The Roles of Forest Fragments and an Invasive Shrub in Structuring Native Bee Communities and Pollination Services in Intensive Agricultural Landscapes

Home , Aesculus glabra, Andrena commoda, Andrena erigeniae, Andrena erythronii, Andrena forbesii, Andrena hippotes

MIAMI UNIVERSITY The Graduate School

Certificate for Approving the Dissertation

We hereby approve the Dissertation

Michael John Minnick

Candidate for the Degree

DOCTOR OF PHILOSOPHY

______Thomas O. Crist, Director

______David J. Berg, Reader

______Amélie Y. Davis, Reader

______David L. Gorchov, Reader

______Jing Zhang, Graduate School Representative ABSTRACT

THE ROLES OF FOREST FRAGMENTS AND AN INVASIVE SHRUB IN STRUCTURING NATIVE BEE COMMUNITIES AND POLLINATION SERVICES IN INTENSIVE AGRICULTURAL LANDSCAPES

Michael J. Minnick

This dissertation examines how an invasive woody plant, Lonicera maackii, temporally and spatially structures native bee communities of forest-edge habitat in agricultural landscapes. In Chapter 1, I measured bee species composition and pollination services ≤200 m from isolated forest patches in response to L. maackii flower removals. Removing flowers released a subset of small-bodied bees and increased pollination services after two years. Pollination services provisioned by large-bodied and generalist bee species (e.g. Bombus spp) increased when nearby plants were adjacent to intact L. maackii flowers. Findings suggest that L. maackii flowers suppress one component of the bee community and attract another to the forest patch that increases usage of the adjacent crop fields. In Chapter 2, I compared two components of the bee community and their responses to L. maackii density, floral resources of the forest patch, and the surrounding landscape. Bees sampled in pan traps were typically small, specialized, and responded to local patch features. Bees sampled in vane traps were larger in body size, social, and responded to landscape composition 3 km from the forest patch. These findings suggest that L. maackii floral resources support weaker foragers within the forest patch as well as larger bees that forage throughout the landscape. Both components of the bee community responded to tree community composition and were vertically stratified in the tree canopy. In Chapter 3, I measured bee diversity and community composition at different vertical strata in response to L. maackii density and flowering period as well as floral resource availability of woody plants. I found that L. maackii supports a component of the vertically stratified bee community which changes interactions with floral resources of the native woody vegetation at different vertical strata. Collectively, my studies demonstrate that L. maackii structures forest-edge bee communities through mechanisms involving functional and life history traits of individual bee species. Therefore, in my Conclusion Chapter, I developed a synthetic model that assigned an Agricultural Landscape Response Index for Bees (ALRIB) value between 0 and 1 to bees of each species that corresponds with their likelihood of responding to the forest patch as an island or as one land cover type within a broader mosaic of different resources. I conclude that L. maackii invasion into forest fragments within intensively managed agricultural landscapes filters the bee community in favor of species that use its floral resources and exhibits an overall homogenizing effect on species diversity.

THE ROLES OF FOREST FRAGMENTS AND AN INVASIVE SHRUB IN STRUCTURING NATIVE BEE COMMUNITIES AND POLLINATION SERVICES IN INTENSIVE AGRICULTURAL LANDSCAPES

A DISSERTATION

Presented to the Faculty of

Miami University in partial

fulfillment of the requirements

for the degree of

Doctor of Philosophy

Department of Biology

Michael J. Minnick

The Graduate School Miami University Oxford, Ohio

2020

Dissertation Director: Thomas O. Crist

Michael John Minnick

2020

TABLE OF CONTENTS

General Introduction ...... 1 1 Introduction ...... 1 2 Bee Pollination and Invasive Plants...... 3 3 Bee Diversity and Modern Agricultural Landscapes ...... 6 4 References ...... 13 Chapter 1: Bee communities and pollination services in adjacent crop fields following flower removal in an invasive forest shrub ...... 26 1 Abstract ...... 26 2 Introduction ...... 28 3 Methods ...... 30 3.1 Study Design ...... 30 3.2 Bee Community ...... 32 3.3 Sentinel Flower Visitation ...... 33 3.4 Sentinel Fruit Production ...... 33 3.5 Analyses ...... 33 4 Results ...... 37 4.1 Bee community & functional traits ...... 37 4.2 Bee visits to sentinel plants...... 38 4.3 Sentinel plant production ...... 38 5 Discussion...... 39 5.1 Bee community & functional traits ...... 39 5.2 Bee visits to sentinel plants...... 41 5.3 Sentinel plant production ...... 42 6 References ...... 46 Chapter 2: Community responses of native bees to landscape composition depend on bee functional traits and seasonal floral resource availability ...... 74 1 Abstract ...... 74 2 Introduction ...... 76 3 Materials and Methods ...... 78 3.1 Site Selection ...... 78 3.2 Bee Community ...... 79 3.3 Local Floral Resource Availability ...... 79

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3.4 Landscape Features ...... 80 3.5 Analyses ...... 81 4 Results ...... 83 4.1 Communities ...... 83 4.2 Bee Community Composition ...... 83 4.3 Habitat Patch and Landscape Composition Effects ...... 84 4.4 Seasonal Effects and L. maackii Density ...... 87 5 Discussion...... 88 5.1 Bee Responses to Resolution and Scale ...... 88 5.2 Seasonal Resource Availability: Flowers, Lonicera maackii, and Trees ...... 90 6 Conclusion ...... 94 7 References ...... 95 Chapter 3: Floral resources of an invasive shrub alter native bee communities at different vertical strata in forest-edge habitat ...... 129 1 Abstract ...... 129 2 Introduction ...... 130 3 Materials and Methods ...... 132 3.1 Site Selection ...... 132 3.2 Bee Community ...... 133 3.3 Woody Plant Community ...... 133 3.4 Analyses ...... 135 4 Results ...... 138 4.1 Bee Abundance and Species Richness ...... 138 4.2 Bee Community Composition ...... 139 5 Discussion...... 140 5.1 Lonicera maackii Affects Vertical Use of Forest Edges ...... 140 5.2 Bees Across Vertical Strata ...... 143 6 Conclusion ...... 146 7 References ...... 147 General Conclusion ...... 180 1 Introduction ...... 180 2 Shifting perception of bee responses to the landscape ...... 181 3 Effects of Lonicera maackii on bees ...... 183

