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The evolution of swimming capacity among migratory and non-migratory populations of the threespine stickleback… Dalziel, Anne Cecilia 2012

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THE EVOLUTION OF SWIMMING CAPACITY AMONG MIGRATORY AND NON-MIGRATORY POPULATIONS OF THE THREESPINE STICKLEBACK (GASTEROSTEUS ACULEATUS) by Anne Cecilia Dalziel BSc., Acadia University, 2001 Msc., Queen's University, 2003 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (ZOOLOGY)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2012  © Anne Cecilia Dalziel, 2012  ABSTRACT Understanding how complex traits evolve is critical for understanding how animals meet environmental challenges. In my dissertation I studied the mechanisms by which prolonged swimming performance (Ucrit), a complex whole-animal performance trait, has evolved among ancestral anadromous-marine and derived non-migratory stream-resident ecotypes of threespine stickleback (Gasterosteus aculeatus). I showed that stream-resident populations from Bonsall and West Creeks have evolved a decreased Ucrit, but via different genetic mechanisms, and that three additional wild stream-resident populations also had low Ucrits. Collectively, these data are consistent with a role for natural selection in the evolution of a reduced capacity for prolonged swimming after freshwater colonization. I next determined which candidate morphological, physiological, and biochemical traits evolved in conjunction with these decreases in Ucrit capacity in Bonsall and West Creek streamresident populations. I found that a number of traits predicted to influence Ucrit in fishes evolved as predicted in both stream-resident populations. To further assess the associations between these candidate traits and Ucrit, I compared the genetic architecture of Ucrit with the genetic architecture of candidate traits by comparing F1 hybrids to pure F1 crosses. I found that a number of candidate traits had a similar genetic architecture as Ucrit, but that many of these traits were population-specific. These data suggest that non-parallel genetic, morphological and physiological mechanisms may contribute to the evolution of similar performance capacities. To test the associations between candidate traits and Ucrit, I correlated traits with Ucrit in Bonsall Creek F2 hybrids. In F2 hybrids the complete linkage of all divergent traits in F1 crosses is partially broken apart. I found that only four candidate traits (ventricle mass, adductor mass, and adductor and abductor citrate synthase activities) significantly regressed against Ucrit in F2 hybrids, accounting for 17.9% of variation in Ucrit. These data suggest that, when dissociated from other traits, many candidate traits do not have a strong effect on Ucrit, additional unmeasured traits are likely to influence Ucrit, and that many traits are necessary to reach a high Ucrit. This dissertation provides a clear empirical example of the patterns of evolution in a complex trait and its underlying mechanisms.  ii  PREFACE A version of chapter 2 has been published as: “Dalziel, A.C., Vines, T.H., and Schulte, P.M. (2012). Reductions in prolonged swimming capacity following freshwater colonization in multiple threespine stickleback populations. Evolution 66, 1226-1239”. I conceived of this project in collaboration with Drs. T.H. Vines and P.M. Schulte and collected fish from natural populations with Dr. T.H. Vines. I performed all the breeding and rearing of fish, and did all laboratory work, performed all analyses, and wrote the original manuscript. Drs. T.H. Vines and P.M. Schulte provided advice on analyses and contributed revisions to this manuscript. A version of chapter 3 has also been published: “Dalziel, A. C., Ou, M., and Schulte, P. M. (2012). Mechanisms underlying parallel reductions in aerobic capacity in non-migratory threespine stickleback (Gasterosteus aculeatus) populations. J. Exp. Biol. 215, 746-759”. I conceived of this project in collaboration with Dr. P.M. Schulte, performed the majority of the laboratory work, analyzed all of the data and wrote this manuscript. In addition to assisting me with project conception, Dr. P.M. Schulte contributed experimental advice and edited this manuscript. M. Ou completed the analysis of gill surface area (Table 2.1) and hemoglobinoxgyen-binding affinity (Appendix B, Fig. B1). All experiments in this dissertation were approved by the UBC animal care committee (A07-0288). Permits for stickleback collection were obtained from the BC Ministry of Environment (Fish Collection Permits NA/SU06-26169 and NA/SU07-38414).  iii  TABLE OF CONTENTS ABSTRACT .................................................................................................................................. ii  PREFACE .................................................................................................................................... iii  TABLE OF CONTENTS ............................................................................................................ iv  LIST OF TABLES ...................................................................................................................... vii  LIST OF FIGURES ..................................................................................................................... ix  ACKNOWLEDGEMENTS ....................................................................................................... xii  DEDICATION ........................................................................................................................... xiv  Chapter 1: Introduction .......................................................................................................... 1  1.1  The evolution of complex, whole-animal performance traits ............................... 1  1.2  Traits contributing to prolonged swimming performance in fish ........................ 5  1.3  Model species: Threespine stickleback (Gasterosteus aculeatus).......................... 7  1.3.1  Stream-resident and anadromous-marine threespine stickleback ....................... 8  1.4  Research questions ................................................................................................. 10  Chapter 2: Reductions in prolonged swimming capacity following freshwater colonization in multiple threespine stickleback populations. ............................................. 12  2.1  Summary ................................................................................................................. 12  2.2  Introduction ............................................................................................................ 12  2.3  Materials and methods ........................................................................................... 15  2.3.1  Fish collection and rearing................................................................................ 15  2.3.1.1  Common-garden reared crosses.................................................................... 15  2.3.1.2  Wild-caught fish ........................................................................................... 16  2.3.2  Measurement of prolonged swimming speed ................................................... 17  2.3.3  Measurement of morphological traits ............................................................... 18  2.3.4  Measurement of standard and maximum metabolic rates ................................ 19  2.3.5  Statistical analyses ............................................................................................ 20  2.4  Results ...................................................................................................................... 21  2.4.1  Prolonged swimming performance ................................................................... 21  2.4.2  Candidate traits ................................................................................................. 22  2.4.2.1  Pectoral fin surface area and shape ............................................................... 22  2.4.2.2  Body shape ................................................................................................... 23  2.4.2.3  Metabolic rates.............................................................................................. 24  2.5  Discussion ................................................................................................................ 25  2.5.1  Evolutionary forces influencing prolonged swimming performance ............... 26  2.5.2  Morphological and physiological traits ............................................................ 28  2.6  Acknowledgments ................................................................................................... 30  Chapter 3: Mechanisms underlying parallel reductions in aerobic capacity in nonmigratory threespine stickleback (Gasterosteus aculeatus) populations............................ 35  3.1  Summary ................................................................................................................. 35  3.2  Introduction ............................................................................................................ 35  3.3  Materials and methods ........................................................................................... 38  3.3.1  Experimental animals ....................................................................................... 38  3.3.2  Collection of blood and tissue samples ............................................................ 39  3.3.3  Analysis of blood and tissue samples ............................................................... 40  iv  3.3.3.1  Blood hemoglobin concentration and mean cellular hemolglobin content .. 40  3.3.3.2  Gill morphometrics ....................................................................................... 40  3.3.3.3  Ventricle and pectoral muscle masses and enzyme activities ...................... 40  3.3.3.4  Muscle histology........................................................................................... 41  3.3.4  Statistical analysis............................................................................................. 43  3.4  Results ...................................................................................................................... 44  3.4.1  Gill morphology ............................................................................................... 44  3.4.2  Hematocrit and mean cellular hemoglobin content .......................................... 45  3.4.3  Ventricle mass and enzyme activities ............................................................... 45  3.4.4  Pectoral muscle mass, fibre-type, and enzyme activities ................................. 46  3.4.5  Associations between ṀO2,max and underlying traits. ...................................... 48  3.5  Discussion ................................................................................................................ 49  3.5.1  Traits associated with reductions in ṀO2,max.................................................... 49  3.5.2  Comparisons among marine populations.......................................................... 54  3.5.3  Genetic basis of maximum oxygen consumption and underlying traits ........... 55  3.5.4  Does the stickleback oxygen cascade demonstrate symmorphosis? ................ 56  3.6  Acknowledgements ................................................................................................. 57  Chapter 4: Correlates of prolonged swimming performance: Using F2 hybrid crosses to study the traits contributing to differences in performance between migratory and nonmigratory threespine stickleback ecotypes ........................................................................... 68  4.1  Summary ................................................................................................................. 68  4.2  Introduction ............................................................................................................ 68  4.3  Materials and methods ........................................................................................... 70  4.3.1  Experimental animals ....................................................................................... 70  4.3.2  Measurement of maximum prolonged swimming speed .................................. 71  4.3.3  Measurement of morphological traits ............................................................... 72  4.3.4  Measurement of physiological and biochemical traits ..................................... 73  4.3.4.1  Sample collection ......................................................................................... 73  4.3.4.2  Measuring ventricle and pectoral muscle masses and enzyme activities ..... 73  4.3.5  Statistical analysis............................................................................................. 74  4.4  Results ...................................................................................................................... 76  4.4.1  Critical swimming speeds of F2 hybrids .......................................................... 76  4.4.2  Effect of individual candidate traits on Ucrit in F2 hybrids. .............................. 76  4.4.3  Effect of multiple candidate traits on Ucrit in F2 hybrids .................................. 76  4.5  Discussion ................................................................................................................ 77  4.5.1  The effect of candidate traits on Ucrit ................................................................ 78  4.5.2  Ucrit and candidate trait values in F1 and F2 lines ............................................ 81  4.5.3  Traits contributing to the capacity for whole-animal performance .................. 82  4.5.4  Conclusions ...................................................................................................... 84  4.6  Acknowledgements ................................................................................................. 84  Chapter 5: General discussion and conclusions .................................................................. 92  5.1  Major findings and implications ........................................................................... 92  5.1.1  Reductions in swimming performance have evolved in parallel in multiple freshwater populations of stream-resident stickleback ..................................................... 92  5.1.2  Parallel reductions in prolonged swimming capacity are determined by different genetic mechanisms in closely related populations of threespine sticklebacks . 93  v  5.1.3  Multiple candidate traits predicted to influence prolonged swimming have evolved in stream-resident populations ............................................................................ 94  5.1.4  Many candidate traits have a similar genetic architecture as Ucrit, but some of these traits differ between populations ............................................................................. 95  5.1.5  Many underlying traits are necessary to achieve a high capacity for Ucrit........ 96  5.1.6  Anadromous marine threespine stickleback populations ................................. 97  5.2  Strengths and limitations of the dissertation research ........................................ 98  5.3  Future research ..................................................................................................... 100  5.4  Concluding thoughts............................................................................................. 103  REFERENCES ..........................................................................................................................104  APPENDICES ............................................................................................................................126  Appendix A - Supplementary material for Chapter 2 ........................................ 126  A.1  Gait transition speed during Ucrit trials .................................................. 126  A.2  Repeatability of critical swimming speed (Ucrit) ................................... 127  A.3  Plasticity of Ucrit: Lab vs. Wild fish ...................................................... 128  A.4  Plate morphology of F1 hybrid crosses ................................................. 131  A.5  Calculation of P-values for linear discriminants ................................... 132  A.6  Discriminant function analysis of pectoral fin shape ............................ 135  Appendix B - Supplementary material for Chapter 3 ........................................ 136  B.1  Whole blood cell hemoglobin oxygen binding affinity ........................ 136  B.2  Ventricle mass: Effect of Sex ................................................................ 138  B.3  Mass of pectoral muscles: Effect of Sex ............................................... 139  B.4  Pectoral adductor and abductor enzymes: Effect of Sex ....................... 141  Appendix C - Supplementary material for Chapter 4 ......................................... 143  C.1  Repeatability of Ucrit in F2 hybrid stickleback ...................................... 143  C.2  Comparisons among F1 and F2 line crosses ......................................... 144  C.3  Relationship between candidate traits and Ucrit: Fixed effects only ...... 148  C.4  Relationship between candidate traits and Ucrit in each family and sex 149  C.5  Effect of ‘background traits’ ................................................................. 156   vi  LIST OF TABLES Table 2.1. Results of body shape linear discriminant analysis ....................................................34 Table 3.1. Gill morphology of laboratory-bred F1 fish from Bonsall Creek parents ...................62 Table 3.2. Hematocrit, hemoglobin concentration, and mean cellular hemoglobin content of laboratory-bred F1 fish ..................................................................................................................63 Table 3.3. Fibre area for red, pink, and white fibres from abductor muscles of laboratory-bred F1 females from West Creek parents ..................................................................................................64 Table 3.4. Fibre area for the largest and smallest red fibres in the adductor muscles of laboratory-bred F1 females from West Creek parents ...................................................................64 Table 3.5. Percentage of each fibre-type in the abductor muscles of laboratory-bred F1 females from West Creek parents ...............................................................................................................65 Table 3.6. Enzyme activities of laboratory-bred F1 females ........................................................66 Table 4.1. Results of discriminant function analysis of six body shape traits in F2 fish ..............89 Table 4.2. Results of linear mixed-model regressions of candidate traits vs. Ucrit in F2 fish .......89 Table 4.3: Correlations among explanatory variables in F2 fish ..................................................90 Table 4.4: Results of multiple linear regression analyses .............................................................91 Table A1. Results of discriminant function analysis of pectoral fin shape................................135 Table B1. Enzyme activities of laboratory-bred F1 crosses .......................................................142 Table C1. Results of linear regression of candidate traits vs. Ucrit in F2 fish .............................148 Table C2. Residual Pectoral fin area regressed against Ucrit in each F2 family and sex ............153 Table C3. Body shape ld1 regressed against Ucrit in each F2 family and sex.............................153 Table C4. Ventricle mass regressed against Ucrit in each F2 family and sex ..............................153 Table C5. Pectoral adductor mass regressed against Ucrit in each F2 family and sex ................153 Table C6. Pectoral abductor mass regressed against Ucrit in each F2 family and sex ................154 Table C7. COX per gram adductor regressed against Ucrit in each F2 family and sex ...............154 Table C8. CS per gram adductor regressed against Ucrit in each F2 family and sex ..................154 vii  Table C9. LDH per gram adductor regressed against Ucrit in each F2 family and sex ...............154 Table C10. COX per gram abductor regressed against Ucrit in each F2 family and sex .............155 Table C11. CS per gram abductor regressed against Ucrit in each F2 family and sex ................155 Table C12. LDH per gram abductor regressed against Ucrit in each F2 family and sex .............155  viii  LIST OF FIGURES Figure 1.1. Connections across levels of biological organization ................................................11 Figure 2.1. Locations of the threespine stickleback populations used in Chapter 2 .....................31 Figure 2.2. Critical swimming speeds of laboratory-bred and wild-caught stickleback ..............31 Figure 2.3. Pectoral fin size and shape of laboratory-bred F1 stickleback crosses ......................32 Figure 2.4. Body shape of laboratory-bred F1 stickleback crosses ..............................................33 Figure 2.5. Metabolic rates of laboratory-bred F1 stickleback crosses .......................................34 Figure 3.1. Locations of the threespine stickleback populations used in Chapter 3 .....................58 Figure 3.2. Residual ventricle mass of laboratory-bred F1 females ............................................58 Figure 3.3. Residual pectoral adductor and abductor mass of laboratory-bred F1 females .........59 Figure 3.4. Succinate dehydrogenase stained pectoral muscles from laboratory-bred F1 females......................................................................................................................................60 Figure 3.5. LDH activity per gram of adductor and abductor muscle from laboratory-bred F1 females......................................................................................................................................61 Figure 4.1. Histogram of residual critical swimming speeds of F2 hybrids ................................85 Figure 4. 2. Relationship between pectoral fin area, body shape and Ucrit in F2 hybrids ............86 Figure 4.3. Relationship between residual ventricle mass, adductor mass, and abductor mass and Ucrit in F2 hybrids ..........................................................................................................................87 Figure 4.4. Relationship between adductor and abductor enzyme activities and Ucrit in F2 hybrids ...........................................................................................................................................88 Figure A1. Relationship between gait transition speed and critical swimming speed ...............126 Figure A2. Repeatability of Ucrit in laboratory-bred F1 crosses after one month .......................127 Figure A3. Critical swimming speed of wild-caught adult, wild-caught juveniles, and laboratorybred F1stickleback .......................................................................................................................130 Figure A4. Plate values for laboratory-bred F1 hybrid crosses from Bonsall and West Creeks 131 Figure A5. Histogram of F-statistics for 10,000 randomizations of family grouping with pectoral fin shape ld1 ...................................................................................................................132 ix  Figure A6. Histogram of F-statistics for 10,000 randomizations of family grouping with pectoral fin shape ld2 ...................................................................................................................133 Figure A7. Histogram of F-statistics for 10,000 randomizations of family grouping with body shape ld1 .............................................................................................................................133 Figure A8. Histogram of F-statistics for 10,000 randomizations of family grouping with body shape ld2 .............................................................................................................................134 Figure B1. Whole blood cell hemoglobin oxygen-binding affinity of wild-caught Bonsall Creek fish ...............................................................................................................................................137 Fig. B2. Residual ventricle mass of laboratory-bred F1 families ................................................138 Fig. B3. Residual adductor and abductor mass of laboratory-bred F1 families ..........................140 Figure C1. Repeatability of Ucrit in F2 hybrid stickleback .........................................................143 Figure C2. Histograms of residual pectoral fin area and body shape linear discriminant 1 values in F2 fish ......................................................................................................................................145 Figure C3. Histograms of residual ventricle mass, residual pectoral adductor mass, and residual pectoral abductor mass in F2 fish ..................................................................................146 Figure C4. Histograms of mass specific enzyme activities in the pectoral muscles of F2 fish ..147 Figure C5. Relationship between pectoral fin surface area and ld1 and residual Ucrit in F2 hybrid stickleback ...................................................................................................................................149 Figure C6. Relationship between tissue masses and residual Ucrit in F2 hybrid stickleback .....150 Figure C7. Relationship between adductor enzyme activities and residual Ucrit in F2 hybrid stickleback ...................................................................................................................................151 Figure C8. Relationship between abductor enzyme activities and residual Ucrit in F2 hybrid stickleback ...................................................................................................................................152 Figure C9. Effect of ‘background’ traits on relationship between candidate traits and Ucrit ......156  x  LIST OF ABBREVIATIONS AIC - akaike information criterion BL – body lengths CPK – creatine phosphokinase COX – cytochrome c oxidase CS – citrate synthase DFA – discriminant function analysis F1 – first generation cross F2 – second generation cross [Hb] – hemoglobin concentration Hb P50 –whole red blood cell hemoglobin-oxygen binding affinity Hct – hematocrit HOAD – 3-hydroxy-o-acylCoA dehydrogenase LDH – lactate dehydrogenase MCHC – mean cellular hemoglobin content mL - milliliter ṀO2,max – maximum oxygen consumption rate MM – marine x marine cross MS – marine x stream-resident cross O2 - oxygen PK – pyruvate kinase PPT- parts per thousand SEM – standard error of the mean STDEV – standard deviation SDH – succinate dehydrogenase SM – stream-resident x marine cross SS – stream-resident x stream-resident cross QTL – quantitative trait locus  xi  ACKNOWLEDGEMENTS My doctoral thesis has been quite the journey and my supervisor, Patricia Schulte, has been an amazing mentor and friend through it all. Thank you for your guidance and support: you have made me a better scientist and I look forward to many more collaborations in the future. I am also grateful to Trish for attracting such a wonderful group of scientists to her lab, including: Anne Todgham, Nann Fangue, Brian Sardella, Milica Mandic, Charles Darveau, Graham Scott, Wendy Vandersteen, Rush Dhillon, Carol Bucking, Jess McKenzie, Tim Healy, Hyein Kim, Jesse Bittmann, Taylor Gibbons, Katja Anttila, Mauricio Urbina and Michelle Ou. I am especially grateful for the guidance I’ve received from Carol Bucking, Charles Darveau, Rush Dhillon, Nann Fangue, and Anne Todgham. My master’s and honour’s thesis supervisors, Dr. Chris Moyes and Dr. Don Stewart, have also continued their mentorship and I thank them for their support. Tim Vines inspired me to start this (second) dissertation project, and has been my partner in crime ever since. I would like to thank Tim for opening my eyes to the wonderful world of stickles, broadening my scientific horizons, and reminding me of how much I loved mucking around in streams. His passion for science and nature kept me motivated when I was in a less enthusiastic frame of mind. I would like to thank Dolph Schluter for sharing his knowledge of evolutionary biology, statistics, and sticklebacks, and for serving on my dissertation committee; he has done much to enrich my experience as a graduate student. As well, Dolph generously opened up his aquatic facilities to my stickleback and I, which gave me the opportunity to collaborate with his phenomenal lab group (especially Tim Vines, Sean Rogers, Arianne Albert, Rowan Barrett, Kerry Marchinko, Luke Harmon, Simone Des Roches, Antoine Paccard, Gina Conte, Matt Arnegard, Laura Southcott, Pat Tamkee, and Joey Courschene). In particular, Sean Rogers’ boundless enthusiasm and intellectual support gave me the confidence to extend my research into fields that were initially outside my comfort zone. I’ve also been very lucky to have Jeffrey Richards on my dissertation committee. Jeff has answered countless questions about animal physiology and biochemistry, and provided invaluable experimental advice. His lab members have also provided stimulating intellectual discussions, experimental advice, extra chemicals, and comic relief. I’d especially like to thank Milica Mandic, Ben Speers-Roesch, and Gigi Lau. Next, I owe many thanks to Rick Taylor, who conducted the experiments from which my dissertation research stems from, and as a committee member, has done much to improve my thesis. Donald McPhail, professor emeritus, also xii  provided the foundation for my work and readily shared his immense knowledge and enthusiasm for the natural history and ecology of the stickleback. All of the faculty members of the comparative physiology and evolutionary biology groups have been generous with their knowledge, technical expertise and equipment and I would like to thank them for making department of Zoology an intellectually stimulating and supportive work environment. I’d also like to thank the Zoology staff, and Bruce Gillespie, Don Brandys, and Patrick Tamkee in particular. Bruce and Don kept my experiments going by fixing un-cooperative swim tunnels, and Pat’s advice helped to keep my fish happy and healthy (cheaply). As any graduate student knows, it is your friends and colleagues that keep you (reasonably) sane. Three women have been critical to my graduate survival: my two SG’s, Aleeza Gerstein and Milica Mandic, and my fellow Acadia alumni, Meaghan MacNutt. Thank you. Your confidence in me kept me going, your advice improved this dissertation, and your friendship has helped to make the past few years a wonderful journey. In addition, I want to thank Dave Toews, Julie Lee-Yaw, Crispin Jordan, Graham Scott, Angela Scott, Matt Siegle, Jon Mee, Les Harris, J.S. Moore, Jess Hill, Alistair Blachford, Sam Yeaman, Leithen M’Gonigle, Gwylim Blackburn, Heather Maughan, Charissa Fung, Emily Coolidge, Catalina Reyes, Erica Eliason, Dan Baker, Matt Regan, Mike Sackville, Rik Block, Gina Conte, Laura Southcott, Jasmine Ono, Tammy Rodella, Stefani Angelopoulos and Briar Howes for their friendship, support, and advice. My parents, Joan and Frank Dalziel, are the reason that I decided to study biology and complete a doctoral thesis. They sparked my love of learning during our summers exploring the beaches of P.E.I., helped me with my science fair projects, tutored me during grade school, and told me to never stop asking ‘why?’. They have continued to give me their unwavering support during my time in graduate school. Thank you. I would also like to thank my grandparents, Jack and Cecilia Dalziel, for sharing their love of learning and providing “Scholarships from the Bank of Grammie and Grampie”, which allowed me to continue with my graduate education. Finally, I would like to thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Society of Zoologists and the Department of Zoology at UBC for funding this research.  xiii  DEDICATION This thesis is dedicated to Joan and Frank Dalziel  xiv  Chapter 1: Introduction 1.1  The evolution of complex, whole-animal performance traits Whole-animal performance capacity can be defined as ‘the ability of an animal to conduct  an ecologically relevant task’ (Irschick et al. 2008), and is a measure of how well a particular task is completed. Whole-animal performance traits, such as running from predators, swallowing prey, or defending a territory, are of great ecological importance. However, there is comparatively little information about how selection acts upon these complex traits (reviewed by Irschick et al. 2007; 2008), or which traits at underlying levels of biological organization contribute to the evolution of performance in natural populations (reviewed by Kingsolver and Huey 2003). Determining the precise mechanisms by which performance evolves is complicated by the fact that many underlying (or subordinate) traits, and interactions among traits, can contribute to differences in whole-animal performance capacity (reviewed by Arnold 1983; Kingsolver and Huey 2003). As a result of this underlying complexity, termed many-to-one mapping (reviewed by Wainwright 2005), there are multiple different ways that a given performance capacity can evolve. A major question arises from this observation: if there are many different paths that can lead to the evolution of performance, what determines which path is taken (Walker 2010)? The answer to this question is likely dependent upon a variety of factors, such as the level of standing genetic variation within a population. If a population contains little genetic variation in one trait, but substantial variation in other traits, then evolution is predicted to proceed along ‘the genetic lines of least resistance’, or via the traits with the greatest standing variation (e.g. Schluter 1996). Interactions among proteins, biochemical pathways, and physiological systems can also lead to developmental and functional constraints or facilitations along particular mechanistic pathways (reviewed by Schwenk and Wagner 2004; Brakefield and Roskams 2006; Walker 2007; Walsh and Blows 2009; Walker 2010; Futuyma 2011). Finally, stochastic evolutionary processes, demographic processes, and the combined sources and strengths of selection acting on the population under study will determine the rate and direction of phenotypic evolution (reviewed by Lahti et al. 2009). Clearly, predicting how performance traits will evolve is a very complex problem, but empirical studies that determine which mechanistic pathways underlie evolutionary variations in performance capacity can provide insight into the relative importance of these processes.  