast, and they share many characteristics with it, most notably the presence of galactolipid-rich membranes and chlorophyll a associated to its photosystems.

Cyanobacteria store energy in the form of cyanophycean starch (Pulz and Gross, 2004). They are generally bigger than non-photosynthetic bacteria, ranging from 0.5 to 30 µm and usually live in groups, colonies or filaments, but can be free-living. Most filamentous morphotypes are non-branching, but some have true branching (Whitton and Potts, 2012). There is high variability in proteins used for photosynthesis between the different classes of cyanobacteria. Their phylogeny is hard to establish because many genetic elements were transferred by HGT.

Cyanobacteria are found in fresh water and oceans, where they are either free-living or grow on other algae as epiphytes (Ferris and Palenik, 1998). They also grow in aero-terrestrial ecosystems as biofilm (Crispim et al., 2003), as parasites to land plants, on animal fur, or in symbiotic associations in lichen, sponges, plants or protists. They can be found in almost any biome, including deserts (Garcia-Pichel and Pringault, 2003), hot springs (Papke et al., 2003) and ice and snow, where they are abundant in cryoconites (Takeuchi et al., 2015). They lack flagella but are capable of motility by ‘gliding’ or ‘swaying’.

Cyanobacteria can form harmful algal blooms (HAB) which are currently occurring at an increasing rate and size globally due to the release of nutrients from industrial and agricultural waste into the water, and global warming. Some taxa produce toxins that kill other phytoplankton and fish and can also be harmful to humans. For example, in China, these blooms have increased 20-fold in frequency since the country began using chemical fertilizers in the 1950s. The transformation of water habitats after the development of a cyanobacteria bloom, called eutrophication, happens naturally in certain circumstances, but it has worsened dramatically by the anthropogenic release of nitrogen and phosphorous during the 20th century (Glibert et al., 2005; Smith and Schindler, 2009). Eutrophication (or hypertrophication) leads to over-consumption of oxygen by bacteria decomposing the algae, rendering the water hypoxic and affecting the biodiversity of that ecosystem (Wang et al., 2016). Nutrient ratios and light exposure are the most important factors dictating the growth of cyanobacteria and algae in general.

Figure 1.4 Picture of a eutrophic pond around Grenoble (E. Maréchal, 2020).

Some filamentous cyanobacteria have evolved specialized cells for nitrogen fixation called heterocysts or diazocytes, cells responsible for photosynthesis called akinetes, and cells

capable of motility for dispersal called hormogonia (Sandh et al., 2012). To fix nitrogen, they use enzymes that only work in anoxic conditions; hence, they need to separate photosynthesis from nitrogen fixation (Tomitani et al., 2006).

There have been many classification publications. Depending on the source, the phylum is divided into different classes or orders. For example, cyanobacteria were divided into 2 classes according to Cavalier-Smith et al., 2002: the Gloeobacteria, who have the particularity of having phycobilisomes in the cytoplasm but no thylakoids, and the Phycobacteria. Komarek et al., 2014, describes a more recent classification that separates the phylum into eight orders. 1.2.2 Archaeplastida (primary endosymbionts) The Archaeplastida supergroup are organisms that evolved after a unique endosymbiosis event during which a cyanobacterium was taken up by a heterotrophic eukaryote. It must be stressed that non-cyanobacterial prokaryotic partners, including Chlamydia-related pathogenic bacteria, have possibly contributed to the primary endosymbiosis, following the so-called MATH (ménage-à-trois hypothesis), a model elaborated to explain why so many genes of non-cyanobacterial origin are involved in chloroplast structure and function (Cenci et al., 2017, Figure 1.5).

Acquisition of the chloroplast following the MATH model

Ancestral Ancestral cyanobacteria cyanobacteria 2 Ancestral Chlamydia

Ancestral Chlamydia

Loss of Chlamydia cells and LGT faciliting chloroplast stable residence

Figure 1.5 Acquisition of the primary chloroplast following the Ménage-à-Trois Hypothesis (MATH, from Maréchal, 2018). In this scheme, the acquisition of the ancestral cyanobacteria coincides with the presence of parasitic Chlamydia, either in distinct (1) or identical (2) phagocytic vacuoles. The presence of Chlamydia cells provides a genetic environment adapted to the residence of a bacterium within a eukaryote. Following HGT, Chlamydia genes are proposed to have facilitated the cyanobacteria-to-chloroplast transition. 24

As mentioned above, this supergroup comprises green algae lineages and land plants, as well as red and ‘blue-colored’ (Glaucophyte) algae (Figure 1.3B). Their plastid is surrounded by two membranes and divides by binary fission synchronized with the host cell division in algae (Miyagishima, 2011).

Cyanobacteria OEM A. Primary endosymbiosis Pept. Envelope unknown heterotrophic IEM eukaryote 1 Thyl.

OEM Pept. 2 OEM IEM 2 Primary chloroplast IEM Glaucophyta OEM (e.g. Cyanophora) Envelope OEM IEM 2 IEM Green algae Thyl. (e.g. Chlamydomonas) Red algae Plants (e.g. Cyanidioschyzon) (e.g. Arabidopsis)

red B. Secondary green lineage endosymbiosis lineage

Secondary plastid OEM? IEM? Up to ? 4 limiting unknown ? membranes heterotrophic eukaryotes 2 Thyl. (…)

4 4/-

Haptophytes 3/- (e.g. Phaeocystis) 4 Apicomplexa 4+Nuc (e.g. Plasmodium) Chromerida 4 (e.g. Chromera) Chlorarachniophytes Heterokonts Euglenozoa (e.g. Bigelowiela) (e.g. Phaeodactylum, (e.g. Euglena) Nannochloropsis)

Figure 1.6 Schematic representation of plastid evolution. (from Petroutsos et al., 2014) A) Primary endosymbiosis. In the upper panel, a single primary endosymbiosis between an unknown heterotrophic eukaryote and a Gram-negative cyanobacterium led to the three primary-plastid-bearing lineages, i.e., the ‘blue’ lineage (Glaucocystophytes), the ‘red’ lineage (red algae) and the green lineage (Green algae and plants, forming together the Viridiplantae clade). The primary plastid is always surrounded by an envelope containing two membranes, vertically inherited from the two membranes limiting the cyanobacterium (see schemes in figures on the left side). An independent endosymbiosis has led to the emergence of Paulinella, not shown in this figure. B) Secondary endosymbiosis. Two types of secondary endosymbiosis involving two different green algae and unrelated unknown heterotrophic eukaryotes led to Euglenozoa and Chlorarachniophytes. A single endosymbiosis between a red alga and a heterotrophic 25 eukaryote probably led to all remaining plastid-bearing protists. Loss of photosynthesis is pervasive in several lineages. The number of membranes limiting primary and secondary plastids is highlighted in yellow: (2), (3) or (4). Phyla that include species that have lost their plastids are indicated: (3/-) and (4/- ). Phyla in which the primary nucleus has been conserved as a nucleomorph are indicated: (4+Nuc). To maintain simplicity, the proposed origin of diatoms and other Chromalveolates from serial secondary endosymbiosis involving both a green and a red alga in (4) are not shown.

It must be noted that the primary chloroplast of all Archaeplastida is delimited by an envelope made of two membranes. This envelope is the site of lipid syntheses necessary for its own expansion and to generate the photosynthetic membrane and thylakoids. Four lipids are conserved from cyanobacteria to primary chloroplasts. Three are unique to photosynthetic membrane, and not present in other eukaryotic membranes, which are monogalactosyldiacylglycerol or MGDG, digalactosyldiacylglycerol, or DGDG and sulfoquinovosyldiacylglycerol or SQDG (Petroutsos et al., 2014). The fourth one, phosphatidylglycerol (PG) is also found in other eukaryotic and prokaryotic membranes. The genes coding for the enzymes producing MGDG and DGDG have been ‘exchanged’ by non- cyanobacteria in the course of evolution. Interestingly the Archaeplastida who have conserved some of the initial genes are a group of acidophilic red algae (Petroutsos et al., 2014).

1.2.2.1 Red algae (Rhodophyta) Rhodophyta were named after the red pigment phycoerythrin, though parasitic forms of Rhodophyta lack this pigment. Species belonging to the green lineage, in particular Chlorophyta, can also appear red, because of the presence of other pigmented molecules, such as carotenoids (e.g. the red blooms at the surface of snow is due to green algae, whose cells are ‘loaded’ with red carotenoids).

Rhodophyta are a diverse and species-rich group of usually photoautotrophic organisms. Most are multicellular and marine dwelling, though some unicellular species exist. It is estimated that less than 3% of Rhodophyta species are capable of living in fresh water (Nan et al., 2017). Their photosynthetic antennae contain chlorophyll a, but also possess phycobillins like cyanobacteria (Mittal et al., 2017). Rhodophyta are widely cultivated to make sushi (e.g. Porphyra for nori) and agar. Unlike green algae, they do not accumulate starch in plastids, but floridean starch in the cytoplasm (Yu et al., 2002). Additionally, there are pit connections between cells of filamentous species. They lack flagella, likely having lost this feature after their separation from other Archaeplastida lineages.

The majority of seaweeds of tropical and temperate climates are red algae, however, they are not abundant in cold climates where secondary endosymbionts (e.g. Heterokonts) and green algae dominate. They have the ability to live in much deeper waters than other algae (Lee, 2018, pp. 84-132). Some are parasitic or epiphytic (Freese et al., 2017). The Rhodophyta classification, similar to that of other algae lineages, evolves constantly, therefore the number of its classes, orders and genera are regularly updated. As of May 2020, NCBI classification lists the following classes: Bangiophyceae, Florideophyceae, Compsopogonophyceae, Rhodellophyceae and Stylonematophyceae (https://www.ncbi.nlm.nih.gov/). A sister group of non-photosynthetic organisms,

Rhodelphis, has been linked to the Rhodophyta in the 2020 Eukaryote Tree of Life (Gawryluk et al., 2019, Burki et al., 2020). 1.2.2.2 ‘Blue-colored’ algae (Glaucophyta) Glaucophyta are a small group of unicellular algae (Figure 1.3B) exclusively found in fresh water, such as in shallow lakes as macrophyte epiphytes (Price et al., 2016). They are present in low abundance. Their plastids, called cyanelles, have a vestigial peptidoglycan layer initially believed to be retained from the cyanobacterial ancestor, although recent phylogenic analyses have shown that genes coding for enzymes synthesizing this layer are of non- cyanobacterial origin (Sato and Takano, 2017). Their photosynthetic antennae contain chlorophyll a and phycobilliproteins likely inherited from the ancestral cyanobacteria, but do not possess cyanobacterial carotenoids. They likely diverged from other groups of algae earlier than red and green lineages. Some species are flagellated while others are not. (Lee, 2018, pp. 80-83)

1.2.2.3 Green algae Green algae together with plants form the super-phylum Viridiplantae, also called the Chloroplastida or Chlorobionta (Lewis & Mccourt, 2004) (Figure 1.3B). Green algae are a paraphyletic clade divided into three lineages: the Chlorophyta, comprising most green algae, the Charophyta, the closest relatives of vascular plants (Mattox and Stewart, 1984), and the latest discovered phylum, the Prasinodermophyta, previously thought to be a class within Chlorophyta (Piganeau, 2020, Table 1.1). They comprise very diverse classes of algae both genetically and morphologically. Their synapomorphic traits (traits conserved in the whole clade) include the storage of starch as the main polysaccharide in the plastid, usually associated with a pyrenoid, stacked thylakoids, and the presence of both chlorophyll a and b in their photosynthetic antennae. Like most unicellular eukaryotes, they also tend to accumulate triacylglycerol in stressful environments.

Chlorophyta Prasinophyta Charophyta

marine, brackish, soil, open ocean, coastal Environment fresh water snow, rock and fresh water and deep marine water

Division closed mitosis closed mitosis open mitosis

Reproduction sexual/asexual asexual, bipartition sexual/asexual

Number of 0-102 0-8 0-2 flagella

macroalgae: blade, tube, siphons, filaments ; filamentous, Morphology coccoid microalgae: coccoid, rod, coccoid ellipsoid

unicellular, unicellular, colonial, Growth form unicellular colonial, symbiotic, parasitic multicellular

picoplankton (<3µm) picoplankton Cell size µm - 1 m to macroalgae (m) (<3µm)

Number of 1-many 1 1-many chloroplasts

Pyrenoid yes/no yes/no no

Number of 6,779 165 756 species

Table 1.1. The three green algae lineages and their specificities. The number of species is based on current knowledge (AlgaeBase, Guiry and Guiry, May 2020).

Green algae cell walls usually have cellulose as the main structural polysaccharide and their main carotenoid is lutein (Lee, 2018, pp. 133-230). The cells can be further protected in harsh environments by a layer of mucilage (Liu et al., 2019). The particularity of green algae is the emergence of chlorophyll b and the loss of phycobillisomes in their plastids. They are widespread across the globe, found in almost every type of environment including the Arctic and deserts (Rocca et al., 2009). They grow in marine, fresh water, and soil environments. Some are transitively airborne. They are dominant in snow, brackish water, fresh water and freshwater-derived glaciers (Williamson et al., 2019). Some green algae are also found in symbiotic associations with fungi, forming lichen (Piercey-Normore & Athukorala, 2017). Certain species have become plant parasites. Green algae, like cyanobacteria, can form blooms in water called green tides, which accumulate in vast amounts and decay on beaches, releasing toxic gases (Charlier and Lonhienne, 1996). The Streptophyta/Chlorophyta separation (dated >1 billion years ago, Lelliaert et al., 2011) correlates with a preference for 28 fresh water/marine habitats, and it is thought that the first adaptations of streptophyte algae to fresh water was a major advantage that lead to the emergence of vascular plants (Becker and Marin, 2009). Chlorophyta The Chlorophyta phylum is one of the three green algae lineages. They are primarily freshwater and aero-terrestrial, but some are marine. It is a very species-rich and diverse phylum (Lee, 2018). Chlorophyta classification is particularly difficult to assess due to the existence of cryptic species, i.e. strains of microscopic cells with no distinguishing features, even ultrastructurally, but which are not cross-fertile. The exhibition of asexual reproduction in some species further complicates identification (Vieira et al., 2016). Some Chlorophyta are distinguished from other algae by their flagella, which is a symmetrical cruciate root system (Stewart and Mattox, 1978). This phylum comprises five classes: Chlorophyceae, Ulvophyceae, Trebouxiophyceae, Chlorodendrophyceae and Pedinophyceae (Figure 1.7).

Figure 1.7 The Chlorophyta classes.

Chlorophyceae have been confirmed as a monophyletic group of microalgae (Fang et al., 2018). They exhibit closed mitosis, a trait which sets them apart from Charophyta. Their life cycles are either monogenetic haploid or digenetic haplo-diploid. Some species grow epiphytically on other algae or surfaces, like the freshwater genus Chaetophora (Guiry and Guiry 2020). The Chlorophyceae is the only class whose monophyly is uncontested, though taxonomy within the class is subject to debate as different markers give different results (Fučíková et al., 2019). This is the largest class with 3,673 described species (Guiry and Guiry, 2020; Table 1.2). Its members can be unicellular, colonial, filamentous or parenchymatous. It is divided into five orders that form two major lineages according to most phylogenies (Czech and Wolf, 2020). The first is a clade composed of Sphaeropleales and Volvocales/Chlamydomonadales. The second comprises the Oedogoniales, Chaetopeltidales, and Chaetophorales. Chlorophyceae have diverse cell organizations, from simple coccoids, presenting from none to a few hundred flagella, to colonies and filaments. The Volvocales order is the largest group within the Chlorophyceae with 21 monophyletic groups. The best- known Chlorophyceae is the first ever sequenced green algae, Chlamydomonas reinhardtii, a 29

model unicellular green alga from the Volvocales order. It is within the Chlamydomonas genus, a polyphyletic clade with 400–600 species (Ettl, 1983) whose phylogeny is still under debate and many of its species are considered to need to be classified in independent genera. This is due to the traditional morphological classification of Chlorophyceae (Susanti et al., 2020. The Chlamydomonas genus is characterized by strains possessing an asteroid chloroplast with a central pyrenoid and hemispherical papilla. Chlamydomonas nivalis, the best-known snow algae, resembles it by its two flagella and single chloroplast (Remias et al., 2016). Within the Volvocales, there is a paraphyletic phylogroup named Chloromonadinia, which comprises mostly freshwater and snow-dwelling species, as well as lesser-known species from soil environments, which differ from freshwater species morphologically (Barcyté et al., 2020). It comprises Chloromonas species often found in snow blooms, Gloeomonas (Nakada et al., 2015), Ixipapillifera (Nakada et al., 2016), and Chlainomonas, whose phylogeny remains unclear (Barcyté et al., 2018). Chlainomonas cells found in snow blooms are represented by two species: Chlainomonas rubra and Chlainomonas kolii. The Chlainomonas cells are larger than the snow-dwelling red cyst-forming strains, and more ellipsoidal in shape. They possess small peripheral plastids, with or without a pyrenoid. Their swimmers (motile stages) have four flagella that are discharged above 2°C within seconds (Remias et al., 2016).

Chlorophyceae Trebouxiophyceae Ulvophyceae Chlorodendrophyceae Pedinophyceae

Size scale µm µm µm - cm <30 µm <10 µm

3,673 909 1,938 44 24

Number 0-102 0-4 0-4 4 1 flagella macroalgae: blade, asymmetrical, ovoid Cell tube, siphons, coccoid, rod, coccoid, rod ellispoid to ellipsoid, often morphology filaments. distinctly flattened Microalgae: unicellular, unicellular, colonial, unicellular, colonial, Growth form unicellular unicellular colonial symbiotic, multicellular parasitic Chloroplast one-many one-many one-many one one number mostly in fresh Fresh water, brackish marine, fresh mostly marine; fresh marine, fresh water, Environment water, the rest water, marine, and water, soil water, soil or soil habitats in soil, snow hypersaline habitats Asexual; Reproduction Asexual; sexual Asexual; sexual Asexual Asexual sexual

Table 1.2. The classes of Chlorophyta and some of their specific features. Based on Guiry and Guiry 2020; Stewart and Mattox, 1978; Li et al., 2019; Lee, 2018; Czech and Wolf, 2020; Ettl, 1983; Pröschold and Leliaert, 2007; Peksa and Skaloud, 2011; Holzinger et al., 2017.

The Ulvophyceae comprise unicellular and most of the multicellular marine green algae. They are a very important part of coastal and benthic environments, but poorly represented in 30 fresh water. The Ulvophyceae class comprises approximately 1,938 species (Guiry and Guiry, 2020; Table 1.2). It presents a great morphological diversity, with unicellular organisms, or multicellular and forming filaments, siphons, blades or tubes, that can be ramified or not, in uni- or bi-seriate structures (Pröschold and Leliaert, 2007).

The Trebouxiophyceae class has ~900 described species (Metz et al., 2019, Table 1.2) and is known for its aero-terrestrial species, especially those that can lichenize (Peksa and Skaloud, 2011). Trebouxiophyceae are widely present in freshwater environments, with a few species found in marine ecosystems. The Asterochloris lineage constitutes one of the most common lichen photobionts worldwide, with 20 lichen genera. Trebouxiophyceae also comprises invertebrate parasites (Yaman, 2008) including non-photosynthetic algae (De Koning and Keeling, 2006). Novel species were discovered on tree barks (Neustupa et al., 2011). The best-known Trebouxiophyceae is Chlorella spp., a model green alga genus constituting the core of Trebousiophyceae. The order Prasiollale within the Trebouxiophyceae class is especially known for its distribution in polar and cold-temperate regions. It has a single family with four genera that have a stellate chloroplast. Some species are subaerial (Rindi et al., 2007). The unicellular coccoid Coccomyxa are found in extreme environments such as very hot (Fucíková et al., 2014), highly acidic (Juárez et al., 2011), very cold (Hodac et al., 2016) and habitats with high heavy metal concentrations (Barcité and Nedbalová, 2017). The Chlorella and Stichococcus orders are small airborne microalgae, omnipresent in terrestrial and aquatic habitats (Hodac et al., 2016). The Coccomyxa taxonomy is also subject to debate and requires further work. The Prasiola are present worldwide, including polar regions, in marine, fresh water and soil environments (Holzinger et al., 2017).

The Chlorodendrophyceae are a small class of unicellular quadriflagellates, retained from the prasinophytes and streptophytes, with 44 described species (Table 1.2). It comprises one order, Chlorodendrales, one family, Chlorodendraceae, and 2 genera, Tetraselmis and Scherffelia (Naik and Anil, 2018). They are found in fresh water, brackish water, marine, and hypersaline habitats.

The Pedinophyceae are also a small class of asymmetric, uniflagellate green algae from marine, fresh water, or soil habitats, with 24 described species (Table 1.2). Its flagellum is unique, long, in a lateral to subapical position, inserted in a shallow groove or flagellar pit, curved backwards around the resting cell, directed backward during swimming, and usually covered by long, delicate flagellar hairs. This class possesses 3 orders: Marsupiomonadales, Pedinomonadales and Scourfieldiales, and four genera: Pedinomonas, Chlorochytridion (synonym of Pedinomonas), Resultor, and Marsupiomonas (Marin, 2012).

Charophyta Charophyta, also called Streptophycophyta, are the second green algae lineage. They are the closest relatives of vascular plants, with whom they share the Streptophyta phylum (Caisová and Gabka, 2009). Charophyta are usually found in fresh water, but can also be found in brackish water, permanent or temporary lakes, pools and rivers, as well as aero- terrestrial environments. Their presence is often considered as a sign of a ‘healthy’ ecosystem (Poikane et al., 2018). Some ancestral forms are thought to be of marine origin, but now do

31 not survive in high salinity (Puche et al., 2015). They dominated fresh water environments during the Permian and early Cretaceous periods but seem to be less diverse today than in geological records (Martin-Closas, 2003). This clade is not as species-rich as the Chlorophyta, nevertheless it has a great morphological diversity. The Charophyta can be unicellular, filamentous or parenchymatous. They have a laterally inserted flagellum with flagellar parallel basal bodies associated with a single broad band of microtubules. Their motile cells have two flagella. They also divide via open mitosis, like plants. The Charophyta have a persistent interzonal spindle during cytokinesis (Mattox and Stewart, 1975). Divergence of land plants from Charophyta was estimated to have taken place between 472.2 to 419.3 Million years ago (Morris et al., 2018). The best-known groups include the Klebsormidiales, the Zygnematales, the Coleochaetales and the Charales.

Prasinodermophyta When the full genome of the marine picoeukaryote Prasinoderma colonial, thought to be a Chlorophyta until recently, was sequenced (Li et al., 2020), its genome revealed that the Palmophyllophyceae class to which it belongs was actually a separate phylum from Chlorophyta, and a sister clade to it and the Streptophyta (Charophyta). It diverged before the split of Chlorophyta and Streptophyta. Members of this phylum have extremely compact, small genomes. The smallest eukaryote is a Prasinodermophyta, Ostreococcus tauri at 0.8 µm in length. They are found in the open ocean, coastal and deep-water, environments (Piganeau, 2020). They can be so abundant that they dominate the photosynthetic biomass in open oceans and coastal systems (Leliaert et al., 2011). 1.2.3 Secondary Endosymbionts The emergence of more complex photosynthetic organelles resulted from the occurrence of two or more events of secondary endosymbiosis (Figure 1.6B). This happened multiple times in different green and red lineages. As an example of these dramatic milestones in the evolution of photosynthetic eukaryotes, an Archaeplastida alga, Rhodophyta or Chlorophyta, was engulfed by a secondary eukaryotic host. Over evolutionary time, the engulfed photosynthetic endosymbiont lost most of its subcellular structures, reduced its genetic material, and remained completely dependent of its host. The reduced engulfed alga thus formed a so-called ‘secondary plastid’. These plastids have three to four membranes and specific machineries to import and export molecules.

The four membranes limiting most secondary plastids are believed to derive, from the outermost to the innermost one, from the phagocytic membrane (the cytoplasmic membrane of the phagocytosing eukaryote), the plasma membrane of the endosymbiont, and the two membranes of the primary plastid envelope (Gould et al., 2008). One of these membranes is believed to have been lost in some secondary endosymbionts containing plastids with three bounding membranes, like in Euglena (Stoebe and Maier, 2002).

Some secondary plastids retained more relic structures from the engulfed primary alga, such as a vestigial nucleus called the nucleomorph, as well as a vestigial cytoplasm, sometimes containing ribosomes, in the periplastid space from the phagocytosed eukaryote (Archibald, 2007; Flori et al., 2016).

The plastid DNA of Heterokonta is more closely related to Rhodophyta plastid DNA indicating its red algae origin, while Euglenophyta (in the Discoba) and Chlorarachniophyte (in the Rhizaria) plastids derive from green algae. In spite of polyphyletic issues, it has been possible to define the TSAR super-group (Telonemids, Stramenopiles, Alveolates, and Rhizaria) which includes diatoms, kelps, dinoflagellates and protozoan parasites, the Haptista group, which comprises the haptophyte algae and the Discoba group, which includes the Euglenophyte algae (Burki et al., 2020) (Figure 1.8).

Figure 1.8 The new tree of life, from Burki et al., 2020. This figure shows the Archaeplastida (highlighted in green), the two super-groups deriving schematically from secondary endosymbiosis events with red algae, TSAR (highlighted in yellow and red) and Haptista (highlighted in brown), and groups deriving from secondary endosymbiosis events with a green alga, such as Discoba in the Excavates, circled with dashed lines.

Some secondary endosymbiotic algae produce harmful toxins that poison shellfish and fish, and subsequently humans when ingested. The thrive of the first secondary endosymbiotic toxic algae coincided with the extinction of many filter feeder species (Lee, 2018).

In this chapter, we only list a few noticeable clades of secondary endosymbionts, being able to spread in marine and fresh water environments.

Diatoms, also called Bacillariophyceae, are one of the most important and abundant groups of phytoplankton and can also be benthic. They occupy a very wide range of ecological niches. They are found mostly in marine environments where they are successful thanks to fast responses to changes in light exposure, and nutrient storage. They are also found in fresh water, brackish water and aero-terrestrial environments (Van Dam et al., 1994). They dominate some freshwater environments along with green algae, both in diversity and biomass (Bellinger and Sigee, 2015, pp 1-4), but are barely present in snow and freshwater-glaciers (Healey, 1978). In both sea and sea ice, pennate diatoms and dinoflagellates thrive in cold habitats both in the Arctic and Antarctic and constitute an important food source for marine species (Jones, 1996; Lee, 2018 pp 345-350; Rózańska et al., 2009; Vancoppenolle et al., 33

2013). They can be quite diversified and abundant, even dominant, especially in microhabitats like high salinity melt ponds (Lee et al., 2012). They have a high diversity in open oceans, as well as along coastlines (Malviya et al., 2016). They are used for ecosystem health monitoring as some species are resistant to pollution while others are not (Visco et al., 2015).

Ochrophyta comprise numerous important photosynthetic phyla. Pelagophyceae are members of the ultraplankton less than 2 µm large. Some are at the origin of so-called ‘brown tides’, which can reach a biomass so thick that they completely block light from penetrating through to lower levels of water bodies. They can survive at very high salinity, as well as low temperature and light. Dictyophyceae, also called golden-brown algae, are ameoboid vegetative cells with rhizopodes, which have mostly marine but also freshwater members. Some Dictyophyceae like the Silicoflagellates are very prominent in cold waters. The Xanthophyceae are a mainly freshwater and terrestrial group with few marine species, and are yellow-green. They can be motile or non-motile and have the ability to form resting spores (Lee, 2018, pp 401-411). Phaeophyceae are multicellular algae and appear brown because of their fucoxanthin pigment. They are almost exclusively found in marine ecosystems, but some species are freshwater-dwelling, and some are found in brackish waters (Lee, 2018, pp 415- 469).

Finally, Dinoflagellates are also a noticeable group of secondary endosymbionts, not dominant in fresh water but they can form blooms, including toxic ones, in eutrophic lakes and ponds (Wehr et al., 2015, pp 1-10). 1.2.4 Photosymbiosis The association of a photosynthetic prokaryote or eukaryote cell with a non- photosynthetic organism is also often encountered in marine and terrestrial environments, owing to a general process called ‘photosymbiosis’. It is considered that both photosynthetic and non-photosynthetic partners benefit from this association, to such an extent that it becomes essential (e.g. Decelle et al., 2019). The concept of species is thus at the limit of its definition, although some controversial classifications of photosymbiotic systems, such as lichens, have led to species-like nomenclatures.

Lichens are symbiotic associations of at least a fungi and an alga, sometimes also joined by bacteria. For a long time, scientists believed that a single fungus and a single alga were involved, but genetic studies have shown that multiple fungi and algae can make up a single ‘species’ of lichen. Lichens are named according to the fungi moiety, or mycobiont, which is not ideal because multiple different algal partners can form an association with a fungus, and the resulting lichen is different phenotypically and biochemically. The algal partner is called the photobiont and can be either a eukaryotic algae (usually, but not exclusively, green algae), or a cyanobacterium, or both. Some species of lichen have a specialized reproductive mechanism in which the mycobiont and the photobiont spread together to colonize a new location, but generally the fungus produces spores on its own, which once spread waits for a photobiont to spread onto it. Lichens are found globally in environments that have growth inhibiting parameters. Since they are slow growing (from less than a millimeter to a few millimeters a year) they require little competition. Marine lichens are found in intertidal spaces (Sanders et al., 2004). Lichens also grow in polluted environments, on slow growing trees, 34 and on light exposed rocks (Chen et al., 2000). They are especially prominent in higher altitude environments because they resist UV exposure and desiccation well (Villar et al., 2005; Bergamaschi et al., 2002).

In the alpine environment (next chapter), algae belonging to all the branches described in this chapter can develop in very diverse habitats. Their life cycles can alternate forms subjected to strong contrasted environmental conditions (from dry to wet and even icy conditions, with or without snow) due to seasonal variations, populating different habitats (from river to wetlands or rocky surfaces), be part of communities with strong species compositional changes, and even alternate free-living with lichen-associated forms. Understanding algal biodiversity at high elevation, spatiotemporal patterns, and the dynamics of populations and communities, requires a basic understanding of the corresponding environments.

Chapter 2. The alpine environment

Alpine environments constitute the world’s highest-altitude terrestrial habitats, covering a total area of 3.3 million km2 (Körner et al., 2017). Despite their global distribution, as they are located on different continents with different origins, these environments share several characteristics. These are discussed below by introducing the generalities of the alpine biome, before addressing the focus of this study, the French Alps. 2.1 The alpine biome Alpine environments are part of the cryosphere, which is composed of the coldest terrestrial environments. They are of great interest because of the amount of fresh water they hold, a resource very unequally distributed across the globe. Mountains are considered the ‘great providers of wild water for all continents’ (Margat, 2011). Mountains that provide more water than the lowland areas are even called ‘water towers’ because of their importance, providing up to 95% of fresh water to human communities (Viviroli et al., 2007). The most famous alpine mountain ranges are the European Alps, the Andes in South America, the Rocky Mountains in North America, and the Himalayas in Asia. The highest mountain peak, Mount Everest in the Himalayas, reaches 8848 m asl. Alpine biomes are classified together with polar regions in the Köppen climate classification (Peel et al., 2007), and they have many similarities. Because of their high altitude, these alpine biomes are characterized by extreme variation in UV exposure (Vinebrook et al., 1996) and temperatures over seasons and diurnal cycles, which cause episodes of freeze-thaw cycles (Remmert and Wunderling, 1970). They also have short growing seasons and zonation where the conditions, such as strong winds and low temperatures, are too harsh for trees (Wilson, 1959). This creates a delimitation called the tree line where they disappear in favor of shrubs (Niessen et al., 1992).

Alpine ecosystems constitute a crucial food and water resource and provide multiple services for humans. For example, the European Alps glaciers provide up to 90% of drinking water to populations living in the valleys below them, as well as irrigation and hydroelectric power. Alpine biomes are also involved in cloud formation and precipitation (Cotton and Anthes, 1992). The rich biodiversity of plants secures soils, protecting populations against natural hazards (Egan and Price, 2017). Beyond those services, alpine ecosystems are greatly associated with cultural identity. Since the 20th century, they provide new services such as tourism and recreation, becoming a major source of livelihood for mountain communities (Palomo, 2017).

However, globally, the effects of climate change are accentuated in these biomes (Beniston, 2003) and especially in the alpine tundra, questioning the durability of these services (Diaz, 2007). Within the alpine biome, there are different types of environments that interact with each other. Fresh water environments comprise lakes and ponds as well as snow and glaciers. Aero-terrestrial biomes are constituted of soil and air that transiently contains organisms, facilitating their transport. Soil environments vary greatly along altitude because 36 they can be bare, or covered in shrubs or dense forests, resulting in very different chemical and biological composition profiles. 2.2 The French Alps geography and topology The European Alps are a crescent-shaped and relatively young range of mountains representative of a collision belt and have a surface area of 190,000 km2 (permanent secretariat of the alpine convention, 2010). Located in south-central Europe and spanning eight countries from France to Slovenia across 1,200 km, the European Alps were formed by the continental collision of the Adriatic (African) and Mesozoic subducting (Eurasian) tectonic plates during the Cretaceous period (Piaz et al., 2002). The Alps are therefore constituted of layers of African, European and Oceanic rock (Schmid et al., 2004). The width of this mountain range is at a mean of 200 km and the mean ridge height is 2,500 m (Cebon et al., 1998). The Alps' highest peak, the Mont Blanc, reaches 4,808 m above sea level. The French Alps span eight French ‘départements’ from Var to Haute-Savoie. The valleys and lakes carved between the mountain ranges are the result of recent ice ages when glaciers flowed and caused rock erosion. The glacier flows also transported rocks and boulders away from their original location as they melted. Moraines, large deposits created at the edge of glaciers as they melt, also prevented water flow, creating alpine lakes of which some are still present today, though they are remnants of the much larger original lakes. Mountains around valleys are steep, characteristic of the passage of glaciers.

2.3 Climate and environmental conditions in the French Alps The French Alps extend through a large area, and thanks to size and topologic complexity, they have many different climatic influences and regional climates as well as contrasted seasonal weather (Dumas, 2013). The alpine climate depends on north Atlantic ocean processes and locally on orographic precipitation and cyclogenesis as well as strong winds (Wanner et al., 1997). Orography (or the topography and relief) plays a strong role in depression and can induce cyclogenesis, cyclonic circulation in the atmosphere. Orographic cyclogenesis is also called lee cyclogenesis. The Alps are so expansive that they mark a separation between two climate zones: the mid-latitude temperate zone and the Mediterranean zone. In the south, the influence of the Mediterranean renders this zone highly cyclogenetic. The relief on the east on the contrary, makes it anti-cyclonic (Buzzi and Tibaldi, 1978).

Figure 2.1 Annual temperatures and precipitation at different altitudes in the French Alps. Data: https://fr.climate-data.org/.

2.3.1 Atmospheric composition and pressure in the Alps The atmosphere on Earth is particular in that it interacts with its oceanic and continental surface, and its composition is largely influenced by biological processes (Fowler et al., 2009). Organic matter composes most of the atmospheric aerosols. These also have elemental carbon, accounting for 15% of carbonaceous aerosol content, though organic carbon dominates. There has also been an increase in recent years in dust quantity reaching the Alps’ atmosphere. Dust is a major contributor of aerosol particulate matter in the atmosphere. Dust originating from the Sahara Desert can undergo mixing with secondary compounds such as ammonium sulfate and nitrate, carboxylic acids, nitrogenous compounds and sea salt during transportation (Wang et al., 2013). The gases in the atmosphere have evolved due to anthropogenic activities, increasing concentrations of carbon dioxide, methane, nitrous oxide and chlorofluorocarbons (Cebon et al., 1998). The atmospheric pressure is directly related to local weather rather than global changes.

Atmospheric aerosols are not only an important factor in airborne environments, but in all environments because precipitation washes them down to Earth.

Nitrogen is a limiting factor in many environments, and anthropogenic activities release four times more nitrogen in the atmosphere than natural processes, modifying balances and therefore ecosystems that depend on the natural cycling of reactive nitrogen (Galloway et al., 2004). The ozone concentration has been decreasing since the 1970s by 0.23% per year. Ozone in the upper troposphere has two origins: leaking from the stratosphere, where most of the ozone lies, and photochemistry. The decrease in photochemistry-originating ozone is

linked to increase in NOx gases in the upper troposphere. Average daily NOx in the springtime

maximum above the Swiss Alps was 50 pptv. The source of the NOx was presumed to be from aircraft emissions, lightning and layer entrainment (Carpenter et al., 2000).

CO2 levels are increasing worldwide, mostly due to anthropogenic activities, there is no evidence that this increase is affecting plants at higher altitude differently than at sea level. In grasslands, a study found that increases in CO2 levels lead to an increase in biomass (Riedo et al., 2000). 2.3.2 Spatial, daily, seasonal and annual variations in temperature Air temperature decreases by about 0.65°C every hundred meters of elevation gained (Cebon et al., 1998). The high-altitude Alps are a harsh environment that experiences temperature extremes seasonally and daily. Altitude gradients provide abrupt ecosystem changes linked to temperature variations. These are especially visible at tree lines and snowlines, whose position is directly related to temperature rather than altitude itself. The zone above the tree line is called the ‘alpine zone’. Depending on the latitude the mountain is located at, it can be between 3,500 and 4,500 m above sea level at the equator, to 2,000-3,000 m in the Alps, and to 1,000-2,000 m in Alaska (Beniston, 2000). It also creates microenvironments that harbor endemic species, which are especially threatened by climate change. Weather and climate are very different depending on the altitude in the French Alps. Figure 2.1 represents temperature and precipitation annually in four different cities from the valley to high altitude. In Grenoble (200 m asl, in the valley) the mean annual temperature (over the period of 1987-2012) is 11.2°C and the type of climate is oceanic. Chamonix, at 1,305 m in the Mont-Blanc massif, has a temperate continental climate and averages 7.3°C annually. Les Menuires is located at 1,805 m asl and has a subarctic climate and an annual temperature of 3.8°C. In Val-Thorens, the highest of the four cities, at 2,300 m asl, the mean annual temperature is 1.1°C, and the climate is characterized as tundra (https://fr.climate- data.org/). There are differences between valleys, depending on the orientation of the massifs around them. The southern French Alps are warmer because of the Mediterranean climate influence (Auffray et al., 2010). At very high altitude, temperatures can be much lower. For example, 200 m south of the col de Dome, at 4,280 m asl on a windy glacier, the annual temperature in 1976 was -14°C (Batifol and Boutron, 1984). 2.3.3 Light and UV exposure Solar ultraviolet radiation (UV) has a low wavelength, below the visible spectrum (180 nm < X < 400 nm, see Figure 2.2). UV radiation intensity depends on many radiative transfer processes in the atmosphere and at the Earth surface, including absorption of radiation by gases like ozone and sulfur dioxide, scattering of radiation by aerosols, clouds and the Earth’s surface. Mountains receive an increased amount of UV since the pathway of transmission is shorter, there is low aerosol load, and there is higher reflectivity of the snow surface (Kerr, 2003). In Northern latitudes, the depletion of the ozone layer caused an increase in UV-B radiation, which is dangerous to DNA molecules (Aldhous, 2000). UV is arbitrarily divided into UV-A (400-320 nm), UV-B (320-280 nm), and UV-C (280-180 nm, see Figure 2.2). UV- A increases by about 9% per 1,000 m of altitude gain and UV-B by 18% (Blumthaler, 2012). UV-C does not currently reach the Earth surface. UV radiation was much higher when Archean life forms first appeared, selecting for strategies that helped these organisms resist the highly mutagenic rays, which affect DNA by creating thymine dimers and other mutations. Their descendants today may have inherited those strategies to survive the higher UV 39 radiation at high altitude. The UV-B component is especially dangerous for DNA, proteins and lipids, and is much stronger where the ozone layer is depleted in the Antarctic Peninsula (George et al., 2001). Mechanisms of UV-resistance are detailed in chapter 4. In alpine lakes, UV is attenuated by absorption by phytoplankton, and by a lesser fraction, by dissolved organic carbon. UV radiation in alpine lakes affects shallow-water algae community composition in cold systems through direct effects (Vinebrooke and Leavitt, 1999).

Figure 2.2 Light spectrum from IR to visible to UV.

2.3.4 Precipitation, snow and winds Precipitation The relationship between precipitation and elevation is complex and depends on time scale, exposure and atmospheric flows (Vionnet et al., 2016). Precipitation is difficult to predict, because its natural variability is much greater than that of temperatures, though measurement efforts have increased in the last few decades in mountains. Precipitation varies greatly between levels seen in the lower valleys (~700 mm per year) to the high altitude (>2,000 mm per year, Auffray et al., 2010). The French Alps, subject to Mediterranean climate, are vulnerable to heavy rainfall because the steep catchments areas can generate floods, mudflows and avalanches (Kiefer Weisse and Bois, 2001). The overflow in rivers can affect regions far from their alpine origin. The driest part of the Alps is in the Aoste Italy region with means below 550 mm per year. Taking our four example cities at different altitudes, we note that precipitation levels increase with altitude. Grenoble averages 856 mm per year; Chamonix, 1055 mm; Les Menuires, 1355 mm; and Val-Thorens mean annual precipitation is 1605 mm (https://fr.climate-data.org/). Snow Snow cover depends on frequency and intensity of snowfalls, atmospheric circulation and temperature. As a consequence, there is a distinct interannual variability (Martin and Etchevers, 2005). Snowfall levels and number of days with snow depend on altitude as well as latitude (Auffray et al., 2010). The percentage of days with more than 20 mm of snow per 40

day at 1,500 m asl varies from 2 to 10% across the French Alps, being at their highest in the Northern regions because of higher precipitation. Variations between massifs were lower on annual data than on seasonal data (Martin and Durand, 2005). Snow depth depends on snowfall, therefore precipitation, but also temperature cycles, exposition and wind, which can cause erosion. Snow annual accumulation can reach several meters at very high altitude, for example 6-8 meters in 1972 near the col du Midi, at 3,560 m asl (Batifol and Boutron, 1984). Figure 2.3 shows that the number of days with more than a meter of snow on the ground is steadily decreasing, though the number of days with snow stays relatively stable.

Figure 2.3 Yearly snowfall at col de Porte, French Alps, 1,326 m asl, since 1961. Figure originating from meteofrance.fr.

Winds Winds in the French Alps are mostly oriented along the north-south axis since the mountains themselves block winds from other directions. Relief in some instances protects against stronger winds, which are only found often at summits (Auffray et al., 2010). 2.3.5 Seismic activity Seismic activity generally occurs at low magnitude (lower than 1.5 daily) but occasionally occurs at higher magnitudes in the southwestern Alps (above 3, and sometimes above 5) occasionnally leading to minor damage, so the region is closely monitored (Thouvenot et al., 1998). Correlated with heavy rainfall, seismic activity can cause landslides and mudslides (Amitrano et al., 2007).

2.3.6 Aero-terrestrial environments and zonation Zonation Zonation is the distribution of species in distinct zones depending on abiotic parameters like atmospheric temperature, precipitation regime, or light exposure. In the Alps, it depends more on temperature gradients along altitude than the altitude itself, though there are many biotic and abiotic factors that dictate their position (Tranquillini, 1979). In the European Alps, this line is also moving upwards in altitude over time due to changes in land anthropic exploitation, namely abandonment of alpine summer pastures (Wallentin et al., 2008). Tree lines can represent an abrupt switch in vegetation from forests and tall trees to low growing vegetation. Even below the tree line, there are changes in tree and shrub species along the altitude gradient (Daubenmire, 1943; Ohsawa, 1993).

In the Alps, there are five main zones (also called belts) determined by climate with, from top to bottom, the alpine tundra, the alpine grasslands above the tree line, the subalpine zone, the mountain zone and the foothill zone under it, all depicted in Figure 2.4. The altitude at which these zones lay depends on temperature, therefore they vary depending on orientation (so-called ‘ubac’, or north-oriented side, in the shade most of the day, and ‘adret’, south- oriented side, more exposed).

The alpine tundra in the French Alps is the zone located at the summit of the mountain, above 3,000 m. It therefore has the coldest temperatures and the highest radiation, as well as strong winds as it does not benefit from protection provided by higher ridges. Vegetation is usually only seasonal, except for specially adapted species like some lichens, microalgae (including snow and ice algae, depicted in red patches on the snow on Figure 2.4) and bryophytes. This zone also comprises glaciers.

Below, up to 2900-3000 m asl, the alpine grasslands and shrublands are a specific biome composed of alpine meadows. It has more vegetation but is also located above the tree line. It mostly has low vegetation such as shrubs and cushion plants.

Under the tree line, up to 2200-2400 m asl, the first zone is the subalpine zone, whose vegetation is mostly coniferous forests. Under it, the mountain zone has very diverse vegetation reaching altitudes up to 1500-1700 m asl.

Lastly, the foothill (also called colline) zone is the lowest, with altitudes up to 900-1100 m asl.

Figure 2.4. Zonation in the French Alps. ‘Adret’, south-oriented side; ‘Ubac’, north-oriented side. Designed in Gimp, inspired by “The formation of the Alps” exhibit from the Muséum de Grenoble.

Chemical composition of aero-terrestrial environments The French Alps receive dust and sand that has traveled thousands of kilometers from the Sahara Desert in North Africa. Soot suspended in the atmosphere can also originate from industrial areas in close or more remote locations. Dust brings minerals and nutrients including nitrogen, carbon and phosphorus, and serves as fertilizer for species that live there (De Angelis and Gaudichet, 1991).

