For the UHURU Pollination Networks Calculated Using R Package Bipartite with Statistics from R Package Lme4

LARGE MAMMALS, RAINFALL VARIATION, AND THE STRUCTURE OF PLANT- POLLINATOR NETWORKS IN AN AFRICAN SAVANNA

TRAVIS JAMES GUY

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2017

To my loving and hilarious family. To my mom for her unending love, patience, positivity, and encouragement. You have shaped me into the person I am today and who, without, I would have accomplished a mere fraction of what I have done. I owe so much to you. To my dad for showing me the world's beauty and igniting my adventurous spirit. To my brother for being my best friend, epic adventure partner, and showing me a world of possibilities. And to anyone who has gone out of their way to make this wonderful world a better place.

ACKNOWLEDGMENTS

First and foremost I thank my friend, mentor, and advisor Dr. Todd Palmer. He stood by me through the good and the bad times, never wavering in his support and encouragement and having immeasurable patience with me. I owe a great deal of my success to Todd. He instilled a passion for field ecology in me 12 years ago at a small field camp in Kenya and has continued to shape me into the ecologist I have become today. Without his brilliance this project would have never been conceived and the finished product would have paled in comparison to what it is now. Todd’s knowledge of

African savanna ecology, scientific processes, psychology, bourbon, and especially life is unsurpassed, and I am grateful he imparted even a fraction of this wisdom on me. In short, I would not have written this thesis nor have had such an amazing and successful graduate career without him.

I am also indebted to Dr. Katherine Baldock for introducing me to the world of pollination webs, bipartite, and network analysis. She was instrumental in the design and methodology of my project and without her guidance, I would still be lost trying to mathematically describe pollination networks. Her patience and persistence in answering question after question being passed across the Atlantic, often through broken skype chat, is beyond admirable. I cannot thank her enough for her help in initial pollinator identification and for sharing with and introducing me to her network of insect taxonomists. She was a most gracious host during my time at the University of Bristol where my network analysis took shape, and she makes a mean cup of British tea.

Simply put, this research would not have occurred without her.

I would also like to thank Dr. Juan Carlos Ruiz Guajardo for jump-starting my project while in Kenya. I am thankful for the knowledge of local flora he imparted on me

and for tidying up my on-the-ground field methods. Similarly, I thank Dr. Gavin

Ballantyne for assistance in flower and pollinator identification and helpful discussion on pollination network structure and metrics.

I am beyond grateful to my Kenyan field assistants Zachary Ntanyaki, Julius

Olengingiro, and Peter Ekai. They worked tirelessly day after long day in the hot sun, always with a smile on their faces, and they saved me a time or two when elephants snuck up on me. Julius’ humor was calming and kept me grounded during the stressful times. I learned a great deal about the natural history of the area from Zachary, and boy could he wield a butterfly net! Peter was the pinning whiz, and he could work magic with forceps, a pin, and a micro pollinator. Without their help, I would still be watching plants and pinning insect. I would like to give a special thanks to all the staff at Mpala

Research Centre. Who knew a field station in the middle of the bush could be so posh?

Identifying 2,242 African insects was a daunting task. I am forever grateful to

Laban Njoroge of the National Museum of Kenya for his assistance in helping me identify all of my specimens to family, for identifying all my coleopteran specimens to species, and for helping me wade through the permits and paperwork to export my insect specimens to the United States. I am also grateful to Dr. Gary Steck and Charles

Whitehill of the Florida Department of Agriculture and Consumer Services for their invaluable advice on safely shipping fragile insect specimens around the world. I would like to extend a huge thanks to all of the insect taxonomists around the globe who worked diligently to identify poorly known African insects to species level. Being able to construct networks using such high-resolution data has helped to make this project world-class. I thank Mary Gikungu (Apoidea), Jane Macharia (Apoidea), the late Joseph

Mugambi (Lepidoptera), Alain Pauly (Halictidae), Connal Eardley (Apidae and

Megachilidae), James Carpenter (Vespidae), Kevin Williams (Vespidae), Wojciech

Pulawski (Sphecidae), Raymond Wahis (Pompilidae), Paolo Rosa (Chrysididae),

Gerard en Toos Schulten (Scolidae), Donald Quicke (Braconidae), Simon Van Noort

(Gasteruptiidae), Adrian Pont (Muscidae), John Deeming (Muscidae and Calliphoridae),

Pierfilippo Cerretti (Tachinidae), Daniel Whitmore (Sarcophagidae), Andrew Whittington

(Syrphidae), Neal Evenhuis (Bombyliidae), Jason Londt (Asilidae), Torsten Dikow

(Asilidae), Gary Steck (Tephritidae), Andy Warren (Lepidoptera), James Hayden

(Lepidoptera), Susan Halbert (Hemiptera), and Dino Martins (Formicidae) for their hard work identifying my insects.

I would like to thank the other members of the Palmer lab—Patrick Milligan, Dr.

Kirsten Prior, and Jessica Gunson—for their helpful discussions and logistic help both in

Kenya and Florida. I am also greatly appreciative of Dr. Jacob Goheen, Dr. Rob Pringle, and Todd for letting me use their UHURU experiment to collect my data. I greatly appreciate the feedback and helpful suggestion provided to be by my committee members Dr. Colette St. Mary and Dr. Benjamin Baiser. I was funded by a Sigma Xi

Grant-in-Aid of Research, A Tropical Conservation and Development Field Research

Grant, and the Brian Riewald Memorial Fund Research Grant and supported by the

National Science Foundation Graduate Research Fellowship Program (Grant No. DGE-

1315138). I thank the Kenyan government for allowing me to conduct research in their beautiful country (Permit No. NACOSTI/P/14/8685/1657).

I would like to extend a special thank you to 8th grade science teacher Dave

Adams for opening my mind to the awe-inspiring nature of science and Dr. Chris Walser

for introducing me to the joys (and struggles) of field ecology. Lucille Ramsey funded my undergraduate trip to Kenya where this journey all began. I will never forget that opportunity she provided for me.

None of this work would have been possible without the support and love of my parents, Judy and Reg. They are the reason I am the person I am today. No matter the circumstance, they were always there for me. My mother has an uncanny ability to lift me out of the lows and put a smile back on my face. She kept me going every time I wanted to quit. I am forever indebted to her for all of her sacrifices and for everything she has given me. Do not underestimate how important in graduate school (or in life) it is to have a brother who is quick to say “Don’t worry, you will figure out R coding. Let’s go skiing!”

Words cannot begin to express the gratitude I have for my girlfriend Elisha

Kayser. She has been there for me during this entire wild ride. My project and life are certainly better because of her. She was a tremendous field assistant in Kenya, often finding solutions to field problems when I could not. She kept the project on target with her amazing logistical support, not to mention she’s an excellent insect pinner and packer. She put up with me being thousands of miles away and at times moved from her beloved mountains to Florida, in essence putting her life on hold so that I could pursue my dreams. I would not have made it through without her emotional support.

She was always willing to drop everything she was doing at any moment to help me out.

Graduate school was a great challenge, but it was certainly made easier because of

Elisha.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 10

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 13

CHAPTER

1 INTRODUCTION ...... 15

2 METHODS ...... 21

Study Site ...... 21 Study Design ...... 21 Sampling Methods ...... 22 Network Metrics ...... 24 Data Analysis ...... 27

3 RESULTS ...... 31

Rainfall ...... 31 Abundance and Richness ...... 31 Network Metrics ...... 32 Rainfall Gradient’s Effect on Herbivory ...... 34 Beta Diversity ...... 35 Interaction Diversity ...... 35

4 DISCUSSION ...... 49

Study Limitations ...... 60 Future Directions ...... 61 Conclusion ...... 63

LIST OF REFERENCES ...... 66

BIOGRAPHICAL SKETCH ...... 86

LIST OF TABLES

Table page

3-1 Pollinator and flower richness and abundance (averages) in the UHURU study plots. Averages with ± s.e.m. are pooled separately by treatment and location...... 46

3-2 Network metric values (averages) for the UHURU pollination networks calculated using R package bipartite with statistics from R package lme4...... 47

3-3 Factor loadings from the principal components analysis of network metrics. Loadings greater than 1/3 (starred) were deemed important ...... 48

LIST OF FIGURES

Figure page

2-1 The UHURU experiment is located on the Mpala nature conservancy in central Kenya, East Africa...... 29

2-2 The UHURU experiment is a replicated experiment consisting of 3 blocks at each of 3 sites set up along a strong rainfall gradient...... 30

3-1 Average floral richness...... 37

3-2 The 6 pollination networks—three total LMH exclusion and three open plots—constructed in the North UHURU plots...... 38

3-3 The 6 pollination networks—three total exclusion and three open plots— constructed in the South UHURU plots...... 39

3-4 Replicate pollination webs combined into metawebs highlight the differences between the treatments and locations...... 40

3-5 Principal components analysis of network metrics...... 41

3-6 Partition of plant A.) and pollinator B.) overall beta diversity (βSOR) into turnover (βSIM) and nestedness (βSNE) components using R package betapart...... 42

3-7 Dispersion NMDS plots of plant overall beta diversity (βSOR), plant turnover component of beta diversity (βSIM), pollinator overall beta diversity (βSOR), and pollinator turnover component of beta diversity (βSIM)...... 43

3-8 Boxplots of whole network interaction dissimilarity (βWN) and overlapping interaction dissimilarity (βOS) values for all pairwise network comparisons...... 44

3-9 Betalink webs showing species and interactions...... 45

LIST OF ABBREVIATIONS

ΒOS Overlapping species dissimilarity

βS Species dissimilarity

βSIM Simpson dissimilarity

βSNE Nested fraction of Sorensen dissimilarity

βSOR Sorensen dissimilarity

βST Species turnover dissimilarity

βW Beta diversity measure (suggested by Whittaker)

βWN Whole network dissimilarity

Blk Experimental block

CTL Experimental control treatment that is open to all herbivores e.g. Exempli gratia et al. Et alia glmer Generalized liner mixed-effect model.

H2’ An index describing the level of “complimentary specialization”. (network metric)

HL Higher level, which indicates the higher level species in a bipartite network. For pollination networks, the higher level species are the pollinators.