1  At least three general experimental approaches can be used to dissect the mechanisms by which performance traits evolve: experimental selection studies, studies within natural populations, and comparative approaches among populations and species (reviewed by Futuyma and Bennett 2009). Laboratory-based selection studies and animal breeding programmes have provided much insight into the mechanisms by which complex traits can evolve by directly selecting for increases or decreases in performance capacity (reviewed by Swallow et al. 2009). For example, the mechanisms contributing to differences in voluntary running activity (Garland et al. 2011), maximal oxygen consumption rates (e.g. Kirkton et al. 2009; Gębczyński and Konarzewski 2011), predatory behavior, the ability to eat a herbivorous diet (Sadowska et al. 2008), and life-history traits (reviewed by Roff and Fairbairn 2007; Zera and Harshman 2009) have been examined in conjunction with the evolution of performance. These studies have informed biologists about the extent to which constraints can influence the mechanisms of evolutionary change and whether similar mechanisms underlie parallel changes in performance capacity (reviewed by Swallow et al. 2009; Garland et al. 2011). However, the complex selective environments that animals experience in the wild cannot easily be replicated during experimental evolution in the laboratory (reviewed by Irschick and Losos 2009), limiting the direct ecological relevance of this work. As a result, studies of variation within natural populations are also needed to provide insights into more complex, ecologically relevant situations. A number of studies have correlated variation in performance with candidate traits in natural populations (e.g. Garland 1984; James et al. 2005; Kolok 1999), but in the majority of these studies it is not known if the observed differences in performance are genetically based or due solely to phenotypic plasticity (but see Saastamoinen et al. 2008, 2009), limiting the ability of these studies to provide insights into evolutionary processes. Comparative experiments that evaluate variation in performance among populations or species provide another approach that can reveal patterns and associations between performance and traits at lower levels of biological organization (Fig 1.1). While early studies often compared highly divergent species to maximize variation between groups of interest (reviewed by Bennett and Huey 1990; Garland and Adolph 1991), phylogenetically based comparisons have become the norm in the past decade (reviewed by Feder et al. 2000; Garland et al. 2005). These interspecific comparisons have provided a wealth of information about the traits associated with the evolution of performance capacity (e.g. Holzman et al. 2008; Hulsey et al. 2008; Mandic et al. 2009). However, even well designed comparative studies among species can only detect patterns 2  of variation and cannot draw causal connections. In addition, the recent finding that different underlying traits are often responsible for the evolution of high (or low) performance in different species (e.g. Alfaro et al. 2005; Vanhooydonck et al. 2006; Bergmann and Irschick 2010), indicates that comparative studies will often overlook subordinate traits that have only evolved in one group (reviewed by Losos 2011). In contrast to inter-specific comparisons, intra-specific comparisons between populations provide a number of benefits. First, there is normally less overall divergence among populations relative to comparisons between species, which reduces divergence in traits that are not mechanistically related to performance (reviewed by Langerhans and Reznick 2009). Second, it is likely that similar functional and developmental constraints act within a species, so it is more likely that similar traits will contribute to variations in capacity, which will facilitate the detection of traits responsible for differences in performance. Finally, it is likely that populations with genetically based differences in capacity can be inter-bred (e.g. Rouleau et al., 2010). The ability to make controlled crosses between populations also allows for the use of genetic approaches, such as quantitative trait locus (QTL) mapping, to identify the genetic loci that contribute to differences in performance among populations (reviewed by Feder et al., 2000; Dalziel et al., 2009). In advanced generation hybrids (F2 and beyond), recombination begins to break down the linkage disequilibrium found between loci in parental and F1 populations. The extent of linkage disequilibrium decreases as the number of generations increases because of an increase in the number of recombination events (reviewed by Mackay, 2001). Therefore, in advanced generation hybrid crosses the effects of individual candidate traits on performance can be tested in a genetic background that is largely randomized. However, even with advanced generation crosses, many linked loci will co-segregate, so it is not possible to equivocally implicate a candidate trait or locus as the cause of differences in performance. The presence of natural hybrid zones among populations of interest, which often contain a wide range of recombinants, may be helpful in distinguishing the effects of candidate traits from closely linked loci (reviewed by Dalziel et al. 2009). These natural hybrid zones will also facilitate the study of the selective pressures acting upon performance and underlying traits (Barton and Hewitt 1989). The recent findings that whole-animal performance can evolve in time-scales relevant to inter-population variation (e.g. Reznick and Bryga 1990; O’Steen et al. 2002; Leviton et al. 2003; Lee et al. 2003a; Ghalambor et al. 2004; Arnott et al. 2006; Herrel et al. 2008; Langerhans 2009), further supports the utility of inter-population comparisons to understanding the 3  mechanisms by which performance evolves. However, the majority of the studies mentioned above have not examined the mechanisms contributing to variation in performance in detail (but see Geffeney et al. 2002), or have used ‘gene to performance’ approaches (Dimichele and Powers 1982a, b; Chappell and Snyder 1984; Saastamoinen et. al. 2008, 2009). ‘Gene to performance’ studies test the effects of particular genetic variants on performance phenotypes (reviewed by Dalziel et al. 2009), so are likely to miss other traits contributing to performance capacity. Locomotor performance has been suggested to be an excellent performance trait in which to study the mechanistic pathways by which performance traits evolve (Bennett 1989; Bennett and Huey 1990; Irschick and Garland 2001). This is because there is a good understanding of the underlying traits that can influence locomotory performance (reviewed by Bennett 1990), and locomotory capacity is predicted to be under selection in a number of species (reviewed by Husak and Fox 2008; Irschick et al. 2008). In particular, the evolution of aerobically- fuelled, endurance exercise capacity has been well studied from an evolutionary perspective: there are a number of experimental evolution studies (reviewed by Swallow et al. 2009), inter-individual comparisons (reviewed by Bennett and Huey 1990; Kolok 1999), and comparisons among species (reviewed by Bennett 1990; Turner et al. 2006) that have measured this performance trait. Some research programmes have also used the ‘gene-to-physiology’ approach to link variation in metabolic enzymes to variation in endurance exercise capacity (Chappell and Snyder 1984; DiMichele and Powers 1982a; Saastamoinen et al. 2009). However, there are no studies which have taken the ‘physiology-to-mechanism’ approach to thoroughly examine the underlying traits that contribute to evolved differences in endurance exercise capacity among natural populations or closely related species capable of hybridization. A major reason for this deficit in the literature is that many studies have not tested for genetically based differences in performance, so it is possible that the observed variation is solely due to phenotypic plasticity (e.g. Kearney et al. 2005; Fangue et al. 2008; Llewelyn et al. 2010; Oufiero and Garland 2011; Eliason et. al. 2011). Other studies have reared animals in a common laboratory environment but have found no differences among populations (e.g. McGuigan et al. 2003; Chappell and Odell 2004; Pon et al. 2007; Johnson et al. 2010; Rouleau et al. 2010), or found that differences between populations were not evident when the effect of family of origin was taken into account (Hendry et al. 2011). The few studies that have detected genetically based differences in endurance exercise performance among populations have all been conducted on 4  fish, suggesting that performance can evolve relatively rapidly in fishes (Taylor and McPhail 1985; Taylor and Foote 1991; Arnott et al. 2006). While these studies have tested for associated variation in whole-animal metabolism or morphological traits, a thorough study of the physiological and biochemical traits that may also influence swimming performance has not previously been performed. In this dissertation, I use an inter-population comparative approach to study the mechanisms by which aerobic exercise capacity (a ‘model’ performance trait) evolves in fishes. Teleost fishes are excellent organisms in which to examine the evolution of aerobic exercise capacity because there are large differences in endurance swimming performance within and between species, and the traits that can facilitate high performance capacities are well understood (reviewed by Webb 1994; Bernal et al. 2001; Hochachka and Somero 2001; Gibb and Dickson 2002). This prior knowledge allowed me to form hypotheses about the candidate traits at underlying levels of biological organization that have evolved to cause variation in performance (see Section 1.2). In addition, the red (slow-twitch, oxidative) and white (fast-twitch, glycolytic) skeletal muscle fibres are spatially divided in fishes, which greatly facilitates mechanistic studies (reviewed by Moyes and West 1995; Johnston et al. 2011). These factors, in combination with the ecological importance of prolonged swimming capacity for many migratory and pelagic species (reviewed by Plaut 2001; Wolter and Arlinghaus 2003), makes teleost fishes ideal organisms in which to examine the mechanisms by which endurance exercise performance have evolved in natural populations. 1.2  Traits contributing to prolonged swimming performance in fish Endurance swimming performance in fishes is a complex performance trait that is  influenced by a number of underlying traits, many of which also contribute to variation in other performance traits (reviewed by Walker 2010). Despite this complexity, comparative physiologists have made great progress in identifying the underlying traits that can contribute to differences in prolonged swimming capacity. Much of this work is based upon comparisons of species having exceptionally high prolonged swimming capacities (e.g. Thunnidae, the tunas) with either their less-exceptional congeners (reviewed by Bernal et al. 2001) or more divergent species with low capacities (reviewed by Somero and Hochachka 2002). Manipulative studies (e.g. Pearson and Stevens 1991; Gallaugher et al. 1995; Brauner et al. 1993; Brauner et al. 2011; Seebacher and Walter 2012) and studies on naturally occurring variation within populations (e.g. 5  Kolok 1992; Reidy et al. 2000; Claireaux et al. 2005) have also been used to determine which traits correlate with differences in prolonged swimming (reviewed by Kolok 1999). Together, this work has made it possible to make a number of specific predictions about the traits that are likely to contribute to differences in endurance swimming in fishes. Prolonged swimming in fishes is defined as aerobically-fuelled endurance swimming that is maintained for between 20 seconds and 200 minutes (reviewed by Plaut 2001). Prolonged swimming is mainly powered by red skeletal muscle fibres that are largely dependent upon aerobically generated ATP (reviewed by McKenzie 2011), so the amount of red muscle and the contractile and metabolic properties of the muscle fibres are predicted to influence swimming capacity (reviewed by Syme 2006). Variations in mitochondrial content, contractile protein content and activity, and the rates of Ca2+ cycling and content of Ca2+ handling proteins can all contribute to differences in muscle fibre contractility and metabolic capacity (reviewed by Gibb and Dickson 2002; Zierath and Hawley 2004). In addition, the fuels needed to produce aerobically generated ATP can also be critical determinants of prolonged swimming capacity. Mitochondrial oxidative phosphorylation provides most of the ATP required during prolonged swimming, so maximal oxygen consumption rate (ṀO2,max) is predicted to be a critical determinant of prolonged swimming capacity (e.g. Reidy et al. 2000). Variation in ṀO2,max could be due to variation in any of the sequential steps of the oxygen cascade. For example, differences in oxygen uptake from the water, transport to the mitochondria within the skeletal muscles that power swimming, and use by the mitochondrial electron transport chain can all effect ṀO2,max (reviewed by Wagner, 1996). As well, the amount of metabolic fuel stored (i.e. fats, carbohydrates, proteins), and type of fuel oxidized, is critical for generating the ATP needed to power muscle contraction and swimming (reviewed by Weber and Haman 2005; Weber 2011). Prolonged swimming in fishes also depends on the ability to overcome drag forces generated by the viscous nature of water. Body shape influences drag by determining the amount of pressure drag that a fish must overcome, and thus directly affects the amount of energy needed to power sustained swimming. As expected, a number of traits that influence body streamlining and stiffness, including a caudal peduncle depth, fineness ratio, head depth, and caudal area, have been found to influence drag (reviewed by Blake 2004; Langerhans and Reznick 2009). Changes in fin and body size, shape and movement kinematics can vary the amount of thrust that can be generated (e.g. Walker and Westneat 2002) and behavioral traits such as the propensity to school  6  and the choice of swimming speed may also have large impacts on the capacity for prolonged swimming in fishes in their natural habitats (e.g. Cooke et al. 2004). Despite an understanding of the traits that can influence prolonged swimming performance in fish, and what traits differ among species with high and low capacities, the particular traits that contribute to the evolutionary diversification of swimming performance in any given lineage of fishes are unclear. As yet, no experimental evolution studies have been conducted to select for improved or decreased swimming performance in fish. As well, swimming performance (reviewed by Davidson 1997) and the underlying traits predicted to influence performance are phenotypically plastic (e.g. Farrell et al. 1991; Hoffmann and Borg 2006; Antilla et al. 2008), so require common garden experiments to test for genetically based differences in performance. These common garden studies have seldom been performed. The few intra-specific comparative studies that have used a common-garden approach and found genetically based differences in the capacity for prolonged swimming (Taylor and McPhail 1985; Taylor and Foote 1991; Arnott et al. 2006), have not thoroughly studied the morphological, physiological and biochemical traits which can influence swimming performance. My dissertation research aims to fill this gap in the literature. 1.3  Model species: Threespine stickleback (Gasterosteus aculeatus) Intra-specific comparative approaches are likely to be the most successful when the  evolutionary histories of the populations of interest are known, so that the direction of trait evolution can be inferred. Therefore, I have selected the threespine stickleback (Gasterosteus aculeatus) as a model species in which to study the mechanisms by which prolonged swimming performance has evolved. A particular advantage of this species is that the evolutionary history and ecology of stickleback have been extensively studied (reviewed by Wootton 1976, Wootton 1984; Bell and Foster 1994; Ostlund-Nilsson et al. 2007). The threespine stickleback is a small teleost fish that lives throughout the northern hemisphere in both freshwater and marine environments (reviewed by Wooton 1976; Wooton 1984; Bell & Foster 1994; Ostlund-Nilsson et al., 2007). On the Pacific coast of North America freshwater populations of threespine stickleback evolved from marine populations that invaded lakes and streams after the retreat of the Cordilleran Ice Sheet approximately 10-12,000 years ago (reviewed by McPhail, 1994). This invasion was accompanied by changes in a range of biotic and abiotic variables, such as predation pressure, diet, temperature, and salinity (reviewed by Bell and Foster 1994). Since this 7  time, there has been a rapid diversification of morphology, behavior, life history and physiology between marine and freshwater stickleback ecotypes, and among different freshwater populations (Wooton 1984; Bell & Foster 1994; McKinnon & Rundle 2002; Ostlund-Nilsson et al. 2007). This diversification has resulted in a species complex with at least eight different morphs or ‘ecotypes’. This includes ancestral marine stickleback populations, which can be divided into migratory marine and anadromous marine populations (Saimoto 1993), and recently evolved freshwater populations. Freshwater populations include stream-resident fish, inlet- and outletlake fish, solitary lake fish, and lake-dwelling benthic and limnetic ecotypes (Hagen 1967; Larson 1976; McPhail 1994; Taylor & McPhail 2000). I have chosen to use this species for two major reasons. The first is that wild stickleback populations show a great deal of variation in prolonged swimming capacity among populations (Taylor and McPhail 1986, Blake et al. 2005; Tudorache et al. 2007; but see Schaarschmidt and Jürss 2003). Secondly, multiple populations of freshwater stickleback have evolved from independent colonizations by marine fish (e.g. Colosimo et al. 2005), which allowed me to study multiple replicate populations and can provide insight into the influence of selection on trait evolution (reviewed by Losos 2011). These factors have made the threespine stickleback species complex an excellent system in which to study the genetic bases for evolutionary variation in a number of morphological (e.g. Peichel et al. 2001; Shapiro et al. 2004; Cresko et al. 2004; Colosimo et al. 2005, Miller et al. 2007; Albert et al. 2008; Chan et al. 2010), and physiological traits (Kitano et al. 2010). I have chosen to compare non-migratory, stream-resident and anadromous-marine populations in my dissertation research, for the reasons outlined in the following section. 1.3.1  Stream-resident and anadromous-marine populations of threespine stickleback  Stream-resident and anadromous-marine (hereafter referred to as “marine”) stickleback populations are found throughout the species’ range and have genetically based differences in a number of traits, including body armour (lateral plate number, pelvic spine length, dorsal spine length) and body shape (Hagen 1967; Bell & Foster 1994; Schluter et al. 2004). In the spring and summer, these two ecotypes live and breed in the same tidal streams; marine fish live in the lower, tidally influenced, reaches of the streams that have relatively fast flowing waters, while stream-resident fish live upstream, and are normally found in slow flowing and heavily vegetated areas (Hagen 1967; Virgil and McPhail 1994; A. Dalziel, unpublished data). In a number of streams throughout the species range stream-resident and marine sticklebacks interbreed to form 8  hybrid zones (e.g. Jones et al. 2006). In British Columbia, Canada, many of these hybrid zones have been present for over 30 years (e.g. Hagen 1967; Vines and Dalziel, unpublished data). After breeding is completed in the late summer, juvenile marine stickleback and surviving adults migrate to the ocean to over-winter, and may travel over 100 km from their breeding grounds (e.g. Quinn and Light 1989; Williams and Delbeek 1989). In the spring of the next year, sticklebacks migrate back to freshwater streams to breed, and while there is some evidence for natal philopatry, (Saimoto 1993), its extent is not known. Thus, marine stickleback must migrate to and from the ocean, forage for prey in the open ocean, and maintain their position in high-flow regions of a stream. On the other hand, stream-resident stickleback remain in freshwater yearround and breed in slower flowing, heavily vegetated, regions of the stream (Virgil and McPhail 1994). Based upon these observations, Taylor & McPhail (1986) hypothesized that marine fish would be better endurance swimmers but that stream fish would have an enhanced fast-start performance. Indeed, in wild-caught fish from the Salmon River in British Columbia, Canada, this was found to be the case (Taylor & McPhail 1986). These decreases in prolonged swimming capacity in stream-resident fish have also been observed in wild European stickleback (Tudorache et al. 2007; but see Schaarschmidt et al. 2003). Also in support of this hypothesis, Hagen (1967) found that marine fish preferred to stay in faster flowing currents while stream fish remained in still waters when brought into the lab. However, swimming performance is phenotypically plastic in threespine stickleback (e.g. Lee et al. 2010), and is improved with training in fishes in general (Davison 1997; Anttila et al. 2008). Therefore, it is not clear if the differences in swimming performance among wild populations of stickleback are due to genetic variation, phenotypic plasticity or a combination of these two factors. Many of the traits predicted to influence prolonged swimming performance are also plastic, including body and fin shape in stickleback (Sharpe et al. 2008), the mass of the pectoral muscles used in prolonged swimming in stickleback (Hoffmann and Borg 2006), and metabolic rate, which varies with changes in dominance and with feeding in fish (e.g. Eliason et al. 2008; Sloman et al. 2000). To determine if differences in performance have evolved, it is necessary to control for environmental variation and breed and rear fish in a common environment.  9  1.4  Research questions The overall objective of my research was to determine if, and how, differences in prolonged  swimming capacity have evolved after freshwater colonization of stream-resident threespine stickleback. My first objective was to determine whether the previously observed differences in swimming capacity among wild populations (e.g. Taylor and McPhail 1986; Tudorache et al. 2007) were genetically based. To test the hypothesis that stream-resident and marine stickleback have genetically based (and thus evolved) differences in performance, I bred and reared pure stream-resident, pure marine, and F1 hybrid crosses in a common laboratory environment and measured their capacity for prolonged swimming (Chapter 2). I found that laboratory crosses of stream-resident stickleback had a lower capacity for prolonged swimming than marine populations, which demonstrated that these stickleback populations were an appropriate system in which to study the mechanisms by which prolonged swimming performance evolved. I also tested for parallel evolution of swimming performance, and tested to see if the genetic architecture of swimming performance was similar among populations (Chapter 2). My subsequent objectives focused upon determining the mechanistic basis for these observed differences in performance. I measured phenotypes at multiple levels of biological organization, and have highlighted the specific levels measured within each Chapter in Fig. 1.1. In particular, I hypothesized that variation in swimming performance would be associated with the evolution of morphological (body shape, pectoral fin size and shape) and physiological traits (standard and maximal metabolic rate) in stream-resident fishes. I also tested for associations between swimming performance and candidate traits by comparing patterns of variation in F1 pure and hybrid crosses (Chapter 2 and 3). I found that maximal metabolic rate (ṀO2,max) varied significantly among populations in Chapter 2, and I measured a number of steps in the oxygen transport cascade predicted to underlie differences in ṀO2,max in Chapter 3. I hypothesized that the underlying candidate traits that are the most costly to maintain, either directly or because of tradeoffs with other functions, such as maintaining ion gradients (e.g. a high gill surface and small diameter muscle fibres, can increase ṀO2,max but result in an increased cost for ion pumping), would evolve most rapidly in stream-resident fish with a lowered ṀO2,max. My final objective was to test for correlations between candidate physiological, morphological and biochemical traits and prolonged swimming performance. To accomplish this objective, I generated a set of F2 hybrid crosses, with a largely randomized genetic  10  background, to test the effect of candidate traits that have evolved in stream-resident stickleback (Chapter 4). In summary, I have addressed the following questions in this dissertation: 1. Has the capacity for prolonged swimming evolved in stream-resident stickleback? (Chapter 2) 2. Has this capacity evolved in parallel in multiple stream-resident populations? (Chapter 2) 3. What candidate morphological and physiological traits have also evolved in streamresident fish, and may limit swimming performance in these populations? (Chapter 2 and 3) 4. Which candidate traits are associated with performance in F1 and F2 hybrid line crosses? (Chapter 2, 3, 4)  Figure 1.1. Connections across levels of biological organization from genotype to performance. The levels of organization measured in each chapter of my dissertation are indicated on the right. Chapter 2 tests for associations between swimming performance, morphology and standard and maximum metabolic rate (ṀO2,max) in F1 line crosses. Chapter 3 tests for associations between ṀO2,max and the physiological, biochemical and cellular traits that can contribute to variations in the oxygen transport and utilization cascade, using F1 line crosses. Chapter 4 examines the effect of the candidate traits measured in previous chapters on swimming performance in F2 hybrids. Note that all experiments were conducted on fish bred and raised in a common laboratory environment.  11  Chapter 2: Reductions in prolonged swimming capacity following freshwater colonization in multiple threespine stickleback populations. 2.1  Summary I compared ancestral anadromous-marine and non-migratory, stream-resident threespine  stickleback (Gasterosteus aculeatus) populations to examine the outcome of relaxed selection on prolonged swimming performance. I reared marine and stream-resident fish from two locations in a common environment and found that both stream-resident populations had evolved lower critical swimming speeds (Ucrits) than marine populations. However, F1 hybrids from the two locations displayed significant differences in dominance, suggesting that the genetic basis for variation in Ucrit differs between locations. To determine which traits evolved in conjunction with, and may underlie, differences in performance capacity I measured a suite of traits known to affect prolonged swimming performance in fishes. While some candidate traits did not evolve (standard metabolic rate and two body shape traits), multiple morphological (pectoral fin size, shape and four body shape measures) and physiological (maximum metabolic rate; ṀO2,max) traits evolved in the predicted direction in both stream-resident populations. However, data from F1 hybrids suggested that only one of these traits (ṀO2,max) had dominance effects similar to those of Ucrit in both locations. Overall, my data suggest that reductions in prolonged swimming performance were selected for in non-migratory populations of threespine stickleback, and that decreases in ṀO2,max may mediate these reductions in performance. 2.2  Introduction After a source of selection on a trait is reduced or removed, stochastic processes such as  mutation accumulation and gene flow result in an increased likelihood of trait reduction or loss (e.g. Mackie et al. 2009). Natural selection can also play an important role in shaping trait evolution after a selective pressure is relaxed, as a result of direct (e.g. cost of trait maintenance) or indirect (e.g. tradeoffs or facilitations) costs and benefits (Maughan et al. 2007; Hall and Colgrave 2008; Lahti et al. 2009). While there is an increased probability of trait loss when selection is relaxed, traits with no standing genetic variation, low maintenance costs, and/or sufficient gene flow from adjacent populations still under selection, may be retained (Lahti et al. 2009). Overall, predicting whether, and how rapidly, a trait will decay after selection is relaxed can be more difficult than predicting the outcome of strong directional selection, because trait evolution is highly dependent upon the remaining sources of selection, costs of trait maintenance, 12  population dynamics, and the trait's functional and genetic architecture (Maughan et al. 2007; Lahti et al. 2009). In addition, many ecologically important traits are complex traits that result from the integration of multiple morphological and physiological traits at underlying levels of biological organization (Arnold 1983; Irschick et al. 2008; Dalziel et al. 2009). Therefore, complex traits can evolve by a variety of different mechanisms. Empirical studies that examine the consequences of relaxed selection on complex traits and determine the mechanistic basis for their evolution can provide information about the functional trade-offs that may constrain evolutionary change, and generate further insight into the genetic and functional basis of adaptation. Whole-organism performance traits are measures of an organism's ability to perform fitnessrelated tasks, such as running and biting, and are examples of ecologically important traits that are influenced by a complex suite of underlying morphological and physiological traits (Ghalambor et al. 2003; Walker 2007; Irschick et al. 2008). There is comparatively little information about how selection acts upon whole-organismal performance traits as compared to morphological traits (reviewed by Irschick et al. 2007; 2008), and information about the mechanisms by which whole-animal performance traits evolve after selection is relaxed in the wild is also lacking (reviewed by Lahti et al. 2009). Here, we examine the evolutionary outcomes of relaxed selection on a whole-organism performance trait, prolonged swimming performance, in stream-resident populations of threespine stickleback (Gasterosteus aculeatus). Ancestral populations of marine stickleback invaded freshwater lakes and streams across the Northern Hemisphere after the retreat of the Pleistocene glaciers (Bell and Foster 1994). Many of the sources and directions of selection changed after freshwater colonization, and since this time freshwater stickleback populations have diverged extensively from their marine ancestors, and from each other (reviewed by McKinnon and Rundle 2002; Bell and Foster 1994; OstlundNilsson et al. 2007). In particular, selection on prolonged swimming performance is thought to have been relaxed after freshwater colonization, as, unlike ancestral marine anadromous populations (hereafter referred to as "marine"), freshwater stickleback no longer migrate to, or over-winter in, the open ocean (e.g. Quinn and Light 1989; Williams and Delbeek 1989). In stream-resident populations another source of selection on prolonged swimming performance, the need to maintain position in a current, is also thought to be relaxed because stream-resident fish live in slow flowing, heavily vegetated regions of streams, while marine fish are found in fast-flowing, tidally influenced regions of the same streams during the breeding season and early 13  juvenile life (Hagen 1967; Taylor and McPhail 1986; Virgil and McPhail 1994; A.C. Dalziel unpublished data). The multiple independent colonizations of freshwater streams along the coast of British Columbia also allows for the study of replicate evolutionary events, which can provide insight into the influence of selection on trait evolution (Schluter et al. 2010; Losos 2011). The underlying morphological and physiological traits that contribute to the capacity for prolonged swimming in fish (reviewed by Beamish 1978; Bernal et al. 2001; Farrell 2002; Langerhans and Reznick 2009), and endurance exercise in vertebrates in general (reviewed by Bennett 1989, 1991), have been well studied. Therefore, it is possible to make specific predictions about the traits that contribute to differences in prolonged swimming and the direction of trait evolution following relaxation of selection. Prolonged swimming is primarily fueled with aerobically generated ATP, and the quantity of aerobically produced ATP potentially available for powering swimming can be estimated by subtracting the standard metabolic rate (SMR; the minimum energy needed to sustain life in a post-absorptive, non-reproductive, unstressed ectotherm at rest) from the maximum metabolic rate (ṀO2,max); this measure is called the aerobic scope. Higher ṀO2,maxes (and thus aerobic scopes) are associated with increases in prolonged swimming capacity among individuals (Reidy et al. 2000), higher endurance capacities among species of vertebrates (Bennett 1989), and higher capacities for endurance running after laboratory selection for this trait in rodents (e.g. Rezende et al. 2006; Gonzalez et al. 2006). Therefore, we predict that prolonged swimming capacity and aerobic scope will be positively correlated in stickleback. Morphological traits that affect thrust and drag should also influence prolonged swimming capacity. Threespine stickleback power prolonged swimming with their pectoral fins in a rowing motion (Walker 2004), and stickleback with larger pectoral fins should be able to swim at higher speeds. Fin shape also influences swimming performance in labriform fish that use drag-based, 'rowing' (Blake 1981), but the influence of subtle differences in shape are not known, so we did not make specific predictions about the direction of fin shape evolution. Traits that reduce drag are also predicted to be positively correlated with the capacity for prolonged swimming. Body shape traits that reduce drag include a thin caudal peduncle, a small caudal area, shallow, stiff bodies, a large anterior body mass, a posteriorly placed shoulder point, and a near optimal fineness ratio (reviewed by Walker 1997; Weihs and Webb 1983; Webb 1982; Blake 2004; Langerhans and Reznick 2009).  14  Previous studies have shown that wild-caught stream-resident stickleback have a reduced capacity for prolonged swimming when compared to their marine ancestors (Taylor and McPhail 1986; Tudorache et al. 2007, but see Schaarschmidt and Jürss 2003). This loss of performance is also associated with changes in candidate morphological (body streamlining and pectoral fin size; Taylor and McPhail 1986; Schaarschmidt and Jürss 2003) and physiological traits (metabolic rate; Tudorache et al. 2007). However, swimming performance is phenotypically plastic in threespine stickleback (Lee et al. 2010), and is improved with training in fish (Davison 1997; Anttila et al. 2008). The traits thought to influence performance are also plastic: body and fin shape traits vary with rearing environment in stickleback (Sharpe et al. 2008), pectoral fin size changes seasonally in threespine stickleback (Hoffmann and Borg 2006), and metabolic rates vary with changes in dominance and with feeding in fish (e.g. Eliason et al. 2008; Sloman et al. 2000). Therefore, it is not known whether the differences seen in wild caught stickleback have a genetic basis, or are due solely to phenotypic plasticity. In this study we ask the following questions: 1) Has the capacity for prolonged swimming evolved in stream-resident stickleback? 2) Has this capacity evolved in parallel in multiple stream-resident populations? And 3) What candidate morphological and physiological traits might have evolved to limit swimming performance in stream-resident fish? We address these questions by rearing stream-resident, marine and reciprocal F1 hybrid stickleback crosses from two streams in a common laboratory environment and measuring prolonged swimming with a critical swimming speed (Ucrit) test. We also measure prolonged swimming in wild-caught stream-resident fish from three additional locations to see if swimming performance has evolved repeatedly after freshwater colonization, which is suggestive of selection. We then measure a number of candidate morphological and physiological traits in our laboratory-reared F1 crosses to gain insight into the mechanisms by which prolonged swimming performance might have evolved. 2.3  Materials and methods  2.3.1 2.3.1.1  Fish collection and rearing Common-garden reared crosses: Bonsall Creek and West Creek stickleback  We collected fish from Bonsall and West creeks (British Columbia, Canada; Fig. 2.1) from May to June in 2006 and 2007. Stream and marine ecotypes hybridize in these streams (Hagen 1967; T.H. Vines and A.C. Dalziel, unpublished data), so we collected our fish from sites > 15  500m from the hybrid zone, where <1% of fish from the opposite population are found. Parents for F1 crosses were identified as either marine or stream-resident based upon morphology (see Hagen 1967; McPhail 1994), and their genotype at markers for two loci under differential selection: Eda, a gene that controls lateral plate morphology (Colosimo et al. 2005) and sodium potassium ATPase, an ion transporter that plays a major role in ion transport (Jones et al. 2006; Shimada et al. 2010), following the methods of Barrett et al. (2008). These fish were used to produce F1 crosses by artificial fertilization following the methods of Marchinko and Schluter (2007). In total, 23 crosses were made from Bonsall Creek parents [(7 pure stream (SS), 5 pure marine (MM), 6 stream mother x marine father (SM) and 5 marine mother x stream father (MS) crosses] and 16 crosses were made from West Creek parents (5 SS, 5 MM, 3 SM, 3MS). Fish were raised in dechlorinated Vancouver tap water brought to 2ppt ± 0.5ppt with Instant Ocean® sea salt. Fish ate live brine shrimp twice per day for their first month, Daphnia and bloodworms (Chironomid larvae) daily for the next 3 months, and Mysis shrimp and bloodworms from 4 months on. All individuals were fed to satiation at every feeding. Families were raised in separate tanks and split to 20 fish per tank at 2 months of age. Fish were reared at a natural photoperiod and temperatures ranging from 11-17°C until March (~9-11 months of age). At this date fish were 3.5- 4.5 cm standard length, a size generally considered to be adult (e.g. Garenc et al. 1998), and were individually marked with elastomer tags (Northwest Marine Technology, Shaw Island, WA, USA). Fish were then transferred to a 15°C environmental chamber with controlled 12L: 12D photoperiod to prevent them from entering a reproductive state, and acclimated to these constant conditions for at least a month. We studied swimming performance in young adults that were not yet sexually mature to reduce the effects reproduction (e.g. Ghalambor et al. 2004). Marine fish migrate prior to reproduction so swimming performance is ecologically relevant at this life-stage. The University of British Columbia animal care committee approved all experimental procedures (A07-0288). 2.3.1.2  Wild-caught fish: Kanaka Creek, Salmon River, and Little Campbell River stickleback  We collected adult stream-resident fish from Salmon River (as in Taylor and McPhail 1986), Little Campbell River, and Kanaka Creek in June 2008 (Fig. 1). Wild-caught fish were held at the same conditions as laboratory-bred adults for 1 month and remained healthy.  16  2.3.2  Measurement of prolonged swimming speed: Critical swimming speed (Ucrit)  We used a critical swimming speed (Ucrit) test to measure prolonged swimming performance (Brett 1964). In this test water speed is increased in a stepwise manner until a fish can no longer maintain its position in the current. Ucrit is predicted to be an ecologically relevant measure of prolonged swimming for fishes that migrate, live in the open ocean or live in high-flow streams (Kolok 1999; Plaut 2001), and performance correlates with migratory difficulty among populations of salmonids (e.g. Lee et al. 2003b). We swam six individually labeled siblings simultaneously in a Brett style 10-L swim tunnel (SWIM-10; Loligo Systems, Hobro, Denmark), at a water temperature of 15°C ± 1°C and salinity of 2ppt. Water speed was calibrated with a vane wheel flow sensor (Höntzch ZSR25). During this trial fish were constantly observed to be sure that they did not draft off of each other, and generally spread themselves across the bottom of the flume. All fish were <0.25% of the cross sectional area of the tunnel so correction for solid blocking effects was not required. The Ucrit trial followed Fangue et al. (2008), with minor modifications. We placed fish in the tunnel and let them acclimate for 30 minutes at 0.5 body lengths/second (BL·s−1). We then performed a training test by increasing the speed at 0.5 BL·s−1 increments every two minutes until failure. Fish then recovered for 3 hours. To measure Ucrit, we increased speed at 0.5BL increments every 2 minutes until water velocity reached 50% of the training failure speed. Thereafter we increased speed every 10 minutes until the fish could not maintain its position in the current and fell back against the end of the tunnel three times. Critical swimming speed was determined using the following formula: Ucrit = Ui + (ti/tii · Uii), where Ui is the highest speed the fish was able to swim for a full 10 minute interval (BL·sec-1), Uii is the incremental speed increase (BL·sec-1), ti is the time the fish swam at the final speed (min), and tii is the prescribed period of swimming per speed (10 min). We also recorded the gait transition speed (Appendix A; Fig. A1), and found that Ucrit was significantly repeatable over one month (Appendix A; Fig. A2). To test for an effect of sex on Ucrit, we dissected fish and sexed them anatomically after our experiments were completed, but only 100 of the original 234 fish swum could be classified unequivocally. Of these 100 fish, 81 fish from 21 of the 39 families has at least one known male and female per family and could be used to test for the effect of sex. We did not detect any effect of sex on Ucrit (data not shown, F1,20 = 0.067, p= 0.799), which is in agreement with Whoriskey and Wootton’s (1987) previous work. Thus, we combined the sexes in all later analyses. Note  17  that Hendry et al. (2011) did find significant effects of sex in older, sexually mature stickleback kept at a summer photoperiod. 