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Plant-Pollinator Interactions of the -Savanna: Evaluation of Community Structure and Dietary Specialization

by Tyler Thomas Kelly

B.Sc. (Wildlife Biology), University of Montana, 2014

Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science

in the Department of Biological Sciences Faculty of Science

© Tyler Thomas Kelly 2019 SIMON FRASER UNIVERSITY SPRING 2019

Copyright in this work rests with the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation. Approval

Name: Tyler Kelly Degree: Master of Science (Biological Sciences) Title: -Pollinator Interactions of the Oak-Savanna: Evaluation of Community Structure and Dietary Specialization Examining Committee: Chair: John Reynolds Professor Elizabeth Elle Senior Supervisor Professor Jonathan Moore Supervisor Associate Professor David Green Internal Examiner Professor

[

Date Defended/Approved: April 08, 2019

ii Abstract

Pollination events are highly dynamic and adaptive interactions that may vary across spatial scales. Furthermore, the composition of species within a location can highly influence the interactions between trophic levels, which may impact community resilience to disturbances. Here, I evaluated the species composition and interactions of and pollinators across a latitudinal gradient, from , British Columbia, Canada to the Willamette and Umpqua Valleys in Oregon and , of America. I surveyed 16 oak-savanna communities within three ecoregions (the Strait of Georgia/ Puget Lowlands, the Willamette Valley, and the Klamath Mountains), documenting interactions and abundances of the plants and pollinators. I then conducted various multivariate and network analyses on these communities to understand the effects of space and species composition on community resilience. In addition, I evaluated the composition and floral visit patterns of a mid-sized mining-, angustitarsata, to understand how foraging preferences and dietary specialization may change across space and with varying floral compositions. I found that spatial scales had an effect on species compositions, the interactions between plants and , and the foraging preferences of pollinators. I learned that some groups of pollinators may provide stability in networks by increasing generalized interactions and reducing specialization. Additionally, the foraging preferences, A. angustitarsata, were conserved across spatial scales, despite fluctuations in plant compositions and abundances. However, A. angustitarsata is likely not oligolectic, a pollen specialist, because of its ability to facultatively forage on additional plants other than its preferred host plants. Overall, my results show that spatial scales can influence the composition and interactions of plants and pollinators, thus influencing the degree to which species interact and the ability of the community to maintain structure after a disturbance.

Keywords: community structure; mutualistic networks; foraging preferences; ecoregions; specialization; pollen

iii Dedication

There is nothing more beautiful

than the diversity of life.

iv Acknowledgements

First and foremost, I want to thank my supervisor Elizabeth Elle for your continued dedication to the progression of science, education, and ultimately your students. I appreciate how much you have guided and challenged me to become a better ecologist and taught me to embrace my passion for natural history both in my writing and my science. I think it is rare to find a supervisor so dedicated to their students that they will drop everything to drive seven hours to help survey pollinators in a rapidly drying season. I would also like to thank Jonathan Moore for providing a meaningful outside perspective to my research and to David Green for acting as my internal examiner and your previous assistance within my coursework.

Much like anything, research ‘takes a village’, and I’d like to give my warmest thanks to all the folks who have provided feedback and support throughout my years at SFU. First, I would like to thank the people who helped me conduct my field studies; Emily Merlo, Elizabeth, Nicola Rammell, Genviève Reynolds, Michel, and Lora Morandin. In particular, want to give many thanks to Emily Merlo, for your spectacular resilience and dedication to continue collecting data despite having to trudge through poison-oak and head-high fields of allergy-inducing grass, not to mention the occasional unwanted tick. I wish you success with all of your endeavors and all the Mexican food your heart desires. Also thanks to Lora, Ben, and Chase for letting us use your lovely homes during the field season. I would like to thank my fellow lab mates, Sandy, Kyle, Michelle, and Allison, I am grateful for the advice and guidance with my study design and in my writing. And I want to give my sincere thanks to the many people who’ve helped me with coding and data analysis, Dan, Kurt, Colin, Jillian, Pascale, Michael, Richard, Philina and all the folks in Statz Beers, I’m so thankful for your assistance and patience.

To all the land managers, office staff, and facilitators who were helpful with all my logistical needs. Thank you to Terry Griswald, H. Ikerd, Jamie Strange, Jason Gibbs, Tiia Haalpalainen, and the folks at Beaty Biodiversity Museum for help with identification. Furthermore, I want to thank the folks at Science World for celebrating diversity and to Julian Christians and all the folks who participated in the first Queer in STEM day at SFU. It is warming to see people dedicated to making science welcoming and accessible to all kinds of people.

v Graduate school is so much about the people you’re with, as it is about the science and I do not think I would be anywhere without the support and friendship of so many amazing and brilliant people Thanks to Seb, Marie, Leslie, Jess, Michael, Colin, Serena, Darren, Deborah, Sarah, Mason, Eveling, Kirsten, Pauline, Kevin, Asim, Danielle, Tiia, Heather, Jayme, Merinde, and Sean for all the laughs, hikes, shenanigans, and late night talks. And I want to give a special thanks to Kurt, Michelle, Phelina, and Dan for not only providing brilliant advice and guidance these last few years, but also truly wonderful friendships. I am so honoured to have gotten to know each of you.

Thank you to my family for your love and support. And ultimately, thank you to my incredible husband Michel for following me half-way across the continent so that I could pursue my dreams. In many regards, I could not have done this without your patience, encouragement, and love.

vi Table of Contents

Approval ...... ii Abstract ...... iii Dedication ...... iv Acknowledgements ...... v Table of Contents ...... vii List of Tables ...... ix List of Figures ...... xi List of Acronyms ...... xiii Glossary ...... xiv Introductory Image ...... xvii

Chapter 1. Introduction ...... 1 References ...... 6

Chapter 2. Effects of community composition on plant-pollinator interaction networks across a spatial gradient of oak-savanna habitats ...... 10 Summary ...... 10 Introduction ...... 10 Methods ...... 13 Overview ...... 13 Study Region & Sites ...... 13 Study Design ...... 14 Abundance Surveys and Habitat Characteristics ...... 14 Community analysis ...... 15 Interaction Network Structure ...... 15 Categorizing species for analysis ...... 17 Stepwise model selection using AIC ...... 18 Results ...... 19 Community Composition ...... 19 Species’ Group Analysis ...... 20 Discussion ...... 21 References ...... 26 Tables ...... 34 Figures ...... 39

Chapter 3. Evaluation of specialization and foraging preferences across ecoregions, in a mid-sized mining bee Andrena angustitarsata (: ) ...... 43 Summary ...... 43 Introduction ...... 44 Methods ...... 46 Study Design ...... 46 Pollen Frequency and Abundance ...... 47

vii Statistical Analysis ...... 48 Apiaceae Distribution Analysis ...... 48 Species-Level Specialization Analysis ...... 49 Floral Preferences Analysis ...... 49 Results ...... 50 Summary Statistics ...... 50 Apiaceae Distribution ...... 51 Species-level Specialization ...... 51 Floral Preferences ...... 52 Discussion ...... 52 Pollen Collection and Plant Visits ...... 53 Species-level Specialization ...... 54 Floral Preferences ...... 55 Conclusion ...... 56 References ...... 58 Tables ...... 63 Figures ...... 66

Chapter 4. Conclusion ...... 69 Summary ...... 69 Future Directions and Caveats ...... 71 Conclusions ...... 73 References ...... 75

Appendix A. List of Models ...... 78

Appendix B. Plant and Pollinator Guilds ...... 85

Appendix C. Plant and Pollinator Species Lists ...... 94

Appendix D. Pollen Family List ...... 102

viii List of Tables

Table 2.1 Ecoregion information, pollinator and plant abundance and richness, from 16 oak-savanna sites in British Columbia, Washington, and Oregon arranged by latitude. Region indicates ecological regions of focus from north to south: Georgia Straight/ Puget Lowlands (GPL), Willamette Valley (WV) and Klamath Mountains (KM). abundance is the number of insects collected within a site after removing species with only a single individual at that site, and richness also does not include singletons. Flower abundance is the total number of counted flowering units from transect surveys...... 34 Table 2.2 Eigenvectors of Principal Component Analysis performed on eight habitat characteristics for 16 sites ranging from BC to OR. Values in the table are the coordinates of variables for PC1. PC1 accounts for 44% of the variation in the data...... 35 Table 2.3 Network metrics for 16 oak-savanna sites in British Columbia, Washington, and Oregon are listed north to south. Network metrics include network specialization (H2) and modularity (both of which are negatively associated with resilience) and interaction strength asymmetry (ISA) and weighted nestedness (WNODF; both of which are positively associated with resilience). For H2, ISA, and modularity, values are bounded between 0 and 1; values closer to 1 indicate a higher degree of that metric in a site. H2, ISA, and WNODF were calculated using the ‘networklevel’ function and modularity was calculated using the ‘computemodules’ function in the Bipartite package in R...... 36 Table 2.4 Plant and pollinator natural history and functional group categories and the group types within them. Each plant and insect species was assigned to a group within the respective plant and pollinator group categories. Only groups with representatives in at least 10 or mores sites were included in my analyses...... 37 Table 2.5 Generalized linear models of network metrics and their relationship to groups for plants and pollinators. Models represent the nine highest predicted models within the ‘top model analysis’(those two delta AIC above the null); the group analysis (performed separately by group category for each of plants and pollinators) is presented in Appendix A. Significant models are indicated by an *...... 38

ix Table 3.1 Abundance of total Andrena angustitarsata collected and included within my analysis ( Analyzed) within three Pacific coastal ecoregions (listed from north to south); the Strait of Georgia/ Puget Lowlands (GPL), the Willamette Valley (WV), and the Klamath Mountains (KM). Bees were collected while visiting within 16 oak-savanna sites (organized alphabetically) and analyzed for pollen contents on their bodies and scopae. The number of bees collected on Apiaceae and (the preferred plants within my study system) is included with the abundance of flower stems and number of pollen grains counted in the scopae from both floral groups. Mean and standard error for GPL and WV ecoregions were calculated for each column. Bees were removed from analysis for the following reasons: 1 only one bee in site, 2 only one site in ecoregion, ^ broken legs, or # sub sample from sites with above 25 collected specimens. * Indicates flowers were observed within the site, however, were not recorded within randomized floral abundance surveys...... 63 Table 3.2 Model descriptions and outputs evaluating ecoregional differences in pollen collection, floral visit patterns, and plant abundances for Andrena angustitarsata collected within 11 oak savanna sites in two ecoregions the Strait of Georgia / Puget Lowlands and the Willamette Valley. Apiaceae frequency is the amount of Apiaceae pollen found on the scopae (model A1.1) and body of (model A1.2) bees, while Apiaceae abundance refers to the number of observed flower stems within a site. Species-level specialization is indicated by d’ and is calculated based on number of visited flower species compared to total number of available species...... 65

x List of Figures

Figure 2.1 Map of 16 oak-savanna sites across three ecoregions in BC, OR, and WA. Six sites are in the Strait of Georgia/ Puget Lowlands, eight sites are in the Willamette Valley, and two sites are in the Klamath Mountains. Northern-most sites are approximately 650km and 5.5° latitude separated from southern-most sites. Shape files for the ecoregions were downloaded from https://www.epa.gov/eco-research/level-iii-and-iv- ecoregions-continental-united-states...... 39 Figure 2.2 NMDS plot of pollinator communities within 16 oak savanna sites. Colour corresponds to ecoregions; Strait of Georgia/ Puget Lowlands (GPL), Willamette Valley (WV), and the Klamath Mountains (KM, Table 2.1). Shapes correspond to province and state locations. Final stress was 0.161 with 2 dimensions. Ecoregion and PC1 significantly correspond to differences on pollinator compositions. Genera and families of insects shown on axis have strong directional loadings and are shown to aid interpretation...... 40 Figure 2.3 NMDS plot of plant communities within 16 oak savanna sites. Color corresponds to ecoregions; Strait of Georgia/ Puget Lowlands (GPL), Willamette Valley (WV), and the Klamath Mountains (KM). Shapes correspond to province and state locations. Final stress was 0.122 with 3 dimensions. Ecoregion significantly corresponds to plant compositonal difference. Families of plants shown on axes have strong directional loadings and are provided for interpretation...... 41 Figure 2.4 Plotted model outputs for the top models of each of four network metrics H2 (network-level specialization), ISA (interaction strength asymmetry), Modularity, and WNODF (weighted nestedness) and corresponding log transformed plant and pollinator groups (Table 2.4). Plots in left column (A, C, E) indicate models with significant group predictions of network metrics, while models in right column are either primarily influenced by an outlier site (Willow Creek, B, and Coburg, D) or were marginally non- significant (F)...... 42 Figure 3.1 Stacked bar charts represent the averaged frequencies of flower stems (floral abundance), flower visits, and the observed on the body (head and anterior thorax) and in the scopae (hind legs and posterior thorax) of 126 Andrena angustitarsata within 11 oak-savanna sites within two ecoregions; (a) Strait of Georgia/ Puget Lowlands and (b) the Willamette Valley. Plant families were summarized into the top five plant and pollen frequencies from flower and pollen surveys. The “other” category includes 27 plant families, each below 5% of total pollen abundance (Appendix D, Table D.1). All values were averaged for each ecoregion to get comparable frequencies...... 66 Figure 3.2 Species-level specialization, d’, representing the ‘visit specialization’ for Andrena angustitarsata within 11 sites in two ecoregions, Strait of Georgia/ Puget Lowlands (GPL, 6 sites) and the Willamette Valley (WV, 5 sites). Species-level specialization is calculated based on potential interaction partners available for a species within a site, higher values indicate the species has more specialized interactions...... 67

xi Figure 3.3 Bonferroni z-scores of plant preferences for Andrena angustitarsata within two ecoregions a) the Strait of Georgia/ Puget Lowlands and (b) the Willamette Valley for 11 oak savanna sites. Points indicate the frequency of plant family (averaged across sites) within the ecoregion in relation to upper and lower confidence intervals of Bonferroni z-scores for scopal pollen collected from 126 bees. Plant abundance points outside of intervals are considered significantly different than the observed pollen collected by bees; points below intervals are preferred, and points above intervals are avoided. The magnitude of difference between points and confidence intervals is based on Chi-Square residuals across all plant groups within a site. Apiaceae and Rosaceae are the only plant families that were significantly preferred (below the z-score CI) for both ecoregions...... 68

xii List of Acronyms

AIC Akaike information criterion GLMM Generalized Linear Mixed-effects Model GPL Strait of Georgia/ Puget Lowlands ISA Interaction strength asymmetry KM Klamath Mountains MANOVA Multivariate analysis of variance NMDS Non-metric multidimensional scaling PCA Principal component analysis PC1 Principal component one WV Willamette Valley

xiii Glossary

Akaike Information A model selection procedure where chosen models have Criterion (AIC) a minimal Kullback-Leibler distance between the model and the truth. Akaike Weight The relative likelihood of a model based on AIC scores. Andrenidae The mining-bee family; the most common genus is Andrena (Hymenoptera: ). Apiaceae The carrot family; prominent members include species in the genera Lomatium, Sanicula, and Heracleum. Apidae The eusocial, digger, and family; prominent members include Apis mellifera and Bombus sp. (Hymenoptera: Apidae). The daisy and sunflower family; prominent members include Hypochaeris sp. and Eriophyllum sp. Bombillidae The bee- family a prominent group of flower visiting species. Brassicaceae The mustard family; prominent members include Barbarea sp. and Capsella sp. Ecoregion The consolidated climate, hydrology, soil/terrain, biota and land-use activates within a region. Categorized by the Level III of the EPA’s land-designation system. The family; prominent members include Vicia sp. Community Structure The composition of species and their interactions. Associated with the function and resilience of the community. Dietary Breadth The degree of floral resources used and collected for survival and nest provisioning. Dietary Specialization The degree to which bees collect floral resources (pollen, nectar, resin, oil, etc.) from a small number of floral hosts for nest provisioning. The sweat and furrow bee family; prominent members include sp. and Lasioglossum sp. (Hymenoptera: Apidae). A group or cluster of flowers arranged on a stem that is composed of a main branch or a complicated arrangement of branches. Interaction Strength The contribution of one species to the reproductive output of its mutualist partner relative to the combined per interaction contributions of all species.

xiv Interaction Strength Network metric that measures the dissimilarity in the Asymmetry (ISA) interaction strength between mutualists, such that one species is more dependent on the relationship than the other species Order of plants consisting of about 24 plant families; prominent families include the mint family, Lamiaceae. The mason and -cutter bee family; prominent members include Osmia sp. and Megachile sp. (Hymenoptera: Apidae). Mesolecty Middle range of pollen species collected by female bees for nest provisioning. Bees that collect pollen from 1-3 plant families and many genera. Modularity (QuanBiMo) Network metric evaluating the degree to which groups of species interact more amongst each other than with the whole network. Mutualistic network The interactions between two or more trophic levels that provide resources and reproductive benefits to the reciprocal species, such as plants and pollinators, plants and frugivores, or ants and plants. Principal Component A multivariate statistical approach that uses orthogonal Analysis transformation to a large set of observed variables (that may be correlated) into a dimensionally reduced set of uncorrelated variables, called principal components. Network Metric The equations and algorithms that characterized the structure of a network. Network-level Network metric that calculates the level of specialization Specialization (H2’) across all interactions in the network. Measure of discrimination, calculated from species-level specialization, in comparison to a random no- specialization web of interactions. Describes ‘visit specialization’. Non-metric An ordination technique where a large set of observed Multidimensional Scaling variables are reduced into an explicit number of (NMDS) dimensions (typically <3). Oak-savanna Open to lightly forested grasslands where Quercus species are dominant tree species. Historically regulated by fire, these range from rocky outcrops to ephemeral wet-prairies Oligolecty Narrow range of pollen species collected by female bees for nest provisioning. Bees that collect pollen from only 1- 4 plant genera within one family. ‘Eclectic oligolecty’, is a subcategory where bees collect pollen from 2-4 genera in 2-3 plant families

xv Polylecty Broad range of pollen species collected by female bees for nest provisioning. Number of plant taxa in the scopae ranges from 4-25% of available plant families. Resilience The ability of a community to retain structure and function after a disturbance. Related to ‘robustness’ in network analyses, which evaluates the ability of the network to restructure as species and interactions are removed. Rosaceae The rose family; prominent prairie genera include Rosa, Fragaria, and Potentilla. Sapindaceae The soapberry family; prominent members include Acer sp. and Aesculus sp. Scopae Specialized pollen collecting hairs on the body and legs of bees. Species-level Network metric that expresses how specialized a given Specialization (d’) species is in relation to what partners are available in the reciprocal trophic level. Syrphidae The hover-fly family a prominent group of flower visiting species; common genera include , Toxomerus, and Sphaerophoria. Visit Specialization The degree to which a species interactions with only a subset of available interaction partners. For example, bees that visit only plants in the pea family. Weighted Nestedness, Network metric that evaluates the non-random degree to (WNODF) which rare and specialized species interact with a core set of generalist species. Weighted by the number of interactions between species.

Note: citations from the Glossary can be found within the reference section of Chapter 1.

xvi Introductory Image

A. Plants

Pollinators

B.

Two visualizations of a plant (blue) and pollinator (orange) interaction network from Bluebird strip, Finley NWR, in the Willamette Valley, OR, USA. (A) Network with size of nodes and lines indicating # interactions, represents how only a few species have most of the interactions with thick edges and large nodes. (B) Network layout with interaction values indicated on edges, represents how many species are weakly connected to highly connected species (see occidentalis and its mutualists). Visuals constructed using ‘gplot’ and ‘ggnet2’ packages.

xvii Chapter 1.