ID Shannon interaction diversity (network metric) i.e. Id est

IE Interaction evenness (network metrick)

ISA Interaction strength asymmetry (network metric) km Kilometer

LL Lower level, which indicates the lower level species in a bipartite network. For pollination networks, the lower level species are the flowers.

lme4 Linear mixed-effect model statistics package lmer Linear mixed-effect model

LMH Large mammalian herbivores and experimental exclusion treatment that excludes all large mammalian herbivores

MEGA Exclusion treatment that allows all herbivores into the plots except for elephants (Loxodonta Africana) and giraffes (Giraffa camelopardalis)

MESO Exclusion treatment that restrict herbivores greater than 120cm in height from entering the plot m Meter mm Millimeter

N North

NACOSTI National Commission for Science, Technology and Innovation

NMDS Non-metric multidimensional scaling

NMK National Museums of Kenya

No. Number

NOAA National Oceanic and Atmospheric Administration

PC Principal Component

PCA Principal Components Analysis

Q Modularity (network metric)

S South s.e.m Standard error of the mean

UHURU Ungulate herbivory under rainfall uncertainty experiment, which is Kiswahili for freedom. yr Year

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

LARGE MAMMALS, RAINFALL VARIATION, AND THE STRUCTURE OF PLANT- POLLINATOR NETWORKS IN AN AFRICAN SAVANNA

Travis James Guy

August 2017

Chair: Todd Palmer Major: Zoology

The earth’s biodiversity is under increasing pressure from anthropogenic change.

Pollination mutualisms underlie much of the evolutionary and ecological success of flowering plants, including agricultural species, with more than 75% of angiosperms relying on animal pollinators. There is growing evidence that these interactions may be particularly susceptible to anthropogenic change; thus, understanding and predicting how pollination mutualisms will respond to perturbations such as wildlife loss and climate variability is an increasingly urgent priority. To evaluate how global change influences pollination at the community level, there is a critical need for manipulative experiments testing how anthropogenic drivers influence the structure of entire pollinator networks. Leveraging a long-term large-mammal exclosure experiment arrayed along a rainfall gradient in central Kenya, I tested the direct and interactive effects of the simulated extinction of large mammalian herbivores and rainfall variation on the structure of pollination networks. I constructed plant-pollinator interaction networks in plots excluding large herbivores and in control plots at high (639 mm/yr) and low (439 mm/yr) rainfall sites and then calculated (1) flower and insect richness and abundance, (2) structural network metrics, and (3) species and interaction dissimilarity.

Large herbivore removal resulted in an increase in richness and abundance of both flowers and pollinators. Network metrics indicate pollination communities were more stable and robust within herbivore exclosures than in plots where large herbivores had access. Rainfall level did not significantly influence insect or flower diversity nor network structure. Plot to plot comparisons, within and across treatments and sites, showed dissimilar species composition and interactions with considerable re-wiring of interactions among shared species. By modifying floral diversity, large mammalian herbivores of East Africa indirectly influence pollination network structure, and consequently, the loss of these large herbivores tends to lead to increased stability and robustness of the pollination networks. Large herbivore effects are consistent across replicates and even across sites that differ in annual rainfall and remain strong despite high turnover in species composition and substantial re-wiring of interactions across all treatments and sites. This study highlights the importance of indirect interactions in structuring pollination webs.

CHAPTER 1 INTRODUCTION

Human modification of the environment is the leading driver of global biodiversity decline (Dirzo and Raven 2003, Thomas et al. 2004, Butchart et al. 2010, Pereira et al.

2010, Barnosky et al. 2011, Pimm et al. 2014), and biodiversity decline can reduce ecosystem and community function (Tilman et al. 1996, Loreau et al. 2001, Cardinale et al. 2002, Hooper et al. 2005, Cardinale et al. 2012). Pollination is a vital terrestrial ecosystem function as pollinators perform ecosystem services necessary for the maintenance of both wild plant communities (Ashman et al. 2004, Aguilar et al. 2006) and agricultural production (Klein et al. 2007, Ricketts et al. 2008); seventy-five to eighty percent of flowering plant species rely on animal pollinators (Klein et al. 2007, Ollerton et al. 2011), including over 90% of tropical angiosperms (Bawa 1990), and insects pollinate 75% of all human food crops that are used across the globe (Klein et al. 2007).

Over the past decade, there has been growing concern about the decline of both wild and domestic pollinators (Potts et al. 2010), highlighting the fact that plant-pollinator networks are particularly susceptible to human-induced changes (Aguilar et al. 2006,

Traveset and Richardson 2006, Dixon 2009).

While considerable attention has been given to the decline of individual pollinator taxa (Biesmeijer et al. 2006, Goulson et al. 2008, van Swaay et al. 2008,

Vanengelsdorp et al. 2008, Potts et al. 2010, Forister et al. 2011, Martins et al. 2013,

Xie et al. 2013), determining how anthropogenic drivers are influencing the interaction structure of entire pollinator communities is crucially important, since these interactions are key determinants of ecosystem function and stability (McCann 2007, Forup et al.

2008, Burkle and Alarcon 2011) and because community function is determined by

more than just the composition of the network components (Laliberte and Tylianakis

2010, Tylianakis et al. 2010). To evaluate the structure, stability, and interactions of diverse groups of plants and their pollinators in a community context, ecologists have adopted tools from network theory to characterize properties of these mutualist networks (Jordano 1987, Bascompte and Jordano 2007). Network analysis has revealed a number of generalities shared by many mutualistic webs, including that they are typically heterogeneous (Jordano et al. 2003), nested (Bascompte et al. 2003), and composed of weak and asymmetric links (Jordano 1987, Bascompte et al. 2006).

Further, network approaches have determined that nestedness (Memmott et al. 2004,

Okuyama and Holland 2008, Bastolla et al. 2009, Thebault and Fontaine 2010,

Tylianakis et al. 2010), generality (Blüthgen et al. 2007, Allesina and Tang 2012, Kaiser-

Bunbury and Bluthgen 2015), a diversity of interactions (Kaiser-Bunbury and Bluthgen

2015), and connectance (Fortuna and Bascompte 2006, Thebault and Fontaine 2010) are structural properties that confer stability and robustness to mutualist networks.

These insights about the factors that stabilize mutualist webs can be used both as an applied tool to tackle questions in community ecology (Memmott 2009), and to guide conservation management (Tylianakis et al. 2010, Kaiser-Bunbury and Bluthgen 2015).

While network approaches can provide broad insights about the relationships between the structural properties of interaction webs and their stability, they can also obscure important ecological details about how specific pairwise interactions may change across space and time. For example, two neighboring plots may have the same species composition, nestedness, connectance, and generality, while the pairs of species that interact within each network differ strongly. Within plant-pollinator

communities, interactions can fluctuate greatly across space and time (Alarcon et al.

2008, Olesen et al. 2008, Dupont et al. 2009, Burkle and Alarcon 2011, Olesen et al.

2011, Carstensen et al. 2014), with pollinators switching plant species in response to available nectar resources, species availability, flowering duration, and synchrony with other plant species as floral blooms change throughout the day and season (Petanidou et al. 2008, Kaiser-Bunbury et al. 2010, Lazaro et al. 2010). Plant-pollinator interaction variability can also be caused by species turnover as network members leave or join the network over space and time (Dupont et al. 2009, Burkle and Alarcon 2011, Olesen et al. 2011). These details about “who interacts with whom” can be evaluated at the network level with a recently developed set of tools (Poisot et al. 2012). Despite such variability in interactions and species composition, however, overall network structure and stability appear to remain fairly constant across space and time (Alarcon et al.

2008, Dupont et al. 2009, Burkle and Alarcon 2011, Olesen et al. 2011).

Because of the challenges of conducting large scale experiments, most studies examining how anthropogenic change influences pollination network interactions have taken a comparative approach, contrasting network structure across landscapes that differ in land-use regime (Weiner et al. 2014), habitat degradation (Evans et al. 2013,

Spiesman and Inouye 2013, Moreira et al. 2015), or have used areas that have been invaded (Traveset and Richardson 2006, Vila et al. 2009, Traveset et al. 2013, Albrecht et al. 2014); or used a modeling approach in the case of climate change (Memmott et al.

2007). Yet to draw robust causal inferences, there is a critical need for manipulative experiments at the level of entire pollinator networks (Kaiser-Bunbury et al. 2010, Burkle and Alarcon 2011). Recently, several experimental studies conducted at the landscape

scale have focused on the impact of invasive plant species, demonstrating that the addition of novel species can have profound impacts on the structure and function of plant-pollinator networks (Lopezaraiza-Mikel et al. 2007, Russo et al. 2014, Kaiser-

Bunbury et al. 2017). While the addition of novel species to communities is a potent driver of ecological change, the loss of species from their native ranges is similarly a global and accelerating problem (Ceballos et al. 2015). Yet to date, no studies have experimentally examined how the loss of species outside of the network influences plant-pollinator networks.

Worldwide declines in mammalian herbivore populations may have particularly profound effects on plant-pollinator communities, since these herbivores can strongly influence the composition, richness, physical structure, life history traits, and successional patterns of plant communities (Knapp et al. 1999, Bakker et al. 2006,

Beguin et al. 2011), as well as plant floral displays, flower characteristics, and pollen quantity and quality (Herrera et al. 2002). The direction of these effects can vary across space and time (Vesk and Westoby 2001), with herbivores increasing plant richness and diversity in some places (McNaughton 1985, Milchunas et al. 1988, Huntly 1991,

Belsky 1992, Hobbs and Huenneke 1992, Collins et al. 1998, Olff and Ritchie 1998), while decreasing plant richness and diversity in others (Milchunas et al. 1988, Hobbs and Huenneke 1992, Olff and Ritchie 1998, Proulx and Mazumder 1998).

East Africa is currently experiencing rapid mammalian herbivore decline with corresponding high extinction risk (Ottichilo et al. 2000, Ceballos and Ehrlich 2002,

Stoner et al. 2007, Western et al. 2009, Ripple et al. 2015, Ripple et al. 2016), and the loss of these herbivores can have potent and widespread effects on African savannas.

For example, herbivore loss has resulted in strong changes in communities of plants

(Augustine and McNaughton 1998, Anderson et al. 2007), rodents (Keesing 1998), reptiles (McCauley et al. 2006), and insects (Pringle et al. 2007), and driven changes in plant-ant interactions (Palmer et al. 2008), nutrient dynamics (Augustine 2003), and ecosystem stability (Goheen and Palmer 2010). Collectively, these studies indicate the loss of herbivores can cause changes in plant community richness and abundance, which has the potential to exert strong indirect effects on pollinator diversity and behavior.

