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Variation in metabolic rate between individuals and species : cryptic physiological tradeoffs underlying… Van Leeuwen, Travis Edward 2010

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Variation in Metabolic Rate between Individuals and Species: Cryptic Physiological Tradeoffs Underlying Habitat Partitioning and Life History Strategies of Juvenile Salmonids by Travis Edward Van Leeuwen B.Sc., Simon Fraser University, 2007  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES  (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September, 2010  © Travis Edward Van Leeuwen, 2010  Abstract Physiological traits such as standard metabolic rate have been shown to vary up to half an order of magnitude between individuals and species and influence key life history tradeoffs in juvenile salmonids (e.g. smolt timing). The purpose of my thesis research was to examine the relationship between food consumption, dominance and disproportionate feeding on physiological traits and adaptive strategies of juvenile salmonids. Results show that SMR is positively correlated with food consumption and growth in juvenile coho salmon which showed a reduction in SMR under low food and an elevation in SMR under high food (Chapter 2). Comparisons between hatchery and wild juvenile steelhead and coho salmon revealed no difference in SMR between wild coho and wild steelhead but a significant difference in the hatchery fish, with hatchery steelhead being higher. In both wild and hatchery populations I found a marked difference in maximal metabolic rate between steelhead and coho, leading to a greater aerobic scope and swim performance in wild steelhead but no difference in the hatchery fish. This result is consistent with a steelhead energy maximizing strategy, habitat partitioning and trade-offs between elevated SMR at higher growth and decreased swim performance. Interestingly, wild steelhead with higher maximum growth, swim performance, and maximum food consumption do not appear to tradeoff increased growth against lower swim performance, as commonly observed for high growth strains. Instead steelhead appear to be trading off higher growth for lower food consumption efficiency; highlighting potential differences in food  ii  consumption and digestion strategies as cryptic adaptations that have received little attention. In experiments conducted in semi-natural stream channels, I found that dominant coho salmon were able to achieve higher absolute growth rates compared to smaller subordinates, at high food ration but suffered significantly lower absolute growth at low food ration as larger dominant fish approached the capacity of their habitat (Chapter 4). Overall I demonstrated that the relationship between physiological traits, life history strategy and dominance rank depended on per capita food consumption rates, habitat characteristics (pools vs riffles) and the absolute size of individuals in a dominance hierarchy.  iii  Table of Contents Abstract ........................................................................................................................................ ii Table of Contents ....................................................................................................................... iv List of Tables ............................................................................................................................. vii List of Figures ........................................................................................................................... viii List of Abbreviations .................................................................................................................. x Acknowledgements .................................................................................................................... xi Chapter One: General Introduction ......................................................................................... 1 Pacific Salmon Life Cycle ............................................................................................................ 1 Life History Strategies .................................................................................................................. 3 Behavioural Strategies ...................................................................................................... 4 Physiological Strategies.................................................................................................... 5 Biochemical Strategies...................................................................................................... 7 Morphological Strategies.................................................................................................. 8 Oxygen Consumption Rate Measures (Respirometry) ................................................................. 9 Closed Respirometry ....................................................................................................... 10 Flow-through Respirometry ............................................................................................ 11 Intermittent Flow-through Respirometry ........................................................................ 12 Thesis Objectives ........................................................................................................................ 13 Chapter Two: Effects of Food Ration and Identity on SMR: Is Dominance a Cause or Consequence of High Metabolic Rate? .............................................................................. 16 Introduction ................................................................................................................................. 16 Methods....................................................................................................................................... 17 Feeding Treatments ........................................................................................................ 19 Measuring Standard Metabolic Rate .............................................................................. 20 Data Analysis .................................................................................................................. 21 Results ......................................................................................................................................... 22 Discussion ................................................................................................................................... 23  iv  Chapter Three: Variation in Metabolic Rate between Juvenile Coho Salmon and Steelhead Trout: Insight into the Physiological Basis of Habitat Partitioning and Adaptive Tradeoffs of Juvenile Salmonids ............................................................................. 33 Introduction ................................................................................................................................. 33 Methods....................................................................................................................................... 37 Experimental Protocols .................................................................................................. 38 Analytical Protocols........................................................................................................ 39 Measuring Standard Metabolic Rate (SMR)................................................................... 39 Measuring Maximal Metabolic Rate (MMR) .................................................................. 41 Measuring Swim Performance (UCrit) ........................................................................... 43 Statistical Analysis and Calculations.............................................................................. 44 Results ......................................................................................................................................... 45 Standard Metabolic Rate (SMR) ..................................................................................... 45 Maximal Metabolic Rate (MMR) .................................................................................... 45 Aerobic Scope (AS) ......................................................................................................... 45 Swim Performance (UCrit) ............................................................................................. 46 Instantaneous Growth Rate............................................................................................. 46 Discussion ................................................................................................................................... 46 Chapter Four: Failure of Physiological Metrics to Predict Dominance in Wild Juvenile Salmon: Habitat Effects on the Allometry of Growth in Dominance Hierarchies .................................................................................. 60 Introduction ................................................................................................................................. 60 Methods....................................................................................................................................... 63 Experimental Fish ........................................................................................................... 63 Experimental Setup ......................................................................................................... 63 Experimental Design ....................................................................................................... 65 Data Analysis .................................................................................................................. 68 Results ......................................................................................................................................... 69 Instantaneous Growth Rate (Interval 1) ......................................................................... 69 Absolute Growth Rate (Interval 1).................................................................................. 69 (Interval 2) ...................................................................................................................... 70 Discussion ................................................................................................................................... 71 Chapter Five: General Discussion ........................................................................................... 80 References .................................................................................................................................. 89  v  Appendix .................................................................................................................................. 101 Growth of Size Matched Juvenile Coho Salmon and Steelhead Trout ......................... 101 Figure A-1 ..................................................................................................................... 103 Animal Care Certificate ................................................................................................ 104  vi  List of Tables Table 3.1. Standard metabolic rate, maximal metabolic rate, aerobic scope, swim performance and growth rate parameter estimates for hatchery and wild reared juvenile steelhead and coho salmon under high and low food .............. 54 Table 4.1. Average mass of steelhead and coho salmon stocked in experimental stream channels along with % body wt. of food given and % satiation of salmon throughout the experiment ......................................................................................... 75  vii  List of Figures Figure 1.1. A comparison of standard metabolic rate values obtained using flow-through respirometry and closed respirometry...................................................................... 14 Figure 1.2. The relationship between standard metabolic rate and time over a period of 18 hours......................................................................................................................... 15 Figure 2.1. A schematic showing sequence of food treatments throughout the experiment ...... 28 Figure 2.2. The relationship between mass and time over a period of 44 days for juvenile coho salmon, held under high and low food ............................................................ 29 Figure 2.3. The relationship between instantaneous growth rate and time over a period of 44 days for juvenile coho salmon, held under high and low food ....................... 30 Figure 2.4. The relationship between standard metabolic rate and time over a period of 44 days for juvenile coho salmon, held under high and low food ................................ 31 Figure 2.5. The relationship between standard metabolic rate and growth rate over the entire experiment ..................................................................................................... 32 Figure 3.1. The relationship between standard metabolic rate and mass for hatchery and wild juvenile steelhead and coho salmon under high and low food ........................ 55 Figure 3.2. The relationship between maximal metabolic rate and mass for hatchery and wild juvenile steelhead and coho salmon under high and low food ........................ 56 Figure 3.3. The relationship between aerobic scope and mass for hatchery and wild juvenile steelhead and coho salmon under high and low food ................................ 57 Figure 3.4. The relationship between swim performance and length for hatchery and wild juvenile steelhead and coho salmon under high and low food ................................ 58 Figure 3.5. The relationship between instantaneous growth rate and mass for hatchery and wild juvenile steelhead and coho salmon under high and low food ................. 59 Figure 4.1. The relationship between instantaneous growth rate and mass for juvenile wild steelhead and coho salmon in experimental stream channels (interval 1) ................................................................................................................ 76 Figure 4.2. The relationship between absolute growth rate and mass for juvenile coho salmon in experimental stream channels (interval 1) .............................................. 77  viii  Figure 4.3. The relationship between absolute growth rate and mass for juvenile coho salmon in experimental stream channels (interval 2) .............................................. 78 Figure 4.4. The relationship between absolute growth rate and mass for juvenile coho salmon in experimental stream channels from (Rosenfeld et al.2005) .................... 79 Figure A-1. The relationship between instantaneous growth rate and mass for size matched wild juvenile steelhead and coho salmon in experimental stream channels................................................................................................................. 103  ix  List of Abbreviations SMR  Standard metabolic rate  MMR  Maximal metabolic rate  AS  Aerobic scope  UCrit  Swim performance test  PC  Plasma cortisol  GH  Growth hormone  ANCOVA  Analysis of co-variance  YOY  Young of the year  x  Acknowledgements I’m truly indebted to my supervisors Dr. Jordan Rosenfeld and Dr. Jeffrey Richards who have given me valuable guidance and knowledge over the last two years, I’m truly grateful. Memories of camping at Porpoise bay and time spent on the Sunshine coast will forever be remembered. I would like to thank Frank Cloutier for offering me a place to stay, Chapman Creek Hatchery and members of the Sunshine Coast Salmon Enhancement Society for providing me a place to carry out research. I would also like to thank Bob Anstead and volunteers at the hatchery for making my time at the hatchery a memorable one. I would like to thank my lab mates Gigi Lau, Ben Speers-Roesch, Milica Mandic, Mark Scott, Chad Ormond and Lili Yao for their help and insight during this project. I would also like to thank Anne Dalziel for her suggestions and help in various aspects of this project and Dr. Dave Bates for his valuable local insight about streams on the Sunshine Coast as well as Dr. Antoine Leduc for help with collection of fish. Lastly I would like to thank my parents Ada Van Leeuwen and Nick Van Leeuwen, brother, Dustin Van Leeuwen and Annie Olszewski for sticking with me and providing encouragement at times it was needed the most.  xi  Chapter One: General Introduction Pacific Salmon Life Cycle Pacific salmon have been heavily studied for decades due to their high economic and socio-economic importance in an attempt to boost diminishing salmon stocks. First attempts focused on hatchery rearing facilities in which fish were raised to the smolt phase and released into the stream. Recently, however, efforts have expanded to include habitat restoration as impacts of hatchery rearing have come under scrutiny. Restoration techniques include the building of side channel rearing habitat and the introduction of debris such as large stumps, log jams and rocks into streams to create refuge and suitable habitat for juvenile salmonids. Most of the scrutiny surrounding hatcheries have focused on three major concerns which include the possibility for genetic alterations associated with artificial selection of brood stock (Taylor 1986), potential effects of artificial rearing on survivability of hatchery released fish (Taylor 1986), and the possible inability of the population of fish to be sustained once the hatchery is removed. There are five species of Pacific salmon in the northeastern Pacific: coho (Oncorhynchus kisutch), chum (Oncorhynchus keta), pink (Oncorhynchus gorbuscha), chinook (Oncorhynchus tshawytscha) and sockeye (Oncorhynchus nerka). Closely related are steelhead trout (Oncorhynchus mykiss) which until recently were classified under the scientific name Salmo gairdneri. All species of salmon occur in many coastal streams in the Pacific Northwest of North America and have adopted a variety of strategies that allow them to exist and co-exist in streams. In this thesis I will focus on adaptive metabolic traits of juvenile steelhead and coho salmon.  