Diversité microbienne associée au cycle du méthane dans les … · 2020. 7. 30. · Cependant,...

Diversité microbienne associée au cycle du méthane

dans les mares de fonte du pergélisol subarctique

Thèse

Sophie Crevecoeur

Doctorat en biologie

Philosophiae doctor (Ph. D.)

Québec, Canada

© Sophie Crevecoeur, 2016

Diversité microbienne associée au cycle du méthane

dans les mares de fonte du pergélisol subarctique

Thèse

Sophie Crevecoeur

Sous la direction de :

Warwick F. Vincent, directeur de recherche

Connie Lovejoy, codirectrice de recherche

iii

Résumé

La fonte et l’effondrement du pergélisol riche en glace dans la région subarctique du

Québec ont donné lieu à la formation de petits lacs (mares de thermokarst) qui émettent des gaz à

effet de serre dans l’atmosphère tels que du dioxyde de carbone et du méthane. Pourtant, la

composition de la communauté microbienne qui est à la base des processus biogéochimiques dans

les mares de fonte a été très peu étudiée, particulièrement en ce qui concerne la diversité et l’activité

des micro-organismes impliqués dans le cycle du méthane. L’objectif de cette thèse est donc

d’étudier la diversité phylogénétique et fonctionnelle des micro-organismes dans les mares de fonte

subarctiques en lien avec les caractéristiques de l’environnement et les émissions de méthane. Pour

ce faire, une dizaine de mares ont été échantillonnées dans quatre vallées situées à travers un

gradient de fonte du pergélisol, et disposant de différentes propriétés physico-chimiques. Selon les

vallées, les mares peuvent être issues de la fonte de palses (buttes de tourbe, à dominance

organique) ou de lithalses (buttes de sol à dominance minérale) ce qui influence la nature du

carbone organique disponible pour la reminéralisation microbienne. Durant l’été, les mares étaient

fortement stratifiées; il y avait un fort gradient physico-chimique au sein de la colonne d’eau, avec

une couche d’eau supérieure oxique et une couche d’eau profonde pauvre en oxygène ou anoxique.

Pour identifier les facteurs qui influencent les communautés microbiennes, des techniques de

séquençage à haut débit ont été utilisées ciblant les transcrits des gènes de l’ARNr 16S et des gènes

impliqués dans le cycle du méthane : mcrA pour la méthanogenèse et pmoA pour la méthanotrophie.

Pour évaluer l’activité des micro-organismes, la concentration des transcrits des gènes fonctionnels

a aussi été mesurée avec des PCR quantitatives (qPCR). Les résultats montrent une forte dominance

de micro-organismes impliqués dans le cycle du méthane, c’est-à-dire des archées méthanogènes et

des bactéries méthanotrophes. L’analyse du gène pmoA indique que les bactéries méthanotrophes

n’étaient pas seulement actives à la surface, mais aussi dans le fond de la mare où les concentrations

en oxygène étaient minimales; ce qui est inattendu compte tenu de leur besoin en oxygène pour

consommer le méthane. En général, la composition des communautés microbiennes était

principalement influencée par l’origine de la mare (palse ou lithalse), et moins par le gradient de

dégradation du pergélisol. Des variables environnementales clefs comme le pH, le phosphore et le

carbone organique dissous, contribuent à la distinction des communautés microbiennes entre les

mares issues de palses ou de lithalses. Avec l’intensification des effets du réchauffement climatique,

ces communautés microbiennes vont faire face à des changements de conditions qui risquent de

modifier leur composition taxonomique, et leurs réponses aux changements seront probablement

différentes selon le type de mares. De plus, dans le futur les conditions d’oxygénation au sein des

iv

mares seront soumises à des modifications majeures associées avec un changement dans la durée

des périodes de fonte de glace et de stratification. Ce type de changement aura un impact sur

l’équilibre entre la méthanogenèse et la méthanotrophie, et affectera ainsi les taux d’émissions de

méthane. Cependant, les résultats obtenus dans cette thèse indiquent que les archées méthanogènes

et les bactéries méthanotrophes peuvent développer des stratégies pour survivre et rester actives au-

delà des limites de leurs conditions d’oxygène habituelles.

v

Abstract

The thawing and collapse of ice-rich permafrost in the subarctic region of Quebec has given

rise to thaw ponds (thermokarst ponds) that emit the greenhouse gases carbon dioxide and methane

to the atmosphere. However, the microbial community composition that underlies biogeochemical

processes in thaw ponds has been little investigated, particularly concerning the diversity and

activity of micro-organisms involved in the methane cycle. The objective of this thesis study was to

determine the phylogenetic and functional diversity of micro-organisms in subarctic thaw ponds,

and the relationships with environmental properties and methane emission. To that aim, we sampled

ten thaw ponds in four different valleys located across a permafrost degradation gradient with

distinct physico-chemical properties. Depending on valley, the ponds were derived either from the

thawing of a palsa (peat-mound) or lithalsa (mineral-mound), which influenced the nature of

organic carbon available for microbial remineralization. During summer, the ponds were observed

to be well-stratified; there were with strong physico-chemical gradients down the water column,

with an upper oxic layer and a bottom low oxygen or anoxic layer. To identify the factors

influencing microbial community composition, we used high throughput sequencing techniques

targeting transcripts of 16S rRNA gene, and additionally targeted genes involved in the methane

cycle: mcrA for methanogenesis and pmoA for methanotrophy. As a proxy of microbial activity, we

also measured the concentration of functional gene transcripts using with quantitative PCR (qPCR).

The results showed a striking dominance of micro-organisms involved in the methane cycle, namely

methanogenic Archaea and methanotrophic Bacteria. The pmoA analyses implied that

methanotrophic Bacteria were not only active in the surface, but also in the bottom waters where

oxygen concentrations were minimal; this was unexpected given their need for oxygen in methane

consumption. In general, the microbial community properties were largely determined by the origin

of the ponds (palsa versus lithalsa), and much less so by the extent of permafrost degradation. The

key environmental variables pH, phosphorus and dissolved organic carbon likely contributed to the

differentiation of microbial community between the palsa and lithalsa valleys. With intensification

of climate warming, these microbial communities will face changing conditions that are likely to

modify their taxonomic composition, and these responses are likely to differ between ponds in the

two landscape types. Oxygenation of the ponds will likely be subject to major shifts in the future

associated with changes in the duration of the ice-free season and the extent of stratification. Such

changes will impact the balance between methanogenesis and methanotrophy, and thereby affect

the net rates of methane emission. However, the results obtained here indicate that methanogenic

vi

Archaea and methanotrophic Bacteria have strategies to survive and remain active beyond the limit

of their usual oxygen preferences.

vii

Table des matières

Résumé ............................................................................................................................................... iii

Abstract ............................................................................................................................................... v

Table des matières ............................................................................................................................. vii

Liste des tableaux ................................................................................................................................ x

Liste des figures ................................................................................................................................. xi

Liste des abréviations et des sigles ................................................................................................... xiii

Remerciements ................................................................................................................................ xvii

Avant-propos .................................................................................................................................. xviii

Chapitre 1. Introduction ................................................................................................................. 1

1.1. Le pergélisol en Arctique .................................................................................................... 2

1.2. Les mares de fonte du pergélisol ......................................................................................... 3

1.2.1. Formation .................................................................................................................... 4

1.2.2. Caractéristiques limnologiques ................................................................................... 4

1.2.3. Implication dans les émissions de gaz à effet de serre ................................................ 6

1.3. Les micro-organismes dans les milieux aquatiques ............................................................ 6

1.3.1. Processus microbiens .................................................................................................. 7

1.3.2. Micro-organismes impliqués dans le cycle du méthane .............................................. 8

1.3.3. Approches pour étudier les micro-organismes .......................................................... 10

1.4. Organisation de la thèse .................................................................................................... 12

1.4.1. Objectifs et hypothèses des chapitres de thèse .......................................................... 12

1.4.2. Sites d’études............................................................................................................. 13

Chapitre 2. Bacterial community structure across environmental gradients in permafrost thaw

ponds: methanotroph-rich ecosystems .............................................................................................. 15

Résumé .......................................................................................................................................... 15

Abstract ......................................................................................................................................... 16

2.1. Introduction ....................................................................................................................... 17

2.2. Materials and methods ...................................................................................................... 19

2.2.1. Study sites and sampling ........................................................................................... 19

2.2.2. Physico-chemical analysis ......................................................................................... 21

2.2.3. RNA collection and extraction .................................................................................. 22

2.2.4. High throughput multiplex tag sequencing ............................................................... 23

2.2.5. Sequence processing and statistics ............................................................................ 23

2.3. Results ............................................................................................................................... 24

2.3.1. Limnological conditions ............................................................................................ 24

2.3.2. Bacterial alpha-diversity ........................................................................................... 25

viii

2.3.3. Bacterial beta-diversity and community composition ............................................... 26

2.3.4. Bacterial dominants and high abundance of methanotrophs ..................................... 27

2.3.5. Bacterial community structure as a function of environmental gradients ................. 30

2.4. Discussion ......................................................................................................................... 32

2.4.1. Bacterial alpha-diversity ........................................................................................... 32

2.4.2. Bacterial dominants ................................................................................................... 34

2.4.3. Attached and free-living Bacteria.............................................................................. 36

2.4.4. Depth gradients and bacterial community composition ............................................ 36

2.4.5. Spatial variation and landscape gradients ................................................................. 37

2.5. Conclusions ....................................................................................................................... 38

2.6. Acknowledgements ........................................................................................................... 39

Chapitre 3. Diversity and activity of methanotrophs in low-oxygen permafrost thaw ponds ...... 40

Résumé .......................................................................................................................................... 40

Abstract ......................................................................................................................................... 41

3.1. Introduction ....................................................................................................................... 42

3.2. Materials and Methods ...................................................................................................... 44

3.2.1. Study site and sampling ............................................................................................. 44

3.2.2. Sampling and physico-chemical measurements ........................................................ 44

3.2.3. RNA sample preparation and sequencing ................................................................. 44

3.2.4. Sample processing for qPCR ..................................................................................... 45

3.2.5. Sequence processing and analysis¸ ............................................................................ 46

3.3. Results ............................................................................................................................... 47

3.3.1. Physico-chemical parameters .................................................................................... 47

3.3.2. Community arrangement and composition ............................................................... 48

3.3.3. Methanotrophic activity ............................................................................................ 50

3.4. Discussion ......................................................................................................................... 53

3.4.1. Physico-chemical parameters .................................................................................... 53

3.4.2. Community composition and arrangement ............................................................... 53

3.4.3. Methanotrophic activity ............................................................................................ 54


Chapitre 4. Environmental selection of planktonic methanogens in permafrost thaw ponds ...... 58

Résumé .......................................................................................................................................... 58

Abstract ......................................................................................................................................... 59

4.1. Introduction ....................................................................................................................... 60

4.2. Results ............................................................................................................................... 62

4.2.1. Limnological conditions ............................................................................................ 62

4.2.2. Archaeal alpha-diversity ........................................................................................... 62

ix

4.2.3. Archaeal community dissimilarities and composition .............................................. 64

4.2.4. Methanogens inferred from the mcrA versus 16S rRNA analyses ............................ 66

4.2.5. Environmental variables and archaeal community clustering ................................... 67

4.3. Discussion ......................................................................................................................... 68

4.4. Methods ............................................................................................................................. 71

4.4.1. Study site and sampling ............................................................................................. 71

4.4.2. Physico-chemical and molecular analysis ................................................................. 72

4.4.3. Bioinformatic analysis ............................................................................................... 72


Chapitre 5. Conclusion générale .................................................................................................. 75

5.1. Dominance des micro-organismes impliqués dans le cycle du méthane .......................... 75

5.2. Influence de l’origine des mares et du gradient de fonte du pergélisol ............................. 77

5.3. Influence des facteurs environnementaux ......................................................................... 78

5.4. Implication scientifique ..................................................................................................... 79

5.5. Perspectives ....................................................................................................................... 81

Bibliographie ..................................................................................................................................... 84

Annexes ........................................................................................................................................... 104

x

Liste des tableaux

Table 2-1. Limnological properties of the sampled thaw ponds: dissolved oxygen (DO),

conductivity (Cond), chlorophyll a (Chl a), dissolved organic carbon (DOC), total suspended solids

(TSS), soluble reactive phosphorus (SRP), and total nitrogen (TN). The surface samples correspond

to 0 m and bottom samples to the second depth for each pond. ........................................................ 21 Table 2-2. Sequencing and diversity statistics for samples grouped according to valley, depth or size

fraction. Values are means (n=3 to 14) with CV (SD as % mean) in parentheses. ........................... 26 Table 2-3. Identity of the 10 most abundant OTUs (defined at a level of 97% similarity) in each

valley following the SILVA taxonomy. Following a BLASTn search, nearest matches and the

providence of representative reads in GenBank were identified. Several groups appear multiple

times because different OTUs match the same group. See Figure 2-5B for their distribution. ........ 28 Table 3-1. Concentrations of pmoA transcripts oxygen and methane in the sampled thaw ponds. -:

no data. .............................................................................................................................................. 51 Table 4-1. Limnological properties of the bottom water (0.5 m above the sediment) for the sampled

thaw ponds. Temperature (T°C), dissolved oxygen (O2), dissolved organic carbon (DOC), total

suspended solids (TSS) and total phosphorus (TP). .......................................................................... 62 Table 4-2. Identity of the 5 most abundant OTUs (defined at a level of 97% similarity) for each

valley following the lowest taxonomic level of the SILVA modified database (Lovejoy et al., 2015).

The group Euryarchaeota, Miscellaneous Euryarchaeotic Group (MEG) and Thermoplasmatales

could not be further assigned. Percent (%) represents the proportion of those single OTUs in the

community for each valley. ............................................................................................................... 66

xi

Liste des figures

Figure 1-1. Distribution du pergélisol en Arctique. Les deux nuances de violet plus foncées

représentent le pergélisol de continu à discontinu tandis que les deux nuances de violet plus claires

le pergélisol de sporadique à isolé (Brown et al., 1997) ..................................................................... 3 Figure 1-2. Un exemple de paysage du pergélisol avec des mares de fonte, montrant la diversité des

couleurs (site BGR, Nunavik; image : CEN/ArcticNet) ..................................................................... 5 Figure 1-3. Processus microbiens tels que ayant lieu dans les mares de fontes du pergélisol. MOD

désigne la matière organique dissoute. ................................................................................................ 8 Figure 1-4. Schéma simplifié des processus de méthanogénèse et méthanotrophie. ........................ 10 Figure 2-1. Location of the three sampling valleys in Nunavik, subarctic Québec, Canada. ........... 20 Figure 2-2. Profiles of temperature (A), dissolved oxygen (B) and pH (C) as a function of the depth

for the 7 studied ponds. ..................................................................................................................... 25 Figure 2-3. Relative abundance of the different phyla. The samples were from the surface (-S) and

bottom (-B) of ponds in the three valleys. The small fraction (left) is for samples in the size range

0.2 to 3 µm, and the large fraction is for 3 to 20 µm. Phyla that were less than 1% of total

abundance are combined under “Other phyla”. ................................................................................. 27 Figure 2-4. Relative abundance of methanotrophic Bacteria. The samples were from the surface (-S)

and bottom (-B) of ponds in the three valleys. The small fraction (left) is for samples in the size

range 0.2 to 3 µm, and the large fraction is for 3 to 20 µm. The taxa are labelled according to the

highest taxonomical level; genera that represented less than 1% of total abundance were grouped

together and labelled by their shared order. ...................................................................................... 29 Figure 2-5. (A) Bray-Curtis dissimilarity cluster analysis with the community data matrix (OTUs

clustering at >97% of identity) for the study ponds. Surface samples are represented by triangles

and bottom samples by circles, either filled (small fraction) or open (large fraction). (B) The

bacterial dominants in each sample identified by their lowest taxonomical level either found on

GenBank or SILVA (see Table 2-3); the size of the filled circle is proportional to their relative

abundance. ......................................................................................................................................... 30 Figure 2-6. Distance-based redundancy analysis ordination plot showing selected environmental

variables that were significantly correlated with sample distribution. Abbreviations for the

environmental variables are given in Table 2-1. ............................................................................... 32 Figure 3-1. Principal component analysis of the environmental variables: temperature (T), dissolved

oxygen (O2), pH, total phosphorus (TP), total suspended solid (TSS), conductivity (Cond),

Chlorophyll a (Chla), total nitrogen (TN) and dissolved organic carbon (DOC) for the 9 sampled

ponds. Colors indicate the different valleys; surface samples are represented by a triangle and

bottom by a circle. ............................................................................................................................. 48 Figure 3-2. Bray-Curtis dissimilarity cluster analysis of the methanotrophic communities. Surface

samples are represented by triangles and bottom samples by circles, either filled (small fraction) or

open (large fraction). Heatmap shows the methanotrophic community composition. ...................... 49 Figure 3-3. Correlation plot of the PLS analysis to see influence of the environmental variables and

methanotrophic genera on the activity of methanotrophs. Chosen environmental variables were

conductivity (Cond), total phosphorus (TP), total suspended solid (TSS), Chlorophyll a (Chla),

soluble reactive phosphorus (SRP), total nitrogen (TN), carbon dioxide concentration (CO2),

methane concentration (CH4), dissolved organic carbon (DOC), oxygen concentration (O2),

temperature (T°C) and pH. ................................................................................................................ 52 Figure 3-4. Concentration of pmoA transcript as a function of methane concentration for bottom and

surface samples (left panel), and for bottom only (right panel). Shading represents ± 95%

confidence intervals. ......................................................................................................................... 52 Figure 4-1. Alpha-diversity measures for the three sampled valleys. Shannon diversity index and

Chao1 species richness index for the BGR, KWK and SAS valleys. The line in each box plot

xii

indicates the median, the box delimits the 25th and 75th percentile, and the whisker is the range.

Diversity indices for SAS valley differed significantly from the two other valleys (p=0.02 for the

Chao1 and p=0.01 for the Shannon index). ....................................................................................... 63 Figure 4-2. UniFrac clustering and composition of archaeal community. Upper dendrogram

representing the phylogenetic unweighted UniFrac distance of the archaeal community for the study

ponds. Filled or open diamond represent respectively small and large fraction. First letter of sample

name correspond to the valley name: S for SAS, K for KWK and B for BGR. The following number

or combination of letter and number indicate the name of the pond. Bubble plot show the relative

abundance of the different archaeal lineages with notably Miscellaneous Euryarchaeotic Group

(MEG), Miscellaneous Crenarcheotic Groups (MCG) and Deep Euryarcheotic Sea Group (DESG).

........................................................................................................................................................... 64 Figure 4-3. Comparison of the relative abundance of the methanogens in the mcrA community and

the 16S rRNA community. The left plot shows the entire methanogenic community and plot on the

right shows the groups representing less than 1% of the reads. The SAS sample is the large fraction

(3-20µm) of the SAS2B ponds and the KWK sample is the small fraction (0.2-3µm) of the KWK23

pond. .................................................................................................................................................. 67 Figure 4-4. Non metric multidimensional scaling (NMDS) of the community composition.

Phylogenetic Unifrac distances are overlaid with environmental variables; (a) dissolved organic

carbon, (b) total phosphorus and (c) pH. ........................................................................................... 68

xiii

Liste des abréviations et des sigles

16S : Petite sous unité ribosomique des bactéries et archées

ADN ou DNA : Acides désoxyribonucléiques ou Deoxyribonucleic acid

ADNc ou cDNA : ADN complémentaire ou complementary DNA

ANOVA : Analysis of variance

ARN ou RNA : Acides ribonucléiques ou Ribonucleic acid

ARNr ou rRNA : Acides ribonucléiques ribosomiques ou ribosomal RNA

BGR : Bundesanstalt für Geowissenschaften und Rohstoffe

BLAST : Basic local alignment search tool

BSA : Bovin serum albumin

CDOM : Coloured dissolved organic matter

CEN : Centre d’études nordiques

CH4 : Méthane

Chl a : Chlorophylle a

CHUL : Centre hospitalier de l'Université Laval

CO2 : Carbon dioxyde

Cond : Conductivity

dATPs : Deoxyadenosine triphosphate

dbRDA : Distance-based redundancy analysis

DMSO : Dimethylsulfoxide

dNTP : Deoxynucleotide triphosphate

DO : Dissolved oxygen

DOC : Carbone organique dissous ou dissolved organic carbon

DOM : Matière organique dissoute ou dissolved organic matter

DSEG : Deep Sea Euryarchaeotic Group

GES : Gaz à effet de serre

GF/F : Glass microfibre filter

H2 : Dihydrogen

H2S : Hydrogen sulfide

H2SO4 : Sulfuric acid

HMM : Hidden Markov Models

HSD : Honest significant difference

IBIS : Institut de Biologie Intégrative et des Systèmes

xiv

KWK : Rivière Kwakwatanikapistikw

LBA : Luria-Bertani agar

MCG : Miscellaneous Crenarchaeotic Group

mcrA : Sous-unité alpha de la méthyl coenzyme M reductase ou alpha subunit of the methyl-

coenzyme M reductase

MEG : Miscellaneous Euryarchaeotic Group

MES : Matière en suspension

mRNA : Messenger RNA

NAS : Rivière Nastapoka

NCBI : National Center for Biotechnology Information

NEB : New England Biolabs

NMDS : Non-metric multidimensional scaling

nt : Nucleotides

O2 : Oxygen

OTU : Operational taxonomic units

pb : Base pair (nucleotide)

PCA : Principal component analysis

PCR : Réaction en chaine par polymérase ou polymerase chain reaction

pH : Potentiel hydrogène

PLFA : Acides gras dérivés des phospholipides ou Phospholipid-derived fatty acids

PLS : Partial least squares

pMMO : Méthane mono-oxygénase soluble

pmoA : Sous-unité alpha de la méthane mono-oxygénase particulaire ou α subunit of the particulate

methane mono-oxygenase

PVP : Polyvinylpyrrolidone

QIIME : Quantitative insights into microbial ecology

qPCR : PCR quantitative

RDP : Ribosomal Database Project

SAS : Rivière Sasapimakwananisikw

SIP : Marquage par isotopes stable ou stable-isotope probing

sMMO : Méthane mono-oxygénase particulaire

SOC : Super Optimal broth with catabolite repression

SRP : Soluble reactive phosphorus

TN : Total nitrogen

xv

TP : Total phosphorus

TSS : Total suspended solid

UTO : Unité taxonomique opérationnelle

V6-V8 : Région hypervariable de l'ARN ribosomique 16S ou hypervariable region of 16S rRNA

gene

VIF : Variance inflation factors

xvi

À ma famille

xvii

Remerciements

Tout d’abord, je tiens à remercier chaleureusement mon directeur Warwick Vincent et ma

codirectrice de thèse Connie Lovejoy. Tout au long du parcours à embûches que constitue le

doctorat, ils m’ont toujours montré un soutien sans faille et aussi une confiance en mon travail qui

dépassait souvent la mienne. J’ai particulièrement apprécié la sagesse, la patience et la gentillesse

de Warwick, qui a toujours vu le côté positif dans toutes les situations, ainsi que le franc-parler,

l’humour et l’attention bienveillante de Connie. Merci à Steve Charette et Jean-Éric Tremblay pour

leurs commentaires très constructifs durant les réunions de comité. Merci aussi à Philippe Constant

d’avoir accepté de faire partie de mon jury. Ensuite je voudrais adresser un merci spécial à Jérôme

Comte, qui a été très présent et disponible tout au long de ma thèse. Ses critiques et conseils

toujours justes m’ont grandement aidée à bâtir une confiance en moi et une maturité scientifique.

Merci à Marianne Potvin pour son aide précieuse au laboratoire et la mise en place de protocoles

adaptés à mes échantillons. Ce doctorat n’aura pas été le même sans le temps passé avec tous les

formidables collègues des labos Vincent et Lovejoy. Dans chacun d’eux, j’ai trouvé des qualités qui

m’ont et continueront de m’inspirer pour devenir une meilleure scientifique. Merci à Paschale,

Bethany et Mary dont l’amitié représente beaucoup pour moi en plus des dîners partagés ensemble

et discussions motivantes. Enfin, j’ai eu la grande chance de partager un bureau avec deux

personnes remarquables : Nathalie et Deo, avec qui l’entraide, le soutien, les débats et discussions

stimulantes ont fait partie du quotidien. Une passion commune pour les soirées karaoké a contribué

à établir un lien fort entre nous dont je serai toujours nostalgique.

Un tout grand merci chargé d’affection à ma famille, Papa, Maman, Isa, Ol, Fred, Julie et

les petits bouts Manon et William pour leur soutien et amour inconditionnel. Merci à mes parents de

toujours me soutenir dans mes choix, même si ça implique le déchirement de vivre éloignés. Merci

à ma Maman et à Antoine d’avoir pris le temps de relire les parties de ma thèse en français. Merci à

ma grande sœur et mon grand frère d’avoir été de si bons modèles dans ma vie de m’avoir toujours

encouragé en plus de me donner de précieux conseils sur la vie universitaire.

Merci à tous mes amis de la Belgique au Canada. Sachez que tous les moments passés

ensemble à s’amuser et à refaire le monde sont autant d’encouragements qui m’ont aidée à

recharger mes batteries pour garder la motivation de continuer mon travail jusqu’au bout. Enfin, je

remercie Antoine de remplir chaque jour de ma vie avec de la joie et des rires. Si j’ai pu mener à

bien cette thèse comme je le voulais, c’est parce que j’ai toujours pu compter sur toi, sur tes conseils

et quoiqu’il arrive, tu as inlassablement continué à me soutenir et à croire en moi.

xviii

Avant-propos

Cette thèse de doctorat dans le programme de biologie est présentée en cinq parties

principales. Tout d’abord, une introduction explique le concept de mare de fonte du pergélisol et

l’importance des micro-organismes impliqués dans ces milieux aquatiques, en particulier dans le

cycle du méthane. Ensuite, les trois chapitres qui constituent le corps de la thèse sont des articles

scientifiques rédigés en anglais dans le but d’être publiés dans des revues scientifiques révisées par

des paires. Enfin, une conclusion générale résume et explore les implications des résultats obtenus

pour les trois articles scientifiques. Les échantillons traités dans cette thèse ont tous été récoltés

dans des mares de fonte du pergélisol dans la région subarctique du Québec dans le but de

caractériser les communautés microbiennes de ces systèmes. Un intérêt particulier a été porté aux

micro-organismes impliqués dans le cycle du méthane afin de mieux cerner leur diversité et activité.

Pour chaque chapitre de thèse, j’ai contribué à la conception et la planification de la problématique

ciblée avec l’aide de Warwick Vincent et de Connie Lovejoy, j’ai effectué la prise d’échantillons

sur le terrain avec l’aide de Warwick Vincent, de Jérôme Comte et d’Alex Matveev, j’ai procédé à

l’analyse en laboratoire et l’analyse bio-informatique avec l’aide de Jérôme Comte et de Connie

Lovejoy et enfin j’ai rédigé tous les chapitres de thèse en tenant compte des contributions des

coauteurs au niveau de l’apport de l’expertise scientifique, des corrections et commentaires. Les

détails des articles scientifiques destinés à la publication se trouvent ci-dessous :

Chapitre 2. Bacterial community structure across environmental gradients in permafrost thaw

ponds: methanotroph-rich ecosystems

Auteurs : Sophie Crevecoeur, Warwick F. Vincent, Jérôme Comte et Connie Lovejoy.

Publié le 18 mars 2015 dans Frontiers in Microbiology, 2015.

Chapitre 3. Diversity and activity of methanotrophs in low-oxygen permafrost thaw ponds

Auteurs : Sophie Crevecoeur, Warwick F. Vincent, Jérôme Comte, Alex Matveev et Connie

Lovejoy

En préparation, destiné à être soumis à un journal scientifique.

Chapitre 4. Environmental selection of planktonic methanogens in permafrost thaw ponds.

xix

Auteurs : Sophie Crevecoeur, Warwick F. Vincent et Connie Lovejoy

Soumis au journal Scientific Reports le 12 février 2016, présentement sous évaluation.

Les résultats de cette thèse ont également été présentés pendant plusieurs conférences

internationales telles que la rencontre "Joint Aquatic Science Meeting" qui inclut la rencontre

annuelle de l’"Association for the Sciences of Limnology and Oceanography" (ASLO), la rencontre

ArcticNet "Arctic Change" et la conférence Embo "Aquatic Microbial Ecology SAME 14". De plus,

des présentations orales ont été effectuées pour présenter les résultats de cette thèse dans plusieurs

colloques prenant place à l’Université Laval comme lors du 81e congrès de l’association

francophone pour le savoir (ACFAS), du colloque annuel de l’institut Hydro-Québec en

environnement, développement et société et du colloque du département de biologie. J’ai

personnellement conçu et présenté chacun de ces exposés oraux et et affiches dont les détails sont

donnés ci-dessous, en tenant compte de la contribution des coauteurs.

Crevecoeur, S., Vincent, W. F., Lovejoy, C. 2015. Methanotrophic Bacteria in permafrost thaw

ponds: potential for reduction of net methane emissions. Affiche présentée à la conférence Embo

“Aquatic Microbial Ecology SAME 14”, Uppsala, Suède.

Crevecoeur, S. 2015. Détection des méthanogènes et méthanotrophes dans les mares de fonte du

pergélisol. Présentation au colloque de biologie de l’Université Laval, Québec.

Crevecoeur, S. 2015. Biodiversité du Grand Nord en pleine mutation. Présentation au colloque

annuel de l’institut Hydro-Québec en environnement, développement et société, Université Laval,

Québec.

Crevecoeur, S., Vincent, W. F., Comte, J., Lovejoy, C. 2014. Molecular detection of methanogens

and methanotrophs in permafrost thaw ponds: implication for greenhouse gas emissions from

subarctic waters. Affiche présentée à la conférence ArcticNet “Arctic Change”, Ottawa.

Crevecoeur, S., Comte, J., Lovejoy, C., Vincent, W.F. 2014. RNA analysis of Bacteria in

permafrost thaw lakes: implications for greenhouse gas emission. Affiche présentée à la conférence

ASLO “Joint Aquatic Science Meeting”, Portland, Oregon, USA.

xx

Crevecoeur, S., Comte, J., Lovejoy, C., Vincent, W. F. 2013. Impact de la fonte du pergélisol dans

la région subarctique du Québec et micro-organismes impliqués. Présentation au 81e congrès de

l’association francophone pour le savoir (ACFAS) et symposium du centre d’étude nordique,

Université Laval, Québec.

1

Chapitre 1. Introduction

Le réchauffement climatique est devenu un des enjeux majeurs de notre époque en raison de

ses impacts et de son intensification. En effet, les émissions anthropogéniques de gaz à effet de

serre venant des combustibles fossiles n’ont cessé d’augmenter depuis les années 1900 (IPCC,

2013). Parmi les conséquences du réchauffement global, on note une accumulation de chaleur dans

les océans (Riser et al., 2016) et une augmentation anormale de la température des eaux profondes

(Purkey et Johnson, 2010; Meehl et al., 2011), une forte diminution du couvert de glace notamment

dans l’océan Arctique (Krishfield et al., 2014) et une fonte globale du pergélisol (Grosse et al.,

2016). Ces changements risquent de s’amplifier dans le futur car les modèles développés pour

l’instant prévoient des augmentations de température allant de 1 à 4°C d’ici 2100 (IPCC, 2013).

Les effets du réchauffement climatique sont en réalité plus intenses dans les régions

nordiques car même de faibles augmentations de température peuvent avoir un impact important sur

la fonte de la neige, de la glace et du pergélisol. En plus, une augmentation globale de la

température de 2°C, qui représente un scénario moyen, correspondrait à une augmentation

d’environ 8°C dans le Nord à cause notamment de différents effets de rétroaction positive comme la

réduction de l’albédo, l’augmentation de l’humidité dans l’air (IPCC, 2013) et la libération de gaz à

effet de serre provenant de la fonte du pergélisol (Vincent et al., 2013).

Ces effets présents et futurs ont attiré l’attention des scientifiques sur les lacs situés dans les

régions nordiques car ces derniers constituent une des caractéristiques majeures du paysage

(Grosse et al., 2013). Il y a de plus en plus d’évidences que ces lacs sont le théâtre d’activités

biogéochimiques intenses, en particulier en ce qui concerne le cycle du méthane, un gaz avec un

potentiel d’effet de serre environ 23 fois plus élevé que le dioxyde de carbone (Bastviken, 2009).

Les lacs et mares arctiques sont parmi les principales sources de libération de méthane dans

l’atmosphère (Wik et al., 2016). En effet, la fonte du pergélisol qui constitue le bassin versant des

lacs nordiques résulte en un plus fort apport de matières organiques qui augmente l’activité

microbienne à l’origine de la synthèse de gaz à effet de serre (Vincent et al., 2013). D’ici la fin du

21ème siècle, et en supposant une augmentation de la période libre de glace, on estime que les

émissions de gaz à effet de serre depuis les lacs et mares nordiques augmenteront de 20 à 54% (Wik

et al., 2016). Plus particulièrement, les mares qui sont formées par la fonte, l’érosion et

l’effondrement du pergélisol riche en glace (processus thermokarstique), représentent un type de

milieu aquatique très biogéochimiquement actif.

2

Cette thèse porte sur l'étude de la composition de la communauté microbienne dans les

mares de thermokarst (mares de fonte) en lien avec les processus biogéochimiques de

l'environnement, plus particulièrement les micro-organismes impliqués dans le cycle du méthane.

Ce chapitre d'introduction commence par la répartition du pergélisol en Arctique et explique les

différentes caractéristiques de mares de fonte. Ensuite, plus de détails sont donnés à propos du rôle

des micro-organismes dans les milieux aquatiques, que ce soit le recyclage de la matière organique

ou leur implication dans les cycles biogéochimiques. Enfin, l'introduction se termine avec

l'organisation de la thèse et la description des sites à l'étude. Cette recherche a eu lieu dans la zone

subarctique du Québec, où une forte augmentation du réchauffement a été observée depuis les vingt

dernières années, et où on constate des changements majeurs dans le paysage, incluant la

dégradation du pergélisol et l’expansion et l’augmentation du nombre de mares de thermokarst

(Bhiry et al., 2011).

1.1. Le pergélisol en Arctique

Le pergélisol, qui se définit comme un sol qui reste gelé (T°C ≤ 0) pendant au moins deux

années consécutives (Dobinski, 2011), recouvre un quart des terres de l’hémisphère nord (Brown et

al., 1998) (Figure 1-1). Le pergélisol contient aussi bien du sol riche en carbone comme de la tourbe

ou du sol riche en minéraux (Vonk et al., 2015). Le pergélisol peut être continu (90-100% du

territoire), discontinu (50-90%), sporadique (10-50%) ou isolé (2-10%) (Brown et al., 1998).

Au Canada, jusqu’à 50% du territoire contient du pergélisol (Smith et Riseborough, 2002).

Dans le nord du Québec (région du Nunavik), tous les types de pergélisol sont présents et suivent un

gradient, en partant du pergélisol continu au nord du 57e parallèle jusqu’au pergélisol sporadique

(défini comme moins de 2% du territoire par Allard et Seguin, 1987) au niveau du 54e parallèle

(Allard et Seguin, 1987). Cependant, avec l’intensification du réchauffement climatique, le

pergélisol est soumis à une fonte estivale plus intense et la limite Sud du pergélisol remonte vers le

Nord d’année en année (Halsey et al., 1995).

3

Figure 1-1. Distribution du pergélisol en Arctique. Les deux nuances de violet plus foncées représentent le

pergélisol de continu à discontinu tandis que les deux nuances de violet plus claires le pergélisol de sporadique à

isolé (Brown et al., 1997)

L’étude de l’impact de la fonte du pergélisol est un enjeu crucial puisque ce milieu

constitue une réserve de carbone globale. En effet, la quantité de carbone organique stockée dans le

pergélisol est pour l’instant deux fois supérieure à celle contenue dans l’atmosphère sous forme de

dioxyde de carbone (Tarnocai et al., 2009). La fonte du pergélisol entraînerait donc une

augmentation considérable de la concentration des gaz à effet de serre dans l’atmosphère avec

comme conséquence une intensification accablante des effets du réchauffement climatique.

1.2. Les mares de fonte du pergélisol

Durant l’été, on peut constater l’apparition de mares de fonte de pergélisol (aussi appelées

mares de thermokarst, voir Jansson et Taş, 2014). Au Canada, ces mares sont si nombreuses

qu’elles constituent le type d’écosystème aquatique le plus répandu dans ses régions arctiques et

subarctiques (Pienitz et al., 2008). En général, les mares de fonte rapetissent et/ou finissent par être

drainées peu de temps après leur formation à cause de l’évaporation et de la dégradation du

pergélisol (Smith et al. 2005; Riordan et al. 2006; Sannel et Kuhry 2011; Andresen et Lougheed

2015). Cependant, la région subarctique du Québec fait exception, car les mares de fonte persistent

d’une année à l’autre (Smith et al., 2005). Ce phénomène s’explique par la présence de limon

imperméable dans le sol, ancien bassin de la mer de Tyrrell présente il y a 8000 ans (Bhiry et al.,

2011).