Carbon in the environment can occur in different forms. Organic carbon composes a large fraction of total atmospheric aerosol. So-called ‘carbon black’ (CB) is a manufactured product resulting from partial combustion that is used in the industry. CB has low extractable organic carbon, and if bound it is not released in the atmosphere and therefore is not a major concern for atmospheric pollution. Soot, often mixed up or grouped with black carbon, also results from incomplete combustion of wood and coal. Soot is an undesired byproduct and is almost elemental, near graphitic but has high extractable organic carbon content. So-called ‘black carbon’ (BC) is often confused with CB and called soot-like but is also a distinct form of carbon (Long et al., 2013). BC is a product of incomplete combustion from engines and is a carbonaceous aerosol that is highly light adsorbing; therefore, it can be monitored in snow (Long et al., 2013). Brown carbon (BrC) is also a light adsorbing form of carbon, and its specificity is that it adsorbs light at higher wavelengths, into the UV range. Both BC and BrC facilitate water evaporation and cloud evaporation by immersing in cloud droplets. Graphite is an inert form of pure elemental carbon (Long et al., 2013; Andreae and Gelencsér 2006; Laskin et al., 2015). The soil holds the highest carbon stock of any environment.

The quantity of organic carbon in the soil is heavily influenced by the type of plant inhabiting it, as well as atmospheric composition and climate. The first 20 cm of the soil had

43 an average of 33%, 42%, and 50% of the total organic carbon measured in the first 1 m of soil for shrublands, grasslands, and forests, respectively. In the second and third meter of soil, the amount of carbon was lowered to 77%, 43% and 56% for shrublands, grasslands, and forests, respectively. Organic carbon content was negatively correlated with sand content, but positively correlated with precipitation. It decreased slightly with temperature (Jobbagy and

Jackson, 2000). Winter CO2 fluxes were positively related with carbon availability in the soil in winter, and atmospheric CO2 was a limiting factor since adding glucose to soil increased

CO2 fluxes, also indicating heterotrophic activity in the soil below 0°C (Brooks et al., 2004). This is especially true at high altitude. This was shown at the tree line limit, as when the temperature was increased by 4°C, CO2 fluxes between vegetation and soil increased by 45% while carbon uptake increased by 12-17% but this is highly species dependent. Warming also resulted in drier soils, similar to what was observed during the heatwave of 2003 (Hagedorn et al., 2010). Pasture and grazing and their effect on the environment Grazing in the Alps has a strong effect on chemical composition of the environment but also on species distribution. These effects are asymmetrical and heterogeneous. They are also detrimental for biodiversity as they concentrate nutrients in specific areas and cause their loss in others, leading to a decline in productivity of crops (Jewell et al., 2007). Open pasture soils have greater microbial biomass and faster mineralization potential, but slower mineralization rates compared to forest pasture soils. Forest pastures on the other hand, have lower nitrogen uptake by microbes and plants. Soils were found to have a high capacity of internal nitrogen restoration after forest clearance since even new pastures had similar rates to old pastures (Hackl et al., 2000). Grazing favors tall grasses rather than stress-tolerating vegetation (Mayer et al., 2009). 2.3.7 Lakes and rivers Alpine lakes have their own characteristics and communities, including for viruses (Hofer and Sommaruga, 2001), which are in a delicate equilibrium. Dust has been shown to act as fertilizer to alpine lake biota (Reche et al., 2009). Changes in nitrogen, sulfur deposition and temperature led to a decrease in inorganic nitrogen but an increase in sulfate, base cations and silica in alpine lakes. An increase in phosphorus concentrations in alpine lakes, brought about by precipitation contaminated by air pollution driven by anthropogenic activities, led to changes in the trophic state of the lakes (Brahney et al., 2015). Alpine lake ecosystems are quite sensitive to climate change (Parker et al., 2009), which can have impacts on pH (Koinig et al., 1998), algae populations (Holzapfel and Vinebrooke, 2005) and bacterial composition (Pérez and Sommaruga, 2006). Alpine lake ecosystems are also threatened by the artificial introduction of new species (McNaught et al., 1999). UV radiation affects alpine lake algae communities in a species and habitat specific manner (Vinebrooke and Leavit, 1996). 2.3.8 Snow, Glaciers and Cryoconites The cryosphere is composed of snow, glaciers and permafrost (frozen ground). It plays an important role in global climate but is threatened by climate warming.

Temperature and snow Temperature on the surface of melting snow is ~0°C, and below the surface is < 0°C independently of the air temperature (Kol, 1968). In the spring when air temperatures are above 0°C, there is evidence that photosynthesis is possible for plants and algae under snow cover (Starr and Oberbauer, 2003). Counter-intuitively, warmer air temperatures reduce the insulation effect of snow cover, which may lead to a decrease in soil temperature under snow cover, and an increase of their frosting. The snow cover in the past decade has been found to melt earlier in the spring and accumulate later in the fall, affecting the freezing and water content of soil (Brooks et al., 2011). Having consistent snow cover depends on precipitation and temperature. It is crucial to limit frost damage to soil-dwelling species (Kudo, 1991). Snow receives ~1,500 µE m-2 s-1 of sunlight in the spring when snow algae are sampled in the Alps (Grinde, 1983). When snow melts, its water content increases to 40-60%. The snowpack depth can drop by up to 10 cm per 24 h depending on the exposition of the snow (Grinde, 1983). Light and albedo in snow and ice Snow and ice are some of the most reflective natural surfaces. The reflectance of sunlight on surfaces is called ‘albedo’. The higher the albedo, the more sunlight is reflected back outwards of the Earth. The lower the albedo, the more energy from the light is absorbed and the more the Earth is warmed. Albedo therefore has a crucial role in the maintainence of Earth's temperature and is receiving increasing attention in light of climate change and especially global warming. Solar energy reaching the snow/ice surface depends on wavelength, zenith angle, grain size, impurity content, and cloud cover (Warren 1982). The less the snow/ice is pure, the darker it becomes, the more its albedo decreases and the more it absorbs heat.

Pollution from precipitation, scavenged in the atmosphere, ends up in the snow and affects its albedo (Zinder et al., 1988). In snow, light is not uniformly absorbed (Kawecka and Drake, 1978). Local solar energy can reach 4,500 µE m-2 s-1 to 6,000 µE m-2 s-1 in the snow in the Alps, which is 3 to 4 times more than at sea level (Gorton et al., 2001). In the Alps, the UV-B radiation flux was found to increase by 1% per year from 1980 to 1990 (Blumthaler and Ambach, 1990). Snow composition Snow composition is affected by aerosols deposited on its surface, such as dust, even at high altitude (Ronseaux and Delmas, 1988), inorganic matter from rock weathering, and pollution due to anthropogenic activity that has steadily contaminated snow and ice all over the world (Hadley and Kirchstetter, 2012; Kawecka and Drake, 1978).

Carbon content in snow and ice is very irregular, varying from ~1 to ~400 ppbC (part per billion carbon) of organic carbon (OC) between samples from the Alps, Antartica and Greenland. In the French Alps at 4,250 m in the Mont Blanc massif, levels of OC averaged 70 ppbC, much higher than in Antarctica and Greenland (respectively 10 and 20 ppbC). The levels of OC in the Alps and Greenland doubled since the pre-industrial era due to an increased input of aerosols and water-soluble gases from anthropogenic activity. They range from 45 to 45

70 ppbC in winter to 90 to 304 ppbC in summer, where the content is higher. In surface snow, measured in the fall, OC was at ~100 ppbC (Legrand et al., 2013 a & b). It was also suggested that fires and fossil fuel combustion in summer could be at the origin of a seasonal higher OC level in the Alps, lowering the snow albedo, and leading to increased warming. BC presence on snow surface is contributed to by anthropogenic activity by 80%. Predictions for the increase of BC in snow indicate that its presence can contribute an increase of 0.10°C in temperature (Flanner et al., 2007; Hadley and Kirchstetter, 2012). Graphitic carbon was detected in snow meltwater in the USA at a concentration of 4.9-15.7 µg/L, whereas polar samples only reached 2.1-2.6 µg/L (Chylek et al., 1987).

Nitrogen is one of the most important nutrients for algae. It was analyzed in the snow and soil at the Lautaret Pass in the French Alps in a study that showed that there was mostly organic N, and total N quantity did not vary spatially. It was concluded that negligible amounts of snow N ended up in the soil underneath compared to other sources, though its mineralization was influenced by snowmelt through changes in temperature and moisture + (Clement et al., 2012). Nitrogen was measured in snow blooms in Svalbard in NH4 form, and found to be variable, from < 0.005 to 1.2 mg N/l. The highest concentrations coincided with - the presence of moss vegetation. In its NO3 form, nitrogen concentration was very low, < 0.1 mg N/l (Spijkerman et al., 2012). Lead pollution in snow in the French Alps was also determined to be caused by engine combustion from local sources.

Phosphate measurements are more rarely done in snow and ice, and are usually found to be below the detection limit, with some exceptions like in Hoham and Duval, 2001, where 3− phosphate was measured at 1.6- 2.2 µg/l of P-PO4. In Svalbard snow blooms, PO4 was measured at <18 μg P/l concentrations (Spijkerman et al., 2012).

Heavy metals are also encountered in snowpack. A study of the French Alps around the city of Grenoble in the beginning of the 2000s at medium altitude up to 1,800 m evaluated mercury (Hg) levels in fresh snow. Concentrations of total Hg were between 13 and 130 pg per gram of snow, and reactive Hg concentrations were < 0.8 pg per gram of snow (Ferrari et al., 2002). A previous study sampled snow at high altitude in the 1970s from Mont Blanc, from 3,560 m to 4,785 m asl showed that there was great variation between samples, and it was concluded that snow (from the surface to 3 m deep) was very pure at the time of sampling, though heavy metals levels were found to be one order of magnitude higher than that of Greenland snow, e.g. 1 to 15 ng per gram of snow for Pb. Lead pollution in snow in the French Alps was determined to be caused by engine combustion from local sources. Lead levels reached 6.0 ng/m3 in 1998-1999 at Alpe du Grand Serre (1,750 m, asl Veysseyre et al., 2001). Therefore, even at that time, and at that altitude, anthropogenic effects were regularly measurable in the past decades. For other elements (Na, Mg, K, Ca, Fe, Al and Mn), levels were similar to ones measured in Greenland and Antarctica. Fe concentrations ranged from 20 to 493 ng per gram of snow (Batifol and Boutron, 1984). Snow acts as a carrier of nutrients to the algae, bacteria and fungi living in it. The most relevant nutrient concentrations measured in literature for this study are in Table 2.1.

Finally, in snow blooms, elements such as Si, Cl, Fe, Ca and K were found at high concentrations both in the snow and in algae sampled at its surface (Fierdingstad et al. 1974).

Snow pH and conductivity Snow pH is usually slightly acidic to neutral and has been measured from 3.2 to 7.6 (Hoham et al., 1983; Newton, 1982; Bischoff, 2007; Prochazkova et al., 2019). Conductivity is usually very low across all studies examined < 10 µS/cm (10 times less than tap water) and recorded at between 6.8-89 µS/cm (Bischoff, 2007; Prochazkova et al., 2019; Remias et al., 2016).

Measure type Concentration Study

NH4+ ranging from <0.005 to 1.2 Spijkerman et al., N mg N/L; NO3- <0.1 mg N/L 2012

Hoham and Duval, 1.6- 2.2 µg/L of P-PO4; <18 μg P 2001; Spijkerman et al., P/L of PO4 3- 2012

~1 to ~400 ppbC of organic carbon Legrand et al., 2013 a C (OC) & b

pH of snowmelt 4.5 to 6.7 Bischoff, 2007

Conductivity of 6.8-37.9 µS/cm; usually < 10 Bischoff, 2007 snowmelt µS/cm

Table 2.1. Relevant nutrient concentrations in snow and ice for microalgae.

Cryoconites Cryoconite holes are cavities in ice with a diameter that ranges from centimeters to several meters. They are usually half a meter deep, representing 0.1 to 10% of the glacier surface (Anesio et al., 2009). Cryoconite holes are a closed biogeochemical system filled with ice, water from melted ice, much darker than the ice around it because it has a much denser biomass (Wharton et al., 1985), as well as mineral and organic debris. Cryoconites occur all over the world. They are usually seasonal, but some are semi-permanent and possess ice lids which keep them isolated from the atmosphere for up to several years (Bagshaw et al., 2007; Anesio et al., 2009). Cryoconites can produce up to 98 Gg of carbon per year altogether. The temperature in a cryoconite is close to 0°C (Anesio et al., 2009). They contain many different types of organisms, from photoautotrophs and bacteria to insect larvae, tardigrades and collemboles (Takeuchi, 2013). 2.4 Climate change and anthropogenic activity impact on the Alps The mean temperature in France in general has seen an increase from its reference since the 1980s, breaking records in intensity and duration (data from meteofrance.fr). That effect is accentuated in the Alps, especially in the spring and summer (Gibelin et al., 2014). The 47

European Alps have seen their average temperatures increase by at least 2°C in the northern Alps and 1.7°C in the southern Alps since the 1980s. The rise in temperatures is at the origin of most of the visible effects on the mountain environment. The rate of warming has increased in recent decades reaching 4°C per 100 years. It is especially accentuated in medium altitudes in the French Alps (1,500-2,000 m asl) where a sharp decrease in snowfall has been observed as a direct result of warming (Laternser and Schneebeli 2003). As a result, the spatial distribution of species has progressed in altitude. Tree lines are expected to move upwards in altitude as the temperature increases, reshaping the Alps and possibly leading to habitat fragmentation and loss of biodiversity (Smith et al., 2009). Precipitation seems more asymmetric but globally stable since the climate-warming trend started, while the cloud coverage was found to be more frequent.

Snowfall has been decreasing in levels overall, and the number of extreme snowfall levels has also fallen drastically, a phenomenon that is predicted to worsen over time because warming decreases the snow/rain ratio (Eihorn et al., 2015).

Glaciers present the most apparent signs of the effects of warming, having shrunk by 30 to 40% in surface area since the Little Ice Age (∼1850), and some glaciers are predicted to totally disappear in the next 50 years (Haeberli and Beniston, 1998; Brugnara, 2020). The cover of glaciers is shrinking, leading to lower river run-off levels, which can lead to floods downstream. The amount of debris flows is predicted to increase significantly as well. Anthropogenic activities also have had a direct and indirect impact on the reshaping of the Alps, such as intense agricultural practices and the management of natural stocks of species, such as a drastic reduction of the population of some animal predators. Shepherds bring their herd to higher altitudes for grazing, and hunters are driving large predators to their extinction (Breitenmoser 1998). The increase of human activity in the French Alps also increases the risk of avalanches and debris flows (Jomelli et al., 2007). Fires are at much higher risk of occurring in the Southern Alps, and at an increased risk at medium altitude in the Northern Alps (Dupire et al., 2017).

Snow composition is also affected by anthropogenic activities. The concentration of + - nitrogen in NH4 and NO3 forms in alpine glaciers, lowland streams, and the atmosphere have increased in the last century, and are predicted to continue to do so. Nitrogen in water is considered a contaminant and is linked to eutrophication and acidification (Piatek et al., 2005).

Abiotic, biotic and anthropogenic factors therefore have a strong influence on high elevation environments and the natural communities who occupy the corresponding habitats. The next chapter focuses on our current knowledge on microalgae living in mountain areas.

Chapter 3. Alpine microalgae biodiversity

Mountain gradients have great environmental variations across small distances, and therefore offer an interesting playground for species distribution research (Sathyakumar et al., 2020). Alpine environments host a great diversity of species relative to their surface area: it is estimated they support one third of all terrestrial species diversity and about twice the number of flowering plant species than expected from their surface (Körner et al., 2017). Alpine environments additionally house many endemic species (Barthlott et al., 2005). The higher the altitude, the harsher the environmental pressure, and the lower the inter-species competition. 3.1 Microalgae diversity in alpine lakes and rivers The diversity of photosynthetic microorganisms populating rivers, streams and lakes is probably the best described amongst mountain environments. By contrast with other habitats, where green algae seem prominent (see below), these aquatic habitats share a high proportion of secondary endosymbionts with marine environments, most notably Heterokonta, including diatoms. Their recorded biodiversity seems to reflect a strong influence of external factors, ranging from the seasonal provision of water from melting snow and glaciers to anthropic activities at their vicinity. Lakes and rivers have distinct communities of algae at different depths, highlighting the importance of abiotic factors on taxonomic composition (Vinebrooke and Leavitt, 1999). Rivers and streams Diatoms are widely studied in alpine lakes and rivers because of their high biomass and potential as biological indicators for ecosystem health (Rimet et al., 2017). They have also been used to study past biodiverity in lake sediments (Pla‐Rabes et al., 2011). It has been shown that their biodiversity in alpine rivers is declining due to the retreat of glaciers and lower flow into rivers (Fell et al., 2018). Streams at high altitude (4,452 m asl) in the Andes, where daily temperature averages less than 0°C, were sampled from June to December at different sites in a study in 2008, and showed that Diatoms were dominant at 95% in biomass with 23 genera, while Chlorophyceae only comprised 3% of total biomass with 17 genera and cyanobacteria 2% with 12 genera. The most abundant diatoms found were Cocconeis sp, Fragilaria sp, Pinnularia sp, and Navicula sp. The most abundant Chlorophycea was Spirogyra sp., and the most abundant cyanobacteria were Anabaena sp, Nostoc sp, and Oscillatoria sp. Richness was highest in the ice-melt period in the stream. It was also higher in urban sites compared to more preserved sites (Salazar-Torres et al., 2012). Dinoflagellates and Xanthophyceae are also found in rivers (Necchi, 2016).

Table 3.1 Main algae represented in different alpine or alpine-like environments

Alpine and peri-alpine lakes In alpine and peri-alpine lakes in a study in Switzerland, both toxin-producing cyanobacteria (Anabaena, Aphanizomenon, Coelos- phaerium, Oscillatoria, Plankthothrix, Pseudoana- baena, Synechococcus, Woronichinia) and non-toxin-producing cyanobacteria (Aphanocapsa, Aphanothece, Chroococcus, Cyanothece, Dactylococcopsis, Gleitlerinema, Gomphosphaeria, Jaaginema, Leptolyngbya, Limnothrix, Lyngbya, Merismopedia, Pannus, Phormidium, Planktolyngbya, Pleurocapsa, Rhabdogloea, Snowella, Spirulina, Synechocystis) were found to be affected by increased temperature in the range predicted by climate change models. Increased air temperature directly affected cyanobacteria through an increased growth rate, and indirectly via the stabilization of the water column that favors buoyant cyanobacteria over other non-buoyant phytoplankton (Gallina et al., 2011).

Green algae, such as Charophyceae of the genus Klebsormidium are abundant in these environments, especially in lower altitude lakes (Gallina et al., 2011; Rimet and Druart, 2018; Novis et al., 2008). Within the green algae lineages, the Charophyceae are considered the first to colonize fresh water and land, where they showed early remarkable adaptation compared with the more marine-oriented Chlorophyta, though the Chlorophyta phylum is now much more diverse than the Charophyta. Amongst Chlorophyta, Trebouxiophyceae are regularly encountered in alpine lakes (e.g. Summerer et al., 2008).

Amongst secondary endosymbionts, in the Heterokonta phylum, Chrysophyceae (previously known as golden algae, Chromulina sp., Dinobryon sertularia and Mallomonas alveolata) and Dinophyceae (Gymnodinium sp) are dominant in biomass in alpine mountain lakes (Italian Alps, Pugnetti and Bettinetti, 1999) while diatom abundance is variable (low in Toletti, 2001, in Italy, lower than green algae and Diatoms in Gallina et al., 2011, in the Swiss Alps, but dominant in Kilroy et al., 2006 in New Zealand and at lower altitude in the Swiss Alps, Rimet and Druart, 2018). Xanthophyceae are present in lakes as well (Maistro et al., 2017). Cryptophyceae are also present in low abundance (Gallina et al., 2011). Secondary endosymbionts of the green lineage are also regulary recorded, such as Euglenoids (e.g. Jahn, 1946).

In lakes in the Italian Alps, biomass is highest in late summer and in deeper water. Community composition was shown to be driven mainly by nutrient composition (Pugnetti - and Bettinetti, 1999). Biomass decreases with altitude and NO3 concentration but increases with conductivity and pH. Biomass is highest in the fall (Toletti, 2001). 3.2 Aero-terrestrial microalgae diversity of cold environments Aero-terrestrial microalgae are true ecosystem engineers, with multifunctional ecological roles in primary production, nitrogen cycling, mineralization, water retention, and stabilization of soils (Karsten and Holzinger, 2014). The highest concentrations of algae in the soil are found in biological soil crusts (Rippin, 2018)

Soils in extreme environments such as the Antarctic desert (an environment very similar to alpine environments, some results from this region are also described here to complement 51 alpine studies) and alpine tundra are thought to harbor the same genera of microorganisms as temperate zones, but with highly specialized species (Pointing et al., 2009). Soil microalgae biomass is variable, from 0.28% of cyanobacteria and no eukaryotic algae in the Antartica desert soil in Robinson Ridge, based on metagenomic shotgun experiments (Ji et al., 2017), to cyanobacterial biomass domination (up to 98.6%, 0.08 to 0.32 mm3/g of dry weight of soil) with twenty-eight morphotypes detected by fluorescence microscopy in the Himalaya (Řeháková et al., 2011). Similar results were documented in the Antarctic Dry Valleys (Cary et al., 2010). Differences in communities of microalgae between samples of soil were found between mountain ranges, vegetation type, and by a small percentage, by altitude in the Himalaya. The filamentous cyanobacterium of the group Oscillatoriales was dominant in alpine meadows, while colonies of Nostocales, Chroococcales and eukaryotic algae were dominant in the subnival zone (Řeháková et al., 2011, Rippin, 2018) as well as in Antarctica (Pushkareva et al., 2016), where Chroococcales and Pseudanabaenales were also found. At high altitude in the Himalaya (< 4,000 m asl) cyanobacteria diversity was low and evolved with altitude: while Chroococales biomass decreased, that of Oscillatoriales and Nostacales increased along the gradient (Čapková et al., 2016).

Green algae are usually present in soil. In the Charophyta phylum, the filamentous taxa Klebsormidium is the most recurrent genus in polar and alpine soils (Van de Vijver, 2002; Rybalka et al., 2009; Karsten and Holzinger, 2014) along with Chlorokybophyceae and Zygnematophyceae (Mikhailyuk et al., 2018). In the Chlorophyta phylum, the Chlorophyceae (Bracteacoccus sp., Scenedesmus rotundus, Chlorosarcinopsis sp., Chlorella sp. and Myrmecia sp.) were found in alpine soil crusts (Karsten and Holzinger, 2014, Rippin, 2018), as well as Trebouxiophyceae (including Stichococcus, Chlorella and Coccomyxa genera) and in particular Raphidonema, in permafrost.

The Heterokonta/Stramenopile Xanthophyceae was found in polar soil in small amounts where green algae and cyanobacteria are also known to grow (Van de Vijver, 2002; Rybalka et al., 2009). Diatoms, Chrysophyceae, Eustigmatophyta and Xanthophyceae are also present in alpine soil crusts (Karsten and Holzinger, 2014). Ulvophyceae, a class of Chlorophyta well known for its marine species, also comprises some terrestrial taxa (Darienko and Pröschold, 2017). 3.3 Microalgae diversity in glaciers, cryoconites and moraines Microalgae and bacteria are the first to recolonize deglaciated soil (primary succession) poor in carbon and nitrogen. As primary producers, they provide an important quantity of organic matter, rendering the terrain inhabitable by other organisms in turn.

Cyanobacteria of the Oscillatoriales and Nostoc groups were most abundant, though only 11 operational taxonomic units (OTU) were found in a dedicated study aiming at characterizing microbes at a glacier foreland (Frey et al., 2013). Nostocales were not detected in young deglaciated soils from 0.5 to 2.5 years old, but occurred in abundance in older soils, peaking at 6 years. Oscillatoriales had the highest frequency in 0.5-1 year-old soil. The Chroococcales peaked in 2.5-year-old soil (Liu et al., 2016). 52

A few OTUs corresponding to eukaryotic algae of the class Trebouxiophyceae, known partners in lichens, were also found, as well as Chlorophyceae and Ulvophyceae, in the evaluation performed by Frey et al., 2013. DNA content increased with distance to the glacier (moraine age, Frey et al., 2013).

A hotspot of biodiversity lies in cryoconites, full of organic debris and microorganisms (Anesio et al., 2017). Because of the density of organisms, its albedo is reduced significantly. Cyanobacteria, eukaryotic algae and a plethora of bacteria and fungi develop within the cryoconites. Eukaryotic algae are more abundant in holes with higher amounts of nutrients, especially with high nitrogen:phosphorus ratios. Cyanobacteria of the Oscillatoriales group are abundant in cryoconites in the Arctic and Alaska, and help structure the cryoconite as well as fix atmospheric nitrogen (Takeuchi, 2013). Nostoc cyanobacteria grow in colonies that protect them from exterior stresses. The filamentous cyanobacteria form granular algal mats/cryoconite granules (Takeuchi, 2013). Eukaryotic algae identified in cryoconite holes were Chlorophyceae (Chlamydomonas nivalis), Trebouxiophyceae (Raphidonema sp) and Charophyta of the Zygnematophyceae group including Mesotaenium berggrenii, Ancylonema nordenskioldii or Cylindrocystis brébissonii (Vonnahme et al., 2016; Takeuchi, 2013, Alaska, identified by microcopy). 3.4 Snow microalgae of ice and snow blooms Snow is not a sterile, lifeless environment; in fact, it is its own ecosystem. During snowmelt in particular, patches of snow become colored red, pink, yellow, orange, grey or green due to the bloom of microalgae. Historically and in the media, pink/red blooms are much better known, and have been dubbed ‘watermelon snow’ (Lewis, 2020, CBS news). A brief description of their discovery and challenges with its taxonomy, proposed hypotheses on snow microalgae life cycles as well as inventories and biodiversity measurements are given in this section. 3.4.1 The discovery of snow algae Aristotle was the first one to publish any work on the red snow phenomenon (Aristotle, “History of Animal”, 4th century BC), which he discovered in a patch of snow that had been lying there for some time. Much later, Captain John Ross, a navy officer and polar explorer in search of a navigable North-West passage near Greenland noticed red patches in snow at the coast and brought samples back. He noticed that as the samples of snow melted, the red particles sedimented at the bottom of the tube. He initially attributed it to iron from meteorites, but later a botanist called Robert Brown added his hypothesis in the appendix of the trip account published in 1818 stating that these red sediments were actually algae which he described as “Algae of unknown genus”. This was the first time we know of that they were identified (Brown, 1818). In the Alps, they were reportedly first discovered in Switzerland (Perty, 1852, reference in German). 3.4.2 The “Chlamydomonas nivalis” overestimation in literature Snow algae from pink/red blooms take on this red coloration because of the production of a red pigment, astaxanthin (detailed in chapter 4), when they form red cysts (resting stage,

53 called aplanospores). That process seems to be common to several species, which makes them indistinguishable under microscopy in that form, leading to many misidentifications in literature and the general belief that they are all the same species: Chlamydomonas nivalis.

Since then, snow algal biodiversity has been addressed by DNA studies and many previously-thought Chlamydomonas nivalis-like algae were differentiated, while many others thought to be distinct became synonyms (Kol, 1968; Prochazkova et al., 2019). Difficulty in differentiating microalgae is not specific to snow ecosystems. An early study in 1997 described phytoplankton as “little balls of different color” but found that they came from different lineages using an 18S genetic marker (Potter et al., 1997).

Based on 18S, ITS2 and Rbcl data compiled from samples collected in many different blooms across the globe (for marker descriptions see Chapter 5), the genus Sanguina was defined within the Chlamydomonadaceae and proposed to form an independent monophyletic lineage (Prochazkova et al., 2019). The authors concluded that most of the literature depicting green algae as issuing from aplanospores, may not actually come from these red cysts and were already green in the snow when sampled and missed in the microscopy performed by other studies, a strong opinion we challenge in my PhD project.

The authors also showed that the Sanguina genus is distinct from Chlainomonas, which also forms aplanospores but has a different morphology. The same goes for Haematococcus, excluded as a synonym of Sanguina. Based on these recent analyses, Chlamydomonas nivalis was proposed to be renamed Sanguina nivalis (Prochazkova et al., 2019).

In the present document, and considering that this taxonomic position may be reevaluated, both names will be used. 3.4.3 Snow algae cycle hypotheses Snow algae only bloom in the spring when snow melts fast, measured to 10 cm of loss of snow depth per 24 h. They are less often found in patches that are more protected from light exposition (Grinde, 1983). Solar light penetration decreases with snow density, which is highest in winter (Curl et al., 1972) so it has been hypothesized that in the spring, the deeper penetration of light enables it to reach the algae, which become more metabolically active and de-encyst. Enough light for photosynthesis was detected until 50 cm in depth (Thomas, 1972).

Several hypotheses on the snow algae life cycle have been considered and are depticted in Figure 3.1, knowing that Chlamydomonas nivalis (or Chlamydomonas nivalis-like) cells divided between 0-2°C, and may not divide at lower temperatures (Hoham, 1975). To be metabolically active and perform photosynthesis, algae require sufficient light intensity as well. The first hypothesis (Figure 3.1, hypothesis 1) states that snow microalgae could be spending their summers (in the absence of snow) and winters (under deep snow cover) in the soil. In this case, they would only reach the snow column by horizontal transport when snow melts and is humid enough to swim to the surface. This is the hypothesis carried by Prochazkova et al., 2019, suggesting the soil serves as a ‘seed bank’. This could be verified by comparing soil, glacier and snow algae populations. It has been argued that snow algae from soil or from the lower layer of snow could not physically reach the surface of the snow 54 solely by horizontal transport, as snowmelt is probably not liquid enough for their motility, and ice crystals should be too big an obstacle for them (Bischoff, 2007). Grinde, 1983, found that algae biomass was lower on days with high light exposition than on cloud cover days, which he suggests is due to the high light intensity rendering it more difficult for the association of the algae to the water layer around the snow crystals, making them wash out more from the top layer rather than stick to the snow surface.

Bischoff, 2007, measured algae position in the snow column and observed that they descended within the column when high light exposure was applied but remained at the surface when protected from the light. When different types of snow were tested (small grain, surface ice, snowmelt large grain etc.), the algae stayed in the type of snow grains found in snowmelt almost exclusively. Furthermore, the humidity of the snow was positively correlated with snow algae concentration. Vertical movement from algae from soil was tested and it was concluded that algae either moved laterally or were carried by the wind to the surface of the snowpack when a path from the soil was blocked.

The second hypothesis (Figure 3.1, hypothesis 2) depicted by Bischoff, 2007, and Grinde, 1983, suggests that there is horizontal transport from the soil to the snow but only if the snow column is thin enough (less than 60 cm deep for instance), then lateral transport is supposed to occur downhill with snowmelt flow. In their experiments, algae were found at the surface of a snow column that was 2 meters deep, and the author concluded it was impossible for soil algae to have gone vertically this far using their cell motility systems, so they either came laterally or were airborne. Laboratory experiments showed that at 0°C, cells were still motile, and were able to move at least 10 cm in the laboratory snow column of homogeneous snow, which does not prove they do in the field, where snow is not homogeneous. The hypothesis assumes that algae develop in the spring in sunny, well lit slopes, where the snow cover is small. There, they swim up to the surface from the soil, and when the snow melts, they are carried with the flow downhill to the surface of patches of deeper snow, where they develop. However, there are patches of snow blooms in parts of the world distant from slopes, so this remains to be cleared.

The third hypothesis (Figure 3.1, hypothesis 3) suggests snow microalgae could simply not be part of the soil ecosystem at all, but only be transported from permanent snow or ice by the wind, lay dormant in heavy snow cover in the winter, and grow in melting snow in the spring. Aerosol traps were installed by Bischoff, 2007, and some red cysts were detected especially when the blooms first appeared; their quantity decreased over time until none were found towards the end of the season, when snow melted completely.

Genetic comparison of snow blooms worldwide with Sanguina species showed no genetic specificity by geographic area, supporting a wind-borne propagation across continents of the red cysts (Prochazkova et al., 2019).

Figure 3.1 Hypotheses on snow algae life cycle and mode of propagation.

3.4.4 Snow algae bloom site of occurrence It has been hypothesized that snow algae blooms occur where more nutrients are concentrated, such as near bird colonies who produce nitrogen-rich excrements (Fujii et al., 2009). However, multiple studies measuring nutrients in snow inside and outside snow algae blooms found no difference in nutrient concentration (Prochazkova et al., 2019; Bischoff, 2007). Nitrate concentration in snow where blooms occurred decreased after appearance of snow algae in the spring, possibly indicating either nitrate consumption or disappearance in flow water. Conductivity variation does not seem to affect snow algae concentration, but a lower pH was negatively correlated with algae presence and concentration in a sensitive manner (a difference of 0.5 in pH mattered in the results, Bischoff, 2007). Sun orientation was not found to be correlated with bloom occurrence (Prochazkova et al., 2019). Nutrient concentration in the snow (nitrogen and phosphate) does not seem to dictate algae presence or even biomass importance, which is unexpected because those are considered limiting factors for growth (Spijkerman et al., 2012). At a particular site in the Swiss Alps, a higher concentration of nitrate was observed at the surface compared to deeper segments, which could affect algae growth (Bischoff, 2007). Atmospheric gases were shown to be a primary source of nutrients for microorganisms in Antartica desert soils, and could be for snow and ice algae as well, either directly or by mutualistic interaction with microorganisms that can fix nutrients present in atmospheric gases. This could make sense as gas scavenging correlates with phototroph presence (Ji et al., 2017). 3.4.5 Snow and ice microalgae diversity in the Alps Snow microalgae blooms occur in melting snow in the spring or summer. As stated above, they can be yellow, orange, green, grey or pink/red. The most commonly found and described type is the red snow bloom, typical of sites highly exposed to UV, like in alpine environments, 56 while green and yellow blooms are more typical of light-sheltered sites like in/near woods. Snow algae are very diverse, with over 450 species described from all over the world. Most are green algae but others, such as diatoms, Euglenae, Chrysophytes and Dinoflagellates, have been detected in small proportions (Kol, 1968; Kawecka and Drake, 1978). Dinoflagellates are able to live in very cold conditions, and are known to appear in fresh water under a thick layer of ice (Rengenfors and Meyer, 1998). There are several definitions of a ‘true’ snow alga, the one used in this document considers the requirement of fulfilling at least part of its life cycle in snow.

Snow algae can reach concentrations of up to ~7×106 cells/ml (Yoshimura et al., 1997; Spijkerman et al., 2012; Williams et al., 2003) but can also be in much lower concentration (∼5,000 cells/mL, Fogg, 1967) in blooms. In white snow, algae concentration can range from a complete absence to as low as 300 cells/mL (satellite data, Williams et al., 2003). Besides a few cyanobacteria, such as Oscillatoriaceae species identified (by microscopy) in a glacier in the Himalaya (Yoshimura et al., 1997), most photosynthetic cells sampled were eukaryotic algae. A study of snow from the French Alps (Mont blanc and Col du Midi) found few cyanobacteria. Cyanobacteria found were similar to Leptolyngbya spp under microscopy but no eukaryotic microalgae except for some empty frustules (shells) of Diatoms in the snow, likely deposited over time (Elter et al., 2007). It is now clear that this study does not reflect the actual biodiversity we started to unravel in the French Alps, initiated in my PhD thesis.

In red blooms, the most abundant snow algae are red, round cysts with mucilage. A big majority of them are 10-25 µm in size. Fewer, bigger cysts are observed with a size of up to 50 µm (Grinde, 1983; personal observations). Samples of snow taken in patches that macroscopically look pristine and devoid of algae, still contain algae, but at a lower concentration (Brown et al., 2016). Snow algae biodiversity studies have shown that the most commonly described eukaryotic algae in snow blooms are Chlamydomonadaceae (a family within the Chlorophyceae) in their encysted (dormant) stage. As discussed above, literature has ‘wrongly’ systematically attributed this to Chlamydomonas nivalis and Chloromonas nivalis. They have been detected using DNA-based methods globally (Austrian Alps, Lutz et al., 2019). Many other snow algae species have been described since, such as other Chlamydomonas species (Swiss Alps, Bischoff, 2007; Antarctica, Davey et al., 2019) Chlainomonas sp. (Antarctica Luo et al., 2020), Chloromonas (Swiss Alps, Bischoff, 2007; Himalaya, Yoshimura et al., 1997, Antarctica Luo et al., 2020; Antarctica, Davey et al., 2019) more specifically Chloromonas brevispina (Austrian Alps, Lutz et al., 2019), Chloromonas platystigma (Austrian Alps, Lutz et al., 2019), Scotiella cryophila (Austrian Alps, Lutz et al., 2019), Ancylonema nordenskioeldii (Austrian Alps, Lutz et al., 2019;Himalaya, Yoshimura et al., 1997), Botrydiopsis constricta (Austrian Alps, Lutz et al., 2019), Chloroidium saccharophilum (Austrian Alps, Lutz et al., 2019), Trochiscia sp. (Himalaya, Yoshimura et al., 1997), Mesotaenium berggrenii (Himalaya, Yoshimura et al., 1997), Cylindrocystis brebissonii (Himalaya, Yoshimura et al., 1997), Koliella sp. (Himalaya, Yoshimura et al., 1997) and Chlorella (Antarctica, Davey et al., 2019). Some rare algae had no comparable sequences in databases and remain unidentified (Lutz et al., 2019). Snow algae analyses in Colorado and Washington states in the USA, showed the 57 presence of communities that are diverse and taxonomically broad, with Chlamydomonadales (74% of OTUs), Microthamniales (20% OTUs), and Chlorellales (6% OTUs) Brown et al., 2016). In a recent study, red snow blooms contained Sanguina nivaloides, and orange blooms, Sanguina aurantia, in various locations in the Austrian Alps. Sanguina aurantia was differentiated by its macroscopic appearance, having an orange hue rather than red, and according to the authors, mucilaginous sheath surrounding the cells, whereas Sanguina nivaloides has cells that are almost always solitary. The comparison of sequences from snow bloom data worldwide showed little genetic difference, and the authors concluded that the dispersal of the cysts was very efficient, explaining the genetic continuity on different continents (Prochazkova et al., 2019). Sanguina spp. have also been detected in Antarctica (Luo et al., 2020). In a study of glaciers, ice algae inventories revealed that Chlamydomonadales dominated, similar to snow. Trebouxiophyceae, dominant in soil environments, are not ‘true’ snow algae, but merely transported by the wind to snow or ice. The filamentous genus Raphidonema is normally present in permafrost, but it was found in a glacier in the Himalaya (Yoshimura et al., 1997). It does not produce astaxanthin (Stibal and Elster, 2005; Leya et al., 2009). In a melting snow red bloom in the Swiss Alps, Trebouxia and Stichococcus genera were identified using 18S rDNA sequencing, while Pseudochlorella was found in Antarctica (Luo et al., 2020). It is likely that they could also have been transported by the wind. Ulvophyceae have also been found on glaciers among flow debris (Darcy et al., 2017) and in green snow blooms in Antarctica (Luo et al., 2020). The Charophyta Zygnematales was also found in high abundance (Anesio et al., 2017).

Chrysophycean algae were shown to be responsible for yellow snow blooms in water- logged snow in Svalbard, North America, Japan, New Zealand and Antarctica (Remias et al., 2013). In this phylum, Ochromonas, Spumella and Hydrurus genera were detected in a green snow bloom in Antarctica (Luo et al., 2020). Variation in diversity can be large on a local scale, as shown in the Swiss Alps, where it was concluded that each site had a distinct community of microalgae (Bischoff, 2007). Chrysopyceae have also been found in sea ice (Ikavalko, 1996), glaciers and icecaps (Lutz et al., 2015). Dinoflagellates were reportedly found in red snow blooms in Ontario, USA (Gerrath and Nicholls, 1974).

Chapter 4. Microalgae acclimation and adaptation mechanisms

Each environment presents its challenges, and none displays ideal growth conditions such as those we use experimentally. Environmental conditions outside optimal range for growth are stressful for organisms who have to cope with them using mechanisms of acclimation or adaptation. This chapter presents known mechanisms for acclimation (reversible changes in response to temporary environmental stress) and adaptation (permanent genetic, and therefore hereditary changes in response to permanent environmental stress that allow the species to durably live in that environment) to environmental conditions in the Alps or similar environments. An environment is considered extreme when growth conditions are beyond average ‘normal’ limits known to support life. Organisms that live in extreme environments are called extremophiles. When there are combined extreme conditions, they are called polyextremophiles (Valledor et al., 2013). A range of physiological and morphological reactions are known for plants to withstand stress, such as oxygen radical scavenging, maintenance of ion uptake and water balance, and re-allocation of carbon, phosphate and nitrogen (Bohnert and Sheveleva, 1998). It is important to note that results from physiological studies described here at the species or genus level might not reflect biological responses happening at the community or ecosystem level due to cascade, indirect or synergistic effects (Bischof et al., 2006). 4.1 Oxidative stress: a multi-stress response 4.1.1 Reactive oxygen species Microalgae live in oxygen-rich environments. They depend on oxygen for respiration and produce it as a by-product of photosynthesis. O2 is a reactive molecule, which produces reactive oxygen species (ROS) during normal cell function. ROS are byproducts of the electron transport chain during cellular respiration, or derived from catabolic oxidases, anabolic processes and peroxisomal metabolism. In microalgae, they are mainly produced in the mitochondria, chloroplast and peroxisome. Their roles include being cellular messengers in redox signaling reactions and protecting against pathogens. Under stress however, ROS are produced in excess, need to be controlled, and the damage they cause, repaired. In chloroplasts, the limitation of CO2 fixation in addition to over‐reduction of the electron transport chain causes ROS production (Suzuki and Mittler, 2006). Signs of ROS toxicity are a decrease in chlorophyll content and growth inhibition. ROS include the superoxide radicals − (•O2 ), hydrogen peroxide (H2O2) and the hydroxyl radical (•OH, the most reactive). Their most important targets are DNA, proteins and lipids (see Figure 4.1). •OH attacks DNA bases by adding to their double bonds, abstracting hydrogen atoms from their methyl groups, or attacking the sugar residue. ROS attack lipids by oxidating them, generating radicals that react in cascade. These lipid oxide radicals can, in turn, react with DNA bases to form mutations. Proteins degraded by ROS are re-synthesized. (Chaterjee and Walker, 2017). Recycling of

glycolate from photorespiration leads to the production of hydrogen peroxide in the peroxisomes.

Figure 4.1. Reactive Oxygen Species (ROS) damage and control. Dioxygen used in respiration and other processes generates reactive oxygen species (ROS) that can cause lesions in DNA and proteins and oxydate lipids, producing lipid peroxyl radicals. To manage ROS, enzymes scavenge these ROS and react with them to produce non-ROS molecules. Antioxidants are also produced by the cell to neutralize ROS.

4.1.2. DNA repair mechanisms When scavenging is not enough to protect cells, DNA lesions and mutations occur. These can be deleterious because they can lead to cell death. ROS creates endogenous damage in microalgae (Bischoff, 2007). Different mechanisms of DNA repair exist for different types of lesions and mutations, represented in Figure 4.2. Lesions in the DNA are detected by lesion- specific sensor proteins, which enable the repair mechanism to take place. Base excision repair (BER) is a mechanism by which oxidated, deaminated or alkylated bases are excised, and then an unmutated base is ligated in its place to repair the mutation in the DNA. The machinery for BER is present in algae, as attested in Chlamydomonas reinhardtii (Morales-Ruiz et al., 2018). When lesions are more extensive and bulkier, the whole nucleotide is removed during 60

nucleotide excision repair (NER). NER is a crucial mechanism to correct cyclobutane pyrimidine dimers. In NER, the entire nucleotide is removed by endonucleases or using RNA polymerase backtracking and replaced in a coordinated way to prevent single strand break repair (SSBR) pathways to be activated instead (Hercegová et al., 2008). Mismatch repair (MMR) is a post-replication repair pathway involved in replication fidelity but also stress- related damage, such as during a virus infection (Wilson et al., 2014). Breaks in the DNA backbone are repaired by the SSBR or the double strand break repair (DSBR) pathways. DNA breaks affect cell fitness, as they can cause transcription to stall, and can collapse DNA replication. SSBs are repaired using the other DNA strand as template, recognizing the opposite strand nucleotide and re-pairing it with a correct matching nucleotide to repair the break. In DSB, the other strand cannot be used, therefore during replication the second DNA molecule produced can be used as model, a process called homologous recombination (HR). When replication is not occurring, the strands are re-attached, which can cause some mutations to happen if nucleotides had been ripped from the molecule. This process is called non-homologous end joining (NHEJ, Chaterjee and Walker, 2017).

Figure 4.2 DNA repair mechanisms as a result of endogenous or exogenous damage. Based on Kao et al., 2006; Chaterjee and Walker, 2017.