Introduction

Pollinators facilitate the reproduction of over 85% of all angiosperms (Ollerton et al. 2011), including many important agricultural species (Aizen et al. 2009). In recent decades, however, pollinators have experienced significant declines in abundance and diversity (Potts et al. 2010), which will likely have adverse effects on the maintenance of the key service of (Ashman et al. 2004). Additionally, the diversity of plants within prairie ecosystems has declined substantially due in part to habitat loss and fragmentation from increased human development (Noss and Scott 1995, MacDougall and Turkington 2004, Vesely and Rosenberg 2010, Trowbridge et al. 2016). The loss of habitat is one of the greatest threats to pollinating insects and herbaceous plants, particularly within prairie ecosystems, which may provide reliable habitat compared to adjacent human modified landscapes (Potts et al. 2010, Spiesman and Inouye 2013). In the of North America, Garry-oak savannas and associated habitats (i.e. wet-prairies, rock outcrops, and upland prairies), a unique ecosystem embedded within coniferous forests, are some of the most endangered habitat types within Canada and the United States because of habitat fragmentation (Fuchs 2001, Vellend et al. 2008). Garry-oak habitats, however, support a relatively high number of plant and pollinator species (Fuchs 2001, Vesely and Rosenberg 2010). In this thesis, I evaluate the plant and pollinator interactions across 16 oak-savannas along a latitudinal gradient, to understand how species composition influences mutualist interactions at the community-level and the species-level.

Pollination events are highly adaptive interactions that may vary across spatial scales (Burkle and Alarcón 2011). Across space, plant-pollinator communities naturally vary in their composition, which may lead to differences in species interactions and foraging behaviors as resources and species increase or decrease in abundance (Dupont et al. 2009, Burkle and Alarcón 2011, Song and Feldman 2014). Ecoregional differences may be an important spatial component to consider (Minckley et al. 1999, Magnusson 2004). Ecoregions are designated based on weather, geology, temperatures, and biotic factors (Cronquist 1982, Wiken et al. 2011). In this thesis, I

1 established sites within three sub-ecoregions; six sites in the Strait of Georgia/ Puget Lowlands (GPL), eight sites within the Willamette Valley (WV), and then two sites within the Klamath Mountains (KM, Cronquist 1982, Wiken et al. 2011). GPL consist of mild maritime climate, with 150-220 frost free days and 9°C mean annual temperature. WV is marked by warm dry summers and wet winters with 165 to 210 frost free days and 10°C mean annual temperature. Finally, KM consists of mild warm summers and winters with 90-250 frost free days and 14°C mean annual temperature (Wiken et al. 2011). The climactic and landscape level differences between ecoregions may highly influence the blooming of plants and the emergence and activity of pollinators, thus impacting structural differences in plant-pollinator communities across ecoregions.

An important area of study within pollination ecology is the specialization of the relationships between plants and pollinators. Specialization has been studied from the perspective of form and function of flowers, the limited resources collected by pollinators (i.e. pollen variety), and as the observed behavioural interactions between pollinators and their food plants. In this thesis, I focus on the latter two aspects of specialization; resources collected by pollinators for nest provisioning and the observed interactions between plants and pollinators. At the species-level, the resources pollinators collect or the plants they visit may vary across space, likely influenced by the composition and species richness of the community (Brosi 2016, Fründ et al. 2016, Armbruster 2017). As resources change, so might the degree to which pollinators forage on particular plants, thus interactions within a species could vary along a gradient from specialized to generalized (Brosi 2016, Fründ et al. 2016, Armbruster 2017). Except for a few generalist species (Apis mellifera and Bombus sp.), few studies have evaluated the foraging behaviour and preferences of pollinators, let alone how these behaviours may change across spatial scales. Specialist species may be at higher risk of as human development increases (Devictor et al 2010, Clavel et al 2011), so understanding the nuances of specialization within a species is critical for the conservation and management of diverse plant and pollinator communities.

At the community-level, the interactions between trophic levels can relay valuable information about community structure. In the last 20 years, pollination ecologists have adopted the use of network analyses to evaluate the complex relationships and interactions between plants and pollinators at the community-level (Bascompte and Stouffer 2009, Vázquez et al. 2009, Elle et al. 2012). Network analyses apply the

2 advances in modern computational abilities to classic paradigms within plant-pollinator evolutionary and ecological theory. A network-based approach evaluates the matrix of interactions throughout a mutualistic community to provide informative metrics describing community structure (Bascompte et al. 2003, Bascompte and Jordano 2007). For instance, most mutualistic networks have some basic underlying principles such that most species are uncommon and poorly connected in the network, and few species are abundant and have many connections (Trøjelsgaard and Olesen 2013, Figure 1.1). Thus, network properties, may have a directional effect on resilience, the ability of a community to retain structure and function after a disturbance (Menz et al. 2011, Vázquez et al. 2012, Goldstein and Zych 2016). Here, I selected four metrics for my analysis; nestedness, interaction asymmetry, modularity, and network-level specialization. In the following paragraphs I define and describe their relationships to resilience and community structure.

Nestedness and interaction asymmetry are thought to have positive relationships with resilience based on simulation studies (Lance et al. 2017, Soares et al. 2017). Nestedness describes the level to which specialists interact with a core set of interconnected generalists species versus interacting with other specialists (Bascompte and Jordano 2007, Beckett 2015). In specialist-specialist interactions, the relationship is vulnerable to the loss of either interactor, whereas in specialist-generalist interactions, the network is more robust to the loss of some interactions and considered more resilient (Bascompte et al. 2003, Menz et al. 2011, Beckett 2015). In a similar fashion, interaction asymmetry measures the dissimilarity in the interaction strength between mutualists, such that one species is more dependent on the relationship than the other species (Bascompte et al. 2006, Elle et al. 2012, Vázquez et al. 2012). Interaction strength is defined as the contribution of one species to the reproductive output of its mutualist partner relative to the combined per-interaction contributions of all species (Vázquez et al. 2012). Specialists for instance interact strongly with their partners, however generalists typically have lower interaction strength with each of their many partners. Interaction asymmetry evaluates these dissimilarities between specialists and generalists across the network. Theoretically with a higher number of generalists within the network there will be higher asymmetry, thus increasing the connectivity and resilience of the network (Soares et al. 2017). Alternatively if there are many specialists within the network then more species will have higher dependence with their interaction

3 partners; higher dependence could mean less resilience to disturbance if species are unable to facultatively switch to new partners (Cumming 2011, Astegiano et al. 2015, Oliver et al. 2015).

Network specialization and modularity are thought to decrease the resilience of the network based on simulation studies (Astegiano et al. 2015, Palacio et al. 2016, Santamaría et al. 2016), because both describe a tendency of species to largely interact within small groups as opposed to interacting with species across the network (Carstensen et al. 2016). Network specialization is used to evaluate the degree to which a species interacts with particular other species in comparison to all available reciprocal species (Blüthgen et al. 2008, Elle et al. 2012). Networks with higher specialization would have higher dependence between interacting species, such that species are more reliant on their reciprocal partners (Ashworth et al. 2004, Blüthgen et al. 2008). Modularity calculates how much groups of species interact within the group as opposed to with species outside of their group in the larger network (Olesen et al. 2007, Beckett 2016). As with network-level specialization, modularity may increase the dependence of species in the network on one another. Modules with high interdependence may be vulnerable to disturbance if nodes within the module are lost. These metrics can be influential, because while most species likely benefit from specialized interactions, communities are more resilient to species loss with higher connectivity and generalist interactions (Astegiano et al. 2015, Palacio et al. 2016, Santamaría et al. 2016).

Our understanding of the dynamics of specialization within plant-pollinator interactions is limited by the lack of study of the range, life history, and foraging patterns of many pollinator species. Analyses of the pollen collected by a species for nest provision provides the most detail into that species’ dietary breadth. Oligolecty is high dietary specialization, as when female bees collect pollen from a narrow range of floral species or genera within the same family to provision their nests (Cane & Sipes 2006). A general criterion for oligolecty is that 90% of the bees in a sample should have pollen loads consisting of approximately 90% of a narrow clade of plants (Cane & Sipes 2006). Like other types of specialization, oligolecty may also occur on a spectrum within a species depending on differences in habitat and community composition. Plant composition may pressure oligolectic bees to be facultative when the preferred host is rare or competition between pollinators is high. Such facultative behavior might vary

4 depending on the floral resources available, and so could also vary across a latitudinal gradient.

In this thesis, I evaluate how plant-pollinator interactions vary across a 650 km spatial gradient at two organizational levels, the community-level and the species-level. I further evaluate the causes and effects of specialization at these two levels by relating groups of plants and pollinators to specialization and evaluating the range of specialization within a single species. In chapter 2, I evaluated how community composition (groups of plants and insects) influenced network metrics, to make inferences about how changes in composition might influence the resilience of the community. In chapter 3, I explore the dynamics of a species’ foraging preference across space, by evaluating the visit-specialization and dietary specialization of a mid-sized mining bee, Andrena angustitarsata. Overall, my thesis aims to evaluate the relationships between pollinators and plants across a spatial gradient by unifying modern computational methods with classical pollination theory.

5 References

Aizen, M. A., L. A. Garibaldi, S. A. Cunningham, and A. M. Klein. 2009. How much does agriculture depend on pollinators? Lessons from long-term trends in crop production. Annals of Botany 103:1579–1588.

Armbruster, W. S. 2017. The specialization continuum in pollination systems: diversity of concepts and implications for ecology, evolution and conservation. Functional Ecology. 31:88–100.

Astegiano, J., F. Massol, M. M. Vidal, P. O. Cheptou, and P. R. Guimarães. 2015. The robustness of plant-pollinator assemblages: Linking plant interaction patterns and sensitivity to pollinator loss. PLoS ONE. 10.

Ashworth, L., R. Aguilar, L. Galetto, and M. A. Aizen. 2004. Why do pollination generalist and specialist plant species show similar reproductive susceptibility to habitat fragmentation? Journal of Ecology 92:717–719.

Bascompte, J., P. Jordano, C. J. Melián, and J. M. Olesen. 2003. The nested assembly of plant- mutualistic networks. Proceedings of the National Academy of Sciences of the United States of America. 100:9383–9387.

Bascompte, J., P. Jordano, and J. M. Olesen. 2006. Asymmetric Coevolutionary Networks Facilitate Biodiversity Maintenance. Science 312:431–433.

Bascompte, J., and P. Jordano. 2007. Plant-Animal Mutualistic Networks: The Architecture of Biodiversity. Annual Review of Ecology, Evolution, and Systematics. 38:567–593.

Bascompte, J., and D. B. Stouffer. 2009. The assembly and disassembly of ecological networks. Philosophical Transactions of the Royal Society B: Biological Sciences. 364:1781–1787.

Beckett, S. J. 2015. Nestedness and Modularity in Bipartite Networks. 1-227.

Blüthgen, N., F. Menzel, and N. Blüthgen. 2006. Measuring specialization in species interaction networks. BMC Ecology 6:1–12.

Blüthgen, N., J. Fründ, D. P. Vázquez, and F. Menzel. 2008. What Do Interaction Network Metrics Tell Us About Specialization and Biological Traits. Ecology. 89:3387–3399.

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9 Chapter 2.

Effects of community composition on plant- pollinator interaction networks across a spatial gradient of oak-savanna habitats

Summary

Distance between habitats can highly influence the composition and corresponding interactions between trophic levels. Mutualistic networks, such as those of plants and pollinators tend to have a core set of properties that often relate to the resilience of the community. Furthermore, networks are highly impacted by the number of specialists and generalists; however, it is unclear how different groups of species with various life-history strategies influence network structure. In this study, I evaluated how the composition of plants and pollinators within 16 oak-savanna sites changed across a latitudinal gradient. In addition, I evaluated how the corresponding abundance of different groups of plants and pollinators affected network metrics related to resilience. I found that the composition of plant and pollinators varied between ecoregions, while pollinator composition further varied with habitat characteristics. Network metrics, showed no difference across spatial scales, however metrics were significantly related to several pollinator groups, such as above ground nesting insects and predatory larvae. Above ground nesting insects had a positive relationship with nestedness and a negative relationship with modularity, while predatory larvae had a negative relationship with modularity. This study emphasizes how spatial scales can influence species compositions, which in turn affects the structure and potentially the resilience of the community.

Introduction

Interactions with pollinators are essential for the reproduction of the majority of native forbs (87.5%, Ollerton et al. 2011) and increasing evidence suggests that many pollinator populations, are experiencing declines around the world (Potts et al. 2010, Vanbergen 2013, Goulson et al. 2015, Bartomeus and Bosch 2018, Lázaro and Tur 2018). The interactions between plants and pollinators may be highly influenced by the

10 composition of species assembled within a site. Specifically for pollinators, the available foraging resources (plant abundances and species) can highly influence their abundance and population within a region (Ogilvie and Forrest 2017). Furthermore, generalists and specialists will vary in abundance and composition across space, which in turn will influence the underlying function of the community (Dupont et al. 2009, Trøjelsgaard and Olesen 2013). In this way, it is important to understand how species compositions and interactions vary across spatial gradients.

Evaluating the relationships between plants and pollinators using a network- based approach is particularly beneficial because network metrics have been shown through simulation studies to be related to the resilience of communities (see methods; Kaiser-Bunbury et al. 2010, De Frutos et al. 2015, Vieira and Almeida-Neto 2015, Santamaría et al. 2016). Resilience is the ability of the community to maintain structure and function after a disturbance (Menz et al. 2011, Vázquez et al. 2012, Goldstein and Zych 2016). In the context of networks, resilience has been evaluated by simulating the loss of species or interactions and quantifying the extent to which the network structure changes or collapses from secondary and tertiary species (e.g. Lance et al 2017). In this study, I use the interpretation from such simulations to infer resilience for communities for which I have constructed interaction networks. Mutualistic networks tend to have conserved structural patterns, which are often associated with the number of generalised and specialized interactions (Vázquez et al. 2015). The number of generalists within a community will increase the resilience of the network by providing interaction redundancy and acting as a safeguard after a disturbance by requiring more species extinctions before major structural and functional changes (Waser et al. 1996, Lance et al 2017).

Species identity or traits may control community interactions and corresponding network structure (Poisot et al. 2015). Groups of species with different life history strategies or functional traits may increase specialization or generalization (Fenster et al. 2004, Blüthgen et al. 2008, Danieli-Silva et al. 2012). For example, Fabaceae have bilaterally symmetrical flowers with banner and keel that effectively constrain visitors to (mostly) large-bodied bees, whereas the radially symmetrical flowers of Asteraceae welcome more varied and generalized visitors (Junker et al. 2013, Schiestl and Johnson 2013). Thus, specific groups of plants and pollinators may influence network structure and functional resilience by increasing generalization or specialization

11 based on similar life history strategies, functional traits, or taxonomic designations (Coux et al. 2016).

Environmental variation may further influence spatial patterns in community structure, or the species composition and interactions within a community. Specifically, variations in habitat characteristics (e.g. slope, rockiness, woody plant cover) or ecoregional climatic differences (e.g. temperature, rainfall, etc.) will influence community composition (McDonald et al. 2005). Plant communities are often associated with climate and habitat characteristics (Locky and Bayley 2010, Wiken et al. 2011); however, mobile pollinators can cross habitat boundaries, so may vary less across space (e.g. Wray and Elle 2015). While community structure in mutualistic communities tends to be conserved across temporal scales (e.g. Olesen et al. 2008, Dupont et al. 2009), less is known about how communities change across spatial scales and with various environmental gradients. Thus, there is a need for studies that compare community structure across broad spatial scales or environmental gradients (Mayer et al. 2011, Pellissier et al. 2018).

The aim of this research was to explore the composition and structure of mutualistic plant-pollinator communities across a large spatial gradient that spanned multiple ecoregions. Specifically, I addressed the following research objectives. First, I asked if the composition of plant or pollinator communities varied with ecoregion or the habitat characteristics of my sites, expecting that plant communities would differ more than those of mobile insects. Second, given differences in community composition among regions, I evaluated whether network structure also varied with ecoregion or habitat characteristics using a multivariate approach. Finally, I evaluated the importance of different groups of plants and pollinators for network metrics related to functional resilience, using a stepwise model selection framework. In this way, I aimed to connect not only species’ group compositions to network structure, but also to community resilience.

12 Methods

Overview

To examine species compositions and interactions across space I selected 16 sites, where I observed insect visits to flowering plants. First I evaluated how community compositions change across space and habitat characteristics using several ordination techniques. Second I calculated four mutualistic interaction network metrics to evaluate the impact of ecoregion, habitat characteristics, and species composition on network structure. I chose four network metrics often interpreted as being associated with community resilience for this research.

Study Region & Sites

I selected 16 sites within oak-savanna habitats across a 6 degree latitudinal gradient (~ 650 km). This is a unique forb- and grass-dominated habitat which ranges from British Columbia to . Due to a combination of conversion to agriculture and fire suppression, this habitat is increasingly rare (Fuchs 2001, Vesely and Rosenberg 2010). In British Columbia, only 1-5% of the original oak-savanna remains in near natural condition, with similar loss of this habitat estimated in Oregon and Washington (5-35% remaining; Fuchs 2001, Altman and Stephens 2012, Schultz et al. 2011). My sites extend from Vancouver Island, British Columbia, Canada to the Umpqua Valley in Oregon, USA (Table 2.1, Figure 2.1). These sites are within three ecoregions; six sites in the Strait of Georgia/ Puget Lowlands, eight sites within the Willamette Valley, and then two sites within the Umpqua Valley (Figure 2.1, Cronquist 1982, Wiken et al. 2011). I selected sites representing different sub-habitat types within oak-savanna habitats when possible, including oak-woodlands, upland savannas, rocky outcrops, and wet-oak prairies (Altman and Stephens 2012). I selected only sites with over 1ha of established forb communities (fragment sizes ranged from 10-600ha), with similar forb diversity and density, as previous work (in oak-savanna ecosystems) within my research group (e.g. Neame et al. 2013, Gielens et al. 2014, Wray and Elle 2014). Site selection was limited by availability and access which left some gaps in my latitudinal gradient (Fig 1).

13 Study Design

In each study location, I performed a systematic survey of the plant-pollinator interaction network within a 1 ha plot. All field work was conducted between April and July of 2017, the bloom time of most of the forbs in this ecosystem. All sites were surveyed twice by each of two net collectors, once in the morning and once in the afternoon on different dates, on days with less than 12 km/h wind and above 13°C. To account for observational bias among flowers and pollinators of different size, colour, and abundance, surveys were performed separately on each flower species in bloom and consisted of 10-minute net collections of insects contacting the reproductive organs of flowers. Only forb and shrub species with >= 50 individuals in bloom and with flowers greater than 2mm in width (or small flowers part of a larger inflorescence) were included, while wind pollinated grasses and large trees were excluded from observations. Each collector observed plant species in a different random order and timers were stopped when processing insects. All collected insects were pinned and identified to the lowest taxonomic level possible. In addition, and rare bumble bees were identified on the wing or from photographs, but not collected.

Flower Abundance Surveys and Habitat Characteristics

Within each plot, I established four 50m survey transects to measure flower diversity and abundance. I counted open flowers within 10 rectangular 2 x ¼ m quadrats randomly stratified at 1-5m increments, as well as, randomly assigned directions (left or right) off each transect on each survey date. I defined “floral units” by species, and these were normally an entire inflorescence, or sometimes a cluster of flowers on a branch of a shrub. I did not include species excluded from net surveys due to flower morphology, but counted all flowers by species even if abundance was too low for net surveys.

At the end of my field season, I measured habitat characteristics using 1 x ¼ m quadrats. I placed 100 quadrats per site along transects of varying lengths, at regular 10m increments, systematically covering the entire 1 hectare plot area for each site. Within each quadrat I calculated the percent cover of open rock, moss, and classes of vegetation (forbs, grasses, shrubs, and trees). I also measured the maximum vegetation height of forbs, grasses, ferns, and shrubs to get an average vegetation height. Finally, I categorized slope for each quadrat into one of four steepness bins (0-15, 15-30, 30-45,

14 45+ degrees). I then used the maximum bin size for each quadrat (15, 30, 45, and 60 degrees) to calculate the average slope for the site.

