The race for lipids : ontogeny of the fine-scale vertical co-distribution of arctic calanoid copepods and their phytoplankton food as elucidated by artificial intelligence coupled with an imaging profiler

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The race for lipids: ontogeny of the fine-scale vertical

co-distribution of arctic calanoid copepods and their

phytoplankton food as elucidated by artificial

intelligence coupled with an imaging profiler

Thèse

Moritz Schmid

Doctorat interuniversitaire en océanographie Philosophiæ doctor (Ph. D.)

Québec, Canada

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The race for lipids: ontogeny of the fine-scale vertical

co-distribution of arctic calanoid copepods and their

phytoplankton food as elucidated by artificial

intelligence coupled with an imaging profiler

Thèse

Moritz Schmid

Sous la direction de:

Louis Fortier, directeur de recherche Marcel Babin, codirecteur de recherche

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Résumé

Le broutage du phytoplancton par les copépodes arctiques effectue le transfert d’énergie des producteurs primaires vers les niveaux trophiques supérieurs. Les interactions prédateur-proie entre le phytoplancton et le zooplancton dans la colonne d’eau sont toutefois difficiles à étudier puisque l’échantillonnage du zooplancton se fait généralement à l’aide de filets qui stratifient grossièrement la colonne d’eau. La détermination des paramètres physiologiques chez les copé-podes, tels que le contenu lipidique, se fait aussi à une résolution verticale grossière. Pour pallier cette limite, ce projet de recherche utilise le LOKI (Lightframe On-sight Key-species Investi-gation), un système de caméra sous-marine fournissant des données à une résolution verticale de 1 m. Un modèle d’identification automatique du zooplancton qui repose sur l’intelligence artificielle a été développé et appliqué à des profils échantillonnés au cours de l’automne 2013 dans la polynie des eaux du nord et le détroit de Nares dans l’Arctique canadien. Le modèle transforme les images du LOKI en information taxonomique et différencie un grand nombre d’organismes et de classes de particules (n=114), incluant les stades de développement des copépodes. Deux études ont été réalisées à partir des images du LOKI identifiées automati-quement. Premièrement, lors d’une dérive Lagrangienne, les distributions verticales à haute résolution (1 m) des copépodes Calanus hyperboreus, C. glacialis et Metridia longa ont été mises en relation avec leurs lipides totaux (TL, mg) et leur abondance lipidique (LF, %). Les copépodites de C. hyperboreus et C. glacialis avec une faible LF effectuent une migration nycthémérale vers les eaux de surface pendant la nuit pour se nourrir, alors que les indivi-dus du même stade de développement avec une haute LF cessent leur migration et restent en profondeur, probablement pour la diapause. La migration pour la diapause chez C. hy-perboreus semblait avoir lieu pour une LF d’environ 50% alors que C. glacialis avait besoin d’une plus grande LF (60%). Un modèle bioénergétique a montré que les femelles du genre Calanus avaient suffisamment de lipides en réserve pour demeurer en diapause pendant plus de 365 jours, soulignant leur capacité à se reproduire à partir de leurs réserves (capital bree-ders). Dans une deuxième étude, le couplage des stades de développement de C. hyperboreus et C. glacialis et de leur nourriture phytoplanctoniques a été étudié à haute résolution ver-ticale dans la polynie des eaux du Nord et le détroit de Nares. Trois types de distributions verticales de copépodes en réponse au maximum de chlorophylle de subsurface (MCS) et au rayonnement photosynthétiquement actif incident ont été identifiés, tous étant conformes à l’hypothèse d’évitement des prédateurs. Aux stations où les abondances de copépodes étaient les plus élevées dans le MCS, C. hyperboreus et C. glacialis (stades C4 et C5) était partitionnés verticalement à fine échelle (1-2 m). Alors que les pics d’abondance de C. hyperboreus C4 et C5 ont été trouvés au cœur du MCS, les pics d’abondance de C. glacialis C4 et C5 étaient juste au-dessus et en dessous de leurs congénères. Le partitionnement pourrait être expliqué

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par une stratégie optimale de recherche de nourriture ou par les préférences alimentaires des copépodes pour les taxons phytoplanctoniques occupant le MCS. Un éclairage nouveau sur le fin couplage vertical entre le phytoplancton et le zooplancton est important pour une meilleure compréhension des effets des changements climatiques sur l’écosystème marin Arctique.

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Abstract

The grazing of phytoplankton by Arctic copepods channels energy from primary producers to higher trophic levels. However, the predator-prey interactions between phytoplankton and zooplankton in the water column are difficult to study since zooplankton sampling still relies heavily on nets that roughly stratify the water column. The quantification of physiological parameters of copepods, such as lipid content, is also made at coarse vertical resolution. To overcome this limitation, this research used the Lightframe On-sight Keyspecies Investigation (LOKI) system, an underwater camera that provides 1 m vertical resolution. An automatic zooplankton identification model, based on artificial intelligence, was developed for the analy-sis of profiles sampled in fall 2013 in the North Water Polynya (NOW) and Nares Strait (NS), in the Canadian Arctic. The model turns LOKI images into taxonomic information and can differentiate 114 taxa (organisms and particles), including the developmental stages of cope-pods. Two studies were carried out based on automatically identified LOKI images. First, during a Lagrangian drift, fine-scale vertical distributions (1-m resolution) of the copepods Calanus hyperboreus, C. glacialis and Metridia longa were studied in relation to their total lipids (TL, mg) and lipid fullness (LF, %). C. hyperboreus and C. glacialis with low LF per-formed diel vertical migration to surface waters at night to feed, while same-stage individuals with high LF ceased migrating and remained at depth to diapause. Migration to diapause in C. hyperboreus occurred at a LF of approximately 50%, while C. glacialis needed a higher LF (60%). A bioenergetics model showed that Calanus females had enough lipids stored to diapause for over 365 days, highlighting their capacity for capital breeding. In a second study, the fine-scale vertical coupling of C. hyperboreus and C. glacialis developmental stages with their phytoplankton food was studied in the NOW and NS. Three types of copepod vertical distributions in response to the subsurface chlorophyll maximum (SCM) and incident pho-tosynthetic active radiation levels were identified, all of them being in accordance with the predator avoidance hypothesis. At stations where copepod abundances peaked in the SCM, C4 and C5 C. hyperboreus and C. glacialis were vertically partitioned on a fine scale (1-2 m). While C. hyperboreusC4 and C5 abundance peaks were found in the core of the SCM, C. glacialis C4 and C5 peaked just above and below their congeners. The partitioning could be explained by optimal foraging theory or the copepods’ feeding preferences for phytoplankton taxa occupying the SCM. Insight into the fine scale vertical coupling of phyto- and zooplank-ton is important for a better understanding of climate change effects on the Arctic marine ecosystem.

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List of Tables

Table 2.1 Stations included in training and testing the models developed in this

study. . . 20

Table 2.2 Image features used in training the machine learning models for prosome length, prosome width and automatic species identification. Usage indi-cates the models: L indiindi-cates the prosome length model, W the prosome width model and AI the automatic species identification model. Image feature set 1 originates from LOKI browser and image feature set 2

orig-inates from GUIDOS toolbox. . . 22

Table 2.3 Important Random Forests model settings for the regression models of

prosome length and width as well as the zooplankton classification model. 28

Table 2.4 Accuracy (Ac) and specificity (Sp) for each of the 63 compressed

cate-gories at the two test stations. . . 32

Table 3.1 LOKI deployments carried out during the Lagrangian drift. . . 47

Table 4.1 Details on the LOKI deployments included in this study. . . 71

Table 4.2 Water column abundances (ind. m-2_{) of Calanus copepod stages.}

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List of Figures

Figure 1.1 The Arctic as delineated by the working group for Conservation of Arctic Flora and Fauna (CAFF) of the Arctic Council. Panel a) Large marine ecosystems of the Arctic Ocean. Data source: AMAP/CAFF/SDWG (2013). Panel b) Arctic Ocean bathymetry. Data source: Becker et al. (2009), Sandwell and Smith (2009). Maps were created using:

http://wwfarcticmaps.org. . . 2

Figure 1.2 General Arctic Ocean water masses between Bering Strait and Fram Strait. From AMAP (1998), after Aagaard and Carmack (1989) and

Bönisch and Schlosser (1995). . . 4

Figure 1.3 The Arctic food web. From Darnis et al. (2012) . . . 6

Figure 1.4 Sea ice extent in recent years compared to the 1981 - 2010 average.