In this study, I used a large-scale herbivore exclusion experiment (The “UHURU” experiment, Goheen et al. 2013) to evaluate how the loss of large mammalian herbivores (LMH) influences plant-pollinator networks in an East African bushland community. This experiment is replicated across a rainfall gradient, allowing for an assessment of whether the effects of LMH on plant-pollinator networks depend on precipitation level, a factor which is known to affect both the richness and productivity of plant communities in semi-arid ecosystems (Sala et al. 1988, Pausas and Austin 2001,

Cornwell and Grubb 2003, Zavaleta et al. 2003, Huxman et al. 2004, Adler et al. 2005,

Adler and Levine 2007, Bai et al. 2008, Wu et al. 2011). Specifically, I compared pollination networks in plots where all LMH were excluded with plots allowing the full complement of herbivores at two rainfall levels, to determine the effects of LMH loss on pollination networks and to evaluate the dependence of these effects on rainfall level.

Specifically I asked, (1) How do LMH loss and rainfall level act and interact to affect the richness and/or abundance of plants and pollinators within these communities? (2)

Does herbivore exclusion, rainfall, or their interaction change pollination network

structure? (3) Does any change in structure resulting from LMH loss, rainfall level, or their interaction influence the resilience and robustness of networks to member extinction?

I also calculated pairwise beta diversity to evaluate the extent of spatial turnover in plant and pollinator species between plots, and to assess whether such turnover corresponded with any changes in network structure between plots. Similarly, I calculated interaction beta diversity to assess the extent of interaction turnover and re- wiring between plots, and to establish whether these features corresponded with any changes in network structure between plots.

CHAPTER 2 METHODS

Study Site

Network sampling was conducted at Mpala Research Centre, which is situated on a private conservancy in Laikipia County located in central Kenya (0°17’N, 37°52’E) at an elevation of about 1600m (Figure 2-1). Mpala sits in the rain shadow of Africa’s second highest mountain, Mount Kenya (5200m), which causes dramatic climatic variability over small spatial scales. Mpala is home to nearly the entire suite of East

African bushland/savanna wildlife. Data for visitation networks were collected between

30 May 2014 and 3 July 2014 and timed to overlap with the end of the long rains and the beginning of the dry season in an attempt to capture the seasonal variation of the plant-pollinator community in the area (Baldock et al. 2011).

Study Design

My study utilized the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment established in September 2008 (Goheen et al. 2013) (Figure 2-2). The

UHURU experiment consists of herbivore exclosures established in three locations along a 20 km long rainfall gradient ranging from 439 mm/yr in the northern low rainfall area to 639 mm/yr in the southern high rainfall area. Each of the three locations along the gradient has three blocks each consisting of randomly assigned 100m x 100m replicates of four herbivory treatments: “total” exclusion, “meso” exclusion, “mega” exclusion, and control. In total exclusion, all herbivores larger than about 5kg in mass and 50cm tall are excluded from the plot using fences with electrified wire. Meso exclusion treatments restrict herbivores greater than 120cm in height from entering the plot. This allows warthogs (Phacochoerus africanus) and dik-dik (Madoqua cavendishi)

access to the plots. Mega exclusion allows all herbivores into the plots except for elephants (Loxodonta Africana) and giraffes (Giraffa camelopardalis). The control plots are unfenced and allow complete access to all herbivores including those mentioned above, impala (Aepyceros melampus), buffalo (Syncerus caffer), and domestic cattle

(Goheen et al. 2013). For the purpose of this study, I constructed visitation networks in the “total” exclusion and control treatments in the low and high rainfall sites in order to test the effects that simulated extinction and climate variability have on visitation network characteristics.

Sampling Methods

To construct visitation networks, I followed methods similar to Baldock, et al.

(2011). I did not construct actual pollination networks per se, but I instead constructed visitation networks and used visitation as a proxy for pollination. Although not all insect visitors to flowers are efficient pollinators, it has been shown that visitation rate is correlated with fruit set (Vazquez et al. 2005, Garibaldi et al. 2013); therefore, visitation can be treated as an acceptable stand-in for pollination.

I conducted a floral abundance survey in each treatment plot one day prior to collecting visitation data. I surveyed a 50m x 50m subsection in the center of each 100m x 100m plot. Each 50m x 50m subsection was further divided up into 10 subplots measuring 10m x 10m to ensure that no flower was missed. In the morning I counted the total number of floral units (defined as an individual flower or composite inflorescence in the case of composite flowers) in each 2500m2 plot for each flowering plant species and recorded which subplot they occupied. Floral abundance was expressed as the total number of open floral units in each 2500m2 plot. In this study, flower species richness indicates the number of plant species with open flowers in the

plot. This differs from plant species richness, which would include plants not in flower.

Plants were identified to species using keys and descriptions in Blundell (1987) and pressed specimens provided by the UHURU project or identified to genus and morphospecies. In the afternoon, I resurveyed the area to account for any afternoon bloomers that would have been missed on a morning only survey. I constructed a total of 12 networks—3 in South control plots, 3 in North control plots, 3 in South total exclusion plots, and 3 in North total exclusion plots. I alternated sampling between

South and North plots, sampling the control plot and the total exclusion plot of one block in the South plots before proceeding to the North plots. The order of blocks to be sampled in both locations was randomly assigned.

The day after the floral survey, I sampled insect visitors at every flower species that had 10 or more floral units in the 50m x 50m subsection of each plot during the previous day’s floral survey. Each flower was observed for a total of 90 minutes—30 minutes in each of 3 time periods (0800-1030; 1030-1300; 1330-1600)—which corresponded to the time periods that Baldock et al. (2011) found to contain the majority of unique links. If a flower was not open in a given time period, it was not given additional time in another time period. When more species of flowers were blooming than was possible to watch in 1 day, the 2 subsequent days following the floral survey were used and flowers were randomly assigned a day to be observed. In the 2 instances of unfavorable weather (cloudy and windy), I postponed the flower observations by 1 day.

In the case where a species had multiple locations with at least 10 floral units within a watchable area (~1 m2), the flowers to be watched were randomly chosen for

each time period. For species with less than 10 floral units in a 1 m2 area, the location to be watched was chosen based on the watchable area with highest concentration of floral units (e.g. an area with 7 open flowers given preference over an area with 3 open flowers).

During the 30 minute flower observations, the observer watched a recorded number of floral units from a distance of ~1m. An attempt was made to capture every insect that touched the reproductive parts of the flower. I had an 87% catch success rate. Any escaped insect was recorded to the lowest taxonomic level possible by naked eye. Captured insects were euthanized in kill jars using ethyl acetate, pinned, dried, and then identified to species or genus and morphospecies by 30 taxonomic experts across the globe familiar with African insects. I initially identified specimens to family and in some instances genus using collections at the National Museums of Kenya. The pollinator abundance of individual species was determined by the total number of visits by each species to flowers in the network and total pollinator abundance is simply the sum of the abundances of all pollinator species in the plot.

The limited sample size is justified by the difficulty in the simultaneous construction of several whole networks where insect reference collections do not exist.

Although increased sample time would surely lead to an increase in the number of visits observed, the structure of plant-pollinator networks has been shown to be robust to sample size across space and time (Nielsen and Bascompte 2007, Petanidou et al.

2008).

Network Metrics

In order to address my questions, I chose 11 frequently used network metrics to characterize the webs that I constructed in the UHURU plots. Metrics denoted by * and ‡

were suggested by Tylianakis et al. (2010) and Kaiser-Bunbury & Blüthgen (2015), respectively, as metrics that help to quantify structural properties that are important for the conservation of ecological networks. Note that weighted metrics incorporate the inflow and outflow of individuals per species; therefore, they are more robust to sampling differences than unweighted metrics and are less sensitive to detecting differences, which make them more conservative in the comparison between plots

(Bersier et al. 2002, Banasek-Richter et al. 2004, Tylianakis et al. 2007). The 11 metrics are described below:

1. Nestedness (N)*: A network is nested if specialist species (species with few partners) interact with a subgroup of species which in turn interact with a core of highly connected species (generalists) (Bascompte et al. 2003). The R package Bipartite calculates nestedness based on matrix temperature using the binmatnest program (Rodriguez-Girones and Santamaria 2006) where 0 means high nestedness (cold) and 100 means chaos (hot) (Dormann et al. 2009).

2. Interaction Strength Asymmetry, weighted (ISA)*: Describes network dependence asymmetry by quantifying the interaction strength imbalance between a species pair and is another way to measure specialization across both trophic levels. Higher dependence means more specialization (Bascompte et al. 2006, Dormann et al. 2009). In our case, it’s a measure of the difference in the dependence of pollinators on plants vs. the dependence of plants on pollinators. Positive outputs indicate that the species in the higher trophic level (in our case, pollinators) have a higher dependence/specialization (Dormann et al. 2009).

3. Weighted Connectance (C)*: Unweighted (qualitative) connectance is the proportion of links that actually occurs out of all the possible links. C=L/(I*J) (Dunne et al. 2002). Weighted connectance takes into account the diversity of the individuals comprising the visitors and the visited. Quantitative weighted web connectance is calculated as linkage density divided by the number of species in the web and is more robust to sampling differences (Tylianakis et al. 2007).

4. Shannon Diversity (ID)‡*: Weighted measure of interaction diversity (Blüthgen et al. 2008, Dormann et al. 2008). It has 2 components: richness (number) of links and interaction evenness (homogeneity of relative interaction frequencies across links) (Kaiser-Bunbury and Bluthgen 2015).

5. Interaction Evenness (IE)‡: Based on Shannon diversity, a weighted measure of the uniformity of interaction frequencies across all links in a web. Higher values,

up to 1, indicate a uniform distribution of interactions while 0 indicates an uneven network (high skewness in the distribution of link frequencies). It is inversely related to network stability, and its qualitative equivalent is connectance (Bersier et al. 2002, Tylianakis et al. 2007, Blüthgen et al. 2008, Rooney and McCann 2012, Tur et al. 2014, Kaiser-Bunbury and Bluthgen 2015).

‡ 6. Network Specialization Index (H2’) : A measure of specialization at the network level. Values range in a specialization-generalization continuum from 0 to 1 with a value of 0 indicating no specialization (extreme generalization) and 1 indicating perfect specialization (Blüthgen et al. 2006a, Dormann et al. 2009, Kaiser- Bunbury and Bluthgen 2015). High network specialization denotes a high reliance of each species on a few exclusive partners. Low specialization denotes higher functional redundancy in the web (Blüthgen et al. 2007). H2’ is unaffected by sampling size and species frequency as it characterizes the distribution of interactions relative to each other, and it is independent of variation in total species diversity and abundance; therefore, H2’ is an ideal metric to compare specialization across different networks (Kaiser-Bunbury and Bluthgen 2015).