2.3.3  Measurement of morphological traits predicted to influence prolonged swimming  Photographs of laboratory-bred stickleback (n=234 fish; six fish per family), were taken within a month of Ucrits. Fish were anesthetized with 0.2 g tricaine methanesulfonate buffered with 0.4 g sodium bicarbonate in 1L of water. The right side of the fish was photographed with a ruler in the field of view. A second photograph was taken of the right pectoral fin maximally spread over a laminated sheet of paper. Pectoral fin area was measured by tracing an outline of the fin using Image J (Fig. 2.3A). We used TPSdig 2.1 (Rohlf 2010) to digitize twelve landmarks onto the stickleback’s body (Fig. 2.4A,B) and six landmarks onto the stickleback’s pectoral fin (Fig. 3C). We aligned and corrected pectoral fin landmarks for differences in geometric size using tpsRelw (Rohlf 2010), following Albert et al. (2008). To analyze pectoral fin shape we performed a linear discriminant function analysis (DFA) on the aligned x and y pectoral fin coordinates, grouping our fish into six cross-types (reciprocal hybrid crosses were pooled), with the MASS package in R (Venables and Ripley 2002). We chose to use DFA so that we could determine how F1 hybrid fin shape compared to the fin shape of pure stream and marine fish along the axis of variation that best distinguishes pure parental types. We also used the landmarks depicted in Fig. 2.4A,B to measure six body shape traits predicted to mediate evolutionary variation in prolonged swimming capacity in fishes (e.g. Hawkins and Quinn 1996; Walker 1997; McGuigan et al. 2003; Seiler and Keeley 2007; Langerhans 2009; Rouleau et al. 2010). These traits were: 1) fineness ratio [maximum body depth (landmarks 5 to 11) divided by standard length (landmarks 2 to 8)], 2) shoulder point [distance from landmark 8 to intersection with line from 5 to 11 (point of maximum depth)], 3) head depth (landmarks 6 to 10), 4) posterior depth at 3rd spine (landmarks 4 to 12), 5) caudal peduncle depth (landmarks 1 to 3), and 6) caudal area (sum of area of the two triangles formed by connecting landmarks 1, 3, 12 and 3, 4, 12). Inter-landmark distances were calculated by TMorphGen6c (IMP suite 2006, Zeldith et al. 2004). We corrected measurements for overall body size by performing a least squared regression against mass and using residuals in all subsequent analyses. Residuals were made positive by the addition of a constant, log10 transformed, and divided by 2 for linear measures and by 3 for caudal area. We performed a DFA on the six body shape measures following our methods for pectoral fin landmarks.  18  Reproductively mature stickleback show sexual dimorphism in body and pectoral fin shape, but there is little differentiation in these characteristics in non-breeding fish (Hoffmann and Borg 2006; Kitano et al. 2007). Our fish did not enter breeding condition, and sex did not affect Ucrit (see results), so we did not include sex as a variable in our morphological analysis. 2.3.4  Measurement of standard and maximum metabolic rates (ṀO2,max)  We measured metabolic rate indirectly via oxygen consumption. Both standard (SMR) and maximum oxygen consumption rates (ṀO2,max) were measured on individual fish in Beamishstyle swim tunnels by intermittent flow respirometry (233mL; SWIM-MINI; Loligo Systems, Hobro, Denmark) modified for use with FOXY fiber-optic oxygen probes (Ocean Optics, Dunedin, FL, USA). Swim tunnels were housed in an external tank to maintain temperature, and a connecting water pump could be turned on to flush the inner tunnel with fully oxygenated water from the outer tank. During measurement of oxygen consumption, the pump was turned off and the tunnel was sealed. Fish were fasted for 24-36 hours pre-trial, temperature was maintained at 15°C ± 1°C, salinity at 2ppt ± 0.3 ppt, and oxygen never dropped below 75% saturation. Oxygen probes were calibrated in air and N2 gas at the start and end of every trial. If calibrations drifted by >5% the data were discarded. Background bacterial respiration was measured daily and subtracted from all measures, and tunnels were cleaned with bleach biweekly. SMR was measured overnight in the dark for 90 minute intervals (with a 10 minute flush with oxygenated water), at a tunnel speed of < 10% of critical velocity: this speed mixed the water but allowed fish to rest at the bottom of the tunnel. The mass specific oxygen consumption rate (μmol•g -1 wet weight•h-1) was calculated from the slope of the oxygen trace (recorded as partial pressure of oxygen in torr), over a 50 minute period within each 90 minute interval. Water oxygen content was corrected for barometric pressure, solubility of oxygen in water at 15°C and 2ppt and calculated based upon fish weight and respirometer volume. SMR was calculated as the average of all 50 minute intervals for which no activity or stress (noted as higher oxygen consumption rates) occurred. ṀO2,max was measured during a ramp-speed trial similar to our Ucrit protocol. Fish were acclimated in the tunnel for 30 minutes at a speed of <10% Ucrit while the oxygen probes stabilized. The speed was then immediately increased to 50% of Ucrit, and oxygen consumption was measured continuously for at least three 30 minute intervals in which the velocity was increased continuously such that the fish was constantly increasing its swimming speed. Over a 19  30 minute interval velocity increased on average by 15% of fish's Ucrit in 2.5-5% increments. Between intervals there was a 5 minute flush to replenish oxygenated water. Oxygen consumption was measured during at least three 30-minute intervals approaching, and reaching, ṀO2,max. We used a continuous increase in speed to accurately capture ṀO2,max. ṀO2,max data was not collected for 1 Bonsall SS family, and SMR data was not collected for 4 Bonsall SM and 3 MS crosses. 2.3.5  Statistical analyses  All statistical analyses were conducted using R v2.11.1 (R Development Core Team 2010). Multivariate analyses of morphology are described in the section “Measurement of morphological traits”. To test the influence of cross-type on Ucrit, pectoral fin surface area, and ṀO2,max, we used a mixed-effects model with individual nested within family (random-effects), and family nested within cross-type (fixed-effect) with the nlme package in R (Pinheiro et al. 2009). All data met the assumptions of homogeneity of variance and normality. Tukey HSD post-hoc tests were used to detect pairwise differences using the multcomp package in R (Hothorn et al. 2008). Because there was an elevated type I error rate when testing the influence of cross-type on the linear discriminants (ld) obtained from DFA, P-values for these measures were obtained from a null distribution of F-statistics. We generated this distribution by randomly assigning family groupings to pectoral fin (aligned x,y coordinates) and body shape (6 traits) values 10,000 times, performing DFAs on these new data, and then comparing ld1 and ld2 values with ANOVA. The resulting F-values generated a null distribution, to which we compared our Fvalues to calculate a P-value (Appendix A, Figs. A5-A8). Because there are no major differences between the results of our nested ANOVA and ANOVA on collapsed family means (data not shown), we conducted the randomizations on the family means of our 39 cross-types, and tested the influence of cross-type on pectoral fin ld1 and ld2 and body shape ld1 and ld2 using a oneway ANOVA on family means, and not a mixed-effects model. Values for reciprocal hybrid crosses were not significantly different for any of our measures, so we collapsed these crosses in all analyses, with the exception of Ucrit (Fig. 2.1A). To compare the Ucrit of our laboratory-bred stream-resident crosses (Fig. 2.2A) to our wildcaught stream-resident fish (Fig. 2.2B) we conducted a one-way ANOVA, using family means as replicates for lab crosses, and individual fish as replicates for wild-caught fish. We compared patterns of variation in Ucrit and candidate morphological and physiological traits to examine 20  associations between underlying traits and performance, and did not use correlation analysis, because of the statistical problems that arise when using non-independent F1 hybrids. We also explicitly tested for differences in dominance between West and Bonsall Creeks for Ucrit, pectoral fin surface area, pectoral fin shape ld1, body shape ld1, and ṀO2,max by modifying our original model into a genetic model with terms for additivity, dominance, location, and interactions between degree of dominance and location (y~location+additive+dominance+location*dominance), with the nlme package in R (Pinheiro et al. 2009). Differences in dominance were tested by examining the interaction between location and dominance. 2.4  Results  2.4.1  Prolonged swimming performance: Critical swimming speed (Ucrit)  Laboratory-raised marine fish had significantly higher Ucrits than did stream-resident fish raised in the same conditions, demonstrating that there are genetically based differences in swimming performance among stream-resident and marine ecotypes (Fig. 2.2A; F7,31 = 21.99, P < 0.0001). Wild stream-resident fish from three additional populations (Fig. 2.2B), reached Ucrits that were not significantly different from those of laboratory-bred stream-resident fish (F4,25 = 1.143, P = 0.359; also see Appendix A, Fig. A3). However, we found that wild-caught marine fish from these three additional sites swam very poorly (Appendix A; Fig. A3). To determine if this was representative of genetic differentiation in swimming performance among marine populations, or a decline in Ucrit post-migration, we compared Kanaka Creek wild-caught marine adult fish post-migration and juvenile fish caught prior to migration and raised to adulthood in the lab. We found that juveniles raised in the lab swam as well as our lab raised crosses from Bonsall and West Creeks (Appendix A, Fig. A3), suggesting that wild caught marine adults have reduced swimming capabilities post-migration, possibly due to senescence. There were no differences in Ucrit between reciprocal hybrid crosses from a given location, suggesting that there were no maternal effects (Fig. 2.2A). Hybrids from West Creek and Bonsall Creek had significantly different Ucrits (Fig. 2.2A), and we found significant differences in dominance between the two locations (with stream alleles dominant in Bonsall and marine alleles dominant in West Creek; t34 = 2.35; P = 0.025). These results indicate that distinct genetic mechanisms are responsible for the differences in Ucrit found between stream-resident and marine ecotypes in the two locations. 21  2.4.2  Candidate traits: What is the mechanistic basis for reductions in prolonged  swimming capacity? Because many traits have evolved following the freshwater colonization of threespine stickleback, there is potential for spurious correlations with swimming performance. To minimize this problem we focused on candidate traits that are known to impact prolonged swimming performance in fish (reviewed by Beamish 1978; Bernal et al. 2001; Farrell 2002; Langerhans and Reznick 2009). This is especially important when considering body shape traits, which have extensively evolved after freshwater colonization (e.g. Walker 1997; Leinonen et al. 2006; Albert et al. 2008; Reid and Peichel 2010). We measured candidate traits in our laboratory-bred F1 crosses to see if these traits: 1) evolved in stream-resident fish, and 2) evolved in the direction predicted to decrease prolonged swimming performance. Specifically, we predicted that stream-resident fish would have smaller pectoral fins, less streamlined bodies (thicker caudal peduncle, larger caudal area, deeper bodies, an anteriorly placed shoulder point, lower fineness ratio, smaller head), and a lower aerobic capacity. Measuring traits in F1 hybrids also allowed us to determine the contributions of additive versus non-additive genetic effects. If an underlying trait has strong functional linkages to Ucrit we would expect these two traits to have a similar genetic basis. While our data could not provide causal evidence that a given trait, or set of traits, was responsible for decreases in swimming performance, our experimental design allowed us to reject the hypothesis of a strong functional linkage for candidate traits that did not evolve as predicted, or did not have a similar genetic architecture as prolonged swimming performance. 2.4.2.1  Pectoral fin surface area and shape  Laboratory-raised stream-resident fish had significantly smaller pectoral fins than did marine fish (Fig. 2.3B; F5, 33 = 7.69, P < 0.001). The pectoral fin sizes of F1 hybrids were intermediate to parental values in both streams (Fig. 2.3B), and there were no difference in dominance among the F1 hybrids from the two locations (t34 = -0.301; P = 0.765). This contrasts with our findings for Ucrit, for which significant differences in dominance were detected between Bonsall (stream alleles dominant) and West Creeks (marine alleles dominant) (Fig. 2.3B and Fig. 2.2A respectively).  22  There were also large differences in pectoral fin shape in laboratory-raised fish, as summarized by pectoral fin shape ld1 (Fig. 2.3C, D). Pectoral fin shape ld1 explained 71.2% of the variation in the position of the six landmarks (see Fig. 2.3C and Appendix A, Table A1 for a summary of factor loadings), and distinguished stream-resident fish from marine fish (Fig. 2.3D; F5,33 = 30.43, P < 0.00001; P-value obtained from null distribution). In general, pectoral fin shape ld1 differentiated the long, triangular, fins of marine stickleback from the shorter, more rounded fins of stream-resident fish, which also had a wider distal-edge (Fig. 2.3C). Because we had no specific a priori predictions for the direction of pectoral fin shape evolution, we used pectoral fin shape ld1 as a measure of shape to test for an association between fin shape and Ucrit performance. West Creek F1 hybrids, which swam as well as marine fish, had more 'marine' shaped fins (high pectoral fin shape ld1), and poorly swimming Bonsall Creek F1 hybrids had 'stream-like' fins (low pectoral fin shape ld1). In Bonsall Creek, pectoral fin shape appeared to have a similar genetic basis as Ucrit (stream alleles dominant) (Figs. 2.2A and 2.3D). In West Creek, F1 hybrids had pectoral fins that were more similar to those of marine fish, but were significantly different from those of both parental cross-types (Fig. 2.3D), suggesting that marine fin shape alleles were not as dominant to stream alleles as the alleles underlying differences in Ucrit, which displayed clear dominance of marine alleles (Fig. 2.2A). Explicit tests of dominance detected no difference in dominance for fin shape among the F1 hybrids from the two locations (t34 = 1.20; P = 0.239), unlike our results for Ucrit. The second discriminant function (pectoral fin shape ld2) differentiated Bonsall Creek fish from West Creek fish, indicating that there was also genetic variation in pectoral fin shape among locations (Fig 2.3D; F5,33 = 7.943, P<0.0001; Pvalue obtained from null distribution; Appendix A, Table A1 displays factor loadings).  2.4.2.2  Body shape  Our composite measure of six body shape traits that influence drag (body shape ld1) explained 82.1% of the variation in these traits (Table 2.1), and significantly distinguished stream-resident from marine fish (Fig 2.4C; F5,33 = 52.28, P < 0.00001, obtained from null distribution). Body shape ld1 mainly summarized variation in fineness ratio, caudal area, head depth and caudal peduncle depth (see Table 2.1 for factor loadings). These traits evolved as predicted by the lower Ucrits of stream-resident fish; stream-resident fish from both creeks had a lower fineness ratio, larger caudal area, smaller head, and thicker caudal peduncle. Of the six candidate body shape traits, two (shoulder point and posterior body depth) did not show 23  genetically based differences among crosses. As with Ucrit, we found significant differences in body shape ld1 among hybrids from West and Bonsall Creeks (Fig. 2.4C), and these differences matched performance values; West Creek hybrids had a more 'marine' body shape (Fig. 2.4C), and swam as well as pure marine fish (Fig. 2.2A), while Bonsall Creek hybrids had a more 'stream-like' body shape (Fig. 2.4C), and did not swim as well (Fig. 2.2A). In Bonsall Creek, body shape ld1 had a similar genetic basis as Ucrit (stream alleles dominant), but in West Creek the genetic basis for body shape ld1 appeared additive (Fig. 2.4C). Thus, the genetic basis for body shape ld1 and Ucrit was similar in Bonsall Creek fish, but different in West Creek fish. In addition, there was no significant difference in dominance for body shape ld1 among the F1 hybrids from the two locations (t34 = 1.199; P= 0.239). West Creek marine crosses also had significantly higher body shape ld1 scores than any other cross (Fig. 2.4C), but did not reach significantly higher Ucrits (Fig. 2.2A), suggesting that a body shape ld1 score as high as a West Creek marine fish is not necessary to reach an equivalent Ucrit. The second discriminant function (body shape ld2) accounted for 12.72% of the variation in our body shape data and mainly differentiated Bonsall Creek fish from West Creek fish, indicating that there is genetic variation in body shape among locations (Fig. 2.4C; F5,33 = 10.44, P < 0.00001, obtained from null distribution). There were also characteristic differences in lateral plate morphology among stream-resident and marine fish (Hagen 1967), but no significant differences in plate number between Bonsall and West Creek F1 hybrids (Mann-Whitney U-test, P = 0.687; Appendix A, Fig. A4), suggesting that plate morphology does not have a strong impact on Ucrit at intermediate plate values.  2.4.2.3  Metabolic rates  There were no genetically based differences in standard metabolic rate (SMR) among crosstypes (Fig. 2.5A; F5,25 = 0.686, P < 0.638). Therefore, any differences in aerobic scope (ṀO2,max SMR) must be mediated by changes in ṀO2,max. Because aerobic scope and Ucrit are predicted to be positively associated we predicted that stream-resident fish would have a lower ṀO2,max. Indeed, stream-resident fish from both creeks had significantly lower ṀO2,maxes, and thus aerobic scope, than did marine fish (Fig. 2.5B; F5,32 = 24.444, P<0.001). There were also significant differences in ṀO2,max between F1 hybrids from West and Bonsall Creeks (Fig. 2.5B), that matched variation in Ucrit; West Creek F1 hybrids reached an ṀO2,max similar to those of pure marine fish (Fig. 2.5B), and swam as well as did pure marine fish (Fig. 2A), while Bonsall Creek 24  F1 hybrids had a low ṀO2,max (Fig. 2.5B) and low Ucrits (Fig. 2.2A). There was a significant difference in dominance among ṀO2,max values for F1 hybrids from the two locations (t33 = 2.241; P = 0.032), similar to the pattern for Ucrit. For both of these traits we detected dominance of stream alleles in Bonsall Creek and dominance of marine alleles in West Creek (Fig 2.2A vs. Fig 2.5B). West Creek marine crosses also had a significantly ṀO2,max than did Bonsall Creek marine crosses, but did not reach significantly higher Ucrits, suggesting that having a ṀO2,max as high as that of a West Creek marine fish is not necessary to reach an equivalent Ucrit. 2.5  Discussion Many of the sources of selection that may act to maintain a high capacity for prolonged  swimming in marine threespine stickleback were relaxed when threespine stickleback colonized freshwater streams. In this study we examined the evolutionary outcome of relaxed selection on this whole-animal performance trait by rearing pairs of stream-resident and anadromous-marine stickleback from two locations in a common laboratory environment. We found that streamresident stickleback from both locations have evolved a lowered capacity for prolonged swimming (measured with a Ucrit test), and that three additional wild-caught stream-resident populations also have low Ucrits. Comparisons of the performance of F1 hybrids to that of their pure parental cross-types suggested differences in the Ucrits of sympatric stream-resident and marine fish occur primarily through non-additive genetic effects, but via different genetic mechanisms in these two populations. We also examined the functional basis for reductions in prolonged swimming performance by measuring the direction of evolution in selected candidate traits predicted to influence swimming capacity. We found that a number of morphological (pectoral fin size and shape and body shape) and physiological traits (maximum metabolic rate; ṀO2,max) evolved as predicted after freshwater colonization and may contribute to evolutionary variation in swimming performance among ecotypes. However, only ṀO2,max also had a genetic basis similar to prolonged swimming capacity in both Bonsall and West Creeks. These data suggest that ṀO2,max is the most likely of our candidate traits to cause evolutionary variations in prolonged swimming performance.  25  2.5.1  Evolutionary forces influencing prolonged swimming performance: Evidence for  selection? Neutral factors, such as mutation accumulation, often influence trait evolution after a source of selection is relaxed, but the influences of direct and indirect fitness effects may also be important (Maughan et al. 2007; Hall and Colgrave 2008; Lahti et al. 2009). The observation of repeated independent evolution of reduced swimming performance in our populations of stickleback suggests a possible role for selective trait reduction following relaxation of selection. This hypothesis is supported by independent observations from other parts of the species range. For example, Tudorache et al. (2007) found that wild stream-resident threespine stickleback from Belgium have a lower prolonged swimming capacity (~6.5 BL/sec) than do sympatric anadromous-marine fish (~8.25 BL/sec). In contrast, Schaarschmidt and Jürss (2003) found that only one of two stream-resident populations from the Baltic Sea had a lower Ucrit than did marine fish from this region, but their fish were tested post-reproduction and had a very low prolonged swimming capacities (Ucrits of ~ 3.5 to 4.5 BL/sec, compared to our values of ~ 6 to 10 BL/sec). Our data suggest that these post-reproductive fish were likely senescent (see Appendix A, Fig A3). Freshwater colonizations of stickleback in eastern Europe and the western Pacific occurred independently (Orti et al. 1994), and our findings that there is a different genetic basis for Ucrit in West and Bonsall Creeks also suggests that these colonizations may be independent. A similar loss of the capacity for prolonged swimming has also been observed in non-migratory populations of sockeye salmon (Oncorhynchus nerka), which evolved from anadromous fish after the last glaciation (Taylor and Foote 1991). These rapid (<12, 000 years ago), and independent, reductions in prolonged swimming performance in stream-resident fishes, often in the face of gene flow from marine populations (e.g. Hagen 1967; Jones et al. 2006), are consistent with a role for natural selection in the evolution of reduced swimming performance. Selective trait reduction following a relaxation of selection is expected to be influenced by two types of factors: direct and indirect fitness effects (Fong et al. 1995; Lahti et al. 2009). Direct fitness effects include the costs of trait maintenance, whereas indirect fitness effects include possible functional and genetic tradeoffs with other performance traits still under selection (Lahti et al. 2009). Such tradeoffs among performance traits are distinct from classic life-history tradeoffs (Roff and Fairbairn 2007a), because they cannot be alleviated by increasing resource acquisition (reviewed by Ghalambor et al. 2003; Walker 2007). Consistent with a role for indirect costs influencing the evolution of prolonged swimming, two performance traits hypothesized to 26  experience strong positive directional selection in freshwater stickleback, juvenile growth rate (Barrett et al. 2008; Marchinko 2009) and burst swimming performance (Walker 1997; Bergstrom 2002), are negatively correlated with the capacity for prolonged swimming performance. Trade-offs between growth rate and prolonged swimming capacity have been detected in many fishes (e.g. Kolok and Oris 1995; Farrell et al. 1997; Billerbeck et al. 2001), including threespine stickleback (Alvarez and Metcalfe 2005; Lee et al. 2010), and trade-offs between burst and prolonged swimming are also found in fish (Langerhans 2009b; Oufiero et al. 2011). In stickleback, burst swimming performance is heritable (Garenc et al. 1998), and wild stream-resident fish are superior burst swimmers, but worse prolonged swimmers than marine fish (Taylor and McPhail 1986), consistent with the hypothesis of a functional trade-off. These trade-offs between burst and prolonged swimming in fish are predicted to occur because the body shapes that maximize prolonged swimming act antagonistically on burst swimming (reviewed by Webb 1982; Weihs and Webb 1983; Blake 2004; Langerhans and Reznick 2009). Indeed, trade-offs in swimming performance are associated with differences in body shape in Western mosquitofish (Gambusia affinis) (Langerhans 2009b). The mechanistic basis for functional trade-offs between growth rate and prolonged swimming are not as well understood, but might be mediated by metabolic energy partitioning. For example, in Atlantic silversides (Menidia menidia) faster growing northern populations have a higher SMR, and thus lower scope for aerobic activity, than do southern populations that can reach higher Ucrits (Arnott et al. 2006). In this study we found evidence for genetically based differences in body shape traits that are predicted to mediate tradeoffs between burst and prolonged swimming (e.g. caudal area, caudal peduncle depth, head size), consistent with a role for tradeoffs influencing the evolution of prolonged swimming. However, we did not find any differences in SMR between stream-resident and marine stickleback, despite the observed higher growth rates in low-plated stickleback (Marchinko and Schluter 2007; Barrett et al. 2009). Because SMR represents the sum of all metabolic processes at rest, and many energy-demanding traits vary between streamresident and marine stickleback, extracting the effects of any one process (e.g. growth) on SMR is challenging. In addition, both burst swimming and growth are associated with lateral plate morphology in stickleback (Bergstrom 2002; Hendry et al. 2011; Marchinko and Schluter 2007; Barrett et al. 2009), so determining which traits mediate performance tradeoffs will require studies that control for variation in plate morphology and other correlated traits, while measuring all three performance traits. 27  2.5.2  Morphological and physiological traits contributing to reductions in prolonged  swimming performance The capacity for prolonged swimming is a whole-organismal performance trait that is influenced by a number of underlying morphological, physiological and behavioral traits (Walker 2010). Therefore, this trait exhibits the phenomenon of many-to-one mapping, as many different combinations of trait values can yield equivalent performance (Wainwright et al. 2005). In addition, many of the traits that influence prolonged swimming performance also contribute to other performance traits, and thus exhibit 'multi-tasking' (e.g. pectoral fins are also used for maneuvering) (Walker 2010). Determining the mechanisms by which a complex trait evolves, given the many available pathways, can provide insight into the selective forces acting on organisms in the wild, and the tradeoffs or facilitations that influence trait evolution (Walker 2007). We found that a number of morphological and physiological traits predicted to impact prolonged swimming have evolved in stream-resident fish, and have evolved in the direction predicted by reductions in Ucrit. The evolution of a smaller, and more rounded pectoral fin, and a less streamlined body shape agree with data from wild stream-resident and marine fish (Taylor and McPhail 1986; Schaarschmidt and Jürss 2003), and these differences are also found in other freshwater threespine stickleback ecotypes that vary in prolonged swimming performance [i.e. benthic vs. limnetic (Blake et al. 2005), among lake stickleback (Hendry et al. 2011); among lakes (Walker 1997)], suggesting this is a common response to a relaxation of selection on prolonged swimming. In addition, we found that stream-resident fish from British Columbia have evolved a lowered ṀO2,max, which agrees with the findings of Tudorache et al. (2007) in wild stickleback from Belgium. However, the standard metabolic rate (SMR) of our streamresident fish did not evolve, in contrast to the findings of Kitano et al. (2010) and Tudorache et al. (2007): both of these studies found that marine fish have significantly higher SMRs than stream-resident fish. This discrepancy among studies is likely due to methodological differences. For example, Tudorache et al. (2007) and Kitano et al. (2010) measured SMR during the day, and Kitano et al. (2010) studied fish acclimated to a different photo-period. In addition, any differences in the amount of time fish were allowed to adjust to the stress of confinement in a metabolic chamber may have affected SMR as there is variation in the respiratory response to confinement stress among stickleback populations (Bell et al. 2010). As expected based upon circadian rhythms in metabolism, our night-time measures of SMR were lower than those of 28  Kitano et al. (2010) and Tudorache et al. (2007). So, while our experiments find that the SMRs of stream-resident and marine threespine stickleback do not vary in freshwater, the work of Kitano et al. (2010) and Tudorache et al. (2007) indicate that the routine or active metabolic rates during the day do differ. We also found genetically based differences in body shape and ṀO2,max between marine populations from Bonsall and West Creeks, such that West Creek marine fish had a significantly more streamlined body shape and higher ṀO2,max. This difference could be associated with ecological differences between these two populations as West Creek marine fish travel at least 35 km down the Fraser River to reach the ocean, while Bonsall Creek stickleback breed only a few kilometres from the mouth of the estuary (Hagen 1967, T. H. Vines and A. C. Dalziel, unpublished data). These data suggest that marine fish may have undergone local adaptations in exercise physiology in response to migratory conditions, similar to populations of salmonid fishes (e.g. Taylor and McPhail 1985; Taylor and Foote 1991; Eliason et al. 2011). In addition, these data suggest that there may have been genetic differences in ancestral marine populations at the time of freshwater colonization. If this is the case, differences in the genetic basis for Ucrit between West Creek and Bonsall Creek F1 hybrids could be due to differences in the genetic architecture of marine populations. To further test for associations between candidate traits and performance, we compared the patterns of inheritance of Ucrit and candidate traits. None of the traits we measured displayed evidence of parental effects, but there was significant variation in dominance among traits, and among locations. These data allowed us to reject a strong, simple, functional linkage between body shape, pectoral fin area, pectoral fin shape and Ucrit in West Creek fish, and pectoral fin area and Ucrit in Bonsall Creek fish. Hendry et al. (2011) also found mixed support for the impact of pectoral fin size on Ucrit, further indicating that the associations between fin morphology and swimming performance might be population specific, or dependent on other unmeasured traits, such as the size of the pectoral muscles that power swimming. In our Bonsall Creek crosses we found that body shape and fin shape were associated with Ucrit in pure and F1 hybrid crosses, indicating that these traits have a similar genetic architecture, and might be important mediators of Ucrit performance. However, these two shape traits did not have a similar genetic architecture as Ucrit in West Creek crosses, indicating that the functional architecture underling Ucrit capacity might vary among locations. The only trait that had the same genetic basis as did Ucrit in both populations was ṀO2,max. This strong association in both locations suggests that ṀO2,max might 29  mediate the evolutionary variation in Ucrit found among migratory and non-migratory threespine sticklebacks. ṀO2,max is itself a complex trait which is dependent on a suite of underlying traits that influence oxygen uptake at the gill, transport to the swimming muscles, and utilization in the mitochondrial electron transport chain. Therefore, changes in any step of the oxygen transport cascade, and any of the many genes that contribute to each of these underlying traits could theoretically reduce ṀO2,max (reviewed by Turner et al. 2006; Montgomery and Safari 2007). By investigating the underlying traits that contribute to ṀO2,max, we can gain further insight into the mechanistic basis for reductions on swimming performance, examine possible trade-offs with other performance traits, and determine if the same underlying traits contribute to evolutionary reductions in performance in multiple populations of non-migratory stickleback. 2.6  Acknowledgments I would like to thank Dolph Schluter for sharing his insights on stickleback biology, R code,  excellent statistical advice, laboratory space to raise fish, and ideas for improving this manuscript; all are much appreciated. I thank A.C. Gerstein for assistance with statistical analyses, her help collecting fish, and her perceptive comments on this chapter. Further thanks to J. Courchesne and P. Tamkee for their assistance with fish care, and S. DesRoches, C. Jordan, M. Regan, B.A. Sardella, and C. Spencer for help collecting sticklebacks. I thank S. M. Rogers for assistance with morphological analyses, E.B. Taylor for his insights on stickleback ecology, C.A. Darveau for experimental advice, the Halalt First Nation for granting us access to Bonsall Creek, and Bill Bonsall for sharing his historical knowledge of this stream. Insightful comments from C.L. Peichel, D.J. Irschick, and two anonymous reviewers helped to greatly improve this chapter. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through discovery and discovery accelerator grants to P.M.S and a Canada Graduate Scholarship to A.C.D.  30  Figure 2.1. Locations of the threespine stickleback populations used in this study. (A) Western North America with the sampling area outlined with a grey hatched square (B) Region within the grey hatched square, with stickleback collection sites marked with grey stars and labeled in italics.  Figure 2.2. Critical swimming speeds of laboratory-bred and wild-caught stickleback. (A) Critical swimming speed (Ucrit) of laboratory-bred F1 families from Bonsall and West Creek parents. The ecotypes of the crosses' parents [stream x stream (SS, white circles; N=7 Bonsall Creek and 5 West Creek families), marine x marine (MM, solid black circles; N=5 Bonsall Creek and 5 West Creek families), stream mother x marine father (SM, grey circles; N=6 Bonsall Creek and 3 West Creek families), and marine mother x stream father (MS, grey circles; N=5 Bonsall Creek and 3 West Creek families)] are presented on the X axis. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among the eight cross-types (P < 0.0001). (B) Ucrit of wild-caught stream-resident fish from three additional populations (see Fig. 1), with collection location (Sal = Salmon River, Kan = Kanaka Creek, LC = Little Campbell River) noted on the X axis. Data are presented as means ± SEM. 31  Figure 2.3. Pectoral fin size and shape. (A) Representative anadromous stickleback with a pectoral fin (shaded grey) spread maximally. (B) Pectoral fin area residuals (corrected for body mass) of laboratory-bred F1 families. Data are presented as in Fig. 2.2A, and reciprocal hybrids from are pooled. Different letters indicate significant differences among cross-types (P < 0.001). (C) Landmarks used to measure pectoral fin shape are labeled 1 to 6. Arrows multiply by four the changes in landmark position that occur among cross-types for pectoral fin shape linear discriminant 1 (ld1), and summarize changes in shape from a stream-resident to marine fish. (D) Plot of pectoral fin shape ld1 and ld2 scores of laboratory-bred F1 families from Bonsall (Bon) and West Creek (West). Different letters indicate significant differences among cross-types for pectoral fin ld1 (P < 0.00001). Different symbols indicate significant differences among crosstypes for pectoral fin ld2 (P < 0.0001). Both P-values were calculated from a null distribution of randomized F-values (Appendix A, Figs. A5-A6). 32  Figure 2.4. Body shape of F1 stickleback crosses. Representative stream-resident (A) and anadromous (B) sticklebacks showing landmarks (numbered circles) used to measure the six body shape variables (from black lines connecting landmarks, see materials and methods). (C) Plot of body shape ld1 and ld2 scores for laboratory-bred F1 families from Bonsall (Bon) and West Creek (West) parents. Abbreviations and data presentation follow Fig. 2.3D . Different letters indicate significant differences among cross-types for body shape ld1 (P < 0.00001). Different symbols indicate significant differences among cross-types for body shape ld2 (P < 0.00001). Both P-values were calculated from a null distribution of randomized F-values (Appendix A, Figs. A7-A8).  33  Figure 2.5. Metabolic rates of F1 stickleback crosses. (A) Standard metabolic rate (SMR) and (B) Maximum metabolic rate (ṀO2,max) of laboratory-bred F1 families from Bonsall and West creek parents. Data are presented as in Fig. 2.4B. Different letters indicate significant differences among cross-types (P < 0.001). There were no significant differences in SMR among cross-types. Table 2.1. Results of body shape linear discriminant analysis. Variance explained by, and factor loadings for, the first five linear discriminant (ld) functions produced in the discriminant function analysis (DFA) of six body shape traits (see methods section for a full description of traits). Results for body shape ld1 and ld2 are displayed in Fig. 2.4C. ld1 ld2 ld3 ld4 ld5 Variance explained Coefficients of linear discriminants: Fineness Caudal peduncle depth Caudal area Posterior depth Shoulder position Head depth 1  82.10%  12.72%  4.50%  0.59%  0.09%  55.99 -8.58 -32.30 -1.53 0.56 16.33  -59.21 -32.28 34.09 -19.36 -2.18 2.58  1.25 3.94 6.40 -10.37 -2.97 -25.69  -25.48 -1.39 -17.32 11.37 -9.56 6.85  48.94 6.49 20.17 -8.20 -2.45 7.21  34  Chapter 3: Mechanisms underlying parallel reductions in aerobic capacity in non-migratory threespine stickleback (Gasterosteus aculeatus) populations. 