Land and Food Systems, Faculty of
DSpace
UBCV
Zhang, Yangfan
Doctor of Philosophy - PhD
University of British Columbia
Vertebrate life is sustained by aerobic metabolism and temporary bouts of anaerobic metabolism. Steady-state aerobic metabolism can be assessed by measuring whole-organism oxygen uptake (á¹Oâ), the core of Fryâs paradigm, which becomes a part of a respiratory phenotype that also includes anaerobic capacity. In fishes, environment profoundly affects respiratory phenotype, perhaps even determining migratory and reproductive successes, partially driven by hypoxia, increased thermal and pathogen load. Understanding how such factors affect capacity, functional costs and mortality needs a more accurate, precise, and comprehensive characterization of a fishâs respiratory phenotype. Hence, I developed an Integrated Respiratory Assessment Paradigm (IRAP) to characterize a respiratory phenotype (its needs, capacities & patterns) for individual fish that responds to Fryâs paradigm (controlling, limiting, masking, directive, and lethal factors). Chapter 2 describes an IRAP protocol that measures 14 metrics in 8 fish simultaneously over 3 days, a high sampling throughput to better bridge physiology-&-ecology. Chapter 3 introduces and validates new analytical approaches that improved the resolution and accuracy of maximum á¹Oâ (á¹Oâmax) estimations in aquatic respirometry. Chapter 4 illustrates the detection sensitivity of the refined IRAP by distinguishing respiratory phenotypes among three rainbow trout strains using differences in á¹Oâmax, absolute aerobic capacity, critical oxygen saturation (Oâcrit) and hypoxia tolerance. Moreover, high aerobic and anaerobic capacities correlated with high hypoxic and thermal tolerances among these strains. Chapter 5 explores how acclimation to hypoxia affects the respiratory phenotype, discovering hypoxia-acclimated European sea bass suppress standard metabolic rate (SMR) and Oâcrit, a respiratory phenotype better suited for exploiting hypoxic habitats. Chapter 6 explores the diagnostic power of the refined IRAP by assessing the load-dependent changes to SMR, Oâcrit, daily energy expenditure and routine á¹Oâ of a high-virulence infectious hematopoietic necrosis virus infection in sockeye salmon and a low-virulence piscine orthoreovirus infection in sockeye and Atlantic salmon. Chapter 7 assesses the repeatability of IRAP indices, showing four traits for aerobic capacity, routine á¹Oâ and time spend above 50% absolute aerobic scope were particularly repeatable. Chapter 8, therefore, synthesizes that high-resolution and reproducible metabolic phenotyping better poises ichthyologists to develop an individual-based metabolic model and integrate it with a Genomic-Physiology-Ecology axis.
INTERPRETING SPECIES, INTRASPECIFIC AND INTRA-INDIVIDUAL VARIABILITY BY COMPREHENSIVELY CHARACTERIZING A FISHâS RESPIRATORY PHENOTYPE WITH VALID MEASURES OF OXYGEN UPTAKE by Yangfan Zhang B.Sc. (1st Honors), Dalhousie University, 2014 M.Sc. The University of British Columbia, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Applied Animal Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Yangfan Zhang, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Interpreting species, intraspecific and intra-individual variability by comprehensively characterizing a fishâs respiratory phenotype with valid measures of oxygen uptake submitted by Yangfan Zhang in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Animal Biology Examining Committee: Anthony Farrell, Department of Zoology & Faculty of Land and Food System, University of British Columbia, Canada Supervisor Marina Von Keyserlingk, Animal Welfare, Faculty of Land and Food System, University of British Columbia, Canada Supervisory Committee Member Denis Chabot, Maurice Lamontagne Institute, Fisheries and Oceans Canada Supervisory Committee Member Jeffery Richards, Department of Zoology, University of British Columbia, Canada University Examiner William Sheel, School of Kinesiology, Faculty of Education, University of British Columbia, Canada University Examiner Additional Supervisory Committee Members: Guy Claireaux, Laboratoire des Sciences de, Université de Bretagne Occidentale & Cabinet d'Annick Girardin, ministre de la mer, France Co-Supervisor iii Abstract Vertebrate life is sustained by aerobic metabolism and temporary bouts of anaerobic metabolism. Steady-state aerobic metabolism can be assessed by measuring whole-organism oxygen uptake (á¹O2), the core of Fryâs paradigm, which becomes a part of a respiratory phenotype that also includes anaerobic capacity. In fishes, environment profoundly affects respiratory phenotype, perhaps even determining migratory and reproductive successes, partially driven by hypoxia, increased thermal and pathogen load. Understanding how such factors affect capacity, functional costs and mortality needs a more accurate, precise, and comprehensive characterization of a fishâs respiratory phenotype. Hence, I developed an Integrated Respiratory Assessment Paradigm (IRAP) to characterize a respiratory phenotype (its needs, capacities & patterns) for individual fish that responds to Fryâs paradigm (controlling, limiting, masking, directive, and lethal factors). Chapter 2 describes an IRAP protocol that measures 14 metrics in 8 fish simultaneously over 3 days, a high sampling throughput to better bridge physiology-&-ecology. Chapter 3 introduces and validates new analytical approaches that improved the resolution and accuracy of maximum á¹O2 (á¹O2max) estimations in aquatic respirometry. Chapter 4 illustrates the detection sensitivity of the refined IRAP by distinguishing respiratory phenotypes among three rainbow trout strains using differences in á¹O2max, absolute aerobic capacity, critical oxygen saturation (O2crit) and hypoxia tolerance. Moreover, high aerobic and anaerobic capacities correlated with high hypoxic and thermal tolerances among these strains. Chapter 5 explores how acclimation to hypoxia affects the respiratory phenotype, discovering hypoxia-acclimated European sea bass suppress standard metabolic rate (SMR) and O2crit, a respiratory phenotype better suited for exploiting hypoxic habitats. Chapter 6 explores the diagnostic power of the refined IRAP by assessing the load-dependent changes to SMR, O2crit, daily energy expenditure and routine á¹O2 of a high-virulence infectious hematopoietic necrosis virus infection in sockeye salmon and a low-virulence piscine orthoreovirus infection in sockeye and Atlantic salmon. Chapter 7 assesses the repeatability of IRAP indices, showing four traits for aerobic capacity, routine á¹O2 and time spend above 50% absolute aerobic scope were particularly repeatable. Chapter 8, therefore, synthesizes that high-resolution and reproducible metabolic phenotyping better poises ichthyologists to develop an individual-based metabolic model and integrate it with a Genomic-Physiology-Ecology axis. iv Lay Summary All aspects of vertebrate life ultimately have an oxygen cost. The metabolism of ectotherms, e.g., fishes - the most diverse vertebrate taxa, is at the mercy of prevailing environmental conditions, influencing biogeographic zonation and even fitness. Hence, studying fish metabolism is valuable for fisheries, aquaculture, and conservation. A fish's steady-state aerobic metabolism is assessed by measuring whole-organism oxygen uptake, from which a fish's respiratory phenotype (its needs, capacities, and behaviours) is characterized to provide valuable insights into how the environment directly affects a fish's performance. Beyond a standardized phenotyping protocol to advance the understandings in intraspecific variability, phenotypic plasticity and metabolic consequences of viral infections, ichthyologists can characterize a respiratory phenotype in a higher resolution, greater throughput, and better reproducibility than previously. Therefore, a metabolic phenotyping system that develops and integrates an individual-based metabolic model with a Genomic-Physiology-Ecology axis will lead to a better understanding of the mechanisms of evolution. v Preface A version of chapter 1 & 8 is in preparation for submission as a systemic review: Y. Zhang, D. McKenzie, K. Anttila, S. Narum, Z. Chen, S. Cooke, G, Claireaux, R. Taylor, A.P. Farrell, Bridging Fryâs ecological paradigm to Darwinian natural selection using respiratory physiology. A version of chapter 3 was previously published as Y. Zhang, M. J. H. Gilbert, A. P. Farrell (2019). Finding the peak of dynamic oxygen uptake during fatiguing exercise in fish. Journal of Experimental Biology. 222, jeb196568. & Y. Zhang, M. J. H. Gilbert, A. P. Farrell, (2020) Measuring maximum oxygen uptake with an incremental swimming test and by chasing rainbow trout to exhaustion inside a respirometry chamber yields the same results. Journal of Fish Biology. DOI: 10.1111/jfb.14311. A version of chapter 4 was previously published as Y. Zhang, T. Healy, W. Vandersteen, P. M. Schulte, A. P. Farrell (2018) A rainbow trout (Oncorhynchus mykiss) strain with higher aerobic scope also has superior tolerance of acute warming and hypoxia. Journal of Fish Biology. 92, 487â503. Chapter 5 is based on work conducted in Dr. G. Claireauxâs laboratory at Ifremer (PFOM-LARN), France. A version of chapter 5 is in preparation for submission as Y. Zhang, F. Mauduit, K. Anttila, L. Pettinau, H. Ollivier, F. Lancien, A. P. Farrell, G. Claireaux, phenotypic plasticity provides an evolutionary edge for sea bass living in hypoxic future. Chapter 6 synthesized physiological work conducted in Dr. K. Garver and Dr. M. Polinskiâs laboratory at Pacific Biological Station of Fisheries and Oceans Canada. A version of chapter 6 and was published as Y. Zhangâ , M. Polinskiâ , P. Morrison, C. Brauner, A.P. Farrell, K. Garver (2019) High-load reovirus infections do not imply physiological impairment in vi salmon. Frontiers in Physiology 10, 114. A version of chapter 6 is under review as M. Polinskiâ , Y. Zhangâ , P. Morrison, G. Marty, C. Brauner, A.P. Farrell, K. Garver, Respiratory assessments of salmon during rhabdovirus and reovirus infection identify a role for lowering the standard metabolic rate in a vertebrate antiviral defence. Physiological assessment in this chapter is part of a large controlled challenge study of PRV impacts on salmonids species in the Pacific Northwest (Garver et al., 2016; Polinski et al., 2016; Di Cicco et al., 2017; Di Cicco et al., 2018; Polinski, et al., 2019). A version of chapter 7 is in preparation for submission as Y. Zhang, F. Mauduit, K. Anttila, L. Pettinau, H. Ollivier, F. Lancien, A. P. Farrell, G. Claireaux, The respiratory phenotype of a pelagic fish has greater temporal stability at its optimal temperature than at a sub-optimal one. Y. Zhang was the primary contributor to the experimental design, data collection, data analysis and manuscript preparation. A. P. Farrell, G. Claireaux, D. Chabot and Marina von Keyserlingk provided supervision and a tremendous amount of supports for science and life. K. Anttila, C. J. Brauner, R. Suarez and P. M. Trish provided ad-hoc supervision, friendships and an extensive network of collaboration. A. P. Farrell, G. Claireaux provided an outstanding mentorship in experimental design and manuscript preparation. K. Garver and M. Polinski were the major contributor to the experimental design in Chapter 6, provided virus inoculum preparation and helped with manuscript preparation. M. Polinski, F. Mauduit, M. J. H. Gilbert, K. Anttila, L. Pettinau, H. Ollivier, F. Lancien, T. M. Healy, W. Vandersteen, P. R. Morrison, J. Richard, and H. Matkin provided valuable assistance in the animal husbandry in the laboratories and data collection. vii All procedures were approved by the national or institutional animal research authority: University of British Columbia animal care committee (A15-0035, A15-0266, A16-0049, A16-0329: experiments in chapter 3 & 4), Ministère Délégué Ã lâEnseignement Supérieur et Ã la Recherche. (Apafis 14576: experiments in chapter 5 & 7), Canadian Council on Animal Care (CCAC) Pacific Region Animal Care Committee (no: 16â019: experiments in chapter 6). â Equal contributor viii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface ..............................................................................................................................................v Table of Contents ......................................................................................................................... viii List of Tables .................................................................................................................................xv List of Figures .............................................................................................................................. xvi List of Symbols and Abbreviations .............................................................................................. xix Glossary ....................................................................................................................................... xxi Acknowledgements ..................................................................................................................... xxii Dedication ....................................................................................................................................xxv Chapter 1: Introduction â what is the bridge between physiology and ecology? ....................1 1.1 Synopsis .......................................................................................................................... 1 1.2 The value of carefully characterizing environmental effects on respiratory phenotype.. 6 1.3 The value of measuring whole-organism metabolic performance curves using respiratory metrics .................................................................................................................... 13 1.3.1 Thermal performance curve .................................................................................. 14 1.3.2 Hypoxia tolerance and the hypoxic performance curve ....................................... 16 1.3.3 Metabolic consequences of infections â a load-dependent response .................... 19 1.4 The value of understanding how temporally stable a respiratory phenotype is ............ 20 1.5 The specific objectives and hypotheses ........................................................................ 23 Chapter 2: Methodology for Integrated Respiratory Assessment Paradigm (IRAP) ...........32 ix 2.1 Synopsis ........................................................................................................................ 32 2.2 My standardized protocol for Integrated Respiratory Assessment Paradigm (IRAP) .. 32 2.3 Calculation á¹O2 and other IRAP indices ..................................................................... 34 2.4 Experimental animals .................................................................................................... 38 2.5 Statistical software and assigning significance ............................................................. 41 Chapter 3: Improving the validity of estimating maximum oxygen uptake 1 ........................46 3.1 Synopsis ........................................................................................................................ 46 3.2 Introduction ................................................................................................................... 47 3.3 Material and Methods ................................................................................................... 50 3.3.1 Experimental design .............................................................................................. 50 3.3.1.1 Ramp-critical swimming speed (ramp-Ucrit) protocol ....................................... 50 3.3.2 Chase-to-exhaustion protocol ............................................................................... 52 3.3.3 Data analysis ......................................................................................................... 53 3.3.3.1 Determining the minimum duration of a sampling window for a valid á¹O2 measurement ..................................................................................................................... 53 3.3.3.2 Dynamic á¹O2 and peak á¹O2active determinations ............................................. 54 3.3.3.3 Statistical analyses ............................................................................................ 55 3.4 Results ........................................................................................................................... 56 3.4.1 A conservative minimum sampling window in a swim-tunnel respirometer ....... 56 3.4.2 Determine a valid minimum sampling window in an intermittent-flow respirometer .......................................................................................................................... 56 3.4.3 The dynamics of á¹O2active near Ucrit and their relationship to burst-and-glide swimming gait ....................................................................................................................... 57 x 3.4.4 Peak á¹O2chase was higher than peak á¹O2rec .......................................................... 57 3.4.5 Peak á¹O2chase and peak á¹O2active are the same and repeatable ............................. 58 3.5 Discussion ..................................................................................................................... 59 3.5.1 Minimum á¹O2 sampling window and dynamic á¹O2 ........................................... 59 3.5.2 An iterative algorithm best resolves á¹O2 dynamics ............................................. 60 3.5.3 Estimating á¹O2max more repeatably & reliably in static and swim-tunnel respirometry .......................................................................................................................... 61 3.5.4 Recommendations in estimating á¹O2max in aquatic respirometry ........................ 63 Chapter 4: A proof-of-principle test for IRAP â Distinguishing intraspecific respiratory phenotypes among strains of rainbow trout 2 ...........................................................................76 4.1 Synopsis ........................................................................................................................ 76 4.2 Introduction ................................................................................................................... 77 4.3 Materials and Methods .................................................................................................. 79 4.3.1 Intermittent-flow respirometry and hypoxia tolerance ......................................... 79 4.3.2 Thermal tolerance ................................................................................................. 79 4.3.3 Statistical analyses ................................................................................................ 80 4.4 Results ........................................................................................................................... 81 4.4.1 Variation in metabolic traits among strains .......................................................... 81 4.4.2 Variation in hypoxia tolerance and upper thermal tolerance among strains ......... 83 4.4.3 Correlations between metabolic traits and hypoxia tolerance among individuals 84 4.5 Discussion ..................................................................................................................... 85 4.5.1 Intraspecific variation in metabolic traits, hypoxia tolerance and upper thermal tolerance â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦86 xi 4.5.2 Evidence for associations among metabolic traits, hypoxia tolerance and upper thermal tolerance ................................................................................................................... 88 4.6 Conclusion .................................................................................................................... 89 Chapter 5: A proof-of-principle test for IRAP â Characterizing respiratory phenotypic plasticity after acclimation to a limiting factor in marine fish 3 ..............................................96 5.1 Synopsis ........................................................................................................................ 96 5.2 Introduction ................................................................................................................... 97 5.3 Materials and methods .................................................................................................. 99 5.3.1 Acclimation procedures ........................................................................................ 99 5.3.2 Characterizing the respiratory phenotype ........................................................... 100 5.3.3 Characterizing a hypoxia performance curve ..................................................... 101 5.3.4 Statistical analyses .............................................................................................. 102 5.4 Results ......................................................................................................................... 102 5.4.1 Hypoxic exposure impairs aerobic scope ........................................................... 102 5.4.2 A new hypoxic phenotype revealed by testing hypoxia-acclimated fish in normoxia â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..103 5.4.3 Re-acclimation of the hypoxic phenotype to normoxia improved aerobic capacity â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..104 5.5 Discussion ................................................................................................................... 104 5.5.1 Hypometabolism in an active marine fish .......................................................... 105 5.5.2 The hypoxic phenotype defends its aerobic capacity ......................................... 107 5.5.3 Phenotypic reversibility and beneficial carryover effects ................................... 109 5.6 Conclusion .................................................................................................................. 110 xii Chapter 6: A proof-of-principle test for IRAP â Diagnosing sublethal costs of viral infection on respiratory phenotype using salmonid models 4 ................................................116 6.1 Synopsis ...................................................................................................................... 116 6.2 Introduction ................................................................................................................. 117 6.3 Materials and Methods ................................................................................................ 121 6.3.1 Experimental infections ...................................................................................... 121 6.3.1.1 PRV infection of Atlantic salmon ................................................................... 121 6.3.1.2 PRV and IHNV infections of sockeye salmon ............................................... 123 6.3.2 Statistical analyses .............................................................................................. 124 6.4 Results ......................................................................................................................... 125 6.4.1 Fish mortality and IHNV load over time ............................................................ 125 6.4.2 Effects of IHNV on the respiratory phenotype of surviving sockeye salmon .... 125 6.4.3 Mortality, PRV loads and histology of Atlantic salmon & sockeye salmon over time â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..126 6.4.4 PRV infection does not affect the respiratory phenotype of sockeye salmon .... 127 6.4.5 PRV infection does not affect the respiratory phenotype of Atlantic salmon .... 127 6.5 Discussion ................................................................................................................... 128 6.5.1 Functional effects of IHNV infections ................................................................ 129 6.5.2 PRV infection does not compromise respiratory performance ........................... 131 6.6 Conclusion .................................................................................................................. 133 Chapter 7: Quantifying the repeatability of IRAP variables for characterizing respiratory phenotype in fishes5 ...................................................................................................................138 7.1 Synopsis ...................................................................................................................... 138 xiii 7.2 Introduction ................................................................................................................. 139 7.3 Material and Methods ................................................................................................. 141 7.3.1 Experimental animals and repeated IRAP assessments ...................................... 141 7.3.2 Precision quantification and repeatability determination ................................... 141 7.4 Results ......................................................................................................................... 142 7.5 Discussion ................................................................................................................... 143 7.6 Conclusion .................................................................................................................. 147 Chapter 8: General discussion and recommendations â respiratory phenotype can bridge physiology and ecology 6 ............................................................................................................151 8.1 Synopsis ...................................................................................................................... 151 8.2 Holistically assessing aerobic and anaerobic capacity in one framework .................. 153 8.2.1 O2crit is less comprehensive than hypoxia performance curve ............................ 154 8.2.2 Future applications of the hypoxia performance curve ....................................... 156 8.3 Diagnose sublethal effects .......................................................................................... 156 8.3.1 Dire demands for capacity apportioning ............................................................. 157 8.3.2 Diagnostic platform ............................................................................................ 158 8.4 Temporal stability is essential for individual-based metabolism model ..................... 160 8.4.1 Operational recommendations ............................................................................ 162 8.5 Future directions ......................................................................................................... 164 Bibliography ...............................................................................................................................169 Appendices ..................................................................................................................................231 Appendix A Chapter 3 ............................................................................................................ 231 A.1 Figures..................................................................................................................... 231 xiv A.2 Table ....................................................................................................................... 233 Appendix B Chapter 4 ............................................................................................................ 234 B.1 Tables ...................................................................................................................... 234 Appendix C Chapter 5 ............................................................................................................ 235 C.1 Tissue and biochemical analyses ............................................................................ 235 C.2 Figures..................................................................................................................... 236 Appendix D Chapter 6 ........................................................................................................... 243 D.1 Figures..................................................................................................................... 243 xv List of Tables Table 2.1 Summary of key parameters in intermittent-flow respirometry system and the key variables in the protocol of Integrated Respiratory Assessment Paradigm (IRAP) in each data chapter. .......................................................................................................................................... 42 Table 3.1 Summary of protocols used to estimate the maximum rate of oxygen uptake in fishes following a chase to exhaustion outside of an intermittent-flow respirometry with a fixed sampling window duration. ........................................................................................................... 73 Table 3.2 Comparison of peak active oxygen uptake (peak á¹O2active) determined with different sampling window durations (1.5 min to 10 min) and two types of mathematical algorithms, an iterative algorithm and a sequential interval regression analysis.. ................................................ 74 Table 3.3 Estimates of peak oxygen uptake (á¹O2; mg O2 h-1 kg-1) determined during a chase to exhaustion inside the intermittent-flow respirometer (peak á¹O2chase) and during recovery after chasing to exhaustion outside the intermittent-flow respirometer (peak á¹O2rec).. ....................... 75 Table 7.1 Summary of the normalized coefficient of repeatability (COR, %) corresponding to Pearson and Spearman correlation coefficients. ......................................................................... 150 xvi List of Figures Figure 1.1 Eco-physiological Temple of Athena. Inter-individual variations, individual-based metabolic model and diagnostic powers are pillars that connect the strong foundations of comparative physiology to field eco-physiology. ......................................................................... 28 Figure 1.2 Maximum oxygen uptake (á¹O2max; a), absolute aerobic scope (AAS; b) and factorial aerobic scope (FAS; c) over a range of testing temperatures in various studies on rainbow trout (Oncorhynchus mykiss) strains.. ................................................................................................... 30 Figure 1.3 Dissociating stability of traits and validity of experimental methodologies using a precision-&-repeatability matrix. .................................................................................................. 31 Figure 2.1 Comprehensively characterizing a fishâs aerobic & hypoxic respiratory phenotype. A representative trace measured by an Integrated Respiratory Assessment Paradigm (IRAP).. ..... 44 Figure 2.2 Respiratory traits to estimate anaerobic capacity. ....................................................... 45 Figure 3.1 A diagram of the modified static respirometer (a) used to monitor the dynamics of rainbow trout Oncorhynchus mykiss oxygen uptake (á¹O2) during a sealed internal (á¹O2chase, b and d) or following an external (á¹O2rec, c and e). ........................................................................ 66 Figure 3.2 An analysis of varying the sampling window duration for an individual estimate of the rate of oxygen uptake .................................................................................................................... 67 Figure 3.3 Effect of sampling window duration (0.5 to 5 min) on estimates of background oxygen uptake (á¹O2; a,b) and exercise á¹O2 elicited during a chase to exhaustion inside an intermittent-flow respirometer protocol (c, d) in rainbow trout (Oncorhynchus mykiss).. ........... 68 xvii Figure 3.4 Examples of oxygen uptake (á¹O2) dynamics (closed symbols) for individual rainbow trout during a critical swimming speed (Ucrit) test with speed (U) is expressed as a percentage of Ucrit. ............................................................................................................................................... 70 Figure 3.5 Comparing the peak oxygen uptake (á¹O2) of rainbow trout Oncorhynchus mykiss elicited through a chase to exhaustion (á¹O2chase) inside an intermittent-flow respirometer or a critical swimming speed test (á¹O2active) within a swim-tunnel respirometer. .............................. 72 Figure 4.1 Whole-animal a) maximum rate of oxygen uptake (á¹O2max), b) standard metabolic rate (SMRq0.2), c) absolute aerobic scope (AAS) and d) factorial aerobic scope (FAS) for three strains of rainbow trout (Oncorhynchus mykiss) parr ................................................................... 91 Figure 4.2 Whole-animal a) incipient lethal oxygen saturation (ILOS) and b) critical thermal maximum (CTmax) for three strains of rainbow trout (Oncorhynchus mykiss) parr ...................... 92 Figure 4.3 Whole-animal a) critical oxygen level (O2crit) and b) accumulated oxygen deficit (AOD) for three strains of rainbow trout (Oncorhynchus mykiss) parr. ....................................... 93 Figure 4.4 Scatter plots of whole-animal a) critical oxygen level (O2crit) versus standard metabolic rate (SMRq0.2) and b) O2crit versus absolute aerobic scope (AAS) for individual parr.. ....................................................................................................................................................... 95 Figure 5.1 The respiratory phenotype in juvenile European sea bass (Dicentrarchus labrax) at 16 oC as derived by measuring á¹O2 after normoxic acclimation (N-N; grey), hypoxic acclimation and tested in hypoxia (H-H; blue), hypoxic acclimation and tested in normoxia (H-N; orange) and re-acclimation of H-H fish to normoxia (HN-N; green).. .................................................... 112 Figure 5.2 Effects of 6-week hypoxia acclimation on the maximal activity of citrate synthase (CS; a) and lactate dehydrogenase (LDH; b) in red and white muscles of the juvenile European sea bass (Dicentrarchus labrax) at 16 oC. .................................................................................. 113 xviii Figure 5.3 Effects of 6-week hypoxia acclimation on respiratory phenotype as characterized by four indices of anaerobic capacity [(a) critical oxygen saturation (O2crit), (b) incipient lethal oxygen saturation (ILOS) and their derived (c) scope for oxygen deficit (SOD), (d) excess post-exercise oxygen consumption (EPOC)] in juvenile European sea bass (Dicentrarchus labrax) at 16 oC. .......................................................................................................................................... 114 Figure 5.4 Limited-oxygen concentration models of normalized oxygen uptake (á¹O2) in normoxia- and hypoxia-acclimated juvenile European sea bass (Dicentrarchus labrax) at 16 oC...................................................................................................................................................... 115 Figure 6.1 Regression analyses for viral load and various levels of metabolic demands in infectious hematopoietic necrosis virus (IHNV; blue) and piscine orthoreovirus (PRV; purple) injected wild sockeye salmon (Oncorhynchus nerka). .............................................................. 135 Figure 6.2 Key traits of the respiratory phenotype of piscine orthoreovirus (PRV) injected sockeye salmon (Oncorhynchus nerka) and Atlantic salmon (Salmo salar).. ............................ 136 Figure 7.1 Linear regressions (a, c) and frequency distributions (b, d) of the Pearson correlation coefficient (p-value and r-value) as a function of the normalized coefficient of repeatability (COR, %). ................................................................................................................................... 149 Figure 8.1 A Genomic-Physiology-Ecology axis presents a simplified network that connects individual phenotypes to ecology and evolution ........................................................................ 166 Figure 8.2 The interdependence of critical oxygen saturation (O2crit), behavioural activity and standard metabolic rate (SMR) in European sea bass (Dicentrarchus labrax) .......................... 167 xix List of Symbols and Abbreviations AMR active metabolic rate AS aerobic scope, a numerical difference between active and routine (or resting) metabolic rate ATP adenosine triphosphate CS citrate synthase DO dissolved oxygen saturation FL s-1 fork length per second Hb hemoglobin HCT hypoxia challenge test HPC hypoxia performance curve HSMI heart and skeletal muscle inflammation Ifremer Institut Français de Recherche pour lâExploitation de la Mer IHNV infectious hematopoietic necrosis virus IRAP Integrated Respiratory Assessment Paradigm LDH lactate dehydrogenase LOE loss of upright (dorso-ventral) equilibrium MMR maximum metabolic rate á¹O2 rate of oxygen uptake á¹O2 dynamics the unsteady state nature for the rate of oxygen uptake when a fish is approaching its maximum exercise performance, either at a chase-to-exhaustion protocol or at a swim-to-fatigue protocol á¹O2active active rate of oxygen uptake xx á¹O2chase rate of oxygen uptake measured during a chase-to-exhaustion protocol á¹O2peak peak in the rate of oxygen uptake á¹O2rec rate of oxygen uptake measured during the recovery period after fish is tested by a chase-to-exhaustion protocol or swim-to-fatigue protocol MS-222 tricaine methanesulfonateâ¨ OCLTT oxygen- and capacity-limited thermal tolerance PRV piscine orthoreovirus RI regulation index Uburst burst swimming speed Ucrit critical swimming speed in a prolonged swimming test Ufinal final speed increment in a critical swimming speed test Umax maximum swimming speed in an incremental acceleration swimming test xxi Glossary Integrated Respiratory Assessment Paradigm (IRAP) indices: AAS absolute aerobic scope, a numerical difference between the maximum rate of oxygen uptake and standard metabolic rate AOD accumulated oxygen deficit DEE daily energy expenditure EPOC excess post-exercise oxygen consumption FAS factorial aerobic scope FSOD factorial scope for oxygen deficit ILOS incipient lethal oxygen saturation á¹O2max maximum rate of oxygen uptake O2crit critical oxygen level O2crit slope slope of the linear regression line when á¹O2 is below O2crit RMR routine metabolic rate SMR standard metabolic rate SOD scope for oxygen deficit T0.5AAS Time spent above 50% of AAS T0.8AAS Time spent above 80% of AAS xxii Acknowledgements I would first like to express gratitude to my supervisor, Prof. Anthony P. Farrell, for selecting me, and guiding me through a wonderful graduate school journey. Tony is a world-class scientist and an incredible supervisor, who constantly inspires me with his unique philosophies, motivates me to explore my potential and has given me every opportunity to excel. Tony never stopped challenging me with a rising standard throughout my graduate school journey. I would like to express my appreciation to my co-supervisor, Prof. Guy Claireaux, for embracing me with his warm hospitality and strict laboratory training in Ifremer, Brest, France. Guy provided me with the spirit and mentality of being a great experimental biologist and engaged me with many mind-opening conversations. I would like to thank Dr. Florian Mauduit, who was a post-doctoral researcher, in Prof. Claireauxâs laboratory, for generously sharing resources, ideas and many memorable summer times in western France. I would like to thank the amazing research scientists in the Fisheries and Oceans Canada (DFO)âs Virology Research Program, Dr. Mark Polinski and Dr. Kyle Garver. They welcomed me with warm hospitality during one year of ferry trips between Nanaimo and Vancouver in British Columbia. They also challenged me to push the boundary of comparative physiology in an extensive inter-disciplinary study of virology and immunology. They left marks throughout my dissertation. To Dr. Matthew Gilbert, as once were M.Sc. and Ph.D. students, we together challenged the status quo of measuring the maximum rate of oxygen uptake (á¹O2max) and follow-up with a series of studies to test our own hypotheses. I am grateful to my official committee, Prof. Marina (Nina) von Keyserlingk and Dr. Denis Chabot, and ad-hoc committee Assoc. Prof. Katja Anttila, for critical feedback during my xxiii research and manuscript preparations, also their generous support in science, as well as life. I would like to thank Assoc. Prof. Cerri Ronaldo, Prof. Dan Weary and Shelley Small for being great mentors, as well as inspirations. All the colleagues in the Department of Zoology UBC made the last two years an absolute blast, especially the great discussions during the coffee and beer gatherings. I would like to thank Farrellites for their supports. A special thanks to Dr. Zhongqi Chen, Dr. Till Harter, Dr. Timothy Healy, James Marchant, Dr. Phillip Morrison, Lucas Pettinau, Aaron Pierce, Dr. Matthew Regan, Ryan Sprenger, Dr. Michael Sackville, Dr. Wendy Vandersteen for sharing insights about my research from their perspectives. A great thanks to Asst. Prof. Katie Marshall, Assoc. Prof. Philip Matthews, Prof. Colin Brauner, Prof. Jeff Richards and Emeritus Prof. Bill Milsom for the great mentorships. A great thanks to the Faculty of Land and Food System for providing the fellowships, and most importantly, great administrative help. To my friends in the Comparative Physiology (ComPhy) group, all the friendships and supports over the past two years of my M.Sc. and four years of my Ph.D. have certainly being a perfectly splendid experience. I am proud and privileged to a part of a close-knit and extraordinary community of comparative physiologists. Besides the scientists at my home institutions and I collaborated with, the journey of graduate school is enriched through the friendships over my time spent on teaching and service in the Department of Zoology, Faculty of Land and Food System and Graduate Student Society at The University of British Columbia and the professional societies (Aquaculture Association of Canada, American Fishery Society, Canadian Society of Zoologists, and Society for Experiment Biology). xxiv To my undergraduate supervisor, Prof. Jim Duston, for sparking my passion for science and being a life-long mentor. To my beloved wife, Ashley Stuart-Zhang, for her unconditional support, especially for all the travelling overseas. To my parents and grandparents in Fuzhou, China, who taught me to pursue my dreams, to be perseverance, and to always stay curious. xxv Dedication To J. Gao, K. Zhang & A. Stuart-Zhang For showing me courage, strength, perseverance, and wisdom 1 Chapter 1: Introduction â what is the bridge between physiology and ecology? 1.1 Synopsis Natural selection seemingly acts similarly to a principle offered by Sir Winston Churchill (1900) âYou must put your head into the lionâs mouth if the performance is to be a success.