4

1.2.1. Formation

Dans les régions Arctiques où le pergélisol est continu, les mares de fonte se développent

dans le centre de polygones et des ruisseaux se forment dans les coins de glace (Breton et al. 2009).

Dans les régions subarctiques par contre, les mares sont issues de buttes de pergélisol (Calmels et

al. 2008). Ces buttes sont appelées palses ou lithalses selon la composition de leur sol,

respectivement à dominance organique ou minérale (Bhiry et Robert 2006; Calmels et al. 2008). De

manière générale, les palses sont plus abondantes dans le pergélisol discontinu que les lithalses car

les conditions climatiques propices à la formation de lithalses sont beaucoup plus restreintes par

rapport aux palses. En effet, des conditions plus froides sont requises pour la formation de lithalses

contrairement aux palses. Dans le nord du Québec, les conditions plus froides le long de la baie

d’Hudson sont propices à la formation de lithalses alors que les conditions plus chaudes à l’intérieur

des terres sont plus propices à la formation de palses, si bien que la majorité des zones de pergélisol

discontinu dans le nord du Québec est dominée par des palses (Pissart 2000; Pissart 2002).

1.2.2. Caractéristiques limnologiques

Dans le nord du Québec, le gradient de fonte du pergélisol influence les caractéristiques du

paysage, la géomorphologie des mares (Bouchard et al., 2014) et crée un gradient de concentration

de la quantité de carbone allochtone contenue dans les mares. En effet, certaines mares de fonte sont

entourées de pergélisol complètement dégradé alors que pour d’autres, environ 50% du pergélisol

environnant est encore gelé. Ce phénomène crée une variation géographique des propriétés

limnologiques des mares le long du gradient de fonte du pergélisol.

À l’échelle d’une vallée, des mares situées à quelques mètres les unes des autres présentent

une grande variabilité de couleurs, reflétant leur diversité limnologique (Figure 1-2). L’étude de

Watanabe et al. (2011) a montré que ces variations étaient dues en majeure partie au rapport entre

les concentrations de carbone organique dissous, qui vient du pergélisol, et de matière en

suspension, qui vient du sol environnant souvent riche en argile et limon, c’est-à-dire des particules

en suspension qui ne sont pas d’origine algale. En outre, la concentration en nutriments et en

chlorophylle a peut être très variable d’une mare à l’autre (Rossi et al. 2013). En général, les mares

de fonte contiennent des concentrations élevées en nutriments allant de 6.2 à 91 µg L-1 pour le

phosphore ce qui correspond aux statuts mésotrophes et eutrophes (Brown et Simpson, 2001). Par

contre, les données de chlorophylle indiquent souvent une faible productivité primaire à cause du

contenu élevé en matière en suspension (MES) qui cause une grande turbidité et diminue donc

l’efficacité de la photosynthèse. Néanmoins, l’abondance du bactérioplancton est assez élevée pour

correspondre à des données propres aux lacs eutrophes (Breton et al., 2009; Laurion et al., 2010).

5

Figure 1-2. Un exemple de paysage du pergélisol avec des mares de fonte, montrant la diversité des couleurs (site

BGR, Nunavik; image : CEN/ArcticNet)

Enfin, durant l’été, les MES absorbent les rayons du soleil ce qui réchauffe la couche

supérieure de la colonne d’eau (épilimnion), créant une stratification thermique qui laisse la couche

d’eau de fond (hypolimnion) plus froide. Même si les mares sont très peu profondes (entre 1 et 3

mètres de profondeur), la stratification reste très stable durant l’été et il n’y a pas de mélange entre

l’épilimnion et l’hypolimnion (Laurion et al., 2010). Le brassage de la colonne d’eau a seulement

lieu au printemps et à l’automne. Ces périodes de brassage peuvent même parfois être incomplètes

et laisser l’hypolimnion isolé des couches d’eaux supérieures (Laurion et al. 2010; Deshpande et al.

2015). Cette forte stratification crée un gradient physico-chimique pendant l’été, car l’hypolimnion

devient hypoxique à anoxique dû à la respiration bactérienne qui consomme presque tout l’oxygène

présent dans la colonne d’eau et aucun brassage ne vient oxygéner l’hypolimnion isolé de

l’atmosphère. Ce gradient physico-chimique a des conséquences pour les formes de vie au sein de la

mare, car il crée des conditions propices au métabolisme anaérobie.

En résumé, les mares de fonte dans la région subarctique du Québec constituent un

environnement hautement variable à différents niveaux, ce qui, d’un point de vue biologique, en fait

6

un système de choix pour étudier la biogéographie ou l’effet d’un gradient physico-chimique sur la

composition des communautés planctoniques.

1.2.3. Implication dans les émissions de gaz à effet de serre

Depuis quelques années, l’attention des scientifiques pour les écosystèmes de mare de fonte

s’est accrue en raison de leur contribution aux émissions de gaz à effet de serre (GES). En effet, le

carbone resté prisonnier dans le sol pendant plusieurs centaines d’années devient alors disponible,

avec la fonte, pour la reminéralisation microbienne. Les émanations de GES depuis les mares de

fonte ne sont habituellement pas incluses dans les bilans globaux de budget de carbone puisque leur

petite taille les rend invisibles aux outils de télédétection (Tranvik et al., 2009). Pourtant, leur

contribution au cycle du carbone est considérable, surtout en ce qui concerne le méthane (Walter et

al., 2007; Sepulveda-Jauregui et al., 2015) qui a environ 23 fois plus de potentiel en terme d’effet de

serre que le dioxyde de carbone (Bastviken, 2009). Une estimation récente évalue la contribution

des lacs et mares nordiques (au-delà du 50e parallèle) à environ 16 Tg de méthane par année (Wik et

al., 2016). Avec l’effet de l’accélération de la fonte du pergélisol, cette contribution peut augmenter

considérablement. En effet, une étude de Walter et al. (2007) a estimé que la fonte totale du

pergélisol sibérien entraînerait l’émission de 49 000 Tg de méthane dans l’atmosphère. Les

émissions de GES depuis les mares de fonte constituent donc une rétroaction positive au

réchauffement climatique (Walter et al. 2006).

Les mares de fonte dans la région subarctique du Québec ont des taux d’émission de GES

très variables (Laurion et al., 2010). De manière générale, le dioxyde de carbone et le méthane

s’accumulent dans le fond des mares avec des concentrations respectivement 8 à 16 fois et 2 à 125

fois plus élevées dans l’hypolimnion qu’à la surface (Breton et al. 2009). Le méthane peut

s’échapper dans l’atmosphère par diffusion ou ébullition. Dans les mares de fonte situées en Alaska

et en Sibérie, le processus d’ébullition est dominant pour le relargage de méthane dans l’atmosphère

(Walter et al., 2008) et le flux diffusif ne représente que 6% des émissions de méthane de mares de

fonte situées en Sibérie (Walter et al., 2006; Par contre, dans les mares de fonte située dans le nord

du Québec, le méthane est émis principalement par diffusion (Matveev et al., 2016).Enfin, le

mélange saisonnier permet la libération d’une grande partie du méthane accumulé dans

l’hypolimnion (Bastviken, 2009).

1.3. Les micro-organismes dans les milieux aquatiques

Les micro-organismes dans les milieux aquatiques sont à la base des processus

biogéochimiques de l’environnement. Dans cette thèse, la diversité des bactéries et des archées a été

7

étudiée. Ces deux domaines de la vie, malgré leur morphologie similaire, sont phylogénétiquement

et physiologiquement distincts (Woese, 1996). Bien que l’écologie bactérienne soit connue depuis

des siècles, les archées ont seulement été classifiées comme un domaine à part entière en 1990

(Woese et al. 1990). À cette époque, la connaissance de leur écologie se limitait aux

environnements extrêmes. Cependant, les travaux de Delong (1998) notamment ont montré

l’existence d’archées non extrémophiles. Il est maintenant reconnu que les archées sont présentes

dans une grande variété d’environnements (Auguet et al., 2010) mais restent néanmoins plus

difficiles à étudier en raison de leur faible abondance, comme c’est le cas dans les milieux

aquatiques (Sievert et al., 2000; Chan et al., 2005).

1.3.1. Processus microbiens

Un des rôles majeurs du plancton microbien dans les écosystèmes d’eau douce est le

recyclage de la matière organique dissoute. Ce procédé est appelé « boucle microbienne », terme

utilisé pour la première fois par Azam et Graf (1983) pour caractériser le rôle des microbes dans

l’océan. Ce terme est maintenant largement utilisé pour les milieux d’eau douce également. Le

principe de ce processus microbien est que les microbes planctoniques (bactéries et archées)

consomment la matière organique dissoute autochtone (excrétion par le phytoplancton ou produit de

la lyse virale) ainsi que la matière organique allochtone venant du bassin versant (figure 1-3). Au

lieu de sédimenter, cette matière organique se trouve ainsi réintroduite dans la chaîne trophique par

le biais du broutage des bactéries par les eucaryotes microbiens hétérotrophes ou mixotrophes. Les

microbes ont donc un rôle prépondérant dans le recyclage de la matière organique dans les milieux

aquatiques (Cotner et Biddanda, 2002; Fenchel, 2008). Les produits finaux de dégradation de la

matière organique amènent à la formation de dioxyde de carbone dans des eaux oxiques, mais

peuvent également mener à la synthèse de méthane en milieu anoxique.

8

Figure 1-3. Processus microbiens tels que ayant lieu dans les mares de fontes du pergélisol. MOD désigne la

matière organique dissoute.

1.3.2. Micro-organismes impliqués dans le cycle du méthane

Environ 75% du méthane contenu dans l’atmosphère est d’origine biogénique (Chen et

Prinn, 2005). Dans l’est de l’Arctique canadien, le méthane peut être d’origine moderne ou vieux de

jusqu’à 4500 ans (Bouchard et al., 2015). Avec la fonte du pergélisol, une partie du méthane ancien

resté prisonnier dans le sol gelé peut s’échapper dans l’atmosphère, tandis qu'une autre partie du

méthane est moderne et nouvellement synthétisé par les archées méthanogènes, stimulées par la

disponibilité en substrat venant de la fonte du sol. Les archées méthanogènes utilisent les substrats

issus de la dégradation anaérobie de la matière organique comme l’acétate, les groupements

méthyle, l’hydrogène et le dioxyde de carbone (Bapteste et al., 2005) pour synthétiser du méthane

en milieu strictement anoxique (Thauer et al., 2008). L’utilisation de ces différents substrats

détermine chez les archées l’utilisation de différentes voies métaboliques. En effet, les archées

méthanogènes peuvent être acétotrophiques, méthylotrophiques ou hydrogénotrophiques selon les

substrats qu’elles utilisent pour procéder à la méthanogenèse. On distingue sept ordres d’archées

méthanogènes (Borrel et al., 2013) appartenant à deux lignées phylogénétiquement distinctes qui

disposent d’un ancêtre commun, mais dont les descendants ne sont pas tous méthanogéniques

(Bapteste et al., 2005). Cette distinction phylogénétique n’est pas liée à une différence de

métabolisme, car tous les ordres de méthanogènes sont capables de procéder à la méthanogenèse

hydrogénotrophique, mais seulement un ordre est capable d’utiliser les trois voies métaboliques

(Bapteste et al., 2005).

9

En général, les archées méthanogènes peuvent être retrouvées dans différents types

d’environnements, entre autres, les rizières (Wu et al., 2009), les tourbières (Yavitt et al., 2011), le

pergélisol de la toundra (Barbier et al., 2012) ainsi que le pergélisol discontinu (Liebner et al.,

2015). Dans les écosystèmes d’eau douce, les méthanogènes sont plus souvent associés avec les

sédiments (Briée et al., 2007; Ye et al., 2009), comme c’est le cas pour les mares de fonte de l’est

de l’Arctique canadien (Negandhi et al., 2013). La présence d’archées méthanogènes dans la

colonne d’eau est moins sûre étant donnée la présence plus probable d’oxygène. Cependant, dû à la

forte stratification et l’établissement d’un gradient physico-chimique, l’hypolimnion des mares de

fonte peut disposer de conditions propices à la méthanogenèse en plus de fournir un apport riche en

substrat venant de la fonte du sol (Laurion et al., 2010).

Une grande partie des flux diffusif de méthane vers l’atmosphère peut être oxydée par des

bactéries méthanotrophes en entrant dans la couche oxique de la colonne d’eau. Ces bactéries ont la

capacité d’utiliser le méthane comme seule source de carbone et d’énergie (Hanson et Hanson

1996). Les bactéries méthanotrophes sont historiquement connues comme appartenant aux branches

alpha et gamma des protéobactéries (Bowman, 2006). Plus récemment, des nouveaux types de

méthanotrophes ont été découverts, comme ceux appartenant au phylum des Verrucomicrobia qui

ont la particularité de croître dans des conditions très acides (Dunfield et al., 2007) ou le phylum

NC10 capable de coupler l’oxydation de méthane avec la dénitrification (Ettwig et al., 2009), se

dispensant de ce fait du besoin d’oxygène comme accepteur d’électrons. Comme les méthanogènes,

les méthanotrophes ne constituent donc pas un groupe monophylétique et l’apparition de ce trait

semble s’être produite plusieurs fois au cours de l’histoire évolutive des bactéries.

Dans les lacs, les bactéries méthanotrophes peuvent consommer entre 30 et 99% du

méthane (Bastviken et al., 2008). Par exemple, dans un lac boréal en Finlande, les méthanotrophes

consommeraient 80% du méthane produit annuellement, qui se diffuse depuis les sédiments dans la

colonne d’eau (Kankaala et al., 2006). Les méthanotrophes ont donc un rôle très important dans la

régulation du climat en réduisant une forte proportion du méthane qui pourrait être émis dans

l’atmosphère.

En résumé, l’équilibre entre l’activité des méthanogènes et des méthanotrophes détermine le

bilan de carbone global et la quantité de méthane émise dans l’atmosphère. Cependant, des

conditions très différentes sont requises pour la réalisation de l’un ou l’autre processus. Alors que

l’oxygène inhibe la méthanogenèse (Bastviken, 2009), il est nécessaire à la méthanotrophie (Hanson

et Hanson, 1996). Les mares de fonte ont la particularité de disposer d’environnements anoxiques et

10

oxiques au sein de la même colonne d’eau, les rendant particulièrement propices au développement

des micro-organismes impliqués dans les processus de synthèse et de consommation du méthane

(Figure 1-4).

Figure 1-4. Schéma simplifié des processus de méthanogénèse et méthanotrophie.

1.3.3. Approches pour étudier les micro-organismes

Depuis les 25 dernières années, le domaine de l’écologie microbienne a été révolutionné par

le développement des techniques moléculaires (Dorigo et al., 2005; Pedrós-Alió, 2006). La

première forme de technique de séquençage a été introduite par Sanger et al. (1977) et permet

d’obtenir jusqu’à 1000 séquences d’un échantillon donné. Plus récemment, les techniques de

séquençage à haut débit ont permis d’augmenter considérablement le nombre de séquences par

échantillon. Par exemple, le pyroséquençage 454 permet d’obtenir de 1x105 à 1x106 séquences par

cycle et le séquençage illumina de 7x106 à 6x109 (Shokralla et al., 2012). Ces techniques passent

par la réalisation d’une PCR, ce qui peut occasionner des biais, mais nécessite aussi de cibler des

gènes à amplifier pour répondre aux questions écologiques. Le gène le plus fréquemment utilisé

pour étudier la diversité des bactéries et des archées est le gène 16S de l’ARN ribosomique (ARNr

16S) qui offre des résultats précis pour l’identification de la diversité microbienne avec les

techniques de séquençage à haut débit (Liu et al., 2007). Cette approche, qui est adéquate pour

étudier la diversité taxonomique des micro-organismes, ne suffit néanmoins pas pour évaluer leur

diversité fonctionnelle. Pour se faire, il est nécessaire d’amplifier des gènes fonctionnels qui

permettent de répondre à des questions concernant l’activité des micro-organismes. L’étude des

gènes fonctionnels permet de relier la diversité microbienne associée aux processus

biogéochimiques de l’environnement (Zak et al., 2006).

11

En ce qui concerne le cycle du méthane, plusieurs gènes ont été étudiés en lien avec les

processus de synthèse et de consommation du méthane, ce qui facilite l’étude moléculaire des

micro-organismes impliqués dans ce cycle. Au niveau du processus de synthèse, la méthyle

coenzyme réductase catalyse l’étape finale de la formation du méthane (Ellermann et al., 1988) et le

gène qui code pour cette enzyme (mcrA) est donc utilisé comme proxy pour étudier les archées

méthanogènes (Luton et al., 2002). En ce qui concerne l’oxydation du méthane, l’enzyme méthane

monooxygénase impliquée dans la première étape de cette réaction existe sous deux différentes

formes : une enzyme particulaire (pMMO), qui est présente chez presque tous les méthanotrophes,

et une enzyme soluble (sMMO) qui est seulement présente chez certains types de méthanotrophes

(Murrell et al., 1998; Kolb et al., 2003). Le gène qui code pour la sous-unité alpha de la pMMO

(pmoA) est plus particulièrement utilisé comme proxy pour étudier les bactéries méthanotrophes

(Kolb et al., 2003). Cependant, à cause du caractère fortement paraphylétique de la méthanotrophie,

il est impossible de disposer d’une séquence d’amorce permettant d’amplifier tous les types de

pmoA (Sharp et al., 2012). La diversité des méthanotrophes au sein d’une même étude est donc

souvent limitée à l’un ou l’autre groupe phylogénétique pour des raisons logistiques. Ces deux

gènes marqueurs ont l’avantage de présenter une phylogénie cohérente avec celle obtenue grâce au

gène 16S.

Au niveau moléculaire, il est possible de cibler l’ADN ou l’ARN du 16S et des gènes

fonctionnels. Le choix d’utiliser une des deux ou les deux molécules dépend de la question de

recherche. Que ce soit pour le 16S ou pour les gènes fonctionnels, le choix d’utiliser l’ADN permet

d’étudier la composition de la communauté et la diversité des bactéries, des archées ou d’un groupe

fonctionnel particulier. L’ADN permet de détecter non seulement les cellules vivantes, mais aussi

les cellules mortes ainsi que des molécules libres d’ADN qui peuvent rester intactes dans des

conditions froides ou anoxiques (voir Charvet et al., 2012). L’ARN permet aussi d’étudier la

composition de la communauté et la diversité des micro-organismes, mais reflète par contre une

communauté potentiellement active (Blazewicz et al., 2013), car les gènes sont à l’état de transcrits

(Paul, 2001). L’étude de l’ARN requiert l’exécution d’une étape supplémentaire en laboratoire de

transformation de l’ARN en ADN complémentaire (ADNc). En ce qui concerne les gènes

fonctionnels, l’amplification de transcrits fournit une indication plus précise sur la présence de

cellules métaboliquement actives (Juottonen et al., 2008). Bien que les techniques de séquençage

évoquées au début de ce paragraphe permettent d’identifier une large portion de la communauté

microbienne, ces approches restent semi-quantitatives. Pour mesurer le nombre de gènes ou de

transcrits dans un échantillon, il faut procéder à une PCR quantitative (qPCR). Dans le cas de cette

12

thèse, la qPCR sur les transcrits de gènes fonctionnels a été utilisée comme technique pour évaluer

quantitativement l’activité des groupes impliqués dans le cycle du méthane (Tuomivirta et al., 2009;

Wilkins et al., 2015).

1.4. Organisation de la thèse

L’objectif général de cette thèse est de comprendre quels sont les facteurs qui influencent la

composition de la communauté microbienne dans les mares de fonte de pergélisol ainsi que de

connaître l’identité des micro-organismes impliqués dans le cycle du méthane. Les trois prochains

chapitres, qui correspondent à des articles scientifiques, se concentrent sur la réalisation de ces

objectifs.

1.4.1. Objectifs et hypothèses des chapitres de thèse

Chapitre 2 (article 1): "Bacterial community structure across environmental gradients in

permafrost thaw ponds: methanotroph-rich ecosystems".

La forte stratification estivale crée un gradient de conditions physico-chimiques à travers la

colonne d’eau des mares de fonte qui influence le métabolisme des micro-organismes et peut

donner lieu à une séparation des communautés bactériennes. De plus, le gradient de fonte et de

dégradation du pergélisol influence la quantité de matière organique allochtone présente dans les

mares, ce qui a aussi un impact sur la composition de la communauté. L’objectif de ce chapitre est

donc de comprendre les facteurs qui influencent la répartition de la communauté bactérienne et

d’identifier plus particulièrement les bactéries méthanotrophes. Plus précisément les hypothèses

suivantes ont été posées : (1) la communauté bactérienne qui se développe dans l’eau de surface des

mares de fonte est différente de la communauté présente dans l’eau de fond à cause du fort gradient

physico-chimique au sein de la colonne d’eau. (2) La composition de la communauté bactérienne

varie en fonction du gradient de fonte et de dégradation du pergélisol. (3) Étant donné les fortes

concentrations en méthane dans l’eau des mares de fonte, les bactéries méthanotrophes représentent

en forte proportion de la communauté bactérienne.

Chapitre 3 (article 2): "Unexpected diversity and activity of methanotrophs in low-oxygen

permafrost thaw ponds".

Le chapitre 3 a permis d’identifier une forte abondance de bactéries méthanotrophes à la

surface et dans le fond des mares de fonte, mais les techniques utilisées dans le chapitre précédent

ne permettent pas de savoir si ces bactéries participent activement à l’oxydation du méthane. Ce

13

chapitre-ci se concentre donc sur l’étude de la diversité fonctionnelle et l’activité des

méthanotrophes pour mieux comprendre leur implication dans le cycle du méthane. L’objectif de ce

chapitre est donc d’étudier la diversité fonctionnelle et l’activité des méthanotrophes ainsi que les

facteurs qui les influencent comme l’origine de la mare ou le gradient de fonte du pergélisol. Ce

chapitre se base sur les trois hypothèses suivantes : (1) la composition de la communauté des

bactéries méthanotrophes dans les mares de fonte est déterminée par l’origine de la mare (fonte

d’une palse ou lithalse) ainsi que par leur localisation au sein du gradient de fonte du pergélisol. (2)

L’activité des méthanotrophes dépend de la composition de la communauté et est régulée par la

disponibilité en oxygène au sein de la colonne d’eau. (3) Les bactéries méthanotrophes ont le

potentiel de réduire les émissions nettes de méthane dans l’atmosphère.

Chapitre 4 (article 3): "Environmental selection of planktonic methanogens in permafrost

thaw ponds".

Après avoir évalué la diversité et l’activité des méthanotrophes dans les chapitres

précédents, ce chapitre se concentre sur les archées méthanogènes. Dans les écosystèmes lacustres,

les conditions propices aux archées méthanogènes, c’est-à-dire l’absence d’oxygène, sont plus

souvent retrouvées dans les sédiments que dans la colonne d’eau. Cependant, dans le cas des mares

de fonte, l’eau du fond de la mare peut devenir hypoxique voir anoxique ce qui crée des conditions

favorables pour la méthanogenèse. De plus, la fonte du pergélisol fournit un apport de substrat qui

peut stimuler l’activité des méthanogènes. Ce chapitre a pour but d’étudier la diversité des

méthanogènes planctoniques ainsi que les facteurs qui l’influencent comme l’origine de la mare ou

le gradient de fonte du pergélisol. Plus particulièrement ce chapitre se base sur trois hypothèses : (1)

l’eau de fond des mares de fonte constitue un habitat favorable pour les archées méthanogènes. (2)

La composition de la communauté des archées dans les mares de fonte dépend de l’origine de la

mare (fonte d’une palse ou d’une lithalse) et de leur localisation à travers le gradient de fonte du

pergélisol. (3) Les mares issues de palses, qui sont plus riches en carbone, favorisent le

développement d’une communauté d’archées plus diverse comparée aux mares issues de lithalse.

1.4.2. Sites d’études

Pour atteindre les objectifs et tester les hypothèses posées dans cette thèse, des mares de

fonte situées dans quatre vallées différentes ont été échantillonnées. Ces vallées sont situées dans la

région subarctique du Québec (Nunavik) à travers un gradient de fonte du pergélisol. Les deux

vallées les plus au nord, se situent près du village inuit d’Umiujaq alors que les deux vallées les plus

14

au sud se trouvent près du village Kuujjuarapik où se côtoient les communautés Cris et Inuits.

Plusieurs mares ont été échantillonnées dans chaque vallée en août 2012 et 2013.

1.4.2.1. Vallée de la rivière Kwakwatanikapistikw (KWK)

La vallée KWK se trouve à 12 km à l’est du village de Kuujjuarapik dans une zone de

pergélisol sporadique (Bhiry et al., 2011). Dans cette vallée, la fonte des lithalses a donné lieu à la

formation de mares de fonte qui recouvrent plus de la moitié de la surface du sol, témoignant de

l’état avancé de dégradation du pergélisol (Bhiry et Robert, 2006).

1.4.2.2. Vallée de la rivière Sasapimakwananisikw (SAS)

La vallée SAS se trouve aussi dans la zone de pergélisol sporadique, à 8 km au sud de

Kuujjuarapik. Cette vallée abrite une tourbière ombrotrophe, c’est-à-dire que l’eau qui l’alimente ne

vient que des précipitations et est donc souvent acide et pauvre et nutriments. Dans cette vallée, le

pergélisol est très dégradé et l’effondrement de palses donne naissance aux mares de fonte (Arlen-

Pouliot et Bhiry, 2005).

1.4.2.3. Vallée “Bundesanstalt für Geowissenschaften und Rohstoffe” (BGR)

La vallée BGR se situe plus au nord à environ 20 km à l’est d’Umiujaq, dans une zone de

pergélisol discontinu et répandu, c’est-à-dire que plus de 50% du territoire est composé de

pergélisol. Cette vallée est recouverte de limon marin postglaciaire et de tourbe, par conséquent les

mares y sont issues de palses et de lithalses (Calmels et Allard, 2004; Calmels et al., 2008). Dans le

cadre de cette étude et pour des raisons logistiques, seules les mares issues de lithalses ont été

échantillonnées dans cette vallée.

1.4.2.4. Vallée de la rivière Nastapoka (NAS)

La vallée NAS représente la vallée la plus au nord échantillonnée pour cette thèse. Elle se

situe dans une zone de pergélisol discontinu et répandu à environ 30 km au nord d’Umiujaq. Les

mares de fonte dans cette vallée ont encore été très peu étudiées. Les mares échantillonnées dans la

vallée NAS dans le cadre de cette étude proviennent de la fonte de lithalses (Seguin et Allard,

1984).

15

Chapitre 2. Bacterial community structure across

environmental gradients in permafrost thaw ponds:

methanotroph-rich ecosystems1

Résumé

La fonte du pergélisol mène à la formation de mares de thermokarst qui émettent

potentiellement du CO2 et du CH4 dans l’atmosphère. Dans la région subarctique du Nunavik (Nord

du Québec, Canada), ces nombreuses mares peu profondes deviennent fortement stratifiées pendant

l’été. Cela crée un gradient physico-chimique de température et d’oxygène, avec une couche d’eau

de surface oxique et une couche d’eau de fond pauvre en oxygène ou anoxique. Notre objectif était

de déterminer l’influence de la stratification ainsi que des propriétés limnologiques et du paysage

sur la structure des communautés bactériennes potentiellement actives dans ces eaux. Les

échantillons pour l’analyse ARN ont été pris dans des mares situées dans trois vallées différentes à

travers un gradient de fonte du pergélisol. Un total de 1296 unités taxonomiques opérationnelles a

été identifié par séquençage d’amplicons à haut débit, ciblant l’ARNs 16S bactérien, préalablement

retrotranscrit en ADNc. Les betaproteobactéries étaient le groupe dominant dans toutes les mares,

avec la plus forte représentation par les genres Variovorax et Polynucleobacter. Les

méthanotrophes étaient aussi parmi les séquences les plus abondantes pour la plupart des sites. Ils

comptaient même pour 27% du total des séquences (médiane de 4.9% pour tous les échantillons),

indiquant l’importance du méthane comme source d’énergie bactérienne dans ces eaux. Des

phototrophes oxygéniques (Cyanobactéries) et anoxygéniques (Chlorobi) étaient aussi fortement

représentés, ces derniers dans les eaux de fond pauvres en oxygène. Des analyses d’ordination ont

montré que les communautés se regroupaient en fonction des vallées et de la profondeur, avec un

effet significatif attribué à l’oxygène, au pH, au carbone organique dissous et aux matières en

suspension. Ces résultats indiquent que les assemblages bactériens dans les mares de fonte du

pergélisol sont filtrés par les gradients environnementaux, et représentent des associations

complexes de taxons fonctionnellement divers qui ont de grandes chances d’affecter la composition

ainsi que la magnitude des émissions de gaz à effet de serre depuis ces abondants plans d’eaux.

1 Citation : Crevecoeur S, Vincent WF, Comte J and Lovejoy C (2015). Bacterial community structure across

environmental gradients in permafrost thaw ponds: methanotroph-rich ecosystems. Front. Microbiol. 6:192.

doi: 10.3389/fmicb.2015.00192

16

Abstract

Permafrost thawing leads to the formation of thermokarst ponds that potentially emit CO2

and CH4 to the atmosphere. In the Nunavik subarctic region (northern Québec, Canada), these

numerous, shallow ponds become well-stratified during summer. This creates a physico-chemical

gradient of temperature and oxygen, with an upper oxic layer and a bottom low oxygen or anoxic

layer. Our objective was to determine the influence of stratification and related limnological and

landscape properties on the community structure of potentially active Bacteria in these waters.

Samples for RNA analysis were taken from ponds in three contrasting valleys across a gradient of

permafrost degradation. A total of 1296 operational taxonomic units were identified by high

throughput amplicon sequencing, targeting bacterial 16S rRNA that was reverse transcribed to

cDNA. Βetaproteobacteria were the dominant group in all ponds, with highest representation by the

genera Variovorax and Polynucleobacter. Methanotrophs were also among the most abundant

sequences at most sites. They accounted for up to 27% of the total sequences (median of 4.9% for

all samples), indicating the importance of methane as a bacterial energy source in these waters.

Both oxygenic (cyanobacteria) and anoxygenic (Chlorobi) phototrophs were also well-represented,

the latter in the low oxygen bottom waters. Ordination analyses showed that the communities

clustered according to valley and depth, with significant effects attributed to dissolved oxygen, pH,

dissolved organic carbon, and total suspended solids. These results indicate that the bacterial

assemblages of permafrost thaw ponds are filtered by environmental gradients, and are complex

consortia of functionally diverse taxa that likely affect the composition as well as magnitude of

greenhouse gas emissions from these abundant waters.

17

2.1. Introduction

One of the impacts of ongoing climate change is the northward migration of the limit of

permafrost soils in subarctic landscapes, and this is leading to changes in the distribution and

abundance of lakes and ponds caused by permafrost thawing and erosion (Vincent et al., 2013b).

These so called thaw ponds (thermokarst lakes and ponds) represent the most widespread aquatic

ecosystem type in the circumpolar Arctic and Subarctic (Pienitz et al., 2008; Koch et al., 2014). In

some northern regions of the Arctic, thaw lakes are disappearing as a result of evaporation and

drainage (Smith et al., 2005), whereas in some southern locations such as subarctic Québec,

Canada, permafrost lakes are expanding in size and numbers through increased permafrost thawing

and erosion (Payette, 2004). Thaw lakes show cycles of expansion, erosion, drainage and

reformation (van Huissteden et al., 2011) that will likely accelerate under warmer climate

conditions (Vincent et al., 2013b).

Thawing permafrost has global implications for carbon biogeochemical cycling, since

carbon that has been sequestered for thousands of years becomes available for microbial

degradation (Tranvik et al., 2009), resulting in the production of greenhouse gases, especially

carbon dioxide and methane. Despite this potential, greenhouse gas emissions from northern lakes

and thaw ponds are often ignored in regional and global carbon budgets. These open waters

potentially represent a source of around 24 Tg of methane emission per year (Walter et al., 2007).

Methane is primarily produced by a few archaeal clades under anoxic conditions (Sower, 2009),

although methanogenesis has also been observed in oxic water columns (Grossart et al., 2011;

Bogard et al., 2014). This biologically generated methane is available to aerobic methanotrophic

Bacteria that occur in the oxic zone or at oxic/anoxic boundaries (Borrel et al., 2011), and this

methane oxidation activity may regulate net greenhouse gas emissions (Trotsenko and Khmelenina,

2005; Bodelier et al., 2013). For example, methanotrophy consumed up to 80% of the methane

produced in a boreal Finnish lake (Kankaala et al., 2006). Thaw ponds can be either sources of

greenhouse gas emissions (Walter et al., 2007; Laurion et al., 2010; Negandhi et al., 2013), or sinks

for carbon sedimentation and storage (Walter et al., 2014), but reasons for these differences are

poorly understood. Conditions that favor methanotrophy will have the net effect of decreasing

methane release to the atmosphere, however little is known about such processes in the abundant

lakes and ponds on degrading permafrost landscapes.

Bacterial communities vary among lakes as a result of differences in physico-chemical and

biological properties such as pH (Lindström et al., 2005), productivity and dissolved organic carbon

(DOC) availability (Lindström and Leskinen, 2002; Yannarell and Triplett, 2004). For this reason

18

even neighboring lakes in the same region may differ in their bacterial community structure

(Casamayor et al., 2000; Gucht et al., 2001). In addition, catchment and underlying soil properties

vary, resulting in differences in the quantity and composition of organic matter entering lakes,

which in turn may affect their bacterial communities (Judd et al., 2006; Kritzberg et al., 2006).

Bacterial community composition in lakes also changes with depth (Shade et al., 2008; Garcia et al.,

2013), especially in meromictic lakes where temperature, salinity and oxygen gradients select for

distinct communities down the water column (Hollibaugh et al., 2001; Comeau et al., 2012).

Notably, a clone library study by Rossi et al. (2013) found distinct bacterial communities in surface

and bottom waters of thaw ponds in Northern Québec.

Arctic and sub-arctic ponds occur across a range of conditions that could select for

particular bacterial taxa. For example, in Northern Quebec varied combinations of dissolved

organic matter and suspended particles result in striking differences in the optical properties of

neighboring thaw ponds (Watanabe et al., 2011) and characteristic bacterial communities could be

associated with the particular optical properties of individual ponds. The extent and stage of

thawing of the surrounding permafrost could also potentially affect bacterial community structure;

some thaw ponds occur in highly degraded permafrost while others are surrounded by more than

50% of intact permafrost. These different stages of permafrost thaw influence the landscape

characteristics (e.g. vegetation cover, open water extent) and the geomorphology of the ponds

(Bouchard et al., 2014), creating a gradient in concentrations of allochthonous dissolved organic

matter (DOM). With thawing more pronounced at the southern limits of permafrost,

bacterioplankton in these regions would have greater access to this external supply of organic

matter. Since microbial communities control biogeochemical processes such as methane balance,

understanding factors that influence bacterial community composition is central for predicting net

greenhouse gas emissions from the thawing landscape. Clone library analysis of four Northern

Quebec ponds reported the presence of methanotrophs (Rossi et al., 2013), but little is known about

how communities might vary over a range of permafrost conditions.

In addition, bacterial alpha-diversity (number of taxa per lake) could be influenced by local

conditions such as depth, light availability, and pond productivity. For example, diversity is

expected to be lower where low oxygen and light conditions select for a few specialist taxa (Shade

et al., 2008). Lower primary productivity has also been linked with a lower diversity (Cardinale et

al., 2006; Ptacnik et al., 2008), and light and nutrient limitation would therefore influence diversity.

On a landscape scale, the diversity of animals and plants decreases at higher latitudes (Rahbek,

1995; Falge et al., 2002). However Fierer and Jackson (2006), found that for soil microbes alpha-

19

diversity is independent of latitude but more related to soil properties, for example pH. If this were

the case for thaw ponds then inherent properties of the landscape could also influence the alpha-

diversity.

The goal of the present study was to investigate the diversity and distribution of potentially

active bacterial communities in subarctic thaw ponds across a gradient of limnological conditions

and permafrost, from discontinuous permafrost in the North to sporadic permafrost in the South. We

applied high throughput sequencing, targeting the V6-V8 hypervariable region of 16S rRNA. The

use of this hypervariable region enabled us to identify community assemblages in three

geographically separate valleys that differed in their underlying stage of permafrost degradation.

Specifically we tested the hypotheses that: (1) different bacterial communities develop at the

surface and bottom of each thaw pond because of the strong physico-chemical gradient through the

water column; (2) bacterial community composition varies across the gradient of permafrost

thawing and degradation; and (3) given the known high concentrations of methane in these waters

(Laurion et al., 2010), methanotrophic Bacteria are well represented in thaw pond bacterial

assemblages.