Community analysis

To interpret habitat characteristics, I used Principal Components Analysis (PCA) to reduce my eight variables (% grass, % forbs, % trees, % shrubs, % rock, % moss, average vegetation height, average slope) to a single principal component. I centered and standardized all habitat characteristics to account for scale differences between variables before analysis. Using Pearson correlations between PC1 and my input variables, I interpret PC1 as distinguishing among sites that are rocky, steep, and with more woody vegetation versus those that are flatter with more forb cover (Table 2.2).

I then used PC1 and ecoregion to interpret the distribution of sites in species- space in an ordination using the ‘envfit’ function with non-metric multidimensional scaling (NMDS). Envit fits environmental vectors or factors onto the species distribution in the NMDS. I performed this analysis separately for plants and pollinators, and compared the vector correlation coefficients to determine the better predictor of community composition (e.g. ecoregion or habitat). Multivariate analyses were conducted using the ‘vegan’ package in R (Oksanen 2015).

Interaction Network Structure

I evaluated whether network properties (and the resilience that is attributed to them) could be predicted by species’ group membership. I constructed insect-plant interaction networks from net surveys combined across two survey dates for each of my 16 sites (Table 2.3). Four network metrics (Network-level Specialization, Modularity, Nestedness, and Interaction Asymmetry) were calculated using the ‘Bipartite’ package, following recommendations on analysing weighted interaction metrics from package documentation (Dormann et al. 2017). I removed all species with only one occurrence within a site to avoid over dispersion. Additionally, I analysed the effect of ecoregion and habitat characteristics on network structure using Multivariate Analysis of Variance (MANOVA), including four network metrics (see next paragraph) as response variables.

15 Network-level Specialization (H2’) characterizes the degree of interaction specialization amongst all members of the network, calculated based on the interaction diversity at the species-level (Blüthgen et al. 2006). This measure evaluates the selectiveness for each species in the network and calculates a comparable inter-network value between 0 (no specialization) and 1 (totally specialized, Blüthgen et al. 2006). Higher values of H2’ are negatively associated with resilience in simulation studies, because with more specialization within the network, function is lost when species are lost. When networks are more generalized, redundancy in connections leads to maintenance of network structure after a disturbance, thus maintenance of function (Soares et al. 2017).

Modularity (QuanBiMo) evaluates the level to which species occur within link-rich clusters, where more interactions occur within the cluster than between the clusters (Dormann and Strauss 2014). Similar to H2’, higher modularity is interpreted as having a negative relationship with resilience in simulation studies, because when clusters of species are isolated from others there is less redundancy in interactions, and loss of species leads to loss of function (Spiesman and Inouye 2013, Goldstein and Zych 2016, Soares et al. 2017).

Weighted Nestedness (WNODF) measures the degree species are organized around an interacting core of generalists (Bascompte and Jordano 2007, Elle et al. 2012). It evaluates the extent to which specialists interact with the main generalists, versus interacting with other specialists. This weighted metric accounts for network size and interaction strength, and is a core pattern within all mutualistic networks (Almeida- Neto and Ulrich 2011). Higher values of WNODF have been shown to have a positive relationship with resilience in simulation studies, because more species are connected to the generalists in the community, increasing overall connectivity (Bascompte and Jordano 2007, Gibson et al. 2011). For example, generalised plants, like creosote bush (Larrea tridentate), are often visited by and support a wide range of specialist pollinators (Minckley et al. 2000) and contribute to increased nestedness in the system.

Interaction Strength Asymmetry (ISA) evaluates the dependence of one species on another (Blüthgen 2010, Dormann et al. 2017). Resilience increases with higher asymmetry in simulations, because when species do not depend on each other equally it is because there are additional interactors that can provide redundancy (Soares et al.

16 2017). In contrast, equal reliance can imply a highly specialized interaction, and so loss of one species leads to loss of the other.

Categorizing species for analysis

This research included 103 plant and 287 animal species, and so I grouped species by life history strategies, functional traits, and taxonomic levels to allow for interpretation of the importance of various traits on network structure. I established four categories for plants and three for pollinators. I defined three to eight groups within the categories for every species (Table 2.4). I excluded groups within my analyses if those groups were only present in less than 10 of my sites (e.g. were in 6 of 16 sites and so were excluded).

To establish plant groups, species traits were taken from databases, such as e- Flora BC and the Jepson Manual (Klinkenberg 2018, Baldwin et al 2014). Plant group categories were flower colour, inflorescence size, inflorescence type, and (Table 2.4). Due to low abundance, red and orange flowers were included within pink and yellow groups, respectively. Yellow or purple flowers are known to attract many pollinator species, and will likely increase connectivity and relate positively to resilience while less attractive colours, such as blue and red, may have a negative relationship with resilience because fewer insects may see them as a resource (Fenster et al. 2004). For inflorescence size, I used the maximum number of flowers produced by an individual, put into size bins of low (1-15), medium (16-50), and large (51+). Larger may attract more pollinators, thus increasing the overall generalization of the network (Coux et al. 2016). I also considered inflorescence structure, as pollinator behaviour may vary between (for example) spikes and , vs. heads and composites. Compact, “easy access” inflorescence types like heads and composites will likely attract more generalized pollinators, so should be positively associated with resilience. Finally, plant taxonomic groups were comprised of plant orders and were used to account for unmeasured variation in plant traits.

Pollinator categories were larval diet, nesting location, and taxonomy (Table 2.4). Larval diet and nesting location were determined using insect reference guides, such as Bug Guide, and ‘The Bees of the World’ (Michener 2007). Kleptoparasite larvae, such as those in Nomada or Sphecodes, were placed within the ‘parasite’ group, because of their

17 dependence on host species. The larval diet may influence adult behaviour and foraging patterns. For instance, adult forms of the predatory larvae dietary group may be more opportunistic foragers (searching for prey rather than particular flower types) and interact mostly with generalist “easy access” flowers. Nesting location was determined by where females made nests or laid eggs. All nests and eggs laid on vegetation were considered ‘above’, and any species with potential for both above- and below-ground nests were placed in ‘both’. My below-ground nesting insects have high numbers of bees that are known to be generalized in their interactions with many plant species (Halictidae, Bombus), and so although nest location isn’t expected to impact network structure directly, I predict this group will increase generalization and so improve resilience. As for plants, I used taxonomic groups to account for unmeasured traits that may impact network interactions. Insect taxonomic groups were families, however some taxonomic groups were established as orders, because specific families were not collected in 10 or more sites, thus were combined at the order level.

Stepwise model selection using AIC

I used single-predictor linear models to evaluate how network metrics were predicted by categories of plants and insects. I logit transformed H2’, ISA, and Modularity, because these values are bounded between 1 and 0. I compared model fits of the relationship between categories of plants and insects and network metrics using Akaike Information Criterion (AIC) to select top models between the four network metrics and each of the seven categories of plants and insects. From the AIC scores, I calculated the delta AIC by subtracting scores of each model from the lowest AIC model. I further calculated an associated Akaike weight for each model and determined the discrepancy between top models and the next best fit models (Wagenmakers and Farrell 2004).

I considered all models within two delta AIC of the lowest fit model to be significantly similar and further rejected all models within two delta AIC of the null. Initial models evaluated the relationship of groups within categories of plants and insects to network metrics. I then compared in a secondary AIC analysis only those groups identified as the ‘best models’ from the analyses of categories of plants and insects, to establish a ‘top model’ for each network metric (Table 2.5, Figure 2.4). Similar to the ‘category’ analyses, within the ‘top model’ analysis I considered all models within two

18 delta AIC of the lowest fit model to be the best predictive model. In this way, I systematically reduced the number of models, then evaluated the model fits of only those top models two delta AIC below the null.

Results

We conducted a total of 1152 netting surveys over 192 hours of observation, and collected a total of 7,395 insects consisting of 287 species, from 103 plant species. Nearly 40% of all insect species were only found within one site (105 spp.), and almost 55% were only found within one of the three ecoregions (151 spp). Apoidea (bees) were the most commonly collected insect group (72%), then Syrphidae (hover-; 15%) and finally all other insect groups (13%). I collected the most insects on flowers of Asteraceae (25%) and Apiaceae (12%). I observed a total of 1,598 unique pairwise species interactions, of which Lasioglossum villosulum and the introduced Hypochaeris radicata were the most frequently observed interaction (8%, n = 593).

The most diverse plant groups in my sites were Asteraceae (11 spp.), Apiaceae (10 spp.), Fabaceae (9 spp.), and (9 spp.). For pollinators, the most diverse groups were Halictidae (42 spp.), Andrenidae (38 spp.), Syrphidae (38 spp.), and Apidae (25 spp.). The plant species with the greatest number of interactions were Plectritis congesta and Ranunculus occidentalis (54 spp. each), while for pollinators it was Bombus vosnesenskii and Apis mellifera (39 and 37 spp.).

Community Composition

Ecoregions and PC1 were significant predictors of community composition for pollinators (Figure 2). Additionally, ecoregion centroids oriented along a latitudinal gradient (R2 = 0.48, P = 0.002). I interpreted PC1 as distinguishing among sites that are rocky, steep, and with more woody vegetation versus those that are flatter with more forb cover (Table 2.2); PC1 directionally loaded on both NMDS1 and NMDS2 (NMDS1 = 0.62, NMDS2 = -0.78, R2 = 0.48, P = 0.019). In general, Andrena spp. and Bombus spp. were more abundant at higher latitudes and Tachinid flies and Toxomerus spp. were more abundant in sites that were flatter and with more forb cover.

19 For plant species, ecoregion was also a significant predictor of species composition with centroids orienting along a latitudinal gradient (R2 = 0.30, P = 0.035). PC1, however was not a significant predictor of species compositions (NMDS1 = - 0.2163, NMDS2 = -0.8054, NMDS3 = 0.5519, R2 = 0.30, P = 0.20). In general, the abundance of and Brassicaceae were higher in northern sites whereas was more abundant in southern sites.

My multivariate analysis of variance (MANOVA) showed no relationship between network metrics calculated for my 16 sites and either PC1 (Wilks lambda = 0.55, P = 0.26) or ecoregion (Wilks lambda = 0.32, P = 0.23).

Species’ Group Analysis

Pollinator groups more often predicted network structure than plant groups. Twelve models were more than two delta AIC below the null model, eight of which were from pollinator groups. Additionally, only three network metrics were significantly predicted by any models, ISA, WNODF, and modularity; specialization (H2’) was not. Finally, within the ‘top model analysis’, three groups were more than two delta AIC below the null model, two pollinator groups (above ground nesting insects and predatory larvae) and one plant group (Lamiales).

WNODF, the weighted nestedness metric, had the greatest number of models (n =5) above the null within the ‘category’ analyses, however, only above ground nesting insects were important within the ‘top model’ analysis. WNODF had a strong positive relationship with above ground nesting insects, suggesting that communities with high abundance of above-ground nesters were associated with networks structures that are thought to confer higher resilience. Above ground nesting insects were 6.3 times more likely to be the best model compared to the next best fit model (Lamiales) for WNODF.

Modularity had the second greatest number of models (n = 4) above the null within the ‘category’ analyses, three of which were important within the ‘top model analysis’: Lamiales, predatory larvae, and above ground nesting insects. Of the top models, all showed a negative relationship with modularity, suggesting a positive relationship with resilience. Lamiales were 1.5 and 2 times more likely to be the best fit model compared to above ground nesting insects and predatory larvae for modularity.

20 However, Lamiales and predatory larvae models contained one site with higher than average abundance for each group. When this site was removed from the analysis, I found that the relationship was still significant for modularity and predatory larvae, however it was not significant for Lamiales.

ISA, the interaction strength asymmetry metric, had three models above the null within the ‘category’ analyses, of which only predatory larvae was important within the ‘top model’ analysis. Predatory larvae showed a negative relationship with ISA, but as for modularity there was a single outlier site; when this was removed, the model was no longer significant.

Finally, for H2, the network-level specialization metric, all models were within two delta AIC of the null for the ‘category’ analysis, thus cannot be considered significantly different than the null. However, above ground nesting insects was predicted as the best fit model at 1.85 delta AIC from the null, and was marginally non-significant (P = 0.07). Similar to modularity, H2 had a negative relationship to above ground nesting insects, suggesting these insects are associated with network structures that have higher resilience.

Discussion

In this study, I evaluated the community compositions of plants and pollinators across the northern extent of Pacific oak-savanna, compared plant-pollinator networks across three ecoregions, and evaluated the effects of groups defined by traits, functions, or taxonomy on four network metrics related to resilience. I found that pollinator communities varied with ecoregion and habitat characteristics, however plant species varied only by ecoregion. Network metrics, did not differ along ecoregional boundaries, though were significantly related to several pollinator and plant groups, such as above ground nesting insects, predatory larvae, and Lamiales. Further, abundance of both above-ground nesting insects and insects with predatory larvae appear to predict community network structure associated with higher resilience.

I found a divergence in pollinator compositions between sites that were flat open forb-rich savannas and sites that were steep rocky outcrops with higher woody plant cover. Pollinators vary in their habitat preferences (Shackelford et al. 2013, Pisanty and

21 Mandelik 2015, Wu et al. 2018), likely leading to the differences I observed among habitats. Specifically, I found more Bombus sp. and Andrena sp. in the Strait of Georgia/ Puget Lowland ecoregion, which were typically steeper and rockier sites, and more Toxomerus sp. and Coleoptera in the southern ecoregions, which were more open, forb rich sites (Figure 2.2). I had expected that mobile pollinators would not vary much across my sites, but my results suggest that habitat differences are indeed important in structuring pollinator communities. Previous research has shown that local and landscape level habitat complexity effects both pollinator richness and abundance (Shackelford et al. 2013); it would be useful to consider the broader landscape in future studies in our region. I additionally found that pollinator communities varied with ecoregion, suggesting the importance of regional climate patterns. This finding may have important implications for potential issues with the timing of pollinator emergence with plant bloom considering the rapid increase in global temperatures and shifting climactic patterns (see Glenny et al. 2018).

Flowering plant community composition within my study region was significantly predicted by ecoregion but, surprisingly, not habitat characteristics. sp. and Brassicaceae sp. were more common in the Strait of Georgia/ Puget Lowlands ecoregion, which tended to include steep rocky sites, while and Iridaceae were more associated with the Willamette Valley and Klamath Mountain ecoregions, which were open, often wetter sites (Figure 2.3). Other researchers have found climate or ecoregion to be important predictors of plant distributions (Locky and Bayley 2010), in addition to habitat level variations, such as water and soil nutrients (Ozinga et al. 2005). I note that the lack of significant habitat associations in my data is likely linked to the variables I did or did not measure. For instance, although some of my sites were under water at the start of the field season, I measured habitat characteristics at the end of the season after standing water no longer remained. Thus, habitat metrics did not capture the difference between wet-savanna sites and sites that remained dry all season. Regardless, my study was able to reveal patterns in oak-savanna plant communities across ecoregions.

Network structure was not different across ecoregions, and did not vary with my measured habitat characteristics, despite spatial differences in pollinator species compositions. This result differed from my predictions. Previous research has found that mutualistic networks appear to be conserved across temporal scales (Olesen et al.

22 2008, Dupont et al. 2009), though few studies have examined the structural patterns of networks across space (Morales and Vázquez 2008, Cuartas-Hernández and Medel 2015, Poisot et al. 2015, Pellissier et al. 2018). Plant and pollinator networks may vary along latitudinal gradients (Pauw and Stanway 2015), and at different altitudes (Watts et al. 2016), but my results did not indicate spatial variation in network properties based on ecoregional boundaries despite both plant and pollinator compositional differences at this scale. This suggests that while species’ identity may turnover between regions the types of interactions (such as those between generalists and specialists) within the network were not influenced by ecoregion or habitat characteristics and were more likely impacted by local factors.

In my study area, the relative abundance of functional and taxonomic groups within a community may be better predictors of network structure than habitat characteristics or ecoregional effects. While many studies have evaluated the impacts of habitat change on different bee groups (Steffan-Dewenter et al. 2002, Cane et al. 2006, Jha and Vandermeer 2009, Williams et al. 2010, Neame et al. 2013), few have evaluated the relationship between group between network structure and community composition as defined by groups designated by traits or taxonomy. Here, I found that above-ground nesting insects, predators, and Lamiales were the most important groups in terms of their influence on network structure. Abundance of above-ground nesters was positively related to nestedness, and was negatively related to modularity and (marginally) network-level specialization. These findings suggest that above ground nesting insects provide interaction stability within networks, which is thought to increase resilience based on the findings of simulation studies (Ferreira et al 2013, Lance et al 2017, Soares et al 2017, Vanbergen et al 2017). One reason for this finding may be that above ground nesters, which include hover-flies, , and the bee, Apis mellifera in my data are often very abundant and have highly generalized floral visit patterns (Giannini et al. 2015, Lucas et al. 2018). While A. mellifera has been shown to have significant impacts on network structure in other research (Dohzono and Yokoyama 2010, Giannini et al. 2015, Mallinger et al. 2017, Montero-Castaño and Vilà 2017), to my knowledge, only one other study has highlighted the effect of Syrphid flies on network structure (Lucas et al. 2018). My study further highlights the biological importance of these species within pollination systems in this region. The importance of Syrphid flies may be due to how their foraging choices differ from those of bees, which

23 are central-place foragers (Williams et al. 2010, Zurbuchen et al. 2010, Olsson et al. 2015). In contrast, Syrphids use plants for both foraging and nesting opportunities, and so are not as spatially limited in foraging as are bees, because flies do not return to a central nest. In addition, because Syrphids are attracted to aphid volatiles, their foraging decisions are highly linked to the presence of aphids on or near plants (Bargen et al. 1998, Smith and Chaney 2007) rather than to the floral cues most commonly used by bees. Thus, my study reveals the key linkage between the identity of pollinators and plant-pollinator network structure.

Predatory larvae had a negative relationship with both modularity and interaction strength asymmetry (ISA) although the ISA finding was highly influenced by a single site with a high abundance of predatory larvae. These findings are consistent with my prediction that the predatory larval group are generalized, opportunistic foragers and likely increase resilience (Mello et al. 2011, Lucas et al. 2018). The predatory larvae category is comprised of many species of Syrphidae (subfamily Syrphinae, including Toxomerus and Sphaerophoria), which lay eggs near to aphid infestations as noted above, as well as predatory wasps that visit flowers for nectar while simultaneously searching for prey (Bargen et al. 1998, Smith and Chaney 2007, Mello et al. 2011). As noted above, predatory Syrphids and wasps are network generalists and so can increase the nestedness and connectivity of the network (Mello et al. 2011, Lucas et al. 2018). These species may reduce modularity because they primarily use flowers for nectar not pollen, thus visit a wide variety of plants with generalized flower or inflorescence morphology such as composites and umbels (Fontaine et al. 2006). Finally, Lamiales was negatively related to modularity, but this result was not robust to removal of a single site with very high abundance of this plant order. I had expected that bilateral flowers in orders like Lamiales might increase specialization in networks because their flowers restrict visits to a subset of flower-visiting insects (Watts et al. 2016, Fontaine et al. 2006), but just one site had high numbers (especially in genera Prunella and ) and so I must disregard this finding. Overall, my study highlights the importance of highly generalized species, and specifically the importance of species often outside of the majority of conservation concern, such as hover-flies, on network structure and community connectivity.

In this study, I show that ecoregion and habitat characteristics are influential predictors of species compositions, however they did not explain the variation in species

24 interaction networks. Group composition, on the other hand, was a better predictor of network structure and was related to several network metrics that are associated with resilience. Though resilience is inferred in the present study based on interpretations available in the literature (Bascompte et al 2003, Santamaría et al 2014, Santamaría et al 2016, Lance et al 2017, Soares et al 2017) understanding the connection between species’ groups and network metrics may provide insight into how variations in species compositions influence the ability of the community to persist after a disturbance. Some groups may be comprised of more generalized floral visitors and thus the composition and interactions of these groups likely influences the resilience of communities (Spiesman and Inouye 2013). However, some groups are affected differentially by habitat disturbances (Steffan-Dewenter et al. 2002, Neame et al. 2013, De Frutos et al. 2015). This indicates a need to associate functional or taxonomic groups of plants and pollinators with network metrics, to better understand how these communities will change over time and after a disturbance (Winfree et al. 2011, Kennedy et al. 2013). Finally, understanding how environmental characteristics and spatial dynamics influence the structure and resilience of prairie communities is essential to the management and conservation of these systems.

