Forestry, Faculty of
DSpace
UBCV
Butler, Natalie Ann
Master of Science - MSc
University of British Columbia
Pacific salmon are now experiencing temperatures in freshwater that can reach or exceed their upper thermal limits. The aims of this thesis are to evaluate the role of acclimation on the Upper Thermal Tolerance (UTL) in stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha), to evaluate how different methods in measuring UTL affect the measured outcome, and determine how acclimation temperature affects post-swim mortality. Both parr and fry life stages were acclimated for two weeks at temperatures spanning present and expected future summer levels (15°C, 18°C, 20°C, 24°C). Using fish from each acclimation temperature, we measured post-swim mortality following an endurance trial (Eswim), whereby swimming fish were exposed to increasing speeds at each temperature, and subsequently returned to their initial acclimation trough and monitored for 48 hours. The effect of acclimation temperature on critical thermal maximum (CTmax) was measured by two means: stationary (CTmax stat) and swimming (CTmax swim). CTmax stat was determined by water evenly heated at 0.3°C/min in a stationary bath, with loss of equilibrium (LOE) as the indicator of CTmax stat. CTmax swim was determined using a swim flume heated at 1°C/30min with fish swimming at a fixed speed of 4 BL/s, and cessation of swimming as the endpoint. Fish acclimated to higher temperatures generally exhibited higher thermal tolerance until 24°C, where fish were not able to acclimate to higher temperatures. Based on CTmax stat trials, parr showed a consistent increase in acclimation up to 24°C, with increasing CTmax temperatures, though in CTmax swim trials, there was a distinct thermal ceiling at that temperature. Fry exhibited a greater sensitivity to increased temperatures, with a drop in CTmax at 24°C for both stationery and swimming trials. There was high post-swim mortality following the 24°C acclimation groups in the Eswim trials in both parr and fry, indicating an upper thermal limit where impaired recovery. This work highlights the need for varied metrics in studying thermal tolerance and the influence that such metrics have on the measured outcome. Warm temperatures exceeding 20°C are now routinely occurring in the study population region, so we expect increasing mortality rates of fish in coming year.
Assessing effects of acclimation temperature on thermal tolerance of stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha) under ecologically relevant temperatures by Natalie Ann Butler BSc, The University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2024 © Natalie Ann Butler, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Assessing effects of acclimation temperature on thermal tolerance of stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha) under ecologically relevant temperatures Submitted by Natalie Ann Butler in partial fulfillment of the requirements for the degree of Master of Science in Forestry Examining Committee: Scott Hinch, Professor, Department of Forest and Conservation Sciences, UBC Supervisor Erika Eliason, Professor, Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara Supervisory Committee Member Colin Brauner, Professor, Department of Zoology, UBC Supervisory Committee Member Jeffrey Richards, Professor, Department of Zoology, UBC Additional Examiner iii Abstract Pacific salmon are now experiencing temperatures in freshwater that can reach or exceed their upper thermal limits. The aims of this thesis are to evaluate the role of acclimation on the Upper Thermal Tolerance (UTL) in stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha), to evaluate how different methods in measuring UTL affect the measured outcome, and determine how acclimation temperature affects post-swim mortality. Both parr and fry life stages were acclimated for two weeks at temperatures spanning present and expected future summer levels (15°C, 18°C, 20°C, 24°C). Using fish from each acclimation temperature, we measured post-swim mortality following an endurance trial (Eswim), whereby swimming fish were exposed to increasing speeds at each temperature, and subsequently returned to their initial acclimation trough and monitored for 48 hours. The effect of acclimation temperature on critical thermal maximum (CTmax) was measured by two means: stationary (CTmax stat) and swimming (CTmax swim). CTmax stat was determined by water evenly heated at 0.3°C/min in a stationary bath, with loss of equilibrium (LOE) as the indicator of CTmax stat. CTmax swim was determined using a swim flume heated at 1°C/30min with fish swimming at a fixed speed of 4 BL/s, and cessation of swimming as the endpoint. Fish acclimated to higher temperatures generally exhibited higher thermal tolerance until 24°C, where fish were not able to acclimate to higher temperatures. Based on CTmax stat trials, parr showed a consistent increase in acclimation up to 24°C, with increasing CTmax temperatures, though in CTmax swim trials, there was a distinct thermal ceiling at that temperature. Fry exhibited a greater sensitivity to increased temperatures, with a drop in CTmax at 24°C for both stationery and swimming trials. There was high post-swim mortality following the 24°C acclimation groups in the Eswim trials in both parr and fry, indicating an upper thermal limit where impaired recovery. This work highlights the need for varied metrics in studying thermal tolerance and the influence that such metrics have on the measured outcome. Warm temperatures iv exceeding 20°C are now routinely occurring in the study population region, so we expect increasing mortality rates of fish in coming year. v Lay Summary Aquatic ecosystems are at the forefront of climate change, and ectotherms are directly affected by temperature through reduced oxygen concentrations, increased metabolic demands, and direct mortality. Additionally, thermal tolerance measurements are affected by various aspects in methodology such as endpoints, temperature ramp rates, and acclimation temperature. In addition, Pacific salmon exhibit adaptation to the localized thermal environment, which highlights the need for population-specific research. The aims of this thesis are to evaluate the role of acclimation on the Upper Thermal Tolerance (UTL) in stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha), to evaluate how different methods in measuring UTL affect the measured outcome, and how acclimation temperature affects post-swim mortality. This thesis adds to the body of research by including specific life-histories and life stages, as well as include novel methods of UTL evaluation.vi Preface This dissertation is part of a larger project addressing the Nechako River under climate change, with my contribution being experiments to measure life-stage specific thermal tolerance values for the model. My primary responsibility was the design and execution of experimental trials, as well as collection and analysis of data. The use of the swim flume is the suggestion of E. Eliason, in which she helped guide the initial set up and use. All experiments were conducted with the support of many collaborators who dedicated their time to assist me. All tables and figures are made of my own creation and data, with overall analysis guidance from E. Lunzmann-Cooke and K. Birnie-Gauvin, though ultimately all finalized analyses in this dissertation are my own. No portion of the text has been submitted to publication, but will be in the future. Those who provided essential help in the form of developing and executing the project, as well as those who significantly guided the manuscript will be listed as co-authors on manuscripts when they are submitted for publication. All protocols were approved by the Animal Care Committee at the University of British Columbia with the Canadian Council for Animal Care, under animal use protocol (experiment #A19-0284, holding #A19-0056). vii Table of Contents Abstract ............................................................................................................................................ iii Lay Summary ....................................................................................................................................v Preface ............................................................................................................................................. vi Table of Contents ........................................................................................................................... vii List of Tables ................................................................................................................................... ix List of Figures .................................................................................................................................. x List of Abreviations ......................................................................................................................... xi Acknowledgements ......................................................................................................................... xii 1 Introduction .................................................................................................................................. 1 2 Methods ....................................................................................................................................... 11 2.1 Experimental Animals .......................................................................................................... 11 2.1.1 Transport, Acclimation, and Holding ........................................................................... 11 2.2 Critical Thermal Maximum (Stationary) .............................................................................. 12 2.3 Critical Thermal Maximum (Swimming) ............................................................................ 13 2.4 Endurance Swim Trials ........................................................................................................ 14 2.5 Acclimation Response Ratio ................................................................................................ 15 2.6 Statistical Analysis ............................................................................................................... 15 3 Results ......................................................................................................................................... 17 3.1 Acclimation and Holding ..................................................................................................... 17 3.2 Acclimation and Critical Thermal Maximum ...................................................................... 17 3.3 Endurance Swim and Latent Mortality ................................................................................ 18 4 Discussion.................................................................................................................................... 20 5 Conlusion .................................................................................................................................... 27 Tables .............................................................................................................................................. 29 viii Figures ............................................................................................................................................ 35 References ...................................................................................................................................... 40 Appendix: Supplementary Material .............................................................................................. 