Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Sezginer, Yayla
Doctor of Philosophy - PhD
University of British Columbia
Through photosynthesis, phytoplankton form the base of marine food webs and drive atmospheric CO₂ uptake through the marine biological carbon pump. Monitoring phytoplankton photosynthetic responses to changing environmental conditions is thus critical to predicting ecosystem feedbacks to climate change. Fast Repetition Rate fluorometry (FRRF) offers rapid measurements of phytoplankton photo-physiology and photosynthetic electron transport rates (ETR), which represent the ultimate source of metabolic energy for carbon fixation. This thesis aims to address several physiological and methodological considerations that presently limit the widespread use of FRRF for high resolution in-situ measurements of phytoplankton ETR. These challenges include diverging approaches to calculate ETR (Chapter 2), the influence of pre-measurement sample handling practices and variable photo-acclimation states on ETR measurements (Chapter 3), and variability in the stoichiometry between ETR and downstream carbon fixation (Chapter 4). The field-based studies presented in this work illustrate how natural light and nutrient gradients influence photo-physiology, FRRF signatures and their interpretation. Results presented in Chapter 2 reveal that the coherence between ETR estimates derived from different algorithms varies with underlying phytoplankton photo-physiology. Additionally, ETR estimates are directly affected by photoprotective non-photochemical quenching (NPQ) that dissipates excess absorbed photo-energy as heat. Chapter 3 investigates the effects of photo-physiology and photo-acclimation states on NPQ relaxation time-scales, providing useful insights for field protocol design for high resolution ETR acquisition. In addition to NPQ, phytoplankton mitigate dangerous accumulation of reductant by diverting electrons towards alternative sinks, decoupling electron transport rates from downstream carbon fixation. Chapter 4 illustrates how this effect manifests as a hyperbolic relationship between ETR and carbon fixation, offering a new approach to predict carbon fixation rates from ETR. The work presented in this thesis addresses specific details of FRRF application, as well as broader questions regarding energy transfer efficiencies between photosynthetic processes, supporting the expansion of high-resolution FRRF-based estimates of biological carbon uptake.
AUTONOMOUS MEASUREMENT OF PHYTOPLANKTON PHYSIOLOGY AND PRODUCTIVITY USING FAST REPETITION RATE FLUOROMETRY by Yayla Sezginer B.Sc., The University of California, Berkeley, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceanography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2025 © Yayla Sezginer, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Autonomous measurement of phytoplankton physiology and productivity using Fast Repetition Rate Fluorometry submitted by Yayla Sezginer in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Oceanography. Examining Committee: Philippe Tortell, Professor, Earth Oceans and Atmospheric Science, UBC (Supervisor) Douglas Campbell, Professor, Ecophysiology, Mount Allison University (Supervisory Committee Member) Maite Maldonaldo, Professor, Earth Oceans and Atmospheric Science, UBC (Supervisory Committee Member) Sean Crowe, Professor, Earth Oceans and Atmospheric Science, UBC (University Examiner) Robert Guy, Professor, Forestry, UBC (University Examiner) Sven Kranz, Professor, BioSciences, Rice University (External Examiner) Additional Supervisory Committee Members: Patrick Martone, Professor, Botany, UBC (Supervisory Committee Member) iii Abstract Through photosynthesis, phytoplankton form the base of marine food webs and drive atmospheric CO2 uptake through the marine biological carbon pump. Monitoring phytoplankton photosynthetic responses to changing environmental conditions is thus critical to predicting ecosystem feedbacks to climate change. Fast Repetition Rate fluorometry (FRRF) offers rapid measurements of phytoplankton photo-physiology and photosynthetic electron transport rates (ETR), which represent the ultimate source of metabolic energy for carbon fixation. This thesis aims to address several physiological and methodological considerations that presently limit the widespread use of FRRF for high resolution in-situ measurements of phytoplankton ETR. These challenges include diverging approaches to calculate ETR (Chapter 2), the influence of pre-measurement sample handling practices and variable photo-acclimation states on ETR measurements (Chapter 3), and variability in the stoichiometry between ETR and downstream carbon fixation (Chapter 4). The field-based studies presented in this work illustrate how natural light and nutrient gradients influence photo-physiology, FRRF signatures and their interpretation. Results presented in Chapter 2 reveal that the coherence between ETR estimates derived from different algorithms varies with underlying phytoplankton photo-physiology. Additionally, ETR estimates are directly affected by photoprotective non-photochemical quenching (NPQ) that dissipates excess absorbed photo-energy as heat. Chapter 3 investigates the effects of photo-physiology and photo-acclimation states on NPQ relaxation time-scales, providing useful insights for field protocol design for high resolution ETR acquisition. In addition to NPQ, phytoplankton mitigate dangerous accumulation of reductant by diverting electrons towards alternative sinks, decoupling electron transport rates from downstream carbon fixation. Chapter 4 illustrates how this effect manifests as a hyperbolic relationship between ETR and carbon iv fixation, offering a new approach to predict carbon fixation rates from ETR. The work presented in this thesis addresses specific details of FRRF application, as well as broader questions regarding energy transfer efficiencies between photosynthetic processes, supporting the expansion of high-resolution FRRF-based estimates of biological carbon uptake. v Lay Summary Marine food webs and global carbon budgets depend on photosynthesis by marine microalgae. Determining how marine photosynthesis may respond to climate change requires instrumentation and methodologies that can measure real-time microalgae responses to their changing environmental conditions. Chlorophyll, the primary pigment responsible for light absorption for photosynthesis, re-emits a fraction of absorbed energy that is not used for photosynthesis as fluorescence. Therefore, chlorophyll fluorescence measurements can offer insights into photosynthesis yields. However, uncertainties surrounding chlorophyll fluorescence method applications have limited this approach in the field. This thesis aims to address a number of these challenges by focusing on specific methodological questions, including the effects of alternative sampling protocols and data analysis approaches on photosynthesis rate estimates from chlorophyll fluorescence data. This thesis also examines how variable environmental conditions affect connectivity between initial light harvesting for photochemistry and carbon fixation rates. vi Preface A version of chapter 2 has been published as: Sezginer, Y. Suggett, D.J., Izett, R.W., & Tortell, P. (2021) ‘Irradiance and nutrient-dependent effects on photosynthetic electron transport in Arctic phytoplankton: A comparison of two chlorophyll fluorescence-based approaches to derive primary photochemistry’, PLoS ONE, 16(12 December), pp. 1–23. doi: 10.1371/journal.pone.0256410. Tortell, P. collected the data with assistance from Izett, R. I conducted the data analysis and wrote the manuscript with contributions from all coauthors. A version of chapter 3 has been published as: Sezginer, Y. Campbell, D., Pillai, S., & Tortell, P. (2023) ‘Fluorescence-based primary productivity estimates are influenced by non-photochemical quenching dynamics in Arctic phytoplankton’, Frontiers in Microbiology, 14. doi: 10.3389/fmicb.2023.1294521. I collected and analyzed the underway data and wrote the manuscript with contributions from all coauthors. Pillai, S. analyzed discrete photopigment data and provided estimates of phytoplankton taxonomic distributions. Campbell, D. provided data interpretation and insights into photodamage influences on chlorophyll fluorescence signals. A version of chapter 4 has been submitted for peer review and publication December 2024 as: Sezginer, Y., Schuler, K., Speciale, E., Marchetti, A., Till, C., Till, R., & Tortell, P. (2024) ‘Photosynthetic electron, carbon and oxygen fluxes within a mosaic of Fe limitation in the California Current Upwelling System’. In submission. vii Data associated with this manuscript have been submitted to the Earth Science database, PANGAEA and will be publicly accessible, pending review. I analyzed the data and wrote the manuscript with inputs from Tortell, P., Schuler, K., Till, C., Marchetti, A., and Speciale, E. Schuler, K. collected and analyzed discrete and underway [N2O] samples. Speciale, E. collected and analyzed macronutrient and meta transcriptomic RNA samples. Till, C. collected and Till, R. analyzed samples for trace metal concentrations. Generative artificial intelligence was not used in any way for any of the aspects of this work viii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ........................................................................................................................... v Preface ...................................................................................................................................... vi Table of Contents ................................................................................................................... viii List of Tables.......................................................................................................................... xiv List of Figures ......................................................................................................................... xv List of Symbols...................................................................................................................... xvii List of Abbreviations .............................................................................................................. xx Acknowledgements .............................................................................................................. xxiii Dedication ............................................................................................................................. xxv Chapter 1: Introduction ........................................................................................................... 1 1.1 Overview and Motivation ............................................................................................ 1 1.2 Photosynthetic processes and responses to environmental stress .................................. 2 1.3 Methods and metrics for quantifying productivity ....................................................... 7 1.4 Theory and application of Fast Repetition Rate Fluorometry (FRRF) .......................... 9 1.5 Calculating PSII electron transport rates .................................................................... 12 1.6 Challenges facing widespread application of FRRF for autonomous in-situ surveys .. 14 1.6.1 Diverging approaches to derive ETRPSII from FRRF measurements ................... 15 1.6.2 Non-photochemical quenching and dark acclimation protocols .......................... 15 1.6.3 Variability in electron requirements for carbon fixation ..................................... 17 1.6.4 Conclusion ........................................................................................................ 18 ix 1.7 Tables and Figures .................................................................................................... 19 Chapter 2: Irradiance and nutrient-dependent effects on photosynthetic electron transport rates in Arctic phytoplankton: a comparison of two fluorescence-based approaches to derive photochemistry ............................................................................................................ 25 2.1 Summary ................................................................................................................... 25 2.2 Introduction ............................................................................................................... 26 2.3 Methods and materials ............................................................................................... 30 2.3.1 Underway sampling ........................................................................................... 30 2.3.2 LIFT-FRRF sample protocols and parameter retrieval ....................................... 32 2.3.3 Electron Transport Rates (ETRPSII) .................................................................... 34 2.3.4 Non-Photochemical Quenching (NPQ) .............................................................. 36 2.3.5 Statistical Analysis ............................................................................................ 37 2.4 Results ...................................................................................................................... 37 2.4.1 Hydrographic properties .................................................................................... 37 2.4.2 Photophysiological Properties ............................................................................ 38 2.4.3 Photosynthesis-irradiance curves and ETRPSII comparisons ................................ 40 2.5 Discussion ................................................................................................................. 41 2.5.1 Spatial variation in photo-physiology................................................................. 42 2.5.2 Light-dependent effects and residual NPQ ......................................................... 44 2.5.3 Decoupling of ETRa and ETRk .......................................................................... 46 2.5.4 Computational considerations ............................................................................ 47 2.6 Conclusions and future recommendations .................................................................. 49 2.7 Tables and Figures .................................................................................................... 51 x Chapter 3: Fluorescence-based primary productivity measurements are influenced by non-photochemical quenching dynamics in Arctic phytoplankton .............................................. 61 3.1 Summary ................................................................................................................... 61 3.2 Introduction ............................................................................................................... 62 3.3 Theory: Effects of NPQ relaxation on ETR estimates ................................................ 65 3.4 Materials and Methods .............................................................................................. 67 3.4.1 Field sampling ................................................................................................... 67 3.4.2 Photo-physiology and NPQ relaxation kinetics .................................................. 69 3.4.3 Photosynthetic electron transport rates (ETRPSII)................................................ 70 3.4.4 Statistical analysis ............................................................................................. 71 3.5 Results and discussion ............................................................................................... 71 3.5.1 NPQ relaxation kinetics ..................................................................................... 71 3.5.2 Correlation between 𝝈𝑷𝑺𝑰𝑰 and FV/FM during NPQ relaxation and implications for ETRPSII estimates ......................................................................................................... 73 3.5.3 Environmental and taxonomic controls on NPQ relaxation kinetics and ETR estimates 75 3.5.3.1 Svalbard Coastal oceanography and photo-physiology ................................... 75 3.5.3.2 Canadian Arctic Archipelago oceanography and photo-physiology ................ 77 3.6 Conclusion ................................................................................................................ 80 3.7 Tables and Figures .................................................................................................... 81 Chapter 4: Photosynthetic electron, carbon, and oxygen fluxes within a mosaic of Fe limitation in the California Current Upwelling System ........................................................ 89 4.1 Summary ................................................................................................................... 89 xi 4.2 Introduction ............................................................................................................... 90 4.3 Materials and methods ............................................................................................... 94 4.3.1 Sampling Sites ................................................................................................... 94 4.3.2 Fast Repetition Rate Fluorometry ...................................................................... 95 4.3.3 In-situ electron transport rates, ETRPSII .............................................................. 96 4.3.4 Non-Photochemical Quenching, NPQ ................................................................ 99 4.3.5 14C-uptake experiments.................................................................................... 100 4.3.6 Net community productivity (NCP) ................................................................. 100 4.3.7 Nutrient concentrations .................................................................................... 104 4.3.8 Transcriptomic analysis ................................................................................... 105 4.3.9 Pigment concentrations and taxonomic composition ........................................ 106 4.4 Results .................................................................................................................... 107 4.4.1 Oceanographic conditions ................................................................................ 107 4.4.2 Photo-physiology............................................................................................. 109 4.4.3 Primary productivity ........................................................................................ 111 4.4.3.1 Gross photochemistry .................................................................................. 111 4.4.3.2 O2/ N′2-derived net community productivity (NCP) ..................................... 111 4.4.3.3 Carbon fixation rates .................................................................................... 112 4.5 Discussion ............................................................................................................... 114 4.5.1 Factors driving the contrasting biogeochemistry of Cape Blanco and Cape Mendocino filaments....................................................................................................... 114 4.5.1.1 Strength of upwelling and relative age of filaments ...................................... 115 4.5.1.2 Nutrient content of upwelling source water .................................................. 115 xii 4.5.2 Environmental and taxonomic influences on physiology and productivity ....... 117 4.5.3 Energy transfer efficiencies between photosynthetic processes ........................ 120 4.5.3.1 Carbon fixation as a function of ETRPSII ...................................................... 121 4.5.3.2 Comparison of ETRPSII and NCP ................................................................. 124 4.6 Conclusion .............................................................................................................. 127 4.7 Tables and figures ................................................................................................... 128 Chapter 5: Conclusion .......................................................................................................... 138 5.1 Key findings and remaining challenges to FRRF-based productivity surveys........... 138 5.1.1 Current ETRPSII caveats and the need for fluorescence-independent validation 138 5.1.2 Balancing the need for NPQ relaxation and representative in-situ measurements 140 5.1.3 FRRF-based ETRPSII as a predictor of C-fixation and net community productivity 142 5.2 Temperature influences on FRRF-derived productivity estimates ............................ 145 5.3 Applying lessons from FRRF to remote sensing applications ................................... 146 5.4 Concluding remarks ................................................................................................ 147 References ............................................................................................................................. 149 Appendices ............................................................................................................................ 182 Appendix A : Supplementary material for Chapter 2 ........................................................... 182 A.1 CTD profiles ....................................................................................................... 182 A.2 Surface PAR and underway photophysiological variable regression analyses ...... 183 Appendix B Supplementary materials for Chapter 3 ............................................................ 184 xiii B.1 Taxonomic compositional data collected on-station in the Canadian Arctic Archipelago .................................................................................................................... 184 B.2 Ancillary oceanographic data for Canadian Arctic station surface samples .......... 185 B.3 Ancillary oceanographic data for Canadian Arctic station subsurface samples ..... 186 Appendix C Supplementary materials for Chapter 4 ............................................................ 186 C.1 Bathymetric map of study region ......................................................................... 187 C.2 Photopigment and species distribution by sub-region ........................................... 187 C.3 Normalized counts of diatom-specific Fea1 transcripts ........................................ 189 C.4 Volumetric carbon fixation rates as a function of electron transport rates. ............ 190 xiv List of Tables Table 1.1 Summary table of marine primary productivity methods and currencies spanning cellular- to ecosystem-level space and time scales. .................................................................... 19 Table 1.2 Summary of FRRF-derived parameters.. ................................................................... 22 Table 2.1 Correlation of underway hydrographic variables. ...................................................... 52 Table 2.2 Correlation of underway photophysiological variables and surface PAR.. ................. 55 Table 3.1 NPQ relaxation parameters (mean ± standard error) for all study regions. ................. 83 Table 3.2 Spearman rank correlation coefficients between temperature (oC), salinity (psu), and surface PAR (µmol photons m-2 s-1) and photo-physiological parameters. ................................. 86 Table 3.3 Relationships between biogeochemical and photophysiological parameters .............. 88 Table 4.1 Summary of photophysiological properties at each sampling environment and their relationship to hydrographic parameters. ................................................................................. 132 xv List of Figures Figure 1.1. Photosynthetic electron transport chain in eukaryotes. ............................................. 19 Figure 1.2 Representative fluorescence transients and associated electron transport activity...... 21 Figure 2.1 Study region map ..................................................................................................... 51 Figure 2.2 Summary of oceanographic conditions. .................................................................... 52 Figure 2.3 Summary of photophysiological conditions. ............................................................. 54 Figure 2.4 Recent light history effects on photo-physiology measured under low light. ............. 55 Figure 2.5 The PSII photochemical efficiency measured under 150 µmol quanta m-2 s-1 in relation to natural surface irradiance at the time of sample acquisition. ..................................... 57 Figure 2.6 Consolidated mean ETRa (red) and ETRk (black) estimates at each light step of the 25 reprocessed P-I curves. ........................................................................................................ 58 Figure 2.7 Comparison of ETRa versus ETRk. ......................................................................... 59 Figure 2.8 Parameters contributing to the ETRk:ETRa ratio ...................................................... 60 Figure 2.9 Spatial distribution of riverine nutrient inputs and photochemical efficiency (Fv/Fm) along the ship track. .................................................................................................................. 60 Figure 3.1 Location of field sampling stations during summer, 2022. ........................................ 81 Figure 3.2 Representative data analysis used to characterize NPQ relaxation time-scales following a one-minute high light exposure. ............................................................................. 82 Figure 3.3 NPQ relaxation effects on σPSII and Fv/Fm................................................................. 84 Figure 3.4 Underway photo-physiology in different water masses surrounding Svalbard. ......... 85 Figure 3.5 Underway measurements of phytoplankton photo-physiology in different water masses along the coast of Baffin Island and the North Water Polynya (NWP). .......................... 87 xvi Figure 4.1 Study site map. ...................................................................................................... 128 Figure 4.2 Continuous surface water hydrographic properties ................................................. 129 Figure 4.3 Distribution of hydrographic properties within sub-regions .................................... 130 Figure 4.4 Diel patterns in photo-physiological properties along the cruise track..................... 131 Figure 4.5 Primary productivity time series along the cruise track. .......................................... 133 Figure 4.6 Parallel measurements of fluorescence- and 14C-based photosynthesis-irradiance experiments conducted on-station ........................................................................................... 134 Figure 4.7 Different primary productivity currencies and their decoupling mechanism ........... 135 Figure 4.8 All station-based data displayed together as ϕe:C plotted against NPQNSV and colored according to Pmax-C................................................................................................................. 136 Figure 4.9 Comparison between fluorescence-based gross photochemistry (GP) and NCP. ..... 137 xvii List of Symbols Term Definition Units CQa Fraction of closed RCIIs fractional ETRPSII RCII electron transport rate e- s-1 RCII-1 Fm, Fmʹ, Maximum Chla representative of a closed RCII pool for dark and light regulated state, respectively Relative Units Fo, F’o Minimum Chla representative of an open RCII pool for the dark and light regulated state, respectively Relative Units Fo,, Fʹ Minimum Chla measured in dark and light regulated state, respectively. Relative Units Fqʹ/Fmʹ Photochemical yield in the light regulated state (Fmʹ - Fʹ)/ Fmʹ Dimensionless Fqʹ/Fmʹ (Emax) Photochemical yield measured under super saturating irradiance Dimensionless Fqʹ/Fvʹ Fraction of functional RCIIs in the open state (Fmʹ - Fʹ)/ (Fmʹ - F’o) Dimensionless Fv/Fm Photochemical yield in the dark regulated state; Dimensionless xviii (Fm-Fo)/Fm NPQNSV Normalized Stern-Volmer parameterization of Non-photochemical quenching; (𝐹'( /𝐹*( ) NPQSV Stern-Volmer parameterization of Non-photochemical quenching; (Fm - Fʹm)/Fm Dimensionless Pmax Maximum electron transport rate e- s-1 RCII-1 Ps Theoretical maximum electron transport rate without photoinhibition e- s-1 RCII-1 qfast, qint., qslow Amplitude of fast, intermediate, and slow relaxing NPQ components dimensionless qN Coefficient of non-photochemical quenching; Ratio of quenched to unquenched fluorescence; 1 - F′v/Fv 𝛼 Light harvesting efficiency 𝛽 Photoinhibition parameter σPSII, σPSIIʹ, Functional absorption cross-section for PSII in the dark and light regulated state, respectively Å2 PSII-1 𝜙.: 𝐶 Electron requirement for carbon fixation e- C-1 𝜙.: 𝐶/𝑛2344 Electron requirement for carbon fixation e- Chl C-1 RCII-1 xix 𝜏Qa QA photosynthetic electron turnover rate s 𝜏6789 , 𝜏;<9 , 𝜏8=>? Mean lifetime of fast, intermediate and slow relaxing NPQ components s r Probability of excitation energy transfer between RCIIs Unitless xx List of Abbreviations ATP…………………………………………… Adenosine triphosphate BB…………………………………………...... Baffin Bay BS…………………………………………...... Barrow Strait CAA…………………………………………... Canadian Arctic Archipelago CCS………………………………………….... California Current System Chl……………………………………………. Chlorophyll ChlF…………………………………………... Chlorophyll fluorescence CTD…………………………………………... Conductivity, Temperature, Depth sensor Cyt b6f………………………………………… Cytochrome b6f E………………………………………………. Excitation energy ETC………………………………………….... Electron transport chain ETR …………………………………………... Electron transport rate FIRe…………………………………………... Fluorescence Induction Relaxation instrument FRRF…………………………………………. Fast Repetition Rate Fluorometry GP…………………………………………..... Gross Photochemistry GPP…………………………………………... Gross Primary Productivity HPLC…………………………………………. High Performance Liquid Chromatography LHC, LHCII…………………………………... PSII Light harvesting complex MLD…………………………………………... Mixed layer depth MT…………………………………………….. Multiple Turnover xxi NADPH ………………………………………. Nicotinamide adenine dinucleotide phosphate NCP…………………………………………... Net Community Productivity; Gross productivity – community respiration NPP………………………………………….... Net Primary Productivity; gross productivity – autotrophic respiration NPQ ………………………………………….. Non-photochemical quenching PAR…………………………………………… Photosynthetically Active Radiation PI ……………………………………………... Photosynthesis vs Irradiance measurements PIGI…………………………………………... Pressure In-Situ Gas Instrument PP…………………………………………...... Primary Productivity PP:PS……………………………………….... Photoprotective: Photosynthetic pigments PsbS…………………………………………... Membrane bound subunit of Photosystem II PTOX…………………………………………. Plastoquinone oxygen oxidoreductase pH……………………………………………... Acidity PSI……………………………………………. Photosystem I PSII…………………………………………… Photosystem II QA ……………………………………………. Primary plastoquinone electron acceptor QB……………………………………………... Secondary plastoquinone electron acceptor PQ……………………………………………... Plastoquinone pool RCII………………………………………….... Photosystem II Reaction Center SIF…………………………………………...... Sun Induced Fluorescence SST……………………………………………. Sea surface temperature xxii ST……………………………………………... Single Turnover SUNA…………………………………………. Seabird nitrate sensor TSG………………………………………….... Thermosalinograph xxiii Acknowledgements I am deeply grateful for the constant support and guidance I received from my supervisor, Philippe Tortell. I am truly inspired by your vision and ability to turn big ideas into realities. I greatly appreciate your teaching style, which has fostered so many field opportunities, unique collaborations across countries and disciplines, and a supportive work environment. I would also like to thank each of my committee members: Maite Maldonaldo, Douglas Campbell, and Patrick Martone. Special thanks to Dave Suggett and Zbigniew Kolber for their patience, humor, and guidance during my early days learning the ways of Fast Repetition Rate fluorometry. Thank you to my lab mates Kate Schuler, Robert Izett, Brandon McNabb, Sacchi Pillai, Christina Kong, and Rebecca Rust, who provided laughter and coffee breaks along with emotional, physical, and scientific support during lab work, cruise mobilizations and fieldwork. Thank you to Ross McCulloch, Jian Guo, Chris Payne and Maureen Soon for sharing your technical expertise and lab support. In addition, I would like to thank the captains, crew, scientific coordinators, and chief scientists of the CCGS Amundsen and R/V Sally Ride for their efforts, which make oceanographic fieldwork possible. I am also grateful for the connections I made in the field, with particular thanks for Gina Nickoloff who is a wonderful cabinmate, friend, scientist, and oyster forager. I am grateful for my dear friends from California who have stayed in-touch, visited, and grown with me even as we moved towards different fields, cities, and countries, and for my new friends who have made Vancouver a home. There are not enough words to express my gratitude for my parents. I totally credit my interest in science and the natural world to my dad who filled my tiny hands with snow, sap, and electrical wires, always encouraging exploration and experimentation. I am equally indebted to xxiv my mom who, for the past 5 years, has let me practice every big presentation with her on zoom the night before. Your unwavering love and support mean everything to me. Lastly, I feel immensely privileged for my experiences among the mountains, forests, sea-ice, and ocean that were my setting, inspiration, playground and refuge. I am very grateful to have been able to work and live on the unceded ancestral territories of the xwməθkwəy̓ əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and Selílwitulh (Tsleil-Waututh) Nations. xxv Dedication To my most loving and demanding teacher Phew........Never againShe’s awake! We must go back to the top she needs to touch snow 1 Chapter 1: Introduction 1.1 Overview and Motivation Oxygenic photosynthesis by marine phytoplankton has redefined Earth’s atmosphere, ocean chemistry, and climate since its evolution approximately 2.5 billion years ago. Early photosynthesis resulted in the oxygenation of the atmosphere and oceans, which enabled the evolution of more diverse eukaryotic and multicellular life forms. Burial of ancient phytoplankton created today’s petroleum reservoirs and sequestered immense amounts of carbon, possibly triggering several snowball Earth episodes (Benoiston et al., 2017). Today, photosynthesis by phytoplankton continues to shape Earth’s biogeochemistry by sustaining marine food webs, maintaining atmospheric oxygen levels and mediating fluxes of inorganic carbon into biomass. Tracking and forecasting phytoplankton interactions with changing environmental conditions is thus vital to understanding ecosystem feedbacks to climate change. Phytoplankton productivity and physiology depend on a number of environmental factors, such as nutrient distributions and light availability. These variables are, in turn, influenced by physical oceanographic properties (surface winds, temperature and salinity), which can vary over short space and time scales. For this reason, understanding real-time phytoplankton responses to their dynamic environment requires measurement systems capable of capturing fine-scale variability. In recent decades, variable Chlorophyll fluorescence (ChlF) techniques, such as Fast Repetition Rate fluorometry (FRRF), have been used for measurements of phytoplankton photochemistry and photo-physiology at the finest spatial and temporal scales (Balch et al., 2021). Energy absorbed by Chl is re-emitted as fluorescence, heat, or photochemistry. Variable ChlF methods exploit the resulting inverse relationship between Chl fluorescence and 2 photochemistry yields to rapidly and non-invasively probe photochemistry processes (Govindjee, 2004). Despite the suitability of FRRFs for in-situ or continuous operation, and their potential for rapid and non-invasive data acquisition, these instruments have typically been used for low resolution, discrete sample analysis. Some studies have used FRRF to examine phytoplankton photo-physiology using continuous ‘underway’ measurements along a ship-track, with the goal of examining spatial and temporal patterns of nutrient limitation (Aardema et al., 2019; Carvalho et al., 2020; Schallenberg et al., 2020). By comparison, fewer studies have used FRRF in underway settings to derive rates of primary production (D. J. Hughes, Campbell, et al., 2018; Schuback, Tortell, et al., 2021; Tortell, Schuback and Suggett, 2023), largely due to several caveats and uncertainties that have limited this approach. This thesis aims to address a number of these knowledge gaps, with the goal of expanding high-resolution FRRF-based primary productivity surveys. The following introductory sections provide an overview of key photosynthetic processes and their responses to environmental stressors, as well as background information on existing approaches to quantify phytoplankton productivity and a review of variable chlorophyll fluorescence methodology. The final section of this introductory chapter presents an outline of the current challenges to underway FRRF operation, which motivate the research presented in subsequent chapters. 