Ontogeny of Population-Specific Phenotypic Variation in the Threespine Stickleback

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2018-01-09 Ontogeny of Population-Specific Phenotypic Variation in the Threespine Stickleback

Pistore, Alexandra

Pistore, A. E. (2018). Ontogeny of Population-Specific Phenotypic Variation in the Threespine Stickleback (Unpublished masters thesis). University of Calgary, Calgary, AB. http://hdl.handle.net/1880/106305 master thesis

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Ontogeny of Population-Specific Phenotypic Variation in the Threespine Stickleback

Alexandra Pistore

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN MEDICAL SCIENCE

CALGARY, ALBERTA

JANUARY, 2018

The Threespine Stickleback (Gasterosteus aculeatus) is a fish commonly used for the study of adaptive radiation, phenotypic plasticity, parallel evolution, and epigenetic mechanisms.

Information regarding stickleback development, however, is largely missing from the literature.

Using 2D and 3D analysis, I characterized skeletal and soft tissue development in four phenotypically diverse populations of stickleback, three marine and one freshwater. Fish as early as 5 days post fertilization can be distinguished by population, and by 90 days post fertilization, stickleback juveniles have developed a nearly complete skeleton and have attained their population-specific phenotype. This research gives some of the first indications of phenotype development in the Threespine Stickleback, and suggests that juvenile stickleback may be a target of selection in the fish’s expansion into new habitats.

Keywords: Threespine stickleback, Gasterosteus aculeatus, phenotype, juvenile, embryo,

development, ontogeny, skeleton, epigenetics

ii Acknowledgements

I absolutely could not have done this work without an army of people at my back.

Thank you to my lab mates/soul sisters Tegan Barry, Chelsey Zurowski, Emma Carroll, and Riley Waytes for making me laugh, for reminding me that there’s always time for crafts, for the countless tea and lunch and hiking dates, and for suffering through early morning gym sessions with me. Hashtags will never be the same. Thank you especially to Tegan, who always had time to answer my questions, and who drove me all over Western Canada to catch fish.

Minivans seem way cooler now.

Thank you to my lab mates Sarah Anderson, Sara Smith, Jori Harrison, Matthew Morris,

Ella Bowles, Stevi Vanderzwan, Brandon Allen, Hayley Britz, Rebecca Green, Chris Percival,

Jacinda Larson, and Francis Smith for all of the hard work that they did before me, for troubleshooting with me, and for offering insight. This thesis was built upon the shoulders of giants. Thank you, too, to Dr. Benedikt Hallgrimsson for sharing his lab with us.

Thank you to my committee of the most powerfully intelligent yet enormously supportive men – Doctors Sean Rogers, John Bertram, and Steve Vamosi – for the meetings, revisions, emails, and time taken out of Christmas holidays. This process has never been unduly stressful thanks to your incredible support.

Thank you to my family and friends for ignoring me when I needed to be ignored, and for asking me about my research even though it held, for them, the interest factor of a dusty countertop.

And finally, thank you to my supervisor and science wizard Heather Jamniczky. The

English language (and all of the others, I suspect, but cannot confirm) do not contain the words necessary to thank you for all that you have done. Team fish!

iii Table of Contents

Abstract ...... ii Acknowledgements ...... iii Table of Contents ...... iv List of Tables ...... vi List of Figures and Illustrations ...... vii List of Symbols, Abbreviations and Nomenclature ...... xv Epigraph ...... xvi

Chapter 1 – Introduction ...... 1 1.1 General Introduction ...... 1 1.2 Development and Evolution ...... 1 1.3 Threespine Stickleback ...... 5 1.4 Thesis Outline ...... 8

Chapter 2 - Methods ...... 10 2.1 Fish Collection ...... 10 2.2 Fish Care ...... 11 2.3 Crossing ...... 12 2.4 Raising Juveniles ...... 13 2.5 Sampling ...... 15 2.6 Genetics ...... 15 2.7 Photography and 2D Imaging ...... 16 2.8 2D Landmarking and Statistical Analysis ...... 16 2.9 MicroCT Scanning and 3D Imaging ...... 18 2.10 3D Landmarking ...... 19 2.11 2D and 3D Morphometrics ...... 20

Chapter 3 - Results...... 22 3.1 2D Morphometrics ...... 22 3.1.1 Consistency of Landmarking ...... 22 3.1.2 Dorsal Component by Population ...... 22 3.1.3 Dorsal Component by Age ...... 24 3.1.4 Lateral Component by Age ...... 28 3.1.5 Dorsal Component Pooled ...... 31 3.1.6 Lateral Component Pooled ...... 33 3.2 3D Morphometrics ...... 35 3.2.1 Consistency of Landmarking ...... 35 3.2.2 By Population ...... 35 3.2.3 By Age ...... 38 3.2.4 Pooled ...... 40

Chapter 4 - Discussion ...... 43 4.1 Population-Specific Variation is Present in 2D Explorations of Threespine Stickleback ...... 44

iv 4.2 Population-Specific Variation is Present in 3D Explorations of Threespine Stickleback ...... 45 4.3 Population-Specific Phenotypes Appear Early ...... 47 4.4 Sexual Dimorphism is not Consistent Across Populations...... 47 4.5 Implications of Lab Rearing Juveniles on Phenotype ...... 50

Chapter 5 - Limitations ...... 52

Chapter 6 - Future Directions and Significance...... 55

Tables ...... 58

Figures ...... 61

Recipes and Protocols ...... 114 Agarose Gel Protocol ...... 114 Brine Shrimp Hatching Protocol ...... 114 Ethylenediaminetetraacetic Acid (EDTA 0.5M, pH=8) Recipe ...... 115 Ginsburg’s Ringer Solution Recipe ...... 115 Hank’s Solution Recipe ...... 115 Polymerase Chain Reaction (PCR) Mix Recipe ...... 116 Polymerase Chain Reaction (PCR) Protocol ...... 116 Tail Digestion Buffer Recipe ...... 116 Tris (1M) Recipe ...... 117

References ...... 118

v List of Tables

Table 1 – A breakdown of the number of fish available for each population at each timepoint. . 58

Table 2 – List of landmarks used to evaluate left/right morphology. Landmarks from this list were placed on the right side of the body and then repeated on the left side...... 58

Table 3 – List of landmarks used to evaluate dorsal morphology...... 59

Table 4 – List of extra landmarks ...... 60

vi List of Figures and Illustrations

Figure 1- Locations of sites sampled on the coast of British Columbia and Vancouver Island, BC. Orange shows Madeira Park locations as well as Klein Lake, while red shows those locations near the Bamfield Marine Sciences Centre...... 61

Figure 2 - Photograph of embryos 3 days post fertilization showing the appearance of a double membrane (red arrow) and neural crest migration (blue arrow) distinguishing successfully fertilized eggs from those that are not fertilized...... 62

Figure 3 – A typical aquarium setup. Note sponge filters, lack of substrate, and plant for enrichment and cover. This aquarium setup does not show the power filter, as these fish are currently too young to safely install one...... 63

Figure 4- Micro Computed Tomography scans of Threespine Stickleback embryos at 35 days post fertilization (A, B), 16 days post fertilization (C) and 9 days post fertilization (D). All scans were reconstructed at a threshold of 5500 to allow adequate comparison of bone density. A, C, and D are lateral views, rostral is right. B is a superior view, rostral is right. Embryos borrowed from E. Bowles...... 64

Figure 5 - Photographs of juveniles showing 2D landmarks placed dorsally (A, C, E) and laterally (B, D, F, G). Fish 5 through 7dpf had 15 dorsal landmarks (A) and 10 lateral landmarks (B). 12dpf had 15 dorsal landmarks (C) and 13 lateral landmarks (D). 30 and 90dpf fish had 23 dorsal landmarks (E). While 30dpf fish had 24 lateral landmarks (F) and 90dpf fish had 30 lateral landmarks (G). Photos show fish in standardized positions exactly as they were landmarked...... 65

Figure 6 - Photographs from the dorsal view of 5, 6, and 7dpf embryos for those populations for which the timepoints are available. Attempts to standardize position were complicated by the spherical nature of the embryos. Note that although the embryos are relatively amorphic, some variation can be seen in head width and length, and eye shape and size. Rostral is left...... 66

Figure 7 - Photographs from the dorsal (12dpf) and lateral (30 and 90dpf) view of 12, 30, and 90dpf juveniles for those populations for which the timepoints are available. Attempts to standardize position were complicated by the extremely small size of the embryos. Note that the variation in eye shape and size, head morphology, and body shape appears to expand with age. Rostral is left...... 67

Figure 8 - MicroCT scans from the lateral view of 30, 60, and 90dpf juveniles for each of the four populations. Note the variation in ossification at the 30dpf timepoint, as well as the variation in phenotype at the 90dpf timepoint. The 60dpf timepoint is available only for the RB population. The most well ossified specimen was chosen from each population at each timepoint for this diagram. Rostral is left...... 68

Figure 9- MicroCT scans of an Oyster Lagoon adult showing 3D landmark placement. 159 landmarks were placed in an attempt to characterize the entirety of the scanned skeleton. (A) Landmarks from the left lateral view – landmarks were placed reciprocally on the vii right side of each specimen; (B) Landmarks from the rostral view; (C) Landmarks from the ventral view; and (D) Landmarks from the dorsal view. Rostral is left (A, C, D) and out of the page (B)...... 69

Figure 10 - Principal components analysis of 2D dorsal landmark data from the Garden Bay Lagoon population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a broad short head with protruding eyes while positive PC1 values correspond to a long narrow head with long eyes. There is a trend towards head lengthening with age. Rostral is left...... 70

Figure 11 - Principal components analysis of 2D dorsal landmark data from the Klein Lake population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a long narrow head with small eyes while positive PC1 values correspond to a broad short head with large eyes. There is a trend towards head lengthening with age. Rostral is left...... 71

Figure 12 - Principal components analysis of 2D dorsal landmark data from the Oyster Lagoon population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a long narrow head while positive PC1 values correspond to a broad short head. There is a trend towards head lengthening with age. Rostral is left...... 72

Figure 13 - Principal components analysis of 2D dorsal landmark data from the Roquefeuil Bay population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a broad short head with large eyes while positive PC1 values correspond to a long narrow head with small eyes. There is a trend towards head lengthening with age. Rostral is left...... 73

Figure 14 - Principal components analysis of 2D dorsal landmark data from the 5dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to marine populations with a broad short head with small eyes while positive PC1 values correspond to the KL population with a longer head with large eyes. Rostral is left...... 74

Figure 15 - Principal components analysis of 2D dorsal landmark data from the 6dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to GB and OL populations with a long head with large eyes while positive PC1 values correspond to the RB population with a short head and smaller eyes. Rostral is left...... 75

Figure 16 - Principal components analysis of 2D dorsal landmark data from the 7dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the outlier GB fish with a broad short head with small eyes while positive PC1 values correspond to the remainder of the populations with longer heads and large eyes. Rostral is left...... 76

Figure 17 - Principal components analysis of 2D dorsal landmark data from the 7dpf timepoint with the GB outlier removed. Wire frame images show phenotype trends viii corresponding to principal component 1. Negative PC1 values correspond to the OL population with a broad short head, short nose, and small eyes while positive PC1 values correspond to the RB population with a longer head with large eyes. The GB population is spread across PC1. Rostral is left...... 77

Figure 18 - Principal components analysis of 2D dorsal landmark data from the 12dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the RB population with a broad short head and large eyes while positive PC1 values correspond to the GB population with a longer head and smaller eyes. The single KL fish included in this analysis locates near 0 on PC1. Rostral is left...... 78

Figure 19 - Principal components analysis of 2D dorsal landmark data from the 30dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a broad, long head with large eyes while positive PC1 values correspond to a narrow, short head with small eyes. Rostral is left...... 79

Figure 20 - Principal components analysis of 2D dorsal landmark data from the 90dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to RB and KL populations with a broad body while positive PC1 values correspond to the GB population with a narrow body. The OL population is spread broadly across PC1. Rostral is left...... 80

Figure 21 - Principal components analysis of 2D lateral landmark data from the 5dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to an arched ventral line and forward rotated pupils while positive PC1 values correspond to a flat dorsal line and caudally rotated pupils. Rostral is left...... 81

Figure 22 - Principal components analysis of 2D lateral landmark data from the 6dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a flat ventral line and dorsally rotated pupils while positive PC1 values correspond to an arched dorsal line and ventrally rotated pupils. Rostral is left...... 82

Figure 23 - Principal components analysis of 2D lateral landmark data from the 7dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to an arched ventral line and narrow, forward rotated pupils while positive PC1 values correspond to a flat dorsal line and wide, caudally rotated pupils. Rostral is left...... 83

Figure 24 – Principal components analysis of 2D lateral landmark data from the 12dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to an arched ventral line, broad snout, and wide head, while positive PC1 values correspond to a flat dorsal line, and a narrow snout and head. Rostral is left...... 84

ix Figure 25 - Principal components analysis of 2D lateral landmark data from the 30dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a concave head, long body, and humped dorsal line while positive PC1 values correspond to a concave head and short body. Rostral is left...... 85

Figure 26 - Principal components analysis of 2D lateral landmark data from the 90dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the GB and OL populations with a small head, and a shallow buccal cavity and body while positive PC1 values correspond to the RB and KL populations with a large head, and a deep buccal cavity and body. Rostral is left...... 86

Figure 27 - Principal components analysis of 2D dorsal landmark data from the 5dpf through 12dpf timepoints pooled. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a long head with large eyes while positive PC1 values correspond to a short head and smaller eyes. The populations do not separate out cleanly. Rostral is left...... 87

Figure 28 - Principal components analysis of 2D dorsal landmark data from the 30dpf and 90dpf timepoints pooled. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the OL and GB populations with a long wide head with large eyes while positive PC1 values correspond to the KL and RB populations with a short narrow head and small eyes. Rostral is left...... 88

Figure 29 - Principal components analysis of 2D lateral landmark data from the 5dpf through 7dpf timepoints pooled. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to an arched ventral line and forward rotated pupils while positive PC1 values correspond to a flat dorsal line and caudally rotated pupils. The populations don’t separate out cleanly. Rostral is left...... 89

Figure 30 - Principal components analysis of 2D lateral landmark data from the 30dpf and 90dpf timepoints pooled. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the GB and OL populations with a concave head and narrow buccal cavity and body while positive PC1 values correspond to KL and RB populations with a convex head and deep buccal cavity and body. Rostral is left...... 90

Figure 31 - Principal components analysis of all 2D dorsal landmark data. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to GB and OL populations with a broad short head and large eyes while positive PC1 values correspond to KL and RB populations with a long narrow head and smaller eyes. Rostral is left...... 91

x while positive PC1 values correspond to a small head with correspondingly small eyes and jaw. Timepoints do not separate out cleanly. Key denotes months of age. 3 corresponds to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left...... 92

Figure 33 - Principal components analysis of 3D landmark data from the 270dpf timepoint in Garden Bay Lagoon population coloured by sex (F is female, M is male). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to female individuals with a short head, small jaw and snout, and broad deep body while positive PC1 values correspond to male individuals with a large head and jaw, long snout, and slender body. Rostral is left...... 93

Figure 34 - Principal components analysis of 3D landmark data from the adult timepoint in Garden Bay Lagoon population coloured by sex (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to male individuals with a large, long head and deep jaw while positive PC1 values correspond to female individuals with a smaller head and jaw. Rostral is left...... 94

Figure 35 - Principal components analysis of 3D landmark data from the Klein Lake population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to juvenile timepoints with a small, concave head with a small jaw while positive PC1 values correspond to the adult timepoint with a large convex head and relatively enormous jaw. Key denotes months of age. 3 corresponds to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left...... 95

Figure 36 - Principal components analysis of 3D landmark data from the 270dpf timepoint in Klein Lake population coloured by sex (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to unknown individuals with small spines and a forward and downward arched body while positive PC1 values correspond to male and female individuals with a larger head and upward arched body. Rostral is left...... 96

Figure 37 - Principal components analysis of 3D landmark data from the adult timepoint in Klein Lake population coloured by sex (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to most individuals with large spines while positive PC1 values correspond to a single female individual with small spines. Rostral is left...... 97

Figure 38 - Principal components analysis of 3D landmark data from the adult timepoint in Klein Lake population coloured by sex without the single outlier (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large head and eye with a deep body and small spines while positive PC1 values correspond to a small head, slender body, and larger spine and pelvic girdle. Rostral is left...... 98

xi Figure 39 - Principal components analysis of 3D landmark data from the Oyster Lagoon population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large, long head and slender body while positive PC1 values correspond to a small, short head and deep body. The timepoints do not separate out cleanly. Key denotes months of age. 3 corresponds to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left...... 99

Figure 40 - Principal components analysis of 3D landmark data from the 270dpf timepoint in Oyster Lagoon population coloured by sex (F is female, M is male). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to all male and some female individuals with a slender body and long slender head while positive PC1 values correspond to other females with a deeper body and shorter head. Rostral is left...... 100

Figure 41 - Principal components analysis of 3D landmark data from the adult timepoint in Oyster Lagoon population coloured by sex (F is female, M is male). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to male individuals with a slender body, robust jaw, large eye, and long head while positive PC1 values correspond to females with a deeper body and shorter head, small eye, and slender jaw. Rostral is left...... 101

Figure 42 - Principal components analysis of 3D landmark data from the Roquefeuil Bay population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to juvenile timepoints 90, 180, and 270dpf with almond shaped eyes, large jaw, and deep body while positive PC1 values correspond to the 60dpf and adult timepoints with large eyes, small jaw, and slender body. Key denotes months of age. 2 corresponds to 60dpf, 3 to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left...... 102

Figure 43 - Principal components analysis of 3D landmark data from the 270dpf timepoint in Roqefeuil Bay population coloured by sex (F is female, M is male). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to male individuals with a large eye, pointed snout, convex head, and forward arching body while positive PC1 values correspond to females with a concave head, squared snout, and upward arching fusiform body. Rostral is left...... 103

Figure 44 - Principal components analysis of 3D landmark data from the adult timepoint in Roquefeuil Bay population coloured by sex (F is female, M is male, U is unkonwn). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to male individuals with a slender body and head, and large spines, while positive PC1 values correspond to females with a deeper body and head, and small spines. Rostral is left...... 104

xii slender body while positive PC1 values correspond to the marine populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left...... 105

