Floral Anthesis Rate and Pollen Limitation in Delphinium Glaucum and Their Consequences for Female Fitness

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2016 Floral Anthesis Rate and Pollen Limitation in Delphinium glaucum and their Consequences for Female Fitness

Cameron-Inglis, Hazel

Cameron-Inglis, H. (2016). Floral Anthesis Rate and Pollen Limitation in Delphinium glaucum and their Consequences for Female Fitness (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26188 http://hdl.handle.net/11023/2980 master thesis

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Floral Anthesis Rate and Pollen Limitation in Delphinium glaucum and their

Consequences for Female Fitness

Hazel Letitia Cameron-Inglis

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

MAY, 2016

Phenological mismatch between plants and their pollinators and/or interspecific competition for pollination can cause insufficient pollen quantity and/or inadequate pollen quality to limit seed production and siring. Inflorescence display size, a dynamic part of pollinator attraction, influences pollinator behaviour and movement. Thus, a rapid increase in display size as plants begin flowering caused by initially rapid flower anthesis should promote recruitment of pollinators to newly flowering individuals and species.

Tests of two assumptions of this hypothesis for Delphinium glaucum revealed: 1) declining anthesis rate during a plant’s flowering period is an intrinsic characteristic of inflorescence architecture, rather than a response to internal resource economy; and 2) pollinator limitation early during the flowering period reduced ovule fertilization and elevated autogamous self-mating. These results suggest that rapidly increasing initial display size largely contributed to the quality, rather than the quantity of female mating.

Consequences for male outcross success remain to be assessed.

ii Acknowledgements

“It takes a village” is a proverb expressing the belief that it takes a community of people to raise a child. I draw a parallel here in that this thesis would not have come to fruition without the guidance, aid and friendship of many people both within and outside of the academic community. This thesis is the culmination of some of the most challenging and rewarding years of my life. Thank-you to everyone who has helped me along this journey.

In particular, I am profoundly grateful to my supervisor, Dr. Lawrence Harder, for his guidance and support. Lawrence has challenged, inspired and trained me to think carefully and critically, to write and speak precisely, to interact with others kindly and patiently, and to conduct and assess research objectively. Thank you Lawrence, for the opportunities you have given me and for your valued mentorship.

Additionally, I am indebted to my supervisory committee members, Dr. Sean

Rogers and Dr. Mary Reid, as well as other professionals, for their contributions to my research. Sean and Mary, along with my external examiner, Dr. David Goldblum contributed insightful comments and advice on my writing and/or research. Moreover,

Sean shared lab space, equipment, and molecular knowledge; Dr. Ed Yeung allowed me to use his equipment for pollen enumeration; John Swann aided with insect identification;

John Buckley erected an electric fence around my study plants; and the Biogeoscience

Institute supplied weather data from the Barrier Lake Research Station.

Many thanks are also owed to my lab mates, fellow students and lab collaborators for their assistance and social support. Dr. Mason Kulbaba and Dr. Wan-Jin Liao developed DNA extraction protocols and/or Delphinium glaucum microsatellites used in

iii this study. Further, Mason gallantly mentored me on molecular techniques and boosted my morale during troublesome field and lab work. Caitlin Tomaszewski tirelessly and cheerfully assisted me with seed and pollen enumeration and DNA extractions. Dr.

Takashi Ida shared plant community phenology data for my study population. Lauren

Sawich was a motivating force of reassurance and cheer throughout the analysis and writing process. These individuals, along with other current and past lab members: Illona

Clocher, Lisa O’Donnell, David Robinson, and Paul Simpson, shared valuable information, resources, feedback or encouragement. Similarly, I am grateful to several individuals outside my lab: Ella Bowles, Brandon Allen, Stevi Vanderzwan, Leanna

Lachowsky, Yan Liu, Sam Robinson and Riley Waytes, for friendly and illuminating conversations relating to pollination, evolution, or molecular techniques.

Above all, I am extremely thankful for my friends and family. My friends Robin

Butler, Erin Leson and Jon Pellett maintained my sanity and focus by providing encouragement, empathy and creative distractions. My parents, Evelyn and Gordon, and my in-laws, Mike and Audrey, steadfastly supported me emotionally and materially. My wonderful children, Erik and Jamie, understandingly sacrificed their time with me during long field, lab and writing days. And my partner in life, love and eclectic hobbies, Joel

Antler, supported me daily by selflessly providing for our needs, constructively listening to my thoughts, wittily encouraging me to laugh, and patiently reminding me to breathe.

Finally, the National Sciences and Engineering Research Council of Canada funded this research through Dr. Lawrence Harder’s Discovery and Accelerator Grants and through my Postgraduate Scholarship M Award.

iv Table of Contents

Abstract ...... ii Acknowledgements ...... iii Table of Contents ...... v List of Tables ...... vii List of Figures and Illustrations ...... viii

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Pollinator attraction, floral display dynamics and their contributions to plant mating ...... 1 1.2 Pollinator limitation and interspecific competition for pollination services ...... 5 1.3 Objectives ...... 7

CHAPTER TWO: PHENOLOGICAL MISMATCH BETWEEN A PLANT AND ITS POLLINATORS AND ITS CONSEQUENCES FOR SEED PRODUCTION ...... 9 2.1 Introduction ...... 9 2.2 Materials and Methods ...... 12 2.2.1 Study species and site ...... 12 2.2.2 Display dynamics ...... 14 2.2.3 Pollen, seed and plant enumeration ...... 15 2.2.4 Pollinator visitation ...... 16 2.2.5 Herbivory ...... 17 2.2.6 Data analysis ...... 18 2.3 Results ...... 20 2.3.1 Pollinators ...... 20 2.3.2 Phenologies ...... 20 2.3.3 Pollen receipt and potential donors ...... 22 2.3.4 Reproductive output ...... 23 2.4 Discussion ...... 27 2.4.1 Phenological mismatch ...... 27 2.4.2 Limits on seed production ...... 28

CHAPTER THREE: DO ARCHITECTURAL EFFECTS OR RESOURCE DEPLETION GOVERN DECLINING ANTHESIS RATES WITHIN INFLORESCENCES? ...... 31 3.1 Introduction ...... 31 3.2 Materials and Methods ...... 34 3.2.1 Experimental protocol ...... 34 3.2.2 Data analysis ...... 38 3.3 Results ...... 39 3.3.1 Floral display and anthesis rate dynamics ...... 39 3.3.2 Effects of bud removal on anthesis rate ...... 41 3.3.3 Effects of flower position and treatment on seed production ...... 43 3.4 Discussion ...... 45

v CHAPTER FOUR: DOES TEMPORAL VARIATION IN POLLINATOR ABUNDANCE AND FLOWER ANTHESIS INFLUENCE THE FREQUENCY OF OUTCROSSING IN DELPHINIUM GLAUCUM? ...... 50 4.1 Introduction ...... 50 4.2 Materials and Methods ...... 53 4.2.1 Study plants and tissue collection ...... 53 4.2.2 Sampling and DNA extraction ...... 53 4.2.3 PCR and microsatellite scoring ...... 55 4.2.4 Genotype troubleshooting ...... 57 4.2.5 Genotyping error rates ...... 59 4.2.6 Data analysis ...... 59 4.3 Results ...... 61 4.4 Discussion ...... 65 4.4.1 Seasonal variation in outcrossing rates ...... 66 4.4.2 Within-inflorescence variation in outcrossing rates ...... 67 4.4.3 Concluding remarks ...... 68

CHAPTER FIVE: IMPLICATIONS FOR PLANT REPRODUCTIVE SUCCESS ...... 70

REFERENCES ...... 73

APPENDIX ...... 95

vi List of Tables

Table 2.1. Parameter estimates for best-fitting models of stigmatic pollen receipt, fertilizations per ovule, seeds per fertilization, and seeds per ovule...... 25

Table 3.1. Results of generalized linear mixed model assessing the effects of inflorescence age and total flower number on display size ...... 39

Table 3.2. Results of generalized linear mixed model assessing the effects of inflorescence age and total flower number on flower anthesis rate ...... 41

Table 3.3. Results of generalized linear mixed models assessing the effects of treatment and covariates for the first 8 open flowers and flower positions 9-16 on anthesis rate ...... 42

Table 3.4. Significance tests and partial regression coefficients for generalized linear mixed models assessing the effects of flower position and covariates in intact inflorescences or treatment and covariates at flower positions 9-16 on seed count and average seed mass in Delphinium glaucum...... 44

Table 4.1. Error rates for Delphinium glaucum genotypes scored at five microsatellite loci...... 59

Table 4.2. Generalized linear mixed models of the influences on the probability that a seed was cross-fertilized for Delphinium glaucum ...... 63

vii List of Figures and Illustrations

Figure 2.1. Delphinium glaucum inflorescence, bud, male phase flower, maturing fruit, pollen grains on stigmas, ovule, and aborted and mature seeds ...... 14

Figure 2.2. Phenologies and corresponding seed set of open female- and male-phase flowers of 24 Delphinium glaucum plants and the abundance of foraging bees during the 2012 flowering period...... 21

Figure 2.3. The effects of the abundance of bumble bees (Bombus spp.) visiting the Delphinium glaucum population on mean pollen receipt per fruit, number of fertilized ovules per fruit, proportion of seeds per fertilization, and number of seeds per fruit ...... 26

Figure 3.1. Illustration of treatments and predicted responses for the architecture and resource-dynamics hypotheses to removal ...... 36

Figure 3.2. Least-squares mean display size and anthesis rate of 24 Delphinium glaucum inflorescences with increasing inflorescence age...... 40

Figure 3.3. Comparisons of observed least-squares mean anthesis rates between treatments for flowers in positions 9-16 and the first 8 flowers to open ...... 43

Figure 3.4. Comparisons of least-squares mean seed count and seed mass between positions 1-8 and positions 9-16 on intact inflorescences and among treatments for positions 9-16 ...... 45

Figure 4.1. Effects of mean foraging bee abundance and flowering day on predicted outcrossing rate ...... 64

Figure 4.2. Influence of bee abundance on the mean proportion of outcrossed seeds, after accounting for variation in population flowering date ...... 65

viii

Chapter One: Introduction

1.1 Pollinator attraction, floral display dynamics and their contributions to plant mating

More than 87% of angiosperms rely on animals to transfer their male gametes between conspecific individuals for outcrossing (Ollerton et al. 2011). Sexual reproduction produces offspring with new combinations of alleles (Agrawal 2006), which facilitates adaptation (Colegrave 2002) and promotes genetic diversity within populations

(Barrett 2003). Outcrossing with non-relatives reduces homozygosity and associated cumulative effects of the expression of recessive deleterious alleles (inbreeding depression: Goodwillie et al. 2005). In exchange for dispersing pollen, pollinators are typically rewarded with nectar and/or pollen (as sources of sugars, protein and additional nutritional factors: Simpson and Neff 1983, Roulston and Cane 2000), as well as other secondary benefits (Sapir et al. 2005, Manson et al. 2010). Although plants and flower visitors sometimes “cheat” by obtaining services or rewards without benefiting the other partner (reviewed in Bronstein et al. 2006, Irwin et al. 2010, Hargreaves et al. 2009), plant deception and floral larceny are possible only through maintenance of plant- pollinator interactions at the community scale (Bronstein et al. 2006).

Although plants and pollinators depend mutually on one another, the exchange of services is not necessarily equal (Morris et al. 2010). Each partner pursues its own selfish interests, so benefits gained are unintentional ecological and evolutionary consequences of their interactions (Mitchell et al. 2009). In particular, pollinator- mediated natural selection promotes plant investment in floral traits that attract pollinators to and orient their movements within inflorescences (Vaughton and Ramsey

1998, Jordan and Harder 2006, Fenster et al. 2009, Iwata et al. 2012). This imposes a cost on reproduction when floral advertisement diverts resources otherwise available to seed production and siring (Charlesworth and Charlesworth 1987, Vaughton and Ramsey

1998). In contrast, pollinators expend energy locating, travelling between and handling flowers to gain resources for growth and reproduction (reviewed in McCallum 2013).

The distribution of floral resources is patchy and quality (e.g., nectar volume and concentration) varies among species and within inflorescences and patches (Best and

Bierzychudek 1982, Goulson 1999, Devoto et al. 2014), resulting in variation in nutritional gains for pollinators. Consequently, the value of reward provided by a plant will affect the proportion of flowers visited within an inflorescence, the duration of visits and the number of unrewarding flowers that a pollinator visits before departing the inflorescence or patch (Best and Bierzychudek 1982, Pleasants 1989, Harder 1990,

Chittka et al. 1997, Ishii et al. 2008). Thus, natural and sexual selection have acted on plants to maximize seed production and siring by optimizing floral attraction

(Klinkhamer and de Jong 1993, Fishman and Hadany 2015) and on pollinators to maximize their foraging returns by optimizing their foraging behaviour (Charlton and

Houston 2010). In particular, characteristics of pollinator perception, cognition and response capacity (discussed below) create the selective environment for the evolution of floral and inflorescence traits.

Pollinator attraction is an important process in reproduction by animal-pollinated plants. The sessile nature of plants, combined with the short growing period in temperate zones, impose spatial and temporal limits on resource acquisition and fruit development.