1.2 Photosynthetic processes and responses to environmental stress Photosynthesis is a complex process of coordinated subcellular reactions that use light energy to convert CO2 into organic matter. The many enzymes and proteins required to drive 3 photosynthetic reactions rely on macronutrient (i.e. nitrate and phosphate) building blocks and micronutrient (e.g. iron, magnesium, manganese) cofactors. As a result, phytoplankton physiology and growth rates vary with light, macro- and micronutrient availabilities, so that global productivity trends are a product of spatial and temporal variability in light and nutrient supplies (Chavez, Messié and Pennington, 2011). In turn, the balance between phytoplankton growth and decomposition determines oxygen, nutrient, and organic carbon concentrations dissolved in seawater. This interdependence between phytoplankton and marine biogeochemistry is one of the primary motivating factors for studying marine phytoplankton photo-physiology and productivity. The following section summarizes key photosynthesis reactions and regulatory responses to varying environmental conditions, which are relevant to the results and discussion presented in Ch 2-4. Photosynthetic light reactions are initiated when a pair of Chlorophyll a (Chl) molecules embedded in the thylakoid membrane of the Photosystem II (PSII) protein complex reaction center (RCII) absorb incoming radiation and enter an excited state. Excited RCIIs release absorbed energy via the photochemistry pathway by transferring an electron to the primary acceptor, QA, which, in turn, controls electron flow to the secondary acceptor QB. Once fully reduced with two electrons, QB binds two protons from the stroma, forming PQH2, which is released from its PSII binding site. PQH2 then diffuses along the thylakoid membrane and binds Cytochrome b6f (Cyt b6f), where the protons are released to the lumen, and oxidized QB returns to PSII. Through linear transport along the electron transport chain, Photosystem I (PSI) is eventually reduced, while a pH gradient develops across the thylakoid membrane (Fig 1.1). Similar to PSII, PSI releases an electron following photochemical excitation, ultimately leading to reduction of NADP+ to NADPH. Water splitting on the lumen-side of PSII replaces the initial 4 electron released by RCII and contributes an additional proton to the lumen. One O2 molecule is released as a byproduct of water splitting following four charge separation events. The building pH gradient drives ATP synthase, and the resulting energy storage molecules, ATP and NADPH, are used by the Calvin-Benson Cycle where CO2 is fixed into glucose, and by other metabolic activities. These reactions stoichiometrically link oxygen evolution and carbon assimilation with RCII electron transport rates (ETRPSII). Although photosynthesis depends on light, excess excitation beyond maximum electron transport rate capacity can accelerate photodamage. At saturating irradiances, the maximum ETRPSII is limited by the turnover rate of QA (Kolber and Falkowski, 1993). When the entire QA pool is reduced and unable to accept additional electrons, photochemically excited RCII may return to ground state by reducing alternative acceptors, producing highly reactive oxygen species (ROS) capable of damaging nearby proteins. Electron transport rates can be limited by a second bottleneck regulated by the diffusion and reoxidation rate of PQH2, which can create an imbalance between PSI reduction and oxidation (Rochaix, 2011). However, phytoplankton have evolved a number of regulatory mechanisms, which act as ‘safety valves’ to release excess energy and maintain redox homeostasis across the electron transport chain (Suorsa, 2015; Shikanai and Yamamoto, 2017). Excited RCII can return to ground state through productive or damaging photochemistry pathways, or by re-emitting absorbed radiation as heat or fluorescence. The first defense against PSII overexcitation is upregulation of PSII heat dissipation, which reduces PSII photochemical and fluorescence yields. Heat dissipation is commonly referred to as non-photochemical quenching (NPQ), due to the apparent quenching of the fluorescence signal without a corresponding increase in photochemistry (Müller, Li and Niyogi, 2001). Downstream of PSII, 5 cyclic electron transport alleviates imbalances between PSII and PSI electron transport rates. Changes in lumen pH and PQ redox chemistry, which result from high ETRPSII, act as a sensor for excess excitation and trigger NPQ and cyclic electron transport, mechanistically linking these two energy dissipation pathways (Shikanai and Yamamoto, 2017). Although NPQ and cyclic electron transport are most directly affected by high light exposure, nutrient limitation and temperature stress can also influence phytoplankton thresholds for excess excitation. For example, nitrate limitation affects protein biosynthesis and repair rates, and can make phytoplankton more susceptible to long term photodamage. Under nitrate limitation, PSII damage outpaces repair rates, leading to a high volume of non-functional RCIIs (Geider et al., 1993). With limited functional RCIIs and reduced capacity for protein repair, NPQ and cyclic transport are upregulated to protect remaining photosynthetic machinery (Saroussi, Wittkopp and Grossman, 2016). Further, nitrate assimilation requires reduction of NO3- to NH4+, and is a significant consumer of NADPH, so that nitrate limitation or ammonia-based growth lead NADPH:ATP imbalances (Brownlee et al., 2022). Cells correct for these imbalances by increasing cyclic electron transport, which supports the thylakoid pH gradient and ATP synthase without producing NADPH. Recent field studies have observed elevated cyclic e- transport and NPQ in nitrate limited assemblages, and subsequent decreases in response to nitrate enrichment (D. J. Hughes, Varkey, et al., 2018). Iron limitation exerts similarly strong effects on cyclic e- transport and NPQ (Schuback, Schallenberg, Duckham, Maldonado, et al., 2015; Ryan-keogh et al., 2020; Schallenberg et al., 2020). Iron limitation hampers Chl biosynthesis and restricts the concentration of critical iron-rich electron transporters (Michael J Behrenfeld and Milligan, 2013). Cytochrome b6f, PSII, and PSI each require 5, 12, and 2 Fe (Blaby-Haas and Merchant, 2013). As a result, Fe-limited 6 phytoplankton have reduced PSI:PSII ratios and a limited capacity for electron transport after initial PSII charge separation (Strzepek and Harrison, 2004). Consequently, NPQ and cyclic electron transport are elevated in Fe-limited assemblages (Schuback, Schallenberg, Duckham and Maldonado, 2015; Ryan-keogh et al., 2020; Schallenberg et al., 2020). In addition to light and nutrient availability, photosynthesis, like all metabolic activities, is strongly affected by temperature (Neori and Holm-Hansen, 1982). Low temperatures slow enzyme kinetics, thereby reducing carbon assimilation, and growth rates. Cold stress, like nutrient limitation and high irradiance, can thus lead to imbalances between excitation absorption and metabolic requirements for photosynthates (Claquin et al., 2008). On the other hand, high temperatures can denature PSII and other key photosynthetic proteins, similar to ROS-induced photoinhibition (Murata et al., 2007). In response to an increased susceptibility to photodamage and energy imbalances, phytoplankton upregulate NPQ and non-linear electron transport pathways when exposed to temperatures below or above their optimal range (Mackey et al., 2013; Aihara, Takahashi and Minagawa, 2016; Zhengke Li et al., 2021). Temperature optima vary significantly across phytoplankton species, and phytoplankton photo-physiology exhibits significant temperature-dependent plasticity within their optimal temperature range. Laboratory studies have demonstrated strong correlations between temperature and cellular Chl to carbon ratios (Geider, 1987; Stramski, Sciandra and Claustre, 2002), while a compilation of field and lab data revealed a negative relationship between temperature and electron requirements for carbon fixation across a range of oceanic environments (Lawrenz et al., 2013). Characterizing such environmental stress effects on photosynthetic efficiency is critical for fluorescence-based productivity applications and for global productivity models as they alter stoichiometries between Chl, ETRPSII, and carbon uptake. 7 1.3 Methods and metrics for quantifying productivity Marine productivity encompasses a broad array of photosynthetic reactions and processes that occur over various spatiotemporal scales; from initial RCII excitation within nanoseconds of illumination, to carbon export and burial over weeks and months depending on mixed layer residence times. Accordingly, there is an equally broad suite of methods to quantify productivity by targeting different photosynthetic processes, resulting in diverse ‘currencies’ of productivity (i.e. electrons, O2, particulate organic carbon), each representing the output of a different photosynthetic function (Balch et al., 2021; Table 1.1). Although FRRF is the main focus of this thesis, other methods are presented and used for comparison. Each method presents its own advantages and limitations, as summarized in the section below. Marine productivity has historically been defined as the rate of biomass production measured in discrete samples by recording changes in bulk oxygen concentrations or the uptake of isotope tracers (14C, or 13C) in light/dark incubation bottles (Nielsen, 1952; Mountford, 1969). Short-term incubations provide estimates of gross primary productivity (GPP), or the rate of total carbon fixation. Over longer incubations, a fraction of the oxygen or tracer incorporated into biomass is consumed by respiration, so that there is some uncertainty about where results lie on the spectrum between GPP and Net Primary Productivity (NPP; GPP – autotrophic respiration; Banse, 2002). Yet, these methods still offer the most direct observations of carbon fixation, and have been fundamental to current understanding of marine primary productivity. Although still used widely today, incubation experiments are limited to observations at periodic snapshots, and may introduce confounding bottle effects associated with sample containment. 8 Ocean observing satellites provide daily global measurements of Chlorophyll concentrations (Chl). Such consistent and broad coverage has enabled opportunistic studies of phytoplankton responses to climate events, such as episodes of ash deposition following forest fires or marine volcanic eruptions (Hamme et al., 2010; Tang et al., 2021), which would be near impossible to sample with a traditional research vessel. Empirical models between satellite-based Chl and NPP are routinely used to provide carbon flux estimates across regional, ocean basin, and global scales (Behrenfeld, Esaias and Turpie, 2002). However, the satellite record is prone to data gaps in areas where cloud and ice cover are prevalent. Further, empirical models rely on assumed relationships between Chl and carbon assimilation efficiencies, which can vary considerably depending on phytoplankton physiology and require calibration against in-situ measurements (Behrenfeld and Falkowski, 1997; Westberry et al., 2016). Consequently, there is an urgent need to expand continuous ship-based (i.e. underway) systems for measuring in-situ productivity and physiology. In recent decades, several approaches have been developed to use chemical tracers of productivity, such as such as ΔO2/Ar ratios (Tortell, 2005; Saba et al., 2011; Arrigo and van Dijken, 2015; Izett and Tortell, 2020) or triple oxygen isotopes (Haskell II et al., 2017; Howard et al., 2017). Measurements of DO2/Ar, alongside sea-air flux parameterizations, are used to estimate net community production (NCP = GPP minus mixed layer respiration), using Ar as a biologically inert analog for physically mediated O2 fluxes (e.g. bubble entrainment, air-sea exchange). Triple oxygen isotopes estimate gross oxygen production (GOP) using mass balance equations to reconstruct photosynthetic- and atmospheric-derived O2 influxes to the mixed layer based on observed 16O, 17O, and 18O ratios and known ratios for photosynthetic and atmospheric sources (Nicholson, Stanley and Doney, 2014). Both oxygen tracer methods represent biological 9 processes integrated over the residence time of O2 in the surface mixed layer (weeks to months, depending on mixed layer depth and wind speeds), and are useful for constructing carbon budgets, but are not well suited to capture short-term fluctuations. Additionally, both methods assume steady state conditions with negligible vertical or horizontal mixing (Nicholson, Stanley and Doney, 2014; Izett, 2021). This assumption is likely to be violated in areas with strong tidal mixing (e.g. Canadian Arctic Archipelago) or coastal upwelling (e.g. California Current). Variable Chl fluorescence techniques, such as FRRF, provide near-instantaneous measurements of phytoplankton photo-physiology and gross photochemistry (Kolber and Falkowski, 1993; Kolber, Prášil and Falkowski, 1998). Gross photochemistry (GP) describes the conversion of light energy absorbed by the Photosystem II (PSII) complex into chemical reducing power. Rates of photosynthetic electron transport (ETRPSII) between PSII reaction centers and primary acceptor molecules are a function of light availability, PSII light absorption, and photochemical conversion efficiencies. These PSII photophysiological properties can be readily measured with FRRF, offering the finest spatiotemporal resolution productivity measurements. In practice, however, a number of critical uncertainties have limited the use of FRRF for high resolution underway productivity measurements. In the following sections I introduce FRRF theory and application, and outline three key sources of uncertainty that are the main focus of study in this thesis. 1.4 Theory and application of Fast Repetition Rate Fluorometry (FRRF) Chlorophyll (Chl) is the primary light harvesting pigment for marine and terrestrial autotrophs. Due to its near-ubiquitous nature, importance in photosynthesis, and distinct bio-optical properties, there is a long history of studying Chl fluorescence, and a wide range of 10 techniques and uses summarized below (see Govindjee, 2004 for a review of historical Chl fluorescence research). Chlorophyll fluorescence from intact cells responds to photosynthetically active radiation (PAR) between 400-700 nm. Photopigments within light harvesting complexes integrated with PSII or PSI absorb PAR and excite the specialized Chl reaction center through radiative transfer. Excited RCIIs return to ground state by releasing absorbed energy through one of three competing pathways: 1) photochemistry, 2) heat, or 3) as fluorescence. Total fluorescence emissions vary with Chl concentrations and have long been used as a proxy for biomass (e.g. in-vivo ship-board fluorometers or remotely sensed sun-induced fluorescence; Lorenzen, 1966; Gower and King, 2004). By contrast, normalized fluorescence yields (i.e. Chl fluorescence normalized to Chl concentrations) are determined by the probability that a photon absorbed by RCII is reemitted as heat, photochemical energy, or fluorescence and are a useful probe of photochemical efficiency (Schreiber et al., 1995). Variable fluorescence techniques, including FRRF, measure fluorescence yields using pulses of excitation light to manipulate the redox state of the electron transport chain, saturating the photochemistry pathway to induce corresponding changes in fluorescence. Specifically, Fast Repetition Rate fluorometers operate by supplying a rapid sequence of excitation pulses (typically 100, 1 𝜇s pulses over ~ 250 µs) at rates that exceed the reoxidation rate of the primary electron acceptor QA (Schuback, Philippe D Tortell, et al., 2021). During the excitation sequence, fluorescence increases as the QA pool is progressively reduced and unable to accept further electrons from RCIIs, closing the photochemistry pathway. Once the QA pool is saturated, fluorescence stabilizes at a maximum level. Following the excitation phase, a relaxation sequence with larger and exponentially increasing intervals between pulses allows fluorescence 11 to return to basal levels as QA reoxidizes. The resulting rise and fall of fluorescence is called a fluorescence transient or single turnover (ST) flash, since the combined excitation and relaxation sequences cause a single turnover event of the entire Qa pool (Fig 1.2). Critically, this method assumes no changes in heat yields over the short duration of an ST flash (~1s). Primary photophysiological parameters (listed in Table 1.2) are extracted by fitting a biophysical model to the ST transient (Kolber, Prášil and Falkowski, 1998). As illustrated in Fig. 1, the amplitude of a fluorescence transient signal is obtained from the difference between the maximum inducible fluorescence, when all RCIIs are closed (Fm), and the minimum fluorescence when all functional RCIIs are open (Fo). The maximum PSII photochemical yield is derived as the normalized variable fluorescence (BCDB>BC = Fv/Fm), and is often used as an indicator for photosynthetic potential or photophysiological stress due to its sensitivity to taxonomic composition, nutrient abundance, and photoinhibition (Falkowski, Lin and Gorbunov, 2017). Other critical primary parameters include the functional absorption area of PSII (𝜎2344) and the turnover rate of Qa (FGHI), which are, respectively, derived from fluorescence induction and relaxation. Light exposure exerts a first-order control on the shape of ST transients and the resulting fit parameters. Photosynthetic organisms adapted to dynamic light environments (e.g. changes in cloud cover or vertical mixing) rapidly optimize light absorption under low irradiance and protect photosynthetic machinery under excess irradiance. To minimize photodamage, NPQ upregulates PSII heat loss by depressing both RCII light absorption and photochemical conversion efficiencies. In the presence of background light, the amplitude of fluorescence transients is compressed, due to lower maximum fluorescence levels (Fmʹ, see Fig 2c). In the light regulated state, when phytoplankton are exposed to background light, some RCIIs are 12 closed before the excitation sequence begins. As a result, the minimum, light-regulated fluorescence (Fʹ) represents a state where the photochemistry path is partially closed, further decreasing the observed amplitude of light regulated fluorescence transients. In practice, it is not possible to directly measure the light-regulated minimum fluorescence when all RCIIs are open (Foʹ), since actinic light will drive photochemistry and some degree of RCII closure. Instead, Foʹ can be measured either immediately after removing actinic light, under the assumption RCIIs will reopen but NPQ effects will remain, or estimated from a combination of dark and light-regulated measurements as Foʹ = Fo/( Fv/Fm + Fo / Fmʹ) (Oxborough et al., 2012). Importantly, the effects of NPQ render all FRRF-derived photophysiological parameters irradiance-dependent. As a result, most FRRF protocols use a dark or low light acclimation period to standardize measurements by eliminating NPQ effects, e.g. Moore et al. (2006), Schuback, Schallenberg, Duckham, Maldonado, et al., (2015), Zhu et al. (2017), Ko et al. (2020). Most protocols recommend low light intensities of red light to stimulate PSI and prevent backflow of electrons (Tortell, Schuback and Suggett, 2023). However, evaluating light-dependent photophysiological characteristics also requires measurements made in the light regulated state. As a result, characterizing the presence and relaxation time-scales of NPQ is critical to robust FRRF-based productivity measurements (see Ch 3). 1.5 Calculating PSII electron transport rates Photosystem II electron transport rates (ETRPSII) are a valuable metric of productivity, as they directly result in ATP and NADPH production, and set an upper limit for downstream carbon fixation. There are several alternative algorithms to calculate ETRPSII from FRRF primary parameters (Schuback, Philippe D Tortell, et al., 2021). Frequently, ETRPSII is expressed as the 13 product of photosynthetically active radiation (PAR; µmol quanta m-2 s-1), the light acclimated functional absorption area of PSII, (𝜎′PSII; m2 PSII-1), and the fraction of functional RCIIs in the open state ( B(CDB(B(CDB(> = B(KB(L; unitless). The parameters 𝜎′PSII and B(KB(L are both measured in light-regulated phytoplankton (Suggett, Moore and Geider, 2011). Due to its dependence on 𝜎′PSII and the amplitude of fluorescence transients, this algorithm has interchangeably been called the ‘amplitude’ or ‘sigma’ algorithm (Schuback, Philippe D. Tortell, et al., 2021). Here, it is referred to as the amplitude approach, ETRa. 𝐸𝑇𝑅7 = 𝐸 × 𝜎′2344 × 𝐹′𝑞𝐹′𝑣 Eq 1.1 The formulation above depends on light-regulated terms 𝜎′PSII and 𝐹′𝑜 that are difficult to measure with confidence (Schuback, Philippe D. Tortell, et al., 2021) as the variable fluorescence signal to noise ratio decreases in the light (see Fig. 1.1c), and background light introduces noise. However, as 𝜎′PSII is defined as the product of the PSII optical cross section, 𝑎2344, and the efficiency of energy delivery from PSII light harvesting pigments to RCII, 𝐹′ 𝑣 𝐹⁄ ′𝑚 (Suggett, Moore and Geider, 2011), ETR can be calculated without 𝜎′PSII and 𝐹′𝑜 terms assuming that NPQ equally affects 𝜎′PSII and 𝐹′ 𝑣 𝐹⁄ ′𝑚 (Suggett, Právsil and Borowitzka, 2010): 𝜎′2344𝜎2344 = 𝑎2344𝑎2344 𝐹′ 𝑣 𝐹⁄ ′𝑚𝐹𝑣 𝐹𝑚⁄ Eq 1.2 Under this assumption, ETR may be rewritten to eliminate the requirement for 𝜎′PSII and 𝐹′𝑜 measurements: 14 𝐸𝑇𝑅7 = 𝐸 × 𝜎2344 × 𝐹′𝑞𝐹′𝑚 × W𝐹𝑣𝐹𝑚XDF Eq 1.3 Importantly, the above expression combines measurements from the dark (low light) and light regulated states, and therefore an offset in time and photo-physiology between measurements that increases uncertainty. Recently, a new ETR algorithm has been proposed (Gorbunov and Falkowski, 2020). This algorithm, known as the kinetic-ETR (ETRk), relies on quantifying the rates of downstream QA photosynthetic electron transport (FYHZ ) based on fluorescence relaxation kinetics. This algorithm does not involve dark measurements, but does require measurements at saturating and supersaturating irradiances (Gorbunov and Falkowski, 2020), 𝐸𝑇𝑅[ = 1𝜏 ]𝐸 × 𝐹Kʹ𝐹Cʹ_/ ]𝐸C7` × 𝐹Kʹ𝐹CʹaC7`_ Eq 1.4 Here, Emax is a supersaturating irradiance at which primary photochemistry rates will be limited by the turnover rate of QA in its saturated state (FG), which is measured as FGbc under saturating irradiance. 1.6 Challenges facing widespread application of FRRF for autonomous in-situ surveys With its high potential for rapid, automated data acquisition, FRRF is uniquely poised to provide the high resolution productivity data needed for climate models and satellite algorithm validation (IPCC, 2023; Henson et al., 2024). Yet, FRRF use is most commonly restricted to discrete sample analysis, rather than high resolution underway surveys. This thesis identifies three main challenges to underway FRRF application: 1) divergence between alternative ETRPSII 15 algorithms, 2) the requirement for lengthy dark NPQ relaxation periods prior to measurement, and 3) natural variability in ETRPSII to carbon fixation ratios that make it challenging to predict carbon uptake rates from fluorescence-based measurements alone. The body chapters of this thesis, Ch 2-4 outlined below, address each of these challenges in turn. 1.6.1 Diverging approaches to derive ETRPSII from FRRF measurements In theory, all the ETR algorithms are analogous, and they have each been validated against independent biosynthesis rate measurements (i.e. culture growth rates, 14C-uptake rates). However, each algorithm is subject to unique methodological considerations, requiring users to determine which best suits their research design. Further, it is not certain how operational and/or environmental factors may influence the coherence between ETR estimates from the different algorithms. Chapter 2 of this thesis provides the first side-by-side comparison of the ETRa and ETRk algorithms in natural phytoplankton assemblages. Samples were collected underway along a cruise track through Barrow Strait in the Canadian Arctic where phytoplankton displayed signatures of photo-physiological stress that varied with proximity to river outlets with high nitrate concentrations. Results from this work revealed potential physiological and methodological drivers of decoupling between ETRa and ETRk. 1.6.2 Non-photochemical quenching and dark acclimation protocols As noted in Sect 1.5, some ETR algorithms require dark-regulated measurements following NPQ processes relaxation. Additionally, NPQ relaxation provides some standardization of field samples that are otherwise affected by varying and uncertain degrees of fluorescence quenching. 16 For this reason, NPQ relaxation protocols are recommended practice (Schuback, Philippe D Tortell, et al., 2021). However, there is little consensus in the literature regarding how long samples should be held for NPQ relaxation, with applied relaxation times ranging from several minutes to an hour, e.g. Alderkamp et al., (2010), Walter, Peters and van Beusekom (2017). Selecting an appropriate NPQ relaxation protocol is further complicated by natural variation in the phytoplankton NPQ relaxation time-scales, which can vary significantly across environmental conditions and between different taxonomic groups, making it difficult to apply a single standard protocol when working with field samples (Xu et al., 2018). Longer relaxation protocols also reduce measurement resolution and likely cause photophysiological shifts in phytoplankton from their in-situ state. Chapter 3 of this thesis presents a series of experiments quantifying the relaxation time-scales of NPQ in natural phytoplankton assemblages across diverse Arctic seascapes where phytoplankton experience extreme and dynamic light conditions. This chapter also presents and tests a mathematical formulation for the use of short-term low light relaxation rather than measurements of dark-regulated parameters in ETRa algorithms. This formulation is based on the pre-existing assumption that NPQ equally affects 𝜎′PSII and 𝐹′𝑣 𝐹⁄ ′𝑚 (Suggett, Prásil and Borowitzka, 2010). Results presented in this chapter demonstrate that NPQ relaxation time-scales were, on average, 20 minutes for surface samples. However, the assumption of equal NPQ effects on 𝜎′PSII and 𝐹′ 𝑣 𝐹⁄ ′𝑚 was violated among samples affected by photoinhibition. These results demonstrate the effect of photoinhibition on ETRPSII estimates and offer important practical considerations for FRRF users. 17 1.6.3 Variability in electron requirements for carbon fixation As discussed, photosynthetic carbon fixation and oxygen evolution rely on ETRPSII. For every CO2 molecule fixed, a minimum four electrons must be extracted from water, which translates to the evolution of one O2 molecule. Therefore, it should be feasible to quantify GPP in carbon or oxygen units from FRRF-derived ETRPSII estimates. Indeed, a number of studies have demonstrated strong relationships between ETR, O2 evolution and CO2 fixation rates (see Lawrenz et al. (2013) and Regaudie-de-Gioux et al. (2014) for compilations of results). However, observed electron requirements for carbon fixation (𝜙.: 𝐶) often far exceed the theoretical minimum value of 4, and vary as a function of phytoplankton taxonomy and environmental conditions. Predicting 𝜙.: 𝐶 thus remains a considerable challenge to FRRF-based GPP estimates. Progress has been made on this front with improved understanding of the factors that influence decoupling between ETRPSII and carbon fixation (Foyer et al., 2012; Schuback, 2016; D. J. Hughes, Campbell, et al., 2018; D. J. Hughes, Varkey, et al., 2018). In particular, non-linear electron transport pathways divert reducing power produced by ETRPSII away from carbon fixation, increasing 𝜙.: 𝐶. Several studies have now demonstrated that such increases in 𝜙.: 𝐶 correlate with increases in NPQ, suggesting co-regulation of these ‘safety valves’ to mitigate excess excitation pressure (Schuback et al., 2015). Since NPQ is readily measured by FRRF, co-variance of NPQ and 𝜙.: 𝐶 presents the possibility of deriving high resolution carbon-based productivity measurements from ETRPSII surveys. However, the relationship between NPQ and 𝜙.: 𝐶 does not appear to hold across nutrient-replete and light limited phytoplankton (Schuback et al., 2017), revealing further work is still required to understand the interacting effects of light and nutrient availability on energy transfer efficiencies between ETRPSII and carbon fixation. 18 In Chapter 4, parallel FRRF, 14C-uptake, and O2/N′2 measurements are used to examine energy transfer efficiencies between ETRPSII, carbon fixation, and net community productivity along a natural gradient of iron limitation within the California Current. In this ocean region, 𝜙.: 𝐶 varied with NPQ among relatively iron-stressed assemblages, whereas 𝜙.: 𝐶 was fairly constant among nutrient replete phytoplankton. Across all assemblages, carbon fixation varied as a hyperbolic function of ETRPSII, offering a potential alternative approach to estimate carbon fixation from fluorescence-based estimates. Yet, the parameters of this relationship varied considerably with physiological and transcriptomic markers of iron-stress. These results contribute new findings to the ongoing effort to understand and predict the relationship between ETRPSII and carbon fixation. 1.6.4 Conclusion Chapter 5 concludes this thesis by summarizing the methodological and physiological effects that influence FRRF-based productivity estimates. Overall, light and nutrient availabilities appear in each chapter as important drivers of productivity, fluorescence signatures and data interpretation. While this thesis offers new insights to advance the field-based application of FRRF for primary productivity studies, each chapter also yields new questions and challenges for further research. Chapter 5 ends with a discussion of the remaining knowledge gaps and potential future directions for the field, including opportunities to incorporate lessons from FRRF into satellite-based gross productivity algorithms. 19 1.7 Tables and Figures Figure 1.1. Photosynthetic electron transport chain in eukaryotes. Black dashed lines indicate linear electron transport between PSII and Calvin Cycle carbon fixation reactions. The red dashed lines indicate non-linear electron transport pathways that decouple ETRPSII from carbon fixation, including 1) Plastid Terminal Oxidase (PTOX) activity, or chloro-respiration, 2) cyclic electron transport around PSII, 3) cyclic electron transport around PSI, 4) pseudo cyclic electron transport around PSI, leading to Mehler reactions, 5) nitrate assimilation and other metabolic activities that consume NADPH and ATP, and 6) photorespiration. Figure created in biorender.com. Table 1.1 Summary table of marine primary productivity methods and currencies spanning cellular- to ecosystem-level space and time scales. 