Source: National Snow and Ice Data Center at www.nsidc.org. . . 9

Figure 1.5 The study area (black rectangle) for this thesis, comprising the North

Water Polynya (NOW) and Nares (Strait). . . 12

Figure 2.1 a) Schematic of LOKI showing its main components 1-4. b) The LOKI system on the right attached to a frame besides a traditional zooplankton net sampler, during a recent deployment in the Canadian Arctic. c) The LOKI camera, showing how plankton passes through the channel for imaging. a and c are adapted from Isitec GmbH. Photo credit for b):

Jessy Barrette. . . 19

Figure 2.2 Stations in the Canadian Arctic used to train (blue points) and test (black points) the models developed here. Images from station 101

(yel-low points) were used in both training and testing. . . 20

Figure 2.3 LOKI images of selected zooplankton taxa. . . 24

Figure 2.4 Orientational subgroups were also included in the automatic identifica-tion model. a-f) show females of Calanus glacialis in different posiidentifica-tions a) lateral position, b) dorsal short position, c-d) dorsal long position, e-f) antenna in front position. To recognize that these images show the same species and stage required the model to be trained on orientational

categories. . . 25

Figure 2.5 The copepod Heterorhabdus sp. measured at different thresholds. The green outline delineates the area included by LOKI browser for image feature extraction. An identification model based on these five

thresh-olds is more robust than when just one threshold is used. . . 26

Figure 2.6 Raw LOKI images on the left compared to their corresponding MSPA images on the right. The MSPA classes core (green), edge (black), bridge (red), branch (orange) and loop (yellow) each characterize

differ-ent structures (e.g., maxillipeds, antennae) of the zooplankton individuals. 27

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Figure 2.7 Results of internally testing the automatic identification model. The confusion matrix shows probabilities of classifications for the 63 com-pressed categories (n=7306). Probabilities range from classifications rarely occurring (<0.01, blue) to happening regularly (1, red). White cells denote cases where no classifications occurred. High values on the diagonal indicate that the detection probability for a species was high.

Low values off the diagonal delineate category-specific misclassifications. 30

Figure 2.8 Prosome lengths and prosome widths predicted by the model, and plot-ted against their measured values. Red and green lines are linear regres-sions of prosome length and width respectively. The black line indicates

perfect agreement. . . 31

Figure 2.9 External testing results for station 101. The confusion matrix shows probabilities of classifications for the 63 compressed categories (n=4820).

See the caption of Fig. 2.7 for more details. . . 35

Figure 2.10 External testing results for station 126. The confusion matrix shows probabilities of classifications for the 63 compressed categories (n=2163).

See the caption of Fig. 2.7 for more details. . . 36

Figure 2.11 Numbers of zooplankton specimens in particular groups identified by the model, compared to those present in the biological samples from stations 101 and 126. Numbers 1-5 show copepodite stages: M = males, F = females, All = sum of all stages in the biological sample. The black line indicates perfect agreement. Points are sized relative to the size of the

taxa . . . 37

Figure 2.12 C. glacialis C5 and C. glacialis C3 / C. hyperboreus C2 binned at 1m

intervals. . . 38

Figure 3.1 Deployments of LOKI during the Lagrangian drift in the North Water Polynya. Inset map: stations with local sampling times given. Sampling

commenced at the Northeast-most station and continued southward. . . 47

Figure 3.2 The LOKI system attached to a sampling frame with additional nets.

Photo credit: Jessy Barrette. . . 48

Figure 3.3 Examples of LOKI images for each study species. Note the smaller size of C. glacialis compared to C. hyperboreus of the same developmental

stage, and the characteristic long urosome of M. longa. . . 50

Figure 3.4 Image of a C. hyperboreus female with its lipid sac highlighted in red. . 51

Figure 3.5 Temperature, salinity, fluorescence and PAR profiles at our sampling stations during the drift study. Values were interpolated using Ocean Data View’s DIVA interpolation. Black lines show the portion of the

water column sampled. Sampling times shown in the temperature panel. 52

Figure 3.6 Vertical distribution of the unresolved Calanus glacialis C2 / C. hyper-boreus C1 and C. glacialis C3 / C. hyperboreus C2 complexes (black bars) and phytoplankton fluorescence (green line) for 5 different profiles over the 24-h duration of a drift study in the North Water. Sampling time and incident PAR (µE m2_s−1_{) at the beginning of a profile are}

given above each panel. Note the differences in maximum depth among

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Figure 3.7 Vertical distribution of Calanus hyperboreus C3 to female (F) copepodite stages (black bars) and phytoplankton fluorescence (green line) for 5 different profiles over the 24-h duration of a drift study in the North Water. Sampling time and incident PAR (µE m2_s−1_{) at the beginning}

of a profile are given above each panel. Note the differences in maximum

depth among profiles. . . 55

Figure 3.8 Vertical distribution of Calanus glacialis C1 and C4 to female (F) cope-podite stages (black bars) and phytoplankton fluorescence (green line) for 5 different profiles over the 24-h duration of a drift study in the North Water. Sampling time and incident PAR (µE m2_s−1_{) at the beginning}

of a profile are given above each panel. Note the differences in maximum

depth among profiles. . . 56

Figure 3.9 Vertical distribution of Metridia longa C4 to female (F) copepodite stages (black bars) and phytoplankton fluorescence (green line) for 5 different profiles over the 24-h duration of a drift study in the North Water. Sampling time and incident PAR (µE m2_s−1_{) at the beginning}

of a profile are given above each panel. Note the differences in maxi-mum depth among profiles. The vertical distributions of the remaining

M. longa stages are given in Appendix B, Fig. B.1. . . 57 Figure 3.10 Total lipids (panel a) and lipid fullness (panel b) for different copepodite

stages and female (F) Calanus hyperboreus, C. glacialis and Metridia longa. 58

Figure 3.11 Total lipid amounts (left panels) and lipid fullness (right panels) in C. hy-perboreus individuals located at different depths. C3 - C5 = copepodite stages 3, 4 and 5, F = females. In each panel, the light blue crosses show daytime (18:45 h) results. The dark blue crosses show nighttime (2:40 h) results. Regression lines for the two datasets (day and night) are shown as light blue and dark blue lines with the 95% confidence

intervals shown as gray areas. . . 60

Figure 3.12 Total lipid amounts (left panels) and lipid fullness (right panels) in C. glacialis individuals located at different depths. C4 and C5 = copepodite stages C4 and C5, F = females. In each panel, the light blue crosses show daytime (18:45 h) results. The dark blue crosses show nighttime (2:40 h) results. Regression lines for the two datasets (day and night) are shown as light blue and dark blue lines with the 95% confidence intervals shown

as gray areas. . . 61

Figure 3.13 Total lipid amounts (left panels) and lipid fullness (right panels) in M. longa individuals located at different depths. C5 = copepodite stage C5, F = females. In each panel, the light blue crosses show daytime (18:45 h) results. The dark blue crosses show nighttime (2:40 h) results. Regression lines for the two datasets (day and night) are shown as light blue and dark blue lines with the 95% confidence intervals shown as gray

areas. . . 62

Figure 3.14 Estimated maximum potential diapause duration for observed develop-ment stages of Calanus hyperboreus and Calanus glacialis. Dotted line:

six month threshold; dashed line: one year threshold. . . 63

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Figure 4.1 Maps of the Canadian Arctic showing the locations where LOKI was deployed in August 2013; NOW = North Water Polynya, NS = Nares

