Science, Faculty of
Zoology, Department of
DSpace
UBCV
Earhart, Madison
Doctor of Philosophy - PhD
University of British Columbia
How organisms respond to environmental change is a fundamental question in biology, critical to our understanding of how animals will cope with climate change. Determining the climate change resilience of a species or population is a challenging task, as resilience can be influenced by phenotypic plasticity and genetic variation. Specifically in aquatic habitats, organisms encounter rapid environmental changes including variations in temperature, oxygen availability, and salinity which occur across different spatial and temporal scales. My thesis aimed to explore how plasticity and adaptation influence resilience to environmental change across these scales in three different fish species. The three species I studied were Atlantic killifish (Fundulus heteroclitus), white sturgeon (Acipenser transmontanus), and rainbow trout (Oncorhynchus mykiss). I measured whole-animal physiology and underlying molecular mechanisms to assess responses to climate change stressors across developmental stages and populations. In killifish and white sturgeon, I found that exposure to climate change stressors during development had lasting phenotypic effects and affected the plasticity of many traits. These studies underlined the importance of considering developmental stage when evaluating stressor effects, as individuals must persist despite environmental changes during sensitive life stages to survive. Also, in white sturgeon and killifish I demonstrated the impacts of simultaneous stressors on whole-animal performance and the underlying genetic mechanisms highlighting the detrimental impact of compounding stressors. To investigate the role genetic variation plays on the ability of fishes to cope with environmental change I assessed how adaptation to different environments can affect the stress response or survival in novel environments. I found that local adaptation can influence the magnitude and plasticity of the stress-axis in killifish, highlighting the potential for adaptive tradeoffs between the stress response, body condition, and survival in the wild. In rainbow trout, I found that adaptation to hatchery environments impacts offspring phenotypes and decreases survival in the wild, highlighting the interplay between adaptation and coping with novel stressors. My thesis contributes to our understanding of the basic biological question, “how do fishes cope with environmental change?”, and also provides evidence for concrete actions and conservation-management recommendations during climate change.
The roles of plasticity and adaptation in response to environmental change across temporal scales in fishes by Madison Earhart BSc, Mississippi State University, 2015 MSc, The University of Manitoba, 2018 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) December 2023 © Madison Earhart, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The roles of plasticity and adaptation in response to environmental change across temporal scales in fishes submitted by Madison Earhart in partial fulfilment of the requirements for the degree of Doctor of Philosophy_________________________________________________ in Zoology___________________________________________________________________- Examining Committee: Patricia M Schulte, Professor, Zoology, UBC Co-supervisor Daniel W Baker, Professor, Fisheries and Aquaculture, Vancouver Island University Co-supervisor Kayla King, Professor, Zoology and Microbiology & Immunology, UBC_________ University Examiner Scott Hinch, Professor, Forestry, UBC____________ University Examiner Katie E Marshall, Assistant professor, Zoology, UBC Supervisory Committee Member, UBC Additional Supervisory Committee Members: Colin J Brauner, Professor, Zoology, UBC Supervisory Committee Member, UBC iii Abstract How organisms respond to environmental change is a fundamental question in biology, critical to our understanding of how animals will cope with climate change. Determining the climate change resilience of a species or population is a challenging task, as resilience can be influenced by phenotypic plasticity and genetic variation. Specifically in aquatic habitats, organisms encounter rapid environmental changes including variations in temperature, oxygen availability, and salinity which occur across different spatial and temporal scales. My thesis aimed to explore how plasticity and adaptation influence resilience to environmental change across these scales in three different fish species. The three species I studied were Atlantic killifish (Fundulus heteroclitus), white sturgeon (Acipenser transmontanus), and rainbow trout (Oncorhynchus mykiss). I measured whole-animal physiology and underlying molecular mechanisms to assess responses to climate change stressors across developmental stages and populations. In killifish and white sturgeon, I found that exposure to climate change stressors during development had lasting phenotypic effects and affected the plasticity of many traits. These studies underlined the importance of considering developmental stage when evaluating stressor effects, as individuals must persist despite environmental changes during sensitive life stages to survive. Also, in white sturgeon and killifish I demonstrated the impacts of simultaneous stressors on whole-animal performance and the underlying genetic mechanisms highlighting the detrimental impact of compounding stressors. To investigate the role genetic variation plays on the ability of fishes to cope with environmental change I assessed how adaptation to different environments can affect the stress response or survival in novel environments. I found that local adaptation can influence the magnitude and plasticity of the stress-axis in killifish, highlighting the potential for adaptive tradeoffs between the stress response, body condition, and survival in the wild. In rainbow trout, I found that adaptation to hatchery environments impacts offspring phenotypes and decreases survival in the wild, highlighting the interplay between adaptation and coping with novel stressors. My thesis contributes to our understanding of the basic biological question, “how do iv fishes cope with environmental change?”, and also provides evidence for concrete actions and conservation-management recommendations during climate change. v Lay Summary Understanding how organisms respond to environmental change is crucial as our planet’s climate is rapidly changing. Specifically in aquatic habitats, organisms are encountering environmental changes including warming temperatures, less available oxygen, and variations in salinity. My thesis examined how fishes respond to climate change stressors across different life stages, populations, and species to assess the variation in levels of resilience. I showed that the ability to rapidly adjust traits to increase tolerance, also known as plasticity, can play a large role in stressor resilience. I also demonstrated tolerance differences across life stages and highlighted the high sensitivity of developing fish to climate change stressors. Finally, I showed the effects adaptation can have on the stress response and the ability to cope with novel environments. As climate change continues to intensify, applied research about how fish will respond to environmental change is key for developing evidence-based management strategies. vi Preface This research was approved by the UBC Animal Care Committee (A20–0070; A19–0284). A version of chapter 2 has been published as: “Earhart, M. L., Blanchard, T. S., Strowbridge, N., Bugg, W. S., & Schulte, P. M. (2022). Gene expression and latitudinal variation in the stress response in Fundulus heteroclitus. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 268, 111188.” I designed this experiment with help from PMS, I also conducted the experiments with help from TSB, NS, and WSB, I analyzed the data, and wrote the manuscript with PMS and had input and edits from all co-authors. A version of chapter 3 is under review: “Earhart, M.L., Thapar, M., Blanchard, T.S., Bugg, W.S., Schulte, P.M. Persistent interactive effects of developmental salinity and temperature in Fundulus heteroclitus.” I designed the experiment with MT and PMS, I conducted the experiments with help from MT, TSB, and WSB, I analyzed the data, and I wrote the manuscript with PMS and had input and edits from all co-authors. A version of chapter 4 has been published as: “Earhart, M. L., Blanchard, T. S., Morrison, P. R., Strowbridge, N., Penman, R. J., Brauner, C. J., Schulte, P.M., & Baker, D. W. (2023). Identification of upper thermal thresholds during development in the endangered Nechako white sturgeon with management implications for a regulated river. Conservation Physiology, 11(1), coad032.” I designed the experiment with help from RJP, PMS, and CJB, I conducted the experiments with help from TSB, PRM, NS, and RJP, I analyzed the data, and I wrote the manuscript with PMS and had input and edits from all co-authors. A version of chapter 5 has been published as: “Earhart, M.L., Blanchard, T.S., Strowbridge, N., Sheena, R., McMaster, C., Staples, B., Brauner, C.J., Baker, D.W., Schulte, P.M. (2023). Heatwave resilience of juvenile white sturgeon is associated with epigenetic and transcriptional alterations. Scientific Reports, 13(1), 15451.” I designed the experiment with help from PMS, I conducted the experiments with help from TSB, NS, RS, CM, and BP I analyzed the data, and I wrote the manuscript with PMS and had input and edits from all co-authors. Chapters 1, 6, and 7 were written by me with PMS. vii Table of Contents Abstract……………………………………………………………………………………………………………………………………………………….iii Lay Summary………………………………………………………………………………………………………………………………………………..v Preface………………………………………………………………………………………………………………………………………………………..vi Table of Contents………………………………………………………………………………………………………………………………………..vii List of Tables…………………………………………………………………………………………………………………………………………………x List of Figures……………………………………………………………………………………………………………………………………………….xi Acknowledgements……………………………………………………………………………………………………………………………………xiii Dedication………………………………………………………………………………………………………………………………………………….xv CHAPTER #. INTRODUCTION ....................................................................................................................... # CHAPTER &. GENE EXPRESSION AND LATITUDINAL VARIATION IN THE STRESS RESPONSE IN FUNDULUS HETEROCLITUS ... #( 2.1 INTRODUCTION ............................................................................................................................................ 16 2.2 METHODS ................................................................................................................................................... 19 2.2.1 Animal husbandry ....................................................................................................................................... 19 2.2.2 Experimental procedure ............................................................................................................................. 19 2.2.3 Blood and tissue sampling .......................................................................................................................... 20 2.2.4 Plasma cortisol ........................................................................................................................................... 20 2.2.5 Gene expression .......................................................................................................................................... 21 2.2.6 Statistical analysis ...................................................................................................................................... 22 2.3 RESULTS ..................................................................................................................................................... 22 2.3.1 Plasma cortisol ............................................................................................................................................ 22 2.3.2 mRNA expression ........................................................................................................................................ 23 2.3.3 Body condition ............................................................................................................................................ 25 2.4 DISCUSSION ................................................................................................................................................ 26 2.5 FIGURES ..................................................................................................................................................... 34 CHAPTER ). PERSISTENT INTERACTIVE EFFECTS OF DEVELOPMENTAL SALINITY AND TEMPERATURE IN FUNDULUS HETEROCLITUS ....................................................................................................................................... +) 3.1 INTRODUCTION ............................................................................................................................................ 43 3.2 METHODS ................................................................................................................................................... 46 3.2.1 Killifish husbandry ....................................................................................................................................... 46 3.2.2 Critical thermal maximum (CTmax) ............................................................................................................ 47 3.2.3 RNA extraction and quantitative real time PCR (qRT-PCR) ......................................................................... 47 3.2.4 Statistical Analysis ....................................................................................................................................... 48 3.3.1 Embryo hatch success ................................................................................................................................. 50 3.3.2 Embryo gene expression ............................................................................................................................. 50 3.3.3 Juvenile morphometrics and CTmax ........................................................................................................... 51 3.3.4 Juvenile gene expression ............................................................................................................................ 52 3.4 DISCUSSION ................................................................................................................................................ 52 3.5 TABLES ....................................................................................................................................................... 59 3.6 FIGURES ..................................................................................................................................................... 60 CHAPTER +. IDENTIFICATION OF UPPER THERMAL THRESHOLDS DURING DEVELOPMENT IN THE ENDANGERED NECHAKO WHITE STURGEON WITH MANAGEMENT IMPLICATIONS FOR A REGULATED RIVER ................................................... (- viii 4.1 INTRODUCTION ............................................................................................................................................ 68 4.2 METHODS ................................................................................................................................................... 71 4.2.1 White sturgeon husbandry ......................................................................................................................... 71 4.2.2 Embryo metabolic rate ............................................................................................................................... 72 4.2.3 Larval morphometric measurements .......................................................................................................... 73 4.2.4 Critical thermal maximum trials .................................................................................................................. 73 4.2.5 RNA extraction, cDNA synthesis, and qPCR ................................................................................................ 74 4.2.6 Statistical analysis ....................................................................................................................................... 75 4.3 RESULTS ..................................................................................................................................................... 76 4.3.1 Mortality ..................................................................................................................................................... 76 4.3.2 Embryo metabolic rate ............................................................................................................................... 76 4.3.3 Larval morphometrics ................................................................................................................................. 77 4.3.4 Larval CTmax ............................................................................................................................................... 77 4.3.4 mRNA transcript abundance ....................................................................................................................... 77 4.4 DISCUSSION ................................................................................................................................................ 78 4.5 TABLES AND FIGURES .................................................................................................................................... 84 CHAPTER .. HEATWAVE RESILIENCE OF JUVENILE WHITE STURGEON IS ASSOCIATED WITH EPIGENETIC AND TRANSCRIPTIONAL ALTERATIONS ................................................................................................................ 0. 5.1 INTRODUCTION ............................................................................................................................................ 95 5.2 METHODS ................................................................................................................................................... 97 5.2.1 White sturgeon husbandry ......................................................................................................................... 97 5.2.2 Heatwave exposure .................................................................................................................................... 97 5.2.3 Thermal tolerance (CTMax) trials ................................................................................................................ 99 5.2.4 Hypoxia tolerance trials ............................................................................................................................ 100 5.2.5 mRNA transcript abundance ..................................................................................................................... 101 5.2.6 Global DNA methylation ........................................................................................................................... 102 5.2.7 Statistical analysis ..................................................................................................................................... 102 5.3 RESULTS ................................................................................................................................................... 103 5.3.1 Heatwave exposed sturgeon exhibit cross tolerance ............................................................................... 103 5.3.2 Transcript abundance and DNA methylation over time ........................................................................... 104 5.3.3 Heatwave exposed sturgeon exhibit increases in mRNA transcript plasticity following thermal and hypoxia stress .................................................................................................................................................... 104 5.3.4 Thermal and hypoxia stress induce rapid changes in global DNA methylation ........................................ 105 5.4 DISCUSSION .............................................................................................................................................. 106 5.5 FIGURES ................................................................................................................................................... 115 CHAPTER (. THE EFFECTS OF DOMESTICATION ON PHENOTYPIC VARIATION AND SURVIVAL IN A NATURAL ENVIRONMENT IN F& RAINBOW TROUT .......................................................................................................................... #)3 6.1 INTRODUCTION .......................................................................................................................................... 130 6.2 METHODS ................................................................................................................................................. 133 6.2.1 Rainbow trout crosses .............................................................................................................................. 133 6.2.2 Experimental ponds .................................................................................................................................. 133 6.2.3 Pond fish trapping ..................................................................................................................................... 134 6.2.4 Hypoxia tolerance assessment .................................................................................................................. 135 6.2.5 Rotenone .................................................................................................................................................. 135 6.2.6 Affymetrix UVicOmyk SNP array ............................................................................................................... 135 6.2.7 Genotyping ................................................................................................................................................ 136 6.2.8 Hybrid index score .................................................................................................................................... 137 6.2.9 Statistical Analysis ..................................................................................................................................... 137 6.2.10 Phenotypes ............................................................................................................................................. 138 6.2.11 Effects of genotype on phenotype .......................................................................................................... 138 ix 6.3 RESULTS ................................................................................................................................................... 138 6.3.1 Wild and domestic strain (F0) differences ................................................................................................ 138 6.3.2 Pond fish (F2) genotypes ........................................................................................................................... 139 6.3.3 Hybrid index score (F2 pond fish) ............................................................................................................. 140 6.3.4 Pond fish phenotypes ............................................................................................................................... 140 6.3.5 Effects of domestication on fish phenotypes ............................................................................................ 141 6.3.6 Pond fish (F2) rotenone ............................................................................................................................ 141 6.4 DISCUSSION .............................................................................................................................................. 142 6.5 FIGURES ................................................................................................................................................... 148 CHAPTER 4. CONCLUSIONS ..................................................................................................................... #.- BIBLIOGRAPHY .................................................................................................................................... #() APPENDICES ........................................................................................................................................ #04 APPENDIX 1. CHAPTER 3 SUPPLEMENTAL FILE 1 ..................................................................................................... 197 APPENDIX 2. CHAPTER 4 SUPPLEMENTAL FILE 1. .................................................................................................... 199 APPENDIX 3. CHAPTER 4 SUPPLEMENTAL FILE 2. .................................................................................................... 203 APPENDIX 4. CHAPTER 4 SUPPLEMENTAL FILE 3. .................................................................................................... 205 APPENDIX 5. CHAPTER 4 SUPPLEMENTAL FILE 4. .................................................................................................... 207 APPENDIX 6. CHAPTER 5 SUPPLEMENTAL FILE 1. .................................................................................................... 208 APPENDIX 7. CHAPTER 5 SUPPLEMENTAL FILE 2. .................................................................................................... 209 APPENDIX 8. CHAPTER 6 SUPPLEMENTAL FILE 1. .................................................................................................... 213 x List of Tables TABLE &.#. LIST OF FORWARD AND REVERSE PRIMERS AND THEIR EFFICIENCIES USED IN THIS STUDY FOR KILLIFISH (FUNDULUS HETEROCLITUS) CRF, CRFBP, CRFR, POMC, GR$, GR%, STAR, MC%R, $&S AND EF$-Α ............................... )) TABLE ).#. LIST OF FORWARD AND REVERSE PRIMERS AND THEIR EFFICIENCIES USED IN THIS STUDY FOR KILLIFISH (FUNDULUS HETEROCLITUS) HSP().$, HSP().%, HSP+)A, HSP+)B, HIF$A, HSC(), EF$-A, AND $&S. ........................ .0 TABLE +.#. PRIMER SEQUENCES FOR WHITE STURGEON (ACIPENSER TRANSMONTANUS), HSP(), HSP+)A, HSP+)B, HSP-(, HIF$A, HB-A, HB-B, HXK, PEPCK-C, PEPCK-M, G.P, IGF%, GH$, RPS/, RPS& ............................................... -+ TABLE (.#. PHENOTYPES ACROSS PARENTAL STRAINS (DOMESTIC F3 FRASER VALLEY AND WILD F3 PENNASK) AND POND FISH (F&) INCLUDING CONDITION FACTOR, WET MASS, LENGTH, AND HYPOXIA TOLERANCE. .................................. #+# xi List of Figures FIGURE #.#. HOW PLASTICITY AND ADAPTATION CAN DETERMINE RESILIENCE TO ENVIRONMENTAL CHANGE, SUCH AS HEATWAVES, ACROSS TEMPORAL SCALES IN FISHES. ........................................................................................ #. FIGURE &.# THE HYPOTHALAMIC-PITUITARY-INTERRENAL (HPI) AXIS IN FISH. ...................................................... )+ FIGURE &.& PLASMA CORTISOL (NG·ML− ") CONCENTRATIONS IN TWO POPULATIONS OF THE COMMON KILLIFISH (FUNDULUS HETEROCLITUS) ACROSS THREE TREATMENTS: CONTROL, ACUTE STRESS, AND REPEATED STRESS. .............. ). FIGURE &.) BRAIN MRNA GENE EXPRESSION OF GENES INVOLVED IN THE HYPOTHALAMIC-PITUITARY-INTERRENAL (HPI) AXIS IN TWO POPULATIONS OF THE COMMON KILLIFISH (FUNDULUS HETEROCLITUS) ACROSS THREE TREATMENTS: CONTROL, ACUTE STRESS AND REPEATED STRESS. ........................................................................................... )( FIGURE &.+ HEAD KIDNEY MRNA GENE EXPRESSION OF GENES INVOLVED IN THE HYPOTHALAMIC-PITUITARY-INTERRENAL (HPI) AXIS IN TWO POPULATIONS OF THE COMMON KILLIFISH (FUNDULUS HETEROCLITUS) ACROSS THREE TREATMENTS: CONTROL, ACUTE STRESS, AND REPEATED STRESS. .......................................................................................... )- FIGURE &.. LIVER MRNA GENE EXPRESSION OF TWO GENES INVOLVED IN THE HYPOTHALAMIC-PITUITARY-INTERRENAL (HPI) AXIS IN TWO POPULATIONS OF THE COMMON KILLIFISH (FUNDULUS HETEROCLITUS) ACROSS THREE TREATMENTS: CONTROL, ACUTE STRESS, AND REPEATED STRESS. .......................................................................................... +3 FIGURE &.( MORPHOMETRIC MEASUREMENTS IN TWO POPULATIONS OF THE COMMON KILLIFISH (FUNDULUS HETEROCLITUS) ACROSS THREE TREATMENTS: CONTROL, ACUTE STRESS, AND REPEATED STRESS. .............................. +# FIGURE ).# EXPERIMENTAL DESIGN. ........................................................................................................... (3 FIGURE ).&. SURVIVAL AND AVERAGE HATCH DAY FOR KILLIFISH FUNDULUS HETEROCLITUS EMBRYOS DURING INCUBATION IN FOUR TREATMENTS. ........................................................................................................... (# FIGURE ).). PCA, GENE CONTRIBUTION AND VECTOR PLOT OF MRNA LEVELS DURING DEVELOPMENT IN KILLIFISH EXPOSED TO FOUR DIFFERENT TREATMENTS (&(°C &3PPT, &(°C )PPT, &3°C &3PPT, AND &3°C )PPT). ..................... () FIGURE ).+. JUVENILE CRITICAL THERMAL MAXIMUM (CTMAX; PANEL A), WET MASS (PANEL B), LENGTH (PANEL C), AND CONDITION FACTOR (PANEL D). ................................................................................................................. (. FIGURE )... PCA, GENE CONTRIBUTION AND VECTOR PLOT OF MRNA LEVELS AT CONTROL AND FOLLOWING CTMAX IN KILLIFISH EXPOSED TO FOUR DIFFERENT TREATMENTS (&(°C &3PPT, &(°C )PPT, &3°C &3PPT, AND &3°C )PPT). ......... (( FIGURE +.# EXPERIMENTAL DESIGN FOR THIS STUDY ...................................................................................... -. FIGURE +.&. CUMULATIVE MORTALITY (%) OF WHITE STURGEON (ACIPENSER TRANSMONTANUS) EMBRYOS AND YOLK-SAC LARVAE THROUGHOUT EARLY DEVELOPMENT. ......................................................................................... -( FIGURE +.). WHITE STURGEON (A. TRANSMONTANUS) EMBRYO OXYGEN CONSUMPTION RATE, ACCLIMATED TO THREE DIFFERENT TEMPERATURES (#+°C – BLUE, #-°C – YELLOW AND &#°C—PINK). .................................................... -4 FIGURE +.+. MORPHOMETRICS OF LARVAL WHITE STURGEON (A. TRANSMONTANUS) ACCLIMATED TO THREE DIFFERENT TEMPERATURES (#+°C – BLUE, #-°C – YELLOW AND &#°C—PINK) ACROSS ATUS. ............................................... -- FIGURE +... WHITE STURGEON (A. TRANSMONTANUS) LARVAL CTMAX AT THREE DIFFERENT ACCLIMATION TEMPERATURES (#+°C – BLUE, #-°C – YELLOW AND &#°C—PINK). .................................................................. 03 FIGURE +.(. PCA OF MRNA ABUNDANCE OF LARVAL WHITE STURGEON (A. TRANSMONTANUS) ACCLIMATED TO THREE DIFFERENT TEMPERATURES (#+°C – BLUE, #-°C – YELLOW AND &#°C—PINK). .................................................... 0# xii FIGURE +.4 WHITE STURGEON (A. TRANSMONTANUS) LARVAL MRNA LEVELS FOR ALL GENES MEASURED AFTER ACCLIMATION TO THREE DIFFERENT TEMPERATURES (#+°C – BLUE, #-°C – YELLOW AND &#°C—PINK) AT CONTROL AND AFTER CTMAX. ...................................................................................................................................... 0+ FIGURE ..# NECHAKO RIVER TEMPERATURE MEASURED AT THE VANDERHOOF STATION (TEAL) AND RECORDED TEMPERATURES FROM THE SIMULATED HEATWAVE (BLACK). .......................................................................... ##. FIGURE ..&. EXPERIMENTAL DESIGN FOR JUVENILE WHITE STURGEON HEATWAVE EXPOSURE ................................ ##( FIGURE ..). CTMAX AND HYPOXIA TOLERANCE IN PRE-HEATWAVE (PRE-HEATWAVE; BLUE) AND HEATWAVE EXPOSED (HEATWAVE; PINK) JUVENILE WHITE STURGEON. ......................................................................................... ##- FIGURE ..+. BASELINE MRNA LEVELS FOR HSP03A, HSP03B, HSP43, AND HIF#A IN JUVENILE WHITE STURGEON OVER TIME DURING THE HEATWAVE THAT HAVE NOT BEEN EXPOSED TO AN ACUTE STRESSOR. ....................................... #&3 FIGURE .... BASELINE GLOBAL DNA METHYLATION LEVELS IN JUVENILE WHITE STURGEON OVER TIME DURING THE HEATWAVE IN FISH THAT HAVE NOT BEEN EXPOSED TO AN ACUTE STRESSOR. ..................................................... #&& FIGURE ..(. FOLD CHANGE IN MRNA TRANSCRIPT LEVELS AT CONTROL LEVELS (CHECKERED BOXES) AFTER CTMAX TRIALS (SOLID BOXES) IN PRE-HEATWAVE (BLUE) AND HEATWAVE (PINK) JUVENILE WHITE STURGEON. .............................. #&+ FIGURE ..4. FOLD CHANGE IN MRNA TRANSCRIPT LEVELS AT CONTROL LEVELS (CHECKERED BOXES) AFTER HYPOXIA TRIALS (SOLID BOXES) IN PRE-HEATWAVE (BLUE) AND HEATWAVE ACCLIMATED (PINK) JUVENILE WHITE STURGEON. ... #&( FIGURE ..- GILL AND HEART DNA METHYLATION FOLLOWING CTMAX AND HYPOXIA TRIALS IN PRE-HEATWAVE (BLUE) AND HEATWAVE (PINK) JUVENILE WHITE STURGEON. .................................................................................... #&- FIGURE (.#. ILLUSTRATION OF CHROMOSOME AND ALLELE RECOMBINATION IN F3, F#, AND F& GENERATIONS OF DOMESTIC AND WILD STRAINS. ................................................................................................................ #+0 FIGURE (.&. WILD (PENNASK) STRAIN AND DOMESTIC (FRASER VALLEY) STRAIN INDIVIDUALS WERE CROSSED TO CREATE PNFV F# INDIVIDUALS. ......................................................................................................................... #+0 FIGURE (.). THE FIXATION INDEX (FST) BETWEEN PENNASK AND FRASER VALLEY POPULATIONS INCLUDING GRANDPARENTS (F3) USED IN THIS STUDY AND ## INDIVIDUALS FROM EACH SOURCE POPULATION. ....................... #.3 FIGURE (.+. PHENOTYPES OF CONDITION FACTOR (A), HYPOXIA TOLERANCE (B), WET MASS (C), AND LENGTH (D) IN THE PARENTAL POPULATIONS, DOMESTIC FRASER VALLEY (FV; ORANGE) AND WILD PENNASK (PN; GREEN). .................. #.# FIGURE (... PRINCIPAL COMPONENT ANALYSES (PCA) OF ALL RAINBOW TROUT GENERATIONS INCLUDING ALL (A) &33K SNPS AND (B) WITH FILTERED SNPS (FST > 3.4). ........................................................................................ #.& FIGURE (.(. PROPORTION OF POND FISH GENOTYPES (%; WILD HOMOZYGOUS, HETEROZYGOUS, DOMESTIC HOMOZYGOUS) FOR FIXED SNPS AT FST =# (A) AND HIGHLY DIFFERENTIATED FST ³3.4 (B) ACROSS THE GENOME AND INDIVIDUAL CHROMOSOMES (C). ............................................................................................................. #.) FIGURE (.4. RELATIVE FREQUENCY DISTRIBUTIONS (%) OF THE PHENOTYPE DISTRIBUTIONS IN POND FISH SEPARATED BY GENOTYPE .......................................................................................................................................... #.. FIGURE (.-. LINEAR REGRESSION OF DOMESTICATION EFFECTS ON CONDITION FACTOR (A), HYPOXIA TOLERANCE (B), WET MASS (C), AND LENGTH (D). ............................................................................................................ #.