1  All salmonid species are anadromous, moving between fresh and salt water during various stages of their life cycle, with all species being semelparous except steelhead which are iteroparous, having the ability to reproduce more than once. Adult salmon typically enter their natal freshwater stream in the fall with coho spawning from July to January, and steelhead spawning considerably later than the rest of the salmonid species from January to April (Withler 1966, Busby et al. 1996). Eggs remain in the gravel and hatch the following spring, with the timing of hatch dependent on temperature, photoperiod, channel hydraulics and biotic factors such as predation risk (Warkentin 1995; Sih and Moore 1993) and inter specific competition. Due to the later spawning time steelhead emerge considerably later (May to June) than the rest of the other species of salmonids including coho (March to April). The emerging fish are known as alevins and for the first 4-5 weeks they acquire nutrients by consuming their yolk. Once the yolk has been consumed young salmonids become known as fry and begin to feed exogenously on invertebrate drift consisting of aquatic and terrestrial insects. During this stage salmonids engage in intra and interspecific competition for preferred feeding territories (Hartman 1965, Chapman 1966). Competition between salmonids is highly size dependent with larger individuals outcompeting and displacing smaller individuals from preferred habitat (Young 2004) possibly contributing to high density-dependent mortality (Armstrong and Nislow 2006). Dependent on species, fry will remain in the stream for a year or more such as coho, steelhead and chinook whereas pink and chum will immediately emigrate to sea. Coho salmon spend between 1-2 years foraging in freshwater whereas steelhead will remain in the stream for approximately 2-3 years. Once the fish reach a threshold size they will then begin the smoltification process and  2  transition to salt water as fish begin to reach the food capacity of the stream due to their larger size and higher per capita food intake. Size of fish after overwintering in their first year is highly dependent on growth rate from the previous summer (Metcalfe and Thorpe 1992), ultimately determining when juvenile salmonids will smolt. Once fry are ready to smolt and transition to sea they undergo dramatic changes which include morphological, physiological and biochemical in order to tolerate stressors associated with entering seawater (eg. excess Sodium). Salmonids will then feed at sea for approximately 1-4 years depending on species before returning to their natal stream to spawn. Once entering the stream salmonids undergo extensive morphological changes and will not feed, relying on lipid stores to drive the highly energetic migration to the spawning grounds. All species of salmon will then die except steelhead and other trout species which may resume feeding after spawning and emigrate to sea where they are known as kelts. Life History Strategies There is considerable variation in life history strategies (e.g. smolt timing) both within and between species which has been linked to physiological traits such as standard metabolic rate (SMR) in Atlantic salmon (Nicieza et al. 1991), making salmonids a model species for exploring relationships between physiology, life history strategies and dominance hierarchies. There are two general life history strategies that may be adopted by juvenile salmonids with much of the findings being documented in Atlantic salmon. The first strategy also known as an early migrant or energy maximizer strategy is to actively forage in order to maximize growth, allowing these fish to smolt and emigrate in the spring (Metcalfe, 1998) after only one year. The second strategy is to adopt the  3  delayed migrant or energy minimizer strategy (Morgan et al. 2000), which is adopted by fish with a lower appetite and reduced food consumption both of which are dependent on productivity of the freshwater ecosystem. These fish will generally remain in the stream for at least 2 years. Both of these strategies have trade-offs, early migrants may be more susceptible to predation due to an increase in time spent foraging and risk-taking behaviour (Finstad et al. 2007; Vollestad and Quinn 2003). Delayed migrants may not endure the same predation risk as they spend more time under refuge, but may experience lower growth rates. Although the benefits and consequences of these general life history strategies are unknown, it is clear that behaviour, physiology and biochemistry play an important role in determining the growth and survival strategy adopted. It is also unclear if mortality is higher in streams than in the ocean and if this differs between delayed and early migrants. For example, will fish that remain in the stream for an extra year have greater survivability than a fish that went to sea in year one? Although these two strategies are general they can be accomplished in a number of ways which include behavioural, physiological, biochemical and morphological strategies, highly dependent upon species requirements, habitat, food availability and competition. Behavioural Strategies Groups of salmonids are well known to establish dominance hierarchies (Reinhardt 1999; Sloman et al. 2000; 2001) with the dominant fish monopolizing access to food. Hierarchies have also been documented to remain temporally stable (Bachman 1984; Abbott et al. 1985; Nakano 1995; Hansen and Closs 2009) generating an expectation that dominant fish should experience higher growth. This has been demonstrated in a number of studies involving brown trout (Lahti et al. 2001), Atlantic  4  salmon (Huntingford et al. 1998; Martin-Smith and Armstrong 2002) and coho salmon (Vollestad and Quinn 2003). However, Vollestad and Quinn (2003) noted that this may only be advantageous when food availability is limiting and monopolization of food possible. Under high food levels when food monopolization is not possible however, Vollestad and Quinn (2003) found a negative relationship between growth and aggression in juvenile coho salmon presumably due to the added energetic costs of swimming, defending territories and stress to the dominant fish associated with defending territories, compared to subordinate fish. Dominant fish growth is also influenced by habitat type and configuration (e.g. pool size and shape), drift concentration, discharge from the upstream riffle (Hansen and Closs 2009; Harvey et al. 2005), density of fish and the size of dominant in relation to the rest of the hierarchy (Chapter 4). Lastly, fish may attempt to limit the energetic costs of swimming associated with holding in a particular habitat (Bisson et al. 1988). This can be accomplished by holding close to a hydraulic refuge, such as undercut banks, downstream side of rocks or in a low velocity pool, while in close proximity to high velocity habitats where prey encounter rates are higher (Nislow et al. 1999; Hayes et al. 2000), allowing maximization of food intake and a reduction in energetic costs associated with holding continuously in a high velocity habitat. Physiological Strategies There are many physiological strategies that could underlie differences in life history strategies in juvenile salmonids which include partitioning energy budgets differently between compartments like standard metabolic rate (SMR), maximal metabolic rate (MMR) and aerobic scope (AS). Juvenile salmonids with a high standard  5  metabolic rate (SMR), the minimal maintenance metabolic rate of ectotherms in a post absorptive state (Fry and Hart 1948; Beck and Gopp 1995; Priede 1985) have been shown to be competitively dominant (Metcalfe et al. 1995; Cutts et al. 2002) through greater aggression, suggesting that these fish need a greater proportion of food to counteract their higher maintenance metabolism. Under high food this may be beneficial as these fish may become more successful at dominating a food supply leading to higher growth. Alternatively, Millidine et al. (2009) found that Atlantic salmon with a higher SMR also had higher digestion rates, and protein turnover due to shorter residence time of food in the gut compared to fish with a lower SMR, which may lead to higher growth if food is in excess as digestion has been shown to be the bottleneck that minimizes energy assimilation when food is in excess (Booth 1990; Hart and Gill 1992), thereby limiting growth. At low food this strategy would become costly due to the higher energetic costs associated with having a higher SMR, assuming that SMR is genetically fixed and unable to be regulated. A lower SMR may be advantageous at low food as it may allow for resistance to starvation (Mueller and Diamond 2001) due to lower maintenance costs. It is unclear if salmonids have a capacity to regulate their SMR or whether SMR is genetically fixed, or scales directly to anabolic and catabolic activities associated with growth and food consumption (Chapter 2). Considerable research has been conducted exploring the relationship between SMR and life history strategies; however the role of maximal metabolic rate (MMR) and aerobic scope (AS) remain relatively understudied, although their contributions to explaining life history strategies may be significant.  6  Fish in higher velocity habitats may be able to increase maximal metabolic rate (MMR; the maximum amount of energy that a fish can sustain aerobically) in order to compensate for an elevated SMR associated with high growth and food consumption. Increasing MMR may allow for maximization of aerobic scope (AS), the difference between MMR and SMR, and is believed to be the energy confines in which an animal must function (Lee et al. 2003). Increasing AS leads to a higher scope for activity that can be carried out routinely, presumably with the extra energy being allocated for growth. Therefore, a high AS may be associated with higher scope for growth (Chapter 3). Both MMR and AS have been shown to be associated with increased athletic (Killen et al. 2007) and swim performance (UCrit) (Plaut 2001) and to explain a number of responses to environmental challenges (ex. thermal stress, migration distances etc;Djawadan et al. 1997; Bochdansky et al. 2005; Lee et al. 2003; Killen et al. 2006). This strategy may be beneficial as prey encounter rates have been shown to be proportional to water velocity (Nislow et al. 1999; Hayes et al. 2000; Chapter 3). Biochemical Strategies Several researchers have focused on the role of growth hormone (GH) and plasma cortisol (PC) in an attempt to explain adaptive strategies in juvenile salmonids (Jonsson et al. 1998; Sloman et al. 2008). Salmonids with high levels of GH tend to be more dominant than subordinates that have a reduced level of GH (Jonsson et al. 1998). Having a high level of GH would be advantageous in procuring a prime feeding position within a hierarchy when food monopolization is possible (Vollestad and Quinn 2003). If monopolization of food is not possible an energetic cost associated with the production of GH would be expected  7  compared to subordinates with lower levels of GH. It is unclear if dominance is a consequence of increased hunger rather than a cause (Metcalfe et al. 1995) as GH has not been shown to elevate SMR and may not be associated with high metabolic demands (Metcalfe et al. 1995) Lastly plasma cortisol (PC) is produced in an attempt to combat stress and has been found to be higher in subordinate fish (Pottinger and Pickering 1992), although some studies have found that dominant fish had higher levels of PC (Sloman et al. 2008). Increased levels of PC is believed to increase respiration and ultimately SMR, therefore juvenile salmonids may adopt strategies to maximize growth and survival in order to minimize PC levels. Maximizing growth and survival in order to minimize PC can be accomplished by finding adequate refuge or adopting alternate feeding strategies such as feeding at night (Fraser and Metcalfe 1997; Alanara et al. 2001), when dominant fish have presumably seeked refuge for the night. Morphological Strategies Like in mammalian taxa (Milton 1981), some juvenile salmonids may have larger or longer digestive organs, increasing assimilation efficiency by increasing residence time of food in the gut (Nicieza et al. 1994). This would be beneficial during a period of low food as less food may be required to achieve an equal caloric intake due to an increase in assimilation efficiency (ability to extract more nutrients out of the food). A shorter residence time may be advantageous during high food (Sibly 1981) as this may lead to a higher energy assimilation rate by maximizing the amount of food past the gut even though nutrients may not be extracted to the same degree as with a high efficiency  8  gut. This is because digestion may be a bottleneck limiting growth by reducing food consumption even though food is ad libitum (Booth 1990; Hart and Gill 1992). Morphology associated with body shape may also be a strategy to minimize energy expenditure for holding in high velocity habitats where flux of prey is high. Body shape has been shown to influence swim performance (Webb, 1984) and to differ between wild and hatchery fish (Taylor, 1986). It has also been shown to be highly variable (Taylor and McPhail 1985) and influenced by habitat hydraulics and life histories (Riddell and Leggett, 1981; Beacham, 1985; Claytor and Verspoor, 1991). A streamlined body and short median fins has been shown to enhance prolonged swimming performance as measured in an UCrit swim performance test, whereas a deeper body shape improves burst swimming performance (Webb, 1982; Taylor and McPhail, 1985; Baumgartner et al 1988). Therefore a streamlined body may be advantageous and minimize energy costs associated with holding in a riffle habitat whereas a deeper body may be more advantageous for holding in a pool habitat as it facilitates rapid turning and acceleration needed for foraging on invertebrates at the surface of pools (Bisson et al. 1988). Oxygen Consumption Rate Measures (Respirometry) Measuring SMR was one of the key metrics used in this thesis to quantify physiological differences between species. Standard metabolic rate is difficult to measure directly (Sloman et al. 2000) although oxygen consumption of resting fish in a post absorptive state is thought to be a close approximation and has been used in a number of studies (Cutts et al. 1998, 2002; Finstad et al. 2007; Lahti et al. 2002; McCarthy et al. 2000). Throughout this thesis several systems were built and techniques  9  applied to accurately estimate SMR, with the pros and cons of these different approaches considered below. There are three common ways of measuring oxygen consumption which include closed respirometry, flow through respirometry and intermittent flow through respirometry, which were all applied during my thesis. The technique used is highly dependent upon characteristics of the study species. For example closed respirometry may be suitable for estimating SMR in docile, sedentary taxa such as sculpins, but not salmonids that are active and prone to spontaneous bursts of activity which degrade accuracy of SMR estimates due to spikes in oxygen consumption. Therefore a considerable amount of time was taken in determining proper methodology for measuring oxygen consumption and ultimately estimating SMR in juvenile salmonids. Closed Respirometry Closed respirometry is probably the simplest of the three methods and was initially used for measuring SMR. Closed respirometry is measured by enclosing a fish in a respirometer and allowing the fish to acclimate in the respirometer overnight, ideally under adequate water flow and oxygen saturation conditions. The next morning an oxygen probe is placed into the respirometer and flow is then stopped. Once the flow is stopped the respirometer becomes static and the oxygen tension inside the respirometer drops as the fish consumes oxygen. The rate at which oxygen tension decreases inside the respirometer can then be used to calculate a metabolic rate. A number of problems were found with using this method for measuring SMR. Juvenile salmonids were very skittish and prone to spontaneous bursts of activity likely associated in part with declining oxygen and lack of water flow in the closed  10  respirometer. Therefore over the short term in which oxygen consumption is measured using closed respirometry significant variation and inaccuracies in SMR were found (Figure 1.1). Although, a larger volume respirometer could be used to prolong the decrease in oxygen consumption measure and minimize the effect of spontaneous activity, significant error associated with bacterial oxygen consumption and oxygen concentration gradients due to inadequate mixing inside the respirometer may be introduced (Steffensen 1989). Flow-through Respirometry Flow- through respirometry involves introducing the fish into a respirometer and allowing the fish to acclimate within the respirometer under a continuous flow. In order to obtain an oxygen consumption measure the flow into the respirometer is reduced until approximately a 10% reduction in oxygen tension between the inlet water and the outlet water can be measured. By measuring the difference between the oxygen tension of the inlet water and the outlet water and the flow rate of water, an oxygen consumption measure can be determined. This method although difficult initially to determine proper flow rates of water proved to be superior as measures were repeatable and allowed for accurate determinations of SMR (Figure 1.1). This method allows for oxygen consumption measures to be determined continuously over very long periods of time due to the constant supply of oxygen saturated water through the respirometer and allowed easy discrimination between true resting oxygen consumption rates (SMR) from spontaneous bursts of activity as the oxygen tension of the water remains relatively stable when measurements are initiated unlike closed respirometry (Figure 1.2).  