Figure 46 - Principal components analysis of 3D landmark data from the 180dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to KL with a large head and jaw, small spines, and slender body while positive PC1 values correspond to the marine populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left...... 106

Figure 47 - Principal components analysis of 3D landmark data from the 270dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to KL with a large head and jaw, small spines, and slender body while positive PC1 values correspond to the marine populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left...... 107

Figure 48 - Principal components analysis of 3D landmark data from the 270dpf timepoint for all populations coloured by sex (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a slender body, large jaw, and small spines while positive PC1 values correspond to a deeper body, short head, and large spines. Sexes do not separate out cleanly. Rostral is left...... 108

Figure 49 - Principal components analysis of 3D landmark data from the adult timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to KL and RB populations with a large head and jaw, small spines, and slender body while positive PC1 values correspond to the GB and OL populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left. . 109

Figure 50 - Principal components analysis of 3D landmark data from the adult timepoint for all populations coloured by sex (F is female, M is male, U is unknown). Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a slender body, large jaw, and small spines while positive PC1 values correspond to a deeper body, short head, and large spines. Sexes do not separate out cleanly. Rostral is left...... 110

Figure 51 - Principal components analysis of all 3D landmark data coloured by population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to KL with a large head and jaw, small spines, and slender body while positive PC1 values correspond to the marine populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left...... 111

Figure 52 - Principal components analysis of all 3D landmark data coloured by age.Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large head and jaw, small spines, and slender body while positive PC1 values correspond to a smaller head and jaw, larger spines, and deeper body. Age points do not separate out cleanly, instead the separation seen corresponds to

xiii population variation. Key denotes months of age. 2 corresponds to 60dpf, 3 to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left...... 112

Figure 53 - Principal components analysis of all 3D landmark data coloured by sex (F is female, M is male, U is unknown).Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large head and jaw, small spines, and slender body while positive PC1 values correspond to a smaller head and jaw, larger spines, and deeper body. Sexes do not separate out cleanly, instead the separation seen corresponds to population variation. Rostral is left...... 113

xiv List of Symbols, Abbreviations and Nomenclature

Symbol Definition 2D 2 dimensional 3D 3 dimensional ANOVA Analysis of Variance BC British Columbia CVA Canonical Variates Analysis CV Canonical Variate DFA Discriminant Functions Analysis DNA Deoxyribonucleic Acid dpf Days post fertilization GB Garden Bay Lagoon HL Hotel Lake Idh Isocitrate Dehydrogenase KL Klein Lake LESARC Life and Environmental Sciences Animal Resource Centre NBF Neutral Buffered Formalin OL Oyster Lagoon PCA Principal Components Analysis PC Principal Component PCR Polymerase Chain Reaction RB Roquefeuil Bay SL Sarita Lake uCT Micro Computed Tomography

xv Epigraph

The stickleback has an open relationship with development….

xvi 1

Chapter 1 – Introduction

1.1 General Introduction

The more we learn about life on earth, the more it becomes clear to us the incredible resiliency that living forms possess. Lifeforms have managed to penetrate the most impossibly hostile environments, and this tenacity has led to the unbelievable variation in form that we see today.

Life that varies from the smallest amoeba to the most enormous redwood tree are often the products of natural selection, but some amount of plasticity within the organismal form is requisite to allow this evolution. The inherent complexity of the evolutionary process makes the study of variation difficult, however, and our quest to understand it inches onward.

Humans are consumed with questions related to this phenomenon of expansion into new habitats.

Why are some species able to adapt more quickly than others? What traits allow species sharing a niche to outcompete one another? Is an organism able to adapt its form throughout its lifetime, or is adaptation left solely to new generations?

My thesis focuses on these questions and attempts to shed light on the adaptive radiation of an exceptionally effective colonizer – the threespine stickleback.

1.2 Development and Evolution

Our quest to comprehend the association between development and evolution was summarized succinctly by Brian Hall when he stated: “Evo-devo reflects a long search to find and understand relationships between the transformation of an organism within a single generation – development, ontogeny, ontogenetic change – and those transformations that occur between generations – evolution, phylogeny, phylogenetic change” (Hall 2003 p492). Although there is a clear relationship between development and phenotype, the role of developmental mechanisms in the patterning of phenotypic variation and the degree to which development facilitates or

2 constrains evolutionary processes remains poorly understood (Hallgrimsson et al. 2007). At the most basic level, development is highly applicable to evolution because development acts upon genetic material to produce the phenotypic variation upon which natural selection can act (Alberch

1982, Raff and Kaufman 1983, Hall 1999, Hallgrimsson et al. 2009).

Ontogenetic studies in species such as dolphins (del Castillo, Flores, & Cappozzo, 2014) ancestral hominids (Cofran 2014), and humans (Smith et al. 2013) illustrate the role of ontogeny in the development of osseous tissues associated with communication and soft tissue protection

(Del Castillo et al. 2014), feeding apparatuses and craniofacial structures (Cofran 2014, Del

Castillo et al 2014, Smith et al. 2013), sexual dimorphism (Del Castillo et al. 2014), and allometry

(Del Castillo et al. 2014, Cofran 2014). Considering the nature of developmental processes, it is doubtful that any aspect of the phenotype escapes ontogenetic influence (Hall 1999). Furthermore, studies of ontogenetic series in related species, such as Homo neanderthalensis and Homo sapiens, demonstrate that characteristic differences in morphology arise very early in development (Ponce de Leon and Zollikofer 2001), suggesting a role for ontogenetic and developmental mechanisms in evolution.

Although developmental processes have the capacity to influence evolution, the relative ability of developmental processes to produce more or less variation, or variation along a certain phenotypic axis, influences the ease with which a population may evolve (evolvability).

Integration and modularity are two ways by which phenotypic variation may be regulated

(Cheverud 1996, Wagner 1996), and so are key determinants of evolvability (Raff and Sly 2000,

Griswold 2006, Jones et al. 2007, Wagner et al. 2007, Hansen and Houle 2008, Hallgrimsson et al. 2009). Morphological integration is achieved by coordinated variation of functionally and developmentally related features of an organism (Olson and Miller 1958). Conversely, modularity

3 refers to the division of systems into partially dissociated components that may themselves be integrated (Hallgrimsson et al. 2009). Integration biases the direction of evolution by producing correlated effects of mutations on phenotypic variation, and modularity limits the effects of mutations to modules of functionally or developmentally integrated traits (Raff 1996, Raff and Sly

2000, Wagner and Mezey 2004, Wagner et al. 2007), thus limiting the potentially deleterious effects of these mutations on fitness (Hallgrimsson et al. 2009).

Phenotypic covariance structure is the signature left behind by the action of these mechanisms, and is produced by overlapping developmental processes (Hendrikse et al 2007,

Hallgrimsson et al 2009). Covariance structure has the opportunity to evolve through changes in the relative variances of covariance-generating processes (Hallgrimsson et al. 2009). Generally, however, there is a tendency for stability in the structure (Hallgrimsson et al. 2009). In the processes of colonization of new environments and environmental change, covariance structure may be disrupted and small mutations can result in large phenotypic consequences (Hallgrimsson et al 2006, Jamniczky and Hallgrimsson 2009). Perhaps then, phenotypic covariance has the potential to facilitate rapid adaptive radiation through the production of phenotypic variation

(Jamniczky et al 2014). Although isolating the mechanisms behind morphological integration and modularity is very difficult to do (Hallgrimsson et al. 2009), studying development may help identify where and when phenotypic variation is being produced.

Because selection acts directly upon phenotype, certain phenotypes produced by certain developmental-genetic structure may be favoured over others. This leads to the concepts of canalization, developmental stability, and novelty. Canalization refers to the suppression of phenotypic variation among individuals (Wagner et al. 1997), whereas developmental stability refers to the suppression of phenotypic variation within individuals, ie. – the reduction of variation

4 that is not of environmental origin (Waddington 1975, Hallgrimsson et al. 2002). These processes affect the magnitude of phenotypic variance and bias the production of variation (Hallgrimsson et al. 2002), and have the capacity to decrease or increase the rate of evolution (Wagner et al. 1999,

Gibson and Wagner 2000). A novelty may be characterized as a “phenotypic trait that is new in composition or context of expression relative to established ancestral traits” (West-Eberhard 2008, p198) or “a qualitatively new structure with a discontinuous origin” (Muller 1990, p101).

Hallgrimsson and colleagues (2012) argue that a trait is novel when its evolution involves a transition between adaptive peaks on a fitness landscape, and to accomplish this transition, there must be a breakdown of ancestral developmental constraints. Therefore, a novel trait is the result of evolution acting upon the phenotype. The concepts of canalization, developmental stability, and novelty illustrate selection indirectly producing changes in developmental architecture.

Phenotypic plasticity also likely plays a role in facilitating such change, though the degree of influence of this mechanism is poorly understood (Sultan and Stearns 2005, Morris and Rogers

2014). Plasticity may be defined as the production of multiple phenotypes from a single genotype over a range of environmental conditions (Sultan and Stearns 2005). Plastic responses may include changes in any phenotype (eg. behaviour, physiology, morphology, growth, life history, and demography), and can be expressed both within the lifetime of an individual or across generations

(Miner et al. 2005). The capacity of an organism to adapt a single genotype to changes in environment confers the organism an increased environmental tolerance (Via et al. 1995). Given appropriate genetic variability, adaptive phenotypic responses to the environment are thought to be able to evolve in populations that regularly encounter environmental change (Via et al. 1995).

The mechanisms outlined here suggest a complex, but nonetheless important association between development and evolution. Development produces phenotypic variation upon which selection can

5 act, and through a variety of processes selection may indirectly effect changes in the developmental architecture.

If development does indeed have a role in evolution, as suggested above, this relationship may be observable in phylogenetic associations. The theory of recapitulation (Haeckel 1905), in which all organisms transition through a ‘phylotypic stage’, is pervasive in developmental biology and many theories involving developmental patterns and mechanisms have been proposed with reference to this stage (Bininda-Emonds et al. 2003). Recent evidence, however, argues against the existence of a phylotypic stage in vertebrates, and favours instead the existence of numerous tightly delimited developmental modules during embryonic growth (Bininda-Emonds et al. 2003).

Bininda-Emonds and colleagues (2003) hypothesize that timing changes (heterochrony) between these modules may have been an important evolutionary mechanism resulting in the present diversity of vertebrates. While the theory of recapitulation seems unlikely at this juncture, the relationship between ontogeny and phylogeny may still exist in examples of heterochrony. For example, heterochrony – both paedomorphic and peramorphic patterns – has been implicated in the evolution of morphology in fishes (Bemis 1984, Winterbottom 1990, Boughton et al. 1991,

Johnson and Brothers 1993, Zelditch et al 2000) illustrating a possible link between developmental stages and evolutionary relatedness.

1.3 Threespine Stickleback

In the time since the last glaciation 10-12 thousand years ago, numerous freshwater lakes and streams have become available as habitat in the northern hemisphere (Bell and Foster 1994,

Peichel 2005). The threespine stickleback (Gasterosteus aculeatus Linnaeus, 1758, stickleback) is a small marine fish that has utilized this opportunity, and subsequently experienced rapid and repeated divergence into the newly available freshwater environments (Bell and Foster 1994,

Peichel 2005). Differences between marine and lacustrine habitats, namely variation in temperature, salinity, dissolved ion composition, predation, diet, and geography (Bell and Foster

1994), have acted to produce distinct stickleback populations with considerable morphological, physiological, life history, and behavioural variation (Wootton 1984). Populations may differ in terms of behaviour, size, body and head shape, skull shape, armor plating, gill rakers, and colouring

(Hagen and Gilbertson 1972, Moodie 1972, Moodie and Reimchen 1976, Gross 1978, Reimchen

1980, Wootton 1976, 1984, Bell et al. 1985, Francis et al. 1986, Schluter and McPhail 1992, Bell et al. 1993, McPhail 1993, Bell and Foster 1994, Bourgeois et al. 1994, Bell and Orti 1994, Walker

1996, Willacker et al 2010). The presence of numerous distinct stickleback populations across the northern hemisphere offers a unique opportunity to research the effect of differing environments on phenotype, behaviour, and life history.

Interestingly, parallel trait evolution has been observed in independent stickleback populations inhabiting similar environments (Schluter 2000, Cresko et al. 2004, Foster and Baker

2004, Shapiro et al. 2004, Erickson et al. 2014). Parallel evolution, the repeated evolution of similar phenotypes in closely related lineages, has been implicated in stickleback armour

(Colosimo et al. 2004) and pelvic (Shapiro et al. 2004) reduction in fish from British Columbia and armour reduction in fish from Alaska (Cresko et al. 2004). This research has produced a general consensus describing the morphology of ancestral marine and derived lacustrine groups

(Bell and Foster 1994). In this abstraction, anadromous populations are thought to be phenotypically similar to and representative of the ancestral marine form, although they are distinguished in the literature as belonging to separate groups (Wootton 1984), leading to the use of anadromous stickleback as a proxy for the marine fish in morphological investigations (Bell and Foster 1994). Further, differing lacustrine environments have produced another dichotomy

7 between shallow-dwelling benthic foragers, and limnetic populations from deeper lakes (Bell

1976, Lavin and McPhail 1985, Gow et al. 2008, Harmon et al. 2009, Park and Bell 2010, Arnegard et al. 2014). The divergence of the stickleback clade into multiple distinct subgroups with diverse phenotypes illustrates the need for scientific distinction between groups throughout the research process, as well as a better understanding of the mechanisms of this divergence.

The swift evolution of the stickleback phenotype is presumed to be due, at least in part, to phenotypic plasticity in response to natural selection (Schluter 2000, Morris and Rogers 2014).

Plasticity has been demonstrated in the limnetic morph relative to the benthic morph in terms of diet-induced morphological (Day et al. 1994) and behavioural (Day and McPhail 1996) plasticity.

Further, a number of candidate genes have been identified for the regulation of phenotypic plasticity in the cichlid, a related teleost species (Schneider et al. 2014). However, the role of development in structuring this phenotypic variation, and potentially facilitating rapid evolutionary transitions remains unclear.

Although length and age standards are commonly used as exclusion criteria to define individual stickleback as having reached ‘adulthood’ (Baker 1994), literature regarding age and size at sexual maturity in different stickleback populations (Baker 1994, Hagen 1967, Mori 1987,

Snyder 1991, Wootton 1994) is inconsistent. Developmental processes have evidently contributed to novel phenotype variants in association with environmental pressures in stickleback populations

(Wootton 1984, Bell and Foster 1994, Bell 1976). The influence of development on the formation of the adult phenotype, however, and the relative amount of phenotypic variation across ontogeny, have not been explored in stickleback despite these factors potentially influencing evolutionary responses to selection (Hallgrimsson et al 2007). Literature regarding skeletal ossification is particularly sparse (Currey et al 2017), though studies of stickleback genetic variation indicate an

8 important role for Ectodysplasin A (Colosimo et al 2005, Coyle et al 2007) and Paired-Like

Homeodomain 1 (Coyle et al 2007) in the development and evolution of osseous armor plate and pelvic structures, respectively. Further, ossification of skeletal components in related teleosts has been shown to occur at 4.8mm in Indostomus paradoxus (Britz and Johnson 2002) and 1.6mm in

Danio rerio (Westerfield 2007). Though the adult phenotype, rate of growth, and environmental optima likely differ between these species and the stickleback, this information may serve as a basic guideline regarding what we might expect to see throughout stickleback development.

1.4 Thesis Outline

I will investigate the hypothesis that stickleback populations from environmentally distinct habitats will exhibit differences in developmental timing – defined here as morphological maturation throughout growth, with a particular focus on skeletal ossification – before reaching sexual maturity and attaining the population-specific skeletal phenotype. Based on information compiled by Baker (1994), marine and lacustrine stickleback should grow more slowly, and reach a smaller overall size by one year, in comparison to the anadromous and stream dwelling fish which should grow more quickly to reach a larger size by one year. Further, Barrett and colleagues

(2008) suggest that stickleback with reduced armor plating grow faster in fresh water. Baker

(1994) identifies the generally higher productivity of marine waters compared to fresh waters as a possible explanation for the large size of anadromous populations relative to freshwater populations. The relatively small size of the marine populations used in the study appears to argue against this hypothesis (Baker 1994). It is possible, therefore, that this size distribution is incorrect due to small sample sizes, or that some other factor such as predation, energy allocation, diet, or ion concentrations, may be influencing fish size. It follows that stickleback that grow more quickly

9 overall may also experience more rapid skeletal and morphological maturation. To address this hypothesis, I propose the following objectives:

1) Establish the population-specific phenotype and size at sexual maturity for marine and

lacustrine populations.

2) Produce a developmental timeline of skeletal growth for each population.

3) Characterize the skeletal phenotypic variation between populations at various stages of

growth.

The majority of classifications regarding the variation between stickleback populations have been carried out via behavioural and morphological observation (McPhail 1993, Ridgway and McPhail 1983, Robinson 2000), measurements of full body, and feature length (McPhail 1984,

1991, Robinson 2000), and counts of structures such as gill rakers and armour plates (McPhail

1984, 1991, Robinson 2000). Some genetic analyses (McPhail 1984) and basic two-dimensional morphometric analyses (Willacker et al. 2010, Rogers et al. 2012, Jamniczky et al. 2014) have been done. Three-dimensional (3D) morphometric techniques have been largely unexplored with regards to the stickleback, though these methods have yielded promising results in the study of phenotype-environment relationships in adult stickleback (Jamniczky et al. 2015a, Jamniczky et al. 2015b, Pistore et al. 2016, Higham et al. 2017), dolphins (Del Castillo et al 2014), and hominids

(Cofran 2014, Smith et al 2013).

I will use the techniques of 3D geometric morphometrics which will allow me to quantify differences in skeletal morphology between stickleback populations. Multivariate statistics will allow for an investigation of the developmental timeline and establishment of phenotypic variation for each population. Finally, I will conclude with a synthesis chapter including the implications of the data presented, comments on future work, and reflections on limitations of the research.