Strait. . . 71

Figure 4.2 Sample LOKI images of C. hyperboreus and C. glacialis. C4, C5 and F

indicate copepodite stages 4, 5 and females respectively. . . 72

Figure 4.3 Remotely sensed surface chl a (mg m-3_{) and ice cover (%) in the North}

Water Polynya and Nares Strait from April to September 2013. Num-bers inside the panels indicate year and month of sampling (courtesy of

Atsushi Matsuoka, Laval University). . . 74

Figure 4.4 Vertical distributions (ind. m-3_{) of select C. hyperboreus and C. glacialis}

stages (black bars), chlorophyll a (green lines), temperature (red lines), and salinity (cyan lines) at station Kane Basin. Sampling time and near surface PAR were 3:15 h and 18 (µE s-1 _m-2_{, respectively). Vertical}

distributions of the remaining C. hyperboreus and C. glacialis stages

are presented in Appendix C, Fig. C.1.. . . 76

Figure 4.5 Vertical distributions (ind. m-3_{) of C. hyperboreus and C. glacialis}

stages (black bars) at stations NOW East (panel a) and Petermann Glacier (panel b) as well as chlorophyll a (green lines), temperature (red lines), and salinity (cyan lines). Sampling time and incident PAR value (µE s-1 _m-2_{) are written on each panel. Vertical distributions of}

C. hyperboreus and C. glacialis males at Petermann Glacier are shown

in Appendix C, Fig. C.2. . . 77

Figure 4.6 Vertical distributions (ind. m-3_{) of C. hyperboreus and C. glacialis}

stages (black bars) at stations NOW West (panel a), NS West (panel b) and NS East (panel c) as well as chlorophyll a (green lines), temperature (red lines), and salinity (cyan lines). Sampling time and incident PAR

value (µE s-1_m-2_{) are written on each panel.} _{. . . .} ₇₉

Figure 4.7 Abundances (ind. m-3_{) of C4 and C5 C. hyperboreus and C. glacialis}

copepods (black bars) in the subsurface chlorophyll maximum (green lines) at NS stations. A) C4 C. hyperboreus (mean prosome length of 4.1 mm) and C. glacialis (mean prosome length of 2.2 mm) at the West station. B) C5 C. hyperboreus (mean prosome length of 5.25 mm) and

C. glacialis (mean prosome length of 2.9 mm) at the East station. . . . 81 Figure 5.1 Numbers of zooplankton individuals identified by the model and

cor-rected according to Solow et al. (2001), compared to those present in the biological samples, at test stations 101 (panel a) and 126 (panel b). The two test stations are described in Chapter 2. Numbers 1-5 show copepodite stages: M = males, F = females, All = sum of all stages. Points are scaled relatively to the size of the developmental stages. The

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Dedicated to Cynthia and Moka

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„Der Natur gegenüberzustehen und seinen Scharfsinn an ihren Rätseln zu erproben, gibt dem Leben einen ungeahnten Inhalt“

„Being face to face with nature and exploring its mysteries with ones keen wit, gives unimaginable meaning to life“

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Acknowledgments

First of all, I want to sincerely thank my supervisor Louis Fortier for giving me this amazing research opportunity. Working in oceanography was my dream for a long time, and doing so in the Arctic and onboard of icebreakers made this into an unforgettable tour de force. Thank you for always having your door open for impromptu meetings, your support throughout the project, opportunities to go to conferences, and many things more. Like many other members of the scientific community, I am very thankful for your leadership in Arctic research and its management, and the multitude of projects you have initiated and facilitated, which make our research possible. Your delicious cooking of freshly hunted game was a welcome change to working hard, and was a great experience of Québécois and Canadian culture. I also want to thank my co-supervisor Marcel Babin for his advice during my PhD, and giving me the opportunity to be part of the excellent Takuvik research group. His expertise on marine optics and phytoplankton was extremely valuable for this study. Special thanks to my committee member Frédéric Maps, for his keen interest in the LOKI project and discussions on copepod ecology, their lipids, and especially fat copepods, as well as to Jean-Éric Tremblay for his much-appreciated advice on my research during multiple committee meetings. I am grateful to Gesche Winkler for acting as external reviewer.

Many thanks to Jordan Grigor and Cyril Aubry for being part of the LOKI team all these years, and among other things, teaching me plankton identification, and giving me comments on my thesis. Maxime Geoffroy and Deo Florence Onda also gave me comments on my thesis, which I am very thankful for. I want to further thank Eric Rehm for discussions on ocean optics and R/Matlab programming, as well as for comments on my thesis. Thanks also to Atsushi Matsuoka who gave me remote sensing related advice and comments on my thesis. Simon Lambert-Girard’s reviews of several applications of mine for postdocs and grants are much appreciated.

I am indebted to my friends and colleagues who helped me with French translations during my PhD time: Paschale Noël Bégin, Caroline Bouchard, Margaux Gourdal, Pierre-Luc Grondin, Sophie Renaut and Julie Sansoulet. Further, I want to thank Joannie Ferland for comments on phytoplankton pigment analysis and carbon measurements, as well as my student assistants Kevin Gonthier and Claudie Lachance for their work on LOKI images. Thank you to Jonathan Gagnon for nutrient-related discussions.

Thank you to my colleagues and friends from the Fortier lab: Gérald Darnis, Thibaud Dezut-ter, Marianne Falardeau-Côté, Fanny Cusset, Catherine Lalande, Mathieu LeBlanc, Gabrielle Nadaï, Marie Parenteau, and Sarah Schembri made these years at Université Laval and at

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sea a great time. Thanks also to our honorary lab member Alexis Burt for helping with the zooplankton sampling onboard the CCGS Amundsen.

I thank the officers and crews of the CCGS Amundsen and CCGS Pierre Radisson for their professionalism at sea. I am very grateful to ArcticNet for managing the scientific operations on the CCGS Amundsen; lead by Martin Fortier and a great team: Colline Gombault, Keith Lévesque, Thomas Linkowski, Shawn Meredyk, Luc Michaud and Simon Morisset. Thank you all for your hard work on the ArcticNet research program. I am also very thankful for the support I received from Québec-Océan: Lynn Bélanger, Guylaine Potvin, Richard Marquis, Sylvain Blondeau, Pascal Guillot and Brigitte Robineau. I am further obliged to Marie-Hélène Forget and Debra Christiansen-Stowe from Takuvik for their great support during the last years.

Funding from the Canada Foundation for Innovation (CFI) and ArcticNet, a Network of Cen-tres of Excellence of Canada, made this project possible. Thanks also to the Canada Research Chair on the Response of Arctic Marine Ecosystems to Climate Warming for equipment sup-port, the Canada Excellence Research Chair (CERC) in Remote Sensing of Canada’s New Arctic Frontier for a postgraduate scholarship, and to Québec-Océan for stipends.

I would like to thank Thomas Hanken and Heiko Lilienthal from iSiTEC, and Hans-Jürgen Hirche from the Alfred Wegener Institute, for developing LOKI. Thank you for your support throughout the years. LianTze Lim’s excellent LaTeX support made the layout of this thesis possible. Thank you very much for that.

It was a great time working alongside my fellow Takuvik colleagues and friends Guislain Bécu, Clémence Goyens, Nathalie Joli, José Luis Lagunas, Julien Laliberté, Michel Lavoie, Philippe Israël Morin, Griet Neukermans, Nicolas Schiffrine, Arnaud Pourchez, Srikanth Ayyala So-mayajula and Marti Gali Tapias. Thanks for your friendship and scientific discussions. I also want to thank Warwick Vincent and Ladd Johnson for science-related discussions and career advice, as well as Falk Huettmann for introducing me to machine learning many years ago. Thanks to my band mates from the Skopitones whom I had the honor to perform with: Jay, Gog, Caporal, Seby, Valerie and Mat; as well as my good friends Philipp Günther and Jean-Philippe Baillargeon for great times outside of university.