53 ix List of Tables Table 1. The calculation of correlation between body size metrics show how body size metrics scale together with growth. Spearman’s rho calculated between mass (g), fork length (FL, mm), and condition factor (CF) for fry and parr......................................................................................29 Table 2. BIC and likelihood ratio test (LRT) of general linear model selection of CTmax swim trials of body size metrics for each life stage, with acclimation temperature included in all models. LRT favoured no body size metric in all cases. In all cases BIC favoured the model excluding the body size metric. Asterisks denote favoured model and chosen models are in bold...............................30 Table 3. Acclimation response ratio (ARR) of parr and fry between CTmax stat and CTmax swim protocols. Loss of acclimation capacity is observed at higher temperatures for fry regardless of CTM, while it is only observed in the CTmax swim methodology for parr...................................................................................................................................................31 Table 4. AIC, BIC, and likelihood ratio test (LRT) for general linear model selection of the CTmax stat trials, all include acclimation temperature as a predictor in addition to the body size metric of interest. LRT favoured no body size metric in all cases. In all cases but CTmax stat parr, BIC favoured the model with no body size metric. Asterisk denotes favoured model selection and chosen models are in bold................................................................................................................32 Table 5. CTmax range and mean ±SD by life stage, test type, and acclimation temperature......................................................................................................................................33 Table 6. Summary mean ± SD of mass (g) and fork length (mm) of mortalities < 24 hours, at 24 hours, and at 48 hours.....................................................................................................................34 Table 7. Summary mean ± SD of mass (g) and fork length (mm) of fry and parr at the end of the two-week acclimation period...........................................................................................................34 x List of Figures Figure 1. View of acclimation heating area, top-down (a) and in-trough (b) .................................35 Figure 2. View of CTmax stat heating containers for both fry (a) and parr (b) ..................................36 Figure 3. Side view of swim flume used for both CTmax swim and Eswim trials.................................37 Figure 4. Mean ± SEM of critical thermal maximum temperatures (CTmax) for fry (a) and parr (b) for CTmax stat (black) and CTmax swim (blue). Brakets between points denote significance difference in CTmax temperature with p-values between acclimation treatment groups listed above. These figures highlight the differences in critical thermal maxima three-fold: how acclimation temperature affects CTmax, how CTM affect CTmax, and how CTmax differs between life stage.................................................................................................................................................38 Figure 5. Percent mortality of fry (a) and parr (b) post-swim for each swim group as separated by within 24 hours (purple), at 24 hours (teal), and at 48 hours (yellow). Percent mortality at each check time is calculated from the mortalities in the pool of remaining fish at that check time. The bottom x-axis is the acclimation temperature group and the top x-axis is the swim flume temperature. In post-swim mortalities for parr (b), mortality at 24 hours was 100%. This highlights the detrimental effect high acclimation temperatures have on recovery of both parr and fry following stressful events..........................................................................................................39 xi List of Abbreviations ACE – Acclimated Chronic Exposure ARR – acclimation response ratio CTM – Critical thermal method CTmax – Critical thermal maximum CTmax stat – Critical thermal maximum, stationary CTmax swim – Critical thermal maximum, swimming CTmin – Critical thermal minimum DO – Dissolved Oxygen HSPs – Heat-Shock Proteins LOE – Loss of equilibrium LT50 – Lethal temperature 50% OCLTT – Oxygen- and capacity-limited thermal tolerance Tcrit – Critical temperature Topt – Optimum temperature UTL – Upper Thermal Limit UILT – Upper Incipient Lethal Temperature xii Acknowledgements I would like to first and foremost thank my supervisory committee for their guidance during my study. Specifically, I would like to thank Scott Hinch for affording me the opportunity to firstly work in the lab, and then take me on as a student. He has been supportive across all aspects of the project and has encouraged me in times of doubt. I am also thankful for Erika Eliason’s enthusiasm for the project and her readiness to problem-solve the numerous challenges that arose. I would like to thank Colin Brauner for his role in the initial formulation of the project and his insights. Without their collective knowledge I would not be able to undertake this project to the degree of success it achieved. I would also like to thank those who were instrumental to the execution of the project. Andrew Lotto for assisting in the procurement of the necessary equipment, setting up the lab space for the experiments, and for his expertise. I would like to thank Brian Hendriks, Noah Kussin-Bordo, Noa Mayer, Dave Scott, Stephanie Lingard, Kim Birnie-Gauvin, and Rachel Penman for their instrumental help during the experiments and for husbandry of the fish in the lab. I am especially thankful for Kim Birnie-Gauvin and Emma Lunzmann-Cooke’s for mentorship regarding data analysis. This project would not have been possible without the funding from the Canadian Natural Sciences and Engineering Research Council 17 (NSERC) and Rio Tinto collaborative Research and Development grant. I was also graciously awarded a Strategic Recruitment Fellowship, the Braham G. Griffith Memorial Graduate Scholarship in Forestry, and the Hugh Robert Duncan Chrisholm Scholarship in Forestry, all of which I am grateful. Last but not least, I would like to thank my friends and family for their encouragement throughout this entire endeavour, and especially my mother for her tireless proofreading of all my drafts.1 1 Introduction As the climate continues to change, rising freshwater temperatures are becoming a major concern for fish as this exerts a direct effect on their physiology. As ectothermic poikilotherms, the body temperature of Pacific salmon (Oncorhynchus spp.) will typically match that of their environment. Temperature is considered “the master variable” (Brett, 1971) due to its influence on dissolved oxygen and other gases, and because of the direct effect it has on metabolism and other physiological processes. Further, water temperature can be thought of as an ecological resource in that fish behave to attain and retain thermally preferable environments in similar ways to the way they may select feeding sites (Magnussen et al., 1979). As such, thermal tolerance and occupancy of thermal niches become important aspects of growth and survivability for Pacific salmon. While fish can maintain homeostasis in the face of warming temperatures through neuroendocrine, hormonal, and molecular responses (e.g., production of heat-shock proteins), whole-animal reductions in growth, condition, or reproduction can manifest if homeostasis cannot be maintained (Barton, 2002). Elevated temperatures reduce food conversion efficiency and lead to reductions in growth (Wendelaar Bonga, 1997; Brett et a., 1982). Reduced energy stores and growth may impact a fish’s ability to compete for resources and recover from exhaustive activities such as avoiding predation and migrating. Increased anti-predator (Kuehne et al., 2012) and foraging (Quigley and Hinch, 2006) behaviours were observed as compensatory responses to increasing thermal stress. Decreased disease resistance is another physiological outcome of thermal stress in fish (Ellis, 1981; Fries, 1986; Wendelaar Bonga, 1997). Further, many infectious diseases have increased virulence or lethality when temperatures increase (Bell, 1986; Carter, 2005). When thermal limits are reached or surpassed, maladaptive coping mechanisms can lead to death (Poletto et al., 2017). 2 Upper thermal limits (UTL) vary among populations, species, and life stages (reviewed in Mayer et al., 2023), however, individuals can exhibit considerable plasticity in upper thermal tolerance arising from acclimation or intrinsic plasticity (Becker and Genoway, 1979; Beitinger et al., 2000; Quigley and Hinch, 2006; Poletto et al., 2017). While somewhat similar, these phenomena present different facets of thermal tolerance. Acclimation refers to a demonstratable compensatory change in response, physiological or behavioural, to a change in the environment that occurs within a generation (Bullock, 1955; Havird et al., 2020), while plasticity encompasses passive responses of the organism to the environment such as the innate change in metabolism as a response to temperature change, as well as acclimation (active thermal plasticity) (Havird et al., 2020). These can be considered bounded by a thermal ceiling, considered to be the highest temperature the fish can endure (critical thermal maxima), past that point organism-level physiological collapse occurs (Farrell, 2002; Becker and Genoway, 1979; Galbreath et al., 2004). In a swim trial study of Credit River juvenile Chinook salmon, Lim et al. (2020) looked at the interplay between incubation temperature, rearing temperature, and time to fatigue. They found that warmer incubation and rearing temperatures lead to longer times to fatigue, though the highest incubation and rearing temperature pairing had a low survival rate. Acclimation allows for the recruitment of coping strategies such as the synthesis of heat shock proteins (HSPs), which are also coping mechanisms as part of the stress response. Palmisano et al. (2000) found increased expression of HSP 90 in the heart, muscle, brain, and gill tissues of Chinook fry after a 5-hour exposure to 21.5°C. However, there is no set timeframe for acclimation itself to occur. Many studies assume acclimation from weeks to days (Beitinger and Bennett, 2000; Zillig et al., 2021; Adams et al., 2022), though Kokanee salmon have demonstrated an acclimation response in as little as 24-hours, with CTmax increasing with acclimation temperature (Mayer et al., 2023). Acclimation responses have been demonstrated beyond the presence of HSPs; with reorganization on the cellular level, such as increases in mitochondria density in both red and white muscle at 3 colder temperatures during long-term acclimation studies (Eggington and Sidell, 1989). Acclimation plays a key role in the way thermal limits are reached and can help increase the plasticity of these limits (Becker and Genoway, 1979; Galbreath et al., 2004). Pacific salmon have incredibly varied life histories, both between- and within-species. Species such as pink salmon (O. gorbuscha) and chum salmon (O. keta) have little residence time in freshwater after emergence. Other species such as sockeye salmon (O. nerka) generally spend a year, or more, as juveniles in nursery lakes before migrating to the ocean. Chinook salmon (O. tshawytscha) have two distinct life-history types: ocean-type and stream-type. Ocean-type Chinook have a freshwater residence time of just a few months before leaving to saltwater, while stream-type Chinook will spend at least one year rearing in freshwater streams. These varied and, in many cases, spatially and temporally isolated Chinook salmon populations allow for distinct adaptations to local conditions to emerge, especially when populations have high natal stream fidelity (Quinn, 2018). Genetic isolation between life history types in Chinook salmon has been demonstrated by Narum et al. (2004) who located a microsatellite locus able to distinguish stream-type from ocean-type Chinook salmon in the Columbia River with a 93% success rate. Eliason et al. (2011) found local adaptations in sockeye salmon (O. nerka) - populations with arduous migrations had greater aerobic scope, larger hearts, and thermal optima consistent with exposure to historic temperatures in their respective migratory streams. Thermal maxima are also thought to be reflective of local temperature regimes (Eliason et al., 2011). Further, Muñoz et al. (2014) demonstrated maternal effects influencing the critical thermal maximum of progeny, with a strong relationship with egg diameter, pointing to indirect genetic effects contributing to the multi-faceted concept of thermal tolerance and its consequences. Thermal tolerance has a long history of study in Pacific salmon (Donaldson and Foster, 1941; Brett, 1952; Brett, 1964). A recent systematic literature review (Mayer et al., 2023) has highlighted the fact that numerous methodologies have been used to assess thermal tolerance, and 4 that thermal limits derived