Accordingly, angiosperms advertise the location of floral rewards to pollinators through

both long-distance signals (e.g., simultaneous display of multiple flowers, showy corollas, scent), which focus pollinator attention on individual plants, and short-distance signals (e.g., nectar guides), which orient pollinators to the location of rewards within the plant (Borges et al. 2003, Howell and Alarcón 2007). Floral traits involved in short- distance signalling and the physical placement of flowers within inflorescences and of rewards within flowers manipulate pollinator behaviour to enhance pollination efficiency

(Lunau 2000, Jordan and Harder 2006, Fenster et al. 2009). Plants produce a variety of cues (reviewed in Chittka and Raine 2006, Schiestl and Johnson 2013) that pollinators, through instinct and learning, associate with rewards, but the effectiveness of specific cues depends on the biology of the pollinator. For example, bees have much less resolved vision than vertebrates, so bees can discern colour only at close distances

(Spaethe et al. 2001, reviewed in Chittka and Raine 2006).

Long-distance attraction is effectively achieved through the aggregated contribution of individual flowers to floral display (Harder and Barrett 1995, Ohashi and

Yahara 2001, Chittka and Raine 2006, Makino et al. 2007). As such, inflorescences are the fundamental unit for plant mating, because plants display multiple flowers within inflorescences throughout the flowering period (Harder et al. 2004). Three characteristics of inflorescence architecture contribute to the detection of floral displays by pollinators: topology, including the number of open flowers and branching patterns within an inflorescence; geometry, the three-dimensional arrangement of flowers along the inflorescence; and phenology, the timing of flower opening (anthesis), wilting and sexual phases of individual flowers (reviewed in Harder and Prusinkiewicz 2013).

In particular, flower phenology influences the number of flowers open simultaneously on an inflorescence (floral display size: Harder and Johnson 2005) making it a dynamic part of pollinator attraction. Specifically, floral display size is a consequence of the rate of sequential flower opening (anthesis rate) and the longevity and size of individual flowers (Ishii and Sakai 2001, Meagher and Delph 2001, Harder and

Johnson 2005, Ishii and Harder 2006). Therefore, small changes in floral longevity or anthesis rate can affect the maximum size of floral display at a given instant (Harder and

Johnson 2005, Gallwey 2011). Larger displays are more visible to pollinators, signal greater availability of rewards, and provide increased foraging efficiency (Harder and

Barrett 1996, Ohashi and Yahara 2001). Consequently, increasing display size promotes pollinator attraction, in part because a large display offers more rewards (Harder et al.

2001). However, larger displays also allow more movement among a plant’s flowers by individual pollinators, and so they generally experience more among-flower self- pollination (geitonogamy: Klinkhamer and de Jong 1993, Harder and Barrett 1995,

Karron et al. 2004, Lau et al, 2008). Geitonogamy reduces opportunities for outcrossing by gamete discounting (Harder and Barrett 1995, Lau et al. 2008, Karron and Mitchell

2012) and, if self-fertilization occurs, it increases the incidence of inbreeding depression

(Lloyd 1992, Husband and Schemske 1996). Therefore, the evolution of display dynamics ultimately balances the marginal costs of pollinator movement within an inflorescence with the marginal benefits gained through increased pollinator attraction

(Harder and Prusinkiewicz 2013, Van Etten and Brunet 2013).

1.2 Pollinator limitation and interspecific competition for pollination services

The dependence of many plants on animals for pollen dispersal makes pollinator scarcity a common reproductive challenge for plants, which is associated with fewer seeds sired and produced (reviewed in Larson and Barrett 2000, Ashman et al. 2004,

Knight et al. 2005). The incidence and severity of such pollen limitation depends on both pollen quantity (i.e., pollen receipt) and quality (Caruso 2000, Bell et al. 2005, Aizen and

Harder 2007, Harder and Aizen 2010). For instance, when stigmas receive abundant pollen, heterospecific and self-pollen can impose qualitative limitation on pollen tube growth (Arceo-Gómez and Ashman 2014) and/or lower seed quality (Bell et al. 2005).

Persistent, prolonged pollinator limitation can have diverse evolutionary consequences

(Harder and Aizen 2010), including shifts to alternative pollen vectors (Johnson 2006), or the evolution of strict self-mating (Schoen and Brown 1991).

Many floral devices mitigate less predictable pollen limitation when interspecific competition for pollinators is high, or pollinators are scarce, by allowing autonomous self-pollination when outcrossing is limited, thereby providing some reproductive assurance (reviewed by Eckert et al. 2006; Weber and Goodwillie 2009, Buide et al.

2015). For example, limited separation of anthers from styles and the complete overlap of sexual phases enables Silene ramosissima to produce many seeds despite infrequent pollinator visitation (Buide et al. 2015). However, the benefits of reproductive assurance will be offset by the degree and timing of inbreeding depression in selfed progeny

(Weber and Goodwillie 2009, Van Etten et al. 2015) and will depend on the magnitude of pollen limitation on seed production (Lahiani et al. 2015).

Quantitative pollen limitation results from an absence of pollen and/or pollinators in the environment. In the first situation, little pollen may be available because few conspecifics are flowering simultaneously (Scobie and Wilcock 2009, Bartkowska and

Johnston 2014). Alternatively, ample compatible pollen may be presented, but few pollen grains are transferred between flowers because pollinator visitation is infrequent due to pollinator population dynamics (Pyke et al. 2011, Kudo 2014), or pollinator foraging behaviour (i.e., pollinators foraging on alternate food sources: Flanagan et al. 2010,

Buide et al. 2015). Plant and pollinator phenologies are rarely perfectly synchronized.

Therefore, pollinator populations will be sustained by foraging on species that overlap in time and space (Ranta et al. 1981, Westphal et al. 2006, Williams et al. 2012). For plant species with specialized pollination systems, or that flower when unpredictable abiotic conditions can affect pollinator abundance, phenological mismatch between a plant species and its pollinators can also cause pollen limitation (e.g., Kudo and Ida 2013).

Plants with generalized pollination systems must compete for services with other species.

Although plants of one species can facilitate the pollination of other species (Moeller

2004, Liao et al. 2011), interspecific competition can be particularly strong when one species occurs at higher density or offers more rewards than another (Kameyama and

Kudo 2015). This problem could be particularly severe for plant species that are rare compared with others in the community, such as plants that flower early in their population’s phenology, or even for the first flowers on individual plants. For example, individual bumble bees can be reluctant to switch to a newly flowering species even when it provides richer rewards (pollinator neophobia: Forrest and Thomson 2009).

Thus, elevated investment in pollinator attraction as a signal of pollinator rewards may be particularly advantageous during initial flowering.

In a recent review of inflorescence architecture, Harder and Prusinkiewicz (2013) coined the term “grand-opening sale” (GOS) to describe a pattern whereby inflorescence display size peaks rapidly within the first few days of a plant beginning to flower and subsequently declines during the remaining flowering period. This pattern, which has been observed for several species, is a consequence of a declining rate of flower opening

(anthesis rate) relative to inflorescence age (Gallwey 2011). An adaptive hypothesis for a

GOS display phenology proposes that a rapid increase in display size promotes recruitment of pollinators to newly flowering individuals and species, but once pollinators are familiar with a species or individual plant, displays can be smaller to limit among-flower self-fertilization (geitonogamy: Gallwey 2011, Harder and Prusinkiewicz

2013).

1.3 Objectives

This thesis evaluates three main conditions of the GOS hypothesis: 1) that plant species experience greater pollinator limitation early during the population flowering phenology; 2) that a declining rate of flower anthesis during the flowering period of individual plants is an intrinsic characteristic, rather than a plastic response to resource availability; and 3) that the GOS pattern enhances reproductive success. I tested these objectives using experimental manipulation and observations of the summer-flowering perennial Delphinium glaucum S. Watson (Ranunculaceae).

This thesis addresses these objectives in the four subsequent chapters. In Chapter

2, I examine the limits on seed production associated with the phenologies of Delphinium glaucum and its bumble-bee pollinators. In particular, I quantified the effects of pollinator abundance on pollen receipt, fertilization success and seed production to evaluate the incidences of pollen quantity and quality limitation. I discuss the consequences of an early phenological mismatch for pollination and seed production. In

Chapter 3, I report the results of a bud-removal experiment designed to test the architectural and resource-depletion hypotheses for declining gradients in anthesis rate within inflorescences. In Chapter 4, I examine the association of the incidences of outcrossing and selfing with pollinator abundance to test predictions concerning qualitative pollen limitation revealed by Chapter 2. Further, I consider the influences of floral display size, which contributes to floral attraction and pollinator movement, on outcrossing rates within inflorescences. Finally, in Chapter 5, I synthesize my findings and consider their implications for the GOS hypothesis and plant reproductive success.

Chapter Two: Phenological mismatch between a plant and its pollinators and its

consequences for seed production

2.1 Introduction

Flowering time influences the mating environment in which an individual plant flowers, which has reproductive consequences for siring and seed production.

Specifically, an individual’s mating environment includes the abundance and characteristics of flower-visiting animals (Delmas et al. 2015), potential mates (Scobie and Wilcock 2009, Delmas et al. 2015), and interspecific competitors (Pleasants 1980,

Bell et al. 2005). Community and population dynamics of plants and their pollinators will fluctuate during a flowering season, which will influence the availability of pollination services, the density, abundance and genetic composition of plants, and the composition of the flowering community (Arceo-Gómez and Ashman 2014).

Both pollinator and mate limitation can cause pollen limitation of seed production. Pollen limitation occurs when fewer ovules are fertilized than can develop into seeds, given the available maternal resources (Harder and Aizen 2010). This can happen because pollen vectors deliver insufficient pollen to fertilize all viable ovules

(quantity limitation: reviewed in Ashman et al. 2004, Knight et al. 2005) and/or because much of the delivered pollen is of poor quality, which fails to germinate or produce tubes that successfully interact with ovules (quality limitation: Aizen and Harder 2007).

Quantity limitation may reflect limited pollinator visitation or availability of pollen and/or inefficient pollen transfer (Ashman et al. 2004, Scobie and Wilcock 2009,

Hargreaves et al. 2012). In contrast, quality limitation involves viability of pollen grains,

pollen genotype (i.e., male gamete inbreeding depression: Losdat et al. 2014, Harder et al. 2016), and pollen/pistil interactions (Németh and Smith-Huerta 2003), including self- incompatibility (Busch and Schoen 2008), competitive processes (Harder et al. 2016), and allelopathic inhibition by heterospecific pollen (Murphy 2000). Note that quantitative aspects of mating associated with inbreeding depression during seed development have been portrayed as a facet of pollen-quality limitation (e.g., Aizen and

Harder 2007), but are better considered offspring-quality limitation, as they depend on the combination of pollen and egg haplotypes, rather than on pollen characteristics in isolation (Harder and Aizen 2010).

Pollen limitation constrains seed production frequently (Burd 1994, Larson and

Barrett 2000, Ashman et al. 2004, Knight et al. 2005), but estimation of the occurrence and severity of its quantitative and qualitative parts is problematic for various reasons

(Ashman et al. 2004, Aizen and Harder 2007). The most common method involves supplementation of pollen receipt of open-pollinated flowers (ideally all flowers on a plant: Ashman et al. 2004) with cross-pollen. However, for self-compatible species, this approach can result in stigmas receiving different proportions of self- and cross-pollen than would occur naturally, causing over-estimation of pollen limitation (Aizen and

Harder 2007). Less ambiguous evidence of pollen limitation for animal-pollinated species could be obtained by assessing whether pollen receipt and seed production vary with pollinator abundance. A positive relation indicates pollen limitation, whereas no association indicates limitation by another factor, such as ovule number or seed resources. A negative relation is also possible at high pollinator abundance, if excessive visitation damages flowers (Aizen et al. 2014, Xi et al. 2016).

Pollination quantity and quality could vary during a species’ flowering phenology for several reasons. Although pollinators significantly contribute to reproductive output of plants, only a small fraction (~1%) of pollen produced in anthers arrives on conspecific stigmas, including pollen involved in autonomous and facilitated self- pollination (Thomson and Thomson 1989, Harder et al. 2000, Johnson et al. 2005, Harder and Routley 2006). Most pollen is fated to be lost during removal and transport (Harder et al. 2000, Harder and Routley 2006), through processes such as wind, rain, pollinator grooming, consumption and deposition on heterospecific stigmas. Thus, uncertainty in pollination due to variation in the numerical abundance of foraging pollinators and the efficiency of pollen transfer contributes to variation in the quantity of pollen deposited on stigmas (pollen quantity limitation). Differences in the frequency and efficiency of pollinator visits during the flowering period contributes to variation in the quantity of pollen delivered to conspecific stigmas (pollen receipt: Aizen and Harder 2007, Harder and Aizen 2010). In contrast, variation in the number of non-related male flowers (i.e., availability and diversity of outcross pollen) influences the quality of conspecific pollen

(Aizen and Harder 2007, Harder and Aizen 2010). Finally, the abundance and relative attractiveness of interspecific competitors can affect both pollen quantity and quality if pollinators preferentially forage on other species and transfer heterospecific pollen (Aizen et al. 2014). Interspecific transfer of pollen between co-flowering plants results in both conspecific pollen loss and heterospecific pollen deposition, although a few studies suggest that the former is more limiting to seed production (Morales and Traveset 2008).

As flowers that open earliest in a species’ flowering season are rare relative to other conspecifics and heterospecifics and are likely unfamiliar to pollinators (Forrest and

Thomson 2009), the first individuals to flower should be most susceptible to pollen

limitation.

In this chapter, I examine the association between the phenologies of the summer-

flowering perennial Delphinium glaucum S. Watson (Ranunculaceae) and its bumble-bee

pollinators and its consequences for pollination and seed production. In particular, I

consider the incidence of pollen limitation and its quantitative and qualitative elements. I

hypothesize that pollinators are scarce, or are foraging on alternate sources, early in a

species’ flowering period. Thus, pollen limitation of D. glaucum plants should be most

severe early in the season, although this could be mitigated by autogamous self-

pollination.

