Genetics and Evolution of Ultraviolet Reflectance in Flowers

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2018-01-25 Genetics and evolution of ultraviolet reflectance in flowers

Liu, Yan

Liu, Y. (2018). Genetics and evolution of ultraviolet reflectance in flowers (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/5440 http://hdl.handle.net/1880/106359 doctoral thesis

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GENETICS AND EVOLUTION OF ULTRAVIOLET REFLECTANCE IN FLOWERS

Yan Liu

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

JANUARY, 2018

Many flowers have ultraviolet (UV) reflectance patterns, which are invisible to humans but visible to pollinators, such as bumblebees and hummingbirds. In bees and hummingbirds, photoreceptors are sensitive to UV wavelengths, and it is therefore necessary to incorporate this variable to model pollinators’ perception and assess floral UV evolution. In this thesis, I explore micro- and macroevolutionary patterns in floral UV patterns, specifically concentrating on the effect of this phenotype on pollinators. I first explore the ways in which UV patterns can be measured and characterized, as well as explore the underlying basis of UV patterning in flowers.

By gathering UV reflectance data (between 300 to 400nm) on 150 species, I found evidence that the phylogenetic distribution of UV trait disparity is consistent with a stabilizing selection model of evolution, but the magnitude of stabilizing selection varies with geography and pollinator syndrome. Mimulus species have become key model species for investigating the genetics of floral adaptations, in part because it is tremendous diversity in floral phenotypes. I firstly estimate genetic diversity in six populations in Alberta and British Columbia. Historical contingency (via geographic and bioclimatic events) provides the evidence of restricted gene flow. Variance in phenotypes depends not only on allelic interactions but also on environmental factors. Variation and heritability of the floral UV reflectance are further explored with experimental interspecific crosses between Mimulus guttatus and Mimulus luteus. By recoding 12 floral traits throughout the parental to F4 generations, I find that phenotypic covariance is strongest between UV reflectance and other floral traits, lending evidence to the idea that UV reflectance in flowers evolves along with other floral traits as a response to selection from pollinators. My research has implications for forecasting plant adaptation through hybridization and polyploidization, which may occur in concert with the evolution of plant-pollinator relationships.

ii Acknowledgements

I am deeply grateful to Dr. Jana Vamosi, my PhD supervisor. First and foremost, she gave me the precious opportunity to study in the PhD program. I worked and learned with her for five years, and I received the direction to my scientific and career development. Jana will always be there with her students when we are having a difficult time. I will never forget every encouragement and support she gave to me when I felt overwhelmed and confused. This dissertation would not have been possible without efforts of my supervisor, Jana.

Thanks go also to my previous and present committee members, Dr. Marcus Samuel, Dr.

Dae-Kyun Ro, Dr. Amanda Melin (Department of Anthropology and Archaeology & Department of Medical Genetics), Dr. Christina Caruso (University of Guelph), Dr. Edward Yeung and Dr.

Brian Kooyman (Department of Anthropology & Archaeology), who provided invaluable discussion and suggestion to my PhD research, candidate exam and thesis writing.

Studying in Jana’s lab, the team has been invaluable in my PhD journey, as both colleagues and friends, especially Soraya Villalobos, Lisa Cahoon, Dr. Yanbing Gong (Visiting Scholar).

Here's to the many years we spent together!

I want to thank my Masters supervisor Dr. Daoyuan Zhang (Chinese Academy of Sciences) and my undergraduate supervisor Dr. Yanmin Wei (Beijing Agricultural University). They are all graceful and knowledgeable female scientists in China. The experience working with them has always encouraged me to be a good female researcher in the future.

I also greatly appreciate Dr. Xiaoxiao Liu (Department of geography), Dr. Hongxiang

Zhang (Chinese Academy of Sciences), Dr. Qiushi Li, Yingying Cong. They are both my friends and academic cooperators, and especially appreciate to my best research partner Dr. Jiancheng

Wang (Chinese Academy of Sciences). How lucky I am to have you in my life.

iii Big thank to Dr. Chendanda Chinnappa, Dr. Sarah E.J.Arnold (FReD database), Dr.

Benjamin Blackman (University of California) and Ian D. Macdonald (plant collector) for their help and support to my PhD research.

I enjoyed the moments to work with the students in Dr. Sean Rogers lab, Dr. Lawrence

Harder lab and Dr. Dae-Kyun Ro lab. Thanks for their help and company, especially Dr. Matthew

Morris, Dr. Ella Bowles, Dr. Qiqun Cheng (Visiting Scholar), and Dr. Mason Kulbaba.

As an international student and first coming to Canada, I got a lot of help and encouragement from many staff in the Department of Biological Sciences. I would like to express my sincere appreciation to Dianne White, Bonnie Smith, Karen Barron and Christine Goodwin.

I am thankful for the hardworking research assistants in my projects, they are Moin

Tinwala, Sophia Shah, Khanjan Pandya, Maegan Lacuata, Safaa Al-Khaz'Aly, Alexander Chiem and Juliana Abbatia.

My research was funded by the grants from Alberta Innovates-Technology Futures (AITF),

Alberta Conservation Association (ACA), and University of Calgary. Mimulus specimen were loaned from University of Calgary Herbarium (UAC), University of Alberta Herbarium (ALTA), and University of Lethbridge Herbarium (LETH). Thank you for all the support!

My lovely parents are always proud of my study and constantly support my decisions. I love them forever! In the end, I want to talk to myself “More powerful more responsibility, you did a great job! Enjoy your next adventure!”

iv Dedication

This thesis is dedicated to my family. My parents, Zhongqiao Liu and Xingai Wang.

谨以我的博士论文，献给爱我的和我爱的人一直以来的期许和关爱！

v Table of Contents

Abstract ...... ii Acknowledgements ...... iii Dedication ...... v Table of Contents ...... vi List of Tables ...... ix List of Figures and Illustrations ...... xi List of Symbols, Abbreviations and Nomenclature ...... xiii

1 CHAPTER 1 GENERAL INTRODUCTION ...... 1 1.1 Introduction of UV reflectance from flowers and pollinator-flower interaction 1 1.2 Introduction of the floral colours transitions and the correlation with floral UV reflection ...... 2 1.3 Flower and pollinator interaction...... 4 1.4 Inner epidermis between UV reflectance and UV absorbing flowers ...... 6 1.5 Introduction to the study system: Mimulus guttatus ...... 7 1.6 Thesis objectives ...... 8

2 CHAPTER 2 The adaptive value of floral UV reflectance: a comparative investigation of floral traits, geography and bee visitation ...... 13 2.1 Introduction ...... 13 2.2 Materials and Methods ...... 18 2.2.1 Study system ...... 18 2.2.2 UV measurement ...... 19 2.2.3 Quantifying phylogenetic signal ...... 20 2.2.4 Comparative analyses ...... 22 2.2.5 Model selection ...... 22 2.2.6 Phylomorphospace approach ...... 24 2.3 Results ...... 24 2.3.1 Phylogenetic distribution of measured traits ...... 24 2.3.2 Comparative analysis (Phylogenetic Independent Contrasts, PIC) ...... 25 2.3.3 Model Selection ...... 26 2.4 Discussion...... 26 2.4.1 Abiotic factors on floral UV reflectance ...... 27 2.4.2 Biotic factors on floral UV reflectance ...... 30 2.4.3 Concluding Remarks ...... 31

3 CHAPTER 3 Conservation genetics of Mimulus guttatus in Alberta: the contribution of geographical and genetically distinct refugia to species management...... 42 3.1 Introduction ...... 42 3.2 Materials and Methods ...... 45 3.2.1 Study System ...... 45 3.2.2 Sampling site...... 46 3.2.3 Genetic markers ...... 48

vi 3.2.4 DNA Isolation and PCR amplification ...... 48 3.2.5 Genetic and Population Structure Analyses ...... 50 3.2.6 Bioclimatic data ...... 51 3.2.7 Method for quantifying UV reflectance ...... 53 3.3 Results ...... 55 3.3.1 Population structure and genetic diversity ...... 55 3.3.2 Redundancy analysis for Bioclimatic data and UV reflectance variance .57 3.3.3 Cluster analysis in UV spectrum ...... 58 3.4 Discussion...... 59

4 CHAPTER 4 Variation of quantitative traits accompanies the change of floral ultraviolet reflectance trait following Mimulus hybridization and allopolyploid evolution ...... 78 4.1 Introduction ...... 78 4.2 Materials and Methods ...... 82 4.2.1 Study system ...... 82 4.2.2 Methods for imagining UV reflectance ...... 84 4.2.3 Calculation of colour loci in the hymenopteran colour hexagon ...... 86 4.2.4 Greenhouse cultivate and mating design ...... 87 4.2.5 Studying Chromosomes at Meiosis...... 89 4.2.6 Phenotypic measurement ...... 89 4.2.7 Analyses of trait variation ...... 91 4.2.8 Heritability...... 92 4.3 Results ...... 92 4.3.1 Chromosome Examination ...... 92 4.3.2 Statistic analysis ...... 93 4.3.2.1 Phenotypic variation in floral UV patterning ...... 93 4.3.2.2 Pairwise correlation and PCA ...... 95 4.3.2.3 Heritability...... 96 4.4 Discussion...... 96 4.4.1 Consequences of polyploidization on shifts in pollinator discriminations 97 4.4.2 Chromosome characterization of a Mimulus allopolyploid ...... 98 4.4.3 Floral UV reflectance in trait correlations ...... 99 4.4.4 Implication ...... 100

5 CHAPTER 5 GENERAL CONCLUSION ...... 113 5.1 Natural variation in floral UV reflectance...... 113 5.2 Macroevolutionary patterns of UV reflectance ...... 113 5.3 Biogeographical patterns in UV reflectance ...... 114 5.4 Genetic patterns in UV reflectance ...... 115 5.5 Future studies ...... 115

APPENDIX A ...... 117

APPENDIX B ...... 125

APPENDIX C ...... 128

vii APPENDIX D ...... 131

REFERENCES ...... 135

viii List of Tables

Table 2.1 Blomberg’s K or Pagel’s λ for the continuous traits visitation for 150 species ...... 33

Table 2.2 Blomberg’s K or Pagel’s λ for the continuous traits visitation for 69 species in Israel ...... 33

Table 2.3 Pairwise correlation coefficients for the continuous variables ...... 34

Table 2.4 Pairwise correlation coefficients for between the discrete and the continuous variables ...... 34

Table 2.5 Results of AIC for Three Competing Models (UV brightness or UVB) ...... 35

Table 2.6 Results of AIC for Three Competing Models (UVmaximum or UVM) ...... 35

Table 2.7 Comparison of different models predicting UV brightness (UVB) and UV maximum (UVM) ...... 36

Table 3.2 AMOVA design and results of binary genetic data for population groups of Mimulus guttatus ...... 67

Table 3.3 Pairwise distance matrix of FST and geographic distance between all populations based on microsatellite genotype. The population codes: CH= Cypress Hills, DM= Del Bonita & Milk rive, WP= Waterton park, CP= Crowsnest Pass, GP= Grande Prairie, CR= Craig Bay ...... 68

Table 3.5 Loadings of environmental traits on the first three principal components for all Mimulus guttatus individuals ...... 70

Table 3.6 Results of partial redundancy analysis for correlation between bioclimatic variables and genetic variation ...... 71

Table 3.7 Results of full redundancy analysis for stepwise forward selection of bioclimatic variables ...... 72

Table 3.8 F-test results were assessed by multivariate analysis ...... 73

APPENDIX B Table 1 PhylANOVA Asterisks indicate significance correlation coefficients . 127

ix APPENDIX C Table 1 Information for each population of Mimulus guttatus used in the study, including population code, cluster number, GPS coordinates and sampling year ... 128

APPENDIX D Table 1 Floral trait correlations matrix in parental Mimulus and F3 F4 population. Spearman’s correlation coefficients were given below the diagonal...... 131

x List of Figures and Illustrations

Figure 1.1 Visible light and ultraviolet reflectance patterns of Mimulus flowers ...... 10

Figure 1.2 Scanning electron microscopy (SEM) images of (a) Mimulus guttatus (strong UV reflectance) and (b) Mimulus hybrids F1 (no UV reflectance) ...... 11

Figure 1.3 Diagram of floral morphological traits measured. Mimulus guttatus (right) dorsal view, with upper corolla removed to show reproductive structures ...... 12

Figure 2.1 Phylogenetic relationships, pollinator visitation associated with UV pigments type and distribution of biotic and abiotic continuous factors ...... 37

Figure 2.2 Pairwise correlation between the discrete and the continuous variables using phylogenetic independent contrasts ...... 39

Figure 3.1 Geographical distribution of the studied species, Mimulus guttatus. Locations of populations were covered by two significant bioclimatic variables, maximum temperature in July (tmax7) in Fig. A and solar radiation in August (srad8) in Fig. B ...... 74

Figure 3.2 Assuming k=3, 4, 5 genetic clusters, barplot showing the probabilities of individuals to each genetic cluster base on 12 microsatellite loci ...... 76

Figure 3.3 Principal coordinate analysis for relationships among population groups. The goodness of fit of the first two principal components was 46.33% ...... 76

Figure 4.1 UV images for UV reflecting flowers M. guttatus and UV absorbing flowers M. lewisii, M. cardinalis and M. ringens...... 102

Figure 4.2 The colour hexagon represents three photoreceptor excitations E(B) E(U) and E(G) mean blue, ultraviolet and green receptors ...... 103

xi Figure 4.4 Meiotic and mitotic chromosomes of M. guttatus and M. luteus. a, c and d were haploid cells (n) after meiotic activity spreading from anthers; b was in diploid cell (2n) in mitotic metaphase spreading from a root tip...... 105

Figure 4.5 Meiotic chromosomes of pollen mother cells in F4 Mimulus fertile hybrid plants ... 106

Figure 4.6 Photographs of representative Mimulus flowers; a: 4 F3 samples variation in corolla pigmented; b: upper photo showed 3 flowers under normal light; lower photo showed M. guttatus with ‘bullseye’ UV pigment (left) or with strong UV absorbance (right) ...... 107

Figure 4.7 Histograms of floral traits in the hybrids (F3 and F4) population. Phenotypic means for M. guttatus and M. luteus are marked with red and blue lines...... 108

Figure 4.8 The degree of colour separation as seen by bees, and the loci of all 687 colour loci are plotted in the colour hexagon ...... 110

Figure 4.9 Box-plot corresponds to the quantiles in the distribution of the floral UV brightness in parental Mimulus and their offspring F3 and F4 ...... 111

Figure 4.10 Principal component analysis (PCA) on 12 phenotypic traits from parental Mimulus to their offspring F3 and F4 ...... 112

APPENDIX A Figure 1 The anthocyanin biosynthetic pathway...... 121

APPENDIX A Figure 2 LC-MS results and Molecular Weight (MW) for inferred chemicals . 122

APPENDIX D Figure 1 Two types of red-pigmentation patterns that exhibited within the same individual in F3 ...... 134

xii List of Symbols, Abbreviations and Nomenclature

Symbol Definition ACIMS Alberta Conservation Information Management System AIC Akaike information criterion AMOVA Analysis of Molecular Variance ANOVA Analysis of variance bio1 Annual temperature bio12 Annual precipitation BM Brownian motion CCA Canonical correspondence analysis CH Cypress Hills CHI Chalcone flavanone isomerase CHS Chalcone synthase CP Crowsnest Pass CTAB Cetyl trimethyl ammonium bromide dbRDA Distance-based redundancy analysis DFR Dihydroflavonol 4-reductase DHM Dihydromyricetin DHQ Dihydroquercetin DIPs Dearomatized isoprenylated phloroglucinols DM Bonita & Milk River EB Early bursts F1 First generation cross F2 Second generation cross F3 Third generation cross F3'5' Flavonoid 3', 5'-hydroxylase F3'h Flavonoid 3'-hydroxylase F4 Fourth generation cross FIS Inbreeding coefficient of individuals relative to the subpopulation FST Fixation index of the subpopulation relative to the total population GP Grande Prairie H2 The broad-sense heritability h2 The narrow-sense Heritability He Expected heterozygosity Ho Observed heterozygosity I Shannon's Information Index CR Craig Bay, BC kya Thousand years ago LC-MS Liquid chromatography–mass spectrometry MG Mimulus guttatus MgSTS Intron-based length polymorphic markers ML Mimulus lutteus Na Number of alleles Ne Number of Effective Alleles nm Nanometer (wavelength range)

xiii OU Ornstein-Uhlenbeck PAL Phenylalanine ammonia lyase PCA Principal components analysis PCR Polymerase Chain Reaction PGLS Phylogenetic generalized least squares PIC Phylogenetic independent contrast pRDA Partial redundancy analysis prec Precipitation QTL Quantitative Trait Locus SEM Scanning electron microscopy srad Solar radiation tmax Maximum temperature tmean Mean temperature tmin Minimum temperature uHe Unbiased expected heterozygosity UK United Kingdom UV Ultraviolet UVB Ultraviolet brightness UVM Ultraviolet maximum WP Waterton Lakes National Park

xiv 1

1 CHAPTER 1 GENERAL INTRODUCTION

1.1 Introduction of UV reflectance from flowers and pollinator-flower interaction

How fast floral traits evolve in response to the prevalence of certain pollinators is an important outstanding question in evolutionary ecology. Most of the previous work on the rate of evolution of floral traits has examined the effect of pollinator behavior (Grant, 1949), yet recent advances in genetics has allowed examinations of the heritability of plant characteristics as well as the genetic correlations with other characters. Pollinators can initiate and maintain reproductive isolation (Stebbins, 1970, Hu et al., 2008), yet their ability to drive evolution depends on the underlying genetics of floral phenotypic traits that are coevolving, such as floral architecture, flower colour and scent, ultraviolet patterning on petals, and nectar rewards (e.g. Ollerton, 1996,

Spaethe et al., 2001, Leonard et al., 2011, Ding et al., 2017).

Our understanding of how floral trait diversification is associated with pollinator preferences has broadened beyond simple categorization of flower colour, size, shape to incorporate traits that are beyond human perception, such as ultraviolet (UV) reflectance and absorbance. Flowers can appear uniform in colour consistent with the human-visible system, yet exhibit marked variation in the UV floral pattern. Although floral UV reflectance has been studied by entomologists (e.g. Briscoe and Chittka, 2001), most plant evolutionary biologists have studied floral reflectance in the visible portion of the spectrum (Rausher, 2008). The wavelengths in the

UV part of spectrum are detected by pollinators from 300 to 400nm, and are thought to function as nectar guides (Briscoe and Chittka, 2001) for only a portion of a given pollinator assemblage and thus potentially contribute to speciation through reproductive isolation. The ecological relevance of UV reflectance in producing a ‘nectar guide’ has received little attention. UV floral patterns include a bullseye pattern, which increase pollinator attraction (Lunau, 1992a). In figure

1.1, a bullseye pattern, shown the flowers in Mimulus genus, is formed from UV absorption in the central part of flowers (at petal bases) and UV reflection on the periphery (or petal apices), the UV pattern characters are thought to act as nectar guides for pollinators (Lehrer et al., 1995, Johnson and Dafni, 1998, Peterson et al., 2015). It is not currently known if only certain functional groups of pollinators have a preference for a bullseye pattern. Various floral UV absorption patterns have been hypothesized to be an important factor for maintaining reproductive isolation between the species that appear similar in visible colour and floral morphology (Koski, 2015).

Measurement of UV reflectance and absorption is a necessary prerequisite for studies of the evolutionary genetics and ecology of the trait in natural systems. I begin this thesis with an overview of the various ways in which previous researchers have examined UV patterns in flowers, ranging in methodology that employs techniques from microscopy, biochemistry, and optics. I provided a detailed overview of how these techniques pertain to the genus Mimulus, which is the specific focus of Chapters Three and Four.

1.2 Introduction of the floral colours transitions and the correlation with floral UV

reflection

Among flowering plants, before the floral UV reflectance was known to facilitate pollinators attraction and pollinators syndrome divergence, various flower colours within the visual spectrum were thought to be the main floral appearance to pollinators and contribute to the interaction between plants and pollinators (Streisfeld and Kohn, 2007, Hopkins and Rausher, 2012).

The colour preferences in pollinators behavior, i.e. the pollinator-mediated selection, evolved with plant rewards, leading to associations between the habitats and competitive processes among the closely related pollinator species and certain floral colours. Multiple floral colours were genetically

3 characterized and observed to act in reinforcing parapatric speciation. For example, in a study of120 Mimulus species, the phylogeny suggests that floral colours and other traits exhibit more divergence between sympatric sister species than allopatric sister species (Grossenbacher and

Whittall, 2011).

Empirical studies (Crow and Kimura, 1970, Schlager and Dickie, 1971) concluded that there are three main factors (migration, mutation and selection) influencing the phylogenetic distribution of traits. Various extrinsic environmental factors are associated with selection pressures on floral trait evolution, for instance, polymorphism in colorful petals may arise fitness during drought and high heat environments. In this thesis, these three factors are explored in terms of their role in the evolution of floral UV reflection in Chapters Two, Three and Four.

Floral colour and the UV pattern on petals perform functional roles to increase pollinator attraction. Mimulus guttatus, the focus of this thesis, appears uniformly yellow colour to the human eye but exhibits a bulls-eye UV pattern from the perspective of bees. We have very little evidence on the diversity of floral colours paired with floral UV reflectance. The anthocyanin pathway provides a useful framework to study the genetic basis of the production of floral pigment in flowering plants. In principle, anthocyanins can produce flowers that appear pink, red, violet or blue, while carotenoids and flavones can produce flowers that appear yellow, red or purple.

Flavonoids are present in anthocyanin pathway which gives rise not only to floral visual pigments

(Grotewold, 2006), but also to reflect or absorb UV light (Thompson et al., 1972, Harborne and

Nash, 1984, Rieseberg and Schilling, 1985, Gronquist et al., 2001). Flavonoid compounds are the main component for floral UV-absorbtion (Harborne and Nash, 1984). The detailed introduction of flavonoid biosynthetic pathway shows in Appendix A.1. Evolutionary change in floral colour transitions have been driven in part by the pleiotropic effects (Rausher, 2008). Analysis the

4 expression value, we can identify mutations in candidate genes that are responsible for the evolution of floral color (e.g. Streisfeld and Rausher, 2008, Smith and Rausher, 2011). The variation of floral UV reflectance in the field is mainly due to genetic differentiation, rather than the plastic response to environmental stress (Koski and Ashman, 2013).

Studies of functional genes influencing floral UV pattern has been observed to overlap with the genetic background on floral colours (e.g. Holton and Cornish, 1995, Dixon et al., 2013). The previous studies on molecular mechanism about UV pattern for resisting abiotic stress are summarized in Appendix A.2.

