{"http:\/\/dx.doi.org\/10.14288\/1.0441012":{"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool":[{"value":"Science, Faculty of","type":"literal","lang":"en"},{"value":"Zoology, Department of","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider":[{"value":"DSpace","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeCampus":[{"value":"UBCV","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/creator":[{"value":"Blanchard, Tessa Samantha","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/issued":[{"value":"2024-04-08T19:30:21Z","type":"literal","lang":"en"},{"value":"2024","type":"literal","lang":"en"}],"http:\/\/vivoweb.org\/ontology\/core#relatedDegree":[{"value":"Doctor of Philosophy - PhD","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeGrantor":[{"value":"University of British Columbia","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/description":[{"value":"Anthropogenic climate change is causing rising average temperatures and increased \r\nthermal variability across aquatic environments. These impacts may be particularly concerning \r\nfor early life-stages of fishes as their thermal windows are thought to be narrower than those of \r\nadults. However, relatively little is known about how these early-life stages will respond to \r\npredicted temperature changes with global warming. Therefore, I investigated the effects of \r\ndifferent temperature regimes on early life-stages and how they respond to these environments \r\nboth acutely and by utilizing developmental plasticity in two subspecies of Fundulus \r\nheteroclitus, a topminnow that inhabits intertidal saltmarshes along the Atlantic coast of North \r\nAmerica. \r\nI generated thermal performance curves (TPC) for development in embryos of two F. \r\nheteroclitus subspecies reared at a series of constant temperatures and found evidence consistent \r\nwith both local adaptation and countergradient variation between the subspecies. I also showed \r\nthat F. heteroclitus reared at different temperatures had altered hypoxia tolerance and hif1\u03b1 \r\nmRNA transcript abundance, but I observed no change in thermal tolerance. This finding \r\ndemonstrates that developmental cross-tolerance can occur in F. heteroclitus. However, these \r\ndifferences did not persist at the age of 1 year, highlighting reversible plasticity. I then examined \r\nhow fluctuating thermal regimes during development affected embryonic and larval phenotypes. \r\nI demonstrated development under fluctuating temperatures can alter performance in ways that \r\ncannot always be predicted based on performance generated at constant temperatures. \r\nFurthermore, I showed the fish reared under fluctuating temperatures had altered growth, thermal \r\ntolerance, and hypoxia tolerance, which were associated with long-lasting transcriptomic effects \r\nthat persisted even in a common environment. However, high thermal variability during \r\ndevelopment had lasting negative consequences on phenotypes as the result of deleterious \r\nplasticity. \r\nTaken together, my research demonstrates that F. heteroclitus utilize developmental \r\nplasticity as a mechanism to cope with changing temperatures during early development. \r\nHowever, there are limitations to this plasticity which are highlighted in the reversible and \r\ndeleterious plasticity I detected.","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO":[{"value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/87709?expand=metadata","type":"literal","lang":"en"}],"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note":[{"value":" TEMPERATURE DURING EARLY DEVELOPMENT ALTERS MORPHOLOGICAL, PHYSIOLOGICAL, AND MOLECULAR PHENOTYPES ACROSS TEMPORAL SCALES IN ATLANTIC KILLIFISH  by  Tessa Samantha Blanchard  B.Sc., University of Ottawa, 2015 M.Sc., University of Guelph, 2017  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF                                                                                                   THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in  The Faculty of Graduate and Postdoctoral Studies  (Zoology)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2024  \u00a9 Tessa Samantha Blanchard, 2024  ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Temperature during early development alters morphological, physiological, and molecular phenotypes across temporal scales in Atlantic killifish  submitted by Tessa Samantha Blanchard in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Zoology  Examining Committee: Patricia Schulte, Professor, Zoology, UBC Supervisor  Katie Marshall, Associate Professor, Zoology, UBC Supervisory Committee Member  Phillip Matthews, Associate Professor, Zoology, UBC University Examiner Bill Sheel, Professor, School of Kinesiology, UBC University Examiner  Additional Supervisory Committee Members: Colin Brauner, Professor, Zoology, UBC Supervisory Committee Member Jeffrey Richards, Professor, Zoology, UBC Supervisory Committee Member   iii  Abstract   Anthropogenic climate change is causing rising average temperatures and increased thermal variability across aquatic environments. These impacts may be particularly concerning for early life-stages of fishes as their thermal windows are thought to be narrower than those of adults. However, relatively little is known about how these early-life stages will respond to predicted temperature changes with global warming. Therefore, I investigated the effects of different temperature regimes on early life-stages and how they respond to these environments both acutely and by utilizing developmental plasticity in two subspecies of Fundulus heteroclitus, a topminnow that inhabits intertidal saltmarshes along the Atlantic coast of North America.   I generated thermal performance curves (TPC) for development in embryos of two F. heteroclitus subspecies reared at a series of constant temperatures and found evidence consistent with both local adaptation and countergradient variation between the subspecies. I also showed that F. heteroclitus reared at different temperatures had altered hypoxia tolerance and hif1\u03b1 mRNA transcript abundance, but I observed no change in thermal tolerance. This finding demonstrates that developmental cross-tolerance can occur in F. heteroclitus. However, these differences did not persist at the age of 1 year, highlighting reversible plasticity. I then examined how fluctuating thermal regimes during development affected embryonic and larval phenotypes. I demonstrated development under fluctuating temperatures can alter performance in ways that cannot always be predicted based on performance generated at constant temperatures. Furthermore, I showed the fish reared under fluctuating temperatures had altered growth, thermal tolerance, and hypoxia tolerance, which were associated with long-lasting transcriptomic effects that persisted even in a common environment. However, high thermal variability during development had lasting negative consequences on phenotypes as the result of deleterious plasticity.   Taken together, my research demonstrates that F. heteroclitus utilize developmental plasticity as a mechanism to cope with changing temperatures during early development. However, there are limitations to this plasticity which are highlighted in the reversible and deleterious plasticity I detected.   iv  Lay Summary    Our world is in the midst of a climate crisis, in which both mean temperature and the frequency and intensity of warming events and thermal variability are rapidly increasing. Therefore, it is critical that we understand how organisms will cope with these changes. This is especially important for embryos, which are a particularly vulnerable life-stage. However, we know surprisingly little about how embryos are going to respond. Across my thesis, I show that early life-stages of Atlantic killifish exhibited both short- and long-term changes in response to different thermal regimes. This was demonstrated through changes in several performance metrics as well as changes at the molecular level. However, development under stressful conditions resulted in negative effects on development and performance, highlighting the existence of thermal limits to plasticity. My work provides fundamental insights that help us understand how embryos can use plasticity to cope with climate change.                 v  Preface  A version of Chapter 2 is under review: \u201cBlanchard, T.S., Earhart, M.L., Shatsky, A.K., and Schulte, P.M. (2023). Intraspecific variation in thermal performance curves for early development in Fundulus heteroclitus\u201d. T.S.B. and P.M.S. conceived the ideas and designed the experiments. T.S.B., A.K.S., and M.L.E. collected the data; T.S.B. and A.K.S. analyzed all data; T.S.B. and P.M.S. drafted the manuscript. All authors contributed to edits and final publication.  The data presented in Chapter 3 was collected in collaboration with M.L. Earhart and N. Strowbridge. T.S.B. and P.M.S. conceived the ideas and designed the experiments. T.S.B., M.L.E. and N.S. collected the data; T.S.B. analyzed all data; T.S.B. wrote the chapter with edits by P.M.S.  A version of Chapter 4 has been prepared for publication as \u201cBlanchard, T.S., Earhart, M.L., Sheena, R., and Schulte, P.M. (in submission). Performance of embryos reared under fluctuating conditions does not conform to predictions based on constant thermal conditions. T.S.B. and P.M.S. conceived the ideas and designed the experiments. T.S.B., R.S., and M.L.E. collected the data; T.S.B. analyzed all data; T.S.B. and P.M.S. drafted the manuscript. All authors contributed to edits and final publication.  The data presented in Chapter 5 was collected in collaboration with M.L. Earhart, L. Campbell, and R. Sheena. T.S.B. and P.M.S. conceived the ideas and designed the experiments. T.S.B., M.L.E, L.C., and R.S. collected the data; T.S.B. analyzed all data; T.S.B. wrote the chapter with edits by P.M.S.  T.S. Blanchard wrote Chapter 1 and 6 with the editorial support of P.M. Schulte.   All experiments in this thesis were approved by the UBC Animal Care Committee: A20-0070.      vi  Table of Contents Abstract\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. iii Lay Summary\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. iv Preface\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... v Table of Contents\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 vi List of Tables\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... ix List of Figures\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... x List of Abbreviations\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 xiii  Acknowledgements\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. xv Chapter 1: Introduction\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 1 1.1. Overview ............................................................................................................................................ 1 1.2. Thermal Biology ................................................................................................................................ 1 1.2.1. Effects of temperature on ectotherms ........................................................................................... 1 1.2.2. Measuring thermal tolerance in fish  ............................................................................................ 3 1.2.3. Implications of a changing climate .............................................................................................. 4 1.2.4. Jensen's inequality ...................................................................................................................... 4 1.3.   Timescales of responses to environmental change .......................................................................... 5 1.3.1. Acute responses ......................................................................................................................... 5 1.3.2. Slower responses: types of phenotypic plasticity ........................................................................... 6 1.3.3. Developmental plasticity ............................................................................................................ 7 1.3.4. Adaptation ................................................................................................................................. 9 1.4.   Temperature and Hypoxia ............................................................................................................. 10 1.4.1. Interactions between temperature and hypoxia ................................................................................... 10 1.4.2. Measuring hypoxia tolerance .................................................................................................... 11 1.4.3. The hypoxia response ............................................................................................................... 12 1.4.4. Cross-tolerance vs. cross-talk .................................................................................................... 14 1.5.   Developmental Responses to Temperature.................................................................................... 14 1.5.1. Development in ectotherm embryos ........................................................................................... 14 1.5.2. Thermal sensitivity of ectotherm embryos .................................................................................. 15 1.5.3. Developmental plasticity of ectotherm embryos .......................................................................... 17 1.6.   Research Organism ........................................................................................................................ 17 1.6.1. Distribution of Fundulus heteroclitus ......................................................................................... 17 1.6.2. Reproduction strategies of Fundulus heteroclitus ........................................................................ 18 1.6.3. Development in Fundulus heteroclitus ....................................................................................... 19 1.6.4. Hatching cues in Fundulus heteroclitus ...................................................................................... 19 1.7.   Thesis Objectives ........................................................................................................................... 20 vii  1.7.1. Chapter two ............................................................................................................................. 20 1.7.2. Chapter three ........................................................................................................................... 21 1.7.3. Chapter four ............................................................................................................................ 22 1.7.4. Chapter five ............................................................................................................................. 22 1.8.   Importance and Implications ......................................................................................................... 23 Chapter 2: Intraspecific variation in thermal performance curves for early development in Fundulus heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 29 2.1. Introduction ...................................................................................................................................... 29 2.2. Methods ........................................................................................................................................... 33 2.2.1. Animal husbandry ........................................................................................................................ 33 2.2.2. Crosses and experimental design ................................................................................................... 34 2.2.3. Larval morphology ....................................................................................................................... 35 2.2.4. Heart rate .................................................................................................................................... 35 2.2.5. Statistical analyses ....................................................................................................................... 36 2.3. Results .............................................................................................................................................. 36 2.3.1. Thermal performance curves for survival ....................................................................................... 36 2.3.2. Developmental rate ...................................................................................................................... 37 2.3.3. Thermal performance curves for developmental rate ....................................................................... 38 2.3.4. Embryonic heart rate .................................................................................................................... 38 2.3.5. Larval morphometrics .................................................................................................................. 39 2.3.6. Thermal performance curves for embryonic growth rate .................................................................. 40 2.4. Discussion ........................................................................................................................................ 40 Chapter 3: Exposure to altered temperature during early development results in reversible improvements in hypoxia tolerance in juvenile Fundulus heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 57 3.1. Introduction ...................................................................................................................................... 57 3.2. Methods ........................................................................................................................................... 60 3.2.1. Animal husbandry ........................................................................................................................ 60 3.2.2. Experimental design ..................................................................................................................... 61 3.2.3. Critical thermal maximum ............................................................................................................ 62 3.2.4. Hypoxia tolerance ........................................................................................................................ 63 3.2.5. Gene expression ........................................................................................................................... 63 3.2.6. Statistical analyses ....................................................................................................................... 64 3.3. Results .............................................................................................................................................. 65 3.3.1. Survival and hatch........................................................................................................................ 65 3.3.2. Thermal tolerance ........................................................................................................................ 65 3.3.3. Hypoxia tolerance ........................................................................................................................ 65 3.3.4. Gene expression ........................................................................................................................... 66 3.4. Discussion ........................................................................................................................................ 66 Chapter 4: Performance of embryos reared under fluctuating temperatures does not conform to predictions based on constant temperatures\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 80 4.1. Introduction ..................................................................................................................................... 80 4.2. Methods ........................................................................................................................................... 83 4.2.1. Animal husbandry ........................................................................................................................ 83 4.2.2. Experimental design ..................................................................................................................... 84 4.2.3. Larval morphology ....................................................................................................................... 85 4.2.4. Embryo and larval gene expression ................................................................................................ 85 4.2.5. Statistical analyses ....................................................................................................................... 87 viii  4.3. Results ............................................................................................................................................. 87 4.3.1. Survival and time to hatch ............................................................................................................ 87 4.3.2. Larval morphology ....................................................................................................................... 88 4.3.3. Embryo mRNA transcript abundance ............................................................................................. 88 4.3.4. Larval mRNA transcript abundance ............................................................................................... 90 4.4. Discussion ........................................................................................................................................ 91 Chapter 5: Persistent deleterious effects of diel thermal fluctuations during early development... 106 5.1. Introduction ................................................................................................................................... 106 5.2. Methods ......................................................................................................................................... 109 5.2.1. Animal husbandry ...................................................................................................................... 109 5.2.2. Experimental design ................................................................................................................... 110 5.2.3. Agitation temperature and critical thermal tolerance...................................................................... 111 5.2.4. Hypoxia tolerance ...................................................................................................................... 112 5.2.5. Metabolism ............................................................................................................................... 113 5.2.6. Gene expression ......................................................................................................................... 114 5.2.7. Statistical analyses ..................................................................................................................... 115 5.3. Results ........................................................................................................................................... 116 5.3.1. Growth and mRNA transcript abundance ..................................................................................... 116 5.3.2. Thermal tolereance and mRNA transcript abundance .................................................................... 117 5.3.3. Hypoxia tolereance and mRNA transcript abundance .................................................................... 119 5.3.4. Resting metabolic rate ................................................................................................................ 120 5.4. Discussion ...................................................................................................................................... 120 Chapter 6: General discussion and conclusions\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 140 6.1. Overview ....................................................................................................................................... 140 6.2. Highlight, Implications, and Contributions ................................................................................... 140 6.2.1. Chapter 2: Thermal performance of Fundulus heteroclitus embryos ............................................... 140 6.2.2. Chapter 3: Developmental plasticity and cross-talk ....................................................................... 141 6.2.3. Chapters 4 and 5: Importance of incorporating thermal variation .................................................... 141 6.2.4. Chapters 2 and 5: Reversible developmental plasticity .................................................................. 143 6.2.5. Chapters 3, 4, and 5: Lasting effects on hif1\u03b1 ............................................................................... 143 6.3. Limitations ..................................................................................................................................... 144 6.4. Future Directions ........................................................................................................................... 145 6.5. Conclusions ................................................................................................................................... 146 References\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 150 Appendix A\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 193 A.1. Chapter 2: Supplementary Materials ................................................................................... 193 A.2. Chapter 3: Supplementary Materials ................................................................................... 200 A.3. Chapter 4: Supplementary Materials ................................................................................... 201 A.4. Chapter 5: Supplementary Materials ................................................................................... 207      ix  List of Tables  Table 2.1. Calculated mean values and 95% confidence intervals (C.I.) for TPC parameters for survival, developmental rate (1\/days), and embryonic growth rate (mm\/days) between four cross-types of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026...... 56 Table 3.1. Primer sequences and amplification efficiencies for genes used in this study for F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.73 Table 6.1. Summary of the findings across my thesis\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.148                     x  List of Figures  Figure 1.1. Hypothetical performance curve showing the predictions for Jensen\u2019s inequality based on the location of the thermal fluctuations along the TPC\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 24 Figure 1.2. Reaction norms demonstrating plasticity in response to different environments\u2026...25 Figure 1.3. Hypothetical scenario showing potential outcomes of developmental plasticity on phenotypes\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 26 Figure 1.4. Example of experimental designs that can be used to measure developmental plasticity\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 27 Figure 1.5. Strategies that embryonic life-stages might utilize in response to environmental stressors during early development\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u202628 Figure 2.1. Shapes of thermal performance curves (TPC)\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u202650 Figure 2.2. Thermal performance curves (TPCs) for survival (%) of embryos in four cross-types of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 51 Figure 2.3. Thermal performance curves (TPC) for developmental rate (1\/days) in four cross-types of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 52 Figure 2.4. Relationship between (A) heart rate (beats\/min) and developmental temperature and (B) Q10 values for heart rate and developmental temperature between four cross-types of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.53 Figure 2.5. Effects of developmental temperature and cross on larval morphology (A) length (mm) (B) weight (mg), and (C) yolk-sac volume (mm3) between four crosses of F. heteroclitus at hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 54 Figure 2.6. Thermal performance curves (TPCs) for embryonic growth rate (mm\/days) in four cross-types of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 55 Figure 3.1. Experimental design for testing the effects of developmental temperature in the southern subspecies of Fundulus heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 74 Figure 3.2. Effects of developmental temperature on (A) days to hatch and (B) survival to hatch in southern F. heteroclitus embryos reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch.. 75 xi  Figure 3.3. Effect of developmental temperature on upper thermal tolerance (CTmax) measured in 6 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 76 Figure 3.4. Effect of developmental temperature on hypoxia tolerance (time to LOE) measured in (A) 6 month old and (B) 1 year old in southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 77 Figure 3.5. Effect of developmental temperature on the relative mRNA transcript abundance of hif1\u03b1, hsc70 and hsp90b in 1 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 78 Figure 3.6. Effect of developmental temperature on the relative mRNA transcript abundance of hif1\u03b1, hsc70 and hsp90b in (A) brain and (B) liver in 6 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u202679 Figure 4.1. Prediction of performance using thermal performance curve for (A) developmental rate and (B) survival as per Jensen\u2019s inequality (C) Treatment water temperature measurements for each diel cycle (26\u00b10 \u00b0C; 26\u00b13 \u00b0C; 26\u00b15 \u00b0C; 26\u00b1 7\u00b0C) that embryos were incubated at during experimentation\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026100 Figure 4.2. Experimental design testing the effects of diel fluctuating developmental temperatures during early development at the embryo and larval stage in the northern subspecies of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026...\u2026... 101 Figure 4.3.  Effects of fluctuating developmental temperatures on (A) survival and (B) time to hatch in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 102 Figure 4.4.  Effects of fluctuating developmental temperatures on (A) larval dry weight at 0 dph, (B) length at 0 dph, (C) yolk-sac volume at 0 dph, and (D) length at 7 dph in northern F. heteroclitus reared 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch\u2026\u2026 103 Figure 4.5. Relative mRNA transcript abundance of (A) hsp27, (B) hsp70.2, and (C) myog that were significantly affected by fluctuating developmental temperatures at the embryo life-stage (7 dpf) in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 104 Figure 4.6. Relative mRNA transcript abundance of (A) hsc70, (B) hsp90b, (C) myog, (D) hif1\u03b1, (E) dnmt1, and (F) dnmt3bb that were significantly affected by fluctuating developmental temperatures at the larval life-stage (7 dph) in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026105 xii  Figure 5.1. Experimental design to examine the lasting effects of fluctuating temperatures during development in the northern subspecies of F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..130 Figure 5.2. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf1, (C) igf2r, (D) igf2, and (E) gh in 1 month post-hatch juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. 131 Figure 5.3. Effects of diurnally fluctuating temperature from fertilization to hatch the (A) agitation temperature (\u00b0C) and (B) CTmax (\u00b0C) in 1 month post-hatch juvenile northern F. heteroclitus (1 month)\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 132 Figure 5.4. The relationship between (A) length (mm) and CTmax (\u00b0C) and (B) length (mm) and agitation temperature (\u00b0C) in 1 month juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026.\u2026... 133 Figure 5.5. Effects of diurnally fluctuating temperature from fertilization to hatch on the mRNA transcript abundance of inducible heat shock genes (A) hsp70.1, (B) hsp70.2, and (C) hsp90a in 1 month juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026134 Figure 5.6. The relationship between (A-C) agitation temperatures (\u00b0C) or (D-F) CTmax and the expression of inducible heat shock genes hsp70.1, hsp70.2, and hsp90a in 1 month juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026135 Figure 5.7. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf2 in the liver and (C) myog in the liver of 3 month post-hatch juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026... 136 Figure 5.8. Effects of diurnally fluctuating temperature from fertilization to hatch on (A) hypoxia tolerance (time to LOE) and the expression of hif1\u03b1 in (B) brain and (C) gills in 3 month juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 137 Figure 5.9. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf1 and (C) igf1bp in the liver and (D) igf2 in the muscle tissue of 6 month old juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026 138 Figure 5.10. Effects of diurnally fluctuating temperature from fertilization to hatch on the oxygen consumption rate (?\u0307?O2) in 6 month juvenile northern F. heteroclitus\u2026\u2026\u2026\u2026\u2026\u2026...139     xiii  List of Abbreviations ANOVA AS cDNA CODD CSR c-TAD CTmax CTmin dARR DNA DNMT DO dpf dph Ek FIH GH HCT HPS HRE HSD HSE HSF HSP HSR IGF IQR LOE LT50 MMR Analysis of variance Aerobic scope Complimentary DNA Carboxy-terminal oxygen degradation domain Cellular stress-response C-terminal transactivation Upper critical thermal limit Lower critical thermal limit Developmental acclimation response ratio Deoxyribonucleic acid DNA methyltransferases Dissolved oxygen Days post-fertilization Days post-hatch Translation kinetic energy Factor inhibiting HIF-1\u03b1 Growth hormone Hypoxia challenge test Hypothalamic\u2013pituitary\u2013somatotropic axis Hypoxia response element Honestly significant difference Heat shock element Heat shock factor Heat shock proteins Heat shock response Insulin growth factor Inter-quartile range Loss of equilibrium Median lethal temperature Maximum metabolic rate xiv  ?\u0307?O2 mRNA MS-222 NaHCO3  N2 NODD O2 OCLTT ODD PHD Pmax PO2 ppt PVC Q10 RMR RT-qPCR SD SEM SMR Tbr Tcrit Topt TPC Tsaf YSV \u03b1 \u00b0C \u00b5g \u00b5l Metabolic rate Messenger RNA Tricaine methanesulfonate Sodium bicarbonate Nitrogen Amino-terminal oxygen degradation domains Oxygen Oxygen and capacity limitation of thermal tolerance Oxygen-dependent degradation domain Prolyl hydroxylases domain Maximal performance Partial pressure of oxygen Parts per thousand Polyvinyl chloride Temperature coefficient  Resting metabolic rate Quantitative reverse transcription polymerase chain reaction Standard deviation Standard error of the mean Standard metabolic rate Thermal breadth Upper critical limit  Thermal optimum  Thermal performance curves Thermal safety margin Yolk-sac volume Alpha Degree Celsius Micro-gram Micro-litre   xv  Acknowledgments  First and foremost, I wanted to extend my sincerest gratitude to my supervisor Dr. Patricia Schulte. Trish, words can\u2019t even begin to describe how thankful I am for your invaluable mentorship and guidance over the last 6 years. This journey wasn\u2019t an easy one and I don\u2019t think I could have gotten through it without you. Whenever things went wrong, as they did many times, you always had a positive outlook and knew how to handle my anxiety with ease. Your scientific insights and unwavering commitment to high scientific standards have been impactful in shaping me into the best scientist I could be. Finally, thank you for supporting my endeavors. From the moment I joined your lab, your encouragement has allowed me to focus on developing my teaching skills, paving the way for a future career in pedagogy.   I also want to extend my appreciation to Dr. Celeste Leander. My time with you in BIOL342 was something I looked forward to each year during my PhD. You have not only enhanced my teaching abilities, but you have shaped me into a more effective instructor. Your dedication to your students and commitment to new pedagogical approaches are something I aspire to achieve one day as an instructor.   As well, I would like to thank my committee, Dr. Katie Marshall, Dr. Jeff Richards, and Dr. Colin Brauner for your unwavering guidance throughout my PhD. Even though most of our committee meetings were held over Zoom, you were always so insightful, and your expertise always made for interesting discussions. Additionally, I would like to thank Dr. Rush Dhillon for all the killifish artwork that I used throughout my thesis.   I am immensely grateful for all the friends I have made across the Zoology department during my time here at UBC. To Bea, Rachael, Nic, Madison, and Will, thank you for the many get togethers and game nights, this truly got me through my PhD. I would like to extend a special thank you to Madison, we started this journey together, and I don\u2019t think I could have done it without you. Since day one, we had a goal to get through this together and tackle all challenges that came our way- which were many. I am happy to say we made it.   To my parents Carole and Ian, and my stepmom Rach, thank you for your endless love and support, even with my decision to move across the country to pursue my dreams. In memory of my stepmom Christine, your love and support will forever resonate in my heart. To my xvi  stepsister Michelle, thank you for sending me all the care packages over the years, you always knew how to brighten my day.   To my soon-to-be husband Brandon, thank you for being my biggest supporter throughout this journey. Your unconditional love and support manifested in many ways, from preparing weekly lunches and dinners, to embracing my late-night work demands or coming to work with me for my endless weekend fish care duties. Lastly, thank you for making the move across the country so we could be together. I'll always be grateful for that. I look forward to our next journey together.  I would like to finish this section by acknowledging that my thesis was conducted at the University of British Columbia, Vancouver Campus, which is situated on the unceded, traditional, and ancestral territories of the x\u02b7m\u0259\u03b8k\u02b7\u0259y\u0313\u0259m (Musqueam) First Nation.   Thank you all.                 xvii  \u201cResearch in developmental plasticity thus deserves to be recognized for what it really is\u2014an investigation of phenomena of fundamental importance to the genesis of all traits, in all organisms.\u201d \u2013 Moczek, 2015 1  Chapter 1: Introduction 1.1. Overview  In the current context of global climate change, aquatic environments will not only experience increased temperatures but also increased temperature variability, and both are likely to pose a challenge for aquatic organisms such as fish (Morash et al., 2018). When considering the impacts of climate change on fishes, most of the literature tends to focus on juvenile and adult life-stages, but in fishes, embryos are thought to be among the most temperature sensitive life-stages (Dahlke et al., 2020). Yet, the field currently falls short in addressing how embryonic life-stages respond to temperature change. Therefore, the goal of my thesis was to investigate the effects of temperature during early development and how fish may cope with these changes both acutely and by utilizing developmental plasticity. Using Fundulus heteroclitus as my model species, I examined the effects of early developmental temperatures (constant and fluctuating) across different life-stages (embryo to adult) from the physiological to the molecular level. By understanding the effects of various thermal regimes during early development across different temporal scales, my chapters contribute to our current understanding of the thermal biology of fishes by 1) characterizing how developmental temperature alters thermal phenotypes; 2) determining whether developmental temperatures have lasting effects at the physiological and molecular levels; and 3) assessing whether developmental plasticity can be reversible. The purpose of this introductory section is to provide some context regarding the current understanding of thermal physiology and developmental plasticity to provide a justification for my questions, hypotheses, and experimental designs.  1.2. Thermal Biology  1.2.1. Effects of temperature on ectotherms Temperature is a measure of the average translational kinetic energy (Ek) of the molecules in a system. Higher temperatures are the result of faster moving molecules which have a higher kinetic energy, whereas lower temperatures have slower-moving molecules with decreased kinetic energy (Schulte et al., 2011). In addition, temperature affects the three-dimensional structure and function of biological macromolecules such as proteins, lipids, and nucleic acids. Because of these two effects, within biological systems, changes in temperature 2  cause changes in the rates of biochemical reactions, with many rate processes increasing by approximately two-fold for every 10 \u00b0C increase in temperature (Hochachka & Somero 2002). This effect of temperature on reaction rates can be quantified using the temperature coefficient (Q10; Behradek, 1930). This parameter measures the effect that a 10 \u00b0C change in temperature has on a specific biological rate (Behradek, 1930). For most biological rates, this value typically falls around two within the permissive temperatures of the organism, but extreme values are often observed at both the high and low ends of a species\u2019 thermal range (J\u00f8rgensen et al., 2022). For example, values below one suggests that the thermal optimum has been exceeded and rates are declining with temperature, pointing to potential lethal effects of temperature due to protein unfolding (Hochachka & Somero 2002). Changes in protein unfolding occur because of the effects of temperature on the weak bonds that stabilize the higher order structures of proteins. These changes in weak molecular interactions can affect many critical processes in organisms including membrane structure and fluidity, enzyme activity, mitochondrial function, protein structure and gene expression (Hochachka & Somero, 2002). Thus, in response to small changes in temperature, organisms must be able to make physiological modifications to maintain homeostasis. The effects of temperatures across biological levels of organization are also thought to play a role in shaping the range at which a species can function and survive (Schulte et al., 2011; Payne et al., 2016; Seebacher & Little, 2021). Within the range of thermal tolerance of an organism, performance is often represented as a thermal performance curve (TPC). In brief, the thermal performance of an organism typically will increase with temperature from its lower thermal limit (CTmin) to a point at which it reaches a maximum performance (Pmax) at an optimum temperature (Topt). Above this temperature, performance tends to decline until a point at which performance is equal to zero, which represents the upper thermal limit (Schulte et al., 2011). This thermal limit is often referred to as the CTmax, but CTmax (or critical thermal maximum) is also term that is used to describe the temperature at which loss of equilibrium occurs during an acute thermal ramp (Becker & Genoway, 1979). As a result, this may or may not occur at the same temperature as the upper thermal limit observed on the TPC for a particular trait, which if often termed Tcrit. Thus, care must be taken to specify the nature of the thermal limit being measured in a particular experiment. TPCs can also be useful in predicting responses of ectotherms to changes in temperature in the context of anthropogenic climate change (Rebolledo et al., 2021). 3  1.2.2. Measuring thermal tolerance in fish   There are two commonly used metrics for assessing acute thermal tolerance in fishes: the incipient upper lethal temperature (IULT) and the critical thermal maximum (CTmax; Beitinger & Bennett, 2000). Measuring the IULT involves exposing groups of fish to different static temperatures to determine the temperature at which a group of fish experience 50% mortality. However, this method is now seldom used for fishes based on concerns about animal welfare.  As a result, CTmax is now the most frequently reported measure of thermal tolerance (Morgan et al., 2018; Desforges et al., 2023; Ern et al., 2023). This method involves increasing temperature at a pre-determined heating rate until the fish loses equilibrium (LOE). The fish is then returned to a permissive temperature and allowed to recover. This method is considered to be of lower ethical concern as it is non-lethal. It is also highly repeatable and produces reliable estimates of upper thermal tolerance (Morgan et al., 2018; Grinder et al., 2020). However, there has been recent discussion and debate around the appropriate or ideal heating rate for determining CTmax and the ecological relevance of this trait (Illing et al., 2020; Desforges et al., 2023; Ern et al., 2023; Penman et al., 2023). The conventional heating rate across most of the literature in fishes is 0.3 \u00b0C\/min (Beitinger & Lutterschmidt, 2011); however, this relatively fast ramping-rate is thought to potentially overestimate CTmax as a result of the delay in equilibrium between water and body temperature (Illing et al., 2020). In contrast, slower ramping rates may result in lower thermal tolerance if fish accumulate more thermal stress during the ramping period (Saravia et al., 2021). However, slower ramping rates may be considered to be more ecologically relevant as they more closely reflect typical heating rates that fishes may experience in the wild (Bartlett et al., 2022; Desforges et al., 2023). As a result, it is important to take into consideration the effects of the selected methods on the results and conclusions drawn from your study (Desforges et al., 2023). In recent years, there has been interest in a novel measurement of upper thermal tolerance known as agitation temperature (McDonnell & Chapman, 2015). Agitation temperature is defined as a behavioural response in where fish display an erratic swimming behaviour, which is thought to be driven by an escape response to seek cooler temperatures (McDonnell & Chapman, 2015; Bouyoucos et al., 2023). This metric has been suggested to be more ecologically relevant than CTmax, but it has only been assessed in a relatively small number of species to date.  4  1.2.3. Implications of a changing climate          During the 20th century, the industrial revolution has resulted in an increase in the amount of CO2 and other greenhouse gases into the atmosphere (Abram et al., 2016). This has caused a catastrophic change to our climate and more specifically, in the warming of our climate. These effects have been particularly evident by changes in sea surface temperatures. Indeed, it has been predicted that sea surfaces temperatures will increase by up to 3 \u00b0C by the end of the century if no action is taken to reduce greenhouse gas emissions (IPCC, 2021; Xu et al., 2022).  