Chapter 2 - Methods

2.1 Fish Collection

Six threespine stickleback populations: Roquefeuil Bay (RB), Garden Bay Lagoon (GB), and Oyster Lagoon (OL) (marine), and Klein Lake (KL), Hotel Lake (HL), and Sarita Lake (SL)

(lacustrine) were collected from bodies of water in British Columbia (B.C.) Canada during the summer of 2015. Roquefeuil Bay and Sarita Lake populations were collected from bodies of water near the Bamfield Marine Sciences Centre, Bamfield B.C., while Garden Bay Lagoon, Oyster

Lagoon, Klein Lake, and Hotel Lake were collected from bodies of water near Madeira Park on the Sunshine Coast of B.C. (Figure 1). Threespine stickleback were captured using Gee minnow traps (6mm mesh size) left in the water for a maximum of 12hr, with care taken to monitor temperature fluctuations such that fish were not left in traps in warm water. All fish and other animals caught in the traps were catalogued, and those animals not required for research were released. All research was conducted under the permits: AC12-0057, 15-0108FR, MRSU15-

168710, FIL2015-0029, and XR109-2015.

For use in 3D morphometrics, 50 fish from each population were euthanized in the field by placing in an overdose of eugenol (clove oil) for a minimum of 10min. Caudal fin clips were taken from each specimen and preserved in 70% ethanol. Bodies were preserved by injecting 10% neutral buffered formalin (NBF) into the abdominal cavity with a 22 ½ gauge needle. Fish were then placed in small tea bags with identifying tags, and laid in a container and covered with NBF.

Care was taken to set the fish in a standardized position for 3D morphometry – straight, with fins and spines laid flat against the body and the buccal cavity closed. After 24hr, the NBF was removed from the container and replaced with 70% ethanol for long term storage.

For use in breeding, 40 fish from each population were collected into clear plastic bags, 10 per bag, with water from their source location treated with Prime Aquarium Water Conditioner

(Seachem) to remove and control ammonia and nitrogenous wastes. The water was supersaturated with oxygen, and the bags were tightly sealed to retain the water and oxygen within. Each bag was then labeled and placed in a cooler with ice. The ice is multipurpose in that it slows metabolism to reduce the amount of waste produced during transport, acts as a mild anaesthetic in conjunction with the high oxygen concentration, and reduces the risk of overheating in the summer temperatures during transport (Berka 1986). Fish were transported by van to the Life and

Environmental Sciences Animal Resource Centre (LESARC) at the University of Calgary and placed in 120L aquaria of previously cycled water in Aquatic Room 08 (Fish Room). The bagged fish were allowed to sit in the aquarium water for 15min to allow for temperature acclimatization before they were removed from the bags. Fish from marine environments were placed in water of

20-25ppt salinity and fish from lacustrine environments were placed in water of 5-6ppt salinity.

Over the next several weeks, salinity in all saltwater aquaria was slowly reduced to 5-6ppt for ease of care and standardization purposes.

2.2 Fish Care

The temperature in the Fish Room was kept at a constant 17ºC and the light dark cycle was maintained at 16:8 (light to dark). Despite this, each aquarium experienced slight variations in temperature, and this was recorded daily with a thermometer. Fish were fed daily with frozen blood worms (Hikari Bio-Pure) to satiation. Water quality was tested weekly with API Ammonia and

Nitrite test kits, and ¼ water volume was changed weekly to control waste levels and reduce stress.

Adult stickleback were housed in 120L aquaria with densities of no more than 20 fish per aquarium. Each aquarium was outfitted with both a power filter and a sponge filter to extract waste

12 and maintain water quality, as well as plastic plants for cover and enrichment. Plastic lids functioned to reduce evaporation and chance of water contamination from outside sources (Figure

3).

Several pathological infections were observed throughout the course of this project, including Fin Rot and numerous parasites such as Myxosporean, Black Spot, and various unidentified worms. Each infection was treated with the appropriate medication, if available.

All mortalities were recorded and each fish was fin clipped and preserved as had been done in the field.

2.3 Crossing

Adult stickleback were identified as ‘in condition’ for breeding by the presence of a bright red throat, blue eyes, and marked aggression (males) and abdominal swelling with slight protrusion of eggs from the urogenital opening (females). Because all of the parental populations used in this study were wild caught, their exact ages cannot be known, and ‘adult’ was simply defined as an ability to reproduce.

Eggs were extracted from the females by applying gentle pressure to the abdomen in a rostro-caudal direction. Extracted eggs were placed in a clean petri dish with care taken not to get the eggs wet, as water activates the eggs. The egg mass was gently flattened with a clean, dry, gloved finger to ensure even dispersal of oxygen to developing embryos. Each female was marked with an identifying elastomer tag and placed back in her aquarium.

Conditioned males were euthanized with an overdose of clove oil for a minimum of 10 minutes, and testes were removed by opening the abdomen with a small cut rostrally from the urogenital opening. Testes are easily recognizable as vibrant blue vermiform structures found against the abdominal wall bilaterally. Each testis was removed individually and either used to

13 fertilize eggs, or preserved in Hank’s Solution (Recipe Appendix). Males were then fin clipped and preserved as had been done in the field.

To fertilize eggs, a testis was minced with a scalpel near the eggs on the petri dish. Drops of water were mixed with the testis to disperse the sperm, and this water-sperm solution was combined with the eggs. The eggs were left to fertilize for 10-15 minutes, and time of fertilization was noted. The fertilized eggs were then covered with a thin layer of E3 solution (Recipe

Appendix) with care taken to ensure the E3 was shallow enough to allow sufficient gas exchange, and the petri dish was placed in a Percival Growth Chamber set to 18ºC and 70% humidity.

For unknown reasons, only four of the six populations – Garden Bay Lagoon, Klein Lake,

Oyster Lagoon, and Roquefeuil Bay – were successfully crossed. This results in a final dataset containing three marine populations (GB, OL, and RB) and one lacustrine population (KL).

2.4 Raising Juveniles

Newly fertilized stickleback embryos were housed in 100mm x 25mm sterile polystyrene petri dishes in a 36L Percival Growth Chamber (Percival, Iowa, USA) and held at 18°C and 70% humidity. The thin layer of E3 solution was changed daily to keep waste levels under control, ensure adequate oxygen exchange, and reduce the chance of fungal growth on the egg mass. At 2-

3 days post fertilization, those eggs that were successfully fertilized became distinguishable from the unsuccessful fertilizations by the appearance of a double membrane and neural crest migration in the successful eggs (Figure 2), and the unsuccessful eggs were removed to further reduce the chance of fungal growth.

When the embryos hatched, around 10 days post fertilization, they were transferred to 10 gallon aquariums in the fish room containing 5-6 ppt salinity water treated with 5 drops of

Methylene Blue (methylthioninium chloride) to reduce risk of fungal infection. Each aquarium

14 contained two large air stones to ensure adequate circulation of water and oxygen dispersal. Upon transfer to the fish room, hatched embryos were fed artemia (brine shrimp) larvae (O.S.I Brine

Shrimp, or San Francisco Bay Brand Brine Shrimp Eggs depending on availability – See Hatching

Methods) twice daily to satiation. Food consumption increased almost daily as the embryos grew.

When the juvenile stickleback reached an average length of 1cm (~30 days post fertilization), one of the air stones in each aquarium was replaced with a sponge filter to help with waste removal, and a small plant was placed in each aquarium to provide cover and enrichment.

Also at this time, ¼ water changes were begun.

At around 2cm average length (~90 days post fertilization) the juvenile stickleback began to exhibit signs of stress related to high density and subsequent poor water quality. To accommodate the growing fish, each family was moved to a 30-gallon aquarium with two sponge filters and 3 plants each (Figure 3). ¼ water changes were continued. In addition to twice daily artemia larvae, finely chopped bloodworms were added to the juvenile's diet to begin the process of switching them to the adult diet of whole bloodworms. Over the next several weeks, artemia were removed from the diet and replaced increasingly with whole bloodworms until the fish were completely comfortable with the adult diet. Upon completion of the diet switch, feedings were reduced to once daily without incident.

At 3cm average length (~180 days post fertilization), an attempt was made to add a power filter to each juvenile aquarium. The power filters were promptly removed, however, when an entire aquarium of juveniles died for no apparent reason. In this context, a ‘aquarium crash’ occurs when a majority of the fish housed within that aquarium die very rapidly. Despite consultation with numerous experienced parties, a definitive cause for the aquarium crash was not determined.

Power filters were not attempted again.

2.5 Sampling

Juvenile stickleback were sacrificed at a number of predetermined time points (5, 6, 7, 12,

30, 60, 90, 180, and 270 days post fertilization) using an overdose of eugenol (clove oil). These timepoints were chosen using available zebrafish literature, as well as preliminary ossification data from a small dataset of juvenile stickleback borrowed from another researcher in the lab (E.

Bowles) (Figure 4). Fish at 60dpf were added to the dataset late in the research when it was realized that a large amount of ossification was occurring between 30 and 90dpf. Therefore, these fish were used only for the 3D morphometrics as they could not be added to the other datasets late.

For those juveniles large enough (180 through 270 days post fertilization), caudal fin clips were taken for genetic testing such as sexing and genotyping the fish. For all other time points, samples were not obtained for genetic testing as removal of the chorion proved unfeasible and the fish were too small to access a fin clip. Fin clips were preserved in 70% ethanol, and fish were preserved in 4% paraformaldehyde/5% glutaraldehyde if 30dpf or younger, and 70% ethanol if

60dpf or older. Fish preserved in 4%PFA/5%Glut are no longer eligible for future genetic testing as the chemicals destroy genetic material.

Due to constraints with breeding success and egg production, not all time points are available for all populations. Notably, 60dpf fish were only obtained in the RB population. See

Table 1 for a comprehensive breakdown of time points available per population.

2.6 Genetics

Genetic sex determination was performed on available caudal fin clips for both adult and juvenile stickleback used in morphometrics. Those fish too young to obtain fin clips from (younger than 180 days post fertilization) were not used in genetic testing. To determine the sex of each fish, individuals were genotyped at the Idh (Isocitrate Dehydrogenase) locus following Peichel at al.

(2004, Vanderzwan 2015). Genomic DNA was extracted from fin clips using a DNEasy kit

(Qiagen), polymerase chain reaction (PCR) was run with the appropriate Idh primers (Protocol

Appendix), and the amplified DNA was run on a 2%w/v agarose gel. Those samples with one band at 302 base pairs were identified as female, and those with two bands at 302 and 271 base pairs were identified as male. If unreadable or absent bands were encountered, the fish was marked as unknown sex.

2.7 Photography and 2D Imaging

Fish from 5dpf through 90dpf timepoints were photographed from three sides – dorsal, and bilaterally – using a digital camera on a tripod for stabilization. All fish within a timepoint were photographed at the same magnification. Lighting was consistent across all timepoints. Low melting point agarose was used to prop fish upright, when necessary, and in an effort to produce a standardized position from which to photograph the spherical embryos (5dpf through 7dpf).

Specimens were scaled by photographing a ruler in each picture. See Figures 6 and 7 for examples of fish at each available age from each population.

2.8 2D Landmarking and Statistical Analysis

Due to the lack of ossified tissue in early embryos and juveniles (5 dpf through 30 dpf), these fish could not be characterized using 3D morphometrics on skeletal variation. Therefore, these fish were photographed and landmarked using 2D morphometric methods. As a cross-over time point and in an effort to compare the 2D to the 3D datasets, the completely ossified 90dpf fish were both 2D and 3D landmarked. This timepoint allows some continuity of the data and allows us to follow the morphometric trends through time.

2D landmarks were selected on homologous points on each embryo with an effort made to represent overall body shape. Due to size and feature differences between embryonic growth

17 stages, a greater number of landmarks were placed on more developed fish. Fish at 5 through 7 days post fertilization were marked with 15 landmarks dorsally and 10 landmarks bilaterally. Fish at 12 days post fertilization were marked with 15 landmarks dorsally and 13 landmarks bilaterally.

Fish at 30 days post fertilization were marked with 23 landmarks dorsally and 24 landmarks bilaterally. Finally, fish at 90 days post fertilization were marked with 23 landmarks dorsally and

31 landmarks bilaterally. See Figure 5 for examples of fish landmarked. To allow ease of statistical evaluation, the landmark numbers were kept consistent both across sides (i.e. Landmark 2 in dorsal view which marks the rostral-most portion of the left eye is in the same place in the photo from the left lateral side) and across age groups (i.e. Landmark 2 marks the rostral-most portion of the eye in 5dpf through 90dpf). Higher landmark numbers in older fish are unique to the older fish.

Due to very small size and lack of definable characteristics, landmarks in the very young embryos are biased toward the head and especially the eye as these features develop first. Landmarks were placed using tpsDig2 version 2.26 (F. James Rohlf, Ecology & Evolution, SUNY at Stony Brook,

2016) by the author to minimize human error.

Each fish was landmarked three times. This allows the landmark placement itself to be evaluated, and ensures that the phenotype trends seen are not due to landmark error. Discriminant function analysis was performed to compare landmark repeats, and to ensure that the individual landmarking (A. Pistore) was able to place landmarks in a repeatable way.

Due to landmark number differences, fish from the dorsal view were first analyzed in smaller groups. Fish were compared by age set (all of the 5dpf fish for each population compared) as well as by population (all age sets for Klein Lake were compared). Furthermore, groups with similar numbers of landmarks were compared. For example, ages 5, 6, 7, and 12 dpf share 15 landmarks from the dorsal view. These fish, therefore, were analyzed as a set. Moreover, ages 30

18 and 90 dpf share 23 landmarks from the dorsal view, so these fish were also analyzed as a set.

Once each smaller set of fish were compared, the entire dataset was analyzed based on shared landmarks. Thus, each of the age sets 5 through 90dpf was compared based on the 15 dorsal landmarks shared in the youngest age groups.

In the lateral view, fish were compared only within groups by age, and by groups with similar landmark numbers (ex. 5, 6, and 7dpf). There was minimal utility in comparing population datasets or the entire dataset of all ages and populations due to strong dissimilarity in landmark number and placement across ages.

2.9 MicroCT Scanning and 3D Imaging

All fish were scanned in one of two micro computed tomography (microCT) instruments, either a Scanco µCT 35 (ScancoAG, Brutisellen, Switzerland) or SkyScan 1173 High Energy

MicroCT (Bruker, Kontich, Belgium) system. Scan parameters were set such that the scans were as close to identical as possible.

Fish were scanned in a straight standardized position, with the mouth closed and the fins and spines held flat against the body cavity. Each individual was wrapped in clear plastic wrap and secured with tape to maintain the standardized position and to reduce desiccation in the warm, dry conditions created by the scanning process. Foam packing was used to further enforce the standardized position and to reduce the potential for movement throughout the scanning process.

Scans completed on the µCT 35 system were contoured on the scan computer, then transferred directly to a Macintosh computer for 3D visualization and landmarking. Scans completed on the SkyScan system were contoured on a separate computer using NRecon, cropped and processed using ImageJ on a high-powered Macintosh computer to reduce file size, then finally transferred to the above Macintosh computer for 3D visualization and landmarking.

50 adults from each population (Oyster Lagoon, Garden Bay Lagoon, Roquefeuil Bay, and

Klein Lake), and 10 juveniles from each population (Oyster Lagoon, Garden Bay Lagoon,

Roquefeuil Bay, and Klein Lake) at each time point if available (5, 6, 7, 12, 19, 30, 60, 90, 180, and 270 days post fertilization), for a total of 600 fish, were scanned. Visualization of the early embryonic timepoints (5dpf through 30 dpf) proved difficult, however, due to the minimal ossification in the specimens (Figure 8). Therefore, only the 60dpf onward timepoints produced useable scans for landmarking. Interestingly, only half of the 60dpf fish had enough ossification in their skeletons to landmark.

To visualize a maximum of phenotypic diversity, the rostral portion of each fish, from the snout to the distal tips of the pelvic spines, was scanned. The caudal portion of the fish was excluded from scanning to reduce time, and because the areas of greatest interest for this study are contained within the rostral portion of the fish.

The raw data were then analyzed using Amira 5.4 (Visage Imaging, Carlsbad, CA) to produce 3D reconstructions of the skeleton of each fish. The isosurface tool was used to produce these images at a threshold of 2500-3500 (µCT) and 7000 (SkyScan).

2.10 3D Landmarking

Landmark-based geometric morphometrics was performed on the 3D imaged skeletons of the fish such that all populations and time points could be compared. 159 landmarks were used for this purpose and were placed as shown in (Figure 9, Tables 2-4). To save time, two individuals (A.

Pistore and T. Barry) landmarked the adult populations (GB, KL, OL and RB). 15 fish from each population were landmarked by both individuals to compare landmark accuracy. Procrustes

ANOVA was used to compare individuals. Procrustes ANOVA examines the contribution of different factors, including measurement error, asymmetry, and population structure to the total

20 variance present in the dataset (Klingenberg et al. 2002, Klingenberg 2011, Klingenberg and

McIntyre 1998).

2.11 2D and 3D Morphometrics

The landmark coordinates were statistically analyzed using MorphoJ version 1.06d

(Klingenberg 2011) to examine the aforementioned hypotheses. Techniques used include

Procrustes Transformation, Multivariate Linear Regression on centroid size, Principal

Components Analysis (PCA), Canonical Variate Analysis (CVA), and Discriminant Function

Analysis (DFA) as described in Pistore et al. (2016). Procrustes Transformation is a method for standardizing specimens into a common space. Artifacts due to rotation, translation, and scaling are removed from the dataset (Klingenberg et al. 2002). Regression functions to standardize the datasets to a measure of size; in this case, centroid size was used as a proxy for organism size. This ensures that variation in shape between populations and individuals is due to morphology alone, and is not influenced by size differences between populations, especially between the large marine specimens and the small lacustrine specimens. All subsequent analyses were carried out on regression residuals. PCA and CVA are similar in that they evaluate variation within (PCA) and between (CVA) populations (Klingenberg 2011). PCA searches for linear relationships between possibly correlated variables, which explain major axes of variation within the data, and produces a new dataset of uncorrelated variables, called Principal Components (PCs) (Klingenberg 2011).