7. Niche Overlap: A weighted measure of the similarity in interaction patterns between species of the same trophic level. Values range from 0—indicating no niche overlap—to 1—indicative of perfect niche overlap (Dormann et al. 2009). HL denotes higher level trophic species (pollinators in this study) and LL denotes lower level trophic species (plants). Higher niche overlap indicates more network generality while lower niche overlap indicates increased specialization.

8. Robustness (R): A measure of the resilience of the network to the removal (extinction) of network members (Rivera-Hutinel et al. 2012). It’s calculated by measuring the area under the secondary extinction curve when species are sequentially removed from the web (Dormann et al. 2008). For my analysis, each sequentially removed species was randomly chosen. First proposed by Memmott (2004) and improved on by Burgos et al. (2007), the idea is that the removal of a certain amount of species from one level will influence the survivability of a certain fraction of species in another trophic level that depend on those species that were initially removed. R ranges from 0 to 1. In cases where the decay curve is concave (R<0.5), the removal of just a small fraction of species results in the secondary extinction of many species. However, in cases where the decay curve is convex (R>0.5), the network is rather robust as the removal of a large fraction of species from one trophic level causes a relatively small secondary extinction of species in other trophic levels. Robustness.HL is a measure of the resilience of the system to the removal of higher level trophic species (pollinators, in this study); whereas, Robustness.LL is a measure of the resilience of the system to the removal of lower level trophic species (plants).

9. Generality‡: Effective (weighted) mean number of lower level (plant) species per higher level (pollinator) species of a network (Bersier et al. 2002, Dormann et al.

2009). Higher values indicate that pollinator species visit a more varied array of plant species, and thus are more general (Alarcon 2010).

10. Vulnerability‡: The lower level analog to generality. Weighted mean number of higher level (pollinator) species per lower level (plant) species of a network (Bersier et al. 2002, Dormann et al. 2009). Higher values indicate that plant species are visited by a more varied array of pollinator species, and thus are more general (Alarcon 2010).

11. Modularity (Q)‡*: A measure of the extent to which a network is organized into modules. Modules are assemblages of strongly interacting species that interact weakly with species in other modules (Olesen et al. 2007). Q ranges from 0 to 1 with a value of 0 indicating that the number of connections within a module is no more than expected by chance. The closer the value is to 1, the more support is given by the data that the network is divided into modules (Dormann and Strauss 2014). Data Analysis

I constructed the 12 networks using the bipartite package (Dormann et al. 2008) in R (version 3.1.2). I also used bipartite to calculate each of the 11 network metrics derived from each of the 12 networks (Dormann et al. 2009). To determine whether exclusion plots differed from control plots and whether wet (South) plots differed from dry (North) plots, I constructed linear mixed models with random intercepts for each metric using R package lme4 (Bates et al. 2015). I also ran linear mixed models with both random slopes and intercepts, but they produced the same results as the models with only random intercepts; therefore, all reported results are from models with only random intercepts. In my models, treatment (exclusion/control) and location (wet/dry) were fixed effects while blocks were a random effect. To determine whether exclusion and/or rainfall had an effect on a given metric, I used a likelihood ratio test to compare the fitted model to a null model without the fixed effect in question.

I checked each model to see that it fit model assumptions. In one case, nestedness, I log transformed the data to satisfy assumptions. To satisfy assumptions, I used a generalized linear mixed model for three additional metrics—niche overlap LL

(Gaussian link = log), generality HL (gamma link = inverse), and vulnerability LL

(gamma link =identity). According to Warton and Hui (2011) metrics expressed in proportions should be logit transformed if the untransformed data do not meet assumptions. One of my proportional metrics, niche overlap HL, did not initially meet model assumptions; therefore, I logit transformed it using the qlogis function in R.

Richness and abundance measures were over dispersed; therefore, I fit them to a negative binomial distribution in a generalized linear mixed model.

To determine the primary metrics describing the variation in structure between plots, I used the principal components analysis (PCA) ordination technique. The first three principal component axes explained 86.3% of the variation: PC1 47.4%, PC2

27.1%, and PC3 11.8%. For each metric, I deemed factor loadings greater than 1/3 to be important. As with the individual metrics, I constructed linear mixed models with random intercepts for each of the 3 principal components using R package lme4 to determine if there were any treatment and location effects.

I wanted to investigate how dissimilar my plots were in both species composition and interaction diversity. I used the betapart package for R to calculate pair-wise dissimilarities (distance matrices) and determine overall beta diversity (measured as

Sorensen dissimilarity, βSOR) and to determine the turnover (measured as Simpson dissimilarity, βSIM) and nestedness (measured as nestedness-resultant fraction of

Sorensen dissimilarity, βSNE) components of beta diversity (Baselga and Orme 2012).

Using the dissimilarity distance matrices from betapart, I assessed species composition differences between treatments and locations via the non-metric multidimensional scaling (NMDS) method in the VEGAN package for R (Dixon 2003). To test for

significant dispersion (calculated using the function betadisper) and mean differences in species composition between treatments and locations, I ran permutation and

PERMANOVA tests, respectively, using the VEGAN package.

I used the betalink package for R (Poisot et al. 2012) to calculate dissimilarity in species composition (βS = species dissimilarity), whole network dissimilarity of interactions (βWN=whole network dissimilarity), dissimilarity of interactions due to species turnover (βST=species turnover dissimilarity), and dissimilarity of interactions established between species common to both compared plots (βOS=overlapping species dissimilarity) using a beta diversity measure (βW) employed widely in ecology (Whittaker

1960).

Figure 2-1. The UHURU experiment is located on the Mpala nature conservancy in central Kenya, East Africa.

Figure 2-2. The UHURU experiment is a replicated experiment consisting of 3 blocks at each of 3 sites set up along a strong rainfall gradient. Each block has 4 treatments: Total exclusion, mesoherbivore exclusion, megaherbivore exclusion, and open (control). We constructed 6 networks in control and 6 networks in total exclusion plots (highlighted by green shading in the inset) at the high and low rainfall sites.

CHAPTER 3 RESULTS

Rainfall

From 2009-2015 UHURU North and South plots averaged 493.5 and 625.2 mm of rain per year, respectively (Kartzinel et al. 2014, Goheen 2017). In 2014, the year of this study, the North plots fell just shy of their average with a total rainfall of 429.4 mm.

The South plots, however, were well below their average with a total rainfall of 444.4 mm, a deviation from the expected pattern normally caused by the rain shadow of

Mount Kenya. In the 5 months (January-May) preceding my study, the North received a total of 83.8 mm of rain and the South received 123.8 mm. This is half as much rain as the average for January-May from 2009-2013: 210.53 mm average for the North and

236.1 mm average for the South (Kartzinel et al. 2014, Goheen 2017). I timed my study to immediately follow the peak rainfall months of April and May; however, 2014 was unusually dry during those months. Average April and May rainfall from 2009-2015 in the North plots was 73.3 and 47.3 mm and in the South plots was 61.1 and 93.5 mm

(Kartzinel et al. 2014, Goheen 2017). Rainfall in April and May preceding my study was

6.1 and 32.9 mm in the North plots and 11.3 and 60.1 mm in the South plots. Rainfall in

June, my most intensive month of sampling, followed the opposite pattern (35.3 mm S vs. 37.9 mm N). There was no rain in July until after I had finished sampling.

Abundance and Richness

During network construction, I observed 83 different flower species. Large herbivore exclusion plots had significantly higher flower species richness (x̅ =25.83±2.24 s.e.m.) than control plots (x̅ =17.0±3.31, Χ2=3.84, p=0.049) (Figure 3-1, a). There was no difference in the number of flower species between North plots (x̅ =19.17±4.24) and

South plots (x̅ =23.67±1.94, Χ2=1.37, p=0.24). I counted 38,017 total floral units in all plots combined. Large herbivore exclusion plots contained significantly more floral units per plot (x̅ =5,288±2102) than control plots (x̅ = 1,048±381, Χ2=11.90, p=0.0006) (Figure

3-1, b). Exclusion networks had 405% more flowers (31,729 versus 6,288) than control plots. There was no significant difference in floral units for site (Χ2=1.81, p=0.18) although North plots (x̅ =4,672±2307) showed a trend towards greater floral abundance than South plots (x̅ =1,664±369). There was, however, a significant interaction between location and treatment for floral abundance (Χ2=5.70, p=0.02).

I captured 2,242 insects visiting flowers, which were comprised of 303 different species. In total I witnessed 2,509 flower visits resulting in a capture success rate of

89.4%. I found visitor species richness to be significantly higher in large herbivore exclusion plots (x̅ =92.5±12.94) than in control plots (x̅ =33.67±5.19, Χ2=14.22, p=0.0002)

(Figure 3-1, c). There was no difference in visitor species richness between North plots

(x̅ =63.83±19.61) and South plots (x̅ =62.33±12.48, Χ2=0.12, p=0.73). The visitor abundance in large herbivore exclusion plots (x̅ =277.17±53.51) was significantly greater than that in control plots (x̅ =69.17±11.36, Χ2=15.83, p=0.00007) (Figure 3-1,d).

Exclusion networks had 301% more insect visits (1,663 versus 415) than control networks. There was no difference in number of insect visitors between North plots

(x̅ =201±72.83) and South plots (x̅ =145±41.29, Χ2=0.79, p=0.37). Insect and flower abundance averages and mixed-model results are displayed in table 3-1.

Network Metrics

I constructed 12 networks during the study (Figures 3-2 and 3-3): 3 exclusion, low rainfall networks; 3 exclusion, high rainfall networks; 3 control, low rainfall networks; and 3 control, high rainfall networks. A visual inspection of the constructed networks

shows obvious differences between the exclusion and treatment plots but less distinction between the wet and dry plots. The replicates joined into metawebs (i.e., the union of all species and interactions for each treatment and each location) make this observation even more pronounced (Figure 3-4).

Metric averages from the bipartite package and mixed-model results are displayed in Table 3-2. Of the 11 networks I calculated, seven showed a significant difference between large herbivore exclusion plots and open plots with one additional metric displaying marginal significance. Only one metric was found to be significantly different between low rainfall (North) and high rainfall (South) sites.

Exclusion plots were more nested, had a greater diversity of interactions

(measured as Shannon diversity), and were less specialized (as measured by H2’, generality.HL, and vulnerability.LL) than control plots. According to the interaction strength asymmetry metric all plots had asymmetric links (positive values of the ISA metric); however, exclusion plots had less dependency (less specialization) than control plots. Exclusion plot webs were more robust to the loss of pollinators, but not the loss of plants, than were control plots. None of these 7 metrics showed any difference between

North and South plots.