11  Intermittent Flow-through Respirometry Intermittent flow-through respirometry has been described by Steffensen (1989) as the best method for determining SMR, due to ease of use compared to flow-through respirometry. It involves enclosing a fish in a respirometer and allowing the fish overnight to acclimate in the respirometer ideally under adequate flow just like with any method of respirometry. In contrast to other methods, intermittent flow-through respirometry uses a solenoid connected to a computer in order to periodically shut off water flow into the respirometer. Once this occurs, oxygen consumption measures are then taken and calculated in the same manner as closed respirometry, where SMR is based on the rate in which oxygen tension decreases over time. With intermittent flowthrough respirometry however, the respirometer is re-opened to air saturated water, before oxygen declines to stressful levels, and brought back to oxygen saturation. The measurement cycle is repeated by stopping flow in order for an oxygen consumption measure to again be taken. This can be repeated over very long time periods which allows for accurate determination of SMR. This method was used when measuring MMR in the swim tunnels (Loligo Systems, Denmark) as it allowed oxygen consumption measures to be taken intermittently throughout swim performance trials as water velocity was sequentially increased. This method would also work very well for measuring SMR, however, the intermittent flow through system used in my study needed to be manually controlled as opposed to a computer generated solenoid described above, making it inconvenient to use intermittent respirometry over night for long periods when fish were at rest.  12  Thesis Objectives Although extensive research with Atlantic salmon has found considerable variation in SMR and a possible link between life history strategies, physiology and growth, the mechanism(s) underlying adaptive trade-offs and life history strategies of juvenile salmonids remain unclear. The objectives of this thesis were: (1) To examine the relative importance of food consumption and individual differences in SMR to observed variation in SMR, and test the causation between SMR and food ration (Chapter 2); (2) To understand the differences in physiology between juvenile hatchery and wild salmonids underlying any adaptive tradeoffs between growth and other performance attributes at high and low food, and determine whether differences in swimming performance matched differences in habitat use between steelhead and coho salmon (Chapter 3); (3) To understand the allometric effects of body size on relative growth rates of fish in dominance hierarchies, and the effects of increasing ration on the relative growth rates of dominant fish and subdominant fish in semi natural stream channels (Chapter 4).  13  Figure 1.1. Comparison of SMR values for individual coho salmon measured using closed (solid) and flow-through respirometry (open). Oxygen consumption measures were taken within a week of each other on coho salmon that were individually separated in aquaria and fed an equivalent food ration prior to both trials.  14  Figure 1.2. The relationship between oxygen consumption rate and time for a single coho salmon measured over an 18 hour period using flow through respirometry. Note: SMR was achieved for a period between 8-10 hours in between two bouts of spontaneous activity that correspond to the lights in the chamber being turned off and on.  15  Chapter 2: Effects of Food Ration and Identity on SMR: Is Dominance a Cause or Consequence of High Metabolic Rate? Introduction Standard metabolic rate (SMR) is the minimal maintenance metabolic rate of ectotherms in a post absorptive state (Beck and Gopp 1995; Priede 1985). SMR, (usually measured in terms of oxygen consumption), is an integrated measure of the physiological energy expenditures involved in tissue maintenance and organism homeostasis, and is analogous to basal metabolic rate in endotherms. Standard metabolic rate has been shown to vary up to half an order of magnitude between individuals for species ranging from fish (Cutts et al. 2002; Finstad et al. 2007; Alvarez et al. 2006) to mammals (Boily 2002; Speakman et al. 2004) to birds (Williams and Vezina 2001) with differences that are repeatable over time (McCarthy 2000; Seppanen et al. 2010; Bech et al. 1999). The consistency of differences in metabolic rate between individuals and the apparent inability of individuals to regulate their SMR (Priede 1985) has led many researchers to conclude that differences in individual SMR are fixed (i.e. genetic), suggesting that this level of individual variation in SMR is adaptive. Differences in SMR within salmon and trout populations have been linked to variation in individual growth, behaviour and life history strategies (e.g. timing of smolt migration; (Metcalfe et al. 1995; Cutts et al. 1999; Forseth et al 1999; McCarthy 2001; Finstad et al. 2007). Metcalfe et al. (1995) and Cutts et al. (2002) have shown that Atlantic salmon with a higher SMR were more likely to be dominant and that SMR was correlated with aggression, presumably due to the higher energetic demands associated with a higher maintenance metabolism. While it is clear that differences in SMR are associated with differences in life history and performance (e.g. growth), it remains 16  unclear whether SMR is the cause of these differences (i.e. higher SMR is genetically fixed), or a consequence of them. For example, there is evidence that SMR increases with ration (O’Connor et al. 2000) and since SMR is an integrated measure of anabolic and catabolic activity and an increase in food ration and ultimately, food consumption and growth could conceivably elevate SMR. Here I provide evidence that variation in SMR is influenced by differences in food ration, which commonly varies in nature between habitats (e.g. pools vs. riffles; Rosenfeld and Boss 2001) and with rank in dominance hierarchies, where the dominant fish commonly receives more food (Sloman and Armstrong 2002). I propose that, rather than high SMR being a cause of dominance, high SMR may simply be a consequence of high food consumption associated with a dominant rank in a competitive hierarchy. In this chapter I examine the relative importance of food ration on SMR in an attempt to compare individual differences in SMR to observed variation in SMR due to food consumption. Oxygen consumption measures of separately reared young of the year (YOY) coho salmon placed on varying food rations over a period of 44 days was used. Objectives of the analysis were to determine (1) whether SMR is elevated under high food independent of specific dynamic action (SDA), (2) the size of a food consumption effect on SMR relative to intrinsic differences in SMR between individuals, and (3) whether juvenile coho salmon are able to down regulate their SMR at very low food levels. Methods Fifty young of the year (YOY) coho salmon were obtained by seining and minnow trapping in Chapman Creek near Sechelt, British Columbia, Canada (Universal  17  Transverse Mercator (UTM) 448100E 5478100N). Fish were transported back to the University of British Columbia and housed indoor under quarantine in a temperature controlled environment chamber at 14 °C. Fish were housed in a 350L flow-through tank supplied with de-chlorinated tap water and acclimated together for approximately three months prior to experimentation. During the acclimation period fish were fed ad libitum a combination of Aqua Pride Trout 48:16 pellets from (Unifeed, Okotoks, Alberta) with the following composition: crude protein 48.0%, crude fat 16.0%, crude fibre 5.0%, sodium 0.40%, calcium 2.5%, phosphorus 1.5%, supplemented with a natural diet of earth worms. Forty fish were weighed and measured to the nearest 0.01g and 0.1cm, respectively (2.6 + 0.5g SD average weight) and stocked individually in either side of 114L tanks divided into two chambers to optimize available space. Tanks were divided in half by a 1mm mesh partition, which allowed fish to see each other and permit circulation of water in the tank. Food however, was too large to pass through the screen divider ensuring each fish received the quantity of food appropriate for their treatment. Fish were housed separately to prevent the development of dominance hierarchies which are inevitable among fish held together, and can lead to large differences in food consumption, even at a fixed ration, as well as associated stress and agonistic behaviours which have all been shown to elevate SMR (Cutts et al. 1998; Metcalfe et al. 1995). Housing fish individually allowed me to control for these factors and isolate the effect of food on SMR. An Aqua clear 200 filter was placed in the middle of the divider, and aquaria were vacuumed siphoned weekly followed by a 50% water change to maintain water quality. A large rock with green flag tape attached (artificial plant) was added for  18  enrichment. Fish were acclimated to a 12L: 12D cycle for two weeks after stocking and observed closely to ensure that fish were feeding and behaving normally with no signs of stress associated with being housed individually. All experimental protocols were approved by the University of British Columbia Animal Care Committee in accordance with the Canadian Council on Animal Care (AUP A09-0051). Feeding Treatments After the two week acclimation period, fish were again measured to the nearest 0.01g and 0.1cm and placed on an initial maintenance food treatment (Figure 2.1). All fish were fed an initial baseline ration of 40% of satiation once daily based on their body weight using the satiation equation from (Brett 1971) for juvenile sockeye salmon ( F ood consumption (% body dry weight) = 14.5 – (5.15 *Log10 (Wet weight of fish (g)). This was done to ensure that all fish received the same level of food based on the allometry of food consumption (smaller fish consume less food than larger fish to achieve an equal degree of fullness, but satiate at a much higher percent of body weight). Food treatments consisted of 75% natural diet (chopped up earth worm) and 25% pellets. Standard metabolic rate was measured after the 14 day baseline period and 20 fish were then placed on a low food diet which consisted of 1% of body wt.·day-1 and 20 fish were placed on a high food diet (satiation) fed once daily based on the satiation equation described above (Brett 1971). After an additional 10 days on each ration, SMR, weights and lengths were measured on all fish. Food treatments were then switched, with the high food group placed on the low food ration and the low food group placed on the high food ration for a further 10 days (Figure 2.1). Standard metabolic rate, weights and lengths were again measured at the end of the treatment. To assess the ability of fish to down  19  regulate metabolism in the absence of food, all fish were then placed on a starvation treatment for 10 days, at the end of which SMR, weights and lengths were again measured (Figure 2.1). Measuring Standard Metabolic Rate Aquaria holding fish were vacuumed siphoned to remove any food and debris the night before fish were placed in respirometry chambers to ensure that fish had sufficient time to evacuate their guts and were unfed 20 hours prior to oxygen consumption measurements; 20 hours post-feeding has been shown to be adequate for the specific dynamic action (SDA) response to subside (McCarthy 2000; Cutts et al 2002). Specific dynamic action is an elevation in metabolic rate from the increased energy demands associated with digestion, immediately following a meal (Kleiber 1961; Alsop and Wood 1997; Jobling 1981). The morning following aquaria cleaning, oxygen consumption measures were taken using flow-through respirometry in the same environmental chamber at 14 °C. Fish were placed into glass respirometry chambers in which water flowed at a constant rate for a minimum of 10 hours prior to experimentation. Respirometers were constructed of 13.0 cm lengths of 2.8 cm diameter glass tubing, covered in black plastic to minimize fish activity during respirometry measurements (Cutts et al. 2002). Measurements of background oxygen consumption for individual respirometry measurements were found to be negligible. Oxygen consumption was measured continuously overnight using a fiber optic oxygen meter (Foxy-Or125 oxygen sensors; Ocean Optics) that was calibrated daily using nitrogen and aerated water at 14 °C. An air-stone in the header tank of the respirometer apparatus ensured oxygen saturation of  20  water entering the respirometry chamber. Water from the header tank flowed through a gravity feed to a splitter box with a magnetic stir bar to prevent formation of oxygen gradients prior to water entering the flexible tubing feeding the three parallel respirometers. Flow to each respirometer was adjusted using micro valves to ensure that there was at least a 10% drop in oxygen tension between the inlet and outlet of the respirometer. Flow was measured by collecting the outlet water for 60s and weighing to the nearest 0.01g periodically throughout the experiment. The rate of oxygen consumption was determined using the following equation (Ege and Krough 1914): MO2(whole)=Vw∆Cw02 / bw where bw is the mass of the fish, Vw is the flow rate of water through the respirometer and ∆Cw02 is the difference between the oxygen tension of inflow water into the respirometer and the oxygen tension of the outflow water. Concentration of oxygen was calculated by taking Po2 (partial pressure of oxygen) corrected for barometric pressure and multiplying by αO2 (umol L-1 torr-1), the solubility coefficient at the observed temperature. Continuous oxygen consumption measurements were averaged over half hour periods and plotted graphically to discriminate periods of complete resting from spontaneous activity, which appeared as distinct spikes in SMR. The lowest one hr duration oxygen consumption value over the full oxygen trace was used as the estimate of SMR. Data Analysis Fish mass and length were measured at the end of each food treatment, at the same time as SMR. Instantaneous growth rates of fish (percent per day) over each of the  21  four treatment intervals were calculated as {[loge(final mass)- loge(initial mass)]/duration} x 100 (Ricker 1975). Growth, mass and SMR data was log transformed to linearalize data and meet assumptions of normality and homogeneity of variance. I tested for the effects of food, fish mass, fish identity, and prior food treatment on growth and SMR using analysis of covariance (ANCOVA), including interactions between food and mass, identity and mass, and identity and food. I used mass-specific values for SMR during the analysis because mass did not differ significantly between treatments throughout the experiment (Figure 2.2). I treated fish identity as blocks (n=40) to determine whether individual fish had consistently high or low SMR values over the multiple treatments. Interaction terms and independent variables that were not significant at p>0.05 were removed from the model. I then evaluated whether there was a difference in SMR between fish on a high and low food treatment using a t-test. A t-test was appropriate because all fish were on the same ration prior to half being placed on high and low food rations. I regressed SMR on growth using all data combined (irrespective of food treatment) to test for any apparent relationship between metabolism and growth for the entire data set. All analysis was conducted using R version 2.8.1 statistical software. Results Mean weight of coho salmon (+ 95% CI) at the beginning of the experiment was 4.29 + 0.81 g and 4.63 + 0.52 g for the two groups and did not differ between groups throughout the experiment (Figure 2.2). There was no significant effect of individual fish identity (F 7, 72  = 2.95, p=0.09) or weight on growth (F 7, 72 = 0.78, p =0.38). Growth was  22  significantly lower in the low food (F 5, 74 = 9.17, p=0.03) and starvation treatments (F 5, 74  = 27.83, p <0.03) than fish on the high food treatment (Figure 2.3).  There was no significant effect of individual fish identity on SMR (F 11, 68 = 0.005, p =0.92), providing no support for intrinsic differences in SMR between individuals. There was also no significant effect of fish mass or prior treatment on SMR. I did find however that SMR was lower for fish on a low food treatment and higher for fish on a high food treatment (t= 3.47, df=19, p <0.003). Standard metabolic rate of fish on the high food treatment was also significantly higher than fish on the starvation treatment (F 5, 74 = 22.90, p <0.001; Figure 2.4). The food by prior treatment interaction (Fig. 4, third set of rations at 20 days) was marginally insignificant for mass-specific SMR (F 5,74 = 2.78, p =0.10), but significant when analyzed as whole body SMR (F 5,74 = 5.37, p =0.02) Growth rate was significantly correlated with SMR for the whole data set (r2= 0.17, p=0.001, n = 20), indicating that fish with a higher SMR had a higher growth rate (Figure 2.5). To determine whether my ration levels achieved the desired degree of satiation, I back calculated estimated food consumption based on observed growth rate and temperature during the experiment using a bioenergetic model for coho salmon from Sullivan et al. (2001). Coho salmon on intermediate food ration were estimated to be growing at 51% satiation whereas coho salmon at high and low food were growing at 69% and 30% of satiation, respectively. Discussion My results demonstrate that the quantity of food consumed directly affects SMR of juvenile coho salmon, and indicates that high food consumption is a cause of elevated  23  SMR rather than a consequence of it. Not surprisingly, I also found that higher ration resulted in higher growth rate, and consequently SMR and growth were positively correlated with fish on the high food treatment having the highest SMR and fish on the starvation treatment the lowest, consistent with O’Connor et al. (2000). Because higher food consumption and growth requires an up regulation of anabolic pathways even when digestion is not taking place, it is not surprising that increased food consumption and growth elevates SMR independent of the costs of digestion (SDA). This positive relationship between SMR and growth has also been demonstrated in a number of earlier studies under high ration (Cutts et al. 1998; Yamamoto et al.1998), but my results indicate that the apparent relationships between SMR and growth illustrated in Figure 2.5 is spurious. Many earlier studies have documented large differences in SMR between individual juvenile salmonids (Cutts et al. 2002; Finstad et al. 2007; Alvarez et al. 2006) which were shown to be consistent and repeatable over time (McCarthy 2000; Seppanen et al. 2010). In contrast with these studies, I could not detect a significant effect of individual fish identity on SMR when fish were held separately and fed individual rations in the absence of competition. This may be due in part to low statistical power to detect individual differences in this study. However, my results indicated that any individual (fixed genetic) differences in SMR are relatively small compared to the effect of ration, which was pronounced. Previous research has shown that salmonids with a high SMR tend to be more dominant and aggressive relative to fish with low SMR (Metcalfe et al. 1995; Cutts et al. 2002). Dominant salmonids generally out-compete lower SMR conspecifics for  24  preferential feeding territories and access to food. This correlation between dominance and SMR has lead researchers to identify high SMR as a cause of dominance (e.g. Alvarez et al. 2006). Most metabolic studies however, are carried out in the laboratory with fish housed in communal tanks, often at high densities leading to competition for food, particularly at rations below satiation. Under these conditions dominance hierarchies readily form, leading to large variation in food consumption, growth, and body size (Cutts et al. 1998; McCarthy et al. 1992) with