The new dataset is a simpler representation of the variation between populations, and allows for more straightforward analysis of the variation data. CVA is similar to PCA, however in this method, variables are combined into Canonical Variates (CVs), which allow for the most robust discrimination between populations. CVs represent a new set of variables, which are then plotted to visualize axes of variation. As with PCA, the new dataset reduces the variation, and allows for

21 more straightforward analysis. DFA examines the separation between two groups of observations, unlike CVA, which may consider more than two groups at one time, and is therefore most useful for comparison of specific groups rather than a general analysis (Klingenberg 2011).

Chapter 3 - Results

3.1 2D Morphometrics

3.1.1 Consistency of Landmarking

Before comparing fish based on 2D landmarks, the landmark placement itself was analyzed. DFA of landmark repeats shows that landmark repeats 1 vs. 2 has a Procrustes distance of 0.02 (p = 0.4890), 1 vs. 3 has a Procrustes distance of 0.02 (p = 0.2990), and 2 vs. 3 has a

Procrustes distance of 0.01 (p = 0.6940). This indicates that the landmark repeats are not statistically different from one another. All statistical analysis henceforth are run on data averaged between the three repeats.

3.1.2 Dorsal Component by Population

The GB dataset includes fish that are 5, 6, 7, 12, and 90dpf. Regression on centroid size for this group reveals that 62.6% of variation is predicted by centroid size (p <0.0001).

The PCA indicates that fish within the GB dataset differ by age (Figure 10), with PC1 explaining 79.3% of the variance and PC2 explaining 9.0%. Negative PC1 values correspond to

5dpf fish, while the most positive PC1 values correspond to 12 and 7dpf fish. 90dpf fish cluster densely in the middle of PC1 near zero. Positive PC1 values are associated with a long, narrow head with a large eye. Conversely, negative PC1 values are associated with a broad short head and smaller, more protruding eyes.

The CVA of this group shows a similar trend. Procrustes distances among groups (shown in brackets) are statistically significant for 5dpf vs 6dpf (0.16, p = 0.0002), 5dpf vs 7dpf (0.18, p

= 0.0010), 5dpf vs 12dpf (0.24, p = 0.0065), 5dpf vs 90dpf (0.17, p < 0.0001), 6dpf vs 12dpf (0.10, p = 0.0236), and 12dpf vs 90dpf (0.11, p = 0.0003). The following comparisons are not significant:

6dpf vs 7dpf (0.18, p = 0.3973), 6dpf vs 90dpf (0.03, p = 0.0734), 7dpf vs 12dpf (0.09, p = 0.1955), and 7dpf vs 90dpf (0.02, p = 0.9648).

The KL dataset includes fish that are 5, 12, 30, and 90dpf. Regression on centroid size for this group reveals that 25.2% of variation is predicted by centroid size (p = 0.0024).

The PCA indicates that fish within the KL dataset differ by age (Figure 11), with PC1 explaining 91.0% of the variance and PC2 explaining 4.1%. Negative PC1 values correspond to

30dpf and 90dpf fish while positive PC1 values correspond to 5dpf and 12dpf fish. Positive PC1 values are associated with a wide short head and large eyes. Negative PC1 values are associated with a narrow, long, pointed head and small narrow eyes.

The CVA of this group indicates that the 12dpf age group is not statistically different from

30dpf (Procrustes distance 0.25, p = 0.0542). All other comparisons are significant: 12dpf vs 5dpf

(0.18, p = 0.0498), 12dpf vs 90dpf (0.19, p = 0.0420), 30dpf vs 5dpf (0.34, p < 0.0001), 30dpf vs

90dpf (0.09, p = 0.0011), and 5dpf vs 90dpf (0.25, p < 0.0001).

The OL dataset includes fish that are 5, 6, 7, and 90dpf. Regression on centroid size for this group reveals that 87.6% of variation is predicted by centroid size (p = 0.0001).

The PCA indicates that fish within the OL dataset differ by age (Figure 12), with PC1 explaining 72.9% of the variance and PC2 explaining 9.1% of the variance. Negative PC1 values correspond to age sets 6dpf, 7dpf, and 90dpf, while age set 5dpf appears to span the entirety of

PC1, from very negative to very positive. Positive PC1 values are associated with a broad short head and large eyes while negative PC1 values are associated with a long narrow head and eyes that are larger rostrally than they are caudally.

The CVA of this dataset shows that 5dpf vs 6dpf (Procrustes distance 0.12, p = 0.0708),

5dpf vs 7dpf (0.11, p = 0.1917), 6pf vs 7dpf (0.03, p = 0.7348), 7dpf vs 90dpf (0.03, p = 0.2253),

24 and 7dpf vs 90dpf (0.02, p = 0.8030) are not statistically significant while 5dpf vs 90dpf (0.10 p =

0.0244) is.

The RB dataset is the most complete with fish that are 5, 6, 7, 12, 30, and 90dpf. Regression on centroid size for this group reveals that 44.8% of variation is predicted by centroid size (p <

0.0001).

The PCA indicates that fish within the RB dataset differ by age (Figure 13), with PC1 explaining 79.4% of the variance and PC2 explaining 10.4% of the variance. 5dpf, 6dpf, and 12dpf cluster towards the negative end of PC1 while 30dpf and 90dpf cluster towards the positive end of

PC1. 7dpf spans zero. Positive PC1 values are associated with a long narrow head and small eyes while negative PC1 values are associated with a short broad head and large eyes.

The CVA of this dataset shows that each comparison is statistically significant. Procrustes distances among groups for each set are as follows: 5dpf vs 6dpf (0.08, p = 0.0022), 5dpf vs 7dpf

(0.19, p < 0.0001), 5dpf vs 12dpf (0.1, p < 0.0001), 5dpf vs 30dpf (0.32, p < 0.0001), 5dpf vs 90dpf

(0.20, p < 0.0001), 6dpf vs 7dpf (0.12, p = 0.0001), 6dpf vs 12dpf (0.09, p = 0.0008), 6dpf vs 30dpf

(0.26, p < 0.0001), 6dpf vs 90dpf (0.41, p < 0.0001), 7dpf vs 12dpf (0.12), 7dpf vs 30dpf (0.16, p

< 0.0001), 7dpf vs 90dpf (0.08, p < 0.0001), 12dpf vs 30dpf (0.31, p < 0.0001), 12dpf vs 90dpf

(0.21, p < 0.0001), 30dpf vs 90dpf (0.13, p < 0.0001).

3.1.3 Dorsal Component by Age

The 5dpf dataset includes fish from GB, KL, OL, and RB populations. Regression on centroid size shows that 7.29% of variation is predicted by centroid size (p = 0.0761).

The PCA indicates that fish within the 5dpf dataset differ by population (Figure 14), with

PC1 explaining 63.7% of the variance and PC2 explaining 14.2%. The graphical output for this

PCA shows that KL clusters towards the positive end of PC1, RB is associated with negative PC1,

25 and OL settles near zero. GB appears to be spread broadly across PC1. Positive PC1 values are associated with a lengthening of the head without much effect on head width, and an enlargement of the eye. Negative PC1 values are associated with a shortening of the head without much effect on width and a smaller eye.

The CVA of this dataset shows GB vs KL (Procrustes distance 0.11, p = 0.0008), KL vs

OL (0.11, p = 0.0073), KL vs RB (0.11, p = 0.0002), and OL vs RB (0.07, p = 0.0349) are all statistically significant while GB vs OL (0.07, p = 0.2338) and GB vs RB (0.01, p = 0.9605) are not.

The 6dpf dataset includes fish from GB, OL, and RB. Regression on centroid size shows that 9.9% of variation is predicted by centroid size (p = 0.0500).

The PCA indicates that fish within the 6dpf dataset differ by population (Figure 15), with

PC1 explaining 50.1% and PC2 explaining 24.7%. Negative PC1 values correspond with OL and

GB fish while positive PC1 values correspond with RB fish. RB is spread over PC1 while OL and

GB are more closely clustered. Positive PC1 values are associated with a shortened head without much effect on eye size or head width while negative PC1 values are associated with a lengthened head and the same effect on eye size and head width.

The CVA of this dataset shows that only OL and RB are statistically different with a

Procrustes distance of 0.08 (p = 0.0042). GB vs OL (0.06, p = 0.0533) and GB vs RB (0.04, p =

0.1205) are not.

The 7dpf dataset also includes fish from GB, OL, and RB. Regression on centroid size shows that 22.5% of variation is predicted by centroid size (p = 0.0019).

The PCA indicates that fish within the 7dpf dataset differ by population (Figure 16), with

PC1 explaining 67.5% of the variance and PC2 explaining 14.0% of the variance. The graphical

26 output for the PC analysis shows an interesting trend. Except for 1 GB fish, all 3 populations are associated with positive PC1 values. The GB exception – fish number 7 - is associated with negative PC1 values. In this analysis, positive PC1 values are associated with a lengthened head, especially in the caudal portion of the head. Negative PC1 values are associated with a broad head that is shortened rostro-caudally. If the dataset is analyzed with GB7 removed (Figure 17), negative

PC1 values are associated with OL while positive PC1 values are associated with RB. GB is spread evenly throughout PC1. Positive PC1 values are associated with a lengthened and narrowed head shape with a large eye. Negative PC1 values are associated with a shortened and broadened head shape.

The CVA of this dataset shows that none of the comparisons GB vs OL (0.03, p = 0.8509),

GB vs RB (0.02, p = 0.9875), and OL vs RB (0.04, p = 0.1469) are statistically significant.

Interestingly, the GB fish that appears to be a population outlier in the PCA graph is not an outlier in the CVA graph.

The 12dpf dataset includes fish from GB and RB, as well as one fish from KL. Regression on centroid size shows that 11.2% of variation is predicted by centroid size (p = 0.2205).

The PCA indicates that fish within the 12dpf dataset differ by population (Figure 18), with

PC1 explaining 71.8% of the variance and PC2 explaining 10.9% of the variance. Negative PC1 values are associated with the RB population while the GB population corresponds to positive PC1 values. The single KL fish sits near zero and appears to be more similar to RB along PC1. Positive

PC1 values are associated with a long and narrow head and eyes while negative PC1 values are associated with a wide round head and large eyes.

The CVA of this dataset shows that GB vs RB (Procrustes distance 0.13, p = 0.0028) is statistically significant while KL vs RB (0.06, p = 0.3357) and GB vs KL (0.09, p = 0.7480) are not.

The 30dpf dataset includes only fish from KL and RB populations. Regression on centroid size shows that 25.6% of variation is predicted by centroid size (p = 0.0033).

The PCA indicates that fish within the 30dpf dataset differ by population (Figure 19), with

PC1 explaining 44.9% of the variance and PC2 explaining 35.6%. There is very little divergence of the two populations along either PC1 or PC2 in the graphical output. Positive PC1 values are associated with a narrow head with eyes that do not protrude beyond the edges of the face. The body is lengthened, with the majority of the length added in the body, behind the head. Negative

PC1 values are associated with a wide head and large eyes. The body is shortened behind the head, making the head appear very large and out of proportion.

The CVA between these two populations shows a Procrustes distance among groups of

0.02 (p = 0.9359). The graphical output for the CVA shows slightly better differentiation between the two groups, with KL clustering towards negative CV1 values and RB clustering towards positive CV1 values, but there still exists some overlap.

Finally, the 90dpf dataset includes fish from GB, KL, OL, and RB. Regression on centroid size shows that 4.27% of variation is predicted by centroid size (p = 0.1634).

The PCA indicates that fish within the 90dpf dataset differ by population (Figure 20), with

PC1 explaining 64.5% of the variance and PC2 explaining 18.8%. Negative PC1 values are associated with KL, OL, and RB while positive PC1 values correspond to GB. RB is spread more across PC1. There is very weak separation of all of the populations in the graphical output. Positive

PC1 values are associated with very minimal change in head shape with some widening of the

28 body about the pelvis. Similarly, negative PC1 values are associated with a narrowed body in the pelvic region with very minimal change to the proportions of the head.

The CVA of this dataset shows that KL vs GB and KL vs RB are statistically significant with Procrustes distances of 0.06 (p = 0.0165) and 0.04 (p = 0.0006) respectively. The remainder of the comparisons are not significant. KL vs OL (0.02, p = 0.4583), GB vs OL (0.05, p = 0.1044),

GB vs RB (0.05, p = 0.0970), and OL vs RB (0.03, p = 0.1626).

3.1.4 Lateral Component by Age

The 5dpf dataset includes fish from all four populations. Regression reveals that 1.52% of variation is predicted by centroid size (p = 0.6801).

The PCA indicates that fish within this dataset differ very little (Figure 21), with PC1 explaining 50.4% of variance and PC2 explaining 34.7% of variance. Populations cannot be distinguished on either PC1 or PC2 using the graphical output, although GB and OL appear to cluster more tightly near zero and KL and RB are more spread out. PC1 values are associated with a lengthening of the snout and a shortening of the caudal portion of the head. The eye size is unchanged, but the pupil is shifted dorsally and caudally. Negative PC1 values are associated with a shortened and upturned snout and a lengthened head caudally. The eye size remains unchanged but the pupil is shifted ventrally and rostrally.

The CVA of this dataset shows that no populations are significantly different from one another. GB vs KL (0.05, p = 0.7539), GB vs OL (0.08, p = 0.5428), GB vs RB (0.10, p =

0.1635), KL vs OL (0.11, p = 0.3644), KL vs RB (0.13, p = 0.0866), and OL vs RB (0.13, p =

0.2658).

The 6dpf dataset includes fish from GB, OL and RB populations. Regression on centroid size shows that 3.0% of variation is predicted by centroid size (p = 0.5453).

The PCA indicates that fish within this dataset differ very little (Figure 22), with PC1 explaining 50.7% of total variance and PC2 explaining 34.6%. Very little can be seen from the graphical output for the PCA of this timepoint, but it is apparent that the OL population clusters more tightly around zero in both PC1 and PC2 while GB and RB are spread more broadly over negative and positive values. Positive PC1 values are associated with a lengthening of the head and an upturning of the snout. The eye size remains relatively unchanged, but the pupil is shifted ventrally. Negative PC1 values are associated with a shortened head and a flattening of the ventral line. The eye is rotated rostrally about the most ventral aspect and the pupil is shifted dorsally.

The CVA of this dataset shows that none of the Procrustes distance among groups are statistically significant. GB vs OL (0.06, p = 0.7552), GB vs RB (0.04, p = 0.8096), and OL vs

RB (0.07, p = 0.5274).

The 7dpf dataset includes fish from GB, OL and RB populations. Regression on centroid size shows that 7.1% of variation is predicted by centroid size (p = 0.1740).

The PCA indicates that fish within this dataset differ minimally (Figure 23), with PC1 explaining 61.4% of total variance and PC2 explaining 25.4%. The graphical output for the PCA follows a similar trend to that seen in 6dpf. OL clusters more tightly in the middle while GB and

RB are spread more broadly. Positive PC1 is associated with an enlargement of the eye in the rostro-caudal plane and a flattening of the ventral line. The pupil is shifted dorso-caudally and becomes more round. Negative PC1 is associated with a smaller eye in the rostro-caudal plane and an upturning of the snout and gills, making the ventral line curve dorsally about the eye. The pupil is shifted rostro-ventrally and is narrowed in the rostro-caudal direction making it appear slit-like.

The CVA of this dataset shows that the populations in this dataset are not statistically significant. GB vs OL (0.10, p = 0.0890), GB vs RB (0.02, p = 0.9974), and OL vs RB (0.11, p =

0.2868).

The 12dpf dataset includes fish from GB, KL and RB populations. Regression on centroid size shows that 10.3% of variation is predicted by centroid size (p = 0.2352).

The PCA indicates that fish within this dataset differ in terms of population (Figure 24), with PC1 explaining 52.8% of total variance and PC2 explaining 24.3%. Both GB and KL populations are associated with negative PC1 values while RB is spread more evenly across PC1.

PC2 appears to split GB from KL, with positive PC2 values associated with GB and KL clustering near zero.

The CVA of this dataset shows that RB is not significantly different from KL or GB in terms of lateral morphology, but GB and KL are different with a Procrustes distance of 0.09 (p <

0.0001).

The 30dpf dataset includes fish from KL and RB. Regression on centroid size shows that

6.5% of variation is predicted by centroid size (p = 0.2461).

The PCA indicates that fish within this dataset differ slightly by population (Figure 25), with PC1 explaining 37.8% of variance and PC2 explaining 16.7%. The graphical output shows that KL clusters more tightly together around zero while RB is spread more evenly between extreme negative PC1 values and positive PC1 values. Positive PC1 values are associated with a deeper body and a larger eye. The cranium is more convex, the mandible is small, and the maxilla is large. The lateral fins are shifted caudally. Negative PC1 values are associated with a slender body with a more concave cranium and small eye. The snout is upturned, lending to the overall concavity of the head. The lateral fins are shifted rostrally.

The CVA of this dataset shows a Procrustes distance of 0.02 (p = 0.3032).

The 90dpf dataset includes fish from all four populations. Regression on centroid size shows that 4.6% of variation is predicted by centroid size (p = 0.0919).

The PCA indicates that fish within this dataset differ by population (Figure 26), with PC1 explaining 38.5% of variance and PC1 explaining 17.9%. Positive PC1 values are associated with the RB population while negative PC1 values are associated with the OL and GB populations. KL appears to be centered near a PC1 of zero with a slight bias towards the positive values. Positive PC1 values are associated with a deepening of the head about the buccal cavity and an enlargement of the mandible. The eye is shifted dorsally into the space provided by a convex cranium. The lateral fins are shifted caudally and the topline of the fish is shortened while the ventral aspect remains largely unchanged. Negative PC1 values are associated with a small buccal cavity and jaw as well as a body that lacks depth. The overall shape of the fish is fusiform. The lateral fins are shifted rostrally.

The CVA of this dataset shows statistically significant comparisons with Procrustes distances among groups as follows: GB vs RB 0.05 (p = 0.0002), KL vs OL 0.04 (p = 0.0021),

KL vs RB 0.04 (p = 0.0030), and OL vs RB 0.06 (p < 0.0001). GB vs KL 0.03 (p = 0.2703) and

GB vs OL 0.03 (p = 0.1438) are not statistically different.

3.1.5 Dorsal Component Pooled

The first pooled set analyzed from the dorsal view was the youngest with shared landmarks.