4.1.3 Antioxidants Antioxidants are molecules that prevent oxidation by ROS. They can be either water or lipid soluble. Several antioxidants usually overlap in their target and/or mode of action in the cell. Antioxidants in microalgae are extracted and used as pharmaceuticals, nutraceuticals, or cosmetics. Algae are widely exploited for this worldwide, using knowledge from their stress reactions to certain environmental conditions like high light, cold temperature and nutrient starvation (Cirulis et al., 2013). Enzymatic antioxidants There are also antioxidative enzymes such as catalase, glutathione (GSH) and superoxide- dismutase (SOD), enzymes that are almost ubiquitous in organisms that perform respiration, a reaction which naturally produces ROS. These enzymes are specialized in dealing with (scavenging, neutralizing) ROS (Karsten and Holzinger, 2014). GSH holds its antioxidant

61 property from its thiol group in its cysteine moiety. The thiol group is a reducing agent that can be reversibly oxidized and reduced (Yokota et al., 1988). Catalase is one of the best- known antioxidant enzymes. It catalyzes the decomposition of H2O2 in the peroxisome. Its interaction with H2O2 is mediated by its iron heme. It has several isoforms in Chlamydomonas reinhartii (Kato et al., 1997). SODs are a group of enzymes that catalize the following - - + reaction: O2 + O2 + 2 H ➔ H2O2 + O2. Several isozymes of SOD exist with different metals at their reaction center: iron (FeSOD), copper-zinc (Cu/ZnSOD) or manganese (MnSOD). All three types have been found in photosynthetic organisms. In plants, it has been shown that expression levels of the different isozymes depend on the type of tissue (Corpas et al., 2006), so they are likely to depend on the strain of microalgae. They are found in different compartments such as the mitochondria or the peroxisome. Pigments Pigments are antioxidants that are exploited and commercialized as nutraceuticals the most (Cirulis et al., 2013; Li et al., 2008). Pigments were used to classify algae, as some types are lineage-specific (Vinebrooke and Leavitt, 1999). However, not all pigments are antioxidative: the best-known pigment, chlorophyll a, has a role in oxygenic photosynthesis. It absorbs light at specific wavelengths and plays a role as electron donor in the electron transport chain as well as in energy transfer through resonance. Details on its specific role are detailed in part 4.3 of this chapter detailing microalgae response to light and UV. Pigments that act as antioxidants are lipid soluble. Antioxidative pigments act by quenching singlet oxygen, hydrogen transfer, or electron transfer (Graßmann, 2005). The best known antioxidative pigments are carotenoids, such as β-carotene, which has a role in photosynthesis as well. Lutein and anthocyanin are other common photoprotective pigments found in green algae. Vitamins There are other antioxidants produced by the cell, such as vitamin C (ascorbic acid), the best-characterized vitamin. It is water-soluble and synthetized in the mitochondria. It has been found in high concentration in higher plants. Production of vitamin C by the green microalga Chlorella pyrenoidosa can reach up to 2 g/L when grown in the right conditions (Running et al., 1994). Vitamin C is thought to reductively regenerate oxidized vitamin E (α-tocopherol, Abe et al., 1999). Tocopherols (such as Vitamin E) are lipid-soluble antioxidants whose concentration increases under various stresses, including cold stress (Míguez et al., 2017; Safafar et al., 2015). It was suggested that cooperative interactions between vitamin E and vitamin C could protect against lipid peroxidation (Abe et al., 1999). Vitamine B1 (also − − known as thiamine) has O2 /OH scavenging properties and is responsible for the recycling of vitamin C through the synthesis of nicotinamide adenine dinucleotide phosphate (NADPH; Subki et al., 2018) Polyphenols Polyphenols are a very diverse class of molecules (> 8,000 compounds) that act as natural antioxidants through single electron transfer and hydrogen atom transfer. In algae, they are phenolic acids but also flavonoids, such as isoflavones, flavanones, flavonols, and

62 dihydrochalcones and there are probably more to be discovered. Tocophenols, methylated phenols, have also been found in microalgae, and were found to be in higher concentration in certain green algae than other algae (Safafar et al., 2015). Vitamin E, synthesized only by photosynthetic organisms, is an α-tocopherol and an important lipid-soluble antioxidant in algae. α-tocopherol has been shown to be important for electron transport reactions and cell membrane stabilization related to membrane permeability and fluidity (Bischof et al., 2006; Carballo-Cárdenas et al., 2003; Durmaz, 2007). 4.2 Acclimation and adaptation to cold temperatures 4.2.1 Generalities Optimal algal growth for mesophiles (temperate climate) is usually 20-30°C. For mesophiles such as Chlamydomonas reinhardtii, temperatures < 3°C rapidly cause cell death (Valledor et al., 2013). An organism capable of living in conditions below 15°C but whose growth optimum is above that is considered ‘psychrotolerant’. Those whose growth optimum is below 15°C are specifically considered ‘psychrophile’ (cold loving; Morgan-Kiss et al., 2006). Temperature decreases with altitude and can vary depending on exposition (see chapter 2, Figure 2.4, ‘Adret’ vs ‘Ubac’). At low temperature, growth rates and metabolic activity usually decrease through cold-induced osmotic, oxidative, energetic, and other stresses (Valledor et al., 2013). Photosynthesis, and therefore primary production, is one of those inhibited processes (Strand and Oquist, 1985).

In snow and at the surface of glaciers, temperatures are close to 0°C, and there are episodes of freeze-thaw cycles, making it an extreme environment. Drastic diurnal temperature fluctuations at high altitude require algae to demonstrate plasticity and dynamic rapid responses. Snow algae have to adapt to these conditions physiologically (Lukes et al., 2014). It is still unknown whether one of the cell stages of some snow algae such as Sanguina (the aplanospores or cysts) are dependent on cold temperatures, which would classify them as ‘cryophiles’ (Prochazkova et al., 2019). Some snow algae, in an environment close to 0°C, −1 have a photosynthetic performance value of approximately 175 μmol of O2 per μg of Chl per hour (Remias et al., 2016). Adaptation to cold involves remodelling of the proteome. This remodelling involves genes responsible for stress sensing, signaling, gene regulation, transcript and protein processing, and degradation. Energy-deficiency signals and specific sugars are suggested to trigger specific responses (Valledor et al., 2013). In Chlamydomonas reinhardtii, cold stress responses include reduced growth and photosynthesis, accumulation of sugars, and changes in membrane composition, as well as in ribosomes, spliceosomes, proteasomes, and sugar signaling pathways (Valledor et al., 2013). Cold temperature exposition also has been shown to increase the pyrenoid starch sheath thickness and reduce the nucleolus density after 24 h while after 72 h, the flagella is lost and starch granules are accumulated. After 120 h the chloroplast was disorganized and the number of starch granules around it was increased. Even though snow algae are well adapted to cold temperatures, some still display non-optimal growth at 0-2°C. When compared with Chlamydomonas reinhardtii, they have shown higher growth and oxygen evolution capacity at 5-15°C, therefore they are much more efficient at lower temperature than mesophiles (Figure 4.8B; Valledor et al., 2013)

4.2.2 Membrane lipid composition and fluidity The lipids of microalgae cell membranes comprise various classes of amphipathic, hydrophobic and polar molecules, classically found in prokaryotic and eukaryotic biomembranes. They include sterols and other isoprenoids, sphingolipids, and most importantly, glycerolipids.

Glycerolipids consist of a 3-carbon glycerol backbone (numbered sn-1, sn-2 and sn-3), esterified to a fatty acid at positions sn-1 and sn-2, and harboring either a polar headgroup at position sn-3 in the case of membrane glycerolipids, or a third fatty acid, making up triacylglycerol (TAG), a storage form that accumulate inside specific organelles called lipid droplets. Based on the polar headgroup, membrane glycerolipid ‘classes’ are defined, with phosphoglycerolipids (or phospholipids) when containing a phosphate, glycosylglycerolipids (or glycolipids) when containing a sugar, or betaine lipids, when containing a betaine headgroup linked by an ether bond.

Each glycerolipid class is characterized by a ‘profile’ of fatty acids, with various carbon chain lengths and number of double bonds (also called ‘desaturations’).

Extraplastidal membranes mainly contain phosphoglycerolipids, synthesized in the endoplasmic reticulum (ER), including phosphatidylcholine (PC) and phosphatidylethanolamine (PE), and in lower proportions, phosphatidylinositol (PI) and phosphatidylserine (PS).

Photosynthetic membranes are composed of three characteristic glycosylglycerolipids, monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and sulphoquinovosyldiacyglycerol (SQDG), as well as the phospholipid phosphatidylglycerol (PG). In higher plants, SQDG is present in low proportions, but in microalgae, it is often a major constituent. MGDG of algae usually contain a higher proportion of polyunsaturated fatty acids (PUFA) than DGDG, and both are more unsaturated than SQDG.

The chloroplast of Chlorophyta (as for all Archaeplastida) is surrounded by two membranes, so green algae and Angiosperms are considered to harbor similar sets of enzymes to generate their membranes. Nevertheless, an important lipid class is not conserved in these two groups. Betain lipids are specific of algae, present in extraplastidial membranes, believed to play a similar structural and functional role as PC. Whereas Angiosperms are devoid of betaine lipids, some algae contain both a betain lipid and PC, whereas some algae are completely devoid of PC. The betaine lipid diacylglyceryl-O-(N,N,N-trimethyl)-homoserine (DGTS) is found in green algae where it constitutes an important component. Another betain lipid, 1,2-diacylglyceryl-3-O-2’(hydroxymethyl)-(N,N,N-trimethyl)-beta alanine (DGTA) is common in many heterokonts. A third betaine lipid, diacylglycerylcarboxylhydroxymethylcholine (DGCC) has also been reported in heterokonts. Usually, the amount of phosphoglycerides such as PC, PE and PG, is lower than that of glycosylglycerides in algae.

As for mitochondrial membranes, a characteristic lipid of the inner mitochondrial membrane is diphosphatidylglycerol (DPG, or cardiolipin; Li-Beisson et al., 2019).

Figure 4.3 Main algae glycerolipid structures and synthesis. A. Glycerolipid structure details. B. Synthesis relationship between glycerolipids.

Low temperature rigidifies lipids from the membrane bilayer, which is detrimental to ion permeability. In response to that, fatty acid (FA) proportions, lipid class content, and levels of unsaturation and branching of FA are modified, thus increasing the fluidity of the membrane. The production of polyunsaturated fatty acids (PUFAs) is even used for chemotaxonomic classification of psychrophilic and psychrotolerant algae (Li-Beisson et al., 2019). A high proportion of unsaturated, unusual short- and medium-chain PUFAs were observed in snow red cysts from Antarctica, demonstrating that it is one of the strategies employed by snow algae (Bidigare et al., 1993; Rezanka et al., 2008).

In a puzzling and non-convincing study that needs to be repeated, snow algae aplanospore lipid analysis in the thylakoid membrane showed that PG might be the most abundant lipid, with 70 mol% at 15°C, and 90 mol% at 5°C. PG FAs were predominantly palmitic (16:0), oleic (18:1) and -linolenic (18:3). The oleic acid decreased when temperature increased, and -linolenic acid increased with temperature (Lukes et al., 2014). 4.2.3 Cryoprotecting molecules Snow, ice and soil, including permafrost, at high altitude are subjected to harsh temperatures near or well below 0°C. Ice crystals can form at freezing temperatures, and destroy cell structures (Valledor et al., 2013). Amongst possible mechanisms activated under these conditions to prevent such damage, algae can produce compounds like polyols, sugars, or ice-nucleating proteins that lower cytosol freezing point (Leya, 2013; Kawecka and Drake, 1978). Formation of aplanospores also contribute to the protection of cells (Prochazkova et al., 2019).

Polyols Polyols are known as ‘stress metabolites’. They are low molecular weight carbohydrates that can act as antioxidants and respiratory substrate, or stabilize proteins under heat stress during energy deficiency. They lower the freezing temperature of the cytosol, giving protection against mechanical damage. Glycerol is a well-known polyol cryoprotectant. It is widely used experimentally to conserve strains, and is produced naturally by microalgae, including snow algae (Roser et al., 1992). Sugars Cryoprotectants are usually water-soluble and need to be as non‐toxic to cells as possible. Sugars have been shown to contribute to freezing tolerance by acting as osmolytes, cryoprotectants, scavengers of ROS and signaling molecules, though this depends on species (Míguez et al., 2017). Sugars are produced naturally by cells in cold environments but are also used effectively for cryopreservation of stocks of algae strains at the laboratory. Sugar molecules are high-molecular weight, non‐permeating molecules and therefore act extracellularly in higher plants. They typically interact with the lipid bilayer during the freezing phase, thus maintaining plasma membrane integrity when cells undergo dehydration. Not all sugars are beneficial, and the type and concentration of sugar matters (Tsai et al., 2018). Sucrose production is part of the cold acclimation strategies of Klebsormidium flaccidum (Nagao et al., 2008) and cold shock response of Chlorella vulgaris (Salerno and Pontis, 1989). Particularly high concentrations of free arabitol, ribitol and mannitol were found in lichen species, though carbohydrate levels (20-60 mg/g dry weight) were similar in temperate species compared to those from Antarctica. In free-living algae, sugar composition and carbohydrate levels were found to be variable, with notably a snow algae containing sucrose, glucose and other non-identified molecules (Roser et al., 1992). Ice-nucleating proteins Ice crystal forms when ice nuclei are generated by homogeneous or heterogeneous nucleation, after which they grow in size. Ice crystal-controlling proteins are also called ‘antifreeze proteins’ (AFPs) and can be glycoproteins (AFGP). They operate by lowering the freezing point of water (Kawahara et al., 2004). AF(G)Ps have been found in green algae in cold environments, including a snow algae from the group Chlamydomonadaceae as well as in fungi, bacteria and lichen (Raymond et al., 2009). Encystment Encystment is a process often seen in snow algae blooms, whereby snow algae take on a coccoid morphology, with a secondary cell wall and sometimes mucilage (see part 4.3.5, Figure 4.7). This can be a morphological protection against cold for some species (Prochazkova et al., 2019). 4.2.4 Lipid storage The main storage lipid of microalgae is triacyglycerol (TAG), a neutral lipid accumulated in lipid droplets (hydrophobic subcellular structures). TAGs are involved in various functional and metabolic processes, including lipid homeostasis, signaling, trafficking and inter- 66 organelle communications (Goold et al., 2015). TAG accumulation during nutrient, temperature or chemical stress is thought to be a protective mechanism reducing ROS damage to photosynthetic membranes (Du ZY and Benning, 2016).

TAG is formed by a combination of de novo synthesis and transfer of FAs and recycled diacyl moiety from membrane lipids. The lipid composition of Chlamydomonas reinhardtii is similar to that of higher plants overall, though it has some unusual fatty acids such as 16:4 Δ 4,7,10,13, pinolenic acid (18:3 Δ 5,12) and coniferonic acid (18:4 Δ 5,9,12,15) as well as Δ5 double bonds in linoleic and α-linolenic acids. Very long chain PUFAs (VLCPUFA) such as arachidonic acid (ARA) or eicosapentaenoic acid (EPA) can occur in some green algae (Li- Beisson et al., 2019). 4.3. Acclimation and adaptation to high-irradiance and UV light UV radiation is radiation in the 400-180 nm range (Figure 2.2, Chapter 2). Light radiation is essential for photosynthesis, but quantity and quality of irradiance can either promote this cellular process or inhibit it if radiation becomes excessive. Radiation is considered stressful for cells under prolonged and intense exposition (Bischof et al., 2006, Figure 4.5A). Excess light exposure can trigger an imbalance between light absorbed and light used by the photosynthetic apparatus provoking over-excitation and risking photooxydative stress. UV radiation can be differentiated into three types based on thresholds of damage: UV-A (320– 400 nm), UV-B (280–320 nm) and UV-C (180–280 nm, see Chapter 2). UV-B is considered especially damaging for biomolecules (UV-chromophores) such as DNA and proteins, which absorb these highly energetic wavelengths. UV-C does not currently penetrate through the ozone layer in the Alps and does not reach the Earth’s surface (more details in Chapter 2; Kerr 2003). UV-A increases by about 9 % per 1000 m of altitude gain and UV-B by 18 %, and therefore is much higher in the Alps at high altitude (Blumthaler 2012). On a day with clear skies, in snow above the treeline, photosynthetically active radiation (PAR) fluence rates can reach 4,500 μmol⋅m−2⋅s−1 and occasionally reach 6,000 μmol⋅m−2⋅s−1 and in these conditions, −3 −1 −11 −1 photosynthetic activity can reach 6 to 34 μg C mm ⋅hr (≈6.5 × 10 μmol CO2⋅s per cell). In the field, no photoinhibition was detected in a study by William et al., 2003. At higher irradiation levels in Antarctica (746 to 1362 mmol PAR m-2⋅s-1), no photoinhibition was detected in a snow bloom (Remias et al., 2013). The effects of UV-B on aquatic ecosystems depend on the optical properties of the body of water (Bischof et al., 2006). 4.3.1 UV intensity depends on the environment In lakes, it has been shown that the different algae communities respond differently to UV exposure depending on whether they are freeliving (phytoplankton) or attached to plants or sediments (periplankton). Phytoplankton, much more exposed to UV, are capable of surviving at much higher UV intensities. The effects of UV radiation on green algae and cyanobacteria were habitat- and taxon-specific, possibly showing differences in photoprotection and photorepair capabilities (Vinebrooke and Leavitt, 1999.

In snow and ice at high altitude, algae are not sheltered from UV and light and therefore receive full solar exposure (Duval et al., 1999). In soil, at the surface, the algae are mostly unprotected and also have to adapt to high light and UV exposure, especially above the treeline in meadows. 4.3.2 Light-intensity related ROS production ROS have been shown to be produced in excess as a result of high light exposure and UV. Algae require sufficient light intensity for photosynthesis, a process that produces a basal level of ROS, but above a certain intensity which is species-dependant. Therefore, algae need to reach a balance in terms of photosynthesis productivity and photoprotection that is adapted to light intensity and fluctuation in the environment in which they live (Niyogi, 1999). 4.3.3 UV-mediated protein damage Proteins can also be altered through aromatic amino acid structure disruption or by cleaving disulfide bonds between cysteine residues, affecting their tertiary structure. These molecular processes can result in decreased photosynthetic activity if proteins involved in photosynthesis such as D1 of the photosystem II (PSII), the ATPase or the enzyme Rubisco in the Calvin cycle are damaged. 4.3.4 DNA photodamage UV-specific DNA damage DNA absorbs radiation through its aromatic residues. Their alteration can lead to a deformation of the DNA structure. UV creates chemical bonds between neighboring pyrimidines (dimerization), reactions that lead to cyclobutane dimers and pyrimidine (6-4)- pyrimidone (6-4)-photoproducts, which introduce errors and mutations when DNA is transcribed or replicated respectively (Martinez-Fernandez et al., 2017; Bischof et al., 2006). Thymine-thymine dimers halt both DNA and RNA polymerases and therefore replication and transcription and can result in mutation and even cell death (see representation of a thymine dimer in Figure 4.2). UV-damage-specific DNA repair mechanisms There are some DNA-repair mechanisms dealing with DNA damage by UV (exogenous damage). Pyrimidine dimers presented in the paragraph above are repaired using photolyase (see Figure 4.2). 4.3.5 Effects of high light and UV on photosynthesis In plants and algae, pigments of the photosynthetic apparatus can also be destroyed by UV exposure, the phycobillins and the chlorophylls being the most sensitive, are subject to photodestruction. Chlorophyll a has been observed to be more affected than Chlorophyll b

(Bischof et al., 2006). All of these effects lead to decreased CO2 fixation and oxygen evolution, up to cell structure deformation and cell death.

Photosynthetic pigments Green algae such as Chlamydomonas spp usually have the following pigments: chlorophylls a and b, and the primary carotenoids, neoxanthin, violaxanthin, antheraxanthin, lutein, zeaxanthin and beta-carotene. 4.3.6 Photoprotective carotenoids Carotenoids are yellow to orange-red terpenoid pigments. They are less sensitive to radiation than chlorophylls or phycobillins and can therefore absorb some of the extra radiation by scavenging and deactivating free radicals (Bischof et al., 2006). Carotenoids in photosynthetic protein complexes are C40 tetraterpenes biosynthesized by a branch of isoprenoid or terpenoid pathway from the C5 isoprene unit (Britton, 1993).

Figure 4.5 Structures of algae photoprotective carotenoids.

Beta-carotenoids Of the primary carotenoids, β-carotene is the best known. β-carotenes in algae can act as antioxidants, scavenging ROS (Bischof et al., 2006). β-carotene is a precursor for Vitamin-A synthesis. In snow algae, β-carotene was detected additionally to other primary carotenoids such as lutein and neoxanthin as well as 3% unknown pigments (see Figure 4.5A, Remias et al., 2013). The xanthophyll cycle The xanthophyll cycle (or V-cycle) is an important short-term photoprotective mechanism known in vascular plants, heterokonts and green algae, and is especially important for adaptation to rapid changes in light exposition (Míguez et al., 2017; Goss and Jakob, 2010; Karsten and Holzinger, 2014). It is the light-dependent conversion (two de-epoxidation steps)

of the xanthophylls violaxanthin (Vx, the double-epoxidated form, Figure 4.5E) to zeaxanthin (Zx, the de-epoxidated form, Figure 4.5D), two oxygenated carotenoids. Upon high light exposure, Vx is converted to Zx via the intermediate, antheraxanthin (Ax, single-epoxidated form, Figure 4.5F) catalysed by a pH-regulated de-epoxidase in the thylakoid lumen and requiring the presence of MGDG (Figure 4.6, Safafar et al., 2015). This process can be inhibited by thiols. The interconversion is rapid, a matter of minutes. In darkness or under low irradiance, the Zx is re-epoxidated in the back reaction. Its role is to achieve thermal dissipation of surplus excitation energy in the photosystem II (PSII) antenna system. This dissipation is variable depending on algae species.

Figure 4.6 The classical xanthophyll cycle and its effects. Violaxanthin pools are present at low light, then de-epoxidated in two steps, leading to accumulation of zeawanthin under high light. This process decreases photosynthesis efficiency, but protects against photoinhibition and photodamage.

In green algae, the contribution of the xanthophyll cycle to heat dissipation, a process registered as non-photochemical quenching (NPQ) of chlorophyll a fluorescence, is considered not as significant as in higher plants (Karsten and Holzinger, 2014). This process requires the presence of a trans-thylakoidal proton gradient and high enough concentrations of Zx. NPQ correlated with Zx+Ax helps to measure the extent of dissipated energy in higher plants, though Zx-dependent NPQ appears to play a minor role in algae, if any. Zeaxanthin per chlorophyll ratios were significantly lower than in higher plants. Measurements of kinetic changes in chlorophyll emission give information on the efficiency of photosynthetic light utilization and fraction of dissipated energy. NPQ is connected to a structural change of the PSII light-harvesting complex (LHCII, Goss and Jakob, 2010). In Chlorococcum, a genus that comprises snow algae species, the synthesis of xanthophyll cycle pigments was counteracted by synthesis of secondary carotenoids astaxanthin and canthaxanthin (Masojídek et al., 2004). Cyanobacteria do not perform the full xanthophyll cycle but can form zeaxanthin from β- carotene under high light exposure via the xanthophyll biosynthesis pathway. The xanthophyll pigments are also known to be involved in protection against oxidative stress damage of lipids and participate in the blue light response. It also seems that zeaxanthin accumulation helps modulating the thylakoid membrane fluidity (Eskling et al., 1997). Pigment analyses showed that red and orange cysts as well as green cells from snow possess all three pigments (Bidigare

70 et al., 2013; Remias et al., 2013). Green algae can also possess other xanthophylls such as loroxanthin or astaxanthine, the latter of which is described below (Safafar et al., 2015). Astaxanthin Some green algae produce large quantities of secondary carotenoids found inside lipid globules surrounding the chloroplast in the cytoplasm. Their accumulation is known to be parallel to a reduction in chlorophyll and primary carotenoid content under conditions of limiting nitrogen. They shield the synthetic apparatus from too high radiation. Secondary carotenoids identified in green algae include astaxanthin, cis-astaxanthin, canthaxanthin, echinenone, hydroxyechinenone, and β-carotene. Astaxanthin is a hydroxy carotenoid generally occurring in a fatty acid ester and synthesized from β-carotene. It absorbs at a maximum of 474 +/- 2 nm in methanol (MeOH) (Bidigare et al., 2013).

Astaxanthin is a keto-carotenoid that absorbs radiation in the near UV-range (absorption maximum found at 478 nm in its ester form, protecting the chloroplast from photodamage and photoinhibition but they also scavenge radicals produced by the radiation (Prochazkova et al., 2019). It has thirteen conjugated double bonds in alternating single-double bonds, which gives it its strong antioxidant properties (Figure 5C; Han et al., 2013). Astaxanthin is produced in large quantities in some snow algae and in the freshwater alga Haematococcaceae, (Luo et al., 2018). It is produced by slightly different pathways depending on the species (Han et al., 2013). In green algae from snow, 90% of the astaxanthin was found in esters making it lipid soluble (Remias et al., 2013). Carotenoid-deficient cells of Chlamydomonas reinhardtii exposed to high light had high cell mortality (Sager and Zalokar, 1958). The effect of UV-B irradiation on algae indicate that UV-sensitive algae had a reduction in VLCPUFAs, such as EPA and DHA, an effect exacerbated by nutrient deprivation (Wang and Chai, 1994). In encysted snow algae carotenoid/chlorophyll a ratio were increased by a factor of 40 compared to non-encysted forms (Bidigare et al., 2013). Snow algae such as Sanguina nivaloides have been shown to tolerate short periods of high UV-B radiation thanks to secondary carotenoids. Sanguina nivaloides shows high photophysiological plasticity with a highly efficient photosystem under low irradiation and no photoinhibition up to 2,000 μmol⋅m−2⋅s−1 (Prochazkova et al., 2018b). 4.3.4 Non-pigment UV-shielding molecules Mycosporine-like amino acids (MAAs) Mycosporine-like amino acids (MAAs) are amino acid (mostly aminocyclohexenimine) derivatives. They are colorless, water-soluble, polar and uncharged at cellular pH or zwitterionic. The ~25–30 MAAs known are very similar, but more variants probably exist. They absorb at UV-A/B wavelengths and dissipate energy in the form of heat without generating photochemical reactions (Karsten and Holzinger, 2014). They are not common in green algae but have been found in Prasiola crispa ssp. Antarctica. Lower temperatures have been experimentally proven to stimulate the MAA production, which is why they are common in Antarctic species (Bischof et al., 2006).

Other molecules Phenolics and hydrolyzable tannins have high antioxidant activity. Phlorotannins for example, found exclusively in brown algae, are found in cell walls and extracellularly to shield cells from UV. They can act as antioxidants intracellularly to prevent cell damage (Bischof et al., 2006). In snow algae, an increase in phenolic compound concentration was found upon UV exposure. Cells were described to be well adapted to UV-A (Duval et al., 2000). 4.3.5 Encystment Some snow algae in red blooms are encysted, a state in which their chloroplast is filled with lipid globules surrounding the chloroplast so much that they mask the cell interior in their mature form. These lipid droplets contain secondary carotenoids, the best known of which is astaxanthin, which gives the alga its characteristic red color. Red snow cysts are spherical, from 10 to 40 µm in diameter, and with (see Figure 4.7B, black arrow) or without mucilage (Figure 4.7A) and/or blunt or sharp protusions. Mucilage has a polyphyletic origin and is very common in coccoid Chlorophyceae algae (Krienitz and Bock, 2012). They contain a nucleus and a single central chloroplast with one naked pyrenoid. Their red color is due to peri- plastidal lipid globules, which contain red-orange secondary carotenoids. It appears that carotenoid/chlorophyll a ratios define the shade of the snow bloom. The chloroplast is not visible under the carotenoid-filled oil droplets in mature cysts (Figure 4.7A), though it can appear in young cysts (Figure 4.7B, chloroplast indicated with a 'c'). The outermost layer in young cysts could be a primary cell wall, which is shed during maturation when the secondary cell wall appears, according to Prochazkova et al., 2019.

Figure 4.7 Microscopy of red snow cysts (Aplanospores) from a red bloom of the French Alps. 10 µL of melted snow were observed under light micrscopy at 630x. The arrow shows mucilage while the letter 'c' represents the chloroplast showing through the oil droplets. Scale: 10 µm. A. Mature red cyst without mucilage. B. Young red cyst with mucilage.

4.4. Photochilling ‘Photochilling’ is the simultaneous exposure to low temperature and high light conditions. Such a stress is encountered in the Alps at high altitude. Organisms in that environment must combine protective mechanisms to survive (Míguez et al., 2017). When multiplying stresses, in conditions of high light the threshold of radiation tolerance decreases because the photosynthetic capacity is already lowered by other stresses, such as cold (Eskling et al., 1997; Karsten and Holzinger, 2014) 4.5 Resistance to dehydration and desiccation Water content can vary extensively in alpine environments, from an abundance after rainfall and snowmelt, to extended periods of dryness or freezing. High UV irradiance and its

72 absorption into the Earth’s surface further exacerbates dryness. In presence of low water content, algae can become dehydrated, which can slow down metabolic processes. Desiccation happens at even lower water content, which can lead to complete stop in metabolic processes (Karsten and Holzinger, 2014, Figure 4.8C). After the snow melts, algae may flow down to the soil, rocks, permafrost or moss, where they are thought to spend the summer, fall and winter, until they can reproduce in spring in the melting snow (see Chapter 3 for more details). During these months, they are subject to even harsher diurnal temperature fluctuations, such as 30°C differences in 24h. Sanguina nivaloides aplanospores are therefore considered desiccation resistant (Prochazkova et al., 2019). Usually, aeroterrestrial green algae have thick cell walls enabling them to resist environmental stresses, including desiccation (Hotter et al., 2018). Mucilage Some encysted snow algae have a mucilage layer made of polysaccharides and surrounding their cell wall. It is thought to contribute to protecting them against desiccation, though this remains to be proven since no correlation has yet been established between mucilage presence and snow water content (Boney, 1981; See Figure 4.7B). TAG accumulation Air-drying of green algae Chlorella kessleri has been shown to stimulate TAG synthesis by about 3-fold. Chlamydomonas reinhardtii showed the same effect in smaller proportions (Shiratake et al., 2013). Cell wall layers and flexibility The cell wall is made up of mostly carbohydrates such as cellulose, with some other compounds such as proteins. Desiccation can cause mechanical damage because of the shrinking of the cell when its water content, and therefore its volume, decreases. The cell wall plays essential roles in water exchange with the environment, cell volume and turgor pressure. Cell wall folding is possible thanks to its flexibility and is an adaptation to this mechanical stress provoked by desiccation. Specific polysaccharides such as β‐galactofuranans and linked O‐methylated mannogalactan have been found in green symbiotic microalgae in their cell walls (Centeno et al., 2016).

Some photobionts (algae moiety of a lichen) as well as free-living green algae have a three-layer cell wall. It was described that an innermost layer had variable thickness, was amorphous in structure, and built up mainly by Golgi-derived hemicelluloses. The outer wall layer above it was uniformly thick and had short, likely cellulosic fibrils embedded in an amorphous matrix. Beyond these two wall layers was an outermost uniformly thick trilaminar wall layer containing sporopollenin in a rigid middle part. Proteins appeared to be embedded in an amorphous, carbohydrate matrix on its inner and outer surfaces (Honegger and Brunner, 1981). Snow algae in their encysted form have a secondary cell wall that provides mechanical protection (Remias et al., 2013)

Lichenization A major adaptation to desiccation as well as other stresses is the symbiotic association of algae with fungi in their lichenized form. Algae cells usually lie between layers of fungi mycelium thus being protected from evaporation of water, UV radiation and photoinhibition. This symbiotic association successfully adapted to terrestrial habitats, including extreme environments such as deserts and high altitude. The most impressive adaptation lies in the remarkable response to desiccation stress, where water comprises only 10% of the fresh weight of the lichen thalli. Lichens are also capable of surviving in a metabolically paused state when water availability is null, until water becomes available again and they can resume their growth. The process of desiccation–rehydration is rapid in these organisms (Centeno et al., 2016). Polyols Polyols, additionally to their cryoprotective role as mentioned above, have putative protective roles against desiccation. It seems that the synthesis of polyols in some green algae was transiently induced by water loss. Their role in protection against desiccation is linked to the fact that they decrease the intracellular water potential (osmotically active). Such polyols as glycerol, erythritol, arabitol, ribitol, mannitol, sorbitol and volemitol have been found in microalgae. The large variety of polyols produced by different species of algae means they can also be used for chemotaxonomy (Hotter et al., 2018; Centeno et al., 2016; Gustavs et al., 2011). Red cysts from snow identified at the time as Chloromonas nivalis had glycerol levels at 0.7 mg per gram of dry weight, its most abundant sugar alcohol (Remias et al., 2013). Some red encysted algae from snow did not have glycerol, but instead had sorbitol (Roser et al., 1992). Sugars Some sugars like sucrose, raffinose, stachyose (Hotter et al., 2018) and callose for some algae (Holzinger et al., 2014) have been shown to increase during dehydration or desiccation (Holzinger and Pichrtová, 2016; Rippin, 2018). They play a role in the maintenance of cell membrane integrity and the “glass state” formation during desiccation (Holzinger et al., 2014). The “glass state” refers to the process of replacing water with sugars to reversibly immobilize the cytosol so that the flux of solutes is extremely low. The sugars in the cytosol responsible for this state stabilize macromolecules and membranes, effectively protecting the cell against desiccation. Sugars can also neutralize toxic compounds that interfere with tolerance to water stress (Azua-Bustos et al., 2014). 4.6 Resistance to nutrient starvation Nutrient starvation stress is also responsible for ROS production, changes in pigment concentrations and photosynthetic efficiency as part of the acclimation, short-term stress response. Long-term nutrient stress triggers an adaptation to these conditions which consists of changes in metabolism which are highly species-specific (Stehfest et al., 2005).

4.6.1 Nitrogen starvation Nitrogen limitation typically results in reduced protein content and an increase in carbohydrate or lipid storage in algae (Stehfest et al., 2005).

Snow and ice algae actively cycle nitrogen (Hamilton et al., 2013). Nitrogen is considered limiting in snow and ice, though experiments have shown that adding nitrogen (nitrate and/or ammonium) to the environment did not enhance carbon fixation. The same was found with addition of phosphate (Hamilton and Havig 2017). TAG accumulation Nitrogen depletion creates stress, which causes an increase in TAG and oil droplets, correlated with a decrease in membrane lipids amount. This has been shown to be due to the mobilisation of membrane acyl groups for TAG formation under stress, involving galactoglyceride lipase in Chlamydomonas reinhardtii (Li-Beisson et al., 2019). In Coccomyxa, more than two thirds of the chloroplast lipids are lost during TAG accumulation produced by the prokaryotic pathway. Fatty acid composition In a study comparing red cysts with green cells from snow, green cells were found to be enriched in saturated fatty acids (72%) while red cysts were enriched in monounsaturated fatty acids (80%). In red cysts, 5% of the fatty acids are selectively esterified to the hydroxy carotenoid, astaxanthin (Bidigare et al., 2013). Antioxidant production increase A study on Microchloropsis (previously Nannochloropsis) oculata showed that decreasing N concentrations led to an increase by 69% in α-tocopherol (Vitamin E), 38% in ascorbic acid (Vitamin C) and 101% in β-carotene accumulation, all three important antioxidants (Herrero et al., 1991; Carballo-Cárdenas et al., 2003; Durmaz, 2007). 4.6.2 Phosphorus starvation Phosphorus is a major nutrient for algal growth as it is essential for phosphoglyceride biosynthesis. Chlamydomonas reinhardtii had reduced levels of all phosphoglycerides upon P-starvation. The PG is taken from the photosynthetic membranes and is replaced by SQDG. In Monodus subterraneus, P-starvation caused increases in DGDG and DGTS (and TAG, though not as much as N-starvation) which accompanied the loss of phosphoglycerides. 4.6.3 Sulfur starvation Sulfur is used for protein and metabolite synthesis. S-starvation can increase concentrations of non-polar lipids in Chlorella, and 85% of the SQDG was found to be broken down. 4.6.5 Depletion in other nutrients Other nutrients (e.g. Co, Cu, Fe, Mg, Mn, Mo, Zn) are present in trace amounts, are sequestered by algae and have a strong influence on algal growth, especially Fe (Hamilton 75 and Havig, 2017). Fe and Zn influence TAG and oil droplet accumulation in Chlamydomonas (Urzica et al., 2013). 4.6.6 Oxygen depletion Microalgae in the soil, sediments and even fresh water are subject to hypoxia (low oxygen levels) and even anoxia (absence of oxygen) due to oxygen consumption during respiration by microorganisms. Under hypoxic conditions, Chlamydomonas accumulates TAG that were enriched in unsaturated FAs (Hemschemeier et al., 2013). 4.7. Metal and heavy metal stress The response to metal stress is, as other stresses are, species-dependant. Similar to other stress responses, a growth rate and chlorophyll content decrease is observed above the sensitivity threshold in algae, as shown with copper (Cirulis et al., 2013). The similarity in the stress response is linked to the fact that metal stress produces ROS, as shown by the rescue experiments using ROS scavenger SOD (Zheng et al., 2011).

Though remote alpine nival zones are thought to be protected from pollution by heavy metals, it has been shown that they are not exempt from it (See chapter 2; Van De Velde et al., 1998; Thevenon et al., 2011). Heavy metals have been shown to induce stress and affect growth, which can have important effects in an already stressful environment where the reproduction window is very limited in time. Cadmium-exposed cells of Koliella Antarctica for example showed at 1 ppm reduced growth rates, decrease in other nutrient uptake, malformation of photosythetic membranes, and at the highest concentration of 5 ppm, cell death (Rocca et al., 2009).

GSH, mentioned as an antioxidant in part 4.1.3, has been shown to be involved in metal stress handling in algae as well as ROS (Cirulis et al., 2013).

4.8 CO2 flows

Atmospheric CO2 is used by microalgae as substrate for photosynthesis. Dissolved CO2 2+ 2+ in fresh water depends on atmospheric CO2 as well as other factors, such as Ca and Mg - 2- and dissociates into bicarbonate (HCO3 ) and carbonate (CO3 ). CO2 flows measured in the snow in winter showed outward flow because of soil respiration under it. A comparison of snow blooms and white snow occurred showed that snow blooms had a net CO2 uptake in the light rate of ≈0.3 μmol⋅m−2⋅s−1 while in the dark it had 0.016 μmol⋅m−2⋅s−1. In white snow, the measure was an order of magnitude less than in the bloom, showing clear photosynthetic activity related to algae concentration. During a whole day time-lapse, the difference between dark-measured rates and peak rates was variable, around ~ 0.1–0.3 μmol⋅m−2⋅s−1, or ~1 × 10−9 μmol per cell⋅s−1. Different wavelengths of light were compared in the field, and red light drove more photosynthesis than white light, while blue and green both drove significantly less. It is thought that the pigment astaxanthin prevented the short-wavelength light from reaching the chloroplast. It is likely that red snow algae cysts get much of their CO2 from respiration within or beneath the snow. In the field in the conditions measured (bright clear day, freezing temperature), there was no evidence of photoinhibition (William et al., 2003). It is possible that the oil accumulated during the spring and summer months by snow algae are 76 an adaptation to a life cycle where the algae lay in the soil until the snow starts to melt in the spring (Szyszka-Mroz et al., 2019). CO2 levels in the atmosphere have increased by 25% since the industrial revolution, largely due to anthropogenic activities (Hanagata et al., 1992). Under very high concentration of CO2, growth rates decrease in different proportions depending on the species tested, until they reach a concentration of CO2 that completely inhibits growth (Fukuju et al., 1992). Microalgae acclimate to these conditions, which can generate secondary inhibitory effects, by degrading proteins that are specialized in low-CO2 response, and by reducing CO2 affinity (Baba and Shiraiwa, 2012). This acclimation is materialized by decreases in the activity of carbonic anhydrase and in the capacity of accumulation of inorganic carbon. No significant difference between high CO2 and low-CO2 conditions was found in relative contents of lipid classes. However, in fatty acids, high-CO2 exposed cells, had higher relative content in 18:2 and lower 18:3 than in low-CO2, therefore the degree of unsaturation decreased (Tsuzuki et al., 1990). In higher plants in the Alps, there is evidence that high CO2 levels negatively affect photosynthesis rates. The same is possible in microalgae, but remains to be demonstrated (Körner and Diemer, 1994). On the other hand, slight increases such as those observed at present time show successful acclimation and an increase of biomass production in higher plants, including at high altitude such as at the treeline. At high altitude, low temperature is more of a limiting factor than carbon; therefore, the increase in biomass is still low. It might not be the same for algae, well adapted to low temperatures (Wieser et al., 2009). 4.9 Non-lichen algae-fungi/bacteria interactions It was shown that, among compounds associated with green snow algae communities was aminoadipic acid, a precursor for penicillin synthesis in fungi that produce α‐aminoadipate and calystegine, an alkaloid known for its involvement in plant–bacterial communication. The authors found fungi in the genera Cryptococcus and Rhizophydiales (Davey et al., 2019). 4.10 Biotic stresses Eukaryotic microalgae are the frequent target of viruses such as megaviruses in marine environments (Wilson et al., 2014). Soil viruses are largely unexplored but could play important roles in this ecosystem (Trubl et al., 2018). Some studies have suggested that the virus communities in soil are locally and globally diverse and distinct from other types of environments (Fierer et al., 2007). Viruses live in extreme environments along with microbes and eukaryotes (Le Romancer et al., 2006) and have been found in sea ice (Wells and Demming, 2006) and Siberian lake ice cover (Zhang et al., 2007). 4.11 Concluding remarks A comparison of green and red snow blooms showed that the dominant metabolites and metabolic pathways in green blooms were associated with nitrogen and amino acid metabolism, while in red blooms it was with osmolyte and fatty acid metabolism, showing different strategies to survive in these difficult environmental conditions. The following figure encapsulates major features of environmental stresses exerted in mountain environments and some of the physiological responses presumed to be triggered in green algae.

Figure 4.8 Environmental stress in the Alps and their adaptation or acclimation strategies.

Chapter 5. Methods for assessing green microalgae biodiversity

Converging evidence shows that there is currently a steep and continuous drop in biodiversity on Earth, the first one caused by human activity, the so-called ‘6th mass extinction’, or ‘Anthropocene extinction’ (Ceballos et al., 2017). It is also considered a major crisis of our time, because of its impact on health and economy. However, most species have yet to be discovered, so to monitor that loss of biodiversity on a global scale, more data is needed on the current state of environments (Thomsen et al., 2015). Additionally, climate change alters the habitability of certain environments, affecting species populations and communities that are important to keep track of. Microalgae can be a very good indicator of changes. Indeed, they adapt quickly, spread rapidly where conditions are favorable to them and they are theoretically favored by the rise in atmospheric CO2 used for photosynthesis. This makes studying their biodiversity in different environments of utmost importance. There are many studies about microalgae diversity, but few of them are focused on alpine environments, especially in the French Alps. Existant studies are mostly focused on lakes, and sometimes soil. This chapter addresses the complex definition of biodiversity and its importance. The different tools available to study microalgae biodiversity are also described, followed by our choices and arguments that guided them for this study. 5.1 Definition and importance of biodiversity Biodiversity is the contraction of ‘biological’ and ‘diversity’ first described in 1986 in The National Forum on BioDiversity by Dr. Walter Rosen (Wilson and Peter, 1988). Its definition varies according to who uses the term to carry their point, but it generally is agreed to correspond to the 'variety of life in a given ecosystem', which is represented by three levels: landscape, number of different taxa (a group of organisms of the same taxonomic rank; examples include ‘Chlamydomonas’, ‘Metazoa’, or ‘Chlorophyta’, Kevin et al., 2013), and intraspecific diversity (genetic diversity; Loreau et al., 2005). Taxonomy refers to the classification of organisms.

The earliest classification was found in Mesopotamia, 5,000 years ago. How organisms were classified intially depended on the reason for their classification (categories of food, medicine, poisons and other types of utilization), and therefore varied extensively. Attempts at implementing a universal classification were found in Aristotle's eidos and genos (now species and genus). Whereas Aristotle’s work on animals’ classification in History of Animal (Aristotle, 4th century BC) and on the quest to their primary living cause in On the Soul (Aristotle, 350 BC) can still be accessed, very little is known on his work on plants. However, the same principles can be found regarding species variety in the work of Theophrastus, his successor at the head of Athen’s Lyceum in Enquiry into Plants (Theophrastus, circa. 350- 287 BC). In this ancient classification, non-animated (or still) organisms like plants were considered inferior in a value pyramid to animated organisms, i.e. animals, with humans at the uppermost position. There are different versions but today's classification has moved away 79 from human uses and value hierarchies and is based on Linnaeus' work, who is at the origin of the current naming system in Systema Naturae and Species plantarum (Linnaeus, 1735; Linnaeus, 1799), as well as Darwin, who defined how to organize groups of organisms depending on their ancestry in On the origin of species (Darwin, 1909; Sundberg and Pleijel, 1994).

Linnaeus first published his classification system in Systema Naturae (Linneaus, 1735), where he used ranks to classify organisms into groups, similar to branches of a tree that divide into smaller narrower branches. The fundamental unit in classification is the species. Ranks start with kingdoms, themselves divided into classes, which, in turn, are divided into orders, then genera, and species, which sometimes are divided into subspecies. It is still very debatable whether this gives an accurate enough description of biological organisms, especially in microalgae (Manhart and McCourt, 1992; Maréchal, 2020). This nomenclature uses unique binomial names to identify species (genus name followed by species name, followed by the name of the discoverer), also called the ‘scientific name’, to label taxa. This classification was based on the observation of morphological traits in species.