20 Method Photosynthetic ‘currency’ scale Measurement frequency Refs. Fast Repetition Rate Fluorometry Gross photochemistry ms – 1hr Single location – 100 kms ms – 1hr (depending on protocol) (Kolber and Falkowski, 1993; Schuback, Philippe D. Tortell, et al., 2021) 14C photosynthetron Approx. Gross carbon fixation Hour Single location 1- 2 hours (Johnson and Sheldon, 2007) H2O- 18O incubations Gross oxygen production Hours – day Single location 3-24 hours (Ferrón et al., 2016) 13C or 14C incubations Gross or net carbon fixation Hours – day Single location 3-24 hours (López-Sandoval et al., 2019) Triple oxygen tracer Gross oxygen production Days - weeks 100 m – 100 kms seconds (Nicholson, Stanley and Doney, 2014) 𝛥O2/Ar or 𝛥O2/N2’ tracer Net community production Days - weeks 100 m – 100 kms seconds (Tortell, 2005; Izett and Tortell, 2020) 21 Satellite ocean color NPP models Net primary production Daily – annual 4km - global Complete orbit every 2 days (Behrenfeld and Falkowski, 1997; Aardema et al., 2019) Figure 1.2 Representative fluorescence transients and associated electron transport activity. a) A typical dark-regulated fluorescence induction-relaxation transient displaying rise in fluorescence from basal (Fo) to maximum fluorescence (Fm). The initial rise in fluorescence is controlled by the rate of initial light absorption, mediated by the functional absorption area of PSII (𝝈𝑷𝑺𝑰𝑰). During the relaxation sequence, fluorescence decreases following a multi-exponential trend, determined by the reoxidation rate of QA (𝝉𝑸𝒂). b) The minimum fluorescence (Fo) corresponds to a state when functional RCIIs are open, and the photochemistry pathway is !!"## 22 enabled, while the maximum fluorescence (Fm) corresponds to a reduced QA pool and closed RCIIs (Panel d). Panel c) displays a fluorescence transient measured under background actinic light, with reduced maximum fluorescence (F'm) due to non-photochemical quenching of fluorescence, and slightly increased initial fluorescence (F') due to partial RCII closure prior to the excitation sequence. Prime notation (') identifies measurements made under actinic light. All schematics are illustrated in biorender. Table 1.2 Summary of FRRF-derived parameters. Primary parameters are determined by fitting ST transients with the biophysical model described by Kolber et al. (1998), commonly completed within FRRF instrument software. Prime symbols denote light-regulated parameters measured under actinic light exposure. Term Definition Units Primary parameters Extracted from biophysical model fit to ST transient Fo,, Fʹ Minimum Chla measured in dark and light regulated state, respectively. Relative Units Fo, F’o Minimum Chla representative of an open RCII pool for the dark and light regulated state, respectively Relative Units Fm, Fmʹ, Maximum Chla representative of a closed Relative Units 23 RCII pool for dark and light regulated state, respectively σPSII, σPSIIʹ, Functional absorption cross-section for PSII in the dark and light regulated state, respectively Å2 PSII-1 Fv/Fm Photochemical yield in the dark regulated state; (Fm-Fo)/Fm Dimensionless Fqʹ/Fmʹ Photochemical yield in the light regulated state (Fmʹ - Fʹ)/ Fmʹ Dimensionless Fqʹ/Fvʹ Fraction of functional RCIIs in the open state (Fmʹ - Fʹ)/ (Fmʹ - F’o) Dimensionless Fqʹ/Fmʹ (Emax) Photochemical yield measured under super saturating irradiance Dimensionless 𝜏Qa QA photosynthetic electron turnover rate s r Probability of excitation energy transfer between RCIIs Unitless Secondary parameters Terms derived from primary parameters ETRPSII PSII electron transport rate e- s-1 RCII-1 NPQSV Stern-Volmer parameterization of Non-photochemical quenching; (Fm - Fʹm)/Fm Dimensionless 24 NPQNSV Normalized Stern-Volmer parameterization of Non-photochemical quenching; (𝐹'( /𝐹*( ) qN Coefficient of non-photochemical quenching; Ratio of quenched to unquenched fluorescence; 1 - F′v/Fv 25 Chapter 2: Irradiance and nutrient-dependent effects on photosynthetic electron transport rates in Arctic phytoplankton: a comparison of two fluorescence-based approaches to derive photochemistry 2.1 Summary We employed Fast Repetition Rate fluorometry for high-resolution mapping of marine phytoplankton photo-physiology and primary photochemistry in the Lancaster Sound and Barrow Strait regions of the Canadian Arctic Archipelago in the summer of 2019. Continuous ship-board analysis of chlorophyll a variable fluorescence demonstrated relatively low photochemical efficiency over most of the cruise-track, with the exception of localized regions within Barrow Strait, where there was increased vertical mixing and proximity to land-based nutrient sources. Along the full transect, we observed strong non-photochemical quenching of chlorophyll fluorescence, with relaxation times longer than the 5-minute period used for dark acclimation. Such long-term quenching effects complicate continuous underway acquisition of fluorescence amplitude-based estimates of photosynthetic electron transport rates, which rely on dark acclimation of samples. As an alternative, we employed a new algorithm to derive electron transport rates based on analysis of fluorescence relaxation kinetics, which does not require dark acclimation. Direct comparison of kinetics- and amplitude-based electron transport rate measurements demonstrated that kinetic-based estimates were, on average, 2-fold higher than amplitude-based values. The magnitude of decoupling between the two electron transport rate estimates increased in association with photophysiological diagnostics of nutrient stress. Discrepancies between electron transport rate estimates likely resulted from the use of different photophysiological parameters to derive the kinetics- and amplitude-based algorithms, and 26 choice of numerical model used to fit variable fluorescence curves and analyze fluorescence kinetics under actinic light. Our results highlight environmental and methodological influences on fluorescence-based photochemistry estimates, and prompt discussion of best-practices for future underway fluorescence-based efforts to monitor phytoplankton photosynthesis. 2.2 Introduction Phytoplankton productivity in polar marine waters is constrained by nutrient and light availability, which fluctuate dramatically across seasonal cycles and shorter time and space scales (Tremblay et al., 2015). In late summer, when sea ice cover is at a minimum and the mixed layer is shallow and highly stratified, phytoplankton are exposed to high solar irradiance and low nutrient concentrations (Tremblay and Gagnon, 2009). Under these conditions, the growth and photosynthetic efficiency of Arctic phytoplankton becomes nitrogen-limited (Tremblay et al., 2006; Lewis et al., 2019; Ko et al., 2020). However, localized regions of elevated productivity can persist where various processes transport nutrients into the mixed layer, including upwelling, tidal mixing, and freshwater input from rivers and glaciers (Ardyna et al., 2011; Randelhoff et al., 2020; Terhaar et al., 2021). Modelling studies suggest that Barrow Strait in the Canadian Arctic Archipelago is one such productivity hotspot, with strong tidal currents and shallow sills driving vertical mixing in a region where Pacific and Atlantic-derived water masses converge (Hannah, Dupont and Dunphy, 2009; K. G. Hughes et al., 2018). Additionally, Barrow Strait receives glacial and land-derived nutrients from the Cornwallis and Devon Island rivers (Young et al., 2015; Brown et al., 2019). Rapid climate change in the Arctic is expected to have complex effects on these nutrient delivery mechanisms through the intensification of coastal erosion (Fritz, Vonk and Lantuit, 2017), increasing river inputs (Peterson et al., 2002) and reduced vertical mixing due to intensifying stratification (P. Coupel et 27 al., 2015; Nummelin et al., 2016). At present, it is unclear how phytoplankton productivity will respond to these anticipated perturbations. Assessing phytoplankton productivity in physically-dynamic marine waters requires high spatial resolution measurements that cannot be obtained from traditional discrete bottle incubation methods, such as 14C uptake experiments. For this reason, oceanographic field studies have increasingly employed continuous sampling of surface water properties using variable chlorophyll a (Chla) fluorescence from Fast Repetition Rate Fluorometry (FRRf) and other related methods to rapidly and autonomously assess phytoplankton photo-physiology and primary photochemistry as a proxy for primary productivity (e.g. Kolber and Falkowski, 1993; Moore et al., 2003; Fujiki et al., 2008; Gorbunov and Falkowski, 2022). Such variable fluorescence techniques rely on the inverse relationship between Chla fluorescence and photochemistry. These processes, along with heat dissipation, comprise the three energy dissipation pathways for light energy absorbed within Photosystem II (PSII) (Kromkamp and Forster, 2003). Fast Repetition Rate Fluorometry operates by supplying rapid excitation light pulses to progressively saturate the photosynthetic pathway and simultaneously induce a measurable Chla fluorescence response—often referred to as a fluorescence transient (Kolber, Prášil and Falkowski, 1998). Analysis of Chla fluorescence transients provides information on the photochemical efficiency and functional absorption cross section of PSII, as well as estimates of the turnover rate of photosynthetic electron transport chain molecules (Moore et al., 2006; Suggett, Právsil and Borowitzka, 2010). Such FRRF transients can be obtained nearly instantaneously, offering opportunities for very high temporal and spatial resolution measurements. 28 Primary photochemistry is typically estimated from variable Chla fluorescence measurements by calculating the rate of photosynthetic electron transport out of PSII (ETRPSII). There are several algorithms that may be applied to derive ETRPSII (Schuback, Philippe D Tortell, et al., 2021). Each algorithm relies on the same principles of light harvesting and photosynthetic electron transport, but arrive at ETRPSII estimates using slightly different, but theoretically equivalent, combinations of photophysiological metrics. As a result, different algorithms confer different field-sampling advantages and challenges. The so-called ‘amplitude’ based approach (abbreviated here as ETRa; sometimes also referred to as the sigma-algorithm), calculates ETRPSII as the product of photosynthetically active radiation (PAR), the functional absorption cross section of PSII in the dark-acclimated state (σijkk), and the photochemical efficiency of PSII normalized by the dark-acclimated maximum photochemical efficiency of PSII (Suggett, Právsil and Borowitzka, 2010). This approach reduces uncertainty in ETRPSII estimates by using σijkk measurements made in the dark-acclimated state, which are subject to less noise than σijkk′ measurements made in the light (Schuback, Philippe D Tortell, et al., 2021). As a result, ETRa is a favorable approach in low biomass waters where low signal-noise measurements represent a considerable challenge. To achieve dark acclimation for ETRa measurements, phytoplankton samples are kept in darkness or very low light to relax non-photochemical quenching processes (NPQ), which upregulate heat dissipation of absorbed light energy, thereby reducing photochemistry and fluorescence yields (Kolber and Falkowski, 1995). Optimal dark acclimation times vary between phytoplankton species and depend to a large extent on the environmental history of samples (Bates, 1985), making it challenging to design widely applicable field protocols for high resolution data acquisition. In practice, applied dark-acclimation periods range from 5–30 29 minutes (e.g. Alderkamp et al., 2010; Walter, Peters and van Beusekom, 2017). Many FRRF field deployments have focused on discrete sample analysis, applying extended dark acclimation periods to ensure samples reach a short-term steady state condition (Moore et al., 2006; Schuback, Schallenberg, Duckham and Maldonado, 2015; Wei et al., 2019). Such discrete sample analysis enables standardized measurements and characterization of light-dependent physiological responses, at the cost of significantly reduced spatial and temporal measurement resolution. In contrast, continuous underway flow-through data acquisition yields high-resolution, real-time measurements of phytoplankton photo-physiology, but creates uncertainty in the light exposure history of phytoplankton transiting through a ship’s seawater supply lines. As a result, samples analyzed in continuous mode are neither fully representative of in-situ photo-physiology or fully dark-acclimated states, and thus incompatible with the ETRa algorithm, which requires both dark- and light-acclimated measurements. Recently, a new fluorescence approach has been developed to derive ETRPSII based on the turnover rate of the primary electron acceptor molecule (Qa) within the photosynthetic electron transport chain (Gorbunov and Falkowski, 2020). In this ‘kinetic’ approach, Qa turnover rates are derived from analysis of fluorescence relaxation time-constants, resulting in the term ETRk. The derivation of ETRk does not depend on dark-acclimated measurements, and can thus significantly increase the frequency of ship-board ETRPSII measurements. The kinetic fluorescence approach for ETRk was originally developed and implemented in mini-Fluorescence Induction-Relaxation (mini-FIRe) instruments, which use similar data acquisition protocols, but a different numerical approach for fluorescence relaxation analysis than FRRF. Gorbunov et al. (2020) observed greater coherence between growth rates of laboratory cultures and FIRe-derived ETRk compared to alternative ETRPSII estimates, suggesting a strong potential 30 for ETRk to quantify in-situ primary photochemistry. To our knowledge, there have been no direct comparisons of FRRF-derived ETRk and ETRa estimates for natural phytoplankton assemblages. In this chapter, we examine the relationship between ETRk and ETRa estimates across a range of hydrographic regimes in the Canadian Arctic Ocean. We employed a hybrid approach to data collection along a ship-track through Lancaster Sound and Barrow Strait, combining semi-continuous flow-through measurements with light response curves on static samples. Continuous sampling enabled us to obtain high-resolution measurements and examine the effects of light history and nutrient status on the physiology of Arctic phytoplankton assemblages. Data from rapid light-response curves allowed direct comparison of FRRf-derived ETRk and ETRa estimates. Our results demonstrate residual light-dependent NPQ effects on dark (low light) sample measurements, and a decoupling of ETRk and ETRa under conditions of phytoplankton photophysiological stress. We relate the spatial patterns in our observations to regional and fine-scale patterns in hydrography and nutrient supply in the eastern Canadian Arctic Archipelago, and discuss the potential effects of different data analysis approaches on ETRPSII-based primary photochemistry estimates. Results from our work will inform future ship-based deployments of FRRf and related techniques to understand spatial patterns in phytoplankton productivity. 2.3 Methods and materials 2.3.1 Underway sampling Arctic Ocean samples and hydrographic data were collected aboard the CCGS Amundsen from August 10–15, 2019, within the eastern region of Lancaster Sound and Barrow Strait, in the waters surrounding Devon and Cornwallis Islands. Access to these sampling regions was granted 31 by the Nunavut Research Institute Scientific Research License (0501119R-M). All FRRF measurements were obtained with a LIFT (Light Induced Fluorescence Transient)-fluorometer (LIFT-FRRF; Soliense Inc.). Water samples were delivered to the LIFT-FRRF sampling cuvette using the ship’s seawater line as a primary supply (BC-4C-MD pump, March MFG Inc, nominal flow rate ∼20 L min-1), combined with two secondary peristaltic pumps. The first pump (Masterflex L/S, model 7518–10), was used to create a continuous sampling loop (∼200 mL min-1) connected via t-fitting to a custom-built peristaltic pump actuated by the LIFT-FRRf software. The FRRF-actuated pump enabled precise synchronization of the sample handling and fluorescence measurements, allowing us to employ a semi-continuous sampling strategy of alternating fluorescence transients and light response curve measurements (details below). In addition to FRRF measurements, in-situ Chla fluorescence, surface water salinity and temperature were measured using a flow-through Seabird thermosalinograph system (SBE 38), equipped with a WETStar fluorometer (WET Labs). The underway Chla fluorescence signal was calibrated against discrete samples collected from surface Niskin bottles to approximate the along-track Chla biomass (mg m-3). Surface PAR measurements were obtained from a QCR-22000 Biospherical Instruments probe mounted above the ship’s super-structure. Biological oxygen saturation, ΔO2/Ar, was measured as a metric of net community production using a Hiden Analytical quadrupole membrane inlet mass spectrometer (MIMS; HAL20) following the approaches outlined by Tortell et al. (2005; 2011). These gas measurements were made continuously on seawater obtained from the same underway lines that supplied the FRRf system. Briefly, seawater was circulated at a constant flow rate past the mass spectrometer’s inlet cuvette consisting of a 0.18 mm thick silicone membrane. Measurements of the mass-to-charge ratios at 32 (O2) and 40 (Ar) atomic mass units were obtained at approximately 20 s. intervals. Air 32 standards, consisting of filtered seawater (<0.2 µm) incubated at ambient sea surface temperature and gently bubbled using an aquarium air pump, were run periodically by automatically switching the inflow water source every 45–90 minutes. For both seawater and air standard measurements, the inflowing water passed through a 6-m heat exchange coil immersed in a constant-temperature 4°C water bath before passing the MIMS inlet. The seawater and air standard O2/Ar ratios ([O2/Ar]sw and [O2/Ar]std, respectively) were used to derive underway ΔO2/Ar by linearly interpolating between air standard measurements. Water column hydrographic profiles were examined using CTD casts to a depth of 200m at 16 stations (Fig 1). Mixed layer depths were derived from a density difference criterion of 0.125 kg m-3 from surface water values. The CTD and underway surface salinity, temperature, Chla, and PAR data were provided by the Amundsen Science group of Université Laval. 2.3.2 LIFT-FRRF sample protocols and parameter retrieval Chlorophyll a fluorescence transients were obtained using a Single Turnover (ST) flash protocol and fit to the biophysical model of Kolber et al. (1998) to derive photosynthetic parameters under dark acclimation and under actinic light, where the latter is denoted with the ′ notation. A summary of the derived parameters and their definitions is given in Chapter 1, Table 1. Specifically, we derived estimates of the functional absorption cross section of PSII (𝜎2344, 𝜎2344( ), the minimum and maximum fluorescence when the reaction center pool is fully open and closed (Fo, F′ and FM, FM′, respectively), and the variable fluorescence (𝐹* = 𝐹n − 𝐹>; 𝐹K( = 𝐹n( − 𝐹′). Single Turnover excitation flashlets were delivered simultaneously by 445, 470, 505, 535, and 590 nm LED lamps. Each curve fit and acquisition was based on fluorescence yields averaged from five sequential ST flashes, each with a total duration of 0.2 s. Each 5-flash sequence was 33 separated by a 1s interval. Curve fitting and fluorescence averaging was completed within LIFT software (Soliense Inc.). Each seawater sample was initially held in the cuvette under low light for five minutes to allow NPQ relaxation. During this low-light acclimation period, five LEDs at peak wavelength excitation of 445, 470, 505, 535, and 590 nm each supplied 1 µmol quanta m-2 s-1 of actinic light, providing a total irradiance of 5 µmol quanta m-2 s-1. Samples were acclimated to low light rather than total darkness to avoid RCII closure and fluorescence quenching associated with back flow of electrons from the PQ pool to Qa (Tortell, Schuback and Suggett, 2023). After this short acclimation period, 40 acquisitions (each consisting of 5 averaged fluorescence transients) were collected. Irradiance was then increased to 150 µmol quanta m-2 s-1 (30 µmol quanta m-2 s-1 per LED) and samples were held for an additional five minutes to acclimate to the higher irradiance level before collecting another 40 acquisitions to measure photo-physiology close to the saturation irradiance for primary photochemistry, previously found to range from 96–213 µmol quanta m-2 s-1 for surface assemblages in the Canadian Arctic during summer (Schuback et al., 2017). The LIFT-FRRF cuvette was then flushed for 60 s with seawater from the ship seawater supply, displacing at least five full cuvette volumes before the pump was turned off to isolate the next sample for measurements. Semi-continuous sampling was interrupted every 90 mins to perform light response curves on static samples. For each light response curve, the actinic irradiance supplied by each LED lamp increased incrementally from 0 to 350 µmol quanta m-2 s-1 for all 5 LEDs to create light steps of 0, 15, 35, 75, 110, 150, 200, 300, 400, 550, 850, 1250, and 1750 µmol quanta m-2 s-1 total actinic light. A total of 25 acquisitions (of 5 averaged fluorescence transients each), were obtained at each light step with 30s pause at each new light level to provide some acclimation 34 time. Fluorescence amplitudes generally stabilized by the third round of data acquisition, and we thus excluded the first two flash sequences at each light level from data analysis. Light response curves were used to calculate ETRPSII as a function of increasing light intensity (see below for calculation details). To produce final photosynthesis-irradiance curves, ETRPSII was plotted against irradiance and fit to the hyperbolic function described by Webb et al., (1974), using a least squares non-linear regression to derive the maximum light-saturated photosynthetic rate (ETRmax), the saturating light intensity (Ek) and the light utilization efficiency (α). Daily blank corrections were performed by analyzing seawater gently passed through a 0.2 µm syringe filter. Soliense software was programmed to subtract the resulting fluorescence intensity from all measurements. Prior to the field deployment, the LIFT-FRRF lamps used to produce actinic light and the probing flashes were calibrated using a Walz spherical submersible micro quantum sensor (Walz, US-SQS-L). 2.3.3 Electron Transport Rates (ETRPSII) The amplitude and kinetics-based ETRPSII algorithms (ETRa and ETRk, both e- s-1 RCII-1) were applied to fluorescence data collected during light response curves to determine ETRPSII at increasing actinic light intensities. ETRa was applied to all 85 light response curves collected along the ship-track to produce photosynthesis-irradiance curves following Webb et al. (1974). At each light level, ETRa (e- RCII-1 s-1) was determined as the quantity of incident light absorbed by PSII directed towards photochemistry (Suggett, Právsil and Borowitzka, 2010); 35 𝐸𝑇𝑅7 = 𝑃𝐴𝑅 × 𝜎2344 × 𝐹K(/𝐹C(𝐹L/𝐹C × 6.022 × 10w Eq. 2.1 Here, PAR (µmol quanta m-2 s-1) is the total actinic light provided by the FRRF LEDs, σPSII (Å2 PSII-1) is the PSII functional absorption cross section measured in dark acclimated samples, and 𝐹K(/𝐹C( divided by Fv/Fm (dimensionless) is the PSII photochemical efficiency measured under actinic light normalized by the dark-measured maximum photochemical efficiency. The constant 6.022*10-3 converts σijkk units from Å2 PSII-1 to m2 PSII-1 and PAR from µmol quanta to quanta. For ETRk calculations, fluorescence data from a subsample of 25 light response-curves with optimal signal-to-noise ratios were re-analyzed using a 3-component multi-exponential model to describe Qa reoxidation kinetics (Kolber, Prášil and Falkowski, 1998). This numerical procedure was applied as a fitting option in the FRRF Soliense Software. 𝐹(𝑡) = 𝐹> + (𝐹C − 𝐹>) 𝐶b7(𝑡) 1 − 𝑝1 − 𝐶b7(𝑡)𝑝 Eq. 2.2 𝜕𝐶b7𝜕𝑡 = 𝐸(𝑡, 𝜆) × 𝜎2344 × 1 − 𝐶b7(𝑡)1 − 𝐶b7(𝑡)𝑝 − 𝐶b7(𝑡)𝛼;/𝜏;w;F Eq. 2.3 Here, F(t) represents the measured fluorescence signal, CQa(t) (dimensionless) is the fraction of available PSII reaction centers closed by excitation flashlets, with CQa(t) equal to 1 when the photochemistry pathway is fully closed. The term p (dimensionless) is the connectivity factor, which describes the likelihood of energy transfer between RCIIs. αi and τi refer to the 36 amplitude and time constant of the ith component of Qa reoxidation, respectively. The value 𝜕𝐶b7/𝜕𝑡 is determined by the balance between primary photochemistry induced by excitation flashlets (E; µmol quanta m-2 s-1) and electron transfer from Qa to secondary electron acceptor, Qb, mediated by three kinetic components. Fluorescence transients were fit to retrieve photophysiological parameters by integrating Eq 2.3 over the length of the ST flash sequence and then iteratively fitting Eq 2.2 to the fluorescence data. Importantly, we note Eq 2.3 differs slightly from that applied by FIRe-based data analysis, in which there is an added term to describe RCII closure induced by actinic irradiances (See ‘Computational Considerations’ in Section 2.5). The rate of Qa reoxidation, τ, was calculated by averaging the time constants of the two primary components of electron transfer from Qa to secondary electron acceptor Qb following the approach of Gorbunov and Falkowski (2020). The kinetic-based ETRPSII was then calculated as, 𝐸𝑇𝑅[ = 1𝜏 × 𝑃𝐴𝑅 × 𝐹K(/𝐹C(𝑃𝐴𝑅nc × 𝐹K(/𝐹C( aC7` Eq. 2.4 Where PARmax is a super-saturating light level chosen as a value three–fold higher than the light saturation parameter, Ek, derived from photosynthesis irradiance curves. 𝐹K(/𝐹C( aC7` is the PSII photochemical efficiency measured under PARmax. The photosynthetic turnover rate, 1/𝜏 (s-1), is taken as 1/τ at saturating irradiance where primary photochemistry is at a maximum (Gorbunov and Falkowski, 2020). 2.3.4 Non-Photochemical Quenching (NPQ) 37 We quantified NPQ as the relative decrease in fluorescence due to increased heat dissipation between samples measured under low light and 150 𝜇mol quanta m-2 s-1, following Bilger and Björkman (1990). 𝑁𝑃𝑄3* = (𝐹C − 𝐹C( )/𝐹C Eq. 2.5 This derivation assumes full relaxation of all NPQ processes in the dark regulated state. However, as demonstrated in the results, this assumption did not hold in our low-light measured samples. For this reason, we used an additional measure of NPQ based on the ratio of photochemical efficiency measured under low and high light, i.e. (𝐹K(/𝐹C( )/(𝐹L/𝐹C). 2.3.5 Statistical Analysis Pair-wise statistical relationships between measured variables were determined using Spearman Rank correlation tests. Samples from two regions of our transect (Lancaster Sound and Barrow Strait) were compared using Kruskal-Wallis test. Lilliefors test rejected the null hypothesis that underway data were normally distributed, so we report median rather than mean values of all photophysiological variables. Deviation from the median was determined as the median absolute deviation. All analyses were completed using Matlab (Mathworks, R2020a). 2.4 Results 2.4.1 Hydrographic properties Sea surface temperature (SST) and salinity varied significantly across our study region, reflecting the influence of different water masses and freshwater inputs. Sea surface temperature ranged from 0.6 to 15.5°C, and salinity ranged from 22.8 to 31.6 PSU. Salinity and SST strongly 38 covaried and exhibited a number of sharp transitions across prominent hydrographic fronts associated with freshwater input (Fig 2.2; Table 2.1). Surface layer (∼7m) phytoplankton biomass, approximated by in-situ Chla fluorescence measurements (non-FRRf), was low throughout the entire transect, with a mean Chla fluorescence equivalent to 0.15 ± 0.04 mg m-3 (n = 7200). These in situ Chla measurements showed significant diel periodicity, likely reflecting daytime fluorescence quenching effects. Both SST and salinity exhibited weak correlations with Chla (Table 2.1). Mixed layer depths recorded at the 16 profiling stations were shallow, with a mean of 9.2 ± 4.5 m. Mixed layer nitrate and nitrite concentrations at profiling stations were frequently below the detection limit (Appendix 2.1, mean 0.03 ± 0.049 µM, n = 16), indicative of post-bloom, nutrient-limited summer conditions. The mixed layer Chla concentration averaged across all 16 profiling stations was 0.40 ± 0.18 mg m-3 (n = 16). There was no statistically significant relationship between station Chla and mixed layer nitrate and nitrite concentrations (ρ = 0.09, p = 0.75, n = 16). Biological oxygen saturation, derived from ΔO2/Ar measurements, was greater than zero across the entire cruise track, implying net autotrophic conditions. Small-scale features in ΔO2/Ar distributions were observed across hydrographic frontal regions with only weak correlation to salinity or temperature measured along the cruise track (Table 2.1). 2.4.2 Photophysiological Properties The photophysiological parameters Fv/Fm and σijkk fluctuated over diel cycles throughout our sampling period (Fig 2.3a and 2.3b), showing daily maxima at night, and decreasing during daylight hours. Both of these variables exhibited a significant negative correlation with actinic surface PAR intensity averaged over the 5 min window prior to sample measurements (Table 2.2; Fig 2.4a and 2.4b). This result is consistent with previously noted in-situ photophysiological 39 diurnal patterns of daytime fluorescence quenching (Xie et al., 2018; Schuback and Tortell, 2019). We note, however, that these Fv/Fm and σijkk measurements were collected after 5 minutes of low-light, indicating the persistence of longer-lived quenching effects remained after this relaxation period. Thus dark-acclimation (i.e. complete NPQ relaxation) was not achieved in our measurement protocol, and the Fv/Fm and σijkk values presented here are representative of an intermediate state between in-situ and dark-acclimated values. Despite signs of strong light-dependent quenching, NPQ, calculated as (𝐹C − 𝐹C( )/𝐹C, was negatively correlated to surface PAR (Fig 2.4c). This surprising result can be explained by the derivation used here for NPQ, which measures the fractional change in NPQ between light and dark (low light) measurements, rather than total NPQ. As a result, this approach does not account for residual quenching present in samples after the five-minute relaxation period. To address this limitation, we used (𝐹K(/𝐹C( )/(𝐹L/𝐹C) to estimate the extent of residual quenching present in samples after five minutes of NPQ relaxation. As expected, this derived variable was well correlated to surface PAR (Fig 2.4d, Table 2.2), reflecting the effects of residual quenching and, potentially, short-term photoinhibition. To further examine light-independent sources of Fv/Fm and σijkk variability, we isolated night-time Fv/Fm and σijkk measurements collected under low ambient surface PAR. Due to the long summer daylight hours in the Arctic, only 1% of data points represent true night when surface PAR = 0. We thus chose light levels ≤ 100 µmol quanta m-2 s-1 to represent night-time conditions. By comparison, midday surface PAR ranged from 400–1000 µmol quanta m-2 s-1. The median night-time Fv/Fm for the entire transect was 0.36 ± 0.03 (n = 214), a value similar to the global average of 0.35 ± 0.11 (Falkowski, Lin and Gorbunov, 2017), but lower than the 0.55 median value previously recorded for late-summer assemblages in the Canadian Arctic (Martin 40 et al., 2010). The lowest Fv/Fm values were recorded at the beginning of the ship track (August 10–12) within Lancaster Sound (0.32 ± 0.03, n = 120), while Fv/Fm increased significantly in Barrow Strait (0.40 ± 0.02, n = 94), indicating greater photosynthetic potential in this region (Fig 3b). In contrast, night-time σijkk did not significantly vary between Lancaster Sound (250 ± 17.2) and Barrow Strait (241.1 ± 15.3) (Fig 2.3a). Note that these absolute σijkkvalues are somewhat lower than those reported in previous studies, likely reflecting our use of simultaneous excitation flashlets centered around 445, 470, 505, 535, and 590 nm. Relative to blue light, not all of these wavelengths are efficiently absorbed by phytoplankton, resulting in an apparent decrease σijkk (Schreiber, Klughammer and Kolbowski, 2012). As discussed below, σijkk values are also subject to physiological, taxonomic and environmental effects (David J Suggett et al., 2009). As observed for Fv/Fm, the photosynthetic efficiency measured under 150 µmol quanta m-2 s-1 actinic light (𝐹K(/𝐹C( F) displayed strong regional differences, increasing from 0.21 ± 0.01 (n = 283) in Lancaster Sound to 0.30 ± 0.02 (n = 198) in Barrow Strait. However, unlike the low-light measurements, 𝐹K(/𝐹C( F did not exhibit a diel signature and was remarkably consistent across samples measured within Lancaster Sound, where 𝐹K(/𝐹C( F was weakly correlated to surface PAR (Table 2; Figs 2.3e and 2.5). Whereas, 𝐹K(/𝐹C( F in Barrow Strait was strongly positively correlated to surface PAR (Table 2.2). Such regional differences in the 𝐹K(/𝐹C( F, surface PAR relationship imply differential capacities of Lancaster Sound and Barrow Strait phytoplankton to photo-acclimate to higher irradiances. 2.4.3 Photosynthesis-irradiance curves and ETRPSII comparisons 41 We used light response curves to compare FRRF-based ETRa and ETRk estimates. In this approach, ETRa (Eq 2.1) was plotted against actinic irradiance (Fig 2.6) to derive the maximum rate of charge separation at RCII (Pmax), the light-dependent increase in the charge separation rates (α), and the saturating light intensity (Ek). Fit parameters from these curves varied considerably, with mean values of Pmax, α, and Ek of 460 ± 345 e- s-1 RCII-1, 1.37 ± 0.57 e- RCII-1 quanta-1 m-2, and 358.0 ± 199.0 µmol quanta m-2 s-1, respectively (n = 85). A subsample of 25 light response curves was re-analyzed using the ‘kinetic’ approach (ETRk; Eq 2.5) and compared with ETRa values. This comparison revealed a strong correlation between the ETRPSII algorithms (ρ = 0.81, p < 0.001; Fig 2.7). However, the kinetics-approach produced consistently higher results than ETRa, with values 1.96 ± 1.2 times greater, on average, than ETRa (Fig 2.6). Along our cruise track, we observed a distinct spatial pattern in the ETRk:ETRa ratio. The highest values (3.26 ± 0.95, n = 14) occurred during the early part of our survey (August 10–13), with significantly lower ETRk:ETRa (1.42 ± 0.16, n = 11) observed near the end of our cruise, particularly in Barrow Strait (Fig 8). More generally, ETRk:ETRa displayed a strong negative correlation with Fv/Fm values (ρ = -0.60, p < 0.01, n = 25). As discussed below, this result suggests ETRk:ETRa decoupling is driven by photophysiological and environmental variability. 