4 Invasive plant effects on a synthetic framework...... 188 4.1 Development of ALRIB ...... 189 4.2 Application of ALRIB ...... 190 5 Conclusion ...... 192 6 References ...... 193 Appendix A ...... 199

LIST OF TABLES

CHAPTER 1 TABLES Table 1. Log-response ratios of the probability of presence and absence for each species (with 95% CI) that demonstrated a difference in abundance between treatments within distances and years. Ratios were calculated as the natural log of the species abundance in control plots subtracted from that in flower-removal plots; a positive mean indicates increased bee abundance in flower removal plots relative to controls; a negative mean indicates a decrease...... 52 Table 2. Pollinator visitation of sentinel plants in response to flower removal of honeysuckle shrubs at forest edges, distance from forest edges, and microclimate factors. Competing model coefficients (± 1 SE) of predictor variables in general linear models with pollinator visitation expressed as binary responses. Each competing model is represented by a row for the occurrence of a pollinator visit from bees (above) and all other taxa (below). All predictors are log-transformed. Wt = cumulative weight of competing models ...... 53

Supplementary Table 1. Sampled bees with mean functional trait values considered in analyses. Total abundance of each species is provided followed by number of females in parentheses. Missing body parts excluded 1-9 females of a species in functional trait analyses (^). Males and unknown species were not included in analyses (^^), although bidentate Nomada morphospecies were assumed specialized foragers. Some traits were suggested in literature but not empirically demonstrated (†). Known to forage on Lonicera spp (*). Species inferred to heavily use L. maackii floral resources are highlighted ...... 58 Supplementary Table 2. Bee species richness and abundance in response to flower removal treatments and distance from forest edges. Coefficients (± 1 SE) of predictor variables honeysuckle flower removal treatment (Tx) and distance into the crop field (Distance) in the best-fitting and competing models in 2013 (above) and 2014 (below)...... 67 Supplementary Table 3. Bee functional diversity in response to flower removal treatments and distance from forest edges. Coefficients (± 1 SE) of competing models explaining functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv) of the bee community in 2013 (above) and 2014 (below)..68 Supplementary Table 4. Sentinel plant pollination success in response to flower removal treatments and distance from forest edges. Best-fitting model coefficients (± 1 SE) for each response in 2013 (above) and 2014 (below). Distance is log-transformed. Flower removal treatment = “Tx”...... 69 Supplementary Table 5. Flower abundance estimations of L. maackii shrubs within five 5 x 5-m quadrats along three edges used in the study. Base circumference of every shrub > 1 cm in diameter was measured within quadrats (data unpublished). Formula (e(β0+β1(dgl) + β2(COV))) extracted from Hassett and McGee (2017), where β0 is the intercept, β1 and β2 are coefficients for the basal diameter (dgl) of each stem and percent canopy cover (COV), respectively. Using parameters derived from stands in New York, USA, β0 = 3.49, β1 = 0.66, and β2 = not significant. Lonicera maackii on edges generally produce more flowers and berries than those in forest interiors (Barriball et al. 2014) likely due to their high sun exposure, so I also assumed β2 = 0. Mean basal diameter of all shrubs within the evaluated 125 m2 area is conservatively represented as the number of L. maackii flowers in 250 m2 of the 10 x 100-m plots in this study (1/2 the area)...... 70

CHAPTER 2 TABLES Table 1. Unsupervised land cover classes contained within the Cropland Data Layer (CDL) database were lumped into eight categories: corn, fallow, flowerless, forest, grassland, impervious, residential, and soybean...... 100

Supplementary Table 1. Lonicera maackii density based on basal area within the 5-m forest edge, site location, aspect of each forest-edge site, size of the associated forest fragment, and width of forest-agriculture interface. Sites are listed in increasing order of L. maackii density...... 106 Supplementary Table 2. Species abundances by trap. Females (F) and males ( (M) ) of each species and morphotype (morpho-) used in analyses. Species are alphabetically ranked within taxonomic family. Total abundance across trapping methods (Total) and average size of females (males) are calculated means of three individuals. Queens of social species were not measured (i.e. Bombus spp) and were counted as workers for this estimation...... 107