disproportionate food consumption and growth by more aggressive dominant fish. When fish were reared individually in this study, preventing the establishment of dominance hierarchies and allowing more precise control of individual ration, I showed that SMR was elevated or lowered depending on food consumption, and I was not able to detect effects of individual identity on SMR. While there likely is a genetic component to individual variation in SMR, this result suggests that increased food consumption in dominance hierarchies is likely a driver of much of the individual variation in SMR observed in earlier studies. Since dominance hierarchies are typically size dependent and stable over time, they should result in consistent and repeatable measures of high or low SMR for individual fish. Therefore I propose that a high SMR for fish reared in communal tanks may simply be a consequence of high food consumption associated with a dominant rank in a competitive hierarchy, rather than high SMR being a cause of dominance. It is likely that the correlation of SMR with food ration we observed in juvenile coho also occurs in other salmonid species and potentially other vertebrate species as well, and may be relevant to understanding interactions between growth and metabolism in diverse areas of ecology. For instance, there is debate concerning the role of  25  metabolism in mediating growth depression under predation risk. A number of studies (e.g. Steiner and Van Buskirk 2009) have demonstrated a depression in metabolic rate in the presence of long-term exposure to predators. This is usually attributed to a selected physiological response for minimizing the costs of anti-predator behavior, such as increased shelter use and reduced foraging by the prey species. However, if the correlation between SMR and food consumption I observed is widespread, then decreased SMR and growth in the presence of predators may simply be a result of decreased food consumption associated with reduced foraging activity. My results also show that juvenile coho have the ability to down regulate their SMR according to food availability and ultimately food consumption. If SMR were inflexible, salmonids with a high SMR would rapidly deplete their energy stores during prolonged food deprivation, as experienced in freshwater during winter in temperate climates. Instead salmonids seem to show a capacity to regulate their SMR in order to maximize growth during periods of high food and minimize weight loss during periods of food deprivation (e.g. O’Connor et al. 2000). There is, however no evidence that the reduction in SMR under food deprivation is an active suppression of metabolism at low food; rather, decreased metabolism is consistent with a simple reduction in anabolic and catabolic physiology associated with reduced food consumption and growth. The interaction between ration and prior treatment (Figure 2.4, third set of rations at 20 days) indicates an asymmetry in the rate of SMR decrease and elevation depending on prior fish condition. Standard metabolic rate increased more rapidly with elevated ration than it declined with reduced ration, suggesting that there is a lag in down regulation of anabolic and catabolic pathways that elevate SMR following a high food  26  ration. Juvenile coho growth in the high food treatment was considerably lower than their physiological maximum. Back calculated food consumption based on a bioenergetic model for coho (Sullivan et al. 2001) indicated that fish in the high ration treatment were well below satiation, despite the intention to feed them to excess. The reasons for lower than expected growth and food consumption, are unclear because fish did not appear stressed, but food consumption might have been higher had fish been fed more than once daily. Were fish consuming even more in the high ration however, the observed difference in SMR between food treatments would likely have been even more pronounced. This study clearly demonstrates that food consumption affects SMR independent of SDA and that juvenile salmonids regulate their SMR in proportion to consumed ration. I propose that microhabitat and dominance-mediated effects on food consumption as well as genetic differences both play a role in individual variation in SMR. Therefore I suggest that studies involving SMR need to be cautious about asymmetric feeding due to formation of dominance hierarchies in communal tanks, as disproportionate feeding may contribute more to variation in SMR than generally thought, and may explain a significant amount of individual variation in SMR that is usually attributed to intrinsic (genetic) factors.  27  High food (10 days)  High food (10 days)  Starvation (10 days)  Intermediate food (40% of satiation diet) (14 days)  Low food (1% of body wt.day-1) (10 days)  Low food (1% of body wt. day-1) (10 days)  Figure 2.1. Schematic showing the sequence of food treatments throughout the experiment  28  Figure 2.2: Mass of juvenile coho salmon over the length of the experiment. Grey and black circles represent different groups of coho salmon placed on contrasting food treatments as illustrated in figure 1. Note: there are no significant differences in mass throughout the experiment (error bars represent 95% CI)  29  Figure 2.3: Instantaneous growth rate of juvenile coho salmon over the length of the experiment. Grey and black circles represent different groups of coho salmon placed on varying food rations. Note: fish on the high food rations had a significantly higher growth rate than fish on the starvation and low food rations throughout the experiment (error bars represent 95% CI).  30  Figure 2.4: Standard metabolic rate (SMR) of juvenile coho salmon over the length of the experiment. Grey and black circles represent different groups of coho salmon placed on varying food rations. Note: fish on the high food ration had a significantly higher SMR than fish on the low food ration for the 10 day interval. Fish on the high food ration (throughout the experiment) had a significantly higher SMR than fish on the starvation treatment (error bars represent 95% CI).  31  Figure 2.5: The relationship between standard metabolic rate (SMR) and growth rate of juvenile coho salmon over the length of the experiment (data for all treatments combined). Note: the correlation between growth rate and SMR is significant at p< 0.05.  32  Chapter Three: Variation in Metabolic Rate between Juvenile Coho Salmon and Steelhead Trout: Insight into the Physiological Basis of Habitat Partitioning and Adaptive Tradeoffs of Juvenile Salmonids Introduction Adaptive trade-offs are fundamental to the evolution of diversity and the coexistence of species (Schluter 1995). Adaptive trade-offs occur when differentiation at one end of a resource or environmental gradient precludes efficient exploitation of resources elsewhere. For example, morphological adaptation to feed on benthic vs. planktonic food resources (gill raker spacing, body morphology, mouth orientation, etc) is one of the most common adaptive trade-offs among freshwater fishes (Schluter 1993; Robinson and Wilson 1994), and matches habitat and resource use along a benthicpelagic gradient. In contrast with morphological traits, physiological adaptations are usually cryptic and are consequently more poorly documented but likely as important in ecological differentiation (Garland and Adolph 1991; Djawadan et al. 1997; Bochdansky et al. 2005; Lee et al. 2003; Killen et al. 2007). Although some physiological adaptations represent direct responses to simple selection gradients (e.g. hypoxia tolerance), others are the result of selection on complex life history traits like growth or cost of transport. Standard metabolic rate (SMR) is a key physiological attribute that represents the integration of a broad suite of metabolic pathways and processes and is known to vary widely in animals (Cutts et al. 2002; Boily 2002; Speakman et al. 2004). Because SMR is positively correlated with growth (Metcalfe et al. 1995; Alvarez and Nicieza 2005) and constitutes a significant proportion of an animals energy budget (Finstad et al. 2007), variation in  33  SMR has been suggested as a major adaptive tradeoff between species (Steyermark 2002), ecotypes (Alvarez et al. 2006), and populations (Garland and Adolph, 1991) along productivity gradients (Mueller and Diamond 2001). A high SMR may facilitate growth when resources are abundant, but elevated metabolic demands could result in decreased growth when resources are scarce (Mueller and Diamond 2001), suggesting that higher metabolic rates should be selected for in environments where resources are abundant and growth rates are high (Steyermark 2002). However, the role of SMR in adaptive tradeoffs related to growth along productivity gradients remains unclear. Maximizing energy intake (food consumption) while minimizing energy expenditures (costs of foraging and transport) is an additional trade-off confronting organisms. While SMR integrates adaptations related to growth and maintenance, the costs of foraging represent active metabolism and the optimal tradeoff among active metabolism, resting metabolism, and energy intake may also change along a resource (productivity) gradient. For instance, taxa adapted to abundant resources may be energy maximizers with higher optimal growth, food consumption, and transport costs, while taxa adapted to habitat with less abundant resources should be energy minimizers, with lower optimal growth, energy intake, and costs of energy acquisition (Arendt and Wilson 1997; Arendt 1997). Juvenile steelhead (Oncorhynchus mykiss) and coho (Oncorhynchus kisutch) salmon occur in sympatry in many coastal streams of northwestern North America and represent model species for exploring the relationships between growth, metabolism, and performance of fish adopting energy maximizing vs. cost minimizing strategies. Both species feed on aquatic and terrestrial invertebrates drifting in the water column, but  34  partition habitat differently in the wild. Coho forage near the surface (Fraser 1969; Johnston 1970) and occupy pools (Hartmann 1965; Bisson et al. 1988; Bugert and Bjorn 1991) while steelhead hold closer to the substrate (Fraser 1969; Johnston 1970) and occupy faster velocity riffle habitat (Hartmann 1965; Bisson et al. 1988; Bugert and Bjorn 1991), although both species prefer and grow faster in deep, low velocity pools than in riffles (Hartman 1965, Quinn and Peterson 1996, Young 2001). In many of these freshwater systems coho emerge earlier than steelhead and therefore take up residence in the preferred low velocity pools (Young 2004), where their larger size and prior residence allows them to effectively displace steelhead into riffles (Young 2004; Hartmann 1965; Bisson et al. 1988; Bugert and Bjorn1991), leading to habitat partitioning. In addition to partitioning habitat along a velocity and depth gradient, coho and steelhead differ morphologically in a manner consistent with their habitat use. Coho salmon have a laterally compressed body form with long median fins that facilitates rapid acceleration and turning which would be advantageous for capturing invertebrate drift at the surface of pools (Bisson et al. 1988). Steelhead however, have a longer more cylindrical body form and shorter median fins that are best suited for holding in faster velocity riffle habitat as it minimizes drag (Bisson et al. 1988). Previous studies also indicate that steelhead have a higher maximum growth rate and food consumption than coho, which is consistent with maximizing energy intake at high foraging costs (Sullivan et al. 2001;Young 2001; Hartman 1965), whereas coho have a lower maximum growth rate and food consumption in pool habitats where costs of swimming are reduced (Sullivan et al. 2001, Young 2001).  35  Adaptive tradeoffs along a productivity gradient also provide a framework for understanding the effect of hatcheries on physiological attributes that affect growth and survival. To better understand the effects of human selection on growth and metabolic tradeoffs in these species, I compared wild steelhead and coho (McNab Creek, with no history of hatchery influence) to a partially domesticated stock of coho and steelhead (Chapman Creek, where a hatchery has been operating for over 20 years). Because hatcheries represent a resource-rich environment that selects for higher juvenile growth rates (Gross 1998), I hypothesized that potential differences in growth, metabolism, and swimming performance between juvenile coho and steelhead would be reduced (relative to wild stocks) in the populations subject to a strong hatchery influence. To understand the potential significance of variation in SMR to growth, differentiation between species and patterns in habitat use along productivity gradients that correspond to an energy maximizing vs. cost minimizing strategy, I tested whether there were predictable differences in metabolism and performance between juvenile coho salmon and steelhead trout. In addition to growth and SMR, I also measured and compared metrics of active metabolism and swimming performance, including maximal metabolic rate (MMR) and aerobic scope (AS) for both wild and hatchery juvenile steelhead and coho salmon under high and low food ration. Aerobic scope is the difference between SMR and MMR and represents the metabolic scope for activity, with a high AS and MMR being correlates of increased athletic ability and swim performance (Killen et al. 2007; Plaut 2001). My three main objectives in this study were (1) To understand the differences in metabolism underlying any adaptive tradeoffs between growth, SMR and swim performance at high and low food levels, (2) to determine  36  whether differences in swimming performance matched differences in habitat use associated with energy maximizing vs. cost minimizing strategies, and (3) to determine whether differences in growth, metabolism, and swim performance between coho salmon and steelhead trout are reduced under selection in a high productivity hatchery environment. My expectations were that steelhead and coho would exhibit a tradeoff between growth and metabolism at high and low food ration, with steelhead growing faster than coho at high food abundance but more slowly at low food. Additionally, I expected that steelhead would exhibit a higher SMR than coho, and a higher swim performance associated with adaptation to swimming in a faster velocity habitat as steelhead are commonly found in riffles. Methods Twenty hatchery steelhead and coho salmon were obtained from Chapman Creek fish hatchery in Sechelt, BC, Canada (UTM 448100E 5478100N) on Aug. 12, 2009. Average initial weights of hatchery steelhead and coho salmon were 1.25 + 0.36g SD and 2.97 + 0.73g SD, respectively. Twenty wild steelhead and coho salmon were captured using a combination of seining, minnow trapping and dip netting from McNab Creek, Port Mellon, BC, Canada (UTM 471738 E 5490574N) under animal capture and transfer permit # 11908 on Aug. 28 2009. The fish collected from McNab Creek are presumed to be of wild origin because there is no history of hatchery releases into the creek. Average weights of wild steelhead and coho salmon were 0.74 + 0.29g SD and 1.95 + 0.60g SD, respectively. Fish were transported to Chapman Creek hatchery and tagged with a visible implant elastomer tag (Northwest Marine Technology, Inc.) so that SMR, MMR, AS,  37  UCrit and growth data could be collected on individuals. Because hatchery fish were larger and available earlier than wild fish, the hatchery fish growth experiment was set up 16 days earlier than the wild fish growth experiment. Fish were held indoor at Chapman Creek hatchery in two approximately 500 L flow-through aluminum troughs supplied with water from Chapman Creek. To optimize space, troughs were divided into four adjacent compartments using 1mm mesh partitions, which allowed hatchery and wild fish to be separated but reared in the same trough. Food however, was too large to pass through the screen divider ensuring fish received the appropriate quantity of food. During the pre-treatment acclimation period, fish were fed ad libitum with Aqua Pride trout 48:16 pellets (Unifeed, Okotoks, Alberta; crude protein 48.0%, crude fat 16.0%, crude fibre 5.0%, sodium 0.40%, calcium 2.5%, phosphorus 1.5%). Average temperature for the hatchery fish growth experiment was 12.3°C + 2.0 SD (Aug. 26-Sept. 20) and 8.30°C + 1.15 SD for the wild growth experiment (Sept 21Oct. 11), which took place later because of the smaller size at age of wild fish. Hatchery fish were acclimated for approximately 2 weeks before food treatments were applied, whereas wild fish were acclimated for approximately 3 weeks to ensure wild fish were adequately eating pelleted food. Fish were held under a 12L:12D light cycle. All fish were weighed and measured to the nearest 0.01g and 0.1cm and were healthy and feeding prior to experimentation. Experimental Protocols My experimental design included high and low food treatments for both wild and hatchery fish of both species (n = 8 experimental units in total). After the initial acclimation period, fish were again measured to the nearest 0.01g and 0.1cm and placed  38  on either high food (satiation) or low food (1% body wt day-1 followed by seven days of starvation) ration. Seven days of starvation was used in the low food treatment as this has been shown to be enough time to elicit a reduction in SMR (chapter 2). High food fish were fed three times daily throughout the experiment and the low food fish were fed once in the morning throughout the experiment prior to the seven day starvation period. I estimated a satiation ration using the equation from (Brett 1971) for juvenile sockeye salmon (Food consumption (% body dry weight) = 14.5 – (5.15 *Log10 (wet weight of fish (g)) to ensure that all fish received the same level of food based on the allometry of food consumption (smaller fish consume less food than larger fish to achieve an equal degree of fullness, but satiate at a much higher percent of body weight). I also used a bioenergetic model (Sullivan et al. 2001) to back calculate estimated food consumption based on growth at the observed rearing temperatures for both coho and steelhead as a form of a posteriori validation of ration. Based on these estimates coho and steelhead at high food were growing at a rate consistent with satiation, thereby controlling for any potential differences in maximum food consumption between species. All experimental protocols were approved by the University of British Columbia Animal Care Committee in accordance with the Canadian Council on Animal Care (AUP A09-0051). Analytical Protocols Measuring Standard Metabolic Rate (SMR) Troughs were vacuumed siphoned to remove food and debris the night before fish were placed in respirometry chambers. This ensured that fish were unfed for 20 hours prior to oxygen uptake measurements, and had sufficient time to evacuate their guts; 20  39  hours post feeding has been shown to be adequate for the specific dynamic action (SDA) response to subside (McCarthy 2000; Cutts et al 2002). Specific dynamic action is an elevation in metabolic rate from the increased energy demands associated with digestion, immediately following a meal (Kleiber 1961; Alsop and Wood 1997; Jobling 1981), and is generally not considered part of SMR. Respiration was measured using flow-through respirometry by placing individual fish into glass respirometry chambers in which water flowed at a constant rate, nine hours prior to oxygen consumption measurements. Respirometers were constructed of 13.0 cm lengths of 2.8cm diameter glass tubing, covered in black plastic to minimize fish activity during respirometry measurements (Cutts 2002). Glass respirometers were used to prevent possible issues with use of plastics (e.g. gas permeability; Stevens 1992). Measurements of background oxygen consumption for individual respirometry measurements were found to be negligible. After a minimum nine hour acclimation in the respirometer, oxygen consumption was measured continuously overnight using a fiber optic oxygen probe (Foxy-Or125 oxygen sensors; Ocean optics) calibrated daily using nitrogen and aerated water at 13°C. An air-stone in the header tank of the respirometer apparatus kept inflow water fully saturated with oxygen. Flow to each respirometer was adjusted using micro valves to ensure that there was at least a 10% drop in oxygen tension between the inlet and outlet of the respirometer. Flow was measured by collecting the outlet water for a period of 60s and weighing to the nearest 0.01g periodically throughout the experiment. Temperature was held constant at 13.1°C + .43 SD in this and all other metabolic protocols, as continuously recorded using a submersible temperature logger.  