â. Acting similarly to natural selection, scientific boundaries are meant to be pushed. An organism must start to fall over an edge to know where the boundary is (Farrell, 2018, pers. comm.). My dissertation concerns whether biologists can effectively use quantitative physiological approaches to assess the likelihood of an animal surviving in a given environment. I address a specific question: can biologists quantify a fishâs respiratory phenotype in a precise and meaningful fashion to define its boundaries of performance? But why is this type of characterization important? While animals living today have certainly triumphed over adversity in the past, they are currently enduring a new challenge resulting from the rapid, anthropogenically driven environmental changes. Thus, biologists are attempting to answer a critical question - which species will triumph in the changing environment? Obviously, accurate projections need valid data input. Therefore, an equally important question is what are the valid data needed for these projections? Currently available models of predicting the impacts of global warming on aquatic organisms use data such as an upper thermal tolerance limit (e.g. Sunday et al., 2012; Dahlke et al., 2020), which has limited ecological relevance (Wang et al., 2014; Jutfelt et al., 2019). Moreover, in most of these models, it is hypothesized that a theoretical âaverage animalâ can be representative of an entire population, even though broad intraspecific variability is well established for most if not all performance traits (Matthews, 1986; Fangue et al., 2006; 2 Overgaard et al., 2011). I will argue, like my predecessors (Fry, 1947; Brett, 1964), that respiratory ecophysiology can make the climate change models of fishes more representative of the actual world by measuring a fundamental currency of life â metabolic rate. When budging the metabolic currency, animals need to at least cover the operational, exploratory and stress costs. If the principal costs rely on the loan of anaerobic metabolism, the associated glycolytic interests are still repaid aerobically. Hence, the intra-specific and inter-individual variability of key metabolic traits are what I consider as part of a key pillar in ecophysiology (Figure 1.1). Diagnostic power is another pillar in ecophysiology. A centuries-old history of respiratory physiology shows that technologies, methodologies, and analytical protocols have improved to make more accurate and precise measurements, along with a higher sampling throughput (key historical advances in this field are summarized in Box 1). As such, my dissertation will show that such improvements are on-going; indeed, I will advance new ideas in this regard. A more important question than how to better measure is what to measure. Characterizing a phenotype that can be directly related to lifetime fitness is my last pillar in ecophysiology (Figure 1.1). While the idea of âsurvival of the fittestâ was first introduced by Charles Darwin (1859) as a driver for natural selection, our ecological toolbox contains few valid measures of individual âfitnessâ beyond lifetime reproductive fitness. Perhaps the insights gained from respiratory physiology can help in this regard because even lifetime reproductive fitness of an entire population can have its predictive limitations, as amply demonstrated by the example of pesticide-resistant insects, which developed resistance to insecticides through mutations in the voltage-gated sodium channel gene and detoxification enzymes (Ranson et al., 2001; Ranson et al., 2000; Martinez-Torres et al., 1999). Hence, an animalâs fitness phenotype can often find 3 explanations in some physiological mechanisms. I hope to illustrate that todayâs respiratory ecophysiologists are well prepared to accurately characterize a respiratory phenotype and contribute meaningfully to predictions at a population level of fish using individual-based metabolic models. My dissertation focuses on teleost fishes (~30,000 species) that represent almost half of all vertebrate species. Fishes display very diverse physiological mechanisms that allow them to live from polar regions (Eastman, 2013) to hot springs (Greenway et al., 2014), from hyperoxic tidepools (Richards, 2011) to oxygen minimum zones (Wishner et al., 2020), from high-altitude lakes (Wang et al., 2003) to oceanic trenches under enormous water pressure (Fujii et al., 2010) and from ion-poor waters of the Amazon (Gonzalez et al., 1997) to hypersaline desert lakes (Sardella et al., 2004). All these successful and broad environmental adaptations radiated from a mass extinction in the Permian era when a drastic decrease in oxygen (i.e., hypoxia) wiped out 96% of marine fishes (Clack, 2007). The subsequent successful radiation involved a respiratory phenotype of a very proficiency and a unique mode of tissue oxygenation (Randall et al., 2014). A respiratory phenotype of a teleost fish can characterize some whole-organism metabolic performance curves across environmental conditions (see details in section 1.3). In general, aerobic capacity is a respiratory trait that has been widely used to study the mechanistic underpinnings for lifetime fitness. Aerobic metabolism is the tightly regulated combustion that powers all sustained activities, and aerobic capacity encompass those activities beyond the basic cellular needs (scope for activity). Aerobic capacity can be measured as a function of an environmental variable to generate a performance curve for a species or population that demarks an environmental range. Aerobic capacity is often maximized over an optimal range then becomes constrained and then non-existent when environmental factors approach the extreme 4 conditions for the animals. Modelling how environmental factors affect the aerobic capacity of fish is now termed the Fry Paradigm (Fry, 1947; Fry, 1971). An aerobic capacity performance curve does not only characterize sublethal constraints on the maximum aerobic scope as sub-optimal conditions with some activities such as growth and reproduction being entirely suppressed, but the performance curve also defines the conditions at which the environment becomes sub-lethal when aerobic capacity can only support basic cellular metabolic needs. Of course, time-limited survival is possible at and beyond the boundaries for aerobic capacity (Pörtner & Farrell, 2008), which may require that basic cellular metabolic needs are supported partially or wholly by anaerobic metabolism, or that basic cellular metabolic needs are down-regulated (Hochachka et al., 1996). Therefore, fully characterizing an individual respiratory phenotype should consider both aerobic and anaerobic capacities. Hence, during my MSc, I coined the Integrated Respiratory Assessment Paradigm (IRAP) which can measure aerobic and anaerobic metrics on multiple fish simultaneously over a three-day protocol (Zhang et al., 2016). IRAP was further refined to quantify anaerobic capacity (Zhang et al., 2017) and agitation behaviours (Zhang et al., 2019). Altogether, IRAP characterizes a fish's respiratory phenotype: needs, capacities, and behavioural patterns. My Ph.D. dissertation will further refine and pioneer proof of principle studies for IRAP. Testing the utility of IRAP as a phenotyping platform starts with distinguishing the strain-level differences. Individual variation in coping with sublethal effects can be a focal point to understand natural selection in individuals. Individual variations are the result of complicated interactions between genes, life histories and the present environment. Indeed, individual variation can affect the chances of populations thriving in future environmental changes. Thus, the better individual phenotypic variation is characterized, the better gene-environment interactions will be 5 understood. Hence, I focus on the measurement techniques that have the potential for high sampling throughput in reliably measuring many temporally stable traits related to fitness. Therefore, my dissertation progresses through a trilogy to better bridge physiology with ecology: I) Improving the validity of standardized respirometry measurements that characterize a fishâs respiratory phenotype (section 1.2; Chapter 2 & 3); II) Examining the power of IRAP to resolve different respiratory phenotypes among strain, with acclimation and with pathogen challenges (section 1.3; Chapter 4, 5 & 6); III) Using a novel precision-&-repeatability matrix to identify which IRAP metrics are temporally repeatable (section 1.4; Chapter 7). Box 1. A brief history of aquatic respirometry Aquatic respirometry has progressively refined definitions, measurement protocols and analytical tools, as well as adapting new technologies. The principles, however, remain the same. Using a stoichiometry of respiratory gas exchange with metabolism, respirometry measures the rate of oxygen uptake (á¹O2) (Lavoisier, 1777) to estimate the rate of ATP generated by aerobic metabolism when an animal is at a steady state. Aquatic respirometry estimates whole-animal á¹O2 by measuring the rate of dissolved oxygen (DO) depletion inside a closed animal housing chamber. The technology used to measure DO has evolved immensely, driving new scientific discoveries and the clarity of definitions. The original Winkler titration method of á¹O2 estimate (Winkler, 1888) was certainly accurate but the challenge of taking water samples and the high water volume of early respirometers meant the single á¹O2 measurement occurred every 30-60 min (Brett, 1964). The advent of Clark-type oxygen electrodes (Qlark, 1956; Gatti et al., 2002) provided continuous measurements of DO with a higher temporal resolution for a single á¹O2 measurement (e.g., every 5 min Bushnell et al., 1994). Currently, the widely accepted use of fibre-optic oxygen probes coupled with intermittent-flow respirometry allows for a more precise DO measurement by having nearly no drift in the calibrations and no oxygen consumptions in the probes. Thus, more dynamic á¹O2 measurements are possible (e.g. every 1.5â3 min; Svendsen et al., 2019; Zhang et al., 2016; Zhang et al., 2018). Nevertheless, some longer measurement periods are still used with fibre-optic oxygen probes (e.g., Norin et al., 2014; Slesinger et al., 2019) typically when water volume is large relative to fish volume. Better regulation of water flow was an important advance. Earlier respirometry used a static, closed single chamber system (Humboldt & Provencal 1809), where metabolic wastes accumulated (Keys, 1930) and the water eventually become hypoxic, conditions that are unlikely to yield accurate estimates of standard metabolic rate when fish are stressed. Hence, a flow-through, single-chamber respirometer was developed using a fixed and known flow rate (Ege & Krogh, 1914) while measuring input and output DO with two oxygen probes. But water stratification, 6 the delay in respirometer washout and different drifts in the input and output oxygen probes reduced measurement accuracy (Keys, 1930). Brett (1962) introduced intermittent-flow respirometry so that metabolic waste accumulation and hypoxic conditions are avoided. Steffensen et al. (1984) introduced a single oxygen probe in water sampling loop ensures water mixing (better á¹O2 measurement accuracy) and computer switching of the water flow into the chamber, removed the problems associated with flow-through respirometry. Today, computers and solenoid valves accurately control the duration of the flushing and measurement cycles (Bushnell et al., 1984; Steffensen 1989), which are in part set by the fish-to-water volume ratio (ideally between 1:20â1:100; Clark et al., 2013). Although intermittent-flow cycles are typically 5â20 min (e.g. Bushnell et al., 1994; Reidy et al., 1995), more than one á¹O2 measurement can be derived from a single measurement cycle. However, to date, no one has provided objective criteria to set a minimum measurement period, something that is clearly dependent on the DO signal-to-noise ratio and something that can benefit from the analytical capabilities of computers. Computer technology and automation also have increased the number of respirometers that can be run simultaneously (Forstner, 1983). á¹O2 measurements have thus become more valid and scalable than half of a century ago (Fry 1946, 1971; Brett, 1964; Beamish, 1964) that portable respirometry systems (Svendsen et al., 2016) allow studies in remote field locations (Gamperl et al., 2002; Farrell et al., 2003; Rodnick et al., 2004; Warnock & Rasmussen, 2014; Hartman & Jensen, 2016; Mochnacz et al., 2017), which help bridge physiology with ecology. 1.2 The value of carefully characterizing environmental effects on respiratory phenotype Adaptations of respiratory phenotypes to match environmentally driven metabolic demands are broadly observed within and across animal taxa: from native Tibetans and Andeans (Homo sapiens, Linnaeus 1758) (Beall, 2007), to high-altitude migrating bar-headed geese (Anser indicus) and resident, high-altitude Andean geese (Chloephaga melanoptera, Eyton 1838) (Scott et al., 2015; Laguë, 2017), to Fraser sockeye salmon populations (Oncorhynchus nerka, Walbaum 1792) (Eliason et al., 2011), and to Idaho redband trout populations (Oncorhynchus mykiss gairdneri, Richardson 1836) (Chen et al., 2018). A respiratory phenotype is typically characterized by multiple metrics. Here I will continue the history of aquatic respirometry (Box 1), illustrating how the recent development of measuring protocols advances the clarity in the definitions. The principle is being explicit about what the actual measurements are, what the inferences are and what the interpretations are. 7 A subsistence cellular metabolism that maintains cellular homeostasis is the basal metabolic rate (BMR). The minimum maintenance metabolic demand for an intact organism in a post-absorptive state that includes basic energy expenditure of circulatory and respiratory functions above BMR is called the standard metabolic rate (SMR; Krogh, 1914; Fry & Hart, 1948). Krogh (1914) considered SMR as the ânearest attainable approximationâ of BMR, whereas the latter is theoretically impossible to be measured in live animals that need to spend energy in circulatory and/or respiratory functions. The newer iteration for BMR includes the minimum costs of circulation and respiration on live endothermic animals (Frappell & Butler, 2004). However, for ectothermic animals, such as fish, the basal metabolism only theoretically exists at its lowest survivable temperature. Hence, SMR is the preferred terminology for the minimum maintenance metabolism of an intact fish when fully acclimated to a given temperature at normoxia. SMR estimates can be easily masked by stress and spontaneous locomotor activity and thereby SMR should be determined in a quiescent (no further decline in á¹O2 for the last 12 hours in a minimum water flow, which is difficult to achieve in a swim-tunnel respirometry), post-absorptive and non-maturing individuals fish, preferably following a reasonable habituation period to the experimental apparatus (Beamish & Mookherjii, 1964). Also, the fish should have experienced the diurnal cycles (Holeton, 1974) and been habituated to respirometers. Yet, few practitioners today allow fish to stay in the respirometers for even a minimum of two diurnal cycles to estimate SMR, even though the minimum duration for obtaining SMR can be longer, depending on the species and life stages. Clearly, SMR should not be confused with the resting metabolic rate. The latter has been used to define a quiescent state as a surrogate for SMR (Norin et al., 2016), but replication of the experimental conditions by other researchers would be difficult, if not impossible. The rejected 8 metabolic cold adaptation hypothesis, which stated that polar fish have an elevated SMR in comparison to temperate and tropical fish (Scholander et al., 1953; Wohlschlag, 1960), is a good example of the need to avoid reporting artificially inflated resting metabolic rate as SMR (Holeton, 1973; Holeton, 1974; Steffensen, 2002). Thus, I estimate SMR using the best available practices that capture the metabolic rate in a quiescent state (Chabot et al., 2016). I also used distribution-based analytical algorithms to parse out the SMR from spontaneous routine activities when the fish is undisturbed inside the respirometer (Steffensen et al., 1994; Chabot et al., 2016; see Chapter 2 for experimental and analytical details). Above the quiescent state is a routine metabolic rate (RMR), which is the average á¹O2 including the costs of spontaneous activity when measured in a stable laboratory environment (Fry, 1971). RMR can be any intermediate metabolic rate between SMR and active metabolic rate (AMR; Brett, 1972). I use RMR to estimate agitation in the respirometer, an index of behavioural activity. Although RMR is reasoned as the more âecologically relevantâ because wild fish is more frequently at an intermediate metabolic state (Randall, 2014, pers. comm., Wood, 2017, pers. comm.), I reason that SMR is equally, if not more, ecologically relevant, as an idling cost of metabolic machinery that fish always carry. Metabolic activities beyond a resting state, such as swimming, feeding, growth, reproduction, etc., tap into a fishâs aerobic capacity to variable degrees. Its ceiling is the maximum aerobic metabolic rate (MMR), estimated by maximum oxygen uptake (á¹O2max). á¹O2max is only equal to MMR when oxygen flux through oxygen transport cascade is steadily consumed by oxidative phosphorylation, a steady metabolic state (Brett, 1962; Zhang & Gilbert, 2017). Otherwise, á¹O2max is likely uncoupled from MMR. For most, but not all fish species (Steell et al., 2019), and especially for salmonids, the maximum aerobic capacity is expressed, 9 and therefore can be measured, during swimming as fish approach their maximum swimming speed. In such conditions, red muscle aerobic, ATP producing, metabolic pathways are fully engaged, mobilizing the entire aerobic metabolic scope of the swimming animal, a likely case of steady-state where Fick principle (Box 2) applies. Using a critical swimming speed (Ucrit) test to assess MMR is, however, time-consuming (last longer than 10 h). To bridge physiology and ecology, a more rapid swimming protocol was developed, the ramp-Ucrit, which takes advantage of the fact that fish can swim for sustained periods of > 60 min at low velocities. The ramp-Ucrit rapidly accelerates the fish to ~50% Ucrit before starting 20-min velocity increments (Jain et al., 1997). While the protocol for a Ucrit test is robust and has been refined, the analytical approach of MMR has not. This is because swimming gait transits from steady to burst-and-coast when approaching Ucrit (Fry & Hart, 1948; Bainbridge, 1958; Brett, 1962). Also, at the final velocity increment, fish can rarely last for 20 min, a criterion for steady-state exercise. This suggests an unsteady state where á¹O2 is no longer equal to the metabolic rate. Hence, active metabolic rate was used to acknowledge that the peak á¹O2 was not measured, instead, an average active metabolic state was estimated (Bainbridge, 1958; Brett, 1962). The term á¹O2max was first used by Jones (1971) in a algorithmic analysis to understand how cardiac and branchial pumps can optimize oxygen supply. Brett (1972) uses the term á¹O2max to estimate the instantaneous aerobic metabolic demand for mixed swimming gaits â steady and burst-and-coast. Because of poor resolution of the previous generations of oxygen probes (Box 1), no study has yet to verify this estimate by increasing the temporal resolution of an á¹O2 measurement to be more commensurate with swimming behaviours, where passive gliding swimming might help with 10 restoring venous oxygen content, ATP and creatine phosphate stores (Farrell & Clutterham, 2003; Peake & Farrell, 2004, 2005, 2006). The term á¹O2max is latter applied to the highest á¹O2 measured after a fish has been chased to exhaustion and placed in a static respirometry, again an unsteady state (Lucas & Priede, 1992). Fish is typically chased for ~3â15 min until it becomes either refractory to touch or loses its righting reflex. á¹O2 measurements typically begin within 1â2 min after this state has been reached, the time needed to transfer the fish in a respirometry chamber and start the á¹O2 measuring system. But some researchers expose the fish to air for a short, standardized period (1 min) to delay and standardize the start of recovery (Norin & Clark, 2016). The á¹O2max measured with a chase-to-exhaustion protocol has become a widely adopted proxy for MMR (Reidy et al., 1995; Clark et al., 2013) in part because of its high versatility of application and in part because the measurement is relatively rapid using an inexpensive respirometer, which allows for a larger sampling throughput. Chasing to exhaustion is also relevant to selection pressures introduced by the catch-&-release from recreational fisheries and the release of by-catches from commercial fisheries (Farrell et al., 2001; Raby et al., 2015) that can introduce a sizeable selection pressure on wild fish. However, a fundamental concern is that á¹O2max is being measured while the fish is in a non-steady state when its respiratory quotient (CO2 exhaled / O2 inhaled) is > 1 and ventilation-&-perfusion is desynchronized (Lauff & Wood, 1997). Hence, this type of á¹O2max estimate does not directly conform to the Fick principle that is based on a steady state (Box 2; Nelson, 2016; Zhang & Gilbert, 2017). Hence, chasing protocol utmost measures á¹O2max as a surrogate as MMR. As I will describe in Chapter 3, an on-going debate exists on whether swimming or chasing protocol is the most valid approach to estimate MMR. Also, the criteria for measuring 11 á¹O2max with a chase-to-exhaustion protocol needs to be more pedantically prescribed for two reasons. Foremost, transferring an exhausted fish to a respirometer could easily miss the peak value because post-chase á¹O2 declines rapidly. Second, with á¹O2 declining rapidly the duration of the á¹O2 measurement window has paramount importance because only a short sampling window will capture a true peak value of a rapidly decaying signal, but it will measure noise if it is too short. Both can compromise the accuracy of the á¹O2max measurements. Chapter 3 will also advance new and more standardized protocols to assure reproducibility of á¹O2max measurements using both the prolonged swimming to fatigue and chase-to-exhaustion protocols. Only when á¹O2max and SMR are reliably measured can the quality of estimating aerobic capacity be assured. Aerobic capacity can be best estimated by absolute aerobic scope (AAS) as the numerical difference between á¹O2max and SMR (Beamish, 1978; Brett & Groves, 1979), or factorial aerobic scope (FAS) as the quotient of á¹O2max and SMR (Clark et al., 2005). Most of the literature in fish biology measured aerobic scope (AS) as the numerical difference between active metabolic rate and resting metabolic rate, a surrogate of AAS. From the above, it is clear that with swimming-to-fatigue and chase-to-exhaustion protocols some or perhaps most of the instantaneous maximum metabolic demand is subsidized by high energy phosphates and anaerobic metabolism; the true oxygen cost of peak anaerobic performance is deferred and can be measured as excess post-exercise oxygen consumption (EPOC). Total EPOC is measured as an integral of post-exercise á¹O2 until full recovery is achieved (Brett, 1964). EPOC has been measured after various swim-to-fatigue protocols (Farrell, 2008; Marras et al., 2010; Svendsen et al., 2010; Svendsen et al., 2015; Cordero et al., 2019) and after various chase-to-exhaustion protocols (Scarabello et al., 1992; Zhang et al., 2018). Post-fatigue EPOC typically has rapid and slow phases and lasts for ~4 h (Lee et al., 12 2003a; Lee et al., 2003b; Lee et al., 2003c). Post-exhaustion EPOC was also thought to last for ~4 h (Scarabello et al., 1991 & 1992), but actually lasts much longer (up to ~17 h) if SMR rather than RMR is used as the baseline (Zhang et al., 2018). In fact, post-exhaustion EPOC can typically show three phases: rapidly declining phase during the first 40 mins (i.e. 16% of EPOC), then a 3â6 h plateau phase (i.e. 53% of EPOC) and finally slow, 6â10 h declining phase to reach SMR plus 10% (i.e. 31% of EPOC; Zhang et al., 2018). The rapid phase is thought to replenish ATP and creatine phosphorylation as well as the hemoglobin and myoglobin oxygen stores (Wood, 1991). The plateau phase is thought to restore the routine status of osmotic, acid-base and cardiovascular variables, as well as plasma lactate and glucose status (Scarabello et al., 1991). The slow phase correlates with lactate oxidation, in situ gluconeogenesis, glycogen restoration and intracellular pH restoration (Zhang et al., 2018). Broader characterizations are needed to understand how universal the 3-phase EPOC phenomenon is. Nevertheless, newest data and the theoretical framework suggest that EPOC can provide insights into the use of and capacity for glycolytic metabolism at the whole-animal level, as Bainbridge (1958) and Brett (1972) speculated that 90% of the energy used to support instantaneous maximum metabolic demands are from glycolysis, which generates ATP more rapidly than oxidative phosphorylation (Pfeiffer et al., 2001; Zheng, 2012). Box 2: Fick equations In the whole animal, five Fick equations describe steady-state oxygen transport at each step in the oxygen cascade system: ventilation, gill diffusion, perfusion, tissue diffusion and cellular respiration. For example, oxygen uptake by a fish from the surrounding water can be related to internal convection by á¹O2 = Q Ã (Cao2 â Cvo2), where á¹O2 is the rate of oxygen uptake, Q is cardiac output, Cao2 is arterial oxygen content and Cvo2 is venous oxygen content. This equation clearly states that a major function of the fish gill is to fully saturate arterial blood with oxygen and ventilation-perfusion matching, which is critical to ensure a steady-state for respiratory diffusion. Specifically, oxygen is extracted at gills, pumped by the heart, and delivered by blood through a circulation system to mitochondria for generating ATP. Thus, á¹O2 only reliably estimates whole-organism metabolic rate when oxygen flux through transport cascade is at a steady state. The equation also elucidates that increased delivery of oxygen to tissues is possible by both increasing Q and decreasing Cvo2, as well as by increasing the capacitance of blood through an increased hemoglobin concentration provided arterial blood is fully saturated. Conversely, hypoxia and anemia both reduce Cao2 and must be compensated by increasing Q and/or decreasing Cvo2 to maintain á¹O2. Fick 13 equations were used in early cardiorespiratory studies of fish to examine regulatory responses during swimming (Stevens & Randall, 1967a, b), when exposed to hypoxia (Holeton & Randall, 1967a, b) and to temperature extremes (Brett, 1952) to provide insights into Q and Cvo2, the physiological variables that could not be reliably measured in 1950â60s. Sensitivity analyses using Fick equations are also useful in predicting where limiting steps exist in the oxygen transport cascade (Scott & Milsom, 2006; Scott et al., 2015). Indeed, Fick equations help appreciate some of the acute responses of fish to increase oxygen supply: hyper-ventilation (Holeton & Randall, 1967; Hughes & Sunders, 1970), increased blood perfusion of gill lamellae (Sundin, 1995), bradycardia to increase time for diffusion at gill lamellae (Randall & Shelton, 1963; Butler & Taylor, 1975; Randall, 1982; Farrell, 2007), increased venous tone to support the venous return to the heart (Sandblom & Axelsson, 2005; Sandblom & Axelsson, 2006), the release of splenic red blood cell stores to raise hemoglobin concentration (Scott & Rogers, 1981; Soldatov, 1996) and redistribute blood to essential tissues (Schwerte et al., 2003). Chronic responses to increasing oxygen supply capacity include: enhancing gill lamellar surface area (Sollid et al., 2003; Brauner & Rombough, 2012) and hemoglobin oxygen affinity (Weber & Jensen, 1988), as well as remodelling cardiac (Gamperl et al., 2001; Gillis & Johnston, 2017) and swimming muscles (McKenzie et al., 2004), and increasing myoglobin content (Bailey & Driedzic, 1986; Driedzic & Gesser, 1994; Acierno et al., 1997). 1.3 The value of measuring whole-organism metabolic performance curves using respiratory metrics For the biology of fishes, effects of environmental factors on whole-organism respiratory performance were characterized as controlling, limiting, masking, directive and lethal, depending on the form of their influence on fish respiratory performance (Fry, 1947; Fry, 1971). For example, while temperature controls the rate of biophysical and biochemical reactions, hypoxia limits the pace of metabolism by limiting the oxygen supply. Conversely, a pathogen load can perhaps either mask the maintenance workload for metabolism or allocate energy to mount energy-consuming immune responses. Thus, environmental factors create zones of tolerance (with optimal and sub-optimal performance contained therein) outside of which fishes temporarily resist lethality (Fry & Hart, 1948; Fry, 1971). Performance curves capture the effect of environmental factors, alone or in combination, on traits and functions that are important for lifetime fitness i.e., survival, tolerance, growth. My dissertation focuses on metabolic performance curves derived by measuring respiratory variables to highlight the value of simultaneously measuring aerobic and anaerobic capacity. Here, I will focus on two environmental factors, temperature (section 1.3.1) and oxygen level (section 1.3.2) 14 and then introduce the potential applications of load-dependent response curve on diagnosing sub-lethal costs of pathogen infections (section 1.3.3). 1.3.1 Thermal performance curve A Fry thermal polygon graphically illustrates how thermal acclimation changes the boundaries of acute thermal tolerance (Fry, 1947). Other physiological functions, such as a thermal performance curve of aerobic capacity, can be placed within the polygon to examine the underlying mechanisms for thermal tolerance. SMR and á¹O2max have different thermal sensitivities, which give rise to different thermal performance curves for SMR and á¹O2max, as well as AAS and FAS. The thermal performance curve for SMR can increases exponentially or linearly, plateau (i.e., Q10 becomes 1; Claireaux & Lagardère, 1999) or sometime continuously increase when approaching an upper thermal limit. á¹O2max performance curves have various shapes. á¹O2max often increases with temperature up to a peak at the optimal temperature (Topt; Pörtner & Farrell, 2008), then either decline at pejus temperatures (Tpej; Pörtner & Knust, 2007) or close to CTmax (Pörtner, 2010; Pörtner et al., 2017). The performance curve for AAS similarly showed four thermal zones are of particular interests: a Topt zone where AAS remains above 90% of its maximum (Chen et al., 2015), Tpej zone where AAS is below 90% of its peak (Frederich & Pörtner, 2000), and the lower and upper critical temperatures (Tcrit) where AAS is zero and the thermal window for aerobic scope is defined (Fry & Hart, 1948; Ferreira et al., 2014). Beyond Tcrit, glycolysis may be engaged and anaerobic end products start to accumulate (Pörtner & Farrell, 2008; Pörtner et al., 2017) causing acidosis and possibly mortality (Wood et al., 1983; Ferguson & Tufts, 1992). FAS performance, however, typically decreases with temperature (Norin et al., 2014; Drost et al., 2016) to a large part because of the increase in SMR (the denominator) with temperature is proportionately greater 15 than that for á¹O2max, and in part because á¹O2max plateaus and even declines at supra-optimal temperatures. The oxygen- and capacity-limited thermal tolerance (OCLTT) hypothesis suggests that sustained thermal performances of fishes are constrained by the oxygen supply capacity at multiple biological levels and that thermal performance curves for an aerobic scope can explain important aspects of biogeographic distributions. To understand how fish either establish in the existing or evolve to new thermal niches, characterizing the inter- and intra-specific variations at various geographic locations becomes an important approach. For the eurythermal fishes, the optimal thermal window for aerobic scope is larger and the thermal performance curve is quite flattened (Healy & Schulte, 2012; Ferreira et al., 2014), while polar fish species would have left-shifted thermal performance curves compared with tropical fish species (see review by Pörtner, 2002; & meta-analysis by Payne & Smith, 2017). For the stenothermal fishes, the optimal thermal window for aerobic scope is narrow and the thermal performance curve has a pronounced bell-shape curve. Multiple populations of sockeye salmon in the Fraser river basin have a thermal optimal range of aerobic performance curves matching the central peak of a unimodal distribution for historical temperatures of natal rivers (Eliason et al., 2011, 2013). A once thought of as a stenothermal species, rainbow trout (Oncorhynchus mykiss, Walbaum 1792), shares the feature of eurythermal species in some geographically isolated populations. Wild southern Californian strain (O. mykiss irideus) has a nearly flat curve of aerobic scope (AS) over 13â25 ËC when fish were held at 13 ËC and acutely exposed to 14â24 ËC (Verhille et al., 2016). When southern Californian populations are warm-adapted in a western Australian hatchery (Molony et al., 2004), they gained a higher thermal tolerance by widening the optimal thermal range of the AS curve. The Australian strain held at 15 ËC can sustain Topt of 16 AS curve up to 20 ËC at acute exposures (Chen et al., 2015). The peak AS at the optimal temperature (15.5 ËC) of the acutely exposed Australian strain is 43% higher than that of the Californian strain, which rivals the peak AAS value of the Wyoming strain that is acclimated and tested at 15 ËC (Dickson & Kramer, 1971). Besides the artificial selection, natural sections in redband trout (O. mykiss gairdneri) also widen the optimal thermal range. A desert strain (warm climate), when held at 15 ËC and acutely exposed to 12â24 ËC, has a 3 ËC wider Topt window than that montane strain (cool climate) (Chen et al., 2018), which are well within the AS curve of the Australian strain (Figure 1.2). Whether the artificial and natural selections work on the same set of genes are still to be researched. To bridge physiology with genomics, Chapter 4 will explore the power of IRAP in resolving respiratory phenotypic variations in three rainbow trout strains. 1.3.2 Hypoxia tolerance and the hypoxic performance curve Fish engage their glycolytic capacity in hypoxia, as peak á¹O2 and aerobic capacity being constrained by the reduction of dissolved oxygen saturation (DO). The minimum DO that a fish can aerobically sustain its whole-organism subsistence metabolic demand is the critical oxygen level (O2crit, Fry, 1947; Fry & Hart, 1948; Fry, 1971). At O2crit, fish has zero aerobic capacity as peak á¹O2 meets SMR. O2crit is determined by monitoring á¹O2 during exposure of a fish to progressive hypoxia (Fry & Hart, 1948; Claireaux & Chabot, 2016). Using SMR as the baseline to determine O2crit, a DO when á¹O2 first declines below SMR (SMR-derived O2crit), assures a rigorous measurement quality: a stable baseline without the behavioural biases (Fry & Hart, 1948). Others have used RMR as the baseline to determine O2crit for a better ecological relevance (Wood, 2018), but fish biologists have recently reached a consensus that SMR-derived O2crit is the most valid (Reemeyer & Rees, 2019; Negrete & Esbaugh, 2019) in part because of the new and robust analytical criteria used to measure SMR (Claireaux & Chabot 2016). IRAP, since its 17 inception in 2016 (Zhang et al., 2016), uses an SMR-derived O2crit, which is continuously used in Chapters 4, 5 & 6 to test its detection sensitivity. For simplicity, I will refer to SMR-derived O2crit as O2crit. The anaerobic capacity at the levels of DO below O2crit has been termed scope for survival (Hochachka, 1990), a mirror concept to scope for activity. With further decreases in DO, á¹O2 decreases and ultimately a fish loses its upright equilibrium - the incipient dissolved oxygen saturation (ILOS) (Fry & Hart, 1948, Claireaux et al., 2013). Such a glycolytic capacity can be quantified by an accumulated oxygen deficit (AOD), an integral of the area bounded by SMR, O2cirt and ILOS (Figure 2.2) that accounts for the duration and severity of hypoxic exposure (Zhang et al., 2017). An assumption for calculating AOD is that SMR is not suppressed in acute hypoxia, which is currently thought as valid for most fishes except the anoxia-tolerant species (see review by Stecyk, 2017). When a fish progressively uses glycolysis, lactate and H+ accumulate (the end products of glycolysis) and life becomes time-limited, in part because glycogen stores are finite (Nonnotte et al., 1993; Maxime et al., 2000; Routley et al., 2002) and in part, because a plasma lactate threshold of ~20 mmol l-1 in fish (Milligan, 1996; Zhang et al., 2017) seems to signal mortality (Black, 1958; Wood et al., 1983). Anoxia-tolerant ectotherms go to an extraordinary length to mitigate metabolic acidosis. For example, goldfish (Carassius auratus, Linnaeus 1758) excrete lactate as ethanol, an H+-consuming process (Shoubridge & Hochachka, 1980) despite the potential drawback of losing carbon energy. Similarly, the freshwater turtle (Chrysemys picta, Schneider 1783) avoids fatal acidosis and buffers the high blood lactate by demineralizing carbonates from its shell (Jackson, 2000). 18 Suppressing SMR is another survival strategy that slows waste accumulation and glycogen depletion. This can be achieved by being in very cold water and by ion channel, spike and translational arrests, and even reduced cardiac activity (Hochachka et al., 1996; Stecyk et al., 2008; Speers-Roesch et al., 2010; Lefevre et al., 2017). Yet, most studies that reported metabolic suppression merely observed a constrained AMR or RMR (by hypoxia in a lethargic state) (Speers-Roesch et al., 2018). This is fundamentally different from measuring suppressed minimum maintenance demands. Only Bushnell et al. (1984) speculated an SMR suppression in hypoxia-acclimated rainbow trout. The SMR was estimated by exercising fish over a range of graded velocities in a swim-tunnel respirometry and extrapolating active á¹O2 to a zero swimming speed. This is not a direct measurement. Hence, Chapters 5 & 6 measure SMR to resolve the suppression of minimum maintenance demands. While O2crit and ILOS are widely used as end-points for assessing hypoxia tolerance in fishes (Nilsson & ÃstlundâNilsson, 2008; Rogers et al., 2016; Wood, 2018) and are part of an IRAP assessment, a hypoxia performance curve (HPC) of a fish can be used to more holistically characterize the respiratory phenotype. An HPC is based on the premise that a fish ultimately can no longer conserve its arterial oxygen saturation during progressive hypoxia (hypoxemia). As a result, peak á¹O2 must decrease as a function of DO despite the numerous acute compensatory mechanisms during progressive hypoxia (see Box 2). An HPC, therefore, represents the relationship between peak á¹O2 and DO, being modelled a limited oxygen concentration curve at a population level (Neill et al., 1994; Claireaux et al., 2000; Claireaux & Chabot, 2016). Determining an HPC is not a trivial experimental challenge and acceptable protocols are still a work in progress with even on-going discussion of whether the linear or nonlinear fits are appropriate (Seibel & Deutsch, 2020). Thus, Chapter 5 will explore the potential of an HPC to 19 resolve the benefits of hypoxic acclimation, which are known for some fish species to increase á¹O2max and aerobic scope (Bushnell et al., 1984; Petersen & Gamperl, 2010), by improving oxygen uptake at the gills, oxygen delivered to the mitochondria and thereby the capacity to aerobically meet metabolic needs (Petersen & Gamperl, 2010; Petersen & Gamperl, 2011). 1.3.3 Metabolic consequences of infections â a load-dependent response Pathogens may kill a proportion of a population, while others in the population may resist the infection, likely with a metabolic cost; others may be exposed but not infected. For the individuals resisting the pathogen load, maintenance metabolism can perhaps be masked (SMR reduced) or energy may be allocated to mount energy-consuming immune responses (a metabolic load above SMR). Thus, respiratory performances may have a load-dependent relationship with pathogens. For example, sea lice infection causes a load-dependent osmotic imbalance and swimming performance impairment in out-migrating pink salmon (Oncorhynchus gorbuscha, Walbaum 1792) smolts (Nendick et al., 2011; Brauner et al., 2012). Pathogen infections can elevate metabolic rate in mammals, but data are very limited. For example, an endotoxin-induced fever increased RMR by 33% ËC-1 in mature ewes (Baracos et al., 1987) and by 31% ËC-1 in humans (Buskirk et al., 1964). Given that most biochemical reactions have a Q10 of 2â3, a 1 ËC increment in body temperature should increase metabolic rate by only 10%; the additional ~20% change in RMR could be attributed to the metabolic cost of fighting the endotoxin, an example of masking factor to the hosts. Whether or not pathogen infections induce dose-dependent respiratory responses is largely unexplored, even though they can certainly impair the cardiorespiratory function and oxygen supply capacity. For example, fungal infections (Ichthyophonus spp. & Saprolegnia spp.) of the heart and gill of salmonids reduced post-exercise á¹O2 during the rapid phase of EPOC 20 (limiting MMR) and Ucrit performance could not be repeated after a 40â60 min of recovery (Randall et al., 1987; Brauner et al., 1994; Jain et al., 1998), unlike a non-infected salmon (Tierney & Farrell, 2004). Parasite-induced cardiac dysfunction reduced á¹O2max and AAS in Atlantic cod (Gadus morhua, Linnaeus 1758) (Behrens et al., 2014) and slightly reduced the Ucrit and CTmax (Powell & Gamperl, 2016), an example of limiting factor. Ectoparasites infections increased the costs of swimming that were speculated to increase SMR (Binning et al., 2012), an example of dose-dependent masking. The multiple mechanisms of hindering the performance of fishes appear to lend the supports to that pathogen infections can affect individual survival at key life stages (Kongtorp et al., 2004; Miller et al., 2014). Viral infection has been proposed to compete for the already fine-tuned aerobic capacity of spawning sockeye salmon (Jeffries et al., 2014). Juvenile sockeye salmon carrying an IHNV load were more prone to predation by bull trout (Salvelinus confluentus, Suckley 1859) (Furey et al, 2016) and a pathogen load was associated with the premature death of the migratory adult Pacific salmonids (Miller et al., 2011). Chapter 6 will therefore explore whether or not IRAP could resolve the effects of a viral infection on the traits of respiratory phenotype in a dose-dependent manner. An essential element of this assessment was the use of a time-matched control group to account for any time-dependent dynamics in the respiratory phenotype. 