Supplementary Table 3. Flowering plant community with open flowers < 2 m from the ground. Species, or groups of species, are alphabetically ranked under their respective taxonomic families. Flower color (Red/Pink/Orange (R), White (W), Yellow (Y), Blue/Purple (B), Green (G)), the number of sites in which flowering stems occurred (out of 12), the number of flowering stems (Herbaceous), clusters of flowers (Vines), and apparent individuals (shrubs and trees), as well as the associated growth habit (Tree (T), Woody Vine (VW), Herbaceous Vine (VH), Herb (H), Woody Shrub (SW), Cane-like Shrub (SC) and Sedge (Se)) are listed. Two colors indicate differences in flower color, with the first letter representing petals, and the second referring to the general appearance of the rest of the flower (green is not included). Here, a plant is considered a vine if it is a climbing vine. Differ in flower morphology, though the same color (*)...... 115 Supplementary Table 4. Tree species abundances within 0.5 ha forest-edge sites ranked by extrapolated L. maackii density (m2/ha). Species are listed alphabetically...... 121 Supplementary Table 5. Top four generalized additive models of abundance (not shaded) and species richness (shaded) for bees of pan traps at fine resolution (0.15 m x 0.15 m) and vane traps at coarse resolution (30 m x 30 m). Models are ranked by AICc. Degrees of freedom include penalized smoothed terms. Important predictors of characteristics of the focal forest patch included the linear variables of abundance, species richness, or evenness of the flowering herbaceous community (flowerabun, flowerrich, flowereven) or abundance or species richness of the tree community (treeabun, treerich) along the transect of forest edge, margin width between the belt transect and adjacent crop field (marginT), and the non-linear effect of the day of the study ( s(time) ). Patch-scale interactions included nonlinear effects of tree abundance or species richness with the day of the study ( ti(time, tree) ) and the linear effects of tree abundance with evenness of the flowering plant community (treeabun:flowereven). Important landscape predictors included the proportion of land cover types, including semi-natural habitat (SNH) that is frequently disturbed (disturbed SNH), margins between soybean/corn fields as well as drainage ways within crop (drainage), fallow fields and crops that are bee- pollinated (fallow), crops that are not bee-pollinated or do not produce a showy flower (flowerless crop), forested SNH (forest), high densities of buildings, roads, parking lots and other impenetrable surfaces (impervious surface), grass lawns (lawn), soybean crop fields (soybean), strips (<10 m wide) of habitat between forest and any other land cover type (stripF), strips of habitat that were not between two crop fields nor adjacent to forest land cover type (stripother), and open water (water). Direction of the relationship between response and predictor is either positive (+) or negative (-) and depicted as superscripts following each linear predictor. Smoother terms are not provided a linear direction. Scale is 2.0 km (*)...... 124

CHAPTER 3 TABLES Table 1. Top four generalized linear mixed models of abundance and species richness for bees. Models are ranked by AICc. All models include a random intercept of vertical stratum (n=3) nested within the unique array of traps (n=20), as well as an autoregressive term for temporal autocorrelation for each array among sampling periods. Predictors included L. maackii density (L. maackii), composite flowering time of mature woody vegetation (Flower timeM), abundance of all woody vegetation (WVA Abundance) and mature woody vegetation (WVM Abundance), basal area of all woody vegetation (WVA BA) and mature woody vegetation (WVM BA), sampling period (Period), and vertical stratum (stratum). Direction of the relationship between response and continuous predictor is either positive (+) or negative (-) and depicted as superscripts following each predictor. Directions of the relationships between levels of categorical predictors and responses are described in the text. Two-way interactions are depicted with an asterisk between the two terms “ * ”...... 152 Table 2. Top five models predicting changes in bee composition. Models are listed in order of decreasing variance explained by the constraining variables. Predictors include L. maackii density (L. maackii), basal area (m2/ha) of mature individuals of woody species with showy flowers (WVM, sho BA), species richness of all woody vegetation (WVA Richness), and composite mean peak flowering time of woody vegetation during the season for all (Flower timeA) and only mature (Flower timeM) stems...... 153

Supplementary Table 1. Lonicera maackii basal area, site locations, aspect of each forest-edge site, and the size of the associated forest fragment...... 159 Supplementary Table 2. Summary statistics and life history attributes of woody species found in the ten forest- edge transects. Sexual maturity, or life stage at which an individual flowers, was assigned for each species based on stem DBH (see Methods for details). Individual tree heights were estimated to determine if a stem

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was short (< 7 m) or tall (> 7 m), as well as maturity when provided information was height-specific. Heights were estimated using a modified form of Weiskittel et al.’s (2016) equation for “American beech” (AB), “ash” (AS), “other hardwood” (OH), “other softwood” (OS), “red maple” (RM), and “sugar maple” (SM) species classes. Since all individuals were on forest-agriculture edges, I assumed canopy closure did not affect tree growth, and assigned a value of 0 to the associated parameters. Predicted values from fitted lines were used when sources offered variable relationships between size (DBH or height) and maturity status within the region (^). All measured understory shrubs were assumed to be mature; maturity was assumed at DBH > 3 cm from personal observation (#), or at DBH > 10 cm (##). Total abundance of each species; estimated number of immature individuals, mean DBH (cm) ± 1 SE of estimated mature and immature individuals, estimated abundance of short (< 7 m) and tall (> 7 m) individuals, estimated flowering time in relation to L. maackii bloom (before/during/after), 1st Principal Component axis represents flowering time relative to other species from PCA (see Methods), and insect relationship with flowers, including showy (S) and entomophilous (E), are listed for each species...... 160 Supplementary Table 3. Life history attributes of bee species, including mean body length of three males, three females, and up to three queens based on availability (each * corresponds to one queen). Sampled bee abundance in the understory (Und), subcanopy (Sub), and canopy (Can) canopy layers, as well as the total abundance of bees (Abundance) and only males (parentheses). Nomada bidentate were assumed oligolects (#). Traits suggested but not empirically demonstrated in the literature (†)...... 167 Supplementary Table 4. Standardized coefficients of structural equation models evaluating indirect effects of L. maackii on bee abundance and species richness mediated through the woody plant community before, during, and after the flowering period of L. maackii. Terms represent bee abundance, abundance of all woody vegetation (WVA Abundance), species richness of all woody vegetation (WVA Richness), L. maackii density (L. maackii), the subcanopy stratum (binary variable), and canopy stratum (binary variable). Model terms were constrained to include only predictors within fitted best generalized linear mixed effects models. A “ : ” indicates an interaction, “ ^ ” indicates a significant interaction with sampling period. ( . P < 0.10, * P < 0.05, ** P < 0.01, *** P < 0.001)...... 172