Thank you to my friends in Germany: Jan Hoeber, Steffi Hoeber, Matthias Löffler, Fabian Peters and Mirjam Peters, who were there for me despite the long distance.

I am more than grateful to my parents Sibylle and Georg, my brother Jan, as well as Oma Renate and Opa Wilfried for always being there for me and for encouraging me in my science career. Mum and dad, thanks for already bringing the ocean close to me the day before I was born, on SF bay!

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My thesis is dedicated to my wife Cynthia, and our hairy companion Moka who brought much love and joy to our time in Québec.

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Foreword

This doctoral thesis comprises a general introduction (Chapter 1), three scientific articles (Chapters 2, 3 and 4), and general conclusions (Chapter 5). Chapter 2 is published in Methods in Oceanography. Chapters 3 and 4 are in preparation for publication:

Chapter 2

Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2016) The LOKI underwater imaging sys-tem and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography 15-16:129-160. http://dx.doi.org/10.1016/j.mio.2016.03.003. (reproduced with permission of the publisher).

Chapter 3

Schmid, M.S., Maps, F., Fortier, L. Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging. This will be submitted to Journal of Plankton Research.

Chapter 4

Schmid, M.S., Fortier, L. Underwater imaging reveals the fine-scale vertical distribution of Calanus hyperboreus and Calanus glacialis in relation to the phytoplankton subsurface maximum. This will be submitted to Journal of Plankton Research.

I designed this research project, and all analyses were either performed by myself or by students under my supervision. I supervised two students for their « initiation à la recherche » in line with the objectives of my doctoral research. For this thesis I participated in research cruises in 2012, 2013 and 2014 onboard the research icebreakers CCGS Pierre Radisson and CCGS Amundsen. All chapters have benefited from the corrections and comments of co-authors or colleagues. Results of these chapters have been presented at the following national and international scientific conferences:

[12] Schmid, M.S., Fortier, L. (2017) Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging. Arctic Frontiers (Tromsø, Nor-way).

[11] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2015) Automatic zooplankton species identification for the greater North Water Polynya region. APECS Online Conference – New perspectives in the Polar Sciences.

[10] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2014) Zooplankton imaging with the Lightframe On-sight Keyspecies Investigation (LOKI) system. 5ème Rencontre des

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Technologies Marines (Benodet, France). * Invited talk, presented remotely via skype. [9] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2014) Automated zooplankton iden-tification for Baffin Bay and adjacent waters – combining in-situ imaging, machine learning and taxonomy to gain insights into the fine-scale dynamics of zooplankton. Arctic Change 2014 (Ottawa, Canada).

[8] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2014) Automated zooplankton identifi-cation for Baffin Bay and adjacent waters. Québec-Océan Annual Meeting (Rivière-du-Loup, Canada). * Awarded best oral presentation

[7] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2013) New perspectives in zooplankton sampling: use of in-situ optical imaging to profile the vertical distributions of taxa. APECS webinar series. Eastern Arctic Research Webinar.

[6] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2013) In-situ imaging of mesozooplank-ton in order to assess fine scale spatiotemporal variability. Colloque Biologie (Québec City, Canada).

[5] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2013) In-situ imaging of mesozooplank-ton in order to assess fine scale spatiotemporal variability: experiences from the BaySys 2012 expedition in Hudson Bay. Arctic Frontiers (Tromsø, Norway). * Awarded out-standing poster award

[4] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2013) In-situ imaging of Arctic plank-ton: automated taxonomic classification using machine learning algorithms. ArcticNet Annual Conference (Halifax, Canada).

[3] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2013) In-situ imaging of Arctic zoo-plankton. Québec-Océan Annual Meeting (Rivière-du-Loup, Canada).

[2] Schmid. M.S., Aubry, C., Grigor, J., Fortier, L. (2012) Assessing spatiotemporal variability in the mesozooplankton using a newly developed plankton imaging system -first experiences from the BaySys 2012 expedition in Hudson Bay. ArcticNet Annual Conference (Vancouver, Canada).

[1] Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2012) Assessing spatiotemporal vari-ability in the mesozooplankton using a newly developed plankton imaging system - first experiences from the BaySys 2012 expedition in Hudson Bay. Québec-Océan Annual Meeting (Montreal, Canada).

I also published a software for processing of LOKI images:

Schmid, M.S., Aubry, C., Grigor, J., Fortier, L. (2015) ZOOMIE v1.0 (Zooplankton Multiple Image Exclusion). https://dx.doi.org/10.5281/zenodo.17928.

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1. Chapter 1 – General Introduction

1.1 The Arctic Environment

Marked seasonal fluctuations in insolation levels leading to changes in ice cover make the Arctic an unique environment. In winter, during the polar night (September to March), light levels are very low, while in summer the sun does not set (midnight sun). Arctic climate is heavily influenced by the generally high albedo of the Arctic surface (e.g., ice), reflecting much of the incoming solar radiation (Frolov et al., 2009).

1.2 The Arctic Ocean

The semi-enclosed Arctic Ocean comprises an area of 14 ∗ 106_km2 _{(AO, Fig. 1.1) and receives}

large volumes of warm Atlantic water with a relatively high salinity (S) of > 34 PSU through Fram Strait (∼2600 m deep) and the Barents Sea. Lower volumes of cold, relatively fresh and seasonally modified Pacific water (S = 31 - 34 PSU), are supplied to the Amerasian Basin of the AO through the shallow (∼45 m deep) Bering Strait (Stein and Macdonald, 2004). The Lomonosov Ridge spans ∼1800 km, dividing the AO into Eurasian and Amerasian Basin (Fig. 1.1). Continental shelves make up a large portion (∼50%) of the AO and these shallow ecosystems are productive areas (Sakshaug, 2004). The Arctic Ocean and adjoining seas traditionally witness a sea ice maximum during March and a minimum during September. This feature is heavily affected by climate change (discussed further in section 1.5). The AO basin has a mean depth of ∼1190 m and its central part is almost perennially covered by a drifting icepack, having a mean thickness of 3 m (Comiso, 2010). During winter the icepack extends to the landmasses and becomes almost double in size. The ice prevents exchange of light and heat between the atmosphere and the ocean, significantly influencing algal growth (Di Prisco and Verde, 2012). Aside from the attached microbial communities, the sea ice is also an important habitat for a number of invertebrates (e.g., amphipods of the genus Gammarus), fishes (e.g., Boreogadus saida, polar cod), and marine mammals (e.g., seals).

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Figure 1.1 The Arctic as delineated by the working group for Conservation of Arctic Flora and Fauna (CAFF) of the Arctic Council. Panel a) Large marine ecosystems of the Arctic Ocean. Data source: AMAP/CAFF/SDWG (2013). Panel b) Arctic Ocean bathymetry. Data source: Becker et al. (2009), Sandwell and Smith (2009). Maps were created using: http://wwfarcticmaps.org.

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The formation of sea ice in the AO drives the formation of deep water. When seawater freezes, salt is rejected, substantially increasing the salinity of surrounding waters (i.e., brine). This salty and cold water sinks and drives deep-water currents, which mark the beginning of the thermohaline circulation (ACIA, 2005). While in the West AO, the Beaufort Gyre (clockwise motion) is largely responsible for the displacement of surface waters and sea ice, the Transpolar Drift, which can change between a clockwise and counterclockwise direction depending on the Arctic Oscillation, facilitates ice export to the Atlantic (Mysak, 2001; Stein and Macdonald, 2004).