This included 5, 6, 7, and 12dpf fish. Regression on centroid size for this group reveals that 11.3% of variation is predicted by centroid size (p = 0.0002).

The PCA indicates that the young fish do not differ very much (Figure 27), with PC1 explaining 68.0% of the variance and PC2 explaining 13.4%. Positive PC1 values are associated

32 with GB and OL populations. Positive PC1 values show fish with broad, shortened heads. KL and

RB are centered around a PC1 of zero, but remain tightly clustered without much spread. Negative

PC1 values show fish with long narrow heads. PC2 separates KL from RB, with GB and OL associated with negative values and a very flattened snout with small eyes, RB centered near zero, and KL associated with positive values and a lengthened snout with large eyes.

The CVA of this dataset shows that GB vs OL has a non-significant Procrustes distance of

0.06 (p = 0.1029), as does OL vs RB (0.04, p = 0.1614). All other comparisons are significant. GB vs KL (0.10, p = 0.0018), GB vs RB (0.07, p = 0.0009), KL vs OL (0.10, p = 0.0001), and KL vs

RB (0.07, p = 0.0025).

The second pooled set analyzed from the dorsal view was the oldest with shared landmarks including 30 and 90dpf fish. Regression on centroid size for this group reveals that 42.0% of variation is predicted by centroid size (p < 0.0001).

The PCA indicates that the older fish differ by population (Figure 28), with PC1 explaining

56.4% of total variance, and PC2 explaining 26.8%. KL fish appear to be associated with positive

PC1 values while OL and GB fish cluster towards negative PC1 values. RB fish are dispersed across PC1 but do cluster more strongly in the positive PC1 range. Positive PC1 values are associated with a wide head from the dorsal view. The body is lengthened caudally and the spine plates are distanced from the head. Negative PC1 values are associated with a narrow head and a shortened body.

The CVA of this dataset shows that nearly all comparisons are significant. Procrustes distances among groups are as follows: GB vs KL 0.11 (p = < 0.0001), GB vs OL 0.5 (p = 0.0320),

GB vs RB 0.07 (p = 0.0006), KL vs OL 0.07 (p < 0.0001), and KL vs RB 0.07 (p < 0.0001). OL vs RB is the only comparison that is not significant with a Procrustes distance of 0.03 (p = 0.1669).

Finally, I analyzed all of the populations and age groups using the 15 landmarks that they shared. Regression on centroid size shows that 40.5% of variation is predicted by centroid size (p

< 0.0001).

The PCA indicates that the pooled fish differ both by age and by population (Figure 31), with PC1 explaining 82.4% of the variance and PC2 explaining 6.9% of the variance. Negative

PC1 values are associated with 5, 6, 7, and 12dpf age points as well as the GB population, while positive PC1 values are associated with 30 and 90dpf age points and the KL population. The OL and RB populations both loosely straddle the zero of PC1. Positive PC1 values are associated with a long, narrow head and small eyes while negative PC1 values are associated with a short, wide head and large eyes.

The CVA of this dataset shows that GB vs KL (Procrustes distance 0.20, p < 0.0001), GB vs OL (0.11, p = 0.0004), GB vs RB (0.12, p < 0.0001), KL vs OL (0.10, p = 0.0046), and KL vs

RB (0.08, p = 0.0070) are statistically significant. Conversely OL vs RB (0.04) has a p value equal to 0.2456.

3.1.6 Lateral Component Pooled

The first pooled set analyzed from the lateral view was the youngest with shared landmarks. This included 5, 6, and 7dpf fish. Regression on centroid size for this group reveals that 1.04% of variation is predicted by centroid size (p = 0.4447).

The PCA indicates that young fish differ most significantly by age (Figure 29), with PC1 explaining 44.8% of the variation in data and PC2 explaining 35.9%. There is very little grouping of populations or ages on the PC graph though 6dpf and OL show the strongest clustering towards a PC1 of zero. Positive PC1 values are associated with a flattening of the ventral line and an

34 enlargement and caudal shift of the pupil while negative PC1 values are associated with an upturning of the snout and a smaller, rostrally shifter pupil.

The CVA of this dataset shows that GB vs KL are separated by 0.07 (p = 0.2221), GB vs

OL (0.06, p = 0.2068), GB vs RB (0.05, p = 0.2462), KL vs RB (0.07, p = 0.0693), and KL vs OL

(0.10, p = 0.0826). The only populations that are statistically different in terms of dorsal morphology are OL vs RB with a separation of 0.09 (p = 0.0498).

The second set analyzed from the lateral view was the oldest with shared landmarks. This included 30 and 90dpf fish. Regression on centroid size for this group reveals that 32.8% of variation is predicted by centroid size (p < 0.0001).

The PCA indicates that older fish differ both by age and by population (Figure 30), with

PC1 explaining 29.9% of the variance and PC2 explaining 16.7%. Negative PC1 values are associated with the 90dpf age group as well as GB and OL populations. These fish appear to have a concave head and a shallow buccal cavity and body. The ventral aspect of the fish is flat while the dorsal aspect is humped. The eye is shifted ventrally and the lateral fins are shifted rostrally.

Positive PC1 values, conversely, are associated with the 30dpf age group as well as the KL and

RB populations. These fish appear to have a convex head and a deepened buccal cavity and body with a large mouth. The lateral fins are shifted caudally and the eye is shifted dorsally.

The CVA of this dataset shows that GB vs KL (Procrustes distance 0.03, p = 0.0624), and

GB vs OL (0.03, p = 0.1872) are not statistically different while GB vs RB (0.03, p = 0.0445), KL vs OL (0.04, p < 0.0001), KL vs RB (0.03, p = 0.0220), and OL vs RB (0.04, p = 0.0028) are statistically different.

3.2 3D Morphometrics

3.2.1 Consistency of Landmarking

Procrustes ANOVA indicates that between population differences (F 20.66, p < 0.0001) are roughly ten times as large as between landmarker differences (F 2.32, p < 0.0001). This indicates that differences between landmarker will not greatly affect morphometric comparisons, and it is acceptable to go ahead comparing fish.

3.2.2 By Population

Each population included in this analysis contains 90dpf, 180dpf, and 270dpf juveniles as well as an adult set. Additionally, the RB population includes a set of 60dpf juveniles.

Regression of the GB population on centroid size reveals that 42.3% of variation is predicted by centroid size (p < 0.0001). The KL population regressed on centroid size has 21.9% predicted by centroid size (p < 0.0001), while the OL population has a 27.3 % predicted by centroid size (p < 0.0001). Finally, the RB population has 34.0% predicted by centroid size (p < 0.0001).

Principal components analysis of the separated populations of fish shows that the different age groups do not separate out strongly along either of the first PC axes in any of the populations.

The GB dataset indicates very minimal separation (Figure 32), with PC1 explaining only 22.0% of the variance and PC2 explaining 14.2%. Positive PC1 values in this population are associated with markedly smaller heads and associated features without much change in the body shape or size, while negative PC1 values are associated with larger heads in relation to body size.

When coloured by sex, PCA of the 270dpf timepoint indicates that males and females can be separated on the first principal component (Figure 33), with negative PC1 values associated with female individuals with a small head and deep body and positive PC1 values associated with male individuals and a large head and slender body. Interestingly, the male individuals appear to

36 group together quite tightly while the female individuals are spread more along PC1. Similarly,

PCA of the adult timepoint coloured by sex shows that males and females can be separated (Figure

34), with negative PC1 associated with male individuals and a large head with slender body and positive PC1 associated with female individuals and a small head with deep body.

In the KL dataset (Figure 35), PC1 explains 17.5% of the variance and PC2 explains 13.7%.

Some separation can be seen in this dataset, with the juvenile age groups (90, 180, and 270dpf) associated with negative PC1 values and the adult age group associated with positive PC1 values.

Positive PC1 values are associated with a shallower cranium, and small jaw, while negative PC1 values are associated with a convex cranium and robust, square jaw.

When coloured by sex, PCA of the 270dpf timepoint indicates that males and females do not separate out along PC1, but they can be separated on PC2 (Figure 36). PC1 appears to distinguish between the known sexes (males and females) and the single unknown individual in the dataset. Negative PC1 values are associated with the unknown individual with a robust jaw, small spines, concave head, and forward arching body while positive PC1 values are associated with the known individuals with large spines, convex head, and upward arching body. Similarly,

PCA of the adult timepoint coloured by sex shows that PC2 separates the males from the females while PC1 appears to separate the majority of the group from an outlier (Figure 37). When this outlier is removed, a similar trend is shown with PC2 separating out the sexes and the two unknown individuals locating in the middle of the two groups (Figure 38). Negative PC1 values correspond to a large head with large eyes and a small pelvis wile positive PC1 values correspond to a small head and a large pelvis.

PC1 in the OL dataset (Figure 39) explains 23.6% of the variance and PC2 explains 14.3% of the variance. In terms of the graphical output, however, a pattern is not discernable. Positive

PC1 in this dataset is associated with a shortened snout and flat cranium while negative PC1 is associated with a lengthened snout and a convex cranium.

When coloured by sex, PCA of the 270dpf timepoint indicates that some separation exists between males and females along PC1 (Figure 40). Negative PC1 is associated with male individuals and some female individuals with a long slender head, large eyes, long snout, and narrow body while positive PC1 values are associated with female individuals with a short broad head, small snout, and deep body. Separation between the sexes is much more apparent in the adult dataset coloured by sex (Figure 41). Negative PC1 values correspond to male individuals with a large head, robust jaw, large spines and pelvis, and slender body while positive PC1 values correspond to female individuals with a petite head, short snout, smaller spines, and deep body.

Finally, in the RB dataset (Figure 42), PC1 explains 27.8% of the variance and PC2 explains 19.0%. This dataset, which includes one extra timepoint of 60dpf, shows the most impressive graphical output of those discussed here. The 90, 180, and 270dpf fish are associated with negative PC1 values. The adult timepoint is tightly clustered on the positive side of zero in

PC1, and the 60dpf juveniles are spread out over a range of the most negative PC1 values. Positive

PC1 values are associated with a great enlargement of the eye with deepening ventrally. The jaw becomes smaller, making the snout appear more pointed and the head appear chisel-shaped.

Negative PC1 values indicate a shrinking of the eye and an enlargement of the jaw making the snout appear rounded.

When coloured by sex, PCA of the 270dpf RB fish indicates that some separation exists between males and females along PC1 (Figure 43). Negative PC1 is associated with most male individuals with a pointed snout, convex head, and forward arching body while positive PC1 is associated with female individuals with a squared snout, tubular head, and upward arching body.

PCA of the adult timepoint coloured by sex (Figure 44) shows that negative PC1 is associated with male individuals with slender heads and bodies and large spines while positive PC1 is associated with female individuals with robust heads and bodies and smaller spines.

When analyzed using a CVA, the GB population is statistically different for all age groups except 90dpf vs adult (Procrustes distance 0.02, p = 0.1752). Procrustes distances are as follows:

90dpf vs 180dpf (0.05, p = 0.0001), 90dpf vs 270dpf (0.05, p < 0.0001), 180dpf vs 270dpf (0.03, p = 0.0121), 180dpf vs. adult (0.03, p = 00009), and 270dpf vs adult (0.03, p = 0.0005). The KL population is statistically significant for all age groups: 90dpf vs 180dpf (0.04, p < 0.0001), 90dpf vs 270dpf (0.03, p = 0.0118), 90dpf vs adult (0.03, p = 0.0002), 180dpf vs 270dpf (0.03, p =

0.0017), 180dpf vs adult (0.05, p < 0.0001), and 270dpf vs adult (0.05, p < 0.0001). Conversely, in the OL population only the 3dpf vs 6dpf comparison is significant (0.04, p = 0.0135), while all other comparisons are not. Finally, all comparisons in the RB population are significant with p values at or near < 0.0001. Procrustes distances are as follows: 60dpf vs 90dpf (0.10), 60dpf vs

180dpf (0.10), 60dpf vs 270dpf (0.09), 60dpf vs adult (0.06), 90dpf vs 180dpf (0.06), 90dpf vs

270dpf (0.04), 90dpf vs adult (0.04), 180dfp vs 270dpf (0.05), 180dpf vs adult (0.07), and 270dpf vs adult (0.05).

3.2.3 By Age

Because only RB fish were sampled at the 60dpf timepoint, no analyses for the 60dpf timepoint are reported here.

Regression on centroid size for 90dpf indicates that 7.5% of variation is predicted by centroid size (p = 0.0229). 13.0% is predicted for the 180dpf timepoint (p = 0.0001), while 25.1% is predicted for the 270dpf and adult timepoints (p < 0.0001).

For the 90dpf timepoint, PCA shows that the fish differ by population (Figure 45). PC1 explains 47.6% of the variance while PC2 explains 11.9% of the variance. Along PC1, the KL population is associated with negative values and the marine populations GB, OL, and RB are associated with positive values. PC2 separates the marine populations with OL associated with the most positive values, GB associated with positive values near zero, and RB associated with negative PC2 values. Positive PC1 values are associated with a shortening of the snout and an upward rotation of the rostral portion of the head, making the snout appear turned up. The opercle is small and rotated forward, and the pelvis and spines are markedly enlarged. The body is deep around the pelvis. From a dorsal view, the head is short and narrow and the spine plates are considerably enlarged. Negative PC1 values are associated with an enlargement, down-turning and deepening of the snout. The head is shallow and long, and the opercle is enlarged and rotated caudally. The pelvis and pelvic spines are small and short. From a dorsal view, the head is lengthened and widened and the pelvis is narrowed. PC2 mostly explains body depth, with positive

PC2 associated with a deepened body and negative PC2 associated with a long narrow body.

PC1 explains 39.8% of the variance for the 180dpf timepoint (Figure 46), while PC2 explains 16.1% of the variance. Once again, KL is associated with negative PC1 values while the marine populations are associated with positive PC1 values. PC2 separates the marine populations with OL spread along the positive PC2 values and GB and RB clustered together in the negative

PC2 values. Positive PC1 values are associated with a shortening of the snout and a deepening of the body about the pelvis. The pelvic spines are enlarged and lengthened. From a dorsal view, the head is short and narrow and the pelvis is wide. Negative PC1 values are associated with an enlarged and downturned snout with a lengthened head. The pelvis is small while the body is narrow and long. From a dorsal view, the head is long and wide while the pelvis is narrow.

In the 270dpf timepoint, PC1 explains 28% of the variance and PC2 explains 23% of the variance (Figure 47). The graphical output is less concise than the earlier two described, but the

KL population is still associated with negative PC1 values and the marine populations associated with positive PC1 values. Similarly, along PC2, RB is associated with positive values while the

GB and OL populations are spread more broadly through the low positive and negative values.

The 270dpf timepoint shows very similar trends to those seen in 90 and 180dpf. Positive PC1 values are associated with a shortened and upturned snout and a deep body with a large pelvis.

Dorsally, the head is narrow and pointed. PC2 is associated with a large downturned snout, large eye, and narrow streamlined body with a small pelvis. Dorsally, the head is widened.

Finally, in the adult dataset PC1 explains 22.3% of the variance and PC2 explains 18.5% of the variance (Figure 49). Unlike the other PCAs described in this section, KL and RB are associated with negative PC1 values and OL and GB are associated with positive PC1 values. PC2 separates RB with more positive values from KL with more negative values. PC1 is associated with marked brachycephaly and a small eye. The body is deep, with a large pelvis and robust pelvic spines. From a dorsal view, the head is narrow and pointed and the pelvis is wide. Negative PC1 values are associated with a long, narrow head with an enlarged eye and beak-like downturned snout. The body is also narrow with a small pelvis and small spines. From a dorsal view, the snout is square and broad, and the head is neither narrow nor wide. The pelvis and spines are small and thin.

3.2.4 Pooled

When pooled together, all populations and all age sets, a regression on centroid size predicts 21.5% (p < 0.0001).

PCA shows that the fish as a whole differ by population more than they differ by age

(Figures 51-53), with PC1 explaining 31.6% of the variance and PC2 explaining 12.7%. In terms of age, PC1 shows a clustering of age sets 90, 180, and 270dpf as well as adults around zero. The

60dpf RB fish are separated out from the rest, and are clustered together at the end of the positive range of PC1. Populations are parsed much more strikingly. KL is associated with negative PC1 values while the marine populations GB, OL, and RB are associated with positive PC1 values.

PC2 separates the marine populations, with positive values associated with RB and negative and zero values associated with GB and OL. The positive PC1 values, which represent the marine phenotypes, appear to be associated with a shortening of the most rostral portion of the face without a strong effect on eye shape. The opercle is smaller and rotated forward. The pelvis and pelvic spines are deepened ventrally, widened laterally, and markedly enlarged. From a dorsal view, the head is narrowed and shortened, making it appear microcephalic. Overall the general shape change in positive PC1 is a smaller head and a larger, deeper body. The negative PC1 values, which represent the KL population, appear to be associated with a lengthening of the rostral face and an enlargement of the eye and nostril. The maxilla is projected forward, giving the fish an overbite.

The opercle and jaw are larger, but the pelvis and pelvic spines are markedly shrunken. From a dorsal view, the head is longer and wider while the dorsal spines are smaller and shorter. Overall the general shape change in negative PC1 is a larger head and a narrower body with smaller spines.

In PC2, positive PC values which represent the RB population, are associated with a narrowing of the body, making the fish appear more streamlined. Minimal change is seen in the head. GB and

OL, which associate with negative PC2 values, have bodies that are much deeper and more robust.

When coloured by sex, PCA of the 270dpf timepoint for all populations pooled shows that sexes do not separate out cleanly along either PC1 or PC2 (Figure 48). Conversely, the adult

42 timepoint for all populations pooled shows that sexes can be separated along PC2 (Figure 50).

Finally, with all timepoints put together for all populations, the variance between populations is much stronger than the variance between sexes, so sexes cannot be separated out in a PCA (Figure

53).

The values from the CVA indicate that every comparison between populations is significant (p < 0.0001). Procrustes distances are as follows: GB vs KL (0.08), GB vs OL (0.02),

GB vs RB (0.03), KL vs OL (0.08), KL vs RB (0.08), and OL vs RB (0.04).