1.3 Flower and pollinator interaction

Pollinators have preferences and tolerances to certain habitats, resulting in variation in the selective agents influencing floral colours if floral colours represent adaptations to different functional groups of pollinators (Grant, 1993, Fenster et al., 2004). The principal pollinators are thought to vary with different floral traits, yet the research thus far has mainly focused on the roles of bees, hummingbirds, or moths (Beardsley et al., 2004, Smith et al., 2008). The diversity of floral colours reflects adaptation to facilitate perception by the color vision systems not only in the insects (invertebrates) as pollinators, but also in vertebrate pollinators such as birds and bats

(Faegri and Van der Pijl, 2013). Pollinator adapted cues that involve floral colour tend to be evolutionarily labile. For instance, evolutionary transitions have been observed in diverse lineages

(Grant and Grant, 1968), where the bee pollination syndrome has transitioned to hummingbird- pollinated flowers at least 129 times in western North America. The ability to study these transitions at the genetic level is now possible, since Bradshaw and Schemske (2003) performed a study where they tested the effects of evolved mutations on alleles which can function to shift

5 adaptive pollinator syndrome from bumblebee-pollinated to hummingbird-pollinated, or vice versa, in Mimulus flowers.

Bees have received much of the attention for studying behavior in pollination preference tests, rather than hummingbirds (Lunau et al., 2011). In Lunau et al (2011), tests with artificial flowers showed that neotropical orchid bees prefer to pollinate red flowers with UV reflectance, and white flowers without UV reflection. On the contrary, hummingbird preferences show less association with flower colours. Bee colour preferences is the optimal system for detecting or identifying floral ecology and evolution (Chittka, 1996). Unlike the trichromatic colour vision in bees, hummingbirds have tetrachromatic colour vision, which is more sensitive to colour spectra.

Bees’ visual system can sense the UV, blue and green, without a red photoreceptor. The detection of red was shifted with the colour loci of the blue and UV-receptor in bees’ vision.

The hummingbird-pollination syndrome has received less attention yet it is thought that hummingbirds prefers long floral tubes, rich nectar rewards, brightness of red colour on petals and less floral odor for a given floral display area (Bradshaw and Schemske, 2003, Wilson and Jordan,

2009). Some evidence suggests that hummingbirds foraging behavior has an inflexible relationship with red flowers (McDade, 1983, Delph and Lively, 1989). Lunau and Maier (Lunau and Maier,

1995), however, provided a different hypothesis that the attractive floral colours functions as defence against nectar-robbing insects. Hummingbirds thus do not have invariant preference for floral color (Lunau et al., 2011), and both white and red flowers are common to the perceptual colour space.

Flies prefer visiting flowers with open nectar and short floral tubes. Unlike that seen with bee habitat, fly-pollination is present in more seasons throughout the year and with wider climatic conditions. In some species (e.g. Hedera helix), the pollination has shifted completely from bees

6 to flies, which may help allow the plants to persist in areas with climatic stress (Kevan, 1972,

Kevan and Baker, 1983). Diptera and Lepidoptera typically increase in importance as pollinators at higher altitude, while hymenopterans decline because of the lower temperature (Shrestha et al.,

2014b). Visual systems of moths suggest that they have labile preferences for floral colour. Most noctuids moths are attracted to the range of the spectrum at shorter wavelengths; other moth clades appear to have no preference for a UV pattern as the nectar guide, such as Macroglossa (Kevan and Baker, 1983). Beetle pollination is associated with flat inflorescences, and the main attractant of beetles is odour, not visual cues (Arnold et al., 2016). Pollen-eating beetles prefer a sweet odour and nectar production rather than flower colours (Faegri and Van der Pijl, 2013). Beetles are considered important pollinators, yet in reality, they often make destructive visits to flowering plants. Other insect groups depend on the odour guides or temperature guides to pollinate under low light condition. For example, in the night-blooming flower, Silene otites, flies and moths are regular visitors. The insects visited for sources of food, but still carry the pollen when they touch the female flowers. In this study, I focus on the ‘bullseye’ UV pattern for pollinator vision, and building the bee visual model to assess floral UV evolution.

1.4 Inner epidermis between UV reflectance and UV absorbing flowers

In previous studies (Brehm and Krell, 1975, Kay et al., 1981), the flavonoid compounds localized on the floral petals were seen as important in the expression of the UV-absorbing phenotype, yet epidermal papillae, like the triangular cones, can also cause UV-absorbing patterning. Earlier studies examined M. guttatus petals by scanning electron microscopy (SEM) to compare different patterns in corolla, lateral and central corolla (Bodbyl-Roels, 2012). This study found denser and sharper conical cells appeared in the UV absorbing regions, while the strong UV

7 reflectance region at petals contained flat cells and less textured than the absorbance part at center corolla. In Figure 1.2 (a), the petals of M. guttatus show strong UV reflectance. The cells associated with UV reflection have a conical, rounded shape, and were densely packed together. Also, the center of the corolla shows UV absorption where there are fewer sharply pointed cells. While these characteristic cell patterns can be driven by natural selection (Bodbyl-Roels, 2012), Gorton and

Vogelmann (Gorton and Vogelmann, 1996) conclude that epidermal papillae do not significantly influence UV phenotypes on corolla displays. In Figure 1.2, I used the Nikon A1R confocal microscope to examine the cell structure between highly UV reflecting flowers in M. guttatus and

UV absorbing flowers in Mimulus hybrids derived from crosses with M. luteus (see Chapter Four).

In M. guttatus, the conical cells were spaced sparsely compared to UV-absorbing flowers. The conical cells have a different phenotype in the UV-absorbing regions in Mimulus hybrids (Figure

1.2 b). Mimulus hybrids have petals with strong UV absorbance, and these are the same regions that have larger, denser and sharper epidermal papillae. Therefore, it seems likely that some UV patterning is caused by cell structure in Mimulus.

1.5 Introduction to the study system: Mimulus guttatus

Mimulus guttatus Fisch. (Phrymaceae), or yellow monkey-flower, is a North American species that has experienced rapid phenotypic evolution, in part due to its distribution along a wide elevation gradient. In southern regions, the species has diverged into annual and perennial ecotypes

(Vickery Jr, 1978). In Alberta, Mimulus guttatus (2n = 28) is a self-compatible wildflower species occupying sunny, wet, mossy places at various middle and low elevation sites in Alberta

(Holmgren, 1983, Moss and Packer, 1983). Though studied heavily in southern regions,

8 populations in Waterton National Park, Crownest Forest, and Cypress Hills have received little study.

The flowers have upper petal lobes that are 2-lipped and lower petal lobes that are 3-lipped.

They are born in leaf-axils, the calyx is tubular, and the lobes are shorter than the floral tube (Figure

1.3). The corolla throat exhibits bilaterally symmetric nectar guides. There are four stamens

(didynamous) and a touch-sensitive stigma with two longitudinal ridges with brushy hairs and numerous reddish-purple spots on the ventral petal, which advertise floral rewards to and guide pollinators (Fenster et al., 2004). Flowering date is from July to early September. Previously, flower colours are widely believed as a signal that directs pollinator behavior. Recently, Mimulus guttatus has been developed a model system for understanding the genetic basis (Yuan et al., 2013a,

Yuan et al., 2013b, Yuan et al., 2014, Wessinger et al., 2014) of a wide suite of floral traits (e.g., colour, shape, rewards, display). It is a classic model system that has been used to elucidate how genetic and phenotypic variation affect pollinator interactions. For example, transcription factors

(e.g. MYB gene, Yuan et al., 2014) and the biochemical precursors (e.g. anthocyanin, carotenoid pigmentation) contribute to pollinator-mediated reproduction more so than does pollinator preference driven by floral pigment evolution. My aim in this thesis was to examine how pigments invisible to the human eye (e.g., UV pigments) evolve in flowering plants. The ultraviolet reflectance spectra was assessed in Mimulus by several methods for comparing interspecific variation in floral UV pattern.

1.6 Thesis objectives

UV patterning in flowers is fascinating in its complexity. Because it is invisible to the human eye, there are still many unanswered questions regarding its geographical and genetic

9 variability. The remaining chapters in this thesis are focused on the estimation of the variation in

UV patterns in flowers and its associations with other floral cues. The variation in floral UV reflectance will greatly extend our understanding of abiotic and biotic factors, which contribute to its phylogenetic distribution (Chapter Two).

By focusing on M. guttatus and its hybrids I was able to further examine the variation in floral traits, especially in UV phenotypes. I used the above techniques to examine the geographical variation in UV patterning in flowers in M. guttatus populations within Alberta and determined whether the genetic structure of populations was associated with the UV phenotype and/or bioclimatic forces (Chapter Three). I then made experimental crosses to examine the inheritance of UV patterns in the genus Mimulus species. In performing these interspecific crosses, I inadvertently produced a new species, which led to an investigation to understand restoration of sexual selection in triploid hybrids. The phenotypic covariance matrices were estimated for traits correlation experiencing different selection pressures, and explored how hybridization and allopolyploidization affects UV reflectance in flowers (Chapter Four). Generally, I was interested in heritability and selection on the UV phenotype and examined floral UV reflectance in ways that would elucidate whether it arises due to selection or responds to environmental factors.

Figure 1.1 Visible light and ultraviolet reflectance patterns of Mimulus flowers

Figure 1.2 Scanning electron microscopy (SEM) images of (a) Mimulus guttatus (strong UV reflectance) and (b) Mimulus hybrids F1 (no UV reflectance). Top images provide a horizontal view to the flower petals and bottom images show the face of the flower petals. In image set (b), epidermal papillae, like the triangular cones, were associated with UV absorbing patterning. The cell shapes are sharper than that seen in (a). The green area is also larger than that seen in (a)

Figure 1.3 Diagram of floral morphological traits measured. Mimulus guttatus (right) dorsal view, with upper corolla removed to show reproductive structures. Floral measures reported in study depicted with brackets

2 CHAPTER 2 The adaptive value of floral UV reflectance: a comparative investigation

of floral traits, geography and bee visitation

2.1 Introduction

Even over evolutionary timescales, closely-related species often retain similarity in characters; i.e., characters can exhibit what is known as phylogenetic signal. Measurements of phylogenetic signal can be useful for detecting statistical non-independence in a trait data set (i.e., the average degree of covariance among trait values for a clade that can be explained by shared phylogenetic history). A variety of indices have been employed to test and evaluate for phylogenetic signal in quantitative traits. After statistically accounting for phylogenetic signal, it is possible to more accurately address questions about the correlations among certain traits

(Felsenstein, 1985), the tendency of characters to shift after the niches changes (Thuiller et al.,

2011), and where shifts occur in rates of change in characters in phylogenies (Losos, 2008).

Phylogenetic signals can emerge when related species respond similarly to a common selection pressure, but natural selection is not a necessary requirement (Gittleman et al., 1996,

Blomberg et al., 2003, Revell et al., 2008). Generally, adaptation in response to natural selection can reduce the value of phylogenetic signal, and produce unclear phylogenetic signatures. In topological phylogenetic trees, related species will tend to be similar under the simplest evolutionary models, such as Brownian motion. Topology is described as a rooted tree shape, which corresponds to the ancestor represented at the root and the offspring at the tips and the branches in the phylogeny depicting the evolutionary relationships among the taxa. Assuming a

Brownian motion model, many previous studies have mapped comparative data onto phylogenetic trees (Ingram and Mahler, 2013). This method of incorporating phylogenetic signal assumes that the trait difference between two species will be directly proportional to the amount of time that has

14 passed since the species shared a common ancestor, and was used to detect adaptive diversification and niche conservatism in a number of traits. More recently, model-based measurement provides an advanced method in phylogenetic comparative analysis, and incorporates the distribution of trait values (Pagel, 1999). Interpreting evolutionary process from empirical patterns by model simulation with a phylogenetic tree, allows for more precision in determining the effect of ecological interactions and opportunity for adaptively radiating lineages.

Traits (also called characters, characteristics, or phenotypes) are features of organisms that arise through the expression of an organism’s genetic makeup in a particular environment.

Variation in floral characters has served as an important model for understanding evolutionary rate and process, which is constrained by natural selection and abiotic components (climatic and geographic factors) (Koski and Ashman, 2016). Increased evolutionary rates can lead to greater convergence (i.e., lower values of phylogenetic signal) if a common stabilizing selective force becomes stronger (Svanbäck and Bolnick, 2005). Testing for the presence of trait rate shifts is a key component for evaluating the consequent questions about niche, coexistence, and ecological strategies (Ackerly, 2009). The other simple and basic method to measure of the rate of evolution for various extrinsic (ecological) and intrinsic (genetic) variables is a trait evolutionary model

(Butler & King 2004), which can be used to test whether trait evolution follows a pattern of adaptive radiation or species specialization. Determining the tempo and mode of evolution in a specific trait, is an important step in understanding the functional role of a trait in the evolutionary process. For instance, organisms diversify in a trait over short timescales as a response to a change in the environment, such as establishing on an isolated island, in a degraded habitat, or during extinction of an interacting species (e.g. pollinators). These changes create new challenges and offer new niches for organisms (Schluter, 2000). Thus, examining how the trait differs in a related

15 species can be used to gain insight into the history of the character, as well as how it might evolve in future. Both extrinsic (ecological) and intrinsic (genetic) factors have received much attention in the investigation of questions about the determinants of diversification and rate of evolution

(Svanbäck and Bolnick, 2005, Ackerly, 2009).

Genes of the flavonoid biosynthesis pathway have been characterized as contributors to a diverse suite of colours (red, pink, purple, and blue) of flowering plants (Tiffin et al., 1998).

Flavonoid compounds are responsible for UV pigments in the nectar guides of flowers (Thompson et al., 1972, Dement and Raven, 1974, Rieseberg and Schilling, 1985, Gronquist et al., 2001).

Recent studies indicate the close ecological associations between flower colour and pollinator behaviour (Muchhala et al., 2014), and between flower colour and geography (Shrestha et al.,

2014a) or bioclimate (Smith and Goldberg, 2015). Therefore, it is reasonable to assume that the effect of selection pressures and environment on flower traits may restrict the UV reflectance phenotype to be associated with certain flower colours. For example, at higher elevations, the temperature is cold with strong solar UV irradiance. Cold stress restricts the production of flavonoids (Rivero et al., 2001). Flavonoids serve as regulator enzymes in the anthocyanin pathway, which plays a role in several ecological functions like controlling plants against UV radiation and pests, or promoting the interaction between the plant and the pollinators (Koes et al.,

1994, Shirley, 1996). The prediction of a significant effect of temperature on UV pattern in flower petals is supported in Potentilleae (Koski and Ashman, 2016). Here, I test whether there exists a correlation between flowers colour and geography in a global dataset, examining whether the effect of geography on UV reflectance is supported at larger spatial scales.

Previous studies have investigated the relationship between pollinator preference and visual floral traits. For example, the floral traits (such as large display size, rich nectar rewards,

16 low in anthocyanin and carotenoid pigments) evolve subsequent to geographic isolation and cause further phenotypic divergence between bee-pollinated Mimulus lewisii and hummingbird- pollinated Mimulus cardinalis (Schemske and Bradshaw, 1999). In another study, Rae and.

Vamosi (2013) examined the variation of UV pattern on flower petals has effect for nectar guides which supports the correlation relationship between UV reflectance trait and pollinator visitation.

Based on the previous results, I hypothesized that the ultraviolet (UV) reflectance is a neglected mechanism of divergence between closely related species, playing a role in pollinator group shifts.

However, UV reflectance also may have a protective function and increase in prevalence in high

UV irradiance environments (e.g., high alpine floras, Koski and Ashman, 2015a, Koski and

Ashman, 2015b). Therefore, in Mimulus species, the diversification of floral traits (e.g., UV reflectance, colour, shape and display) may occur as the consequence for plants survival in novel niches. Different resources can be a key component in adaptive radiations, where a number of closely-related species originate relatively rapidly (Darwin and Mayr, 1859, Erwin, 2008, Tokeshi,

2009). The range of underutilized niches can offer an upper limit to species, but species can continue to proliferate into diverse phenotypes if they adapt to interact with different pollinators

(Walker and Valentine, 1984, Benton and Emerson, 2007). Ecological opportunity predicts that biotic components of the environment can lead to enhancement of the phenotypic variance and extent of phenotypic divergence among types, especially in conjunction with geographic divergence or environmental stress (Simpson, 1955, Schluter, 2000).

Determining whether UV reflectance in flowers is a consequence of strong selection requires an understanding of models of macroevolution. The Brownian motion (BM) model posits that there is no optimal phenotype, and so the trait will (1) appear to evolve randomly by genetic drift and clade-specific directional selection (Felsenstein, 1988), and (2) exhibit a low phylogenetic

17 signal according to an analogous random walk along the lineage. The advanced models depend on the BM model which are consistent with more nuanced and realistic factors in evolving lineages

(mutation, selection and genetic drift) (Martins and Hansen, 1997, Estes and Arnold, 2007, Revell et al., 2008). When high values for phylogenetic signals are observed, this pattern indicates that related species are ecologically similar. In the Ornstein-Uhlenbeck (OU) model, the trait has a selective optimum and selective pressures act to drive the trait to this optimal value. If different clades have different optima, then drift and natural selection can cause adaptive radiation via morphological diversification (Butler et al., 2004). Under the OU model, natural selection and complex environmental factors, such as the change of geographical structure and climate, generates a pattern of rapid change in a trait over short time periods, but over longer temporal and spatial scales, stabilizing selection can tightly constrain the overall morphospace (i.e. morphological spaces) (Felsenstein, 1973, Lande, 1976, Eldredge et al., 2005). The OU model can be produced similar evolutionary changes in older or younger clades, but this signature is obscured

(i.e. lower evolutionary rate) over long time scales (Hansen, 1997, Estes and Arnold, 2007). The

OU model tends to optimally partition morphological disparity and species diversity due to the recent events, whereas the early bursts (EB) model assume that most of the total trait disparity is generated in early divergences of a clade (Blomberg et al., 2003, O'Meara et al., 2006, Harmon et al., 2010). The EB model thus envisions an “early burst” evolutionary process in which evolution is generated rapidly early in the radiation, often occurring within an established biota of competitors, predators, and/or new habitats (Yoder et al., 2010). But subsequently EB trait evolution slows down as niches are filled, followed by reduced diversification.

Because UV patterning in flowers is beyond the visual spectrum for humans, few studies have examined the relationship between altitude gradients, UV reflectance and pollinator

18 preferences (Koski and Ashman, 2015a). In this chapter, I aim to explore the following hypotheses:

1) Because the fitness associated with floral UV reflectance is heavily influenced by pollinator functional groups, floral UV reflectance will not exhibit a pattern of neutral evolution (BM); 2) At the macroevolutionary scale, the magnitude of stabilizing selection will vary with geography and with pollinator syndrome; 3) I predict that species that inhabit lower altitudes will exhibit flowers that have little UV reflectance or have UV-absorbing flowers.

2.2 Materials and Methods

2.2.1 Study system

I used data from the Floral Reflectance Database (http://reflectance.co.uk) (Arnold et al.,

2010) to generate a dataset of plant attributes, which includes altitudinal gradient, flower size

(corolla width and tube length), plant height, bee visitation. I separated flower size into two measurements: corolla width and tube length. Plant height, an important variable in floral phenotype, is the measurement of the erect stem from the ground. Flowers were categorized as pollinated by small, medium, or large bees, bumblebees, wasps, flies, beetles, butterflies, hawkmoths and/or hummingbirds. For the purposes of this project, I code plant species as “1” if they were pollinated by bees and “0” if pollinated by other pollinator groups.

The Floral Reflectance Database was found to have sufficient data for 146 species

(Wyszecki and Stiles, 1982). I then searched the literature, scanned other database websites or specimen in herbarium or pictures to enrich the dataset with the missing morphologic variables and verify the existing assessments. Four Mimulus species (M. guttatus, M. lewisii, M. cardinalis and M. luteus) were measured from my own specimens growing in the University of Calgary greenhouse. The final dataset obtained a phylogenetic tree with 150 species in different genus.

2.2.2 UV measurement

UV spectra were also quantified by Ocean Optics Jaz 2000-Series spectrometer with a

UV/VIS xenon light source and XSR fiber optic reflection probe (Ocean Optics, Dunedin, Florida,

USA) for every 0.22nm between 300~700nm wavelengths. Whole spectrum exploratory analyses indicate reflectance curve and amplitude from 300~700nm, which were performed across the region divided by the mid-wave nadir for UV characteristic (Kemp, 2006, Kemp and Rutowski,

2007). UV reflectance spectra were summarized using segment classification. I used the labels

“brightness” and “hue” to calculate UV variables as perceived by pollinators. This system quantifies UV into UV hue and UV brightness. UV hue is calculated as the wavelength corresponding to the highest peak from 300 to 400 nm, UV brightness is the total area under the reflectance curve from 300 to 400 nm. For instance, the visible colour of flowers is yellow to humans but transmits UV light as “UV-green” colour to the prospective bees (Gumbert et al., 1999,

Arnold et al., 2010). The UV spectral reflectance spectra extracted from Floral Reflectance

Database (http://reflectance.co.uk) (Chittka and Menzel, 1992, Peitsch et al., 1992) can be classified in terms of variance measurements which include flower colour, chroma, UV brightness and UV hue (Endler, 1990). The range of UV hue is small, which cannot illustrate the correlation with various biotic and abiotic factors clearly. UV hue was limited in wavelength range around

300 nm, but it has larger range in the ordinate (reflectance percentage). For clearly the correlation with other factors, I collected the maximum reflectance percentages, which were corresponding to

UV hue, instead of UV hue wavelength. In this thesis, I used UV maximum on behalf of the corresponding UV hue.

I concentrated on UV brightness and UV maximum in this study. The spectral reflectance values from wavelengths between 300 and 700nm are transformed in continuous (UV brightness and UV maximum) and discrete data (UV presence/absence). I quantified the UV reflectance curve between 300 and 700nm in two continuous variables (UV brightness/maximum). UV brightness is calculated as the summed reflectance amplitudes from 300nm to 400 nm wavelength. UV maximum is calculated as the highest UV reflectance value in the unimodal curve from 300nm to

400 nm wavelength. To attract pollinators, the UV reflectance flowers often show a bulls-eye pattern (Kemp and Rutowski, 2007). To gain a measurement of UV reflectance flowers in these patterned flowers, I measured the petal apex reflecting and UV-absorbing in the center. In a simplified comparative analysis, I converted the data to a binary trait, using the code “1” for UV reflecting flowers. In contrast, UV-absorption flowers were represented by “0”. The UV- absorption flowers do not have a UV pattern in petals and is considered dark colour from the perspective of pollinators.

2.2.3 Quantifying phylogenetic signal

I used Phylomatic (http://www.phylodiversity.net/phylomatic) (Webb and Donoghue,

2005) to generate a phylogeny for the 150 species in my spectral dataset (Webb and Donoghue

2005) using Stroedtree (Zanne et al. 2014, plants), which supplied time-calibrated branch lengths and visualized the resulting tree using ‘ape’ (Paradis et al., 2004) ‘geiger’ (Harmon et al., 2008)

‘diversitree’ (FitzJohn, 2012) R packages.