3.1  Summary Non-migratory, stream-resident populations of threespine stickleback have a lower  maximum oxygen consumption (ṀO2,max) than do ancestral migratory marine populations. Here, I examined laboratory-bred stream-resident and marine crosses from two locations (West and Bonsall Creeks), to determine which steps in the oxygen transport and utilization cascade evolved in conjunction with, and thus have the potential to contribute to, these differences in ṀO2,max. I found that West Creek stream-resident fish have larger muscle fibres (not measured in Bonsall fish), Bonsall Creek stream-resident fish have smaller ventricles, and both streamresident populations have evolved smaller pectoral adductor and abductor muscles. However, many steps of the oxygen cascade did not evolve in stream-resident populations [gill surface area, hematocrit (Hct), mean cellular hemoglobin content, and the activities of mitochondrial enzymes per gram ventricle and pectoral muscle], arguing against symmorphosis. I also studied F1 hybrids to determine which traits in the oxygen cascade have a similar genetic architecture as ṀO2,max . In West Creek ṀO2,max, abductor and adductor size all showed dominance of marine alleles, while in Bonsall Creek ṀO2,max and ventricle mass showed dominance of stream-resident alleles. I also found genetically-based differences among marine populations in Hct, ventricle mass, pectoral muscle mass and pectoral muscle pyruvate kinase activity. Overall, reductions in pectoral muscle mass evolved in conjunction with reductions in ṀO2,max in both stream-resident populations, but the specific steps in the oxygen cascade that have a similar genetic basis as ṀO2,max, and are thus predicted to have the largest impact on ṀO2,max, differ among populations. 3.2  Introduction Maintaining a high capacity for aerobic exercise can be an important contributor to  Darwinian fitness (reviewed by Husak and Fox 2008; Irschick et al. 2008). One trait that can limit the capacity for aerobic exercise is an animal's maximum capacity for aerobic energy metabolism (Bennett 1989; Bennett 1991), typically measured as maximum oxygen consumption (ṀO2,max). In spite of high phenotypic plasticity, ṀO2,max can be heritable (e.g. Garland and Bennett 1990; Nespolo et al. 2005), and experience selection in wild populations (e.g. Hayes and O'Connor 1999; Jackson et al. 2001). In addition, selection studies in rodents have shown that aerobic capacity can rapidly evolve (Henderson et al. 2002; Rezende et al. 2006; Sadowska et al. 35  2008; Gębczyński and Konarzewski 2011). Mechanistically, ṀO2,max is determined by a variety of structures and processes related to oxygen uptake from the environment, transport to the working muscles and utilization by the mitochondrial electron transport chain (reviewed by Taylor and Weibel 1981; Wagner 1996). However, the relative importance of each step in this oxygen cascade in determining ṀO2,max may vary with an individual's physiological status (reviewed by Wagner 1996; Wagner 2010), and among individuals, populations, and species (e.g. Frappell et al. 2002). Inter-specific comparisons of metabolically active species to less athletic species have shown that differences in ṀO2,max are often associated with multiple changes in the oxygen transport and utilization cascade (reviewed by Suarez 1996; Hoppeler and Weibel 1998; Bernal et al. 2001; Turner et al. 2006). These inter-specific studies suggest that the oxygen transport and utilization cascade evolves in a 'symmorphotic' fashion, such that all steps in the oxygen cascade are well matched in capacity, and no single trait sets the upper limit for ṀO2,max (sensu Taylor and Weibel 1981). However, evolutionary theory (e.g. Garland 1998; Hansen et al. 2006), and recent intra-specific empirical studies (e.g. Gonzalez et al. 2006; Chappell et al. 2007; Gębczyński and Konarzewski 2011), argue that this is not the case. In particular, experimental evolution studies in rodents suggest that inter-specific comparisons simply miss the step-wise evolution of aerobic capacity that can be observed at shorter time scales (e.g. Henderson et al. 2002; Howlett et al. 2009; Kirkton et al. 2009; Gębczyński and Konarzewski 2011). However, little is known about how maximum aerobic performance and underlying traits have evolved among natural populations that experience more complex selective environments, relative to experimental evolution studies, with multiple divergent and concurrently acting sources of selection (but see an attempt by Odell et al. 2003). Determining which step(s) underlie differences in ṀO2,max among natural populations can provide mechanistic information about the traits limiting ṀO2,max in a particular species, provide insight into the costs of having a high ṀO2,max, and establish the pace at which this ecologically relevant trait evolves in the wild. When studying these questions, intra-specific comparisons among closely related populations may be preferable to inter-specific studies, because there is a reduced likelihood of divergence in traits not mechanistically related to ṀO2,max (reviewed by Langerhans and Reznick 2009). In addition, individuals with genetically based differences in performance capacity can be interbred (e.g. Seiler and Keeley 2007; Rouleau et al. 2010), which allows for the use of forward  36  genetic approaches to identify and test the impacts of ‘candidate’ traits predicted to contribute to differences in performance (Feder et al. 2000; Dalziel et al. 2009). Threespine stickleback are small teleost fish found across the Northern Hemisphere (reviewed by Bell and Foster 1994; Ostlund-Nilsson et al. 2007). In British Columbia, Canada, multiple freshwater populations of threespine stickleback evolved from marine ancestors after the Cordilleran Ice Sheet receded, approximately 10-12,000 years ago (reviewed by McPhail 1994). Since this time, freshwater threespine stickleback populations have diverged from each other, and from their marine ancestors, in a number of morphological, behavioural and physiological traits (reviewed by Mckinnon and Rundle 2002; Ostlund-Nilsson et al. 2007), but crosses between divergent populations are still possible. The multiple independent colonizations of freshwater streams also allows for the study of replicate evolutionary events, which can provide insight into the influence of selection on trait evolution (reviewed by Losos 2011). We have previously shown that two populations of stream-resident stickleback in British Columbia have evolved reductions in maximum oxygen consumption (ṀO2,max) compared to sympatric anadromous-marine populations (hereafter referred to as “marine”) (Chapter 2). Wild streamresident stickleback from Belgium also show reductions in ṀO2,max (Tudorache et al. 2007), which indicates that reductions in maximum oxygen consumption have evolved rapidly (<12,000 years) and in parallel after freshwater colonization, and is suggestive of a role for selection in trait loss (Lahti et al. 2009). Here, we examine a number of steps in the oxygen transport and utilization cascade to determine the mechanistic basis for the reductions in ṀO2,max in stream-resident stickleback. Because many traits in the oxygen cascade are phenotypically plastic in fish (e.g. Hoffmann and Borg 2006; Farrell et al. 1991; Henriksson et al. 2008), we have utilized fish raised in a common laboratory environment to minimize the effects of phenotypic plasticity (Chapter 2). Specifically, we measured traits related to the capacity for oxygen uptake from the water (gill surface area), oxygen transport to the working muscle (blood hemoglobin concentration, hematocrit, and ventricle size), oxygen diffusion from the blood to the mitochondria (muscle fibre-size), and oxygen use at the mitochondria within the swimming muscles (pectoral muscle size and muscle aerobic capacity, which we assessed by measuring fibre-type composition and the activities of mitochondrial enzymes as proxies for mitochondrial content). We predicted that the traits that are the most costly to maintain, either directly or because of tradeoffs with other functions, such as  37  maintaining ion gradients (e.g. a high gill surface and small diameter muscle fibres), would evolve most rapidly in stream-resident fish. 3.3  Materials and methods  3.3.1  Experimental animals  The fish used in this study were collected, bred and raised as described in Chapter 2. Briefly, our crosses were made from fish collected at Bonsall Creek on Vancouver Island and West Creek on mainland British Columbia (Fig. 3.1) (BC Ministry of Environment Fish Collection Permits NA/SU06-26169 and NA/SU07-38414). In this experiment we studied 22 crosses made from Bonsall Creek parents [(5 pure stream (SS), 6 pure marine (MM), 6 stream mother x marine father (SM) and 5 marine mother x stream father (MS) crosses] and 17 crosses from West Creek parents (5 SS, 6 MM, 3 SM, 3 MS crosses). Fish were raised in de-chlorinated Vancouver tap water brought to 2ppt ± 0.5ppt with Instant Ocean® sea salt, and were fed live brine shrimp twice per day for their first month, Daphnia and bloodworms daily for the next 3 months, and Mysis shrimp and bloodworms (Chironomid larvae) from 4 months on. Each cross was raised in a separate 30 gallon tank and families were reduced to 20 fish per tank at the age of 2 months to keep all crosses at similar densities. Fish were reared at a natural photoperiod and laboratory temperatures (~11-17°C) until March (~9-11 months of age). At this age they were transferred to a 15°C environmental chamber with controlled 12L:12D photoperiod (the natural photoperiod for our collection sites in March) to prevent fish from entering reproductive state. The maximum aerobic metabolic rates (ṀO2,max; measured as μmol· wet weight·g-1·h-1) of these fish were originally presented in Chapter 2. We used exercise to induce ṀO2,max because stickleback are highly motivated by the presence of the experimenter and swim well in experimental flumes (Taylor and McPhail 1986; Chapter 2). While ṀO2,max can be reached post-prandially in some sluggish species of fish, such as cod, swimming-results in higher estimates of ṀO2,max in more active fishes, such as salmonids (reviewed by Claireaux et al. 2005). After collecting data for the measures described in Chapter 2, fish were left to acclimate at 15°C and 12L:12D for at least another month before they were terminally sampled for the biochemical measures presented in this paper. We found that a related physiological trait, prolonged swimming performance (Ucrit), is highly repeatable after a month (r2=0.901, p<0.001) (Chapter 2), indicating that the 1 month interval between assessment of ṀO2,max and the underlying traits should not significantly affect our interpretations. At the time of sampling the majority of crosses (31 of 39) were 38  approximately one year and five to seven months of age, but eight West Creek crosses (3 SS, 3 MM, 1 SM, and 1 MS) were approximately two and a half years of age. No fish displayed evidence of senescence at the time of sampling (A.C. Dalziel, personal observations). 3.3.2  Collection of blood and tissue samples  Stickleback were individually sacrificed by placing fish in a container of tank water with an overdose of anaesthetic (1 g/L tricaine methanesulfonate buffered with 2 g/L sodium bicarbonate). As soon as a fish lost equilibrium (< 30 sec), it was removed it from the anaesthetic, blotted dry, and weighed. To collect blood we severed the caudal peduncle with a razor blade just posterior to the cloaca and collected blood in two capillary tubes for the determination of hemoglobin concentration [Hb], and hematocrit (Hct) (see “Analysis of blood and tissue samples” for further information). We then dissected out the heart, pectoral adductor and pectoral abductor muscles with the aid of a dissecting microscope. All tissues were snap frozen in liquid nitrogen, and stored at -80°C. We next removed the left gill basket, rinsed gills with distilled water, added gills to Karnovsky's fixative (2.5% glutaraldehyde and 2% formaldehyde in 0.1 M Sodium Phosphate Buffer, pH 7.2), and stored samples at 4°C. We used a second set of fish from the same crosses to collect pectoral muscle samples for histological measurements. These fish were also terminally anaesthetized with an overdose of anaesthetic (1 g/L tricaine methanesulfonate buffered with 2 g/L sodium bicarbonate), and after they lost equilibrium (< 30 sec), the fish were removed from the anaesthetic, blotted dry, and weighed. The full left pectoral girdle was removed from the fish, coated in mounting medium (Fisher Histoprep; Fisher Scientific, Nepean, ON, Canada), and placed on a cork disk at the pectoral fin insertion point with the pectoral muscle perpendicular to the disk plane. This muscle preparation was rapidly frozen in 2-methylbutane (cooled in liquid N2), wrapped in foil and stored at -80°C. We were unable to collect data from all of our crosses for every measurement. In particular, we do not have gill morphology data for any West Creek crosses, or histological data for any Bonsall Creek crosses. The University of British Columbia animal care committee approved all breeding and experimental procedures (A07-0288).  39  3.3.3  Analysis of blood and tissue samples  3.3.3.1  Blood hemoglobin concentration [Hb] and mean cellular hemolglobin content  (MCHC) We collected Hct samples in heparanized micro-hematocrit capillary tubes (Fisher Scientific, Nepean, ON, Canada), sealed the tubes, stored them on ice until a dissection was complete, and then centrifuged the capillary tubes at 5,000g for 3 min and recorded Hct. To measure blood [Hb], we collected blood in 5 μL micro blood collecting tubes (Fisher Scientific, Nepean, ON, Canada), immediately added this blood sample to 1 mL of Drabkins' reagent (Sigma-Aldrich, Oakville, ON, Canada), and stored samples at 4°C. We measured [Hb] spectrophotometrically at A540 (Blaxhall and Daisley 1973), and calculated mean cellular hemoglobin content (MCHC) as [Hb]/Hct. 3.3.3.2  Gill morphometrics  Our measurements of gill morphology followed Hughes (1984), and were conducted using a Leica MZ16A stero-microscope (Lecia Microsystems GmbH, Wetzlar, Germany). Measurements were made in Image J (Rasband 2011). We used the second gill arch to conduct all measurements. There was variation in filament length along a given gill arch, so each arch was divided into three equal sections (anterior, middle, posterior). We counted the total number of filaments in each section and measured the length of three randomly chosen filaments in each of the three sections (11x magnification). The linear spacing between lamellae along the filaments was determined by measuring the distance covered by 10 lamellae on six randomly chosen filaments (50x magnification). Lamellar area was calculated by breaking lamellae off the filaments and measuring the area of 5 randomly chosen lamellae per sample (115x magnification). Total gill surface area was calculated as, Area = LnB where L is the total filament length (mm) on all four gill arches (each with two rows of filaments), n is the number of lamellae per millimeter on both sides of the filament, and B is the average bilateral surface area of the lamellae (mm2). 3.3.3.3  Ventricle and pectoral muscle masses and enzyme activities  Frozen ventricles, pectoral adductor and pectoral abductor muscles were weighed and immediately added to 20 volumes (pectoral muscles) or 79 volumes (ventricles) of chilled homogenization buffer (50 mM hepes, 1 mM EDTA and 0.1% Triton X-100; pH 7.4), and homogenized in 4mL Wheaton glass homogenizers kept on ice. We measured enzyme activities for cytochrome c oxidase (COX; EC 1.9.3.1, complex IV in the electron transport chain), citrate 40  synthase (CS; EC 2.3.3.1. a citric acid cycle enzyme), creatine phosphokinase (CPK; EC 2.7.3.2, an enzyme which reversibly catalyzes the transfer of phosphate between ATP and creatine phosphate, a compound which stores energy and facilitates intra-cellular ATP transfer), pyruvate kinase (PK; EC 2.7.1.40, an enzyme in the glycolytic pathway), lactate dehydrogenase (LDH; EC 1.1.1.27, an enzyme which catalyzes the inter-conversion of pyruvate and NADH to lactate and NAD+, which is critical for replenishing NAD+, and maintaining high glycolytic flux during cellular hypoxia), on whole cell extracts from the ventricle, pectoral adductor and pectoral abductor at 25°C (following Moyes et al. 1997; McClelland et al. 2005). We optimized all assays to ensure that substrates were not limiting and modified protocols for measurement using a plate spectrophotometer (Spectramax 190, Molecular Devices, Sunnyvale, CA, USA). Final concentrations for each assay were as follows: COX (50 μM reduced cytochrome c, 0.5% Tween 20, in 50mM Tris, pH 8), CS (0.15 mM DTNB, 0.15 mM Acetyl CoA, and 0.5 mM oxalacetic acid in 50 mM Tris, pH 8), PK (0.15 mM NADH, 5 mM ADP, 100 mM KCl, 10 mM MgCl2, 10μM fructose 1,6-bisphosphate, 50 U/mL LDH, 5mM phosphoenol pyruvate in 50 mM Tris, pH 7.4), LDH (5 mM NADH, 25mM pyruvate in 50 mM Tris, pH 7.4), CPK (50 mM creatine phosphate, 3 mM ADP, 1.5mM NADP+, 20mM glucose, 12 mM AMP, 25 mM MgCl2, 2 U/uL Hexokinase, 1.5 U/mL GPDH in 50 mM Tris, pH 7.4). We assayed COX on fresh homogenates that had been kept on ice for less than one hour, and measured all other enzymes and total protein on tissue homogenates that were frozen once at -80°C, thawed, and kept on ice for less than two hours, because preliminary experiments indicated that this approach maximized enzyme activities. All samples were assayed in triplicate, and background reaction rates (no substrate present) were subtracted for CS, PK, LDH, and CPK. We measured the protein content in all tissue homogenates with Bradford Reagent (Sigma-Aldrich, Oakville, ON, Canada), and excluded samples with protein concentrations that were greater than 1 standard deviation from the tissue average protein content. Protein concentrations in homologous tissues were similar in all cross-types (data not shown). 3.3.3.4  Muscle histology  In addition to studying muscle metabolic characteristics by measuring the activities of metabolic enzymes predicted to reflect muscle fibre-type composition, we also directly examined fibre-type composition of pectoral adductor and abductor muscles in our West Creek crosses. Generally, fish have three types of muscle fibres: red or slow-twitch fibres (small fibres with high oxidative and low glycolytic capacities, similar to mammalian type I fibres), pink or 41  intermediate fibres (intermediate sized fibres with high oxidative and high glycolytic capacities, similar to mammalian type IIA/IIX fibres), and white or fast-twitch fibres (large fibres with low oxidative and high glycolytic capacities, similar to mammalian type IIB fibres) (reviewed by Johnston et al. 2011). Much more is known about the metabolic differences found among mammalian fibre-types, so we also incorporated knowledge about homologous mammalian fibres when making our predictions (reviewed by Zierath and Hawley 2004). We predicted that stickleback red fibres would have high CS and COX activities (which we use as proxies for “aerobic capacity”), pink fibres would have similar or slightly lower CS and COX activities, and white fibres would have the lowest activities of these mitochondrial enzymes. Red fibres were also predicted to have the lowest activities of LDH (which we use as a proxy for “glycolytic potential in oxygen-limited conditions”), PK (a proxy for “glycolytic potential”), and CPK (a proxy for phosphocreatine levels). White fibres are predicted to have the highest activities of CPK of all three fibre-types. Our predictions for PK and LDH activities in white and pink fibres were less clear. The mammalian literature suggests that white fibres should have the highest activities of PK, and LDH, but in Common Carp (Cyprinus carpio) pink fibres have higher (Johnston et al. 1977) or similar LDH activities as white fibres (Jabarsyah et al. 2000). Te Kronnie et al. (1983) found that the axial musculature of threespine stickleback (Gasterosteus aculeatus) is primarily composed of white fibres, with some pink fibres, and no red fibres. To date, the fibre-type composition of pectoral abductors and adductors had not been separately studied in threespine stickleback due to their small size and similar 'red' phenotype (e.g. Guderley and Couture 2005; Orczewska et al. 2010). We examined the fibre-type composition of the pectoral muscles used for prolonged swimming (Taylor and McPhail 1986; Walker 2004) as a proxy for muscle mitochondrial content, which can influence maximum oxygen utilization potential. Frozen pectoral muscle samples were sectioned (10 µm) transverse to fibre length in a −20°C cryostat (Leica CM3050 S, Leica Microsystems, Nussloch, Germany), and sections for histological analysis were taken at the midpoint of the pectoral muscle. These sections were transferred to glass slides and stored at -80°C. Sections were stained for succinate dehydrogenase (SDH) activity following the protocols of Scott et al. (2009), and then photographed using light microscopy (Olympus FSX100). We measured the cross sectional area of at least 20 white, pink, and red muscle fibres in the pectoral adductor muscle and 20 of the largest and 20 of the smallest red fibres in the abductor muscles to quantify muscle size 42  heterogeneity in the adductor, with Image J (Rasband 2011). We also measured the relative area of pink, white, and red fibres in the abductor muscle (there are no white fibres in the adductor muscle) using Image J (Rasband 2011). We attempted to calculate the capillary density in pectoral muscles with an alkaline phosphatase stain. We verified this method for stickleback (A.C. Dalziel and R.H. Dhillon, unpublished), and Mummichog (Fundulus heteroclitus) caudal steaks (Dhillon and Schulte 2011), but were unable to reliably quantify capillary density in pectoral muscle samples, possibly because capillaries in stickleback pectoral muscles are very tortuous (A.C. Dalziel, unpublished). 3.3.4  Statistical analysis  All statistical analyses were conducted using R v2.11.1 (R Development Core Team 2010). We first tested for effects of fish size on all of our measurements. If measurements were significantly correlated with mass, we corrected for size by calculating the residuals from a least squared regression against mass. In the case of ṀO2,max we calculated residuals from a least squared regression of the log10 of ṀO2,max against the log10 of mass because ṀO2,max is normally found to scale exponentially with size (reviewed by Weibel and Hoppeler 2005), and this transformation linearizes the data. We used untransformed data for all other measurements because we had no a priori hypothesis for these relationships, and linear regressions provided good fits to the data. To test the influence of cross-type on our variables we used a mixed-effects model with individual nested within family (random-effects), and family nested within crosstype (fixed-effect) with the nlme package in R (Pinheiro et al. 2009). All data met the assumptions of homogeneity of variance and normality. Tukey HSD post-hoc tests were used to detect pair-wise differences using the multcomp package in R (Hothorn et al. 2008). The number of families measured for each variable is listed in the figure or table legends, and the number of individuals per family sampled is as follows: for blood variables (Hct, [Hb], MCHC) we measured at least three males and three females from each family, for tissue masses we sampled at least three females per family, for histological measures we sampled at least two females per family, and for measurements of enzyme activity we sampled at least two females per family. Our data is not paired at the level of the individual, so comparisons among traits can only be made at the levels of family and cross-type. However, because individual data is nested within family, individual variability is incorporated into our mixed-effects models.  43  The focus of this paper is on the effect of ecotype (i.e. stream-resident vs. marine), and not sex, so we focused on female fish for the majority of our measures (histology, enzymes and tissue masses). However, we did collect sufficient data from both sexes for gill morphology and blood variables and for these measures we present data for both sexes and divide cross-types into a male and female groups in statistical analyses. For the remainder of our measured variables (histological data, tissue masses, and enzyme activities), we did not collect a sufficient number of males from all cross-types so we presented data for, and performed statistical tests on, females only. In the cases where we were able to measure enough males to also investigate the effect of sex in a sub-set of cross-types (ventricle and pectoral muscle mass, and pectoral enzyme activities), we present male and female data as an online supplement (Appendix B, Figs. B2-4 and Table B1), and have highlighted any significant effects of sex in the main text. We also explicitly tested for an effect of location (i.e. Bonsall vs. West Creeks), by using a mixed-effects model with location as a fixed-effect, and cross-type, family and individual as nested random-effects. Differences in dominance between West and Bonsall Creeks crosses were detected by modifying our original statistical model into a genetic model with terms for additivity, dominance, location, and interactions between degree of dominance and location (y~location+additive+dominance+location*dominance), with the nlme package in R (Pinheiro et al. 2009). Differences in dominance between Bonsall and West creeks were tested by examining the interaction between location and dominance. We did not use correlation analysis to detect associations between ṀO2,max and candidate morphological and physiological traits because of the statistical problems that arise when using non-independent F1 hybrids. While our data cannot provide causal evidence that a given trait, or set of traits, is responsible for decreases in ṀO2,max, our experimental design does allow us to reject the hypothesis of a strong functional linkage for candidate traits that did not evolve as predicted, or did not have a genetic architecture similar to that of prolonged swimming performance. 3.4  Results  3.4.1  Gill morphology (both sexes; Bonsall Creek crosses only)  There were no significant differences among cross-types in filament number (Table 1; F5,30= 2.215; P = 0.0788), lamellar spacing (Table 1; F5,30=1.134; P = 0.3639), or bilateral lamellar area (Table 1; F5,30=2.2816; P = 0.0716). There were significant differences in total filament length among cross-types (Table 1; F5,30= 2.737; P = 0.0375), but this variation did not result in 44  significant differences in overall gill surface area among Bonsall Creek laboratory raised crosses (Table 1; F5,30= 2.475; P = 0.0543). 3.4.2  Hematocrit (Hct) and mean cellular hemoglobin content (MCHC) (both sexes;  Bonsall Creek and West Creek crosses) We found significant differences in Hct among cross-types (Table 2; F11,271 = 4.989, P < 0.0001). All differences detected by post-hoc tests were between West and Bonsall Creek crosses, suggesting an effect of location and not ecotype. The effect of location was significant in our mixed-effects model explicitly testing this (t10 = 5.595; P = 0.0002). There were also significant differences in [Hb] among cross-types (Table 2; F11,280 = 4.5744; P =0.0001), but differences in [Hb] are influenced by Hct, as, all else being equal, samples with a higher Hct will have a higher [Hb] for a given volume of blood. When corrected for hematocrit, we found no differences in [Hb] per red blood cell (MCHC) (Table 2; F11,255= 2.092, P = 0.0213). We also conducted preliminary measurements of whole-blood-cell hemoglobin P50 (Hb P50) from wildcaught Bonsall creek marine and stream-resident fish, and did not find significant differences among ecotypes (Appendix B, Fig. B1). 3.4.3  Ventricle mass and enzyme activities (females only; Bonsall Creek and West Creek  crosses) We found significant differences in residual ventricle mass among cross-types, with Bonsall Creek marine crosses having significantly larger hearts than those of all other crosses except West Creek marine crosses (Fig. 3.2; F5,30 = 3.460; P = 0.0138; females only). While West Creek marine fish had slightly larger ventricles than did stream-resident fish from West Creek, this difference was not significant. We did not find a significant effect of location (t4= -0.887; P = 0.4253; females only) or sex (F1,6= 0.4487; P = 0.5278) on ventricle mass (Appendix B, Fig. B2). We measured the activities of cytochrome c oxidase (COX) and citrate synthase (CS) as markers for mitochondrial content, pyruvate kinase (PK) and lactate dehydrogenase (LDH) as markers for glycolytic capacity, and creatine phosphokinase (CPK) as a marker for ATP transfer potential in the ventricle. We found no significant differences among cross-types for CS (F5,30 =2.0557; P = 0.0991), PK (F5,30 =0.8933; P = 0.4981), LDH ( F5,30 =1.9132; P = 0.1216), or CPK (F5,30 =1.370; P = 0.2634) activity per gram ventricle (Table 3.4). We did find significant differences among cross-types for COX activity per gram ventricle (F5,30 =8.1045; P < 0.001), and post-hoc 45  tests indicated that five of the six significant differences were between West Creek and Bonsall Creek crosses. The effect of location on ventricle COX activity was significant (t4 = 2.8035; P = 0.0486 ), with West Creek stream-resident and hybrid fish having a slightly higher activity of COX/g ventricle than all Bonsall Creek cross-types, and also West Creek marine crosses. 3.4.4  Pectoral muscle mass, fibre-type (females only; West Creek crosses only), and  enzyme activities (females only; Bonsall Creek and West Creek crosses) We found significant differences in residual adductor (F5,32 = 14.499; P < 0.0001), and abductor mass (F5,32 = 11.251; P < 0.0001), among cross-types (Fig. 3.3). Marine fish from both locations had larger adductor and abductor muscles than sympatric stream-resident crosses. We did not find a significant effect of location on adductor (t4 =1.362 ; P = 0.244) or abductor mass (t4 = 1.41 ; P = 0.2307). We also examined pectoral muscles to see if there were metabolic differences in the muscles of stream-resident and marine fish. In general, a higher mitochondrial content should allow for higher use of oxygen in the muscle, and should facilitate a higher ṀO2,max . The mitochondrial content of muscles is tightly correlated with fibre-type (reviewed by Zierath and Hawley 2004), and can also be assessed by measuring the activity of muscle metabolic enzymes (e.g. Reichmann et al. 1985). We measured fibre-type composition in West Creek crosses (Fig. 3.4), and found that stickleback pectoral muscles were composed of three different fibre types, which we generally classified by fibre size and relative oxidative capacity (SDH staining intensity; Fig. 3.4). White fibres had a large cross sectional area (~3000-4000 μm2) and were lightly stained for SDH, pink fibres were of intermediate size (~1200-2000 μm2) and intermediate SDH staining, and red fibres were small (~ 600-1000 μm2) and darkly stained for SDH (Fig. 3.4A; Table 3.3 and 3.4). The abductor muscle, which powers the recovery stroke, was composed of red, pink and white fibres (Fig. 3.4A). Stream-resident fish had a significantly lower percentage of red fibres in their pectoral abductor muscle than did marine fish, and hybrids had an intermediate percentage of red fibres (Table 3.5; F2,11= 4.154; P = 0.0453). The adductor muscle, which powers the thrust-generating stroke, was composed of primarily red fibres (~ 600-1000 μm2), but we also detected some fibres that were intermediate to red and pink fibres in size (~900-1800 μm2) and staining, which we called 'red-pink' fibres (Fig. 3.4B, C, D). We found qualitative differences in the number of 'red-pink' fibres (A.C. Dalziel, personal observations), and  46  quantitative differences in their size (Table 3.4), such that stream-resident fish appeared to have more, larger, 'red-pink' fibres than marine fish. We next investigated the metabolic capacity of pectoral muscles by measuring the activities of a suite of metabolic enzymes (Table 3.6: CS, COX, PK, LDH, and CPK). These measures allowed us to compare our histological and enzymatic results in West Creek fish, so that we could gain insight into the metabolic characteristics of Bonsall Creek muscles (as we did not have histological samples from these crosses). Our histological data suggested that the adductor muscle is mainly composed of red-fibres (high oxidative and low glycolytic capacity), but that the adductors of West Creek stream-resident fish also had a population of 'red-pink' fibres (high oxidative and glycolytic capacity) that were not present in marine fish (Fig. 3.4; Table 3.4). Therefore, we predicted that stream-resident fish would have similar activities of mitochondrial enzymes (CS, COX) and higher activities of glycolytic enzymes (PK, LDH). As predicted, we found that West Creek stream-resident fish had similar activities of CS and COX, and higher LDH activities per gram adductor (Table 3.6), but we did not detect differences in PK activity among ecotypes (Table 3.6). In general, our histological and enzymatic data for the pectoral adductor muscle were concordant. Our histological data for the abductor muscles demonstrated that West-Creek streamresident fish had a lower percentage of red-fibres than did West Creek marine fish (Table 3.5; due to a combination of more white and pink fibres in stream-resident fish). Thus, we predicted that West Creek stream-resident fish should have lower activities of aerobic enzymes (CS, COX), and higher activities of glycolytic enzymes (PK, LDH) and CPK in their abductor muscle. We did not find any significant differences in COX, CS, PK, or CPK activities per gram abductor, but our predictions for LDH activities were met (Table 3.6). Overall, our histological and enzymatic data for West Creek abductor muscles did not match the predictions from the histological data perfectly, but both measures were able to detect fibre-type differences in the abductor and adductor muscle. Therefore, measuring LDH activities per gram adductor and abductor muscle in Bonsall Creek fish reflected differences in fibre-type composition. Our combined enzymatic data for Bonsall and West Creek crosses displayed no significant differences among any cross-types in adductor COX or CS activity (Table 3.6; COX, F5,32 =1.534, P = 0.2069; CS, F5,32 =1.2841, P = 0.295), and no differences in adductor PK and CPK activity among ecotypes (Table 3.6; PK, F5,31 =4.389; P = 0.0039; CPK, F5,31 =3.7887, P = 0.0086). There were also no significant differences in COX (Table 3.6; F5,32 =0.4954; P = 7773) 47  or CS (Table 3.6; F5,32 = 1.9332; P = 0.1162) activities per gram of abductor muscle, and no ecotype-specific differences in CPK and PK activities per gram abductor muscle (Table 3.6; CPK, F5,31 = 4.0223, P = 0.0063; PK, F5,32 = 4.9442, P = 0.0018 ). However, there were significant differences among ecotypes in LDH activity per gram adductor (Fig. 3.5A; Table 6; F5,32 = 6.773; P = 0.0002), and abductor (Table 3.6; F5,31 = 10.2467; P = 0.0001; Fig. 3.5B), such that marine crosses from both locations had significantly lower LDH activities than did streamresident crosses from both locations (Table 3.6; Fig. 3.5A). The presence of similar activities of mitochondrial (CS, COX) and glycolytic (PK) enzymes, but different activities of LDH, suggests that stream-resident fish from both populations had a higher proportion of pink, fast-oxidative glycolytic fibres in their adductor and abductor muscles. We also found some differences in enzyme activity between the pectoral adductor and abductor muscles. The adductor muscles from all cross-types had slightly higher COX, equivalent CS and CPK, and lower LDH and PK activities per gram than did the abductor muscles. These findings generally agree with our histological findings that the adductor (composed of red and some red-pink fibres) is more aerobic, and less glycolytic, than the abductor (composed of red, pink, and white fibres). In addition, we studied the effect of sex on the size and enzyme activities of pectoral muscles. We found no effect on adductor or abductor size (Appendix B, Figs.B2-B3), but we found significant differences in enzyme activities among sexes (Appendix B, Table B1). Males had lower PK activities in their abductor and adductor muscles and slightly higher abductor and adductor COX, abductor CS, adductor CPK, and lower abductor LDH activities (Appendix B, Table B1). Our histological data also provided us with information on muscle fibre size (Table 3.3-3.4), which may influence the oxygen cascade by affecting the diffusion distance from the capillary to the mitochondria. We found that stream-resident fish from West Creek had significantly larger white (Table 3.3; F2,13=14.449; P < 0.001), and pink fibres (F2,13=5.752 ; P = 0.0204), in their abductor muscle. The size of 'red-pink' fibres in West Creek stream-resident fish was also larger than these fibres in marine fish (F2,13=5.942; P = 0.0147). 3.4.5  Associations between maximum oxygen consumption (ṀO2,max) and underlying  traits related to oxygen transport and utilization. By measuring traits in F1 hybrids, we were also able to study the genetic architecture (i.e. additive versus non-additive genetic effects) of candidate traits and compare them to our findings 48  for ṀO2,max (Chapter 2). If a given trait has a strong functional linkage to ṀO2,max we expect it to evolve in the predicted direction and to have a similar genetic basis as ṀO2,max. Thus, we can reject the hypothesis of a strong functional linkage between a candidate trait and ṀO2,max for traits that did not evolve as predicted after freshwater colonization, or that did not have a similar genetic architecture as ṀO2,max. We have previously shown that the lower ṀO2,max in Bonsall and West Creek stream-resident fish are the result of loci displaying strong dominance effects, such that stream alleles are dominant in Bonsall Creek F1 hybrids and marine alleles are dominant in West Creek F1 hybrids (Chapter 2). Many traits which have evolved in streamresident fish did not fit these patterns, such as fibre size (Table 3.3 and 3.4) in West Creek crosses, the percentage of red muscle in the abductor muscle (Table 3.5) of West Creek crosses, and abductor and adductor mass in Bonsall Creek crosses (Fig. 3.3). In West Creek crosses abductor mass, adductor mass, and the activity of LDH per gram of adductor and abductor all displayed similar dominance effects as ṀO2,max. In Bonsall Creek, only ventricle mass and LDH per gram of abductor displayed similar dominance effects as ṀO2,max. The only trait that displayed similar dominance effects as ṀO2,max in both locations was the activity of LDH per gram of abductor muscle (based upon post-hoc tests), but when we explicitly tested for differences in dominance among locations we found a non-significant interaction (t31 = -1.965; P = 0.0583). Note that reciprocal F1 hybrid crosses did not vary in ṀO2,max or any of the traits measured in this study (results not shown). These data suggest that mitochondrially-encoded genes and/or parent-of-origin dependent genomic imprinting do not significantly influence these traits. 