1.4 The value of understanding how temporally stable a respiratory phenotype is Most disciplines of biology typically study the mean of the population and overlook individual variation (see reviews by Bennett, 1987; Nikinmaa & Anttila, 2019), which potentially overlooks important aspects for natural selection: the larger the diversity, the greater chance that some individuals can survive environmental perturbations and sustain the population (Ives & 21 Carpenter, 2007). Individual variations within a population can reflect both inter-individual variations as well as temporal variation because animals are constantly changing at a cellular level, as manifested in developmental plasticity and in ageing. Hence, nearly all traits are dynamic. For ectotherms, some can adjust over time through acclimation/acclimatization and some of these adjustments may be encoded in the genes and might become adaptation if they are expressed in the offspring. Improving the understanding of phenotypic plasticity needs an advance in the knowledge of shifting baselines, temporal stabilities. The stability of a respiratory phenotype over a temporal scale of days to months is an important consideration, and if such phenotypic characterizations have meaningful and reproducible applications in ecology. While cross-sectional study designs rely more on measurement accuracy, precision, and sample size than repeatability for statistical power, a longitudinal design must also maximize the signal-to-noise ratio in measuring temporally stable traits to repeatedly characterize biological changes in a meaningful manner. Hereby, a repeated-measure design using standardized protocols explores temporal stability by comparing the repeatability of the trait along with its variance. With this type of experimental design, various statistical tools can assess repeatability (see Chapter 7). However, the repeatability of fish respirometry variables is still poorly studied. AAS, á¹O2max and resting metabolic rate were repeatable after 5 weeks in brown trout (Salmo trutta, Linnaeus 1758) (using Kendallâs coefficient of concordance: r â¥ 0.52, p â¤ 0.002; Norin & Malte, 2011). Resting metabolic rate, but neither AAS nor á¹O2max, was repeatable for up to 6 weeks in barramundi (Lates calcarifer, Bloch 1790) held under low salinity, high temperature and hypoxia (Norin et al., 2016). Hence, the repeatability of the many respiratory metrics obtained holistically with IRAP has not been tested. 22 Chapter 7, therefore, will explore the use of a novel precision-&-repeatability matrix to disentangle measurement error from intra-individual variation, whereby a correlation coefficient (Pearsonâs or Spearmanâs) plotted on the y-axis measures repeatability and the coefficient of repeatability (COR = twice a standard deviation for the differences between two repeated measurements) plotted on the x-axis quantifies the range of differences between two tests, a measure of precision ( Figure 1.3). Often, COR is normalized as a percentage to an average value of a tested trait to account for any differences in absolute magnitude. My expectation is that a trait with a Normalized coefficient of repeatability (%)Repeatability (p-value)ImprecisePreciseUnrepeatableRepeatableThresholdThresholdâ¢ Invalid protocolsâ¢ Unstable traits Protocol needs a refinementbefore concluding traits stability â¢ Invalid protocolsâ¢ Stable traitsRepeatability detected despite invalid protocols due to strong selection pressureâ¢ Valid protocolsâ¢ Unstable traitsTrait might not experiencestrong selection pressureâ¢ Valid protocolsâ¢ Stable traitsTraits can bridge to ecology to evolutioni)ii)iii)iv) 23 COR value below a threshold in which the measurements are more likely to be repeatable, and hence of greater value in longitudinal studies. I acknowledge experimental protocols affect the accuracy and precision of measurement (Krogh, 1916), whereby a cornerstone is to maximize the signal-to-noise ratio and to standardize the experimental protocols. Measurement errors and lack of repeatability may overlook small differences in individuals or populations (Type I statistical error). As a whole, my dissertation standardized protocols, improved reproducibility and aided quantitative comparisons. Only then, I can reliably detect trait stability, suggesting a strong selection pressure and the streamlined underlying mechanisms, and trait instability, suggesting a weak selection pressure and intertwined dependent mechanisms. 1.5 The specific objectives and hypotheses My dissertation builds on the recently established platform of Integrated Respiratory Assessment Paradigm (IRAP) for respiratory phenotyping (Zhang et al., 2017) that packages advancements in respiratory physiology (Fry, 1947; Brett, 1972; Farrell, 2007) and static respirometry systems (Steffensen, 1989; Claireaux & Lagardère, 1999; Svendsen et al., 2016) into one protocol and one system. Iterations of the IRAP protocol (Zhang et al., 2016, Zhang et al., 2017, Zhang et al., 2018) were the first attempt to comprehensively and simultaneously measure aerobic and anaerobic capacities together in individual fish. IRAP, perhaps, measures numerous aerobic and anaerobic indices in the shortest period possible to provide insights into metabolic needs, capacity, and behavioural patterns. IRAP starts with a standardized exhaustive chasing protocol that generates á¹O2max and EPOC within the first 12 hours, while the fish habituates to the respirometer. The ensuing two diurnal cycles (36 h) in the respirometer allow fish to experience and reach a post-absorptive, minimum maintenance state to reliably measure SMR and concluded with a hypoxia challenge test to measure O2crit, AOD, ILOS and even HPC. A 24 respiratory phenotype is, therefore, characterized by 4 indices of aerobic performance, 6 indices of glycolytic capacity estimates and 4 indices of behavioural activity for agitation (see Chapter. 2 for details of each index). Phase I: standardizations and improvements. Chapter 2 is a general methodology chapter where I fully describe the IRAP protocols, variable testing conditions and analytical tools that were used in the subsequent five data chapters, which employ various fish species to broadly apply this respirometry technique. My objective in chapter 3 was to specifically improve the validity of estimating á¹O2max, an aerobic metric that still needed refinement and standardization. I introduced a new technique for chasing a fish inside a closed respirometer and introduced a new analytical approach to determine the minimum duration for a sampling window to reliably estimate á¹O2. In doing so, I tested the following hypothesis: estimates of á¹O2max can be independent of the chasing and swimming protocols provided the methodologies for determining peak á¹O2 are valid. Phase II: testing for the diagnostic power of IRAP with three proof-of-principle studies (resolving intra-specific variations, phenotypic plasticity and sublethal costs). My objective in chapter 4 was to distinguish the respiratory phenotype among strains of rainbow trout. Intra-specific variations studies are usually conducted on the geographically isolated wild populations, where the phenotypic variations are the results of genotypic variations, life histories and environment. Hence, the most rigorous test for detecting intra-specific variations is differentiating among hatchery-raised strains of the same species. At this regard, a common garden testing narrows the phenotypic variation to genotypic variation. Here, I tested the following hypothesis: IRAP can resolve the intra-specific phenotypic variations of metabolic traits underlying the thermal and hypoxia tolerances in rainbow trout. 25 My objective in chapter 5 was to characterize the plasticity of the respiratory phenotype of sea bass after acclimation to an environmental limiting factor. Besides standing genotypic variations, physiological processes exhibit tremendous plasticity, and this plasticity is, perhaps, one of the greatest sources of raw material for evolutionary changes. Studying respiratory phenotypic plasticity in hypoxia acclimation and testing conditions might provide new insights for living in an oxygen-limited environment. I tested the following hypothesis: IRAP can resolve the phenotypic plasticity in aerobic and anaerobic capacity for marine fish. My objective in chapter 6 was to diagnose shifts in respiratory phenotype in associations with viral infections in salmon. Sublethal effects often gradually shift the distribution of the phenotype over generations. If the diagnostic power of the phenotyping platform can detect sublethal costs, biologists can better study a persistent force of natural selection â a viral infection. The challenge is whether detection sensitivity and testing throughput can keep up with the rapidly changing dynamics of infections. Hence, I use IRAP to diagnose the sublethal effects of two viruses of the greatest concern for wild Pacific salmon and salmon aquaculture. I tested the following hypothesis: IRAP can resolve the metabolic consequences of a viral infection. Phase III: Which IRAP metrics are repeatable and what temporally stable traits can benefit future eco-physiological research. My objective in Chapter 7 was to study the repeatability for each of the 14 IRAP metrics and to understand the precision of the measurements. Does the current IRAP protocol detect everything to be seen? How can temporal stability of traits and measurement repeatability of metrics to be dissociated? Using a novel precision-&-repeatability matrix, I tested the following hypothesis: precision and repeatability are positively correlated when measurements are repeated on the same individuals. 26 Chapter 8 synthesizes my discoveries and proposed integration of an individual-based metabolic model with a Genomic-Physiology-Ecology axis for ecological physiology as a future research direction, field ecophysiology. Respiratory phenotyping should be reproducible so that ecophysiological investigations conducted in different locations and time can be reliably synthesized to understand the adaptation strategies for the most diverse vertebrates â teleost fishes. 27 High throughputBiochemistry machineriesDiagnostic powerRespiratoryphenotypeInter-individual variationsAccuracy, Precision,RepeatabilityIndividual-based metabolic modelAerobic & anaerobiccapacityPhysiologicalPlasticityGeneticvariationsPhenotype characterizationGenotypesEcological-levelfunctional performances 28 Figure 1.1 Eco-physiological Temple of Athena. Inter-individual variations, individual-based metabolic models and diagnostic powers are pillars that connect the strong foundations of physiology, biochemistry and genomics to field eco-physiology. Field eco-physiology characterizes individual phenotypes to understand the functional performances of populations and species at an ecological level and at their natal habitats. Inter-individual variations originate from physiological plasticity and genetic variations. Individual-based metabolic model measures the environmental effects on respiratory phenotype that includes aerobic and anaerobic capacity. Diagnostic power is a result of accurate, precise, repeatable measurements and high sampling throughput. 29 02004006008001000á¹O2max (mg O2 h-1 kg-1 ) 02004006008001000AAS (mg O2 h-1 kg-1 ) 0 5 10 15 20 25 30024681012Testing temperature (oC)FAS(a)(b)(c)13 °C 22 g California, Tuolume O.mykiss, measured RMR, Verhille et al., 201615 °C 37 g Western Australia, O.mykiss, measured RMR, Chen et al., 2015, 15 °C 2.5 g Idaho, Fawn O. mykiss gairdneri, measured RMR, Chen et al., 2018, 15 °C 2.5 g Idaho, Little Jacks O. mykiss gairdneri, measured RMR, Chen et al., 201815 °C 2.5 g Idaho, Keithley O. mykiss gairdneri, measured RMR, Chen et al., 2018 Acclimated to testing temperature 79-398 g, Utah, DeSmet O. mykiss, measured SMR, Dickson & Kramer 1971Acclimated to testing temperature 137-264 g, Utah, Hull-Erickson O. mykiss, measured SMR, Dickson & Kramer 1971 30 Figure 1.2 Maximum oxygen uptake (á¹O2max; a), absolute aerobic scope (AAS; b) and factorial aerobic scope (FAS; c) over a range of testing temperatures in various studies on strains of rainbow trout (Oncorhynchus mykiss) and redband trout (Oncorhynchus mykiss gairdneri) . The data are presented as mean ± 95% C.I. calculated from either Gaussian three-parameter function (Y = amplitude*exp(-0.5*((X-mean)/SD)^2)) or linear regressions models (Y = intercept + slope*X). Figure legends state the acclimation temperature, body mass, location, strains, original studies and whether standard metabolic rate (SMR) or resting metabolic rate (RMR) are measured. The studies had fish of different mass, settings, testing protocols and analytical techniques, as well as acclimation and temperatures (indicated), but not all of the differences in the curves can be attributed to these. A red vertical dash line marks 18 °C as the 7 Day Average of the Daily Maxima criterion for the management of rainbow trout habitat in the Pacific Northwest (U.S. Environmental Protection Agency, 2003). Rainbow trout, redband trout are in blue, green respectively, where the aerobic capacity thermal performance curves are measured by acute exposure. The performance curves measured by the acclimation studies are in orange. Patterns of lines differentiate the strains. The range of the curve is determined by testing temperature ranges. 31 Figure 1.3 Dissociating stability of traits and validity of experimental methodologies using a precision-&-repeatability matrix. Normalized coefficient of repeatability is a proxy for measurement precision at the x-axis, and p-value from correlation coefficients tests is a proxy for repeatability at the y-axis. This forms four zones of interests: i) valid protocols and stable traits; ii) valid protocols and unstable traits; iii) invalid protocols and stable traits; iv) invalid protocols and unstable traits. The threshold for repeatability is a p-value of 0.05 at correlation coefficients tests, whereas the threshold of precision is under development. Precision and repeatability are expected to be positively correlated. Normalized coefficient of repeatability (%)Repeatability (p-value)ImprecisePreciseUnrepeatableRepeatableThresholdThresholdâ¢ Invalid protocolsâ¢ Unstable traits Protocol needs a refinementbefore concluding traits stability â¢ Invalid protocolsâ¢ Stable traitsRepeatability detected despite invalid protocols due to strong selection pressureâ¢ Valid protocolsâ¢ Unstable traitsTrait might not experiencestrong selection pressureâ¢ Valid protocolsâ¢ Stable traitsTraits can bridge to ecology to evolutioni)ii)iii)iv) 32 Chapter 2: Methodology for Integrated Respiratory Assessment Paradigm (IRAP) 2.1 Synopsis Chapter 2 contains a detailed description of the IRAP protocol that transcends all my subsequent data chapters (Chapters 3 to 7). Also, parameters in the intermittent-flow respirometry system and key variables in the IRAP protocol of each data chapter are summarized (Table 2.1). The intent is to avoid unnecessary duplication of information in the data chapters, where details specific to that experiments are presented. 2.2 My standardized protocol for Integrated Respiratory Assessment Paradigm (IRAP) The integrated respiratory assessment paradigm (IRAP) is a protocol that uses automated intermittent-flow respirometry to assess the respiratory phenotype in fish species that has been refined over time (Zhang et al., 2016; Zhang et al., 2018; Zhang et al., 2017, Zhang et al., 2019). I used IRAP for all my experiments and the following is an overview of the elements of the protocol that were common to all the data chapters in my thesis. IRAP simultaneously tests eight fish that are housed in individual Loligo®-type (Loligo®Systems, Denmark, loligosystems.com) respirometer chambers. The size of the chamber was matched to the fish size (water volume : fish ratio see Table 2.1) to optimize detection sensitivity for the change in water DO inside of the respirometer due to fish oxygen uptake when the respirometer was in the closed mode (Clark et al., 2013a). All eight chambers were immersed in a temperature-controlled freshwater or seawater bath (volumes found in Table 2.1) which was connected via a pump (Eheim 600) to a gas exchange column that delivered aerated water to the respirometers in the open mode. DO was maintained above 70 % sat throughout an IRAP protocol except during a hypoxia challenge test (HCT). For an HCT, 33 controlled injection of nitrogen rather than air into the bottom of the exchange column progressively decreased DO in the outer bath in a controlled fashion. All fish were fasted for at least 2 days before being placed in the respirometer (Table 2.1), where á¹O2 was continuously monitored for at least 3 days during three states (Figure 2.1). The IRAP protocol started by individually chasing a fish to exhaustion. By monitoring post-exhaustion á¹O2 over a 16-h recovery period under normoxic conditions, estimates of á¹O2max and EPOC with DO were generated. Monitoring of á¹O2 was then continued in an undisturbed, normoxic fish for at least a 2-day quiescent period (Table 2.1) to generate a valid estimate of SMR and some behaviour-related traits (e.g. RMR). The IRAP protocol ended with an HCT to assess hypoxia tolerance by estimating O2crit and determining ILOS (the DO at a loss of dorso-ventral equilibrium (Claireaux et al., 2013) (Figure 2.1). Fish were typically removed immediately from the respirometer chamber, revived in fully aerated seawater, and placed in a recovery tank for assessment prior to being returned to the holding tank (> 95% survival for all experiments) unless they were euthanized for tissue samplings. The chase-to-exhaustion methodology varied somewhat among the chapters (see Table 2.1). In chapters 4 and 6, fish were individually chased to exhaustion by hand in a 20 L bucket and given a standardized 1-min air exposure during the transfer to the respirometer. In Chapters 3 and 5, fish were individually exhausted within the respirometer using a customized chasing device (a 14-cm flexible plastic flapper at the mid-point of the chamber). This new chasing method improved IRAP by providing continuous, on-line monitoring of á¹O2 while the fish was chased to observe the peak in á¹O2 and eliminated the need for air exposure (Zhang et al., 2020). The HCT was the same for all chapters. Each flushing cycle with deoxygenated water made the fish progressively hypoxic. Water DO in the outer bath was reduced over a 45 min 34 period to a moderate level of hypoxia for that species (i.e. about twice O2crit; Table 2.1). The rate of deoxygenation then slowed (0.15 ± 0.02% sat. min-1) until the fish lost its dorso-ventral equilibrium when the protocol was terminated. Background respiration of each empty respirometer chamber was measured for 20 min at the end of each IRAP trial and found to be negligible relative to SMR in Chapter 4 (< 1%; 7 °C well water) & in Chapter 6 (< 1%; 11 °C disinfected seawater). In Chapter 5 (16 or 25 °C seawater), background respiration exceeded this 1% threshold and the fishâs á¹O2 was corrected by subtracting the background á¹O2 value. Background respiration was minimized by thoroughly disinfecting the entire apparatus with either Virkon Aquatic (10 g lâ1; Syndel Canada; www.syndel.ca) for a minimum of 6 h (Chapter 4 & 6), or diluted (10%) household bleach (Chapter 5) for 30 min between IRAP trials. 2.3 Calculation á¹O2 and other IRAP indices á¹O2 was continuously and automatically monitored on-line using computer software (AquaResp v.3, Denmark, Aquaresp.com) that processed water DO measurements in the respirometers (a 1 Hz sampling rate) from an optical oxygen probe associated with each respirometer (Robust Oxygen Probe OXROB10, Pryoscience, pyro-science.com). The optodes were calibrated to 0% saturation (water saturated with sodium sulphite and bubbled with nitrogen gas) and 100% saturation (fully aerated water) at the start of each experiment (PyroScience Manual-Oxygen Sensor Fiber-optic & Contactless, V1.014). á¹O2 values were calculated for 60-120 s periods whenever the respirometer was sealed (periods lasting 3-10 min; Table 2.1). Details of the measurement cycles (flush period, stabilization period and sealed period) are found in Table 2.1. The slope of the decrease in DO over time met a minimum requirement for linearity (i.e., R2 > 0.9) to calculate á¹O2. 35 Maximum oxygen uptake (á¹O2max) was assigned to the highest á¹O2 as á¹O2max (provided R2 >0.9), which often, but not always, occurred during the first measurement cycle post-exhaustion. In Chapters 3 & 5, the á¹O2 values were calculated by a sequential interval (60â120 s) regression algorithm using AquaResp v.3 software (Table 2.1). In Chapters 4, 6 & 7, á¹O2max was calculated after a conventional exhaustive chase outside the respirometer protocol using Eqn 2.1. In Chapters 3 & 5, á¹O2max was calculated off-line after chasing the fish inside the respirometer using an iterative algorithm coupled with an objectively determined minimum and valid sampling window duration (Table 2.1) using Eqn. 2.2 (calculated in LabChart v8.1). Peak á¹O2 was better identified using the iterative algorithm with a rolling regression that repeatedly advanced the sampling window by a single DO measurement through the entire measurement period when the respirometer was closed, and the algorithm selects the maximum á¹O2 with R2 > 0.95 (Zhang et al., 2020). The variance of the background á¹O2 (~20 min) of the respirometer was examined as a function of the duration of the sampling window to assign a valid minimum sampling window for this off-line analysis. The mean, S.D. and C.V. of the background á¹O2 were calculated over sampling window durations between 0.5 min and 5 min. The stable C.V. and S.D. were the principal criteria used to select a valid minimum sampling window duration for the peak á¹O2 calculation. Secondary criteria were an R2 > 0.95 (a higher standard than the manufacturerâs software, i.e., AquaResp) and no negative á¹O2 values (which would suggest noise was being measured). á¹O! = $"!"[$,($'()]"+[$,($'()] â &'# â '$) â *%+ (- â .$)0 Sequential interval Eqn. 2.1 36 Peak á¹O! = max 8$"!"[,,(,'()]"+[,,(,'()] â &'# â '$) â *%+ (- â .$)0 9 Rolling regression Eqn. 2.2 Where units for á¹O2 are mg O2 h-1 kg-1, :&% :'0 is the change in O2 saturation over time, Vr is the respirometer volume, Vf is the assumed fish volume, So is the solubility of O2 (calculated by AquaResp v.3 software) in the experimental temperature, salinity and atmospheric pressure, t is a time constant of 3600 s (per hour), Mf is fish mass, a is the sampling window duration (s), i is 1 DO sample forward from the end of previous sampling window, and n is 1 DO increment from the first DO measurement at a set sampling frequency of 1 Hz, albeit the frequency sometimes varied with computer recording capacity. The application of an iterative algorithm (Eqn. 2) is a recent advancement in aquatic respirometry (Rummer et al., 2016; Harianto et al., 2019; Morozov et al., 2019). My contribution is merely setting a valid minimum sampling window for an iterative algorithm to capture meaningful dynamics in á¹O2 that might otherwise be missed (Zhang et al., 2019). SMR, the minimum maintenance metabolic rate of an intact fish, was calculated with an established quartile method (Chabot et al., 2016), which reports SMR as the 20th percentile (q0.2) of all á¹O2 values recorded for two diurnal cycles after the first 12-h recovery period. Typically, SMR was estimated from > 300 á¹O2 values (Table 2.1) and therefore provided a valid baseline for calculating other IRAP indices (AAS, FAS, total EPOC and O2crit). Aerobic capacity was assessed using two derived indices: absolute aerobic scope (AAS = á¹O2max â SMR, Claireaux et al., 2005); and factorial aerobic scope (FAS = á¹O2max / SMR; Clark et al., 2005). Excess post-exercise oxygen consumption (EPOC), a measure of effort during exhaustive swimming, is the sum of all á¹O2 values subtracting SMR plus 10% during the entire recovery 37 EPOC period â until the time when á¹O2 first returned to SMR plus 10% (Zhang et al., 2018). This strategy reduced the overestimate of EPOC and its duration due to spontaneous fish activity (Zhang et al., 2016). The speed of recovery from exhaustion was directly assessed by EPOC duration (EPOCdur), which is the time (in hours) for á¹O2 values to reach a quantity of SMR plus 10% beginning from the end of the exhaustive chase. Routine activity and agitation in the respirometer were assessed over two diurnal cycles after the recovery period with three indices. Routine metabolic rate (RMR) was the overall average á¹O2 during this period. Time spent above 50% or 80% of AAS (T0.5AAS or T0.8AAS) was the percentage of á¹O2 values above 50% or 80% á¹O2max during this period (Zhang et al., 2019). All the IRAP metrics mentioned in this paragraph were derived from the á¹O2 that are directly calculated from AquaResp software using the conventional sequential algorithm. The quality of PO2 traces was checked as described by (Chabot et al., 2020). The HCT generated six indices to assess anaerobic performance and anaerobic capacity (Figure 2.2). The slower phase of progressive deoxygenation generated at least 10 á¹O2 data points before O2crit and then ILOS were reached, the DO saturation at which dorso-ventral equilibrium was lost (Claireaux et al., 2013). A linear regression applied to these data allowed off-line determination of O2crit (the DO when the regression line intercepts SMR; Claireaux & Chabot, 2016) and the slope of the linear regression line (O2crit slope) when á¹O2 is below O2crit. Slope validation required that the line did not pass below any á¹O2 recorded in normoxia and the intercept at the y-axis was negative (Claireaux & Chabot, 2016). Three derived indices were: scope of oxygen deficit (SOD = O2crit â ILOS); factorial scope of oxygen deficit (FSOD = O2crit / ILOS) and accumulated oxygen deficit (AOD = â O2crit â ILOS'()*"'(0 ), as the area bounded by the SMR and observed á¹O2 curve over the time interval of O2crit and ILOS (Zhang et al., 2017). 38 2.4 Experimental animals Chapter 3 tested a hatchery-reared, river-dwelling strain (wild Blackwater strain, Blackwater River, British Columbia) of rainbow trout (Oncorhynchus mykiss, Walbaum 1792), a well-studied teleost species in ecology and physiology, with a history as a model in fish biology and exercise physiology. They were obtained from Fraser Valley Trout Hatchery (Abbotsford, British Columbia, Canada; Freshwater Fisheries Society of BC) and held at 14 oC in 200-L circular tanks containing dechlorinated Vancouver tap water in the Zoology Aquatic Facility. Water temperature was monitored daily and fish were fed a daily maintenance ration of commercial trout pellets (Skretting Canada Inc., Vancouver, BC, Canada). Animal holding and experimental procedures were approved by the University of British Columbia Animal Care Committee (A16-0049, A15-0035 and A15-0266). Chapter 4 tested three strains of rainbow trout (A, B and C) reared by Miracle Springs Inc. (Mission, British Columbia, Canada). Rainbow trout is a good candidate species in which to study intraspecific variations of respiratory traits because several of these traits have been independently demonstrated to differ among strains of this species (e.g., Carline & Machung, 2001; Scott et al., 2015; Chen et al., 2015). Strain A had been batch-spawned on October 25th, 2015 and November 8th, 2015 using eggs pooled from 5 females fertilized with milt from 5 males on each date (10 females and 10 males total). Strain B had been batch spawned on November 17th, 2015 in 6 groups using eggs pooled from 3 females fertilized with milt from 4 males for each group and December 3rd, 2015 in 2 groups using eggs pooled from 6 females fertilized with milt from 3 males for each group (30 females and 30 males total). For Strain A and B, eggs were incubated in Heath trays with flow-through well water at a temperature of 8.0 °C until hatch (336 degree days). Strain C was spawned on December 31st, 2015 as single-pair crosses, with eggs 39 from each female split in half and each half was fertilized with milt from a different male (20 females and 20 males total). Strain C was crossed and initially reared at a commercial aquaculture facility in Rochester, Washington, the U.S.A. at a temperature of 10 °C. The fry from these crosses were transported to Miracle Springs Inc. at the end of April 2016. Fry from all three strains were reared for 8 months at Miracle Springs Inc. in circular holding tanks (0.2 m3) under natural photoperiod at 8.3 ± 0.1 °C and fed with the same diet (EWOS, sized appropriately for life-stage; www.ewos.com). Dissolved oxygen saturation was maintained at > 80 % sat. throughout rearing. Mean holding densities for the three strains were 2.4 ± 0.1 kg m-3 in April 2016 and 7.5 kg m-3 in January 2017 (immediately prior to the start of experiments). All experimental trials were conducted at Miracle Springs Inc. in 2017 between January and February while the fish were fed a maintenance diet (ration: 0.5% of body mass over 5 days). Prior to any experimental trial, fish were fasted for 120 h to reach a post-absorptive stage (Eliason & Farrell, 2014). Animal holding and experimental procedures adhered to an approved UBC animal care protocol (A16-0329). Chapters 5 & 7 tested juvenile European sea bass (Dicentrarchus labrax, Linnaeus 1758), a valuable species for recreational and commercial fisheries in Western Europe and a popular aquaculture species in Southern Europe and Mediterranean areas. A cohort of European sea bass (N = 80) was acclimated to laboratory conditions in a 2000-L indoor tank for 2 months, during which they were fed ad libitum twice weekly (Le Gouessant, Lambale, France) and subcutaneously implanted with a radio frequency identification tag after anesthesia (MS-222; 100 mg Lâ1), i.e., seven weeks before the hypoxia acclimation experiment started. The other cohort of juvenile European sea bass were distributed between two 500-L tanks (sub-optimal & optimal temperatures: N = 30 & 30). These tanks were supplied with open flow 40 seawater. Six weeks before the temperature acclimation period started, water temperature increased from 16 °C to 25 °C (the thermal optimum) over 7 days. Fish were then maintained at their respective acclimation temperatures for at least 4 weeks prior to the first respirometry assessment at 25 °C. Fish were fasted for 48 h prior to the IRAP protocol. After completing the IRAP, all fish were retrieved in a fully aerated seawater and placed in a recovery aquarium before being returned to the holding tank after 1 h. The second respirometry assessment occurred 4 weeks after the completion of the first assessment. Chapter 6 tested Atlantic salmon (Salmo salar, Linnaeus 1758), the most extensively cultured salmon species worldwide, and sockeye salmon (Oncorhynchus nerka, Walbaum 1792), the iconic salmon species in the Pacific Northwest. A cohort of the domesticated Mowi-McConnell strain of Atlantic salmon (Withler et al., 2005), developed specifically for use by the British Columbia aquaculture industry, was obtained from a commercial hatchery on Vancouver Island, Canada. A Pitt River strain of wild sockeye salmon was obtained from a restocking hatchery (Pitt Meadows, BC, Canada). All fish were transported to the Pacific Biological Station (a research station of Fisheries and Oceans Canada), where the infection studies were conducted under the permission and guidelines of the Canadian Council on Animal Care (CCAC) Pacific Region Animal Care Committee (no: 16â019). Pre-transport screening of 20 fish found no PRV infections using quantitative PCR (qPCR) and no other virus was propagated on CHSE-214 or EPC diagnostic fish cell lines. Both species were reared for 4 months in municipal dechlorinated freshwater at 8°C before being transitioned over four weeks to sand-filtered, UV-irradiated, seawater (28-32 ppt) at 11°C ± 0.5°C, a water temperature previously shown to be associated with PRV infections in both laboratory and field environments (Kongtorp et al., 2006; Wessel et al., 2017). Both species were divided equally into six circular experimental tanks (350 L each 41 tank; 40 fish per tank) supplied with 15 L per min flow-through, sand-filtered, UV-irradiated 11°C seawater and held for 10-20 days (Atlantic salmon) or 30-40 days (sockeye salmon) prior to virus injection and the start of experimentation. Experiments began shortly after seawater transfer was completed because of a belief that Atlantic salmon most often develop heart and skeletal muscle inflammation (HSMI) at this time (Lovoll et al., 2012) and wild sockeye salmon first encounter piscine orthoreovirus (PRV) and infectious hematopoietic necrosis virus (IHNV) when they migrate to the sea (Rudakova et al., 2007; Morton et al., 2017). Fish were fed EWOS dry pellet rations at 0.5% body weight per day throughout the study and DO was maintained above 90% air saturation (> 8.2 mg L-1). The Atlantic salmon (N = 25; body mass by bulk weight estimate: ~75 g) were tested in March-July 2017 and the sockeye salmon (N = 25; body mass by bulk weight estimate: ~110 g) were tested in September-December 2017 using the same experimental procedures were used unless otherwise was stated. 2.5 Statistical software and assigning significance Significances were assigned when α < 0.05. All analyses were conducted using SPSS v. 26 (IBM Corp., USA, ibm.com) and Prism v.8 (GraphPad Software, USA, graphpad.com). Respirometry data analyses were performed in R v.3.3.3 software and R studio (RStudio team, 2015) using the fishMO2 package (Chabot et al., 2016). Analyses performed in each data chapter are specified in the statistical analyses section of that chapter. 42 Table 2.1 Summary of key parameters in intermittent-flow respirometry system and the key variables in the protocol of Integrated Respiratory Assessment Paradigm (IRAP) in each data chapter. Ch.3 á¹O2max O. mykiss Ch.4 O. mykiss Ch.5 D. labrax Ch.6 S. salar Ch.6 O. nerka Ch.7 D. labrax Fasting period before IRAP assessment (h) 48 120 48 72 72 48 Outer bath volume (L) 768 768 500 768 768 500 Size of respirometer (L) 2.3 2.3 2.3 2.3 4.2 2.3 Chamber to fish ratio 33:1 33:1 35:1 27:1 38:1 35:1 Testing temperature (â¦C) 14 ± 0.5 7.4 ± 0.5 16 ± 0.5 11 ± 0.5 11 ± 0.5 25 ± 0.5 Total IRAP duration (Days) N.A. 4.5 3 4.5 4.5 3 Experimental period to acquire SMR (Days) N.A. 3.5 2 3.5 3.5 2 Chasing period outside respirometers (min) 19.7 ± 2.1 6 N.A. 7 6 N.A. Air exposure outside respirometers (min) N.A. 1 N.A. 1 1 N.A. Chasing period inside respirometers (min) 26.7 ± 2.2 N.A. 10 N.A. N.A. 10 á¹O2max sampling window duration (s) 60-90 110 120 110 110 120 Protocols of hypoxia challenge test N.A. Reach 30 % sat. in 90 min & 0.15 ± 0.02 % sat. min-1 Reach 31 % sat. in 45 min & 0.15 ± 0.02 % sat. min-1 Reach 40 % sat. in 45 min & 0.15 ± 0.02 % sat. min-1 Reach 40 % sat. in 45 min & 0.15 % ± 0.02 sat. min-1 Reach 40 % sat. in 45 min & 0.15 ± 0.02 % sat. min-1 Flush-stabilization-measure (s) for á¹O2max N.A. 25-70-110 120-60-420 25-70-110 35-50-110 120-60-420 Flush-stable-measure (s) for SMR N.A. 50-115-345 120-60-420 50-115-345 65-110-345 120-60-420 Flush-stable-measure (s) in hypoxia N.A. 50-85-225 120-60-420 50-85-225 50-85-225 120-60-420 Sampling window in sequential interval regression (s) N.A. 110 N.A. 110 110 N.A. Sampling window in iterative algorithm (s) 60 N.A. 126 N.A. N.A. 126 Amounts of data points to calculate SMR N.A. ~ 830 > 300 ~ 600 ~ 600 > 300 Transitory DO from fast to slow ramp (% sat.) / O2crit (% sat) N.A. 2 2 2 2 2 43 0 15 30 45 60 750100200300400500Time (h)á¹O2 (mg O2 h-1 kg-1 ) Chased toexhaustionRecoveryUndisturbed Hypoxiachallengetestá¹O2max : Maximum capacity to take up O2SMR: Minimum O2 needsAbsolute aerobic scope:(AAS = [á¹O2max- SMR])Agitation: Time spent at > 50% AAS (or any %)Routine life: Daily energy expenditure (DEE)O2 consumedduring recovery(EPOC)DO for critical O2saturation (O2crit)DO for incipient lethal O2 saturation (ILOS) 44 Figure 2.1 Comprehensively characterizing a fishâs aerobic & hypoxic respiratory phenotype. A representative trace is measured by an Integrated Respiratory Assessment Paradigm (IRAP). Fish were first chased to exhaustion to measure maximum oxygen uptake (á¹O2max) in an intermittent-flow respirometer. á¹O2 during the post-exercise recovery was measured as excess post-exercise oxygen consumption (EPOC) and duration of EPOC (EPOCdur) was also calculated (see Figure 2.2 for details). Over the two diurnal cycles approximately > 300 á¹O2 values were recorded to have a valid amount of sample size to estimate standard metabolic rate (SMR) using the 20th percentile. During this quiescent period, behavioural activities (agitation) were estimated with time spend above 50% or 80% AAS (T0.5AAS or T0.8AAS). Also, all the 300 á¹O2 values were averaged to estimate routine metabolic rate (RMR) and total oxygen consumed as daily energy expenditure (DEE). IRAP concluded with a standardized hypoxia test (Claireaux et al., 2013) to estimate critical oxygen saturation (O2crit) as derived from SMR and the incipient lethal oxygen saturation (ILOS) at which loss of dorso-ventral equilibrium was observed. The demonstration for calculating the derived indices of estimate anaerobic capacity is in Figure 2.2. Part of the IRAP trace measured for estimating the respiratory traits were noted with colour coded labels. Cartons demonstrate the physiology stated of fish in human equivalence when the traits are measured. 45 Figure 2.2 Respiratory traits to estimate anaerobic capacity. Representative traces for oxygen uptake rate (á¹O2) versus time (h) displaying the areas (shaded grey) used to calculate excess post-exercise oxygen consumption (EPOC; a) and accumulated oxygen deficit (AOD; b), with the additional scope for oxygen deficit (SOD) and factorial scope for oxygen deficit (FSOD). The red regression line went through the data points used for determining the linear regression of determining critical oxygen saturation (O2crit). 0 2 4 6 8 10Time after exhaustive chase (h)050100150200250300350Mâ O 2 (mg O 2 hâ1 kgâ1 )EPOCSMRq0.2SMRq0.2+10%Observed MO2a0 1 2 3 4Time of hypoxic exposure (h)050100150200250Mâ O 2 (mg O 2 hâ1 kgâ1 )AODSMRq0.2O2critILOSObserved MO2bScope for oxygen deficit (SOD) = O2crit â ILOS Factorial Scope for oxygen deficit (SOD) = O2crit / ILOS 46 Chapter 3: Improving the validity of estimating maximum oxygen uptake 1 3.1 Synopsis Chapter 3 studied the dynamics of active á¹O2 in rainbow trout that were either swam to fatigue or chased to exhaustion with the intention of improving á¹O2max determinations. To do so, I needed a standardized methodology for establishing a minimum sampling window for an á¹O2 determination, one that reliably estimates signal to noise ratio. I used an analysis of the background respirometer á¹O2 for this purpose. I also invented a protocol to chase a fish to exhaustion while measuring á¹O2, eliminating the possibility of missing peak á¹O2. These technical and analytical improvements then allowed me to test the hypothesis that estimates of á¹O2max are independent of the testing protocols, a topic that is still actively debated. In support of my hypothesis and provided peak á¹O2 was determined with an iterative algorithm and a minimum sampling window, peak á¹O2active measured during swimming-to-fatigue was the same as peak á¹O2chase measured during chase-to-exhaustion inside a respirometer. Moreover, these á¹O2max values were repeatable for the same individuals. In contrast, a conventional interval regression algorithm could underestimate á¹O2max by 23% or 31% in swim-tunnel or static respirometers, respectively. Thus, my new analytical approach and a more valid chase-to-exhaustion protocol bring a higher precision and accuracy to estimate á¹O2max in fish. 1 Chapter 3 is a synthesis of two previously published articles (Appendix E.1): I) Y. Zhang, M. J. H. Gilbert, A. P. Farrell (2019) Finding the peak of dynamic oxygen uptake during fatiguing exercise in fish. Journal of Experimental Biology. 222, jeb196568. II) Y. Zhang, M. J. H. Gilbert, A. P. Farrell (2020) Measuring maximum oxygen uptake with an incremental swimming test and by chasing rainbow trout to exhaustion inside a respirometry chamber yields the same results. Journal of Fish Biology. DOI: 10.1111/jfb.14311 47 3.2 Introduction The maximum rate of oxygen uptake (á¹O2max) is widely used to evaluate cardiorespiratory fitness for human health (e.g., Bonomi et al., 2020), to assess ecological performance in ectotherms (e.g., Pörtner & Farrell, 2008), and to study the divergent evolution of oxygen transport cascade system in different environments (e.g., Lague et al., 2017). Among these disciplines, human kinesiology has developed the most standardized practices to reliably and reproducibly measure á¹O2max, which is typically assessed by a graded exercise until fatigue is reached (Albouaini et al., 2007; Dlugosz et al., 2013) after oxygen uptake reaches a peak and plateaus despite a further increase in the imposed workload (Taylor et al., 1955). Yet, debate continues over the best testing protocol (Robergs et al., 2010; Beltz et al., 2016; Poole and Jones 2017; Van Breda et al., 2017; Green & Askew, 2018) to capture this transient peak while assuring reproducibility. The same challenge is shared when estimating á¹O2max in fish, as indicated in recent reviews of testing protocols (Clark et al., 2013; Norin & Clark, 2016; Zhang & Gilbert, 2017). The gold standard for estimating á¹O2max in fish similarly uses graded exercise tests and dates back over 70 years (Fry & Hart, 1948; Brett, 1964). A critical swimming speed (Ucrit) test incrementally increases swimming velocity until the fish fatigues. The duration of each velocity increment is currently 20â75 min (i.e., prolonged swimming) to assure a steady-state to estimate a maximum sustainable swimming speed and á¹O2 while swimming against a constant workload (i.e., water velocity; U) after an initial adjustment period to a new U. However, swimming gait changes to incorporate more bouts of burst swimming as a fish approaches its Ucrit because the capacity of aerobic red muscle fibres to sustain a steady swimming speed has been exceeded; thus the assumption of steady-state breaks down (Rome et al., 1985; Peake & Farrell, 2004, 48 2005, 2006). Because á¹O2 when fish is in an active swimming state (á¹O2active) is expected to become more dynamic as swimming gait shifts during the final speed increment (Ufinal), the peak in á¹O2 (á¹O2peak) may be more transient than previously thought. Also, the duration of Ufinal is often much shorter than for the slower velocity increments. Despite these theoretical concerns, no previous fish study has attempted a high-resolution measurement of á¹O2active to examine á¹O2 dynamics when swimming gait changes near Ufinal, which is one objective of the present chapter. Historically, high-resolution measurement of á¹O2active during closed swim tunnel respirometer was not possible simply because measuring dissolved oxygen (DO) in water required many minutes. The decline in DO could be followed for 30â60 min for a single estimate of á¹O2active using the Winkler method (Wells, 1932; Fry & Hart, 1948; Brett, 1964), periods that greatly exceed brief bouts of burst swimming. Fiberoptic oxygen sensors (optodes) have now lifted such constraints with their fast response time (<15 s) and a high measuring frequency (â¤ 1 s-1; pyroscience.com/index.html), which is much faster than the original Winkler method (Winkler, 1888; Krogh, 1914; Keys, 1930) and likely the subsequent Clark-type electrodes (formerly a 2â5 min response time, but more recently â¤ 20 s; Qlark, 1956; Steffensen et al., 1984; Bushnell et al., 1994; Gatti et al., 2002; Glazer et al., 2004; Tengberg et al., 2006). Thus, optode technology provided me with the instrument to examine the dynamic nature of á¹O2peak during a Ucrit protocol. An accepted alternative approach for measuring á¹O2max in fishes is the chase-to-exhaustion protocol, which rapidly transfers an exhausted fish to a respirometer and follows á¹O2 during the recovery period (á¹O2rec) (e.g. Roche et al., 2013; Svendsen et al., 2014). However, á¹O2rec initially decays very rapidly post-exhaustion (Figure 3.1), which introduces the danger of missing á¹O2peak, especially during fish transfer into the respirometer from the chasing aquarium. 