I tested for the presence of phylogenetic signal (the degree to tell closely resemblance between related species on the phylogenetic tree) for bee visitation, and geographic (altitude) and floral traits (UV reflectance, plant height, tube length and corolla width) parameters for all 150

21 species for which I have values of UV reflectance. Blomberg et al.'s (2003) K and Pagel's (1999)

λ are two quantitative measures of the value of phylogenetic signal. Values of K < 1 result from less resemblance between related species due to a high evolutionary rate and K > 1 results from more related species being similar due to low evolutionary rate or stabilizing selections. λ is a scaling parameter for the correlations between branch length between any two species and their trait values, thus λ is not expected to be over 1. Values of 0 < λ < 1 are interpreted to mean, while closely-related species may be more similar than expected by chance, there is little evidence that the trait is under natural selection; λ=1 means the evolutionary process closely approximated by the BM model. Pagel’s λ can be used to estimate phylogenetic signal of discrete data (the presence/absence of UV reflectance and bee pollinated). Both of these methods are implemented by the ‘multiPhylosignal’ and ‘phylosig’ function in the R phylogenetics package ‘phytools’

(Revell, 2012) which is open-source from the Comprehensive R Archive Network, CRAN (Team,

2014). (The detailed introduction about equation of phylogenetic signal statistic, K, showed in

Appendix B.)

Pollinators in different geographical areas can put different selection pressures on a lineage.

Abiotic selection pressures (temperature, precipitation and habitat etc.) can vary along geographic gradients and can contribute to UV reflectance variation in floral traits (Koski and Ashman, 2015a,

Koski and Ashman, 2016). In order to reduce the influence of environment, I removed 69 species from the set of 150 species tree such that only those that were present in Israel remained. I measured the K value for the analysis with only these 69 species in Israel and tested whether I obtained similar results as in the broad-scale geographic analysis.

2.2.4 Comparative analyses

Any flower characters that relate to a pollinator’s visual system, such as floral display and or a UV bullseye pattern, can be conceived as an adaptation for enhancing pollinator foraging efficiency (Lunau, 1992b, Conner and Rush, 1996, Thompson, 2001, Koski and Ashman, 2014).

Considering the adaptation can be costly, UV reflectance should be associated with floral species that generally invest more in traits for attracting pollinators. The hypothesis about positive associations between UV reflectance and floral display size was assessed using comparative analysis.

In an initial assessment with altitudinal position, floral traits, and bee visitation, I tested whether associations exist between these traits using phylogenetic regression (Felsenstein, 1985) and phylogenetic ANOVA (showed in Appendix B Table 1). To test for the presence of a phylogenetic correlation between UV quantitative variation and each predictor variable separately,

I generated the intercepts and slopes from the linear regressions of Phylogenetic Independent

Contrast (PIC) scores across the 150 species tree with the ‘pic’ function in the ape package. I also performed phylogenetic least-squares regression using the ‘nlme’ function in ape. Generalized least squares is a method for estimating the fit of a linear model when the errors exhibit uneven variance (heteroscedasticity), or when the errors are correlated.

2.2.5 Model selection

The K statistic is thought to be a very useful method for estimating the phylogenetic signal in continuous traits. This metric can be used to compare trait evolution in different clades underlying different evolutionary models but does not estimate correlations between traits. To estimate correlations between traits or between geographic distribution and trait values, previous

23 studies (Smith et al., 2008, Revell, 2010, Harmon et al., 2010, Koski and Ashman, 2016) have used the PGLS approach with different evolutionary models (BM, OU and early bursts).

Interestingly, the best evolutionary model for a given trait may differ from the best model that incorporates a relationship between that trait and another trait. To solve this problem, it is important to compare alternative evolutionary models when performing "phylogenetically correct" statistical analyses (Revell, 2010). Using PGLS, phylogenetic least-squares regression, you can examine a number of alternative evolutionary models for explaining correlations.

I used the packages ‘nlme’ and ‘geiger’ in R to compare the likelihood of the data under the Brownian motion model (corBrownian), the single peak Ornstein Uhlenbeck model

(corMartins), and the early-burst/late-burst model (corBlomberg). The Brownian motion (BM) model assumes that trait divergence should be correlated with time since a pair of species shared a common ancestor on the phylogeny (Felsenstein, 1985). A strong phylogenetic signal in a trait increases the chances that the phylogenetic distribution of traits follows a BM model. The

Ornstein- Uhlenbeck (OU) model is used to evaluate the evolutionary rate of the trait underlying stabilizing selection (Martins, 2000, Blomberg et al., 2003, Hansen et al., 2008). After or during adaptive radiation, the different evolutionary signals are constrained through selective optima, not random genetic drift in phenotypic space (Martins, 2000, Blomberg et al., 2003, Hansen et al.,

2008). The parameter (a) of an OU model provides an estimate of acceleration or deceleration in evolutionary rate. A higher value represents a trait with low phylogenetic signal. When parameter

(a) is zero, the model is equivalent to the BM model. Phylogenetic signal varies from strong (a=0) to zero (a=∞). An “early burst” evolutionary process represents a process where evolution is rapid early in the radiation but slows down as niches are filled in the phenotypic space. A rapid evolutionary rate appears in the younger clades over a short period of time, but because of the

24 deceleration in evolutionary rate, phenotypes do not exhibit the expected amount of divergence over the long term.

The Akaike information criterion (AIC; Akaike, 1992, Akaike, 1998) can be used to interpret the conditional probabilities of each potential model which facilitates model comparison.

AIC scores provide a way to examine which model maximizes the probability of the observed data.

Higher log likelihood and lower AIC values indicate better model support. AICi is valid for sufficiently large data sets, and AICc is valid for finite data sets (Sugiura, 1978, Hurvich and Tsai,

1995). In addition, ∆i(AIC), AICi – minAIC, is used to estimate AICc weights, which offers further evidence on the best model (Wagenmakers & Farrell, 2004).

2.2.6 Phylomorphospace approach

A "phylomorphospace" plot provides a 2D or 3D visualization of species values for two traits, where the species are linked by their phylogenetic relationships (Sidlauskas, 2008). The coordinates of the nodes of the phylogeny correspond to their ancestral state reconstruction values for each trait. I used this technique to visualize the trajectory of trait evolution during diversification, assuming Brownian motion (BM) or Ornstein Uhlenbeck (OU) models. As opposed to simple “morphospace” plots, phylomorphospaces can illustrate ancestor-descendant trajectories of trait evolution.

2.3 Results

2.3.1 Phylogenetic distribution of measured traits

The plotted backbone phylogenies with discrete and continuous trait data are shown in

Figure 2.1. For 150 species, Pagel’s λ was higher than 0 for the discrete data UV reflectance

(λ=0.2635349, logL=-102.53) and bee visitation (λ=6.62E-05, logL=-47.72035). I got the similar results for 69 species, UV reflectance (λ=6.67E-05, logL=-49.19213) and bee visitation (λ=4.44E-

05, logL=2.4026).

The K statistic is used as a standardized unit of estimation of how trait values are distributed with regard to the phylogenetic relationships of species and can reveal overall patterns of evolutionary conservatism or adaptive radiation. In Table 2.1, K was more than 0 for the continuous data UV reflectance (UV maximum and UV brightness) which means the distribution of UV variance is considered due to other factors for 150 species, and does not depend solely on phylogenetic relationships. I obtained similar values in the taxa of 69 species (Table 2.2). K was significantly higher than 0 for height and corolla width in both groups.

2.3.2 Comparative analysis (Phylogenetic Independent Contrasts, PIC)

Corolla width exhibited a significant positive association with flower tube length (r2=0.25,

P<0.001). In my phylogenetically-informed analysis, floral traits variables were positive correlated with UV variation and bee visitation (Figure 2.2).

In examining the pairwise correlations between any two continuous variables (UV brightness, UV maximum, altitude, tube length, corolla width and plant height), with the variables log transformed (Figure 2.2 and Table 2.3), I found that UV reflectance has a significant positive correlation with flower size, and plant height. From the correlation results between continuous variables and discrete variables (the presence/absence of UV reflectance and bee pollinated)

(Figure 2.2 and Table 2.4), I found that bee visitation has a positive correlation with UV reflectance.

The PIC results also suggest there was no association between altitude and bee visitation or between altitude and UV reflectance.

As seen in Figure 2.3, the phylomorphospace illustrates the trajectories of evolutionary model and shows a general increase in diversity from roots to tips (i.e., the values at the tips occupy a greater circumference of the morphospace than the internal nodes).

2.3.3 Model Selection

To examine the evolutionary rates of floral traits independently, I first compared AIC values among three models for UV brightness and UV maximum to determine the best fit (see

Table 2.5 and 2.6), finding that the OU model had the most support (i.e. lower AIC value of the three models). The PGLS analysis constrained to incorporate an Ornstein-Uhlenbeck model which is then likely the most appropriate model for UV traits. Using the Akaike weight ratio, I estimate that the OU model is

푊푊푂푈(퐴퐼퐶) ≈ 30731 푊푊퐸퐵(퐴퐼퐶) times as likely as the EB model. The parameters alpha of UV brightness and UV maximum are

0.25 and 0.35, which suggests that stabilizing selection influences evolution of the traits.

To address whether variables such as altitude, floral traits and/or bee visitation predicted quantitative variation in UV brightness and UV maximum, I ran sets of models for UVB and UVM

(Table 2.7) assuming BM and OU models of trait evolution. In the OU models, competition variables, except plant height, are important contributor for the best model of UV reflectance.

2.4 Discussion

This analysis of global patterns in flowering plant species is consistent with previous studies that have found that UV patterns influence bee visitation and foraging (Rae and Vamosi,

2013). My analysis with phylogenetic regression and model estimation for multiple correlation analysis indicates that abiotic (altitude gradient) and biotic factors (floral traits and bee visitation) both appear to structure UV pattern variation. This is consistent with previous findings of a positive effect of altitude on UV bullseye patterns (Koski and Ashman, 2015a). I found that, while altitude had only a weak effect, there was a trend for highly UV reflecting flowers to inhabit lower altitudes, potentially because those habitats are richer in bees. Previous studies have focused on the function of UV absorption on the petal in terms of its effects on absorbing solar UV-irradiance (Tevini et al., 1991, Day et al., 1993, Middleton and Teramura, 1993, Gronquist et al., 2001, Koti et al., 2004), but this research on UV patterning indicates that there is a stronger relationship with pollinators

(Koski and Ashman, 2016). Thus, my prediction that bees prefer to pollinate higher-UV- reflectance flowers was supported by the results. Flower size and plant height may act in conjunction with UV floral patterns to attract bee visitation, but these phylogenetic correlations may just be due genetic correlations (explored in more detail in Chapter Four).

2.4.1 Abiotic factors on floral UV reflectance

The PIC results indicated that all variables except altitude are associated with floral UV reflectance. As well, they indicate no correlation between either UV reflectance or bee visitation and altitude in PIC results, which was the opposite of the result of phylogenetic ANOVA (results in Appendix B Table 1). These contrasting results suggest that presence of multiple predictors influencing UV traits and examining a single correlation without accounting for underlying correlates can be problematic. I outline potential reasons for how underlying correlates can influence simple comparative analyses below.

(1) Geographical sampling

In my study, traits values for the 150 species were from Norway, Germany, Austria, Italy,

Israel, Canada and Brazil. The environment status, such as temperature, is different at various latitude, but same altitude. For example, the species grow in Brazil from 700 to 3000 m above sea level, which are under different selection pressures than the species growing in Norway from 800 to 1630m. In other words, although I was mainly interested in the effects of altitude, temperature could have influenced the results. In a study examining the yellow-flowered species Argentina anserina, flowers with a larger area of bulls-eye on the petals occurred disproportionately at high altitudes, (Koski and Ashman, 2015a). Therefore, while limiting the species and sample locations can add precision regarding the effects of altitude on floral evolution, these studies often lack generality. In my study, when we broaden the number of species type and locations, we find that the effects of altitude are not as strong as other variables.

(2) Adaptive pleiotropic effects

Thermal stress increases along altitudinal clines such that plants persist under cold stress more at the highest altitudes. Production of flavonoid compounds can ameliorate cold stress

(Rivero et al., 2001, Bita and Gerats, 2013), and flavonoid compounds induce UV absorption pigmentation on the petals (Thompson et al., 1972, Brehm and Krell, 1975, Rieseberg and

Schilling, 1985, Gronquist et al., 2001). Cold stress thus provides selection pressure for flowers to exhibit a dark colour (Falconer et al., 1996, Armbruster, 2002, Bernhardt et al., 2016). A dark coat can generate heat to defend against cold stress, thus allowing the individuals with dark phenotypes to survive and reproduce. While UV patterning on flowers may have initially evolved to function as UV protection or cold stress amelioration, the potential `creative' role of pleiotropy may have

29 led to the trait being co-opted for other functions. For example, considering that higher altitude can select for higher amounts of UV absorption pigments (Koski and Ashman, 2015a), populations of Argentina anserina living at high altitudes experience a corresponding shift in pollinator assemblages from hymenopterans to dipterans. Similarly, in the alpine species Viola pedata, bees preferred to forage on bicoloured flowers, because the defensive pigmentation (dark purple) produces warmer posterior petals which bees prefer (Bernhardt et al., 2016). However, other studies have found that bees prefer to forage on flowers with less UV absorption (Rae and Vamosi,

2013) so the relationship appears to be species- and habitat-specific. Therefore, at a global scale, the finding of no association among altitude, floral morphologies and UV reflectance suggests that selection pressures are complex and context-specific. The phenotypic correlations are explored in more detail in Chapter Four.

(3) Influences of diversification

Some insight into the main function of UV patterning in flowers may come from consideration of the evolutionary models with the greatest support. The effect of altitude was a significant factor in models of UV brightness (where the OU model was most supported) but not

UV maximum (where the BM model was most supported). Taking these phylogenetic models into account suggests that UV brightness may be a trait with stronger stabilizing selection pressures acting upon it (i.e., to survive cold stress) while UV maximum may depend on the pollinator community and play a role in reproductive isolation (Adderley and Vamosi, 2015) and diversification (Levin, 2000). This is consistent with my finding that the presence of floral UV reflectance attracted different pollinator assemblages, and was associated with different display size along altitude gradients.

2.4.2 Biotic factors on floral UV reflectance

In my comparative analysis, corolla width of flowers exhibits a positive association with flower tube length (r2=0.25, P<0.001), indicating that increases in flower display level often occur in both the length and width direction. From the PIC results, the prediction that macroevolutionary reflectance of ultraviolet (UV brightness and UV maximum) on the flower petals would be associated with other floral traits (tube length, corolla width and plant height) was supported.

While the comparative analysis between floral traits and UV reflectance as a continuous value is more persuasive than the analysis with UV reflectance as a discrete variable, both analyses indicate that UV is an important component of floral display. Nevertheless, the r2 value was close zero and is therefore considered weaker than the correlations between other floral components. The relationship between UV reflectance and floral display may not have a linear relationship, because of the context-dependent influences described above.

Nevertheless, the results suggest that UV reflection has a genetic component and may be under strong selective forces, as seen with other floral traits (Warren and Mackenzie, 2001,

Wessinger and Rausher, 2012, Wessinger et al., 2014, Ding et al., 2017). In the OU models, all variables except plant height are potentially important. Thus, the results support my prediction that the evolutionary variation of UV reflectance was determined by natural selection, which simultaneously influences other floral traits and pollinator visitation. Thus, I predict the patterns observed with altitude are a by-product of other, more important relationships; in the other word, higher UV reflecting flowers are observed at lower altitudes, because bees prefer to pollinate at lower altitudes. The PGLS analysis suggests that the OU model is the most appropriate model of evolution for these traits, indicating that it is a trait subject to stabilizing selection pressures. Using

AIC model selection (Akaike, 1974), the optimality fitting model for floral traits also suggests the

OU model for these 150 species taxa. I will examine whether there is sufficient genetic variation for selection to act upon in Mimulus guttatus in Chapter Four.

2.4.3 Concluding Remarks

Biotic and abiotic effects are both factors associated with the flower UV reflectance variation. In my study, I found that the UV reflectance phenotype tended to be associated with yellow flowers, which would generally be considered flowers with the bee pollination syndrome.

These colour preferences are far from universal. For example, in a previous study (Wessinger et al., 2014), results failed to find that the red flowers of Penstemon barbatus had high hummingbird visitation. Hummingbird vision has red receptors, whereas other pollinators are poorly attracted

(McCarthy et al., 2015). The authors concluded that floral traits and corresponding pollination syndrome evolve independently and that environment had a greater influence on floral colour.

Several studies provide compelling evidence, such as from the investigations of biochemistry and genetics of pigments show repeated evolutionary transitions that contribute to the adaptive divergence of floral traits (Bradshaw and Schemske, 2003, Hoballah et al., 2007, Wessinger et al.,

2014, Ding et al., 2017). For example, I found several explanations from Wessinger and Rausher

(2012) that floral colour morphs are often associated with variants of anthocyanin pathway genes.

While there is still uncertainty over whether abiotic or biotic factors (namely, geography and bee visitation) provide the main selection agents, this study was able to retrieve evidence that the phylogenetic distribution of UV trait disparity is consistent with the OU evolutionary model.

OU models of evolution over macroevolutionary time scales suggest that the dominant evolutionary force is stabilizing selection towards floral traits at adaptive optima. Floral UV

32 reflectance is an important contribution to the evolutionary divergence of intra/interspecies taxonomic relationships. The relative importance of these correlations (floral traits, altitude and pollinator syndromes) forms the basis of following chapters of this thesis.

Table 2.1 Blomberg’s K or Pagel’s λ for the continuous traits visitation for 150 species. Asterisks indicate significance correlation coefficients. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 K PIC.variance. PIC.variance.rnd.m PIC.variance. PIC.variance. Pagel’s λ logL obs ean P Z Altitude 0.098 6.20E+04 1.21E+05 0.234 -0.470 7.63297E-05 -1235.910 Height 0.407 4.42E+02 3.84E+03 0.003* -0.671 0.9893727 -905.152 Tubelength 0.101 3.81E+00 8.40E+00 0.057 -0.923 0.5579057 -514.142 Corollawidth 0.129 6.22E+00 1.68E+01 0.011* -0.975 0.8038969 -552.272 Brightness 0.080 3.07E+00 5.40E+00 0.129 -0.923 6.61898E-05 -482.533 UV 0.067 1.15E-03 1.67E-03 0.259 -0.661 0.2765238 123.090 maximum

Table 2.2 Blomberg’s K or Pagel’s λ for the continuous traits visitation for 69 species in Israel. Asterisks indicate significance correlation coefficients. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 K PIC.variance. PIC.variance.rnd. PIC.variance.P PIC.variance. Pagel’s λ logL obs mean Z Altitude 0.113 2.29E+04 2.50E+04 0.412 −0.311 6.67E-05 -540.488 Height 0.556 2.31E+02 1.19E+03 0.007* −0.852 0.9882917 -417.048 Tubelength 0.175 7.36E-01 1.24E+00 0.088 −0.982 6.67E-05 -198.389 Corollawidth 0.416 2.16E+00 8.44E+00 0.001** −1.188 0.9314328 -250.842 Brightness 0.066 3.65E+00 2.32E+00 0.919 1.434 6.67E-05 -220.599 UV 0.072 1.23E-03 8.53E-04 0.885 1.261 5.05E-05 52.968 maximum

Table 2.3 Pairwise correlation coefficients for the continuous variables. Asterisks indicate significance correlation coefficients. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Altitude Tube Length Corolla Width Plant height Brightness 0.003 0.068** 0.078*** 0.053** UVmaximum 0.005 0.140 *** 0.095*** 0.022 .

Table 2.4 Pairwise correlation coefficients for between the discrete and the continuous variables. Asterisks indicate significance correlation coefficients. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Altitude Tube Corolla Plant Brightness UVmaximum Length Width height Bee 1.884e-06 0.028* 0.121*** 0.112*** 0.028* 0.114*** UV 0.005 0.007 0.002 0.194*** 0.116*** 0.028*

Table 2.5 Results of AIC for Three Competing Models (UV brightness or UVB). EB model: a=0.1070329; OU model: alpha =0.2492987 (alpha parameter for OU models is about 0.25 for UV brightness model); Time: 30730.99426 Model log likelihood AICc ∆ AICc AICc Weights 1 Brownian Motion -555.759 1115.600 146.641 1.436419e-32 2 Early Burst -491.730 989.625 20.666 3.253938e-05 3 Ornstein- -481.400 968.959 0.000 9.999675e-01 Uhlenbeck

Table 2.6 Results of AIC for Three Competing Models (UVmaximum or UVM). EB model: a = 0.06365846; OU model: alpha = 0.3503823 (alpha parameter for OU models is about 0.35 for UV maximum model); Time: 4.93E+12 (4.93*1012) Model log likelihood AICc ∆ AICc AICc Weights 1 Brownian Motion 36.113 -68.144 171.358 6.165910e-38 2 Early Burst 93.607 -181.048 58.454 2.026673e-13 3 Ornstein- 122.834 -239.503 0.000 1.000000e+00 Uhlenbeck

Table 2.7 Comparison of different models predicting UV brightness (UVB) and UV maximum (UVM). Presence of a given variable in a model is indicated with ‘1’ and absence is indicated with ‘0’ Traits AIC Altitude Tube Height Corolla Bee Brownian Ornstein- Motion Uhlenbeck UVB10 1 1 0 1 1 1091.891 950.191 UVB9 1 0 0 0 1 1114.740 961.517 UVB12 0 1 0 1 1 1099.683 963.622 UVB8 1 1 0 1 0 1093.855 965.291 UVB2 0 1 0 0 0 1114.720 965.947 UVB5 0 0 0 0 1 1113.269 967.046 UVB4 0 0 0 1 0 1114.878 967.477 UVB6 1 1 0 0 0 1107.458 967.947 UVB7 1 0 0 1 0 1099.057 968.642 UVB11 1 1 1 1 0 1106.237 968.700 UVB1 1 0 0 0 0 1115.829 969.604 UVB3 0 0 1 0 0 1117.443 970.569 UVB13 0 0 1 0 1 1114.884 970.934

UVM12 0 1 0 1 1 -125.890 -253.204 UVM8 1 1 0 1 0 -120.126 -252.609 UVM10 1 1 0 1 1 -127.717 -251.317 UVM6 1 1 0 0 0 -89.203 -249.885 UVM2 0 1 0 0 0 -74.702 -248.745 UVM4 0 0 0 1 0 -71.997 -248.031 UVM11 1 1 1 1 0 -111.953 -246.372 UVM7 1 0 0 1 0 -91.864 -244.414 UVM9 1 0 0 0 1 -81.441 -242.973 UVM5 0 0 0 0 1 -84.417 -241.475 UVM3 0 0 1 0 0 -66.250 -239.861 UVM1 1 0 0 0 0 -66.561 -239.588 UVM13 0 0 1 0 1 -83.981 -237.732 Parameter estimate(s): alpha=1

Figure 2.1 Phylogenetic relationships, pollinator visitation associated with UV pigments type and distribution of biotic and abiotic continuous factors, which include altitude gradient (Alt), tube width (Tub), corolla width (Corolla), plant height (Height), UV Brightness (Bright), UV reflectance of UV hue (UVMAX). Colour codes indicate separate discretely valued traits, pollinator visitation and UV pigments type, which mapped on this three. Red is no-bee pollinated and blue is bee pollinated species, black is UV reflecting flowers and grey is UV absorbing flowers. Diameter of bars are proportional to the degree of continuously valued traits based on data for measurements from Floral Reflectance Database and empirical measurements. Traits values with optimization adjustments

Figure 2.2 Pairwise correlation between the discrete and the continuous variables using

phylogenetic independent contrasts

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Relative Bee Contrast Relative Bee Contrast Relative UV Contrast

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Figure 2.3 Phylomorphospace projections of a phylogeny generated with morphological diversification. From top to bottom figures, 2D visualization of species values for the traits between UV brightness & Corolla width, UV brightness & Tube length and UV bringtness & plant height. In these phylomorphospace, using colour to visualize ancestral state reconstructions for continuous traits, the ancestral state (internal node) estimates keep colour black and extant species values are the tip colour red

3 CHAPTER 3 Conservation genetics of Mimulus guttatus in Alberta: the contribution of

geographical and genetically distinct refugia to species management

3.1 Introduction

Effective conservation is fostered by increased knowledge of the geography of genetic diversity. Levels of genetic variability in species are influenced by the complex interaction of multiple processes, including competitive exclusion among species (Symonds et al., 2010), biotic insularity (MacArthur and Wilson, 1963), habitat complexity (Price et al., 2011) and population size (Kisel and Barraclough, 2010, Liu et al., 2010b). Environmental change on a broad temporal scale is a natural process that can lead to local adaptation, speciation, and extinction. The flora of

Alberta is young. Much of Alberta was covered by a massive ice sheet 15,000 to 16,000 years ago.