The effects of changes in overall mean temperatures have received extensive attention in fishes (e.g., Nilsson et al., 2010; Geffroy & Wedekind, 2020; Little et al., 2020; Alfonso et al., 2021; Lindmark et al., 2022). However, anthropogenic effects are also causing changes in patterns of thermal variation both temporally and spatially, including increased frequencies of marine heatwaves (Kotz et al., 2021; Xu et al., 2022). These changes in thermal variation also occur across multiple time scales such as changes within a day (i.e., diel cycles), across weeks or a month (i.e., heatwaves) or over the course of a year (i.e., seasons). For example, the amplitude of diel variation in air temperature has increased across different climate regions anywhere from 0.3 to 1.4 \u00b0C between 1975 and 2013 (Wang & Dillon, 2014; Kefford et al., 2022). As well, these patterns in thermal variability are also becoming more stochastic and unpredictable as the result of climate change. As changes in thermal variation coincide with current changes in mean temperatures, it is surprising how few studies have investigated the impacts of thermal variability in ectotherms, and more specifically in fishes.  1.2.4. Jensen\u2019s inequality          To better predict how organisms will respond to changes in climate, it is critical to understand the implications of thermal variation in the context of thermal performance curves (TPCs). To make predictions about the effects of thermal variation based on thermal performance curves that have been generated based on organisms acclimated to a constant temperature, is important to consider Jensen\u2019s inequality theorem, which is a mathematical description of the effects of averaging on non-linear functions (Ruel & Ayres, 1999; Denny, 2017). When applied to thermal performance curves, Jensen\u2019s inequality leads to the conclusion that thermal variability in an environment can either increase or decrease performance in comparison to a constant temperature with the same mean depending on where that variability 5  falls along a TPC (Fig. 1.1). At the simplest level it predicts that if the mean of a fluctuating environment falls above the TPC inflection point (concave portion of the curve) then average performance is predicted to be lower than at a constant temperature with the same mean (Fig. 1.1; dashed red line). On the contrary, if the mean of a fluctuating environment falls within the convex portion of the TPC then performance is predicted to be higher (Fig. 1.1; dashed blue line) than at a constant temperature with the same mean (Ruel & Ayres, 1999; Martin & Huey, 2008). Lastly, if the mean of the thermal fluctuation falls within the linear portion or the curve, it is predicted that the average performance to be the same under fluctuating and constant temperatures. Similar to the assumptions for TPCs (Sinclair et al., 2016), the effects of Jensen\u2019s inequality are also dependent on factors such as the thermal tolerance of the species, the relationship between the performance trait and temperature or whether the TPC is affected by plasticity that buffers the effects of thermal variation through shifting the TPC to ensure that performance is sustained (Morash et al., 2018). 1.3. Timescales of responses to environmental change 1.3.1. Acute responses (the heat shock response)   When an organism is faced with an acute stressor (e.g., temperature, toxins, oxidative stress), the cellular stress response (CSR) is one of the first pathways to be activated (K\u00fcltz, 2020; Somero, 2020). Within the CSR is the well-studied heat shock response (HSR; Lindquist, 1986; Feder & Hofmann, 1999). The HSR is a universal response present across most organisms, with the exceptions of some Antarctic fishes and invertebrates (Buckley & Somero, 2009). The HSR is comprised of the up-regulation of molecular chaperones, also known as heat shock proteins (reviewed by Feder & Hofmann, 1999; Basu et al., 2002; Currie, 2011). Heat shock proteins (HSPs) are a family of conserved proteins found across all organisms to date (Feder & Hofmann, 1999; Currie, 2011). There are three major families of HSPs that have been classified: the HSP90 (85-90kDa), HSP70 (68-7kDa), and the smaller HSPs (16-47kDa, e.g., HSP27, HSP40). Across these families, HSP70 is the most highly conserved and is also the most inducible to temperature stress (Lindquist, 1986). In response to thermal stress, these molecular chaperones are responsible for ensuring proper folding of proteins and preventing aggregation of damaged proteins to help maintain cellular homeostasis (Feder & Hofmann, 1999; Pessa et al., 2023). This rapid upregulation of HSPs to thermal stress is specifically controlled by heat shock 6  factors (HSF1); more specifically, they are regulated by HSF1 binding to promoter regions known as heat shock elements (HSEs; Morimoto et al., 1992) located on hsp genes, which then activates the transcriptional process (Buckley & Hofmann, 2002). The relationship between temperature and transcription levels of hsps has been highly studied across species, revealing a strong correlation (Feder & Hofmann, 1999; Basu et al., 2002). Furthermore, the levels of hsp induction have also been found to be correlated with the thermal tolerance in many organisms (Mosser et al., 1987; Mosser & Bols, 1988; Basu et al., 2002). The expression of hsps can differ across tissues and in their thermal inducibility (Ton, temperature at which the expression of hsps increases; Fangue et al., 2006, 2011). However, not all heat shock proteins respond to thermal stress (i.e., constitutive HSPs) and instead these HSPs play a role in regulation of the cell cycle, cell death, signalling pathways, and other cellular processes (Currie, 2011). Furthermore, heat shock proteins are not only regulated by thermal stress but also can be influenced by a variety of abiotic (e.g., osmotic stress, heavy metals; Iwama et al., 1999) and even some biotic factors (e.g., social interactions\/predators (Kagawa et al., 1999; Currie et al., 2010) and disease (Young, 1990; Ackerman & Iwama, 2001)). The HSR is a highly adapted response that is unique to each organism which is often reflective of the environment they inhabit. For example, the Antarctic emerald rockcod, Trematomus bernacchii, lack the ability to induce hsps in response to thermal stress which is thought to be driven by adaptations to colder environments (Buckley & Somero, 2009). Furthermore, this adaptive variation in the HSR is also apparent within species (i.e., intraspecific variation) in which several studies have identified differences in the induction of hsps between northern and southern populations (Healy et al., 2010; Gleason & Burton, 2015). Overall, the HSR is a complex response that helps organisms cope with thermal stress by minimizing protein damage and maintaining homeostasis, and it is an integral mechanism for species to respond to our warming environment. Even though we have a strong understanding of the HSR and its role in thermal tolerance in response to thermal stressors and acclimation, there still remains much to be understood on the effects of developmental temperatures on the HSR.  1.3.2. Slower responses: types of phenotypic plasticity Phenotypic plasticity is defined as the ability of a single genotype to produce different phenotypes in response to environmental variation (West-Eberhard, 1989). Plasticity is thought to play a role in reducing extinction and increasing diversification of populations that are undergoing rapid environmental change (Taborsky, 2017). Plasticity is often illustrated using a 7  reaction norm plot, which is a simple way of visualizing genotype by environment interactions (G \u00d7 E; West-Eberhard, 1989). Models for plasticity predict that plasticity is typically favoured when the environment the organism experiences is heterogeneous, when the cues may be predictable of future environments or when the cost of plasticity is small (Uller, 2008). As with most things, plasticity is also associated with costs and limitations to the organism (DeWitt et al., 1998). If this was not the case, organisms would be able to express any preferred or beneficial trait across all environments with no associated consequences (DeWitt et al., 1998). However, this is not always possible due the trade-off between the costs and benefits of plasticity. Some of these costs include maintenance costs, which are the costs associated with maintaining machinery to respond to environmental cues within the environment (Van Buskirk & Steiner, 2009). Thus, in the face of climate change, organisms face a trade-off as it is not always beneficial to change their phenotype to survive current environmental stressors.  Plasticity can occur at different levels of organization from whole-animal behaviour to physiological and molecular changes (West-Eberhard, 1989). Similarly, across time scales there are various types of phenotypic plasticity that an organism can display such as acclimation\/acclimatization, developmental plasticity, and transgenerational plasticity. Acclimation\/acclimatization is defined as a short-term plastic change in a phenotype that can alter fitness in a new environment (Munday, 2014). This change in phenotype typically occurs over the course of days, weeks, or months, however; this process is usually considered to be reversible once the environment returns to its original conditions (Earhart et al., 2022a). Another type of plasticity is developmental plasticity, defined as a change in phenotype in response to environmental variation during early development that can have persistent effects on adult phenotypes (West-Eberhard, 2005). Lastly, there is transgenerational plasticity which occurs when the environment experienced by the parents shapes the phenotype of their offspring or further subsequent life-stages (Munday, 2014). Throughout my thesis, I specifically examined the effects of developmental plasticity, which I discuss in more detail below.  1.3.3. Developmental plasticity  Stressors during early development can be a cue that triggers phenotypic change (Lea et al., 2018). In some cases, these phenotypic changes can be lasting, through the process of developmental plasticity. Developmental plasticity has been suggested to be an important factor 8  in the evolution of novel phenotypes (West-Eberhard, 2005). Developmental plasticity can be beneficial, deleterious, or neutral to an organism\u2019s fitness or performance (Fig. 1.2; Vagner et al., 2019). In the case of beneficial plasticity, the early environmental conditions match that of the environment experienced by later life-stages thus maximizing fitness. In contrast, deleterious plasticity arises if there is a mismatch between the environment experienced during development and adulthood overall reducing fitness (Seebacher & Grigaltchik, 2014). Furthermore, the degree of plasticity that an organism may show during development is thought to be dependent on when the stressor is present within the environment (i.e., there may be critical windows during which developmental plasticity occurs). These critical windows represent when an organism\u2019s phenotype is most susceptible to alteration as the result of plasticity through environmental regulation (Burggren & Mueller, 2015). Furthermore, Lindstr\u00f6m (1999) proposed that the earlier the environmental effect is present, the more impactful the long-term outcomes may be. However, in scenarios where the environmental stressor exceeds the thermal tolerance of the organism, this can incur developmental stress (i.e., cellular stress). As a result, it is predicted that plasticity under developmental stress would likely be maladaptive to the organism (O\u2019Dea et al., 2019).   With developmental plasticity, there is the potential for three outcomes within an organism\u2019s phenotype \u2013 irreversible plasticity, partially reversible plasticity, and reversible plasticity (Fig. 1.3; Burggren, 2020). The most studied of these is irreversible plasticity, which occurs when a switch in phenotype during development occurs and then the phenotype remains the same throughout the remainder of the life of an organism. In contrast, reversible plasticity is a switch in phenotype that occurs during early development which then reverts back to the \u201coriginal\u201d phenotype in later life-stages (Burggren, 2020). Even though reversible plasticity has been less studied, it is thought to potentially reduce the costs of plasticity when a mismatch with the environment occurs in later life-stages. This mismatch occurs when the environment experienced during early development results in a phenotype that is not beneficial in subsequent life-stages (Taborsky, 2007).     Across the literature, developmental plasticity has been measured in fishes using various experimental designs (Pottier et al., 2022a; Fig. 1.4). The differences across experimental designs reflect variations in the timing of the environmental stressor (e.g., temperature). 9  Additionally, these differences also depend on the authors' definition of the developmental window of an organism. These experimental designs include exposing organisms to thermal stressors during: B) a critical window during embryonic development, C) the embryo stage (pre-hatch), D) only the larval stage, E) the embryo to larval stage, F) the embryo to juvenile stage (before acclimation to a new temperature) before being re-acclimated to control conditions. Across my thesis, all my data chapters utilize the third experimental design (C), where the thermal stressor was only present during embryonic development (pre-hatch). This design was selected because I considered the most critical part of development to be when embryos are developing in the egg and are unable to disperse.  1.3.4.  Adaptation  Although fishes can be found at temperatures ranging from -1.9 \u00b0C to almost 40 \u00b0C, individual fish species are typically adapted to function within a relatively narrow range of temperatures, and as result species distributions are highly dependent on environmental temperature (Hochachka & Somero 2002). We can define adaptation as the result of selection on favorable phenotypes that can be inherited through multiple generations resulting in a shift in allele frequency over time that increases fitness in the changing environment (Donelson et al., 2019). Understanding the nature of these adaptive changes is an important goal of thermal biology.  Organisms typically deal with temperature in different ways depending on their ability to maintain body temperature independently from environmental temperatures (Schulte et al., 2011). Generally, we differentiate between \u201cendotherms\u201d and \u201cectotherms\u201d based on how they determine their body temperature. Endotherms utilize internal metabolic heat production to maintain body temperatures in response to temperature change (Hochachka & Somero 2002). However, ectotherms do not generate and retain sufficient metabolic heat to maintain a body temperature different from the environmental temperature, and thus ectotherms are considered to be more sensitive to changes in environmental temperature (Hochachka & Somero 2002). Most fishes are ectothermic; however, there are some exceptions such as tuna, billfish, and opah that have evolved specialized physiological mechanism for either regional or global endothermy (Schulte et al., 2011). The focus of my thesis is on the \u201ctypical\u201d fish strategy of ectothermy. 10  The breadth of temperatures that fishes can tolerate is dependent on many different aspects such as their life stage, age, and population; however, it is often correlated to their distribution and habitat (Rombough, 1997). Fishes that can tolerate a wider range temperature are termed \u2018eurytherms\u2019, whereas fishes that can only tolerate a narrow temperature range are termed \u2018stenotherm\u2019 (Hochachka & Somero 2002). These thermal characteristics are proposed to have their own set of costs and benefits to an organism. Eurythermal fish can live in a broader range of habitats but have increased energetic costs, whereas stenothermal fish have decreased energetic cost but reside in a much narrower range of habitats (Logan & Buckley, 2015). There is currently an extensive field of research examining the tolerance and performance of fishes in response to temperature change in juvenile and adult fishes (e.g., Wardle, 1980; Johnston & Dunn, 1987; Nilsson et al., 2010; Healy & Schulte, 2012; Pisano et al., 2019). As temperatures are predicted to increase over the next century, these studies are needed to provide insight into how adult fishes may cope with their changing environment (IPCC, 2021).  1.4. Temperature and Hypoxia 1.4.1. Interactions between temperature and hypoxia  Changes in temperature do not act in insolation in the natural environment but instead act alongside or in synergy with other environmental stressors that may exacerbate the stress response of organism in a changing climate (McBryan et al., 2013). In aquatic environments, one such interaction is between temperature and hypoxia (McBryan et al, 2013; Earhart et al., 2022a). In brief, hypoxia is typically defined as oxygen levels that fall below 3 mg O2\/L for marine and below 6 mg O2\/L for freshwater environments (Farrell & Richards, 2009). Oxygenation of water bodies is highly impacted by temperature such that the amount of dissolved oxygen (DO) in water decreases as temperature increases. Changing temperature also alters water circulation patterns and mixing, which can also affect oxygenation (Earhart et al., 2022a). As well, with current and predicted increases in global mean temperatures, dissolved O2 levels are expected to further decrease. For example, the predicted 2-3 \u00b0C increase in temperature is expected to further decrease ocean oxygen levels by up to ~7% (Keeling et al., 2010). However, the largest impact of higher temperatures on hypoxia is through a combined effect of increased temperature and eutrophication. Eutrophication occurs when excess nutrients are added to an aquatic environment, for example through human activities such as increased agricultural 11  run-off of nutrients into water bodies (Smith & Schindler, 2009). This poses a problem because this increase in nutrients removes a nutritional constraint on micro-organisms such as phytoplankton and zooplankton and allows massive increases in the metabolism of microorganisms as temperature rises, resulting in oxygen depletion in the aquatic environment. Furthermore, unpredictable thermal events such as heatwaves can lead to more frequent severe hypoxic events (Earhart et al., 2022a).   At the level of the organism, both temperature and hypoxia have also been shown to have interactive effects. The primary mechanism is through their combined effects on metabolic rate, in which increases in temperature increase metabolic demand but simultaneously limits the capacity for ATP turnover as a result of lower oxygen availability (McBryan et al., 2013). This effect on metabolism can also influence other biological mechanisms such as cardiovascular performance, respiratory function, mitochondrial function, energy allocation, and growth (Earhart et al., 2022a). Furthermore, the combined effects of temperature and hypoxia can interact in various ways. The first is considered an \u201cadditive\u201d effect where the measured effects are the sum of the two stressors independently whereas \u201csynergistic\u201d refers to the effects of the two stressors together is higher than the two stressors independently. Lastly, there can also be an \u201cantagonistic\u201d effect where the interactive effects of both stressors are less than one stressor in isolation (McBryan et al., 2013; Earhart et al., 2022a). In response to changing environments, the \u201csynergistic\u201d effect of temperature and hypoxia is thought to the most concerning one for organisms (McBryan et al., 2013).  1.4.2. Measuring hypoxia tolerance  Measuring hypoxia tolerance provides insight into the capacity of an organism to maintain function under reduced oxygen availability (Bickler & Buck, 2007; Rogers et al., 2016). This potentially allows predictions about how species will handle hypoxic episodes in their natural environment. There are various methods for assessing hypoxia tolerance in fishes, each with their own advantages and disadvantages. There are four primary types of hypoxia tolerance tests that have been widely used in fishes. The first method is the critical oxygen tension (Pcrit), which is defined as the point where oxygen consumption rate, typically standard metabolic rate (SMR), becomes dependent on the amount of oxygen (O2) in the environment (Ultsch et al., 1978). In other words, Pcrit represents when a fish switches from being oxy-12  regulators to oxy-conformers (Ultsch & Regan, 2019). This metric has been shown to be highly correlated with various respiratory traits across the O2 cascade (Mandic et al., 2009). However, this method has also received some criticism based on several factors and limitations that were highlighted by Wood (2018). Recently an alternative method for assessing the relationship between O2 consumption rate and environment O2 levels was introduced known as regulation index (RI; Mueller & Seymour, 2011). This method determines how well an organism can oxyregulate by measuring the area under the curve between ?\u0307?O2 and a range of O2 levels. The benefit of this method is that is it not restricted to the data fitting a single pattern as seen in Pcrit (Mueller & Seymour, 2011). The last two methods are considered hypoxia challenge tests (HCT), which are used to measure hypoxia tolerance, defined as when the fish lose equilibrium (LOE; Zhang & Farrell, 2022). The simplest HCT is time to LOE, which measures the time it takes for a fish to lose LOE at fixed level of O2 saturation (McBryan et al., 2016; Zhang & Farrell, 2022). The final method is known as the incipient lethal O2 saturation (ILOS) test, which measures the percentage of DO at which the fish exhibits LOE (Wood, 2018; Zhang & Farrell, 2022), generally measured during a trial in which oxygen levels are decreased linearly over time. Each of the metrics listed above have their own benefits and limitations and each provides different insights into the tolerance of a species.  1.4.3. The hypoxia response  In response to hypoxia, fish can respond across many biological levels as the result of both acute and chronic exposures to sustain performance, with greater changes across the O2 cascade occurring with longer exposures (Richards, 2009). Long-term changes across the O2 cascade can include metabolic suppression, altered gill morphology, changes in heart function and hemoglobin properties or changes in mitochondria function as a few examples; however, these responses vary across species (Pollock et al., 2007; Mandic et al., 2009; Richards, 2009). Short-term responses, on the other hand, are considered to be the first line of defense for a fish to tolerate lower oxygen levels (Zhang & Farrell, 2022). These responses can include changes in respiration such as increased ventilation at the gills and\/or changes in perfusion of the gills and specifically within the lamellae (Sundin, 1995; Perry et al., 2009; Tzaneva et al., 2011). At the cardiovascular level, hypoxia can elicit tachycardia (i.e., increase in heart rate) which increases circulation and favors O2 diffusion at the tissues (Farrell, 2007). The spleen has also been shown 13  to increase the release of red blood cells, ultimately increasing the O2-carrying capacity of the blood (Gallaugher et al., 1992). Fish may also show behavioural changes such as surface respiration or decreasing activity to reduce the amount of O2 being utilized (Rogers et al., 2016).   Many rapid responses to hypoxia occur at the molecular level and more specifically involve changes in protein and mRNA transcript abundance in response to hypoxia. The HIF pathway in particular is known to play a role in cellular O2 homeostasis and is thought to be critical for responding to hypoxic conditions (Mandic et al., 2021). HIF is a heterodimeric protein composed of two subunits, HIF-\u03b1 which is considered the O2-regulated unit and HIF-\u00df. HIF-\u03b1 specifically contains both a C-terminal-transactivation (c-TAD) domain and an O2-dependent degradation (ODD) domain (Ivan et al., 2001; Jaakkola et al., 2001). The stability and function of HIF-\u03b1 is regulated by two different independent hydroxylases proteins, prolyl hydroxylase (PHD) and factor inhibiting HIF (FIH). They regulate HIF-\u03b1 by hydroxylating residues on either the c-TAD domain via FIH or the ODD domain via PHD (Ciechanover, 1998). Under normoxia, HIF-\u03b1 is rapidly degraded by the VHL-mediated ubiquitin proteases and thus, does not accumulate in the cell. Furthermore, PHDs will also hydroxylate the proline residues of HIF-\u03b1 on the carboxy- and amino-oxygen degradation domain (NODD and CODD; Zhu et al., 2013). However, under hypoxic conditions, there is a decrease in hydroxylation, as a result VHL can no longer degrade HIF-\u03b1 and therefore HIF-\u03b1 will accumulate in the cell. HIF-\u03b1 will then enter the nucleus of the cell and dimerize with the HIF-\u00df subunit which will activate hypoxia responsive elements (HREs) and activate the transcription of several genes in response to hypoxia (for a review see Zhu et al., 2013; Mandic et al., 2021). In addition to this well-known post-translational regulatory mechanism for HIF, there is also evidence that, hif1\u03b1 mRNA transcript abundance is responsive to changes in oxygen level, particularly in fishes (Terova et al., 2008; Geng et al., 2014; Wang et al., 2021). This suggests that changes in hif1\u03b1 mRNA transcript abundance can act as an indicator of the activation of the hypoxia response.  HIF-\u03b1 modulates the transcription of various genes in response to changes in O2 levels, causing increases in glycolysis and angiogenesis, which increases anaerobic and aerobic energy supply, respectively (Richards, 2009). The HIF signalling pathway also regulates many other downstream cellular signalling pathways involved in metabolism and hemoglobin function in fishes (Richards, 2009). This suggests that the HIF signalling pathway plays an important role in 14  the hypoxia response in fishes (Terova et al., 2008; Geng et al., 2014; Wang et al., 2021). The importance of the HIF signalling pathway to the hypoxia response has been clearly demonstrated in zebrafish, where hif1\u03b1 knock-outs showed reduced hypoxia tolerance (Joyce & Perry, 2020; Mandic et al., 2020). The HIF signalling pathway has also been found to have an interactive effect with temperature. Across several species, warmer temperatures elicit higher hif1\u03b1 transcription (Olsvik et al., 2013; Levesque et al., 2019). However, the role of HIF1-\u03b1 in thermal tolerance is less clear, a recent study on zebrafish found that hif1\u03b1 knock-outs did not have altered thermal tolerance (Joyce & Perry, 2020). This highlights that in zebrafish, HIF1-\u03b1 does not play a direct role in determining the upper thermal tolerance.  1.4.4. Cross-tolerance vs. cross-talk  As temperature and hypoxia are tightly linked in aquatic environments, it has been suggested that signalling pathways across the cellular-stress response (CSR) in response to these two abiotic factors may potentially be inter-connected. This relationship has been defined across the literature in two forms: cross-tolerance and cross-talk. In brief, cross-talk can be defined as when a shared signalling pathway activates different protective mechanisms against different stressors (Sinclair et al., 2013). On the other hand, cross-tolerance is typically defined as when exposure to one stressor can enhance mechanisms that also protect against another stressor, typically at the cellular level (Sinclair et al., 2013). Under both scenarios, the increase in tolerance to one stressor causes increases in tolerance to another stressor. Throughout my thesis, both of these terms will be used as I explore the relationship between thermal stress and its effects on hypoxia tolerance at the whole-animal and cellular level.  1.5. Developmental Responses to Temperature 1.5.1. Development in ectotherm embryos  Embryos are key to a species\u2019 persistence and survival. In most fishes, life starts inside an egg, in which they spend a portion of their time developing (Du & Shine, 2022). Development is a set of highly coordinated processes consisting of both cellular differentiation and proliferation, each of which are carefully programmed steps but that occur at different rates (Mueller et al., 2011). Although it is well-known that genes passed down from the parents are critical for determining the traits of embryos and the resulting offspring, many embryonic traits are affected by non-genetic parental effects, often via maternal effects. The primary transmission of this non-15  genetic maternal information to the egg occurs through epigenetic processes or by altering mRNAs (Kappeler & Meaney, 2010). For example, during the early blastula stages of development, key maternal mRNAs that are deposited in the egg are required for successful development (Sullivan et al., 2015; Lubzens et al., 2017). Non-genetic maternal effects can also include passing on proteins, hormones, and energy stores that can further support the development of the egg (Green, 2008). Non-genetic paternal effects have also been shown to alter the development of the fertilized egg, likely through changes in DNA methylation, but this has been less studied across the literature (Crean et al., 2013).  However, even though parental effects play a key role in shaping offspring fitness, direct environmental effects can also play a significant role in offspring development. Environmental effects are particularly critical at the embryo stages because at this time most fish are unable to disperse to avoid unfavourable conditions but must still undergo energetically expensive and highly sensitive developmental processes. Therefore, it is critical that embryos have the ability to respond to changes in their environment for survival and to enhance fitness. Ectothermic embryos have many active responses to help deal with environmental variation (Fig 1.5). The first potential response to unfavourable environments maybe be to escape; for example, embryos may induce early hatching to move away from stressors, a strategy which has been demonstrated in both amphibians (Warkentin, 2007; Poo et al., 2023) and some fishes (Wedekind & M\u00fcller, 2005; Wisenden et al., 2022). However, this is only possible under circumstances where embryos are developed enough to survive outside the egg. Secondly, embryos may instead induce mechanisms to tolerate stressors (Du & Shine, 2022). For example, under drought conditions, some fishes will go into a diapause state until conditions become favourable to hatch (Martin & Podrabsky, 2017). Lastly, embryos may rely on plasticity to cope with environmental stressors by inducing defense mechanisms or adjusting their phenotype to match that of the environment. For example, in response to thermal stressors, embryos may elicit a heat shock response to help repair any damage as the result of heat-stress and potentially adjust their thermal tolerance (Krone et al., 1997; Stefanovic et al., 2016; Whitehouse et al., 2017). 1.5.2. Thermal sensitivity of ectotherm embryos  Temperature is thought to be the most important abiotic factor influencing development (Mueller et al., 2015). Temperature is a strong predictor of many key embryonic traits including 16  developmental rate, yolk-sac volume and utilization, and size at hatch (Pepin, 1991; Klimogianni et al., 2004). In the wild, fish normally lay their eggs within temperature ranges that are in the middle of embryonic thermal tolerance (Herzig & Winkler, 1986). However, every species has their own thermal window which encompasses the minimum, optimal and maximal temperature for growth and developmental rate (Rombough, 1997). Higher temperatures typically result in embryos that develop at a faster rate whereas lower temperatures slow down development (Pepin, 1991). Development at non-optimal temperatures can lead to higher mortality rates and higher deformities that may result in hatchlings with decreased fitness at later life stages (Das et al., 2006; Brown et al., 2011). The thermal tolerance of an organism changes throughout their lifespan, with very early life stages typically having a narrower thermal breadth than both their larval and adult stages (Rombough, 2011; Dahlke et al., 2020). Rombough (1997) suggested that the limit of temperature tolerance of embryos to be roughly \u00b15.8 \u00b0C for both tropical and temperate species. In addition, a recent study by Dahlke et al. (2020) found that embryonic life stages of 71 species had a smaller thermal window by an average of 20 \u00b0C compared to the subsequent life stages. They also predicted that up to 40% of species will not be able to survive within their current geographical range because of this vulnerability during early developmental stages. Thus, with aquatic environmental temperatures predicted to increase by 2-3 \u00b0C over the next century it is critical to understand the effects of temperature on early development (IPCC, 2021).  There are various hypotheses in the literature proposing why embryos have such a narrow thermal range compared to other life stages. The first hypothesis is that embryos are unable to temperature compensate because they are unable to regulate membrane fluidity or regulate metabolic rate in response to a change in temperature (Rombough, 1997). The second hypothesis is that fish embryos are oxygen-limited because they do not have the cardiorespiratory capacity to meet O2 demands in thermal extremes (Dahlke et al., 2020). Finally, it is thought that embryos are already functioning near their maximum aerobic capacity because of the high energetic costs of developmental processes and cannot increase aerobic energy supply further to cope with the costs of temperature change (Dahlke et al., 2020). Being able to cope with temperature variation requires many physiological and biochemical modifications that could be energetically demanding and may not be possible during embryonic development, making embryos more susceptible to thermal stress than other life-stages. 17  1.5.3. Developmental plasticity of ectotherm embryos Environmental temperatures during early development are known to influence the physiology, morphology, and molecular responses of fishes in ways that have the potential to persist across life stages. Many studies have shown that any shift in temperature away from an organism\u2019s optimal temperature during development can result in phenotypic variation in subsequent life-stages. Variation in thermal environments during early development have been shown to alter morphological traits such as body shape by inducing changes in the vertebrae and fin ray numbers (Lindsey & Harrington, 1972; Murray & Beacham, 1989) or altering muscle phenotype such as the proportion of red muscle in adults (Johnston et al., 1997; Macqueen et al., 2008; Campos et al., 2013a). Higher temperatures during development have also been shown to affect sex ratios in fishes such that warmer incubation temperatures resulted in a higher proportion of males (Rougeot et al., 2008; Donelson & Munday, 2015). At the physiological level, incubation temperatures during development have been shown to alter metabolic rate (Schaefer & Walters, 2010; Donelson et al., 2011), swimming performance (Batty et al., 1993; Burt et al., 2011; Scott & Johnston, 2012), and thermal tolerance (Chen et al., 2013; Moyano et al., 2017; Spinks et al., 2019) in later life stages. At the molecular level, developmental temperatures can alter many regulatory genes involved in growth, development, thermal stress, and metabolism that can have lasting effects on phenotypes (Metzger & Schulte, 2018; Ripley et al., 2023). Overall, altered temperatures during development can lead to many physiological and molecular changes that may be beneficial or deleterious to an organism.  1.6. Research Organism   In my thesis, I have chosen to use the Atlantic killifish, Fundulus heteroclitus, as the study system for my research. In this section, I review some of the characteristics that make this species a good choice for studying developmental plasticity.  1.6.1. Distribution of Fundulus heteroclitus Atlantic killifish, Fundulus heteroclitus, are topminnows that inhabit intertidal salt marshes and estuaries along a steep latitudinal thermal gradient on the Atlantic coast of North America from New Brunswick to Florida (Hardy, 1978). There are two known subspecies, northern (Fundulus heteroclitus macrolepidotus) and southern (Fundulus heteroclitus heteroclitus) that differ genetically, morphologically, physiologically, and behaviourally (e.g., 18  (Scott et al., 2004; Fangue et al., 2006, 2009; Healy et al., 2010; McBryan et al., 2016; Earhart, et al., 2022b). Both killifish subspecies exhibit high site fidelity, rarely migrating more than 1 km and typically having a home range of ~30 m (Abraham, 1985). The salt marshes in which they spend their lives undergo both daily and seasonal temperature variation and these variations differ with latitude. On average, southern populations experience temperatures of approximately 10 \u00b0C higher than their northern counterparts (Schulte, 2007). In addition to this latitudinal thermal variation, salt marshes undergo extreme daily thermal variation of as much as 15 \u00b0C during tidal cycles (Schulte, 2007). Overall, adult F. heteroclitus can tolerate temperatures between -1.5 \u2013 44 \u00b0C, but it is proposed that subspecies of F. heteroclitus have undergone local adaptation to their particular environments and thus each subspecies uses only part of this overall range (Schulte et al., 2011). This variation between the subspecies makes them an ideal model for studies of thermal adaptation and local adaptation (Nordlie, 2006). Furthermore, populations of F. heteroclitus exhibit another pattern known as countergradient variation, observed in traits such as growth rate and developmental rate (Conover, 1990; DiMichele and Westerman, 1997; Schultz et al., 1996). Countergradient variation is often defined as when populations from higher latitudes demonstrate higher performance than populations from lower latitudes when assessed at a common temperature. This pattern is thought to be indicative of an adaptation to seasonality, with the more rapid development and growth rates occurring in higher latitude populations (northern F. heteroclitus) compensating for a shorter breeding\/growing season (Conover, 1990; Conover & Present, 1990). 1.6.2. Reproduction strategies of Fundulus heteroclitus Fundulus heteroclitus also display an unusual type of reproductive behaviour. They employ a semilunar spawning strategy which coincides with tidal cycles such that females lay their eggs at high tides in the highest part of the salt marsh (DiMichele & Westerman, 1997). However, it has been found that northern populations do not display as strong semilunar patterns as observed in the southern species (McMullin et al., 2009). Reproduction in F. heteroclitus is thought to be initiated by a courtship behaviour (Newman, 1907) and can also be triggered by both temperature and\/or photoperiod. More specifically, in males the primary driver of gamete production is temperature, whereas females respond to both photoperiod and temperature, with photoperiod being important for maintaining gamete production (Taylor, 1986). The length of reproductive season also differs between subspecies. The southern fish have a longer breeding 19  season of around 7 months (March to September) in which they normally spawn within the temperature range of 15-30 \u00b0C. In contrast, the northern subspecies have a shorter spawning season, typically 2 months (May-June) with a smaller temperature range of 15-25 \u00b0C. Furthermore, the number of spawns per year also differ, with the southern subspecies spawning 6+ times\/year and the northern subspecies only spawning 3-4 times\/year (Taylor, 1986). These spawning cycles can last upwards of 5+ days which is thought to coincide with change in high tides (Abraham, 1985). Eggs are laid on different substrates depending on the subspecies: northern fish typically deposit their eggs on algal mats or sandy beaches whereas the southern species lay their eggs on oyster beds or between the leaves of marsh grass (Taylor, 1986). As a result of this strategy, the eggs of both subspecies of killifish spend a portion of their developmental period in air and will not hatch till the incoming high tide (Abraham, 1985).   1.6.3. Development in Fundulus heteroclitus  The developmental stages of F. heteroclitus have been well documented (Armstrong & Child, 1965). A total of 39 developmental stages have been identified from the time of fertilization to hatch. During early development, Fundulus eggs are considered to be oxygen-independent as they can still successfully develop in presence of cyanide (Crawford & Wilde, 1966), but development becomes oxygen-dependent after blastulation, around stage 15 (Armstrong and Child, 1965; Crawford and Wilde, 1966). The developmental thermal range of F. heteroclitus varies with its latitudinal distribution. Embryos from the northern subspecies have a developmental thermal range shifted towards colder temperatures and southern embryos have a developmental thermal range shifted towards warmer temperatures. However, across constant developmental temperatures, it has been shown that northern embryos typically develop faster than their southern counterparts (DiMichele and Westerman, 1997). At the embryonic level, studies have identified many morphological and physiological differences between the subspecies (e.g., egg size, metabolism; DiMichele & Taylor, 1980; DiMichele & Powers, 1984, 1991; DiMichele & Westerman, 1997).  1.6.4. Hatching cues in Fundulus heteroclitus     In F. heteroclitus, hatching is thought to be triggered by the release of the enzyme chorionase through the opening of the mouth which helps digest the chorion for hatch (DiMichele & Taylor, 1980). In the wild, F. heteroclitus embryos are exposed to many abiotic 20  factors that can play a role in determining when they hatch (i.e., hatching cues). For example, a study by DiMichele & Taylor et al. (1980) examined what abiotic cues trigger hatching in F. heteroclitus. They examined the effects of temperature, salinity, pH, hydration, and dissolved oxygen on hatching initiation. In this study, they found that the two main mechanisms initiating hatching were O2 concentration and hydration, whereas pH, temperature and salinity were found to be less important. Furthermore, they also reported that eggs did not hatch in response to these cues if it was before a certain developmental period. Another study that examined the effects of temperature and salinity found that the highest percentage of hatching was found at 20 \u00baC, however, salinity had no effect (Tay & Garside, 1975). However, the biggest factor that affects hatching appears to be air exposure. F. heteroclitus embryos incubated in air for 6 days hatched around 3 days sooner than embryos that were immersed during incubation, thus suggesting constant immersion may delay hatching (Tingaud-Sequeira et al., 2009). This finding correlates with the spawning behaviour of F. heteroclitus as eggs would get hydrated during the incoming spring tide (Abraham, 1985).  1.7. Thesis Objectives As highlighted in the introductory section of my thesis, many studies have begun to address the lasting effects of temperature on embryonic development with a primary focus on these effects under constant temperatures (e.g., Moyano et al., 2017; Spinks et al., 2019; Illing et al., 2020; Del Rio et al., 2021), yet very little is known about the effects under temperature fluctuations. As animals typically experience thermal variation in their natural habitat, it is important to understand how developing fishes respond to thermal fluctuations to make better predictions on how they will respond to climate change. To address these gaps, the overall goal of my PhD thesis was to advance our current limited understanding of development plasticity as a mechanism to cope with changing environments during early development in fish in both constant and fluctuating environments. To achieve this overarching goal, I set out to address four objectives that I have highlighted below within each of my chapters, alongside my methodological approaches for each chapter.  