40  The rate of oxygen consumption was determined using the following equation (Ege and Krough 1914): MO2=Vw∆Cw02 where Vw is the flow of water through the respirometer and ∆Cw02 is the difference between the oxygen tension between water entering and leaving the respirometer. Concentration of oxygen was calculated by correcting Po2 (partial pressure oxygen) for barometric pressure and multiplying by αO2 (umol L-1 torr-1), the solubility coefficient at the observed temperature. Continuous oxygen consumption measurements were averaged over half hour periods and plotted graphically. Periods of complete resting were readily discriminated from spontaneous activity, which appeared as distinct spikes in SMR, and SMR was estimated using the lowest one hr duration oxygen consumption values observed during the respirometry trial. Measuring Maximal Metabolic Rate (MMR) After SMR was measured overnight, fish were transferred into two identical mini swim tunnels (Loligo Systems, Denmark) and acclimated for 1 hour at very low velocity (~ 0.3 BL sec-1). Black plastic was placed over the middle of the swim tunnel to allow an area of refuge where fish normally held during swimming trials. Fish were made to swim at different velocities by adjusting the speed of a small motor driving an impeller built into one end of the swim tunnel prior to oxygen consumption measures. An air stone in the holding tank of the swim tunnel kept water fully saturated with oxygen. Water was periodically exchanged to prevent build up of nitrogenous waste. Measurements of background oxygen consumption for individual respirometry measurements were found to be negligible.  41  Swim tunnels were used to swim the fish to exhaustion in order to determine MMR. Oxygen consumption of fish swam to exhaustion is believed to be a close approximation of MMR, which was measured using closed respirometry. Oxygen consumption was measured after 10 min of prolonged swimming at the desired velocity using a fiber optic oxygen probe (Foxy-Or125 oxygen sensors; Ocean optics). A reduction in oxygen tension of 20% was used as a threshold to terminate oxygen consumption measurements and flush the tunnel with oxygenated water prior to increasing the velocity of water inside the tunnel for the next oxygen consumption measure. This stepwise swimming at higher velocities was repeated until the fish began to show signs of fatigue and needed to be repeatedly encouraged to swim using a bright light or by reversing the flow. At this point oxygen consumption rate was measured until the fish could no longer swim, resulting in a decrease in oxygen consumption and termination of the swimming trial. The highest oxygen consumption rate usually corresponded to the highest water velocity in which the fish swam for a complete time interval and was used to estimate MMR. The rate of oxygen consumption was determined using the following equation: (Ege and Krough 1914) MO2=Vw.∆Cw02 ∆t where Vw is the volume of water in the respirometer, ∆Cw02 is the change in oxygen tension of the water and ∆t the time period associated with the drop in oxygen tension within the tunnel (Steffensen 1989). Concentration of oxygen was calculated as described early for calculating SMR  42  Measuring Swim Performance (UCrit) After MMR trials were completed, both fish were transferred together into a larger swim tunnel with known velocity calibrations. Both fish were then subjected to a UCrit swim performance test to assess differences in endurance swimming between species as outlined in Brett (1964). Fish were acclimated to the tunnel at a linear velocity of 0.3 BL sec-1 for a minimum of three hours prior to the UCrit test, providing adequate time for recovery (Jain et al. 1998; Kolok 1991) from the previous MMR trial that was used as a pre swim. Pre swims are commonly used to familiarize fish with swimming and have been used in a number of studies (Fangue et al. 2008; Lee et al. 2003; Taylor and McPhail 1985). Fish were swum in pairs in a Beamish-style swim tunnel (Loligo Systems, Denmark). Although fish swam in pairs, agonistic behaviours were not observed, nor were there differences in swim performance between fish swum separately or together (determined prior to experimentation on several test fish). Velocity of water inside the tunnel was controlled by a rheostat and calibrated using a flow meter. During acclimation and the swim performance test, ¾ of the swim tunnel was covered in black plastic to provide cover and lower stress of test fish. For the UCrit test water velocity was increased by 0.3 BL sec -1 every 10 minutes in a stepwise fashion as outlined by Fangue et al. (2008) until fish fatigued. Fish were considered fatigued and the test terminated if three repeated attempts to force the fish to continue swimming were not successful. UCrit was calculated using the following equation from Brett (1964): UCrit=Ui + (ti x Uii) tii where Ui is the highest speed the fish swam during the full time period, Uii is the incremental speed increase, ti is the time at which the fish swam at the final speed, and tii  43  is the prescribed period of swimming at each velocity (10 min). Due to the small size of the fish in relation to the cross sectional areas of the swim tunnel, UCrit values were not corrected for a solid blocking effect. Statistical Analyses and Calculations Instantaneous growth rates of fish (percent per day) were calculated as {[loge(final mass)- loge(initial mass)]/duration} x 100 (Ricker 1975). AS was determined by subtracting MMR from SMR. All data except UCrit were log transformed to linearalize the data and meet assumptions of normality and homogeneity of variance. Growth data was log transformed using a constant (1.60 for wild fish and 1.75 for hatchery fish) to allow transformation of negative growth values associated with the low food treatments. I tested for the effects of food, individual fish mass, and species on SMR, MMR, AS, UCrit and growth using analysis of covariance (ANCOVA), including interactions between food and mass, food and species, and mass by species. Interaction terms and independent variables that were not significant at p>0.05 were removed from each model. Absolute rather than mass-specific values for SMR, MMR, AS, UCrit and growth were used with mass as a covariate due to the large variation in fish size. Data for all food treatments and species were first analyzed together. If a significant food by species interaction was found, coho and steelhead data were separated and further analyzed independently. All analysis was conducted using R version 2.8.1 statistical software.  44  Results Mean mass of wild coho and steelhead (+ SD) at the end of the experiment was 3.89 + 0.56 g and 1.70 + 0.32 g at high food and 2.92 + 2.23g and 0.92 + 0.27 g for the low food treatment, respectively. Mean mass of hatchery coho and steelhead (+ SD) at the end of the experiment was 5.40 + 1.28 g and 3.33 + 0.60 g for high food and 3.50 + 0.87g and 1.64 + 0.60 g for the low food treatment, respectively. Mass was highly significant in all models (Table 3.1). Standard Metabolic Rate (SMR) The high food treatment substantially elevated SMR for both wild coho and steelhead (Figure 3.1, Table 3.1), but high food significantly elevated SMR only for steelhead (F 2, 10 = 5.68, p=0.04) among the hatchery fish (Figure 3.1, Table 3.1). Contrary to expectation, there was no difference in SMR between wild coho and steelhead (F 4, 22 = 2.92, p=0.10). There was, however a significant interaction between food and species for hatchery fish (F 4, 22 = 17.8, p<0.001), with hatchery steelhead having a significantly higher SMR than hatchery coho (F 2, 10 = 5.68, p=0.04) at high food, but a significantly lower SMR than hatchery coho at low food (F 2, 11 = 5.55, p=0.04). Maximal Metabolic Rate (MMR) Steelhead had a significantly higher MMR than coho for both wild (F 5, 25 = 53.6, p<0.001) and hatchery fish; (F 4, 23 = 5.56, p=0.03) (Figure 3.2, Table 3.1). Aerobic Scope (AS) Wild steelhead had a significantly higher AS than wild coho at both high and low food (F 2, 12 = 10.09, p=0.008). The high food treatment significantly decreased AS in  45  wild fish (F 2, 12 = 9.84, p=0.004; Table 3.1, Figure 3.3), but there was no significant effect of food or species on AS in hatchery fish (Table 3.1, Figure 3.3). Swim Performance (UCrit) Wild steelhead had a significantly higher UCrit than wild coho (F 4, 25 = 18.1, p<0.001) (Table 3.1; Figure 3.4). There was no significant food effect on UCrit (Table 3.1; Figure 3.4) or difference between hatchery steelhead and coho (F 4, 24 = 0.03, p=0.87). Instantaneous Growth Rate Mean growth rates of wild coho and steelhead were 1.91 + 0.68 % body wt. day-1 and 3.08 + 0.33 % body wt. day-1 at high food and -1.00 + 0.25% body wt. day-1 and 1.01 + 0.40 % body wt. day-1 for the low food treatments, respectively. Mean growth rates of hatchery coho and steelhead (+ SD) were 1.79 + 0.45 % body wt. day-1 and 3.35 + 0.50 % body wt. day-1 at high food and -0.96 + 0.37 % body wt. day-1 and -0.90 + 0.29 % body wt. day-1 for the low food treatments, respectively. Instantaneous growth rate of steelhead was significantly higher than coho growth in both the wild (F 3, 25 = 9.22, p=0.006) and hatchery fish (F 3, 37 = 22.1, p<0.001; Table 3.1, Figure 3.5). As expected, fish on the high food ration also grew faster. Discussion Contrary to expectation, SMR of wild steelhead was not higher than wild coho at either food level, providing little evidence for a tradeoff between SMR and growth at high and low food. My results also demonstrate that the quantity of food consumed directly affects SMR similarly for both coho and steelhead, where higher food consumption leads to a higher SMR consistent with results in Chapter 2.  46  Earlier studies have shown an adaptive tradeoff involving growth and swimming performance (UCrit) between populations along a latitudinal gradient (countergradient variation; Conover and Present 1990), with faster growing northern populations exhibiting lower swimming capacity and higher vulnerability to predation (Billerbeck et al. 2001; Lankford et al. 2001; Chiba et al. 2007). In contrast, southern populations with a longer growing season have lower growth rates and an increased swim performance which is associated with lower mortality rates. This tradeoff between higher growth rate and lower swimming capacity was not evident between wild coho and wild steelhead in my study; wild steelhead both grew as fast or faster than wild coho and also appeared to have a higher UCrit, AS, and MMR. Maximal metabolic rate is the maximum amount of energy that a fish can ouput aerobically and is primarily limited by the ability of the cardiovascular system to deliver oxygen to mitochondria in the muscles. Wild steelhead had a higher MMR than wild coho, resulting in wild steelhead having a higher AS given that SMR was not statistically different between species. Aerobic scope represents the excess energy that an organism can allocate to growth or active metabolism (i.e. the energy available in excess of maintenance metabolism) and is an important physiological parameter that has been used to explain a number of responses to environmental extremes (e.g. thermal stress) and challenges (e.g. long migration distances; Djawadan et al. 1997; Bochdansky et al. 2005; Lee et al. 2003; Killen et al. 2007). A large AS is believed to allow fish to carry out multiple physiological functions simultaneously, including digestion, growth and swimming (Killen et al. 2007). Conceivably, a high AS may directly facilitate increased growth through greater excess available energy when swimming costs are low (as in my  47  study where fish were reared in troughs with very little current). Alternatively, a high AS may allow fish more energy for defending territories, swimming, and active prey capture leading to greater food consumption and growth. Therefore, my AS results suggest that the negative relationship between growth and swim performance determined by Billerbeck et al. (2001); Lankford et al. (2001) and Chiba et al. (2007) in northern populations showing counter gradient variation may be the result of a lower aerobic scope compared to southern populations which was not tested in their studies. A reduced aerobic scope could be caused by elevated SMR at high growth and food consumption levels, or a greater investment in a larger digestive system at the cost of reduced structural investment in cardiovascular and transport tissue that enhances swimming ability. A low growth efficiency, as manifest by juvenile wild steelhead (e.g. Sullivan et al. 2001), may appear non-adaptive on first consideration. Reduced growth efficiency, however may become adaptive at high food availability (Sibly 1981), as a high food consumption/short gut residence strategy may maximize energy assimilation per unit time if food quality is high (Millidine et al. 2009). Digestion has been shown to be the bottleneck that minimizes energy assimilation when food is in excess (Booth 1990; Hart and Gill 1992), thereby limiting growth. Conceivably, an adaptive trade-off between growth efficiency and growth rate could exist at high and low food. A fixed low growth efficiency would become maladaptive in resource poor environments where a strategy based on high food consumption and rapid extraction of labile energy would be ineffective.  48  Differences in physiology between wild steelhead and wild coho appear consistent with energy maximizing vs. energy minimizing strategies (Arendt and Wilson 1997; Arendt 1997). Previous studies have shown that steelhead have higher food consumption (Sullivan et al. 2001) and tend to hold in higher velocity habitats (riffles; Young 2001, Hartman 1965), compared to coho that have lower food consumption and tend to hold in lower velocity habitats (pools where energy expenditures are minimal; Hartman 1965; Bisson et al. 1988, Young 2001). A higher MMR, AS and swim performance make wild steelhead better suited for holding in faster velocity habitats compared to wild coho. Holding in faster velocity habitats may allow steelhead with a lower growth efficiency and shorter gut residence time the ability to maximize food consumption and take advantage of higher prey encounter rates per volume of water associated with riffles (Nislow et al. 1999; Hayes et al. 2000) relative to pools. This strategy may allow wild steelhead to offset the cost of a shorter growing season due to a later emergence time compared to wild coho (steelhead are spring spawners, coho are fall spawners whose fry hatch 4-6 weeks earlier than steelhead). Consistent with northern temperate species like Atlantic silversides (Menidia menidia), salmonids are also under strong selection to exceed a threshold body size for overwintering (Post and Parkinson 2001; Post and Evans 1989; Shuter and Post 1990; Schultz and Conover 1997); however, wild steelhead with a higher MMR may be able to compensate for an elevated SMR associated with increased food consumption and high growth rates while still maintaining swim performance, unlike Menidia menidia. Differences between steelhead and coho in SMR, AS, growth and swim performance appeared reduced in hatchery reared fish compared to wild fish. Hatcheries  49  represent a resource rich environment in which selection for higher growth rates occur (Petersson et al. 1996; Fleming et al. 2002; Sundstrom et al. 2005) and therefore indirectly higher SMR, since SMR is correlated with increased growth rate and food consumption (Chapter 2). The relatively homogenous environment of a hatchery where both species are raised at low velocities and high productivity over multiple generations would be expected to cause convergent selection and reduce differences in physiology between species. The smaller difference in swimming performance between steelhead and coho in hatchery fish may be the result of decreased AS in hatchery steelhead associated with an elevated SMR. This is consistent with other studies (Bams 1967; Brett et al.1958) which have shown that hatcheries select for higher growth rate at the cost of reduced swimming performance. It is also consistent with the countergradient variation tradeoffs described above, and suggests that a tradeoff between growth rate and swimming ability may be intrinsic to growth variation between populations within a species. The only caveat for the conclusion that differences between species were more pronounced in wild fish is that the wild fish experiment occurred 3 weeks later at lower temperature (12.3 vs. 8.3˚C). Because steelhead are somewhat more cold adapted, a lower temperature could enhance differences in performance between the species, although all metabolic tests