from different methods can generate very different outcomes and perspectives on thermal tolerance. First introduced by Cowles and Bogert (1944), critical thermal maximum (CTmax) and minimum (CTmin) study approaches generate a continual and consistent change in temperature from an acclimation temperature until loss of equilibrium (LOE) is achieved. CTmax has been used to show the extension of the thermal with warm acclimation ceiling in many cases (Becker and Genoway, 1979; Galbreath et al., 2004). This approach, while not considered to produce ecologically relevant results, has undergone various changes and modifications in order to more directly measure specific aspects of thermal tolerance (Isaza and Rodgers, 2022; Blasco et al., 2020; Geist et al., 2010; Galbreath et al., 2004). For example, the upper incipient lethal temperature (UILT) approach measures an abrupt change from one acclimation temperature to another, with death as the endpoint. The UILT focuses on the lethal response to temperature change, as opposed to the previous focus on the incremental steps towards physiological disorganization. Galbreath et al. (2004) note that UILT allows for the measurement of some chronic exposure to temperature and more closely reflects a lived experience, although the rapid change to experimental temperature is not necessarily ecologically relevant. To help reduce the abrupt temperature change, Zale (1984) developed the acclimated chronic exposure (ACE) protocol, in which a slow heating rate was used prior to a 60-day UILT test. This approach allowed for the study of incipient thermal limits but also more closely simulated a lived thermal experience. High temperatures can have direct lethal consequences, through multi-faceted and complex phenomena involving both acute and chronic thermal exposures. While there is no single predictive physiological framework which can integrate all the causes of thermal-based mortality, the oxygen- and capacity-limited thermal tolerance (OCLTT) model is one that has been very useful for linking thermal varying physiological processes to fish survival (Farrell, 2002). The OCLTT model utilizes the concept of ‘aerobic scope’ which is the difference between resting and 5 maximum metabolic rates, and represents the availability of aerobic metabolism for a fish to use for various activities. Aerobic scope peaks at some intermediate temperature (termed Topt) and declines to zero (termed Tcrit, considered an upper thermal limit) at high temperatures where tissues are unable to receive enough oxygen for any level of activity, resulting in death. Collapse of cardiac output has been linked to collapse of metabolic scope, as insufficient oxygen supply to the heart will in turn reduce its functional capacity until it is no longer able to support the organism (Farrell, 2002). There are fine-scale aspects of physiology which can influence oxygen transport. Haemoglobin binding ability decreases with increasing temperature (Samuelsen et al., 1999), thus reducing the overall efficacy of oxygen transport. In addition, increased haematocrit has also been linked to increased CTmax (Muñoz et al., 2017), and can be a proxy measure for haemoglobin present in whole blood. However, some research presented on artificially increased haematocrit in the blood through increased exercise training did not result in increased CTmax (Isaza and Rodgers, 2022). As temperatures approach Tcrit, a reduction or cessation of feeding is also common thus fish may not be able to meet their energy needs as food conversion efficiency and ration size diminish, leading to starvation (Brett et al., 1982; Brett, 1962). Respirometry is the method in which oxygen consumption is measured to estimate metabolism and calculate metabolic scope. Developed early in fisheries science (Fry and Hart, 1948; Brett, 1962), field-based respirometry has become more common as mobile facilities and equipment become more available (Farrell et al., 2003; Rodnick et al., 2004). Considered to be closely representative of the physiological thermal ceiling, there is still much debate regarding the universality of the OCLTT (Ern et al., 2016; Pörtner et al., 2017; Jutfelt et al., 2018). Other research supports that CTmax may be linked to temperature effects over multiple systems. Ern, Andreassen, and Jutfelt (2023) outline pathways in which acute warming may lead to loss of equilibrium over the molecular, cellular, organ, and organismal levels. The temperature-dependent deterioration of electrical excitability hypothesis (TDEE), Vornanen (2016, 2020) reports temperature-dependent imbalances in thermal 6 resistance in sodium-potassium (Na+/K+) ion pumps, where Na+ channels, more thermally sensitive than K+ channels, compromise the initiation of action potentials at elevated temperatures. More specifically this can affect the heart, where electrical excitation responsible for cardiac pumping becomes disorganized. This subsequent mis-match of action potential can also affect systems throughout the organism that rely on electrical excitability, such as neuron firing and muscle control (Ern, 2023; Vornanen, 2020). Mayer et al. (2023) found that some methodologies identify thresholds below lethal limits, such as when high temperatures create barriers for migration and smoltification, and alter preference or avoidance of specific habitats. Marine and Cech (2004) found that juvenile Sacramento River Chinook salmon reared in elevated temperatures (21-24°C and 17-20°C) demonstrated impaired smoltification through reduced gill Na+, K+-ATPase activity and these treatments had greater osmoregulatory impairment than the colder (13°C-16°C) group, supporting previous work on other anadromous salmonids (Adams et al., 1973; Wedemeyer et al., 1980). Thermal barriers to spawning migration have been measured in many ways, namely through tagging and counts at fish fences, weirs, or dams (Goneia et al., 2006; MacDonald et al., 2010). These measures result in relatively lower thermal threshold values and are more valuable for management as they represent thermal environments that are more ecologically relevant. Preference and avoidance studies also yield low temperatures as they represent a fish’s selection for preferred thermal habitat. These types of studies can be performed either in the field using temperature-logging tags (Armstrong et al., 2016), or in laboratory settings with the use of systems such as the shuttle-box, where fish select environments to occupy based on given choices (Christensen et al., 2021). These metrics can be easily influenced by confounding factors such as predation and competition, especially in the field environment (Crowder and Magnuson, 1982). Thermal agitation metric is another example of a sub-lethal method of measuring thermal tolerance. Measured as the temperature at which there is an increased amount of activity in 7 response to increased temperature, assumed to be seeking thermal refugia, the thermal agitation metric highlights temperatures at which routine behaviour is altered but to a much lower threshold than LOE (McDonnell and Chapman, 2015; Turko et al., 2020). Related to this is the agitation window, which is the difference between the agitation temperature and CTmax, and highlights the temperature margin available until CTmax is reached. Turko et al. (2020) found that the thermal agitation temperature was 5-6°C below CTmax in redside dace (Clinostomus elongatus), and Bouyoucos et al. (2023) evaluated physiological metrics in spiny dogfish (Squalus suckleyi) in relation to this metric and found the onset of cellular stress responses, as well as peak heart rate at this temperature. It is important that methodologies which assess thermal tolerance have some element of ‘ecological relevance’ so that results of studies can help identify how actual thermal experience in the wild may affect the physiology, behaviour, or survival of salmon. There are a variety of methodological approaches assessing thermal tolerance, across a continuum of ecological relevance (reviewed in Mayer et al., 2023). Methodologies such as CTmax and UILT do not reflect the fact that fish in the wild may migrate or move away from high temperatures or through competition be pushed into thermally unfavourable environments, thus these approaches have low ecological relevance. Additionally, extreme endpoints are not practical for management purposes since fish experiencing LOE in the natural environment cannot recover. CTmax represents an upper limit as a response to changing temperature, while the UILT represents a limit achieved from a sudden exposure. Loss of equilibrium is the defined endpoint for CTmax, and 50% mortality at a given temperature (LT50) is the endpoint of the UILT. Neither of these limits are completely representative of relevant ecology, and extreme endpoints do not give enough insight for use by fisheries managers. Once an animal has reached a state in which it is no longer able to right itself, there are few actions that can taken to help them recover and survive. Neither of these tests convey how functional thermal tolerance is realized in the wild. Field studies such as the study of 8 thermal migration barriers may offer more relevant metrics for measuring thermal tolerance in terms of measured values reflecting the lived thermal experience. Temperatures at which certain life stage changes are impeded are also a more relevant measure. High temperatures have been reported to lead to premature smolting, migration blocks or shifts in timing, and even desmoltification (Carter, 2005). Lower thresholds allow for a more conservative reported thermal limit and can offer much more relevance and flexibility in terms of management. Because of the myriad of metrics available and the varying information they convey, investigating the issue through multiple lenses is important to be able to get a more accurate picture of thermal tolerance. Speers-Roesch and Norin (2016) also emphasize the importance of using multiple metrics when measuring thermal tolerance. Blending field study data with laboratory-derived data can help bridge what approximate thermal ranges are in controlled environments with what is realized in their natural environment. With regards to the use of CTmax and UILT experiments for evaluating thermal tolerance, these widely used protocols are perceived to allow for a standardized evaluation of thermal tolerance and the establishment of an extreme thermal ceiling that modifications to protocols or between-species results can be more easily compared (Desforges et al., 2023). In British Columbia, riverine peak summer temperatures have been increasing (Morrison et al., 2002), and a climate model by Islam et al. (2019) found that the Fraser River’s temperatures had increased approximately 1°C from 1950-2015, translating into a doubling of the number of summer days where the water temperature exceeded 20°C. Such consequences mean more days where water is reaching the upper temperature limit of many salmonids (Richter and Kolmes, 2005; Mayer et al., 2023), and to a lesser degree, reduced oxygen in the water. Muñoz et al. (2015) predicted that current warming scenarios could result in a 17% chance of catastrophic loss of Chinook salmon (O. tshawytscha) by 2100, or 98% in a worst-case-scenario warming. The Columbia River, a major system located to the south of the Fraser River, is predicted to have a 9 5°C increase by the late 21st century with worst-case-scenario warming of 24% more warming in the summer (Rupp et al., 2017). The current and projected warming of rivers in the Pacific Northwest region raises large concerns for the sustainability of Pacific salmon populations. However, little is known regarding the thermal tolerance and limits of British Columbia interior populations, although studies have been conducted on adult life stages (Van Wert et al., 2023). Thus, measuring thermal tolerance of juvenile life stages is important, particularly in interior populations. Juvenile stages are important to study because the freshwater habitat that these fish rear in can be dynamic, ephemeral, and incredibly susceptible to anthropogenic change (Zillig et al., 2021). Outmigration success of juvenile salmon can also be dependent on hydrological conditions highlighting the need for protection of variable phenotypic expression and diversity in a changing climate (Sturrock et al., 2015). Chinook salmon are culturally and socioeconomically important to British Columbia. They