Generally, the western AO vertical structure can be divided into four prominent layers. Fresh-water input from precipitation, melting ice and large river deltas (e.g., Yenisei, McKenzie and Yukon) helps form the buoyant Polar Mixed layer (PML; Fig. 1.2), which is the topmost layer, with a salinity of 27 – 31 PSU. Underneath the PML are nutrient-rich Pacific Waters (S = 31 – 33 PSU), while Atlantic Water is lower in nutrients and more saline (S > 34 PSU). Canada Basin Deep Water (CBDW) is the densest and coldest of the layers (Shimada et al., 2001; Shimada et al., 2005; McLaughlin et al., 2006). These four water masses are divided by haloclines which maintain the vertical structure of the water column. Specifically, the Pacific Halocline and the Atlantic Halocline (Steele et al., 2004). The Pacific Halocline is especially important for biological productivity. Periodically cooling surface waters can lead to mixing and a shoaling of the Pacific Halocline, bringing much needed nutrients to surface waters (Steele et al., 2004), which are in turn taken up by phytoplankton.

During summer, nitrate in the PML reaches very low levels, which is an unfavorable condition for lager phytoplankton species (Li et al., 2009). Hence, phytoplankton tends to follow the nitracline that forms usually at the bottom of the photic zone (Nishino et al., 2011). Here, low light conditions lead to increased pigment accumulation in the phytoplankton cells (Martin et al., 2010), forming the subsurface chlorophyll maximum (SCM; Tremblay et al., 2008; Martin et al., 2012). Because cells increase their amount of pigments substantially at these low light conditions, the SCM does not necessarily have to coincide with the productivity maximum (Martin et al., 2010). In the Arctic the SCM contributes up to 70% of annual primary production (Martin et al., 2013).

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Figure 1.2 General Arctic Ocean water masses between Bering Strait and Fram Strait. From AMAP (1998), after Aagaard and Carmack (1989) and Bönisch and Schlosser (1995).

1.3 The trophic web of the Arctic Ocean

Organismal diversity in the AO is generally lower than in other parts of the world ocean (ACIA, 2005), and although there are several endemic, ice-associated (i.e., sympagic) species living in the Arctic, like amphipods, the degree of endemism in the Arctic is considered low (Dunton, 1992).

Strong seasonality in illumination and subsequent sea ice cover in the Arctic marine ecosystem leads to bouts in primary production of autotrophic algae (Ji et al., 2013). Blooms of under-ice algae appear in spring and are succeeded by open water phytoplankton blooms once under-ice retreats. Traditionally, the Arctic witnesses only a spring/summer phytoplankton bloom. Due to climate change, fall phytoplankton blooms, indicators of more temperate seas, have been observed more and more in the AO (see section 1.5). The contribution of phytoplankton and ice-algae growth to primary production averages 12 - 50 g C m-2 _yr-1_{and 5 - 10 g C m}-2 _yr-1_,

respectively (Legendre et al., 1992; Leu et al., 2011), but ice-algae can be higher in areas of thick ice (Gradinger, 2009). This organic matter under the ice is an important food source for fauna living below the ice, including the benthos (David et al., 2015; Kortsch et al., 2015). The timing and development of phytoplankton blooms (i.e., its phenology) highly depends on the receding of the ice edge, which allows light that can be used for photosynthesis to penetrate deeper into the water column (Zenkevitch, 1963; Falk-Petersen et al., 2009; Tremblay et al., 2012). Phytoplankton diversity in the AO includes ∼1874 phytoplankton and ∼1027 sympagic taxa (Poulin et al., 2011), while worldwide diversity of phytoplankton is estimated at ∼5000 phytoplankton species (Bluhm et al., 2011). Primary production in the Arctic (> 329 ∗ 106 _t

C yr-1_{) is rather low compared to other oceans, The Bering Sea alone, for instance, contributes}

> 300 ∗ 106 _{t C yr}-1 _{in primary production in a smaller area (Sakshaug, 2004). Within the}

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AO, regional contributions also vary substantially (Sakshaug, 2004). For example, primary production in the Barents Sea is ∼136 ∗ 106 _{t C yr}-1_{, while the Chukchi Sea in the western}

AO contributes ∼42 ∗ 106 _{t C yr}-1_{. Phytoplankton blooms in highly productive areas support}

a large secondary production of zooplankton as well as benthic organisms (Hopcroft et al., 2010; Grebmeier et al., 2006a; Nishino et al., 2016).

While bottom-up control of phytoplankton via nutrient availability (foremost nitrogen; Trem-blay and Gagnon, 2009; TremTrem-blay et al., 2011) is a strong controlling factor, top-down control by grazing of zooplankton can also determine the extent of the phytoplankton population (Banse, 2013). 5000 invertebrate species are currently estimated to inhabit the AO, of which most by far are benthic (91%) organisms, 8% are pelagic and only 1% are sympagic (CAFF, 2013). Zooplankton comprise a wide variety of heterotrophic organisms that have a size range of ∼0.002 to ∼200 mm and have relatively limited swimming capabilities, thus relying much on ocean currents for dispersal. One of the key zooplankton groups in the AO trophic web are copepods, with members of this group being herbivores, carnivores and omnivores. Copepods represent up to 80% of the Arctic zooplankton biomass (Auel and Hagen, 2002; Søreide et al., 2008; Darnis and Fortier, 2014).

Within the copepods the most important group is likely the Calanus genus, key species in the ecosystem due to their ability to turn carbohydrates and proteins from algae into high-energy lipids (i.e., wax esters). These lipids make Calanus such an important food source for higher trophic levels (Falk-Petersen et al., 2009). For instance, age-0 polar cod (Boreogadus saida) receives 92% of its carbon intake from preying on the copepod species Calanus hyperboreus, C. glacialis, Metridia longa, and Pseudocalanus spp. (Falardeau et al., 2014). The endemic polar cod comprises up to 95% of the AO pelagic fish assemblage (Benoit et al., 2008; Fortier et al., 2015) and channels up to ∼75% of energy from the zooplankton to higher trophic levels (Welch et al., 1992). Thus, it very likely is the most important of the ∼250 fish species in the AO (CAFF, 2013). Young (i.e., age-0) B. saida tend to aggregate in the epipelagic layer, while older individuals (i.e., age-1+) are found deeper in the water column (Geoffroy et al., 2016). B. saida is the staple food of seabirds, seals and narwhals (Figure 1.3).

While the part of the trophic web as described above relies much on the availability of sunlight and is most active during summer time, the microbial food web is also highly active during winter time (Forest et al., 2011; Darnis et al., 2012). The microbial food web is in general less well studied, but molecular studies show a high diversity of its different components including archaea, bacteria (Galand et al., 2009) and various eukaryotic microbes (Lovejoy et al., 2007; Lovejoy and Potvin, 2011). Constituents of the microbial food web are preyed upon by microzooplankton (20 - 200 µm; Fig. 1.3), which are in turn a food source for larger zooplankton (Seuthe et al., 2011; Darnis et al., 2012).

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Figure 1.3 The Arctic food web. From Darnis et al. (2012)

1.4 Coupling of zooplankton with the abiotic environment,

and their prey and predators

High biomass of microalgae can support a large zooplankton population (Tremblay et al., 2011). Furthermore, when primary production is high (i.e., during phytoplankton blooms), elevated secondary production of zooplankton usually follows with a delay of several weeks. In the North Water Polynya (NOW), which is situated between Greenland and the Canadian Arctic Archipelago (see also section 1.9, ’Study area’), the phytoplankton bloom usually starts in the eastern sector in April/May and then progresses west- and northwards with the receding sea ice (Klein et al., 2002; Ringuette et al., 2002). Hence, because zooplankton has more time to grow and develop in the East, zooplankton abundances there are higher than in the West of the NOW (Klein et al., 2002; Ringuette et al., 2002). When phytoplankton is not available in time for zooplankton grazing, zooplankton production often remains low. This can have negative consequences for higher trophic levels (Leu et al., 2011; see section 1.5 for climate change induced mismatch) and demonstrates how important the timing of the availability of the microalgae resource (i.e., under-ice algae and pelagic phytoplankton) is for the entire AO marine ecosystem.