2.5 Discussion The primary focus of our work was to quantify phytoplankton photo-physiology and photochemical yields along our ship-track using active Chla fluorescence methods. With this in mind, we applied a sampling and analysis strategy to support both amplitude-based and kinetic analysis of FRRF data to derive ETR estimates. In the following, we first discuss the spatial 42 patterns in FRRF-based measurements in relation to nutrient concentrations and light histories of phytoplankton assemblages encountered along our sampling transect. We then examine potential factors leading to the uncoupling of ETRPSII estimates, including environmental and physiological variability, and potential influences of different data analysis methods. We conclude by discussing the implications of our results for future ship-board FRRf deployments. 2.5.1 Spatial variation in photo-physiology Across our survey region, we observed notable spatial patterns in FRRf data, with low 𝐹L/𝐹C and 𝐹K(/𝐹C( F values in Lancaster Sound, and significantly higher values in Barrow Strait. Low 𝐹L/𝐹C values are typical in the late-summer Arctic, and have been shown to increase in response to nitrate (but not phosphate) enrichments (Mills et al., 2018; Lewis et al., 2019; Ko et al., 2020). This result, coupled with the nutrient depletion observed at profiling stations along our cruise track (Table 2.2), suggests that the low 𝐹L/𝐹C values we observed likely reflect nitrogen deficiency. Iron (Fe)-limitation has also been shown to exert a strong negative effect on 𝐹L/𝐹C, coincident with increases in σijkkvalues. These Fe-dependent effects result from a combination of physiological responses (Schuback, Schallenberg, Duckham and Maldonado, 2015; Roncel, Antonio A González-Rodríguez, et al., 2016; Viljoen et al., 2018) and taxonomic shifts towards smaller cells (David J. Suggett et al., 2009). In contrast, the low 𝐹L/𝐹Cvalues recorded in the Lancaster Sound were not associated with high σijkk, rather night-time σPSII and Fv/Fm values had a weak positive correlation (ρ = 0.17, p < 0.05, n = 214). Based on these results, and the proximity of our sampling to land-based Fe sources (Colombo et al., 2020), 43 we ruled out iron limitation as a likely cause for the low photo-efficiencies observed from August 10–13 in Lancaster Sound. Notwithstanding the low background nutrient concentrations measured at profiling stations, chemical analyses from the Canadian Arctic Archipelago Rivers Program and Canadian Arctic GEOTRACES program have revealed elevated nitrate and nitrite concentrations within several rivers that discharge into Barrow Strait (Fig 2.9; Brown et al., 2019, 2020). In this region, we observed low surface water salinity and elevated Fv/Fm, suggesting a link between river input and increased photosynthetic efficiency, which we ascribe to nutrient inputs. Additionally, the greatest mixed layer depths were found at the two CTD profiling stations situated between Cornwallis Island and Devon Island (Table 2.2), indicating enhanced mixing associated with strong tidal currents, in agreement with model predictions of elevated mixing in Barrow Strait (K. G. Hughes et al., 2018). These observations suggest that spatial differences observed in FRRf-derived photo-physiology may reflect elevated nutrient availability in Barrow Strait, resulting from a combination of river inputs and mixing effects. Several mechanisms can explain the apparent effects of increased nitrogen availability on phytoplankton photo-physiology. First, increased nitrogen availability enables protein synthesis needed to repair photo-inactivated reaction centers (Murata et al., 2007; Zhenzhen Li et al., 2021). Indeed, phytoplankton in Barrow Strait appeared to have an increased ability to acclimate to higher light levels, as evidenced by the strong positive relationship between 𝐹K(/𝐹C( F and surface PAR (ρ = 0.67, p < 0.01, n = 94; Fig 2.5) observed in this region. By comparison, 𝐹K(/𝐹C( F exhibited only a weak relationship to surface PAR in Lancaster Sound where 𝐹K(/𝐹C( F was significantly reduced and relatively constant (Fig 2.5). This result suggests that, 44 phytoplankton in Lancaster Sound had a reduced capacity to maintain protein repair rates needed to prevent lasting photodamage at higher light levels. In addition to direct physiological effects, localized nutrient loading may indirectly affect FRRF signatures by stimulating a shift from small to larger phytoplankton species, for instance from nano-flagellates to diatoms (Mills et al., 2018). Unfortunately, we lack information on phytoplankton assemblage composition, and thus cannot directly examine any potential taxonomic effects on photophysiological signatures. However, such a shift from small to large cells would be expected to drive a decrease in σPSII, concurrent with increases in Fv/Fm (David J. Suggett et al., 2009), which we did not observe. Previous pigment analyses conducted in the Canadian Arctic Archipelago found that Lancaster Sound and Barrow Strait were both dominated by diatom species, followed by dinoflagellates, in summer (Viljoen et al., 2018). We thus infer that the spatially-divergent Fv/Fm values, coupled with persistently low σPSII, primarily reflect photophysiological nutrient effects in relatively large cells. 2.5.2 Light-dependent effects and residual NPQ We observed strong residual NPQ effects after five minutes of low light acclimation (Figs 2.3 and 2.4). Notably, the extent of these quenching effects was a predictable function of the short-term light history experienced by in situ phytoplankton assemblages (Fig 3d). Previous studies examining the drivers of NPQ variability (Algarra and Rudiger, 1993; Salvucci and Crafts-Brandner, 2004; Trimborn et al., 2014) suggest that the magnitude of NPQ effects at a given light level is tied to a number of environmental factors (e.g. temperature and CO2 concentrations), phytoplankton taxonomy and physiological status. Given these sources of variability, NPQ relaxation times needed for robust Fv/Fm measurements are expected to differ significantly across ocean regimes. In cold waters, such as those encountered along our ship 45 track, NPQ relaxation is slower (Sherman et al., 2020), and this may have contributed to the longer-lived quenching observed in our low-light samples. As the spatial and temporal resolutions of Fv/Fm and ETRa measurements are constrained by such acclimation periods, it is recommended that future FRRf field deployments conduct experiments using natural assemblages to determine the regional minimum relaxation period necessary to achieve steady-state dark-acclimation. This acclimation step can then be incorporated into underway FRRF protocols, resulting in more robust ETRa estimates, albeit with reduced measurement frequency. Such routine determinations of the minimum NPQ relaxation time requirements have not been commonly carried out for marine phytoplankton (Moore et al., 2006). As a result, there is little systematic knowledge of the global variability of NPQ relaxation times. Adopting such pre-study tests (or within protocol) as standard practice would improve current understanding of environmental and taxonomic controls on NPQ relaxation kinetics. Moreover, it may be necessary to adjust the length of the dark-acclimation period to reflect changing conditions over the duration of a cruise. We thus recommend that future work incorporate semi-regular assessments of dark-acclimation times into field-sampling protocols. This is an avenue I explore thoroughly in the following chapter. Ultimately, the appropriate low light/dark acclimation time depends on the research question of interest as sampling protocols with shorter acclimation periods provide higher resolution datasets more reflective of in-situ photophysiological conditions, whereas longer acclimation periods reduce NPQ effects on σPSII and Fv/Fm values, and thus simplify data interpretation. 46 2.5.3 Decoupling of ETRa and ETRk Across our study region, ETRk significantly exceeded ETRa (Fig 2.6), with the ratio of ETRk:ETRa varying strongly in response to phytoplankton photophysiological conditions (Fig 2.7). The ratio between ETRk and ETRa depends on a number of variables: 𝐸𝑇𝑅[: 𝐸𝑇𝑅7 = 𝐹L/𝐹C𝜎2344 × 𝜏 × 𝐹K(/𝐹C( aC7` × 6.022 × 10w Eq. 2.6 Importantly, all of the terms defining ETRk:ETRa are potentially responsive to shifts in nutrient abundances and phytoplankton taxonomic composition. Nutrient enrichment experiments have demonstrated increasing τQa with nutrient deficiency and elevated actinic irradiance (Mills et al., 2018; Gorbunov and Falkowski, 2020). Further, σPSII can also change with nutrient availability, but the observed percent change in σPSII following short-term nitrate enrichment is small compared with that of Fv/Fm (Mills et al., 2018; Ko et al., 2020). Baseline fluorescence, may also influence the terms used to define ETRk:ETRa. Photo-damaged, inactive, and dead material containing Chl are still capable of fluorescing and contribute to the non-variable portion of the FRRF signal, reducing normalized fluorescence amplitudes, Fv/Fm and 𝐹K(/𝐹C( F (Tortell, Schuback and Suggett, 2023). Indeed, analysis of our data revealed a strong correlation between Fv/Fm and 𝐹K(/𝐹C( F, (ρ = 0.93, p < 0.001, n = 25). However, in one sample, we recorded the highest Fv/Fm but the lowest 𝐹K(/𝐹C( F. This sample also showed the highest ETRk:ETRa value (Fig 2.7). After removing this outlier, the correlation between Fv/Fm and ETRk:ETRa strengthened from ρ = -0.60 before removal to ρ = -0.86 (p < 0.01 for both, n = 25 and 24, respectively). This outlier could potentially have been relatively nutrient replete but low-light acclimated, which would explain the high Fv/Fm but low 𝐹K(/𝐹C( F. In general, we conclude 47 that differential environmental and taxonomic sensitivities of the variables used to derive ETRa and ETRk can lead to discrepancies between these two productivity photosynthesis metrics and variability in ETRk:ETRa. Further investigation of ETRk and ETRa divergence is critical to inform our understanding of electron requirements for carbon assimilation and biomass production, particularly under nutrient-limiting conditions (D. J. Hughes, Varkey, et al., 2018). For instance, Schuback et al. (2015) found that iron-limited phytoplankton assemblages exhibited elevated ETRa and greater decoupling between ETRa and C-assimilation rates compared to iron-enriched assemblages. High ETRa in iron-limited samples were attributed to the higher σPSII values in iron limited samples. Since ETRk does not directly include σPSII, we speculate that C-assimilation may show less decoupling with this photosynthesis metric under low iron conditions. This hypothesis remains to be tested in future studies. 2.5.4 Computational considerations Beyond the physiological and taxonomic effects described above, the computational procedures used to analyze Chla fluorescence relaxation kinetics and derive turnover rates of electron transport molecules may also have a direct effect on the observed relationship between ETRk and ETRa. We derived τQa parameters used to calculate ETRk using the FRRF fluorescence transient fitting approach, as outlined in Eq 2.3, Section 2.3.3. This approach relies on numerically fitting the rate of change in fraction of closed RCII (CQa), driven by electron fluxes in and out of Qa. 𝜕𝐶b7𝜕𝑡 = 𝑒;< − 𝑒>9 Eq. 2.7 48 Here ein is equivalent to the rate of primary photochemistry induced by excitation flashlets, and eout is controlled by Qa reoxidation. Within FRRF Soliense software, ein is formulated as, 𝐸(𝑡, 𝜆) × 𝜎2344 × 1 − 𝐶b7(𝑡)1 − 𝐶b7(𝑡)𝑝 Eq. 2.8 By comparison, FIRe-based analysis of fluorescence transients deviates from FRRf by including an additional term to describe reaction center closure by background actinic light (PAR) (Gorbunov and Falkowski, 2020). As a result, Eq 2.9 is modified as: 𝐸(𝑡, 𝜆) × 𝜎2344 × 1 − 𝐶b7(𝑡)1 − 𝐶b7(𝑡)𝑝 + 𝑃𝐴𝑅(𝜆) × 𝜎2344 × 1 − 𝐶b7(𝑡)1 − 𝐶b7(𝑡)𝑝 Eq. 2.9 The FRRF based analysis does not include a PAR term, as it is presumed that constant background light influences the baseline of the fluorescence signal, but does not contribute to dynamic changes in fluorescence measured over the course of an ST flash [Z. Kolber, pers. comm.]. In this interpretation, CQa(t) represents the fraction of initially available reaction centers closed by excitation pulses, such that CQa (t = 0) always equals 0. Gorbunov and Falkowski (2020) conducted a primary analysis of differences in τQA values retrieved from FRRF and FIRe fluorescence relaxation analyses. Their results showed that when the effect of background light was explicitly included in numerical formulations, FIRe-derived τQA values displayed strong actinic light dependencies, increasing with irradiance until plateauing around saturating irradiances (Ek). By contrast, their FRRf- 49 derived τQA values varied little with actinic irradiance and resulted in a markedly shorter photosynthetic turnover time. Our own FRRF-derived τQA values displayed a weak relationship with applied actinic irradiances (ρ = 0.17, p < 0.01, n = 203). It is possible that applying the FIRe model fit to our own data may have also yielded slower photosynthetic turnover times, and therefore lower ETRk estimates, but it is unclear to what extent the alternative model may have affected our ETRk results and the observed decoupling between ETRk and ETRa. Going forward, it will be important to separate physiological drivers of ETRk and ETRa decoupling from offsets resulting from the use of different mathematical numerical approaches to data analysis. Discrepancies between ETRk and ETRa that cannot be explained by different derivations of τQa must be attributable to differences between the two ETRPSII algorithms themselves. This raises the important question of which approach is most accurate, as neither has been established as a “gold standard”. Addressing this issue will require parallel independent measurements of PSII activity, such as gross oxygen evolution measurements from 18O experiments, as preformed previously for ETRa, e.g. David J. Suggett et al., (2009), but not ETRk. Such fluorescence-independent validations of ETRa and ETRk are critically lacking, and will help elucidate the taxonomic and environmental influences on FRRF-based productivity photochemistry measurements (D. J. Hughes, Campbell, et al., 2018) This, in turn, will be of significant practical utility to FRRF users seeking to derive ship-based primary productivity estimates. 2.6 Conclusions and future recommendations Fast Repetition Rate fluorometry offers a means to rapidly assess both physiological status and photosynthetic electron transport rates of phytoplankton. The aim of this study was to evaluate an autonomous protocol for high resolution FRRf measurements of phytoplankton 50 physiology, and to compare two alternative models for deriving primary productivity photochemistry estimates from FRRF data. Our results demonstrate significant residual NPQ effects after five minutes of low light acclimation, suggesting the need for extended low light acclimation periods, which would significantly decrease measurement frequency. Our findings also illustrated localized regions of elevated Fv/Fm, likely linked to local nitrogen loading by freshwater inputs and tidal mixing. Although the amplitude-based and kinetic-based derivations of photosynthetic electron transport rates were well correlated, absolute agreement between estimates appeared to be affected by phytoplankton photo-physiology, with the two models diverging under nutrient-limited conditions. As a first step, resolving discrepancies between ETRa and ETRk will require consensus regarding the analysis of raw fluorescence transient data. This aim is fundamental for consistent data reporting among the growing community of FRRF and FIRe users (Schuback, Philippe D Tortell, et al., 2021). Second, ETRa and ETRk should be validated against fluorescence-independent measures of productivity. Considering that measurement of ETRk does not require a dark acclimation step, short (∼ 5 min.) acclimation steps should be sufficient to accurately derive this term. Based on these findings, the ETRk model, if validated against independent productivity metrics, may be advantageous for high resolution evaluations of in-situ photosynthetic rates under ambient light conditions. Yet, discrepancies in the methods used to derive 𝜏 from fluorescence kinetics must be resolved within the community first. 51 2.7 Tables and Figures Figure 2.1 Study region map. The inset globe shows the geographic region of interest outlined in blue. The detailed map shows the numbered CTD stations sampled in August 2019, overlayed on bathymetric contours. We use the 200m bathymetric contour line around 92°W to separate Lancaster Sound and Barrow Strait. The ship track is shown in red. The complete list of geographic coordinates sampled is available in the Polar Data Catalogue (doi: 10.5884/12715). The map was produced in Matlab (R2020a) using the publicly available m_map package 52 Figure 2.2 Summary of oceanographic conditions. Hydrographic conditions, phytoplankton biomass and biological oxygen saturation measured in surface waters (∼ 7 m depth) along the cruise track. Shaded area on Aug. 13–14 marks measurements made in Barrow Strait. Table 2.1 Correlation of underway hydrographic variables. Results of Spearman Rank correlation analyses among each underway hydrographic variable are displayed. ** is used to indicate p values < 0.01. In all instances n = 7200. Note, this sample number does not account for autocorrelation. 53 Salinity (psu) Temp. (°C) Chla (mg m-3) 𝚫O2/Ar Salinity (psu) -- 0.70** 0.06** 0.09** Temp. (°C) -- -0.17** -0.12** Chla (mg m-3) -- 0.19** 𝚫O2/Ar -- 54 Figure 2.3 Summary of photophysiological conditions. Semi-continuous FRRf measurements of photo-physiology (black dots) are superimposed over the in-situ surface PAR (blue line). A loess smoothing function was applied to photo-physiological measurements (red line). Shading denotes the Barrow Strait portion of the transect. 55 Figure 2.4 Recent light history effects on photo-physiology measured under low light. FRRf-derived photophysiological parameters measured under low light are plotted against in-situ surface PAR at the time of sample acquisition. Panels (a) and (b) show σPSII and Fv/Fm measurements made after 5 min of low light exposure. Panels (c) and (d) show NPQ and residual quenching measured after low light treatment. Lines of best fit are shown for Barrow Strait (solid line) and Lancaster Sound (dashed line). Full regression analyses results are reported in Appendix 2.2. Error bars show standard error, but are often concealed by size of data symbols. Table 2.2 Correlation of underway photophysiological variables and surface PAR. Spearman rank correlation coefficients between underway photophysiological parameters and a. b. c. d. 56 recent surface PAR exposure, analyzed by region and for the total dataset. ** is used to indicate p << 0.01. Photophysiological variables Surface PAR Lancaster Sound n = 283 Barrow Strait n = 198 Total n = 481 FV/FM -0.72** 0.27** -0.72** σijkk -0.79** -0.61** -0.50** NPQ -0.66** -0.65** -0.65** F′q/F′m150: FV/FM 0.81** 0.66** 0.68** F′q/F′m150 0.21** 0.67** -0.01 57 Figure 2.5 The PSII photochemical efficiency measured under 150 µmol quanta m-2 s-1 in relation to natural surface irradiance at the time of sample acquisition. Lines of best fit are shown for Barrow Strait (solid line) and Lancaster Sound (dashed line). Full regression analyses results are reported in Appendix 2.2. Error bars show standard error, but are often concealed by size of data symbols. 58 Figure 2.6 Consolidated mean ETRa (red) and ETRk (black) estimates at each light step of the 25 reprocessed P-I curves. Error bars represent the standard error of all individual measurements from all curves at each light step. Curves were produced using the photosynthesis-irradiance function described by Webb, Newton and Starr (1974). 59 Figure 2.7 Comparison of ETRa versus ETRk.Each point represents the mean ETRPSII value at a given light intensity within a Photosynthesis-Irradiance curve, and error bars are the standard error. The dashed line indicates a 1:1 relationship. 60 Figure 2.8 Parameters contributing to the ETRk:ETRa ratio measured in Lancaster Sound (LS) versus Barrow Strait (BS). Horizontal lines within each boxplot represent the median. The upper and lower edge of each box demarks upper and lower quartiles, respectively, while whiskers extend over the entire data range, excluding outliers. Outliers, determined as data points falling over 1.5x the interquartile distance away from box edges, appear as unfilled circles. p values in each subplot are results from 2-group Kruskal-Wallis tests. All data shown here was collected during the 25 reprocessed P-I curves. Figure 2.9 Spatial distribution of riverine nutrient inputs and photochemical efficiency (Fv/Fm) along the ship track. River contributions of nitrate and nitrite are indicated by the size of grey bubbles. The largest inputs of nitrate and nitrite in the region are concentrated in the strait between Cornwallis and Devon Islands, coincident with observations of raised Fv/Fm values. Larger circles are used to denote night-time measurements of Fv/Fm, whereas smaller triangles denote day-time measurements. 61 Chapter 3: Fluorescence-based primary productivity measurements are influenced by non-photochemical quenching dynamics in Arctic phytoplankton 3.1 Summary Chlorophyll fluorescence-based estimates of primary productivity typically include dark or low-light pre-treatments to relax non-photochemical quenching (NPQ), which influences the relationship between PSII photochemistry and fluorescence yields. The time-scales of NPQ relaxation vary significantly between phytoplankton taxa and across environmental conditions, creating uncertainty in field-based productivity measurements derived from fluorescence. To address this practical challenge, we used fast repetition rate fluorometry to characterize NPQ relaxation kinetics in Arctic Ocean phytoplankton assemblages across a range of hydrographic regimes. Applying numerical fits to our data, we derived NQP relaxation life times, and determined the relative contributions of various quenching components to the total NPQ signature across measurement sites. Relaxation kinetics were best described as a combination of fast-, intermediate- and slow-relaxing processes, operating on time-scales of seconds, minutes, and hours, respectively. Across sampling locations and depths, total fluorescence quenching was dominated by the intermediate quenching component. Our results demonstrated an average NPQ relaxation rate of 20 ± 1.9 minutes, with faster relaxation among high light acclimated surface samples relative to low-light acclimated sub-surface samples. We also used our results to examine the influence of NPQ relaxation on estimates of photosynthetic electron transport rates (ETR), testing the commonly held assumption that NPQ exerts proportional effects on light absorption (PSII functional absorption cross section, sPSII) and photochemical quantum efficiency (FV/FM). This assumption was violated in a number of phytoplankton assemblages that 62 showed a significant decoupling of sPSII and FV/FM during NPQ relaxation, and an associated change in ETR estimates. Decoupling of sPSII and FV/FM was most prevalent in samples displaying symptoms photoinhibition. Our results provide insights into the mechanisms and kinetics of NPQ in Arctic phytoplankton, with important implications for the use of FRRF to derive non-invasive, high-resolution estimates of photosynthetic activity in polar marine waters. 3.2 Introduction For over two decades, single-turnover active Chlorophyll fluorescence (ST-ChlF) techniques have been used for rapid, non-invasive photochemistry measurements, exploiting the inverse relationship between Photosystem II (PSII) photochemical and fluorescence yields (Kolber, Prášil and Falkowski, 1998). In applying ChlF measurements, it is important to account for competitive heat dissipation processes, known as non-photochemical quenching (NPQ), which decrease ChlF without an associated increase in photochemistry (Müller, Li and Niyogi, 2001). In broad terms, NPQ is upregulated under excess excitation conditions when PSII energy absorption outpaces downstream photosynthesis. Observable NPQ results from several underlying ChlF quenching mechanisms (Triantaphylidès and Havaux, 2009) whose presence and relative amplitudes vary across taxa and environmental conditions. Rapid energy-dependent quenching (qE) is induced by lumen acidification during photosynthetic electron transport, which leads to protonation of PsbS binding sites on light harvesting complexes (LHC; Lavaud, 2007). The resulting LHC conformational changes increase PSII heat losses, thereby competitively downregulating charge separation. Lumen acidification also promotes de-epoxidation of LHC xanthophyll pigments, converting them to fluorescence quenching, anti-oxidant forms (Fernández-Marín et al., 2021). 63 In some phytoplankton groups, not including diatoms, ‘slow ’NPQ (qT) involves migration of mobile LHC units from PSII to PSI during state transitions to balance incoming excitation energy throughout the electron transport chain (Owens, 1986; Chukhutsina, Büchel and Van Amerongen, 2014). Finally, long-term quenching (qI) results from photo-inactivation or photodamage, which induces PSII core protein synthesis and repair (Campbell and Serôdio, 2020). Collectively, these and other quenching mechanisms mitigate PSII over-reduction and associated damage. Irrespective of the underlying mechanisms, NPQ directly impacts ChlF-based estimates of PSII photosynthetic electron transport rates (ETR), and low light or dark NPQ relaxation periods are commonly applied prior to sample measurement to minimize these effects. NPQ relaxation protocols can be optimized in laboratory experiments with individual phytoplankton species, but diverse NPQ responses across taxa (Goss and Lepetit, 2015; Croteau et al., 2021) and variable environmental conditions (Zhenzhen Li et al., 2021) make it difficult to identify appropriate NPQ relaxation time-scales for field measurements. Different field studies have thus employed a wide variety of protocols, with dark or low light applications ranging from 5 – 60 minutes (e.g. Alderkamp et al., 2010; Walter, Peters and van Beusekom, 2017; Schallenberg et al., 2020). The use of lengthy NPQ relaxation times is particularly challenging for continuous, underway sampling, potentially leading to photo-physiological shifts from in-situ states, and reduced measurement frequency. Despite the potentially confounding effects of NPQ on ChlF measurements, the NPQ signal itself provides valuable photo-physiological information. Among oceanographers, there is interest in NPQ as a proxy for the electron requirements for carbon fixation (Schuback, 64 Schallenberg, Duckham and Maldonado, 2015; Schuback et al., 2016, 2017; D. J. Hughes, Varkey, et al., 2018), and as an indicator of oxidative stress associated with iron limitation (Ryan-keogh et al., 2020; Schallenberg et al., 2020). Other environmental factors, including high light, low nutrients and low temperature, can lead to oxidative stress. Such conditions exist across much of the Arctic Ocean, where phytoplankton are subject to extreme oceanographic conditions. In the Arctic summer, high solar radiation and long daylight hours within the stratified and nutrient-poor surface layer contribute to strong NPQ signatures (Lacour et al., 2018; Lewis et al., 2019). During the summer – fall transition, light availability decreases rapidly and wind-mixing resupplies the surface layer with nutrients (Ardyna et al., 2014). Historically, in situ studies of Arctic phytoplankton have focused on the mid-summer season. However, on-going climate changes are expected to increase the frequency of Arctic fall-blooms associated with longer ice-free seasons (Ardyna et al., 2014; Lewis, Van Dijken and Arrigo, 2020), motivating further study of phytoplankton photo-physiological properties outside the mid-summer months. To date, several field studies have demonstrated the effects of environmental variability on NPQ (David J. Suggett et al., 2009; Gorbunov et al., 2011; Alderkamp et al., 2013; Schallenberg et al., 2020), but direct studies of NPQ relaxation kinetics have largely been restricted to laboratory cultures of single phytoplankton species (Roháček et al., 2014; Blommaert, Chafai and Bailleul, 2021) or land plants (Long et al., 2022). In this study, we describe NPQ relaxation kinetics in natural Arctic phytoplankton across a range of environmental conditions and taxonomic composition. We also present a mathematical framework to justify short NPQ relaxation protocols for ST-ChlF-based ETR estimates, and provide the first in-situ analysis of NPQ effects on derived photochemistry estimates. Our results 65 expand current understanding of NPQ relaxation dynamics in natural marine phytoplankton assemblages, and provide novel insights into the effects of low-light acclimation protocols on fluorescence-based primary photochemistry estimates. 3.3 Theory: Effects of NPQ relaxation on ETR estimates To derive ETR from ST-ChlF measurements, photosynthetically available radiation (PAR, µmol photons m-2 s-1) is multiplied by the functional absorption cross section of PSII (𝜎′PSII, m2 photon-1, or Å2 PSII-1), and the fraction of PSII reaction centers open for photochemistry (F'q/F'v, dimensionless). ETR = 𝑃𝐴𝑅 ∗ 𝜎2344( ∗ 𝐹′𝑞/𝐹′𝑣 Eq. 3.1. The prime notation (') indicates measurements made under background light, which drives both photochemistry and NPQ induction, decreasing ChlF yields. Lower ChlF yields decrease measurement signal to noise ratios, affecting the statistical quality of derived parameters, particularly 𝜎′PSII (Schuback, Philippe D. Tortell, et al., 2021). For this reason, samples are often exposed to low light prior to ChlF measurements to allow for re-opening of PSII and NPQ relaxation. Light-regulated terms in Eqn. 1 can then be substituted by ‘dark-regulated ’equivalent terms for higher confidence ETR estimates (Suggett D, Moore M, 2010). 𝐸𝑇𝑅 = 𝑃𝐴𝑅 × 𝜎2344 × 𝐹′𝑞 𝐹′𝑚 × (𝐹* 𝐹n )DF Eq. 3.2. Importantly, this formulation assumes NPQ has an equal effect upon PSII photochemical efficiency (FV/FM) and 𝜎PSII. This assumption is based on the definition of 𝜎PSII as the product of 66 the PSII optical absorption area (𝑎2344) and FV/FM (Kolber, Prášil and Falkowski, 1998; Kromkamp and Forster, 2003), such that: ( = ∗7∗7 Eq. 3.3. From the relationship presented in Eq. 3.3, it follows that robust ETR estimates can be obtained without extended NPQ relaxation periods,  = ∗7∗7 Eq. 3.4. Here, ′′ notation implies measurements under low-light or darkness, when NPQ is only partially relaxed. Under these conditions, samples are not fully acclimated to low light, but the PSII pool reopens for photochemistry and rapidly reversible NPQ components are relaxed, improving data signal to noise. Substituting terms derived in Equation 3.4, Equation 3.2 can be rewritten as: 𝐸𝑇𝑅 = 𝑃𝐴𝑅 × 𝜎((2344 × 𝐹(𝑞 𝐹(𝑚 × 𝐹((𝑣 𝐹((𝑚 DF Eq. 3.5. This modified approach does not require full NPQ relaxation, thus enabling rapid measurements amenable to high-frequency autonomous data collection. Such high-resolution measurements are necessary to characterize in-situ phytoplankton responses to dynamic environments and fine-scale oceanographic variability (e.g. narrow hydrographic fronts or upwelling plumes). 67 3.4 Materials and Methods 3.4.1 Field sampling We present results from two ship-based surveys of Arctic phytoplankton assemblages during summer and early fall, 2022. During June 24 – July 7, 2022, surface seawater was collected from an underway supply system aboard the Le Commandant Charcot during a circumnavigation of the Svalbard Archipelago (Fig. 3.1). This cruise was operated primarily for tourism, with limited opportunities for discrete scientific sampling, and limited ancillary data. Later in the season, additional discrete and continuous sampling was conducted in the Canadian Arctic (CAA; Fig. 3.1) aboard the CCGS Amundsen from September 23 to October 16, 2022, as part of the ArcticNet program. During both cruises, photophysiological properties of phytoplankton assemblages were monitored with a bench-top LIFT Fast Repetition Rate Fluorometer (FRRF; Soliense Inc.) using a single-turnover (ST) flash protocol, as described in Ch 2. Background fluorescence blanks were prepared by gently filtering samples through 0.2 um GF/F filters. Blanks were prepared for each discrete sample, and once daily for continuous underway data, with derived blank values subtracted from all measurements. Seawater for continuous, underway sampling was obtained from the ship’s surface intake supply, drawn from a nominal depth of ~ 7 m. A peristaltic pump actuated by Soliense LIFT software was used to draw water into the measurement cuvette in synchronization with autonomous data acquisition routines. The FRRF was programmed to continuously collect 10 successive ST-ChlF transient measurements per sample without background illumination. Between samples, the cuvette was flushed with water from the underway line for 2 minutes. This underway sampling was interrupted every 4 hours by NPQ relaxation experiments (see section 3.4.2), and by rapid light curves (PvE measurement; data not shown here). In the CAA, 68 additional discrete samples were collected using Niskin bottles from the surface (~ 1 m) and subsurface Chl maxima (10-40 m). In addition to FRRF measurements, the ship’s continuous seawater line supplied an underway flow-through system for hydrographic measurements on both vessels. The flow-through system on the CCGS Amundsen included a Seabird thermosalinograph (SBE 38), and a WETstar fluorometer (WET Labs) for [Chla] estimates. On Le Commanant Charcot, temperature and salinity were measured with a SBE 45 Seabird thermosalinograph, but no fluorometer was available. Underway hydrographic measurements in the CAA were supplemented with on-station CTD casts. Mixed layer depths were calculated with a density-difference criterion of 0.02 kg m-3, following Noh and Lee (2008). Niskin bottle sampling was used to calibrate a CTD mounted nitrate sensor (Seabird SUNA) and Chla fluorometer (Seapoint chlorophyll fluorometer). Depth profile data were provided by the Amundsen Science group of Université Laval, and are available from the Polar Data Catalogue (10.5884/12713). On the CAA cruise, we collected samples for photosynthetic pigment analysis, as a source of phytoplankton taxonomic information. For these samples, two 1L dark Nalgene bottles were filled directly from Niskin bottles from the surface and subsurface chlorophyll maxima depth. Within 1 hour of sampling, samples were filtered under low light onto 45mm GF/F filters (Whatman, 0.7 𝜇m pore size) and immediately placed in a -80oC freezer. Filters were shipped on dry ice to the Estuarine Ecology Laboratory at the University of South Carolina for high performance liquid chromatography analysis of pigment concentrations. Pigment data were analyzed using CHEMTAX software to identify the relative abundances of phytoplankton groups 69 (Wright and Jeffrey, 2006; Higgins, Wright and Schlüter, 2011). An initial pigment matrix, specific for Arctic phytoplankton was taken from Coupel et al. (2015). To determine photoprotective to photosynthetic ratios (PP:PS) of carotenoid concentrations, the total concentrations of Alloxanthin, Carotene, Diadinoxanthin, Diatoxanthin, Zeaxanthin, and Antheraxanthin were divided by the total concentrations of 19’-butanoyloxyfucoxanthin, fucoxanthin, 19’-hexanoyloxyfucoxanthin, and peridinin. 