Due to suspected noise of some landmark points, spine landmarks in particular, the datasets were next analyzed without the highly variable (in comparison to other landmarks) points.

Differences between regression, PCA, and CVA between the cleaned dataset and the raw dataset were minimal, however, so the data containing the extra landmarks are not reported here.

Chapter 4 - Discussion

In this thesis, threespine stickleback morphology was characterized to establish developmental timelines for four different populations of fish. Results suggest that stickleback populations may begin to exhibit population-specific phenotypes as early as a few days post fertilization. Given the extremely small size and apparent amorphous nature of embryonic fish, this finding is surprising and intriguing. Furthermore, µCT scans of juvenile fish indicate that a completely ossified skeleton is formed somewhere between 30 and 90dpf, much later than the population-specific phenotypes begin to appear in the data. This suggests that stickleback soft tissues pattern phenotypic variation long before the skeleton has begun to form.

Although the enormous amount of variation within the threespine stickleback species is widely accepted and increasingly well characterized (Hagen and Gilbertson 1972, Moodie 1972,

Moodie and Reimchen 1976, Gross 1978, Reimchen 1980, Wootton 1976, 1984, Bell et al. 1985,

Francis et al. 1986, Schluter and McPhail 1992, Bell et al. 1993, McPhail 1993, Bell and Foster

1994, Bourgeois et al. 1994, Bell and Orti 1994, Walker 1996, Willacker et al 2010), very little is known about stickleback developmental timelines. Most of the literature used to inform the decisions made in this thesis, for example, came from studies in related teleost species (Britz and

Johnson 2002, Westerfield 2007), as stickleback-specific information could not be found. Since the completion of data collection for this paper, a single article has appeared in the literature exploring the timing of armor, pelvic, and spine ontogeny in threespine stickleback (Currey et al.

2017). Currey et al.’s (2017) paper explores questions related to the ones presented here, but was published too late to inform my experimental design. The lack of understanding of stickleback life cycles is further evidenced by the discordance between different definitions of ‘adult’ in the literature (Baker 1994, Hagen 1967, Mori 1987, Snyder 1991, Wootton 1994). Length and age

44 standards are commonly used as exclusion criteria to denote ‘adulthood’ (Baker 1994) despite the inherent variation seen across stickleback populations (Hagen and Gilbertson 1972, Moodie 1972,

Moodie and Reimchen 1976, Gross 1978, Reimchen 1980, Wootton 1976, 1984, Bell et al. 1985,

Francis et al. 1986, Schluter and McPhail 1992, Bell et al. 1993, McPhail 1993, Bell and Foster

1994, Bourgeois et al. 1994, Bell and Orti 1994, Walker 1996, Willacker et al 2010). For my research, adulthood was simply assumed to be fish in condition for sexual reproduction. Because these fish were wild caught, their exact ages are not known.

While phenotypic plasticity in response to natural selection is presumed to play a role in the threespine stickleback’s ability to rapidly and effectively colonize new environments (Schluter

2000, Morris and Rogers 2014) the timeline along which environmental pressures act, and the role of development in structuring the necessary phenotypic variation, remains unclear. Many of the studies exploring these concepts have used adult threespine stickleback (Day et al. 1994, Day and

McPhail 1996) and other related teleost species (Schneider et al. 2014). It seems intuitive, though, to follow these mechanisms to the source – development and ontogeny. Due to the inherent flexibility of soft tissues in developing fishes, it is possible that juvenile stickleback are under stronger selection than adults. These growing fishes, then, are an obvious area to focus our research.

4.1 Population-Specific Variation is Present in 2D Explorations of Threespine Stickleback

Due to lack of ossified tissue, fish from timepoints 5dpf through 90dpf were analyzed using

2D morphometric methods. In terms of dorsal morphology, the youngest fish analyzed (5dpf through 12dpf) differ more strongly by age than by population (Figures 10-31). Although CVA shows statistical significance to the between-population comparisons for all but OL vs RB, PCA shows very weak separation by population along the first principal components (Figures 30, 31).

This suggests that at these very early timepoints, population specific phenotypes are likely forming, but are not yet developed enough to produce more variation than the age differences do.

Interestingly, even within the youngest (5dpf) fish, the four different populations can be distinguished by PCA (Figures 14, 21). Although surprising, this finding suggests that fish as early as a few days post fertilization have already begun to diverge phenotypically, long before the skeleton begins to ossify.

Another interesting trend observed in the 2D data was the dissimilarity in population phenotypes observed in the dorsal versus the lateral landmark sets. While the dorsal sets appear to present the emergence of phenotypic variation between populations as early as 5dpf (Figure 14), the lateral sets do not show divergence until much later (Figures 21-25). This is especially interesting when we consider that the same fish were used for each view. The finding of early divergence in dorsal landmarks and later divergence in lateral landmarks suggests that the dorsal aspect of the fish develops variation first. This is not surprising when we consider the general trajectory of embryonic growth, regardless of species. Animals tend to develop important midline structures such as the head and central nervous system first, and lateral structures such as appendages much later (Roberts 1971). The threespine stickleback is likely to follow this ubiquitous pattern.

4.2 Population-Specific Variation is Present in 3D Explorations of Threespine Stickleback

3D morphometrics, completed on fish 60dpf and older, indicates that by 90dpf or 3 months of age juvenile stickleback have produced a nearly complete skeleton and have attained the population specific adult form (Figure 45). Both PCA and CVA reflect this trend, and also suggest that while KL and RB populations possess distinct morphology, GB and OL populations are more similar. This is unsurprising as the GB and OL populations represent fish from close geographical

46 locations along the coast, with presumably more similar environmental pressures, genetic relatedness, and gene flow. KL population represents a lacustrine phenotype. The RB population is a marine fish from completely open ocean waters that differ tremendously from Oyster and

Garden Bay Lagoons, so its dissimilarity from the other three populations is not unexpected.

The most striking thing about the 3D dataset is perhaps the 60dpf RB fish. Not only do these fish possess extreme variation in ossification (half of the fish having nearly complete skeletons and half having nothing but a cleithrum), but the 60dpf timepoint appears to be significant for a shift in population morphology. PCA of the entire 3D dataset (Figure 52) shows that timepoints 90dpf through adulthood are nearly indistinguishable from one another, suggesting that by 90dpf the juveniles have attained a morphology matching that of the adult populations included in the dataset. While it does cluster near the rest of the RB dataset, the 60dpf timepoint for RB appears to be distinct. Although conclusions cannot be extrapolated to the other three populations included in this study, it does appear that for RB fish at least, 60dpf could be the timepoint at which the population specific form is developing, but not yet finalized. This trend is reflected even more clearly in the separately analyzed RB dataset (Figure 42).

When analyzed separately, the marine populations retain the trend described above

(Figures 32-34, 39-44). With the exception of the 60dpf timepoint, juvenile fish are nearly indistinguishable from adult fish based on skeletal morphology. This supports the hypothesis that they have attained the adult form. A separate PCA of the KL population, however, shows a slightly different trend (Figures 35-38). It appears that KL can be distinguished by age along PC1, which suggests that KL fish may not attain the adult form as early as their saltwater counterparts. This finding is unexpected given that the literature suggests that fish with reduced armor plating should grow faster in fresh water than fish with extensive plating (Baker 1994). This finding is supported,

47 however, by the hypothesis that larger fish will experience more rapid skeletal and morphological maturation. The KL fish are generally smaller than the anadromous and marine fish, so perhaps their delayed maturation is simply a reflection of their smaller size.

4.3 Population-Specific Phenotypes Appear Early

Although this research was designed around the assumption that the threespine stickleback attains an adult-type phenotype early in development, the immediacy of phenotypic divergence in the developing fish was surprising. As discussed in the results section, populations as early as 5dpf can be distinguished by PCA (Figures 14, 21) especially from the dorsal view. Embryos at this stage are little more than heads on yolk sacs, and certainly contain no discernible hard tissue. From an evolutionary perspective, the appearance of early phenotypes suggests that selection is acting on juvenile fish as well as their adult counterparts. Conceptually, the idea that selective pressures are acting on ‘little fish in big ponds’ makes a lot of sense. The tiny, friable juveniles that have not yet developed adult armor or spines, and do not possess the speed required to escape predators, are likely a food source for everything in the vicinity, including piscivorous fish (Bell and Foster

1994), aquatic insects, and predatory birds (Bell et al. 1993).

4.4 Sexual Dimorphism is not Consistent Across Populations

An interesting trend observed in this data was the differences in sexual dimorphism across populations. Madeira Park populations – Garden Bay Lagoon and Oyster Lagoon – appeared to possess sexual dimorphism as early as 270dpf, with male fish displaying the typical large head, long snout, robust jaw, and slender body, and females displaying the relatively small head and snout, smaller jaw, and robust body (Figures 33, 40). Roquefeuil Bay and Klein Lake populations, however, were not as cleanly separated by sex (Figures 36-38, 43, 44). Some of the expected

48 differences in head and body shape were apparent in these populations, but sexual dimorphism did not have as strong an effect on variation.

The geographic proximity of the GB and OL populations, as discussed, makes phenotypic and life history similarities unsurprising. It is unclear, however, how the RB and KL populations fit into this picture with respect to sexual dimorphism. Perhaps the darker, deeper waters of Klein

Lake and Roquefeuil Bay make the appearance of the male stickleback less important in the female’s selection of a mate. We know that male stickleback are responsible not only for egg fertilization, but also for nest construction and egg protection while females simply produce and deposit the eggs (Whoriskey and FitzGerald 1994). Perhaps this division of labour is more pronounced in the Madeira Park stickleback, necessitating more variation in sexual phenotype.

Research by Reimchen (1980, 2004) also suggests that some populations of stickleback display so much sexual dimorphism that the two sexes occupy separate niches within a single habitat. In the populations Reimchen studied, female stickleback occupy open water niches while male fish prefer benthic niches (1980, 2004). This habitat preference is also associated with differing selective pressures in terms of predation and diet (Reimchen 1980, Reimchen and Nosil 2004). It follows that populations such as these display marked sexual dimorphism. Madeira Park stickleback could perhaps have similar niche divergence and therefore similar effects on sexual dimorphism. Finally, it is possible that the trends seen here are simply an artefact of small sample size. More research is needed to parse out these questions.

It is interesting to note that the observed sexual dimorphism within the stickleback skeleton was developed long after the first indications of skeletal phenotypic divergence between populations. This perhaps suggests that different mechanisms are involved in the patterning of population-specific phenotype vs sexual dimorphism, and that the presence of dimorphism within

49 a population is less related to early adaptability and survival than is the population-specific phenotype. Sexual dimorphism, it seems, is not adaptive in the very young stickleback. The appearance of sexual dimorphism in the juvenile skeleton is perhaps related to the age of sexual maturity, or potentially coincides with the time at which the sexes begin to occupy separate niches within the ecosystem as discussed above. Conversely, population-specific phenotype may be beneficial much earlier on, and is likely related more strongly to successful adaptation to environmental pressures and subsequent survival. It is also possible that the population-specific phenotype within the skeleton must be relatively well developed to form a sexually dimorphic fish.

Perhaps the population-specific phenotype is the foundation upon which other, more advanced skeletal phenotypes such as sexual dimorphism, are built.

Differences in sexual dimorphism between the populations used in this study suggests that some component of environment makes dimorphism more or less favourable. Because the production and maintenance of sexually dimorphic traits – brilliant blue eyes and red throats in male stickleback for example – is costly, it is possible that the benefit to fastidious mate selection is not enough to outweigh the value of a well-adapted feeding apparatus and ability to efficiently evade predators in some populations. Perhaps the populations with less marked dimorphism are under stronger selection pressures from predation or food competition, and cannot afford to ‘waste energy’ on sexual dimorphism. It is also possible that these populations are using methods of mate selection less visible to the human eye and more suited to the environments in which they live.

While highly visible males “make sense” in the open waters of Oyster Lagoon and Garden Bay

Lagoon, perhaps the shallower waters of Klein Lake better lend themselves to olfactory or auditory methods of mate selection. These phenotypes, of course, would not be obvious in the design of my study.

4.5 Implications of Lab Rearing Juveniles on Phenotype

The observation that all of the juveniles reach a phenotype statistically similar to that of the wild-caught adults suggests that the practice of lab rearing of fish in wildly different conditions than they would naturally encounter does not significantly affect the population-specific phenotype, at least not in the F1 populations. This is useful to know given the frequent necessity of lab-rearing fish for research.

It is important to note, however, that the very characteristic that makes the stickleback an ideal species for the study of evolution also makes for some difficulty interpreting the data collected here. Each population used in this research was utilized in the same way. Adult fish were collected in the wild, brought back to the lab and crossed, and their F1 progeny were lab raised under common garden conditions. Although this setup represents the ‘best we could do’ in a sense, and steps were taken to ensure that each population was treated similarly, it is difficult to say for certain that each population responded to the situation in the same way. Realistically, this research cannot be done in the field.

The fact that two out of the six populations originally collected for this project never came into condition in the lab – and therefore had to be excluded from the study – suggests that the fish experienced the lab environment in a non-uniform manner. The dissimilarities experienced by the adult populations likely also extends to their progeny. The developing stickleback not only come from a genetic background geared towards the surroundings from which their parents originated, but they also possess a ‘plastic toolbox’ meant to help them adapt to unexpected environments.

The classic, population-specific phenotypes seen in the wild populations are likely produced by an interplay between the stickleback’s genetic background and its plastic responses to the environment in which it grows. Our current knowledge shortage makes prediction of the

51 mechanisms at play very difficult. It is possible, however, to predict that at least some component of the phenotype may have become obscured by its growth in an unnatural environment.

Chapter 5 - Limitations

There are a number of limitations in this study. The lack of ossified tissue in 5dpf through

60dpf age sets made 3D landmarking of the very young juveniles impossible. Although 2D morphometrics is a tried and true method of comparing shape data, 3D morphometrics was the goal of this study and comparison of an entire, completed set of stickleback populations through all of the timepoints would have been more ideal. An attempt was made to stain the young embryos with IKI using methods used in previous papers (Pistore et al. 2016) but the stickleback chorion proved IKI impenetrable and could not be removed without mutilating the fish.

Other potential methods for characterizing phenotype in unossified fishes include: optical projection tomography (OPT) scanning, measurement of features by hand, and general characterization of phenotype by description. These methods are either less accessible and more time consuming (OPT), or less precise (measurement and description) than the chosen 2D and 3D characterization, however. Furthermore, they can be hampered by fish-specific factors such as rotation, measurement error, and investigator fatigue in the case of measurement; and skin pigment inhibiting optical measurement in the case of OPT.

Although it may be the best method available to us, 3D geometric morphometrics has its limitations as well. Unfortunately, the shape data associated with an entire organism is much too complex to characterize and statistically analyze with modern computers. Landmarking of the organism, therefore, simplifies the shapes within biology and makes statistical analyses possible.

The simplification of the organismal form, however, results in an inherent loss of phenotypic information, and results in statistical analyses of incomplete form. Furthermore, variation between, and even within populations of stickleback makes accurate placement of landmarks difficult. This is illustrated by the statistically significant difference between the two 3D landmarkers (T. Barry

53 and A. Pistore) described earlier. Although the two individuals were landmarking the same fish, with the same set of landmarks, and had discussed the landmark placement thoroughly, we were still unable to produce landmark sets that matched. The variation introduced into the dataset by landmark placement, therefore, is another limitation of this method.

Working with live animals is always challenging, and the threespine stickleback is no exception. The relative incompleteness of the different timepoints reflects issues encountered not only in obtaining fish in our lab in Alberta, but also in keeping the adult fish alive to reproduce, bringing adult fish into condition, effectively crossing adults and fertilizing embryos, and keeping successfully fertilized embryos alive for as many as 9 months. The RB population is easily the hardiest, most suited to life in a lab, and most fertile, and this can be seen in the near perfect completeness of this dataset.

On a related note, the limitations of working with live fish also placed restrictions on sample size. While it would have been nice to have a more complete dataset, as discussed above, an ideal study design would also include far more fish at each timepoint. This would allow for the appropriate power in statistical analysis. It is possible that, given an appropriately large sample size, more of the comparisons between populations would have been significant. The barriers to massive sample sizes, however, are numerous and include the cost of housing and feeding hordes of fish, the time constraints related to caring for them, the short breeding season and intolerance to lab environments restricting egg production in adults, and a simple lack of space, among others.

Perhaps the most disappointing shortcoming of this study is the lack of a complete 60dpf dataset. The 60dpf timepoint was added as an afterthought when it became clear that the majority of ossification was occurring between 30 and 90dpf. Unfortunately, by the time the need for this

54 timepoint was recognized, only the hardy little RB population was still coming into condition and producing eggs.

Another limitation is the inclusion of only a single lacustrine population. Additional lacustrine populations – Hotel and Sarita Lakes - were captured in the field and successfully brought back to the lab, but no successful crosses were obtained. The inclusion of only a single lacustrine population in this study results in a very narrow view of lacustrine variation. As discussed earlier, lake environments are sufficiently different to produce two recognized subgroups of stickleback form - shallow-dwelling benthic foragers, and limnetic populations from deeper lakes (Bell 1976, Lavin and McPhail 1985, Gow et al. 2008, Harmon et al. 2009, Park and

Bell 2010, Arnegard et al. 2014). The lack of lacustrine populations in this study makes characterization of these subgroups and others very difficult.

Chapter 6 - Future Directions and Significance

In a perfect world, this research would be expanded to include a greater sample of both marine and lacustrine populations, as well as to consider the addition of anadromous populations of stickleback. This would allow for the characterization of a greater range of stickleback form, and would help shed light on the concept of parallel evolution in this species.

In the future, a more complete age range – especially timepoints in the critical ossification stages of the stickleback between 30dpf and 90dpf – from a larger set of populations would help parse out the differences in development between marine vs anadromous vs lacustrine and give a more clear picture of development across age groups. Ideally, the fish should all be compared using a single modality, even more ideally a 3D modality. To achieve this goal, advances in technology would need to occur such that pre-ossified, cartilaginous precursor skeletons could be visualized in a uCT and landmarked.

It would also be interesting to expand this study beyond the controlled environment of the lab to different developmental environments. The effects of salinity, temperature, light, etcetera on development would be an important and enlightening direction to follow.