( FIGURE (.0. THE NUMBER OF F& RAINBOW TROUT IN EACH GENOTYPE (DOMESTIC HOMOZYGOUS, HETEROZYGOUS, OR WILD HOMOZYGOUS) FOLLOWING THE ADDITION OF ROTENONE. .................................................................... #.4 xiii Acknowledgements I’d first like to acknowledge and thank the Saik'uz, Wet'suwet'en, xwmeθkweye̓m, Sḵwxw̱ú7mesh and Tsleil-Waututh Nations for their continued stewardship of the natural world and for the opportunity to live, play, and research on these unceded territories. Trish, thank you for your unwavering support, patience, and wisdom over the last five years, I truly could not have done this without you. A PhD is difficult during the best of times and your guidance has been invaluable through some of the hardest years of my life. You’ve taught me how to be not only a better scientist but also how to be a caring and phenomenal supervisor. The combination of independence and support you gave me allowed me to truly pursue what I love and for that I will be forever grateful. Katie, Dan, and Colin, thank you all for your insights, flexibility, and guidance as my committee over these last five years. With the pandemic I had to re-imagine most of my thesis from what I originally proposed and I truly appreciate your help throughout this process to get to this point. Our meetings, including my comprehensive exams, were an invaluable learning experience that I hope to pass on through my career. To all my gracious and helpful collaborators, this thesis would not have been possible without you. A special thank you to Bob, Carlo, and Steve, the folks at the Nechako White Sturgeon Recovery initiative and Freshwater Fisheries Society of BC. To Stella, Sravya, and everyone I TA’d with, thank you for an incredible 5 years of teaching. It was wonderful to learn from you all. Our grading parties, lunches, and all the laughs and emotional support made the PhD process much more enjoyable. To all of my past teachers and mentors thank you for your guidance and insights throughout my education, for without incredible teachers I wouldn’t be here. A special thank you to Peter and Gary, thank you for believing in me and teaching me how to do research and always supporting me once I left your labs. My comphy friends and the Schulte lab, thank you for your friendship and support, it really made this an unforgettable time. A special thank you to Tessa, Nic, Bea, and Rachael, your steadfast support through every experiment, hardship, and success has meant the world to me. Thank you all for everything you do inside and outside of science, I love you all! xiv To my family, mom, dad, Nick, thank you for your continued support, and for always believing I can do anything I set my mind to. Dad, thank you for instilling in me my love of the natural world, its creatures, and the importance of work hard play hard. Mom, thank you for teaching me how to be persistent and tenacious, and for always having my back, I wouldn’t be here without you both. Nicky thank you for always being there and always making me laugh, you are the best brother I could ask for. To my grandma, Bernie thank you for your support and belief in me throughout my life, you have always been a safe, comforting place for me to turn to and I will always be grateful for that. To my extended family, cousins, Aunt Jean, and Uncle Steve, thank you all for your enthusiasm and for your interest and support throughout this degree, it means everything to have the support of my family. Best friends, I mean where would I be without you all! Tessa and Brandon thank you for endless game nights and stay-cations, life with you in Vancouver has been a blast. Alyssa and Kristen, y’all are my rock, thank you for being loyal to the soil and for always loving me no matter what. Throughout this PhD you two have always brought me back down to earth and remind me constantly of what is important, thank you for everything. To the best friends I lost during this degree, Justin and Luke, I miss you both so much every day. Luke, your kindness, patience, and infectious love of the natural world is something I try to emulate every day. Some days after losing you both it was difficult to find the will to continue with this degree. But Justin I hear you in my head everyday telling me “you can do it Maddy”, and I did it and I hope you’re proud. Finally, to Will, my best friend and love of my life thank you for your unconditional love and support for the last five years. Thank you for the endless scientific conversations, paper edits, and celebrations during my degree. Without you it would not have been possible or worthwhile, so thank you and I love you! xv Dedication For the preservation and restoration of our natural world “Action on behalf of life transforms. Because the relationship between self and the world is reciprocal, it is not a question of first getting enlightened or saved and then acting. As we work to heal the earth, the earth heals us.” Robin Wall Kimmerer, Braiding Sweetgrass Chapter 1. Introduction Understanding how organisms respond to environmental change is a fundamental question in biology. In natural environments organisms are exposed to multiple abiotic factors that can function as stressors either in isolation or simultaneously (Gunderson et al., 2016), which can cause reductions in performance and fitness (Schulte, 2014). These abiotic factors can include natural variations in temperature, oxygen, pH, salinity, UV light, as well as anthropogenic elements like heavy-metals and organic toxicants. Depending on the nature and intensity of a stressor, organisms may be negatively impacted through sub-lethal, or non-lethal effects, that impede normal life processes, or stressors may result in debilitation that ultimately ends in mortality (Akbarzadeh et al., 2018). Understanding how organisms respond to environmental change and their thresholds for how they deal with assorted stressors is a fundamental question in biology (Pigllucci, 1996). As such, questions pertaining to how evolution shapes the ways in which animals respond to environmental change, and if variable environments select for physiologically-tolerant organisms have been of particular interest for decades (Pigllucci, 1996; Schulte, 2014). These questions, however, are challenging as the responses of an organism depend on both the temporal and spatial scale of environmental change and the characteristics of the organisms experiencing the change (Angilletta and Sears, 2011; Schulte, 2014). As the climate changes, our planet’s organisms will be faced with many environmental stressors threatening biodiversity (Cahill et al., 2013). Specifically in aquatic habitats, organisms are encountering many environmental changes including warming temperatures, less available oxygen, decreases in pH, and variations in salinity with these changes occurring across different spatial as well as temporal scales. For example, climate warming is happening on the scale of centuries affecting the entire globe, but heatwave events are typically localized, occurring for a matter of days to weeks, and the different rates of environmental change across these scales impacts organisms in distinct ways (Calvin et al., 2023). The rapid, intense weather events exacerbated by climate change are very challenging for organisms to endure and may pose a larger risk to global biodiversity than chronic global warming (Smale et al., 2019; Laufkötter et al., 2020; Woolway et al., 2021). Additionally, both chronic warming and extreme acute weather 2 changes typically cause rapid variations in multiple stressors at once which can push organisms to their physiological limits. If aquatic organisms, such as fishes, cannot cope with a rapidly changing environment it can result in increases in stress which decreases performance, fitness, and ultimately survival. Determining the resilience of a species or population to climate change stressors is a crucial but challenging task, as resilience can be determined through different types of phenotypic plasticity and by genetic variation (or potential for adaptation; Huey et al., 2012; Norberg et al., 2012; Buckley and Huey, 2016; Earhart et al., 2022; Schulte and Healy 2022). As such, this thesis aims to explore how plasticity and adaptation determine resilience to environmental change across temporal scales in different fish species (Figure 1.1). In this introductory chapter, I begin by providing some of the fundamental theoretical background that underpins my research, and I close the chapter with a brief summary of my approach, experimental systems, and the goals of each of my research chapters. Phenotypic plasticity Phenotypic plasticity has been defined numerous ways across different disciplines, but here I define phenotypic plasticity as the ability of an organism to change its phenotype in response to a change in the environment, without a change in the underlying genotype (Whitman and Agrawal, 2009; Earhart et al., 2022). Specifically, I consider only active biochemical changes that are regulated by an individual, such as alterations in gene expression, hormone balance, or enzyme function, rather than passive responses such as an increase in the rate of an enzymatic reaction due to acute increases in temperature (Havird et al., 2020). This type of response has sometimes been termed passive plasticity, but I prefer to make a clear distinction between the regulated and unregulated responses of an organism. Phenotypic plasticity can be divided into three general classes, distinguished by the time scales across which they operate: plasticity through acclimation, developmental plasticity, and transgenerational plasticity; and all three types of plasticity may be key in playing different roles in climate change resilience across temporal scales in organisms (Forsman, 2015; Seebacher et al., 2015; Burggren, 2018; Donelson et al., 2018). This thesis investigates the first two types, acclimation plasticity 3 and developmental plasticity, and as such these will be mainly discussed in this introductory chapter. Plasticity by acclimation to different environmental variables acts across relatively short timescales (days, weeks or months) and can be reversible if the environment returns to its previous circumstances (Earhart et al. 2022). As climate change worsens, there will be an increase in the frequency and severity of extreme weather events that also exist on similar short timescales, like days, weeks or months (IPCC, 2023). Thus, phenotypic plasticity is one of the main ways an organism, such as fish, can use to tolerate these novel environments created by intense, rapid changes which can modify important aquatic variables significantly, such as water temperature, oxygen availability, and salinity (Huey et al., 2012; Buckley and Huey, 2016; Snell-Rood et al., 2018; Morley et al., 2019a). Acclimation to an environmental variable, such as warm temperatures, allows organisms to adjust their physiology to cope with more extreme and rapid changes in environmental temperature (Gunderson and Stillman, 2015; Stillman, 2019) that have been increasingly observed with rising global temperatures (Lopez et al. 2018; Morley et al. 2018). As such, acclimation plasticity in animals like fishes can result in significant phenotypic variation within and across populations and plays an important role in shaping species’ responses to climate change (Burggren, 2018; Vagner et al., 2019; Earhart et al., 2022; Schulte and Healy 2022) Developmental plasticity occurs when the environment during developmental critical windows induces long-lasting changes in the phenotype later in life (Burggren and Mueller, 2015; Earhart et al., 2022; Pottier et al., 2022). There are also important interactions between acclimation and developmental plasticity (Beamen et al. 2016) such that developmental environmental conditions like altered temperatures can affect the capacity for acclimation later in life (Seebacher et al. 2014; Earhart et al. 2022). Many developing organisms, including most fishes, have a limited ability to move away from unsuitable conditions and cannot use behavioral mechanisms to avoid or cope with environmental stressors. Thus, they must use plasticity to cope, which is energetically costly and thus may have impacts later in life. Understanding how developing organisms respond to climate change scenarios is critical for our ability to predict which species or populations will be resilient or alternatively, which ones need protection. 4 Plasticity through cross tolerance Another possible indicator of a population’s or species’ ability to withstand rapid environmental change through acclimation plasticity is the ability to accrue cross tolerance (Todgham and Stillman, 2013; Collins et al., 2021a, 2021b; Earhart et al., 2022), a phenomenon that occurs when the mechanisms that are enhanced to protect against one stressor also elicit protection against a second stressor (Sinclair et al., 2013). Changes in our environment happen simultaneously, as climate-change related stressors like warming or hypoxia often co-occur with one another (IPCC, 2023), which results in compounding stressors that can be a challenge for organisms to endure (Todgham and Stillman, 2013; Jackson et al., 2016; Simmons et al., 2021; Earhart et al., 2022). This is why measuring cross-tolerance across multiple stressors may be crucial for understanding of how organisms cope with concurrent stressors. For example, when a fish is acclimated to a warm environment, they typically increase their thermal tolerance, but this may also increase their tolerance to other co-occuring stressors like hypoxia. Temperature and oxygen are intrinsically linked, with increases in environmental temperature also decreasing environmental oxygen availability (Verberk et al., 2011) that occurs simultaneously with a temperature induced increase in metabolic rates in ectothermic organisms (Earhart et al., 2022). As such, to deal with this mis-match in oxygen supply and metabolic demand when faced with thermal stress, fishes could gain increased tolerance to low oxygen through acclimation to warm temperatures, which would be an example of cross-tolerance. In the studies that have measured the potential for cross-tolerance, it seems that this ability is species, population, and even stressor specific (Fangue et al., 2011; Earhart et al., 2022; Jensen and Benfey, 2022). As such, it may not be feasible for all species or populations to exhibit cross-tolerance in the face of mounting stressors, leaving them more vulnerable to the compounding impacts of multiple stressors. Measuring plasticity through acclimation By characterizing a population’s plasticity in response to acclimation to various environmental stressors we can make more robust predictions on whether a population has the tolerance to withstand our changing climate (Huey et al., 2012; Gunderson and Stillman, 2015; 5 Seebacher et al., 2015; Buckley and Huey, 2016; Gunderson et al., 2016; Donelson et al., 2019; Kelly, 2019; Morley et al., 2019a). Acclimation to warm temperature in particular can lead to reductions in thermal sensitivity of physiological processes which can increase tolerance thresholds (Seebacher et al., 2015). One way to test an organism’s acclimation capacity is through tolerance tests, where a tolerance trait is measured to compare performance prior to and following acclimation to a given environmental parameter (Earhart et al., 2022). For example, to quantify an increase in thermal tolerance following acclimation to increasing temperatures, the acclimation response ratio (ARR; Claussen, 1977) is commonly used (Gunderson and Stillman, 2015; Morley et al., 2019a; Pottier et al., 2022). ARR is the change in upper thermal tolerance relative to the change in mean acclimation temperature and therefore is a metric of physiological plasticity and acclimation capacity. In fishes, thermal tolerance is typically quantified using the critical thermal maximum test, or CTmax, which involves increasing environmental water temperature at a constant, predetermined rate until the fish experiences loss of equilibrium. The temperature at which the fish loses equilibrium is then considered its upper thermal tolerance, or CTmax. Typically, as mean acclimation temperature increases, upper thermal tolerance also increases, up until an organism’s physiological limits are breached. Unfortunately, ARRs are typically much lower than one, meaning an increase in acclimation temperature by 1°C does not cause an increase in upper thermal tolerance of 1°C (Gunderson and Stillman, 2015; Morley et al., 2019b). Thus, there is a limit to the extent to which acclimation plasticity can buffer organisms from extreme increases in temperature, but it may still play an important role in helping a species cope in the short-term with intense weather events. In addition to changes in thermal tolerance, whole-organism plasticity in response to environmental change can be measured in a number of different ways. Other important whole-animal metrics that can inform researchers about how well an organism is coping with environmental change include growth, body condition, fecundity, metabolic scope (the difference between maximal and resting metabolic rate), and immune capacity, as they are fundamental indicators of fitness (Donaldson et al., 2008; Pincheira-Donoso and Hunt, 2017). My thesis measured many of the above metrics including thermal and hypoxia tolerance, growth, 6 condition, and metabolism to determine whole organism plasticity in response to different stressors. Measuring developmental plasticity To accurately assess if a trait is developmentally plastic, an organism, such as a fish, must be exposed to the environmental condition of interest during early life and then removed from that variable following development (Nettle and Bateson, 2015). The immediate and persistent effects of exposure to that variable can then be measured in the organism as it continues to develop into adulthood. For example, to assess the effects of warming on developmental plasticity, groups of fish embryos would be exposed to different temperatures (warm and cold) during development. Following hatch, these fish would be put into common conditions and then the effects of early development on the plasticity of certain traits such as thermal tolerance or hypoxia tolerance could be assessed later in life (Nettle and Bateson, 2015; Burggren, 2020). Although the effects of developmental conditions on future phenotypes is a pressing question, it can be difficult to interpret climate change resilience through developmental plasticity studies for a few reasons. For example, inferring how fishes in the wild may use developmental plasticity to cope with environmental change can be difficult because in nature adverse environmental conditions may persist following development. For this reason, it can be difficult to extrapolate the role developmental plasticity plays and it may be difficult to disentangle acclimation plasticity from developmental plasticity in the wild. As such, it is currently still unclear the role developmental plasticity can play in coping with climate change conditions and if these changes are beneficial. Stressor thresholds across biological levels of organization Often when measuring performance in response to increasing temperature, there will be a range of temperatures in which performance metrics will increase until they reach a peak which is considered the optimal temperature (Schulte et al., 2011). Past this optimal temperature, as warming continues, there is a sub-lethal threshold where physiological performance begins to decline, which is an indication that this environment is becoming too 7 stressful for the organism to maintain optimal physiological performance (Schulte et al., 2011; Jeffries et al., 2018; Blasco et al., 2020). Once warming approaches a lethal limit, organisms will eventually succumb to the stress and death is imminent (Akbarzadeh et al., 2018). However, different whole-organism traits have different tolerance thresholds and some traits may be better suited than others for predicting fishes’ tolerance to environmental change. It is important to understand the thresholds of different traits, as defining thermal limits across many traits with different sensitivities is critical for predicting climate change resilience across species. In addition to measuring whole-animal physiological traits, to further highlight an organism’s capacity to cope with environmental change we must also measure traits across multiple levels of biological organization. The underlying machinery that orchestrates physiological change must acclimate to enhance physiological plasticity and promote tolerance at the whole-organism level. Measuring the plasticity of molecular mechanisms like gene expression in response to environmental change can illuminate the rapid and effective manipulation of the cellular environment during times of stress (Komoroske et al., 2015, 2021; Connon et al., 2018; Jeffries et al., 2018). If cellular processes begin to fail, RNA polymerase and ribosomal functions can break down ultimately leading to reduced transcriptional, proteomic, and whole-organism plasticity. For example, when environmental temperature increases, the speed of cellular processes also typically increases until a thermal threshold is reached and these processes begin to fall apart (Jeffries et al., 2018). In order for organisms like fishes to respond transcriptionally to warming they must first rely on thermally-responsive molecular pathways. If the necessary machinery is not in place due to breaching of thermal limits, mRNA changes cannot successfully modify the proteome or physiology of the animal (Smith et al., 2013; Akbarzadeh et al., 2018; Bugg et al., 2022). Further, the transcriptional response is more sensitive and reactive to warming than conventional physiological measurements (e.g. whole animal traits like CTmax) and as such measuring a transcriptional profile provides great insight into the physiological status of fish (Komoroske et al., 2015; Connon et al., 2018; Jeffries et al., 2018; Keagy et al., 2023). 8 Gene expression and environmental change Gene expression is the first phenotype, the foundation for all other phenotypic modifications and thus, transcriptomes provide a unique opportunity to link functional genetic variation and environmental change over time and space, associated with a phenotypic response (Keagy et al., 2023). There are different avenues a researcher can take to measure the transcriptome, or gene expression, including RNA sequencing (RNA-seq) and quantitative reverse transcription polymerase chain reaction (RT-qPCR). RNA-seq is a technique used to sequence RNAs and through bioinformatic analysis can provide transcript counts to estimate transcript abundance. This is a useful tool for exploratory experiments that are used to generate biomarkers or to identify biological pathways through measuring the transcriptome mRNA abundance to investigate an organisms’ functional response to various environmental variables (Komoroske et al., 2015; Connon et al., 2018, 2018; Jeffries et al., 2018; Keagy et al., 2023). A more targeted, quicker, and less-expensive approach to measuring transcriptomic response is RT-qPCR. This technique can illuminate the responsiveness of specific target genes to environmental change and is commonly used to measure the mRNA abundance of particular genes of interest that have previously been identified as candidate genes that are important in responding to a given stressor. For example, when a fish is exposed to warming there are certain genes, such as heat shock proteins, which are known to be sensitive to increasing temperature (Somero, 2020). Measuring the mRNA response of these genes can thus provide a snapshot into the cellular response and its link to the phenotypic response. Gene expression connects genotype to cellular and whole-organism physiology, demonstrating the potential for adaptive phenotypes during environmental change, thus, serving as a functional link across space and time (Somero, 2010; Connon et al., 2018; Keagy et al., 2023). By measuring mRNA expression in organisms exposed to environmental change, we can associate the plasticity of the cellular response simultaneously with the plasticity of other traits allowing us to understand the factors that determine genotype-environment interactions, a central goal in biology (Auld et al., 2009). Many studies emphasize the importance of plasticity at the whole-organism level in response to stressors like warming, but what is the basis for this plasticity? The molecular phenotypes required to increase whole-animal plasticity are not well 9 understood, and phenotypic plasticity at the gene expression level is likely critical (Stern et al., 2007; Smith et al., 2013; Kenkel and Matz, 2016; Oomen and Hutchings, 2017). Gene expression plasticity can be measured gene by gene, by grouping genes into pathways or networks, or by examining entire transcriptome shifts in expression patterns. However, the expression of genes is quite dynamic (or plastic) because their regulation can be affected by internal factors like genetics, physiology, and development as well as external factors like environmental change. Comparing mRNA patterns across individuals or populations exposed to different stressors can elucidate adaptive aspects of transcriptional plasticity as there are different frameworks in which gene expression can respond to environmental change (Jeffries et al., 2018; Rivera et al., 2021). For example, in two populations exposed to heat stress the more tolerant one may have a frontloading pattern (Collins et al., 2021a), where that population has a higher baseline expression in heat-stress genes but reduced plasticity in response to acute thermal stress. This may be adaptive in scenarios, for example, where individuals are living in an environment near their thermal limit, with little variability in environmental temperature (e.g. reef fish or corals near the equator). In other cases more tolerant individuals may exhibit gene expression dampening which is reduced plasticity regardless of baseline levels (Bay and Palumbi, 2015). While frontloading or dampening may be important for specific genes, an individual’s ability to cope with new conditions through physiological plasticity is likely intrinsically linked to their ability to shift the profile of the transcriptome (Logan and Cox, 2020). Through investigating the plasticity of multiple levels of biological organization we can create a more complete picture of how organisms will cope with environmental variation caused by climate change. Epigenetic effects The genome can be functionally altered through a variety of processes that facilitate transcriptional plasticity and ultimately physiological plasticity. Epigenetic processes, like histone modification and DNA methylation, lead to functional changes in the genome that will ultimately affect how mRNAs are expressed without altering the actual genetic sequence (Deans and Maggert, 2015). How these epigenetic processes underlie plasticity or tolerance to environmental change is even less understood than the role of transcriptional plasticity. 10 However, in principle it makes sense that plastic epigenetic mechanisms, which are sensitive to environmental change, could increase plasticity at other levels of biological organization like the transcriptome or whole-animal tolerance (Entrambasaguas et al., 2021). DNA methylation refers to the chemical process in which a methyl group is added to the C-5 position of the cytosine ring of DNA (Moore et al., 2013). The addition of the methyl group to the DNA is catalyzed by DNA methyltransferases (Dmnts). There are two main classes of Dmnts. The first class (Dmnt1), maintains the DNA methylation pattern during DNA replication from the parental DNA strand to the new daughter strand (Moore et al., 2013; Metzger and Schulte, 2016) and the second class can create new methylation patterns to DNA and are known as de novo Dmnts (Dmnt3). The de novo Dmnts can actively methylate or de-methylate parts of the genome to decrease or increase expression of certain genes in response to environmental change (Moore et al., 2013; Metzger and Schulte, 2016). How quickly these epigenetic marks can be added in response to something like thermal stress or the effect these changes can have on transcriptional and whole-organismal plasticity is mostly unknown, particularly in fishes. We can further understand how organism tolerance to changing environments is created through measuring the plasticity of these underlying molecular mechanisms which can give us insight into how these phenotypes are selected upon and if they are adaptive to environmental change. Limitations of phenotypic plasticity With all types of phenotypic plasticity, across biological levels, there is a limit to the degree to which animals can respond to environmental variation. No organism is ideally or infinitely plastic, indicating that there must be limits to the evolution of phenotypic plasticity, or that being plastic is costly (DeWitt et al., 1998; Auld et al., 2009; Murren et al., 2015). There is evidence in fishes that plasticity can only help fish cope with climate change to a certain extent, which is extremely concerning for global biodiversity (Earhart et al., 2022). In 1998 DeWitt et al., proposed a “costs and limits” framework which suggests that although there are clear ecological and evolutionary benefits of being plastic, there are obviously limitations and costs. The latter is quantified as a reduction in fitness that occurs in a plastic individual when compared to a non-plastic individual that express the same trait (DeWitt et al., 1998; Murren et al., 2015). Limits of 11 plasticity are often defined as when a plastic genotype cannot produce the same phenotype as a non-plastic genotype. There are many potential costs of plasticity including the energetic costs of regulating plasticity, production costs for creating inducible structures, information costs where learning about the environment may be risky, and developmental instability where the environmental conditions during development differs from the environment experienced as an adult (DeWitt et al., 1998). In addition to costs there are also limitations to the benefits of plasticity including: information reliability in which a plastic organism may produce a maladaptive phenotype, or lag-time limits where delays in responding to environmental change preclude a phenotypic response which can reduce fitness (DeWitt et al., 1998; Auld et al., 2009; Murren et al., 2015). There is also mounting evidence that acclimation and developmental plasticity alone will not be enough to adequately buffer fishes from the effects of climate change like warming and hypoxia (Earhart et al., 2022). Thus, organisms may need to rely on adaptation as well as plasticity to cope with climate change, however, this may be problematic as rates of evolutionary change may not keep pace with our rapidly changing environment. Adaptation to environmental change Another way organisms cope with environmental change is through adaptation, a dynamic evolutionary selection process that increases fitness to a given environment. In contrast to plasticity, adaptation typically occurs over many generations and is due to genetic sequence modification, e.g. genomic inversions and single nucleotide polymorphisms (SNPs). For example, individuals that are better suited for their environment will have higher fitness than less-suitable individuals and the genomic sequences of the fit individuals can be passed down through generations. Additionally, plasticity may be selected upon, and genetic divergence in plastic responses play an important role in adaptive responses to climate change throughout time (Kelly, 2019). Over time, the genomes comprised of these sequences that are adapted to environmental change will make a population more suited for that specific environment. There is substantial evidence of local adaptation along thermal gradients in fish. For example, populations of Fundulus heteroclitus are exposed to different temperature regimes in 12 the northern part of their range compared to those in the southern range. These populations not only differ in their phenotypes, but also their underlying genetic sequences that code for these differences, and these populations are considered locally-adapted (Schulte, 2007, 2014; Brennan et al., 2018). As such, the southern population is more thermally tolerant as they have been exposed to warmer temperatures over many generations, and this higher tolerance is in part from changes in SNPs (Healy et al., 2018). For organisms to adapt to rapid environmental change it is likely climate change pressures will select for populations that have increased or divergent plastic responses and resilience or tolerance to multiple stressors (Somero, 2010; Franks and Hoffmann, 2012; Huey et al., 2012; Kelly, 2019). However, there is evidence in fishes that adaptation to warmer temperatures can cause a decrease in thermal plasticity (Morgan et al., 2020) which suggests fish already living near their thermal limits will struggle to adapt to climate change. As such, there are clear limitations on the potential for both plasticity and adaptation to rescue organisms from the current mass extinction being caused by human-induced climate change. In addition to abiotic environmental change in natural settings, artificial environments can also cause rapid adaptation in organisms (Lorenzen et al., 2012). For example, to combat declining fish populations over the last century humans have grown fish in hatcheries, and the environment in these hatcheries is quite different from wild environments. The fish grown in hatcheries have different morphology, physiology, and transcriptomic responses compared to wild fish (Marchetti and Nevitt, 2003; Álvarez and Nicieza, 2003; Tymchuk and Devlin, 2005; Baerwald et al., 2016; Le Luyer et al., 2017; Gavery et al., 2019; Rodriguez Barreto et al., 2019; Venney et al., 2021; Koch et al., 2023). These differences could be due to acclimation or developmental plasticity as a result of the hatchery environment if wild broodstock are not regularly introduced to the hatchery breeding program. However, over multiple generations it is possible that the relaxed selection pressures of artificial environments (e.g. no predation, food availability, and bare tanks) could cause changes in the genetic sequence of these hatchery individuals. Likewise, it is also possible that artificial environments select for particular traits like fast growth rate. In fact, there is mounting evidence that fishes released from hatcheries can be genetically distinct from their wild counterparts (Jonsson et al., 2003; Araki et al., 2007, 2008; 13 Skaala et al., 2012, 2019; Glover et al., 2017; Crowley et al., 2022) suggesting they have adapted to this indoor environment and may no longer be suited for the wild. As hatchery fish are created to supplement wild populations, releasing individuals that are no longer adapted for these natural, ever-changing environments would be counter-productive to their purpose. However, it is still uncertain how adaptation through hatchery-domestication can occur in fishes and how domesticated gene sequences affect fish fitness and survival in the wild. As such, supplementing wild populations with hatchery-individuals that are no longer adapted to environmental change may cause more harm as these genetic sequences could be passed down through reproduction. Unlike plasticity, that can occur rapidly, adaptation (depending on the generational time) can take months to years to decades. As such, while species may be able to rely on adaptation to survive chronic increases in global warming, adaptation is most likely not fast enough to deal with the extreme weather events which are increasing in severity and frequency. There are many factors that can determine if adaptation will be rapid enough to respond to environmental change including generational time, gene flow, and genetic sweeps. However, even if adaptation can rapidly change an organism’s phenotype, there are inherent tolerance limits to environmental change. Organisms that already live near their upper thermal thresholds may struggle to adapt with the current pace of climate warming, especially if there is an adaptation/plasticity trade off. It is possible however, that adaptation to environmental change may select more resilient, or plastic, phenotypes within a population, which can lead to an overall increase in stressor tolerance limits (Morley et al. 2018). Thus, individuals with higher plasticity could be selected for, making plasticity an adaptive trait to environmental change. Thesis Aims In this thesis I investigated how plasticity and adaptation across spatial and temporal timescales affect the ability of three fish species to cope with environmental change. The three species I studied were Atlantic killifish (Fundulus heteroclitus), white sturgeon (Acipenser transmontanus), and rainbow trout (Oncorhynchus mykiss). 14 In chapters 2 and 3 I used killifish, Fundulus heteroclitus, which have a large range along the east coast of North America, and as such present an opportunity to investigate the effects of adaptation along a latitudinal thermal gradient on different traits and plasticity. In chapter 2 I examined the potential for local adaptation in the stress response and gene expression in two populations of Fundulus found along the east coast of North America. In chapter 3, I investigated the persistent effects of developmental temperature and salinity on F. heteroclitus, to assess the role developmental plasticity plays in forming various phenotypes into adulthood through measuring whole-organism traits and underlying mRNA levels. In chapters 4 and 5 I used white sturgeon, Acipenser transmontanus, which are an extremely plastic, long-lived species of conservation concern. Thus, understanding their capacity for acclimation and plasticity is crucial for predicting their resilience to our changing environment. In chapter 4 I explored the potential for acclimation plasticity during development in white sturgeon, and its effects on upper thermal thresholds of different traits and the transcriptomic mechanisms underlying these traits. In chapter 5, I used a real-time, environmentally accurate heatwave exposure to assess the role plasticity plays in heatwave resilience of white sturgeon and how plasticity of mRNAs and DNA methylation influences the whole-animal response. Lastly, in chapter 6 I studied rainbow trout, Oncorhynchus mykiss, which are a fundamental species to commercial, recreational, and subsistence fishing that are native to western North America, but are found on every continent except Antarctica, due to stocking for aquaculture and recreational fishing. There are many genomically-distinct strains of trout and as such they are an excellent species in which to investigate the influence of genomic adaptation on traits and fitness. Specifically, I explored the effects of hatchery-domestication through adaptation in F2 crosses of between wild and domesticated rainbow trout, and how possession of domesticated alleles affects phenotypes and survival in a wild environment. 15 Figure 1.1. How plasticity and adaptation can determine resilience to environmental change, such as heatwaves, across temporal scales in fishes. 16 Chapter 2. Gene expression and latitudinal variation in the stress response in Fundulus heteroclitus 2.1 Introduction The stress response is a set of complex behavioural and physiological changes that occur at multiple levels of biological organization in response to a wide variety of potentially harmful biotic and abiotic stimuli. In vertebrates, the stress response is usually assessed by quantifying the release of synthesized catecholamines and glucocorticoids (Wendelaar-Bonga, 1997; Iwama et al., 1998; Schulte, 2014). Glucocorticoid synthesis is regulated by the activation of the hypothalamic pituitary-interrenal (HPI) axis in fish or the hypothalamic-pituitary adrenal (HPA) axis in tetrapods (Wendelaar-Bonga, 1997). This involves a suite of molecular and physiological responses in multiple organs starting in the brain that ultimately leads to the binding of glucocorticoid hormones (such as cortisol) to glucocorticoid receptors (GRs) in many target tissues (Fig. 2.1). Cortisol binding to its receptors initiates a wide range of biochemical and physiological changes, many of which are involved in energy reallocation to ensure the animal has the energy resources to respond to and recover from the stressor. This axis is tightly regulated by negative feedback loops driven by cortisol binding to GRs in hypothalamic, pituitary and interrenal cells. Multiple studies across a range of vertebrate taxa have demonstrated that the reactivity of the HPA/HPI axis and the extent of glucocorticoid release consistently and repeatably differs between individuals, suggesting that individuals may differ in their “stress-coping styles” (Koolhaas et al., 1999; DeKoning et al., 2004; Rosengren et al., 2018). For example, there are well-established strains of rainbow trout (Oncorhynchus mykiss) that can be categorized as either “high-responders” or “low responders”, based on the extent of their plasma cortisol response to a standardized stressor (Pottinger and Carrick, 1999). These stress phenotypes have been maintained over multiple generations suggesting that interindividual differences in stress coping style may be genetically based (Pottinger and Carrick, 1999; LeBlanc et al., 2012). Similar patterns have been observed in a variety of fish species, including striped bass, European sea 17 bass, Atlantic cod, and gilthead sea bream (Castranova et al., 2005; Samaras et al., 2016; Samaras and Pavlidis, 2018; Hori et al., 2012a, 2012b, Tort et al., 2001). However, the underlying mechanisms that drive differences in stress reactivity remain poorly understood (Pottinger and Carrick, 1999; Wong et al., 2015; Moltesen et al., 2016; Alfonso et al., 2019). Typically, organisms that differ in stress-reactivity also differ in other aspects of their phenotypes, including differences in metabolism, immunity, and fecundity (Koolhaas et al., 1999), and thus HPA/I axis reactivity is thought to be closely related to fitness (Breuner et al., 2008; Crespi et al., 2013). Consistent with this idea, populations and subspecies from different environments have been shown to differ in stress reactivity, which suggests that either local adaptation to environmental conditions or epigenetic effects from exposure to different environments may shape the stress response (Addis et al., 2011; Angelier et al., 2011; Dahl et al., 2012). However, the evolutionary significance of variation in the stress hormone axis is not well understood, as the activation of the stress response can result in both beneficial and deleterious effects (Pickering, 1992; Pottinger, 2008; Schreck, 2010). Most studies of variation between high and low responding stress phenotypes in fish have been conducted in domesticated aquaculture strains (Pottinger and Carrick, 1999; LeBlanc et al., 2012; Castranova et al., 2005; Samaras et al., 2016; Samaras and Pavlidis, 2018; Hori et al., 2012a, 2012b; Tort et al., 2001), and little is known about the mechanistic basis of naturally occurring variation in stress-coping styles across populations. Here, we use populations of the Atlantic killifish, Fundulus heteroclitus, that have previously been shown to exhibit differences in their response to a standardized stressor (DeKoning et al., 2004) to address this issue. This species is found across a latitudinal thermal gradient on the east coast of North America and northern and southern populations differ in many aspects of their morphology, behavior, physiology, and genetics (Powers and Schulte, 1998), including their stress response. Killifish from southern populations are more stress responsive than their northern counterparts, with higher levels of plasma cortisol following either a standardized stressor or injection of cortisol (DeKoning et al., 2004). These populations also differ in their glycolytic enzymatic activity (LDH-B, ALD, PFK), indicating a difference in the capacity of the liver to metabolically respond to a stressor (Schulte et al., 2000; DeKoning et al., 2004). In particular, LDH-B enzyme activity differs 18 between the populations at rest but not following a stressor and it is possible these differences are explained by sequence variation in a glucocorticoid responsive element in the Ldh-B gene (Schulte et al., 2000; DeKoning et al., 2004). These findings suggest that northern and southern killifish populations differ in both their stress response and their metabolic response to stress, making them an ideal natural system in which to investigate the molecular basis and functional significance of underlying differences in stress-reactivity of natural populations. To achieve this objective, we first examined whether the previously observed differences in stress reactivity between killifish populations are more evident in response to a single acute stressor or to a repeated stressor. Next, we assessed the molecular basis for variation in the cortisol response to stress between populations by measuring changes in mRNA levels of eight genes involved in the HPI axis. We measured various genes in the whole- brain, head kidney and liver following both acute and repeated stress, to test the hypothesis that variation at multiple steps along the HPI axis is associated with the observed differences in stress-reactivity between populations (Fig. 2.1). Specifically, in the brain we assessed the expression of corticotropin releasing factor (CRF), CRF binding protein (CRFBP), CRF receptor (CRFR), proopiomelanocortin (POMC), and both glucocorticoid receptors (GR1 and GR2). The CRF pathway is the first step of the stress cascade in the HPI axis as CRF is released from the nucleus preopticus area upon recognition of a stressor. CRF is chaperoned by its binding protein, CRFBP, which is known to bind to CRF making it inactive, and the receptor binds to CRF which initiates the following steps in the pathway) (Flik et al., 2006). The next step includes POMC, which is cleaved into many active peptides, including adrenocorticotropic hormone (ACTH). ACTH travels from the pituitary to the head kidney or interrenal gland initiating steroidogenesis (Takahashi and Kawauchi, 2006). In the head kidney we measured the mRNA expression of melanocortin 2 receptor (MC2R), and steroidogenic acute regulatory protein (StAR). We chose to assess these genes as MC2R is the receptor for ACTH which begins steroid synthesis and StAR is a rate-limiting step in cortisol production (Stocco et al., 2005). Finally, in the liver, kidney, and brain we assessed the expression of GR1 and GR2 as these receptors are key in regulating the HPI axis through negative feedback (Suarez-Bregua et al., 2018). Overall, we predicted that southern killifish would exhibit higher levels of expression in key genes in the HPI axis, consistent with their greater stress 19 reactivity. Finally, we assessed whether increased stress reactivity is associated with beneficial or deleterious physiological impacts by measuring changes in body condition in response to acute and repeated stress in both populations. 