3.4.2 Photo-physiology and NPQ relaxation kinetics Primary photophysiological parameters (See Appendix 3.1) were derived by fitting the biophysical model of Kolber, Prášil and Falkowski (1998) to ChlF transients produced by the ST flash protocol. Excitation flashlets consisted of 25,000 𝜇mol photons m-2 s-1 light pulses centered around 445 nm with 1 𝜇s duration separated by dark intervals of 2.5 𝜇s. Physiological parameters were monitored during NPQ relaxation curves, which were initiated in freshly collected samples exposed for one-minute to 500 𝜇mol photons m-2 s-1 irradiance supplied simultaneously by 5 colored lamps (445, 470, 505, 530, and 590 nm), providing 100 𝜇mol photons m-2 s-1 each. Following high light exposure, low intensity, far-red actinic light (5 𝜇mol photons m-2 s-1; 730 nm) was applied to samples to relax NPQ. During the relaxation period, the coefficient of non-photochemical quenching was calculated as the ratio of quenched to unquenched variable fluorescence, 𝑞𝑁(𝑡) = 1–𝐹L(((𝑡)/𝐹L (Roháček, 2010). Total qN is the sum of underlying quenching components. Following the approach of Roháček (2010), we fit a three-component exponential decay model to the observed qN time series to describe NPQ relaxation kinetics and the relative contributions of fast, intermediate and slow quenching (Fig 3.2): 70 𝑞𝑁(𝑡) = 𝑞𝑁(𝑞6789𝑒D I + 𝑞;<9𝑒D + 𝑞𝑠𝑙𝑜𝑤𝑒− 𝑡𝑇𝑠𝑙𝑜𝑤) Eq 3.6. The three components of NPQ have relaxation life-times of 𝜏6789, 𝜏;<9, and 𝜏𝑠𝑙𝑜𝑤. The initial amplitudes of qfast, qint, and qslow sum to 1, and total quenching immediately following high light exposure equals qN0. The total NPQ relaxation life time was calculated as the weighted mean of the individual relaxation life times: 𝜏K¢ = 𝑞6789 ∗ 𝜏6789 + 𝑞;<9.∗ 𝜏;<9 + 𝑞8=>? ∗ 𝜏8=>? Eq. 3.7. For all measurements, curve fits with R2 values < 0.9 were excluded from further analysis. To avoid overfitting, we applied Akaike’s Information Criterion to compare our three-component model against a two-component model. With few exceptions, the three-component model outperformed the two-component model, justifying the generalized fitting of fast, intermediate and slow relaxation components. 3.4.3 Photosynthetic electron transport rates (ETRPSII) Photosynthetic electron transport rates were calculated according to Eq. 3.5. The light-regulated photochemical yield of PSII was taken as the average B£B measured during the high light exposure period. Values of 𝜎′′2344 and 𝐹*((/𝐹n(( recorded throughout the relaxation period were used to follow the change in ETR(500 𝜇mol photons m-2 s-1) during NPQ relaxation. 71 3.4.4 Statistical analysis We performed Kruskal-Wallis and multi-comparison tests to compare NPQ relaxation kinetic parameters between CAA surface, subsurface, and Svalbard Archipelago phytoplankton assemblages. Pearson correlation coefficients were used to assess the co-variation in 𝜎((2344 and 𝐹L((/𝐹C(( during NPQ relaxation. Spearman rank correlations were used to examine relationships between environmental and photophysiological variables. All curve fitting was performed using least squares methods using Matlab (R2020a). 3.5 Results and discussion 3.5.1 NPQ relaxation kinetics Across all samples, application of a short high light treatment (500 µmol photons m-2 s-1) induced a strong initial NPQ response, with qN0 ranging from 0.80 to 1 and showing an average relaxation life-life (𝜏K¢) of 20.0 ± 1.9 minutes. Significant differences in qN0 were detectable between sampling regions. The greatest values of qN0 were observed in the subsurface CAA (average = 0.93 ± 0.01), followed by the surface CAA (average = 0.90 ± 0.01), and surface Svalbard (average = 0.88 ± 0.01) samples (Table 3.1). We observed a positive correlation between the magnitude of qN0 and the life time for relaxation (r = 0.47, p << 0.01), with the highest 𝜏K¢ in the subsurface CAA, followed by the surface CAA, and surface Svalbard samples. The total qN relaxation signal was well described across sampling locations as a combination of three kinetic relaxation components operating on time-scales of seconds (qfast), minutes (qint.), and hours (qslow). Variability in 𝜏K¢ among samples was driven by differences in the relative amplitudes of the individual quenching components. For example, qslow was minimal in Svalbard samples where qN relaxed fastest. Relative to the Svalbard samples, the contribution 72 of qslow to total qN was two-fold higher in CAA surface samples, and four-fold higher in subsurface CAA samples. The reverse pattern was apparent in qint, whose contribution to qN was greatest in Svalbard samples, and lowest in CAA subsurface samples. In contrast, qfast did not display significant differences between sampling regions. Notable regional differences in NPQ dynamics appeared to be related to variability in mean daily irradiance exposure (Table 3.1). Across the three datasets, mean daily irradiance displayed a positive correlation with qint (r = 0.39, p << 0.01), and negative correlations with slow qslow (r = -0.45, p << 0.01) and 𝜏K¢ (r = -0.46, p << 0.01). This result provides evidence that light acclimation status influences NPQ relaxation dynamics, with rapidly-relaxing components of NPQ preferentially upregulated under higher light conditions. Although the amplitudes of qfast, qint and qslow varied between samples, relaxation lifetimes (𝜏𝑓𝑎𝑠𝑡, 𝜏𝑖𝑛𝑡, 𝜏𝑠𝑙𝑜𝑤) for each component were remarkably similar (Table 3.1), supporting their interpretation as reflecting distinct mechanisms. Consistent with previous observations (Malnoë, 2018), 𝜏6789 was ~30s, while 𝜏;<9 was around 5 minutes, and 𝜏𝑠𝑙𝑜𝑤 was several hours. Previous studies of NPQ kinetics have attributed fast relaxation quenching (lifetimes ranging from ~10 to 100 seconds) to rapid energy-dependent quenching (qE) associated PsbS de-protonation. Intermediate quenching with life times around 10 minutes (Schansker, Tóth and Strasser, 2006; Roháček et al., 2014) have been interpreted as state-transition related quenching (qT), while and slow quenching relaxing (over several hours) has been attributed to long-lived photoinhibition (qI; Nilkens et al., 2010; Roháček, 2010). We observed that intermediate quenching was the most significant contributor to qN0 across all samples, with relaxation life times faster than LHC de-phosphorylation rates required to reverse state transitions (McCormac, 73 Bruce and Greenberg, 1994), but within range of zeaxanthin epoxidation rates (Nilkens et al., 2010). We thus postulate that the intermediate component of NPQ relaxation, commonly attributed to qT in other studies, may be associated with xanthophyll cycling dynamics (qZ) in our samples, rather than with state transitions. This idea is supported by the significantly higher PP:PS ratios in surface CAA samples (with high qT), relative to subsurface samples. Further, qT pathways are not known to exist in diatoms (Lavaud, 2007), which were prevalent in our study regions, particularly in southern Baffin Bay (See section 3.5.3.2, and Appendix 3.2). Diatoms have instead been shown to exhibit strong xanthophyll-regulated quenching (Blommaert, Chafai and Bailleul, 2021), consistent with our observations. 3.5.2 Correlation between 𝝈𝑷𝑺𝑰𝑰 and FV/FM during NPQ relaxation and implications for ETRPSII estimates As expected, NPQ relaxation led to a consistent increase in 𝐹L((/𝐹C(( following the removal of high light (Fig 3a). In contrast, there was much greater variability in 𝜎′′2344 responses to NPQ relaxation, with instances of both increasing and decreasing 𝜎′′2344 over the time-course of low light exposure. As a result, changes in 𝐹L((/𝐹C(( and 𝜎′′2344 were sometimes uncoupled during NPQ relaxation, with correlation coefficients ranging from 0.98 to -0.89 (Fig. 3.3c) across different measurement locations. Divergence between 𝐹L((/𝐹C(( and 𝜎′′2344 exerted a direct effect on derived ETR estimates during NPQ relaxation. As hypothesized, samples with synchronous changes in 𝐹L((/𝐹C(( and 𝜎′′2344 displayed minimal changes in ETR estimates (Eqn. 3.5) over the time course of the experiment (Fig 3.3b). In contrast, ETR decreased by as much as 50% in samples where 𝐹L((/𝐹C(( and 𝜎′′2344 showed significant uncoupling. These results challenge the notion that ETR estimates are unaffected by NPQ relaxation time due to coordinated 𝐹L((/𝐹C(( and 𝜎′′2344 responses. 74 The expectation of equal NPQ effects on 𝜎𝑃𝑆𝐼𝐼 and FV/FM is based on the definition of 𝜎𝑃𝑆𝐼𝐼 as the product of FV/FM and the absorption coefficient of PSII photochemistry, 𝑎𝑃𝑆𝐼𝐼. In turn, 𝑎𝑃𝑆𝐼𝐼, is determined by the ratio of the absorption coefficient of light harvesting complexes (𝑎𝐿𝐻𝐶) and the concentration of functional PSII reaction centers (Silsbe et al., 2015). During ST-ChlF protocols, 𝑎𝑃𝑆𝐼𝐼 is typically assumed to be constant over the relatively short time-course of measurements. In practice, however, a decrease in 𝜎𝑃𝑆𝐼𝐼 during NPQ relaxation could be explained by increasing numbers of functional PSII reaction centers, as cells recover from photoinhibition, so that 𝑎𝐿𝐻𝐶 is 'diluted' across more PSII centers. We examined the potential role of photoinhibition in the NPQ signal, based on the correlation between 𝐹>(( and 𝐹C(( during NPQ relaxation curves. Regulated NPQ (qE and qZ) is expected to cause proportional quenching of Fo and Fm, while photoinhibition leads to increased Fo relative to Fm (Gilmore, Hazlett and Debrunner, 1996; Müller, Li and Niyogi, 2001). Our analysis showed that the relationship between 𝜎′′2344 and NPQ was related to underlying patterns in 𝐹>(( and 𝐹𝑚′′ . Across all samples, 𝐹>(( and 𝐹C(( increased during NPQ relaxation periods, but subtle differences in 𝐹>(( and 𝐹C(( recovery kinetics led to some variability in the relationship between the two terms, with correlation coefficients ranging from 0.55 to > 0.99. Out of 125 NPQ relaxation experiments, 39 samples displayed perfectly synchronized 𝐹>(( and 𝐹𝑚′′ responses (𝜌 >= 0.99). Among these samples, 𝜎′′2344 increased over the time-course of NPQ relaxation and showed a strong negative correlation with NPQ (𝜌 =-0.87). These samples also displayed tightly coupled 𝜎′′2344 and F′′v/F′′m responses (𝜌 = 0.80), near constant ETR estimates during the NPQ relaxation time course (𝜌 = -0.02), and only a small contribution of long-lived photoinhibitory quenching (qslow) to the overall qN signal (qslow = 0.02 ± 0.01). In contrast, samples with less 75 synchronized 𝐹>(( and 𝐹C((responses (𝜌 <= 0.90) displayed higher degrees of qslow (0.08 ± 0.01), and weaker relationships between 𝜎′′2344 and F′′v/F′′m (𝜌 = 0.20). In these latter samples, ETR estimates were more sensitive to dark relaxation life time, decreasing in response to relaxation life time (𝜌 = -0.35). These results suggest that the assumption of constant 𝑎2344 may be violated due to plasticity in the concentration of functional RCIIs under conditions of initial photoinhibition followed by recovery. It follows from this that choice of NPQ relaxation protocol will have a particularly strong influence on ETR results in samples exposed to potential photoinhibitory light regimes. 3.5.3 Environmental and taxonomic controls on NPQ relaxation kinetics and ETR estimates The considerable variability we observed in NPQ relaxation kinetics reflects differences in the photo-acclimation state of phytoplankton assemblages across our study region. This photophysiological variability is, in turn, driven by hydrographic and taxonomic properties associated with spatial and temporal (summer vs. fall) differences between samples. To minimize confounding seasonal effects, we conducted separate analysis of environmental and taxonomic effects on NPQ relaxation dynamics and ETR for the CAA and Svalbard datasets. 3.5.3.1 Svalbard Coastal oceanography and photo-physiology The Svalbard Archipelago is situated on the eastern boundary of Fram Strait, where the bulk of water mass transfer occurs between the Arctic and Atlantic basins (Walczowski, 2014). Off the west coast of Svalbard, the West Spitsbergen Current (WSC) delivers warm, saline Atlantic water north to the Arctic Basin, while off the east coast, the East Spitsbergen Current 76 (ESC) carries cold, fresher Arctic water south. These currents mix with glacial melt water in the many fjords penetrating the Svalbard coastline. Due to the distinct hydrographic features of these different water masses, we were able to use underway temperature and salinity measurements to divide data into WSC, ESC and Fjord subregions (Fig 3.4a). Photophysiological properties varied with hydrographic properties such as temperature, salinity and PAR (Table 3.2), leading to significant spatial heterogeneity along our cruise track (Fig 3.4c-d). Most notably, 𝜎2344 varied significantly from east to west, with a mean value of 1309 ± 7.15 Å2 RCII-1 in the WSC, compared to 745 ± 8 and 696 ± 7 Å2 RCII-1 for fjords and the ESC, respectively. In contrast, there were no significant differences in FV/FM between any of the subregions, with mean values falling within 0.33 to 0.36 for the three subregions. Variability in 𝜎𝑃𝑆𝐼𝐼 had a significant effect on NPQ relaxation kinetics and ETR behavior across the subregions (Table 3.2). In ESC-influenced waters with lower 𝜎2344, ETR was relatively stable during NPQ relaxation experiments, with a mean percent change of -2.5 ± 5.0 %. By comparison, ETR estimates in WSC and fjord waters were more sensitive to NPQ relaxation, with 𝛥ETR of -10.1 ± 7.0 %. and -24.1 ± 3.6%, respectively. Differences in 𝛥ETR were significant between Fjord and ESC populations (p << 0.01). Variability in phytoplankton taxonomy may partially explain the spatial differences in photo-physiology around Svalbard. Different phytoplankton taxa have distinct light harvesting pigment compositions, and the functional PSII absorption area, 𝜎𝑃𝑆𝐼𝐼, also shows taxon-specific variability (David J Suggett et al., 2009; Gorbunov et al., 2020). Although FV/FM also varies with species composition, it appears to be more strongly affected by nutrient concentrations, and is thus widely used as an indicator of environmental conditions (Ryan-Keogh et al., 2013; Ko et 77 al., 2020). The significant differences we observed in 𝜎𝑃𝑆𝐼𝐼 but not in FV/FM suggest that variability between subregions of the Svalbard Archipelago are due to changes in taxonomic composition, rather than direct environmental effects. Although we lack direct estimates of phytoplankton taxonomy for the Svalbard region (due to limited opportunities for discrete sampling), previous studies in this area have noted differences in phytoplankton composition between ESC and WSC influenced waters. Kilias et al. (2014) observed a high percentage of haptophytes in WSC study sites, with chlorophytes more abundant at a study site affected by the ESC. The differences in 𝜎𝑃𝑆𝐼𝐼 between the ESC and WSC reported here agree well with expected values for haptophytes and chlorophytes. Laboratory monocultures of green algae yielded 450 nm-specific measurements of 𝜎𝑃𝑆𝐼𝐼 nearing 600 Å2 RCII-1, while haptophytes were closer to 1000 Å2 RCII-1 (Gorbunov et al., 2020). Without ancillary data, we cannot unequivocally determine whether observed differences in 𝜎𝑃𝑆𝐼𝐼, and ultimately ETR behavior, are driven by differences in nutrient availability or in phytoplankton taxonomy for the Svalbard samples. As discussed below, such analysis is, however, possible for the CAA samples. 3.5.3.2 Canadian Arctic Archipelago oceanography and photo-physiology Sampling in the Canadian Arctic Archipelago (CAA) took place in the late summer to early fall (Sept 23 – Oct 15, 2022) in Baffin Bay, primarily in waters influenced by the Baffin Island current. This water mass transports Pacific-derived Arctic water exiting Nares Strait and Lancaster Sound south to the Labrador Sea along the east coast of Baffin Island (Münchow, Falkner and Melling, 2015). In the north, the cruise track intersected the North Water Polynya (NWP), where southbound Arctic water mixes with warmer, saltier Atlantic water carried by the West Greenland Current. The polynya, referred to by local communities as Pikialasorsuaq or ‘great upwelling’, has long been recognized as a productivity hotspot (Ribeiro et al., 2021). As 78 discussed below, biogeochemical and photophysiological differences were apparent between the different oceanographic settings of the NWP and Southern Baffin Bay. Depth profiles of seawater density within the NWP (stations with latitude > 76oN) confirmed enhanced vertical mixing in the NWP. The average mixed layer (ML) depth in the NWP was 41.5 ± 16.9 m. By comparison, stratification was stronger in Southern Baffin Bay, where the ML was 20.4 ± 2.22 m (Fig 3.5b). As expected, differences in stratification intensity between Baffin Bay and the NWP were associated with differences in surface water nutrient concentrations; [NO3] was elevated in NWP surface waters compared to Baffin stations, with mean concentrations of 3.46 ± 1.11 and 0.37 ± 0.34 𝜇M, respectively (Fig 3.5b). The higher nutrient availability in the NWP surface waters was associated with elevated FV/FM values (Table 3.3; Fig 3.5e). However, the highest biomass was observed in sub-surface waters of central Southern Baffin Bay (Fig 3.5c), in a deeper chlorophyll maximum where [Chl] peaked at 8.1 mg m-3 and the associated FV/FM was 0.48. This elevated biomass may have contributed to the depleted nitrate values in Baffin Bay surface and subsurface waters. Across our CAA sampling stations, the ratio of photoprotective to photosynthetic pigments (PP:PS) was the strongest predictor of NPQ relaxation dynamics (Table 3.3), reflecting the influence of light-acclimation state on NPQ responses. PP:PS also displayed strong correlations with 𝜎2344, albeit with an opposite relationship in surface waters (positive correlation) and subsurface waters (negative correlation). The positive correlation between PP:PS and 𝜎2344 in surface samples is not immediately intuitive, as photoprotective pigments are not excitonically coupled to RCII. However, samples with relatively few photosynthetic pigments are expected to have limited [RCII], leading to elevated 𝜎2344 as 𝜎2344 is dependent on 79 the ratio of 𝑎¬­® to [RCII] (see section 3.5.2). In turn, 𝜎2344 was the greatest determinant of ETR behavior during NPQ relaxation experiments among surface samples. Surface 𝜎2344 was negatively correlated with 𝛥ETR (𝜌 = -0.75, p <<0.01), such that samples with lower 𝜎2344 displayed only small changes in ETR during NPQ relaxation experiments. In contrast, samples with higher 𝜎2344 values demonstrated large decreases in ETR estimates as a function of NPQ relaxation time. The relationship between 𝜎2344 and 𝛥ETR observed here is similar to that reported for Svalbard above. In contrast to surface waters, there was no statistically significant correlation between 𝜎2344 and 𝛥ETR in subsurface samples (𝜌 = 0.34, p > 0.05). We did, however, observe a significant relationship between PP:PS and 𝛥ETR. Samples with lower concentrations of photo-protective pigments (i.e. lower PP:PS ratios) exhibited larger decreases in ETR estimates during the time-course of NPQ relaxation (Table 3.3). This result can be understood in the context of cellular photo-protective capacity. Cells with lower concentrations of photoprotective pigments would be more prone to long-lived photoinhibition during high light treatments (Choudhury and Behera, 2001), which could explain the observed decreases in 𝜎′′2344 (and thus ETR) during NPQ relaxation (See section 3.5.2). These results imply that light acclimation state and species composition, plays an important role in determining the stability of ETR during NPQ relaxation experiments. Beyond the use of PP:PS and 𝜎2344 as indicators of cellular photo-protective capacity, these light harvesting properties provide some taxonomic explanation for the observed patterns in 𝛥ETR. Diatom-dominated stations expressed the lowest 𝜎2344 values and 𝛥ETR closest to zero, whereas higher 𝜎2344 were linked to stations where picoplankton were dominant, and where 80 absolute magnitudes of 𝛥ETR were greater (Appendix 3.2). This result suggests photosynthetic rate estimates are less sensitive to NPQ relaxation effects in diatom-dominated assemblages, compared to picoplankton dominated communities. Assumptions of proportional NPQ effects on 𝜎2344 and FV/FM, and thus NPQ effects on photosynthetic rate estimates, were developed in early fluorescence studies conducted on eukaryotic species (Gorbunov et al., 2001; Suggett D, Moore M, 2010). However, previous studies have shown these assumptions do not hold in prokaryotes (Xu et al., 2018), and our results provide field-based evidence that they are also violated in marine environments with taxonomically-mixed phytoplankton assemblages. 3.6 Conclusion The purpose of this study was to characterize the effects of natural oceanographic variability on NPQ relaxation kinetics and the subsequent effects of NPQ relaxation on ETR estimates. Contrary to the assumption that NPQ relaxation time should have no influence on ETR estimates, we observed divergent FV/FM and 𝜎𝑃𝑆𝐼𝐼 responses during NPQ relaxation experiments, leading to significant NPQ-dependent variability in ETR estimates. We thus conclude that NPQ relaxation time is, indeed, an important consideration for reliable ETR estimates. On average, we found that 20 minutes of low light exposure was sufficient to relax short-lived quenching mechanisms, while 45 minutes was sufficient for samples acclimated to low light environments. These results have important implications for the application of FRRF in underway surveys of photochemistry. Distinct NPQ dynamics occur across varying light environments and phytoplankton taxonomic composition, and can vary strongly across different hydrographic subregions. With an increasing dataset of NPQ dynamics across a range of oceanographic regions, it may be possible to build empirical models defining appropriate protocols for NPQ relaxation for field-based studies. 81 3.7 Tables and Figures Figure 3.1 Location of field sampling stations during summer, 2022. Underway NPQ relaxation curves were conducted at locations indicated by red circles along ship survey tracks (black line) in the Canadian Arctic Archipelago, CAA (A) and Svalbard Archipelago (B). In the CAA, NPQ relaxation curves were also measured at discrete oceanographic stations indicated by blue triangles. Nutrient and pigment data are available for these stations. The CAA and Svalbard sampling regions are shown as red and blue boxes, respectively, on the upper map. 82 Figure 3.2 Representative data analysis used to characterize NPQ relaxation time-scales following a one-minute high light exposure. Data obtained from repeated FRRF analysis are shown as white points, while the black line represents the model fit to the data, combining fast 83 (𝐪𝐟𝐚𝐬𝐭), intermediate (𝐪𝐢𝐧𝐭) and slow (𝐪𝐬𝐥𝐨𝐰) components of NPQ, which are displayed in green, blue, and red, respectively. Table 3.1 NPQ relaxation parameters (mean ± standard error) for all study regions. The fractional contribution of each quenching component to the total qN0 amplitude is denoted as qFast, qInt., and qslow, while the half-lives of each quenching component are denoted as 𝝉𝒇𝒂𝒔𝒕, 𝝉𝒊𝒏𝒕, and 𝝉𝒔𝒍𝒐𝒘. The ratio of photoprotective to photosynthetic pigments is abbreviated as PP:PS. CAA subsurface – Sept/Oct (n = 19) CAA surface - Sept/Oct (n = 48) Svalbard surface - June/July (n = 58) qN0 0.93 ± 0.01 0.90 ± 0.01 0.88 ± 0.01 𝜏K¢ 45.54 ± 4.32 min 17.76 ± 2.09 min 13.28 ± 1.42 min qFast 0.15 ± 0.01 0.15 ± 0.03 0.11 ± 0.02 𝜏6789 0.45 ± 0.13 min 0.59 ± 0.12 min 0.46 ± 0.07 min qInt. 0.69 ± 0.05 0.81 ± 0.03 0.87 ± 0.02 𝜏;<9. 5.84 ± 2.85 min 5.83 ± 0.81 min 5.93 ± 0.50 min qSlow 0.16 ± 0.02 0.04 ± 0.01 0.02 ± 0.00 𝜏8=>? 267 ± 30.6 min 188 ± 28.2 min 217 ± 20 min Mean surface PAR 5.0 ± 1.9 𝜇mol photons m-2 s-1 76.5 ± 1.3 𝜇mol photons m-2 s-1 1,0780 ± 224 𝜇E 𝜇mol photons m-2 s-1 PP:PS 0.18 ± 0.01 0.30 ± 0.02 (n = 19) N/A 84 Figure 3.3 NPQ relaxation effects on σPSII and Fv/Fm. A-B) Representative time-series of 𝝈𝑷𝑺𝑰𝑰 and FV/FM measurements during NPQ relaxation, showing strong coupling (A) and decoupling (B) of these variables following low light exposure. The percent change of 𝝈𝑷𝑺𝑰𝑰 (blue triangles), FV/FM (red circles), and ETR (black squares) are plotted with respect to NPQ relaxation time in minutes, with values normalized to 1 at the beginning of the low light measurement period. C) Distribution of F′′V/F′′M (blue) and 𝝈′′𝑷𝑺𝑰𝑰 (orange) changes during NPQ relaxation experiments. Changes in FV/FM were always positive, whereas both positive and negative changes in 𝝈𝑷𝑺𝑰𝑰 were observed. D) The effect of 𝝈𝑷𝑺𝑰𝑰 and FV/FM decoupling on 85 derived ETR estimates. Samples with strongly correlated 𝝈𝑷𝑺𝑰𝑰 and FV/FM responses showed little change in ETR estimates during the time-course of NPQ. Figure 3.4 Underway photo-physiology in different water masses surrounding Svalbard. A) Underway measurements of phytoplankton photo-physiology in different water masses surrounding Svalbard. Seawater density calculated from salinity and temperature measurements (Millero and Poisson, 1981) illustrates the hydrographic difference between the West and East Spitsbergen Current (WSC and ESC, respectively). The WSC was defined as saltier than 35 psu and warmer than 6oC (Aagaard, Foldvik and Hillman, 1987). Fjords were assigned as water with a salinity < 33 psu. All intermediate water was classified as ESC. Gaps in thermosalinograph (TSG) data occurred during sampling in heavily ice-covered waters. Approximate paths of the 86 WSC (red) and ESC (white) are adapted from Vihtakari et al. (2019). Panels (B,C) show continuous underway 𝝈𝑷𝑺𝑰𝑰 and FV/FM measurements. Panel (D) shows the fractional change in ETR recorded throughout NPQ relaxation experiments. Table 3.2 Spearman rank correlation coefficients between temperature (oC), salinity (psu), and surface PAR (µmol photons m-2 s-1) and photo-physiological parameters. P values < 0.05 are indicated with * and p values << 0.01 with **. Sample number n = 58 in all cases. 𝜎2344 Fv/Fm qN 𝜏K¢ qfast qint qslow ΔETR Temp 0.73** -0.55* 0.37 0.10 0.30* -0.33* 0.06 -0.07 Sal 0.59* 0.16 0.71** -0.23 -0.14 0.13 0.09 0.34* PAR 0.37 -0.57** -0.17 -0.10 0.59** -0.55* -0.35 0.28 87 Figure 3.5 Underway measurements of phytoplankton photo-physiology in different water masses along the coast of Baffin Island and the North Water Polynya (NWP). A) Map of Baffin Bay showing bathymetry and surface currents. The southbound Baffin Island Current (BIC) is shown in blue, and the northbound West Greenland Current (WGC) in red. The grey shaded area highlights the NWP. B) Mean nitrate depth profiles are displayed for Baffin Bay stations (red), and NWP stations (black). The standard error is indicated by the shaded area. The 88 mean mixed layer depth for both regions is indicated by X marker. C) Colors indicate the dominant taxa group for the subsurface (squares) and surface (circles), while marker size indicates the total chlorophyll concentration. The FV/FM and 𝝈𝑷𝑺𝑰𝑰 for underway surface samples are shown in subplots D and E, respectively. F) The fractional change in ETR over the NPQ relaxation time (30 minutes) for surface samples. Table 3.3 Relationships between biogeochemical and photophysiological parameters. Spearman rank correlation coefficients between temperature (oC), salinity (psu), and PAR (𝝁mol photons m-2 s-1), PP:PS (dimensionless), NO3 (𝝁M), and Chl (𝝁g L-1) and photo-physiological parameters in the Canadian Arctic Archipelago. P values < 0.05 are indicated with *. P values << 0.01 are **. Column n details the number of samples considered. 𝜎2344 Fv/Fm qN 𝜏K¢ qfast qint qslow ΔETR n Surface Temp 0.26 -0.29 -0.42* 0.08 0.11 -0.19 0.16 0.22 48 Sal -0.53** 0.19 0.15 0.18 0.26 -0.23 0.36 0.47* 48 PAR 0.19 -0.19 -0.17 -0.11 -0.06 -0.07 -0.05 0.09 48 PP:PS 0.58** -0.09 -0.38 -0.15 -0.43* 0.39* -0.11 -0.18 19 NO3 -0.34 0.51* -0.05 -0.09 -0.12 0.16 0.11 -0.19 19 Chl -0.06 0.30* 0.24 0.32 -0.23 0.27 0.02 -0.02 19 Sub-surface PAR 0.26 -0.43 -0.19 -0.38 -0.01 0.17 -0.25 -0.22 19 PP:PS -0.45* 0.62** 0.00 -0.15 -0.45* 0.17 0.22 -0.58* 19 NO3 -0.14 0.37 -0.11 0.00 -0.11 0.31 -0.11 -0.17 19 Chl 0.23 -0.42 0.37 0.04 0.26 0.03 -0.20 0.16 19 89 Chapter 4: Photosynthetic electron, carbon, and oxygen fluxes within a mosaic of Fe limitation in the California Current Upwelling System 4.1 Summary We compare primary productivity estimates based on different photosynthetic ‘currencies’ (electrons, O2 and carbon) collected from the dynamic coastal upwelling waters of the California Current. Fast Repetition Rate Fluorometry and O2/N2′ measurements were used to collect high-resolution underway estimates of photosynthetic electron transport rates and net community productivity, respectively, alongside on-station 14C uptake experiments to measure gross carbon fixation rates. Our survey captured two upwelling filaments at Cape Blanco and Cape Mendocino with distinct biogeochemical signatures and iron availabilities, enabling us to examine photosynthetic processes along a natural iron gradient. Significant differences in photo-physiology, cell sizes, Si:NO3- draw-down ratios, and molecular markers of Fe-stress indicated that phytoplankton assemblages near Cape Mendocino were Fe-stressed, while those near Cape Blanco were Fe-replete. Upwelling of O2-poor deep water to the surface complicated O2-based net community productivity estimates, but we were able to correct for these vertical mixing effects using continuous [N2O] surface measurements and depth-profiles of Ã['Å]Ã[¢Å']. Vertical mixing corrections were strongly correlated to sea surface temperature, which serves as an N2O-independent proxy for upwelling. Following vertical mixing corrections, all three productivity estimates reflected trends in Fe-stress physiology, indicating greater productivity near Cape Blanco compared to Cape Mendocino. For all assemblages, carbon fixation varied as a hyperbolic function of electron transport rates, but the derived parameters of this relationship were highly variable and significantly correlated with physiological indicators of Fe-stress (𝜎2344, 90 FV/FM, Si:NO3- and diatom-specific PSI gene expression), suggesting that iron availability influenced the coupling between photosynthetic electron transport and subsequent carbon fixation. Net community productivity showed strong coherence with daily-integrated photosynthetic electron transport rates across the entire cruise track, with no apparent relationship with Fe-stress. This result suggests that fluorescence-based estimates of gross photochemistry are still a good indicator for bulk primary productivity, even if Fe-limitation influences the stoichiometric relationship among productivity currencies. 4.2 Introduction Along the eastern boundaries of ocean basins, coastal upwelling delivers nutrient-rich deep water to the euphotic zone (Bograd et al., 2023), sustaining high phytoplankton growth rates and primary productivity. Despite representing less than 1% of the surface ocean, these productive upwelling ecosystems, support ~20% of global fishery catches (Pauly and Christensen, 1995), and play a disproportionate role in ocean carbon uptake through the ‘biological carbon pump’(Mathis et al., 2024). Quantifying rates of primary productivity (PP) within eastern boundary currents is thus vital for accurate carbon budgeting and fishery yield predictions (Marshak and Link, 2021), yet this remains challenging due to the highly dynamic nature of these systems. The California Current system (CCS) is one of the best studied eastern boundary currents, extending from British Columbia, Canada, to Baja California, Mexico. Upwelling in the CCS occurs during spring and summer when northerly winds drive Ekman transport of surface water offshore. Within the upwelling season, short-term changes in windspeed and direction can dampen or reverse upwelling signals on the scale of hours to days, while complex coastline 91 geometry directs wind flow, creating upwelling hotspots in the lee of capes (Castelao and Luo, 2018). Underlying bathymetric features and deep-water composition further influence the nature of upwelling filaments and the availability of macro and micro nutrients. In regions with shallow and wide continental shelves, sediment deposition provides a primary source Fe and other micronutrients (Deutsch et al., 2021). In contrast, waters overlying steep narrow shelves retain less Fe, and PP in these regions can be limited by Fe availability despite the presence of upwelling conditions (Biller et al., 2013). The resulting ‘mosaic of Fe limitation’ influences the distribution of phytoplankton biomass and productivity across the CCS (Hutchins et al., 1998; Till et al., 2019). Resolving ecosystem responses to such a heterogenous environment requires high resolution measurements. Traditionally, primary productivity has been measured using discrete bottle incubations where the net change in dissolved O2 or particulate organic carbon is measured over time. Shorter incubations approximate gross primary productivity (GPP), whereas longer incubations allow time for respiration to act upon tracers, yielding estimates somewhere between GPP and net PP (NPP = GPP – respiration). Although bottle incubations are still widely used to directly observe carbon fixation rates, the resulting measurements can be ambiguous in terms of GPP vs. NPP, while also providing low sampling resolution and posing potential containment artefacts (Banse, 2002). To avoid these challenges, a number of high through-put PP proxies have been developed based on advances in dissolved gas measurements, bio-optical techniques, and satellite-based ocean color observations (IOCCG, 2022). These diverse PP methodologies target different photosynthetic processes, from subcellular light absorption to ecosystem scale carbon export. 