This study has contributed to our understanding of development in the threespine stickleback, allowing us insight into the amazing variation observed in the species and offering important data to scientists completing stickleback research in the future. To my knowledge, this is one of the first studies to attempt to quantify stickleback development and ontogeny. Our success may help pave the way for similar research in the future.

Furthermore, the data from this study suggests that threespine stickleback begin to diverge phenotypically much earlier than expected and long before the hard tissues of their skeleton have ossified. This is interesting from a species perspective. As already stated, the threespine

56 stickleback is nearly unparalleled in its ability to adapt to, colonize, and flourish in new environments. Perhaps the fish’s success in this venture is related to its early expression of phenotype as shown here, and its flexible relationship with evolution.

A strong understanding of development and ontogeny in this species will help future researchers make more informed decisions about their choice of representative populations to study and may help form more concrete definitions of ‘adult’. The results from my research suggest that very early development patterns the phenotype of the adult fish. This information may inform future scientists hoping to lab-rear juvenile stickleback.

The data presented here has also given us insight into the patterning of sexual dimorphism in the threespine stickleback. Stickleback sexual dimorphism has been extensively studied in the literature. Different populations of stickleback have displayed dimorphism in ornamentation

(Yong et al. 2016), niche occupation (Reimchen 1980, Reimchen and Nosil 2004), nest construction and egg husbandry (Reimchen 1980), feeding mechanics (McGee and Wainwright

2013), protein expression (Viitaniemi and Leder 2011), body shape (Leinnonen et al. 2006), and armor (Moodie 1972, Reimchen 1980) and are known to vary across habitats (Spoljaric and

Reimchen 2008) and over time (Aguirre et al 2008). Although much has been revealed about sexual dimorphism in the adult threespine stickleback, the developmental patterning of dimorphism remains largely undiscovered. In the populations studied here, the relatively late

(270dpf vs 5dpf) appearance of sexual dimorphism compared to adult-specific phenotype suggests that not only are the two developmentally distinct, but that population-specific phenotype is more important to the survival of juvenile fish. This is unsurprising, since sexual dimorphism is a costly practice and likely isn’t beneficial to juvenile stickleback.

From a bigger picture perspective, this research is some of the first in the long process of understanding the relationship between development and evolution. If the early appearance of phenotypic patterning in the threespine stickleback is directly related to its ability to rapidly evolve, it would follow that other highly evolvable, phenotypically plastic species might display similar early development and ontogeny. Furthermore, we might expect species that are less evolutionarily successful to produce phenotypes later in development, or by different mechanisms. Similarly, an understanding of the relationship between adult skeletal phenotype and sexual dimorphism in the stickleback can likely be extended to other species. Very broadly, a greater understanding of ontogeny and development as it relates to evolution can be applied to humans as well. We, along with stickleback, have been very successful at colonizing diverse habitats across the globe, although we have the advantage of culture and technology. Perhaps the little fish will allow us to gain greater insight into our own species.

Tables

Table 1 – A breakdown of the number of fish available for each population at each timepoint. Time Point GB KL OL RB 5dpf 10 10 4 10 6dpf 10 4 10 7dpf 10 4 10 12dpf 10 1 10 30dpf 10 10 10 10 60dpf 10 90dpf 10 10 10 10 180dpf 10 10 10 10 270dpf 10 10 10 10 Adult 50 50 50 50 Total 130 101 102 140 Table 2 – List of landmarks used to evaluate left/right morphology. Landmarks from this list were placed on the right side of the body and then repeated on the left side. Landmark Description 1 Anterior tip of dentary 2 Anterior tip of premaxilla 3 Anterior tip of maxilla 4 Anterior corner of nasal ventrolateral process 5 Dorsal corner of nasal-lateral ethmoid suture 6 Dorsal maximum of lachrymal 7 Lacrimal-prefrontal suture on orbital 8 Anterior tip of articular 9 Ventral maximum of lachrymal 10 Dorsal tip of articular 11 Ventral-most tip of articular 12 Lacrimal-second orbital suture 13 Anterior tip of preoperculum 14 Dorsal-most tip of supraorbital 15 Ventral-most tip of sphenotic 16 Dorsal-most tip of third suborbital 17 Posterior minimum of third suborbital 18 Ventral-most tip of third suborbital 19 Anterior minimum of preoperculum 20 Anterior dorsal-most tip of preoperculum 21 Posterior maximum of preoperculum, first ridge 22 Ventral dorsal-most tip of preoperculum 23 Dorsal-most tip of interoperculum 24 Ventral maximum of preoperculum, second ridge

25 Ventral-most tip of interoperculum 26 Dorsal-most tip of suboperculum 27 Ventral maximum of suboperculum 28 Posterior tip of suboperculum 29 Dorsal-most tip of operculum 30 Anterior maximum of operculum 31 Anterior minimum of operculum 32 Ventral-most tip of operculum 33 Posterodorsal tip of operculum 34 Opercular hinge angle 35 Posterior tip of pterotic 36 Anterior tip of ectocoracoid 37 Posterior tip of ectocoracoid 38 Anterior tip of pelvic plate 39 Anterior midline of pelvic plate at suture point 40 Minimum of pelvic plate at trochlear joint 41 Maximum of pelvic process 42 Posterior tip of pelvic process 43 Anterior minimum of ascending process of pelvic plate 44 Anterodorsal maximum of ascending process of pelvic plate 45 Posterodorsal maximum of ascending process of pelvic plate 46 Posteroventral maximum of ascending process of pelvic plate at trochlear joint 47 Dorsal-most tip of pelvic spine 48 Ventral-most tip of pelvic spine 49 Posterior tip of pelvic spine 50 Midline of plate 4 at lateral pores 51 Ventral tip of plate 4 52 Midline of plate 5 at lateral pores 53 Ventral tip of plate 5 54 Midline of plate 6 at lateral pores 55 Midline of plate 7 at lateral pores

Table 3 – List of landmarks used to evaluate dorsal morphology. Landmark Description 1 Left posterior tip of first basal plate 2 Right posterior tip of first basal plate 3 Left ventral maximum of first basal plate 4 Right ventral maximum of first basal plate 5 Left anterior tip of first basal plate 6 Right anterior tip of first basal plate 7 Midline joint insertion of first dorsal spine 8 Left joint insertion of first dorsal spine 9 Right joint insertion of first dorsal spine

10 Left-most tip of first dorsal spine 11 Right-most tip of first dorsal spine 12 Posterior tip of first pelvic spine 13 Left posterior tip of second basal plate 14 Right posterior tip of second basal plate 15 Left ventral maximum of second basal plate 16 Right ventral maximum of second basal plate 17 Left anterior tip of second basal plate 18 Right anterior tip of second basal plate 19 Midline joint insertion of second dorsal spine 20 Left joint insertion of second dorsal spine 21 Right joint insertion of second dorsal spine 22 Left-most tip of second dorsal spine 23 Right-most tip of second dorsal spine 24 Posterior tip of second pelvic spine

Table 4 – List of extra landmarks Landmark Description 1 Right ventral-most tip of premaxilla 2 Left ventral-most tip of premaxilla 3 Right ventral-most tip of maxilla 4 Left ventral-most tip of maxilla 5 Right midpoint expansion of sphenotic 6 Left midpoint expansion of sphenotic 7 Right dorsal-most tip of posttemporal 8 Left dorsal-most tip of posttemporal 9 Right ventral-most tip of posttemporal 10 Left ventral-most tip of posttemporal 11 Right dorsal-most tip of cleithrum 12 Left dorsal-most tip of cleithrum 13 Right ventral-most tip of cleithrum 14 Left ventral-most tip of cleithrum 15 Right maximum of supraoccipital 16 Left maximum of supraoccipital 17 Right base of supraoccipital crest 18 Left base of supraoccipital crest 19 Dorsal-most tip of premaxilla 20 Rostral-most tip of supraoccipital 21 Caudal-most tip of supraoccipital 22 Tip of supraoccipital crest 23 Rostral-most tip of first dorsal plate 24 Rostral-most tip of second dorsal plate 25 Caudal-most part of parasphenoid in minimum between supporting arms

Figures

A B D

E F

Garden Bay Lagoon Klein Lake Oyster Lagoon Roquefeuil Bay

5dpf

6dpf

7dpf

Garden Bay Lagoon Klein Lake Oyster Lagoon Roquefeuil Bay

12dpf 30dpf 90dpf

30dpf 60dpf 90dpf Figure 8 - MicroCT scans from the lateral view of 30, 60, and 90dpf juveniles for each of the four populations. Note the variation in ossification at the 30dpf timepoint, as well as the variation in phenotype at the 90dpf timepoint. The 60dpf timepoint is availabl e only for the RB population. The most well ossified specimen was chosen from each population at each timepoint for this diagram. Rostral is left.

Roquefeuil Bay Oyster Lagoon Klein Lake Garden Bay Lagoon Garden Bay Klein Lake Lagoon Oyster Bay Roquefeuil

A B Figure 9- MicroCT scans of an Oyster Lagoon adult showing 3D landmark placement. 159 landmarks were placed in an attempt to characterize the entirety of the scanned skeleton. (A) Landmarks from the left lateral view – landmarks were placed reciprocally on the right side of each specimen; (B) Landmarks from the rostral view; (C) Landmarks from the ventral view; and (D) Landmarks from the dorsal view. Rostral is left (A, C, D) and C out of the page (B).

Figure 17 - Principal components analysis of 2D dorsal landmark data from the 7dpf timepoint with the GB outlier removed. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to the OL population with a broad short head, short nose, and small eyes while positive PC1 values correspond to the RB population with a longer head with large eyes. The GB population is spread across PC1. Rostral is left.

Figure 25 - Principal components analysis of 2D lateral landmark data from the 30dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a concave head, long body, and humped dorsal line while positive PC1 values correspond to a concave head and short body. Rostral is left.

Figure 32 - Principal components analysis of 3D landmark data from the Garden Bay Lagoon population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large head with large eyes and jaw while positive PC1 values correspond to a small head with correspondingly small eyes and jaw. Timepoints do not separate out cleanly. Key denotes months of age. 3 corresponds to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left.

Figure 39 - Principal components analysis of 3D landmark data from the Oyster Lagoon population. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to a large, long head and slender body while positive PC1 values correspond to a small, short head and deep body. The timepoints do not separate out cleanly. Key denotes months of age. 3 corresponds to 90dpf, 6 to 180dpf, and 9 to 260dpf while A stands for adult. Rostral is left.

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Figure 45 - Principal components analysis of 3D landmark data from the 90dpf timepoint. Wire frame images show phenotype trends corresponding to principal component 1. Negative PC1 values correspond to KL with a large head and jaw, small spines, and slender body while positive PC1 values correspond to the marine populations with a smaller head and jaw, larger spines, and deeper body. Rostral is left.

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Recipes and Protocols

Agarose Gel Protocol

1) Mix 1g agarose and 100mL 0.5 Tris-borate-EDTA (TBE) buffer in a 250mL flask.

2) Microwave on high for 20 seconds at a time until fully dissolved, swirling between.

3) Allow to cool for 5-10 minutes.

4) Add 10µL SYBR Green and stir.

5) Pour into a mould, remove any bubbles with a pipette tip, and add appropriate lanes.

6) Allow to cool and set for 15 minutes until gel is slightly cloudy.

7) Add the gel to the electrophoresis machine and cover completely with used TBE buffer.

8) Load: 5µL of product + 1µL of blue juice (ThermoFisher Scientific) per lane. Add

appropriate ladder.

Brine Shrimp Hatching Protocol

1) Create ~25ppt salinity water by mixing Instant Ocean Aquarium Salt with water.

2) Add ~1Tbsp of artemia cysts per 2L of salt water in a hatching cone.

3) Set an air stone in the very bottom of the hatching cone for adequate circulation and shine

a desk lamp on the hatching cone to provide heat.

4) Allow the hatching cone to circulate for 48 hours, then remove the air stone and allow the

shrimp to settle. Leave the desk lamp shining on the cone.

5) After 5-10 minutes, all unusable waste products should have risen to the top, and there

should be a dense orange cloud of hatched artemia swimming close to the light from the

lamp. Using a turkey baster, suck up the orange cloud and surrounding salt water, and

transfer it to another holding container.

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6) These hatched artemia may be either: fed immediately to juvenile fish, frozen in ice cube

trays for future feeding, or kept in the secondary holding container with an air stone

circulating for up to 5 days.

Ethylenediaminetetraacetic Acid (EDTA 0.5M, pH=8) Recipe

1) Add 186.1g disodium ethylenediaminetetraacetate·2H2O to 800mL of H2O.

2) Stir vigorously with a magnetic stir bar.

3) Adjust the pH to 8.0 with NaOH (~20g NaOH pellets).

4) Dispense into aliquots and sterilize by autoclaving.

Ginsburg’s Ringer Solution Recipe

900mL ddH2O

6.6g NaCl

0.25g KCl

0.30g CaCl2

0.20g NaHCO3- add last

Mix well and bring up to 1L, autoclave.

Hank’s Solution Recipe

49.5mL Ginsburg’s ringer solution

0.25mL Gibco antibiotic/antimycotic (Invitrogen cat# 15240-096, 100x concentration)

0.25mL Gentamycin sulfate hydrate (Invitrogen cat# 15750-060, 10mL, 50mg/mL liquid)

Mix thoroughly by inversion in a falcon tube

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Polymerase Chain Reaction (PCR) Mix Recipe

IDH Primers developed by Peichel et al. 2004

Per sample:

6.5µL ddH2O

1µL NEB

0.25µL deoxyribose nucleoside triphosphate (dNTP)

0.5µL forward primer

0.5µL reverse primer

0.2µL Taq polymerase

1µL sample DNA

Polymerase Chain Reaction (PCR) Protocol

1st cycle: 1 minute 45 seconds 95°C

45 seconds 56°C

45 seconds 68°C

2nd-36th cycles: 45 seconds 94°C

45 seconds 59°C

45 seconds 68°C

Final: 5 minutes 68°C

Hold: 4°C

Tail Digestion Buffer Recipe

5mL Tris (pH=8.0)

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10mL NaCl (5M)

10mL EDTA (0.5M)

25mL SDS (10%)

450mL ddH2O

Tris (1M) Recipe

1) Dissolve 121.1g Tris base in 800mL H2O.

2) Allow solution to cool to room temperature, then adjust pH to 8.0 by adding concentrated

HCl.

3) Adjust the volume to 1L by adding H2O.

4) Dispense into aliquots and sterilize by autoclaving.

118

References

Aguirre WE, Ellis KE, Kusenda M, Bell MA (2008) Phenotypic Variation and Sexual Dimorphism in Anadromous Threespine Stickleback: Implications for Postglacial Adaptive Radiation.

Biological Journal of the Linnaean Society 95: 465-478

Alberch P (1982) Developmental Constraints in Evolutionary Processes. In: Bonner JT (editor)

Development in Evolution. Springer, Berlin and New York: p 313-332

Arnegard ME, McGee MD, Matthews B, Marchinko KB, Conte GL, Kabir S, Bedford N, Bergek

S, Chan YF, Jones FC, Kingsley DM, Peichel CL, Schluter D (2014) Genetics of Ecological

Divergence During Speciation. Nature 0: 1-5

Baker JA (1994) Life History Variation in Female Threespine Stickleback. Oxford University

Press, New York

Barrett RDH, Rogers SM, Schluter D (2008) Natural Selection on a Major Armor Gene in

Threespine Stickleback. Science 322: 255-257

Bell MA (1976) Evolution of Phenotypic Diversity in Gasterosteus aculeatus Superspecies on the

Pacific Coast of North America. Systematic Zoology 25: 211-227

Bell MA, Orti G (1994) Pelvic Reduction in Threespine Stickleback from Cook Inlet Lakes:

Geographical Distribution and Intrapopulation Variation. Copeia 1994: 314-325

Bell MA, Francis RC, Havens AC (1985) Pelvic Reduction and its Directional Asymmetry in

Threespine Stickleback from the Cook Island Region, Alaska. Copeia 1994: 314-325

Bell MA, Orti G, Walker JA, Koenings JP (1993) Evolution of Pelvis Reduction in Threespine

Stickleback: A Test of Competing Hypotheses. Evolution 47: 906-914

Bell MA, Foster SA (1994) The Evolutionary Biology of the Threespine Stickleback. Oxford

University Press, New York

119

Bemis W (1984) Paedomorphosis and the Evolution of the Dipnoi. Paleobiology 10: 293-307

Berka R (1986) The Transport of Live Fish: A Review. EIFAC. Food and Agriculture Organization of the United Nations. Rome.

Bininda-Emonds ORP, Jeffery JE, Richardson MK (2003) Inverting the Hourglass: Quantitative

Evidence Against the Phylotypic Stage in Vertebrate Development. Proceedings of the Royal

Society of London B 270: 341-346

Boughton DA, Collette BB, McCune AR (1991) Heterochrony in Jaw Morphology of Needlefishes

(Teleostei: Belonidae). Systematic Zoology 40: 329-354

Bourgeois JF, Blouw DM, Bell MA (1994) Multivariate Analysis of Geographic Covariance

Between Phenotypes and Environments in the Threespine Stickleback, Gasterosteus aculeatus, from the Cook Inlet Area, Alaska. Canadian Journal of Zoology

Britz R, Johnson GD (2002) “Paradox Lost”: Skeletal Ontogeny of Indostomus paradoxus and its

Significance for the Phylogenetic Relationships of Indostomidae (Teleostei, Gasterosteiformes).

American Museum Novelties 2282: 1-43

Cheverud JM (1996) Developmental Integration and the Evolution of Pleiotropy. American

Zoologist 36: 44-50

Cofran Z (2014) Mandibular Development in Australopithecus robustus. American Journal of

Physical Anthropology 154: 436-446

Colosimo PF, Hosemann KE, Balabhadra S, Villarreal GJ, Dickson M, Grimwood J, Schmutz J,

Myers RM, Schluter D, Kingsley DM (2005) Widespread Parallel Evolution in Sticklebacks by

Repeated Fixation of Ectodysplasin Alleles. Science 307: 1928-1933

120

Colosimo PF, Peichel CL, Nereng K, Blackman BK, Shapiro MD, Schluter D, Kinglsey DM

(2004) The Genetic Architecture of Parallel Armour Plate Reduction in Threespine Sticklebacks.