3.5  Discussion  3.5.1  Traits associated with reductions in ṀO2,max: stream-resident versus marine crosses  Differences in ṀO2,max among individuals, populations or species could be due to variation in any of the sequential steps of the oxygen cascade: (1) convection of oxygen across the respiratory surface (2) diffusion from the respiratory surface to the blood, (3) convection through the blood, (4) diffusion from the capillary to the mitochondria, or (5) use of oxygen in the electron transport chain (reviewed by Taylor and Weibel 1981; Wagner 1996). We examined a series of traits in the oxygen cascade to determine if they evolved in conjunction with decreases in ṀO2,max. The theory of symmorphosis predicts that all steps in the oxygen cascade will have evolved to match the lowered ṀO2,max in stream-resident fish, because maintaining an excess 49  capacity in any of these traits is energetically costly. Evolutionary theory predicts that traits which are costly to maintain, either for energetic reasons or due to functional trade-offs with other performance traits (Lahti et al. 2009), should be the first to evolve after stream-resident fish evolved a lower ṀO2,max. These two hypothesis both argue that maintenance costs of 'excess capacity' should drive evolutionary change, but evolutionary theory does not predict that all traits in the oxygen cascade will evolve together, and also makes predictions about the order of evolutionary change (e.g. which traits will evolve first). We have used mechanistic knowledge about possible tradeoffs with other performance traits [e.g. the osmo-respiratory compromise influencing fish gill size (Sardella and Brauner 2007), and muscle fibre size (Johnston et al. 2005; Jimenez et al. 2011)] and the energetic costs of trait maintenance [(e.g. cost of maintaining large cardiac and skeletal muscle masses (e.g. Daan et al. 1990; Zera et al. 1998)] to form a set of predictions as to which oxygen cascade traits may have evolved in stream-resident fish in the relatively short (<12,000 years) time since they diverged from marine stickleback. Specifically we predicted that stream-resident populations with a lower ṀO2,max would have evolved smaller gill surface areas, lower hematocrits (Hct), smaller hearts, smaller pectoral muscles, and larger pectoral muscle fibres than their migratory marine ancestors. Contrary to our predictions, we found no differences in gill surface area among streamresident and marine ecotypes reared in a common laboratory environment at a salinity of 2 ppt (Table 3.1). This was surprising, because in addition to the potential impact on ṀO2,max, we predicted that a smaller gill surface area would experience positive selection in freshwater in stickleback. This is because the fish gill is the primary site of osmoregulation and respiration, causing a trade-off between these two tasks (reviewed by Sardella and Brauner 2007), and after colonization of freshwater, a new source of selection was applied in stickleback: selection for survival in freshwater streams and lakes during the cold winter months. Ancestral marine stickleback cannot successfully osmoregulate under these conditions, and experience high mortality rates in freshwater at cold temperatures (Schaarschmidt et al. 1999). Thus, it has been hypothesized that stream-resident fish experienced strong selection for survival in the cold, low salinity waters, which they now inhabit (e.g. Heuts 1945, Guderley 1994, Schaarschmidt et al. 1999). Indeed, lake-resident stickleback populations can survive at lower temperatures than can marine stickleback in freshwater (Barrett et al. 2011). These ecological changes suggest that smaller gills, which are predicted to be favoured during osmoregulatory challenges, would be selected for in freshwater. It is possible that other 50  traits related to oxygen uptake and ion leakage at the gill, which we did not measure in the current study, such as lamellar blood-to-water diffusion distance, gill perfusion, or ventilation rate may have evolved in threespine stickleback after freshwater colonization (e.g. Henriksson et al. 2008). Fish gills are also highly plastic, and able tobe remodeled when exposed to changes in salinity, oxygen concentrations, and temperature (reviewed by Nilsson 2007), so it is also possible that any differences in gill surface area among ecotypes would only be observed after acclimation to cold, freshwater. Our predictions for gill surface area also complicated by the differences in growth rate found among low plated (stream-resident phenotype) and fully plated (marine phenotype) stickleback, such that low-plated fish have a faster growth rate in freshwaters (Marchinko and Schluter 2007; Barrett et al. 2009). Thus, it is possible that a high capacity for oxygen uptake is needed to maintain the high growth rates of freshwater ecotypes, so that smaller gill surface area is not favoured in these populations. Finally, it is possible that evolutionary constraints (e.g. a lack of genetic variation; reviewed by Garland and Carter 1994; Feder et al. 2000; Brakefield and Roskam 2006; Futuyma 2010), have limited the evolution of gill surface area and related physiological traits. Decreases in hematocrit (Hct) and mean cellular hemoglobin content (MCHC) could also contribute to reductions in ṀO2,max by reducing the oxygen carrying capacity of the blood. Increases in Hct increase blood viscosity (e.g. Egginton 1996) and the energetic costs of pumping blood. However, these costs may not be significant at the Hct levels and temperatures common for stickleback (Gallaugher et al. 1995). We did not find any differences in Hct or MCHC among stream-resident and marine crosses (Table 3.2). We also measured the wholeblood hemoglobin-oxygen binding affinity (Hb P50) of wild Bonsall Creek stream-resident and marine fish, and did not find significant differences among ecotypes (Appendix B, Fig. B1). Together, these data suggest that differences in the oxygen carrying capacity of the blood have not evolved in conjunction with decreases in ṀO2,max in stream-resident stickleback. Reductions in cardiac output can also decrease ṀO2,max via decreases in oxygen convection. Indeed, cardiac output is correlated with ṀO2,max in salmonids (e.g. Claireaux et al. 2005; Eliason et al. 2011), and if all else is equal, ventricle size should correlate with cardiac output. Traits that are costly to maintain may undergo particularly rapid evolution for trait loss, when selection is relaxed (Lahti et al. 2009). Thus, ventricle mass might be predicted to evolve rapidly if the energetic costs of maintaining a large heart are high. Indeed, heart size is positively associated with basal metabolic rate among species of birds (e.g. Daan et al. 1990) and strains of laboratory 51  mice (e.g. Konarzewski and Diamond 1995; Brzek et al. 2007), suggesting high maintenance costs. However, heart mass is also correlated with the mass of other metabolically active organs (Daan et al. 1990; Konarzewski and Diamond 1995; Brzek et al. 2007), and is not always correlated with basal metabolic rate (e.g. Chappell et al. 2007), suggesting that the metabolic costs of maintaining a large heart are context dependent. The differences in growth rate among stickleback ecotypes also complicate our predictions for ventricle mass evolution, as a high capacity for convective oxygen transport may also be needed to maintain high growth rates. We found variation in ventricle size among ecotypes, but these effects differed among locations: Bonsall Creek stream-resident crosses had significantly smaller hearts than did sympatric marine crosses, but West Creek stream-resident crosses had ventricles that did not differ significantly from those of sympatric marine crosses (Fig. 3.2). However, we found no differences between marine and stream-resident crosses in the activity per gram ventricle of CS or COX (Table 3.6), suggesting that there has been no evolutionary changes in cardiac aerobic capacity after freshwater colonization. These findings suggest that ventricle size may contribute to decreases in ṀO2,max in Bonsall Creek but not West Creek stream-resident fish, and also argue that having a ventricle as large as that of a Bonsall Creek marine fish is not necessary for reaching a high ṀO2,max. After oxygen is transported to the working muscle, it must diffuse from the capillary to the muscle mitochondria. If all else is equal, smaller fibres will decrease the diffusion distance to the mitochondria when compared to larger fibres (Kinsey et al. 2011), but incur higher costs from maintaining ion gradients, resulting in an energetic trade-off (“optimal fibre number hypothesis”; e.g. Johnston et al. 2005; Jimenez et al. 2011). We predicted that if selection for a high oxygen diffusion rate was relaxed, stream-resident fish with larger fibres would have a decreased cost of maintaining ion gradients and be at a selective advantage. In agreement with these predictions, we found that stream-resident fish from West Creek had larger pink and white fibres in their abductor (Table 3.3), and had larger 'red-pink' fibres in their adductor muscles (Table 3.4). There was also a non-significant trend towards larger red abductor and adductor fibres in stream-resident fish (Table 3.3 and 3.4). Further studies examining the ion transporter content of stickleback muscles are needed to determine if changes in muscle size impact the costs of ion regulation as they do in American lobster (Homarus americanus) muscle fibres (Jimenez et al. 2011), and measurements of mitochondrial placement within a fibre, myoglobin content  52  and muscle capillarity are needed to examine other possible differences in oxygen diffusion capacity (e.g. Scott et al. 2009; Kinsey et al. 2011). The final step in the oxygen transport cascade involves the reduction of oxygen by cytochrome c oxidase (COX) in the mitochondrial electron transport chain, and the size and mitochondrial content of the working muscles generally correlates with ṀO2,max (reviewed by Wagner 1996; Hoppeler and Wiebel 1998). In labriform swimmers such as threespine stickleback, prolonged swimming is powered with the pectoral muscles (Taylor and McPhail 1986; Walker 2004), with the pectoral adductor powering the forward thrust, and the pectoral abductor powering the recovery stroke (Thorsen and Weastneat 2005). If the energetic costs of maintaining large pectoral muscles are high, then we would predict that stream-resident fish, which have a lower ṀO2,max than marine stickleback populations (Chapter 2), would evolve smaller pectoral muscles, with a lower aerobic capacity per gram muscle. Indeed, large, metabolically active muscles are energetically expensive for sand crickets to maintain (e.g. Zera et al. 1998): sand crickets (genus Gryllus) that develop into a long-winged form capable of flight have larger flight muscles which oxidize more lipids, higher muscle metabolic rates, higher whole-animal standard and maximum metabolic rates, and smaller gonads than short-winged forms with smaller muscles (Zera et al. 1997; Zera et al. 1998; Crnokrak and Roff 2002; Nespolo et al. 2008). As predicted, we found that pectoral muscle size was reduced in both streamresident populations (Fig 3.3). To assess the aerobic capacity of stickleback pectoral muscle we measured two traits that are correlated with mitochondrial content: fibre-type composition (Zierath and Hawley 2004), and the activities of two mitochondrial enzymes, CS and COX (e.g. Reichmann et al. 1985). We found that West Creek stream-resident fish had a lower percentage of red fibres in their abductor muscle (Table 3.5), and more pink fibres in their adductor muscle (Fig.3.4; Table 3.4) than West Creek marine fish (Bonsall Creek crosses were not studied by this method), but we did not find evidence for differences in CS and COX activities per gram tissue (Table 3.6). These data suggest that stream-resident fish have evolved a higher percentage of pink fibres, which have high glycolytic and aerobic capacites, so do not reduce the capacity for oxygen utilization in the pectoral muscle. Overall, these data indicate that differences in pectoral muscle size, but not mitochondrial content per gram pectoral muscle, are associated with a decreased ṀO2,max in stream-resident stickleback. We hypothesize that the energetic costs of maintaining large pectoral muscles, with small diameter fibres, may have resulted in selection for parallel reductions in pectoral muscle mass in stream-resident threespine stickleback populations. 53  In summary, reductions in the aerobic capacity of stream-resident stickleback are associated with reductions in a number of candidate oxygen cascade related traits, including a reduced pectoral muscle size in Bonsall and West Creek stream-resident fish, differences in pectoral muscle fibre size in West Creek stream-resident fish (not measured in Bonsall Creek crosses), and decreases in ventricle size in Bonsall Creek stream-resident fish. Previous studies on wild stream-resident and marine sticklebacks from another population in the Fraser river, BC, Canada (Salmon River), have identified similar decreases in pectoral muscle mass and heart mass (C.A. Darveau and P.W. Hochachka, unpublished data; Hochackha and Somero 2002), and wild stream-resident fish from the Baltic Sea tributaries also have smaller pectoral muscles than sympatric marine fish (Schaarschmidt and Jürss 2003). Overall, these data, in combination with our results, suggest that decreases in pectoral muscle size have evolved in parallel with decreases in ṀO2,max in both Pacific and Atlantic stream-resident threespine stickleback populations. We also found evidence for the evolution of fibre-type composition, but not mitochondrial content (i.e. activities of CS and COX per gram muscle) in the pectoral muscles of stream-resident fish. Changes in pectoral fibre-type that do not influence mitochondrial content (measured using CS and COX as proxies) should not impact ṀO2,max directly, but these changes in fibre-type may impact swimming performance (e.g. Chapter 2). Previous studies by Darveau and Hochachka (unpublished data; Hochachka and Somero, 2002) and Schaarschmidt and Jürss (2003), have found similar differences in LDH activities per gram muscle in wild-caught stream-resident vs. marine stickleback. Overall, these independent, changes in ṀO2,max, pectoral muscle size and fibre type (indicated by LDH activity), which occurred in less than 12,000 years in the face of gene flow from marine populations (e.g. Hagen 1967; Jones et al. 2006; T. H. Vines and A. C. Dalziel, unpublished data), are consistent with a role for natural selection in the evolution of these physiological traits in stream-resident stickleback. 3.5.2  Comparisons among marine populations  Local adaptation to migratory conditions may occur in anadromous fish that display natal philopatry. For example, Eliason et al. (2011) found that the cardiac physiology of sockeye salmon in the Fraser River in BC, Canada, is correlated with migratory difficulty. Marine populations of threespine stickleback also display some degree of homing (Saimoto 1993), and are genetically differentiated (although much less so than are freshwater populations: Withler and McPhail 1985; Colosimo et al. 2005; Jones et al. 2012), so there is the potential for local 54  adaptation to migratory conditions in this species as well. Our two marine populations vary in the difficulty of their anadromous migrations: West Creek marine fish travel at least 35 km down the Fraser River to reach the ocean, but Bonsall Creek stickleback breed only 1-2 kilometres from the mouth of the estuary (Hagen 1967, T. H. Vines and A. C. Dalziel, unpublished data). We found that multiple traits related to oxygen transport and utilization, muscle metabolic capacity, and swimming performance were higher in West Creek marine crosses than in Bonsall Creek marine crosses, including hematocrit values (Hct) in female fish (Table 3.2), pectoral muscle mass (Fig. 3.3), pectoral muscle pyruvate kinase (PK) activity (Table 3.6), and body streamlining (Chapter 2). However, Bonsall Creek marine fish had larger ventricles than did West Creek marine fish. These data suggest that marine populations do form genetically distinct populations and, with the exception of ventricle mass, their phenotypes are generally consistent with our predictions for local adaptation to migratory conditions. In addition, the combination of a large ventricle (high capacity for blood convection), but a smaller pectoral muscle mass (lower capacity for oxygen use) in Bonsall Creek, and small ventricles (lower capacity for blood convection), and larger pectoral muscles (higher capacity for oxygen use) in West Creek fish, further argues against a tight matching of the capacities of all steps in the oxygen transport cascade at any given point in evolutionary time (i.e. symmorphosis). Overall, marine populations are able to reach similar maximum metabolic rates (Fig. 2.2; Chapter 2), but likely use different underlying mechanisms to reach high performance values, a phenomenon called many-to-one mapping (reviewed by Wainwright et al. 2005; Walker 2010). If ancestral marine populations also displayed physiological variation at the time of freshwater colonization, any current differences among freshwater populations may also be due to differences in the genetic 'starting point' at the time of colonization. It is possible that the relatively limited number of families from each population (five to 11 crosses of each cross-type), might not express the full amount of variation present in these natural populations. However, the fact that we observed cross-types with significantly different ṀO2,maxes and significant differences in which oxygen cascade traits associate with a given capacity for ṀO2,max, clearly argues that a high ṀO2,max is not always reached by the same mechanisms. 3.5.3  Genetic basis of maximum oxygen consumption and underlying traits  If an underlying trait has had a major effect on ṀO2,max, then we would expect that the loci contributing to variation in this trait will also account for a large amount of the variation in 55  ṀO2,max. Thus, any traits that are strongly mechanistically related to ṀO2,max should have a similar genetic basis to that of ṀO2,max, which we have found to show dominance of marine alleles in West Creek F1 hybrids, and a dominance of stream alleles in Bonsall Creek F1 hybrids. While there are parallel decreases in pectoral muscle size in both stream-resident populations, the traits that had a similar genetic basis as did ṀO2,max differ among locations. In West Creek, abductor and adductor masses matched the genetic architecture of ṀO2,max (i.e. dominance of marine alleles) and in Bonsall Creek, ventricle mass matched the genetic architecture of ṀO2,max (i.e. dominance of stream alleles). Thus, none of our oxygen-cascade related traits had a similar genetic basis as MO2,max in both locations. These data argue that while there are some oxygen cascade related traits that have repeatedly evolved in stream-resident fish (reductions in pectoral muscle size), the particular traits predicted to have a large impact on ṀO2,max (e.g. those that share a similar genetic basis) differ among populations. These findings argue that reductions in ṀO2,max may also happen in a variety of different ways (e.g. many-to-one mapping). 3.5.4  Does the stickleback oxygen cascade demonstrate symmorphosis?  The hypothesis of symmorphosis argues that there should be a “quantitative match of design and function parameters within a defined functional system” (Weibel et al. 1991). Thus, if the stickleback respiratory system evolved in a symmorphotic manner, we would expect that changes in ṀO2,max were accompanied by changes in all steps of the oxygen cascade from the gill to the mitochondria within the swimming muscles. Overall, our data strongly argue against a tight matching of capacity at all steps of the oxygen cascade in threespine stickleback. Our first example comes from our comparisons of stream-resident and marine ecotypes. Despite large differences in ṀO2,max between these ecotypes (Chapter 2), a number of steps in the oxygen cascade do not differ, including gill surface area (Table 3.1), MCHC, Hct (Table 3.2), hemoglobin P50 (Appendix B, Fig.B1), and the activities of mitochondrial enzymes per gram of pectoral muscle (CS and COX; Table 3.6). Our next example is from our comparisons of marine populations which can reach a similar ṀO2,max, but which vary in a number of oxygen cascade related traits [e.g. pectoral muscle size (Fig. 3.3), ventricle size (Fig.3.2), and Hct (Table 3.2)]. In particular, Bonsall Creek marine fish show evidence of a high capacity for blood convection (i.e. large ventricle), but lower capacity for oxygen use (i.e. a smaller pectoral muscle mass), while West Creek fish show evidence of a lower capacity for blood convection (i.e. small ventricles), but higher capacity for oxygen use (i.e. larger pectoral muscles). Together, these 56  comparisons argue against a tight matching of the capacities of all steps in the oxygen transport cascade at any given point in evolutionary time. Physiological systems often influence more than one ecologically important task. This 'multi-tasking' can lead to trade-offs among performance traits, and is one of the major reasons why perfectly matched systems (as predicted under symmorphosis) are unlikely to evolve in natural populations (Weibel et al. 1991; Garland 1998). For example, because at least one of the traits involved in the fish respiratory system also has another dominant function and is expected to experience trade-offs among functions (e.g. the gill is the primary site of both osmoregulation and respiration), we would predict that the respiratory system in fish is less likely to display strict symmorphosis than in mammals (Weibel et al. 1991). However, even in the mammalian respiratory system there is evidence for mismatches between structure (i.e. lung capacity) and function (i.e. ṀO2,max) (Weibel et al. 1991). Garland (1998) has reviewed a number of further reasons why symmorphosis is unlikely to occur in nature, which include, but are not limited to: limitations to biological materials, limitations to selection, the importance of sexual selection, and the importance of stochastic evolutionary processes. So why is it common to find evidence for the matching of design and function in inter-specific comparisons? We argue that this observation is simply a result of the different time scales available for divergence between interand intra-specific comparisons. Inter-specific studies observe the many changes that have occurred over longer evolutionary time-scales, while intra-specific studies are more likely to detect the first few 'steps' of evolutionary change. This point is best exemplified by selection studies in rodents which clearly display that there has been very rapid, step-wise evolution of aerobic capacity would have been missed if not studied every few generations (e.g. Henderson et al. 2002; Howlett et al. 2009; Kirkton et al. 2009; Gębczyński and Konarzewski 2011). 3.6  Acknowledgements We thank C.A. Darveau for inspiring us to do this research and for providing experimental  advice. We are grateful to D. Schluter for statistical advice and sharing his stickleback rearing facilities. We thank G.R. Scott for many helpful discussions and technical assistance with muscle fibre-type measurements, and R.S. Dhillon for his help with histological sampling and assays. We also thank A.Y. Fong, C. Reyes and C.S. Porteus for their assistance with microscopy and histological preparations, W.K. Milsom for use of his cryostat, J.G. Richards for assistance measuring gill morphology, and D.S. Srivastava for use of her microscope. 57  Figure 3. 1. Locations of the threespine stickleback populations used in this study. (A) Western North America with the sampling area outlined with a gray hatched square (B) Region within the gray hatched square, with stickleback collection sites marked with white stars and labeled in italics.  Figure 3.2. Residual ventricle mass of laboratory-bred F1 threespine stickleback females from Bonsall and West Creek parents plotted against the residuals of log maximum oxygen consumption (MO2,max). Pure stream x stream crosses (SS; N = 5 Bonsall families and 5 West Creek families) are indicated with white circles, pure marine x marine (MM; N = 6 Bonsall families and 4 West Creek families) are indicated by solid black circles, and hybrid crosses (H; N = 11 Bonsall families and 5 West Creek families) are indicated with gray circles. The collection location is also noted next to each data point: W = West Creek, and B= Bonsall Creek. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among the six cross-types for residual ventricle mass (P < 0.05). Different symbols indicate significant differences among cross-types for the residuals of log MO2,max (from Chapter 2; P < 0.05). 58  Figure 3.3. Residual pectoral (A) adductor and (B) abductor mass of laboratory-bred F1 threespine stickleback females from Bonsall and West Creek parents plotted against residuals of log maximum oxygen consumption (MO2,max). Abbreviations and data presentation follows Fig. 3.2. N = 5 Bonsall Creek SS, 11 H and 6 MM families and 5 West Creek SS, 5 H, and 6 MM families.  59  Figure 3.4. Succinate dehydrogenase (SDH) stained pectoral muscles from laboratory-bred F1 threespine stickleback females from West Creek parents. SDH, complex II in the mitochondrial electron transport chain, is used as a marker for oxidative capacity. (A) Representative section of the full pectoral muscle cut transverse to fibre length (produced from stitched images), with excerpt from the abductor muscle to display examples of red (dark gray arrow), pink (light gray arrow), and white (white arrow) fibres. Representative images of adductor muscles from (B) pure stream, (C) F1 hybrid, and (D) pure marine fish.  60  Figure 3.5. LDH activity per gram of (A) adductor and (B) abductor muscle from laboratorybred F1 threespine stickleback females from Bonsall and West Creek parents, and plotted against residuals of log maximum oxygen consumption (MO2,max). Abbreviations and data presentation follows Fig.3.2. N = 5 Bonsall Creek SS, 11 H and 6 MM families and 5 West Creek SS, 5 H, and 6 MM families.  61  Table 3.1. Gill morphology of laboratory-bred F1 threespine stickleback families from Bonsall Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). There was a significant effect of mass on filament length, so residual filament lengths were used for statistical tests, but raw data are also presented here for clarity. Different letters indicate significant differences among groups for a given measure (P < 0.05). If letters are not included, there are no significant differences among the 6 groups  Bonsall Stream Female (N = 3 families) Bonsall Stream Male (N = 4 families) Bonsall Hybrids Female (N = 10 families) Bonsall Hybrids Male (N = 10 families) Bonsall Marine Female (N= 5 families) Bonsall Marine Male (N = 6 families)  Mass  Total number of Filaments  2.24 ± 0.52  41.8 ± 0.4  1.99 ± 0.26  43.2 ± 0.9  2.09 ± 0.19  43.4 ± 0.8  2.13 ± 0.16  41.4 ± 0.8  1.69 ± 0.10  44.6 ± 0.8  1.73 ± 0.13  43.3 ± 0.7  Average filament length (mm) for each gill section 1.063 ± 0.139 1.620 ± 0.342 0.931 ± 0.090 1.071 ± 0.022 1.848 ± 0.071 0.967 ± 0.076 1.159 ± 0.076 1.917 ± 0.093 0.962 ± 0.031 1.138 ± 0.062 1.897 ± 0.100 1.118 ± 0.084 1.069 ± 0.060 1.841 ± 0.068 1.057 ± 0.041 1.068 ± 0.064 1.814 ± 0.065 1.077 ± 0.038  Total filament length (mm)  Residual total filament length  Lamellar spacing (lamellae/mm filament)  Lamellar area (μm2)  Raw Gill surface Area (cm2)  Residual total Gill surface Area  111.68 ± 12.72  -15.75 ± 0.17B  55.42 ± 0.52  15.53 ± 0.89  38.73 ± 4.92  -2.15 ± 1.80  120.40 ± 7.02  -0.86 ± 2.82AB  55.83 ± 1.04  16.02 ± 1.35  43.73 ± 3.85  5.43 ± 3.76  123.55 ± 7.14  -0.16 ± 3.57AB  57.04 ± 1.05  13.70 ± 1.03  38.82 ± 4.53  -0.50 ± 3.33  122.75 ± 6.54  -1.85 ± 4.90AB  54.68 ± 0.75  12.05 ± 0.68  32.48 ± 2.65  -7.22 ± 2.23  124.42 ± 7.13  10.28 ± 6.90A  56.80 ± 1.11  14.21 ± 0.33  40.23 ± 2.79  4.88 ± 2.96  120.59 ± 3.40  5.52 ± 2.48A  57.3 ± 0.43  13.83 ± 0.53  38.28 ± 1.42  2.54 ± 1.87  62  Table 3.2. Blood hematocrit (Hct), hemoglobin concentration ([Hb]), and mean cellular hemoglobin content (MCHC) of laboratory-bred F1 threespine stickleback families from Bonsall and West Creek parents. Values are the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). N is the number of families sampled for each cross-type. Different letters indicate significant differences among groups for a given measure (P < 0.05). If letters are not included, there are no significant differences among the 12 groups. [Hb] (mM) Hct (% RBC) MCHC ([Hb]/Hct) (N = 5 families) (N = 5 families) Bonsall Stream (N = 5 families) female 1.29 ± 0.14AD 38 ± 3CD 3.42 ± 0.14 BC ABCD male 1.48 ± 0.12 41 ± 3 3.69 ± 0.17 (N = 5 families) (N = 5 families) (N = 5 families) West Stream ABCD AB female 1.59 ± 0.07 49 ± 2 3.23 ± 0.09 1.56 ± 0.09ABCD 49 ± 3AB male 3.23 ± 0.05 (N = 10 families) Bonsall Hybrid (N = 10 families) (N = 11 families) female 1.33 ± 0.05ABCD 42 ± 2BCD 3.20 ± 0.07 ABCD ABC male 1.44 ± 0.04 45 ± 1 3.28 ± 0.09 (N = 5 families) (N = 5 families) (N = 5 families) West Hybrid female 1.64 ± 0.13ABCD 49 ± 1AB 3.35 ± 0.27 ABCD male 1.57 ± 0.09 46 ± 2ABC 3.44 ± 0.31 (N = 5 families) (N = 5 families) Bonsall Marine (N = 5 families) female 1.27 ± 0.08AC 37 ± 2D 3.34 ± 0.10 BD ABC male 1.44 ± 0.06 43 ± 2 3.36 ± 0.09 (N = 6 families) (N = 6 families) (N = 6 families) West Marine female 1.67 ± 0.09ABCD 51 ± 1A 3.30 ± 0.17 male 1.64 ± 0.19ABCD 47 ± 3AB 3.53 ± 0.20  63  Table 3.3. Fibre area (and residuals) for red, pink, and white fibres from abductor muscles of laboratory-bred F1 threespine stickleback females from West Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). There was a significant effect of mass on all values, so residuals were used for statistical tests, but raw data is also presented here for clarity. Different letters indicate significant differences among groups for a given measure (P < 0.05). If letters are not included, there are no significant differences among the three groups.  Stream (N = 4 families) Hybrid (N = 6 families) Marine (N = 5 families)  Red fibre area (μm2)  Residual red fibre area  Pink fibre area (μm2)  Residual pink fibre area  White fibre area (μm2)  Residual white fibre area  780.3±92.6  35.0±66.8  1950.9±250.4  204.7±93.4A  4228.1 ±489.6  758.1±177.2A  955.6±109.8  194.5±100.5  2085.6±210.7  311.4±188.0A  3674.8± 149.1  125.7±186.5A  640.7±88.7  -82.0±72.9  1400.3±106.2  -293.2± 97.8B  2667.2 ±224.2  -690.4±218.3B  Table 3.4. Fibre area (and residuals) for the largest and smallest red fibres in the adductor muscles of laboratory-bred F1 threespine stickleback females from West Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). There was a significant effect of mass on all values, so residuals were used for statistical tests, but raw data is also presented here for clarity. Different letters indicate significant differences among groups for a given measure (P < 0.05). If letters are not included, there are no significant differences among the three groups. Stream (N = 4 families) Hybrid (N = 6 families) Marine (N = 5 families)  Smallest red fibre area (μm2) 972.5 ± 199.2  Residuals red fibre area 130.8 ± 180.4  Largest red-pink fibre area (μm2) 1821.4 ± 189.5A  Residuals red-pink fibre area 347.1 ± 190.9A  1043.8 ± 104.4  185.4 ± 91.9  1800.7 ± 322.1A  284.4 ± 302.5A  667.3 ± 39.2  -150.7 ± 39.2  904.6 ± 59.9B  -529.5 ± 42.7B  64  Table 3.5. Percentage of each fibre-type (and residuals) in the abductor muscles of laboratorybred F1 threespine stickleback females from West Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). There was a significant effect of mass on all values, so residuals were used for statistical tests, but raw data is also presented here for clarity. Different letters indicate significant differences among groups for a given measure (P < 0.05). If letters are not included, there are no significant differences among the three groups. % red fibres West stream (N = 4 families) West hybrid (N = 5 families) West marine (N = 5 families)  64.17 ± 3.48  Residual % red fibres -6.85± 3.28A  % pink fibres 11.60 ± 1.50  Residual % pink fibres 0.82 ± 1.65  % white fibres 24.23 ± 4.59  Residual % white fibres 6.03 ± 4.60  71.49 ± 3.11  0.13± 3.33AB  9.83 ± 1.18  -1.06 ± 1.15  18.69 ± 2.58  0.93 ± 2.88  77.18 ± 2.42  5.46 ± 2.61B  9.47 ± 1.82  -1.54 ± 1.71  13.35 ± 1.14  -3.92 ± 0.99  65  Table 3.6. Enzyme activities of laboratory-bred F1 females from Bonsall Creek and West Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). The number of families used to calculate enzyme activities are as follows: N = 5 Bonsall Creek SS, 11 H and 6 MM families and 5 West Creek SS, 5 H, and 6 MM families for ventricle enzyme activities. N = 4 Bonsall Creek SS, 11 H and 6 MM families and 5 West Creek SS, 5 H, and 6 MM families for pectoral muscle enzyme activities. Different letters indicate significant differences among groups for the indicated enzyme within a single tissue (P < 0.05). If letters are not included, there are no significant differences in enzyme activity within the tissue among the six groups. U Enzyme/g Ventricle  U Enzyme/g Adductor  U Enzyme/g Abductor  Cytochrome c oxidase (COX) Bonsall Stream  39.01 ± 3.14C  34.52 ± 5.23  31.15 ± 5.81  West Stream  62.20 ± 7.49AB  36.42 ± 5.08  34.41 ± 4.47  Bonsall Hybrids  47.17 ± 3.79C  34.87 ± 2.67  32.15 ± 2.07  West Hybrids  87.00 ± 10.1A  40.38 ± 5.65  34.42 ± 2.84  Bonsall Marine  51.00 ± 3.8BC  36.62 ± 3.03  30.23 ± 5.07  West Marine  47.06 ± 6.3BC  44.46 ± 4.04  32.99 ± 4.25  Citrate Synthase (CS) Bonsall Stream  11.20 ± 0.76  11.38 ± 1.41  10.87 ± 0.80  West Stream  14.95 ± 1.29  13.81 ± 1.71  13.70 ± 0.87  Bonsall Hybrids  13.41 ± 1.04  11.45 ± 0.86  11.67 ± 0.74  West Hybrids  15.98 ± 1.40  13.86 ± 2.17  13.20 ± 1.29  Bonsall Marine  14.45 ± 0.72  12.11 ± 0.87  12.06 ± 0.71  West Marine  12.46 ± 1.36  12.94 ± 0.97  11.01 ± 0.88  Pyruvate Kinase (PK) Bonsall Stream  47.43 ± 1.58  73.93 ± 7.91ABC  76.94 ± 10.10CD  West Stream  42.66 ± 4.60  88.61 ± 8.73A  112.54 ± 11.70A  Bonsall Hybrids  43.12 ± 2.55  73.09 ± 5.08BC  83.83 ± 6.28BCD  West Hybrids  48.78 ± 3.53  88.45 ± 4.02A  104.51 ± 6.45AB  Bonsall Marine  44.93 ± 1.35  53.52 ± 4.50C  65.34 ± 5.61D  West Marine  45.03 ± 2.73  83.33 ± 6.87AB  98.87 ± 8.70ABC  66  U Enzyme/g Ventricle  U Enzyme/g Adductor  U Enzyme/g Abductor  Lactate dehydrogenase (LDH) Bonsall Stream  39.73 ± 5.67  22.26 ± 1.85A  31.61 ± 0.76AB  West Stream  44.94 ± 4.02  18.05 ± 2.23AB  36.45 ± 3.20A  Bonsall Hybrids  49.36 ± 2.86  14.39 ± 2.17ABC  28.62 ± 2.08ABC  West Hybrids  44.00 ± 5.91  9.94 ± 1.40BCD  22.32 ± 3.24BCD  Bonsall Marine  43.58 ± 6.01  6.65 ± 1.17D  14.58 ± 3.59D  West Marine  31.61 ± 2.12  4.94 ± 1.13D  13.70 ± 2.11D  Creatine Phosphokinase (CPK) Bonsall Stream  298.22 ± 48.07  589.05 ± 71.37AB  647.67 ± 60.06AB  West Stream  202.45 ± 23.71  797.67 ± 39.86A  846.09 ± 25.72A  Bonsall Hybrids  296.58 ± 20.32  755.61 ± 27.40A  703.21 ± 55.71A  West Hybrids  281.59 ± 30.85  749.36 ± 37.17A  692.96 ± 53.05A  Bonsall Marine  355.59 ± 23.75  593.84 ± 62.84B  471.81 ± 37.27B  West Marine  309.47 ± 47.90  723.57 ± 44.16AB  621.79 ± 82.65AB  67  Chapter 4: Correlates of prolonged swimming performance: Using F2 hybrid crosses to study the traits contributing to differences in performance between migratory and non-migratory threespine stickleback ecotypes 4.1  Summary Determining which morphological, physiological, and biochemical traits contribute to  differences in whole-animal performance among populations can be difficult when many candidate traits vary between groups with high and low capacities. I have previously found that migratory (anadromous marine) and non-migratory (stream-resident) threespine stickleback (Gasterosteus aculeatus) have genetically based differences in prolonged swimming performance (Ucrit) that are associated with differences in a number of traits (e.g. pectoral fin size, body shape, pectoral muscle and heart size, and metabolic enzyme activities) predicted to influence Ucrit. Here, I use F2 hybrid crosses to determine which candidate traits correlate with differences in Ucrit in a largely randomized genetic background. I found that four of 11 candidate traits significantly regressed against Ucrit in F2 hybrids and that the combined effects of ventricle mass, pectoral adductor mass, and the activity of CS per gram adductor muscle accounted for 17.9% of variation in Ucrit. Overall, these data provide additional support for a causal role of variation in muscle and heart mass in intra-specific differences in Ucrit, but suggest that many candidate traits do not have a strong effect on Ucrit when disassociated from other traits. These data indicate that Ucrit is a complex performance trait for which many underlying traits are necessary to achieve high performance capacity and that other, unmeasured, traits might also affect Ucrit. 