The distribution of zooplankton is strongly influenced by hydrographic features, including

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water masses and clines (Daly and Smith, 1993). Zooplankton organisms are often able to actively adjust their vertical position in the water column, for instance over the day-night cycle (diel vertical migration or DVM; Longhurst, 1976). Relative and absolute changes in light intensities in the water column are the likely triggers for DVM behavior, while the ultimate goal is thought to be to avoid visual predators (Hays, 2003). While DVM, especially in copepods, is most frequently observed in late summer and autumn (Wallace et al., 2010), recent studies also observed migrations during the polar night when ambient light levels are very low (Berge et al., 2009, 2015; Last et al., 2016). Predators can tune their behavior to the migration patterns of their zooplankton prey (Hays, 2003). DVM thus plays an essential role in structuring the pelagic community and the trophic web.

Predator-prey coupling of zooplankton with lower (i.e., phytoplankton) and higher trophic levels (i.e., fish) is also of high importance on smaller vertical scales (decimeters to several meters). For instance, at thin layers (i.e., dense aggregations with vertical extents in the decimeters to meters range, but horizontal extents of up to several kilometers) predator-prey interactions can be complex. Thin layers can be made up of bacteria, particles, phyto-and zooplankton, phyto-and fish (Durham phyto-and Stocker, 2012), phyto-and a thin layer made up of a prey species is often focused on by its predators (Cowles, 2003; McManus et al., 2003; Durham and Stocker, 2012). Copepods, for instance, are often observed exploiting thin phytoplankton layers (Durham and Stocker, 2012). These observations are supported by the zooplanktons’ ability for highly selective feeding (Price and Paffenhoefer, 1985; Vanderploeg, 1994). Herbi-vore copepod species have different phytoplankton prey size preferences (Frost, 1972), whereby larger herbivores/omnivores generally prefer bigger cells as prey. While Calanus spp. feed most efficiently on prey of an equivalent spherical diameter (ESD) of 30-40 µm (Levinsen et al., 2000), the lower size limit is around 10 µm ESD for C. glacialis and C. hyperboreus, and 5 µm ESD for the smaller C. finmarchicus (Levinsen et al., 2000).

1.5 Adaptations of zooplankton in the Arctic

Among the copepods, large herbivore Calanus are highly adapted to the AO environment, as demonstrated for instance by their seasonal vertical migration (SVM) including diapause (Hirche, 1997; Falk-Petersen et al., 2009; Darnis and Fortier, 2014). This SVM includes copepods being in surface waters during spring/summer to feed and accumulate lipids, before descending to diapause at depth. During diapause in fall and winter Calanus copepods live off substantial lipid reserves (>70% of total body weight; Scott et al., 2000) in a condition of reduced metabolism. Diapause can last for up to 10 months (July to April) in C. hyperboreus (Darnis and Fortier, 2014; Matsuno et al., 2015). Among the different lipids, wax esters are the main long-term energy deposit in zooplankton (Lee et al., 2006). Other copepods such as the omnivore M. longa do not perform SVM or go into diapause, instead they feed and reproduce year-round (Kosobokova, 1983; Darnis and Fortier, 2014). Hence, M. longa does

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accumulate a lower amount of lipids than Calanus (Lee, 1975; Ashjian et al., 1995).

Reproduction in copepods is also adapted to the AO. C. hyperboreus reproduces at depth in winter and only uses stored lipids (capital breeding; Falk-Petersen et al., 2009; Hirche, 2013). C. glacialis, on the other hand, utilizes newly gained energy from grazing on ice-algae and pelagic phytoplankton in spring, and stored lipids, for spawning (Hirche, 1989; Tourangeau and Runge, 1991; Hirche and Kattner, 1993; Søreide et al., 2010; Wold et al., 2011). C. hyperboreus eggs, which are released at depth, float to the water-ice interface where first nauplii stages can feed on under ice-algae (Conover and Huntley, 1991). Since C. glacialis does not release eggs while still at depth, young C. hyperboreus C1 and C2 stages are usually observed earlier in the year than young C. glacialis copepodites (Darnis and Fortier, 2014). These differences in spawning are examples of the ecological niches and the partitioning of available food and habitat (Gause, 1934; Ross, 1986), enabling the co-existence of seemingly very similar species.

In the AO ecosystem, SVM contributes substantially to the downward transport of carbon, because lipid-rich copepods descend to depth and respire during diapause (Darnis and Fortier, 2012; Darnis et al., 2017). Through this process, Calanus copepods in the AO can export carbon at a rate of 3.1 g m-2 _yr-1 _{beyond 100 depth during certain months of the year. This}

active downward export can exceed the passive flux of particulate organic carbon (POC) (Darnis and Fortier, 2012). Fast sinking fecal pellets from crustaceans and other zooplankton can also contribute substantially to the carbon export (Turner, 2015).

1.6 Climate change in the Arctic

The effects of climate change on the terrestrial and marine environments of the Arctic region include changes in species ranges as well as phenological changes of terrestrial vegetation and marine phytoplankton blooms (Parmesan and Yohe, 2003; Hinzman et al., 2005). Inflow of warm Atlantic and Pacific waters into the AO and receding sea ice are the main drivers for sea surface temperature to rise 2-3 times faster than in temperate seas (i.e., Arctic amplification; Screen and Simmonds, 2010; Spielhagen et al., 2011). Furthermore, cold Arctic waters can take up more carbon dioxide than warmer temperate waters, which led to a twofold increase in ocean acidification in the Arctic in recent years (Bates et al., 2011). While the extent of sea-ice is decreasing rapidly (Fig. 1.4), models predict that the AO could be completely ice-free by 2045 (Laliberté et al., 2016). This would have devastating consequences for ice reliant species such as sympagic amphipods and narhwals (ACIA, 2005). Temperatures in fall are predicted to increase by as much as 13°C in the Arctic by the end of the century, if no greenhouse gas mitigation efforts take place (Overland et al., 2014). Together, climate change effects in the AO lead towards an ocean that becomes more similar to temperate seas (i.e., Atlantification and Pacification of the Arctic; Wassmann et al., 2006; Shimada et al., 2006; Woodgate et

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al., 2006), with less sea-ice, and the development of fall phytoplankton blooms (Ardyna et al., 2014). Ardyna et al. (2014) showed that fall phytoplankton blooms are becoming more frequent features of the AO because nutrients are available later in the season. Loss of sea-ice increases sea surface - atmosphere interactions, resulting in more intense wind induced mixing that can penetrate the water column deeper than usual, causing upwelling of nutrients to surface layers.

Figure 1.4 Sea ice extent in recent years compared to the 1981 - 2010 average. Source: National Snow and Ice Data Center at www.nsidc.org.

Due to the prolonged growing season (Arrigo et al., 2008), annual phytoplankton primary pro-duction has increased by more than 20% since 1998 (Arrigo and van Dijken, 2011; Bélanger et al., 2013), and freshwater input has increased due to melting ice and stronger river discharge. This latter observation has been implicated in the surface freshening and increased stratifi-cation that limit nutrient availability in surface waters (Peterson et al., 2002; McLaughlin and Carmack, 2010), which in turn has been linked to shifts in the cell sizes of the dominant phytoplankton groups. Under nutrient depleted conditions, smaller cells thrive because they are more effective at assimilating nutrients (Li et al., 2009). More severe shifts towards smaller

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phytoplankton cells are predicted for the future AO.

Such severe changes in phytoplankton populations would have significant implications for higher trophic levels, for instance due to prey size preferences of zooplankton. Currently, di-atoms are sustaining lipid rich C. glacialis and C. hyperboreus copepods that in turn sustain bird populations (e.g., little auk, Alle alle). Under a general Atlantification pattern of the Barents Sea, changes (e.g., in phytoplankton cell size) would favor a dominance of C. fin-marchicus, which accumulates fewer lipid reserves. This type of prey, with lower lipids, would in turn favor herring and minke whales, instead of the little auk (Falk-Petersen et al., 2007). Several other impacts of climate change on zooplankton distributions, survival and phenology, have been observed in the Arctic, or modeled, and have the potential to cause ecosystem shifts (Melle and Skjoldal, 1998; Richardson, 2008; Doney et al., 2012).