2.2 Methods 2.2.1 Animal husbandry F. heteroclitus, common killifish, were collected from a northern population, containing individuals of the subspecies F. heteroclitus macrolepidotus, in Hampton, New Hampshire by Aquatic Research Organism Inc. (Hampton, NH, USA). and from a southern population, containing individuals of the subspecies F. heteroclitus heteroclitus, from Jekyll Island, Georgia in 2015. Fish were transported to the University of British Columbia and housed in a large recirculating brackish water system (20ppt) in eight 138-gal tanks for two years. Prior to the start of the experiment, fish were moved to four recirculating 30-gal glass aquaria containing fully aerated, 20ppt brackish water held at a temperature of 15°C ± 1 with a 12 h:12d light cycle. All animals were allowed to acclimate to their glass aquaria for four weeks prior to starting the trials. The four aquaria were designed as either an experimental tank where fish would be exposed to a stressor or a control tank where fish would be undisturbed except for sampling. Each tank split in half with a mesh divider to house 16 northern killifish (average weight: 4.1 ± 1.2 g) on one side and 16 southern killifish (average weight: 4.5 ± 1.1 g) on the other (two control and two experimental tanks for a total of 128 fish). Fish were fed twice a day to satiation with Tetramin flake food and tanks were cleaned for debris and uneaten food daily. All fish were reared and experiments were completed in accordance with the guidelines of the Canadian Council on Animal Care under an approved animal care protocol (A20–0070). 2.2.2 Experimental procedure To assess the response to an acute stressor we randomly selected four fish from each population and tank (n = 8 fish per population; four per tank) and transferred them to buckets containing either northern or southern individuals and well-aerated water at the acclimation conditions. These two buckets were then agitated for 30 min (Leech and Taylor, 1980), by 20 shaking the bucket to mimic a transport stress, and the fish were sampled immediately afterwards. To assess the response to repeated stress, we selected four fish from each population and tank (n = 8 fish per population; four per tank) and exposed them to the same stress condition as per the acute treatment, but in this case the fish were returned to the acclimation tank after exposure to the stressor. The bucket agitation was repeated on these fish every day for seven days, beginning on the same day as the acute treatment. Following the last stressor treatment, the fish were immediately sampled. Sampling and bucket agitation were completed between 9 am and 12 pm for both acute and repeated stress experiments to limit effects of diurnal rhythms. Eight control fish from each population were sampled from the tanks and immediately anesthetized at each of the acute and the final repeated stress timepoints. Fish were euthanized with an overdose of buffered anesthetic, MS-222 (0.5 g·L− 1) and sampled as described below. The remaining fish in the tanks were used for an independent experiment and data are not included here. 2.2.3 Blood and tissue sampling Following anesthesia, weight, length, and sex were recorded for each individual and blood samples were taken via caudal tail severance using heparinized capillary tubes. At least 15 μl of plasma was collected from each individual, although typically 0.25–0.5 ml of blood per fish could be collected in multiple capillary tubes. Blood was then centrifuged at 10,000g for 5 min, and plasma was removed and stored at -80°C. The fish were then dissected to remove the brain (including the pituitary), liver and head kidney, which were preserved in 500 μl RNAlater (Invitrogen; Thermo Fisher Scientific). Samples were kept at room temperature for 24 h and then transferred to -80°C. 2.2.4 Plasma cortisol Plasma cortisol was measured using a commercially available ELISA kit (Cayman; 500,360) with a minimum detection limit of 35 pg/ml (Larter and Rees, 2017; Reemeyer et al., 2019). Following the manufactures instructions, samples were run in duplicate and at two different dilutions to ensure samples fell within the range of the standard curve. Intra-assay variation was 21 7.4% and inter-assay variation was 6.7%. Following colour development, the absorbance was measured at 420 nm using a 96-well plate spectrophotometer (SpectraMax 190, Molecular Devices). 2.2.5 Gene expression Total RNA was extracted from the whole-brain (including the pituitary), liver and head kidney for each individual using a RNeasy kit following the manufacturer's instructions (Qiagen; Hilden, Germany). Total RNA concentration and purity was determined for all samples using a Nanodrop 2000c (Thermo Scientific). RNA samples were then stored at -80°C until further use. cDNA was synthesized from 1 μg of DNase treated RNA using a qScript cDNA synthesis kit following the manufacturer's instructions (Quantabio; Beverly, Massachusetts), in a thermocycler (Bio-Rad; Hercules, California) under the following conditions: 1 cycle of 22°C for 5 min, 1 cycle of 42°C for 30 min, and 1 cycle of 85°C for 5 min. The cDNA was then stored at – 30°C until further use. Following cDNA synthesis, qPCR was conducted using a 10-fold cDNA dilution, in nuclease-free water, for all genes except for 18 s which was diluted 25- fold. Real-time quantitative polymerase chain reaction (RT-qPCR) for all genes was performed using Bio-Rad SsoAdvanced Universal SYBR-Green Supermix, containing Sso7d DNA polymerase, an optimized buffer, dNTPs (5 ul per sample), and nuclease free water (3 ul per sample) (ThermoFIsher; Waltham, Massachusetts), as well as 0.5 μl of each primer and 1 μl of cDNA in a total volume of 10 μl. There were 8 genes of interest and 2 reference genes measured across the three tissues (Table 1; Fig. 2.1). Target gene primers were designed using the killifish (mummichog) reference genome on Ensembl and all were assessed for possible secondary structures and non-target binding. Primer efficiency was determined using a 1:10 standard curve dilution of pooled cDNA from all treatments (Table 1), and amplicon size was assessed by gel electrophoresis. Reference gene expression was not different across populations or treatments and both no reverse transcriptase and no template controls were run on each plate. All reactions were run in a RTqPCR machine (Bio-Rad CFX96) on a 96 well plate under the same conditions: 2 min at 95°C, 40 cycles of 15 s at 95°C, 30 s at 60°C, and 30 s at 73°C, a decrease for 1 min to 60°C and 30 s at 22 72°C. Melt curves were determined by denaturation for 15 s at 95°C, a decrease for 1 min down to 60°C and then a gradual increase of 0.075°C·s− 1 to 95°C. Amplification data were analyzed using standard curves created for each gene and the data for genes of interest were normalized to the geometric mean of expression of the two reference genes, 18s and EF1-α (Vandesompele et al., 2002). 2.2.6 Statistical analysis Data were analyzed using two-way ANOVA with treatment (control or stress) and population (northern or southern) as factors within the acute timepoint and in a separate two-way ANOVA within the repeated stress timepoint, using the respective time-matched controls. If the ANOVA detected significant effects, these tests were then followed by planned comparisons, first comparing differences within populations between time-matched control and stress treatments and second comparing between populations in the same treatment, both corrected with a Sidak correction (α = 0.05). Data were tested for normality and homogeneity of variance using a Shapiro-Wilks and Levene's test, respectively, as well as visual inspection of normality (Q-Q) plots. If the data violated either assumption, a log or exponent transformation was applied. 2.3 Results 2.3.1 Plasma cortisol In the acute treatment, plasma cortisol was significantly different between the populations (P = 0.0247), across treatments (P = 0.0234) and there was an interaction between these factors (P = 6 × e− 6 Fig. 2.2). Planned comparisons demonstrated and increase in cortisol in both acutely-stressed populations when compared to their control groups (Southern: P = 2 × e− 6; Northern: P = 1 × e− 6 ). Additionally, the southern population had a higher acute cortisol response than the northern population (P = 0.0017). In the repeated stress treatment, plasma cortisol was different between treatments (P = 5 × e− 10), but there was no effect of population (P = 0.6212) or their interaction (P = 0.5856). Like acutely-stressed fish, planned comparisons confirmed a higher cortisol response in repeated-stress fish compared to their respective controls (Southern: P = 3 × e− 7 ; Northern: P = 2 × e− 10) however, there was no difference 23 between populations during repeated stress. At rest, baseline cortisol did not differ between the two populations for northern and southern populations, in either time-matched control. 2.3.2 mRNA expression Brain gene expression In the acute treatment, CRF mRNA levels were significantly different between populations (P = 0.0124) but there was no effect of treatment (P = 0.1702) or an interaction between the factors (P = 0.5053; Fig. 2.3A). Planned comparisons did not detect any differences between populations in control fish and small differences between populations in acutely-stressed fish (P = 0.0498), and there was no difference between control and acutely-stressed fish in either population. In the repeated stress treatment, there were no significant effects of population (P = 0.6017), stress (P = 0.1152), or their interaction (P = 0.2019). Overall, in the acute treatment, CRFBP expression was significantly different between northern and southern fish (P = 0.0002) with southern fish having higher levels than northern fish, but there was no effect of treatment or their interaction (P = 0.5125; P = 0.6727; Fig. 2.3B). Planned comparisons detected this difference in CRFBP levels between populations in the controls (P = 0.0262) and the acute stress group (P = 0.0035). In the repeated stress treatment, CRFBP expression was significantly affected by population (P = 0.0008) but not treatment (P = 0.2828) or their interaction (P = 0.0765). Planned comparisons detected a difference between populations in the control groups (P = 0.0088) but not between fish exposed to repeated stress. In contrast to CRF and the binding protein, CRFR expression in the acute treatment was not significantly affected by treatment (P = 0.0520), population (P = 0.5587) or their interaction (P = 0.8691); Fig. 2.3C). In the repeated stressor ANOVA, there was a small effect of treatment (P = 0.0437), but no effect of population (P = 0.3577) or their interaction (P = 0.9915), and there were no differences detected in the planned comparisons. There were no differences in POMC at the acute or repeated stress timepoints in population (P = 0.9109; P = 0.8688), treatment (P = 0.9707; P = 0.5713) or their interaction; P = 0.4812; P = 0.4734; Fig. 2.3D). Overall in the acute treatment, GR1 brain gene expression was affected by population (P = 0.0485), but not by treatment (P = 0.3736) or their interaction (P = 0.7663; Fig. 2.3E), and no differences were detected in planned comparisons. 24 The repeated stress treatment had no significant effect of any factor (P = 0.4661; P = 0.2251; P = 0.3589). Similar to GR1, two-way ANOVA within the acute stress treatment indicated that GR2 gene expression was affected by population (P = 0.0196) but not treatment (0.0505), or their interaction (P = 0.5190; Figure 2.3.F), but no differences were detected in planned comparisons. In the repeated stress treatment, there was an effect of treatment (P = 0.0004) and an interaction (P = 0.0075) but no population effect (P = 0.4802). Planned comparisons revealed a difference between populations in the controls at the repeated stress timepoint (P = 0.0364). Head kidney gene expression There was an overall effect of population on MC2R gene expression at the acute timepoint (P = 0.003) but no effect of treatment or their interaction (P = 0.4925; P = 0.6140; Fig. 2.4A). Southern fish had higher mRNA levels, and planned comparisons detected these differences as significant during acute stress (P = 0.0280). Similarly, in the repeated stress treatment, two-way ANOVA detected a population effect (P = 0.0280) but no effect of treatment or their interaction (P = 0.6719; P = 0.9178), but these were not detected in planned comparisons. Overall, at the acute timepoint, StAR gene expression was not affected by population (P = 0.385), treatment (P = 0.8984) or their interaction (P = 0.4831; Fig. 2.4B). However, at the repeated stress timepoint, there was an overall effect of population (P = 0.0318) but no effect of either treatment (P = 0.8306) or their interaction (P = 0.8926), but these were not detected in planned comparisons. There were no differences in GR1 head kidney mRNA expression at the acute timepoint between populations (P = 0.3606), treatments (P = 0.54), or the interaction (P = 0.9374) between factors (Fig. 2.4C). Two-way ANOVA at the repeated stress timepoint similarly revealed no effect of population (p = 0.2120), treatment (P = 0.1920) or their interaction (P = 0.5280). Unlike GR1 expression, there was an effect of the interaction between population and treatment in the expression of GR2 at the acute timepoint (P = 0.0035) but no effect of population (P = 0.1123) or treatment (P = 0.8007), Fig. 2.4D). Planned comparisons revealed a significant difference between the populations, with the southern population having higher gene expression at in fish exposed to acute stress (P = 0.0092) but not in the controls. Planned comparisons also showed a difference between control and acute stress in the northern 25 fish (P = 0.0251). Two-way ANOVA in fish exposed to repeated stress detected no significant effects of population (P = 0.5416), treatment (P = 0.0719) or the interaction (P = 0.8124). Liver gene expression There was an overall effect of population at the acute timepoint on GR1 expression in the liver (P = 8 × e− 6 ; Fig. 2.5A) but no effect of either treatment or the interaction (P = 0.2813; P = 0.4229). Planned comparisons revealed differences between populations at both the control (P = 0.0159) and acute (P = 0.0014) time points with the southern population's GR1 levels being 3 to 4-fold higher than in the northern population. At the repeated stress timepoint, GR1 expression was also affected by population (P = 0.0047) but not treatment (P = 0.6267) or their interaction (P = 0.5571). The population effect was confirmed by the results of planned comparisons with the controls differing between the populations (P = 0.0175). In contrast, GR2 expression was not affected by population, treatment or their interaction at the acute (P = 0.3033; P = 0.9824; P = 0.0728); Fig. 2.5B) or repeated stressor timepoints (P = 0.8536; P = 0.6076; P = 0.5792). 2.3.3 Body condition Wet mass was significantly affected by treatment at the acute timepoint (P = 0.0455; Fig. 2.6A), but was not affected by population or their interaction (P = 0.6285; P = 0.6931). There were no differences in mass detected in planned comparisons tests between groups. At the repeated stress timepoint, there were no effects of population (P = 0.6965), treatment (P = 0.2893) or the interaction of factors (P = 0.754). Length was not affected by population, treatment, or their interaction in the acute (P = 0.2246; P = 0.0511; P = 0.6318) or repeated stress treatments (P = 0.4231; P = 0.4916; P = 0.6053; Fig. 2.6B). At the acute timepoint, Fulton's condition factor (K) had a significant population effect (P = 6 × e− 6 ; Fig. 2.6C) but was not affected by treatment or their interaction (P = 0.2825; P = 0.1466). Planned comparisons revealed southern killifish had a 35% higher condition factor following an acute stressor (P = 0.0001), but the populations did not differ in the control groups. The repeated stress timepoint exhibited the same pattern, with an effect of population on condition factor (P = 0.0014) but not treatment (P = 0.2288) or their interaction (P = 0.0588). Planned comparisons showed southern 26 killifish had higher condition than northern killifish following the repeated stress (P = 0.0011) and no differences were seen in the controls. Further, repeated stress decreased condition in the northern population, as stressed fish had 20% lower condition factor following the week of repeated stress compared to the controls (P = 0.0230), whereas the southern killifish's condition was not affected by repeated stress. 2.4 Discussion Consistent with previous reports of variation in the stress hormone axis among killifish populations, here we show that fish from a southern population have a more reactive cortisol response than fish from a northern population when exposed to a common stressor. As we predicted, southern killifish also exhibited higher levels of expression at key genes in the HPI axis, including CRF and CRFBP in the brain, MC2R and StAR in the head kidney, and GR1 in the liver. One of the most striking difference in expression between the populations was in CRF binding protein in the brain, with levels being higher in southern killifish. This was unexpected because this protein is thought to bind and inactivate CRF and high expression might be expected to dampen the response of the HPI axis. Unlike the clear differences between the populations in mRNA levels for multiple genes, there were relatively few, and somewhat inconsistent, effects of either acute or repeated stress on the expression of the genes we measured. However, stress had strikingly different effects on body condition between the killifish populations. Northern killifish were unable to maintain body condition in the face of repeated stress, which may be an indication of disrupted metabolic homeostasis, whereas southern killifish were able to maintain condition throughout the study. The differences we observed in the mRNA abundance of genes in the CRF pathway between populations may be of particular importance, as these genes are known to regulate both the cortisol response and appetite (Bernier and Peter, 2001; Bernier and Craig, 2005; Conde-Sieira et al., 2018; Backstrom ¨ et al., 2021). These differences may explain the observed differences in body condition following stress between these populations. CRF-producing cells found in the nucleus preopticus start the cascade of the stress response by releasing the CRF peptide. The extent of this CRF signal, which induces the rest of the stress response, is reliant on 27 both the presence and abundance of CRFBP, which is thought to bind and inactivate CRF, and the number of CRF receptors available for binding (Flik et al., 2006). Thus, one might predict increases in plasma cortisol would coincide with increases in CRF-pathway gene expression levels. However, this is a complex feedback loop, as mRNA expression of CRF, CRFBP and CRFR have been shown to change in response to a variety of stressors across vertebrates, with their abundance dependent on the severity and duration of the stressor (McClennen et al., 1998; Doyon et al., 2005; Vindas et al., 2017). In addition to this complexity, measuring CRF, CRFBP and CRFR in whole-brain tissue can be challenging to interpret as CRF can be differentially regulated in different parts of the brain, such that one area may increase in expression another may decrease (Flik et al., 2006). In the current study, CRF expression was different between the two populations only in the acute stress treatment, and expression increased significantly only in the northern population following the repeated stressor. Stress-responsivity of CRF expression has also been observed in rainbow trout exposed to different stressors, with increases in CRF expression observed following a repeated chase event (Doyon et al., 2005) and exposure to hypoxic conditions (Bernier and Craig, 2005). However, there are also studies where genes associated with the CRF pathway were not altered following a stressor in rainbow trout (Moltesen et al., 2016) or in common carp (Huising et al., 2004), most likely due to differences in the stressor. Interestingly in the current study, CRFBP was also differentially expressed between the two killifish populations with the southern population having greater gene expression at all time points, significantly so in both controls and following the acute stressor. There was no effect of stress treatment on CRFBP expression in southern fish, whereas the northern fish downregulated the expression of CRFBP following an acute stressor and expression increased after a repeated stressor. The response of the northern population is similar than what is reported in rainbow trout, where CRFBP gene expression increased after repeated chasing events but not an acute stressor (Doyon et al., 2005). Abundance of CRFBP has been implicated in controlling the amount of available CRF and could be key in controlling the negative feedback response in the brain (McClennen et al., 1998; Doyon et al., 2005; Denver, 2009). The differences between brain control of the HPI axis between the northern and southern populations suggests a difference in 28 capacity for stress and subsequent cortisol production as well as a possible fundamental change in how stress is handled in the negative feedback loop with CRFBP. However, it is possible the interpretation of the negative feedback response is confounded with measuring these genes in whole-brain tissue that may have area-specific regulation. The complex negative feedback within the HPI axis includes response loops at each tissue involved in the stress response. In this study, one of the key regulatory mechanisms in the negative feedback response that is different between the killifish populations are the glucocorticoid receptors. Here we show population differences in both glucocorticoid receptor 1 in the liver and glucocorticoid receptor 2 in the head kidney. Specifically, the southern killifish have higher GR1 liver mRNA expression at the control and acute timepoints, with a similar (although non-significant) pattern following repeated stress, suggesting a higher capacity for cortisol-responsive liver metabolism in general and following a stressor. Previous studies in the common killifish show the southern population have higher levels of liver metabolic enzymes following a stressor when compared to northern killifish (DeKoning et al., 2004) and GR1 could be playing a direct role in those enzymatic differences. In the head kidney, the populations have contrasting responses to stress in GR2 expression. The southern population did not change GR2 expression in response to either acute or chronic stress, whereas the northern population decreased GR2 expression in response to acute stress. As upregulation of GR2 has been reported in the interrenal gland of rainbow trout following a stressor (Kiilerich et al., 2018), the downregulation of GR2 in the northern population could suggest a decreased capacity of the negative feedback system when compared to the southern population. In the liver, GR1 mRNA expression is also dissimilar between the populations, reinforcing the possible differences between their negative feedback loops. However, interpretation of the negative feedback loop can be difficult in a composite tissue, such as the head kidney, which contains sparse interrenal cells. Here, the differences in GRs in the head kidney and liver may illustrate essential differences to the way these populations cope with stress as cortisol levels and associated stress related genes are regulated by GR abundance and vice versa (Aluru and Vijayan, 2009). In addition to differences in the negative feedback loop there are also expression changes in the genes involved in cortisol production. In the head kidney, MC2R, the receptor for 29 ACTH, expression is higher in the southern population across the acute and repeated stress experiments. Additionally, there is an effect of population, only following repeated stress, on the expression of StAR, one of the rate-limiting steps in cortisol synthesis. Together these differences in mRNA levels suggest the southern population has the ability to synthesize more cortisol than the northern population. Differences in these two genes have also been observed when comparing fish species with differing responses to stress. For example, increased MC2R expression coincides with increased cortisol production in high response European sea bass when compared to low response sea bass (Samaras and Pavlidis, 2018) and in rainbow trout exposed to a stressor (Aluru and Vijayan, 2008). The gene expression of StAR is also commonly paired with measuring circulating cortisol levels in fish and increases in StAR typically parallel increases in cortisol in response to stress (Hori et al., 2012a, 2012b; Jeffrey et al., 2014; Skrzynska et al., 2018; Wei et al., 2018; Culbert et al., 2018; Earhart et al., 2020). However, in this study, there were no differences in expression within either population between the control and stressed treatments but the southern population did have higher expression overall. There have also been studies in other teleosts that do not show an increase in StAR mRNA expression following a stressor, which may be because of sampling timing or differences in the regulation of other rate-limiting genes in the HPI axis (Geslin and Auperin, 2004; Nematollahi et al., 2009; Jeffrey et al., 2014; Madaro et al., 2015). In this study, in addition to the brain CRF-pathway changes and liver GR1 expression, the differences in plasma cortisol synthesis could be driven by the observed head kidney expression changes in MC2R and GR2. The other main contributors controlling the metabolic response to stress are the glucocorticoids and their receptors (Vijayan et al., 2003; Rose et al., 2010). Cortisol and GRs form a negative feedback loop at every level in the HPI axis and have the ability to influence the abundance of one another, making their relationship a complex one (Vijayan et al., 2003). In a study with rainbow trout livers, cortisol application increased the GR mRNA abundance and activated the GR signaling pathway which also increased liver glycogen, and plasma glucose (Aluru and Vijayan, 2007; Faught and Vijayan, 2016). GR abundance has also been demonstrated to affect gluconeogenesis in zebrafish GR knockouts where the stress-induced plasma glucose increase was abolished and muscle glucose uptake was enhanced (Faught et al., 2016). Both 30 cortisol and GRs control gluconeogenesis not only in the movement and allocation of glucose but also proteins as well as fats (Mommsen et al., 1999). The differences in cortisol following a stressor between the populations in this study could influence the abundance of liver GRs, ultimately controlling differences in metabolic processes. It is also possible that the sequence differences in the glucocorticoid responsive element of metabolic enzymes in northern and southern killifish causes them to differently metabolically respond to stress (Schulte et al., 2000; DeKoning et al., 2004). In any case, the stark differences in condition and circulating cortisol, most likely driven by brain CRF expression, head kidney expression of genes regulating cortisol synthesis, and head kidney and liver GR expression, suggest a fundamental difference in the way these populations initiate a stress response and possibly the corresponding metabolic response. The genes described above are known to change in response to stress; however, changes in mRNA expression do not always directly correlate with protein abundance. Specifically, the mRNA expression of GRs is not always a direct parallel with GR protein levels (Alderman et al., 2012; Jeffrey et al., 2012; Sathiyaa and Vijayan, 2003). In cortisol treated rainbow trout, there was a higher liver GR mRNA abundance but a lower protein content following a chronic stressor (Sathiyaa and Vijayan, 2003). This illustrates the auto-regulation of GR mRNA by protein abundance because as the proteins are used and degraded, which genomically induces mRNA expression. Further, the abundance of GR proteins is known to affect the mRNA abundance of other genes in the HPI axis (Aluru and Vijayan, 2009; Alderman et al., 2012). In rainbow trout, decreased protein GR levels coincided with decreased CRF mRNA levels and decreased plasma cortisol levels (Alderman et al., 2012). Together, this suggests an interaction not only between GR mRNA, cortisol, and other genes in the HPI axis but also complex interactions with GR protein levels. Additionally, differences in plasma cortisol can also be caused by changes in timing of release or clearance in the blood. Thus, it is possible the differences we demonstrate in gene expression in the brain, kidney, and liver are not the complete story in the HPI axis differences between the two killifish populations. Even though protein levels may not match the mRNA levels measured here, we still demonstrate population differences at both the gene expression level and whole-body physiological measurements in response to stress. 31 Initiating a stress response has many physiological consequences and arguably one of the most important ones is maintaining and allocating energy to handle the stressor. In the current study, not only are there population differences in cortisol and associated gene expression, but there are also differences in the effects of stress on body condition. We demonstrated that following seven days of stress fish from the northern population were in worse condition than the southern population indicating an inability to maintain energy reserves during repeated stress. These differences in body condition could be due to alterations in the CRF pathway and changes in GR abundance. Together, CRF, CRF-BP and GRs are not only involved in the HPI axis but are also implicated in metabolic processes (Bernier and Peter, 2001; Bernier et al., 2012; Conde-Sieira et al., 2018). Bernier and Peter (2001) illustrate the effects of CRF peptides and CRFR antagonists by injecting fish with CRF peptides, which caused a decreased food uptake as well as decreased appetite. A study in food-deprived rainbow trout showed a decrease in hypothalamus CRFBP mRNA abundance and an increase in CRF mRNA abundance resulting in a suppression of appetite (Bernier et al., 2012). In the current study, the northern population increased CRF during the repeated time point, where the southern killifish did not, and the northern killifish also had a loss of condition at the same time point. Further, the southern fish had increased levels of CRFBP in the brain during the control and acute stress timepoints. This protein is thought to bind to CRF (Behan et al., 1995), increasing food intake. Although not quantified, this could suggest the northern fish may have decreased food uptake in response to stress in contrast to the southern population which may have maintained or increased food uptake and appetite throughout the study. Killifish are not the only species to differ in their stress response along a latitudinal gradient, in fact this has been described across many vertebrates, including Willow Warblers (Silverin et al., 1997), White Crowned Sparrows (Wingfield et al., 2008), Cane Toads (Assis et al., 2020) and Eastern Red-backed Salamanders (Novarro et al., 2018). While all these populations listed span different distances, the studies all have similar results, the northern populations are less responsive to stress than the southern populations. This phenomenon suggests there is a benefit to having variation in the stress response along latitudinal gradients and that this intrapopulation variation in the stress response is actively selected upon. One hypothesis to 32 explain this phenomenon is the shorter breeding season paired with more extreme and unpredictable temperatures experienced by northern individuals selects for a reduced stress response in these populations (Silverin et al., 1997; Wingfield et al., 2008; MacDougall-Shackleton et al., 2013). This, in turn, would reserve more energy for survival instead of responding to stressors, rather than expending energy to return to homeostasis following a stressor, like the southern populations. Another plausible hypothesis to explain this occurrence proposes that the ability of ectotherms to complete physiological tasks is dependent on temperature and can often co-vary with latitude as a result of developmental plasticity or local adaptation (Deutsch et al., 2008; Novarro et al., 2018). The question is, what makes the response to stress different? Is it something as simple as increased cortisol production, or something more nuanced like changes in the negative feedback loop? The results of this study indicate it is not one part of the axis that is acted on, but instead multiple steps including their complex feedback loops and interactions are selected upon. Traits with large individual variation, such as HPI negative feedback, allow for selection to occur across a wide range of phenotypes and environments. There is often large variation in negative feedback ability between individuals that is linked to variation in subsequent behavior, reproduction and survival (Alderman et al., 2012; Culbert et al., 2018; Lattin and Kelly, 2020). As such, it is imperative we continue to study the individual variation in HPI/HPA negative feedback and its possible adaptative trade-offs between condition, reproduction and survival in wild populations (Lattin and Kelly, 2020). In this study, we demonstrate multiple differences in the HPI axis within populations of the same species that are separated by latitude. The differences in the feedback loop, underlined by changes in gene expression of many genes at three tissues, subsequently affected responses to both acute and repeated stress and body condition in the killifish. This demonstrates the importance of intraspecific variation in the cortisol mediated stress response, and the potential role of local adaptation of this axis to environmental conditions across latitudinal gradients. 33 Table 2.1. List of forward and reverse primers and their efficiencies used in this study for Killifish (Fundulus heteroclitus) crf, crfbp, crfr, pomc, gr1, gr2, star, mc2r, 18s and ef1-α. Efficiencies are listed as %. Tissues used for each primer are listed: B=whole-brain, L=liver and HK= head kidney. Gene Forward primer Reverse primer Efficiency (%) Amplicon size Tissue crf CCTTGGAGAGGAGTATTTCATC GGATAGTACAAGGATGGGAAAG 96% 69 B crfbp TCGAGAACAACGACGTCTCC CATGTCCAGGCAACGCAAAG 110% 113 B crfr TAGCACTGGCAGGATTGTGG ATGCATTCTCTGTACGCGCT 96% 90 B pomc CGGCTAAACGCTCCTACTCCA GTGTAGACCTTGACGGGGCG 97% 82 B gr1 CAAGACCAGAAGCCCTTTAG ATACCTCTTCCCGCCTATC 100% 75 B, L, HK gr2 GTGTGTGTGTGTCTCCTAAC CAGCTCACGACATATCATCTAC 94% 136 B, L, HK StAR AGATCCGAGCGACAGAAA CCTGGAAAGCAAGAGAACATAA 100% 115 HK mc2r GGGCATTTGTCGTGTGTTGG CACATGCAGCTGGAACAAGG 93% 110 HK 18s TTCAGCCACACGAGATTGAG ATCAACGCGAGCTTATGACC 98% 86 B, L, HK ef1-α TCACCATCAGCTTTTATAAAG GACGCTCCATTGCCTACTGTC 107% 92 B, L, HK 34 2.5 Figures Figure 2.1 The Hypothalamic-pituitary-interrenal (HPI) axis in fish. Following the recognition of a stressor, the HPI stress response begins with the release of corticotropin releasing factor (CRF), a neuropeptide generally produced in neurons of the preoptic area that extend to the pituitary (Cerda-Reverter and Canosa, 2009; Hauger et al., 2007). The binding of CRF to its receptors on the pituitary initiates the release of proopiomelanocortin (POMC) from the pituitary, a precursor polypeptide to the molecule adrenocorticotropin hormone (ACTH). ACTH then binds to melanocortin 2 receptors (MC2R) on the interrenal cells of the head kidney initiating steroidogenesis of the hormone cortisol. Inside the interrenal cells the production of cortisol requires the movement of cholesterol to the inner-mitochondrial membrane by steroidogenic acute regulatory protein (StAR) (Stocco et al., 2005). This is considered one of the rate limiting steps in steroidogenesis and cortisol synthesis after which cortisol is released from the interrenal cells into the bloodstream. Once in circulation, cortisol binds to glucocorticoid receptors (GRs), GR1 and GR2 which are found in every tissue. Illustration created by Rashpal Dhillon. 35 Figure 2.2 Plasma cortisol (ng·ml− 1) concentrations in two populations of the common killifish (Fundulus heteroclitus) across three treatments: control, acute stress, and repeated stress. The box plot line represents the median, the box extends from the 25th to 75th percentiles, and whiskers are 5–95% confidence intervals. The control treatments are outlined in green and stress treatments are in blue. Acute stress is on the left and repeated stress is on the right. P-Values from two-way ANOVA are reported for each timepoint (Pop = Population; Trt = Treatment; Int = Interaction). Asterisks represent significant differences (P < 0.05) between control and stress treatments within populations. A and B letters represent significant differences between populations within control groups (P < 0.05). X and Y letters represent significant differences between populations in the stress treatments (P < 0.05; N = 7–8). Northern Southern Northern Southern0200400600Cortisol ng.ml-1ControlStressxy****Acute RepeatedPop: P=0.0247*Trt: P=0.0234*Int: P=6e-6*Pop: P=0.6212Trt: P=0.5e-10*Int: P=0.5856 36 Figure 2.3 Brain mRNA gene expression of genes involved in the hypothalamic-pituitary-interrenal (HPI) axis in two populations of the common killifish (Fundulus heteroclitus) across three treatments: control, acute stress and repeated stress. Panel A is CRF mRNA expression, panel B is CRFBP gene expression, panel C is CRFR expression, panel D is POMC expression, panel E is GR1 expression, and panel F is GR2 expression. The box plot line represents the median, the box extends from the 25th to 75th percentiles, and whiskers are 5–95% confidence intervals. The Northern Southern Northern Southern012345CRF mRNA expressionAcute RepeatedabAPop: P=0.0124*Trt: P=0.1702Int: P=0.5053Pop: P=0.6017Trt: P=0.1152Int: P=0.2019Northern Southern Northern Southern0.00.51.01.52.0CRF R mRNA expressionAcute RepeatedCPop: P=0.0520Trt: P=0.05587Int: P=0.8691Pop: P=0.3577Trt: P=0.0437*Int: P=0.9915Northern Southern Northern Southern0123GR 1 mRNA expressionAcute RepeatedEPop: P=0.0485*Trt: P=0.3736Int: P=0.7663Pop: P=0.4661Trt: P=0.2251Int: P=0.3589Northern Southern Northern Southern02468CRF BP mRNA expression ControlStressxyabAcute RepeatedabBPop: P=0.0002*Trt: P=0.5125Int: P=0.6272Pop: P=0.0008*Trt: P=0.2828Int: P=0.0765Northern Southern Northern Southern01234POMC mRNA expressionAcute RepeatedDPop: P=0.9109Trt: P=0.9707Int: P=0.5713Pop: P=0.8688Trt: P=0.5713Int: P=0.4734Northern Southern Northern Southern0.00.51.01.52.02.5GR 2 mRNA expressionAcute Repeated*abFPop: P=0.0196*Trt: P=0.0505Int: P=0.5910Pop: P=0.4802Trt: P=0.0004*Int: P=0.0075* 37 control treatments are in grey and stress treatments are in blue. Acute stress is on the left and repeated stress is on the right. P-Values from two-way ANOVA are reported for each timepoint (Pop = Population; Trt = Treatment; Int = Interaction). Asterisks represent significant differences (P < 0.05) between control and stress treatments within populations. A and B letters represent significant differences between populations within control groups (P < 0.05). X and Y letters represent significant differences between populations in the stress treatments (P < 0.05). All data are normalized to the geometric mean of two control genes (18S, EF1a). CRF (N = 6–8), CRFBP (N = 7–8), CRFR (N = 6–8), POMC (N = 7–8), GR1 (N = 6–8), GR2 (N = 6–8). 