2.2. Materials and methods

2.2.1. Study sites and sampling

Samples were collected from 1 to 13 of August 2012 from three different subarctic valleys

(Figure 2-1), which were chosen to represent a gradient of thawing permafrost. Ponds from these

valleys have been investigated since 2006 and nomenclature follows that of earlier studies (Calmels

et al., 2008; Breton et al., 2009; Laurion et al., 2010).

20

Figure 2-1. Location of the three sampling valleys in Nunavik, subarctic Québec, Canada.

The ponds were designated with a three letter prefix and a unique number or number letter

combination. The KWK (55°16’N, 77°46’W) and SAS (55°13’N; 77°42’W) ponds are in two

separate river valleys close to the village of Whapmagoostui-Kuujjuarapik in the sporadic

permafrost zone, while the BGR ponds (56°37′N; 76°13′W) are in the Sheldrake River valley close

to the village of Umiujaq, Québec, in the discontinuous permafrost region. The ponds have formed

in thawing permafrost mounds that are primarily organic (peat) or mineral; the term palsa refers to

organic mounds and lithalsa to mineral mounds (Gurney, 2001; Calmels et al., 2008). KWK and

BGR ponds originated from the thawing of lithalsas and SAS ponds from palsas. BGR and KWK

ponds are surrounded by shrubs (Salix planifolia and Betula glandulosa) and sparse trees (Picea

mariana, Picea glauca, Larix laricina) (Calmels et al., 2008; Breton et al., 2009). The SAS valley

vegetation, in contrast, is dominated by Carex and Sphagnum (Arlen-Pouliot and Bhiry, 2005;

Bhiry et al., 2011). Two ponds were selected from the BGR valley (1 and 2), three from the KWK

valley (1, 6 and 23) and 2 from the SAS valley (1B and 2A) (Table 2-1). Ponds were sampled from

an inflatable boat held near the center of the ponds using ropes tethered to the shore. Temperature,

21

conductivity, dissolved oxygen and pH profiles were taken using a 600R multiparametric probe

(Yellow Spring Instrument). Surface water samples were collected directly into submerged acid and

sample rinsed 4-L Cubitainers and near-bottom samples were collected using a horizontally

mounted Van Dorn bottle (Wilco) and immediately transferred to 4-L Cubitainers. Care was taken

to overfill the Cubitainers when sampling the bottom water and all the Cubitainers were capped to

minimize exchange with the atmosphere. The filled Cubitainers were then transported back to the

laboratory by helicopter and processed within 2 h.

Table 2-1. Limnological properties of the sampled thaw ponds: dissolved oxygen (DO), conductivity (Cond),

chlorophyll a (Chl a), dissolved organic carbon (DOC), total suspended solids (TSS), soluble reactive phosphorus

(SRP), and total nitrogen (TN). The surface samples correspond to 0 m and bottom samples to the second depth for

each pond.

2.2.2. Physico-chemical analysis

Water samples for dissolved organic carbon (DOC), soluble reactive phosphorus (SRP),

major ion analysis were filtered through a MilliQ™ water pre-rinsed 47-mm diameter 0.22-µm pore

size acetate filters (Whatman). DOC concentrations were analysed using a Shimadzu TOC-5000A

carbon analyzer calibrated with potassium biphthalate. Three blank filters of Milli-Q water passed

through the filters were also analysed along with the samples and these small background values

were subtracted. Water samples for total phosphorus (TP) and total nitrogen (TN) were preserved

with H2SO4 (0.15% final concentration) and analysed using standard methods (Stainton et al., 1977)

Pond Depth (m)

DO (mg

L-1)

DO

(%satu-

ration)

pH Cond (µS

cm-1)

Chl

a (µg

L-1)

DOC (mg

L-1)

TSS (mg

L-1)

SRP (µg

L-1)

TN (mg

L-1)

SAS1B 0

1

6.37

1.54

63

15

6.0

5.63

93

212

4.9

2.9

15.5

16.2

27

33

2.7

3

0.9

1.8

SAS2A 0

2.4

5.75

0.26

64

2

6.2

5.58

97

300

1.7

18.1

14.9

18.9

2.6

16

3.1

4.1

0.7

1.6

KWK1 0

1.8

9.69

0.50

101

4.2

6.66

6.22

63

150

10.9

10.3

12

12

26

141

3.7

12.6

0.6

1.0

KWK6 0

3

9.94

1.82

97

17

6.36

6.35

82

112

3.3

27.1

5.2

5.2

8.2

16

1.3

1

0.4

0.7

KWK23 0

3.2

9.8

0.36

97

2.7

6.44

6.09

29

259

1.9

7.2

7.8

10.9

8.3

74

5.5

133.6

0.4

2.7

BGR1 0

3.2

10.0

4.06

101

37

7.38

7.56

168

190

0.9

1.1

3.5

3.3

2.4

3.8

2.4

2.2

0.2

0.4

BGR2 0

1

9.43

3.47

94

34

7.31

7.17

209

387

2.4

3.8

9.3

8.7

13

57

3.4

4.5

0.4

1.2

22

at Institut National de la Recherche Scientifique (Quebec City, QC, Canada). Total suspended solids

were collected by filtration onto preweighed 47 mm GF/F filters (Whatman) that had been

precombusted at 500°C for 4 h. The GF/F filters were then oven dried for 2 h at 60°C and

reweighed. Samples for chlorophyll a (Chl a) were filtered onto a GF/F 25 mm filters (Whatman)

and stored at -80°C. Chl-a concentrations were determined using high performance liquid

chromatography (ProStar HPLC system, Varian, Palo Alto, CA, USA) following the protocol of

Bonilla et al., (2005).

2.2.3. RNA collection and extraction

Water samples were first prefiltered through a 20-µm mesh to remove larger organisms and

then filtered sequentially through a 3-µm pore size, 47-mm diameter polycarbonate filter (DHI lab

product) and a 0.2 µm Sterivex unit (Millipore) with a peristaltic pump. From 100 to 600 mL of

water were filtered and the filtration was stopped after 2 hours to minimize RNA degradation. The

size fractionation was employed to distinguish between particle-attached Bacteria on the 3-µm filter

and free living Bacteria on the 0.2-µm filter (Crump et al., 1999; Galand et al., 2008; Mohit et al.,

2014). Both filters were preserved in RNAlater (Life Technologies) and the filters were stored at

-80°C until processed. The PowerWater Sterivex DNA Isolation Kit was used for extracting the

RNA from the Sterivex units for the BGR1, KWK6 surface, KWK23 surface and SAS1B surface.

The large fraction for the same samples was extracted using the PowerWater DNA Isolation kit

(MoBio). Protocols were adapted for RNA analysis by adding 1% to 2% (final concentration) of

beta-mercaptoethanol as a preliminary step. The extraction column was loaded with 50% ethanol

(final concentration) to fix the RNA to the column. RNase-free water was used for the final elution

step. The co-extracted DNA was then digested with the RTS DNase Kit (MoBio). Following

problems with potential polyphenol contamination in some samples, the remaining samples were

extracted with the AllPrep DNA/RNA Mini Kit (Qiagen). This protocol was modified by the

addition of cross-linked polyvinylpyrrolidone (PVP, Alfa Aesar) to a final concentration of 10%

before loading the samples onto the lysate homogenization column. Prior to use, the PVP was

sterilized with UV light and was then added as a reagent to remove potential contaminating organic

matter (humic acids and polyphenols). For all samples, the extracted RNA was converted to cDNA

immediately with the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems-

Ambion). The concentrations and quality of cDNA were checked on a 1% agarose gel; cDNA was

not detected from the large fractions of KWK23 and the two SAS ponds, and these samples were

therefore not further processed. All cDNA was stored at -80°C until analysis.

23

2.2.4. High throughput multiplex tag sequencing

The V6-V8 region of the bacterial 16S rRNA that had been converted to cDNA was

amplified using the 454 primers as described in Comeau et al. (2011). PCR was carried out in a total

volume of 20 µL, the mixture contained HF buffer 1X (NEB), 0.25 µM of each primer, 200 µM of

each dNTPs (Life Technology), 0.4 mg mL-1 BSA (NEB), 1 U of Phusion High-Fidelity DNA

polymerase (NEB) and 1 µL of template cDNA. Two more reactions with 5X and 10X diluted

template were also carried out for each sample, to minimize potential primer bias. Thermal cycling

began with an initial denaturation at 98°C for 30 s, followed by 25 cycles of denaturation at 95°C

for 10 s, annealing at 50°C for 30 s, extension at 72°C for 30 s and a final extension at 72°C for 420

s. The three dilution reactions were pooled and purified with a magnetic bead kit Agencourt

AMPure XP (Beckman Coulter) and then quantified spectrophotometrically with the Nanodrop

1000 (Thermo Fisher Scientific). The amplicons were sequenced on two 1/8 plates of the Roche 454

GS-FLX using the “PLUS” chemistry at the IBIS/Laval University, plate-forme d’analyses

Génomiques (Québec, QC). Raw 454 sequences have been deposited in the NCBI database under

accession number SRP050189.

2.2.5. Sequence processing and statistics

Sequences were analysed using the UPARSE pipeline (Edgar, 2013). For quality filtering,

the sequences were truncated at 340 and 300 bp for the first and second 1/8 plate runs to keep 75%

of the reads at the 0.5 expected error rate. Singletons as well as chimeras were then removed and

operational taxonomic units (OTUs) were determined at the ≥97% similarity level. Taxonomic

assignment of these OTUs was performed using the Mothur classifier (Schloss et al., 2009) with a

0.8 confidence threshold based on the SILVA reference database (Pruesse et al., 2007) modified to

include sequences from our in-house, curated northern 16S rRNA gene sequence database.

Shannon, Simpson and Chao1 diversity indexes were estimated for each sample using Quantitative

Insights Into Microbial Ecology (QIIME) pipeline (Caporaso et al., 2010) by creating multiple

rarefaction statistics. Species richness and evenness were estimated with Shannon and Simpson

indexes while the Chao1 index provides an estimate of true richness (Colwell, 2012). Three-way

analysis of variance (three-way ANOVA) was used to assess differences in the diversity indices

between valleys, fractions and depths. As the Simpson index is a proportion, the data were arcsin

transformed to achieve normality. An a posteriori Tukey HSD test was run to identify differences

between valleys.

Beta-diversity analysis was used as a means of comparing ponds; for this analysis the

dataset was re-sampled to ensure the same number of reads per sample (3071), which corresponded

24

to the sample with the fewest number of sequences. beta-diversity was derived using the command

multiple_rarefaction_even_depth.py in QIIME. The rarefaction curve for OTUs reached a plateau

between 2000 and 4000 reads, indicating that our selection of 3071 reads provided a reasonable

representation of the community.

Downstream statistical analyses were performed in R (version 3.0.1) using the package

Vegan (Oksanen et al., 2013). The community data matrix was square root transformed before

running the statistical analyses. Beta-diversity was assessed by the use of Bray-Curtis dissimilarity

to estimate compositional difference among sampling sites. Distance-based redundancy analysis

(dbRDA) was performed to test and identify the influence of environmental variables on the

composition matrix. For the latter analysis, explanatory variables were selected via a forward

selection and highly correlated variables were removed from the analysis to ensure that collinearity

would not reduce analysis quality. The variance inflation factors (VIF) were calculated for each

variable and none of the coefficients exceeded the value of 5, we note that multicollinearity should

be examined when the VIF exceeds 10 (Borcard et al., 2011). Compositional differences among

valleys, depths and fraction were tested with permutation tests (999 permutations) on the Bray-

Curtis metric using the function Adonis in the Vegan package (Oksanen et al., 2013).

We defined ‘bacterial dominants’ as the ten most abundant OTUs from each valley. Each

OTU was submitted to a separate BLASTn search in NCBI GenBank

(http://blast.ncbi.nlm.nih.gov/Blast.cgi) to identify the nearest match. In some cases a single OTU

corresponded to two separate genera. We constructed a reference tree using longer sequences from

clone libraries (S. Crevecoeur, unpublished data) and GenBank to resolve these uncertainties. The

short reads were mapped onto the reference tree using the ParInsert command available in QIIME.

In many cases, several different OTUs had matches to a single genus.

2.3. Results

2.3.1. Limnological conditions

Temperatures decreased down the water column, with markedly colder bottom waters in all

of ponds (Figure 2-2A). The surface waters were well oxygenated except for the SAS sites, which

had only 5 to 6 mg L-1 of dissolved oxygen (DO) at the surface (Table 2-1, Figure 2-2B). DO

concentrations fell to lower values with depth (Figure 2-2B). Ponds were hypoxic (<2 mg L-1) at the

near-bottom sampling depths, with the exception of the bottom of the BGR ponds where dissolved

oxygen levels were around 3-4 mg L-1 (Table 2-1). There was a pH gradient across the three valleys:

25

the SAS valley ponds were the most acidic with pH values from 5.3 to 6.2, while those in the KWK

valley ranged from 6.0 to 6.7 and in the valley from 7.1 to 7.5. In all ponds, pH decreased slightly

with the depth (Figure 2-2C). The other measured limnological variables also showed large

differences between valleys, ponds and depths (Table 2-1). Conductivity was higher in the bottom

waters, by up to an order of magnitude in KWK23. DOC concentrations varied from <4 mg L-1 in

the blue-green colored surface waters of BGR1 to >15 mg L-1 in the black SAS ponds. The total

suspended solids varied by up to an order of magnitude, even among ponds from the same valley;

(for example, SAS1B versus SAS2A and BGR1 versus BGR2), and concentrations increased with

depth in all ponds. The indicators of trophic state (Chl a, SRP and TN) were mostly in the

oligotrophic to mesotrophic range, with higher values near the bottom of some the ponds, notably

KWK23 (SRP and TN), and SAS2A and KWK6 (Chl a).

Figure 2-2. Profiles of temperature (A), dissolved oxygen (B) and pH (C) as a function of the depth for the 7 studied

ponds.

2.3.2. Bacterial alpha-diversity

The sequencing statistics for individual ponds are given in Annex 1. For the overall study,

112,479 sequences (reads) were obtained, yielding a total of 1296 OTUs (excluding singletons,

defined as OTUs that only occurred once in the entire data set). Three-way ANOVA for the

different sample groupings (Table 2-2) showed that there was no significant difference in alpha-

diversity as estimated by either the Shannon or Simpson indices between depths, size fractions or

valleys. However, the Chao1 index showed significant differences between valleys (p=0.018) and

fraction (p=0.004), and a significant interaction between valleys and depth (p=0.01). The mean

26

species richness of the KWK ponds was 32 % higher than in the BGR ponds, and the Tukey HSD

test showed that the significant difference between valleys was only between KWK and BGR

(p=0.015). The mean Chao1 index for the large fraction was 22% greater than for the small fraction

(Table 2-2).

Table 2-2. Sequencing and diversity statistics for samples grouped according to valley, depth or size fraction.

Values are means (n=3 to 14) with CV (SD as % mean) in parentheses.

Sample group OTUsa Shannona Simpsona Chao1a

SAS 183 (11)

4.35 (13) 0.88 (4) 210 (8)

KWK 211 (27) 5.09 (10) 0.91 (8) 224 (23)

BGR 156 (39) 4.58 (16) 0.89 (6) 170 (38)

Surface 176 (26) 4.98 (14) 0.92 (5) 194 (25)

Bottom 196 (34) 4.56 (13) 0.87 (7) 210 (31)

Small 169 (34) 4.66 (14) 0.90 (7) 187 (30)

Large 216 (21) 4.96 (13) 0.92 (7) 229 (22) aCalculated with an OTU definition of 97% similarity.

2.3.3. Bacterial beta-diversity and community composition

All ponds were dominated by Proteobacteria followed by Bacteroidetes, Verrucomicrobia,

and Actinobacteria (Figure 2-3). The maximum Proteobacteria representation (80% of reads) was in

the SAS2A surface community. Within the Proteobacteria, Betaproteobacteria was the dominant

class, except at the bottom of KWK23 where Gammaproteobacteria dominated. Chlorobi dominated

the bottom of KWK1 and SAS2A. Gemmatimonas, Lentisphaerae and Spirochaetes were found in

low abundance (about 1% of the community) within the KWK valley. There was little difference

between the small and large fractions, with the exception of the BGR1 bottom community that had

a high proportion (25% of reads) of cyanobacteria in the large fraction only.

27

Figure 2-3. Relative abundance of the different phyla. The samples were from the surface (-S) and bottom (-B) of

ponds in the three valleys. The small fraction (left) is for samples in the size range 0.2 to 3 µm, and the large

fraction is for 3 to 20 µm. Phyla that were less than 1% of total abundance are combined under “Other phyla”.

2.3.4. Bacterial dominants and high abundance of methanotrophs

A total of 21 separate taxa were found in the top ten OTUs from each valley (Table 2-3),

which accounted for 38% of the total number of reads. The two most abundant OTUs corresponded

to two Betaproteobacteria, Variovorax and Polynucleobacter. These two taxa were in all samples,

and accounted for up to 30% of the community reads in each sample. Another OTU in the family

Puniceicoccaceae (Verrucomicrobia) was more abundant at the BGR valley and represented up to

40% of the reads in the surface large fraction of BGR2. One OTU of Pelodictyon (Chlorobiaceae)

represented up to 50% of the bottom community reads for KWK1 and another OTU of Pelodictyon

was the most abundant taxon in the bottom of SAS2A.

28

Table 2-3. Identity of the 10 most abundant OTUs (defined at a level of 97% similarity) in each valley following the

SILVA taxonomy. Following a BLASTn search, nearest matches and the providence of representative reads in

GenBank were identified. Several groups appear multiple times because different OTUs match the same group.

See Figure 2-5B for their distribution.

Number of

reads

Silva taxonomy %

Identity

Isolation

source

Accession

number

Genbank

taxonomy

7461 Comamonadaceae 100 Wheat

phyllosphere

KF054966 Variovorax soli

6253 Polynucleobacter 100 Lake Grosse

Fuchskuhle

KC702668 Polynucleobacter

5757 Puniceicoccaceae 100 Yellowstone

Lake

HM856500 Opitutae

3748 Pelodictyon 99 Lake

chemocline

AM086645 Pelodictyon

clathratiforme

2454 Methylobacter 99 Thaw pond

hypolimnion

JN656724 uncultured gamma

proteobacterium

1881 Rhodoferax 100 Soil GQ421098 uncultured

Rhodoferax

1539 Rubrivivax 99 Foodplain FM886868 Rubrivivax

gelatinosus

1317

Comamonadaceae 99 Waterfall KM035968 Curvibacter

1275 ACK-M1 100 Irrigation

water

JX657295 uncultured

actinobacterium

1260 Nostocaceae 99 Eutrophic

pond

FN691914 Dolichospermum

curvum

1256 Sediminibacterium 99 Yellowstone

Lake

HM856392 uncultured

Sediminibacterium

1250 Sediminibacterium 100 Lake

epilimnion

HQ532649 uncultured

Bacteroidetes

1137 Pelodictyon 97 Lake

chemocline

AM086645 Pelodictyon

clathratiforme

1085 Limnohabitans 99 Daphnia

Digestive

tract

HM561466 uncultured

Limnohabitans

1003 Sediminibacterium 99 Yellowstone

Lake

HM856387 uncultured

Sediminibacterium

947 Methylotenera 99 Biodeteriora-

ted wood

KC172609 uncultured

Methylophilaceae

761 Chitinophagaceae 99 Lake

epilimnion

HQ532140 uncultured

Bacteroidetes

739 Synechococcales 99 Meromictic

lake

AB610891 Synechococcus

637 Methylobacter

100 Thaw pond

hypolimnion

JN656784 uncultured gamma

proteobacterium

477 Methylococcaceae 96 Landfill

cover soil

HF565143 Methylobacter

450 Polaromonas 99 stems KF385223 uncultured

Polaromonas

29

Finer taxonomic analysis revealed the presence of methanotrophic bacterial groups at all sites

(Figure 2-4), with 0.1 to 27% of OTUs matching Methylobacter (5% on average per site). Other

methanotrophic Bacteria belonging to the family Methylococcales (Methylocaldum, Methylomonas,

Crenothrix, or unknown Methylococcales) were found in all ponds, although sometimes in low

proportions (<1%). An exception was in the bottom waters of SAS1B where methanotrophs

accounted for 23% of the sequences, more than half of which were in the Methylococcales.

Sequences corresponding to a newly discovered order of methanotrophic Bacteria,

Methylacidiphilales (Verrucomicrobia), were found at the KWK and SAS valleys, in smaller

proportions compared to other methanotrophs; for example, 1-6% of the total reads in the KWK6

surface samples. Amongst the dominant OTUs, methanotroph sequences were relatively abundant

in most samples (Figures 2-4, 2-5), with a median representation of 4.9% of total reads per sample,

and a maximum of 25% for one single Methylobacter OTUs in the KWK23 bottom community

reads. Another OTU corresponding also to Methylobacter was present at the BGR valley along with

the OTU belonging to the order Methylococcaceae.

Figure 2-4. Relative abundance of methanotrophic Bacteria. The samples were from the surface (-S) and bottom (-

B) of ponds in the three valleys. The small fraction (left) is for samples in the size range 0.2 to 3 µm, and the large

fraction is for 3 to 20 µm. The taxa are labelled according to the highest taxonomical level; genera that represented

less than 1% of total abundance were grouped together and labelled by their shared order.

30

Figure 2-5. (A) Bray-Curtis dissimilarity cluster analysis with the community data matrix (OTUs clustering at

>97% of identity) for the study ponds. Surface samples are represented by triangles and bottom samples by circles,

either filled (small fraction) or open (large fraction). (B) The bacterial dominants in each sample identified by their

lowest taxonomical level either found on GenBank or SILVA (see Table 2-3); the size of the filled circle is

proportional to their relative abundance.

Some of the most abundant OTUs matching genera Rhodoferax, Rubrivivax,

Limnohabitans, Curvibacter, the Actinobacteria ACK-M1, the three Sediminibacterium OTUs and

the methylotrophic genus Methylotenera were distributed evenly across samples. The filamentous

cyanobacterium Dolichospermum was highly abundant only in the surface of KWK1 and KWK6

where it contributed 20% of the community. Finally, OTUs matching Synechococcus, Polaromonas

and a taxon in the Chitinophagaceae (Bacteroidetes) were more abundant in samples from the BGR

and KWK valleys (Figure 2-5B).

2.3.5. Bacterial community structure as a function of environmental gradients

Community structure mostly followed a regional pattern as ponds from the same valley

clustered together in the Bray-Curtis dissimilarity dendrogram (Figure 2-5). There were some

31

exceptions, with the bottom of KWK1 and 23 clustering closer to the SAS sites and apart from the

other KWK samples. Within the BGR cluster, surface and bottom communities clustered separately

while the two depths clustered together at the SAS sites. In terms of community composition, with

the exception of the more aerobic BGR waters, the bottom communities were strikingly different

from those at the surface because of the presence of anoxygenic phototrophs, specifically

Chloroflexi and Chlorobi. Small and large fractions from the same sample always clustered

together. A permutation test was used to test for differences between valleys, depth and size fraction

and indicated significant differences in community composition according to valley (p=0.001) and

depth (p=0.009) as well as a significant interaction between valleys and depth (p=0.011), indicating

that the difference between surface and bottom communities depended on valley location. Pairwise

comparison indicated that amongst the significant differences between sites, communities in the

KWK valley were significantly different from those in the BGR and SAS valleys (p=0.001 and

p=0.015) but BGR and SAS were not significantly different. Permutation tests were carried out to

compare surface versus bottom communities, and all four dominant phyla (Proteobacteria,

Bacteroidetes, Verrucomicrobia and Actinobacteria) showed significant differences between depths

(p<0.05). There were no significant differences between fractions (p=0.5).

The dbRDA ordination (Figure 2-6) explained 64% of the variation and confirmed the

regional pattern. The first horizontal axis was significantly correlated with pH, DO, TSS and DOC

while the vertical axis was significantly correlated with chl a and conductivity. KWK samples were

more dispersed compared to the SAS and BGR samples. The distribution of the BGR sites was

mostly explained by the higher pH in this valley. The BGR sites and all of the surface KWK

samples were separated from the other samples including bottom layers from KWK ponds and

appeared associated with the higher concentrations of dissolved oxygen, while the bottom of KWK1

and 23, the SAS sites and the bottom of KWK6 were at the low end of the oxygen gradient. The

SAS valley was differentiated by its higher DOC, and the bottom samples from KWK1 and

KWK23 by their higher concentrations of TSS and SRP. The surface of the KWK sites and KWK6

bottom were characterised by lower conductivity and higher chl a.

32

Figure 2-6. Distance-based redundancy analysis ordination plot showing selected environmental variables that

were significantly correlated with sample distribution. Abbreviations for the environmental variables are given in

Table 2-1.

2.4. Discussion

2.4.1. Bacterial alpha-diversity

The subarctic thaw ponds at all sites were thermally stratified, consistent with previous

observations in summer (Laurion et al., 2010). There were large vertical gradients in chemical

properties, with a surface oxic layer (epilimnion) overlying low oxygen bottom waters

(hypolimnion). These gradients provided a wide range of potential bacterial habitats within a single

pond. The ponds contained diverse bacterial assemblages, with the number of OTUs (99-307 per

sample) overlapping that reported for much deeper, stratified water bodies. For example, 280-425

OTUs were reported from a meromictic lake in the High Arctic using the same primers for tag

pyrosequencing the 16S rRNA gene (Comeau et al., 2012), and 67-223 OTUs were reported for

thermally stratified German lakes (Garcia et al., 2013). The Shannon and Simpson diversity indices

in the thaw ponds were greater than those reported form German lakes, which were all less than 4.1

33

for the Shannon index and 0.36 for the Simpson index (Garcia et al., 2013). However, the

taxonomic richness of these thaw pond communities was much lower than that in soils. For

example, 4781-6231 OTUs were reported in 10 g samples of German grassland soils (Will et al.,

2010) and 1496-1857 OTUs in the same quantity of High Arctic soil crusts (Steven et al., 2013;

singletons excluded). Previous studies have documented this large disparity between lakes and soils

(Lozupone and Knight, 2007; Tamames et al., 2010), suggesting fundamental difference in

microhabitats in the highly structured matrix of soils relative to aqueous planktonic environments.

Given the large potential input of soil particles and their associated Bacteria from permafrost

degradation, higher levels of diversity and species richness might have been expected in these

waters. However the total number of OTUs (1296) for our entire study including all 22 samples was

17% of that observed in the High Arctic soil crust study (7432), and within the range for other

aquatic ecosystems.

The Shannon and Simpson diversity indices showed that all samples were equally diverse,

with no significant differences among valleys, depths or size fractions. Steven et al. (2013) similarly

observed in their soil crust analyses of 6 sites in the High Arctic that differences in diversity indices

among sites were small relative to comparisons with other systems, including Antarctic soils. It

would be of great interest to compare bacterial diversity in these northern thaw ponds with the

shallow ponds of Antarctica, but to our knowledge such data are lacking. The significant difference

of Chao 1 index between KWK and BGR valleys indicated that species richness was higher at the

lower latitude site. Species richness values for the surface waters were the same order of magnitude

for the three valleys, however there was a significant interaction of species richness between sites

and depth with species richness of bottom water increasing from BGR to KWK and SAS. This also

corresponds to a gradient in DOC, with highest concentrations in the SAS ponds, and may reflect

differences in bacterial productivity.

RNA vs DNA templates have been used to distinguish between active and inactive cells

(Jones and Lennon, 2010) and to assess potential growth rates (Campbell and Kirchman, 2013). 16S

rRNA is thought to provide an estimate of ‘potentially active Bacteria’ (Blazewicz et al., 2013),

which would be a subset of the community represented in the environmental DNA. The latter may

include dead cells, spores and even free DNA that remains intact in the dark, cold, polar

environment (e.g., Charvet et al., 2012). For this reason it is thought that the use of DNA can lead to

overestimation of diversity, while rRNA may provide a more conservative and accurate estimation

of diversity.

34

2.4.2. Bacterial dominants

For the overall data set, the dominant phylum was Proteobacteria and Betaproteobacteria

the dominant Class, as in many freshwater ecosystems (Newton et al., 2011). Two genera within

that Class were particularly abundant, each on average representing around 10% of the total number

of reads: Polynucleobacter and Variovorax. Both have attributes that make them well-suited to the

heterogeneous combination of allochthonous (terrestrial) and autochthonous (aquatic) organic

carbon compounds that are likely to occur in these waters. Polynucleobacter is a cosmopolitan

genus that often dominates planktonic freshwater communities. It produces extremely small cells

that are capable of growing aerobically on a wide range of complex media (Hahn, 2003), although

genomic analyses of free-living and symbiotic strains of P. necessarius indicate a small genome

size and the absence of certain functions, including an inability to use sugars as a carbon and energy

source (Boscaro et al., 2013).

Variovorax includes taxa with diverse nutritional and energy acquisition strategies,

including the ability to break-down and use a wide variety of plant-derived molecules, as well as

denitrification, sulfate reduction and autotrophic CO2 fixation. Genomic analysis of a plant-

associated V. paradoxus strain has shown that it has a remarkable combination of features for both

heterotrophic and autotrophic lifestyles, and although it lacks the genes for methane

monooxygenase, it is known to enhance the activity of methanotrophs in consortia (Han et al., 2011;

and references therein). This strain also has three genes encoding aerobic carbon monoxide

dehydrogenase, which could be of value given that high levels of carbon monoxide are known to be

produced by photochemical reactions in high latitude, DOC-rich waters (Xie et al., 2009).

Subarctic thaw ponds emit methane (Laurion et al., 2010), and the presence and diversity of

methanotrophic Bacteria is of particular interest. Consistent with our hypothesis, methanotrophs

were found in all samples, and included three of the most abundant OTUs: two strains of

Methylobacter, and one OTU with affinities to the family Methyloccocaceae. The methanotrophic

Bacteria all belonged to Type I methanotrophs which are in the Gammaproteobacteria and the

Verrucomicrobia. No Bacteria belonging to Type II methanotrophs in the Alphaproteobacteria were

identified. Methanotrophic communities are known to be sensitive to temperature, with Type I

methanotrophs developing under low temperature conditions (Wartiainen et al., 2003; Börjesson et

al., 2004; Wagner et al., 2005; Graef et al., 2011) and Type II under higher temperatures (Mohanty

et al., 2007). The absence of Type II methanotrophs from even the warmer surface waters of the

thaw ponds in summer implies selection by the low temperature conditions that prevail throughout

the water column during most of the year.

35

Methanotrophs contributed a relatively high percentage of the total number of sequences in the thaw

ponds, with a median of 4.9%, and a maximum of 27% in the bottom waters of KWK23. This is

particularly high relative to other lakes, for example up to 2% in a meromictic lake in the High

Arctic (Comeau et al., 2012) and only up to 3% of sequences in the plankton of eutrophic Lake

Plusssee in Germany (Eller et al., 2005). This high relative abundance is closer to that observed in

tundra soils (Vecherskaya et al., 1993) and anoxic lake sediments (Costello and Lidstrom, 1999),

indicating the biogeochemically distinct nature of thaw pond ecosystems, with continuously high

inputs of methane as a bacterial energy source.

Two groups of bacterial phototrophs were conspicuous members of the thaw pond

assemblages: Cyanobacteria and Chlorobi. The presence and abundance of sequences for these taxa

varied among the samples, in part associated with depth and valley specific characteristics (see

below). Cyanobacteria are often dominants of high latitude aquatic ecosystems, especially mat-

forming Oscillatoriales and picoplanktonic taxa in the genus Synechococcus (Vincent, 2000). In the

thaw pond data set, Oscillatoriales were little represented (with the exception of the bottom of

SAS1B), as might be expected in a planktonic environment, which was in contrast to High Arctic

soil crusts where Oscillatoriales are among the dominants. Mat-forming taxa are unlikely to thrive

on the sediments of the subarctic thaw ponds given their high turbidity and the poor penetration of

photosynthetically active radiation to the bottom (Watanabe et al., 2011). The picocyanobacterial

group Synechococcales was represented in many of the ponds, although accounted for only 0.7% of

the total bacterial reads. A surprisingly more abundant cyanobacterial group in two of the ponds

was the Nostocaceae, with strong affinities (>99%) to the nitrogen fixing taxon Dolichospermum

curvum (formerly known as Anabaena curvum). Colonial cyanobacteria are largely absent from the

plankton in other waters of the north and south polar regions (Vincent, 2000), again underscoring

the distinctive properties of permafrost thaw ponds as an ecosystem type.

Pelodictyon was the fourth most abundant OTU in the overall data set, accounting for 3.3%

of all sequences. This green sulfur Bacterium (Chlorobi) is often observed under anoxic conditions

in stratified freshwater lakes at depths where there is sufficient light for photosynthesis as well as

high concentrations of its electron donor hydrogen sulfide. Chlorobi were earlier reported from 16S

rRNA gene clone libraries of the KWK valley (Rossi et al., 2013), and their importance in the deep

low-oxygen waters in many of the lakes suggest that anaerobic photosynthesis likely contributes

significantly to the production of these subarctic ecosystems.

36

2.4.3. Attached and free-living Bacteria

In a wide range of marine systems, particle-attached and free-living bacterial communities

are morphologically and phylogenetically distinct (e.g., Crump et al., 1999; Lapoussière et al.,

2011; Mohit et al., 2014). Similarly in some freshwaters, for example Lake Erie (Mou et al., 2013)

and Lake Bourget (Parveen et al., 2011) differences in attached and free-living bacterial

communities have been reported. Here we found little difference between the communities from our

large and small fractions, which should have selected for attached and free-living bacterial

communities respectively. The lack of difference in some of the samples may have been due to

extremely small particulates in some of these ponds (ca. 1 µm; Watanabe et al., 2011) that would

have passed through the 3-µm filter along with their attached bacterial flora, masking any

differences. However, Chao1 diversity was significantly greater in the >3 µm size class and this

would be more consistent with the >3-µm filters retaining both attached and many free living

bacteria if the filters became blocked. Our inability to filter more than 500 mL of sample suggest

this latter explanation is more likely, and it is also consistent with the presence of picocyanobacteria

(Synechococcales) in some of the large fraction samples, notably from BGR1 (Figure 2-5).

Irrespective of the cause, more detailed size fractionation or a microscopy approach would be

needed to accurately resolve the difference between particle-attached and free-living bacteria in

these highly turbid ponds.

2.4.4. Depth gradients and bacterial community composition

The thaw ponds were stratified with pronounced gradients in temperature and oxygen.

Although the ponds are shallow, the high concentrations of CDOM and small wind fetch mean that

they stratify early in spring and remain so over the summer (Watanabe et al., 2011). In fact, there is

evidence that some ponds may not mix to the bottom at all in some years (Laurion et al., 2010).

Within valleys, surface and bottom communities clustered well apart from each other, with the

exception of the SAS site where surface waters were depleted in oxygen compared to the other sites

(Figure 2-5, Table 2-1). In terms of community composition, Actinobacteria were often poorly

represented at the bottom of all ponds, consistent with their preference for more oxygenated waters

(Allgaier and Grossart, 2006; Taipale et al., 2009). Anaerobic sulphate reducers, including the

Deltaproteobacteria Geobacter, Anaeromyxobacter and Desulfovibrio, were found in small

proportions in the bottom of ponds. Other bacterial dominants with matches to the family

Chitinophagaceae (Bacteroidetes) including Sediminibacterium have the ability to produce H2S (Qu

and Yuan, 2008), consistent with an active sulfur cycle and anaerobic conditions.

37

Some of the largest depth-dependent differences were for the phototrophic taxa, in keeping

with the rapid attenuation of photosynthetically available radiation in these waters (Watanabe et al.,

2011), as well as the depth variations in chemical properties of the ponds. The anoxygenic

phototroph Pelodictyon was one of the most abundant OTUs, but was restricted to low oxygen

bottom waters, as expected, and was absent from the more oxygenated BGR sites. This

photosynthetic sulfur Bacterium is adapted to low light and anoxic, H2S-containing waters (Gich et

al., 2001). Cyanobacteria in contrast, tended to be in the upper euphotic zone, with the KWK sites

especially having a greater relative representation of cyanobacteria in the surface compared to the

bottom of the ponds. The most striking difference was in the surface waters of KWK6 where

Nostocaceae were the dominant cyanobacterial group, indicating the likely growth of colonial,

potentially nitrogen-fixing species in these surface waters. However, for other ponds the large

fraction of BGR1 bottom sample was dominated by Synechococcales, possibly indicating the

growth of smaller celled cyanobacteria able to maintain populations in the better illuminated bottom

waters of BGR1, where concentrations of light-attenuating materials (Chl-a, DOC, TSS) were less

than most of the other ponds. Oscillatoriales, which are filamentous, were found in the bottom of

another pond, SAS1B, possibly indicating the sedimentation of aggregates of filaments or sections

of mat from shallower depths.