1.7.1. Chapter two  The primary objective of chapter two was to characterize the thermal performance of embryonic development by constructing TPCs and assessing larval morphology in both 21  subspecies of F. heteroclitus and their reciprocal crosses. More specifically, I was interested if patterns of thermal performance across subspecies were consistent with a hypothesis of local adaptation or countergradient variation. I hypothesized that the thermal performance of a species is dependent on the temperature of their local environment (subspecies effect) due to processes such as local adaptation or countergradient variation. As a result, I predicted that the southern species would be more warm-tolerant (right-shift in their TPC) relative to the northern subspecies. Based on previous research, I also predicted that the northern subspecies would show a higher developmental rate (vertical shift in their TPC) consistent with countergradient variation. Secondly, I also hypothesized that that development at temperatures approaching thermal limits would cause changes in developmental timing that would result in unfavourable larval phenotypes (deleterious plasticity). From this hypothesis, I predicted that incubation of embryos near their thermal limits would result in larvae that were smaller and had decreased yolk-sac size. I tested these hypotheses by rearing embryos across a range of constant developmental temperatures until hatch. This chapter was fundamental to my thesis by establishing the TPC of embryonic development in F. heteroclitus because it allowed me to select appropriate developmental temperatures for subsequent chapters.  1.7.2. Chapter three  The primary objective of chapter three was to determine if developmental temperatures have lasting effects on thermal and hypoxia tolerance at the whole-animal and cellular levels in the southern subspecies of F. heteroclitus, and to determine if any developmentally plastic phenotypes were reversible. I hypothesized that developmental temperatures would alter the thermal performance of juvenile fish as a result of beneficial developmental plasticity. From this, I predicted that fish exposed to warmer temperatures during development would have improved thermal performance (e.g., higher CTmax) and altered hsp expression. Furthermore, I hypothesized that developmental temperatures would alter the hypoxia tolerance of juvenile fish through cross-tolerance. From this hypothesis, I predicted that fish exposed to warmer developmental temperatures would have greater hypoxia tolerance and higher hif1\u03b1 mRNA transcript abundance. To test these hypotheses, embryos were developed at either a constant temperature of 20 \u00b0C or 26 \u00b0C until hatch followed by rearing at a common constant temperature of 20 \u00b0C for 12 months. 22  1.7.3. Chapter four  The primary objective of chapter four was to determine whether the effects of diel thermal fluctuations on embryonic development can be predicted using data from experiments performed at constant temperatures (chapter two) based on Jensen\u2019s inequality. This chapter was performed using the northern subspecies of F. heteroclitus and temperatures were selected based on the results of Chapter two. As well, I also examined the effects of fluctuating temperatures on larval morphology and changes in mRNA transcript abundance for genes involved in the heat shock stress response (HSR), genes related to growth and metabolism, those involved in DNA methylation, and genes related to hypoxia tolerance. I hypothesized that rate processes under fluctuating temperatures would be influenced by the shape of the TPC taking into account the effects of Jensen\u2019s inequality. Thus, I predicted that rate processes would be slower under the fluctuating regimes I examined relative to those determined under constant temperatures with the same mean. As well, I hypothesized that fish reared under fluctuating temperatures would allocate more fuel to developmental processes due to increased metabolic demands. From this, I predicted that fish reared under the greatest thermal fluctuations would hatch smaller and with less yolk than fish reared at constant temperatures. Lastly, I hypothesized that exposure to constant versus fluctuating environments during embryonic development would result in changes at the transcriptome level that would persist as a result of developmental plasticity. Therefore, I predicted that mRNA transcript abundance would differ between individuals reared under constant and fluctuating developmental temperatures, even after larvae were acclimated to a common temperature. To measure the effects of thermal variation during development, embryos were incubated at either a constant (26\u00b10 \u00b0C) temperature or one of three thermal regimes with daily fluctuations (26\u00b13 \u00b0C, 26\u00b15 \u00b0C or 26\u00b17 \u00b0C) until hatch. Once hatched, larvae across all treatments were held at a common temperature of 26\u00b10 \u00b0C.  1.7.4. Chapter five  The primary objective of chapter five was to identify whether fluctuating temperatures during early development had lasting effects on various phenotypes at the whole-animal and cellular levels. For this chapter, I used the same clutches of larvae that hatched in chapter 4 and reared them out for 6 months at 26\u00b10 \u00b0C. I assessed a variety of traits including thermal tolerance, hypoxia tolerance, metabolism, and growth rate to get a holistic understanding of the 23  effects of early developmental temperatures. In this chapter, I hypothesized that temperature fluctuations experienced during early development result in lasting beneficial plasticity. From this, I predicted that fish reared under fluctuating temperatures would have increased performance as juveniles (e.g., increased thermal and hypoxia tolerance, higher growth rate, lower metabolic rate) relative to juveniles that were reared at constant temperatures. As well, I hypothesized that fluctuating temperatures would have lasting effects on the transcriptome. Therefore, I predicted that juveniles would have altered mRNA transcript abundance and the plasticity of their mRNA relative to juvenile fish that had been developed at constant temperatures. 1.8. Importance and Impact  In response to a changing environment, organisms can respond by migrating away to a more favourable habitat, exhibiting phenotypic plasticity in response to their environment such that performance improves, or through evolutionary adaption; or if none of these are possible, organisms face the probability of dying (Somero, 2010). However, when considering how fishes will respond it is important to consider the specific life-stage that is faced with the environmental stressor. In particular, the embryonic life-stage is considered the most vulnerable life-stage due to its inability to migrate away from an environmental stressor (Dahlke et al., 2020). Yet, how fishes utilize developmental plasticity to cope with their changing environment is not-well studied, and even less is known about the effects of thermal fluctuations (e.g., Donelson et al., 2011; Moyano et al., 2017; Spinks et al., 2019; Illing et al., 2020; reviewed by Vagner et al., 2019). However, aquatic environments are not static, but experience high thermal variability and this is predicted to further increase with climate change (IPCC, 2021). My thesis advances our current limited understanding of how organisms utilize developmental plasticity to cope with both constant and diel fluctuating thermal environments, a process that I believe may be crucial for a species survival.   24   Figure 1.1. Hypothetical performance curve showing the predictions for Jensen\u2019s inequality based on the location of the thermal fluctuations along the TPC. In scenario 1, thermal fluctuations (dashed blue line) fall within the convex region of the curve and therefore performance under fluctuating conditions (Pfluc) is predicted to be higher than performance under constant conditions (Pconst) with the same mean. In scenario 2, thermal fluctuations fall along the linear portion of the curve, which is predicted to result in no difference between performance under fluctuating and constant temperatures. Lastly, in scenario 3, thermal fluctuations (dashed red line) fall within the concave region of the curve and therefore performance under fluctuating conditions (Pfluc) is predicted to be lower than performance under constant conditions (Pconst) with the same mean.  25   Figure 1.2. Reaction norms demonstrating plasticity in response to different environments. (A) Displays beneficial plasticity, such that the new phenotype has increased performance in the new environment. (B) Displays no effect of plasticity, as performance remains the same across both environments. (C) Displays deleterious plasticity, such that the new phenotype has decreased performance in the new environment.         26   Figure 1.3. Hypothetical scenario showing potential outcomes of developmental plasticity on phenotypes. In response to an environmental stressor experienced during development, organisms may show (A) no plasticity, (B) irreversible plasticity, in which a phenotypic change persists throughout the lifespan, (C) partially reversible plasticity, in which the new phenotype partially reverts back to the original phenotype and (D) reversible plasticity, in which a phenotype fully reverts back to the original phenotype.     27   Figure 1.4. Example experimental designs that can be used to measure developmental plasticity. Design A represents a control experiment in which the fish are not exposed to a change in temperature. Design B represents an experiment that tests for critical windows during embryonic development by exposing embryos for a brief window during development. In design C, the thermal exposure is only present during the embryonic stage until hatch. Design D assesses the effects thermal stressor during only the larval stage. In design E, both the embryo and larval stage are exposed to the thermal stressor before being re-acclimated to control conditions. Lastly, in design F, the thermal stressor is present from the embryo to the juvenile life-stage before being re-acclimated as adults. Note that designs in which the altered thermal environment is present throughout life (not shown here) cannot be used to identify developmental plasticity, because they cannot separate the effects of developmental plasticity and acclimation.    28   Figure 1.5. Strategies that embryonic life-stages might utilize in response to environmental stressors during early development. Embryos may try to (1) escape by hatching early to move away or (2) tolerate (or avoid) these stressful conditions through inducing diapause, which pauses development. Alternatively, embryos may utilize plasticity by providing a defense mechanism (3) (i.e., repairing DNA, increasing HSP production) or adjusting their phenotype (4) to be better suited to the new environment (i.e., change their morphology, increase metabolic rate).     29  Chapter 2: Intraspecific variation in thermal performance curves for early development in Fundulus heteroclitus 2.1. Introduction  Understanding the effects of temperature change on organisms is likely to be critical for forecasting the impacts of climate change; however, most work examining the effects of temperature on aquatic organisms such as fish has been conducted on adults, and much less is known about the thermal sensitivities of embryos (Llopiz et al., 2014; Dahlke et al., 2020). This issue is of great importance because including information about the effects of temperature on early developmental stages can have substantial impacts for predictions of climate change vulnerability across populations (Radchuk et al., 2013; Levy et al., 2015; Kingsolver & Buckley, 2020). This is important because some of the currently available data for fishes suggest that early developmental stages are often more vulnerable to thermal extremes than later life stages (Rombough, 1997; Flynn & Todgham, 2018; Dahlke et al., 2020). However, the evidence for greater sensitivity to thermal extremes during early development is mixed (Tangwancharoen & Burton, 2014; Przeslawski et al., 2015; Truebano et al., 2018; Pandori & Sorte, 2019; Rebolledo et al., 2020; Collin et al., 2021). For example, in fishes, the conclusion from a meta-analysis that early developmental stages may be more sensitive to thermal extremes (Dahlke et al., 2020) has been challenged on several grounds including differences in the metrics chosen to reflect thermal tolerance across life stages, and the lack of comprehensive testing across multiple acclimation\/developmental temperatures in embryos and larvae (Pottier al., 2022b). Thermal performance curves (TPC) provide a conceptual framework in which to assess the effects of temperature on organismal performance and fitness across levels of biological organization (Schulte et al., 2011). These curves generally have a characteristic shape (Fig. 2.1A), with performance increasing relatively gradually towards a maximum, followed by a steep decline with increasing temperatures (Schulte et al., 2011). TPCs can be described using a few simple metrics including the CTmin and CTmax, which are the low and high temperatures where performance is equal to zero, the Topt, which is the temperature at which performance is maximized (Pmax), and the thermal breadth Tbr, which is the range over which performance reaches some specified percentage of Pmax (often either 50% or 80% of Pmax).  30  TPC shape and parameters can be altered by both plasticity and adaptation (Schulte et al., 2011; Seebacher and Little, 2021), and estimating the capacity for plasticity and adaptation in TPCs is likely to be vital for understanding population responses to climate change (Angilletta, 2006; Sinclair et al., 2016). However, relatively few studies in aquatic organisms, and particularly in early developmental stages, have assessed performance traits across a sufficient number of temperatures to provide good estimates of the shape of the TPC. As a result, many studies of climate change vulnerability have relied on estimates of CTmin and CTmax (Dahlke et al., 2020), although changes in these parameters do not necessarily capture information about the nature of the shift across the entire TPC. Comprehensive characterization of TPC shapes has been suggested to be critical in determining which taxa will be winners and losers in the face of climate change (Sinclair et al., 2016; T\u00fcz\u00fcn & Stoks, 2018). In this context, understanding how the shape of TPCs can be altered by plasticity or local adaptation is an important consideration for predicting a species likely resilience to climate change (Sinclair et al., 2016). There is a substantial body of theory that has attempted to describe potential changes in TPC shape in response to thermal adaptation or acclimation. One such change involves a horizontal shift of the curve (Fig. 2.1B). For example, a right-shift of the TPC in a species living in warm environments could represent beneficial plasticity or local adaptation. If this horizontal shift occurs without a shift in peak height, this implies that thermal adaptation (or plasticity) is sufficient to completely compensate for the thermodynamic effects of temperature on performance. Alternatively, a variety of studies have detected an upwards vertical shift of the TPC (Fig. 2.1C) with a higher Pmax in warm adapted taxa (Alruiz et al., 2023). This pattern is sometimes referred to as the \u201chotter is better\u201d hypothesis, as it suggests that performance of low-temperature adapted taxa may be constrained due to limits on the ability of acclimation or adaptation to fully compensate for thermodynamic restrictions imposed by low temperatures (Knies et al., 2009; Angilletta et al., 2010). On the other hand (Fig. 2.1D), other studies have detected an upwards shift of the TPC in taxa from higher latitudes (i.e., colder environments) when compared at a common temperature with lower latitude populations, a pattern termed countergradient variation (Conover & Schultz, 1995). Countergradient variation is thought to represent an adaptation to seasonality, rather than temperature per se (Conover et al., 1990; Conover & Present, 1990; Yamahira & Conover, 2002). For example, it is hypothesized that an increased growth rate in organisms from higher latitudes represents a compensation for 31  the much shorter growing season, allowing individuals at high latitudes to reach a similar size as conspecifics at lower latitudes despite lower temperatures and shorter summer seasons. Finally, the breadth of the TPC could differ between populations (Fig. 2.1E), which has been suggested as an adaptation to the extent of thermal variability. In addition, there has been much consideration given to whether there is a trade-off between increased thermal breadth and increased maximum performance, which is a type of generalist-specialist trade-off that has been termed the \u201cjack-of-all-temperatures\u201d but \u201cmaster of none\u201d hypothesis (Huey & Hertz, 1984; Huey & Kingsolver, 1989; Gilchrist, 1995).  The above hypotheses for adaptive and plastic variation in TPC are not an exhaustive list, and the various changes in TPC shape are not mutually exclusive, as horizontal, vertical, and breadth changes can occur in various combinations. The constraints and trade-offs reflected in these shape changes have been widely discussed (Huey & Kingsolver, 1989, 1993; Angilletta et al., 2002; Gardiner et al., 2010; Buckley & Kingsolver, 2021; Montagnes et al., 2022) but there remains limited consensus as to the dominant forces shaping the evolution of TPC shapes, with various studies supporting different interpretations (Frazier et al., 2006; Morrison & Hero, 2003; Conover et al., 2009; Angilletta et al., 2010; Sanford & Kelly, 2011; Sinclair et al., 2012; McElroy, 2014; S\u00f8rensen et al., 2018; Kontopoulos et al., 2020; Pettersen, 2020; Liu et al., 2022; Dwane et al., 2023). Thus, the lack of comprehensive data for TPCs at early life stages, in organisms such as fishes presents a major challenge for assessing these alternatives across life stages. To address the limited data available for TPC shapes during early development in fishes, and to assess the nature of TPC evolution across latitudes, here we examine changes in the shape of the TPC for early developmental stages across populations of a small teleost fish, the Atlantic killifish, Fundulus heteroclitus. Atlantic killifish inhabit intertidal salt marshes along the Atlantic coast of North America There is a steep latitudinal thermal gradient along this coast such that the temperature experienced by southern populations are approximately 10\u00b0C warmer on average than those experienced by their northern counterparts throughout the year (Schulte, 2007). There are two recognized subspecies: F. heteroclitus macrolepidotus that live in the northern part of the species range and F. heteroclitus heteroclitus that live in the south. Substantial work on the adults of these subspecies demonstrates that the two differ genetically, morphologically, 32  physiologically, and behaviorally (Fangue et al., 2006; Powers & Schulte, 1998; Fangue et al., 2009, 2009; Healy & Schulte, 2012; McBryan et al., 2016) with clear evidence of both local adaptation (Fangue et al., 2006; Brennan et al., 2016) and countergradient variation (Schultz et al., 1996; Fangue et al., 2009a) depending on the trait examined.  In addition to being a model system for the study of thermal adaptation, F. heteroclitus was also an important model organism for developmental biology in the earliest years of the field, starting with work by Thomas Hunt Morgan in the 1890s (Morgan, 1893; Morgan 1895; Atz, 1986). Subsequently, several studies have revealed latitudinal variation in embryonic characteristics including embryonic morphology and size (Brummett, 1966; Able & Castagna, 1975; DiMichele & Taylor, 1980; Brummett & Dumont, 1981; Morin & Able, 1983; Marteinsdottir & Able, 1988). Similarly, there is evidence for latitudinal variation in the thermal biology of F. heteroclitus embryos. For example, several studies have observed that northern embryos develop faster than those from the south when compared at a common temperature, which is consistent with a pattern of countergradient variation (DiMichele & Westerman, 1997; McKenzie et al., 2017), and there is some evidence that northern killifish have a lower maximum temperature for development than southern killifish (DiMichele and Westerman, 1997), suggestive of local adaptation. However, previous studies have not assessed embryonic development across a sufficient number of temperatures to model the shape of the TPC, which makes it difficult to be confident regarding conclusions about adaptation to local conditions of temperature or seasonality, and it is not possible to estimate the thermal breadth for developmental processes.   To assess whether TPCs for northern and southern Fundulus heteroclitus demonstrate patterns consistent with local adaptation and\/or countergradient variation, we characterized multiple traits (e.g., survival, heart rate, larval morphometrics) during embryonic and larval development in a northern and southern population F. heteroclitus and their respective reciprocal crosses. A total of eight developmental temperatures (15, 18, 21, 24, 27, 30, 33, and 36\u00b0C) were selected to capture the temperatures likely to be encountered during early development across the species range to ask: 33  1) Are patterns of TPC variation for early development across populations consistent with predictions for local adaptation or countergradient variation? 2) Is the breadth of the TPC for early development narrower than that observed in adults? 3) Does embryonic heart rate reflect observed differences in development rate across populations? 4) Are patterns of variation in the larval morphology across populations consistent with predictions for local adaptation or countergradient variation? 2.2. Methods 2.2.1. Animal husbandry Adult Fundulus heteroclitus macrolepidotus were collected during the Fall of 2019 by Aquatic Research Organisms from Hampton, New Hampshire (42\u00b055'N, 70\u00b051'W) and Fundulus heteroclitus heteroclitus from Jekyll Island, Georgia (31\u00b002'N 81\u00b025'W) using baited Gee\u2019s G-40 minnow traps. After transport to the University of British Columbia, Vancouver, Canada, fish were quarantined for 1 month in 454 L static tanks with daily water changes at 20 \u00b0C, 12L:12D and 25 ppt salinity, made with Instant Ocean Sea Salt (Instant Ocean, Spectrum Brands, Blacksburg, VA) and dechlorinated Vancouver tap water. Fish were then transferred to a recirculating system under the same conditions. Fish were fed once daily with PE Mysis Flakes Saltwater Fish Food (Piscine Energetics, BC) until satiation. To achieve breeding conditions, during the spring of 2021, fish from each population were transferred to 60 L tanks held at 18 \u00b0C, 12L:12D and 25 ppt, that were well aerated with air stones. Each tank had 5 females (4 tanks of females\/population) and 5 males (2 tanks\/population) for a total of 30 fish across 12 tanks. Males and females of each population were kept in separate tanks to reduce aggressive behaviors which increased under spawning conditions. To induce spawning, tank temperatures were increased 1 \u00b0C every other day until a final temperature of 25 \u00b0C was reached and photoperiod was simultaneously increased by 15 mins\/day to a photoperiod of 16L:8D. Breeding fish were fed a diet of blood worms and Mysis shrimp (Hikari) twice a day to satiation. Animal experimentation was performed in accordance with an approved University of British Columbia animal care protocol A20-0070.  34  2.2.2. Crosses and experimental design A total of four different cross-types of F. heteroclitus were made \u2013 pure northern, pure southern and their reciprocal hybrid crosses (N\u2640 \u00d7 S\u2642 and S\u2640 \u00d7 N\u2642). Fertilization was completed in vitro as previously described (McKenzie et al., 2017). In brief, both males and females were placed under light anesthetic using MS-222 (1 g\/L of Syncaine\u00ae buffered with 2 g\/L NaHCO3) Light pressure to the abdomen was used to release the milt or eggs from adults. Twenty females and ten males from each population were used for this experiment. On any given spawning day, eggs from all spawning females from the same population were mixed and then randomly divided into Petri dishes containing a shallow layer of saltwater (~10 mL) held at 25 \u00b0C and 25 ppt. Milt from all males from that spawning day was then mixed and divided across the Petri dishes. Eggs and sperm were allowed to incubate for 1 hour to achieve fertilization. Mixing gametes allowed us to maximize the genetic diversity of the embryos used in the experiment, without confounding treatment effects with family effects. Eggs were then examined under a microscope for the presence of a fertilization envelope (Armstrong and Child, 1965).           On any given spawning day, embryos were then divided into pools of 60-130 from each cross-type and incubated in Petri dishes (2-4 Petri dishes per temperature and cross-type) containing 10 mL of 25 ppt dechlorinated water with diluted (0.0003%) methylene blue (as a fungicide) at a range of temperatures from 15 \u00b0C to 36 \u00b0C in increments of 3 \u00b0C and 12L:12D light cycle in temperature-controlled incubators (MIR-154, PHCbi, Tokyo, Japan). Water quality measurements and water changes (90-100%) were performed daily with a 30 minute air exposure period to induce hatching if the embryos were ready to hatch (stages 32-36; Armstrong and Child, 1965), as determined by visual inspection under a microscope (presence of buccal movement; Marteinsdottir and Able, 1992). Petri dishes were checked twice daily for non-viable embryos, (identified by opaque white eggs or absence of a heartbeat) and for new hatches. Once hatched, larvae were euthanized with MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO3), fixed in 10% neutral buffered formalin for 24 h, and stored in 70% EtOH at 4\u00b0C until analysis. To assess whether the more rapid development and growth that has previously been observed in northern fish is associated with changes in metabolic parameters, a second set of clutches was generated to allow measurement of embryonic heart rate. Gametes were collected 35  by breeding 15 females and 10 males from each population. These embryos were incubated at a range of temperatures from 15 \u00b0C to 33 \u00b0C in increments of 3 \u00b0C as described above. To account for differences in developmental timing across temperatures, heart rate measurements were performed at the same developmental stage across all temperatures: stage 30-31 as characterized by Armstrong and Child (1965) at which point the heart chambers are fully differentiated. A total of eight individuals per cross-type and developmental temperature were randomly selected for measurement.  2.2.3. Larval morphology To assess the effects of temperature on larval morphology, fixed larvae (0dph) were digitally imaged using a DinoLite Digital Microscope (Dino-Lite Edge 5MP, Torrance, CA, USA) at a magnification of 40x. Total length and yolk-sac volume were measured for each larva using ImageJ, and all larvae were evaluated for deformities across all temperatures and crosses. Dry weight was measured by placing larvae in a drying oven at 60 \u00baC for 2 h. Dried larvae were then cooled for 20 mins and weighed using a microbalance (Mettler Toledo XPR2, Columbus, OH, USA). To estimate yolk-sac volume (YSV), we used the formula YSV = (\u03c0\/6)YsL\u22c5YsD\u00b2, in which YsL represents yolk-sac length and YsD represents yolk-sac diameter (Blaxter & Hempel, 1963). Embryonic growth rate was calculated by dividing length by the days to hatch for each larva. 2.2.4. Heart rate           Heart rate measurements were performed by video recording live embryos in a Petri dish using a DinoLite Digital microscope (Dino-Lite Edge 5MP, Torrance, CA, USA). Temperature was maintained by placing the Petri dish containing the embryo on a drilled aluminum block through which temperature regulated water was circulated (Lauda Brinkmann RMT-6, Delran, NJ, USA). A digital thermometer (Hanna Chektemp1, Laval, Quebec, Canada) was placed in the Petri dish to ensure temperature remained stable during measurement (\u00b1 0.5 \u00baC). Embryos were tested at their developmental temperature and were given 20 minutes to rest after transfer to the microscope before measurements began to account for handling stress. For all heart-rate measurements, 1 minute videos were recorded in triplicate with 1 minute intervals between each 36  video. Heart rate was then determined by counting the number of heart beats per minute (beats\u00b7min-1).  2.2.5. Statistical analyses          Statistical analyses were performed using GraphPad Prism (version 9.3.0) and RStudio (version 4.2.1). We estimated TPC shape for survival (%), developmental rate (1\/days), and embryonic growth rate (mm\/days) using the program rTPC (Padfield et al., 2021). We ran a total of 10 best-fit models for each curve, based on an initial run of the 24 total models, and the best fit models were determined using Akaike information criterion (AIC) for each curve. We then chose a single model with the lowest mean AIC, and a best-fit TPC curve between all crosses, was selected for comparison among crosses. Different models were selected for each phenotype: survival (\u201cRatkowsky_1983\u201d), developmental rate (\u201cONeill_1972''), and embryonic growth rate (\u201cWeibull_1995\u201d). From each model, key parameters were predicted for each curve (e.g., thermal optimum, CTmax, Q10, safety margin, and thermal breadth (80% of Pmax) with the exception of embryonic growth rate for which only Topt could be calculated, due to the shape of the curve. To obtain confidence intervals for the estimates, we used non-parametric bootstrapping case resampling implemented in rTPC to estimate model uncertainty (Padfield et al., 2021).   Significant correlations between heart rate and developmental temperature were identified using a pairwise Spearmen\u2019s correlation test (\u03b1=0.05). To assess the effects of developmental temperature on embryo and larval phenotypes, we used a two-way ANOVA with cross and temperature as factors. If significant effects were detected, we used a Tukey\u2019s post hoc test for multiple comparisons. To check for normality and homogeneity of variance in our data, Shapiro\u2013Wilk and Bartlett\u2019s tests were also run. In the case where the assumptions were not met, we log transformed the data, which allowed assumptions to be met. All data are presented as mean \u00b1 s.e.m. and tests were all evaluated at an alpha level of 0.05. 2.3. Results 2.3.1. Thermal performance curves for survival TPCs for survival estimated using rTPC are displayed in Fig. 2.2 and TPC parameters and 95% confidence intervals between the four cross-types are shown in Table 2.1. Regardless of 37  cross-type, F. heteroclitus embryos did not survive to hatch when incubated at 36 \u00b0C. In general, both cross-types with southern mothers had a higher Topt, with pure southern having a Topt of 30.3\u00b0C and reciprocal southern cross (S\u2640 \u00d7 N\u2642) having a Topt of 30.9 \u00b0C. In contrast, the reciprocal northern cross (N\u2640 \u00d7 S\u2642) displayed an intermediate Topt of 28.9 \u00b0C and the pure northern cross displayed the lowest Topt of 25.2 \u00b0C, and the confidence interval for this estimate did not overlap with those of either the pure southern cross-type, or the cross-type with a southern mother (Table 2.1). This pattern between cross-types demonstrates a right-shift of the TPC in populations with a southern mother. Embryos from the pure northern crosses also displayed a marked decrease in survival from 30 to 33 \u00b0C (Fig. 2.2A). CTmax estimated from the TPC was not substantially different between cross-types (Table 2.1). Although making accurate estimates of CTmax is difficult as we have no data above 33\u00b0C, because embryos did not survive to hatch at 36 \u00b0C, and thus our estimates of CTmax reflect extrapolation of the curve beyond the available data. On the other hand, at low temperatures, embryos of the pure northern cross performed well, whereas survival was poor for embryos of the pure southern cross below 24 \u00b0C (Fig. 2.2D). Furthermore, the cross N\u2640 \u00d7 S\u2642 had improved survival at high temperature and poor survival at the lowest temperature, similar to the pure southern cross. However, at moderately low temperatures (18 and 21 \u00b0C), this cross displayed an intermediate pattern with survival declining by ~25% compared to ~50% declines in survival in the pure southern cross. There were also slight differences in curve shape depending on the population, with pure northern crosses having a higher (i.e., higher survival) and broader shape than the pure southern cross which had a lower and narrower shape, resulting in a larger thermal breadth for the pure northern cross. 2.3.2. Developmental rate Developmental rate (calculated as the reciprocal of time to hatch; Table A2.1) was significantly affected by temperature (F7,1000=1165.83, P<0.0001), cross (F3,1000=113.79, P<0.0001) and their interaction (F21,1000=23.58, P<0.0001). We directly compared the maximum developmental rate observed regardless of temperature among the crosses. The pure northern cross had the highest maximum developmental rate (0.130 \u00b1 0.001 days-1), which was observed at 30 \u00b0C, whereas the pure southern cross had the lowest maximum developmental rate (0.096 \u00b1 0.003 days-1), which was observed at 33 \u00b0C. The reciprocal crosses tended to be most similar to 38  the development rate of their maternal parent, with the N\u2640 \u00d7 S\u2642 reciprocal cross having a maximum developmental rate of 0.112 \u00b1 0.002 days-1 at 30 \u00b0C, and the S\u2640 \u00d7 N\u2642 reciprocal cross having a maximum developmental rate of 0.096 \u00b1 0.003 days-1 at 33 \u00b0C. 2.3.3. Thermal performance curves for developmental rate TPCs for developmental rate estimated using rTPC are displayed in Fig. 2.3, and TPC parameters and 95% confidence intervals between the four cross-types are shown in Table 2.1. In general, both crosses with southern mothers had a higher Topt, and a smaller safety margin (calculated as CTmax - Topt) than did the crosses with northern mothers, suggesting a right shift of the TPC in the southern population. Curve shape also differed depending on the maternal origin, with crosses from northern mothers having a taller and narrower shape and crosses from southern mothers having a lower and broader shape, resulting in a larger thermal breadth (~1 \u00b0C larger, Table 2.1). There were no differences in CTmax between the four crosses, although making accurate estimates of CTmax is difficult due to the lack of data above 33 \u00b0C, because embryos did not survive to hatch at 36\u00b0C, and thus our estimates of CTmax reflect extrapolation of the curve beyond the available data. Overall, the shapes of the TPCs differed between crosses as a result of both vertical and horizontal shifts. 2.3.4. Embryonic heart rate          Heart rate was positively correlated with developmental temperature (Fig 2.4A, Table A2.6, R2=0.99; P<0.0001 for all cross-types). We predicted that embryos from the northern cross would have a more rapid heart rate than those from the southern cross to support their more rapid developmental rate. However, only the reciprocal northern cross (N\u2640 \u00d7 S\u2642) was found to have a significantly different slope relative to all other crosses which was most likely driven by this cross-type maintaining a higher heart rate at the higher developmental temperatures. Q10 values for heart rate were ~4 for both maternal southern crosses from 15 to 21 \u00b0C then decreased to ~2.5 from 21-24 \u00b0C (Fig 2.4B). By contrast, the Q10 values for both maternal northern crosses decreased from 4 to 3 between 15 and 21 \u00b0C. From 24-30 \u00b0C, Q10 values for heart rate were stable at ~2.0 for all crosses other than the pure southern cross which had a Q10 value of ~1.6. At 33 \u00b0C, all crosses had a Q10 value of less than 2.0.   39  2.3.5. Larval morphometrics          Larval length at hatch (Fig. 2.5A, Fig. A2.1) was significantly affected by temperature (F5,173 = 43.80, P < 0.0001), cross (F3,173 = 103.20, P<0.0001) and their interaction (F15, 173 = 3.08, P<0.0001). Across all temperatures, the pure northern cross was significantly smaller in length at hatch compared to the pure southern cross. On average, the length of pure northern crosses ranged between 5 mm and 6.5 mm at hatch across temperatures whereas the pure southern cross hatched at 6 mm to 8 mm in length. The reciprocal cross with a northern mother had an intermediate length at hatch across most temperatures (6-7 mm), whereas the reciprocal cross with a southern mother was more similar in length to the pure southern cross across most temperatures. Higher temperatures resulted in smaller length at hatch for all crosses. The effect of temperature on length at hatch was particularly striking for the pure southern crosses at lower temperatures (18 \u00b0C and 21 \u00b0C). The p-values for larval weight at hatch for all pairwise comparisons of the two-way ANOVA are shown in Table A2.2 and Table A2.3.          Larval weight at hatch (Fig. 2.5B, Fig. A2.1) was significantly affected by temperature (F5,168 = 2.95, P=0.014), cross (F3,168 = 74.88, P<0.0001), and their interaction (F15,168 = 4.99, P<0.0001). In general, temperature had a smaller effect on weight at hatch within a cross compared to its effects on length. In both maternal northern crosses, larvae hatched with a significantly lower weight at 18 \u00b0C relative to 33 \u00b0C (P<0.05). Whereas, in the maternal southern crosses, the only significant effect of temperature on larvae weight was found between larvae hatched at 27 \u00b0C and 33 \u00b0C (P<0.05). As well, patterns for larval weight among crosses were similar to those for length, with pure northern larvae having significantly lower weight (~0.40 mg) at hatch compared to pure southern larvae (~0.60 mg) across most developmental temperatures. However, near their thermal limits of development (33 \u00b0C), there were no significant differences in weight between the northern and southern crosses (P>0.05). For the reciprocal crosses, their weights at hatch were comparable to their maternal parent cross for most temperatures. The p-values for larval weight at hatch for all pairwise comparisons of the two-way ANOVA are shown in Table A2.2 and Table A2.4. Yolk-sac volume (YSV) at hatch (Fig. 2.5C, Fig. A2.1) was significantly affected by temperature (F5,175 = 30.56, P < 0.0001), cross (F3,175 = 19.29, P<0.0001), and their interaction (F15,175 = 2.16, P=0.002). In the pure northern cross, YSV increased with temperature, whereas in 40  the pure southern cross, YSV increased up to 24 \u00b0C but then remained stable as temperature increased. Both reciprocal crosses exhibited similar patterns in response to temperature as the pure southern cross increased up to 24 \u00b0C and then remained stable. As well, at 33 \u00b0C, the pure northern cross had a larger YSV than the pure southern cross (P<0.05). In general, the reciprocal cross with a northern mother hatched with a smaller yolk sac compared to all the other cross-types. The p-values for larval YSV at hatch for all pairwise comparisons of the two-way ANOVA are shown in Table A2.2 and Table A2.5. 2.3.6. Thermal performance curves for embryonic growth rate   TPCs for embryonic growth rate (larval length divided by days to hatch) estimated using rTPC are displayed in Fig. 2.6, and 95% confidence intervals for Topt between the four cross-types are shown in Table 2.1, as this was the only curve parameter that could be accurately estimated due to the shape of the TPCs. Overall, crosses with a southern mother had higher Topt, with the pure southern cross-type having a Topt of 31.2 \u00b0C and the reciprocal southern cross-type (S\u2640 \u00d7 N\u2642) having a Topt of 32.7 \u00b0C. By contrast, both the reciprocal northern cross-type (N\u2640 \u00d7 S\u2642) and the pure northern cross-type displayed lower and similar Topt of 29.0 \u00b0C and 28.8 \u00b0C, respectively. Embryos from crosses with a northern mother also displayed a marked decrease in growth rate from 30 to 33 \u00b0C (Fig. 2.6A and 2.6B). In contrast, embryos from crosses with a southern mother maintain growth rate at higher developmental temperatures (Fig. 2.6D and 2.6E), highlighting a potential right-shift of the TPC in populations with a southern mother. There were also slight differences in curve shape depending on the population, with crosses with a northern mother having a taller TPC (i.e., higher growth rate) than crosses with a southern mother (i.e., lower growth rate) across most developmental temperatures.  2.4. Discussion The thermal tolerance of early-life stages in fishes is likely to be an important factor in predicting a species vulnerability to climate change, but there are limited data examining the thermal tolerance of development in fishes, with most data available for species that are used in aquaculture or for which there is hatchery breeding (Pauly & Pullin, 1988; Beacham & Murray, 1989; Murray et al., 1990; Rombough, 1997; Ojanguren & Bra\u00f1a, 2003; Nissling, 2004; Burt et al., 2011; Llopiz et al., 2014; Flynn & Todgham, 2018). In this study, we examined the thermal 41  tolerance during embryonic development and the effects of temperature on developmental rate in two populations of F. heteroclitus and their reciprocal crosses. Our TPC modeling revealed a vertical shift (with the northern cross-type having a higher Pmax for developmental rate), and horizontal shifts in several traits (higher Topt in the southern cross-type for survival, developmental rate, and embryonic growth rate), as well as a change in thermal breadth (with the southern cross-type having a wider Tbr for developmental rate but not for survival). These data suggest that both local adaptation and countergradient variation may be factors influencing the thermal tolerance and development of F. heteroclitus embryos. Are patterns of variation in the TPC for early development across populations consistent with predictions for local adaptation or countergradient variation? There have been relatively few studies examining latitudinal or other geographic variation in the shape of thermal performance curves for early developmental stages, and most of the available work has been performed in insects (Khelifa et al., 2019; Alruiz et al., 2023). By contrast, there are limited data for geographical variation in TPC shapes for the early developmental stages of fishes, although there is some evidence for geographic variation in embryonic TPCs for salmonids (Beacham and Murray, 1989); however, this variation is not associated with latitude.  In this study, we observed clear differences in the shapes of the TPCs for survival, developmental rate, and embryonic growth rate between northern and southern populations of Atlantic killifish. Consistent with the prediction of local adaptation to temperature on the shape of TPC, the pure southern cross-type had a slight horizontal shift for all TPCs compared to the pure northern cross-type such that the Topt for the southern cross was ~2.5 \u00b0C higher (i.e. developmental rate, embryonic growth rate) or ~5.7 \u00b0C higher (i.e., survival) than that of the northern cross-type. Note that this difference in Topt for development and growth is similar in extent to the observed differences in CTmax at the adult stage between these populations, (Fangue et al., 2006) and the difference between subspecies in embryonic survival is substantially larger. In contrast, our data did not fit the prediction for local adaptation in maximum developmental rate or embryonic growth rate (Pmax). According to the \u201chotter is better hypothesis\u201d, locally adapted warm-water populations would be predicted to have a higher maximal performance than populations from colder temperatures (Huey and Kingsolver, 1989). However, we found that the pure northern cross-type had a higher Pmax compared to the pure southern cross-type. Furthermore, the pure northern cross-type had a faster developmental rate 42  and higher embryonic growth rate across all developmental temperatures with the exception of their thermal limit of 33 \u00b0C, which is consistent with the predictions of the theory of countergradient variation. This theory suggests that the fast developmental rate and higher embryonic growth rate of fish from the northern population is likely driven by their short breeding season selecting for faster development rates at the lower temperatures consistent with higher latitudes (Taylor, 1986).  We also observed a difference in the breadth of the TPC for developmental rate between pure northern and pure southern crosses, with the pure southern cross having about 1 \u00b0C larger thermal breadth than the pure northern cross for developmental rate. It has been hypothesized that if generalist-specialist trade-offs or other environmental effects do not constrain temperature adaptation, then populations adapted to warmer temperatures would have a larger thermal breadth (Knies et al., 2009). Alternatively, differences in thermal breadth between species have been suggested to be driven by environments with more thermal variability (Dowd et al., 2015). However, both populations experience similar ranges in thermal variability within their habitats both on an annual and a daily basis (data from NOAA, NERRS, 2023) which makes this hypothesis unlikely. Interestingly, the observed patterns are consistent with \u201cjack-of-all temperatures but master of none\u201d hypothesis that posits the existence of a trade-off between maximal performance and thermal breadth (Huey and Hertz, 1984; Huey and Kingsolver, 1989; Angilletta, 2009) which is a form of generalist-specialist trade off. However, the observed difference in thermal breadth between the two populations is small, and may, at least in part, be a result of the way that thermal breadth is calculated. Consistent with best practices in the field, we computed thermal breadth at a set percentage of the maximum performance (in this case 80% of maximum developmental rate, Pmax). However, the two populations differ substantially in Pmax, but only across a narrow thermal range (27-30 \u00b0C, Figure 3, Supp Table 1). At other temperatures, the developmental rates are quite similar between the two populations. Thus, if we, for example, computed thermal breadth at a common performance level, rather than at a common percentage of maximum performance, thermal breadth would not differ between the crosses. In addition, if we consider survival to hatch instead of developmental rate, a different picture emerges. Thermal breadth for survival was ~3 \u00b0C smaller for the pure southern cross-type than the pure northern cross-type. This is because fish from the pure northern cross-type maintain high survival (>50%) across a broad thermal range (15-30 \u00b0C), whereas fish from the southern 43  cross-type have generally lower survival (<50%), and only maintain reasonably high survival from 24-33\u00b0C (~50%), suggesting a narrower thermal breadth in southern fish for this critical fitness related trait.  