UNIVERSITY of CALIFORNIA, SAN DIEGO Pollinator Effectiveness of Peponapis Pruinosa and Apis Mellifera on Cucurbita Pepo a Thesis

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UNIVERSITY OF CALIFORNIA, SAN DIEGO

Pollinator Effectiveness of Peponapis pruinosa and Apis mellifera on Cucurbita pepo

A Thesis submitted in partial satisfaction of the requirements for the degree Master of Science

Biology

Jessica Audrey Davids

Committee in charge:

Professor David Holway, Chair Professor Joshua Kohn Professor James Nieh

2018

Jessica Audrey Davids, 2018

The Thesis of Jessica Audrey Davids is approved and it is acceptable in

quality and form for publication on microfilm and electronically:

______

______Chair

University of California, San Diego

2018

iii

TABLE OF CONTENTS

Signature Page…………………………………………………………………………… iii Table of Contents………………………………………………………………………... iv List of Tables……………………………………………………………………………... v List of Figures……………………………………………………………………………. vi List of Appendices………………………………………………………………………. vii Acknowledgments……………………………………………………………………... viii Abstract of the Thesis…………………………………………………………………… ix Introduction………………………………………………………………………………. 1 Methods…………………………………………………………………………………... 3

Results…………………………………………………………………………………...... 7 Estimated pollen deposition….…………………………………………..……...... 7 Fruit set……………………………………………………...…………………...... 7

Discussion………………………………………………………………………………....9 References………………………………………………………………………………. 12 Tables…………………………………………………………………………………… 15 Figures…………………………………………………………………………………... 16 Appendices……………………………………………………………………………… 20

iv LIST OF TABLES

Table 1. Behaviors of bees videotaped during visits to squash flowers………….…….... 15

v LIST OF FIGURES

Figure 1. Comparison of time spent on stigmas during single visits of Cucurbita pepo by Apis mellifera and Peponapis pruinosa. The bottom and top of each box represent the first and third quartiles, respectively, and the middle line represents the median. The bottom and top whiskers represent the minimum and maximum values, respectively….16

Figure 2. Pollen deposition on stigmas of Cucurbita pepo flowers as a function of time spent on stigmas during single visits by both Apis mellifera and Peponapis pruinosa. The regression line includes both species…...………………………….………………...…. 17 Figure 3. Pollen deposition on female stigmas of Cucurbita pepo as a function of Julian day by both Apis mellifera and Peponapis pruinosa. Each regression line represents data from each bee species. …………….…………….………..……….....………………….. 18 Figure 4. Comparison of single visit pollen deposition on female stigmas of Cucurbita pepo by Apis mellifera and Peponapis pruinosa bee species. Box and whisker plots as in Fig.1………………………………………………………...……………………………19

vi LIST OF APPENDICES

Appendix 1. Soil Moisture Data. This table summarizes soil moisture readings collected twice a week for the duration of the project. Each plant received four soil moisture readings per date, within an hour of watering, resulting in a mean per plot per date. The mean of all soil moisture readings spanning the duration of the project ...…………………...…..….. 20 Appendix 2. Protocols for behavioral analysis of single visits. …………………..……....22 Appendix 3. Fate of squash flowers allowed to set fruit. (HB = Apis mellifera, PP = Peponapis pruinosa (♀)).……………………………………...……………………...... 23 Appendix 4. Single Visit Pollen Deposition Data. The data associated with every single visit sacrificed for the pollen deposition component of the study. Pollinator taxa designations: HB = Apis mellifera, PP = Peponapis pruinosa (♀). For additional information on data collection see Appendix 5…………………………...... ………..…...24 Appendix 5. Single visit pollen transfer procedures. A detailed summary of stigma storage & preservation and pollen counting protocols……….………………....…….....27 Appendix 6. Fruit Data. Fruit resulted from single visits by pollinators on flowers allowed to set fruit. Pollinator taxa designations: HB = Apis mellifera, PP = Peponapis pruinosa (♀). For additional information on data collection see Appendix 7………………....…....29 Appendix 7. Fruit Processing Protocols. A detailed description of fruit handling protocols including fruit harvesting, fruit volume determination, seed weighing, counting and storage, and fruit sugar content estimation. ………………………………………………31 Appendix 8. Data from Behavioral Analysis of Videos. This table includes the data associated with behavior from each experimental single visit. Where Visit Code = (Plant Number. Flower Number. Pollinator [HB = Apis mellifera, PP = Peponapis pruinosa], Pollination Date. Fate of Flower [Fruit = F, No fruit = N, Stigma = S])..…...………..…...34 Appendix 9. R Code used for statistical analyses…………….. …………...……..………38

vii ACKNOWLEDGMENTS

I would like to thank my undergraduate research assistants (Aaron Ta, Andrian

Krastev, Blaine Novak-Pilch, Breana Garcia, Chandler Pourvahidi, Chantal

Sengsourinho, Carline Hua, Crystal Chan, Danny Rummani, Jessica Miranda, Johana

Leon, Kara Powell, Kiara Suzuki, Kylie Etter, Rosalba Herrera, Tori Renfro, Valerie

Inthavong, Vanessa Heredia, William Bauer) for dedicating many hours of their time to help me both in the field and in the lab. I would like to thank the members of the Holway

Lab, especially Jess Gambel, who helped me have a productive field season and guided my thesis to its completion by helping me with my analyses and R coding. Finally, I’d like to thank the members of my committee for their guidance through every step of this process, especially my committee chair, David Holway.

viii ABSTRACT OF THE THESIS

Pollinator Effectiveness of Peponapis pruinosa and Apis mellifera on Cucurbita pepo

Jessica Audrey Davids

Master of Science in Biology

University of California, San Diego, 2018

Professor David Holway, Chair

Differences between specialist and generalist pollinators provide insight into the evolution of specialization in plant-pollinator interactions. Plants of the genus Cucurbita

(Cucurbitaceae) are visited by both generalist pollinators (e.g., Apis mellifera) and by specialist pollinators (e.g., Peponapis pruinosa). Previous studies have estimated pollinator effectiveness of Apis mellifera (honey bees), and Peponapis pruinosa (squash bees) in agricultural Cucurbita species, but none have investigated behavioral differences that underlie variation in effectiveness. In the summer of 2017, I conducted single visit pollinator effectiveness trials on 21 acorn squash (Cucurbita pepo) plants at the UC San