Debate on classification persists today on the definition of taxa, and even on the concept of species, given that there is also intra-species genetic diversity. This is natural, since lineages are not cut and neat in nature, but rather occur in as a spectrum of evolutionary change. Some distinct species can cross, generating for instance novel allodiploid hybrids (e.g. the diatom Fistullifera solaris, Tanaka et al, 2015). In numerous prokaryotic and eukaryotic algal species, such as Synechococcus (Pittera et al., 2017) and Ostreocossus (Grimsley et al., 2010), cells can propagate without sexual reproduction and diversify in the form of ecotypes and cryptic species. Classification is used to simplify the parameters and to try to extract patterns to facilitate the study of evolution. Now, classification is based on DNA by the means of phylogeny, recently reviewed by Scornavacca et al., 2020. The study of DNA has shattered certainties in classification, and led to changes in taxonomy, making it very unstable and ever changing, especially with microorganisms like microalgae (Adl et al., 2005; Adl et al., 2019; Burki et al., 2020).

Biodiversity can be seen as a multitude of living lineages with varied functional architectures. Within each species, there are a variety of phenotypic traits; this results from a combination of genes and genomes, with flows of gene alleles and genotypes, genetic materials transferred vertically or horizontally, etc., providing the potential for the emergence of novel functional architectures, traits, species, enabling the biosphere to acclimate, adapt and evolve. Interdependence is key to comprehend the importance of biodiversity in ecosystems. The most obvious example of interdependence is trophic networks, where photosynthetic organisms capturing organic elements via photosynthesis (primarily carbon, via atmospheric CO2 reduction into glucose, but also phosphorous, nitrogen, sulfur, etc.) are consumed by grazers and subsequently carnivorous animals, as well as by all sorts of heterotrophic organisms such as bacteria, fungi, etc. Consumption by humans is much more intense since biodiversity not only serves to provide a stock of species for food but is also a source of natural or cultivated/elevated feedstock for fibers, materials for a broad variety of uses, energy, medicinal compounds, pigments and other chemicals, etc. Photosynthetic organisms are also critical for the quality of the atmosphere, being at the origin of the high 80 level of oxygen (Ward et al., 2016; Olson et al., 2016). Microorganisms are also used to transform matter for a broad range of purposes, from wastewater cleaning, including processes with microalgae like Chlorella (Vo et al., 2019) to enzymatic fermentations by lactic bacteria and yeasts to process food (e.g. bread and cheese) and drinks (e.g. alcoholic beverages) (Tofalo et al., 2020).

The importance of knowing, monitoring and conserving biodiversity has been known for a very long time and declared ‘urgent’ in the Report of the United Nations (UN) Conference on Environment and Development in 1992. In this report, the UN requires Member States to invest in means to conserve biodiversity. These means include increasing the number of educational programs, efforts into collecting data on land, water and air pollution, the dynamics of destruction of natural environments replaced by agricultural or urban areas, as well as research to understand and learn how to protect species. Other drivers of biodiversity destruction include climate change and habitat changes, sometimes driven by populations moving into new habitats because of wars or because they were chased away by other populations (Pereira et al., 2012). Again in 2015, the Paris Agreement stated that parties are “noting the importance of ensuring the integrity of all ecosystems, including oceans, and the protection of biodiversity, recognized by some cultures as Mother Earth, and noting the importance for some of the concept of “climate justice”, when taking action to address climate change”. Ecosystems were mentioned again in articles 7 and 8 of the agreement. In the 1990’s, the importance of protecting the ecosystems and their biodiversity was made clear, but 23 years later, little progress has been made and UN guidelines remain unchanged, showing how little action has been taken, though research continually shows degradation of biodiversity caused by human activity (United Nations report, 2015). The United nations even declared the ‘decade of biodiversity’ in 2011 (UN website, news/un/org), stating that the loss of biodiversity would affect poor populations first, but ultimately will affect all of humanity. 5.2. Biodiversity measurements Biodiversity is deceptively multidimensional and complex. No single method to measure it is suitable across studies, and no single parameter can measure all its facets. Biodiversity measurements are a proxy for how communities assemble, evolve over time when monitored, and spread over spatial parameters. Diversity was first partitioned into α, β and γ diversity by Whittaker in 1960, to characterize its levels. The simplest way to measure biodiversity is to use α diversity. It is a measure of the number of different species (or taxa) in an environment. It can be counted in absolute or relative numbers. This measure is used across most ecological studies, but it has to be done carefully, as sampling effort differences can bias the result. A common way to minimize this bias is to use rarefaction or accumulation curves to standardize data (see Figure 5.1 below, where the number of species added per sample added is depicted; it is at a sharp incline in the beginning when the main species are discovered, but as more samples are added, few new species are discovered since the common ones were present in other samples; only the rarest ones are left, and the curve flattens).

Figure 5.1. Example of an accumulation curve. On the X axis, the cumulative number of samples added and on the Y axis, the corresponding cumulative number of species added by each sample. When the maximum number of species is attained, new samples do not add new species and the curve flattens.

Depending on the study, data can be standardized either by sample, individual, or a hybrid of the two (Gotelli and Colwell, 2001). It is considered important to have consistency in ecological studies to be able to compare them and make global ecological inferences (Manoylov, 2014). With microalgae it is especially difficult to capture rare species, because they can lay dormant in very small numbers, e.g. in the form of cysts (or aplanospores), and only become detectable in very specific conditions (Sharma et al., 2011).

Functional diversity is a measure of the role of groups of taxa to address services ecosystems fulfill, which is used to predict the impact of the absence/presence of organisms in an ecosystem (Colwell, 2009). Rank-abundance plots are used to observe how even the species abundances are for different taxa. Typically, few very abundant taxa represent most of the biomass observed, alongside a series of rarer taxa, which are easily missed by any technique used. An exhaustive biodiversity measure is, in practice, very difficult to obtain because rare taxa can easily be missed in some samples and appears on the off sample. This makes it difficult to assess whether the rare taxon is truly from that area or transported there prior to sampling. This is the case with snow, where Trebouxiophyceae were thought to dwell, but do not seem to fulfill any part of their life cycle in, and therefore can not truly be called snow algae as they are merely carried there by the wind (see Chapter 3).

To make up for incomplete sampling ‘richness estimation’ calculations were developed. Linear regressions were initially used, but now most scientists use a version of the well-known Chao richness estimator, which is non-parametric (Chao, 2009). The Chao estimator extrapolates data to estimate the total richness and make up somewhat for what might have been missed, since the total theoretical number is unknown. The total number of species on earth is approximated; it is especially difficult to predict for microscopic organisms like microalgae, and its prediction keeps increasing as new taxa are discovered (De Clerck et al., 2013). In a given sample, the total number of species is generally underestimated since there are many singletons (a taxon only recorded once in the sample), whose origins are difficult to trace, or missed altogether. In ecology, it is very common to measure diversity within a given 82 habitat with α diversity indices (MacArthur, 1965). The best-known α diversity indices are Shannon (1) and Simpson (2) diversity indices (below).

(1) Shannon diversity index:

(2) Simpson diversity index:

They combine species richness with species evenness but do not rank taxa the same way. Accordingly, they have a different sensitivity to rare taxa. Species abundance evenness affects the perception of the diversity of an environment: an environment with one extremely abundant species and three rare species will be considered less diverse compared to an environment with four evenly abundant species. Such examples are detailed by Marcon, 2015. Both Shannon and Simpson indices increase with richness or evenness, Shannon index being more sensitive to species evenness whereas that of Simpson is more sensitive to species richness (Colwell, 2009). Additional indices are reviewed by Colwell, 2009 and Marcon, 2015.

The mathematical formulas used to describe diversity try to take into account the information that was missed during sampling (up to a certain point), and therefore ressemble mathematics of entropy. The duality of entropy and diversity was generalized (Marcon and Hérault, 2015) and Hill unified the different biodiversity indices by creating the Hill numbers based on HCDT (or Tsallis) entropy. Therefore, we can measure biodiversity using Hill numbers and vary its number q. Shannon and Simpson indices are integrated in the Hill numbers equation; the exponential of the Shannon index is the Hill number with with q~1 and the Simpson index is the Hill number with q=2 respectively. With an increase in the q parameter of the Hill number, the weight of rare species decreases.

Phylogenetic analyses enable the study of degrees of difference between organisms. For example, two species of different phyla will be much more different than two species of the same genus. Adding phylogenetic analyses allows one to appreciate aspects of diversity that the aforementioned indices do not take into account.

At a higher scale, to compare biodiversity between samples, β diversity is measured as a distance between environments. The most commonly used are the Bray–Curtis dissimilarity, based on abundance or read count data, and the Jaccard distance, based on presence/absence data (Marcon, 2015).

Finally, γ diversity compares α diversity on a higher scale study. γ diversity is the union of local α diversity measurements on a regional scale (Marcon, 2015).

5.3 Experimental tools for biodiversity studies Natural sciences, schematically based on observations, phenomenology, correlations and elaborations of hypotheses, have evolved into modern biology, which is experimental, often evaluating working hypotheses deriving from naïve observations. Initially, plants and animals were described and drawn by biologists and inventories were made by hand. Since microscopes can help identify characteristic features (synapomorphies), the task has expanded to the world of microorganisms, but with important limitations for cells presenting similar morphological traits. The study of isolated and cultivable microorganisms to assess life cycles, possible alternations of generations and life stages, identification of sexual dimorphism, but also genomic comparisons (molecular phylogenies) and chemical traits (chemotaxonomy) are now combined to help define taxa. Academic collections of cultivable species and electronic databases for OMIC information have succeeded to printed floras and reference collections of fixed samples in natural history museums. An important challenge is now to develop specific resources to cultivate strains purified from live samplings, ideally with cryopreserved aliquots, and to distribute and share these strains with the scientific and teaching communities addressing questions ranging from species development to ecophysiology, dynamics and functioning of populations and communities, and ecology of specific habitats. 5.3.1 Microscopic observation The first studies of biodiversity were inventoring the environment, making herbaria (a physical collection of specimens) from collected samples of plants and algae, and writing descriptions from sampling sites, dates, and macro-observations that enable identification. They constitute a valuable source of knowledge, with some herbaria being duplicated and distributed worldwide. Many studies point out that, because of the decline of expert taxonomic expertise, it has become more difficult to obtain correct identifications of algae (Thomsen et al., 2015). Nevertheless, there is now online access to more standardized taxonomy literature in the ‘era of cybertaxonomy’, with online resources like AlgaeBase (Guiry and Guiry, 2020), the Index Nominum Algarum (INA; Silva and Moe) and AlgaTerra (Jahn and Kusber, 2020). De Clerck et al. 2013 stated that the number of taxonomists is increasing on the contrary, and the number of new species discovered as well. Inventorying macroalgae and identifying them macroscopically is possible, with the exception of some taxa whose study of their cell cycle is necessary. Unfortunately, microalgae are more difficult to conserve and identify, requiring an expert algae taxonomist and imaging systems with high resolution (microalgae sizes span from 1 µm to 50 µm diameter), sometimes even confocal or electronic microscopy (Škaloud and Radochová, 2004), and even that can be unhelpful. Microalgae conservation is limited to those that are indeed cultivable and maintained in academic resources like the Culture Collection of Algae and Protozoa (CCAP, https://www.ccap.ac.uk/), the Roscoff Culture Collection (RCC, http://roscoff-culture- collection.org/), the University of Texas collection of algae (UTEX, https://utex.org/), or the Culture Collection of Cryophilic Algae maintained in Potsdam (CCCryo, http://cccryo.fraunhofer.de/web/strains/), which means they have to be grown in the laboratory or kept in frozen stocks. Some cells have distinct structures such as the ability to form colonies, the shape and position of their chloroplasts and pyrenoids, mitotic apparatus,

84 flagella apparatus, mitochondria shape and location (Metting et al., 1996). However, these may change depending on their stage in the cell cycle. Some microalgae have ultrastructural criteria that require extensive training in taxonomy to identify. Cyanobacteria identification relies on the presence of a sheath and heterocysts in filaments sampled, which are not in each cell (see chapter 1). If the identification is not performed immediately upon sampling, conservation of microalgae may affect cells' state, rendering identification more difficult. Methods of sample fixation, such as the use of formaldehyde, can destroy some structures, while Lugol's iodine can mask them (Manoylov, 2014). Observational inventories of microalgae require collecting the sample and counting the number of each taxa using a microscope. There have been reports of deviations between different taxonomy schools that led to differences in results from taxonomist to taxonomist (Manoylov, 2014). Observational inventories can be done routinely with species that have distinct characteristics (such as some diatoms), but most microalgae are nearly impossible to identify as they lack obvious discriminatory structures or elusive cell cycles (microalgae usually reproduce asexually), and due to the presence of cryptic species (Vieira et al., 2016). Microscopic observation of microalgae is also hampered by the fact that a majority of taxa are rare. It is very difficult to sample rare taxa consistently, and it would require an unrealistic sampling effort (Manoylov, 2014).

In snow, studies such as Spijkerman et al., 2012 only used microscopy to count algae using Mallassez slides, and to differentiate the types of dormant cells (red or orange cysts) but failed to identify unambiguously the species. Stibal et al., 2017 used microscopy to identify particles in snow as algae but could not characterize taxa. In a description of snow and ice phototrophs and microorganisms from Antartica, the Andes and the Alps, cyanobacteria, algae and fungi were observed using light, electron and fluorescence microscopy and placed in different categories. Those were cyanobacteria and non-diatom micro-algae, diatom valves and their segments, bacteria – coccoid, rod, and red clusters, fungi – spores and hyphae, yeast, and pollen (Maupetit et al., 1994). Other studies used microscopy to determine algae species as well (Smith and Olson, 2007; Yoshimura et al., 1997), but most now agree that a dual method of combining microscopy with DNA-based analysis is necessary to conserve consistency between studies (Somogyi, B., et al., 2013). The consensus is now that DNA- based tools are a requirement to identify microalgae. 5.3.2 Barcoding Barcoding is used to identify and classify organisms using portions of their DNA (Hebert et al., 2003). It uses different algorithms for downstream classification (Leliaert et al., 2014). It radically changed the phylogeny of algae that was previously based on morphological traits (Krienitz et al., 2012; 2015). It has become a significantly more accurate option to identify incomplete macroalgae specimens as well as microalgae in a consistent manner faster than microscopy independently of the life stage of the algae sampled (Hall et al., 2010). With the rapid advancement of sequencing methods, the cost of these techniques has been decreasing steadily. DNA-based methods make it possible for non-taxonomists to identify strains they collected.

Barcoding is a polymerase chain reaction (PCR)-based technique, amplifying a small region of the target algae DNA, or ‘barcode’, used to compare to a database of DNA sequences that belong to known species, in order to identify the target species (Hebert et al., 2002). The process of barcoding starts with sampling a specimen and isolating it from co-sampled contaminants. It is then grown in the lab to increase its biomass, a process which can be complicated by the specificities that different species require for their growth. In macroalgae, it can be challenging to remove epiphytes (smaller algae that grow on them), but a more pressing problem is concerns abou the fact that most organisms do not grow in vitro. After successful isolation and culturing, DNA is extracted, then amplified and sequenced. Initially the DNA amplified was cloned before it could be sequenced, but this is no longer the case. The sequences obtained are compared to a database of previously identified species, and matched. Extraction and even PCR conditions usually need to be adjusted, and some strains can require considerable efforts. A new study from 2020 claims it is possible to bypass the culture and DNA extraction, and amplify barcodes directly from samples, which would work with well-designed markers and sufficient biomass (Fei et al., 2020). A variant of the traditional technique of barcoding is the single-cell sequencing, where a single cell’s DNA is extracted then amplified using usual barcode markers (Muramoto et al., 2008). Currently, there is no real consensus on DNA extraction methods, or choice of barcode marker. An ideal marker should have a low intra-specific divergence, but a high inter-specific divergence so that taxa are not wrongly taken to be different species, or wrongly identified as the same, since such false assessments would affect the estimation of biodiversity (Dentinger et al., 2011). It is quite difficult to find a universal algae barcode because green algae are evolutionarily diverse, being a polyphyletic lineage that started diverging early on (De Clerck et al., 2013). Some universal primer designs were attempted for eukaryotes, plants, and algae (described in this chapter) and recommended on barcode of life database (BOLD) Systems (www.barcodinglife.com). To design new primers, sufficient data on many microalgae is required to know which region is conserved enough that the primers will bind to DNA extracted from all taxa, but also a region variable enough that taxa can be separated.

Genomes of green algae and especially plastomes vary in organization and in content, with differences in introns, repeated sequences, and number of copies, making it challenging to design universal algae primers (Turmel and Lemieux, 2017, 157–193). Ideally, the region amplified should be large enough that a phylogeny can be produced (Taberlet et al., 2018). In barcoding, it is much easier to adjust the conditions for each taxon and even adjust the primers to be taxon-specific because only one taxon will be amplified at a time. Adjustments take time but are worthwhile for proper identification, as shown in Hall et al., 2010. The different barcodes used and their ability to amplify green microalgae is discussed in this chapter. A huge increase in number of algae DNA sequences on databases like GenBank (https://www.ncbi.nlm.nih.gov/genbank/) has been observed, providing more and more genetic information to generate better barcodes and phylogenies (De Clerck et al. 2013). A drawback of techniques that require sequencing is that every step may introduce errors and false positives. A considerable increase in the number of improperly named algae sequences in the databases has occurred since sequencing has bloomed (Meiklejohn et al., 2019). Consequently, the reference libraries contain a vast amount of errors that are not straightforward to manipulate when processing data. The coverage of reference libraries is an 86 important limiting factor in identifying unknown sequences, and the design of barcodes and any other genetic-based identification technique relies on their completeness and accuracy. This is especially an issue for microalgae, where entire families lack representation in references databases, while others are over-represented, leading to biases in analyses. 5.3.3 Metabarcoding To circumvent the inconveniences of barcoding, DNA metabarcoding was developed. This technique, the term of which was coined in 2011 (Pompanon et al., 2011), was first used by microbiologists but has now been developed for diet analysis, past biodiversity studies as well as for monitoring current biodiversity. It is a mixture of barcoding (which only amplifies a single species at a time) with high throughput sequencing (HTS) to analyze a community of organisms in a sample (See Figure 5.2A). There is still debate over its sensitivity. Its expansion has been relatively slow because of high costs of sequencing and the fact that it requires a combination of many skills from the experimental stage to the full analysis. However, those costs are decreasing, and standardized methods have slowly started to appear over the years. The aim of this technique is to obtain taxonomic information based on DNA of many taxa from a single environmental sample, allowing the study of entire communities and even the comparison of communities across large geographical areas. Unlike for barcoding, the different taxa are not purified from the sample and their DNA extracted separately, but the total sample DNA is extracted (the origin of that DNA is discussed in this chapter) and amplified by PCR using DNA metabarcodes.

Metabarcodes are different from classical barcodes because they have to be sensitive to target taxa, but at the same time, ignore non-target taxa such as epiphytes that are also present in samples. For example, when targeting algae, a eukaryotic marker will amplify not only algae but also co-sampled vascular plants and fungi. Those contaminants can be separated downstream in the bioinformatic analysis, but their higher biomass tends to shadow the algae to such an extent that they are hardly amplified at all, and the information about their presence can be lost (Taberlet et al., 2018). Handling of metabarcoding data must be done carefully. Raw data is obtained in considerable amounts, and there are many steps that introduce errors with this technique. The PCRs themselves may introduce errors at each cycle, primers and sequences can associate and/or form chimeras creating artificial sequences of DNA, DNA molecules present on the experimenter can contaminate the samples, pipetting can leak DNA sequences from well to well making it difficult to know where they originated and if sequences are actually present in the sample, and finally, sequencing can be erroneous as well. Metabarcoding experiments require dedicated equipment and training to avoid contaminations. The choice of the bioinformatic pipeline steps depends on the goal of the study, the most critical of which are eliminating erroneous PCRs by using PCR replicates. Having a positive control (“mock” community) is indispensable to know how sensitive each step is and to create the best-suited pipeline for the samples (Calderón-Sanou et al., 2019). There are multiple tools available to remove these errors. Their choice depends on the goal of the study. Originally, most scientists used Roche 454 pyrosequencing, but since Roche stopped updating and maintaining these equipments, the choice shifted to Illumina sequencing.

However, although there have been efforts made to standardize metabarcoding, there is no standard for DNA preservation, extraction, or marker-design. The choice of marker is based on the goal of the study, the target taxa, and the completeness of databases. Many sequence data on databases are ‘environmental data’ and have not been checked thoroughly, so they can be unreliable, and are generally filtered out. Additionally, a considerable quantity of data is not made available globally but kept locally due to costs of hosting it online (Hadi et al., 2016). Most studies use single markers for a given group of taxa because of the cost of using multiple markers.

Figure 2.2. Metabarcoding process workflow. A. Metabarcoding experiment steps, starting with sampling, then DNA extraction and PCR, sequencing and finally taxonomic assignation. B. Scheme of an “ideal” metabarcode, which is generally around 100 bp long.

5.3.4 Additional techniques To bypass the amplification issues of barcoding and metabarcoding, some studies use non-PCR methods. Such methods can be based on markers other than DNA. A gross estimation of types of algae can be done by extracting pigments and separating them by high performance liquid chromatography (HPLC). This allows for the differentiation of cryptophytes (using alloxanthin), diatoms (diatoxanthin), dinoflagellates (peridinin), cyanobacteria (zeaxanthin), filamentous cyanobacteria (myxoxanthophyll and oscillaxanthin), Chlorophyta (lutein, Chlorophyll b, and derivatives), as well as chromophytes (chrysophytes, diatoms, some dinoflagellates; fucoxanthin, Chl c). For green algae, this does not go further than phylum identification, and therefore is ill-suited for green algae biodiversity studies (Vinebrooke and Leavitt, 1999). Techniques that still base themselves on DNA without PCRs include genome skimming, a shotgun sequencing technique with low overall coverage but deep coverage for high copy fractions of the genome such as in mitochondria and plastid genomes (Dodsworth 2015). This requires good quality DNA from separate cultures obtained from environmental samples, so it is time- and resource-consuming, and will not enable recovery of taxa that do not grow in culture. Metagenome skimming is used to bypass the need for isolation and laboratory culture by working on environmental 88 samples directly. Total DNA is extracted, and then a whole genome shotgun library is prepared and sequenced. The organisms are separated and identified in the bioinformatics pipeline (example with Lichen samples in Greshake et al., 2016; Ji et al., 2017 with desert soil microorganisms). Other techniques are mitochondrial metagenomics, which are applied to mixes of organisms (Crampton-platt., 2016; Turmel et al., 2020) and environmental samples of algae (Cai et al., 2018). Mitochondrial enrichment by centrifugation has also been tested with some success in arthropods (Zhou et al. 2013). More recently, Jo et al., 2019 adapted this technique in so-called PCR-free small-organelles enriched metagenomics (SoEM) on filtered seawater containing algae, with an efficiency claimed to outperform metabarcoding. However, as these techniques rely on having good quality DNA, they do not pick up very rare organisms despite a considerable sampling effort in habitats such as soil. They also rely on a reasonable number of organisms, and with closely related species, such as microalgae, formation of chimeras represents an issue. They also miss the presence of past organisms: they are therefore a snapshot at a given moment, unlike metabarcoding can be in some encironments. In environments where the turnover is very important, non-PCR methods capture the biodiversity in the limiting timeframe of sampling. 5.4 Operational taxonomic units (OTU) The datasets of many genetic sequences generated during sequencing of environmental samples present new challenges. A species can not be summed up by a single sequence because of intra-species genetic diversity. To make sense of this new concept of environmental sampling, operational taxonomic units (OTU) were first suggested to group closely related taxa when numerical taxonomy began (Sokal and Sneath, 1963; Blaxter et al., 2005). Molecular operational taxonomic units (MOTU) define the sequences, and not species, issued by sequencing of DNA from environmental samples. The interpretation of MOTU data is complex, and to simplify and aggregate data to extract meaning, ecologists use taxonomical assignation, attributing a taxon name from a reference database of sequences by means of sequence alignment and similarity. There is no absolute threshold to define if a MOTU belongs to a certain species (Schloss and Westcott, 2011). 5.5 Environmental microalgae DNA Microalgae can be found in many different environments. DNA can be in various lengths and cellular and molecular contexts, and can degrade differently depending on conditions in that environment, altogether affecting its detection. 5.5.1 Intracellular, extracellular and total DNA An environmental sample contains various nutrients, pollutants, as well as whole organisms with their own intact intracellular DNA. This DNA is obtained by bulk sample analysis, but is accompanied by extracellular DNA released during cell death and lysis, caused by either apoptosis or necrosis. This latter DNA decays rapidly, breaking down to fragments as small as 100 base pairs (bp). Total DNA is the mixture of both of those: intracellular and extracellular DNA (Levy-Booth et al., 2007). The choice of the method of DNA extraction for the study depends on the objectives. For example, in the case where current biodiversity is desired, it is more useful to use methods that favor the isolation of intracellular DNA. 89

5.5.2 eDNA detection Environmental DNA (eDNA) detection in samples depends on how well it is preserved in that environment. DNA breaks down at different speeds depending on environmental parameters. The type of environment sampled will affect how well and how long the DNA can still be detected. The morphology, architecture and chemical composition of the limiting membrane and cell wall determine how easily the cell integrity will be disrupted, thus how rapidly its DNA is released and degraded. For example, cysts are much more resistant, with high quality DNA, which can persist for hundreds of years (Boere et al., 2009). In water environments, eDNA can still be detected a few days to a few weeks following cell death, with a few days of difference between fresh and sea waters (Green et al., 2011). The weather can also negatively affect the state of the DNA. Warmer temperatures and higher UV exposure tend to promote DNA degradation (Tsuji et al., 2016, Green et al., 2011). Environments also contain varying amounts of excreted enzymes like exonucleases, or other DNA-degrading chemicals, mostly produced by bacteria. DNA can also be taken up by organisms as a source of nitrogen, phosphorous and carbon. On the other hand, there are also some factors which prevent degradation, such as inorganic and organic particles that the DNA can be adsorbed on (such as sand and clay; Pietramellara et al. 2009). This explains why eDNA in sediments last much longer, especially in anoxic conditions.

In the sediments, DNA can be detected for a very long time, even thousands of years. Only the top few centimeters contain live organisms (Capo et al., 2015). In the soil, DNA can be detected between a few days to dozens of years later (Pietramellara et al., 2009; Yoccoz et al., 2012), but DNA from organisms that lived there recently is always more abundant than DNA from organisms that had previously colonized that environment. In permafrost, the DNA can even be detected many thousands of years later, as the conditions are ideal for its preservation (Willerslev et al., 2014). 5.6 Currently used DNA markers for algae The first markers used for algae were either eukaryotic or plant. Markers for metabarcoding analyses tend to be much smaller than for barcoding, because DNA is rapidly damaged in the environment. The markers require the highest resolution possible, meaning that they will distinguish between different taxa at different levels, ideally down to the species level. Markers should target a region that is highly inter-specific, meaning that it varies from species to species, but flanked by two regions, which are well conserved in the group of interest. Markers in coding regions are interesting because they are easier for targeting specific groups, like photoautotrophs. However, because of redundancy in the genetic code these regions tend to have intra-specific variation, yet not enough variation between taxa because proteins have conserved sequences to retain their function properly. For that reason, non- coding region markers can be more advantageous.

Gene markers Marker COI RBCL TUFA Marker 500-600 400-1400 600-900 size (bp) Type barcoding barcoding barcoding/metabarcoding experiment snow algae; Chlorophytes: Target Chlorophyceae; Chlorophyceae and Green algae group Charophyceae Trebouxiophyceae; Charophytes amplification not best for barcoding; Good very good to differentiate species and always obtained; inter-species haplotypes; may require taxa-specific Assessment region too variable for differenciation; not as primers; best for barcoding or with good taxonomical adapted for other markers in metabarcoding assignation metabarcoding Vieira et al., 2016; Hall Fučíková et al., 2011; Muramoto et et al., 2010; Marcelino et Fučíková et al., 2011; al., 2008; Prochazkova et al., 2019; Studies al., 2016; Zou et al., Hall et al., 2010 Patel et al., 2018; Zou et al., 2016; 2016; Fučíková et al., Hadi et al., 2016; Hall et al., 2010 2011

Table 5.1. Main markers on the coding region used in PCR-based techniques for green algae. The MatK marker is not included, as it does not amplify Chlorophyta.

Non-coding markers

rps11- Marker 18S 23S ITS ITS1 ITS2 rpl2 16S cluster Marker size 400-1700 300-600 180-500 650-1350 200-400 5000 200-600 (bp)

Type barcoding/metab barcoding/metab barcoding/metab barcoding/metab barcoding/metab barcodin barcoding/metab experi arcoding arcoding arcoding arcoding arcoding g arcoding ment

all eukaryotes; phototrophs, Prokaryotes, Target Chlorop including all including green Green algae Green algae Green algae including group hyta green algae algae cyanobacteria

Good amplification but good highly variable Good Success Good Good sometimes amplification; makes it hard to amplification, of amplification; amplification; unable to incomplete taxa identify species universality; can amplific useful for Assess- Not as complete differentiate coverage in precisely; be too variable ation is assignation and ment as 18S, but are between taxa; database; not secondary for accurate variable, phylogeny if the best used can ideal by itself for structure helps species not ideal marker is large together underestimate phylogeny with phylogeny identification marker enough biodiversity

Groendahl et al., Hall et al., 2010; 2017; Hall et al., Marcelino et al., Vieira et al., 2010; Marcelino Provan 2016; Ahmad et 2016; Fučíková Marcelino et al., et al., 2016; Patel Hall et al., 2010; et al., al., 2013; et al., 2011; Hadi 2016; Zou et al., Studies et al., 2018; Hall et al., 2010 Prochazkova et 2004; Sherwood and et al., 2016; Zou 2016; Ahmad et Cahoon et al., al., 2019 Vieira et Presting, 2007; et al., 2016; Frey al., 2013 2018; al., 2016 Cahoon et al., et al., 2013 Prochazkova et 2018; al., 2019

Table 5.2. Main markers on the non-coding region used in PCR-based techniques for green algae.

Another important factor is the amount of documentation on the selected region available in databases. To identify species, reference sequences are required. There are some universal markers, but ultimately the choice entirely depends on the goal of the study and its needs (resolution, coverage). In numerous cases, it is recommended to use a multi-marker approach, though it is not always possible logistically. Unique markers are therefore often selected carefully and used as a compromise (Taberlet et al., 2018).

5.6.1 COI marker The COI (or cox1) marker is in a mitochondrial Cytochrome C Oxidase subunit I gene. It is described as a universal barcode used to amplify animal DNA and is mostly used for barcoding because it is too long in its entirety for metabarcoding (~600 bp). As a marker of a coding region, it has good variability making it able to distinguish species, but it can be poorly 92 conserved in some groups. Some metabarcoding studies use a shortened segment of the COI marker, in a ‘mini barcode’ version (Leray et al., 2013, Elbrecht et al., 2016). It continues to be used for plants (Hebert et al., 2003; Saddhe and Kumar, 2018). This gene contains several introns in some Charophyta algae (Turmel et al.,2002) and in Chlorophyta lineages (Pombert et al., 2006), although it does not usually have any in metazoans except in some sponges (Schuster et al., 2017). It was found to be very variable in some studies, and not able to amplify all tested groups, so it was not recommended for barcoding. Hall et al., 2010 tested multiple markers including COI, and found that it did not amplify most of their green algae taxa. 5.6.2 RbcL marker RbcL is the gene encoding the large subunit of the ribulose 1,5-bisphosphate carboxylase/Oxydase (RuBisCO, a CO2 fixing enzyme) in green plants and autotrophic algae. The RbcL marker is a fragment of around 500-800 bp encoded in plastids (Hollingsworth et al., 2009), but minibarcode versions were developed for uses in metabarcoding (Little, 2014). It has been widely used for plants in barcoding (CBOL Plant Working Group, 2009) and for metabarcoding with some success, though not perfect (Bell et al., 2017; Mallott et al.,2018). It has also been used for red and green algae from both Chlorophyta and Charophyta lineages (Sanders et al., 2003). As a coding region marker, it is quite stable for a gene but can still be too variable and require taxa-specific primers, though it shows good resolution (Rindi et al., 2007; Fučíková et al., 2011) and sometimes has introns (Hanyuda et al., 2000, McManus et al., 2012). Conclusions from barcoding studies on green algae confirm that it has good resolution but that it required some taxa-specific or internal primers (Vieira et al., 2016; Fučíková et al., 2011; Hall et al., 2010; Hadi et al., 2016; Zou et al., 2016; Patel et al., 2018). It has been used to show that a snow bloom contained multiple snow algae haplotypes, so it shows good potential for snow algae studies (Muramoto et al., 2008). 5.6.3 TufA marker TufA is a synthesis elongation factor EF-Tu encoded in the Chlorophyta plastid (described first in Händeler et al., 2010). It was used in barcoding studies where it was reported to perform at least as well as RbcL (Vieira et al., 2016; Hall et al., 2010; Marcelino et al., 2016; Fučíková et al., 2011), better in some cases (Saunders and Kucera, 2010), but did not generate accurate phylogenetic groups in others (Zou et al., 2016). Its issue is that the databases do not have as many reference sequences for this marker as for other markers like RbcL. The gene was transferred to the nuclear genome in some Charophyta and in vascular plants (Baldauf et al., 1990). Multiple groups are generating data for metabarcoding studies using TufA as a marker, enabling scientists to have a better database, so it has promise, but is probably better suited in combination with other markers (Rossetto Marcelino and Verbruggen, 2017; Sauvage et al., 2017). 5.6.4 MatK marker MatK is the maturase K gene, formerly known as orfK. It is located within the intron of the chloroplast gene trnK and is ~1,500 base pair-long and seems to be conserved, at least in part, across the plant kingdom (Hilu and Liang, 1997). It is used for plants in metabarcoding 93 studies as well as barcoding studies (Hollingsworth et al., 2009) and is recommended as a barcode for plants (CBOL Plant Working Group, 2009), though it presents its share of drawbacks (Fatima et al., 2019). It is found in some Charophytes, but is absent in Chlorophytes, so it will only capture the Charophyte lineage of green algae (Pombert et al., 2005; Sanders et al., 2003), and is possibly restricted to Charophytes and Embryophytes (a group tentatively called the Plasmodesmophytes) 5.6.5 The chloroplast 16S and 23S The 16S and 23S are both rRNA genes encoded in the chloroplast. The Universal Plastid Amplicon (UPA), or 23S, is a portion of the chloroplast ribosomal large subunit 50S and has been used for vascular plants and algae. It has been found to be less variable than other markers, but as its name suggests, it has ‘remarkable universality’ and amplifies cyanobacteria, red and green algae (Sherwood and Presting, 2007). Its length is 400-600 bp. In barcoding and metabarcoding studies of green algae, these markers are used quite successfully for both Chlorophytes and Charophytes (Hall et al., 2010; Ahmad et al., 2013) as well as cyanobacteria (Marcelino et al., 2016), and overall as a photoautotroph protist marker, though its recovery of biodiversity is not exhaustive (Cahoon et al., 2018; Sherwood and Presting, 2007).

The 16S is a portion of the small subunit chloroplast 30–35S. It has nine hypervariable regions (V1–V9). Similar to the 23S, the 16S is easy to amplify, and retrieves a good portion of the biodiversity compared to other markers (Zou et al., 2016; Ahmad et al., 2013), though it has trouble differentiating closely related species (Marcelino et al., 2016). Both the 16S and the 23S markers are close to inverted repeats in the chloroplastic genome, which could explain why they are quite conserved and less variable (Sherwood and Presting, 2007). 5.6.6 The 18S marker The 18S is a universal marker for eukaryotes and encodes the gene that is part of the small subunit of the ribosome. It is the nuclear equivalent of the 16S described above. It has been used for land plants (Hamby et al., 1988) as well as algae, though it generally also amplifies other non-target eukaryotic DNA as contaminants and it tends to be biased towards heterotrophs (Kirkham et al., 2011). It has been shown to underestimate diversity (Marcelino et al., 2016). For that reason, it is generally used for multi-taxa studies (Groendahl et al., 2017; Cahoon et al., 2018), though it has low resolution and so has difficulty differentiating species (Hall et al., 2010; Cho et al., 2017). The number of copies of the 18S varies between species. The variable region 4 (V4) of the 18S varies in length a great deal, making alignments difficult and its intra-species variability make grouping of OTUs difficult (Flynn et al., 2015). It is the most commonly used marker for phylogenies because of its universality and the secondary structure of the rRNA can also be used to reinforce results (Czech and Wolf, 2020). 5.6.7 Non-coding region markers Non-coding DNA regions were also tested for the design of potential markers and were interesting especially for evolutionary genetics (Taberlet et al., 1991). The most used region is the internal transcribed spacer (ITS). This marker from the nuclear ribosomal operon is

94 between the small and the large ribosomal subunits, including the 5.8S, in the polycistronic rRNA precursor transcript. It is a widely used barcode for land plants and algae and is considered universal. Its length is very variable, from 100 bp to 1,000 bp, rendering it difficult to align. It has intra-species variation, which varies in amount depending on taxa, so it can sometimes be difficult to cluster taxa together with accuracy. Secondary structure information helps counter that (Schultz and Wolf, 2009). Its popularity is partly due to the high rate of nucleotide substitution which enables the comparison of recently diverged taxa. Just like the 18S, the ITS marker tends to amplify non-target taxa as well because of its high amplification susceptibility. The number of copies of the ITS also varies between species. Because of its universality, it was broadly used, and databases have many sequences of algae as reference. They even have their own formatted database for metabarcoding along with other plants in Banchi et al., 2020.

The ITS is used extensively for barcoding, and its main advantage is that its structure can be used additionally to its sequence for phylogeny, though it is too big in its entirety for a metabarcoding experiment. The full ITS has been used for barcoding green algae studies and while it proved efficient in some of them (Zou et al., 2016; Hegewald et al., 2013), it also fell short in others who had trouble discriminating at the species level (Caisová et al., 2013), amplifying sequences (Vieira et al., 2016), or unwillingly aligning irrelevant sequences (Fučíková et al., 2011). The ITS comprises several regions in eukaryotes, which are referred to as ITS1 (corresponding to the prokaryote ITS located between the 16S and 23S, but in eukaryotes located between the 18S and 5.8S rRNA genes, see Figure 2.3) and ITS2, which originated as an insertion that interrupted the ancestral 23S. During rRNA maturation the ITS pieces are excised. ITS2 is a 150-300 bp region that has been referred to as a ‘gold standard’ for plant identification because it is easy to amplify. It is a small marker, therefore ideal for metabarcoding, and it is performant at discriminating plant species (Chen et al., 2010; Li et al., 2011; Hall et al., 2010; Hadi et al., 2016). In algae, it was used to assess the taxonomy with some success using the sequence and the secondary structure of the ITS2 (Bock et al., 2011; Buchheim et al., 2011). The ITS2 is not as referenced in databases as the 18S for green algae, especially of soil and snow algae. It was mostly used on snow blooms from the United States samples, and once for European Alps snow blooms in Prochazkova et al., 2019, a fact not known at the time of choosing of our own markers. ITS2 was able to distinguish between orange cysts and red cyst from orange and snow blooms, with a difference in sequence identities ≥95.1% inter-bloom and ≥96.5% intra-bloom. This was much more discriminating than 18S, and even RbcL, making it an interesting possibility for further studies in the French Alps. The authors have also been able to show intra-specific diversity by studying haplotypes, of which they found 18 different ones with ITS2 (Prochazkova et al., 2019). Another study of snow algae compared the 18S and ITS2 markers. They amplified between 80,000 and 120,000 MOTUs each. The 18S marker had 48% of sequences attributed to algae, while the ITS2 marker had 69%. The ITS had less complete database of reference sequence, and therefore had a larger proportion of unidentified algae than the 18S. Their most abundant algae as well as a few others had an ambiguous identification (Lutz et al., 2019).

Other non-coding regions studied include the rps11-rpl2 cluster (Provan et al., 2004), which had variable success depending on the plant tested, lacked sequences in the databases, and failed when tested on green algae (Vieira et al., 2016).

Figure 5.3. Scheme of the internal transcribed spacer (ITS) in the nuclear ribosomal operon.

5.7 Concluding remarks Alpine green microalgae dwell in fresh water, soil, snow and glaciers. This project focuses on all of them but glaciers. As mentioned above, a microscopy-only study vastly underestimates biodiversity, and can only detect a fraction of the microalgae species. Therefore, a dual-analysis combining both microscopy and DNA-based identification has been selected for this study. The large selection of samples and environments sampled require universal primers and a compromise in cost-per-sample. Chlorophyta have been reported in snow and glaciers in higher abundance and frequency than other algae (see Chapter 3), their study would enable comparing biodiversity of this major phototroph group in different environments and therefore was selected as the target group. Metabarcoding was chosen to accommodate for the large number of samples because it is more cost effective and efficient for biodiversity studies as it does not require lab culture and isolation of all strains and captures the trace of algae that do not grow in culture. As for markers, results reported in Table 5.1 and the ecoPrimers software (Riaz et al., 2011) were used to guide to the best choice. Two new markers were designed: one for the Chlorophyta phylum and one for its largest class, the Chlorophyceae. The Chlorophyceae group was shown to be prominent in snow in literature. Database completeness and universality of amplification in a multi-environment (soil, lakes and snow) study like the one presented in this work, guided the choice to the 18S and 23S regions. Universality is especially important because metabarcoding studies in the French Alps are sparse and inexistent on snow microalgae in that region. A detailed description of the markers is given in Chapter 6.

PART 2. RESULTS

Chapter 6

6.1 Preamble Alpine environments are at high altitude and characterized by a gradient of temperature and UV exposition. The changes in environmental conditions are much sharper than along a latitudinal gradient, with often strata of habitats and species specifically adapted to them. As the temperatures increase globally, the environmental conditions shift upwards in altitude, modifying these habitats. Most visibly, the treeline, a limit at which vascular trees are able to grow, was shown to be higher than before. How have these changes already affected the biodiversity of high-altitude environments? How will it be affected in the future? As for the first question, we can only compare with data previously measured. But in the case of microalgae, few inventories have been performed, especially when it comes to soil communities. Soil delivers ecosystem services that many species rely on, including downstream human populations. Furthermore, in the European Alps, land use has and still is changing. To answer the second question, it is important to acquire data on present microalgae species. It is therefore crucial to develop new tools and techniques to maintain and/or restore these ecosystems (Mooney et al., 2009).

Until now, soil algae studies have mostly focused on biological soil crusts. The best way to get an idea of overall biodiversity is to use metabarcoding as most microorganisms cannot be grown in vitro at present time, and this technique provides a snapshot in time of all the target organisms a marker can amplify and identify. Most markers targeting green algae were created for marine or freshwater studies and are not appropriate for the study of alpine soil algae. Existing eukaryotic markers developed at LECA were initially tested on databases (Euka01, Euka02 and Euka03, Taberlet et al., 2018), and only one of them was able to amplify green algae, Euka03. Its resolution was limited, however. Therefore, for this project, two new metabarcoding markers were designed to target the Chlorophyta phylum of green algae, and its largest class, the Chlorophyceae. The Chlorophyta marker was designed by modifying the Euka02 marker. The Chlorophyceae marker was designed using the ecoprimers software (http://metabarcoding.org/ecoprimers; Riaz et al., 2011).

Green algae are composed of three distinct lineages, the Chlorophyta, the Charophyta and the Prasinophyta. The Chlorophyta phylum was chosen as the focus of this study. It is the densest, most diverse, most studied and widespread. This provides an advantage, as species from this phylum are more referenced on databases, making it easier to study. Chlorophyceae is the largest and most diverse class of Chlorophyta. These two new markers are presented and validated for the study of soil green algae biodiversity in the article in part 6.2 of this chapter.

We know that different species respond differently to altitude (c.f. Grytnes et al., 2006), with vascular plants for example forming a bell-shaped richness curve with a peak in richness while bryophytes do not follow a specific pattern overall. Such a study has not yet been

98 performed on soil green algae, so this study brings also light on this matter for the first time on Chlorophyta. 6.2 Article 1 Stewart A., Rioux D., Boyer F., Gielly L., Pompanon F., Saillard A., Tuiller W., The ORCHAMP Consortium, Valay J-G, Maréchal E., Coissac E. (2021, submitted.) Altitudinal zonation of green algae biodiversity in the French Alps.

N.B: Figures in this article are numbered independantly of the thesis figures.

Altitudinal zonation of green algae biodiversity in the French Alps

Adeline Stewart1,2,3, Delphine Rioux3, Fréderic Boyer3, Ludovic Gielly3, François Pompanon3, Amélie Saillard3, Wilfried Thuiller3, The ORCHAMP Consortium, Jean- Gabriel Valay2, Eric Maréchal1,*, Eric Coissac3,*

1 - Univ. Grenoble Alpes, CEA, CNRS, INRAe, IRIG, LPCV, 38000 Grenoble.

2 - Univ. Grenoble-Alpes, CNRS, Jardin du Lautaret, 38000, Grenoble, France

3 - Univ. Grenoble-Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, 38000, Grenoble, France.