In Alberta, the isolated sites of Waterton Lakes and Cypress Hills are considered putative locations of glacial refugia or, alternatively, represent key locations of early migration of plant species from the South following glacier retreat. Thus far, few studies have investigated the demographic history, evolutionary history and geographic distribution of vascular plant species adapted to the landscapes distributed in Alberta. A few of Alberta’s rare vascular plants have widespread distributions within North America but are uncommon wherever they are found (Kershaw, 2001).

To monitor and manage rare species successfully, we must understand both the biology of the species and the reasons for its rarity. Many factors determine the distribution of rare species. Some rare Alberta species may have persisted as isolated populations.

Spatial genetic structure and distribution can be affected by climatic fluctuations and can show evidence that allows us to track the effects of climatic change for a species (Comes and

Kadereit, 1998, Stewart et al., 2010). Many studies (Wen and Shi, 1993, Naqvi et al., 1994) have recorded ice sheet retreats and expansions in glacial–interglacial cycles, providing insight into the

43 response of plant population contraction and expansion. The evolutionary history of plant species that have resided in glacial refugia during the Pleistocene cycles are generally thought to include the following two steps. Firstly, the founder effect and genetic drift make initial small populations lose many alleles, which can lead to genetic homozygosity during glacial expansion. Secondly, plant species persist in refugial place during glacial periods due to the maintenance of sufficient population size that reserves existing genetic variation (Comes and Kadereit, 1998, Liu et al.,

2010b). Thus, the sampling sites with plant populations that still harbour high levels of genetic diversity and numerous alleles are identified as putative glacial refugia.

Mimulus (Phrymaceae) has been the subject of intensive ecological and evolutionary genetic research for over 50 years. Species within Mimulus have become key model systems for investigating the genetics of speciation (Hiesey, 1971, Bradshaw et al., 1998, Sweigart et al.,

2006), inbreeding depression (Dudash and Carr, 1998), mating system evolution (Leclerc-Potvin and Ritland, 1994, Fishman et al., 2002, Sweigart et al., 2003), ecological adaptations (Macnair and Christie, 1983, Angert et al., 2005) and cytological patterns of evolution (Vickery Jr, 1978,

Beardsley et al., 2004). M. guttatus in particular is extremely variable throughout its range in its abundance, ranging from being considered invasive to rare (Dudash et al., 1997, Boedeltje et al.,

2004), making it an ideal species to test hypotheses regarding the contribution of demographic history to present-day rarity, which can then influence how we prioritize the conservation of this native species. Past dispersal has been observed to play a role in current genetic variation, with invasive populations having higher genetic diversity than native populations (Dudash et al., 1997,

Liu et al., 2010a). M. guttatus produces many small seeds, and its success is aided by seed buoyancy, high survival rates, and rapid growth (Boedeltje et al., 2004). Considering the

44 colonization success of this species, the rarity of this species in Alberta is therefore likely influenced by time and rarity of habitat.

Rare species within Alberta known to occur in specific regions are documented by the

Alberta Conservation Information Management System (ACIMS). According to ACIMS, seven

Mimulus species have ranges that extend into Alberta. With the exception of M. lewisii, Mimulus species (including M. guttatus, M. breweri, M. floribundus, M. glabratus, M. ringens, M. tilingii) are rare species in Alberta. M. guttatus in particular is restricted to three locations: northern Rocky

Mountains, Cypress Hills, and the foothills. These three places have different habitats, alpine, montane and foothills due to different climatic conditions. In high altitude sites, plants experience extreme abiotic conditions such as a high amount of solar radiation and low temperatures (Emery et al., 1994, McEwen and Vamosi, 2010). Additionally, the lowland environments are generally characterized having more precipitation and nutrient content, leading to greater amounts of competition from other species (Emery et al., 1994, McEwen and Vamosi, 2010).

The distribution of genetic diversity can be used to infer historical migration and identify the expansion range of a species under long-term survival in refugia. M. guttatus can readily be used in genetic analysis, making it an ideal organism to test hypotheses regarding the number and location of Pleistocene refugia in Alberta, as well as testing postglacial expansion routes. Climate oscillation, glacial cycles, and ice sheet movement among these habitats are factors that have likely moulded the current distribution and biodiversity of M. guttatus. Considering the dispersal capacity of a species such as M. guttatus, gene flow across these barriers that could rapidly erase this historical signature. Gene flow often opposes the effect of natural selection, which can exhibit phenotypic differentiation among species (Bradshaw, 1965, Mayr, 1970).

Floral UV patterning is one trait that has been investigated in M. guttatus for potentially

45 playing a role in pollination or tolerance of solar radiation. It is known that UV exposure varies with latitude and longitude (Koski and Ashman, 2016) and therefore different sites likely vary in the selection pressure for UV patterning. I hypothesize the sites that share gene flow exhibit similar phenotypes even despite under different environments. I also hypothesize that the genetic structure of M. guttatus in Alberta will reflect its dispersal along coastal migration routes. Previous studies have found that the refugial populations were likely located along the Coast Mountains and south of the Rocky Mountains (Avise, 2000, Godbout et al., 2008). The wave of migrants crossing from coastal British Columbian (BC) to Alberta (AB), the migration routes colonize interior AB from southwestern BC as the ice sheet retreated.

With a similar genetic toolbox I also determined: 1) whether the level of genetic diversity in populations of M. guttatus in Alberta mirror the report from Alberta Conservation Information

Management System (ACIMS), 2) the optimal genetic index that enables the characterization of the observed distribution patterns in M. guttatus, 3) the locations regarding the presumed refugia in Alberta, such as Waterton National Park and Cypress Hills, 4) the directions and magnitude of gene flow that have affected population genetic diversity, and (5) whether bioclimatic forces are associated with population differentiation, particularly in terms of the degree of floral UV reflectance. These data will allow managers to better assess the conservation priority for different populations of M. guttatus in Alberta.

3.2 Materials and Methods

3.2.1 Study System

Mimulus guttatus DC. (Phrymaceae, historically Scrophulariaceae, order Lamiales), the yellow monkey flower, is an annual or perennial herb native to western North America (Lowry et

46 al., 2009). It ranges from the Pacific Coast to the Rocky Mountains (Vickery Jr, 1978). While M. guttatus is common in the United States, Mimulus is considered a rare species in Alberta according to the Conservation Status Ranks in Alberta, in the ACIMS database. The perennial M guttatus species, grows in permanently wet habitat, can exhibit annual life cycles under osmotic stress in dry habitats (Hu et al., 2007, Hassine et al., 2008). Yellow flowers, with insect-visible UV patterns on the petals, can attract high numbers of visitors, namely solitary bees and bumblebees. One individual can produce more than 500 seeds. Capsules fruits are broadly oblong character with 7-

12mm long, which enfold the numerous seeds with 0.4-0.5mm long (Vickery, 1952, Vickery Jr,

1978). The weight of Mimulus seeds is less than 0.02mg.

3.2.2 Sampling site

For this study, I concentrated on populations of M. guttatus that occupy alpine and prairie habitats in Alberta. We also collected M. guttatus from Craig Bay, Parksville, BC. The wildflower genus Mimulus maintains tremendous diversity and abundance in these source populations in

British Columbia. I sampled M. guttatus from 60 sites in 4 populations in 2014 and then supplemented this sampling with 36 M. guttatus historical specimens, which were collected between 1986 and 1999 (site information shown in APPENDIX C Table 1). The six known extant population sites with M. guttatus are shown in Figure 3.1 (A and B). Fresh leaves were collected and dried quickly in silica gel for DNA extraction. Care was taken to sample different individuals and avoid sampling leaves that were from same slender creeping stems. Voucher specimens from each of the different populations are deposited in the University of Calgary Herbarium.

I collected M. guttatus from four populations, noting features that affect abundance as follows: (i) During the survey of Waterton Lakes National Park, M. guttatus grew along the small

47 stream in spruce forest on the east facing slope in Cameron lake north western trail. The population area was smaller than that of previous records (Kuijt, 1982, Moss and Packer, 1983, Douglas et al., 2001, Kershaw, 2001) and specimens. (ii) In Cypress Hills, I collected an individual plant every

~25 m along Nichol Springs, Reesor Creek and Battle Creek. All samples were taken from along the riparian zone. Compared with the other sampling locations, sites in Cypress Hills hosted abundant M. guttatus patches in habitats that ranged from boreal, montane, to prairie. (iii) For the

Adanac Road site in Crowsnest Pass, the habitats can be described as a roadside montane valley basin. This site was seepage flowering meadow adjacent a ditch, with an abandoned gravel pit in the vicinity. In terms of environmental disturbance, Crowsnest Pass would be considered to have the most anthropogenic activity, being close to coal mines, and suffering severe river floods. The distribution of the Mimulus genus has a more limited range in Crowsnest Pass.

Distribution records from herbaria and online databases were consulted for field sampling, but many M. guttatus populations were absent from previously recorded locations. Therefore, to enlarge the dataset, I collected leaf tissue from the herbarium specimens located in the collections at the University of Calgary, University of Alberta and University of Lethbridge. In the labels of these specimens, M. guttatus habitats were recorded as wet, open sites, including seepage areas, meadows, waterfall spray zones, streambanks, springs, gravel bars, rock ledges and crevices, ditches and clearings in the lowland, steppe, montane and subalpine zones in Alberta. The specimens, their geographical coordinates, and collection date are provided in Appendix C Table

3.2.3 Genetic markers

Following Vallejo-Marin and Lye (2013), I used 14 codominant markers (Table.3.1) to estimate the genetic diversity within and between 6 populations, sampled from a total of 96 individuals. Fourteen codominant markers, including 7 microsatellite makers (AAT motif;

KELLY and WILLIS, 1998) and 7 M. guttatus sequence tagged sites or MgSTS (intron-based length polymorphic markers) (Vallejo-Marin and Lye, 2013). As in the previous study (Vallejo-

Marin and Lye, 2013), I obtained the same fragment results that occurred in three peaks per locus for all samples that were amplified by AAT365 and STS657. The other 12 primers were found to amplify variable fragment sizes in the diploid samples.

3.2.4 DNA Isolation and PCR amplification

The genomic DNA was extracted from dried leaf tissue, which was stored in silica gel, by using the cetyl trimethyl ammonium bromide (CTAB) /chloroform DNA extraction protocol modified from Doyle (1987). DNA quantity was measured by NanoDrop ND 1000 (Thermo

Scientific), which was used to measure nucleic acid concentration and purity of nucleic acid samples. I also used one other method for a visual assessment of DNA quantity, using gel electrophoresis on a 0.8% agarose gel to observe potential DNA degradation in the archived samples.

Polymerase chain reaction (PCR) amplifications were carried out in 10 l reaction volumes consisting of 1l of 10 × ThermoPol buffer (New England Biolabs), 0.6 l of 25 mM MgCl2, 0.5l of 10 mM dNTPs (Invitrogen), 0.1 l of 10 M forward primer (Integrated DNA Technologies),

0.4 l of 10 M reverse primer (Integrated DNA Technologies), 1 l of genomic DNA (100 ng/l),

0.1 l of Taq DNA polymerase (5 U/l, New England Biolabs), 0.1l of BSA (10mg/ml), and 5.8

l of ddH2O. 0.4 l of the fluorescent FAM-, NED-, VIC-, and PET- labelled primers (Applied

Biosystems), and the quantity of the fluorescent forward primer keeps same as the reverse primers’. Amplification was performed by ABI C-1000 and S-1000 thermocycler (Bio-Rad, Inc.,

Hercules, CA) with protocol: 94°C for 3 minutes (min) 30 cycles of (94°C for 30 seconds (s)

52°C for 45s 68°C for 45s) 8 cycles of (94°C for 30s 53°C for 45s 68°C for 45s)

68°C for 10 min 4°C forever. PCR products were run in 2 % w/v low-melting-point agarose with SYBR®-safe DNA Gel Stain (Invitrogen, Carlsbad, CA, USA) to visualize allele sizes (in base pairs). Genotype and allele size can be visualized and scored by using a ABI 3500XL capillary sequencer under GeneScan 500 LIZ size standard (Applied Biosystems). I added 1 l of the PCR products to each 10 l formamide/standard solution. Then the mixture solution was denatured at

95 ° C for 5 min.

Fragment analysis was conducted using GenemapperTM 4.0 software (Applied

Biosystems), which exports raw peak sizes per locus referencing the size standard and the suitable allele range. Before calling alleles, it selects the colour that matches to the fluorescent - labelled primer. The proper peak sizes per locus were scored and then refined manually. Subsequently used the allele matrices for these 96 individuals in genetic population structure and genetic variation analyses. Detect and align the presence of null alleles and fragments errors in genotyping matric using Micro-Checker (Van Oosterhout et al., 2004).

3.2.5 Genetic and Population Structure Analyses

I modified the adjusted genotype data for different analysis using software by MStools

(Kavan and Man 2011). Several approaches were used to determine the population structure and genetic variance indices based on groups identified in the AMOVA (Analyses of molecular variance) with Arlequin version 3.11 (Excoffier et al., 2005) where the significance tests are based on 1000 permutations. AMOVA was used to test historical demographic expansion. Deviations from Hardy–Weinberg proportions and linkage equilibrium were analyzed with GENEPOP 4.2

(Rousset, 2008) based on the data matrices of alleles in 10,000 dememorization steps. I computed

FST (Wright’s fixation index), Geographic Distance, Nei Unbiased Genetic Identity based on the data matrices of alleles using GenAlEx 6.5 (Peakall and Smouse, 2006). FST is a measure of genetic differentiation. Unbiased expected heterozygosity (uHe) was calculated from GenAlEx 6.5, which is the value to identify the population diversity, specifically to identify the refugial locations. It is assumed that there is higher level of genetic variation in refugial locations, and relatively lower degrees of genetic variation in the more recently established populations.

The colonization history was evaluated using binary data, and STRUCTURE software was used to visualize the consequences of population divergence. A cluster analysis was utilized to estimate the degree of admixture among populations using the program STRUCTURE version

2.3.4 (Pritchard et al. 2000), estimating the likelihood of K= 2-5 for genetic clusters corresponding to the number of populations, using 200,000 burn-in iterations and 200,000 MCMC iterations.

STRUCTURE HARVESTER web version 0.6.94 (Earl, 2012) was used to process the

STRUCTURE output, which was utilized to visualize the clusters within each population.

BOTTLENECK 1.2.02 (Cornuet and Luikart, 1996) used the matrices of alleles and performed

Wilcoxon sign tests to identify the effective population size of each population. BOTTLENECK

51 can be used to predict the probability of population expansion or contraction, especially through founder effect and genetic drift events. Two possible mutation models, IAM (the infinite allele model, Kimura and Crow, 1964) and SMM (the strict one-step stepwise mutation model, Ohta and

Kimura, 1973) were used for simulation population distribution. If the population has been in bottlenecks, which leads to the allele diversity reduced more quickly than expected heterozygosity

(Cornuet and Luikart, 1996).

3.2.6 Bioclimatic data

I recorded the geographical coordinates during field surveys and downloaded the 17 bioclimatic variables from the WorldClim database (http://www.worldclim.org/download)

(Hijmans et al., 2005). (1) perc08: precipitation in August; (2) prec07: precipitation in July; (3) avg(perc) : mean precipitation in summer months; (4) wc2srad07: solar radiation in July; (5) wc2srad08: solar radiation in August; (6) avg(srad): mean solar radiation in summer months; (7) bio1: Annual temperature; (8) bio12: Annual precipitation; (9) tmean7: mean temperature in July;

(10) tmean8: mean temperature in August; (11) avg(tmean): mean temperature in summer months;

(12) tmax7: maximum temperature in July; (13) tmax8: maximum temperature in August; (14) avg(tmax) mean maximum temperature in summer months; (15) tmin7: minimum temperature in

July; (16) tmin8: minimum temperature in August; (17) avg(tmin): mean minimum temperature in summer months. The monthly solar radiation was collected dataset from Beckmann et al.

(Beckmann et al., 2014). The unit of temperature is °C × 10, the unit of precipitation is mm, and the unit of solar radiation is kJ m-2 day-1.

I used redundancy analysis (RDA) to determine linear combinations between the two matrices (FST and environmental variables), performing an extension of multivariate linear

52 regression in the explanatory variables, and visualizing the linear correspondence of variables to the other matrix (Hair et al., 1998, Legendre et al., 2011, Oneal et al., 2014). Distance-based redundancy analysis (dbRDA) (Legendre and Anderson, 1999) inherits from RDA, using pairwise measures of population differentiation such as FST matrices with the calculation of dissimilarities (distances). In this chapter, the explanatory variables are represented by environmental variables, while the response variable is the FST matrix which results from the allele frequencies of the microsatellite markers. dbRDA can be used for transformations such as genetic differentiation (FST) into PCoA axes, or to use PCoA to linearize genetic variables. In this process, it retains all variables that have positive eignvectors, and runs an RDA with PCoA axes. dbRDA has been used to quantify the variation explained by environmental variables, so this analysis can explain how genetic diversity is affected by environmental variables. PCoA and dbRDA all perform ordinary metric scaling (Oksanen et al., 2007). RDA (function vare.cca and ordistep in

‘vegan’, Oksanen et al., 2007) worked as ANOVAs, calculated variance components F-ratios and corresponding P-values. In ordination methods (Oksanen et al., 2012), the variance components explained whether variation of environmental factors was assisted with genetic variation in microsatellite loci conditioned on geographic distance matrix (latitude and longitude). Higher F- ratios mean stronger relationships between the climatic variables and genetic variation.

Model selection using ordination tests (RDA, CCA, and dbRDA) were conducted according to forward or backward selection via the ‘cca’ and ‘ordistep’ functions in ‘vegan’.

Akaike information Criterion (AIC) -like statistics for ordination provide similar functions for

RDA. Tests like dbRDA and RDA cannot use the AICs, because they are not based on likelihoods or Sum of Squares. However, this function provides an alternative method, utilizing permutation

P values. Functions ordistep (based on P values) can be used to select variables in a stepwise

53 manner. I retained the model with the environmental variables that best explained significant portions of allele variation.

I utilized principal components (PCs) to simplify dimensionality of environmental variables using PROC FACTOR and PROC PRINCOMP in SAS v9.3 (SAS Institute©, Cary, SC,

USA). In principal components analysis, the environmental variables include precipitation, solar radiation, annual mean temperature, annual precipitation, mean temperature, maximum temperature and minimum temperature. I optimized the precipitation and temperature data by using the specific month of sampling. The remaining climate components were combined for the

‘dbRDA’ and ‘capscale’ functions in ‘vegan’. Full and partial dbRDAs all use function ‘capscale’.

I ran dbRDA, conditioned on the geographical coordinates, i.e. the effects of the geographical coordinates of sampling independent from the explanatory variables. Euclidean distances were considered in the test, after computing a Pearson or Spearman correlation matrix to identify distance metric in "euclidean", "mahalanobis", "manhattan", "gower”, and significance of F was assessed after 1000 permutations. All statistical analyses were performed in R version 3.4.0.

3.2.7 Method for quantifying UV reflectance

M. guttatus has yellow tubular corollas, which are marked with many reddish-purple dots in the flaring throat. The corolla is strongly two-lipped, composed of a two-lobed upper lip and a three-lobed lower lip. Before gaining a reading from the spectrometer, I split flowers into their upper and lower halves. I then obtained three spectrometry readings, one of the top and two of the bottom petals. Because the ‘bullseye’ pattern is present in Mimulus flowers, the readings, which were taken in the center at the opening flower, are always around 0 for reflectance. Reflectance ratios close to 0 are interpreted that no UV reflectance is occurring. The exception to this was very

54 small Mimulus flowers where edge and center readings were taken instead. Some have ‘near edge’ readings to ensure that readings were taken that captured both UV reflective and absorptive areas.

The spectrometer is standardized before readings with white and dark black standards. To decrease noise in the readings, a spectrometry reading of the dark standard was taken and subtracted from the UV spectra readings. After removing the dark standard readings from the spectra data, the readings were divided up into those from the UV reflectance (300~400nm) and those from the non-UV reflectance region (400~700nm), which based on pollinator vision. The ‘petal lobes’ section represents functionally important to the discrete and quantitative variation in the UV bullseye. The ‘petal lobes’ was an average of upper and lower lips for each flower. I found the strength of UV absorption in lower petal is slight more than upper and bilateral petals consistent with the finding from previous studies (Bodbyl-Roels, 2012, Rae and Vamosi, 2013).

I detected whether UV reflectance at UV hue (UV maximum) and UV brightness are significant differences between groups (k=3) and groups (k=4) via PROC GLM in SAS v9.3. UV maximum and UV brightness were calculated from reflectance spectrum of the deposited flowers, which were collected from six M. guttatus populations. I obtained the spectrum with Ocean Optics

Jaz 2000-Series spectrometer, and transferred the spectrum to UV maximum and UV brightness

(see the method in Chapter Two). Cluster analysis was used to group spectra via dis() and hclust() function in R version 3.4.0. I then constructed a dendrogram showing pairwise distances between the UV spectrum categories and population groups. A Mantel test revealed that UV maximum was highly correlated with UV brightness, so I chose to only categorize the UV spectrum with UV maximum for the cluster analysis.

3.3 Results

3.3.1 Population structure and genetic diversity

The percentage of polymorphic loci, which was used to estimate the variation within populations, was 100.0%, 100.0% and 83.33% for Waterton Lake National Park, Crowsnest Pass and Cypress Hills, respectively. STRUCTURE analysis suggested K=3 has highest support, whereas K=4 distinct clusters explained the most variance. When I assume a deviation from HWE,

K+1 is better fit for the number of genetic clusters (Figure 3.2). The number of distinct genetic clusters within the data was therefore deemed to be 3 or 4 for further analysis. STRUCTURE identified three major clusters WP, CH and CP. PCoA was used to identify and describe clusters of genetically similar individuals by Genalex software (Figure 3.3). PCoA identified the groups corresponding to the results from STRUCTURE.