I found no difference in interaction evenness or niche overlap between exclusion plots and control plots or between North and South plots. According to the weighted connectance metric, control plots trended toward higher connectance (p=0.07), but there was no difference in connectance between North and South plots. While there was no difference in modularity between exclusion and control plots, South plots were more modular than North plots.

The principal components analysis is shown in Figure 3-5 and the factor loadings given in Table 3-3. Using 1/3 as the factor loading cutoff, nestedness (-0.41), Shannon diversity of interactions (0.41), generality.HL (0.38), robustness.HL (0.36), H2’ (-0.34), and interaction strength asymmetry (-0.34) were the metrics most strongly associated with PC1. This axis, thus, represents a generalized/specialized and nestedness axis with networks in plots that have higher PC1 values being more general and nested. PC2 was correlated with interaction evenness (-0.50), robustness.LL (-0.47), weighted connectance (-0.40), and vulnerability.LL (-0.31). This axis has no clear interpretation.

PC3 is strongly correlated with modularity (-0.83); thus, networks in plots that have higher PC3 values are less modular.

According to the mixed effect model, exclusion plots had significantly higher values for the PC1 axis than control plots, indicating that exclusion plots are more generalized and nested than control plots (Χ2=9.85, p=0.002). There was no difference in PC1 values for location (Χ2=0.02, p=0.89). PC2 values did not differ for treatment

(Χ2=0.06, p=0.80) or location (Χ2=0.64, p=0.42). PC3 values were higher in North compared to South plots (Χ2=8.0, p=0.005) indicating that North plots are less modular.

There was no difference in PC3 values for treatment (Χ2=0.28, p=0.59).

Rainfall Gradient’s Effect on Herbivory

There was no added positive or negative effect on herbivory caused by the precipitation gradient for any of the network metrics. Additionally there was no added effect on herbivory due to precipitation for insect richness, insect abundance, or flower richness. I did find a significant interaction between treatment and location for the abundance of floral units (Χ2=5.70, p=0.017) indicating that low precipitation strengthens the effect of herbivory as it relates to floral abundance. Exclusion plots in

the low rainfall North averaged 7,355 more open flowers than control plots compared to exclusion plots in the high rainfall South that averaged 1,125 more open flowers than control plots (Figure 3-1, b).

Beta Diversity

Overall beta diversity was high across all plots for both plants (βSOR=0.83) and pollinators (βSOR=0.92). Similarly it was high between all site comparisons (control to control, LMH to LMH, control to LMH, and North to South) for both plants and pollinators

(Figure 3-6). The majority of the beta diversity was due to species turnover as compared to the nested loss of species for both plants and pollinators (Figure 3-6).

NMDS dispersion plots indicate a trend towards a difference in species composition between North and South plots (Figure 3-7). The PERMANOVA test indicates that North plots had a significantly different species composition in both overall beta diversity (βSOR) and the turnover component of beta diversity (βSIM) for both plants

(βSOR: F=2.92, p=0.007; βSIM: F=4.90, p=0.002) and pollinators (βSOR: F=1.83 p=0.003;

βSIM: F=2.88, p=0.001). There was no difference in βSOR or βSIM between control and exclusion plots for either plants (βSOR: F=1.32 p=0.18; βSIM: F=0.94, p=0.52) or pollinators (βSOR: F=1.20 p=0.13; βSIM: F=0.0, p=1.0). Betadisper permutation tests, however, indicate no differences between plot types or sites for overall beta diversity nor for the turnover component for either plants (βSOR: F=0.40, p=0.73; βSIM: F=0.04, p=0.98) or pollinators (βSOR: F=0.58, p=0.64; βSIM: F=0.41 p=0.75).

Interaction Diversity

Pairwise whole network dissimilarity of interactions (βWN) was high between all plots (global pairwise average βWN=0.97), and βWN was high regardless of whether the comparison was between replicates, between treatments, or between locations (Figure

3-8). When species were shared between networks, a majority of the whole network interaction dissimilarity was due to the re-wiring of interactions. Overlapping species dissimilarity (βOS), which explicitly defines the spatial interaction re-wiring as it ignores the part of the network turnover that is caused by species turnover, accounted for the largest portion of βWN (global pairwise average βOS=0.74) indicating that there was a substantial amount of re-wiring occurring and that the dissimilarity in interactions was not simply due to species turnover. This considerable amount of re-wiring occurred regardless of whether the plot comparisons were between replicates of the same type of plot or whether the comparisons were across treatments or locations (Figure 3-8).

Figure 3-9a shows the interactions of two replicate networks and highlights the significant amount of re-wiring occurring, even between two networks in the same location and subject to the same treatment whereas figure 3-9b shows a scenario with low re-wiring relative to the average amount of rewiring occurring between plots in my study.

The results of betapart combined with betalink indicate that there was a high amount of species turnover between all plots; however, for plots that shared species, interactions were very dissimilar and caused, for the most part, by the re-wiring of interactions.

A. B. 12500 30

s c 10000

n 20 d

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C. D.

n 400

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Figure 3-1. Average floral richness A.), floral abundance B.), pollinator richness C.), and pollinator abundance D.) for each treatment at North and South sites in UHURU. Statistical analyses testing for significant differences pooled data across treatment and location; however, data are not pooled here so as to be more informative. Exclusion plots had significantly higher richness and abundance than control plots for both flowers and pollinators. There was no significant difference between locations for any of the diversity measures. Data are mean ± s.e.m.

Figure 3-2. The 6 pollination networks—three total LMH exclusion and three open plots—constructed in the North UHURU plots. Pollinators are in the upper level and indicated by blue bars whereas flowers are on the lower level and indicated by green bars. Bar thickness corresponds to the total number of interactions the given species was involved in.

Figure 3-3. The 6 pollination networks—three total exclusion and three open plots— constructed in the South UHURU plots. Pollinators are in the upper level and indicated by blue bars whereas flowers are on the lower level and indicated by green bars. Bar thickness corresponds to the total number of interactions the given species was involved in.

Figure 3-4. Replicate pollination webs combined into metawebs highlight the differences between the treatments and locations. Pollinators are in the upper level and indicated by blue bars whereas flowers are on the lower level and indicated by green bars. Bar thickness corresponds to the total number of interactions the given species was involved in.

Figure 3-5. Principal components analysis of network metrics. The PC1 axis corresponds to generalization/specialization and nestedness while the PC3 axis strongly corresponds to modularity. See table 3-3 for factor loadings. Key to plots: N = North plot, S = South plot, Ctrl = control (open) plot, LMH = large mammal exclusion plot. Numbers 1 through 3 indicate the replicate (i.e. block) number

A. Plants North North North South South South Controls Exclusions Ctrl:Ctrl LMH:LMH Ctrl:LMH Ctrl:Ctrl LMH:LMH Ctrl:LMH S:N S:N

e 1.00

v 0.75

r 0.50

i 0.25

d 0.00 SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR

B. Pollinators North North North South South South Controls Exclusions Ctrl:Ctrl LMH:LMH Ctrl:LMH Ctrl:Ctrl LMH:LMH Ctrl:LMH S:N S:N

e 1.00

v 0.75

i r 0.50

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d 0.00 SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR SIM SNESOR

Dissimilarity Component SIM.turnover SNE.nestedness SOR.overall

Figure 3-6. Partition of plant A.) and pollinator B.) overall beta diversity (βSOR) into turnover (βSIM) and nestedness (βSNE) components using R package betapart. Analysis indicates that beta diversity is high and that this high dissimilarity is mostly due to species turnover.

Figure 3-7. Dispersion NMDS plots of plant overall beta diversity (βSOR), plant turnover component of beta diversity (βSIM), pollinator overall beta diversity (βSOR), and pollinator turnover component of beta diversity (βSIM) indicating trends in dispersion differences between North and South plots. Key to plots: N = North plot, S = South plot, Ctrl = control (open) plot, LMH = large mammal exclusion plot. Numbers 1 through 3 indicate the replicate (i.e. block) number.

Dissimilarity Component OS WN

North North North South South South Controls Exclusions Ctrl:Ctrl LMH:LMH Ctrl:LMH Ctrl:Ctrl LMH:LMH Ctrl:LMH S:N S:N

1.00

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Figure 3-8. Boxplots of whole network interaction dissimilarity (βWN) and overlapping interaction dissimilarity (βOS) values for all pairwise network comparisons. The boxplots indicate that most of the interaction dissimilarity is the result of βOS, the re-wiring of interactions. * indicates the sample mean.

Figure 3-9. Betalink webs showing species and interactions. A.) Side by side network comparison between 2 replicate plots, South exclusion 1 and South exclusion 3, that illustrates the high level of re- wiring between plots (βWN=0.98, βOS=0.78). Blue circles are members of the South exclusion 1 network; green circles are members of the South exclusion 3 network; and red circles are shared species between the two networks. Blue lines indicate an interaction in South exclusion 1; green lines indicate an interaction in South exclusion 3; and the red line indicates an interaction found in both networks. There are numerous shared species between the plots but only 1 shared interaction indicating a high level of re-wiring. In a situation with low re-wiring, you’d expect most interactions between species common to both networks to be red. In this instance, almost every interaction between species common to both networks is either blue or green. B.) Side by side network comparison between to different treatment, plots, North control 2 and North LMH 2, that still have some re-wiring but that have relatively low re-wiring compared to other plots in the study (βWN=0.81, βOS=0.22). The plots share several species as indicated by the red circles but unlike in A., the plots also share several interactions as indicated by the red lines.