In the KWK and SAS waters, methanotrophs were more abundant in the bottom waters,

consistent with the earlier reported profiles of methane in the ponds. For example in KWK23,

where the difference between surface and bottom samples was particularly striking, methane

concentrations increase sharply at the bottom of the pond by several orders of magnitude (Laurion

et al., 2010). Methanotrophs are aerobic and unable to sustain growth under completely anoxic

conditions (Chowdhury and Dick, 2013), and our observations imply that the deep pond habitat

provides a favourable combination of high methane and adequate oxygen. Interestingly, in the BGR

ponds where there was less difference between surface and bottom oxygen, there was a higher

proportional abundance of methanotrophs in the surface waters; this might suggest dependence on

methane production from decomposition of macrophytes in the littoral zone, or via plant-microbe

interactions in this region (Laanbroek, 2010).

2.4.5. Spatial variation and landscape gradients

Our results showed a clustering of ponds according to the individual valleys (Figure 2-5),

implying environmental filtering of community composition based on landscape related properties

(Figure 2-6). The distance based redundancy analysis pointed to DOC concentrations and pH as

controlling factors, both of which are known to influence bacterial community structure (e.g.,

38

Lindström et al., 2005; Fierer and Jackson, 2006). The higher pH in the BGR ponds separated them

from the ponds in the other two valleys, although not all microbial groups followed expected

relationships. Verrucomicrobia have been associated with low pH conditions (Lindström et al.,

2005), but this group was relatively abundant in the BGR pond with the highest pH, and poorly

represented in the SAS pond with the lowest pH. However the pH range in this study was narrower

than in Lindström et al. (2005).

Dissolved organic matter origin and source has been reported to influence bacterial

community structure and function (Kritzberg et al., 2006). DOC had the greatest influence on

community composition at the SAS site, where the ponds originated from organic palsas and DOC

concentrations were higher and likely different in composition from the DOC at the other two

valleys, where the ponds were formed by lithalsa thawing. The permafrost thaw gradient was also

associated with the availability of DOC. The BGR valley is surrounded by discontinuous permafrost

and >50% of the soil carbon would be frozen, and not available for degradation. In contrast, the

southern, more degraded KWK and SAS valleys, ponds would be influenced by the eroding

permafrost (Bouchard et al., 2014), and the input of allochthonous DOC would be more substantial.

Photochemical breakdown of some of the more recalcitrant soil organic materials to lower

molecular weight compounds (Laurion and Mladenov, 2013) may additionally enhance substrate

availability in these waters.

2.5. Conclusions

Permafrost thaw lakes and ponds are a prominent feature of the northern landscape and are

strong emitters of greenhouse gases. Because of their abundance on the landscape and wide

distribution they are also useful for investigating the influence of large scale versus small scale

environmental gradients. We found that permafrost gradients influenced the landscape properties, in

turn driving bacterial community composition. Within, a pond the physico-chemical stratification

creates oxygen gradients that favor different microbes. In permafrost thaw lakes, the variety of

allochthonous substrates derived from terrestrial vegetation and soils, and autochthonous sources

including oxygenic photosynthesis by cyanobacteria, microalgae and macrophytes and anoxygenic

photosynthesis by green sulfur Bacteria, likely provide a heterogeneous range of organic substrates

available to diverse heterotrophic taxa. Methanotrophs were among the most abundant sequences at

most sites, indicating the potential importance of methane as a bacterial energy source in these

waters. Their activities likely reduce the net emission of methane, in the process contributing to the

CO2 efflux from these ecosystems. The functionally diverse bacterial taxa in these abundant

‘biogeochemical hot spots’ across the subarctic landscape likely have a strong effect on the net

39

emission of both greenhouse gases, as the result of their metabolism of organic carbon from

multiple sources. Ancient permafrost soils are now being increasingly thawed, eroded and

mobilised as a result of the rapid warming of the North. The diverse bacterial communities

identified here will likely assure that at least part of these new transfers from land to water are

ultimately converted to CO2 and released to the atmosphere.

2.6. Acknowledgements

We acknowledge the Natural Sciences and Engineering Research Council of Canada

(NSERC) funding for Discovery Grants to WFV and CL, the Discovery Frontiers grant ADAPT

and Canada Research Chair support to WFV, the Network of Centres of Excellence ArcticNet

support for WFV and CL, and the Fonds de recherche du Québec-Nature et technologies (FRQNT)

for funding for the Centre d’études nordiques (CEN). Computing support was provided by

CLUMEQ/Compute Canada.

We are also grateful to Claude Tremblay at the Whapmagoostui-Kuujjuarapik CEN station,

the pilots of Canadian Helicopter Ltd., Anna Przytulska-Bartosiewicz, Bethany Deshpande,

Paschale Noël Bégin and Alex Matveev for help in the field, Marianne Potvin for laboratory

assistance and the development of protocols, Adam Monier for aid with bioinformatics advice,

Marie-Josée Martineau for pigment analysis, and two anonymous reviewers and the editor for their

valuable comments on previous versions of the manuscript.

40

Chapitre 3. Diversity and activity of methanotrophs in

low-oxygen permafrost thaw ponds

Résumé

Les lacs et mares dérivés de la fonte du pergélisol sont de forts émetteurs de dioxyde de

carbone et de méthane dans l’atmosphère, mais on en connait peu sur les processus d’oxydation du

méthane dans ces eaux. Dans cette étude, nous avons examiné la distribution et l’activité des

bactéries méthanotrophes dans les mares de fonte provenant de l’érosion de deux types de

pergélisol : dominé par la tourbe ou minéral. Nous avons émis l’hypothèse que la composition de la

communauté est influencée par le type de pergélisol, et que l’activité des méthanotrophes dépend de

la disponibilité en oxygène. Nos analyses des transcrits du gène pmoA par séquençage Illumina et

PCR quantitative ont montré que les communautés étaient composées de lignées diverses et

potentiellement actives. Les méthanotrophes de Type I, particulièrement le genre Methylobacter,

dominait toutes les communautés, mais il y avait cependant une séparation taxonomique claire entre

les deux types de pergélisols, ce qui indique un contrôle environnemental de la structure des

communautés. L’activité des méthanotrophes mesurée avec la concentration en transcrits de pmoA

est corrélée avec la conductivité, le phosphore et les matières totales en suspension. L’activité des

méthanotrophes a aussi été détectée dans les eaux de fond pauvres en oxygène, où elle était

négativement corrélée avec la concentration en méthane, ce qui suggère une déplétion du méthane

par les méthanotrophes. Le réchauffement climatique en cours pourrait induire une plus longue

période de fonte de la glace dans les mares de fontes et changer l’oxygénation, ce qui pourrait

altérer l’activité méthanotrophique et les émissions de méthane dans l’atmosphère.

41

Abstract

Lakes and ponds derived from thawing permafrost are strong emitters of carbon dioxide and

methane to the atmosphere, but little is known about the methane oxidation processes in these

waters. Here we investigated the distribution and activity of methanotrophic Bacteria in thaw ponds

in two types of eroding permafrost landscapes in subarctic Québec: peatlands and mineral soils. We

hypothesized that community composition is influenced by the surrounding landscape, and that

methanotrophic activity is a function of oxygen conditions. Our analysis of pmoA transcripts by

Illumina amplicon sequencing and quantitative PCR showed that the communities were composed

of diverse and potentially active lineages. Type I methanotrophs, particularly Methylobacter,

dominated all communities, however there was a clear taxonomic separation between the two

landscape types, indicating environmental control of community structure. Methanotrophic activity

as measured by pmoA transcript concentrations was correlated with conductivity, phosphorus and

total suspended solids. Methanotrophic activity was also detected in low-oxygen bottom waters,

where it was inversely correlated with methane concentrations, suggesting methane depletion by

methanotrophs. Ongoing warming may induce a longer ice-free season and changes in oxygenation

that could alter methanotrophic activity and methane emissions to the atmosphere.

42

3.1. Introduction

Climate warming has resulted in the thawing and collapse of permafrost landscapes

(thermokarst erosion), producing changes in the frequency and size of the resultant thaw lakes. In

some areas, permafrost thaw lakes are draining and infilling, while in other northern regions they

are expanding (Wrona et al., 2016 and references therein). Northern lakes and ponds are strong

emitters of methane (Walter et al., 2008; Wik et al., 2016), and emission rates are especially high

from small thermokarst water bodies in subarctic permafrost peatlands (Matveev et al., 2016). Many

of these waters are known to become highly stratified during summer, with marked gradients of

temperature and oxygen down through the water column (Breton et al.,2009). This stratification is

likely to have an impact on methane production (methanogenesis) and loss processes

(methanotrophy) that are both dependent on temperature and oxygen availability.

Methane emissions to the atmosphere are the net result of microbial methanogenesis and

methanotrophy, and in many environments, the activity of methanotrophic Bacteria may impose a

strong control on emission flux rates. Methanotrophs have the distinctive ability to use methane as

their sole source of carbon and energy (Hanson and Hanson, 1996) and have the capacity to oxidize

up to 60% of the yearly methane produced at a global scale (Reeburgh et al., 1993). In rice fields,

for example, the proportion of methane oxidized by methanotrophs varies from 20% to 90% (Epp

and Chanton, 1993; Gilbert and Frenzel, 1995; Khalil et al., 1998). In soil and freshwater

ecosystem, up to 95% of the methane produced is consumed by oxidation (Frenzel et al., 1990; Le

Mer and Roger, 2001; Bastviken et al., 2008). For example, in a boreal humic lake, 80% of the

methane produced in the sediments was estimated to be consumed by methanotrophs in the water

column (Kankaala et al., 2006).

Less is known about the importance of methanotrophy in Arctic and Subarctic ecosystems.

In the Arctic Ocean, a ‘methane filter’ of aerobic and anaerobic methane oxidizers in the sediments

is thought to substantially moderate the current fluxes of methane to the water column and

atmosphere, and methanotrophs may be an increasingly important source of biomass for Arctic

benthic food webs in the future (James et al., 2016). In High Arctic soils, Type II methanotrophs in

the upper soil profile appear to be sufficiently active to fully consume all the methane, and the soils

are net methane sinks (Stackhouse et al., 2015). This contrast with northern lakes, which in general

appear to be net sources of methane (Wik et al., 2016). Although permafrost thaw lakes in the North

are particularly well known for their high production rates of methane, little attention has been

given to their methane oxidation potential.

43

The advent of molecular techniques has provided the means to identify micro-organisms in

many habitats, and has revealed a remarkable diversity of microbial taxa in all domains of life

(Pedrós-Alió, 2006). The application of functional genomics is now allowing links to be drawn

between microbial diversity and biogeochemical processes (Zak et al., 2006), including for

microbes involved in the methane cycle. The functional gene coding for one of the enzyme proteins

involved in bacterial methane oxidation, specifically the alpha-subunit of particulate methane

mono-oxygenase (pmoA), is increasingly used as a phylogenetic marker to study methanotroph

community composition and activity (McDonald and Murrell, 1997; Degelmann et al., 2010). Due

to phylogenetic and metabolic differences, methanotrophs are commonly separated into two types

(Bowman, 2006). Type I methanotrophs (Gammaproteobacteria) use the ribulose monophosphate

(RuMP) pathway for carbon assimilation while Type II methanotrophs (Alphaproteobacteria) uses

the serine pathway. Type I taxa are sometimes further divided into Type Ia (Methylobacter-related)

and Type Ib (Methylococcus-related) depending on their phylogenetic affiliation (McDonald et al.,

2008). Recently, new groups of methanotrophs have been discovered that are phylogenetically

distant from Type I and II; for example, the genus Crenothrix, which belongs to the

Gammaproteobacteria but has a phylogenetically divergent pmoA (Stoecker et al., 2006); the

verrucomicrobial order Methylacidiphilales (Dunfield et al., 2007); and the NC10 phylum that has

the ability to couple methane oxidation with denitrification (Ettwig et al., 2009).

Our goal in the present study was to identify factors that control the community structure

and activity of methanotrophic Bacteria in permafrost thaw ponds, and to address this goal by way

of phylogenetic and quantitative PCR (qPCR) analysis of pmoA transcripts. We conducted this

research in ponds that lie in two types of permafrost landscape types in subarctic Québec: peatlands

with raised mounds of organic material (palsas) and shrub-tundra landscapes with raised mounds of

inorganic soils (lithalsas). These landscapes lie at the southern end of the permafrost range, and the

ice-rich mounds are thawing and degrading rapidly, giving rise to palsa- and lithalsa-derived

thermokarst lakes that differ in their limnological properties such as dissolved organic carbon

concentrations (Przytulska et al., 2016). We hypothesized that the lakes in the two landscape types

would also differ in their methanotroph community structure. Additionally we tested the hypothesis

that active methanotrophy (as measured by pmoA transcripts) would be a feature of all of these

lakes, with the potential to reduce net methane emissions, and that the distribution of

methanotrophic activity down the water column would be determined by oxygen availability.

44

3.2. Materials and Methods

3.2.1. Study site and sampling

Samples were collected during two field visits, 1 to 13 August 2012 and 31 July to 19

August 2013, from four subarctic valleys in northern Québec. The KWK (55°16’N; 77°46’W) and

SAS (55°13’N; 77°42’W) valleys are located in the sporadic permafrost region, where permafrost

covers less than 2% of the soil. The nearest village is Whapmagoostui-Kuujjuarapik. BGR

(56°37’N; 76°13’W) and NAS (56°55’N, 76°22W) valleys are situated 100 km north of the two

other valleys, in the discontinuous permafrost region, close to the village of Umiujaq, Quebec

(details in Crevecoeur et al. 2015). The SAS valley ponds are the result of thawing of palsas, peat-

rich permafrost mounds (Bhiry and Robert 2006) while NAS, BGR and KWK ponds originated

from thawing of lithalsa, mineral permafrost mounds (Lajeunesse and Allard 2003; Calmels et al.,

2008; Bhiry et al., 2011).

3.2.2. Sampling and physico-chemical measurements

Profiles of temperature, dissolved oxygen (DO), and pH were taken with a 600R

multiparametric probe (Yellow Spring Instrument). Detection limit for dissolved oxygen with the

probe is 0.2 mg L-1. Bottom samples were collected using a horizontally mounted Van Dorn bottle

(Wilco) and immediately transferred to acid washed 4-L Cubitainers that were rinsed with sample

water prior to filling. Cubitainers™ were overfilled placed in coolers and brought back to the

laboratory. Methane concentrations were measured with the headspace technique as described in

Matveev et al. (2016). Water samples for physico-chemical analysis (DOC, TSS and TP) were

processed as in Crevecoeur et al. (2015).

3.2.3. RNA sample preparation and sequencing

The water for RNA analysis was prefiltered through a 20 µm Nitex screen and then

sequentially filtered through a 3 µm Nuclepore™ polycarbonate (PC) filter and a 0.2 µm Sterivex™

unit (Millipore) to separate large (particle-attached) and small fraction (free-living) of planktonic

micro-organisms. Filter were conserved in RNAlater (Life Technologies) and stored at -80 ºC

before extraction. RNA was extracted with the AllPrep DNA/RNAMini Kit (Qiagen) modified to

include an additional step using polyvinylpyrrolidone (PVP, Alfa Aesar) to minimize potential PCR

inhibition. RNA was converted to cDNA using the High Capacity cDNA Reverse Transcription Kit

(Applied Biosystems-Ambion). The quantity and quality of cDNA was checked on a 1% agarose

gel; samples with adequate cDNA for further processing (Annex 2) were stored at -80° C.

45

Amplification of pmoA was performed with a two-step dual-indexed PCR approach

modified for Illumina instruments with two consecutive sets of primers (Annex 3). In the first step,

the gene specific portion was fused to the Illumina TruSeq sequencing primers and PCR was carried

out in a total volume of 25 µL that contained HF buffer 1X (NEB), 0.25 µM of each primer, 200

µM of each dNTPs (Life Technology), dimethylsulfoxide (DMSO, NEB) at a final concentration of

3 %, 1 U of Phusion High-Fidelity DNA polymerase (NEB) and 1 µL of template cDNA. To

minimise primer bias, two more reactions with 5 and 10 fold diluted template were also carried out

for each sample. Temperature and duration of thermal cycling were started with an initial

denaturation at 98°C for 30 s followed by 35 cycles of denaturation at 98°C for 10 s, annealing at

56°C for 30 s, extension at 72°C for 30s and a final extension at 72°C for 300 s. The three dilution

reactions were pooled together and purified using the Axygen PCR cleanup kit (Axygen). Quality

and quantity of the purified PCR product were checked on a 1% agarose gel. Fifty to 100 fold

dilution of this purified product was used as a template for a second PCR step with the goal of

adding barcodes (dual-indexed) and missing sequence required for Illumina sequencing. This

second PCR was done in triplicates under the same conditions as the first PCR but with 15 cycles.

Triplicates were pooled together and purified as above and then quantified spectrophotometrically

with the Nanodrop 1000 (Thermo Fisher Scientific). Barcoded amplicons were pooled in equimolar

concentration for sequencing on the Ilumina MiSeq at the Plateforme d’Analyses Génomiques

(IBIS, Université Laval, Québec, Canada), using primers that contain Illumina-specific sequences

protected by intellectual property law (Oligonucleotide sequences © 2007-2013 Illumina, Inc.). The

raw Illumina sequences have been deposited in the Short Read Archive database under the

bioproject PRJNA317391.

3.2.4. Sample processing for qPCR

Standards were prepared from PCR product produced under the same conditions as the first

reaction PCR of the dual-indexed PCR for Illumina. Triplicate samples of PCR product were

purified with a Feldan PCR purification kit. Amplicons were cloned using a Stratagene cloning kit

following the manufacturer’s instructions, with the following modifications: a polyA tail was added

to the amplicons using Feldan polymerase and buffer and dATPs (final concentration 0.175 nM)

and 26 to 30 ng were used for the ligation reaction. Transformed cells were incubated 1 h in SOC at

37°C for recovery before plating on agar plates. Positive clones were incubated again in LB media

containing 7% glycerol for 16h at 37°C. To verify that the target gene was amplified, the T3/T7

PCR reaction was performed on the grown clones and 11 of them were sent to sequencing facilities

at the Sequencing Center of Laval University Hospital Center (CHUL; Quebec City, QC, Canada).

46

Standards were then prepared by amplifying a positive clone with T3 and T7 primers. Amplicons

were checked on a 1% agarose gel and the band corresponding to the length of the amplicon

(around 700 pb) was cut and purified with a gel purification kit (Qiagen). Another T3/T7 PCR was

performed on the purified amplicon and was purified with a PCR purification kit (Feldan).

Concentrations were measured on a NanoDrop™ spectrometer. Each reaction (standard and

samples) was run in triplicate. Standards for the calibration were diluted 10 times to cover measures

from 107 to 101 copies µL-1. Potential inhibition was checked by running 10 and 100 fold dilutions

of the sample, which covered the expected copy number range. QPCR reactions for standards and

samples were performed in 20 µL reactions containing 5 µL of template, 500 µM of each primer

(PmoA169F and PmoA661r), 1 X Ssofast EvaGreen™ supermix and nuclease free water on a

Chromo4 thermal cycler (Bio-Rad) with the following steps: initial denaturation (30 s at 95°C) and

then 40 cycles of denaturation (5 s at 95°C), annealing (30 s at 55°C) and elongation (20 s at 72°C).

3.2.5. Sequence processing and analysis¸

PmoA reads from the Illumina amplicon sequencing were analyzed using the FunGene

pipeline (Fish et al., 2013). Reads shorter than 400 pb were discarded and chimera were checked

and removed with UCHIME. Sequences were translated to amino acid and compared to the pmoA

reference sequence with FrameBot for detecting frameshift errors and sequences with inframe

STOP codons, which were removed. Remaining high quality sequences were aligned with

HMMER3 and then clustered at 93% similarity. A custom pmoA database was constructed by

downloading pmoA sequences from the Functional Gene Repository v.8.0. Reference sequences

were checked against the NCBI nr database and the reference database was manually inspected to

ensure all types of known methanotrophs were represented. Taxonomic affiliation of the

representative sequences of pmoA OTUs were done in Qiime using the assing_taxonomy.py

command (Caporaso et al., 2010). For unassigned sequences, a Neighbour Joining 1000 bootstraps

tree following the Poisson model was constructed to allow assignation of sequences to type Ia, Type

Ib or type II methanotrophs by phylogenetic affiliations. Rarefaction curves were of OTUs were

based on 93% similarity using the command alpha_rarefaction.py available in Qiime. The dataset

was re-sampled 100 times to ensure the 37000 reads per sample, which was the minimum number

of reads per a sample less 10%, using the command multiple_rarefaction_even_depth.py in Qiime.

The community data matrix was square root transformed before running the Bray-Curtis

dissimilarity measurement. The Ward method was used for clustering and Bray-Curtis distances

were squared as recommended in Murtagh and Legendre (2014). A principal component analysis

(PCA) was carried out on the environmental variables with the function rda in the Vegan package

47

(Oksanen et al., 2013). Variables that were not normally distributed were log transformed in order

to meet test assumptions.

Testing for differences in the qPCR data required a non-parametric test since the values

could not meet normality assumptions even after log transformation. The Kruskal-Wallis test was

then used to assess differences in methanotrophic activity between valleys, depth and fraction. Only

the samples with corresponding read data were taken into account in this analysis and surface

samples that fell below limit of detection for the qPCR measurements were considered as missing

data. To evaluate which environmental variables and groups of methanotrophs (X-variable)

contributed to the activity of methanotrophs (Y-variable) we used a partial least squares regression

approach (PLS) available in the R package mixOmics (Lê Cao et al., 2009; González et al., 2011),

and that transform X into latent variables to explain to maximum variance of Y. For this analysis,

OTUs were binned into phylogenetic groups, i.e. genera, and unclassified Methylococcaceae were

phylogenetically assigned to Type Ia and Type Ib following their placement in the phylogenetic

tree. The PLS analysis has the advantage of remaining robust in the face of collinearity and missing

data (Wold et al., 2001). The missing values in X were replaced by values from model prediction

using the NIPALS algorithm. Prior to analysis, environmental data that were not normally

distributed were log transformed as well as the Y variable. Methanotroph data that were zero

inflated were fourth root transformed as recommended in Wold et al. (2001). The result of the PLS

were then plotted on a correlation plot to identify variables correlated with the expression of the

pmoA. The relationship between the qPCR data (the two fractions summed to estimate the global

methanotrophic potential of the surface or bottom of the pond) and CH4 concentration was assessed

for the bottom samples. qPCR data were square root transformed to meet normality assumption.

Pearson correlation values were then calculated using the Vegan package in R (Oksanen et al.,

2013).

3.3. Results

3.3.1. Physico-chemical parameters

Surface waters of the ponds were warmer with higher oxygen concentrations (Figure 3-1)

compared to bottom waters. All the ponds were thermally stratified at the time of sampling and

oxygen was depleted in the bottom. Surface and bottom BGR1 and 2 were oxic (DO > 3 mg L-1) all

the way to the bottom while the other pond bottoms were hypoxic to anoxic (See Annex 4). Ponds

from the northern BGR and NAS valley had higher pH values (from 7 to 8), while pH was more

acidic in the southern valleys (from 5 to 6). The bottom waters of the ponds in the KWK and SAS

48

valleys had higher nutrient concentrations, with highest concentrations of total nitrogen and

phosphorus in the bottom of KWK23; 2.74 mg N L-1 and 170.5 µg P L-1 for the phosphorus. The

TSS values followed the same trend, with the highest value at the bottom of the KWK1 pond of

140.77 mg L-1. Ponds from the SAS valley contained higher DOC concentrations, which ranged

from 15.5 mg L-1 at the surface of SAS 1B to 21.5 mg L-1 for the bottom of SAS2B (Figure 3-1,

Annex 4).

Figure 3-1. Principal component analysis of the environmental variables: temperature (T), dissolved oxygen (O2),

pH, total phosphorus (TP), total suspended solid (TSS), conductivity (Cond), Chlorophyll a (Chla), total nitrogen

(TN) and dissolved organic carbon (DOC) for the 9 sampled ponds. Colors indicate the different valleys; surface

samples are represented by a triangle and bottom by a circle.

3.3.2. Community arrangement and composition

A total of 1,850,808 reads were retained after quality control and cleaning, these

corresponded to a total of 985 OTUs at 93% similarity. The semi-parabolic profile of the rarefaction

curve suggested a good representation of the methanotroph diversity within each valley. SAS and

49

KWK valleys reached plateaus at higher level that compared to the BGR and NAS valleys. On

average, SAS valley samples plateaued above 300 OTUs and KWK valley around 200. BGR and

NAS valley followed with plateau around 200 and 150 OTUs (see Annex 5). There was no

significant difference in alpha-diversity Shannon and Choa1 indices between the valleys.

The methanotrophic community differed between landscape type, that is between palsa and

lithalsa valleys (Figure 3-2); a test based on 9999 permutations showed that the community

structure differed significantly among the different valleys (p=0.001). There was no significant

difference between the two depths of sampling or between the two size fractions (p>0.05).

Figure 3-2. Bray-Curtis dissimilarity cluster analysis of the methanotrophic communities. Surface samples are

represented by triangles and bottom samples by circles, either filled (small fraction) or open (large fraction).

Heatmap shows the methanotrophic community composition.

All the pond communities were dominated by type I methanotrophs. The most abundant

genus was Methylobacter, which accounted for up to 92% of the reads in the small fraction of

bottom of SAS2B (Figure 3-2). The group ‘unclassified’ and other Methyloccocaceae contained

sequences phylogenetically assigned to Type Ia (above 1% of the reads at all sites) and Type Ib

(below 1% of the reads for most sites) and other genera such as Methylovulum, Methylosoma and

Methylococcus that represented less than 1% of reads in each sample and were then grouped with

unclassified and other Methylococcaceae. The genus Methylomonas was in low abundance in the

50

lithalsa ponds, more represented in the palsa ponds and abundant in the surface of SAS2B small and

large fraction (87% and 41% of the reads, respectively). The genus Methylomicrobium was present

in each sample, ranging from less than 1% in the large fraction of BGR2 bottom to 11% of the reads

in the large fraction of SAS1B bottom. Unlike Methylomonas, the genus Methylosarcina was more

represented in the lithalsa sites and reached its highest abundance in the small and large fraction of

the bottom of the NASH ponds (5% and 4% of the reads, respectively). Compared to the type I, the

type II methanotrophs were low in terms of abundance and of diversity. Only three genera of type II

methanotrophs were identified. Methylocystis was in highest abundance (5 % of the reads) in the

large fraction of SAS1B bottom and was absent from several samples in the palsa and lithalsa

valley. Methylosinus was present in even lower abundance and reached a maximum of 2%

abundance in the small fraction of SAS2B surface. Finally, Methylocapsa was only found in

SAS1B, SAS2A and SAS2B where it represented less than 1% of the reads.

3.3.3. Methanotrophic activity

The activity of the methanotrophic community was inferred by the concentration of pmoA

transcripts per ml (Table 3-1). Regression coefficients of the calibration curves were between 0.991

and 0.999, qPCR efficiency was between 82 and 115% and the limit of detection was 10 pmoA

transcript copies. Number of pmoA transcripts per volume varied from 2.2 x 102 for the surface

small fraction of BGR1 to 7.6 x 106 for the bottom large fraction of SAS1B. The Kruskal-Wallis test

did not detect any pattern of influence of number of pmoA transcript in function of the valley or the

fraction. However, there was a clear trend that measurements were higher in 2012 compared to

2013. The results showed potential methanotrophic activity not only in oxygenated surface waters

but also in the hypoxic to anoxic bottom waters of the ponds. This included the bottom waters

SAS2B and SAS2A where oxygen concentrations were below our limit of detection (0.2 mg L-1).

51

Table 3-1. Concentrations of pmoA transcripts oxygen and methane in the sampled thaw ponds. -: no data.

Pond Depth Year pmoA transcripts mL-1 Oxygen

Methane

Small Large (µM) (µM)

-

BGR1 Surface 2013 2.2 x 102 4.3 x 103 617 -

BGR1 Bottom 2013 5.2 x 103 3.4 x 103 188 -

BGR2 Surface 2012 4.4 x 106 3.8 x 105 589 20

BGR2 Bottom 2012 1.6 x 106 9.4 x 104 217 229

NASH Bottom 2012 1.6 x 103 1.6 x 104 123 -

KWK1 Bottom 2012 1.0 x 106 2.1 x 105 31 -

KWK6 Bottom 2012 6.4 x 105 3.6 x 105 114 -

KWK12* Bottom 2013 6.1 x 102 - 17 351

KWK23 Bottom 2012 1.3 x 106 4.3 x 105 22 -

SAS1A Surface 2013 1.6 x 103 3.9 x 102 - -

SAS1A Bottom 2013 4.1 x 102 4.9 x 102 - -

SAS1B Bottom 2012 2.6 x 105 7.6 x 106 103 -

SAS2A Surface 2012 2.8 x 104 2.2 x 106 359 3

SAS2A Bottom 2012 1.3 x 106 5.9 x 106 16 102

SAS2A* Surface 2013 6.6 x 103 5.0 x 103 135 -

SAS2A* Bottom 2013 1.5 x 103 2.5 x 104 12 323

SAS2B Surface 2013 3.1 x 103 1.4 x 104 326 3

SAS2B Bottom 2013 2.8 x 104 2.6 x 105 12 292

*no Illumina data

The influence of environmental variables and community composition on the activity of

methanotrophs was evaluated by way of PLS analysis. The first two latent variables of the PLS

analysis explained cumulatively 61% of the variation (Figure 3-3). The concentration of pmoA was

mostly related to conductivity, total phosphorus, total suspended solids and the abundance (number

of reads) of Methylocystis. There was no linear relationship between CH4 concentration and

methanotrophic activity. However, there was a strong, negative, nonlinear relationship between

these two variables in the bottom waters: for the square root of pmoA transcript concentrations and

CH4 concentration, R2=0.996, p<0.001(Figure 3-4).

52

Figure 3-3. Correlation plot of the PLS analysis to see influence of the environmental variables and

methanotrophic genera on the activity of methanotrophs. Chosen environmental variables were conductivity

(Cond), total phosphorus (TP), total suspended solid (TSS), Chlorophyll a (Chla), soluble reactive phosphorus

(SRP), total nitrogen (TN), carbon dioxide concentration (CO2), methane concentration (CH4), dissolved organic

carbon (DOC), oxygen concentration (O2), temperature (T°C) and pH.

Figure 3-4. Concentration of pmoA transcript as a function of methane concentration for bottom and surface

samples (left panel), and for bottom only (right panel). Shading represents ± 95% confidence intervals.

53

3.4. Discussion

3.4.1. Physico-chemical parameters

The subarctic permafrost thaw ponds sampled here were highly heterogeneous in their

limnological properties, with large differences even among ponds located in a same valley. All the

ponds showed physico-chemical gradients down their water columns associated with the summer

stratification. These ponds are known to go through two periods of mixing during the year: in early

spring (May) and during the fall (September). However, that mixing does not always extend

through the entire water column (Laurion et al., 2010; Deshpande et al., 2015). During the summer,

the mixed layer of the ponds is restricted to the epilimnion and venting of gases is not observed

from the bottom of the ponds (Laurion et al., 2010). For the KWK valley, for which we have the

longest record, conditions were hypoxic to anoxic during the summers 2006 (Laurion et al., 2010),

2007 and 2009 (Rossi et al., 2013), and again in 2011. The strong summer stratification and lack of

oxygen in the bottom waters suggest that the ponds in both the KWK and SAS valleys are

favourable environments for methane production by microbial action. Although BGR1 bottom was

anoxic in 2005 (Breton et al., 2009) and 2011, bottom waters were not hypoxic in 2012 and 2013,

suggesting more interannual variation in the BGR valley. However, anoxic conditions are persistent

during winter for BGR1 (Deshpande et al., 2015). The limnological properties of the ponds were

consistent with their origins either palsa or lithalsa with the influence of the DOC on the highly

organic SAS valley ponds and also the more acidic pH due to the high amount of organic acid.

3.4.2. Community composition and arrangement

Our results strongly indicate that the main driver of methanotroph community composition

in permafrost thaw ponds was the origin of pond formation (i.e., from palsas or lithalsas) more than

the depth or the extent of permafrost thawing. For example, the methanotrophic communities

retrieved from the KWK valley that lies in sporadic permafrost were more similar to those found

hundreds of km away in the BGR and NAS valleys that lie in discontinuous and widespread

permafrost than the communities in SAS (sporadic permafrost). However, the link between the

composition of methanotrophic community and the composition of dissolved carbon needs to be

more extensively investigated.

In this study, the size fractionation had no effect on the methanotrophic community

composition, while in seawater environments the same size fractionation protocol revealed distinct

microbial communities (Galand et al., 2008; Mohit et al., 2014). Our results may be due to similar

communities in the free living and particle associated fractions, perhaps because the permafrost

54

waters are rich in particles. It may also reflect methodological limitations, however, with the

clogging of the 3 µm filters due to the high concentration of suspended materials, thereby retaining

some of the free-living cells.

The methanotrophic community in all of the thaw ponds was dominated by type I

methanotrophs. In general, type I methanotrophs are the dominant group in colder environments

(Graef et al., 2011), with some species described as psychrotolerant such as Methylobacter

tundripalidum (Wartiainen et al., 2006; Martineau et al., 2010) and some truly psychrophilic taxa

such as Methylobacter psychrophilus (Omelchenko et al., 1996). Previous studies in soils and

sediments have shown that type II methanotrophs are to be more abundant and more active when

temperature raise around 20°C (Mohanty et al., 2007; Urmann et al., 2009) while type I

methanotophs are active in colder environments (Börjesson et al., 2004; Graef et al., 2011). He et al.

(2012) recorded a shift from type I to type II methanotrophs with increasing temperature of Arctic

lake sediments, and also a shift in the community composition of within each type. Methanotrophic

Bacteria are likely to be positively influenced by increased temperatures. The intensification of

global warming has the potential create a shift of the methanotrophic community since higher

temperature will favour type II methanotrophs over type I.

The primer chosen for the pmoA transcripts would have specifically targeted the Gamma-

and Alpha-proteobacteria methanotrophs, and other methanotrophs possessing a distinct pmoA

enzyme such as the Verrucomicrobia genus Methylacidiphilum (Dunfield et al., 2007; Op den Camp

et al., 2009), the Gammaproteobacteria genus Crenothrix (Stoecker et al., 2006) and the denitrifying

methanotrophs belonging to the NC10 phylum (Ettwig et al., 2009; Luesken et al., 2011) were not

detected. A previous study on bacterial community in permafrost thaw ponds targeting the 16S

rRNA indicated the presence of methanotrophs Verrucomicrobia in relatively high abundance (from

1 to 6% of the reads) in the KWK and SAS valley as well as one Crenothrix OTU in low abundance

in the BGR, KWK and SAS valley (Crevecoeur et al., 2015). The current study then does not take

into account the presence of those latter groups. No members of the phylum NC10 have been

identified to date in the thaw ponds via either RNA (this study, Crevecoeur et al., 2015) or DNA

analysis (Comte et al., 2016).

3.4.3. Methanotrophic activity

Few other studies have assessed the number of pmoA transcript in lakes. One such study in

a methane-rich environment, Lake Kivu based on DNA found from 1.1 x 103 to 2.9 x 104 copies of

pmoA ml-1 (İnceoğlu et al., 2015). Studies analysing the concentration of pmoA transcripts have

55

mostly targeted soil or semi-aquatic ecosystems. A study on a Tibetan wetland measured around 106

copies of pmoA g-1 of dry weight of soil (Yun et al., 2010), while a study on a boreal peatland found

pmoA transcript numbers from 105 to 107 per gram of soil (Freitag et al., 2006), within the same

range that we found in thaw ponds.

There was no obvious relationship between the diversity and the activity of methanotophs.

This difference between valleys and the origin of the ponds (palsa or lithalsa) does not influence

methanotrophic activity as it does diversity. This decoupling between diversity and activity might

be explained by a high level of functional redundancy within the community. Other studies showed

that rate of functional redundancy in bacterial communities can be high and that gene expression

strongly depends on environmental variables (Comte et al., 2013; Trimmer et al., 2015). However,

functional redundancy for methanotrophs was not supported by another study using PLFA-SIP that

found a link between activity and community composition (Bengtson et al., 2009), in contrast to the

present study.

Contrary to our initial hypothesis, the presence or absence of oxygen did not seem to

influence methanotrophic activity estimated by transcripts. High concentrations of transcripts of

pmoA were recorded in the low-oxygen (hypoxic or anoxic) bottom waters of the ponds, in addition

to in the surface waters. There is increasing evidence that methanotrophs are micro-aerobic. Blees et

al. (2014) evaluated the vertical distribution of methanotrophs through the water column of an

alpine lake and found methanotrophs way below the oxycline where the lake is supposedly anoxic.