49 To compensate, a standardized air exposure (1â2 min) during the fish transfer is often used in an attempt to delay the peak á¹O2rec (Table 3.1). Despite these two accepted protocols, an on-going debate in fish biology centres on whether the swim-to-fatigue Ucrit protocol or a chase-to-exhaustion protocol provides the most valid estimate of á¹O2max (Reidy et al., 1995; Sylvestre et al., 2007; Roche et al., 2013; Rummer et al., 2016; Killen et al., 2017; Hvas & Oppedal, 2019; Slesinger et al., 2019). While some of the debates focus on the inability of certain fishes to perform a Ucrit test, no-one has considered whether peak á¹O2rec is missed during fish transfer, which was my second objective in this chapter. To do so, I tested a novel protocol of chasing rainbow trout to exhaustion inside the respirometer while measuring á¹O2 during the imposed workload (á¹O2chase). I predicted that á¹O2chase is higher than á¹O2rec. Moreover, by testing the same fish with swim-to-fatigue Ucrit protocol and a chase-to-exhaustion protocol, a repeated-measures design would improve the statistical power to test the hypothesis that estimates of á¹O2max can be independent of the testing protocol provided the methodology used for determining peaks in á¹O2 is valid. Independent of the testing protocol, the validity of measuring the dynamics of á¹O2 is ultimately influenced by the duration of the sampling window for the measurement, given that water is well mixed in the respirometer. Too long a sampling window would underestimate á¹O2active, an averaging effect. Indeed, no criteria have been established to set a minimum sampling window to reliably measure á¹O2 dynamics without introducing measurement noise. For example, sampling windows used with a chase-to-exhaustion protocol vary by 120-fold, from 0.5 min to 60 min (Ern et al., 2017; Lucas & Priede, 1992). Therefore, my third objective was to innovate a standardized method to optimize a ratio of á¹O2 signal against instrumental noise so that á¹O2max could be more reliably assigned. To do so, I applied I) various statistical 50 approaches to analyze the background á¹O2 of the respirometer to determine a valid minimum sampling window; II) an iterative algorithm rather than a conventional sequential interval regression algorithm to better resolve á¹O2 dynamics. 3.3 Material and Methods 3.3.1 Experimental design Trials were conducted with 14 fish (body mass: 69.1 ± 3.2 g) that were fasted for 48 h. Using a repeated-measures experimental design, the swimming-to-fatigue and chasing-to-exhaustion protocols were performed exactly 1 week apart (an ample recovery time) with half of the fish tested first with a fatigue swimming test and the other half tested first with an exhaustive chase test. Each fish was chased to exhaustion outside the static respirometer and then, after recovering for a minimum of 20 h inside the respirometer, it was re-chased to exhaustion while inside the respirometer. Thus, peak á¹O2chase, peak á¹O2rec and peak á¹O2active were measured on each fish. 3.3.1.1 Ramp-critical swimming speed (ramp-Ucrit) protocol A fish was placed in a 10-L swim-tunnel respirometer (Loligo Systems, Tjele, Denmark; water volume to fish mass ratio =144 : 1) in a thermally regulated laboratory (14 ËC) where it habituated for 1 h to the swimming section (40 Ã 10 Ã 10 cm) at a water velocity (U) of ~0.5 body length (bl s-1). The swim tunnel was shaded with an opaque curtain to minimize visual disturbances through the transparent swim section of the respirometer. A ramp-Ucrit protocol (Jain et al., 1997) increased U up to ~40% of Ucrit (4 cm s-1 every 1 min for 5 min), after which U was increased in the increments of 9 cm s-1 (i.e., ~10 % of Ucrit) every 20 min until the fish reached a fatigue velocity (Figure. S3.1), as previously recommended (Farlinger & Beamish, 1977; Hammer, 1995). A fish was deemed fatigued when it remained on the rear grid of the swimming section for > 5 s. A ramp-Ucrit protocol assumes that salmonids can swim aerobically 51 for sustained periods at < 50% Ucrit (Beamish, 1978; Hammer, 1995). Following the ramp, the present study had 5â7 U increments of 20 min duration before fish became fatigued; the total test duration was < 130 min. Validation of the ramp-Ucrit protocol (and its earlier versions Randall et al., 1987; Brauner et al., 1994) has been performed for wild and hatchery-reared salmonids in field and laboratory conditions (Farrell et al., 1998; Jain et al., 1998; Lee et al., 2003a, b). Water DO (expressed as % air saturation) was measured (~1 Hz sampling frequency) with a fibre-optic oxygen probe (Robust Oxygen Probe OXROB10, PyroScience GmbH, Aachen, Germany) mounted in the center of the flow straightening vanes ahead of the swimming section to ensure proper water mixing. A flush pump (Universal 2400, EHEIM GmbH & Co KG, Deizisau, Germany), attached via a one-way outflow valve to the swim tunnel, was manually controlled to keep DO > 80% each time the respirometer was closed for a á¹O2 measurement period, which varied depending on the imposed U. Flushing the swim tunnel was deliberately avoided during the transition to the Ufinal so the dynamics of á¹O2active could be followed immediately after transition to the Ufinal. Swim behaviour was recorded with an infrared camera (1000TVL, Zosi Technology, Hong Kong, China) mounted ~1 m above the swim tunnel and was semi-quantitatively analyzed using a behavioural event logging software (BORIS; Friard & Gamba, 2016). Four mutually exclusive swimming modes were scored to account for all observed swimming behaviours, extending an earlier semi-quantitative assessment (Farrell & Clutterham 2003). They were: 1) maintaining station (M) using a steady swimming mode and with only minor drift; 2) burst-and-coast swimming (B) involving repeated cycles of forward progress and backwards drift; 3) intense burst swimming (I) where a fish sustained its swimming effort against the grid at the front of the swim section without gaining any forward ground speed; and 4) resting (R) when a 52 fish ceased swimming activity and drifted back against the rear grid and remained there for <5 s, otherwise the Ucrit protocol stopped and Ucrit was assigned {Ucrit = U + [(t/ti) Ã Ui], where U is the penultimate speed a fish can maintain, Ui is the speed increment, ti is the time interval for a speed increment, and t is the time swam in the Ufinal increment}. The total time spent in each of these four swimming modes is reported as a proportion of the 20-min swimming period at that U. 3.3.2 Chase-to-exhaustion protocol A 2.25 l, Loligo®-type static respirometer chamber (water volume : fish ratio = 33 : 1; Loligo®Systems, Denmark, loligosystems.com) was modified by adding a chasing device (a soft brush with a 12-cm metal stem bent at a right angle and sealed through a 1.5 cm diameter port on top of the respirometer at the mid-point; Figure 3.1). Turning this device induced the fish to perform turning, darting and burst swimming behaviours until it became exhausted and refractory to agitation. The respirometer was submerged in a water reservoir (2.4 Ã 0.8 Ã 0.4 m) that was fully aerated throughout the trial and held at 14 ËC (i.e., their holding temperature). Freshwater was replaced daily to limit bacterial growth. A flush pump (Universal 3400, EHEIM, eheim.com), controlled by a computer (AquaResp v.3, Denmark, Aquaresp.com), intermittently circulated water from the reservoir through the respirometer throughout the experiment. DO was continuously recorded (~1 Hz sampling frequency) in an external recirculation loop, equipped with a separate pump (Universal 600, EHEIM, eheim.com; for effective mixing inside the respirometer) and containing an optical oxygen probe (Robust Oxygen Probe OXROB10, response time < 15 s, Pryoscience, pyro-science.com). The testing protocol involved first individually chasing a fish to exhaustion outside the respirometer by tail pinching in a 50-l cooler that had space for burst swimming until it became refractory for >5 s (19.7 ± 2.1 min). The exhausted fish was then quickly transferred to the 53 respirometer within 10 s without air exposure (i.e., immersed in a 1 l container). The exhausted state was maintained by re-chasing the fish inside the respirometer for 30 s with the chasing device during the sealing of the respirometer. Post-exercise á¹O2 measurements began thereafter (Figure 3.1) and followed for a minimum of 20 h to ensure recovery to ~SMR plus 10% (Zhang et al., 2018a). The fish was then re-chased to exhaustion without removing it from the respirometer while á¹O2 was monitored on-line in real time (á¹O2chase), as calculated every 0.5-min by AquaResp v.3 (Denmark, Aquaresp.com) with a sequential interval regression analysis (R2 = 0.98 ± 0.01). During this chase, the periodic burst swimming exponentially increased á¹O2 over the first ~5 min before the elevated á¹O2 fluctuated and showed transient peaks of á¹O2 over a further ~15 min (Figure 3.1) until the fish became refractory to touch and á¹O2chase decreased. Chasing lasted 26.7 ± 2.2 min and produced at least one repeated peak in á¹O2, typically appearing 5â20 min after the chase started. The respirometer was also manually flushed to maintain water DO â¥ 80% saturation. 3.3.3 Data analysis 3.3.3.1 Determining the minimum duration of a sampling window for a valid á¹O2 measurement The background á¹O2 of a swim-tunnel respirometer provides a reasonably stable á¹O2 for a 20-min period and is routinely recorded in fish respirometry. Thus, this very low signal-to-noise ratio á¹O2 can provide a conservative estimate of the shortest sampling window duration (peak á¹O2active is at least 70-times larger). The á¹O2active of a fish swimming steadily at an intermediate water velocity (e.g., 50% Ucrit) provided a much larger signal for such analysis but risked more inherent noise from the water flow. I used both signals to determine the minimum duration of a sampling window for a valid á¹O2 measurement, i.e., the minimum variance around the á¹O2 54 estimate. Five independent, 20-min DO traces of background á¹O2 were tested using sampling windows duration from 0.8 min to 5.0 min, which generated 25 to 4 estimates, of the same background á¹O2 signal with the same inherent noise. For each sampling window duration, the mean, standard deviation (S.D.) and coefficient of variation (C.V.) and coefficient of determination (R2) were determined for the background á¹O2 signal. Likewise, for á¹O2active at ~50% Ucrit, sampling window durations of 0.20 min to 1.5 min generated 76 to 10 estimates, respectively, of the same á¹O2active for similar statistical analysis. á¹O2 data were logarithmically transformed to achieve normality and homogeneity of residuals and analyzed with a one-way ANOVA with Tukey post-hoc tests to compare the mean, S.D., C.V. and R2 of the pooled data as a function of sampling duration. 3.3.3.2 Dynamic á¹O2 and peak á¹O2active determinations Detailed analysis (n=14 fish) of the dynamic nature of á¹O2 was performed at Ufinal unless Ufinal was too short for full analysis of the sampling window durations, in which case the penultimate step was used for the analysis (see Figure 3.4) á¹O2active increased with U (Appendix A.1, Figure. S3.1) and changes in swimming gait always occurred during Ufinal and the penultimate U. For comparison, a detailed analysis is presented for two representative fish while they swam steadily at an intermediate U and mostly maintained station. All reported á¹O2 values were re-analyzed off-line in different ways and with different sampling window durations, but they were always referenced to the start of the U increment. A conventional sequential interval regression analysis (Eqn. 2.1 in Chapter 2; calculated in Excel v16.3, Redmond, WA, USA) calculated á¹O2active using consecutive sampling windows of 1.5-min, 2.0-min, 2.5-min, 3.0-min, 5-min or 10-min in duration (except when interrupted by a respirometer flush period). The 1.5-min estimates of á¹O2active (the conservative and minimum sampling window) were averaged to generate a ~20- 55 min estimate. The mean R2 for each determination was always > 0.93 regardless of the sampling window duration. An iterative rolling regression (termed an iterative algorithm; Eqn 2.2 in Chapter 2; calculated in LabChart v8.1, ADInstruments, Colorado Springs, CO, USA) that repeatedly advanced by a single DO measurement was also used to measure maximum á¹O2 during the Ucrit test. Peak á¹O2active was assigned to the highest á¹O2active with the minimum sampling window duration. Mean values of peak á¹O2active estimated by various methods were compared with a one-way repeated ANOVA and after logarithmic transformation if necessary, to meet the assumptions of normality of residuals. I predicted the iterative algorithm with a minimum sampling window would generate the highest estimate of peak á¹O2active, in which case, I would reject the null hypothesis that the estimate of á¹O2max was independent of the duration of the sampling window and the calculation method. 3.3.3.3 Statistical analyses For exhaustive chasing and fatigue swimming protocols, supervised repeated-measures ANOVA (mixed-effects model) with a balanced sample size was conducted to individually address each of our a priori objectives regarding the effect of sampling window duration, analytical algorithm and exercise protocol on peak á¹O2 estimates. This statistical approach minimized the likelihood of a type II error. A Shapiro-Wilkâs test and diagnostic plots were used to confirm that the logarithmically transformed data met the assumptions of the statistical test. The ANOVA was followed by paired t-tests for specific comparisons of interest (Table 2) with Holm-Sidak correction of p-values to control for multiple comparisons. The supervised analysis was supplemented with a more global analysis of peak á¹O2 as a function of sampling window, analytical algorithm and chasing protocol (Appendix A.1: Table S3.1). 56 3.4 Results 3.4.1 A conservative minimum sampling window in a swim-tunnel respirometer The average background á¹O2 value was independent of the sampling window duration (0.8â5 min; Figure 3.2). However, variability in the background á¹O2 estimate was clearly dependent on the duration of the sampling window. Both S.D. and C.V of the mean background á¹O2 decreased as sampling window duration increased, with C.V. stabilizing at â¥ 1.5 min, and S.D. stabilizing at â¥ 2.5 min (Figure 3.2a, b). Thus, the minimum sampling window duration for a very low á¹O2 signal was between 1.5 and 2.5 min, depending on whether C.V. or S.D. was used. S.D. and C.V. of the mean for a larger á¹O2active signal while fish was swimming steadily (Figure 3.2c, d) both increased when the sampling window duration was < 1.5 min. This was likely due to a greater level of noise given that spurious, negative á¹O2active values appeared with the 0.5-min sampling window. Also, R2 decreased significantly (p â¤ 0.015) with a sampling window duration of < 0.5 min (Figure 3.2). Therefore, a conservative minimum sampling window for the analysis of peak á¹O2active was 1.5 min. 3.4.2 Determine a valid minimum sampling window in an intermittent-flow respirometer The variability of the background á¹O2 estimate depended on the duration of the sampling window. The range, the C.V., the S.D. of the mean background á¹O2, as well as the variance in R2 for the determination, all decreased as the duration of the sampling window increased. The mean C.V. stabilized at â¥ 1.5 min and S.D. stabilized at > 2.0 min (p > 0.05; Figure 3.3) similar to the analysis of the background á¹O2 of the swim tunnel. Moreover, R2 for the linear regressions was always > 0.95, even with a 0.5-min sampling window duration. Therefore, while I used 1.0 min as my minimum sampling window to determine peak á¹O2active in the present 57 study (Figure 3.3), I also provided estimates for sampling windows of 0.5â1.5 min for comparison (Table 3.3; Figure 3.5). 3.4.3 The dynamics of á¹O2active near Ucrit and their relationship to burst-and-glide swimming gait á¹O2active of rainbow trout swimming at Ufinal was dynamic despite the constant imposed water velocity and in contrast to the lower, steady á¹O2active associated with a steady swimming gait at an intermediate U (Figure 3.4). The dynamic nature of á¹O2active was best resolved with a sampling window duration of 1.5 min in combination with the iterative algorithm. Importantly, the estimate of peak á¹O2active with a 1.5-min iterative algorithm was higher (p â¤ 0.022) than all the other analytical methods by 6â23% (Table 3.2). Whenever swimming gait varied during Ufinal, á¹O2active usually changed (Figure 3.4). Therefore, I examined semi-quantitatively the association with swimming behaviour. Beyond the individual variation among fish (Figure 3.4), á¹O2 decreased when a fish rested on the rear grid, as might be expected. I also discovered clear spikes in á¹O2active that coincided with the swimming modes that produced (or should produce) a ground speed, i.e., combining burst and intense burst swimming, or transitioning to burst swimming. Therefore, burst-and-coast swimming behaviours likely produced the dynamics in á¹O2active which were also associated with peak á¹O2active. Consequently, swim-tunnel respirometry will underestimate á¹O2max if the dynamic nature of á¹O2active is not accounted for. 3.4.4 Peak á¹O2chase was higher than peak á¹O2rec When analyzed with an iterative algorithm, peak á¹O2chase was 19â23% higher (p < 0.05) than the peak á¹O2rec estimate (Table 3.3), a difference largely independent of sampling window duration (0.5â1.5 min). Notably, an iterative algorithm and a 1-min sampling window produced a peak 58 á¹O2chase that averaged 31% higher (Table 3.3) than the peak á¹O2rec assessed with a conventional interval regression analysis method. 3.4.5 Peak á¹O2chase and peak á¹O2active are the same and repeatable Peak á¹O2chase (663 ± 43 mg O2 h-1 kg-1) determined with an iterative algorithm and a 1.0-min sampling window (R2 = 0.99 ± 0.005) was statistically indistinguishable from peak á¹O2active (707 ± 34 mg O2 h-1 kg-1) determined with an iterative algorithm and a 1.5-min sampling window. Furthermore, á¹O2max values determined in this manner for individual fish were repeatable for the two testing protocols (Figure 3.5; r â¥ 0.77 and p < 0.05 for the Pearsonâs and Spearmanâs correlation tests). However, if peak á¹O2active was determined with a 1.0-min sampling window, the mean C.V. for á¹O2active increased to 22% (Figure 3.5), resulting in an 18% higher mean peak á¹O2active than peak á¹O2chase analyzed in the same manner (p < 0.01; Figure 3.5). Also, individual repeatability was reduced (only the Pearsonâs correlation test remained significant; r = 0.68 and p < 0.05). Therefore, with my testing apparatus, a 1.0-min sampling window was less valid for estimating the peak á¹O2active than a 1.5-min sampling window. Consistent with the analysis of peak á¹O2active, the importance of using an iterative algorithm to estimate peak á¹O2chase was highlighted by the peak á¹O2active estimates being 23â28% greater (p < 0.05) for a 1.0-min sampling window when compared with a conventional sequential interval regression analysis with sampling windows of 0.5â1.5 min (Table 3.2). Even with an iterative algorithm, the estimate of peak á¹O2chase had a higher variance (C.V. = 28%) with a 0.5-min sampling window compared with a 1.0-min sampling (C.V. = 19%) and peak á¹O2chase significantly decreased (p < 0.05) with a 1.5-min sampling window (Figure 3.5). Thus, the minimum sampling window to reliably estimate peak á¹O2chase proved to be 1 min. 59 3.5 Discussion 3.5.1 Minimum á¹O2 sampling window and dynamic á¹O2 Only when experimental protocols and analytical approaches minimize measurement errors can true biological variation and repeatability be revealed. Hence, I explored a new experimental protocol and analytical approaches to improve the accuracy of estimating á¹O2active in fishes, which described á¹O2 dynamics even in a Ucrit swimming test. One of my objectives was to determine a minimum sampling window for an á¹O2 measurement to maximize the resolution of á¹O2 dynamics and avoid underestimating peak values, without unnecessarily introducing noise and variance that might create type II statistical errors. While the minimum sampling window duration could easily change with different testing apparatus and different fish species, the key criteria that I applied here represent recommendations for others to follow for their particular apparatuses and species: C.V. and S.D. should be stable (Figure 3.2 & 3.3), R2 should be > 0.95 and no single á¹O2 value can be negative. These criteria represent an advance on using the R2 value as the sole quality criterion for regression analysis of DO (Svendsen et al., 2016; Chabot et al., 2016). The possibility of underestimating á¹O2active with too long a sampling window duration is then avoided. In the present studies, the variances of the background á¹O2 and á¹O2active signals were minimized when the sampling window duration was 1.0 to 1.5 min. This duration may have technical and biological merits as well. Both the water mixing time in the swim tunnel (e.g. Rodgers et al., 2016) and the blood transit time around the circulatory system of a fish have similar durations (1â2 min: Cameron and Polhemus, 1974; Maren and Swenson, 1980; Hughes et al., 1981; Harter et al., 2018). With a higher measurement resolution, I revealed á¹O2 dynamics, with peaks of á¹O2active being associated with periods of burst-and-glide swimming near Ucrit. Steady-state swimming, as 60 expected, produced a steady á¹O2 after an initial adjustment period to a new U. These results suggest that the deferred oxygen cost of glycolysis during burst-and-coast swimming may be more rapidly and at least partially repaid, rather than being fully deferred to EPOC. Burst swimming is largely powered by anaerobic metabolism in fast-twitch, white muscles (Rome et al., 1984; Rome et al., 1985; Lawrence et al., 1990; Peake & Farrell, 2004), as evidenced by the accumulation of tissue lactate (Milligan, 1996), and is associated with a temporary decrease in venous oxygen content (Farrell & Clutterham, 2003). Therefore, when this oxygen-depleted venous blood reaches the gills, it could temporarily increase á¹O2active provided cardiac output and arterial oxygen content were unchanged. Alternatively, white muscle recruitment could consume additional oxygen during burst-and-coast swimming (Moyes et al., 1992). 3.5.2 An iterative algorithm best resolves á¹O2 dynamics Another objective was to demonstrate that an iterative algorithm resolves á¹O2 dynamics better than an interval regression algorithm, regardless of the duration of the sampling window. Indeed, others are reporting this approach since I started my dissertation (Rummer et al., 2016; Harianto et al., 2019). Revealing á¹O2 dynamics requires off-line analysis, while on-line sequential block analysis with the manufacturerâs software remains very useful for monitoring á¹O2. Importantly, my approach avoided underestimating peak á¹O2active during Ufinal by 16% in the present experiment. An iterative algorithm will also open the possibility for refining quantitative assessments of oxygen costs associated with swimming kinetics and exercise states (e.g., Peake & Farrell, 2005) and will relax the concerns about analyzing the rapid declines in á¹O2 observed after exhaustion (e.g., Scarabello et al., 1992; Zhang et al., 2018) and fatigue (Farrell et al., 1998; Wagner et al., 2005 Farrell, 2007; Eliason et al., 2013). A more quantitative analysis of 61 á¹O2active dynamics during and after swimming behaviours could provide better mechanistic and biomechanics insights. 3.5.3 Estimating á¹O2max more repeatably & reliably in static and swim-tunnel respirometry By chasing a fish to exhaustion within a respirometer, peak á¹O2rec can be reliably recorded and generated an 18% higher peak á¹O2chase than peak á¹O2rec measured when fish were transferred post-exhaustion. This result highlights the technical challenges of capturing peak á¹O2 in an exhausted fish. Although I did not specifically evaluate the impact of using air exposure to delay the recovery of the fish, others may wish to examine this. Also, I acknowledge that my testing protocol may have introduced a potential trial order effect because á¹O2chase was always measured 20 h after á¹O2rec (perhaps a training effect to produce the difference). While the 20-h recovery period used in the present study should have allowed full recovery of á¹O2 (Zhang et al., 2018) and exercise performance (either Ucrit or á¹O2max) is repeatable about 1 h after the first test and often slightly lower (Jain et al., 1997; Jain et al., 1998; Farrell et al., 1998; Farrell et al., 2003; McKenzie et al., 2007), trial order should be tested in future studies. However, trial order is not a concern for the comparison of peak á¹O2active and á¹O2chase because the order of these two tests was alternated. Peak á¹O2chase was detected within the first 5 to 20 min post-exhaustion in the present study with rainbow trout, but this time period may not apply to all fishes and life stages, which is all the more reason for continuous monitoring of á¹O2 well beyond the initial decline, as previously recommended (Clark et al., 2013b). For example, peak á¹O2 was delayed by up to 20 min in 1â9 g Atlantic salmon (Cutts et al., 2002) and by 5â8 h in sexually matured coho salmon 62 (Oncorhynchus kisutch, Walbaum 1792) (Clark et al., 2012). All the same, peaks can still be missed during flushing periods and fish may not be motivated to reach á¹O2max. As others pointed out, determining á¹O2max with a chase-to-exhaustion protocol offers several advantages over a swim-to-fatigue protocol. They include a much faster measurement of peak á¹O2 (possibly <1 h), the possibility of testing non-athletic fish species, use of less sophisticated equipment (Clark et al., 2013; Norin & Clark, 2016) and an easier field application of fish respirometry (Lapointe et al., 2018). While studies of phenotypic and individual variation, which usually requires high throughput, are promoting the further study of chasing fish inside a respirometer. Improvements and variations on the actual testing apparatus are needed to see if it applies to all fish species including large specimens. In my hands and for rainbow trout, the best á¹O2max estimate was independent of exercise protocol and repeatable. The exercise protocol needed to be valid and the á¹O2 data needed to be properly interrogated off-line with an iterative algorithm and a minimum sampling window. All the previous comparisons of peak á¹O2chase and á¹O2active used a longer sampling window and the vastly different sampling window durations lack justifications. For example, Hvas & Oppedal (2019) used a 3â5 min sampling window for both á¹O2active and á¹O2rec in Atlantic salmon and reported peak á¹O2active was 52% higher than peak á¹O2rec. Slesinger et al. (2019) used a sampling window of 10 min for peak á¹O2active but 5â10 min for peak á¹O2rec and reported that peak á¹O2active was 49% higher than peak á¹O2rec in black sea bass (Centropristis striata, Linnaeus. 1758). Likewise, Roche et al. (2013) used a 5-min sampling window with a repeated-measures design and reported that peak á¹O2active was 36% higher than peak á¹O2rec in coral reef fishes. Rummer et al. (2016) used a 5-min interval regression analysis for peak á¹O2active and a 1-min iterative algorithm for peak á¹O2rec with a repeated-measures design and reported that peak 63 á¹O2active was 38% higher than peak á¹O2rec in coral reef fishes. Conversely, Sylvestre et al. (2007) reported that peak á¹O2active and peak á¹O2rec were the same for Atlantic cod (Gadus morhua, Linnaeus 1758), but the sampling window was 20â30 min for both measurements, durations far too long based on the present work. Reidy et al. (1995) also used a 20-min sampling window and reported that peak á¹O2rec was ~35% higher than peak á¹O2active in Atlantic cod. The willingness of fish to exercise to exhaustion was not tested, but controlled by repeated measures designs, in the current study. Some individuals may be more willing to exercise, either because of motivation or anaerobic threshold. The topic of how neurological central governance affects stress tolerance is a merging interest in animal welfare (Lagisz et al., 2020)and kinesiology (Mayer & Thiel, 2018). Altogether with the advanced understanding of metabolic capacity, fish biologists are one step closer to understand how wild fish make decisions for their natural behaviour (Hein et al., 2020). 3.5.4 Recommendations in estimating á¹O2max in aquatic respirometry Based on the work in this chapter, I propose the following guiding principles as the best practices for reliably estimating á¹O2max independent of the exercise protocol. By doing so, I hope to encourage experimenters to adopt more standardized analytical and protocols to estimate á¹O2max with the same rigour as now used to estimate SMR in fishes (Steffensen, 2002; Chabot et al., 2016; Zhang et al., 2016). The 1st principle is that the experimental protocol of á¹O2max should maintain a constant workload during the measurements. This allows á¹O2 to exhibit a plateau with multiple peaks and providing the measures that are internally repeatable for the analyses in the following two principles. 64 The 2nd principle is that the analysis should establish the minimum and valid sampling window so that the variance for estimating a steady á¹O2 remains low and unaffected by the measurement noise. This analysis can be easily and conveniently done by using the background á¹O2 of the respirometer because the noise associated with this signal is inherent to á¹O2 measuring instruments and is routinely recorded with any respirometry studies. The 3rd principle is that analysis should combine an off-line iterative algorithm with the minimum sampling window to precisely identify peak á¹O2. This approach can be applied to the rapidly declining á¹O2 immediately after exhaustive exercise and the more stable but still dynamic á¹O2active just before a fish reaches fatigue in an incremental swimming test. The potentially substantial error introduced by deviating from this second principle is a potentially large underestimate of peak á¹O2. 65 66 Figure 3.1 A diagram of the modified static respirometer (a) used to monitor the dynamics of rainbow trout Oncorhynchus mykiss oxygen uptake (á¹O2) during a sealed internal (á¹O2chase, b and d) or following an external (á¹O2rec, c and e) chase. Individual representative á¹O2 profiles are presented and highlight the peak in á¹O2chase (b; indicated by a red dot) and the rapid decay of á¹O2 when a fish was placed into a respirometer after being chased to exhaustion outside of the respirometer (c). The manual flush periods are indicated by grey bars for the exhaustive chase within the respirometer(b). Group average of á¹O2chase (d) or á¹O2rec (e) are shown as mean Â± s.e.m for the same 10 individuals and were calculated using a 1-min sequential interval regression algorithm. The dashed grey horizontal line represents á¹O2rec obtained with a more common 3-min sequential interval regression algorithm as a reference (Photo credit: M. Gilbert). 67 Figure 3.2 An analysis of varying the sampling window duration for an individual estimate of the rate of oxygen uptake (á¹O2; n=5): (a) and (b) are for a 20-min background in the swim-tunnel respirometer, while C and D are for a 15-min measurement of á¹O2active of a rainbow trout swimming steadily at ~50% of critical swimming speed (Ucrit). (a) Means and standard deviations (S.D.) for the individual á¹O2 determinations (mean Â± s.e.m.). (b) Coefficient of variation (C.V.) for the individual á¹O2 determinations (mean Â± s.e.m.). (c) Mean and C.V. for the individual á¹O2active determinations (mean Â± range). (d) Coefficient of determination (R2) for the individual á¹O2active determinations (mean Â± S.D.). Statistical differences (p < 0.05; one-way ANOVA) are indicated by either an asterisk or the different letters, while the horizontal black lines indicate stable minimum values for S.D. and C.V.105150 08162432* *** **S.D. (mg O2 h-1 kg-1)Mean á¹O2S.D.á¹O2 (mg O2 h-1 kg-1 )0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.505001000150020002500C.V. (%)***-2000-1500-1000-5000500100015002000148 80 49 36 26 15 C.V. (%) á¹O2 (mg O2 h-1 kg-1 )0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.000.00.20.40.60.81.0R2a aabbcbccSampling window duration (min)(a) (b) (c) (d) 68 Figure 3.3 Effect of sampling window duration (0.5 to 5 min) on estimates of background oxygen uptake (á¹O2; a,b) and exercise á¹O2 elicited during a chase to exhaustion inside an intermittent-flow respirometer protocol (c, d) in rainbow trout (Oncorhynchus mykiss). The four panels present: (a) the mean (n=6), the mean of the standard deviation (S.D.; n=6) for background á¹O2 determinations; (b) the mean of the coefficient of variation for background á¹O2 (n=6); (c) mean, minimum and range for á¹O2 values during a chase to exhaustion (n=10); and (d) the mean values for the coefficient of variation and coefficient of determination (R2; n=10) for á¹O2 values during the internal chase to exhaustion. All á¹O2 values were calculated with a sequential interval regression analysis and presented as mean Â± s.e.m. Different letters assign a statistical difference among the measurement periods (p < 0.05 calculated by one-way ANOVA with Tukey post-hoc tests). Our suggested conservative and aggressive minimum sampling window duration are indicated by purple and red vertical dashed lines, respectively. 036912150102030405060Mean O2 (mg O2 h-1 kg-1 )bStandard deviation (mg O2 h-1 kg-1)cdbbcdd def defdef def def def ef fadefdef0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5010002000300040005000Coefficient of variation (%)abcbcd bcd dabcdabcabcbcdbcdabcdabcdabcdabcdabcdabcda-2000-100001000200030004000O2 (mg O2 h-1 kg-1)Mean MinimumRangeabcd de e ef fa a aa a a a a0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.750501001502000.50.60.70.80.91.0Coefficient of variation (%)R2abcd d d ded e(c)(d)Sampling window duration (min)(a)(b) 69 0 4 8 12 16 20 2402503004506007509001050Time (min)88% UcritR-B-IR-M-BM-B-I1.5 min: R2 = 0.923 min: R2 = 0.982 min: R2 = 0.942.5 min: R2 = 0.975 min: R2 = 0.96á¹O2 (mg O2 h-1 kg-1 ) 0 4 8 12 16 20 240100200300400Time (min)46% Ucrit1.5 min: R2 = 0.825 min: R2 = 0.9810 min: R2 = 0.992.5 min: R2 = 0.932 min: R2 = 0.913 min: R2 = 0.95M-BR-I M-Bá¹O2 (mg O2 h-1 kg-1 ) 0 4 8 12 16 20 2402503004506007509001050Time (min)1.5 min: R2 = 0.872 min: R2=0.932.5 min: R2=0.963 min: R2=0.965 min: R2 = 0.99M-BM-BB-I89% Ucrit 100% Ucritá¹O2 (mg O2 h-1 kg-1 ) 0 4 8 12 16 20 2402503004506007509001050Time (min)1.5 min: R2 = 0.773 min: R2 = 0.965 min: R2 = 0.9710 min: R2 = 0.992 min: R2 = 0.862.5 min: R2 = 0.91100% Ucrit EPOCB-IB-IR-Iá¹O2 (mg O2 h-1 kg-1 ) 0 4 8 12 16 20 2402503004506007509001050Time (min)1.5 min: R2 = 0.935 min: R2 = 0.972 min: R2 = 0.862.5 min: R2 = 0.9110 min: R2 = 0.993 min: R2 = 0.96100% Ucrit EPOCB-I R-BR-M R-BB-Iá¹O2 (mg O2 h-1 kg-1 ) 0 4 8 12 16 20 2402503004506007509001050Time (min)1.5 min: R2 = 0.783 min: R2 = 0.965 min: R2 = 0.9710 min: R2 = 0.992 min: R2 = 0.862.5 min: R2 = 0.91100% Ucrit EPOCB-IR-BR-BB-IR-M-Bá¹O2 (mg O2 h-1 kg-1 ) RMBI0 4 8 12 16 20 2402503004506007509001050Time (min)89% Ucrit1.5 min: R2 = 0.962 min: R2 = 0.982.5 min: R2 = 0.983 min: R2 = 0.995 min: R2 = 0.99M-B M-B-IR-B-Iá¹O2 (mg O2 h-1 kg-1 ) MBRMBIRMBIMBIRMBI0 4 8 12 16 20 240100200300400Time (min)52% Ucrit1.5 min: R2 = 0.782.5 min: R2 = 0.903 min: R2 = 0.935 min: R2 = 0.9710 min: R2 = 0.992 min: R2 = 0.87R-M-B M-Bá¹O2 (mg O2 h-1 kg-1 ) MBA BEG HFDCMBI(a) (b) (c) (d) (e) (f) (g) (h) 70 Figure 3.4 Examples of oxygen uptake (á¹O2) dynamics (closed symbols) for individual rainbow trout during a critical swimming speed (Ucrit) test with speed (U) is expressed as a percentage of Ucrit. (a) & (b) steady swimming at an intermediate U. (c) â (h) the final or penultimate U before fatigue. Some analysis continued into recovery (EPOC: excess post-exercise oxygen consumption; open symbols). Grey block = respirometer being flushed. Different sampling window durations (1.5 to 10 min) used in a sequential interval regression analysis have different coloured lines (see the key, which also shows the coefficient of determination (R2) for each measurement method). The line of identity (grey dotted) is for the average of all 1.5-min á¹O2 values for the entire swimming step (as shown in Fig. S1). The pie diagram represents the proportion of time the fish spent using different swimming behaviours: rest (R), maintain stationary (M), burst (B), intense burst (I). The horizontal blue bars and associated letters indicate when these different swimming behaviours predominated. 71 O2chase 1-min O2active 1.5-min0200400600800100012001400Peak O2 (mg O2 h-1 kg-1)Pearson: r=0.78, p=0.0080 Spearman: r=0.77, p=0.013 O2chase 1min O2active1min0200400600800100012001400Peak O2 (mg O2 h-1 kg-1)Pearson: r=0.68, p 920.0=Spearman: r=0.50, p 41.0=0.5-min1-min1.5-min1-min1.5-min0200400600800100012001400Peak O2 (mg O2 h-1 kg-1)abaO2active O2chaseabb cC.V.:15%28% 20% 19% 22%(a)(b)(c) 72 Figure 3.5 Comparing the peak oxygen uptake (á¹O2) of rainbow trout (Oncorhynchus mykiss) elicited through a chase to exhaustion (á¹O2chase) inside an intermittent-flow respirometer or a critical swimming speed test (á¹O2active) within a swim-tunnel respirometer. (a) Violin plots of peak á¹O2 (dotted line=25-75 percentile; dash line = median; left & right boundaries show the frequency distribution of data; upper & lower boundaries = minimum & maximum) estimated for peak á¹O2active analyzed with a 1.0- and 1.5-min iterative algorithm as well as for peak á¹O2chase analyzed with an iterative algorithm and sampling windows of 0.5-, 1.0- and 1.5-min. The coefficient of variations (C.V.) is presented for each analytical approach. Different letters denote statistical differences in peak á¹O2 (p < 0.05; one-way repeated-measure ANOVA with Holm-Sidak adjusted paired t-tests; a). Pearson and Spearmanâs correlation tests for repeatability of peak á¹O2chase (blue dots) and peak á¹O2active (black dots) using sampling windows of 1-min or 1.5-min duration. Solid lines denote the individual repeatability (p < 0.05 in Pearson and Spearmanâs correlation tests), whereas dashed lines denote a lack of individual repeatability (p > 0.05 in Pearson or Spearmanâs correlation tests). 73 Table 3.1 Summary of protocols used to estimate the maximum rate of oxygen uptake in fishes following a chase to exhaustion outside of an intermittent-flow respirometry with a fixed sampling window duration. Transfer time Duration of á¹O2 sampling window (min) Intentional air exposure âNoneâ or duration (min) Reference Unclear from methods 60 None Lucas & Priede 1992 5 s 20 None Reidy et al.,1995 Unclear from methods 15 None Mallekh & Lagardère, 2002 âImmediately startâ 15 None Lefrancois & Claireaux, 2003 Unclear from methods ~20 None Sylvestre et al., 2007 Unclear from methods 10 None Jordan & Steffensen, 2007 6 s 1 None Norin & Malte, 2012 <10 s 5 1 Roche et al., 2013 8 s 10 None Norin et al., 2014 < 20 s 3 None Svendsen et al., 2014 Unclear from methods 3 None Norin & Clark, 2016 < 10 s 1 1 Rummer et al., 2016* Intentional air exposure 1.8 2 Zhang et al., 2016 Unclear from methods 0.5 1 Ern et al., 2017 Intentional air exposure 3.7 2 Zhang et al., 2017 Intentional air exposure 1.8 1 Zhang et al., 2018b Intentional air exposure 1.8 1 Zhang et al., 2019b Unclear from methods 3â5 None Hvas & Oppedal, 2019 < 1 min 5â10 None Slesinger et al., 2019 * Rummer et al., 2016 used an iterative analysis while all other studies summarized used a conventional interval analysis. 74 Table 3.2 Comparison of peak active oxygen uptake (peak á¹O2active) determined with different sampling window durations (1.5 min to 10 min) and two types of mathematical algorithms, an iterative algorithm and a sequential interval regression analysis. Values are presented as mean Â± s.e.m. for both the absolute value of peak á¹O2active and the percent difference from the peak á¹O2active using the 1.5-min iterative algorithm where n=14 except for the 10 min estimate of á¹O2max, where n=11. Footnote. Different superscripted letters denote a statistically significant difference (p < 0.05) among the absolute values (a repeated one-way ANOVA). Iterative algorithm Sequential interval regression analysis 1.5-min 1.5 min 2 min 2.5 min 3 min 5 min 10 min Peak á¹O2active (mg O2 h-1 kg-1) 731.5 ± 29.9a 689.9 ± 28.6b 674.3 ± 31.2bc 668.0 ± 33.9bc 638.7 ± 28.1c 620.8 ± 28.9cd 502.7 ± 49.7e Difference from 1.5-min iterative algorithm (%) -5.6 ± 1.0 -7.9 ± 1.1 -9.0 ± 1.7 -12.7 ± 1.5 -15.2 ± 1.6 -23.3 ± 1.6 75 Table 3.3 Estimates of peak oxygen uptake (á¹O2; mg O2 h-1 kg-1) determined during a chase to exhaustion inside the intermittent-flow respirometer (peak á¹O2chase) and during recovery after chasing to exhaustion outside the intermittent-flow respirometer (peak á¹O2rec). The estimates were determined using both an iterative algorithm and an interval regression analysis with multiple á¹O2 sampling window durations (0.5, 1.0 & 1.5 min). Statistical differences between analytical approaches are denoted by asterisks, between chasing protocols are denoted by daggers, and between chasing and analysis combined are denoted by pound signs as determined by Holm-Sidak corrected paired t-tests. These differences are presented as a percentage of peak á¹O2chase. Values are presented for the same Oncorhynchus mykiss (n=10) as mean Â± s.e.m and statistically significant differences were assigned when p < 0.05. 0.5 min 1.0 min 1.5 min Peak á¹O2chase â iterative algorithm 788 ± 69 663 ± 43 626 ± 39 Peak á¹O2chase â interval regression 548 ± 46* 506 ± 47* 471 ± 47* Difference between analyses (%) -28 ± 6 -23 ± 7 -24 ± 7 Peak á¹O2rec â iterative algorithm 619 ± 50â 535 ± 42â 501 ± 43â Difference in chasing protocols (%) -23 ± 6 -18 ± 6 -19 ± 7 Peak á¹O2rec â interval algorithm 484 ± 47# 457 ± 40# 456 ± 40# Combined difference in chasing and analysis (%) -39 ± 6 -31 ± 6 -27 ± 7 76 Chapter 4: A proof-of-principle test for IRAP â Distinguishing intraspecific respiratory phenotypes among strains of rainbow trout 2 4.1 Synopsis Characterizing biological variations needs precise measurements. The most rigorous test for resolving intra-specific variation is differentiating strains of the same species reared under common-garden conditions, which narrows the origin of phenotypic variation to genotypic variation. I hypothesize that IRAP can resolve the intra-specific phenotypic variations of metabolic traits underlying the thermal and hypoxia tolerances in rainbow trout. I compared parr from three strains of the hatchery-raised rainbow trout to examine the intraspecific variations. Variation in absolute aerobic scope (AAS), critical oxygen level (O2crit), incipient lethal oxygen saturation (ILOS) and critical thermal maximum (CTmax) generally exhibited consistent differences among the strains, suggesting the possibility of functional associations among these traits at a strain level. This possibility was further supported at the individual level by a positive correlation between ILOS and O2crit, and a negative correlation between O2crit and AAS. These results indicate that intraspecific differences in hypoxic and thermal tolerances among strains of rainbow trout may be primarily determined by differences in the ability to maintain oxygen uptake, and that variations in aerobic scope in normoxia likely play a role in determining the ability of these fish to sustain metabolism aerobically when water oxygen saturation is decreased. 