Table 3-1. Pollinator and flower richness and abundance (averages) in the UHURU study plots. Averages with ± s.e.m. are pooled separately by treatment and location. Metric Metric Chi- Metric Metric Chi- Chi- Model treatment location interaction Metric Average Average squared Average Average squared square Used p-value† p-value† p-value‡ Exclusion Control statistic North South statistic statistic Pollinator glmer 92.50 33.67 0.0002* 14.220 63.83 62.33 0.7332* 0.116 0.2149* 1.538 species (negative ±12.94 ±5.19 ±19.61 ±12.48 richness binomial) Pollinator glmer 277.17 69.17 0.0001* 15.834 201.00 145.33 0.3741* 0.790 0.4051* 0.693 abundance (negative ±53.51 ±11.36 ±72.83 ±41.29 binomial) Flower glmer 25.83 17.00 0.0499* 3.842 19.17 23.67 0.2426* 1.365 0.2118* 1.559 species (negative ±2.24 ±3.31 ±4.24 ±1.94 richness binomial) Floral unit glmer 5288.17 1048.00 0.0006* 11.902 4672.33 1663.83 0.1791* 1.806 0.0170* 5.702 abundance (negative ±2102.75 ±381.36 ±2307.29 ±36.12 binomial)

† = Likelihood ratio test with random intercepts ‡ = Likelihood ratio test with random intercepts for treatment*location * = indicates statistical significance at p≤0.05

Table 3-2. Network metric values (averages) for the UHURU pollination networks calculated using R package bipartite with statistics from R package lme4. The last column indicates which network (treatment and/or location) that the given metric provides support for as being more robust or stable given the metric values and statistical significance. Averages with ± s.e.m. are pooled separately by treatment and location. Network that the metric Metric Metric Chi- Metric Metric Chi- Chi- treatment location interaction provides Metric Model Used Average Average squared Average Average squared square p-value† p-value† p-value‡ evidence to Exclusion Control statistic North South statistic statistic being more stable/robust nestedness lmer (log 14.277 28.158 0.0007* 11.544 21.682 20.753 0.9435* 0.005 0.6811* 0.169 exclusion transformed) ±1.94 ±3.40 ±4.51 ±3.76 interaction lmer 0.533 0.650 0.0266* 4.918 0.622 0.561 0.2003* 1.640 0.7557* 0.097 exclusion strength ±0.05 ±0.05 ±0.07 ±0.07 asymmetry weighted lmer 0.062 0.103 0.0701* 3.282 0.093 0.073 0.3444* 0.894 0.4929* 0.470 --§ connectance ±0.01 ±0.02 ±0.02 ±0.01 Shannon diversity lmer 3.747 2.722 0.0004* 12.741 3.129 3.340 0.3348* 0.930 0.7199* 0.129 exclusion ±0.18 ±0.17 ±0.33 ±0.22 interaction lmer 0.537 0.548 0.7201* 0.128 0.543 0.543 0.9991* 0.000 0.3287* 0.954 -- evenness ±0.01 ±0.03 ±0.03 ±0.02 lmer 0.785 0.882 0.0390* 4.261 0.804 0.863 0.1854* 1.754 0.7316* 0.118 exclusion H2 ±0.03 ±0.04 ±0.05 ±0.02 niche overlap HL lmer (logit 0.162 0.275 0.1479* 2.094 0.281 0.156 0.1909* 1.710 0.5643* 0.332 -- transformed) ±0.05 ±0.09 ±0.10 ±0.03 niche overlap LL glmer 0.059 0.083 0.2545* 1.298 0.092 0.050 0.1117* 2.530 0.4854* 0.487 -- (gaussian, ±0.02 ±0.03 ±0.03 ±0.01 link=log) lmer 0.564 0.535 0.0253* 5.000 0.549 0.550 0.9243* 0.009 0.6659* 0.186 exclusion robustness.HL ±0.01 ±0.01 ±0.01 ±0.01 lmer 0.788 0.747 0.2126* 1.554 0.781 0.754 0.4216* 0.646 0.8952* 0.017 -- robustness.LL ±0.01 ±0.03 ±0.03 ±0.01 generality.HL glmer (Gamma, 1.823 1.256 0.0024* 9.215 1.666 1.413 0.1168* 2.460 0.4433* 0.588 exclusion link=inverse) ±0.26 ±0.08 ±0.30 ±0.09 vulnerability.LL glmer (Gamma, 7.793 4.790 0.0060* 7.538 6.473 6.110 0.9460* 0.005 0.8620* 0.030 exclusion link=identity) ±1.10 ±0.56 ±1.23 ±0.96 lmer 0.433 0.451 0.8325* 0.005 0.314 0.570 0.0083* 6.969 0.5677* 0.327 North modularity ±0.07 ±0.10 ±0.09 ±0.03

† = Likelihood ratio test with random intercepts ‡ = Likelihood ratio test with random intercepts for treatment*location * = indicates statistical significance at p≤0.05 § = control, if significance is taken at p=0.1

Table 3-3. Factor loadings from the principal components analysis of network metrics. Loadings greater than 1/3 (starred) were deemed important Metric PC1 (47.4%) PC2 (27.1%) PC3 (11.8%) interaction strength asymmetry -0.338* -0.312* 0.000* weighted connectance -0.305* -0.396* 0.000* Shannon diversity 0.405* 0.000* -0.181* interaction evenness 0.000* -0.498* -0.287* H2' -0.338* 0.299* -0.273* robustness.HL 0.363* -0.219* 0.000* robustness.LL 0.216* -0.473* 0.000* generality 0.383* 0.000* 0.309* vulnerability 0.114* -0.338* 0.000* modularity 0.000* 0.000* -0.834* nestedness -0.407* -0.140* 0.000*

CHAPTER 4 DISCUSSION

My results indicate that the loss of large herbivores from bushland communities increases the richness and abundance of both pollinators and floral resources and increases the stability, complexity, and robustness of plant-pollinator networks.

Exclusion plots were less specialized (as supported by the metrics H2’, generality, vulnerability, and interaction strength asymmetry—and summarized by PC1), more nested, contained weaker interaction dependencies, and were composed of a higher diversity of interactions. Control plots were marginally more connected. Contrary to expectations, however, rainfall level had little to no effect on richness and abundance of network members nor did it affect the structure of plant-pollinator networks, aside from modularity. There was a high level of species turnover across all plots and a high level of interaction dissimilarity. When species were shared between networks, the interaction dissimilarity was mostly due to re-wiring.

Of the 11 metrics that I looked at, 7—nestedness, interaction strength asymmetry, Shannon diversity, H2’, robustness (HL), generality, and vulnerability— support the conclusion that the removal of large herbivores results in networks that are more resilient and robust to network specific disturbances or extinctions since theory predicts and studies have shown that network stability is increased by generality

(McCann et al. 1998, Sole and Montoya 2001, Memmott et al. 2004, Bascompte et al.

2006, Blüthgen et al. 2007, Kaiser-Bunbury et al. 2010, Allesina and Tang 2012,

Campbell et al. 2012, Kaiser-Bunbury and Bluthgen 2015), nestedness (Bascompte et al. 2003, Memmott et al. 2004, Fortuna and Bascompte 2006, Bastolla et al. 2009,

Thebault and Fontaine 2010, Tylianakis et al. 2010, Pocock et al. 2012, Russo et al.

2014), interaction asymmetry (Emmerson and Yearsley 2004, Wootton and Emmerson

2005, Bascompte et al. 2006, Tylianakis et al. 2010), and higher levels of interaction diversity (Kaiser-Bunbury and Bluthgen 2015). This aligns with the conclusion drawn from PC1; the networks with higher PC1 values—the exclusion plots—have higher generality and nestedness. Of the remaining metrics, 3—interaction evenness, niche overlap, and modularity—did not differ significantly between exclusion and control plots; however, exclusion plots showed a trend towards lower modularity, a feature which has also been suggested to stabilize pollination networks (Thebault and Fontaine 2010).

My results mirror findings from a recent large-scale manipulative study that investigated differences in pollination network structure between restored (invasive plants removed) sites and unrestored sites in the Seychelles (Kaiser-Bunbury et al.

2017). They found that manipulative restoration led to greater pollinator richness and abundance as well as increased interaction diversity (measured using Shannon diversity). They also found that manipulated plots were more generalized (measured using H2’) and that there was no difference in interaction evenness.

Modularity was the only metric that differed significantly across the precipitation gradient. Modularity was lower in North plots, suggesting that drier plots might be more stable; however, none of the other 10 metrics differed significantly between sites, suggesting that rainfall level did not strongly influence plant-pollinator network structure.

Meta-studies using networks from disparate geographic locations have found that annual precipitation both influenced (Trojelsgaard et al. 2015) and had no affect

(Dupont et al. 2009) on pollination network structure. Likewise, studies using strong

climatic gradients found precipitation to both influence (Gonzalez et al. 2009) and not influence (Devoto et al. 2005) network structure.

The strong effect of herbivores on plant-pollinator networks appears to be due in part to the suppressive effect of herbivores on floral resources, particularly in the understory community. The higher floral richness and abundance I found in herbivore exclusion plots could potentially be attributed to several mechanisms. First, it is well known that herbivory can reduce plant reproduction (Marquis 1984, Hendrix 1988,

Karban and Strauss 1993, Ruohomaki et al. 1997, Mothershead and Marquis 2000,

Barber et al. 2012), including herbivory by large mammals (Hendrix 1988, Bastrenta

1991, Gomez and Zamora 2000a, Herrera 2000, Brookshire et al. 2002, Gomez and

Zamora 2002, Cote et al. 2004, Gomez 2005, Maron and Crone 2006, Goheen et al.

2007). Herbivore-induced plant reproduction declines are often the result of vegetative plant-part removal that decreases the amount of energy and resources available for reproduction (Stephenson 1980, Louda 1984, Marquis 1984) or reproductive structure removal (Breedlove and Ehrlich 1968, Hendrix 1979, Louda 1982, Gomez and Zamora

2000b, a, Herrera 2000, Gomez 2005, Maron and Crone 2006, McCall and Irwin 2006).

The reduced reproduction caused by herbivory has also been associated with the loss of photosynthate and associated compensatory regrowth (Strauss and Agrawal 1999), induced response generation such as allocating resources for defense or regrowth

(Redman et al. 2001, Gomez and Zamora 2002, Cipollini et al. 2003), and indirectly by reducing plant population density (Vazquez and Simberloff 2004). Herbivores can also influence floral abundance via herbivore-mediated plant mortality by preventing a plant from reaching a reproductive stage or shortening the plant’s life (Hendrix 1988) via the

consumption of above ground material or trampling (Adams 1975, Hulme 1994,

Palmisano and Fox 1997, Vazquez and Simberloff 2004).

Second, it is possible that herbivores reduce overall species richness, leading to a decrease in floral richness in control plots. In another study conducted in the same area, Young et al. (2013) found that plots without wildlife had slightly higher species richness, as well as strong increases in total vegetative cover. However, this result contrasts with a previous study in the UHURU plots that found no treatment effect of mammal exclosures on plant diversity (Goheen et al. 2013). In other systems, herbivores have been shown to decrease plant species richness (Waser and Price

1981, Proulx and Mazumder 1998, Kohyani et al. 2008) in some cases (but see Belsky

1992, Hartnett et al. 1996, Collins et al. 1998, Proulx and Mazumder 1998, Kohyani et al. 2008).