92 At the smallest spatial and temporal scales, Photosystem II (PSII) electron transport rates (ETRPSII) quantify light absorption and conversion to chemical energy for a variety of metabolic activities including carbon fixation. Measurements of ETRPSII can be obtained from active chlorophyll fluorescence techniques, which exploit the inverse relationship between PSII fluorescence and photochemical yields to enable non-invasive and high-frequency measurement from underway seawater lines (Kranz et al., 2020; Sezginer et al., 2023) or autonomous platforms (e.g. Carvalho et al., 2020). Downstream of ETRPSII, carbon fixation can be directly measured using bottle incubations, or approximated from empirical algorithms relating NPP to remotely sensed Chlorophyll (Chl) concentration, sea surface temperature (SST), and photosynthetically available radiance (PAR) (Behrenfeld and Falkowski, 1997; Behrenfeld et al., 2005; Saba et al., 2011). Finally, net community productivity (NCP), represents the difference between GPP and community-wide respiration and can be equated to carbon export out of the mixed layer. This term can be derived from measurements of biological oxygen saturation, ΔO2/Ar, using Ar-normalization of O2 to correct for physical influences on gas disequilibria (e.g. temperature or salinity changes or bubble injection) to isolate the biological signal (Craig and Hayward, 1987; Cassar et al., 2009). Assuming steady-state conditions, O2 fluxes from the mixed layer represent a balance between net biological production and sea-air exchange, allowing the calculation of NCP from sea-air flux estimates. Steady state assumptions are violated in upwelling systems, such as the CCS, where O2 fluxes in the mixed layer are also affected by vertical mixing. However, such vertical mixing effects can be corrected using N2O as a tracer of O2-depleted deep water, as oxygen-consuming N2O production pathways are photo-inhibited at the surface, resulting in fixed subsurface N2O to O2 ratios in the ocean (Cassar, Nevison and Manizza, 2014). 93 The various measurement techniques described above yield PP estimates in a number of different ‘currencies’, i.e. carbon, oxygen and electrons, each with different integration time scales (seconds to weeks). In theory, combining PP quantification approaches can fill data gaps (e.g. cloud or ice interference with satellite data, or missing years in time series operations), and provide deeper understanding of ocean metabolism. In practice, incorporating alternative measurement approaches requires understanding of conversion rates between the various PP currencies. Predicting these conversion factors is challenging, as they vary in response to environmental conditions and phytoplankton taxonomy and physiology (Halsey and Jones, 2015; Schuback et al., 2017; D. J. Hughes, Varkey, et al., 2018; Hughes et al., 2021). For example, the ETRPSII : GPP ratio often exceeds the theoretical stoichiometry of 4 (Halsey and Jones, 2015), implying that redox potential generated at PSII is used for functions other than carbon fixation, such as nitrogen uptake or cyclic electron transport. Similarly, differences between ETRPSII and NCP represent combined O2 consumption pathways, including cellular community-wide respiration, chloro- and photorespiration and pseudo cyclic electron transport. Investigating the drivers of decoupling between currencies can thus improve conversion rate estimates, and also provide insights into energy transfer efficiencies between different components of the photosynthetic process. Here we present simultaneous PP measurements in the CCS collected using high resolution, underway sampling techniques along a cruise track between Newport, OR and San Francisco, CA. Underway measurements of ETRPSII and NCP were conducted during the May-June upwelling season of 2023 and were complemented with parallel measurements of ETRPSII and 14C-GPP collected at oceanographic stations. Our results demonstrate fine-scale spatial patterns in GPP and NCP associated with variability in localized upwelling filaments, with 94 particularly notable differences observed across gradients of dissolved iron concentrations resulting from varying coastal bathymetry. These results enable us to examine variability in productivity currency conversion factors across natural Fe availability gradients within the California Current System, with potential application to other complex coastal waters. 4.3 Materials and methods 4.3.1 Sampling Sites Measurements were collected along the Oregon and Northern California coast during the Phytoplankton UPwelling Cycle (PUPCYCLE II) expedition, onboard the R/V Sally Ride from May 27 – June 11, 2023. A key objective of the program was to examine the evolution of phytoplankton blooms in recently upwelled waters, and we specifically targeted two upwelling plumes off Cape Blanco and Cape Mendocino, which were identified by low sea surface temperature (SST < 12oC; Figure 4.1). Along the cruise track, temperature, salinity, and chlorophyll fluorescence were monitored by the ship’s underway system, supplied by a seawater supply line with a nominal intake depth of approximately 5m. Nitrate concentrations were measured continuously with a Seabird SUNA sensor calibrated against NaNO3 standards immediately prior to the cruise. In addition to standard oceanographic variables, the seawater supply line was also used for continuous underway measurements of phytoplankton photo-physiology and ETRPSII using a Fast Repetition Rate fluorometer (FRRF; Soliense Inc.), and NCP using a custom-built Pressure In-Situ Gas Instrument (PIGI), as described below. Daily CTD and rosette casts were conducted one hour before local sunrise, and samples were collected at four depths targeting 1%, 10%, 22%, and 46% of surface solar irradiance levels. Photosynthesis-Irradiance (PI) curves (measured with Fast Repetition Rate fluorometry– 95 see below), chlorophyll a (Chl) and nutrient concentrations (NO3-, Si, PO4, Fe) were measured at all four depths. Samples collected from 1% and 46% irradiance levels were also incubated for 14C-based PI curves (see sect. 2.5), with sub-samples collected for High Performance Liquid Chromatography (HPLC)-based analysis of phytoplankton pigments. 4.3.2 Fast Repetition Rate Fluorometry A bench-top Soliense Inc. Fast Repetition Rate Fluorometer (FRRF) was configured for underway collection of chlorophyll a fluorescence transients following Sezginer et al., (2021). Samples were introduced to the measurement cuvette by an integrated peristaltic pump. The pump was used to flush the measurement cuvette for 2.5 minutes before isolating a sample for analysis. Following a one-minute dark period to relax short lived non-photochemical quenching, five single-turnover fluorescence transients were collected from each sample in the dark. Each single-turnover transient included a sequence of 100 sub-saturating excitation light pulses of 1.5 𝜇s duration, and a 1 𝜇s interval between pulses. The excitation phase is designed to stimulate photochemistry and gradually reduce the pool of primary electron acceptor molecules, Qa (Kolber, Prášil and Falkowski, 1998). As the Qa pool is reduced, further electron exchange between PSII and Qa is prevented, closing the photochemistry pathway and causing a concurrent increase in PSII fluorescence yields. The excitation phase is followed by a relaxation sequence consisting of 127 light pulses with an initial 20 𝜇s interval. During the relaxation phase, the interval between light pulses increases exponentially, enabling Qa reoxidation between pulses to gradually reopen the photochemistry pathway, such that fluorescence yields return to their basal levels. The biophysical model described by Kolber, Prášil and Falkowski (1998) was fit to the 96 resulting fluorescence transient to derive the maximum quantum yield of PSII (FV/FM), functional absorption area of PSII (𝜎2344), and turnover rate of the primary electron acceptor Qa (𝜏b7). 4.3.3 In-situ electron transport rates, ETRPSII Continuous FRRF sampling was interrupted every 12 samples (~3hrs) to conduct a Photosynthesis-Irradiance (PI) curve characterizing light-dependent changes in photo-physiology and photochemistry. Across the entire cruise, we collected 91 PI curves. Each PI curve was initiated with a fresh sample, and consisted of 11 light levels, increasing from 0 – 850 𝜇mol photons m-2 s-1. Light was supplied evenly by the five actinic LEDs within the FRRF (445, 470, 505, 530, and 590 nm) calibrated against a handheld WALZ ULM-500 PAR meter prior to deployment. At each light level, ETRPSII was calculated following Suggett and Moore (2010): 𝐸𝑇𝑅2344 = 𝑃𝐴𝑅 ∗ 𝜎2344( ∗ 𝐹K(𝐹L( ∗ 6.033 ∗ 10Dw Eq 4.1. In this formulation, the rate of photon delivery to the pool of PSII reaction centers (RCII) is derived as the product of photosynthetically active radiation (PAR; units 𝜇mol photons m-2 s-1) supplied by the LED lamps, and the wavelength-specific functional absorption area of RCII (𝜎2344( ; units of Å2 PSII-1). The conversion efficiency from light to photochemical energy depends on the fraction of open RCII, measured as the dimensionless ratio between the fluorescence amplitude measured under actinic light when photochemistry is active (𝐹K( = 𝐹C( − 𝐹() and that measured under actinic light if all RCII were oxidized, (𝐹L( = 𝐹C( − 𝐹>(). In practice, measuring the minimum fluorescence when the RCII pool is completely open (𝐹>() is challenging, as actinic 97 light always drives some degree of photochemistry and reduction of RCII. Alternatively, 𝐹>( can be derived as Fo/(FV/FM + Fo/FÉ( ), following Oxborough et al., (2012). The prime notation (ʹ) refers to FRRF parameters derived under actinic light. The constant 6.033 ∗ 10Dw converts PAR to units of quanta m-2 s-1, and 𝜎2344( to units of m2 RCII-1, so the final units of ETRPSII are quanta s-1 RCII-1. For a complete description of FRRF derived parameters, see Schuback et al. (2021) and (Tortell, Schuback and Suggett, 2023). For each light curve measured, ETRPSII was plotted against PAR and fit with the photosynthesis-irradiance model of Platt et al. (Platt, Gallegos and Harrison, 1980): 𝐸𝑇𝑅2344 = 𝑃8 ]1 − 𝑒DÊ2cË2 _ 𝑒DÌ2cË2 Eq. 4.2. During the initial light-limiting part of the curve, 𝐸𝑇𝑅2344 increases linearly with PAR with a slope of 𝛼. As PAR increases to saturating levels, 𝐸𝑇𝑅2344 stabilizes at maximum levels, Pmax. The light saturation index, Ek is derived as Pmax/𝛼. When phytoplankton are affected by photoinhibition, 𝛽 describes the decrease in 𝐸𝑇𝑅2344 at high light levels (i.e. >> Ek). In the absence of photoinhibition, Ps = Pmax. When photo-inhibition is present (𝛽 > 0), Ps represents the theoretical maximum potential 𝐸𝑇𝑅2344. When 𝛽 > 0, Pmax is derived as: 𝑃C7` = 𝑃8 W 𝛼𝛼 + 𝛽X W 𝛽𝛼 + 𝛽XWÌÊX Eq 4.3. To evaluate 𝐸𝑇𝑅2344 over the cruise track, we linearly interpolated derived values of 𝛼, 𝛽, and Ps to the sampling resolution of continuous PAR measurements from the ship’s 98 meteorological tower (Biospherical Inst. QSR-240P). The mean light intensity in the mixed layer (PARin-situ) was estimated by accounting for light attenuation with depth, quantified by the diffuse attenuation coefficient (Kd = ln(PAR0/PARmld)/(mld - 0) ), where PAR0 is PAR measured at the surface and PARmld is PAR measured at the mixed layer depth (mld) (Domingues and Barbosa, 2023). For each CTD cast (n = 28), mld was determined using a density difference criterion of 0.125 kg m-3, and PARmld was measured with a Biospherical QSP-200 PAR sensor mounted to the CTD rosette. Both Kd and mld were linearly interpolated to the resolution of continuous PAR measurements. Finally, in-situ PAR was estimated following Domingues and Barbosa (2023) as, 𝑃𝐴𝑅;< 8;9 = 𝑃𝐴𝑅(1 − 𝑒DÍÎn¬Ï)(𝐾Ñ ∗ 𝑚𝑙𝑑)DF Eq 4.4 To compare ETRPSII with NCP (Sect. 2.6), we converted ETRPSII from e- RCII-1 s-1 to volumetric units of mmol O2 m-2 d-1. This conversion requires an estimate of the chlorophyll content of RCII, which is known to vary significantly across phytoplankton in response to taxonomic and environmental influences (Greene et al., 1992; Murphy et al., 2017; Aardema et al., 2024). Following previous authors (Kolber and Falkowski, 1993; Schuback et al., 2015), we assumed a possible range of Chl to RCII ratios of 400 to 700, yielding upper and lower bounds of Chl-normalized ETRPSII. 𝐺𝑃 = 𝐸𝑇𝑅2344 ∗ 86400 ∗ (𝐶ℎ𝑙: 𝑅𝐶𝐼𝐼)DF ∗ 𝐶ℎ𝑙 ∗ 𝑚𝑙𝑑 ∗ 14 Eq 4.5. 99 To obtain volumetric units, Chl-normalized ETR was multiplied by mixed layer Chl concentrations (mmol Chl m-3 * mld), assuming homogenous [Chl] throughout the mixed layer. Multiplying by 86400 converts from s-1 to d-1. Given that four charge separation events are required per O2 evolved, ETR was divided by 4 for final gross photochemistry (GP) estimates in terms of mmol O2 m-2 d-1. Appendix 4.4 provides details of error propagation for determination of total uncertainty. 4.3.4 Non-Photochemical Quenching, NPQ Under excess irradiance, light supply to PSII outpaces maximum downstream electron transport rates, creating the potential for dangerous reactive oxygen species (ROS) to accumulate (Müller, Li and Niyogi, 2001). To mitigate excess excitation, photoautotrophs have evolved a number of photoprotective mechanisms, including non-photochemical quenching (NPQ), which dissipates excitation absorbed by PSII as heat, thereby reducing PSII photochemical and fluorescence yields. Previously, NPQ has been quantified from FRRF data as Stern-Volmer quenching, defined as the relative decrease in PSII fluorescence in response to light exposure: NPQSV = (𝐹C − 𝐹(C)/𝐹C( . However, this formulation does not account for longer-lived NPQ mechanisms that may still be active during dark measurements following recent high light exposure. To overcome this limitation, we used the normalized Stern-Volmer parameter (NPQNSV), calculated as 𝐹>(/𝐹L( (McKew et al., 2013). For each PI curve measured, NPQNSV was plotted against PAR and fit with a single component exponential curve. Out of 91 curve fits, 95% had R2 > 0.90 and 87% had R2 > 0.95. In-situ NPQNSV was then estimated by mapping the resulting curve fits on to in-situ PAR values. 100 Similarly, for each PI curve, the fraction of closed RCII, 𝐹K(/𝐹L(, was plotted as a function of PAR and fit with a single component exponential curve, with 92% of curves having an R2 > 0.90. Following the same procedure used to evaluate in-situ NPQNSV, in-situ 𝐹K(/𝐹L( was approximated by mapping the resulting curve fit onto in-situ PAR values. 4.3.5 14C-uptake experiments During daily station sampling, 200 mL were collected from 1% and 46% light level depths (approximately 50 and 10 meters, respectively) into acid washed 250 mL bottles for 14C incubations. Samples were immediately spiked with 150 𝜇Ci of H14CO3 (Perkin Elmer), and inverted to homogenize the contents of the bottles. The homogenized media was then aliquoted into 20 mL borosilicate scintillation vials, which were incubated over 3 hours in a custom-built photosynthetron at 7 light levels from 0 – 650 𝜇mol photons m-2 s-1. At the end of the incubation, the entire content of the vials was filtered onto 25mm GF/F filters with a nominal pore size of 0.7 𝜇m. Filters were fumed with 10% HCl for 24 hours to remove any inorganic carbon prior measuring activity on filters with an on-board scintillation counter (Beckman LS 6500). Immediately after spiking samples, three vials were filtered for triplicate time zero measurements. Three 100 𝜇L aliquots were also taken from the initial 200 mL sample and treated with 100 𝜇L of 3M NaOH to measure total 14C counts. Disintegrations per minute were converted into hourly C fixation rates according to Knap et al., (1996). 4.3.6 Net community productivity (NCP) 101 We measured NCP based on mixed layer concentrations of O2 and N2 obtained from the Pressure of In-Situ Gas Instrument (PIGI), following Izett and Tortell, 2021 and Izett et al., 2021. This method estimates NCP from the biological oxygen saturation anomaly, ΔO2/N2′, using N2′ as an analog for Argon (Ar) to correct for physical effects on O2 saturation. In this method, net community productivity is equated to the sea-air flux of O2 as determined by the biological saturation anomaly (ΔO2/N2′) scaled by the [O2] in equilibrium with the atmosphere ([O2]sat), and the O2 piston velocity (𝑘'Ù). NCP = 𝑘'Ù ∗ Δ ÚÙÛÅ( ∗ [OÙ]ÜÝ Eq. 4.6. The PIGI enables cost effective measurements of ΔO2/ N2′ using an oxygen optode and a gas tension device rather than a mass spectrometer, which is more commonly used to measure ΔO2/Ar (Izett and Tortell, 2020). In this method, N2′ is derived as an approximation of Ar, using model calculations that quantify differences between Ar and N2 concentrations due primarily to solubility changes and bubble processes. A full description of the 1D model applied to estimate N2′ is available in Izett and Tortell (2021). The model uses ancillary data, including windspeed, mixed layer depth, temperature, salinity, and sea level pressure, to estimate changes in mixed layer Ar and N2 concentrations over one residence time period prior to sampling. We applied a residence time of 14 days for this region where mixed layer gas residence times are strongly influenced by the timescales of upwelling events (Austin and Barth, 2002). Ancillary datasets required for N2′ calculations were obtained from a combination of satellite observations and model products, and are compiled in the Supplement S1, alongside descriptions of each data 102 source. The 1D model calculations and code are available at https://github.com/rizett/O2N2_NCP_toolbox with example calculations. Additional corrections to NCP estimates were made to account for vertical mixing fluxes, which transport low O2 water to the surface (Izett et al., 2018). Previous studies have omitted ΔO2/Ar data collected in known upwelling areas, where the assumption of limited vertical mixing fluxes on ΔO2/Ar variability is violated (e.g. Stanley et al., 2010). To address this limitation, Cassar, Nevison and Manizza (2014) developed an approach to use surface measurements of N2O to quantify vertical transport of low O2 waters. In marine environments, there is a strong stoichiometric relationship between apparent oxygen utilization and N2O, which is produced as a by-product of subsurface oxygen-consuming N remineralization pathways (Elkins et al., 1978). These N2O production pathways are thought to be photo-inhibited within the euphotic zone (Horrigan, Carlucci and Williams, 1981; Olson, 1981), so that excess N2O concentrations in the mixed layer serve as a tracer for vertical influxes of O2-depleted subsurface water. We note that several recent studies have observed nitrification within the euphotic zone, challenging the assumption that N2O production is limited to subsurface waters (Grundle, Juniper and Giesbrecht, 2013; Smith et al., 2014). However, a sensitivity analysis by Izett et al. (2018) determined the potential effects of euphotic zone nitrification introduced a nearly negligible error to NCP estimates in regions with high productivity and high mixing rates, as is the case in the CCS. We thus used the approach of Cassar, Nevison and Manizza (2014) and Izett et al., (2018), to correct for vertical mixing following Eq. 7. NCP = 𝑘'Ù ∗ (ÞÚÅßà ∗ [OÙ]ÜÝ − [áÅâ [âÅ ∗ ã[ÚÅ]äÃ[¢Å']ä ∗ [𝑁Ù𝑂]æ) 7. 103 This mixing correction uses surface measurements of the N2O biological concentration ([N2O]B), the ‘supply ratio’ of oxygen saturation, given by the vertical gradient of biological O2 to N2O (ã[ÚÅ]äÃ[¢Å']ä), and the ratio of gas transfer velocities ([áÅâ [âÅ ). Biological concentrataions, indicated by the ‘B’ superscript, are derived by isolating and removing physical solubility effects from measured gas concentrations. The surface water biological saturation of N2O, [N2O]B, was derived based on the difference between the N2O saturation anomaly ([N2O]sat) and changes in N2O solubility due to recent heat fluxes ([N2O]meas - [N2O]sat – [N2O]thermal). Heat flux effects on solubility, [N2O]thermal, were derived following Keeling and Shertz (1992), with corrections from Jin et al. (2017). Surface [N2O]meas was continuously measured from the surface seawater supply with an integrated cavity output spectroscopy (OA-ICOS) gas analyzer (Los Gatos Research, N2O/CH4 Analyzer, Model Number: 913–0055) coupled to a gas extraction module (Schuler and Tortell, 2023). The region-specific supply ratio, ã[ÚÅ]äÃ[¢Å']ä, was calculated by taking the slope of subsurface [O2]B plotted against subsurface [N2O]B. The compiled data across the cruise track resulted in a supply ratio of −1.6 ∗ 10ç ± 0.3 ∗ 10ç mmol O2 (mmol N2O)-1, which is similar to previous measurements for the Northeast Pacific (−1.8 ∗ 10ç; Izett et al., 2018) and global basins (−1.5 ∗ 10ç ;(Cassar, Nevison and Manizza, 2014). Following Cassar, Nevison and Manizza (2014), we assumed a constant ÍáÅâ ÍâÅ ratio of 0.92. We note that several recent studies have observed nitrification within the euphotic zone, challenging the assumption that N2O production is limited to subsurface waters (Grundle, Juniper and Giesbrecht, 2013; Smith et al., 2014), and potentially leading to overestimates in our vertical mixing-corrected NCP estimates. Previous observations in the CCS reported a range of depth-integrated mixed layer nitrification rates between 0.3 – 2 mmol NH4+ m-2 d-1, resulting in 104 consumption of 0.6 – 4 mmol O2 m-2 d-1 (Stephens et al., 2020). Following the approach of Izett et al. (2018) we used a range of realistic N2O:O2 stoichiometries to estimate potential upper and lower bounds of mixed layer N2O production. We determined mixed layer N2O production likely ranged between 0.09 – 0.23 𝜇mol N2O m-2 d-1, which would yield offsets in our final NCP estimates between 1.2 and 3.3 mmol O2 m-2 d-1. Total uncertainty due to sources of error in other derived parameters was determined by following Izett (2021). 4.3.7 Nutrient concentrations Samples collected during daily productivity casts were analysed for dissolved NO3- + NO2-, PO43-, and silicic acid concentrations. Thirty mL were collected from niskin bottles and filtered onto GF/F filters, using acid-washed syringes into 20mL HDPE scintillation vials. Samples were kept frozen before analysis on a OI Analytical Flow Solutions IV auto analyzer by Wetland Biogeochemistry Analytical Services at Louisiana State University. Detection limits were 0.09 µmol L-1 for nitrate + nitrate, 0.02 µmol L-1 for phosphate and 0.02 µmol L-1 for silicic acid. Reference standards for dissolved nutrients in seawater were used to ensure quality control. Samples for iron analysis were collected with a rosette of Teflon-coated OTE-bottles during a separate cast directly after the daily sampling cast. After recovery, OTE-bottles were taken directly to a trace metal-clean sampling van where they were pressurized with filtered compressed air. Surface samples (~3 m depth) were also collected with a tow-fish system plumbed into the trace metal van, as in Bruland et al. (2005). All samples were passed through pre-cleaned 0.2 micrometer Supor membrane Acropak capsule filters into trace metal cleaned bottles (Cutter et al., 2014). Samples were acidified to pH 1.8 with optima HCl, and analyzed post-cruise with a flow injection analysis method (Lohan, Aguilar-Islas and Bruland, 2006), with 105 modifications as in Biller et al. (2013). Briefly, this method involved pre-concentrating the Fe at pH 2 with Toyopearl Chelate-650 resin and eluting into a reaction stream containing the colorimetric agent N,N-dimethyl-p-phenylenediaminedihydrochloride (DPD). The absorbance of the reaction stream was measured with a flow-through spectrophotometer. Calibration was performed with a standard addition curve, and blanks were assessed using acidified MilliQ. Reference samples analysed to assess accuracy compared well to consensus values: SAFe D1 0.70 +/- 0.04 nmol Fe/kg, n=12 compared with consensus value 0.67 +/- 0.04 nmol/kg, and GEOTRACES GSC 1.51 +/- 0.07 nmol/kg (n=11) compared with consensus value 1.53 +/- 0.11 nmol/kg. 4.3.8 Transcriptomic analysis Surface niskins (46% surface irradiance) collected during daily productivity casts were subsampled for RNA extraction. Approximately 2.5L to 4L of seawater were filtered onto 0.8 µm Pall Supor filters (142 mm) using a peristaltic pump, and then flash frozen in liquid nitrogen and stored at -80 °C. RNA was extracted using the RNAqueous-4PCR kit, following manufacturer instructions with the incorporation of a bead beating step during RNA lysis. All RNA samples were sent to GENEWIZ for library preparation and sequencing with PolyA tail selection. Sequencing was performed on an Illumina HiSeq 4000 with a 2x150 bp configuration. GENEWIZ provided raw paired-end read sequences for each sample. Raw reads were trimmed using Trim Galore 0.6.10 (Martin, 2011) and quality control was determined with FastQC (Andrews, 2010). A de novo metatranscriptome assembly was conducted using rnaSPAdes 3.15.5 (Bushmanova et al., 2019) and CD-HIT-EST (Li and Godzik, 2006). Contigs were annotated using the Marine Functional Eukaryotic Reference Taxa 106 (MarFERReT) database (Groussman et al., 2023), which provides NCBI taxonomic annotations (Federhen, 2012) and Pfam 34.0 functional annotations (Mistry et al., 2021). Samples were mapped against the MarFERReT DIAMOND sequencing aligner and its compatible BLASTX command (e-value < 1e-06) (Buchfink, Xie and Huson, 2015). Trimmed samples were aligned using Salmon (Patro et al., 2017). The package tximport (Soneson, Love and Robinson, 2016) was used to generate a comprehensive table of read count data for each sample and each contig. Only counts taxonomically mapping to Bacillariophyta (i.e., diatoms) were included. The normalized counts for all genes were then calculated using DESeq2’s median of ratios method (Love, Huber and Anders, 2014). Normalized counts of the low iron-inducible periplasmic protein (Fea1) (Allen et al., 2007), which shows high similarities to Iron Starvation-Induced Protein 2A (ISIP2A) (Behnke and LaRoche, 2020), was used as an indicator for iron stress (Marchetti et al., 2017). 4.3.9 Pigment concentrations and taxonomic composition During daily productivity casts, duplicate 1L dark Nalgene bottles were filled with water from niskin bottles collected at 1% and 46% PAR level depths. Under low ambient light, samples were filtered onto 47mm GFF filters (Whatman, nominal pore size 0.7 𝜇m). Filters were immediately flash frozen and stored in an onboard -80oC freezer. Samples were shipped on dry ice to the Pinckney Estuarine Ecology Photopigment Analysis Facility at the University of Southern California. There, photopigment concentrations were determined with high performance liquid chromatography following (Pinckney et al., 2001). Pigment compositions are reported in Appendix 2. 107 In addition to pigment sampling, Light microscopy was used to identify and enumerate dominant phytoplankton taxa. For microscopic cell counts, 25-50 mL subsamples preserved in Lugol’s solution were concentrated by sedimentation using Utermöhl chambers for >24h (Lund, Kipling and Le Cren, 1958). Cell counts of recognizable dinoflagellate and diatom genera were carried out using an Olympus CKX-31 inverted microscope in at least ten fields of view per sample at 200x and 400x magnification. 4.4 Results 4.4.1 Oceanographic conditions Across the cruise track, sea surface temperature (SST) ranged from 8.5 to 14 oC (Fig. 4.2), with strong coastal to offshore gradients (Fig. 1 and 2). The lowest SST were observed within near-shore upwelling plumes, which were associated with high salinity (> 33 psu). Along the entire cruise transect, salinity was negatively correlated with SST (ρ = -0.73, p << 0.01), as expected for upwelling regions. Sharp hydrographic fronts were apparent along coastal to offshore transects. Moving offshore, SST rapidly increased, while salinity dropped, changing by as much as 2 degrees and 0.5 psu, respectively, within a span of 5 km. These results indicate the presence localized on-shore upwelling plumes, as compared to more homogenous off-shore waters. Within the upwelling plumes, NO3- concentrations were elevated, reaching maximum concentrations of 20.5 𝜇M and displaying a positive relationship with salinity (ρ = 0.89, p << 0.01) and a negative relationship with SST (ρ = -0.76, p << 0.01). Off-shore, NO3- decreased to concentrations below the SUNA detection limit (~ 0.3 𝜇M), highlighting the difference in nutrient availability between the oligotrophic offshore waters and productive coastal upwelling 108 environments. Chlorophyll concentrations varied between 0.04 to 5.6 mg m-3 and exhibited a statistically significant (though weak) positive relationship with NO3- (ρ = 0.30, p << 0.01). In addition to the coastal-offshore gradient, surface water hydrography also differed between the two distinct upwelling plumes we sampled. These plumes were identified as low SST in the lees of Cape Blanco and Cape Mendocino (Fig 4.1). Both plumes exhibited an upwelling signature, but the apparent intensity of upwelling (as reflected in SST, salinity, NO3-, and Chl) was significantly stronger within the northern Cape Blanco plume (Fig. 4.3). Most apparently, SST was several degrees cooler at Cape Blanco (median of 9.6 ± 0.4 oC) than at Cape Mendocino (median of 11.5 ± 0.01 oC). Nitrate concentrations were highly variable across both plumes, but the mean NO3- at Cape Blanco (9.4 ± 0.8 𝜇M) was nearly twice as high as that of Cape Mendocino (5.2 ± 0.4 𝜇M). Chlorophyll concentrations were elevated at both plumes relative to offshore waters, with a median of 1.9 ± 0.01 mg m-3 around Cape Blanco and 1.4 ± 0.01 mg m-3 at Cape Mendocino. Both these Chl concentrations are well below that which can be supported by the available NO3- concentrations (1 𝜇M NO3- can typically yield 1 𝜇g chl L-1), indicating that the phytoplankton blooms were likely in the early phases of development following upwelling. Underway surface measurements were accompanied by on-station discrete sampling for NO3-, PO4, Si, and Fe concentrations. As expected, NO3- was highly correlated with PO4 (𝜌 = 0.99, p << 0.01) and SiO2 (𝜌 = 0.92, p << 0.01). The ratio of NO3-:PO4 was 7.4 ± 2.0, and less than half the expected Redfield ratio of 16. This result is consistent with observations of low NO3-:PO4 (~2-3) in the North Pacific attributed to high subsurface denitrification rates (Tyrrell and Law, 1997). In contrast with the strong covariance observed among macronutrients, surface Fe distributions were not correlated with surface NO3- concentrations (𝜌 = 0.35, p = 0.6). At 109 Cape Blanco, Fe concentrations varied from 1.2 – 2.0 nM, whereas Cape Mendocino concentrations were significantly lower, ranging from 0.21 – 0.65 nM. Offshore Fe concentrations were relatively high, with a surface concentration of 0.29-0.42 nM. These waters exhibited low macronutrient concentration. In section 5.1 we explore the potential causes for different nutrient signatures in the various water masses we sampled. 4.4.2 Photo-physiology Along the cruise track, phytoplankton photophysiological properties displayed spatial variability associated with hydrographic gradients, superimposed on significant diel cycles. In particular, we observed strong diel signatures in the expression of various photo-protective mechanisms. Generally, we observed decreases in the PSII functional absorption area, 𝜎2344, throughout the day, followed by recovery overnight (Fig. 4.4a). The maximum photochemical efficiency of PSII (FV/FM) similarly decreased during the day and peaked overnight. Measurements of NPQNSV displayed an inverse diel pattern to those of 𝜎2344 and FV/FM, reflecting adjustments to the allocation of absorbed energy between competitive photochemistry and thermal dissipation pathways. However, the magnitude of diel variability in FV/FM, 𝜎2344, and NPQNSV signals displayed significant variability between subregions, as discussed below. These observations agree with previous diel cycle studies of the region (Schuback and Tortell, 2019). Overall, photosynthetic parameters derived from semi-continuous PI curves also exhibited diel patterns that mirrored those of FV/FM and 𝜎2344. The maximum, light-saturated, ETRPSII (Pmax) and the light utilization efficiency under light limiting conditions (𝛼) both peaked during the midday when in-situ irradiance was highest. The light saturation level, Ek, tracked 110 surface light availability, while the photoinhibition parameter (𝛽), data not shown, peaked during midday and dissipated overnight. As with the continuous underway data, results from these discrete PI curves match the previous diel observations of Schuback and Tortell (2019). We note, however, that there is potential for some convolution of temporal and spatial variability, as the ship spent more time offshore in the night, and on-shore during the daytime. It is thus possible, that some of the diel cycling partially reflects different photo-physiological signals between coastal and offshore waters. Beyond diel signals, we also observed significant gradients in photophysiological parameters in relation to oceanographic conditions. In general, FV/FM, Pmax, and 𝛼 displayed positive relationships with upwelling indicators, i.e. salinity, macronutrients, and decreased sea surface temperature (Table 4.1), suggesting that vertical transport of nutrient-rich water to the surface supported high photochemical yields. In contrast, signs of upwelling were associated with decreased 𝜎2344 and NPQNSV. However, despite general trends between photo-physiological parameters and upwelling, there were significant differences in photo-physiological properties between the Cape Blanco and Cape Mendocino upwelling plumes. At Cape Blanco, mean values of NPQNSV and 𝜎2344 were significantly lower than at Cape Mendocino, while FV/FM, Pmax, 𝛼, and 𝛽 were all higher at this site, compared to Cape Mendecino (Table 4.1). Photophysiological properties at Cape Mendocino were much closer to those observed in offshore non-upwelling waters, with mean FV/FM values that were lower than offshore, despite elevated macro-nutrient concentrations. This result, combined with low Fe concentrations at Cape Mendocino, suggests that phytoplankton at Cape Mendocino were Fe-stressed despite the presence of upwelling conditions (see Discussion Section 4.5). 