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LIST OF FIGURES

GENERAL INTRODUCTION FIGURES Figure 1. Island versus landscape views of bee communities and their pollination services in agricultural environments. The island model emphasizes patch area and distances between patches within an inhospitable matrix, while the landscape model views semi-natural habitat patches as part of a larger mosaic of land cover types that, together, influence biodiversity at any given location...... 22 Figure 2. Predicted responses of the bee community to invasive plant flowers. Invasive plant inflorescences attract bees within a given distance (blue: lesser attraction; red: greater attraction) and may facilitate visitation and pollination services to nearby plants when invasive flowers are at low densities (a). A more intense floral display (depicted as greater flower density) may saturate the pollinator community and suppress visitation and pollination services to nearby plants (b). These hypotheses are shown in two dimensions but apply to the height dimension as well. As populations of invasive plants spread to provide the dominant source of local floral resources, the species composition of the bee community should shift in favor of populations that acclimate to use the invader’s floral resources (c)...... 23 Figure 3. Aerial imagery of agricultural landscapes focused on regions with intensively managed maize. Images are from 10,000 m above sea level. Relative image locations and year taken are clockwise from far left: Colombia (2017), Midwestern United States (2014), Germany (2017), SE China (2017), SE Australia (2019), Zambia (2017)...... 24 Figure 4. Publications per year on wild bees in agricultural landscapes (grey bars), as well as those that analyzed ≤ 2 (dark blue) or > 2 land cover types (light blue) in the surrounding matrix...... 25

CHAPTER 1 FIGURES Figure 1. Fitted best models demonstrating the relationship between distance from forest edge with honeysuckle shrubs (x-axis), and honeysuckle floral-removal treatment. Bee abundance and richness are from pan trap samples in 2013 (a, b) and 2014 (c, d), respectively. Circles and solid lines represent control plots while triangles and dashed lines represent flower removal plots. Raw values are grey points and means are black points...... 62 Figure 2. Multivariate associations between species functional traits and environmental factors weighted by the bee community on RLQ axes (co-inertia analysis) for 2013 (left) and 2014 (right). Points and lines represent relative positioning of functional traits (dashed lines; triangles) and environmental factors (solid lines; black circles). Species scores are plotted (circles)...... 63 Figure 3. Predicted values and 95% CI of expected occurrence of a bee visitation to cucumber flowers during an observation (y-axis) in response to the number of open flowers on sentinel plants (x-axis) between control (solid line) and honeysuckle flower removal (dashed line) plots. Shifts in responses are demonstrated under conditions of no wind (left) and maximum observed wind (right). Grey shapes are jittered. Solid circles represent plants associated with control plots and triangles plants with floral removal plots...... 64 Figure 4. Relationships between sentinel plant pollination, flower-removal treatments, and distance from forest edge. Confidence intervals (vertical bars) are for mean (black shapes) number of seeds per plant (a & e), seeds per fruit (b & f), fruit (c & g), and production measured as fruit volume (d & h). Lines represent fitted values of the best model between treatments (circles and solid lines = control; triangles and dashed lines = flower removal) with distance from the forest edge in 2013 (a-d) and 2014 (e-h). If floral removal is not in best model, mean relationships of treatments are represented by a solid line...... 65

Supplementary Figure 1. Invasion of Amur honeysuckle (Lonicera maackii) along the forest-crop edge at one of the five study sites (left) and two common visitors of L. maackii flowers: large mining bee, Andrena sp (top right); the honeybee, Apis mellifera (bottom right) ...... 71 Supplementary Figure 2. Constrained ordination using distance-based redundancy analysis (dbRDA) of bee species composition in flower-removal and control plots. Biplot arrow represents direction of increasing distance from forest edges with honeysuckle shrubs (significant predictor). Confidence ellipses (95%) are provided for honeysuckle floral removal (dashed) and control (solid) samples. Points represent samples taken at different distances (size of the point) from forest edges where honeysuckle shrubs were with (circle) or without (triangle) flowers...... 72

Supplementary Figure 3. Fitted values with 95% CI of expected frequency of a visitation event by a potential non- bee pollinator to one of three cucumber flowers within a 10-minute observation period (y-axis) due to wind speed (x-axis). Jittered grey points represent raw occurrence values, were “1” represents at least one visitation event and “0” represents no visitor observed...... 73

CHAPTER 2 FIGURES Figure 1. Study sites in the agricultural matrix of SE Indiana and SW Ohio, USA (a). Relative area of landcovers within ovals scaled by 0.1, 0.5, 1.0, 2.0, or 3.0 km radius from terminal ends of belt transects were used in models by classifying landcovers of the landscape (b) into a discrete set of categories using the Cropland Data Layer (CDL) from 2015 (c)...... 101 Figure 2. Land cover types at spatial scales of 0.1, 0.5, and 1.0 km were analyzed using the coarse (30 x 30-m) resolution (corn = corn; forest = forest, grassland = grassland, imp = impervious, res = residential) of Cropland Data Layer data from 2015 (a), as well as the fine (0.15 x 0.15-m) resolution (cornFC = cornFC, dSNH = disturbed SNH, drain = drainage, f & forest = forest, g = grassland, i = imperviousfine, la = lawn, sf = stripF, so = stripother, water = water) of delineated aerial photographs taken in 2015 (b). Star represents the center of the 100-m belt transect)...... 102 Figure 3. Fitted regression splines (red lines) on raw data points of abundance (a and c) and species richness (b and d) of bees sampled in pan traps (a and b) and vane traps (c and d) throughout the year. Letters on x-axis represent first letter of the month, beginning with April...... 103 Figure 4. Non-metric multi-dimensional scaling representing the dissimilarities among species composition of vane trap (dark gold) and pan trap (yellow) samples of a site (large circle). Lines represent 95% confidence ellipses; black dots represent species scores. (K = 2; stress = 0.17) ...... 104 Figure 5. Kernel density of bee body length of specimens sampled in vane traps (dark gold) superimposed onto pan traps (yellow). Small bees sampled in vane traps likely represent bees that use resources closer to vane traps (i.e. not low-lying). Each individual was assigned a body length based on sex and species (see Supplementary Table 2 for details)...... 105