38 Figure 2.4 Head kidney mRNA gene expression of genes involved in the hypothalamic-pituitary-interrenal (HPI) axis in two populations of the common killifish (Fundulus heteroclitus) across three treatments: control, acute stress, and repeated stress. Panel A is MC2R gene expression, panel B is StAR mRNA expression, panel C is GR1 expression, and panel D is GR2 expression. The box plot line represents the median, the box extends from the 25th to 75th percentiles, and whiskers are 5–95% confidence intervals. The control treatments are in grey and stress treatments are in blue. Acute stress is on the left and repeated stress is on the right. P-Values from two-way ANOVA are reported for each timepoint (Pop = Population; Trt = Treatment; Int = Northern Southern Northern Southern0123MC2R mRNA expressionxyAcute RepeatedAPop: P=0.003*Trt: P=0.4925Int: P=0.6140Pop: P=0.0280*Trt: P=0.6719Int: P=0.9178Northern Southern Northern Southern1.01.52.02.53.0GR 1 mRNA expressionAcute RepeatedCPop: P=0.3606Trt: P=0.54Int: P=0.9374Pop: P=0.2120Trt: P=0.1920Int: P=0.5280Northern Southern Northern Southern0.00.51.01.52.0StAR mRNA expressionControlStressAcute RepeatedBPop: P=0.385Trt: P=0.8984Int: P=0.4831Pop: P=0.0318*Trt: P=0.8306Int: P=0.8926Northern Southern Northern Southern0.00.51.01.52.0GR 2 mRNA expressionAcute Repeated*xyDPop: P=0.1123Trt: P=0.5280Int: P=0.0035*Pop: P=0.5416Trt: P=0.0719Int: P=0.8214 39 Interaction). Asterisks represent significant differences (P < 0.05) between control and stress treatments within populations. A and B letters represent significant differences between populations within control groups (P < 0.05). X and Y letters represent significant differences between populations in the stress treatments (P < 0.05). All data are normalized to the geometric mean of two control genes (18S, EF1a). MC2R (N = 7–8), StAR (N = 6–8), GR1 (N = 7–8), GR2 (N = 6–8). 40 Figure 2.5 Liver mRNA gene expression of two genes involved in the hypothalamic-pituitary-interrenal (HPI) axis in two populations of the common killifish (Fundulus heteroclitus) across three treatments: control, acute stress, and repeated stress. Panel A GR1 mRNA expression and panel B is GR2 expression. The box plot line represents the median, the box extends from the 25th to 75th percentiles, and whiskers are 5–95% confidence intervals. The control treatments are in grey and stress treatments are in blue. Acute stress is on the left and repeated stress is on the right. P-Values from two-way ANOVA are reported for each timepoint (Pop = Population; Trt = Treatment; Int = Interaction). Asterisks represent significant differences (P < 0.05) between control and stress treatments within populations. A and B letters represent significant differences between populations within control groups (P < 0.05). X and Y letters represent significant differences between populations in the stress treatments (P < 0.05). All data are normalized to the geometric mean of two control genes (18S, EF1a). GR1 (N = 6–8), GR2 (N = 7–8). Northern Southern Northern Southern051015GR 1 mRNA expressionabxyabAcute RepeatedPop: P=0.8e-6*Trt: P=0.2813Int: P=0.4229Pop: P=0.0047*Trt: P=0.6267Int: P=0.5571Northern Southern Northern Southern0.00.51.01.52.02.5GR 2 mRNA expressionControlStressAcute RepeatedPop: P=0.3033Trt: P=0.9824Int: P=0.0728Pop: P=0.8536Trt: P=0.6067Int: P=0.5792A B 41 Figure 2.6 Morphometric measurements in two populations of the common killifish (Fundulus heteroclitus) across three treatments: control, acute stress, and repeated stress. Panel A is the wet mass (g), panel B is the total length (cm), and panel C is the condition factor (K). The box plot 42 line represents the median, the box extends from the 25th to 75th percentiles, and whiskers are 5–95% confidence intervals. The control treatments are in grey and stress treatments are in blue. Acute stress is on the left and repeated stress is on the right P-Values from two-way ANOVA are reported for each timepoint (Pop = Population; Trt = Treatment; Int = Interaction Asterisks represent significant differences (P < 0.05) between control and stress treatments within populations. A and B letters represent significant differences between populations within control groups (P < 0.05). X and Y letters represent significant differences between populations in the stress treatments (P < 0.05). Weight, length, condition (N = 8). 43 Chapter 3. Persistent interactive effects of developmental salinity and temperature in Fundulus heteroclitus 3.1 Introduction In aquatic environments, exposure to multiple abiotic stressors including temperature, dissolved oxygen, pCO2, and salinity are worsening due to the impacts of climate change (Walther et al., 2002; Caldeira and Wickett, 2003; Diaz and Breitburg, 2009; Altieri and Gedan, 2015; Loder et al., 2015; Friedman et al., 2017; Breitburg et al., 2018). Unfortunately, most of the evidence describing the impacts of these stressors on aquatic organisms such as fish is derived from experiments evaluating these stressors in isolation; however, real-world environmental change involves the interactions of many stressors (Todgham and Stillman, 2013; Przeslawski et al., 2015; Gunderson et al., 2016; Earhart et al., 2022). The complex interactions among multiple stressors have the potential to result in “ecological surprises” (Lindenmayer et al., 2010) causing significant uncertainty in our ability to predict the impacts of climate change on organisms and ecosystems (Piggott et al., 2015; Orr et al., 2020). In principle, when an organism is exposed to simultaneous stressors, the resulting effect on performance can be additive, antagonistic, or synergistic (Todgham and Stillman, 2013; Gunderson et al., 2016; Earhart et al., 2022). Additive effects result when the cumulative effects of multiple stressors are equal to the sum of each stressor in each isolation, antagonistic effects occur if the combined effect is less than the sum of the effects in isolation, and synergistic effects occur if the combination of stressors has a larger effect on performance than expected from their isolated impacts. Synergistic effects are often observed when organisms are exposed to climate change stressors such as high temperatures in combination with changes in hypoxia, or high pCO2 (Przeslawski et al., 2015; Earhart et al., 2022). Thus, to evaluate the impacts of cumulative climate change related stressors, methods using combined stressor exposures can more accurately evaluate realistic impacts on fishes. The effects of combined stressors may be particularly important for the early life stages of fishes, which are often considered to be more sensitive than are adult stages because of their 44 high metabolic and the intrinsic sensitivity of delicate early developmental processes (Przeslawski et al., 2015; Dahlke et al., 2020; Pottier et al., 2022). In addition, developing fishes have limited ability to swim away from unsuitable conditions, and thus cannot use behavioral mechanisms to avoid or cope with environmental stressors. It is possible that developing fishes may be able to recruit various forms of plasticity to offset the impacts of environmental stressors. However, plasticity is energetically costly and the high metabolic demand of early developmental stages and their reliance on endogenous food reserves may constrain the amount of energy allocated to plastic responses (Pottier et al., 2022). In addition to plasticity during development, environmental exposures during sensitive early development stages can induce a type of long-lasting change that results in effects that persist later in life, and even into adulthood, a process which is termed developmental plasticity (Earhart et al., 2022; Pottier et al., 2022). It is important to note that some developmentally plastic traits are reversible and depend on the phenotype and the type of exposure being examined (Earhart et al., 2022). In this study, we examine the interactive effects of temperature and salinity during early development, and the potential for long-lasting developmental plasticity. To our knowledge, there are no other studies in fishes examining the possibility of persistent developmental effects of temperature and salinity on various phenotypes later in life. In fact, the effects of interacting stressors on developmental plasticity and the possibility of persistent effects have seldom been examined in fishes. Of the studies comparing combined stressor effects in fishes, most of the literature encompasses the consequences of hypoxia or pCO2 in combination with nonoptimal temperatures but there is far less research on the interactive effects of changing salinity with other stressors (Heckwolf et al., 2018). A few studies to date have demonstrated that the combination of thermal and osmotic stress can have either additive or synergistic effects on the physiology of fishes at the adult stage, demonstrating the potential importance of interactive effects (Ban et al., 2014; Przeslawski et al., 2015; Jackson et al., 2016), but in general this combination of stressors has received relatively limited attention, and very little work has been performed on early developmental stages. This oversight is unfortunate, as salinity has profound effects on the physiology, performance, range distribution, and survival of fishes (Morgan and 45 Iwama, 1991; Bœuf and Payan, 2001; Ern et al., 2014; Evans and Kültz, 2020), and human impacts are causing changes in salinity in many habitats that may include either desalinization or saltwater intrusion, depending on the habitat (Loder et al., 2015; Lauchlan and Nagelkerken, 2020). In this study, we investigated the short term and persistent effects of exposure to changes in salinity and temperature and their interaction during development in Fundulus heteroclitus, a species of killifish found in marshes and estuaries along the east coast of North America. These habitats exhibit substantial temporal and spatial variation in both temperature and salinity, such that there are diurnal, tidal, and seasonal fluctuations in both water temperature and salinity. To survive in these habitats all life stages, including embryos, must be able to cope with these interacting stressors and there is evidence of substantial local adaptation to both temperature and salinity in this species (Schulte, 2007; Healy et al., 2018). We exposed killifish embryos from the southern subspecies (Fundulus heteroclitus heteroclitus) to four different combinations of salinity and temperature to test the interacting effects of these stressors at multiple levels of biological organization during development and to test the persistent effects of these exposures after returning to common conditions four months following hatch. These treatments were chosen to simulate warm (26°C) and cooler (20°C) temperatures with a combination of either brackish water (20ppt) or freshwater (3ppt). Embryos of the southern subspecies are capable of developing at temperatures from 20-30°C (DiMichele and Westerman, 1997) and at salinities from 0-30ppt (Dimichele and Taylor, 1980) In general, the southern subspecies is considered to be warm-adapted and to be more successful at acclimating to higher salinities, at least when tested at the adult stage, and as such the 26°C 20ppt treatment is likely to be the least stressful combination of developmental conditions used in this study. During development we measured mRNA levels of multiple genes throughout embryogenesis, hatch success and timing, and at four months after hatch we measured juvenile size, critical thermal maximum (CTmax), and mRNA transcript levels before and after an acute thermal stressor. We aimed to investigate the following questions: (1) Is the combination of low temperature and low salinity worse for killifish than the stressors in isolation? (2) How is the 46 molecular response, hatch time, and survival affected by developmental conditions? (3) Are there persistent effects of developmental conditions on the size or thermal tolerance of juvenile fish after several months in common conditions? And (4) Do juvenile fish exposed to different developmental environments have persistent changes in mRNA transcript levels that reflect any observed differences during development? 3.2 Methods 3.2.1 Killifish husbandry Fundulus heteroclitus were collected from the southern part of their range in Jekyll Island, Georgia, USA in September 2019 and shipped to the University of British Columbia for subsequent research. Following quarantine, adult fish were held in a recirculating aquatic system at 20°C and 20ppt with a 12:12 light dark cycle. Fish were fed fish flake food daily and water quality was checked three times a week. To induce spawning, adult killifish were transferred to 20-gallon tanks at 20°C 20ppt, and temperature was increased 0.5°C·day-1 until a temperature of 25°C was achieved. During this time adult killifish were fed bloodworms and Mysis shrimp (Hikari) twice daily until satiation. In June 2020, we fertilized eggs from seven females with sperm from seven males using previously described methods (McKenzie et al., 2017). Briefly, both gravid males and females were lightly sedated with anesthetic (MS-222 0.5g·L-1 buffered with sodium bicarbonate) and stripped of sperm and eggs, respectively. Eggs from all females were mixed together in a petri dish (25°C 20ppt) and the sperm from all males was added. The eggs and sperm incubated for an hour prior to checking for a fertilization envelope under a microscope. Embryos were then randomly separated into four treatments 2 hours following fertilization: 20°C & 20ppt, 20°C & 3ppt, 26°C & 20ppt, 26°C & 3ppt (Fig. 3.1). The killifish embryos were held in two replicate Petri dishes per treatment, with ~47 embryos per dish, in temperature-controlled incubators (MIR-154, PHCBI; set to 20°C or 26°C) with daily water changes to maintain oxygenation and salinity. For the first three days of incubation, the water in each petri dish contained diluted methylene blue (0.0003%) as a fungicide and water was changed daily. Embryos were monitored daily for hatching with the number of embryos that hatched and the day of hatch recorded. 47 Following hatch, the individual larvae were moved to a common environment of 20°C and 20ppt in a recirculating zebrafish rack. Larvae from separate developmental treatments were kept in different tanks, with 3 replicate tanks for each treatment. The larval killifish were able to feed immediately following hatch, and as such they were fed freshly-hatched brine shrimp (Artemia) three times daily and water quality was checked twice weekly. All experiments were performed according to approved animal use protocol A20-0070. 3.2.2 Critical thermal maximum (CTmax) Four months post hatch, the maximum thermal tolerance of juvenile killifish was assessed as CTmax, using loss of equilibrium (LOE) as the trial endpoint (Fangue et al., 2006). Killifish were placed into individual beakers inside a water bath and the temperature was gradually increased at a rate of 0.3°C/minute until fish displayed LOE. All beakers had an air stone to maintain air saturation and each also had their own thermometer. Following CTmax, juveniles were euthanized with an overdose of buffered MS-222 (0.5g·L-1) and wet mass (nearest g) and length (nearest mm) were recorded for each fish. Whole-body samples were then placed in RNAlater and left in the fridge (4°C) for 24 hours prior to being placed in a -80°C freezer, for subsequent analysis. CTmax trials were conducted over two days between 10 am and 12 pm to limit circadian changes (Healy and Schulte, 2012). Following the trial on the last day, immediately following removal from their tanks, control killifish were euthanized as described above, to assess control levels of mRNA. 3.2.3 RNA extraction and quantitative real time PCR (qRT-PCR) RNA extraction was performed using a Qiagen RNeasy Mini Kit according to the manufacturer's instructions. RNA extraction and quantitative real time PCR (qRT-PCR) were conducted for both embryos and juveniles (control and post CTmax) to examine the mRNA levels during development and to test for persistent developmental plasticity in mRNA levels. For the embryo RNA extraction process, 3 embryos were pooled for each sample to ensure sufficient material for detection of mRNA expression. For juvenile fish, whole-body homogenates were 48 used which included all internal organs. A Nanodrop 2000 (Thermofisher) was then used to quantify the amount and purity of the extracted RNA. cDNA was synthesized using a qScript cDNA synthesis Kit (Quantabio) following the manufacturer’s instructions. DNase I (Thermofisher) treatment was conducted for each sample prior to cDNA synthesis. A thermocycler (T100 thermal cycler BioRad) was used for cDNA synthesis as follows: 1 cycle of 22 °C for 5 min, 1 cycle of 42 °C for 30 min, 1 cycle of 85 °C for 5 min, followed by a hold at 4°C. Prepared cDNA was stored at -30°C for future use. To determine primer efficiency and to construct standard curves, we used pooled cDNA with efficiency tests run for all genes in both a 1:10 and 1:5 dilution series to determine the most efficient dilution for subsequent analysis of all samples (table 1). Hsp70-1, hsp70-2, hsc70, hsp90a, and b-actin were run with cDNA diluted to 1:10, while 18s was run with cDNA diluted to 1:25, and cDNA for the hsp90b assay was not diluted. Both reference genes, 18s and b-actin were checked for stability across all treatments and were not different between groups (Chapman and Waldenström, 2015). All reactions were completed using a RT-qPCR machine (Bio-Rad CFX96) in a 96-well plate using SYBR master mix under the following recommended cycling conditions: 2 min at 95°C, 40 cycles of 15s at 95°C and 30s at 58°C, and melt curves were produced by denaturation for 15s at 95°C, a decrease for 10s down to 60°C and then a gradual increase of 0.5°C s−1 to 95°C. Amplification data were normalized to the geometric mean of expression of the stable reference genes and then analyzed using the 2△△CT method (Schmittgen and Livak, 2008); the control group used to normalize data was the 26°C 20ppt group. 3.2.4 Statistical Analysis Embryo life stage Embryo survival was analyzed using a Cox proportional-hazards model to evaluate hatch differences across treatments and generate survival curves in R (R Core Team, 2013). We assessed time to hatch using a two-way ANOVA with temperature, salinity, and their interaction as fixed factors. Two statistical analyses for mRNA abundance were completed. First, we performed a principal component analysis (PCA) using the “factomineR” and “factoextra” (Lê et al., 2008; 49 Kassambara and Mundt, 2021) packages in R. All developmental data (5, 10 and 15dpf) were analyzed in a PCA to assess how mRNA levels change during development and between incubation treatments. Additionally, three-way ANOVAs were used to analyze mRNA expression data for each gene during embryogenesis, with dpf, temperature, and salinity, and their interaction included in the model as fixed factors. To test for differences within a developmental time point we analyzed each day (5, 10 and 15) separately with a Tukey’s HSD test. To test for differences within an incubation treatment across developmental days we analyzed each treatment separately (26°C 20ppt, 26°C 3ppt, 20°C 20ppt, and 20°C 3ppt) with a Tukey’s HSD test. Juvenile life stage Changes in wet mass, length, and CTmax data were tested using two-way ANOVA with developmental temperature and salinity as fixed factors. As was the case for analyses at the embryo life stage, two statistical analyses for mRNA abundance were completed. A PCA was performed in R using the same packages listed above. Data for mRNA levels were divided into a control group and a post CTmax group to assess any persistent changes in mRNA levels at rest and following a thermal stressor. Second, we analyzed juvenile control and post-CTMax gene expression data for each gene with three-way ANOVA with temperature, salinity, and CTmax exposure, and their interactions as fixed effects. We then divided the data into control and CTmax groups to determine differences between developmental groups at control or after a thermal stressor with a Tukey’s HSD. To assess differences between control and CTmax within a developmental group we divided the data into developmental subgroups and tested for differences with a Tukey’s HSD. All ANOVAs were completed in Graphpad Prism (Version 9.0.2) and Shapiro-Wilk and Levene’s tests were used to assess normality of data and homogeneity of the variance, respectively. Normality was also visually inspected using residual plots. If assumptions were violated either a log, exponent, or square root transformation was applied to the data. Following ANOVAs, data were corrected with Tukey's HSD tests. All statistical analyses were performed with a significance level of 0.05. Graphpad Prism (Version 9.0.2) was used to graph all the data. 50 3.3 Results 3.3.1 Embryo hatch success The Cox-proportional hazard model determined survival was significantly different between the 26°C treatments and the 20°C treatments (P < 0.0001) as both 26°C treatments had increased survival compared to their 20°C counterparts (Fig. 3.2a). Salinity affected survival of embryos in the 20°C group only, where embryos in freshwater had lower survival than fish in brackish water (P < 0.001). Two-way ANOVA revealed effects of temperature, salinity, and their interaction on time to hatch (Fig. 3.2b; P < 0.0001, P < 0.0001, P < 0.001, respectively). Both 26°C treatments completed hatch by 15dpf, whereas the 20°C treatments did not begin hatching until 15dpf. Additionally, there was a significant effect of salinity between the 20°C groups, where the 20°C 20ppt group hatched before the 20°C 3ppt group. There was no difference in hatch day between the different salinity treatments at 26°C. 3.3.2 Embryo gene expression In the PCA during embryogenesis, PC1 explained 32.6% of the variance and PC2 explained 24.6% (Fig. 3.3). The genes that contributed the most to the variance were hsp90a, hsp90b, hsp70.2, and hsc70. We observed the largest difference in the putatively most stressful treatment, 20°C 3ppt, where at 5dpf this group is located in the top left quadrant distant from the other treatments. This suggests larger differences in mRNA levels in this low salinity and temperature incubation group at this developmental stage. All 5dpf embryo treatments are in the top half of the PCA where by 15dpf the treatments have shifted to the bottom half of the PCA, suggesting PC2 variance may be mostly explained by developmental time. In the 26°C treatment groups, there were relatively modest differences across developmental time, suggesting this temperature incubation creates a different mRNA response during development than in the 20°C treatments, which exhibit much larger shifts during development. In fact, there are minor differences between 10dpf and 15dpf in both 26°C groups, where the 20°C groups demonstrate large changes in space between all three developmental timepoints. 51 We used a three-way ANOVA with temperature, salinity, and dpf as the fixed factors to evaluate differences in mRNA levels during embryogenesis for each gene (See appendix 1). For hif1a mRNA levels, three-way ANOVA detected an effect of time (P < 0.05), temperature (P < 0.05), and an interaction of temperature and salinity (P < 0.05). Three-way ANOVA also detected significant effects of the interaction of time and temperature (P < 0.01) for hsp90a mRNA levels but all other factors were not statstically significant (P > 0.05). Additionally, hsp90b levels were significantly affected by all three factors, developmental time (P < 0.0001), temperature (P < 0.0001) and salinity (P < 0.05) The interaction of time and temperature (P < 0.0001) was also shown to significantly effect hsp90b transcript levels. Only temperature (P < 0.05) significantly affected hsc70 transcript abundance during embryogenesis and all other factors were not significant (P > 0.05). Abundance of hsp70.1 mRNA was affected by the interaction between developmental time and temperature (P < 0.05) which was determined by three-way ANOVA, and all other factors were not significant (P > 0.05). Finally, hsp70.2 mRNA abundance was affected by the interaction between temperature and salinity (P < 0.05). 3.3.3 Juvenile morphometrics and CTmax CTmax was assessed for fish from the four developmental treatments and LOE was observed in all treatments between 37°C- 39°C. No significant differences were found in CTmax (Fig. 3.4a) among developmental temperature, salinity, or their interaction (P > 0.05) as determined by two-way ANOVA. We also used two-way ANOVA to detect lasting differences in wet mass (g; Fig. 3.4b), length (mm; Fig. 3.4c), and condition factor (K; Fig. 3.4d) of the juvenile fish. There were lasting effects of developmental temperature and salinity, but not their interaction, on wet mass (g; Fig. 3.4b) and length (mm; Fig. 3.4c) in juvenile killifish (P < 0.05) where killifish reared in cooler temperatures (20°C) were larger than the 26° counterparts, and killifish reared in lower salinity were also larger than fish reared in 20ppt. Condition factor (K; Fig. 3.4d) was not significantly affected by any of the factors in the two-way ANOVA. 52 3.3.4 Juvenile gene expression In the PCA for juvenile killifish prior to and after CTmax, PC1 explained 41.6% of the variance and PC2 explained 32.2%. The genes that contributed the most to this variance were hsp90b, hif1a, and hsp90a. Four months after transfer to a common environment, we still observed differences in baseline mRNA levels. In the PCA, the 26°C 20ppt group is in the top right corner where all three other developmental groups are in the middle left of the PCA. This suggests the 26°C 20ppt group differed in their baseline mRNA levels. Following CTmax, all four groups shift to the bottom right corner. Both of the 20ppt groups have a larger spatial change following CTmax, suggesting developmental salinity effects mRNA plasticity in response to an acute thermal stressor. We used a three-way ANOVA with developmental temperature, developmental salinity, and CTmax as the factors to determine significant effects on mRNA abundance in juvenile killifish prior to and following CTmax (See Appendix 1). The transcript abundance of hif1a was significantly affected by two interactions, developmental temperature and CTmax (P < 0.01) and developmental salinity and CTmax (P < 0.05). All other factors were not detected as significant (P > 0.05). The mRNA abundance of hsp90b had a similar overall pattern to hif1a mRNA abundance, and also was significantly affected by the interaction between developmental temperature and CTmax (P < 0.05). Similarly, hsp70.2 also had a significant interaction between developmental temperature and CTmax (P < 0.001), and also followed a comparable pattern to hif1a and hsp90b. The overall abundance of hsp70.2, hsp70.1, and hsp90a was only significantly affected by CTmax (P < 0.0001) and no other factors. There were no significant differences detected by the two-way ANOVA for hsc70 mRNA levels. 3.4 Discussion In this study we demonstrate developmental and persistent effects from embryonic incubation in various salinities and temperatures on killifish physiology. During embryogenesis, killifish that were incubated in 20°C took twice as long to hatch as embryos incubated at 26°C. Salinity affected survival and the number of hatched larvae in the 20°C treatments, with the lowest hatch rate and survival in the 20°C 3ppt treatment, but this effect was not observed at 53 the higher temperature. This highlights that the combination of low temperature and salinity had synergistic effects on survival and time to hatch, but that this interactive effect is conditional, depending on the particular combination of stressors. Additionally, we observed differences in baseline mRNA levels throughout development dependent on temperature, salinity, and their interaction. During development, hsp70.2, hsp90a, and hsp90b differed the most between treatments, with an interactive effect of temperature and salinity such that the 20°C 3ppt treatment typically had higher expression than the other groups, indicating that these fish may be responding to acute and chronic stress as these genes are known to increase during thermal and salinity stress in fish (Fangue et al., 2006; Schulte, 2007; Tine et al., 2010; Evans and Kültz, 2020; Yusof et al., 2022). At the juvenile life stage, we observed differences in the wet mass and length of the killifish, highlighting the persistent effects of developmental conditions, which is evidence of developmental plasticity, such that the killifish incubated in cooler temperatures and lower salinity were larger than their warm and high salinity counterparts. However, there were no differences in thermal tolerance at the juvenile stage suggesting the environments during incubation did not have persistent effects on maximum thermal tolerance at the whole-organism level. Four months after hatch, juvenile killifish still had persistent differences in mRNA levels in control fish and in fish following CTmax. Interestingly, the two genes that contributed the most to the differences observed at the juvenile life stage were hsp90b and hif1a suggesting persistent molecular changes in genes associated with chronic stress. Our data suggest that incubation in stressful combinations of low temperature and low salinity affected hatch and mRNA levels during developmental exposure and that these differences had persistent effects on body size and mRNA levels at the juvenile life stage. The combination of low temperature and low salinity has worse impacts on killifish embryo survival and hatching rate than the stressors in isolation Exposure to multiple stressors at once can be challenging for fish to endure, especially during the delicate embryo life stage (Dahlke et al., 2020). Here, the worsened effects of the combination of low temperature and salinity on killifish was apparent across multiple traits and had persistent effects at the juvenile stage. We observed decreases in survival and longer time to 54 hatch in embryos that were exposed to the combination of low temperature and salinity, indicating these stressors had a synergistic effect on these traits. These stressors in isolation have been shown to affect embryo development and hatching rate in other aquatic species. For example, decreased embryo survival in response to low salinity exposure has also been identified in other marine and estuarine species (Hassell et al., 2008; Shi et al., 2008; Delorme and Sewell, 2014; Ackerly et al., 2023). Unfortunately, climate change is increasing freshwater intrusion and rainfall, ultimately exposing both estuarine and intertidal species to lower salinities more often (Lauchlan and Nagelkerken, 2020). Likewise, temperatures below the thermal optimum have been shown to affect fish embryo development and survival (Murray and McPhail, 1988; Wiegand et al., 1989; Rana, 1990; Ojanguren and Braña, 2003). Most thermal stress studies in fishes focus on climate warming effects, but as our weather patterns become more extreme and freshwater intrusion continues fish may also experience longer exposures to temperatures below their thermal optimum. There are a few studies investigating the effects of the combination of low temperature and salinity on fish embryo survival and hatching rate and effects are dependent on the species and the habitat of those species. In most species, this combination is synergistic and has negative effects on both survival and hatch (Laurence and Rogers, 1976; Laurence and Howell, 1981; Marangos et al., 1986; Walsh et al., 1991; Thiyagarajan and Qian, 2003; Berlinsky et al., 2004; Fockedey et al., 2006; Cowart et al., 2009; Abdo-de la Parra et al., 2012). In other species the effects of low temperature and salinity are negligible on hatch and survival of embryos (Watanabe et al., 1999; Hassell et al., 2008; Brown et al., 2011). As killifish are an estuarine species, they are considered extremely tolerant to osmolality and thermal changes (Schulte, 2007), but in this study we demonstrate that during development even they are susceptible to the combination of these stressors. This is concerning for other, less-tolerant estuarine and tidal fish species and the effect of these concurrent stressors on embryo survival and hatch rate warrants further investigation in other species. Incubation in different temperatures and salinities effect mRNA levels and mRNA plasticity during development 55 In this study we demonstrate changes at the molecular level in genes associated with thermal and osmotic stress during embryogenesis. Specifically, during development hsp90a, hsp90b and hsp70.2 drove most of the observed variation between our treatments. The embryos in the 20°C 3ppt treatment demonstrate larger changes in mRNA levels during development, suggesting they are responding to a more stressful environment than the other treatments using alterations in gene expression that affect mRNA levels. Changes in hsps are typically thought to be associated with warm thermal stress, however here we demonstrate that these genes are also responsive to cold temperature stress during development, perhaps due to their activity as chaperones. Changes in hsp mRNA levels in response to cold stress have also been seen in other adult fishes and highlight the importance of the heat shock response to both warm and cold water acclimation (Iwama et al., 1999; Donaldson et al., 2008). Salinity stress has also been demonstrated to increase hsp mRNA levels in aquatic organisms (Moreira-de-Sousa et al., 2018; Zabinsky et al., 2019; Ai et al., 2020; Evans and Kültz, 2020; Ghelichpour et al., 2020; Pourmozaffar et al., 2020; Yusof et al., 2022). As such, it is not surprising the killifish have differences in these genes and demonstrates the ability of the embryos to mount a molecular response to multi-stressors during development and highlights the plasticity of these fish at an early life stage. There are persistent effects of developmental conditions on the size of juvenile individuals but not thermal tolerance Four months following hatch, and after long term exposure to common conditions, the juvenile killifish were different in size highlighting persistent developmental plasticity at the whole organism level. The killifish that were incubated in cooler temperatures during development were larger than the fish incubated in warmer temperatures, even after four months at a common temperature 20°C. Across ectotherms, acclimation to warm temperatures is known to drive reductions in body size (Verberk et al., 2011; Audzijonyte et al., 2019; McKenzie et al., 2021; Weeks et al., 2022), and this is thought to be the basis of the temperature-size rule (Atkinson, 1994). Likewise, rearing ectotherms in cooler temperatures typically results in larger body size than individuals reared in warm conditions (Atkinson and 56 Sibly, 1997). However, it is less clear if warm or cool temperature exposure only during embryogenesis has persistent effects on size in later life stages in fishes. Here we demonstrate effects of cold developmental temperature on both mass and length. Unfortunately, in our study fish size was not measured immediately following hatch, so it is unclear if the differences in size observed at the juvenile stage are the result of persistent effects as a result of differences in size at hatch, or whether they are the result of differences between groups in growth rate following hatch. For example, in some ectotherms exposure to cooler developmental temperatures can cause smaller body size at the time of hatch, but eventually the organism can compensate for this reduction in growth and become larger than individuals reared in warmer conditions (Ali et al., 2003). Regardless, developmental temperature had persistent effects on size in juvenile killifish (either due to effects on size at hatch or growth rates following hatch) which could affect subsequent phenotypes such as behavior, reproduction, and ultimately fitness (Krause and Godin, 1994; Atkinson and Sibly, 1997; Wootton et al., 2022). Additionally, individuals incubated in lower salinities, regardless of temperature, were also larger than fish incubated at higher salinities. This is significant as salinity fluctuations occur frequently in the estuarine environments common killifish inhabit. The effect low salinity has on fish body size is not as well studied as the effects of low temperature. One study in yellowtail flounder (Limanda ferruginea) developed in various salinities demonstrated decreases in length in fish that were exposed to lower salinities (Laurence and Howell, 1981). As such, it is unclear as to why low developmental salinity has persistent effects on juvenile size months later and this is worthy of further investigation. As with developmental temperature, the impacts of salinity on body size can have remarkable effects on other phenotypes and ultimately can influence fitness (Atkinson, 1994; Krause and Godin, 1994; Atkinson and Sibly, 1997; Wootton et al., 2022). To our knowledge, there are no other studies that have exposed embryos to the combination of low temperature and salinity during development with persistent effects on body size months later. Thus, our study results are critical for our understanding of how developmental conditions can affect whole organism phenotypes, even for an extremely tolerant estuarine species that is adapted to large environmental fluctuations. Unlike body size, thermal tolerance (measured as CTmax) was not different between 57 juvenile killifish that were incubated under different developmental conditions. A meta-analysis by (Pottier et al., 2022) across ectothermic species determined that the mean effect size for developmental temperature impacts on CTmax did not differ from zero, although there is substantial variation among species (which may depend, at least in part, on study design). However, persistent effects of developmental temperature are vastly understudied and it is possible that there are effects of incubation temperature later in life in ectotherms(Earhart et al., 2022; Pottier et al., 2022). Although we did not detect persistent differences in CTmax, it is important to note this is only one way to measure thermal tolerance and there could be differences in other metrics that were not assessed. Further, killifish are very tolerant to changes in temperature, and exhibit substantial plasticity in CTmax at the adult life stage (Fangue et al., 2006; Healy and Schulte, 2012) and as such it is possible that the temperatures used in this study were not extreme enough to induce persistent changes in thermal tolerance into the juvenile life stage. The effects of the combination of thermal and osmotic stress on thermal tolerance are not as well studied; however, in tadpoles acclimated to stressful temperatures and salinities, there was a reduction in thermal tolerance (Chuang et al., 2022). The effects of these multi-stressors on developmental plasticity of thermal tolerance in other fish species is extremely unclear, as to our knowledge there are no other studies that have investigated this. Thus, it is imperative that future research investigates the effects of developmental thermal and osmotic stress in other estuarine and marine fish species, as thermal tolerance is critical for fishes to cope with our changing climate. Juvenile fish exposed to different developmental environments have persistent changes in mRNA levels Developmental conditions had persistent effects on mRNA levels four months after hatch in juvenile killifish both before and after an acute thermal stressor. Interestingly, we demonstrate lasting differences in both hsp90b and hif1a mRNA levels between killifish that were incubated at different temperatures and salinities throughout embryogenesis. The mRNA responses of these two genes differ the most between the 26°C 20ppt and the 20°C 3ppt treatments, with opposite responsive trajectories at rest and following CTmax, between the two 58 treatments. Exposure to stressful temperatures earlier in life has been shown to influence mRNA levels of hsp90b and hif1a later in life in other fishes as well, indicating previous thermal stress can have lasting effects on transcriptional responses (Bugg et al., 2021). Additionally, the juvenile fish resulting from embryos exposed to low temperature and salinity have an increased transcriptional response in hsp70.2 and hif1a following an acute thermal stressor. This indicates that exposure to low salinity and cold temperature early in life affects the mRNA plasticity of these genes in response to thermal stress months after exposure to these initial stressors. While thermal tolerance was not different between the treatments, the underlying mRNA levels were and this demonstrates that there were important underlying changes in gene expression that might result in changes in other whole-organism phenotypes. Our data suggest that thermal and osmotic stress during development causes long-term transcriptional changes, which can have far reaching impacts on the physiology and fitness of fishes (Schulte, 2014; Jeffries et al., 2018; Komoroske et al., 2021). These differences in mRNA levels could also influence other phenotypes not measured here and could ultimately have profound effects on their physiology, performance, and survival. Conclusion Our study demonstrates that developmental conditions, even in a tolerant estuarine species, can have persistent effects across multiple levels of biological organization. Their lack of comparable literature highlights the need for a more comprehensive investigation of cumulative, climate change related impacts affecting the survival and fitness of estuarine species during early life. This is especially important because of the persistence of these effects into the juvenile life stage, and illuminates the potential impacts on fish physiology and survival in habitats experiencing desalinization or salt water intrusion, increases and changes in rainfall patterns, and temperature anomalies. As our climate changes and estuarine conditions are predicted to increase in variability, through alteration of both temperature and salinity, this study suggests that there will likely be impacts both on developing fish and during later life, even in an exceptionally tolerant species such as the common killifish. 