111 4.4.3 Primary productivity 4.4.3.1 Gross photochemistry In-situ ETRPSII, followed a notable diel cycle due to its first order dependency on irradiance (Eq 4.1, Fig. 4.5a). However, the relationship between PAR and ETRPSII was spatially variable across the cruise track, reflecting differences in 𝛼BËËB , Pmax, and Ek between Cape Blanco and Cape Mendocino (Table 4.1; Fig. 4.4). The amplitude of diel ETRPSII was greatest near Cape Blanco, despite lower average mixed layer PAR (Fig 4.4a). At Cape Blanco, maximum ETR averaged 977±83 e- RCII-1 s-1, as compared to maximum values of only 600 ± 42 e- RCII-1 s-1 at Cape Mendocino. The higher maximum ETR at Cape Blanco is in good agreement with the observations of higher photochemical yields in this region (Table 4.1). 4.4.3.2 O2/ N′2-derived net community productivity (NCP) In addition to ETRPSII, we estimated NCP from underway O2/N′2 measurements. Prior to correcting for vertical mixing, more than 80% of derived NCP values were less than 0, suggesting net heterotrophic conditions over most of the cruise track. The most negative uncorrected NCP values were observed near Cape Blanco, despite the high gross photochemistry rates measured in this region. The apparent decoupling between NCP and GPP can be largely explained by vertical mixing of low O2 waters, which artificially depress O2-derived NCP estimates (see Sect. 4.2.4). After applying the N2O-based mixing correction, we found that the majority of the cruise track (73% of measurements) exhibited net autotrophy, with the highest value recorded within the Cape Blanco filament. For the most part, net heterotrophy only existed at night in warmer off-shelf waters. The mean corrected NCP was 80 ± 218 mmol O2 m-2 d-1, within range of previous 112 observations of late-spring NCP within the California Current (Kranz et al., 2020). The large standard deviation reflects the large diel and spatial variability observed along the cruise track. The highest NCP estimates we obtained (> 500 mmol O2 m-2 d-1) are on the upper end of previous measurements. Values above 100 mmol O2 m-2 d-1 have only been observed in the most productive coastal waters (Wang et al., 2020; Niebergall et al., 2023), further emphasizing the high productivity of the CCS. The N2O-derived mixing correction term was strongly correlated to N2O-independent indicators of upwelling, namely temperature (𝜌 = -0.77, p << 0.01). Uncertainty in vertical-mixing corrected NCP due potential mixed layer nitrification (see sect. 4.2.6) represented between 1.5 – 4.2% of our mean corrected NCP value. These results give confidence that high surface concentrations of N2O are a valid marker of upwelling and transport of O2-poor subsurface water into the mixed layer. The maximum correction factor, 1200 mmol O2 m-2 d-1, was observed within the cold upwelling filament near Cape Blanco, where uncorrected NCP was below -500 mmol O2 m-2 d-1. This result highlights the impact of vertical fluxes on O2-based NCP estimates in upwelling regions. 4.4.3.3 Carbon fixation rates At nine discrete sampling stations, 14C-based PI curves were measured in parallel with ETRPSII at the surface and at the base of the euphotic zone. Volumetric carbon fixation rates varied significantly between stations and depths. Maximum carbon fixation rates (carbon-based Pmax) ranged from 0.4 to 96 𝜇g C L-1 hr-1. Over 85% of the variability in carbon fixation rates could be explained by differences in biomass, which varied from 0.11 to 9 mg Chl L-1. Chlorophyll concentrations in near-surface waters were, on average, four times higher than those 113 at the base of the euphotic zone, implying the bulk of carbon fixation took place in the mixed layer. On average, the bottom depth of the euphotic zone was 14 ± 12 meters deeper than the bottom depth of the mixed layer. To compare carbon-based GPP estimates against parallel ETRPSII measurements, carbon fixation rates were normalized to chlorophyll and converted to units of C chl-1 s-1. Chlorophyll normalized carbon fixation rates were positively correlated with Fe (𝜌 = 0.40, p < .05), FV/FM (𝜌 = 0.56, p < .05), and Si:NO3- ratios (𝜌 = 0.60, p < .05), and negatively correlated with 𝜎2344 (𝜌 = -0.68, p < .05). By comparison, carbon fixation rates were not significantly correlated with NO3- or PO4 concentrations or salinity. Notably, carbon fixation consistently saturated at lower light intensities than ETRPSII. The average Ek for ETRPSII was 5 ± 2.8 times greater than Ek for carbon fixation. As a result, carbon fixation did not scale linearly with ETRPSII, but rather demonstrated a hyperbolic relationship at each station and depth sampled. At sub-saturating light levels, carbon fixation increased linearly with ETRPSII, until approaching an asymptote as ETRPSII continued to increase while carbon-fixation remained stationary (Fig 4.6). This result indicates that at light levels beyond the saturating index for carbon-fixation, ETRPSII provides reducing power in excess Calvin-Benson Cycle requirements. Previous studies have noted a similar nonlinear relationship between carbon fixation and ETRPSII, consistent with an upregulation of alternative electron pathways under high light levels (Suorsa, 2015; Zhu et al., 2017; Schuback and Tortell, 2019). This non-linear relationship between C fixation and ETR has been cited as a key limitation to the widespread use of FRRF for autonomous high resolution GPP estimates. Although our results demonstrate a clear hyperbolic relationship between these rates, the parameters describing this relationship were variable across stations. In sect. 4.5.3, we further examine the relationship between carbon 114 fixation and ETRPSII, and contextualize the apparent differences in carbon-electron decoupling with available physiological, and environmental data. 4.5 Discussion As expected, our continuous underway measurements revealed strong spatial and temporal variability in biogeochemical properties across the California Upwelling system. In particular, we observed large diel cycles, and coastal-offshore gradients in biogeochemical properties, with two distinct upwelling filaments in the vicinity of Cape Mendocino and Cape Blanco. Differences in nutrient availability between sample sites appeared to exert a strong influence on photo-physiology, gross photochemistry, gross primary productivity and net community productivity. In this section, we explore the potential underlying causes of biogeochemical differences across our survey region, with a focus on iron gradients across the two distinct upwelling filaments. We also discuss the direct and indirect influence of iron availability and other environmental variables on phytoplankton photo-physiology, and energy transfer efficiencies between photosynthetic processes. 4.5.1 Factors driving the contrasting biogeochemistry of Cape Blanco and Cape Mendocino filaments The significant differences in surface oceanographic conditions between upwelling filaments may have been driven by differences in 1) the strength and timing of upwelling at the two Capes, or 2) differences in the nutrient content of the sub-surface upwelling source waters. We investigated these two possibilities by examining the NOAA coastal upwelling transport index (CUTI) as a proxy for upwelling strength during and prior to the sampling period, and by 115 evaluating nutrient depth profiles to examine the upwelling source waters at the two capes. Our analysis suggests that both factors likely contributed to the apparent differences between Cape Blanco and Cape Mendocino biogeochemistry, providing evidence that Fe and Si concentrations were particularly affected by bathymetric features that influence Fe supply. 4.5.1.1 Strength of upwelling and relative age of filaments To examine differences in the timing and strength of upwelling between the two filaments, we tracked CUTI for a 10 day interval prior to sampling (Jacox et al., 2018). Throughout the sampling period, the strength of upwelling at Cape Blanco varied from 1.3 to 2.4 m d-1 of vertical transport (positive values indicate upwelling) and peaked 6 days prior to our arrival, when vertical transport rates were 3.8 m d-1. During our sampling period at Cape Mendocino, vertical transport varied from -0.2 to 3.3 m d-1. Upwelling conditions persisted in this region, with vertical transport rates > 2 m d-1 between June 1 and 4, four to one days prior to our arrival. However, after June 4, the vertical mixing index at Cape Mendocino rapidly reversed to weak downwelling (-0.1 to -0.3 m d-1) during the last few days of sampling, emphasizing the dynamic nature of this sampling environment. These results support the hypothesis that colder and more nutrient rich water near Cape Blanco was attributable to stronger and more consistent upwelling in this region in the interval prior to our sampling. In contrast, Cape Mendocino was transitioning from upwelling to downwelling during our sampling period. 4.5.1.2 Nutrient content of upwelling source water Nutrient depth profiles offer additional insight into the nutrient concentrations of upwelling filament source waters. Unfortunately, nutrient samples were only collected down to 116 the base of the euphotic zone, missing the deeper source waters. Nonetheless, measurements at the base of the euphotic zone (40-50m) enable us to compare subsurface nutrient concentrations. Mean concentrations of [NO2- + NO3-] between 40 and 50m were significantly (p < 0.05) higher at Cape Blanco (24.5 ± 4.3 𝜇M) than at Cape Mendocino (16.5 ± 5.7 𝜇M). Similarly, phosphate concentrations between 40-50m were significantly greater around Cape Blanco (2.6 ± 0.3 𝜇M) compared to Cape Mendocino (2.1 ± 0.4 𝜇M; p = 0.02). Relative to nitrate and phosphate, larger differences were observed in Fe and Si concentrations between the two capes. The 40-50m silicic acid concentration at Cape Blanco (30.0 ± 8.6 𝜇M) was nearly double that observed at Cape Mendocino (16.3 ± 8.2 𝜇M; (p << 0.01), while, Fe concentrations between 40-50m at Cape Blanco (6.8 ± 4.1 nM) were more than three-fold higher than those at Cape Mendocino (1.8 ± 2.3 nM; p = 0.01). These results support the hypothesis that the two upwelling plumes were seeded by different water masses with distinct nutrient concentrations. Differences in underlying bathymetric features between Cape Blanco and Mendocino likely contributed to the observed differences in Fe and Si availability. Cape Blanco sits over a broad section of the continental shelf (> 30km wide) composed of highly erodible sedimentary rocks with mineral rich sand-silt layers originating from the Klamath Mountains (Spigai, 1971). The broad shelf continues south until the triple junction of the North American, Pacific, and Gorda plates which forms the submarine Mendocino escarpment, a narrow ridge extending west from Cape Mendocino along the transform fault (Menard and Dietz, 1952). Importantly, the shelf rapidly narrows to less than 5 km at the latitude of Cape Mendocino (Appendix 4.1). Differences in shelf width have important implications on sub-regional iron availability. Previous work by Biller et al. (2013) demonstrated that shelf width correlated with greater Fe bioavailability in the water layer directly overlying the seafloor. This trend was evident in our study as well, with a 𝜌 117 = 0.57 correlation between shelf width and Fe concentrations at the bottom of the euphotic zone for on-shelf stations. Yet, with only nine stations on-shelf stations, this correlation was not statistically significant. In contrast with NO3- and PO43-, which are resupplied to the surface by upwelling, remineralized Fe is rapidly removed from the water column, such that Fe supply to the surface can be significantly decoupled from macronutrients (King and Barbeau, 2011; Bruland, Middag and Lohan, 2014). As a result, differences in upwelling strength at Cape Blanco and Cape Mendocino likely account for the differences in PO43- and NO3- between the two sites, while contrasting shelf features can explain the larger differences in Fe availability. These observations fit within the theory that the California Current contains a ‘mosaic’ of Fe limitation, where patches of Fe-poor water may persist even in the presence of upwelling conditions (Hutchins et al., 1998; Till et al., 2019). In the following section, we present several lines of evidence that photo-physiological properties of phytoplankton assemblages were influenced by iron gradients. 4.5.2 Environmental and taxonomic influences on physiology and productivity Environmental gradients exert direct effects on physiology and productivity by determining the supply of essential nutrients that support phytoplankton growth and the maintenance of photosynthetic proteins. As a cofactor in many biological redox reactions, Fe plays a particularly important role in the photosynthetic electron transport chain and nutrient uptake pathways. Several lines of evidence suggest that iron stress was a key factor shaping phytoplankton productivity and photo-physiology across our study site. As noted above, there was a significant difference in Fe concentrations between Cape Blanco (high Fe) and Mendocino (low Fe) associated with variability in the shelf width. The difference in Fe-availability was 118 strongly correlated with Si:NO3- (ρ = 0.85, p << 0.0.1), likely reflecting excess Si uptake by iron-limited diatoms (M. Franck et al., 2000; Sarthou et al., 2005). Further evidence of Fe-stress at Cape Mendocino was obtained from ancillary transcriptomic analysis, which demonstrated elevated expression of the Fe assimilation gene Fea1 (Appendix 4.3), which has previously been cited as a marker of Fe-stress (Allen et al., 2007). Together, these observations indicate the onset of Fe stress at sites with reduced Fe-availability. Below we outline the apparent taxonomic and physiological responses to apparent Fe stress. Beyond directly affecting phytoplankton physiology, Fe gradients can indirectly influence phytoplankton physiology and productivity by driving taxonomic shifts towards species that are adapted to low Fe conditions. Although diatoms were the most dominant group across the study area, their relative contribution was significantly lower around Cape Mendocino compared to Cape Blanco (Appendix 4.2). At Cape Mendocino, we observed a taxonomic shift towards smaller phytoplankton, including smaller diatoms and dinoflagellates. Smaller cells sizes afford larger surface area to volume ratios, facilitating nutrient uptake at lower concentrations (Sunda and Huntsman, 1997). Moreover, stations near Cape Mendocino had high abundances of Pseudo-nitzschia, while none were observed near Cape Blanco. Pseudo-nitzschia is a well-studied diatom with a number of physiological adaptations to Fe-limitation, causing it to be favoured over other diatoms under low Fe conditions (Lampe et al., 2018). These shifts in phytoplankton assemblages towards smaller sizes and low Fe specialists suggest bottom-up environmental controls driving taxonomic composition. Cell size and nutrient status influence the optical properties and photo-physiology of phytoplankton. Large cells are prone to pigment packaging effects, which decrease Chl-specific absorption as intracellular Chl concentrations increase and surface area to volume ratios 119 decrease. This effect causes reduced 𝜎2344, as was observed near Cape Blanco (Table 4.1). Nutrient limitation, particularly for Fe, can also lead to accumulation of photo-inactive or damaged RCII (Roncel, Antonio A. González-Rodríguez, et al., 2016). Inactive RCII still absorb light, but do not contribute to photochemistry. This drives high 𝜎2344, which is proportional to the light harvesting complex absorption coefficient normalized by active RCII concentrations (Oxborough et al., 2012; Zhengke Li et al., 2021), and low FV/FM, due to inactive RCII contributing to the FM but not FV signal (Schuback, Philippe D Tortell, et al., 2021). Both of these commonly cited indicators of Fe stress were observed around Cape Mendocino and also offshore (Table 4.1). The taxonomic and nutrient-dependent effects on photo-physiology described above are expected to directly impact ETRPSII (Eq. 4.1). Previous studies have noted higher ETRPSII among Fe-limited phytoplankton, presumably due to increased 𝜎2344 (Schuback et al., 2015). However, we observed greater ETRPSII in the relatively Fe-rich waters near Cape Blanco (Table 4.1), likely due to high 𝐹K(/𝐹L((𝑃𝐴𝑅), which represents the proportion of open RCII at a given light level (Suggett, Moore and Geider, 2011). Low NPQ observed in the Cape Blanco region (Fig. 4.4d) likely enabled 𝐹K(/𝐹L( to remain high under high light levels. Indeed, 𝐹K(/𝐹L( measured during underway Photosynthesis-Irradiance curves and mapped onto in-situ irradiances demonstrated that 𝐹K(/𝐹L( was higher around Cape Blanco compared to Cape Mendocino (Table 4.1). It is well recognized that iron limitation exacerbates high light stress and NPQ (Ryan-keogh et al., 2020; Schallenberg et al., 2020), and the high NPQ at Cape Mendocino compared to Cape Blanco provides further evidence that Cape Mendocino assemblages were affected by Fe stress. Iron limitation also impacts photosynthetic processes downstream of ETRPSII. In this study, maximum carbon fixation rates (Pmax determined during 14C PI experiments) displayed a strong correlation 120 with Si (𝜌 = 0.63, p << 0.0.1) and the ratio of Si:NO3- (𝜌 = 0.60,p << 0.0.1) in the water column. Overall, our results suggest that Fe availability gradients between Cape Blanco and Mendocino influenced local community composition and physiology with consequential effects on C and Si cycles. Differences in taxonomic composition, photo-physiology, nutrient quotas and productivity all serve as evidence that the community shifted towards Fe-limitation in proximity to Cape Mendocino. Due to the differential sensitivity of ETRPSII, carbon fixation, and NCP to Fe-availability, we hypothesized that Fe-limitation would lead to a decoupling between these different PP currencies. We explore this hypothesis below with direct comparisons of ETRPSII, C-fixation, and NCP. 4.5.3 Energy transfer efficiencies between photosynthetic processes Measurements of primary productivity in different ‘currencies’ (electrons, carbon, and oxygen) each reflect the rates of different photosynthetic processes. Comparison of these alternative productivity metrics can yield information on energy transfer efficiencies across different components of the photosynthetic pipeline (Fig 4.7). Water-splitting and carbon fixation require a minimum 4 charge separation events required to produce one O2 and fix one CO2. Yet a number of non-linear electron transport pathways can divert reducing power from carbon fixation, decoupling ETRPSII from GPP, while oxygen consumption by respiration and and non-linear electron transport pathways further decouple ETRPSII from NCP (Fig 4.7). The ratio between ETRPSII and GPP thus provides information on the magnitude of non-linear electron transport, while the ratio between ETRPSII and NCP reflects the sum of non-linear electron transport and respiration. In practice, interpreting the decoupling between ETR, GPP, 121 and NCP is complicated by differences in the temporal scales between measurement approaches, as well as the various assumptions implicit in each method. In the following sections, we directly compare parallel productivity measurements to examine energy transfer efficiencies across photosynthetic processes, taking care to note important methodological considerations. 4.5.3.1 Carbon fixation as a function of ETRPSII Non-linear electron transport pathways (Fig 4.7) maintain redox homeostasis when ETRPSII exceeds downstream energy requirements for growth and metabolism. The energy balance between PSII and PSI becomes disrupted under high irradiance, when PSII absorbs energy in excess of PSI electron transport rates, and/or under nutrient limitation when biosynthesis of electron transporters is limited (Schuback et al., 2015; Roncel, Antonio A. González-Rodríguez, et al., 2016; D. J. Hughes, Varkey, et al., 2018). Iron limitation, in particular, exerts acute constraints on the synthesis of Photosystem I (PSI) and Cytochrome b6f (Cyt b6f), which require 12 and 5 Fe atoms each (Raven, Evans and Korb, 1999). As a result, Fe-limited phytoplankton have high levels of PSII relative to PSI, exacerbating energy imbalances between PII and PSI, and necessitating upregulation of non-linear electron transport pathways (Michael J. Behrenfeld and Milligan, 2013). We therefore hypothesized that Fe-stress would increase decoupling between C-fixation and ETRPSII. One of the primary findings of this work is that C-fixation varies as a hyperbolic function, with curves shapes defined by the maximum carbon fixation rate (Pmax-C) and the saturation constant (Ksat). These parameters were highly variable between samples (Fig. 4.6). Samples with high Pmax-C and Ksat, (e.g. Station 6) showed more linear relationships between C-fixation and ETRPSII, and a nearly constant electron requirement for carbon fixation (𝜙.: 𝑐/𝑛2344, 122 units = -e Chl C-1 RCII-1). By contrast, in samples with low Pmax-C and Ksat, carbon-fixation quickly saturated with respect to ETRPSII, resulting in an increase in 𝜙.: 𝑐/𝑛2344 with increasing light levels (e.g. Station 7). Determining sources of Pmax-C and Ksat variability therefore provides significant utility in predicting the stoichiometry of electron requirements for gross carbon fixation. enabling fluorescence-based GPP estimates. Previous studies have documented the importance of different environmental, taxonomic and physiological parameters in driving variability in 𝜙.: 𝑐/𝑛2344, but efforts to develop empirical algorithms predicting 𝜙.: 𝑐/𝑛2344 remain ongoing (Lawrenz et al., 2013). Recent progress has been made on this front by Schuback et al., (2015, 2016, 2017) who noted a consistent relationship between NPQNSV and 𝜙.: 𝑐/𝑛2344 in the surface waters of the Northeast Pacific and Canadian Arctic, suggesting that carbon fixation can be estimated using FRRF-based NPQ and ETRPSII measurements alone. Subsequent studies have applied the NPQNSV ∝ 𝜙.:®/𝑛2344 relationship observed by Schuback et al. (2015) to collect high-resolution fluorescence-based GPP estimates, e.g. Kranz et al. (2020). Others, however, have noted that the NPQNSV ∝ 𝜙.:®/𝑛2344 relationship does not hold for all taxa (Hughes et al., 2021), light conditions (Schuback et al., 2017), or environments where phytoplankton grow on more reduced N forms like NH4+ (Fei et al., 2024). In our study, NPQNSV was positively correlated with 𝜙.:®/𝑛2344 (𝜌 = 0.55, 𝑝 ≪ 0.01). However, the linear relationship proposed by Schuback et al. (2017; 2019) did not adequately predict 𝜙.:®/𝑛2344 for our samples (R2 = -0.41), nor did we find a single line of best fit that could describe all of our data (R2 = 0.30). Rather, we found that 𝜙.:®/𝑛2344 scaled directly with NPQNSV only for samples with low Pmax-C (=< 0.3 C Chl-1 s-1). In contrast, for samples with high 123 Pmax-C (> 0.3 C Chl-1 s-1), 𝜙.:®/𝑛2344 remained relatively constant across increasing light, NPQNSV, and ETRPSII (Fig 4.8). By analogy with the Michaelis-Menten enzyme kinetics model, variability in Pmax-C can be explained as the product of the enzyme concentration and maximum reaction rate (Choi, Rempala and Kim, 2017). In our case, Pmax-C reflects the entire suite of proteins that facilitate the conversion of chemical energy to organic matter. Reduced concentrations of PSI and Cyt b6f expected under Fe-stress would therefore reduce Pmax-C. Indeed, Pmax-C was significantly correlated to physiological markers of Fe-stress, 𝜎2344 (𝜌 = -0.68, p << 0.01), FV/FM (𝜌 = 0.56, p = 0.02), and Si:NO3- (𝜌 = 0.60, p = 0.01). Additionally, meta transcriptomic analysis of diatom RNA revealed significant positive correlations (p < 0.05) between Pmax-C and the expression level of different PSI subunits (psaE, psaL, psaM), with correlation coefficients of 𝜌 = 0.71, 0.70, and 0.93, respectively. In contrast, there were no detected correlations between Pmax-C and Cyt b6f, however Cyt b559a, a subunit of PSII, also demonstrated a strong positive correlation with Pmax-C (𝜌 = 0.76, p < 0.05). While the precise functional roles of Cyt b559a are still not certain, previous studies have demonstrated its potential role in PSII assembly and photoprotective cyclic electron transport around PSII (Chiu and Chu, 2022). Finally, it is worth noting Ksat and Pmax-C displayed a positive correlation (𝜌 = 0.70, p << 0.01). In addition to non-linear electron transport, 𝜙.:®/𝑛2344 is also directly affected by the number of Chl energetically coupled to RCII (1/𝑛2344). Directly measuring 1/𝑛2344 requires specialized O2 flash yield instrumentation (Suggett et al., 2009), which was unavailable for this study. However, in-situ Chl concentrations, normalized to FRRF-derived proxies for [RCII] (∝ Fo/𝜎2344) following the approach of Oxborough et al. (2012), can be used to examine variability in 1/𝑛2344between samples. With a known instrument calibration factor, Ka, either provided by 124 instrument manufacturers or determined independently by O2 flash yield measurements, this approximation could be used to estimate the absolute value of 1/𝑛2344. It is well established that Chl:RCII (1/𝑛2344) ratios increase under low light, to maximize light absorption (Greenbaum and Mauzerall, 1991). In our measurements, the proxy for 1/𝑛2344 varied significantly between sample depths, with higher 1/𝑛2344 at the bottom of the euphotic zone compared to surface depths, confirmed by a t-test comparison of population means (p << 0.01). Iron limitation is also expected to increase Chl:RCII. Although iron limitation lowers total cellular Chl content, Chl is more likely to be energetically coupled to RCII rather than PSI reaction centers (Greene et al., 1992). Accordingly, 1/𝑛2344 displayed a negative correlation with Fea1 expression in surface samples (𝜌 = 0.72, p <0.05, n = 9), which we used as a proxy for iron limitation. We thus conclude that Fe-stress likely contributed to variability in 𝜙.:®/𝑛2344 in addition to influencing non-linear electron transport. The hyperbolic relationship between carbon fixation and electron transport was unaffected by 1/𝑛2344, which was assumed to be constant for individual samples throughout the course of photosynthesis-irradiance experiments (Appendix 4). However, this assumption may be violated under high light, due to photoinactivation of RCII (Campbell and Serôdio, 2020). A robust understanding of 𝜙.:®/𝑛2344 variability requires direct [RCII] measurements collected in parallel with ETRPSII and carbon fixation measurements. 4.5.3.2 Comparison of ETRPSII and NCP As opposite end members of the productivity spectrum (Fig. 4.7), ETRPSII quantifies gross photochemical energy production, while NCP represents the net accumulation of photosynthetic carbon or oxygen remaining after accounting for all sources of mixed layer respiration. To directly compare ETRPSII and NCP, we converted ETRPSII from units of e- RCII-1 125 s-1 to mmol O2 m-2 d-1 (Eq. 4.5) by assuming each RCII was functionally coupled to 400 – 700 Chl pigments (Kolber and Falkowski, 1993; Schuback et al., 2015) and 4 charge separation events per gross O2 evolved. The resulting O2-based gross photochemistry values varied between 0 – 4000 mmol O2 m-2 d-1 (Fig. 4.5c), coincident with the range of previously reported values for the CCS (Kranz et al., 2020). On average, NCP accounted for 17 ± 8% of gross photochemistry, indicating ~80% of oxygen produced at PSII by water splitting reactions was consumed within the mixed layer through autotrophic and heterotrophic respiration. In contrast to 𝜙.:®/𝑛2344, there were no significant differences in NCP:ETRPSII between Cape Blanco, Cape Mendocino, or offshore, suggesting minimal effects of nutrient limitation on decoupling between ETRPSII and NCP.. Although NCP is constrained by gross photochemistry, NCP was greater than ETRPSII over 29% of the cruise track. This apparent contradiction can be explained by differences time-scales between instantaneous ETRPSII measurements, and NCP, which is integrated over O2 residence times in the mixed layer (~1–2 weeks). Sustained net autotrophy can lead to accumulation of O2 in the mixed layer, such that measured O2 fluxes indicate high levels of NCP despite short-term decreases in ETRPSII (e.g. overnight). Regardless of the large differences in integration time-scales and metabolic sources of decoupling, NCP showed strong coherence with ETRPSII (Fig 4.9). Direct comparison between continuous underway measurements of ETRPSII and NCP yielded a moderate positive correlation (𝜌 = 0.43, p << 0.01). To account for some of the decoupling introduced by the strong diel dependence of ETRPSII, we also compared ETRPSII and NCP measurements integrated over 24hr bins. This comparison indicated a much stronger relationship between ETRPSII and NCP (𝜌 = 0.92, p << 0.01; Fig 4.9). Across daily integrated time-scales, NCP linearly increased as a function of ETRPSII with a ~15% energy transfer efficiency and a predicted NCP of -0.55 mmol 126 O2 m-2 when ETRPSII is zero. This efficiency estimate is within range of previous studies that have compared gross oxygen production and NCP using triple oxygen isotope and O2/Ar methods (Haskell II et al., 2017; Howard et al., 2017), despite the differences in integration time scales between ETR and the triple oxygen isotope method. Further, a sensitivity analysis found no significant changes in the derived energy transfer efficiency between ETR and NCP integrated over 24, 48, 72, and 96-hour bins. These results suggest the utility of FRRF to estimate gross oxygen productivity as an alternative to triple oxygen isotopes or other discrete methods, e.g. H218O tracer experiments. Despite the inherent dependency of net oxygen production on gross oxygen production, the strength of the correlation between NCP and ETRPSII and the consistency of ETRPSII:NCP across offshore, Cape Blanco and Cape Mendocino subregions is surprising given the vast suite of potential methodological and physiological sources of uncoupling (Fig 7). However, the derivations of NCP and GP both have similar dependencies on mixed layer Chl concentration. To obtain FRRF-derived GP estimates in comparable units of mmol O2 m-2 d-1, we multiplied in-situ ETRPSII by mixed layer Chl concentrations (Eq 5). While mixed layer Chl concentrations are not explicitly included in NCP calculations (Sect 2.6), biomass is expected to be a primary driver of bulk productivity. If Chl-normalized NCP is instead compared against GP expressed in units of mmol O2 Chl-1 d-1, the correlation between 24h binned and instantaneous NCP and ETRPSII estimates decrease to 𝜌 = 0.22 and 0.35, respectively. We therefore conclude that it remains challenging to derive gross and net carbon fluxes from FRRF measurements alone, but paired ETRPSII and Chl measurements can provide useful constraints for NCP estimates. 127 4.6 Conclusion Consistent with previous observations, our results indicate a patchwork of Fe-stress within the coastal upwelling waters of the California Current, providing evidence for physiological Fe-stress within an upwelling filament near Cape Mendocino. Differences in iron availability between upwelling filaments appear to be linked to bathymetric features that influence sediment loading, and variable micronutrient content of sub-surface upwelling source waters. Paired fluorescence- and 14C-based photosynthesis-irradiance measurements indicated strong connectivity between ETRPSII and carbon-fixation in Fe-replete phytoplankton, and greater decoupling in these rates for Fe-limited assemblages, with greater associated variability in 𝜙.: 𝐶/𝑛2344. Recently, there has been significant focus on understanding 𝜙.: 𝐶/𝑛2344 variability to expand FRRF-based GPP surveys (D. J. Hughes, Campbell, et al., 2018). Our results suggest that nutrient replete phytoplankton are able maintain near constant 𝜙.: 𝐶/𝑛2344 under increasing excitation pressures due to their ability to efficiently transfer energy between PSI and PSII. Under these circumstances, NPQNSV is not a good predictor of 𝜙.: 𝐶/𝑛2344. However, where nutrient limitation necessitates enhanced non-linear electron transport pathways to maintain energy balance between PSII and PSI, 𝜙.: 𝐶/𝑛2344 does scale with NPQNSV and excess excitation pressure. With the accumulation of further data across a range of oceanographic conditions, it may be possible to derive more robust empirical relationships between NPQNSV and 𝜙.: 𝐶/𝑛2344, which could be used to derive GPP in C-based units from FRRF measurements. In addition, our results show a strong general coherence between daily integrated ETRPSII and NCP measurements, suggesting that ETRPSII may have significant utility as an indicator of bulk primary productivity. We thus conclude that high-resolution, ship-board measurements hold 128 significant potential to explore fine-scale variability in surface water primary productivity in complex coastal waters. 4.7 Tables and figures Figure 4.1 Study site map. Location of the cruise track (white line) and discrete sampling stations (white circles) during the PUPCYCLE expedition. The black bathymetric contour line represents the 200 m isobath. The study region and surrounding waters are colored by a. daily NASA Aqua Modis Level 3 satellite retrievals of Chl concentration (Chl) averaged over the cruise period (May 27 – June 11, 2024) b. NASA Aqua Modis Level 3 satellite retrievals of Sea Surface Temperature (SST) averaged over the cruise period. 129 Figure 4.2 Continuous surface water hydrographic properties. Cape Mendocino is abbreviated as Cape Mendo. 130 Figure 4.3 Distribution of hydrographic properties within sub-regions. The line inside each boxplot represents the median, the whiskers display the 75th percentile. Points outside the whiskers represent outliers. Panel (e) displays the spatial distribution of offshore (off), Cape Blanco, and Cape Mendocino (Mendo) data. Samples with SST < 12oC and south of 41.5oN were considered part of the Cape Blanco plume while samples with SST < 12oC north of 41.5oN were considered part of the Cape Blanco plume. Samples falling outside these criteria were designated as offshore samples. Criteria were chosed by studying Fig 4.1. 