4.2  Introduction The ability to complete whole-animal performance tasks, such as running from a predator or  courting a mate, are determined by the integration of multiple traits at underlying levels of biological organization (reviewed by Arnold 1983; Kinsolver and Huey 2003). This underlying complexity (i.e. many-to-one mapping; Wainwright et al. 2005), makes it possible for performance traits to evolve in a variety of ways. Determining which, of the many possible, underlying traits have evolved to cause differences in performance among populations and species provides insights into the physiological mechanisms by which ecologically relevant traits evolve (reviewed by Dalziel et al. 2009), and the functional tradeoffs and facilitations that influence the evolution performance in natural populations (reviewed by Walker 2007; 2010). Here, I utilize an advanced generation (F2) hybrid cross between migratory (anadromous marine) 68  and non-migratory (stream-resident) threespine stickleback (Gasterosteus aculeatus) populations to identify the candidate traits that are correlated with the capacity for prolonged swimming. The capacity for endurance exercise is predicted to influence survival and reproduction in a number of species (reviewed by Husak and Fox, 2008; Irschick et al. 2008), including anadromous fishes (reviewed by Fraser et al. 2011). Much is known about the underlying morphological (reviewed by Webb 1982; Weihs and Webb 1983; Blake 2004; Langerhans and Reznick 2009), and physiological (reviewed by Jones and Randall, 1978; Kolok 1999; Bernal et al. 2001; Farrell 2002) traits that can improve prolonged swimming capacity, but less is known about the specific traits that have evolved to cause differences in prolonged swimming capacity within and among natural populations. This is in large part because swimming performance (e.g. Lee et al. 2010), and the underlying traits which can influence prolonged swimming capacity (e.g. Hoffmann and Borg 2006; Sharpe et al. 2008; Anttila et al. 2008) are phenotypically plastic. Therefore, experiments that control for environmental differences (i.e. “common garden” experiments that can determine if traits are genetically based), are needed to identify the traits that cause differences in swimming capacity. Threespine sticklebacks are small teleost fish that live in fresh and salt waters throughout the northern hemisphere (Wootton 1984; Bell and Foster 1994; Ostlund-Nilsson et al. 2007). On the Northern Pacific Coast of North America, marine fish colonized lakes and streams and established a number of freshwater populations when these habitats were uncovered after the Cordilleran Ice Sheet receded, approximately 10-12,000 years ago (McPhail, 1994). We have previously shown that the differences in prolonged swimming capacity detected between wild stream-resident and anadromous marine (hereafter referred to as “marine”) stickleback populations (Taylor and McPhail 1986; Tudorache et al. 2007; but see Schaarschmidt and Jürss 2003), are genetically based (Chapter 2). In addition, we have found that a number of morphological, physiological and biochemical traits predicted to impact prolonged swimming have evolved in conjunction with differences in performance in stream-resident threespine stickleback populations (Chapter 2, 3). For example, stream-resident fish from Bonsall Creek (Vancouver Island, BC, Canada) have smaller pectoral fins, a less streamlined body shape, smaller heart ventricles, and smaller and more glycolytic pectoral muscles than do migratory marine stickleback (Chapter 2, 3). Any of these traits could, in principle, cause reductions in swimming performance in stream-resident fish.  69  In natural populations of stream-resident and marine stickleback all of the traits which have diverged between populations will co-vary. Therefore, comparing a stream-resident to a marine population cannot unambiguously show which trait(s) cause a lowered swimming capacity. To address the issue of co-variation among all candidate traits in comparisons between ecotypes we have generated F2 hybrid crosses between stream and marine stickleback populations from Bonsall Creek in this study. In advanced generation hybrid crosses recombination breaks down much of the linkage disequilibrium found among loci (and phenotypic traits) in the parental populations. However, closely linked genes will still segregate as a group. This recombination allows us to test the effect of a candidate trait (and tightly linked loci) on prolonged swimming performance in fish with a largely randomized genetic background. In this study we specifically tested for correlations between several morphological (pectoral fin area and body shape as proxies for thrust generation and drag), physiological (ventricle and pectoral muscle mass as proxies for cardiac output and swimming muscle aerobic capacity), and biochemical [activity of citrate synthase (CS), cytochrome c oxidase (COX), and lactate dehydrogenase (LDH) as proxies for mitochondrial content and muscle fibre-type composition] traits and critical swimming speed (Ucrit) in F2 hybrid stickleback to identify some of the underlying mechanistic causes of the observed variation in Ucrit between stream-resident and marine populations. 4.3  Materials and methods  4.3.1  Experimental animals  The grandparents of the F2 hybrid fish used in this study were collected from wild populations living in Bonsall Creek on Vancouver Island (Chapter 3; Fig. 3.1) (BC Ministry of Environment Fish Collection Permits NA/SU06-26169 and NA/SU07-38414). First generation (F1) pure and hybrid crosses between wild marine and stream-resident stickleback were bred in the spring and summer of 2006 and 2007 (further details described in Chapter 2). A single marine x stream (MS) F1 cross from 2007 was used to produce the three second generation (F2) hybrid families used in the current study in April of 2009, so that the three MS F2 families all share the same grandparents, but have different parents. We used this crossing design to limit the number of alleles at each locus to facilitate future quantitative trait locus (QTL) mapping studies. The first MS F2 family was composed of 69 fish (34 females, 38 males, 6 fish of indeterminate sex), the second F2 family of 75 fish (41 females, 29 males, 6 fish of indeterminate sex), and the 70  third family of 78 fish (31 females, 27 males, 11 fish of indeterminate sex), for a total of 222 fish. Full details of our breeding protocols are presented in Chapter 2. We raised fish in dechlorinated Vancouver tap water brought to 2ppt ± 0.5ppt with Instant Ocean® sea salt, and fed fish live brine shrimp twice per day for their first month, Daphnia and bloodworms daily for the next 3 months, and Mysis shrimp and bloodworms (Chironomid larvae) from 4 months on. We reared fish at a natural photoperiod and laboratory temperatures (~11-17°C) until March (~11 months of age). At this age we transferred fish to a 15°C environmental chamber with a controlled 12L: 12D photoperiod (the natural photoperiod for our collection sites in March) to prevent fish from entering reproductive state. To reduce the impact of tank-effects, we divided three 100 gallon tanks into three sections with mesh, and then put 1/3rd of each family into each of the three tanks. When fish reached a size of ~3.5 cm we individually tagged each fish and removed the mesh to give the fish additional space. These tags remained visible throughout the experiment and were used to individually identify each fish during subsequent measurements. The University of British Columbia animal care committee approved all breeding and experimental procedures (A07-0288). 4.3.2  Measurement of maximum prolonged swimming speed: Critical swimming speed  (Ucrit) We used a critical swimming speed (Ucrit) test to assess prolonged swimming performance in our stickleback (Brett 1964). In this test, water speed is increased in a step-wise manner until a fish can no longer maintain its position in the current. Ucrit correlates with migratory difficulty among populations of salmonids (e.g. Lee et al. 2003), and is also predicted to be an ecologically relevant measure of prolonged swimming for other species of fish that migrate, forage in the open ocean, or live in high-flow streams (Kolok 1999; Plaut 2001), as do migratory marine threespine stickleback. To measure Ucrit, we swam six individually labeled siblings in a Brett style 10-L swim tunnel (SWIM-10; Loligo Systems, Hobro, Denmark), at a water temperature of 15°C ± 1°C and salinity of 2ppt and calibrated water speed with a vane wheel flow sensor (Höntzch ZSR25). The Ucrit trial followed the methods I used in Chapter 2, with some modifications. We placed fish in the tunnel to acclimate for 30 minutes at 0.5 body lengths/second (BL·s−1) and then performed a training test by increasing the speed at 0.3 BL·s−1 increments every two minutes until the first fish in the group failed. We then let the fish recover for one hour. During our Ucrit trial, we 71  increased speed by 0.5 BL increments every two minutes until the fish fell back against the end of the tunnel three times. Critical swimming speed was determined using the following formula: Ucrit = Ui + (ti/tii · Uii), where Ui is the highest speed the fish was able to swim for a full 10 minute interval (BL·sec-1), Uii is the incremental speed increase (BL·sec-1), ti is the time the fish swam at the final speed (min), and tii is the prescribed period of swimming per speed (2 min). All fish were <0.25% of the cross sectional area of the tunnel so we did not need to correct for solid blocking effects. Fish swam spread throughout the flume, and were constantly observed to be sure that they did not draft. The reduced time increment used in these trials (2 min vs. 10 min in Chapter 2) and decreased step-wise speed increase (0.3 BL/sec vs. 0.5 BL/sec) was not found to significantly affect Ucrit in preliminary studies (data not shown), and significantly reduced the time needed to run the trials. This was important as we needed to Ucrit test, photograph and sample all 222 F2 fish in less than a month, to be sure that we measured all traits during the period within which we know Ucrit is highly repeatable in these populations (Appendix A; Fig. A2), and to minimize the possibility of age-related effects. We also re-swam a sub-set of 34 fish two weeks after the initial Ucrit trial to further test the repeatability of Ucrit in F2 hybrid stickleback, and found that swimming performance was significantly repeatable over this time period (Appendix C; Fig. C1). At the time of Ucrit trials our F2 fish were one year and two to three months of age and the F1 crosses measured in Chapter 2 ranged in age from eleven months to one year and three months of age. We found that there was still an effect of size (despite the incorporation of standard length in the calculation for Ucrit) so all analyses were performed using the residuals of regressions of Ucrit against standard length (see statistical analysis for details). 4.3.3  Measurement of morphological traits predicted to influence Ucrit  Photographs of F2 stickleback (n=222 fish from three F2 families), were taken less than three weeks after we measured Ucrit. We anesthetized fish with 0.2 g tricaine methanesulfonate buffered with 0.4 g sodium bicarbonate in one L of water and photographed the right side of the fish with a ruler in the field of view. We took a second photograph of the right pectoral fin maximally spread over a laminated sheet of paper to measure pectoral fin area, and measured this area by tracing an outline of the fin in Image J. We used TPSdig 2.1 (Rohlf 2010) to digitize twelve landmarks onto the stickleback’s body that could be used to measure body shape traits (see Chapter 2). We chose to measure five body shape traits predicted to mediate evolutionary variation in prolonged swimming capacity in fishes (reviewed by Blake 2004; Langerhans and 72  Reznick 2009) and found to vary between F1 stream-resident and marine stickleback crosses (Chapter 2). These traits were: 1) fineness ratio (maximum body depth divided by standard length), 2) head depth, 3) posterior depth at 3rd spine, 4) caudal peduncle depth, and 5) caudal area. Linear measures were collected from landmarks with TMorphGen6c (IMP suite 2006, Zeldith et al. 2004). We corrected measurements for overall body size by performing a least squared regression against mass and using residuals in all subsequent analyses. Residuals were made positive by the addition of a constant, log10 transformed, and divided by two for linear measures and by three for caudal area in preparation for multivariate analyses. To obtain a composite measure of body shape we performed a linear discriminant function analyses (DFA) on the six body shape traits with the MASS package in R (Venables and Ripley 2002), after 'training' with data from pure marine and stream-resident fish collected by Dalziel et al (2011). At the time photos were taken both F2 and F1 fish (measured in Chapter 2) were less than one month older than they were in Ucrit trials. 4.3.4 4.3.4.1  Measurement of physiological and biochemical traits predicted to influence Ucrit Sample collection  We terminally sampled F2 hybrid stickleback for tissue collection less than a month after we measured Ucrit (fish were one year and three to four months of age). We sacrificed fish by placing them in an overdose of anaesthetic (1 g/L tricaine methanesulfonate buffered with 2 g/L sodium bicarbonate), and as soon as a fish lost equilibrium (< 30 sec), it was blotted dry and weighed. We then removed the heart and pectoral adductor and abductor muscles with the aid of a dissecting microscope. All tissues were snap frozen in liquid nitrogen and stored at -80°C. The F1 crosses measured in Chapter 2 were approximately one year and five to nine months of age at the time of biochemical sampling, so were between one and six months older than F2 fish. 4.3.4.2  Measuring ventricle and pectoral muscle masses and enzyme activities  We weighed frozen ventricles and pectoral muscles (g), and immediately added pectoral muscles to 20 volumes of chilled homogenization buffer (50 mM hepes, 1 mM EDTA and 0.1% Triton X-100; pH 7.4) in 4mL Wheaton glass homogenizers kept on ice. We measured enzyme activities for cytochrome c oxidase (COX; EC 1.9.3.1, complex IV in the electron transport chain, which is found on the inner mitochondrial membrane), citrate synthase (CS; EC 2.3.3.1. a citric acid cycle enzyme found in the mitochondrial matrix), lactate dehydrogenase (LDH; EC 73  1.1.1.27, a glycolytic enzyme found in the cytosol, which catalyzes the inter-conversion of pyruvate and NADH to lactate and NAD+, and allows for high glycolytic flux during cellular hypoxia), and pyruvate kinase (another glycolytic enzyme), on whole cell extracts at 25°C using the non-limiting substrate concentrations listed in Chapter 3. We have found that a higher activity of LDH/g abductor and adductor muscle is indicative of a higher proportion of pink (fast-oxidative glycolytic) fibres (Chapter 3), so we used this measure as a proxy for fibre-type composition in the pectoral muscles. Pectoral muscle physiology is predicted to influence swimming performance in stickleback, which use these muscles to power prolonged swimming (Walker 2004). While the activity of pyruvate kinase (PK; a glycolytic enzyme in the cytosol) per gram adductor and abductor muscle did not significantly vary between stream-resident and marine F1 crosses, there was a trend towards lower PK in marine crosses and we found that males had lower PK activities than females (Chapter 3). We primarily measured PK in F2 hybrids to further test for an effect of sex on PK activity. CS and COX are indicators of mitochondrial content, and pectoral muscles with higher mitochondrial content should be able to produce more aerobically generated ATP to fuel prolonged swimming. We did not find significant differences in CS and COX among stream-resident and marine stickleback F1 crosses, but did see a slightly higher activity of these enzymes in marine crosses, so measured these enzymes in F2 fish to further examine the impact of mitochondrial enzyme content on Ucrit (Chapter 3). As well, we have found a number of non-synonymous sequence differences in the mitochondrial DNA of stream-resident and marine populations in the genes for cytochrome b, NADH dehydrogenase subunit 2, and ATP synthase subunit 6 (A.C. Dalziel and H. Kim, unpublished data). Because COX is composed of nuclear and mitochondrially encoded protein subunits, low COX activity may be indicative of possible hybrid incompatibilities due to interactions between mitochondrial and nuclear genes (Burton et al. 2006). 4.3.5  Statistical analysis  All statistical analyses were conducted using R v2.11.1 (R Development Core Team, 2010). We first tested for an effect of fish size on all of our measurements by testing for a significant correlation with body mass (candidate traits) or body length (Ucrit). If measurements were significantly correlated with mass, we corrected for size by calculating the residuals from a least squared regression against mass (all candidate traits) or standard length (Ucrit). To compare data  74  collected for F1 line crosses in Chapters 2 and 3 (pure stream, pure marine, and reciprocal F1 hybrid crosses), we calculated residuals with the same linear equations as for our F2 individuals. We examined the effect of each of our eleven candidate traits (fixed effects) on residual Ucrit (response variable) with family and sex as nested random effects, with the nlme package in R (Pinheiro et al. 2009). Our eleven candidate traits were: residual pectoral fin area, body shape ld1, residual ventricle mass, residual adductor mass, residual abductor mass, residual COX activity/g adductor, residual CS activity/g adductor, LDH activity/g adductor, residual COX activity/g abductor, residual CS activity/g abductor, LDH activity/g abductor. We also modeled the effect of each candidate trait against Ucrit with a fixed effect only model (no nested random effects) to obtain an estimate for the fraction of variation in Ucrit explained by each explanatory variable (R2; Appendix C; Table C1). We also examined correlations among explanatory traits, and accounted for multiple comparisons by adjusting our cut-off for significant P-values to a corrected P-value based upon the false discovery rate calculated by the “Brainwaver” package in R (Achard 2010). We next conducted a multiple linear regression to examine the combined predictive power of our explanatory traits and interactions among these traits. We only included variables which were significantly associated with Ucrit (preceding paragraph: adductor CS, abductor CS, ventricle mass, adductor mass). To examine the impact of these candidate explanatory traits on Ucrit, we followed a model selection approach suggested by Zuur et al. (2009) for linear mixed effects models, which begins with a model with as many of our explanatory variables as possible (fixed effects) and their interactions, then finds the optimal random variable structure, next finds the optimal fixed effects structure, and finally examines, interprets and validates the optimal model. We examined our fixed effects structure by using step-wise multiple linear regression (forward and reverse) to determine with explanatory variables should be included in the model. We then examined the significance an interaction between adductor mass and CS activity, which was strongly predicted by biological mechanism. We also examined all other possible models including all two and three way interaction using the stepAIC protocol implemented by the MASS package (Venables and Ripley 2002) in R, and compared akaike information criterion (AIC) values (data not shown). However, this preliminary analysis did not suggest that any other interactions were significant (data not shown). We could not directly test the contributions of additive and additive-dominance models of composite gene action to phenotypic variation in Ucrit and candidate traits with the joint-scaling 75  regression technique (Lynch and Walsh 1998), or examine line variances to test for segregation variance (high variance in F2 hybrids), because we only had a small number of closely related F2 families (n=3). In addition, F1 and F2 lines were reared in different years, and thus under slightly different environments. However, we have displayed our data in a manner that allows for a visual comparison of line means in Appendix C (Figs. C2-C4). 4.4  Results  4.4.1  Critical swimming speeds (Ucrits) of F2 hybrids  Ucrit was significantly repeatable over a two week time period in F2 hybrid stickleback (Appendix C, Fig. C1: t=8.27, p < 0.001, r2=0.671), and there was no effect of sex ( F1,187 = 1.754, p = 0.187), family (F2,187 = 2.252, p = 0.108) or the combination of sex and family (F5,185 = 1.813, p = 0.1122), on Ucrit (residuals of regression against standard length). Second generation F2 hybrids expressed a range of performance phenotypes similar to F1 hybrids, none reached Ucrits as high as the mean Ucrit for marine F1 crosses, and less than 16 of 222 F2 fish had Ucrits as low as the mean for F1 stream crosses (Fig.4.1A). F2 hybrids also had mean Ucrits that were similar to those of F1 hybrid stickleback (Fig. 4.1B: F3,22 = 25.10, p < 0.0001), and both hybrid lines had Ucrits which were slightly more similar to stream-resident than marine crosses. 4.4.2  Effect of individual candidate traits on Ucrit in F2 hybrids.  We found that four of the eleven candidate traits had a significant relationship when regressed against Ucrit using a mixed-effects linear model (Figs. 4.2-4, Table 4.2). Traits with a significant effect on Ucrit in our F2 hybrid crosses included residual ventricle mass (Fig. 4.3A), residual adductor mass (Fig. 4.3B), residual CS per gram adductor (Fig. 4.4A) and abductor (Fig. 4.4B) [results of the regressions for each family and sex are presented in Appendix C in Tables C2-C12]. 4.4.3  Effect of multiple candidate traits on Ucrit in F2 hybrids  We next assessed the ability of our explanatory variables, and interactions among variables, to predict Ucrit by conducting a multiple linear regression. We only included explanatory variables which had a significant effect on Ucrit (Section 4.4.2). To prevent multicollinearity (reviewed by Slinker and Glantz 1985), we removed abductor CS activity, as it was tightly correlated with adductor CS activity (Table 4.3). We removed abductor CS, and not adductor CS, because the latter had stronger predictive power in our single variable analyses. After removing 76  correlated traits we were left with three explanatory variables in our full multivariate model: ventricle mass, adductor mass, and adductor CS. We found that the optimal structure for our model did not include the random effects of family of origin or sex (data not shown, following procedures outlined by Zuur et al. 2009). Therefore, we used a multiple linear regression including fixed effects only. As expected from our single trait analyses, stepwise multiple linear regressions (forward and reverse) found that all three explanatory variables should be included in the model. We also included the interaction between adductor mass and CS activity in one of our models, because this interaction is predicted to determine the overall ‘aerobic capacity’ of the adductor muscle. We found that a model including the explanatory variables of residual ventricle mass, residual adductor mass, residual adductor CS activity, and the interaction between adductor mass and CS activity per gram adductor resulted in the best fit (Table 4.4, model 1), but that this model was not significantly different from a model without the interaction term (Table 4.4, model 2). Therefore, the reduced model (model 1: adductor CS + ventricle mass + adductor mass) was selected as the best fit model, and explained 17.9% of the variation in Ucrit in F2 hybrids. 4.5  Discussion Determining which morphological, physiological and biochemical traits cause differences in  whole-animal performance capacity can be difficult when many traits differ between populations with high and low capacities. This difficulty arises because all candidate traits co-vary in pure populations, so it is not possible to determine the effect of any single trait on performance by simply comparing these populations. However, there are a number of experimental designs which can isolate the effect of a particular candidate trait on performance, including physiological manipulations, reverse genetics, and forward genetic techniques (reviewed by Dalziel et al. 2009). In this study we used a forward genetics approach to identify the traits that have a significant effect on Ucrit in F2 hybrids between stickleback populations with high (marine) and low (stream-resident) Ucrits. We found that four of 11 candidate traits that we measured significantly regressed against Ucrit in F2 hybrids and that the combined effects of ventricle mass, pectoral adductor mass, and the activity of CS per gram adductor muscle accounted for 17.9% of variation in Ucrit. Overall, these data provide additional support for a causal role of variation in muscle and heart mass in intra-specific differences in Ucrit. However, the correlations between these candidate traits and 77  Ucrit could be due to other loci in close physical linkage with the loci responsible for these traits. The observation that seven of the 11 candidate traits did not have a significant relationship with Ucrit in F2 hybrids suggests that many traits that differ among stream-resident and marine stickleback (e.g. pectoral fin area, body shape, the activity of LDH in pectoral muscles) do not have a strong effect on Ucrit when disassociated from other traits (i.e. in a randomized genetic background). These data suggest that there may be traits which were not measured in this study but have a strong effect on Ucrit, and that many traits are necessary to achieve high swimming performance. 4.5.1  The effect of candidate traits on Ucrit  We regressed each candidate trait against Ucrit to determine if any of the traits we measured had a strong effect on Ucrit when expressed in F2 hybrids with a range of values for other traits. We found that only four of the 11 traits measured significantly regressed against Ucrit in F2 hybrids (i.e. Ventricle mass, Adductor mass, CS activity per gram adductor and CS activity per gram abductor). When we examined the impact of all independent (non-correlated) candidate traits with a multivariate regression we found that the combination of ventricle mass, adductor mass, and the activity of CS per gram adductor muscle accounted for 17.9% of the variation in Ucrit in F2 hybrids. These data are consistent with the hypothesis that differences in muscle and heart mass contribute to the differences in Ucrit between stream and marine stickleback ecotypes, but argue that many of the traits that differ between ecotypes (e.g. pectoral fin area, body shape, the activity of LDH in pectoral muscles) do not have a strong effect on Ucrit when they are disassociated from all the other traits that have diverged between stream and marine populations. In addition, we found evidence to suggest that a novel trait, CS activity per gram adductor muscle, contributes to differences in Ucrit among stickleback populations. In our studies of F1 crosses, we found that F1 marine fish had slightly higher mitochondrial enzyme activities (CS, COX) in their pectoral muscles, but these differences were not significant (Chapter 3). Thus, we had not predicted that the activity of CS in the pectoral adductor muscle would be a strong predictor of Ucrit. The findings that CS is a significant predictor of Ucrit in F2 hybrids might be due to increased statistical power in the current study or may indicate that the effects of CS activity differ in pure and randomized genetic backgrounds. While the activity of COX, another mitochondrial enzyme, was significantly correlated with CS activity (r=0.399 in adductor and r=0.413 in abductor muscle), regressions between adductor and abductor COX and Ucrit were not 78  significant. This discrepancy may have arisen because COX activity may not be reflective of COX enzyme content while CS activity is reflective of CS content. COX is a multimeric protein that is composed of nuclear and mitochondrial encoded subunits (reviewed by Capaldi, 1996), and is allosterically regulated (e.g. Kadenbach et al. 1997), while CS is encoded by a single nuclear gene, and is a homodimer with no known covalent modifications (Wiegand and Remington 1986). Therefore, CS activity may be a better proxy for enzyme content, and thus mitochondrial content, oxidative capacity, and aerobic performance. It is clear from this study that no single candidate trait that we measured is sufficient to confer a high Ucrit in F2 hybrid stickleback, and that even a combination of our candidate traits can only explain about 17.9% of the variation in swimming performance. One possible reason for the poor predictive power is that we did not measure all of the traits that contribute to variation in Ucrit in F2 hybrid stickleback. For example, Taylor and McPhail (1986) found that wild stream-resident fish had lower fin beat frequencies than marine stickleback, and we found that this was also true for fish reared in a common garden (A.C. Dalziel, unpublished data). However, we did not measure any of the kinematic traits that can impact Ucrit (e.g. Walker 2004). As well, many of the traits we measured are proxies for traits known to influence prolonged swimming in fish (reviewed by Kolok 1999), such as maximal metabolic rate (e.g. Reidy et al. 2000), cardiac performance (e.g. Claireaux et al. 2005), skeletal muscle metabolic and contractile properties (e.g. Antilla et al. 2008), and drag (reviewed by Langerhans and Reznick 2009). While proxies for many of these traits have been themselves significantly correlated with prolonged swimming performance, such as COX activities of the cardiac and skeletal muscles in largemouth bass (Kolok 1992), it is likely that these proxy traits are not fully representative of the physiological traits we aimed to study. For example, we measured ventricle mass as a proxy for cardiac output, but differences in heart rate (e.g. Eliason et al. 2011) and ventricle shape (e.g. Claireaux et al. 2005) may also influence this trait. In addition, pectoral muscle size and mitochondrial enzyme activity per gram muscle were used as proxies for muscle contractile properties [e.g. fatigue resistance and contraction rate (e.g. Syme et al. 2006)], but differences in a number of other traits, such as muscle fuel storage (reviewed by Gibb and Dickson 2002), and the content of calcium handling proteins (e.g. James et al. 2011; Seebacher and Walter 2012), can also influence muscle power output and endurance. In addition, many of our candidate traits are predicted to influence maximal metabolic rate (ṀO2,max), but we have not measured all traits in the oxygen transport and utilization. Many other 79  traits predicted to influence ṀO2,max were measured in F1 crosses, and were not found to be significantly different between stream-resident and marine crosses (e.g. gill surface area, hematocrit, mean cellular hemoglobin content, hemoglobin-oxygen binding affinity) (Chapter 3). However, our findings that CS per gram adductor was a significant predictor of Ucrit in F2 hybrids, but not significantly different in F1 crosses, suggests that this could be the case for other traits that varied in F1 crosses, but not significantly, such as hemoglobin-oxygen binding affinity (Chapter 3). The small size of stickleback also makes measuring other traits related to oxygen transport and utilization (reviewed by Wagner 1996) difficult, but these traits may also contribute to differences in ṀO2,max, and Ucrit (e.g. measuring cardiac output directly). Finally, we do not believe that behavioral differences contribute to variation in Ucrit in this forced, laboratory-based measure of performance (A.C. Dalziel, unpublished observations), but such traits may be critical to performance in more ecologically relevant prolonged swimming tasks (e.g. migratory success). One way to find any additional traits that contribute to variation in Ucrit is to take an ‘unbiased’ approach, such as conducting a quantitative trait locus (QTL) mapping study. Such an experiment should be able to uncover loci that contribute to variation in Ucrit, but for which there are no a priori predictions for the effect of a trait on performance (reviewed by Dalziel et al. 2009). Another factor that may have decreased our ability to detect an effect of candidate traits on performance is the relatively small range of Ucrit values, and of some of our candidate traits, in our F2 hybrid families relative to the differences between stream-resident and marine populations. Because we did find some variation among families in Ucrit, performing our experiments on a larger number of F2 families, and families bred from independent grandparental lines, might be one way to obtain a wider range of values for Ucrit and candidate traits. In principle, it is also possible that the effects of a given candidate trait on Ucrit might be dependent on the value of one or more "genetic background" trait(s) (i.e. there might be complex epistatic interactions among the traits that influence Ucrit). While our multiple linear regression should detect simple interactions that are constant throughout the data range, they may not detect more complex interactions. Evidence for interactions among traits can come from experiments displaying that the effect of a focal trait varies depending on the genetic background in which it is expressed (reviewed by Demuth and Wade 2006). To explore this possibility, we split our data into two groups based upon the value of a “background” trait, and then reexamined regressions of the first trait against Ucrit. We present this data exploration for the three candidate traits found to regress significantly against Ucrit in F2 hybrids: ventricle mass, adductor mass, and adductor 80  CS/g in Appendix C (Fig. C10). We found that the relationship between candidate traits and Ucrit varied depending on the value of a second ‘background’ trait. For example, in the F2 fish with the largest (top 50%) and smallest (bottom 50%) adductors, the R2 of adductor CS/g regressed against Ucrit varied from 0.06 vs. 0.15, and the contributions of each family to this overall relationship varied, further suggesting that genetic background, which also varies among families, needs to be further examined (Appendix C, Fig. C10). These data suggest that our current method of testing a given trait in a randomized genetic background is not the best way to study the impacts of a given trait on Ucrit. Instead, other forward genetics crossing designs (reviewed by Dalziel et al. 2009), reverse genetic methods such as gene insertions or deletions (e.g. Colosimo et al. 2005; Chan et al. 2010) and physiological manipulations (e.g. Seebacher and Walter 2012) to examine the impact of a particular trait, or combination of traits, in a pure stream, pure marine, and F1 hybrid backgrounds may prove to be more informative. Unfortunately, some of these approaches will be difficult to complete in this system due to the relatively long generation time of stickleback, the need to measure adult performance, and the complex regulation of many of the physiological traits and small size of threespine stickleback. 4.5.2  Ucrit and candidate trait values in F1 and F2 lines  We also compared the trait values for Ucrit among line crosses (F1 stream, F1 marine, F1 hybrid, and F2 hybrids; Fig. 4.1). F2 hybrids had a range of Ucrits, and mean Ucrit, similar to that of F1 hybrids. No F2 fish reached Ucrits as high as the mean Ucrit for pure marine crosses and very few (< 16/222) reached Ucrits as low as the mean for pure stream crosses. The observation that no F2 fish reached Ucrits as high as those of marine fish is likely due to a dominance of streamresident alleles, similar to our findings in F1 hybrids, and not because of intrinsic hybrid incompatibilities: Hybrids between stream-resident and marine stickleback are viable in the laboratory (e.g. Hagen 1967; Schluter et al. 2004), and adult hybrids are commonly found in Bonsall Creek (Hagen, 1967; Vines and Dalziel, In preparation) and throughout the species range (e.g. Jones et al. 2006). We could not explicitly test for composite genetic effects (e.g. additive, dominance, epistasis) or increased variation in F2 lines when compared to F1 lines (i.e. segregation variance, see Schluter et al. 2004) because we only have data for 3 closely related F2 families, but our data can provide some insight into the genetic basis for differences in Ucrit. For example, we found that no F2 hybrids reached Ucrits outside the range of parental lines (i.e. transgressive segregation; Fig. 1A), and we did not find any evidence for segregation variance in 81  F2 hybrids (e.g. higher variance in F2 lines than in F1 pure and hybrid lines), which indicates that the difference in Ucrit between marine and stream-resident stickleback is likely due to a relatively large number of genes (Lande 1981). In contrast to our findings for Ucrit, F2 hybrids expressed trait values outside of the range of parental lines for a number of candidate traits (i.e. residual ventricle mass, abductor mass, adductor and abductor CS and COX activities, body shape ld1, and residual pectoral fin area; Appendix C; Figs. C2-C4). Such transgressive segregation can be due to epistasis or the action of complementary genes, which occurs when genes with ‘positive’ effects are distributed among parental lines causing the phenotype in parental lines to be less than the absolute maximum value because the ‘positive’ effects at some loci are negated by the ‘negative’ effects at other loci (reviewed by Lynch and Walsh 1998; Rieseberg et al. 1999). In addition, we found that F2 hybrids displayed a large range of trait values for ventricle masses, abductor masses, and body shapes, which might be indicative of segregation variation (Appendix C; Fig. C2-C4). Together, our data on trait ranges and variance suggest that the patterns of genetic variation in some candidate traits might differ from the patterns of variation in Ucrit, and further argues that these candidate traits do not have strong mechanistic links to Ucrit. However, these data must be interpreted with caution, because we reared F1 and F2 fish in different years (F2 fish were bred and raised two years later), and it is possible that environmental differences between years affected our results. We were able to control temperature, salinity, light:dark conditions, and feeding between years (see methods in Chapter 2), but there were differences in fish density that could have affected growth rates, and thus, metabolic traits (e.g. Guderley et al. 1994, 2001). It is also possible that differences in age at the time of sampling affected our biochemical measurements, as some F1 fish were up to six months older than F2 fish when these traits were measured (up to one year and six to nine months of age vs. one year and three to four months of age). In particular, the higher mitochondrial enzyme activities (CS and COX) in the pectoral muscles of F2 may be due to senescence in our F1 fish, as CS activity is known to decline in axial muscles by two years of age in migratory marine stickleback (Guderley et al. 1994). 