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33 Tables

Table 2.1 Ecoregion information, pollinator and plant abundance and richness, from 16 oak-savanna sites in British Columbia, Washington, and Oregon arranged by latitude. Region indicates ecological regions of focus from north to south: Georgia Straight/ Puget Lowlands (GPL), Willamette Valley (WV) and Klamath Mountains (KM). Insect abundance is the number of insects collected within a site after removing species with only a single individual at that site, and richness also does not include singletons. Flower abundance is the total number of counted flowering units from transect surveys. Sites Province/ Abbrev. Region Latitude Insect Insect Plant Plant State Abun. spp. Abun. spp. Cowichan Garry Oak BC GO GPL 48.8 518 43 437 15 Mount Tzuhalem BC MZ GPL 48.8 335 34 382 17 Mill Hill BC MH GPL 48.6 264 41 522 15 Oak Haven Park BC OH GPL 48.5 501 38 544 17 Mima Mounds WA MO GPL 46.9 206 32 732 14 WA RP GPL 46.9 378 36 597 16 Lacamas Lake Park WA LLA WV 45.6 623 49 402 17 Lacamas Prairie WA LC WV 45.6 376 41 658 18 Jefferson Farm OR JF WV 44.8 702 45 231 15 Kingston Prairie OR KI WV 44.8 564 50 293 14 Bluebird Strip OR BL WV 44.4 414 32 353 15 Bellfountain Rd OR BR WV 44.4 359 34 215 15 Willow Creek OR WC WV 44.0 561 39 529 13 Coburg Ridge OR CO WV 44.0 487 40 486 21 North Bank OR NB KM 43.3 197 31 415 19 Popcorn Swale OR PO KM 43.3 456 40 592 13

34 Table 2.2 Eigenvectors of Principal Component Analysis performed on eight habitat characteristics for 16 sites ranging from BC to OR. Values in the table are the coordinates of variables for PC1. PC1 accounts for 44% of the variation in the data. Habitat Characteristics PC1

% Rock Cover 0.43

% Forb Cover -0.43

% Grass Cover -0.11 % Tree Cover 0.32

% Shrub Cover 0.34

% Moss Cover 0.41

Average slope 0.34

Avg. Veg. Height -0.33

35 Table 2.3 Network metrics for 16 oak-savanna sites in British Columbia, Washington, and Oregon are listed north to south. Network metrics include network specialization (H2) and modularity (both of which are negatively associated with resilience) and interaction strength asymmetry (ISA) and weighted nestedness (WNODF; both of which are positively associated with resilience). For H2, ISA, and modularity, values are bounded between 0 and 1; values closer to 1 indicate a higher degree of that metric in a site. H2, ISA, and WNODF were calculated using the ‘networklevel’ function and modularity was calculated using the ‘computemodules’ function in the Bipartite package in R. Sites H2’ Modularity WNODF ISA

Cowichan Garry Oak 0.56 0.56 14.50 0.17 Mount Tzuhalem 0.57 0.58 10.08 0.13 Mill Hill 0.57 0.60 5.86 0.18 Oak Haven Park 0.50 0.50 11.18 0.09 Mima Mounds 0.53 0.57 10.09 0.13 Rocky Prairie 0.56 0.54 9.33 0.12 Lacamas Lake Park 0.66 0.64 11.59 0.12 Lacamas Prairie 0.48 0.50 12.39 0.11 Jefferson Farm 0.58 0.60 15.19 0.15 Kingston Prairie 0.63 0.57 10.21 0.14 Bluebird Strip 0.67 0.60 8.04 0.13 Bellfountain Rd 0.47 0.51 17.48 0.06 Willow Creek 0.44 0.44 17.83 0.12 Coburg Ridge 0.42 0.45 11.55 0.04 North Bank 0.53 0.59 6.87 0.13 Popcorn Swale 0.54 0.54 18.64 0.23

36 Table 2.4 Plant and pollinator natural history and functional group categories and the group types within them. Each plant and insect species was assigned to a group within the respective plant and pollinator group categories. Only groups with representatives in at least 10 or mores sites were included in my analyses. Nesting Location Larval Diet Taxonomic Above Ground Detritivore Andrenidae Aquatic Apidae Below Ground Parasite Bombiliidae Both Pollen Coleoptera Predator Halictidae Megachilidae Pollinator Categories Other Diptera Syrphidae Flower Color Inflorescence Size Inflorescence Type Taxonomic Blue small (0-15) Cyme Pink medium (16-50) Head/Composite Purple large (50 +) Solitary White Spike/ Yellow Umbel Lamiales

Flower Categories

37 Table 2.5 Generalized linear models of network metrics and their relationship to groups for plants and pollinators. Models represent the nine highest predicted models within the ‘top model analysis’(those two delta AIC above the null); the group analysis (performed separately by group category for each of plants and pollinators) is presented in Appendix A. Significant models are indicated by an *.

Top Models DAIC Akaike Weight Network-level specialization H2 ~ Above 1.86 0.17 Weighted Nestedness WNODF ~ Above* 0.00 0.68 WNODF ~ Lamiales 3.69 0.11 WNODF ~ Apidae 4.63 0.07 WNODF ~ Coleoptera 4.77 0.06 WNODF ~ Parasite 4.82 0.06 WNODF ~ Null 7.03 0.02 Interaction Strength Asymmetry ISA ~ Predator* 0.00 0.81 ISA ~ Syrphidae 4.39 0.09 ISA ~ Purple 4.88 0.07 ISA ~ Null 7.13 0.02 Modularity mod ~ Lamiales* 0.00 0.40 mod ~ Above* 0.80 0.27 mod ~ Predator* 1.34 0.20 mod ~ Asterales 2.73 0.10 mod ~ Null 5.24 0.03

38 Figures

Figure 2.1 Map of 16 oak-savanna sites across three ecoregions in BC, OR, and WA. Six sites are in the Strait of Georgia/ Puget Lowlands, eight sites are in the Willamette Valley, and two sites are in the Klamath Mountains. Northern-most sites are approximately 650km and 5.5° latitude separated from southern-most sites. Shape files for the ecoregions were downloaded from https://www.epa.gov/eco- research/level-iii-and-iv-ecoregions-continental-united-states.

39

Figure 2.2 NMDS plot of pollinator communities within 16 oak savanna sites. Colour corresponds to ecoregions; Strait of Georgia/ Puget Lowlands (GPL), Willamette Valley (WV), and the Klamath Mountains (KM, Table 2.1). Shapes correspond to province and state locations. Final stress was 0.161 with 2 dimensions. Ecoregion and PC1 significantly correspond to differences on pollinator compositions. Genera and families of insects shown on axis have strong directional loadings and are shown to aid interpretation.

40 1.5

Iridaceae

1.0

Prov./ State

0.5 BC WA OR

0.0 Ecoregion

NMDS2 GPL WV KM −0.5

PC1 −1.0 Asparagaceae Brassicaceae −1 0 1 Brassicaceae NMDS1 Boraginaceae

Figure 2.3 NMDS plot of plant communities within 16 oak savanna sites. Color corresponds to ecoregions; Strait of Georgia/ Puget Lowlands (GPL), Willamette Valley (WV), and the Klamath Mountains (KM). Shapes correspond to province and state locations. Final stress was 0.122 with 3 dimensions. Ecoregion significantly corresponds to plant compositonal difference. Families of plants shown on axes have strong directional loadings and are provided for interpretation.

41

Figure 2.4 Plotted model outputs for the top models of each of four network metrics H2 (network-level specialization), ISA (interaction strength asymmetry), Modularity, and WNODF (weighted nestedness) and corresponding log transformed plant and pollinator groups (Table 2.4). Plots in left column (A, C, E) indicate models with significant group predictions of network metrics, while models in right column are either primarily influenced by an outlier site (Willow Creek, B, and Coburg, D) or were marginally non-significant (F).

42 Chapter 3.

Evaluation of specialization and foraging preferences across ecoregions, in a mid-sized mining bee Andrena angustitarsata (Hymenoptera: Andrenidae)

Summary

With growing concern over the loss of global biodiversity, specialist species may be at high risk of extinction or removal. The dietary preferences and requirements, however, for most bee species are poorly understood. Yet a species’ dietary breadth, the range of plant species a bee visits or collects pollen from, may change as preferred host plants increase or decrease in abundance or with the presence of alternative host plants. Recently the mid-sized mining-bee Andrena angustitarsata was described as an eclectic oligolege species (collecting pollen from < 3 plant families) on Apiaceae species in two regions of Vancouver Island British Columbia, Canada, however floral visit records from a larger geographic range indicate the species is a generalist. In this study, I evaluate the dietary breadth and foraging preferences of the mining bee Andrena angustitarsata across 16 sites including not only British Columbia but also Washington and Oregon, United States of America. While bees indicated a strong preference for both Apiaceae and Rosaceae within two ecoregions the level of visit specialization increased in the Strait of Georgia/Puget Lowland ecoregion where Apiaceae plants were more abundant. Spatial patterns did influence the relative specialization of bees, however bees maintained strong preference for particular flower families (Apiaceae and Rosaceae) regardless of local abundance. I conclude that the dietary breadth of Andrena angustitarsata is closer to mesolecty or even polylecty, however the species shows strong preference for only two plant families and so is not as generalized as previous records suggest. These results contribute to the growing need to understand pollinator foraging behaviours in order to provide informed management and conservation decisions that benefit both plants and insects.

43 Introduction

Floral preferences and dietary breadth are known to impact pollinator foraging behaviours within an area (Westrich and Schidt 1986, Müller and Kuhlmann 2008). Specialization on floral resources (dietary specialization) may improve foraging efficiency and nest productivity (Strickler 1979, Williams 2003, Sipes and Tepedino 2005), while generalization may allow bees to persist and nest in habitats with few preferred resources (lower quality). Out of the approximately 20,000 described bee species most are likely polylectic (pollen generalists, provisioning nests with pollen from multiple plant genera and families), and fewer oligolectic (pollen specialists, provisioning from one to three genera in one plant family), though few bee species have had the pollen they collect evaluated (Wcislo and Cane 1996, Cane and Sipes 2006, Michener 2007, Fowler 2016). The dietary breadth of a species may affect its ability to persist in a habitat if its preferred plant partners are absent (Bommarco et al. 2010, Winfree et al. 2014). Dietary specialists (oligolectic bees) may not be able to persist if their host plants are removed (Winfree et al. 2014), and even some polylectic bees with strong foraging preferences (Davis et al. 2012) may be susceptible to declines in the absence of host plants (Ashworth et al. 2004, Davis et al. 2012). In light of concerns over declining native and wild bee populations (Potts et al. 2010, Vanbergen 2013, Goulson et al. 2015), evaluating the floral interactions, floral preferences, and foraging behaviours of bees (especially potential dietary specialists) across spatial scales is essential to understanding how habitat and regional characteristics will impact bee populations (Williams et al. 2001, Olesen et al. 2008, Burkle and Alarcón 2011, Davis et al. 2012).

The dietary breadth of a species may not be conserved across space and may depend on the available floral resources. Thus, bees are likely to exhibit facultative foraging behaviours (e.g. selecting resources based on availability); even species with strong foraging preferences may switch to alternative host plants when their preferred flowers are absent (Wood and Roberts 2017). Theoretically, for a species to be oligolectic its floral preferences should remain constant between habitats and with different floral assemblages. However, considering that specialization is rare within northern temperate regions, facultative foraging would seem more likely even for oligolectic species (Sipes and Tepedino 2005). Dietary specialists may be excluded from otherwise suitable habitats if their preferred plant host is absent, unless they have

44 adaptive facultative foraging behaviours or effective diapause, where bees wait for their foraging hosts to bloom (Cane and Sipes 2006). Such behaviours would allow species to persist in habitats with fewer preferred plant species, yet little is known about facultative foraging for most bees and even less so across spatial gradients (Sipes and Tepedino 2005, Fründ et al. 2010).

Once pollen analysis is complete, it is possible not only to evaluate how dietary specialization might change across the landscape, but also how it might relate to visit specialization, using metrics from network-based methods. For instance, the species- level specialization index, d’, characterizes the degree to which a species interacts with all potential partners within the network (Blüthgen et al. 2006). Bees with high d’ values have lower overall connections to the whole network, indicating more specialized foraging behaviour. The d’ index is typically representative of the visit specialization of a species and may be a useful statistic in comparison to the species’ dietary specialization, the resources collected by the bee (pollen content in the scopae), providing insight on whether behaviour and resource use align.

An alternative method to evaluate foraging behaviour is to apply classic habitat- use versus availability metrics, commonly used for many vertebrate species (Neu et al. 1974, Byers et al. 1984), but rarely used for invertebrates. Some studies have compared pollen collected by solitary bees to available forage (flowers) as a means of evaluating foraging preferences (Davis et al. 2012, Jha et al. 2013, Ritchie et al. 2016). Utilization- availability indices can provide additional information, because they indicate the relative preference for each plant species within a locality and thus can provide insight into how the magnitude of preference changes across plant communities. If a bee species has specialized dietary preferences then their use of particular plant species should be high, regardless of the abundance and availability of that plant species.

Determining where a bee fits within the specialization-generalization spectrum is both qualitatively and descriptively challenging. However, by using multiple approaches that evaluate pollen collection, visit patterns, and available forage we may be able to more accurately describe the dietary breadth of a species. In this study, I evaluate the dietary breadth of a common mid-sized mining bee, Andrena angustitarsata. This species of solitary bee is an eclectic oligolege (collect pollen from 2-4 genera in 2-3 plant families) on plants in the carrot family, Apiaceae within natural and urban habitats on

45 Vancouver Island, BC (Wray and Elle 2015). Specialization is common in Andrenidae (Chambers 1946, Michener 2007, Fowler 2016), however, collection records from museums and digital references like Discover Life suggest A. angustitarsata is a floral generalist, in contrast to pollen use data from BC (Schuh et al 2010). It may be that A. angustitarsata are facultative in their foraging across their range, depending on the abundance of their preferred host. Here, I evaluate the foraging preference of Andrena angustitarsata using pollen analysis, visit patterns, and plant abundances across three ecoregions in the Pacific Northwest. First, I ask if female bees collect differential quantities of Apiaceae pollen among the ecoregions. I also evaluate whether pollen on the body (that may adhere from visits to nectar sources) and in the scopae (purposefully collected to provision the nest) differ. Second, I evaluate whether ecoregion or the abundance of Apiaceae predicts the species-level specialization index, d’, measured using visit patterns, and whether this index predicts dietary specialization. Finally, I calculated a utilization index to evaluate which plant families are preferred/avoided relative to their availability in the habitat.

Methods

Study Design

As a part of a larger research project (Chapter 2), all insect-plant interactions were originally observed within 16 oak-savanna fragments in three ecoregions, the Strait of Georgia/ Puget Lowlands (GPL), the Willamette Valley (WV), and the Klamath Mountains (KM, Commission for Environmental Cooperation 1997, Wiken et al 2011). In this study, I only evaluated the floral interactions of Andrena angustitarsata, within 11 sites were the bees were in high enough abundance within GPL and WV (Table 3.1). Ecoregions aim to characterize the landscape delineating boundaries based on climatic, hydrological, topographical, and biotic factors (Commission for Environmental Cooperation 1997, Wiken et al 2011). Study design and floral abundance surveys are described in more detail in Chapter 2. In summary, each site was sampled twice during the flowering season, between April and July of 2017. Within each sampling period, two people conducted two 10min observations of each species, collecting all floral visitors interacting with the reproductive organs of flowers. Observations were conducted once in the morning and once in the afternoon on different days and in non-

46 overlapping order between collectors. Furthermore, to assess the influence of floral abundances on pollen and visit specialization, I conducted separate floral abundance surveys for each site. Although ~7500 insects from ~300 plant species were documented in this work, here I focus only on female A. angustitarsata and their floral associates.

I subsampled all sites with over 25 A. angustitarsata, which were comprised of three sites in GPL; Cowichan Garry Oak Preserve, Mount Tzuhalem, and Oak Haven Park, by randomly selecting 15 specimens from each of these sites for analysis.

Pollen Frequency and Abundance

For most solitary bee species, little is known about diet beyond lists of plant species from which the species has been collected; this does not provide insight into dietary specialization. Visit patterns may not be representative of a species’ true dietary breadth, because even specialist bees will visit a variety of plants for nectar while only collecting pollen from a few species, and floral records for male bees provide no insight into dietary specialization, as they do not provision nests (Wcislo and Cane 1996, Cane and Sipes 2006, Tur et al. 2014, Fowler 2016). Furthermore, the majority of papers evaluating dietary breadth of a species refer to visit patterns and do not evaluate the pollen collected by bees to provision their nests. Pollen analysis is necessary to provide insight beyond visit records to differentiate among plants used for nectar and plants used for provisioning nests (Cane and Sipes 2006, Tur et al. 2014).

I identified pollen from two locations on each specimen, ‘the body’ defined as the head and the anterior thorax, and ‘the scopae’ which includes the hind legs and thoracic scopae (poster of the thorax). Pollen from the body was collected using small cubes of fuchsin gel to both dab pollen grains stuck to the head and the top, front, and lateral sides of the thorax, as well as dye pollen grains for ease of identification (Kearns and Inouye 1993, Toshack and Elle in press, Bosch et al. 2009). Gel was then melted to a microscope slide with 10-15uµl ethanol added to increase the spread of pollen across the slide and a cover slip was added (Kearns and Inouye 1993). ‘Scopal pollen’ was only collected from regions with specialized scopal hairs, avoiding the front pairs of legs and abdomen. Scopal pollen was removed from each bee by pipetting 10µl ethanol onto the scopal hairs on the legs and thorax, then using a new pipette tip to suction off saturated

47 pollen. If not all pollen was removed, the remainder was scraped from the scopal regions and added to the pollen-ethanol solution. I then vortexed the solution for 5 seconds, pipetted 10-15µl on to a microscope slide, and added melted fuchsin gel to assist in pollen identification (Kearns and Inouye 1993, Güler and Sorkun 2007).

Pollen grains were identified to the family level at 400x magnification, using a pollen reference collection I prepared from flowers from the study region, as well as online pollen identification databases such as Pollen Wiki and The Global Pollen Project (Martin and Harvey 2017, Stebler 2019). To maintain a unified sampling structure one person counted up to 100 pollen grains for each sample (Kearns et al.1998). To get a reasonably representative sample of pollen I divided each slide into four quadrants and counted up to 25 pollen grains within each quadrant along transects alternating direction vertically and horizontally and alternating starting at the edge of the slide or the centre. I included 12 slides that had between 51 and 99 grains, but discarded any slides with a pollen count of <50 (6 slides from ‘body’ samples).

Statistical Analysis

All statistical analyses were conducted in R version 3.5.0 (R Development Core Team, 2016).

Apiaceae Distribution Analysis

To assess which factors influenced the frequency of Apiaceae pollen on the bees among ecoregions and pollen locations (body & scopae) I used two generalized linear mixed effect models (one with scopal pollen data, model A1.1 and one with body pollen data, model A1.2), with site within ecoregion as a random effect using a binomial family distribution in both (‘GLMM’, R package ‘lme4’, Bates et al. 2015, Bolker et al. 2008). I further offset the models by the log abundance of Apiaceae found in the site and I weighted them by total pollen counted to account for the max 100 pollen grain sub- sample from each slide. I evaluated pollen location, because I predict that pollen on the body of the bees would coincide with plant visit patterns, while pollen within the scopae would likely be more concentrated with Apiaceae pollen. Additionally because the Apiaceae may not be evenly distributed across ecoregions, I evaluated the difference in the abundance of Apiaceae plants between the ecoregions, using a generalized linear

48 model, with a negative binomial distribution, offset by the total flower abundance for each site (model A1.3, ‘GLM.NP’, R package ‘MASS’, Venables and Ripley 2002).