An AMOVA revealed high inference power and corroborated the STRUCTURE results.

However, I obtained different results in the AMOVA analysis using different settings of the K value (i.e., when population divergence was broken down into two categories K=3 and K=4).

There were lower differentiations among groups when K=3 genetic clusters were identified (Table

3.2A). However, the AMOVA analysis revealed a high proportion of variation between all populations (Table 3.2B); 44% of variation proportion occurred among populations, accompanied by 56% of variation within populations. The microsatellite data reveals a significant fixation index of the subpopulation relative to the total population (FST) of 0.438 and significant inbreeding coefficient of individuals relative to the subpopulation (FIS) genetic differentiation of 0.321.

Pairwise FST values differed from zero between populations and corresponded to geographic distance (Table 3.3). The highest FST for a given geographic distance was between the

CH and WP populations. Following Wright (1978), genetic differentiation is considered high when

FST ranges more than 0.25; genetic differentiation is considered moderate-strong when FST ranges from 0.16 to 0.25; genetic differentiation is considered moderate when FST ranges from 0.06 to

0.15; and genetic differentiation is considered weak when FST is less than or equal to 0.05. Most pairwise values were strongly differentiated. The CP population exhibited less genetic differentiation from the CH and DM population. WP population exhibited very high differentiation from other populations.

Further, I calculated several genetic indices (number of alleles, effective Alleles, observed heterozygosity, expected heterozygosity, fixation index) for different populations using the program GENALEX v.6 (Peakall and Smouse, 2006) (Table.3.4). Expected heterozygosity (He) indices are higher than observed heterozygosity (Ho), which indicates somewhat reduced diversity within populations, although not significantly higher. In Cypress Hills (CH) and Crowsnest Pass

(CP) populations, He values are significantly higher than Ho, which suggests that the diversity of

M. guttatus is declining. The populations do not differ significantly in fixation indices (F).

Significantly negative F values would indicate that the population has experienced population size expansion or natural selection (Fu, 1997).

Significant deviations from Hardy-Weinberg equilibrium (HWE) were found for all microsatellite loci, which can be caused by the occurrence of null alleles, potential scoring errors, short alleles, or population size reduction (Wattier et al., 1998). Using the optimal K=4 is clusters calculated in STRUCTURE. Wilcoxon sign tests were calculated by running 10,000 replications, under three different microsatellite mutation models: the two-phase model (T.P.M; Di Rienzo et al., 1994), and the stepwise mutation model (S.M.M; Chakraborty and Nei, 1977), the infinite allele model (I.A.M; Kimura and Crow, 1964) with the parameters settings (the variance of T.P.M

= 30, probability = 70%). Two-phase mutation model (T.P.M) provided the best fit to explain the

57 population change at microsatellite loci (Di Rienzo et al., 1994). I chose T.P.M and S.M.M model for Wilcoxon Sign tests, which revealed that the observed heterozygosity frequencies were significantly lower than the expected heterozygosity frequencies under S.M.M in Waterton Lake

National Park population (WP, P= 0.03418) and Crowsnest Pass (CP, P= 0.04248). There are no significant deviations from expected heterozygosity frequencies in T.P.M model. The results suggest that the size of both populations (WP and CP) is experiencing recent declines.

3.3.2 Redundancy analysis for Bioclimatic data and UV reflectance variance

The sum of the first three principal components (PCs) explained 96.51% of the variation in the environmental variables (Table 3.5). All but annual precipitation had moderately high loadings on PC1 and PC2 with temperature and solar radiation loading positively and precipitation loading negatively on PC1 but annual precipitation (Table 3.5). Based on the factor loadings, PC1 and PC2 performed with good inference power to summarize important variation in temperature, solar radiation and precipitation.

Partial redundancy analysis (pRDA) revealed that temperature variables were significantly associated with microsatellite loci variation after controlling for spatial coordinates (Table 3.6).

Moreover, the maximum temperature, in flowering months (July and August), was significantly associated with variation in the molecular markers. All precipitation variables but annual precipitation exhibited no correlation with microsatellite loci variation.

The RDA with stepwise forward addition of environmental variables (Table. 3.7) revealed that the temperature variables can explain significant portions of genetic variation (the response variables) in full RDA and partial RDA. The results of full RDA are similar to partial RDA, which identified five environmental variables (maximum temperature in July and August, annual mean

58 temperature, mean temperature in July and solar radiation in August) were important explanatory variables to understand genetic connectivity.

3.3.3 Cluster analysis in UV spectrum

The distance cluster analyses based on the UV maximum yielded four categories, which did not visually correspond to the genetic structure (Figure 3.4). UV brightness exhibits a significant positive correlation with UV maximum (Pearson’s correlation coefficients, Mantel tests, r=0.96, p<0.0001). A generalized linear model identified the effect of genetic structure groups and population on floral UV reflectance of M. guttatuts, finding that the genetic structure does have a significant effect but the population does not (Table 3.8). The hierarchical clustering of the UV spectrum among the four genetic structure groups, roughly shows that the magnitude of UV spectrum is in line with the effect of geography among population groups. Floral irradiance is similar in UV spectrum based on the closer pairwise distance between population groups. For example, in Figure 3.4, population group WP (purple) exhibited a similar magnitude of UV reflectance as the population group CP+DM (green). They were the sister group to a cluster. For the two population groups with the largest average distance, the floral traits are weakly constrained

(e.g., the magnitude of UV reflectance in M. guttatus is different between population group WP

(purple) and GP+BC+KAK (pink)). Shifts between different latitudes have been associated with divergence in the floral UV spectrum. Lower latitude is correlated with increased UV maximum reflectance. At lower latitudes, the mean of the reflectance percentage of UV maximum is 45.16% in population WP. At higher latitudes, the mean of the reflectance percentage of UV maximum is

28.69% in population GP.

3.4 Discussion

The geographical range of a species partly determines its extinction risk, and is impacted by a number of factors with anthropogenic and bioclimatic causes (Mooney and Cleland, 2001).

Endangered species often have small population sizes and fragmented ranges, which leads to altered levels of asexual reproduction (Van Kleunen et al., 2015), adaptive divergence

(Dobzhansky and Dobzhansky, 1937, Rieseberg, 2001), loss of performance traits (Wessinger et al., 2014), gene deletion and mutation (Lowry and Willis, 2010, Noor et al., 2001, Rieseberg, 2001), or even rapid production of new species (Kirkpatrick and Barton, 2006). This study indicates that

M. guttatus has suffered some of these effects from human activities and harsh environments in certain regions of Alberta. This detailed analysis of genetic variation, population structure and population divergence using twelve codominant markers in Mimulus (Lowry et al., 2008, Vallejo-

Marin and Lye, 2013) revealed the most likely locations of glacial refugia for this species. The relationship between bioclimatic variables and population genetic differentiation can be used to interpret how important bioclimatic variables are in driving the evolution and distribution patterns in M. guttatus.

(1) The level of genetic variance in populations of M. guttatus in Alberta

Mimulus guttatus is a rare species in Alberta with three populations (CH, WP and CP) that appear as highly distinct from other populations. This pattern suggests a postglacial migration where three predominant genotypes were derived from different ancestral lineages. The genetic diversity results provide evidence that the northward wave of migrants had experienced vicariance event and genetic drift, after having established the glacial refugium in Cypress Hills (CH).

Relatively higher genetic diversity in the refugial descendants is often interpreted as a signal that

60 the population survived at relatively high population sizes during a glaciation period, where these large population sizes allowed for the diversification of a distinct lineage.

Population fragmentation and insularity can cause wild populations to experience demographic and genetic bottlenecks. Genetic diversity declines faster under bottlenecks (Taylor et al., 2003), which can be used to estimate the magnitude and pattern about genetic change associated with migration (Kaneshiro, 1980, Templeton, 1980). A demographic bottleneck is interpreted from the reduction in the population size resulting in a reduction in number of effective alleles (Ne) (Leberg, 1992, Menotti-Raymond and O'Brien, 1993). In the test of possible historical bottlenecks, population size in WP and CP experienced significantly decreases under the stepwise mutation model (S.M.M), which suggests that two populations have undergone bottlenecks, and did not show significant heterozygote deficiency under other model assumptions, yielding similar conclusions to other species studied previously (Valdes et al., 1993, Godbout et al., 2008). The population size at CP appears to have been affected by genetic drift. I speculate that routes of gene flow in M. guttatus are now interrupted by anthropogenic and natural damage, such as coal mining, construction of a cobble dam, and a severe river flood that, combined with climate oscillations, are determining factors contributing to the Mimulus population size decline, and lineage fragmentation.

Because of limited sampling, population GP and DM showed a lack of alleles with low statistical power for genetic clustering. Considering the physical barriers (i.e., the Rocky

Mountains), I hypothesized that population GP represent descendants from the British Columbia region. Pollen, seed dispersal and gene flow are restricted by the high altitude of the Rockies

(Thompson and Anderson, 2000) yet some studies have revealed that high altitude sites were not barriers historically (Anderson, 1996).

(2) Possible glacial refugia and postglacial colonization of inland Alberta

In Table 3.3, the population pairwise FST revealed that the population WP was the most genetically differentiated population. Therefore, population CH was unlikely to be the ancestral lineage of the population WP. The genetic structure analyses are consistent with the pairwise population estimates. There is evidence that the Wisconsin glaciation refugia included the large southern ice-free areas south in the United States (Dyke and Prest, 1987). The population differentiation research of lodgepole pine (Godbout et al., 2008) provided some evidence for northward colonization from the refugium in Montana and North Dakota (Mehringer Jr et al.,

1977). After the ice sheet retreated, descendants would migrate northward along the Rocky

Mountains. The wave of migrants expanded their range around the Waterton Lake population and likely established this disjunct population. Even if Mimulus could be sampled more completely in populations WP and CP, contemporary barriers maintained by periodic floods and coal mining likely overwhelm any potential effects of geographic distance on genetic distance, as well as compromise the quality of the habitat for these plants.

The surveyed M. guttatus populations showed observed heterozygosity (HO) values that were significantly lower than expected heterozygosity (HE) which is indicative of allopatric fragmentation. This pattern is consistent with the high levels of genetic differentiation between

WP and CH reflecting different ancestral lineages that occupied and expanded during the northward colonization along the Rocky Mountains (Williams et al., 2004). Another wave of migrants came from the coast in Southwestern BC which is another putative refugium inland

(Ward et al., 2003). Descendants of these ancestral refugia likely contributed to a large historical population size (Norris et al., 2006) that shaped the distribution of present- day populations in

62 different environments (Muellner et al., 2006, Cuenca et al., 2008). The overall level of genetic variation between south coast and north of Grande Prairie has been previously observed, and the alleles were sequenced to detect possible homoplasy. Glacial advances and retreats can provide species with numerous splitting and hybridization opportunities, therefore characterizing genetic diversity in Northern mountainous regions requires additional study such as examinations of the fossil record.

(3) Population structure and bioclimatic variables

Climatic oscillations can also influence the distribution isolation and bottlenecks observed in a population. M. guttatus occurs over a wide range of habitats from alpine to lowland environment (McEwen et al., 2013), and local adaptation has resulted in coastal and inland ecotypes (Oneal et al., 2014). Previous studies on M. guttatus have focused on genetic diversity

(Vallejo-Marin and Lye, 2013), local adaptation (Lowry et al., 2008) and floral UV polymorphism

(Peterson et al., 2015) separately. The attempt of this study is to integrate the population genetic diversity with the analysis of bioclimatic variation and floral UV polymorphism. Because of seasonal variation in environmental variables, we collected M. guttatus samples in its flowering season and incorporated the environmental factors corresponding to sampling within that month.

The first principal component or genetic variation in M. guttatus was largely comprised of climatic variation (Table 3.5). Temperature and solar radiation variables are more environmentally differentiated across a vast range of climatic components. The redundancy analysis (Table 3.6) confirmed that the maximum temperature variable was the most environmental differentiated across climatic conditions and exhibited significant correlation with genetic variation. Therefore, temperature is likely to be the most important climatic factor in producing population genetic

63 structure in M. guttatus in Alberta. Annual precipitation and solar radiation exhibited moderate correlations with genetic variation. In Alberta, Mimulus species flower early and are restricted to microsites with high soil moisture conditions during the dry summer. The habitat requirements of

M. guttatus are likely a predominant reason why maximum temperature and solar radiation in the flowering season shape population genetic structure of M. guttatus.

(4) Population genetic diversity and UV pigmentation

The magnitude of UV spectrum from the size of UV bulls-eye also varies with the climate and geography (Koski and Ashman, 2015b). The differences found with the UV reflectance character of Mimulus among different genetic groups may be due to these traits being adaptive floral characters. Variation in UV characteristics may suggest alternate local adaptation to different climates or pollinators. This study represents the first attempt to examine the association between gene flow in M. guttatus and the UV floral reflectance phenotype. In other species (e.g., in

Argentina anserine (Rosaceae)), researchers found that the increasing bullseye size functioned for floral protection, and at highest latitude a large proportion of bullseye size protected the flower from UV radiation. Gloger (1833) had found birds’ feather turned to be darker colour with increasing proximity to the Equator. Depend on Gloger’s rule. Our results reinforce the idea of

Gloger’s rule based on the data from Mimulus species (Gloger, 1833, Cuthill, 2015, Koski and

Ashman, 2015b), yellow monkey flower with increasing UV reflectance flowers tended to grow in areas of lower latitude. The populations in Alberta experience extremely cold and dry habitats, and presumably persist through rapid evolution in stress tolerance or phenotypic plasticity (Morris et al., 2014, Rezansoff et al., 2015). However, in M. guttatus, flowers have a lower proportion of petal area with bullseye pigmentation under strong solar irradiance; and larger potions of petal area

64 with bullseye pigmentation under weak solar irradiance. After checking UV floral polymorphism in Mimulus species, I found that red and reddish floral pigmentation appeared UV-absorbing; yellow floral pigmentation appeared UV-reflectance. Yellow and reddish pigmentation in the human-visible spectrum may have evolved to attract the different pollinator assemblages that predominate within different elevations. Alternatively, species may have evolved darker pigmentation such as red (and reddish) at higher latitude in cold temperature, because dark coatings absorb more solar radiation.

(5) Assess the implications for conservation for M. guttatus

Numerous studies on phylogeographic and demographic history after glacial recolonization can be combined with analysis of allele frequencies. Dispersal away from refugia can place isolated fragments in geographic proximity and enhance hybridization opportunities across the lineages. This was likely an important process that determines the spatial distribution of diversity of M. guttatus that has been ongoing throughout the Quaternary (2.59 million years to the present) (Hewitt, 1996), especially since the last glacial maximum (c. 31–15 kya) (Varga and

Schmitt, 2008). There is much evidence for dramatic changes in distribution of species in glacial- interglacial cycles from records of pollen, pollinators’ fragments and fossil records.

For a rare species, avoiding inbreeding depression and escaping competitive pressures, dispersal is favored solution when population experiences selection or constraints forces. Dispersal can help population avoid homogeneous mating (Venable and Brown, 1988, Olivieri et al., 1995).

Anthropogenic and geographical distance prevent gene flow, which was likely a factor causing population size decline and lineage fragmentation in this species. Field surveys revealed damage at M. guttatus sites including coal mining, construction of a cobble dam, and a severe river flood

65 that combined with climate oscillations. In Alberta, long distances between the Mimulus guttatus populations reduced the possibility for between-population outcrossing. For reducing inbreeding depression, previous studies (Madsen et al., 1999, Liu et al., 2010a, Willi et al., 2007) showed effects of outcrossing is important genetic rescue for endangered species.

To monitor and manage focal species, especially rare species, successfully, we must understand both the demographic history of the species. On the basis of genetic data, we can estimate the influence that past glacial periods have had in placing geographical barriers that determine the potential for range dispersal and the current geographic distribution of populations.

The results identified population Waterton Lake National park and Crowsnest Pass as priority areas for high conservation value. The colonization routes, throughout from south to north, have overlap with priority conservation areas. Human damage reduction and nature reserve establishment from

Waterton Lake National Park could potentially be implemented northward along the Rocky

Mountains, which should be considered in conservation planning if there are similar patterns revealed with other species in the future.

Table 3.1 Co-dominant genetic markers used for genetic analysis. Marker type: SSR = microsatellite (AAT motif), STS = Mimulus guttatus sequence tagged sites or MgSTS (intron-based length polymorphic markers). Linkage group refers to the M. guttatus genome. (Vallejo-Marin and Lye, 2013) Name Marker Forward Sequence Reverse Sequence Fluorescent Multiplex type label AAT217 SSR CCACAGAGAGGATTGGGTGT TGAGCAGCTAAAAATGGAGG FAM 1 AAT225 SSR ATTCCGACTGGTTTCATTCA CTTCCGACTAATCAGTAGAACAACA PET 1 AAT230 SSR AATTTCACGTGCCAATCTGA CCCTGGGTTAGCACTTAGCA NED 1 AAT240 SSR CCCCTTTTAACCACTATATAATAACC AGTGTGTGGGATTGAAAAGAA FAM 1 AAT267 SSR ATCCAATTCTTTGGAATAACATC TCACTTCATTACAAGTGACCTAGC NED 1 AAT278 SSR TGAGACTGTTTGGTGTGCAG GGAAGAAGACGATAGGGCTG FAM 2 AAT356 SSR CAGCAACGGCCTCACTAATG GGCGGAACCAGAATTTTATG VIC 1 MgSTS234 STS CGGTAACATCACCCTCTTCC CCGATTTCTCCCTTCTTGG FAM 2 MgSTS321 STS ACAAACTGCGGAACCAACC GCATTCATGACCGTCTAATCG NED 2 MgSTS430 STS CATTGCCTTGGAGTCTCG CCGATTAAAACCTTCTAGTGATGG NED 2 MgSTS657 STS TGCTTCTATCAGCTCCTCTGC TTCGACGTCAATGTCAGACC PET 2 MgSTS681 STS AACGGCACCACATCATGG AATAGCGAGGAAACCTAATGC PET 2 MgSTS685 STS GACAATGTAGTGGTTCCTGTGG CATCGATCTCGTAATGTTTGC FAM 2 MgSTS84 STS CCACCGAAGAAGTTGAAACC GCCTTAATAGGACCCCCAAC VIC 2

Table 3.2 AMOVA design and results of binary genetic data for population groups of Mimulus guttatus A. K=3 K=4 Source of Sum of Variance Percentage Sum of Variance Percentage variation squares components variation squares components variation Among 219.024 0.943 Va 21.659 256.627 1.093 Va 25.797 groups Among 59.693 1.046 Vb 24.031 22.090 0.778 Vb 18.372 populations within groups Within 434.875 2.365 Vc 54.310 434.875 2.365 Vc 55.831 population Total 713.591 4.355 713.591 4.236

B. Source d.f. Sum of Mean Estimated % Squares Squares Variance Variance Among 5 279.361 55.872 1.839 44% populations Within 186 435.083 4.727 2.363 56% populations Total 191 714.444 4.202 100%

Pairwise Population FST Values/Geographic Distance

CH DM WP CP GP CR

0.000 173.886 286.427 300.380 871.454 1014.958 CH

0.316 0.000 118.850 146.451 813.655 857.975 DM

0.480 0.296 0.000 46.932 746.290 739.393 WP

0.139 0.140 0.309 0.000 699.454 716.008 CP

0.269 0.245 0.335 0.173 0.000 684.654 GP

0.369 0.299 0.335 0.208 0.196 0.000 CR

Table 3.4 Genetic diversity in M. guttatus populations. Several genetic indices, including number of alleles (Na), number of Effective Alleles (Ne), Shannon's Information Index (I), observed heterozygosity (HO), expected heterozygosity (He), unbiased expected heterozygosity (uHe) and Fixation index (F). The population codes: CH= Cypress Hills, DM= Del Bonita & Milk River, WP= Waterton Park, CP= Crowsnest Pass, GP= Grande Prairie, CR= Craig Bay, BC

Pop N Na Ne I Ho He uHe F

CH Mean 31.000 2.583 1.576 0.526 0.161 0.307 0.312 0.420

SE 0.000 0.358 0.145 0.103 0.047 0.060 0.061 0.137

DM Mean 2.000 1.833 1.672 0.505 0.292 0.344 0.458 0.096

SE 0.000 0.167 0.146 0.096 0.096 0.064 0.085 0.214

WP Mean 18.917 3.417 1.770 0.662 0.356 0.366 0.376 0.163

SE 0.083 0.379 0.189 0.105 0.119 0.060 0.062 0.184

CP Mean 30.000 4.167 1.997 0.800 0.281 0.448 0.456 0.436

SE 0.000 0.366 0.180 0.093 0.088 0.055 0.056 0.146

GP Mean 9.000 2.583 1.934 0.701 0.333 0.430 0.455 0.251

SE 0.000 0.288 0.159 0.107 0.083 0.062 0.065 0.117

CR Mean 5.000 2.167 1.807 0.574 0.383 0.357 0.396 -0.007 SE 0.000 0.271 0.214 0.123 0.122 0.073 0.082 0.204

Table 3.5 Loadings of environmental traits on the first three principal components for all Mimulus guttatus individuals. prec. = precipitation, srad. = solar radiation, annual temp. mean = annual mean temperature, annual prec. = annual precipitation, temp. mean = mean temperature, temp. max = maximum temperature, temp. min = minimum temperature Environmental variables

PC1 PC2 PC3

Eigenvalue 3.453 1.972 1.331

% variation 49.330 28.170 19.010 temp. mean 0.948 -0.020 0.314 temp. max 0.874 -0.431 0.151 temp. min 0.859 0.282 0.382 annual temp. mean 0.714 0.663 -0.151 srad. 0.330 -0.723 -0.545 annual prec. 0.020 0.850 -0.510 prec. -0.660 0.149 0.695

Table 3.6 Results of partial redundancy analysis for correlation between bioclimatic variables and genetic variation. tmax7 = maximum temperature in July. tmax8 = maximum temperature in August. bio1 = annual mean temperature. tmean7 = mean temperature in July. srad8 = solar radiation in August. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Variance F (P) tmax7 1085.17 41.592 *** tmax8 1941.25 74.404 *** bio1 1826.62 70.001 *** tmean7 203.19 7.788 ** bio12 90.23 3.459 * avg.perc. 69.15 2.651 . srad8 9.01 0.345

Table 3.7 Results of full redundancy analysis for stepwise forward selection of bioclimatic variables. tmax7 = maximum temperature in July. tmax8 = maximum temperature in August. bio1 = annual mean temperature. tmean7 = mean temperature in July. srad8 = solar radiation in August. avg.tmean. = average for mean temperature. tmean8 = mean temperature in August. bio12 = annual precipitation. avg.srad. = average for solar radiation. srad7 = solar radiation in July. perc8 = precipitation in August. tmin7 = minimum temperature in July. avg.tmin. = average for minimum temperature. tmin8 = minimum temperature in August. avg.perc. = average for precipitation. prec7 = precipitation in July. avg.tmax. = average for maximum temperature. * p  0.050, ** p  0.010, *** p  0.001

Model AIC tmax7 + tmax8 + bio1 + tmean7 + srad8 763.82*** + avg.tmean. 764.04 + tmean8 764.04 + bio12 764.09 + avg.srad. 764.54 + srad7 764.54 + perc8 764.58 + tmin7 764.73 + avg.tmin. 765.07 + tmin8 765.05 + avg.perc. 765.08 + prec7 765.46 + avg.tmax. 763.82

Table 3.8 F-test results were assessed by multivariate analysis. * P< 0.05, ** P<0.01, *** P<0.001 Effect UV maximum UV brightness

Group (k=4) 3.59* 3.23*

Population 0.63 0.51

Group (k=3) 5.28** 4.76*

Population 0.52 0.43

Figure 3.1 Geographical distribution of the studied species, Mimulus guttatus. Yellow dots represented sampling location in Alberta and British Columbia. Locations of populations were covered by two significant bioclimatic variables, maximum temperature in July (tmax7) in Figure A and solar radiation in August (srad8) in Figure B. In Figure A, high temperature was shown in dark colour. Transmission from Southeast to Northwest, the low temperature was shown by light grey colour in the map. In Figure B, light grey colour represented strong solar radiation and covered Southeast region of the map. Transmission from Southeast to Northwest, the magnitude of solar radiation turned to weak. The regions in dark grey tend to obtain low solar radiation. The population codes: CH= Cypress Hills, DM= Del Bonita & Milk River, WP= Waterton Park, CP= Crowsnest Pass, GP= Grande Prairie, CR= Craig Bay, BC A.