2.2 Materials and Methods

2.2.1 Study species and site

Delphinium glaucum, commonly known as tall, mountain, or Sierra larkspur,

typically grows in moist to mesic open forests, willow thickets and transition zones

between forests and grasslands of the Rocky Mountains and western boreal forests

(Looman 1984). This species ranges from California, Nevada and Utah in the south and

extends north to Alaska, Yukon and the Northwest Territories and east from the Coast-

Cascade Mountains to Saskatchewan and Montana (Looman 1984, Welsh and Ralphs

2002). Delphinium glaucum has been documented less frequently in Nevada, Wyoming

and Saskatchewan (Looman 1984, Welsh and Ralphs 2002). Of the four tall larkspur

species (height: 100 – 200 cm) in western North America, only D. glaucum occurs in

Canada (Welsh and Ralphs 2002). Delphinium glaucum is diploid, with 2n=16.

Delphinium glaucum is a perennial, hermaphroditic herb ranging from 35 – 120 cm high and flowers in mid to late summer (Looman 1984). Plants typically have 1-8 vertical flowering stems (racemose inflorescences). Each inflorescence produces 10-60 bilaterally symmetrical, purple to blue, spurred flowers with two nectaries located at the tip of the floral spur. Larger inflorescences can produce a few lateral branches with 1-10 flowers, which typically open after flowers on the main axis have finished flowering.

Only flowers along the main inflorescence axis were considered in this study, but very few of the sampled individuals had lateral branches. Flowers develop acropetally – in the basal to distal direction along the inflorescence (Fig 2.1A). Individual flowers within inflorescences have a distinct male phase, during which the 20-30 anthers dehisce sequentially, followed by a briefer female phase when the stigmas of the three carpels are receptive (Ishii and Harder 2012). On some inflorescences, a few uppermost flowers do not proceed to female phase.

I collected data from 114 individuals in a small portion of a large (> 10,000 plants) D. glaucum population at Sibbald Flats (51.04°N, -114.87°W), Alberta, during summer 2012. Flowering phenology was tracked daily for all flowers on 24 individuals

(described below), whereas 90 individuals were used to assess floral anthesis rates (see

Chapter 3). From the end of June until late August, flowering by animal-pollinated plants at Sibbald Flats was dominated by Delphinium glaucum and Knautia arvensis. Cattle grazing began in the meadow after August 11, but the study plot was enclosed within a temporary ~ 250 m2 electric-fence that was erected on August 10.

Figure 2.1. Delphinium glaucum inflorescence (A, B), bud (late phase: C), male

phase flower (G), maturing fruit (D), pollen grains on stigmas (E, F), ovule, and

aborted and mature seeds (H). Panel A shows an inflorescence opening from

bottom to top with the uppermost flowers in bud stage. The inflorescence in Panel B

is progressing from female (uppermost positions) to fruit with the stigmas collected

in the bottom positions. The coloured string separates positions by day of opening.

In panel H, the first and third positions from the left are aborted seeds, the second

position is an ovule (darkened with age) and the fourth position is a mature seed.

The scale line (H) is 10 mm total.

2.2.2 Display dynamics

To assess display dynamics of D. glaucum plants during the flowering period, I

chose 24 plants at 5-m intervals along four parallel 25-m transects, separated by 10 m (6

plants per transect). The first transect was established at a haphazardly chosen point

within the research plot and extended in a randomly selected starting direction (147°).

All plants sampled within the research plot were identified with brown flags and small

white jeweller’s tags to minimize distraction of pollinators. For each plant, I daily

recorded the reproductive stage (bud, flower, or fruit, Fig. 2.1) and sexual phase (male, or

female) of every flower (July 7, 2012 - August 4, 2012) until the last flower had set fruit.

Typically, the first anther dehisces within one day of anthesis, anthers dehisce

sequentially and an anther’s pollen is depleted within one day of dehiscence (pers. obs).

Expansion of stigmas usually coincides with the end of anther dehiscence within a

flower. Thus, I categorized a flower to be in male phase from flower opening until all but

one anther had dehisced, and in female phase from complete anther dehiscence until all

petals had wilted. I classified a flower as a fruit immediately after all sepals and petals

wilted or after a light touch of the flower caused the remaining sepals and petals to fall.

2.2.3 Pollen, seed and plant enumeration

To measure pollen receipt per flower, I collected stigmas of the newest senesced

flower daily (at least one per inflorescence per day that new fruits appeared) in

microcentrifuge tubes, containing 70% ethanol. Stigmas were collected after all petals

and sepals had fallen to ensure they were no longer receptive (Fig 2.1). Pollen grains

were stained with basic fuchsin dye and the numbers of pollen grains on each stigma

were manually counted using a compound microscope (Fig. 2.1E, 2.1F, 40X; Kearns and

Inouye 1993). Pollen counts for all of a flower’s stigmas were summed to calculate a

total pollen count for each flower.

I collected fruits as they matured and placed them individually in paper coin

envelopes. Fruits were stored at room temperature and the numbers of surviving seeds,

aborted seeds and unfertilized ovules were counted in each fruit. I distinguished aborted

seeds from ovules and mature seeds by assessing colour, shape and size (Fig. 2.1H).

Although, ovules can darken to tan or light brown with age, in fresher samples

unfertilized ovules are typically white, small (~ 0.5 mm at widest point) and have a flat,

teardrop shape. Mature seeds are full, pale to dark brown, ~ 2 mm wide by 2.5 mm long

and are irregularly wedge-shaped with the edges on the flat side frequently tapering down

to light-coloured, papery wings. The total number of fertilizations was calculated from

the number of intact plus aborted seeds. I considered a seed to be aborted if it showed

any sign of development (i.e., increasing in size, filling or changing of shape, or

darkening of pigment compared to other ovules), but did not attain the characteristics of a

mature seed. For example, an early aborted seed may have been a similar length to an

ovule, but appeared plumper, slightly rounder, or darker, whereas a late aborted seed may

have reached the size of a mature seed but appeared to be somewhat shrivelled, flatter, or

very pale. Total fruit mass (g) and total seed mass (g) were weighed on a Mettler Toledo

303E analytical balance to the nearest 0.0001 g. I counted all buds on each inflorescence

before flowering commenced as a measure of plant size because it was a trait not

influenced by resource manipulation.

2.2.4 Pollinator visitation

The abundance and identities of foraging pollinators were typically surveyed

along the transects described above at 10 am and 3 pm each day between July 7 and

August 10, 2012. Exceptions occurred on July 15, 16, 23, August 2 and 5, when I

conducted single surveys because of rain or strong winds (on July 23) within 1 h before

or during the scheduled observation period. Although a few plants continued flowering

along the transect after August 10, pollination surveys were concluded then because most

of the meadow was mowed to allow cattle grazing within the area (D. glaucum,

especially its fruits, is toxic to cattle: Gardner and Pfister 2000). Using methods for

surveying pollinators along a line transect (Dafni et al. 2005), I walked each transect

slowly (about 10 m min-1) and recorded the identity of every insect observed probing D.

glaucum flowers within 2 m of each transect. Only bumble bees were observed foraging

on D. glaucum during surveys. Bumble bees were identified to species based on the

colour pattern of body hair. On survey days, I wore muted colours (e.g., brown, grey, or

green) and did not use scented products or insect repellent.

2.2.5 Herbivory

To avoid confounding effects of floral herbivory on seed enumeration (this

Chapter), or resource availability (Chapter 3), I excluded flowers subjected to herbivory

in analyses of seed and ovule counts. I recorded instances of flower herbivory based on

observations of flowers during the flowering period and examination of fruits during seed

enumeration. Specifically, I noted the positions of missing or damaged flowers due to

herbivory. During the flowering period, I observed an adult moth ovipositing on a D.

glaucum flower and caterpillar larvae consuming whole flowers or fruits (2.89% [n=16]

and 5.86% [n=95] of open transect and experimental flowers, respectively). Adult flies

(tentatively identified as Botanophila spp. or a related genus in the Anthomyiini: J. Swann

pers. com.) were observed visiting D. glaucum flowers and both fly larva and signs of

seed consumption (i.e., empty seed coats) were discovered when counting seeds. These

flies or a similar genus have previously been documented as seed predators of D. barbeyi

(Elliott and Irwin 2009). Typically, fruits subject to predispersal seed predation (18.2%

[n=86] and 28.9% [n=419] of non-aborted transect and experimental fruits, respectively)

had a hole in the proximal end of one carpel. In these carpels only empty seed coats

remained.

2.2.6 Data analysis

Statistical analyses involved nonlinear mixed models, as implemented in the

NLMIXED procedure of SAS/STAT 13.2 (SAS Institute Inc. 2014). For each

dependent variable (pollen receipt, PR; fertilizations per ovule, F/O; seeds per

fertilization, S/F; seeds per ovules, S/O), I compared the fits of two functions of bee

abundance ( bees: number of bees observed foraging on D. glaucum per minute per 200

m2): a positive asymptotic function,

yˆ  a1 ebbees0.001 , (1)

and a constant function,

yˆ  , (2)

where y represents a specific dependent variable. For pollen receipt, y was transformed

using ln(receipt). For eq. 1, a estimates the mean asymptote for all plants and b

positively affects the rate of approach to the asymptote. For eq. 2, λ is the maximum

likelihood estimate of the mean response to bee abundance. As I sampled multiple

flowers per plant, some variation in dependent variables could arise from plant-specific

effects, rather than bee abundance. I accounted for among-plant variation by estimating

plant-specific yˆi  yˆ  ui , where yˆ is the overall mean and ui is the mean deviation for

2 plant i. The ui were assumed to be normally distributed with mean 0 and variance s . To account for overdispersion, F/O, S/F and S/O were modeled using beta-binomial distributions and the logit link function, whereas PR was modeled using a negative- binomial distribution and the ln link function (Richards 2008). The beta- and negative- binomial distributions each involve a parameter , which affects the within-plant variance in addition to its association with the mean. Maximum-likelihood techniques were used to estimate model parameters (a, b, , s2 and  ).

I selected the most parsimonious model for each dependent variable using

Akaike’s Information Criterion (AIC), which balances the trade-off between model complexity and how well a model fits the data (Richards 2008). I calculated the difference in AICs between the constant and asymptotic models. If the difference was greater than 6, I selected the model with the lowest AIC (Richards 2008). Otherwise, both models could be candidate explanations, but the asymptotic model was excluded based on parsimony, if the constant model fit better (i.e., lower AIC). I also considered bee:flower ratio (bees / total observational flowers day-1) as an alternate independent variable to bee abundance in models for F/O and S/O. Conclusions (i.e., choice of constant vs. asymptotic models) did not differ between these analyses. Moreover, models with bee abundance fit slightly better than those with the bee:flower ratio (F/O:

∆AIC=1.2, S/O: ∆AIC=4.1). Accordingly, I designated bee abundance as an appropriate independent variable.

2.3 Results

2.3.1 Pollinators

Bombus flavifrons and B. californicus were the predominant visitors (53.4% and

37.8% of the observed visits [n=451 and n=319], respectively), followed by B.

nevadensis (7.8% [n=66] of observed foraging visits). Bombus appositus, B. moderatus

(queens) and a single species of skipper (Hesperiidae) foraged infrequently (1.1 % of

visits combined [n=9]). Bombus flavifrons visited D. glaucum throughout its flowering

period, beginning on the first day that observation plants flowered (July 7). In contrast,

B. californicus and B. nevadensis were first observed visiting these plants on July 11 and

July 12, respectively, and then became more common

2.3.2 Phenologies

The overall abundance of open D. glaucum flowers on the 24 observation plants

peaked on July 21, 6 days earlier than that of its bumble-bee pollinators (Fig. 2.2A).

Very few bees

Figure 2.2. Phenologies (A, C) and corresponding seed set (B) of open female-

(n=569, upper pink area) and male-phase flowers (n=569, lower blue area) of 24

Delphinium glaucum plants and the abundance of foraging bees (Bombus spp., solid line) during the 2012 flowering period. The sum of female- and male-phase flowers on a given day represents the total available flowers on the 24 sampled plants (panel

A). The solid orange line in panel B shows the bee:flower ratio on days that resulted in seed set. Each line in panel C corresponds to the phenology of an individual plant with a common vertical scale of 0 to 36 (flowers). Bees were observed foraging on D. glaucum inflorescences (survey and non-survey) within 1 m either side of four 25 m transect lines. The dashed black line in panel A indicates pollinator observations

after the survey plants finished flowering. Plants denoted with a red asterisk did

not produce viable seeds.

(mean ± SE = 0.08 ± 0.02 bees min-1, 21 bees) were observed during the first 11 days of

flowering. During this period, almost 32% (n=169) of flowers entered or completed

female phase. Visiting bee abundance increased from 0.09 bees min-1 (2 bees) on July 7

to 3.54 bees min-1 (85 bees) during the peak on July 28. Mean (±SE) visitor abundance

during the 35 observation days was 1.08 ± 0.176 bees min-1 (844 bees total); however

mean bee abundance after the first 11 days increased to 1.54 ± 0.195 bees min-1 (24 days,

823 bees total). Because of this asynchrony, the pollinator:flower ratio was low as

flowering increased, but high as flowering waned (Fig. 2.2B).