Diego Biology Field Station in San Diego County, California to link interspecific

ix behavioral differences of pollinators to their effectiveness. Female squash bees spent more than seven times longer per single visit in contact with squash stigmas and in turn deposited more than ten times more pollen compared to generalist honey bees. For all trials combined pollen deposition and fruit set increased with time bees spent on receptive stigmatic surfaces. Single visits by squash bees were more likely to result in fruit set compared to honey bees (85% vs. 12%). In terms of single visits, these results indicate that Apis mellifera are less effective at pollinating acorn squash compared to female Peponapis pruinosa. The results of this study differ from previous studies because

Apis mellifera on our study site forages for pollen from a variety of different plant species in the surrounding landscape. In agricultural systems, in contrast, honey bees may have fewer options for pollen foraging.

x Introduction

Investigating the key differences that make some pollinators more efficient than others is important because of the potential implications for potential changes in agricultural and land management practices needed to bolster specific species of pollinators (Garibaldi 2013). The use of single visit trials is a common means by which to estimate pollinator effectiveness (Artz and Nault 2011, Cane et al. 2011, King et al. 2013,

Tepedino 1981, Thomson and Goodell 2001). Previous studies have used seed set, pollen removal, fruit set, seed set, seed weight, fruit development rate to assess pollinator effectiveness (Artz and Nault 2011, King et al. 2013, Tepedino 1981, Thomson and

Goodell 2001). Single visit measures of pollinator effectiveness can clarify contributions made by individual species (Canto-Aguilar & Parra-Tabla 2000, Garibaldi et al. 2013,

Hung et al. 2017, Winfree 2007). Single visit measures of pollinator effectiveness are also of interest as many crops have differing dependence on insect pollinators and managed honey bees in the United States have been in decline in recent decades (Ollerton

2011, Smith et al. 2013). Studies that focus on pollinator effectiveness are particularly insightful when they include measures of pollinator behavior.

Acorn squash (Cucurbita pepo) is an agricultural crop that attracts both generalist and specialist pollinators. Cucurbita pepo is monoecious and produces unisexual flowers that are visited by a diversity of pollinators (McGregor 1976, Michelbacher 1964), including super generalist honey bees and specialist squash bees. Previous studies have compared the effectiveness of single visits by bees to female flowers of C. pepo (Artz and

Nault 2011, Cane 2011, Tepedino 1981), but the behavior of bees during single visits has

1 received relatively little attention in these studies, which focus on visit duration, fruit set, fruit growth, and stigma contact. A gap in understanding thus exists with respect to the visitation behavior of individual pollinators and how it relates to pollinator effectiveness.

In particular, single visit pollen deposition by Apis mellifera compared to Peponapis pruinosa in previous studies on C. pepo was not found to be different (Artz and Nault

2011, Tepedino 1981). An open pollination study used video surveillance to measure visitation frequency, visit duration and temporal activity patterns of bees to investigate the connection between pollen deposition and visitation on open pollinated flowers on C. pepo but did not link pollen deposition with bee behavior (Phillips and Gardiner, 2015).

My study investigated the effectiveness of single visits between specialist and generalist pollinators that visit female flowers of acorn squash (Cucurbita pepo). The primary motivation was to discern differences between generalist (Apis mellifera) and specialist and (Peponapis pruinosa (♀)) pollinators on acorn squash. I compared pollinator effectiveness of Apis mellifera and Peponapis pruinosa (♀) in three different ways: behavior from video recordings, pollen deposition count, and fruit set. This study relates the behavior of bees during single visits with stigmatic pollen deposition and resulting fruit set. With managed honey bees in decline in some parts of the world (Smith et al. 2013), it is imperative to assess the effectiveness of native bees as pollinators to anticipate potential impacts of how systemic bee population shifts may affect pollination services in agricultural systems (Winfree et al. 2007).

Methods

I studied acorn squash plants (Cucurbita pepo) at the UC San Diego Biology

Field Station (32.885931, -117.229641) between June and September 2017. I planted seeds in hand-tilled plots; plants (plots planted: n = 25; plants used in experiment due to gopher damage: n = 21) were spaced 1.2 m apart from one another in a 5 x 5 plant grid.

Three weeks after germination, I initiated weekly measures of plant growth that continued until the end of the experiment. I watered plants daily to maintain soil moisture levels with an average of 43.1% VWC (sd = 7.7, n = 1428) (Appendix 1). I used a Field

Scout TDR100 Soil Moisture Reader to estimate the percentage volumetric water content

(VWC). Individual measurements were repeated four times per plant; the mean of these four samples was used as an estimate of soil moisture for that plant for a given time period (Appendix 1).

Acorn squash flowered from late July to late August. I conducted single visit pollinator effectiveness trials throughout this period. Plants were surveyed daily for floral buds that looked like they would open the following day (i.e., ones with yellowing tips).

These floral buds were bagged for the next day using a paper pollinating bag

(Canvasback #S27 shoot bags; 5 x 2.5 x 18 cm) to exclude pollinators. The following day, I removed bags from all male flowers within 10 minutes of sunrise. This was done so that pollen would not be completely stripped from the plants by prior pollinators. This was done so that the start of experiment at sunrise replicated the pollen availability when the flowers first opened. There was not enough ambient light in the system prior to sunrise to record the single visits. The experimental procedures on female flowers were

3 executed within one hour of sunrise, after bags had been removed from the male flowers.

Every female flower provided a potential single-visit replicate. I repeated this experimental protocol among plants with female flowers on a given day until all female flowers received a single visit or until one hour past sunrise. For each single visit trial, I used a GoPro HERO Session video camera (model C3141326666972) to record pollinator behavior for the entirety of each single visit. The camera was placed on a tripod and positioned or held by a volunteer such that the camera provided an unobstructed view of the flower. Prior to the start of each single visit trial and before the pollen bag was removed from a focal female flower, the GoPro was turned on and set to record such that it was 0.5 to 1.5 m away from the flower. These protocols ensured that the entirety of every single visit to be recorded. Once a single visit trial began, the camera was repositioned such that it was approximately 15 cm away from the flower to allow for an unobstructed view of the sigmatic lobes and the bee’s movements within the flower.

To obtain single visits by specific pollinators, I used an aspirator to blow puffs of air at incoming bees (usually Apis mellifera and P. pruinosa (♂)). This approach provided an effective and unobtrusive means to deter unwanted bees from the immediate vicinity of flowers (Nabors et al., 2018). Moreover, while a focal bee was visiting a flower during a single visit, I used aspiration to prevent other bees from visiting the flower. I considered a single visit to be a visit by either individual A. mellifera and P. pruinosa (♀) that entered the opening of the flower (Table 1). Trials in which more than one bee entered a flower were discarded (Appendix 2).