The effects of herbivores on plant richness may be context dependent, and rely critically on factors such as rainfall: for example, the removal of herbivores in dry environments is predicted to release non-tolerant plants from herbivory thereby increasing plant community richness (Olff and Ritchie 1998). Over time, strong herbivory can reduce richness due to herbivory-induced succession that results in a few tolerant or well-defended plant species (Davidson 1993, Anderson and Briske 1995). In addition, decreases in plant diversity due to herbivory are often associated with nutrient poor environments, where nutrient limitation may prevent regrowth after herbivory

(Proulx and Mazumder 1998, Bakker et al. 2006). The red sand soils of the UHURU experiment are relatively nutrient poor (Pringle et al. 2016) which in concert with low moisture levels may reduce plants’ ability to recover from high levels of herbivory.

Mirroring floral richness and abundance results, pollinator richness and abundance were higher in plots where large herbivores had been excluded. Variation in pollinator richness between exclusion and control plots may simply reflect underlying patterns in floral richness, if higher floral richness creates a broader potential for niche differentiation and a concomitant reduction in interspecific competition among pollinators. Previous studies have reported that insect diversity (Murdoch et al. 1972,

Haddad et al. 2001, Ribas et al. 2003) and specifically pollinator diversity (Gathmann et al. 1994, Potts et al. 2003, Ebeling et al. 2008, Albrecht et al. 2010, Scherber et al.

2010, Scriven et al. 2013) increases with plant diversity. Higher flower richness could also potentially attract a wider range of pollinators through apparent facilitation: similar looking flowers, via Müllerian convergence, could facilitate pollination by raising the apparent floral density of a species (Feldman et al. 2004); generalist flowers could draw in pollinators that specialist flowers rely on; and a greater floral diversity could signal higher resource complementarity, and thus optimal procurement of multiple floral resources to pollinators that would in turn shape their foraging decisions (Ghazoul

2006).

Increases in pollinator richness might also result from higher floral abundance within herbivore exclusion plots. For example, higher floral abundance may increase pollinator diversity by increasing total nectar availability to a level sufficient to support additional species (“resource rarity hypothesis”, MacArthur 1969, Abrams 1995) or by allowing rare pollinator species to become abundant enough to persist (“consumer rarity hypothesis”, Hutchinson 1959, Macarthur 1965, Abrams 1995) (Siemann 1998) as it is generally believed that higher food resource availability (i.e. energy) per area promotes

a greater abundance of consumers per area, assuming food is the limiting resource

(Fretwell and Lucas 1969, Wright 1983). In other systems, increased food abundance corresponds to increased consumer density in apparent bottom-up control (Dempster and Pollard 1981, Adler 1998, Marques et al. 2000, Prevedello et al. 2013, reviewed in

Boutin 1990), including that higher pollinator abundance results from higher floral abundance (Dreisig 1995, Matteson et al. 2013, Scriven et al. 2013, Wilkerson et al.

2013, Tadey 2015) and increased pollinator visitation frequency results from a higher density of flowers (Bosch and Waser 2001).

While it is likely that changes in pollinator richness and abundance in this study resulted from the influence of large herbivores on floral abundance and richness, there are several other mechanisms by which herbivores can affect plant traits and thus shape the interaction between plants and their pollinators including through flowering phenology (Strauss et al. 1996), flower display (Gomez and Zamora 2000b), floral morphology (Lehtila and Strauss 1999, Samocha and Sternberg 2010), quality and quantity of floral nectar (Krupnick et al. 1999), and pollen production per flower (Frazee and Marquis 1994, Strauss et al. 1996). Evaluating whether any of these mechanisms contributed to the patterns observed in this study will require further study.

The loss of large herbivores strongly altered the interactions and structure of pollination networks—leading to increased generalization, nestedness, interaction asymmetry, and interaction diversity—metrics which typically indicate greater stability and robustness in mutualistic networks. My findings are congruent with the prediction that increased diversity should increase the stability of mutualistic networks (Thebault and Fontaine 2010, Sauve et al. 2014), and consistent with studies by Lazaro et al.

(2010) and Albrecht et al. (2010) that found that the degree of network generalization was positively related to floral abundance and richness. One potential explanation for this structural difference between treatments is that the addition of more individuals and more species to the pool of both plants and pollinators allows for a greater diversity of interactions—as indicated by observed increases in the Shannon interaction diversity metric—and thus a greater chance for species to incorporate new links subsequently increasing generalization. This would be an expected result under optimal foraging theory; For example, with an increase in floral diversity in exclusion plots, pollinators should increase their generality in order to minimize foraging time and plants would increase their generality as they are thus visited by a greater array of pollinators

(Macarthur and Pianka 1966, Heinrich 1976, Pyke 1984). Optimal foraging theory suggests another mechanism for the observed generality increase from control to exclusion plots; as pollinator abundance increases certain flowers may more rapidly be depleted of floral resources, resulting in individual pollinators increasing their dietary breadth (Fontaine et al. 2008).

The betalink results indicate a high degree of interaction re-wiring between plots, indicating that both plants and pollinators in the study area have the capacity to interact with multiple partners and increase their generality. Given an environment where flowering can be short-lived and is dependent on variable and often scarce and ephemeral rainfall, it may be beneficial for pollinators to be plastic and opportunistic and for plants to attract a wide assortment of pollinators. In plots with greater richness and abundance, species capable of interacting with multiple species may realize more of these potential interactions, thereby increasing their generality (Kaiser-Bunbury et al.

2010). This is congruent with the neutrality hypothesis (Vazquez 2005, Petanidou et al.

2008), which states that network patterns are mainly driven by random encounters, such that abundant species will opportunistically interact more frequently and with a greater variety of species. Highly mobile and opportunistic pollinators may be less likely to visit areas with low floral abundance (control plots) than areas with greater floral resources (exclusion plots), such that plants in control plots depend more on specialized pollinators that show high levels of fidelity (Johnson and Steiner 2000).

The more generalized and nested architecture of herbivore exclusion plots may also suggest increased functional performance of the pollinator community, since more general networks have greater functional redundancy and lower mutual dependencies, the degree to which one partner relies on the other for survival (Tur et al. 2013, Kaiser-

Bunbury et al. 2017). Additionally, higher generalization in networks leads to greater niche complementarity and ‘sampling effects’, which increase the probability that a highly effective pollinator will be a visitor to a given plant (Blüthgen and Klein 2011). As such, these network properties have important conservation implications, since maintaining pollination communities that are resilient and robust to member species loss or decline while maintaining high functionality is a goal for managers, especially considering the value of pollinator services to both agriculture and wild systems (Potts et al. 2010).

Contrary to predictions, I found little difference in network architecture between high and low rainfall sites, modularity being the only significant structural difference.

This result was surprising, since plots at the higher rainfall South site have higher species richness, diversity, and density of understory vegetation (Goheen et al. 2013). I

found no significant difference in floral richness between North and South plots and, interestingly, a trend towards higher floral unit abundance in the North compared to the

South (though this trend was driven by a single outlier plot in the North). Therefore, while overall plant richness and density are higher in the South, flower richness and abundance, important measures from a pollinator’s perspective, were roughly equivalent between North and South during the period of this study. The likely explanation for this is that rainfall was relatively equal between the two sites during June of 2014 (37.92 mm North compared to 35.29 mm South), the month I sampled most intensely. In arid-adapted plants, rainfall can trigger flowering, especially when climate is unpredictable (Bowers and Dimmitt 1994, Ghazanfar 1997, Penuelas et al. 2004).

The pulse-reserve hypothesis addresses how isolated precipitation events stimulate plant growth and reproduction and predicts that there are ‘biologically important’ rain events that stimulate plant reproduction (Noy-Meir 1973, Ogle and Reynolds 2004); under what is termed the “threshold-delay” model, arid adapted plants, especially succulents, forbs, and grasses, respond within 1 to 2 days to these pulses (Ogle and

Reynolds 2004). Studies have confirmed that semiarid-adapted plants can rapidly flower following precipitation events (Sala and Lauenroth 1982, Crimmins et al. 2011).

Consequently, the 5 June rain event that occurred early in our sampling season in both the North (37.84 mm) and South (19.64 mm) plots paired with the 20 June rain event in the South (15.66 mm) are likely responsible for the floral richness and abundance patterns I observed. The more arid-adapted plants of the North might have responded more strongly to the rain pulse, especially since the pulse was of greater magnitude in the North. In contrast to forbs, woody plants’ responses to rain pulses are slower and

typically of a lower magnitude (Noy-Meir 1973), which, in light of low levels of rainfall in the months preceding my study, may explain why the overstory Acacia species had a relatively low floral abundance in my plots despite the June rain events.

While landscape-scale variation in vegetative species richness is likely to influence pollination networks at large spatial and temporal scales, my results hint that rainfall pulses and their frequency might be more important in influencing floral richness and abundance, and thus pollination network structure, than annual precipitation patterns at local scales over short time periods. Climate models predict overall increases in precipitation in the Laikipia region of Kenya (Ziervogel et al. 2008,

Butterfield 2011), and an increase in intensity of droughts and rainfall events (IPCC

2013). My data suggest that increased variability in rainfall may exert strong short-term effects on pollination networks, and future studies conducted under other precipitation scenarios will be helpful in evaluating the relative importance of short-term variation in precipitation vs. long term averages in structuring plant-pollinator networks in these communities.

All individual networks, regardless of whether they were neighboring replicates or varied by treatment and/or location, were fairly dissimilar to each other in species composition. This dissimilarity was caused predominantly by species turnover as opposed to the loss of a subset of species. Previous studies have shown high spatial or temporal dissimilarity in species composition across networks (Alarcon et al. 2008,

Burkle and Alarcon 2011, Olesen et al. 2011, Carstensen et al. 2014), but these dissimilarities were found across large spatial scales (at least several kilometers) and/or long time scales (across months/seasons/years). In this study, I found high species

dissimilarity across replicates sampled 5 - 12 days apart and separated by less than

200 m. Interestingly, NMDS analyses of beta diversity suggest greater dissimilarity between North and South sites than between control and exclusion plots (see Figure 3-

7). Despite higher dissimilarity in beta diversity between North and South sites, network structure was very similar between these two sites. There were strong differences in network structure between exclusion and control plots even though these plot types differed less than North to South sites in species composition.