2.3.3 Pollen receipt and potential donors

Pollen deposited on stigmas appeared to be predominately conspecific. Although

pollen grains were not identified to species, pollen grains deposited on collected stigmas

were uniform in size and shape (Fig. 2.1E, 2.1F). Potential heterospecific pollen donors

whose flowering times overlapped with that of D. glaucum at Sibbald Flats included

Cerastium arrense, Achillea millefolium, Oxytropis splendens, Campanula rotundifolia,

Oxytropis sericea, Aster laevis and Knautia arvensis (pers. obs., T. Ida, unpublished

data). None of these species are close relatives of D. glaucum, hence pollen grain

differences should be noticeable. Given their floral tube lengths, the long-tongued

pollinators of D. glaucum would most likely visit O. splendens, O. sericea and C.

rotundifolia. Although, I observed individual Bombus flavifrons workers foraging on

K. arvensis and O. splendens flowers, I saw no switching by individual pollinators

between D. glaucum and coflowering heterospecific plants during the flowering period.

While individual bees sometimes switch between species, this observation was consistent

with typical bee foraging behaviour (constancy: Waser 1986). Collectively, these

observations suggest that stigmas received little heterospecific pollen; however the

absence of heterospecific pollen is not certain.

2.3.4 Reproductive output

On average (±SE), the 24 survey plants produced 15.8 ± 0.97 seeds per fruit

(n=360; in fruits without herbivory) on 21 female phase flowering days (July 11 – July

31) after adjusting for variation in ovule number. Mean seed set increased from 10.1 ±

1.75 (n=117) seeds during the first 11 days of bee visitation to 14.4 ± 2.37 seeds (n=251)

after the first 11 days of bee visitation (Fig. 2.2B).

Bee abundance on days when specific flowers were in female phase had

contrasting effects on different aspects of seed production (Table 2.1, Fig. 2.3). For

pollen receipt, the constant model (AIC=2878.8) fit better than the asymptotic model

(AIC=2879.8), suggesting no effect of pollinator abundance (Fig. 2.3A). In contrast,

ovule fertilization exhibited a clear asymptotic relation (AIC=1879.8: constant model,

AIC=1898.5), increasing from a mean of about 13 fertilizations per flower when bees

were rare to about 20 fertilizations if bee abundance exceeded 1 bee min-1 per 200 m2

(Fig. 2.2B, Fig. 2.3B). At the asymptote, 79.6 (± 2.1) % of ovules were fertilized. The

subsequent development of zygotes into seeds did not vary asymptotically with bee

abundance (seeds/fertilization: asymptotic AIC=1103.0, constant=1101.0), with an

average of 92% development success (Fig. 2.3C). Overall, seed production exhibited an

asymptotic relation to bee abundance (AIC=2146.3: constant model, AIC=2161.5), largely reflecting the association observed for ovule fertilization, although the asymptote was approached more slowly (Fig. 2.3D).

Table 2.1. Parameter estimates (95% confidence intervals) for best-fitting models of stigmatic pollen receipt,

fertilizations per ovule, seeds per fertilization, and seeds per ovule. a estimates the asymptote, b affects how quickly the

asymptote is reached,  is a measure of within-plant variability, and s2 is among-plant variance. The regression for

pollen receipt considered a negative binomial distribution and ln link function, whereas those for the other variables

considered beta-binomial distributions and the logit link function. LCL and UCL = lower and upper confidence limits.

a b  s2

Model LCL UCL LCL UCL LCL UCL LCL UCL

25 Pollen receipt 50.61 -- 12.95 0.1622

40.81 60.42 -- -- 10.44 15.46 0.0453 0.2792

Fertilizations per ovule 1.361 2.702 0.1747 3.6634

1.094 1.629 1.386 4.015 0.1350 0.2144 0.0915 7.2352

Seeds per fertilization 2.457 -- 0.2536 20.13

2.165 2.749 -- -- 0.1697 0.3376 -3.548 43.81

Seeds per ovule 0.9173 1.524 0.3456 1.1058

0.6294 1.2053 0.465 2.583 0.2768 0.4143 -0.0449 2.2625

Figure 2.3. The effects of the abundance of bumble bees (Bombus spp.) visiting the

Delphinium glaucum population on mean ( SE) pollen receipt per fruit (A), number of fertilized ovules per fruit (B), proportion of seeds per fertilization (C), and number of seeds per fruit (D). Bees were observed foraging along four 25-m transects while the sampled D. glaucum flowers were in female phase. Seed number and fertilizations were adjusted for total ovule number. Fruits were included if there was no evidence of herbivory (n=360); whereas pollen on stigmas were counted in a subset of these fruits (n=307).

2.4 Discussion

2.4.1 Phenological mismatch

The phenologies of the Delphinium glaucum plants and their pollinators were not synchronous for the study population (Fig. 2.1). Despite many open flowers, pollinator visitation was very low during the first 11 days of flowering. In contrast, the abundance of bumble bees visiting D. glaucum continued increasing for a week after flower abundance began declining.

Such phenological mismatch between a plant and its pollinators could reflect

bumble-bee population dynamics and/or pollinator preference. As the season progressed, fewer queens and more worker bees were observed foraging (pers. obs), suggesting that

D. glaucum flowered during the initial, exponential growth phase of bee-colony development (Goulson 2003, Westphal et al. 2006). Such an effect could be common for plant species served by pollinators that respond numerically to flower abundance, such as primitively eusocial Hymenoptera. The shift in visitor abundance may also reflect pollinator preference for more attractive alternatives (Pleasants 1980, Chittka et al. 1997,

Mitchell et al. 2009, Seifan et al. 2014).

Pollinator preference could also contribute to phenological mismatch. This could occur because flowering individuals of a newly blooming species are initially at low density and so are less attractive to pollinators than more common species (Klinkhamer and de Jong 1990, Seifan et al. 2014) and/or because pollinators are reluctant to forage on newly opened species (neophobia: Forrest and Thomson 2009). Simple density dependence could contribute to the initial delay in visitation as a plant species comes into flower; however, it does not explain the continued increase in visitation after peak

flowering. In contrast, the behavioral inertia associated with neophobia could cause both aspects of phenological mismatch. Thus, the earliest flowering individuals are the most vulnerable to pollinator neophobia, even when pollinators are abundant in the environment. Regardless of the cause of initial pollinator resistance to visit a newly flowering species, plants should benefit from floral and inflorescence traits that enhance their attractiveness.

2.4.2 Limits on seed production

The low pollinator abundance during early flowering of D. glaucum caused pollen

limitation of seed production. Flowers that opened early, when pollinator visitation was lowest produced 50% fewer seeds than flowers that opened later when visitation exceeded 1 bee min-1 (Fig. 2.2B, 2.3D). The specific influences of pollinator abundance on the processes involved in seed production during early flowering suggest that poor pollen performance after deposition, rather than insufficient pollen quantity, during early flowering caused pollen limitation. Pollen receipt did not vary significantly with the number of foraging bees observed per minute. Instead, pollinator abundance positively affected the success of ovule fertilization, suggesting that proportionately more of the pollen received by early flowers was ineffective. In addition, the consistent development of most zygotes into seeds, regardless of pollinator abundance, rules out differential effects associated with offspring quality. In contrast to the qualitative pollen limitation during early flowering, the invariance in seed production once bees visited plants frequently indicates a switch to resource limitation. Ovule limitation can be ruled out, if all ovules were viable, because an average of 20% of ovules remained unfertilized.

Poor ovule fertilization during early flowering could result from abiotic effects on pollen performance or intrinsic pollen quality. Temperature is the most likely abiotic influence, as it positively affects adhesion of pollen to stigmas and germination (Hedhly et al. 2005) and pollen-tube growth rates (e.g., Jefferies et al. 1982, Marquard 1992; reviewed in Hedhly 2011). However, this explanation seems unlikely for the D. glaucum observations, because minimum daily temperatures declined, rather than increased during the flowering period of 2012 (based on records from the University of Calgary Barrier

Lake Field Station, 11.8 km from the study site: Appendix 2.1).

Pollinator visitation and pollen receipt are often correlated (Engel and Irwin 2003,

Price et al. 2005), so that the lack of an association for D. glaucum coupled with an asymptotic relation of ovule fertilization to pollinator visitation suggests a shift in pollen quality. Specifically, this combination of results could arise if stigmas of early flowers received proportionally more poor-quality pollen that those of later flowers. Two aspects of pollen quality could be involved. Pollinators visiting D. glaucum early, when its flowers were relatively uncommon, may have also been visiting other species, and so delivered more heterospecific pollen grains than later in the season (see Knight et al.

2005). However, this explanation seems unlikely, as few heterospecific pollen grains were visible on stigmas. Alternatively, stigmas of early flowers may have received relatively more self-pollen than later flowers. With infrequent visitation, more pollen could have remained in anthers when stigmas became receptive, increasing the likelihood of self-pollination without the aid of a pollen vector (autogamy). This response underlies the role of autogamy in providing reproductive assurance when pollinators are infrequent

(e.g., Vaughton and Ramsey 2010, de Vos et al. 2012). However, the extent to which

autogamy actually provides reproductive assurance depends on the ability of self-pollen to fertilize ovules and of selfed zygotes to develop into seeds. The asymptotic relation of ovule fertilization, but not of seed development, to pollinator abundance points specifically to poor pollen quality as the cause of pollen limitation for early D. glaucum flowers.

The approach used in this study to identify the limits on seed production has several advantages over alternative methods. Most importantly, this approach allows direct assessment of quantitative pollen limitation, ovule limitation and offspring quality limitation, as well as indirect assessment of qualitative pollen limitation and the possibility of inferring resource limitation. In addition, field implementation is straightforward, as no manipulations are required. Instead, insights on the entire process of seed production can be revealed by pollinator observations, quantification of flowering phenology and collection of stigmas and seeds from naturally pollinated plants. This approach avoids the application of unnatural pollen mixtures and the need to pollinate all flowers to avoid resource redistribution among developing fruits that compromise pollen- supplementation experiments (Ashman et al. 2004, Aizen and Harder 2007). It also explicitly quantifies the key features of the relations of seed production to pollinator visitation and pollen performance, rather than treating pollination and post-pollination processes as black boxes.

Chapter Three: Do architectural effects or resource depletion govern declining

anthesis rates within inflorescences?

3.1 Introduction

Outcrossing by animal-pollinated plants requires that they attract pollinators to transfer their pollen to conspecifics. Pollinator visit frequency depends on features of a plant’s pollination environment, including the abundances of potential pollinators

(Chapter 2) and other plant species served by those pollinators (Moeller 2004, Liao et al.

2011, Arceo-Gómez and Ashman 2014, Devaux et al. 2014, Lázaro et al. 2015), and can

be modified by a plant’s intrinsic characteristics, such as the size and number of flowers displayed simultaneously (Vaughton and Ramsey 1998, Thompson 2001, Harder and

Johnson 2005, Ishii and Harder 2006, Lázaro et al. 2015). Competition with other plant species for pollinator service can be especially problematic if pollinators restrict their visits to flowers of a single species (constancy: Waser 1986), even when more rewarding alternatives are available. However, flower constancy is not predetermined across all individuals of a pollinator species, as individuals may switch to different flower types on occasion (Waser 1986). Nevertheless, at least bumble bees are reluctant to forage on novel species (neophobia: Forrest and Thomson 2009). Therefore, individuals that flower at the beginning of a population’s flowering season, when pollinators are foraging on other plant species, may be at a particular disadvantage if other species flower simultaneously (Chapter 2).

The number of flowers that plants display simultaneously may help counteract neophobia. Plants with more open flowers generally attract more pollinators (Ohashi and

Yahara 2001), so large initial displays should enhance a plant’s attractiveness to otherwise neophobic pollinators. A plant’s floral display size is governed by the phenology of its flowers (Ishii and Sakai 2001, Meagher and Delph 2001), specifically the number of flowers opened per day (anthesis rate) and how long those flowers live

(longevity: Harder and Johnson 2005). Small changes in floral longevity or anthesis rate can affect the temporal pattern and maximum display size of inflorescences (Harder and

Johnson 2005, Gallwey 2011). Total display size typically increases as new flowers open and declines as flowers senesce. Nevertheless, during the initial phase of pollinator attraction, before any flowers senesce, display size is governed by anthesis rate.

Therefore, a high initial anthesis rate should help overcome neophobia of unfamiliar pollinators. Consistent with this expectation, Gallwey (2011) found that anthesis rate declined as inflorescences aged for five of 11 study species, all of which produced vertical racemes.

According to this hypothesis, a plant’s floral display size should be adaptively dynamic, changing during the flowering season in association with pollinator abundance and interest (Gallwey 2011). Anthesis rate and floral longevity are both aspects of floral development, so adaptive dynamics could be implemented through developmental controls. Flower position alone can influence development (Diggle 2003, 2014, Liu and

Huang 2012) in a manner determined long before flowering begins (Liu and Huang

2012). In a recent survey of published studies, Diggle (2014) found that gradients in floral traits within inflorescences were typically attributable to such position effects. The mechanism responsible for positional variation along a plant axis is unknown; however, one hypothesis is that positional variation reflects adaptation to differences in mating

opportunities, or other conditions, among flower positions (Diggle 2003). If so, a decline in anthesis rate from early to late flowers could exist to enhance display size early on and thus represents an intrinsic characteristic of inflorescence architecture (Harder and

Prusinkiewicz 2013).