After I recorded single visits at individual flowers, I either (1) let the flower develop to determine whether or not it would set fruit (n =38 flowers) (Appendix 3), or

(2) sacrificed the flower to estimate stigmatic pollen deposition (n = 57 flowers)

(Appendix 4, Appendix 5). To determine a flower’s fate, I used the following set of criteria. I always allowed the first female flower on each plant to mature into a fruit following a single visit. If this flower set fruit (Appendix 6), then no other female flowers on that plant were allowed to set fruit and any subsequent female flowers were used to estimate stigmatic pollen deposition. If the first female flower aborted its fruit, then the next female flower produced by that plant was allowed to set fruit. Depending on whether or not this second flower set fruit, subsequent flowers on that plant were either allowed to set fruit (e.g., all previous female flowers aborted) or were sacrificed to estimate stigmatic pollen deposition (e.g., a previous female flower had set fruit).

Flowers allowed to fruit were covered with an organza mesh bag (15 x 23 cm) cinched below the ovary to exclude other floral visitors. Each mesh bag was removed after 3 days; by this time the flower had senesced, and further pollination was no longer possible. Fruits that did set were harvested 50 days after their pollination date by cutting the stem 3-5 cm above the fruit (Appendix 7). Female flowers used to estimate stigmatic pollen deposition were removed by cutting below the flower, just under the bulb. Stigmas were removed with a razor and placed in a 50 mL Falcon tube containing 100% ethanol with 3 drops of diluted Basic Fuchsin, 80+%, pure, certified dye. Tubes containing stigmas were labeled and refrigerated at between 4°C and -8°C. Pollen grains were

5 counted under a dissecting scope (40-50X) after stigmas had been in solution for at least three days.

Videotapes were transcribed to obtain behavioral data for single visits made by individual Apis mellifera and Peponapis pruinosa (♀). Behaviors recorded included the following: walk, fly, nectar, stigma, rub legs, grooming (Table 1, Appendix 8). In addition, the following information was documented: visit time of day, visit date, total visit duration, and the proportion of stigma lobes touched (Appendix 8).

All statistical analyses were run in R (R Core Team 2016). Given that repeated measures were made on female flowers on the same plant, our analyses treat plant as a random effect in linear mixed models and generalized linear mixed models. Data were log transformed where indicated to reduce potential problems caused by unequal variance

(see Results). In the analyses that pertain to stigmatic pollen deposition, we used linear mixed models to test whether pollinator type (Apis mellifera or Peponapis pruinosa), pollination date, or pollinator behavior (e.g., duration of time spent on stigma) affected stigmatic pollen deposition. We also used linear mixed models to test for behavioral differences between A. mellifera and P. pruinosa. For the analyses of fruit set, we used generalized linear mixed models to test whether or not fruit set is dependent upon pollinator type (A. mellifera or P. pruinosa), pollination date, or stigmatic pollen deposition. R code for all analyses is included in Appendix 9.

Results

Estimated pollen deposition

Compared to honey bees, female squash bees spent more than eight-fold longer per visit on squash stigmas (Fig. 1; linear mixed model: effect of pollinator type t92 =

7.34, P < 0.0001). In this analysis the amount of time spent on a stigma was log transformed. Overall, increasing time spent on stigmas during single visits led to greater pollen deposition on stigmas (Fig. 2; linear mixed model: t54 = 5.73, P < 0.0001).

Pollination date also significantly affected pollen deposition (Fig. 3; linear mixed model: t54 = -2.92, P < 0.01) with pollen deposition decreasing over time. Yet, this trend was driven solely by Apis mellifera visits (linear mixed model: t43 = -2.72, P < 0.01) and was not significant for Peponapis pruinosa visits. Squash bee single visits in turn deposited more than ten-fold more pollen on squash stigmas compared to the amount deposited during single visits by honey bees (Fig. 4; linear mixed model: t54 = 4.66, P < 0.0001). In this last analysis estimated pollen deposition was log transformed.

Fruit set

The amount of time that bees spent on stigmas per visit significantly increased the likelihood of fruit set (generalized linear model: z = 3.04, P < 0.01). For flowers allowed to go to fruit, female squash bees, compared to honey bees, spent more than sevenfold longer per visit on squash stigmas (linear mixed model: effect of pollinator type t31 =

5.38, P < 0.0001). Accordingly, single visits by squash bees were significantly more

7 likely to result in fruit set compared to single visits by honey bees (85% vs 12%; generalized linear model: z = 3.23, P < 0.002).

Discussion

My study investigated the differences in effectiveness between generalist and specialist bee species. I found the specialist pollinator to be ten times more effective with respect to single visit pollen deposition. This was due to differences in behavior between the two pollinator types with female squash bees more frequently contacting the stigmas and contacting them for a longer than honey bees. Increased contact with the stigma was associated with increased pollen deposition and fruit set.

Previous studies differed in their findings with respect to pollen deposition by squash bees on Cucurbita. Canto-Aguilar and Parra-Tabla (2000) found that female squash bees (Peponapis limitaris) deposited more pollen on stigmas when compared to male squash bees and female honey bees. While the findings of Canto-Aguilar and Parra-

Tabla (2000) are in line with the findings of my study, other studies on Peponapis pruinosa found no differences in stigmatic pollen deposition between squash bees

(Peponapis pruinosa) and honey bees (Artz and Nault 2011, Tepedino 1981). A possible explanation as for why the results for my study diverge from previous studies, may be that Apis mellifera have greater floral options at our field site. Apis mellifera likely forages for pollen on other plant species in the surrounding landscape.

Previous studies have also found no difference in fruit set resulting from female

P. pruinosa versus A. mellifera single visits (Tepedino 1981). While honey bees were consistently observed recruiting to open pollinated acorn squash flowers at my site, their behavior during single visits demonstrated limited contact with receptive stigmatic

9 surfaces. Honey bees prefer female flowers, whereas squash bees prefer male flowers

(Phillips and Gardiner 2015, Tepedino et al. 1981). In addition, the findings of Tepedino et al. (1981), that female C. pepo flowers produce more nectar compared to male flowers, may explain the lower pollen deposition demonstrated by honey bees in this study and may indicate that nectar was a resource that enhanced recruitment (Tepedino et al. 1981).

Additionally, the pollination efficiency of honey bees may be influenced by the availability of other resources in the area (Phillips and Gardiner, 2015). Honey bees collect the large Cucurbita pollen grains with difficulty (Michelbacher et al. 1964), however no such difficulty was found in prior single visit studies on Cucurbita (Tepedino et al. 1981), which suggests that A. mellifera may collect squash pollen in some areas but not others.

This study was limited to female bees of both species and didn’t consider male squash bees that are known to pollinate C. pepo (Cane et al. 2011). In addition,

Xenoglossa, another specialist species of squash bee, and Agapostemon, a generalist sweat bee, visited squash flowers at my field site, but were not included due to their relative rarity. This study started relatively late in the squash growing season, and trials were conducted within one hour of sunrise. Honey bees increase their activity on

Cucurbita later in the day (Michelbacher et al. 1964). Future research should investigate how these temporal changes alter pollen deposition and viability in this study system.