Arctic herbivore copepods are depending heavily on the timing of ice-algae and phytoplankton blooms. A temporal mismatch of hatching copepod nauplii (e.g., of C. glacialis) and the phytoplankton bloom can lead to low food availability for the developing nauplii and can have severe implications for the growth of these young copepods, ultimately leading to much lower abundances later in the season (Leu et al., 2011). Additional temporal mismatch scenarios are anticipated with climate change and will likely have consequences for entire trophic webs (Søreide et al., 2010; Leu et al., 2011). For instance they could lead to a disruption of lipid accumulation in Calanus copepods, and thus shorten the maximum time that copepods can remain in diapause (Maps et al., 2014), potentially leading to their starvation (Auel et al., 2003).

1.7 Development of optical sampling techniques for

zooplankton

Traditionally, zooplankton are sampled with net samplers, some of which have multiple nets that can be used to stratify the water column (Wiebe and Benfield, 2003). For example, the stratifying sampler HydroBios Multinet has 9 different opening and closing nets, permitting a coarse vertical resolution of the water column (e.g., several meters to several hundred meters). To overcome the limitations of net sampling, in-situ imaging systems for zooplankton, which provide high vertical resolution (∼1 m) have been developed since the 1980s (Ortner et al., 1979; Davis et al., 1992a; Jaffe et al., 2001). The high vertical detail of the data resulting from underwater imaging makes it possible to study the fine scale distributions (∼1 m) of zooplankton along hydrographic gradients such as fronts and clines (Haury et al., 1978; Valiela, 1995). Studies using plankton imaging systems include research on plankton patchiness (Davis et al., 1992b), the drivers behind fine-scale aggregations of gelatinous zooplankton (Luo et al., 2014), interactions between organisms in thin layers (Greer et al., 2013), and global estimates of the biomass of rhizaria (Biard et al., 2016). Several systems are nowadays being used

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by scientists. They include the Video Plankton Recorder (VPR; Davis et al., 1992a), the ZOOplankton VISualization and Imaging System (ZOOVIS; Benfield et al., 2003), the In-situ Ichthyoplankton Imaging System (ISIIS; Cowen and Guigand, 2008), the Underwater Vision Profiler 5 (UVP5; Picheral et al., 2010), and the Lightframe On-sight Keyspecies Investigation (LOKI) system (Schulz et al., 2010). The different systems have different characteristics and limitations based on their design and optical setup (Schulz et al., 2010; Schulz, 2013). For instance, the amount of water that is imaged per unit time varies between samplers (Luo et al., submitted), which is important in determining the representativeness of images for abundance estimations (Davis et al., 1992a; Benfield et al., 1996; Cowen and Guigand, 2008). The design of most samplers aims at imaging the zooplankton as undisturbed as possible, thus imaging particles and organisms drifting freely in the water (e.g., UVP and ISIIS). LOKI, however, includes a net that is attached to the camera inflow and concentrates zooplankton from the surrounding water similar to a traditional zooplankton net (Schulz et al., 2010; Hirche et al., 2014). Due to its unique optical setup that includes a relatively small channel (length = 31.3 mm, width = 20.75 mm, volume = 2.6 cm3_{), where images are taken, LOKI produces}

images of excellent resolution for animals in the size range of the mesozooplankton (Schulz et al., 2010). These LOKI images are of higher quality than images produced currently by most other imaging systems. As underwater imaging systems are designed to detect and photograph every encountered organism, large amounts of images (i.e., millions, highly depending on the imaging system) are generated during a scientific cruise and subsequently have to be analyzed.

1.8 Artificial intelligence for the automatic identification of

plankton underwater imagery

Apart from obtaining detailed vertical distribution profiles of plankton, the main goal when working with underwater imaging systems is the automatic taxonomic identification of organ-isms in the many collected images (Rolke and Lenz, 1984). To achieve this, automatic identifi-cation models are developed by "teaching" them the relationships between pixel distributions on images and the underlying taxa (i.e., they are trained). This enables the trained model to automatically identify particles and organisms documented on images. Whilst the earliest methods for the automatic identification of zooplankton were based on linear discriminant analysis (Jeffries et al., 1980), more modern approaches are based on machine learning (ML) and deep learning algorithms (Dieleman et al., 2015; González et al., 2016; Luo et al., submit-ted), both of them subcategories of artificial intelligence. Artificial Neural Networks (ANNs; Simpson et al., 1991; Culverhouse et al., 1996) and combinations of ANNs with Support Vec-tor Machines (SVMs; Hu and Davis 2005, 2006) were implemented for plankton imaging, but Random Forests (RF; Breiman, 2001a, 2001b) established itself quickly as the best performing and fastest approach for automatic zooplankton and phytoplankton identification (Gorsky et al., 2010). The basis for RF are decision trees. With the latest developments in deep learning

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(LeCun et al., 2015), driven by companies such as Google and Microsoft, more computation-ally costly Convolutional Neural Networks (CNNs) are now also available for the automatic identification of zooplankton and show much potential (Dieleman et al., 2015; Orenstein et al., 2015; Luo et al., submitted). In most studies only a small number of taxonomic classes (e.g., 3-20 classes) were identified by automatic identification models (Hu and Davis, 2006; Bell and Hopcroft, 2008; Gorsky et al., 2010; Bi et al., 2015) and classification accuracy was often lower than the accepted threshold of 67-83% (Culverhouse et al., 2003; Hu and Davis, 2005). Recent studies, however, have developed models that can identify 70 (Orenstein et al., 2015) and even 121 classes (Dieleman et al., 2015) with high accuracy. Although the focus here is on automatic identification models for zooplankton, it should not go unnoticed that great advancements have also been achieved in the automatic identification of phytoplankton (Sosik and Olson, 2007; Laney and Sosik, 2014; Orenstein et al., 2015). Analyzing phyto-and zooplankton images automatically can increase the analytical capabilities of laboratories (Benfield et al., 2007).

1.9 Study area

This thesis is based on research conducted in the North Water Polynya (NOW) and Nares Strait (NS) up to the Petermann Glacier at 80°N, between Greenland and Ellesmere Island in the Canadian Arctic (Fig. 1.5).

Figure 1.5 The study area (black rectangle) for this thesis, comprising the North Water Polynya (NOW) and Nares (Strait).

The NOW was extensively investigated during the International North Water Polynya Study

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from 1997 to 1999 (see special issues in Deep-Sea Research and Atmosphere and Ocean). The NOW is one of the largest (∼90,000 km2_{) recurrent polynyas (i.e., open water surrounded by}

sea-ice) in the Arctic (Barber et al., 2001; Fortier et al., 2001; Deming et al., 2002; Tremblay et al., 2006a, 2006b). It is formed typically every year mostly by latent heat (i.e., westerly wind-generated polynya), while its growth is also facilitated by sensible heat (i.e., easterly fluxes of heat from water and atmosphere; Deming et al., 2002; Ingram et al., 2002). Water masses in the NOW include Surface Water, Upper- and Lower Arctic water, Northern Baffin Bay Atlantic Water and Atlantic Transition Water (Bâcle, 2000). Arctic Water originates in central parts of the AO, and gets to the NOW via the Lincoln Sea and through Smith Sound. Northern Baffin Bay Atlantic Water originates from the West Greenland Current and is thus relatively warm (Bâcle, 2000). NS connects the Lincoln Sea and Baffin Bay, playing an important role in the exchange of heat and freshwater between the two (Rabe et al., 2010). The Petermann Glacier is a tidewater glacier situated on the Greenland coast that is strongly coupled to the surrounding ocean (Münchow et al., 2016). Atlantic water reaches the Petermann Glacier via the Lincoln Sea, where it has to pass a 290 m deep sill in order to enter NS. Petermann Glacier is changing substantially due to climate change and is poised to change further with rising temperatures (Rignot and Steffen, 2008; Münchow et al., 2016). It lost two large parts (130 km2 _{and 260 km}2_{) of its terminus due to calving events in 2010 and}

2012.