They also still measured methane consumption at this depth, implying that methanotrophs can

survive and even grow during periods of prolonged anoxia. Idea concept is emerging that a

consortium of micro-organisms may allow aerobic methanotrophs to sustain their activity in anoxic

water. Methanotrophs could be for example coupled with phototrophs (Milucka et al., 2015; Oswald

et al., 2015), or denitrifiers (Beck et al., 2013; Liu et al., 2014). All the latter studies that reported

methanotrophic activity under anoxic conditions were based on incubation experiments, which do

not accurately reproduce in situ conditions (Radajewski et al., 2003). Here, we provided evidence

from in situ sampling that methanotrophs could stay active in waters that contain as little as 1 or 2%

oxygen. Their presence in the bottom could be a compromise to avoid grazing and be closer to the

methane source, since methane increases by orders of magnitude down the water column in many of

these ponds (Matveev et al., 2016). Furthermore, a small amount of oxygen can possibly diffuse to

the bottom from time to time when mixing of the epilimnion creates a supply of oxygen close to the

thermocline, and when internal waves propagate across the oxycline, as observed in these waters

(Deshpande et al., 2015).

56

Only the year of sampling had a significant effect on methanotrophic activity. This

interannual variation in methanotrophic activity indicates the need for seasonal and multi-year

monitoring of limnological properties and their effect on methanotrophic activity. Contrary to our

second hypothesis, methanotrophic activity did not follow the beta-diversity patterns and was little

influenced by oxygen availability.

Other factors that more strongly influenced the methanotrophic community composition

were identified by the PLS analysis: phosphorus, conductivity and total suspended solids.

Phosphorus has been shown to have an effect of enhancing microbial CH4 oxidation in soil (Zhang

et al., 2011). Phosphorus could increase methanotrophic potential by the enhancement of microbial

biomass (Liu et al., 2012) which is substantial in thaw ponds since up to 27% of the bacterial

community is composed by methanotrophs in some ponds (Crevecoeur et al., 2015). The link with

the TSS suggests that methanotrophic activity would be related by the presence of particles. In

general, sinking particles are considered to be microbial hot-spots (Grossart and Simon, 1998). An

oxic/anoxic interface can develop at the surface of sinking particles (Reiche et al., 2011) that is a

favourable environment for methanotrophy.

PLS analysis suggested that the methanotroph genus that was the most related to pmoA

expression was Methylocystis. This genus represented a small fraction of the reads (no more than

5%), which contrasted with the most abundant genus Methylobacter that did not seem to relate to

the methanotrophic activity. This highlights the potential importance of the “rare biosphere”, as

described in an increasing number of studies (Galand et al., 2009; Logares et al., 2009; Pedrós-Alió,

2012). Suweis et al. (2013) found by way of a modelling approach that rare species were positively

correlated with ecosystem resilience. Hence the disappearance of a rare species, which by definition

is more sensitive to extinction, can result in decreased ecosystem stability. In the thaw ponds, our

results suggest that methane consumption is driven by rare taxa that may be vulnerable to

perturbation by ecosystem change. Methylocystis is rare in the thaw pond ecosystems, but has been

found in high abundance in a Japanese wetland (Narihiro et al., 2011) and a sphagnum peatland

(Kip et al., 2011). The acid tolerance of Methylocystis may be the reason why it was more abundant

in the acidic SAS peatland valley. Another reason that might explain the influence of Methylocystis

on pmoA transcription is the fact that some Methylocystis are able to develop a parallel fermentative

metabolism in low oxygen conditions and so may be more adapted to those conditions

(Vecherskaya et al., 2009). The link between methanotrophic activity and methane concentration

was influenced by the depth of sampling. While no clear trend was observed for the surface, a

negative relationship was found for the bottom samples. This means that methanotrophs could have

57

the potential to reduce net methane emission from the ponds, but may also imply that where the

concentration of methane is high; the conditions are not propitious for pmoA transcription. A

negative relationship between CH4 fluxes and number of pmoA transcripts has also been found at a

peatland site (Freitag et al., 2010). In contrast, Kankaala et al. (2006) showed a positive linear

relationship between methanotrophic activity and CH4 concentration, suggesting that is this boreal

lake, methanotrophs are limited by CH4 availability. Hence the factors that influence the

relationship between methanotrophic activity and methane concentration are still not fully

understood and may vary among lake types.

In conclusion, our results show that the methanotrophic communities of subarctic thaw

ponds are mainly influenced by landscape type (organic palsa versus mineral lithalsa), and that they

are dominated by Type I methanotrophs that are characteristic of low-temperature environments.

Ongoing climate warming may disturb this community composition and favour methanotrophs

adapted to higher temperature, but the extent of change in the community is likely to depend on the

origin of the pond. Higher temperature will lead to stronger stratification and longer periods of

hypoxic and anoxic conditions in the bottom of thaw ponds, but also to longer periods of ice-free

conditions and mixing in spring and fall. The distribution and detection of methanotrophic activity

at the bottom of the ponds suggests they have strategies for maintaining activity even under low

oxygen conditions. Changes in temperature, mixing, oxygenation and nutrient supply will likely

affect the activity of methanotrophs in the future, and thereby alter the net emission of methane

from these waters to the atmosphere.



(NSERC) funding for Discovery Grants, the Discovery Frontiers grant ADAPT, Canada Research

Chair program, the Network of Centres of Excellence ArcticNet, and the Fonds de Recherche du

Québec-Nature et Technologies (FRQNT) for funding for the Centre d’Études Nordiques (CEN).

Computing support was provided by CLUMEQ/Compute Canada. We thank Marianne Potvin for

laboratory assistance and the development of protocols. We are also grateful to Claude Tremblay at

the Whapmagoostui-Kuujjuarapik CEN station, the pilots of Canadian Helicopter Ltd., and Anna

Przytulska-Bartosiewicz, Bethany Deshpande and Paschale Noël Bégin for help in the field.

58

Chapitre 4. Environmental selection of planktonic

methanogens in permafrost thaw ponds

Résumé

Le réchauffement et l’érosion thermique du pergélisol riche en glace résultent en la

formation de mares de fonte qui peuvent devenir des importants émetteurs de méthane dans

l’atmosphère. Ici nous avons examiné les méthanogènes et les autres archées, dans deux types de

mares de fonte qui sont formées soit par l’effondrement de buttes de pergélisol dominé par la tourbe

(palses) ou des buttes de sol minérales (lithalses) dans le Québec subarctique, Canada. En utilisant

des techniques de séquençage à haut débit à partir de la région hypervariable de l’ARNr 16S, nous

avons déterminé la structure taxonomique et la diversité des communautés d’archées dans l’eau près

du fond, et analysé les transcrits du gènes mcrA dans deux sites. Les mares de tous les sites étaient

fortement stratifiées, avec des eaux de fond hypoxiques à anoxiques. La communauté des archées

étaient dominées par les Euryarchaeota, plus particulièrement les taxons méthanogéniques

Methanomicrobiales et Methanosarcinales, indiquant une communauté de méthanogènes

planctoniques potentiellement active. L’ordre des Methanomicrobiales comptait pour la plupart des

transcrits du gène mcrA dans les deux mares. La communauté des archées différait de manière

significative entre les mares issues de lithalses ou de palses, avec une plus forte diversité alpha dans

les mares de palses riches en carbone organique, et une différence prononcée dans la structure des

communautés. Ces résultats indiquent la présence largement répandue des archées produisant du

méthane dans les mares de fonte, avec une sélection environnementale de taxons en fonction du

type de pergélisol.

59

Abstract

The warming and thermal erosion of ice-containing permafrost results in thaw ponds that

can be strong emitters of methane to the atmosphere. Here we examined methanogens and other

Archaea, in two types of thaw ponds that are formed by the collapse of either permafrost peat

mounds (palsas) or mineral soil mounds (lithalsas) in subarctic Quebec, Canada. Using high-

throughput sequencing of a hypervariable region of 16S rRNA, we determined the taxonomic

structure and diversity of archaeal communities in near-bottom water samples, and analyzed the

mcrA gene transcripts from two sites. The ponds at all sites were well stratified, with hypoxic or

anoxic bottom waters. Their archaeal communities were dominated by Euryarchaeota, specifically

taxa from the methanogenic orders Methanomicrobiales and Methanosarcinales, indicating a

potentially active community of planktonic methanogens. The order Methanomicrobiales accounted

for most of the mcrA transcripts in the two ponds. The archaeal communities differed significantly

between the lithalsa and palsa ponds, with higher alpha diversity in the organic-rich palsa ponds,

and pronounced differences in community structure. These results indicate the widespread

occurrence of planktonic, methane-producing Archaea in thaw ponds, with environmental selection

of taxa according to permafrost landscape type.

60

4.1. Introduction

Archaea are widely distributed throughout the biosphere and play key roles in

biogeochemical cycling processes, including methane production (Jarrell et al., 2011).

Methanogenic Archaea are responsible for a large fraction of organic carbon decomposition under

anaerobic conditions (Kuntz et al., 2015), and their metabolic activities in lakes and natural

wetlands may account for more than 30% of total methane emissions to the atmosphere (Kirschke et

al., 2013). In high latitude northern regions, diverse archaeal communities have been reported in

soils (Jansson and Taş, 2014), wetlands (Juottonen et al., 2008), lakes (Pouliot et al., 2009), rivers

(Galand et al., 2008), and marine environments (Galand et al., 2009; Comeau et al., 2011).

Permafrost landscapes in many parts of the Arctic contain abundant lakes and ponds that have been

formed by thawing and collapse of ice-rich soils, and these so-called thaw or thermokarst lakes

(Jansson and Taş, 2014) are strong emission sources of both CH4 and CO2 (Walter et al., 2007).

With ongoing climate warming in northern regions, the thawing of permafrost soils may lead to

increased mobilisation and transfer of ancient carbon reserves into such thaw ponds, where some of

this organic material would be available for decomposition by methanogens (Vonk et al., 2015). To

date, studies of archaeal communities in permafrost aquatic ecosystems have focused on pond

sediments in the High Arctic (Negandhi et al., 2013) and wetland soils in the Subarctic (Liebner et

al., 2015), while the presence, diversity, and substrate preferences of methanogens in the water

column have been little explored.

Methanogenic Archaea use different pathways for methanogenesis that depend on the

carbon substrate and electron donor. The two substrates that are the most commonly used are

H2/CO2 and acetate through hydrogenotrophic and acetoclastic methanogenesis, respectively. A

third, less common methylotrophic pathway requires the use of a methyl group as substrate

(Bapteste et al., 2005). The hydrogenotrophic pathway is found in almost all orders of methanogens

(Methanococcales, Methanopyrales, Methanobacteriales, Methanosarcinales, Methanomicrobiales,

Methanocellales and the recently discovered Methanomassiliicoccales (Bapteste et al., 2005; Borrel

et al., 2013), while the genus Methanosaeta in the order Methanosarcinales is an obligate acetotroph

(Barber et al., 2011). Only the order Methanosarcinales has been shown to contain genera or species

able to use the three different pathways (Bapteste et al., 2005). Both hydrogenotrophic and

acetotrophic taxa of Archaea have been detected in the soil of Subarctic wetlands (Liebner et al.,

2015) and High Arctic thaw ponds (Negandhi et al., 2013).

In the Hudson Bay region of subarctic Québec, thermokarst lakes and ponds have been

increasing in size and number over the last three decades in response to rapid warming (Bhiry et al.,

61

2011). These thaw ponds emit CH4 to the atmosphere (Laurion et al., 2010), implying active

methanogenic communities. Although there is some evidence of methanogenesis in oxic waters

(Grossart et al., 2011; Bogard et al., 2014), methanogenesis by Archaea usually occurs under anoxic

conditions (Thauer et al., 2008), and is especially likely in anoxic sediments. However, the bottom

waters of Quebec subarctic thaw ponds are generally hypoxic and sometimes anoxic during summer

(Laurion et al., 2010; Breton et al., 2009; Rossi et al., 2013), providing conditions for methanogenic

activity. In addition prolonged anoxia occurs throughout the water column during winter when the

ponds are ice covered (Deshpande et al., 2015). Episodic mixing events occur especially during

autumn prior to freeze-up, but also in spring and occasionally during summer (Laurion et al., 2010).

These mixing events are likely to accelerate the ventilation of CH4 into the atmosphere and

oxygenate the water column (Deshpande et al., 2015), which would disrupt the redox conditions

conducive to anaerobic methanogenesis. The sensitivity to these ponds to mixing raises the question

of whether methanogens are active members in the microbial water column plankton.

The substrates available for methanogens in thaw ponds may vary depending on the

landscape characteristics of the surrounding catchment. The extent of thawing and associated

permafrost erosion will influence the quantity of allochthonous organic matter, while soil properties

will influence the nature and lability of the organic matter. In the Quebec Subarctic region, most

thaw ponds originate from one of two different types of permafrost landscapes either from peat

covered palsa mounds or mineral lithalsa mounds (Gurney, 2001; Calmels et al., 2008). Eroding,

carbon-rich palsa soils are likely to release large amounts of organic matter (Kiikkilä et al., 2014),

some of which can be biologically or photochemically broken down (Laurion and Mladenov, 2013)

into substrates that may stimulate microbial activity including archaeal methanogenesis.

The aims of the present study were to determine the diversity and any environmental

partitioning of archaeal communities in subarctic thaw ponds. Specifically, we evaluated the

following hypotheses: 1) the bottom waters of these ponds provide a suitable habitat for

methanogenic Archaea; 2) permafrost landscape type (palsa versus lithalsa) and the extent

permafrost degradation, affects archaeal community structure; and 3), the carbon enriched

conditions of the palsa catchments favors greater archaeal diversity compared to lithalsa dominated

catchments. We tested these hypotheses using microbial plankton samples taken from ponds in

three different permafrost valleys in subarctic Quebec, and the communities were identified using

high-throughput sequencing (Illumina MiSeq) of the V6-V8 hypervariable region of archaeal 16S

rRNA. We further examined the potential metabolic diversity of methanogenic Archaea by high

throughput sequencing of mRNA transcripts of the mcrA gene. This gene codes for subunit A of the

62

methyl-coenzyme M reductase and has been used as a proxy for studying taxonomic richness and

community composition of methanogens, including across a palsa wetland gradient in subarctic

Norway (Liebner et al., 2015). Given that many of these thaw ponds contain high concentrations of

suspended sediments (Laurion et al., 2010), we also tested for differences in archaeal taxonomic

structure between particle-attached and free-living communities.

4.2. Results

4.2.1. Limnological conditions

All the ponds were thermally stratified during the period of sampling, with low or near zero

values of dissolved oxygen at the bottom (Table 4-1). Ponds from the KWK (lithalsa) and SAS

(palsa) valleys were hypoxic to anoxic at the bottom. Values for pH were higher in the BGR

(lithalsa) valley, followed by the KWK valley and then the SAS valley, where the waters were

slightly acidic. DOC values tended to be higher for ponds from the SAS valley, although KWK1

and KWK23 also had relatively high concentrations of DOC. The highest TSS values were recorded

from KWK1 and KWK23 followed by BGR2. Total phosphorus concentrations were the highest in

the KWK ponds and in BGR2.

Table 4-1. Limnological properties of the bottom water (0.5 m above the sediment) for the sampled thaw ponds.

Temperature (T°C), dissolved oxygen (O2), dissolved organic carbon (DOC), total suspended solids (TSS) and total

phosphorus (TP).

Pond Year T°C O2

(mg.L-1)

O2

(% sat.)

pH DOC

(mg L-1)

TSS

(mg L-1)

TP

(µg L−1)

BGR1 2013 9.7 3 37.1 7.3 2.7 10.4 19.3

BGR2 2012 11 3.5 32.7 7.2 8.7 57.4 148.9

KWK1 2012 6.4 0.5 4.2 6.2 12 140.8 87.8

KWK6 2012 8.2 1.8 17.5 6.3 5.2 16 99.9

KWK23 2012 4.4 0.4 2.7 6.1 10.9 73.6 170.5

SAS1B 2013 11.7 1 14.2 6.2 16.2 18.4 26.1

SAS2A 2012 4.6 0.3 2 5.6 18.9 16.2 41.5

SAS2B 2012 5.7 0.5 3.8 4.5 21.5 7.7 25.8

4.2.2. Archaeal alpha-diversity

The surface water samples yielded archaeal 16S rRNA sequences from only 3 of the 16

pond samples (SAS2A, SAS2B and BGR2; see Annex 6), and no mcrA transcripts could be

amplified from any of these surface samples. We therefore focused our subsequent analyses

exclusively on 16 bottom water samples (8 ponds, two fractions). These yielded a total of 747,149

reads after quality filtering and removal of singletons, and corresponded to 473 OTUs. The cDNA

63

for mcrA was successfully amplified and sequenced from two bottom samples: the small (<3-µm)

fraction of KWK23 and the large (>3-µm) fraction of SAS2B. The mcrA transcripts yielded

112,320 reads corresponding to 142 OTUs. The 16S rRNA rarefaction curve plateau was higher in

the SAS than the BGR and KWK ponds, with 200 and 250 OTUs on average for the SAS valley and

100 and 150 OTUs on average for the BGR and KWK valleys (see Annex 7). Some individual SAS

samples did not reach a plateau above 250 OTUs, suggesting that these communities may have been

under-sampled. ANOVA analysis showed Chao1 (P=0.020) and Shannon (P=0.0125) indices

differed significantly among valleys. Tukey HSD tests showed that SAS differed from BGR

(P=0.04 for the Shannon index and P=0.027 for the Chao1 index) and KWK (P=0.003 for the

Shannon index and P=0.01 for the Chao1 index), with no significant difference between KWK and

BGR. For the Shannon index, the median in the SAS valley was 5.7 with a range from 4.7 to 6.4,

while the medians for BGR and KWK valley were 4.3 and 4.0 respectively, with ranges from 3.5 to

5 and 3.2 to 4.5. For the Chao1 index, the median in the SAS valley was 318 and ranged from 290

to 339. The medians for the BGR and KWK valleys were 182 and 185 respectively, with ranges

from 98 to 213 and 80 to 230. So the SAS ponds had higher archaeal diversity and species richness

than those in the two other valleys (Figure 4-1).

Figure 4-1. Alpha-diversity measures for the three sampled valleys. Shannon diversity index and Chao1 species

richness index for the BGR, KWK and SAS valleys. The line in each box plot indicates the median, the box delimits

the 25th and 75th percentile, and the whisker is the range. Diversity indices for SAS valley differed significantly

from the two other valleys (p=0.02 for the Chao1 and p=0.01 for the Shannon index).

64

4.2.3. Archaeal community dissimilarities and composition

An unweighted UniFrac distance analysis of the bottom water 16S rRNA data showed that

the SAS palsa valley clustered apart from the lithalsa valleys KWK and BGR (Figure 4-2). A

permutation test (9999 permutations) showed that the difference between communities was

significant for valleys only (P=0.001). There were no significant differences between the small and

the large fractions and or between the different years of sampling. Pairwise comparisons confirmed

that SAS was significantly different from KWK (P=0.003) and BGR (P=0.001), while KWK and

BGR were not significantly different from each other.

Figure 4-2. UniFrac clustering and composition of archaeal community. Upper dendrogram representing the

phylogenetic unweighted UniFrac distance of the archaeal community for the study ponds. Filled or open diamond

represent respectively small and large fraction. First letter of sample name correspond to the valley name: S for

SAS, K for KWK and B for BGR. The following number or combination of letter and number indicate the name of

the pond. Bubble plot show the relative abundance of the different archaeal lineages with notably Miscellaneous

Euryarchaeotic Group (MEG), Miscellaneous Crenarcheotic Groups (MCG) and Deep Euryarcheotic Sea Group

(DESG).

65

The overall archaeal community composition was mostly composed of methanogens and

other Euryarchaeota (Figure 4-2). Methanogens were represented mainly by the order

Methanomicrobiales, which composed 13 to 69% of the reads for the large fraction of KWK1. The

second dominant group of methanogens was the Methanosarcinales, with a greatest relative

abundance of 39% of reads in the large fraction of SAS2B. Two other orders of methanogens

(Methanocellales and Methanobacteriales) represented less than 1% of the relative abundance of the

total community, with the Methanocellales only found in the SAS valley. The two dominant orders

of methanogens, Methanomicrobiales and Methanosarcinales, constituted on average 55% of the

KWK valley community, followed by the 45% of the SAS valley community and then 38% of the

BGR valley community. In general there were more methanogens in the large fraction than in the

small fraction, except for KWK23 and BGR1 where Methanomicrobiales were more abundant in

the small fraction. The 16S rRNA sequences for the small number of surface water samples

similarly contained a high percentage of representatives from the Methanomicrobiales and

Methanosarcinales, in both the large and small fractions (Annex 6).

In addition to the putative methanogenic groups mentioned above, other Euryarchaeota in

bottom waters included a large proportion of unclassified Euryarchaea and the Miscellaneous

Euryarchaeotic Group (MEG), which accounted for from 6% in the large fraction of SAS2B to 69%

of reads in the small fraction of BGR2. In the KWK samples Thermoplasmata were relatively more

abundant and represented up to 10% of the reads in the large fraction of KWK1. Finally,

Halobacteriales and the Deep Sea Euryarchaeotic Group (DSEG) were found in almost all samples,

but represented less than 1% of the reads. Crenarchaeota were also recovered and included the

Miscellaneous Crenarchaeotic Group (MCG), which was more common in the SAS valley and

represented up to 13 % of the reads in the large fraction of SAS2A. Other Crenarchaeota groups

(group C3, Thermoprotei and Crenarchaeotic Marine Benthic Group B) were <1% of all reads but

were present in all three valleys. Finally, the phylum Thaumarchaeota was present in the two BGR

valley ponds, one KWK and one SAS sample. Unclassified Archaea ranged from <1% in the small

fraction of KWK23 to 7% of reads for the small fraction of SAS1B (Figure 4-2).

Methanogenic Archaea were consistently among the five most abundant OTUs (defined at a

level of 97% similarity) for each valley. The two most abundant OTUs across all bottom water

samples were Methanoregula and Methanosaeta (Table 4-2). The KWK and SAS valleys shared

another abundant OTU of Methanoregula and the two other of the most abundant OTUs in the SAS

valley were also in the genus Methanoregula. The five most abundant OTUs in the SAS valley were

exclusively methanogenic Archaea. Other abundant OTUs in KWK and BGR valleys belonged to

66

the MEG and had high identity percentages with sequences isolated from a petroleum hydrocarbon-

contaminated aquifer. Other OTUs in the top five were either unclassified Euryarchaeota (BGR) or

Thermoplasmatales (KWK). The most abundant OTUs in the valleys showed no specificity to a

particular habitat type with high homology with sequences isolated from diverse freshwater and

marine environments.

Table 4-2. Identity of the 5 most abundant OTUs (defined at a level of 97% similarity) for each valley following the

lowest taxonomic level of the SILVA modified database (Lovejoy et al., 2015). The group Euryarchaeota,

Miscellaneous Euryarchaeotic Group (MEG) and Thermoplasmatales could not be further assigned. Percent (%)

represents the proportion of those single OTUs in the community for each valley.

Number

of reads

Valley Silva taxonomy %

identity

Accession

number

Genbank

taxonomy

Isolation source

68710

KWK

(22%)

BGR (14%)

SAS (11%)

Methanoregula 100 GU224062 uncultured

archaeon

lake water

44091 SAS(12%)

KWK(8%)

BGR(6%)

Methanosaeta

100 LN626810

uncultured

archaeon

Marine bioreactor

24882 KWK(8%)

SAS (3%)

Methanoregula 99 JN397914 uncultured

archaeon

spring pit

22248 KWK

(10%)

BGR (9%)

MEG

99 AY294412 uncultured

archaeon

hydrocarbon-

contaminated aquifer

20494

BGR(14%)

MEG

98 AY294414 uncultured

archaeon

hydrocarbon-

contaminated aquifer

19308

SAS(4%)

Methanoregula 98 KJ955705 uncultured

Methanoregula

hydrocarbon-

contaminated sediment

17934

BGR(8%)

Euryarchaeota

90 AB019748 uncultured

archaeon

deep-sea hydrothermal

vent

10585 SAS(3%) Methanoregula

98 JF304133 uncultured

archaeon

outfall sediment

9430 KWK (6%)

Thermoplasmatales 93 EU910624 uncultured

euryarchaeote

sediment

4.2.4. Methanogens inferred from the mcrA versus 16S rRNA analyses

The mcrA reads were strongly dominated by the Methanomicrobiales and contained a much

lower proportion of other methanogenic groups compared to the 16S rRNA community (Figure 4-

3). Methanomicrobiales accounted for 70% of mcrA reads versus 40% of the 16S rRNA reads in the

SAS sample, and 99% of mcrA reads versus 89% of 16S rRNA reads in the KWK sample.

Conversely, the proportion of Methanosarcinales was lower in the mcrA sequences with 30% of

mcrA versus 58% of 16S rRNA for the SAS sample and 0.3% of the mcrA versus 11% of 16S rRNA

reads for the KWK sample. The Methanobacteriales and Methanocellales that were recovered at

67

low relative abundance in the 16S rRNA reads were below 1% of reads in the mcrA results. The

order Methanomassiliicoccales was not detected in the 16S rRNA sequences but was found in the

mcrA community, with 0.3% of reads for the SAS sample and 0.03% of reads for the KWK samples

(Figure 4-3).

Figure 4-3. Comparison of the relative abundance of the methanogens in the mcrA community and the 16S rRNA

community. The left plot shows the entire methanogenic community and plot on the right shows the groups

representing less than 1% of the reads. The SAS sample is the large fraction (3-20µm) of the SAS2B ponds and the

KWK sample is the small fraction (0.2-3µm) of the KWK23 pond.

4.2.5. Environmental variables and archaeal community clustering

NMDS ordination based on the unweighted UniFrac phylogenetic distance was carried out

to determine how the measured environmental variables may have influenced archaeal community

structure. The two dimensional NMDS had a stress value of 0.085, which indicated good

representation of the community arrangement. The ordination of the bottom water communities was

significantly correlated with three environmental variables: TP (P=0.006), DOC (P=0.008) and pH

68

(P=0.006). The ordination was consistent with the community dendrogram analysis (Figure 4-2),

with the SAS valley communities being phylogenetically distinct relative to the two other valleys

(Figure 4-4). Clustering followed the isopleths of three variables; higher DOC (Figure 4-4a) and

lower TP (Figure 4-4b) and lower pH (Figure 4-4c) contributed to the separation of the SAS valley

from the other two valleys.

Figure 4-4. Non metric multidimensional scaling (NMDS) of the community composition. Phylogenetic Unifrac

distances are overlaid with environmental variables; (a) dissolved organic carbon, (b) total phosphorus and (c) pH.

4.3. Discussion

The bottom waters of all of the thaw ponds sampled in the present study harbored Archaea,

with a major fraction of the 16S rRNA reads (up to 60%) assigned to methanogenic taxa. These

results imply that archaeal methane-producers are likely to occur at relatively high concentration in

the microbial plankton and would contribute to the extreme accumulation of methane reported from

these ponds. For example, Laurion et al. (2010) measured up to 100 µmol CH4 L-1 at the bottom of

KWK23. In our study, Methanomicrobiales and Methanosarcinales dominated both 16S rRNA and

the mcrA transcripts. Both orders are widely reported from planktonic environments (see Auguet et

al., 2010), including boreal lakes (Jurgens et al., 2000). The two orders have also been found in

northern peatland soils (Jansson and Taş, 2014) and wetlands (Liebner et al., 2015), implying

habitat plasticity.

The putative high concentrations of methanogens based on the relative abundance of reads

would be consistent with the highly stratified water columns that create conditions for the

persistence of hypoxia or anoxia in the hypolimnion of the shallow ponds. Although the ponds were

thermally stratified at the time of sampling in summer, there is seasonal and interannual variability

in pond oxygen tensions, ranging from prolonged anoxia during the 6 months of winter ice-cover, to

69

full water column oxygenation during autumn mixing (Deshpande et al., 2015). There is evidence

that Methanomicrobiales in Arctic and peatland soils may be tolerant of variable redox conditions

(Hoj et al., 2006), which may contribute towards their occurrence and success in these subarctic

thaw ponds. This group of hydrogenotrophic methanogens may also be favored by the high

concentrations of CO2 that accumulate in the bottom waters of these lakes, for example up to 500

µmol CO2 L-1 in the anoxic bottom waters of KWK23 (Laurion et al., 2010). Extreme low

temperature environments are reported to favor acetotrophic methanogens over hydrogenotrophic

taxa, in sediments (Nozhevnikova et al., 1997) and soils (Metje and Frenzel, 2007), however at least

in summer, temperatures in the ponds were within the range that would favor the hydogenotrophic

taxa.

In the present study we used RNA as a template and amplified 16S rRNA rather than using

DNA, which would amplify the gene only. The rational was that the 16S rRNA would be a better

target the Archaea that were actively growing and producing proteins. There are reported

inconsistencies, however, between ribosomes (rRNA content) and growth rates and our RNA

approach only provides a measure of potentially active cells (Blazewicz et al., 2013). In the present

study, the mRNA of mcrA and 16S rRNA from the BGR ponds were inconsistent, with high

proportions of Methanomicrobiales 16S rRNA reads and no success in the amplification of the

transcripts of mcrA, which would indicate a higher probability of active methanogenesis (Juottonen

et al., 2008). The lack of successful amplification of the mcrA may be the result of methodological

problems, for example the delay between sampling and filtration because of the remoteness of the

BGR valley, and degradation of the messenger RNA. Alternatively it may indicate that viable

methanogens were present but that their methane-producing activity was suppressed at the

transcriptional level, for example by the ambient oxygen levels that were higher than in the other

ponds. Similarly, our detection of 16S rRNA reads for methanogens in the surface samples from a

small number of the ponds and the absence of mcrA transcripts in these samples would be consistent

with evidence from elsewhere that methanogens remain viable in oxygenated waters, but that such

conditions inhibit methanogenesis (Bastviken, 2009).

There was a clear effect of permafrost landscape type (palsa versus lithalsa) on archaeal

diversity and community structure. The permutation test on the UniFrac matrix showed that there

were no significant differences in archaeal community structure between the two lithalsa valleys

that differed in extent of permafrost degradation, from sporadic permafrost in the south (KWK) to

discontinuous permafrost in the north (BGR), but that SAS significantly differed from them both.

The thawing of an organic-rich palsa would release both particulate and dissolved organic carbon

70

into the pond water, and the SAS communities were well separated, clustering at the upper end of

the DOC gradient (Figure 4-4a). Soil archaeal communities are strongly shaped by carbon

availability and composition (Hansel et al., 2008), and methanogenic activity is related to organic

matter quantity and quality (Borrel et al., 2011). The degradation of the SAS palsas may result in a

not only more carbon but also a greater variety of organic substrates for methanogens compared to

the more mineral lithalsa soils. The greater variety of substrates could potentially support the more

diverse communities, and the greater proportion of Methanosarcinales. The five most abundant

OTUs for the SAS valleys were all methanogenic Archaea, but with different carbon strategies,

from the hydrogenotrophic genus Methanoregula (Borrel et al., 2011) to the obligate acetotrophic

genus Methanosaeta (Smith and Ingram-Smith, 2007). The newly discovered methanogenic order

Methanomasiliicoccales was detected in both ponds by the mcrA analysis, but at an order of

magnitude higher proportional abundance in the SAS sample.

The subarctic thaw lakes sampled here contained variable, often high concentrations of

suspended solids (Table 4-1). In systems elsewhere, such particles may influence community

structure. For example using the same fractionation protocol as in the present study, Galand et al.

(2008) reported greater diversity of Archaea in the particle-rich waters of an Arctic river and its

receiving coastal waters compared to the adjacent, more oligotrophic marine system. Here, we

failed to detect any systematic difference between the two fractions, in either diversity or

community structure. This may be due to methodological limitations, with the clogging of the 3 µm

filters and retention of the free-living fraction, or may simply reflect a lack of partitioning as a

function of particle size. In the more aerobic BGR ponds, particles may offer a refuge to anaerobic

methanogens as may occur in the ocean (Marty, 1993), but there was no evidence of methanogenic

enrichment in this fraction.

Other environmental variables that may select for specific archaeal taxa are pH and

inorganic nutrients. Archaeal diversity in soil can decrease with increasing pH (Tripathi et al.,

2013), in our water samples the SAS valley also had the lowest pH, consistent with such a pattern,

despite the narrow pH range between our ponds and sites. Phosphorus availability may also

influence diversity or select for certain groups. A study on rice roots indicated that high phosphate

concentrations inhibit members of the family Methanosarcinaceae and favored

Methanobacteriaceae (Lu et al., 2005), but the latter group was present only in low abundance in the

thaw ponds.

71

In conclusion, Euryarchaeota, were found in the microbial plankton of thaw ponds and had

a high proportion of the methanogenic orders Methanobacteriales and Methanosarcinales. The mcrA

reads indicated that the Methanomicrobiales dominated, and pointed to the importance of the

hydrogenotrophic pathway for methanogenesis in these waters. There was a distinct separation

between palsa and lithalsa sites, suggesting that the greater supply and diversity of carbon substrates

in the palsa ponds selected for a significantly different, more diverse archaeal community than in

the lithalsa ponds. These results imply that permafrost landscape type exerts a strong environmental

filtering effect on archaeal community structure in these northern aquatic environments.

4.4. Methods

4.4.1. Study site and sampling

Samples were collected 1 to 13 August 2012 and 31 July to 19 August 2013 from three

different valleys on the eastern side of Hudson Bay, in northern Quebec, Canada. The KWK

(55◦16’N; 77◦46’W) and SAS (55◦13’N; 77◦42’W) valleys are located near the village of

Whapmagoostui-Kuujjuarapik, in a region of sporadic permafrost where permafrost covers less than

2% of the landscape. The BGR valley (56◦37’N; 76◦13’W) is situated 100 km north of the two other

valleys, in the discontinuous permafrost region, close to the village of Umiujaq. The SAS valley is

covered in peatland and ponds from the thawing of organic palsas (Bhiry and Robert, 2006), while

BGR and KWK ponds originated from lithalsas (Calmels et al., 2008). Two to three ponds were

selected from each valley: BGR1 and BGR2, KWK1, 6, 23 and SAS1B, 2A and 2B. Pond number

and names were chosen to be consistent with previous literature for this region (Laurion et al., 2010;

Breton et al., 2009; Calmels et al., 2008; Crevecoeur et al., 2015; Comte et al., 2015). All of the sites

were accessed by helicopter and the ponds were sampled from an inflatable boat positioned over the

central region of maximum depth. Profiles of temperature, dissolved oxygen (DO), and pH were

taken with a 600R multiparametric probe (Yellow Spring Instrument). Bottom samples of the

microbial plankton communities were collected using a horizontally mounted Van Dorn bottle

(Wilco) positioned 0.5 m above the sediments. The water was immediately transferred to acid-

washed, 4-L Cubitainers® that were rinsed three times with sample water prior to filling, and were

overfilled to avoid oxygenation Surface samples were also collected in rinsed Cubitainers, held 0.2

m beneath the pond surface. All Cubitainers were capped and placed in coolers after filling, and

were returned to the laboratory by helicopter for immediate filtration.

72

4.4.2. Physico-chemical and molecular analysis

Water samples for physico-chemical analysis (DOC, TSS and TP) and for molecular

analysis (RNA) were processed as in Crevecoeur et al. (2015). The samples for nucleic acids were

extracted with the AllPrep DNA/RNAMini Kit (Qiagen) modified to include an additional step

using polyvinylpyrrolidone (PVP, Alfa Aesar) to minimize potential PCR inhibition. RNA was

converted to cDNA using the High Capacity cDNA Reverse Transcription Kit (Applied

Biosystems-Ambion). Amplification of the V6-V8 region of 16S rRNA and mcrA was performed

using the sequence specific regions described respectively in Comeau et al. (2011) and Luton et al.

(2002) using a two-step dual-indexed PCR approach modified for Illumina instruments. In a first

step, the gene specific portion was fused to the Illumina TruSeq sequencing primers (Annex 8) and

PCR was carried out in a total volume of 25 µL that contained HF buffer 1X (NEB), 0.25 µM of

each primer, 200 µM of each dNTPs (Life Technology), dimethylsulfoxide (DMSO, NEB) at a final

concentration of 3 %, 1 U of Phusion High-Fidelity DNA polymerase (NEB) and 1 µL of template

cDNA. To decrease potential primer bias, two more reactions with 5 and 10 fold diluted template

were also carried out for each sample. Temperature and duration of thermal cycling were started

with an initial denaturation at 98°C for 30 s followed by 40 cycles of denaturation at 98°C for 10 s,

annealing at 55°C for 10 s, extension at 72°C for 30 s and a final extension at 72°C for 300 s. The

three dilutions reaction were pooled together and purified using the Axygen PCR cleanup kit

(Axygen). Quality and quantity of the purified PCR product were checked on a 1% agarose gel.

Fifty to 100 fold dilution of this purified product was used as a template for a second PCR step with

the goal of adding barcodes (dual-indexed) and missing sequence required for Illumina sequencing

(Annex 8). This second PCR was done in triplicates under the same conditions as the first PCR but

with 13 cycles. Triplicates were pooled together and purified as above and then quantified

spectrophotometrically with the Nanodrop 1000 (Thermo Fisher Scientific). Barcoded amplicons

were pooled in equimolar concentration for sequencing on the Ilumina MiSeq at the Plateforme

d’Analyses Génomiques (IBIS, Université Laval, Québec, Canada). Please note that primers used in

this work contain Illumina specific sequences protected by intellectual property (Oligonucleotide

sequences © 2007-2013 Illumina, Inc.). The raw Illumina sequences have been deposited in the

Short Read Archive database under the accession number SRP069874.