are considered the most important salmon of the marine recreation fishery (Kristianson and Strongitharm, 2006; Counterpoint Consulting, 2014). Numerous Fraser River Chinook salmon conservation units (i.e. meta-populations) have been designated by COSEWIC as endangered, with the majority of these being ‘stream-type’ populations indicating an imperative need to examine thermal tolerance of their juvenile life stage which spends over a year rearing in freshwater. Indeed, Chinook salmon are among the most widely-studied Pacific salmon species in terms of thermal tolerance yet only a small number of studies have examined the juvenile life stage, and few examined thermal tolerance with ecologically relevant methods (reviewed in Mayer et al., 2023). Further, there has been little research emphasis on the impact of population-level acclimation and adaptation on thermal tolerance (Mayer et al., 2023), and Zillig et al. (2020) noted a lack of research assessing thermal tolerance across populations of the same species, which leads to the potential erroneous use of thermal tolerance measurements for management. They also undertook thermal assessment of populations of varying latitudes using aerobic scope, CTmax, 10 and temperature-dependent growth (Zillig et al., 2020). They make a specific case that Californian Chinook salmon encounter temperatures well above published guidelines, with added facets of population-specific thermal tolerance. They found that Coleman River fall-run and Sacramento winter-run demonstrated significant acclimation capacity up to 20°C, while neither Feather River runs had this benefit (Zillig et al., 2023). Thus, gaps exist in our understanding of both species-, population-, and life stage-specific thermal tolerance in Chinook salmon, and we are lacking more mechanistic investigations of thermal failure points, and effects of chronic high temperature exposure. My research intends to address some of these gaps. My objectives are to: (1) investigate the role of acclimation temperature on the upper thermal tolerance (CTmax) of stream-type Chinook salmon fry and parr from an interior British Columbia population, (2) assess multiple approaches of thermal tolerance measurement through comparison of a less ecologically relevant metric (CTmax stat) and a more ecologically relevant metric (CTmax swim) and, (3) investigate the role of temperatures spanning the current wild temperature range on latent mortality. Being able to adapt protocols such as the CTmax to better reflect the lived experience of salmonids is a valuable tool in which standardized and widely used protocols can become more ecologically relevant (Steinhausen et al., 2008; Blasco et al., 2020). I hypothesize that acclimation temperature will affect the plasticity of upper thermal tolerance and that increased acclimation temperature will allow for an increased CTmax, and, that the least ecologically relevant metric will reveal higher thermal tolerance. Lastly, I hypothesize that higher acclimation temperatures will illicit increased latent mortality after physically exerting events due to impaired recovery. 11 2 Methods 2.1 Experimental Animals Each individual was used for either CTM protocol or the endurance swim experiments. All protocols were approved by the Animal Care Committee at the University of British Columbia with the Canadian Council for Animal Care, under animal use protocol (experiment #A19-0284, holding #A19-0056). 2.1.1 Transport, Acclimation, and Holding From the Nicola River, BC, Canada, 610 Chinook parr (2019 brood year; 6.88 ± 2.42g; 87 ±7.5mm) were collected October 15, 2020 and 1288 fry (2021 brood year; 0.71 ± 0.22g; 41 ± 4.3mm) were collected May 1st, 2021 from the Spius Creek Hatchery and driven 3.5 hours to the Pacific Salmon Conservation and Ecology Laboratory at the University of British Columbia (UBC) in 1500 litres of water, 10°C, with dissolved oxygen (DO) greater than 85% the entire trip. Upon arrival at UBC, four 375-litre recirculating acclimation troughs received 125 parr or 200 fry each at random. The remaining fish were placed in flow-through acclimation troughs at ambient temperature (one tank per acclimation temperature; Fig. 1a, b). All acclimation trial troughs began at 15°C. Temperature was slowly increased to the target acclimation temperatures of 15°C, 18°C, 20°C, and 24°C over three days, (increase of 3°C/day until target temperature was reached). Fish were held at their acclimation treatment temperature for a minimum of two weeks before experimentation began. Temperature was monitored and maintained with an Inkbird ITC-306 temperature controller and 800W titanium heaters (Aquatop model TH-800), and 300W heaters (Hydor THEO HY-T11402). A perforated hose was run through each trough and weighed down to ensure proper aeration and to avoid stratification. A water pump (Tunze Silence 1073.008) at the 12 outflow recirculated water. Temperature gradients in each trough were monitored with a Veegee alcohol thermometer at the outflow and a tidbit V2 temperature data logger at the midway. DO was maintained above 75% saturation at outflow and greater than 80% at inflow, measured with an Oxyguard Handy Polaris or a YSI Pro. Water flow was monitored using a Gardena Digital flow meter, and maintained between 0.5-1.7l/min. Oxygen and temperature gradients, nitrites, nitrates, and ammonia were checked twice per day Monday-Friday, and once per day Saturday and Sunday. Nitrites, nitrates, and ammonia were all below the minimum detection range throughout each study. Fish were fed four times a day Monday to Friday, and once or twice a day Saturday/Sunday with EWOS Pacific Plus complete feed for salmonids (1.5mm) for parr and fry received Skretting Bio-Vita (mix of sizes #0 and #1). Feed ration was calculated based on the weekly mass of each acclimation treatment and temperature effects on metabolism. Fish acclimated to 15°C, 18°C, 20°C, 24°C were fed 1.5%, 2.0%, 2.5%, and 3.5% of their body mass, respectively (Diana 1995). The troughs were partially covered with insulating foam to create shaded area and fluorescent light was set to a naturalistic photoperiod, adjusted with diurnal cycles every two weeks with the change in sunset. 2.2 Critical Thermal Maximum (Stationary) Fifteen fish from a given acclimation treatment were placed in a stationary bath. Parr were placed in a 40-litre cooler with four heating sticks (Hydor THEO HY-T11402, and Aquatop TH-C500) affixed to the bottom of the bath, two pumps on the bottom of the bath at opposite ends, two pumps suctioned onto opposite walls, and two air stones at the bottom (Fig. 2a). Pump intakes were covered with filter foam to prevent fish from getting caught. Fry were placed in a 17-litre cooler with two Aquatop TH-C500 affixed to the bottom and the same pump configuration as the parr. A mesh lining was used to separate the fry from the heating area entirely (Fig. 2b). The 13 initial temperature matched that of the trough and fish were allowed to acclimate for 30 minutes in the bath before the trial began. After the acclimation period, water was heated at 0.3°C/min and monitored with a YSI temperature probe, moving slowly around the bath to ensure that the water was heating evenly. CTmax stat was determined with LOE, once the fish was no longer able to right itself. Upon LOE the individual was netted and then euthanized with Tricaine Methanosulfonate (MS-222). Temperature at LOE, length and mass were recorded. This process was repeated for all acclimation treatments over the course of two days. 2.3 Critical Thermal Maximum (Swimming) The goal of the CTM swim test was to swim a group of fish at a fixed, aerobic swimming speed while slowly increasing the water temperature until the fish quit swimming (Steinhausen et al., 2008, Blasco et al., 2020). This is considered a more ecologically relevant assessment because it helps reflect energy expenditure that a juvenile salmonid may experience, as opposed to CTmax stat, which is an environment which allows fish to conserve energy. A total of 30 fish from a given acclimation treatment were placed in a flow-through swim flume system (3,200cm long× 15cm wide × 25cm high) (Eliason et al., 2017); 45-litre swim space, roughly 300 litres; at their acclimation temperature and allowed to adjust to the flume for 30 minutes at a swimming speed of 1BL/s. The speed was increased every 5 minutes by 1BL/s until the target swimming speed of 4BL/s was reached. Once at the target swimming speed, the water temperature was increased by 1℃ every 15 minutes until the water reached 20℃, then the rate of warming decreased to 1℃ every 30 minutes, using a 300W heating stick (Aquatop TH-C500), a 1300W heating stick (Pong-Dang heater AJT13K), and a chiller (EcoPlus commercial chiller HGC728707). Fish were considered to have quit swimming, and thus reached CTmax swim, after 15 seconds of continuous body contact with the back grate of the flume and did not swim off the grate if prodded once with 14 the net. If the fish swam off the grate and continued to swim during removal, it was allowed to keep swimming. Once a fish reached CTmax swim, it was removed from the trial by netting and CTmax time and temperature were written down. The fish was immediately anaesthetized using MS-222, measured for mass and length, then fish were euthanized by concussion and decapitation. This process was repeated for each acclimation temperature treatment over four days. 2.4 Endurance Swim Twenty-five fish per acclimation temperature treatment were tested at all four temperatures, placed in the swim flume and allowed to acclimate at 1 BL/s for 30 minutes before the trial began. After 30 minutes, water velocity was increased from 1 BL/s to 7 BL/s (parr) or 10 BL/s (fry) over the course of three hours for parr and four hours for fry. Temperature was continuously maintained at either 15℃, 18℃, 20℃, or 24℃ +/- 0.8℃. To ensure that fish were swimming in the middle of the flume where flow was most laminar, light was shown at both extremities of the swim space, and a black garbage bag was clamped to the top and far side of the flume to create a more desirable area in the middle. Fish were removed when they dropped out, using the same criteria as the CTmax swim trials. At removal, CTmax time and speed were recorded, the individual was anesthetized to measure length and mass. Then, fish were placed in a mesh bucket with air stones in their initial acclimation trough and monitored for recovery at 24-hours and 48-hours, during which time any mortalities were recorded. Check time refers to the time post-swim where mortality can occur (0 hours, 24 hours, and 48 hours). After 48 hours, the fish were released into a sectioned off end of their acclimation trough, isolated from any fish still naïve from the flume. These fish were then euthanized at the end of the study using MS-222. 15 2.5 Acclimation Response Ratio The acclimation response ratio (ARR) is defined here as a degree change in CTmax per degree change in acclimation temperature, as seen in (1) (Claussen, 1977). This is a way of quantifying the ratio between the upper thermal limit and the level of acclimation. For example, an ARR of one means that for every one-degree change in acclimation temperature, there is a one-degree change in CTmax. This metric is helpful in gauging how ectotherms may respond to changes in the environment, and in some cases, indicate the potential for long-term adaptation to changing temperatures (Morley et al. 2018). 