Studying the impacts of time would involve studying how temporal changes both within day and season may alter pollen deposition, pollen viability and could be accomplished by starting earlier in the season and further staggering plant germination times. The

10 investigation of the impact of time could also include changing experimental time of day and start times after uncovering the male flowers. The time changes in male flower availability by removing exclusion bags may have little impact on the study as there are native species of Cucurbita at the study site that will be available to pollinators.

Future directions for this experiment could include the use of a higher fruit yield and rate of fruit development, strain of Cucurbita pepo, as it would facilitate more avenues to investigate pollen limitation through fruit growth measurements, a higher flower replicate value per plant, and the ability to sacrifice more flowers for pollen viability studies by allowing the grains to germinate. The findings of this study reinforce the case that estimates of pollinator effectiveness in one environment do not always translate into other systems.

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Tables

Table 1. Behaviors of bees videotaped during visits to squash flowers.

Behavior Description Additional Qualifications Shifting body without leg Simultaneous movement of movement or leg Walk leg and of body movement with stationary body doesn’t count Legs/antennae can still Simultaneous beating of touch the flower as long as Fly wings while body is it is clear they are not suspended in the air supporting the bee's weight Not including when the bee Head directed downwards turns and incidentally faces Nectar towards the base of the the base of the stigma stigma and held there

If it touches the stigma Whenever bee touches the unusually, such as if a part Stigma receptive, yellow part of other than the leg brushes the stigma against the stigma, it is noted as such When the bee’s hind legs The latter is timestamped in are rubbed either against the notes to acknowledge Rub Legs one another or against its that it is different from two abdomen legs being rubbed together

When the bee uses its legs Grooming to brush any part of its face -- or antennae If the bee leaves the water Imagine flower filled with and gets wet again, on any water, the start time of a part of their body, it is no single visit is the moment longer considered a single Single Visit the bee gets wet, the end visit. These multiple visits time is the moment that the will not be used in the bee leaves the water. analysis.

Figures

Figure 1.

Figure 2.

Figure 3.

Figure 3. Pollen deposition on female stigmas of Cucurbita pepo as a function of Julian day by both Apis mellifera and Peponapis pruinosa. Each regression line represents data from each bee species.

Figure 4.

Figure 4. Comparison of single visit pollen deposition on female stigmas of Cucurbita pepo by Apis mellifera and Peponapis pruinosa. Box and whisker plots as in Fig. 1.

Appendices

Appendix 2. Protocols for behavioral analysis of single visits.

To define a single visit, analysts were asked to imagine the flower filled with water and if a bee breached the imaginary water line that fills the flower at any time it was considered the start of a visit. The moment the bee left the, was no longer in the imaginary water with any body part at any time, that is considered the end of a single visit.

Worksheets were created for each video to consistently identify and quantify bee behavior. The total number of stigma lobes on the flower were counted and recorded in the worksheet. The entirety of each bee visit was analyzed in one second increments.

Each second was assessed to determine which behaviors the bee was demonstrating during that second: touching stigma, nectaring, grooming, rubbing legs, walking, or flying. Additionally, the total number of stigma lobes that the bee touched were recorded.

Notes were made on pollen presence/absence on the bee, bee species, and any behaviors not otherwise on the worksheet. Totals were then calculated for the total number of seconds in which the bee demonstrated each behavior and the total length of the visit.

Each video was watched by a minimum of two different individuals and up to four individuals, with an average of three individuals. The behavior analysis of these individuals were then compared and areas where their worksheets differed were watched by me.

Appendix 3. Fate of squash flowers allowed to set fruit. (HB = Apis mellifera, PP = Peponapis pruinosa (♀)).

Polination Fruit Set Plant Date (day Pollinator (Yes/No) of year) 1 223 HB No 1 234 HB No 2 222 HB No 2 242 HB No 3 220 PP Yes 4 212 PP Yes 5 213 HB No 5 230 HB No 5 237 HB No 5 246 HB No 7 233 HB Yes 7 215 HB No 8 226 PP Yes 9 217 PP Yes 10 223 HB No 10 230 HB No 10 236 HB No 10 245 HB No 11 211 PP Yes 12 219 PP Yes 12 212 HB No 13 223 HB Yes 13 216 HB No 14 225 PP Yes 14 219 PP No 15 245 HB No 16 216 HB No 16 230 HB No 18 214 PP Yes 19 216 HB No 19 225 HB No 19 238 HB No 20 212 PP Yes 23 239 HB Yes 23 220 HB No 24 216 PP Yes 25 220 PP Yes 25 212 PP No

Appendix 4. Single Visit Pollen Deposition Data. The data associated with every single visit sacrificed for the pollen deposition component of the study. Pollinator taxa designations: HB = Apis mellifera, PP = Peponapis pruinosa (♀). For additional information on data collection see Appendix 5

Total Pollination Flower Pollen Plant Date (day Pollinator Number Count of year) (grains) 1 1 218 HB 70 1 1 221 HB 16 1 1 224 HB 3 1 2 231 HB 88 1 1 231 HB 255 1 2 234 HB 1 1 1 235 HB 92 1 4 236 HB 3 1 2 236 HB 9 1 1 237 HB 213 1 1 242 HB 1 2 1 214 HB 71 2 2 214 HB 98 2 1 225 PP 975 2 3 242 HB 74 2 1 243 HB 314 2 1 245 HB 92 2 2 245 HB 96 5 2 214 HB 365 5 1 234 PP 1506 5 1 238 HB 24 5 1 242 HB 2 7 1 226 PP 407 7 1 232 PP 153 7 1 235 HB 3 7 2 236 HB 0 7 1 236 HB 112 7 1 246 HB 0 8 1 213 PP 1657 9 2 216 HB 110 9 1 216 HB 464 9 1 243 HB 0 10 1 235 HB 0 10 1 237 HB 255 10 1 238 HB 83 10 2 245 HB 36 12 1 214 HB 20 12 2 217 HB 27 13 1 219 PP 101 13 1 224 HB 278 14 1 221 HB 28 14 1 222 PP 751 14 1 229 HB 31

Appendix 4. Single Visit Pollen Deposition Data, Continued

15 1 212 PP 969 15 1 236 HB 3 15 2 236 HB 9 16 1 221 HB 6 16 1 222 HB 90 19 1 218 PP 537 19 1 219 HB 66

19 1 220 HB 157 19 1 225 PP 221 19 1 232 HB 0 19 1 239 HB 11 19 1 246 HB 0 23 1 223 PP 699 25 2 222 PP 2081

Appendix 5. Single visit pollen transfer procedures. A detailed summary of stigma storage & preservation and pollen counting protocols.