2 A version of chapter 4 was previously published as (Appendix E.2): Y. Zhang, T. Healy, W. Vandersteen, P. M. Schulte, A. P. Farrell (2018) A rainbow trout (Oncorhynchus mykiss) strain with higher aerobic scope also has superior tolerance of acute warming and hypoxia. Journal of Fish Biology. 92, 487â503. 77 4.2 Introduction Hypoxia has dramatic negative effects on organismal performance and survival (e.g., Richards et al., 2009) and so variation in hypoxia tolerance is likely one important determinant of biogeographic distributions in fishes (Childress & Seibel, 1998; Chapman et al., 2002; Richards, 2011). Consequently, substantial research effort has been aimed at understanding the mechanistic basis of variation in hypoxia tolerance among fish species. This research has identified interspecific differences in hypoxia tolerance that are correlated with differences in metabolic rate (Mandic et al., 2009), gill surface area (Chapman et al., 2002), hemoglobin (Hb) P50 (Speers-Roesch et al., 2012), Hb concentration (Chapman et al., 2002), Hb multiplicity or subunit heterogeneity (Brix et al., 1999), hematocrit (Chapman et al., 2002), tissue glycogen concentrations (Speers-Roesch et al., 2013), maximal anaerobic enzyme activity (Farwell et al., 2007; Mandic et al., 2013), maintenance of cardiovascular function (Gesser, 1977; Speers-Roesch et al., 2012), maintenance of brain ATP concentration (Speers-Roesch et al., 2013), and metabolic suppression (Hochachka et al., 1996). In general, mechanisms identified in these studies highlight that intraspecific variations in hypoxia tolerance are typically associated with differences in indices that reflect either the capacity to extract oxygen, the capacity to sustain metabolic demands aerobically under low oxygen conditions, or capacity to suppress metabolic demands (Richards, 2011). Although many studies have focused on interspecific variation in hypoxia tolerance, relatively few have examined the physiological mechanisms that underlie intraspecific variation in hypoxia tolerance among populations or individuals of the same species. Intraspecific variation provides the raw materials on which natural selection acts, and thus is a key component of the responses of organisms to environmental changes (Bernatchez, 2016). In addition, studies 78 of intraspecific variation allow direct tests of the extent to which physiological traits covary, which can provide insights into the functional underpinnings of key traits (e.g. Bennett, 1987; Metcalfe et al., 2016). Previous intraspecific comparisons in fish have demonstrated that mechanisms like those listed above for interspecific variation may also underlie intraspecific differences in hypoxia tolerance (e.g., Chapman et al., 2002; Martinez et al., 2004; Martínez et al., 2009; Davies et al., 2011; McBryan et al., 2016). However, the extents to which differences in metabolic rate, aerobic scope, the capacity to maintain oxygen uptake, and the capacity to sustain metabolism anaerobically contribute to intraspecific variation in hypoxic and thermal tolerance have not been clearly established. The main goals of this chapter were to use individuals and strains of hatchery-raised rainbow trout parr to assess the relationships between (1) critical oxygen level (O2crit) and standard metabolic rate (SMR), maximum oxygen uptake rate (á¹O2max) or absolute aerobic scope (AAS), and (2) incipient lethal oxygen saturation (ILOS; a measure of hypoxia tolerance) and critical oxygen level (O2crit) or accumulated oxygen deficit (AOD. These traits were measured as components of IRAP (Zhang et al., 2016 & 2017), which also includes calculations of factorial aerobic scope (FAS), excess post-exercise oxygen consumption (EPOC), scope for oxygen deficit (SOD), and factorial scope for oxygen deficit (FSOD) allowing strain-level differences in these traits to be assessed as well. Furthermore, the critical thermal maximum (CTmax) of rainbow trout parr was determined for each strain in an independent test to allow preliminary comparisons between patterns of strain-level variations in upper thermal tolerance, hypoxia tolerance and absolute aerobic scope in normoxia. 79 4.3 Materials and Methods 4.3.1 Intermittent-flow respirometry and hypoxia tolerance Respiratory phenotyping and hypoxia tolerance assessments by IRAP protocol are fully described in Chapter 2. In total, the IRAP protocol was completed for 24 fish (11 for Strain A, 7 for Strain B and 6 for Strain C). The body masses of fish from Strain A, B and C used in the IRAP trials were 73 ± 3 g, 69 ± 3 g and 66 ± 5 g, respectively. Water temperature was monitored by a temperature probe (Shielded Submersible Temperature Sensor TSUB36, Pyroscience; www.pyro-science.com) placed in the reservoir, and throughout all trials, the temperature was 7.4 ± 0.5 °C. The experimental apparatus was in an isolated and quiet room. 4.3.2 Thermal tolerance Upper thermal tolerance was measured using a standard critical thermal limit methodology for fish (Beitinger et al., 2000). The CTmax was determined for 10 additional fish from each strain of rainbow trout (body masses was 30 ± 2 g, 18 ± 2 g and 24 ± 4 g, respectively, for Strain A, B and C). CTmax trials were conducted in a 130 l polypropylene tank. For each trial, five fish from each strain (15 fish total per trial) were fin clipped to allow for strain identification, and then transferred to the testing apparatus, which contained freshwater at a starting temperature of 7.4 ± 0.5 °C. Water temperature in the apparatus was monitored throughout the trials by a digital thermometer placed inside the test tank (Thermo Fisher Scientific; www.thermofisher.com). Trials began after the fish had adjusted to the apparatus for 10 min. All trials started at 11:15 am (~4 h after sunrise). 45 °C freshwater was gradually introduced to the test tank to increase the temperature at a constant rate of warming (0.3 ± 0.03 °C min-1). Thermal stratification was avoided by vigorous aeration of water in the testing tank. When a fish lost equilibrium (generally after 45-65 min of warming), it was removed from the apparatus, weighed, and placed in an 80 individual recovery tank containing 7.4 ± 0.5 °C freshwater. The temperature at which a fish lost equilibrium was recorded as its critical thermal maximum (CTmax). All fish recovered following the thermal tolerance trials, and the strain of each fish was determined following recovery by visual inspection of the fishâs fin clip. 4.3.3 Statistical analyses All statistical analyses were conducted in R (v3.4.0; R Development Core Team, 2017; www.r-project.org). Differences in body mass have the potential to result in allometric scaling effects on physiological measurements. However, the mean body mass of the fish used in this study did not differ among the strains (one-way ANOVA, p > 0.05), and after the values for SMRq0,2, SMRLow10, á¹O2max, AAS, EPOC and AOD were converted to mass-specific values body mass was not a significant covariate of any of the traits measured in this study (ANCOVA, p â¥ 0.348). This suggests that the effects of allometric scaling in the current study were negligible due to the similar body masses among the three strains and that normalizing SMRq0,2, SMRLow10, á¹O2max, AAS, EPOC and AOD to body mass (i.e., using mass-specific values) adequately accounted for variation in these traits. Thus, body mass was not included as a covariate in the analyses for the effects of strain in this study. The data for SMRq0,2, SMRLow10, á¹O2max, EPOC, O2crit, ILOS and SOD met the assumptions of normality and homogeneity of variances (test by Shapiro-Wilk tests and Bartlettâs tests, respectively), and the data for AAS, FSOD and AOD met these assumptions after log transformation. As a result, differences among strains for these traits were assessed by one-way ANOVA using the car R package (v2.4.1). Pairwise comparisons between strains were conducted by Tukeyâs HSD post-hoc tests using the multcomp R package (v1.4.6). Differences in FAS and CTmax among strains were tested by Kruskal-Wallis ANOVA, for the assumptions of normality or homogeneity of variances for parametric tests. Pairwise comparisons between 81 strains for FAS and CTmax were conducted by Nemenyi post-hoc tests using the PMCMR R package (v4.1). Relationships between O2crit and SMRq0.2, á¹O2max or AAS were assessed by fitting two general linear models [equations (1) and (2)]. O!"#$% = #& + #'%SMR(&.!) + #!%*ÌO!*+,) + #-(strain)+ 4 Eqn 4.1 O!"#$% = #& + #'(AAS) + #!(strain) + 4 Eqn 4.2 Following model fitting, effects of SMRq0.2, á¹O2max or AAS were tested by ANOVA. Note that separate models were required to test for effects of SMRq0.2 and á¹O2max, and AAS on O2crit, because AAS is calculated directly from SMRq0.2 and á¹O2max, which results in extreme multicollinearity if these traits are included as explanatory variables in the same general linear model. Relationships between ILOS and O2crit or AOD were assessed by fitting the general linear model below [equation (3)] followed by ANOVA. ILOS = #& + #'(O!"#$%) + #!(AOD) + #-(strain) + 4 Eqn 4.3 Prior to fitting the general linear models above [Eqn 4.1â 4.3], preliminary analyses with full factorial versions of these models (i.e., with all possible interaction terms) were fit to the data. In these analyses, none of the interaction terms were significant. Thus, the model reduction was utilized and the interaction terms were removed from the models used in the final analyses [Eqn 4.1-4.3; Appendix B: Table S4.1-4.3]. 4.4 Results 4.4.1 Variation in metabolic traits among strains Intraspecific differences of certain metabolic traits were detected among the three strains of rainbow trout. While á¹O2max was significantly different among the strains (p < 0.001), SMR did 82 not differ among strains (SMRq0.2: p > 0.05; Figure 4.1b; SMRLow10: p > 0.05; Table S4.1). Post-hoc tests indicated that the majority of strain variation in á¹O2max was a consequence of higher á¹O2max in Strain C (500.2 ± 26.6 mg O2 h-1 kg-1) than in either Strain A or B (348.8 ± 24.3 and 314.5 ± 22.2 mg O2 h-1 kg-1, respectively; p < 0.01), but no significant difference in á¹O2max was detected between Strain A and B (p > 0.05). Consistent with the lack of variation in SMRq0.2 among strains (49.4 ± 1.5 mg O2 h-1 kg-1 for all individuals), AAS demonstrated the same pattern of variation among strains as was observed for á¹O2max, and this variation in AAS among strains was statistically significant (p < 0.001; Figure 4.1). There was no difference in AAS between Strain A and B (301.1 ± 23.6 and 265.1 ± 23.6 mg O2 h-1 kg-1, respectively; p > 0.05), and AAS was higher in Strain C (448.8 ± 25.8 mg O2 h-1 kg-1) than in either Strain A or B (p < 0.01). Similarly, FAS was significantly different among strains (p < 0.01; Figure 4.1d) with higher FAS in Strain C (9.9 ± 0.7) than in Strain A (6.7 ± 0.9; p < 0.01), and no significant difference in FAS between Strain B (7.4 ± 0.5) and either Strain A or C (p > 0.05). Interestingly, EPOC was also significantly different among the three strains of rainbow trout (p < 0.05; Table S4.1). However, the pattern of variation among the strains was different from the pattern of variation observed for á¹O2max. Although EPOC was significantly higher in Strain C than Strain B (p < 0.05), there was no difference in EPOC between Strain A and either Strain B or C (p > 0.05). Taken together, these results indicate that Strain C of rainbow trout has superior maximum aerobic capacity compared to Strain A and B, which results in an elevated aerobic scope in Strain C compared to the other strains. 83 4.4.2 Variation in hypoxia tolerance and upper thermal tolerance among strains Both hypoxia tolerance and upper thermal tolerance were significantly different among the rainbow trout strains (ILOS: p < 0.001; Figure 4.2; CTmax: p < 0.001; Figure 4.2b). Moreover, the patterns of variation among strains in both tolerance measures were generally similar to those observed for á¹O2max and AAS (see Figure 4.1). On average, Strain C tolerated the lowest oxygen saturation (7.0 ± 0.6 % sat.) and highest acute temperature (26.6 ± 0.2 °C) of the three strains (Strain A: 11.1 ± 0.4 % sat. and 23.7 ± 0.7 °C for ILOS and CTmax, respectively, and Strain B: 8.3 ± 0.7 % sat. and 24.8 ± 0.5 °C for ILOS and CTmax, respectively). For ILOS, the differences between Strain A and C, and Strain A and B were significant (p < 0.01), whereas the ILOS of Strain C was not significantly different from that of Strain B (p = 0.247). For CTmax, the differences between Strain C and either Strain A or B were significant (p < 0.05), whereas the CTmax of Strain A was not significantly different from that of Strain B (p > 0.05). In addition to measuring hypoxia tolerance (ILOS), the IRAP protocol includes measurements of the ability of fish to maintain oxygen extraction at low oxygen saturations (i.e., O2crit) and to maintain survival at oxygen tensions that restrict oxygen uptake (i.e., AOD, SOD, FSOD). Among the strains, there was significant variation in O2crit (p < 0.001; Figure 4.3a), but not in AOD (p > 0.05; Figure 4.3; 12.8 ± 2.1 mg O2 kg-1 for all individuals), SOD (p > 0.05; Table S4.1) or FSOD (p > 0.05; Table S4.1). Furthermore, the pattern of variation in O2crit among strains was similar to the pattern of variation in ILOS. The O2crit of each strain was significantly different from the O2crit values of the others (p â¤ 0.004) with the lowest O2crit in Strain C (9.7 ± 0.3 % sat.), the highest O2crit in Strain A (14.4 ± 0.4 % sat.), and an intermediate O2crit in Strain B (11.9 ± 0.5 % sat.). The similarity between the patterns of variation in ILOS and O2crit and the lack of variation in AOD, SOD, and FSOD among strains suggest that hypoxia tolerance in 84 rainbow trout may primarily be determined by the ability of these fish to maintain oxygen uptake at low environmental oxygen saturations. 4.4.3 Correlations between metabolic traits and hypoxia tolerance among individuals Although the parallel patterns of variation in hypoxia tolerance (ILOS & O2crit), AAS and á¹O2max among the strains of rainbow trout in this study could potentially be due to functional relationships among these traits, comparisons among only three strains cannot provide definitive conclusions as to whether these traits are functionally associated or even correlated with each other. However, because the IRAP protocol includes measurements of SMR, á¹O2max, AAS, FAS, EPOC, O2crit, AOD, SOD, FSOD and ILOS for each fish, the relationships among these traits can be explored further by assessing the extent to which the traits covary among individuals. Here, the potential for associations between O2crit and SMR, á¹O2max or AAS, and between ILOS and O2crit or AOD were assessed using general linear models [Eqn 4.1â 4.3]. Given the similarities among the patterns of variation in O2crit, á¹O2max and AAS among the strains of rainbow trout, differences in metabolic rate and aerobic scope in normoxia may play a role in determining O2crit in this species. Surprisingly, variation in á¹O2max did not significantly covary with variation in O2crit among individuals (p > 0.05; Appendix B: Table S4.1 & S4.2), whereas variation in SMR had a significant effect on variation in O2crit (p < 0.05; Figure 4.4a; see Appendix B: Table S4.1 & S4.2). These results are somewhat in contrast to the lack of strain variation in SMR and the generally parallel patterns of variation in O2crit and á¹O2max among strains (Figure 4.1, Figure 4.3). However, variation in AAS among individuals had a significant effect on variation in O2crit (p < 0.05; Figure 4b; Appendix B: Table S4.1 & S4.2), which is consistent with the similar patterns of variation in AAS and O2crit among strains (Figure 4.1, Figure 4.3). 85 In addition, the similarity between the patterns of variation in O2crit and ILOS among the strains highlighted the possibility that O2crit may play a role in determining hypoxia tolerance in these fish, and variation in O2crit had a significant effect on variation in ILOS among the individual fish used in this study (p < 0.001; Figure 3c; Appendix B: Table S4.1 & S4.2). Interestingly, if both strains and O2crit were included as explanatory variables (Eqn 4.3), strain was not a significant factor contributing to variation in ILOS (ANOVA, p > 0.05; Appendix B: Table S4.1 & S4.2), suggesting that the majority of strain-level variation in ILOS (Appendix B: Table S4.1 & S4.2) is due to strain-level variation in O2crit. However, variation in AOD also had a significant effect on variation in ILOS among individuals (p < 0.001; Figure 3d; Appendix B: Table S4.1 & S4.2), indicating that differences in AOD may also contribute to differences in ILOS among individuals although ILOS varies among strains, whereas AOD does not (Figure 4.2, Figure 4.3). In combination, these results suggest that differences in aerobic scope in normoxia potentially play a role in determining variation in critical oxygen level and ultimately in hypoxia tolerance in rainbow trout. 4.5 Discussion This study demonstrated significant correlations between hypoxia tolerance (i.e., ILOS) and the capacity to maintain oxygen uptake in hypoxia (i.e., O2crit), and between O2crit and absolute aerobic scope (AAS) in normoxia in rainbow trout. Together, these results suggest that metabolic rate and aerobic scope may play key roles in determining hypoxia tolerance in rainbow trout, and highlight the potential for functional associations among these traits. Consistent with these possibilities, similar patterns of variation existed for ILOS, O2crit, á¹O2max and AAS among the three strains of rainbow trout. In addition, strain differences in these four traits are generally parallel to differences in upper thermal tolerance (i.e., CTmax). Therefore, enhanced AAS is 86 possibly a common underlying mechanism that improves the tolerances of both hypoxia and high temperature in rainbow trout. 4.5.1 Intraspecific variation in metabolic traits, hypoxia tolerance and upper thermal tolerance Intraspecific differences in metabolic rate, aerobic scope, critical oxygen level, hypoxia tolerance and upper thermal tolerance are not novel observations in fish generally, and several previous studies have assessed at least one of these traits in domestic or wild strains of rainbow trout from the U.S.A., Canada, Austria, Italy, Australia and Japan (Bidgood & Berst, 1969; Klar et al., 1979; Lee & Rinne, 1980; Wieser et al., 1985; Scarabello et al., 1992; Strange et al., 1993; Alsop & Wood, 1997; Currie et al., 1998; McGeer et al., 2000;Myrick & Cech, 2000; Myrick & Cech, 2005; Carline & Machung, 2001; Faust et al., 2004; Molony et al., 2004; Rodnick et al., 2004; Ineno et al., 2005; Galbreath et al., 2006; LeBlanc et al., 2011; Recsetar et al., 2012; Scott et al., 2014; Chen et al., 2015; Verhille et al., 2016). In particular, these studies have demonstrated substantial variation in upper thermal tolerance, hypoxia tolerance, routine á¹O2 and á¹O2max in rainbow trout, which could be indicative of a large amount of intraspecific variation within this species. However, relatively few studies have compared strains within a single experiment, and direct comparisons among studies are confounded by differences in rearing conditions, life stages, diets, photoperiods and holding temperatures all of which have the potential to affect physiological traits. For example, Chen et al. (2015) estimated that as much as 74 % of the variation in CTmax among studies in rainbow trout could be attributed to differences in acclimation temperature alone, suggesting that, at least for CTmax, phenotypic plasticity rather than intraspecific divergence is responsible for the majority of previously observed trait variation in this species. Regardless, the majority of the trait values presented in the current study span 87 similar ranges to those previously reported for rainbow trout, suggesting they are likely reasonable estimates of these traits. In contrast, the FAS values observed here are moderately higher than those reported elsewhere (Scarabello et al., 1992; Alsop & Wood, 1997; McGeer et al., 2000; Chen et al., 2015; Verhille et al., 2016) potentially as a result of either higher acclimation temperatures (Clark et al., 2013a), or the use of routine á¹O2 in previous studies, rather than SMR, to estimate FAS. The results of the present study clearly demonstrate intraspecific variation in á¹O2max, AAS, FAS, EPOC, O2crit, ILOS and CTmax among three strains of rainbow trout. In contrast, the majority of the previous studies that have independently examined variation in metabolic traits, hypoxia tolerance or upper thermal tolerance among strains of rainbow trout have found little evidence of intraspecific variation (e.g. Myrick & Cech, 2000; Rodnick et al., 2004; Scott et al., 2014). For instance, Scott et al. (2014) observed no effect of strain on variation in CTmax, time to loss of equilibrium in hypoxia, routine á¹O2 or O2crit in adults of four different British Columbia, Canada strains of rainbow trout, although time to loss of equilibrium in hypoxia was affected by strain in juveniles. In combination, the results of the previous studies cited above suggest that intraspecific variation in these traits may not be widespread among most strains of rainbow trout, despite the differences observed in the present study. However, upper lethal temperatures (determined by 50% mortality) are higher in high-temperature selected strains than in control strains in Australia (Molony et al., 2004) and Japan (Ineno et al., 2005), and intraspecific variation in CTmax has been observed between a wild and a domestic strain of rainbow trout from the U.S.A. (Carline & Machung, 2001). Similarly, differences in CTmax and routine á¹O2 have been observed among families of hatchery rainbow trout in Australia (Chen et al., 2015), and differences in hypoxia tolerance have been detected between groups of rainbow trout with 88 different lactate dehydrogenase B genotypes (Klar et al., 1979), indicating the potential for genetically based variation in these traits. Taken together, these results suggest (1) that there are intraspecific differences in metabolic rate, hypoxia tolerance and upper thermal tolerance in rainbow trout, but (2) that the detection of these differences is dependent on the specific strains that are compared, as several strains do not differ in these traits. 4.5.2 Evidence for associations among metabolic traits, hypoxia tolerance and upper thermal tolerance Only one previous study has directly assessed intraspecific variation in metabolic rate, critical oxygen level, hypoxia tolerance and upper thermal tolerance among strains of rainbow trout (Scott et al., 2015), and the strains used in that study demonstrated few intraspecific differences. Therefore, the current study is the first to demonstrate differences in all these traits concurrently among a set of rainbow trout strains. Although it is not possible to disentangle entirely the genetic and epigenetic effects on the measured traits because Strain C was reared at a different facility under slightly different thermal conditions than the other two strains for the first 4 months of life, the design of the current study allows tests of the potential for associations among these traits by comparisons among strains and individuals. The strain that had the highest mean ILOS (Strain A) in the present study also had the lowest mean á¹O2max and AAS, and the strain that had the lowest mean ILOS (Strain C) had the highest mean á¹O2max and AAS. These patterns are consistent with the potential of an association between hypoxia tolerance and AAS or á¹O2max in normoxia, but comparisons among three strains alone are relatively weak evidence for this potential association, and although differences between Strain A and C in these traits were consistently resolved in post-hoc tests, Strain B was not significantly different from Strain A for AAS, whereas Strain B was not significantly 89 different from Strain C for ILOS. However, one strength of the current study is the ability to evaluate trait correlations among individuals for ILOS, O2crit, AOD, SMR, á¹O2max and AAS, which were measured on the same fish as components of the IRAP protocol, and these analyses reveal additional support for the possibility of a functional association between normoxic aerobic scope and hypoxia tolerance. Potentially the clearest finding of the present study was a tight correlation between ILOS and O2crit both among individuals and among strains. Thus, the capacity of a rainbow trout individual to maintain SMR aerobically at low oxygen saturations appears to play a major role in determining the minimum oxygen saturation that the individual can tolerate. As such, it is possible that ILOS may ultimately be a time-dependent phenotype determined in part by the duration that fish experience oxygen saturations below O2crit. If this is the case, then O2crit could also be thought of as an index of hypoxia tolerance in this species. Indeed, O2crit has been used as a measure of hypoxia tolerance in a variety of species of fish (e.g., Speers-Roesch et al., 2013; Rogers et al., 2016). In this regard, post-hoc tests demonstrated that Strain A had the highest O2crit, and Strain C had the lowest O2crit of the three strains in the present study, which is consistent with Strains A and C having the poorest and best tolerance of hypoxia, respectively. Furthermore, variation in O2crit was also correlated with both SMR and AAS measured in normoxia among individuals. In general, rainbow trout individuals with high values of AAS had low values of O2crit, which suggests that differences in AAS in normoxia may influence the abilities of á¹O2max individuals to maintain SMR aerobically when exposed to hypoxia. 4.6 Conclusion In comparison to the extent to which relationships between metabolic traits and hypoxia tolerance can be assessed using the data presented in the current study, the extent to which 90 relationships between these traits and upper thermal tolerance can be examined are limited, because CTmax was not determined for the same individuals as those used in the IRAP trials. However, variation in CTmax among strains was parallel to variation in á¹O2max and AAS among strains, and Strain C had the best tolerances of both hypoxia and high temperatures. These results suggest the possibility of associations among these traits in rainbow trout, but comparisons among only three strains are insufficient to provide firm conclusions on the relationships of these traits and CTmax, and this potential association should be assessed further by examining individual-level correlations in the future. Furthermore, although the potential for limitation of aerobic scope to play a role in determining CTmax has been demonstrated in some species (e.g., Chen et al., 2015), it is not the case in all species (e.g., Wang et al., 2014; Ern et al., 2016). Nevertheless, the present study clearly demonstrates the intraspecific variation in á¹O2max, AAS, O2crit, ILOS and CTmax among three strains of rainbow trout. Correlations among traits at the individual level indicate that there are likely functional associations between AAS, O2crit, and ILOS in this species. This opens the new venue for examining intra-specific variations and correlations for the traits of aerobic and anaerobic capacity. 91 Figure 4.1 Whole-animal a) maximum rate of oxygen uptake (á¹O2max), b) standard metabolic rate (SMRq0.2), c) absolute aerobic scope (AAS) and d) factorial aerobic scope (FAS) for three strains of rainbow trout (Oncorhynchus mykiss) parr - Strain A (orange), Strain B (green) and Strain C (blue). Box plots present 25% and 75% percentiles (median - bar; mean - diamond) and the upper and lower whiskers extend to the maximum and minimum values, respectively, that fall within 1.5 times the interquartile range from the 75% and 25% percentiles, respectively. Different letters above the box plots indicate denote statistically significant differences among groups for a measurement.A B C0100200300400500600700StrainMâ O 2max (mg O 2 hâ1 kgâ1 ) AABaA B C0100200300400500600700StrainAAS (mg O 2 hâ1 kgâ1 ) AABcA B C020406080100StrainSMR q0.2 (mg O 2 hâ1 kgâ1 )A A AbA B CStrainFAS0481216 AABBd 92 Figure 4.2 Whole-animal a) incipient lethal oxygen saturation (ILOS) and b) critical thermal maximum (CTmax) for three strains of rainbow trout (Oncorhynchus mykiss) parr - Strain A (orange), Strain B (green) and Strain C (blue). Box plots present 25% and 75% percentiles (median - bar; mean - diamond) and the upper and lower whiskers extend to the maximum and minimum values, respectively, that fall within 1.5 times the interquartile range from the 75% and 25% percentiles, respectively. Different letters above the box plots indicate denote statistically significant differences among groups for a measurement. A B CStrainILOS (% sat.)051015 ABBaA B CStrainCTmax (°C)015202530A A Bb 93 Figure 4.3 Whole-animal a) critical oxygen level (O2crit) and b) accumulated oxygen deficit (AOD) for three strains of rainbow trout (Oncorhynchus mykiss) parr - Strain A (orange), Strain B (green) and Strain C (blue). Box plots present 25% and 75% percentiles (median is the middle bar and mean the diamond symbol) and the upper and lower whiskers extend to the maximum and minimum values, respectively, that fall within 1.5 times the interquartile range from the 75% and 25% percentiles, respectively. Different letters above the box plots indicate denote statistically significant differences among groups for a measurement. Scatter plots of whole-animal c) incipient lethal oxygen saturation (ILOS) versus O2crit and d) ILOS versus AOD for individual parr - Strain A (orange squares; dotted lines), Strain B (green circles; solid lines) and A B C05101520StrainO 2crit (% sat.)ABCa0 5 10 15 20051015O2crit (% sat.)ILOS (% sat.)Strain AStrain BStrain CcA B C01020304050StrainAOD (mg O 2 kgâ1 )AAAb0 10 20 30 40 50051015AOD (mg O2 kgâ1)ILOS (% sat.)Strain AStrain BStrain Cd 94 Strain C (blue triangles; dashed lines). Lines display lines of best fit for each strain as determined from general linear models for ILOS with strain and either O2crit (c) or AOD (d) as factors. 95 Figure 4.4 Scatter plots of whole-animal a) critical oxygen level (O2crit) versus standard metabolic rate (SMRq0.2) and b) O2crit versus absolute aerobic scope (AAS) for individual parr - Strain A (orange squares; dotted lines), Strain B (green circles; solid lines) and Strain C (blue triangles; dashed lines). Lines display lines of best fit for each strain as determined from general linear models for O2crit with strain and either SMR (a) or AAS (b) as factors. 0 20 40 60 8005101520SMR (mg O2 hâ1 kgâ1)O 2crit (% sat.)Strain AStrain BStrain Ca0 100 200 300 400 500 60005101520AAS (mg O2 hâ1 kgâ1)O 2crit (% sat.)Strain AStrain BStrain Cb 96 Chapter 5: A proof-of-principle test for IRAP â Characterizing respiratory phenotypic plasticity after acclimation to a limiting factor in marine fish 3 5.1 Synopsis Current models of climate change predict that an increased frequency of hypoxic episodes and an expansion of marine oxygen minimum zones will pose significant threats for fish. I reasoned that marine fishes like European sea bass (Dicentrarchus labrax) that naturally face deoxygenated conditions might already possess phenotypic plasticity to deal with changing oxygen levels. As such, I investigate whether sea bass adopt a new respiratory phenotype when acclimated to hypoxia. I hypothesize that standard metabolic rate (SMR) is suppressed in hypoxia to reduce the reliance on glycolysis, and successful foraging in hypoxic coastal zones would require that the new phenotype does not sacrifice absolute aerobic scope (AAS). To test these two hypotheses, and to quantify phenotypic reversibility when the fish return to a normoxic environment, I characterized and compared the normoxic respiratory phenotype of sea bass with that after acclimation to hypoxia and also after hypoxia-acclimated fish were re-acclimated back to normoxia. When tested in normoxia hypoxia-acclimated fish had a 19% lower SMR and the same AAS compared with normoxia-acclimated fish, and when tested in hypoxia, they had a better aerobic performance than normoxia-acclimated fish. Thus, the new hypoxic phenotype clearly had improved aerobic capacity. Further, when the hypoxic phenotype was re-acclimated back to normoxia, it had 26% higher AAS than normoxia-acclimated fish, suggesting an impressive carry-over effect. Thus, sea bass possess considerable respiratory plasticity that benefits life in a hypoxic environment by reducing maintenance metabolism while retaining a high aerobic capacity. Such respiratory plasticity, which has not been shown before in another fish species, could provide an evolutionary edge for sea bass in the more hypoxic future, although I acknowledge that some trade-offs that are not evident in current results might exist. 3 A version of chapter 5 is in preparation for submission as Y. Zhang, F. Mauduit, K. Anttila, L. Pettinau, H. Ollivier, F. Lancien, A. P. Farrell, G. Claireaux, phenotypic plasticity provides an evolutionary edge for sea bass living in a hypoxic future. 97 5.2 Introduction Animal populations can respond to environmental selection pressure either by moving to a more favourable location, by changing their phenotype, or by evolving over multiple generations. In marine habitats, a major driving force is oxygen availability because exposure to situations where dissolved oxygen is reduced (hypoxia) is common (Deutsch et al., 2015). Hypoxic environments challenge an animalâs requirement to balance its oxygen supply with metabolic demand, a fundamental component of metabolic regulation (Hochachka & Matheson, 1992; Huss & Kelly, 2005). Hypoxia can be so severe that oxygen supply might not even meet the needs for basic functions. Some ectotherms, nevertheless, have evolved different strategies to survive severe hypoxia and even anoxia, which happen regularly in aquatic ecosystems (see review by Stecyk, 2017). For example, freshwater turtles and crucian carp (Carassius carassius, Linnaeus 1758) tolerate anoxia at bottom of small ponds for 5 months when they enter dormancy during winter by suppressing standard metabolic rate (SMR) by up to 90% (Ultsch & Jackson, 1982). Although this extreme hypometabolic state reduces demand as a tolerance mechanism eliminating the immediate need for oxygen and greatly reducing the reliance on glycolysis, the trade-offs include an inevitable constraint on locomotion (see review Stecyk et al., 2008) and suppression of cardiac output, which is also seen in hypoxia-tolerant common carp (Cyprinus carpio, Linnaeus 1758) and tilapia (Oreochromis spp) (Stecyk & Farrell, 2006; Speers-Roesch et al., 2010; Lague et al., 2012),. Not all fish species, however, are able to tolerate hypoxia so well. Hypoxia-sensitive species typically defend oxygen supply in hypoxia and do not suppress SMR like anoxia-tolerant species. For example, hypoxia-acclimated Atlantic cod maintained maximum oxygen uptake (á¹O2max) and enhanced oxygen delivery by elevating heart rate, hemoglobin concentration and 98 tissue oxygen extraction (Petersen & Gamperl, 2010; 2011). Likewise, hypoxia-acclimated rainbow trout enhanced oxygen delivery by improving blood supply to the heart and locomotory muscles during hypoxia (McKenzie et al., 2004; Gamperl et al., 2001; Gillis & Johnston, 2017). These strategies, however, are not effective at the low oxygen level experienced by anoxia-tolerant fishes that reduce SMR. The importance of understanding the full repertoire of strategies used by fishes to cope with hypoxia lies with the threat to marine fishes by the predicted increase in the frequency of hypoxic episodes and larger oxygen minimum zones (OMZs), especially in coastal areas (Schmidtko et al., 2017; Yasuhara et al., 2019; IUCN report). However, many coastal marine fishes vertically migrate diurnally between pelagic normoxic zones and OMZs (Gjøsaeter, 1984; MacKenzie & Mariani, 2012). Thus, an unresolved question is whether marine fishes can evolve to improve hypoxia tolerance at the same pace of the predicted increasing frequency of hypoxic episodes, or do they already have strategies to effectively forage in hypoxic habitats. Some fish species, like European sea bass, already successfully exploit hypoxic conditions in estuaries and coastal lagoons without apparently sacrificing the capacity to swim powerfully and capture prey in hydraulically dynamic coastal waters (Pickett & Pawson, 1994; JourdanâPineau et al., 2010). Hence, the current study aims to investigate sea bass respiratory phenotype and whether phenotypic plasticity in respiratory capacity relates to what is known of sea bass. Their hemoglobin-oxygen affinity (P50 = ~13 torr; Pichavant et al., 2003) is in between that of the hypoxia-tolerant common carp (P50 = 7 torr; Roy & Lykkeboe, 1978) and that of active, hypoxia-sensitive rainbow trout (P50 = 22 torr; Wood & Lomholt, 1976), suggesting sea bass could have a relatively high capacity to deliver oxygen to actively working tissues in a hypoxic environment. 99 I hypothesize that, similar to anoxia-tolerant species, sea bass can save energy after acclimation to hypoxia by establishing a hypometabolic state, which then reduces its reliance on glycolytic capacity in hypoxic water, but without compromising aerobic capacity and hence foraging in both hypoxic and normoxic pelagic zones. Further, this hypoxic phenotype should revert back to the normoxic respiratory phenotype on return to normoxia, because a hypometabolic state of reduced protein synthesis can hinder growth. To test the hypothesis, I fully characterized the whole-organism respiratory phenotype in both normoxia and hypoxia, and also after re-acclimation to normoxia, by coupling a whole-organism integrated respiratory assessment paradigm (IRAP; Zhang et al., 2019) with measurements of key metabolic enzyme activities (Childress & Somero, 1979; Dalziel et al., 2012). Collectively, my goal was to characterize the organismal and molecular aspects of physiological phenotypic plasticity that might prepare marine fishes to respond to the more hypoxic future. 5.3 Materials and methods 5.3.1 Acclimation procedures Fish were distributed (normoxic: n = 50; hypoxic: n = 50) into two flow-through (fresh, thermoregulated (16°C) seawater) 500-L aquaria. Control normoxic fish were maintained with dissolved oxygen (DO) at ~98 % sat., while DO in the hypoxia acclimation tank was reduced at a rate of 10 % sat. h-1 until ~50 % sat., which was then maintained with a custom-built, 50-L gas-equilibrium column. Fresh, thermoregulated seawater entered the column from the top (300 L h-1) while nitrogen gas was injected at the bottom. DO of 50% sat. is known to accommodate peak oxygen uptake of specific dynamic action in European sea bass (Zambonino-Infante et al., 2017). Hypoxic acclimation lasted six weeks. Hypoxia-acclimated (H-group) and normoxia-acclimated (N-group) sea bass had similar relative liver mass (see Appendix C.2: Figure S5.4), suggesting 100 that the hypoxic phenotype maintained a good nutritional condition during the 6-week acclimation period. The longer acclimation (typically 3-4 weeks of acclimation period in fish biology) is to allow fish to fully manifesting any potential tissue remodelling. After the acclimation, the respiratory phenotype of normoxia-acclimated (N-group; n = 28) and hypoxia-acclimated (H-group; n = 32) sea bass was tested by IRAP assessment. To further evaluate the reversibility of the hypoxia-acclimated phenotype, 13 additional hypoxia-acclimated fish were returned to the normoxic aquarium and reared for a further 4 weeks before the other IRAP assessment (HN-N-group). After the IRAP measurements, fish were all recovered and maintained in either normoxia or hypoxia without any mortalities for at least a further 2 weeks to recover from IRAP before being euthanized with a blow to the head. Red and white skeletal muscle were sampled to analyze aerobic and anaerobic enzymes (citrate synthase & lactate dehydrogenase) activities (N: n = 23; H: n = 13; HN-N: n = 13; see Appendix C.1 for procedures for enzyme activity assays). Procedures for enzyme activity assays are detailed in the supplementary material. Blood, ventricle and liver were sampled to measure hematocrit, relative ventricular mass and relative liver mass, respectively. Thus, I reduced the fish number required, and increased statistical power by taking tissue samples from the same fish that were used for IRAP measurements. All the experiments were carried out according to French animal care procedures (Apafis 14576). 5.3.2 Characterizing the respiratory phenotype The respiratory phenotype of individual fish was established with an automatic intermittent-flow respirometry system (Steffensen, 1989) as detailed in Chapter 2. The experiments were conducted in 16 ± 0.5 °C seawater. Sea bass were individually exhausted within the respirometers while monitoring á¹O2 on-line (Zhang et al., 2020). A minimum sampling window 101 duration (2-min; see Appendix C.2: Figure S5.1) for determining á¹O2max was determined as described in Chapter 2. Control normoxic sea bass were characterized only in normoxic water (N-N; n = 28; 61.5 ± 2.0 g), while hypoxia-acclimated sea bass were characterized in either hypoxic water (50% sat.; H-H; n = 16; 63.6 ± 5.7 g), or normoxic water (H-N; n = 16; 74.7 ± 4.5 g). The normoxia-reacclimated fish (HN-N; n = 13; 63.3 ± 6.1g) were tested in normoxia. The four fish groups had the same body size (see Appendix C.2: Figure S5.5). After successfully reviving all the fish, I returned them to the recovery tanks. 5.3.3 Characterizing a hypoxia performance curve A hypoxia performance curve (HPC) was measured in normoxia- (n=8) and hypoxia- (n=8) acclimated fish after at least a 7-day recovery from the IRAP protocol, which was considered ample, especially since sea bass regularly swim and encounter hypoxia in nature. Full recovery from the exhaustive chase takes < 24 h (Milligan, 1996; Zhang et al., 2018) and would have been completed during the IRAP assessment. For the HPC, fish were placed in the respirometers described above and left to habituate for 30 min in flush mode (with ~95 % sat. 16 ËC seawater). Thereafter, fish were exercised for 10 min inside the respirometer (see Chapter 3) to generate a á¹O2max using AquaResp at DO of 95% sat.. A á¹O2 value was subsequently generated every 5 min with the measurement cycle (30-s sealed stabilization period, 120-s sealed measurement period and 150-s flush period) with the fish recovering and water DO decreasing progressively (see Chapter 2). Fish were then mildly re-exercised inside the respirometer for 5 min to maintain a peak á¹O2 (á¹O2peak: see Appendix C.2: Figure S5.2) while DO continued to decrease, generating a new á¹O2peak at a lower DO during a 5 min recover period. The fish was exercised again to generate the subsequent á¹O2peak as DO reduces. The protocol mildly exercising the fish as DO decreased progressively continued for 125 min, generating 11 á¹O2peak values when DO 102 was greater than O2crit (from 95% to 20% sat.). Fish were removed immediately from the respirometers and returned to the well-aerated aquaria. á¹O2peak values were calculated offline and used for HPC modeling. An HPC for individual fish plotted á¹O2peak at each DO to which a one-phase association regression model was applied (Mueller & Seymour, 2011; see Appendix C.2: Figure S5.2). Only individuals with 0.65 â¤ R2 â¤ 0.99 (n=3 were rejected) were combined to generate the best-fit models for the HPC of the normoxia- and hypoxia-acclimated groups. 