𝐴𝑅𝑅 = ∆஼்௠௔௫∆் (1) 2.6 Statistical Analysis All data were analyzed with R 4.2.1 (2021) and RStudio IDE version 2023.03.0+386, and a significance level of α = 0.05 was applied for all statistical tests. All measures are given as mean ± standard deviations unless otherwise noted. Spearman rank correlation was used to examine the relationship between mass, fork length, and condition factor (body size metrics). As all metrics were found to be correlated (Table 1), only one body size metric at a time was added to a general linear model alongside acclimation temperature. Outliers were assessed using Cook’s distance and visual observation on a quartile-quartile plot, and no datapoints were removed (Cook’s D < 0.5). Normality and homoscedasticity were evaluated when needed with Shapiro-Wilk test and Levene’s test respectively. Analysis of variance was performed to assess the influence of acclimation temperature on body size metrics for each life stage; showing that acclimation temperature influenced body size metric in most cases. General linear models were developed for objective one to determine the 16 difference in CTmax, with body size metric and acclimation temperature as predictors. Interactions between acclimation temperature and body size metric were found not to be significant (P > 0.05) and were excluded from the final models. Finally, Bayesian information criterion (BIC) and Log likelihood were used in model selection, with the final models omitting body size metric (Table 2). Post hoc Tukey tests were carried out to assess differences between significant CTmax temperatures. CTmax stat and CTmax swim were assessed in a similar manner, with the outcome as CTmax temperature and predictors as test type (CTmax stat and CTmax swim) and life stage (fry and parr). When evaluating CTmax between fry and parr, each were first grouped by test type (CTmax stat or CTmax swim). Due to fundamental differences in methodology, they were not considered together. T-tests were then performed for each test type with life stage as a predictor of CTmax. Post-swim mortality was assessed as percent mortality of each swim group (acclimation temperature and flume temperature combination), and check time (within 24 hours, at 24 hours, and at 48 hours). 17 3 Results 3.1 Acclimation and Holding Mean (± SD) treatment temperatures over the course of the study in each trough for fry were 23.61 ± 0.44℃, 19.25 ± 0.42℃ 17.03 ± 0.66℃, 14.98 ± 0.92℃ and for parr were 21.5 ± 1.81℃, 18.46 ± 1.39℃, 16.46 ± 0.84℃, and 14.92 ± 0.14℃. Holding trough temperatures were left to fluctuate with city water source temperatures for both life stages. The discrepancies between the target acclimation temperatures and the measured temperatures in parr are largely due to temperature dropping quickly overnight when the city water was colder during October to December. Overall, there was a downward trend in body size metrics, though it was not significant (Table 7 and Table S6). In fry, there was a short period of mortality across all acclimation temperatures as fish began exogenous feeding which then tapered off; however higher mortality was sustained in the 24℃-acclimation group throughout the study. In parr, no in-trough mortality was observed until 13 days into the acclimation period (November 5th, 2020), and low levels of mortality in the 24℃ occurred until two large mortality events happened 26 and 27 days into the study period (November 18th and November 19th, 2020), where 24 and 36 parr died respectively. 3.2 Acclimation and Critical Thermal Maxima With test types pooled together, fry overall had a higher thermal tolerance (P <0.001). However, in CTmax stat trials there was no significant (P <0.25) difference between fry and parr, with mean values only ~0.25℃ apart. Additionally, parr had a significantly (~1.78℃) lower CTmax swim than fry (P <0.001). Acclimation temperature significantly affected CTmax temperature (CTmax stat P < 18 0.001, CTmax swim P < 0.001). Additionally, CTmax stat was significantly higher than CTmax swim in both fry and parr (P < 0.001; in both cases). Acclimation response ratio (ARR) (Table 4) calculated for fry showed a positive acclimation response between acclimation temperatures until 24℃ acclimation treatment for both test types, after which there was a loss of acclimation capacity, demonstrated by the lower mean CTmax temperatures during those trials (Figure 1a). Parr show a similar trend of ARR in the CTmax swim trials, but the CTmax stat trials show a positive acclimation response for all increases in acclimation temperature, also reflected in measured CTmax (Figure 1b). Acclimation temperature was found to be a significant predictor of CTmax temperature (CTmax stat P < 0.001, CTmax swim P = 0.0153) (Table 5). In parr, the general trend in CTmax across acclimation treatments showed a positive relationship between CTmax temperature and acclimation temperature (Table 6), for both test types until the 24°C-acclimation temperature is reached, after which there was a significant decrease in CTmax in the swim trial (Figure 1a). In fry, there was a sudden and significant drop in CTmax temperature at 24°C in CTmax stat. In the CTmax swim, there were similarities in the thermal ceiling reached in the 24℃-acclimation temperature (Figure 1b). 3.3 Endurance Swim and Latent Mortality Performance overall was comparable across acclimation treatments and life stages, with most individuals completing the full swim trial and the only fry post-swim mortalities occurred in the 24℃-acclimation treatment (Figure 5a). The only significant parr swim group mortalities were the 24℃-acclimation treatments tested at 15℃ and 24℃ (P = 0.032 and P < 0.001 respectively). Check time was not a significant predictor of mortality (P > 0.05 for both checks at 24 hours and 48 hours). Most of the parr post-swim mortalities were in the 24℃-acclimation treatment, though 19 a small amount occurred in the 20℃-acclimation treatment (Figure 5b). All fry major mortality events involved the 24℃-acclimation swim groups (P < 0.001, P = 0.01, P = 0.01; P = 0.015 for 15℃, 18℃, 20℃, and 24℃ flume temperatures, respectively), and check time at 48-hours was significantly (P = 0.046) different than the within 24 hours check time for fry. There was no significant difference in mortality between the checks within 24-hours and at 24 hours (P > 0.05). Body size metrics taken at mortality (Table 6) show little difference (< 1cm) between mortalities across check times, though a slight downward trend in these measurements was observed in parr. 20 4 Discussion One of my main objectives was to investigate the role of acclimation temperature on the upper thermal tolerance of Chinook fry and parr. I found that two-week exposure to elevated temperatures caused elevated mortality not only during the acclimation holding period, but also post-swim during the endurance trials (Fig. 5a and b). The inability for many 24°C-acclimated fish to recover from endurance trials suggests a potential energy limitation and high stress these fish may have prevented recovery at high temperatures, especially compared to the lower temperature-acclimated swim groups exposed to high flume temperatures. It is well-documented that acclimation temperature affects growth rate and food conversion efficiency (Brett, 1971; Brett et al., 1982), and during times of prolonged stress, fewer resources can be put towards reserves, affecting recovery (Wendelaar Bonga, 1997). Acclimation temperature overall did not have a large effect on mass, fork length, or condition factor, though there was a slight downward trend in body size metrics with fish being smaller in the 20°C and 24°C acclimation treatments at the two-week mark, despite consistent feed adjustments for a maintenance ration. Fulton’s condition factor (Ricker, 1975) helps quantify the role of chronic temperature on the relationship between length and mass as this morphometric analog of energy reserves in the body has been shown to be positively correlated with lipid stores in some fish (Herbinger et al., 1991; Chellappa et al., 1995; Mozár et al., 2015). While performing regressions with body condition factor against CTmax could help visualize the relationship between body condition and plasticity of the upper thermal tolerance, body condition was not a significant predictor of CTmax in this study, though other studies have used this metric to correlate survival time in a similar thermal challenge (Robinson et al., 2008). Acclimation response ratio (ARR) is a helpful metric gauging how ectotherms acclimate to the thermal environment over a short period of time. In this research we observed that the 21 calculated loss of acclimation capacity corroborates with the measured CTmax, further highlighting the thermal ceiling reached for both life stages, as well as the role acclimation temperature plays on the upper thermal tolerance of these salmon. Morley et al. (2018) performed an extensive meta-analysis of reported ARR for 319 species, using it as a metric for upper thermal tolerance and estimating thermal safety margins for groups of species with modeled global climate change. Mid-latitude fish (25°-55°), where salmon are found, had an estimated overall ARR of 0.06. In the present study, the ARR ranged from -0.49 – 0.45 (mean 0.05), depending on the acclimation temperature, life stage, and test type. This metric is a simple way to estimate and report thermal tolerance information though high variability in species and methodology makes it difficult to extrapolate these to finer-scale predictions. The mortality in the acclimation troughs was notable throughout the trial period. Overall, the 24°C-acclimated fish had considerably more mortality during the two-week acclimation period, as well as during the trial period, compared to the other three acclimation treatments. There was a noticeable decline in body condition visually past the two-week mark in the highest acclimation group, which can indicate that metabolic demands driven by the water temperature were not being entirely met despite the calculated maintenance ration. Very few individuals were euthanized due to poor body condition or morbidity following the animal care guidelines. The second objective of this study was to assess multiple approaches to measuring thermal tolerance — in this case CTmax stat and CTmax swim. This study demonstrated the difference methodology makes in measuring CTmax, and that objectives need to be considered when designing thermal limit studies. Here there was a significant difference in CTmax swim between fry and parr, but not for CTmax stat. This highlights the importance of taking methodology into consideration when using thermal tolerance values from multiple sources, as when test types were pooled together, fry had a significantly higher CTmax overall. This topic has come up in debate 22 during some literature reviews (Dahlke et al., 2020; Pottier et al., 2022; Dahlke et al., 2022), and should be considered when synthesizing thermal tolerance data across different sources. The final objective of the study was to investigate the role of temperatures spanning their current natural range on post-swim mortality. Post-swim mortalities observed in this study highlight the role of acclimation temperature in recovery after extended physical exertion. The mortalities occurring exclusively in the higher temperature acclimation treatments (20°C and 24°C) suggest that acclimation and recovery environments may play an important role in survival of these events, as lower temperature treatments (15°C and 18°C) performed to the same degree as the higher temperature treatments in the endurance swim trials but did not experience any mortality. This mortality can be influenced by the amount of energy reserves in the animal for recovery, as fish acclimated to higher temperatures may have a smaller reserve (Wendelaar Bonga, 1997; Brett et al., 1982). Research has shown that temperature and stress can affect digestion and nutrient absorption (Volkoff and Rønnestad, 2020), and the fish in the higher acclimation temperature treatments would not only have fewer resources to draw from when recovering from the endurance swim, in addition to maintaining the thermal coping mechanisms in the recovery environment. While traditional CTM carries little semblance to the thermal experience in the wild, it is a common metric that can be easily compared across species, and absolute values measured can help with comparisons between groups. Modified methodologies such as changes in ramp rate (Galbreath et al., 2004), acclimation time (Lutterschmidt and Hutchinson, 1997), and adjustments such as the addition of a swim flume (Blasco et al., 2020) can affect the final measured response. Desforges et al. (2023) explore the ways in which a global standard in measurement may be inhibiting accurate measurement of thermal response as it ignores many inherent differences in thermal response based on species and individuals. They identify ramp rate as a focal criticism of standard CTM, as ramp rate variation can trigger different thermal responses; acute warming will 23 trigger short-term responses such as neuroendocrine changes, and chronic warming is likely to induce longer-term systemic responses and partial acclimation to the treatment temperatures (Desforges et al., 2023). Differences in morphology have been shown to alter LOE during CTmax, as well as life stage, which was supported in this study. Specifically, in CTmax