Stigma Storage and Preservation

After a single visit by a pollinator, the flower was removed from the plant by cutting just below the bulb of the flower. The flower was placed in a pollen bag

(Canvasback #S27) and labeled. Less than one hour later, the stigma was removed by removing all petals from the flower, holding the flower so that the stigma was centered above a 50 mL Falcon tube, and cutting just above the nectary with a razor blade. 70%

/100% (issues with 70% EtOH early in the study led to the switch to 100%) was added to the 50 mL Falcon tube until the ethanol just covered the stigma. 3 drops of diluted Basic

Fuchsin, 80+%, pure, certified dye was then added to the 50 mL Falcon tube and the cap was replaced to the top of the Falcon tube. The Falcon tube was then gently swirled several times, until the dye was mixed into the ethanol completely. The Falcon tube was then placed under refrigeration (between 4°C and -8°C).

Pollen Counting

Pollen grains were counted after stigma had been in solution for at least three days. The counting process was accomplished by removing the Falcon tub from the refrigerator. A piercing tool was then used to take the stigma out of the 50mL Falcon tube. One half of a petri dish was marked on the bottom, outside with two lines, forming quadrants. This was repeated if needed due to too much fluid. The stigma was then placed inside of one half of an unmarked petri dish. The number of lobes on the stigma

were counted and recorded, as were any peculiarities. A razor was used to cut one of the lobes off the stigma. The lobe was placed in one half of a petri dish and the razor was used to cut the lobe into small pieces. The petri dish was then placed under a dissecting microscope. A dissecting microscope was then used to examine the lobe pieces and count and record the number of stained pollen grains attached to each stigma piece using a hand tally counter. This was repeated for each lobe on the stigma. After all stigma lobes were cut and pollen grains counted, the remaining solution from the 50mL Falcon tube was poured into a petri dish with marked quadrants, more petri dishes were used when necessary. Ethanol was used to rinse the remaining solution and pollen from the falcon tube and cap into the marked petri dish. Care was taken not splash. This was especially difficult with the cap, but a necessary step. The dissecting microscope was used to count and record the stained pollen grains in the different quadrants in the solution. The solution and stigma lobe pieces were then returned to the 50mL Falcon tube. Ethanol was used to remove the remaining solution and pollen grains in the petri dishes back into the

Falcon tube. All razors, piercing tools, and tweezers were rinsed with ethanol into the original, labeled 50mL Falcon tube. All petri dishes were examined under the scope and any remaining pollen grains were counted and documented. Lastly, the cap was checked to ensure that it was screwed on tightly, a piece of tape with the counter’s initials and the date was placed the cap, and the tube was returned to refrigerator.

Appendix 6. Fruit Data. Fruit resulted from single visits by pollinators on flowers allowed to set fruit. Pollinator taxa designations: HB = Apis mellifera, PP = Peponapis pruinosa (♀). For additional information on data collection see Appendix 7.

7.1

11.6 15.4 15.8 15.8 13.5 13.5 15.5 15.9 14.5 13.3 12.4 14.0 13.6

Brix

(°Bx)

Average Average

loped

-- --

23.0 24.5 23.8 23.6 18.2 22.0 23.0 18.0 19.5 13.0 38.9 11.1

(mg)

Average Average

Seed Weight

Undeve

ped

-- --

23 49 91 18 78 13 78

214 118 198 161 272

Total

Seed Weight

Undevel

1 2 0 9 5 5 9 7 1 4 1 0 7 7

Seed Count

Undeveloped

9.8

99.5 99.9 9 96.9 91.5 92.8 86.1 94.3 99.4 91.9 95.1 70.7

Seed (mg)

119.5 124.8

Mature Mature

Weight Weight

Average Average

Seed (mg)

9676 2995 8993 9380 1929 7795

Total

14027 10465 15373 11699 20058 15471 15113 14994

Mature Mature

Weight Weight

81 24 90 94 21 82

141 108 168 126 233 164 152 212

Seed

Count

Mature Mature

345 293 154 306 277 332 352 307 366 343 397 145 338 353

(cm^3)

Volume Volume

Average Average

333 303 150 296 266 333 338 295 359 331 373 140 330 313

Weight Weight

Harvest Harvest (grams)

PP PP PP PP PP PP PP PP PP PP PP

HB HB HB

Pollinator

lination lination

220 212 233 226 217 211 219 223 225 214 212 239 216 220

year)

Date (day of of Date (day

3 4 7 8 9

11 12 13 14 18 20 23 24 25

Plant

Appendix 7. Fruit Processing Protocols. A detailed description of fruit handling protocols including fruit harvesting, fruit volume determination, seed weighing, counting and storage, and fruit sugar content estimation.

Fruit Harvesting

After 50 days from the pollination date, sheers were used cut the stem about 3 to 5cm from the top of the fruit. The fruit was placed in an organza mesh bag (15cm by 23cm) and clearly labeled. That same day, the fruit was then gently rinsed with water and dried using paper towels. Photographs of the top, bottom and sides of the fruit were taken.

These photographs included including the fruit’s label and a ruler for scale. A food scale was then used to weigh the fruit (in grams). Digital calipers were then used to measure the fruit. The following measurements were taken: height (mm) (next to the stem to the tip where the flower used to attach), diameter (mm) (widest part, around its fruit body).

Additional notes on fruit appearance were recorded. The fruit was then placed back into the organza mesh bag and stored in a cool dry place until they could be processed.

Fruit Volume Determination

Fruit volume was calculated by water displacement in a container with fill lines at 20mL increments. The container was filled to the 1000mL fill line. As the fruits floated, a wire fruit dunking device was used to submerge the fruit. The wire fruit dunking device was lowered into the water until the water line met a specific mark on the device ensuring that the fruit was completely submerged. The device had no significant impact on water volume readings. The second water volume level reading was recorded. The fruit was

removed from the water, dried and the process was repeated a minimum of three times.

Fruit volume was calculated by subtracting the starting volume from the volume of the water containing the submerged fruit. Calculations using data collected using digital calipers were used to calculate the volume were used to determine which method was best for calculating the volume of acorn squash fruits. It was determined that, due to the irregular shape of acorn squash, the displacement methods yielded more accurate results.

Fruit Processing

Fruits were cut in half. A spatula was used to scrape out all seeds and pulp from the center of the fruit. The seeds and pulp were placed in a strainer. Water was gently run over the strainer to clean off the seeds and begin to separate the seeds from each other.