4.4.3. Bioinformatic analysis

Sequences of 16S rRNA were analysed using the UPARSE pipeline for Illumina paired-end

reads (Edgar, 2013) involving merging pair-ends, quality filtering with a 1.0 expected error rate,

dereplication of sequences, sorting sequences by size, removing singletons, clustering OTUs at

73

≥97%, discarding chimeras, indexing OTUs names and creating OTU tables. The downstream

analyses were done within the Qiime pipeline (Caporaso et al., 2010). Taxonomic assignment of

these OTUs was performed using the mothur classifier (Schloss et al., 2009) with a 0.8 confidence

threshold based on the SILVA reference database (Pruesse et al., 2007) modified to include

sequences from our curated 16S rRNA gene sequence database (Lovejoy et al., 2015). Amplicons

of mcrA were analysed with the FunGene pipeline of RDP server

(http://fungene.cme.msu.edu/FunGenePipeline/) (Fish et al., 2013). Reads shorter than 150 nt were

discarded and chimeras were checked and removed with UCHIME. Sequences were translated and

compared to the mcrA reference sequence with FrameBot for correcting frameshift errors,

sequences having in-frame stop codons were discarded. Amino acid sequences were aligned with

HMMER3 and then clustered at 84% similarity that corresponds to OTU level of 97% for 16S

rRNA (Yang et al., 2014). A custom mcrA database was constructed by downloading mcrA

sequences from the Functional Gene Repository v.8.0 with score no lower than the HMM training

sequences. Reference sequences were checked against the NCBI nr database and the in house

database was manually curated to ensure all families of methanogens were represented. Taxonomic

affiliation of the representative sequences of mcrA OTUs was defined by alignment with the

reference database using the BLASTp algorithm. A Neighbour Joining (1000 bootstrap) tree

following the Poisson model was constructed to assign taxonomy to sequences hitting uncultured

methanogens.

Shannon and Chao1 diversity indexes for the 16S rRNA were estimated using the command

alpha_diversity.py available in Qiime. Data were tested for normal distribution using the Shapiro-

Wilk test and homoscedasticity with the Bartlett test. Three-way analysis of variance (three-way

ANOVA) was used to assess differences in diversity indexes between valleys, fractions, and year.

An a posteriori Tukey HSD test was run to identify differences between valleys. For beta-diversity

analyses, OTU tables were subsampled 100 times at 22,200 reads for 16S rRNA and 30,800 reads

for mcrA. This number of reads corresponds each time to the smallest number of reads per sample

minus 10%. A tree for the 16S rRNA was constructed in Qiime using the method Fasttree in order

to calculate the unweigthed Unifrac phylogenetic distance (Lozupone and Knight, 2005) using the

command beta_diversity.py.

Further statistical analyses were done with R (R Core Team, 2013) (version 3.0.1) and

Qiime. A non-metric multidimensional scaling (NMDS) with the UniFrac distance was also used to

visualize the influence of the environmental variables on the community. The NMDS was

performed in R using the function monoMDS with a principal coordinates analysis done with the

http://fungene.cme.msu.edu/FunGenePipeline/)

74

function wcmdscale as a starting configuration. Correlations between the environmental variables

and the ordination were tested in R with the envfit function. The selected significant environmental

variables were then plotted on the ordination using the ordisurf function. These functions were

employed via the vegan package (Oksanen et al. (2013). Vegan: Community Ecology Package. R

package version 2.0-8. http://CRAN.R-project.org/package=vegan; last accessed January 2016).

Statistical significance of difference between communities was assessed using permutation test

adonis on the UniFrac distance matrix under the function compare_category.py. Pairwise

comparisons were then processed using the function make_distance_boxplot.py. The resulting p-

values were corrected with Bonferroni. Both of these commands are available in Qiime (Caporaso

et al., 2010). The five most abundant OTUs for each valley were subsequently submitted to a

BLASTn against the GenBank nr database to assess identification and isolation source of the closest

matches.



(NSERC) funding for Discovery Grants to WFV and CL, the Discovery Frontiers grant ADAPT

and Canada Research Chair support to WFV, the Network of Centres of Excellence ArcticNet

support for WV and CL, and the Fonds de Recherche du Québec-Nature et Technologies (FRQNT)

for funding for the Centre d’Études Nordiques (CEN). Computing support was provided by

CLUMEQ/Compute Canada. We thank Jérôme Comte and Adam Monier for help in bioinformatics

and statistic. We are also grateful to Claude Tremblay at the Whapmagoostui-Kuujjuarapik CEN

station, the pilots of Canadian Helicopter Ltd., Anna Przytulska-Bartosiewicz, Bethany Deshpande,

Paschale Noël Bégin, and Alex Matveev for aid in the field, Marianne Potvin for laboratory

assistance and the development of protocols.

http://cran.r-project.org/package=vegan

75

Chapitre 5. Conclusion générale

Cette thèse a permis de mieux comprendre l’organisation des communautés microbiennes

dans les mares de fonte en plus de mettre en lumière des résultats inédits sur la présence et l’activité

des micro-organismes planctoniques impliqués dans le cycle du méthane. Les principales

contributions de cette thèse ainsi que les perspectives qu’elle ouvre pour de futures recherches sont

détaillées dans cette conclusion générale. Même si les observations rapportées ici sont spécifiques

au nord du Québec, les mares de thermokarst sont extrêmement abondantes à travers tout l’Arctique

(Grosse et al., 2013). Les découvertes faites dans le cadre de ce travail suggèrent l’existence de

caractéristiques générales qui peuvent être appliquées à ce type d’écosystème d’eau douce

prédominant dans le Nord.

5.1. Dominance des micro-organismes impliqués dans le cycle du

méthane

Pour les deux domaines de la vie étudiés ici, on voit une forte dominance dans la

communauté de micro-organismes impliqués dans le cycle du méthane, c’est-à-dire les bactéries

méthanotrophes et les archées méthanogènes. Tout d’abord, en ce qui concerne les méthanotrophes,

leur présence a été attestée dans tous les échantillons en très grande proportion (jusqu’à 27% de la

communauté dans certains échantillons) et seulement des méthanotrophes de Type I ont été

identifiés avec le gène 16S. Cette proportion est très élevée si on la compare avec d’autres

écosystèmes aquatiques où les méthanotrophes représentent environ 2 à 3% de la communauté

planctonique (Eller et al., 2005; Comeau et al., 2012). Les proportions que l’on retrouve dans les

mares de fonte s’apparentent donc plutôt à ce qui a été observé dans les sédiments du lac

Washington (environ 15% de la communauté) (Costello et Lidstrom, 1999), dans la tourbe (environ

6%) (Serkebaeva et al., 2013) ou dans le sol de la toundra (de 1 à 23%) (Vecherskaya et al., 1993).

Les méthanotrophes font aussi partie des unités taxonomiques opérationnelles (UTOs) les plus

abondantes, plus particulièrement le genre Methylobacter.

Ensuite, l’amplification du transcrit du gène fonctionnel pmoA dans la plupart des

échantillons au chapitre 3 confirme l’activité métabolique des méthanotrophes détectés avec le

transcrit du gène 16S au chapitre 2. La composition de la communauté obtenue avec les deux gènes

est assez similaire, car dans les deux cas, la communauté est composée presque essentiellement de

méthanotrophes de Type I avec une dominance prononcée du genre Methylobacter. Cependant,

l’analyse de la communauté à partir des transcrits du gène pmoA permet aussi de détecter la

présence de méthanotrophes de Type II en très faible abondance. Ici le gène fonctionnel permet

76

d’avoir des résultats plus complets sur la composition de la communauté des méthanotrophes, mais

ces résultats peuvent aussi être dus au fait que deux techniques de séquençage différentes ont été

utilisées. En effet, le pyroséquençage 454 utilisé au chapitre 2 offre une moins grande profondeur

de séquençage que le séquençage Illumina MiSeq utilisé au chapitre 3 (Shokralla et al., 2012).

Enfin, les archées méthanogènes sont aussi retrouvées en forte abondance dans tous les

échantillons de fond (et quelques échantillons de surface). Cela souligne l’importance des

méthanogènes planctoniques dans les mares de fonte subarctiques contrairement aux mares de fonte

arctiques où les méthanogènes ont surtout été trouvés dans les sédiments (Negandhi et al., 2013).

Dans nos échantillons, la proportion des archées méthanogènes peut atteindre environ 70% de la

communauté, alors que dans les autres écosystèmes d’eau douce les méthanogènes ne représentent

en général pas plus de 30% (Auguet et al., 2010). La forte proportion que l’on observe ici

correspond plutôt à celle trouvée dans le sol des rizières (entre 37 et 88%) (Lee et al., 2015) ou dans

la couche supérieure des sédiments d’un lac méromictique (jusqu’à 90%) (Borrel et al., 2012). Les

méthanogènes sont en général plutôt retrouvés dans les sédiments ou le sol, bien que le groupe

Methanomicrobiales, qui fait partie d’un des groupes les plus abondants dans nos échantillons, est

considéré comme prédominant dans les écosystèmes d’eau douce (Auguet et al., 2010). Les archées

méthanogènes font aussi partie des UTOs les plus abondantes dans les mares de fontes surtout en ce

qui concerne les genres Methanoregula et Methanosaeta qui disposent de métabolismes différents,

respectivement hydrogénotrophe et acétotrophe obligatoire.

Contrairement aux méthanotrophes, il n’a pas été possible d’amplifier le transcrit du gène

fonctionnel mcrA pour tous les échantillons et de mesurer l’activité des méthanogènes avec la

qPCR. Il est donc difficile de savoir si les méthanogènes identifiés dans le fond des mares de fonte

sont vraiment actifs. Néanmoins, l’amplification du transcrit du gène mcrA dans deux échantillons

permet de supposer que les méthanogènes peuvent être actifs au moins dans ces deux mares-là. En

comparant les communautés identifiées avec les transcrits du gène 16S rARN et du gène mcrA, on

remarque une composition de la communauté assez similaire, mais les proportions de

Methanomicrobiales augmentent avec le mcrA, ce qui suppose que ce groupe participe plus

activement à la méthanogenèse et donc que la voie métabolique dominante pour la synthèse de

méthane est hydrogénotrophique. De nouveau, l’analyse de la communauté à l’aide du gène

fonctionnel amène la détection d’un groupe qui n’était pas reporté avec le 16S rARN, c’est-à-dire

les Méthanomassiliicoccales. Cette fois, cette différence n’est pas due à une limitation technique

puisque les deux transcrits de gènes ont été séquencés avec la plate-forme Illumina MiSeq.

77

Les micro-organismes impliqués dans le cycle du méthane représentent donc une forte proportion

de la communauté microbienne planctonique dans les mares de fonte. L’identification des micro-

organismes par le biais de l’analyse des gènes fonctionnels est compatible avec l’analyse du gène

16S rARN, mais semble permettre l’identification d’une plus grande diversité de micro-organismes

impliqués dans le cycle du méthane.

5.2. Influence de l’origine des mares et du gradient de fonte du

pergélisol

Les compositions des différentes communautés microbiennes étudiées dans cette thèse sont

principalement influencées par la vallée d’échantillonnage. À plus grande échelle, on voit une

distinction assez claire entre les communautés de mares issues de palses (à dominance organique)

ou de lithalses (à dominance minérale). Par contre, l’influence du gradient de fonte du pergélisol est

beaucoup moins marquée. En effet, les communautés microbiennes de la vallée KWK semblent

structurellement et phylogénétiquement plus proches des communautés des vallées BGR et NAS,

alors que ces dernières ne se trouvent pas dans la même zone de dégradation du pergélisol. La

nature du carbone plutôt que la quantité de l’apport du carbone allochtone a donc plus d’influence

sur la communauté. Pour la communauté des archées, le riche apport en carbone organique

allochtone favorise même une plus grande diversité telle qu'observée dans la vallée SAS. Ce type de

relation a déjà été remarqué pour le bactérioplancton (Landa et al., 2013) et l’archéoplancton

(Galand et al., 2008) et dépend de la diversité de la matière organique dissoute disponible pour la

dégradation microbienne. En effet, le carbone organique dissous peut prendre différentes formes (de

labile à récalcitrant). Le carbone organique labile stimule le bactérioplancton métaboliquement actif

et, une fois ce substrat dégradé, les matières organiques semi-labiles et récalcitrantes peuvent être

décomposées par des taxons spécialistes, ce qui augmente la diversité microbienne dans son

ensemble (Li et al., 2012; Nelson et Wear, 2014). En résumé, la diversité de substrat de la matière

organique et non sa quantité, mène à une plus grande diversité microbienne (Galand et al., 2008;

Hansel et al., 2008). Dans le contexte de fonte du pergélisol, il est difficile de savoir comment la

réactivité de la matière organique dissoute va évoluer, mais la labilité du carbone est censée

augmenter avec la fonte (Hodgkins et al., 2014).

Dans l’ensemble, la composition des communautés microbiennes étudiées dans cette thèse

est principalement influencée par l’origine de la formation des mares plutôt que par le gradient de

dégradation du pergélisol. Cela reflète l’interdépendance entre la communauté microbienne et la

nature du carbone disponible pour la dégradation alors que la quantité du carbone relargué dans les

mares semble avoir moins d’impact sur les communautés microbiennes.

78

5.3. Influence des facteurs environnementaux

Les différences de communautés entre les mares issues de palses et de lithalses sont aussi

liées à des variables environnementales clefs. En effet, les mares issues de palses sont en général

plus acides et moins productives (plus faible concentration en phosphore total et en chlorophylle a)

et plus riches en carbone organique dissous que les mares issues de lithalses. Ces variables sont déjà

reconnues dans la littérature pour leur forte influence sur les communautés microbiennes. Tout

d’abord, l’origine allochtone ou autochtone du carbone organique dissous influence la composition

des communautés microbiennes (Kritzberg et al., 2006). Ensuite, le pH est un élément essentiel

pour la caractérisation des communautés microbiennes et a été identifié comme le facteur qui

explique le mieux la distribution biogéographique des communautés bactériennes dans le sol (Fierer

et Jackson, 2006) et dans les lacs (Lindström et al., 2005). Enfin, le phosphore est souvent un

élément limitant pour la croissance des microbes dans les écosystèmes d’eau douce (Vadstein,

2000) et l’apport de phosphore combiné à d’autres nutriments a le potentiel de modifier la

composition de la communauté (Newton et McMahon, 2011). Dans la vallée SAS, les

concentrations en phosphore sont moins élevées que dans les autres vallées. Le phosphore est

d’ailleurs en général un élément limitant dans les tourbières où la dégradation du carbone est

contrôlée par la disponibilité en phosphore (Lin et al., 2014). L’apport en phosphore stimule aussi le

potentiel d’oxydation du méthane (Liu et al., 2012) et a été identifié ici comme un des facteurs qui

influence l’activité des méthanotrophes mesurée à partir du nombre de transcrits de pmoA. L’effet

stimulant du phosphore sur l’activité bactérienne en général se répercute donc sur les

méthanotrophes qui constituent une forte proportion de la communauté microbienne des mares de

fonte. Les matières en suspension semblent aussi être impliquées dans l’activité des méthanotrophes

alors que ce facteur a très peu d’impact sur la composition des communautés microbiennes (pas de

différence de composition de la communauté entre les fractions de taille). Il est possible qu’un

assemblage complexe de microbes soit associé aux particules (Grossart et Simon, 1998) où des

archées méthanogènes profiteraient de microzones anoxiques au sein des particules pour produire

du méthane (Marty, 1993), qui serait ensuite directement oxydé par des bactéries méthanotrophes à

la surface des particules. Dans le cas de cette étude, l’activité des méthanotrophes ne suit pas le

même patron que la composition de la communauté. L’absence de lien apparent entre la diversité et

la fonction des bactéries méthanotrophes peut s’expliquer par le concept de redondance

fonctionnelle, où différentes espèces détiennent le même rôle dans l’environnement les rendant

interchangeables (Rosenfeld, 2002).

79

En bref, certaines variables environnementales clefs comme la concentration en carbone

organique dissous, la concentration en phosphore et le pH, contribuent à la distinction des

communautés microbiennes entre les mares issues de palses et de lithalses. À l’exception du

phosphore, des variables différentes influencent l’activité des méthanotrophes comme les particules

en suspension et la conductivité, ce qui illustre un découplage entre la diversité et la fonction des

bactéries méthanotrophes. Il n’est donc pas possible de tirer des conclusions sur l’expression d’un

trait microbien en se basant uniquement sur la diversité de sa communauté.

5.4. Implication scientifique

Les effets du réchauffement climatique sont ressentis de manière beaucoup plus intense

dans les régions arctiques et subarctiques. Si la tendance se maintient, le réchauffement pourrait

contribuer à une augmentation de 30% des taux d’émission de méthane des lacs et mares nordiques

(Wik et al., 2016). Dans la région de Kuujjuarapik, des anomalies de températures sept fois plus

élevées que la moyenne ont été mesurées durant les dernières années (Bhiry et al., 2011). Ces

augmentations de températures peuvent avoir plusieurs conséquences. Par exemple, la stratification

thermique observée durant l’été dans les mares de fonte risque d’être renforcée par des températures

atmosphériques plus chaudes et de s’établir pendant plus longtemps. Ce renforcement de la

stratification peut mener aussi à un gradient physico-chimique plus intense au sein de la colonne

d’eau, où les conditions anoxiques vont prévaloir dans le fond (Vincent, 2009). Pour l’instant,

l’anoxie n’était pas systématiquement présente dans le fond des mares échantillonnées durant l’été.

Les mares plus au nord des vallées BGR et NAS, bien que stratifiées, étaient oxiques dans le fond

de la colonne d’eau au moment de l’échantillonnage. À l’avenir, on peut donc s’attendre au

développement de conditions hypoxiques à anoxiques dans le fond des mares des vallées situées

plus au nord ainsi qu'à un renforcement et une expansion de la zone anoxique pour les mares des

vallées situées plus au sud. Ces changements de conditions vont perturber les communautés

microbiennes et favoriser l’établissement de microbes au métabolisme anaérobie comme le groupe

des Chlorobi, déjà présent en forte abondance dans les fonds des mares des vallées KWK et SAS.

Au vu des résultats obtenus dans cette thèse, ce changement de communauté sera aussi influencé

par l’origine des mares, c’est-à-dire palse ou lithalse.

Les effets du changement climatique vont donc amener des conditions plus propices pour la

synthèse de méthane dans la colonne d’eau des mares de fonte. Les archées méthanogènes qui

constituent déjà le groupe d’archées dominant dans ces mares aura alors le potentiel de s’activer.

Pour l’instant, l’absence d’amplification du transcrit du gène fonctionnel mcrA dans la plupart des

échantillons ne nous permet pas de supposer que la forte proportion d’archées méthanogènes

80

présente dans le fond des mares participe activement à la synthèse de méthane au moment de

l’échantillonnage. Dans ce cas, le méthane émis depuis les mares peut provenir de méthanogènes

qui seraient plutôt actifs dans les sédiments, comme cela a été observé pour les mares de fonte de

l’Arctique canadien (Negandhi et al., 2013) ou résulter du dégazage du vieux méthane resté

prisonnier dans le sol gelé (Bouchard et al., 2015). Si les méthanogènes planctoniques deviennent

actifs, il y a donc un très fort potentiel pour une augmentation de la synthèse de méthane et donc

une libération d’une plus grande quantité de méthane dans l’atmosphère.

Au final, la quantité de méthane qui s’échappe des mares dans l’atmosphère dépend de

l’équilibre entre l’activité des méthanogènes et des méthanotrophes. Dû à leur métabolisme aérobie,

les méthanotrophes doivent trouver un compromis entre la disponibilité en méthane et en oxygène,

mais les seuils de concentration de ces gaz à partir desquels les méthanotrophes deviennent actifs

sont méconnus. De plus en plus d’évidences amènent à penser que les bactéries méthanotrophes

sont en fait microaérobiques (Vecherskaya et al., 2009; Blees et al., 2014) et peuvent tenir leur

apport en oxygène via des associations avec d’autres types d’organismes comme des phototrophes

(Milucka et al., 2015; Oswald et al., 2015) ou des dénitrificateurs (Beck et al., 2013; Liu et al.,

2014) pour rester actives même dans des conditions très pauvres en oxygène. De plus, la pression de

broutage par les protistes étant moins forte dans les eaux hypoxiques, les méthanotrophes peuvent

ainsi éviter la prédation. Seulement, avec l’intensification du réchauffement climatique, les mares

de fonte vont faire face à une augmentation de la durée des périodes de fontes des glaces et de

stratification, ce qui perturbera les conditions d’oxygénation. Alors qu’une plus forte oxygénation

pourrait avantager les bactéries méthanotrophes par rapport aux archées méthanogènes, des plus

fortes conditions de stratification pourraient au contraire altérer les processus d’oxydation du

méthane et en favoriser la synthèse par les archées méthanogènes. Les résultats de cette thèse

permettent cependant de conclure que les micro-organismes impliqués dans le cycle du méthane

sont capables de développer des stratégies pour rester présents et actifs jusqu’aux limites de leurs

conditions optimales en oxygène. En somme, les écosystèmes de mares de fonte sont au centre des

perturbations que subit l’environnement à cause du réchauffement global. Les communautés

microbiennes risquent de subir des changements dans leur composition qui vont dépendre en grande

partie de l’origine de la mare (palse ou lithalse). De plus, l’équilibre entre la méthanogenèse et la

méthanotrophie risque d’être perturbé par l’impact du réchauffement climatique favorisant

potentiellement la méthanogenèse par rapport à la méthanotrophie. Les méthanotrophes semblent

néanmoins disposer de stratégies pour rester actifs dans des eaux très faibles en oxygène.

81

5.5. Perspectives

Tout au long de cette thèse, les fractions de tailles utilisées pour distinguer les

communautés de micro-organismes libres ou attachés aux particules ont mené à l’identification de

communautés semblables. Pourtant, dans la littérature, ces fractions de tailles ont été utilisées avec

succès pour différencier les communautés libres et attachées (Crump et al., 1999; Galand et al.,

2008; Mohit et al., 2014). Dans cette thèse, la composition des deux communautés est restée

similaire pour les bactéries, les archées et les méthanotrophes. Par contre, la richesse spécifique de

la communauté bactérienne était plus élevée dans la grande fraction, ce qui suppose que des

bactéries libres se sont retrouvées sur les filtres de 3 µm, augmentant ainsi la diversité et masquant

les différences de composition entre les deux communautés. En effet, les mares de fonte contiennent

énormément de grosses particules en suspension de 10 µm à 1 cm de diamètre qui peuvent

rapidement boucher le filtre de 3 µm et ainsi retenir toute la communauté libre ou attachée sur ce

filtre (Deshpande et al., 2016). La présence de particules plus petites que 3 µm (Watanabe et al.,

2011) peut aussi amener à la présence de bactéries attachées aux particules sur le filtre de 0.22 µm.

Dans le futur, il serait souhaitable de définir de nouvelles fractions de tailles adaptées aux systèmes

riches en matière en suspension comme les mares de fonte. La caractérisation de la composition des

communautés microbiennes à partir du gène ARNr 16S est une technique largement utilisée pour

laquelle des outils bio-informatiques et bases de données complètes facilitent l’analyse. Néanmoins,

ces techniques demandent de passer par une étape de PCR qui peut initier des biais dans la

composition de la communauté microbienne selon le choix des amorces (Hong et al., 2009) et sur

l’estimation de l’abondance totale et relative des OTUs (Engelbrektson et al., 2010). En outre,

l’utilisation de gènes fonctionnels amène à l’identification d’une plus grande diversité fonctionnelle

qui n’est pas toujours détectée avec l’ARN 16S. Pour avoir un meilleur aperçu de la diversité

taxonomique et fonctionnelle dans les mares de fonte, il serait approprié d’utiliser les techniques de

métagénomique et métatranscriptomique, qui permettent de séquencer la quasi-totalité des gènes ou

transcrits présents dans l’échantillon (Thomas et al., 2012; Logares et al., 2014). Ces deux

techniques ont déjà révélé un potentiel de découverte de nouveaux organismes non repérés par les

techniques de séquençage d’amplicons (Gilbert et al., 2008).

À l’avenir, il serait crucial de mieux comprendre quels sont les seuils d’activité des

méthanogènes et des méthanotrophes. Mesurer l’activité des micro-organismes est une tâche assez

difficile. Dans cette thèse, l’approche qui a été utilisée est la mesure des transcrits, donc l’étape

précédant la traduction de l’ARN messager en protéine. L’information concernant la régulation des

gènes fonctionnels mcrA et pmoA dans les cellules est assez méconnue. L’analyse des mécanismes

82

de régulation de la transcription et de la traduction du gène pmoA en laboratoire représente un défi,

car il n’est pas possible de le cloner entièrement puisque certaines parties sont toxiques pour la

bactérie Escherichia coli. La concentration en cuivre semble être un des principaux facteurs qui

régule l’expression du gène pmoA (Gilbert et al., 2000; Knapp et al., 2007). L’étude des transcrits

permet donc de savoir si la cellule est métaboliquement active et prête à synthétiser la protéine

nécessaire à la réaction, mais dans notre cas il n’est pas possible de savoir si le méthane est

vraiment synthétisé ou consommé par les micro-organismes. Une autre approche pour évaluer

l’activité des micro-organismes consiste à mesurer le processus de synthèse ou de consommation du

méthane en aval. Par exemple, pour mesurer le taux de production du méthane, il est possible

d’incuber un substrat et ensuite de mesurer les rendements de production de méthane en fonction du

temps comme dans les études de Metje et Frenzel (2007), Grossart et al. (2011) et Wilkins et al.

(2015). Les deux dernières références combinent ces résultats avec des approches moléculaires.

Wilkins et al. (2015) trouvent même une corrélation positive entre le nombre de transcrits de mcrA

et le taux de production du méthane et supposent donc que l’on peut déduire l’un à partir de l’autre.

Pour la méthanotrophie, beaucoup d’études utilisent l’approche des isotopes stables ("stable-isotope

probing", SIP), qui implique de fournir aux méthanotrophes un substrat marqué par un isotope

stable (13C) et de détecter la présence de cet isotope dans l’ARN ou les acides gras dérivés des

phospholipides (PLFAs). La technique du PLFAs-SIP est plus efficace pour détecter l’oxydation de

méthane par les méthanotrophes, car la limite de détection de carbone marqué dans les acides gras

est un ordre de grandeur plus bas que dans l’ARN (Bengtson et al., 2009). Ces techniques

permettent de confirmer si le méthane a bien été consommé et incorporé dans les structures

bactériennes. Cependant, l’application de ces techniques demande la mise en place d’expériences

d’incubation qui ne reflètent pas exactement les conditions in situ, mais correspond plutôt à un

potentiel de méthanotrophie pour le milieu (Radajewski et al., 2003). La combinaison des

techniques de SIP et la mesure moléculaire des transcrits du gène permettrait donc d’avoir une

image plus complète des processus métaboliques avant et après leur réalisation. Il serait aussi

nécessaire d’investiguer de manière plus complète les facteurs qui influencent l’activité des micro-

organismes. En effet, dans cette thèse, l’activité des méthanotrophes est seulement partiellement

expliquée par les variables environnementales mesurées, ce qui met en lumière l’importance

potentielle d’autres types de variables pour expliquer l’activité des méthanotrophes comme, par

exemple, le contrôle descendant ("top-down") du broutage par les protistes ou le contrôle ascendant

("bottom-up") de la lyse virale.

83

Aussi, la composition et la fonction des communautés microbiennes restent encore

inconnues durant les autres périodes de l’années, notamment pendant les événements de mélange

automnaux et printaniers ainsi que durant la période de stratification inverse qui s’établit pendant

l’hiver. Les conditions de brassage de la colonne d’eau risque de perturber les communautés de

méthanogènes et méthanotrophes, ainsi que d’induire une possible ventilation du méthane vers

l’atmosphère sans que les méthanotrophes ne puissent efficacement le consomer. Par contre, durant

les conditions de stratification hivernale, l’entièreté de la colonne d’eau de certaines mares peut

alors devenir anoxique (Deshpande et al., 2015) ce qui contibue à l’établissement de condition plus

favorables pour les méthanogènes, bien que la diminution de la température peut aussi amener une

diminution de la production de méthane (Yvon-Durocher et al., 2014). Ces conditions risque d’être

moins optimale pour les méthanotrophes étant donné l’absence d’oxygènes, et les changements de

température pourrait amener à l’établissement d’une communauté de méthanotrophes différente

plus adaptés aux froid comme les méthanotrophes de Type II (He et al., 2012).

En résumé, de futures recherches sur les mares de fonte devraient s’orienter vers

l’utilisation de techniques de séquençage plus complètes comme la métagénomique et la

métatranscriptomique pour mieux identifier la diversité phylogénétique et fonctionnelle de la

communauté microbienne. L’étude moléculaire de l’activité des micro-organismes impliqués dans

le cycle du méthane devrait aussi être couplée à des expériences d’incubation d’isotopes stables afin

d’avoir une vue d’ensemble plus exhaustive de l’activité métabolique de méthanotrophes et

méthanogènes. Il serait aussi intéressant d’appliquer les approches utilisées dans cette étude à une

plus grande gamme de conditions géographiques dans les régions Subarctique et Arctique, et

d’étendre ce travail tout au long de l’année pour étudier le cycle du méthane durant les autres

saisons.

84

Bibliographie

Allard, M., and Seguin, M. K. (1987). Le pergélisol au Québec nordique : bilan et perspectives.

Géographie Phys. Quat. 41, 141–152. doi:10.7202/032671ar.

Allgaier, M., and Grossart, H. (2006). Diversity and seasonal dynamics of actinobacteria

populations in four lakes in northeastern germany. Appl. Environ. Microbiol. 72, 3489–3497.

doi:10.1128/AEM.72.5.3489.

Andresen, C. G., and Lougheed, V. L. (2015). Disappearing Arctic tundra ponds: Fine-scale

analysis of surface hydrology in drained thaw lake basins over a 65 year period (1948-2013).

J. Geophys. Res. Biogeosciences 120, 466–479. doi:10.1002/2014JG002778.

Arlen-Pouliot, Y., and Bhiry, N. (2005). Palaeoecology of a palsa and a filled thermokarst pond in a

permafrost peatland, subarctic Québec, Canada. The Holocene 15, 408–419.

doi:10.1191/0959683605hl818rp.

Auguet, J.-C., Barberan, A., and Casamayor, E. O. (2010). Global ecological patterns in uncultured

Archaea. ISME J. 4, 182–190. doi:10.1038/ismej.2009.109.

Azam, F., and Graf, J. S. (1983). The ecological role of water-column microbes in the sea. Mar.

Ecol. Prog. Ser. 10, 257–263.

Bapteste, E., Brochier, C., and Boucher, Y. (2005). Higher-level classification of the Archaea:

evolution of methanogenesis and methanogens. Archaea 1, 353–363.

doi:10.1155/2005/859728.

Barber, R. D., Zhang, L., Harnack, M., Olson, M. V, Kaul, R., Ingram-Smith, C., and Smith, K. S.

(2011). Complete genome sequence of Methanosaeta concilii, a specialist in aceticlastic

methanogenesis. J. Bacteriol. 193, 3668–3669. doi:10.1128/JB.05031-11.

Barbier, B. A., Dziduch, I., Liebner, S., Ganzert, L., Lantuit, H., Pollard, W., and Wagner, D.

(2012). Methane-cycling communities in a permafrost-affected soil on Herschel Island,

Western Canadian Arctic: active layer profiling of mcrA and pmoA genes. FEMS Microbiol.

Ecol. 82, 287–302. doi:10.1111/j.1574-6941.2012.01332.x.

Bastviken, D. (2009). “Methane,” in Encyclopedia of Inland Waters, ed. G. Likens (Oxford:

Elsevier), 783–805.

Bastviken, D., Cole, J. J., Pace, M. L., and Van de Bogert, M. C. (2008). Fates of methane from

different lake habitats: Connecting whole-lake budgets and CH4 emissions. J. Geophys. Res.

113, G02024. doi:10.1029/2007JG000608.

Beck, D. A. C., Kalyuzhnaya, M. G., Malfatti, S., Tringe, S. G., Glavina Del Rio, T., Ivanova, N.,

Lidstrom, M. E., and Chistoserdova, L. (2013). A metagenomic insight into freshwater

methane-utilizing communities and evidence for cooperation between the Methylococcaceae

and the Methylophilaceae. PeerJ 1, e23. doi:10.7717/peerj.23.

Bengtson, P., Basiliko, N., Dumont, M. G., Hills, M., Murrell, J. C., Roy, R., and Grayston, S. J.

(2009). Links between methanotroph community composition and CH4 oxidation in a pine

forest soil. FEMS Microbiol. Ecol. 70, 356–366. doi:10.1111/j.1574-6941.2009.00751.x.

85

Bhiry, N., Delwaide, A., Allard, M., Bégin, Y., Filion, L., Lavoie, M., Nozais, C., Payette, S.,

Pienitz, R., Saulnier-Talbot, É., and Vincent, W. F. (2011). Environmental change in the Great

Whale River region, Hudson Bay: five decades of multidisciplinary research by Centre

d’études nordiques (CEN). Ecoscience 18, 182–203. doi:10.2980/18-3-3469.

Bhiry, N., and Robert, C. (2006). Reconstruction of changes in vegetation and trophic conditions of

a palsa in a permafrost peatland, subarctic Québec, Canada. Ecoscience 13, 56–65.

doi:10.2980/1195-6860(2006)13[56:ROCIVA]2.0.CO;2.

Blazewicz, S. J., Barnard, R. L., Daly, R. A., and Firestone, M. K. (2013). Evaluating rRNA as an

indicator of microbial activity in environmental communities: limitations and uses. ISME J. 7,

2061–2068. doi:10.1038/ismej.2013.102.

Blees, J., Niemann, H., Wenk, C. B., Zopfi, J., Schubert, C. J., Kirf, M. K., Veronesi, M. L., Hitz,

C., and Lehmann, M. F. (2014). Micro-aerobic bacterial methane oxidation in the chemocline

and anoxic water column of deep south-Alpine Lake Lugano (Switzerland). Limnol.

Oceanogr. 59, 311–324. doi:10.4319/lo.2014.59.2.0311.

Bodelier, P. Le, Meima-Franke, M., Hordijk, C. A., Steenbergh, A. K., Hefting, M. M., Bodrossy,

L., von Bergen, M., and Seifert, J. (2013). Microbial minorities modulate methane

consumption through niche partitioning. ISME J. 7, 2214–2228. doi:10.1038/ismej.2013.99.

Bogard, M. J., Del Giorgio, P. A., Boutet, L., Chaves, M. C. G., Prairie, Y. T., Merante, A., and

Derry, A. M. (2014). Oxic water column methanogenesis as a major component of aquatic

CH4 fluxes. Nat. Commun. 5, 5350. doi:10.1038/ncomms6350.

Bonilla, S., Villeneuve, V., and Vincent, W. F. (2005). Benthic and planktonic algal communities in

a High Arctic Lake: pigment structure and contrasting responses to nutrient enrichment. J.

Phycol. 41, 1120–1130. doi:10.1111/j.1529-8817.2005.00154.x.

Borcard, D., Gillet, F., and Legendre, P. (2011). Numerical Ecology with R. Springer. New York

doi:10.1007/978-1-4419-7976-6.

Börjesson, G., Sundh, I., and Svensson, B. (2004). Microbial oxidation of CH4 at different

temperatures in landfill cover soils. FEMS Microbiol. Ecol. 48, 305–312.

doi:10.1016/j.femsec.2004.02.006.

Borrel, G., Jézéquel, D., Biderre-Petit, C., Morel-Desrosiers, N., Morel, J.-P., Peyret, P., Fonty, G.,

and Lehours, A.-C. (2011). Production and consumption of methane in freshwater lake

ecosystems. Res. Microbiol. 162, 832–847. doi:10.1016/j.resmic.2011.06.004.

Borrel, G., Lehours, A.-C., Crouzet, O., Jézéquel, D., Rockne, K., Kulczak, A., Duffaud, E., Joblin,

K., and Fonty, G. (2012). Stratification of Archaea in the deep sediments of a freshwater

meromictic lake: vertical shift from methanogenic to uncultured archaeal lineages. PLoS One

7, e43346. doi:10.1371/journal.pone.0043346.

Borrel, G., O’Toole, P. W., Harris, H. M. B., Peyret, P., Brugère, J.-F., and Gribaldo, S. (2013).

Phylogenomic data support a seventh order of methylotrophic methanogens and provide

insights into the evolution of methanogenesis. Genome Biol. Evol. 5, 1769–1780.

doi:10.1093/gbe/evt128.