5.3.4 Statistical analyses All IRAP variables were statistically compared among the treatment groups using ANOVA with Tukey-Kramer post-hoc tests. If necessary, data were logarithmically transformed to meet the assumptions of normality of residuals. The best-fit model used individual á¹O2peak values normalized as a percentage of the individual á¹O2max (reduce variations in á¹O2peak), which were arcsine transformed to compare the normoxia- and hypoxia-acclimated groups using independent sample t-tests (absolute values see Appendix C.2: Figure S5.3). To minimize the type II error for HPC validation, priority hypotheses were tested by supervised t-tests to for only the relevant comparisons. 5.4 Results 5.4.1 Hypoxic exposure impairs aerobic scope When the normoxia-acclimated fish tested in normoxia (N-N) are compared with the fish acclimated and tested in hypoxia (H-H; 50% DO), no significant differences emerged for any of the IRAP-derived respiratory indices, except for á¹O2max and the derived variables AAS and FAS, which were significantly lower in hypoxia (p â¤ 0.01; Figure 5.1). Thus, the dissolved oxygen level used for hypoxic acclimation was limiting for sea bass. Even so, the HPC for hypoxia-acclimated fish revealed a similar á¹O2peak at 50% sat. as the H-H fish (387.5 ± 13.0 vs. 103 319.9 ± 15.7 mg O2 h-1 kg-1, respectively; p = 0.07). Therefore, á¹O2max was similarly reduced by 21% with 50% sat. hypoxia in two independent experiments, suggesting that hypoxia acclimation had not impaired á¹O2max, at least with 50% sat. hypoxia. Also, hematocrit, relative ventricular mass (p â¥ 0.07; see Appendix C.2: Figure S5.4) and maximum lactate dehydrogenase capacities in red and white muscles (p â¥ 0.93, Figure 5.2) were unchanged after hypoxic acclimation, being similar for the H- and the N-groups. 5.4.2 A new hypoxic phenotype revealed by testing hypoxia-acclimated fish in normoxia Hypoxia-acclimated fish when tested in normoxia (H-N) had a significantly lower SMR compared with the N-N group with a 19% lower subsistence metabolism (88.0 ± 2.1 vs. 109.3 ± 3.5 mg O2 h-1 kg-1, p = 0.001; Figure 5.1). This hypometabolic state was evident even in the post-recovery, on-line á¹O2 traces of individuals and when these data were pooled á¹O2 (Figure 5.1). Also, the hypometabolic state was independent of any spontaneous activity because the variance of RMR of the quiescent H-N group was identical to that for the N-N and H-H groups (p â¥ 0.22; Figure 5.1). Furthermore, the SMR of the H-N group was 9% lower than that of H-H group (p < 0.001; Figure 5.1), a difference likely attributable to the additional subsistence cost of obtaining oxygen from water with 50% less oxygen. As suspected for the above, the hypoxia-acclimated sea bass did not sacrifice the aerobic capacity, as revealed by both á¹O2max and AAS being similar for the H-N and N-N groups (p â¥ 0.99, Figure 5.1). Also, with a lower SMR and a similar á¹O2max, FAS for H-N was significantly greater than that for N-N (Figure 5.1). Several results suggested that the hypoxic phenotype reduced reliance on glycolytic capacity during acute exposure to hypoxia. When compared with N-N group, the H-N fish displayed a significantly reduced O2crit (28%; 12.9 ± 0.5 vs.18.0 ± 0.7 % sat., p < 0.0001), ILOS 104 (19%; 5.0 ± 0.4 vs. 6.2 ± 0.3 % sat., p = 0.039) and SOD (34%; 7.9 ± 0.4 vs. 11.9 ± 0.8 % sat., p = 0.001; Figure 5.3). Moreover, hypoxia-acclimated fish had a left-shifted HPC and maintained a 44% higher percentage of AAS between 27â46% DO sat. (0.0004 â¤ p â¤ 0.049) than the normoxia-acclimated fish (Figure 5.4). These changes suggest that the hypoxic phenotype benefited from improved oxygen delivery to tissues, improvements that sometimes were best revealed by testing the hypoxia-acclimated sea bass in normoxia. 5.4.3 Re-acclimation of the hypoxic phenotype to normoxia improved aerobic capacity A 4-week re-acclimation of the hypoxic phenotype to normoxia only partially restored the initial normoxic phenotype. Indeed, beneficial carryover effects from hypoxic acclimation persisted after 4 weeks in normoxia, as revealed by the HN-N group having a significantly higher á¹O2max (18%; 503.4 ± 27.2 vs. 427.3 ± 11.7 mg O2 h-1 kg-1), AAS (27%; 402.5 ± 23.3 vs. 318.0 ± 13.7 mg O2 h-1 kg-1) and FAS (26%; 5.1 ± 0.2 vs. 4.0 ± 0.2, p â¤ 0.0086; Figure 5.1), as well as a higher citrate synthase capacity in the red muscle (p = 0.049, Figure 5.1) when compared with the N-N group. While the O2crit of the HN-N group remained 10% lower than the N-N group (p = 0.001; Figure 5.3), SMR, ILOS and SOD of the HN-N group had all returned to a level statistically indistinguishable to that of the original N-N phenotype (p â¥ 0.17; Figure 5.1c & 3b,c). Lastly, the EPOC of the HN-N group was 38% lower than that of the N-N group (p = 0.05; Figure 5.3). 5.5 Discussion I demonstrated that sea bass has respiratory plasticity that would benefit its life history (living and foraging in both hypoxic and hydraulically active habitats) and perhaps in the future with progressively more hypoxic oceans. Hypoxia acclimation clearly created a new respiratory phenotype for sea bass, one in which SMR was suppressed and aerobic scope was enhanced. 105 This new phenotype would reduce the dependence on glycolytic capacity in hypoxia and reduced the limitation hypoxia places on á¹O2max and aerobic scope. While a 4-week re-acclimation to normoxia fully reversed the depressed SMR of the hypoxic phenotype, valuable carryover effects remained - an enhanced aerobic capacity and an enhanced aerobic enzyme capacity in red muscles when compared to a normoxic fish that had not been acclimated to hypoxia. Collectively, these results support my hypothesis that hypoxic sea bass take advantage of lowering SMR and defending aerobic capacity better in moderate hypoxia, phenotypic plasticity that might give an evolutionary advantage for sea bass in a more hypoxic future. Such abilities, therefore, should be considered in projections for future population dynamics and biogeographic distribution patterns of sea bass, and warrant experiments to see if other marine fishes share these traits. 5.5.1 Hypometabolism in an active marine fish Hypoxia-acclimated sea bass reduced SMR by 19% when tested in normoxia. As far as I know, this is the first time such a metabolic suppression has been demonstrated outside of anoxia-tolerant fish species. How their maintenance metabolism was reduced is a matter of speculation. Based on previous work, possibilities include translational arrest (Cassidy et al., 2018), a reduction of Na+ K+-ATPase activity (Hulbert & Else, 2004) and better mitochondrial coupling (Rolfe & Brown, 1997), although these mechanisms are not entirely mutually exclusive. A reduction in Na+ K+-ATPase activity seems less likely because it would affect the function of all cells and yet hypoxia-acclimated sea bass maintained a normal RMR unlike the dormancy states that exist when channel arrest is found (Hulbert & Else, 2004). Similarly, increasing the efficiency of oxidative phosphorylation while reducing O2 demand for producing ATP also seems unlikely as there are no previous examples of such responses in hypoxia acclimation (Du 106 et al., 2016). In contrast, translational arrest for protein turnover would reduce ATP demand and is a mechanism commonly used by anoxia-tolerant fishes (Gracey et al., 2001; Hand, 1998; Buck & Hochachka, 1993; Fähling, 2009). Even acute hypoxia exposure has been shown to induce translational arrest in Amazonian cichlids (Astronotus ocellatus, Agassiz 1831) (Cassidy et al., 2018), Arctic char (Salvelinus alpinus, Linnaeus 1758) (Cassidy & Lamarre; 2019), and jumbo squid (Dosidicus gigas, d'Orbigny 1835) (Seibel et al., 2014). Suppression of digestion, growth and muscle development reported in fishes, including sea bass, reared in hypoxia (Goldberg et al., 1975; Dupont-Prinet et al., 2010; Zambonino-Infante et al., 2017) could also in part reflect translational arrest. However, while the translational arrest is the most likely mechanism to explain the 19% decrease in SMR in hypoxia-acclimated sea bass, it is probably not the only mechanism because the hypoxia-acclimated sea bass maintained liver mass and citrate synthase capacities in red and white muscles. Further studies are, thus, needed to reveal at the molecular level how sea bass can reduce SMR in hypoxia and how fast molecular signalling pathways can establish a whole-organism hypoxic phenotype, as originally proposed by Hochachka et al. (1996). The energy-saving observed previously in cunner (Tautogolabrus adspersus, Günther 1862) during hypoxia was a result of inactivity (Speers-Roesch et al., 2018). However, I am confident that the metabolic suppression in hypoxia-acclimated sea bass was not due to them becoming more lethargic in the respirometer. The hypoxic and normoxic phenotypes had the same level of spontaneous activity when tested in normoxia. Also, the analytical method for measuring SMR eliminates most of the concerns of including spontaneous activities, especially when I measured fish for two diurnal cycles without disturbance. I am also confident that any stress of acutely moving a hypoxia-acclimated sea bass into a normoxic environment was not a 107 contributing factor, because stress elevates rather than reduces metabolic rate. Also, SMR was determined following a 24-h recovery period. Less clear, however, is whether or not the phenotypic plasticity observed here can occur rapidly, perhaps over a period of a day, which would certainly benefit sea bass that live in periodically hypoxic habitats and also introduces the possibility that the phenotype of the hypoxia-acclimated fish might change while being tested in normoxia. While the time course of the acclimation responses should be studied to resolve this possibility, we do know that SMR can return to the normoxic phenotype after 4 weeks of re-acclimation to normoxia. A potential benefit of the observed metabolic suppression is to offset the increased ventilation cost of hypoxia water as indicated by SMR of hypoxia-acclimated fish being 9% higher when measured in hypoxia than in normoxia. If simply assume that all other parameters in the oxygen transport cascade system being equal, sea bass would need to breathe twice as much seawater with water DO of 50% sat. Whether the difference between SMR measurements of hypoxia-acclimated fish in hypoxia and normoxia is solely the added cost of breathing hypoxic water (Farrell & Steffensen, 1987) or an additional osmo-respiratory compromise (Wood et al., 2019) is unclear and further studies are needed. 5.5.2 The hypoxic phenotype defends its aerobic capacity My second main finding was that hypoxia-acclimated sea bass when tested in normoxia maintained the same á¹O2max and AAS as their normoxia-acclimated counterparts. Previously, only hypoxia-sensitive species such as rainbow trout (Bushnell et al., 1984), Atlantic cod (Petersen & Gamperl, 2010; 2011) and red bream (Pagrus auratus, Forster, 1801) (Cook et al., 2013) have been shown to maintain á¹O2max and aerobic capacity when tested in normoxia after hypoxia-acclimation. This shared ability will benefit sea bass whenever they exploit a hypoxic 108 habitat and especially after the return from exploiting such habitats for prolonged periods because a clear carryover effect (increased aerobic scope) was seen after hypoxia-acclimated sea bass were re-acclimated to normoxia. Hypoxia is a well-established limiting environmental factor (Beamish 1971). Even hypoxia-acclimated sea bass reduced their á¹O2max, AAS and FAS in 50% sat. water. However, an HPC can more precisely quantify the constraint on the aerobic scope as water becomes progressively more hypoxic (Neill et al., 1994; Claireaux & Chabot, 2016). Indeed, hypoxia-acclimated sea bass had left-shifted HPC, indicating they could maintain a greater aerobic scope than normoxia-acclimated fish at the lower water oxygen levels, a clear benefit to the ecological needs of sea bass foraging and exploiting hypoxic coastal waters. Explaining the mechanism(s) used by hypoxia-acclimated sea bass to defend aerobic scope will require further study but should consider known responses to hypoxia in fishes, such as gill lamellar remodelling (Sollid et al., 2003; Brauner & Rombough, 2012; Anttila et al., 2015) to increase the oxygen diffusion time in gill (Randall & Shelton, 1963; Randall, 1982), changes in hemoglobin-oxygen binding (Weber & Jensen, 1988) and increased venous tone to mobilize more oxygen (Sandblom & Axelsson, 2005, 2006), and remodelling of cardiac (Gamperl et al., 2001; Gillis & Johnston, 2017) and swimming muscles (McKenzie et al., 2004) to increase capillarity. Such mechanisms would also explain the improved capacity of hypoxia-acclimated sea bass to tolerate hypoxia â a lower O2crit and ILOS. Previous studies of other hypoxia-acclimated fishes have similarly seen decreases in O2crit (Routley et al., 2002; Timmerman & Chapman, 2004; Fu et al., 2011; Remen et al., 2013; Cook et al., 2013) and ILOS (Petersen & Gamperl, 2011; Yang et al., 2013; Dan et al., 2014). Consequently, hypoxia-acclimated sea bass can defer their reliance on glycolytic 109 capacity in hypoxic water, a trait that could give them an evolutionary advantage if the oceans become more hypoxic in the future. In the present study, I added sensitivity to model the HPC by reducing biological variation. Rather than using different fish at various DO levels (Neill et al., 1994; Claireaux & Lagardère, 1999; Claireaux et al., 2000; Lefrancois & Claireaux, 2003), I generated an HPC for a single fish using a repeated-measures design. While my methodology might suffer from potentially underestimating á¹O2max at moderate levels of hypoxia (hence I used the term á¹O2peak), sea bass were clearly conforming to DO at levels well above the DO50. The repeated-measures design also means fewer fish are needed to generate a valid HPC. My second improvement to modelling the HPC was to normalize each á¹O2peak value to a percentage of its á¹O2max, thereby generating an HPC fitted to 100% AAS (see Appendix C.2: Figure S5.2). This analytical approach then lends itself to determining DO50, the DO for 50% of AAS (see Figure 5.4), an index that could be broadly used to compare fish species, much like the P50 for a blood-oxygen dissociation curve. The quality control for this new approach is that á¹O2peak at DO50 was identical to á¹O2max of hypoxia-acclimated fish tested in 50% DO water. Thus, metabolic waste accumulation may not have been a problem with the repeated-measures design that I used. Because my new approach resolved a 21% difference in DO50 (38 vs. 48 % sat.; p = 0.0031; Figure 5.2), the DO50 approach holds promise for the broader application in physiology, ecology and evolution. 5.5.3 Phenotypic reversibility and beneficial carryover effects The third main result in this chapter was the beneficial carryover effects of an elevated aerobic energy production capacity and improved hypoxia tolerance being retained from the hypoxia acclimation even after 4 weeks in normoxia, although SMR was restored. For example, they 110 might reduce their reliance on glycolysis for intensive exercise, especially since EPOC was lower for the HN-N group than for the N group. Likewise, retaining a low O2crit and ILOS would provide an immediate benefit for re-entry into a hypoxia zone. In contrast, not reversing the hypometabolic state could be maladaptive in normoxic conditions as it might hinder important bioenergetic traits, such as growth (Pardee, 1974). Considering the cyclic nature of the environment, studies in phenotypic reversibility are very important but extremely scarce (see review by Burggren, 2020). 5.6 Conclusion European sea bass, a pelagic fish that naturally experiences cyclic hypoxic episodes, can adjust its phenotype when acclimated to hypoxia by becoming hypometabolic and by defending oxygen delivery better in hypoxia without compromising aerobic capacity. The results are discovered in part by introducing new methods to determine DO50, an approach that may help broad comparisons among fish species. Shifting SMR to a hypometabolic phenotype relieved the metabolic dependence on glycolytic capacity during severe hypoxia without compromising normal aerobic capacity. Therefore, deferring the time-limited glycolysis and slowing the accumulation of debilitating anaerobic end-products might be underappreciated by ecological modelling in this era of oceanic deoxygenation, whereby phenotypic plasticity and the environmental preconditioning are overlooked. 111 N-N H-H H-N HN-N0100200300400500600700á¹O2max (mg O2 h-1 kg-1 ) bcba0 10 20 30 400100200300400500Time (h)N-NH-HH-NHN-Ná¹O2 (mg O2 h-1 kg-1 ) 0 10 20 30 400100200300400500Time (h)á¹O2 (mg O2 h-1 kg-1 ) N-NH-NHN-N H-HN-N H-H H-N HN-N0204060Variance for RMR (S.D.) abbaabN-N H-H H-N HN-N050100150200SMR (mg O2 h-1 kg-1 ) aabab(e)(c)(a)(b)N-N H-H H-N HN-N0100200300400500600AAS (mg O2 h-1 kg-1 ) bcbaN-N H-H H-N HN-N02468bcaaFAS(d)(g)(f) 112 Figure 5.1 The respiratory phenotype in juvenile European sea bass (Dicentrarchus labrax) at 16 oC as derived by measuring the rate of oxygen uptake (á¹O2) after normoxic acclimation (N-N; grey), hypoxic acclimation and tested in hypoxia (H-H; blue), hypoxic acclimation and tested in normoxia (H-N; orange) and re-acclimation of H-H fish to normoxia (HN-N; green). These demonstrative traces and five indices describe the spontaneous activity and aerobic capacity. (a) A continuous á¹O2 trace of representative individual fish is presented for four treatment groups for the first 40 h of an Integrated Respiratory Assessment Paradigm (IRAP) testing protocol. Their individual standard metabolic rate (SMR; solid horizontal lines) and maximum oxygen uptake (á¹O2max; dotted horizontal lines) are presented for reference. White-&-grey segments indicate summer diel cycles in western France (15 L:9 D). (b) For illustrations, á¹O2 traces were pooled as a function of time and present as mean Â± s.e.m. for each treatment group. Group mean values are presented as boxplots (bar is 25-75 percentile, whiskers are 10-90 percentile, line is median and â+â is mean, n = 13â28) for (c) SMR, (d) spontaneous activity [S.D. of the RMR measured after excess post-exercise oxygen consumption and before hypoxia challenge test] (e) á¹O2max, (f) absolute aerobic scope (AAS = á¹O2max â SMR) and (g) factorial aerobic scope (FAS = á¹O2max / SMR). These analyses were based on individual á¹O2 traces, as shown in (a) after the fish was exercised inside the intermittent-flow respirometer. Phenotypic plasticity is indicated by comparing N-group (grey) with H-group (blue). Reversibility is indicated by comparing N-group (grey) with HN-N-group (green). Different letters denote a statistical significance (one-way ANOVA with Tukey-Kramer post-hoc tests, : < 0.05). 113 Figure 5.2 Effects of 6-week hypoxia acclimation on the maximal activity of citrate synthase (CS; a) and lactate dehydrogenase (LDH; b) in red and white muscles of the juvenile European sea bass (Dicentrarchus labrax) at 16 oC. Values were present in the boxplot (bar is 25-75 percentile, whiskers are 10-90 percentile, the line is median and â+â is mean, n = 13â23). Phenotypic plasticity is indicated by comparing normoxia-acclimated fish (N; grey) with hypoxia-acclimated fish (H; blue). Reversibility is indicated by comparing normoxia-acclimated fish (grey) after re-acclimate to normoxia for 4 weeks and tested in normoxia (HN-N; green). Different letters denote a statistical significance (two-way ANOVA with Tukey-Kramer post-hoc tests, : < 0.05) among the treatment groups in each muscle group. N H HN-N N H HN-N0.00.10.20.30.4CS activity (umol/min/mg protein)ababa a aRed muscle White muscleN H HN-N N H HN-N0.00.51.01.52.02.5LDH activity (umol/min/mg protein)Red muscle White musclea a aa a a(a) (b) 114 Figure 5.3 Effects of 6-week hypoxia acclimation on respiratory phenotype as characterized by four indices of anaerobic capacity [(a) critical oxygen saturation (O2crit), (b) incipient lethal oxygen saturation (ILOS) and their derived (c) scope for oxygen deficit (SOD), (d) excess post-exercise oxygen consumption (EPOC)] in juvenile European sea bass (Dicentrarchus labrax) at 16 oC. Values were present in the boxplot (bar is 25-75 percentile, whiskers are 10-90 percentile, line is median and â+â is mean, n=13â28). Phenotypic plasticity is indicated by comparing normoxia-acclimated fish tested in normoxia (N-N; grey) with hypoxia-acclimated fish tested either in hypoxia (H-H; blue) or in normoxia (H-N; orange). Reversibility is indicated by comparing normoxia-acclimated fish (grey) after re-acclimate to normoxia for 4 weeks and tested in normoxia (HN-N; green). Different letters denote a statistical significance (one-way ANOVA with Tukey-Kramer post-hoc tests, : < 0.05). N-N H-H H-N HN-N0250500750100012501500a abbabEPOC (mg O2 kg-1) N-N H-H H-N HN-N051015202530O2crit (% sat.)abc bcN-N H-H H-N HN-N0510152025SODaabbabN-N H-H H-N HN-N03691215ILOS (% sat.)abbab(a) (b)(c) (d) 115 Figure 5.4 Hypoxia performance curve quantified by the limited-oxygen concentration models of normalized oxygen uptake (á¹O2) in normoxia- and hypoxia-acclimated juvenile European sea bass (Dicentrarchus labrax) at 16 ËC. á¹O2 values are measured and then normalized as a percentage of AAS (% AAS) in each individual. Mean Â± s.e.m. for á¹O2 at a given dissolved oxygen saturation of the water (% sat.) were used to characterize a group HPC model using one-phase association equations [normoxia phenotype: y = -39.5 + (130.0 + 39.5) Ã [1-e(-0.005 Ã x)]; R2 = 0.88, AIC=339.8; hypoxia phenotype: y = -85.8 + (88.3 + 85.8) Ã [1-e(-0.057 Ã x)]; R2 = 0.81, AIC=412.6]. The solid curves are one-phase association regression models and the shaded areas are the 95% confidence intervals of these curves. Asterisks denote the statistical significances between arcsine transformed % AAS between normoxia- and hypoxia-acclimated fish at each dissolved oxygen saturation (independent sample t-tests, : < 0.05). Blue dash lines graphically illustrate the comparison of dissolved oxygen (DO) for 50% of AAS (DO50). DOAAS-500 20 40 60 80 100020406080100Dissolved oxygen (% sat.)Hypoxia phenotype R2=0.81 AIC=412.6Normoxia phenotype R2=0.88 AIC=339.8% AAS***** 116 Chapter 6: A proof-of-principle test for IRAP â Diagnosing sublethal costs of viral infection on respiratory phenotype using salmonid models 4 6.1 Synopsis Natural selection more probably acts over a series of sublethal costs to gradually shift the distribution of the phenotype and allele frequency over generations. If the diagnostic power of a phenotyping protocol (e.g. IRAP) can detect the sublethal shifts, biologists are better poised to reveal the subtle yet persistent force of natural selection. A major overlooked force in natural selection is viral infections. Hence, I tested the hypothesis that IRAP can resolve the metabolic consequences of a viral infection. I used two viruses, the high-virulence, low-prevalence infectious hematopoietic necrosis virus (IHNV) as a positive control and the low-virulence, high-prevalence piscine orthoreovirus (PRV), which is of concern for wild sockeye salmon conservation and Atlantic salmon aquaculture. IRAP detected load-dependent effects of IHNV infections in wild sockeye salmon on SMR, RMR, DEE and O2crit. Individual fish that were resolving the IHNV load suppressed their SMR, RMR and consequently, O2crit and DEE also decreased significantly. However, PRV infection had no major or sustained effects on the respiratory phenotype of Atlantic salmon, even after being given a hypoxic challenge and re-testing, but SMR decreased by 12% in sockeye salmon, which was too shallow a metabolic suppression to affect aerobic and anaerobic capacity, recovery from an exhaustive exercise, behavioural activities and hypoxia tolerance. The diagnostic power of IRAP to characterize load-dependent sublethal effects of viral infections should enable better evidence-based policy developments to implement precautionary measures to manage wild salmon conservation on the west coast of Canada. 4 Chapter 6 is a synthesis of one published article and a manuscript in preparation: I) Y. Zhangâ , M. Polinskiâ , P. Morrison, C. Brauner, A.P. Farrell, K. Garver (2019) High-load reovirus infections do not imply physiological impairment in salmon. Frontiers in Physiology 10, 114. II) M. Polinskiâ , Y. Zhangâ , P. Morrison, G. Marty, C. Brauner, A.P. Farrell, K. Garver, Respiratory assessments of salmon during rhabdovirus and reovirus infection identify a role for lowering the standard metabolic rate in a vertebrate antiviral defence. Under review â Equal contributor 117 6.2 Introduction Infectious pathogens are a growing threat to public health and wildlife, hence an important component for governing society and conserve biodiversity. Globally, human infectious disease has averaged 5.2 events per year from 1940 to 2004, whereby an event is defined by the first pathogen emergence in the human population that leads to diseases (Jones et al., 2008). For diseases, mortality can be the accumulated consequences of subtle sublethal effects. Thus, if sublethal effects go undetected, unexpected catastrophes may follow. Among pathogens, animal viruses are expected to inflict metabolic costs to the infected hosts, a sublethal effect. For example, virally infected organisms typically initiate antiviral immune responses, inflammation, host-directed apoptosis and suppression of metabolic pathways for macromolecule synthesis (Hand & Hardewig, 1996; Kaminskyy & Zhivotovsky, 2010; Yada & Tort, 2016), all of which should have detectable metabolic consequences at the whole-organism level. Indeed, typhoid-vaccinated humans increased metabolic rate by 16% while mounting a fever response (Cooper et al., 1992). Similarly, Sprague Dawley rats mounting fever and inflammatory responses after turpentine injections increased metabolic rate by 28% (Cooper et al., 1994). However, not all studies have seen such costs. No detectable change in metabolic rate was seen in typhoid-vaccinated humans with increased white cell counts (Cooper et al., 1992). Similarly, no detectable change in the metabolic rate occurred in humans with increased allelic polymorphism erythrocyte when vaccinated with Plasmodium-target antigens for malaria resistance (Miller, 1994). However, the absence of a metabolic change could justify that, or a low diagnostic power being unable to resolve the cost, or a metabolic suppression of some sort to offset the increased cost of immune responses. 118 Fish are no exception to the metabolic costs when acting as hosts to pathogens (Harvell et al., 1999). Sea lice infestation has load-dependent costs on the swimming performance of pink salmon (Oncorhynchus gorbuscha, Walbaum, 1792) (see review by Brauner et al., 2012). The threshold of inducing the sublethal effects was higher than that used in the mathematical models, which predicted louse-induced mortality would cause an imminent collapse of Broughton pink salmon populations (in British Columbia, Canada) (Krkosek et al., 2007; Krkošek et al., 2011). The population collapse has not happened in 2019 (Brooks & Jones, 2008; Cline et al., 2019). Although history has demonstrated the inferior predictive power of association studies and simplistic modelling approach, association studies have now led to speculation that viruses found on farmed fish are a ticking time bomb that could collapse wild Pacific salmon populations, particularly sockeye salmon, by preventing a once-in-a-lifetime spawning event and result in zero lifetime fitness (Olsen et al., 2015; Takano et al., 2016; Garseth et al., 2013; Morton et al., 2017; Di Cicco et al., 2018). Pathogen transmission from farmed salmon to wild salmon has been suggested as the most prominent threat for fish conservation in Canada and some European countries (Krkošek et al., 2011). Given the on-going and broadening concerns about pathogenic infections in wild salmon, my objective is to test the principle that a valid and high-throughput IRAP protocol has sufficient diagnostic power to detect sublethal effects of pathogen infections on the respiratory metrics. I hypothesized that IRAP can resolve the metabolic consequences of a viral infection in salmonids. Such hypothesis testing needs positive control. Viruses are described by their virulence and prevalence, their ability to transmit through a population. Therefore, I tested a high virulence (their ability to induce harm on a host) and low prevalence virus (their ability to transmit through a population), the infectious hematopoietic necrosis virus (IHNV) as a positive control. I also 119 tested a high prevalence and low-virulence virus (piscine orthoreovirus, PRV) because of the emerging concern for transmission between farmed and wild salmon (OIE, 2017), but for which sub-lethal effects are largely unknown although the knowledge of pathological changes is increasing. IHNV is endemic to wild sockeye salmon, a keystone species in the northeast Pacific Ocean. Government agencies have been managing the transmission of IHNV among wild, hatchery-reared and farmed salmon for almost 50 years (Amend et al., 1969). Even so, IHNV has periodically caused large economic losses in salmon aquaculture (Garver et al., 2013), and as a result, all marine-reared Atlantic salmon aquaculture companies now voluntarily vaccinate their salmon against IHNV (Wade, 2017). Within the first week of an IHNV infection, an acute infectious hematopoietic necrosis (IHN) develops, often resulting in significant fish mortality (Rudakova et al., 2007). However, the proportion of the infected population that withstand the initial phase of infection, eventually clears the IHNV load and survive (Traler et al., 1997), suggesting multiple phenotypic mechanisms determining individual susceptibility or resistance to an IHNV infection. Mechanistically, IHNV infections often trigger an up-regulated antiviral response, initiating inflammatory processes and amplified apoptotic signalling and heightened RNA degradation. Conversely, resistant sockeye salmon can suppress cell proliferation and macromolecule synthesis (Polinski et al., 2019). Yet, functional assessments on sublethal metabolic consequences of IHNV infections are lacking. PRV has a global distribution in wild and cultured salmon populations and has been detected in nearly all salmonid species (Garseth et al., 2013; Marty et al., 2015; Olsen et al., 2015; Takano et al., 2016; Morton et al., 2017; Julio et al., 2018). The prevalence in free-swimming wild Pacific salmon is generally 5% or lower (Polinski & Garver, 2019). However, 120 PRV prevalence in Atlantic salmon farms in BC now approaches 100% once salmon are transferred to ocean net pens, a prevalence that starts to increase after about 6 months in seawater (Di Cicco et al., 2017; Polinski et al., 2019). Thus, PRV is currently the transmissible virus of most concern for salmon aquaculture countries (Wessel et al., 2017). Unlike IHNV, PRV load increases more slowly and persists. PRV produces microscopic viral inclusion bodies in erythrocytes where it preferentially replicates. PRV load reaches a peak around the 3â4 weeks post-infection and high PRV titres persist (Finstad et al., 2014; Wessel et al., 2015; Garver et al., 2016; Wessel et al., 2017). The persistent phase can sometimes cause a disease known as the heart and skeletal muscle inflammation (HSMI; Wessel et al., 2017) in a small proportion of infected populations of farmed Atlantic salmon (< 0.17%) and Pacific salmon (< 0.25%) (Polinski & Garver, 2019). HSMI is diagnosed as moderate to severe myocarditis in compact and spongy layers of cardiac muscle and moderate to severe myo-degeneration and necrosis in red muscle fibres (Kongtorp et al., 2004). In the northwest Pacific, the mortality of HSMI-like cardiopathy has been reported only at one farm site (peak at 0.55 % for the whole farm) that happened simultaneously with handling stress, algal bloom and fluctuating dissolved oxygen (Di Cicco et al., 2017). Over the past 25 years, cardiomyopathies in farmed salmon in British Columbia were associated with a cumulative mortality of below 0.4% (Polinski & Garver 2019). However, controlled laboratory infection studies (injecting PRV into salmon) have never induced HSMI (Polinski et al., 2016; Garver et al., 2016; Polinski et al., 2019). A PRV infectious study on Norwegian Atlantic salmon concluded that the hypoxia-acclimated, PRV-infected group had the same hypoxia tolerance as control fish but the PRV-infected group had a reduced cardiac thermal performance (Lund et al., 2017), despite the study had no time-matched control and unbalanced treatment groups, and hypoxia acclimation alone could explain such a shift. 121 However, the functional assessments of á¹O2max and AAS on PRV-infected wild sockeye salmon are lacking to test this suggestion. Thus, by subjecting Atlantic and sockeye salmon smolts to the IRAP protocol I will contribute to substantial knowledge gaps concerning wild salmon conservation. To be comprehensive, my functional assessments followed domesticated Atlantic salmon for 21 weeks after a PRV injection to capture all phases of the viral infections (early entry and dissemination, peak viremia, and latent persistence) and made comparisons to time-matched, sham-injected controls. Wild Fraser River sockeye salmon were subsequently followed for 9 weeks after either an IHNV injection or a PRV injection. By knowing the progression of the viral loads over time, I could conduct load-dependent analyses of the metabolic consequences of the infections over several weeks. 6.3 Materials and Methods While I mention complementary data generated by collaborators in my discussions of viral load and histopathology, the data are not included here as I did not make the measurements. 6.3.1 Experimental infections The PRV and IHNV laboratory challenge models using intraperitoneal (ip) virus injection were developed in Pacific Canada (Garver et al., 2016; Polinski et al., 2016; Polinski et al., 2019). 6.3.1.1 PRV infection of Atlantic salmon An isolate of PRV from a commercial freshwater salmon hatchery on Vancouver Island, Canada (designated 16-005ND) has provided a consistent, well-characterized laboratory model for inducing high-load viremia in the Mowi-McConnell strain of Atlantic salmon (Garver et al., 2016; Polinski et al., 2019). This isolate is representative of the PRV1 subtype which is widely and naturally found in both wild and farmed salmon of western North America (Kibenge et al., 2013; Siah et al., 2015). PRV has resisted all attempts of in vitro propagation, and no purified 122 viral particles were available at the time of the experiments with Atlantic salmon. Therefore, inoculates of PRV 16-005ND (the PRV treatment group) were prepared from infected red blood cells and injected as previously described (Polinski et al., 2019). Briefly, the blood of commercially produced salmon that had been frozen at -80 °C was thawed, diluted 1:10 in HBSS, sonified for 80 s in 10 s bursts with 30 s rests on ice and clarified via centrifugation at 2000 Ã g for 5 min at 4°C. Inoculate sourced from PRV-infected fish was passed four times through 30â50 g Atlantic salmon (3â4 weeks incubation period per pass) to increase the inoculating dose. PRV treatment group in which sonified, clarified blood and diluted with HBSS at a final concentration of 3.25 Ã 107 copies PRV L1 reverse-transcribed genome segments were injected (0.1 ml total injection volume). PRV inoculates were identically prepared between each passage. To properly differentiate physiological changes due to PRV infection, I used two control groups to identify any time-dependent changes in respiratory physiology over the 21-week monitoring period: 1) a blood-injected control (BC) group in which sonified and clarified Atlantic salmon blood diluted in Hankâs balanced salt solution (HBSS) was injected (total injection volume 0.1 mL) to control for any effects associated with the injection of a foreign blood homogenate and 2) a saline-injected control (SC) group followed the same injection protocol. Fish for the three treatment groups were evenly distributed into three sets of duplicate holding tanks, with a staggered start date, starting with SC, so that the IRAP assessment could be started every 5 days for each treatment group. Therefore, time-matched data are reported as either weeks post-challenge (wpc) rather than calendar days. An IRAP assessment (n=8 fish for each sampling event) lasted 5 days and data are reported as 1, 3, 9, 18 and 21 wpc, which was the same time points for post-infection tissue sampling (n=8 fish for each sampling event). The only 123 exception is that at 18 wpc, fish was revived after the HCT and were re-tested in 21 wpc to test whether or not hypoxia stress could trigger PRV-associated respiratory harm. 6.3.1.2 PRV and IHNV infections of sockeye salmon Purified PRV infectious particles became available to test with sockeye salmon, which eliminate the need for a blood control group. PRV infectious particles were purified by collecting PRV-infected blood from a commercial hatchery (isolate 16-005ND; Garver et al., 2013). The blood was sonicated, clarified, and injected ip into a cohort of 10 naïve Atlantic salmon, as previously described. Following a three-week holding period at 11°C in seawater, fish were euthanized and peripheral blood collected and sonicated to purify viral particles via a cesium chloride gradient ultracentrifugation. Following 24 h dialysis in modified Dulbeccoâs phosphate-buffered saline without magnesium and calcium, particle purity was visualized via electron microscopy and reverse-transcribed copies of PRV L1 genomic material were enumerated using qPCR as a proxy of viral titer. Glycerol was added to a final volume of 10% and aliquots were stored at -80°C. IHNV (BC93-057 isolate genogroup U) was propagated and quantified using Epithelioma papulosum cyprinid (EPC) cells using standard techniques as previously described (Garver et al., 2013). Specifically, virus stock with less than three in vitro passages was used to inoculate a monolayer of EPC cells at a multiplicity of infection of 0.001. Cells were cultured in Minimal Essential Media supplemented with 10% fetal bovine serum (MEM-10) at 15°C. The culture media was harvested by centrifuging at 3000 g for 15 min once a total cytopathic effect was reached. Viral titer within the supernatant was determined by plaque assay using EPC monolayers (Garver et al., 2013) before being stored at -80°C. PRV and IHNV stocks were thawed and media was added to achieve a final concentration of 1% Glycerol, 9% Dulbeccoâs phosphate-buffered saline (without magnesium 124 and calcium) and 90% MEM-10. These stocks were then diluted 1:100 â 1:10,000 with Hanks balanced salt solution to achieve a target dose of either 1 Ã 106 PRV reverse-transcribed L1 genomic copies or 5 Ã 105 pfu of IHNV per 100 µl injection. Media without a virus was identically prepared to act as an SC. Duplicate tanks of sockeye salmon (50 fish per tank) were anesthetized in 75 mg per l tricaine mesylate (MS-222) and each fish was individually administered a 100 uL intraperitoneal injection of either the PRV or IHNV inoculate. Again, the starting date of the trials was staggered by 5 days so that an IRAP assessment could be first completed for the SC and data were time-matched as week post-challenge (wpc) for experiments: 1, 4 and 9 wpc. Given the expectation that some fish would quickly die of the IHNV infection and most drastic immune responses of the survivors are manifest in the 1st week (Polinski et al., 2019), sufficient fish numbers (n=16) for the first IRAP assessment at 1 wpc were assured by infecting the second cohort of sockeye salmon (duplicate tanks of 50 fish per tank) with IHNV five days after the first cohort, and 8 infected individuals were subsequently tested at 4 wpc and at 9 wpc. 6.3.2 Statistical analyses Effects of PRV and IHNV infections on respiratory variables were compared at each discrete sampling event using two-way ANOVA followed by Holm-Å ídák post-hoc tests between virus-infected and appropriate control groups. Because no substantial difference in respiratory traits was observed between saline-injected control and blood-injected control (see Appendix D.1: Figure S6.6), only data for the blood-injected control for Atlantic salmon are presented in this chapter for simplicity. Time-dependent changes in the blood-injected control group were assessed using one-way ANOVA with Holm-Å ídák post-hoc tests. The relationship between 125 respiratory variables and either PRV load or IHNV load (logarithmic transformed) was described by fitting a one-phase association regression model. 6.4 Results 6.4.1 Fish mortality and IHNV load over time IHNV-infected sockeye salmon accumulated 24% mortality within 4 wpc, with most of this mortality occurring during the third wpc (see Appendix D.1: Figure S6.1). IHNV was not detected in control fish but detected in 88% of 16 infected fish and reached levels as high as 1.4 Ã 106 copies per ug kidney RNA. As expected, sockeye salmon that survived the trial resisted the IHNV infection by progressive clearing the IHNV load. Thus, by 4 wpc, only 25% of fish remained infected and the average IHNV load had declined by 10,000-fold, decreasing to 7.5 Ã 102 copies per ug kidney RNA (see Appendix D.1: Figure S6.3). 6.4.2 Effects of IHNV on the respiratory phenotype of surviving sockeye salmon While the extensive time-matched comparisons only revealed trivial effects of IHNV infection, individual fish resolved the IHNV infection at different rates and carried a wide range of IHNV loads at 1 wpc and 4 wpc (see Appendix D.1: Figure S6.6), which could confound statistical comparisons based solely on sampling time. Hence, I performed regression and correlation analyses between IHNV load and the respiratory indices. I discovered that SMR, RMR, DEE & O2crit all showed a significant dependency as a function of IHNV load in the survivors as revealed by a one-phase regression equation (hyperbolic) when compared with 95% CI for the vehicle control group (Figure 6.1). Any effects on SMR seen in sockeye salmon that were resolving the IHNV infection likely had cascade effects for DEE, RMR and O2crit because SMR comprises 85% of RMR and DEE (see Appendix D.1: Figure S6.2) and is required to determine O2crit. 126 The important aspect of a positive relationship between SMR and IHNV load (best-fitted model: R2 = 0.40, Pearson: r = 0.66, p = 0.0031; Figure 6.1) was that individual fish with the lowest IHNV load had an SMR value that fell below the lower 95% CI for the control group even though those individuals with the highest IHNV load fell within the 95% CI band for vehicle control. The transition apparently occurred when the host had around 1 Ã 103 copies per ug kidney RNA. Moreover, the infection phenotype with a suppressed SMR was dominated by fish at 1 wpc, the survivors of the early infection phase. Thus, it appears that fish that were quickly resolving the IHNV infection (low viral load) suppressed SMR. Likewise, RMR was significantly and positively related to IHNV load (best-fitted model: R2 = 0.30, Pearson: r = 0.56, p = 0.013), as was DEE (best-fitted model: R2 = 0.38, Pearson: r = 0.63, p = 0.051; Figure 6.1) and O2crit (best-fitted model: R2 = 0.62; Pearson: p = 0.0003; Figure 6.1). 