59 3.5 Tables Table 3.1. List of forward and reverse primers and their efficiencies used in this study for Killifish (Fundulus heteroclitus) hsp70.1, hsp70.2, hsp90a, hsp90b, hif1a, hsc70, ef1-a, and 18s. Gene Name Forward primer Reverse Primer Efficiency (%) hsp70.1 TGACAACGGGAAGCCTAAAG GCCTCGGCTATTTCTCTCATT 97% hsp70.2 CACCTCAACAGAGGTCAATCA CAGTCCTAAACCAGCAGAGAC 109% hsp90a GACGAGATGGTTTCCCTCAAA GCACCAAGATAAGGAGGAGATG 92% hsp90b GACCTTCAAGTCCATCCTGTTC GTCGTTCTTCTTGGAGCCATAC 99% hif1a GAGAAAGAGGTTCCTCTCAGAAC CTCCTTTAGCTCGCTCAGTTT 100% hsc70 CTGCTGGAGATACTCATCTTGG GTCCTTCTTGTACTTGCGTTTG 98% ef1-a TCACCATCAGCTTTTATAAAG GACGCTCCATTGCCTACTGTC 105% 18s TTCAGCCACACGAGATTGAG ATCAACGCGAGCTTATGACC 105% 60 3.6 Figures Figure 3.1 Experimental design. Seven killifish (Fundulus heteroclitus) males and seven killifish females were group spawned, all eggs and sperm were mixed together and separated into 8 petri dishes, (2 per treatment) following fertilization. The embryos were divided into four treatments, 26°C 20ppt, 26°C 3ppt, 20°C 20ppt, and 20°C 3ppt. throughout development (5dpf, 10dpf, and 15dpf) mRNA levels were assessed across all four treatments. Following hatch, all larvae were put into a zebrafish rack under common conditions, 20°C 20ppt. Four months following hatch thermal tolerance and mRNA levels were assessed in juvenile killifish. Illustration by Madison Earhart. 4ਡmNnShRਡlaSeQmRNAਡleUelRਡਡRiYeਡmeaRTQemenSRCTmaWReUenਡmaleRandਡfemaleRgQNTOਡROaVned 61 Figure 3.2. Survival and average hatch day for killifish Fundulus heteroclitus embryos during incubation in four treatments. Panel A depicts survival across 4 treatments: 26°C 20ppt (pink 1 6 11 16 21 26 31 36 41020406080100Hatch DayHatch (%)26°C 20 PPT20°C 20 PPT26°C 3 PPT20°C 3 PPTabc26 2010152025304050Temperature (Co)Hatch Day (dpf)ab*26°C 20 ppt26°C 3 ppt20°C 20 ppt20°C 3 pptA)B) 62 triangles), 26°C 3ppt (red circles), 20°C 20ppt (dark blue triangles), 20°C 3ppt (light blue circles). Different letters indicate a significant difference between treatment groups. Panel B depicts median hatch day (whiskers are min to max) across the 4 treatments, 26°C treatments are pink and 20°C treatments are blue, checker patterned bars indicate a salinity of 3ppt. Letters indicate a significant difference across temperatures and the asterisks indicate a significant difference in salinity within a temperature. 63 Figure 3.3. PCA, gene contribution and vector plot of mRNA levels during development in killifish exposed to four different treatments (26°C 20ppt, 26°C 3ppt, 20°C 20ppt, and 20°C 3ppt). each point represents the centroid of data for 12 to 15 extractions, each including two to three hifĕahspĝĔbhscěĔhspěĔƣĕhspĝĔahspěĔƣĖ 64 individuals. Different shapes represent different developmental timepoints (triangles are 5dpf, diamonds are 10dpf, and circles are 15dpf). And colors indicate the temperature and salinity treatment (dark red is 26°C 20ppt, light red is 26°C 3ppt, dark blue is 20°C 20ppt, and light blue is 20°C 3ppt). 65 Figure 3.4. Juvenile critical thermal maximum (CTmax; panel a), wet mass (panel b), length (panel c), and condition factor (panel d). Colors indicate the temperature and salinity treatment (dark red is 26°C 20ppt, light red is 26°C 3ppt, dark blue is 20°C 20ppt, and light blue is 20°C 3ppt). Asterisks indicate significant differences between developmental treatments (P < 0.05). 26 2036373839Temperature (Co)CTmax (°C)26 200.00.10.20.30.40.5Temperature (Co)Wet Mass (g)temp: p < 0.05salinity: p < 0.05✱26 20152025303540Temperature (Co)Length (mm)temp: p < 0.05salinity: p < 0.05✱20°C 20 ppt26°C 20 ppt26°C 3 pptA) B)C)26 200.00.51.01.52.0Temperature (Co)Condition Factor (K)D)20°C 3 ppt 66 Figure 3.5. PCA, gene contribution and vector plot of mRNA levels at control and following CTmax in killifish exposed to four different treatments (26°C 20ppt, 26°C 3ppt, 20°C 20ppt, and 67 20°C 3ppt). Each point represents the centroid of data for n=4-14 for treatment. Different shapes represent either control mRNA levels (triangles) or mRNA levels following CTmax (circles). Colors indicate the temperature and salinity treatment (dark red is 26°C 20ppt, light red is 26°C 3ppt, dark blue is 20°C 20ppt, and light blue is 20°C 3ppt). 68 Chapter 4. Identification of upper thermal thresholds during development in the endangered Nechako white sturgeon with management implications for a regulated river 4.1 Introduction River ecosystems are extremely sensitive to increases in temperature and other climatic variables associated with climate change (Palmer et al., 2008, 2009; Watts et al., 2015; Pletterbauer et al., 2018). Indeed, the effects of warming are already detectable in fish communities in rivers (Daufresne and Boët, 2007), and projected future increases in air temperature (IPCC, 2021) will only increase the stress on these freshwater ecosystems (Albert et al., 2021). However, around the world, many rivers are affected by dams (Grill et al., 2019), and although these dams have well-documented negative effects by altering flow regimes, changing sediment composition, and blocking fish passage, they also present an opportunity to help regulate river water temperature through managed release of cool water from reservoirs (Olden and Naiman, 2010; Rheinheimer et al., 2015; Chen and Olden, 2017). In western North America, thermal regulation of rivers through dam releases is often focused on mitigating harm to salmonids (Macdonald et al., 2012; Munsch et al., 2020; Anderson et al., 2022; FitzGerald and Martin, 2022). However, this approach neglects other native, and often imperiled, fish species (Zarri et al., 2019). For example, in the Nechako river in British Columbia, Canada, a government mandate requires operators of the Kenney Dam to maintain river water temperatures <20°C in July and August to protect the sockeye salmon (Oncorhynchus nerka) during their spawning migration (Levy and Nicklin, 2018; Nechako River, 2022; Summer Temperature Monitoring Program, 2022). This is a critical temperature for the sockeye, and without this protection, high pre-spawn mortality occurs (Levy and Nicklin, 2018). However, there is another critically endangered species inhabiting this river: Nechako white sturgeon (Acipenser transmontanus) (Nechako River, 2022). Unfortunately, relatively little is known about the thermal limits of white sturgeon, although what data exist suggest that 20°C may be above 69 optimal temperatures for spawning and early development (Wang et al., 1985; Webb et al., 1999; Hildebrand et al., 2016). In particular, sturgeon early-life stages during the first two months of development are extremely sensitive to external stressors like flow, sediment and temperature (Wang et al., 1985; McAdam et al., 2005; Baker et al., 2014; Boucher et al., 2014; Bugg et al., 2020; Brandt et al., 2022; Yoon et al., 2022), and these cumulative environmental changes may contribute to the observed long-term recruitment failures for white sturgeon in the Nechako river. Specifically in this river system, increase in fine sediment like sand, increases in temperature and changes in flow affecting spawning grounds are hypothesized to be the driving factors of failed recruitment. The recruitment failure in the Nechako river has been observed since the 1960s, and as such without supplementation intervention, the population is headed toward extinction (Nechako White Sturgeon Recovery Initiative, 2021). However, river temperature regulation on the Nechako is only required during the July and August migration and spawning season for sockeye salmon, and sturgeon spawn earlier in the summer (May and June in the Nechako River; Hildebrand et al., 2016), and thus the timing of current regulations is not likely to adequately protect vulnerable sturgeon embryos and larvae. As such, in this study, our objective was to define suitable temperatures across multiple levels of biological organization in Nechako white sturgeon at two of the most vulnerable and understudied developmental stages: embryos and yolk-sac larvae. We aimed to identify upper thermal thresholds, or tipping points, above the thermal optimum, at which performance begins to decrease as temperature increases. At these sublethal thermal thresholds, a variety of changes occur across biological levels of organization (Schulte, 2014; Jeffries et al., 2018). However, different processes—whether at the level of transcriptional, cellular or the whole organism—may have distinct thermal thresholds influencing their function. Determining the sublethal temperature thresholds for species of conservation concern is critical for developing useful management regimes because performance is impacted long before lethal thermal thresholds are reached, and reduced performance can have consequences for fitness (Schulte, 2014). Thus, distinguishing the lethal and sublethal temperatures for developing white sturgeon survival and physiological performance is critical to informing regulatory practices 70 controlling river temperatures in the Nechako river. This is of particular importance for the more vulnerable life stages in the Nechako river, like the spawning adults, embryos, and larvae because of the failed recruitment observed in this system. To aid in identifying these thermal thresholds for Nechako white sturgeon embryos and larvae, we measured transcriptional and whole-animal physiological responses after temperature acclimation and acute thermal stress across early development. We incubated developing Nechako river white sturgeon to three different, ecologically relevant, temperatures throughout embryogenesis and yolk-sac absorption: 14, 18 and 21°C. These temperatures were chosen based on previously documented river temperatures: for example, in 2021, Nechako river temperatures were at 14°C in June, during the critical embryogenesis stage and reached 18°C by mid-June during the yolk-sac absorption stage. We additionally chose 21°C to test the efficacy of the summer temperature monitoring program for the white sturgeon because the Nechako river reached 21°C by the end of June in both 2021 and 2022 (Environment Canada, 2022). The monitoring program for sockeye salmon focuses on avoiding temperatures >20°C during July and August (Levy and Nicklin, 2018) and white sturgeon embryos and larvae are experiencing temperatures higher than this before these months. Thus, warm water exposures in this system could be playing a role in the failed recruitment for white sturgeon. Our objective in this study was to define sublethal and lethal thermal thresholds in white sturgeon embryos and yolk-sac larvae to aid in recommendations for temperature regulation through dam management that may help with conserving the Nechako white sturgeon. We measured whole-animal physiological performance through embryo metabolism, yolk-sac larvae morphometrics, and larvae maximum thermal tolerance (CTmax). To test for differences in cellular performance between developmental temperatures, we measured the transcript abundance of 13 genes in the yolk-sac larvae at rest and after an acute thermal stressor. Genes were selected for their roles in thermal stress, hypoxia and blood oxygenation, energy allocation, and growth, to provide insight into multiple physiological processes that could be affected by exposure to warmer temperatures. 71 4.2 Methods 4.2.1 White sturgeon husbandry On May 25, 2021, gametes were collected from wild-caught male and female white sturgeon at the Nechako White Sturgeon Recovery Initiative (NWSRI) in Vanderhoof, British Columbia, Canada (54.0140° N, 124.0130° W). This population is critically endangered, and as such has limited spawning adult individuals (<100 spawning fish). Due to this endangered status, we collected eggs from two females and milt from three males to create a total of six families (3 half-sib families from each female). Eggs were fertilized at 13°C, within 30 min of collection, and were then immediately spread across six Petri dishes (~200 embryos per dish). Embryos from each family were evenly distributed across the petri dishes. Embryos adhered to the petri dishes within 3 min (Earhart et al., 2020), and the embryos were then transported back to the Initiative for the Study of the Environment and its Aquatic Systems (INSEAS) facility at the University of British Columbia, fully immersed in coolers supplied with air stones and HOBO temperature loggers (Onset Computer Corporation, Bourne, MA, USA). To maintain temperatures <14°C, cool, dechlorinated water from the NWSRI was added to the coolers when necessary throughout the 10-h transit. On arrival, embryos in the petri dishes were transferred to an incubator (model: MIR-154, PHCBI), held at 14°C with a 12:12 light cycle. At 1 day post-fertilization (dpf) four of the six petri dishes were moved to a separate incubator held at 18°C, and the next day, at 2 dpf, two of those petri dishes were moved to a third incubator held at 21°C. As such, by 2 dpf, the embryos were in three different temperature treatment groups—14, 18 and 21°C (2 petri dishes per temperature)—and they were held at these temperatures for the remainder of the experiment. This schedule of increasing embryo incubation temperatures over 2 days was chosen to avoid complete mortality from thermal shock because fertilization was conducted at 13°C (Wang et al., 1985). Note that this design allowed for equal representation of all families across all developmental temperatures. To allow for appropriate comparisons of developmental rates between treatments, accumulated thermal units (ATU) were calculated (dpf × T°C) throughout the experiment (Rombough, 1985; Jay et al., 2020). 72 All petri dishes were held inside 9 L containers filled with water bubbled with air from an air stone and circulated with a pump to maintain dissolved oxygen levels. After hatch, we added bio-balls to the tanks for substrate, and all larvae were kept in the 9 L containers described earlier. Water quality measurements and ~30% water exchanges were completed twice daily to ensure ammonia did not rise >0.5 ppm throughout the experiment. Temperatures were checked multiple times daily, with temperature in each incubator monitored and recorded via HOBO temperature loggers. Mortalities were recorded daily in each treatment throughout the experiment. All embryos and larvae in this study were reared and sampled under guidelines established by the Canadian Council for Animal Care and approved by the Animal Care Committee at the University of British Columbia under Protocol A19–0284. 4.2.2 Embryo metabolic rate Measurements of whole-embryo metabolic rate were conducted the day before hatch (105 ATU) for each group (Fig. 4.1). We chose to use embryo metabolic rate as an indicator of thermal tolerance rather than critical thermal maximum (CTmax) because sturgeon embryos are not completely transparent, making the identification of CTmax end points challenging. Metabolic rate may be a particularly sensitive indicator of thermal challenges in embryos because of their high metabolic demand during development (Dahlke et al., 2020). Individual embryos were randomly selected from both Petri dish replicates and placed into 1.8 mL custom glass micro-respirometry chambers with magnetic stir bars below a false-bottom mesh to ensure mixing during the trial. All metabolic rate measurements were completed at the embryos’ respective incubation temperature, 14, 18 or 21°C, by placing chambers into an incubator. Three trials were run per day and were conducted between 8 am and 4 pm. After being placed in the metabolic chamber, embryos were allowed to recover for 30 min, to account for handling stress, before the beginning of the metabolic rate measurements. The oxygen probe was placed into the chamber, and then the chamber was sealed with many layers of parafilm to secure the oxygen probe. We assessed metabolic rate through measuring the reduction in oxygen levels using NeoFox fibre optic probes (FOXY system, Ocean Optics, Dunedin, FL, USA). Oxygen levels were recorded every 10 s until air saturation reached 70% inside the chambers (~1.5–2.5 h). After 73 each trial, fish were weighed using a microbalance (Mettler Toledo XPR2). Fibre optic probes were calibrated twice weekly using 100% air-saturated water by vigorously bubbling air into the water and 0% air-saturated water created by the addition of sodium sulfite (1 g/100 ml). Chambers were rinsed with 70% ethanol between trials to kill bacteria, and background oxygen consumption was measured after each trial and found to be negligible (<1% MO2). We measured the change in oxygen concentration over time, which was corrected for both the volume of the respirometer and embryo wet mass to give the oxygen consumption rate (μmolO2·g−1·h−1). We chose a coefficient of determination (R2 value) of 0.9 as our threshold for MO2 data (Chabot et al., 2021). A total of nine individual embryos were measured from each acclimation temperature (4–5 individuals from each Petri dish replicate). 4.2.3 Larval morphometric measurements Length, wet mass, and yolk-sac volume were measured at 113, 162, 213 and 271 ATU after they were euthanized with an overdose of buffered MS-222 (0.5 g·l−1). Total length was measured by analyzing photographs, taken through a dissecting microscope at 5× magnification, in ImageJ using the “draw line” tool. All images included a reference ruler (millimetres), and all measurements were taken from the top of the head to the tip of the tail. Wet mass was measured to the nearest 0.000001 g (Mettler Toledo XPR2) after thoroughly drying each individual fish with a Kimwipe. Yolk-sac volume was measured by analysing the dissecting microscope photos on ImageJ by taking measurements of the diameter (YsD) and the length of the yolk-sac (YsL). Yolk-sac volume was then calculated by this formula (Blaxter and Hempel, 1963): Yolksac volume (YsV) = (π/6)YsL × YsD2. 4.2.4 Critical thermal maximum trials To assess the maximum thermal tolerance of the yolk-sac larvae, we performed CTmax trials at 270 ATUs for each temperature treatment (Fig. 4.1). This coincided with the time of yolk-sac absorption and the start of yolk-plug ejection before exogenous feeding. For each trial, 25 individuals were randomly selected from the incubators and placed into a mesh fish breeder net (Hagen, Montreal, Quebec, CA) that was held inside a 20 L experimental bucket containing 74 aerated, circulating, dechlorinated water set to the acclimation temperature of the treatment being tested (14, 18 or 21°C). Sturgeon larvae were held for an hour before the start of the trial to allow time to recover from handling stress (Bugg et al., 2020). Trials were run by increasing temperature 0.3°C·min−1 with immersed titanium heater sticks (finnex TITANIUM 300+) until the larvae lost equilibrium (LOE) and were unable to right themselves after a disturbance (i.e. touching their caudal tail with a blunted probe; Bard and Kieffer, 2019; Yoon et al., 2019; Bugg et al., 2020; Penman et al., 2023). Air saturation was maintained >95% throughout the trial through bubbling air through air stones. Immediately after LOE, the sturgeon was removed from the breeder net and euthanized with an overdose of buffered MS-222 (0.5 g·l−1), and whole-body samples were snap-frozen and stored at −80°C until further analysis. At this time, we also sampled control fish (fish not exposed to an acute stressor) from each of the incubation tanks in the same way for mRNA analysis. 4.2.5 RNA extraction, cDNA synthesis, and qPCR Whole-body total RNA was extracted using a RNeasy kit (Qiagen, Hilden, Germany). For each sample, total RNA concentration and purity were determined using a Nanodrop 2000c (Thermo Scientific). Extracted RNA samples were then stored at −80°C until future use. After extraction, RNA was treated with DNase to remove any DNA in the sample. Synthesis of cDNA was performed using a qscript synthesis kit (Quantabio, Beverly, MA, USA) with 1 μg of DNase-treated RNA in a thermocycler (Bio-Rad, Hercules, CA, USA) as follows: 1 cycle of 22°C for 5 min, 1 cycle of 42°C for 30 min, 1 cycle of 85°C for 5 min, followed by a hold at 4°C. After cDNA synthesis, samples were diluted 10-fold with nuclease-free water for subsequent qPCR analysis. cDNA was then stored at −30°C until further use. Real-time quantitative polymerase chain reactions (RT-qPCRs) for all genes were completed in a total volume of 10 μl per well with Bio-Rad Sso Advanced Universal SYBR-green supermix (5 μl per sample), nuclease-free water (3 μl per sample), primers (100 μM 0.05 μl in 0.95 μl nuclease-free water), and 1 μl cDNA. We measured the mRNA abundance of 13 target genes and two reference genes (Table 1) in whole-body samples, and abundance was normalized to the expression of these two stable reference genes. Reference genes did not differ across 75 different temperature treatments, ATUs or between CTmax and control samples (Chapman and Waldenström, 2015). Primers were designed using a white sturgeon transcriptome (Doering et al., 2016) and were assessed for secondary structures and non-target binding. Primer efficiency was determined by generating a 1:10 standard curve for each gene using pooled cDNA. Both no template and no reverse transcriptase controls were run on every qPCR plate, and no contamination was detected. All reactions were completed using an RT-qPCR machine (Bio-Rad CFX96) in a 96-well plate under the following SYBR-recommended cycling conditions: 2 min at 95°C, 40 cycles of 15 s at 95°C and 30 s at 58°C, and melt curves were produced by denaturation for 15 s at 95°C, a decrease for 10 s down to 60°C, and then a gradual increase of 0.5°C·s−1 to 95°C. Amplification data were analysed using standard curves created for each gene, and the data for genes of interest were normalized to the geometric mean of expression of the two reference genes (Vandesompele et al., 2002; Earhart et al., 2022b). 4.2.6 Statistical analysis Mortality differences between treatments were analysed via Cox proportional hazards model in R v4.1.2 (R core team, 2013) using the “survival” and “survminer” packages (Therneau, 2015; Kassambara et al., 2019) to assess the effect of temperature on mortality rate across ATU. To assess the specific differences between temperatures, the “pairwise_survdiff” function from the “survminer” package was applied with a Bonferroni correction. For all analysis of variance (ANOVA), residuals were analysed with both Shapiro-Wilks and Levene tests to assess data normality and homogeneity of variance. Normality was also assessed by visually inspecting Q-Q plots. If assumptions of either test were violated, data were transformed with a log transformation before the ANOVA; this was only the case for yolk-sac volume data. All ANOVAs were completed in GraphPad Prism 9 with a significance level of 0.05. See appendix 2 for all ANOVA tables. Morphometrics (mass, length and yolk-sac volume) were analysed with a two-way ANOVA with acclimation temperature, ATU, and their interaction included in the model as fixed effects. After the ANOVA, multiple comparison tests were performed and corrected with a Tukey honest significant difference (HSD) test. 76 Embryo metabolic rate data and larval CTmax data were analysed with a one-way ANOVA followed by a multiple-comparison test and corrected with a Tukey HSD test. Acclimation response ratios were also calculated for CTmax data across acclimation temperatures by subtracting the average CTmax of the 14°C acclimation treatment from 18°C (or the CTmax of 18°C from 21°C) and dividing that by the change in acclimation temperature between treatments: Acclimation response ratio (ARR) = (CTmax18°C − CTmax14°C) / Δ°C Two different statistical analyses for mRNA abundance were completed. First, a two-way ANOVA was completed for each gene, with acclimation temperature, CTmax and their interaction as the fixed effects. We additionally performed a principal component analysis (PCA) using the “factomineR” (Lê et al., 2008) and “factoextra” (Kassambara and Mundt, 2021) packages in R. Data were subset into two groups: (1) mRNA levels at control and (2) mRNA levels following CTmax to analyze how yolk-sac larvae transcriptional responses differed following only acclimation and after an acute thermal stressor. 4.3 Results 4.3.1 Mortality Mortality was significantly impacted by temperature as determined by a Cox proportional hazards model (Fig. 4.2). Almost all mortality, regardless of acclimation temperature, was observed before hatch around the time of neurulation. However, at 21°C, there was significantly more mortality when compared with 14°C (15.9 ± 3.48% vs 28.2 ± 3.8%; P < 0.0001), and 18°C exhibited an intermediate level of mortality (21.9 ± 0.04%), which was not significantly different from either 14 or 21°C. 4.3.2 Embryo metabolic rate One-way ANOVA revealed a significant effect of acclimation temperature on embryonic metabolic rate (Fig. 4.3; P = 0.01). Both the 18- and 21°C-acclimated embryos had a higher metabolic rate than the 14°C-acclimated group, but there was no difference between the 18 and 21°C groups, suggesting a thermal limitation of embryo metabolic rate. 77 4.3.3 Larval morphometrics All morphometric data were analysed via two-way ANOVA. White sturgeon larval length was significantly affected by acclimation temperature (Fig. 4.4a; P = 0.0006), ATU (P < 0.0001) and their interaction (P < 0.0001). Similarly to length, two-way ANOVA revealed a significant effect of acclimation temperature (P = 0.0003), ATU (P < 0.0001) and their interaction (P < 0.0001) on wet mass of larval sturgeon Fig. 4.4b), and yolk-sac volume (Fig. 4.4c; P < 0.0001) was affected by the same factors. Sturgeon sampled from each temperature grew over time; however, the 21°C larvae were larger than both 14 and 18°C groups at each ATU sampling time with the exception of 113 ATU. In addition, yolk-sac volume was reduced in the 21°C larvae, suggesting that it was more quickly depleted compared with the 18 and 14°C larvae. This suggests that the 14 and 18°C acclimation groups used less energy from the yolk because they maintained more of their yolk-sac size across ATUs. 4.3.4 Larval CTmax Maximum thermal tolerance was significantly affected by temperature (Fig. 4.5; P < 0.0001) as detected by one-way ANOVA. The CTmax temperatures were 21.9, 27.5 and 30.4°C for the 14, 18 and 21°C acclimation groups, respectively. The acclimation response ratio for the yolk-sac larvae was quite impressive, with an ARR of 1.4 between the 14 and 18°C acclimation groups and an ARR of 0.9 between the 18 and 21°C group. These results indicate that for each degree change in acclimation temperature, there was about a degree (°C) increase in CTmax. 4.3.4 mRNA transcript abundance In the PCA for control larvae mRNA data, principal component 1 explained 62% of the observed variation in mRNA abundance, whereas principal component 2 explained 12% of the variation. The main gene contributions for PC1 and PC2 (principal component; Fig. 4.6a; Appendix 3) were pepck-m, hsp90b, ifg2, hba, hif1a, hsp90a and hsp70. The PCA for the control mRNA data highlighted separation between the 14 and 18°C acclimation group, with the 18°C fish shifting to the right on principal component 1 (Fig. 4.6a). However, at 21°C, the fish were more similar to the 14°C fish, suggesting a thermal threshold at 18°C, with cellular performance 78 beginning to decrease at 21°C. In the PCA for CTmax larvae, mRNA data principal component 1 explained 65% of the variance and principal component 2 explained 10%. The genes contributing the most to PC1 and PC2 were pepck-m, hif1a, hsp90a, igf2, hba, hxk, hsp90b and hsp70 (Fig. 4.6b; Appendix 3). Examination of the CTmax principal component analysis revealed a different pattern (Fig. 4.6b), with all three temperature treatments inducing similar responses to acute thermal stress. These data suggest there were more differences in the mRNA transcriptional response between fish at rest from exposure to warm temperatures and that these fish respond similarly when faced with thermal stress. In both groups, pepck-m is contributing the most differences identified between acclimation groups. However, the rest of the genes contributing to observed variation differ between groups at rest and after CTmax. For example, hsp90b contributes more at the control point, whereas hif1a drove more of the variation in response to CTmax. 4.4 Discussion In this study, we highlight multiple thermal thresholds across different levels of biological organization for developing Nechako white sturgeon. Typically, non-linear response patterns to increasing temperature represent sublethal thermal thresholds and show an inverted U-shape trajectory (Jeffries et al., 2018), and this is demonstrated here across multiple phenotypes as temperatures surpassed 18°C. Development in 21°C had sublethal and lethal effects on both white sturgeon embryos and yolk-sac larvae. After only 2 days at 21°C, embryo metabolic rate was suppressed, demonstrating an inability of white sturgeon to maintain metabolic rate at high temperatures during embryogenesis. There was also an increase in embryo mortality at 21°C with >30% mortality of white sturgeon embryos, highlighting a lethal thermal threshold. After hatch, the sublethal consequences continued at 21°C because larval sturgeon exhibited decreased thermal plasticity during CTmax and a dampened transcriptional response during development. Thus, 21°C causes an increase in mortality during embryogenesis with sublethal responses in both whole-animal physiology as well as cellular performance in Nechako white sturgeon embryos and larvae. It is striking that 21°C represents an important thermal threshold in both embryos and larvae across very different sets of traits. This is of concern because the 79 Nechako river has been reaching temperatures in excess of 20°C by the end of June, which persist throughout the summer months (end of September; Environment Canada, 2022). Our collective evidence suggests temperatures >18°C represent a sublethal limit for multiple phenotypes including transcriptional responses and whole-animal physiology; however, this threshold was not the same for growth. Sturgeon larvae were larger at 21°C than at 18°C, suggesting they may continue to increase their growth rate but at the cost of maintaining their metabolic rate and thermal plasticity. After hatch, larval sturgeon acclimated to 21°C used their yolk-sac faster and were consistently longer than the 14 and 18°C groups, indicating different energy allocation between groups. Maintaining growth despite stressful temperatures is demonstrated across sturgeon species (Earhart et al., unpublished data; Hung et al., 1993; Allen et al., 2006; Kappenman et al., 2009; Boucher et al., 2014; Poletto et al., 2018; Bugg et al., 2020; Brandt et al., 2022) and may indicate the importance of growth during early-life stages to avoid predation and ultimately to reach maturation size (Bjornson et al., 2020). In any case, the sublethal thermal limit for growth seems to be higher than the limits for the other metrics measured in this study; however, this comes at a cost. Plasticity of the transcriptional response is key in responding to changing environments like warming (Wellband and Heath, 2017; Earhart et al., Unpublished data), but acclimation to these temperatures at an early developmental stage decreases this necessary plasticity in white sturgeon. Here, we measured the mRNA levels of 12 different genes, and all of them have an inverted U-shape at control measurements (Fig. 4.7); however, they are not always statistically significant. Conversely, after CTmax, there is little difference in how groups respond, suggesting acclimation did not influence the transcriptional response of these genes after acute thermal stress. However, the patterns seen after acclimation only demonstrate a decrease in cellular performance with increasing temperature because the transcriptional response in 21°C-acclimated fish is diminished. This difference is also evident in the mRNA PCA, where the 14 and 21°C acclimation groups were close together, but the 18°C group is separated. Together, these results support 18°C as a thermal boundary and 21°C as a sublethal temperature for larval white sturgeon mRNA transcriptional activity. This consistent pattern across all genes characterized suggests there may be an active, temperature-sensitive transcriptional control mechanism 80 influencing the abundance of these mRNAs. A control mechanism, such as DNA methylation, which has recently been demonstrated to change in response to thermal acclimation across sturgeon species (Earhart et al., unpublished data; Bugg et al., 2022; Penny et al., 2023). The inverted-U response has also been observed in other species of fish, identified through reduced mRNA transcription in response to increasing temperature, signifying a reduction in cellular performance may be common among fishes undergoing thermal stress (Jeffries et al., 2018; Bugg et al., 2020, 2022; Mackey et al., 2021; Stavrakidis-Zachou et al., 2021). The reduction in transcriptional plasticity in response to acclimation to 21°C is likely playing a role in the reduced physiological performance also demonstrated by the larval sturgeon. Not only do we see an inverted U-shape response in the mRNA levels, but this is also detected at the whole-animal level through measurements of whole-embryo metabolism and larval thermal plasticity (ARR). The white sturgeon embryos increased their metabolic rate between the 14 and 18°C acclimation groups, but did not increase from 18 to 21°C. This suggests the embryos could not further increase their metabolic rate despite acclimation to a warmer temperature, highlighting a sublethal threshold for embryonic metabolism. Because embryos have metabolically intensive developmental demands (Dahlke et al., 2020), a suppression or decrease in metabolic rate can aid in identifying these sublethal temperatures because decreases in metabolic rate are usually a survival tactic to endure above-optimum temperatures. Fishes typically suppress their metabolic rate as a mechanism to survive in challenging environments, and this is observed when temperatures begin to reach sublethal and lethal levels (Richards, 2010; McBryan et al., 2013; Jeffries et al., 2018). The metabolic suppression demonstrated in this study has also been identified in other subarctic Canadian sturgeon populations that were acclimated to 20 and 24°C, suggesting a common metabolic response to these elevated temperatures for northern sturgeons (Bugg et al., 2020, 2022). However, even at their temperature thresholds, the larval sturgeon in this study demonstrated remarkable plasticity across levels of biological organization to thermal acclimation during their most sensitive life stages. Although larvae at 21°C had decreased plasticity compared with fish at 18°C, the acclimation capacity (measured as ARR) at either temperature is much more impressive than what is reported in other fish species (Gunderson 81 and Stillman, 2015; Morley et al., 2019). The ARRs reported here, 1.4 (between 14 and 18°C) and 0.9 (between 18 and 21°C), are twice as high compared with other fishes (Morley et al., 2019) and thus highlights the outstanding ability of larval white sturgeon to accrue thermal tolerance through acclimation. In fact, across North American sturgeon species, there are reports of impressive increases in thermal tolerance as demonstrated by relative elevation of CTmax after thermal acclimation (Wilkes, 2011; Zhang and Kieffer, 2014; Bard and Kieffer, 2019; Rodgers et al., 2019; Bugg et al., 2020; Penman et al., 2023; Earhart et al., Unpublished data). This large acclimatory capacity of sturgeons compared with other fishes may be a result of having large genomes regulating their thermal plasticity, and this would greatly benefit an ancient, long-lived species (Fontana et al., 2004; Ellis et al., 2014; Bugg et al., 2020). In addition, in this study we observed impressive thermal plasticity in white sturgeon yolk-sac larvae because they demonstrated the highest reported ARR of any sturgeon species or life stage, emphasizing a potential role for development to impact thermal plasticity because ARR likely decreases with age (Wilkes, 2011; Bard and Kieffer, 2019; Bugg et al., 2020; Penman et al., 2023; Earhart et al., 2023). This increased plasticity observed during early life, in which exposure to high temperatures during critical developmental windows can cause long-lasting impacts on phenotype, will ideally make the fish more suited for its future environment (Burggren and Mueller, 2015; Burggren, 2020; Earhart et al., 2022a). This type of plasticity may be especially important for long-lived species with long generational times like white sturgeon because they must rely heavily on plastic physiological and molecular responses to cope with a changing environment rather than adaptation through rapid generational time. There is a limit, however, to this acclimatory capacity and subsequent plasticity. In many northern sturgeon species, decreases in thermal plasticity in these populations are often observed as temperatures reach 20°C (Zhang and Kieffer, 2014; Bugg et al., 2020; Penman et al., 2023), possibly indicating an innate thermal threshold for beneficial plasticity (Earhart et al., 2022a). Indeed, acclimation to warm temperatures near an organism’s thermal limits affects traits like thermal performance and plasticity (McBryan et al., 2013; Little et al., 2020; Earhart et al., 2022a). As expected, acclimation to warmer temperatures did increase thermal tolerance (Morley et al., 2019) in the larval sturgeon, but the extent of plasticity induced by warm 82 acclimation was lower as temperature increased. The ARR between 18 and 21°C was 0.5 lower than between 14 and 18°C, demonstrating a temperature threshold for thermal plasticity, although these ARRs are both still higher than what is reported in other species. A decrease in thermal plasticity after acclimation to temperatures past the optimum has been observed across fish species and suggests that there is a hard limit to thermal plasticity through acclimation capacity (Seebacher et al., 2015; Rohr et al., 2018; Morgan et al., 2020; Earhart et al., 2022a). Our findings indicate that in white sturgeon, a species with delayed maturity and long generation times, long-term acclimation to warming river temperatures will not increase plasticity indefinitely to compensate for the rapidly warming environments they inhabit. The sublethal thresholds demonstrated here for Nechako white sturgeon embryos and larvae highlight the need to reassess the summer temperature monitoring program for the Nechako river. Based on the temperatures investigated in this study, where fish reared at 18°C were less negatively affected than those at 21°C, it raises the question whether 20°C is an appropriate threshold for white sturgeon. Additional studies are needed to assess the long-term impacts of warming temperature during development because even 18°C could have negative physiological impacts later in life. Furthermore, the current 20°C threshold has been breached by 2°C in recent years, such that the Nechako river will reach 22°C during the middle of the summer. To accommodate for when temperatures surpass regulatory thresholds and to create a more inclusive, holistic monitoring program for the Nechako that will protect both salmonids and sturgeons, we should consider lowering the temperature threshold <20°C to 18°C. Lowering the limit to 18°C would help with embryo survival and also help the larval sturgeon develop their phenotypic plasticity, which is of crucial importance. Importantly, this lowered threshold in the Nechako to help young-of-the-year sturgeon and spawning salmon is crucial because both species have reduced physiological performance at temperatures >18°C (Macdonald et al., 2012; Levy and Nicklin, 2018). Finally, temperature monitoring should start in the beginning of June rather than July because the sturgeon are most vulnerable during this time, and climate change is contributing to shifts in seasonality while also continuing to rapidly increase the rate and intensity of temperature change. As a critically endangered species with complete and continuing recruitment failure, ensuring the Nechako white sturgeon have suitable 83 temperatures during the summer reproductive and developmental months may help encourage larval survival and reduce one of the many stressors they must overcome to successfully spawn, survive and ultimately recruit future generations. 