Boscaro, V., Felletti, M., Vannini, C., Ackerman, M. S., Chain, P. S. G., Malfatti, S., Vergez, L. M.,

86

Shin, M., Doak, T. G., Lynch, M., and Petroni, G. (2013). Polynucleobacter necessarius, a

model for genome reduction in both free-living and symbiotic bacteria. Proc. Natl. Acad. Sci.

U. S. A. 110, 18590–5. doi:10.1073/pnas.1316687110.

Bouchard, F., Francus, P., Pienitz, R., and Laurion, I. (2014). Subarctic thermokarst ponds :

investigating recent landscape evolution and sediment dynamics in thawed permafrost of

northern Québec (Canada). Arctic, Antarct. Alp. Res. 46, 251–271.

doi:http://dx.doi.org/10.1657/1938-4246-46.1.251.

Bouchard, F., Laurion, I., Preskienis, V., Fortier, D., Xu, X., and Whiticar, M. J. (2015). Modern to

millennium-old greenhouse gases emitted from freshwater ecosystems of the eastern Canadian

Arctic. Biogeosciences 12, 11661–11705. doi:10.5194/bgd-12-11661-2015.

Bowman, J. (2006). “The methanotrophs—the families Methylococcaceae and Methylocystaceae.,”

in The Prokaryotes, eds. M. Dworkin, S. Falkow, E. Rosenberg, K. H. Schleifer, and E.

Strackebrandt (Springer, New York.), 266–289.

Breton, J., Vallières, C., and Laurion, I. (2009). Limnological properties of permafrost thaw ponds

in northeastern Canada. Can. J. Fish. Aquat. Sci. 66, 1635–1648. doi:10.1139/F09-108.

Briée, C., Moreira, D., and López-García, P. (2007). Archaeal and bacterial community

composition of sediment and plankton from a suboxic freshwater pond. Res. Microbiol. 158,

213–27. doi:10.1016/j.resmic.2006.12.012.

Brown, J., Ferrians, Jr., O. J., Heginbottom, J. A., and Melnikov, E. S. (1997). Circum-Arctic map

of permafrost and ground-ice conditions. Washington, DC: U.S. Geological Survey in

Cooperation with the Circum-Pacific Council for Energy and Mineral Resources. Circum-

Pacific Map Series CP-45, scale 1:10,000,000, 1 sheet.

Brown, J., and Ferrians, O.J., Heginbottom, J.A. Melnikov, E. S. (1998, revised February 2001).

Circum-arctic map of permafrost and ground ice conditions. Boulder, CO Natl. Snow Ice

Data Center. Digit. Media.

Brown, T., and Simpson, J. (2001). Managing phosphorus inputs to urban lakes I . Determining the

trophic state of your lake. Urban Lake Manag. 3, 771–781.

Calmels, F., and Allard, M. (2004). Ice segregation and gas distribution in permafrost using

tomodensitometric analysis. Permafr. Periglac. Process. 15, 367–378. doi:10.1002/ppp.508.

Calmels, F., Allard, M., and Delisle, G. (2008). Development and decay of a lithalsa in Northern

Québec: A geomorphological history. Geomorphology 97, 287–299.

doi:10.1016/j.geomorph.2007.08.013.

Campbell, B. J., and Kirchman, D. L. (2013). Bacterial diversity, community structure and potential

growth rates along an estuarine salinity gradient. ISME J. 7, 210–20.

doi:10.1038/ismej.2012.93.

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer,

N., Peña, A. G., Goodrich, K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Jeremy,

E., Ley, R. E., Lozupone, C. A., Mcdonald, D., Muegge, B. D., Reeder, J., Sevinsky, J. R.,

Turnbaugh, P. J., and Walters, W. A. (2010). QIIME allows analysis of high-throughput

community sequencing data. Nat. Methods 7, 335–336. doi:10.1038/nmeth.f.303.QIIME.

87

Cardinale, B. J., Srivastava, D. S., Duffy, J. E., Wright, J. P., Downing, A. L., Sankaran, M., and

Jouseau, C. (2006). Effects of biodiversity on the functioning of trophic groups and

ecosystems. Nature 443, 989–992. doi:10.1038/nature05202.

Casamayor, E. O., Schäfer, H., Bañeras, L., Pedrós-Alió, C., and Muyzer, G. (2000). Identification

of and spatio-temporal differences between microbial assemblages from two neighboring

sulfurous lakes: comparison by microscopy and denaturing gradient gel electrophoresis. Appl.

Environ. Microbiol. 66, 499–508. doi:0099-2240/00/$04.00?0.

Chan, O. C., Claus, P., Casper, P., Ulrich, A., Lueders, T., and Conrad, R. (2005). Vertical

distribution of structure and function of the methanogenic archaeal community in Lake Dagow

sediment. Environ. Microbiol. 7, 1139–1149. doi:10.1111/j.1462-2920.2005.00790.x.

Charvet, S., Vincent, W. F., Comeau, A., and Lovejoy, C. (2012). Pyrosequencing analysis of the

protist communities in a High Arctic meromictic lake: DNA preservation and change. Front.

Microbiol. 3, 422. doi:10.3389/fmicb.2012.00422.

Chen, Y. H., and Prinn, R. G. (2005). Atmospheric modeling of high- and low-frequency methane

observations: Importance of interannually varying transport. J. Geophys. Res. 110, 1–27.

doi:10.1029/2004JD005542.

Chowdhury, T. R., and Dick, R. P. (2013). Ecology of aerobic methanotrophs in controlling

methane fluxes from wetlands. Appl. Soil Ecol. 65, 8–22. doi:10.1016/j.apsoil.2012.12.014.

Colwell, R. K. (2012). “Biodiversity: concepts, patterns, and measurement,” in The Princeton

Guide to Ecology, ed. S. A. Levin (Princeton, NJ), 257–263.

Comeau, A. M., Harding, T., Galand, P. E., Vincent, W. F., and Lovejoy, C. (2012). Vertical

distribution of microbial communities in a perennially stratified Arctic lake with saline, anoxic

bottom waters. Sci. Rep. 2, 604. doi:10.1038/srep00604.

Comeau, A. M., Li, W. K. W., Tremblay, J.-É., Carmack, E. C., and Lovejoy, C. (2011). Arctic

Ocean microbial community structure before and after the 2007 record sea ice minimum.

PLoS One 6, e27492. doi:10.1371/journal.pone.0027492.

Comte, J., Fauteux, L., and del Giorgio, P. A. (2013). Links between metabolic plasticity and

functional redundancy in freshwater bacterioplankton communities. Front. Microbiol. 4, 112.

doi:10.3389/fmicb.2013.00112.

Comte, J., Lovejoy, C., Crevecoeur, S., and Vincent, W. F. (2016). Co-occurrence patterns in

aquatic bacterial communities. Biogeosciences 12, 10233–10269. doi:10.5194/bgd-12-10233-

2015.

Comte, J., Monier, A., Crevecoeur, S., Lovejoy, C., and Vincent, W. F. (2015). Microbial

biogeography of permafrost thaw ponds across the changing northern landscape. Ecography

38, 001–010. doi:10.1111/ecog.01667.

Costello, A. M., and Lidstrom, M. E. (1999). Molecular characterization of functional and

phylogenetic genes from natural populations of methanotrophs in lake sediments. 65, 5066–

5074. doi:0099-2240/99/$04.00+0.

Cotner, J. B., and Biddanda, B. A. (2002). Small players, large role: microbial influence on

88

biogeochemical processes in pelagic aquatic ecosystems. Ecosystems 5, 105–121.

doi:10.1007/s10021-001-0059-3.

Crevecoeur, S., Vincent, W. F., Comte, J., and Lovejoy, C. (2015). Bacterial community structure

across environmental gradients in permafrost thaw ponds: methanotroph-rich ecosystems.

Front. Microbiol. 6, 192. doi:10.3389/fmicb.2015.00192.

Crump, B. C., Armbrust, E. V., and Baross, J. A. (1999). Phylogenetic analysis of particle-attached

and free-living bacterial communities in the columbia river, its estuary, and the adjacent

coastal ocean. Appl. Environ. Microbiol. 65, 3192–3204. doi:0099-2240/99/$04.00?0.

Degelmann, D. M., Borken, W., Drake, H. L., and Kolb, S. (2010). Different atmospheric methane-

oxidizing communities in European beech and Norway spruce soils. Appl. Environ. Microbiol.

76, 3228–35. doi:10.1128/AEM.02730-09.

Delong, E. F. (1998). Everything in moderation : Archaea as “non-extremophiles.” Curr. Opin.

Genet. Dev. 8, 649–654. doi:10.1016/S0959-437X(98)80032-4.

Deshpande, B. N., Crevecoeur, S., Matveev, A., and Vincent, W. F. (2016). Bacterial production in

subarctic peatland lakes enriched by thawing permafrost. Biogeosciences Discuss. 32, 1–49.

doi:10.5194/bg-2016-32.

Deshpande, B. N., MacIntyre, S., Matveev, A., and Vincent, W. F. (2015). Oxygen dynamics in

permafrost thaw lakes: Anaerobic bioreactors in the Canadian subarctic. Limnol. Oceanogr.

60, 1656–1670. doi:10.1002/lno.10126.

Dobinski, W. (2011). Permafrost. Earth-Science Rev. 108, 158–169.

doi:10.1016/j.earscirev.2011.06.007.

Dorigo, U., Volatier, L., and Humbert, J.-F. (2005). Molecular approaches to the assessment of

biodiversity in aquatic microbial communities. Water Res. 39, 2207–18.

doi:10.1016/j.watres.2005.04.007.

Dunfield, P. F., Yuryev, A., Senin, P., Smirnova, A. V, Stott, M. B., Hou, S., Ly, B., Saw, J. H.,

Zhou, Z., Ren, Y., Wang, J., Mountain, B. W., Crowe, M. A., Weatherby, T. M., Bodelier, P.

L. E., Liesack, W., Feng, L., Wang, L., and Alam, M. (2007). Methane oxidation by an

extremely acidophilic bacterium of the phylum Verrucomicrobia. Nature 450, 879–882.

doi:10.1038/nature06411.

Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads.

Nat. Methods 10, 996–998. doi:10.1038/nmeth.2604.

Eller, G., Känel, L., Krüger, M., Eller, G., Ka, L., and Kru, M. (2005). Cooccurrence of aerobic and

anaerobic methane oxidation in the water column of Lake Plußsee. Appl. Environ. Microbiol.

71, 8925–8928. doi:10.1128/AEM.71.12.8925.

Ellermann, J., Hedderich, R., Böcher, R., and Thauer, R. K. (1988). The final step in methane

formation. Eur. J. Biochem. 172, 669–677. doi:10.1111/j.1432-1033.1988.tb13941.x.

Engelbrektson, A., Kunin, V., Wrighton, K. C., Zvenigorodsky, N., Chen, F., Ochman, H., and

Hugenholtz, P. (2010). Experimental factors affecting PCR-based estimates of microbial

species richness and evenness. ISME J. 4, 642–647. doi:10.1038/ismej.2009.153.

89

Epp, M. A., and Chanton, J. P. (1993). Rhizospheric methane oxidation determined via the methyl

fluoride inhibition technique. J. Geophys. Res. 98, 18413–18422. doi:10.1029/93JD01667.

Ettwig, K. F., van Alen, T., van de Pas-Schoonen, K. T., Jetten, M. S. M., and Strous, M. (2009).

Enrichment and molecular detection of denitrifying methanotrophic bacteria of the NC10

phylum. Appl. Environ. Microbiol. 75, 3656–62. doi:10.1128/AEM.00067-09.

Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier, P., Bernhofer, C.,

Burba, G., Clement, R., Davis, K., Elbers, J., Goldstein, A., Grelle, A., Granier, A.,

Guomundsson, J., Hollinger, D., Kowalski, A., Katul, G., Law, B., Malhi, Y., Meyers, T.,

Monson, R., Munger, J., Oechel, W., Paw, K., Pilegaard, K., Rannik, U., Rebmann, C.,

Suyker, A., Valentini, R., Wilson, K., and Wofsy, S. (2002). Seasonality of ecosystem

respiration and gross primary production as derived from FLUXNET measurements. Agric.

For. Meteorol. 113, 53–74. doi:10.1016/S0168-1923(02)00102-8.

Fenchel, T. (2008). The microbial loop – 25 years later. J. Exp. Mar. Bio. Ecol. 366, 99–103.

doi:10.1016/j.jembe.2008.07.013.

Fierer, N., and Jackson, R. B. (2006). The diversity and biogeography of soil bacterial communities.

Proc. Natl. Acad. Sci. U. S. A. 103, 626–31. doi:10.1073/pnas.0507535103.

Fish, J. A., Chai, B., Wang, Q., Sun, Y., Brown, C. T., Tiedje, J. M., and Cole, J. R. (2013).

FunGene: the functional gene pipeline and repository. Front. Microbiol. 4, 291.

doi:10.3389/fmicb.2013.00291.

Freitag, T. E., Chang, L., and Prosser, J. I. (2006). Changes in the community structure and activity

of betaproteobacterial ammonia-oxidizing sediment bacteria along a freshwater-marine

gradient. Environ. Microbiol. 8, 684–696. doi:10.1111/j.1462-2920.2005.00947.x.

Freitag, T. E., Toet, S., Ineson, P., and Prosser, J. I. (2010). Links between methane flux and

transcriptional activities of methanogens and methane oxidizers in a blanket peat bog. FEMS

Microbiol. Ecol. 73, 157–165. doi:10.1111/j.1574-6941.2010.00871.x.

Frenzel, P., Thebrath, B., and Conrad, R. (1990). Oxidation of methane in the oxic surface-layer of

a deep lake sediment (Lake Constance). FEMS Microbiol. Ecol. 73, 149–158.

doi:10.1111/j.1574-6968.1990.tb03935.x.

Galand, P. E., Casamayor, E. O., Kirchman, D. L., and Lovejoy, C. (2009). Ecology of the rare

microbial biosphere of the Arctic Ocean. Proc. Natl. Acad. Sci. U. S. A. 106, 22427–22432.

doi:10.1073/pnas.0908284106.

Galand, P. E., Lovejoy, C., Pouliot, J., and Vincent, W. F. (2008). Heterogeneous archaeal

communities in the particle-rich environment of an arctic shelf ecosystem. J. Mar. Syst. 74,

774–782. doi:10.1016/j.jmarsys.2007.12.001.

Garcia, S. L., Salka, I., Grossart, H.-P., and Warnecke, F. (2013). Depth-discrete profiles of

bacterial communities reveal pronounced spatio-temporal dynamics related to lake

stratification. Environ. Microbiol. Rep. 5, 549–55. doi:10.1111/1758-2229.12044.

Gich, F. B., Borrego, C. M., Martinez-Planells, A., Steensgaard, D. B., Garcia-Gil, J., and

Holzwarth, A. R. (2001). Variability of the photosynthetic antenna of a Pelodictyon

clathratiforme population from a freshwater holomictic pond. FEMS Microbiol. Ecol. 37, 11–

90

19. doi:10.1111/j.1574-6941.2001.tb00848.x.

Gilbert, B., and Frenzel, P. (1995). Methanotrophic bacteria in the rhizosphere of rice microcosms

and their effect on porewater methane concentration and methane emission. Biol. Fertil. Soils

20, 93–100. doi:10.1007/BF00336586.

Gilbert, B., McDonald, I. R., Finch, R., Stafford, G. P., Nielsen, A. K., and Murrell, J. C. (2000).

Molecular analysis of the pmo (particulate methane monooxygenase) operons from two type II

methanotrophs. Appl. Environ. Microbiol. 66, 966–975. doi:0099-2240/00/$04.00?0.

Gilbert, J. A., Field, D., Huang, Y., Edwards, R., Li, W., Gilna, P., and Joint, I. (2008). Detection of

large numbers of novel sequences in the metatranscriptomes of complex marine microbial

communities. PLoS One 3, e3042. doi:10.1371/journal.pone.0003042.

González, I., Lé Cao, K.-A., and Déjean, S. (2011). mixOmics: Omics data integration project.

Available at: http://www.mixomics.org.

Graef, C., Hestnes, A. G., Svenning, M. M., and Frenzel, P. (2011). The active methanotrophic

community in a wetland from the High Arctic. Environ. Microbiol. Rep. 3, 466–472.

doi:10.1111/j.1758-2229.2010.00237.x.

Grossart, H. P., and Simon, M. (1998). Bacterial colonization and microbial decomposition of

limnetic organic aggregates (Lake snow). Aquat. Microb. Ecol. 15, 127–140.

doi:10.3354/ame015127.

Grossart, H.-P., Frindte, K., Dziallas, C., Eckert, W., and Tang, K. W. (2011). Microbial methane

production in oxygenated water column of an oligotrophic lake. Proc. Natl. Acad. Sci. U. S. A.

108, 19657–19661. doi:10.1073/pnas.1110716108.

Grosse, G., Goetz, S., McGuire, A. D., Romanovsky, V. E., and Schuur, E. A. G. (2016). Changing

permafrost in a warming world and feedbacks to the Earth system. Environ. Res. Lett. 11,

040201. doi:10.1088/1748-9326/11/4/040201.

Grosse, G., Jones, B., and Arp, C. (2013). “Thermokarst lakes, drainage, and drained basins,” in

Treatise on Geomorphology, ed. J. F. Shroder (San Diego), 325–353.

Gucht, K. Van Der, Sabbe, K., De Meester, L., Vloemans, N., Zwart, G., Gillis, M., and Vyverman,

W. (2001). Contrasting bacterioplankton community composition and seasonal dynamics in

two neighbouring hypertrophic freshwater lakes. 3, 680–690. doi:10.1046/j.1462-

2920.2001.00242.x.

Gurney, S. D. (2001). Aspects of the genesis, geomorphology and terminology of palsas: perennial

cryogenic mounds. Prog. Phys. Geogr. 25, 249–260. doi:10.1177/030913330102500205.

Hahn, M. W. (2003). Isolation of strains belonging to the cosmopolitan Polynucleobacter

necessarius cluster from freshwater habitats located in three climatic zones. Appl. Environ.

Microbiol. 69, 5248–5254. doi:10.1128/AEM.69.9.5248.

Halsey, L. A., Vitt, D. H., and Zoltai, S. C. (1995). Disequilibrium response of permafrost in boreal

continental western Canada to climate change. Clim. Change 30, 57–73.

doi:10.1007/BF01093225.

91

Han, J. I., Choi, H. K., Lee, S. W., Orwin, P. M., Kim, J., LaRoe, S. L., Kim, T. G., O’Neil, J.,

Leadbetter, J. R., Lee, S. Y., Hur, C. G., Spain, J. C., Ovchinnikova, G., Goodwin, L., and

Han, C. (2011). Complete genome sequence of the metabolically versatile plant growth-

promoting endophyte Variovorax paradoxus S110. J. Bacteriol. 193, 1183–1190.

doi:10.1128/JB.00925-10.

Hansel, C. M., Fendorf, S., Jardine, P. M., and Francis, C. A. (2008). Changes in bacterial and

archaeal community structure and functional diversity along a geochemically variable soil

profile. Appl. Environ. Microbiol. 74, 1620–1633. doi:10.1128/AEM.01787-07.

Hanson, R. S., and Hanson, T. E. (1996). Methanotrophic bacteria. Microbiol. Mol. Biol. Rev. 60,

439–471. doi:0146-0749/96/$04.00?0.

He, R., Wooller, M. J., Pohlman, J. W., Quensen, J., Tiedje, J. M., and Leigh, M. B. (2012). Shifts

in identity and activity of methanotrophs in arctic lake sediments in response to temperature

changes. Appl. Environ. Microbiol. 78, 4715–4723. doi:10.1128/AEM.00853-12.

Hodgkins, S. B., Tfaily, M. M., McCalley, C. K., Logan, T. A., Crill, P. M., Saleska, S. R., Rich, V.

I., and Chanton, J. P. (2014). Changes in peat chemistry associated with permafrost thaw

increase greenhouse gas production. Proc. Natl. Acad. Sci. U. S. A. 111, 5819–5824.

doi:10.1073/pnas.1314641111.

Hoj, L., Rusten, M., Haugen, L. E., Olsen, R. A., and Torsvik, V. L. (2006). Effects of water regime

on archaeal community composition in Arctic soils. Environ. Microbiol. 8, 984–996.

doi:10.1111/j.1462-2920.2005.00982.x.

Hollibaugh, J. T., Wong, P. S., Bano, N., Pak, S. K., Prager, E. M., and Orrego, C. (2001).

Stratification of microbial assemblages in Mono Lake, California, and response to a mixing

event. Hydrobiologia 466, 45–60. doi:10.1023/A:1014505131859.

Hong, S., Bunge, J., Leslin, C., Jeon, S., and Epstein, S. S. (2009). Polymerase chain reaction

primers miss half of rRNA microbial diversity. ISME J. 3, 1365–1373.

doi:10.1038/ismej.2009.89.

van Huissteden, J., Berrittella, C., Parmentier, F. J. W., Mi, Y., Maximov, T. C., and Dolman, A. J.

(2011). Methane emissions from permafrost thaw lakes limited by lake drainage. Nat. Clim.

Chang. 1, 119–123. doi:10.1038/nclimate1101.

İnceoğlu, Ö., Llirós, M., Crowe, S. A., García-Armisen, T., Morana, C., Darchambeau, F., Borges,

A. V., Descy, J.-P., and Servais, P. (2015). Vertical distribution of functional potential and

active microbial communities in meromictic Lake Kivu. Microb. Ecol. 70, 596–611.

doi:10.1007/s00248-015-0612-9.

IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I

to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Cambridge, eds. T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A.

Nauels, Y. Xia, V. Bex, and P. M. Midgley Cambridge, United Kingdom and New York, NY,

USA doi:10.1017/ CBO9781107415324.005.

James, R. H., Bousquet, P., Bussmann, I., Haeckel, M., Kipfer, R., Leifer, I., Niemann, H.,

Ostrovsky, I., Piskozub, J., Rehder, G., Treude, T., Vielstädte, L., and Greinert, J. (2016).

92

Effects of climate change on methane emissions from seafloor sediments in the Arctic Ocean:

A review. Limnol. Oceanogr., in press.

Jansson, J. K., and Taş, N. (2014). The microbial ecology of permafrost. Nat. Rev. Microbiol. 12,

414–425. doi:10.1038/nrmicro3262.

Jarrell, K. F., Walters, A. D., Bochiwal, C., Borgia, J. M., Dickinson, T., and Chong, J. P. J. (2011).

Major players on the microbial stage: why Archaea are important. Microbiology 157, 919–

936. doi:10.1099/mic.0.047837-0.

Judd, K. E., Crump, B. C., and Kling, G. W. (2006). Variation in dissolved organic matter controls

bacterial production and community composition. Ecology 87, 2068–79. doi:10.1890/0012-

9658(2006)87[2068:VIDOMC]2.0.CO;2.

Juottonen, H., Tuittila, E.-S., Juutinen, S., Fritze, H., and Yrjälä, K. (2008). Seasonality of rDNA-

and rRNA-derived archaeal communities and methanogenic potential in a boreal mire. ISME

J. 2, 1157–1168. doi:10.1038/ismej.2008.66.

Jurgens, G., Glöckner, F. O., Amann, R., Saano, A., Montonen, L., Likolammi, M., and Münster, U.

(2000). Identification of novel Archaea in bacterioplankton of a boreal forest lake by

phylogenetic analysis and fluorescent in situ hybridization. FEMS Microbiol. Ecol. 34, 45–56.

doi:10.1016/S0168-6496(00)00073-8.

Kankaala, P., Huotari, J., Peltomaa, E., Saloranta, T., and Ojala, A. (2006). Methanotrophic activity

in relation to methane efflux and total heterotrophic bacterial production in a stratified, humic,

boreal lake. Limnol. Oceanogr. 51, 1195–1204. doi:10.4319/lo.2006.51.2.1195.

Khalil, M. A. K., Rasmussen, R. A., and Shearer, M. J. (1998). Effects of production and oxidation

processes on methane emissions from rice fields. J. Geophys. Res. 103, 25233–25239.

doi:10.1029/98JD01116.

Kiikkilä, O., Smolander, A., and Ukonmaanaho, L. (2014). Properties of dissolved organic matter in

peatland: implications for water quality after harvest. Vadose Zo. J. 13.

doi:10.2136/vzj2013.08.0155.

Kip, N., Ouyang, W., van Winden, J., Raghoebarsing, A., van Niftrik, L., Pol, A., Pan, Y.,

Bodrossy, L., van Donselaar, E. G., Reichart, G.-J., Jetten, M. S. M., Damsté, J. S. S., and Op

den Camp, H. J. M. (2011). Detection, isolation, and characterization of acidophilic

methanotrophs from Sphagnum mosses. Appl Env. Microbiol 77, 5643–54.

doi:10.1128/AEM.05017-11.

Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G., Dlugokencky, E. J.,

Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler, L., Cameron-Smith, P., Castaldi, S.,

Chevallier, F., Feng, L., Fraser, A., Heimann, M., Hodson, E. L., Houweling, S., Josse, B.,

Fraser, P. J., Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C., Naik, V.,

O’Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R. G., Rigby, M.,

Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T., Simpson, I. J., Spahni, R., Steele, L.

P., Strode, S. A., Sudo, K., Szopa, S., van der Werf, G. R., Voulgarakis, A., van Weele, M.,

Weiss, R. F., Williams, J. E., and Zeng, G. (2013). Three decades of global methane sources

and sinks. Nat. Geosci. 6, 813–823. doi:10.1038/ngeo1955.

93

Knapp, C. W., Fowle, D. A., Kulczycki, E., Roberts, J. A., and Graham, D. W. (2007). Methane

monooxygenase gene expression mediated by methanobactin in the presence of mineral

copper sources. Proc. Natl. Acad. Sci. U. S. A. 104, 12040–12045.

doi:10.1073/pnas.0702879104.

Koch, J. C., Gurney, K., and Wipfli, M. S. (2014). Morphology-dependent water budgets and

nutrient fluxes in Arctic thaw ponds. Permafr. Periglac. Process. 25, 79–93.

doi:10.1002/ppp.1804.

Kolb, S., Knief, C., Stubner, S., and Conrad, R. (2003). Quantitative detection of methanotrophs in

soil by novel pmoA-targeted real-time PCR assays. Appl. Environ. Microbiol. 69, 2423–2429.

doi:10.1128/AEM.69.5.2423.

Krishfield, R. A., Proshutinsky, A., Tateyama, K., Williams, W. J., Carmack, E. C., McLaughlin, F.

A., and Timmermans, M.-L. (2014). Deterioration of perennial sea ice in the Beaufort Gyre

from 2003 to 2012 and its impact on the oceanic freshwater cycle. J. Geophys. Res. Ocean.

119, 1271–1305. doi:10.1002/2014JC010192.Received.

Kritzberg, E. S., Langenheder, S., and Lindström, E. S. (2006). Influence of dissolved organic

matter source on lake bacterioplankton structure and function--implications for seasonal

dynamics of community composition. FEMS Microbiol. Ecol. 56, 406–417.

doi:10.1111/j.1574-6941.2006.00084.x.

Kuntz, L. B., Laakso, T. A., Schrag, D. P., and Crowe, S. A. (2015). Modeling the carbon cycle in

Lake Matano. Geobiology 13, 454–461. doi:10.1111/gbi.12141.

Laanbroek, H. J. (2010). Methane emission from natural wetlands: Interplay between emergent

macrophytes and soil microbial processes. A mini-review. Ann. Bot. 105, 141–153.

doi:10.1093/aob/mcp201.

Lajeunesse, P., and Allard, M. (2003). The Nastapoka drift belt, eastern Hudson Bay: implications

of a stillstand of the Quebec-Labrador ice margin in the Tyrrell Sea at 8 ka BP. Can. J. Earth

Sci. 40, 65–76. doi:10.1139/e02-085.

Landa, M., Cottrell, M., Kirchman, D., Blain, S., and Obernosterer, I. (2013). Changes in bacterial

diversity in response to dissolved organic matter supply in a continuous culture experiment.

Aquat. Microb. Ecol. 69, 157–168. doi:10.3354/ame01632.

Lapoussière, A., Michel, C., Starr, M., Gosselin, M., and Poulin, M. (2011). Role of free-living and

particle-attached bacteria in the recycling and export of organic material in the Hudson Bay

system. J. Mar. Syst. 88, 434–445. doi:10.1016/j.jmarsys.2010.12.003.

Laurion, I., and Mladenov, N. (2013). Dissolved organic matter photolysis in Canadian arctic thaw

ponds. Environ. Res. Lett. 8, 035026. doi:10.1088/1748-9326/8/3/035026.

Laurion, I., Vincent, W. F., MacIntyre, S., Retamal, L., Dupont, C., Francus, P., and Pienitz, R.

(2010). Variability in greenhouse gas emissions from permafrost thaw ponds. Limnol.

Oceanogr. 55, 115–133. doi:10.4319/lo.2010.55.1.0115.

Lê Cao, K. A., González, I., and Déjean, S. (2009). IntegrOmics: An R package to unravel

relationships between two omics datasets. Bioinformatics 25, 2855–2856.

doi:10.1093/bioinformatics/btp515.

94

Lee, H. J., Jeong, S. E., Kim, P. J., Madsen, E. L., and Jeon, C. O. (2015). High resolution depth

distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and

rhizosphere soils of a flooded rice paddy. Front. Microbiol. 6, 639.

doi:10.3389/fmicb.2015.00639.

Li, D., Sharp, J. O., Saikaly, P. E., Ali, S., Alidina, M., Alarawi, M. S., Keller, S., Hoppe-Jones, C.,

and Drewes, J. E. (2012). Dissolved organic carbon influences microbial community

composition and diversity in managed aquifer recharge systems. Appl. Environ. Microbiol. 78,

6819–6828. doi:10.1128/AEM.01223-12.

Liebner, S., Ganzert, L., Kiss, A., Yang, S., Wagner, D., and Svenning, M. M. (2015). Shifts in

methanogenic community composition and methane fluxes along the degradation of

discontinuous permafrost. Front. Microbiol. 6, 356. doi:10.3389/fmicb.2015.00356.

Lin, X., Tfaily, M. M., Green, S. J., Steinweg, J. M., Chanton, P., Imvittaya, A., Chanton, J. P.,

Cooper, W., Schadt, C., and Kostka, J. E. (2014). Microbial metabolic potential for carbon

degradation and nutrient (nitrogen and phosphorus) acquisition in an ombrotrophic peatland.

Appl. Environ. Microbiol. 80, 3531–40. doi:10.1128/AEM.00206-14.

Lindström, E. S., Agterveld, M. P. K., and Zwart, G. (2005). Distribution of typical freshwater

bacterial groups is associated with pH, temperature, and lake water retention time. Appl.

Environ. Microbiol. 71, 8201–8206. doi:10.1128/AEM.71.12.8201.

Lindström, E. S., and Leskinen, E. (2002). Do neighboring lakes share common taxa of

bacterioplankton? Comparison of 16S rDNA fingerprints and sequences from three geographic

regions. Microb. Ecol. 44, 1–9. doi:10.1007/s00248-002-0007-6.

Liu, J., Sun, F., Wang, L., Ju, X., Wu, W., and Chen, Y. (2014). Molecular characterization of a

microbial consortium involved in methane oxidation coupled to denitrification under micro-

aerobic conditions. Microb. Biotechnol. 7, 64–76. doi:10.1111/1751-7915.12097.

Liu, L., Gundersen, P., Zhang, T., and Mo, J. (2012). Effects of phosphorus addition on soil

microbial biomass and community composition in three forest types in tropical China. Soil

Biol. Biochem. 44, 31–38. doi:10.1016/j.soilbio.2011.08.017.

Liu, Z., Lozupone, C., Hamady, M., Bushman, F. D., and Knight, R. (2007). Short pyrosequencing

reads suffice for accurate microbial community analysis. Nucleic Acids Res. 35, e120.

doi:10.1093/nar/gkm541.

Logares, R., Bråte, J., Bertilsson, S., Clasen, J. L., Shalchian-Tabrizi, K., and Rengefors, K. (2009).

Infrequent marine-freshwater transitions in the microbial world. Trends Microbiol. 17, 414–

422. doi:10.1016/j.tim.2009.05.010.

Logares, R., Sunagawa, S., Salazar, G., Cornejo-Castillo, F. M., Ferrera, I., Sarmento, H., Hingamp,

P., Ogata, H., de Vargas, C., Lima-Mendez, G., Raes, J., Poulain, J., Jaillon, O., Wincker, P.,

Kandels-Lewis, S., Karsenti, E., Bork, P., and Acinas, S. G. (2014). Metagenomic 16S rDNA

Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and

structure of microbial communities. Environ. Microbiol. 16, 2659–2671. doi:10.1111/1462-

2920.12250.

Lovejoy, C., Comeau, A., and Thaler, M. (2015). Curated reference database of SSU rRNA for

95

northern marine and freshwater communities of Archaea, Bacteria and microbial eukaryotes.

Nordicana D23.

Lozupone, C. A., and Knight, R. (2007). Global patterns in bacterial diversity. Proc. Natl. Acad.

Sci. U. S. A. 104, 11436–11440. doi:10.1073/pnas.0611525104.

Lozupone, C., and Knight, R. (2005). UniFrac : a new phylogenetic method for comparing

microbial communities. Appl. Environ. Microbiol. 71, 8228–8235.

doi:10.1128/AEM.71.12.8228.

Lu, Y., Lueders, T., Friedrich, M. W., and Conrad, R. (2005). Detecting active methanogenic

populations on rice roots using stable isotope probing. Environ. Microbiol. 7, 326–336.

doi:10.1111/j.1462-2920.2004.00697.x.

Luesken, F. A., Zhu, B., van Alen, T. A., Butler, M. K., Diaz, M. R., Song, B., Op den Camp, H. J.

M., Jetten, M. S. M., and Ettwig, K. F. (2011). pmoA primers for detection of anaerobic

methanotrophs. Appl. Environ. Microbiol. 77, 3877–80. doi:10.1128/AEM.02960-10.

Luton, P. E., Wayne, J. M., Sharp, R. J., and Riley, P. W. (2002). The mcrA gene as an alternative

to 16S rRNA in the phylogenetic analysis of methanogen populations in landfill. Microbiology

148, 3521–3530. doi:10.1099/00221287-148-11-3521.

Martineau, C., Whyte, L. G., and Greer, C. W. (2010). Stable isotope probing analysis of the

diversity and activity of methanotrophic bacteria in soils from the Canadian high Arctic. Appl.

Environ. Microbiol. 76, 5773–84. doi:10.1128/AEM.03094-09.

Marty, D. G. (1993). Methanogenic bacteria in seawater. Limnol. Oceanogr. 38, 452–456.

doi:10.4319/lo.1993.38.2.0452.

Matveev, A., Laurion, I., Deshpande, B. N., Bhiry, N., and Vincent, W. F. (2016). High methane

emissions from thermokarst lakes in subarctic peatlands. Limnol. Oceanogr., in press.

McDonald, I. R., Bodrossy, L., Chen, Y., and Murrell, J. C. (2008). Molecular ecology techniques

for the study of aerobic methanotrophs. Appl. Environ. Microbiol. 74, 1305–15.

doi:10.1128/AEM.02233-07.

McDonald, I. R., and Murrell, J. C. (1997). The particulate methane monooxygenase gene pmoA

and its use as a functional gene probe for methanotrophs. FEMS Microbiol. Lett. 156, 205–

210. doi:http://dx.doi.org/10.1111/j.1574-6968.1997.tb12728.x.

Meehl, G. A., Arblaster, J. M., Fasullo, J. T., Hu, A., and Trenberth, K. E. (2011). Model-based

evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat. Clim.

Chang. 1, 360–364. doi:10.1038/nclimate1229.

Le Mer, J., and Roger, P. (2001). Production, oxidation, emission and consumption of methane by

soils: a review. Eur. J. Soil Biol. 37, 25–50. doi:10.1016/S1164-5563(01)01067-6.

Metje, M., and Frenzel, P. (2007). Methanogenesis and methanogenic pathways in a peat from

subarctic permafrost. Environ. Microbiol. 9, 954–964. doi:10.1111/j.1462-2920.2006.01217.x.

Milucka, J., Kirf, M., Lu, L., Krupke, A., Lam, P., Littmann, S., Kuypers, M. M., and Schubert, C.

J. (2015). Methane oxidation coupled to oxygenic photosynthesis in anoxic waters. ISME J.,

96

1–12. doi:10.1038/ismej.2015.12.

Mohanty, S. R., Bodelier, P. L. E., and Conrad, R. (2007). Effect of temperature on composition of

the methanotrophic community in rice field and forest soil. 62, 24–31. doi:10.1111/j.1574-

6941.2007.00370.x.

Mohit, V., Archambault, P., Toupoint, N., and Lovejoy, C. (2014). Phylogenetic differences in

summer attached and free-living bacterial communities in a temperate coastal lagoon, revealed

via high throughput 16S rRNA gene sequencing. Appl. Environ. Microbiol. 80, 2071–2083.

doi:10.1128/AEM.02916-13.