stat trials, parr had a 0.25 °C higher CTmax than fry but did not exhibit a reduction in ARR, whereas fry did. In CTmax swim trials, parr had a ~1.78°C lower CTmax than fry. In addition, endpoints such as LOE or LT50 will affect the reported thermal limit, emphasizing the importance of reporting measurement methods when considering data for informing management practices. There is a growing body of literature regarding the lack of ecological relevance standard CTM experiments have (Steinhausen et al., 2008; Blasco et al., 2020; Mayer et al., 2023; Desforges et al., 2023), especially in a management setting, and studies used to support management decisions should be chosen or conducted in a manner that represents the questions asked. Based on the findings of this study, managers should utilize literature that reflects their populations of interest, whether it be research focused on their system, or centered around comparable species and systems. Outlined in Desforges et al. (2023), adjustments to protocols to help make them more ecologically relevant the establishment of tolerance ranges, and as mentioned in Mayer et al. (2023) measures of thermal tolerance can vary by endpoint chosen. For example, it would be more appropriate to assess system temperatures for returning spawners by temperatures in which migration is blocked (Connor et al., 2019), as opposed to more extreme measures such as CTmax. In the framework of ecological realism, this research spans different levels of the lived thermal experience of salmonids. While it does not address thermal tolerance on a mechanistic level, the different experiments help piece together thermal tolerance in the population. The holding aspect of the study allowed for an assessment of the effects of chronic temperature exposure, especially at temperatures close to the published lethal maximum (Richter and Kolmes, 2005). The effects of temperature on body size metrics show how these chronic 24 exposures affect the metabolic needs of the salmon, especially how the 24°C acclimation fish were unable to maintain body mass despite being fed a calculated maintenance ration (Diana, 1995). The classic CTmax stat methodology allows the measure of absolute thermal limits, where under all circumstances physiological collapse occurs. While it does not reflect the lived thermal experience of the salmon, it provides an absolute measure that can be used to compare with other absolute thermal thresholds from different species or life stages. The CTmax swim helps develop a lower and more realistic thermal threshold, where burst and coast swimming patterns are seen before the endpoint CTmax swim, behaviourally indicating the transition from aerobic to anaerobic metabolism. The endurance swim work helps infer the effects of acclimation temperature and stressor temperature during a prolonged swim trial. Specifically, the failed recovery of the 24°C-acclimation regardless of flume temperature and the successful recovery of the other acclimation temperature fish despite being swum in 24°C water highlighted the effects of high temperature exposure and the compound effects of multiple stressors. Together this work helps management assess the multifaceted effects of temperature on salmon, as chronic high temperature exposures may not lead to death immediately, but they may reduce their ability to withstand and recover from additional stressors. Beyond the more immediate and direct effects of temperature on mortality, temperature also plays a role in the immune system and influences environmental toxicity. Bell (1986) demonstrated that breakouts of the protozoan parasite, Ichthyophthirius multifiliis, are more likely in temperatures above 15.6°C, especially in Chinook salmon, with optimum temperatures for outbreak between 21.2-26.7°C. This parasite is known for direct feeding on tissues, and high pathogen loads cause openings for secondary infections by opportunistic pathogens. Certain chemical compounds are also found to have additive effects with increases in temperature such as increased ammonia concentration, and decreased pH demonstrated to simulate increased growth and metabolic demand (Morgan et al., 2001) in rainbow trout (O. mykiss), and chronic exposure to 25 higher acidity with a +2°C above experimental ambient showed detrimental effects to ion regulation in low ration treatments, and increased temperature with increased ammonia during simulated summer temperatures showed an inhibitory effect of liver protein metabolism and greater oxygen demand. While acclimation plays an important role in thermal tolerance, it in and of itself does not pass down to subsequent generations. In general, it takes four to five generations for adaptations to develop, highlighting the tight timescale the fish have to adapt to warmer conditions. While there is some evidence of indirect genetic effects linked to thermal tolerance (Muñoz, 2014), there is little is known regarding how well adaptation will be able to keep up with the rapidly changing climate, if at all. In addition, temperature is intimately linked to life stage, in which salmon development timing is altered with changes in temperature. Warmer winters can lead to early emergence, which may mean that fry emerge before optimal food availability, and as demonstrated in this study, increased metabolic demand which may not be met in the environment (Crozier et al., 2008). Sparks et al. (2017) found that cold-incubated sockeye salmon took 2.5 times longer to develop than their warm-temperature reared counterparts, either from a constant or variable temperature regime. Unequal community responses to temperature change may result in ecological mismatches (Visser and Both, 2005), which highlights the need for a yardstick, a metric which measures the shifts in species and their environment in response to climate change (Visser and Both, 2005). Future research could investigate areas that we could not with our experimental design. Specifically, examining how thermal change associated with the post-swim recovery environment is an important consideration. We maintained the same post-swim temperatures as the acclimation environment, but in nature, salmonids often select thermal refuges after conducting strenuous swimming particularly through warm water, which is thought to be an adaptation to limit negative effects of high temperature exposure (Minke-Martin et al., 2018). Had we used that approach, we 26 may have seen lower post-swim mortality at high swim temperatures. Future work using the CTmax stat approach would benefit from the use of the thermal agitation temperature, as well as a more direct measure of organismal change during this process to better assess the mechanisms of change. Directly evaluating the effects of acclimation temperature on aerobic scope may help achieve an even more mechanistic understanding of how temperature affects thermal limits through the lens of the OCLTT, and helps us determine how CTmax values compare to aerobic scope criteria. Lastly, acclimation ramp rate may affect thermal tolerance measures and is certainly an ecologically important variable. We kept our rate constant; however slow ramp rates could cause fish to exhibit an acclimation response whereas faster ramp rates may create a large lag between water and internal temperature affecting measurements of CTmax (Penman, 2023; Galbreath et al., 2004; Becker and Genoway, 1979). 27 5 Conclusion This study demonstrated differences in CTmax across acclimation temperatures, methodologies, and life stages for an interior population of stream-type Chinook salmon. While acclimation temperature helped extend the measured CTmax for parr with the stationary trials, this was not reflected in the swim trials, and a failure to acclimate was observed when continuous physical exertion was required. This highlights a necessity for thermal tolerance studies to be designed in a way that reflects the key management objectives. Fry showed an increase in measured CTmax with increase in acclimation temperature up to 20°C, and a thermal ceiling at 24°C regardless of test type, supporting the difference in acclimation and thermal tolerance response that can occur across life stages in Pacific salmon. Additionally, the post-swim mortality monitoring demonstrates considerable recovery capacity from colder-acclimated fish as they were able to recover fully from extended swims in water up to 24°C. The post-swim mortalities themselves are telling, as they almost exclusively affected 24°C-acclimated fish regardless of life stage and flume temperature, showing that the chronic exposure to temperatures close to their upper range and the return to that temperature post-exercise can severely reduce survival regardless of the temperature during the exercise period. While this study helped add to the repertoire of literature demonstrating the role of acclimation temperature on CTmax, it also shone a light on the effect CTM has on CTmax. Overall, these results support the need for targeted and deliberate choice in methodology when measuring and reporting thermal tolerance metrics, and a consideration given to using methodologies that are as ecologically relevant as possible. Our different methodologies produced different estimates of upper thermal limits. For example, at 24°C there was a 2.8°C difference between CTmax stat and CTmax swim. While our results point to an increase in plasticity in the upper thermal limit with 28 warmer thermal acclimation, there appears to be is a ‘ceiling’ at 24°C wherein acclimation treatment demonstrated decreased swimming ability with poor swimming performance and higher mortality compared to the other acclimation treatments. This specific upper thermal limit has also been identified for adult Chinook salmon from interior BC populations (Van Wert et al. 2023). Given that summer temperatures in some of BC’s interior streams are now routinely reaching 24°C, we would expect to see increasing levels of mortality of both adults and juveniles in future years as stream temperatures continue to warm, reaching and exceeding this temperature. 29 Tables Table 1. The calculation of correlation between body size metrics show how body size metrics scale together with growth. Spearman’s rho calculated between mass (g), fork length (FL, mm), and condition factor (CF) for fry and parr. Life stage Body size metric combination rho Fry (n=181) mass & FL 0.88 (P < 0.001) mass & CF 0.24 (P = 0.0014) FL & CF -0.17 (P = 0.025) Parr (n=162) mass & FL 0.90 (P < 0.001) mass & CF 0.67 (P < 0.001) FL & CF 0.33 (P < 0.001) 30 Table 2. BIC, and likelihood ratio test (LRT) of general linear model selection of CTmax swim trials of body size metrics for each life stage, with acclimation temperature included in all models. LRT favoured no body size metric in all cases. In all cases BIC favoured the model excluding the body size metric. Asterisks denote favoured model and chosen models are in bold. Fry BIC LogLik Mass 415.9 -223.50 Fork Length 414.6 -215.50 Condition Factor 417.2 -212.65 No body size metric 412.4* -226.38* Parr BIC LogLik Mass 577.5 -328.97 Fork Length 578 -329.01 Condition Factor 577.4 -326.94 No body size metric 573.0* -329.08* 31 Table 3. Acclimation response ratio (ARR) of parr and fry between CTmax stat and CTmax swim protocols. Loss of acclimation capacity is observed at higher temperatures for fry regardless of CTM, while it is only observed in the CTmax swim methodology for parr. ∆ T acclimation (°C) Fry Parr CTmax stat 15 –18 0.27 (n=29) 0.16 (n=30) 18 –20 0.31 (n=30) 0.07 (n=30) 20–24 -0.32 (n=30) 0.17 (n=30) CTmax swim 15–18 0.19 (n=60) 0.005 (n=62) 18–20 0.08 (n=60) 0.45 (n=63) 20–24 -0.06 (n=60) -0.49 (n=40) 32 Table 4. BIC, and likelihood ratio test (LRT) for general linear model selection of the CTmax stat trials, all include acclimation temperature as a predictor in addition to the body size metric of interest. LRT favoured no body size metric in all cases. In all cases but CTmax stat parr, BIC favoured the model with no body size metric. Asterisk denotes favoured model selection and chosen models are in bold. Fry BIC LogLik Mass 176.8 -76.201 Fork length 176.4 -75.990 Condition factor 177.4 -76.491 No body size metric 173.5* -76.534* Parr BIC LogLik Mass 65.8 -17.470 Fork length 59.9 -16.876 Condition factor 58.5 -16.955 No body size metric 56.3* -17.932* 33 Table 5. CTmax range and mean ±SD by life stage, test type, and acclimation temperature. Life stage Test type Acclimation temperature CTmax range Mean± SD Fry Stationary 15 27.9℃-29.5℃ 28.9℃ ± 0.52 (n=14) 18 28.4℃-30.1℃ 29.7℃ ± 0.42 (n=15) 20 29.5℃-30.7℃ 30.3℃ ± 0.31 (n=15) 24 24.4℃-30.9℃ 29.1℃ ± 1.67 (n=15) Swim 15 27.2℃-28.30℃ 28.1℃ ± 0.82 (n=30) 18 27.5℃-29℃ 28.6℃ ± 0.29 (n=30) 20 28.5℃-29.2℃ 28.8℃ ± 0.189 (n=30) 24 24℃-29.4℃ 28.6℃ ± 1.05 (n=30) Parr Stationary 15 28.80℃-29.50℃ 29.15℃ ± 0.20 (n=15) 18 29.10℃-29.90℃ 29.62℃ ± 0.30 (n=15) 20 28.40℃-30.30℃ 29.77℃ ± 0.47 (n=15) 24 30.00℃-31.10℃ 30.46℃ ± 0.31 (n=15) Swim 15 21.50℃-28.00℃ 26.8℃ ± 1.75 (n=30) 18 22.30℃-28.30℃ 26.8℃ ± 1.52 (n=32) 20 23.10℃-29.10℃ 27.7℃ ± 1.38 (n=30) 24 24℃-28.30℃ 25.7℃ ± 1.51 (n=9) 34 Table 6. Summary mean ± SD of mass (g) and fork length (mm) of mortalities < 24 hours, at 24 hours, and at 48 hours. Fry Under 24h 24h 48h Mass 0.64 +/- 0.085 (n=2) 0.54 +/- 0.075 (n=4) 0.62 +/- 0.11 (n=7) Fork Length 39.5 +/- 0.5 36.2 +/- 1.43 36.9 +/- 0.99 Parr Mass 6.39 +/- 0.56 (n=20) 5.77 +/- 0.18 (n=25) 4.98 +/- 0.48 (n=14) Fork Length 85.4 +/- 1.65 83.0 +/- 1.11 76.0 +/- 1.59 Table 7. Table 6. Summary mean ± SD of mass (g) and fork length (mm) of fry and parr at the ed of the two-week acclimation period. Life stage Acclimation temperature Mean mass (g)± SD Mean fork length (mm)± SD Fry 15 (n=14) 0.63 ± 0.05 ± 18 (n=15) 0.68 ± 0.08 ± 20 (n=15) 0.70 ± 0.10 ± 24 (n=15) 0.64 ± 0.12 ± Parr 15 (n=15) 7.12 ± 2.36 85.5 ± 8.3 18 (n=15) 6.98 ± 1.51 86.5 ± 3.6 20 (n=15) 6.89 ± 1.81 88.3 ± 7.0 24 (n=15) 6.26 ± 1.97 85.3 ± 8.3 35 Figures Figure 1. View of acclimation heating area, top-down (a) and in-trough (b). a b 36 Figure 2. View of CTmax stat heating containers for both fry (a) and parr (b). a b 37 Figure 3. Side view of swim flume used for both CTmax swim and Eswim trials. 