* Correspondence:

EM, [email protected] EC, [email protected]

Keywords: Green alga; Chlorophyta; metabarcoding; mountain environment; soil; biodiversity; high elevation

100

Abstract In temperate regions, mountain environments are marked by an altitudinal zonation of habitat types. They are home to a multitude of terrestrial green algae, who have to cope with abiotic conditions specific to high elevation, e.g., high UV irradiance, alternating desiccation, rain and snow precipitations, extreme low temperatures and diurnal variations in temperature, and chronic scarceness of nutrients. Even though photosynthetic green algae are key primary producers colonizing open areas and potential markers of climate change, their overall biodiversity in the Alps has been poorly studied so far. Here, we investigated whether the spatial distribution of green algae followed the altitudinal zonation of the Alps, based on the assumption that algae can spread via airborne spores and settle in their preferred habitats under the pressure of parameters correlated with elevation. We did so by focusing on selected representative elevational gradients at distant locations in the French Alps, where soil samples were collected at different depths. We showed that alga DNA represented a relatively low proportion of the overall eukaryotic diversity as measured by a universal Eukaryote marker. We designed two novel green algae metabarcoding markers to amplify the Chlorophyta phylum and its Chlorophyceae class, respectively. Using our newly developed markers, we showed that elevation was a strong correlate of species and genus level distribution. Altitudinal zonation was thus determined for about fifty species, with proposed accessions in reference databases. In particular, Planophila laetevirens and Bracteococcus ruber related species as well as the snow alga Sanguina genus were only found in soil starting at 2,000 m above sea level. Analysis of the vertical distribution in soils further highlighted the importance of pH and nitrogen/carbon ratios. This metabolic trait may also determine the Trebouxiophyceae over Chlorophyceae ratio. Guidelines are discussed for future, more robust and precise analyses of environmental algae DNA in soil in mountain ecosystems, to comprehend the distribution of algal species and dynamics in response to environmental changes.

101

Introduction Green algae are unicellular, colonial or multicellular photosynthetic organisms that are ubiquitous in almost all ecosystems. They have evolved in two major lineages, one referred to as Chlorophyta, what has been traditionally called green algae, another referred to as Charophyta, containing a smaller but often geographically widespread number of taxa (Lewis and McCourt, 2004; Domozych et al., 2016). Recently, a third lineage was introduced, covering marine Prasinodermophyta, which diverged before the split of Chlorophyta and Streptophyta (Li et al., 2020), and that was not considered in the present work. The majority of green alga species are found in aero-terrestrial habitats, living freely or in lichen in association with fungi (Rindi et al., 2010), from wet to dry areas (Lewis and McCourt, 2004; Holzinger et al., 2017). Some species can occupy non-liquid water systems, most notably snow and ice (Remias, 2012; Lukes et al., 2014; Hisakawa et al., 2015; Holzinger et al., 2016; Lu et al., 2016; Hoham and Remias, 2019). Knowledge on the biodiversity and distribution of green algae in mountain environments is fragmented and overall poor, some habitats having attracted more attention, such as lakes (Jacquemin et al., 2019) or snowpack at high elevation (Hoham and Remias, 2019), others being barely explored. Mountain environments are marked by the tight apposition of contrasted habitats, structured by the topography, elevation, temperature, exposure to sunlight, wind, precipitations, etc., with abiotic conditions considered as ‘extreme’ such as high light intensity, extreme negative temperatures, high UV irradiance, strong winds, desiccation, extreme diurnal variations in temperatures and chronic scarceness of nutrients (Geremia et al., 2016). The spatio-temporal structuration of mountain habitats and communities depends on altitudinal zonation and exposure to sun, extreme temperature, wind and precipitation, the two latter being possible vectors for external climatic, abiotic (e.g. noxious gases, nutrient-rich dusts, carbon-rich industrial sews) and biotic (e.g. pollen, bacterial, algal or fungal spores) influences. In environments undergoing rapid evolutions, photosynthetic algae can act as pioneering organisms, able to develop autotrophically on empty surfaces, and being the primary producers allowing the subsequent foundation of trophic networks. As an example, green algae have been shown to be primary colonizers after glacier’s retreat (Kastovska et al., 2005; Hotaling et al., 2017). It is also expected, although not demonstrated yet, that the current increase in atmospheric CO2 could be beneficial to the development of photosynthetic algae and as such, act positively on the efficiency of colonization. Green algae are therefore expected to be ‘markers’ of climate change. In mature ecosystems, once established in their habitat, green algae can proliferate to such an extent that they form so called blooms, which can be determinant in further evolutions of their environment. For instance, at the surface of snowpack,

102 green algal blooms are detected by the pigmentation of resistant cells (cysts), containing red carotenoids, such as astaxanthin (Holzinger et al., 2016). The ‘red snow’ thus formed reduces the albedo, triggering an increase of superficial temperature and accelerating snow melting (Lutz et al., 2016; Di Mauro et al., 2020). In this aspect, green algae are therefore also expected to be ‘actors’ of environmental changes. If we aim to comprehend the dynamics of ecosystems, data on microbial communities, and most notably on photosynthetic microorganisms, are critical. Spatiotemporal distribution of green algae is thus a major information we need, to address changes in mountain areas exposed to drastic seasonal variations and to irreversible transformations triggered by climate evolution. These data are currently missing. The Alps provide a wide range of small-scale ecosystems (Geremia et al., 2016). As elevation increases, several environmental parameters, like temperature, humidity, light intensity or UV irradiance, also change. This gives an opportunity to study adaptability and specificity of algae to these factors. The distribution of plant species in mountain environment is generally assumed to be mostly constrained by abiotic factors (Benton, 2009; Martinez-Almoyna et al., 2020; Körner, 2021) and it could be the same for microalgae, as suggested by a microscopy-based study of green microalgae performed in the Swiss Alps along an elevational gradient (Rehakova et al., 2011). To study microalgae communities, some research groups have performed sampling, followed by microscopic observation of microalgae (e.g. Rehakova et al., 2011) or DNA-barcoding analyses (e.g. Hall et al., 2010). None of these studies has provided a comprehensive taxonomic assessment of sampled green algae. Cell morphology is not sufficient to identify species, and information on life cycles, biochemical traits or genomic sequences would be requested to refine characterizations. Efforts are needed in this direction. DNA-based analysis is currently the best compromise to obtain more exhaustive information on microalgae communities (Lutz et al., 2016; Groendahl et al., 2017). Metabarcoding is based on assigning amplified molecular operational taxonomic unit (MOTU) sequences from an environmental sample (eDNA) to a database of sequences. Its success depends on the effectiveness of the markers to amplify targeted taxa, the effectiveness of the PCRs and sequencing, the status of the reference database (Ficetola et al., 2010; Ficetola et al., 2016) and the quality of the bioinformatic pipeline (Calderòn-Sanou et al., 2020). Markers for green algae of the Chlorophyta phylum were designed in several studies (e.g. Vieira and Bagatini, 2016; Zou et al., 2016; Pfendler et al., 2018) but most previous works use more general eukaryotic markers like ITS (internal transcribed spacer, Heeger et al., 2018) or COX1 (cytochrome oxidase I, Ward et al., 2005). The use of general eukaryotic markers reflects the fact that databases contain more of these sequences available for assignments. None of these markers has proven ideal for the study of green algae. They have issues such as too high or too

103 low variability, presence of introns, absence of data in the databases, or amplification of only a part of the community (Vieira and Bagatini, 2016). Here, we evaluated green microalgae biodiversity, focusing on Chlorophyta, in selected elevational gradients in the French Alps from 1,250 to 3,000 meters high, from forests at lowest stages, to a variety of other habitats such as heathlands, grasslands and rocky areas at high elevations (Figure 1). Presence of green algae was monitored at different depths in the soil. The sampling campaign was part of a large project aiming at understanding biodiversity and its drivers and dynamics over time in the Alps, called Orchamp, and which provided the samples (https://orchamp.osug.fr/home). To that purpose, we validated two new markers for metabarcoding studies, a Chlorophyta phylum marker “Chlo01” designed in the V7 region of the 18S ribosomal RNA, to cover most of the green algae and a Chlorophyceae marker “Chlo02” designed in the 23S ribosomal RNA chloroplast sequence. We then asked whether green algae could be detected with these markers and if we could point some possible environmental drivers of their distribution patterns and community structure.

104

Material and Methods

Soil sampling During the summer of 2016, 158 soil samples were collected in the French Alps along elevation gradients covering elevations from 1,250 m to 2,940 m at five different sites: Chamrousse (CHA) 45.098692°N, 5.885508°E, elevations from 1,250 m to 2,180 m; Loriaz (LOR) 46.038079°N, 6.918759°E, from 1,370 m to 2,330 m; Anterne (ANT) 46.009245°N, 6.805825°E from 1,400 m to 2,370 m; Ristolas (RIS) 44.724622°N, 7.031392°E, from 1,900 m to 2,890 m and Vieux Chaillol (VCH) 44.721000°N, 6.187555°E, from 2,150 m to 2,940 m. (Figure 1).

Figure 1. Aerial view of the five sampled elevational gradients and their topography (1,250 m to 2,940 m). Selected sites cover the full range of environments and habitats found in the Alpine altitudinal zonation and a representative geographical distribution. DNA was extracted from soil collected at each sampling location and used for an evaluation of Chlorophyta communities by DNA metabarcoding. Satellite images from Nasa.

The four lowest sites include environments composed of forests at the lowest elevations, and grasslands and pastures at the highest elevations. At their highest elevations, only RIS and VCH

105 reach the nival zone, and the latter has no forest at its base. The sampling was done along the elevational gradients approximatively every 200 m. For each level, sampling was performed in triplicate at two different soil horizons: the litter (soil not totally decomposed) between 0 and 10 cm depth, and the deep soil between 10 and 25 cm depth (soil totally decomposed).

Environmental variables In addition to the sampling site (Site), eight environmental variables were measured for each sample: elevation above sea level in meters (Elevation); soil pH (pH) and organic matter content (Organic Matter) assessed using standard protocols (Robertson, et al., 1999); total soil carbon (Carbon) and nitrogen (Nitrogen) measured using a Flash EA1112 (Thermo Scientific) elemental analyzer (Martinez-Almoyna et al, 2020); the Carbon over Nitrogen ratio (C/N ratio). Finally, two categorical variables: the type of environment (Environment) with two modalities, forest or open-area, and the soil horizon (Horizon) with two modalities, litter or deep soil, complete this description. It is common for environmental variables to be multi- colinear with respect to each other. To overcome this problem, a subset of continuous environmental variables was selected using the variance and inflation factor (VIF) criterion: VIF = 1/(1 - R2) where R2 is the coefficient of determination of the multiple linear model of one of the explanatory variables explained by the others. Variables with a VIF greater than 5 (Sheather, 2009) were iteratively removed. At the end of the selection process, Elevation, pH, Nitrogen and C/N ratio were retained, while Carbon and Organic matter, which are highly correlated with Nitrogen, were removed (Figure S1).

DNA Metabarcoding markers Two new DNA metabarcodes were designed for this analysis. The first one targets Chlorophyta (Chlo01), the second targets Chlorophyceae (Chlo02). To design these new metabarcodes, 1628 complete chloroplast sequences were downloaded from the NCBI database (https://www.ncbi.nlm.nih.gov/, November 2017) comprising 74 Chlorophyta plastid genomes, including 23 Chlorophyceae genomes. The ecoPCR software (http://metabarcoding.org/ecopcr) and the ROBIBarcodes R package (https://git.metabarcoding.org/obitools/ROBIBarcodes) were used to refine the corresponding primer sequences, assess the conservation of the priming sites using sequence logos (Schneider et al., 1990), and estimate in silico the taxonomical resolution as proposed in Ficetola et al., 2010. The sequence library used to realize those tests was the entire EMBL sequence database (release 139, Amid et al., 2020). The hybridization temperature was empirically determined using OligoCalc (http://biotools.nubic.northwestern.edu/OligoCalc.html) following recommendations in Taberlet et al., 2018. Chlo01 was adapted from the eukaryotic marker 106

Euka02 and corresponds to the V7 variable region of nuclear 18S rRNA gene (Taberlet et al., 2018). Corresponding primers were modified to make them specific of Chlorophyta. Chlo02 was designed using the ecoPrimers software (http://metabarcoding.org/ecoprimers; Riaz et al., 2011). A third marker, Euka03 (Euka03F: CCCTTTGTACACACCGCC, Euka03R: CTTCYGCAGGTTCACCTAC) targeting all eukaryota, was used to assess relative proportion of algae eDNA within the eukaryota super kingdom (Taberlet et al., 2018).

Algae Mock community for positive controls A mock community constituted by 13 green unicellular marine algae species from the Roscoff Culture Collection (RCC, http://www.roscoff-culture-collection.org) were used as template for the PCR positive controls. According to the order presented in Table S1, the DNA concentration for each species was adjusted to half of the previous one. The community was expected to be similar in concentration and complexity to our samples. If the thirteen species could theoretically be amplified by Euka03 and Chlo01, only 3 species were expected to be amplified by and Chlo02. The DNA of each species constituting the mock community was extracted using the Macherei Nagel NucleoSpin Plant II extraction kit according to the instruction manual (https://www.mn-net.com/).

Soil DNA extraction, PCR amplification and sequencing Extracellular DNA was extracted from 15 g of soil or litter as described previously (Taberlet et al., 2012). PCRs were then performed in triplicates for each sample, in parallel with extraction blanks, with no template soil and PCR blanks, with no template DNA (negative controls) and positive control. After an initial denaturation at 95°C for 10 min, 40 cycles (38 for Chlo01) of amplification were run: denaturation 95°C, 30 s; hybridization 30 s; elongation 72°C 1min. Hybridization temperature was respectively 55°C, 50°C, and 55°C for Chlo01, Chlo02, and Euka03. Each PCR product was individually tagged according to Taberlet et al., 2018. This enabled the pooling of up to 1,052 PCRs per sequencing libraries. Pooled PCRs were purified using the Qiagen MinElute PCR Purification Kit for Chlo01 and Euka03 and the Qiagen QIAquick PCR Purification Kit for Chlo02 (https://www.qiagen.com/). Sequencing libraries were prepared and sequenced (2×125 bp paired-end reads) by Fasteris, (Geneva, Switzerland), using their MetaFast protocol.

Design of a reference database of green algae sequences The reference sequence databases used for taxonomic assignment were extracted using ecoPCR (Ficetola et al., 2010, Boyer et al., 2016) from the EMBL database (version 140, 2019; Amid

107 et al., 2020), using Chlo01, Chlo02 or Euka03 primers as queries. EcoPCR results were filtered using OBITools (Boyer et al., 2016) to keep only the sequences annotated with an unambiguous family and genus. Strictly identical sequences were merged and their taxonomical annotations summarized at the lowest common ancestor. Sequences containing ambiguous nucleotides were also discarded. The cleaned reference databases for Chlo01, Chlo02, and Euka03 are constituted respectively by 1444, 744, and 17207 sequences. The Chlo01 database represents 295 genera and 62 families belonging the Chlorophyta. The Chlo02 database represents 42 genera and 19 families belonging the Chlorophyceae. The Euka03 database represents 5179 genera and 2488 families belonging the Eukaryota.

Read filtering and processing The reading pairs were assembled, and demultiplexed to be separated by sample. The sequences were then de-replicated to obtain the number of reads of each sequence variant in each PCR. These steps and the following were realized using the OBITools software (Boyer et al., 2016) following the protocol by Taberlet et al., 2018. According to the amplicon lengths estimated from our reference databases for each marker, sequences shorter than 65 bp and longer than 200 bp for Chlo01 and Euka03, and 130 bp for Chlo02 were discarded. Rare sequence variants never represented by more than 10 reads in a PCR were discarded. Punctual errors generated during PCR cycles were discarded using the obiclean (Boyer et al., 2016). Sequence variants were taxonomically annotated using the ecotag and the reference database described above. Only MOTUs annotated in the target clade of its marker were conserved. At this stage, any MOTU that was more abundant in the negative PCR controls than in any of the samples was annotated as a contaminant and discarded.

Removing of unsuccessful PCRs Of all the PCRs analyzed, some provide unreliable results. They were detected according to two criteria, the number of reads associated with a PCR, and considering a sample, the similarity between PCR replicates. Based on the distribution of the number of reads per PCR observed for each marker, PCRs with more than 200 reads for the markers Chlo01 and Chlo02, and 1,000 reads for Euka03 were considered unsuccessful and rejected. The reproducibility of PCR replicates was estimated by the distance between a replicate and the barycenter of the replicates for that sample. Distances were estimated using Euclidean distances computed on the Hellinger transformed data (square roots of the relative frequencies), which corresponds to a correlation distance. Distribution of these distances is used to detect potential outliers.

Data analyses and statistics 108

Further filtering and data analysis were run using R (v.3.6.2, R Core Team, 2019) using the ROBItools package (https://git.metabarcoding.org/obitools/ROBITools) for managing OBITools data files, ggplot2 (Wickham et al., 2011) for graphics, the ade4 package (Dray and Dufour, 2007) for every multidimensional scaling and the Vegan package (Oksanen et al., 2013) for computing Hellinger transformation (square rooted relative frequencies), relative frequencies, and Permutational multivariate analyses of variance (PERMANOVA). The iteratively reweighted least squares (IRLS) procedure for estimating outlier robust linear models was computed with the robustRegBS function of the robustreg package, implementing the methods presented in (Hubert, 1981).

Taxonomical diversity The diversity of algal communities was estimated for metabarcoding data using Hill numbers, with q =1 here (the exponential of the Shannon entropy index). A Hill number is the effective number of species composing a theoretical community, which would be perfectly even, and having the same diversity as the community studied. A taxonomic diversity measured by a Hill number (qD) takes less and less account of rare species when the q parameter increases. In the case of metabarcoding data, using q = 1 penalizes not only the rarest species, but also the many false taxa generated during PCR amplification that occur at low read frequencies. As a result, the taxonomic diversity 1D values estimated from DNA metabarcoding data are relatively congruent with those estimated from conventional inventories (Calderòn-Sanou et al. 2020). The relationships between diversity and environmental parameters were measured by discretizing the gradients into seven levels. The strength of the relationship was estimated with a one-factor ANOVA and its significance was tested with the Kuskal-Wallis method.

Community turnover The composition turnover between communities was estimated using Euclidean distances calculated on the Hellinger transformed contingency table of sequence reads, per MOTU and samples. We then projected those pairwise distances using principal coordinate analysis (PCOA). The strength of the correlations between community changes and environmental variables was estimated using Redundancy Analysis (RDA) using the Vegan R package. The environmental variables were centered and scaled for the analysis. The optimal model was selected using a forward-backward selection procedure implemented in the ordistep function. Partitioning of the community changes variance was performed using the varpart function. Permutation-based estimate of p-values rely on 999 permutations.

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Niche inference Niches of the MOTUs identified at the species or genus levels were estimated using the Outlying Mean Index (OMI) method (Doledec et al., 2000) as implemented in the niche function of the ADE4 R package. This method describes the niche according to three terms: its marginality, its marginal tolerance and its residual tolerance. Marginality measures the distance of the center of a taxon's niche from the center of the environmental space, which would represent a ubiquitous species. Marginal tolerance measures the width of the niche along its the marginality axis, defined by the vector connecting the center of the environmental space and the center of the taxon’s niche. The marginal tolerance measures the width of the niche in the orthogonal plan to the marginality axis. Dolédec et al., 2000, measured the specialization of a taxon by the non-zero marginality of its niche. In our case a taxon could also be considered as specialized, if its marginality was null but its marginal tolerance was lower than that expected for a taxon uniformly distributed in the environmental space. Therefore, a taxon was defined as specialized if its marginality was not null or if its marginal tolerance is smaller than expected under uniform distributions. Both conditions were tested by permutation (n=999) following the procedure implemented in the rtest function.

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Results Design and validation of Chlorophyta and Chlorophyceae DNA markers. There are three main lineages of green algae: Chlorophyta, Prasinophyta and Charophyta, the latter of which also is close to land plants (Kapraun, 2007). We focused on the Chlorophyta phylum, an important and diverse lineage of green algae, and were particularly interested in the Chlorophyceae class, which we expected to yield the greatest diversity of microalgae. The developed markers were termed Chlo01 and Chlo02. Similarly to Euka02 (Taberlet et al., 2018), the Chlo01 marker corresponds to the V7 region of the 18S nuclear rRNA gene. Its length ranges from 80 to 180 bp. The primer pair Chlo01F: AGTTGGTGGGTTGCCTTGT, Chlo01R: CACAGACCTGTTATTGCCTC has an estimated hybridization temperature of 55°C. The Chlo01 marker theoretically discriminates 24 % of the sequences at the species level, 43 % at the genus level, 53 % at the family level, 62 % at the order level and 74 % at the class level (Figure S2). The Chlo02 marker corresponds to a sequence included in the 23S chloroplastic rRNA gene. Its length ranges from 91 to 94 bp. The primer pair Chlo02F: RCTTAGTCCCGGCCATT, Chlo02R: CTAAGTGGWAAAGGATGTG has an estimated hybridization temperature of 50°C. The Chlo02 marker discriminates 47 % of the sequences at the species level, 65% at the genus level, and 73 % at the family level (Figure S2).

Sequencing results We used the three markers to amplify DNA extracted from soil samples collected along the five elevation gradients. After filtering, the Chlo01 marker amplified 4,080 MOTUs represented by ~5.8 million reads, including 566 Chlorophyta MOTUs corresponding to 3.3 million reads (see Table 1). The Chlo02 marker amplified 8,580 MOTUs, corresponding to 6.3 million reads; among them, 61 MOTUs belonged to Chlorophyceae, represented by less than 0.2 million reads. The Euka03 marker amplified 8,743 MOTUs, represented by 5.7 million reads, including 4,108 Eukaryota MOTUs corresponding to 4.1 million reads and 37 Chlorophyta MOTUs representing only 14,829 reads.

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Table 1. Amplified MOTU and read counts by each marker for all samples.

Number of MOTU and Number of MOTU and Number of green algae Marker reads in raw data reads after filtering MOTU and reads 1,280,988 MOTUs 4,080 MOTUs 566 MOTUs Chlo01 12,849,650 reads 5,763,181 reads 3,305,241 reads 2,392,669 MOTUs 8,580 MOTUs 61 MOTUs Chlo02 32,640,026 reads 6,210,043 reads 186,616 reads 1,902,234 MOTUs 8,743 MOTUs 37 MOTUs Euka03 13,604,341 reads 5,700,798 reads 14,829 reads

Chlorophyta DNA represents a minor fraction of soil DNA To evaluate the relative part of algae eDNA present in soil samples, data obtained with the Euka03 marker were analyzed. Figure 2 shows the fraction of fungi, Streptophyta (mostly vascular plants) or Chlorophyta reads amplified by the Euka03 marker. While fungi and vascular plants occupy on average a high fraction of the reads, 59% (sd = 19%) and 21% (sd = 17%) respectively, Chlorophyta represent less than 3.3% of the reads in every PCR and 0.6% on average. In fact, only 18.6% of the PCRs with the Euka03 marker had some Chlorophyta reads (Figure 2). That trend is the same at every sampling site, even if the abundance of algae seems to increase at sites that cover higher elevations. Due to this low abundance of reads, which could result in under-sampling of diversity, and due to the low taxonomic resolution of Euka03, only 37 MOTU of Chlorophyta were identified. Of these, 7 belong to Chlorophyceae and 16 to Trebouxiophyceae. That low abundance of Chlorophyta eDNA is also confirmed by the marker Chlo01. Despite the fact that this marker is supposed to be highly specific to that clade (Figure S2), we observed that many sequences were not annotated as Chlorophyta. Such high artifactual amplifications are commonly observed when target DNA concentration is very low in PCRs. Using an IRLS procedure, a linear model explaining 25% of the variance can be established on a logarithmic scale between the relative frequencies of Chlorophyta reads estimated by Euka03 and Chlo01 (Figure 3).

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Figure 2. Relative frequencies of three eukaryotic clades; Fungi, Streptophyta, and Chlorophyta, among the sampling sites. The lower and upper limits of the boxes correspond to the first and third quartile, respectively, while the bold center line marks the median. The whiskers delineate the confidence interval defined as 1.5 times the difference between the first and third quartile. Outlier PCRs ranging outside of that interval are marked with dots.

Figure 3. On a logarithmic scale, the relative frequencies of Chlorophyta reads estimated by the two markers Chlo01 and Euka03 are correlated. The linear model (dotted line) was estimated using an iteratively reweighted least squares procedure (IRLS) to underweight the influence of the few outliers PCRs. The blue scale indicates the weight associated to each PCR after the convergence of the algorithm.

Diversity of the Chlorophyta communities

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The scarcity of Chlorophyta eDNA, confirmed by both Euka03 and Chlo01, can be explained either by a low biomass of algae in Alpine terrestrial environments or by our poor ability to extract algal eDNA from soil samples. Whatever the reason, this rarity limits the completeness of our sampling. Therefore, we certainly sampled only the most abundant taxa. This must be kept in mind when analyzing the data. For checking at minima, the quality of the 1D estimation for algae and considering our data filtering stringency, we estimated 1D for positive controls carried out on the mock community of 10 marine species of Chlorophyta. According to its composition, the theoretical diversity of the mock community was 1D = 4.0 using the marker Chlo01 and 1.8 using the marker Chlo02 since only three of the ten species are Chlorophyceae. For Chlo01 the 72 replicates of the positive control gave a mean diversity 1D = 3.83 species (sd = 0.016), which is slightly underestimated. For Chlo02, the same positive controls gave a mean diversity 1D = 1.330 species (sd = 0.0016) instead of the theoretical 1.8. Over all five elevation gradients, the mean diversity observed for a sample for Chlorophyta (Chlo01) was 1D = 9.58 (sd = 0.018 for 321 PCRs), and for Chlorophyceae (Chlo02) 1D = 2.224 (sd = 0.0075 for 187 PCRs). A rough estimate of regional diversity (ɣ), by cumulating the results of the five elevational gradients, gave 1D = 49.03 for Chlorophyta and 1D = 13.40 for Chlorophyceae. The β diversity estimated as ɣ/α can be evaluated to 5.11 sites for Chlo01 and 6.02 sites for Chlo02. These two values were to be related to the number of studied gradients, i.e. five, and indicate that most of the MOTUs are site specific. Among the 566 MOTUs identified by Chlo01, 367 are present on only one gradient, 76 on two, the remaining 123 being observable on at least three gradients. For Chlo02, among the 61 MOTUs detected, 35, 9 and 17 MOTUs appear respectively in one, two, or three or more gradients (Figure 4). There is a strong link in that dataset between the endemism of a MOTU and its rarity, the MOTUs occurring in one or two sites only are also those having the lowest frequencies of occurrences at these sites (Figure 4). Therefore, the high endemism observed was probably related to the low coverage of the sampling. With the exception of the CHA gradient, which shows atypical results, among the four non-collinear environmental variables, pH (Figure 5A), elevation (Figure 5B), and nitrogen content (Figure 5C) are significantly related to diversity. They explained 22%, 9% and 22% of the variance of 1D, respectively.

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Figure 4. Endemism of the MOTUs according to their maximum frequency of occurrence in one of the gradients. The higher a MOTU has a high frequency of reads on at least one gradient, the more likely it will be present in many gradients.

At the 5% threshold, the C/N ratio had no detectable effect on algal community diversity. The litter, which was richer in nitrogen, carbon and organic matter had a mean algal diversity 1D =11.8 (sd = 0.43) significantly higher (Mann-Withney p-value = 10-15) than that of the deep soil layer 1D = 6.7 (sd = 0.43). On the other hand, forest environments did not present a significantly different diversity from open environments (Mann-Withney p-value = 0.54), although the former were also richer than the latter in Nitrogen, Carbon and Organic Matter.

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Figure 5. Impact of environmental parameters on algae community diversity. Each gradient is divided in seven parts. This division allows for mixing in a single part, samples from several sites. The bold horizontal bars indicate the mean of the diversity for the covered interval. The whiskers delimitate the 95% confidence interval of that mean. The four p-values are adjusted for multiple tests using the false discovery rate method (Benjamini and Hochberg, 1995). The determination coefficients R2 measure the part of the variance explained by the sliced gradients using one way ANOVA.

Main components of Chlorophyta communities The Chlorophyta taxa identified with Chlo01 belong to four classes: Trebouxiophyceae, Chlorophyceae, Ulvophyceae and Pedinophyceae. They corresponded respectively to 82.3%, 11.1%, 1.6%, and 0.02% of the reads of this marker. Pedinophyceae, the rarest clade, was detected only on the RIS gradient in only two PCRs (Figure 6). Trebouxiophyceae, and 116

Chlorophyceae, the two most abundant classes see their relative abundance evolving as a function of soil pH and elevation (Figure 7). Because of their large dominance and the relative measure of their abundance, the decrease of one class mechanically increased the other. It was therefore not possible from these results to decide between the different hypotheses of substitution of one class by the other, the rarefaction of one class, or the increase of the other. As for the variation in diversity presented below, the two factors had a significant effect, with here, again, a higher variance explained by pH (R2=0.22) than that explained by elevation (R2=0.026). On the other hand, no effect of nitrogen or C/N ratio was detected on this variation in abundance between the two classes.

Figure 6. Distribution of the four taxonomical classes of Chlorophyta. Trebouxiophyceae and Chlorophyceae are the two main clades. Pedinophyceae just occurs sporadically in two PCRs on the RIS gradient.

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Figure 7. Trebouxiophyceae and Chlorophyceae relative abundance was impacted by soil pH (A) and elevation (B). The upper blue lines indicate the tendency of Trebouxiophyceae relative abundance when pH or elevation increase. The opposite trend is materialized for the Chlorophyceae by the two bottom blue lines.

Figure 8. Redundancy analysis (RDA) of the Chlorophyta community against four environmental variables Nitrogen, Elevation, C/N ratio and pH.

The impact of Nitrogen, Elevation, C/N ratio and pH was significant but small The impact of environmental variables on Chlorophyta community variation (Figure 8). Sites were used as a covariate. The model selection retained the four variables considered as significant. However, the explanatory power of these variables on the variance of the communities is low (adjusted R2 = 0.063). In decreasing order of influence, the variables

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Nitrogen, Elevation, C/N ratio and pH have respectively an adjusted partial R2 of 0.018, 0.016, 0.010 and 0.0091.

Niche description of the MOTUs identified at the species and genus levels. Fifty-one and forty-five MOTUs were assigned to a species or genus respectively. For each of these 96 taxa, the optimal range for each of the four environmental variables was determined. For each of the variables, it was possible to identify taxa with optimal ranges spanning the entire environmental gradient. Figure 9 shows the optimal elevational range of all identified genera, from lowest to highest altitude. While Symbiochloris, Desmococcus, Chloroidium, Apatococcus, Trentepolia were associated with low elevation, Actinochloris, Sanguina, Scotinosphaera and Sponchiochloris are preferentially found at high elevations (Figure 9). Forty-six of the 96 taxa tested (22 species and 24 genera) had a niche significantly specialized compared to the tested span of environmental variables. The niche of these taxa was compared by performing a Principal Component Analysis (PCA) where each of these taxa was defined by the center of its optimum interval for each of the variables (Figure 10). The first axis of the PCA carried most of the variance (82.3%). Chlorophyceae were significantly more localized to the left on this axis than Trebouxiophyceae (Mann-Whithey p-value = 2.5x10-6). This position corresponded to a preference for higher pH and elevations, whereas Trebouxiophyceae prefer a higher C/N ratio and higher nitrogen. This was consistent with the impact of pH and elevation on the relative abundance of these two classes of Chlorophyta (Figure 7).

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Figure 9. Read relative frequency along elevation for each genus identified. Arrows indicate the median. The range in grey centered on the pic of density is where the MOTU is the most abundant. P-values were evaluated using the Mann-Withney test. Bold taxon names indicate a significative p-value at 0.05.

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Figure 10: Principal component analysis of the 46 taxa with specialized niche. Taxa are positioned are positioned according to the OMI of their niche.

Discussion This work addressed the potential altitudinal zonation of green algae in mountain areas in temperate regions in the Northern hemisphere, taking the French Alps as study case. To date, and to our knowledge, no such systematic investigation has been attempted. We benefitted from the availability of soil eDNA samples, obtained by the Orchamp consortium. Five gradients have been sampled at distant locations, covering elevations from about 1,250 to 3,000 meters (Figure 1). It must be noted that only two of the five gradients reached the niveal zone, nevertheless, they provide information on Chlorophyta clades present at the highest elevations. We based our study on several assumptions. The first one is that soil samples could provide information on the presence of species regardless of seasonal variations, which may alter relative abundance, and even determine the absence or presence of some species at the time of sampling. The second assumption was that elevation gradients could be compared, and that an elevation in one site may correspond, approximately, to an elevation in another site. We hypothesized that, due to their role as primary producers and as pioneer species in open areas, algal DNA could be detected in most sites. The presence of Chlorophyta was indeed confirmed in the five elevational gradients, and in most of the soil samples. However, based on 121 a first evaluation using the Euka03 eukaryotic marker, it was clear that Chlorophyta DNA occurred in an extremely low proportion (Figure 2, Table 1). We designed, and validated, two new markers for metabarcoding studies, the Chlorophyta phylum marker Chlo01 in the V7 region of the 18S ribosomal RNA, to cover most of the green algae, and the Chlorophyceae marker Chlo02 in the 23S ribosomal RNA chloroplast sequence. Both improved our detection of algal DNA and the identification of algal MOTUs, still highlighting an extremely low proportion of Chlorophyta DNA in soil samples (Table 1). The low coverage of green algae might be attributed to the higher proportion of DNA from other organisms, which present a higher biomass. Microbial communities develop in soil away from light exposure, and are therefore expected to be dominated by heterotrophic species feeding off of available organic carbon and other nutrients. Multicellular eukaryotes are also present with substantial levels in biomass, like fungi, animals or plant roots. The low proportion of algal DNA may explain why most of the MOTUs we detected appeared site-specific (Figure 4). This limitation should be taken into account, and hopefully corrected in future, more comprehensive analyses. Here, we therefore considered that the detected clades were probably the most abundant ones in algal communities, and that the distribution patterns we detected reflected strong trends. Since numerous green algae have the capacity to form airborne spores (Tesson et al., 2016), allowing them to be transported by ascending winds, and since many stressful environmental parameters such as extreme temperatures and high UV light exposure are correlated with elevation, we wondered whether altitudinal zonation may be a major determinant of spatial occupancy and of biodiversity, regardless of the sampling sites. Among the four non-collinear environmental variables we monitored, pH (Figure 5A), elevation (Figure 5B), and nitrogen content (Figure 5C) were significantly related to diversity. Thus, our prior assumption that elevation could be compared between sites proved to be acceptable, as this parameter appeared as one of the plausible determinants of algal distribution. Nevertheless, it was not sufficient, not even prominent, since pH and nitrogen appeared as likely more important. It must be noted that the CHA gradient showed some atypical results compared to other sites, which may be due to the location of this site, facing a highly dense urban area and likely influenced by winds streaming from the Rhône valley (Figure 1). When focusing on Chlorophyta classes, Trebouxiophyceae and Chlorophyceae appeared as the two most abundant ones in all our samples, consistent with their prominence in aero-terrestrial habitats. Their relative abundance was strikingly correlated with soil pH and elevation (Figure 7), highlighting again these two parameters as determinant. We refined our analysis on the fifty-

122 one and forty-five MOTUs we could assign to a species or genus, respectively, attempting to determine the optimal range for each of the four environmental variables. The distribution at the genus level is not simple to analyze, as genera encompass a number of species, which can be distinct between samples, and/or having overlapping niches hiding more specific distributions at the species levels. Some genera, like Stichococcus, Coccomyxa, Xylochlorus, Trebouxia, Dictiochloropsis, Myrmecia, Pseudochloroella or Bracteacoccus were detected at nearly all elevations, and the pattern of their distribution rather suggest that they are cosmopolitan genera (Figure 9). This does not exclude that, within these genera, some species may have emerged as highly specific of certain niches. Further studies, at the species and/or ecotype levels are therefore needed for these large clades. Desmococcus, known to comprise species that are tolerant to desiccation (Lüttge and Büdel, 2010) or covering artificial hard surfaces in urban areas in central Europe (Hallman et al., 2016), are associated with low elevations (Figure 9), possibly connected to a broader geographic distribution in valleys. Symbiochloris, comprising free-living and/or lichenized algae (Škaloud et al., 2016) are also associated with low elevation, but data do not allow determining whether corresponding species are lichen photobionts or not. Interestingly, the Sanguina genus corresponding to species causing red snow blooms, i.e. S. nivaloides and S. aurantia (Procházková et al., 2019), is found at elevations higher than ~2,000 m, with an optimal occurrence at ~2,400 m. This finding is consistent with the proliferation of S. nivaloides and S. aurantia in the snow cover encountered at these elevations. It also highlights that the soil can possibly be a long-term reservoir for these snow algae in the summer season. Eventually, the two genera preferentially found at high elevations were Scotinosphaera, described previously in various habitats in low elevations as well (Škaloud et al., 2013) and Spongiochloris, based on a small number of occurrence in two sites (Figure 9). The airborne spreading of Spongiochloris has been reported in previous reports (Tesson et al., 2016), which may explain a transport of this taxon reported in various locations in desertic or mountain sites to such high elevation. When focusing on species-level MOTUs (Figure S3), obtained patterns needs to be considered with caution due to the lack of reference genomes of Chlorophyta in existing databases, and possible misannotations. Still, the number of accessions previously recorded in mountain areas or in polar regions is striking, including Chloromonas nivalis (optimal elevation at ~1,800 m; Procházková et al., 2018); Ploeotila sp. CCCryo 086−99 (which is closely related to Sanguina species; Procházková et al., 2019), detected here in one sample at ~2,000 m; Trebouxiophyceae sp. SC2−2 (first described in glacial refugia in Antarctica; De Wever et al., 2009), here quite cosmopolitan, with an optimal elevation at ~2,100 m; Sphaerocystis sp. CCCryo 133−01 (described in moss fields along snow melt in the Spitzberg, in the CCCryo collection; Leya,

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2020), here with an optimal elevation at ~2,150 m; Trebouxia sp. UTEX SNO74 (previously recorded as Chlamydomonas nivalis, based on a collection in the snow; Matsuzaki et al., 2018), here with an optimal elevation at ~2,500 m. In the latter case, the distribution of Trebouxia sp. UTEX SNO74 is broad, including occurrence at lower elevation, suggesting that species associated to this accession might be tolerant, but not specific, to the conditions found in high elevations. With optimal elevation higher than 2,500, species include Planophila laetevirens (previously detected in various locations in the Alps as well as high latitudes; Schmidt and Darcy, 2015), Bracteococcus ruber (recently detected in alpine mountains in New Zealand; Novis and Visnovsky, 2012) and Spongiochloris spongiosa. Interestingly, in one occurrence at ~3,000 m, Bracteacoccus aerius was detected. This species known to stick to dust in air suspension, may have reached the top of this mountain site via ascending winds. Altogether, obtained data support that species-level MOTUs are likely associated with an altitudinal zonation. Other environmental factors may be also important, in combination, as shown by the distribution patterns we also obtained with pH, nitrogen and C/N (supplementary figures S3-S9). We do not exclude that the taxonomic assessments presented in this study may be biased, first by a high level of similarity between the amplified DNA with that of a close but different species/accession in the reference database, and secondly by an overrepresentation of psychrophile species in the reference database. Our search for significant correlations highlighted that clades belonging to the Chlorophyceae were distinct from Trebouxiophycea by their preference for higher pH and elevations. By contrast, Trebouxiophyceae appeared to prefer a higher C/N ratio and higher nitrogen (Figure 10), suggesting that soil nutrients were determinant as well. When we considered all sites, the algal diversity was actually significantly higher in the litter (soil not totally decomposed) compared to the deep soil layer underneath. The litter is richer in nitrogen, carbon and organic matter and is only partially exposed to light. Based on their compositions, soils may therefore favor species being both phototroph and heterotroph, which has been known for a long time to comprise numerous Chlorophyta species (Parker et al., 1961). The capacity to combine phototrophy and heterotrophy, and in the case of synergies between these two energetic metabolisms, mixotrophy, seems therefore a possible strategy for algae to spread in the soil, compared to algae from lakes and rivers, which may simply rely on strict phototrophy. This metabolic capacity may therefore also be determinant at the level of genera and/or species.

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Concluding remarks Metabarcoding is a tool of choice to study microalgae communities in such large areas and territories as the different mountain massifs that make up the French Alps. The main difficulty compared to other microbial phyla lies in the lack of molecular markers and the lack of reference genomes in databases. The Chlo01 and Chlo02 green microalgae markers developed here successfully amplified green algae from Alpine soil samples. They also amplified DNA from marine green alga strains from the RCC public collection used as positive controls. They will be extremely useful for future studies. The amount of microalgae DNA is very small in the soil. To get a solid overview of the biodiversity of microalgae in the Alpine soil, the sampling effort should be increased as well as the number of PCR technical replicates, resembling the type of effort used for ancient or freshwater DNA (Valentini et al., 2016). Despite this technical limit, we assumed that detected DNA corresponded to the most abundant species, and we were therefore still able to draw some conclusions from this preliminary work. Firstly, our sampling sites allowed us to test whether elevation was a major, if not the most prominent, determinant of spatial distribution, based on the assumption that algae would mainly spread via airborne spores (Tesson et al., 2016) and sit in their preferred habitats under the pressure of parameters correlated with elevation, such as decreasing temperature levels and exposure to increasing UV light. A putative decline of biodiversity due to the extreme conditions in highest elevations was not evidenced. This indicates that photosynthetic eukaryotic algae are present in all niches, and that their diversity can be a source of pioneering species colonizing open areas, such as those opened by the retreat of glaciers. Comparison of read frequency along elevational gradients suggested that elevation, but also pH and soil N in combination contribute to the spatial distribution of green algae. Future works will therefore be needed to investigate the impact of the geological context, since all sites investigated here were crystalline and acidic. Different distribution patterns might therefore be encountered in soils covering calcareous and alkaline rocks in pre-alpine massifs. Vertical differences in green alga biodiversity supported the fact that factors other than light were determinant in the presence of species in soil, possibly acting as essential local reservoirs for a long-term occupancy of this habitat. In particular, the C/N ratios seems determinant in the case of Trebouxiophyceae, and future work will be needed to refine the role of this parameter in relation with the energetic metabolism of species, being not only phototrophic, but also heterotrophic and/or mixotrophic. At the species/accession level, an altitudinal zonation was evidenced, again with pH being determinant in the distribution pattern in a more refined manner. Some species seem cosmopolitan whereas others appear specific to some elevations and corresponding habitats; it

125 is possible that there is an altitudinal zonation of microbial communities in broader sense, and that there is a relationship with multicellular organisms who are also specific to certain elevations. Based on this work, some of the accessions we highlighted need to be assigned taxonomically with greater precision, to be considered as potential markers of ecosystems’ evolution. Altogether, this study will help draw up guidelines for future, more robust and precise analyses of environmental green algae DNA, from the analysis of more local patterns in some habitats such as forests, meadows, lakes, streams, glaciers, etc, to larger scale comparisons of remote sites in Alpine massifs. In addition to organic carbon, that seems essential for heterotrophic/mixotrophic species over obligate photoautotrophs, light, N or pH, other factors like temperature, other nutrients including iron, phosphorus, etc, or the availability of water streaming from the network of rivers, lakes and/or runoff from snow/ice melting, etc, need to be considered as well. Future analyses of this group of primary producers, integrating various spatial and temporal scales could therefore help address the evolution of mountain habitats and ecosystems, strongly affected by the effects of climate change.

Author Contributions AS and WT collected the samples along the Orchamp gradients. LG, AS and DR performed DNA extractions and dilutions; AS and DR performed PCRs. FB, AS and EC performed data filtering; AS and EC performed data analyses; FP provided expertise in environmental DNA analyses; JGB, EM and EC conceived the project; AS, EM and EC contributed to the writing of the article.

Funding This work was supported by CNRS (Mission pour l’Interdisciplinarité) and National Research Agency (Alpalga ANR-20-CE02-0020, Oceanomics ANR-11-BTBR-0008, GlycoAlps ANR- 15-IDEX-02, GRAL Labex ANR-10-LABEX-04, and EUR CBS ANR-17-EURE-0003, GlobNets ANR‐16‐CE02‐0009) and from ‘Investissement d'Avenir’ grants managed by the ANR (Montane: OSUG@2020: ANR‐10‐LAB‐56).

Acknowledgements Authors wish to thank Ian Probert (Roscoff Culture Collection, Station Biologique de Roscoff, France) who provided marine green algae strains used as a control in this study, Isabelle Domaizon (INRAE, Thonon, France) for fruitful discussions and the consortium in charge of the Orchamp program for guidance throughout the project, DNA samples and soil analyses data (https://orchamp.osug.fr).

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Supplementary figures

Figure S1. Distributions of environmental variables and their relationships. The density diagrams on the diagonal show the distribution of each variable across the different sampling sites. The scatter plots in the lower triangle of the matrix show the relationships between the variables, and the panels in the upper triangle summarize the linear correlations between the variables. The colors are related to the sampling site and the correlation panels provide their relationship to the sampling sites.