Maternal and paternal effects on development rate and survival To understand the implications of maternal and paternal effects on the shape of the TPC, we also examined the TPC for the reciprocal crosses of both populations. We predicted the reciprocal crosses would show an intermediate-shaped TPC for all phenotypes. However, we found that the TPC for developmental rate of the reciprocal crosses with a southern mother was not different from that of the pure southern cross-type (having a similar Topt and Tbr). This suggests strong southern maternal effects on the shape of the TPC for developmental rate. On the other hand, the reciprocal cross with a northern mother had an intermediate Topt and Pmax which suggests that southern alleles from the male parent may be playing a role in both the horizontal (Topt) and vertical (Pmax) shift in the TPCs. However, the Tbr of this cross was more similar to that of their female parent, suggesting a maternal effect for developmental rate. Another notable finding from our data is the strong genotype effect on embryonic survival. Unlike developmental rate, both reciprocal crosses displayed intermediate phenotypes for survival (i.e., Topt, Tbr, and Pmax), which is in support of our prediction. Furthermore, we found that cross-types with a southern parent (maternal and\/or paternal) displayed a low survival at colder acclimation temperatures (15-21 \u00b0C). However, this incubation temperature did not always result in death of the embryos as we could still observe a heartbeat despite the fact that the embryos did not hatch. It is likely that under low temperature conditions the F. heteroclitus embryos went into a state of developmental arrest similar to diapause to await conditions more suitable for hatching. Diapause is a common occurrence during the development of many species of killifish (Podrabsky et al., 2010; Pola\u010dik and Vrt\u00edlek, 2023). The occurrence of diapause is particularly well studied in the annual killifishes, such as Austrofundulus limnaeus. In these species, fish may undergo up to three consecutive periods of diapause at specific morphological stages (diapause I, II, or III), which enables them to survive the dry season until they receive an appropriate hatching cue such as rewetting by the rainy season (Wourms, 1972; Podrabsky et al., 2001). Non-annual killifish such as Fundulus heteroclitus do not undergo developmental arrest equivalent to diapause I, II, or III, but postponement of hatching of variable duration (ranging 44  from a few days to several months), which is superficially similar to diapause III, has been described in a variety of non-annual killifish species including F. heteroclitus (Thompson et al., 2017). However, gene expression profiles in non-annual killifish during these developmental delays differ from those observed in diapause III in annual killifishes, suggesting that it is not a \u201ctrue\u201d diapause state (Thompson et al., 2017).  Is the breadth of the TPC for early development narrower than that observed in adults? There has been a vigorous discussion surrounding the suggestion that the early life-stages of fishes have a narrower thermal breadth than juvenile and adult life stages (P\u00f6rtner & Farrell, 2008; Dahlke et al., 2020; Rebolledo et al., 2021; Pottier et al., 2022a). A key metric often used to estimate an organism\u2019s thermal tolerance at the adult stage is the upper thermal tolerance (CTmax) and lower thermal tolerance (CTmin; Becker & Genoway, 1979; Schulte et al., 2011). However, these parameters have seldom been measured in fish embryos, and instead thermal breadth is often measured as LT50 (median lethal temperature in static exposures) at this life stage (Cowan et al., 2023). Prior meta-analyses of variation in thermal breadth across life stages (Dahlke et al., 2020) may be confounded by different combinations of metrics across life stages, such as LT50 for developmental stages and CTmax for adults (Pottier et al., 2022b).  Because of the extensive data available for the thermal breadth of adult F. heteroclitus, here we are able to directly compare previously obtained LT50 for adult fish to that of the embryos measured here. From our data, we estimate that the upper LT50 of pure northern embryos is around 33 \u00b0C as there is a sharp decline in survival at that temperature, whereas the LT50 of southern embryos is between 33 \u00b0C and 36 \u00b0C. In comparison, previous work on the LT50 of adult F. heteroclitus found that the northern population have a LT50 of 36.4 \u00b0C and the southern population has a LT50 38.2 \u00b0C (Fangue et al., 2006). This suggests that the embryos in both populations have a lower upper lethal temperature limit than adults, suggesting they are slightly more sensitive to increasing temperatures. As for lower thermal limits, there are no specific estimates of LT50 for adults. However, previous studies have shown that adult F. heteroclitus can acclimate to temperatures near freezing (2 \u00b0C; Fangue et al., 2006), and at least in the northern parts of the species range, water temperatures are typically close to 0 \u2103 for long periods during the winter, although temperatures below 10 \u2103 are rare in the southern parts of the 45  species range. In our study, we found that F. heteroclitus embryos derived from southern parents have decreased survival below 24 \u00b0C, whereas embryos derived from northern parents maintained over 50% survival at 15 \u00b0C (the lowest temperature which we tested) suggesting their lower thermal limits falls somewhere below 15 \u00b0C.  These data suggest that F. heteroclitus embryos have a narrower thermal breadth for survival than do adults of this species. However, this reduction in thermal breadth is primarily influenced by reductions in cold tolerance, and tolerance of high temperatures is only slightly reduced in embryos compared to adult fish. This observation is consistent with the life history of this species. F. heteroclitus has a life span of 3-5 years, and thus adults must be capable of surviving through the winter. On the other hand, their breeding season is restricted to the spring and summer, and thus embryos are highly unlikely to experience cold temperatures but may experience critically high temperatures. Overall, our data provides evidence that the embryo life stages of both subspecies of F. heteroclitus have a narrower thermal tolerance than their subsequent adult life-stages. This supports the idea that early-life stages may have a smaller thermal window than do adults (Dahlke et al., 2020). However, the implications of this observation in the context of climate change are not as simple as has been suggested as the bulk of the reduction in thermal breadth is due to limited tolerance of cold temperatures by embryos. On the other hand, climate change is likely to result in increased thermal variability (including an increased incidence of \u201ccold snaps\u201d, as well the more often considered increase in the frequency of heatwaves (Canning-Clode et al., 2011; Roitberg & Mangel, 2016). This emphasizes the importance of considering the life history of an organism and the timing of reproduction when attempting to integrate information about the variation in thermal performance curves across life stages into predictions of vulnerability to climate warming. Does embryonic heart rate reflect observed differences in development rate across populations? One of the most striking differences we observed in embryonic development between northern and southern killifish was the faster developmental rate in embryos from pure northern crosses. This difference has been observed in a several studies (DiMichele and Westerman, 1997; McKenzie et al., 2017), and here we asked whether this increased developmental rate was 46  associated with an increased heart rate, as increased heart rate could support the increased aerobic metabolic demand of more rapid development. As expected, developmental temperature had a strong effect on embryonic heart rate as demonstrated by a strong positive correlation between heart rate and temperature. However, the effect of cross-type on heart rate was less evident compared to the effect of cross-type on developmental rate, with only the reciprocal northern cross having significantly different slope relative to all other crosses. This difference was largely driven by the reciprocal northern cross maintaining a high heart rate at higher developmental temperatures. This suggests that the higher developmental rate of the northern fish may not be supported by increased aerobic metabolism. However, other metrics of cardiac performance that were not measured in this study, such as stroke volume, could be different between cross-types and this possibility requires further investigation. Differences in developmental rate between the northern and southern crosses were particularly evident at developmental temperatures from 27-30 \u2103, and there is no evidence that heart rate was greater in embryos from fish with northern mothers than those from southern mothers. Previous work by DiMichele and Westermann (1997) suggested that differences in developmental rate between the populations largely occur prior to blastulation, whereas development rates are more similar later in development. Prior to the mid-blastula transition, F. heteroclitus embryos are resistant to cyanide (Crawford & Wilde, 1966), suggesting that metabolism of these early stages may be reliant primarily on glycolytic pathways. By contrast, as embryos develop past the mid-blastula transition, they become increasingly reliant on aerobic metabolism (Mendelsohn & Gitlin, 2008), and developmental rates across cross-types are more similar. This observation is consistent with studies of embryos from geographically intermediate populations that differ in genotype at the lactate dehydrogenase-B (Ldh-B) locus (DiMichele & Powers, 1991; Paynter et al., 1991). These studies demonstrated that metabolic differences during the first 12 to 24 h after fertilization are largely responsible for development-rate in individuals bearing either the northern or southern Ldh-B genotypes (DiMichele & Powers, 1991; Paynter et al., 1991).  To the extent that heart rate is a proxy for rates of aerobic metabolism, our data suggest that this may not differ between F. heteroclitus embryos from different populations at later stages of development. However, we only measured heart rate at a single time-point late in development and did not directly measure metabolic rate. Examination of Q10 patterns between crosses also provides insight into the thermal sensitivity of heart rate in embryos. We found that 47  colder temperatures (<24 \u00b0C) resulted in Q10 values between 3 and 4, whereas between 24 and 33 \u00b0C Q10 values remained between 1.5 and 2, suggesting that embryo heart rate has a much higher sensitivity at colder temperatures. Indeed, this pattern is especially true for crosses with southern mothers. Increases in Q10 at low temperatures are often observed, and recent developments using macromolecular rate theory suggest that increased Q10 is a result of underlying biochemical processes and is evidence that the temperature is below the optimum for the biological process being studied (Alster et al., 2016).  Effects of temperature during early development on larval phenotypes across populations Previous studies have detected differences in larval phenotypes among populations of F. heteroclitus (Marteinsdottir & Able, 1992). Consistent with these previous studies, we found that larvae derived from northern parents were smaller than those from southern parents, regardless of developmental temperature. These contrasting phenotypes between the pure crosses may, at least in part, be explained by differences in egg size, as southern fish produce eggs with a larger egg diameter (Marteinsdottir & Able, 1988) and larval size at hatch is correlated with egg size in F. heteroclitus (Marteinsdottir & Able, 1992). In addition, embryos of the southern population have more oil droplets within their yolk-sacs, which may lead to differences in energy allocation, usage, and growth (Morin & Able, 1983). The differences in egg size and larval size between populations have been suggested to be driven by local adaptation of life history strategies (Taylor, 1986). For example, the shorter breeding season in the north may impose selection for a higher clutch size because of the reduced opportunities for multiple rounds of reproduction, and this may result in a trade-off in egg size, and thence in larval size (Taylor, 1986). In our study, the weight of larvae derived from reciprocal crosses matched that of the pure cross with the same subspecies mother, but both reciprocal crosses (i.e., regardless of the origin of their mother) had an intermediate length at hatch and were more similar in length to the pure southern larvae for most developmental temperatures. This observation suggests that while the large egg size of southern mothers is clearly important in determining larval characteristics, it cannot be solely responsible for the observed differences in the sizes of the resulting larvae.  Within a given developmental temperature, most crosses had a similar yolk-sac volume at hatch. This is surprising given the clear effects of cross-type on larval length. This suggests that 48  there may be differences in growth efficiency or energy allocation among crosses. In particular, larvae from reciprocal crosses with a northern mother tended to have lower yolk-sac volume at hatch at most developmental temperatures. This is suggestive of an inability to match energetic demand to the more limited energy supply provided by the smaller and less energy-dense yolk contributed by the northern mother.  Larval phenotypes such as hatchling size and yolk-sac volume (YSV) were also strongly influenced by incubation temperatures during embryogenesis, as is the case in many fish species (Pepin et al., 1997; Jordaan et al., 2006; Politis et al., 2017). We found that decreasing temperatures resulted in larvae hatching at a longer length for all crosses and higher weight at hatch for both maternal northern crosses. A negative relationship between length at hatch and developmental temperature that has been found in many species of developing fish (Ojanguren & Bra\u00f1a, 2003; Martell et al., 2005; Brown et al., 2011; Jay et al., 2020) and this phenomenon could possibly be explained by longer incubation periods at lower temperatures resulting in a more advanced developmental stage and larger size at hatch, which aligns well with our findings (Ojanguren and Bra\u00f1a, 2003). We also observed differences in larval weight at hatch across temperatures, which could be the result of differences in yolk-sac absorption efficiency driven by differences in metabolic rates (Kamler, 2008). In crosses with a northern mother, YSV and larvae weight follow the same positive relationship such that larvae that hatched at warmer temperatures also had larger YSV. In contrast, crosses with a southern mother had no clear relationship between larval weight and YSV at hatch. This suggests that northern and southern embryos may be utilizing yolk differently across different developmental temperatures. To further assess this possibility, we estimated embryonic growth rates by dividing size at hatch by days to hatch at each temperature (Fig. 2.6). Maximum growth rate (Pmax) was highest around ~29 \u00b0C in the cross-types with a northern mother highlighting potential differences in yolk-utilization. The possibility of differences in yolk utilization efficiency, energy allocation, or usage across populations is thus a fruitful avenue for future work. Conclusions In summary, here we provide evidence that TPC shape varies between latitudinally distinct populations of F. heteroclitus, with patterns consistent with both local adaptation (i.e., Topt for development rate, survival, embryonic growth rate) and countergradient variation (i.e., in 49  Pmax for developmental rate). As well, we found evidence that embryos from two subspecies of F. heteroclitus have a narrower thermal breadth than their adult life-stages, which partially supports the hypothesis that embryos are a particularly thermally sensitive life-stage (Dahlke et al., 2020; Rebolledo et al., 2021), although the implications for this observation in the context of climate change are less clear, as the bulk of difference in the thermal breadth between life stages was the reduced cold-tolerance of embryos. Lastly, we also found that the pure northern cross-type develops faster, hatches larger, and has higher survival across most developmental temperatures. This emphasizes the importance of including information about the thermal biology of early-life stages and enough developmental incubation temperatures to help make clear predictions about how organisms are likely to cope with changing environments.       50   Figure 2.1. Shapes of thermal performance curves (TPC). (A) TPC parameters: CTmin and CTmax , the low and high temperatures where performance is equal to zero; Topt the optimal temperature where performance is maximized (Pmax); thermal breadth (Tbr), the range where performance is greater than some specified percentage of Pmax; thermal safety margin (Tsaf),  the temperature difference between Topt and CTmax.  The shape of the TPC between warm and cold acclimated populations may demonstrate (B) local adaptation, (C) \u201chotter is better\u201d hypothesis, (D) countergradient variation and (E) \u201cjack of all temperatures but master of none\u201d hypothesis. Illustration by Madison Earhart.   51   Figure 2.2.  Thermal performance curves (TPCs) for survival (%) of embryos in four cross-types of F. heteroclitus; (A) northern female x northern male, (B) northern female x southern male, (D) southern female x southern male, and (E) southern female x northern male across eight developmental temperatures (15, 18, 21, 24, 27, 30, 33 and 36\u00b0C). (C) Overlaid TPCs for all four cross-types. All curves are fitted with the Ratkowsky 1983 model. Bootstrapping was used to calculate 95% confidence intervals for the TPC curves, shown in light grey. Individual data points represent replicate Petri dishes (N=2-4).      52   Figure 2.3. Thermal performance curves (TPC) for developmental rate (1\/days) in four cross-types of F. heteroclitus; (A) northern female x northern male, (B) northern female x southern male, (D) southern female x southern male, (E) southern female x northern male across eight developmental temperatures (15, 18, 21, 24, 27, 30, 33 and 36\u00b0C). (C) Overlaid TPCs for all four cross-types. All curves are fitted with O\u2019Neill 1972 model. Bootstrapping was used to calculate 95% confidence intervals, shown in light grey. Each point represents the days to hatch of a given individual. Note that F. heteroclitus tend to hatch synchronously so the points for many individuals are overlayed (N=7-100 individuals, depending on cross-type and temperature).    53   Figure 2.4. Relationship between (A) heart rate (beats\/min) and developmental temperature and (B) Q10 values for heart rate and developmental temperature between four cross-types of F. heteroclitus. (A) Data are presented as means \u00b1 SD. Significant relationships were identified using a pairwise Spearmen\u2019s correlation test (\u03b1=0.05). * Denotes that the slope is significantly different from all other slopes (P<0.001). (B) Points represent Q10 values calculated from means in panel (A) for each developmental temperature. The dotted line represents a Q10 value of 2.0.  54   Figure 2.5. Effects of developmental temperature and cross on larval morphology (A) length (mm) (B) weight (mg), and (C) yolk-sac volume (mm3) between four cross-types of F. heteroclitus at hatch. All data presented as means \u00b1 s.e.m. (N=8). Different letters denote significant differences between temperatures within a cross-type (P<0.05). * Denotes significant differences between southern and northern cross at the same developmental temperature (P<0.05). 55   Figure 2.6. Thermal performance curves (TPCs) for embryonic growth rate (mm\/days) in four cross-types of F. heteroclitus; (A) northern female x northern male, (B) northern female x southern male, (D) southern female x southern male, and (E) southern female x northern male across six developmental temperatures (18, 21, 24, 27, 30, and 33\u00b0C). (C) Overlaid TPCs for all four cross-types. All curves are fitted with the Weibull 1985 model. Bootstrapping was used to calculate 95% confidence intervals, shown in light grey. Individual data points represent each individual hatched larva (N=8-10 per cross-type and temperature). 56  Table 2.1. Calculated mean values and 95% confidence intervals (C.I.) for TPC parameters for survival, developmental rate (1\/days), and embryonic growth rate (mm\/days) between four cross-types of F. heteroclitus.    Northern\u2640 x Northern\u2642 Northern\u2640 x Southern\u2642 Southern\u2640 x Northern\u2642 Southern\u2640 x Southern\u2642  Survival (%) Thermal Optimum (\u00b0C) 25.2 28.9 30.3 30.9 95% C.I. 24.80 \u2013 26.75 26.54 \u2013 30.30 28.15 \u2013 32.51 29.29\u2013 31.74 CTmax (\u00b0C) 36.1 36.3 36.1 35.8 95% C.I. 35.26 \u2013 36.51 35.80 \u2013 38.28 35.82 \u2013 44.27 35.62 \u2013 36.59 Safety Margin (\u00b0C) 10.9 7.32 5.80 4.91 95% C.I. 8.86 \u2013 11.03 5.60 \u2013 11.61 2.96 \u2013 9.22 4.08 \u2013 7.23 Thermal Breadth (\u00b0C) 10.7 8.80 7.28 6.83 95% C.I. 9.55 \u2013 10.85 7.70 \u2013 9.86 6.08 \u2013 8.64  6.00 \u2013 7.77  Developmental Rate (1\/days) Thermal Optimum (\u00b0C) 29.15 30.41 31.56 31.65 95% C.I. 28.91 \u2013 29.32 30.17 \u2013 30.70 31.12 \u2013 31.93 31.43 \u2013 32.02 CTmax (\u00b0C) 35.96 35.85 35.99 35.99 95% C.I. 35.74 \u2013 36.38 35.78 \u2013 35.89 35.97 \u2013 36.00 35.97 \u2013 36.00 Q10 3.55 2.48 1.96 2.00 95% C.I. 3.41 \u2013 3.72 2.28 \u2013 2.66 1.80 \u2013 2.13 1.81 \u2013 2.19 Safety Margin (\u00b0C) 6.82 5.42 4.44 4.34 95% C.I. 6.50 \u2013 7.41 5.20 \u2013 5.67 4.07 \u2013 4.85 3.98 \u2013 4.49 Thermal Breadth (\u00b0C) 6.92 6.95 8.11 7.93 95% C.I. 6.76 \u2013 7.07 6.63 \u2013 7.30 7.88 \u2013 8.44 7.42 \u2013 8.25  Embryonic Growth Rate (mm\/days) Thermal Optimum (\u00b0C) 28.8 29.0 31.2 32.57 95% C.I. 28.16 \u2013 29.16 28.34 \u2013 29.40 29.35 \u2013 33.00 29.55 \u2013 33.00 57  Chapter 3: Exposure to altered temperature during early development results in reversible improvements in hypoxia tolerance in juvenile Fundulus heteroclitus 3.1.  Introduction Embryonic development is a highly sensitive time period, especially in aquatic ectotherms (Rombough, 1997; Burggren & Mueller, 2015). For example, embryonic life stages have been hypothesized to have smaller thermal windows compared to adults, which limits the range of temperatures at which they can survive or successfully develop (Dahlke et al., 2020). Furthermore, unlike later life-stages, most fish embryos lack the ability to behaviourally thermoregulate due to their inability to actively control their movement, which limits their ability to find optimal developmental temperatures. In addition, their underdeveloped cardiorespiratory systems may constrain their ability to cope with the increased metabolic demands associated with high temperatures (Cowan et al., 2023). Together, these characteristics suggest that knowledge of the thermal biology of embryonic life-stages may be critical for predicting how species may respond to the warming associated with climate change. However, most of the available data on the acclimation capacity and plasticity of ectotherms in response to temperature change is focussed at juvenile or adult life-stages, whereas less known about the plasticity of developing fishes (Earhart et al., 2022a), limiting our ability to predict the consequences of anthropogenic warming. Understanding the effects of the environment that an organism experiences during early development may be particularly critical because early environmental exposures can lead to altered phenotypes in later life-stages through carry-over effects (Jonsson & Jonsson, 2014; Vagner et al., 2019). These effects can be beneficial and increase the chances of survival or reproductive success (Jonsson & Jonsson, 2014; Vagner et al., 2019) or can be maladaptive or result in mismatched phenotypes in which the phenotype produced during early development does not match the optimum phenotype for the adult environment (Taborsky, 2017). These carry-over effects occur as a result of developmental plasticity, which is a process that allows a single genotype to produce various phenotypes in response to environmental changes that occur during early development (West-Eberhard, 2005). Developmental plasticity is often described in the literature as an irreversible process in which the altered phenotype is present throughout the lifespan of an organism (Burggren, 2020). However, the persistence of this phenotype may 58  depend on various factors such as the severity, timing, and length of the exposure to the stressor, (Burggren & Mueller, 2015; Mueller, 2018) and the possibility of reversible developmental plasticity is often overlooked (Burggren, 2020).  Reversible plasticity may be the result of an interaction between developmental plasticity and acclimation capacity. For example, some phenotypes, as the result of developmental plasticity may be partially or fully reversible when an organism is able to readjust their phenotype through acclimation in later juvenile or adult stages (Burggren, 2020). In addition, developmental plasticity can alter the acclimation response in adult organisms (Scott & Johnston, 2012; Beaman et al., 2016). Detecting developmental plasticity, and particularly reversible developmental plasticity, can be challenging and most studies to date use only a single sampling time-point for a specific phenotype, which makes it impossible to detect reversible plasticity (Pottier et al., 2022a). While there have been few studies to investigate reversible plasticity in various phenotypes including pigmentation in larval newts (Polo-Cavia & Gomez-Mestre, 2017) and egg size in butterflies (Brakefield et al., 2007), whether phenotypes derived from developmental plasticity are reversible in later life-stages still remains mostly unknown in fishes.  Temperature-induced plasticity during development has been shown to modify traits such as thermal tolerance in fishes (Chen et al., 2013; Spinks et al., 2019; Del Rio al., 2019; Illing et al., 2020; Del Rio et al., 2021). However, studies examining the critical thermal maximum (CTmax), a common method to quantify thermal tolerance (Lutterschmidt & Hutchinson, 1997), have found mixed results (Pottier et al., 2022a) with studies identifying decreased (Chen et al., 2013), increased (Del Rio et al., 2019) or no significant changes (Spinks et al., 2019; Illing et al., 2020) in CTmax when fish are developed at higher temperatures. The substantial differences across studies and extent of developmental plasticity in response to temperature could either be the result of inter-specific differences in plasticity or could be caused by differences in study design, such as test temperatures, the age of the fish during the trial, and if there are particular \u201ccritical windows\u201d for developmental plasticity (Burggren & Reyna, 2011). Alternatively, scenarios where no difference is observed could be the result of the timing of sampling point, if plasticity is reversible and fish reverse their phenotype through acclimation in later life stages.  In adult life-stages, thermal acclimation has been shown to alter hypoxia tolerance in some fish species (He et al., 2015; McBryan et al., 2016; Jung et al., 2020; McArley et al., 2020; 59  Jensen & Benfey, 2022). This is an example of the phenomenon of cross-tolerance or cross-talk (Sinclair et al., 2013). Beneficial cross-tolerance occurs when an exposure to one stressor increases the performance of an organism to another stressor by inducing a mechanism that is protective against both stressors (Todgham et al., 2005; Sinclair, et al., 2013), whereas cross-talk occurs when shared signaling pathways activate different mechanisms of protection against each stressor. In addition to beneficial cross-tolerance or cross-talk, it is also possible for prior exposure to one stressor to result in decreased performance to another. For example, acclimation to higher temperatures has been shown to decrease hypoxia tolerance in some species (Remen et al., 2013; McDonnell & Chapman, 2015; Shi et al., 2018). There have been several proposed mechanisms that may explain cross-tolerance or cross-talk between temperature and hypoxia in fishes, but the mechanisms underlying this phenomenon still remain relatively unknown (Earhart et al., 2022a). For example, temperature induced increases in gill surface area in response to warm acclimation have been proposed to explain cross tolerance to hypoxia in Fundulus heteroclitus (McBryan et al., 2016). Alternatively, some studies suggest that cross-tolerance or cross-talk may be driven by the cellular stress response through the heat shock pathway and its potential interaction with the hypoxia-inducible factor (hif1\u03b1; Todgham et al., 2005; Levesque et al., 2019). However, whether exposure to warmer temperatures during development results in cross-tolerance or cross-talk in response to later-life exposure to hypoxia has yet to be investigated.  Therefore, the goal of this study was to assess whether developmental temperatures alter thermal and hypoxia tolerance in later life, and if so whether this plasticity is reversible or irreversible. To explore this question, we used the southern subspecies of Fundulus heteroclitus, the Atlantic killifish, which is a topminnow found in estuarine and salt marsh habitats along the East Coast of North America (Hardy, 1978). In these habitats they experience both daily and seasonal temperature variation, and are acutely exposed to changes in temperature, oxygen, and salinity (Sidell et al., 1983). Most studies to date in F. heteroclitus have examined the effects of temperature acclimation on wild-caught, lab-acclimated fish, and these studies have identified substantial capacity for acclimation (Fangue et al., 2006; Healy et al., 2010; Healy & Schulte, 2012; McBryan et al., 2016). In this study, we address the following questions: 1) Does exposure to different temperatures during early developmental shape adult thermal tolerance as the result of developmental plasticity? 60  2) Does exposure to different developmental temperatures result in cross-tolerance to hypoxia at later-life stages? 3) Are the effects of developmental plasticity reversible?  4) Does developmental thermal exposure result in lasting changes in gene expression? We hypothesized that F. heteroclitus would exhibit substantial capacity for developmental plasticity as a component of their adaptation to their variable estuarine environment, and thus we predicted that developmental temperatures would alter the thermal and hypoxic performance of adult fishes as a result of beneficial developmental plasticity. To test this hypothesis, we exposed F. heteroclitus embryos from the southern subspecies to either 20 \u00b0C or 26 \u00b0C during embryonic development. In the context of their natural environment, 26 \u00b0C is considered a typical developmental temperature, while 20 \u00b0C is considered cooler for this subspecies (NOAA, NERRS, 2023). Once hatched, fish were raised at a common temperature of 20 \u00b0C for 1 year and assessed both molecular (hif1\u03b1, hsc70, and hsp90b) and whole-organism phenotypes (thermal tolerance, hypoxia tolerance) at several timepoints across the juvenile life-stage (Fig. 3.1).   3.2.  Methods 3.2.1. Animal husbandry Adult Fundulus heteroclitus heteroclitus were collected during the Fall of 2019 from Jekyll Island, Georgia. Fish were initially held in 454 L fibreglass tanks in a recirculating system and held at the following conditions 20 \u00b0C, 12L:12D and 25 ppt, made with Instant Ocean Sea Salt (Instant Ocean, Spectrum Brands, Blacksburg, VA, USA). Fish were fed once a day with PE Mysis Flakes Saltwater Fish Food (Piscine Energetics, Vernon, BC, Canada) until satiation. In the summer of 2020, a total of 40 parental fish were transferred (20 females and 20 males) into 60 L tanks, with each tank having either 5 males or 5 females per tank for a total of 8 tanks. Males and females each were held in separate tanks to reduce aggressive behaviors which increased during breeding seasons. Each tank was held at the following conditions: 18 \u00b0C, 12L:12D, 25 ppt, and were well aerated with air stones. To achieve spawning conditions, tank temperatures were increased by 1 \u00b0C every other day until a final temperature of 25 \u00b0C was achieved. Photoperiod was also slowly increased by 15 mins\/day until a photoperiod of 16L:8D 61  was achieved. Fish were fed a diet of blood worms and Mysis shrimp (Hikari, Hayward, CA, USA) twice a day until satiation to provide the additional nutrition needed to support gamete production and spawning. Animal experimentation was performed in accordance with approved University of British Columbia animal care protocol A20-0070. 3.2.2. Experimental design To spawn fish, we used an in vitro fertilization protocol as previously described (McKenzie et al., 2017). In brief, both mature females and males were placed under a light anesthetic using MS-222 (1 g\/L of Syncaine\u00ae buffered with 2 g\/L of NaHCO3). Light pressure was applied to the abdomen to release the milt or eggs from the adults. A total of 11 families were created; however, only 8 families had enough viable eggs hatch for each treatment. Milt and eggs (~40-160) from each family were added to individual Petri dishes with ~10 mL of 25 ppt dechlorinated water and allowed to incubate for 1 hour to achieve fertilization. Eggs were then examined under microscope for the presence of a fertilization envelope (Armstrong and Child, 1965). Embryos from each family were divided between two treatments: a cool temperature (20 \u00b0C) and a warm temperature (26 \u00b0C). We defined these temperatures as \u201ccool\u201d and \u201cwarm\u201d, respectively based on the thermal performance curves for embryo development in this species, as 20 \u00b0C is well below the thermal optimum for development, and close to the minimum temperature for development of this subspecies, whereas 26 \u00b0C is closer to the thermal optimum (see chapter 2). Petri dishes (8\/treatment) were placed in temperature-controlled incubators (MIR-154, PHCbi, Tokyo, Japan) at their respective incubation temperature under a 12L:12D light cycle. Each family Petri dish contained 20-80 embryos\/dish that were held at a salinity of 25 ppt in ~10 mL of dechlorinated water with (0.0003%) methylene blue as a fungicide. Embryos were held under these conditions until hatch. Water changes (90-100%) were performed every day to discard any dead embryos and water quality parameters were also checked. Embryos were given a daily 30 minute air exposure period as embryos neared hatching (based on embryonic morphology) as air exposure and subsequent immersion is a natural cue for F. heteroclitus hatching (Marteinsdottir and Able, 1992). Time to hatch (days) and survival (% hatch) were recorded from each family and both temperature treatments. Once hatched, larvae from each treatment were divided across 5 tanks\/developmental treatment with approximately equal numbers of larval fish (30-40) from each family per tank . Each tank (9.5 L) was held at a 62  common temperature 20 \u00b0C, a salinity of 25 ppt and a light cycle of 12L:12D. For whole-animal analyses, fish were subjected to thermal and hypoxia tolerance assessments at either 6 months and\/or 1 year of age as described below (Fig. 3.1). For molecular analyses, fish were sampled at both 1 month and 6 months to detect any lasting effects of developmental temperatures on transcriptional levels as indicated below (Fig. 3.1). Fish were raised to the age of 1 year, which is considered the late juvenile or early adult stage, as F. heteroclitus are capable of breeding as yearlings, although fecundity is typically low at this age due to a smaller size (Kneib, 1986). 3.2.3. Critical thermal maximum (CTmax) Critical thermal maximum (CTmax) was measured in juvenile fish at 6 months of age (Fig 3.1). For each trial, 8 fish (4 fish from each developmental temperature) were individually transferred to 1 L containers with 11 cm x 11 cm plastic mesh windows that were labelled (A-H). Individual containers were placed in a larger 130 L experimental tank with recirculating water using multiple pumps (VicTsing 400GPH) to ensure adequate mixing and aerated with air stones to maintain oxygen concentrations (100% air saturation) during each trial. Fish were allowed to recover from handling stress in the containers for 30 mins prior to the trial while temperature was maintained at 20 \u00b1 0.5 \u00b0C. CTmax was measured using the method described by Fangue et al. (2006) in which temperature was increased in the experimental tank at a rate of ~0.3 \u00b0C\/min using multiple titanium heating sticks (Aquatop 400W, 500W, Brea, CA, USA). Individual thermometers (Hanna ChekTemp1 Digital Thermometers, Laval, Quebec, Canada) were placed into each container to record temperature measurements. CTmax was recorded as the temperature at which the fish were no longer able to right themselves, also defined as loss of equilibrium (LOE). The point at which each fish reached CTmax was determined by the same observer for each trial and the observer remained blinded to the experimental treatments to reduce biases. After LOE was reached, fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L NaHCO\u2083), weight and length were recorded and then fish were frozen in liquid nitrogen (N2). A total of 6 trials were conducted over the course of one week between 10 am and 2 pm to minimize effects of the diurnal cycle (Healy & Schulte, 2012), resulting in a total of 24 fish per temperature being assessed for CTmax. There was no significant effect of trial within a treatment and therefore trials were combined in subsequent analyses.   63  3.2.4. Hypoxia tolerance \u2013 time to LOE  Hypoxia tolerance was measured in juvenile fish at both 6 months and 12 months of age (Fig. 3.1). For each trial, 8 fish (4 fish from each developmental temperature) were individually transferred to 1 L containers with 11 cm x 10 cm plastic mesh windows and were individually labelled (A-H). Individual containers were then placed into a 50 L plexiglass tank with eight submersible pumps (VicTsing 80GPH) ensuring adequate water circulation throughout the tank. To ensure that fish were not able to obtain oxygen from the water surface, a plastic mesh was placed ~3 cm below the water surface for each individual container (McBryan et al., 2016). Additionally, bubble wrap was used to cover the entire tank and each individual container to prevent oxygen from diffusing into the tank during the trial. Fish were allowed to recover from the handling stress for 30 mins before the experiment began and temperature was maintained at their holding temperature of 20 \u00b1 0.5 \u00b0C. For each trial, N2 gas was bubbled into the water in the plexiglass tank to reduce the partial pressure of O2 (PO2) from 21.2 kPa (100% air saturation) to 0.4 kPa (2% air saturation) over the course of 40-50 mins as previously described (McBryan et al., 2016; Healy et al., 2018). Once a PO2 of 0.4 kPa was achieved, a timer was started, and O2 levels were held at that level by manually adjusting the flow of N2. O2 levels were continuously monitored using a YSI dissolved oxygen probe (Xylem Inc, Yellow Springs, OH, USA). We quantified time to LOE as the time (mins) after reaching 0.4 kPa that fish were no longer able to right themselves after gentle movement of the beaker. Time to LOE was determined by the same observer for each trial and the observer remained blinded to the experimental treatments to reduce biases. After LOE was reached, fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO3), weight and length were recorded and then fish were frozen in liquid N2. A total of 6 trials were conducted over the course of one week between 10 am and 2 pm, resulting in a total of 24 fish tested per treatment. There was no significant effect of trial within a treatment and therefore trials were combined in subsequent analyses. 3.2.5. Gene expression   For gene expression, control fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO3) and sampled at both 1 month and 6 months (Fig. 3.1). Both whole-body (1 month) and the brain and liver (6 months) were preserved in RNAlater\u00ae (Thermo Fisher Scientific, Waltham, MA, USA), held at 4 \u00b0C for 24 h and then were stored at -80 \u00b0C until 64  RNA extractions. Total RNA was extracted using TRIzol\u00ae Reagent (Invitrogen, Waltham, MA, USA) to isolate the aqueous RNA phase and the RNeasy kit to wash the RNA following the manufacturers protocol (Qiagen; Hilden, Germany). RNA concentration and purity were determined using spectrophotometry with a Nanodrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA). RNA samples were stored at -80 \u00b0C until further analysis. We synthesized cDNA using 1 \u00b5g of total RNA per sample using the methods described by Earhart et al. (2022b). cDNA was stored at -30 \u00b0C until further analysis. We measured the expression of three target genes (hif1\u03b1, hsc70, hsp90b) using real-time quantitative polymerase chain reaction (RT-qPCR). Gene-specific primers were designed using the F. heteroclitus (i.e., mummichog) reference genome on Ensembl. Primer efficiency and specificity was assessed across all genes for each tissue by gel electrophoresis and using a 1:10 standard curve dilution from pooled cDNA across both treatments (Table 3.1). RT-qPCR reactions were performed using 5\u00b5l of Bio-Rad SsoAdvanced Universal SYBR-Green Supermix, 3 \u00b5l of nuclease-free water, 0.5 \u00b5l of forward and 0.5 \u00b5l of reverse primers, and 1 \u00b5l of cDNA for a total of 10 \u00b5l. cDNA was diluted 1:10 with nuclease-free water for all target genes (hif1\u03b1, hsc70 and hsp90b) and housekeeping genes (ef1\u03b1, b-actin) prior to being added to the plate. No reverse transcriptase and no template controls were also included on each plate. RT-qPCR (Bio-Rad CFX96, Mississauga, ON, Canada) was performed with an initial denaturing temperature at 95 \u00b0C for 2 mins, followed by 40 cycles of 15 s at 95 \u00b0C, and 30 s at 58 \u00b0C. Melt-curve analysis was performed denaturation at 95 \u00b0C for 15s, followed by 1 min at 60 \u00b0C and then a gradual increase to 95 \u00b0C in 0.5 \u00b0C increments. All housekeeping genes (ef1\u03b1, b-actin) were checked for stability across treatments prior to analysis. Data for each gene was quantified using the standard curve for each gene and the expression of target genes was normalized to the geometric mean of the housekeeping genes (Vandesompele et al., 2002). 3.2.6. Statistical analyses To assess the effects of developmental temperature on survival, CTmax, time to LOE, and gene expression, a two-sided unpaired parametric t-test was used. To check for normality and homogeneity of variance in our data, Shapiro\u2013Wilk and Bartlett\u2019s tests were conducted. Statistical analyses were performed using GraphPad Prism (version 10.3.0). All data are presented as mean \u00b1 s.e.m. and tests were all evaluated at an alpha level of 0.05. 65  To assess effects of developmental temperature on time to hatch, we ran an ANOVA using a linear mixed-effects model (LMM), to account for effects of family\/Petri dish. The model was fitted using the 'lmer' function from the 'lme4' package in R (version 4.2.1). This model incorporated developmental temperature as a fixed effect and family\/Petri dish as a random effect to account for family specific variation.  3.3.  Results 3.3.1. Survival and hatch  Time to hatch was significantly impacted by developmental temperature (t(355)=-15.03, P<0.001, Fig. 3.2A). On average, embryos from the warmer developmental temperature (26 \u00b0C) hatched faster, at about 18 \u00b1 0.34 days, whereas embryos from the colder developmental temperature (20 \u00b0C) hatched in 25 \u00b1 0.35 days. Furthermore, the linear mixed model revealed a significant random effect of family (\u03c7\u00b2= 17.38, P<0.001, Fig. A3.1) on time to hatch. The survival of embryos until hatch was also significantly impacted by developmental temperature (t(14)= 6.033, P<0.0001, Fig. 3.2B). Embryos that developed at 26 \u00b0C had a higher survival with ~70% survival to hatch compared to 47% survival to hatch for embryos developed at 20 \u00b0C.   3.3.2. Thermal tolerance  The upper critical thermal maximum (CTmax) of 6 month old juveniles was not affected by developmental temperature (t(45)= 1.480, P>0.05, Fig. 3.3). CTmax for juvenile fish was 37.4 \u00b0C for fish that had developed at 20 \u00b0C and 37.7 \u00b0C for fish that developed 26 \u00b0C. 3.3.3. Hypoxia tolerance (Time to LOE)  Developmental temperature had a significant effect time to LOE in 6 month old juvenile fish (t(45)= 2.071, P<0.05, Fig. 3.4A). Fish reared at 20 \u00b0C had a lower time to LOE at ~44 minutes, whereas fish reared at 26 \u00b0C reached LOE after ~64 minutes, indicating higher tolerance. However, at 1 year of age, although the trend was in the same direction, developmental temperature no longer had a significant effect on time to LOE (t(40)= 1.545, P>0.05, Fig. 3.4B). Fish reared at 20 \u00b0C had a time to LOE of ~35 minutes and fish reared 26 \u00b0C reached LOE after ~47 minutes.    