PLoS Biology 2(5): 635-641

Coyle SM, Huntingford FA, Peichel CL (2007) Parallel Evolution of Pitx1 Underlies Pelvic

Reduction in Scottish Threespine Stickleback (Gasterosteus aculeatus). Journal of Heredity 98(6):

581-586

Cresko WA, Amores A, Wilson C, Murphy J, Currey M, Phillips P, Bell MA, Kimmel CB,

Postlethwait JH (2004) Parallel Genetic Basis for Repeated Evolution of Armour Loss in Alaskan

Threespine Stickleback Populations

Day T, McPhail JD (1996) The Effect of Behavioural and Morphological Plasticity on Foraging

Efficiency in the Threespine Stickleback (Gasterosteus sp.) Oecologia 108(2): 380-388

Day T, Pritchard J, Schluter D (1994) A Comparison of Two Sticklebacks. Evolution 48(5): 1723-

1734

Del Castillo DL, Flores DA, Cappozzo HL (2014) Ontogenetic Development and Sexual

Dimorphism of Franciscana Dolphin Skull: A 3D Geometric Morphometric Approach. Journal of

Morphology 275: 1366-1375

Erickson PA, Glazer AM, Cleves PA, Smith AS, Miller CT (2014) Two Developmentally

Temporal Quantitative Trait Loci Underlie Convergent Evolution of Increased Branchial Bone

Length in Sticklebacks. Proceedings of the Royal Society B 281: 1-8

Foster SA, Baker JA (2004) Evolution in Parallel: New Insights from a Classic System. Trends in

Ecology and Evolution 19(9): 456-459

121

Franchini P, Fruciano C, Spreitzer ML, Jones JC, Elmer KR, Henning F, Meyer A (2014) Genomic

Architecture of Ecologically Divergent Body Shape in a Pair of Sympatric Crater Lake Cichlid

Fishes. Molecular Ecology 23: 1828-1845

Francis RC, Baumgartner JV, Havens AC, Bell MA (1986) Historical and Ecological Sources of

Variation Among Lake Populations of Threespine Stickleback, Gasterosteus aculeatus, Near Cook

Inlet, Alaska. Canadian Journal of Zoology 64: 2257-2265

Fruciano C, Tigano C, Ferrito V (2012) Body Shape Variation and Colour Change During Growth in a Protogynous Fish. Environmental Biology of Fishes 94: 615-622

Gibson G, Wagner G (2000) Canalization in Evolutionary Genetics: A Stabilizing Theory?

Bioessays 22: 372-380

Gow JL, Rogers SM, Jackson M, Schluter D (2008) Ecological Predictions Lead to the Discovery of a Benthic-Limnetic Sympatric Species Pair of Threespine Stickleback in Little Quarry Lake,

British Columbia. Canadian Journal of Zoology 86: 564-571

Griswold CK (2006) Pleiotropic Mutation, Modularity and Evolvability. Evolution &

Development 8: 81-93

Gross HP (1978) Natural Selection by Predators on the Defensive Apparatus of the Threespine

Stickleback, Gasterosteus aculeatus. Canadian Journal of Zoology 56: 398-413

Haeckel E (1905) The Riddle of the Universe at the Close of the Nineteenth Century. Translated by Joseph McCabe. Harper & Brothers Publishers, New York and London

Hagen DW (1967) Isolating Mechanisms in Threespine Stickleback (Gasterosteus aculeatus).

Journal of the Fisheries Research Board of Canada 24: 1637-1692

Hagen DW and Gilbertson LG (1972) Geographic Variation and Environmental Selection in

Gasterosteus aculeatus in the Pacific Northwest, America. Evolution 26: 32-51

122

Hall BK (1999) Evolutionary Developmental Biology. Kluwer, Dordrecht

Hall BK (2003) Evo-Devo: Evolutionary Developmental Mechanisms. The International Journal of Developmental Biology 47: 491-495

Hallgrimsson B, Brown JJ, Ford-Hutchinson AF, Sheets HD, Zelditch ML, Jirik FR (2006) The

Brachymorph Mouse and the Developmental-Genetic Basis for Canalization and Morphological

Integration. Evolution and Development 8: 61-73

Hallgrimsson B, Lieberman DE, Young NM, Parsons T, Wat S (2007) Evolution of Covariance in the Mammalian Skull. Novartis Foundation Symposium. Vol. 284, pp. 164. Chichester, New York.

John Wiley

Hallgrimsson B, Jamniczky HA, Young NM, Rolian C, Parsons TE, Boughner JC, Marcucio RS

(2009). Deciphering the Palimpsest: Studying the Relationship Between Morphological

Integration and Phenotypic Covariation. Evolutionary Biology 36: 355-376

Hallgrimsson B, Jamniczky HA, Young NM, Rolian C, Schmidt-Ott U, Marcucio RS (2012) The

Generation of Variation and the Developmental Basis for Evolutionary Novelty. Journal of

Experimental Zoology Part B: Molecular and Developmental Evolution 318(6): 501-517

Hallgrimsson B, Willmore K, Hall B (2002) Canalization, Developmental Stability, and

Morphological Integration in Primate Limbs. Yearbook of Physical Anthropology 45: 131-158

Hansen TF, Houle D (2008) Measuring and Comparing Evolvability and Constraint in Multivariate

Characters. Journal of Evolutionary Biology 21: 1201-1219

Harmon LK, Matthews B, Des Roches S, Chase JM, Shurin JB, Schluter D (2009) Evolutionary

Diversification in Stickleback Affects Ecosystem Functioning. Nature 458: 1167-1170

Hendrikse JL, Parsons TE, Hallgrimsson B (2007) Evolvability as the Proper Focus of

Evolutionary Developmental Biology. Evolution and Development 9:393-401

123

Higham TE, Jamniczky HA, Jagnandan K, Smith SJ, Barry TN, Rogers SM (2017). "Comparative dynamics of suction feeding in marine and freshwater three-spined stickleback, Gasterosteus aculeatus: kinematics and geometric morphometrics." Biological Journal Of The Linnaean

Society: 1-11

Jamniczky HA, Hallgrimsson B (2009) A Comparison of Covariance Structure in Wild and

Laboratory Muroid Crania. Evolution 63: 1540-1556

Jamniczky HA, Harper EE, Garner R, Cresko WA, Wainwright PC, Hallgrimsson B, Kimmel CB

(2014) Association Between Integration Structure and Functional Evolution in the Opercular Four-

Bar Apparatus of the Threespine Stickleback, Gasterosteus aculeatus (Pisces: Gasterosteidae).

Biological Journal of the Linnaean Society 111: 375-390

Jamniczky HA, Barry TN, Rogers SM (2015). "Eco-evo-devo in the study of adaptive divergence: examples from threespine stickleback (Gasterosteus aculeatus)." Integrative and Comparative

Biology 55(1): 166-178

Jamniczky HA, Campeau S, Barry TN, Skelton J, Rogers SM (2015). "Three-Dimensional

Morphometrics for Quantitative Trait Locus Analysis: Tackling Complex Questions with Complex

Phenotypes." Evolutionary Biology 42(3): 260-271

Johnson GD, Brothers EB (1993) Schindleria: A Paedomorphic Goby. Bulletin of Marine Science

52: 441-471

Jones AG, Arnold SJ, Burger R (2007) The Mutation Matrix and the Evolution of Evolvability.

Evolution: International Journal of Organic Evolution 61(4): 727-745

Klingenberg CP (2011) MORPHOJ: An Integrated Software Package for Geometric

Morphometrics. Molecular Ecology Resources 11: 353-357

124

Klingenberg CP, Barluenga M, Meyer A (2002) Shape Analysis of Symmetric Structures

Quantifying Variation Among Individuals and Asymmetry. Evolution 56: 1909-1920

Klingenberg CP, McIntyre G (1998) Geometric Morphometrics of Developmental Instability: Analyzing Patterns of Asymmetry with Procrustes Methods. Evolution 52(5):1363–1375 Lavin PA, McPhail JD (1985) The Evolution of Freshwater Diversity in the Threespine

Stickleback (Gasterosteus aculeatus): Site-Specific Differentiation of Trophic Morphology.

Canadian Journal of Zoology 63: 2632-2638

Leinonen T, Cano JM, Makinen HS, Merila J (2006) Contrasting Patterns of Body Shape and

Neutral Genetic Divergence in Marine and Lake Populations of Threespine Sticklebacks. Journal of Evolutionary Biology 19: 1803-1812

McGee MD, Wainwright PC (2013) Sexual Dimorphism in the Feeding Mechanism of Threespine

Stickleback. Journal of Experimental Biology 216:835-840

McPhail JD (1984) Ecology and Evolution of Sympatric Sticklebacks (Gasterosteus):

Morphological and Genetic Evidence for a Species Pair in Enos Lake, British Columbia. Canadian

Journal of Zoology 62: 1402-1408

McPhail JD (1991) Ecology and Evolution of Sympatric Sticklebacks (Gasterosteus): Evidence for a Species Pair in Paxton Lake, Texada Island, British Columbia. Canadian Journal of Zoology

70: 361-369

McPhail JD (1993) Ecology and Evolution of Sympatric Sticklebacks (Gasterosteus): Origin of the Species Pairs. Canadian Journal of Zoology 71: 515-523

Miner BG, Sultan SE, Morgan SG, Padilla DK, Relyea RA (2005) Ecological Consequences of

Phenotypic Plasticity. Trends in Ecology and Evolution 20(12): 685-692

125

Moodie GEE (1972) Predation, Natural Selection, and Adaptation in an Unusual Threespine

Stickleback. Heredity 28: 155-167

Moodie GEE and Reimchen TE (1976) Phenetic Variation and Habitat Differences in Gasterosteus

Populations of the Queen Charlotte Islands. Systematic Zoology 25: 49-61

Mori S (1987) Divergence in Reproductive Ecology of the Threespine Stickleback, Gasterosteus aculeatus. Japanese Journal of Ichthyology 34: 165-175

Morris MRJ, Rogers SM (2014) Integrating Phenotypic Plasticity Within an Ecological Genomics

Framework: Recent Insights from the Genomics, Evolution, Ecology, and Fitness of Plasticity. In:

Landry CR, Aubin-Horth N (eds) Ecological Genomics. Springer Netherlands, Dordrecht, pp 73-

105

Mousseau TA, Roff DA (1987) Natural Selection and the Heritability of Fitness Components.

Heredity 59: 181-197

Muller GB (1990) Developmental Mechanisms at the Origin of Morphological Novelty: A Side

Effect Hypothesis. In: Nitecki MH (editor). Evolutionary Innovations. Chicago: University of

Chicago Press p 99-130

Olson EC, Miller RA (1958) Morphological Integration. University of Chicago Press. Chicago.

Park PJ, Bell MA (2010) Variation of Telencephalon Morphology of the Threespine Stickleback

(Gasterosteus aculeatus) in Relation to Inferred Ecology. Journal of Evolutionary Biology

23:1261-1277

Peichel CL (2005) Fishing for Secrets of Vertebrate Evolution in Threespine Sticklebacks.

Developmental Dynamics 234: 815-823

126

Peichel CL, Ross JA, Matson CK, Dickson M, Grimwood J, Schmutz J, Myers RM, Mori S,

Schluter D, Kingsley DM (2004) The Master Sex-Determination Locus in Threespine Sticklebacks is on a Nascent Y Chromosome. Current Biology 14: 1416-1424

Pistore AE, Barry TN, Bowles E, Sharma R, Vanderzwan SL, Rogers SM, Jamniczky HA (2016).

"Characterizing phenotypic divergence using three-dimensional geometric morphometrics in four populations of threespine stickleback (Gasterosteus aculeatus; Pisces: Gasterosteidae) in Katmai

National Park and Preserve, Alaska." Canadian Journal of Zoology 94(7): 463-472

Ponce de Leon MS, Zollikofer CPE (2001) Neanderthal Cranial Ontogeny and its Implications for

Late Hominid Diversity. Nature 412: 534-538

Raff RA (1996) The Shape of Life. The University of Chicago Press, Chicago

Raff RA, Kaufman TC (1983) Embryos, Genes, and Evolution. MacMillan Publishers, New York

Raff RA, Sly BJ (2000) Modularity and Dissociation in the Evolution of Gene Expression

Territories in Development. Evolution & Development 2(2): 102-113

Reimchen TE (1980) Spine Deficiency and Polymorphism in a Population of Gasterosteus aculeatus an Adaptation to Predation? Canadian Journal of Zoology 58: 1232-1244

Reimchen TE, Nosil P (2004) Variable Predation Regimes Predict the Evolution of Sexual

Dimorphism in a Population of Threespine Stickleback. Evolution 58(6): 1274-1281

Ridgway MS, McPhail JD (1983) Ecology and Evolution of Sympatric Sticklebacks

(Gasterosteus): Mate Choice and Reproductive Isolation in the Enos Lake Species Pair. Canadian

Journal of Zoology 62: 1813-1818

Roberts R (1971) A Guide to Vertebrate Development 6th ed. Burgess

Robinson BW (2000) Trade Offs in Habitat-Specific Foraging Efficiency and the Nascent

Adaptive Divergence of Sticklebacks in Lakes. Behaviour: 865-888

127

Rohlf FJ (2002) NTSYSpc: Numerical Taxonomy System, Version 2.10y. Setauket, NY: Exeter

Publishing Ltd.

Schluter D (2000) The Ecology of Adaptive Radiations. Oxford University Press, Oxford

Schluter D and McPhail JD (1992) Ecological Character Displacement and Speciation in

Sticklebacks. The American Naturalist 140(1): 85-108

Schneider RF, Li Y, Meyer A, Gunter HM (2014) Regulatory Gene Networks that Shape the

Development of Adaptive Phenotypic Plasticity in a Cichlid Fish. Molecular Ecology 23: 4511-

4526

Shapiro MD, Marks ME, Peichel CL, Blackman BK, Nereng KS, Jonsson B, Schluter D, Kingsley

DM (2004) Genetic and Developmental Basis of Evolutionary Pelvic Reduction in Threespine

Sticklebacks. Nature 428: 717-723

Smith HF, Ritzman T, Otarola-Castillo E, Terhune CE (2013) A 3-D Geometric Morphometric

Study of Intraspecific Variation in the Ontogeny of the Temporal Bone in Modern Homo sapiens.

Journal of Human Evolution 65: 479-489

Snyder RJ (1991) Migration and Life Histories of the Threespine Stickleback: Evidence for

Adaptive Variation in Growth Rate Between Populations. Environmental Biology of Fishes 31:

381-388

Spoljaric MA, Reimchen TE (2008) Habitat-Dependent Reduction of Sexual Dimorphism in

Geometric Body Shape of Haida Gwaii Threespine Stickleback. Biological Journal of the Linnaean

Society 95: 505-516

Sultan SE, Stearns SC (2005) Environmentally Contingent Variation: Phenotypic Plasticity and

Norms of Reaction. In: Hall B, Hallgrimsson B (editors) Variation: A Central Concept in Biology.

Elsevier Academic Press Burlington MA, San Diego CA, London UK.

128

Valentin AE, Penin X, Chanut JP, Sevigny JM, Rohlf FJ (2008) Arching Effect on Fish Body

Shape in Geometric Morphometric Studies. Journal of Fish Biology 73: 623-638

Via S, Gomulkiewicz R, De Jong G, Scheiner SM, Schlichting CD, Van Tienderen PH (1995)

Adaptive Phenotypic Plasticity: Consensus and Controversy. Tree 10(5): 212-217

Waddington CH (1975) The Evolution of an Evolutionist. Ithaca, NY. Cornell University Press.

Wagner GP (1996) Homologues, Natural Kinds and the Evolution of Modularity. American

Zoologist 36: 36-43

Wagner GP, Booth G, Bagheri-Chaichian H (1997) A Population Genetic Theory of Canalization.

Evolution 51(2): 329-347

Wagner GP, Chiu CH, Hansen TF (1999) Is Hsp90 a Regulator of Evolvability? Journal of

Experimental Zoology 285: 116-118

Wagner GP, Mezey JG (2004) The Role of Genetic Architecture Constraints in the Origin of

Variational Modularity. In: Schlosser G, Wagner GP (editors). Modularity in Development and

Evolution. P 338-358. Chicago and London. The University of Chicago Press

Wagner GP, Pavlicev M, Cheverud J (2007) The Road to Modularity. Nature Genetics 8: 921-931

Walker J (1996) Principal Components of Shape Variation Within an Endemic Radiation of

Threespine Stickleback. In: Marcus LF, Corti M, Loy A, Naylor G, Slice DE, eds. Advances in

Morphometrics, New York: Plenum, 321-334

West-Eberdard MJ (2008) Toward a Modern Revival of Darwin’s Theory of Evolutionary

Novelty. Philosophy of Science 75: 899-908

Westerfield M (2007) The Zebrafish Book, 5th Ed. University of Oregon Press, Eugene

Whoriskey FG, FitzGerald GJ (1994) Ecology of the Threespine Stickleback on the Breeding

Grounds. The Evolutionary Biology of the Threespine Stickleback 188-206

129

Willacker JJ, Hippel von FA, Wilton PR, Walton KM (2010) Classification of Threespine

Stickleback Along the Benthic-Limnetic Axis. Biological Journal of the Linnaean Society 101:

595-608

Winterbottom R (1990) The Trimmatom nanus Species Complex (Actinopterygii, Gobiidae):

Phylogeny and Progenetic Heterochrony. Systematic Zoology 39: 253-265

Wootton RJ (1976) The Biology of the Sticklebacks. Academic Press, London.

Wootton RJ (1984) A Functional Biology of Sticklebacks. Croom Helm, London

Wootton RJ (1994) Energy Allocation in the Threespine Stickleback. Oxford University Press,

New York

Yong L, Peichel CL, McKinnon JS (2015) Genetic Architecture of Conspicuous Red Ornaments in Female Threespine Stickleback. G3 Genes-Genomes-Genetics 6(3): 579-588

Zelditch ML, Sheets HD, Fink WL (2000). Spatiotemporal Reorganization of Growth Rates in the

Evolution of Ontogeny. Evolution 54: 1363-1371