38 Figure 4. Mean ± SEM of critical thermal maximum temperatures (CTmax) for fry (a) and parr (b) for CTmax stat (black) and CTmax swim (blue). Brakets between points denote significance difference in CTmax temperature with p-values between acclimation treatment groups listed above. These figures highlight the differences in critical thermal maxima three-fold: how acclimation temperature affects CTmax, how CTM affect CTmax, and how CTmax differs between life stage. a b 39 Figure 5. Percent mortality of fry (a) and parr (b) post-swim for each swim group as separated by within 24 hours (purple), at 24 hours (teal), and at 48 hours (yellow). Percent mortality at each check time is calculated from the mortalities in the pool of remaining fish at that check time. The bottom x-axis is the acclimation temperature group and the top x-axis is the swim flume temperature. In post-swim mortalities for parr (b), mortality at 24 hours was 100%. This highlights the detrimental effect high acclimation temperatures have on recovery of both parr and fry following stressful events. a b 40 References Adams, O. A., Zhang, Y., Gilbert, M. H., Lawrence, C. S., Snow, M., & Farrell, A. P. (2022). An unusually high upper thermal acclimation potential for rainbow trout. Conservation Physiology, 10(1), coab101. Adams, B. L., Zaugg, W. S., & McLain, L. R. (1973). Temperature effect on parr-smolt transformation in steelhead trout (Salmogairdneri) as measured by gill sodium-potassium stimulated adenosine triphosphatase. Comparative Biochemistry and Physiology Part A: Physiology, 44(4), 1333-1339. Armstrong, J. B., Ward, E. J., Schindler, D. E., & Lisi, P. J. (2016). Adaptive capacity at the northern front: sockeye salmon behaviourally thermoregulate during novel exposure to warm temperatures. Conservation Physiology, 4(1), cow039. Barton, B. A. (2002). Stress in fishes: a diversity of responses with particular reference to changes in circulating corticosteroids. Integrative and comparative biology, 42(3), 517-525. Becker, C. D., & Genoway, R. G. (1979). Evaluation of the critical thermal maximum for determining thermal tolerance of freshwater fish. Environmental Biology of Fishes, 4(3), 245-256. Beitinger, T. L., Bennett, W. A. (2000) Quantification of the role of acclimation temperature in temperature tolerance of fishes. Environmental biology of fishes, 58, 277-288. Beitinger, T. L., Bennett, W. A., & McCauley, R. W. (2000). Temperature tolerances of North American freshwater fishes exposed to dynamic changes in temperature. Environmental biology of fishes, 58(3), 237-275. 41 Bell, M. C. (1986). Fisheries handbook of engineering requirements and biological criteria (p. 209). Fish Passage Development and Evaluation Program, Corps of Engineers, North Pacific Division. Blasco, F. R., Esbaugh, A. J., Killen, S. S., Rantin, F. T., Taylor, E. W., & McKenzie, D. J. (2020). Using aerobic exercise to evaluate sub-lethal tolerance of acute warming in fishes. Journal of Experimental Biology, 223(9), jeb218602. Bouyoucos, I.A., Weinrauch, A.M., Jeffries, K.M., & Anderson, W.G. (2023). Physiological responses to acute warming at the agitation temperature in a temperate shark. Journal of Experimental Biology, 226(19), 10.1242/jeb.246304. Brett, J. R. (1952). Temperature tolerance in young Pacific salmon, genus Oncorhynchus. Journal of the Fisheries Board of Canada, 9(6), 265-323. Brett, J. R. (1962). Some considerations in the study of respiratory metabolism in fish, particularly salmon. Journal of the Fisheries Research Board of Canada, 19(6), 1025-1038. Brett, J. R. (1964). The respiratory metabolism and swimming performance of young sockeye salmon. Journal of the Fisheries Research Board of Canada, 21(5), 1183-1226. Brett, J. R. (1971). Energetic responses of salmon to temperature. A study of some thermal relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). American zoologist, 11(1), 99-113. Brett, J. R., Clarke, W. C., Shelbourn, J. E., & Pacific Biological Station. (1982). Experiments on thermal requirements for growth and food conversion efficiency of juvenile chinook salmon Oncorhynchus tshawytscha. Dept. of Fisheries and Oceans, Fisheries Research Branch, Pacific Biological Station. 42 Bullock, T.H. (1955). Compensation for Temperature in the Metabolism and Activity of Poikilotherms. Biological Reviews. 30(3), 311-342. Carter, K. (2005). The effects of temperature on steelhead trout, coho salmon, and Chinook salmon biology and function by life stage. California regional water quality control board, 1-26. Chellappa, S., Huntingford, F. A., Strang, R. H. C., & Thomson, R. Y. (1995). Condition factor and hepatosomatic index as estimates of energy status in male three‐spined stickleback. Journal of Fish Biology, 47(5), 775-787. Christensen, Emil AF, Lars EJ Andersen, Heiðrikur Bergsson, John F. Steffensen, & Shaun S. Killen. "Shuttle-box systems for studying preferred environmental ranges by aquatic animals." Conservation Physiology 9, no. 1 (2021): coab028. Claussen, D. (1977). Thermal acclimation in ambystomatid salamanders. Comparative Biochemistry and Physiology--Part A: Physiology, 58(4), 333-340. Connor, W.P., Tiffan, K.F., Chandler, J.A., Rondorf, D.W., Arnsberg, B.D., & Anderson, K.C. (2019). Upstream Migration and Spawning Success of Chinook Salmon in a Highly Developed, Seasonally Warm River System. Reviews in Fisheries Science & Aquaculture, 27(1), 1-50. 10.1080/23308249.2018.1477736. Counterpoint Consulting. (2014). Economic dimensions of British Columbia’s Pacific salmon resource: final report. Pacific Salmon Foundation. Cowles, R. B., & Bogert, C. M. (1944). A preliminary study of the thermal requirements of desert reptiles. Bulletin of the AMNH; v. 83, article 5. 43 Crowder, L. B., & Magnuson, J. J. (1982). Thermal habitat shifts by fishes at the thermocline in Lake Michigan. Canadian Journal of Fisheries and Aquatic Sciences, 39(7), 1046-1050. Crozier, L.G., Hendry, A.P., Lawson, P.W., Quinn, T.P., Mantua, N.J., Battin, J., Shaw, R.G., & Huey, R.B. (2008). Potential responses to climate change in organisms with complex life histories: evolution and plasticity in Pacific salmon. Evolutionary Applications. 1(2), 252-270. 10.1111/j.1752-4571.2008.00033.x. Dahlke, F. T., Wohlrab, S., Butzin, M., & Pörtner, H. O. (2020). Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science, 369(6499), 65-70. Dahlke, F., Butzin, M., Wohlrab, S., & Pörtner, H. O. (2022). Reply to: methodological inconsistencies define thermal bottlenecks in fish life cycle. Evolutionary Ecology, 36(2), 293-298. Desforges, J. E., Birnie‐Gauvin, K., Jutfelt, F., Gilmour, K. M., Eliason, E. J., Dressler, T. L., McKenzie, D.J., Bates, A.E., Lawrence, M.J., Fangue, N., & Cooke, S. J. (2023). The ecological relevance of critical thermal maxima methodology for fishes. Journal of Fish Biology, 102(5), 1000-1016. Diana, J. S. (1995). Biology and ecology of fishes. Biological Sciences Press, Carmel. Ind. Donaldson, L. R., & Foster, F. J. (1941). Experimental study of the effect of various water temperatures on the growth, food utilization and mortality rates of fingerling sockeye salmon. Progressive Fish-Culturist, 7(51), 39–39. Egginton, S., & Sidell, B. D. (1989). Thermal acclimation induces adaptive changes in subcellular structure of fish skeletal muscle. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 256(1). 44 Ellis, A. (1981). Stress and the modulation of defense mechanisms in fish. Stress and fish, 147-169. Eliason, E. J., Clark, T. D., Hague, M. J., Hanson, L. M., Gallagher, Z. S., Jeffries, K. M., Gale, M.K., Patterson, D.A., Hinch, S.G., & Farrell, A. P. (2011). Differences in thermal tolerance among sockeye salmon populations. Science, 332(6025), 109-112. Eliason, E. J., Gale, M. K., Whitney, C. K., Lotto, A., & Hinch, S. G. (2017). Intraspecific differences in endurance swim performance and cardiac size in sockeye salmon (Oncorhynchus nerka) parr tested at three temperatures. Canadian Journal of Zoology, 95(6), 425-432. Ern, R., Andreassen, A.H., & Jutfelt, F. (2023). Physiological Mechanisms of Acute Upper Thermal Tolerance in Fish. Physiology, 38(3), 141-158. 10.1152/physiol.00027.2022. Ern, R., Norin, T., Gamperl, K.A., & Esbaugh, A.J. (2016). Oxygen dependence of upper thermal limits in fishes. Journal of Experimental Biology, 219(21), 3376-3383. 10.1242/jeb.143495.;5 Farrell, A. P. (2002). Cardiorespiratory performance in salmonids during exercise at high temperature: insights into cardiovascular design limitations in fishes. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 132(4), 797-810. Farrell, A. P., Lee, C. G., Tierney, K., Hodaly, A., Clutterham, S., Healey, M., S.G. Hinch & Lotto, A. (2003). Field‐based measurements of oxygen uptake and swimming performance with adult Pacific salmon using a mobile respirometer swim tunnel. Journal of Fish Biology, 62(1), 64-84. Fries, C. R. (1986). Effects of environmental stressors and immunosuppressants on immunity in Fundulus heteroclitus. American Zoologist, 26(1), 271-282. 45 Fry, F. A., & Hart, J. S. (1948). Cruising speed of goldfish in relation to water temperature. Journal of the Fisheries Board of Canada, 7(4), 169-175. Galbreath, P. F., Adams, N. D., & Martin, T. H. (2004). Influence of heating rate on measurement of time to thermal maximum in trout. Aquaculture, 241(1-4), 587-599. Geist, D. R., Deng, Z., Mueller, R. P., Brink, S. R., & Chandler, J. A. (2010). Survival and growth of juvenile Snake River fall Chinook salmon exposed to constant and fluctuating temperatures. Transactions of the American Fisheries Society, 139(1), 92-107. Goniea, T. M., Keefer, M. L., Bjornn, T. C., Peery, C. A., Bennett, D. H., & Stuehrenberg, L. C. (2006). Behavioral thermoregulation and slowed migration by adult fall Chinook salmon in response to high Columbia River water temperatures. Transactions of the American Fisheries Society, 135(2), 408-419. Havird, J. C., Neuwald, J. L., Shah, A. A., Mauro, A., Marshall, C. A., & Ghalambor, C. K. (2020). Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: Why methodology matters. Functional Ecology, 34(5), 1015-1028. Herbinger, C. M., Friars, G. W. (1991). Correlation between condition factor and total lipid content in Atlantic salmon, Salmo salar, L., parr. Aquacultal Fish Management. 22, 527–529. Isaza, D. F. G., & Rodgers, E. M. (2022). Exercise training does not affect heat tolerance in Chinook salmon (Oncorhynchus tshawytscha). Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 270. 