4.5.3  Traits contributing to the capacity for whole-animal performance  Other studies that have attempted to correlate prolonged swimming in fishes with underlying morphological, physiological and biochemical traits have had mixed success (Kolok 1992; Kolok and Farrell 1994; Garenc et al. 1999; Gibb and Dickson 2002; Odell and Chappell 2003; 82  Claireaux et al. 2005). For example, intra-individual variation in ṀO2,max in Trinidadian guppies (Poecilia reticulata) was not significantly correlated with any of the six candidate traits (swimming muscle, heart and gill size and muscle CS, LDH and myofibrillar ATPase activities) measured by Odell et al. (2003) after corrections for multiple comparisons. Gibb and Dickson (2002) found that muscle aerobic enzyme activities (red muscle, white muscle and heart CS activity, red muscle and heart 3-hydroxy-o-acylCoA dehydrogenase activity and myoglobin content) were not significantly correlated with swimming performance in two scombrid fishes [(kawakawa tuna (Euthynnus affinis) and chub mackerel (Scomber japonicus)]. However, Kolok (1992) was able to predict up to 73% of variation Ucrit capacity in wild-caught largemouth bass (Micropterus salmoides) by measuring variation in red muscle COX, condition factor and gill filament density. Interestingly, in this experiment on wild-caught fish, the predictors of endurance swimming (a set velocity test) varied with season, suggesting that trait plasticity, and not just genetically based differences in mean values, may be critical to performance (Kolok 1992). Studies of burst swimming performance in fishes have also found that the predictors of swimming performance can vary within a population over time. For example, Garenc et al. (1999) found that axial muscle COX and PK activities were significant predictors of burst swimming capacity in adult threespine stickleback, but not in juvenile fish, suggesting that changes associated with reproduction in adults may result in muscle enzyme levels limiting performance. Many studies of intra-individual differences in endurance exercise capacity and ṀO2,max in other vertebrate species have successfully linked underlying traits with performance (e.g. Garland 1984; Garland and Else 1987; Garland and Bennett 1990; Longphre and Gatten 1994; Chappell et al. 1999; Hammond et al. 2000), but other studies have been unable to link performance to any underlying candidate traits (e.g. Bennett et al. 1989; Chappell et al. 2007). These studies, in combination with our results, argue that determining the predictors of locomotory capacity is a complex problem, and that even when predictors can be identified they are often dependent upon species, genetic background, age, sex, reproductive status, and season. These studies also highlight the importance of performing experiments under ecologically relevant conditions if differences in morphology/physiology and performance are to be linked to fitness (reviewed by Arnold 1983; Kingsolver and Huey 2003).  83  4.5.4  Conclusions  Overall, our results from F2 hybrids, in combination with comparisons with F1 crosses (Chapters 1 and 2), suggest that no single trait we measured is sufficient to confer a high Ucrit in threespine sticklebacks, but that a combination of ventricle mass, adductor mass and adductor CS activity can explain 17.9% of the variation in Ucrit in F2 hybrids. These data suggest that other, unmeasured traits also contribute to differences in swimming performance between streamresident and marine stickleback ecotypes. The data from our three F2 crosses also suggests that many loci contribute to variation in swimming performance between stream-resident and marine threespine stickleback. 4.6  Acknowledgements I thank D. Schluter, E. B. Taylor, and J.G. Richards for statistical advice and helpful  comments on earlier versions of this manuscript. I thank W.E. Vandersteen and S.M. Rogers for advice on crossing designs and helpful discussions of line cross analyses.  84  Figure 4.1. (A) Histogram of residual critical swimming speeds (Ucrits) of three F2 hybrid families with the grand mean and full range of Ucrits reached by all F1 individuals represented by circles and colored bars. (B) Grand means ± STDEV of F1 and F2 line crosses. F2 line means are compiled from data from the three F2 families (N = 69-78 fish per family) and F1 line means are grand means compiled from family means (N= 7 F1 stream families, 5 F1 marine families, and 11 F1 hybrid families, with N = 6 fish per family). Data for F1 crosses is from Chapter 2.  85  Figure 4.2. The relationship between Ucrit and (A) residual pectoral fin area and (B) body shape ld1 in F2 fish. The thick black line represents the fitted values for whole population (with family and sex as nested random effects) for residual pectoral fin area (F1,179 = 0.485, p = 0.487) and body shape ld1(F1,175 = 0.009, p = 0.922) (Table 4.2). The coloured lines represent the fitted values for each family and sex (red hatched line = family 1 females; solid red line = family 1 males; blue hatched line = family 2 females; solid blue line = family 2 males; green hatched line = family 3 females; solid green line = family 3 males, information on these regressions can be found in Appendix C; Tables C2 and C3).  86  Figure 4.3. The relationship between Ucrit and residual (A) ventricle, (B) adductor, and (C) abductor mass in F2 fish, with data presented as in Figure 4.3. Thick black lines represent the fitted values for residual ventricle (F1,177 = 7.456, p = 0.007), adductor (F1,187 = 11.621, p = 0.001), and abductor mass (F1,183 = 2.385, p = 0.124) (Table 4.2). The coloured lines represent the fitted values for each family and sex (red hatched line = family 1 females; solid red line = family 1 males; blue hatched line = family 2 females; solid blue line = family 2 males; green hatched line = family 3 females; solid green line = family 3 males, information on these regressions can be found in Appendix C; Tables C4-C6). 87  Figure 4.4. The relationship between Ucrit and (A) residual CS, (C) residual COX, and (E) LDH activity per gram adductor muscle in F2 fish, and (B) residual CS, (D) residual COX, and (F) LDH activity per gram abductor muscle in F2 fish. Data are presented as in Figure 4.2. Thick black lines represent the fitted values for residual CS per gram adductor (F1,184 = 28.101, p < 0.001), residual COX per gram adductor (F1,187 = 1.040, p = 0.309), LDH per gram adductor (F1,187 = 0.911, p = 0.341), residual CS per gram abductor (F1,183 = 10.66, p = 0.001), residual COX per gram abductor (F1,186 = 1.246, p = 0.266), and LDH per gram abductor (F1,183 = 0.112, p = 0.738) (Table 4.2). The coloured lines represent the fitted values for each family and sex (red hatched line = family 1 females; solid red line = family 1 males; blue hatched line = family 2 females; solid blue line = family 2 males; green hatched line = family 3 females; solid green line = family 3 males, information on these regressions can be found in Appendix C; Tables C7C12). 88  Table 4.1. Variance explained by, and factor loadings for, the linear discriminant (ld) function produced in the discriminant function analysis (DFA) of six body shape traits (see methods section for a full description of traits). Ld1 values for F2 fish were obtained by training with data from pure stream and pure marine crosses: marine fish have low and stream-fish have high ld1 scores. Coefficients of linear discriminants: -97.054 Fineness 61.704 Caudal peduncle depth 32.557 Caudal area -37.250 Posterior depth -36.315 Head depth Table 4.2. Results of linear mixed-model regression of candidate traits (fixed effects) versus Ucrit. Family and sex were included as nested random effects. R2 values are from models with fixed-effects only (Appendix C, Table C2). Trait \ parameter R2 Slope p-value F-value (df) 0.000 -3.020 0.487 0.005 (1,179) Pectoral fin surface area (residuals) 0.000 -0.003 0.941 0.009 (1,175) Body shape ld1 Ventricle mass 0.050 0.046 0.002 9.935 (1,175) (residuals) Adductor mass 0.053 0.492 < 0.001 11.250 (1,184) (residuals) 0.008 0.053 0.124 2.385 (1,183) Abductor mass (residuals) 0.000 0.016 0.313 1.023 (1,184) COX/g adductor (residuals) CS/g adductor 0.125 0.303 < 0.001 28.101 (1,184) (residuals) 0.002 -0.064 0.331 0.948 (1,184) LDH/g adductor 0.000 0.021 0.230 1.450 (1,183) COX/g abductor (residuals) CS/g abductor 0.051 0.246 0.001 10.660 (1,183) (residuals) 0.000 0.010 0.738 0.112 (1,183) LDH/g abductor  89  Table 4.3: Correlations among explanatory variables (fixed-effects only). When corrected for multiple comparisons, the cut-off for significant P-values is 0.00347. Significantly correlations are bolded. Pectoral fin area Body shape ld1 Ventricle mass Adductor mass Abductor mass Adductor CS Adductor COX Adductor PK Adductor LDH Abductor CS Abductor COX Abductor PK Abductor LDH  Pectoral fin area ---  Body shape ld1 r = -0.181 p =0.0147 --  --  --  Ventricle mass r = 0.134 p =0.0729 r =-0.0154 p = 0.840 --  --  --  --  Adductor mass r = 0.0521 p = 0.480 r =-0.0193 p = 0.796 r = 0.092 p = 0.217 --  --  --  --  --  Abductor mass r =0.114 p =0.121 r =-0.0827 p = 0.268 r = 0.150 p =0.0434 r = 0.565 p<0.0001 --  --  --  --  --  --  Adductor CS r = 0.0194 p = 0.792 r = 0.333 p< 0.0001 r = 0.128 p =0.0841 r = 0.237 p<0.0001 r = 0.219 p=0.0024 --  --  --  --  --  --  --  Adductor COX r = 0.171 p =0.0197 r = 0.0603 p = 0.418 r = 0.0648 p = 0.385 r =-0.0752 p = 0.301 r = 0.135 p= 0.0624 r = 0.399 p<0.0001 --  --  --  --  --  --  --  --  Adductor PK r =0.290 p<0.0001 r =-0.078 p=0.295 r =0.018 p=0.811 r =-0.194 p=0.0071 r = -0.118 p=0.104 r =210 p=0.0035 r =0.222 p=0.0021 --  --  --  --  --  --  --  --  --  Adductor LDH r = 0.0665 p = 0.367 r =-0.0635 p = 0.394 r = -0.144 p =0.0523 r = -0.211 p=0.0034 r = -0.119 p = 0.102 r=-0.0898 p = 0.217 r = 0.161 p = 0.026 r =0.321 p<0001 --  --  --  --  --  --  --  --  --  --  Abductor CS r -0.0168 p = 0.820 r = 0.349 p <0.0001 r = 0.127 p =0.0884 r = 0.276 p=0.0001 r = 0.251 p=0.0005 r = 0.793 p<0.0001 r = 0.376 p<0.0001 r =-0.0069 p=0.925 r = -0.121 p =0.0973 --  --  --  --  --  --  --  --  --  --  --  Abductor COX r = 0.165 p =0.0247 r = 0.0203 p = 0.786 r = 0.0526 p = 0.482 r=-0.0639 p = 0.381 r = 0.0944 p = 0.195 r = 0.313 p<0.0001 r = 0.754 p<0.0001 r =0.117 p=0.108 r = 0.111 p = 0.126 r = 0.413 p<0.0001 --  --  --  --  --  --  --  --  --  --  --  --  Abductor PK r =0.339 p<0.0001 r =-0.119 p=0.111 r =0.0542 p=0.468 r =-0.165 p=0.023 r =-0.0912 p=0.211 r =0.141 p=0.0528 r =0.193 p=0.0076 r =0.844 p<0.0001 r =0.275 p=0.0001 r =-0.025 p=0.731 r =0.134 p=0.065 --  --  --  --  --  --  --  --  --  --  --  --  --  Abductor LDH r=0.254 p=0.0005 r = -0.133 p =0.0745 r = 0.0024 p = 0.974 r = -0.202 p=0.0051 r =-0.0544 p = 0.456 r = 0.0718 p = 0.325 r = 0.317 p<0.0001 r =0.648 p<0.0001 r = 0.611 p<0.0001 r =-0.0659 p = 0.366 r=0.224 p=0.0018 r = 0.720 p<0.0001 --  90  Table 4.4: Results of multiple linear regression analyses Model 1: Ucrit~ventricle mass + adductor mass + adductor CS + adductor mass:adductor CS Adjusted R2 F-statistic (df) P-value Coefficients:  18.8% 11.03 (4,169) 0.0000000568 Estimate Std.Error t-value P-value (Intercept) -0.148 0.190 -0.780 0.436 Ventricle mass 0.029 0.014 2.139 0.034 Adductor mass 0.250 0.151 1.653 0.100 Adductor CS 0.261 0.062 4.212 < 0.001 Addutor mass: Adductor CS 0.067 0.039 1.724 0.087  Model 2: Ucrit~ventricle mass + adductor mass + adductorCS 17.9% 13.56 (3,170) 0.0000000566 Estimate -0.075 0.032 0.274 0.276  Std..Error 0.186 0.014 0.151 0.062  t-value -0.403 2.387 1.816 4.461  P-value 0.688 0.018 0.071 <0.001  Model comparison: F2,169= 2.971, p = 0.087  91  Chapter 5: General discussion and conclusions Understanding the mechanisms by which complex performance traits evolve in natural populations is one of the requirements for predicting how animals will respond to future environmental challenges (reviewed by Naish and Hard 2008; Futuyma 2010; Feder et al. 2010). However, there are relatively few empirical studies that have uncovered the specific mechanisms by which whole-animal performance traits have evolved among natural populations (but see Langerhans 2009; Lee et al. 2011; Hendry et al. 2011). The major objectives of my dissertation were to determine if the capacity for prolonged swimming evolved in stream-resident threespine stickleback (Gasterosteus aculeatus) populations, and if so, determine which traits contribute to differences in performance between stream-resident and ancestral marine stickleback populations. Thus, my dissertation was designed to provide a much needed empirical study of the morphological, physiological, and biochemical mechanisms by which endurance exercise capacity evolves among natural populations. 5.1  Major findings and implications Collectively, my research provides a number of insights into the evolution of endurance  exercise capacity, and of complex performance traits more generally. In this section I review some of my major findings in the context of my thesis objectives and discuss how this research contributes to our understanding of how whole-animal performance capacity evolves. 5.1.1  Reductions in swimming performance have evolved in parallel in multiple  freshwater populations of stream-resident stickleback (Chapter 2) I found that the differences in performance detected between wild stream-resident and marine stickleback populations (Taylor and McPhail 1986, Tudorache et al. 2007; but see Schaarschmidt and Jurss 2003), have a genetic basis by breeding and rearing stream-resident and marine stickleback crosses from two different locations in a common laboratory environment and then measuring their capacity for Ucrit (Chapter 2; Bonsall and West creeks, British Columbia, Canada, Fig. 2.2A). In addition, I demonstrated that Ucrits in wild-caught stream-resident stickleback from three additional locations in British Columbia were also lower than those of sympatric marine stickleback (Chapter 2; Salmon River, Kanaka Creek, Little Campbell River, Fig. 2.2B and Fig. A3). Overall, these data suggest that reductions in the capacity for prolonged 92  swimming have evolved in parallel in multiple stream-resident populations, and are consistent with a role of natural selection in the evolution of reduced swimming performance. My studies, in combination with Barrett et al.’s (2011) findings that freshwater stickleback have evolved a lower thermal tolerance, indicate that changes in whole-animal performance have evolved relatively quickly (i.e.< 12,000 years) in threespine stickleback. Other studies comparing populations which have recently ( < 200 years ago) experienced environmental changes display that performance can evolve more rapidly in natural populations (e.g. Lee et al. 2003a; Levinton et al. 2003; Langerhans 2009), or after experimental introductions (e.g. Herrell et al. 2008). In combination with studies measuring the response to laboratory-based selection experiments, which have found that large differences in whole-animal performance capacities can evolve in less than 40 generations (Swallow et al. 2009; Lee et al. 2011), these studies argue that many complex performance traits are highly evolvable. 5.1.2  Parallel reductions in prolonged swimming capacity are determined by different  genetic mechanisms in closely related populations of threespine sticklebacks (Chapter 2) I determined the genetic architecture responsible for differences in prolonged swimming capacity between stream-resident and marine stickleback populations in Bonsall and West Creeks by comparing the swimming performance of pure F1 stream-resident, pure F1 marine, and reciprocal F1 hybrid crosses (Chapter 2; Fig. 2.2A). I found significant differences in dominance between the two locations (with stream alleles dominant in Bonsall and marine alleles dominant in West Creek), which indicates that distinct mechanisms are responsible for the reductions in Ucrit in these two stream-resident populations. These data suggest that even populations which evolve from quite similar genetic starting points can take different genetic routes to reach similar phenotypic values. The evolution of similar phenotypes, by different genetic mechanisms, is further evidence for a role of natural selection in driving trait evolution, and also suggests that evolutionary constraints are not so strong that only one ‘genetic route’ to performance can be taken (Losos 2011).  93  5.1.3  Multiple candidate traits predicted to influence prolonged swimming have evolved  in stream-resident populations (Chapters 2 and 3) Because the underlying morphological and physiological traits that can affect prolonged swimming in fish are well understood (reviewed by Bernal et al. 2001; Farrell 2002; Langerhans and Reznick 2009), I was able to make clear predictions about the candidate traits that might influence swimming capacity in stickleback, and the direction in which these traits should evolve in stream-resident stickleback with a reduced capacity for prolonged swimming. Of the eighteen unique traits for which I could make clear predictions, I found that seven (body shape, pectoral fin shape, pectoral fin size, ṀO2,max, pectoral adductor and abductor muscle size, skeletal muscle fibre size (only measured in West Creek), and the activity of LDH/g pectoral muscle) evolved in the direction predicted to be associated with a decreased Ucrit in both stream-resident populations (Bonsall and West creeks). In addition, ventricle size evolved as predicted in Bonsall Creek stream-resident fish, but not in West Creek fish. I found no significant differences between stream-resident and marine populations in standard metabolic rate, gill surface area, hematocrit, mean cellular hemoglobin, hemoglobin oxygen binding affinity, ventricle aerobic (CS, COX) enzyme activities per gram tissue, ventricle anaerobic enzyme (PK, LDH) activities per gram tissue, ventricle CPK activity per gram tissue, pectoral muscle aerobic enzyme (CS, COX) activities per gram tissue, and pectoral muscle CPK activity per gram tissue. Together, these data indicate that many subordinate traits are associated with evolutionary differences in performance, and that these morphological and physiological traits can also evolve relatively quickly (< 12,000 years since divergence of stream and marine populations). The evolution of a number of underlying traits in conjunction with the evolution of performance is commonly found in selection studies as well. For example, studies examining the mechanisms by which a high capacity for voluntary wheel running and ṀO2,max evolve in rodents have found that a suite of underlying traits have evolved in response to selection on these traits in less than 40 generations in the case of the former experiment (e.g. Rhodes et al. 2005; Rezende et al. 2006; Jonas et al. 2010), and 20 generations in the latter experiment (e.g. Howlett et al. 2009; Kirkton et al. 2009). This rapid evolution of a number of traits predicted to influence performance suggests that it will be difficult to determine the order in which underlying traits evolved in wild populations. Therefore, experimental introductions to natural or semi-natural environments will be crucial for determining the `steps` by which complex phenotypes evolve in the wild.  94  The evolution of many of the same traits in both of the stream-resident populations that I studied also suggests that these traits are associated with freshwater adaptation, and have not evolved by purely stochastic evolutionary processes. However, it is important to note that many other sources of selection have changed after freshwater invasion in threespine stickleback, and selection has not been solely for reductions in swimming performance. For example, freshwater sticklebacks need to osmoregulate in freshwater instead of saltwater during the winter (reviewed by Guderley 1994), escape from invertebrate predators over the winter (e.g. Marchinko 2009), and eat different types of food (reviewed by Hart and Gill 1994). Therefore, it is possible that the candidate traits that have evolved in both stream-resident populations (i.e. ṀO2,max, pectoral muscle size, pectoral fin size, body shape) evolved as a result of selection on other performance traits, and not swimming capacity. It is also possible that traits which ‘multi-task’ and mediate tradeoffs with other ecologically important tasks (e.g. osmoregulation) may have been evolutionarily constrained by these functional tradeoffs (e.g. gill surface area). 5.1.4  Many candidate traits have a similar genetic architecture as Ucrit, but some of these  traits differ between populations (Chapters 2 and 3) By comparing pure F1 and F1 hybrid crosses I was also able to compare the genetic architecture of candidate traits with that of swimming performance. Traits with a major effect on performance are predicted to share a genetic basis with performance, so this comparison provides additional evidence that a candidate trait is mechanistically related to Ucrit. I found that the traits that had a similar genetic architecture as Ucrit in Bonsall Creek were pectoral fin shape (Chapter 2), body shape (Chapter 2), ṀO2,max (Chapter 2), ventricle mass (Chapter 3), and the activity of LDH/g abductor muscle. In West Creek, pectoral fin shape (Chapter 2), ṀO2,max (Chapter 2), pectoral muscle masses and LDH/g pectoral muscle (Chapter 3) had a similar genetic architecture as Ucrit. Therefore, a subset of the candidate traits which evolved in stream-resident fish are predicted to have a strong influence on Ucrit in both populations (e.g. pectoral fin shape, ṀO2,max, and the activity of LDH/g abductor), while other traits that are predicted to strongly influence Ucrit were found to be important in only one population (e.g. ventricle mass and body shape in Bonsall Creek and pectoral muscle size in West Creek). Together, these data suggest that there is a mixture of shared and divergent mechanisms contributing to the evolution of Ucrit in these two stream-resident populations. This hypothesis is consistent with my findings of a different composite genetic basis of Ucrit in these two locations (Section 5.1.2), and suggests that 95  there are different ways for prolonged swimming performance to evolve in sticklebacks (i.e. many-to-one mapping; Wainwright et al. 2005). The finding that similar performance capacities are reached by different underlying mechanisms is a common occurrence in inter-specific comparisons (e.g. Carroll et al. 2004; Alfaro et al. 2005; Collar and Wainwright 2006; Stayton 2006; Strobbe et al. 2009; Bergman and Irschick 2010), intra-specific comparisons (e.g. Law and Blake 1996), and among selected lines of rodents in experimental evolution studies (e.g. Garland et al. 2011). Therefore, my results, in combination with these other studies, suggest that many-to-one mapping is common for a variety of performance traits, and that in the examples studied to date, evolutionary constraints rarely limit performance traits to evolve in only one specific way (Wainwright et al. 2005). 5.1.5  Many underlying traits are necessary to achieve a high capacity for Ucrit  (Chapter 4) In my F1 line crosses (in Chapters 2 and 3), all of the traits which have diverged between stream-resident and marine populations are in complete linkage disequilibrium, and thus co-vary. Therefore, any traits that vary between stream-resident and marine populations may be associated with Ucrit in F1 line crosses simply because of this linkage disequilibrium, and not necessarily because they are mechanistically related to Ucrit. To reduce the number of these spurious associations, I only studied candidate traits that are known to impact prolonged swimming performance in fish (see Chapters 2 and 3). However, this methodology cannot completely eliminate spurious associations between candidate traits and Ucrit. To further test the associations between candidate traits and prolonged swimming performance found in Chapters 2 and 3, I generated a set of F2 hybrid crosses from Bonsall creek stream-resident and marine grandparents (Chapter 4). In F2 hybrid crosses, recombination breaks down some of the linkage disequilibrium found in F1 hybrids, and allowed me to study the effect of each candidate trait on prolonged swimming performance in fish with largely randomized genetic backgrounds. I found that four of the eleven candidate traits I measured in Bonsall Creek F2 hybrids were significant predictors of Ucrit capacity (residual ventricle mass, residual adductor mass, activity of CS per gram adductor and the activity of CS per gram abductor muscle; Chapter 4). Of these four traits only ventricle mass was also found to be strongly associated with Ucrit in Bonsall F1 crosses (i.e. shared a similar genetic architecture; Chapter 3), suggesting that associations between Ucrit and pectoral fin shape, body shape, and the activity of LDH/g abductor muscle that 96  I observed in Bonsall Creek F1 hybrids (Chapter 3), are not sufficient to have an effect on Ucrit in a randomized genetic background. The second trait that was strongly associated with Ucrit in F2 hybrids, adductor mass, did evolve to a reduced size in Bonsall stream-resident fish (Chapter 3), but did not have a similar genetic basis as Ucrit in Bonsall Creek F1 hybrids. The third and fourth traits that significantly correlated with Ucrit in F2 fish were the activities of CS per gram adductor and abductor, which was unexpected; while these trait were higher in F1 marine fish than streamresident fish, they were not significantly so. Together, these three traits explained 17.9% of variation in Ucrit. Overall, these data indicate that Ucrit is a complex performance trait for which many underlying traits are necessary to achieve high performance capacity and that other, unmeasured, traits also affect Ucrit. My finding that many traits contribute to differences in Ucrit capacity in F2 hybrids is not surprising in light of the data collected from experimental evolution studies which have found that multiple candidate subordinate traits quickly evolve in conjunction with performance (discussed in 5.1.3). These data suggest that swimming performance truly is a ‘complex’ trait, and that determining all of the subordinate traits contributing to variation in Ucrit will be difficult. 5.1.6  Anadromous marine threespine stickleback populations show genetically based  differences in morphological and physiological traits associated with swimming capacity (Chapters 2 and 3) The majority of work using threespine stickleback as a model to address questions in evolutionary ecology has compared marine to freshwater populations or compared sympatric freshwater populations, whereas less attention has focused upon variation among marine populations (reviewed by Bell and Foster 1994; Ostlund-Nilsson et al. 2007). While there is less differentiation among marine populations than there is among freshwater populations (e.g. Withler and McPhail 1985; Hohenlohe et al. 2010; Jones et al. 2012; note that overall genetic variation within a given marine population is normally much higher than that for freshwater populations), my work suggests that there may be substantial local adaptation in anadromous marine populations. I have found that there are genetically-based differences between Bonsall and West Creek marine populations in body shape, ṀO2,max (Chapter 2), pectoral muscle mass, hematocrit (in females only), and pectoral muscle pyruvate kinase (PK) activities (Chapter 3). The pervasive local adaptation found in the salmonid fishes, including many of the species of Pacific salmon which co-exist with stickleback in the Pacific Ocean (reviewed by Taylor 97  1991; Fraser et al. 2011), suggests that local adaptation can evolve rapidly in migratory fishes with a degree of natal philopatry (reviewed by Quinn 2005). While there is evidence for homing in marine stickleback (e.g. Saimoto 1993), the extent of natal philopatry is still unknown. I hope to continue to study marine stickleback populations to determine the extent of natal philopatry in this species and test for physiological differentiation among populations with different migratory requirements (section 5.3). 5.2  Strengths and limitations of the dissertation research In my dissertation I used a critical swimming speed (Ucrit) test to measure the capacity for  prolonged swimming in stickleback. Ucrit is predicted to be an ecologically relevant measure of performance for pelagic planktivorous fishes, migratory fishes, and fishes that must maintain their position in high flow streams (Plaut 2001; Wolter and Arlinghaus 2003), such as marine threespine stickleback. However, I have not directly tested the ecological relevance of Ucrit for migratory marine stickleback by determining if this laboratory based measure of performance is correlated with performance in the wild. Such tests are critical to assessing the ecological relevance of laboratory based performance measures (reviewed by Irschick and Garland 2001; Irschick 2003), because many animals do not use maximal performance capacities in the wild or can compensate for differences in laboratory based measures of performance (e.g. Irschick et al. 2005; Husak and Fox 2006). In addition, ecological performance should be tested for links to fitness by conducting mark/recapture studies of fish with known performance capacities in wild stream-resident and marine populations, but this has not yet been done for any species of migratory fish (reviewed by Plaut 2001). While data from field experiments are needed to directly assess the ecological relevance of Ucrit, studies on wild salmonid populations (E. J. Eliason, personal communication), and comparisons among populations (Lee et. al. 2003), have found Ucrit to be positively correlated with increases in migratory difficulty, and support the ecological relevance of this measure of performance. A major strength of my dissertation is that I tested for genetically based differences in performance by rearing fish in a common laboratory environment. However, as a result of the relatively long generation time of threespine stickleback (~ eight to ten months to reach adulthood), I measured performance in F1 crosses whose parents were collected from the wild, and experienced a range of environmental differences. Therefore, it is possible that parental (reviewed by Badyaev and Uller 2009), or transgenerational epigenetic effects (e.g. Greer et al. 98  2011) contribute to the differences in performance found in my F1 fish. However, my findings that reciprocal F1 hybrids did not vary in Ucrit or any other candidate traits, argues that paternal effects and sex-specific epigenetic variation do not have a strong effect on these traits (Chapter 2 and 3). I used the comparative method to determine which candidate traits evolved in conjunction with differences in performance in stream-resident stickleback populations in West and Bonsall Creeks (reviewed by Sanford et. al. 2002; Garland et al. 2005). One of primary strengths of comparative methods is the use of naturally evolved populations, which is not the case with other methodologies such as laboratory based experimental evolution studies (reviewed by Futuyma and Bennett 2009). However, a major issue for all studies attempting to find the traits that contribute to variation in performance is that of unmeasured traits: how can you be sure you have measured all important variables? I tried to examine a range of candidate variables, but it is likely that I missed candidate traits that also influence Ucrit in these populations (Chapter 4). To determine which candidate traits are most strongly associated with Ucrit capacity I used F1 and F2 hybrid crosses, which is rarely done in combination with comparative studies. I first compared F1 hybrids to pure parental crosses to compare the genetic architecture of Ucrit and candidate traits. However, because F1 hybrid line crosses are not independent from parental forms, I was not able to correlate candidate traits with Ucrit in my F1 crosses (even with cross type as a random nested variable). As well, the linkage disequilibrium among all traits in the parental populations made it impossible to test the effect of a given candidate trait in isolation of all of the other traits which have also evolved in stream-resident threespine stickleback. To address this problem I generated F2 hybrid crosses and correlated candidate traits against Ucrit in a randomized genetic background (Chapter 4). I found a number of traits to be significantly correlated with Ucrit, but the overall predictive power of all of my candidate traits was only ~ 17.9%. This may be due to unmeasured traits (see the paragraph preceding this), but might also be due to complex interactions among traits which cannot be detected by a multiple regression analysis (i.e. co-adaptive epistasis, or dominance-by dominance epistasis; reviewed by Carlborg and Haley 2004). Different forward genetic crossing designs, such as the production of recombinant inbred lines or recombinant congenic strains, may be better able to detect epistatic interactions (reviewed by Demuth and Wade 2006). However, a limitation with most forward genetic methods is that only a small portion of the naturally occurring variation is sampled. For example, in my F2 crosses I could only study the variation present in a single stream-resident and 99  a single marine grandparental fish (Chapter 4). One way to resolve this issue is to examine wild advanced generation stream-resident and marine hybrids found in natural hybrid zones (e.g. Jones et al. 2006), which I hope to do in the future. Finally, in this dissertation I used a combination of comparative methods, and correlational analysis to determine the effects of underlying traits on swimming performance. However, these methods cannot detect causation. To detect causation, manipulative experiments that test the effect of each candidate trait in isolation, and various combinations of candidate traits, are needed. While some candidate traits, such as fin size, can be manipulated by phenotypic engineering (e.g. Sinervo et al. 1992) other candidate traits (e.g. ventricle mass) will be difficult to manipulate in adult fish by currently available methods for phenotypic engineering, physiological manipulation, and reverse genetics (reviewed by Dalziel et al. 2009). 5.3  Future research Through this research I identified a number of candidate traits that have evolved in  conjunction with, and were significantly correlated with, differences in performance. However, my experiments with F2 hybrids from Bonsall Creek suggested that there are other, unmeasured, traits that also contribute to variations in Ucrit (Chapter 4). In the future, I plan to combine studies of additional candidate traits, such as swimming kinematic measures (Walker 2004), with ‘unbiased’ approaches to trait detection, such as quantitative trait locus (QTL) mapping. A major benefit of such unbiased approaches is that they are not limited to only studying traits that have been previously shown to influence swimming in fish. The genetic resources currently available for threespine stickleback make such methods possible (e.g. Peichel et al. 2001; Leder et al. 2009), and QTL mapping approaches have been used to successfully detect the genetic bases for a number of morphological traits in threespine stickleback (Peichel et al. 2001; Shapiro et al. 2004; Cresko et al. 2004; Colosimo et al. 2005; Miller et al. 2007; Albert et al. 2008; Chan et al. 2010). I am currently collaborating with Dr. Sean Rogers to perform QTL mapping studies on prolonged swimming performance in threespine stickleback. As might be predicted for such a complex performance trait (Nikinmaa and Waser 2007; Mackay et al. 2009), I have not found any loci with effects as strong as those for morphological traits (e.g. Colosimo et al. 2005) in my preliminary studies. However, I plan to continue to search for the loci that may contribute to differences in Ucrit among marine and freshwater populations by combining quantitative genetic approaches with ecological genomics methods. 100  The genetic resources available for threespine stickleback have also facilitated ecological genetic (e.g. Hohenlohe et al. 2010; Shimada et al. 2010; DeFaveri et al. 2011; Jones et al. 2012) and candidate gene studies (e.g. Kitano et al. 2010). I hope to use the genomic data available from these studies to select candidate genes and test the impacts of these selected loci on swimming performance in ‘gene to physiology’ approaches (reviewed by Feder et al. 2010). The presence of natural hybrid zones between stream-resident and marine threespine stickleback populations also provides an opportunity to measure the strength of selection on candidate loci (Hagen, 1967; Jones et al. 2006). Dr. Tim Vines and I have intensively sampled the hybrid zone between stream-resident and marine stickleback populations in Bonsall Creek, and have been able to measure the strength of selection on a number of loci that contribute to the evolution of morphological traits (Vines et al., In preparation). In the future, I hope to use this data set to examine selection on candidate ‘physiological’ genes predicted to influence swimming performance in threespine stickleback. An additional avenue for future research is to study the role of phenotypic plasticity, and the interactions between plasticity and genetically based differences in Ucrit. In this dissertation I assessed Ucrit at a single temperature (15ºC) and salinity (2 ppt) because my primary objective was to test for genetically-based differences in performance and candidate traits. However, Ucrit is a phenotypically plastic trait that varies with factors such as temperature, salinity, and training (e.g. Lee et al. 2010; Antilla et al. 2008). These abiotic factors also vary among many stickleback populations, and an intriguing possibility is that the reaction norms for Ucrit have also evolved in stream-resident populations (e.g. Hutchings 2011). For example, it is not known if the reduced Ucrit of stream-resident stickleback can be improved with training (e.g. Farrell et al. 1991; Antilla et al. 2008), or if the divergence between stream-resident and marine fish in Ucrit is even greater in seawater. Conducting ‘reciprocal transplant’ studies, where both ecotypes are tested in freshwater and seawater, or trained and untrained groups are compared, can highlight the importance of phenotypic plasticity to performance capacity. In addition, training studies of both ecotypes will provide insight into the question of whether evolved differences in performance can limit the responses to training, and if a ‘shared use’ of biochemical and physiological pathways results in tradeoffs between plasticity and evolutionary change (reviewed by Moyes and Hood 2003). A third avenue for future research is to measure selection on swimming performance in wild stickleback populations by integrating my laboratory-based measures of swimming 101  performance into the morphology-performance-fitness paradigm (Arnold 1983; Kingsolver and Huey 2003). Such integration will require a phased research programme with a laboratory-based component to examine the links between morphological traits and performance within streamresident and marine populations, a field-based component to tests the effect of laboratory based performance traits on ecological performance, and further field studies to measure the effect of performance on fitness in each population (reviewed by Arnold 1983; Irschick 2003). This is no easy task, especially for small migratory fishes such as threespine stickleback for which tracking technologies used in larger fishes are not as viable (e.g. Cooke et al. 2008). However, with an increased understanding of the genetic and physiological bases for variations in performance, this task will become more feasible, as candidate loci can be studied among age classes and generations (Endler 1986). In addition, such studies may be easier to complete for freshwater populations, which remain in a small area year round, and for which semi-natural experiments are more easily conducted (e.g. Barrett et al. 2008). The colonization of freshwater from marine environments is common in other post-glacial fishes (reviewed by McDowall 1988), and other animals (reviewed by Lee and Bell 1999), and is common in ‘invasive’ species of commercial concern (Lee and Gelembiuk 2008). The threespine stickleback species complex is an excellent system in which to study the evolution of a suite of complex traits after freshwater colonization. In particular, studies examining the evolution of osmoregulatory performance (e.g. Schaarschmidt and Jurss 1999), thermal tolerance (e.g. Barrett et. al. 2011) and interactions between osmoregulation, temperature and swimming performance among populations will provide insight into the tradeoffs among the many performance traits which have evolved after freshwater colonization, and the relative roles of various selective pressures on the evolution of the integrated phenotype in stickleback. In this dissertation I studied the evolution of low performance capacity in stream-resident populations, but my research (see section 5.1.5) also suggests that threespine sticklebacks can be used to study the evolution of an increased capacity for prolonged swimming. For example, comparisons of anadromous populations of stickleback that migrate long and short distances within the same river systems in British Columbia, may be a good model for examining the mechanisms by which higher capacities evolve. I hypothesize that upstream populations will have evolved a higher aerobic capacity with increases in migratory distance. Indeed, preliminary data from coho salmon (Taylor and McPhail 1985), sockeye salmon (Eliason et al. 2011), and  102  my comparisons of two threespine stickleback marine populations with differences in migratory difficulty (West and Bonsall Creeks) are consistent with this hypothesis. 