131 Figure 4.4 Diel patterns in photo-physiological properties along the cruise track. Grey shading indicates night-time, when surface PAR < 5 𝝁mol photons m-2 s-1. a. FV/FM (black) and 𝝈𝑷𝑺𝑰𝑰 (red), b. Pmax (black) and 𝜶 (red), c. Ek (black) and NPQNSV (red) are displayed with respect to sampling time. High sea states between 06/04 – 06/06 impacted the seawater intake, resulting in the data gap. 132 CAPE BLANCO CAPE MENDOCINO OFF-SHORE SST N= 71604 SAL N=71604 NO3- N= 989 FE N=40 SI N= 40 PAR N= 71604 FV/FM N= 1438 0.47±0.01A 0.39 ± 0.08B 0.41± 0.08C -0.55* 0.27* 0.39* 0.32 0.52 -0.04 𝝈𝑷𝑺𝑰𝑰, N= 1438 545 ± 5A 655 ± 11B 647 ± 5C 0.58* -0.38* -0.45* -0.55 -0.60 -0.12 PMAX, N = 91 977±83A 600 ± 42B 600 ± 91B -0.67* 0.40* 0.50* 0.45 0.78* 0.19 𝜶, N = 91 4.8±0.3A 3.3 ± 0.3B 3.6 ± 0.4B -0.57* 0.19 0.24 0.37 0.45 -0.11 𝜷, N = 91 1.55 ± 0.47 1.00 ± 0.36 0.98 ± 0.43 0.44* 0.19 0.31 0.19 0.56 0.09 EK, N = 91 210±19A 172 ± 20B 176 ± 15B -0.38* 0.33* 0.41* 0.62 0.72* 0.37* NPQNSV, N = 91 0.75±0.05A 1.33 ± 0.2B 1.2 ± 0.2B 0.66* -0.26 -0.46 -0.36 -0.62 0.58* 𝑭𝒒( /𝑭𝒎( N = 91 0.21 ± 0.10 0.31 ± 0.14 0.33 ± 0.13 0.34* -0.18 -0.28 -0.55 -0.31 0.84* Table 4.1 Summary of photophysiological properties at each sampling environment and their relationship to hydrographic parameters. Left side of the table displays the median ± median absolute deviation for each photophysiological parameter according to sampling environment. Superscripts denote groups with significantly different medians. Right side of the table displays spearman rank correlation coefficients for each photophysiological parameter against environmental parameters. * indicates p < 0.05. Due to differences in sampling frequencies, the number of observations (N) varied across different parameters. For correlation analyses, paired observations were matched in space and time to the lowest resolution measurement. Cape Mendocino is abbreviated as Cape Mendo. 133 Figure 4.5 Primary productivity time series along the cruise track. a) ETRPSII (black line) derived from FRRF-measured PI curve parameter interpolated to match NCP sampling frequency. Grey patches indicate the mean PAR available within the mixed layer (PARin-situ). The bar on the bottom is coloured by the sampling latitude. The Cape Blanco is designated as latitude > 40.5 and is coloured red, while Cape Mendocino is designated as latitude < 40.5 coloured blue. b) Uncorrected NCP (grey) and mixing-corrected NCP (black). The dashed red line denotes the boundary between net autotrophy (NCP > 0) and net heterotrophy (NCP < 0). The color bar on the bottom illustrates the sample temperature. c) mixing-corrected NCP (black, right y-axis) and gross photochemistry (GP), shown in red (left y-axis). GP is calculated by converting ETRPSII from units e- RCII-1 s-1 to mmol O2 m-2 d-1. The grey shading around the red line displays the range of GP based on an assumed range of 400 – 700 Chl:RCII. 134 Figure 4.6 Parallel measurements of fluorescence- and 14C-based photosynthesis-irradiance experiments conducted on-station. The top left displays the sample locations colored by maximum Chl-specific surface carbon fixation rates. The black contour line displays the 200m isobath. The other panels show carbon fixation rates plotted against ETRPSII. Surface samples are represented by open circles while sub-surface samples are depicted as closed circles. Each curve is fit with the function, 𝑪 𝒇𝒊𝒙𝒂𝒕𝒊𝒐𝒏 = 𝑪𝒎𝒂𝒙∗𝑬𝑻𝑹𝑷𝑺𝑰𝑰𝑲𝒔𝒂𝒕ú𝑬𝑻𝑹𝑷𝑺𝑰𝑰 , where Cmax is the maximum carbon fixation rate, and Ksat is the saturation constant beyond which changes in carbon fixation with respect to ETRPSII become increasingly non-linear. Carbon fixation data were not collected at Station 1. All curve fits had an R2 > 0.9. 135 Figure 4.7 Different primary productivity currencies and their decoupling mechanisms. The three productivity currencies of interest are indicated with respect to the spatial time scales they represent. Numbered red pathways denote decoupling mechanisms including 1) chloro-respiration 2) cyclic transport around PSII 3) cyclic transport around PSI 4) pseudo-cyclic electron transport and Mehler reactions 5) reductant consuming nutrient uptake pathways 6) photorespiration and 7) Community-wide respiration. Figure produced in Biorender. 1 s 1 h 1 monthLog TimeETRPSII!e:c/"PSII Decoupling Mechanisms: • Non-linear -e transport• NADPH consumption by nutrient uptake pathways• Physiological changes in Chl: RCII!e:NCP Decoupling Mechanisms: • Community-wide respiration• Differences in integration time scales• Lack of steady-state mixed layer conditionsC fixation NCPCalvin CycleATPADPRubiscoNADPHNADP+CO2 O2GlucoseNADPHNADP+6O2air-sea fluxesHorizontal Verticalfluxes Respiration7 136 Figure 4.8 The relationship between ϕe:C/n_PSII, NPQNSV and Pmax-C. The electron requirement for carbon fixation, 𝜙e:C/𝑛2344, measured as the ratio of ETRPSII to Chl-normalized carbon fixation, is plotted against NPQNSV. Each datum is colored by the sample’s Pmax-C, shown by the red/blue colorbar. Lines of best fit are drawn through points with Pmax-C <= 0.3 and > 0.3 mol C mol Chl-1 s-1, with the colorbar indicating Pmax-C. 137 Figure 4.9 Comparison between fluorescence-based gross photochemistry (GP) and NCP. The left subpanel directly compares continuous underway ETRPSII and NCP measurements (𝝆 = 0.43, p << 0.01), while the right panel compares daily-integrated measurements of ETRPSII and NCP measurements (𝝆 = 0.92, p << 0.01). A line of best fit (y = 0.14*GP – 0.55) is drawn through the positive NCP data points. The error bars represent the total uncertainty of each measurement determined by propagating uncertainty in each input variable. For GPP, total uncertainty represented as the uncertainty in Chl:RCII. For NCP, uncertainty was quantified from uncertainty in the deep water supply ratio of O2:N2O, and modelled differences between [N2] and [Ar]. Assumption biases (e.g. no horizontal advection of O2) also represents a potential large source of uncertainty but was not quantified. A line of best fit is drawn through the positive NCP points on the right plot with the equation y = 0.14 * GPP – 0.55 (R2 = 0.82). 138 Chapter 5: Conclusion Climatologists and oceanographers have repeatedly called for broader in-situ data coverage to accurately characterize phytoplankton contributions to global carbon cycles, and better understand potential responses of primary productivity to changing ocean conditions (Bindoff et al., 2019; IPCC, 2023). Fast Repetition Rate fluorometry is positioned to meet this need with readily automated protocols to measure phytoplankton photo-physiology and productivity that could be deployed more widely on autonomous floats, gliders, and ships of opportunity. However, further understanding of environmental controls on photosynthetic efficiency and Chl fluorescence signatures is needed before FRRF can be applied as a robust tool for high-resolution productivity estimates across diverse oceanographic regions. Towards that end, this thesis has presented several case studies demonstrating the influence of light and nutrient availability on fluorescence-based productivity estimates, revealing certain methodological and physiological biases. In this final chapter, I synthesize the resulting conclusions, explore remaining limitations and areas for future research, and discuss potential applications of this work beyond FRRF. 5.1 Key findings and remaining challenges to FRRF-based productivity surveys 5.1.1 Current ETRPSII caveats and the need for fluorescence-independent validation In Ch 2, I provided the first comparative analysis of two widely used ETRPSII algorithms, the amplitude-based and more recent kinetic approaches, on natural phytoplankton assemblages. This study found that results from the two algorithms were well correlated, but their absolute 139 values diverged from one another as much as 15-fold, depending on phytoplankton physiology. The apparent dependency of ETRa:ETRk on phytoplankton physiology results from the different physiological parameters used in the two algorithms; FV/FM and 𝜎2344 in the case of ETRa, and 𝜏b7 and 𝐹K(/𝐹C( F in the case of ETRK. Further, the underlying derivation of these critical primary parameters is affected by differences in software and calculation methods across various FRRF instruments, highlighting the need for standardized methodologies across the FRRF community. Although Ch 2 identified the drivers of decoupling between ETRa and ETRk, it did not compare the PSII photochemistry estimates derived from these algorithms against a fluorescence-independent benchmark estimate of primary productivity. For this reason, it remains unclear which algorithm yields more accurate results under different circumstances. Both ETRPSII algorithms have been compared against 14C-uptake rates or phytoplankton growth rates measurements (DJ, HL and TM, 2009; Gorbunov and Falkowski, 2020). However, as this thesis and numerous previous studies have demonstrated (Lawrenz et al., 2013; Schuback et al., 2015; Schuback et al., 2017; D. J. Hughes, Varkey, et al., 2018), both of these measurements of NPP are routinely decoupled from ETRPSII through non-linear electron transport pathways and autotrophic respiration. Further, because both ETRPSII algorithms rely on the same fundamental underlying assumption of an inverse relationship between fluorescence and PSII photochemical yields, they produce highly correlated results. As such it is important not just to compare ETRPSII results against alternative fluorescence-independent metrics, but to compare exact magnitudes of PSII activity in matching units. Doing so requires estimation of the photosynthetic size unit (i.e. Chl RCII-1), since ETRPSII is reported in units of e- RCII-1 s-1 whereas oxygen evolution or carbon fixation rates are reported volumetrically or per Chl (DJ, HL and TM, 2009). 140 The most direct approach to estimate the photosynthetic size unit is via the oxygen flash yield method (Silsbe et al., 2015). In this approach, an electrode or micro-optode measures instantaneous changes in O2 concentration in response to pulses of excitation light, which may come from an FRRF’s LEDs. Assuming 4 charge separation events for each O2 molecule, the ratio of oxygen evolution (O2 L-1 s-1) to ETRPSII (FçO2 RCII-1 s-1), measured simultaneously, gives the concentration of RCII (Murphy et al., 2017). The circular nature of requiring ETRPSII estimates to measure [RCII] and [RCII] estimates to validate alternative ETRPSII algorithms presents a challenge. However, with a known instrument calibration factor, [RCII] can alternatively be estimated from FRRF measurements alone, following the formulation presented by Oxborough et al. (2012) with modifications by Murphy et al. (2017) to account for variability across photo-acclimation states and taxa. Ideally, combined FRRF-derived estimates of [RCII], and oxygen flash yield-based estimates of instantaneous oxygen evolution rates may be used to determine which ETRPSII provides the most accurate results. I recommend the oxygen flash yield approach over alternative methods for measuring gross oxygen production (e.g. H2O-18O tracer experiments) because the oxygen flash yields can be integrated with FRRF systems for truly simultaneous measurements, reducing the chances of error introduced by bottle artefacts and differences in measurement integration times. 5.1.2 Balancing the need for NPQ relaxation and representative in-situ measurements Each chapter of this thesis identified the influence of NPQ on underway FRRF measurements, visualized as diel fluctuations in the key primary parameters FV/FM and 𝜎2344. In all cases, this light-dependent source of variability complicates the interpretation of FRRF data 141 as an indicator for nutrient stress. In the most extreme cases, as seen in Ch 3, the response of FV/FM and 𝜎2344 to NPQ relaxation were uncoupled by apparent photo-inhibition and inactivation of PSII, with propagating effects on ETRPSII derivations. This decoupling suggests the need longer dark acclimation periods to standardize measurements and limit NPQ effects, as have been applied in most FRRF field studies (e.g. Lewis et al., 2019; Schallenberg et al., 2020). Yet, such extended NPQ relaxation protocols clearly cause photophysiological shifts in phytoplankton from their in-situ states and dramatically reduce measurement frequency, negating some of the primary advantages of the FRRF approach. Given the challenges outlined above, researchers must balance the need for high spatial resolution measurements with the requirement for sufficient NPQ relaxation, and this requires knowledge of NPQ relaxation time scales. In Ch 3, I applied an autonomous underway protocol to monitor NPQ relaxation and quantify NPQ relaxation time scales in near real-time. The data presented here suggests that 20 minutes is an appropriate NPQ relaxation time for Arctic surface samples. However, NPQ relaxation time scales will vary with taxonomic, environmental and physiological conditions (Lacour et al., 2018; Fisher et al., 2020). Therefore, future researchers should incorporate protocols for monitoring NPQ relaxation dynamics and determining relaxation time scales within their regions of interest. Measurements to monitor NPQ relaxation can be collected autonomously during routine recommended low light relaxation periods within the instruments sample cuvette, exerting no additional cost on measurement frequency during semi-continuous underway operation. Adopting such NPQ monitoring protocols can dramatically expand the sparse global dataset of NPQ relaxation dynamics, increasing understanding of environmental influences on NPQ relaxation, and important information on phytoplankton photo-physiology. 142 Beyond its impacts on FRRF measurements, understanding NPQ provides insight into broader photosynthetic processes in phytoplankton. Studies suggesting NPQ as a proxy for iron limitation, e.g. Ryan-keogh et al. (2020) and Schallenberg et al. (2020), imply that NPQ may be as useful as other indicators of nutrient stress like FV/FM with less confounding influences. Additionally, due to the connection between NPQ and other ‘safety valves’ for releasing excess energy, measuring NPQ can offer valuable utility as a predictor of electron requirements for carbon fixation (Schuback et al., 2015; Schuback and Tortell, 2019; Wei et al., 2019), at least among nutrient-stressed surface samples (Schuback et al., 2017; Ch 4). Finally, improving understanding of global NPQ dynamics is critical to leveraging satellite-based measurements of Chl fluorescence line height (nFLH) data, which represent the most complete time-series of phytoplankton physiology with global coverage (Behrenfeld et al., 2009). With these considerations in mind, the future of FRRF research hinges on characterizing NPQ dynamics in relation to various sources of environmental and physiological variability. 5.1.3 FRRF-based ETRPSII as a predictor of C-fixation and net community productivity A primary objective of this thesis, and the FRRF-user community, is to apply FRRF technology as a tool for high resolution productivity surveys (Kolber and Falkowski, 1993; D. J. Hughes, Campbell, et al., 2018; Tortell, Schuback and Suggett, 2023). These kinds of surveys have been limited in the past by uncertainty in the conversion factor between ETRPSII and carbon-based GPP, NPP, or NCP, which varies between taxa and physiological states (Lawrenz et al., 2013). Although ETRPSII is valuable in itself as a measure of photosynthetically-derived reductant available for key metabolic activities, conversion to carbon units is critical for comparison with historical productivity datasets and incorporation into carbon budgets and 143 climate models (D. J. Hughes, Campbell, et al., 2018). In Ch 4, I paired high throughput FRRF and O2/NÙ( measurements and periodic 14C-uptake experiments for parallel analysis of ETRPSII. GPP, and NCP. The results, collected across sharp hydrographic fronts within the California Current Upwelling System, revealed a hyperbolic relationship between ETRPSII and carbon fixation, and a relatively constant relationship between ETRPSII and NCP integrated over 24-hour bins. The hyperbolic relationship between ETRPSII and carbon fixation, though not previously reported, is perhaps not surprising. When comparing carbon- and electron-based photosynthesis-irradiance curves, which themselves have a hyperbolic shape, previous studies have routinely noted higher light saturation indices for ETRPSII compared to carbon fixation (Hancke et al., 2015; Schuback et al., 2015; Schuback and Tortell, 2019). As a result, carbon fixation is expected to increase linearly with ETRPSII at low light levels and remain fixed at irradiances between the carbon-fixation and ETRPSII light saturation indices, while ETRPSII continues to increase. The resulting hyperbolic shape is consistent with the understanding that non-linear electron transport pathways are upregulated under high excitation pressure. The hyperbolic relationship between ETRPSII and C fixation, if better parameterized across a range of ocean regions and conditions, offers a new approach to predict GPP from FRRF measurements. The variable carbon – electron curve shape is likely to be influenced by samples photo- and nutrient-acclimation states, which will determine light saturation indices and maximum rates of ETRPSII and carbon fixation, respectively. The remaining challenge is identifying readily measurable predictors of the electron-carbon curve shape. Although NPQ provides information on phytoplankton ‘safety valve’ responses to excess excitation and has therefore been suggested as a proxy for e:C, the apparent correlation between NPQ and e:C does 144 not appear to hold for light-limited (Schuback et al., 2017) or nutrient replete (Ch 4) phytoplankton. Taking a different approach, Ch 4 demonstrates how meta-transcriptomics can be used to identify genes and proteins highly correlated with electron-carbon curve shape parameters, helping elucidate the molecular mechanisms and pathways determining electron requirements for carbon fixation. Future research should be directed towards deeper understanding of the molecular pathways decoupling carbon fixation from ETRPSII and microbial ecophysiology to develop more mechanistic models of e:C that can be applied across diverse ocean regimes, light and nutrient availabilities. For example, phytoplankton grown on more reduced forms of nitrogen, such as NH4+, are expected to have lower electron requirements for carbon fixation, whereas assemblages grown on more oxidized N forms (N2 or NO3-), should have higher e:C, as additional reductant is consumed by either N2 fixation (diazotrophs) or NO3- reduction (Scherer, Almon and Böger, 1988; Timmermans, Stolte and de Baar, 1994; D. J. Hughes, Campbell, et al., 2018). Although NPQ is unlikely to be able to differentiate assemblages based on their dominant N assimilation pathways, molecular biomarkers can and therefore can improve predictions of e:C. In light of significant anthropogenic eutrophication altering total amounts and distributions of reduced nitrogen in marine environments (Glibert et al., 2016), it is critical to understand the impact of various nitrogen sources on marine photosynthetic processes. At present, it is clear that more research and ancillary measurements are required to infer carbon-based photosynthesis estimates from FRRF measurements. Nonetheless, the strong coherence between FRRF-based ETRPSII and NCP demonstrated in Ch 4 suggests that such efforts hold significant promise in further developing FRRF approaches for ocean productivity studies. 145 5.2 Temperature influences on FRRF-derived productivity estimates Temperature exerts strong direct effects on photosynthetic processes through thermodynamic regulation of enzyme kinetics. Temperature further indirectly affects productivity by regulating stratification and mixed layer depths, which influence light and nutrient availabilities in the upper mixed layer. A key caveat of this thesis is the limited analysis of direct temperature effects on ChlF signatures, photo-physiology, and phytoplankton productivity, particularly for the chapters concerning Arctic assemblages. Previous studies have observed decreases in PSII photochemical yields, longer lived NPQ, increases in 𝜙e:C, and decreases in Chl:C under low temperatures (Geider, 1987; Lawrenz et al., 2013; Lacour et al., 2018; Zhengke Li et al., 2021). Consequently, the physiological and methodological drivers of ChlF variability examined in this thesis are all likely to be influenced by temperature. More generally, there are relatively few studies investigating the effects of temperature on FRRF signatures and derived parameters compared to those investigating the effects of nutrient and light availability (e.g. D. J. Hughes, Varkey, et al., 2018; Mills et al., 2018; Lewis et al., 2019; Gorbunov and Falkowski, 2020; Ko et al., 2020; Greene et al., 1992; Schuback, et al., 2015; Roncel, Antonio A. González-Rodríguez, et al., 2016; Viljoen et al., 2018; Trimborn et al., 2019; Schallenberg et al., 2020; Zhu et al., 2017; Schuback and Tortell, 2019). Given the importance of temperature as an environmental driver of phytoplankton photo-physiology and the rapid rise in global sea surface temperatures (Bindoff et al., 2019) and the increasing frequency and intensity of marine heatwaves (Laufkötter, Zscheischler and Frölicher, 2020), future studies should strive to directly investigate the effects of temperature on ChlF 146 5.3 Applying lessons from FRRF to remote sensing applications Currently, global estimates of marine productivity rely on empirical algorithms between NPP and satellite-measurements of Chl, sea surface temperature and other remote sensing data products (Lee et al., 2015). However, carbon to chlorophyll ratios vary depending on taxa, as well as light and nutrient availability (Geider, MacIntyre and Kana, 1997; Wang et al., 2009). Moreover, empirical algorithms, which were built primarily on data from North Atlantic assemblages where there is a longer history of oceanographic research, are biased and tend to underestimate NPP in more Fe-limited waters, such as the north Pacific and Southern Ocean (Westberry et al., 2016). As an alternative approach, remotely sensed Fluorescence Line Height (FLH) products offer the same resolution and coverage as satellite-based Chl and SST data, while also containing further physiological information, such as nutrient limitation status (Behrenfeld et al., 2009). Similar to FRRF and other variable Chl fluorescence techniques, Chl-normalized FLH measurements represent the balance between fluorescence, photochemistry, and heat dissipation. At present, FLH is most commonly used as an indicator for Fe-limitation, similarly to FV/FM measurements (e.g. Browning et al., 2023; Lin and Letscher, 2025). However, if NPQ can be accounted for, there is significant potential to expand the use of FLH to estimate global gross photochemistry using algorithms similar to those developed for variable Chl fluorescence techniques. Accounting for NPQ, which varies seasonally and between ocean environments, is not trivial. Yet, geostationary observing platforms that can observe diel and seasonal variations in NPQ offer a means to further develop frameworks to isolate photochemical yields from FLH measurements (O’Malley et al., 2014). Although work on this front has stagnated, the upcoming 147 launch of a new hyperspectral geostationary platform (Geostationary Littoral Imaging and Monitoring Radiometer, Launch Date: 2026-2027) has reinvigorated interest in FLH as a tool to quantify global phytoplankton photo-physiology and photochemistry yields (A. Manino, Pers. Comms.). Future efforts to develop FLH-based productivity algorithms will greatly benefit from the work of FRRF community, which can provide key insights into NPQ dynamics and coherence between FLH-based photochemistry estimates and carbon uptake rates. While FLH measurements can greatly expand our data coverage, satellite-based measurements are limited to surface waters and ice and cloud free pixels. These data gaps overlap with some of the most productive waters, as phytoplankton frequently concentrate at the edge of spring ice melts and deep chlorophyll maxima where nutrients are more available (Martin et al., 2010; Matrai et al., 2013; Tremblay et al., 2015). A new generation of submersible FRRFs offers a means to fill these critical data gaps by enabling in-situ measurements from autonomous gliders and floats (e.g. Carvalho et al., 2020). Additionally, gliders and floats can be used to track water parcels in a Lagrangian fashion, isolating spatial sources of variability that often confound interpretation of time-series data (Johnson et al., no date). As such, further development of gliders and floats equipped with FRRF sensors may significantly advance our understanding of phytoplankton photo-physiology and productivity in productive and historically under sampled waters. 5.4 Concluding remarks The number of researchers using variable Chl fluorescence within the oceanographic community has steadily grown over the past quarter century, moving from a relatively small cohort of specialized users, to a wider community of researchers motivated to address broad questions around biologically mediated energy fluxes in the global oceans (D. J. Hughes, 148 Campbell, et al., 2018; Gorbunov and Falkowski, 2022). As instrument manufacturers, software and numerical approaches continue to diversify, it becomes ever more critical to develop standards to ensure comparability and reproducibility of FRRF-based photochemistry data (Tortell, Schuback and Suggett, 2023). These key objectives for the FRRF community, highlighted by the Scientific Committee on Oceanic Research (SCOR) Working Group 156, dovetail closely with the arc of research presented in this thesis. Here, I have presented new insights into methodological and physiological drivers of variability across different fluorescence-based photochemistry estimates and resulting derivations of electron – carbon stoichiometry in marine photosynthesis. 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(2017) ‘Relationship between light, community composition and the electron requirement for carbon fixation in natural phytoplankton’, Marine Ecology Progress Series, 580, pp. 83–100. doi: 10.3354/meps12310. 181 182 Appendices Appendix A : Supplementary material for Chapter 2 A.1 CTD profiles Sampling station locations are indicated as LS for Lancaster Sound or BS for Barrow Strait. Station mixed layer depth (MLD), mean mixed layer nitrate and nitrite concentration, and Chl a concentration within the mixed layer are shown. Location CTD Station Latitude (°) Longitude (°) MLD (m) [NO3- + NO2-] (µM) Chl a (mg/m-3) LS 1 74.316 -80.517 6 < 0.02 0.43 LS 2 74.107 -80.511 9 < 0.02 0.21 LS 3 73.987 -80.468 9 < 0.02 0.25 LS 4 75.482 -78.643 8 < 0.02 0.12 BS 5 74.222 -93.517 10 < 0.02 0.20 BS 6 74.361 -93.556 6 < 0.02 0.39 BS 7 74.599 -93.717 10 < 0.02 0.41 LS 8 74.371 -89.621 9 0.087 0.50 BS 9 74.483 -93.640 9 0.063 0.77 LS 10 74.486 -80.508 8 < 0.02 0.45 LS 11 74.159 -80.470 6 0.189 0.24 LS 12 73.816 -80.508 7 < 0.02 0.58 BS 13 74.951 -92.388 24 0.018 0.58 BS 14 75.014 -93.115 14 0.048 0.28 183 LS 15 75.523 -79.748 12 0.047 0.54 LS 16 74.234 -86.160 6 < 0.02 0.21 A.2 Surface PAR and underway photophysiological variable regression analyses All p values were < 0.001. Standard error is reported for the regression intercept and coefficient, respectively. Photophysiological variable Surface PAR Lancaster Sound n = 283 Barrow Strait n = 198 𝐹L/𝐹C 𝑦 = 0.34 − 1.4𝐸 − 4𝑥 SE = 0.0029, 8.5E-3 R2 = 0.45 𝑦 = 0.40 + 5.34𝐸− 5𝑥 SE = 0.003, 0.024 R2 = 0.06 𝜎2344 𝑦 = 233.92 − 0.19𝑥 SE = 2.65, 9.6E-5 R2 = 0.64 𝑦 = 237.7 − 0.24𝑥 SE = 4.27, 1.8E-5 R2 = 0.42 NPQ 𝑦 = 0.16 − 2.4𝐸 − 4𝑥 SE = 0.006, 1.9E-5 R2 = 0.37 𝑦 = 0.26 − 5.7𝐸 − 4𝑥 SE = 0.009, 5.2E-5 R2 = 0.45 𝐹L/𝐹C:𝐹K(/𝐹C!"# 𝑦 = 0.61 + 4.7𝐸 − 4𝑥 SE = 0.006, 2.0E-5 R2 = 0.67 𝑦 = 0.68 + 5.0𝐸 − 4𝑥 SE = 0.0029, 4.9E-5 R2 = 0.42 𝐹K(/𝐹C!"# 𝑦 = 0.21 + 3.5𝐸 − 5𝑥 SE = 0.002, 7.4E-6 R2 = 0.07 𝑦 = 0.27 + 0.24𝐸− 4𝑥 SE = 0.004, 2.5E-5 R2 = 0.37 184 Appendix B Supplementary materials for Chapter 3 B.1 Taxonomic compositional data collected on-station in the Canadian Arctic Archipelago Chemtax results derived from pigment samples is expressed as fractional abundance. < dl symbolizes below detection limit. Station Diat. Dino. c3-flag Crypt Chryso Hapto Pras-2 Pras-3 Chloro Surface KA1 < dl 0.02 < dl 0.07 0.01 0.80 < dl < dl 0.10 KA3 0.19 0.10 0.04 0.09 0.07 0.08 < dl 0.37 0.06 KA5 0.18 0.13 0.04 0.06 0.08 0.40 0.06 0.04 < dl KB5 < dl 0.11 0.19 0.05 0.07 0.01 < dl 0.56 < 0.01 KB3 0.12 0.08 0.21 0.08 0.04 0.04 < dl 0.42 < dl KB1 < dl 0.05 0.03 0.10 0.06 0.63 < dl 0.03 0.10 KC5 0.15 0.12 0.06 0.08 0.04 0.09 < dl 0.42 0.03 KC3 0.30 0.06 0.05 0.07 0.05 0.09 0.06 0.32 < dl KC1 < dl 0.04 0.24 0.02 0.02 0.67 < dl < dl 0.01 KD1 0.03 0.06 < dl 0.09 0.04 0.69 < dl 0.05 0.04 KD3 0.13 0.10 < 0.01 0.08 0.07 0.22 < dl 0.36 0.03 KD5 0.04 0.10 0.02 0.09 0.08 0.14 0.02 0.46 0.05 CLYDE 0.03 0.06 0.04 0.08 0.07 0.14 0.04 0.54 0.05 N324 0.18 0.13 0.20 0.05 0.06 0.04 < dl 0.33 < dl N323 0.26 0.15 0.24 0.05 0.04 0.05 < dl 0.22 < dl N103 0.04 0.08 0.16 0.16 0.07 0.14 0.01 0.32 0.03 N105 0.34 0.09 0.09 0.09 0.05 0.09 0.04 0.21 < 0.01 N110 0.07 0.10 0.28 0.12 0.03 0.07 < dl 0.32 0.01 N116 < dl 0.09 0.08 0.10 0.09 0.12 0.01 0.51 < dl Subsurface KA1 < dl < dl < dl 0.04 < dl 0.66 < dl 0.25 0.06 KA3 0.34 0.13 0.32 0.07 0.03 0.03 0.06 < dl 0.02 KA5 0.01 0.12 0.37 0.14 0.07 0.16 0.06 < dl 0.06 KB5 < dl 0.11 0.05 0.09 0.08 0.21 0.05 0.41 < 0.01 KB3 0.19 0.16 0.37 0.07 0.04 0.05 < 0.01 0.11 0.01 KB1 < dl 0.09 0.42 0.08 0.02 0.30 0.02 < dl 0.07 KC5 < dl 0.14 0.14 0.05 0.11 0.43 0.02 0.04 0.06 KC3 0.54 0.07 0.34 0.03 < dl < 0.01 0.01 < dl 0.01 185 KC1 < dl < dl < dl 0.02 < dl 0.84 0.01 0.11 0.03 KD1 < dl 0.05 < dl 0.03 0.06 0.66 < dl 0.18 0.01 KD3 0.50 0.08 0.30 0.04 0.01 < dl 0.01 0.07 < dl KD5 0.62 0.04 0.27 0.02 < dl < dl 0.02 0.03 < 0.01 CLYDE < dl 0.10 0.14 0.10 0.06 0.12 0.08 0.37 0.04 N324 0.53 0.08 0.31 0.03 < dl < dl 0.05 < 0.01 < dl N323 0.12 0.12 0.50 0.03 0.01 < dl 0.02 0.20 < dl N103 0.43 0.08 0.12 0.12 0.02 0.07 0.08 0.08 < dl N105 0.15 0.12 0.02 0.18 0.07 0.21 0.23 0.02 < dl N110 < dl 0.11 0.09 0.18 0.06 0.16 < 0.01 0.35 0.05 N116 < dl 0.09 0.03 0.10 0.08 0.16 0.11 0.44 < dl B.2 Ancillary oceanographic data for Canadian Arctic station surface samples Station Sample Depth (m) Lat (oN) Lon (oE) MLD (m) Chl (mg m-3) NO3 (𝜇M) PP:PS KA1 1 66.61 -61.19 19 0.34 4.1 0.21 KA3 1 66.73 -59.61 15 0.58 < dl 0.43 KA5 1 66.88 -57.96 28 1.46 < dl 0.34 KB5 1 67.59 -59.02 21 0.70 0.01 0.36 KB3 1 67.33 -60.28 19 0.39 < dl 0.44 KB1 1 67.06 -61.51 38 0.18 0.07 0.24 KC5 1 68.15 -59.97 21 0.65 < 0.01 0.47 KC3 1 67.75 -61.27 13 0.25 < dl 0.29 KC1 1 67.35 -62.53 27 0.22 < dl 0.15 KD1 1 67.47 -63.69 10 0.21 < dl 0.18 KD3 1 68.24 -62.60 20 0.62 < dl 0.36 KD5 1 69.00 -61.41 14 0.35 0.29 0.32 CLYDE 1 70.35 -68.45 6 0.42 < dl 0.34 N324 1 73.83 -79.60 26 0.82 0.03 0.22 N323 1 74.15 -79.30 25 0.90 < dl 0.23 N103 1 76.18 -76.98 81 0.31 5.39 0.26 N105 1 76.12 -75.76 18 0.21 3.96 0.23 N110 1 76.30 -73.62 58 0.46 4.23 0.27 N116 1 76.38 -70.52 9 0.70 0.25 0.29 186 B.3 Ancillary oceanographic data for Canadian Arctic station subsurface samples Station Sample Depth (m) Lat (oN) Lon (oE) MLD (m) Chl (mg m-3) NO3 (𝜇M) PP:PS KA1 19 66.61 -61.19 19 0.55 4.1 0.21 KA3 24 66.73 -59.61 15 8.1 < dl 0.15 KA5 27 66.88 -57.96 28 3.4 < dl 0.20 KB5 22 67.59 -59.02 21 1.07 0.31 0.27 KB3 27 67.33 -60.28 19 6.8 0.39 0.17 KB1 40 67.06 -61.51 38 0.53 0.03 0.14 KC5 25 68.15 -59.97 21 4.4 0.14 0.16 KC3 23 67.75 -61.27 13 3.7 < dl 0.12 KC1 25 67.35 -62.53 27 0.54 < dl 0.16 KD1 40 67.47 -63.69 10 0.35 < dl 0.14 KD3 24 68.24 -62.60 20 4.8 0.24 0.13 KD5 22 69.00 -61.41 14 6.1 0.83 0.11 CLYDE 10 70.35 -68.45 6 0.45 < dl 0.24 N324 35 73.83 -79.60 26 2.4 0.04 0.17 N323 27 74.15 -79.30 25 1.6 < dl 0.16 N103 35 76.18 -76.98 81 0.24 4.3 0.22 N105 45 76.12 -75.76 18 0.36 6.1 0.24 N110 17 76.30 -73.62 58 0.48 5.7 0.30 N116 5 76.38 -70.52 9 0.72 < dl 0.25 Appendix C Supplementary materials for Chapter 4 187 C.1 Bathymetric map of study region The black contour line indicates the 200m isobath. The horizontal bar graph demonstrates the shelf width at the aligned latitude. C.2 Photopigment and species distribution by sub-region 188 a-c) display non-Chl pigment distribution by mass for Cape Blanco (CB), Cape Mendocino (CM), and Offshore (Off). d-f) display the taxonomic distribution of diatom and dinoflagellate groups visible for microscope counts. 189 C.3 Normalized counts of diatom-specific Fea1 transcripts Diatom-specific expression of Fe assimilation gene Fea1. Bar height displays Fea1 count means by station. Error bars display the standard deviation. Stations 1-4 were in close proximity of Cape Blanco, 6 and 10 were offshore while the remaining stations were in proximity of the Cape Mendocino upwelling filament. 190 C.4 Volumetric carbon fixation rates as a function of electron transport rates. Open circles represent surface measurements while closed circles represent samples collected from the bottom of the euphotic zone. Electron transport rates were converted to volumetric units by multiplying ETRPSII by the fluorescence-derived proxy for [RCII] ∝ Fo/𝜎2344 (Oxborough et al., 2012). The volumetric ETR values presented here are off by an unknown factor, Ka, which represents the FRRF instrument calibration factor required to estimate exact magnitudes of [RCII]. However, the proxy results provide valuable information on variability in 1/𝑛2344. 0 50 100ETR (e- s-1 L-1)012C fixation (C L-1 s-1)Station 20 50ETR (e- s-1 L-1)00.51C fixation (C L-1 s-1)Station 30 10 20ETR (e- s-1 L-1)00.5C fixation (C L-1 s-1)Station 40 5ETR (e- s-1 L-1)00.050.1C fixation (C L-1 s-1)Station 50 50ETR (e- s-1 L-1)00.5C fixation (C L-1 s-1)Station 60 50ETR (e- s-1 L-1)00.050.1C fixation (C L-1 s-1)Station 70 10 20ETR (e- s-1 L-1)00.05C fixation (C L-1 s-1)Station 80 5 10ETR (e- s-1 L-1)00.020.04C fixation (C L-1 s-1)Station 90 100 200ETR (e- s-1 L-1)00.20.4C fixation (C L-1 s-1)Station 10
Thesis/Dissertation
eng
Oceanography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tortell, Philippe Daniel, 1972-
Autonomous measurement of phytoplankton physiology and productivity using fast repetition rate fluorometry
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