6.4.3 Mortality, PRV loads and histology of Atlantic salmon & sockeye salmon over time The cumulative mortality for PRV-infected Atlantic and sockeye salmon (2%) was the same as that for the control groups (3%). At 1 wpc, only 37.5% of 8 Atlantic salmon had a detectable PRV load, increasing to a 100% prevalence at 4 wpc, when PRV load peaked at 1 Ã 109 copies ug-1 blood RNA (Appendix E.3). This high PRV load persisted through to 21 wpc, staying above 1 Ã 109 copies ug-1 blood RNA (see Appendix D.1: Figure. S6.3 & Appendix E.3). In sockeye salmon, PRV reached a peak of 1 Ã 108 copies ug-1 blood RNA at 4 wpc, staying above 1 Ã 106 copies ug-1 blood RNA until 9 wpc. PRV was not detected in any control fish. Histopathology of PRV-infected Atlantic salmon revealed cytoplasmic transient inclusions characteristic of reovirus factories in up to 8% of erythrocytes (Appendix E.3). While mild heart and/or skeletal muscle inflammation was diagnosed in PRV- infected salmon, the prevalence and severity of heart inflammation in the BC group were comparable (p > 0.39) at 1, 127 4 and 21 wpc (Appendix E.3). Nevertheless, PRV-infected fish had a higher cumulative median severity score (1 or 2 out of a scale of 3) for heart inflammation (p = 0.02) at 9 wpc (Appendix paper E.3) and the only fish with a moderate heart inflammation (2 fish) or skeletal muscle inflammation (1 fish) were in the PRV treatment group (Appendix E.3). In PRV-infected sockeye salmon, histopathology revealed a mild muscle inflammation (i.e., score = 1) in one of eight fish sampled at 1 wpc, a mild cardiac inflammation in half of the eight fish sampled at 4 wpc, and no cardiac and muscle inflammation was observed in the 9 wpc. 6.4.4 PRV infection does not affect the respiratory phenotype of sockeye salmon Respiratory indices of SC and PRV-infected sockeye salmon changed significantly over the 9-week infection trial. Nevertheless, most respiratory indices did not differ between control and PRV-infected sockeye salmon when compared at the three testing times. There were four exceptions (Figure 6.2, see Appendix D.1: S6.4). Only at 1 wpc (early phase), PRV-infected sockeye salmon had a 40% lower EPOC (p = 0.007) compared with controls. Only at 4 wpc and 9 wpc (peak and persistent phases, respectively), PRV-infected sockeye salmon had a 12% lower SMR (p â¤ 0.042) compared with controls. However, this suppression in SMR had no load-dependent cascade effect on RMR, DEE and O2crit (Figure 6.1). Only at 9 wpc, PRV-infected sockeye salmon had a 14% lower RMR (p â¤ 0.036) compared with controls (Figure 6.2). PRV load was not significantly related to any measured respiratory indices that did change in IHNV infections: SMR, RMR, DEE and O2crit (Figure 6.1; PCC: p â¥ 0.22). 6.4.5 PRV infection does not affect the respiratory phenotype of Atlantic salmon Some of the respiratory traits in both the saline-injected control (SC) and blood-infected control (BC) groups of Atlantic salmon (SMR, FAS, EPOC, EPOCdur, RMR, T0.5AAS, T0.8AAS and ILOS) varied significantly over time (see Appendix D.1: Figure S6.5). For example, BC injection alone 128 significantly (p < 0.05 family-wise error adjusted) contributed to aerobic capabilities (23 ± 4% increase in SMR; 24 ± 2% decrease in FAS) and exhaustion recovery (26 ± 5% decrease in EPOCdur) at 10 wpc relative to SC injection. Thus, the examination was restricted to time-matched comparisons between BC and PRV-infected groups. The respiratory phenotype of PRV-infected Atlantic salmon is summarised in Figure S6.6 (see Appendix D.1) and was not statistically different from that of the SC group for any measured variable for any time-matched point of the study. Indeed, regardless of whether BC treated Atlantic salmon did or did not contain mild heart inflammation, there was no significant impairment to any of the physiological performance measurements (Appendix E.3). Also, no respiratory indices were statistically different between BC and PRV treatment groups at 21wpc (Figure 6.2) despite being exposed to an HCT 3 weeks earlier. Instead, fish in all treatment groups improved their hypoxia tolerance (ILOS decreased by 11 %, O2crit decreased by 10 %; Figure 6.2) as a result. This repeated-measure design suggests that PRV infection of Atlantic salmon did not prevent phenotypic plasticity in response to the prior hypoxic exposure. Consequently, PRV had no major or sustained effects on any respiratory indices measured in Atlantic salmon in any infection phases. 6.5 Discussion According to the Fry paradigm, the virus can be masking (e.g. loading) and lethal factors, which are observed in the present study. While IHNV was certainly a lethal factor for 28% of the sockey AAS e population tested, it surprisingly did not limit á¹O2max and AAS, the metabolic load did not increase in sockeye salmon that survived the IHNV infection. Instead, I provide clear evidence that individual sockeye salmon more rapidly resolved the IHNV infection via suppressing SMR below the time-matched controls. This was an unexpected finding and is the 129 first direct measurement to my knowledge that fish use whole-organism metabolic suppression to fight any infection, not just viral ones. Thus, the modest suppression of SMR in IHNV infection may indicate that SMR, if properly measured, has the potential to resolve sublethal pathogenic infections. Like the metabolic suppression seen in sea bass acclimated to hypoxia (Chapter 5), the fact that this response was detected by IRAP speaks to the resolution power of the technique and a great promise as a whole-organism diagnostic platform for sublethal effects. While significant, consistent but unanticipated responses for IRAP indices were detected for the high-virulence, low-prevalence IHNV, the same cannot be said for the high-prevalence, low-virulence PRV. I saw either sockeye salmon or Atlantic salmon had mortality and despite the Atlantic salmon being held for an additional 3 weeks after an HCT, none of the IRAP indices were consistently changed in PRV-infected salmon. Nor did I see any viral load-dependency with any of the respiratory indices even though the viral load persisted through the trials. The finding is consistent with the previous controlled infectious challenges trials that concluded pervasive PRV in the global wild and farmed salmon populations have low virulence (Garver et al., 2016; Polinski et al., 2019). 6.5.1 Functional effects of IHNV infections Among the IHNV survivors, two clear phenotypes were revealed by the load-response analysis: the susceptible phenotypes with a high viral load (largely at 4 wpc) and the resistant phenotypes with a low viral load that were resolving the infection faster than the susceptible phenotype. The resistant phenotype suppressed SMR, while those susceptible phenotypes with a high load did not. Given the enormous efforts made by others (Steffensen, 1989; Farrell, 2008; Chabot et al., 2016; Claireaux & Chabot, 2016) and myself (Zhang et al., 2016; Zhang et al., 2018) to improve the experimental and analytical protocols of estimate SMR in fish, the precision of SMR 130 measurements has now reached a calibre where subtle changes can be revealed experimentally. Therefore, I firmly believe that false-negative discoveries were largely eliminated, and changes as small as 12% can be statistically resolved. I offer the following potential explanations. SMR, as subsistence cellular metabolism for maintaining cellular and whole-organism homeostasis, can decrease only if some aspects of metabolism are turned down. Related supporting evidence exist. Atlantic salmon families that are resistant to infectious pancreatic necrosis virus downregulated gene expressions for metabolism and tissue differentiation (Reyes-López et al., 2015). Similarly, RMR was suppressed in Drosophila C virus-infected fruit fly (Drosophila melanogaster, Meigen 1830) (Arnold et al., 2013). Also, sockeye salmon resistant to IHNV infection showed a global transcriptional downregulation for pathways involved in protein synthesis, DNA transcription, and macromolecule metabolism (Polinski et al., 2019), which can limit resources required for robust viral replication in target tissues, thereby lowering the viral load. Across animal taxa, suppressing minimum maintenance metabolism is typically a survival strategy for animals to live in recourse-limited conditions, such as anoxia and starvation (Hochachka, 1990; Guppy & Withers, 1999; Richards, 2010; Heuton et al., 2018). Perhaps, a viral infection is another stressful condition where the strategy of energy conservation can increase survival. However, the observed whole-animal metabolic outcomes can still be an amalgamation of the suppression of basic needs and the additional costs in the immune responses. That high IHNV load group had the same metabolic costs as the control group was possible when global downregulations of metabolic pathways (hypometabolism) offset the costs of anti-viral and pro-inflammatory responses (hypermetabolism). In this regard, the hyperbolic curve of SMR, RMR and DEE may reflect different upregulated costs for antiviral molecular defences from low to 131 high IHNV load. An integration of whole-organism metabolic assessment, targeted measures for the immune genes (Appendix paper E.3) and global scanning for macromolecule pathways will be necessary to probe the exact cellular metabolic and immune pathways underlying the whole-organism metabolic rate. Although viral infection surprisingly did not compromise aerobic capacity measured during the brief bouts of intensive exercise, whether prolonged or sustained exercise is compromised remains to be studied. The latter mode of locomotion is arguably more related to the migration. 6.5.2 PRV infection does not compromise respiratory performance The high-load viremia and temporal infection dynamics resulting from injection of a PRV-loaded blood homogenate or purified virus in this study closely mirrored previous challenge trials with Atlantic salmon in Pacific Canada (Garver et al., 2016; Polinski et al., 2019). This study stimulated a maximum systemic PRV carrying capacity was likely reached at approximately 10-100 billion PRV copies per mL blood â a load that is equal to, if not higher, than loads generated in natural or aquaculture systems (Lovoll et al., 2012; Garseth et al., 2013; Marty et al., 2015). Therefore, my laboratory study simulated a relevant âworst-caseâ model for assessing physiological harm associated with PRV load intensity. The âworst-caseâ scenario of viremia did not result in any measurable compromises in the respiratory phenotype. Here, I provide evidence that respiratory phenotype is unaffected by these erythrocyte inclusions and correspondingly high PRV transcriptional loads even with the erythrocytes having transient innate immune responses as observed previously (Polinski et al., 2019). Transient cytoplasmic inclusions of erythrocytes are a common and primary attribute of acute systemic PRV infection and indicative of viral replication at the entrance and early peak phases (Finstad et al., 2014; Wessel et al., 2015; Polinski et al., 2019). Importantly, these innate responses cannot 132 be energetically expensive in Atlantic salmon because neither SMR nor erythrocyte function was affected. Furthermore, The North American genotype of PRV1 in our study produced mild and occasionally moderate heart inflammation in a Pacific domesticated strain of Atlantic salmon (Garver et al., 2016; Polinski et al., 2019). Like the previous studies in the PRV subtype from Pacific Canada, the PRV infected domesticated Atlantic salmon here also did not develop HSMI, as the severity of inflammation was indistinguishable from control fish. It was certainly not associated with compromised heart function; otherwise, the metabolic functions of PRV-infected salmon would not have been as robust as those of controls. Globally, a Norwegian genotype of PRV, which caused moderate heart lesions (classified as HSMI) in a cohabitation model system, produced similar PRV loads. The more plausible explanation is that at least some subtypes of PRV may have found a near commensal relationship with hosts. The juxtaposition of these regionally divergent findings supports three important conclusions: (i) The Atlantic salmon heart is functionally resilient to mild (present study) and perhaps moderate inflammation (Lund et al., 2017); whether severe inflammation affects cardiac performance has yet to be tested. (ii) There is a regional disparity in PRVâs ability to cause respiratory harm. Identifying how much of this regional variability is dependent on viral genotype, host genotype, or environmental factors requires further investigation. Thus, PRV infections are unlikely to be the sole cause that impairs cardiorespiratory functions of wild salmon at a sufficient severity that hinders their migratory successes, as claimed by the association studies (Olsen et al., 2015; Takano et al., 2016; Garseth et al., 2013; Morton et al., 2017; Di Cicco et al., 2018). (iii) PRV emerges as an atypical example of a virus with putative pathogenic properties because the total quantity of virus within an infected organism appears to 133 have little to no bearing on whether the organism becomes diseased and experiences physiological harm. 6.6 Conclusion After decades of developments in respiratory physiology and the respirometry system, the present study proved the principle that IRAP can diagnose the sub-lethal effects of viral infections to a sensitivity that even links the metabolic demands with viral loads in a load-dependent manner. The key discovery here is that fish have enough metabolic capacity to cope with PRV viremia while simultaneously performing challenging physiological tasks. In addition, the high-throughput physiological characterization excels at testing all stages of viral infections with time-matched controls. The thoroughness of respiratory phenotyping shines at screening many aspects of metabolic energy demands and expenditures for the sub-lethal effects of viral infections. Collectively, these approaches assure the rigour for the conclusion that the PRV subtype from Pacific Canada, like most reoviruses, appears to be generally low (Garver et al., 2016; Polinski et al., 2019). More importantly, a new interface of integrating cellular metabolic pathways and whole-organism metabolic performance is now available to study the metabolic costs of viral infections. Therefore, whole-organism functional assessments should be implemented broadly to inform the risk assessments and policy development for fish health and wildlife management. 134 100 102 104 106 108 1010020406080100120SMR (mg O2 h-1 kg-1 ) r = -0.21, p = 0.21100 102 104 106 108 10100510152025PRV copies per ug blood RNA (log)O2crit (% sat.)r = 0.15, p = 0.28100 102 104 106 108 1010020004000600080001000012000DEE (mg O2 kg-1)r = 0.25, p = 0.16100 102 104 106 108 10100306090120150RMR (mg O2 h-1 kg-1 ) r = -0.03, p = 0.450306090120SMR (mg O2 h-1 kg-1 ) y = -5.8 + (73.8 + 5.8) â¢(1 - exp(-0.69 â¢ x)); R2 = 0.40 r = 0.66, p = 0.0031103100100.1102 104 105 106 1070510152025IHNV copies per ug kidney RNA (log)O2crit (% sat.)r = 0.78, p = 0.0003y = -85.4 + (17.5 + 85.4) â¢ (1 - exp(-1.5 â¢ x)); R2 = 0.62 103100100.1102 104 105 106 10701000200030004000DEE (mg O2 kg-1d-1 )r = 0.63, p = 0.051y = -1105 + (2197 + 1105) â¢ (1 - exp(-0.76 â¢ x)); R2 = 0.38 103100100.1102 104 105 106 1070306090120150RMR (mg O2 h-1 kg-1 ) r = 0.56, p = 0.013y = -34.7 + (90.6 + 34.7) â¢ (1 - exp(-0.78 â¢ x)); R2 = 0.30 103100100.1102 104 105 106 107(A)(B)(C)(D)(E)(F)(G)(H)(a) (a) b) (c) (d) (e) (f) (g) (h) 135 Figure 6.1 Regression analyses for viral load and various levels of metabolic demands in infectious hematopoietic necrosis virus (IHNV; blue) and piscine orthoreovirus (PRV; purple) injected wild sockeye salmon (Oncorhynchus nerka). (a, e) standard metabolic rate (SMR), (b, f) routine metabolic rate (RMR), (c, g) daily energy expenditure (DEE) and (d, h) critical oxygen saturation (O2crit) were correlated with viral load in sockeye salmon. The grey horizontal line and grey shaded area are the mean value ± 95% confidence interval for the sham-injected control group. The control group (without a viral load on the x-axis) was presented only as a reference value for the respiratory indices on the y-axis. The regression analyses used fish sampled 1- and 4-week post-IHNV injection, and 1-, 4- and 9-week post PRV injection (the specific sample date is indicated by a different symbol, as described in the key). Best-fitted hyperbolic equations (coloured solid lines and shaded area to denote the 95% confidence interval) and their R2 are shown at the top of the panel when a significant relationship existed. Spearmanâs correlation coefficients (r) are provided at the bottom of each panel. 136 Figure 6.2 Key traits of the respiratory phenotype of piscine orthoreovirus (PRV) injected sockeye salmon (Oncorhynchus nerka) and Atlantic salmon (Salmo salar). Line plots (mean Â± s.e.m.; n = 8) of metabolic indices measured at 3 and 5 discrete sampling events for sockeye 1 4 9075150225300375450á¹O2maxAASSMR* *á¹O2 (mg O2 h-1 kg-1 ) 1 4 90306090120036RMR (mg O2 h-1 kg-1 ) T0.5AAST0.8AASRMR% of time*1 4 9030060090012001500EPOC (mg O2 kg-1)*1 4 9101214161820% air saturation of waterILOSO2crit*PRVControl1 3 9 18 21075150225300375450AASSMRá¹O2 (mg O2 h-1 kg-1 ) á¹O2max1 3 9 18 2106012018024002040RMR (mg O2 h-1 kg-1 ) * RMR% of timeT0.5AAST0.8AAS1 3 9 18 21030060090012001500EPOC (mg O2 kg-1)1 3 9 18 21010203040% air sat. of water ILOSO2critControlPRVWeek post challenge (wpc) Week post challenge (wpc)(A)(B)(C)(D)(E)(F)(G)(H)Sockeye salmon Atlantic salmon(a) (b) (c) (d) (e) (f) (g) (h) 137 (panels aâd) and Atlantic salmon (panels eâh), respectively. Coloured lines are the PRV-injected group. Grey lines are saline control in sockeye salmon and blood control in Atlantic salmon. Significant difference (â; p < 0.05) in mean value between control and PRV injected (PRV) treatment groups are indicated at each time point as determined by HolmâÅ ídák post-hoc tests (α = 0.05). In Atlantic salmon, data at 21week post-challenge (wpc) is a repeated measure of fish assessed at 18 wpc. (a, e) á¹O2max, maximum metabolic rate; SMR, standard metabolic rate (0.2-quartile method); AAS, absolute aerobic scope; (b, f) RMR, routine metabolic rate; T0.5AAS, time spent above 50% of AAS; T0.8AAS, time spent above 80% of AAS (c, g) EPOC, excess post-exercise oxygen consumption; (d, h) O2crit, critical oxygen level; ILOS, incipient lethal oxygen saturation). 138 Chapter 7: Quantifying the repeatability of IRAP variables for characterizing respiratory phenotype in fishes5 7.1 Synopsis Transcending previous data chapters, aerobic capacity and O2crit estimated by the more standardized experimental metrics often detected biologically meaningful differences. These include intra-specific variations, metabolic suppression after a hypoxia acclimation or during a phase of viral resistance, and viral-load dependent costs in basic and routine needs. Can this be the truly null effects on other metrics, or other indices were not precise enough to resolve the biological differences? Using the two repeated-measure studies in European sea bass (Chapter 5) and Atlantic salmon (Chapter 6), Chapter 7, therefore, quantified repeatability by interclass Pearsonâs correlation coefficient test and precision by a normalized coefficient of repeatability. This allowed me to test the hypothesis that precision and repeatability are positively correlated. Specifically, using these two different studies on two different fish species, I discovered that aerobic capacity (FAS, SMR, AAS, á¹O2max) and behavioural activities (RMR, T50AAS) were always repeatable and metrics for anaerobic capacity (ILOS, AOD, O2crit, O2crit slope, EPOC) were sometimes repeatable. To this end, the results showed that, in thermal optima, traits with COR of less than 53% had three-time higher chances of being repeatable than those had COR of larger than 53%. On average, a minimum of 60% of the traits was repeatable in each study. I, however, cannot conclude that the standardized analytical approach always yields repeatable measurement, because O2crit was not repeatable in sea bass despite its COR was 33%. The repeatable O2crit in Atlantic salmon had a COR that was only 13%. Clearly, experimental protocols for measuring anaerobic indices need to be further refined before concluding that the poorer repeatability in anaerobic traits is due to biological variations. 5A version of chapter 7 is in preparation for submission as Y. Zhang, F. Mauduit, K. Anttila, L. Pettinau, H. Ollivier, F. Lancien, A. P. Farrell, G. Claireaux, The respiratory phenotype of a pelagic fish has greater temporal stability at its optimal temperature than at a sub-optimal one. 139 7.2 Introduction Natural selection over multiple generations can lead to stable traits, allowing individuals with an adaptive phenotype to prevail in the local environments of their biogeographic range and possibly passing on their genes and epigenetic modifiers to their offspring (local adaptation). Because evolution acts upon the temporally stable and adaptive traits over a temporal time scale (Coope, 2004), I can further reason that those important traits should be stable over time in a stable, optimum environment. Thus, well-defined, independent traits should be repeatable over periods of days to months provided they are measured by a valid protocol in an optimal environment. The whole-animal capacity for ATP production, the most fundamental currency of life, is clearly an important phenotype and one that should be repeatable. IRAP metrics attempt to provide some insights for whole-animal capacity for ATP production. Testing traits stability needs accurate, precise, and repeatable measurements. Repeatable laboratory estimates are the bedrock of reproducibility in biological science. Refining the IRAP methodology was a goal of my doctoral dissertation, potentially bringing even higher accuracy and precision to estimating respiratory traits than those regarded as the best practices in the field (e.g., Clark et al., 2013; Chabot et al., 2016; Claireaux & Chabot, 2016). At the outset of my pursuit, IRAP had already benefitted from new and better protocols and analyses in the ceiling (á¹O2max; Clark et al., 2013) and the floor (SMR; Chabot et al., 2016) of aerobic capacity. Similarly, the transition point (O2crit) between aerobic and anaerobic metabolism (Claireaux & Chabot, 2016) and the endpoint (ILOS) for anaerobic tolerance (Claireaux et al., 2013) are now characterized with much higher accuracy and precision. Chapter 3 provided guiding principles for best practices for estimating á¹O2max. The present chapter considers the repeatability of all IRAP metrics because only five IRAP metrics have been tested for repeatability. To clarify the 140 difference between a metric and a trait, only a metric is demonstrated to be temporally repeatable should the metric be called a trait. Transcending my chapters, the standardized metrics for aerobic capacity and the O2crit protocol often detected biologically meaningful differences. Also, across literature, most repeatability is manifest at the metrics for aerobic capacity and aerobic exercise performance, and some studies showed repeatability for hypoxia tolerance (estimated by time to loss of equilibrium during hypoxia) (see review by Killen et al., 2016). The objective of this chapter is to understand if the detection sensitivity for biological difference relates to the repeatability of the measurements. In this regard, a repeated-measures design that remeasures the same individuals provides great statistical power. I have two datasets that fill this criterion: respiratory phenotype repeated measured at the thermal optima for European sea bass and Atlantic salmon in two independent studies. Therefore, in Chapter 7, I quantified the temporal stability of IRAP metrics by a normalized coefficient of repeatability (COR) that measures intra-individual variability that also inevitably includes measurement errors. To disentangle whether a lack of temporal stability is due to measurement errors, I return to the precision-&-repeatability matrix (see Chapter 1) to provide guiding principles in dissociating measurement errors from intra-individual variations. Among repeatable traits, I predicted that COR has a threshold, below which warrants a higher chance of repeatability. Therefore, I calculated normalized COR and repeatability for metrics of IRAP. I hypothesize that the precision of measurements and repeatability of indices are positively correlated. 141 7.3 Material and Methods 7.3.1 Experimental animals and repeated IRAP assessments The general details of the IRAP protocol and the holding conditions for European sea bass and Atlantic salmon (which had a similar body mass, ~75 g) are detailed in Chapter 2. The data for Atlantic salmon is taken from Chapter 6 because IRAP testing was repeated for the same individuals (control groups) at weeks 18 and 21 weeks (3 weeks apart) and at their optimum temperature (11 oC). The statistical power of the present analysis was increased by pooling data for the saline-control and blood-injected control groups because their respiratory phenotypes were nearly identical (see Appendix D.1: Figure S6.6). The methodology used to generate these data and the data themselves therefore are found in Chapter 6. Respiratory phenotyping of the same sea bass was repeated 4 weeks apart at their optimum temperature (25 ËC) in Brest, France in the summer of 2018. Experimental and IRAP procedures were detailed in Chapter 2, where fish holding facility and fish handling procedures were the same as Chapter 5. 7.3.2 Precision quantification and repeatability determination The range of variance for a metric is estimated by the coefficient of repeatability (COR). COR is widely adopted in kinesiology to assess the measurement validity for new apparatuses (Glaner & Silva, 2011; Lafountain et al., 2016; Garland et al., 2017) or measurement precision for new protocols (Wisén & Wohlfart, 2004; Cooper et al., 2009; Cooper et al., 2005; Eriksen et al., 2014). COR was calculated as two times the standard deviations for the differences between two repeated measurements (Wergel-Kolmert et al., 2002). The normalized coefficient of repeatability was a percentage for the mean values calculated from the two repeated measures (Eqn. 7.1). Normalized COR estimates the range of errors around the mean to estimate precision. For simplicity, normalized COR will be referred to as COR. 142 Normalized COR = ! Ã 0[â(4!56)^!] :â(< = >)/ ! Ã 100% Eqn. 7.1 Where B@ is each value of the difference between two repeated measurements from the population, C is the mean of the difference between two repeated measurements, N is the sample size, : is an average of a trait measured at the 1st test, # is an average of a trait measured at the 2nd test. Because precision needs to be assessed based on absolute values, only Pearsonâs correlation coefficient values (PCC) were correlated with COR. PCC assesses the repeatability of absolute values and estimates the reproducibility of measurements. Also, Spearmanâs correlation coefficients (SCC) were conducted to assess the repeatability of rank orders of subjects. 7.4 Results All the aerobic metrics (á¹O2max, SMR, AAS, FAS, RMR) measured in normoxia were repeatable in European sea bass and Atlantic salmon. Specifically, the repeatable á¹O2max estimates had a COR range of 14â31% in both species (PCC: p â¤ 0.027, r â¥ 0.49; SCC: r â¥ 0.41, p < 0.0001). Likewise, the repeatable SMR estimates had a COR range of 13â33% in both species (PCC: p â¤ 0.014, r â¥ 0.50; SCC: p â¤ 0.043, r â¥ 0.40). The derived aerobic capacity metrics (AAS: COR = 17â53% & FAS: COR = 20â26%) were also repeatable (PCC, AAS: p â¤ 0.037, r â¥ 0.46 & FAS: p â¤ 0.0045, r â¥ 0.63; SCC, AAS: p â¤ 0.04, r â¥ 0.45 & FAS: p â¤ 0.008, r â¥ 0.59). Also, the two behavioural activity metrics (i.e., RMR: COR = 18â27%; T0.5AAS: COR = 23â108%) were repeatable (PCC, RMR: p â¤ 0.0056, r â¥ 0.62 & T0.5AAS: p â¤ 0.028, r â¥ 0.49; SSC, RMR: p â¤ 0.02, r â¥ 0.5; T0.5AAS: p â¤ 0.05, r â¥ 0.43). In contrast, the IRAP metrics measured under hypoxic conditions were less repeatable, some varied between two species and some had higher COR ranges. O2crit (COR = 13%) was repeatable as absolute values in Atlantic salmon (PCC: p = 0.038 r = 0.46), but not as individual rank orders (SCC: p = 0.11 r = 0.33) and not repeatable in European sea bass (COR = 33%; 143 PCC: p = 0.081, r = 0.34; SCC: p = 0.23 r = 0.18). Moreover, ILOS (COR = 16%) and EPOC (COR = 105%) were repeatable in Atlantic salmon (ILOS: PCC: p = 0.007, r = 0.6; SCC: p = 0.004 r = 0.65 & EPOC: PCC: p = 0.037, r = 0.46; SCC: p = 0.032, r = 0.48) but not European sea bass (ILOS: COR = 95%; PCC: p = 0.27, r = 0.15; SCC: p = 0.16, r = 0.24 & EPOC: COR = 122%; PCC: p = 0.24, r = 0.18; SCC: p = 0.23, r = 0.18). Whereas, AOD (COR = 100%) was repeatable in European sea bass (PCC: p = 0.006, r = 0.56; SCC: p = 0.014, r = 0.505), but not Atlantic salmon (COR = 168%; PCC: p = 0.14, r = 0.28; SCC: p = 0.06, r = 0.41). Likewise, repeatable O2crit slope had a COR of 37% when measured in sea bass (PCC: p = 0.037, r = 0.42; SCC: p = 0.043, r = 0.41). Yet, SOD (COR = 53â93%) and FSOD (COR = 16â61%) were not repeatable in neither species (PCC: p â¥ 0.10, r â¤ 0.33; SCC: p â¥ 0.082, r â¤ 0.33). Overall, 46% of 13 metrics (FAS, SMR, AAS, á¹O2max, RMR, T50AAS) were repeatable in both species with the Pearson r-values that were always higher than 0.4, 38% of metrics were repeatable in at least one species (ILOS, AOD, O2crit, O2crit slope, EPOC) and 15% of indices (FSOD, SOD) were not repeatable in any species. Hence, anaerobic metrics generally had poorer repeatability (5 out 13 repeatable tests) than aerobic metrics (12 out of 12 repeatable tests) under an optimal thermal condition. Hence, the aerobic metrics can be regarded as aerobic traits, whereas anaerobic measurements remain as metrics. Furthermore, when COR was < 53%, 78% of metrics were repeatable but only 22% of the metrics were repeatable when COR was > 53%. The smaller the COR value, the higher probability the metrics were repeatable (p â¤ 0.04; R2 â¥ 0.17; Figure 7.1). 7.5 Discussion I analyzed the repeatability of the IRAP metrics when the protocol was performed 3â4 weeks apart at thermal optima for two fish species belonging to a different genus. I found support for 144 my hypothesis that the precision of measurements and repeatability of metrics are positively correlated (Figure 7.1). Four aerobic traits (SMR, á¹O2max, AAS & FAS) and two behavioural traits (RMR, T0.5AAS) were repeatable in Atlantic salmon and European sea bass, with COR ranging from 14 to 53%, except T0.5AAS which had a COR range of 80â108%. The precision threshold of these two experiments appeared to be a COR of 53%, a soft criterion. Therefore, the chance of repeatability increases when metrics are measured more precisely. Disentangling intrinsic biological variations from measurement errors in any respirometry study is inherently challenging due to the lack of a gold standard for calibration. To determine if a trait is stable requires a valid experimental protocol coupled with a robust analytical algorithm to provide precise and accurate values. Hence, I revisit the precision-&-repeatability matrix presented in Chapter 1. Repeatability was determined by a criterion of p-value less than 0.05 in a correlation coefficient test, a hard criterion. By superimposing the precision-&-repeatability matrix and Figure 7.1, I discovered that SMR, RMR, á¹O2max, AAS & FAS appear to fall in the category of stable traits measured by a valid protocol, whereas T0.5AAS fell in the category of stable traits measured by a non-standardized protocol. A comparison of the present findings with previous temporal stability studies in fish is inevitably limited because neither COR nor a range of metabolic metrics was examined at the individual level to the extent of the present study. However, studies of aerobic exercise performance (Midgley et al., 2007) and validations of new testing protocols (Glaner & Silva, 2011) in kinesiology routinely evaluate validity using repeated-measures design and estimate variability with normalized COR. For example, VÌO2max and RMR in untrained humans had a COR of 33% and 20â35% when remeasured after 4 weeks and 1â2 days, respectively (Wisén & Wohlfart, 2004; Cooper et al., 2009). My COR values for estimating á¹O2max and RMR (á¹O2max: 145 14â31%; RMR: 18â27%) were at the same calibre as kinesiology. Notably, á¹O2max shared the same level of high precision as SMR (23â26%) which is estimated by a standardized protocol (Chabot et al., 2016). Also, the repeatability of aerobic and behavioural traits are comparable and consistent with previous studies in fish. Repeatability of AMR, RMR and aerobic scope (AS = AMR â RMR) of brown trout (Salmo trutta, Linnaeus 1758) was maintained for five weeks (Kendallâs non-parametric coefficient of concordance: r â¥ 0.52, p â¤ 0.002), but progressively declined over 10 weeks and completely disappeared after 15 weeks (Norin & Malte, 2011). Moreover, RMR, but neither AMR nor AS, were repeatable in barramundi (Lates calcarifer, Bloch 1790) at low salinity (10 ppt), high temperature (35 °C) and in hypoxia (45% sat.) for 2â6 weeks (SCC: 0.39â0.71; Norin et al, 2016). In contrast to the traits of aerobic capacity, none of the anaerobic metrics was repeatable in both studies, suggesting most metrics related to glycolytic capacity generally have poorer repeatability. ILOS and EPOC were not repeatable in European sea bass, and AOD was not repeatable in Atlantic salmon. These metrics all had COR of larger than 95%. The test of stability for anaerobic traits is currently confounded by the validity of the protocols. O2crit was repeatable in Atlantic salmon but only approached repeatability in European sea bass. O2crit, with a COR range of 13â33%, is likely measured by a valid protocol but the index is perhaps is not very repeatable. Repeatability tests for EPOC and AOD, as the estimates of glycolytic capacity, are not available in the literature. Being consistent with my results, lactate dehydrogenase concentration and activity were not always repeatable over a month (correlation coefficient: 0.09â0.66; Martínez et al., 2002). In contrast, the duration when fish maintain an upright equilibrium in hypoxia, another anaerobic endpoint, was repeatable for 18 months in European sea bass (PCC: 0.48â0.62; Joyce et al., 2016) and for 6 months in striped bass (Morone saxatilis, 146 Walbaum 1792) as individual rank orders (Kendallâs non-parametric coefficient of concordance; W = 0.53 p = 0.012; Nelson et al., 2019). In my study, a related ILOS was only repeatable in Atlantic salmon. Clearly, more experiments are needed for the metrics of anaerobic capacity. For example, the EPOC value is easily inflated by spontaneous activity during recovery. A possible solution is to model the area under the curve using a quantile regression algorithm (Chabot et al., 2016). However, challenges for EPOC analysis are accounting for the three phases (Zhang et al., 2018) and establishing the necessary objective criteria, because the shape and duration of EPOC vary with species, life stages and types of exercise. Furthermore, hypoxia severity and time of exposure form a dichotomy for estimating glycolytic capacity. AOD that accounts for both factors was repeatable in sea bass, whereas SOD and FSOD that do not account for the time dimension were not repeatable. Despite the rate of hypoxia induction was manually regulated to achieve the best inter-trial consistency of hypoxia induction for the estimates of glycolytic capacity, COR of AOD was 100â168 %, and COR of ILOS was 95% in sea bass, approximately twice of the precision threshold (COR = 53%). This means if a species has a small anaerobic capacity, the meaningful biological differences can be missed in empirical studies. The immediate validation for AOD should test the assumption that SMR is not suppressed in an acute hypoxia challenge test, a stable baseline. Then the next developmental phase for anaerobic capacity is assuring that the proposed metric exactly measures capacity. The capacity of an animal can be temporally stable whereas the costs of functions are less likely so. Only when some more repeatable anaerobic metrics are available, fish biologists can better understand the temporal stability of the anaerobic traits. 147 7.6 Conclusion I proved that the best practices of aquatic respirometry protocol achieve the same calibre of precision as the well-established respirometry assessments in kinesiology. Fish biologists worldwide are well poised to use standardized respirometry protocols and generate more accurate estimates of aerobic capacity and behavioural traits of agitation. Moreover, by enabling fish biologists to move beyond characterizing aerobic capacity and start to estimate anaerobic capacity, IRAP expands our understanding of a respiratory phenotype. 148 0 50 100 150 200010203040COR (%)Frequency of Pearson p-value (%)Pearson p < 0.05Pearson p â¥ 0.050 50 100 150 2000.00.10.20.3COR (%)Pearson p-valueAASFASá¹O2max RMRSMRT>50%AASAODEPOCFSODILOSO2critO2crit slopeSODy = 0.0009x+0.001p = 0.01 R2 = 0.25Aerobic: Anaerobic:0 50 100 150 200010203040COR (%)Frequency of Pearson r-value (%) Pearson r â¤ 0.4Pearson r > 0.40 50 100 150 2000.00.20.40.60.81.0COR (%)Pearson r-valueAASFASá¹O2max RMRSMRT>50%AASAODEPOCFSODILOSO2critO2crit slopeSODy = -0.002x + 0.6p = 0.04 R2 = 0.17Aerobic: Anaerobic:(a) (b)(c) (d) 149 Figure 7.1 Linear regressions (a, c) and frequency distributions (b, d) of the Pearson correlation coefficient (p-value and r-value) as a function of the normalized coefficient of repeatability (COR, %) for Atlantic salmon (Salmo salar) and European sea bass (Dicentrarchus labrax). The measurements were obtained in the optimum temperatures (11 ËC for S. salar & 25 ËC for D. labrax). Metrics are repeatable if p < 0.05, which happened to have r > 0.4. Panels a & c presented the data for the theoretical precision-&-repeatability matrix (see Chapter 1). The lower left section encompasses the stable traits measured by valid protocols, the upper left section encompasses the unstable traits measured by valid protocols, the upper right section encompasses the unstable traits measured by invalid protocols, and the lower right section encompasses the stable trait measured by invalid protocol. Aerobic traits include absolute aerobic scope (AAS), factorial aerobic scope (FAS), maximum oxygen uptake (á¹O2max), standard metabolic rate (SMR), time spent above 50% AAS (T0.5AAS) and routine metabolic rate (RMR). Anaerobic metrics include scope for oxygen deficit (SOD), the factorial scope for oxygen deficit (FSOD), incipient lethal oxygen saturation (ILOS), accumulated oxygen deficit (AOD), critical oxygen saturation (O2crit), linear regression slope for determining O2crit (O2crit slope) and excess post-exercise oxygen consumption (EPOC). 150 Table 7.1 Summary of the normalized coefficient of repeatability (COR, %) corresponding to Pearson and Spearman correlation coefficients. Pearson correlation coefficient was used to examine the repeatability of absolute values for IRAP metrics. Spearman correlation coefficient was only provided to examine the repeatability as individual rank orders. The values are COR (%). Colour represents the range of p-values from the repeatability tests (green: p â¤ 0.05 yellow: 0.05 < p â¤ 0.1 red: p > 0.1). Pearson correlation coefficient Spearman correlation coefficient Atlantic salmon 11 ËC European sea bass 25 ËC Atlantic salmon 11 ËC European sea bass 25 ËC Aerobic indices FAS 20 26 20 26 SMR (mg O2 h-1 kg-1) 23 26 23 26 AAS (mg O2 h-1 kg-1) 17 53 17 53 á¹O2max (mg O2 h-1 kg-1) 14 31 14 31 Behavioural indices RMR (mg O2 h-1 kg-1) 27 18 27 18 T50%AAS (h) 80 108 80 108 Anaerobic indices O2crit slope â 37 â 37 ILOS (% sat.) 16 95 16 95 AOD (mg O2 kg-1) 168 100 168 100 O2crit (% sat.) 13 33 13 33 EPOC (mg O2 kg-1) 105 122 105 122 FSOD 16 61 16 61 SOD (% sat.) 93 53 93 53 151 Chapter 8: General discussion and recommendations â respiratory phenotype can bridge physiology and ecology 6 8.1 Synopsis âIchthyologists are poised with, while challenged by studying the most specious group of vertebrates to understand the cogs of evolution.â âââ Yangfan Zhang 6A version of chapter 1 & 8 is in preparation for submission as a systemic review: Y. Zhang, D. McKenzie, K. Anttila, S. Narum, Z. Chen, S. Cooke, G, Claireaux, R. Taylor, A.P. Farrell, Bridging Fryâs ecological paradigm to Darwinian natural selection using respiratory physiology. 152 The general objective of my dissertation is to provide tools to better bridge physiology and ecology using diverse teleost fish species as a model system. My overarching goal was to reliably characterize inter-individual phenotypic variations with accurate, precise, and high-throughput measures related to functions and performances. I focused on fishâs respiratory phenotype using the IRAP phenotyping platform I advanced earlier (Zhang et al., 2016; Zhang et al., 2017), which had leveraged on over 70 years of advancement in fish respiratory physiology (Fry, 1947; Fry & Hart, 1948; Brett, 1952; Fry, 1971; Brett, 1964; Brett, 1967; Brett, 1973; Farrell & Clutterham, 2003; Farrell, 2007a; Farrell, 2007b; Steinhausen et al., 2008; Enders & Boisclair, 2016; Nelson, 2016) and the latest aquatic respirometry technologies (Steffensen, 1989; Claireaux & Lagardère, 1999; Svendsen et al., 2016; Clark et al., 2013; Norin & Clark, 2016b; Claireaux & Chabot, 2016; Chabot et al., 2016; Metcalfe et al., 2016; Svendsen et al., 2016). At the outset of my doctoral dissertation, I identified that advancing Fryâs paradigm needs a valid and high-throughput characterization for a respiratory phenotype of aerobic and anaerobic capacity because oxidative phosphorylation and glycolysis directly link to lifetime fitness. Respirometry technology is on the verge of a breakthrough to mechanistically bridge physiology and ecology on an individual basis. The technical linchpins are accuracy, precision, repeatability, large sample size, stable baseline, and high diagnostic power (Figure 8.1; Chapters 2 & 3). Three proof-of-principle studies demonstrated that the respiratory phenotyping platform can identify intra-specific variation (Chapter 4), describe phenotypic plasticity by acclimation (Chapter 5) and diagnose the sublethal effects of infections (Chapter 6). Chapter 7 demonstrated the reproducibility of respirometry phenotyping when applying the best available protocols and 153 identifying the temporally stable traits for aerobic capacity and behavioural traits of agitation at an individual level. Timely and accurately diagnosing sublethal effects already had important conservation and management implications on fisheries and aquaculture in the northwest Pacific (Canadian Science Advisory Secretariat reports Ã 2; Polinski & Garver, 2019; Mimeault et al., 2019). The diagnoses of the viral-load dependent effects of viral infections on respiratory phenotype are key to understand the migratory success of Pacific salmon. Also, the diagnoses proved that low-virulent PRV infection had no major or sustained effects on the respiratory phenotype of sockeye and Atlantic salmon. Expanding on the discoveries, I will highlight the novel contributions and future directions in I) unifying aerobic and anaerobic capacity in one framework; II) diagnosing sublethal effects; III) foraging individual-based metabolism model with a Genomic-Physiology-Ecology axis (Figure 8.1). 