Species-Level Specialization Analysis

I calculated species-level specialization indices (d’, using the ‘bipartite’ package for each plant-pollinator interaction matrix, Blüthgen et al 2006) to evaluate how interaction specialization is influenced by the abundance of Apiaceae in addition to how well interaction specialization predicts pollen specialization. I evaluated how well ecoregion (model A2.1) and the abundance of Apiaceae (model A2.2, scaled and centred) predict species-level specialization using generalized linear models with binomial distributions, separately because of a confounding relationship between ecoregion and Apiaceae abundance (GLM, R Package ‘stats’, R Core Team). Finally, I assessed how well d’ predicts the amount of Apiaceae within the scopae of the bees using a generalized linear mixed model with site nested within ecoregion as a random effect, using a binomial family distribution (model A.3). I further weighted the model to account for the total counted pollen for each slide (because some slides had fewer than 100 pollen grains counted) thus adjusting for the relative influence of each observation based on total pollens counted.

Floral Preferences Analysis

Finally, I evaluated the preference for different plant groups within ecoregions by conducting a utilization-availability analysis to determine the statistical validity that bees are collecting from or avoiding plants in proportion to the plant’s abundance in the environment (Neu et al. 1974, Byers et al. 1984, Bartomeus 2014). I summed pollen and vegetation quantities into plant families each with above 5% abundance in both the pollen counted and the floral abundance (Apiaceae, Asteraceae, Brassicaceae, , Rosaceae). Additionally, all other plants/pollen types (below 5%) were compiled into an ‘other’ category (Appendix D, Table D.1). I averaged pollen samples within a site, then average both floral abundances and pollen for each ecoregion from sites. I used Pearson’s Chi Square test to estimate the magnitude of preference for each plant group, by calculating confidence intervals around Bonferroni z-scores of the observed pollen abundance within each plant family, compared to the abundance of each plant family within the habitat (Neu et al. 1974, Byers et al. 1984, Davis et al 2012, Bartomeus 2014). This index evaluates the magnitude to which floral abundance is

49 above, below, or equal to the amount of the resource used, in this case the pollen collected in the scopae of the bees. Flower abundances outside the confidence intervals indicate a significant effect while the magnitude of the preference or avoidance was calculated using residuals from the chi-square test.

Results

Summary Statistics

I collected a total of 332 female Andrena angustitarsata from 14 out of 16 sites, making them one of the most frequenctly collected species within my thesis. I collected A. angustitarsata in all six sites in GPL (276 bees), five of eight sites in WV (49 bees), and one of two sites in KM (7 bees). Furthermore, the two northern most sites had the highest abundances of bees, with the Cowichan Garry Oak Preserve having 111 individuals, followed by Mount Tzuhalem with 78. On average I collected 5.6x more A. angustitarsata in GPL (46 ± 17.11 bees / site) than WV (6.13 ± 2.77 bees / site). One of two sites in KM, North Bank, had zero A. angustitarsata so I excluded KM from further analysis. I also excluded all sites with only one observed bee (Coburg Ridge and Bellfountain Road, in the WV). Finally, I excluded three bees with broken hind legs or body parts (Table 3.1). After exclusions, I counted 24,241 pollen grains from 126 bees (120 bees for body) within 11 sites in GPL and WV.

Across both ecoregions, Apiaceae was the most collected pollen, with bees over all sites having on average 48% Apiaceae pollen in their scopae and 49% on the body (Figure 3.1). Rosaceae was the second most prevalent and consistently observed pollen type in the scopae (10.7%) and the body (0.08%) of the bees across all sites. Apiaceae was also the most visited plant family, having 62% of all plant visits; followed by Brassicaceae 11%, and Rosaceae 5.5% (Figure 3.1). Overall, I observed bees visiting 22 plant species within 12 plant families, meanwhile I observed pollen from 31 different plant families on the body or scopae of the bees. Of the 31 plant families found within the pollen samples of the bees, only four of these represented more than 5% of the total pollen collected in the scopae (n = 12,546): Apiaceae 48%, Rosaceae 12%, Sapindaceae 8%, and Brassicaceae 5.5%. Similarly only three plant families comprised 5% or more of the total pollen found on the body (n = 11,677): Apiaceae 50%, Rosaceae 9.5%, and Asteraceae 5%.

50 Apiaceae Distribution

The frequency of Apiaceae pollen on the bees was not different between ecoregions for both scopal pollen (model A1.1) and body pollen (model A1.2), when accounting for the abundance of Apiaceae in the environment. While the amount of Apiaceae pollen on the bees was slightly lower in WV than GPL, the effect of ecoregion was marginally non-significant for both models (Table 3.2). Conversely, GPL on average had 3.5x more Apiaceae pollen in the scopae (732.67 ± 229.39) than WV (206.14 ± 157.59, Table 3.1). Additionally, 52% of all bees from all sites had a majority of Apiaceae pollen within their scopae, followed by 9% of bees having a majority of Rosaceae and 8% Sapindaceae. These quantities vary between ecoregions. In GPL, ~60% of the bees have majority of Apiaceae pollen followed by ~10% having a majority of Sapindaceae, while in WV, ~40% of the bees have a majority of Apiaceae pollen followed by 20% having a majority Rosaceae.

The abundance of Apiaceae plants was significantly different between ecoregions (model A.1, Table 3.2) with GPL having on average 40x higher Apiaceae plants (87.5 ± 48.52) than WV (6.88 ± 4.46, Table 3.1). While Apiaceae was low in abundance within WV, Rosaceae was on average nearly 60x more abundant within WV (85.88 ± 60.63) than GPL (1.5 ± 1.5, Table 3.1). However within both regions, Apiaceae and Rosaceae (as well as many other plants observed within pollen samples) were not always captured within our vegetation surveys despite being present within or adjacent to my sites. In some cases, many of the plants observed within pollen samples but missing from vegetation surveys, were trees (like maples, Sapindaceae) , shrubs thought to be wind pollinated (poison oak, Anacardiaceae), and other large shrubs that were not sampled for pollinators due to logistical difficulties (Appendix D, Table D.1).

Species-level Specialization

On average across the sites, species-level specialization (d’, a measure of visit specialization ranging between 0, no specialization, to 1, total specialization), for A. angustitarsata ranged between 0.19 (Jefferson Farm in WV) and 0.75 (Mount Tzuhalem in GPL), with an average of 0.43 ± 0.05. However, d’ was significantly different between ecoregions, with about 1.5x higher d’ values in GPL than WV (A2.1 in Table 3.2, Figure 3.2). In GPL, the average d’ value was 0.52 ± 0.07 and in WV it was 0.32 ± 0.05.

51 Furthermore, the abundance of Apiaceae had a significantly positive effect on d’ (model A2.2 in Table 3.2). However, d’ did not predict the amount of Apiaceae pollen in the scopae, indicating a divergence between visit specialization and dietary specialization (model A2.3 in Table 3.2).

Floral Preferences

Overall, for both ecoregions, the bees had significantly different preferences for the six plant groups (GPL, X2 = 742.18, df = 5, p < 0.0001; WV, X2 = 2046.7, df = 5, p < 0.0001). Apiaceae and Rosaceae within both ecoregions were the only significantly preferred plant groups while all other plants were significantly avoided or were not significantly preferred or avoided (Figure 3.3). Based on chi residuals, Apiaceae had the highest magnitude of preference in the WV followed by Rosaceae, the opposite pattern was observed within GPL with Rosaceae having a larger magnitude than Apiaceae. Many plants that were visited or found within pollen samples, such as Brassicaceae in GPL or Ranunculaceae in WV, were significantly under-selected in comparison to their abundance, indicating that bees do not prefer these plants but instead use them only occasionally.

Discussion

Across the three sets of analyses I conducted within this study, each evaluates the influence of Apiaceae and other plants on the dietary breadth of Andrena angustitarsata. The first analysis found that the Apiaceae abundance varies across ecoregions, while the frequency of Apiaceae pollen on the bees more conserved across space. In the second analysis, species level specialization was different between ecoregions and had a positive relationship to the abundance of Apiaceae. However d’ had no relationship with the frequency of Apiaceae in the scopae of the bees. Finally, in the third analysis, I found Apiaceae and Rosaceae pollen were the only plant families positively selected for despite their abundance within each site. Overall my results indicate that A. angustitarsata has a wider dietary breadth than observed within previous geographically limited work (Wray and Elle 2015) on this species. However, my results show that A. angustitarsata displays strong foraging preferences for primarily Apiaceae plants followed by Rosaceae. Furthermore, bees collected Apiaceae in similar amounts

52 between ecoregions regardless of Apiaceae abundance, though their degree of visit specialization increased with Apiaceae abundance. This indicates a disconnect between visit specialisation and dietary specialisation (pollen), and further that the pollen selection of the bees is conserved across space while their visit patterns may be more variable. Finally, the results of the utilization-availability analysis further supports the previous two analyses, in that Apiaceae is a primary source of pollen for A. angustitarsata, though the species will facultatively forage on other species.

Pollen Collection and Plant Visits

Bee visit patterns suggest that A. angustitarsata is polylectic however, pollen frequencies and plant visits indicate that the bees have strong preferences for Apiaceae (mostly Lomatium sp.), though other plants are visited and collected in differential amounts likely depending on their local abundances. Sapindaceae and Brassicaceae were prevalent in pollen samples within GPL and Anacardiaceae and Sapindaceae were common in WV, though uncommon within the 1ha study area in each site. My results align with, to some extent, previous work (Wray and Elle 2015), but suggest more facultative use of resources than previously estimated. Apiaceae was overwhelmingly the most abundant pollen found on A. angustitarsata, consisting of 82-98% of all the pollen collected by the bees across the sites followed by Rosaceae (~13%) in the years of the previous study (2008-2010, 2012), while I observed 53.6% Apiaceae and 8.2% Rosaceae in GPL and 36.5% Apiaceae and 22.1% Rosaceae in WV in this study (2017). In both studies, Apiaceae and Rosaceae were the most commonly selected plants over all sites, however in lower frequencies in the current study.

Bee and flower populations often fluctuate greatly from year to year (Williams et al. 2001, Dupont et al. 2009, Souza et al. 2018), which may affect how much certain flower pollens are collected. Bee emergence and peak flower bloom may fluctuate between years (depending on weather), leading to a slight phenological mismatch between the bees and their preferred floral resources (Petanidou et al 2014). During my study, I experienced a temporally later and shorter spring than in the previous study years (2010 and 2012) with similar number of days with >1mm precipitation across the region in March and April (15.3 ± 2.15 days for 2017, 13.8 ± 1.13 days for 2010 and 14 ± 1.25 days for 2012), followed by higher temperatures in May and June (highest monthly temperatures; 26.2 ± 0.92 for 2017, 21 ± 0.66 for 2010, and 22.2 ± 0.64 for 2012) based

53 on Environment and Climate Change Canada data from 24 weather stations on Vancouver Island, where sites were studied in all years (Government Canada 2019). These weather patterns can lead to late bee emergence and flower bloom (due to spring precipitation) then rapid desiccation of flowers as temperature rises, potentially compressing the phenology of flower bloom. Either the availability of more diverse resources in a compressed bloom season, or lack of availability of preferred flower sources due to desiccation, may lead to bees visiting a greater variety of plants, than they might in a year with more favourable weather patterns (Hegland et al. 2009, Petanidou et al. 2014). The differences in pollen collection from year to year, or region to region, suggest that to accurately evaluate the dietary preferences and foraging behaviours of a bee species requires both spatial and temporal analysis (Sipes and Tepedino 2005). Future studies on bee foraging behaviours should include both spatial and temporal scales, especially within fragmented or other isolated habitats with lower potential of species turn over (Steffan-Dewenter and Schiele 2008).

Species-level Specialization

Species-level visit specialization was significantly predicted by the amount of Apiaceae in the environment, indicating that as Apiaceae increases in abundance, A. angustitarsata tend to have more specialized interactions. However, species-level specialization was not a good predictor of the amount of Apiaceae in the scopae. This suggests that dietary specialization and visit specialization are similar, but surprisingly may not be strongly linked. Visit patterns can only indicate part of the bee’s foraging history, which includes nectar as well as pollen foraging (Wcislo and Cane 1996, Cane and Sipes 2006, Tur et al 2014). In my study, I observed more plant families within pollen samples than from visit records, however many of these families were plants not typically considered ‘prairie species’ and often on the edges of my sites, such as Anacardiaceae (poison oak, considered wind pollinated), Sapindaceae (maple) and Salicaceae (willow). While I did not include these species within my prairie surveys and cannot make many inferences upon them, future studies on bee dietary preferences may need to consider the surrounding habitat matrix to fully assess a species’ foraging behaviours and plant preferences.

Species-level visit specialization was also significantly different between the two ecoregions, with GPL having nearly 2x higher specialization levels than WV, even

54 though the frequency of Apiaceae flowers and Apiaceae pollen within bee scopae was not significantly different between ecoregions (Figure 3.2). For some solitary bee species, the pollen content provided to larvae may have significant impacts on growth and development, and novel pollen (those not typically preferred by the bees) can lower larval growth and increase the time to development (Williams 2003). Specialization on Apiaceae may enable A. angustitarsata to have higher reproductive success which may increase their population size overtime (Johnson and Steiner 2000, Minckley et al. 2000). I found higher abundances of A. angustitarsata in sites with relatively abundant Apiaceae (such as Cowichan Garry Oak Preserve), indicating that though the bees exhibit facultative foraging behaviours they may have greater population sizes and possibly higher fitness in areas with high abundance of their preferred foraging plants. Currently, however, I can only speculate on potential reproductive outcomes of specialization for A. angustitarsata, based on the assumption that solitary bee species do not travel far from where they emerged and where they nest (Gathmann and Tscharntke 2002, Greenleaf et al. 2007).

Floral Preferences

The frequencies of pollen utilized from the top five plant families indicate significant preferences for Apiaceae and Rosaceae in relation to the abundance of those plants in the environment (Figure 3.3). All other plant groups were collected in similar frequencies to floral availability or significantly under selected relative to availability (Figure 3.3). Rosaceae had the highest magnitude of preference in GPL followed by Apiaceae and opposite pattern was observed in WV with Apiaceae having the highest preference. The magnitude reversal is likely due to low abundance of Rosaceae within GPL and low abundance of Apiaceae in WV, thus although these species were low in abundance the bees still over-selected it in both ecoregions. This suggests that while A. angustitarsata will visit and collect pollen from a diversity of plant families, they over- select Apiaceae and Rosaceae, leading me to conclude that their categorical dietary breadth is closer to mesolecty or eclectic oligolecty, with regional variation (Cane and Sipes 2006).

55 Conclusion

Andrena angustitarsata exhibit strong foraging preferences for Apiaceae based on visits, pollen samples, and abundances of Apiaceae in the habitat. They further show preferences for Rosaceae, however this was only apparent after pollen analysis. Meanwhile the bees appear to select other plant families in relation to the plant’s abundance within the habitat. It is clear that A. angustitarsata exhibit facultative foraging behaviours across their range, which may aid them to reproduce even within “lower quality” habitats (e.g. Clarke and Robert 2018), such as those with little Apiaceae or Rosaceae. My results suggest that evaluating the dietary breadth for a bee species should include not only the frequencies to which a species visits and collects pollen from flowers, but also the habitat matrix and any external effects, such as weather, that may influence foraging behaviours.

Pollen specialization is thought to increase foraging efficiency, allowing bees to produce larger nests than can be achieved with a more generalist diet (Strickler 1979). However, facultative foraging strategies may enable bees to reproduce in climactically challenging or otherwise less ideal sites or years. Because specialization requires a species’ food source to be abundant and reliably accessible, it is adaptive for bee species to adjust their diet depending on habitat quality (in this case, the availability of preferred plant partners), and from year to year (Goldstein and Zych 2016). Furthermore, bees may exhibit facultative foraging preferences to increase nutritional content, specifically protein or sugars. Pollen is the only source of proteins for bees, however the protein content of pollen varies considerably (Roulston et al. 2000). It may be that while Apiaceae have flowers with unrestricted access to pollen and nectar rewards the protein content of Apiaceae pollen may be low (~30% protein), especially compared to Rosaceae (~42% protein, Roulston et al. 2000).

Overall, this study highlights the nuances of specialization within a species, and how spatial patterns (as well as temporal) can influence foraging behaviours and dietary breadth. Only by conducting a comprehensive analysis of the floral visit patterns and pollen collection of female bees can we determine the extent to which foraging behaviours are facultative or obligate. In this study, A. angustitarsata exhibits polylectic behaviours across the species, however clearly shows strong foraging preferences for specific plant families. Thus, it may be more important to understand and evaluate the

56 floral preferences and the magnitude of those preferences for a bee species, to understand its ecological needs and potential conservation requirements.

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62 Tables

Table 3.1 Abundance of total Andrena angustitarsata collected and included within my analysis (Bees Analyzed) within three Pacific coastal ecoregions (listed from north to south); the Strait of Georgia/ Puget Lowlands (GPL), the Willamette Valley (WV), and the Klamath Mountains (KM). Bees were collected while visiting flowers within 16 oak-savanna sites (organized alphabetically) and analyzed for pollen contents on their bodies and scopae. The number of bees collected on Apiaceae and Rosaceae (the preferred plants within my study system) is included with the abundance of flower stems and number of pollen grains counted in the scopae from both floral groups. Mean and standard error for GPL and WV ecoregions were calculated for each column. Bees were removed from analysis for the following reasons: 1 only one bee in site, 2 only one site in ecoregion, ^ broken legs, or # sub sample from sites with above 25 collected specimens. * Indicates flowers were observed within the site, however, were not recorded within randomized floral abundance surveys. Site

on

of of

. . Rosaceae in Scopae Total Pollen Counted Scopae stems) Abun stems) Apiaceae in Rosaceae (# Total Bees Bees Analyzed Apiaceae Collected # Bees Abun Apiaceae (# Strait of Georgia/ Puget Lowlands Cowichan 111 15# 12 292 0 988 128 1500 Garry Oak Mill Hill Park 10 10 5 17 0 373 74 1000

Mima Mounds 6 6 3 13 9 167 100 600

Mount 78 15# 8 173 0 769 128 1500 Tzouhalem Oak Haven 50 15# 7 0* 0 393 116 1500

Rocky Prairie 21 21 18 30 0 1706 125 2100

Mean ± SE 46 ± 13.67 ± 8.83 ± 87.5 ± 1.5 ± 732.67 111.83 1366.67 17.11 2.09 2.21 48.52 1.50 ± ± 8.73 ± 229.39 209.23

63

on of of

Site . . Total Bees Collected Bees Analyzed # Bees Apiaceae Abun Apiaceae stems) (# Abun Rosaceae stems) (# Apiaceae in Scopae Rosaceae Scopae in Total Pollen Counted Willamette Valley Bluebird Strip 6 4^ 0 0* 1 0 184 373

Bellfountain 1 01 0 20 69 0 83 100 rd. Coburg Ridge 1 01 0 1 9 0 40 100

Jefferson 24 23^ 19 33 71 1118 234 2291 Farm Kingston 0 0 0 0* 31 na na na Prairie Lacamas 9 9 3 1 0* 300 336 900 Prairie Lacamas Lake 3 3 0 0* 2 0 44 300 Park Willow Creek 5 5 3 0* 504 25 86 500

Mean ± SE 6.13 ± 3.17 ± 3.13 ± 6.88 ± 85.88 ± 206.14 143.86 652 ± 2.77 1.19 2.32 4.46 60.63 ± ± 42.11 292.04 157.59 Klamath Mountains North Bank 0 0 0 0* 0* na na na

Popcorn 7 02 7 0* 0 553 67 700 Swale

64 Table 3.2 Model descriptions and outputs evaluating ecoregional differences in pollen collection, floral visit patterns, and plant abundances for Andrena angustitarsata collected within 11 oak savanna sites in two ecoregions the Strait of Georgia / Puget Lowlands and the Willamette Valley. Apiaceae frequency is the amount of Apiaceae pollen found on the scopae (model A1.1) and body of (model A1.2) bees, while Apiaceae abundance refers to the number of observed flower stems within a site. Species-level specialization is indicated by d’ and is calculated based on number of visited flower species compared to total number of available species. Mod Resp. Predictor Coef. Z-score df Lower Upper CI p-value Est. CI A1.1 Apiaceae Intercept -0.056 -0.060 1 -2.146 2.035 0.952 (scopae) Freq ~

Ecoregion -2.754 -1.944 1 -6.369 0.184 0.052 (WV) A1.2 Apiaceae Intercept -0.080 -0.131 1 -1.447 1.286 0.896 (body) Freq ~ Ecoregion -1.805 -1.937 1 -3.980 0.158 0.053 (WV) A1.3 Rel. Freq. Ecoregion -2.453 -3.220 12 -3.652 -0.480 0.001 Apiaceae (GPL) Abun.~ Ecoregion -2.787 -2.733 12 -5.043 -0.729 0.006 (WV) A2.1 d’ ~ Intercept 0.181 0.816 124 -0.252 0.620 0.414

Ecoregion -1.023 -2.581 124 -1.824 -0.262 0.0098

A2.2 d’ ~ Intercept -0.476 -2.133 124 -0.921 -0.043 0.0329

Abun. 0.005 2.370 124 0.001 0.009 0.018 Apiaceae (veg) A2.3 Apiaceae Intercept -2.944 -1.379 1 -8.216 1.619 0.168 Freq ~ d’ 3.788 0.832 1 -6.185 14.657 0.405

65 Figures

a. Strait of Georgia/ Puget Lowlands 100

75

50

25

Plant Families 0 Apiaceae Asteraceae b. Willamette Valley Brassicaceae 100 Ranunculaceae Frequency Rosaceae Other Sp.