66  3.3.4. Gene expression  To determine whether developmental temperatures had lasting effects on the heat shock and hypoxia responses, we measured mRNA transcript abundance in three genes across two time-points. Developmental temperature had a significant effect on relative mRNA transcript abundance for hif1\u03b1 in 1 month old juvenile fish (t(18)= 2.337, P=0.031, Fig. 3.5). Fish that had developed at 20 \u00b0C had significantly higher mRNA transcript abundance of hif1\u03b1 relative to fish that developed at 26 \u00b0C. There was no significant effect of developmental temperatures on the mRNA transcript abundance for both hsc70 (t(18)= 1.060, P=0.303, Fig. 3.5) and hsp90b (t(18)= 0.8250, P=0.420, Fig. 3.5).  At 6 months of age, there was no significant effect of developmental temperature on the mRNA transcript abundance in the brain for hif1\u03b1 (t(18)= 1.748, P=0.098, Fig. 3.6A), hsc70 (t(18)= 1.142, P=0.269, Fig. 3.6A) or hsp90b (t(18)= 1.024, P=0.319, Fig. 3.6A) or in the liver (hif1\u03b1: t(18)= 0.2250, P=0.825, Fig. 3.6B; hsc70: t(18)= 1.060, P=0.463, Fig. 3.6B; and hsp90b: t(18)= 1.356, P=0.192, Fig. 3.6B). 3.4.  Discussion   The environment experienced by early life-stages can have lasting effects on juvenile phenotypes, altering the way juvenile and adult life-stages respond to environmental stressors (Jonsson & Jonsson, 2014; Vagner et al., 2019; Pottier et al., 2022), but few studies have examined the persistence of these changes or the potential for cross-tolerance or cross-talk between temperature and hypoxia. Therefore, we tested the effects of two developmental temperature regimes (either 20 or 26 \u00b0C from just after fertilization to hatch) on thermal and hypoxia tolerance and potential underlying molecular mechanisms at later life stages after the fish had been held at a common temperature of 20 \u00b0C from 1 month to a year. Consistent with our previous work (see chapter 2), 20 \u00b0C was a deleterious temperature for embryonic development and embryos reared at 20 \u00b0C had both a lower survival and spent significantly longer developing. We did not detect an effect of developmental temperatures on thermal tolerance (CTmax) later in life, but we did detect beneficial cross-tolerance (or cross-talk) such that hypoxia tolerance (time to LOE) was higher in 6 month old juvenile fish reared at warmer temperatures (26 \u00b0C) during development. However, when hypoxia tolerance was re-measured at the adult stage (1 year), there was no longer an effect of developmental temperature indicating 67  reversible developmental plasticity. At the molecular level, developmental temperatures had very few effects on the expression of constitutive genes involved in the heat shock response. By contrast developmental temperature affected the expression of hif1\u03b1 in 1 month old juvenile fish. Together, these data demonstrate that early developmental temperatures have short-term effects on juvenile phenotypes, with evidence of cross-talk or cross-tolerance between temperature and hypoxia, but these phenotypic changes do not necessarily persist into adulthood, at either the molecular or physiological levels.  Early developmental temperature does not alter juvenile CTmax or hsp mRNA transcript abundance   The ability to physiologically alter thermal tolerance in response to developmental temperatures is thought to be a beneficial plastic response to help organisms cope with warming environments. However, across the literature, the persistent effects of developmental temperature on thermal tolerance still remain unclear since the responses of fishes to developmental temperature vary significantly (Chen et al., 2013; Spinks et al., 2019; Illing et al., 2020; Pottier et al., 2022a). A recent meta-analysis by Pottier et al. (2022a) examined the effects of developmental temperatures on thermal tolerance (CTmax) by measuring the developmental acclimation response ratio (dARR), or the change in CTmax for every degree change in developmental temperature. Across 138 species, average dARR was 0.13 \u00b0C per 1 \u00b0C change in developmental temperature, but the dARR for persistent effects (after animals had been returned to a common temperature) was much smaller (0.048), and not significantly different from zero. Based on this dARR for persistent developmental effects, we predicted that embryos raised at warmer developmental temperatures would have a CTmax around 0.29 \u00b0C higher than embryos reared at the cooler developmental temperatures, and our results are remarkably consistent with this finding. Killifish developed at the warmer temperature had a CTmax 0.3 \u00b0C higher than fish that had developed at the colder temperature, and this difference was not statistically significant. However, this analysis of persistent effects combines several experimental designs with thermal exposures at either the embryo or juvenile stage. When studies in which only the embryo stage are considered, the dARR was -0.082 (Pottier et al., 2022a), implying a negative effect of embryonic exposure, although again this value was not significantly different from zero.  68  Although the mean dARR for persistent effects of developmental temperatures on CTmax is only 0.048 (or -0.082), the range across studies is very large, with values falling anywhere from -1 to +1 (Pottier et al., 2022a). The reasons for these differences remain unknown and could be the result of difference in species, life-stage, age that the trials were performed, or the specific choice of temperatures relative to the species-specific thermal performance curve. In support of this, a meta-analysis by O\u2019Dea et al. (2019) found that larger differences in developmental temperature do not lead to larger shifts in phenotypic means suggesting a limitation driven by the costs of plasticity; it might be less costly to have a fixed phenotype and avoid the possibility of a mismatched phenotype that can lead to a decrease in fitness later in life (O\u2019Dea et al., 2019).   We quantified thermal tolerance in 6 month old juveniles, and given the extreme plasticity of CTmax in adult F. heteroclitus (Fangue et al., 2006), there is a high probability that the juvenile fish we studied might have simply acclimated to the common mean temperature of 20 \u00b0C. Previous work in F. heteroclitus has modeled the relationship between CTmax and acclimation temperature (Fangue et al., 2006). Using this existing model for the relationship between thermal tolerance and acclimation temperature in adult F. heteroclitus, we would predict CTmax should be around 38.7 \u00b0C for southern fish acclimated to 20 \u00b0C and around 40.6 \u00b0C for fish acclimated to 26 \u00b0C (Fangue et al., 2006). We found CTmax values between 37.4-37.7 \u00b0C, almost 1 \u00b0C lower than predicted values for adult fish acclimated to 20 \u00b0C (Fangue et al., 2006). One explanation for this discrepancy could be differences in temperature sensitivities across life-stages or could be a result of persistent developmental effects resulting from lab rearing relative to the previous studies that have examined CTmax only in wild-caught adults acclimated to laboratory conditions. Therefore, it would be useful for future studies to investigate whether developmental temperatures or laboratory conditions have persistent effects on CTmax by measuring CTmax across several time points from larval to adult life-stages in both lab-reared and wild-caught fish.   The relationship between thermal tolerance and the cellular stress response has been extensively characterized in fishes (Somero, 2020). However, to date, little is known about whether developmental temperature has lasting effects on components of the cellular stress response at the molecular level. We assessed the effects of developmental temperature on the mRNA transcript abundance of hsc70 and hsp90b, which are both constitutively expressed genes involved in cellular processes such as protein folding and assembly and cellular homeostasis 69  (Hochachka & Somero, 2002). We found that developmental temperatures had no significant effect on hsc70 and hsp90b mRNA transcript abundance in both 1 month old and 6 month old juveniles. It is important to note that both developmental temperatures for this study were below the thermal optimum for developmental rate (~31 \u00b0C) in this subspecies, with 20 \u00b0C falling near their lower limit for development. Under colder temperatures, it is thought that the rates of protein folding may decrease, requiring an increase in molecular chaperones such as hsc70 and hsp90 to help maintain protein folding (Teigen et al., 2015). Even though 20 \u00b0C may be a colder temperature for development in this subspecies, adult killifish spend several months a year at this temperature and thus would not be considered thermally stressful acclimation temperature (NERRS, 2023). Therefore, to further understand if 20 \u00b0C is a thermally stressful temperature during development future studies should investigate the expression of heat shock genes during the developmental period at colder temperatures.  Early developmental temperature alters juvenile but not adult hypoxia tolerance   Due to the known interactive effects of temperature and hypoxia in adult fish (Earhart et al., 2022a), we asked whether developmental temperatures altered hypoxia tolerance in juveniles. We found that fish developed at warmer temperatures had better hypoxia tolerance than fish developed at colder temperatures for 6 months only. This demonstrates that hypoxia tolerance of F. heteroclitus can potentially be shaped by early life environments as the result of beneficial developmental plasticity. Currently, to our knowledge, only one other study, in Chinook salmon, has examined the lasting effects of temperature during early development on hypoxia tolerance (Del Rio et al., 2019). In contrast to our results, they found that fry reared at warmer temperatures (14 \u00b0C) had a lower time to LOE than fry reared at colder temperatures (10 \u00b0C) under normoxic conditions. It is important to note, however, that these fish were held at these conditions for the entirety of their development up to the point of testing, which limits the ability to isolate the effects of developmental plasticity and acclimation effects on the phenotype. Thus, our study is the first to conclusively demonstrate that altered thermal exposure during development can have short-term effects on hypoxia tolerance even after the fish have been returned to common conditions. Because we did not detect a significant effect of early developmental temperature on CTmax, our results are likely more consistent with a phenomenon of cross-talk, in which temperature activated a shared signaling pathway that activated a 70  mechanism causing an increase in hypoxia tolerance, rather than cross-tolerance, which would imply a mechanism that affects both thermal and hypoxia tolerance.  To explore a potential mechanism that could underlie the increased hypoxia tolerance we observed, we examined the mRNA transcript abundance of hif1\u03b1. HIF1\u03b1 is one of the subunits of the transcriptional factor HIF and is known to play a role in mitigating hypoxia tolerance in fishes as well as downstream effects on other genes involved in metabolism (Mandic et al., 2021). For example, a study by Joyce & Perry (2020) found that zebrafish with hif1\u03b1 knockouts had lower hypoxia tolerance than wild type but that this knockout had no effect on thermal tolerance. However, several studies have demonstrated that hif1\u03b1 can be regulated by temperature (Rissanen et al., 2006; Levesque et al., 2019; Earhart et al., 2023), suggesting it may be a candidate for a mechanism underlying cross-talk between thermal exposures and hypoxia tolerance. However, it is important to note that a change in hif1\u03b1 does not always result in a change in HIF1\u03b1 protein levels and vice versa (Murphy et al., 2023). Therefore, we must be cautious when making predictions about the relationship between hif1\u03b1 mRNA abundance and hypoxia tolerance (Richards, 2009). We found evidence of lasting changes in hif1\u03b1 mRNA abundance such that fish that were reared at 20 \u00b0C as embryos had significantly higher hif1\u03b1 transcript abundance as 1 month old juveniles compared to fish that were reared at 26 \u00b0C. Lower hif1\u03b1 expression at warmer temperatures has also been found in adult Atlantic salmon in response to thermal acclimation, but this was not correlated to changes in hypoxia tolerance at the whole-animal level (Olsvik et al., 2013). Only one other study has examined the effects of developmental temperature on hif1\u03b1 expression after a return to a common temperature (Levesque et al., 2019). In this study, zebrafish embryos exposed to warmer developmental temperatures had higher hif1\u03b1b transcript abundance, which is the opposite direction observed in our study, and this did not confer changes to hypoxia tolerance in larvae (Levesque et al., 2019). Whether the differences between these studies is a result of species-specific effects, differences in the thermal exposures utilized, differences in the life-stages exposed and tested, or differences in the metric of hypoxia tolerance assessed (time to LOE versus critical oxygen tension, Pcrit) remains unknown. However, it is intriguing that in both studies developmental thermal exposures had a transient effect on the levels of hif1\u03b1 mRNA transcript abundance. This potential cross-talk effect of developmental temperature on hif1\u03b1 expression is clearly worthy of further investigation.    71  Are the effects of developmental plasticity reversible?   Developmental plasticity is often described as an irreversible process that results in a permanent change to adult phenotypes. Indeed, many studies of developmental plasticity have focused on phenotypes that are considered irreversible as the result of developmental plasticity such as life-history traits (i.e., fecundity, color, morphology). As a result, the possibility of reversible developmental plasticity is often overlooked. However, the idea of reversible plasticity is highly plausible, particularly for phenotypes that are able to continuously change throughout their lifespan in response to environmental change (Lande, 2014). For example, it is well established that some traits are reversible in response to acclimation to different temperatures such as gill remodelling in fishes, and changes in CTmax with temperature (Sollid & Nilsson, 2006; Bowden et al., 2014; McBryan et al., 2016). Indeed, adult plasticity in physiological traits is very common, especially in organisms that experience both daily and seasonal fluctuations within their environment (Brahim et al., 2019), and this reversibility of developmentally induced phenotypes has been suggested to be an important mechanism for coping with climate change (Burggren, 2020). Thus, to understand the adaptive potential of developmental plasticity it is important to examine whether a change in phenotype is irreversible or reversible. Since we found differences in hypoxia tolerance in 6 month old juveniles, we were interested if this phenotype persisted to the adult life-stage (~1 year). We found that hypoxia tolerance was no longer different in adult killifish that had been developed at different temperatures, indicating reversible plasticity. The scarcity of studies in this area demonstrates the need for more experiments using designs that can reveal potential patterns of reversible plasticity across the lifespan of fishes.  Conclusions  Our study is one of the first to show evidence of either cross-talk or cross-tolerance driven by developmental temperatures in hypoxia tolerance as the result of developmental plasticity. Furthermore, we also found evidence of reversible plasticity at both the molecular (hif1\u03b1) and physiological levels (hypoxia tolerance) in F. heteroclitus after being acclimated to common temperatures for 1 year. However, developmental temperatures had no effects on thermal tolerance as we observed no significant effects for CTmax or changes in mRNA transcript abundance involved in the cellular stress response suggesting that F. heteroclitus display limited developmental plasticity in their thermal tolerance, or that developmental effects are rapidly reversed by acclimation even in relatively early juvenile stages. Together, our data emphasize the 72  importance of study designs that incorporate repeated sampling timepoints across life-stages to better understand the reversible and lasting effects of developmental plasticity and determine whether developmental plasticity holds the potential to allow animals to rapidly respond to anthropogenic climate change.                     73  Table 3.1. Primer sequences and amplification efficiencies for genes used in this study for F. heteroclitus. Gene Sequence 5\u2019 to 3\u2019 Efficiency (%) Tissue hif1\u03b1 F: TCTCACAAAGACTCACCATCAC R: TGTGTTTCTACCCACACGAAG 109 98 104 Whole-body  Brain Liver hsp90b F: GACCTTCAAGTCCATCCTGTTC R: GTCGTTCTTCTTGGAGCCATAC 99 99 98 Whole-body  Brain Liver hsc70 F: CTGCTGGAGATACTCATCTTGG R: GTCCTTCTTGTACTTGCGTTTG 107 97 102 Whole-body  Brain Liver ef1\u03b1 F: GGGAAAGGGCTCCTTCAAGT R: ACGCTCGGCCTTCAGCTT 100 101 104 Whole-body  Brain Liver b-actin F: CCTCCAAGACACCCAACAAC R: TAACGCCTCCTTCTTCATCGTTC 100 98 103 Whole-body  Brain Liver     74    Figure 3.1. Experimental design for testing the effects of developmental temperature in the southern subspecies of Fundulus heteroclitus. Embryos were exposed to either 20 \u00b0C or 26 \u00b0C from fertilization to hatch and then reared at common temperature of 20 \u00b0C.  We then measured a series of traits in 6 month old juveniles (thermal tolerance and hypoxia tolerance), and 1 year old sub-adults (hypoxia tolerance). Tissues were also sampled from 1 month old fish (whole body) and brain and liver for 6 month old juveniles to quantify lasting changes in mRNA transcript abundance in response to developmental temperature.75    Figure 3.2. Effects of developmental temperature on (A) days to hatch and (B) survival to hatch in southern F. heteroclitus embryos reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values. Days to hatch were analyzed using a linear mixed model (N=8 families and with ~10-40 embryos per family). Survival to hatch was analyzed using an unpaired parametric t-test (N=8). ****Denotes significant difference (P<0.001) between developmental temperatures.   76   Figure 3.3. Effect of developmental temperature on upper thermal tolerance (CTmax) measured in 6 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N =23-24).             77   Figure 3.4. Effect of developmental temperature on hypoxia tolerance (time to LOE) measured in (A) 6 month old and (B) 1 year old in southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N =23-24). *Denotes significant difference (P<0.05) between developmental temperatures.    78   Figure 3.5. Effect of developmental temperature on the relative mRNA transcript abundance of hif1\u03b1, hsc70 and hsp90b in 1 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N =10). *Denotes significant difference (P<0.05) between developmental temperatures.  79   Figure 3.6. Effect of developmental temperature on the relative mRNA transcript abundance of hif1\u03b1, hsc70 and hsp90b in (A) brain and (B) liver in 6 month old southern F. heteroclitus that were reared at either 20 \u00b0C or 26 \u00b0C from fertilization to hatch. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N =10).      80  Chapter 4: Performance of embryos reared under fluctuating temperatures does not conform to predictions based on constant temperatures 4.1. Introduction  Climate change is resulting in increases in mean temperatures, the frequency and severity of extreme events, and in thermal variability (IPCC, 2021). Predicting how organisms will respond to these changes is thus of urgent importance, as climate change is already causing biodiversity loss, and these losses are only expected to accelerate (Pecl et al., 2017; Talukder et al., 2022). However, to date, most studies have focused on the impacts of increased mean temperatures (Schaefer & Walters, 2010; Scott & Johnston, 2012; Moyano et al., 2017; Del Rio et al., 2019; Spinks et al., 2019; Illing et al., 2020) and much less is known about the impacts of increased thermal variability on biochemical and physiological traits, organismal performance, and fitness (Niehaus et al., 2012; Morash et al., 2018). This is particularly concerning because increases in temperature variability have been suggested to result in more physiological consequences and costs to maintain biological function than increases in mean temperature (Morash et al., 2018).  The lack of data on the effects of thermal variability on organismal performance and physiological function is particularly acute for embryonic life stages (Massey & Hutchings, 2021). This is a critical issue because embryonic life stages are thought to be particularly sensitive to temperature change (Dahlke et al., 2020). In addition, the environment experienced during development can cause long-lasting changes through the process of developmental plasticity and lead to difference in in life history traits (i.e., growth and development; Schaefer & Ryan, 2006; Rodgers et al., 2018; Pisano et al., 2019) and physiological function later in life (Johnston et al., 2001; Schaefer & Walters, 2010; Scott & Johnston, 2012; Del Rio et al., 2019; Illing et al., 2020). Much is known about the impacts of constant incubation temperatures on embryonic development across a range of taxa (Pepin et al., 1997; Klimogianni et al., 2004; Kamler, 2008; Politis et al., 2017). In contrast, the literature examining the effects of fluctuating temperatures during early development has mainly focused on insects (e.g., Kingsolver et al., 2009; Williams et al., 2012; Cavieres et al., 2018), with some studies on reptiles and amphibians (Shine et al., 1997; Du et al., 2009; Li et al., 2013; Kern et al., 2015) whereas less is known about these effects in fishes (Lim et al., 2017; Eme et al., 2018; Del Rio et al., 2021).  81  Temperature effects on physiological rate processes and organismal performance are typically non-linear. Rates tend to increase with an accelerating trajectory, reach a maximum temperature at some thermal optimum, and then decline rapidly, which is reflected in the typical shape of a thermal performance curve (TPC). It has been argued that TPCs generated from experiments performed at constant temperatures can be used to make predictions about the effects of thermal fluctuations if this TPC shape is considered using Jensen\u2019s inequality theorem (also known as the \u201cfallacy of the average\u201d), which is a mathematical description of the effects of averaging on non-linear functions (Ruel & Ayres, 1999; Denny, 2017). When applied to TPCs, Jensen\u2019s inequality leads to the conclusion that thermal variability in an environment can either increase or decrease performance in comparison to a constant temperature with the same mean depending on where that variability falls along a TPC. At the simplest level it predicts that if the range of the fluctuations extends above the TPC inflection point (i.e., above the thermal optimum, Topt) then average performance is predicted to be lower than at a constant temperature with the same mean. On the contrary, if the range of the fluctuations is below the TPC inflection point near the convex portion of the curve, then performance is predicted to be higher than at a constant temperature with the same mean. Lastly, if the range of the fluctuating environment falls within the linear portion of the curve, then no differences in performance between fluctuating and constant temperatures would be expected (Ruel & Ayres, 1999; Martin & Huey, 2008).  Jensen\u2019s inequality acts as a useful null hypothesis against which to test the effects of thermal variability compared to performance at constant temperatures. However, there are many factors that could influence predictions based on Jensen\u2019s inequality. In particular, this type of analysis neglects the possibility of plasticity in the TPC, which could result in changes in thermal breadth, horizontal or vertical shifts in the TPC, or changes in slope due to changes in thermal sensitivity (Morash et al., 2018). Unfortunately, there are relatively few studies where TPCs for both constant and fluctuating temperatures are available to address these issues, even in adult organisms (e.g., Bernhardt et al., 2018; Morash et al., 2018; Marshall et al., 2021), and even fewer in developing organisms (Niehaus et al., 2012; Massey & Hutchings, 2021).  Here, we utilized the northern subspecies of Fundulus heteroclitus, the Atlantic killifish, a small topminnow found in salt marshes on the east coast of North America to test the hypothesis that the effects of thermal fluctuations can be predicted using data from experiments 82  performed at constant temperatures, taking into account Jensen\u2019s inequality. F. heteroclitus embryos are typically deposited on algal mats or sandy beaches in shallow water, which exposes them to a substantial daily thermal variation (Taylor, 1986), with temperature changing by as much as 15\u00b0C as the result of tidal cycles (Schulte, 2007). Thus, this species is well adapted to fluctuating environments. We therefore used this species to ask: 1) Can embryonic survival and development under fluctuating temperatures be predicted from the TPC at constant temperature taking into account Jensen\u2019s inequality? 2) How do fluctuating temperatures during development affect larval morphology? 3) Do thermal fluctuations affect transcriptome profiles in embryos? 4) Do these early-life environments have lasting effects on the transcriptome?  We have recently constructed complete TPCs for early development in F. heteroclitus at constant temperatures (see chapter 2). In the current study, we exposed developing embryos to diurnally fluctuating developmental temperatures (26\u00b10 \u00b0C 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, and 26\u00b17 \u00b0C). The mean temperature is just below (developmental rate) or slightly above (survival) the predicted thermal optimum (Topt), and the maximum daily fluctuation is consistent with the largest fluctuations seen in the natural habitat, resulting in exposure to temperatures well above the Topt. To apply non-linear averaging via Jensen\u2019s inequality, one can simply draw a line between the rates at the extreme temperatures of the fluctuating regime on the TPC generated from constant temperature acclimation, with the average being the rate at the midpoint of this line (Denny, 2017). In the context of the current experiment, we therefore predicted that rate processes should be slower under all fluctuating regimes relative to those determined under constant temperatures, with the deviation being greater for the more extreme fluctuations (Fig. 4.1A). Survival during development also exhibits a TPC with similar shape (Fig. 4.1B) and thus we also predicted that survival should decline with increasing thermal fluctuations.  We also assessed the effects of fluctuating temperature on larval phenotypes such as size at hatch, which is known to be strongly influenced by developmental temperature in fishes (Gray, 1928; Rombough, 1988, 1994; Pepin, 1991; Pepin et al., 1997; Ojanguren & Bra\u00f1a, 2003; Klimogianni et al., 2004; Kamler, 2008; Mueller et al., 2015; Politis et al., 2017; Mitz et al., 2019). Much less is known about the effects of fluctuating thermal regimes on size at hatch, and 83  a wide range of predictions have been made including minimal effects because the effects of high and low temperatures might simply cancel each other out (Mitz et al., 2021), or reductions in energy reserves and body size at hatching due to increased energetic costs of coping with thermal fluctuations (Pettersen et al., 2024). In addition, to assess potential mechanisms underlying the effects of thermal fluctuation in early development, we also assessed the mRNA transcript abundance of genes related to the processes we predicted may be impacted by thermal fluctuations, including genes in the heat shock stress response (HSR), genes related to growth and metabolism, genes related to hypoxia tolerance, and genes involved in DNA methylation. Finally, we assessed gene expression in larvae one week after hatching, after they had been returned to a constant temperature of 26 \u00b0C, to assess whether fluctuating temperatures during development have lasting effects, as most studies that have examined the persistent effects of developmental temperatures on the transcriptome have focused on constant rearing temperatures. Together, these experiments provide a comprehensive assessment of the effects of diel thermal fluctuation during early development and suggest that experiments performed at constant temperatures cannot be used to accurately forecast responses to thermal fluctuations. 4.2. Methods 4.2.1. Animal husbandry Fundulus heteroclitus macrolepidotus were collected by Aquatic Research Organisms (Inc.) in May 2021 from Hampton, New Hampshire (42\u00b054\u203246\u2033N). Upon arrival to the lab, fish were held in 454 L fibreglass tanks in a recirculating system. The system was maintained at 20 \u00b0C, a photoperiod of 12L:12D and artificial seawater was held at a salinity of 25 ppt by mixing dechlorinated water with Instant Ocean Sea Salt (Instant Ocean, Spectrum Brands, Blacksburg, VA, USA). Fish were fed once daily to satiation with Plankton and Krill Flakes (AngelFins, Guelph, ON, Canada). In March of 2022, a total of 64 parental fish (48 females and 16 males) were transferred into 60 L tanks kept at a temperature of 18 \u00b0C, a light cycle of 12L:12D, salinity of 25 ppt that and aerated with air stones. Each tank had 6 females and 2 males to reduce aggressive behaviour between males for a total of 8 tanks. To achieve spawning conditions, we first mimicked winter conditions by decreasing the temperature 2 \u00b0C\/day until a final temperature of 10 \u00b0C and simultaneously decreased photoperiod by 15 mins\/day until an 8L:16D was achieved. Fish were held in these \u201cpseudo-winter\u201d conditions for 1 month. To induce 84  spawning in the tanks, we then mimicked a change in season from winter to spring by gradually increasing the temperature by 3 \u00b0C\/week over a course of a month to achieve a final holding temperature of 24 \u00b0C, simultaneously increasing day length by 15 mins\/day until a photoperiod of 16L:8D was achieved. Fish were fed a mixed diet of blood worms and Mysis shrimp (Hikari, Hayward, CA, USA) twice a day until satiation to provide an enriched diet to support spawning. Animal experimentation was performed in accordance with approved University of British Columbia animal care protocol A20-0070. 4.2.2. Experimental design Fish were allowed to naturally spawn in the tanks by providing artificial nests made with polyvinyl chloride (PVC) pipe as described by McKenzie et al. (2017). Three artificial nests were placed into each tank at night and were checked every morning for the presence of eggs, as this species tends to spawn just before the beginning of the photophase. Eggs were then collected in Petri dishes with 10 mL of 25 ppt dechlorinated water. We then examined each egg for the presence of a fertilization envelope (Armstrong and Child, 1965). Embryos from each spawning batch (day) were pooled, mixed, and then divided equally across Petri dishes (30-50 embryos per dish), which were then distributed across all treatments. Each day 1-3 Petri dishes were exposed to each treatment, and the experiment was repeated across 8 spawning days. This approach was taken to maximize the genetic diversity of the embryos used in the experiment, without confounding treatment effects with family effects, although this design does not allow us to directly assess the effect of family. Experimental treatments were exposure to 26 \u00b1 0 \u00b0C, 26 \u00b1 3 \u00b0C, 26 \u00b1 5 \u00b0C, or 26 \u00b1 7 \u00b0C until hatch to test for this critical window, and these exposures were performed in temperature-controlled incubators (MIR-154, PHCbi, Tokyo, Japan) on a 12L:12D light-cycle. Sinusoidal temperature fluctuations were generated on a diel, 24 h cycle, with the highest temperature occurring at the middle of the photophase (see Fig. 4.1C). The temperature for each incubator was monitored using temperature loggers (Onset HOBO TidbiT MX2203 Wireless Temperature Data Loggers, Bourne, MA, USA) and recorded every 30 minutes. Each treatment had a total of 8-10 replicate Petri dishes with ~30-50 embryos per dish containing 10 mL of 25 ppt dechlorinated water with diluted (0.0003%) methylene blue (as a fungicide). Water quality was checked daily, and 90-100% water changes were completed daily across all Petri dishes. Embryos were then given a 30 minute air exposure period as a hatching cue at later developmental stages that were near hatching (stages 32-36; Armstrong & Child, 1965). 85  Embryonic stage was assessed through visual inspection of the embryos under a microscope for the presence of buccal movement (Marteinsdottir & Able, 1992), which is thought to induce hatching by the release of the enzyme chorionase. All Petri dishes were checked daily for dead embryos which were discarded as well for new hatchlings. Both time-to-hatch (days) and survival (%) were recorded from each treatment. Replication is at the level of the Petri dish. Once hatched, larvae from each treatment group were divided across individual tanks on a Zebrafish rack (5 tanks\/treatment) with 30-50 larval fish per tank (9.5 L). The Zebrafish rack was held at 26 \u00b0C, 25 ppt and 12L:12D. Petri dish of origin was no longer tracked. For whole-animal analyses, fish were sampled 0 dpf and 7dpf as described below (Fig. 4.2). For molecular analyses, fish were sampled at both 7 dpf and 7 dph to detect the effects of fluctuating temperatures on the transcriptome as indicated below (Fig. 4.2). 4.2.3. Larval morphology To examine the effects of fluctuating temperature on larval morphology at hatch, we sampled larvae at hatch (0 days post-hatch), with one larva per Petri dish (8-10\/treatment) and at 1-week post-hatch (7 days post-hatch; dph, 8-10 fish\/treatment). Larvae were euthanized (3 g\/L of Syncaine\u00ae buffered with 3 g\/L of NaHCO3) then fixed in 10% neutral buffered formalin for 24 hr and stored in 70% EtOH at 4 \u00b0C. We digitally imaged larvae (0 dph and 7 dph) using a DinoLite Digital Microscope (Dino-Lite Edge 5MP, Torrance, CA, USA) at a magnification of 40x. We measured length (mm; 0dph and 7dph) and yolk-sac volume (YSV; mm3; 0 dph) for each larva using ImageJ. To measure YSV, the following equation was used: YSV = (\u03c0\/6)YsL\u22c5YsD\u00b2, in which YsL signifies yolk-sac length and YsD yolk-sac diameter (Blaxter & Hempel, 1963). As well, dry weight (mg; 0 dph) was measured in whole larvae (including the yolk sac) by placing them in a drying oven held at 60 \u00baC for 2 h, then dried larvae were then cooled for 20 min at room temperature and weighed on a microbalance (Mettler Toledo XPR2, Columbus, OH, USA).  4.2.4. Embryo and larval gene expression Total RNA was extracted from 7 dpf embryos. We pooled 3 embryos\/dish and sampled at the same time each day, when all the incubators had been at a temperature of 26 \u00b0C for almost 2 h (8-10 pools\/treatment). Pooled embryos were immediately flash frozen in liquid N2 and stored at -80 \u00b0C until RNA extractions were performed. We also sampled 1 week old larvae by 86  euthanizing them in MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO3), then immediately followed by flash freezing in liquid N2 and stored at -80 \u00b0C until RNA extractions were performed. At this point, fish from the 8-10 Petri dishes had been distributed across 5-6 tanks\/treatment and samples were taken from all tanks for a total of 8-10 larvae\/treatment. RNA was isolated from individual larvae. All RNA was extracted using TRIzol\u00ae Reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer\u2019s instructions and the aqueous phase was purified using RNeasy columns, according to the manufacturer\u2019s protocol (Qiagen; Hilden, Germany). We measured RNA concentration and purity via spectrophotometry on a Nanodrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA). Samples were then stored at -80 \u00b0C until further analysis.  cDNA was synthesized using 1 \u00b5g of total RNA per sample as described by (Earhart et al., 2022b). After synthesis, cDNA was stored at -30 \u00b0C until further analysis. We measured the expression of 16 target genes involved in the heat shock pathway (hsp90b, hsc70, hsp27, hsp60, hsp70.1, hsp70.2), growth and development (igf1, igf2, myog), DNA methylation (dnmt1, dnmt3ab, dnmt3ba, dnmt3bb), and metabolism and hypoxia (ldhb, hif1\u03b1, cs) using real-time quantitative polymerase chain reaction (RT-qPCR).  Primer efficiency was determined for all genes using a standard curve of serial 1:5 dilutions from pooled cDNA across all treatments and developmental stages (Table A4.1). We performed RT-qPCR using the following reagents, 5 \u00b5l of Bio-Rad SsoAdvanced Universal SYBR-Green Supermix, 0.5 \u00b5l of forward and 0.5 \u00b5L of reverse primers, 3\u00b5l of nuclease-free water, and 1 \u00b5l of cDNA for a total of 10 \u00b5l. No reverse transcriptase and no template controls were included on the cDNA plate. Dilutions for each sample are found in Supplemental Table 1. Gene expression was assessed in a 96-well plate by RT-qPCR (Bio-Rad CFX96, Mississauga, ON, Canada) with the following thermal cycling protocol: initial denaturation at 95 \u00b0C for 2 mins, followed by 40 cycles of 15 s at 95 \u00b0C, and 30 s at 58 \u00b0C. Melt-curve analysis was performed denaturation at 95 \u00b0C for 15 s, followed by 1 min at 60 \u00b0C and then a gradual increase to 95 \u00b0C in 0.5 \u00b0C increments. All reference genes (ef1\u03b1, 18s, b-actin) were checked for stability across treatments prior to analysis. Data for each gene were analyzed using the 2\u25b3\u25b3CT method in which expression was normalized to the geometric mean of the reference genes and then the control group as baseline (26\u00b10 \u00b0C; Schmittgen & Livak, 2008).   87  4.2.5. Statistical analyses To examine the effects of developmental temperature on survival, larval morphology, and gene expression, we used a one-way ANOVA with temperature as the factor. When significant effects were found, a Tukey\u2019s HSD post hoc test for multiple comparisons was used. We tested for normality using a Shapiro-Wilk test and homogeneity of variance using a Bartlett\u2019s test. In cases where the assumptions were not met, data were transformed using a square root transformation.  In the case where assumptions were not met after transformation, a Kruskal-Wallis\u2019s test with a Dunn\u2019s pairwise test was used (e.g., hsp70.2 mRNA transcript abundance). All statistical analyses were performed using GraphPad Prism (version 10.0.2) and tests were all evaluated at an alpha level of 0.05. To assess the effects of thermal regimes on days to hatch, we conducted an ANOVA using a linear mixed-effects model (LMM), to account for any effects of Petri dish. This model was fitted using the 'lmer' function from the 'lme4' package in R Studio (version 4.2.1). In this model, we incorporated developmental temperature as the fixed effect and Petri dish as a random effect to account for dish-specific variation. When significant effects were found, we performed post hoc pairwise comparisons using estimated marginal means (EMMs) to investigate specific differences between thermal regimes. This was accomplished using the emmeans() function from the emmeans package in R Studio (version 4.2.1).  4.3. Results 4.3.1. Survival and time to hatch  Embryo survival to hatch was found to be significantly affected by developmental thermal regime (P=0.015, Fig. 4.3A). Survival (% hatched) was highest in embryos incubated at constant temperature (26\u00b10 \u00b0C) at ~80% and significantly different from that of the highest fluctuation treatment (26\u00b17 \u00b0C) from which only ~65% hatched. The low (26\u00b13 \u00b0C) and the intermediate (26\u00b15 \u00b0C) fluctuating temperatures had ~78% and ~71% survival, respectively.   Time to hatch was also significantly affected by developmental thermal regime (P< 0.001, Fig. 4.3B). Contrary to the predictions from Jensen\u2019s inequality, which suggested that hatching should be delayed at the highest temperature fluctuation (26\u00b17 \u00b0C), embryos in this group hatched significantly earlier (~11.3 days) than all other temperature groups (~11.8-12.2 88  days). Additionally, the linear mixed model revealed a significant random effect of Petri dish (\u03c7\u00b2= 38.81, P<0.001, Fig. A4.1) on time to hatch across developmental temperatures. For F-values and DF see Table A.4.3. 4.3.2. Larval morphology  Larval weight at hatch (0 dph) was significantly affected by early developmental thermal regime (P=0.004, Fig. 4.4A). In contrast to our predictions, larvae that developed in the intermediate and high temperature fluctuations weighed significantly more at hatch relative to larvae that developed at a constant temperature. Developmental temperature also significantly affected larval length (P=0.010, Fig. 4.4B). Embryos that developed at the higher fluctuating temperatures were significantly longer at hatch compared embryos that developed at a constant temperature. As an indicator of growth efficiency, we also measured larval yolk-sac volume at hatch (0 dph). Yolk-sac volume at hatch (0 dph) was significantly affected by fluctuating temperatures during early development (P< 0.001, Fig. 4.4C). Larvae that developed in the constant and low fluctuation treatments had a smaller YSV at hatch relative to larvae the developed at the intermediate and higher fluctuating temperatures.  Patterns in larval size were still present in larvae after 1 week of being returned to a constant, common developmental temperature, as 7 dph larvae raised under the highest fluctuating temperatures were significantly longer than larvae that developed at constant temperatures (P=0.028, Fig. 4.4D). For F-values and DF see Table A.4.3. 4.3.3. Embryo mRNA transcript abundance  To assess whether thermal fluctuations during early development resulted in a cellular stress response, mRNA transcript levels were measured for multiple heat shock proteins. Thermal fluctuations during embryonic development had a significant effect on the relative transcript abundance of hsp27 (P<0.001, Fig. 4.5A). Embryos that developed at the low (26\u00b13 \u00b0C) and intermediate (26\u00b15 \u00b0C) fluctuations had significantly lower hsp27 mRNA transcript abundance than embryos that developed at the highest fluctuation (26\u00b17 \u00b0C), but there was no significant difference relative to embryos that developed at a constant temperature. We also found a small but significant induction of hsp70.2 in embryos that developed at the at the highest 89  fluctuation (26\u00b17 \u00b0C) compared to all other temperatures (P=0.036, Fig. 4.5B) as determined by a Kruskal-Wallis test. No significant effects of developmental temperature at the embryo life stage were found in hsc70, hsp90b, hsp70.1, and hsp60 (P>0.05, Fig. A4.2). For F-values, DF, and P-values see Table A.4.2.  To assess mechanisms associated with differences in growth and development between fluctuating temperatures, we measured the mRNA abundance of several genes known to be key regulators of growth and development. Developmental temperature fluctuation during embryonic development had a significant effect on the relative transcript abundance of myog (P=0.007, Fig. 4.5C), a muscle-specific transcription factor that induces muscle growth and development. Embryos that developed at the low and intermediate fluctuating temperatures had a lower myog mRNA abundance than embryos that developed in the high fluctuating temperatures. However, at the embryo life stage, we detected no significant effect of developmental temperature fluctuations on the relative transcript abundance of either igf1 or igf2 (P>0.05, Fig. A4.2), two genes that are critically important for growth in fish. For F-values, DF, and P-values see Table A.4.2.  To assess potential mechanisms associated with effects of developmental temperature fluctuations on metabolism and the potential for interactions between developmental temperature and hypoxia, we measured mRNA transcript abundance of hif1\u03b1, a gene associated with the hypoxic response, ldhb a gene involved in anaerobic metabolism that has been shown to be very important for specifying developmental rate in this species (DiMichele & Powers, 1984; DiMichele et al., 1986), and cs, a gene involved in aerobic metabolism. Developmental temperature fluctuations had no significant effect on the relative mRNA abundance in hif1\u03b1, ldhb, and cs (P>0.05, Fig. A4.2) at the embryonic life-stage as determined by a one-way ANOVA. For F-values, DF, and P-values see Table A.4.2.  To assess the potential impacts of developmental temperature fluctuations on processes that might affect developmental plasticity, such as DNA methylation, we measured mRNA abundance for genes coding for dnmts, enzymes that regulate DNA methylation. There was no significant effect of developmental temperatures on the relative transcript abundance in dnmt1, dnmt3ab, dnmt3ba, or dnmt3bb (P>0.05, Fig. A4.2M) at the embryo life-stage as determined by a one-way ANOVA. For F-values, DF, and P-values see Table A.4.2. 