The seeds and pulp were then placed on paper towels to soak up excess water. Mature seeds were then separated from pulp and undeveloped seeds. Mature seeds were defined as being large and hard, while undeveloped seeds were described as thin, or full of air inside an opaque husk. Once all seeds were separated and counted they were placed in separate clean organza mesh bags labeled either mature or undeveloped along with the fruit’s plant number and pollination date, seed count, and processing date. The bag was cinched and placed on a paper towel with the seeds uniformly spread to dry. These steps were taken to avoid mold.

Seed Processing

After at least 2 weeks of drying, seeds were manually recounted and then weighed on a scale in milligrams cup. All mature seeds for that fruit were weighed (in mg) by placing them in the previously tared secondary container, a paper Dixie cup. This was repeated for undeveloped seeds for that same fruit. Only one bag of seeds was open at a time.

Once counted and weighed, all seeds were returned to their labeled organza mesh bag and stored indefinitely in the Holway lab.

Fruit Sugar Content

While seeds were drying, the two halves of the fruit were taken and used to measure the approximate sugar content or BRIX level of the fruit flesh. This was accomplished by scraping fruit flesh from the center of the fruit half, placing that fruit flesh scraping on a refractometer, pressing down on the cover to liquidate the flesh, and holding the refractometer up to the light. This was repeated four times per fruit, two times on each half. This was a qualitative measurement and, as it was not measured on the fruit harvest day, may have been influenced by storage conditions.

Appendix 8. Data from Behavioral Analysis of Videos. This table includes the data associated with behavior from each experimental single visit. Where Visit Code = (Plant Number. Flower Number. Pollinator [HB = Apis mellifera, PP = Peponapis pruinosa], Pollination Date. Fate of Flower [Fruit = F, No fruit = N, Stigma = S]). For information on how videos were analyzed, see Appendix 2.

Average Time Total Time Time Time Time Time Visit Proportion Spent Length Spent on Spent Spent Spent Spent VisitCode Time of Stigma Rubbing of Visit Stigma Nectaring Grooming Walking Flying (AM) Lobes Legs (seconds) (seconds) (seconds) (seconds) (seconds) (seconds) Touched (seconds) 11.1.PP.211.F 7:11 0.65 6 6 5 0 0 3 2 4.1.PP.212.F 6:18 0.60 189 189 161 18 59 23 2 12.1.HB.212.N 7:44 0.00 87 0 77 5 0 11 1 15.1.PP.212.S 7:05 1.00 473 470 362 97 0 85 1 20.1.PP.212.F 6:31 0.51 106 106 73 31 0 8 2 25.1.PP.212.N 6:42 0.62 60 60 43 15 1 7 2 5.1.HB.213.N 6:20 1.00 86 13 77 0 0 33 2 8.1.PP.213.S 6:23 0.58 128 128 126 2 2 6 2 2.1.HB.214.S 6:20 1.00 74 10 62 1 2 24 7 2.2.HB.214.S 6:30 0.63 159 11 149 1 0 34 2 5.2.HB.214.S 6:42 0.93 105 47 87 3 0 38 6 12.1.HB.214.S 6:50 0.78 82 12 76 0 0 32 4 18.1.PP.214.F 6:15 0.69 13 13 11 0 0 5 2 7.1.HB.215.N 6:14 0.54 244 11 211 1 0 88 1 9.1.HB.216.S 6:48 0.74 110 57 93 1 0 22 3 9.2.HB.216.S 6:59 0.62 109 6 90 7 0 25 3 13.1.HB.216.N 6:41 0.74 99 3 90 0 0 18 1 16.1.HB.216.N 6:06 0.43 152 3 125 0 0 56 0 19.1.HB.216.N 6:31 1.00 107 7 87 0 0 48 1 24.1.PP.216.F 6:24 0.68 134 133 115 17 12 4 2 9.1.PP.217.F 6:54 0.78 12 11 8 4 0 10 1 12.2.HB.217.S 6:59 0.96 58 4 51 1 3 28 1 1.1.HB.218.S 6:06 0.23 103 1 89 0 0 48 4 19.1.PP.218.S 6:59 0.91 131 131 99 26 0 9 2 12.1.PP.219.F 6:14 0.75 119 116 113 2 2 18 1 13.1.PP.219.S 6:50 0.93 73 72 58 14 0 7 2 14.1.PP.219.N 6:37 0.67 137 137 115 18 4 3 2 19.1.HB.219.S 7:01 1.00 222 54 182 4 0 91 1 3.1.PP.220.F 6:14 1.00 82 65 52 2 5 51 2 19.1.HB.220.S 6:35 1.00 145 20 122 0 0 86 4 23.1.HB.220.N 6:22 1.00 126 27 98 0 0 65 1 25.1.PP.220.F 6:12 0.54 94 94 91 2 9 7 2 1.1.HB.221.S 6:20 1.00 105 19 90 0 0 47 2 14.1.HB.221.S 6:29 1.00 6 5 0 0 0 6 2 16.1.HB.221.S 6:26 0.00 9 0 0 0 0 8 3 14.1.PP.222.S 6:40 1.00 216 215 186 20 11 27 2 25.2.PP.222.S 6:15 1.00 145 145 118 16 27 16 2 2.1.HB.222.N 6:16 0.78 184 3 153 0 0 67 5 16.1.HB.222.S 6:58 1.00 103 11 88 1 0 34 3 1.1.HB.223.N 6:30 0.00 67 0 60 0 0 34 1 10.1.HB.223.N 6:22 0.41 97 3 78 0 0 46 2 13.1.HB.223.F 6:10 1.00 108 85 78 1 10 90 3