75

50

25

0 Flower Stems Flower Visits Body Pollen Scopal Pollen

Figure 3.1 Stacked bar charts represent the averaged frequencies of flower stems (floral abundance), flower visits, and the observed pollens on the body (head and anterior thorax) and in the scopae (hind legs and posterior thorax) of 126 Andrena angustitarsata within 11 oak- savanna sites within two ecoregions; (a) Strait of Georgia/ Puget Lowlands and (b) the Willamette Valley. Plant families were summarized into the top five plant and pollen frequencies from flower and pollen surveys. The “other” category includes 27 plant families, each below 5% of total pollen abundance (Appendix D, Table D.1). All values were averaged for each ecoregion to get comparable frequencies.

66 0.6 el v e 0.4 Species−l Specialization, d'

0.2

0.0

Strait of Georgia/ Willamette Puget Lowlands Ecoregion

Figure 3.2 Species-level specialization, d’, representing the ‘visit specialization’ for Andrena angustitarsata within 11 sites in two ecoregions, Strait of Georgia/ Puget Lowlands (GPL, 6 sites) and the Willamette Valley (WV, 5 sites). Species-level specialization is calculated based on potential interaction partners available for a species within a site, higher values indicate the species has more specialized interactions.

67 a. Strait of Georgia/ Puget Lowlands 0.6

0.4

0.2 erence f

0.0

b. Willamette Valley Foraging Pre (Bonferroni z-scores) 0.6

0.4

0.2

0.0 Apiaceae Asteraceae Brassicaceae Ranunculaceae Rosaceae Other Plants Plant Families Figure 3.3 Bonferroni z-scores of plant preferences for Andrena angustitarsata within two ecoregions a) the Strait of Georgia/ Puget Lowlands and (b) the Willamette Valley for 11 oak savanna sites. Points indicate the frequency of plant family (averaged across sites) within the ecoregion in relation to upper and lower confidence intervals of Bonferroni z-scores for scopal pollen collected from 126 bees. Plant abundance points outside of intervals are considered significantly different than the observed pollen collected by bees; points below intervals are preferred, and points above intervals are avoided. The magnitude of difference between points and confidence intervals is based on Chi-Square residuals across all plant groups within a site. Apiaceae and Rosaceae are the only plant families that were significantly preferred (below the z-score CI) for both ecoregions.

68 Chapter 4.

Conclusion

Summary

Spatial scales are an important consideration in ecology, despite the challenge of conducting research over large distances (Willig et al. 2003, Morales and Vázquez 2008, Burkle and Alarcón 2011, Moreira et al. 2015). Within pollination ecology, spatial scale can influence the abundance and composition of species within a community and their interactions with their mutualistic partners (Morales and Vázquez 2008, Dupont et al. 2009). Furthermore, changes in community structure (the way the community is composed and interacts) may influence how resilient the community is to disturbance (Moreira et al. 2015, Goldstein and Zych 2016, Santamaría et al. 2016). Thus different communities may have different levels of resilience, an important consideration for fragmented habitats, which may not be able to recruit new species easily (De Frutos et al. 2015, Xiao et al. 2016).

In this thesis, I evaluated how communities of plants and pollinators varied across a latitudinal gradient and how changes in community composition influenced network structural components related to community resilience (Chapter 2). I found that spatial scales had an effect on species compositions, interactions between plants and insects, and the foraging preferences of pollinators. Species composition can highly influence the types of interactions within the community, because some species will inevitably be more dominant (many interactions and high abundance) than others (Hooper et al. 2005, Burkle and Knight 2012, Kemp et al. 2018). I found that the composition of species was different between ecoregions, with the Strait of Georgia/ Puget Lowlands (GPL) having higher abundance of Bombus sp., Andrena sp. and Brassicaceae, whereas the southern ecoregions, the Willamette Valley (WV) and the Klamath Mountains (KM) had more non-bee pollinators, such as Syrphidae and Coleoptera, and plants associated with wet-lands like Boraginaceae and Iridaceae. Additionally, pollinator compositions changed with sub-habitat from wet-prairies to rocky outcrops, however plant compositions only changed between ecoregions.

69 Insect group composition influenced network structural components associated with community resilience. Some groups of pollinators may influence the resilience of the community by increasing nestedness and decreasing modularity (and potentially specialization) within the network. Above ground nesting insects and insects with predatory larvae likely increase resilience within the community via increased connectivity (nestedness) and reduced fragmentation (modularity) of the network structure. These groups contained high numbers of many generalist species, such as Apis mellifera and Syrphidae, that provide stability via diffuse and redundant interactions with the plant community. Species richness and composition remain vital to pollination systems because they influence not only the interactions of the community, but also ecosystem functioning and resilience (Burkle and Knight 2012).

At the species-level, the abundance of available resources (especially preferred resources) can change the number of interacting partners a species has. In Chapter 3, I found that while bee species may interact with a variety of plants to collect pollen and nectar, they may still have strong foraging preferences to particular plant families. Within the GPL and WV ecoregions, Andrena angustitarsata maintained a foraging preference for Apiaceae and Rosaceae plants, regardless of the abundance of those plants within the ecoregions. Additionally, as Apiaceae increased within my sites, so did the degree of species-level specialization for A. angustitarsata. However, the number of plant species increased from observed floral visit patterns to pollen collected on the bees, indicates A. angustitarsata may not be oligolectic -- at least not across the entirety of its range -- and is likely closer to mesolecty. Regardless of the categorical designation of the dietary breadth of A. angustitarsata, it is clear that the species prefers specific plant families.

Available foraging plants are one of the main regulators of bee populations (Roulston and Goodell 2011). Additionally, the available resources are highly influential to the population and composition of bee populations (Ogilvie and Forrest 2017). I observed more A. angustitarsata within sites with relatively abundant Apiaceae, potentially indicating an increase to their reproductive output which could impact population growth. Understanding the spatial dynamics of bee foraging preferences and behaviours remain understudied yet valuable to bee abundances and compositions within a region.

70 Future Directions and Caveats

As with any research evaluating diverse communities with often poorly resolved species identities, multiple decisions need to be made to make field work tractable that may wind up having impacts on interpretation. In my thesis, I used plant trait databases to establish plant groups in chapter 2, however, plants can have highly variable phenotypic traits within and between populations making database estimates less accurate. This lower resolution of floral traits may have influenced the group dynamics between plant groups and network metrics. Floral traits continue to be a challenging aspect of pollination ecology and future research should continue to explore the dynamics of how floral traits influence pollinator interactions, abundances, and population growth.

In my field work, I focussed on plants based on standard pollination survey methods, excluding wind pollinated plants, large trees and bushes (including poison oak), as well as extremely small flowers presented singly (not in an inflorescence) with less than 2mm wide corollas. Bees obviously visit these plants at least occasionally, as observed within pollen samples in Chapter 3, such as wind-pollinated birch trees (Betulaceae), poison-oak bushes (Anacardiaceae), and large maple trees (Sapindaceae). Additionally, I may have netted and collected pollinators off a particular plant species, however that plant was not observed in the vegetation surveys. For instance, Apiaceae was in most of my sites however, because of the naturally patchy dispersion of plants, was not observed within some vegetation surveys. This finding highlights how challenging it can be to evaluate foraging preferences when sampling designs and survey choices do not always capture all plants present in an environment. These survey issues are likely unavoidable, however with time-permitting, increasing the number vegetation plots per site may mitigate some of mismatch between observations of pollinator visits and vegetation surveys. In my lab work, I categorized pollen types by family, as is typical for this work due to the similarity in pollen morphology within genera and families. The use of DNA and metabarcoding techniques may improve the ability to evaluate the exact species bees are collecting pollen from. However DNA techniques remain cost-prohibitive for most pollination studies and require a carefully established reference collection.

71 Furthermore, to achieve the highest possible number of communities for analysis, I was only able to sample each site twice during the bloom period and only twice during each sample. In part there was a trade-off between number of times each site was sampled, and the robust, labour-intensive survey method I used. My survey methods focused on observing each plant species individually, whereas other pollination surveys employ transects, trapping devices, or single-timed observations of the whole flowering community within a given area (Gibson et al. 2011). Each of these methods greatly reduces the overall amount of time needed to complete a single survey, whereas my surveys took between 3-4 hours to complete. My methods, however, account for sampling biases, in that collectors will naturally collect from busy, attractive, and abundant flowers and are more likely to miss flowers with less frequent more specialized visits. Furthermore, trapping protocols, of course, provide no information at all about visit patterns. Additionally, I had to consolidate my samples across the entire field season to maintain a useful number of interactions for each site. Obviously, this means in calculating some metrics I may have ‘forbidden links’, or interactions that are not spatially or temporally viable (Olesen et al. 2011), in my full, cross-season networks. While forbidden links are a vexing issue within pollination ecology, evaluating both the spatial and temporal dynamics of plant-pollinator networks simultaneously is logistically challenging.

Metrics and algorithms like those in network ecology are very useful for consolidating large complex data sets, however as with many statistics they need to be used appropriately or they can over or under represent the true nature of a community. It is essential to have multiple inferences for each and work within the boundaries of the algorithms. For example, the Bonferroni z-scores are useful when sample sizes are similar and above zero, however they can lead to high variation with low sample sizes. Additionally, in my thesis I use the concept of resilience, to contextualize each of network metrics in Chapter 2. However resilience, to my knowledge, is primarily calculated from simulation and theoretical studies (Bascompte et al 2003, Blüthgen et al 2006, Santamaría et al 2016, Lance et al 2017, Vanbergen et al 2017). Despite this fact network metrics remain valuable indicators of environmental quality and community structure (Soares et al 2017, Vanbergen et al 2017). Finally, network ecology over the last 20 years has exploded computationally with now dozens of metrics to choose from. Research outcomes could have different interpretations depending on the metrics

72 selected. A careful understanding of network metrics in relation to research goals is essential.

An interesting step forward, beyond my thesis, would be to evaluate the repercussions of spatial dynamics within plant-pollinator interactions. In particular, it would be beneficial to move beyond the single species I studied to understand how facultative additional bee species (and potentially other taxa) are in their foraging behaviours and how this influences their reproductive success and population growth. Furthermore, future projects could evaluate how spatial scales coupled with various human induced changes will impact the composition and interactions of plant-pollinator communities within native and fragmented ecosystems (see Knight et al 2018). Understanding the spatial dynamics of pollinator foraging behaviours and preferences may provide insight into how habitat filtering processes and anthropogenic change influence both plant and pollinator population dynamics.

Conclusions

My thesis applies a series of metrics and mathematical algorithms to plant- pollinator interactions at a unique spatial-scale, to provide insights into the importance community structure and dietary preferences across space. My work highlights the importance of spatial scales within highly diverse and dynamic plant-pollinator communities, and emphasizes the importance of species compositions in the structure of these communities, which likely impacts function. As habitats undergo intensive anthropogenic changes, the impacts to plant-pollinator communities will substantially influence the composition of species and their interactions within these communities. Establishing and maintaining communities with the understanding of species functional groups, dietary preferences, and network paradigms is essential to plant and pollinator conservation. Ultimately my thesis contributes to the growing body of literature regarding mutualistic networks and pollinator foraging ecology.

In chapter 2, I showed that some groups of pollinators may have an important role in connecting and increasing resilience within networks, by increasing generalization and reducing specialization. Generalist species are important within networks because they often interact with the specialist species in the site. This produces a nested network structure that provides stability to the network via resources and services to a wide

73 range of species (Vázquez et al. 2009, Song et al. 2017). It is likely that having a diverse set of generalists within a community optimizes network resilience because different foraging behaviours and strategies provide connections to more specialized species. (Poisot et al. 2015). In chapter 3, my research shows that foraging preferences are conserved across space, indicating bees may benefit from more specialized interactions with plants. The resources available for a species will naturally vary across spatial scales, which makes fragmented habitats a particular issue for specialist pollinators incapable of facultative foraging and with short dispersal ranges (Greenleaf et al. 2007, Clavel et al. 2011). Furthermore, fragmented habitats may undergo some level of habitat filtering processes, selecting for more generalist species capable of surviving in the potentially “lower quality” habitats between fragments, thus, fragmented habitats will likely have a greater effect on specialist species (Sargent and Ackerly 2008, Bizecki Robson et al. 2018). Together these studies enhance the understanding of plant- pollinator interactions across space and organizational levels.

74 References

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77 Appendix A.

List of Models

Table A.1 Models and AIC values from stepwise model selection analysis in Chapter 2. The four network metrics, H2, WNODF, ISA, and Modularity arrange respectively, are divided into subtables of each category analysis for pollinators and plants between the nesting, larval diets, insect taxonomic, flower colour, inflorescence size, inflorescence type, and flower taxonomic categories (arranged respectively) and their associated groups, along with null model.

1-Network Level Specialization, H2 Pollinator Categories Nesting Groups D AIC Akaike Weight H2 ~ above 0.000 0.424 H2 ~ below 1.747 0.177 H2 ~ null 1.858 0.168 H2 ~ aquatic 2.302 0.134 H2 ~ both 2.963 0.096

Laval Diet Groups H2 ~ predator 0.000 0.258 H2 ~ pollenivore 0.494 0.202 H2 ~ null 0.617 0.189 H2 ~ detritivore 0.870 0.167 H2 ~ parasite 1.958 0.097 H2 ~ herbivore 2.183 0.087

Insect Taxonomic Groups H2 ~ null 0.000 0.193 H2 ~ halictidae 0.599 0.143 H2 ~ syrphidae 1.022 0.116 H2 ~ 1.251 0.103 H2 ~ coleoptera 1.363 0.098 H2 ~ other fly 1.382 0.097 H2 ~ megachilidae 1.567 0.088 H2 ~ andrenidae 1.608 0.087 H2 ~ apidae 1.911 0.074

78 1-Network Level Specialization, H2 (continued) Plant Categories Flower Colour Groups D AIC Akaike Weight H2 ~ null 0.000 0.283 H2 ~ pink count 0.850 0.185 H2 ~ white count 1.045 0.168 H2 ~ yellow count 1.236 0.153 H2 ~ blue count 1.967 0.106 H2 ~ purple count 1.999 0.104

Flower Inflorescence Size Groups H2 ~ null 0.000 0.355 H2 ~ high count 0.552 0.269 H2 ~ low count 0.788 0.239 H2 ~ medium count 1.902 0.137

Flower Inflorescence Type Groups H2 ~ null 0.000 0.297 H2 ~ cyme count 1.178 0.165 H2 ~ solitary count 1.274 0.157 H2 ~ raceme count 1.300 0.155 H2 ~ head count 1.886 0.116 H2 ~ umbel count 2.000 0.109

Flower Taxonomic Groups H2 ~ Liliales count 0.000 0.234 H2 ~ Lamiales count 0.391 0.192 H2 ~ Asterales count 0.547 0.178 H2 ~ Ranunculales count 1.398 0.116 H2 ~ Geraniales count 1.787 0.096 H2 ~ null 1.909 0.090 H2 ~ Asparagales count 2.668 0.062 H2 ~ Fabales count 3.891 0.033

79 2-Weighted Nestedness, WNODF Pollinator Categories Nesting Groups D AIC Akaike Weight WNODF ~ above 0.000 0.928 WNODF ~ null 7.027 0.028 WNODF ~ below 7.687 0.020 WNODF ~ aquatic 8.374 0.014 WNODF ~ both 9.020 0.010 Laval Diet Groups WNODF ~ parasite 0.000 0.370 WNODF ~ herbivore 0.741 0.255 WNODF ~ null 2.206 0.123 WNODF ~ pollenivore 2.754 0.093 WNODF ~ detritivore 2.845 0.089 WNODF ~ predator 3.327 0.070

Insect Taxonomic Groups WNODF ~ apidae 0.000 0.270 WNODF ~ coleoptera 0.145 0.251 WNODF ~ other fly 1.695 0.116 WNODF ~ bombyliidae 2.021 0.098 WNODF ~ null 2.402 0.081 WNODF ~ syrphidae 2.943 0.062 WNODF ~ megachilidae 3.296 0.052 WNODF ~ andrenidae 3.863 0.039 WNODF ~ halictidae 4.395 0.030

Plant Categories Flower Colour Groups WNODF ~ yellow count 0.000 0.367 WNODF ~ null 1.308 0.191 WNODF ~ blue count 1.443 0.178 WNODF ~ purple count 2.444 0.108 WNODF ~ pink count 2.905 0.086 WNODF ~ white count 3.305 0.070

Flower Inflorescence Size Groups WNODF ~ null 0.000 0.368 WNODF ~ medium count 0.092 0.351 WNODF ~ high count 1.849 0.146 WNODF ~ low count 1.993 0.136

80 2-Weighted Nestedness, WNODF (continued) Flower Inflorescence Type Groups D AIC Akaike Weight WNODF ~ null 0.000 0.292 WNODF ~ head count 0.701 0.206 WNODF ~ solitary count 1.427 0.143 WNODF ~ raceme count 1.556 0.134 WNODF ~ cyme count 1.853 0.116 WNODF ~ umbel count 1.988 0.108

Flower Taxonomic Groups WNODF ~ Lamiales count 0.000 0.518 WNODF ~ Asterales count 2.542 0.145 WNODF ~ null 3.337 0.098 WNODF ~ Liliales count 3.703 0.081 WNODF ~ Geraniales count 4.780 0.047 WNODF ~ Ranunculales count 5.192 0.039 WNODF ~ Fabales count 5.323 0.036 WNODF ~ Asparagales count 5.336 0.036

3-Interaction Strength Asymmetry, ISA Pollinator Categories Nesting Groups ISA ~ above 0.000 0.324 ISA ~ null 0.143 0.302 ISA ~ both 1.561 0.149 ISA ~ below 2.091 0.114 ISA ~ aquatic 2.141 0.111

Laval Diet Groups ISA ~ predator 0.000 0.916 ISA ~ null 7.130 0.026 ISA ~ parasite 7.315 0.024 ISA ~ pollenivore 8.392 0.014 ISA ~ herbivore 9.021 0.010 ISA ~ detritivore 9.025 0.010