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Figure S2. Markers for Chlorophyta (Chlo01) and Chlorophyceae (Chlo02) for metabarcoding. A, C, E: Chlorophyta. B, D, F: Chlorophyceae marker. A, B: Primer sequences. The size of the letters represents the degree of conservation across tested algae sequences. C, D: amplicon size and their respective occurrence across tested algae sequences. E, F: Proportion of target vs non- target taxa amplified depending on the number of mismatches allowed on primer sequences.

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Figure S3. Distribution of species along elevation gradient.

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Figure S4. Distribution of species along pH gradient.

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Figure S5. Distribution of genera along pH gradient.

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Figure S6. Distribution of species along Nitrogen gradient.

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Figure S7. Distribution of genera along Nitrogen gradient.

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Figure S8. Distribution of species along C/N ratio.

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Figure S9. Distribution of genera along C/N ratio.

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Table S1. Roscoff Culture collection (RCC) green microalgae selected as controls for the present study; Concentrations and quantity of DNA added to the Positive control Mix in PCRs for metabarcoding are indicated below resulting in a two-fold serial dilution.

RCC Euka03 Chlo01 Chlo02 DNA quantity Taxid Taxon ID (ng) RCC 7 133490 Picochlorum atomus + + - 8000 RCC + + + 3052 Chlamydomonas sp 4000 443 RCC + + - 3165 Tetraselmis striata 2000 130 RCC 6 3047 Dunaliella tertiolecta + + + 1000 RCC + + - 114055 Chlorella vulgaris 500.0 537 RCC Pycnococcus + + - 41880 250.0 581 provasoli RCC + + + 41891 Coccomyxa sp 125.0 891 RCC + + - 29646 Stichococcus sp 62.50 1055 RCC Tetraselmis + + - 34154 31.25 1563 convolutae RCC + + - 36882 Pyramimonas sp 15.63 2501 RCC + + - 188557 Acrochaete sp 7.813 2960 RCC + + - 88271 Picocystis salinarum 3.906 3402 RCC + + - 1418015 Pseudochloris sp 1.953 4743

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Chapter 7

7.1. Preamble Mountain environments are changing drastically due to climate change, and our knowledge of some of their green microalgae is still poor, giving us little to go on to measure changes in the future. While soil algae in an altitude gradient in different types of environments were our focus in Chapter 6, here addressed some other environments, namely lakes and snow.

The effects of altitude on freshwater algae community composition are a reflection of how these environments are going to change with the increasing temperatures to help us predict the evolution of the alpine landscape. However, freshwater green algae have not been studied yet along an altitude gradient of lakes using metabarcoding.

Snow algae form visible blooms in patches of the snow at high altitude in the spring and summer during snow melting. These blooms can vary in hue from red, pink and orange to grey and green, depending on the dominant species. Snow algae have piqued the interest of scientists since the 1900s but even more so since the 2000s. Since then, a steady increase in the number of publications per year about snow algae has occurred (Figure 7.1). This interest is especially important now that the cold environment they grow in are in danger of disappearing due to the global increase of temperature and its secondary effects.

Figure 7.1 Number of publications on snow algae since the 1980s on Pubmed. This figure was generated using the RISmed R package using the keyword “snow algae” in November 2020 with a cap at 90000 publications starting in reverse chronological order.

Until recently, snow algae diversity had been studied using mostly microscopy and barcoding. However, recent studies using metabarcoding (e.g. Lutz et al., 2019) have shown 141 that their diversity had been largely underestimated. It was first believed that red blooms were monospecies, and due to a large concentration of Chlamydomonas nivalis cells. Since then, many other species have been described. Recently, the Sanguina genus was described and found worldwide in red snow blooms (Procházková et al., 2019). As we observed red cysts in our snow samples under microscopy, we also wondered if we would find DNA evidence for the presence of C. nivalis, or Sanguina spp., of monospecies populations or diversified communities.

There are also different hypotheses on the means of propagation of the snow algae, as the snow melts. Hypotheses include the soil being a reservoir of cysts, followed by horizontal or lateral transfer through the snow column during spring. To test this, we sampled soil near a snow bloom. Other possibilities are lateral dispersion from permanent ice at higher altitude, or wind dispersal (Bischoff, 2007). We also sampled white snow, with no visible bloom, to look for the possible presence of algae in lower concentration. Why blooms occur in certain patches and not others is also not understood, and comparing them might provide insight. One main possibility is nutrient availability, so carbon, nitrogen, phosphorus and iron, as the main necessary nutrients for algae growth, were measured in both a bloom and outside a bloom to test this hypothesis.

Knowing what factors influence growth in the environment also helps us understand their physiology. Snow algae are known to produce molecules that are of biotechnological interest. Microalgae can be used to produce large quantities of oil (TAGs) in lipid droplets that are used to produce biofuels. In general terms, microalgae present a promising alternative to fossil fuels, but their large-scale production worldwide is still challenging. They need to be grown in very large quantities, but are much easier to culture than crops as they take much less space and care, and do not compete with needs for food production. The culture media is inexpensive and microalgae can be grown in non-arable land in outdoor culture pools. However, to maximize production, microalgae require specific conditions, including temperature. Maintaining heated warehouses would consume too much energy and be counterproductive. Therefore, cryotolerant strains might offer a line of investigation for cultures outdoors in colder climates. Collecting more information on different species that colonize these environments will provide knowledge for both basic and applied questions.

The following article validates the markers designed and presented in chapter 6 for multi environment studies and brings more complete information on the inventory of green algae in the European Aps by adding two new environments, lakes and snow.

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7.2 Article 2 Stewart A., Kallend A., Rioux D., Boyer F., Miquel M., Lionnet C., Bureau S., Tisserand D., Pompanon F., Saillard A., Bonin A., Domaizon I., Valay J-G, Maréchal E., Coissac E. (2021, in prep.) A comparative study of Chlorophyta biodiversity in high altitude European alpine lakes, snow algal bloom and soil

N.B: Figures in this article are numbered independantly of the thesis figures.

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A comparative study of Chlorophyta biodiversity in high altitude European alpine lakes, snow algal bloom and soil

Adeline Stewart1,2,3, Auria Kallend3, Delphine Rioux3, Christian Miquel3, Clément Lionnet3, Sarah Bureau4, Delphine Tisserand4, François Pompanon3, Aurélie Bonin3, Isabelle Domaizon5, Eric Coissac3,*, Jean-Gabriel Valay2, Eric Maréchal1,*

1Laboratoire de Physiologie Cellulaire et Végétale, Unité mixte de recherche 5168 Univ. Grenoble-Alpes, Centre National de la Recherche Scientifique, Commissariat à l’énergie atomique et aux énergies alternatives, INRAE; IRIG; CEA Grenoble; 17 rue des Martyrs, 38000, Grenoble, France

2Station de Recherche du Jardin du Lautaret, Unité mixte de service 2925 Univ. Grenoble- Alpes, Centre National de la Recherche Scientifique; 2233 rue de la piscine, Domaine Universitaire, 38610, Gières, France

3Laboratoire d’Ecologie Alpine, Unité mixte de recherche 5553 Univ. Grenoble-Alpes, Université Savoie-Mont Blanc, Centre National de la Recherche Scientifique

4Institut des Sciences de la Terre, Unité mixte de recherche, Université Grenoble Alpes and Centre National de la Recherche Scientifique, Grenoble, France; OSUG-C (Maison des Géosciences), 1381, rue de la Piscine, 38610 GIERES

5Centre Alpin de Recherche sur les Réseaux Trophiques et Ecosystèmes Limniques, Unité mixte de recherche 42 Université Savoie Mont-Blanc, INRAE, 74200 Thonon les Bains, France

* Correspondence:

EM, [email protected]

EC, [email protected]

Keywords: Green algae; Chlorophyta; Chlorophyceae; Trebouxiophyceae; Sanguina; Metabarcoding; Alpine environment; snow algae; biodiversity; Alpine lake

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Abstract Mountain environments, marked by an altitudinal zonation of ecosystems, are affected by climate change, which impact on the corresponding biodiversity is currently difficult to predict. By contrast with vascular plants and the macro-fauna, knowledge on the structure and dynamics of microbial communities in mountain ecosystems is currently missing, and therefore no scenario can be anticipated regarding their evolution. Among them, microscopic photosynthetic green algae can populate all ecosystems, from soil to freshwater habitats such as lakes, snowpack and glacier surface. Green algae are the most diverse algae in aero- terrestrial environments, especially the Chlorophyta lineage, but their study is limited by the lack of genomic data to design appropriate markers. Using recently designed metabarcoding markers for Chlorophyta and the Chlorophyceae class, we investigated the biodiversity in green algae communities in 20 lakes at different representative locations in the French Alps, during the summer season. A single algal bloom site was also selected as a study case, based on the currently non-predictable appearance of this phenomenon, in early summer. Soil at the vicinity of the algal snow bloom was also analyzed. Biodiversity in lake samples exhibited higher proportions of Chlorophyceae and Trebouxiophyceae over Ulvophyceaea and a great inter-sample variability, which did not correlate with depth below the surface, lake altitude or any clear geographical pattern. Snow bloom samples (red snow) highlighted the highest diversity of Chlorophyceae. Soil highlighted the highest biodiversity of Trebouxiophyceae. The Sanguina genus was a specific clade in the red snow, confirmed by DNA analyzes and consistent with microscopy observations. Soil in the vicinity of red snow contained very few reads of Sanguina, suggesting that in soil, Sanguina life cycle may not rely on an active cell division. This study confirms the use of a previously designed Chlorophyta marker for multi- environment studies and paves the way for long term spatiotemporal surveys of freshwater and aeroterrestrial Chlorophyta in the Alps. Introduction Climate change strongly affects mountain environments in temperate regions, such as in the European Alps, as the increase in temperature (+2°C compared to an average +1.4°C outside of the Alps, Durand et al., 2009). Ecosystems in the Alps are complex and models are still improving to predict how they might evolve in longer terms in response to the continued effects of climate change (Austin et al., 2011). Some microorganisms are among the first to respond to delicate changes, yet little is known about their communities and diversity, let alone how they will respond to later stages of warming. It is therefore crucial to study and characterize the species living in these fragile habitats to gauge their biodiversity and predict how they will be affected in the future. In turn, it will help to predict how changes in these communities will affect the habitats harboring them, and impact ecosystem services benefiting humans (Egan and Price, 2017; Cotton and Anthes, 1992; Viviroli et al., 2007).

Green microalgae, in particular, play an important role in these environments as primary producers and colonizers, fixing carbon and to some extent nutrients like nitrogen and phosphorus, and enabling other organisms to grow (Hotaling et al., 2017). They interact closely with bacteria, fungi and vascular plants (Krug et al., 2020). Green algae are constituted of three lineages: the Chlorophyta (the largest and most diverse phylum), the Charophyta (the 145 closest lineage to land plants), and the Prasinophyta (comprising few species of marine picoplankton) (Li et al., 2019). Focusing on Chlorophyta classes, Chlorophyceae is the most diverse one and comprises marine, freshwater and aero-terrestrial species (Guiry and Guiry, 2020). Ulvophyceae is known for its multicellular and unicellular marine species, but less is known about its freshwater and aero-terrestrial representatives. Ulvophyceae can form photosymbiotic associations with fungi as lichen (Darienko and Proschold, 2017). Trebouxiophyceae is the second most described class in freshwater environments, also comprising marine and aero-terrestrial species; it is the most common photobiont encountered in lichen (Krienitz and Bock, 2012). To survive in the Alps, green microalgae are adapted to high UV and light irradiation, large and rapid temperature changes, freezing, and in some environments, desiccation and nutrient scarceness (Geremia et al., 2016).

The environmental criteria driving the spatial and temporal dynamics of green algae in mountain regions are currently unknown. In a recent comprehensive study of soil performed in the French Alps, no altitudinal zonation could be detected similar to that observed with vascular plants, but other factors, such as presence of organic carbon source, seemed to be important determinant factors (Stewart et al., in prep).

Freshwater algae have been studied in more depth, yet most studies used microscopy or markers such as pigments rather than DNA-based identification (Vinebrooke and Leavitt, 1999). Alpine lakes are subject to biotic and abiotic parameters specific to their location, but they have scarcely been studied with a focus on green algae biodiversity at different altitudes. They are interesting as they present small-scale ecosystems with large differences between planktonic and benthic communities (Vinebrooke and Leavitt, 1999).

Snow algae are the exception and have recently received increasing attention (Procházková et al., 2019; Lutz et al., 2019). Interest has increased in the context of global warming because of their impact on snow albedo: algae blooms in seasonal snow and permanent ice in the springtime cause visible coloring of the snow surface and contribute to its melting (Stibal et al., 2017). Snow algae blooms can be green, grey, yellow, orange or red, the latter being the best-known as it is the most frequently reported (Remias et al., 2016; Procházková et al., 2019; Bidigare et al., 1993). This phenomenon is part of a positive feedback loop that was set into motion due to global climate warming favoring algae growth (Ganey et al., 2017). Many Chlorophyceae have been characterized as snow algae. In the current understanding, though Trebouxiophyceae have been reported in snow as well, they are apparently unable to accomplish their cell cycle in this environment; they are therefore not considered as ‘true’ snow algae (Yoshimura et al., 1997). Snow algae taxonomy has evolved considerably since DNA-based studies became popular. Chlamydomonas nivalis was thought to be at the origin of most snow blooms but many other snow algae have been identified in the last decade, including Sanguina nivaloides and other Sanguinaea species (Procházková et al., 2019). The actual role of C. nivalis as a major red snow alga is therefore currently questioned.

Snow algae blooms occur in patches in the snow, and bloom location occurrence factors are still unknown. Snow algae can occur in white snow without causing a bloom, but in much lower cell concentrations (Williams et al., 2003; Brown and Jumpponen, 2019). Snow algae

146 cell cycles are still poorly understood. It is not known whether the algae are transported from permanent ice to seasonal snow by the wind or if the algae lay dormant in the soil and then colonize snow by vertical and/or lateral travel when the snow starts to melt by way of their flagella and melt flow (Bischoff, 2007; Procházková et al., 2019).

In this study, high altitude lakes, snow, and soil in the Northern French Alps were sampled to address green microalgae communities in these environments. Lakes were sampled at different altitudes to determine if this parameter could contribute to shaping green microalgae communities in this environment, and how influential it was. To that purpose, 20 lakes were selected at different representative locations in the French Alps. A single algal bloom site was also selected as a study case in this work, based on the currently difficulty to predict appearance of this phenomenon. Snow was sampled both in patches were a snow algae bloom occurred (red snow) and outside of blooms (white snow) to compare communities. Soil in the vicinity of red snow was also sampled to address algae present in the soil where the snow had melted. All samples were collected in early summer. Material and Methods Snow and soil sampling Snow and soil sampling were performed in early summer 2019 at two sites located near Col du Lautaret, in the French Alps (Figure 1). The first site was Col des Rochilles (CR, 45°05' 02.8" N 6°28' 13.8" E) at 2,500 m above sea level (asl), in a ~10% slope with SW exposure. The second site was Col de Cerces (CC, 45° 04' 45.5" N 6° 28' 36.2" E) at 2,574 m asl, on flat terrain. Biological duplicates were taken at three depths in the snow: at the surface, 20 cm and 40 cm (right above the soil) along a vertical column. CR had patches of red snow, indicating the presence of snow bloom, and samples of snow were taken from both red patches (snow bloom) and white patches (outside snow bloom). Duplicate soil samples were taken at the surface, 3 meters away from the snow bloom sample site. CC did not show any visible snow bloom, but the samples were taken at the same coordinates where a snow bloom was recorded in 2017 and 2018 at the same period. All samples were collected by using a 50 mL tube inserted in the snow or soil, handled with sterile gloves and immediately closed and placed in a cooling box before being frozen at 20°C later that day at the laboratory.

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Figure 1. Map of the location of the massifs where lakes, soil and snow were sampled. 1. Cerces-Rochilles 2. Chablais 3. Aravis 4. Grandes Rousses 5. Beaufortain 6. Vanoise Secteur Tigne 7. Vanoise Bonneval 8. Vanoise Termignon Bonneval. Satellite photo from NASA.

Lake sampling Lakes were sampled during the summer of 2016 during a survey of freshwater algae at INRAE, Thonon-les-Bains, UMR CARRTEL (See Table 1). Water was collected at the surface using a 0.2 µm filter. Sites ranged in altitude between 1,660 m to 2,769 m asl. Set of marine green algae used as positive control for DNA metabarcode analyses The positive control (CPos) used was made up of 13 marine microalgae species from the Roscoff Culture Collection (RCC, http://www.roscoff-culture-collection.org), listed in Table S1. The CPos used was similar in concentration and complexity to that expected in collected samples, but distinguishable from them, therefore constituting a mock community of algae. DNA extraction Extracellular DNA from snow and from RCC cultures for the CPos was extracted using the Qiagen DNeasy Plant Pro Kit (https://www.mn-net.com/) following the manufacturer’s instructions. Soil DNA was extracted using the Qiagen DNeasy PowerSoil Kit following the provided protocol (Lazarevic et al., 2013). For snow bloom samples, containing cyst that might prevent DNA release, before amplification, the column filters from the kit were checked under fluorescence microscopy to verify complete cyst lysis: no remaining red cysts were found, only cell debris. For the soil extracts, extracellular DNA was extracted from 15 g of soil as described previously (Taberlet et al., 2012).

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Lake Altitude Lake name Code Massif depth (m) (m) Lac de Roy LA.2 Chablais 1,660 11 Lac de Tavaneuse LA.7 Aravis 1,805 5.9 Lac d'Arvoin LA.8 Chablais 1,670 7 Lac Blanc de l'Herpie LA.18 Grandes Rousses 2,525 36.1 Lac Noir de Poutran LA.19 Grandes Rousses 2,047 12.1 Lac Besson-Rond LA.20 Grandes Rousses 2,070 10.3 Lac de Presset LA.24 Beaufortain 2,514 19.2 Lac Cornu de Forclaz LA.25 Beaufortain 2,457 6.5 Grand lac du Chardonnet LA.32 Vanoise Secteur Tigne 2,384 6.7 Lac du Grataleu LA.33 Vanoise Secteur Tigne 2,512 7.3 Lac Noir du Carro LA.38 Vanoise Bonneval 2,750 11.3 Lac Blanc du Carro LA.39 Vanoise Bonneval 2,753 6.2 Vanoise Termignon Lac Long LA.43 2,467 7 Bonneval Vanoise Termignon Lac Rond LA.44 2,500 12 Bonneval Vanoise Termignon Lac du Pelve LA.45 2,574 6.5 Bonneval Vanoise Termignon Lac de la Leisse middle one) LA.51 2,769 9.9 Bonneval Vanoise Termignon Lac supérieur de Lanserlia LA.52 2,760 8.5 Bonneval Lac Rond des Drayères LA.54 Cerces-Rochilles 2,446 11.9 Lac du Grand Ban LA.55 Cerces-Rochilles 2,465 9 Lac Sainte Marguerite du Thabor lowest in LA.61 Cerces-Rochilles 2,508 4.5 altitude

Table 1. Lake sample sites and their characteristics.

PCR amplification and sequencing Two green algae markers were used for PCR amplification: Chlo01 targeting the Chlorophyta phylum (Fw AGTTGGTGGGTTGCCTTGT Rv CACAGACCTGTTATTGCCTC) and Chlo02 targeting the Chlorophyceae class within the Chlorophyta phylum (Fw RCTTAGTCCCGGCCATT, Rv CTAAGTGGWAAAGGATGTG) (Stewart et al., submitted). Four PCR replicates were performed. Negative control PCRs with no template DNA as well as extraction blanks with no sample input were also used. PCRs were performed in triplicates for each sample, in parallel with extraction blanks with no template soil and PCR blanks no template DNA (negative controls) and positive control. After an initial denaturation at 95°C for 10 min, 40 cycles (38 for Chlo01) of amplification were run: denaturation 95°C, 30s; hybridization 30 s; elongation 72°C 1min. Hybridization temperatures were respectively 55°C and 50°C for Chlo01 and Chlo02. Each PCR product was individually tagged according to Taberlet et al., 2018. This enabled the pooling of up to 1052 PCRs per sequencing libraries. Pooled PCRs were purified using the Qiagen MinElute PCR Purification Kit for Chlo01 and the Qiagen QIAquick PCR Purification Kit for Chlo02

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(https://www.qiagen.com/). Sequencing libraries were prepared and sequenced (2×300 bp paired-end reads) by Fasteris, (Geneva, Switzerland), using their MetaFast protocol. Data filtering DNA was sequenced from both 5’ and 3’ ends in a paired-end approach. The first step of data filtering consists in the pairing of those reads. The second step is demultiplexing to separate reads by sample. The third step is dereplication to obtain counts for each read, using the Obitools software following the protocol by Taberlet et al., 2018. The fourth step is a read size filtering based on in silico estimates of the marker size to remove any obvious PCR and sequencing errors. The theoretical read sizes were predicted using the ecoPCR software: 70- 250 bp for algae with Chlo01, 80-150 bp for algae with Chlo02. As a first attempt to limit false positive detections, underrepresented sequences were discarded, with a threshold defined arbitrarily below 12 counts per PCR in a fifth step. To address the assignation of some reads to unknown taxa, unsupervised classification of sequences, which is a non-taxonomy- supervised approach, was performed using the Obiclean package from Obitools in a sixth step (Boyer et al., 2016). Sequences were aligned, compared and linked when they had a single nucleotide difference. This formed groups of sequences that were then ordered from most abundant to rarest. The most abundant, termed “head” sequences, represent the true original sequence, while the sequences connected to them, the “internal” sequences, are considered as errors introduced by PCR, but originating from true sequences. The sequences not linked to any other sequence are “singleton” sequences. These are also likely errors and are filtered out in a less conservative approach. Then the reference databases were used to identify each head sequence with a classification and its associated taxid (or taxonomy id) using the ecotag function from Obitools. Using the R software (v.3.6.2, R Core Team, 2019), data was filtered with a more stringent approach with a cutoff at 12,000 reads per PCR. The sequences assigned with above 90% sequence similarity to their reference sequence taxon were kept, the others discarded. Data analysis Further filtering and data analysis were run using R (v.3.6.2, R Core Team, 2019) via Rstudio (https://rstudio.com, version 1.2.5033) with the following packages: ROBItools, Vegan (Oksanen et al., 2013), ggplot2 (Wickham et al., 2011) for visualization, ade4 (Dray and Dufour, 2007) for principal component analysis. The sample diversity was measured as Hill’s numbers for q=1 (Chao et al., 2014). Statistical significance for biodiversity was calculated using the Mann-Whitney variation of the Wilcoxon test as data did not obey normal law and were not paired. Permutational multivariate analyses of variance (PERMANOVA) were done using the Vegan adonis function, based on Euclidean of distances with 999 permutations followed by Tukey’s HSD test. α = 0.05; NS (not significant), p-value > 0.05; *, p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001; ****, p-value < 0.0001. Snow carbon, nitrogen, iron and phosphate concentration Concentrations of carbon and nitrogen in snow samples were measured with a TOC/TN analyzer (Shimadzu TOC-L/TNM-L, Shimadzu, Korneuburg, Austria) at the ISTerre analyticial platform, Grenoble, France. Samples were kept at -20°C before analysis, then 150 thawed in room temperature water. Samples were filtered with a 0.22 µm filter before analysis. Two biological repeats and two technical repeats were used for each sample (n = 4). Controls included ultra pure water and filter controls. Iron Fe2+ and total soluble phosphate were measured using inductively coupled plasma using atomic emission spectroscopy (ICP-AES, Shimadzu, ICPE 9800 series) with ionized argon gas for the plasma at 7,000 K. Microscopy analyses Snow samples were observed using optical microscopy (Zeiss AxioScope A1 fluorescence microscope). In brief, ~45 mL of melted snow was centrifuged for 10 min at 3,000 rpm and the supernatant transferred to a new tube. The cells were resuspended in 200 µL TAP medium (Gorman and Levine 1965). Ten microliters of the resuspended cells were placed between slide and coverslip and observed at 200- and 1,000-fold magnification. Results Sampled lakes, snow bloom, bloomless snow and soil exhibit distinct green microalgae communities In this work, we assumed that all samples collected during summer seasons could be reasonably compared. Since we based our study on a comprehensive survey of 20 lakes in representative locations and altitudes in the French Alps and a small number of snow-covered sites (Figure 1), we took into account this evident sample size bias when comparing these distinct types of habitats.

Number of OTUs Number of OTUs Number of Marker /reads /reads algae OTUs In raw data After filtering /reads

12,572 551,153 OTU 13,828 OTU OTU Chlo01 25,962,301 reads 22,633,337 reads 19,528,825 reads

4225 OTU 605,242 OTU 15,627 OTU Chlo02 9,361,635 32,187,575 reads 27,086,611 reads reads

Table 2. Number of OTUs or reads in the raw data, after filtering and after keeping only algae OTUs for Chlo01 and Chlo02.

In all extracts, both Chlo01 and Chlo02 successfully amplified algae DNA. The two markers produced similar numbers of reads and OTUs (~500,000 and ~30,000 respectively,

151 see Table 2) but Chlo01 yielded more algae OTUs after filtering than Chlo02 (~12,000 and ~9,000 respectively), which makes sense as it is a broader marker. In a previous study, the two markers were used for soil microalgae successfully (Stewart et al., submitted). Here, both Chlo01 and Chlo02 also amplified algae DNA from lakes and snow (see Table 3), illustrating that these markers were appropriate for algae DNA amplification in all types of environments.

Compiled number of OTUs and reads per PCR

Marker lake samples soil samples snow samples

Chlo01 ~1,408 OTU ~538 OTU ~1,026 OTU

Chlorophyta ~111,095 reads ~180,444 reads ~127,745 reads

Chlo02 ~658 OTU ~46 OTU ~281 OTU

Chlorophyceae ~84,329 reads ~187 reads ~37,340 reads

Table 3. OTU evaluation in each environment for Chlo01 and Chlo02.

Lake samples contained more OTUs than any other sampled environments, but this did not correlate with higher read levels (Table 3). Soil yielded far fewer green algae OTUs than lakes and snow. This was in line with results from a previous study, which reported in particular that Chlorophyceae template DNA was very low in the soil, causing low replicability in PCRs with the Chlo02 marker (Stewart et al., submitted).

The fraction of Chlorophyta evaluated with the Chlo01 marker was plotted versus PCR replicate similarity, with each data point representing a collected sample (Figure S1). For a given sample, highly reproducible PCR results are characterized by a similarity value closer to 1, showing the robustness of the analysis, whereas samples with a very low target DNA content often fail to meet this criterion and have PCR similarity values <<1. Some soil samples and snow collected outside blooms showed lowest replicability, and also low fractions of Chlorophyta DNA (Figure S1). By contrast, snow collected within blooms and most lake samples had high replicability levels and a high fraction of Chlorophyta DNA, from 0.7 up to nearly 1.

A PCA was performed with the community matrix according to sample origin (Figure S2). The permanova statistical test discriminated the types of environments with an r2 of 0.32581 with a p-value <0.001. The Tukey post hoc test on the comparisons between environments on the PCA shows that the second axis discriminates lake environments from the three other environments but axes 1 and 3 discriminated lakes from red and white snow only (Table S2). However when we tried to discriminate lakes from each other as well as from other environments, we found that lake communities were as different from each other as they were from separate environments. This explained why the lake data points were much more scattered on the PCA than those from the snow and soil sampling sites (Figure S2, Tukey data not shown). This likely reflects the number and diversity of sampling sites for lakes, whereas 152 snow bloom sampling was performed only on one location. Our work cannot address the question of inter-sample variability in snow blooms. Regardless of this difference in sampling size and complexity, with only one site, the number of Chlorophyta and Chlorophyceae OTUs measured in a snow bloom was about two thirds and half that measured in all sampled lakes, respectively, showing that snow was an unsuspectedly rich transient ecosystem that could compare to more permanent habitats. Sampled lakes highlighted the highest biodiversity of Chlorophyta, soil being enriched in Trebouxiophyceae and snow bloom in Chlorophyceae. Biodiversity was measured in the different environments sampled using Hill’s numbers for q=1 (Mendes et al., 2008) (Figure 2). P-values for the wilcoxon statistical test on the significance of differences in read counts and diversity are indicated in Table S3 for Chlo01, Table S4 for Chlo02. Considering the Chlo01 marker reporting Chlorophyta diversity, lakes were the most diverse, but also had the most variable index, likely due to the large variety of sampling sites. The snow bloom and soil samples highlighted the second highest biodiversity, above bloomless snow, but with far less variability, likely related to the small number of sampling locations.

The Chlo02 marker showed different results for the Chlorophyceae biodiversity. This marker suggests that soil was the most diverse, while diversity in lakes and bloomless snow were similar in range and higher than the analyzed snow bloom. To verify if the Chlorophyta diversity pattern was different from the Chlorophyceae because other classes of algae drove that diversity differently or if the markers showed different patterns because they did not amplify the same algae, the biodiversity was recalculated using the Chlo01 dataset in a subsample of OTUs classified as Chlorophyceae, Trebouxiophyceae and Ulvophyceae respectively (Figure 3).

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Figure 2. Biodiversity in sampled lakes, red snow, white snow, and soil. A. Chlo01 Chlorophyta marker. B. Chlo02 Chlorophyceae marker. Snow and soil were from Col des Rochilles only. Biodiversity was evaluated using Hill’s numbers for q=1. NS (not significant), p-value > 0.05; *, p- value < 0.05; **, p-value < 0.01. ***, p-value < 0.001. ****, p-value < 0.0001.

In lakes, the highest biodiversity of Chlorophyta was attributable to the Chlorophyceae and Trebouxiophyceae classes (Figure 3A and B, Table S5 and S6 for p-values of statistical tests). Soil expectedly contained a higher Trebouxiophyceae diversity than Chlorophyceae, in line with a previous comprehensive study performed in five altitudinal gradients in the French Alps (Stewart et al., submitted). Soil and lakes both had Ulvophyceae, nearly on par with Trebouxiophyceae (Figure 3C, Table S8).

The algae currently considered as able to complete part of their life cycle in the snow are Chlorophyceae (Stibal and Elster, 2005). Consistently, red snow exhibited a very high diversity of Chlorophyceae (Figure 3A), but few Trebouxiophyceae could still be detected (Figure 3B), whereas nearly no Ulvophyceae occurred (Figure 3C). In this simple study, the red snow samples had the highest biodiversity in Chlorophyceae compared to all other environments. A variety of snow algae have been described in the literature (Yoshimura et al., 1997; Lutz et al., 2019; Procházková et al., 2019), but it is interesting that, even with a simple sampling site like in this work, this environment would have higher Chlorophyceae diversity than a compiled sampling set performed in different high-altitude lakes. White snow had much lower diversity of Chlorophyceae than red snow but had similar levels of Trebouxiophyceae and slightly higher Ulvophyceae. Based on this comparison, Trebouxiophyceae and Ulvophyceae could possibly be present in the snow by chance, carried to snow surface by the wind, and be unable to develop further.

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Figure 3 Biodiversity in different environments focused on the Chlorophyceae, Trebouxiophyceae and Ulvophyceae classes (evaluated using Hill’s numbers for q=1). A. Chlo01 marker considering only Chlorophyceae data. B. Chlo01 marker considering only Trebouxiophyceae data. C. Chlo01 marker considering only Ulvophyceae data. D. Chlorophyceae vs Trebouxiophyceae read proportions normalized per PCR in the different environments.

To further compare the Chlorophyceae and the Trebouxiophyceae, the dominant classes here, the corresponding normalized reads were compared. In Figure 3D, the dotted red line highlights an equal proportion of reads for the two classes: the data above have a higher proportion of Trebouxiophyceae and below, a higher proportion of Chlorophyceae. The only environment that contained a higher proportion of Trebouxiophyceae was the soil. All snow data were the more distant to the limit, clustered together on the Chlorophyceae side, confirming that snow communities were mainly composed of Chlorophyceae algae. Lakes were variable, but still had higher proportions of Chlorophyceae over Trebouxiophyceae. Chlorophyceae of the Sanguina genus drive differences in red snow compared to soil communities When lakes were removed from the dataset and the PCA recalculated, soil, red snow, and white snow were distinctly separated along two axes (Figure 4). The first axis of the PCA significantly discriminated the samples inside the red snow from the other samples (Tukey’s statistical tests in Table S8, permanova R2 of 0.56422 with a p-value < 0.001), whereas the second axis significantly discriminated the samples in the soil from those in the snow. There was no overlap between the communities of the different types of environment. There were 155 no significant differences between the snow communities at the two sites sampled outside blooms (data not shown). Chlamydomonadaceae OTUs were more representative of white snow samples while it was Sanguina and Chloromonas for red snow and Trebouxiaceae for soil.

Figure 4. PCA of Chlo01 marker of samples according to their environment. Taxa names represent the taxa with the highest contribution in differentiating the environments. Lake data were not considered.

Chlamydomonadaceae are a family, which Sanguina is a part of, but these OTUs were not identified further than at the family level based on their sequence, so they could be different Sanguina genotypes or species, or different snow algae altogether. Sanguina is a genus of snow algae described in Procházková et al., 2019. Presence of both red and orange sanguina-like cells (possibly representing Sanguina nivaloides and Sanguina aurantia respectively) have been observed using optical microscopy in red snow samples (Figure 5).

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Figure 5. Sanguina-like cells observed in red snow collected at Col des Rochilles. Following sampling as described in the Methods section, an aliquot of cells was placed between slide and coverslip and observed at 1,000 (B and C) and 200 fold magnifications (A). Red arrow and B: Sanguina nivaloides-like. Orange arrow and C: Sanguina aurantia-like. In photograph C., chlorophyll appears in green.

Chlorophyta diversity in snow vertical columns In Table 4, the total number of of OTUs and reads were compared between red snow and white snow. The Chlorophyta marker showed that the highest number of detected OTUs was at 20 cm in depth in the red snow samples, whereas it was highest at the surface outside blooms. The biodiversity was calculated on normalized data per PCR using Hill’s numbers for q=1 in Figure 6, p-values of statistical test in Table S9. The Chlorophyta biodiversity, no matter the depth, was significantly higher within the red snow compared to the white snow samples. Within red snow, biodiversity was significantly higher at 20 cm in depth. There was no significant difference between surface and 40 cm samples.

40 cm Sample 20 cm below at snow surface below snow origin snow surface surface

~1,049 ~962 OTU 913 OTU OTU Red snow ~94,860 reads ~89,162 reads 114,248 reads

~1,014 ~1021 OTU ~1,149 OTU OTU White snow ~149,130 reads 164,400 reads ~137,916 reads

Table 4. Number of OTUs or reads per PCR within and outside red snow for the Chlo01 Chlorophyta marker.

Together, these data suggest that the green alga populations analyzed here in a single snow bloom, particularly of the Sanguina genus, were possibly achieving their life cycles within the depth of the snowpack column, not only as close as possible to its surface. Unexpectedly, the pattern deduced from Chlorophyceae obtained within OTUs amplified with the Chlo01 marker was not confirmed with the Chlo02 marker designed to amplify Chlorophyceae (Figure S4A and Table S10). This was surprising, since most described snow algae belong to Chlorophyceae. This discrepancy in Chlorophyceae evaluation using the two markers was also observed in snow samples shown in Figure 2. We examined more closely OTUs obtained with both markers in the case of snow samples and observed that Sanguina taxa were not in the list of identified OTUs in the Chlo02 dataset. It is therefore possible that this marker is not adapted for all Chlorophyceae amplification, based on an imperfect reference database composed of mostly marine algae. This reflects one of the current limits of 157

DNA metabarcoding detection of green algae and stresses the need to use multiple markers whenever possible. It also highlights the urgent need to implement public databases with more green algae genomic data. It is thus possible that reference databases for the 23S region were still lacking representation within snow algae. The latter is definitely the case for Sanguina and snow aplanospore studies, which have been described using 18S and ITS markers (Procházková et al., 2019).

Figure 6. Biodiversity in red snow different depths. Snow samples were collected at Col des Rochilles. Hill numbers were calculated for q = 1 (equivalent to the exponential of Shannon diversity). NS (not significant), p-value > 0.05; *, p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001. ****, p-value < 0.0001.

Distinct Chlorophyta communities in lakes, independent on altitude or evident geographical patterns Sampling depth below lake surface, lake altitude, geographic proximity as well as the massif from which the samples originated were examined as potential drivers of lake green algae biodiversity, using PCA and biodiversity indexes. Lake communities were analyzed via PCA to evaluate whether the different lakes highlighted indistinguishable community patterns or could aggregate into groups (Figure S5A). There were no clear groups according to the Calinski-Harabasz criterion, besides lakes 2 and 8, which were more distinct from the other lakes. The GPS coordinates were considered for each lake, but there was no match to the PCA (Figure S5B). There was also no clustering according to altitude (Figure S5C). Along Axis 2 and 4 of the PCA, the samples slightly clustered according to massif (Figure 7A and Table 158

S11 for Tukey post hoc p-values), indicating that a larger scale geographical distribution might be determinant, but not the only factor defining freshwater green microalgae communities.

Biodiversity was examined per lake and ranked based on altitude of sampling. Biodiversity varied among samples seemingly uncorrelated with this only criterion (Figure 7B). Grouping of the samples by altitude (a first group ranged from 1,500 to 2,500 m asl while a second spanned 2,500 to 3,000 m asl) showed similar biodiversity in both groups (Figure S6A). By the same token, biodiversity was similar among groups of lakes according to the sampling depth below the surface (Figure S6C). Biodiversity of the different massifs was estimated by grouping samples together (Figure S6B), highlighting high variability among the samples. Nevertheless, and pointing the need for future analyses to confirm this possible trend, the Vanoise Secteur Tigne and Vanoise Termignon Bonneval were significantly less diverse than the other massifs.

Figure 7. PCA of the samples by massif and biodiversity of every lake along the altitude gradient for the Chlo01 marker. A. First factorial plane of PCA on which all replicates of each lake sample are projected and colored by massif of sampling. B. Biodiversity (evaluated using Hill’s numbers for q=1) of each lake sampled in order of altitude of sampling site and colored by massif.

Discussion This work was performed based on markers recently developed to explore the biodiversity of Chlorophyta, and three of its majors classes, Chlorophyceae, Trebouxiophyceae and Ulvophyceae, in the complex geographical and environmental structure of mountain massifs in the European Alps. Sampled sites allowed a comprehensive analysis of lakes, and a focused case study of red snow and soil samples at its vicinity.

Lake algae communities appeared distinct from aero-terrestrial communities. Unexpectedly, whereas the distribution of vascular plants in mountain habitats shows an elevational zonation and geographical patterns based on other criteria, such as exposure to light, soil composition, habitat structures, etc, we could not detect clear indication on similar patterns in lake green algae communities. Lake communities were extremely different from 159 each other and did not cluster according to any abiotic factor tested here. Lakes had higher biodiversity overall, especially a higher diversity of green algal classes than the other environment we tested, likely reflecting the bias in sample size difference. Chlorophyceae was the most abundant class in lakes, but Trebouxiophyceae were common, confirming other studies of alpine freshwater green algae (Tolotti, 2001; Summerer et al., 2008; Krienitz and Bock, 2012; Vieira et al., 2016). Altitude did not affect alpha-biodiversity of green microalgae communities as a whole, similar to other studies of alpine lake biodiversity on invertebrates, but likely affects the distribution at the species or genus level. (Zaharescu et al., 2016) and previous comprehensive studies in soil in altitudinal gradient (Stewart et al., in prep). Communities were also not defined strongly by lake depth, or any clear geographical proximity pattern.

Snow algae communities had not been studied in the French Alps yet, and this study paves a way to improve their exploration, first by confirming a Chlorophyta marker or combination of markers as appropriate and useful for their study. In particular, this focused study case confirms that the Sanguina genus described in North and South America as well as throughout continental Europe (Procházková et al., 2019), is also present in the studied site in Col des Rochilles. It lacks the resolution of longer markers and did not identify the precise species of Sanguina, but, combined with other longer markers, can be interesting for more in depth taxonomy studies. With other Chlamydomonadaceae, Sanguina taxa set red snow communities apart from white snow and soil. These results are in line with the hypothesis that communities of green microalgae are different between red snow where a visible algae bloom occurs, and snow where the cell concentration is too low to be visible (white snow). Most snow algae species described in literature are from the Chlorophyceae class (Lutz et al., 2019). The Chlorophyta marker indicated that algae from other Trebouxiophyceae were detected in both red and white snow, but very little of their DNA was recovered. It is likely that detected Trebouxiophyceae presence was opportunistic, with little or no capacity to complete their life cycles, brought by the wind, as previously suggested by Stibal and Elster, 2005.

In this study case of snow bloom, carbon, iron nitrogen and phosphate measurements were also performed to evaluate whether the visible blooms were due to nutrient concentrations in the snow. There was no significant difference between the samples in carbon concentration, while iron, nitrogen and phosphate measurements were beneath detection levels (Table S3). Therefore, similar to other studies, a high nutrient level did not appear to dictate where algae blooms occured in snow (Bischoff, 2007; Prochazkova et al., 2019; Spijkerman et al., 2012). Nevertheless, we do not exclude that a high nutrient concentration prior to bloom development, might be determinant, not detected any more when the bloom has formed and exhausted its environment from nutrients. Seasonal spatiotemporal analyses in random locations, where red snow are likely to develop, are therefore still required to address this hypothesis.

Soil sampled at the vicinity of red snow highlighted distinct Chlorophyta community structures. Firstly, Trebouxiophyceae proportions were higher than Chlorophyceae only in the soil. Secondly, Sanguina DNA was detected in the soil in all samples and replicates, but at less than 50 reads per PCR, ruling out cross-sample contamination. This result suggests that soil may act as a reservoir of cells during summer and winter months, when there is either no 160 snow, or the snow water content is too low but with a very small level of cells. No aplanospores were observed during microscopy examination of the corresponding soil samples. Since DNA can be adsorbed by inorganic and organic particles (Pietramellara et al. 2009), Sanguina DNA in soil could either reflect a life stage with very slow division rate or DNA settled there after cell death. In future work, the soil who could represent a niche for Chlorophyceae, capable to populate other permanent or transient habitats in the Alps, such as lakes, snowpack, glaciers, etc., and deserves much more attention than it had until now. The challenge is now to identify appropriate sites for long term and regular surveys, addressing this issue. References Austin, M. P., & Van Niel, K. P. (2011). Improving Species Distribution Models For Climate Change Studies: Variable Selection And Scale. Journal Of Biogeography, 38(1), 1– 8. Https://Doi.Org/10.1111/J.1365-2699.2010.02416.X

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Zaharescu, D. G., Burghelea, C. I., Hooda, P. S., Lester, R. N., & Palanca-Soler, A. (2016). Science Of The Total Environment Small Lakes In Big Landscape: Multi-Scale Drivers Of Littoral Ecosystem In Alpine Lakes. Science Of The Total Environment, The, 551– 552(April 2006), 496–505. Https://Doi.Org/10.1016/J.Scitotenv.2016.02.066 Author Contributions AS, SB and DT performed snow nutrient analysis. DR and CM performed DNA extractions, dilutions and PCRs; CL, AS and AK performed data filtering; AS and AK performed data analysis supervised by AB; FP and AB provided expertise in environmental DNA analyses; JGV lead the sampling campaign at Col du Lautaret; JGB, EM and EC conceived the project; all authors contributed to the writing of the article. Funding This work was supported by CNRS (Mission pour l’Interdisciplinarité) and National Research Agency (Oceanomics ANR-11-BTBR-0008, GlycoAlps ANR-15-IDEX-02, GRAL Labex ANR-10-LABEX-04, and EUR CBS ANR-17-EURE-0003).

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Acknowledgements Authors wish to thank Ian Probert (Roscoff Culture Collection, Station Biologique de Roscoff, France) who provided marine green algae strains used as a control in this study, Isabelle Domaizon (INRAE, Thonon, France) for expertise in freshwater green algae and for all lake DNA samples and Juliette Jouhet (LPCV, CEA) for fruitful discussions as well as ISTERRE for help with the snow nutrient analysis.

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Supplementary tables and figures RCC Euka03 Chlo01 Chlo02 DNA quantity Taxid Taxon ID (ng) RCC 7 133490 Picochlorum atomus + + - 8000 RCC + + + 3052 Chlamydomonas sp 4000 443 RCC + + - 3165 Tetraselmis striata 2000 130 RCC 6 3047 Dunaliella tertiolecta + + + 1000 RCC + + - 114055 Chlorella vulgaris 500.0 537 RCC Pycnococcus + + - 41880 250.0 581 provasoli RCC + + + 41891 Coccomyxa sp 125.0 891 RCC + + - 29646 Stichococcus sp 62.50 1055 RCC Tetraselmis + + - 34154 31.25 1563 convolutae RCC + + - 36882 Pyramimonas sp 15.63 2501 RCC + + - 188557 Acrochaete sp 7.813 2960 RCC + + - 88271 Picocystis salinarum 3.906 3402 RCC + + - 1418015 Pseudochloris sp 1.953 4743

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Red snow White snow Soil

White snow 0.9941793 - -

Axis 1 Soil 0.9003088 0.9402273 -

Lake 0.0064150 0.0009308 0.5559323

White snow 0.8325777 - -

Axis 2 Soil 0.5213409 0.8001544 -

Lake <0.0000001 <0.0000001 0.0037592

White snow 0.9864115 - -

Axis3 Soil 0.8161393 0.8905387 -

Lake 0.0039112 0.0007047 0.6265546

Table S2. Tukey post hoc multiple comparison of means p-values on the PCA in Figure S1 for different environments.