were performed at an intermediate temperature of 13.0˚C where temperature related differences in performance between species should have been minimal. In contrast, interspecific differences between coho and steelhead that allow higher maximum steelhead growth do not appear to involve a tradeoff between growth rate and swimming ability, but rather between growth rate and growth efficiency. Bioenergetic  50  models (e.g. Sullivan et al. 2001) predict that steelhead have a much lower growth efficiency than coho, and have to consume approximately 50% more food to achieve equivalent growth. This suggests that adaptive tradeoffs between these species involve different digestion strategies that influence growth efficiency, where steelhead have a high food consumption strategy that strips out more labile energy with lower efficiency than coho, allowing them to achieve higher growth at high food abundance. Different digestive strategies to maximize energy assimilation are common among vertebrates (Hume 1989; Milton 1981; van Gils et al. 2007), but remain poorly documented among fish (see Nicieza et al. 1994 and Millidine et al. 2009 for exceptions), but I suggest that they may underlie many of the adaptive tradeoffs related to growth that allow differentiation and coexistence of ecologically similar species like salmonids. This apparent adaptive tradeoff between high growth and low growth efficiency has also been demonstrated in other species and ecotypes of salmonid. Similar to the contrast in habitat use and growth efficiency observed between coho and steelhead, juvenile anadromous brook trout have been shown to have a more streamlined body shape, occupy higher velocity habitats, and show increased food consumption and lower growth efficiencies than sympatric resident juveniles (Morinville and Rasmussen 2008; 2003). This suggests that adaptive tradeoffs between species (steelhead vs. coho) and ecotypes (resident vs. anadromous) of sympatric salmonids differ from those between populations on a latitudinal gradient. One limitation of this study is the difference in size between species and between hatchery and wild fish. Difference in emergence times between steelhead and coho salmon make it difficult to size match fish without reducing coho growth by rearing at  51  lower temperatures or under food deprivation until steelhead are of similar size. This was not done as chronic effects of food deprivation and temperature on metabolic rate were unknown. Another option was to select smaller coho to size match with larger steelhead, but this could also introduce artifacts as differences in size may be the result of individual differences in physiology, and selecting fish from opposite ends of the size distribution could distort differences between species. While conclusions with respect to metabolic values are size-corrected and therefore robust, my growth results should be interpreted with caution because steelhead were smaller than coho and the allometry of growth predicts higher growth rates of smaller fish; consequently slopes of the lines in Figure. 3.5 should be negative. The positive slopes in Figure. 3.5 indicate the presence of dominance hierarchies where larger fish monopolize resources, even at high food (see Chapter 4). Since both wild and hatchery steelhead were systematically smaller than coho, higher steelhead growth is also confounded with smaller body size, making it difficult to unambiguously attribute higher steelhead growth to a species effect. Although differences in morphology have been used to explain differences in habitat partitioning, physiology provides new insight into the metabolic tradeoffs underlying habitat partitioning, with coho adopting an energy minimizing strategy and steelhead adopting an energy maximizing strategy. Adaptive trade-offs associated with these life history strategies are complex, although steelhead may be maximizing MMR to offset costs of an elevated SMR associated with high food consumption and growth in order to maintain a high AS and swim performance in energetically costly habitats (riffles), contrary to trade-offs seen between populations along a latitudinal gradient (e.g.  52  Conover and Present 1990). Differences between hatchery and wild fish were variable, with more pronounced differences in wild fish compared to hatchery fish. With higher growth rates and SMR hatchery fish showed no difference in AS and swim performance between species, consistent with inter-specific tradeoffs between growth and swim performance observed in Menidia menidia. The most distinctive emergent tradeoff between wild coho and wild steelhead appears to involve growth efficiency, similar to the metabolic tradeoff which appears to exist between brook trout ecotypes adopting resident and anadromous life histories (Morinville and Rasmussen 2003), suggesting that this strategy may be fairly general among salmonids. Divergent digestive strategies are common and well documented among terrestrial vertebrates (Hume 1989; Milton 1981; van Gils et al. 2007), and may be an important but overlooked aspect of adaptive strategies of juvenile salmonids, and fish in general.  53  Dependent Variable Log SMR  Log MMR  Log AS  UCrit  Log Growth  Fish Type H  Log Mass (x) 0.52*Log Mass  W  0.77*Log Mass  H  0.95*Log Mass  W  1.64*Log Mass  H  0.90*Log Mass  W  1.17*Log Mass  Length  H  3.53*Length  W  3.08*Length  H  0.53*Log Mass  W  0.74*Log Mass  Food Level -0.05(L) 0(H) -0.26(L) 0(H) 0.39(L) 0(H) 0.16(L) 0(H) -0.55(L) 0(H) -0.73(L) 0(H)  Species  Log Mass*Food  Log Mass*Species  Species*Food  Intercept  n  F  P value  0.10(St) 0(Co) 0.09(St) 0(Co) 0.54(St) 0(Co) 5.04(St) 0(Co) 0.21(St) 0(Co) 0.34(St) 0(Co)  -0.59*Log Mass(StL,CoL) 0(StH),(CoH) -  -0.41*Log Mass(StH),(StL) 0 (CoH),(CoL) -0.50*Log Mass(StH),(StL) 0 (CoH),(CoL) -  -0.22(StL) 0 (StH),(CoL),(CoH)  0.95  27  59.56  <0.001  0.85  27  90.11  <0.001  1.15  27  105.70  <0.001  0.70  27  42.08  <0.001  1.05  27  60.52  <0.001  0.73  27  18.81  <0.001  17.55  27  23.37  <0.001  16.45  27  4.80  0.02  0.20  38  186.10  <0.001  -  29  60.86  <0.001  -0.36(StL) 0 (StH),(CoL),(CoH) -  Table 3.1: The effect of food level, species, mass and the interactions: mass*food, mass*species and species*food on various physiological parameters: standard metabolic rate (SMR; umol hr-1), maximal metabolic rate (MMR; umol hr-1), aerobic scope (AS; umol hr-1), swim performance (UCrit; cm sec-1) and growth rate (% body wt. day-1) for both wild (W) and hatchery (H) steelhead (St) and coho (Co) at high (H) and low (L) food. Parameters for significant variables are presented. Sample equation for predicting SMR for hatchery coho salmon under high food is as follows: SMR=0.52*(Log Mass) + 0.95.  54  Hatchery  Wild  Figure 3.1.The relationship between standard metabolic rate and mass for juvenile steelhead (squares) and coho salmon (triangles) under high (closed) and low food (open) food treatments. Figure on left represents fish that were reared in a hatchery environment and figure on right represents wild fish. Lines represent a best fit line based on parameter estimates. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: SMR was significantly higher for fish on the high food ration compared to the low food ration in the wild fish. Hatchery coho also had a significantly higher SMR at high food and a significantly lower SMR at low food compared to hatchery coho. See text for further statistical analysis and table 3.1 for further parameter significance.  55  Hatchery  Wild  Figure 3.2.The relationship between maximal metabolic rate and mass for juvenile steelhead (squares) and coho salmon (triangles) under high (closed) and low (open) food treatments. Figure on left represents fish that were reared in a hatchery environment and figure on right represents wild fish. See text for statistical analysis and table 3.1 for parameter significance. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: steelhead had a significantly higher MMR than coho in both hatchery and wild fish. See text for further statistical analysis and table 3.1 for further parameter significance.  56  Hatchery  Wild  Figure 3.3.The relationship between aerobic scope and mass for juvenile steelhead (squares) and coho salmon (triangles) under high (closed) and low (open) food treatments. Figure on left represents fish that were reared in a hatchery environment and figure on right represents wild fish. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: Wild steelhead had a significantly higher AS than wild coho. See text for further statistical analysis and table 3.1 for further parameter significance.  57  Hatchery  Wild  Figure 3.4.The relationship between swim performance and length for juvenile steelhead (squares) and coho salmon (triangles) under high (closed) and low (open) food treatments. Figure on left represents fish that were reared in a hatchery environment and figure on right represents wild fish. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: wild steelhead had a significantly higher UCrit than wild coho. See text for further statistical analysis and table 3.1 for further parameter significance.  58  Wild  figures on right represent wild fish. See text for statistical analysis  ation (open) food treatments. Figures on left represent fish that were  and Length and for juvenile Steelhead (squares) and Coho Salmon  Hatchery  Figure 3.5.The relationship between growth rate and mass for juvenile steelhead (squares) and coho salmon (triangles) under high (closed) and low (open) food treatments. Figure on left represents fish that were reared in a hatchery environment and figure on right represents wild fish. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: steelhead had a significantly higher growth rate in both the hatchery and wild fish at both high and low food. See text for further statistical analysis and table 3.1 for further parameter significance.  e.  59  Chapter 4: Failure of Physiological Metrics to Predict Dominance in Wild Juvenile Salmon: Habitat Effects on the Allometry of Growth in Dominance Hierarchies Introduction Territoriality is one of the best examples of interference competition and generally results in dominant individuals gaining preferential access to food, shelter or mates. Territoriality occurs across a broad range of taxa (e.g. Huntingford and Turner 1987; Harwood et al. 2003) and is especially common among juvenile drift-feeding salmon which rapidly establish size-based dominance hierarchies both in laboratory settings (Reinhardt 1999; Sloman et al. 2000; 2001) and in the wild (Nakano 1995). Dominance hierarchies form soon after emergence as juveniles engage in intra and inter specific competition for preferred feeding territories (Hartman 1965, Chapman 1966, Cutts et al. 1999). It is generally accepted that dominance is advantageous as preferential access to food (Cutts et al. 1999) facilitates increased growth (Metcalfe et al. 1995) as well as survival (Huntingford and Turner 1987), since mortality rate tends to decline with fish size (Post and Parkinson 2001; Post and Evans 1989; Shuter and Post 1990; Schultz and Conover 1997). The ability to monopolize access to food in dominance hierarchies that are temporally stable (Bachman 1984; Abbott et al. 1985; Nakano 1995; Hansen and Closs 2009) generates an expectation that dominant fish should experience high growth. Laboratory experiments have generally found positive correlations between dominance and growth (e.g. Metcalfe et al. 1995; Thorpe et al. 1992; Cutts et al. 1999; Sloman et al. 2001), with some notable exceptions (e.g. Huntingford and Garcia de Leaniz 1997). However, despite the expectation of higher growth of dominant fish from laboratory  60  studies, relationships between dominance, growth and physiological diagnostics in wild salmonids have been mixed. While several studies have shown positive correlations between dominance and physiological metrics of performance, such as growth hormone levels (GH; Johnsson and Bjornsson 1994), or standard metabolic rate (SMR; Priede 1985), other studies have found no relationship between dominance and physiological condition in wild populations, or negative relationships (e.g. Alvarez and Nicieza 2005, Sloman et al. 2008). Contrary to expectation, dominant wild fish may exhibit lower growth rates (Harvey et al. 2005, Hansen and Closs 2009), SMR (Alvarez and Nicieza 2005) and higher cortisol (stress) levels than subordinates (Sloman et al. 2008). This has caused puzzlement, since dominants are typically expected to have higher SMR, growth rates (Alvarez and Nicieza 2005; Cutts et al. 1999; Roskaft et al. 1986) and lower cortisol levels, which are usually found in high concentrations in subordinates (Ejike and Schreck 1980). These conflicting results between correlates of dominance in the lab and field are attributed to the general complexity of natural habitats (Alvarez and Nicieza 2005; Sloman et al. 2008), although the specific mechanism(s) underlying variation in the benefits of dominance remain unclear. Various studies have shown that greater time spent foraging, risk-taking, and subsequent exposure to predation by dominant or faster-growing fish (Nakano 1995; Finstad et al. 2007) results in higher mortality rates for dominants, but this should not cause lower growth or elevated stress. Vollestad and Quinn (2003) demonstrated that being dominant may only be advantageous when food availability is limiting or predictable, allowing monopolization of the food supply. Under high or unpredictable  61  food levels, Vollestad and Quinn (2003) found a negative relationship between growth and dominance in juvenile coho salmon, presumably because the food supply was insufficient to offset the greater energetic costs and stress of territorial defense. In this chapter I build on the observations of Vollestad and Quinn (2003) to demonstrate that inconsistencies in the apparent benefits of dominance are not unexpected but a logical outcome of the allometry of growth and differential energy intake among fish in a dominance hierarchy. Hierarchies are not all of equal benefit to dominant and subordinate fish and net energy intake in a hierarchy will depend on body size and energetic demands of a fish relative to the food supply, which will vary with habitat type and configuration (e.g. pool size and shape, drift concentration and discharge from the upstream riffle; Hansen and Closs 2009; Harvey et al. 2005). A large fish in a habitat with abundant food that it can monopolize will experience higher growth than a subordinate, particularly if they are of similar size; however, a large dominant fish in an unproductive habitat may experience lower growth than a smaller subdominant which has a smaller absolute energy requirement for growth (Hansen and Closs 2009). As part of a larger study to assess metabolic differences in juvenile steelhead trout and coho salmon (Chapter 3), I reared fish at different rations in a set of artificial stream channels. Because fish increased in size throughout the experiment, it also provided an opportunity to analyze size-based patterns of growth to understand (1) the allometric effects of body size on relative growth of fish in dominance hierarchies, and (2) the effects of increasing ration (and the ability of the dominant to monopolize resources) on the relative growth rates of dominants and subdominants. Based on the allometry of juvenile salmonid growth (e.g. Rosenfeld and Taylor 2009), I expect that 1) relative  62  growth rates of dominants will decrease as fish increase in size and approach the capacity of their habitat, 2) enhanced ability of the dominant to monopolize resources will increase the disparity in growth between the dominant and subordinates, and 3) size disparity within a dominance hierarchy will promote higher growth of subdominants because of the lower energetic requirements of smaller individuals. Methods Experimental Fish Twenty four wild steelhead and coho salmon were captured using a combination of dip netting, minnow trapping and seining from McNab Creek, Port Mellon, BC, Canada (Universal Transverse Mercator (UTM) 471738 E 5490574N). The fish collected from McNab Creek are presumed to be of wild origin because there is no history of hatchery releases into the creek. Wild steelhead and coho salmon averaged, 0.53 + 0.19g SD and 1.99 + 0.64g SD in weight and 3.93 cm + 0.40 SD and 5.72 cm + 0.58 SD in fork length (FL), respectively, at time of collection. Fish were transported to Chapman Creek and individually marked with visible implant elastomere (Northwest Marine Technology, Inc.) so that growth of individuals could be monitored. Average water temperature for the first growth interval (Aug. 29 - Sept. 24, 2009) of the experiment was 14.0°C + 1.4 SD and 9.7°C + 1.8 SD for the second interval (Sept 24 - Oct. 7, 2009). Experimental Setup This experiment was set up as part of a broader study to assess differences in growth and metabolism between juvenile steelhead trout and coho salmon; however, it became apparent that allometric patterns in growth over time, as described below, could provide unique insight into the costs and benefits of dominance within hierarchies.  63  The experiment was conducted in twelve outdoor artificial stream channels installed in a side channel of Chapman Creek, Sechelt, BC, Canada (Universal Transverse Mercator (UTM) 448100E 5478100N). Channels were constructed and designed in a similar fashion to Rosenfeld et al. (2005) although at a larger scale. Channels were 2.5 m long, 1m wide and 60 cm deep, and constructed out of 6.4 mm plywood coated with epoxy resin and lined with plastic sheeting to prevent leakage. Channel boxes were supported above the stream on wooden frames arranged in three rows of four channels in a staircase design (see Rosenfeld et al. 2005 for schematic). Water from each channel spilled into a smaller box that was 60cm long, 1m wide and 60 cm deep, to dispel turbulence before water entered the next experimental channel downstream. A 6mm mesh screen at both ends of each channel prevented fish escape. Water gravity fed into the channels through an intake pipe from Chapman Creek and a header box upstream of the channels. Flow through each of the 12 channels was held constant throughout the experiment at approximately 7.5 L·s-1. Stream channels were filled with equal amounts of 2 - 4 cm diameter river washed gravel arranged so that each channel had an upstream riffle (average water depth + SD, 6 + 2 cm, average velocity, 16 + 6 cm/sec) and a downstream pool (max depth 26 cm, average water depth 20 + 5cm, velocity 3 + 2 cm/sec) of approximately equal lengths. Habitat in each channel was characterized by measuring velocity and depth with a Marsh-McBirney model 2000 flowmeter at four points along ten transects spaced 20 cm apart in each channel. Channels were run for approximately two weeks prior to stocking fish to allow colonization of aquatic invertebrates, and covered with a coarse plastic net to protect against predators.  