81 3-Interaction Strength Asymmetry, ISA (continued) Insect Taxonomic Groups D AIC Akaike Weight ISA ~ syrphidae 0.000 0.402 ISA ~ bombyliidae 1.676 0.174 ISA ~ andrenidae 2.701 0.104 ISA ~ null 2.744 0.102 ISA ~ apidae 3.844 0.059 ISA ~ halictidae 4.524 0.042 ISA ~ other fly 4.611 0.040 ISA ~ coleoptera 4.695 0.038 ISA ~ megachilidae 4.731 0.038

Plant Categories Flower Colour Groups ISA ~ purple count 0.000 0.522 ISA ~ null 2.250 0.170 ISA ~ blue count 3.170 0.107 ISA ~ yellow count 3.955 0.072 ISA ~ pink count 4.157 0.065 ISA ~ white count 4.222 0.063

Flower Inflorescence size Groups ISA ~ null 0.000 0.453 ISA ~ medium count 1.570 0.207 ISA ~ high count 1.954 0.171 ISA ~ low count 1.970 0.169

Flower Inflorescence Type Groups ISA ~ null 0.000 0.329 ISA ~ solitary count 1.146 0.185 ISA ~ raceme count 1.970 0.123 ISA ~ head count 1.995 0.121 ISA ~ cyme count 2.000 0.121 ISA ~ umbel count 2.000 0.121

82 3- Interaction Strength Asymmetry, ISA (continued) Flower Taxonomic Groups D AIC Akaike Weight ISA ~ null 0.000 0.234 ISA ~ Ranunculales count 0.688 0.166 ISA ~ Asparagales count 1.248 0.126 ISA ~ Fabales count 1.660 0.102 ISA ~ Liliales count 1.667 0.102 ISA ~ Lamiales count 1.884 0.091 ISA ~ Asterales count 1.901 0.091 ISA ~ Geraniales count 1.970 0.088

4-Modularity Pollinator Categories Nesting Groups Mod ~ above 0.000 0.793 Mod ~ null 4.447 0.086 Mod ~ aquatic 5.599 0.048 Mod ~ both 6.003 0.039 Mod ~ below 6.312 0.034

Larval Diet Groups Mod ~ predator 0.000 0.717 Mod ~ null 3.905 0.102 Mod ~ parasite 5.196 0.053 Mod ~ detritivore 5.444 0.047 Mod ~ pollenivore 5.716 0.041 Mod ~ herbivore 5.808 0.039

Insect Taxonomic Groups Mod ~ megachilidae 0.000 0.217 Mod ~ syrphidae 0.291 0.187 Mod ~ null 0.591 0.161 Mod ~ andrenidae 1.669 0.094 Mod ~ bombyliidae 1.836 0.087 Mod ~ coleoptera 2.191 0.072 Mod ~ other fly 2.530 0.061 Mod ~ apidae 2.558 0.060 Mod ~ halictidae 2.573 0.060

83 4-Modularity (continued) Plant Categories Flower Colour Groups D AIC Akaike Weight Mod ~ pink count 0.000 0.235 Mod ~ null 0.091 0.224 Mod ~ yellow count 0.107 0.223 Mod ~ white count 1.406 0.116 Mod ~ blue count 1.571 0.107 Mod ~ purple count 1.804 0.095

Flower Inflorescence Size Groups Mod ~ null 0.000 0.405 Mod ~ high count 0.628 0.296 Mod ~ medium count 1.996 0.149 Mod ~ low count 1.999 0.149

Flower Inflorescence Type Groups Mod ~ null 0.000 0.266 Mod ~ raceme count 0.022 0.263 Mod ~ cyme count 1.194 0.147 Mod ~ head count 1.562 0.122 Mod ~ solitary count 1.908 0.103 Mod ~ umbel count 1.981 0.099

Flower Taxonomic Groups Mod ~ Lamiales count 0.000 0.636 Mod ~ Asterales count 2.726 0.163 Mod ~ Asparagales count 5.151 0.048 Mod ~ null 5.242 0.046 Mod ~ Ranunculales count 5.292 0.045 Mod ~ Geraniales count 6.749 0.022 Mod ~ Liliales count 6.936 0.020 Mod ~ Fabales count 6.959 0.020

84 Appendix B.

Plant and Pollinator Guilds

Table B.1 List of plant species organized by families with their associated groups and respective categories; flower colour, inflorescence size, inflorescence type, and taxonomic .

Plant Species Plant Family Taxonomic Colour Inflor. Size Inflor. Type Daucus pusillus Apiaceae Apiales white high umbel Heracleum maximum Apiaceae Apiales white high umbel Lomatium bradshawii Apiaceae Apiales yellow high umbel Lomatium dissectum Apiaceae Apiales purple high umbel Lomatium nudicaule Apiaceae Apiales yellow high umbel Lomatium triternatum Apiaceae Apiales yellow high umbel Lomatium utriculatum Apiaceae Apiales yellow high umbel Sanicula bipinnatifida Apiaceae Apiales purple medium umbel Sanicula crassicaulis Apiaceae Apiales yellow medium umbel Sanicula graveolens Apiaceae Apiales yellow high umbel Camassia leichtlinii (white) Asparagaceae Asparagales white medium raceme Camassia leichtlinii Asparagaceae Asparagales purple medium raceme Asparagaceae Asparagales purple medium raceme Achillea millefolium Asteraceae Asterales white high head Agoseris aurantica Asteraceae Asterales yellow high head Anthemis cotula Asteraceae Asterales white high head deltoidea Asteraceae Asterales yellow high head Cirsium arvense Asteraceae Asterales purple high head Erigeron speciosus Asteraceae Asterales blue high head Eriophyllum lanatum Asteraceae Asterales yellow high head Hypochaeris radicata Asteraceae Asterales yellow high head Asteraceae Asterales white high head gracilis Asteraceae Asterales yellow medium head Taraxacum officinale Asteraceae Asterales yellow high head Bellis perennis Asteraceae Asterales white high head Crepis capillaris Asteraceae Asterales yellow high head Mahonia aquifolium Berberidaceae Raunculales yellow high shrub scorpioides Boraginaceae blue medium cyme Nemophila menzeisii Boraginaceae Boraginales white low solitary Plagiobothrys spp Boraginaceae Boraginales white medium cyme Barbarea orthoceras Brassicaceae Brassicales yellow medium raceme

85 Plant Species Plant Family Taxonomic Colour Inflor. Size Inflor. Type Capsella bursa-pastoris Brassicaceae Brassicales white medium raceme Cardamine penduliflora Brassicaceae Brassicales white low raceme Cerastium arvense Brassicaceae Brassicales white low cyme Lepidium heterophyllum Brassicaceae Brassicales white medium raceme Campanula rotundifolia Campanulaceae Asterales purple low raceme Symphoricarpos mollis Caprifoliaceae Dipsacales pink high shrub Symphoricarpos sp. Caprifoliaceae Dipsacales pink high shrub Symphoricarpus albus Caprifoliaceae Dipsacales pink high shrub macrophylla white low solitary Silene hookeri Caryophyllaceae Caryophyllales pink low cyme Sedum spathulifolium Crassulaceae yellow medium cyme Marah oregana Cucurbitaceae Cucurbitales white medium raceme Arctostaphylos uva-ursi Ericaceae white high shrub Fabaceae Fabales yellow high shrub hirsutus Fabaceae Fabales pink low solitary Lathyrus sphaericus Fabaceae Fabales pink low solitary Lotus pinnatus Fabaceae Fabales yellow low umbel albicaulis Fabaceae Fabales purple medium raceme Lupinus sulphureus Fabaceae Fabales purple medium raceme Vicia sativa Fabaceae Fabales purple low solitary Vicia tetrasperma Fabaceae Fabales purple low raceme Vicia villosa Fabaceae Fabales purple medium raceme Lathyrus aphaca Fabaceae Fabales yellow low solitary Trifolium repens Fabaceae Fabales pink medium head Erodium cicutarium Geraniales purple low solitary Geranium columbinum Geraniaceae Geraniales purple low solitary Geranium dissectum Geraniaceae Geraniales pink low solitary Geranium molle Geraniaceae Geraniales pink low solitary tenax Iridaceae Asparagales purple low solitary Sisyrinchium idahoense Iridaceae Asparagales blue low umbel Prunella vulgaris Lamiaceae Lamiales purple medium raceme Allium acuminatum Liliales pink medium umbel Allium amplectens Liliaceae Liliales pink medium umbel Brodiaea hyacinthina Liliaceae Liliales white medium umbel tolmiei Liliaceae Liliales white low solitary Dichelostemma capitatum Liliaceae Liliales blue low umbel oregonum Liliaceae Liliales yellow low solitary Fritillaria affinis Liliaceae Liliales purple low solitary Linum lewisii Linaceae Liliales blue low raceme

86 Plant Species Plant Family Taxonomic Colour Inflor. Size Inflor. Type Sidalcea campestris Malvaceae Malvales white medium raceme Sidalcea virgata Malvaceae Malvales pink medium raceme Zygadenus venenosus Melanthiaceae Liliales yellow high raceme gracilis Onograceae pink low raceme Clarkia purpurea Onograceae Myrtales purple low raceme Epilobium ciliatum Onograceae Myrtales white low raceme Parentucellia viscosa Lamiales yellow medium raceme Orobanche uniflora Orobanchaceae Lamiales purple low solitary Linanthus bicolor Ericales pink low solitary linearis Polemoniaceae Ericales white medium head Claytonia perfoliata Portulacaceae Caryophyllales white medium raceme Montia linearis Portulacaceae Caryophyllales white medium raceme Montia parvifolia Portulacaceae Caryophyllales white low raceme Dodecatheon hendersonii Ericales pink low umbel Dodecatheon pulchellum Primulaceae Ericales pink low umbel Aquilegia formosa Ranunculaceae Raunculales pink low solitary menziesii Ranunculaceae Raunculales purple medium raceme Delphinium nuttallii Ranunculaceae Raunculales purple medium raceme Ranunculus aquatilis Ranunculaceae Raunculales yellow low solitary Ranunculus occidentalis Ranunculaceae Raunculales yellow medium cyme Ranunculus orthorhynchus Ranunculaceae Raunculales yellow low cyme Ranunculus alismifolius Ranunculaceae Raunculales yellow low cyme Fragaria virginiana Rosaceae Rosales white low solitary Potentilla glandulosa Rosaceae Rosales yellow medium cyme Potentilla gracilis Rosaceae Rosales yellow medium cyme Rosa pisocarpa Rosaceae Rosales pink high shrub Galium boreale Rubiaceae white high cyme parviflorum Saxifragales white low raceme Saxifraga integrifolia Saxifragaceae Saxifragales white medium head levisecta Scrophulariaceae Lamiales yellow medium raceme grandiflora Scrophulariaceae Lamiales blue medium raceme Collinsia parviflora Scrophulariaceae Lamiales blue medium raceme Digitalis purpurea Scrophulariaceae Lamiales purple high raceme Mimulus alsinoides Scrophulariaceae Lamiales yellow low solitary Mimulus guttatus Scrophulariaceae Lamiales yellow low raceme Veronica officinalis Scrophulariaceae Lamiales blue medium raceme Veronica scutellata Scrophulariaceae Lamiales blue medium raceme Veronica serpyllifolia Scrophulariaceae Lamiales blue medium raceme Plectritis congesta Valerianaceae Dipsacales pink medium head

87 Plant Species Plant Family Taxonomic Colour Inflor. Size Inflor. Type Viola praemorsa Violaceae Malpighiales yellow low solitary Viola adunca Violaceae Malpighiales purple low solitary

88 Table B.2 List of insects collected within 16 oak-savanna sites, organized by insect order then taxonomic group. Other categories include nest location and larval diet. Insect Species Nest Location Larval Diet Taxonomic Hymenoptera: Bees, Wasps, & Ants Andrena amphibola below pollenivore Andrenidae Andrena angustitarsata below pollenivore Andrenidae Andrena anisochlora below pollenivore Andrenidae Andrena astragali below pollenivore Andrenidae Andrena auricoma below pollenivore Andrenidae Andrena buckelli below pollenivore Andrenidae Andrena caerulea below pollenivore Andrenidae Andrena candida below pollenivore Andrenidae Andrena chlorogaster below pollenivore Andrenidae Andrena chlorura below pollenivore Andrenidae Andrena cressonii below pollenivore Andrenidae Andrena cupreotincta below pollenivore Andrenidae Andrena evoluta below pollenivore Andrenidae Andrena hemileuca below pollenivore Andrenidae Andrena lupinorum below pollenivore Andrenidae Andrena microchlora below pollenivore Andrenidae Andrena nigrocaerulea below pollenivore Andrenidae Andrena nivalis below pollenivore Andrenidae Andrena perplexa below pollenivore Andrenidae Andrena prunorum below pollenivore Andrenidae Andrena saccata below pollenivore Andrenidae Andrena salicifloris below pollenivore Andrenidae Andrena subtilis below pollenivore Andrenidae Andrena thaspii below pollenivore Andrenidae Andrena transnigra below pollenivore Andrenidae Andrena vicinoides below pollenivore Andrenidae Andrena w-scripta below pollenivore Andrenidae Panurginus atriceps below pollenivore Andrenidae Anthophora pacifica below pollenivore Apidae Apis mellifera above pollenivore Apidae Bombus appositus both pollenivore Apidae Bombus bifarius both pollenivore Apidae both pollenivore Apidae Bombus flavifrons below pollenivore Apidae Bombus griseocollis both pollenivore Apidae

89 Insect Species Nest Location Larval Diet Taxonomic Bombus huntii below pollenivore Apidae Bombus melanopygus both pollenivore Apidae Bombus mixtus both pollenivore Apidae Bombus sitkensis below pollenivore Apidae Bombus vandykei below pollenivore Apidae Bombus vosnesenskii below pollenivore Apidae acantha above pollenivore Apidae Ceratina micheneri above pollenivore Apidae Ceratina nanula above pollenivore Apidae Eucera acerba below pollenivore Apidae Eucera edwardsii below pollenivore Apidae Eucera frater below pollenivore Apidae Nomada sp. 1 below parasite Apidae Nomada sp. 2 below parasite Apidae Nomada sp. 4 below parasite Apidae Colletes kincaidii below pollenivore Colletidae Hylaeus modestus above pollenivore Colletidae Agapostemon texanus below pollenivore Halictidae Agapostemon virescens below pollenivore Halictidae Halictus confusus below pollenivore Halictidae Halictus farinosus below pollenivore Halictidae Halictus ligatus below pollenivore Halictidae Halictus rubicundus below pollenivore Halictidae Halictus tripartitus below pollenivore Halictidae Lasioglossum below pollenivore Halictidae (Evylaeus) sp. 1 Lasioglossum below pollenivore Halictidae (Evylaeus) sp. 3 Lasioglossum below pollenivore Halictidae (Evylaeus) sp. 5 Lasioglossum below pollenivore Halictidae (Evylaeus) sp. 6 Lasioglossum albipenne below pollenivore Halictidae Lasioglossum cressonii below pollenivore Halictidae Lasioglossum incompletum below pollenivore Halictidae Lasioglossum knereri below pollenivore Halictidae Lasioglossum laevissimum below pollenivore Halictidae Lasioglossum nevadense below pollenivore Halictidae Lasioglossum olympiae below pollenivore Halictidae Lasioglossum pacificum below pollenivore Halictidae Lasioglossum pruinosum below pollenivore Halictidae

90 Insect Species Nest Location Larval Diet Taxonomic Lasioglossum sisymbrii below pollenivore Halictidae Lasioglossum titusi below pollenivore Halictidae Lasioglossum villosulum below pollenivore Halictidae Lasioglossum zonulum below pollenivore Halictidae Sphecodes sp. 13 below parasite Halictidae Sphecodes sp. 14 below parasite Halictidae Sphecodes sp. 16 below parasite Halictidae Sphecodes sp. 4 below parasite Halictidae Hoplitis grinnelli above pollenivore Megachilidae Megachile melanophaea above pollenivore Megachilidae Osmia albolateralis above pollenivore Megachilidae Osmia atrocyanea above pollenivore Megachilidae Osmia coloradensis above pollenivore Megachilidae Osmia cyanella above pollenivore Megachilidae Osmia dolerosa above pollenivore Megachilidae Osmia gabrielis above pollenivore Megachilidae Osmia kincaidii above pollenivore Megachilidae Osmia lignaria above pollenivore Megachilidae Osmia malina above pollenivore Megachilidae Osmia nemoris above pollenivore Megachilidae Osmia odontogaster above pollenivore Megachilidae Osmia sculleni above pollenivore Megachilidae Osmia trevoris above pollenivore Megachilidae Osmia tristella above pollenivore Megachilidae rubifloris above pollenivore Megachilidae Aporus sp. below parasite Other Hym. Cephidae sp. above herbivore Other Hym. Chrysura sp. above parasite Other Hym. Dolichovespula arenaria above predator Other Hym. Ichneumonidae sp. 1 above parasite Other Hym. Ichneumonidae sp. 4 above parasite Other Hym. Polistes dominula above predator Other Hym. Priocnemis sp. above parasite Other Hym. Vespula consobrina above predator Other Hym. Diptera: Bee-flies & Hover-flies Bombylius aestruus below parasite Bombyliidae Bombylius major below parasite Bombyliidae Conophorus columbiensis below detritivore Bombyliidae Conophorus sackenii below detritivore Bombyliidae

91 Insect Species Nest Location Larval Diet Taxonomic Dasysyrphus sp. above predator Syrphidae Eristalis arbustorum aquatic detritivore Syrphidae Eristalis brousii aquatic detritivore Syrphidae Eristalis hirtus aquatic detritivore Syrphidae Eristalis obscurus aquatic detritivore Syrphidae Eristalis tenax aquatic detritivore Syrphidae Eristalis transversa aquatic detritivore Syrphidae Eupeodes latifasciatus above predator Syrphidae Eupeodes volucris above predator Syrphidae Helophilus aff. nasonii aquatic detritivore Syrphidae Melanostoma mellinum above predator Syrphidae Merodon equestris above herbivore Syrphidae Paragus sp. above predator Syrphidae Parasyrphus sp. above predator Syrphidae Pipiza sp. above predator Syrphidae Platycheirus stegnus above predator Syrphidae Pseudosyrphid sp. above predator Syrphidae Scaeva pyrastri above predator Syrphidae Sphaerophoria contigua above predator Syrphidae Sphaerophoria above predator Syrphidae novaeanglia Sphaerophoria sulphuripes above predator Syrphidae Sphaerophoria weemsi above predator Syrphidae Toxomerus marginatus above predator Syrphidae Toxomerus occidentalis above predator Syrphidae Volucella bombylans above detritivore Syrphidae Rhagio sp. above predator Other fly Rhamphomyia sp. 1 above predator Other fly Rhamphomyia sp. 2 above predator Other fly Scathophagidae sp. - - Otherf ly Tachina sp. 1 above parasite Other fly Tachinidae sp. above parasite Other fly Thecophora sp. 2 below parasite Other fly Zodion sp. above parasite Other fly Coleoptera: Beetles Anastrangalia laetifica above herbivore Coleoptera Anthaxia sp. above herbivore Coleoptera Brachysomida californica below herbivore Coleoptera Coleoptera sp. 3 - - Coleoptera

92 Insect Species Nest Location Larval Diet Taxonomic Cortodera sp. below herbivore Coleoptera Criocerinae spp. above herbivore Coleoptera Diabrotica below herbivore Coleoptera unidecimpunctata Elateridae sp. 1 above herbivore Coleoptera Elateridae sp. 2 above herbivore Coleoptera Elateridae sp. 3 above herbivore Coleoptera Elateridae sp. 4 above herbivore Coleoptera Elateridae sp. 5 above herbivore Coleoptera Elateridae sp. 6 above herbivore Coleoptera Elateridae sp. 7 above herbivore Coleoptera Molorchus sp. above herbivore Coleoptera Scirtidae spp. aquatic detritivore Coleoptera Stenocorus nubifer above herbivore Coleoptera