Number of reads Biodiversity

Red Red Lake Soil Lake Soil snow snow

Soil 0.01853 - - 0.0006481 - -

Red snow 0.8933 0.01853 - 1.453e-06 0.5076 -

White 0.8933 0.2616 0.05635 2.2e-16 0.0003763 3.016e- snow 14

Table S3. Wilcoxon rank sum test with continuity correction for the Chlo01 marker comparing read counts and biodiversity between the different environments.

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Number of reads Biodiversity

Lake Soil Red snow Lake Soil Red snow

6 Soil 3.784x10 - - 0.01489 - -

Red 0.09348 3.232x105 - 2.475x1011 1.901x107 - snow

White 1.012x107 1.925 x105 0.001533 0.1428 0.0008223 1.464x108 snow

Table S4. Wilcoxon rank sum test with continuity correction for the Chlo02 marker comparing read counts and biodiversity between the different environments.

Number of reads Biodiversity

Red Lake Soil Lake Soil Red snow snow

Soil 0.007732 - - 0.0003036 - -

Red 0.5576 0.006522 - 2.739x107 1.901x107 - snow

White 0.002028 0.0003557 0.03061 4.562x107 0.008424 8.82x1016 snow

Table S5. Wilcoxon rank sum test with continuity correction for the Chlo01 marker within the Chlorophyceae class only comparing read counts and biodiversity between the different environments.

Number of reads Biodiversity

Red Red Lake Soil Lake Soil snow snow

Soil 0.003138 - - 0.1201 - -

6 5 9 Red snow 4.753x10 4.705x10 - 4.849x10 0.02474 -

White 6.339x1012 1.372x105 0.1514 3.006x1014 0.005061 0.5254 snow

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Table S6. Wilcoxon rank sum test with continuity correction for the Chlo01 marker within the Trebouxiophyceae class only comparing read counts and biodiversity between the different environments.

Number of reads Biodiversity

Red Red Lake Soil Lake Soil snow snow

Soil 0.2793 - - 0.356 - -

Red 7.491x1 0.012 - 5.947x10 0.00042 - snow 08 47 9 92

White 3.801x1 0.029 0.0056 6.486x10 0.01832 0.003374 snow 010 45 6 9

Table S7. Wilcoxon rank sum test with continuity correction for the Chlo01 marker within the Ulvophyceae class only comparing read counts and biodiversity between the different environments.

White Red snow snow

White 0.0000091 - Axis 1 snow Soil 0.8746007 0.0003163

White 0.1676061 - Axis 2 snow Soil 0.0019628 0.0635581

Table S8. Tukey post hoc test p-values on the first two axes of the PCA of Col des Rochilles soil and snow. Calculated using the TukeyHSD function of the factoextra R package.

Reads Biodiversity

20 cm 40 cm 20 cm 40 cm

Red Surface 0.5204 0.7984 0.02028 0.372 snow 20 cm - 0.5204 - 0.009065

White Surface 0.7791 0.9591 0.7929 0.5635 snow 20 cm - 0.7473 - 0.4309

Table S9. Wilcoxon rank sum test with continuity correction for the Chlo01 marker comparing read counts and biodiversity between different depths in red or white snow.

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Reads Biodiversity

20 cm 40 cm 20 cm 40 cm

Surface 0.7789 0.8665 0.007212 0.5453 Red snow 20 cm - 0.9591 - 0.04681

White Surface 0.7789 0.8665 0.0289 0.2786 snow 20 cm - 0.9591 - 0.6126

Table S10. Wilcoxon rank sum test with continuity correction for the Chlo02 marker comparing read counts and biodiversity between different depths in red or white snow.

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Axis 1 Axis 2 Axis 3 Axis 4

Beaufortain-Aravis 0.0608048 0.0075966 0.0000018 0.1305737

Cerces-Rochilles-Aravis 0.9641186 0.8701837 0.9749882 0.0096111

Chablais-Aravis <0.0000001 0.9999448 0.0002155 0.9999971

Grandes Rousses-Aravis 0.974015 0.9233511 0.7567223 0.9981911

Vanoise Bonneval-Aravis 0.8323803 0.7448019 0.8965095 0.9901024

Vanoise Secteur Tigne-Aravis 0.9999938 0.9992858 0.9998114 0.7296497

Vanoise Termignon Bonneval-Aravis 0.9985167 0.9922139 0.9990506 0.8790569

Cerces-Rochilles-Beaufortain 0.1232529 0.02813 0.0000001 <0.0000001

Chablais-Beaufortain <0.0000001 0.0021409 0.8111982 0.0113077

Grandes Rousses-Beaufortain 0.1033393 0.0165891 0.0000015 0.0976633

Vanoise Bonneval-Beaufortain 0.5155315 0.1556693 0.0000049 0.001019

Vanoise Secteur Tigne-Beaufortain 0.023417 0.0043913 0.0000001 0.8785134

Vanoise Termignon Bonneval-Beaufortain 0.0110291 0.00074 <0.0000001 0.3427065

Chablais-Cerces-Rochilles <0.0000001 0.9230713 0.0000487 0.0011152

Grandes Rousses-Cerces-Rochilles 1 0.9999992 0.9894877 0.0000012

Vanoise Bonneval-Cerces-Rochilles 0.9986701 0.9998641 0.9996552 0.0150925

Vanoise Secteur Tigne-Cerces-Rochilles 0.9777438 0.9752554 0.9981174 0.0000001

Vanoise Termignon Bonneval-Cerces-Rochilles 0.9954831 0.9801761 0.9969164 <0.0000001

Grandes Rousses-Chablais <0.0000001 0.9667968 0.0008139 0.9468803

Vanoise Bonneval-Chablais <0.0000001 0.8012131 0.0013527 0.9957907

Vanoise Secteur Tigne-Chablais <0.0000001 0.9999988 0.000029 0.3092474

Vanoise Termignon Bonneval-Chablais <0.0000001 0.9995988 0.0000006 0.4149438

Vanoise Bonneval-Grandes Rousses 0.9971826 0.9986066 0.999995 0.5328987

Vanoise Secteur Tigne-Grandes Rousses 0.9860989 0.9924915 0.853946 0.8622296

Vanoise Termignon Bonneval-Grandes Rousses 0.998101 0.9961738 0.7229704 0.9721188

Vanoise Secteur Tigne-Vanoise Bonneval 0.8369825 0.8992033 0.9669932 0.0638822

Vanoise Termignon Bonneval-Vanoise Bonneval 0.893099 0.8976932 0.9442346 0.070567

Vanoise Termignon Bonneval-Vanoise Secteur 0.9999015 0.9999968 1 0.9983231 Tigne

Table S11. Tukey post hoc test p-values on the first two axes of the PCA of Col des Rochilles soil and snow. Calculated using the TukeyHSD function of the factoextra R package.

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Figure S1. Fraction of reads identified as Chlorophyta in relation with PCR replicate similarity.

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Figure S2. PCA of Chlo01 marker of samples according to their environment. CR: Rochilles Pass, CC: Cerces Pass, LA: lakes; in: inside snow bloom sample, out: outside snow bloom sample, sol: soil sample.

Figure S3. Contribution of the ten most important OTUs to axe 1 of the PCA from Figure 4.

Figure S4. Contribution of the ten most important OTUs to axe 2 of the PCA from Figure 4.

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Figure S5. Biodiversity in snow blooms, bloomless snow at different depths for the Chlo02 Chlorophyceae marker.

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Figure S6. PCA and relative geographic position of the lakes for the Chlo01 marker. A. First factorial plane of PCA on which all replicates of each lake sample are projected. Eigenvalues in appendix 4. B.Relative geographic position of the lakes where the samples were taken. C. PCA with coloring according to altitude group: 1500-2500 m and 2500-3000 m.

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Figure S7. Effects of altitude, massif and lake depth on lake biodiversity of Chlorophyta. A. Biodiversity of the samples according to altitude groups (1500-2500 m and 2500-3000 m). B. Biodiversity according to massif. C. Biodiversity according to lake depth. NS p > 0.05; * p < 0.05; ** p < 0.01. *** p < 0.001. **** p < 0.0001. Whenever statistics are not indicated, the result was NS. 176

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Chapter 8. Isolation and preliminary study of axenic strains of snow algae collected in the Lautaret region and creation of a reference culture collection

Introduction Microalgae are found globally in nearly every type of environment, including in snow and ice where they are easily detected when they form pigmented blooms as they are present in high concentrations. Snow algae have been described worldwide, and new species are still being described and little is known about them. It is hypothesized that snow blooms could be a result of horizontal and/or lateral relocation of algae that are present in the soil, with soil acting as a reservoir of cells. It is also possible that some algae could be transported by the wind, based on the detection of aplanospores in wind traps (Bischoff, 2007). One of the basic questions we addressed in this PhD project was which microalgae populate snowpacks in the French Alps in the region of the Col du Lautaret and how they could cope with the multitude of stressful conditions characterizing snow. The time dedicated to this project was evidently not sufficient to address them all satisfactorily and comprehensively, so this chapter provides a set of preliminary works to build future strategies. The requirement of a culture collection of local snow algae. To address the relevance of biotic or abiotic parameters in the physiology and development of algal strains, model species need to be considered and manipulated in laboratory or field conditions. There are several collections of snow algae, including the ‘Culture collection of Algae at the University of Texas at Austin’, UTEX (Starr and Zeikus, 1987) and CCCryo (Culture Collection of Cryophilic Algae Fraunhofer IZI-BB), a culture collection of cryophilic algae isolated from green blooms. CCCryo does have one Chloromonas reticulata strain from snow from the Vanoise National park, and one strain from a river in France, but from the Pyrenean range of mountains. It is not possible to study algal strains from an external collection in a given natural environment, without risking the spreading of an invasive species, so a clear limitation to any longer-term study is the availability of a collection of local strains. We thus put an important effort in initiating a so- called Lautaret Culture Collection. The col du Lautaret is home to a historic botanical garden created in 1899 by Grenoble University. Innovative plant research is led there, with strong links to the Laboratoire de Physiologie Cellulaire et Végétale (LPCV) and the Laboratoire d’Ecologie Alpine (LECA). Lautaret garden (Grenoble Alpes University and CNRS) is a unique infrastructure for short and long-term experimental observation and manipulation of temperate mountain ecosystems. It provides facilities and expertise to scientists working at 178 various levels. Here, strains from snow samples collected around the Col du Lautaret with the assistance of Lautaret garden staff were isolated in 2017, 2018 and 2019. Test cultures to define the right culture conditions to culture snow algae were conducted using samples collected with the assistance of Lautaret garden staff and the network “Refuges Sentinelles” In search of abiotic factors triggering the conversion of green algae into blooms of orange/red-pigmented and lipid-rich cysts. The development of dense algal population in the snow is a puzzling question. In high elevation, snow and glaciers environments are subject to harsh conditions such as low temperatures, high UV and light exposition and low nutrient availability. Leaching of soil, dust, run-off from pastures, animal dejections and snowmelt flow are thought to bring nutrients to snow patches. One could consider that high nutrient levels could be determinant for the density of algal populations and microbial communities. Thus far, all studies looking at the relationship between nutrient concentration and presence of a snow bloom have found no correlation (Bischoff, 2007; Spijkerman et al., 2012). The topography of the environment could also play an important role in dictating where snow algae blooms occur, but we miss comprehensive data to address this possibility. Having purified strains in hand, we attempted to develop experimental conditions that may mimic some of the stresses exerted in nature (temperature, light, nutrients’ availability), trying to identify some adaptive mechanisms, including the accumulation of carotenoids and other cellular features reminiscent of the conversion into pigmented cysts. Could biotic factors be involved in the physiology status of snow algae? In addition to abiotic parameters, biotic interactions, mediated by secreted molecules, may also trigger triacylglyceride (TAG) production. For instance, some phyto-hormones, which may act like infochemicals, such as brassinolides, were found to enhance TAG production in Chlorella (Liu et al., 2018). Little has been made in that direction in the study of snow algae. Here we addressed the possibility that epibrassinolide might influence the growth in snow algae strains. Likewise, another type of phytohormons, i.e. auxin, has proved to act synergistically with brassinosteroids in controlling growth and metabolic status of some green microalgae (Bajguz and Piotrowska-Niczyporuk A, 2013). Thus, we also investigated the effect of indole-3-acetic acid (IAA) on the growth of snow algae collected in the vicinity of col du Lautaret. Synergistic effects on growth were examined. A variant of IAA, dichlorophenoxyacetic acid (2,4D), used as a herbicide, was also tested. Mining the genomes Finally, we would have benefitted from the availability of reference genomes of snow algae strains, to search for specific genomic features, families of genes, pathways that could differ with related species living in lower altitudes. Such specific feature identified by bioinformatics comparisons would have been valuable to develop working hypotheses on some still unknown adaptation mechanisms. Unfortunately, no such dataset is currently available. Longer-term studies would therefore benefit from such information, and we put

179 efforts on our axenic strains to produce sufficient amounts of DNA for genome sequence characterization.

Methods Sampling campaign protocol Location This study is the result of 3 consecutive sampling seasons in June 2017, June 2018 and June 2019. The first site was Col des Rochilles (CR, 45°05' 02.8" N 6°28' 13.8" E) at 2,500 m above sea level (asl), which was in a slope. The second site was Col de Cerces (CC,45° 04' 45.5" N 6° 28' 36.2" E) at 2,574 m asl, on flat terrain. The third site was Mi Pons (MP, 45° 03' 49.9" N 6° 28 06.5" E) at 2,450 m asl. The fourth site was Mi Pons B, near the third site but distinct (MPB, 45°03' 40.5" N 6°28 06.2" E), also at 2,450 m asl (Figure 8.1). A fith site was sampled in June 2017 only, the Mines site (Mines, 45° 1' 57.32" N 6° 29' 13.60" E) at 2,230 m asl. CR and CC were sampled all three years. MP and MPB only in 2017 and 2018.

Figure 8.1 Map of the snow sampling sites in the western region of the European Alps. 1. Col des Rochilles. 2. Col de Cerces. 3. Mi Pons. 4. Mi Pons B.

The samples used to calibrate culture conditions originated from Reflab, the refuge laboratoire de haute montage led by Laboratoire d’excellence Innovation & Territoires de Montagne (LabEx ITEM) and the Parc national des Écrins. Samples were taken in snow just underneath the surface to avoid dust using a 50 mL sterile tube at the refuge Adèle Planchard in 2017, 2018 and 2019.

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Snow sampling A hole was dug in the snow at the bloom site at all three sampling campaigns (Figure 8.2B) but also at the nearest white snow patch with no visible bloom in the 2019 campaign. The hole was 50 cm deep (Figure 8.2D, where the hole reached the ground under the layer of snow). Snow was sampled by inserting a 50- or 15-mL polycarbonate tube into the snow using sterile gloves and capping the tube immediately (Figure 8.2G, visible red cells). Duplicates for each experiment type were collected exactly under the surface, at 20 cm in depth, and at 40 cm in depth. To avoid top-to-bottom contamination from digging from the snowpack surface, the snow front was also dug laterally. The samples were kept in a cooler until they could be either frozen (for the metabarcoding and snow nutrient analyses) or kept at 4°C under light exposure for imaging following fixation (for cell counts) or transferred to culture conditions (for strain purification).

Figure 8.2 Contextual landscape views of the sampling site. A. Red snow bloom at Col des Rochilles (2,496 m). B. Digging of the hole for snow collection at Col des Rochilles. C. Collection site at Cl de Cerces. D. Soil samples collected near the bloom site at Col des Rochilles. E. View of the hole dug for snow sampling, 50 cm deep, at Col des Rochilles. F. Snow collected at the surface in the bloom site at Col des Rochilles. G. Snow collected at the surface at the bloom site at Col des Rochilles after melting, showing the aggregation of aplanospores at the bottom of the tube. H. Close-up of the sampling zone in C. River sampling Rivers at the Col du Lautaret were sampled in the summer of 2020 at the surface of the Guisane river at six locations with a 50 mL sterile tube opened underwater. Fifty mL of river water was collected for each. The samples were stored at 4°C and stored at low light. The cells 181 were centrifuged at 3,000 rpm for 10 minutes and the supernatant discarded. The cells were resuspended in 200 µL TP medium (Gorman and Levin, 1965) and 5 µL was plated on an agar plate with the same medium supplemented with acetate, TAP (Gorman and Levin, 1965). Colonies were picked from each plate and streaked on their own plate and observed using fluorescence microscopy. Lichen sampling Lichen thalli were sampled at col des Rochilles using a knife and paper to absorb the moisture. The photobionts were loosely separated from the rest of the thalli using a razor blade on a glass slide and observed under light and fluorescence microscopy. Microscopy observations, chloroplast detection by chlorophyll fluorescence, cytosolic oil droplet detection by Nile Red staining Oil droplets are observed using nile red (NR) staining at 370 nm excitation. 40 µL of NR solution (2.5 mg/mL in DMSO) was added to 160 µL of culture. Cells were incubated for 20 min at room temperature before being observed under the microscope or fluorescence was measured by spectroscopy. Nile red fluorescence is observed at 530/580 nm. Chlorophyll fluorescence is observed after excitation using 370 nm laser and observed at 440/680 nm. Snow samples and cultures were observed using fluorescence optical microscopy (Zeiss AxioScope A1). Ten microliters of suspended cells were placed between slide and coverslip and observed at 1,000 and 200-fold magnification. Snow algae cell counts Snow samples were melted at room temperature at the laboratory the same day as the sampling. Paraformaldehyde (PFA) was added to a final 4% vol/vol concentration in each tube to fix the cells, which were then kept at 4°C. Sixteen microliters of the fixed mix were mounted on a Malassez counting chamber and the cells were counted twice for each sample.

To monitor alga development during cultivation in growth chambers, cells were either counted with the same procedure using a Malassez counting chamber, or alternatively a Luna- FL cell counter from Logos Biosystems or measurement of the optical density at 730 nm (OD730) using a Spark multimode microplate reader from Tecan Life Science. All counting methods were compared and provided significantly similar result. Snow algae culture and isolation of axenic strains For solid cultures, cells were streaked on TP or TAP medium (Gorman and Levine 1965) agar plates using disposable plastic loops. TP has no source of digestible carbon. TAP medium corresponds to the same composition, supplemented with acetic acid (see table).

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For 1 L TAP Tris base 2.4 g (1) NaNO3 - (2) NH4Cl 15.0 g

MgSO4(7H2O) 4.0 g

CaCl2(2H2O) 2.0 g NaCl - KCl - (3) K2HPO4 0.3 g (3) KH2PO4 0.14 g

Na2EDTA(2H2O) 50 mg

ZnSO4/ZnCl2 22 mg

H3BO3 11.4 mg

MnCl2 5.1 mg

FeSO4/FeCl3 5.0 mg

CoCl2 1.6 mg

CuSO4 1.6 mg

MoO3 1.10 mg Glacial Acetic Acid(4) 1 ml pH adjusted to 7.0

Table 8.1 Table of the composition of snow algae culture media. (1) Element added in experiments testing the growth of algae according to the source of nitrogen given. (2) Elements removed in –N cultures. (3) Elements removed in –P cultures. (4) Element removed in TP medium.

For liquid cultures, cells were primarily inoculated from a single colony picked at the surface of a solid medium plate, transferred into a flask containing 10 mL of TP medium, supplemented with vitamins (vitamin H at 8.18 nM, vitamin B12 at 2.94 nM and vitamin B1 at 0.594 mM final concentrations) sterilized by filtration (0.2 µm diameter). Algae were cultivated in growth chambers (Infors) at 20°C under continuous light at 80 µE⋅m-2⋅s-1, under gentle shaking at 100 rpm. Growth was assessed by cell counting on aliquots, using either Malassez counting cells or a Luna cell counter as described above, in triplicates. Strains were maintained in 10 mL subcultures, inoculated with 1 million cells per mL. For strain isolation and axenisation, 45-mL of melted snow was centrifuged at 800 x g for 10 min at 4°C and the supernatant transferred to a new tube for safekeeping. The cells were suspended in 200 µL TP medium Then, 5-30 µL of the resuspended algae were deposited on a petri dish of TP minimum media agar complemented with vitamins (vitamin H at 8.18 nM, vitamin B12 at 2.94 nM and vitamin B1 at 0.594 mM final concentrations) and spread with a disposable 183

plastic loop (Figure 8.3). They were incubated under continuous light (80 µE⋅m-2⋅s-1) at 20 °C in a vertical growth chamber (Sanyos) and observed daily until green colonies appeared. Colonies were picked, isolated and plated in either minimum media TP agar with vitamins, or minimum media TP agar with vitamins supplemented with a fungicides and antibiotics (nalidixic acid at 0,015 mg/mL (15 mg/mL stock), azoxystrobin at 0.01 mg/mL final (1 mg/mL stock) and carbenicillin at 0,1 mg/mL (100 mg/mL stock) in different combinations). The algae underwent axenisation cycles by repeating this procedure for up to a year depending on how they reacted to the antibiotics, by observing the plates with an inverted microscope and picking colonies developing with lowest levels of apparent bacterial or fungi contaminants. The strains were confirmed axenic following epifluorescence imaging after nuclei staining using 4',6-diamidino-2-phenylindole (DAPI, 1 g⋅mL-1 in 20% DMSO), after excitation at 350 nm, and capture of fluorescence at 450-490 nm.

Figure 8.3 Isolation (axenisation) of strains of snow algae. Snow was sampled at the surface, 20 cm and 40 cm in depth within red snow (visible algae bloom). The collected cells were pelleted after centrifugation at 800 x g for 10 minutes at 4°C and suspended in 200 µL fresh TP liquid medium. Five to thirty microliters were then plated on TP agar solid medium and incubated at 20°C under continuous light at 80 µE⋅m-2s-1. The cells were observed under an inverted microscope to follow colony formation of green microalgae, which were picked for an additional round of cultivation on solid medium. The strains were then streaked until complete purification from bacterial or fungi contaminants.

Indole-3-acetic acid and Epi-brassinolide treatments Indole-3-acetic acid (IAA) was purchased from Sigma and a concentrated solution was stored in Ethanol 100% at a concentration of 250 µmol/mL. Epibrassinolide (EB) was 184 purchased from CliniSciences and stored in dimehtylsulfoxide (DMSO) at a concentration of 100 mmol⋅mL-1. Dichlorophenoxyacetic acid (2,4D) from Sigma was stocked at 0.12 g/L. Treatments were performed on cultures in sold media. After melting of TP agar and cooling, 25 mL of TP agar was mixed with 1-3 µL of IAA in ethanol and/or 2.5-7.5µL of EB in DMSO or 3.65 mL-10.95 mL of 2,4D. 5 µL of culture was deposited at the surface of the agar and plates were left open for 5 minutes under sterile air flow. Plates were then incubated under continuous light at 80 µE.m-2⋅s-1 at 20 °C for 30 days. Development of algae was monitored biweekly. Glycerolipid extraction and analysis Glycerolipids were extracted from freeze-dried cells grown in 50 mL of medium. About 1-10 x109 cells were required for a triplicate analysis. First, cells were harvested by centrifugation at 1,500-3,000 x g for 10 min at 20°C then immediately frozen in liquid nitrogen. Once freeze-dried, the pellet was suspended in 4 mL of boiling ethanol for 5 minutes to prevent lipid degradation, and lipids were extracted by addition of 2 mL methanol and 8 mL chloroform at room temperature. The mixture was then saturated with argon and stirred for 1 hour at room temperature. After filtration through glass wool, cell debris were rinsed with 3 mL chloroform/methanol 2:1, v/v, and 5 mL of NaCl 1% were then added to the filtrate to initiate phase separation. The chloroform phase was dried under argon before solubilizing the lipid extract in 1 mL of chloroform. Total glycerolipids were quantified from their fatty acids (FAs): in a 10 μL aliquot fraction, a known quantity of saturated 15-carbon FA (15:0) was added and all FAs were methanolyzed into methyl esters (FAME) by a 1 hour incubation in 3 mL 2.5% H2SO4 in pure methanol at 100°C (Jouhet et al., 2003). The reaction was stopped by addition of 3 mL water, and 3 mL hexane were added for phase separation. After 20 min of incubation, the hexane phase was transferred to a new tube. FAMEs were extracted a second time via the addition, incubation and extraction of another 3 ml hexane. The combined collected hexane fractions (6 ml) were argon-dried and FAMEs were suspended in 30 μL hexane for analysis by gas chromatography coupled with flame ionization detection (GC-FID, Perkin Elmer), using a BPX70 (SGE) column. FAMEs were identified by comparison of their retention times with those of standards (Sigma) and quantified by the surface peak method using 15:0 for calibration. Extraction and quantification were performed with at least three biological replicates. Pigment extraction and analysis Cells were harvested by centrifugation at 600 x g for 10 min at room temperature then immediately frozen in liquid nitrogen. Then cells were suspended in 200 µL of a Methanol/Tris solution (1/100 v/v Methanol/Tris-HCl 10 mM, pH 7.5) and mixed vigorously using a vortex. Samples were dried under argon, in the dark. The dry pellet was then suspended in HPLC-grade methanol or ethyl acetate, collected in a fresh glass tube and dried under argon. The pellet was resuspended in Methanol/Tris and methanol or ethyl acetate alone and dried under argon until the pellet became white. The supernatants collected at each step were pooled and centrifuged at 600 x g for 2 min at room temperature. Pigments were dried under argon and stored at -20°C until analyses. The samples were analyzed as in Allorent et al., 2013 by HPLC using a C30 reverse-phase column (250 x 4.6 mm) from YMC Co (Interchim). The 185 mobile phases used were methanol (A), water/ methanol (20/80 by volume) containing 0.2% (w/v) ammonium acetate (B), and tert-methyl butyl ether (C). The gradient used was 95% A/5% B for 12 min, a step to 80% A/5% B/15% C at 12 min, followed by a linear gradient to 30% A/5% B/65% C by 30 min. The identities of the chlorophylls and carotenoids were assigned based on retention time. Quantification was achieved by evaluating the area below each peak. Flow cytometry

Cells were grown in 5 mL liquid medium, until concentration reached 1x106 cells⋅mL-1. A 3-mL sample was then harvested by centrifugation at 2,000 x g for 5 min at room temperature. The cell pellet was washed once in 3 ml phosphate buffer saline (PBS) 1 X and collected by centrifugation at 2,000 x g for 5 min at room temperature. The cells were then fixed and permeated in cold 70% ethanol, added drop by drop to the pellet while mixing vigorously using a vortex. Fixed cells were incubated for 1 hour at 4°C, then washed once in PBS and collected by centrifugation at 850 x g. The cells were then treated with 50 µL RNAse (RNAse A, Sigma) at 100 µg⋅mL-1 to remove any trace of RNA, as PI fixes on all nucleic acids and we only want to visualize DNA. Then, 200 µl propidium iodide (PI, 50 µg/ml) were added and the cells were incubated in the dark for 30 minutes at room temperature before being injected in a flow cytometer (BD FACSCalibur, BD Biosciences). Photosynthesis analysis To determine photosynthesis parameters in cell cultures, room temperature fast chlorophyll fluorescence kinetics were measured. Chlorophyll fluorescence was measured using a PAM (Speedzen MX, JBeamBio) Chl fluorescence photosynthesis analyzer. Parameters were the same as described in Allorent et al., 2013. A 50 mL volume of cell culture grown at 20°C was transferred to a black 96-well plate and dark incubated for 30 min before measurements. Excitation was performed in the blue range ( = 450 nm; F0). F0 is the steady- state fluorescence in dark-adapted cultures, Fm is the maximal fluorescence after a saturating light pulse with green light (520 nm) of dark-adapted cultures, Fm’ is the maximal fluorescence after a saturating light pulse with green light (520 nm) of light-adapted cultures, and Fv is the difference between Fm and F0. With these parameters, the maximum efficiency of energy conversion of PSII (or maximum quantum yield of PSII) can be calculated as Fv/Fm = (Fm − Fo)/Fm formula (Geller et al., 2018). The non photochemical quenching of energy (NPQ) was calculated with the formula Y (NPQ) = F/Fm′ − F/Fm (Kramer et al., 2004). DNA extraction DNA was extracted using a method described in Morabito et al., in prep. Genome sequencing The algae were sequenced using the Illumina HiSeq 2500 technology with a paired-end sequencing library. Raw reads were cleaned with Trimmomatic v.0–36 and assembled by the newly developed commercial pipeline Reconstructor, www.sequentiabiotech.com/omicstools/pipeline. The full genome sequencing and annotation is planned to be done with a hybrid Illumina/PacBio sequencing method using Illumina 186

Novaseq 6000 and third generation sequencing technology PacBio RSII. The Illumina sequences short reads and PacBio sequences long reads. This combination increases coverage and accuracy (Rhoads and Au, 2015). Results Snow is a rich environment that harbors a diversity of microorganisms Snow samples collected at high elevation sites in the vicinity of the Col du Lautaret, observed under fluorescence microscopy, contained a multitude of photosynthetic and non- photosynthetic cells of various sizes, mixed with uncharacterized debris and particles. There were mainly algae, fungi (see Figure 8.4A-R) and bacteria. In spite of methodic and tedious scrutiny, no Chlamydomonas nivalis morphotype could be detected, over the three years of sampling at the sites. The Sanguina-like aplanospore cells were the most abundant in surface samples and gradually decreased in occurrence with depth of sampling (own observations based on pigmentation of snow). The snow was only visibly red in the first 2-5 cm in depth. Aplanospores of 10-50 cm in size and of a deep red color resembling those of Sanguina nivaloides as well as orange cysts similar to those of Sanguina aurantia were both present but detected in microscopy only at the surface and at 20 cm in depth (Figure 8. 4G-R). Red cysts and, to a lesser extent, green cells in snow samples were observed in high concentration in surface samples, and much more rarely in deeper snow samples, sometimes not at all in microscopy. According to Prochazkova et al., 2019a, Sanguina cells cannot be cultured in the laboratory by any means discovered thus far, and only other green algae grow. Other species are also known to form red-colored cysts. It is therefore expected that only a subset of algae species collected will be cultivable. After sampling, the algae collected in the snow from all sites were grown at the laboratory in TP media, which is commonly used for Chlamydomonas reinhardtii and Chlamydomonas nivalis, and likely appropriate for snow algae.

As a positive control of our life sample cultivation procedure, Chlamydomonas nivalis UTEX 2824 was successfully propagated and maintained in parallel conditions.

When plated, even samples containing no detectable cells by microscopy examination of aliquots contained a minimum of 2-30 cultivable green algae colonies per 5 µL of cells concentrated 250 times. Those samples did contain cells but were too low in concentration for detection.

After being isolated and grown on agar plates, the green algae observed had distinct morphologies from those observed in microscopy of initial fresh snow samples. This is either because the cells that are cultured were too low in concentration to be observed under microscopy, or because the change in conditions of culture, namely temperature and media, affected the morphology of the algae. A description of all morphotypes and the corresponding collection sites where they could be detected is provided in Table 8.1.

In one long-term observation performed over a time course of two months under the microscope, where the petri dish was left taped to the inverted microscope and therefore left at room temperature, cysts changed color over time from red to brown, and no conversion into

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green cells could be observed and directly evidenced. Green cells inoculated in parallel on solid medium colonize the plate until they recover it in that same time.

Figure 8.4 Variety of microorganisms detected in snow samples. A-F: Putative fungi cells. G-L: Snow samples from the surface and 20 cm in depth presenting red and orange aplanospore-like cysts, green algae cells, contaminants and debris. Scale bar: 10 µm. All photos are from 1000-fold magnification.

A first analysis of morphotypes, with an attempt to narrow down the identification of corresponding taxa, was performed with the help of Alain Couté (Muséum d’Histoire Naturelle, Paris).

Figure 8.5 Morphotypes found in snow blooms at the region of the Col du Lautaret. Ten microliters of liquid culture of the algae in TP medium were observed under light microscopy at 1,000-fold magnification. Bar = 10 µm.

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After culture at 20°C at 80 µE⋅m-2⋅s-1 (Table 8.2), the morphotype that was apparently encountered and possibly common in almost all samples were rods of 4-10 µm long with a single chloroplast, resembling Stichococcus spp. (Trebouxiophyceae). We do not exclude that multiple species may correspond to this morphotype. Some rods grow in a filament in solid media but are detached like the other rod cultures in liquid cultures under agitation.

The second most common morphotype was small coccoids of ~5 µm in diameter. Some of them exhibited multiple fissions with up to four or eight daughter cells, but at their biggest do not exceed 10 µm in diameter. There were also medium sized coccoids of ~10 µm that performed multiple fission with up to 4-8 cells. Another common morphotype is large coccoids that perform multiple fission and reach their biggest size at up to 30 µm in diameter, before releasing a multitude of small daughter cells.

Some strains are coccoid in shape but differ from other cultures as they grow in clumps and are not observed alone. One strain found had clumps of 2-3 cells exclusively, surrounded by a membrane or envelope, while other strains grew in a multiple of two. There were also oval shaped cells of about 4 µm, which had multiple fission and build up to round mother- cells of up to 15-20 µm.

The most elusive morphotype is a filamentous unbranched, non-siphoneous green alga, found in Mi Pons and Mi Pons B in 2017 only.

Some strains, like CR1concA (Figure 8.5) from Col des Rochilles snow surface, have shown some stages with two flagella, in the presence of acetate, whereas in the absence of acetate they agglutinate and do not separate into single cells.

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Table 8.2 Green algae morphotypes collected from snow around col du Lautaret. Snow collected at the four different sites was plated and cultured at 20°C at 80 µE.m-2.s-1 in TP agar plates and then observed under light microscopy at 1000 fold magnification. NA: sites not sampled in 2019.

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The elaboration of the Lautaret Culture Collection, LCC Many colonies (a mean of 20 per sample) were isolated from snow and subsequently cultured for examination. Most colonies were coccoid and rod-shaped. Out of all the colonies, sixteen strains were shortlisted for further analysis based on morphology. Four of them were rod-shaped (CC1C, MP2A, MP3A, Mines5B), two were filamentous (MP2Afil2, MPB2A), one was composed of clumps of 2-3 cells (CR1concA), three were oval (ACC4C, Mines5D1, CC6A), two were small coccoids (ACC4D, CC1B), two were medium coccoids (ACR2dilC4A, ACR2A1B1) and large coccoid (MP1concB) (Figure 8.6). They constitute the first strains of the Lautaret Culture Collection, LCC. Their characteristics are summed up in Table 8.3. They have one chloroplast per cell after division, though multiple are visible during division as some perform multiple fission.

Figure 8.6 Representative morphotypes of LCC strains. Ten microliters of liquid culture of the algae in TP medium were observed under light microscopy at 1,000-fold magnification. Bar = 10 µm.

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LCC_number code_name Massif Altitude Sample year Snow depth Morphotype Length

1 MP1ConcB MI PONS 2,450 m 2017 Surface coccoid 10-40 µm

2 MP3A MI PONS 2,450 m 2017 20 cm rod 8-10 µm

3 MP2A MI PONS 2,450 m 2017 Surface rod 8-10 µm

4 Mines5D1 Mines 2,230 m 2017 40 cm oval 4-12 µm

5 MPB2A MI PONS B 2,450 m 2017 Surface filamentous 10-15 µm

6 MP2Afil2 MI PONS 2,450 m 2017 Surface filamentous 10 µm

7 CC1C Col Cerces 2,550 m 2017 Surface rod 8-10 µm

8 CC1B Col Cerces 2,550 m 2017 Surface small coccoid 6-8 µm

9 CC6A Col Cerces 2,550 m 2017 40 cm coccoid 4-10 µm

10 Mines5A Mines 2,230 m 2017 40 cm coccoid 8-10 µm

11 Mines5B Mines 2,230 m 2017 40 cm rod 8-10 µm

Col des coccoid 12 CR1concA Rochilles 2,462 m 2017 Surface clumps 6-10 µm

13 ACC4C Col Cerces 2,550 m 2018 20 cm oval 4-20 µm

14 ACC4D Col Cerces 2,550 m 2018 20 cm small coccoid 6-8 µm

Col des 15 ACR2dilC4D Rochilles 2,462 m 2018 Surface coccoid 6-10 µm

Col des 16 ACR5AB Rochilles 2,462 m 2018 40 cm rod 8-10 µm

Col des 17 ACR2A1B1 Rochilles 2,462 m 2018 Surface coccoid 8-10 µm Table 8.3 LCC strain sampling details and characteristics.

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Preliminary study of algae collected in river fresh water in the Lautaret area

Table 8.4 River algae from Col du Lautaret area. Ten microliters of culture of the algae in TP medium were observed under light microscopy at 1,000 fold magnification. Bar = 10 µm.

In this non-comprehensive and very preliminary sampling of rivers, four morphotypes were observed: small coccoid, large coccoid, oval grouped by four, and filamentous. The river strains were all different from the snow strains. Genome size estimations using flow cytometry The genome size of all selected LCC strains and Chlamydomonas nivalis UTEX 2824 used as a reference strain was investigated using flow cytometry after staining with propidium iodide dye (Figure 8.7). Chlamydomonas reinhardtii, a genetic model species whose genome size is known (110 Mbp, haploid, Merchant et al., 2007) was used as higher end control size. Microchloropsis gaditana, which has a small genome size (30 Mbp, Schwarz et al., 2018), was used as lower end control size (Table 8.5). The cells were also observed under microscopy after treatment to investigate cell integrity and verify fluorescence following staining with PI (data not shown). FACS graphs show one to several peaks, depending on the cell’s stage in its division process, as many of these strains perform multiple fission (MP1concB, ACC4C, Mines5D1, ACR2dilC4A, Mines5A and CC6A).

Unexpectedly, the genome size of Chlamydomonas nivalis UTEX 2824, was evaluated to be seven to ten times bigger than Chlamydomonas reinhardtii genome, between 700 and 1,200 Mbp. Snow algae from the LCC seemed to range between 20 and 120 Mbp.

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Preliminary estimated size PI fluorescence (known size) Mbp

Microchloropsis gaditana 5 (30)

Chlamydomonas reinhardtii 30 (110)

Chlamydomonas nivalis UTEX 2824 100-300 730-1,200

CR1concA 2-4 ~20

MP1concB 20 70-120

MP2A 20 70-120

ACC4C 15 55-90

CC1C 10 36-60

ACR2dilC4A 15 55-90

CC6A 10 36-60

Mines5D1 10 30-60

Mines5A 20 70-120

Mines5B 6 20-36

Table 8.5 Genome size estimation of LCC strains based on Chlamydomonas reinhardtii and Microchloropsis gaditana using propidium iodide and flow cytometry.

This first evaluation served to select strains to study in more detail, obtain a gross estimation of genome sizes and calibrate a comprehensive genome annotation of some selected algal strains, outsourced to Sequentia, Spain.

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Figure 8.7 Flow cytometry analysis of genome sizes of snow algae using propidium iodide. Three million cells were grown in TP media at 20°C then centrifuged and wahed with PBS. The cells were fixed with ethanol 70% for 30 min at 4°C. RNAse was used to remove RNA and the DNA was stained using PI for 30 min at RT.

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Full genome sequencing of Chlamydomonas nivalis/typhlos UTEX 2824 and three selected LCC strains The reference strain Chlamydomonas nivalis/typhlos UTEX 2824 does not have a fully sequenced genome yet, therefore as the ultimate snow algae reference, it was important to start with it. As mentioned in the introduction, this strain is currently under taxonomic correction, likely belonging to C. typhlos (communication from Thomas Leya, Potsdam, Germany), a species also described in snow samples. Three of our LCC strains with different morphotypes were also selected for full genome sequencing.

We used a hybrid method for sequencing with both Illumina and PacBio to increase coverage and sequence quality. Illumina was used first to determine the actual genome size and heterozygosity as well as to identify the closest algae species, based on reference databases (Table 8.6).

Chlamydomonas nivalis/typhlos UTEX 2824 has a very large genome, confirming the trend from our FACS experiment, though it had been largely overestimated. The C. nivalis/typhlos genome was found to be about 202 Mbp in size and haploid.

MP1concB was estimated at 23.5 Mbp, and identified as most similar to Auxenochlorella protothecoides.

CR1concA was estimated at 20 Mbp but was not a pure culture, so not identified with certainty. The clean reads indicate that the alga is related to Coenochloris signiensis.

Finally, the MP2A strain was estimated at 51 Mbp in genome size. Its taxonomy is complex, the algal genome matches to many other algae, which makes it likely a new species. The alga that matched the most (only about 6%) was Stichococcus bacillaris.

Chlamydomonas nivalis/typhlos MP1concB MP2A CR1concA UTEX 2824

Heterozygosity 0.10% 0.28% 0.22-0.23% 0.09-0.10%

Genome Repeat ~25 ~4 ~8 ~60 Length (Mbp)

Genome Unique ~175 ~19 ~43 ~104 Length (Mbp)

Table 8.6 Selected strains genome heterozygosity estimation, repeat genome length and unique genome length.

The annotation of these genomes is a longer-term perspective of this PhD thesis.

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Snow algae carbon source preference and heterotrophic capacity Algae are phototrophs converting light into the requested chemical energy (ATP) and + reducing power (NADPH, H ) to capture and reduce atmospheric CO2 at the basis of their carbon metabolism. In our investigation of green alga biodiversity based on DNA barcoding, we found that organic carbon in soil might be determinant in the distribution of some species, based on their capacity to grow heterotrophically and/or mixotrophically (Chapter 6). In addition, since not all wavelengths of light can penetrate deep in snow (Richardson and Salisbury, 1997), some strains isolated from snow might be able to grow in the dark given a carbon source such as glucose or acetate in a twelve-day growth test. We evaluated therefore this capacity (Figure 8.7).

In the dark, glucose enabled the growth of all strains tested, and the growth of CC6A was especially boosted, even higher than in the light. MP1concB was the only strain to retain a green coloration in the dark, even in the absence of a carbon source.

We found that in most cases in the light, glucose presence increased growth noticeably. This suggested that the strains that remained green and grew better in the presence of glucose could possibly be mixotroph, using both photosynthesis and reduced carbon sources for their carbon metabolism.

Other strains, like CC6A and MPB2A preferred glucose. The conversion into heterotrophic metabolism coincided with a yellowing of the culture for CC6A and MPB2A, likely because chlorophyll was degraded.

Provision of acetate was not as efficient to sustain heterotrophic growth as glucose did. Acetate either inhibited growth, Mines5B, MP3A, CC1B and CC6A, did not significantly affect it for Mines5A, Mines5D1, MP2A or significantly increased growth for MP1concB, CC1C, CC6A and CR1concA. MPB2A growth was not inhibited by acetate, but cells lost their green coloration in a manner similar to that in presence of glucose.

Most LCC strains proved therefore to be able to perform photoautrophically in the light and heterotrophically in the dark, provided the appropriate reduced carbon source, here glucose, was available. Future studies include the fine analysis of reduced carbon source possibly present in the snow or natural habitats of corresponding species.

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Figure 8.8 Growth of some LCC strains in the light or in the dark with acetate or glucose as carbon source. Duplicates of 5µL of culture were deposited on agar plates and grown for a 30 days under continuous light at 80 µE.m-2.s-1 or in the dark under aluminum foil on either TP media or supplemented with 2% glucose or 2% acetate, w/vol.

Preliminary study of the impact of nutrient starvation on snow algae Cells of selected LCC strains and the standard Chlamydomonas nivalis/typhlos UTEX 2824 were cultured in solid media for 30 days, plated on petri dishes with agar TP medium with or without nitrogen and/or phosphate to assess the effect of nutrient depletion on growth (size of colony) and culture phenotype (color) to the deficiency (Figure 8.8).

In the absence of N, colony sizes were reduced on solid media for ACC4C, ACC4D, ACR2dilC4A, ACR5A3, MP2A, MPB2A and UTEX2824, showing that growth was strongly altered. The other strains did not show a reduced colony size indicating that growth was not impaired visibly for these strains (CR1concA, Mines5D1, MP3A, MP1concB and CC1C). Colony pigmentation changed from green to pale yellow in all strains except CR1concA and MP1concB, indicating a drop in chlorophyll concentrations, and was the most severe for the filamentous strain MPB2A, which seemed to lose almost all pigmentation.

On agar plates, only CR1concA colonies became orange (Figure 8.9). Interestingly, in liquid cultures, both CR1concA and MP1concB cells became orange (Figure 8.10). Since MP1concA was able to grow and still had visible chlorophyll pigments on TP with no source of carbon in the dark, it is possible that it is able to consume the agar, or trace carbon molecules in the agar powder used for petri dishes, and therefore was not as stressed as CR1concA on Figure 8.9.

P depletion on agar plates did not induce a distinct phenotype, either alone or in combination with N depletion. When cells were plated and subcultured on –P agar plates for

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six months, cells still showed no distinct growth impairment (not shown). This led us to perform preliminary liquid culture studies, which indicate that agar possesses trace P concentrations sufficient to sustain normal growth (data not shown). CR1concA reacted to stress faster than other strains unless it was supplied with a reduced carbon source like acetate in the media.

Figure 8.9 LCC and UTEX 2824 response to nitrogen and phosphate starvation on agar plates. Five µL of culture were deposited on agar plates with TP media (CTRL) or without N and/or P (-N, -P, -N-P) and dried for 10 minutes. Cells were cultured for 30 days before pictures were taken.