84 4.5 Tables and Figures Table 4.1. Primer sequences for white sturgeon (Acipenser transmontanus), hsp70, hsp90a, hsp90b, hsp47, hif1a, hb-a, hb-b, hxk, pepck-c, pepck-m, g6p, igf2, gh1, rps5, rps8. Target genes were chosen based on their roles in the response to temperature stress, hypoxia, energy allocation, and growth. rps5 and rps8 were used as reference genes because they showed stable expression across all treatments. Efficiencies are listed as a percentage (%). Gene name Gene category Forward Primer Reverse Primer Efficiency (%) hsp70 Temperature CCATGAACCCCAGCAACACT TGCACAACAGAGTCGTCGTA 100.8 hsp90a Temperature CCTTGATTGCCTCCTCTGTT GACTCATTCCAACCGCATCTA 103.4 hsp90b Temperature ACTTGGTCCTTGCTCTCACC GCGATACCACAGCTCTCAGT 104.9 hsp47 Temperature ACCTGTAATAAGTCCGCATCTC GTGTACGAACCACCCAAGAA 96.5 hif1a Hypoxia GCATCTGAGGATAGTGGTAAAG CTGTTGGCAGTAGGAGAATG 99.6 hb-a Blood oxygenation CGAATTGTCACCAGGTTCTAT GTGAGAGGCATCTTTGTAGTT 105.4 hb-b Blood oxygenation GCTTGCACCAGGGATTT ACTAAACACCAGCCATCTTAC 104.7 hxk Energy allocation GAAGCCGCCAGAACAATAA GTACACCAAGACCCACTTTAG 100.2 pepck-c Energy allocation CGTATGCCCTGACCTTAATC CGACAATAACTTGAGACACAATC 100.5 pepck-m Energy allocation CAGTCAGCGAGTTCGTTTC CTTTCCAGTGTTCCCAGTATC 97.5 g6p Energy allocation CGCTCTGCTTCTCCAATAG CCCTAACAACCTCACACTAAA 98.9 igf2 Growth GGAGAATTACACCAGCAAGAA CACAGAAAGGACGCCAATAA 95.1 gh1 Growth CGACCGAGTGTTTGAGAAA AGTGAGCTTCAGCAAAGTAG 104.9 rps5 Reference ACTCGACCCGAATTGGACG GTTGACTCTACGCAGGGGG 99.9 rps8 Reference GGGCGACCCAATTCATACTT CCAGGGACAACTGGCATAAA 102.7 85 Figure 4.1 Experimental design for this study. Embryos and larvae were acclimated (in duplicate) to one of three temperatures: 14, 18 or 21°C for the duration of the experiment. Embryo metabolic rate was measured at 105 ATU. Larval critical thermal tolerance (CTmax) was measured at 270 ATU before exogenous feeding. Samples for mRNA measurement were taken from control fish and fish after CTmax for quantification of mRNA gene abundance. Illustration by Madison Earhart. 86 Figure 4.2. Cumulative mortality (%) of white sturgeon (Acipenser transmontanus) embryos and yolk-sac larvae throughout early development. Acclimation temperatures are represented by different colors, 14°C in blue, 18°C in yellow and 21°C in pink. Time of hatch is indicated by the dashed vertical lines on the figure. Letters represent significant differences between acclimation temperatures (P < 0.05, Cox proportional hazards model). Data are expressed as percentage cumulative mortality from time of fertilization to yolk-plug ejection (0–280 ATU; n = 400–450; 2 petri dishes per temperature each containing all families). 87 Figure 4.3. White sturgeon (A. transmontanus) embryo oxygen consumption rate, acclimated to three different temperatures (14°C – blue, 18°C – yellow and 21°C—pink). Measurements were conducted the day before hatch in each treatment (105 ATU). Letters that differ represent significant differences between acclimation temperatures. Data are expressed as median with quartiles and individual data points are shown (n = 8). 88 Figure 4.4. Morphometrics of larval white sturgeon (A. transmontanus) acclimated to three different temperatures (14°C – blue, 18°C – yellow and 21°C—pink) across ATUs. Panel A is length, panel B is wet mass and panel C is yolk-sac volume. Asterisks represent differences 89 between temperatures within ATUs. Letters that differ represent significant differences across time within an acclimation temperature. Data are expressed as a median with quartiles and individual data points are shown (n = 7–33). 90 Figure 4.5. White sturgeon (A. transmontanus) larval CTmax at three different acclimation temperatures (14°C – blue, 18°C – yellow and 21°C—pink). Measurements were conducted at the start of yolk-plug ejection in each treatment (270–273 ATU). Letters that differ represent significant differences between acclimation temperatures. Data are expressed as median with quartiles and individual data points are shown (n = 23–27). 91 Figure 4.6. PCA of mRNA abundance of larval white sturgeon (A. transmontanus) acclimated to three different temperatures (14°C – blue, 18°C – yellow and 21°C—pink). Panel A is a PCA of mRNA levels and the genes that contribute to the PCs in acclimated and control fish. Panel B is a PCA of mRNA levels and genes that contribute to the PCs in acclimated fish after CTmax trials. Gene contribution figures are colored by different gene function: energy allocation (pink), temperature stress (dark purple), hypoxia and blood oxygenation (blue) and growth (orange). 92 The red dashed line on both gene contribution figures indicates the default average contribution expected for each gene to the overall observed variation. 93 94 Figure 4.7 White sturgeon (A. transmontanus) larval mRNA levels for all genes measured after acclimation to three different temperatures (14°C – blue, 18°C – yellow and 21°C—pink) at control and after CTmax. Significant differences between acclimation temperatures at control or after CTmax are denoted by an asterisk. Significant differences between control and CTmax measurements within an acclimation temperature are denoted by letters. Significant two-way ANOVA effects are listed on the top of each individual gene panel. 95 Chapter 5. Heatwave resilience of juvenile white sturgeon is associated with epigenetic and transcriptional alterations 5.1 Introduction Heatwaves, prolonged but discrete periods of unusually high temperatures, pose a major threat to organisms, ecosystem health, and global biodiversity (Vasseur et al., 2014; Stillman, 2019; Román-Palacios and Wiens, 2020). In aquatic environments, increases in temperature often co-occur with decreased oxygen (hypoxia) as a result of multiple interacting mechanisms (Pörtner, 2001; Verberk et al., 2011; Schulte, 2015), and these events are particularly challenging for aquatic ectotherms. For example, warming increases the rate of biological reactions, increasing aerobic metabolic demand, which necessitates an increase in oxygen supply to maintain energetic balance at a time when aquatic oxygen levels are in decline (Pörtner, 2001; Deutsch et al., 2015; Schulte, 2015; Earhart et al., 2022). Exposures to the deadly combination of high temperature and hypoxia associated with heatwaves are likely to become more frequent, longer lasting, and more extreme across a wider range of aquatic habitats as climate change worsens (Diaz and Rosenberg, 2008; Altieri and Gedan, 2015; Breitburg et al., 2018). During an extended heatwave, organisms may use phenotypic plasticity to help offset the effects of increased temperatures. In many fish species, acclimation to warm conditions results in a number of beneficial changes including increased thermal tolerance (Earhart et al., 2022); but whether this plasticity also induces increased tolerance to hypoxia is unclear (Collins et al., 2021b). The ability to produce this cross-tolerance may be critically important because acute exposure to high temperature reduces hypoxia tolerance, and acute exposure to low oxygen reduces thermal tolerance (Earhart et al., 2022). Whether exposure to heatwave conditions can mitigate these acute effects, and the potential mechanisms underlying resilience, remain unknown. Resilience to heatwaves would require active molecular and physiological changes, such as alterations in gene expression (Logan and Buckley, 2015; Earhart et al., 2022), which could in part, be influenced by epigenetic mechanisms such as DNA methylation (Skjærven et al., 2014; Logan and Buckley, 2015; Anastasiadi et al., 2017; Metzger and Schulte, 2017; Xinxing 96 Wang et al., 2021). However, it is unknown whether changes in DNA methylation can occur quickly enough to confer plasticity during rapid and intense climate events such as heatwaves. Understanding the extent of tolerance to the interacting impacts of high temperature and hypoxia is particularly important for species of conservation concern, such as sturgeon, ancient fishes that are considered to be one of the most endangered group of vertebrates on the planet (Haxton and Cano, 2016; Congiu et al., 2023). Many species of sturgeon can live over 100 years and thus these fish must be able to cope with changes in environmental conditions throughout their lives (Sturgeon slipping towards extinction, 2022). Thus, the ability to withstand episodes of thermal and hypoxic stress, or to induce plasticity to cope with these events, may play a key role in the survival of these iconic fish in a changing world. In this study, we investigated the interacting effects of temperature and hypoxia on an endemic, endangered population of white sturgeon (Acipenser transmontanus) (Hildebrand et al., 2016) from the Nechako river in British Columbia (BC), Canada. This population is estimated to have declined from approximately 5,000 individuals to less than 400 spawning adults since the 1960s. Almost all wild sturgeon in the population are over 50 years old (Nechako White Sturgeon Recovery Initiative., 2004), and there has been no recruitment in this population since the late 1960’s. Although adult sturgeon breed and produce embryos in the Nechako there is little or no survival of these juvenile fish during the first year of life (Committee on the endangered status of wildlife in Canada (COSEWIC), 2013, 2013). Hatchery supplementation with juvenile fish released at one to two years of age is currently used to avoid extirpation of this population. To investigate the extent of resilience in response to heatwave conditions across levels of biological organization during their critical first year of life we used individuals from this supplementation program exposed to a simulated heatwave in the laboratory. Specifically, we asked: 1. Do juvenile sturgeon exposed to heatwave conditions exhibit cross tolerance to acute exposure to high temperature and hypoxia? 2. Do mRNA levels and global DNA methylation change over time during a heatwave? 3. Does exposing fish to heatwave conditions affect the plasticity of mRNA levels in response to an acute stressor? 97 4. Does acute exposure to high temperature and hypoxia induce rapid changes in global DNA methylation, and is this response different in fish exposed to a heatwave? 5.2 Methods 5.2.1 White sturgeon husbandry In May 2021 gametes from wild-caught white sturgeon to be used as wild broodstock in the supplementation program were collected at the Nechako White Sturgeon Recovery Initiative (NWSRI) in Vanderhoof, British Columbia (54.0140° N, 124.0130° W). Eggs from two females were individually fertilized with sperm from each of three males in freshwater at 13°C within 30 minutes of collection. It is important to note that this endangered population has limited adult individuals that are able to be used for hatchery spawning; 3 half-sib families were created from each female for six families in total. Immediately following fertilization embryos were randomly spread across six egg mats and within three minutes, the embryos were firmly adhered to the mats (Earhart et al., 2020). Embryos on egg mats were transported to the InSEAS aquatic facility at the University of British Columbia (UBC) in coolers, supplied with air stones to maintain oxygen levels and HOBO temperature loggers (Onset Computer Corporation; Bourne, MA, USA) to monitor temperature. Cool, dechlorinated water from the NWRSI was periodically added to coolers to maintain temperatures below 14°C for the duration of the 10 h trip. At UBC, embryos on egg mats were transferred to six 9L plastic tanks in a freshwater recirculating system (13°C), and remained at this temperature until the start of the simulated heatwave. Once the sturgeon hatched and started feeding, freshly hatched artemia (Artemia International LLC; Texas, USA) were supplied three times daily. Juvenile sturgeon were also reared in the same six 9L tanks throughout this experiment. All fish in this study were reared and sampled under guidelines established by the Canadian Council for Animal Care and approved by the Animal Care Committee at the University of British Columbia under Protocol A19-0284. 5.2.2 Heatwave exposure In late June of 2021 an extreme heatwave occurred across British Columbia, including in the Nechako river, which is where the parental sturgeon were captured. We took advantage of 98 the availability of near real-time temperature data from the Nechako river to perform an experiment to assess the resilience of white sturgeon to these heatwave conditions (Real-Time Hydrometric Data Graph for NECHAKO RIVER AT VANDERHOOF (08JC001) [BC] - Water Level and Flow - Environment Canada, 2022). We exposed sturgeon to a simulated lab-based heatwave (Fig. 5.1) which allowed us to assess heatwave tolerance and subsequent resilience to other acute stressors during the life stage that would have been exposed to the heatwave in the wild. To begin the heatwave exposure, temperature was increased with titanium aquarium heater sticks at a rate of 2.3°C·day-1 for three days, to simulate the rate of increase seen in the Nechako river during the 2021 heatwave (Fig. 5.1). Following these three days, water was at 20 ± 1°C and juvenile white sturgeon were held at this temperature for 17 days, creating a 20-day heatwave in total. Temperature was recorded every 30 minutes with HOBO temperature loggers (Onset Computer Corporation; Bourne, MA, USA) throughout the experiment. Pre-heatwave (immediately prior to the onset of heatwave conditions) and after the heatwave (following heatwave exposure), thermal and hypoxia tolerance were assessed on a subsample of fish, and pre-heatwave, mid-heatwave, and at the end of the heatwave fish were sampled for assessment of mRNA levels and DNA methylation. We also assessed mRNA levels and DNA methylation following the acute thermal and hypoxia stressors in both pre-heatwave and heatwave exposed sturgeon (Fig. 5.2). Due to the limited number of fish we were permitted to use from this critically endangered population, we did not have sufficient fish to hold a group of sturgeon at 13°C throughout the trial. While this is a limitation of the experimental design, our goal was to assess the resilience of sturgeon to heatwave exposure and subsequent secondary stressors. Thus, comparisons between fish at the start of the experiment (at 13°C) and at various times during the 20-day heatwave exposure should be regarded with caution as we cannot formally disentangle potential effects of 20 days of holding from the effects of heatwave exposure. However, prior work in our laboratory suggests that the measured traits are relatively stable to laboratory conditions during this life-stage with only 10 days separating our measurements. 99 5.2.3 Thermal tolerance (CTMax) trials Prior to and after the heatwave (Fig. 5.2) two CTMax trials were conducted, either under normoxic conditions, at 100% oxygen saturation (10.54 to 7.43 mg·L-1), or under moderately hypoxic conditions, at 70% oxygen saturation (7.38 to 4.9mg·L-1). For every CTMax trial, 25 fish were randomly selected from the six tanks and placed in a mesh fish net breeder tank (Hagen, Montreal, Quebec, CA) in a 20 L experimental bucket containing aerated, circulating, dechlorinated water at the acclimation temperature of the treatment being tested (i.e. 13°C or 20°C). Fish were held in the experimental bucket for an hour prior to the trial to allow recovery from handling stress (Bugg et al., 2020). CTMax trials were conducted at the same time every day, between 10 am and 12 pm, to minimize diurnal effects on physiology (Lankford et al., 2003). CTMax was assessed by increasing temperature by 0.3°C.min-1 until the fish lost equilibrium (LOE) and were unable to right themselves (Bugg et al., 2020; Penman, 2021). At the time of LOE, temperature was recorded and the fish was euthanized by immersion in an overdose of tricaine methanesulfonate solution (250 mg·L−1; MS-222, Syndel Laboratory, Vancouver, Canada) buffered with an equal volume of sodium bicarbonate. Whole-body samples were then preserved in either 96% ethanol (DNA samples) or snap-frozen (RNA samples). For the normoxic CTMax trial (n=25), oxygen levels were maintained at 95±2% air saturation or higher by bubbling air through a porcelain gas diffuser, continuously monitored using a ProDO oxygen meter (Yellow Spring instruments, Yellow Springs, Ohio, USA). For the hypoxic CTMax trial (n=25), nitrogen gas was introduced through a porcelain gas diffuser, acutely dropping oxygen at the rate of 1.5%.min-1 until 70±2% air saturation was reached. As the absolute concentration of oxygen dissolved in water is different between temperatures of 13°C and 20°C, the oxygen concentration for pre-heatwave trials started at 7.38 mg·L-1 and ended at 5.48 mg·L-1 and heatwave trials started at 6.37 mg·L-1 and ended at 4.9 mg·L-1 . The nitrogen flow rate was adjusted throughout the trial to compensate for temperature effects on oxygen saturation. 100 5.2.4 Hypoxia tolerance trials Hypoxia tolerance was assessed as the oxygen level at LOE following acute exposure to declining oxygen levels. All trials were conducted at the same time each day between 10 am and 12 pm (Lankford et al., 2003). Fish were removed from their holding tanks and placed into a mesh breeder net (Hagen, Montreal, Quebec, CA) inside the experimental tank which contained 100L of aerated, dechlorinated, circulating water set to the respective acclimation temperature. Fish were permitted one hour to recover from handling stress prior to the trial. Oxygen was then lowered by bubbling in nitrogen to achieve a decline in dissolved oxygen of 1.5%·min-1 (Strowbridge et al., 2021). Both oxygen levels and temperature were continuously measured throughout the trial and nitrogen bubbling was manually adjusted to achieve the desired decline in oxygen. To test the effects of both acclimation and acute temperature exposure, independent hypoxia tolerance trials were conducted at 13°C, 17°C and 20°C. Thus, pre-heatwave fish were tested at their acclimation temperature (13°C) or during acute exposure to increased temperatures of 17°C or 20°C. Heatwave fish were tested at their acclimation temperature (20°C) or during acute exposure to decreased temperatures of 17°C or 13°C (Fig. 5.2). For assessment of pre-heatwave fish, for each hypoxia trial, 50 fish were randomly selected from the six holding tanks (8-9 fish per tank) and added to a mesh breeder net with the water temperature at 13°C. Depending on the trial, during the 1h rest period prior to hypoxia tolerance assessment, temperature was either maintained at 13°C, or acutely increased to either 17°C or 20°C at a rate of 0.3°C·min-1 and maintained for the remainder of the trial with aquarium heater sticks. Once each fish reached LOE, they were removed from the tank, euthanized, and the whole-body was preserved in either 96% ethanol or snap-frozen, as previously described (n=25 for each sampling method). For assessment of heatwave fish, for each temperature trial, 30 sturgeon were randomly selected from the experimental tanks and hypoxia tolerance was assessed as described above. For those trials involving acute temperature exposure, water temperature was acutely decreased to either 17°C or 13°C using a recirculating chiller and ice packs at a rate of 0.3°C·min-1 during the hour prior to the trial and then maintained at that temperature for the rest of the trial. The 101 hypoxia tolerance assay was then conducted and at LOE fish were sampled as described above (n=15 for each sampling method). 5.2.5 mRNA transcript abundance Total RNA was extracted from the gills and heart using a RNeasy kit (Qiagen; Hilden, Germany). Total RNA concentration and purity was determined for each sample using a Nanodrop 2000c (Thermo Scientific). RNA samples were then stored at -80°C until further use. cDNA was synthesized from 1μg of DNAse treated RNA with a qscript cDNA synthesis kit (Quantbio; Beverly, Massachusetts), according to the manufacturer’s instructions. Following synthesis, cDNA was diluted 10-fold with nuclease-free water for subsequent real-time quantitative polymerase chain reaction (RT-qPCR) analysis. cDNA was then stored at -30°C until further use. RT-qPCR was performed in a total volume of 10μl per well with Bio-Rad Sso Advanced Universal SYBR-green supermix (5μl per sample), nuclease free water (3μl per sample), primer (1μl per sample; 0.1μl primer in 0.9μl nuclease free water), and 1μl cDNA. We assayed four target genes, hsp90a, hsp90b, hsp70 and hif1a, which were selected for their known roles in acute thermal and hypoxia responses in fishes. Thus, we predicted that these genes would increase in expression following both heatwave acclimation and acute thermal and hypoxia stress [26,60]. We also assessed the expression of three potential reference genes in both tissues (rpl7, rps8, and rps5). Primers were designed using a white sturgeon transcriptome (Doering et al., 2016) and all were assessed for secondary structures and non-target binding. Primer efficiency (Appendix 6) was determined using a standard curve generated from serial 1:10 dilutions of pooled cDNA. Both no template and no reverse transcriptase controls were run on each qPCR plate, and no genomic DNA or other contamination was detected. All reactions were completed in an RT-qPCR machine (Bio-Rad CFX96) in a 96-well plate as follows: 2 min at 95°C, 40 cycles of 15 s at 95°C, 30 s at 60°C, and 30 s at 73°C, a decrease for 1 min to 60°C and 30 s at 72°C. Melt curves were determined by denaturation for 15 s at 95°C, a decrease for 1 min down to 60°C and then a gradual increase of 0.075°C·s-1 to 95°C. Only reference genes that had stable expression across treatments were used in subsequent analysis (rpl7 and rps8 for gill and 102 all three for heart). Amplification data were normalized to the geometric mean of expression of the stable reference genes and then analyzed using the 2△△CT method; the control group used to normalize data was the pre-heatwave baseline expression group (Schmittgen and Livak, 2008). 5.2.6 Global DNA methylation Global DNA methylation was measured in baseline samples from gills and hearts in pre-heatwave fish, fish exposed to 1 week of the heatwave (mid-heatwave), and fish exposed to the entirety of the heatwave (heatwave). Additionally, global DNA methylation was assessed in samples following the CTMax and hypoxia trials in pre-heatwave and heatwave sturgeon. Gills and heart were dissected from fish preserved in ethanol under a dissecting microscope and DNA was then extracted using a DNAeasy blood and tissue extraction kit (Qiagen, Germantown, Maryland, USA). Total DNA purity and concentration was assessed by gel electrophoresis and using a Qubit fluorometer (Invitrogen). Global methylation was assessed using a 5-mC DNA ELISA Kit (Zymo Research, Irvine, California, USA). Samples were run in duplicate and a standard curve was generated on each plate; intra-assay variation was 4.33% and inter-assay variation was 7.03%. Following color development, absorbance was measured at 450 nm using a 96-well plate spectrophotometer (SpectraMax 190, Molecular Devices). Global DNA methylation levels were normalized using the sterlet sturgeon genome, as this is the only sturgeon genome available (Du et al., 2020). 5.2.7 Statistical analysis All statistical analyses were completed using GraphPad Prism 9 with a significance level of 0.05. Normality and homogeneity of variance were assessed using Shapiro-Wilks, Levene’s tests, and visual inspection of Q-Q plots, if assumptions were violated data were transformed. Following all ANOVAs, multiple comparisons tests were performed with a Tukey’s HSD test. CTMax data (Fig. 5.3a) were analyzed using Mann-Whitney rank tests to determine significance within a treatment (100% vs 70%) or across treatments (pre-heatwave vs heatwave) as the data could not be transformed to meet assumptions for parametric testing. 103 Hypoxia tolerance data (Fig. 5.3b) were log-transformed prior to analysis via two-way ANOVA with acclimation temperature (pre-heatwave or heatwave) and temperature during trial as fixed effects. mRNA transcript levels (Fig. 5.4) for pre-heatwave, mid-heatwave, and heatwave timepoints were analyzed via one-way ANOVA. Baseline gill and heart global DNA methylation (Fig. 5.5) was also analyzed via one-way ANOVA. Each gene’s mRNA level following CTMax (Fig. 5.6) and hypoxia exposure (Fig. 5.7) was analyzed via two-way ANOVA (with heatwave exposure and CTMax or hypoxia as factors). Both the gill and heart DNA methylation following CTMax and hypoxia (Fig. 5.8) were analyzed via two-way ANOVA (with heatwave exposure and CTMax or hypoxia as factors). 5.3 Results 5.3.1 Heatwave exposed sturgeon exhibit cross tolerance Prior to exposure to heatwave conditions (pre-heatwave), acute hypoxic exposure reduced CTMax (Fig. 5.3a; P < 0.0001), with CTMax declining from 28.2°C to 24.6°C. Sturgeon exposed to heatwave conditions had a higher CTMax regardless of oxygen level (P < 0.0001). After heatwave exposure, there was no difference in CTMax (P > 0.05) between fish tested in normoxia (32.02°C) and acute hypoxia (32.22°C). Thus, induced cross-tolerance offset the acute effects of moderate hypoxia (70% O2 saturation) on CTMax in heatwave exposed fish. Both exposure to acute temperature increase during the hypoxia trial (P < 0.0001) and heatwave exposure (P < 0.0001) significantly affected hypoxia tolerance, with a significant interaction between these two factors (P < 0.001). Prior to heatwave exposure (pre-heatwave fish), oxygen saturation at LOE increased from 26.9% to 37.4% to 46.6% with acute exposure to temperatures of 13°C, 17°C, and 20°C, respectively (P < 0.0001 for all comparisons), indicating severely diminished hypoxia tolerance at higher temperatures (Fig. 5.3b). Heatwave exposure offset these effects such that fish acutely tested at 20°C had similar hypoxia tolerance to pre-heatwave fish tested at 13°C (LOE at 26.9% vs 27.4%; P > 0.9999). Thus, juvenile sturgeon exposed to heatwave conditions demonstrate complete compensation for the effects of acute high temperature exposure on hypoxia tolerance. In addition, we observed less than 1% 104 mortality during heatwave exposure, which highlights the substantial resilience of juvenile sturgeon to heatwave exposure and exposure to secondary acute stressors. 5.3.2 Transcript abundance and DNA methylation over time In the gill, gene mRNA levels stayed stable over time (Fig. 5.4a), with an increase in only one gene, hsp90a, at the mid-heatwave time point followed by a decrease in expression at the end of the heatwave (P < 0.05). Gill DNA methylation showed the opposite pattern of change across time (Fig. 5.5a; P < 0.0005), decreasing from pre-heatwave to mid-heatwave (P < 0.05) and then increasing from mid-heatwave to heatwave, returning to pre-heatwave levels (P < 0.0005). Heart mRNA transcript abundance changed over time (Fig. 5.4b), with an increase in transcript abundance at the mid-heatwave time point and a decrease in transcript abundance by the end of the heatwave, with significant effects detected by one-way ANOVA for hsp90a (P < 0.005), hsp90b (P < 0.01), hsp70 (P < 0.005), and hif1a (P < 0.05). Global DNA methylation of the heart also changed over time (Fig. 5.5b; P < 0.005), but less so than in the gill. Mid-heatwave, DNA methylation decreased in the heart (P < 0.005). However, unlike in the gill, heart DNA methylation did not return to baseline at the heatwave timepoint (P < 0.05). 5.3.3 Heatwave exposed sturgeon exhibit increases in mRNA transcript plasticity following thermal and hypoxia stress There were significant effects of having been exposed to a CTMax test on the mRNA abundance of hsp90a, hsp90b, and hsp70 but not hif1a (Appendix 7). Heatwave exposure significantly affected the abundance of hsp90a, hsp70, and hif1a, with a significant interaction between these factors for all genes except hsp90b (Appendix 7). In general, fish exposed to heatwave conditions had larger increases in mRNA abundance in response to CTMax, such that heatwave fish had significantly higher mRNA abundance than their relative control levels as detected by post-hoc tests for hsp90a, hsp90b, hsp70, and hif1a (Fig. 5.6a). These data suggest that heatwave exposed sturgeon exhibit increased gill transcriptional plasticity in response to acute temperature stress. 105 Generally, the changes in heart mRNA abundance following CTMax were smaller than changes in the gill (Fig. 5.6b). Two-way ANOVA detected significant effects of CTMax only on the mRNA abundance of hsp90a and hsp70 and there was no significant effect of heatwave exposure on the mRNA abundance of any gene. However, there were significant interactions for the two factors for both hsp90a and hsp70 (Appendix 7), with heatwave exposed fish showing greater transcriptional response when compared to their relative controls. Two-way ANOVA detected significant effects of hypoxia exposure on the mRNA abundance of hsp90b and hsp70 in the gill, but no significant effects of heatwave exposure or interaction between these factors for any of the heat shock protein genes (Appendix 7). By contrast, for hif1a there were no significant main effects but there was a significant interaction between them. Only hsp70 increased significantly in response to acute hypoxia exposure during hypoxia trials in pre-heatwave fish. (Fig. 5.7a) In contrast, in fish exposed to the heatwave all genes were significantly upregulated after the hypoxia trial. There were few changes in heart mRNA levels in response to acute hypoxic exposure during hypoxia tolerance trials. Only hsp90a and hsp70 were detected as significantly hypoxia responsive by two-way ANOVA. No significant effects of heatwave exposure were detected for any gene and only hsp90a exhibited a significant interaction between acute hypoxia exposure and the heatwave (Fig. 5.7b; Appendix 7). In pre-heatwave fish, only hsp70 was upregulated in the heart following acute hypoxia. In fish exposed to the heatwave, hsp90a, hsp70 and hif1a were significantly higher than their relative controls following hypoxia trials. 5.3.4 Thermal and hypoxia stress induce rapid changes in global DNA methylation In this study and others (Marsh and Pasqualone, 2014; Skjærven et al., 2014; Sun et al., 2016; Anastasiadi et al., 2017; Metzger and Schulte, 2017; Ryu et al., 2018; Lallias et al., 2021; Venney et al., 2021; Xinxing Wang et al., 2021; Smith et al., 2022), DNA methylation demonstrates responsiveness to warm temperature acclimation, but whether rapid changes in methylation occur in response to acute high temperature and hypoxia exposures, and whether heatwave exposure affects these responses is unknown in fishes. For gill DNA methylation, two-way ANOVA detected significant effects of CTMax (Fig. 5.8a; P < 0.0005), no significant effect of 106 acclimation condition (pre-heatwave vs heatwave; P > 0.05), and a significant interaction between the two factors (P < 0.005). In pre-heatwave fish, acute exposure to high temperature during a CTMax trial did not affect gill DNA methylation, whereas in heatwave fish, CTMax exposure resulted in a rapid and significant decrease in gill DNA methylation. Similarly, there was a significant effect of hypoxia exposure (P < 0.01), no significant effect of heatwave exposure (P > 0.05), and a significant interaction between these factors (P < 0.05) on global gill DNA methylation. In pre-heatwave fish, gill DNA methylation increased from 56.43% to 73.32% (Fig. 5.8a) in response to acute hypoxia exposure, whereas it was high (75.35% to 77.74%) in heatwave fish both under normoxic conditions and after acute hypoxic exposure (Fig. 5.8a). In the heart, DNA methylation was significantly affected by both CTMax (P < 0.0001) and heatwave exposure (P < 0.01), with no interaction between the factors. In both pre-heatwave and heatwave fish, CTMax exposure caused a statistically significant decrease in heart DNA methylation (Fig. 5.8b; pre-heatwave: 51.26% to 26.27% and heatwave: 38.42% to 16.32%). Similarly, heart DNA methylation was significantly affected by both acute hypoxia (P < 0.005) and heatwave exposure (P < 0.01) with no interaction between the factors. In general, heart DNA methylation was lower following acute hypoxic exposure in both pre-heatwave and heatwave conditions although this effect was not detected in post-hoc tests (Fig. 5.8b; pre-heatwave: 51.26 to 36.3% and heatwave: 38.42% to 24.9%). Together, these data demonstrate the ability of DNA methylation to rapidly change in response to both acute temperature increase and hypoxia exposure, highlighting tissue-specific modifications that are enhanced by heatwave exposure. 