5.4  Concluding thoughts: Understanding the mechanisms by which complex traits evolve At many times in my dissertation I have worried that biologists studying the evolution of  complex phenotypes in the wild might “suffer the fate of implosion from complexity…” (Kingsolver and Huey 2003). With so many basic questions about how genetic model organisms work (e.g. C. elegans, D. melanogaster) still waiting to be answered, is it currently possible to understand that mechanisms by which performance evolves in a non-model species? While this task will be difficult, there are a number of research programmes that have made progress in the ‘gene to performance’ approach by testing the impacts of genetic variation on higher levels of biological organization (examples reviewed in Dalziel et al. 2009). In addition, there has been progress made in linking whole-animal performance to underlying physiological and biochemical traits (e.g. Lee et. al. 2011). Of course, the complexities of organismal design, in combination with recent work highlighting the importance of phenotypic plasticity (Mockzek et al. 2011) and epigenetic regulation (Danchin et al. 2011) for a number of complex phenotypes (e.g. Greer et. al. 2011; Rajakumar et al. 2012) means that this will continue to be a difficult task. However, collaborations among the molecular and developmental biologists who work to determine the links between genotype and biochemical and developmental phenotypes, comparative physiologist and biomechanists who aim to link variation in morphology and physiology to performance, and evolutionary biologist and ecologists capable of measuring the fitness consequences of variation in ‘ecological performance’ (Irschick 2003; Irschick and Reznick 2009), will be the key to learning about the mechanisms by which animals function in natural populations and how they change over time (reviewed by Dalziel et al. 2009; Satterlie et al. 2009; Mykles et al. 2010).  103  REFERENCES Achard, S. (2010). brainwaver: Basic wavelet analysis of multivariate time series with visualization and parameratization using graph theory. In R package version 1.5. Albert, A. Y. K., Sawaya, S., Vines, T. H., Knecht, A. K., Miller, C. T., Summers, B. R., Balabhadra, S., Kingsley, D. M. and Schluter, D. (2008). 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This transition results in a shift from aerobically fueled pectoral fin swimming (powered by the red, slow-twitch pectoral muscles) to a combination of aerobically and non-aerobically fueled swimming (by the white, fast-twitch caudal muscles). Thus, the gait transition speed might be a good proxy for maximum aerobic swimming capacity. We measured gait transition as the speed at which 3 of the 6 siblings swum in a trial began to burst at least 3 times per minute. This was recorded for 24 of the 39 laboratory-raised families of sticklebacks: 2 Bonsall pure stream (SS) families, 4 Bonsall stream mother x marine father (SM) families, 2 Bonsall marine mother x stream father (MS) families, 1 Bonsall pure marine (MM) family, 5 West SS families, 3 West SM families, 3 West MS families, and 4 West MM families. We used Pearson's correlation to test the significance, and measure the strength, of the linear relationship between Ucrit and gait transition speed. The strong positive correlation between gait transition and Ucrit indicates that Ucrit is also a good proxy for maximum aerobically fueled swimming speed in this species (Fig. A1). In addition, we found that oxygen consumption continued to increase until Ucrit was reached.  Figure A1. Relationship between gait transition speed and critical swimming speed (Ucrit), y = 0.980x + 1.474, d.f.= 23, p=0.951, r2=0.905, p<0.001. Dots represent family means.  126  A.2-Repeatability of critical swimming speed (Ucrit) To test for repeatability of swimming performance we re-swam (Ucrit 2) a subset of our laboratory-raised fish one month after we initially measured Ucrit (Ucrit 1). In particular, we reswam 43 fish from 14 of our Bonsall creek families (3 fish from MM1; 3 from MM2; 3 from MM3; 2 from MM4; 3 from MM5; 3 from MM6; 3 from SS1; 5 from SS2; 5 from SS4; 2 from SS5; 2 from MS1; 3 from MS2; 3 from SM1; 3 from SM2). We used Pearson's correlation to test the significance, and measure the strength, of the linear relationship between Ucrit 1 and Ucrit 2. The strong positive correlation between Ucrit 1 and Ucrit 2 indicates that Ucrit is repeatable over this time span (Fig. A2).  Figure A2. Repeatability of Ucrit after one month. y=1.173 + 0.814x, r2=0.901, p<0.001. Dots represent individual fish.  127  A.3-Plasticity of Ucrit: Lab vs. Wild fish Our studies on wild populations from Salmon River, Kanaka Creek, Little Campbell River (Figure 2.1), also allowed us to assess plasticity in swimming performance. We initially collected this data to test for parallel evolution of swimming performance, and not to specifically study plasticity, so comparisons are often made among locations. However, our findings that there are no significant differences in Ucrit among ecotypes from different locations (Figure 2.1A) suggest that this comparison should be informative. We compared the Ucrit of our lab-bred crosses from Bonsall and West creeks to wild-caught adult fish collected from Kanaka Creek, Little Campbell River, Salmon River in June 2008 (to test the effect of environment in early development and adulthood, and prior migration, on Ucrit), and wild-caught juveniles from Kanaka Creek, Little Campbell River, Salmon River collected in July 2008 and raised in the lab to adulthood (to test the effect of environment in early development on Ucrit). Wild-caught fish were all kept in dechlorinated Vancouver tap water brought to 2ppt ± 0.5ppt with Instant Ocean® sea salt in a 15°C environmental chamber under a 12L:12D photoperiod. Adults were fed Mysis shrimp and bloodworms daily and were acclimated to laboratory conditions for at least 1 month prior to Ucrit measurements (n=6 per population). Juveniles were fed chopped bloodworms and Daphnia daily for approximately 4 months and then Mysis shrip and bloodworms for the next 4 months. After 8 months in the lab the juvenile fish had reached adult size (4-5 cm SL), and Ucrit was measured (n=6 per population). Wild-caught adult stream-resident fish were healthy, but wild-caught adult marine fish were heavily parasitized and difficult to keep alive in the laboratory. All juvenile fish were healthy and easily kept alive in the lab. Comparisons among populations and treatments (e.g. wild caught, lab-bred and raised, or wild-caught and lab-raised) were conducted with one-way ANOVA, after Ucrit values for lab-bred fish were collapsed into family means, so data would be comparable to that for wild-caught individuals (F13,67 = 16.40, p < 0.001; Fig. S3). We found that wild-caught adult marine fish from Salmon River, Kanaka Creek and Little Campbell River did not swim as well as lab-bred and raised marine crosses from Bonsall and West Creeks (Figure A3). Poor swimming performance (e.g. Oufiero and Garland 2009), in combination with the overall poor health of the adult marine fish, suggested that our wild marine fish had begun to senesce after returning to breed. Adult marine fish collected from Bonsall and West Creeks had similar problems with parasitism and survival in the lab (A.C. Dalziel, unpublished data). Our results contrast with those of Taylor and McPhail (1986), who found that 128  wild-caught, adult marine fish from the Salmon River had superior endurance swimming performance compared to stream-resident fish. Differences in collection time may explain this discrepancy: Taylor and McPhail (1986) collected fish that had just arrived on the breeding grounds (E.B. Taylor, personal communication), while we sampled our adults in June, when most migratory fish had already been on the breeding grounds for at least month, and could have already reproduced. Our wild marine fish were also at least 1 year of age, which is near the maximum lifespan of marine threespine sticklebacks from the Netherlands (Van Mullem and Van der Vlugt 1964). Although there is variation in lifespan among and within stickleback populations, studies on marine fish from the St. Lawrence River in Quebec, Canada indicate that reproduction induces senescence in both one and two year old fish (Dufresne et al.1990). Our wild-caught marine fish were all reproductively mature (fish displayed breeding colouration, and when dissected many females were gravid and males had large testes), had migrated, and were at least 1 year of age. To test for the combined role of age, breeding, and prior migration, we measured the Ucrit 810 month old wild-bred, lab-raised fish that had not migrated, and were not yet reproductively mature. We found no significant differences in the Ucrit between wild-caught, lab-raised marine fish and lab-bred and raised marine fish, suggesting that differences in early developmental environment do not have a major effect on Ucrit (Figure A3). Thus, age, reproductive status, and/or prior migration appear to negatively impact Ucrit in marine fish, and measures of performance in wild-caught marine sticklebacks must take these variables into account. In contrast to our results for the marine ecotype, we found no significant differences in Ucrit among wild-caught stream adults, wild-bred and lab-rasied stream fish, or lab-bred and raised stream fish (Figure A3). These finding suggest that stream-resident fish may senesce more slowly than marine sticklebacks.  129  Figure A3. Critical swimming speed (Ucrit) of (A) wild-caught adult stream-resident (stream) and marine sticklebacks, (B) wild-caught juveniles that were raised to adulthood in the laboratory, and (C) sticklebacks bred and raised in a common lab environment (data is from Figure 2A). Data are presented as the means of all individuals (A,B) or families (C) ± SEM. Different letters indicate significant differences among populations. Bon = Bonsall Creek, Kan = Kanaka Creek; LC = Little Campbell River; Sal= Salmon River; West= West Creek.  130  A.4-Plate morphology of F1 hybrid crosses Because our fish were not stained, we could not directly measure plate sizes and numbers. Therefore, we measured the plate morphology of hybrid crosses by assigning a plate value from 1 to 4 using the photographs taken to measure morphological traits. A plate value of one was assigned fish with 8 or fewer plates that did not extend more than 1 plate past the 2nd dorsal spine (the stream-resident, “low” plate phenotype). A plate value of four was assigned to fish with 30 or more plates, which covered the full length of the fish's body (the marine, “fully” plated phenotype). A plate value of two was assigned to fish that had lateral plates extending to the 3rd dorsal spine, and a plate value of three was assigned to fish with plates extending past the 3rd dorsal spine into the caudal peduncle region. Of the 102 hybrids tested [6 siblings from the 17 hybrid families (6 SM and 5 MS crosses from Bonsall Creek, and 3 SM and 3 MS crosses from West Creek)] only one fish had a plate score of four and 11 fish had a plate score of one. The majority of hybrid fish had plate scores of two or three. There was a great deal of variation among and within families of a given cross-type (data not shown). We collapsed individual data into family means and also pooled reciprocal hybrid cross-types from each site because there were no differences in Ucrit among reciprocal crosses. We found no significant differences in 'platedness' between hybrids from Bonsall or West Creeks (Fig. A4; Mann-Whitney U-test, p = 0.687).  Figure A4. Plate values for F1 hybrid crosses from Bonsall and West Creeks. Dots represent family means.  131  A.5-Calculation of P-values for body shape and pectoral fin shape linear discriminants Because DFAs generate a linear combination of variables that maximize the differences between groups, there is an elevated chance of detecting differences among groups in the absence of true differences (type I error) when testing the influence of cross-type on body and pectoral fin shape ld1 and ld2. Thus, to determine a P-value for the linear discriminant we performed a randomization test. In these randomizations, family groupings were randomly assigned to raw pectoral fin (aligned x,y coordinates) and body shape (6 traits) values, a DFA was run on this data, and an ANOVA was run on the generated ld1 and ld2 values. This process was repeated 10,000 times, and the resulting F-values were used to generate a null distribution. We then compared our original F-values to this distribution to calculate the true P-value (Figs. A5-A8). We found a significant effect of cross-type in all one-way ANOVAs: pectoral fin shape ld1 (F5,33 = 30.43; P < 0.00001), pectoral fin shape ld2 (F5,33 = 7.94; P < 0.0001), body shape ld1 (F5,33 = 52.28; P < 0.00001), body shape ld2 (F5,33 = 10.44; ; P < 0.00001). In all four cases (body shape ld1 and ld2, pectoral fin shape ld1 and ld2), the p-value calculated from these null distributions were equivalent to or less than our original p-values [pectoral fin shape ld1 (P < 0.00001), pectoral fin shape ld2 (P < 0.0001), body shape ld1 (P < 0.00001), body shape ld2 (P < 0.00001)].  Figure A5. Histogram of F-statistics for 10,000 randomizations of family grouping with pectoral fin shape ld1, with the actual F-statistic indicated with an arrow.  132  Figure A6. Histogram of F-statistics for 10,000 randomizations of family grouping with pectoral fin shape ld2, with the actual F-statistic indicated with an arrow.  Figure A7. Histogram of F-statistics for 10,000 randomizations of family grouping with body shape ld1, with the actual F-statistic indicated with an arrow.  133  Figure A8. Histogram of F-statistics for 10,000 randomizations of family grouping with body shape ld2, with the actual F-statistic indicated with an arrow.  134  A.6-Discriminant function analysis of pectoral fin shape Table A1. Variance explained by, and factor loadings of, the five linear discriminant (ld) functions produced in the discriminant function analysis (DFA) of pectoral fin shape using the 6 landmarks shown in Fig. 2.3C. x1 and y1 refer to the x and y coordinates for the first landmark, and subsequent numbers match the rest of the landmarks. Data for pectoral fin shape ld1 and ld2 are displayed in Figure 2.3C. ld1  ld2  ld3  ld4  ld5  Variance explained  71.2%  16.5%  8.7%  2.3%  1.4%  Coefficients of linear discriminants: x1 y1 x2 y2 x3 y3 x4 y4 x5 y5 x6 y6  0.25 -1.97 27.54 -6.45 -11.30 6.12 -9.89 -0.04 39.88 -3.47 -17.36 3.90  29.89 30.75 -5.67 -14.30 -34.33 -18.69 2.49 2.33 19.33 -21.16 -10.52 24.49  -3.46 15.11 7.19 39.78 -5.50 -7.96 -6.45 -1.95 13.17 -6.32 1.56 -0.51  -5.69 7.79 -18.41 -50.57 16.99 35.42 -6.96 -2.52 18.52 -7.88 0.29 16.84  -9.82 -42.71 38.20 25.78 -28.78 8.87 6.25 -1.62 8.82 -1.65 -1.02 15.11  135  Appendix B - Supplementary material for Chapter 3 B.1-Whole-blood-cell hemoglobin oxygen-binding affinity (Hb P50): Wild-caught Bonsall Creek stickleback We collected wild juvenile stickleback from Bonsall Creek in August of 2009, when fish were between one and four months of age, and raised these fish in the lab under the same conditions as juveniles from laboratory-bred crosses (see materials and methods in main text). We measured whole-blood-cell hemoglobin-oxygen binding affinity (Hb P50) following the procedures described in Henrikkson et al. (2008) and Mandic et al. (2009) using a custom-made PWee 50 at 15ºC. The PWee 50 uses dual absorption at wavelengths of 393 and 435 nm to measure percent saturation at different PO2s set by a Corning 192 Precision Gas Mixer. Fish were sacrificed with an overdose of anaesthetic (1 g/L tricaine methanesulfonate buffered with 2 g/L sodium bicarbonate). Once the fish lost equilibrium (< 30 sec), it was removed it from the anaesthetic, blotted dry, and weighed. We then collected blood by severing the caudal peduncle from the body, at a point just past the cloaca. Blood was collected in two heparanized capillary tubes, and one tube was used to determine hematocrit (Hct), and the blood from the other tube was expelled into a 1.5mL tube, kept at 4ºC, and used to analyze Hb P50. Blood samples were analyzed within 1 hour of collection. To measure Hb P50 less than 1μl of blood was sandwiched between two gas permeable membranes (YSI, Yellow Springs, Ohio, USA) and loaded into a the PWee 50, which was kept in a 15°C incubator. CO2 was kept constant at 0.5%, but O2 levels were varied throughout the experiment, ranging from 0 to 100% O2, to determine the per cent saturation of hemoglobin at different O2 levels. The differences in absorbance at 393 and 435nm were recorded the at nine to fourteen different O2 tensions to construct a linear section of the Hill plot for the determination of Hb P50. We measured the Hb P50 of 5 stream-resident and 5 marine fish. We found no significant differences in Hb P50 between Bonsall Creek stream-resident and marine fish (Fig. B1; t8= -1.457; P = 0.183).  136  Figure B1. Whole blood cell hemoglobin oxygen-binding affinity (Hb P50) of wild-caught Bonsall Creek fish. Values are ecotype means ± SEM. (N=5 per ecotype).  137  B.2-Ventricle mass of Bonsall and West Creek crosses : Effect of Sex Ventricle mass was measured following the methods outlined in the main text, and we corrected for size by calculating the residuals from a least squared regression against mass. To test the effect of sex we include data for a subset of crosses for which there are at least 2 males and 2 females from each family. Overall, we have data for sex effect for four Bonsall Creek MM families, four West Creek SS families, three West Creek hybrid families, and three West Creek MM families. While males generally had slightly larger hearts than did females, there was no significant effect of sex on ventricle mass (F1,6=0.4487; P = 0.5278). There were significant differences among cross-types (F7,50=5.671; P =0.0200 ), with Bonsall Creek marine males and females having larger hearts than those of West Creek stream males and females, and West Creek hybrid females, while West Creek marine males had significantly larger hearts than did West Creek stream females (Fig. B2). Note that in these cross-types means for female ventricle mass from Fig. B2 will differ from those in Fig. 2 because this analysis only included a subset of families for which a sufficient number of males and females were measured.  Fig. B2. Residual ventricle mass of laboratory-bred F1 families from Bonsall and West Creek parents. F = females (light circles), M = males (dark circles), BMM = Bonsall marine x marine crosses, WSS = West stream x stream crosses, WH = West stream x marine and marine x stream crosses, WMM = West marine x marine crosses. Data are presented as the grand means ± SEM. of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among cross-types (P < 0.05). 138  B.3-Mass of pectoral muscles in Bonsall and West Creek crosses: Effect of Sex Pectoral adductor and abductor masses were measured following the methods outlined in the main text, and were corrected for size by calculating the residuals from a least squared regression against mass. To test the effect of sex we included data for a subset of crosses for which there were at least 2 males and 2 females from each family. During the breeding season male threespine stickleback will fan nests containing their developing eggs with their pectoral fins. This fanning behavior supplies oxygen to the eggs, and may result in different selective forces acting upon pectoral muscle size in males and females. Overall, we had sufficient data to test for a sex effect for three Bonsall Creek MM families, two Bonsall Creek SS families, five West Creek SS families, three West Creek hybrid families, and six West Creek MM families. While males had slightly larger muscles than did females, there was no significant effect of sex on adductor (F1,8= 0.268; P = 0.6183), or abductor mass (F1,8= 0.634; P = 0.4488). We found significant differences among cross-types in adductor (F9,91 = 8.006; P <0.0001) and abductor mass (F9,91 = 10.243; P <0.0001) (Fig. S3). Note that in these cross-types, means for female adductor and abductor masses in Fig. S3 may differ from those in Fig. 3 because this analysis only included a subset of families for which a sufficient number of males and females were measured.  139  Fig. B3. Residual adductor (A) and abductor (B) mass of laboratory-bred F1 families from Bonsall and West Creek parents. F = females (light circles), M = males (dark circles), BSS = Bonsall stream x stream crosses, BMM = Bonsall marine x marine crosses, WSS = West stream x stream crosses, WH = West stream x marine and marine x stream crosses (pooled), WMM = West marine x marine crosses. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among cross-types (P < 0.05).  140  B.4- Pectoral adductor and abductor enzymes from West Creek crosses: Effect of Sex Enzyme activities were measured following the methods outlined in the main text. To test the effect of sex we have used a subset of West Creek crosses (3 SS, 3 MM), and sampled at least 3 males and 3 females from each family. There was no effect of sex on the activities of adductor CS (F1,33= 0.00131 ; P = 0.9713) and LDH (F1,33= 2.946; P = 0.0955), and a significant effect of sex on PK (F1,33= 79.92 ; P < 0.0001), COX (F1,33= 5.550; P = 0.025), and CPK (F1,33= 9.0907; P = 0.0049), such that males had slightly higher adductor COX and CPK activities, and much lower PK activities. We found no differences among-cross types for adductor COX (F3,29= 2.015; P = 0.1337) , and CS (F3,31= 0.42754; P = 0.7347) activities, but significant differences for PK (F3,31= 28.939; P < 0.0001) and LDH (F3,31= 45.419 ; P < 0.0001) and CPK (F3,31= 3.1997; P = 0.0369) (Appendix B, Table B1). Differences among cross-types for PK and CPK only occur between the sexes, while streamresident crosses had higher LDH than marine crosses. There was no effect of sex on the activities of abductor CPK (F1,33= 0.3157; P = 0.0578), and a significant effect of sex on COX (F1,31= 5.101; P = 0.0311), LDH (F1,32= 4.354; P = 0.045), PK (F1,33= 103.6744; P < 0.0001), and CS (F1,33= 4.26529; P = 0.0468), such that males had slightly higher abductor COX and CS activities, and slightly lower PK and LDH activities. We found no differences among-cross types for abductor COX (F3,29=1.74576; P = 0.1796), CS (F3,31= 2.66673; P = 0.065), or CPK (F3,31= 2.0392; P = 0.1288). Similar to the adductor muscle, we found that there are significant differences among cross-types for LDH (F3,30= 10.71614; P < 0.0001), and PK (F3,31= 34.0468; P <0.0001) activities, such that stream-resident crosses also have much higher abductor LDH, and males from stream crosses have lower LDH/g abductor than females. Similar to our findings in the adductor muscle, differences in PK activity among cross-types occur between sexes (Appendix B, Table B1). Overall, the major enzymatic difference in the pectoral muscle of males and females is that males have a lower activity of PK, and slightly higher activities of CS and COX in both the abductor and adductor (Appendix B, Table B1).  141  Table B1. Enzyme activities of laboratory-bred F1 crosses from Bonsall and West Creek parents. Data are presented as the grand means ± SEM of all family means, but statistical tests included all individuals in a nested ANOVA model (see results text). Different letters indicate significant differences among groups from a given muscle, for the selected enzyme (P > 0.05). U enzyme/g adductor U enzyme/g abductor Cytochrome c oxidase (COX) West Stream - female 43.80 ± 4.13 38.52 ± 6.32 male 51.41 ± 3.93 44.30 ± 1.57 West Marine - female 49.04 ± 3.65 33.89 ± 6.15 male 55.25 ± 5.48 42.35 ± 4.69 Citrate synthase (CS) West Stream - female 15.84 ± 2.12 14.25 ± 1.36 male 16.10 ± 2.26 16.44 ± 1.61 West Marine - female 13.82 ± 1.25 11.45 ± 1.31 male 14.04 ± 0.67 13.00 ± 0.68 Pyruvate Kinase (PK) West Stream - female 100.50 ± 8.33A 122.28 ± 12.01A BC male 65.16 ± 1.57 76.80 ± 3.31BC AB West Marine - female 84.77± 8.01 104.11 ± 12.07AB male 60.35 ± 8.47C 63.04 ± 9.75C Lactate dehydrogenase (LDH) West Stream - female 17.48 ± 1.46A 38.88 ± 5.10A A 19.78 ± 1.20 30.18 ± 3.65B male West Marine - female 3.84 ± 0.47B 12.66 ± 2.57C B male 5.11 ± 0.71 12.14 ± 2.06C Creatine phosphokinase (CPK) West Stream - female 804.00 ± 26.82 849.10 ± 46.62 male 920.46 ± 36.35 908.31 ± 17.64 West Marine - female 760.35 ± 56.98 728.98 ± 39.81 male 848.44 ± 63.29 748.63 ± 57.03  142  Appendix C - Supplementary material for Chapter 4 C.1-Repeatability of Ucrit in F2 hybrid stickleback  Figure C1. Repeatability of Ucrit in F2 hybrid stickleback. Ucrit 2 was measured two weeks after Ucrit 1 [Ucrit2 = 0.660*(Ucrit1) + 3.015, d.f.= 33, t=8.27, p < 0.001, r2=0.671].  143  C.2-Comparisons among F1 and F2 line crosses Male and female F2 sticklebacks had relatively large pectoral fins, with a mean pectoral fin areas (residuals of regression against mass) larger than that of F1 stream-resident and hybrid crosses (Fig. C2A: F3,22 = 11.53, p < 0.0001). In addition, no F2 fish had pectoral fins as small as the mean pectoral fin area of stream-resident fish, and the range of pectoral fin areas expressed by F2 hybrids spanned just beyond the mean pectoral fin area of marine F1 crosses (Fig. C2A). F2 hybrid sticklebacks had a mean body shape ld1 score (Table 4.1) that was intermediate to stream-resident and marine values and not significantly different from F1 marine crosses or F1 hybrids (Fig. C2B: F3,22 = 9.23, p = 0.0004). The range of body shape ld1 values expressed by F2 hybrids spanned slightly beyond the mean body shape ld1 scores of both marine and streamresident F1 crosses (Fig. C2B). F2 sticklebacks had relatively large ventricles and pectoral muscle adductors: the mean ventricle mass of F2 hybrids (residuals of regression against mass) was significantly higher than the mean tissue masses of F1 stream-resident and hybrid crosses (Fig. C3A: F3,20 = 6.65, p = 0.0027), and the mean adductor mass of F2 hybrids (residuals of regression against mass) was significantly larger than F1 stream-resident crosses (Fig. C3A: F3,21 = 5.81, p = 0.0047). The range of ventricle sizes expressed by F2 hybrids spanned beyond the mean masses of F1 marine and stream-resident crosses (Fig. C3A).The range of adductor masses in F2 hybrids was within the range of values expressed by marine F1 crosses, and no F2 hybrids possessed adductors as small as the mean adductor masses for F1 hybrid and stream-resident crosses (Fig. C3B). Alternatively, the mass of the pectoral abductor muscle in F2 hybrids was not significantly different than any F1 crosses (Fig. C3C: F3,21 = 1.23, p = 0.325), but F2 hybrids expressed a range of abductor sizes that spanned well beyond the means for F1 stream-resident and marine crosses (Fig.C3C). F2 hybrids also displayed mean activities of mitochondrial enzymes [CS and cytochrome c oxidase (COX), residuals of regression against mass] in their pectoral adductor (Fig. C4A,C: CS: F3,20 = 7.62, p = 0.0014; COX: F3,20 =13.77, p < 0.0001) and abductor muscles (Fig. C4B,D: CS: F3,21 =7.54, p=0.0013; COX: F3,21 =12.18, p<0.0001) that were higher than those of all F1 crosses, except marine abductor CS activities (Fig C4A-D). CS and COX activities spanned far beyond the upper enzyme activities of F1 crosses, and no individual F2 fish had CS or COX activities as low as the mean for stream-resident F1 crosses (Fig. C4A-D). Alternatively, glycolytic enzyme activities (PK, LDH) in the pectoral muscles of F2 fish were generally 144  intermediate to stream and marine activities (LDH: Fig. C4G; F3,20 = 6.98, p=0.0021 and Fig. C4H; F3,20 = 3.84, p =0.0254; PK Fig. C4E, F). However, the mean LDH activity in the adductor muscles of F2 fish was significantly lower than that of F1 stream-resident crosses, and no F2 fish had LDH activities per gram adductor muscles that reached the mean LDH activity in streamresident fish (Fig. C4G).  Figure C2. Histograms of residual (A) pectoral fin area and (B) body shape linear discriminant 1 (ld1) values in F2 fish. The full range of Ucrits reached by all F1 individuals are represented by thick colored bars, and the mean Ucrit ± STDEV for F1 line crosses are denoted with circles and thin lines (data from Chapter 3; green=stream-resident crosses, orange = F1 hybrid crosses, blue = marine crosses).  145  Figure C3. (A) Histograms of residual (A) ventricle mass (B) residual pectoral adductor mass, and (C) residual pectoral abductor mass of F2 fish . Grand means ± stdev of residual tissue masses in F1 crosses (data from Chapter 3) are displayed in color (green=stream-resident crosses, orange = F1 hybrid crosses, blue = marine crosses).  146  Figure C4. Histograms of residual citrate synthase (CS) activity per gram adductor (A) and abductor (B), residual cytochrome c oxidase (COX) activity per gram adductor (C) and abductor (D) muscle, pyruvate kinase per gram adductor (E) and abductor (F) muscle, and lactate dehydrogenase (LDH) activity per gram adductor (G) and abductor (H) muscle in F2 fish. There is a strong effect of sex on PK activities (E,F), resulting in a bimodial distribution. Grand means ± stdev of enzyme activities in F1 crosses (data from Chapter 3) are displayed in color (green=stream-resident crosses, orange = F1 hybrid crosses, blue = marine crosses).  147  C.3 Relationship between candidate traits and Ucrit : Fixed effects only Table C1. Results of linear regression of candidate traits vs. Ucrit (fixed effects only). Trait \ parameter R2 p-value F statistic (df) Y= -2.494*x - 0.049 0.000 0.557 0.557 (1, 184) Pectoral fin surface area (residuals) Y= 0.0155*x - 0.012 0.000 0.757 0.096 (1, 180) Body shape ld1 Y= 0.0441*x - 0.147 Ventricle mass 0.050 0.0055 7.890 (1, 182) (residuals) Y= 0.495*x - 0.0421 Adductor mass 0.053 0.0008 11.620 (1, 189) (residuals) Y= 0.0542*x - 0.0320 0.008 0.110 2.581 (1,188) Abductor mass (residuals) Y= 0.0128*x - 0.0504 0.000 0.388 0.749 (1, 189) COX/g adductor (residuals) Y= 0.303*x - 0.0323 CS/g adductor 0.125 0.0000003 28.100 (1, 189) (residuals) Y= -0.0746*x + 0.645 0.002 0.243 1.373 (1, 189) LDH/g adductor Y= 0.0154*x – 0.0417 0.000 0.356 0.858 (1, 188) COX/g abductor (residuals) Y=0.248*x – 0.0311 CS/g abductor 0.051 0.001 11.200 (1, 188) (residuals) Y=0.0045*x – 0.144 0.000 0.863 0.02982 (1, 188) LDH/g abductor  148  C.4 Relationship between candidate traits and Ucrit in each family and sex  Figure C5. The relationship between Ucrit and (A) residual pectoral fin area and (B) body shape ld1 in F2 fish. The thick black line represents the fitted values for whole population (with family and sex as nested random effects) for residual pectoral fin area (F1,179 = 0.485, p = 0.487) and body shape ld1(F1,175 = 0.009, p = 0.922). Colored lines represent the fitted curves for each individual group (open red circles with red hatched line = family 1 females; closed red circle with solid red line = family 1 females; open blue triangle with blue hatched line = family 2 females; closed blue triangle with blue solid line = family 2 males; open green circle and green hatched line = family 3 females; closed green circle and green solid line = family 3 males, with further information in Tables C3 and C4).  149  Figure C6. The relationship between Ucrit and residual (A) ventricle, (B) adductor, and (C) abductor mass in F2 fish, with data presented as in Figure C5. Thick black lines represent the fitted values for residual ventricle (F1,177 = 7.456, p = 0.007), adductor (F1,187 = 11.621, p = 0.001), and abductor mass (F1,183 = 2.385, p = 0.124) (Table 4.2). Further information is in Tables C5 to C7.  150  Figure C7. The relationship between Ucrit and (A) residual CS, (B) residual COX, and (C) LDH activity per gram adductor in F2 fish, with data presented as in Figure C5. Thick black lines represent the fitted values for residual CS (F1,184 = 28.101, p < 0.001), residual COX (F1,187 = 1.040, p = 0.309), and LDH (F1,187 = 0.911, p = 0.341) activity per gram adductor (Table 4.2). Further information for regressions is in Tables C8-C10.  151  Figure C8. The relationship between Ucrit and (A) residual CS, (B) residual COX, and (C) LDH activity per gram abductor in F2 fish. Thick black lines represent the fitted values for residual CS (F1,183 = 10.66, p = 0.001), residual COX (F1,186 = 1.246, p = 0.266), and LDH (F1,183 = 0.112, p = 0.738) activity per gram adductor (Table 4.2). Further information for regressions is in Tables C9-C11.  152  Table C2. Residual Pectoral fin area (x, explanatory variables) regressed against Ucrit (y, response variable) in each F2 family and sex. R2 P F Family 1- F 0.041 0.153 2.163 (1,26) Family 1- M 0.002 0.313 1.061 (1,25) Family 2- F -0.024 0.740 0.112 (1,37) Family 2- M -0.030 0.653 0.207 (1,26) Family 3- F -0.011 0.411 0.697 (1,27) Family 3- M -0.005 0.372 0.821 (1,33) Table C3. Body shape ld1 (x, explanatory variables) regressed against Ucrit (y, response variable) in each F2 family and sex. R2 P F Family 1- F 0.115 0.050 4.257 (1,24) Family 1- M 0.048 0.147 2.247 (1,24) Family 2- F 0.044 0.109 2.699 (1,36) Family 2- M 0.113 0.045 4.434 (1,26) Family 3- F 0.071 0.087 3.155 (1,27) Family 3- M -0.019 0.552 0.362 (1,33) Table C4. Residual Ventricle mass (x, explanatory variables) regressed against Ucrit (y, response variable) in each F2 family and sex. R2 P F Family 1- F 0.231 0.007 8.794 (1,25) Family 1- M 0.039 0.822 0.052 (1,24) Family 2- F 0.026 0.168 1.976 (1,36) Family 2- M 0.023 0.213 1.630 (1,26) Family 3- F 0.109 0.042 4.556 (1, 28) Family 3- M 0.027 0.741 0.111 (1, 33) Table C5. Residual pectoral adductor mass (x, explanatory variables) regressed against Ucrit (y, response variable) in each F2 family and sex. R2 P F Family 1- F 0.030 0.702 0.149 (1,28) Family 1- M -0.040 0.967 0.002 (1, 25) Family 2- F -0.027 0.944 0.005 (1,37) Family 2- M -0.009 0.394 0.750 (1,27) Family 3- F 0.125 0.029 5.300 (1,29) Family 3- M 0.112 0.028 5.301 (1,33)  153  Table C6. Residual pectoral abductor mass (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F 0.027 0.190 1.800 (1, 28) Family 1- M 0.010 0.274 1.252 (1,24) Family 2- F -0.027 0.948 0.004 (1,37) Family 2- M 0.007 0.283 1.199 (1,27) Family 3- F 0.104 0.043 4.495 (1,29) Family 3- M 0.042 0.124 2.487 (1,33) Table C7. Residual COX per gram adductor (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F -0.036 0.973 0.001 (1,28) Family 1- M -0.038 0.836 0.043 (1,25) Family 2- F 0.046 0.101 2.827 (1,37) Family 2- M -0.036 0.859 0.032 (1,27) Family 3- F 0.003 0.305 1.088 (1,29) Family 3- M -0.006 0.380 0.790 (1,33) Table C8. Residual CS per gram adductor (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F -0.013 0.433 0.632 (1,28) Family 1- M -0.023 0.527 0.412 (1,25) Family 2- F 0.133 0.013 6.812 (1,37) Family 2- M -0.033 0.754 0.100 (1,27) Family 3- F 0.166 0.013 6.951 (1,29) Family 3- M 0.229 0.002 11.100 (1,33) Table C9. LDH per gram adductor (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F 0.005 0.295 1.137 (1,28) Family 1- M 0.004 0.302 1.109 (1,25) Family 2- F 0.024 0.724 0.127 (1,37) Family 2- M 0.032 0.712 0.140 (1,27) Family 3- F 0.111 0.038 4.738 (1,29) Family 3- M 0.085 0.049 4.171 (1 33)  154  Table C10. COX per gram abductor muscle (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F 0.022 0.551 0.364 (1,28) Family 1- M 0.032 0.642 0.222 (1,24) Family 2- F 0.030 0.147 2.195 (1,37) Family 2- M 0.035 0.811 0.058 (1,27) Family 3- F 0.033 0.852 0.035 (1,29) Family 3- M 0.017 0.519 0.426 (1,33) Table C11. CS per gram abductor muscle (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F 0.009235 0.269 1.270 (1,28) Family 1- M 0.02646 0.556 0.356 (1,24) Family 2- F 0.07295 0.053 3.990 (1,37) Family 2- M 0.03698 0.969 0.002 (1,27) Family 3- F 0.05834 0.102 2.859 (1,29) Family 3- M 0.02004 0.202 1.695 (1,33) Table C12. LDH per gram abductor muscle (x, explanatory variables) regressed against Ucrit (y, response variable), in each F2 family and sex. R2 P F Family 1- F 0.007 0.280 1.211 (1,28) Family 1- M 0.003 0.349 0.914 (1,24) Family 2- F 0.017 0.550 0.363 (1,37) Family 2- M 0.031 0.692 0.160 (1,27) Family 3- F 0.013 0.444 0.601 (1,29) Family 3- M 0.091 0.044 4.389 (1,33)  155  C.5- Effect of ‘background traits’ on the relationships between a focal trait and Ucrit  Figure C9. The relationship between Ucrit and (A,B) residual ventricle mass, (C,D) residual adductor mass, and (E,F) residual adductor CS/g for the highest 50% of values (B, D, F), and lowest 50% of values (A,C,E) of a second ‘background’ trait listed on the X axis. The thick black line represents the fitted curve for all values, and colored lines represent the fitted curves for each individual F2 family (red = family 1; blue = family 2; green = family 3).  156  

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