64  Two 45 cm X 30 cm plexiglass panels coated with solar guard reflective film were installed above each channel to allow observations of fish from a concealed location. Covered canopies of dark plastic sheeting were constructed between the channels to maximize the reflective properties of the film and allow better viewing of fish in the stream channels. Experimental Design Experimental treatments included two levels of food for each of two salmonid species (juvenile coho and steelhead) applied over two sequential time intervals (26 and 14 day’s duration) from Aug 29 – Oct. 7 2009. This allowed me to assess the allometry of growth within dominance hierarchies under contrasting degrees of food limitation (habitat capacity) determined by food abundance. Fish were weighed at the end of each time interval to track the allometry of growth as fish increased in size and became increasingly limited by the capacity of the channel habitat. Food levels in the second time interval were increased in the high food treatment to assess how the benefits of dominance responded to increased ration. Because coho hatch approximately 4-6 weeks earlier than steelhead, coho juveniles are larger at any given time, so that the contrast between species in this experiment was largely one of initial body size. Four juvenile steelhead or coho were systematically assigned to each of the twelve channels, for a density of 1.6 fish·m-2. In the first time interval, the high food treatment involved supplementing the first and fourth rows of channels (n=6) with natural invertebrate drift three times daily collected from drift nets set overnight in Chapman Creek. To increase contrast in food availability, invertebrate drift in the low food treatment (second and third rows of channels, n=6) was reduced by filtering half of the  65  inflow volume entering the channels through a 250um mesh net. To determine ration in the different treatments, I used a bioenergetic model for juvenile steelhead and coho salmon (Sullivan et al. 2001) to back calculate estimated food consumption based on observed growth at the end of each time interval (Table 4.1). Because of their small initial size, steelhead at high and low food were at satiation (i.e. experiencing maximum growth rates) in interval 1, whereas coho at high and low food were growing at 90% and 60% of estimated satiation, respectively. During the second time interval the high food treatment was increased by supplementing drift with blood worms (frozen chironomids) in order to meet the increasing demand of growing fish and maintain the high food treatment near satiation. During the second interval steelhead and coho at high food were estimated to be growing near satiation, whereas coho and steelhead on low food were growing at 43% and 51% of satiation respectively. Invertebrates for supplementing prey abundance in the high food treatments were collected 3 times daily by lifting 12 drift nets placed in the side channel of Chapman Creek. Invertebrates and blood worms were then incrementally added in equal amounts to the head of each of the high food channels (n=6) by overflow from inclined buckets receiving water from a 12V bilge pump. Blood worms were boiled for 10-20 minutes and refrozen prior to use to prevent release of exotic pathogens into receiving waters. All but two fish were recovered at the end of the experiment. Data on coho growth from an earlier stream channel experiment (Rosenfeld et al. 2005) allowed me to retrospectively assess the effects of habitat configuration and fish density on relative growth of dominant and subdominant fish. Experiments from Rosenfeld et al. (2005) differed from mine in several ways. First, channels were  66  narrower and shallower (60cm wide and 20 cm deep, compared to 1m wide and 30 cm deep in this study); narrower channels should increase the ability of the dominant to monopolize drifting prey. Second, fish densities in Rosenfeld et al. (2005) were as high as 12 fish·m-2 (compared to 1.6·m-2 in this study); higher fish density should negatively impact the smallest fish in a hierarchy, because of greater prey depletion by a larger number of fish upstream. Based on the model from Sullivan et al. (2001; see above), coho from Rosenfeld et al. (2005) experiment were at 77% satiation at high food and 16% satiation at low food. Dominance rank of fish in the stream channels was assigned based on final size of fish. Juvenile salmonids are known to be extremely aggressive and quickly establish dominance hierarchies (Reinhardt 1999; Sloman et al. 2000; 2001) that are primarily size based (Chapman 1966; Nakano 1995; Young 2003; 2004). Size only becomes a poor predictor of dominance when fish are nearly size-matched, and factors like prior residence or outcome of aggressive encounters become important (Sloman and Armstrong 2002). Channels were deliberately stocked with a range of fish sizes (average weight difference between the smallest and largest fish was (1.59 + 0.63g), and this relatively large range in size within each channel makes it reasonable to infer that dominance was well correlated with size. Causal observations during the experiment confirmed strong dominance hierarchies within the stream channels with the largest fish defending the most profitable position.  67  Data Analysis Daily instantaneous growth rates of fish (percent per day) were calculated as {[loge(final mass)- loge(initial mass)]/duration} x 100 (Ricker 1975). Absolute growth rate was calculated as (final mass)-(initial mass)/duration. The slope of the absolute growth (g·day-1) vs. individual mass relationships from each treatment was used as a conservative index of the benefits of dominance. A growthbody mass relationship with a positive slope indicates that growth of dominants exceeds that of subordinate fish, while a negative slope would indicate lower absolute growth of dominant fish despite their competitive advantage. I used absolute rather than relative growth (% body weight day-1; Figure 4.1) because relative growth systematically declines with fish size even at maximum growth rates. I tested for the effects of food, species, channel, and individual fish mass (average weight over the growth interval) on absolute growth using analysis of covariance (ANCOVA), including a food by mass and species by mass interaction term. If a significant interaction was found, coho and steelhead data were separated and analyzed independently. Interaction terms and independent variables that were not significant at p>0.05 were removed from the model. All data passed assumptions of normality and homogeneity of variance. Analysis was conducted using R version 2.8.1 statistical software. Since each stream channel included a pool-riffle sequence, combining data from 3 replicate channels with the same treatment is roughly analogous to sampling 3 pool-riffle  68  sequences in a natural stream and assessing the emergent relationships between growth and individual mass. Results Instantaneous Growth Rate (Interval 1) I found that smaller fish had a higher instantaneous growth rate (F 4, 41 = 8.21, p=0.007; Figure 4.1), as expected based on the allometry of growth (i.e. maximum growth declines allometrically with body size). Steelhead had higher growth than coho (F 4, 41 = 34.9, p<0.001), but this was confounded with the smaller body size of steelhead relative to coho (Figure 4.1). There was also a significant mass by food interaction (F 4, 41 = 4.11, p=0.049; Figure 4.1), with steelhead showing no difference in instantaneous growth at high and low food (F 3, 18 = 0.66, p=0.43), while coho growth was significantly reduced on the low food ration. Absolute Growth Rate (Interval 1) There was a significant interaction between body mass and food with species combined (F 5, 40 = 6.20, p=0.02; Figure 4.2), indicating that the effect of body mass on growth varied with ration. For steelhead (smaller on average than coho) there was no difference in growth at high and low food levels (F 3, 18 = 0.17, p=0.68), and a positive slope indicated that dominants grew faster at both rations. Analysis of the coho data separately however, revealed an interaction between body mass and food (F 3, 20 = 5.00, p=0.04), indicating that dominant (larger) coho at high food experienced higher absolute growth than subordinates (positive slope), whereas dominant coho had lower absolute growth at low food (negative slope; Figure 4.2).  69  Interval 2 There was a significant interaction between mass and food level (F 4, 37 = 39.96, p<0.001; Figure 4.3) across the entire data set. Although steelhead grew faster on the high ration (Figure 4.3), slopes were similarly positive at both high and low food (F 3, 17 = 0.12, p=0.73), but this was not the case for coho. Analysis of the coho data separately revealed a significant interaction between food and mass (F 3, 18 = 24.58, p<0.001); the positive slope at high food indicated that larger (dominant) coho did better than subordinates when food was abundant, but the relationship became negative at low food, with the largest fish exhibiting the lowest absolute growth (Figure 4.3). This was the same pattern as in the first time interval, except that growth rates of all fish on the low food diet declined in interval 2 as their body size increased (to the extent of the largest fish losing weight). In contrast, the increase in ration in the high food treatment (relative to interval 1) increased the slope of the coho growth line at high food (Figure 4.3), despite the increase in average fish size. Analysis of Rosenfeld et al. (2005) data revealed similar patterns in absolute growth within dominance hierarchies. Consistent with my study, dominant larger coho had higher absolute growth than subdominants at high food (significant mass by food interaction, F 3, 18 = 26.6, p<0.001). Unlike the present study, however the smallest coho at low food were unable to achieve higher growth rates than larger fish (i.e. negative slope in Figures 4.2 and 4.3), presumably because the narrower channel in Rosenfeld et al. (2005) allowed more effective monopolization of the drift by the dominant, and a higher density of nonspecific’s reduced per capita energy intake for downstream subordinates.  70  Discussion By controlling channel structure and fish density in artificial streams, I was able to examine how fish size and the allometry of growth affect perceived benefits of dominance in competitive hierarchies. I show that conflicting results among correlates of dominance in the literature are not unexpected, but are the outcome of allometric effects of body size on energy requirement and absolute growth of fish in a dominance hierarchy, which depend on habitat capacity and the ability of dominant fish to monopolize resources. Consistent with expectation, I found that dominant fish experienced higher growth rates than subordinates when food was abundant. At low food (i.e. lower habitat capacity) coho clearly demonstrated a negative relationship between absolute growth rate and dominance (Figures 4.2 and 4.3), and subordinates achieved higher absolute growth rates. This result can be attributed to the higher net energetic requirement of the larger dominant fish compared to smaller subordinates, which have a lower absolute energy requirement and per capita food consumption, allowing them to achieve greater growth on a smaller ration. The ability of smaller fish to grow at a lower absolute ration was validated by near maximal growth of small steelhead at rations that were inadequate to satiate larger coho (Figures 4.1 and 4.2). Steelhead, were very small relative to channel size during the first growth interval, similar to recently emerged salmonids. Steelhead were able to achieve equal and near-maximal growth at both low and high food in the first growth interval due to their small size and low net energetic requirement relative to channel habitat capacity. Presumably, had steelhead been allowed to grow large enough to  71  approach the capacity of the stream channels growth of dominant steelhead would have declined below that of subdominants, as with coho. Although data from Rosenfeld et al. (2005) show patterns of dominant growth at high and low food levels that are similar to this study, there are key differences. First, at high food they found a greater disparity in growth between subordinate and dominant, so that the smallest fish experience near zero growth despite their lower energy requirements. Second, at low food the smallest coho did not experience greater growth than dominants, as was the case in this study. These differences with my study suggest that the smaller width of the channels used in Rosenfeld et al. (2005) allowed the dominant to acquire an even greater proportion of the food supply due to shorter capture distances and subsequent increases in prey interception rates (Piccolo et al. 2007). In my channels, however, we see a distinct negative relationship between absolute growth and average mass, suggesting that dominant fish were less successful in monopolizing available prey (Figure 4.2 and 4.3), likely due in part to lower prey detection associated with a wider channel, and possibly a decreased predictability of the food supply at lower food levels (Vollestad and Quinn 2003). Density of fish should also affect relative growth rates of fish in a dominance hierarchy, particularly for smaller fish since upstream predation will act as a filter reducing abundance of drifting prey to subdominants. This likely contributed to the much lower growth rates of smaller subordinates in data from Rosenfeld et al. (2005; Figure 4.4), and supports the inference that variation in fish density between habitats and streams will also contribute to differences in relative growth and condition of fish in dominance hierarchies.  72  My data also suggests that a larger size disparity within a dominance hierarchy will promote higher growth of subdominants because of the lower energetic requirement of smaller individuals. This suggests that an extended hatch time of juvenile salmonids (resulting in differential fish size) can reduce intraspecific competition and increase cohort production, provided that dominant and subdominant fish can serially recruit to new habitats that deliver more energy as they grow. When this is not the case (i.e. in small streams where deeper habitat is limiting), growth of dominant fish will decline as they approach the limits of their habitat (Rincon and Lobon-Cervia 2002), and static dominance hierarchies will continue to suppress growth of subordinates. A number of studies have failed to find consistent positive correlations between dominance and growth, or other physiological diagnostics of condition in wild fish. Dominants are expected to have higher SMR, growth (Alvarez and Nicieza 2005; Cutts et al. 1999; Roskaft et al. 1986) and lower cortisol levels, but studies have shown that dominant fish in the wild may exhibit lower growth (Harvey et al. 2005; Hansen and Closs 2009), SMR (Alvarez and Nicieza 2005) and higher cortisol levels (Sloman et al. 2008) than subordinates. This study clearly shows that the relative benefits of dominance depends on the size of the dominant relative to the capacity of the habitat, which will depend on prey abundance and the configuration of the habitat (e.g. pool width or depth, length of the upstream riffle; Hansen and Closs 2009). Similarly, relative growth rates of subordinates (and therefore their SMR and stress levels) will depend on their size relative to prey availability, as well as the density of upstream conspecifics and the ability of the dominant to monopolize resources. Unlike most laboratory environments, natural stream channels encompass a variety of habitats that differ in their ability to support fish  73  production. Even among a single habitat class like pools, individual habitat units differ greatly in the energy available to drift-feeding fish, and the number and size of fish that can be supported (Hansen and Closs 2009). Although a number of studies have explored the costs and benefits of dominance, they have not explicitly considered how the allometry of fish growth relative to habitat capacity can reverse the expected growth and condition of dominant and subordinate fish. Here I show that allometric effects of body size on relative growth rate in dominance hierarchies can account for much of the discrepancy among fitness-related correlates of dominance between fish in the wild and laboratory, and demonstrate the importance of considering ecological processes within the context of a natural habitat template.  74  Avg. mass (St)  Avg. St (% sat.) mass (Co)  Co (% sat.)  Drift  Frozen Blood worms  Interval 1 High food Low food  0.55g 0.52g  2.09 1.90  100% 100%  90% 60%  Suppl.+ ½ Ambient  N/A N/A  Interval 2 High Food Low Food  1.35 1.35  2.82 2.26  100% 43%  100% 51%  Suppl.+ ½ Ambient  18% body wt. day-1 0  Table 4.1. Average mass of steelhead (St) and coho (Co) salmon stocked in experimental stream channels with estimated food ration expressed as % satiation during interval 1 and 2 of the growth experiment. Note: Blood worms were not used as supplemental food during interval 1.  75  Figure 4.1. The relationship between instantaneous growth rate and average mass (interval 1) for juvenile steelhead (squares) and coho salmon (circles) in supplemented/high food (solid) and non-supplemented/low food (open) in artificial stream channels. Note: smaller fish had a significantly higher growth rate than larger fish. Also, coho had a significantly lower growth rate at low food than high food.  76  Figure 4.2. The relationship between absolute growth rate and average mass (interval 1) for juvenile steelhead (squares) and coho salmon (circles) in supplemented/high food (solid) and non-supplemented/low food (open/dashed) in artificial stream channels. Note: Larger dominant coho had a significantly higher growth rate at high food than smaller subordinate coho.  77  Figure 4.3. The relationship between absolute growth rate and average mass (interval 2) for juvenile steelhead (squares) and coho salmon (circles) in supplemented/high food (solid) and non-supplemented/low food (open/dashed) in artificial stream channels. Note: Larger dominant coho had a significantly higher growth rate compared to smaller subordinate coho at high food. stream channels  78  Figure 4.4. The relationship between absolute growth rate and average mass for juvenile Steelhead (squares) and coho salmon (circles) in supplemented/high food (solid) and non-supplemented/low food (open/dashed) in artificial stream channels from Rosenfeld et al. (2005). Note: dominant larger coho had a significantly higher growth rate at high food than subordinates.  