5.4 Discussion In this study we demonstrate that juvenile white sturgeon exposed to heatwaves are resilient to this stressor during this critical life period. The heatwave exposed sturgeon exhibited cross-tolerance to subsequent acute stressors associated with changes in epigenetic marks and mRNA levels in response to acute thermal and hypoxia stress. Of particular note, sturgeon 107 demonstrate the capacity to rapidly change global DNA methylation in response to acute heat or hypoxic stress over the course of a few hours. This study illustrates the extent of, and mechanisms underlying, resilience in juvenile white sturgeon in response to climate change scenarios during their most vulnerable year of life. Ultimately, these physiological mechanisms may have significant benefits in dealing with the increases in heatwave duration and frequency associated with climate change. Exposure to heatwave conditions induced cross-tolerance to high temperature and hypoxia As expected, based on many studies of the effects of thermal acclimation on thermal tolerance in fishes (Morley et al., 2019a), exposure to increased temperature during the simulated heatwave resulted in an increase in thermal tolerance in juvenile sturgeon. Although the observed differences in CTmax between pre-heatwave and heatwave fish could, in principle, be due to the 20 days between these two measurements, it is much more likely that this increase in thermal tolerance is due to thermal acclimation associated with the heatwave exposure. Not only is it well documented across fish species that warm acclimation increases thermal tolerance (Morley et al., 2019a; Earhart et al., 2022), this response to thermal acclimation has also been reported in the same population of sturgeon at different ages (Cheung, 2019; Penman, 2021; Earhart et al., 2023). Increases in CTmax following various thermal acclimation temperatures are demonstrated across white sturgeon populations (Lee et al., 2016; Rodgers et al., 2019) and other sturgeon species (Wilkes, 2011; Zhang and Kieffer, 2014; Bard and Kieffer, 2019; Rodgers et al., 2019; Bugg et al., 2020; Penman, 2021; Earhart et al., 2023). In fact, sturgeon have higher capacity for acclimation, measured as the acclimation response ratio (ARR), and show unusual plasticity and resilience to rapidly increasing temperatures compared to other fishes (Gunderson and Stillman, 2015; Morley et al., 2019a). Further, this acclimation capacity is even more pronounced in early developmental stages, where white sturgeon at the yolk-sac larvae stage have an ARR of over 1 (Earhart et al., 2023). In this study, juvenile sturgeon exhibited an ARR of 0.55, which is higher than average for a temperate fish species (Morley et al., 2019a), but similar to that of slightly older white sturgeon 108 (Penman, 2021) indicating that white sturgeon have substantial capacity for plasticity and are remarkably tolerant to temperature changes. Much less studied is the possibility that thermal acclimation also results in cross tolerance to hypoxia (Collins et al., 2021b; Earhart et al., 2022), a phenomenon that occurs when the mechanisms that are enhanced to protect against one stressor also elicit protection against a second stressor (Earhart et al., 2022). Here, we demonstrate clear evidence of cross-tolerance in juvenile sturgeon after heatwave exposure highlighting the resilience of this species in response to extremely stressful environmental conditions. Importantly, sturgeon were able to completely compensate for the negative effects of acute temperature increases on hypoxia tolerance. Prior to the heatwave, acute exposure to 20°C resulted in a near-doubling of oxygen saturation at LOE compared to fish tested at 13°C (i.e., acute high temperature exposure greatly reduced hypoxia tolerance). In fish exposed to heatwave conditions, hypoxia tolerance at 20°C was not different from that of pre-heatwave fish tested at 13°C, indicating complete compensation of this trait (Precht, 1958). In comparison, most fish species demonstrate little or no compensation of hypoxia tolerance with warming, and if they do, compensation is generally only partial and only occurs across a relatively small range of temperatures (McBryan et al., 2013; Jensen and Benfey, 2022). Juvenile sturgeon thus exhibit a much greater degree of cross-tolerance than has been demonstrated for most fish species. The extent of cross-tolerance is also evident from the observation that heatwave exposed sturgeon had the same CTMax under both normoxia and hypoxic conditions. Thus, heatwave exposed fish were able to completely compensate for the negative effects of moderate hypoxia on thermal tolerance. Although we cannot formally rule out the possibility that the observed differences between heatwave exposed and pre-heatwave fish were due to the 20 days in the laboratory, given that thermal acclimation has been shown to result in cross-tolerance to hypoxia in at least some fish species (McBryan et al., 2016; Earhart et al., 2022), we suggest that the observed cross-tolerance in heatwave exposed fish represents a plastic response. It is notable that these results are based on exposure to temperatures mimicking recorded river temperatures, whereas most previous work has been performed using constant laboratory exposures. These results indicate that sturgeon during their first year of life 109 have the ability to accrue cross tolerance to withstand environmentally realistic multi-stressor challenges. Exposure to heatwave conditions causes long-lasting changes in mRNA levels and global DNA methylation The underlying mechanisms that induce resilience and cross-tolerance following thermal acclimation are poorly understood in fishes (Logan and Buckley, 2015; Earhart et al., 2022). In this study we measured baseline mRNA levels for several key genes predicted to be involved in the response to acute high temperature and hypoxic exposure before (pre-heatwave), during (mid-heatwave), and after (heatwave) exposure to heatwave conditions. In all genes measured in the gill, transcript abundance showed a similar pattern over time, with only hsp90a increasing halfway through the heatwave but returning to baseline levels following the entirety of the heatwave exposure. In the heart, genes had an increase in mRNA levels halfway through the heatwave exposure period and a decrease in levels, back to pre-heatwave levels or below, by the end of the heatwave (Rivera et al., 2021). While this could be due to the 10 days between measurements, it is much more likely this change in baseline mRNAs is a result of heatwave exposure. Increases in heat shock protein (hsp) and hypoxia-inducible-factor-1 (hif1a) transcript abundance are a common response to thermal stress in fishes (Fangue et al., 2006; Komoroske et al., 2015; Jeffries et al., 2018; Rivera et al., 2021), which suggests that mid-heatwave the sturgeon may have been thermally stressed. By the end of the heatwave mRNA levels returned to or below pre-heatwave levels, and this decrease in abundance over time could indicate chronic thermal stress or complete acclimation to a warmer temperature (Fangue et al., 2011; Logan and Buckley, 2015; Rivera et al., 2021). Paired with the observed changes in whole animal tolerances, which exhibit substantial compensation, and low levels of mortality (less than 1%) throughout the heatwave, we suggest that the changes in mRNA levels after 20 days of chronic heatwave exposure reflect a completed acclimation response. We also measured global DNA methylation at the same time points to assess whether this regulatory mechanism influencing mRNA abundance also changed over the course of the 20- 110 day heatwave exposure. Changes in DNA methylation during warming have been observed across many organisms from polychaetes (Marsh and Pasqualone, 2014), to coral (Weizman and Levy, 2019; Smith et al., 2022), to teleost fishes (Metzger and Schulte, 2017; Ryu et al., 2018; Morley et al., 2019a; Lallias et al., 2021; Venney et al., 2021) and here we show this pattern also occurred in white sturgeon, an ancient fish species. Thus, we suggest that while it may be possible these differences are due to the 10 days in between measurements, it is much more likely these changes are due to heatwave exposure. However, it should be noted that the whole-genome methylation levels in juvenile sturgeon are lower than what is typically reported in teleost fishes (Metzger and Schulte, 2016), although still much higher than is typical for invertebrates (Suzuki and Bird, 2008), suggesting a fundamental difference in the role of DNA methylation as an epigenetic regulatory mechanism across taxa. In this study, gill methylation levels decreased mid-heatwave. Generally in vertebrates, decreases in methylation are associated with increases in transcription activity, and thus this pattern is suggestive of an overall increase in transcription (Metzger and Schulte, 2017) in response to increased temperatures. Methylation returned to baseline levels by the end of the heatwave, possibly signifying complete tolerance to the increased temperature. Conversely, heart DNA methylation decreased mid-heatwave and stayed at this level through the end of the heatwave, demonstrating tissue-specific responses of DNA methylation to heatwave exposure. We further suggest that the remarkable physiological resilience that white sturgeon exhibit in response to a heatwave is, at least in-part, influenced by changes in molecular underpinnings such as transcriptional activity and DNA methylation. Heatwave exposed sturgeon exhibit substantial plasticity of mRNA levels in response to an acute stressor The observed changes in whole-organism physiology in response to acute thermal and hypoxic stressors (e.g., increases in CTMax and decreases in oxygen saturation at LOE) in response to heatwave exposure in juvenile sturgeon were associated with a plastic transcriptional response following exposure to acute warming. An increase in gill hsp transcriptional activity after acute thermal stress following warm acclimation has also been 111 demonstrated by other animals (Fangue et al., 2011; Bugg et al., 2020; Manzon et al., 2022), including sturgeon, suggesting high temperature acclimation may increase future thermal tolerance through increases in gill transcriptional plasticity. In the heart, changes in mRNA transcript abundance were not as responsive to acute thermal stress as in the gill; but the heatwave exposed sturgeon had increased plasticity in the mRNA levels of both hsp90a and hsp70. Similarly, in other organisms both the heart and gill increase hsp transcription after heat stress (Fowler et al., 2009; Cardoso et al., 2015; Liu et al., 2019), but with varying levels of plasticity in the gill versus the heart depending on the species and duration and intensity of heat stress. The findings presented here and across other fish species suggest that increasing transcriptional plasticity in response to acute temperature change following thermal acclimation is a common defense strategy. As such, climate events like heatwaves may increase juvenile sturgeon tolerance when they encounter future acute temperature changes. Interestingly, there were differences in the extent of trait variation among individuals between pre-heatwave and heatwave exposed fish. For CTmax, pre-heatwave fish exhibited much more variation than did fish exposed to the heatwave. This pattern suggests that there could be inter-individual variation in the extent of plasticity in response to heatwave exposure. It is also consistent with the idea of “plastic floors and concrete ceilings” (Sandblom et al., 2016) which suggests that upper thermal tolerance measured as CTmax has a relatively fixed upper limit above which no further plasticity can be induced. We hypothesize that heatwave exposed sturgeon are reaching this ceiling and as a result individuals with relatively low pre-heatwave CTmax express greater plasticity than individuals with relatively high starting CTmax. In contrast, we do not observe this pattern of reduced variation post-heatwave at the molecular level under baseline conditions. Indeed, variance in gene expression levels were greater in heatwave exposed fish following CTmax exposure. This suggests a lack of strict coupling across these levels of biological organization, although additional experiments would be necessary to rigorously test this idea. Together these differences at the molecular and whole-organism levels highlight the complexity of making functional linkages across biological levels of organization. The ability of the juvenile sturgeon to fully compensate for the negative effects of high temperature on hypoxia tolerance following heatwave exposure is very rare in fishes (Havird et 112 al., 2020; Earhart et al., 2022) and it is likely that transcriptional plasticity is playing a role in this phenomenon. Exposure to acute hypoxia resulted in substantial (~5-fold) increases in hsp70 levels in both gill and heart in pre-heatwave and heatwave sturgeon. Increases in hsp70 abundance in response to hypoxia have also been demonstrated in other sturgeon species and thus induction of hsp70 appears to be a common response to hypoxic stress in sturgeon (Ni et al., 2014; Xiao-wen Wang et al., 2021) and other fishes (Delaney and Klesius, 2004; Wang et al., 2017). Acute hypoxia stress resulted in substantial increases in hif1a in sturgeon that were exposed to the heatwave, which could be partially influencing the hypoxia tolerance demonstrated by these fish. Hif1a is a subunit of a transcription factor that regulates the expression of genes involved in metabolism and oxygen supply, and is known to orchestrate the cellular response to hypoxia (Richards, 2009; Mandic et al., 2020). In other aquatic animals, the mRNA abundance of hif1a changes in response to thermal acclimation (Rissanen et al., 2006; Bugg et al., 2020) and subsequent or co-current hypoxia exposure (Levesque et al., 2019; Beemelmanns et al., 2021), but the effect these changes have on cross-tolerance differs among species. Regardless, in this study, the heatwave exposed sturgeon demonstrated complete compensation of hypoxia tolerance and increased hif1a mRNA plasticity during the hypoxia trial, highlighting the contribution hif1a may have in promoting this cross-tolerance. Acute exposure to high temperature and hypoxia induces rapid changes in global DNA methylation, and this response is enhanced in heatwave exposed sturgeon For the first time in fish, we demonstrate rapid changes in global DNA methylation over the course of an hour in response to acute thermal and hypoxia stress. Not only do we see rapid plasticity in methylation, these changes are tissue-specific, different between stressors, and different between pre-heatwave and heatwave exposed fish. In response to acute thermal stress, heatwave fish rapidly decreased gill DNA methylation to half that of baseline levels. By contrast, DNA methylation levels were not affected by acute high temperature exposure of pre-heatwave fish. The rapid decrease in global gill DNA methylation in heatwave-acclimated fish suggests that they may be able to recruit greater transcriptional responses to acute thermal 113 stress, which is consistent with our observation of increased mRNA plasticity in gill following CTMax in heatwave exposed sturgeon. In the heart, global DNA methylation decreased in response to acute high temperature exposure in both heatwave-acclimated and pre-heatwave fish, highlighting the similarities and differences between these tissues. Acute hypoxia exposure was associated with high levels of global DNA methylation in the gill. In the pre-heatwave fish, this was the result of an increase in DNA methylation as a result of acute hypoxic exposures, whereas in the heatwave exposed fish, DNA methylation was high under normoxic conditions, and remained so following acute hypoxia exposure. This pre-conditioning of global DNA methylation also has the potential to play a role in the observed cross-tolerance of the heatwave exposed fish. Contrasting patterns of DNA methylation in response to acute hypoxic exposure were observed in the heart, where DNA methylation slightly decreased from baseline levels in response to acute hypoxia exposure in both heatwave and pre-heatwave fish. Similar patterns of decreases in heart DNA methylation in response to hypoxia have been observed in other fish species (Léger et al., 2021), suggesting that this may represent a common response to hypoxic stress in fish hearts. The difference in DNA methylation in response to thermal and hypoxia stress in the gill may indicate opposing cellular strategies to cope with these stressors. The gill is the interface between the external and internal environment and hypoxic conditions are known to damage this tissue (Richards, 2009). As high levels of DNA methylation are often associated with decreases in transcriptional activity in vertebrates, this would be consistent with the observation of decreases in transcriptional activity in response to cellular stress (Jeffries et al., 2018). Further, in this study and others hypermethylation has been demonstrated in response to environmental stressors such as thermal stress(Metzger and Schulte, 2017; Swaegers et al., 2023). It has been suggested that an inactivation of generalized gene expression may conserve energy for activation of stress-responsive genes, and as such a hyper-methylated state may be beneficial for stressor tolerance. The heart, however, shows an opposite pattern, suggesting that there may be different thresholds for suppression of transcriptional activity to environmental stress across tissues. 114 Conclusion The collective evidence from this work demonstrates that juvenile white sturgeon exhibit substantial resilience in response to heatwave conditions during a critical life stage. Heatwave-exposed sturgeon exhibit a high level of cross-tolerance, in whole animal physiological responses to acute thermal and hypoxia stress. The complete compensation observed during acute thermal and hypoxia exposures is unusual among fish species and is clear evidence of the impressive tolerance of white sturgeon. The observed heatwave resilience in juvenile white sturgeon was associated with changes in mRNA levels and DNA methylation. White sturgeon demonstrated molecular plasticity, including a rapid induction of changes in DNA methylation in response to acute stressors. This is the first study to demonstrate these rapid changes in methylation in fishes as a response to acute thermal or hypoxia stress. These data highlight a previously unappreciated mechanism of thermal compensation, but questions remain concerning whether this capacity is unique to sturgeon and associated with the fact that they are an ancient, long lived, polyploid species. Lastly, these findings have environmentally relevant consequences for understanding the thermal sensitivity of a critically endangered species during their most vulnerable year of life. As heatwaves are one of the largest threats to global biodiversity, it is encouraging that, as demonstrated in this study, this iconic, critically endangered white sturgeon population has the physiological capacity to cope with extreme thermal stress. This study highlights the resilience of white sturgeon to heatwave events, and emphasizes the importance of measuring animal tolerance to extreme climate change events using realistic thermal exposures in endemic, endangered species. 115 5.5 Figures Figure 5.1 Nechako river temperature measured at the Vanderhoof station (teal) and recorded temperatures from the simulated heatwave (black). Temperatures taken during the British Columbia heatwave in June and July 2021. 10121416182022Temperature (°C)Nechako riverSimulated heatwaveDays0 10 20 116 Figure 5.2. Experimental design for juvenile white sturgeon heatwave exposure. Juvenile sturgeon acclimated to 13°C were exposed to a simulated heatwave mimicking heatwave recorded in the Nechako river in June 2021. Temperature was increased to 20°C over the course of three days and then maintained at that level for 20 days. Hypoxia and thermal tolerance 117 (CTMax) were assessed in pre-heatwave (13°C) fish and those exposed to 20°C for 20 days. Hypoxia trials were conducted at the acclimation temperature and after acute exposure to 13°C, 17°C or 20°C, acute increases to 17°C and 20°C for pre-heatwave fish and acute decreases 17°C and 13°C for Heatwave fish. CTMax trials were conducted at either 70% or 100% air saturation. Sturgeon gills and heart were sampled at pre-heatwave, mid-heatwave, and heatwave to assess baseline mRNA levels and global DNA methylation. Following hypoxia and CTMax trials stress induced levels of mRNA and global DNA methylation were also assessed (pre-heatwave and heatwave fish). Illustration by Madison Earhart. 118 Figure 5.3. CTMax and hypoxia tolerance in pre-heatwave (pre-heatwave; blue) and heatwave exposed (heatwave; pink) juvenile white sturgeon. Panel A represents CTMax under normoxia (100% air saturation) or hypoxia (70% air saturation). Asterisks indicate CTMax differences between normoxic and hypoxic test conditions within pre-heatwave or heatwave fish respectively. Panel B shows % air saturation at the time of loss of equilibrium in pre heatwave 13°C17°C20°C13°C17°C20°C020406080Temperature during hypoxia trial (°C)Oxygen at LOE (%)abcxyy100% 70%100% 70%1520253035Oxygen level during trial (%) CTMax (°C)*A)B)Pre heatwaveHeatwave 119 and heatwave sturgeon at three different temperatures. Letters indicate differences between temperatures during the hypoxia trial, within each acclimation group. For both panels, data are expressed as a median with quartiles and individual data points are shown (n=25-50). 120 Figure 5.4. Baseline mRNA levels for HSP90a, HSP90b, HSP70, and HIF1a in juvenile white sturgeon over time during the heatwave that have not been exposed to an acute stressor. Data are normalized to the reference genes (see text) and presented as fold change compared to the mean of the control group (pre-heatwave). Pre-heatwave (blue), mid-heatwave (yellow), and heatwave (pink). For both panels, data are expressed as a median with quartiles and individual 121 data points are shown (n=5-8). Panel A is gill mRNA and panel B is heart mRNA. Letters that differ indicate significant differences between days within a gene (P < 0.05). 122 Figure 5.5. Baseline global DNA methylation levels in juvenile white sturgeon over time during the heatwave in fish that have not been exposed to an acute stressor. Pre-heatwave (blue), mid-heatwave (yellow), and heatwave (pink). Panel A is gill and panel B is heart. Letters that differ Preheatwave Mid heatwave Heatwave020406080100Gill DNA methylation (%)abaPre heatwaveMid heatwaveHeatwavePreheatwave Mid heatwave Heatwave020406080100Heart DNA methylation (%)abbAB 123 indicate significant differences between days as determined by Tukey HSD (P < 0.05). For both panels, data are expressed as a median with quartiles and individual data points are shown (n=7-10). 124 Figure 5.6. Fold change in mRNA transcript levels at control levels (checkered boxes) after CTMax trials (solid boxes) in pre-heatwave (blue) and heatwave (pink) juvenile white sturgeon. Panel A 125 shows gill mRNA levels and panel B shows heart mRNA levels. All mRNA levels are normalized to reference genes. An asterisk indicates differences between control mRNA levels and post-CTmax mRNA levels for each gene. Significance was determined via two-way ANOVA and a Tukey’s HSD (P < 0.05). For both panels, data are expressed as a median with quartiles and individual data points are shown (n=4-8). 126 Figure 5.7. Fold change in mRNA transcript levels at control levels (checkered boxes) after hypoxia trials (solid boxes) in pre-heatwave (blue) and heatwave acclimated (pink) juvenile white sturgeon. Panel A shows gill mRNA levels and panel B shows heart mRNA levels. All mRNA levels hsp90a-5051015Relative mRNA fold change after hypoxiahsp90a hsp90b hsp70 hif1a hsp90b hsp70 hif1a** ***HypoxiaControl PreheatwaveHeatwave-5051015Relative mRNA fold change after hypoxiahsp90a hsp90b hsp70 hif1a hsp90a hsp90b hsp70 hif1a* ***AB 127 are normalized to reference genes. An asterisk indicates differences between control mRNA levels and post-hypoxia trial mRNA levels for each gene. Significance was determined via two-way ANOVA and a Tukey’s HSD (P < 0.05). For both panels, data are expressed as a median with quartiles and individual data points are shown (n=4-8). 128 Figure 5.8 Gill and heart DNA methylation following CTMax and hypoxia trials in pre-heatwave (blue) and heatwave (pink) juvenile white sturgeon. Panel A shows gill DNA methylation and panel B shows heart DNA methylation. Letters that differ indicate differences between control, CTMax, or hypoxia mRNA levels within a group (pre-heatwave or heatwave fish). Significance was 129 determined via two-way ANOVA and a Tukey’s HSD (P < 0.05). For both panels, data are expressed as a median with quartiles and individual data points are shown (n=5-10). 130 Chapter 6. The effects of domestication on phenotypic variation and survival in a natural environment in F2 rainbow trout 6.1 Introduction Pacific salmonids are the cornerstone of social-ecological systems that have supported Indigenous peoples and ecosystems around the north Pacific for thousands of years (Atlas et al., 2021). They are a keystone group of fishes that are responsible for transporting nutrients from the oceans and rivers to the land (Hyatt and Godbout, 1999; Helfield and Naiman, 2006), and are a key part to the food chain, feeding many animals from orcas, to sturgeon, to bears. As such, their continued existence is vital (Atlas et al., 2021). Unfortunately, salmonid populations have been in decline for several decades in British Columbia, Canada as a result of anthropogenic pressures such as habitat degradation, overfishing, and climate change (Price et al., 2017). Due to population declines and crashing fisheries, salmonids are commonly stocked from hatcheries for conservation, commercial, and recreational purposes and millions of hatchery-reared salmonids are released yearly across British Columbia. In the short-term, these hatchery-released fish have become an integral part in sustaining fisheries, but in the long-term they have had serious, detrimental impacts on wild populations (Naish et al., 2007) and in reality, the success of hatchery-reared individuals in natural environments is not always high. Unfortunately, hatchery-reared fish often have a very low survival rate in the wild, and the ones that do survive and reproduce may decrease the fitness of wild stocks, either by breeding with them (Jonsson et al., 2003; Miller et al., 2004; Araki et al., 2007, 2008; Shedd et al., 2022), or by competing for resources (Fleming and Gross, 1993; Tatara and Berejikian, 2012; Sylvester et al., 2019). This reduced fitness has been attributed to the differences in the phenotype of domesticated salmonids (Christie et al., 2016), relative to their wild ancestors. In hatchery programs that do not regularly introduce wild broodstock, there is a potential for hatchery fish to experience relaxed selection pressures on traits crucial to survival in the wild, and this causes the intentional or unintentional selection of traits that maximize fitness in the hatchery environment, but that may be poorly suited to life in the wild. 131 Indeed, hatchery salmonids are known to exhibit a series of phenotypes typical of domestication, including differences in in growth, behaviour, morphology, and transcriptomic responses when compared to their wild counterparts (Johnsson et al., 1996; Devlin et al., 2001, 2013; Marchetti and Nevitt, 2003; Álvarez and Nicieza, 2003; Tymchuk and Devlin, 2005; Tymchuk et al., 2007, 2009; Reinbold et al., 2009; White et al., 2013; Bellinger et al., 2014; Christensen et al., 2014; Campbell et al., 2015). Fish reared in indoor environments across generations may accumulate alleles that reduce fitness in natural environments, either through neutral processes acting on alleles that are not negative in a hatchery environment, but deleterious in the wild, or through positive selection on alleles that enhance fitness in the hatchery but that have negative effects in the wild. However, domestication in salmonids is relatively recent when compared to other domesticated animals like cows, pigs, or dogs (Frantz et al., 2020) and as such it is unclear the extent to which the observed phenotypic differences have a genetic basis. If these phenotypes are driven by relaxed/domestication selection pressures, there would be changes in allele frequency over many generations of adaptation to hatchery rearing, eventually creating domesticated alleles resulting in what is called a “domestication signature” in the genome (Sun et al., 2014; Gutierrez et al., 2016). There is also a possibility these differences arise from epigenetic or transcriptomic modifications as a result of phenotypic plasticity (Tymchuk et al., 2009; Devlin et al., 2013; White et al., 2013; Baerwald et al., 2016; Le Luyer et al., 2017; Gavery et al., 2019; Rodriguez Barreto et al., 2019; Venney et al., 2021; Koch et al., 2023). Indeed, it has been observed that hatchery-offspring exhibit epigenetic differences compared to wild fish even after only a single generation in the hatchery (Le Luyer et al., 2017; Koch et al., 2023; Venney et al., 2023). When domesticated hatchery reared fish are released into wild environments, there is the possibility that they may breed with wild fish. The genetic and phenotypic impacts of the introduction of domesticated alleles into wild populations have been extensively studied in Atlantic salmon (Glover et al., 2017) through experimental studies in the laboratory, in seminatural settings, and in the wild (Skaala et al., 2012, 2019; Bolstad et al., 2017; Bicskei et al., 2020; Perry et al., 2020; Crowley et al., 2022; Islam et al., 2022; Perriman et al., 2022). The 132 overall conclusion from these studies is that gene flow between domesticated and wild salmon is likely to change life-history and fitness-related traits, leading to reduced population growth. Less is known about these genetic impacts in Pacific salmon, although a recent study in chum and masou Pacific salmon suggests that increasing the percentage of wild genes in hatchery fish improves the survival of captive-bred offspring in the wild (Sahashi and Morita, 2022). To test the impacts of domesticated alleles, and their effects on phenotypes and survival in a natural setting in Pacific salmon, we created F2 offspring between wild and domesticated strains of rainbow trout (a pacific salmonid) to generate individuals with a variety of allelic combinations (Fig 6.1). We chose to use rainbow trout because they are an important Pacific salmonid species that is stocked around the world (MacCrimmon, 1971) and is thus, economically and recreationally important globally. There are multiple subspecies, ecotypes and strains of rainbow trout as a result of extensive introductions and different environmental selection pressures. Furthermore, the segregated breeding of hatchery-reared fish, exposed to long-term captivity, has created a captive phenotype in some trout strains which is remarkedly different then the wild phenotype (Pulcini et al., 2013). As rainbow trout are a native species to British Columbia (BC) that is also extensively stocked in the region, there is an opportunity to compare wild strains that have not experienced genetic admixture to hatchery domesticated lines of trout that are stocked across BC. There have been many studies defining phenotypic (Scott et al., 2015; Thompson et al., 2015; Allen et al., 2016; Watson et al., 2019), transcriptomic (Tymchuk et al., 2009; Devlin et al., 2013), and genetic (Vandersteen et al., 2012; McBryan, 2017) differences between the two strains used in this study: Pennask, the wild strain and Fraser Valley, the hatchery domesticated strain. These two strains are extremely different as the Fraser Valley strain has a clear “domesticated” phenotype, where the wild Pennask strain has never been supplemented, making these two strains ideal for testing the effects of domesticated alleles on survival and phenotypic variation in a natural environment. We made F1 crosses to then create F2 crosses to assess their phenotypic variation and survival in a natural environment. We stocked 10,000 F2 individuals into two replicate, semi-natural ponds and six months after stocking we captured the F2 trout to assess 133 genotype and its association with survival and phenotypic variation (Fig. 6.2). We aimed to answer the following questions: 1. How much genetic variation is there between the wild and domestic strains? 2. Do surviving fish in the ponds represent a random sample of F2 fish? 3. How much phenotypic variation is there among F2 fish? 4. Are phenotypes influenced by the proportion of domesticated alleles? 6.2 Methods 6.2.1 Rainbow trout crosses The F0 fish from the two source populations (Pennask and Fraser Valley) were crossed at The Pacific Sciences Enterprise Centre (PSEC, Fisheries and Oceans Canada) in West Vancouver in 2016. Eggs from four wild Pennask mothers were crossed with sperm from four domesticated Fraser Valley fathers to generate F1 offspring (Fig. 6.2). F1 offspring were held at the PSEC facility for three years until the generation of F2 crosses. In February 2020, F2 crosses were generated by collecting gametes from 9 F1 females and 9 F1 males. Eggs and sperm were mixed in a large plastic bag for 10 minutes in 10°C dechlorinated, oxygenated water to create F2 offspring (Fig. 6.2). Fertilized embryos were then treated with ovadine for 10 additional minutes and rinsed multiple times to reduce fungal growth. Fertilized embryos were then added to Heath trays and mortalities were removed daily. Water was maintained at 10°C in the Heath trays and was well oxygenated. Once the embryos hatched, alevin were transported to the University of British Columbia (UBC) in coolers maintained at 10°C and supplied with air stones for aeration of the water. In April 2020, following transport all of the F2 rainbow trout were stocked into replicate ponds at the UBC, described below. Approximately 5000 trout were stocked into each replicate pond, for a total of 10,000 fish across two ponds (Fig. 6.2). 6.2.2 Experimental ponds The ponds used in this experiment are semi-natural ponds located on UBC’s south campus. Although the ponds are man-made all other inputs are natural, i.e., temperature, 134 oxygen saturation, food availability, predation, and habitat structure. We used two replicate ponds (700,000 liters each) numbered pond 11 and pond 12, which were equal in size, but slightly differed in the substrate composition. Pond 11 had more plants with sandy, silty soil where the bottom of pond 12 was comprised of more gravel. The ponds were fertilized with potassium, phosphate, and nitrates one month prior to the addition of fish to increase productivity. The ponds were checked weekly for temperature, oxygen and signs of predation or rainbow trout presence. 6.2.3 Pond fish trapping In September 2020, six months after stocking, pond fish were captured with baited fyke nets and minnow traps (Fig. 6.2). Nets were baited with a variety of different baits including frozen krill bait (Jerry’s Bait and Tackle) and bloodworms (Haiki). Fyke nets were set overnight in one pond and the following morning trapped fish were added to pond-side recirculating tanks. There were three 20-gallon tanks pond-side that had recirculating pond water and each tank also had an airstone to ensure aeration. Captured trout were left in these tanks for 24 hours to allow them to recover from capturing and handling stress. The fyke net was then set in the second pond that evening and trapped fish were added into pond-side tanks the following morning, and this cycle repeated over the course of seven days. Following the 24-hour rest period rainbow trout were assessed for multiple phenotypes including hypoxia tolerance, morphometrics, coloration, length, and weight (Fig. 6.2) in the following order: after LOE in hypoxia the fish was sedated in MS-222 (0.25g·L-1 buffered in sodium bicarbonate), following sedation an overhead photo was taken of the fish next to a color card and ruler and then it was euthanized with MS-222 (0.5g·L-1 buffered in sodium bicarbonate). After the photo, the fish was weighed, and length was measured, and then a fin clip was taken from each fish. The fin clip was added to a vial with 95% EtOH and the entire fish was then wrapped in tinfoil and added to liquid nitrogen for future use. 135 6.2.4 Hypoxia tolerance assessment Hypoxia tolerance was assessed as the air saturation level at the time the fish displayed loss of equilibrium (LOE) following acute exposure to declining oxygen levels. All trials were conducted at the same time each day between 9am and 12pm. The temperature of the hypoxia trial tanks was the same as the pond, as the water in the tank was directly pumped in from the ponds. The temperature of the water in the tanks across the trials throughout the week ranged from 22-24°C. In total, six trials were run, three trials for each pond on alternating days. After recovery for 24 hours following capture from the ponds, the pump recirculating water from the pond to the tank was turned off and an additional pump used to mix the water within the tanks. Next, oxygen was lowered by bubbling in nitrogen into the tank to achieve a decline in air saturation of 1.5%·min-1. Nitrogen bubbling was manually adjusted throughout the trial by continuously measuring oxygen levels throughout the trial with a YSI oxygen meter (Pro20). Once the fish reached LOE, they were removed from the tank, euthanized, and sampling was completed as described above. 6.2.5 Rotenone To ensure that trapping and netting did not miss large groups of fish which would skew genotype analysis, after capture of the pond fish we added rotenone to the ponds. Rotenone is used as an insecticide or a pesticide to remove unwanted fish from ponds or lakes. In this case, we added rotenone to both of the ponds to cause any remaining fish to swim to the surface, and we then caught these fish on the pond surface once they had loss of equilibrium. To ensure the fish was properly euthanized, they were then placed in a buffered MS-222 bath (0.5mg·L-1 buffered in sodium bicarbonate). The trout were then weighed, length was measured, and a fin clip was taken from each individual for genotyping. 6.2.6 Affymetrix UVicOmyk SNP array All fish that were captured from the ponds, as well as grandparents (F0), parents (F1), and additional individuals from the source populations (Pennask and Fraser Valley) were 136 genotyped using a 200k rainbow trout single nucleotide polymorphism (SNP) chip that had been previously developed by collaborators. The affymetrix Omyk 200k SNP chip was designed using 66 genomes of different rainbow trout strains (Appendix 8) to identify SNPs for inclusion on the chip. Briefly, seven priority groups of SNPs were classified, where group 1 SNPs represented highly polymorphic variations validated in wild rainbow trout populations with an allele frequency between 0.5 and 0.95. Groups 2-5 were composed of >45 million SNPs from 74 individuals that were then sorted into different SNP groups based on their frequency, commonality, and distance between markers. Group 6 SNPs included a small number of variants to maximize differentiation between coastal, interior, and California origin trout and group 7 were SNPs on the rainbow trout sex-determining gene, sdY. Following recommended designation from Affymetrix for SNP chip design, a total of 201,544 SNPS were finalized for the chip. 6.2.7 Genotyping In total we genotyped 379 individuals on the SNP chip, 302 individuals captured in pond trapping (F2), 30 individuals captured after rotenone (F2), 8 grandparents (F0), 18 parents (F1), 11 individuals from the wild populations (Pennask), and 11 individuals from the domestic population (Fraser Valley). We chose to include individuals from the source populations to ensure the diagnostic SNPs we identified were in fact differences between wild and domestic strains and not familial differences. Once genotyped on the chip, the genotype data for each individual across each SNP was generated, where alleles were coded in a binary format where 0=AA, 1=AB, 2=BB, and -1= no call. These data were then used for the following analyses. Genotypes for the pond fish were determined by assigning each allele (AA or BB) for each SNP to either a domestic homozygous allele, a wild homozygous allele, or a heterozygous allele. If the domestic individuals (F0) had AA for a given allele, this was considered the domesticated allele and if the wild individuals (F0) had BB for a given allele this was considered a wild allele. As such, these genotypes were assigned to each allele for each SNP across pond fish. 137 6.2.8 Hybrid index score A hybrid index score can be used to assess the extent of hybridization in a population or to determine whether hybrid individuals are more like one or other of the parental strains. The hybrid index score (h) for F2 individuals was determined by using the following formula (Neff and Smith, 1979): (𝑃𝑒𝑛𝑛𝑎𝑠𝑘 𝑚𝑒𝑎𝑛 𝑃𝐶1 𝑠𝑐𝑜𝑟𝑒 − ℎ𝑦𝑏𝑟𝑖𝑑 𝐹2 𝑚𝑒𝑎𝑛 𝑃𝐶1 𝑠𝑐𝑜𝑟𝑒)(𝑃𝑒𝑛𝑛𝑎𝑠𝑘 𝑚𝑒𝑎𝑛 𝑃𝐶1 𝑠𝑐𝑜𝑟𝑒 − 𝐹𝑟𝑎𝑠𝑒𝑟 𝑉𝑎𝑙𝑙𝑒𝑦 𝑚𝑒𝑎𝑛 𝑃𝐶1 𝑠𝑐𝑜𝑟𝑒) 6.2.9 Statistical Analysis Fixation index (FST) Hierfstat v0.5-11 was used to investigate patterns of genetic variation and differentiation between groups (Goudet 2004). The fixation index (FST) was investigated between the Pennask and Fraser Valley populations using both the F0 grandparents and individuals from the populations to establish baseline levels of differentiation between crossed groups in this study, with the function boot.ppfst used over 1000 iterations to identify 95% confidence intervals. We also identified FST to find SNPs of high differentiation (FST ³ 0.7) for further analysis. These source population individuals were included in the analysis because of confounding within the F0 fish, where all wild individuals were female and all domestic individuals were male. Principal component analysis (PCA) We used the R package pcadapt v4.3.5 to visualize PCAs and identify potential SNPs under selection (Luu et al. 2017). A given SNP was only accepted as significant at a false discovery rate-adjusted p-value (q) < 0.05, while the function get.pc was used to find the principal component in which a SNP drove variation. SNP filtering Following the FST analysis between wild and domestic individuals, SNPs in the F2 pond fish were filtered based on SNP FST. SNPs were filtered at two different FST levels, one was at an FST ³ 0.7 which reduced the SNP list from 201,554 to 16,591. This was further filtered for SNPs 138 annotated to a location and known chromosome, reducing the SNPs with high differentiation to 13,899. The second group of filtered SNPs was for FST = 1 SNPs which reduced the SNP list to 33 SNPS, this was again filtered further for chromosome annotation which left 21 SNPS with an FST =1 and an annotated position. 6.2.10 Phenotypes Many phenotypes were collected from the pond fish and four are included in this study (condition, weight, length, and hypoxia tolerance – the remaining phenotypes were assessed by an MSc student who collaborated on this project). Histograms of each phenotype were created to illustrate the phenotypic variation amongst pond fish. These phenotypes were also assessed in 50 individuals from each parental population and comparisons between populations were done with Mann-Whitney tests on Graphpad Prism v10. All phenotypes for the parental populations, which were raised in a hatchery, were also determined at the age of 6 months to be consistent with the age of the pond fish at the time of measurement. 6.2.11 Effects of genotype on phenotype To determine the effects of genotype on various phenotypes we performed linear regressions. There was no significant relationship between phenotypes and hybrid index as using all the SNPs, therefore we generated a % domestication score using genotypes of fixed SNPs. Specifically, we assessed the proportion of homozygous domesticated alleles in SNPs with an FST =1 across individuals and plotted that against a phenotype. For example, for a given individual if 11 out of 22 SNPs had a domesticated genotype that individual would be considered 52% domesticated for this analysis. 