Figure 3.2 Assuming k=3, 4, 5 genetic clusters, barplot showing the probabilities of individuals to each genetic cluster base on 12 microsatellite loci. A given clusters are shown different colours. CH, DM, WP, CP, GP and CR represent Cypress Hills, Del Bonita & Milk River, Waterton park, Crowsnest Pass, Grande Prairie, Craig Bay population sites

Figure 3.3 Principal coordinate analysis for relationships among population groups. The goodness of fit of the first two principal components was 46.33%. Different colour symbols represented different geographic populations, and the population codes: CH= Cypress Hills, DM= Del Bonita & Milk River, WP= Waterton Park, CP= Crowsnest Pass, GP= Grande Prairie, CR= Craig Bay

Principal Coordinates (PCoA)

CH DM WP

CP Coord.2 GP CR

Coord. 1

Figure 3.4 Dendrogram was constructed for UV maximum based on distance cluster analyses for M. guttatus spectrum in seven populations. The coloured labels on the upright depicting floral UV spectrum relationships among population groups of M. guttatus. Coloured labels on the dendrogram represent population structures which are denoted by pink, yellow green, green, purple text, respectively. And the population codes: W= Waterton Park, GP= Grande Prairie, BC= British Columbia, KAK= Kakwa Provincial Park, CH= Cypress Hills, CP= Crowsnest Pass, DM= Del Bonita & Milk River

4 CHAPTER 4 Variation of quantitative traits accompanies the change of floral

ultraviolet reflectance trait following Mimulus hybridization and allopolyploid

evolution

4.1 Introduction

Mimulus has an extensive north-south distribution ranging from southern Chihuahua to the

Aleutian Islands and from the Pacific Coast to the Rocky Mountain (Delmer, 1974). Although

Mimulus has developed as an excellent model system for the experimental investigation in evolutionary processes since 1947 (Vickery Jr, 1978), few have investigated the allopolyploid formation between M. guttatus and M. luteus. M. guttatus and M. luteus became well established throughout the United Kingdom (UK) in the 1800’s (Silverside, 1990). The hybrid M. robertsii was described in 1990 and distinguished from the parental plants (M. guttatus and M. luteus), and is now well dispersed within the British Isles (Preston et al., 2002, Stace, 2010, Vallejo-Marin,

2012). Subsequent studies (Vallejo-Marin, 2012, Edger et al., 2017) have reported that a new

British allopolyploid species Mimulus peregrinus (Phrymaceae) that differs morphologically from its ancestral progenitor species, M. guttatus and M. luteus, has now formed. This species has also now been used to study evolutionary history and new speciation of polyploid taxa. In this chapter,

I outline how I have developed a new allopolyploid taxon within the Mimulus genus via allopolyploidization. In particular, I use this system to understand plant evolution with respect to floral traits and UV patterning.

Polyploidy is a particularly common process that occurs in flowering plants (angiosperms)

(Leitch and Leitch, 2008). Polyploid organisms are generated due to nondisjunction of chromosomes, which are classified into autopolyploids and allopolyploids (Ramsey and

Schemske, 1998). The plants within polyploid lineages may become more common than the plants

79 with diploid chromosome complements under natural field conditions (Stebbins, 1980).

Interspecific hybridization generates allopolyploids. Allopolyploids exhibit considerably complex evolutionary lineages with aneuploidy, which were induced by meiotic irregularities (Ramsey and

Schemske, 2002). The meiotic irregularities include univalent, biovalent and multivalent pairing

(Ozkan et al., 2001) and unbalanced gametes (Ramsey and Schemske, 2002). Aneuploidy has been identified during cell division. Aneuploidy is caused when the pairs of chromosomes do not separate properly in metaphase of meiosis (Birchler and Newton, 1981). Compared to the diploid state, extra or missing chromosomes incur fewer deleterious effects in polyploids. However, allopolyploids may experience reduced fertility. To restore proper chromosome pairing, bivalent formation and removal of maladapted genes must occur rapidly (Liedl and Anderson, 1993, Leitch and Leitch, 2008). Because Mimulus grows readily in the lab, it is possible to observe the progeny during initial filtering process and then examine the resulting genetic changes and the diversity effects on floral phenotypes, also can be observed.

Interspecific hybridization may trigger changes in anthocyanin pigmentation. The anthocyanin pathway provides a genetic basis to study the production of floral pigments.

Flavonoids serve as regulator enzymes in this pathway, which can express several ecological functions like protecting plants against UV radiation and pests, as well as promoting the interaction between the plant and the pollinators (Koes et al., 1994, Shirley, 1996). Therefore, floral colour shifts can alter evolutionary trajectories. McCarthy et al (2015) predicted a priori that floral colour evolution is constrained by polyploidy and interspecific hybridization when it results in pollinator shifts. Genes with pleiotropic effects in the pigmentation biosynthetic pathways are involved in expressing signals that provide colour discrimination by pollinators (Dyer and Chittka, 2004b,

Smith et al., 2008, McEwen and Vamosi, 2010). Floral colour evolution had been driven by, or

80 resulted in, pollinator shifts (McCarthy et al., 2015). In this chapter, I focus on floral UV reflectance, and characterize how floral UV reflectance shifts due to polyploidization.

Chittka (1994) employed a two-dimensional diagram to measure colour spectra according to hymenopteran vision. Hymenopterans have trichromatic colour vision, which includes UV, blue and green receptors (Von Helversen, 1972, Menzel and Backhaus, 1991, Peitsch et al., 1992). A summary of the spectrum loci is provided in this Chapter. Briefly, 33% of species examined thus far have flowers located in Blue-Green zone, 17% have flowers located in each of the Blue and

Green zone, 12% have flowers located in UV-Green zone, and 11% have flowers located in UV-

Blue zone (Chittka et al., 1994). The rarest (4%) of species in the wild have pure UV reflectance flowers, which is what Mimulus guttaus exhibits. In a study of UV reflectance in the butterfly

Colias eurytheme, Kemp and Rutowski (2007) have tested UV brightness and pigments exhibiting from the dorsal wing, and found the range of heritability (H2 or h2) of UV and pigments is 0.4 ~

0.9. Higher heritability values indicate that much of the variation in the trait depends on variation in genetic alleles. In this chapter, I will evaluate the genetic components of signal content about floral UV reflectance in genus Mimulus.

The genus Mimulus (Phrymaceae) comprises around 150 species organized in 10-12 sections (McMinn, 1946, Vickery Jr, 1978). M. guttatus and M. luteus are not sister species, yet are in the same section and are considered relatively closely related (Cooley et al., 2008,

Grossenbacher and Whittall, 2011). Numerous studies of reciprocal crosses tested the effects of genetic architecture on local adaptation in M. guttatus (Bradshaw et al., 1995, Fishman et al., 2002,

Bouck et al., 2007, Lowry and Willis, 2010). They have examined the effect of locally adaptive traits of M. guttatus in terms of the Quantitative Trait Loci (QTL) contributing to floral organ sizes, vegetative traits, life history and salt spray tolerance (Boyce, 1954, Hall et al., 2006, Lowry et al.,

2008, Wu et al., 2010). Further assays of phenotypic covariance between floral traits and nonfloral traits (e.g. vegetative and physiological traits) are necessary to determine the evolutionary response to selection pressures. For example, in coastal perennial and inland annual monkeyflower species, the genetic basis of flowering time, vegetative and floral traits divergence was governed by multiple pleiotropic QTLs (Hall et al., 2006, Hall and Willis, 2006). The population genetic models have been developed by antagonistic pleiotropy of genes, which affects how the loci evolve one trait in local habitat but are constrained in foreign habitats (Strauss et al., 1999, Kawecki and Ebert,

2004, Gardner and Latta, 2006, Lowry, 2010). In this chapter, I established a set of recombinant inbred lines within the greenhouse and obtained detailed traits correlations within the close relatives M. guttatus and M. luteus to better understand the genetic mechanisms underlying divergent phenotypic selection.

Variance components are standardly used to estimate the relative importance of heredity versus environment. Heritability is defined as the proportion of the phenotypic variance (VP) that

2 is attributed to genetic variation (VG) or H = VG/VP. Given its definition as broad-sense heritability, the value of heritability depends on the effects of dominance and epistasis. The phenotypic covariance between progenies is used to determine the degree of resemblance between progenies and parents. The traits related to reproductive fitness generally express lower heritabilities (e.g., grain yield) than do traits considered important for survival (e.g., plant height) (Visscher et al.,

2008). Narrow-sense heritability (h2) measures the proportion of phenotypic variation in a trait that is associated with the additive effects of genes (Hill et al., 2008). Variance in phenotypes depends not only on allelic interactions but also on environmental factors. The estimation of heritability tends to be imprecise unless based on character data from hundreds of families, so I collected numerous Mimulus samples for measuring in my greenhouse experiment. Generally, high values

82 of heritability would indicate that the trait is able to respond to selection pressures, and may adapt rapidly to changing conditions (e.g., pollinator assemblages shift). On the contrary, with low value of heritability, the morphological traits would include a large amount of additive genetic variation association with the fitness under environmental factors (Fowler et al., 1997).

In the current study, I investigate: (i) how chromosomal changes affect the meiotic process and consequently the sexual fertility in triploid hybrids; (ii) when a triploid and any other hybrid are successfully obtained, what is the phenotypic correlation between floral UV reflectance and other traits; (iii) does the heritability of floral UV reflectance and the magnitude of floral UV reflectance change among different generations in the allopolyploidization process; and (iv) do the changing levels of variation in colour spectra allow us to understand the ecological advantages of polyploidy in Mimulus and provide insight into the prevalence of polyploids among angiosperms.

4.2 Materials and Methods

4.2.1 Study system

Mimulus guttatus is a species that has been found to maintain 14 pairs of chromosomes (2n

=28), yet still has a relatively small genome size (~430 Mb). Therefore, the chromosomes are small and difficult to count. It is a model system to use in intra- and interspecific crosses; M. guttatus flowers are predominantly outcrossing (with an outcrossing rate is 0.6-0.9, Lin and Ritland, 1996), and are easily propagated in the greenhouse. M. guttatus is a native species in western North

America that displays a bee pollination syndrome (Vickery Jr, 1978). The width of corolla tube throat is 2-3cm (see Figure 1.3), and the whole flower appears bright yellow with numerous maroon spots and densely yellow-hairy in the flaring center of lower lobes. M. guttatus displays

83 strong UV reflectance on the corolla petals and UV absorbance in tube throat which manifests as a ‘bullseye’ pattern to pollinators (see Chapter One). There are 4 didynamous stamens and a bilobed stigma (Sweigart et al., 2006). After flowering, the calyx tube inflates with the capsule

(7~12 mm long) to form numerous small seeds (0.4~0.5 mm long). The flowering time is from

July to early September in Alberta. M. guttatus plants are about 6 dm in height, with the degree of branching depending on their environment. Leaves with several large veins from the base are normally ovate or elliptical, and the leaf size is up to 4 ×4 cm with the toothed margin. The upper leaves are sessile, and the lower ones are petiolate. Perennial M. guttatus grows from a creeping stolon, bears roots at the nodes, smooth and sparsely hairy.

Mimulus luteus (2n = 4x = 60-62) is a perennial herbaceous species that is abundant in

North and South America, and is also naturalized in some European countries (Medel et al., 2003,

Stace, 2010). The 5-lobed flowers display irregular red and purple blotches on the bright yellow background colour. The corolla shows strong UV absorbance, amounting to having pigmented regions on the petals according to the vision of its pollinators. Studies on M. luteus indicate that its pollinators include bees, butterflies and humming birds (Medel et al., 2003). Differences in the floral traits between M. luteus and M. guttatus include flower size, seasonality, and nectar reward.

Seed production per fruit capsule is about 300 and with larger seed size than M. guttatus. Leaves are similar phenotypic characters as M. guttatus, but thicker and larger size up to 10 folds. After comparing DNA markers, M. luteus is thought to have a close phylogenic relationship with M. guttatus (Grossenbacher and Whittall, 2011), nevertheless they have distinct cytological differentiation.

Mimulus robertsii (2n = 3x = 44-45) is the triploid hybrid between M. guttatus and M. luteus (Silverside, 1990), which is a newly described species of Mimulus in the UK. Despite being

84 vegetatively vigorous, M. robertsii individuals have low reproductive success due to sterile pollen and seeds. In a previous genetic study (Vallejo-Marin and Lye, 2013), M. robertsii appears to be strongly isolated reproductively in the wild from either of its progenitors. In my greenhouse inbreeding experiment with M. guttatus and M. luteus, hybrid flowers exhibit very heavily blotched corollas, and the irregular red blotches are not heritable. In the F1 generation, the variable hybrid populations display various red-pigmented patterns. The appearance of vigorous growth is reflected in its large and very broad leaves, even larger than in M. luteus.

Mimulus peregrinus (2n = 6x = 92) shares a close affinity with M. robertsii, but possesses the duplicated genome size and twice the number of chromosomes. It is reported as a new allopolyploid species in the UK (Vallejo-Marin, 2012). The corolla displays uniformly yellow flowers with a weak bullseye pattern in the UV spectrum (i.e., moderate UV reflectance at petal tips and absorbance close to the center). It is also spotted with red on the central lower lobe. The flowers of M. peregrinus are larger than that seen in M. guttatus, and the pollen and seeds are viable. M. peregrinus grow with large healthy leaves. The upper leaves are ovate, and the basal ones are spatulate.

4.2.2 Methods for imagining UV reflectance

(1) UV gel imager

As the Figure 4.1 shown, I used a standard filter (for Sybr Safe gel stain), and set the same exposure for every image so that images were standardized for easy comparison (I did replicate 10 and 15 second exposures for each flower). The gel imager emitted light with a wavelength of

~300nm. Two flowers were sampled per plant; one was cut in half and photographed the upper and lower corolla, and another was balanced upwards, to get a view similar to a bee approaching

85 the flower. To get the depth of field I operated with the aperture, and was careful to limit the amount of pollen I flicked on to the gel rig as it creates a lot of fluorescent background noise.

(2) Camera with UV filter lens

Modern cameras are usually designed to block UV and IR transmission, and improve visible light pass through the camera lenses (Tetley and Young, 2008) but this can be altered to get a perception of the UV patterning of flowers. I used a Nikon camera that was designed to polish the coatings which used to reduce UV transmission (Tetley and Young, 2008). To obtain a bee visual model of UV reflectance pattern of flowers, bee vision was shown by Nikon digital camera

D50 with UV transmitting filters (i.e., a Schott BG38 filter to block infrared (IR) and a Hoya U340 filter to block visible light). The UV transmitting filters are good for recording UV radiation, and the spectrometer test (see Section 2.2.2) gives a measurement of the reflectance spectra between

330 to 390 nm, which together offer a good assessment of UV transmissions that a pollinator would perceive (see Figure 1.1(a-d)). Once I confirmed that the UV transmitting filters removed IR and visible light, I did replicate 20 seconds exposures for each flower. The UV transmission was captured by the camera sensor as a grayscale photograph, which can then be used to interpret the

UV patterns on the flower. The presence or absence of UV reflectance is polymorphic among different portion of flowers (upper or lower petals) and also among flowers in different Mimulus species. For example, in the Figure 1.1e of M. guttatus, the side petals with brightness white colour mean strong UV reflectance, and the black central flower means UV absorption pattern, which composed bullseye image. In the Figure 1.1e of M. dentatus, the petals with gray colour mean weak UV reflectance.

4.2.3 Calculation of colour loci in the hymenopteran colour hexagon

The wavelengths of the UV part of spectrum were detected by pollinators from 300 to

400nm by spectrometer (as described in Chapter Two). I used a different method to measure how well pollinators can discriminate a flower from the variable illumination. This method transfers the floral reflectance curves to colour loci in a pollinator visual model. The colour loci were calculated in a hexagon diagram that represents the visual model for hymenopteran species (a well- established system for studies on light reflectance and absorption in flowers) (Chittka et al., 1997,

Chittka et al., 2001, Dyer and Chittka, 2004a, Dyer and Chittka, 2004b, Dyer and Chittka, 2004c,

Rae and Vamosi, 2013, Peterson et al., 2015). The light reflectance by the respective photoreceptor of pollinators is calculated according the following equations,

700 = 1/ ( ) ( ) ( ) (1) 푅 ∫300 퐼퐵 휆 푆 휆 퐷 휆 푑휆

700 = ( ) ( ) ( ) (2) 푃 푅 ∫300 퐼푆 휆 푆 휆 퐷 휆 푑휆 where R represents the sensitivity to background illumination in the different types of photoreceptors; IB(), the spectral reflectance of the background; S(), the spectral reflectance of the sensitivity to UV, Blue, Green receptors respectively [data from(Arnold et al., 2010)]; D(), the spectral reflectance of illuminant distribution [data from(Arnold et al., 2010)], IS(), the spectral reflectance of the stimulus; P, the transduction of photoreceptor absorption. Effective quantum flux (P) into receptor excitations (E) is given by

퐸= 푃/(푃 + 1) (3)

The spectrum from 300 to 700 nm is subdivided into six types of photoreceptors of bee eyes (UV, UV-blue, blue, blue-green, green and UV-green), which depend on which of the three colour receptors of bees (UV, blue or green). In colour hexagon, there are four domains: the

87 wavelength ranges in 300~400nm termed for UV; the wavelength ranges 400~500nm termed for blue; the wavelength ranges in 500~600nm termed for green and 600~700nm termed for red.

In Chittka’s equations (Chittka, 1992), determine the colour loci on the bee visual hexagon, which consists the UV-receptor at 340nm, the blue-receptor at 430nm, and the green-receptor at

540nm. The coordinates (x/y) are converted by the receptor excitations (E) by

푥 = sin 60° ∗ (퐸(퐺) − 퐸(푈)) (4)

푦 =퐸(퐵) − 0.5 ∗ (퐸(푈) + 퐸(퐺)) (5)

E(U), E(B) and E(G) are plotted as vectors with 120 angles between each other which are independent values between 0 to 1 (Figure 4.2). A value of 0 means no receptor excitation and values 1 means maximal receptor excitation. The colour loci (x and y) were determined by three vectors combination. For example, point u= E(U)/E(B)/E(G) = 1/0/0; point b= E(U)/E(B)/E(G) =

0/1/0; point g= E(U)/E(B)/E(G) = 0/0/1. Point ub= E(U)/E(B)/E(G) = 1/1/0, which means the UV and blue receptors equally strong, but stimulate the green receptor very weak.

4.2.4 Greenhouse cultivate and mating design (Figure 4.3)

I grew M. guttatus (MG) plants as maternal families and M. luteus (ML) as paternal families, raising 15 offspring from each parent in the University of Calgary greenhouse. The seeds and Mimulus plants were sown in Pro Mix HP potting soil (Premier Tech, Québec, Canada) with

Peters General Purpose 20-20-20 fertilizer (JR Peters Inc., Allentown, Pennsylvania, USA), setting the growth chamber at 16 h moderate light (21°C) and 8 h dark (16°C). The pots were placed in trays with in water (approximately around 4 cm water depth). I randomly selected 10 pairs of parental individuals for crossing. After germination, I transplanted the small individuals from

1206RBK Traditional Insert Black to 3.5-in2 square individual pots. Germination rates were low

88 in some crosses due to many factors, (e.g., an ongoing aphid infestation in the greenhouse, growth chamber maintenance issues, etc.) so the resulting number of progeny varied.

The mating design was as follows: I first randomly chose 10 pairs of the two parental species and conducted reciprocal crosses on M. guttatus and M. luteus. For example (1) M. guttatus

× M. luteus (MG×ML), (2) M. luteus × M. guttatus (ML×MG). I obtained abundant seeds of F1 hybrids from MG×ML. But the crossing between maternal ML× paternal MG was not successful.

M. guttatus as the maternal parent and M. luteus as the paternal parent are successful than the reciprocal crosses, possibly because asymmetric reproductive barriers appear to occur in the process of interploidy hybridization (Roberts, 1964, Vallejo‐Marín and Hiscock, 2016). F1 plants

(MG×ML) had similar floral phenotypes, with a corolla size similar to ML. Irregular red blotches on the background yellow petals, and they exhibited no UV reflectance, much like ML. There were

6×12 pots for every tray, and 10 trays represented 10 hybrids families respectively. I grew 20 F1 seeds in each pot and then I transferred the viable seedlings to larger pots. Most of the F1

(MG×ML) individuals were sterile, although among the many F1 individuals, I was able to observe a very small number of individuals that developed an inflated fruit capsule. These seeds generated

12 individuals in the F2 generation, they were self-fertilized to produce inbred lines. To determine the variability of hybrid F1 generating fewer fertile seeds, I repeated the above crossing procedure and again observed very low fertility (6 individuals in the F2 generation).

The F2 generation did not exhibit the same low level of fertility and produced F3 seeds.

These F3 individuals were self-fertilized to yield the F4 population. The ancestry of the F3 and F4 lines were documented and floral characteristics were measured (F3: 16 lines; F4: 5 lines). To further investigate genetic correlation relationship in Mimulus hybrids, I gathered further phenotypic information at different generations and among a variety of lineages, obtaining an

89 additional backcross F3 (low UV reflectance) to one of the selfed progenies of the M. guttatus

(high UV reflectance) parent (not shown). The reciprocal cross (ML×MG) was conducted simultaneously, but since they had poor quality seeds and fewer F1 (ML×MG) hybrids were no phenotype different with ML. Hence, I used only MG× ML crosses in study.