Figure 8.10 Culture color change during nitrogen starvation for MP1concB and CR1concA. 200µL of liquid culture of TP+N or TP-N were put in a white 96 well plate after 44 days of culture. 199

Cells from cultures on solid media (CC6A, ACC4C, ACC4D, ACR2dilC4A, ACR5A3, CC1B, Mines5A, Mines5D1, MP2A, MP3A) or liquid cultures (CR1concA, MP1concB) from control conditions (+N) and nitrogen depleted conditions (-N) were observed under microscopy (Figure 8.10) to see if the nutrient depletion affected individual cell phenotypes. CR1concA and MP1concB cells, were bright orange. The cultures on solid media showed a discoloring in microscopy, correlated with the macroscopic pale yellow appearance. In some cultures (CC6A, ACC4C, ACR2dilC4A, MP3A, MP2A, Mines5D1, Mines5A, CC1B), lipid droplets were visible inside cells. Further examination could determine whether TAG accumulates in these lipid droplets, which is a known physiological response to nitrogen deprivation.

Figure 8.11. Microscopy of LCC cells under normal or nitrogen deprived conditions. Cells were taken from petri plates using a 1 mL tip and dipped in 50 µL TP media to resuspend them or taken from liquid culture flasks before 10 µL were deposited on a slide and observed under light microscopy at 1,000 fold magnification. Scale bar: 10 µm.

As N starvation lead to a phenotypical change in MP1concB and CR1concA, marked by the development of an orange pigmentation, both strains as well as two strains that did not show such pigment phenotypes (Mines5B, MP3A) were used to compare fatty acid composition in normal conditions or following nitrogen starvation (Figure 8.13). Fatty acids ranged from 14- to 18-carbon in length (C14 to C18).

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The experiment was only performed once and needs to be repeated. Nevertheless, in this preliminary dataset, FA profiles were clearly modified in nitrogen deprived cultures in all four tested strains. The strains did not all respond in identical ways.

Figure 8.13 Total fatty acid composition in LCC strains in normal and nitrogen deprived conditions with no carbon source. Cells were cultured in either TP or TP-N for 44 days at 20°C and exposed to 80 µE.m-2s-1 light intensity in a 12:12 light:dark cycle. All measurements are of FA quantity in nmol per million cells except for CR1concA which is expressed in nmol of FA divided by fresh weight. This experiment was performed only once, and should be repeated in the future.

In this analysis of FA profiles, a guideline is based on the classically high level of PUFAs (16:2, 16:3, 18:2, 18:3) in plastid membrane glycerolipids, mostly galactolipids, and the classically high level of SFAs (16:0, 18:0) and MUFAs (16:1, 18:1) in cytosolic TAG, in the form of oil droplets. Most strains show a decrease in fatty acid species measured when

201 nitrogen deprived (MP3A, Mines5D1, MP1concB, CR1concA), but CC6A had higher 18:1 levels and Mines5B had higher FA quantity in16:0, 18:1, 18:2, 18:3.

The strains showing a phenotype marked by an orange pigmentation triggered by the starvation in N, MP1concB and CR1concA, had higher levels of acyl-lipids per cells, overall marked by a high proportion of PUFAs, indicating that if oil accumulates, plastids remain well developed and TAG is therefore not produced at the expense of photosynthetic membrane development. The decrease of FAs in N-starved MP1concB might also indicate that carbon metabolism is not only directed to TAG as a carbon storage form and that starch or other carbohydrate storage polymers might accumulate as well as a way to cope with the excess in carbon following the imbalance in nitrogen in the primary metabolism.

By contrast, strains which do not clearly exhibit such accumulation of carotenoids (Mines 5B, CC6A, Mines5D1, MP2A and MP3A), seem to exhibit a relative increase in the proportion of SFAs and MUFAs possibly reflecting a strong cell stress response with carbon metabolism partitioned in the direction of TAG production at the expense of chloroplast development.

This preliminary work points to possibly distinct metabolic strategies to survive and develop in various supplies of nutrients. These strategies will need to be verified, confirmed and investigated with care in the future. Chlamydomonas nivalis/typhlos UTEX 2824 under nitrogen stress and high light We completed this first investigation of the effect of nutrient deprivation on snow algal cells by a preliminary abiotic multi-stress study, combining nitrogen removal with exposure to an excess of light. To that purpose, Chlamydomonas nivalis/typhlos UTEX 2824 was cultured under low light (LL, 100 µE.m-2.s-1) and high light (HL, 400 µE.m-2.s-1) either in control media (TP) or nitrogen deprived media (TP-N). C. nivalis/typhlos was cultured for 24 days and the cell concentration, size, phenotype, chlorophyll and NR fluorescence and photosynthetic ability were measured three times a week. At the end of the cultures, cells were analysed for pigment composition.

Imaging of cells during the nitrogen stress time course (Figure 8.14) shows that at day 3, starved C. nivalis did not show any difference from replete control. No visible lipid droplets appeared, based on NR fluorescence.

At day 5, many oil droplets were visible in all nitrogen deprived cells. They were visible even without the fluorescence of the NR. Subsequent analysis showed that in nitrogen deprived cultures, droplets became very large and occupied a large proportion of the cell volume. In C. nivalis/typhlos cells cultivated in HL or LL with nitrogen droplets were also observed, but with smaller sizes. At LL, they seemed more numerous than at HL. Under HL+N condition, cells appeared less healthy, and the green pigmentation seemed lower than under LL+N condition, reflecting a strong multi-stress level.

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Figure 8.14 Combined effects of nitrogen deprivation and high light on Chlamydomonas nivalis/typhlos UTEX 2824 phenotype and TAG accumulation. Fluorescence imaging of treated cells highlights chlorophyll fluorescence (440/680nm), allowing the detection of chloroplasts and Nile Red staining (530/580nm) of oil droplets enriched in TAG. Bars = 10 µm.

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Photosynthetic activity based on PSII efficiency was assessed using the Fv/Fm ratio. This ratio represents the maximum quantum yield of PSII and is usually around 0.6-0.7. In a study by Zheng et al., 2020, the Chlamydomonas nivalis/typhlos UTEX 2824 strain that we also used had a Fv/Fm of 0.6 at 22°C, when grown in TAP medium. Nitrogen deprived strains were immediately affected with Fv/Fm falling to a value at ~0.1. We cultivated C. nivalis/typhlos in TP instead of TAP, and the Fv/Fm value was stable around 4-5 in HL+N and LL+N conditions and consistently fell below 0.2 in N-starved conditions (data not shown). Based on this experiment performed only once, and which should be repeated to provide statistical robustness, HL may amplify the impact of N deprivation on oil accumulation. However, no clear conversion into orange-pigmented form could be observed. Future work will be based on this experimental setting so as to explore the effects of other multi-stress conditions in the formation of oil droplet, combined with the accumulation of pigments and eventually formation of a cyst. Two LCC strains undergo conversions into orange-pigmented forms following nitrogen deprivation Very interestingly, MP1concB and CR1concA were two strains that became orange under nitrogen deprivation. CR1concA was apparently more sensitive to this type of abiotic stress, even turning orange in control conditions after a long culture time, possibly consuming all medium nitrogen faster, or being able to convert into a pigmented form following the deprivation of other nutrients, such as phosphorus, sulfur, etc.

MP1concB did not turn orange in TP-agar. We hypothesized that this strain was able to use trace amounts of nitrogen from the agar.

Under fluorescence microscopy, the cells turned yellow-bright orange (Figure 8.16). A pigment analysis was performed for MP1concB and CR1concA under nitrogen deprived conditions at low light. The orange coloration of cells indicated a carotenoid accumulation. The peaks corresponding to chlorophyll a were not always visible using the same pigment extraction technique, therefore the +N samples are missing in this first analysis. Some ketocarotenoid fatty acid esters were also detected but not identified. Plant growth hormone indole-3-acetic acid (IAA) and epibrassinolide and their effects on snow algae growth Finally, we tested some possible biotic factors on algal development, which could be generated in highly dense communities in the form of infochemicals, molecules emitted and acting as cell-to-cell signals. We focused on auxin (IAA) and brassinosteroids (epibrassinolides, or EBs), which combined effects have been described in previous works on green microalgae: EBs were found to enhance TAG production in Chlorella (Liu et al., 2018) and to operate synergistically with auxin (Bajguz and Piotrowska-Niczyporuk A, 2013). We performed a preliminary comparative study, with acute treatments at high doses of infochemicals in the µM range.

IAA is a plant growth hormone and its effect on snow algae growth is shown in Figure 8.15. IAA was diluted in ethanol. Control treatments showed no effects of ethanol alone. IAA 204 did seem to have a negative effect on the development of some strains in a dose dependent manner (10 and 30 µM), but had no visible effect on others. In all cases, under low nitrogen condition, addition of IAA did not lead to any conversion of any of the alga tested into orange form. Rather, the CR1concA strain (numbered 13 in Figure 8.17) seemed to lose its ability to form orange colonies under low N, consistent with IAA altering CR1concA growth.

Epibrassinolide (EB) seemed to have a positive impact on growth at 10 µM but a negative impact at 30 µM. This was not dependent on DMSO as the control shown no impact on growth at either concentration (highest concentration shown). Interestingly, under low N, 10 µM EB did not alter the ability of CR1concA to form orange colonies, seemingly amplifying the number of orange colonies obtained without phytohormone addition (Figure 8.15). One can therefore hypothesize a cross talk between EB sensing and low N response in the conversion of CR1concA into pigmented cells.

Combinations of EB and IAA did not show any clear synergistic and antagonistic impacts, mostly leading to decreased growth or no effects.

To check whether IAA was either an unspecific toxic molecule or a specific inhibitor of algal growth sensed by a dedicated receptor, dichlorophenoxyacetic acid (2,4D) was provided at 10 and 30 µM. 2,4D seemed to have a positive effect on growth on some strains, despite it being used as a herbicide in plants. This suggests that IAA may be a specific inhibitor of growth, acting on population size control, and that 2,4D may titer an IAA specific receptor, triggering an opposite positive effect.

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Figure 8.15 Effects of growth hormones and brassinolide on snow algae growth in normal and nitrogen- deprived conditions. Concentrations are expressed in µM. Eb: Epibrassinolide. IAA: indole-3-acetic acid.1. ACR1B. 2. ACR1A1B1. 3. MP2A. 4. MP3A. 5. Mines5B. 6. CC1C. 7. ACR2dilC4A. 8. ACC4C. 9. CC1B. 10. Chlamydomonas reinhardtii. 11. Mines5D1. 12. MP1concB. 13. CR1concA. 14. Mines5A. 15. CC6A. 16. Chlamydomonas nivalis/typhlos UTEX 2824.

Following this study of multiple biotic factors, future works will focus on strains highlighting a response to EB and IAA, either showing a stimulation of growth and/or an inhibition. On a selected model system, more in-depth study of the level of stress response needs to be examined with care at the level of photosynthesis efficiency, oil droplet accumulation in the cytosol, possible accumulation of some carotenoids, and combinations with other abiotic and/or biotic stress. This work paves the way to more comprehensive works on well -elected strains having now in hands genomic information and preliminary ecophysiology data.

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Discussion and perspectives Collected strains show a remarkable diversity of species and morphotypes. Snow blooms can take different appearances: the best-known and found worldwide are red snow blooms, where aplanospores, bright red cysts, occur in high concentration, and are thought to be due to Sanguina nivaloides and other species of the larger Chlainomonas group (Prochazkova et al., 2019; Engstrom et al., 2020). Orange, green, yellow, and grey blooms have also been reported, with no aplanospores, and they can contribute to the staining of snow (Yoshimura et al., 1997; Krug et al., 2020; Remias et al., 2005).

Snow algae found near the Col du Lautaret presented an unexpectedly large genetic diversity during the metabarcoding study in Chapter 7. This diversity was also observed in their morphology with filamentous, coccoid, rod shaped algae, etc. There were both members of the Chlorophyta lineage, and filamentous members of the Charophyta lineage Klebsormidia (MPB2A). If we consider aplanospores to be specific to the Sanguina genus, we could hypothesize this taxon to be in highest concentration at the surface of snow. The possibility of other Chlorophyta to form pigmented cells and cysts in snow blooms is also considered. Further DNA testing would reveal if both species of Sanguina described in Prochazkova et al., 2019 are in fact present, as is suspected since both red and orange cysts were observed. As mentioned in Chapter 7, Sanguina genus presence was confirmed by DNA, but not identified down to the species level in Col des Rochilles. The species could be both Sanguina aurantia and Sanguina nivaloides, or it could be a yet unknown species of Sanguina.

Both Chloropyceae and Trebouxiophyceae classes of Chlorophyta were collected in snow samples, observed and cultured in the laboratory. It is thought that Trebouxiophyceae are not true snow algae as they do not accomplish any part of their cell cycle in snow (Stibal and Elster, 2005), but may be transported to snow from permafrost. Unexpectedly, no Chlamydomonas nivalis/typhlos cells were observed in any of the samples over the three-year period, when sampling was performed. This could be because sampling was not done at the right time for these cells, but it could also be that this alga is not present at these sites. Additional sampling campaigns over the course of the spring to summer months would confirm that.

In previous studies by others, Sanguina and Chloromonas were only found in late summer samples from high alpine sites above 1,500 m in Canada (British Columbia) with 18S and rbcl markers in Engstrom et al., 2020, and it would be interesting to perform an altitude gradient sampling campaign focusing on snow algae in the French Alps, to verify whether altitude could indeed play an important role in snow algae biodiversity patterns.

Collected cysts/aplanospores were thought to de-encyst and lead to some of the green algae morphotypes. Further tests are needed to prove this fully. Our time lapse results (not shown) do not invalidate this claim from Prochazkova et al., 2019. However, the level of green algae developing after plating red bloom samples in all our trials indicate that other species that Sanguina populate in sufficiently high amount this ecosystem.

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Somes laboratory conditions trigger the accumulation of oil droplets and carotenoids in a number of LCC strains Red snows are thought to occur in more exposed snowfields, as the encystment characterized by a high concentration of lipid globules filled with secondary carotenoid astaxanthin around the chloroplast is likely an adaptation to especially high UV and light exposition (Han et al., 2013). In snow algae, astaxanthin is mostly found in esterified mono- and diesters forms with fatty acids (Remias et al., 2010; Fujii et al., 2010). Though astaxanthin is not exclusive to snow algae as it is produced for instance by the common green alga Haematococcus pluvialis under stress conditions, it is associated with high light found in snowfields and glaciers (Han et al., 2013). Carotenoids have a primordial role in photoprotection both actively (primary carotenoids such as β-carotene) and passively (secondary carotenoids such as astaxanthin). Therefore, pigment composition and concentration are expected to drive differences between types of blooms. To date, the role of each snow algal species in the development of colored cysts with specific pigment profiles has not been assessed methodically and systematically.

In a study of aplanospores of uncharacterized algae collected in a red snow bloom (then called Chlamydomonas nivalis, though this naming is now controversial, Prochazkova et al., 2019a), HPLC analyses showed presence of chlorophylls a and b and primary carotenoids, typical of green algae. The xanthophyll cycle pigments deriving from -carotene (violaxanthin, antheraxanthin and zeaxanthin) were also detected, and their sum was one third of lutein levels, deriving from -carotene. The quantity of astaxanthin and its fatty acid ester derivatives was found to be 7-25 times that of chlorophyll a in snow algae (Remias et al., 2005). Carotenoid and chlorophyll concentration increased after sun simulator treatment in both snow and soil algae in a study comparing the two. Pigment concentration increase was higher in the snow alga than the soil alga tested. The quantity of chlorophyll a was not different under high light (HL, 730 µmol.m-2.s-1, 1.43 W.m-2 UV-B) compared to controls, but secondary carotenoids, already high in the snow alga, increased under high light from 203 µg.g–1 in dry matter (DM) to 235 µg.g-1 in DM (Remias et al., 2010). Although these pigments are classically involved in the structure and functioning of photosynthesis, aplanospores exhibited lowered photosynthetic rates after a three-day exposure to 1.43 W.m-2 (high) UV- B. In this study, only violaxanthin was found among the xanthophyll cycle pigments, by contrast with analyses from Remias et al., 2005, though this apparent discrepancy might be due to the variation in pigment profiles at various stages of the xanthophyll de-epoxidation cycle (Remias et al., 2009). The FA associated with astaxanthin were mostly palmitic (16:0) and oleic (18:l Δ9) acid. Depending on environmental conditions or species, the astaxanthin was in higher proportions in monoester forms (cysts from sample in Hermit Island, Antarctica) or in diester forms (cysts from samples in Wyoming). This could be because the two sets of cysts represented different species of snow algae that were not discernible morphologically, or because they were the same species but in different environmental conditions. FA were selectively esterified to astaxanthin as total FA composition was different to astaxanthin esters FA. An estimated 5% of the total FA pool is associated with the astaxanthin esters (Bidigare et al., 1993).

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Snow algae that did not form aplanospores or other colored cysts, in so-called green snow blooms, were also analysed for pigments during control and HL conditions in Remias et al., 2009. During control conditions, chlorophyll a/b and lutein were the most abundant pigments. In high light conditions, all Chlorophyceae strains exhibited an increase in astaxanthin and canthaxanthin that caused a yellow to orange pigmentation of the cells. Noticeably, Trebouxiophyceae cells did not accumulate any secondary carotenoids. For a long time, it was thought that red aplanospores were the mature stage of green and orange cells observed in the field (Bidigare et al., 1993). As Sanguina are thought to be unable to grow in conditions tested so far, and red cysts have not yet been obtained from green algae found in the field, this is not proven.

Here, our results for the pigment analysis remains unfinished but are consistent with a biosynthesis of carotenoids, which can accumulate in some circumstances. The fact that we could not separate lutein from astaxanthin was a drawback that will be revisited in the future. In order to identify the carotenoids, a saponification step will be explored for our strains. For the MP1concB and CR1concA grown in nitrogen replete conditions, the pics were difficult to identify and different techniques are to be tried next. It is also planned to study the difference in lipid composition between aplanospores from the field and the snow algae strains we isolated, especially the orange strains MP1concB and CR1concA, as lipid composition did not seem to differ between red and green blooms in Davey et al., 2019.

Glycerolipid composition and FA profiles of each glycerolipid class are known to change when algae are subjected to UV, light (Spijkerman et al., 2012), or temperature (Lukes et al., 2014) variations and nutrient deprivation stress (Lu et al., 2013). Glycerolipids have a multitude of roles within cells. They are basic structural building blocks for all bilayer membranes, can act as intermediates in carbon metabolism and form storage lipid droplets, or be involved in signaling processes. In microalgae, glyco-glycerolipids (i.e. mono- and digalactosyldiacylglycerol, MGDG and DGDG, and sulfoquinovosyldiacylglycerol, SQDG) are key components of photosynthetic membranes (Dörmann and Hölzl, 2009; Boudière et al., 2014; Petroutsos et al., 2014). FA are esterified to the glycerol backbone of glycerolipids. A study of total FA contained in cells from red and green snow blooms in Svalbard, low altitude but high latitude locations, revealed that FA variability between sample locations was driven by C18:1n-9, C18:2n-6, and C18:3n-3 (Spijkerman et al., 2012), which are FA commonly encountered in plastid galactolipids. It would be important to assess whether these FAs could be also present in triacylglycerol (TAG) accumulating in cytosolic oil droplets. Usually, TAG accumulates using de novo synthesized FAs (saturated and monounsaturated FAs, SFAs and MSFAs), but in some cases they can be composed of FAs recycled from membrane glycerolipids (with higher levels of polyunsaturated FAs or PUFAs). One could consider FA profiles as a proxy for the developmental/physiological status of algae in the snow. TAG being likely to accumulate both SFAs and MUFAs, one may wonder if the presence of a single double bond in FAs may be determinant as well. In a study comparing green and red snow, green snow cells were shown to be enriched in SFAs, accounting for 72% of the total FA, whereas red snow cells had 80% MUFAs (Bidigare et al., 1993).

When algal cells grow in temperate conditions and with sufficient nutrient levels, PUFA can represent up to 75% in FA profiles, such as in the snow alga Chloromonas brevispina 209

(Řezanka et al., 2008a). A high level of PUFAs is considered an adaptation to cold temperatures as in membranes, PUFA-enriched glycerolipids increase fluidity (Procházková et al., 2019b). When field and culture strains were cultured under normal, nitrogen deprived (-N) and high light (HL) conditions to mimic abiotic factors found in the field, the –N HL induced an increase in C18:1n-9 FA attributed to a stall in growth by the authors (Spijkerman et al., 2012). As mentioned above, this increase in MUFA is likely to simply reflect an increase in TAG.

Again, our preliminary study is consistent with an accumulation of oil in some species subjected to environmental stresses. More in-depth studies are now necessary on well-selected strains to address the precise remodeling of lipid classes operating following stresses, and to define whether one general scheme is observed for lipid remodeling or whether multiple scenarios are encountered. Focused study of C. nivalis/typhlos UTEX 2824 The UTEX 2824 strain of Chlamydomonas nivalis/typhlos was already investigated for cold tolerance by Zheng et al., 2020. In a preliminary growth study, all the strains of the LCC tested had lower growth rates at 4°C than at 20°C, meaning they were likely not strictly psychrophiles, similar to what was found in the Chlamydomonas nivalis/typhlos study both at 12°C and at 4°C. Some of the strains barely grew at all, ACC4D for instance, a small coccoid. Snow is 0-1°C, therefore neither of these studies is enough to show how the algae react in natura. Growth may occur on specific states of the snow, in locally heated interstitial water, whereas proximity of ice combined with other stresses may trigger cyst formation. Further testing is required to investigate which combination of abiotic and/or biotic stress might operate on this balance between actively developing cells and resting spores, especially at lower temperatures because it has been shown that flagella disappeared at as low as 3°C for some snow algae (Remias et al., 2016). Genome sequencing of LCC strains paves the way for future investigations Finally, we now have in hand an initial collection of 15 strains isolated in the snowpack at high elevations in the French Alps, with three fully sequenced genomes (completed with the first genome of C. nivalis/typhlos). Procedures to grow cells, determine their phenotypes, from their cell morphology, lipid and carotenoid contents, photosynthetic status, etc, have been validated during this PhD thesis. Multiple abiotic stresses (nutrient starvation, high light exposure, low temperature) and biotic interaction via infochemicals (auxins, brassinosteroids) have been investigated and proved to trigger responses. This paves therefore the way for more comprehensive ecophysiology and functional analyses of selected strains of the Lautaret Culture Collection, likely to highlight different strategies developed by green algae to live in this unique habitat. The LCC will also be a unique resource for educational and scientific purposes.

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PART 3. CONCLUSION AND DISCUSSION

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Conclusion and perspectives

The integrity of mountain environments is being affected by climate change, with dramatic alterations to the multitudes of habitats found at high elevations, marked by shortening snow seasons, lowering of lake water levels and the accelerated retreat of glaciers. High-elevation ecosystems are being endangered, however mid- and long-term evolutions are difficult to predict since our basic knowledge of their biodiversity is still missing. This is particularly striking concerning photosynthetic unicellular organisms, also called microalgae, who belong to different branches of the prokaryotic and eukaryotic evolution and play a key role as primary producers, especially in pioneer areas where vascular plants do not grow.

Algae populate nearly every type of environment, including those considered as extreme at high altitudes. Schematically, alpine environments are characterized by sharp gradients of decreasing temperatures, increasing light and UV exposition, and low or irregular supplies of water in the soil or low nutrient content in snow and ice. Algae biodiversity in these environments still needs to be evaluated, and the factors that drive its composition and spatiotemporal distribution are poorly understood. Green eukaryotic algae and cyanobacteria dominate freshwater and aeroterrestrial environments. In the specific case of rivers and lakes, other important branches of photosynthetic eukaryotes are also prominent, including diatoms, not studied in this thesis. Amongst green algae, the Chlorophyta phylum is especially diverse and to our knowledge, ‘true’ snow algae are solely from its Chlorophyceae class. Designing and confirming two new green microalgae markers There are both genetic and non-DNA based markers to study microalgae biodiversity (see Chapter 5; Vinebrooke and Leavitt, 1999), but most are inappropriate for large datasets of samples (Taberlet et al., 2018). Drawbacks for the use of non-DNA markers range from their intrinsic variability to heavy logistic requirements for sampling and measurements, and limitations for comparisons of different types of environmental samples (soil, fresh water and snow).

When using DNA-based markers, the design of long sequences would seem ideal for better identification of organisms. However, since the detection of target DNA requires a sufficient minimal amount, and since extracellular DNA degrades rapidly in the environment, the technique is usually limited to being used on cultures of microorganisms. If environmental samples must be cultivated to be analyzed, one should consider that not all microorganisms can actually be grown in vitro. Common markers for eukaryotic algae include the ITS, RbcL and 18S markers, detailed in Chapter 5, and some have been tested in multiple studies, including ours. However, they were not designed to allow fine distinctions at the class, genus, or ideally species level in green algae, especially for species in the soil.

We have been able to design and confirm two additional DNA markers that have successfully amplified green algae in soil, lakes and snow. The first marker, Chlo01, is on the 215

V7 of the 18S and amplifies Chlorophyta, and the second, Chlo02 is on the chloroplast 23S and allows the identification of different taxa in its most important class, Chlorophyceae. The two markers successfully amplified algae DNA in all the samples in all environments.

In soil, there was very little Chlorophyta DNA (and consequently an even smaller proportion of Chlorophyceae DNA). The eukaryotic marker Euka03 showed that Chlorophyta represented less than 3.3% of the reads in each PCR, while fungi and vascular plants occupied most of the reads. This means that the target DNA for Chlo01 and Chlo02 was in very low concentration. In technical terms, this led to PCRs that did not replicate well, and an incomplete assessment of the total biodiversity present in the soil. However, it was found that Chlorophyceae were not as predominant in the soil as previously thought, and that instead Trebouxiophyceae were the most abundant and diverse class.

Lakes showed the highest biodiversity of Chlorophyta among all environments tested overall, though it was very variable from lake to lake. They had a more balanced representation of Chlorophyceae, Trebouxiophyceae and Ulvophyceae compared to all other environments we studied.

The evaluation of snow biodiversity also highlighted the necessity to combine multiple DNA markers. For instance, it was recently suggested that the genus of green algae called Sanguina, was a major genus forming aplanospores in the snowpack (Prochazkova et al., 2019). Sanguina were found in high concentration in the snow using the Chlo01 18S Chlorophyta marker. As the 23S Chlorophyceae marker does not have representation of Sanguina in the reference databases, it could not identify this genus. Most 23S sequences are from marine or freshwater algae, so this marker is likely more appropriate for studies in these environments, until databases are more complete.

Since metabarcoding is biased by the completeness of corresponding databases it relies on for identification, it has been suggested that non-DNA based markers should be used in addition to metabarcodes to identify at least the proportions of the main classes of Chlorophyta. The appropriateness of such additional markers has yet to be validated. It is already recommended to use a combination of DNA-based markers as well as DNA-free markers in cyanobacteria biodiversity studies (Komárek, 2006). Among the possible markers, fatty acids present an interesting option. Lipid profiles are already used in combination with DNA (Coolen et al., 2004). Longer term perspectives of this PhD thesis include the evaluation of such chemotaxonomic markers.

Finally, based on all works presented in this PhD thesis, it would have been valuable to also design a Trebouxiophyceae marker, as the first part of this project focused on soil algae, and we found that this class was dominant in this environment. However, this class is not monophyletic (Lemieux et al., 2014), and taxa are still shifting between classes, so it might be difficult to design a unique marker covering all Trebouxiophyceae. More work on the taxonomy is still required to make more sense of this class, and the Chlorophyta in general.

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Exploring green algae biodiversity in soils, lakes and snow in the French Alps revealed a complex and multifactorial spatiotemporal distribution Using these DNA markers, we analyzed two major datasets. The first dataset is from the Orchamp altitudinal gradient soil samples. Soil was sampled both at the surface (litter) and below (topsoil, partially decomposed soil). The environments span from forests to clearings. They yielded 3.3 million Chlorophyta reads accounting for almost 566 OTUs with the Chlo01 marker, while Chlo02 yielded ~180,000 Chlorophyceae reads for 61 OTUs. The second dataset was from lake samples collected in the summer of 2016 in the French Alp by the Thonon-les-bains CARRTEL laboratory as well as soil and snow samples from the region of the Col du Lautaret. Collectively, this dataset yielded 19 million reads for 12,000 OTUs with the Chlo01 marker, 9 million reads for 4,000 OTUs with the Chlo02 marker.

Abiotic factors are all the chemo-physical parameters exerted on the environment at all structural scales. In the Alps, altitude indirectly affects abiotic factors like temperature, precipitations, snow levels, light irradiance, UV levels, etc. Combined with soil topography, from horizontal areas to steep slopes on mountainsides, water supplies and soluble nutrients such as nitrogen or phosphorus can be stagnant, permanent or variable, washed away more or less rapidly. These abiotic factors are likely to impact microalga biodiversity.

The sampling of soil over several altitude gradients during this project showed that altitude, along with pH, C/N ratio, nitrogen, are all abiotic factors that come into play in the distribution of species and genera of green algae. These factors do not explain all of the variance in communities, and other unknown factors come into play. This is in contrast with the prominent role played by altitude in the distribution of vascular plants, described in past studies (Grytnes et al., 2006; Bruun et al., 2006).

For lakes, altitude was not found to be much of a driver of biodiversity, though the altitude gradient for lakes is not constructed the same way soil gradients are. Lakes are more dispersed across the Alps, which means environmental conditions between lakes on the gradient are even more different than for our soil study. This confirms other studies on altitude gradients (Zaharescu et al., 2016).

As for snow blooms, it would also be interesting to compare biodiversity at different altitudes. Since no systematic survey of snow bloom spatiotemporal distribution has been performed in the Alps, we cannot address this issue yet. Snow nutrient content has been studied in several publications in order to identify if it could explain why algae bloom in certain spots or not, and if it could provide insight on differences in algae composition at different spots. There were no clear patterns explaining why algae bloom in certain fields and not others, as shown in past studies in other mountain locations (Fujii et al., 2009). In our study, we could not detect any difference in carbon content between white and red snow, including white snow where a bloom occurred the two previous years. It appears that carbon, nitrogen and phosphate levels alone are not sufficient to explain the presence of algae blooms (Prochazkova et al., 2019; Bischoff, 2007). Angle of sunlight was also not correlated with

217 bloom occurrence (Prochazkova et al., 2019). As we were only able to sample one bloom for the metabarcoding study, we could not compare biodiversity between sites. It could make sense to have different nutrient compositions in snow, as it was suggested that the presence of birds might affect blooms. Outside of nutrients brought from animals, most are brought from dust (Reche et al., 2009) or snowmelt from higher snowfields. In the future, it would be important to survey red snow occurrence at the regional scale, with a fine record of seasonality. Based on this, appropriately selected sites where blooms form in higher to lower snowfields should be compared.

It is still not completely understood how snow algae propagate, though it is likely a combination of aerosol, horizontal and lateral propagation through the air and snowmelt respectively (Bischoff, 2007). Both our studies confirmed that Sanguina DNA is present in soil above 2,000 m asl. Future challenges include an investigation of alga distribution in all environments, including airborne particles, to evaluate hypotheses regarding long-distance propagation, resting stages over long periods in harsh environments and connectivity between contrasted habitats. Establishing a new snow algae culture collection Snow samples collected in three campaigns in 2017, 2018 and 2019, were cultured at the laboratory and green algae isolated and purified (see Chapter 8). One of the major achievements in this thesis project was defining a core of fifteen independent axenic strains in a new culture collection, called Lautaret Culture Collection, or LCC.

In the preliminary experiments described here, the strains have apparently diverse responses to different stresses exerted in laboratory conditions, such as nitrogen and phosphate depletion, and/or high light exposure. Two strains accumulated carotenoids so that the cultures became bright orange under nitrogen starvation conditions, while most other strains seemed to have a decrease in chlorophyll, and their cultures appeared yellow. They also had different lipid profiles under nitrogen stress.

The reference alga Chlamydomonas nivalis/typhlos from the UTEX collection (UTEX 2824) was also examined under nitrogen stress and showed that it accumulated TAG after 5 days under those conditions in droplets that increased in size over time. Under high light in nitrogen replete conditions, there was also an accumulation of TAG and possibly brown carotenoids in visible brown droplets under microscopy, but those did not increase in size over time. Under both high light and nitrogen deprivation, the results were similar to the low light without nitrogen. The nitrogen deprivation also led to an increase in lutein pigment while high light led to an increase in zeaxanthin, a carotenoid linked to light stress. Finally, a preliminary experiment testing the effects of epibrassinolide, indole-3-acetic acid (IAA) and dichlorophenoxyacetic acid showed a variety of responses in the different strains on growth. Why a new culture collection? There are multiple well established microalgae culture collections, such as the Culture Collection of Algae and Protozoa (CCAP, https://www.ccap.ac.uk/), the Roscoff Culture Collection (RCC, http://roscoff-culture-collection.org/) or the University of Texas collection

218 of algae (UTEX, https://utex.org/), but only a few include or are dedicated to snow algae at the time of this project, such as the Culture Collection of Cryophilic Algae Fraunhofer IZI- BB (CCCryo; http://cccryo.fraunhofer.de/web/strains/), and the Culture collection of Algae at the University of Texas at Austin (UTEX, Starr and Zeikus, 1987), mentioned in Chapter 8. Before and during this PhD project, snow algae in the region of the Col du Lautaret were collected and cultured in the laboratory and fifteen strains were isolated. This was more than expected based on previous publications in which snow samples were cultured (Hoham, 1975; Bischoff, 2004). Having strains in hands, ecophysiological studies in laboratory conditions can now be performed, but cultivation in natura can also be attempted. For obvious bioethical reasons, one cannot introduce an external species in a field site, and it is not possible for instance to inoculate the model alga C. nivalis/typhlos UTEX 2824, sampled in North America, in a snowpack in the European Alps. We therefore need local strains.

Lichens are widespread and particularly diverse in the Alps because they fill niche environments, including pioneer areas where no other eukaryotic species develop, such as the surface of rocks. Here, lichens were also sampled at the site of snow blooms, but the photobiont was not separated and isolated in order to culture the algae. It would be interesting to complete a regional collection in the future and include cultivable lichen photobionts as well. Lichens are slow growing, a few milimeters per year maximum, so they can become outcompeted in less stressful environments (Piercey-Normore and Athukorala, 2017).

Six river samples were also collected around the same region and the isolation and selection process was started. Based on our work and published data, algae from rivers are expected to differ from those collected in snow.

Finally, soil samples were also examined under microscopy in this PhD study, but until now, no algae were observed in spite of low-level detection of DNA. As a perspective of this work, it is planned to attempt to isolate some life samples and isolate microalgae. Identification of the strains The identification of isolated algae was attempted using morphological traits, consulting specialist colleagues, including Alain Couté from Museum National d’Histoire Naturelle. Although there are exceptions, most green microalgae cannot be identified very precisely with this information alone, even with electronic microscopy. Therefore, the 18S rDNA was sequenced, and expected to help narrow it down, but was mostly inconclusive. In order to have enough information to better identify the species, genome skimming experiments are planned in the future, which will give us the genome of the plastids and the mitochondria. This will allow for enough data to align the algae sequences and identify them. As detailed, subsets of the strains are having their entire genome sequenced, which will enable their identification, possibly even determining if they constitute new species. Genome sequencing The genome size of the first strains of the collection as well as the standard snow alga Chlamydomonas nivalis/typhlos UTEX 2824 was first evaluated using propidium iodide staining in a flow cytometry experiment. Propidium iodide stains nucleic acids in a semi-

219 quantitative manner, and with RNAse treatments, only the stained DNA is detected (Chapter 8). In parallel, DAPI (4′,6-diamidino-2-phenylindole) staining (data not shown) under fluorescence microscopy revealed that most strains underwent conventional mitosis processes, in which two daughter cells were generated from a cell division. Several strains performed multiple internal fissions (karyokinesis) prior to cell division (cytokinesis), as several nuclei were present within single cells. In such strains, the number of nuclei increased with the size of the cell. Using this information, we expected to detect several peaks of PI fluorescence in flow cytometry for each nuclei count. The cultures were not synchronized, so it can be assumed that all stages of division would be present at the time of analysis. From that, we deduced an approximate genome size. The only factor we could not check was the ploidy. We used two algae of larger (Chlamydomonas reinhardtii) and smaller (Microchloropsis gaditana) genome sizes, respectively 110 and 30 Gbp, to calibrate the estimation. Based on this calibrated experiment, the various strains had different estimated genome sizes, though most were estimated to be around 20-50 Mbp. Three of the fifteen strains are undergoing full genome sequencing, identifying one of them as an Auxenochlorella spp. One strain had sequences identified to many other species and might constitute a new species itself.

Four strains, i.e. three unknown LCC strains, MP2A, MP1concB and CR1concA and Chlamydomonas nivalis/typhlos UTEX 2824 were selected for full genome sequencing using a hybrid sequencing method involving Illumina short reads and PacBio long reads for better accuracy. It was found that the larger genomes had been overestimated in Propidium Iodide staining experiments, such as the one for MP2A, 50 Mbp instead of the estimated 70 Mbp, and Chlamydomonas nivalis/typhlos, at ~200 Mbp instead of the 700 Mbp estimated. Regardless, the C. nivalis genome is almost double that of the larger genome used for our estimations, and sister species of Chlamydomonas reinhardtii. The full analysis of the genomes being still in progress, we do not know what makes up such a difference in genome size between the two species yet. It is possible that the difference is in sequences tied to the regulation of the expression of genes linked to the adaptation to conditions in the snow such as high light, cold temperatures and other stresses.

Based on genomic data, some taxonomic assessment could be refined. The MP1concB strain was thus identified as an Auxenochlorella spp. The MP2A strain had sequences identified to many other species and might constitute a new species itself.

The analyses of obtained genomic data is one of the major long term perspectives of the current thesis project, and is expected to provide insights on species populating specific mountain areas such as snow, and reveal possible genes and/or genomic features, pathways or other biological processes that could act as determinants for the adaptation to low temperature, high light, high UV levels, etc.

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Algae-bacteria-fungi mutualistic interactions and the question of specific holobionts Noticeably, in our attempts to obtain axenic strains, some algae could not be completely separated from bacteria and/or fungi, even in media complemented with vitamins. This might indicate the existence of necessary interactions between these organisms that are mutually beneficial. Strong interactions between fungi, bacteria and algae have been reported in the past. The best example resides in lichen, which are composed of at least an alga and a fungi species, but can also associate more than two species and include bacteria as well (Cooper and Smith, 2015). These mutualistic interactions also exist outside of the lichenized forms. In our experiments, we used growing media and culture conditions, which may not be completely representative of the conditions in the field. The biodiversity of bacteria and fungi in the snow is planned to be examined using metabarcoding as well; the experiments have been performed but the analysis has yet to be completed. The importance of these interactions lies in the ability to enhance biomass, as shown in Krug et al., 2020 in co-cultures experiments. Nutrients and vitamins are not the only molecules that can be transferred: molecules involved in intra- and inter-species communication, so called infochemicals, can also be produced and shared, enhancing or inhibiting growth in complex mechanisms controlling population sizes within communities. This has been studied between higher plants communicating via volatile organic compounds (Reviewed in Pichersky et al., 2006). Some rhizobacteria are capable of promoting plant growth and suppressing the growth of pathogens (e.g. Berg et al., 2016). The identification of microalga infochemicals is in its infancy (e.g. Bajguz and Piotrowska- Niczyporuk A, 2013; Liu et al., 2018). Here, we made a preliminary evaluation of the potential role of such infochemicals like brassinosteroids or auxins (Chapter 8). A future challenge is to identify how algae, fungi and bacteria communicate, particularly in the snow. A long-term perspective would be to address this complex series of questions with similar works as described in the current project, but with a focus on bacteria and fungi.

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The following paintings are botanical illustrations of some snow algae from the Lautaret Culture Collection. They were painted with watercolor paints from Winsor and Newton and L’Aquarelle Sennelier, then scanned.

Photos are a mostly non-biased representation biological objects, they are a snapshot in time of organisms. Illustrations allow the artist to show more on a single image and integrate knowledge from hundreds of hours of observation of the organisms. They add value to the microscopy images more typical nowadays of botanical representations of species (Kur, 2018).

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CEA Grenoble

Laboratoire de Physiologie Cellulaire et Végétale

UMR 5168 CEA / CNRS / Université Grenoble Alpes – UMR 1417 INRA

17, avenue des Martyrs 38054 Grenoble cedex 9

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Evaluation of the biodiversity of green microalgae in alpine ecosystems

Algae populate nearly every type of environment, from freshwaters and oceans to soil, rock surfaces, ice, snow, etc. They are primary producers at the base of trophic networks, and can play a pioneering role in the conquest of new habitats. In temperate regions, mountain environments are characterized by a sharp gradient of decreasing temperatures, increasing light and UV exposition, and low water content in the soil or low nutrient content in the snow and ice. Green eukaryotic microalgae dominate freshwater and aeroterrestrial environments. Their biodiversity in the Alps is still mostly unknown, and the factors that drive it, poorly understood. Mechanisms of adaptation of green algae to the vast array of conditions in the Alps are barely explored. To narrow the gap in our knowledge, we addressed the biodiversity of green algae in the French Alps. We focused on Chlorophyta and its main class, the Chlorophyceae. Two new markers were designed for a metabarcoding study of Chlorophyta and Chlorophyceae in soil, lakes and snow. The level of Chlorophyta DNA proved to be extremely low in soil samples, dominated by Trebouxiophyceae. Sampling of soil over several altitude gradients from 1,000 to 3,000 m, at five distinct locations in the French Alps, showed that altitude was determinant along with pH, nitrogen and carbon/nitrogen ratio for the distribution at genus and species levels. It is the first time that altitudinal zonation was demonstrated for some species. Lakes had more balanced proportions and the highest diversity of algae overall, with more Chlorophyceae and Trebouxiophyceae than Ulvophyceaea. Chlorophyceae constituted almost all snow algae at a selected site at 2,500 m altitude, including species of the Sanguina genus. Snow algae in the region of the Col du Lautaret were collected from 2017 to 2019, cultured in the laboratory, isolated and axenized. The fifteen strains isolated constitute a new Lautaret Culture Collection. The classical snow alga, Chlamydomonas nivalis, was not found in snow sites therefore a C. nivalis strain from the University of Texas collection was used as a reference. We made a preliminary analysis of physiological responses of multiple strains, giving insight for future comprehensive characterizations. The full genome of C. nivalis and three isolated strains were sequenced and revealed potential new species. This project confirmed the use of two new green algae markers, paves the way for more thorough aeroterrestrial algae studies and will allow in depth molecular biology of some selected snow algae strains in the future.

Evaluation de la biodiversité des algues vertes dans l’ecosystème alpin.

Les algues regroupent des organismes photosynthétiques divers, procaryotes et eucaryotes, peuplant la quasi-totalité des milieux, des eaux douces et océans, aux sols, surfaces rocheuses, neiges etc. Parmi celles-ci, les algues vertes constituent un groupe particulièrement répandu et divers dans les écosystèmes aéro-terrestres. Les algues sont des producteurs primaires à la base des réseaux trophiques et peuvent jouer un rôle pionnier dans la conquête de milieux. Notre compréhension de l’évolution des écosystèmes alpins est néanmoins limitée par notre manque de connaissance de la microflore photosynthétique. La biodiversité des Chlorophyta a été examinée par des analyses de metabarcoding. Les Chlorophyceae étant la classe la plus importante et diverse, nos études se sont focalisées sur ce groupe. Dans un premier temps, deux nouveaux marqueurs des Chlorophyta et des Chlorophyceae, Chlo01 et Chlo02, ont donc été conçus. Ces marqueurs ont été exploités pour analyser des échantillons de sols collectés sur des gradients altitudinaux, au niveau de cinq sites différents des Alpes Françaises, de 1,000 à 3,000 m. Notre analyse montre une faible présence d’ADN de Chlorophyta dans le sol, celui-ci étant plutôt dominé par les Trebouxiophyceae. L’altitude, le pH, le ratio de C/N, et l’azote affectent la distribution de certaines espèces. C’est la première fois qu’une distribution selon l’altitude est démontrée pour certaines espèces de microalgues vertes. Nous avons ensuite évalué la biodiversité des algues vertes dans les lacs alpins, et sur un site à 2,500 m d’altitude présentant un bloom d’algues des neiges. Les lacs présentent une biodiversité riche, avec une plus grande proportion de Chlorophyceae et de Trebouxiophyceae que d’Ulvophyceaea. Le site de neige rouge présentait quasi-exclusivement des Chlorophyceae tandis que le sol environnant était dominé par les Trebouxiophyceae. Le genre Sanguina était abondant dans les échantillons de neige, confirmant nos analyses microscopiques montrant la présence de formes enkystées pigmentées. Enfin, des échantillons d’algues collectées de 2017 à 2019 dans des neiges rouges dans la région du col du Lautaret ont été mises en culture au laboratoire puis isolées et axénisées. Elles présentent une diversité inattendue. Nous avons ainsi établi une collection originale de 15 lignées isolées de la neige, formant le cœur d’une collection nouvelle pour la recherche et l’éducation, dont trois lignées ont déjà été soumises à un séquençage génomique pour de futures études.

Keywords/Mots-clefs: Chlorophyta; Chlorophyceae; Alps; snow algae; Culture collection; Metabarcoding; Chlamydomonas nivalis.

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