Supplementary Figure 1. Differences in AICc scores of best models for bee abundance (a and c) and species richness (b and d) responses of samples from pan traps (a and b) and vane traps (c and d) across spatial scales. Competing models were ∆AICc < 2. Coarse resolution models were performed at all spatial scales, while fine resolution models only included 0.1, 0.5, and 1 km scales...... 125 Supplementary Figure 2. Changes in effects of either tree species richness (a) or abundance (b-d) on bee abundance (a & c) or species richness (b & d) of pan traps (a & b) or vane traps (c & d) as the season progressed (April, July, and November shown). Magnitude of the response by bees is represented by a blue- yellow color gradient, where a negative response is indicated by blue and a positive response is represented by yellow. Pixels of gray represent uncertainty due to a lack of observations. Contour lines were overlaid for ease of interpretation. The y-axis was natural-log transformed, and both axes were centered within the analysis...... 126 Supplementary Figure 3. Partial residual plots of the smoothing effect of season in best models for pan trap bee abundance (a), pan trap bee species richness (b), vane trap bee abundance (c), and vane trap bee species richness (d). Season is a continuous predictor and marked by the first letter of the month, where the first “A” indicates April and “N” indicates November. Intervals (between dashed lines) represent two standard errors from the fitted value (black line). Partial residuals are plotted in the background. Red shaded region represents the time period in which L. maackii flowered (May 11- June 3)...... 127 Supplementary Figure 4. Partial residuals from the best model explaining bee abundance of pan traps (a) and species richness of vane traps (b) that were used to fit the season predictor refit to L. maackii density. Only partial residuals from 11 May – June 3 were used to represent the time period when L. maackii was flowering...... 128

CHAPTER 3 FIGURES Figure 1. Best model predictions of bee abundance as a function of L. maackii density. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis...... 154

Figure 2. Best model predictions of bee abundance as a function of woody vegetation abundance for each of the three sampling periods (A): before, during, and after the flowering period of L. maackii, as well as each of the three vertical strata (B): understory, subcanopy, and canopy. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis...... 155 Figure 3. Bee species richness at each canopy layer. Averages and 95% CI of best model predictions. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis. Not significant (NS), *** P < 0.005, **** P < 0.001...... 156 Figure 4. Best model predictions of bee species richness as a function of the species richness of woody vegetation for each of the three sampling periods: before, during, and after the flowering period of L. maackii. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis...... 157 Figure 5. Bee composition from RLQ analysis. Visualization of maximized correlations of a co-inertia analysis between species trait (Q: nesting substrate (Soil Nesting; Wood Nesting), sociality (Social; Solitary), Body Length; dashed lines and triangles) and environment (R: sampling period relative to L. maackii flowering (Before; During; After), vertical strata from which bees were sampled (Understory; Subcanopy; Canopy), species richness of woody vegetation (WV Richness), percentage of woody vegetation estimated < 7 m tall (<7 m tall), basal area of woody vegetation with showy flowers (WVsho BA), L. maackii density (L. maackii), and mean peak flowering time of woody vegetation derived from PCA (See methods; Flower time); solid lines and circles) matrices weighted by species abundances (L: species-site matrix) superimposed on species scores (grey circles)...... 158

Supplementary Figure 1. Map depicting region in which study took place. All sites were located within the featured area, with one site at 39.558ºN, -84.899ºW (see Supplementary Table 1 for site details) magnified to show an example of an isolated forest patch in the landscape (inset)...... 174 Supplementary Figure 2. An array of six traps at heights of 1, 4, 7, 10, 13, and 16 m from the ground. Traps are connected with black parachord (300 lb test) at the top of each blue vane (holes provided by manufacturer). Knots in rope used to secure positioning of each trap. The parachord is anchored into the ground with a stake (not visible). Propylene gycol is in bottom of traps. Inset: close-up of trap at 16 m and the location where parachord is looped around branch (red circle). Looped parachord returns to ground and is secured around the trunk of a tree (not shown)...... 175 Supplementary Figure 3. PCA of the woody vegetation community that flowered before, during, and after (black dots) the L. maackii bloom weighted by total species abundance. Species scores (red dots) on the first Principal Component were used for analyses to represent the mean relative peak flowering time (Supplementary Table 2). Magnification shows species scores near origin (inset)...... 176 Supplementary Figure 4. Species scores of RLQ axes. See Figure 4 for details. Six species of Bombus were removed and are located in the direction of the arrow in the order of B. impatiens, B. pensylvanicus, B. fervidus, B. bimaculatus, and B. auricomis...... 177 Supplementary Figure 5. Predictions of mature tree abundance as a function of densities of L. maackii at sites with early (solid), mid (dashed), and late (dotted) peak flowering times...... 178 Supplementary Figure 6. Mean (± SE) bee abundances after the flowering period of L. maackii between forest edges with and without T. americana trees. Observed abundances (hollow circles) are jittered 0.1 log unit. Models included data from after L. maackii bloom. Likelihood ratio test was used to evaluate significance of T. americana presence. Y-axis is natural-log transformed...... 179

GENERAL CONCLUSION FIGURES Figure 1. Generalized linear model predictions of the proportion of wild bee publications that analyzed the landscape as a function of year published. Black circles represent raw data. Shaded region indicates 95% CI of predicted values...... 194 Figure 2. Proportion of studies that analyzed the surrounding matrix that used different maximum distances (radii) from sampling location to evaluate bee abundance and diversity responses to area of land covers. Bars represent increments of 0.1 km except for three bars representing 3.1–4.0 km, 4.1–5.0 km and 5.1–15.0 km...... 195 Figure 3. ALRIB values of bee communities in adjacent crop fields in response to flower removals of L. maackii shrubs at five sites over two years. Statistics are based on a linear model fit of the removal treatment to ALRIB responses...... 196