Mou, X., Jacob, J., Lu, X., Robbins, S., Sun, S., and Ortiz, J. D. (2013). Diversity and distribution

of free-living and particle-associated bacterioplankton in Sandusky Bay and adjacent waters of

Lake Erie Western Basin. J. Great Lakes Res. 39, 352–357. doi:10.1016/j.jglr.2013.03.014.

Murrell, J. C., Mcdonald, I. R., and Bourne, D. G. (1998). Molecular methods for the study of

methanotroph ecology. FEMS Microbiol. Ecol. 27, 103–114. doi:10.1016/S0168-

6496(98)00063-4.

Murtagh, F., and Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: which

algorithms implement Ward’s criterion? J. Classif. 31, 274–295. doi:10.1007/s00357-014-

9161-z.

Narihiro, T., Hori, T., Nagata, O., Hoshino, T., Yumoto, I., and Kamagata, Y. (2011). The impact of

aridification and vegetation type on changes in the community structure of methane-cycling

microorganisms in Japanese wetland soils. Biosci. Biotechnol. Biochem. 75, 1727–1734.

doi:10.1271/bbb.110265.

Negandhi, K., Laurion, I., Whiticar, M. J., Galand, P. E., Xu, X., and Lovejoy, C. (2013). Small

thaw ponds: an unaccounted source of methane in the Canadian High Arctic. PLoS One 8,

e78204. doi:10.1371/journal.pone.0078204.

Nelson, C. E., and Wear, E. K. (2014). Microbial diversity and the lability of dissolved organic

carbon. Proc. Natl. Acad. Sci. U. S. A. 111, 7166–7167. doi:10.1073/pnas.1405751111.

Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D., and Bertilsson, S. (2011). A guide to the

natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49.

doi:10.1128/MMBR.00028-10.

Newton, R. J., and McMahon, K. D. (2011). Seasonal differences in bacterial community

composition following nutrient additions in a eutrophic lake. Environ. Microbiol. 13, 887–

899. doi:10.1111/j.1462-2920.2010.02387.x.

Nozhevnikova, A. N., Holliger, C., Ammann, A., and Zehnder, A. J. B. (1997). Methanogenesis in

sediments from deep lakes at different temperatures (2–70°C). Water Sci. Technol. 36, 57–64.

doi:10.1016/S0273-1223(97)00507-6.

Oksanen, J., Blanchet, G. F., Kindt, R., Legendre, P., Minchin, P., O’Hara, R. B., Simpson, G. L.,

Solymos, P., Stevens, H. H., and Wagner, H. M. (2013). Vegan: Community Ecology Package. Version 2.0-10. Available at: http://vegan.r-forge.r-project.org

Omelchenko, M. V., Vasileva, L. V., Zavarzin, G. A., Saveleva, N. D., Lysenko, A. M., and

97

Mityushina, L.L., Kamelenina, V.N., Trotsenko, Y. A. (1996). A novel psychrophilic

methanotroph of the genus Methylobacter. Microbiology 65, 339–343.

Op den Camp, H. J. M., Islam, T., Stott, M. B., Harhangi, H. R., Hynes, A., Schouten, S., Jetten, M.

S. M., Birkeland, N.-K., Pol, A., and Dunfield, P. F. (2009). Environmental, genomic and

taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1,

293–306. doi:10.1111/j.1758-2229.2009.00022.x.

Oswald, K., Milucka, J., Brand, A., Littmann, S., Wehrli, B., Kuypers, M. M. M., and Schubert, C.

J. (2015). Light-dependent aerobic methane oxidation reduces methane emissions from

seasonally stratified lakes. PLoS One 10, e0132574. doi:10.1371/journal.pone.0132574.

Parveen, B., Reveilliez, J.-P., Mary, I., Ravet, V., Bronner, G., Mangot, J.-F., Domaizon, I., and

Debroas, D. (2011). Diversity and dynamics of free-living and particle-associated

Betaproteobacteria and Actinobacteria in relation to phytoplankton and zooplankton

communities. FEMS Microbiol. Ecol. 77, 461–76. doi:10.1111/j.1574-6941.2011.01130.x.

Paul, J. H. (2001). Gene expression by mRNA analysis. Methods Microbiol. 30, 395–408.

doi:10.1016/S0580-9517(01)30055-7.

Payette, S. (2004). Accelerated thawing of subarctic peatland permafrost over the last 50 years.

Geophys. Res. Lett. 31, 1–4. doi:10.1029/2004GL020358.

Pedrós-Alió, C. (2006). Marine microbial diversity: can it be determined? Trends Microbiol. 14,

257–263. doi:10.1016/j.tim.2006.04.007.

Pedrós-Alió, C. (2012). The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 449–466.

doi:10.1146/annurev-marine-120710-100948.

Pienitz, R., Doran, P. T., and Lamoureux, S. F. (2008). “Origin and geomorphology of lakes in the

polar regions,” in Polar Lakes and Rivers: Limnology of Arctic and Antarctic Aquatic

Ecosystems, eds. W. F. Vincent and J. Laybourn-Parry (Oxford: Oxford University Press), 25–

41.

Pissart, A. (2000). Remnants of lithalsas of the Hautes Fagnes , Belgium : A summary of present-

day knowledge. Permafr. Periglac. Process. 11, 327–355. doi:10.1002/1099-

1530(200012)11:4<327::AID-PPP370>3.0.CO;2-Q.

Pissart, A. (2002). Palsas , lithalsas and remnants of these periglacial mounds. A progress report.

Prog. Phys. Geogr. 4, 605–621. doi:10.1191/0309133302pp354ra.

Pouliot, J., Galand, P. E., Lovejoy, C., and Vincent, W. F. (2009). Vertical structure of archaeal

communities and the distribution of ammonia monooxygenase A gene variants in two

meromictic High Arctic lakes. Environ. Microbiol. 11, 687–699. doi:10.1111/j.1462-

2920.2008.01846.x.

Pruesse, E., Quast, C., Knittel, K., Fuchs, B. M., Ludwig, W., Peplies, J., and Glockner, F. O.

(2007). SILVA: a comprehensive online resource for quality checked and aligned ribosomal

RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196.

doi:10.1093/nar/gkm864.

Przytulska, A., Comte, J., Crevecoeur, S., Lovejoy, C., Laurion, I., and Vincent, W. F. (2016).

98

Phototrophic pigment diversity and picophytoplankton abundance in permafrost thaw lakes.

Biogeosciences 13, 13–26. doi:10.5194/bgd-12-12121-2015.

Ptacnik, R., Solimini, A. G., Andersen, T., Tamminen, T., Brettum, P., Lepisto, L., Willen, E., and

Rekolainen, S. (2008). Diversity predicts stability and resource use efficiency in natural

phytoplankton communities. Proc. Natl. Acad. Sci. 105, 5134–5138.

doi:10.1073/pnas.0708328105.

Purkey, S. G., and Johnson, G. C. (2010). Warming of global abyssal and deep Southern Ocean

waters between the 1990s and 2000s: Contributions to global heat and sea level rise budgets.

J. Clim. 23, 6336–6351. doi:10.1175/2010JCLI3682.1.

Qu, J.-H., and Yuan, H.-L. (2008). Sediminibacterium salmoneum gen. nov., sp. nov., a member of

the phylum Bacteroidetes isolated from sediment of a eutrophic reservoir. Int. J. Syst. Evol.

Microbiol. 58, 2191–2194. doi:10.1099/ijs.0.65514-0.

R Core Team (2013). R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna. Available at: http://www.R-project.org/

Radajewski, S., McDonald, I. R., and Murrell, J. C. (2003). Stable-isotope probing of nucleic acids:

A window to the function of uncultured microorganisms. Curr. Opin. Biotechnol. 14, 296–

302. doi:10.1016/S0958-1669(03)00064-8.

Rahbek, C. (1995). The elevational gradient of species richness: a uniform pattern? Ecography 2,

200–205. doi:10.1111/j.1600-0587.1995.tb00341.x.

Reeburgh, W. S., Whalen, S. C., and Alperin., M. J. (1993). “The role of methylotrophy in the

global methane budget,” in Microbial growth on C1 compounds, eds. J. C. Murrell and D. P.

Kelly (Intercept, Andover, United Kingdom), 1–14.

Reiche, M., Lu, S., Ciobota, V., Neu, T. R., Nietzsche, S., Rösch, P., Popp, J., and Küsel, K. (2011).

Pelagic boundary conditions affect the biological formation of iron-rich particles (iron snow)

and their microbial communities. Limnol. Oceanogr. 56, 1386–1398.

doi:10.4319/lo.2011.56.4.1386.

Riordan, B., Verbyla, D., and McGuire, A. D. (2006). Shrinking ponds in subarctic Alaska based on

1950-2002 remotely sensed images. J. Geophys. Res. Biogeosciences 111, G04002.

doi:10.1029/2005JG000150.

Riser, S. C., Freeland, H. J., Roemmich, D., Wijffels, S., Troisi, A., Belbéoch, M., Gilbert, D., Xu,

J., Pouliquen, S., Thresher, A., Le Traon, P.-Y., Maze, G., Klein, B., Ravichandran, M., Grant,

F., Poulain, P.-M., Suga, T., Lim, B., Sterl, A., Sutton, P., Mork, K.-A., Vélez-Belchí, P. J.,

Ansorge, I., King, B., Turton, J., Baringer, M., and Jayne, S. R. (2016). Fifteen years of ocean

observations with the global Argo array. Nat. Clim. Chang. 6, 145–153.

doi:10.1038/nclimate2872.

Rosenfeld, J. S. (2002). Functional redundancy in ecology and conservation. Oikos 98, 156–162.

doi:10.1034/j.1600-0706.2002.980116.x.

Rossi, P., Laurion, I., and Lovejoy, C. (2013). Distribution and identity of Bacteria in subarctic

permafrost thaw ponds. Aquat. Microb. Ecol. 69, 231–245. doi:10.3354/ame01634.

99

Sanger, F., Nicklen, S., and Coulson, A. R. (1977). DNA sequencing with chain-terminating

inhibitors. Proc. Natl. Acad. Sci. U. S. A. 74, 5463–7. doi:10.1073/pnas.74.12.5463.

Sannel, A. B. K., and Kuhry, P. (2011). Warming-induced destabilization of peat

plateau/thermokarst lake complexes. J. Geophys. Res. Biogeosciences 116, G03035.

doi:10.1029/2010JG001635.

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski,

R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G.,

Van Horn, D. J., and Weber, C. F. (2009). Introducing mothur: open-source, platform-

independent, community-supported software for describing and comparing microbial

communities. Appl. Environ. Microbiol. 75, 7537–7541. doi:10.1128/AEM.01541-09.

Seguin, M. K., and Allard, M. (1984). Le pergélisol et les processus thermokarstiques de la région

de la rivière Nastapoca, Nouveau-Québec. Géographie Phys. Quat. 38, 11–25.

doi:10.7202/032532ar.

Sepulveda-Jauregui, A., Walter Anthony, K. M., Martinez-Cruz, K., Greene, S., and Thalasso, F.

(2015). Methane and carbon dioxide emissions from 40 lakes along a north–south latitudinal

transect in Alaska. Biogeosciences 12, 3197–3223. doi:10.5194/bg-12-3197-2015.

Serkebaeva, Y. M., Kim, Y., Liesack, W., and Dedysh, S. N. (2013). Pyrosequencing-based

assessment of the bacteria diversity in surface and subsurface peat layers of a northern

wetland, with focus on poorly studied phyla and candidate divisions. PLoS One 8, e63994.

doi:10.1371/journal.pone.0063994.

Shade, A., Jones, S. E., and McMahon, K. D. (2008). The influence of habitat heterogeneity on

freshwater bacterial community composition and dynamics. Environ. Microbiol. 10, 1057–67.

doi:10.1111/j.1462-2920.2007.01527.x.

Sharp, C. E., Stott, M. B., and Dunfield, P. F. (2012). Detection of autotrophic verrucomicrobial

methanotrophs in a geothermal environment using stable isotope probing. Front. Microbiol. 3,

303. doi:10.3389/fmicb.2012.00303.

Shokralla, S., Spall, J. L., Gibson, J. F., and Hajibabaei, M. (2012). Next-generation sequencing

technologies for environmental DNA research. Mol. Ecol. 21, 1794–1805. doi:10.1111/j.1365-

294X.2012.05538.x.

Sievert, S. M., Ziebis, W., Kuever, J., and Sahm, K. (2000). Relative abundance of Archaea and

Bacteria along a thermal gradient of a shallow-water hydrothermal vent quantified by rRNA

slot-blot hybridization. Microbiology 146, 1287–1293. doi:10.1099/00221287-146-6-1287.

Smith, K. S., and Ingram-Smith, C. (2007). Methanosaeta , the forgotten methanogen ? Trends

Microbiol. 15, 150–155. doi:10.1016/j.tim.2007.02.002.

Smith, L. C., Sheng, Y., MacDonald, G. M., and Hinzman, L. D. (2005). Disappearing Arctic lakes.

Science 308, 1429.

Smith, M. W., and Riseborough, D. W. (2002). Climate and the limits of permafrost: A zonal

analysis. Permafr. Periglac. Process. 13, 1–15. doi:10.1002/ppp.410.

Sower, K. R. (2009). “Methanogenesis,” in Encyclopedia of Microbiology, ed. M. Schaechter (San

100

Diego, CA), 265–286.

Stackhouse, B. T., Vishnivetskaya, T. A., Layton, A., Chauhan, A., Pfiffner, S., Mykytczuk, N. C.,

Sanders, R., Whyte, L. G., Hedin, L., Saad, N., Myneni, M., and Onstott, T. C. (2015). Effects

of simulated spring thaw of permafrost from mineral cryosol on CO2 emissions and

atmospheric CH4 uptake. J. Geophys. Res. Biogeosciences 120, 1764–1784.

doi:10.1002/2014JG002802.Received.

Stainton, M., Capel, M. J., and Armstrong, F. A. (1977). The Chemical Analysis of Fresh Water.

Winnipeg: Canadian Fisheries and Marine Service. Special Publication 25, 1-168.

Steven, B., Lionard, M., Kuske, C. R., and Vincent, W. F. (2013). High bacterial diversity of

biological soil crusts in water tracks over permafrost in the high arctic polar desert. PLoS One

8, e71489. doi:10.1371/journal.pone.0071489.

Stoecker, K., Bendinger, B., Schöning, B., Nielsen, P. H., Nielsen, J. L., Baranyi, C., Toenshoff, E.

R., Daims, H., and Wagner, M. (2006). Cohn’s Crenothrix is a filamentous methane oxidizer

with an unusual methane monooxygenase. PNAS 103, 2363–7. doi:10.1073/pnas.0506361103.

Suweis, S., Simini, F., Banavar, J. R., and Maritan, A. (2013). Emergence of structural and

dynamical properties of ecological mutualistic networks. Nature 500, 449–452.

doi:10.1038/nature12438.

Taipale, S., Jones, R., and Tiirola, M. (2009). Vertical diversity of bacteria in an oxygen-stratified

humic lake, evaluated using DNA and phospholipid analyses. Aquat. Microb. Ecol. 55, 1–16.

doi:10.3354/ame01277.

Tamames, J., Abellán, J. J., Pignatelli, M., Camacho, A., and Moya, A. (2010). Environmental

distribution of prokaryotic taxa. BMC Microbiol. 10, 85. doi:10.1186/1471-2180-10-85.

Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G., and Zimov, S. (2009).

Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochem.

Cycles 23, 1–11. doi:10.1029/2008GB003327.

Thauer, R. K., Kaster, A.-K., Seedorf, H., Buckel, W., and Hedderich, R. (2008). Methanogenic

Archaea: ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6,

579–591. doi:10.1038/nrmicro1931.

Thomas, T., Gilbert, J., and Meyer, F. (2012). Metagenomics - a guide from sampling to data

analysis. Microb. Inform. Exp. 2, 3. doi:10.1186/2042-5783-2-3.

Tranvik, L. J., Downing, J. A., Cotner, J. B., Loiselle, S. A., Striegl, R. G., Ballatore, T. J., Dillon,

P., Finlay, K., Fortino, K., Knoll, L. B., Kortelainen, P. L., Kutser, T., Larsen, S., Laurion, I.,

Leech, D. M., Mccallister, S. L., Mcknight, D. M., Melack, J. M., Overholt, E., Porter, J. A.,

Prairie, Y., Renwick, W. H., Roland, F., Sherman, B. S., Schindler, D. W., Sobek, S.,

Tremblay, A., Vanni, M. J., Verschoor, A. M., Wachenfeldt, E. Von, and Weyhenmeyer, G.

A. (2009). Lakes and reservoirs as regulators of carbon cycling and climate. Limnol.

Oceanogr. 54, 2298–2314. doi:10.4319/lo.2009.54.6_part_2.2298.

Trimmer, M., Shelley, F. C., Purdy, K. J., Maanoja, S. T., Chronopoulou, P.-M., and Grey, J.

(2015). Riverbed methanotrophy sustained by high carbon conversion efficiency. ISME J. 9,

2304–2314. doi:10.1038/ismej.2015.98.

101

Tripathi, B. M., Kim, M., Lai-Hoe, A., Shukor, N. A. A., Rahim, R. A., Go, R., and Adams, J. M.

(2013). pH dominates variation in tropical soil archaeal diversity and community structure.

FEMS Microbiol. Ecol. 86, 303–311. doi:10.1111/1574-6941.12163.

Trotsenko, Y. A., and Khmelenina, V. N. (2005). Aerobic methanotrophic bacteria of cold

ecosystems. FEMS Microbiol. Ecol. 53, 15–26. doi:10.1016/j.femsec.2005.02.010.

Tuomivirta, T. T., Yrjälä, K., and Fritze, H. (2009). Quantitative PCR of pmoA using a novel

reverse primer correlates with potential methane oxidation in Finnish fen. Res. Microbiol. 160,

751–756. doi:10.1016/j.resmic.2009.09.008.

Urmann, K., Lazzaro, A., Gandolfi, I., Schroth, M. H., and Zeyer, J. (2009). Response of

methanotrophic activity and community structure to temperature changes in a diffusive

CH4/O2 counter gradient in an unsaturated porous medium. FEMS Microbiol. Ecol. 69, 202–

212. doi:10.1111/j.1574-6941.2009.00708.x.

Vadstein, O. (2000). Heterotrophic, planktonic bacteria and cycling of phosphorus. Adv. Microb.

Ecol. 16, 115–167. doi:10.1007/978-1-4615-4187-5_4.

Vecherskaya, M., Dijkema, C., Saad, H. R., and Stams, A. J. M. (2009). Microaerobic and

anaerobic metabolism of a Methylocystis parvus strain isolated from a denitrifying bioreactor.

Environ. Microbiol. Rep. 1, 442–449. doi:10.1111/j.1758-2229.2009.00069.x.

Vecherskaya, M. S., Galchenko, V. F., Sokolova, E. N., and Samarkin, V. A. (1993). Activity and

species composition of aerobic methanotrophic communities in tundra soils. Curr. Microbiol.

27, 181–184. doi:10.1007/BF01576018.

Vincent, W. F. (2000). “Cyanobacterial dominance in the polar regions,” in The Ecology of

Cyanobacteria, eds. B. A. Whitton and M. Potts (Dordrecht : Kluwer Academic Publisher),

321–340.

Vincent, W. F. (2009). “Effects of Climate Change on Lakes,” in Encyclopedia of Inland Waters,

ed. G. E. Likens (Oxford : Elsevier), 55–60.

Vincent, W. F., Laurion, I., Pienitz, R., and Walter Anthony, K. M. (2013a). “Climate impacts on

Arctic Lake ecosystems,” in Climatic Change and Global Warming of Inland Waters: Impacts

and Mitigation for Ecosystems and Societies, eds. C. R. Goldman, M. Kumagai, and R. D.

Robarts, 27–42.

Vincent, W. F., Lemay, M., Allard, M., and Wolfe, B. B. (2013b). Adapting to permafrost change: a

science framework. Eos, Trans. Am. Geophys. Union 94, 373–375.

doi:10.1002/2013EO420002.

Vonk, J. E., Tank, S. E., Bowden, W. B., Laurion, I., Vincent, W. F., Alekseychik, P., Amyot, M.,

Billet, M. F., Canário, J., Cory, R. M., Deshpande, B. N., Helbig, M., Jammet, M., Karlsson,

J., Larouche, J., MacMillan, G., Rautio, M., Walter Anthony, K. M., and Wickland, K. P.

(2015). Reviews and Syntheses: Effects of permafrost thaw on arctic aquatic ecosystems.

Biogeosciences Discuss. 12, 10719–10815. doi:10.5194/bgd-12-10719-2015.

Wagner, D., Lipski, A., Embacher, A., and Gattinger, A. (2005). Methane fluxes in permafrost

habitats of the Lena Delta: Effects of microbial community structure and organic matter

quality. Environ. Microbiol. 7, 1582–1592. doi:10.1111/j.1462-2920.2005.00849.x.

102

Walter, K. M., Chanton, J. P., Chapin, F. S., Schuur, E. A. G., and Zimov, S. A. (2008). Methane

production and bubble emissions from arctic lakes: Isotopic implications for source pathways

and ages. J. Geophys. Res. 113. doi:10.1029/2007JG000569.

Walter, K. M., Smith, L. C., and Chapin, F. S. (2007). Methane bubbling from northern lakes:

present and future contributions to the global methane budget. Philos. Trans. A. Math. Phys.

Eng. Sci. 365, 1657–1676. doi:10.1098/rsta.2007.2036.

Walter, K. M., Zimov, S. a, Grosse, G., Jones, M. C., Anthony, P. M., Chapin, F. S., Finlay, J. C.,

Mack, M. C., Davydov, S., Frenzel, P., and Frolking, S. (2014). A shift of thermokarst lakes

from carbon sources to sinks during the Holocene epoch. Nature 511, 452–456.

doi:10.1038/nature13560.

Walter, K. M., Zimov, S. A., Chanton, J. P., Verbyla, D., and Chapin, F. S. (2006). Methane

bubbling from Siberian thaw lakes as a positive feedback to climate warming. Nature 443, 71–

75. doi:10.1038/nature05040.

Wartiainen, I., Hestnes, A. G., McDonald, I. R., and Svenning, M. M. (2006). Methylobacter

tundripaludum sp. nov., a methane-oxidizing bacterium from Arctic wetland soil on the

Svalbard islands, Norway (78 degrees N). Int. J. Syst. Evol. Microbiol. 56, 109–13.

doi:10.1099/ijs.0.63728-0.

Wartiainen, I., Hestnes, A. G., and Svenning, M. M. (2003). Methanotrophic diversity in high arctic

wetlands on the islands of Svalbard (Norway)-denaturing gradient gel electrophoresis analysis

of soil DNA and enrichment cultures. Can. J. Microbiol. 49, 602–612. doi:10.1139/w03-080.

Watanabe, S., Laurion, I., Chokmani, K., Pienitz, R., and Vincent, W. F. (2011). Optical diversity

of thaw ponds in discontinuous permafrost: A model system for water color analysis. J.

Geophys. Res. 116, G02003. doi:10.1029/2010JG001380.

Wik, M., Varner, R. K., Walter Anthony, K., MacIntyre, S., and Bastviken, D. (2016). Climate-

sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9,

99–106. doi:10.1038/ngeo2578.

Wilkins, D., Lu, X.-Y., Shen, Z., Chen, J., and Lee, P. K. H. (2015). Pyrosequencing of mcrA and

archaeal 16S rRNA genes reveals diversity and substrate preferences of methanogen

communities in anaerobic digesters. Appl. Environ. Microbiol. 81, 604–13.

doi:10.1128/AEM.02566-14.

Will, C., Thürmer, A., Wollherr, A., Nacke, H., Herold, N., Schrumpf, M., Gutknecht, J., Wubet,

T., Buscot, F., and Daniel, R. (2010). Horizon-specific bacterial community composition of

German grassland soils, as revealed by pyrosequencing-based analysis of 16S rRNA genes.

Appl. Environ. Microbiol. 76, 6751–9. doi:10.1128/AEM.01063-10.

Woese, C. R. (1996). Phylogenetic trees: Whither microbiology? Curr. Biol. 6, 1060–1063.

doi:10.1016/S0960-9822(02)70664-7.

Woese, C. R., Kandler, O., and Wheelis, M. L. (1990). Towards a natural system of organisms:

proposal for the domains Archaea, Bacteria, and Eucarya. Proc. Natl. Acad. Sci. U. S. A. 87,

4576–4579. doi:10.1073/pnas.87.12.4576.

Wold, S., Sjostrom, M., and Eriksson, L. (2001). PLS-regression : a basic tool of chemometrics.

103

Chemom. Intell. Lab. Syst. 58, 109–130.

Wrona, F. J., Johansson, M., Culp, J. M., Jenkins, A., Mård, J., Myers-Smith, I. H., Prowse, T. D.,

Vincent, W. F., and Wookey, P. A. (2016). Transitions in Arctic ecosystems: Ecological

implications of a changing hydrological regime. J. Geophys. Res. Biogeosciences 121.

doi:10.1002/2015JG003133.OnlineOpen.

Wu, L., Ma, K., and Lu, Y. (2009). Rice roots select for type I methanotrophs in rice field soil. Syst.

Appl. Microbiol. 32, 421–428. doi:10.1016/j.syapm.2009.05.001.

Xie, H., Bélanger, S., Demers, S., Vincent, W. F., and Papakyriakou, T. N. (2009).

Photobiogeochemical cycling of carbon monoxide in the southeastern Beaufort Sea in spring

and autumn. Limnol. Oceanogr. 54, 234–249. doi:10.4319/lo.2009.54.1.0234.

Yang, S., Liebner, S., Alawi, M., Ebenhöh, O., and Wagner, D. (2014). Taxonomic database and

cut-off value for processing mcrA gene 454 pyrosequencing data by MOTHUR. J. Microbiol.

Methods 103, 3–5. doi:10.1016/j.mimet.2014.05.006.

Yannarell, A. C., and Triplett, E. W. (2004). Within- and between-lake variability in the

composition of bacterioplankton communities : investigations using multiple spatial scales.

Appl. Environ. Microbiol. 70, 214–223. doi:10.1128/AEM.70.1.214.

Yavitt, J. B., Yashiro, E., Cadillo-Quiroz, H., and Zinder, S. H. (2011). Methanogen diversity and

community composition in peatlands of the central to northern Appalachian Mountain region,

North America. Biogeochemistry 109, 117–131. doi:10.1007/s10533-011-9644-5.

Ye, W., Liu, X., Lin, S., Tan, J., Pan, J., Li, D., and Yang, H. (2009). The vertical distribution of

bacterial and archaeal communities in the water and sediment of Lake Taihu. FEMS

Microbiol. Ecol. 70, 107–20. doi:10.1111/j.1574-6941.2009.00761.x.

Yun, J., Ma, A., Li, Y., Zhuang, G., Wang, Y., and Zhang, H. (2010). Diversity of methanotrophs in

Zoige wetland soils under both anaerobic and aerobic conditions. J. Environ. Sci. 22, 1232–

1238. doi:10.1016/S1001-0742(09)60243-6.

Yvon-Durocher, G., Allen, A. P., Bastviken, D., Conrad, R., Gudasz, C., St-Pierre, A., Thanh-Duc,

N., and del Giorgio, P. A. (2014). Methane fluxes show consistent temperature dependence

across microbial to ecosystem scales. Nature 507, 488–491. doi:10.1038/nature13164.

Zak, D. R., Blackwood, C. B., and Waldrop, M. P. (2006). A molecular dawn for biogeochemistry.

Trends Ecol. Evol. 21, 288–295. doi:10.1016/j.tree.2006.04.003.

Zhang, T., Zhu, W., Mo, J., Liu, L., and Dong, S. (2011). Increased phosphorus availability

mitigates the inhibition of nitrogen deposition on CH4 uptake in an old-growth tropical forest,

southern China. Biogeosciences 8, 2805–2813. doi:10.5194/bg-8-2805-2011.

104

Annexes

Annexe 1. Sequencing results of the number of reads and number of OTUs and diversity indexes calculated with

QIIME for each sample. Samples are designated by pond name followed by the position in the water column

(surface, S; bottom, B), and then by fraction (small, S; large, L).

Sample Reads OTUs Shannon Simpson Chao1

SAS1B-S-S 3837 204 5.17 0.93 218

SAS1B-B-S 4384 198 4.41 0.89 223

SAS2A-S-S 3242 169 4.07 0.87 195

SAS2A-B-S 4063 163 3.91 0.84 188

KWK1-S-S 3071 116 5.02 0.94 128

KWK1-B-S 10562 307 4.03 0.72 340

KWK1-S-L 6114 185 5.44 0.95 199

KWK1-B-L 7807 291 4.80 0.82 315

KWK6-S-S 3832 182 5.82 0.96 213

KWK6-B-S 3639 182 5.16 0.94 222

KWK6-S-L 5856 202 4.98 0.91 223

KWK6-B-L 4916 238 5.30 0.94 256

KWK23-S-S 5502 186 5.60 0.96 219

KWK23-B-S 3577 223 5.18 0.91 241

BGR1-S-S 4374 127 4.82 0.93 150

BGR1-B-S 3922 102 4.48 0.89 114

BGR1-S-L 6058 261 6.09 0.97 275

BGR1-B-L 5993 209 5.43 0.94 222

BGR2-S-S 3922 99 4.13 0.89 110

BGR2-B-S 4164 106 3.86 0.85 116

BGR2-S-L 6936 204 4.26 0.80 235

BGR2-B-L 6708 141 4.02 0.85 153

105

Annexe 2. Ponds sampled during the 2012 and 2013 field campaign and the availability of data.

Pond Depth Year cDNA Illumina

sequencing data

qPCR data

Small Large

BGR1 Surface 2013 X X X X BGR1 Bottom 2013 X X X X

BGR2 Surface 2012 X X X X

BGR2 Bottom 2012 X X X X

NASH Surface 2012

NASH Bottom 2012 X X X X

KWK1 Surface 2012

KWK1 Bottom 2012 X X X X

KWK6 Surface 2012


KWK12 Surface 2013

KWK12 Bottom 2013 X

KWK23 Surface 2012


SAS1A Surface 2013 X X X

SAS1A Bottom 2013 X X X

SAS1B Surface 2013

SAS1B Bottom 2012 X X X X

SAS2A Surface 2013 X X X

SAS2A Bottom 2013 X X X

SAS2A Surface 2012 X X X X

SAS2A Bottom 2012 X X X X

SAS2B Surface 2013 X X X X

SAS2B Bottom 2013 X X X X

106

Annexe 3. Properties of the pmoA primers fused with the Trueseq sequencing primers.

Primer Targeting

region

Trueseq primers References

PmoA

169f

pmoA ACACTCTTTCCCTACACGACGCTCTTCCGATCT-

GGNGACTGGGACTTCTGG

(Kolb et al.,

2003)

pmoA

661r

pmoA GTGACTGGAGTTCAGACGTGTGCTCTTCCGATC

T- CCGGMGCAACGTCYTTACC

(Kolb et al.,

2003)

Generic forward

second-PCR primer

AATGATACGGCGACCACCGAGATCTACAC[index

1]ACACTCTTTCCCTACACGAC

Generic reverse

second-PCR primer

CAAGCAGAAGACGGCATACGAGAT[index2]GTG

ACTGGAGTTCAGACGTGT

107

Annexe 4. Physico-chemical properties of the surface and bottom (0.5 m above the sediments) of the sampled

ponds. Temperature (T°C), Conductivity in µS cm-1 (Cond), dissolved oxygen in mg L-1(O2), pH, total nitrogen in

mg L-1 (TN), total phosphorus in µg L-1 (TP), dissolved organic carbon in mg L-1 (DOC), total suspended solids

(TSS), Chlorophyll a in µg L-1 (Chla), concentration of carbon dioxide in µM (CO2) and concentration of methane

in µM (CH4). -: no data

Pond Depth T°C Cond O2 pH TN TP SRP DOC TSS Chla CO2 CH4

BGR1 surface 20.0 0.1 9.9 8.8 - 22.2 - - 1.6 0.9 22.6 1.1

BGR1 bottom 9.7 0.1 3.0 7.3 - 19.4 - - - 1.2 - -

BGR2 surface 15.0 0.2 9.4 7.3 0.5 49.1 3.4 9.3 13.1 2.4 20.2 0.4

BGR2 bottom 11.0 0.4 3.5 7.2 1.2 148.9 4.5 8.7 57.4 3.8 229.2 2.6

NASH surface 18.3 89.0 9.7 7.6 0.6 30.5 6.2 4.1 18.2 2.1 - -

NASH bottom 7.1 0.1 1.7 7.3 0.6 64.8 6.3 3.7 18.3 2.0 - -

KWK1 surface 17.9 0.1 9.7 6.7 0.6 67.9 3.7 12.0 20.3 10.9 - -

KWK1 bottom 6.4 0.2 0.5 6.2 1.1 87.8 12.6 12.0 140.8 10.3 - -

KWK6 surface 14.0 0.1 9.9 6.4 0.4 29.8 1.3 5.2 7.3 3.3 - -

KWK6 bottom 8.3 0.1 1.8 6.4 0.7 99.9 1.0 5.2 16.0 27.1 - -

KWK23 surface 14.7 0.0 9.8 6.4 0.4 57.1 5.5 7.8 6.4 1.9 - -

KWK23 bottom 4.4 0.3 0.4 6.1 2.7 170.5 133.6 10.9 73.6 7.2 - -

SAS1A surface 18.5 - - - - - 3.3 - 2.8 12.6 - -

SAS1A bottom 13.5 0.1 6.4 6.0 0.9 15.9 3.1 15.5 27.1 5.0 - -

SAS1B surface 9.1 0.2 1.7 5.6 1.8 29.1 3.0 16.2 33.1 3.0 - -

SAS1B bottom 9.1 0.2 1.7 5.6 1.8 29.1 3.0 16.2 33.1 3.0 - -

SAS2A surface 19.9 0.1 5.8 6.2 0.7 10.9 3.0 14.9 2.6 1.4 470.8 3.4

SAS2A bottom 4.6 0.3 0.3 5.6 1.6 41.5 4.1 18.9 - 18.1 1954.9 101.7

SAS2B surface 16.4 0.1 5.2 6.8 - 24.1 - - - 8.6 290.0 3.0

SAS2B bottom 6.1 0.1 0.2 6.4 - 58.2 3.0 - - 2.7 3872.2 292.0

108

Annexe 5. Rarefaction curves of the pmoA. OTUs were clustered at 93%.

109

Annexe 6. 16S rRNA and methanogenic orders recovered from the surface water samples, and percent

representation in the sequences.

Taxonomy SAS2A SAS2B BGR2

Large Small Large Small Large Small

Methanomicrobiales 23 4 8 2 51 41

Methanosarcinales 18 < 1 33 <1 8 <1

Methanobacteriales 2 0 1 <1 2 <1

Methanocellales <1 0 0 0 <1 <1

Unclassified Euryarchaeota 40 69 41 64 23 36

MEG 7 21 12 26 9 18

Thermoplasmata 4 < 1 < 1 < 1 4 < 1

Halobacteriales < 1 < 1 <1 <1 <1 2

DESG < 1 0 < 1 2 < 1 < 1

MCG 2 < 1 1 < 1 < 1 0

Group C3 < 1 < 1 < 1 1 < 1 < 1

Marine Benthic Group B 0 < 1 < 1 0 < 1 < 1

Thermoprotei 0 0 0 0 < 1 < 1

Unclassified Crenarchaeota 0 0 < 1 0 < 1 0

Thaumarchaeota 0 0 0 0 0 0

Unclassified Archaea 3 3 3 4 1 1

110

Annexe 7. Rarefaction curves of the 16S rRNA. OTUs were clustered at 97%.

111

Annexe 8. Properties of the V6-V8 16S rRNA and mcrA primers fused with the Trueseq sequencing primers.

Primer Targeting

region

Trueseq primers References

A-956F V6-V8 ACACTCTTTCCCTACACGACGCTCTTCCGATCT-

TYAATYGGANTCAACRCC

Comeau et al.,

2011

A-1401R V6-V8 GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-

CRGTGWGTRCAAGGRGCA

Comeau et al.,

2011

MLF mcrA ACACTCTTTCCCTACACGACGCTCTTCCGATCT-

GGTGGTGTMGGDTTCACMCARTA

Luton et al.,

2002

MLR mcrA GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-

TTCATTGCRTAGTTWGGRTAGTT

Luton et al.,

2002

Generic forward

second-PCR primer

AATGATACGGCGACCACCGAGATCTACAC[index1]AC

ACTCTTTCCCTACACGAC

Generic reverse

second-PCR primer

CAAGCAGAAGACGGCATACGAGAT[index2]GTGACTG

GAGTTCAGACGTGT

Diversité microbienne associée au cycle du méthane dans les … · 2020. 7. 30. · Cependant,...

Documents

Transcript of Diversité microbienne associée au cycle du méthane dans les … · 2020. 7. 30. · Cependant,...