6.3 Results 6.3.1 Wild and domestic strain (F0) differences The two populations (Pennask and Fraser Valley) were genetically distinct with an overall FST of 0.444 (0.443 to 0.446 95% CI) between wild and domestic individuals (Fig. 6.3). Out of 201,554 SNPs used in the present data, 16,591 had high differentiation (FST ³ 0.7), but only 139 13,899 were annotated to a known chromosome. There were 33 SNPs completely fixed between the domestic and wild strains, with an FST =1 but only 21 of these were annotated to a known chromosome. The two parental populations were also phenotypically distinct (Fig. 6.4 a-d) and Mann-Whitney U tests detected significant differences in condition factor (P<0.0001), hypoxia tolerance (P< 0.05), wet mass (P<0.0001), and length (P<0.0001). 6.3.2 Pond fish (F2) genotypes In total 302 fish were recaptured from the 10,000 fish originally stocked. There were 133 fish from pond 11 and 169 fish from pond 12. We assessed if surviving pond fish genotypes represented a random distribution by analyzing F2 individuals in a PCA with all 200k SNPs along with both F0 populations and the F1 parents (Fig. 6.5a). There were four main groups of fish defined on the PCA. PC1 explained 18.2% of the variance and domesticated and wild individuals are divided across this axis. PC2 explained 10.1% of the variance and this axis highlighted differences across groups of wild genotype individuals. The PCA illustrates that surviving pond fish genotypes were not representative of a random sample of F2 genotypes and in fact, F2 fish closely grouped with either domesticated F0 grandparents or wild F0 grandparents (Fig. 6.5a). There were 150 pond fish that grouped with the domesticated grandparents and 161 pond fish that grouped with wild grandparents when assessing genotype across all SNPs. We then did an additional PCA using the subset of highly differentiated SNPs (FST ³0.7). PC1 explained 7.9% of the variance and PC2 explained 5.6% of the variance (Fig. 6.5b). Using only these highly differentiated SNPs resulted in the pond fish forming two distinct groups, rather than 4, but these continued to group with either F0 domestic individuals or F0 wild individuals. There were 141 pond fish that grouped with the domesticated grandparents and 172 pond fish that grouped with the wild grandparents when assessing genotype across highly differentiated SNPs. From assessing the PCAs, it appeared that there were very few heterozygous individuals that fell in between the domesticated and wild groups so we explored this further. We used two subsets of SNPs (FST ³0.7 or FST =1) to assess the genotype proportion of each SNP across 140 individuals, where the genotype was either domestic homozygous, heterozygous, or wild homozygous (Fig. 6.6a). Theoretically, at any given fixed SNP, without selection (either intrinsic as a result of genomic incompatibilities, or extrinsic as a result of ecological selection), one would expect the genotypes to be 25% domestic homozygous, 50% heterozygous, and 25% wild homozygous. However, that was not the case in the pond fish, which suggests evidence of selection at both subsets of SNPs. For SNPs with an FST =1 there were 22.4% domestic homozygous alleles, 31.2% heterozygous alleles, and 38.7% wild homozygous alleles (Fig. 6.6a). This suggests individuals that had more wild genotype had higher survival in the ponds and individuals with more domesticated or heterozygous alleles were selected against. When assessing the same question with the subset of SNPs where FST ³0.7 the selection signature was not as strong but still exists, with 23.8% domestic homozygous, 47.6% heterozygous, and 28.6% wild homozygous (Fig. 6.6b). We then assessed these genotypes (SNPs at FST ³0.7) across chromosomes to investigate if there were certain parts of the genome that were more domesticated, heterozygous, or wild, but there were no large differences between chromosomes (Fig. 6.6c). Of note, there were very few SNPs (FST ³0.7) that were above 50% heterozygous genotype across individuals, suggesting that heterozygosity was not favorable in a semi-wild environment. 6.3.3 Hybrid index score (F2 pond fish) The expected hybrid index score for F2 hybrids is 0.5 and in this study the hybrid index score for the pond fish was 0.47. This suggests the pond fish are more like the wild population than the domesticated population. 6.3.4 Pond fish phenotypes The phenotypes, hypoxia tolerance, condition, wet mass, and length, did not differ between ponds so the two pond groups were analyzed together. Frequency distribution histograms were created for each phenotype (Fig. 6.7). Condition factor (K) ranged from 0.55 to 1.3 in the pond fish and the mode was 0.85 (Fig. 6.7a). Hypoxia tolerance ranged from 10%-22% (air saturation at LOE) with the mode at 17% (Fig. 6.7b). Wet mass ranged from 0.5-6.5g with the 141 majority of fish weighing 2.0g (Fig. 6.7c). Lastly, length ranged from 40-95mm and the mode was 60mm (Fig. 6.7d). Interestingly, most of the phenotypes measured in the pond fish were quite similar to the phenotypes measured in the Pennask (F0 wild population) and different from the Fraser Valley (F0 domesticated population) phenotype reared in the hatchery (Table 1). Table 6.1. Phenotypes across parental strains (domestic F0 Fraser Valley and wild F0 Pennask) and pond fish (F2) including condition factor, wet mass, length, and hypoxia tolerance. *converted to mg·L-1 as temperature was different between hypoxia trials. Phenotype Domestic (F0) Wild (F0) Pond fish (F2) Condition factor (K) 1.3 0.82 0.86 Wet mass (g) 69.74 2.67 2.51 Length (mm) 174.2 68.32 60.7 Hypoxia tolerance (mg·L-1*) 1.05 0.99 1.27 6.3.5 Effects of domestication on fish phenotypes Following a linear regression analysis on all four phenotypes (Figure 6.8), condition factor was the only phenotype significantly affected by domestication (P<0.0001; R2=0.03). Neither hypoxia tolerance (P=0.2; R2=0.004), wet mass (P=0.9; R2= 0.0005); length (P=0.2; R2= 0.004) were affected by the proportion of domestication at fixed SNPs. 6.3.6 Pond fish (F2) rotenone We captured an additional 30 fish following the addition of rotenone to the ponds, 9 from pond 11 and 21 fish from pond 12 (Fig. 6.9). The genotypes of the fish that were left in the ponds were 1 domestic homozygous fish, 11 heterozygous fish, and 17 wild homozygous fish. Genotypes for each individual were determined by assessing what genotype was the most common across SNPs with an FST =1. 142 6.4 Discussion In this study we demonstrate that second-generation hybrid offspring with domesticated alleles have lower survival in a semi-natural environment and that there are clear differences between the phenotypes of individuals carrying increased proportions of domesticated alleles. We further demonstrate that there is substantial genetic and phenotypic differentiation between the wild and domesticated strains of rainbow trout investigated here. Our data suggests that if hatchery-domesticated and wild strains mate, their offspring may be less fit for the natural environment as domesticated alleles affected survival. Thus, we must start re-considering segregated hatchery practices and their effects on fish domestication, especially when these fish are being used to supplement wild populations. Indeed, in British Columbia Fraser Valley domestic fish are generally only released as sterile triploids to minimize this risk. The wild and domestic strains are genetically distinct The Pennask and Fraser Valley populations are known to be phenotypically distinct (Tymchuk et al., 2009; Devlin et al., 2013; Scott et al., 2015; Thompson et al., 2015; Allen et al., 2016; Watson et al., 2019) and, in this study, we show they are also genetically distinct with an FST = 0.44. This FST is quite high for populations in the same species and highlights how different these domestic and wild strains are from each other. In fact, a FST value over 0.25 is considered very high genetic variation between groups, where an FST of 0-0.05 is considered little genetic variation, 0.05-0.15 is moderate, 0.15-0.25 is a large genetic variation, and above 0.25 is considered very large (Wright, 1984). As such, over time domestication selection has created genetically-distinct fish that is now very different from its wild counterparts. In other rainbow trout populations across California the average FST between populations was 0.127 (Berg and Gall, 1988), across multiple commercial trout strains in France the average FST was around 0.1 (D’Ambrosio et al., 2019), and across eight trout lodge commercial strains FST =0.13 (Liu et al., 2017). It can be difficult to compare the fixation index for the wild and domestic populations to wild rainbow trout studies, as there is no gene flow between Pennask and Fraser Valley populations and as such it would be expected that they would have a higher FST. However, even 143 when compared to the studies comparing between domesticated strains or across domesticated and wild fish strains (Skaala et al., 2004), the observed genetic differences in this study are large. These large genetic differences most likely underlie the divergent phenotypes we also observed in these two strains. The Fraser Valley population has the classic domesticated fish phenotype, where they are larger with higher condition factor than their wild counterparts. It has been suggested that these domesticated phenotypes may, at least in part, be due to plasticity in response to the hatchery environment, but with the observed phenotypic differences between the strains under common garden conditions in the hatchery and the high level of genetic differences between the domesticated and wild strain illustrated in this study, it is likely these phenotypes are genetically based and can affect fitness of their offspring in the wild. This supports the hypothesis that relaxed selection pressures in a hatchery environment over time have led to fixed genetic differences in the domestic population and these changes may not be suitable for fitness in a natural environment. As such, there are many implications that arise when considering introduction of domesticated alleles into the wild if fertile domesticated strains are stocked given the impacts those alleles may have on future offspring. Pond fish with wild alleles had better survival in a semi-natural environment In this study we demonstrate that fish with wild-type alleles survived better in the semi-natural environment of the ponds than individuals with domestic alleles. Without selection pressures we would have expected the genotypes of pond fish to follow a Hardy-Weinberg equilibrium 1:2:1 distribution in an F2; however, that is not what we observed. Instead, our data are suggestive of selection where the pressures in a wild environment better matched the wild genotype in the hybrid offspring. This suggests not only are domesticated fish less fit for a wild environment, but their offspring through multiple generations, even when crossed with a wild strain, are also less fit. A decrease in fitness has been demonstrated in other hatchery salmonids (Miller et al., 2004; Araki et al., 2007, 2008; Skaala et al., 2019; Sylvester et al., 2019; Islam et al., 2022; Shedd et al., 2022), and here we observe the same result as F2 fish harboring more domesticated alleles had lower survival compared to fish with wild alleles. There are many 144 selection pressures in a natural environment that do not occur in hatcheries, such as food scarcity, predation, variation in temperature and oxygen, and niche habitat competition. Further, the hybrid index for the pond fish was 0.47, which is slightly lower than the expected score of 0.5. When the hybrid index score is more or less than 0.5, it indicates the hybrids are more like one of the parental populations instead of an even mix of both (Neff and Smith, 1979). In this case, a score of 0.47 indicated the pond fish were more like the wild parental population than the domestic population. This further solidifies the result that surviving pond fish were more like the wild population and these selection pressures favored wild-type alleles. Interestingly, there are also fewer heterozygous individuals then would be expected without selection. There are many SNPs that are heterozygous in up to 50% of individuals, but very few above this threshold. This lack of full heterozygous alleles may be an example of genomic incompatibility or an example of ecological selection, where environmental factors determine the survival and fitness of hybrid individuals varying in phenotypic traits (Vamosi et al., 2000; Thompson et al., 2022). Genomic incompatibility can reduce genetic exchange in hybrids resulting in hybrid inviability, however, we do observe some SNPs that are heterozygous across 50% of individuals and there a few individuals that are mostly heterozygous. Therefore, while it is possible the lack of heterozygosity could be from genomic incompatibilities, it is more likely a result of ecological selection. The direction and intensity of ecological selection on hybrids is dependent on the specific environment in which individuals exist and its possible in an indoor environment the heterozygous individuals would have survived in higher numbers. This would also be an excellent way to test the relative importance of genomic incompatibility vs ecological selection. If what we observe in this study it is in fact genomic incompatibility (and not ecological incompatibility) the lack of fully heterozygous hybrids would persist indoors because there would not be ecological selection. In either case, there are more domestic individuals that survived in the ponds compared to heterozygous individuals which suggests a domesticated genotype (and possibly phenotype) must have been more advantageous than a heterozygous genotype. Thus, it appears that hybrid F2 fish with dominant parental phenotypes, i.e. wild or 145 domestic, fared better in a wild environment than heterozygous individuals, suggesting that even the domesticated genotype increased survival when compared to heterozygous individuals. High variation in phenotypes across pond fish In addition to the genetic variation across surviving pond fish there is also substantial phenotypic variation across the measured traits. As the parental strains are extremely different in these phenotypes it is not surprising to observe substantial variation in F2 fish. This highlights that fish with different sizes and tolerances were able to survive in the ponds; however, the extent of variation across F2 fish is not as large as the differences in phenotypes observed between the parental strains. In the absence of selection, we might expect F2 hybrid fish to express all possible combinations of phenotypes. Alternatively, F2 hybrid fish may be more similar to one of the source populations or may even exhibit phenotypes outside the range of the parental population in a phenomenon called transgressive segregation (Rieseberg et al., 1999). In this study, the phenotypes of the pond fish were more similar to the wild parental population. Whether this difference is a result of the preponderance of wild alleles in the surviving fish or to plastic effects due to the pond environment remains to be determined. In any variable, natural environment, phenotypic plasticity plays a large role in a fish’s ability to cope with change and in this study, it is possible some of the trait variation observed here is a result of phenotypic plasticity. As the ponds are a natural, ever-changing environment the phenotypes observed in the F2 fish may look very different if the fish were instead reared inside in a hatchery. In the pond fish, the domesticated phenotype was not as prevalent and the trout were much more similar to the wild Pennask parental phenotype even if there were domesticated alleles present. This suggests the environmental conditions, such as food availability and predation, in the ponds could have shaped a wild-like phenotype in the F2 hybrids. Some phenotypes were affected by the proportion of domesticated alleles The proportion of domesticated alleles affected important phenotypes of the pond fish including physiological metrics and behavior. First, condition factor was higher in individuals with 146 more domesticated alleles than individuals with more wild alleles. This is very intriguing, as we know that generally in artificial environments domesticated fish have higher condition than wild fish, and this has been demonstrated between the two parental population phenotypes in other studies (Tymchuk and Devlin, 2005; Devlin et al., 2013) and in the current study. Additionally, given the initial high stocking density of the ponds (compared to naturally occurring populations in ponds of the same size) it is also possible that differences in size or condition could affect intraspecific predation within the ponds, as cannibalism and dominance hierarchies are commonly described in salmonids (Duk et al., 2017). It has been hypothesized that these differences in condition or size would not exist in a wild environment regardless of the genetic predisposition because of the limited resources in a natural setting (Tymchuk and Devlin, 2005). However, in this study we demonstrate that even in a semi-wild environment fish with domesticated alleles maintain higher condition factor than their wild counterparts. It is possible that the initial stocking density, which was higher than a natural environment, may have given the domesticated, aggressive, phenotype an early advantage compared to the more wild fish. This suggests that size and condition are not only genetically determined but also maintained across environments with different selection pressures. Second, we demonstrate that catchability, a behavioral phenotype, was affected by the proportion of domesticated alleles in pond fish. Anti-predator behavior is known to have a genetic basis in fishes, which may influence individual responses to baited nets or hooks (Klefoth et al., 2013). As hatchery-domesticated fish are commonly used to stock ponds and lakes frequented by recreational anglers, it has been demonstrated across salmonid species that domesticated strains are more vulnerable to angling than their wild conspecifics (Flick and Webster, 1962; Dwyer and Piper, 1984; Dwyer, 1990; Mezzera and Largiadèr, 2001; Klefoth et al., 2013). Specifically, the Fraser Valley domesticated population used in this study is known in the angler community to be a very easy fish to catch, and is purposely stocked in lakes for easy fishing (FFSBC, 2023). This strain is known for their risk-taking, aggressive behavior and as such, can be easy to bait or catch compared to other rainbow trout strains. Here, we defined catchability by the number of individuals remaining after trapping and adding rotenone to the ponds, i.e. fish that were caught prior to the addition of rotenone were considered more 147 catchable than fish that were caught only after adding rotenone. Of the 30 fish captured after rotenone addition, 29 of these individuals were heterozygous or wild genotype, with only one domestic genotype individual. This is different than the proportion of genotypes following the original trapping, where 22% of the individuals had a highly domesticated genotype. This suggests that genotype has a strong effect on our ability to catch fish and that even at the F2 generation this behavioral change persists, illustrating the strong influence domesticated alleles can have on the behaviour of hybrids. The aggressive and bold behavior of fish with a domesticated genotype may be one of the reasons less domesticated fish survived in the ponds as they may have been more heavily predated on than the wild genotype fish. In a study comparing predation rates between hybrid (domestic x wild) steelhead trout to wild trout, F1 hybrid trout were more likely to take risks than wild fish, highlighting the potential for damaging behavioral changes if domestic and wild fish interbreed (Johnsson and Abrahams, 1991). In hybrid coho salmon crosses (F1 and F2) there were also behavioral differences in the hybrid individuals, where the more domesticated fish had a reduced anti-predator response then the wild-type fish (Tymchuk et al., 2006). These studies and our current study demonstrate that behavioural changes linked to domestication persist through multiple generations, even when mixed with wild individuals, and this could be extremely problematic when considering anti-predator responses in offspring of wild fish and hatchery fish. Conclusions In this study we demonstrated that hybrid F2 individuals with domesticated alleles had lower survival in a wild environment, highlighting the deleterious effects of domestication on fitness. Additionally, possession of domesticated alleles was associated with differences in condition factor and behavior even in a wild setting. These results are extremely concerning when considering the fate of offspring of hatchery-reared individuals and endemic populations in the wild. It is important to note that this study may be worst case-scenario when considering the level of domestication (Nugent et al., 2022) in the Fraser Valley population that has been hatchery-reared for over 30 generations. In addition, in British Columbia hatchery programs take 148 several approaches to mitigate the potential deleterious effects of introducing domesticated alleles into wild populations, including the use of integrated hatchery management in which wild broodstock is generally used and, the stocking of sterile individuals in the case of domesticated stocks. Nevertheless, my data highlight the importance of considering the detrimental impacts of introducing domesticated alleles into wild populations through releasing hatchery fish, and the impacts this has on our wild, endangered populations. 6.5 Figures 149 Figure 6.1. Illustration of chromosome and allele recombination in F0, F1, and F2 generations of domestic and wild strains. Domestic (Fraser Valley) strain shown in red and the wild (Pennask) strain is shown in green. Allelic combination possibilities increase with each generation. Figure 6.2. Wild (Pennask) strain and domestic (Fraser Valley) strain individuals were crossed to create PNFV F1 individuals. F1 individuals were crossed to create F2 individuals. These F2 individuals were then stocked into semi-natural replicate ponds with 5000 fish in each pond. The trout were left in the ponds for 6 months prior to trapping. Fish were trapped over the course of a week using fyke nets and minnow traps and phenotypes (hypoxia tolerance, morphometrics, color, and size) were assessed pond-side. Fin clips were taken for genotyping on the 200k SNP chip panel. 150 Figure 6.3. The fixation index (FST) between Pennask and Fraser Valley populations including grandparents (F0) used in this study and 11 individuals from each source population. The FST between the domestic and wild populations was 0.44. Colors are alternated between chromosomes where odd chromosomes are red and even and unknown chromosomes are blue. 1 2 ė Ę ę Ě ě Ĝ ĝ 10 11 12 1ė 1Ę 1ę 1Ě 1ě 1Ĝ 1ĝ 2021 22 2ė 2Ę 2ę 2Ě 2ě 2Ĝ2ĝ unknownChromosomeƴ1ƣ0ƴ0ƣę0ƣ00ƣę1ƣ0Fst 151 Figure 6.4. Phenotypes of condition factor (A), hypoxia tolerance (B), wet mass (C), and length (D) in the parental populations, domestic Fraser valley (FV; orange) and wild Pennask (PN; green). Mann-Whitney U tests detected significant differences and these are denoted with asterisks (P<0.05). 152 Figure 6.5. Principal component analyses (PCA) of all rainbow trout generations including all (A) 200k SNPs and (B) with filtered SNPs (FST > 0.7). Domestic grandparents (F0) are orange dots, wild grandparents (F0) are grey dots, parents (F1) are light blue dots, and pond fish (F2) are navy blue dots. 153 Figure 6.6. Proportion of pond fish genotypes (%; wild homozygous, heterozygous, domestic homozygous) for fixed SNPs at FST =1 (A) and highly differentiated FST ³0.7 (B) across the genome 154 and individual chromosomes (C). Panel C shows the genotypes (SNPs FST ³0.7) for wild, heterozygous, and domestic alleles across all chromosomes. 155 Figure 6.7. Relative frequency distributions (%) of the phenotype distributions in pond fish separated by genotype. Homozygous wild fish are designated by green bars, heterozygous designated by blue bars, and homozygous dominant by orange bars. Panels show condition factor (A), hypoxia tolerance (B), wet mass (C), and length (D). 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5010203040Condition factor (K)Relative frequency (percentages)0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.50102030Wet mass (g)Relative frequency (percentages)10 11 12 13 14 15 16 17 18 19 20 210102030Air saturation at LOE (%)Relative frequency (percentages)HeterozygousHomozygous domesticHomozygous wild40 45 50 55 60 65 70 75 80 85 90 950102030Length (mm)Relative frequency (percentages)A)C)B)D) 156 Figure 6.8. Linear regression of domestication effects on condition factor (A), hypoxia tolerance (B), wet mass (C), and length (D). Domestication (%) was determined by the proporiton of domesticated homozygyous alleles at fixed SNPs (FST =1). P-value for each regression is listed on the figure and dotted lines represent 95% confidence intervals. 0 20 40 60 800.00.51.01.5Domestication %Condition Factor (K)P<0.00010 20 40 60 800510Domestication %Wet mass (g)P=0.90 20 40 60 80510152025Domestication %Air saturation at LOE (%) P=0.20 20 40 60 80406080100Domestication %Length (mm)P=0.2A)C)B)D) 157 Figure 6.9. The number of F2 rainbow trout in each genotype (domestic homozygous, heterozygous, or wild homozygous) following the addition of rotenone. The genotype was determined by assessing the proportion of domesticated, heterozygous, or homozygous alleles for fixed SNPs (FST =1) in fish that remained after trapping and following addition of rotenone to the ponds. domestic homozygous heterozygous wild homozygous05101520GenotypeNumber of fishdomestic homozygousheterozygouswild homozygous 158 Chapter 7. Conclusions Throughout this thesis I investigated the roles of plasticity and adaptation in response to environmental change across different temporal scales in fishes. My thesis chapters span a wide breadth of topics with a common thread of understanding the interface between genes and the environment. Broadly, the main goal of my thesis was to explore how environmental change interacts with development, different stressors, phenotypic plasticity, and adaptation in different fishes. Another important goal in designing my research was to do applied research that will impact and help further our knowledge in fields of climate change effects and conservation. To do this I asked fundamental biological questions but in the context of climate change stressors and I used innovative molecular tools but in species of conservation concern. Through this framework I was able to contribute to our understanding of foundational biology but also provide evidence of concrete actions that may be used to create conservation management recommendations. We as humans have caused a new mass extinction event and as such I believe it is essential scientists prioritize applied, climate-driven, and conservation research. In my chapters I prioritized these topics in different ways. In chapters 2 and 6, I explored how adaptation to different environments affects the plasticity of the stress response and coping with novel environments. Chapters 3 and 4 were dedicated to investigating the sensitivity of developmental stages to climate change stressors like temperature change or salinity differences. Chapters 4 and 5 were focused on the conservation of an endangered species and investigated the impacts of ecologically-relevant climate change stressors to ultimately help manage these populations. Lastly, in chapters 3 and 5 I investigated the impacts of simultaneous stressors because as our climate changes fish will face multiple kinds of stress at once. I focused on measuring responses to environmental change across different levels of biological organization because plasticity at the molecular level is critical to creating responses in the whole organism. Because of this in chapters 2, 3, 4, and 5 I evaluated responses at the mRNA level in response to different stressors across various life-stages. Investigating gene expression proved to be invaluable in these studies and was extremely useful in understanding responses to different stressors. I repeatedly found that mRNA plasticity is crucial in responding to change and it really is the “first phenotype” (Keagy et al., 2023). In 159 response to thermal, hypoxia, and osmotic stress higher levels of plasticity were key in maintaining or improving whole-animal performance. I also found a high degree of mRNA plasticity in response to acclimation which then translated to higher plasticity following acute stressors, a promising finding when considering a individual’s ability to cope with climate change. Specifically, in the sturgeon chapters (4 and 5) my research demonstrated the important role thermal acclimation plays in mRNA plasticity and how we can use these measurements to determine thermal thresholds for species of conservation concern. This is a really exciting avenue of research as it creates the opportunity to measure stressor thresholds non-lethally through small tissue samples for mRNA analysis. Also, in chapter 3, I was able to highlight persistent effects of developmental conditions on individual’s mRNA plasticity later in life. This was especially important as these changes were persistent in a species known to be highly tolerant of environmental changes. This study emphasized the significant and important implications of phenotypic plasticity in fishes that are exposed to unideal developmental conditions due to climate change. After completing most of this research and considering the prominence of mRNA plasticity across the chapters, I began to explore the plasticity of the mechanism regulating mRNA levels. Thus, in chapter 5 I added another molecular component by measuring DNA methylation in conjunction with gene expression and whole-animal plasticity. Highlighting how plastic and rapid DNA methylation changes can occur in response to environmental change was one of my main contributions to fundamental science from this thesis. It was thrilling to observe this underappreciated molecular mechanism in response to thermal stress and further emphasized the importance of plasticity across genetic machinery. Examining organismal responses to thermal stress is a critical part of climate change research and as such I assessed the effects of changing temperatures in three chapters of this thesis. My research echoes the literature in that increasing temperatures have a profound effect on fishes and some species will be more resilient to change than others. In the white sturgeon chapters, I found that there are differences in sensitivity to warming temperatures dependent on the life stage. Specifically, I found that exposure to warm temperatures during the earliest stages of life had negative effects on whole-animal and mRNA plasticity but when exposed with a 160 heatwave later in life there were increases in these metrics. This underscores the importance of considering developmental stage when evaluating the effects of thermal stress as individuals must persist through changes during sensitive life stages for them to survive. In addition to measuring increasing temperatures, I also assessed the effects of cool temperature during development in a warm-adapted species (chapter 3). This is also relevant to climate change research because increases in severe weather events will cause cold weather anomalies to rise in frequency. Interestingly, exposure to cool temperature during development had lasting effects on both mRNA levels and size months after exposure. This study highlights the importance of studying relevant climate stressors specific for the study species, which may include responses to cold temperatures. The results from chapter 3 also exemplify the physiological impacts of multiple stressors, which is an understudied avenue of climate change research. The effects of cold temperature combined with low salinity on development were worse than in isolation, and had lasting impacts on later life stage physiology in a very tolerant, estuarine fish species. To date, there are very few studies assessing the persistent impacts of developmental temperature and salinity in estuarine or tidal fish species and more research in this area is urgently needed. I further investigated the impacts of concurrent stressors in chapter 5, however in this study they were acute changes rather than developmental incubation conditions. The impacts of simultaneous rapid warming and hypoxia proved to be greatly detrimental for sturgeon not previously exposed to a heatwave and decreased both acute thermal and hypoxia tolerance. However, sturgeon exposed to a heatwave impressively tolerant to these co-stressors and accrued impressive cross-tolerance through heatwave acclimation making them more resilient to multiple stressors. These two studies again highlight the importance of developmental sensitivity and acclimation history when assessing plasticity to environmental change. In addition to the role plasticity plays in coping with environmental change, two of my thesis chapters assess the role of adaptation. One chapter (2) focused on how adaptation affects the stress response and the other (6) investigated how adaptation to artificial environments can affect survival and phenotypes in the wild. Interestingly, I found that local adaptation to a thermal gradient affected the stress response, in that fish adapted to a warmer, southern 161 environment were more responsive than their northern counterparts. These differences persisted at the whole-animal, hormone, and mRNA response. This research opens up an avenue of climate change-related questions pertaining to adaptative tradeoffs between a responsive stress axis with body condition and survival in the wild. The rainbow trout chapter addresses a different conservation question pertaining to our use of hatcheries to supplement endangered fish populations and how fish adapted to novel environments like hatcheries can cause harm when mixed with wild fish. This was an important question I wanted to answer in my thesis because currently one of the main management strategies to maintain wild fish stocks are supplementation programs that are releasing fish that are very different than their wild counterparts. Through using genetic tools like a SNP-chip, I was able to assess complex but applied genetic questions and exemplify the negative effects domestication can have on our wild fish. Broadly, this study also underlines the basic question: what are the effects of adaptation to a static environment when that population is then exposed to a novel or changing environment? This question can be applied to other climate change research as our planet’s organisms are and will continue to face a novel, rapidly changing environment that they may not be adapted for. There are many pressing questions that still need to be answered especially in species of conservation concern. A lot of the research investigating climate change effects on fishes is done in the laboratory and more research in wild populations is desperately needed. This is especially the case when considering endangered populations and in developing fishes. It can prove to be more difficult to do these studies in the wild as there are inherently more variables, there are more permits necessary, and for the most part these studies in the wild fish cannot be euthanized for physiological measurements. As such, we must use cutting-edge techniques and non-lethal sampling to begin to validate different sampling methods to investigate the variety of environmental stressors our fishes face in the wild. These types of studies will be meaningful if researchers are able to combine bioinformatics, physiology, and ecology. These studies will also require collaboration outside of academia and will be even more powerful by working together with First Nations, industry and the government. By measuring the physiological impacts across multiple levels of biological organization through non-lethal sampling such as blood samples, gill 162 biopsies, mucus samples, and more, we can start to determine the resilience of our natural populations over time and make more meaningful predictions about how fish will respond to climate change. Finally, I want to conclude with the importance of conducting applied collaborative research to address our climate change and conservation crises. I fully believe applied science that is impact-oriented must be prioritized across our scientific communities and that basic science questions will still be answered, just with an additional purpose. 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Colors indicate the temperature and salinity treatment (dark red is 26°C 20ppt, light red is 26°C 3ppt, dark blue is 20°C 20ppt, and light blue is 20°C 3ppt). Different letters (ABC) denote significant differences between treatments within a developmental time point. Different letters (XYZ) denote significant differences within a 5 10 15012345Days Post Fertilizationhif1a mRNA levelstime: p = 0.02temp: p = 0.02temp x salinity: p = 0.04xxxy xyy y5 10 150510152025Days Post Fertilizationhsp90b mRNA levelstime: p < 0.0001temp: p < 0.0001salinity: p = 0.02time x temp: p < 0.0001aabab ba aabbaabb ab,zxyy5 10 150246Days Post Fertilizationhsp70.1 mRNA levelstime x temp: p = 0.04 5 10 15012345Days Post Fertilizationhsc70 mRNA levelstemperature: p = 0.01xxyya aabb5 10 1501234Days Post Fertilizationhsp90a mRNA levelstime x temp: p = 0.003 xxyy5 10 1502468Days Post Fertilizationhsp70.2 mRNA expressiontemp x salinity: p = 0.01aabb baaabb26°C 20 PPT26°C 3 PPT20°C 20 PPT 20°C 3 PPT 198 treatment across developmental time points. Significant three-way ANOVA effects and interactions are listed on each gene panel. 199 Appendix 2. Chapter 4 supplemental file 1. 200 201 202 203 Appendix 3. Chapter 4 supplemental file 2. Vector plot and dimension loadings for control PCA in white sturgeon yolk sac larvae. hsp70hsp90ahsp90bhsp47hbbigf2g6ppepckcpepckmhxkhbagh1hif1a−1.0−0.50.00.51.0−1.0 −0.5 0.0 0.5 1.0Dim1 (62%)Dim2 (12.2%)Variables − PCA 204 Vector plot and dimension loadings for CTmax PCA in white sturgeon yolk sac larvae. hsp709 ahsp90bhsp47hbbigf2g6ppepckcpepckmhxkhbagh1hif1a−1.0−0.50.00.51.0−1.0 −0.5 0.0 0.5 1.0Dim1 (65.3%)Dim2 (10.2%)Variables − PCA 205 Appendix 4. Chapter 4 supplemental file 3. MO2 chambers for measuring embryo metabolic rate. Picture on the left shoes entire design, picture on the right shows a single embryo in each MO2 chamber. 206 CTMax set up for measuring thermal tolerance in white sturgeon yolk sac larvae. Sturgeon are held in a breeder net, inside a bucket filled with oxygenated, dechlorinated water. There are multiple pumps circulating water, heater sticks, an air stone, and thermometers. 207 Appendix 5. Chapter 4 supplemental file 4. Developmental timing over days for each incubation temperature in white sturgeon embryos and yolk sac larvae. One dash = one day. 208 Appendix 6. Chapter 5 Supplemental file 1. Primer sequences for white sturgeon, Acipenser transmontanus, hsp70, hsp90a, hsp90b, hif1a, rpl7, rps8, and rps5. Gene Forward primer Reverse primer Efficiency (%) hsp70 CCATGAACCCCAGCAACACT TGCACAACAGAGTCGTCGTA 100.7 hsp90a CCTTGATTGCCTCCTCTGTT GACTCATTCCAACCGCATCTA 103.4 hsp90b ACTTGGTCCTTGCTCTCACC GCGATACCACAGCTCTCAGT 104.9 hif1-a GCATCTGAGGATAGTGGTAAAG CTGTTGGCAGTAGGAGAATG 99.5 rpl7 TCACAAAGGCCAACTTGGGT CGAGATCCGCTTGTCCAGAA 101.9 rps8 GGGCGACCCAATTCATACTT CCAGGGACAACTGGCATAAA 102.8 rps5 ACTCGACCCGAATTGGACG GTTGACTCTACGCAGGGGG 99.9 209 Appendix 7. Chapter 5 Supplemental file 2. ANOVA table for all ANOVAs completed in this study in juvenile white sturgeon exposed to a heatwave. 210 211 212 213 Appendix 8. Chapter 6 Supplemental file 1. Supplemental figure 1. The 66 rainbow trout genomes used to create the 200k SNP chip adapted from collaborators.
Thesis/Dissertation
eng
Zoology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Schulte, Patricia M.
Baker, Daniel W
The roles of plasticity and adaptation in response to environmental change across temporal scales in fishes
Text