4.2.5 Studying Chromosomes at Meiosis

Flower anthers are the site of production of haploid gametes, and it is therefore good material for chromosome counting for plants where the majority of somatic chromosomes are small, such as M. luteus (2n=4x=60-62). In most case, the chromosome number was determined in the pollen mother cell. The chromosomes were counted in the meiotic division stage (at approximately the same time 11am each day). In metaphase, the pollen cell is enlarged which facilitates counting. The procedure consisted of fixing the immature buds in ethanol, prefixation, fixation, hydrolysis, dissection and staining, followed (Hiremath and Chinnappa, 2015) method.

To reduce clumping of chromosomes, paradichlorobenzene-saturated solution was used for prefixation. The fixations required Carnoy’s Fixative I (3 parts absolute ethanol and 1 part glacial acetic acid) for 24 hours. The following pretreatments included hydrolysis in 1N HCl at 60 C for

10-15 min and staining with 2% Acetocarmine or 2% Acetoorcein. The morphology and number of chromosome were examined using a Leica microscope DM2500 with stereo light at 1000 times magnification, and the digital images were captured using a Leica DFC290 digital camera (Leica,

Germany).

4.2.6 Phenotypic measurement

Twelve phenotypic traits were measured, including floral UV brightness, tube width,

90 corolla width, corolla length, stigma length, short stamen length, long stamen length, nectar sugar

(nectar volume and sugar concentration), flowering period, leaf width, leaf length and leaf thickness. To test whether floral UV brightness was correlated with suites of these 11 traits, I separated the traits measurements into the following categories: floral-related traits (comprised of tube width, corolla width and corolla length), vegetative-related traits (comprised of leaf width, leaf length and leaf thickness) and reproduction-related traits (comprised of stigma length, short stamen length and long stamen length).

I measured the morphological traits using the digital calipers. Nectar was collected by 2 ml glass capillary tubes (Drummond Scientific, Broomall, PA, USA), and the volume was measured using the digital calipers. The nectar sugar concentration was measured using the hand held sugar refractometers (Bellingham and Stanley Eclipse 0–50 or 45–82, Kent, UK). Floral UV reflectance can be collected by Ocean Optics Jaz 2000-Series spectrometer with a UV/VIS xenon light source and XSR fiber optic reflection probe (Ocean Optics, Dunedin, Florida, USA) and classified in three types of photoreceptors of hymenopteran vision model (UV, Blue and Green). We collected floral UV data with uniform room conditions and background materials.

The hymenopteran vision model can be represented with the bee colour. For the analyses of floral trait variation, I quantified floral UV reflectance in UV brightness and the colour coordinate, which were calculated from the spectral reflectance wavelengths between 300 and

700nm. UV brightness and colour coordinate are independent characteristics that can represent inter-individual UV variation in or among different generations. UV brightness was calculated as the summed reflectance percentage from 300 to 400 nm. Using the aforementioned spectrometer, the spectral reflectance was quantified for the reflectance proportion from 300 to 700 nm, and converted into colour coordinate in hymenopteran hexagon model. I generated ultraviolet

91 reflectance images to directly visualize discrete polymorphism in the presence or absence of floral

UV reflectance by filtering the contamination lights and transmitting the wavelengths of the bee vision range.

Ultraviolet spectra measurements were taken for each generation in parental Mimulus and their hybrids. Preliminary measurements indicated that there was little variation in the values among individuals in parental Mimulus and I therefore collected fewer measurements of the parental generation compared to the hybrids (N is around 200). I did note that, in M. guttatus flowers, the side and top petals exhibit higher UV reflectance than the bottom petals occasionally as seen in other studies (Rae and Vamosi, 2013). To account for this possibility, I collected UV reflectance in the top petal, or I collected twice from top and bottom petals if there is significant difference in magnitude.

4.2.7 Analyses of trait variation

I calculated Spearman’s correlation between pairwise combinations of traits values among

MG, ML, F3 and F4 generation (Evans, 1996), considering the correlation, measured as r, moderate between 0.40 and 0.59, strong between 0.60 and 0.79, and very strong is between 0.80 and 1.00. After initial analysis of pairwise correlation, I then explored whether UV brightness exhibited tighter correlations with some combinations of traits using standard least squares method. There was a total of 12 different diversity variables used in principal component analysis

(PCA). The principal components are derived from an eigenvalue decomposition of the metric of trait covariance. All analyses were done using SAS JMP (version 12.0, SAS institute, Cary, NC).

4.2.8 Heritability

I then examined the inheritance patterns of UV pigments in a cross between MG (high UV reflectance) and ML (high UV absorptance). Previous studies indicate that UV reflectance inheritance is Mendelian (Peterson et al., 2015), yet my initial observations indicated that there was continuous variation in the level of UV brightness. UV brightness was measured from a total of 1386 individual flowers from F3 (16 lines) and 316 individual flowers from F4 (5 lines).

Heritability percentage in broad sense was estimated using the lme4 package (Bates et al., 2014) in R v. 3.1.3 (R Development Core Team 2014) and the proportion of variance explained due to genetic effects by following formula (Visscher et al., 2008):

Heritability (h2) = variance(line) / [variance(line) + residual variance]

The interaction between lineage and generation can be considered a residual variance component in the formal as follows:

1 1 h2 = variance(line) / [variance(line) + variance (line × generation) + residual variance] 2 2

4.3 Results

4.3.1 Chromosome Examination

Figure 4.4 shows chromosomes in pollen mother cell of immature anthers. Therefore, the chromosome number represents the haploid genome (n) which is preferable for chromosome counts in species with small genome size or a large number chromosomes, such as M. luteus. In

Figure 4.4(a, c, d) chromosome counts were carried out in anther pollen mother cells. In Figure

4.4b, chromosome counts were carried out in root tip cells. In Figure 4.4a, the gametophytic count

93 of M. guttatus is n=14. Figure 4.4b is mitotic metaphase chromosomes of M. guttatus 2n=28.

Figure 4.4c and d are meiotic metaphase chromosomes of M. luteus, n=30~31. Meiotic metaphase stages were used to count chromosome numbers of F4 hybrids. The chromosome numbers varied from 2n= 72~78. Figure 4.5e shows 35 bivalents and 2 univalents (2n=72); Figure 4.5f shows 31 bivalents and 11 univalents (2n=73); Figure 4.5g shows 38 bivalents and 2 univalents (2n=78);

Figure 4.5h shows meiotic metaphase-1 chromosomes with majority bivalents and some univalent.

In F4 generation, the number of chromosome is not constant in chromosome number.

Nevertheless, despite the lack of stabilization at the F4 generation, the formation of these higher univalents still results in viable seed.

4.3.2 Statistic analysis

4.3.2.1 Phenotypic variation in floral UV patterning

The phenotypes of F4 exhibited less variation than F3. In the F3 generation, the corolla size was intermediate between the corolla size of MG and ML (Figure 4.6a). Floral pigment patterns were restricted within a given line, even two types of red-pigmentation patterns that exhibited within the same individual (APPENDIX D Figure 1). The UV patterns in most of the inbreed lines is UV absorbance like ML. The F4 flowers were generally larger than MG flowers, having a similar flower size to ML. Their UV reflectance included three main types as follows: no

UV, lower and moderate UV (Figure 4.6b). The seeds for inbred lines from F2 to F4 generation were fertile with a larger size than MG seeds and their appearance looked dark black and firm. The phenotypic distribution for 12 traits was continuous and unimodal (except leaf thickness; see

Figure 4.7). The parental traits values were averaged and marked on the histograms (Figure 4.7).

For all traits except flowering period and leaf thickness, F3 and F4 phenotype were intermediate

94 between the two parental phenotypes.

In the colour hexagon, the coordinates suggest evolutionary phenotype of Mimulus flowers transitions among generations, with maintenance in the zone that is well discriminated by hymenopterans. The colour hexagon models inform us how bees perceive the object by receiving the signal of ultraviolet reflectance. The bee vision model was categorized into six colour zones, which includes ultraviolet, UV-blue, blue, blue-green, green and UV-green (Chittka, 1992).

Distribution of flower colour loci can be overlaid upon the spectral hexagon to assess the bees’ colour perception (Chittka et al., 1994). In Figure 4.8, the colour loci of different generations are depicted in the bee vision model. The spectra of most individuals were distributed in the sectors of the hexagon that code for the bees seeing the flowers as UV or Blue-Green, with some variation due to errors in data collecting.

As expected, the colour loci from MG and ML lay in different spectral zones. MG individuals clustered in UV and UV-Green, whereas the loci of ML individuals clustered in Blue-

Green. The hexagon model clearly distinguished the respective combination of colour along opposite axes (Chittka, 1992). The colour loci of individuals of the F2 and F3 generation showed colour loci compressed along certain axes in the hexagon, and tended towards the UV spectral area. In F1 hexagon model, the colour cloud exhibited variation along a single straight line that crossed the two opponent areas (UV and Blue-Green spectral areas).

To display a quantile box-plot (Figure 4.9), the median of F3 and F4 took the middle observation and lay between parental Mimulus. F3 and F4 had larger spread of the distribution of continuous floral UV data, comparing the parental Mimulus. By taking the box-plot of floral UV reflectance, we have seen that implementation of a procedure can vary substantially from generation to generation.

4.3.2.2 Pairwise correlation and PCA

Floral UV brightness exhibited weak phenotypic correlations among traits in parental

Mimulus (Appendix D table 1). In F3 and F4 individuals, by contrast, the strongly correlated traits were UV brightness, sugar nectar, short and long stamen lengths. General linear regression models showed that all direction of the significant correlations was consistent with the reproduction- related traits (the combinations of stigma length, short stamen length and long stamen length) (t

=2.55, P =0.0113*), flowering period (t =2.57, P =0.0108*) and nectar sugar content (t =24.78,

P<0001*) in F3. Similar correlation results were found with nectar sugar (t =4.61, P =0.0013*) in

F4. In this chapter, I focused on the strong correlation between UV brightness and other traits.

Other strong correlations should be considered for future research. These results were further corroborated using principal component analysis (PCA), further demonstrating the tendency of

UV to part of a suit of traits involved in floral evolution (Figure 4.10).

The PCA data set consisted of a large number of interrelated attributes (Figure 4.10). The

ﬁrst two components of PCA total accounted for 99.9% of variability in all four generational populations. Attribute correlations or similarities are indicated by the angles between attribute vectors. Directionally parallel vectors (small angle) are very similar, while vectors moving back in the opposite direction (large angle) indicate negative correlations. There are numerous metrics available for measuring an attribute’s relative importance. The same length gives each attribute the same influence on PCA. Perpendicular vectors represent no association (e.g., low correlation between UV reflectance and leaf size in F3 and F4).

Interestingly, the traits associated with floral UV reflectance, were different between generations. In F3 and F4 generation, floral UV brightness was tightened with long and short

96 stamen length, and nectar sugar (which were strongly and signiﬁcantly correlated with each other; see also results as in Spearman’s correlation coefficients). After hybridizing, floral UV reflectance traits were consistently associated with reproduction-related traits, nectar in particular. Nectar sugar tended to be strong correlated with floral UV brightness indicating a possible genetic basis for this correlation (either through hitchhiking or pleiotropy).

Although I expected that corolla size showed significant effect to family and high correlations with other traits, corolla size was unimportant variable and only weakly correlated with floral UV reflectance using PCA. Flowering period was portrayed in shorter vectors to represent its weak effect with floral UV brightness. Stigma length showed a negative correlation and no significant relationship with other traits.

4.3.2.3 Heritability

The heritability of the floral UV brightness variation was estimated as h2 = 0.6, which accounted for the differences among the inbred lines and generations (F3 with 16 lines, and F4 with 5 lines), and then included the residual variance. I obtained a value of h2 = 0.58 if the calculation only includes the F3 inbred lines (the differences are caused by the fact that the F3 and

F4 would have experienced slightly different environments). Therefore, heritability of floral UV brightness range is 0.58~0.60 in Mimulus hybrids.

4.4 Discussion

Understanding how plants respond to selection pressures is aided by the artificial creation of hybrids and synthetic allopolyploids in the greenhouse. The Mimulus allopolyploids developed here can be further used in the study of the genetic basis of floral mophological changes. One

97 finding is that allopolyploids with the same parental ancestors can still be very different at the genomic level. My unique genetic model in Mimulus has fewer chromosomes than hexaploid (M. peregrinus, 2n = 6x = 92, Vallejo-Marin, 2012). However, this new model exhibits similar, stable floral morphology as well as the same self-fertility as M. peregrinus. The results suggest that stabilization occurs at the F4 generation, with the production of higher proportions of viable seeds.

In this study, I found that high values of heritability in UV reflectance were associated with genetic diversity. Gene expression for floral UV reflectance is tightly linked a few individual genes, which may complicate our interpretation of heritability. While the interpretation of the value for heritability should be approached with caution, several methods included in this chapter can be used to support the idea of a genetic basis to floral UV reflectance in Mimulus allopolyploids as well as provide insight on the potential ecological advantages of polyploidy and polyploid- generated angiosperm diversity.

4.4.1 Consequences of polyploidization on shifts in pollinator discriminations

Adaptations to different pollinator assemblages in Mimulus can include divergence in flower size, red dots/patches on the corolla (von Bohlen, 1995, Totland and Schulte-Herbrüggen,

2003). Pollinator discrimination has been observed to lead to interspecific reproductive isolation

(Cooley et al., 2008). According to Kiang (Kiang, 1972), M. guttatus flowers are pollinated mainly by bumblebees, and taxa in the M. luteus has a generalized pollination system because of the distinction in floral anthocyanin pigments. Therefore, floral phenotypic evolution may have been driven by, or resulted in, pollination system shifts in the past.

I observed limited variability in the hexagon colour vision model, as the colour loci were distributed in opponent visional categories, mainly shifting linearly along one axis. The optimal

98 signal transfer would be increasing the intensity to a pure UV pattern. The Mimulus hybrid line established a novel suite of floral traits display and future research should study what effects this has on pollinator visitation. Colour locus evolution can be affected by gene flow, genetic drift or pleiotropic selection on floral pigment genes (Chittka et al., 2001, Rausher, 2008). The vision model can help elucidate the effects of genomic changes on flower-pollinator interactions

(Beardsley et al., 2003, Clare et al., 2013).

4.4.2 Chromosome characterization of a Mimulus allopolyploid

Polyploidization is acknowledged as a major contributor to angiosperm diversification

(Ramsey and Schemske, 2002, Soltis, 2005). Genera such as Mimulus are comprised of diploid species as well as species formed through hybridization and polyploidization (Vickery, 1995,

Beardsley et al., 2004, Vallejo-Marin, 2012). Data on chromosome number shifts comes from many research areas, which include taxonomy, plant breeding, genetics, and phylogenetics yet in most cases the mechanism of polyploidy formation is unknown. The neo-allopolyploid plants produced in this study presents highly fertile pollen and seeds, and floral and vegetative characteristics traits that are intermediate between M. guttatus and M. luteus. In plants, the anther and ovules produce haploid microspore or megaspores, respectively, for sexual reproduction. In hybrids, at the first stage of meiosis (Meiosis I), maybe univalents, biovalents, and multivalents are formed, which result in unstable chromosomal numbers. The variation in chromosome number are resulting in some haploid cells with a balanced chromosome complement, may be the reason for rare restoration of sexual fertility in triploid hybrids.

4.4.3 Floral UV reflectance in trait correlations

Polyploid-generated angiosperm diversity has been partly driven by, or resulted in, floral evolution (Vamosi and McEwen, 2012). Previous studies have highlighted the roles of genome duplication, transgressive expression, and/or heterosis in determining the prevalence of allopolyploids. Polyploidy in a clade may confer advantages that result in increased species diversity, due to decreased in extinction due to reduced possibility of inbreeding depression for example (Thompson and Lumaret, 1992, Crow and Wagner, 2005, Rapp et al., 2009). A recent study (Edger et al., 2017) indicates that polyploidy in Mimulus led to increased persistence and survival for 140 years. Creating novel genetic combinations reduced the risk of genetic bottlenecks

(Mavrodiev et al., 2015, Servick et al., 2015, Vallejo-Marín et al., 2017). Heterosis can contribute to the novel phenotypic variation, which may favor the adaptation of a neo-allopolyploid.

Phenotypic correlation, or the degree of association with quantitative traits (Falconer, 1975,

Conner, 2002), was strong between floral UV metrics with other floral characteristics is likely evidence of common contributions to the attraction function of floral display, and thus, UV reflectance is expected to diverge if closely related species experience different selection pressures.

The UV brightness heritability was estimated to be 0.58~0.60, which is considered a moderate to high level of heritability, so much of the differences in this trait is due to variation in genotypes.

The evolution of floral UV brightness may depend on selection on pleiotropic effects of floral pigment genes.

The phenotypic correlations also indicate that genes for UV reflectance are in close proximity to other floral traits in the genome. In particular, UV traits were tightly correlated with reproduction-related traits including stamen length and nectar sugar content in the hybrid population. Pollinator visitation is often dependent on flower visual displays and nectar rewards

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(Raine et al., 2006). The F4 generation showed more variation in floral traits than parental Mimulus.

Leaf character was also correlated with UV brightness. The discrimination function in pollinator vision might depend on the background and illumination colours (e.g. leaf and solar light).

My results also suggest that hybrids were more similar to M. guttatus in corolla size and I speculate that hybrids would attract similar pollinators to M. guttatus if planted in the wild. In previous studies (Conner and Rush, 1996, Galloway et al., 2002), floral corolla and display size have significant effects on pollinator visitation, driving much of pollinator preference for a given species (Thompson, 2001, Thompson, 2005). For example, hummingbirds often visit species with a large sized corolla, regardless of the volume of nectar rewards (Feinsinger, 1976). Finally, my results indicate that flowering period (phenology) had weak correlations and is likely a trait that has a plastic response to the environmental conditions. My results are consistent with many previous studies in this respect (Galloway, 1995, Hall et al., 2006, Hall and Willis, 2006), which have demonstrated that phenology is more dependent on environmental than genetic variation.

4.4.4 Implication

My research here explored how hybridization and allopolyploidization affects UV reflectance in flowers. Through examination of phenotypic correlations and heritability, I gathered the evidence to suggest that selection can likely act on UV reflectance traits, especially if the selection on floral traits is mediated by pollinator attraction. I thus speculate that UV reflectance has contributed to plant evolutionary history. The effects on pollinator attraction through hybridization is likely a neglected aspect of plant speciation research that will benefit from further study on the genetic basis of adaptive evolution. Selection and hybridization are two triggers to lineage diversification (Rieseberg et al., 1999). Floral UV reflectance is an inherited trait that

101 provides an important signal to pollinators and is thus likely to be involved in pollination syndrome shifts. Furthermore, shifts in UV reflectance may simultaneously affect a plant’s defense functions against UV damage, and/or resistance to pests (Shirley, 1996, Kumar and Pandey, 2013, McCarthy et al., 2015). Newly originated polyploid individuals will have novel genetic developmental flavonoid biosynthesis pathways, which will likely have many consequences to pollinator attraction and survival in natural settings.

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Figure 4.1 UV images for UV reflecting flowers M. guttatus and UV absorbing flowers M. lewisii, M. cardinalis and M. ringens (bottom UV gel image taken by Alex Twyford)

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Figure 4.2 The colour hexagon represents three photoreceptor excitations E(B) E(U) and E(G) mean blue, ultraviolet and green receptors. The vector length can vary from 0 (no excitation) to 1 (maximal excitation)

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Figure 4.3 Breeding design for neo-allopolyploid Mimulus. The floral patterns of Mimulus guttatus (A) and M. luteus (B). They were crossed to produce an F1 (C), then F2 were self- pollinated to make F3, which were self-pollinated to make F4 (D). A: M. guttatus 2x=2n=28; B: M. luteus 2x=4n=60~62; C: F1 hybrids 2x=3n=45; D: F4 2x=68~90. The sections of histograms suggest some possible chromosome segmental changes in F2, F3, F4

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Figure 4.4 Meiotic and mitotic chromosomes of M. guttatus and M. luteus. a, c and d were haploid cells (n) after meiotic activity spreading from anthers; b was in diploid cell (2n) in mitotic metaphase spreading from a root tip. a: M. guttatus, meiotic metaphase-I, n=14; b: M. guttatus, mitotic metaphase chromosomes from root tips, 2n=28; c: M. luteus, meiotic metaphase-I, n=31; d: M. luteus, meiotic metaphase-I, n=31

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Figure 4.5 Meiotic chromosomes of pollen mother cells in F4 Mimulus fertile hybrid plants. e: 35 bivalents + 2 univalents, 2n=72; f: 31 bivalents + 11 univalents, 2n=73; g: 38 bivalents + 2 univalents, 2n=78; h: meiotic metaphase-I chromosomes showed majority bivalents and some univalents chromosomes

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Figure 4.7 Histograms of floral traits in the hybrids (F3 and F4) population. Phenotypic means for M. guttatus and M. luteus are marked with red and blue lines, respectively

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Figure 4.8 The degree of colour separation as seen by bees, and the loci of all 687 colour loci are plotted in the colour hexagon. (a) the loci of M. guttatus (N=75) and M. luteus (N= 9) are marked with red and blue dots, respectively (b) the loci of F1 (N=196) and F2 (N=200) are marked with red and blue dots, respectively (c) the loci of F3 (N=207)

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Figure 4.9 Box-plot corresponds to the quantiles in the distribution of the floral UV brightness in parental Mimulus and their offspring F3 and F4, using the following abbreviations: ML, Mimulus luteus; MG, Mimulus guttatus

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Figure 4.10 Principal component analysis (PCA) on 12 phenotypic traits from parental Mimulus to their offspring F3 and F4. Loadings for 12 phenotypic traits which included UV brightness, tube width, corolla width, corolla length, stigma length, short stamen length, long stamen length, nectar sugar, flowering period (FP), leaf width, leaf length and leaf thickness. The eigenvalues of the first two principal components was 99.9% in four generations

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5 CHAPTER 5 GENERAL CONCLUSION

As seen in the three preceding projects (Chapters Two, Three, and Four), UV patterning in flowers is highly variable within and between species. Below, I consider my findings on how floral UV reflectance can evolve in flowering plants and suggest future approaches for studying the likely consequences of this trait in natural communities.

5.1 Natural variation in floral UV reflectance

Floral UV reflectance can be made visible and quantified by using a UV transmitting filter and/or a spectrometer. The patterns seen in floral UV reflectance, in Mimulus especially (the

“bullseye” pattern) suggest that the trait contributes significantly to the phenotypic diversity observed in nature. My study of the variation in UV reflectance suggests that this phenotype is probably mediated by plant-pollinator interactions due to the dramatic shifts observed in pollinator perception of floral colour when there is a peak in the UV part of the spectrum. However, this observation stayed alone in insufficient to rule out the roles of bioclimatic variables (associated with biogeographic distribution). At the mechanistic level, the UV phenotype in M. guttatus was observed to have associations with biochemical and microstructural traits. Because floral UV reflectance could be driven with either an association with different pollinator groups

(hummingbirds, Hymenoptera, Lepidoptera and Diptera), or affect a plant’s defense functions against UV damage and pests, these findings motivated alternative hypotheses to pollinator attraction and survival in the following chapters.