Figure 4. ALRIB of bee communities (April-November) at forest edges fitted to different densities of L. maackii. Each point represents the bee community of a site. Shaded area indicates the 95% CI ...... 197 Figure 5. ALRIB of bee communities throughout the canopy strata along forest edges before, during, and after the flowering period of L. maackii fitted to different densities of L. maackii. Each point represents the bee community of a site. Shaded area indicates the 95% CI...... 198

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DEDICATION To those who expressed unconditional love through these years of hard work and long hours:

Jennifer Cunningham-Minnick Diana Minnick John Minnick Steven Minnick Karen Cunningham Tim Cunningham Cameron Cunningham

And our new arrival who’s love pushed me across the finish line:

Lillian Cunningham-Minnick

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ACKNOWLEDGEMENTS

First I want to thank Dr. Thomas Crist, my advisor, for turning my love of exploring mysteries of the natural world into a career. His teachings and patience have shown me how to scientifically address and statistically disentangle some of earth’s most intricate relationships. I thank him for supporting, and at times tolerating, my ambitious pursuits while softly guiding me around pitfalls that I often realized only in hindsight. Through his wisdom, I have grown professionally as a scientist and learned to see the world through an additional lenses. I also thank Dr. Valerie Peters, my mentor, for introducing me to the world of bees and providing me direction as a new graduate student. Her persistence and hard-work ethic started me down a path of not only asking ambitious and rewarding questions but also developing the experimental designs that could answer them. I want to thank my committee members for their patience and working with me through the early years, as I was an incoming graduate student with stage fright and no academic background in ecology. Whether they saw potential or just hoped it was there, I may never know. But I am grateful for all their perspectives and counsel. I also thank Dr. Bruce Steinly for the opportunity to learn to teach, the great conversations during busy days, and treating me as a fellow entomologist. Finally, a very special thanks to retired greenhouse manager Jack Keegan. It is an understatement that the graduate and undergraduate students of the Crist lab helped me reach this goal. They were steadfast in their support whether it was carrying 5-gallon jugs of water into the middle of a blistering hot soybean field, meticulously pinning thousands of bees, brainstorming catchy talk titles, or simply socializing. I wish to give a special thanks to Dr. Kaitlin Campbell, who showed me the world of beekeeping, shared most of my successes and failures at Miami, and is an amazing friend and conference buddy. I also want to thank Dr. Michael Mahon, who’s enhanced motivation, interest in statistics, and friendship no doubt contributed to my success. Friendship of, and discussions with, Gwendolyn Lloyd were instrumental in these last years as we feverishly wrote together. The vital assistance and hard work of motivated undergraduates made these time-consuming and labor-intensive projects possible. I thank all of them, but am particularly proud and appreciative of Troy Meyer, Anita Schaeffer, Heather Brewster, and Kelsey Donahue for working directly with me for years and developing their own projects. The friendliness of the graduate students within the Biology Department made this position fun. Though I lack the space here to mention even a fraction of those that brought a daily smile to my face, I give special thanks to Drs. Ancilleno Davis, Tyler Hoskins, Jill Korach, and Ashley Walters that were with me over seven years ago, as well as McKenna Burns who has become a great friend over the last few years. It was a gift to work with private landowners and local growers of the area. Through their curiosity, generosity, and trust, I accessed and physically manipulated their properties during four successful years of field experiments. Therefore, their participation was vital for the scientific findings within this manuscript. I thank each of them and hope that more emerging scientists share similar experiences with those that our work directly pertains to. Finally, I thank my family for their unending patience, love, and dedication to my success. They endured the milestones and setbacks of my graduate experience. Specifically, I thank my mother and father, Diana and John Minnick, and mother- and father-in-law, Karen Cunningham and Tim Cunningham, for their unconditional love and support despite my absence or weariness at family events. I also thank Diana and Karen for baby-sitting my daughter, Lillian, days on end for the last year while I wrote this dissertation. I am most appreciative of the never- ending love and support of my wife, Jen. For nearly eight years, she tolerated the late nights and weekends of me staring under the microscope in the lab and working on the computer through the night preparing presentations, analyzing data, and writing manuscripts. No one more fully understands the hard work, sacrifices, rewards, and hardships that accompanied the work within this dissertation. To everyone that helped shape my graduate experience, thank you.

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General Introduction

1 Introduction In response to a growing human population, we need sustainable solutions to increase the efficiency of agriculture and mitigate negative impacts to the biodiversity and ecosystem processes of the world (Foley et al. 2011). In the late 20th century, the human population was nearly 6 billion people with the expectation of 9 billion by 2050 (United Nations 1999). At that time, 12% of the Earth’s unfrozen land was occupied by croplands which were predicted to increase > 25% to 1.9 billion ha to meet the need of doubling crop production in the next half century (Foley et al. 2011; Tilman et al. 2001, 2011). Updated projections increased to 9.7 billion people by 2050 (United Nations 2019). Despite a large body of research investigating food security solutions over the past two decades, the impacts of agriculture and its management practices on the biodiversity and ecosystem services responsible for food production are anticipated to worsen without immediate intervention (Kehoe et al. 2017; Ramankutty et al. 2018). Modern agriculture reduces biodiversity, simplifies complex species interactions, and alters ecosystem functions, which can degrade essential ecosystem services for society (Didham et al. 1996; Sánchez-Bayo and Wychkuys 2019; Steffan-Dewenter and Westphal 2008). Research addressing the effects of agriculture on biodiversity and ecosystem services has taken many approaches and proposed a number of workable solutions (Chopin et al. 2019; Nelson et al. 2009; Ramankutty et al. 2018). Most methods that demonstrated success enhanced the quality of habitat around or within production areas, including land management for select pollinators in programs such as Integrated Crop Pollination (Isaacs et al. 2017), conservation buffers around production areas (Lovell and Sullivan 2006), general ecological intensification of agricultural areas (Kovács-Hostyánszki et al. 2017), prairie strips within production areas (Schulte et al. 2017), and other land sparing and land sharing techniques (Phalan et al. 2011; Ponisio et al. 2019; Tscharntke et al. 2012). Results show that practitioners can increase crop production by conserving or restoring habitat for biodiversity (Hobbs et al. 2008; Kremen et al. 2002; Power 2010), but the need to implement successful methods at broad scales is urgent given the current biodiversity decline (Ceballos et al. 2015). The best approaches may be scale-dependent due to differing responses of taxa to features of fragmented agricultural landscapes (Tscharntke et al.