90  4.3.4. Larval mRNA transcript abundance   To assess whether there were persistent effects of developmental thermal fluctuations after one week of larval development at a common constant temperature, we also measured gene expression in 7 dph larvae. Developmental temperature fluctuations had significant effects on the mRNA transcript abundance of both hsc70 (P<0.001, Fig. 4.6A) and hsp90b (P=0.004, Fig. 4.6B;). Larvae that developed at the highest fluctuating temperature had lower hsp90b mRNA transcript abundance than embryos that developed a constant temperature, and larvae that developed at the intermediate fluctuating temperature had lower hsc70 mRNA abundance relative to all other developmental temperatures. Developmental temperatures did not have an effect on the relative mRNA transcript abundance of hsp27, hsp60, or hsp70.1 (P>0.05, Fig. A4.3) in 1 week larvae as determined by a one-way ANOVA. For F-values, DF, and P-values see Table A.4.2. Note, that although we detect clear effects of developmental temperature fluctuations on gene expression in larvae, the patterns observed in the 1 week larvae were not the same as those present at the embryonic stage. Developmental temperature fluctuations had a significant effect on the relative mRNA transcript abundance of myog in 1 week larvae (P=0.005, Fig. 4.6C) such that larvae that had developed at intermediate and high fluctuations had lower myog mRNA abundance than larvae that developed at a constant developmental temperature. There were no significant effects at the larval stage on the relative mRNA transcript abundance in either igf1 or igf2 (P>0.05, Fig. A4.3) as determined by a one-way ANOVA. For F-values, DF, and P-values see Table A.4.2. At the larval life-stage, developmental temperature fluctuations had a significant effect on hif1\u03b1 transcript abundance (P=0.002, Fig. 4.6D). Larvae that developed at both the intermediate and high fluctuating temperatures had significantly lower hif1\u03b1 mRNA transcript abundance compared to larvae that developed at constant temperatures. There was no significant effect of developmental temperatures on the relative mRNA transcript abundance in cs or ldhb (P>0.05, Fig. A4.3) in larvae after 1 week of being held at common temperatures. For F-values, DF, and P-values see Table A.4.2.  Lastly, we also found that developmental temperature fluctuations had lasting effects on both dnmt1 (P= 0.006, Fig. 4.6E) and dnmt3bb (P=0.031, Fig. 4.6F) mRNA transcript abundance. Larvae that had developed at the intermediate and higher fluctuating temperatures 91  had a lower dnmt1 mRNA abundance compared to larvae that developed at a constant temperature. Similarly, larvae that developed at intermediate fluctuations had lower dnmt3bb mRNA abundance than larvae that developed at constant temperatures, whereas there was no significant effect of developmental temperature on the relative mRNA transcript abundance in dnmt3ab and dnmt3ba (P>0.05, Fig. A4.3) at the larval stage. For F-values, DF, and P-values see Table A.4.2. 4.4. Discussion  Incorporating thermal variation into experimental designs to assess the thermal performance of animals has been suggested to be crucial for making better predictions about organismal responses to both natural and human-modified environments (Morash et al., 2018). However, there is limited data available assessing the effects of variable temperatures during early development in many groups of ectotherms, and particularly in fishes. In this study, we show that predictions regarding survival and performance during development generated from fish raised at constant temperatures do not always capture observed patterns in performance and survival in variable environments, even when the \u201cfallacy of the average\u201d is considered using Jensen\u2019s inequality. Interestingly, despite the differences in performance that we observed between constant and fluctuating regimes, there were very few differences at the molecular level between embryos across different levels of thermal fluctuation, and more differences among treatment groups were apparent in 1 week old larvae, even after all embryos had been held under a common, constant, thermal regime from the time of hatch. These data clearly show that exposure to fluctuating temperatures during early development impacts performance in ways that can be difficult to predict based on performance at constant temperatures, and that thermal fluctuations can have long-lasting transcriptomic effects that persist even in a common environment. Taken together, the results of our study highlight the critical importance of incorporating more environmentally relevant conditions into laboratory studies of organismal performance. Performance at constant temperature predicts survival but not performance at fluctuating temperatures  Using TPCs generated for fish reared at a constant temperature, we were able to use Jensen\u2019s inequality to make clear predictions about the expected survival of embryos exposed to 92  fluctuating temperatures. In alignment with our predictions, we found that embryos exposed to the largest fluctuations had lower survival to hatch compared to embryos reared at constant temperatures. This lower survival suggests that this developmental temperature was thermally stressful for developing embryos, which is not surprising as the fluctuations brought the embryos ~4 \u00b0C above their thermal optimum (see chapter 2). Prior studies looking at the effects of fluctuating temperatures on survival in ectothermic embryos have yielded a range of patterns, including no significant effect of different fluctuating regimes (Warner & Shine, 2011; Lim et al., 2017; Eme et al., 2018) and lower survival in eggs incubated in at higher fluctuations versus constant temperatures (Andrewartha et al., 2010). Similarly, in three species of tadpoles, effects on survival were dependent on the species examined and the amplitude of the fluctuation (Kern et al., 2015). However, in many of these cases, the full TPC for survival in embryos incubated at constant temperatures was not known, which makes it difficult to predict the effects of thermal fluctuations. On the other hand, taken together these data suggest that when fluctuating temperatures extend well above the thermal optimum for a species, it is likely to have negative effects on embryo survival.  Unlike the case for embryo survival, the observed effects of fluctuating temperatures on developmental rate were not in alignment with predictions based on the TPC at constant temperature, even when taking Jensen\u2019s inequality into account. We predicted that embryos incubated under fluctuating conditions would develop more slowly than those developed under constant conditions. In contrast to this prediction, we found that embryos exposed to the largest fluctuations developed faster than embryos developed under constant, low, and intermediate fluctuations. Again, prior work in reptiles and amphibians has revealed variable effects of exposure to diel thermal fluctuations during development, depending on the species and the extent of the fluctuation (Du et al., 2009; Andrewartha et al., 2010; Niehaus et al., 2012; Kern et al., 2015; Pettersen et al., 2024). Moreover, Kern et al, (2015) suggested that the observed species-specific responses could be the result of differences in their natural environment, such that the species for which development is accelerated by exposure to daily thermal fluctuations are native to fluctuating environments. This hypothesis is consistent with our observations for F. heteroclitus, as this species is native to a highly variable environment (Schulte, 2007). However, most previous studies lack information on the shape of the TPC at constant temperature, making it challenging to form clear predictions based on Jensen\u2019s inequality. The one previous study 93  conducted in developing amphibians that utilized TPC data from constant thermal conditions also found that predictions based on constant temperatures did not reflect responses under fluctuating conditions (Niehaus et al., 2012). One possible explanation for the lack of fit between the predictions from embryos reared at constant temperatures and those exposed to daily thermal fluctuations is plasticity in the shape of the thermal performance curve induced by the fluctuating regime.  Fluctuating temperatures during development affect larval morphology We found that embryos exposed to the largest daily thermal fluctuations hatched at a greater length and weight and with a larger yolk-sac than did embryos reared under constant conditions. This result is surprising because it has been proposed that organisms may allocate more fuel to development in fluctuating environments (Niehaus et al., 2012; Petterson et al., 2024). By contrast, our data suggest that F. heteroclitus embryos are able to grow more and use less of their yolk reserve than embryos held under constant conditions. Thus, our data suggest that embryos exposed to the larger daily temperature fluctuations are allocating and\/or utilizing their energy stores differently than embryos reared under constant temperatures. Indeed, our results do not align with any of the prior predictions of the likely effects of thermal fluctuations during development. For example, some authors have predicted that diel temperature fluctuations would have limited effects on size at hatch based on the idea that the effects of high and low temperatures across the fluctuating temperatures would potentially cancel each other out (Mitz et al., 2021). However, this prediction does not factor in potential non-linear effects of temperature, and it does not consider the range of the fluctuating regime relative to the thermal optimum. In general, the costs of development are thought to exhibit a concave function with a clear optimum temperature at which the costs of development are minimized (Petterson et al., 2024). Any fluctuation around this optimum would thus increase the costs of development and result in either smaller yolk-sac at hatch or a smaller size at hatch (Petterson et al., 2024) due to trade-offs between yolk-sac volume remaining at hatch and a size at hatch (Johns et al., 1981; Brown & Taylor, 1992; Rombough, 1997). This trade-off is thought to have important ecological implications because hatching with a large yolk-sac reserve should provide an advantage during early larval development if external resources are limited as it can act as a primary energy source (Brown & Taylor, 1992), whereas hatching at a larger body size is thought to be advantageous because it may help larvae escape predators (Huss et al., 2007; Pettersen et al., 2018). F. 94  heteroclitus embryos appear to be able to maximize both remaining yolk sac volume and larval size at hatch when reared under our highest fluctuating thermal regime. There are at least two potential explanations for this phenomenon. First, it is possible that our fluctuating thermal regimes were not centered around the thermal optimum for the cost of development (Petterson et al., 2024). However, this would imply that the thermal optimum for the costs of development is not the same as the thermal optimum for developmental rate or survival, as our predictions based on Jensen\u2019s inequality would suggest that development should be more costly under our fluctuating regimes if these optima are aligned. Alternatively, this apparent superior energetic efficiency under fluctuating temperatures may be the result of F. heteroclitus being adapted to living in highly variable environments that have resulted in the evolution of mechanisms that enhance yolk conversion efficiency under fluctuating conditions. On the other hand, low survival under the highest fluctuations suggest that these conditions are stressful for killifish.  Thermal fluctuations modestly affect transcriptome profiles in embryos  It is well known that the transcriptome profile of fishes is affected by early developmental temperature when tested at constant temperature exposures (Scott & Johnson, 2012; Metzger & Schulte, 2018). However, very little is known about how gene expression may change in response to variable temperatures during early development in fishes. Our study captured the effects on fluctuating temperature on a selection of key genes involved in the heat shock response, growth and muscle development, metabolism and regulating DNA methylation. At the embryonic life-stage, we found that only 3 genes (hsp 27, hsp70.2, and myog) were significantly altered in response to fluctuating temperatures suggesting relatively limited effects of fluctuating thermal regimes on mRNA transcript abundance in embryos. The heat shock response is thought to not only play an important role in an organism\u2019s tolerance and response to temperature (Currie et al., 2000; Fangue et al., 2006) but is also important for early developmental processes (Krone et al., 1997). Multiple studies have detected upregulation of heat shock proteins in response to thermal stressors during early development; however, the type of heat shock proteins upregulated is species-dependent (Krone et al., 1997; Stefanovic et al., 2016; Whitehouse et al., 2017; Sessions et al., 2021). In our study, hsp27 and hsp70.2 were affected by developmental temperature at the embryo life-stage. Both hsp27 and hsp70 are inducible heat shock proteins whose expression increases substantially in response to 95  thermal stress in F. heteroclitus adults (Healy et al., 2010). In contrast to our prediction, we found that both low and intermediate fluctuating temperatures had lower hsp27 abundance relative to embryos reared at higher fluctuating temperatures. On the other hand, there was a small (~2-fold) induction of hsp70.2 in embryos exposed to the highest fluctuating temperature relative to all other developmental temperatures. This suggests that this fluctuating temperature regime exposed embryos to a high enough temperature to elicit a cellular stress response that induced hsp70.2. However, this fold-change is quite small for hsp70.2 as it is known to exhibit very substantial upregulation (50 to 200-fold) in response to either a heat shock (Healy et al., 2010) or upper thermal tolerance test (Fangue et al., 2006) in adult F. heteroclitus. There are several possible explanations for the relatively limited impact of thermal fluctuations on the heat shock response in embryos. First, it is possible that our fluctuating thermal regime did not expose fish to sufficiently high temperatures for a sufficient length of time to induce a complete cellular stress response. Our highest fluctuating regime exposed fish to a maximum temperature of 33 \u00b0C for a period of 2 hours\/day. In adult F. heteroclitus, hsp27 is highly induced in the muscle after 2 hours at 34 \u00b0C (Healy et al., 2010), which suggests that our embryonic exposures would be likely to induce this gene. Similarly, a study on adult annual killifish, Austrofundulus limnaeus, detected strong induction of hsp27 in response to large diel fluctuations (\u00b117 \u00b0C, up to 37 \u00b0C; Podrabsky & Somero, 2004). Alternatively, it has been suggested that exposure to repeated thermal stress can attenuate the HSR response (Coulter et al., 2015; Whitehouse et al., 2017; Sessions et al., 2021), and thus the fact that we sampled embryos after they had been exposed to the thermal fluctuation for 7 days (at 7 dpf) might account for the limited induction. Finally, it is possible that the turnover rate of these mRNAs is very fast, and thus mRNA abundance might increase at the peak of the thermal fluctuation and then return to baseline (or below) at the time of sampling, which occurred at a phase in the thermal cycle when all embryos had been exposed to the common temperature of 26 \u00b0C for at least 2 h.  In embryos, we also detected a significant effect of thermal fluctuation on the myog gene, which is known to play a role in muscle differentiation during development and thought to play a role in determining fish size (Campos al., 2013a,b). Early life-stages in fishes have been shown to increase expression of myog in response to warmer (Campos et al., 2013b; Anastasiadi et al., 2017) or colder (Wilkes et al., 2001; Aidos et al., 2020) temperatures, suggesting a species dependent response. However, to our knowledge, there are no studies that have investigated this 96  in response in embryos developing at variable temperatures. Our results show that both low and intermediate fluctuations had lower myog mRNA transcript abundance than embryos raised under high fluctuations. These results do not align with differences in size at hatch as we found that embryos incubated at higher thermal fluctuation also had increased body weight and length. In fishes, myog expression has been shown to be tightly correlated to the number of muscle fibres, rather than body size per se (Johnston et al., 2009). Therefore, future research should examine the relationship between fluctuating temperatures during early development and changes in muscle fiber type and number.   Early life thermal fluctuations have lasting effects on the transcriptome Developmental temperatures are thought to have persistent effects on organismal phenotypes as the result of developmental plasticity (West-Eberhard, 2005). Therefore, to test for persistent molecular effects we also measured changes in mRNA transcript abundance in 1 week old larvae that were held at a common constant temperature from the time of hatch. We detected six genes (hsc70, hsp90b, myog, hif1\u03b1, dnmt1, and dnmt3bb) whose expression was significantly affected by prior early developmental exposure to fluctuating temperatures, with most being downregulated relative to larvae reared at constant temperatures. Interestingly, only one gene (myog) was affected at the embryo and the larval life-stage, whereas all other genes were detected as significantly differentially expressed at the larval stage only.  Both hsc70 and hsp90b are constitutively expressed and thought to be involved in normal cellular function such as protein synthesis and development (Hochachka & Somero, 2002).  hsc70 displayed a U-shaped pattern, with a lower expression at the intermediate fluctuation relative to all other development temperatures, whereas hsp90b exhibited a more linear response, with a decrease in mRNA transcript abundance as fluctuation amplitude increased. This suggests that thermal fluctuations during embryonic development may have a lasting effect on the cellular stress response which may impact cellular processes such as protein folding (Fangue et al., 2006; Daugaard et al., 2007).   We found that both intermediate and higher fluctuations during embryo development resulted in a downregulation of hif1\u03b1 at the larval stage. Loss of hif1\u03b1 function by knockout in zebrafish has been shown to impair hypoxia tolerance suggesting that hif1\u03b1 can play a direct role in an organism\u2019s ability to cope with hypoxia (Mandic et al., 2021). This opens the possibility 97  that thermal fluctuations during embryo development could result in long-lasting impacts on hypoxia tolerance. Although thermal stress and hypoxic stress may be related in fishes, these interactive effects are not often studied (Earhart et al., 2022a) and little is known about the effects of thermal stress on hif1\u03b1 expression, with some studies detecting effects at the mRNA level (Levesque et al., 2019), and others failing to do so (Rissanen et al., 2006; Olsvik et al., 2013; Pandey et al., 2021). There has been one study in fish larvae (Levesque et al., 2019), which found that zebrafish embryos incubated at warmer temperatures exhibited a 2-fold increase in hif1\u03b1 expression. This contrasts with what we observed in F. heteroclitus embryos exposed to fluctuating temperatures, where hif1\u03b1 expression was not induced by thermal fluctuations. There are very few studies examining the effects of fluctuating temperatures on hif1\u03b1 expression even in adult fish, and the only current study, to our knowledge, is in adult barramundi (Lates calcarifer) in which acclimation to fluctuating conditions did not significantly alter hif1\u03b1 mRNA transcript abundance relative to constant temperature with the same mean, although acclimation to constant higher and lower temperatures had an effect (Scheuffele et al., 2024). Thus, our study may be the first to assess the potential for persistent effects of fluctuating developmental temperature on hif1\u03b1 expression.   Early development and muscle growth are regulated by several genes, including genes involved in the IGF system (i.e., igf1, igf2) and myogenic regulatory factors (i.e., myog; Aidos et al., 2020). As previously discussed, myog was the only gene that was significantly affected by thermal fluctuations at both the embryo and larval stages although the patterns were slightly different in each case. At the larval stage we observed downregulation of myog at both the intermediate and highest fluctuating temperatures, and this pattern is consistent with the morphological observations, as larvae were significantly longer at 7 dph which would be more consistent with an upregulation of a growth promoting gene. However, myog is thought to be more involved in muscle differentiation than in muscle growth (Aidos et al., 2020). Similarly, higher expression of igf1 is predicted to be correlated with growth (Campos et al., 2013a; Aidos et al., 2020), and we found no significant effect of fluctuating temperature on either igf1 or igf2 to support the morphology data. However, igf levels during development in fish are known to exhibit temporal variability across life-stages which may possibly be masking the effects of temperatures (Triantaphyllopoulos et al., 2020).  98  Persistent effects of developmental temperature are thought to be mediated by epigenetic mechanisms such as changes in DNA methylation (Metzger & Schulte, 2017; Laubach et al., 2018). This process is regulated by DNA methyltransferases (DNMTs) which function by altering the methylation state of genes, which in turn can influence transcription of that gene (Radford, 2018). There are two primary types of DNMT, dnmt1 which helps maintain existing methylation patterns and dnmt3 which assists with environmentally responsive DNA methylation within organisms (Rai et al., 2006). However, there are a few studies that have looked the mRNA transcript abundance in fish larvae in response to different developmental temperatures and these have obtained mixed results with either increased (Anastasiadi et al., 2017) or decreased (Campos et al., 2013b) dnmt1 and dnmt3 abundance in response to warmer temperatures. A study by Carballo et al., (2018) found that temperature only had a significant effect on dnmt3aa at hatch, which might suggest that there may be differences across developmental stages in their response to thermal stress.   In F. heteroclitus, mRNA transcript abundance of two DNMTs (dnmt1 and dnmt3bb) was altered in larvae from embryos that were reared at fluctuating temperatures. These changes provide a plausible mechanism for the persistent effects of thermal fluctuations during development at later life stages. Although is important to note that there are many different regulatory enzymes (such as TET enzymes) involved in regulating DNA methylation that were not examined in this study (Radford, 2018), and there are other potential epigenetic mechanisms such a histone modification that were not examined here. Future studies should examine the role of other DNA methylation genes, as well as quantifying on methylation patterns themselves as previous studies have shown lasting effects of (constant) developmental temperatures on the epigenome (Anastasiadi et al., 2017; Metzger & Schulte, 2017).   Conclusions  Our study is one of few to investigate the effects of fluctuating temperatures at both the physiological and molecular level in developing fishes. Our results demonstrate that embryos reared under diel fluctuating thermal regimes elicit different physiological and molecular responses compared to fish exposed to a constant temperature. We found mixed support for the null hypothesis that using Jensen\u2019s inequality and TPCs generated at constant temperatures could predict the effects of fluctuating temperatures on organismal performance. We had predicted that 99  increased fluctuation would result in decreased developmental rate and lower survival; however, we only found support for the latter. These data suggest that embryonic phenotypic plasticity may alter the shape of the TPC under fluctuating temperatures. Interestingly, embryos raised at the most extreme fluctuating regimes hatched larger and with a larger yolk-sac suggesting there may be a benefit to developing under those conditions for this species. At the molecular level, we also found that fluctuating temperatures can induce many lasting changes to the transcriptome that can potentially result in changes to how they respond to environmental stressors at the whole-animal level. It is important to note that changes in mRNA transcript abundance do not always correspond to changes in protein levels and a phenotypic response (Buccitelli & Selbach, 2020). However, we believe this work is still important for providing a foundation for understanding how early-life stages respond to variable environments. Overall, this study highlights the importance of incorporating thermal variability to help make better predictions on how developing embryos will respond to our changing environment.        100   Figure 4.1. Prediction of performance using thermal performance curve for (A) developmental rate and (B) survival as per Jensen\u2019s inequality. It is predicted that average performance will decrease as temperature fluctuations near the concave portion of the curve (performance: 26\u00b13 \u00b0C > 26\u00b15 \u00b0C > 26\u00b17 \u00b0C) compared to performance with the same constant average temperature (26\u00b10 \u00b0C). (C) Treatment water temperature measurements for each diel cycle (26\u00b10 \u00b0C; 26\u00b13 \u00b0C; 26\u00b15 \u00b0C; 26\u00b17 \u00b0C) that embryos were incubated at during experimentation.   101   Figure 4.2. Experimental design testing the effects of diel fluctuating developmental temperatures during early development at the embryo and larval stage in the northern subspecies of F. heteroclitus. Embryos were exposed to either 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, and 26\u00b17 \u00b0C until hatch then reared at common temperature of 26\u00b10 \u00b0C. At the embryo life-stage, we measured days to hatch, survival and changes in mRNA transcript abundance across a selected group of genes. At the larval-stage, we also measured changes in length, weight, yolk-sac volume, and changes in mRNA transcript abundance.            102   Figure 4.3. Effects of fluctuating developmental temperatures on (A) survival and (B) time to hatch in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch. (A) Box plots represent the 25% and 75% IQR percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values. (B) Data is presented as means \u00b1 s.e.m. Days to hatch were analyzed using a linear mixed model (N=8-9 Petri dishes and with ~10-50 embryos per dish). Different letters denote significant differences between developmental temperature fluctuations.    103   Figure 4.4.  Effects of fluctuating developmental temperatures on (A) larval dry weight at 0 dph, (B) length at 0 dph, (C) yolk-sac volume at 0 dph, and (D) length at 7 dph in northern F. heteroclitus reared 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch. Bolded horizontal lines represent the mean \u00b1 s.e.m and dots represent each individual data point. Different letters denote significant differences between developmental temperature fluctuations (P<0.05; N = 10).         104   Figure 4.5. Relative mRNA transcript abundance of (A) hsp27, (B) hsp70.2, and (C) myog that were significantly affected by fluctuating developmental temperatures at the embryo life-stage (7 dpf) in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median, and individual data points are shown. Different letters denote significant differences between developmental temperature fluctuations (P<0.05; N = 4-8).            105   Figure 4.6. Relative mRNA transcript abundance of (A) hsc70, (B) hsp90b, (C) myog, (D) hif1\u03b1, (E) dnmt1, and (F) dnmt3bb that were significantly affected by fluctuating developmental temperatures at the larval life-stage (7 dph) in northern F. heteroclitus reared at 26\u00b10 \u00b0C, 26\u00b13 \u00b0C, 26\u00b15 \u00b0C, or 26\u00b17 \u00b0C from fertilization until hatch. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median, and individual data points are shown. Different letters denote significant differences between developmental temperature fluctuations (P<0.05; N = 7-8).  106  Chapter 5: Persistent deleterious effects of diel thermal fluctuations during early development 5.1.  Introduction  Aquatic environments exhibit natural variation in multiple environmental factors including oxygen, salinity, and temperature (Morash et al., 2018). Among these factors, temperature is thought to be among the most impactful for aquatic ectotherms (Schulte et al., 2011; Neubauer & Andersen, 2019). Temperature change can be experienced across a variety of temporal scales including long-term changes (i.e., climate change), seasonal changes, short-term exposures (i.e., heatwaves), and diurnal cycles (Le Roy & Seebacher, 2018). In the context of anthropogenic climate change, the majority of work has been done on the predicted effects of increased mean global temperatures, but both temperature variation and extreme weather events are expected to increase in prevalence as climate change progresses (IPCC, 2021), and much less is known about how ectotherms will respond to increased temperature variability in their natural habitat. This increase in thermal variability is particularly concerning because many studies have suggested that physiological performance under constant temperature may not accurately predict performance under fluctuating temperatures (Colinet et al., 2015; Bernhardt et al., 2018; Marshall et al., 2021).  In fishes, the majority of studies on the effects of acclimation to fluctuating temperatures have focussed on juvenile and adult life-stages (Morash et al., 2018; Massey and Hutchings, 2021). These studies have shown that acclimation to fluctuating temperature can alter physiological traits such as growth (Li et al., 2021), swimming performance (Peng et al., 2014; Rodgers et al., 2018), metabolism (Morash et al., 2018; Guzzo et al., 2019), and thermal tolerance (Peng et al., 2014; Rodgers et al., 2018; Salinas et al., 2019; Li et al., 2021). It has been hypothesized that acclimation to fluctuating conditions leads to higher energetic costs for the organism compared to constant temperatures and this increased energetic cost is thought to drive phenotypic change (Ruel & Ayres, 1999). However, the evidence for increased costs due to exposure to fluctuating temperatures is mixed, as some studies have detected higher metabolic rates under acclimation to fluctuation temperatures relative to constant temperatures (Lyytik\u00e4inen & Jobling, 1998; Beauregard et al., 2013), whereas other studies have found that fish exposed to fluctuating temperature have lower (Morash et al., 2018) or no change in 107  metabolic rate compared to fish acclimated to constant temperatures (Morissette et al., 2021). Similar to metabolic rate, the effects of thermal fluctuation on growth are also highly varied, with studies showing decreased (Coulter et al., 2016; Prakoso et al., 2021), increased (Spigarelli et al., 1982; Coulter et al., 2016) or no significant change (Thomas et al., 1986; Dhillon & Fox, 2007; Peng et al., 2014; Morissette et al., 2021) in growth under temperature fluctuations when compared to constant temperatures, depending on the species tested. The effects of exposure to thermal fluctuation on upper thermal tolerance (usually measured as CTmax) are also mixed. Several studies have found higher CTmax under fluctuating temperature versus stable temperatures with the same mean (Peng et al., 2014; Corey et al., 2017; Salinas et al., 2019), whereas a study by Rodgers et al. (2018) found no significant difference between thermal regimes on CTmax. This discrepancy between studies is thought to potentially arise from differences in the proximity of the acclimation temperature to the species-specific thermal optimum and upper lethal temperature, differences in the metabolic rate and\/or differences in life-history among species.   Much less is known about the effects of diel fluctuating temperatures on early life stages in fishes. This is important because early life-stages are thought to have a higher thermal sensitivity (Dahlke et al., 2020; Massey and Hutchings, 2021; Pottier et al., 2021a). In addition, unlike adults, at the embryonic stage fishes are generally unable to actively seek out preferred thermal habitats, which makes them more vulnerable to climate change stressors. The effects of fluctuating temperatures during embryogenesis have received some attention (Bestgen & Williams, 1994; Patrick et al., 2013; Coulter et al., 2015; Eme et al., 2018; Ashton et al., 2021). Together, these studies have shown a wide range of phenotypic effects on embryo and larval development depending on the species and the type of fluctuating regime employed (e.g., diel, random, etc.). For example, fluctuating temperatures have been shown to increase (Eme et al., 2018; Ashton et al., 2021), decrease (Bestgen & Williams, 1994), or have no effect (Patrick et al., 2013; Coulter et al., 2015) on embryo survival. As well, these fluctuating temperatures also translated into differences in larval length, mass, or yolk-sac volume at hatch in several species of fish (Kupren et al., 2011; Lim et al., 2017; Eme et al., 2018). Overall, these studies demonstrate that fluctuating temperatures have varied effects on both embryonic and larval traits, highlighting the diverse responses across species.  108  In addition to the direct effects of temperature on early developmental stages, early life-stages are known to have flexible phenotypes, such that early environmental perturbations can have lasting effects on phenotype across subsequent life-stages through developmental plasticity (West-Eberhard, 2005). Under constant thermal regimes, studies have shown lasting effects on phenotypic traits such as swimming performance (Batty et al., 1993; Burt et al., 2011), metabolic rate (Schaefer & Walters, 2010; Donelson et al., 2011), and thermal tolerance (Moyano et al., 2017; Spinks et al., 2019; Illing et al., 2020; Del Rio et al., 2021). However, our current understanding of the effects of diel fluctuating temperatures during early development on subsequent life stages is quite limited. Previous studies have found that zebrafish reared under variable temperatures during development had higher CTmax compared to fish reared at constant temperatures with the same mean (Schaefer & Ryan, 2006; Massey, 2022). This suggests that fish reared under variable and constant temperatures may have different phenotypic responses, but the limited studies in this area make it difficult to draw general conclusions.   Therefore, the goal of this study was to assess whether fluctuating temperatures have lasting effects on juvenile phenotypes as the result of developmental plasticity. To address this, we used Fundulus heteroclitus, the Atlantic killifish, which is a top minnow that lives in estuaries and salt marshes along the East coast of North America from New Brunswick to northeastern Florida. These habitats exhibit both daily and seasonal variation in temperature (Schulte, 2007). Furthermore, F. heteroclitus are known to lay their eggs during the high tides in these salt marshes and embryos spend a portion of their development at low tide in very shallow water or even exposed to air, which exposes them to even greater variations in temperature (DiMichele and Westerman, 1997). Many studies have examined the plasticity of various phenotypic traits including thermal tolerance, hypoxia tolerance and metabolism in adult F. heteroclitus acclimated to constant temperatures (Fangue et al., 2006; Healy et al., 2010; McBryan et al., 2016). However, relatively little is known about whether F. heteroclitus embryos utilize developmental plasticity to cope with fluctuating temperatures during development and if so, whether this results in irreversible phenotypic effects through developmental plasticity. In addition, there is also the possibility of cross-tolerance to hypoxia given that we observed this phenomenon in response to exposure to different constant temperatures during development in F. heteroclitus (Chapter 3). Thus, here we set out to address the following questions:  Does exposure to fluctuating temperatures during early development:  109  1. have lasting effects on growth and the mRNA transcript abundance of regulatory genes involved in growth in later life-stages? 2. alter thermal tolerance and the heat shock response in later life-stages? 3. induce cross-tolerance to hypoxia and alter mRNA transcript abundance of hypoxia related genes? 4. affect metabolic rate at later-life stages?   I hypothesized that temperature fluctuations experienced during early development result in lasting beneficial plasticity. To address these questions, we exposed embryos after fertilization to one constant temperature (26\u00b10 \u00b0C) and three diurnally fluctuating temperatures (26\u00b13 \u00b0C, 26\u00b15 \u00b0C, and 26\u00b17 \u00b0C) with the same mean. Note this was the same rearing experiment as chapter 4, as clutches were raised to measure other physiological traits described below. These temperatures were chosen based on previous thermal performance curves (TPC) modelled across constant developmental temperatures in the northern subspecies of F. heteroclitus (see chapter 2). Once hatched, embryos from each developmental temperature were then raised at a common temperature of 26\u00b10 \u00b0C. To understand whether fluctuating temperatures have lasting effects on physiological traits, we assessed effects on thermal tolerance (CTmax), hypoxia tolerance, growth, and metabolic rate across several developmental time points (Fig. 5.1). Furthermore, to test the hypothesis that fluctuating temperatures would have lasting effects on the transcriptome, we assessed changes in gene expression for genes to determine underlying mechanisms driving these phenotypic changes (Fig. 5.1). 5.2 Methods 5.2.1. Animal husbandry   Adult killifish from the northern subspecies, Fundulus heteroclitus macrolepidotus, were collected in May 2021 from Hampton, New Hampshire (42\u00b054\u203246\u2033N) by Aquatic Research Organisms (Inc.). Fish were shipped to the University of British Columbia and held in 454 L fibreglass tanks in a recirculating system which was kept at a temperature of 20 \u00b0C, a photoperiod of 12L:12D, and 25 ppt salinity. Fish were fed to satiation once daily with Plankton and Krill Flakes (AngelFins, Guelph, ON, Canada). To begin breeding, in March of 2022, we transferred a total 64 parental fish (48 females and 16 males) into 60 L tanks, with 6 females and 2 males per tank for a total of 8 tanks. This density and sex ratio was used to minimize male-110  male and male-female aggression, which can be extreme in breeding individuals. All tanks were held at a temperature of 18 \u00b0C, a light cycle of 12L:12D, salinity of 25 ppt and aerated with air stones. After 1 week, we then decreased tank temperatures by 2 \u00b0C\/day until a final temperature of 10 \u00b0C was achieved to provide a \u201cpseudo-winter\u201d. Since photoperiod plays an important role in gonadal maturity in this species (Taylor, 1986), we also decreased photoperiod by 15 mins\/day until light cycle of 8L:16D was achieved. Once these conditions were reached, fish were held for 1 month. Tanks were then slowly switched from \u201cpseudo-winter\u201d to \u201cpseudo-spring\u201d conditions to induce spawning. This was achieved by slowly increasing the temperature by 3 \u00b0C\/week while simultaneously increasing the light cycle by 15 mins\/day. This was completed over the course of a month until a final tank temperature of 24 \u00b0C and a photoperiod of 16L:8D was reached. Under spring conditions, fish were fed twice a day a mixed diet of blood worms and Mysis shrimp (Hikari) until satiation to ensure an enriched diet for spawning. Animal experimentation was performed in accordance with approved University of British Columbia animal care protocol A20-0070. 5.2.2. Experimental design Note that we used the same clutches as those that were spawned in Chapter 4 of this thesis. Spawning occurred naturally by providing the fish with artificial nests that were made with polyvinyl chloride (PVC) pipe as described by McKenzie et al., (2017). Artificial nests (3\/tank) were placed inside each tank every night and were checked every morning for the presence of eggs. We collected eggs across from all nests and pooled them together from that day. Every egg was then examined under the microscope for the presence of a fertilization envelope (Armstrong and Child, 1965), and non-viable eggs were discarded. During each spawning day, fertilized embryos were mixed and pooled together and then divided equally into Petri dishes (30-50 embryos\/dish) and then equally divided across the four treatments. This process was repeated across 8 spawning days, for a total of 8-10 Petri dishes incubated at each treatment. We chose this approach to maximize the genetic diversity across embryos, without confounding the effects of treatment and family, although we are not able to directly assess the effects of family using this design. Experimental treatments included incubated embryos at 26 \u00b1 0 \u00b0C, 26 \u00b1 3 \u00b0C, 26 \u00b1 5 \u00b0C, or 26 \u00b1 7 \u00b0C in temperature-controlled incubators (MIR-154, PHCbi, Tokyo, Japan) on a 12L:12D light-cycle until hatch. We generated sinusoidal temperature fluctuations with one diel cycle occurring over the course of 24 h, with the highest temperature 111  for each treatment occurring in the middle of the day. We monitored temperature in each incubator by recording temperature every 30 minutes using temperature loggers (Onset HOBO TidbiT MX2203 Wireless Temperature Data Loggers, Bourne, MA, USA). Embryos in each Petri dish were held in 10 mL of dechlorinated water diluted (0.0003%) methylene blue (as a fungicide) and a salinity of 25 ppt. Petri dishes were checked daily for non-viable embryos, which were removed, and 90-100% water changes and water quality measurements were also completed daily. Once embryos were near hatching (stages 32-36; Armstrong & Child, (1965), they were given a 30 minute air exposure period as a hatching cue every morning. All Petri dishes were checked daily for new hatchlings. Once embryos hatched, larvae from each developmental fluctuation group were equally divided across tanks (5 tanks\/treatment) with ~30-50 larvae per 9.5L tank. All tanks within the Zebrafish rack were all maintained under the same conditions: temperature of 26\u00b10 \u00b0C, a salinity of 25 ppt and on a 12L:12D cycle. For whole-animal performance, juvenile fish were subjected to either a thermal tolerance, a hypoxia tolerance, or a metabolic rate assessment at 1 month, 3 month and 6 months, respectively, as described below (Fig. 5.1). For molecular analyses, fish were also sampled at 1, 3 and 6 months to measure any lasting effects of developmental temperatures on mRNA transcript abundance as described below (Fig. 5.1). 5.2.3. Agitation temperature and critical thermal tolerance (CTmax)   To evaluate the lasting effects of developmental fluctuations on thermal tolerance we measured two traits related to sensitivity to acute temperature change: agitation temperature and upper thermal tolerance (CTmax). Agitation temperature is the temperature at which fish first begin exhibiting signs of agitated swimming and escape behaviour during a thermal ramp (McDonnell & Chapman, 2015; Turko et al., 2020; Bouyoucos et al., 2023) and is thus a behavioural response to thermal stress, whereas CTmax is the temperature at which a fish exhibits loss of equilibrium (LOE) during a thermal ramp and is considered to be a metric of thermal tolerance (Beitinger & Bennett, 2000). Both traits were assessed in 1 month old fish. For each trial, twelve fish were randomly selected from two out of the four developmental temperatures (total of 24 fish) across multiple tanks. Fish were then transferred into 4 different breeding nets (6 fish per net\/treatment) that were placed inside a larger 50 L experimental tank. Two of the breeding nets were designated for the assessment of agitation temperature and the other two nets were designated for CTmax sampling. The experimental tank was continuously aerated to 112  maintain oxygen concentrations (100% air saturation) and 6 pumps (VicTsing 80GPH) were used to maintain adequate water circulation during the trials. Each breeding net had individual thermometers (Hanna ChekTemp1 Digital Thermometer, Laval, Quebec, Canada) to measure changes in temperature. Fish were allowed to habituate to the breeding nets for 30 mins before trials began. During this time, temperature was maintained at 26 \u00b1 0.5 \u00b0C and a salinity of 25 ppt. We measured both agitation temperature and CTmax using the ramping method as described by Fangue et al. (2006) in which temperature was increased at a rate of ~0.3 \u00b0C\/min. Ramping was achieved by using multiple titanium heating sticks (multiple Aquatop 400W and 500W) and occurred at rates between 0.28-0.32 \u00b0C\/min for all trials. Both agitation temperature and CTmax were determined by a single observer for all trials and the observer was blinded to the experimental treatments for all trials to reduce biases. After agitation temperature or CTmax was achieved, we euthanized each fish using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO\u2083) and recorded length. We also sampled for control fish (no exposure to a thermal stressor) to allow assessment of baseline mRNA transcript abundance. Each fish was then preserved in RNALater\u00ae (Thermo Fisher Scientific, Waltham, MA, USA), kept at 4 \u00b0C for 24 h and then stored at -80 \u00b0C until RNA extractions were performed. We ran a total of 8 trials over the course of two weeks between 10am and 3pm to minimize any diurnal effects on thermal tolerance (Healy et al., 2010). No significant effect of trial was found within a treatment and therefore trials were combined in subsequent analyses. 