Alternatively, a systematic decline in the rate of flower opening during an inflorescence’s flowering period could represent a physiological response to a plant’s internal resource availability. Flowering dynamics are influenced by the interplay between resource availability and resource costs of flowers and fruits (Schoen and

Ashman 1995, Meagher and Delph 2001). Resources available to a given flower along the inflorescence axis are contingent on the number of flowers and fruits drawing upon the available pool of maternal resources (Stephenson 1981, Lee 1988). The bottommost flowers within acropetal inflorescences have spatial and temporal priority when accessing leaf and root assimilates as they are transported along the main axis. Thus, resource competition between developing flowers could create gradients in floral traits (Bränn and

Lehtilä 2007). Physiologically, flower opening involves rapid cell expansion (van Doorn and van Meeteren 2003; van Doorn and Kamdee 2014). Thus, energetic requirements of flower opening could contribute to resource competition within the inflorescence (Harder and Prusinkiewicz 2013). In this scenario, anthesis rate declines during a plant’s flowering period because early flowers and fruits deplete accessible resources within the inflorescence that are required to achieve flower opening.

In this chapter, I test the architectural and resource-depletion hypotheses for declining gradients in anthesis rate within inflorescences. Among the species studied by

Gallwey (2011), Delphinium glaucum (Ranunculaceae) exhibited the strongest systematic

decline in anthesis rate with inflorescence age. Therefore, I used this species to investigate the possible influences of architectural effects and changing resource dynamics on anthesis rate. I specifically manipulated resource availability within inflorescences by removing flowers from inflorescences either before anthesis or after senescence and tracking subsequent flower phenology. I predict that if declining anthesis rate adaptively enhances initial display size, anthesis rate will be an intrinsic feature of inflorescence architecture that manifests as positional effects. Alternatively, if the decline arises plastically from resource competition, removal of resource sinks will make more resources available for subsequent positions, weakening, or eliminating the decline.

3.2 Materials and Methods

General characteristics of Delphinium glaucum, details of the study location, and methods of fruit and seed collection are as described in Chapter 2. Recall that flowers on the main inflorescence axis typically open sequentially from bottom to top, thus flowering order corresponds to flower position. Individual D. glaucum flowers typically wilt after 6-7 days (mean ± SD = 6.3 ± 1.32, 7.3 ± 1.07, for the experimental [n=2257] and transect [n=569] flowers, respectively), so only anthesis rate governs display size for a plant’s first week of flowering. Thus, to eliminate interactions with floral longevity I considered only the first 16 flowers on an inflorescence, which approximately equates to the first four to five days of anthesis for each inflorescence.

3.2.1 Experimental protocol

The dependence of anthesis rate on floral architecture and/or resource allocation was assessed experimentally for 90 D. glaucum plants in 30 randomized blocks. I

sampled only single-ramet plants to eliminate confounding effects of hierarchical allocation of resources between ramets (see Schoen and Dubuc 1990). To determine the number of ramets for each plant, I examined the direction of each root stalk when multiple inflorescences grew in a cluster. Two blocks of three plants (assigned to treatments described below) were chosen each day for 15 consecutive days beginning

July 7, 2012. Plants within each block were selected based on developmental stage (i.e., buds at the same stage of development), size (i.e., basal stem diameter and height) and proximity (i.e.,  1 m separation). The blocks of experimental plants were scattered

around the transects described in Chapter 2, but were located at least 1 m from any transect plant.

The three inflorescences in each block were randomly assigned without replacement to three treatments (“Intact” [Fig. 3.1A], “Bud-ex” [bud removal, Fig. 3.1A], or “Fruit-ex” [fruit removal]). For the Bud-ex plants, the bottom eight buds were removed before inflorescence elongation, whereas for the Fruit-ex treatment the bottom eight fruits were removed within a day of flower senescence while the fruits were still immature. Flower anthesis at positions 9-16 preceded flower senescence at positions 1-8, therefore Fruit-ex plants were not included as a separate treatment for the analysis of anthesis rates. Flowers at equal positions (bud positions 1-8) on Intact and Fruit-Ex flowers opened at the same rate after adjusting for plant size (t223.7=0.23, P>0.05), thus they were combined for anthesis rate analyses.

Figure 3.1. Illustration of (A) treatments and (B) predicted responses for the architecture and resource-dynamics hypotheses to removal. Immature buds on the main axis of the inflorescence were removed at positions 1-8 from Bud Ex. inflorescences before inflorescence elongation; thus the first flower to open is located at position 9 on manipulated inflorescences. The architecture hypothesis predicts that the first 8 flowers to open after bud removal will open at the same rate as equivalent positions (B-1, orange dashed line) on intact (gray line) inflorescences.

Alternatively, the resource-dynamics hypothesis predicts anthesis rate to be equal between the first 8 flowers to open on intact and Bud-Ex. inflorescences (B-2, blue dash-dotted line).

I evaluated these hypotheses in three ways. First, to test the architecture hypothesis (Fig. 3.1B), I compared the average anthesis rates of the same positions (bud positions 9-16) on Bud-ex plants to Intact and Fruit-ex plants. If the architecture hypothesis is true, then anthesis rates will not significantly differ at equal bud positions.

Second, to test the resource depletion hypothesis, I compared anthesis rates for the first eight flowers to open on Bud-ex plants (bud positions 9-16) to those of the first eight flowers (bud positions 1-8) on Intact and Fruit-ex plants. If the resource hypothesis is true, then the first eight flowers on Bud-ex plants will open at the same rate as the first eight flowers on Intact plants. Third, to examine evidence of resource interference by lower resource sinks, I evaluated the effects of freed resources on total seed set and average seed mass within Intact inflorescences (positions 1-8 vs. 9-16) and among all 3 treatments (positions 9-16). If the resource hypothesis is true, reproductive output (seed numbers and/or mass) will be significantly lower at the higher positions compared to lower positions within Intact plants and significantly lower for Fruit-ex and Bud-ex plants than for Intact plants. Fruits require more resources than flowers (Stephenson 1981), therefore I predict that there will be less difference in reproductive output between Fruit- ex and Bud-ex plants than between Intact plants and Fruit-ex or Bud-ex plants.

Experimental plants flowered from July 9, 2012 – August 19, 2012. I counted the numbers of open flowers, fruits and dead or missing flowers on each inflorescence daily during this period. I later determined the daily number of new flowers by comparing the cumulative flowers that had opened on a given day (including those that were dead or finished flowering), ot, to the cumulative number from the preceding day, ot-1, rather than by comparing the counts of open flowers alone. Specifically, the number of new flowers

that opened on day t is nt  ot  ot1  wt  wt1 , where wt and wt-1 are total numbers of fruit + dead + missing flowers on days t and t-1, which accounted for the decline in open flowers due to fruiting or other losses. I assigned a sequential position to each new flower

on the date it first opened (the beginning of its male phase) and a corresponding sequential position to each new fruit (end of female phase). Floral longevity was calculated as the difference between the end of female phase and the beginning of male phase.

3.2.2 Data analysis

Statistical analyses involved generalized linear mixed models (Stroup 2012), as implemented in the Glimmix procedure of SAS/STAT 13.2 (SAS Institute Inc. 2014).

Analyses of anthesis rate and display size for transect and experimental plants considered

a Poisson distribution and a ln link function. For transect plants, anthesis rate or display size were modeled with inflorescence age and total flower number as continuous independent variables. For experimental plants, anthesis rate was modeled with treatment (intact and bud removal) as a categorical factor and total bud number as continuous independent variable. Total bud number was used as a measure of size, rather than flower number, which was subject to manipulation in experimental plants. As described in the preceding section, the effects of treatment on anthesis rate were examined for 1) the first 8 flowers to open regardless of position and 2) flowers at positions 9-16 among treatments.

The effects of resource interference by lower resource sinks were examined with two analyses of average seed mass and seed count. I measured seed number and mass as described in Chapter 2. Fruits subjected to herbivory were excluded from the analyses of resource interference. Comparison of these variables for positions 1 to 8 to those of positions 9 to 16 for intact plants examined the natural variation in seed set between lower and mid positioned flowers. In contrast, comparison of these variables for 38

positions 9 to 16 among all treatments (intact, bud removal, fruit removal) tested whether resources were reallocated to higher positions after bud and fruit removal. Analyses of seed count considered a negative binomial distribution and logit link function. Average seed mass was modeled using a normal distribution and identity link function. Position

(1-8 or 9-16) or Treatment (Intact, Bud-ex or Fruit-ex) was included as a categorical factor and total bud number was included as a continuous independent variable in each model. In the analysis of seed count, ovule number was also included as a continuous independent variable. These analyses excluded fruits with evidence of seed herbivory (as described in Chapter 2).

3.3 Results

3.3.1 Floral display and anthesis rate dynamics

Display size increased rapidly during the initial flowering of individual inflorescences (Fig. 2.2C, Fig 3.2, Table 3.1). Overall mean display size after adjusting for differences in plant size was 15.1 ± 0.37 flowers (n=24). Mean display size typically peaked on day 3 at 17.6 ± 0.50 flowers (n=24, Fig. 3.2).

Table 3.1. Results of generalized linear mixed model assessing the effects of inflorescence age and total flower number on display size (total flowers/day).

Effect Partial Regression Coefficient ( SE) Test statistic

Inflorescence age -0.021  0.003 F1,3799=66.96***

Log(total flowers) 0.835  0.054 F1,3799=241.84***

*** P < 0.001

Figure 3.2. Least-squares mean ( SE) display size (A) and anthesis rate (B) of 24

Delphinium glaucum inflorescences with increasing inflorescence age. Display size and anthesis rate were adjusted for differences in total flower number.

Anthesis rate declined with increasing inflorescence age as flowers opened sequentially along the main inflorescence axis. Anthesis rate also varied positively with total flower number (Table 3.2). After adjusting for differences in total flower number, anthesis rate slowed by 50% during the first 9 days of flowering (representing 87.6% of flowers [n=169], Table 3.2, Fig. 3.2).

Table 3.2. Results of generalized linear mixed model assessing the effects of inflorescence age and total flower number on flower anthesis rate (new flowers/day).

Partial Regression Anthesis rate (new Effect Coefficients ( SE) flowers/day)

Inflorescence age -0.121  0.019 F1,144=41.84***

Log(total flowers) 0.718  0.111 F1,144=41.88***

*** P < 0.001

3.3.2 Effects of bud removal on anthesis rate

Mean anthesis rate varied with flower position and total bud number in the experimental inflorescences. After accounting for differences in total bud number, anthesis rate for positions 9-16 did not differ significantly between the bud removal plants and intact plants (Table 3.3, Fig. 3.3A). Conversely, after accounting for differences in total bud number the first 8 flowers (positions 9-16) to open after bud removal on the manipulated plants opened significantly more slowly than the first 8 flowers on intact plants (positions 1-8; Table 3.3, Fig. 3.3B).

Table 3.3. Results of generalized linear mixed models assessing the effects of treatment (intact or bud removal) and covariates for A) the first 8 open flowers and

B) flower positions 9-16 on anthesis rate (new flowers/day). Total bud number was included as a covariate for all models.

Partial regression

Effect coefficient ( SE) Test statistic

A. First 8 Open Flowers

Treatment -- F1,81.1=6.28*

ln(total buds) 0.614  0.174 F1,86.2=12.43***

B. Positions 9 to 16

Treatment -- F1,77.7=0.15

ln(total buds) 1.166  0.158 F1,60.4=54.59***

* P < 0.01, *** P < 0.001

Figure 3.3. Comparisons of observed least-squares mean (± SE) anthesis rates

between treatments for (A) flowers in positions 9-16 and (B) the first 8 flowers to open. Anthesis rate was adjusted for variation in plant size, as measured by total bud number.

3.3.3 Effects of flower position and treatment on seed production

Total bud number, a proxy for plant size, had no effect on seed count and average seed mass (Table 3.4, Fig. 3.4). Total number of ovules varied between positions and between treatments in the analyses of seed count. Mean reproductive output did not vary within intact inflorescences. There were no significant changes in mean seed mass and mean seed number between positions 1-8 and positions 9-16 flowers within intact inflorescences (Table 3.4, Fig. 3.4A, Fig. 3.4B). Likewise, mean seed count and mean average seed mass did not significantly vary between treatments at positions 9-16 (Table

3.4, Fig. 3.4C, Fig. 3.4D).

Table 3.4. Significance tests and partial regression coefficients (± SE) for generalized linear mixed models assessing the effects of A) flower position and covariates in intact inflorescences or B) treatment (intact, bud removal and fruit removal) and covariates at flower positions 9-16 on seed count and average seed mass in Delphinium glaucum. Total bud number was included as a covariate for all models. Ovule number was included as a covariate for seed count only.

Partial regression Average

Effect Seed count coefficient (± SE) seedmass

A. Intact inflorescences (positions 1-8 vs. 9-16)

Position F1,96.3=0.33 F1,1=2.23

Log(ovules) F1,41.8=375.99*** 1.103 ± 0.057 --

Log(total buds) F1,27.1=1.85 F1,1=3.06

B. Positions 9-16 (all treatments)

Treatment F2,257=0.09 F2,1=0.30

Log(ovules) F1,257=797.86*** 1.117 ± 0.040 --

Log(total buds) F1,53.1=0.90 F1,1=0.13

*** P < 0.001

Figure 3.4. Comparisons of least-squares mean ( SE) seed count (A, C) and seed mass (B, D) between positions 1-8 and positions 9-16 on intact inflorescences (A and

B) and among treatments for positions 9-16 (panels C and D). Means were adjusted for variation in plant size, as measured by total bud number and ovule number

(seed count only).