2012; Wiens 1995). Therefore, we need to fill some knowledge gaps to provide more complete and accurate predictions of biodiversity and ecosystem service responses to agriculture and landscape features of novel ecosystems (Hobbs et al. 2014). At the forefront of what is called the ‘sixth mass extinction’ are the effects of land-use land-cover change and invasive species on pollinators, especially bees, due to the pollination services required by angiosperms (Potts et al. 2010 and 2016; Thomas et al. 2004). The intimate relationships between bees and plants suggest that bees will not only respond to changes in resource availability following habitat loss and fragmentation, but also to the invasion of novel plant species that alter the composition, timing, and location of available resources. Considering the extensive flow and naturalization of alien plant species among continents (Kleunen et al. 2015), bees are likely affected by the floral resources and induced abiotic changes of invasive plant species which induce sudden changes to evolved ecological relationships and may further shift the taxonomic and functional communities of bees as well as other pollinators (Beck et al. 2006). A recent flood of studies addressed spatial responses of the bee community to agricultural landscapes, primarily focusing on management schemes to preserve or restore bee diversity within the landscape (Coutinho et al. 2018; Graham and Nassauer 2019; Marja et al. 2019; Nicholson et al. 2019; Ponisio et al. 2019; Sirami et al. 2019). Research on effects of invasive plant species on bee communities is growing as well (Vanbergen et al. 2018). As discussed in Bjerknes et al. (2007) few studies have addressed spatial and temporal dynamics of bee communities in response to invasive plant species within agricultural landscapes. To institute successful management practices that conserve bee diversity and their pollination services in novel ecosystems (Dicks et al. 2016; Hobbs et al. 2014; Potts et al. 2016), we need to understand how invasive plant species and land-use land-cover changes determine the composition and functionality of bee pollinators (Ewers and Didham 2006; Senapathi et al. 2017; Steffan- Dewenter and Westphal 2008). Here, I provide an overview of bee interactions with native and invasive plants in the larger context of studies that view habitat islands within an agricultural matrix and contrast it with an emerging view of landscapes as mosaics of land cover types that differ in their suitability to different bee species. I then describe a synthetic view of bee interactions with landscape features consistent with the current literature while emphasizing gaps in our knowledge of bees in agroecosystems (Figure 1). Finally, I provide three data chapters, each describing a field study

2 that provided insights on how invasive plants interact with semi-natural remnants in agriculture- dominated landscapes to structure the spatial dynamics of bee communities and their pollination services.

2 Bee Pollination and Invasive Plants Consisting of over 4,000 species in North America and an estimated 20,000 worldwide, bees (Apoidea) are the primary pollinators of angiosperms (Michener 2000; Potts et al. 2010). Relationships between angiosperms and pollinators are remarkable examples of species interactions and coevolution that can be disrupted in a human-dominated world (Biesmeijer et al. 2006; Burkle et al., 2013; Thomas et al. 2004). Bees have evolved with flowering plants to form a diverse range of morphologies, behaviors, and life histories (Michener 2000). In response, flowering plants evolved a wide spectrum of attractants to animal pollen-vectors (Parachnowitsch and Kessler 2010; Rosas-Guerrero et al. 2014; Stebbins 1970), including varying patterns of flower morphology (Fenster 1991), UV reflectance (Papiorek et al. 2016), petal color (Stanton et al. 1986; Weiss 1991), fragrant volatile compounds (Knauer and Schiestl 2015), and nectar composition (Southwick et al. 1981). Different combinations of attractants used by flowering plants leads to niche partitioning of bee pollinators (Pleasants 1980) and intriguing specializations to reduce competition among plants (e.g. mimicry (Peter and Johnson 2008)) and pollinators (Weiss 1991). The coevolutionary arms race between bees and angiosperms results in bee species with different combinations of functional and life history attributes, as well as plants with different flowering morphologies and phenologies (Pleasants 1980; Rosas-Guerrero et al. 2014; Sandring and Agren 2009). Provisioning of pollination services to native and nonnative species of plants in agricultural landscapes therefore depends in part on the diverse functional properties of bee species developed through this coevolutionary process. Studies suggest that pollinator communities with greater functional diversity result in better pollination services (Albrecht et al. 2012; Fründ et al. 2013; Woodcock et al. 2019). Therefore, pollination success often depends on behaviors and other functional attributes of the pollinators and plants. Bee species losses in agricultural landscapes result in non-random changes in the composition of functional attributes of the bee community (Coutinho et al. 2018; Forrest et al. 2015; Rader et al. 2014). Despite the high mobility of bees (Greenleaf et al. 2007), the species composition of bee communities often