Appendix 8. Data from Behavioral Analysis of Videos, Continued. 23.1.PP.223.S 6:38 0.86 22 19 21 0 0 19 2 1.1.HB.224.S 6:51 0.74 124 5 113 3 0 27 0 13.1.HB.224.S 6:15 1.00 265 119 246 0 0 79 3 2.1.PP.225.S 6:26 0.92 153 152 150 2 40 13 2 19.1.HB.225.N 6:18 0.15 130 2 113 0 0 54 2 14.1.PP.225.F 6:15 0.94 93 93 91 2 24 18 2 19.1.PP.225.S 6:37 1.00 116 116 72 16 0 85 2 7.1.PP.226.S 6:20 0.73 38 37 37 0 0 10 3 8.1.PP.226.F 6:15 1.00 142 142 125 10 6 42 2 14.1.HB.229.S 7:10 0.00 169 1 140 9 6 24 1 5.1.HB.230.N 6:19 0.31 232 3 198 1 0 77 2 10.1.HB.230.N 6:33 0.00 155 0 135 2 0 43 3 16.1.HB.230.N 6:28 0.05 80 0 65 5 0 18 1 1.1.HB.231.S 6:27 1.00 128 51 106 0 0 65 2 1.2.HB.231.S 6:37 1.00 104 9 77 0 0 45 1 7.1.PP.232.S 6:23 0.76 139 139 136 1 2 19 2 19.1.HB.232.S 6:32 1.00 145 8 112 0 0 77 3 7.1.HB.233.F 6:34 1.00 79 13 50 0 0 40 2 5.1.PP.234.S 6:27 0.93 219 219 187 29 24 9 2 1.1.HB.234.N 6:37 1.00 53 7 34 0 0 26 3 1.2.HB.234.S 6:44 1.00 67 5 45 1 0 31 0 1.1.HB.235.S 6:30 1.00 93 7 69 0 0 42 4 7.1.HB.235.S 6:26 1.00 145 17 120 0 0 62 6 10.1.HB.235.S 6:22 0.89 106 4 85 0 0 42 3 7.1.HB.236.S 6:43 0.10 71 0 56 0 0 32 2 7.2.HB.236.S 6:49 0.52 157 3 142 1 0 54 3 10.1.HB.236.N 6:33 0.00 118 0 87 0 0 53 1 15.1.HB.236.S 6:57 0.50 95 4 85 0 0 27 1 15.2.HB.236.S 7:02 0.60 42 6 29 0 0 41 1 1.2.HB.236.S 6:54 1.00 141 7 116 3 0 43 2 1.4.HB.236.S 7:08 0.07 103 0 76 4 0 40 0 1.1.HB.237.S 6:25 0.00 116 0 101 9 0 21 2 5.1.HB.237.N 6:33 0.00 112 0 93 0 0 22 2 10.1.HB.237.S 6:40 1.00 100 51 76 0 0 41 2 5.1.HB.238.S 6:43 1.00 100 23 79 0 0 55 3 19.1.HB.238.N 6:34 0.72 68 22 56 0 0 30 3 10.1.HB.238.S 6:25 0.89 126 3 100 0 0 40 2 19.1.HB.239.S 6:34 1.00 49 26 37 0 0 27 1 23.1.HB.239.F 6:28 1.00 83 22 64 0 0 28 2 1.1.HB.242.S 6:25 0.57 7 5 0 0 0 6 3 2.1.HB.242.N 6:39 0.61 45 11 31 0 0 25 3 2.3.HB.242.S 6:49 0.87 152 18 123 0 0 70 3 5.1.HB.242.S 6:57 0.00 83 0 72 0 0 25 3 2.1.HB.243.S 6:29 0.67 49 47 38 0 0 27 3 9.1.HB.243.S 6:33 0.00 44 0 34 1 0 20 1 2.1.HB.245.S 6:57 0.00 71 0 60 1 0 15 1 2.2.HB.245.S 7:00 1.00 107 44 75 0 0 57 2

Appendix 8. Data from Behavioral Analysis of Videos, Continued. 10.1.HB.245.N 6:42 1.00 98 10 84 0 0 40 1 10.2.HB.245.S 6:51 0.93 9 3 0 0 0 9 2 15.1.HB.245.N 7:05 1.00 84 41 76 0 0 43 4 7.1.HB.246.S 6:36 0.00 79 0 61 2 0 42 3 5.1.HB.246.N 6:40 1.00 73 9 37 0 0 60 1 19.1.HB.246.S 6:56 0.00 33 0 24 0 0 17 2

Appendix 9. R Code used for statistical analyses.

Pollen Deposition: #Figure 1 model=lmer(LogStigmaTime~Pollinator * PollinationDate + (1|Plant), data = jd) summary(model) model=lmer(LogStigmaTime~Pollinator + PollinationDate + (1|Plant), data = jd) summary(model) plot(LogStigmaTime~Pollinator, data=jd, main="Single Visit Time Spent on Stigmas", xlab="Pollinator Type", ylab="Log Stigma Time", names = expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), col=c("darkolivegreen3", "darkorange"))

PP=subset(jd, Pollinator=="PP")

HB=subset(jd, Pollinator=="HB") mean(PP$StigmaTime) mean(HB$StigmaTime)

#Figure 2 model=lmer(LogPollenCount~LogStigmaTime * PollinationDate + (1|Plant), data = jd) summary(model)

#remove interaction: model=lmer(LogPollenCount~LogStigmaTime + PollinationDate + (1|Plant), data = jd) summary(model)

cols1=c("darkolivegreen3", "darkorange") cols_t1<-cols1[jd$Pollinator] plot(LogPollenCount~LogStigmaTime, data=jd, main="Single Visit Pollen Deposition with Time Spent on Stigmas", xlab="Log Stigma Time", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1) abline(lm(LogPollenCount~LogStigmaTime, data = jd), lwd=2) legend("bottomright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("darkolivegreen3", "darkorange"))

#in gray: cols1=c("gray36", "gray70") cols_t1<-cols1[jd$Pollinator]

#plot stigma time vs pollen count plot(LogPollenCount~LogStigmaTime, data=jd, main="Single Visit Pollen Deposition with Time Spent on Stigmas", xlab="Log Stigma Time", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1) abline(lm(LogPollenCount~LogStigmaTime, data = jd),lwd=2) legend("bottomright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("gray36", "gray70"))

#Figure 3

#Pollinator affect how much pollen on stigma?

model=lmer(LogPollenCount~Pollinator * PollinationDate + (1|Plant), data = jd) summary(model)

#remove interaction: model=lmer(LogPollenCount~Pollinator + PollinationDate + (1|Plant), data = jd) summary(model) plot(LogPollenCount~Pollinator, data=jd, main="Single Visit Pollen Deposition on

Stigmas", xlab="Pollinator Type", ylab="Log Pollen Deposition", names = expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), col=c("darkolivegreen3", "darkorange")) plot(LogPollenCount~Pollinator, data=jd, main="Single Visit Pollen Deposition on

Stigmas", xlab="Pollinator Type", ylab="Log Pollen Deposition", names = expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), col=c("gray36",

"gray70")) pollen=subset(jd, Pollen=="P")

PPpollen=subset(pollen, Pollinator=="PP")

HBpollen=subset(pollen, Pollinator=="HB") mean(PPpollen$PollenCount) mean(HBpollen$PollenCount)

#Figure 4

#model code same as in Figure 3

#plot it:

cols1=c("darkolivegreen3", "darkorange") cols_t1<-cols1[jd$Pollinator] plot(LogPollenCount~PollinationDate, data=jd, main="Single Visit Pollen Deposition with Pollination Date", xlab="Pollination Date", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1, xlim=c(210, 250), ylim=c(0.0, 5.0)) abline(lm(LogPollenCount~PollinationDate, data = jd),lwd=2) legend("topright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("darkolivegreen3", "darkorange"))