5.2 Macroevolutionary patterns of UV reflectance

If UV patterning functions to attract pollinators, I expected that the UV phenotype should

114 be associated with species that invest more resources in attracting pollinators. I also expected to observe a lower phylogenetic signal of the floral UV trait if the phenotype is a result of environment influences (e.g., if the ‘bullseye’ pattern is imposed by thermal stress or solar irradiance). Through the comparative analysis of UV quantitative variation and each predictor variable, the results supported my predictions that (1) higher UV- reflecting flowers occur at lower altitudes, and (2) bees were the prevalent pollinator group at lower altitudes. This chapter also considered the best model of evolution for the traits and found that all variables except plant height are likely under stabilizing selection forces. One unexpected result of this chapter is that UV reflectance was associated with mating system, and is therefore possibly an important contributor to the evolutionary divergence between sister-species pairs with different levels of selfing.

5.3 Biogeographical patterns in UV reflectance

Alberta is a province with very heterogenous topography, which presents an interesting system for examining traits that vary with altitude. M. guttatus is a species that is common elsewhere but listed as rare in Alberta and exhibits a fragmented distribution. In this chapter, I examined how floral UV phenotypes varied with geography in Alberta and explored how this offered evidence toward certain eco-evolutionary demographic models in flowering plants. My models suggest that adaptive divergence of floral UV reflectance followed M. guttatus colonization events. I also found that environmental variables (especially temperature) were significantly associated with microsatellite loci variation after controlling for spatial coordinates.

While variation in UV reflectance was somewhat geographically clustered, floral UV divergence appears to arise both by genetic drift and ecological specialization of populations.

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5.4 Genetic patterns in UV reflectance

Lineages can become reproductively isolated as a result of chromosomal changes. The effect of hybridization can determine how phenotypic traits evolve, especially in the early generation stages. Applying multivariate analyses in parental and hybrid generations, I characterized how phenotypic correlations varied between my experimental interspecific crosses between M. guttatus (where UV patterns are present) and M. luteus (where UV patterns are absent).

In F1 and F2 hybrids, the correlation between floral traits other than UV reflectance varied much less than the correlation between floral UV reflectance with reproductive traits (stamens and nectar reward). F1 hybrids experienced a severe fitness disadvantage (low seed set), yet recovered over the F3 and F4 generations. Higher heritability in UV reflectance in the resulting Mimulus allopolyploids likely reflects the increased the additive genetic variation caused by hybridization.

In a natural system, similar allopolyploids may experience advantages caused by this polyploidy- generated diversity.

5.5 Future studies

The Mimulus genus proved to be an excellent system in which to explore the effects of chromosomal rearrangements on floral UV pigmentation shifts. Research on floral UV reflectance could be an important component of floral colour modifications observed in natural systems, and the flavonoid biosynthesis pathways provides a biochemical framework to link genotypic and phenoypic shifts. Future field studies could concentrate on elucidating the role of pleiotropic selection on floral pigment genes. Further phylogenetic analyses could examine how chromosomal changes are involved in floral colour shifts and rapid adaptive radiations. UV patterning on flowers

116 is a complex trait with many unanswered questions regarding its biogeographical distribution and evolutionary history.

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6 APPENDIX A

ADDITIONAL INTRODUCTION AND FIGURES

1. Introduction of biochemical pathway

Flower colour is complex trait, which proceeds primarily through regulatory mutations in the anthocyanin pathway (APPENDIX A Figure 1). Flavonoids are secondary natural products derived from phenylalanine, which plays a role in several ecological functions like protecting plants against UV reflection and pests, and promoting the interaction between the plant and the pollinators (Koes et al., 1994, Shirley, 1996). The enzyme activity and expression level of candidate genes in anthocyanin pathway, determine that the multiple mutations have caused gain or loss of function phenotypes. Chalcone synthase (CHS) can be activated by UV radiation and other environmental stimuli in flavonoid biosynthesis. CHS is localized on the epidermal cells.

Except regulator genes, the six core structural genes of the flavonoid biosynthesis pathway have been cloned and characterized in diverse colours (red, pink, purple, and blue) of flowering plants

(Tiffin et al., 1998). These six core structural genes include up stream genes code for enzymes

(e.g., chalcone synthase [CHS], chalcone flavanone isomerase [CHI], and flavanone 3-hydroxylase

[F3H]), Dihydroflavonol 4-reductase [DFR] and downstream genes code for enzymes (e.g., anthocyanidin synthase [ANS] and UDP glucose flavonoid 3-oxy-glucosyltransferase [UF3GT]).

Inactivation of hydroxylating enzymes Flavonoid 3'-hydroxylase [F3'h] and Flavonoid 3', 5'- hydroxylase [F3'5'h] results the shift from pelargonidin with one hydroxyl group to cyanidin with two hydroxyl groups, from cyaniding to delphinidin with three hydroxyl groups, respectively.

Following the above pathway, the flower colour is shifted from redder to bluer. In previous researches (Tucker and Lundrigan, 1993, Rausher et al., 1999, Rausher, 2008), the upstream genes tend to produce precursors for one or more important non-anthocyanin flavonoid pathways, and

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DFR changes substrate-bind region to dihydroquercetin [DHQ] and dihydromyricetin [DHM]. The anthocyanin regulatory genes play a greater role for flower colour modification than structural genes in adaptive evolution of anthocyanin pathway. The downstream enzymes (regulatory genes for contributing core flavonoid structures) are specific to the anthocyanin pathway, which evolve more rapidly than upstream genes (structural genes) (Rausher, 2008, Rausher et al., 1999).

Floral UV pigments covary with other colour pigments. Two classes of flavonoids, flavonols and chalcones have been observed to contribute to UV absorption (Dement and Raven,

1974, Koes et al., 1994, Shirley, 1996). To characterize how the flavonoid pathway serves attractive and defensive functions in floral UV pigments (Winkel-Shirley, 2001), I restricted my focus to the particular UV pigment pattern on the flower and HPLC was carried out for analyzing the compounds of flavonoid which are responsible for the UV-absorbing pigments with coevolution of UV-sensitive insects under variant environment stress like UV radiation and cold.

Anthocyanins and carotenoids with the compounds of flavonoids, flavonols and chalcones are important in flower colour (Rausher, 2008).

In my study, Mimulus UV reflecting flowers and non-UV reflecting flowers were analyzed by high performance liquid chromatography. Liquid chromatography–mass spectrometry (LC-MS, or alternatively HPLC-MS) is an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography (or HPLC) with the mass analysis capabilities of mass spectrometry (MS). The samples include Mimulus guttatus, which has high floral UV reflectance, M. luteus which has high UV absorption, and the hybrids from M. guttatus and M. luteus which have UV-absorbing and –reflecting flowers. The dried flower buds were crushed and stirred in methanol for 24 h, and then the resulting slurry was extracted and detected by using an

Agilent Technologies (Santa Clara, California) 6410 Triple Quad LC-MS/MS with a 1200 Series

119 liquid chromatography system equipped with an electron spray ionization source and an Eclipse

Plus C18 1.8 μ m 2.1×50 mm column (Agilent). As the (+)-LC-MS (QqQ) data (APPENDIX A

Figure 2), herbacetin (MW=302) and quercetin glucoside (MW = 464, can be quercetin 3- glucoside or quercetin 7-glucoside) showed significantly more abundant in UV absorption flowers.

In a previous study, it was found that the mixture of flavonol glucosides function for nectar guides, such as “bullseye” pattern of a flower (Hypericum calycinum) (Baby et al., 2013), which character intense ultraviolet absorption in 340~380nm (Gronquist et al., 2001). On the other hand, I found that Sulfurein (MW = 270.2) and Chinesin I (MW = 444.6) were associated with higher UV reflectance in flowers. While these results were preliminary in nature, they suggest that fruitful lines of future research may be acquired with an authentic standard to confirm the identity of these peaks.

2. Molecular mechanism about UV pattern for resistance abiotic stress

Both dearomatized isoprenylated phloroglucinols (DIPs) and flavonoids have the capacity to attract a variety of pollinators, given that the UV light between 340nm to 380nm is discernable to pollinators. For example, DIPs in high concentrations in the ovarian wall and the anthers of the flower Hypericum calycinum, have been characterized as the main pigments conveying floral UV patterns (Gronquist et al., 2001). The previous studies focused on DNA recombination and mutation from plant to repair UV damage, but little research has investigated the molecular mechanisms or genetic basis for floral UV patterning on petals. For example, DIPs and hypercalin

(A, B, C) were distinct chemically from floral UV pigments, and also had a defense function in that they deterred insect larvae (Gronquist et al., 2001). In Mimulus, further testing of anti- herbivory activity could investigate whether the chemical compounds, from UV-absorption

120 pigments from M. guttatus and M. luteus flowers act as herbivore antifeedants, in much the same way as DIPs that can be used to kill Utethisa ornatrix moths (Gronquist et al., 2001).

The flowers petals are directly and indirectly affected by UV-B radiation and thermal stresses, which can cause protein denaturation, membranes disruption, and gene modification. The tolerance to UV radiation depends on various feedback mechanisms, such that the plant may reduce damage by creating a floral UV pattern. UV-B radiation stress can drive biosynthesis of flavones and flavonols, leading to herbivore and pathogen resistance (Winkel-Shirley, 2001). UV radiation breaks the epicuticular layer of the petal cell, which stimulates the compounds of the secondary metabolites to reflect and absorb UV light. And the function of absorbing UV can be a useful method to attract pollinators. In researching the flavonoid pathway genes (Taylor and

Briggs, 1990, Ehmann et al., 1991, Alokam et al., 2002), others have observed a correlation between anthocyanin biosynthesis and the transcript level of Chalcone synthase (CHS) and phenylalanine ammonia lyase (PAL) under the control of UV receptors, blue-light receptors and phytochrome.

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APPENDIX A Figure 1 The anthocyanin biosynthetic pathway. Pathway enzymes are indicated in parentheses: CHS, chalcone synthase; CHI, chalcone-flavanone isomerase; F3H, flavanone 3-hydroxylase; DFR, dihydroflavonol reductase; ANS, anthocyanidin synthase; UF3GT, UDP glucose flavonoid 3-oxy-glucosyltransferase. (Reference Rausher et al. 1999)

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APPENDIX A Figure 2 LC-MS results and Molecular Weight (MW) for inferred chemicals

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7 APPENDIX B

ADDITIONAL INTRODUCTION AND TABLE

1. Bee colour hexagons represents the visual perception of insects and in part drives bee pollinator behavior (Chittka, 1992). I transferred information from the bee colour hexagon to a code value

(the presence/absence of UV reflectance) using information from the Floral Reflectance Database

(Arnold et al., 2010). The combination of three-colour opponent diagrams (UV, UV-Blue, UV-

Green) relate to UV reflectance through bee vision, can then be reduced to categorical value (Bee and UV Columns). Alternatively, UV reflectance can remain a continuous value, e.g. UV brightness and UV maximum values obtained from spectrometer readings (Ocean Optics, Dunedin,

Florida, USA). The presence of UV can be effectively extracted from bee colour hexagons (UV reflectance has a positive association with UV brightness and maximum)

2. The equation of phylogenetic signal statistic, K, of Blomberg et al. (Blomberg et al., 2003), as following

K= observed/ expected

The ratio of K equals the ratio of mean squared error divided by the contrasts variation. The ratio of the mean squared error is measured from the phylogenetic correct mean (MSE0) over the mean squared error of data with the variance-covariance matrix (Yoder et al.). The expected MSE ratio is supposed under BM. The statistical results for significant value of phylogenetic signal are lower than the accurate value by various factors which include errors from estimation of phylogeny, branch length, fitness etc. These factors may cause the departure from Brownian motion. If the candidate tree describes little covariance within the tip data which leads to relatively small MSE0, and smaller value of MSE0/MSE means weak phylogenetic values. On the contrary, if the variance-

126 covariance pattern is observed in the data which is explained by the candidate tree, so MSE will be relatively small which leads to a large value of MSE0/MSE (strong phylogenetic signals). K >

1 means high level of phylogenetic signal. The tendency of related species is more similar form each other along the candidate tree than expected which means the trait is distributed in the evolutionary process in accordance with the inheritance in ancestral state estimation, not associate with other factors such as environmental influence. On the contrary, K < 1 means accessing traits correlation not only needs to account for relationship among the resemble species, but also considering about other environmental and ecological drivers.

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APPENDIX B Table 1 PhylANOVA Asterisks indicate significance correlation coefficients. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Discrete Bee UV

Continuous F value P F value P Altitude 0.003923308 0.959 8.627821 0.02 * Tube length 0.006706095 0.937 3.378033 0.133 Corolla width 0.228412 0.635 0.03778159 0.899 Plant height 6.795053 0.011* 1.01471 0.437 UV brightness 4.014811 0.039 * 50.33897 0.001*** UV maximum 2.196097 0.151 21.63671 0.001***

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8 APPENDIX C

ADDITIONAL INTRODUCTION AND TABLE

APPENDIX C Table 1 Information for each population of Mimulus guttatus used in the study, including population code, cluster number, GPS coordinates and sampling year, the population codes: CH= Cypress Hills, DM= Del Bonita & Milk River, W= Waterton Park, CP= Crowsnest Pass, GP= Grande Prairie, CR= Craig Bay, BC; Accession codes: UAC = University of Calgary, ALTA = University of Alberta, LETH = Univeristy of Lethbridge

Population Cluster N W Year Accession Code No. sampled No. CH1 1 49.6375 110.31917 2014 CH2 1 49.63778 110.31889 2014 CH3 1 49.61806 110.09472 2014 CH4 1 49.61806 110.09472 2014 CH5 1 49.65861 110.10028 2014 CH6 1 49.65712 110.10866 2014 CH7 1 49.65706 110.10911 2014 CH8 1 49.65679 110.10963 2014 CH9 1 49.65651 110.11048 2014 CH10 1 49.65667 110.11016 2014 CH11 1 49.65748 110.10848 2014 CH12 1 49.65761 110.10839 2014 CH13 1 49.6576 110.10834 2014 CH14 1 49.65775 110.10775 2014 CH15 1 49.65773 110.10699 2014 CH16 1 49.637693 110.318017 2014 CH17 1 49.637583 110.317955 2014 CH18 1 49.637716 110.37999 2014 CH19 1 49.637659 110.317811 2014 CH20 1 49.6357595 110.317901 2014 CH21 1 49.62681 110.19954 2014 CH22 1 49.62681 110.19954 2014 CH23 1 49.62681 110.19954 2014 CH24 1 49.62681 110.19954 2014 CH25 1 49.62681 110.19954 2014 CH26 1 49.663476 110.302354 1947 ALTA15500 CH27 1 49.63584 110.14037 1999 ALTA106161 CH28 1 49.666203 110.271367 1945 ALTA46490 CH29 1 49.663416 110.272512 1960 UAC23295 CH30 1 49.666831 110.10699 1964 UAC23296

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CH31 1 49.6146653 110.262847 1999 ALTA106162 DM1 2 49.028654 112.788885 1960 ALTA122478 DM2 2 49.14356 112.08603 1951 ALTA262121 W1 3 49.01399 114.05161 2014 W2 3 49.01399 114.05161 2014 W3 3 49.01399 114.05161 2014 W4 3 49.01399 114.05161 2014 W5 3 49.01399 114.05161 2014 W6 3 49.01399 114.05161 2014 W7 3 49.08 114.9 1969 LETH2933 W8 3 49.1 114.3 1970 LETH4121 W9 3 49.08 113.51 1970 LETH4321 W10 3 49 113.5 1970 LETH4391 W11 3 49.4 114.5 1970 LETH4657 W12 3 49.1 114.3 1970 LETH4726 W13 3 49.1 114.05 1970 LETH5010 W14 3 49.2 113.47 1970 LETH5170 W15 3 49.029927 113.935177 1966 ALTA32157 W16 3 49.13333 114.15 1969 ALTA45916 W17 3 49.16667 114.08333 1977 UAC23301 W18 3 49.16667 114.08333 1977 UAC23302 W19 3 49.666831 114.22318 1953 ALTA15498 CP1 4 49.53241 114.38268 2014 CP2 4 49.53241 114.38268 2014 CP3 4 49.53241 114.38267 2014 CP4 4 49.53241 114.38267 2014 CP5 4 49.53241 114.38267 2014 CP6 4 49.53241 114.38275 2014 CP7 4 49.53241 114.38275 2014 CP8 4 49.53242 114.38275 2014 CP9 4 49.53242 114.38275 2014 CP10 4 49.53239 114.38277 2014 CP11 4 49.53239 114.38277 2014 CP12 4 49.53239 114.38277 2014 CP13 4 49.53239 114.38277 2014 CP14 4 49.53235 114.38278 2014 CP15 4 49.53235 114.38278 2014 CP16 4 49.53234 114.3828 2014 CP17 4 49.53234 114.3828 2014 CP18 4 49.53234 114.3828 2014 CP19 4 49.53225 114.38287 2014

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CP20 4 49.53225 114.38287 2014 CP21 4 49.53226 114.38286 2014 CP22 4 49.53212 114.38296 2014 CP23 4 49.53212 114.38296 2014 CP24 4 49.53212 114.38296 2014 CP25 4 49.418486 114.48214 ~1960 UAC23294 CP26 4 2005 UAC58641 CP27 4 49.13333 113.85 1970 ALTA45918 CP28 4 49.501482 114.22318 1960 UAC23293 CP29 4 49.225 114.125 1970 ALTA131449 CP30 4 49.454494 114.41398 1971 ALTA39057 GP1 5 54.13882 119.9636 2006 ALTA124176 GP2 5 54.89404 119.87661 ? ALTA130480 GP3 5 54.984455 119.992796 2008 UAC84491 GP4 5 54.89855 119.89692 2008 UAC84493 GP5 5 54.89408 119.87624 2008 UAC84495 GP6 5 54.984455 119.992796 2008 UAC84492 GP7 5 54.91418 119.34278 2008/7 UAC84490 GP8 5 54.85348 119.49184 2006/8 UAC58217 GP9 5 54.89291 119.93501 2008 UAC84494 CR1 6 49.303837 124.252157 2014 CR2 6 49.303837 124.252158 2014 CR3 6 49.303837 124.252159 2014 CR4 6 49.303837 124.25216 2014 CR5 6 49.303837 124.252161 2014

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9 APPENDIX D

ADDITIONAL TABLES AND FIGURE

APPENDIX D Table 1 Floral trait correlations matrix in parental Mimulus and F3 F4 population. Spearman’s correlation coefficients were given below the diagonal

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M. guttatus Correlations UV tube corolla corolla leaf leaf leaf stigma short long flowering nectar brightness width width length width length thickness stamen stamen period sugar UV brightness 1.000 tube width 0.121 1.000 corolla width 0.017 0.636 1.000 corolla length 0.003 0.625 0.835 1.000 leaf width -0.174 -0.012 0.096 0.206 1.000 leaf length -0.151 0.069 0.014 0.011 0.689 1.000 leaf thickness -0.290 -0.232 -0.115 -0.104 0.521 0.540 1.000 stigma -0.079 0.022 0.121 0.178 -0.035 -0.117 0.261 1.000 short stamen -0.027 0.013 0.075 0.126 0.115 -0.140 -0.079 0.004 1.000 long stamen -0.089 0.019 0.042 0.151 0.109 -0.055 -0.095 -0.030 0.754 1.000 flowering period 0.074 -0.239 -0.063 0.057 0.334 0.203 0.206 0.062 0.114 0.000 1.000 nectar suger -0.377 -0.080 -0.280 -0.191 0.226 -0.133 -0.527 -0.507 -0.174 0.145 -0.190 1.000

M. luteus Correlations UV tube corolla corolla leaf leaf leaf stigma short long flowering nectar brightness width width length width length thickness stamen stamen period sugar UV brightness 1.000 tube width -0.446 1.000 corolla width -0.343 0.839 1.000 corolla length -0.054 0.704 0.833 1.000 leaf width 0.468 -0.144 -0.356 -0.331 1.000 leaf length 0.836 -0.366 -0.617 -0.463 0.618 1.000 leaf thickness 0.241 0.249 0.393 0.127 0.128 -0.046 1.000 stigma -0.202 -0.709 -0.636 -0.331 0.040 0.108 -0.179 1.000 short stamen 0.083 0.192 0.225 0.195 -0.116 -0.050 0.352 -0.629 1.000 long stamen 0.084 0.264 0.228 0.205 -0.127 -0.006 0.265 -0.552 0.314 1.000 flowering period -0.263 -0.166 -0.140 -0.119 -0.061 0.070 -0.718 0.253 -0.039 0.168 1.000 nectar suger -0.012 0.215 0.403 0.758 -0.167 -0.240 -0.412 -0.442 0.293 0.340 -0.093 1.000

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F3 Correlations UV tube corolla corolla leaf leaf leaf stigma short long flowering nectar brightness width width length width length thickness stamen stamen period sugar UV brightness 1.000 tube width -0.143 1.000 corolla width 0.015 0.712 1.000 corolla length 0.019 0.498 0.640 1.000 leaf width 0.243 0.165 0.297 0.209 1.000 leaf length 0.309 0.031 0.149 0.091 0.653 1.000 leaf thickness 0.521 -0.109 0.060 0.038 0.025 0.086 1.000 stigma -0.299 0.197 0.075 -0.014 -0.148 -0.191 0.111 1.000 short stamen 0.692 0.006 0.018 0.027 0.261 0.268 0.419 -0.206 1.000 long stamen 0.781 -0.007 0.036 0.032 0.388 0.444 0.491 -0.237 0.854 1.000 flowering period 0.157 -0.100 -0.020 -0.019 0.090 0.138 0.339 0.076 -0.056 -0.045 1.000 nectar suger 0.869 -0.113 0.042 0.081 0.301 0.362 0.534 -0.251 0.769 0.920 0.115 1.000

F4 Correlations UV tube corolla corolla leaf leaf leaf stigma short long flowering nectar brightness width width length width length thickness stamen stamen period sugar UV brightness 1.00 tube width 0.00 1.00 corolla width 0.09 0.29 1.00 corolla length 0.00 0.21 0.75 1.00 leaf width 0.04 -0.06 0.07 -0.05 1.00 leaf length 0.18 0.11 0.11 0.09 0.50 1.00 leaf thickness -0.06 0.06 0.11 0.02 0.52 0.43 1.00 stigma 0.38 0.06 0.15 0.06 -0.03 0.19 -0.02 1.00 short stamen 0.78 0.02 0.04 0.00 0.07 0.17 -0.04 0.32 1.00 long stamen 0.85 0.04 0.05 -0.02 0.03 0.14 -0.07 0.41 0.88 1.00 flowering period 0.20 -0.06 -0.03 -0.04 -0.01 -0.03 0.03 -0.11 0.17 0.18 1.00 nectar suger 0.90 0.13 -0.14 -0.04 0.35 0.12 0.58 -0.16 -0.57 -0.11 0.02 1.00

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APPENDIX D Figure 1 Two types of red-pigmentation patterns that exhibited within the same individual in F3

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