93 Appendix C.

Plant and Pollinator Species Lists

Table C.1 Counts of plant stems by family from floral abundance surveys. Abundances of plants summarised across sites for three ecoregions Strait of Georgia/ Puget Lowlands (GPL, 6 sites), the Willamette Valley (WV, 8 sites), and the Klamath Mountains (KM, 2 sites), in addition to total abundance of each species across all sites. Plant Species Plant Family GPL WV KM Total Lomatium bradshawii Apiaceae 0 1 0 1 Lomatium dissectum Apiaceae 0 1 0 1 Lomatium nudicaule Apiaceae 0 33 0 33 Lomatium triternatum Apiaceae 5 0 0 5 Lomatium utriculatum Apiaceae 468 19 0 487 Sanicula bipinnatifida Apiaceae 0 1 0 1 Sanicula crassicaulis Apiaceae 49 0 0 49 Sanicula graveolens Apiaceae 3 0 0 3 Camassia leichtlinii Asparagaceae 13 18 60 31 Camassia quamash Asparagaceae 343 126 2 471 Achillea millefolium Asteraceae 15 7 0 22 Agoseris aurantica Asteraceae 0 0 1 1 Anthemis cotula Asteraceae 0 2 0 2 Bellis perennis Asteraceae 0 12 0 12 Cirsium arvense Asteraceae 0 0 4 4 Crepis capillaris Asteraceae 2 0 0 2 Eriophyllum lanatum Asteraceae 121 83 0 204 Erigeron speciosus Asteraceae 0 9 0 9 Hypochaeris radicata Asteraceae 103 846 0 949 Leucanthemum vulgare Asteraceae 68 282 6 356 Madia gracilis Asteraceae 0 21 85 106 Taraxacum officinale Asteraceae 1 1 0 2 Myosotis scorpioides Boraginaceae 0 762 765 1527 Plagiobothrys spp Boraginaceae 0 1646 81 1727 Capsella bursa-pastoris Brassicaceae 1325 2 0 1327 Cerastium arvense Brassicaceae 307 0 0 307 Campanula rotundifolia Campanulaceae 5 0 0 5 Symphoricarpus albus Caprifoliaceae 12 0 0 12 Symphoricarpos sp. Caprifoliaceae 168 0 0 168 Silene hookeri Caryophyllaceae 0 3 0 3

94 Plant Species Plant Family GPL WV KM Total Marah oregana Cucurbitaceae 0 20 0 20 Arctostaphylos uva-ursi Ericaceae 2 0 0 2 Cytisus scoparius Fabaceae 79 0 0 79 Lathyrus aphaca Fabaceae 0 0 9 9 Lathyrus hirsutus Fabaceae 0 0 191 191 Lathyrus sphaericus Fabaceae 0 0 17 17 Lotus pinnatus Fabaceae 0 22 0 22 Lupinus albicaulis Fabaceae 64 0 0 64 Trifolium repens Fabaceae 50 0 9 59 Vicia sativa Fabaceae 87 406 83 576 Vicia tetrasperma Fabaceae 0 210 60 270 Erodium cicutarium Geraniaceae 0 0 10 10 Geranium columbinum Geraniaceae 0 19 11 30 Geranium dissectum Geraniaceae 62 153 2 217 Geranium molle Geraniaceae 68 4 6 78 Iris tenax Iridaceae 0 5 0 5 Sisyrinchium idahoense Iridaceae 0 20 12 32 Prunella vulgaris Lamiaceae 1 15 0 16 Allium acuminatum Liliaceae 4 0 0 4 Allium amplectens Liliaceae 0 89 0 89 Brodiaea hyacinthina Liliaceae 1 9 0 10 Calochortus tolmiei Liliaceae 0 3 0 3 Dichelostemma capitatum Liliaceae 0 26 2 28 Erythronium oregonum Liliaceae 0 1 0 1 Fritillaria affinis Liliaceae 3 0 0 3 Linum lewisii Linaceae 0 480 7 487 Sidalcea campestris Malvaceae 0 57 0 57 Sidalcea virgata Malvaceae 0 285 1 286 Zygadenus venenosus Melanthiaceae 1 27 0 28 Clarkia purpurea Onograceae 0 51 0 51 Epilobium ciliatum Onograceae 0 5 53 58 Orobanche uniflora Orobanchaceae 16 0 0 16 Parentucellia viscosa Orobanchaceae 0 683 0 683 Collomia linearis Polemoniaceae 0 20 0 20 Claytonia perfoliata Portulacaceae 70 0 0 70 Montia linearis Portulacaceae 0 10 0 10 Dodecatheon hendersonii Primulaceae 71 0 0 71 Dodecatheon pulchellum Primulaceae 0 3 0 3 Aquilegia formosa Ranunculaceae 0 5 0 5

95 Plant Species Plant Family GPL WV KM Total Delphinium menziesii Ranunculaceae 122 0 0 122 Delphinium nuttallii Ranunculaceae 3 0 0 3 Ranunculus aquatilis Ranunculaceae 2 0 189 191 Ranunculus occidentalis Ranunculaceae 216 1034 44 1294 Ranunculus orthorhynchus Ranunculaceae 0 7 0 7 Fragaria virginiana Rosaceae 9 71 0 80 Potentilla glandulosa Rosaceae 0 30 0 30 Potentilla gracilis Rosaceae 0 586 0 586 Galium boreale Rubiaceae 0 5769 0 5769 Lithophragma parviflorum Saxifragaceae 2 0 0 2 Saxifraga integrifolia Saxifragaceae 2 54 0 56 Scrophulariaceae 10 84 0 94 Collinsia grandiflora Scrophulariaceae 13 7 0 20 Collinsia parviflora Scrophulariaceae 284 95 0 379 Mimulus alsinoides Scrophulariaceae 2 0 0 2 Mimulus guttatus Scrophulariaceae 7 34 0 41 Veronica officinalis Scrophulariaceae 1 0 0 1 Veronica scutellata Scrophulariaceae 0 3 59 62 Plectritis congesta Valerianaceae 1709 2817 0 4526 Viola adunca Violaceae 13 0 0 13

96 Table C.2 Count of pollinator species from flower netting surveys organized by family. Abundnaces of insects summarised across sites within three ecoregions Strait of Georgia/ Puget Lowlands (GPL, 6 sites), the Willamette Valley (WV, 8 sites), and the Klamath Mountains (KM, 2 sites), in addition to total number of specimens collected across sites. Insect Species Insect Family GPL WV KM Total Coleoptera: Beetles Anthaxia sp. Buprestidae 0 0 4 4 Anastrangalia laetifica Cerambycidae 0 2 0 2 Brachysomida californica Cerambycidae 0 0 2 2 Cortodera sp. Cerambycidae 0 88 2 90 Molorchus sp. Cerambycidae 0 0 2 2 Stenocorus nubifer Cerambycidae 0 0 4 4 Criocerinae spp. Chrysomelidae 0 0 3 3 Diabrotica unidecimpunctata Chrysomelidae 0 9 18 27 Coleoptera sp. 3 Coleoptera 0 0 2 2 Elateridae sp. 1 Elateridae 3 0 0 3 Elateridae sp. 2 Elateridae 3 0 0 3 Elateridae sp. 3 Elateridae 7 0 0 7 Elateridae sp. 4 Elateridae 0 0 14 14 Elateridae sp. 5 Elateridae 10 0 0 10 Elateridae sp. 6 Elateridae 0 0 10 10 Elateridae sp. 7 Elateridae 2 0 0 2 Scirtidae spp. Scirtidae 3 0 0 3 Diptera: Flies Eulonchus tristis Acroceridae 0 5 0 5 Anthomyiidae sp. Anthomyiidae 10 12 3 25 Bombylius aestruus Bombylidae 0 2 0 2 Bombylius major Bombylidae 49 144 5 198 Conophorus columbiensis Bombylidae 15 0 0 15 Conophorus sackenii Bombylidae 11 38 0 49 Calliphora aldrichia Calliphoridae 0 0 5 5 Calliphora vomitoria Calliphoridae 0 0 2 2 Dalmannia sp. Conopidae 0 13 0 13 Thecophora sp. 2 Conopidae 0 2 0 2 Zodion sp. Conopidae 2 7 0 9 Pseudosyrphid sp. Diptera 0 2 0 2 Empis sp. Empididae 0 19 0 19 Hilara sp. Empididae 5 0 0 5 Rhamphomyia sp. 1 Empididae 25 59 0 84

97 Insect Species Insect Family GPL WV KM Total Rhamphomyia sp. 2 Empididae 0 17 0 17 Lonchaeidae sp. Lonchaeidae 9 0 0 9 Muscidae (metallic) sp. 1 Muscidae 0 5 0 5 Rhagio sp. Rhagionidae 0 7 4 11 Scathophagidae sp. Scathophagidae 0 5 11 16 Dasysyrphus sp. Syrphidae 10 0 0 10 Eristalis arbustorum Syrphidae 5 29 11 45 Eristalis brousii Syrphidae 0 2 3 5 Eristalis hirtus Syrphidae 7 137 30 174 Eristalis obscurus Syrphidae 0 14 2 16 Eristalis tenax Syrphidae 6 5 0 11 Eristalis transversa Syrphidae 3 0 0 3 Eupeodes latifasciatus Syrphidae 0 12 0 12 Eupeodes volucris Syrphidae 3 2 0 5 Helophilus aff. nasonii Syrphidae 0 29 2 31 Melanostoma mellinum Syrphidae 23 5 4 32 Merodon equestris Syrphidae 6 4 0 10 Paragus sp. Syrphidae 2 2 0 4 Parasyrphus sp. Syrphidae 0 2 0 2 Pipiza sp. Syrphidae 0 7 0 7 Platycheirus stegnus Syrphidae 9 98 0 107 Scaeva pyrastri Syrphidae 0 20 0 20 Sphaerophoria contigua Syrphidae 5 0 0 5 Sphaerophoria novaeanglia Syrphidae 4 0 0 4 Sphaerophoria sulphuripes Syrphidae 0 150 16 166 Sphaerophoria weemsi Syrphidae 0 2 0 2 Toxomerus marginatus Syrphidae 2 142 36 180 Toxomerus occidentalis Syrphidae 16 90 42 148 Volucella bombylans Syrphidae 54 2 0 56 Epalpus signifer Tachinidae 2 0 0 2 Gymnosoma fulginosa Tachinidae 0 5 4 9 Peleteria sp. Tachinidae 0 3 0 3 Tachina sp. 1 Tachinidae 0 5 0 5 Tachinidae sp. Tachinidae 0 15 4 19 Hymenoptera: Bees, Wasps, & Ants Andrena amphibola Andrenidae 0 6 0 6 Andrena angustitarsata Andrenidae 278 98 7 383 Andrena anisochlora Andrenidae 0 5 2 7 Andrena astragali Andrenidae 0 0 41 41

98 Insect Species Insect Family GPL WV KM Total Andrena auricoma Andrenidae 46 22 13 81 Andrena buckelli Andrenidae 13 0 0 13 Andrena caerulea Andrenidae 27 9 4 40 Andrena candida Andrenidae 2 2 0 4 Andrena chlorogaster Andrenidae 0 46 0 46 Andrena chlorura Andrenidae 18 0 0 18 Andrena cressonii Andrenidae 5 20 0 25 Andrena cupreotincta Andrenidae 2 0 0 2 Andrena evoluta Andrenidae 0 0 3 3 Andrena hemileuca Andrenidae 2 2 0 4 Andrena lupinorum Andrenidae 0 2 0 2 Andrena microchlora Andrenidae 18 30 0 48 Andrena nigrocaerulea Andrenidae 32 62 0 94 Andrena nivalis Andrenidae 30 4 0 34 Andrena perplexa Andrenidae 0 6 0 6 Andrena prunorum Andrenidae 0 2 0 2 Andrena saccata Andrenidae 33 0 0 33 Andrena salicifloris Andrenidae 46 9 0 55 Andrena subtilis Andrenidae 10 0 0 10 Andrena thaspii Andrenidae 8 0 0 8 Andrena transnigra Andrenidae 6 0 0 6 Andrena vicinoides Andrenidae 2 0 0 2 Andrena w-scripta Andrenidae 2 0 0 2 Panurginus atriceps Andrenidae 0 0 25 25 Anthophora pacifica Apidae 3 0 0 3 Apis mellifera Apidae 163 313 91 567 Bombus appositus Apidae 0 10 0 10 Bombus bifarius Apidae 58 0 0 58 Bombus californicus Apidae 14 71 19 104 Bombus flavifrons Apidae 58 66 5 129 Bombus griseocollis Apidae 0 12 0 12 Bombus huntii Apidae 0 0 2 2 Bombus melanopygus Apidae 107 17 4 128 Bombus mixtus Apidae 148 97 9 254 Bombus sitkensis Apidae 24 2 0 26 Bombus vandykei Apidae 0 0 7 7 Bombus vosnesenskii Apidae 104 76 42 222 Ceratina acantha Apidae 52 66 20 138 Ceratina micheneri Apidae 0 7 0 7

99 Insect Species Insect Family GPL WV KM Total Ceratina nanula Apidae 0 4 0 4 Eucera acerba Apidae 2 0 0 2 Eucera edwardsii Apidae 0 21 0 21 Eucera frater Apidae 6 12 3 21 Nomada sp. 1 Apidae 3 15 0 18 Nomada sp. 2 Apidae 8 22 0 30 Nomada sp. 4 Apidae 2 17 4 23 Cephidae sp. Cephidae 0 10 0 10 Chrysura sp. Chrysididae 0 2 0 2 Colletes kincaidii Colletidae 0 2 0 2 Hylaeus modestus Colletidae 0 9 3 12 Agapostemon texanus Halictidae 0 12 0 12 Agapostemon virescens Halictidae 2 28 0 30 Halictus confusus Halictidae 0 10 0 10 Halictus farinosus Halictidae 0 49 0 49 Halictus ligatus Halictidae 0 123 22 145 Halictus rubicundus Halictidae 10 39 0 49 Halictus tripartitus Halictidae 6 22 17 45 Lasioglossum (Evylaeus) sp. 1 Halictidae 11 40 0 51 Lasioglossum (Evylaeus) sp. 3 Halictidae 0 4 0 4 Lasioglossum (Evylaeus) sp. 4 Halictidae 62 647 0 709 Lasioglossum (Evylaeus) sp. 5 Halictidae 11 4 0 15 Lasioglossum (Evylaeus) sp. 6 Halictidae 82 16 0 98 Lasioglossum albipenne Halictidae 3 13 0 16 Lasioglossum cressonii Halictidae 33 22 2 57 Lasioglossum incompletum Halictidae 10 140 6 156 Lasioglossum knereri Halictidae 2 55 0 57 Lasioglossum laevissimum Halictidae 0 5 0 5 Lasioglossum nevadense Halictidae 14 23 0 37 Lasioglossum olympiae Halictidae 119 140 9 268 Lasioglossum pacificum Halictidae 28 10 0 38 Lasioglossum pruinosum Halictidae 0 12 0 12 Lasioglossum sisymbrii Halictidae 5 9 0 14 Lasioglossum titusi Halictidae 13 152 25 190 Lasioglossum villosulum Halictidae 62 647 0 709 Lasioglossum zonulum Halictidae 0 34 0 34 Sphecodes sp. 13 Halictidae 2 0 0 2 Sphecodes sp. 14 Halictidae 0 2 0 2 Sphecodes sp. 16 Halictidae 2 0 0 2

100 Insect Species Insect Family GPL WV KM Total Sphecodes sp. 4 Halictidae 4 2 0 6 Ichneumonidae sp. 1 Ichneumonidae 0 4 0 4 Ichneumonidae sp. 4 Ichneumonidae 0 4 0 4 Hoplitis grinnelli Megachilidae 0 5 0 5 Megachile melanophaea Megachilidae 4 0 0 4 Osmia albolateralis Megachilidae 2 3 0 5 Osmia atrocyanea Megachilidae 0 5 2 7 Osmia coloradensis Megachilidae 13 0 0 13 Osmia cyanella Megachilidae 2 0 0 2 Osmia dolerosa Megachilidae 6 9 0 15 Osmia gabrielis Megachilidae 0 0 6 6 Osmia kincaidii Megachilidae 4 0 0 4 Osmia lignaria Megachilidae 53 11 0 64 Osmia malina Megachilidae 0 4 0 4 Osmia nemoris Megachilidae 0 2 0 2 Osmia odontogaster Megachilidae 2 0 0 2 Osmia sculleni Megachilidae 0 4 0 4 Osmia trevoris Megachilidae 0 5 0 5 Osmia tristella Megachilidae 0 2 0 2 Megachilidae 0 0 7 7 Aporus sp. Pompilidae 0 2 0 2 Priocnemis sp. Pompilidae 8 8 0 16 Dolichovespula arenaria Vespidae 3 0 0 3 Polistes dominula Vespidae 2 5 3 10 Vespula consobrina Vespidae 3 0 0 3 Lepidoptera: Moths & Butterflies Adela septentrionella Adelidae 31 6 0 37 Erynnis propertius Hesperiidae 2 0 3 5 Glaucopsyche lygdamus Lycaenidae 3 0 0 3

101 Appendix D.

Pollen Family List

Table D.1 Average count of pollen from the body (head and front of thorax) and scopae of 126 female Andrena angustitarsata (120 from the body), within two ecoregions: Strait of Georgia/ Puget Lowlands (GPL, 6 sites) and the Willamette Valley (WV, 5 sites). Plant families with at least 5% of the total pollen abundance (of a pollen location or ecoregion) are listed first, followed by all plant families included within the “other pollen” category (listed alphabetically). Several plant families with at least 5% of the total abundnace were placed into the “other pollen” category because they were not observed within vegetation surveys: Anacardiaceae (poison-oak), Salicaceae (willows), and Sapindaceae (maples).

Pollen families with 5% or GPL WV greater total abundance Scopal Body Scopal Body Apiaceae 53.610 52.671 37.909 39.976 Asteraceae 0.915 2.519 6.068 10.049 Brassicaceae 8.220 6.785 0.023 0.000 Ranunculaceae 1.061 1.886 5.773 6.463 Rosaceae 8.183 5.759 20.091 16.098 Other pollen families with less than 5% of total Anacardiaceae 0.000 0.038 8.091 6.317 Asparagaceae/ Liliaceae 3.012 3.177 0.295 0.585 Betulaceae 3.951 4.139 0.705 0.024 Boraginaceae 0.000 0.000 0.091 0.000 Caprifoliaceae 0.000 0.000 0.023 0.024 Caryophyllaceae 2.012 3.557 0.182 0.049 Convolvulaceae 0.268 0.266 0.000 0.122 Cucurbitaceae 0.012 0.013 0.636 0.000 Ericaceae 0.012 0.063 0.068 0.073 Fabaceae 0.098 2.076 0.273 0.341 Fagaceae 0.707 0.962 0.068 0.000 Geraniaceae 0.024 0.139 0.000 0.000 Malvaceae 0.049 0.038 0.000 0.146 Montiaceae 0.098 0.165 1.659 1.098 Onograceae 0.000 0.000 0.000 0.049 Orobanchaceae 0.000 0.000 0.023 0.000

102 Other pollen families with GPL WV less than 5% of total Scopal Body Scopal Body Papavervaceae 0.012 0.304 0.114 0.171 0.037 0.025 0.841 0.000 0.256 0.949 0.386 1.244 Polygonaceae 0.037 0.000 0.023 0.000 Primulaceae 0.305 1.013 1.227 1.390 Rubiaceae 0.000 0.000 4.523 3.146 Salicaceae 5.451 6.000 1.432 1.098 Sapindaceae 8.951 2.924 6.341 3.610 Saxifragaceae 0.073 0.089 0.045 0.171 Valerianaceae 2.634 2.937 2.227 2.585 Unknown 0.012 0.076 0.045 0.049

103