3.4 Discussion

As anticipated, Delphinium glaucum exhibited a “grand-opening sale” pattern of flower display (Harder and Prusinkiewcz 2013). Inflorescences rapidly peaked in display size due to a declining rate of flower anthesis relative to flower position and inflorescence age. This general pattern of flower anthesis was consistent between

individuals and with several other species having vertical racemes (Gallwey 2011). In particular, these results supported the observations of Gallwey (2011) from the same population three years earlier, demonstrating between-year consistency in display pattern.

The resource competition hypothesis proposes that floral traits decline due to pre-emption of resources by lower positioned resource sinks (Lee 1988, Medrano et al. 2000). Under this hypothesis, removal of resource sinks (i.e., developing flower buds) frees resources that can be allocated to flowers in subsequent positions. Therefore, flower anthesis rates within Intact and Bud-ex inflorescences were predicted to be equal in the first 8 flowers to open, regardless of their positional sequence. This expectation was not supported in this study. Instead, the rate of flower opening differed between treatments according to spatial position along the inflorescence rather than temporal sequence of opening

(Fig. 3.1).

Removal of fruits and flowers early in their development can alleviate resource competition between flowers (reviewed by Stephenson 1981, Lee 1988; also see

Medrano et al. 2000, Guitián et al. 2001, Kliber and Eckert 2004, Pritchard and Edwards

2005, Torices and Méndez 2010). For example, removal of early flowers from

Pancratium maritimum improved fruit and seed set of intermediate and late opening flowers (Medrano et al. 2000). Likewise, removal of the stigmas of primary flowers from

Aquilegia canadensis inflorescences to reduce pre-emption of resources by early flowers, increased seed production of later flowers, whereas reduction of available resources through defoliation decreased fruit set and the number of seeds per fruit (Kliber and

Eckert 2004). Because mature fruits can be up to five orders of magnitude larger than an ovary at flower anthesis, a greater benefit should have been evident from the removal of

flowers as opposed to fruits (Stephenson 1981). However, seed number and size did not significantly differ within intact inflorescences, or among treatments at equivalent positions, which indicates the bottommost positions did not interfere with seed production in the more distal eight positions. Furthermore, positional effects of flower anthesis were not disrupted by the removal of the bottommost buds before inflorescence elongation. I found significant positional effects on floral anthesis in D glaucum. These results suggest that intrinsic architectural effects, rather than resource competition, best explain variation in anthesis rate for this species. Thus, the dynamics of anthesis rate within Delphinium glaucum inflorescences supports the architectural hypothesis, but not the resource dynamics hypothesis.

Numerous studies have documented species-specific architectural effects for many floral traits that are not solely attributable to resource competition (reviewed in Ashman and Hitchens 2000, Diggle 2003, 2014). For instance, declines in petal limb length and ovule number in Silene acutifolia did not vary after the removal of basal and lateral flowers (Buide 2008). Variation in floral trait size does not affect all floral organs similarly. In Delphinium glaucum, sepal length, ovule number and female phase duration decline with flower position, whereas anther and stamen number, and male-phase duration increase distally along the inflorescence (Ishii and Harder 2012).

Several explanations have been proposed for architectural effects. First, morphological variation along the inflorescence axis may govern positional effects

(Diggle 1995, Wesselingh and Arnold 2003). For example, declines in internal vasculature may restrict the allocation and delivery of resources along the inflorescence

(Van Steveninck 1957, Wolfe and Denton 2001, Buide 2008). Van Steveninck (1957)

first observed that fruits developed more slowly at distal positions of Lupinus luteus inflorescences than basally. Correspondingly, he noticed vascular elements were reduced or absent at the distal locations and suggested that resources were not being transported adequately to the top. Likewise, Wolfe and Denton (2001) found the positional decline in fruit set was correlated with stem thickness just below the base of the fruit. In contrast,

Liu and Huang (2012) found positional variation in internal vasculature size in

Adenophora jasionifolia inflorescences; however thinning treatments did not significantly enlarge vascular tissue despite increasing seed production in basal flowers with early thinning. Thus, declines in vasculature size within inflorescences may be architectural effects themselves, rather than the cause of positional variation within other traits (Diggle 2003, Liu and Huang 2012). Similarly, the findings of this study do not seem to support a decline in vasculature reducing anthesis rate within the first 16 positions, because seed set and seed mass did not decline with position. Perhaps the reduction in the number of flowers opening per day reflects an increasing limit to the number of flowers that can be serviced by vasculature along the inflorescence.

Gradients in phytohormones along an inflorescence might also result in architectural effects (Diggle 2003). Several classes of plant hormones (also known as plant growth compounds) promote and/or inhibit growth and development, depending on the identity and concentration of the hormone (Lee 1988). For example, ethylene, gibberellins and auxins regulate genes associated with the initiation of flower opening or closure within a plant (van Doorn and Kamdee 2014). Auxins promote shoot elongation, and fruit formation, whereas gibberellins influence the rate of growth in shoots and bud germination and cytokinins stimulate plants to rapidly produce new cells (reviewed in

Lee 1988, Santner et al. 2009, Wang and Irving 2011). In contrast, ethylene can accelerate fruit ripening, cell and tissue death or slow upward growth in stems, whereas

ABA slows or inhibit growth completely (Wang and Irving 2011). Interestingly, application of cytokinin to basal flowers in Telopea speciosissima inflorescences had no effect on the spatial pattern of fruit maturation, even though architectural effects were evident (Whelan and Denham 2009). Future studies may reveal the role of specific hormones on gradients in floral characteristics and on floral display dynamics.

The results of this experiment demonstrate that floral anthesis rate is an inherent characteristic of inflorescences independent of the effects of resource availability. As such, anthesis rate and the genes that control its expression may be subject to selection.

Rapid initial anthesis rate quickly builds displays that should promote recruitment of pollinators to newly flowering individuals and species (Gallwey 2011, Harder and

Prusinkiewicz 2013). In a meta-analysis of architectural effects in floral traits, Diggle

(2014) found extensive evidence for positional variation in traits associated with floral attraction (e.g., corolla size); however those associated with pollen exchange with pollinators (e.g., corolla tube length) varied little. A few additional studies have found similar positional variation in traits associated with aspects of plant phenology, such as sex-phase duration and flower longevity (Sargent and Roitberg 2000, Ishii and Harder

2012, van der Meer and Jacquemyn 2015), which may contribute to male and female fitness (e.g., outcrossing rates). The findings of this study add anthesis rate to the suite of traits subject to architectural control of within-inflorescence variation. This study is unusual in that it also provides a specific functional explanation for the observed pattern of variation.

Chapter Four: Does temporal variation in pollinator abundance and flower anthesis

influence the frequency of outcrossing in Delphinium glaucum?

4.1 Introduction

Animal-pollinated plants use diverse mating strategies. A large proportion of self-compatible, hermaphroditic plant species reproduce through both self- and cross- fertilization (mixed mating: Goodwillie et al. 2005). Self-pollination can occur as pollen vectors move between flowers within a plant (geitonogamy), or within flowers

(autogamy), either with or without vector facilitation. Furthermore, geitonogamous

deposition of pollen results in the loss of pollen optimally positioned on pollinators for transfer to other plants (pollen discounting: reviewed in Barrett 2002; Harder and Barrett

1995, Karron and Mitchell 2012) and an additional limit to seed production if self-pollen inactivates ovules (ovule discounting: reviewed in Barrett 2002; Sage et al. 2006).

Although autonomous autogamy may provide reproductive assurance when pollinators or mates are limited (Lloyd 1992, Kalisz et al. 2004, Morgan et al. 2005, Eckert et al. 2006), geitonogamy is considered a non-adaptive consequence of pollinator attraction (Lloyd

1992, Harder and Barrett 1995, Eckert 2000).

Self-fertilization (selfing) increases homozygosity within offspring, which can lead to the expression of recessive deleterious alleles (inbreeding depression: Husband and Schemske 1996). The cumulative effects of deleterious alleles increases seed abortion (Levin 1984, Husband and Schemske 1995, Mahy and Jacquemart 1999, Liao et al. 2009), lowers seed germination (Willis 1993a, 1993b, Husband and Schemske 1995,

Ramsey and Vaughton 1998) and reduces seedling survival (Husband and Schemske

1995, Willis 1993b); however inbreeding depression is generally strongest between fertilization and seed maturation and declines with subsequent life stages as the expression of deleterious alleles selectively eliminates less fit offspring (Husband and

Schemske 1996, Yuan et al. 2013).

Flowering plants exhibit large variation in the frequency of selfing within and among species (reviewed in Goodwillie 2005, Igic and Kohn 2006, Brys et al. 2013), populations (Delmas et al. 2015) and individuals (Barrett et al. 1994, Zalucki et al. 2013,

Yin et al. 2016). Mating system is strongly influenced by complex relations between pollinator behaviour, population characteristics, and flower and/or inflorescence traits

(reviewed in Harder and Prusinkiewicz 2013). Population characteristics such as patch size and floral sex ratios have important repercussions for pollen quantity and quality limitation because they affect pollinator limitation and mate availability (Delmas et al.

2015). For example, infrequent pollinator visitation in large patches of Rhododendron ferrugineum resulted in higher outcrossing and high pollen quantity limitation, whereas frequent pollinator visitation in small patches caused high selfing due to low mate availability (i.e., pollen quality limitation: Delmas et al. 2015). In contrast, a decline in pollinator service during the flowering period of Incarvillea sinensis was associated with an increase in delayed self-pollination, despite increasing flower densities (Yin et al.

2016).

Pollinator behaviour, and hence the incidence of self-pollination, is influenced by inflorescence-level display dynamics. Plants with larger floral displays generally attract more pollinators (reviewed by Ohashi and Yahara 2001), which can promote receipt of outcross pollen, but individual pollinators typically visit more flowers on large displays

(reviewed by Ohashi and Yahara 2001), resulting in a higher incidence of geitonogamy

(Harder and Barrett 1995, Karron et al. 2004). For example, Ishii and Harder (2006) found that both the number of visits to D. glaucum inflorescences by bumble bees and the number of probes within inflorescences increased at a decelerating rate with increasing number of flowers. Within-inflorescence variation in floral traits, such as the size and number of floral parts (reviewed in Diggle 2003, 2014) and flower phenology (Harder and Johnson 2005, Gallwey 2011, Chapter 3), can also influence pollinator behaviour and opportunities for cross-pollination. Inflorescence display size is a consequence of individual flower size, the rate of sequential flower opening (anthesis rate) and the longevity of individual flowers (Meagher and Delph 2001, Harder and Johnson 2005,

Gallwey 2011). Small changes in floral longevity or anthesis rate can affect the temporal pattern and maximum size of the floral display within an inflorescence (Harder and

Johnson 2005, Gallwey 2011, also see Chapter 3). Thus, the rate of flower anthesis may contribute to within-inflorescence variation in selfing rates.

Flowering phenology has important repercussions for the mating of individual flowers because pollinator availability, floral characteristics, population characteristics and environmental conditions vary during the flowering period (Murawski et al. 1990,

Petanidou et al. 2008, Austen et al. 2015). Consequently, the influence of ecological and environmental factors on outcrossing frequency of individual flowers is context dependent. In Chapter 2, I reported a phenological mismatch between early flowering

Delphinium glaucum plants and their pollinators that caused pollen limitation of seed production early during the flowering season. I interpreted the increasing seed production, despite equal pollen receipt, as a signal of a shift from an initial high

frequency of autonomous self-pollination to later higher cross-pollination. In this chapter, I test this hypothesis, examining the association between pollinator abundance and the frequency of selfed and outcrossed seeds during the flowering period of the same plants. Further, I consider the influences of display size, which affects floral attraction and pollinator movement, to outcrossing rates within inflorescences. Overall, I expect the lowest proportion of outcrossed seeds when pollinators are rare, but this pattern may be less evident if increased display size from rapid anthesis results in more geitonogamous visitation.

4.2 Materials and Methods

4.2.1 Study plants and tissue collection

General characteristics of Delphinium glaucum, details of the study location, and population flowering phenology, as well as methods for bee surveys and measurement of male and female phenologies of each flower on survey plants are described in Chapter 2.

Recall that the timings of male and female phases were recorded daily for each flower on

24 survey inflorescences. Leaf tissue, which I collected from each plant, was placed in non-chlorinated paper coffee filters, and stored individually in freezer bags containing 15 g of silica. For long-term storage, leaf tissue was transferred to a -80 °C freezer. I collected all fruits as they matured and stored fruits individually at room temperature in paper coin envelopes.

4.2.2 Sampling and DNA extraction

Mature seeds were randomly sampled from each of the 22 plants that produced fruits (see Fig. 2.2). To minimize confounding influences of flower longevity on display

size, I sampled seeds only from fruits produced by flowers from the first 7 to 8 days of anthesis of each inflorescence (see Chapter 3). I pooled a plant’s fruits that opened on the same day (beginning of male phase) and randomly selected 10 of the pooled seeds from days 1, 3, 5, and 7. If the pool contained no seeds for one of these days, then seeds were sampled from the subsequent day (e.g., day 2 instead of day 1), if available. Almost

14.3% (n=98) of sampled seeds were empty, so I replaced them with filled seeds (n=57) from the remainder of the relevant seed pool.

Genomic DNA was extracted from maternal leaf tissue and mature seeds. I extracted maternal DNA from about 20 g of dried leaf tissue (n=24) using a modified cetyl-tri-methyl-ammonium bromide (CTAB) protocol (M.W. Kulbaba 2013, unpublished protocol adapted from S. Kalisz 2006 and Doyle and Doyle 1987).