8.2 Holistically assessing aerobic and anaerobic capacity in one framework Some ecological studies demonstrate that glycolysis supports the chance of surviving in life-&-death situations. For instance, escaping burst swimming can affect the chance of surviving predation (Ghalambor et al., 2004; Witt et al., 2015). Activities of lactate dehydrogenase affect the inhabitation of marine fishes in oxygen minimum zones (Childress & Somero, 1979; Childress & Seibel, 1998). A pair of equilibrium enzymes that modulate glycolytic flux can affect thermal adaptation (Pierce & Crawford, 1997). Hence, studying anaerobic and aerobic capacity in one animal (individual-based) and one protocol (IRAP) has great value. IRAP, as a physiological experimental protocol, advances ecological physiology by combining individual assessments of aerobic and anaerobic capacity. Ideally, the metrics for anaerobic capacity should be repeatable. However, the finding of unrepeatable O2crit in European 154 sea bass was unexpected, given that SMR was repeatable and O2crit measurement protocol was proven by other studies to be valid (Reemeyer & Rees, 2019; Negrete & Esbaugh, 2019). However, these studies have not tested the repeatability of O2crit and only used RMR as a baseline to derive O2crit. Because the reproducibility of different RMR levels is low, O2crit derived by RMR has lower repeatability than the one derived by SMR. Below are some analyses to understand the reasons for the O2crit unrepeatability and approaches for improvements. 8.2.1 O2crit is less comprehensive than hypoxia performance curve O2crit is affected by activity and SMR. Mining the data collected for Chapters 5 & 7, I discovered that active sea bass in hypoxia tend to have a lower O2crit as long as the amount of oxygen consumption exceeded ~100 mg O2 kg-1, independent of acclimation temperatures. Warm-acclimated fish are only half as active in hypoxia as that of cool-acclimated. This possibly relates to the nearly halved amount of oxygen consumed during the hypoxia challenge test in the warm temperature. When cool- and warm-acclimated fish were pooled, the calmer fish in normoxia had the lower SMR and O2crit (Figure 8.2). These calmer fish in normoxia are however more active in hypoxia and having a lower O2crit, a novel observation to the best of my knowledge. The related interdependence of behaviours and RMR was evident in sea bass that RMR and activities in normoxia were positively related to risk-taking behaviours (Killen et al., 2011). Also, O2crit depends on SMR variations induced by the IHNV load (Chapter 6). Clearly, many traits can affect O2crit, lending the supports that O2crit might not be a stable metric. Even though I had less success with the IRAP measurements made in hypoxia than those measured in normoxia, a holistic assessment of a fishâs metabolism should still include aerobic and anaerobic capacity. A key metric in anaerobic capacity is O2crit. Yet, the largest critique for O2crit is concentrating a very complex aerobic to anaerobic metabolic continuum at a single point 155 (Wood, 2018). To resolve this, measuring the hypoxia performance curve (HPC) provides a much more complete view (Fry & Hart, 1948; Neill et al., 1994). As a future improvement for O2crit, my dissertation also refined HPC by maintaining fish at a peak á¹O2 (Chapter 5) inside the static respirometers with a chasing device (Chapter 3; Zhang et al., 2020) while regulating the reduction of DO following the standardized hypoxia challenge test (Claireaux et al., 2013). Exercising fish in the respirometer every 10 min should keep fish in an active state to measure HPC as fish typically need ~30 mins to recover the immediate oxygen and high-energy phosphate stores (Zhang et al., 2018). Measuring one á¹O2 every ~ 10 min at the same individual also improves measuring precision than the previous studies (Claireaux & Lagardère, 1999; Claireaux et al., 2000; Lefrancois & Claireaux, 2003) where different individuals of a population were measured over the range of DO from normoxia to hypoxia. The measurements of individual HPC can advance the understandings of individual variations on whole-organism oxygen supply capacity in hypoxia to a similar level as the. individual-based blood oxygen equilibrium curve (OEC). The mid-point of a hyperbolic HPC when the dissolved oxygen can only maintain 50% AAS is DO50 (see Chapter 5). DO50 is the whole-animal analogue for the oxygen partial pressure when hemoglobin is 50% saturated (P50; Weber & Lykkeboe, 1978; Weber et al., 1987), which is analogous to the Michaelis-Menten constant for the concentration of substrate required for an enzyme to achieve 50% of maximum reaction velocity (Michaelis & Menten, 1913). Likewise, the HPC (whole animal) is analogous to the OEC (tissue), which is analogous to the Michaelis-Menten saturation curve (cell). Thus, HPC is likely well-grounded in multiple levels of animal physiology, an idea will need thorough testing. 156 8.2.2 Future applications of the hypoxia performance curve HPC opens the venue to more precisely calculate a regulation index (RI, Alexander & McMahon, 2004; Mueller & Seymour, 2011). RI measures the area under the HPC in proportion to the HPC area of a hypothetically perfect oxygen regulator that maintains á¹O2max at all DO levels. By far, only one study thoroughly compared RI with other metrics of hypoxia tolerance. However, the RI measure, which did not assure that individuals all reaching the peak á¹O2 (a standardization), concluded that O2crit, ILOS and time to LOE are different assessments of hypoxia tolerance for the same individuals of Fundulidae species (Borowiec et al., 2020). Although the conclusion is plausible, future experiments using a standardized HPC characterization can test if RI, DO50, O2crit and ILOS may reach a similar conclusion in determining anaerobic capacity and hypoxia tolerance. Also, further improvements in methodology should minimize the number of measurements for accurate modelling of the LOC curve. Overall, HPC provides a bioenergetics perspective that metabolic demands are met via a continuous spectrum from predominately aerobic capacity to predominately anaerobic capacity as oxygen is becoming more limiting. Also, HPC shows the transition on whole-organism oxygen-carrying capacity occurs when the cardiorespiratory compensations gradually lose the ability to saturate arterial blood as the ambient oxygen becoming more limiting (Fick principle). 8.3 Diagnose sublethal effects The field of toxicology has advanced enormously since the time of Paracelsus over 500 years ago, who said âAll things are poisons, for there is nothing without poisonous qualities. It is only the dose that makes a thing poison.â Thus, for centuries, toxicologists have sought dose-dependent relationships. What has changed in the past 50 years has been the endpoints and 157 measurements that are used for such relationships, especially by pharmacologists and environmental toxicologists who now seek sub-lethal effects that affect performance (Rand & Petrocelli, 1985). Physiologists who study temperature and hypoxia still routinely use a loss of upright equilibrium (LOE) as a surrogate for lethality (Claireaux et al., 2013; Lutterschmidt & Hutchison, 1997). Using mortality and LOE as an ecological endpoint hinders our ability to prepare, persevere and predict the outcomes of a changing environment. Hence, I and the others (e.g. Zhang et al. 2017; Mauduit et al. 2016 and Anttila et al. 2017) focus on reliably quantifying sublethal effects. For this reason, Chapter 6 studied the sublethal effects of PRV. Concerns are that the migratory and reproductive successes of some wild fish populations have been heavily challenged by pollution, overfishing, hypoxia, and rising water temperature, thereby any sublethal effects that go under the radar can be the last straw break the camelâs back. 8.3.1 Dire demands for capacity apportioning To understand how changes in metabolic demands may relate to sublethal effects, I used the whole-organism viral infection challenge test as a model system to study how subtle yet consistent shifts in individual phenotype translate to the fitness-related traits at a population level. If viruses are nearly everywhere and exist for a good length of natural history, the resistance to viral infection should be a major and intricate part of natural selection (Sheldon & Verhulst, 1996). In a sense, the species alive today are the descendants of the survivors from fighting the virus. Metabolic demands should factor in the costs of viral resistance. In fact, sockeye salmon showed load-dependent effects on metabolic demands, where fish that were resolving IHNV load suppressed SMR as a principal effect. The precise and accurate SMR estimate lead to the discoveries of the suppressions in other dependent metrics (RMR, O2crit, DEE). 158 In sublethal conditions, the root causes of mortality often are that metabolic demands are subsidized by time-limited glycolytic capacity when aerobic capacity is not sufficient. The limitation can be metabolic capacity apportioning, where glycolysis is subsequently engaged when either maximum aerobic capacity is reduced, or the demands are increased. This continuum has a far-reaching implication in understanding the sublethal effects. For example, the field metabolic rate of terrestrial animals is around 2-fold of SMR (Karasov, 1992), suggesting some negative consequences of always living at a full capacity. Furthermore, post-exercise recovery has a á¹O2 plateaus at 2-fold SMR (Zhang et al., 2018) and peak á¹O2 for digestion is also at 2-fold SMR (Adams, 2020). Besides, marine fishes barely distribute in the hypoxic environments that constrain their FAS to be less than 2-fold SMR (Deutsch et al., 2015). This suggests that fish typically sustain a major activity that costs no more than twice of SMR, which is well within an aerobic capacity and leaving an extensive portion of that capacity as a biological safety margin. It is not yet clear whether the safety margin is due to the limitations on metabolic capacity apportioning or a mitigation strategy for glycolytic dependence. 8.3.2 Diagnostic platform Following the rapidly changing dynamics of viral infections needs a high-throughput diagnostic platform. Characterizing eight individual fish at each sampling period provides prognoses about how viral infections affect hosts. The thoroughness in the numbers of variables measured (n=14) provides a holistic assessment of PRV infection. Moreover, the sampling throughput for early IHNV infection increased from 8 to 16 to cover the individual variability for IHNV, an achievable weekly throughput to resolve the viral load-dependent curves. In a longitudinal study, such as disease challenge trials, traits change with biotic and abiotic factors, and time. Hence, a high sampling throughput provides a high statistical power to identify biologically meaningful 159 changes and an experimental capacity to run time-matched control(s). This greatly mitigates the time effects in muddling any treatment effects. Sampling more specimens at a cross-sectional study also assure that the observed changes are inter-individual variation, otherwise, intra-individual variations can always introduce the uncertainties. Although the larger sample size is almost always desirable, to what point the increasing sample size reaches a point of diminishing return? The answers are in statistical principles and possibilities of phenotypic divergences. If a population has a minimum of two phenotypic responses and each has an equal chance of present, reaching enough statistic power in the most basic Studentâs t-test needs at least 12 specimens at a cross-sectional study, 6 specimens per group. If a time-matched control of 6 specimens is needed, a cross-sectional study at least needs a sampling throughput of 18 specimens. Moreover, sublethal effects in distressed situations often have a load-dependent relationship, where 20-30 specimens are needed to best determine a linear and/or non-linear regression. Therefore, a sampling throughput of 18 to 30 specimens at a cross-sectional study is the most ideal in bridging physiology and ecology. The measurement resolution required by some research questions sometimes needs an astronomical sample size. For example, if all the historical cardiopathy observed in British Columbia (Canada) is hypothetically caused by PRV and given that the cumulative mortality is less than 0.4% in farmed salmon (Polinski & Garver 2019), detecting one responder needs a sampling throughout of at least 250 specimens and identify eight responders needs at least 2000 specimens. Thus, current testing throughput on IRAP only resolved that there is neither a whole nor a substantial population effect of PRV on the respiratory phenotype. Tracking a subtle sublethal effect at an individual level will need scalability. A modular static respirometry system simultaneously measures a cohort of 4 fish, albeit with slightly staggered starting times, which 160 can be a future direction. The animal housing chambers have been increasing from 4 (e.g. Forstner, 1983), 6 (e.g. Daoud et al., 2007), 8 (e.g. Zhang et al., 2016), 16 (e.g. Strowbridge, Gilbert, Zhang et al., in prep.) to 32 (e.g. McKenzie pers. comm. 2018). The only constrain for the sample size is the amount of equipment and labour that can be concentrated during an experiment, which is definitely manageable with logistic planning and funding support. In summary, sampling throughout for in vivo measurements of functional performances in the current intermittent-flow respirometry system is much higher than those from the conventional swim-tunnel respirometry protocols that estimate four aerobic traits for one fish each day. The sampling throughput and measuring thoroughness in the current respiratory phenotyping platform is essential to characterize how animals act, acclimatize and adapt, via measurements for the changes of the mean as well as individual variations (Nikinmaa & Anttila, 2019). In a broader sense, the ability to diagnose sublethal effects helps to identify what and understand how and why some extinction events may happen. 8.4 Temporal stability is essential for individual-based metabolism model The associations between genotypes and the environment often miss the important links of functional assessments. The challenges in ecological physiology are expanding the understandings of individual physiology to the ecological performances of the whole population (see commentary by Jutfelt et al., 2018). An important, yet missing, pier is a physiological platform that reliably measures ecologically relevant and temporally stable traits in many individuals simultaneously. Respiratory phenotype reflects the most fundamental capacity for living organisms and its underpinning cardiorespiratory systems that link oxygen uptake at respiratory surfaces and cellular oxygen consumptions at mitochondria. Such conserved aerobic 161 capacity for ATP generation, a foundation for Fryâs paradigm (Fry, 1947), is temporally stable and encoded in the genes (Chen et al., 2018), thereby is likely heritable. Yet, energy dissipation remains to be the primary sources of uncertainty in the individual-based model (IBM) (Buckley, 2000) that studies environmental effects on movement, activity, growth and recruitment at temporal and spatial scales (Rose et al., 1999; Gibson et al., 2019) using dynamic energy budget equations (Kitchell et al., 1977; Huse & Giske, 1998). Hence, future researchers can focus on the individual-based metabolism model (IBMM) using respiratory phenotype data collected by IRAP. Moreover, the better predictive powers of IBMM can come from characterizing individual aerobic and anaerobic capacity during acute exposures and acclimation to environmental conditions. For instance, aerobic thermal performance curves, which are measured in acute exposures in nearly all recent studies, should have been measured in acclimation studies like Brett (1964) and Dickson & Kramer (1971). Foraging IBMM with a Genomic-Physiology-Ecology axis (Figure 8.1) can be instrumental for predicting environmental effects on fish species. Although my dissertation proposes an IBMM as the basis for the axis, other traits (e.g. growth rate, fecundity, fertility and courtship; Sluijs et al., 2008, Friberg & Arnqvist, 2003) may also adopt the Genomic-Physiology-Ecology axis. Animals can move to different habitats to mitigate the sublethal effects, albeit the animals might not best adapt to the new habitats as they do with the previously adapted ones. Besides migrations, one of the crossroads of moving from ecological-level functional performances to survival is phenotypic plasticity that is either genotypical or physiological origin and changes over time as developmental plasticity or ageing. Ultimately, natural selection acts upon stable traits, either within, across life stages or over generations. Survivors that successfully reproduce stand a chance to pass the fitness-related traits to offspring. 162 Genotypes are important vehicles to carry forward the information for phenotypic expressions to complete a cycle of new-born, development, mortality, and evolution. Leveraging on the exponentially increased processing power of modern computers and algorithms of machine learning, human intelligence is augmented for achieving an ecologically driven IBMM. A Human Physiome project that spearheads individual medical treatments based on genomic and physiology information is inaugurated in this decade (Omholt & Hunter, 2016). Because evolution has resulted in a dynamic harmony between animals and the environment, the important question is can animals evolve at the rate of climate change by plasticity and/or adaptation? Respiratory physiology can bridge ecological genomics and evolutionary ecology by providing a major mechanistic understanding of how natural selection acts upon individuals. 8.4.1 Operational recommendations Due to the diversity of teleost species, university laboratories simply do not have the recourses nor human powers to carry out the extensive number of controlled studies, mesocosms studies and wildlife surveys. Hence, a collaborative effort from laboratories in universities, governments, fisheries, and aquaculture sectors is needed to tackle this extensive challenge. The foundation for such a global effect is a standardized protocol that reliably and reproducibly measures biological patterns. Currently, data collected by IRAP in two independent studies (Zhang et al., 2019; Zhang et al. in prep.) at different locations (7710 km apart) and times (~1.5 yr apart) demonstrated the potential to directly compile and compare the respiratory phenotype for meta-analyses. To achieve the level of standardization, measurement noise should be minimized to maximize measurement precision and identify true intra-individual variations. Further, the reference baseline should be stable to quantify true phenotypic plasticity. The measurements should have a high measurement sensitivity to detect a sub-lethal effect. Without 163 minimal noise, maximal precision and stable baselines, biologists are blindfolded when trying to distinguish experimental errors from the true biological variability and stability. As eliminating either instrumental or experimental noises might not be possible in the foreseeable future, quantifying the range of errors in a standardized approach becomes a priority. The recommended principles for conducting modern aquatic respirometry are regulating workloads for testing animals, increasing measurement resolution, and measuring undisturbed animals. Besides following the suggested principles of practices, ecological physiologists can acknowledge and potentially present instrumental and experimental errors as the sources of variations. Hence, I recommend COR to be estimated for the experimental apparatus by at least one testing trial that is often conducted alongside the main experiments. This will provide a range of precision for other practitioners to interpret the results. More importantly, this allows quality assurance for meta-analyses using the data compiled from various laboratories Furthermore, many phenotypic characterizations need to be done in the fields to best reflect the conditions of animals in nature. If the experimental conditions of field studies are less controlled than the laboratory studies, compromised measurement precision is expected. In this regard, the higher sampling throughput of IRAP will help to reduce the type II error by increasing the statistical power. Also, the Bland-Altman plots of laboratory measurements can be the reference baselines for field measurements to estimate the measurement errors. Moreover, if time is limited in field studies, the temporally stable traits measured in normoxia by IRAP should be prioritized. If time is sufficient, the 4-h additional time investment in the hypoxia challenge test generates six more traits for anaerobic capacity. If practitioners are only interested in the ability to defend aerobic capacity in hypoxia, HPC can be measured independently over 4 h instead of three days for O2crit. These considerations, together with the portable respiratory 164 phenotyping platform, have been adopted in the studies of various remote locations (Antarctica, the Arctic, and desert conditions of Idaho and Western Australia), opening an avenue to study inter-individual variations more reliably in field ecophysiology. 8.5 Future directions Same as how I started this story with a quote from Sir Winston Churchill, I will end this story with a quote of Sir Churchill from 1942, amid Second World War, âNow this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.â. I, therefore, conclude with five hypotheses in respiratory ecophysiology: I hypothesize that the consistent selection pressure exists on the respiratory phenotype and showing a strong genetic basis. I hypothesize that aerobic and anaerobic capacity either have trade-offs or co-evolved, depending on the species, life histories and environment. I hypothesize that the traits of anaerobic capacity are equally stable as the traits of aerobic capacity. I hypothesize that anaerobic capacity experiences an equally strong selection pressure as aerobic metabolic capacity, given that animals rely on the anaerobic capacity for time-limited survival in severe hypoxia, high temperature and exhaustive exercise. I hypothesize that the temporal stability of traits decreases in sub-optimal environments where natural selection can act on the diverse phenotypes to increase the chance of adaptability. 165 á¹O2max, AAS, FASEPOC, Recovery capacityRespiratory phenotypeCost of routine behaviours Phenotypic plasticityInter- & intra- individual variationsEcological-levelFunctional performancesEvolutionSublethal effectsTemporal stabilityof traitsSurvivorsStable baselineRepeatabilityExceed thecapacity of plasticityDiagnostic powerDevelopmental Plasticity & ageingIndividual-based metabolic model (IBMM)Large sample sizeHeritable traitsReproductionSMR, ILOS, PcritP50crit, Defend aerobic metabolism in hypoxiaAOD, Anaerobic capacity of handling glycolysisTolerance for metabolic acidosisCost of defence against pathogensGrowth, DigestionFunctions:Capacity:Accuracy, precisionGenotypesBiogeographic distributionGenotypical & physiologicalorigins 166 Figure 8.1 A Genomic-Physiology-Ecology axis presents a simplified network that connects individual phenotypes to ecology and evolution. The advancements in aquatic respirometry allow ichthyologists to characterize the capacity and functions of a respiratory phenotype, while the validity of measurements is denoted from strong to weak on a colour scale of green-orange-red. Bridging from physiology to ecology needs a large sample size of accurate measurements to cover the probability of which traits or individuals are been selected. The diagnostic power allows sublethal effects to be identified. The underlined labels are the proven bridges by my dissertation to connect physiology and ecology. More works are clearly needed to fully understand this Genomic-Physiology-Ecology axis. 167 0 100 200 300 400 500 600 700010203040Amount of O2 uptake above SMR (mg O2 kg-1)O2crit (% sat.)16 oC Week 016 oC Week 4 25 oC Week 0 25 oC Week 4 Amount of O2 uptake in AASAmount of O2 uptake in AAS0 5100 200 300 4000200400600RMR (mg O2 h-1 kg-1)0 5 10 15 200200400600T>50%AAS (h)Amount of O2 uptake above SMR (mg O2 kg-1)0 5 70 140 210 2800200400600SMR (mg O2 h-1 kg-1)(a)(b) (c) (d) 168 Figure 8.2 The interdependence of critical oxygen saturation (O2crit), behavioural activity and standard metabolic rate (SMR) in European sea bass (Dicentrarchus labrax). Fish were acclimated to either 16 or 25 oC and the same fish from each acclimation temperature were repeatedly measured after 4 weeks. The activity level in hypoxia was measured by the amount of oxygen (O2) uptake above SMR during a hypoxia challenge test. 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Oncology Letters, 4(6), 1151â1157. https://doi.org/10.3892/ol.2012.928 231 Appendices Appendix A Chapter 3 A.1 Figures Figure S3.1 Rates of oxygen uptake (á¹O2) in rainbow trout during a ramp (to ~1.5 body length (BL) s-1) Ucrit test with 20-min water velocity (U) increments. Mean values (Â± s.e.m; 14 fish). Detailed analysis of á¹O2active dynamics was performed on the individual data indicated by gray circles and lines. á¹O2 values were analyzed with a conventional sequential interval regression analysis. 0 1 2 3 4 50150300450600750900U ( BL s-1) á¹O2 (mg O2 h-1 kg-1 ) 232 Figure S3.2. An example of dissolved oxygen (DO; % sat.) trace of the first two measurement phases when exhaustively chasing rainbow trout Oncorhynchus mykiss inside a static respirometer. An iterative algorithm identified the steepest slope (red) over the 1-min sampling window duration to estimate the peak oxygen uptake (á¹O2). The expanded segment (above) provides a more detailed view of the measurement noise associated with the steepest DO trace. Note occasional brief increases or near plateaus in DO. 0 100 200 300 400 5000658090100Time (s)Flush pump onFlush pump offWait period86878889Dissolved oxygen (% sat.) 233 A.2 Table Table S3.1. Linear mixed-effects model of peak oxygen uptake (á¹O2; mg O2 h-1 kg-1) as a function of analysis method (iterative vs. interval), exercise protocol (chase inside vs. outside respirometer), and sampling window duration (0.5, 1.0, and 1.5 min) with subject ID included as a random effect. The interval analysis method and chase outside the respirometer were set as reference conditions with a 1.0 min sampling window. All possible combinations of fixed effect interactions were tested the final model was chosen using the restricted maximum likelihood approach and also had the lowest Bayesian information criterion value. P-values were determined using Satterthwaite's approximation for degrees of freedom (df). Fixed effect Estimate Â± s.e.m df t p Constant 452±43 12.41 10.476 <0.001 Chase Inside 42±21 105 2.004 <0.05 Iterative algorithm 85±21 105 4.049 <0.001 Sampling window: 0.5min 70±18 105 3.804 <0.001 Sampling window: 1.5min 27±18 105 1.454 >0.05 Chase Inside * Iterative algorithm 98±30 105 3.296 <0.001 Subject ID was included as a random effect marginal R2 = 0.29 conditional R2 = 0.78 234 Appendix B Chapter 4 B.1 Tables Table S4.1. Analysis of variance table (ANOVA) for variation in critical oxygen level (O2crit) in rainbow trout Oncorhynchus mykiss (Walbaum 1792) parr with strain, standard metabolic rate (SMRq0.2) and maximum rate of oxygen uptake (á¹O2max) as explanatory variables [equation (1)]. Table S4.2. Analysis of variance (ANOVA) table for variation in critical oxygen level (O2crit) in rainbow trout Oncorhynchus mykiss (Walbaum 1792) parr with strain and absolute aerobic scope (AAS) as explanatory variables [equation (2)]. Sum of squares Degrees of Freedom F-value P-value (Intercept) 27.268 1 F1,19 = 30.276 < 0.001 Strain 32.925 2 F2,19 = 18.279 < 0.001 SMRq0.2 5.946 1 F1,19 = 6.602 < 0.05 á¹O2max 2.521 1 F1,19 = 2.799 > 0.05 Residuals 17.112 19 Sum of squares Degrees of Freedom F-value P-value (Intercept) 179.749 1 F1,20 = 163.299 < 0.001 Strain 29.958 2 F2,20 = 13.608 < 0.001 AAS 4.816 1 F1,20 = 4.376 < 0.05 Residuals 22.015 20 235 Appendix C Chapter 5 C.1 Tissue and biochemical analyses Fish at the end of the acclimation period were sacrificed with a blow on the head and blood was immediately sampled from the caudal vein into a heparinized syringe to determine hematocrit (Sigma 201m micro hematocrit centrifuge) and hemoglobin concentration was analyzed with an EnSpire 2300 Multilabel Reader (Perkin Elmer, Turku, Finland). Red, white and cardiac muscle and liver tissue were sampled, weighed and frozen with liquid nitrogen before storage at -80°C pending further analyses. The aerobic and anaerobic energy metabolic capacity of the skeletal muscles was evaluated by measuring citrate synthase (CS, EC 2.3.3.1) and lactate dehydrogenase (LDH, EC 1.1.1.27) activities, respectively. Red and white muscles were homogenized in 19 and 6 vol. homogenization buffer (0.1% Triton, 50 mM Hepes, 1 mM EDTA, pH 7.4) for CS activity, and in 19 and 40 vol. homogenization buffer for LDH. Both the assays were performed in triplicate (randomized) at room temperature using the EnSpire 2300 Multilabel Reader and subtracting the background reaction rate (Dalziel et al., 2012). The concentration of protein in muscle homogenates were analyzed with BCA protein assay kit (ThermoFisher, Waltham, MA, USA) to express enzyme activity as g-1 protein. The relative ventricular and liver mass were then calculated as a percentage of animal mass. 236 C.2 Figures 0102030405060708090S.D. (mg O2 h-1 kg-1 )aabb bb bb bb b b b b b b0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5020406080100120Sampling window duration (min)C.V. (%)aaabb bc cc ccc c c c c c(a)(b)16oC 237 Figure S5.1. An analysis of variation in sampling window duration for the individual estimates of the rate of oxygen uptake (á¹O2). 20-min background á¹O2 traces (n=5) in the respirometer at 16 oC were used for the signal-to-noise ratio analyses. We calculated (a) standard deviations (S.D.) and (b) coefficient of variation (C.V.) for the compiled á¹O2 from each trace at the given sampling window duration. We compared the S.D. and C.V. across different sampling windows using one-way ANOVA with Tukeyâs post-hoc tests (" < 0.05). Values are presented as mean Â± s.e.m.. We assigned 2-min as the conservative sampling minimum duration, as indicated by a vertical dashed line (red). The criteria are selecting the shortest sampling window duration where S.D. and C.V. were stable and before the values drastically increased, as previously prescribed in Zhang et al., 2019. 238 Figure S5.2. Individual one-phase association regression on normalized hypoxia performance curve (HPC) model. The rate of oxygen uptake (á¹O2) is normalized to a percentage (%) of absolute aerobic scope (AAS) at a dissolved oxygen saturation of the water (DO, % sat.). The solid line is the regression model and the dash lines are 95% confidence intervals. R2 and AIC scores are provided to evaluate the goodness of fit for the model. Three individuals (i.e. hypoxia 6, normoxia 1, normoxia 4) were excluded from further analysis. 0 20 40 60 80 100020406080100DO (% sat.)Hypoxia 1R2=0.65AIC=73.3% AAS0 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 5R2=0.79AIC=70.90 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 1Model does not fit, excluded0 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 5R2=0.95AIC=57.70 20 40 60 80 100020406080100DO (% sat.)Hypoxia 2R2=0.86AIC=65.9% AAS0 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 6Model does not fit, excluded0 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 2R2=0.93AIC=62.30 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 6R2=0.89AIC=64.70 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 3R2=0.82AIC=68.30 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 7R2=0.99AIC=38.70 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 3R2=0.99AIC=39.70 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 7R2=0.99AIC=45.80 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 4R2=0.83AIC=68.40 20 40 60 80 100020406080100DO (% sat.)% AASHypoxia 8R2=0.99AIC=42.70 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 4Model does not fit, excluded0 20 40 60 80 100020406080100DO (% sat.)% AASNormoxia 8R2=0.68AIC=74.3 239 Figure S5.3. Modelling hypoxic performance curve (HPC) model of oxygen uptake (á¹O2) and critical oxygen saturation (O2crit) for normoxia- and hypoxia-acclimated European sea bass (Dicentrarchus labrax) at 16 oC. (a) The same fish were exercised inside a static respirometer while the dissolved oxygen saturation of the water (DO, % sat.) was progressively reduced to perform a standardized hypoxia challenge test. Mean Â± s.e.m. for á¹O2 at a given DO (mean Â± s.e.m.) were used to characterize a group HPC model. The solid lines are one-phase association regressions (curve model) and the shaded areas are the 95% C.I. of these curves: one-phase association regression: normoxia-acclimated y = -52.8 + (642.4 + 52.8) Ã [1-e(-0.089 Ã x)] (R2 = 0.82); hypoxia-acclimated y = -268.3 + (454.9 + 268.3) Ã [1-e(-0.21 Ã x)] (R2 = 0.81). We compared á¹O2 (independent-sample t-tests) at each DO and found not acclimation effect. 0 20 40 60 80 1000100200300400500600Dissolved oxygen (% sat.)HypoxiaR2=0.81 AIC=653.6NormoxiaR2=0.82 AIC=576.4á¹O2 (mg O2 h-1 kg-1 ) 240 Figure S5.4. Effects of 6-week hypoxia acclimation on hematocrit (a), relative ventricular mass (b) and relative liver mass (c) in juvenile European sea bass (Dicentrarchus labrax) at 16 oC. Values were present in boxplot (bar is 25-75 percentile, whiskers are 10-90 percentile, line is median and â+â is mean, n = 6â13). Phenotypic plasticity is indicated by comparing normoxia-acclimated fish (N; grey) with hypoxia-acclimated fish (H; blue). Reversibility is indicated by comparing normoxia-acclimated fish (grey) after re-acclimate to normoxia for 4 weeks and tested in normoxia (HN-N; green). Different letters denote a statistical significance (one-way ANOVA with Tukey-Kramer post-hoc tests, " < 0.05) among the treatment groups in each muscle group. N H HN-N0204060Hematocrit (%)a aaN H HN-N0.050.060.070.080.09Relative ventricular mass (%) a aaN H HN-N0.00.51.01.52.02.5Relative liver mass (%)abab(a) (b) (c) 241 050100150Mass (g)a a a a12151821Fork length (cm)a a a aN-N H-N HN-N H-H1.21.51.82.1Condition factora a a a(a)(b)(c) 242 Figure S5.5. Effects of 6-week hypoxia acclimation on body size metrics [(a) mass, (b) fork length, (c) condition factor] in juvenile European sea bass (Dicentrarchus labrax) at 16 oC. Values were present in boxplot (bar is 25-75 percentile, whiskers are 10-90 percentile, line is median and â+â is mean, n = 13â28). Phenotypic plasticity is indicated by comparing normoxia-acclimated fish tested in normoxia (N-N; grey) with hypoxia-acclimated fish tested either in normoxia (H-N; orange) or in hypoxia (H-H; blue). Reversibility is indicated by comparing normoxia-acclimated fish (grey) after re-acclimate to normoxia for 4 weeks and tested in normoxia (HN-N; green). Different letters denote a statistical significance (one-way ANOVA with Tukey-Kramer post-hoc tests, " < 0.05). 243 Appendix D Chapter 6 D.1 Figures 91 4020406080100Week post challengeProportion of fish (%)IHNV loadNo loadPRV load05060708090100Survival rate (%)IHNVPRVControl(A)(B) 244 Figure S6.1. Survival rate and infectious percentage in virus challenge trial for sockeye salmon (Oncorhynchus nerka). (A) Accumulated survival rate (%) of control, piscine orthoreovirus (PRV) injected, infectious hematopoietic necrosis virus (IHNV) infected sockeye salmon (B) percentage fish with and without IHNV & PRV load over the course of the challenge trial. Grey bars mark the period when the experimental protocol of an Integrated Respiratory Assessment Paradigm was conducted. 245 Figure S6.2. Apportioning of total energy expenditure in quiescent sockeye salmon (Oncorhynchus nerka) as measured by 81 h of continuous oxygen uptake recordings in a respirometer at weeks 1, 4 and 9 post-challenge with either piscine orthoreovirus (PRV) or infectious hematopoietic necrosis virus (IHNV). infected sockeye salmon were measured. Total energy expenditure (the combined height of the shaded and open bars) is apportioned to standard metabolic rate (SMR, shaded bar) and to spontaneous activity (open bar). Values are presented as mean Â± s.e.m. (n=8 at each sampling time for each treatment). Two-way ANOVA was conducted and detected no significant difference. 1 4 9 1 4 9 1 4 902000400060008000Oxygen uptake (mg O2 kg-1) over 81 hSMRActivityControl PRV IHNVWeek post challenge 246 Figure S6.3. Piscine orthoreovirus (PRV) and infectious hematopoietic necrosis virus (IHNV) load in experimentally infected sockeye salmon (Oncorhynchus nerka). Gray bars indicate the period when the experimental protocol of an integrated respiratory assessment paradigm was conducted. The dashed line demonstrates a developmental trajectory to a no IHNV load at 9 weeks post challenge (wpc). 0 1 2 3 4 5 6 7 8 9 1010010210410610810101001021041061081010Weeks post challenge (wpc)PRV load (copies per ug blood RNA)PRVIHNVIHNV load(copies per ug kidney RNA) 247 Dimension 1 (31% variance)Dimension 2 (14% variance)-10 -5 0 5 10-6-4-20241 wpc 4 wpc 10 wpc 18 wpcDimension 1 (31% variance)Dimension 2 (14% variance)-10 -5 0 5 10-6-4-2024SC BC PRVContribution dimension 1 (%)LengthTime pointConditionWeightSexTreatmentTankRespirometer02468DimensionsExplained variance (%)1 2 3 4 5 6 7 8010203040Contribution dimension 2 (%)Time pointLengthConditionWeightTreatmentSexTankRespirometer0246810Contribution dimension 3 (%)TankTreatmentWeightConditionSexLengthTime pointRespirometer05101520(A) (B)(C) (D)(E) (F) 248 Figure S6.4. Exploration of variance in integrated respiratory assessment paradigm (IRAP) associated data using principal component analysis. Component scores are grouped by (A) time point or (B) treatment in reference to the two principal dimensions of variance. (C) The contribution to total variance associated with each of the eight principal dimensions. (D-F) Contributions of eight putative predictor variables to the total variance within the first three principal dimensions of variance. Dotted lines correspond to an expected (uniform) contribution. 249 AOD1 4 10 18 21020406080mg O2 â¢ kg-1FSOD1 4 10 18 211.01.21.4O2crit â¢ ILOS-1***SOD1 4 10 18 210369Air saturation (%)**EPOC1 4 10 18 2150010001500mg O2 â¢ kg-1EPOCdur1 4 10 18 2161218hours*MO2max 1 4 10 18 21300350400450500mg O2 â¢ h-1 â¢ kg-1SMRq0.2 1 4 10 18 2150100150mg O2 â¢ h-1 â¢ kg-1**AAS1 4 10 18 21200250300350400mg O2 â¢ h-1 â¢ kg-1 *FAS1 4 10 18 21246MO2max â¢ SMRq0.2-1 **RMR1 4 10 18 21100150200mg O2 â¢ h-1 â¢ kg-1**T0.5MO2max1 4 10 18 210153045Time spent (%)*T0.8MO2max1 4 10 18 210369Time spent (%)(A) (B) (C)(D) (E) (F)(G) (H) (I)O2crit1 4 10 18 2115202530Air saturation (%)(J)ILOS1 4 10 18 21152025Air saturation (%)(K) (L)(M) (N)Week post challenge Week post challengeSCBC 250 Figure S6.5. Respiratory phenotype of saline control (SC) and blood control (BC) inoculated Atlantic salmon (Salmo salar). Bar and whisker plots (bar = 25-75 percentile; whiskers = min-max; line = median; plus = mean; n = 8) of respiratory indices measured at 5 discrete sampling events. Significant difference (* p < 0.05; ** p < 0.01; *** p < 0.001) in mean value between saline injected (SC) and blood control injected (BC) treatment groups are indicated at each time point as determined by uncorrected Fisherâs LSD comparison tests. Significant difference (p < 0.05) following family-wise p-value multiplicity adjustment (Bonferroni, Sidak, Holm-Sidak tests; α = 0.05) is indicated in bold. Data at 21 wpc is repeated measure of fish assessed at 18 wpc. (A) á¹O2max = maximum metabolic rate; (B) SMRq0.2 = standard metabolic rate (0.2-quartile method); (C) AAS = absolute aerobic scope; (D) FAS = factorial aerobic scope; (E) EPOC = excess post-exercise oxygen consumption; (F) EPOCdur = EPOC duration; (G) RMR = routine metabolic rate; (H) T0.5AAS= time spent above 50% of á¹O2max; (I) T0.8AAS = time spent above 80% á¹O2max; (J) O2crit = critical oxygen level, (K) ILOS = incipient lethal oxygen saturation; (L) SOD = scope of oxygen deficit; (M) FSOD = factorial SOD; (N) AOD = accumulated oxygen deficit. 251 1 4 90100020003000mg O2 â¢ kg-1DEE AOD1 4 9051015202530mg O2 â¢ kg-1* *FSOD1 4 90.91.01.11.21.31.41.5O2crit â¢ ILOS-1SOD1 4 90369Air saturation (%)*EPOC1 4 94008001200160020002400mg O2 â¢ kg-1*EPOCdur1 4 939152127hours1 4 9200250300350400450mg O2 â¢ h-1 â¢ kg-1á¹O2max SMRq0.2 1 4 9405060708090100mg O2 â¢ h-1 â¢ kg-1***AAS1 4 9150200250300350400mg O2 â¢ h-1 â¢ kg-1FAS1 4 92468MO2max â¢ SMRq0.2-1RMR1 4 95060708090100110120mg O2 â¢ h-1 â¢ kg-1* *T0.5MO2max1 4 9036912151821Time spent (%)T0.8MO2max1 4 9-10123Time spent (%)(A) (B) (C)(D) (E) (F)(G) (H) (I)O2crit1 4 910121416182022Air saturation (%)*(J)ILOS1 4 9101214161820Air saturation (%)(K) (L)(M) (N)Week post challenge Week post challengeVCPRVIHNV(O)Week post challenge 252 Figure S6.6. A respiratory phenotype of vehicle control (VC), piscine orthoreovirus (PRV) and infectious hematopoietic necrosis virus (IHNV) in inoculated sockeye salmon. VC is the common time-matched control for both virus infected groups. Bar and whisker plots (bar = 25-75 percentile; whiskers = min-max; line = median; plus = mean; n = 8) of respiratory indices measured at 5 discrete sampling events. Significant difference (* p < 0.05; ** p < 0.01; *** p < 0.001) in mean value between saline-injected control (SC) and blood-injected control (BC) treatment groups are indicated at each time point as determined by uncorrected Fisherâs LSD comparison tests. Significant difference (p < 0.05) following family-wise p-value multiplicity adjustment (Bonferroni, Sidak, Holm-Sidak tests; α = 0.05) is indicated in bold. Data at 21 wpc is a repeated measure of fish assessed at 18 wpc. (A) á¹O2max = maximum metabolic rate; (B) SMRq0.2 = standard metabolic rate (0.2-quartile method); (C) AAS = absolute aerobic scope; (D) FAS = factorial aerobic scope; (E) EPOC = excess post-exercise oxygen consumption; (F) EPOCdur = EPOC duration; (G) RMR = routine metabolic rate; (H) DEE = daily energy expenditure; (I) T0.5AAS = time spent above 50% of AAS; (J) T0.8AAS = time spent above 80% AAS; (K) O2crit = critical oxygen level, (L) ILOS = incipient lethal oxygen saturation; (M) SOD = scope of oxygen deficit; (N) FSOD = factorial SOD; (O) AOD = accumulated oxygen deficit.
Thesis/Dissertation
eng
Applied Animal Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Interpreting species, intraspecific and intra-individual variability by comprehensively characterizing a fishâs respiratory phenotype with valid measures of oxygen uptake
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