79  Chapter Five: General Discussion The goals of the research presented in this thesis were to gain a greater understanding of the effects of maintenance (SMR) and active metabolism (MMR) on growth, swim performance and habitat selection of juvenile salmonids. In particular I focused on understanding the relative importance of food consumption and individual differences in metabolism, to test causation between SMR and food consumption (Chapter 2). I also examined adaptive trade-offs between physiological metrics, growth and swim performance between steelhead and coho salmon fed high and low food rations in an attempt to explain habitat partitioning between juvenile steelhead and coho salmon and how this differs between wild and partially domesticated strains of salmonid (Chapter 3). Lastly I considered the allometric effects of body size and increasing ration on relative growth rates of dominant and subordinate fish in dominance hierarchies (Chapter 4). In this chapter I will first recap the current literature and assess how the findings of these experiments further enhance the understanding of the current literature surrounding this area of research and discuss limitations of each study and potential future directions of research arising from this work. Standard metabolic rate is usually the dominating part of the energy budget of animals (Finstad et al. 2007) and has been shown to vary by up to a half an order of magnitude within (Cutts et al. 2002; Finstad et al. 2007; Alvarez et al. 2006) and between species, (Blaxter 1989) with differences that are repeatable over time (McCarthy 2000; Seppanen et al. 2010). With considerable differences in SMR within species and among taxa and the apparent inability of individuals to decrease their SMR (Priede 1985) has led  80  to the conclusion that differences in SMR are genetically fixed and therefore capable of selection (Pakkasmaa et al. 2006; Arnott et al. 2006). Recently however, inconsistencies surrounding correlates of SMR between laboratory and wild studies have lead researchers to the suggestion that variation in SMR may be microhabitat related (Lahti et al. 2002; Alvarez et al. 2006; Finstad et al. 2007) as opposed to intrinsic (genetically fixed). My results from chapter two demonstrated that the quantity of food consumed directly affects SMR of juvenile coho salmon and indicates that higher food consumption is a cause of elevated SMR. I also demonstrated that any intrinsic (genetic) effects between individuals are relatively small compared to the effects of ration. Lastly I demonstrated that salmonids have the ability to decrease their SMR depending on food availability and food consumption. These findings suggest that dominance which has been shown to be linked to high SMR may simply be a consequence of high food consumption associated with a dominant rank in a competitive hierarchy as opposed to a cause. Furthermore, with the decrease in SMR at low food and the demonstrated ability of salmonids to regulate their SMR, the trade-off between high SMR and food deprivation may not be as severe as presented in the literature. My ability to detect effects of food consumption on SMR is primarily due to the simple experimental design. In my study I used individually separated YOY coho salmon, allowing me to explicitly control food consumption and prevent formation of dominance hierarchies. Other studies investigating correlates of SMR hold fish in communal tanks, often at high densities, leading to competition for food and  81  disproportionate food consumption amongst fish, making it difficult to infer cause and effect. Although my study was done in a controlled manner there are still several limitations. I only used coho salmon during this experiment therefore we need to be cautious about applying our findings across species as differences in physiology and food consumption may evoke a whole suite of different responses in maintenance metabolism (SMR) than was observed in our study. Although steelhead from chapter three also elicited a similar elevation in SMR with increased food consumption, providing support that my findings may be robust among salmonids. My study was also conducted only at one temperature (14 °C). It is possible that at different temperatures, which has been shown to influence SMR and digestive rates, may lead to a different response than was observed in my study. Lastly my experiment was done in a very controlled fashion that strictly tested the effect of food ration on SMR. It is unclear if the outcome of our study would change if I were to carry out the experiment in the wild where differences in habitat, channel hydraulics and fish density are pronounced and may have an affect on SMR. Using the experimental stream channels from the experiment in chapter four a similar experiment but under more realistic conditions could be done in order to further address the limitations of this study. Although my study has limitations I suggest that studies involving SMR need to be cautious about disproportionate feeding due to dominance hierarchies in communal tanks, especially studies involving SMR as results may be confounded by individual variation in ration.  82  In chapter three I examined adaptive trade-offs between physiological metrics (SMR, MMR, AS), growth, and swim performance on fish held on high and low food rations in an attempt to explain habitat partitioning between juvenile wild and partially domesticated steelhead and coho salmon. Wild steelhead and wild coho salmon have been shown to occur in sympatry in many coastal streams in northwestern North America. Both species, however have been shown to use habitat differently in the wild. Coho forage near the surface (Fraser 1969; Johnston 1970) and occupy pools (Hartmann 1965; Bisson et al. 1988; Bugert et al. 1991) while steelhead, forage close to the substrate (Fraser 1969; Johnston 1970) and occupy riffles (Hartmann 1965; Bisson et al. 1988; Bugert et al. 1991). It has been demonstrated that these two species differ morphologically in a manner consistent with their habitat partitioning. Bisson et al. (1988) proposed that coho salmon have a laterally compressed body form with long median fins which would be optimally suited for pool habitat as it facilitates rapid turning and acceleration which would be advantageous for capturing invertebrate drift at the surface of pools. Steelhead however, have a longer more cylindrical body form and shorter median fins that would be best suited for holding in riffle habitat as it minimizes drag (Bisson et al. 1988). Previous studies also indicate that steelhead have a higher maximum growth rate and food consumption than coho (Sullivan et al. 2001). Although differences in morphology and food consumption between these two species seem to explain habitat partitioning in the wild, the adaptive trade-offs associated with their physiology and cost maximizing versus cost minimizing strategies were poorly understood.  83  I found that wild steelhead had a higher MMR, AS, growth and swim performance than wild coho. In the hatchery fish however, I found that steelhead had a higher SMR, MMR and growth rate than coho but did not show any difference in AS and swim performance. These results demonstrate that rearing environment may affect the physiology of juvenile salmonids. Hatchery steelhead had a higher SMR and MMR than coho but no difference in AS and swim performance. It is thought that swim performance between hatchery steelhead and coho did not differ due to steelhead potentially being selected for higher growth (and therefore higher SMR) in the hatchery at the cost of reduced swim performance, consistent with other studies that have found a negative correlation between SMR and swim performance (Billerbeck et al. 2001; Lankford et al. 2001; Chiba et al. 2007) which may be a trade-off associated with high food consumption and growth between populations within a species. Wild steelhead however, showed no difference in SMR but a marked difference in MMR leading to a higher AS and swim performance, indicating that wild steelhead are physiologically suited for holding in faster velocity habitats (riffles) compared to coho. This result is also consistent with morphological studies conducted by Bisson et al. (1988). Although my study is consistent with habitat partitioning, adaptive trade-offs between species at high and low food do not involve the same trade-off between growth and swim performance, as growth rate and swim performance for wild steelhead was higher at both high and low food compared to wild coho. The physiological trade-off between steelhead and coho appears to involve growth efficiency, with steelhead needing to consume more food than coho to achieve similar growth rates, but are able to achieve higher maximum growth when food is abundant. Consequently, wild steelhead, seem to  84  have adopted an energy maximizer strategy, consistent with high growth, food consumption and transport costs. Therefore, wild steelhead with a higher MMR, AS and swim performance may have adopted a strategy that allows for the ability to occupy higher velocity riffles, which have been shown to have higher food per volume of water compared to pool habitat (Nislow et al. 1999; Hayes et al. 2000). This strategy may allow wild steelhead to maximize growth in a truncated growing season and compensate for their later emergence time relative to other salmonid species. Interestingly steelhead, contrary to other species, may be compensating for an elevated SMR from higher food consumption by having a higher MMR and AS, thereby maintaining swim performance. Unfortunately direct comparisons between wild and hatchery fish were not able to be made due to a later start time for the hatchery fish experiment as it was more difficult to obtain wild fish than anticipated. Therefore water temperatures between the two experiments differed as stream water used for holding fish was not temperature controlled and began to cool near the end of summer. This made it difficult to effectively test the effect of hatchery rearing on the physiology of juvenile steelhead; instead I had to look at differences in patterns between species, i.e. wild/hatchery effects were confounded with temperature to some extent. Lastly, my experiment was conducted in experimental indoor troughs, which did control for habitat effects but make it difficult to apply our results to more complex habitats in the wild. It would be beneficial to carry out a similar experiment with hatchery and wild fish together allowing direct comparisons between wild and domesticated stocks. This may allow for a better understanding of adaptive trade-offs associated with physiological metrics as hatchery fish would represent fish that would presumably have elevated  85  metabolism associated with selection for higher growth rate and food consumption. Using the experimental stream channels from chapter four we could do a similar experiment but under more realistic conditions in order to further examine the benefits of an energy maximizing strategy versus an energy minimizing strategy. It is possible that steelhead adopting an energy maximizing strategy may not achieve higher growth in the wild compared to coho due to higher energy expenditures associated with holding in riffle habitat that may have been masked in the laboratory experiment due to common rearing conditions. Future directions examining adaptive-trade offs between steelhead and coho should focus on differences in growth efficiency, since this seems to be one of the key differences between species that may facilitate higher steelhead growth at high food. In this study fish were fed a commercial grade pellet which may have masked differences in digestive efficiency between steelhead and coho. If steelhead, do have a lower growth efficiency but a shorter gut residence time, differences may become more apparent if fish were fed natural invertebrate drift as opposed to a highly digestible pellet. This is because steelhead may have been able to extract more nutrients out of the food in a shorter time period than would normally be possible if feeding on natural invertebrate drift high in chitin. In chapter four I focused on understanding dominance hierarchies by principally examining the allometric effects of body size and increasing ration on relative growth rates of dominant and subordinate fish in dominance hierarchies. Competition between salmonids is highly size dependent (Cutts et al. 1999) although previous combative contests, rearing environment, prior residence and genetics  86  may play a role (Sloman and Armstrong 2002). Larger individuals have been shown to outcompete and displace smaller individuals from preferred habitat (Young 2004). It is generally accepted that being the dominant fish among a cohort is advantageous as it may allow preferential access to food (Cutts et al. 1999), leading to increased growth rates (Metcalfe et al. 1990) and ultimately greater survival (Huntingford and Turner, 1987). Results from my study further enhanced the findings of Vollestad and Quinn (2003). I demonstrated that when dominant fish have the ability to monopolize a feeding station such as when food is supplied from a point source or when the size of the pool is small, dominant fish have higher absolute growth rates than subordinates. At low food in a larger habitat however, dominant, larger fish endured a cost associated with a reduced ability to monopolize a feeding station allowing subordinates to scramble for food and achieve higher growth rates than dominants. I also presented a new hypothesis to explain the negative relationship found at low food between absolute growth rate and average mass. Because subdominant fish are usually smaller than dominant fish, subdominant fish may experience higher absolute growth rates than dominants despite lower food consumption rates, as smaller fish need a lower absolute ration to achieve an equal degree of gut fullness and achieve higher absolute growth rates compared to larger individuals that will reach the food capacity of their habitat sooner. In conclusion performance attributes (e.g. growth) depend on the absolute size of individuals in dominance hierarchies and per capita food consumption rates in the hierarchy, which will vary with microhabitat, fish density and stream productivity and may explain physiological correlates that may at first seem puzzling.  87  The major limitation with this last chapter is that I did not explicitly examine patterns of dominance hierarchies, instead I explored trade-offs in growth between wild steelhead and wild coho salmon, when interesting patterns of allometry became apparent. If I were directly exploring dominance hierarchies I would have used small and large fish of the same species instead of using steelhead as small fish and coho as large fish to investigate patterns of allometry. One could argue that differences between species may contribute to differences in observed dominance hierarchy formation and patterns due to differences in aggression and competitive behaviour. In order to better assess dominance hierarchies I could have increased the disparity in fish sizes within a single species. It would also be interesting to directly investigate the effect of physical habitat structure on dominance hierarchies which would allow better estimates of habitat effects on growth in dominance hierarchies. My studies clearly indicate that experiments investigating correlates among physiological metrics need to be cautious about the effects of food consumption, fish density and the allometric structure of dominance hierarchies, since all of these factors can contribute to variation in commonly measured performance metrics like growth, SMR, cortisol, or other metrics of fish condition. Overall my masters thesis addresses a number of inconsistencies in the literature surrounding the link between physiology, adaptive trade-offs and dominance hierarchies in juvenile salmonids and demonstrates the importance of incorporating physiology into the understanding of ecologically important processes, which is often overlooked.  88  References Abbott, J.C., Dunbrack, R.L. Orr, C.D. (1985) The interaction of size and experience in dominance relationships of juvenile steelhead trout (Salmo gairdneri). Behaviour, 92: 241-253 Alanara, A., Burns, M.D., Metcalfe, N.B. 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Food was increased by additionally supplementing with mealworms dispensed with belt feeders to try and ensure satiation. The trial was run from September 25, 2009 to October 17, 2009 at an average temperature of 9 °C. Growth of coho was significantly higher than steelhead at both food levels. This was surprising, since I thought that I was providing enough food to satiate steelhead, but apparently this was not the case since their growth rate was well below their expected maximum, while coho were growing at a rate consistent with their expected maximum. Results support the conclusion that either 1) steelhead and coho were on the same ration, and because steelhead have a lower growth efficiency observed steelhead growth was lower; or 2) steelhead were not eating the excess food provided (e.g. mealworms) to the same extent as coho, hence their lower growth rates. 1) is consistent with published observations of lower steelhead growth efficiency, but is puzzling because steelhead growth rate appeared lower than in the second trial in chapter four. It is possible that invertebrate drift amounts had declined, so that overall food supplementation was reduced, or that growth was reduced at the lower temperatures, but this would have been expected for coho as well. Alternatively, digestibility of meal worms may be low and therefore steelhead, with a potentially shorter gut residence time, were unable to extract nutrients  101  from the meal worms as effectively as coho, making the increase in food unavailable to steelhead.  102  Figure A-1  Figure A-1. The relationship between instantaneous growth rate and average mass for wild juvenile steelhead (squares) and coho salmon (triangles) reared in artificial stream channels in Chapman Creek under high (closed) and low (open) food treatments. Long dashed and dot dashed lines represent steelhead on high and low food, whereas solid and short dashed lines represent coho on high and low food respectively. Note: coho salmon had a significantly higher growth rate than wild steelhead trout at low and high food.  103  Animal Care Certificate  104  

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