Similar to other polynyas, the NOW is a biological hotspot, as open water occurs here earlier in the season (∼3 months) than in surrounding areas, sustaining an early and intense phyto-plankton bloom (Stirling, 1980; Ingram et al., 2002; Klein et al., 2002; Ringuette et al., 2002; Tremblay et al., 2002). These phytoplankton blooms in turn sustain a high secondary pro-duction of zooplankton, with copepods representing >80% of that propro-duction (Ringuette et al., 2002). The most abundant copepod taxa in the NOW include C. hyperboreus, C. glacialis, Pseudocalanus spp., M. longa, Microcalanus pygmaeus, Oithona similis, and Oncaea borealis (Ringuette et al., 2002). They feed many higher trophic levels (e.g., little auks, Karnovsky and Hunt, 2002). Other abundant top predators are ringed seals (Holst et al., 2001), narwhals and polar bears (Heide-Jørgensen et al., 2013). Largely due to the high abundance of marine mammals, subsistence hunting of Canadian and Greenlandic Inuit in the NOW has a long tradition (Heide-Jørgensen et al., 2013).

1.10 Aims and objectives

The central aim of the research presented in this doctoral thesis was to improve our under-standing of 1) the role of lipids in the vertical migration behavior of Arctic copepods and 2) the vertical coupling of dominant Arctic copepod taxa with their phytoplankton food, using fine-scale vertical distributions (1 m resolution) obtained by underwater imaging. To reach these goals, the study utilized an underwater camera system called the Lightframe

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On-sight Keyspecies Investigation (LOKI) system, which was deployed during the ArcticNet sampling campaign in autumn 2013 in the Canadian Arctic, more specifically in the North Water Polynya (NOW) and Nares Strait (NS). The coupling between primary and secondary production, and subsequent lipid accumulation in copepods, is of high importance for the whole AO ecosystem, but threatened by climate change. Our knowledge on the vertical cou-pling between phyto- and zooplankton is mostly based on coarse vertical resolution data and relatively limited. Therefore, enhancing our knowledge in these areas is crucial for under-standing potential future climate change effects on the Arctic marine ecosystem. The core of the thesis comprises three main chapters (2-4) that are presented as scientific publications. In Chapter 2 a machine learning model for the automatic identification of zooplankton in-dividuals on LOKI images was developed for the study area. The objective was to create a machine learning model that can successfully identify life stages of copepod species, thus being able to provide taxonomically detailed data for studying the ecology of key species of the AO zooplankton. The model was rigorously tested for its ability to accurately identify species life stages on images. All further chapters are based on automatically identified images from the LOKI system, obtained at 1 m vertical resolution for the whole water column.

Chapter 3 studied the fine-scale vertical distribution of the key copepod species C. hyperboreus, C. glacialis and M. longa during a 24 h Lagrangian drift in the NOW. In-situ images showing individuals of the three copepods species were analyzed for their lipid content (mg of lipids in prosome) and lipid fullness (% of prosome filled with lipids). The primary objective was to study the role of lipids in copepod individuals in diel vertical migration (DVM) and seasonal vertical migration (SVM) patterns, and to determine the necessary lipid load for Calanus copepods to descend/migrate to diapause.

The main objective of Chapter 4 was to investigate the fine vertical scale coupling of C. hyperboreus and C. glacialis developmental stages with their phytoplankton prey along a transect spanning from the NOW to the Petermann Glacier. Copepod species compositions under differing phenological states of phytoplankton blooms were studied.

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2. Chapter 2 – The LOKI underwater

imaging system and an automatic

identification model for the detection of

zooplankton taxa in the Arctic Ocean

2.1 Résumé

Nous avons déployé le système «Lightframe On-sight Keyspecies Investigation» (LOKI), un nouveau système d’imagerie sous-marine fournissant une qualité d’image à la fine pointe de la technologie, dans l’Arctique canadien au cours de l’automne 2013. Un modèle mathématique d’apprentissage automatique a été construit afin d’identifier automatiquement le zooplancton des images du LOKI. Le modèle distingue avec succès 114 catégories différentes de zooplancton et de particules. La classification taxonomique à haute résolution inclut de nombreuses espèces, de nombreux stades, ainsi que des sous-groupes sur l’orientation ou la conformation de l’animal dans les images. Un modèle de régression par apprentissage automatique de la longueur du prosome (R2 ₌ _{0.97) a été utilisé comme prédicteur clef dans le modèle d’identification}

automatique. La validation interne du modèle d’identification sur des données tests a démontré que le modèle performait dans l’ensemble avec une haute précision (86%) et spécificité (86%). Ceci a été confirmé par des matrices de confusion pour des tests externes de résultats, basés sur l’identification automatique de 2 stations d’échantillonnage complètes. Pour la station 101, pour laquelle les images avaient aussi été utilisées pour l’apprentissage, la précision et la spécificité étaient de 85%. Pour la station 126, dont les images n’avaient pas été utilisées pour l’apprentissage du modèle, la précision et la spécificité étaient de 81%. De plus, des comparaisons entre les résultats du modèle et les identifications au microscope du zooplancton des échantillons des deux stations tests étaient en accord pour la plupart des taxons. La qualité des images du LOKI permet de construire des modèles d’identification automatique précise de détails taxonomiques très élevés, qui joueront un rôle critique dans des études futures de la dynamique du zooplancton et de son couplage avec les autres niveaux trophiques.

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2.2 Abstract

We deployed the Lightframe On-sight Keyspecies Investigation (LOKI) system, a novel under-water imaging system providing cutting-edge imaging quality, in the Canadian Arctic during fall 2013. A Random Forests machine learning model was built to automatically identify zooplankton in LOKI images. The model successfully distinguished between 114 different categories of zooplankton and particles. The high resolution taxonomical tree included many species, stages, as well as sub-groups based on animal orientation or condition in images. Re-sults from a machine learning regression model of prosome length (R2 ₌_{0.97) were used as}

a key predictor in the automatic identification model. Internal validation of the automatic identification model on test data demonstrated that the model performed with overall high accuracy (86%) and specificity (86%). This was confirmed by confusion matrices for external testing results, based on automatic identifications for 2 complete stations. For station 101, from which images had also been used for training, accuracy and specificity were 85%. For station 126, from which images had not been used to train the model, accuracy and specificity were 81%. Further comparisons between model results and microscope identifications of zoo-plankton in samples from the two test stations were in good agreement for most taxa. LOKI’s image quality makes it possible to build accurate automatic identification models of very high taxonomic detail, which will play a critical role in future studies of zooplankton dynamics and zooplankton coupling with other trophic levels.

The race for lipids : ontogeny of the fine-scale vertical co-distribution of arctic calanoid copepods and their phytoplankton food as elucidated by artificial intelligence coupled with an imaging profiler

The race for lipids: ontogeny of the fine-scale vertical

co-distribution of arctic calanoid copepods and their

phytoplankton food as elucidated by artificial

intelligence coupled with an imaging profiler

The race for lipids: ontogeny of the fine-scale vertical

co-distribution of arctic calanoid copepods and their

phytoplankton food as elucidated by artificial

intelligence coupled with an imaging profiler

Résumé

Abstract

Contents

List of Tables

List of Figures

Acknowledgments

Foreword

1. Chapter 1 – General Introduction

1.1

The Arctic Environment

1.2

The Arctic Ocean

1.3

The trophic web of the Arctic Ocean

1.4

Coupling of zooplankton with the abiotic environment,

and their prey and predators

1.5

Adaptations of zooplankton in the Arctic

1.6

Climate change in the Arctic

1.7

Development of optical sampling techniques for

zooplankton

1.8

Artificial intelligence for the automatic identification of

plankton underwater imagery

1.9

Study area

1.10

Aims and objectives

2. Chapter 2 – The LOKI underwater

imaging system and an automatic

identification model for the detection of

zooplankton taxa in the Arctic Ocean

2.1

Résumé

2.2

Abstract