Interactions between and across networks were also highly dissimilar. Even when networks shared common species, there was still a high interaction dissimilarity indicating that re-wiring was responsible for a greater proportion of the interaction dissimilarity than was species turnover. These results corroborate previous studies

(Petanidou et al. 2008, Lazaro et al. 2010, Olesen et al. 2011) that found that pollinators are highly plastic in their resource use.

Together, these results suggest that despite high species turnover and a high degree of interaction re-wiring, similar network structures emerged within exclosure plots at both sites, and within control plots at both sites. Although the mechanisms that underlie the emergence of very similar network properties within treatments despite high compositional and interaction dissimilarity is not clear, differences in floral and pollinator richness between control vs. exclosure plots may be important: in the “preferential attachment” model of network formation (Barabasi and Albert 1999), new species entering the network have a higher probability of interacting with species that already have many links (i.e. generalists), resulting in a ‘rich-gets-richer’ process which generates higher generality and nestedness in networks with higher species richness.

My study demonstrates that large herbivores can exert strong effects on the structure of plant-pollinator networks by modifying floral richness and abundance, and suggests that the loss of these large herbivores can increase the stability of pollination networks. These results underscore the potential importance of indirect interactions in structuring pollination webs; while previous experimental studies have shown that the direct modification of networks (e.g., removal of invasive plants Lopezaraiza-Mikel et al.

2007, Kaiser-Bunbury et al. 2017) can impact pollination, results from this study highlight the strong role that external species can play in modifying pollination networks.

Study Limitations

My study is a snapshot of the plant-pollinator community taken during one portion of the year. I attempted to sample over 2 seasons—the end of the long rains and the dry season immediately following—however, the long rains failed to arrive during the study period. Therefore, my networks consist of the plant-pollinator community during a single season. During my observations, acacias (the dominant tree taxa at this site) flowered only weakly. Whether network structure would change when the various acacia species were in full bloom is an open question. Because plant and pollinator communities change rapidly and fluctuate greatly both within and between years, as well as spatially as resource availability changes and plants and pollinators go through their phenological cycles (Kaiser-Bunbury et al. 2010), results from this study cannot be confidently extrapolated across an entire year or outside of this East African savanna ecosystem. Despite this, my results provide robust evidence that large herbivores can exert powerful indirect effects on plant-pollinator networks.

My results from betalink and betapart analyses should similarly be treated with caution, given that complete sampling is very difficult in a large-scale experiment with a

highly diverse suite of plants and pollinators. Because flowering patches could only be observed for 90 minutes for each network, and given the rarity of certain pollinators, it is almost certain that species and links in networks were missed. Thus, a link observed in one network but not observed in another might still occur outside of the 90-minute observation window. However, the quantitative, weighted network metrics (e.g., network specialization, Shannon diversity, generality/vulnerability) I used in this study are largely independent of network size and sampling effort (Bersier et al. 2002, Blüthgen et al.

2006b).

Future Directions

My study is the first to experimentally investigate how widespread and ongoing losses of large mammalian herbivores affect pollination networks and to explore the context-dependence (i.e., rainfall variation) of those effects. While there has been much excellent work investigating the properties of pollination networks and their implications for both network stability (Memmott 1999, Bascompte et al. 2003, Memmott et al. 2004,

Blüthgen et al. 2006b, Bascompte and Jordano 2007, Bastolla et al. 2009, Thebault and

Fontaine 2010, Blüthgen and Klein 2011) and conservation (Tylianakis et al. 2010,

Russo et al. 2014, Kaiser-Bunbury and Bluthgen 2015), large-scale experimental studies (e.g., Kaiser-Bunbury et al. 2017) are urgently needed to evaluate how anthropogenic drivers (e.g., species loss, climate change, biological invasions) will alter plant-pollinator networks and their associated ecosystem services. Future studies might also profitably evaluate networks across longer time-series to capture the temporal variation in network structure. This is a time intensive task, especially when sampling at such a taxonomically high-resolution scale as the present study; thus, if year-long

sampling is not feasible, an attempt should be made to construct networks from each flowering season in the study’s location.

It would also be informative to evaluate the effects of intermediate levels of human-induced changes on plant-pollinator networks. Due to time and resource constraints, I constructed networks at extreme levels of simulated herbivore extinction and on the high and low end of a precipitation gradient. In the future, studies at UHURU should also construct pollination networks in the other two exclusion levels: meso exclusion (herbivores greater than 120cm excluded) and mega exclusion (elephants and giraffes excluded), to establish whether different functional groups of herbivores exert different indirect effects on network structure (e.g., see Olff and Ritchie 1998). For example, Pringle et al. (2014) found that different size classes of browsers (dik-dik, impala, elephant) consumed shrubs in different ways, with each having distinct direct and indirect effects on plant density and productivity. Using all 3 treatment levels in

UHURU would help establish which herbivores exert the strongest effects on pollination networks, and by what mechanisms.

While understanding how anthropogenic changes affect pollination network architecture is informative, especially for predicting how networks will respond to future perturbation, an important next step is to determine how these changes affect the ecosystem functions and services provided by pollination webs. Thus, attempts should be made to evaluate how different anthropogenic drivers affect plant community productivity (e.g., above and below ground biomass, seed and fruit set), especially since the services pollinators provide are so important to both humans (Klein et al. 2007,

Ricketts et al. 2008) and to biodiversity maintenance (Ashman et al. 2004, Aguilar et al.

2006). Whether changes in network structure translate into changes in function and productivity may in part depend upon the network’s “response diversity” (Elmqvist et al.

2003). Response diversity, the diversity of responses to environmental change by a group of species that contribute to the same ecosystem function, is crucial to the resilience and maintained function of an ecosystem (Elmqvist et al. 2003). Depending on how varied and dynamic a pollination web’s response diversity is, changes in network structure may or may not result in a difference in ecosystem services or productivity. Despite its role in ecosystem resilience, response diversity in plant-animal interaction networks has not been well studied (Tylianakis et al. 2010).

To achieve a complete understanding of ecosystem processes, future studies should also attempt to conduct studies that combine mutualistic and trophic as well as other interactions (e.g. host-parasitoid) into single webs (Sander et al. 2015), which may provide important insights into community dynamics (Mougi and Kondoh 2012). While ecologists have begun combining food and mutualistic webs (Fontaine et al. 2011, Kefi et al. 2012, Mougi and Kondoh 2012), such studies are still in the minority. Future studies, especially manipulative approaches like the present study, should combine multiple interaction networks where possible.

Conclusion

As large mammals continue to decline in East Africa my results suggest that plant-pollinator networks in savannas may in some cases retain, if not strengthen, the structural properties that enable these communities to persist and perform important ecosystem services. An important caveat here is that in my study, native browsers were not replaced with domestic browsers (e.g., goats, camels), which can exert very different effects on vegetation (e.g., Pryke et al. 2016); how “refaunation” (conversion

from wildlife-dominated to livestock-dominated ecosystems) affects pollination networks remains an open question. My results also suggest that moderate changes in total annual precipitation do not necessarily affect the structure of plant-pollinator networks, although sampling across longer time-series will be necessary to determine the potential generality of this conclusion.

This study highlights the powerful indirect effects that large herbivores can exert on the broader savanna communities in which they are embedded (e.g. (Palmer et al.

2008). In Laikipia, where this study was conducted, total wild mammalian herbivore biomass (excluding elephants) decreased 37% between 1990 and 2005 with sharp declines in waterbuck (Kobus ellipsiprymnus), Thomson’s gazelle (Gazella thomsoni), buffalo (Syncerus caffer), eland (Taurotragus oryx), and hartebeest (Alcelaphus buselaphus) (Georgiadis et al. 2007). In Kenya between 1977 and 2016, the average rate of decline of 18 common large (greater than 15 kg) herbivore species was 68.1%

(e.g., impala Aepyceros melampus 84.1%, Grevy’s zebra, Equus grevyi 87.8%, eland

78.0%, elephant 42.6% Loxodonta africana, wildebeest Connochaetes taurinus 64.2%, giraffe Giraffa cemelopardalis 67.0%, and Thomson’s gazelle 75.4%) (Ogutu et al.

2016). My results also provide a striking example of how variable pollination networks can be across even small geographic distances and short time spans.

As anthropogenic factors increasingly drive ecosystem changes, it is imperative to determine what effects they will have on communities so that conservation managers can make informed decisions. As restoration ecology shifts its focus to restoring interactions rather than just species (Henson et al. 2009, Heleno et al. 2010, Fontaine et al. 2011, Kaiser-Bunbury et al. 2017), network approaches will be crucial for

understanding, conserving, and restoring communities and ecosystems, especially given that network structure (and potentially function) can be preserved despite variation in species composition. For example, the approach I used could be applied to study how pollinators are affected by the overpopulation of ungulates in certain protected areas of North American, taking advantage of currently established ungulate exclosures (Baker et al. 1997, Schoenecker et al. 2004). Our study serves as a model to address the effects of anthropogenic change on species interactions using a long- term, large-scale replicated experiment in conjunction with a taxonomically high- resolution network approach.

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BIOGRAPHICAL SKETCH

Travis Guy was born and raised in the Rocky Mountains of Colorado. As such, his passions include the mountains, skiing, snow, wildlife, adventure travel, and conservation. He graduated summa cum laude from the College of Idaho in 2006 with a

Bachelor of Science in biology. While an undergraduate, he developed a passion for field ecology while working in the resource management division of Rocky Mountain

National Park and while conducting research at Shoals Marine Lab in Maine, Heron

Island Research Station in Australia, and Mpala Research Centre in Kenya, where he met his master’s degree advisor. After graduation Travis began a circuitous journey on his way back to academia. Immediately after graduation he worked in the research division of Rocky Mountain National Park, in the ski industry for Vail Resorts, and then as a research scientist for Sapidyne Instruments, a biotech firm. Travis then backpacked for 2 years through Asia. On this trip, his passion for conservation grew, and he decided he eventually wanted to pursue a career in ecology and conservation.

Upon returning to the USA, Travis became the project manager for a study on bighorn sheep nutrition with North Dakota State University, which furthered his interest in field ecology. He applied to graduate school; however, he deferred so that he could work in science support for the United States Antarctic Program at McMurdo Station for 2 field seasons. During his graduate career at the University of Florida, he worked as a NOAA climate science technician at Summit Station, Greenland, a field technician for the

Kenya Rangelands Wild Dog and Cheetah Project, and as a naturalist for the Oceanic

Society. He was also awarded the prestigious National Science Foundation Graduate

Research Fellowship. Travis graduated from the University of Florida with a Master of

Science in zoology and a minor in Wildlife Ecology and Management in 2017.