5.2.4. Hypoxia tolerance   The lasting effects of developmental fluctuations on hypoxia tolerance were quantified in juvenile fish at 3 months old (Fig. 5.1). The experimental tank consisted of a rectangular 50 L plexiglass tank which contained twelve plastic containers (120mL) with their lids and sides replaced with plastic mesh windows (6 cm \u00d7 4 cm). For each trial, 3 fish from each developmental temperature (total of 12 fish) were placed into labeled (A-D) containers and submersed into the tank below water level to prevent surface respiration. Submersible pumps (VicTsing 80GPH) were placed around the experimental chamber to ensure adequate water circulation throughout the tank. The water surface for the experimental tank was covered with bubble wrap to prevent oxygen from the air diffusing into the tank during the trials (McBryan et al., 2016). Fish were allowed to habituate to the containers for 30 mins prior to the beginning of each trial. Prior to and during experimentation, temperature was maintained at 26 \u00b1 0.5 \u00b0C and a 113  salinity of 25 ppt. Water PO2 was decreased by bubbling N2 gas into the water of the experimental tank from 21.2 kPa (100% air saturation) until 0.8 kPa (4% air saturation) over the course of 50 mins as previously described (McBryan et al., 2016; Healy et al., 2018). Previous studies in adult F. heteroclitus have used 0.4 kPa (McBryan et al., 2016); however, during preliminary trials we found that fish lost equilibrium before a PO2 0.4 kPa was reached and therefore a PO2 of 0.8 kPa was used. Once a PO2 of 0.8 kPa was reached, a timer was started, and PO2 levels were held constant at 0.8 kPa by adjusting the flow of N2 gas. During all trials, oxygen levels were monitored continuously using a YSI dissolved oxygen probe (Xylem Inc, Yellow Springs, OH, USA). Time to LOE was measured as the time (minutes) after reaching 0.8kPa that fish were no longer able to maintain an upright position after gentle movement of the container. Time to LOE were determined by a single observer for all trials and the observer was blinded to the experimental treatments for all trials to reduce biases. After time to LOE was reached, fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO\u2083) and length was recorded for each fish. A total of 4 trials were conducted over the course of two-weeks between 10am and 3pm. No significant effect of trial within a treatment was found and therefore trials were combined in subsequent analyses. Control fish were also sampled at this time point (that did not go through any hypoxia stressors) to assess changes in mRNA transcript abundance. Control fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO\u2083) and liver, brain, gill, and muscle tissue were all dissected from each fish. Tissues were then preserved in RNALater\u00ae (Thermo Fisher Scientific, Waltham, MA, USA), held at 4 \u00b0C for 24 h and then stored at -80 \u00b0C until RNA extractions. 5.2.5. Metabolism  Resting oxygen consumption rate (?\u0307?O2) was measured in juvenile fish at 6 months of age for all developmental fluctuating temperatures groups using closed respirometry. The apparatus was a custom built 31 mL plexiglass chamber that had a false-bottom mesh with magnetic stir bars beneath to ensure proper mixing during each trial to prevent oxygen stratification. All measurements were completed at the acclimation temperature of 26\u00b10.5 \u00b0C (measured by the Pt100 temperature sensor) and at a salinity of 25 ppt. Prior to each trial, fish were fasted for 24 h and then individually placed into each chamber and allowed to acclimate overnight (~16-18 h) under flow-through conditions. Oxygen levels in the chamber were measured using PreSens Microx 4 and oxygen sensor spots placed inside each chamber (PreSens 114  Precision Sensing GmbH, Germany). Oxygen consumption was measured every 10 seconds until the chamber reached 70% air-saturation (~20 mins). Background respiration was measured for 10 mins after each trial and was found to be negligible (<1% ?\u0307?O2). Chambers were rinsed with 70% EtOH between trials to minimize bacterial growth and probes were calibrated weekly using 100% air-saturated water and sodium sulfite (1 g\/100 mL). Oxygen consumption was measured as the slope of O2 levels over time and corrected for both chamber volume and mass of the fish (\u00b5mol O2\u00b7g\u22121\u00b7h\u22121). The first 30 seconds was removed from each trial for the probes to equilibrate and a coefficient of determination of 0.97 was used (Chabot et al., 2021). After each trial, fish were euthanized using MS-222 (3 g\/L of Syncaine\u00ae buffered with 6 g\/L of NaHCO\u2083) and both length and mass were recorded. Both liver and muscle tissue were dissected for further mRNA analysis. Liver and muscle tissue were then preserved in RNALater\u00ae (Thermo Fisher Scientific, Waltham, MA, USA), held at 4 \u00b0C for 24 h and then stored at -80 \u00b0C until RNA extractions. 5.2.6. Gene expression  Total RNA was extracted from several tissues and various time-points throughout this experiment. For thermal tolerance, we extracted whole-body RNA from 1 month-old juvenile fish at a control time-point and after the fish had reached their agitation temperature or CTmax. We measured the expression of 7 genes involved in the heat shock response (hsc70, hsp70.1, hsp70.2, hsp90a, hsp90b, hsp27, and hsp60) that were normalized to two housekeeping genes (b-actin and 18S). For hypoxia tolerance, we extracted RNA from both brain and gill tissue from 3 month old juveniles under normoxic conditions. We measured the expression of hif1\u03b1 and normalized expression to ef1\u03b1 and 18S. For growth, we extracted whole-body RNA from 1 month old juveniles, and from liver and muscle tissue at both 3 and 6 months of age in juvenile fish. We measured the expression of 8 selected genes involved in growth (gh, igf1, igf2, igf1r, igf2r, igf1bp, igf2bp, and myog) that were normalized to ef1\u03b1 and 18S. The same protocol described below was used for all samples. Note that all normalization genes were measured for all samples, but only those that were consistently expressed in a given experiment were used for normalization.  We extracted RNA using TRIzol\u00ae Reagent (Invitrogen; Waltham, MA, USA) followed by RNeasy columns (Qiagen; Hilden, Germany), using the manufacturer\u2019s protocols. The RNA 115  concentrations and purity were assessed using spectrophotometry on a Nanodrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA) and samples were stored at -80 \u00b0C. cDNA was synthesized using 1 \u00b5g of RNA for each sample as described by Earhart et al. (2022) and then samples were stored at -30 \u00b0C. Total mRNA abundance was measured for all selected genes using real-time quantitative polymerase chain reaction (RT-qPCR). A standard curve using a 1:5 dilution from pooled cDNA for each experiment and each tissue was used to determine the primer efficiency for all genes (Table A5.1). RT-qPCR for all genes was performed using the following components, 5 \u00b5l of Bio-Rad SsoAdvanced Universal SYBR-Green Supermix, 3 \u00b5l of nuclease-free water, 0.5 \u00b5l of forward and 0.5 \u00b5L of reverse primers, and 1 \u00b5l of cDNA from each sample for a total well volume of 10 \u00b5l. As well, no reverse transcriptase and no template controls were also run on each cDNA plate. Gene expression was measured using a RT-qPCR (Bio-Rad CFX96, Mississauga, ON, Canada) with the following temperature cycling protocol: denaturing at 95 \u00b0C for 2 mins, followed by 40 cycles of 15 s at 95 \u00b0C, and 30s at 58 \u00b0C. Melt-curve analysis protocol included a denaturation at 95 \u00b0C for 15s, followed by 1 min at 60 \u00b0C and then a gradual increase to 95 \u00b0C in 0.5 \u00b0C increments. Prior to analysis, all housekeeping genes for each experiment and tissue were checked for stability across developmental temperatures. Analysis for gene expression was determined using the 2\u25b3\u25b3CT method in which expression was normalized to the geometric mean of the selected housekeeping genes and the control developmental temperature as baseline (26\u00b10 \u00b0C; Schmittgen & Livak, 2008). 5.2.7. Statistical analyses  Statistical analyses were performed using GraphPad Prism (version 10.0.2). Data are presented as means \u00b1 s.e.m. For all whole-animal traits (thermal tolerance, growth, hypoxia, and metabolism), data was analyzed using a one-way ANOVA with developmental temperature as the independent factor and \u03b1 was set to 0.05. If significant effects were found, a Tukey\u2019s HSD post-hoc test for multiple comparisons was conducted. All statistical analyses were tested for both homogeneity of variance using Bartlett\u2019s test and normality using a Shapiro-Wilk test.   For mRNA abundance, either a one-way ANOVA (growth and hypoxia genes) with developmental temperature as the independent or a two-way ANOVA (heat shock genes) with developmental temperature, thermal stressor and their interaction was conducted. If significant effects were found, a Tukey\u2019s HSD post-hoc test for multiple comparisons was conducted. All 116  data were tested for both homogeneity of variance using Bartlett\u2019s test and normality using a Shapiro-Wilk test. Lastly, significant correlations were identified using a pairwise Spearman\u2019s correlation test (\u03b1=0.05). 5.3. Results 5.3.1. Growth and mRNA transcript abundance  To assess the effects of diel fluctuating developmental temperatures on growth, we measured changes in length in 1 month old juveniles. Fluctuating developmental temperature had a lasting effect on growth (P<0.001, Fig. 5.2A, Table A5.3) such that fish exposed to the widest thermal fluctuations during early development were longer than those exposed to all other developmental temperature regimes. At the transcriptome level, fluctuating developmental temperature had significant persistent effects on the mRNA transcript abundance for igf1 (P<0.001, Fig 5.2B), igfr2 (P=0.001, Fig 5.2C), igf2 (P=0.045, Fig 5.6D), and gh (P<0.001, Fig 5.2E). Juvenile fish exposed to the widest thermal fluctuation during development had significantly higher igf1 mRNA transcript abundance than juvenile fish that developed at the low and intermediate fluctuation regimes. As well, there were also persistent effects on igf2 mRNA transcript abundance such that juvenile fish that were exposed to the widest fluctuations during development had higher levels than fish that developed in the regime with low fluctuation. Similarly, mRNA transcript abundance for igfr2 were higher in fish that developed at intermediate and high fluctuations compared to fish that developed at constant temperatures. Fish reared at intermediate and high fluctuating temperatures also had a ~4-5 fold higher mRNA transcript abundance of gh than juvenile fish reared at constant temperatures. No significant effect of fluctuating developmental temperatures was detected for igf1r and igf1bp (P>0.05, Fig A5.1). For F-values, DF, and P-values see Table A.5.2.   Fluctuating developmental temperatures had persistent effects on length in 3 month old juvenile fish (P<0.001, Fig. 5.7A, Table A5.3). Similar to 1 month old juveniles, fish developed under the widest thermal fluctuations were longer than fish exposed to all other developmental thermal regimes. At the transcriptome level, fluctuating temperatures had lasting effects on only two genes. In the liver, igf2 mRNA transcript abundance was higher in fish developed under the highest fluctuations relative to fish developed at low fluctuations and constant developmental temperatures (P=0.003, Fig. 5.7B). In the muscle, myog mRNA transcript abundances were 117  significantly lower in juvenile fish that developed at the highest fluctuations compared to fish that developed at either constant or intermediate fluctuations (P<0.001, Fig. 5.7C). No significant effects of fluctuating developmental temperatures were found for igf1, igf1r, igf2r, and igf1bp (P>0.05, Fig. A5.2) in liver tissue. As well, in the muscle tissue developmental temperatures had no effect on igf1, igf2, igf1r, igf2r, and igf1bp (P>0.05, Fig. A5.2). For F-values, DF, and P-values see Table A.5.2.  In 6 month old juveniles, the effects of fluctuating developmental temperature on length were no longer present (P=0.218, Fig. 5.9A, Table A5.3). However, there were still significant effects at the transcriptome level in both liver and muscle. In the liver, developmental thermal fluctuations had significant effects on the mRNA transcript abundance of igf1 (P=0.016, Fig. 5.9B) and igf1bp (P=0.036, Fig. 5.9C) transcripts. Both genes displayed similar trends, such that juveniles that were reared under the low fluctuation regimes had lower mRNA transcript abundance for both igf1 and igf1bp compared to juvenile fish that were reared at constant temperatures. However, developmental thermal fluctuations had no effect on the transcript abundance of igf2, igf1r, igf2r, and igf2bp (P>0.05, Fig. A5.3). In the muscle tissue, only igf2 was affected by developmental thermal fluctuations (P=0.024, Fig. 5.9C). Igf2 displayed an inverse U-shaped pattern, and the only significant difference was found fish that developed at intermediate fluctuations relative to fish that developed at constant developmental temperatures. No significant effects were found for igf1, myog, igf1r, igfr2, and igf1bp (P>0.05, Fig. A5.3) in muscle tissue in 6 month old juveniles. For F-values, DF, and P-values see Table A.5.2. 5.3.2. Thermal tolerance and mRNA transcript abundance  Developmental exposure to thermal fluctuations had a significant effect on agitation temperature in 1 month juvenile fish (P< 0.001, Fig. 5.3A). Juvenile fish that were reared with low levels of thermal fluctuation had a ~2 \u00b0C higher agitation temperature compared to fish that were developed under high fluctuations and ~1 \u00b0C higher agitation temperature than fish developed at constant temperatures. Furthermore, fish developed under intermediate fluctuations had ~1.5 \u00b0C higher agitation temperature than fish that were reared at high fluctuations during development. For F-values and DF see Table A.5.3.  Upper thermal tolerance (CTmax) was also significantly affected by fluctuating developmental temperatures (P< 0.001, Fig. 5.3B). In 1 month old juvenile fish that were reared 118  under high fluctuations during early development had a significantly lower CTmax relative to all other developmental treatments. On average, juvenile fish from the constant, low, and intermediate reached LOE at a temperature of ~40.3 \u00b0C whereas fish exposed to the high fluctuation regime reached LOE at ~38.5 \u00b0C. For F-values and DF see Table A.5.3.  Because developmental thermal fluctuations had a lasting effect on body size (Fig. 5.3A) we also explored whether there was a relationship between body size and either agitation temperature or CTmax. CTmax was negatively correlated with body size such that individuals that were smaller in length had a higher CTmax (R2= 0.167, P=0.004, Fig. 5.4A). However, this relationship was largely driven by the effects of developmental thermal fluctuations, such that individuals exposed to the widest thermal fluctuations were larger and had a lower CTmax. If this relationship is examined within each developmental temperature group, fish reared under constant temperatures showed a positive correlation (R2= 0.320, P=0.014, Fig. A5.4), whereas fish reared under intermediate fluctuating temperatures showed a negative correlation (R2= 0.424, P=0.003, Fig. A5.4). Alternatively, no significant relationship between CTmax and length was found in fish reared under low (R2= 0.022, P=0.571, Fig. A5.4) or high fluctuating temperatures (Fig. A5.4, R2= 0.217, P=0.051). However, there was no significant correlation between body size and agitation temperature (R2= 0.035, P=0.114, Fig. 5.4B). For F-values and DF see Table A.5.5.  To assess whether fluctuating developmental temperatures altered the transcriptomic response to acute thermal stressors, we measured changes in the expression in a series of heat shock genes in control fish and in fish exposed to either their agitation temperature or CTmax. Both developmental thermal regime and acute exposure to a thermal stressor had significant effects on the mRNA transcript abundance for hsp70.1 (P=0.002, P<0.001, Fig. 5.5A),  hsp70.2 (P=0.0016, P<0.001, Fig. 5.5B), and hsp90a (P=0.021, P<0.001, Fig. 5.5C) and there was no significant interaction between developmental thermal regime and thermal stressor (P>0.05). Specifically, for hsp70.1 mRNA transcript abundance increased between fish that were exposed to control, agitation temperature and CTmax. However, no significant difference between expressions were found between control and agitation stressors for both control and low fluctuation groups. Significant differences between developmental thermal regimes were only detected between fish that were reared at control (26\u00b10 \u00b0C) and the highest fluctuating (26\u00b17 \u00b0C) temperature in fish that had been exposed to their agitation treatment. Similarly, mRNA 119  transcript abundance of hsp70.2 also increased in response to thermal stressors from control to CTmax in all developmental treatments. No significant effects of developmental thermal regime were detected for this gene. Lastly, mRNA transcript abundance of hsp90a increased in response to thermal stressors in each developmental group with the exception of mRNA transcript abundance between agitation temperature and CTmax in the low fluctuation group. Effects of developmental thermal regime were detected for levels of hsp90a in fish exposed to their agitation temperature between fish reared under low and high fluctuations. Developmental temperature, thermal stress or their interaction had no effect on the mRNA transcript abundance of hsc70, hsp90b, hsp27, and hsp60 (P>0.05, Fig A5.5). For F-values, DF, and p-values see Table A.5.4.  Because fish were sampled immediately after they reached their respective agitation temperature or CTmax, and these metrics of thermal tolerance differed among individuals and across developmental treatment groups, we explored the relationship between the acute exposure temperature experienced by each individual and their hsp expression levels. There was a positive relationship between agitation temperature and the expression of several inducible heat shock genes: hsp70.1 (R2= 0.27, P=0.004, Fig. 5.6A), hsp70.2 (R2= 0.75, P<0.001, Fig. 5.6B), and hsp90a (R2= 0.82, P<0.001, Fig. 5.6C). As well, a positive relationship was found between CTmax and the expression of inducible heat shock genes hsp70.1 (R2= 0.27, P=0.007, Fig. 5.6D), hsp70.2 (R2= 0.26, P=0.007, Fig. 5.6E), and hsp90a (R2= 0.27, P=0.006, Fig. 5.6F). However, these relationships were weaker than those found with agitation temperature. For F-values and DF see Table A.5.5. 5.3.3. Hypoxia tolerance and mRNA transcript abundance  Exposure to thermal fluctuations during development had significant lasting effects on the hypoxia tolerance in 3 month old juvenile fish (P = 0.001, Fig. 5.8A). Juvenile fish developed at the intermediate and high fluctuating regimes had significantly lower time to LOE, such that they could only maintain equilibrium for ~4 minutes compared to ~13 minutes for fish raised at constant temperatures. Fish that were developed at low fluctuations were not significantly different compared to any other developmental treatment and had an average time to LOE of ~8 minutes. For F-values and DF see Table A.5.3. 120   To examine potential molecular mechanisms that could be associated with hypoxia tolerance, we assessed changes in hif1\u03b1 mRNA transcript abundance. Fluctuating developmental temperatures had a significant effect on hif1\u03b1 mRNA transcript abundance in the brain (P=0.003, Fig 5.8B). Juvenile fish that developed at intermediate fluctuating temperatures had significantly higher hif1\u03b1 mRNA transcript abundance than fish that developed at constant and low fluctuating temperatures. Also, juvenile fish that developed at the highest fluctuating temperatures had significantly higher brain hif1\u03b1 mRNA transcript abundance than fish that developed at constant temperatures. However, there was no significant effect of developmental temperatures on the mRNA transcript abundance of hif1\u03b1 in the liver (P=0.958, Fig 5.8C). For F-values and DF see Table A.5.2. 5.3.4. Resting metabolic rate  Fluctuating developmental temperature had no significant effect on resting metabolic rate in 6 month old juvenile fish (P=0.077, Fig. 5.10). However, there was a modest trend such that metabolic rate was slightly higher in fish developed in the high fluctuation regime, with an average ?\u0307?O2 of 10.6\u00b5mol\u00b7g-1\u00b7h-1 for juvenile fish developed at constant temperatures and 12.4\u00b5mol\u00b7g-1\u00b7h-1 in the highest fluctuation. For F-values and DF see Table A.5.3. 5.4. Discussion   The lasting effects of developmental plasticity are thought to play a critical role in shaping how organisms will respond to their changing environment. However, little is known about the lasting effects of fluctuating temperatures, which is what most aquatic organisms experience in their natural environment. To address this gap in the literature, our study set out to explore the effects of diel fluctuating temperatures during early development on an array of physiological traits, and on the mRNA transcript abundance of key genes associated with these traits. Our study is thus one of the first to demonstrate that rearing fish under different fluctuating regimes during early development can have lasting effects at both the physiological and molecular level and that both putatively beneficial and putatively deleterious plasticity are present. We found that fish exposed to high fluctuations during development had both reduced thermal and hypoxia tolerance suggesting negative effects of these temperatures on performance traits. These lasting changes in phenotype were associated with differences at the transcriptome level across multiple genes, including several heat shock protein genes and hif1\u03b1. In contrast, 121  growth rate was positively affected by exposure to thermal fluctuations during development, and this change in phenotype was associated with changes in the expression of several genes across the GH\/IGF axis. Together, these data provide clear evidence that organisms raised under fluctuating temperatures have different phenotypes than fish raised under constant temperatures.  Fluctuating temperatures have short-term effects on body size   Exposure to higher thermal variability during development is predicted in some organisms to incur energetic costs due to changes in metabolic demands and changes across various biological levels leading to altered growth rates relative to constant temperatures (Imholt et al., 2011; Coulter et al., 2016; Bolta\u00f1a et al., 2017; Morash et al., 2018). Furthermore, where this temperature variation falls along an organism\u2019s TPC may shape this response such that temperatures that fall above their TPC would likely be more energetically expensive relative to temperatures that fall below (Morash et al., 2021). In the context of our experiment, this would suggest that fish that experienced the highest fluctuation regime during development may have incurred a greater metabolic cost and thus would be predicted to be smaller than fish developed at constant temperature. Instead, we found that fish reared under the highest fluctuation during early development had larger body sizes relative to all other developmental thermal regimes. However, this trend was only present in 1 and 3 month old juveniles and did not persist in 6 month juveniles. In contrast to our work, a study on zebrafish exposed to greater thermal variability during development were smaller compared to those developed at stable temperature which suggest a potential higher energetic cost for development under this thermal regime (Schaefer & Ryan, 2006). However, fish were incubated under these conditions for 100 days (i.e., long past hatch) which would incur more energetic costs than the 10-12 days exposure prior to hatch that fish experienced in our experimental design.  The lack of a persistent effect of developmental thermal fluctuation in 6 month old juveniles could be the result of compensatory growth in the other developmental groups, which would imply a catch-up period observed due to increased growth in the later life stages (Ali et al., 2003). Alternatively, the highest fluctuation group could have also experienced a reduction in growth between 3 months and 6 months of age. Based on changes in the overall growth across developmental temperatures we find support for the latter. When examining differences in length across time-points, fish reared under the highest fluctuations showed almost a 50% reduction in 122  growth (between 3 and 6 months) relative to all other developmental temperatures. Overall, we show that developmental temperatures have short term effects that increase the body size of F. heteroclitus, with potential later effects resulting in reduced growth.   Growth in fishes is a polygenic trait and is primarily controlled by the hypothalamic-pituitary somatotropic (HPS) axis (Triantaphyllopoulos et al., 2020). This axis is predominantly regulated via the interplay of both growth hormone (GH) and insulin-growth-factors (IGFs) which trigger cell proliferation and are central to the regulation of body size in fishes (Triantaphyllopoulos et al., 2020). Growth hormone is known to act as the primary regulator of both IGF-1 production and secretion across many tissues in fishes (Triantaphyllopoulos et al., 2020). The interaction between GH and IGFs has been found to be affected by temperature in fishes. For example, exposure to increased temperature led to an upregulation of both gh and igf1 mRNA transcript abundance alongside increased growth in some species of fish (Gabillard et al., 2003, 2006; Silverstein et al., 2000). The relationship between GH and IGF-2 is less clear in the literature and is thought to play more of a role in metabolism or nutritional status rather than temperature (Ndandala et al., 2022). Even though the effects of temperature have been highly studied on the IGF\/GH pathway in fishes, less is known about the effects of fluctuating temperatures. In response to temperature acclimation under fluctuating conditions, studies have shown both an increase in gh mRNA transcript abundance (Li et al., 2021) or no differences in ghr1 or igf-1 (Scheuffele et al., 2024) in response to fluctuating temperature relative to constant temperatures with the same mean. However, our study is one of the first to examine the lasting effects of developmental temperatures on the GH\/IGF axis. We found that fish that developed under both intermediate and high fluctuations had 4-5-fold increase of gh mRNA transcript abundance in 1 month old juveniles. Furthermore, a similar pattern was also found in the mRNA transcript abundance of igfr2 whereas both igf1 and igf2 exhibited a different transcript response to developmental temperatures. Furthermore, we found that igf2 was still significantly up-regulated in the liver in 3 month old juveniles whereas igf2 were back at control levels in 6 month old juveniles developed under the highest fluctuation regime similar to the patterns that we observed in growth. Although mRNA transcript abundance does not necessarily reflect protein levels, mRNA transcript abundance of igf1 and igf2 are correlated with plasma levels of both IGF1 and IGF2 in rainbow trout (Gabillard et al., 2003). Overall, our data shows evidence 123  that developmental temperatures have lasting effects on the GH\/IGF pathway, and that IGF-2 may be an important factor in growth in F. heteroclitus. Exposure to fluctuating temperatures alters thermal tolerance  In fishes, acclimation of juveniles and adults to higher temperatures often results in improved thermal tolerance as a result of beneficial plasticity (e.g., Li et al., 2021; McDonnell et al., 2021; Sakurai et al., 2021), although the effects of acclimation to fluctuating temperatures are less clear. In general, studies across a wide range of taxa have suggested that acclimation to fluctuating temperatures results in higher CTmax than acclimation to a constant temperature with the same mean (reviewed in Gunderson et al., 2017) but studies of this phenomenon in fishes are surprisingly rare. The few studies that have examined the effects of fluctuating temperatures in fishes have often found contrasting results, such that thermal fluctuations either led to no change in CTmax (Rodgers et al., 2018; Li et al., 2021) or an increase in CTmax (Peng et al., 2014; Salinas et al., 2019) relative to fish reared under constant temperatures with the same means. In scenarios where no significant difference was found, it is hypothesized that fish acclimated to the mean temperature rather than the extremes (Rodgers et al., 2018). However, factors such as the duration, frequency, and magnitude of the fluctuations can play a significant role in shaping their thermal response. It is even less clear whether developmental plasticity results in beneficial plasticity in response to daily thermal fluctuations. We found the effects of developmental thermal fluctuations were different across the thermal traits we quantified; there was some evidence for beneficial developmental plasticity in response to daily thermal fluctuations in agitation temperature, but not for CTmax.   Fish developed with exposure to low levels of thermal fluctuation (26\u00b13 \u00b0C) had significantly higher agitation temperatures than fish developed at a constant temperature with the same mean, and fish exposed to the greatest level of developmental thermal fluctuations (26\u00b17 \u00b0C) had the lowest agitation temperature. This suggests that thermal variation during early development may result in physiological changes that cause fish to become behaviourally agitated at different temperatures. However, whether it is beneficial to have a lower or higher agitation temperature is debatable. Agitation temperature is likely an indicator of the temperatures that elicit an avoidance response, and thus is a component of behavioral thermoregulation in fishes. Higher agitation temperature could be beneficial because it reduces 124  the risk of being exposed to suboptimal (or even lethal) temperatures. On the other hand, it may also involve trade-offs, because if agitation temperature is too low, fish may unnecessarily induce an avoidance response, which might reduce opportunities for foraging and other behaviours that enhance fitness. Studies have only recently begun to quantify agitation temperature in fishes (McDonnell & Chapman, 2015), relatively little is known about the effect of acclimation temperature on this trait, even in adults. However, some studies have suggested that it is relatively insensitive to thermal acclimation (McDonnell & Chapman, 2015). On the other hand, developmental temperature has been shown to substantially increase agitation temperature (McDonnell et al., 2019), although in this latter study, the experimental treatments were applied throughout life and thus the effects of developmental plasticity and thermal acclimation cannot be clearly separated. This finding emphasizes the need for additional studies examining this trait.  For upper thermal tolerance (CTmax), we found that fish reared under the highest fluctuating regimes during development had ~1.8 \u00b0C lower CTmax than fish reared under constant and lower fluctuating regimes during development. This observed decrease in CTmax may be the result of the highest thermal regimes exposing the embryos to temperatures above their thermal optimum (Topt) for a third of their day (~8 h). This high thermal stress has also been shown to modify the expression of genes involved in the heat shock response (see chapter 4) which potentially may have altered the response to juvenile fish to high thermal stress as daily induction of HSPs are thought to result in increased energetic costs (Feder & Hofmann, 1999). Examining whether fish express developmental plasticity in CTmax in response to fluctuating temperatures during development is a relatively unexplored area and there are currently only two known studies on zebrafish in the literature (Schaefer & Ryan, 2006; Massey, 2022). However, both experimental designs slightly differed from our design (in terms of the length of developmental exposure) making it difficult to make direct comparisons. The study by Schaefer & Ryan (2006) found that fish that developed and acclimated to fluctuating regimes had higher thermal tolerance (CTmax) depending on the acclimation temperatures. On the other hand, Massey (2022) found that zebrafish that were reared under fluctuating temperatures had a slightly higher thermal tolerance (~0.3 \u00b0C) than fish reared under constant temperatures. It is interesting that both studies found increased CTmax under higher thermal variability relative to constant temperatures, yet we found decreased thermal tolerance. These differences may be due 125  to the proximity of these temperatures to the upper thermal tolerance of each species, the duration of the exposure, or differences in adaptive plasticity across species. Taken together, all of these studies point to a much smaller effect of thermal fluctuations during development compared to acclimation to these conditions as adults.   From our data, it is also possible to calculate the \u201cCTmax\u2013agitation window\u201d, which can be viewed as the safety margin for acute exposure to high temperatures. It has been suggested that a smaller window suggests adaptation to acute high temperature exposure (Wells et al., 2016), or alternatively that smaller thermal windows may provide less buffer time to escape acute temperature changes (Bouyoucos et al. 2023). In general, killifish reared at lower fluctuations had the smallest thermal window (3.6 \u00b0C) whereas control fish had the largest thermal window (4.8 \u00b0C). Our experimental design (in which agitation temperature and CTmax were measured in different individuals) does not allow us to determine whether these effects are statistically significant, but it does suggest that this phenomenon is worthy of additional study. Fluctuating temperatures have minimal effects on the plasticity of heat shock mRNA transcript abundance   Since we found that fluctuating temperatures had different effects on the two thermal tolerance traits we measured, we can predict that agitation temperature and CTmax have potentially different underlying mechanisms. Thermal regimes during early development can alter molecular mechanisms which can ultimately lead to changes in physiological performance in later life-stages (Metzger & Schulte, 2018; Ripley et al., 2023). Therefore, we examined changes in the heat shock response in response to acute thermal stressors (agitation temperature and CTmax) to understand if they played a role in the observed differences in thermal tolerance traits. We found that developmental thermal fluctuations had minimal effects on the plasticity of mRNA transcript abundance in response to these thermal stressors. The only two observed differences across development were found in response to agitation temperature for hsp70.1 and hsp90a. The expression of inducible heat shock genes is highly dependent on exposure temperature as we demonstrated with significant relationships between hsp mRNA transcript abundance and agitation temperature and CTmax. As a result, the differences in mRNA transcript abundance between developmental treatments are most likely due to differences in agitation temperature that we observed across treatments.  126   Even though the developmental thermal regime had minimal effects on mRNA transcript abundance, we found that both stressor types resulted in a strong induction of heat shock genes. It is well documented in the literature that CTmax results in the induction of heat shock genes in fishes (e.g., Fangue et al., 2006; Fangue et al., 2011; Saravia et al., 2021; Penman et al., 2023); however, whether agitation temperature alters the mRNA transcript abundance of heat shock genes in fishes remains largely unknown. We found that exposure to the agitation temperature increased the mRNA transcript abundance of hsp70.1, hsp70.2, and hsp90a, demonstrating that agitation temperature elicits a strong cellular stress response in F. heteroclitus. In some genes, we saw between ~5-10-fold changes in mRNA transcript abundance, highlighting that agitation temperature is thermally stressful and may even be considered a sub-lethal threshold for organisms. Within some developmental treatment groups, we found that the expression of hsps did not further increase from agitation to CTmax suggesting they potentially reached their maximum induction. This suggests that other mechanisms may also be driving the loss of equilibrium observed for CTmax in F. heteroclitus. In support of this, a recent study by Bouyoucos et al. (2023) found that agitation temperature elicited an increase in hsp70 mRNA transcript abundance in the brain, gills and heart relative to dogfish that did not go through a thermal stress. Furthermore, this study concluded that agitation temperature was driven by changes in the cellular stress response and difference CTmax was driven primarily by changes in anaerobic metabolism. This may be the case for F. heteroclitus to which different mechanisms may be involved in thermal tolerance traits. Exposure to fluctuating temperatures does not induce cross-tolerance   We found that exposure to intermediate and high fluctuations during early development resulted in lower hypoxia tolerance in juveniles relative to fish reared under constant temperatures. Typically, cross-tolerance is defined as when one stressor increases the tolerance of another stressor (Todgham et al., 2005). However, we have found the opposite response, where thermal stress in response to fluctuating temperatures resulted in impaired hypoxia tolerance. This diminished response was surprising as F. heteroclitus are subjected to high thermal variability within their natural habitat and are predicted to be highly adapted to thermal variation. 127   As adults, many species of fish show decreased hypoxia tolerance in response to higher temperatures, which is a phenomenon that is thought to be driven by the lower oxygen solubility at higher temperatures and the increased metabolic demands (He et al. 2015; McDonnell & Chapman 2015; McBryan et al. 2016; Zhou et al., 2019). However, at least in some species, warm acclimation (McBryan et al., 2016) or prior exposure to heat shock can offset these effects (Todgham et al., 2005). Very few studies have examined the effects of early developmental temperature, fluctuating temperatures during development, or the interaction between these two variables on hypoxia tolerance. A recent study in adult F. heteroclitus found that acclimation to diel fluctuations improved the tolerance of hypoxia relative to fish held under constant temperatures, such that they had a higher time to LOE across a larger range of temperatures (Ridgway & Scott, 2023). However, this study was focused on the effects of acclimation at the adult life stage. The single current study that has examined the lasting effects of fluctuating temperatures during early development is a study by Massey (2022). This study found that zebrafish embryos exposed to fluctuating temperatures during early development had a lower Pcrit (higher hypoxia tolerance) as adults relative to fish that developed at constant temperatures independent of acclimation temperature. However, Massey (2022) kept developing fish under these conditions for 29 days. By contrast, we found that an exposure of ~10-11 days (until hatch) was sufficient to elicit a detrimental effect on hypoxia tolerance in F. heteroclitus. As such, other parameters such as critical windows or the time spent near or above the thermal optimum may be important for driving phenotypic changes.   To further understand the lasting effects of fluctuating temperatures on the hypoxic response, we also quantified the effects of developmental thermal fluctuations on hif1\u03b1. Temperature and hypoxic stress are thought to be related at the cellular level, possibly through an interaction between hsp and hif1\u03b1 (Todgham et al., 2005; Levesque et al., 2019). Furthermore, hif1\u03b1 has been shown to be responsive to temperature changes (Rissanen et al., 2006; Pandey et al., 2021). We found that fish reared under both intermediate and high fluctuations had higher hif1\u03b1 mRNA transcript abundance in the brain compared to fish reared under constant temperatures. It is not surprising that we found differences in the brain and not the gills as the brain is known to be particularly sensitive to hypoxia, with a decreased ATP production, depletion of glycogen stores and increased lactate production (DiAngelo & Heath, 1987; Speers-Roesch et al., 2013). However, the fact that we found an increase in hif1\u03b1 in groups that had 128  decreased hypoxia tolerance was unexpected. During a hypoxic response, hif1\u03b1 binds to HRE (hypoxia-responsive elements) in the promoters of hypoxia-responsive genes which triggers a series of downstream transcriptional effects (Richards, 2009). In response to hypoxia, hif1\u03b1 mRNA transcript abundance has been shown to increase the activation of the hypoxia signalling pathway and is thought to be a protective mechanism (Rissanen et al., 2006; Terova et al., 2008). As such, some studies have found that increased hif1\u03b1 confers increased tolerance (see review Mandic et al., 2021) whereas we found the opposite, increased hif1\u03b1 and decreased tolerance. However, it is important to highlight that many other mechanisms are involved in the hif1\u03b1 pathway or hypoxic response (i.e., heart morphology, gills, or hemoglobin affinity) that may be contributing to this decrease in tolerance. Lastly, it is also highly possible that changes in gene expression did not confer changes to the protein level and would require further investigation. Interestingly, we found that 7 dph larvae (see chapter 4) had lower mRNA transcript abundance of hif1\u03b1 in the fish developed in the higher fluctuation regime, whereas in 3 month old juveniles, we found the opposite response. As hif1\u03b1 is also known to play a critical role in development, these observed differences may be associated with different developmental demands. Fluctuating temperatures do not have persistent effects on metabolic rate  Development under fluctuating temperatures was found to have no lasting effect on resting metabolic rate in juveniles. However, there was still a slight trend that two highest fluctuating regimes had slightly higher ?\u0307?O2 relative to fish reared under constant temperatures (P=0.07). ?\u0307?O2 is often considered a developmentally plastic trait and has been shown to be responsive to developmental temperatures at both the embryo and larval stages. However, most studies examining the effect of warmer constant developmental temperatures have found decreased ?\u0307?O2 (Schaefer & Walters, 2010; Donelson et al., 2011; Durtsche et al., 2021). However, the potential lasting effects under fluctuating temperatures remain mostly unknown. Massey (2022) showed that zebrafish reared under fluctuating temperatures during development had ?\u0307?O2 levels are about ~15% lower than fish that were reared under constant conditions. In general, having a lower metabolic rate is thought to be beneficial to organisms by allowing organisms to reallocate energy to other traits. However, given that changes in ?\u0307?O2 were measured in fish that had been acclimated to a common environment for 6 months, it is possible that we found no effect due to potential effects of reversible plasticity in which fish re-129  acclimated to their new environment. Alternatively, we only measured changes in resting metabolic rate (RMR) and as a result we may have missed other measurements in aerobic metabolism that could have been altered by developmental plasticity. For example, a study that heat shocked juvenile lake trout (Salvelinus namaycush) over many consecutive days found that maximum metabolic rate (MMR) and aerobic scope (AS) but not RMR were altered in response to the heat shock suggesting these traits may be more plastic in some species (Guzzo et al., 2019). Therefore, it is possible that F. heteroclitus may have altered other traits involved in metabolic rate that were not measured in this study.  Conclusions   Overall, we found that thermal variability during early development in F. heteroclitus had both short-term beneficial plasticity (growth, agitation temperature), deleterious plasticity (CTmax and time to LOE) or no effect on plasticity (metabolic rate) depending on the physiological trait that we examined. This demonstrates that each physiological trait may be independently influenced by developmental temperatures which could be further explained by different physiological traits having different thermal performance curves (Kellermann et al., 2019). Changes at the whole-animal level were also found alongside changes at the transcriptome levels, where we found lasting changes in the mRNA abundance of genes involved in both hypoxia and growth. The reduced phenotypic effects observed in the 6 month juvenile, highlights the possibility that these physiological and molecular changes are reversible once acclimated to a common temperature for a certain period of time. Furthermore, our study suggests that thermal variability may not always result in beneficial plasticity and in some cases may result in deleterious plasticity. This deleterious plasticity may be the result of a trade-off based on the energetic costs incurred at high thermal variability. Future studies should continue to examine the effects of fluctuating temperatures during development as there still remain many gaps in the literature that have been highlighted throughout our study. Considering which traits may result in beneficial vs deleterious plasticity is an important avenue of future work to understand how organisms will respond to increased variability as the result of climate change. 