Quantities of reagents remained the same as the Kalisz (2006) protocol, but this protocol was modified and tested to use less tissue (20 g vs. 60 g). An incubator was used in place of a water bath and liquid nitrogen was not used for tissue grinding. Prior to DNA extraction from seeds, I removed the seed coat (maternal tissue) with a scalpel after seeds were soaked in water overnight at 4 °C. DNA from the embryo and endosperm of seeds was extracted using QuickExtract™ Seed DNA solution (Epicentre, Madison, WI; but see below). I estimated the quantity and quality of DNA in extractions using a Nanodrop

ND-1000 Spectrophotometer (Wilmington, DE) and prepared 100 µL working solutions for use in PCR by diluting stock solutions with sterile deionized water to 40 ng/µL of

DNA.

PCR inhibitors interfere with PCR amplification by directly or indirectly interacting with DNA, DNA polymerase, or essential DNA polymerase cofactors (i.e.,

Mg2+; reviewed in Wilson 1997). Inhibitory compounds are common in many sample types such as soil, blood, and tissues, as well as, materials and reagents used during DNA processing or purification (Wilson 1997). Polyphenolic compounds, polysaccharides and secondary metabolites produced in plant tissue can be particularly problematic (Azmat et al. 2012, Schori et al. 2013, Sene and Admassu 2013). Although the QuickExtract™ protocol is simpler and faster than the CTAB extraction protocol, it does not include a purification step. Therefore, to minimize inhibitory effects of secondary plant metabolites and other potential inhibitors present in the QuickExtract™ stock solutions, I re-extracted DNA from troublesome seed extractions (n=82), using a modified CTAB extraction protocol for seeds (M.W. Kulbaba 2014, unpublished protocol). This protocol was similar to that used for maternal tissue (Kulbaba 2013) except for the following modifications: the volumes of CTAB buffer and chloroform added to the QuickExtract™ extractions were decreased to 300 µL each, the subsequent mixtures were incubated for

2-4 h before adding chloroform, the aqueous phase was incubated on ice overnight after adding ammonium acetate and isopropanol, and the volumes of the 70% and 95% ethanol washes were reduced to 500 µL.

4.2.3 PCR and microsatellite scoring

I genotyped maternal plants and individual seeds using five polymorphic microsatellite markers (D360, D321, DC1, DC2 and DC3, see Appendix 4.1) developed for Delphinium glaucum (M.W. Kulbaba, W.-J. Liao, S.M. Rogers, L.D. Harder, unpublished manuscript). Oligonucleotides, produced by Integrated DNA Technologies

(Coralville, IA), were labelled with M13 sequences (5’-TGTAAAACGACGGCCAGT-

3’) at the 5’ end of the D360 and DC1 forward primers (Schuelke 2000) and at the 5’ end with 6-FAM for the remaining forward primers (D321, DC2, and DC3).

Microsatellites were amplified in 10-µL PCR reactions using BioRad C-1000, S-

1000, and MJ-mini thermocyclers (BioRad, Hercules, CA). For primers end-labelled with 6-FAM, reactions contained 60 ng of template DNA, 2.0 mM Mg2+ in 1x

ThermoPol Buffer, 0.5mM of forward and reverse primers, 0.2 mM dNTPs and 0.6 Units of Taq polymerase (New England Biolabs, Ipswich, Massachusetts). Thermocycling of these reactions consisted of an initial denaturing stage of 94 ºC for 3 min, followed by 30 cycles of 94 ºC for 30 s, annealing temperature of 55-57 ºC for 30 s, 68 ºC for 1 min, and a final extension of 68 ºC for 5 min. I multiplexed M13 labelled primers for D360 and

DC1 in single 10-μL reactions containing 80 ng of template DNA, 2.0 mM Mg2+ in 1x

ThermoPol Buffer, equi-molar concentrations (0.66mM) of both NED (fluorescent dye,

ABI Biosystems) labeled M13 primer and non-labeled reverse primer, 0.125mM forward primer, 0.2 mM dNTPs, and 0.75 Units of Taq polymerase. Thermocycling of multiplexed M13-labeled markers involved 94 ºC for 3 min, 30 cycles of 94 ºC for 30 s,

57 ºC for 30 s, 68ºC for 1 min, followed by 8 cycles of: 94 ºC for 30 s, 53 ºC for 30 s, 68

ºC for 45 s, and a final extension period of 68 ºC for 10 min. For reactions using M13- labelled primers that were not multiplexed (i.e., repeat amplification during troubleshooting), I used only 60 ng of DNA and 0.6 Units of Taq and adjusted annealing temperature for thermocycling to 58 ºC or 61 ºC for D360 and DC1 respectively. I visually inspected PCR products for efficacy and contamination on a 2% 0.5X TBE agarose gel.

I used an Applied Biosystems 3500xl Automated Sequencer with GeneScan™

500 LIZ internal size standard (Applied Biosystems, Foster City, CA) to separate PCR products for fragment analysis. I multiplexed amplified PCR products labelled with different fluorescent dyes (i.e., 6-FAM, HEX and NED) or non-overlapping product sizes using the same dye (i.e., for the multiplexed PCR products). I scored genotypes using

GeneMapper V.4.1 (Applied Biosystems, Foster City, CA). To minimize scoring discrepancies from multiple observers, genotypes were scored by me alone (Pompanon et al. 2005). To facilitate interpretation of the electropherograms, I scored offspring in family groups, which included maternal sibships and the mother.

4.2.4 Genotype troubleshooting

Delphinium glaucum contains considerable secondary metabolites (Gardner and

Pfister 2000) that could have caused nonspecific amplification and interfered with scoring. Further, the concentration of DNA in seed extractions was much lower than in maternal leaf extractions. Thus, 40-80 ng of DNA was not always available for offspring

PCR. Accordingly, I removed samples that failed PCR amplification despite attempted optimization of PCR conditions (i.e., adjustment of temperature, primer concentrations and DNA concentrations) and repeated dilution of stock solutions, and if available, I randomly sampled alternate seeds as replacement. I included samples that failed to amplify at one or more loci after at least two attempts, but amplified successfully at other loci, in subsequent analyses with missing loci. I cross-referenced genotypes of all seeds with maternal DNA and repeated amplification of individuals with impossible genotypes

(i.e., that did not match at least one maternal allele) at specific loci at least twice. For some electropherograms, non-specific amplification made scoring difficult. To improve

certainty in genotype results and to discern the correct alleles in the presence of stutter and peaks due to nonspecific amplification, I aligned replicate electropherograms by individual and compared them with the maternal genotype to determine the most likely allele. If impossible genotypes were obtained at more than one locus, I excluded individuals from analysis; otherwise I changed genotypes that differed at only one locus to a missing genotype for that locus. For family group 11A, a consistent genotype for at least one allele was not attainable for the parent and all offspring at locus D321, therefore

I entered all genotypes at this locus as missing for both mother and offspring.

All individuals had an apparent 220-bp allele at locus DC3, and no homozygotes were observed for the other alleles, suggesting that the allele at 220 bp might be an artefact. To assess this possibility, PCR product from one maternal and one offspring sample, both apparently homozygous for 220, was sequenced on an Applied Biosystems

3730XL capillary sequencer by University Core DNA Services at the University of

Calgary after I prepared samples for sequencing. High-fidelity Taq polymerase (New

England Biolabs, Ipswich, Massachusetts) was substituted in the PCR reaction and annealing temperature was adjusted to 62 ºC for PCR product prepared for sequencing.

All other constituents and thermocycling conditions remained as described above. I ran the PCR product on a 0.5X TBE 2.0% low-melting point agarose gel and excised the

DNA fragment from the gel using a sterilized scalpel. I purified DNA from the excised gel using a PureLink™ Quick Gel Extraction kit (Life Technologies, Carlsbad, CA).

Sequencing reactions (12 µL) contained 50 ng of DNA and 3.2 pmol of Page-purified, unlabelled DC1 primer. This analysis detected a repeated sequence of 6 TC motifs, verifying the existence of an allele at DC3 of 220 bp.

4.2.5 Genotyping error rates

I repeated amplification and genotyping using fresh dilutions for all maternal plants (n=22) and 71 (10.1%) randomly selected offspring to estimate genotyping error rate. If tissue remained, I re-extracted DNA from maternal plants. Re-extraction of seeds was not possible due to the small amount of seed tissue. I estimated the per-allele error rate for each locus (Table 4.1) as the number of allele differences divided by the number of comparisons (Pompanon et al. 2005). Genotypes at specific loci that did not amplify during the original or repeat amplification were not included in the count of comparisons.

Table 4.1. Error rates for Delphinium glaucum genotypes scored at five microsatellite loci.

Number of Number of Error Rate Locus Allelic Alleles (per allele) Differences Compared D360 0.0001* 0 180 D321 0.006 1 170 DC1 0.017 3 176 DC2 0.019 2 168 DC3 0.022 4 180 *No errors detected. Error rate assumed to be a minimum of 0.0001

4.2.6 Data analysis

I estimated female outcrossing rates (t: proportion of seeds that were cross- fertilized) using both MLTR (Ritland 2002) and COLONY (Wang et al. 2012) from genotypes of 22 mothers and 704 offspring (up to 40 seeds per family). For comparison with an estimate of the outcrossing rate for the Sibbald population from 2010 (W.-J. Liao

and L.D. Harder, unpublished data) and to confirm COLONY results, population-level multi-locus outcrossing rates (tm) were first estimated using MLTR with the standard error based on 1000 bootstrap replicates. Outcrossing rates were compared between

MLTR and COLONY using their 95% confidence intervals.

COLONY uses maximum likelihood to construct a pedigree from putative parents and offspring and provides estimates of selfing rate (s = 1 – t) and inbreeding coefficient

(F: Wang et al., 2012). This analysis accounted for the estimated genotyping error rates

(Table 4.1). The known mother and maternal sibships were entered for each offspring.

All 24 observational plants were included as potential fathers and the estimated probability of including a father in the genotyped pool was set to 0.1. In addition to estimating the population selfing rate, COLONY estimated the probability that each offspring was self- or cross-fertilized and these seed-specific data were used in subsequent statistical analysis. The probability estimates for all but 3 seeds were either 0 or 1, indicating high certainty in assignment of mating outcomes.

The relevant period for assessing female mating outcomes is a flower’s first day of female phase, but fruits for seed sampling were pooled based on the first day of male phase of the associated flowers, which occurred an average (±SD) of 4.90 ± 0.87 days prior to female phase (n=230 flowers). Because of variation in male-phase duration, each pooled seed sample could include seeds fertilized over a range of “female days”, creating uncertainty in the actual fertilization date and associated mating conditions. I accommodated this by representing a seed’s fertilization date and the associated numbers of foraging bees per observation minute, population floral display size, inflorescence

floral display size, and flower position by the weighted averages for all seeds in the pool from which a specific seed was sampled.

Statistical analyses examined influences on the probability that a seed was cross- fertilized with generalized linear mixed models (Stroup 2012), as implemented in the

Glimmix procedure of SAS/STAT 13.2 (SAS Institute Inc. 2014). Analyses considered a binomial distribution and a logit link function and treated plant as a random effect.

Continuous independent variables initially included bee abundance, day of a flower’s female phase corresponding to both initial flowering in the population and initial

flowering of the inflorescence, inflorescence male-phase display size and flower position, as well as all two-variable interactions. To minimize confounding effects of multi- collinearity, I used backward elimination to exclude non-significant terms (α=0.05), given the condition that a variable could be excluded only if it was not part of a significant interaction.

4.3 Results

Female outcrossing rates during 2012 were estimated based on a total of 55 alleles over 5 loci, ranging from 4 to 25 per locus (Appendix 4.1). The overall MLTR multilocus estimate of outcrossing rate (tm ± SD) was 0.801 ± 0.015 (n=728). Family level estimates of tm ± SE ranged from 0.592 ± 0.092 to 1.006 ± 0.007. In COLONY, the overall tm (with 95% confidence limits) was 0.769 (LCL: 0.737, UCL: 0.800), but dropped to 0.712 (LCL: 0.678, UCL: 0.746) after accounting for genotyping error rates

(n=728). The incidence of biparental inbreeding was 0.233 ± 0.011 (± SD), 0.131 (LCL:

0.111, UCL: 0.152) and 0.169 (LCL: 0.150, UCL: 0.192), as estimated by MLTR,

COLONY without error rates and COLONY with error rates, respectively.

The probability that seeds were cross-fertilized was not constant, but varied significantly with flowering date and bee abundance (Table 4.2, Fig. 4.1). Cross- fertilization probability varied strongly and positively with bee abundance (Fig. 4.1, Fig.

4.2), which was low early in the season, but increased rapidly beginning about 10 days after flowering began (see Fig. 2.1). After accounting for the direct effect of flowering date, the average proportion of outcrossed seeds increased from about 0.53 when bees were rare to 0.94 when they were common (Fig. 4.2). This dominant effect is supported by the fact that the simplest model that included only bee abundance fit almost as well as the model that also included flowering day (AIC = 5.35), so both models warrant consideration (decision criterion, AIC < 6; Richards 2008). In contrast, a model that considered only flowering date fit the data very poorly (AIC = 15.91). In contrast to the effects of flowering day and bee abundance, the probability of cross-fertilization did not vary significantly with the day of a flower’s female phase corresponding to initial flowering of its inflorescence, flower position, or the inflorescence’s male-phase display size (Table 4.2: initial model).

Table 4.2. Generalized linear mixed models of the influences on the probability that a seed was cross-fertilized for

Delphinium glaucum. Initial model AIC=824.20; final model AIC=818.18.