#in gray: cols1=c("gray36", "gray70") cols_t1<-cols1[jd$Pollinator]

#plot pollination date vs pollen count plot(LogPollenCount~PollinationDate, data=jd, main="Single Visit Pollen Deposition with Pollination Date", xlab="Pollination Date", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1, xlim=c(210, 250), ylim=c(0.0, 5.0)) abline(lm(LogPollenCount~PollinationDate, data = jd),lwd=2) legend("topright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("gray36", "gray70"))

PP=subset(jd, Pollinator=="PP")

HB=subset(jd, Pollinator=="HB")

#HB: model=lmer(LogPollenCount~ PollinationDate + (1|Plant), data = HB)

summary(model)

#PP: model=lmer(LogPollenCount~ PollinationDate + (1|Plant), data = PP) summary(model)

#plot with separate regression lines from each bee species cols1=c("darkolivegreen3", "darkorange") cols_t1<-cols1[jd$Pollinator]

#plot pollination date vs pollen count plot(LogPollenCount~PollinationDate, data=jd, main="Single Visit Pollen Deposition with Pollination Date", xlab="Pollination Date", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1, xlim=c(210, 250), ylim=c(0.0, 5.0)) abline(lm(LogPollenCount~PollinationDate, data = HB),lwd=2, col=c("darkolivegreen3")) abline(lm(LogPollenCount~PollinationDate, data = PP),lwd=2, col=c("darkorange")) legend("topright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("darkolivegreen3", "darkorange"))

#in gray: cols1=c("gray36", "gray70") cols_t1<-cols1[jd$Pollinator]

#plot pollination date vs pollen count

plot(LogPollenCount~PollinationDate, data=jd, main="Single Visit Pollen Deposition with Pollination Date", xlab="Pollination Date", ylab="Log Pollen Deposition", pch=19, cex=1.2, col=cols_t1, xlim=c(210, 250), ylim=c(0.0, 5.0)) abline(lm(LogPollenCount~PollinationDate, data = HB),lwd=2, col=c("gray36")) abline(lm(LogPollenCount~PollinationDate, data = PP),lwd=2, col=c("gray70")) legend("topright", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("gray36", "gray70"))

Fruit Set

#make zero the mean for Pollination Date (so values center around zero):

PollinationDateNew=(jd$PollinationDate-mean(jd$PollinationDate))

#have PollinationDateNew replace PollinationDate in model

#Total time touching stigma affect prob producing fruit? model= glmer(FruitYorNBinomial~LogStigmaTime * PollinationDateNew + (1|Plant), family = binomial, data=jd) summary(model)

#interaction not sig

#run without interaction: model= glmer(FruitYorNBinomial~LogStigmaTime + PollinationDateNew + (1|Plant), family = binomial, data=jd) summary(model)

#plot stigma time vs probability:

#color: cols1=c("darkolivegreen3", "darkorange") cols_t1<-cols1[jd$Pollinator] plot(FruitYorNBinomial~LogStigmaTime, data=jd, main="Probability of Fruit

Production with Time Spent on Stigmas", xlab="Log Stigma Time", ylab="Fruit

Probability", pch=19, cex=1.2, col=cols_t1) legend("right", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("darkolivegreen3", "darkorange"))

#gray: cols1=c("gray36", "gray70") cols_t1<-cols1[jd$Pollinator] plot(FruitYorNBinomial~LogStigmaTime, data=jd, main="Probability of Fruit

Production with Time Spent on Stigmas", xlab="Log Stigma Time", ylab="Fruit

Probability", pch=19, cex=1.2, col=cols_t1) legend("right", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("gray36", "gray70"))

#plot pollination date vs probability: cols1=c("darkolivegreen3", "darkorange") cols_t1<-cols1[jd$Pollinator] plot(FruitYorNBinomial~PollinationDate, data=jd, main="Probability of Fruit Production with Pollination Date", xlab="Pollination Date", ylab="Fruit Probability", pch=19, cex=1.2, col=cols_t1)

legend("right", expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), pch=19, cex=1.2, col=c("darkolivegreen3", "darkorange"))

#Stigma time in just plants allowed to go to fruit fruit=subset(jd, Fruit=="A") model=lmer(LogStigmaTime~Pollinator * PollinationDate + (1|Plant), data = fruit) summary(model)

#without interaction: model=lmer(LogStigmaTime~Pollinator + PollinationDate + (1|Plant), data = fruit) summary(model)

#plot in color: plot(LogStigmaTime~Pollinator, data = fruit, main="Single Visit Time Spent on Stigmas in Flowers Allowed to Fruit", xlab="Pollinator Type", ylab="Log Stigma Time", names = expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), col=c("darkolivegreen3", "darkorange"))

PPfruit=subset(fruit, Pollinator=="PP")

HBfruit=subset(fruit, Pollinator=="HB") mean(PPfruit$StigmaTime) mean(HBfruit$StigmaTime)

#Pollinator affect probability of producing a fruit? model= glmer(FruitYorNBinomial~Pollinator * PollinationDateNew + (1|Plant), family = binomial, data=jd)

summary(model) model= glmer(FruitYorNBinomial~Pollinator + PollinationDateNew + (1|Plant), family

= binomial, data=jd) summary(model)

#pollinator affect proportion lobes touched? model=lmer(ProportionLobesTouched~Pollinator + (1|Plant), data = jd) summary(model) plot(ProportionLobesTouched~Pollinator, data=jd)

#run with PollinationDate in model: model=lmer(ProportionLobesTouched~Pollinator * PollinationDate + (1|Plant), data = jd) summary(model)

#interaction not sig so run without: model=lmer(ProportionLobesTouched~Pollinator + PollinationDate + (1|Plant), data = jd) summary(model) plot(ProportionLobesTouched~Pollinator, data = jd, main="Proportion Stigma Lobes

Touched in Single Visits", xlab="Pollinator Type", ylab="Proportion Stigma Lobes

Touched", names = expression(italic("Apis mellifera"), italic("Peponapis pruinosa")), col=c("darkolivegreen3", "darkorange"))

#proportion lobes touched affect pollen count? model=lmer(LogPollenCount~ProportionLobesTouched + (1|Plant), data = jd)

summary(model)

#run with PollinationDate in model: model=lmer(LogPollenCount~ProportionLobesTouched * PollinationDate + (1|Plant), data = jd) summary(model) model=lmer(LogPollenCount~ProportionLobesTouched + PollinationDate + (1|Plant), data = jd) summary(model)