46 Islam, S. U., Hay, R. W., Déry, S. J., & Booth, B. P. (2019). Modelling the impacts of climate change on riverine thermal regimes in western Canada’s largest Pacific watershed. Scientific reports, 9(1), 1-14. Jutfelt, F., Norin, T., Ern, R., Overgaard, J., Wang, T., McKenzie, D. J., & Clark, T. D. (2018). Oxygen-and capacity-limited thermal tolerance: blurring ecology and physiology. Journal of Experimental Biology, 221(1). Kristianson, G. & Strongitharm, D. (2006). The evolution of recreational salmon fisheries in British Columbia. Pacific Fisheries Resource Conservation Council. Kuehne, L. M., Olden, J. D., & Duda, J. J. (2012). Costs of living for juvenile Chinook salmon (Oncorhynchus tshawytscha) in an increasingly warming and invaded world. Canadian Journal of Fisheries and Aquatic Sciences, 69(10), 1621-1630. Lim, D. D., Milligan, C. L., & Morbey, Y. E. (2020). Elevated incubation temperature improves later‐life swimming endurance in juvenile Chinook salmon, Oncorhynchus tshawytscha. Journal of Fish Biology, 97(5), 1428-1439. Lutterschmidt, W. I., & Hutchison, V. H. (1997). The critical thermal maximum: history and critique. Canadian Journal of Zoology, 75(10), 1561-1574. Macdonald, J. S., Patterson, D. A., Hague, M. J., & Guthrie, I. C. (2010). Modeling the influence of environmental factors on spawning migration mortality for sockeye salmon fisheries management in the Fraser River, British Columbia. Transactions of the American Fisheries Society, 139(3), 768-782. Magnuson, J. J., Crowder, L. B., & Medvick, P. A. (1979). Temperature as an ecological resource. American Zoologist, 19(1), 331-343. 47 Marine, K. R., & Cech Jr, J. J. (2004). Effects of high water temperature on growth, Smoltification, and predator avoidance in juvenile Sacramento RiverChinook Salmon. North American Journal of Fisheries Management, 24(1), 198-210. Mayer, N.B., S.G. Hinch, and E.J. Eliason. 2023. Thermal tolerance in Pacific salmon: A systematic review of species, populations, life stages, and methodologies. Fish and Fisheries. In press. Minke-Martin, V., Hinch, S.G, Braun, D.C., Burnett, N.J., Casselman, M.T., Eliason, E.J., Middleton, C.T. 2018. Physiological condition and migratory experience affect fitness-related outcomes in adult female sockeye salmon. Ecology of Freshwater Fish 27(1): 296-309. Morgan, I. J., McDonald, D. G., & Wood, C. M. (2001). The cost of living for freshwater fish in a warmer, more polluted world. Global Change Biology, 7(4), 345-355. Morley, S.A., Peck, L.S., Sunday, J.M., Heiser, S., Bates, A.E. (2019). Physiological acclimation and persistence of ectothermic species under extreme heat events. Global Ecology and Biogeography, 28(7), 1018-1037. Morrison, J., Quick, M. C., & Foreman, M. G. (2002). Climate change in the Fraser River watershed: flow and temperature projections. Journal of Hydrology, 263(1-4), 230-244. Mozsár, A., Boros, G., Sály, P., Antal, L., & Nagy, S.A. (2015). Relationship between Fulton’s condition factor and proximate body composition in three freshwater fish species. Journal of Applied Ichthyology, 31, 315-320. 48 Muñoz, N. J., Anttila, K., Chen, Z., Heath, J. W., Farrell, A. P., & Neff, B. D. (2014). Indirect genetic effects underlie oxygen-limited thermal tolerance within a coastal population of chinook salmon. Proceedings of the Royal Society B: Biological Sciences, 281(1789). Muñoz, N. J., Farrell, A. P., Heath, J. W., & Neff, B. D. (2015). Adaptive potential of a Pacific salmon challenged by climate change. Nature Climate Change, 5(2), 163-166. Muñoz, N. J., Farrell, A. P., Heath, J. W., & Neff, B. D. (2017). Hematocrit Is Associated with Thermal Tolerance and Modulated by Developmental Temperature in Juvenile Chinook Salmon. Physiological and Biochemical Zoology, 91(1), 757-762. 10.1086/695556. Narum, S. R., Powell, M. S., & Talbot, A. (2004). A distinctive microsatellite locus that differentiates ocean-type from stream-type Chinook salmon in the interior Columbia River basin. Transactions of the American Fisheries Society, 133(4), 1051-1055. Palmisano, A. N., Winton, J. R., & Dickhoff, W. W. (2000). Tissue-specific induction of Hsp90 mRNA and plasma cortisol response in chinook salmon following heat shock, seawater challenge, and handling challenge. Marine Biotechnology, 2(4), 329-338. Penman, R. J., Bugg, W., Rost-Komiya, B., Earhart, M. L., & Brauner, C. J. (2023). Slow heating rates increase thermal tolerance and alter mRNA HSP expression in juvenile white sturgeon (Acipenser transmontanus). Journal of Thermal Biology, 103599. Poletto, J. B., Cocherell, D. E., Baird, S. E., Nguyen, T. X., Cabrera-Stagno, V., Farrell, A. P., & Fangue, N. A. (2017). Unusual aerobic performance at high temperatures in juvenile Chinook salmon, Oncorhynchus tshawytscha. Conservation physiology, 5(1). Pörtner, H. O., Bock, C., & Mark, F. C. (2017). Oxygen-and capacity-limited thermal tolerance: bridging ecology and physiology. Journal of Experimental Biology, 220(15), 2685-2696. 49 Pottier, P., Burke, S., Drobniak, S. M., & Nakagawa, S. (2022). Methodological inconsistencies define thermal bottlenecks in fish life cycle: a comment on Dahlke et al. 2020. Evolutionary Ecology, 36(2), 287-292. Quigley, J. T., & Hinch, S. G. (2006). Effects of rapid experimental temperature increases on acute physiological stress and behaviour of stream dwelling juvenile chinook salmon. Journal of Thermal Biology, 31(5), 429-441. Quinn, T. P. (2018). The behavior and ecology of pacific salmon and trout. Seattle; Bethesda, Maryland: University of Washington Press. Ricker, W. E. (1975). Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada, 191, 1-382. Richter, A., & Kolmes, S. A. (2005). Maximum temperature limits for Chinook, coho, and chum salmon, and steelhead trout in the Pacific Northwest. Reviews in Fisheries science, 13(1), 23-49. Robinson, M. L., Gomez-Raya, L., Rauw, W. M., & Peacock, M. M. (2008). Fulton's body condition factor K correlates with survival time in a thermal challenge experiment in juvenile Lahontan cutthroat trout (Oncorhynchus clarki henshawi). Journal of Thermal Biology, 33(6), 363-368. Rodnick, K. J., Gamperl, A. K., Lizars, K. R., Bennett, M. T., Rausch, R. N., & Keeley, E. R. (2004). Thermal tolerance and metabolic physiology among redband trout populations in south‐eastern Oregon. Journal of Fish Biology, 64(2), 310-335. Rupp, D. E., Abatzoglou, J. T., & Mote, P. W. (2017). Projections of 21st century climate of the Columbia River Basin. Climate Dynamics, 49(5), 1783-1799. 50 Samuelsen, E.N., Imsland, A.K., & Brix, O. (1999). Oxygen binding properties of three different hemoglobin genotypes in turbot (Scophthalmus maximus Rafinesque): Effect of temperature and pH. Fish Physiology and Biochemistry, 20(2), 135-141. 10.1023/A:1007755525270. Sparks, M.M., Westley, P.A.H., Falke, J.A., & Quinn, T.P. (2017). Thermal adaptation and phenotypic plasticity in a warming world: Insights from common garden experiments on Alaskan sockeye salmon. Global Change Biology, 23(12), 5203-5217. 10.1111/gcb.13782 Speers-Roesch, B., & Norin, T. (2016). Ecological significance of thermal tolerance and performance in fishes. Functional Ecology, 30(6), 842-844. Steinhausen, M. F., Sandblom, E., Eliason, E. J., Verhille, C., & Farrell, A. P. (2008). The effect of acute temperature increases on the cardiorespiratory performance of resting and swimming sockeye salmon (Oncorhynchus nerka). Journal of Experimental Biology, 211(24), 3915-3926. Sturrock, A. M., Wikert, J. D., Heyne, T., Mesick, C., Hubbard, A. E., Hinkelman, T. M., Weber, P.K., Whitman, G.E., Glessner, J.J., & Johnson, R. C. (2015). Reconstructing the migratory behavior and long-term survivorship of juvenile Chinook salmon under contrasting hydrologic regimes. PLoS One, 10(5), e0122380. Turko, A.J., Nolan, C.B., Balshine, S., Scott, G.R.,& Pitcher, T.E. (2020). Thermal tolerance depends on season, age, and body condition in imperilled redside dace Clinostomus elongatus. Journal of Conservation Physiology, 8(1), 10.1093/conphys/coaa062. Van Wert, J. C., Hendriks, B., Ekström, A., Patterson, D. A., Cooke, S. J., Hinch, S. G., & Eliason, E. J. (2023). Population variability in thermal performance of pre-spawning adult Chinook salmon. Conservation Physiology, 11(1), coad022. 51 Visser, M.E., & Both, C. (2005). Shifts in phenology due to global climate change: the need for a yardstick. Proceedings of the Royal Society B: Biological Sciences. 272(1581), 2561-2569. 10.1098/rspb.2005.3356. Volkoff, H., & Rønnestad, I. (2020). Effects of temperature on feeding and digestive processes in fish. Temperature, 7(4), 307-320. Vornanen, M. (2020). Feeling the heat: source–sink mismatch as a mechanism underlying the failure of thermal tolerance. Journal of Experimental Biology, 223(16). 10.1242/jeb.225680. Vornanen, M. (2016). The temperature dependence of electrical excitability in fish hearts. Journal of Experimental Biology, 219(13), 1941-1952. 10.1242/jeb.128439. Wedemeyer, G. (1982). Effects of environmental stressors in aquacultural systems on quality, smoltification and early marine survival of anadromous fish. In North Pacific aquaculture symposium (pp. 155-170). Wendelaar Bonga, S. E. (1997). The stress response in fish. Physiological reviews, 77(3), 591-625. Zale, A. V. (1984). Applied aspects of the thermal biology, ecology, and life history of the blue tilapia, Tilapia aurea (Pisces: Cichlidae) (Doctoral dissertation, University of Florida) Zillig, K.W., Lusardi, R.A., Moyle, P.B., Fangue, N.A. (2021). One size does not fit all: variation in thermal eco-physiology among Pacific salmonids. Rev Fish Biol Fisheries 31, 95–114 (2021). Zillig, K.W., Lusardi, R.A., Cocherell, D. E., Fangue, N.A. (2023). Interpopulation variation in thermal physiology among seasonal runs of Chinook salmon. Canadian Journal of Fisheries and Aquatic Sciences, 80(1), 1-13. 10.1139/cjfas-2022-0133. 52 Zillig, K. W., Cocherell, D. E., & Fangue, N. A. (2020). Interpopulation Variation among Juvenile Chinook Salmon from California and Oregon. The United States Environmental Protection Agency Region, 2020-07. 53 Appendix: Supplementary Material Table S1. Mean ± SE of mass, fork length, and condition factor for fry and parr with each acclimation temperature overall. Acclimation Temp (°C) Fry Parr Mass(g) 15 0.69 ± 0.14 (n=44) 7.43 ±2.64 (n=45) 18 0.77 ±0.17 (n=45) 7.05 ± 2.79 (n=47) 20 0.85 ±0.23 (n=45) 6.35 ± 1.73 (n=46) 24 0.52 ±0.17 (n=45) 6.57 ±2.25 (n=24) Fork length (mm) 15 41±3 87±8 18 42±4 86± 8 20 43±5 86± 6 24 37±3 86± 9 Condition Factor 15 0.098± 0.01 0.106±0.01 18 0.102± 0.01 0.104±0.02 54 Acclimation Temp (°C) Fry Parr 20 0.104± 0.02 0.096±0.01 24 0.094± 0.02 0.099±0.01 55 Table S2. R code of models and post hoc analysis chosen for comparison of acclimation temperature on CTmax. Dropout_temp refers to the temperature at CTmax. CTmax stat, fry m21<- glm(data=CTmax_stat_fry, formula =dropout_temp ~ acclimation_temp_c_factor) m21_eff<-emmeans(m2,spec="acclimation_temp_c_factor") m21_tukey<-contrast(m2_eff, method="pairwise") CTmax swim, fry m20<- glm(data=CTmax_swim_Fry, formula =dropout_temp ~ acclimation_temp_c_factor) m20_eff<-emmeans(m3,spec="acclimation_temp_c_factor") m20_tukey<-contrast(m3_eff, method="pairwise") CTmax stat, parr m22<-glm(data=CTmax_stat_parr, formula =dropout_temp ~ acclimation_temp_c_factor m22_eff<-emmeans(m11,spec="acclimation_temp_c_factor") m22_tukey<-contrast(m11_eff, method="pairwise") CTmax swim, parr m23<-glm(data=CTmax_swim_parr, formula =dropout_temp ~ acclimation_temp_c_factor m23_eff<-emmeans(m12,spec="acclimation_temp_c_factor") m23_tukey<-contrast(m12_eff, method="pairwise") 56 Table S3. R code for test type and life stage models and post hoc evaluation on the outcome CTmax temperature. Dropout_temp refers to the temperature at CTmax. Life stage comparison as separated by test type glm(data= CTmaxStat , formula = dropout_temp ~ life_stage) t.test(dropout_temp ~ life_stage, data = CTmaxStat) glm(data= CTmaxSwim, formula = dropout_temp ~ life_stage) t.test(dropout_temp ~ life_stage, data = CTmaxSwim) Table S4. R code for mortality models and post hoc evaluation on the outcome CTmax temperature. Fry post-swim mortality mort_fry <-glm(data=Eswim_mort_percentage_fry,formula=mortality ~ swim_group+ check) Parr post-swim mortality mort_parr <-glm(data=Eswim_mort_percentage_parr,formula=mortality ~ swim_group+ check) Table S5. Analysis of variance between body size metrics (mass, fork length, and condition factor) and acclimation temperature for fry (a,c,e) and parr (b,d,e)
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hinch, Scott G.
Assessing effects of acclimation temperature on thermal tolerance of stream-type juvenile Chinook salmon (Oncorhynchus tshawytscha) under ecologically relevant temperatures
Text