Intial model† Final model

Partial regression Partial regression

Source of variation coefficient ( SE) Test statistic coefficient ( SE) Test statistic

Intercept 2.036  0.811 t1,21=2.51* 1.755  0.493 t1,21=3.56**

Foraging bee abundance 0.897  0.219 F1,673=16.80*** 0.863  0.213 F1,677=16.36***

Within-population flowering day -0.153  0.0556 F1,673=7.55** -0.122  0.046 F1,677=7.18**

Male display size (inflorescence) -- F1,673=0.65 -- --

Within-inflorescence flowering day -- F1,673=0.01 -- --

Total flowers (survey plants) -- F1,673=0.49 -- --

Position -- F1,673=0.20 -- --

Plant 2 1 = 17.48*** = 20.45***

*P<0.05, ** P < 0.01, *** P < 0.001, †full initial model with all 2-way interactions: see Appendix A4.2

Figure 4.1. Effects of mean foraging bee abundance (bees observed min-1 per 200 m2) and flowering day (1 = July 7) on predicted outcrossing rate (see Table 4.1 for estimated regression coefficients). The first flower entered female phase on day 4.

The dark grey solid line illustrates the variation in observed bee abundance.

Figure 4.2. Influence of bee abundance on the mean ( SE) proportion of outcrossed seeds, after accounting for variation in population flowering date (n=701).

4.4 Discussion

As predicted in Chapter 2, genotyping of offspring reveals many seeds fertilized when bees visited infrequently at the beginning of the flowering period were the product of self-mating. Thus, the low seed production during this period, despite abundant pollen receipt (Chapter 2), apparently reflects poor zygote quality. That this seed failure resulted specifically from autogamy, rather than geitonogamy, is supported by the almost exclusive outcrossing when bees were common and the lack of influence of male-phase

display size on the probability of outcrossing. I now consider other evidence for seasonal variation in outcrossing and its consequences.

4.4.1 Seasonal variation in outcrossing rates

Although outcrossing rates have been measured for 100s of species (Goodwillie et al. 2005), only two previous studies have examined variation in outcrossing during flowering seasons. Morinaga et al. (2003) attributed a seasonal increase in outcrossing rate of Heloniopsis orientalis (Liliaceae) to a pollinator shift from flies to solitary bees.

Yin et al. (2016) suggested that a seasonal decline in pollinator abundance cause a similar

decline in outcrossing for Incarvillea sinensis (Bignoniaceae), but they did not measure pollinator activity. Thus, my study is the first to demonstrate explicitly the role of pollinator abundance in variation in outcrossing.

Interestingly, the mechanisms of self-pollination differ between these three cases.

Morinaga et al. (2003) found that Heloniopsis orientalis is incapable of autonomous autogamy and they attributed the higher selfing when flies were abundant to frequent geitonogamy, although they did not address the possibility of facilitated autogamy. In contrast, both studies that associated low outcrossing with limited pollinator availability identified mechanisms of autonomous autogamy as the relevant pollination mode (Yin et al. 2016, this study). Such autonomous self-pollination would provide partial reproductive assurance, depending on the severity of inbreeding depression during seed development.

Seasonal changes in outcrossing likely explain the 10% difference in overall outcrossing rates estimated in the Sibbald Flats population during 2010 (tm ± SD = 0.899

± 0.037; W.-J. Liao and L.D. Harder, unpublished manuscript) and 2012 (0.801 ± 0.015;

this study). The 2010 estimate is based on seeds produced by flowers that were in female-phase during a 5-day period during late-peak flowering (July 25-29), when pollinators would have been relatively abundant. My study similarly detected high outcrossing rates during the equivalent period with abundant pollinators (Fig. 4.2).

However, my overall estimate also included fruits pollinated early during the flowering period when autogamous selfing was relatively common. Thus, the contrast between the

2010 and 2012 estimates likely represents differences in the temporal characteristics of the samples, rather than fundamental annual differences. This highlights the importance of considering sampling time when comparing population-level estimates of parameters.

4.4.2 Within-inflorescence variation in outcrossing rates

The lack of significant variation in outcrossing rates with either flower position or the number of male-phase flowers on inflorescences suggests no within-inflorescence variation in outcrossing rates. Rather than demonstrating an absence of contribution to female fitness, this likely indicates that the occurrence of geitonogamous self-pollination is low. Protandrous, hermaphroditic racemes such as D. glaucum feature several traits that prevent geitonogamy (Back et al. 1996). Specifically, dichogamy, the temporal separation of male and female phase, and the bottom-up foraging patterns of bees along inflorescences act in concert to reduce the transfer of self-pollen between flowers of an inflorescence (Corbet et al. 1981, Harder et al. 2000). For example, a manipulative experiment with Eichornia paniculata demonstrated that selfing rates increased when bees moved upward from female-above-male inflorescences compared to male-above- female inflorescences (Harder et al. 2000). On the other hand, Campanula americana

experienced greater geitonogamy with increasing display size, but dichogamy did not reduce intra-floral visitation (Galloway 2002).

Numerous studies have demonstrated that larger plants experience increased geitonogamy (Harder and Barrett 1995, Eckert 2000, Galloway 2002, Karron et al. 2004,

Williams 2007). However, technical challenges in unambiguously estimating within- individual selfing rates have limited the number of studies in natural populations at finer scales of analysis (Ivey and Wyatt 1999, Ritland 2002, Williams 2007). Nevertheless,

Williams (2007) demonstrated that outcrossing rates in Delphinium barbeyi, a close relative of D. glaucum, decreased significantly with increasing display size. In fact, selfing rates in large multi-stalked inflorescences were twice as high as that of plants with only a single ramet and more than 60% of selfing rates could be attributed to between- inflorescence visitation. In direct contrast, I examined outcrossing within single ramets, which eliminated the confounding influence of among-ramet geitonogamy. As opposed to the findings of Williams’ study, D. glaucum had very high outcrossing rates later in the flowering period (~94%), suggesting that the influences of autogamy and within- inflorescence geitonogamy are minimal when foraging bees are common.

4.4.3 Concluding remarks

This is the first study to relate variation in outcrossing rates during a season directly to pollinator abundance. The results reveal complex relations between pollinator behaviour, population characteristics, and flower and/or inflorescence traits and their combined effects on outcrossing rates within a population of Delphinium glaucum. A negative relation of the female outcrossing rate to increasing display size from rapid flower anthesis was not evident. Experimental manipulation may reveal negative effects

of rapid anthesis on female fitness, but this is not consistent with observed natural variation in inflorescence pattern, because mechanisms to mitigate geitonogamy within inflorescences appear to be effective. Overall, these results suggest Delphinium glaucum benefits from at least partial reproductive assurance from autonomous autogamy when pollinators are scarce. Future study will reveal the severity of inbreeding depression in selfed seeds. Nonetheless, maximal outcrossed seed set is achieved when pollinators are common.

Chapter Five: Implications for Plant Reproductive Success

This thesis tested two key assumptions of the adaptive hypothesis for GOS display phenology and assessed the advantage of GOS phenology for female reproductive success. Consistent with the first assumption, Delphinium glaucum experienced pollinator limitation early during its flowering period, which reduced ovule fertilization

(Chapter 2) and outcrossing (Chapter 4). These results demonstrate environmental influences on differential survival and reproduction of offspring, especially in relation to pollinator abundance. Furthermore, in support of the second assumption, experimental manipulation of resource dynamics indicated that anthesis rate is an intrinsic characteristic of inflorescence architecture (Chapter 3). Thus, anthesis rate could be subject to natural and sexual selection, although the genetic control and heritability of anthesis rates have yet to be determined for any species. Despite strong support for underlying assumptions of the GOS hypothesis, whether the GOS display pattern enhances reproductive success is less clear. Neither seed production (Chapter 3) nor female outcrossing rate (Chapter 4) varied significantly with flower position, anthesis rate or inflorescence display. However, the findings of this thesis are consistent with an adaptive value for female seed production and male siring success.

That rapid anthesis of a plant’s initial flowers did not enhance female performance may reflect three features of Delphinium glaucum (and many other bumble- bee pollinated species): protandry, vertical inflorescences and acropetal flowering.

Individual D. glaucum flowers exhibit protandry, whereby male phase precedes female phase within flowers. Protandry delays the onset of a plant’s female function until several days after it first displays flowers. In D. glaucum, initial flowers have a four- to

five-day male phase, so that inflorescence display increases considerably before flowers enter female phase. Thus, the effects of rapid initial anthesis for building displays and attracting pollinators will have been largely realized before a plant’s flowers begin receiving pollen, limiting the possible GOS benefits for female success.

The combination of protandry and acropetal (bottom to top) flowering with the tendency of bumble bees to move upward on vertical inflorescences (e.g., Corbet et al.

1981) should also limit the consequences of increasing display size for the female selfing rate. Although previous studies have demonstrated that large displays increase geitonogamous self-pollination (Harder and Barrett 1995, Karron et al. 2004), they involved species in which individual flowers have no separation between female and male function, so that every flower can simultaneously present and receive pollen. In contrast, protandry on vertical acropetal inflorescences reduces geitonogamy (Harder et al. 2000), because bees generally proceed from female to male flowers. As illustrated in

Chapter 4, when pollinators were abundant (and autogamy seems unlikely) female outcrossing rates reached 94%, indicating that this combination of floral and inflorescence traits averted geitonogamy very effectively. This incidence of outcrossing was almost double that observed when foraging bumble bees were scarce. Thus, if increasing display size during early flowering enhanced pollinator attraction, it largely contributed to the quality, rather than the quantity of female mating.

GOS display may have greater significance for male fitness (siring success) through increased pollen export, which was not measured in this study. The floral sex ratio shifts in protandrous populations, such as those of D. glaucum, from male to female- biased during the flowering period (Brookes and Jesson 2010, Ishii and Harder 2012,

Austen and Weis 2014), so that male mating opportunities are more limited for early- flowering plants and sexual selection for males is much stronger. Such selection should favour initial rapid anthesis to increase a plant’s male competitiveness. To date, no studies have examined temporal variation of paternity within or among individuals in natural environments. In part, this has been limited by technical challenges in assigning paternity, but continual development of polymorphic markers and improved statistical techniques (Bernasconi 2003, Wang et al. 2012) make this an important and feasible future direction of research.

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APPENDIX

Figure A2.1. Bee abundance associated with minimum (A) and average (C) daily temperatures and minimum (B) and average (D) daily temperatures during the 2012 flowering period. Hourly temperature records were obtained from the

Biogeoscience Institute (University of Calgary) for the Barrier Lake Research

Centre, located 11.8 km from the study site. Average temperature was calculated per day. Minimum temperature during the 24-h period was selected as the

appropriate measure due to the negative influence of low temperature on pollen tube germination and growth (reviewed in Hedhly 2011). Average temperature was calculated to show mean temperatures influencing fertilization. Measures were shown against bee abundance to relate pollen performance with receipt of outcross pollen.

Table A4.1. Primer sequences, repeat motif, annealing temperature (Ta), and allele count for five microsatellite loci designed

for Delphinium glaucum. Range describes the allelic size range in base pairs (bp). Allele count and range are for the study

population. Primer sequences, repeat motifc and annealing temperature supplied by M.W. Kulbaba (unpublished data).

c Locus Primer Sequences Repeat Motif Ta (ºC) Total Alleles Range (bp)

F: TGTAAAACGACGGCCAGTTCCCAGATCTCCACAACTACAACG AC 58 7 164-176 D360a R: CGACTAGTTATTTCGGTCACACACG

F: TCACGCATTACTACTAGGCACAGG b AC 57 14 164-194 D321 R: GTGACGTGTACAAAGAAACGACGG F: TGTAAAACGACGGCCAGTCCAACACAAACTCACCCAAC a AG 61 4 215-227 DC1 R: AGAAAAATCTAAAAGACGCA F: TTGGCTTGCATATAAAAG b

97 AG 55 25 160-218 DC2 R: TGTGAGCAATAATGGTGA

F: ATCCAACCGCTCGTGTCA b TC 57 5 220-230 DC3 R: TTCCTCCTTCAGCCCTAC a possessed 5’ M13 sequence TGTAAAACGACGGCCAGT b 6-FAM fluorescent labels appended at 5’ end of forward primer c Repeat motif for DC3 obtained from sequencing data; others provided by M.W. Kulbaba

Table A4.2. Initial full generalized linear mixed model of the influences on the probability that a seed was cross-fertilized for Delphinium glaucum including all 2- way interactions. AIC=840.55.

Source of variation Test statistic

Intercept t1,21=-0.72

Bee abundance (bees) F1,658=1.43

Flowering day F1,658=1.66

Male display size F1,658=0.47

Inflorescence flowering day F1,658=0.83

Total flowers F1,658=3.39

Position F1,658=1.65

Bees x flowering day F1,658=0.01

Bees x male display F1,658=1.60

Bees x infl. flower day F1,658=2.43

Bees x total flowers F1,658=0.02

Bees x position F1,658=1.55

Flowering day x male display F1,658=0.90

Flowering day x infl. flower day F1,658=0.86

Flowering day x total flowers F1,658=0.25

Flowering day x position F1,658=0.16

Male display x infl. flower day F1,658=0.18

Male display x total flowers F1,658=2.16

Male display x position F1,658=0.42

Infl. flower day x total flowers F1,658=3.14

Infl. flower day x position F1,658=0.01

Total flowers x position F1,658=3.84

Plant 2 1 = 8.11**

**P<0.01