130   Figure 5.1. Experimental design to examine the lasting effects of fluctuating temperatures during development in the northern subspecies of F. heteroclitus. Embryos were exposed to either 26\u00b1 0 \u00b0C, 26\u00b1 3 \u00b0C, 26\u00b1 5 \u00b0C or 26\u00b1 7 \u00b0C diurnally until hatch and then reared at common temperature of 26\u00b1 0 \u00b0C. We then measured a series of traits in 1 month (thermal tolerance, length), 3 month (hypoxia tolerance, length) and 6 month old (metabolic rate, length) juveniles. Several tissues were also sampled to measure any lasting changes in mRNA transcript abundance in response to fluctuating developmental temperatures for the series of traits investigated.131  Figure 5.2. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf1, (C) igf2r, (D) igf2, and (E) gh in 1 month post-hatch juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences between developmental temperatures as determined by a one-way ANOVA (P<0.05; A: N=18; B-E: N=6-8).   132   Figure 5.3. Effects of diurnally fluctuating temperature from fertilization to hatch the (A) agitation temperature (\u00b0C) and (B) CTmax (\u00b0C) in 1 month post-hatch juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences between developmental temperatures as determined by a one-way ANOVA (P<0.05; N = 18).   133   Figure 5.4. The relationship between (A) length (mm) and CTmax (\u00b0C) and (B) length (mm) and agitation temperature (\u00b0C) in 1 month juvenile northern F. heteroclitus. Significant relationships were identified using a pairwise Spearman\u2019s correlation test (\u03b1=0.05). Each data point represents an individual from the trial across each developmental temperature group fitted with a linear model (solid line) and the 95% confidence intervals (dashed lines) are displayed.      134   Figure 5.5. Effects of diurnally fluctuating temperature from fertilization to hatch on the mRNA transcript abundance of inducible heat shock genes (A) hsp70.1, (B) hsp70.2, and (C) hsp90a in 1 month juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences within developmental temperatures as determined by a two-way ANOVA (P<0.05; N =3-8). *Denotes significant differences across developmental temperatures within a thermal stress treatment. 135   Figure 5.6. The relationship between (A-C) agitation temperatures (\u00b0C) or (D-F) CTmax and the expression of inducible heat shock genes hsp70.1, hsp70.2, and hsp90a in 1 month juvenile northern F. heteroclitus. Significant relationships were identified using a pairwise Spearman\u2019s correlation test (\u03b1=0.05). Each data point represents an individual from the trial across each developmental temperature group fitted with a linear model (solid line) and the 95% confidence intervals (dashed lines) are displayed. 136    Figure 5.7. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf2 in the liver and (C) myog in the liver of 3 month post-hatch juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences between developmental temperatures as determined by a one-way ANOVA (P<0.05; A: N =18; B-E: N=6-8).     137   Figure 5.8. Effects of diurnally fluctuating temperature from fertilization to hatch on (A) hypoxia tolerance (time to LOE) and the expression of hif1\u03b1 in (B) brain and (C) gills in 3 month juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences between developmental temperatures as determined by a one-way ANOVA (P<0.05; A: N= 18; and B-C: N=8).        138   Figure 5.9. Effects of diurnally fluctuating temperature from fertilization to hatch on the (A) length (mm) and changes in mRNA transcript abundance in (B) igf1 and (C) igf1bp in the liver and (D) igf2 in the muscle tissue of 6 month old juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. Different letters denote significant differences between developmental temperatures as determined by a one-way ANOVA (P<0.05; A: N =18; B-E: N=6-8).     139   Figure 5.10. Effects of diurnally fluctuating temperature from fertilization to hatch on the oxygen consumption rate (?\u0307?O2) in 6 month juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N=10-11). No significant effect was found as determined by a one-way ANOVA (P>0.05).         140  Chapter 6: General discussion and conclusions 6.1. Overview  Throughout my dissertation, I aimed to investigate the effects of temperature during early development and examine how fish may respond to these changes by utilizing developmental plasticity. This was achieved by designing a series of experiments that measured these effects under various thermal regimes and across many levels of biological organization to provide a more in-depth understanding of developmental plasticity. In each data chapter, I established clear objectives that progressively built upon one another, contributing to my overarching aim. Furthermore, given the limited research in this field, a primary focus of my thesis was to address some of the current knowledge gaps across the literature. Below, I will highlight the main core findings from each of my chapters (which can also be found in Table 6.1), discuss the implications of the key findings from each chapter and how they contribute to future research in the field. 6.2. Highlights, Implications, and Contributions 6.2.1. Chapter 2: Thermal performance of Fundulus heteroclitus embryos    Chapter two is the foundational chapter of my thesis, in which I set out to establish a better understanding of the effects of temperature on early development in F. heteroclitus. I realized early-on that there was limited data available on the performance of F. heteroclitus embryos across a wide range of developmental temperatures. Given this gap, identifying the ideal temperature range for my experiments would have posed several challenges. Therefore, in this chapter, I set out to model various thermal performance curves (TPCs) across both subspecies of F. heteroclitus and their reciprocal crosses. Understanding the shape of an organism\u2019s TPC along a latitudinal gradient, especially at the developmental stage, can assist in predicting a species resilience to climate change (Sinclair et al., 2016). I found that the shape of the TPCs showed evidence of both local adaptation (i.e., right-shifted Topt in the southern subspecies) and countergradient variation (i.e., higher Pmax in the northern subspecies). As well, I found differences across morphological traits between northern and southern subspecies, with northern fish hatching at a smaller size across most temperatures, which is thought to be a trade-off with a faster developmental rate. Lastly, in this chapter I found that the thermal breadth for F. heteroclitus embryonic life-stages was narrower than their adult life-stages, supporting the idea 141  that embryos may be a more vulnerable life-stage. Together, the data reported in this chapter established a framework that I was able to utilize for my subsequent chapters.  6.2.2. Chapter 3: Developmental plasticity and cross-talk  In chapter three, I tested the hypothesis that developmental temperatures alter the thermal and hypoxia tolerance performance of fishes as a result of beneficial developmental plasticity. As the effects of developmental temperatures on thermal tolerance had been previously studied in several species of fish, the major contribution of this chapter was to explore the potential for cross-tolerance through developmental plasticity hypoxia tolerance, an area of research that had not previously been explored. In this chapter I showed that there was limited developmental plasticity for thermal tolerance, as developmental temperature was found to have no effect on thermal tolerance. This finding was not surprising as it is consistent with several other studies (e.g., Spinks et al., 2019; Illing et al., 2020). On the other hand, the most interesting and important finding from this chapter was that developmental temperatures had lasting effects on hypoxia tolerance and mRNA transcript abundance of hif1\u03b1. These data demonstrate that developmental cross-tolerance can occur in fishes. However, I also showed that these differences were no longer present when I re-measured these traits in subsequent life-stages, which demonstrates the reversible nature of this plasticity. Together, this chapter supports the hypothesis that F. heteroclitus utilize developmental plasticity in response to environmental change but only in terms of cross-tolerance to hypoxia. This beneficial cross-tolerance has the potential to allow fishes to cope with altered oxygen levels during juvenile life. Given the importance of environmental hypoxia for the fitness of fishes and its prevalence as a result of anthropogenic impacts (Earhart et al., 2022a), these data suggest a novel mechanism via which fish may be able to cope with climate change. 6.2.3. Chapters 4 and 5: Importance of incorporating thermal variation  As biologists, we recognize that animals in their natural environments do not inhabit static environments but rather reside in heterogeneous environments that undergo continuous change. Yet, as biologists, we continue to investigate these effects under constant temperatures in laboratory experiments and then use these data to make predictions on how organisms will respond to changing environments (Morash et al., 2018). Therefore, the goal of the last two chapters of my thesis was to investigate the effects of thermal fluctuations during early 142  development, spanning from the embryo to juvenile life-stages. As this topic is novel to the field, both chapter four and five provide key insights into the mechanisms that fish may utilize and the phenotypic changes they may undergo in response to thermal fluctuations.   In chapter 4, I found mixed support for predictions about the effects of thermal variation derived from TPCs generated from static exposures and Jensen\u2019s inequality, as I found faster developmental rate (inconsistent) but lower survival (consistent) under higher thermal variation. Furthermore, I found that thermal variation led to changes at both the morphological (e.g., length, YSV) and molecular level (e.g., hsp70.2, hsp27, myog, hif1\u03b1) in both the embryo and larval life-stages (chapter 4). At the juvenile life-stage (chapter 5), I found that some physiological traits (e.g., thermal and hypoxia tolerance) were altered by developmental temperature whereas other traits were not (e.g., metabolism). In addition, these lasting changes were accompanied by molecular changes in the expression of several genes (e.g., hif1\u03b1, gh, myog, igf1, igf2). However, the effects of developmental temperatures were less evident in genes involved in the heat shock response, suggesting other mechanisms may be involved in thermal tolerance (i.e., CTmax). Together, these data highlight that the mechanisms fish use under constant and fluctuating temperatures are inherently different. Furthermore, not only did I find changes in response to both intermediate and high fluctuating conditions, but I also found several changes in response to low fluctuations (e.g., agitation temperature, igf1 mRNA transcript abundance) relative constant temperatures. This further demonstrates that even small changes in thermal variation can induce different phenotypic responses.   I think it is also important to highlight a main takeaway from chapter 5, that high thermal variability during early development resulted in deleterious plasticity, reflected by a decrease in both thermal and hypoxia tolerance as juveniles. However, whether these negative effects persist until the adult life-stage remains unknown. Even so, it is still important that we understand the limits of temperature variation that an organism can tolerate before the traits in question become detrimental to an organism\u2019s fitness. This is especially true in species where this thermal limit falls near projected changes in temperature within their environment. Together, I believe that the results from chapter 4 and 5 reinforce the idea that more studies should be incorporating thermal variation into their work if we want to further advance our current limited understanding of organisms' adaptive responses to thermal variability. 143  6.2.4. Chapters 2 and 5: Reversible developmental plasticity  The potential for reversible developmental plasticity has only been mentioned across the literature in a handful of studies (Burggren, 2020). I found, however, evidence of reversible developmental plasticity in both southern (chapter 2) and northern (chapter 5) subspecies of F. heteroclitus and within several traits (e.g., hypoxia tolerance, growth). In studies of developmental plasticity, most study designs lack repeated measurements of traits across time within an experiment (Burggren, 2020; Pottier et al., 2022a). Under these experimental designs, the only potential outcomes for developmental plasticity are limited to either irreversible plasticity or an absence of plasticity. Yet, this window of reversibility is thought to be present as soon as the stressor is no longer present, all the way to the adult life-stage and therefore is often overlooked (Burggren, 2020). Additionally, the idea that a trait may not be present throughout the lifespan of an organism is not surprising, especially if there are high costs associated with this new trait. Overall, the findings from my work shed light on the need to better understand whether phenotypes derived from developmental plasticity are indeed irreversible or whether they only persist for a certain period of time.  6.2.5. Chapters 3, 4, and 5: Lasting effects on hif1\u03b1  One critical, cross-cutting finding of my thesis emerges from three of my data chapters (chapter 3, 4, and 5). Across these chapters, I found that developmental temperature had significant effects on hypoxia tolerance (chapters 3 and 5) and on the mRNA transcript abundance of hif1\u03b1 (chapter 3, 4, and 5). Developmental temperature regimes (constant and fluctuating) were found to have lasting effects (1-3 months) on the mRNA transcript abundance of hif1\u03b1 in both the northern and southern subspecies. The directional changes for both hypoxia tolerance and mRNA transcript abundance were similar across chapters, such that fish that displayed a higher hypoxia tolerance had also lower baseline mRNA abundance of hif1\u03b1. Oftentimes, hif1\u03b1 mRNA transcript abundance is assessed after fish have been acutely or chronically exposed to hypoxia (Terova et al., 2008; Baptista et al., 2016; Whitehouse & Manzon, 2019; Wang et al., 2021). However, I was interested in measuring baseline levels of hif1\u03b1 and whether changes driven by temperature change may confer differences in whole-anima hypoxia-tolerance (i.e., also known as cross-talk or cross-tolerance). This is not the first time that cross-tolerance has been detected in F. heteroclitus, as warm acclimation has been shown to 144  improve hypoxia tolerance (McBryan et al., 2016). Therefore, it is possible that this observed cross-talk across studies in F. heteroclitus is an adaptation that they have selected as the result of living in these highly variable environments. However, it is important to note that these changes in hif1\u03b1 mRNA transcript abundance are not necessarily translated into changes at the protein level and therefore, caution must be taken when drawing conclusions for these chapters.  Overall, I believe that the findings from my thesis contribute to a greater understanding of how early life-stages may utilize developmental plasticity in response to changing temperatures. In the remainder of this chapter, I will highlight the limitations of my thesis (section 6.3) and the potential future avenues of research (section 6.4). 6.3. Limitations  There are several limitations within my thesis that I think should be acknowledged as they may impact the conclusions drawn from my research.   The most apparent limitation to my thesis is that across all my chapters I used wild-caught fish that were acclimated to lab conditions for a period of time before being used for my experiments. When examining the effects of an environmental stressor on developmental life-stages, there is always the possibility of having confounding factors from the parental fish used in the study. It is well known that the environment the parents experience can have lasting effects on their offspring through inter-generational plasticity or transgenerational plasticity (Le Roy & Seebacher, 2018; Earhart et al., 2022a; Massey & Dalziel, 2023). These include changes from the whole-animal (e.g., size, developmental time; Panagiotaki & Geffen, 1992; Green & McCormick, 2005) to the molecular level (Metzger & Schulte, 2018), and even changes at the epigenome level (e.g., DNA methylation; Berbel-Filho et al., 2020; Venney et al., 2020, 2023). Since the thermal history of these parental fish is unknown, it is difficult to disentangle whether the observed differences in phenotypes are solely driven by developmental plasticity or if there are any transgenerational effects. To minimize the possibility of parental effects, I tried to maximize the number of females and males used in each chapter to increase genetic diversity of the offspring, and thus they should have simply contributed to random noise in my data, but I cannot entirely exclude the possibility of impacts of parental environment.   A second important limitation of my work is that all the molecular changes in my thesis were quantified by measuring changes in mRNA transcript abundance using RT-qPCR. Even 145  though mRNA transcript abundance is often used to quantify changes in gene expression, these levels do not always correlate with changes in protein levels (Maier et al., 2009; Buccitelli & Selbach, 2020). Therefore, without measuring changes in protein levels, it is difficult to postulate that mRNA confers changes in whole-animal tolerance. Furthermore, across my chapters, I selected a handful of specific genes to examine to address each research question. However, due to this selective process, I may have overlooked other genes that may have been affected by developmental thermal regimes. To address this problem, future studies should utilize molecular techniques such as RNA-Seq to provide a better understanding of the transcripts responding to developmental temperatures and proteomics to understand how these changes may translate to the structure and function of various proteins. 6.4. Future Directions  Throughout my thesis, I have gained better insight into the interaction of temperature and developmental plasticity in F. heteroclitus. However, I believe many biological questions still remain to be answered that I will further discuss below.   One difficulty that I faced when writing my thesis was the absence of studies examining the effects of temperature on early development, more specifically under thermal fluctuations. Alongside a recent thesis by Massey (2022), my work may be considered one of the foundational studies in this field. This does highlight the novelty of my thesis; however, it also demonstrates the critical need for more studies examining the effects of thermal variation on these sensitive early-life stages. These studies should aim to explore the impacts of thermal variation across many biological levels of organization and different phenotypes, offering a more comprehensive understanding of the mechanistic effects of developmental plasticity. In my opinion, the most interesting future avenue of research is the lasting developmental effects on the epigenome, such as DNA methylation or histone modifications (Venney et al., 2021). Lastly, examining the persistence of these phenotypes such as how long do these changes last (i.e., reversible, or irreversible) and whether these changes can be transgenerational should also be addressed. This avenue of research is just starting to emerge; however, more studies are needed to better understand the adaptive capacity of developmental plasticity.   In terms of research questions that remain specifically for F. heteroclitus as a research organism, a few questions emerged during my thesis. The first avenue of research would be 146  developing a more comprehensive understanding of critical windows in F. heteroclitus embryo development. Several studies have begun to address these critical windows in fishes and have found interesting results, such that exposure to a stressor at different developmental stages can alter developmental rate (Mueller et al., 2015; Del Rio et al., 2021), growth (Mueller et al., 2015; Del Rio et al., 2021) and even metabolic rate (Eme et al., 2015; Del Rio et al., 2021). As there has already been clear identification of the embryonic life-stages of F. heteroclitus, dividing these stages (i.e., blastulation, neurulation) for exposure to environmental stressors and measuring whether there are lasting effects would be a straightforward experiment that would likely yield important insights. Secondly, F. heteroclitus has been a good model organism across the literature for understanding how local adaptation plays a role in shaping phenotypes by utilizing both northern and southern subspecies. As a result, I have been interested in determining if part of the variation in phenotypes between these two subspecies can be explained by their respective developmental environments.  6.5. Conclusions  In the face of our rapidly changing environment, is it essential that we continue to develop a better understanding on how species are currently responding to these changes and will respond in the future. However, the response of early developmental life-stages is rarely accounted for when making predictions; yet these early-life stages are the lifeline to a species persistence (Vagner et al., 2019). My thesis has contributed substantially to understanding how early life-stages may utilize developmental plasticity to respond to thermal stress. In summary, the key findings of my thesis are that: 1. Fundulus heteroclitus subspecies demonstrate patterns consistent with both local adaptation and countergradient variation in response to latitude for multiple traits in early developmental stages. 2. Cross-tolerance (or cross talk) is a possible outcome of developmental plasticity. 3. Developmental plasticity is not necessarily irreversible, and that reversible developmental plasticity may be common for physiological traits in fishes. 4. Predictions based on TPCs generated at constant temperatures are not adequate to predict performance at fluctuating temperatures. 147   Together, I believe that my thesis represents a significant advancement in our current limited understanding of the impacts of developmental plasticity from a physiological and transcriptomic perspective to cope with changing temperatures across temporal scales. As a scientist, I am excited to see the new directions ahead for the field of developmental physiology and how this knowledge can be used to help with species persistence in the face of climate change.     148   Chapter Subspecies Life-Stage Temperatures Trait Effect on Phenotype   2   Northern (N) Southern (S)    15, 18,21,24, 27,30,33, and 36 \u00b0C   Physiological (N) - Horizontally-shifted TPC, \u2193 Topt (all TPCs), \u2193Tbr for developmental rate and \u2191Tbr for survival (S) - Vertically-shifted TPC, \u2191 Topt (all TPCs), \u2191Tbr for developmental rate and \u2193Tbr for survival \u23af In heart rate between crosses Morphological (N) - \u2193 length and \u2193 weight at hatch (S) - \u2191 length and \u2191 weight at hatch   3    Southern    20 and 26 \u00b0C   Physiological  \u23af CTmax between 20 \u00b0C and 26 \u00b0C  \u2191  Hypoxia tolerance in 26 \u00b0C (6 months) \u23af Hypoxia tolerance (1 year)  Molecular  \u23af in heat shock genes (hsc70, hsp90b)   \u2191 hif1\u03b1 mRNA transcript abundance in 20 \u00b0C (1 month) \u23af in hif1\u03b1 mRNA transcript abundance (6 months)   4    Northern    26 \u00b10, \u00b13, \u00b15 And \u00b17 \u00b0C  Morphological  \u2191 Length at 0dph and 7dph in 26 \u00b17 \u00b0C \u2191 Weight in 26 \u00b17 \u00b0C \u2191 YSV in 26 \u00b17 \u00b0C  Molecular  \u0394 in mRNA transcript abundance for hsp27, hsp70.2, and myog (7 dpf) \u0394 in mRNA transcript abundance for hsc70, hsp90b, hif1\u03b1,  myog, dnmt1 and dnmt3ba (7 dph)    5     Northern       26 \u00b10, \u00b13, \u00b15 And \u00b17 \u00b0C   Physiological   \u2191Agitation temperature in 26 \u00b13\u00b0C (1 month) \u2193 CTmax in 26 \u00b17 \u00b0C (1month) \u2193 Time to LOE for 26 \u00b15 and 26 \u00b17 \u00b0C (3 months) \u2191 Length in 26 \u00b17\u00b0C (1 and 3 months), \u23af in 6 months \u23af in ?\u0307?O2  Molecular   \u2191 gh and igf2r in 26 \u00b17 \u00b0C in whole-body (1 month) \u2191 hsp70.1, hsp70.2, and hsp90a with agitation (1 month) \u2193 hif1\u03b1 in 26 \u00b15 \u00b0C and 26 \u00b17 \u00b0C in the brain (3 months) \u2193 igf1 and igf1bp in the liver of 26 \u00b15\u00b0C (6 months) 149  Table 6.1. Summary of the findings across my thesis. This table illustrates the subspecies (e.g., northern, or southern), the life-stage (e.g., embryo, larval or juvenile\/adult), the thermal regimes (e.g., constant or fluctuating) and the traits (e.g., physiological, morphological, and molecular) that were used in each chapter of my thesis. For each chapter, I summarized the general findings represented by \u2193 (decrease), \u2191 (increase) or - (no change) in the trait.                       150  References Able, K. W., & Castagna, M. (1975). Aspects of an undescribed reproductive behavior in Fundulus heteroclitus (Pisces: Cyprinodontae) from Virginia. Chesapeake Science, 16(4), 282. https:\/\/doi.org\/10.2307\/1350946  Abraham, B.J. (1985) Species Profiles: Life histories and environmental requirements of coastal fishes and invertebrates (Mid-Atlantic): mummichog and striped killifish. U.S. Fish Wildlife Service Biological Report.  82(11.40). 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Cross 15\u00b0C 18\u00b0C 21\u00b0C 24\u00b0C 27\u00b0C 30\u00b0C 33\u00b0C N\u2640 x N\u2642 0.025 \u00b1 0.001a 0.039 \u00b1 0.004 a 0.060 \u00b1 0.001 a 0.096 \u00b1 0.002 a 0.128 \u00b1 0.002 a 0.130 \u00b1 0.002 a 0.091 \u00b1 0.002a N\u2640 x S\u2642 0.023 \u00b1 0.002a 0.034 \u00b1 0.002 a 0.045 \u00b1 0.001 b 0.074 \u00b1 0.003 b 0.099 \u00b1 0.004 b 0.112 \u00b1 0.002 b 0.095 \u00b1 0.006 a S\u2640 x N\u2642 0.016 \u00b1 0.001 b 0.036 \u00b1 0.001 a 0.045 \u00b1 0.001 b 0.074 \u00b1 0.002 b 0.078 \u00b1 0.002 c 0.088 \u00b1 0.003 c 0.096 \u00b1 0.003 a S\u2640 x S\u2642 0.018 \u00b1 0.002 b 0.026 \u00b1 0.003 b 0.047 \u00b1 0.003 b 0.078 \u00b1 0.002 b 0.076 \u00b1 0.002 c 0.087 \u00b1 0.003 c 0.096 \u00b1 0.003 a *Different letters denote significant differences within a temperature between cross-type (P<0.05).                 194   Figure A2.1. Effects of developmental temperature and cross on larval morphology (A) length (mm) (B) weight (mg), and (C) yolk-sac volume (mm3) between four crosses of F. heteroclitus at hatch. Box plots represent 25% and 75% IQR percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values (N =7-10). Different letters denote significant differences (P<0.05) between cross type within a given developmental temperatures. 195  Table A2.2. P-values for planned comparison between cross type within a given temperature for larval morphometrics (e.g., length, weight. yolk-sac volume) at hatch.    Cross 18\u00b0C 21\u00b0C 24\u00b0C 27\u00b0C 30\u00b0C 33\u00b0C LENGTH N\u2640 x N\u2642 vs. N\u2640 x S\u2642 0.0024 0.0005 0.0009 <0.0001 <0.0001 0.0037 N\u2640 x N\u2642 vs. S\u2640 x N\u2642 <0.0001 <0.0001 0.0271 <0.0001 0.0091 0.0009 N\u2640 x N\u2642 vs. S\u2640 x S\u2642 <0.0001 <0.0001 0.0002 <0.0001 <0.0001 <0.0001 N\u2640 x S\u2642 vs. S\u2640 x N\u2642 0.2650 0.0028 0.7882 0.0025 0.2103 0.8865 N\u2640 x S\u2642 vs. S\u2640 x S\u2642 <0.0001 0.0001 0.9345 0.0200 0.1734 0.4395 S\u2640 x N\u2642 vs. S\u2640 x S\u2642 0.0099 0.6105 0.4700 0.8847 0.0008 0.9032 WEIGHT N\u2640 x N\u2642 vs. N\u2640 x S\u2642 0.0826 0.0455 0.9998 0.9672 0.9994 0.0800 N\u2640 x N\u2642 vs. S\u2640 x N\u2642 0.0026 0.0038 0.0062 <0.0001 0.1249 0.9286 N\u2640 x N\u2642 vs. S\u2640 x S\u2642 <0.0001 <0.0001 0.0093 <0.0001 0.0472 0.9889 N\u2640 x S\u2642 vs. S\u2640 x N\u2642 <0.0001 <0.0001 0.0080 <0.0001 0.0959 0.2796 N\u2640 x S\u2642 vs. S\u2640 x S\u2642 <0.0001 <0.0001 0.0118 <0.0001 0.0345 0.1612 S\u2640 x N\u2642 vs. S\u2640 x S\u2642 0.0556 0.2225 0.9993 0.9328 0.9770 0.9912 YOLK-SAC VOLUME N\u2640 x N\u2642 vs. N\u2640 x S\u2642 0.6586 0.1519 0.0703 0.5647 0.0005 <0.0001 N\u2640 x N\u2642 vs. S\u2640 x N\u2642 0.8351 0.9323 0.5167 0.1249 0.9112 0.1097 N\u2640 x N\u2642 vs. S\u2640 x S\u2642 0.9998 0.9075 0.9577 0.4325 0.1279 0.0001 N\u2640 x S\u2642 vs. S\u2640 x N\u2642 0.2172 0.4185 0.0013 0.0042 0.0077 0.1053 N\u2640 x S\u2642 vs. S\u2640 x S\u2642 0.6330 0.0441 0.0223 0.0311 0.4004 0.9952 S\u2640 x N\u2642 vs. S\u2640 x S\u2642 0.8793 0.6071 0.8329 0.8846 0.4286 0.2084 *Bolded p-values denote significant differences between cross type within a temperature.  196  Table A2.3. P-values for planned comparison between developmental temperatures within a given cross type for length at hatch.  *Bolded p-values denotes significant differences between temperatures within a given cross type.          Temperature N\u2640 \u00d7 N\u2642 N\u2640 \u00d7 S\u2642 S\u2640 \u00d7 N\u2642 S\u2640 \u00d7 S\u2642 18\u00b0C vs 21\u00b0C 0.8908 0.9912 0.8968 0.9709 18\u00b0C vs 24\u00b0C 0.2731 0.3956 0.0001 <0.0001 18\u00b0C vs 27\u00b0C 0.0002 0.0402 0.8953 <0.0001 18\u00b0C vs 30\u00b0C 0.0006 0.1088 <0.0001 <0.0001 18\u00b0C vs 33\u00b0C 0.0001 <0.0001 <0.0001 <0.0001 21\u00b0C vs 24\u00b0C 0.8954 0.7832 <0.0001 <0.0001 21\u00b0C vs 27\u00b0C 0.0113 0.1813 0.2856 0.0021 21\u00b0C vs 30\u00b0C 0.0218 0.3685 <0.0001 0.0004 21\u00b0C vs 33\u00b0C 0.0075 0.0005 <0.0001 <0.0001 24\u00b0C vs 27\u00b0C 0.1924 0.8902 0.0116 0.8397 24\u00b0C vs 30\u00b0C 0.2714 0.9840 0.5776 0.9778 24\u00b0C vs 33\u00b0C 0.1368 0.0348 0.8772 0.4207 27\u00b0C vs 30\u00b0C >0.9999 0.9987 <0.0001 0.9969 27\u00b0C vs 33\u00b0C >0.9999 0.4006 0.0003 0.0215 30\u00b0C vs 33\u00b0C   0.9995 0.1935 0.9971 0.0808 197  Table A2.4.  P-values for planned comparison between developmental temperatures within a given cross type for weight at hatch.  *Bolded p-values denotes significant differences between temperatures within a given cross type.                Temperature N\u2640 x N\u2642 N\u2640 x S\u2642 S\u2640 x N\u2642 S\u2640 x S\u2642 18\u00b0C vs 21\u00b0C 0.9690 0.9939 0.9838 >0.9999 18\u00b0C vs 24\u00b0C 0.4335 0.0004 0.6004 0.8890 18\u00b0C vs 27\u00b0C 0.9874 0.2389 0.0809 0.9763 18\u00b0C vs 30\u00b0C 0.8916 0.0127 0.9998 0.1524 18\u00b0C vs 33\u00b0C 0.0057 0.0060 0.9918 0.0551 21\u00b0C vs 24\u00b0C 0.8942 0.0030 0.9416 0.8683 21\u00b0C vs 27\u00b0C 0.7065 0.5604 0.3370 0.9827 21\u00b0C vs 30\u00b0C 0.9998 0.0629 0.9284 0.1362 21\u00b0C vs 33\u00b0C 0.0624 0.0336 0.8018 0.0480 24\u00b0C vs 27\u00b0C 0.1301 0.2846 0.8809 0.4548 24\u00b0C vs 30\u00b0C 0.9701 0.9243 0.4167 0.7569 24\u00b0C vs 33\u00b0C 0.5115 0.9754 0.2499 0.4895 27\u00b0C vs 30\u00b0C 0.5226 0.8654 0.0380 0.0219 27\u00b0C vs 33\u00b0C 0.0005 0.7471 0.0154 0.0057 30\u00b0C vs 33\u00b0C   0.1252 >0.9999 0.9996 0.9983 198  Table A2.5. P-values for planned comparison between developmental temperatures within a given cross type for yolk-sac volume at hatch.  *Bolded p-values denotes significant differences between temperatures within a given cross type.                Temperature N\u2640 x N\u2642 N\u2640 x S\u2642 S\u2640 x N\u2642 S\u2640 x S\u2642 18\u00b0C vs 21\u00b0C 0.6292 0.9950 >0.9999 0.3464 18\u00b0C vs 24\u00b0C 0.0005 0.0491 0.0002 0.0002 18\u00b0C vs 27\u00b0C 0.0085 0.0168 0.0002 <0.0001 18\u00b0C vs 30\u00b0C <0.0001 0.0125 0.0002 0.0072 18\u00b0C vs 33\u00b0C <0.0001 0.0013 0.0015 0.0581 21\u00b0C vs 24\u00b0C 0.0817 0.1818 0.0001 0.2106 21\u00b0C vs 27\u00b0C 0.3971 0.0777 0.0001 0.1273 21\u00b0C vs 30\u00b0C <0.0001 0.0609 0.0002 0.6828 21\u00b0C vs 33\u00b0C <0.0001 0.0094 0.0013 0.9781 24\u00b0C vs 27\u00b0C 0.9707 0.9990 >0.9999 0.9999 24\u00b0C vs 30\u00b0C 0.3224 0.9969 >0.9999 0.9747 24\u00b0C vs 33\u00b0C 0.0121 0.8926 0.9986 0.6008 27\u00b0C vs 30\u00b0C 0.0585 >0.9999 >0.9999 0.9211 27\u00b0C vs 33\u00b0C 0.0008 0.9838 0.9947 0.4420 30\u00b0C vs 33\u00b0C   0.7401 0.9922 0.9993 0.9688 199  Table A2.6. F-value, DF, and P-values for linear regressions for heart rate across developmental temperatures.  Cross-Type F-value DF P-value N\u2640 \u00d7 N\u2642 4995 1,54 <0.001 N\u2640 \u00d7 S\u2642 6059 1,54 <0.001 S\u2640 \u00d7 S\u2642 4964 1,53 <0.001 S\u2640 \u00d7 N\u2642 6500 1,53 <0.001                       200  A.2. Chapter 3: Supplementary Materials   Figure A3.1. Effects of family on days to hatch in southern F. heteroclitus across two developmental temperatures. (A) Boxplot showing the effects of family on hatch days at 20 \u00b0C; (B) Boxplot showing the effects of family on hatch days at 26 \u00b0C; and C) Reaction norms for days to hatch between families that developed at 20 \u00b0C and 26 \u00b0C. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median (N=8-40 eggs\/family).        201  A.3. Chapter 4: Supplementary Materials Table A4.1. List of primers and their respective efficiencies and dilutions for genes used in this study for F. heteroclitus.  Gene Sequence  5\u2019 to 3\u2019 Efficiency (%) Dilution hsp90b F: GACCTTCAAGTCCATCCTGTTC R: GTCGTTCTTCTTGGAGCCATAC 104 1:25 hsc70 F: CTGCTGGAGATACTCATCTTGG R: GTCCTTCTTGTACTTGCGTTG 101 1:125 hsp27 F: AACAAGCCACCCATCGAGTACT R: GACGTGAGGTCCTGCTCTTCTT 101 1:25 hsp60 F: GACGAGCTGGAGATCATTGAGG R: GGTAGGCATCCTGGAACTCACA 105 1:25 hsp70.1 F: TGACAACGGGAAGCCTAAAG R: GCCTCGGCTATTTCTCTCATT 107 1:25 hsp70.2 F: CACCTCAACAGAGGTCAATCA R: CAGTCCTAAACCAGCAGAGAC 107 1:5 dnmt1 F: CATCGGACTGGAGATTAAGAAA R: TTAGTCAGAGACCTCCATCTT 97 1:25 dnmt3ab F: GGTTATGTGGTTCGGAGATG R: TGGTTGGTGAAAGGCATTAT 104 1:25 dnmt3ba F: GAGCTGATTGTAGACCTGTATG R: CTGTGCTTAGTGATGTCTGG 92 1:25 dnmt3bb F: CTACCTGGTTCTAAGGGATCT R: CCACACCCACAGAGATAGA 97 1:25 igf1 F: CGATGTGCTGTATCTCCTGTAG R: CTGTTGCCGTCGGAGTC 90 1:25 igf2 F: CTTCTATTTCAGTAGGCCAACCA R: GGTCACAGCTACGGAAACAA 97 1:25 myog F: CTCTTTGAGACCAACCCTTAC R: CCATCATGGAGTTTCTGTCTT 96 1:25 ldhb F: CAACGTCTTCAAGTGCATCATC R: TTGGACACCACCAGGATTG 95 1:25 cs F: CTATGGTGGTATGAGAGGAATG R: GACACTCTGGAATGCTGTAG 93 1:25 hif1\u03b1 F: GAGAAAGAGGTTCCTCTCAGAAC R: CTCCTTTAGCTCGCTCAGTTT 100 1:25 18S F: TTCCGATTAACGAACGAGAC R: GACATCTAAGGGCATCACAG 92 1:625 ef1-\u03b1 F: GGGAAAGGGCTCCTTCAAGT R: ACGCTCGGCCTTCAGCTT 96 1:25 b-actin F: CCTCCAAGACACCCAACAAC R: TAACGCCTCCTTCTTCATCGTTC 96 1:25      202   Figure A4.1. Effects of Petri on days to hatch in northern F. heteroclitus across (A) 26 \u00b1 0\u00b0C (C26), (B) 26 \u00b1 3\u00b0C (V3), C) 26 \u00b1 5\u00b0C (V5), and D) 26 \u00b1 7\u00b0C (V7) temperature fluctuations. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median (N=~10-50 eggs\/Petri dish).         203   Figure A4.2. Genes involved in the heat shock response ((A) hsc70, (B) hsp90b, (C) hsp70.1, and (D) hsp60), growth and development ((E) igf1 and (F) igf2), hypoxia and metabolism ((G) hif1\u03b1, (H) cs, and (I) ldhb), and methylation ((J) dnmt1, (K) dnmt3ab, (L) dnmt3ba, and (M) dnmt3bb) that were not significantly affected by fluctuating temperature at the embryonic life-stage in northern F. heteroclitus. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median, and individual data points are shown (N = 7-8). 204   Figure A4.3. Genes involved in the heat shock response ((A)hsp27, (B) hsp60, (C) hsp70.1), growth and development ((D) igf1, (E) igf2), metabolism ((F) cs, (G) ldhb), and methylation ((H) dnmt3ab, (I) dnmt3ba) that were not significantly affected by fluctuating temperature during early development at the larval life-stage in northern F. heteroclitus. Box plots represent the 25% and 75% IQR, whiskers represent the minimum and maximum, horizontal bar is the median, and individual data points are shown (N = 7-8).       205  Table A4.2. F-value, DF, and P-values for a one-way ANOVA testing the effects of fluctuating developmental temperatures on mRNA transcript abundance in genes involved in heat shock, growth, metabolism\/hypoxia, and DNA methylation.  Gene Group Stage Gene F-value DF P-value Heat shock  Embryo hsp27 7.385 3,26 <0.001   hsp70.2 8.587* 3* 0.036   hsp70.1 2.338 3,25 0.098   hsp90b 1.853 3,25 0.163   hsp60 2.312 3,26 0.1   hsc70 3.918 3,26 0.07        Larval hsp27 2.159 3,28 0.12   hsp70.1 2.492 3,26 0.08   hsp90b 5.705 3,27 0.004   hsp60 1.826 3,28 0.18   hsc70 8.495 3,27 <0.001       Growth Embryo igf1 0.985 3,27 0.41   igf2 0.885 3,27 0.46   myog 5.045 3,26 0.007        Larval igf1 1.864 3,27 0.16   igf2 1.663 3,27 0.2   myog 5.311 3,27 0.005       Metabolism and hypoxia Embryo hif1\u03b1 1.429 3,26 0.26   cs 1.394 3,27 0.27   ldhb 0.919 3,27 0.44        Larval hif1\u03b1 6.57 3,27 0.002   cs 1.887 3,27 0.16   ldhb 2.247 3,27 0.11       DNA methylation Embryo dnmt1 2.805 3,26 0.06   dnmt3ab 1.297 3,27 0.3   dmnt3ba 2.774 3,26 0.06   dmnt3bb 1.039 3,27 0.39        Larval dnmt1 5.108 3,27 0.006   dnmt3ab 2.599 3,27 0.07   dnmt3ba 1.612 3,26 0.21   dnmt3bb 3.435 3,27 0.031 *Denotes H-value obtained by performing a Kruskal-Wallis test 206  Table A4.3. F-value, DF, and P-values for a one-way ANOVA testing the effects of fluctuating developmental temperatures across various physiological parameters.   Physiological Traits Age F-value DF P-value Survival 0 dph 4.126 3,29 0.015 Days to Hatch 0 dph 10.334 3,1045 <0.001 Length  0 dph 4.373 3,36 0.01 Weight 0 dph 5.293 3,36 0.004 YSV 0 dph 10.71 3,36 <0.001 Length  7 dph 3.389 3,36 0.028                      207  A.4. Chapter 5: Supplementary Materials Table A5.1. List of primers and their respective efficiencies for each tissue and genes used in this study for F. heteroclitus.  Gene Sequence 5\u2019 to 3\u2019 Tissue Efficiency (%) hsp90b F: GACCTTCAAGTCCATCCTGTTC R: GTCGTTCTTCTTGGAGCCATAC Whole-body  99 hsc70 F: CTGCTGGAGATACTCATCTTGG R: GTCCTTCTTGTACTTGCGTTG Whole-body  110 hsp27 F: AACAAGCCACCCATCGAGTACT R: GACGTGAGGTCCTGCTCTTCTT Whole-body  99 hsp60 F: GACGAGCTGGAGATCATTGAGG R: GGTAGGCATCCTGGAACTCACA Whole-body  99 hsp70.1 F: TGACAACGGGAAGCCTAAAG R: GCCTCGGCTATTTCTCTCATT Whole-body  107 hsp70.2 F: CACCTCAACAGAGGTCAATCA R: CAGTCCTAAACCAGCAGAGAC Whole-body  107 hsp90a F: GACGAGATGGTTTCCCTCAAA R: GCACCAAGATAAGGAGGAGATG Whole-body  102 igf1 F: CGATGTGCTGTATCTCCTGTAG R: CTGTTGCCGTCGGAGTC Whole-body Liver Muscle 100 103 95 igf2 F: CTTCTATTTCAGTAGGCCAACCA R: GGTCACAGCTACGGAAACAA Whole-body Liver Muscle 94 105 101 myog F: CTCTTTGAGACCAACCCTTAC R: CCATCATGGAGTTTCTGTCTT Muscle 98 gh F: CTGTCAGAACTGAAGACAGGAA R: GGTGGAGGTGTCCGTAAAG Whole-body 98 igf1r F: GGAGGATACAAAGCCCACTAAA R: TCCAGGTCTTCCACTGATTTG Whole-body Liver Muscle 97 103 104 igf2r F: GATCCACACTCATCAGCTTCTC  R: GGTGCATTCTGGACTCTCATT Whole-body Liver Muscle 109 99 96 igf1bp F: GAGAAAGGAGCTGGAGACTAGA R: TCTCTGGACATTCAGGAGGTAA Whole-body Liver Muscle 91 91 109 igf2bp F: TCTCTGACTAGAGGAGGGAATG R: GACGGAAACCAGCGAGTAAA Liver  102 hif1\u03b1 F: GAGAAAGAGGTTCCTCTCAGAAC R: CTCCTTTAGCTCGCTCAGTTT Gills\/Brain 97 18S F: TTCCGATTAACGAACGAGAC R: GACATCTAAGGGCATCACAG Whole-body Liver Muscle Gills\/Brain 95 96 91 93 b-actin F: CCTCCAAGACACCCAACAAC R: TAACGCCTCCTTCTTCATCGTTC Whole-body  98 ef1a F: GGGAAAGGGCTCCTTCAAGT R: ACGCTCGGCCTTCAGCTT Muscle Liver Gills\/Brain 100 101 104 208    Figure A5.1. Effects of fluctuating developmental temperatures on the mRNA transcript abundance of growth genes (A) igf1r and (B) igf1bp in 1 month old juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. No significant effects were found as determined by a one-way ANOVA (P>0.05)          209   Figure A5.2. Effects of fluctuating developmental temperatures on the mRNA transcript abundance of growth genes in the liver (A) igf1, (B) igf1r, (C) igf2r, (D) igf2bp and (E) igf1bp and in the muscle tissue (F) igf1, (G) igf2, (H) igf1r, (I) igf2r, and (J) igf1bp in 3 month old juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. No significant effects were found as determined by a one-way ANOVA (P>0.05). 210   Figure A5.3. Effects of fluctuating developmental temperatures on the mRNA transcript of growth genes in the liver (A) igf2, (B) igf1r, (C) igf2r, and (D) igf2bp and in the muscle tissue (E) igf1, (F) myog, (G) igf1r, (H) igf2r, and (I) igf1bp in 6 month old northern juvenile F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown. No significant effects were found as determined by a one-way ANOVA (P>0.05)211     Figure A5.4. The relationship between length (mm) and CTmax (\u00b0C) in 1 month old juvenile northern F. heteroclitus. Significant relationships were identified using a pairwise Spearman\u2019s correlation test within each developmental temperature (\u03b1=0.05). Each data point represents an individual from the trial across each developmental temperature group fitted with a linear model (solid line).     212   Figure A5.5.  Effects of fluctuating developmental temperatures on the expression on heat shock genes (A) hsc70, (B) hsp90b, (C) hsp27, and (D) hsp60 in 1 month juvenile northern F. heteroclitus. Box plots represent 25% and 75% percentiles (horizontal line, median); the upper and lower whiskers extend to the minimum and maximum values and individual data points are shown (N=6-8).             213  Table A5.2. F-value, DF, and P-values for a one-way ANOVA testing the effects of fluctuating developmental temperatures on mRNA transcript abundance in growth and hypoxia genes.  Trait Tissue Age Gene F-value DF P-value Growth   1 month      Whole-body  igf1 8.232 3,26 0.0005  Whole-body  igf2 3.079 3,26 0.6749  Whole-body  igf1r 2.886 3,26 0.0548  Whole-body  igf2r 7.308 3,26 0.001  Whole-body  igf1bp 2.011 3,26 0.1371  Whole-body  gh 4.996 3,24 0.0078 Growth   3 months      Liver  igf1 2.508 3,25 0.082  Liver  igf2 6.049 3,25 0.003  Liver  igf1r 0.7542 3,25 0.5303  Liver  igf2r 1.672 3,26 0.1974  Liver  igf1bp 2.264 3,26 0.1047  Liver  igf2bp 0.4485 3,26 0.7204 Growth  3 months      Muscle  igf1 0.6944 3,24 0.5645  Muscle  igf2 0.9885 3,24 0.4149  Muscle  igf1r 0.2124 3,24 0.8868  Muscle  igf2r 0.5402 3,24 0.6594  Muscle  igf1bp 1.79 3,24 0.1759  Muscle  myog 13.82 3,24 <0.0001 Hypoxia  3 months      Brain  hif1\u03b1 6.06 3,28 0.0026  Gills  hif1\u03b1 0.1027 3,28 0.9578 Growth  6 months      Liver  igf1 4.115 3,27 0.0158  Liver  igf2 1.299 3,28 0.2943  Liver  igf1r 0.8161 3,28 0.4958  Liver  igf2r 2.239 3,27 0.1066  Liver  igf1bp 3.269 3,27 0.0365  Liver  igf2bp 0.09968 3,27 0.9595 Growth  6 months      Muscle  igf1 1.76 3,27 0.1786  Muscle  igf2 3.758 3,27 0.0224  Muscle  igf1r 1.06 3,27 0.3822  Muscle  igf2r 0.08922 3,27 0.9654  Muscle  igf1bp 0.2914 3,27 0.8312  Muscle  myog 2.489 3,27 0.0817  214  Table A5.3. F-value, DF, and P-values for a one-way ANOVA testing the effects of fluctuating developmental temperatures on physiological parameters.   Physiological Trait Age F-value DF P-value Length 1 month 16.59 3,67 <0.0001  3 months 10.08 3,67 <0.0001  6 months 1.539 3,43 0.2181 CTmax 1 month 26.65 3,68 <0.0001 Agitation 1 month 9.225 3,68 <0.0001 Hypoxia 3 months 5.772 3,67 0.0014 Metabolic Rate 6 months 2.466 3, 38 0.077                       215  Table A5.4. F-value, DF, and P-values for a two-way ANOVA testing the effects of fluctuating developmental temperatures and stressor-type on mRNA transcript abundance.  Gene Factor F-value DF P-value hsc70 Interaction 0.312 6,72 0.9287  Stressor 0.4596 2,72 0.6334  Developmental 0.9574 3,72 0.4177      hsp70.1 Interaction 0.6841 6,68 0.663  Stressor 80.17 2,68 <0.0001  Developmental 5.627 3,68 0.0017      hsp70.2 Interaction 0.729 6,60 0.628  Stressor 135.1 2,60 <0.0001  Developmental 3.718 3,60 0.0161      hsp90a Interaction 0.5994 6,62 0.7297  Stressor 342 2,62 <0.0001  Developmental 3.504 3,62 0.0205      hsp90b Interaction 0.1046 6,72 0.9957  Stressor 0.1372 2,72 0.872  Developmental 1.385 3,72 0.2542      hsp27 Interaction 1.059 6,63 0.3964  Stressor 2.003 2,63 0.1435  Developmental 0.2435 3,63 0.8657      hsp60 Interaction 0.3984 6,62 0.8773  Stressor 0.2977 2,62 0.7436  Developmental 1.451 3,62 0.2366         216  Table A5.5. F-value, DF, and P-values for linear regressions with agitation temperature and CTmax.   Relationship F-value DF P-value Agitation vs length 2.568 1,69 0.1136 CTmax vs length 14.03 1, 70 0.0004 Agitation vs hsp70.1 9.926 1,26 0.0041 Agitation vs hsp70.2 73.82 1,25 <0.0001 Agitation vs hsp90a 121.3 1,26 <0.0001 CTmax vs hsp70.1 8.719 1,24 0.0069 CTmax vs hsp70.2 8.575 1,24 0.0074 CTmax vs hsp90a 9.1 1,24 0.006   ","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/hasType":[{"value":"Thesis\/Dissertation","type":"literal","lang":"en"}],"http:\/\/vivoweb.org\/ontology\/core#dateIssued":[{"value":"2024-05","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt":[{"value":"10.14288\/1.0441012","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/language":[{"value":"eng","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline":[{"value":"Zoology","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/provider":[{"value":"Vancouver : University of British Columbia Library","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/publisher":[{"value":"University of British Columbia","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/rights":[{"value":"Attribution-NonCommercial-NoDerivatives 4.0 International","type":"literal","lang":"*"}],"https:\/\/open.library.ubc.ca\/terms#rightsURI":[{"value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","type":"literal","lang":"*"}],"https:\/\/open.library.ubc.ca\/terms#scholarLevel":[{"value":"Graduate","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/contributor":[{"value":"Schulte, Patricia M.","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/title":[{"value":"Temperature during early development alters morphological, physiological, and molecular phenotypes across temporal scales in Atlantic killifish","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/type":[{"value":"Text","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#identifierURI":[{"value":"http:\/\/hdl.handle.net\/2429\/87709","type":"literal","lang":"en"}]}}