3 changes at narrow spatial scales (Williams et al. 2001; Reverté et al. 2019). Nonetheless, interactions between plants or pollinators and the surrounding environment likely occur at different scales (Bossenbroek et al. 2004), and it has been challenging for investigators to quantify community patterns of flower visitation and pollination by bees in both habitat remnants and surrounding land uses. Therefore, it is often unclear which plant-pollinator links, species or functional attributes, within pollination networks are most important to pollination services and plant reproduction (Valdovinos 2019; Vázquez et al. 2005). In general, one must focus on the plant perspective to determine its most important pollinators. An effective pollinator is one that spreads conspecific pollen from other plant individuals onto the receptive stigma of a flower from the same species. However, to balance nutritional needs, bees provision their brood with pollen and nectar from various species of plants (Hass et al. 2018; Smith et al. 2019). Therefore, from the pollinator perspective, multiple plant species with high quality and quantity of flower nectar and pollen are advantageous near nesting sites (Makino and Sakai 2007; Gathmann & Tscharntke 2002). These numerous and overlapping interactions between bee and flower produce complex pollination networks that continuously structure plant and pollinator communities (Rosas-Guerrero et al. 2014). Pollination systems are therefore driven by local environmental conditions and plant- pollinator feedbacks as well as those in the surrounding landscape (Revilla and Křivan 2018; With 2019). A functionally redundant bee community in the landscape provides resistance and resilience of local pollination networks to some degree of disturbance frequency and magnitude (Kaiser-Bunbury et al. 2011). Land-use and land-cover changes, such as the conversion of natural land cover into row crops, may cause sudden, large-scale changes to bee communities (Kaiser-Bunbury et al. 2010). Alternatively, changes can be subtle and persistent. For instance, alien plants can offer floral resources of their own and integrate into the pollination network, causing shared links with long established plant-pollinator interactions and some mutualisms to be lost over time (Biesmeijer et al. 2006; Burkle et al. 2013; Montero-Castaño and Vilá 2012; Vanbergen et al. 2018). Competition between invasive and native herbaceous plants for pollinators is well-studied (Carvalheiro et al. 2014; Morales and Traveset 2009; Stout and Tiedeken 2017; Traveset and Richardson 2006). Generally, dense and showy inflorescences containing large quantities of nectar and pollen are produced by invasive flowering plants, which may compete with native

4 species for pollinators (Muñoz and Cavieres 2008; Williams et al. 2011). However, some studies showed that these interactions can facilitate visitation of pollinators (Figure 2a; Chung et al. 2014; Feldman et al. 1998; McKinney and Goodell 2011; Molina-Montenegro et al. 2008) or divert pollinators away from nearby flowering species (Figure 2b; Baskett et al. 2011; Goodell and Parker 2017; Nicholson et al. 2019), leading to increased or decreased pollination of neighboring plants, respectively (Morales and Traveset 2009). Some investigators hypothesized that these contradictory effects on nearby plants are dependent on the intensity of the floral display by the invader (Figure 2a & 2b; Kaiser-Bunbury et al. 2011; Herron-Sweet et al. 2016). In addition to mechanisms of pollen competition on nearby plants, saturating bee pollinators with floral resources can lower the maximum visits per flower per time (maximum pollination success) and suppress pollination of nearby plants. Flowers on plants in the vicinity of the invader may then exhibit decreased pollination success rates (Essenberg 2012; Goodell and Parker 2017; Grindeland et al. 2005; Mitchell 1994; Mitchell et al. 2004). Alternatively, if flowers and their associated pollinators are not in the area, the ability to self-pollinate or clone can mitigate population decline until pollinators are available and may explain an advantage of self-compatibility found in many invasive plants with intensive and often extensive floral phenology (Chrobock et al. 2013; Corli and Sheppard 2019). Invasive plants also compete with natives indirectly by altering the surrounding microhabitat to less-favorable conditions for flower production or bee foraging (Bjerknes et al. 2007; McKinney and Goodell 2010). Over time the selected use of invasive flowers and reduced visitation to native plants may filter the species composition and abundance of the bee community. Overall bee diversity is often reduced due to a simplification of the native plant community (Winfree et al. 2009), yet the abundance of generalist pollinators may increase if they benefit from flowers of the invaders (Hegland and Boeke 2006; Vilà et al. 2009). Therefore, high densities of flowering invasive plants may lead to novel pollinator-plant relationships and structure local bee communities in favor of species that exploit these novel floral resources (Figure 2c; Aizen et al. 2008; Bartomeus et al. 2013; Burkle et al. 2013; Kunin and Iwasa 1996; Makino and Sakai 2007; Vanbergen et al. 2018). Crop plants are often dense and mass-flowering resources of pollen and nectar, and their presence in the landscape may have strong effects on bee communities in semi-natural habitats (Riedinger et al. 2015; Westphal et al. 2003). Inflorescences of some crops attract generalist pollinators to their flowers and this visitation generally benefits production (Garibaldi et al.

2013; Kleijn et al. 2015; Klein et al., 2007), emphasizing the economic importance of pollinator abundance in agricultural landscapes (Calderone, 2012; Klein et al. 2007; Woodcock et al. 2019). For example, soybean and canola flowers are frequently visited by bees and a subset of these pollinators enhance soybean and canola seed set (Cunningham-Minnick et al. 2019; Gill and O’Neal 2015; Milfont et al. 2013; Perrot et al. 2018). In turn, certain bee populations may increase in abundance, suggesting novel mutualisms between bees and intensively managed crops (Cunningham-Minnick et al. 2019; Knapp et al. 2019; Westphal et al. 2003). Although not as expansive as soy or canola fields, floral resources of other crops are also suspected to support wild bee assemblages (Martins et al. 2018). Therefore, alien and invasive plants likely play a role in filtering resident bee communities (Bartomeus et al. 2013), though the species composition shifts are largely unknown and there is evidence of resiliency in the plant-pollinator networks of semi-natural habitats with invasive plants or those adjacent to flowering crops (Magrach et al. 2018).

3 Bee Diversity and Modern Agricultural Landscapes Globally, agricultural land-use and land-cover changes have created mosaic landscapes composed of semi-natural and natural habitat remnants (i.e. woodlands, wetlands, and grasslands), strips of moderately disturbed landcover, and larger areas of cultivated crops (Figure 3). The conversion of natural land cover to agricultural uses with frequent disturbance or pesticide applications have displaced preferred nesting habitat and food resources into isolated and patchily distributed remnants (Fahrig 2003). Loss of bee diversity and their pollination services have ensued following conversion of ‘natural’ landcovers to agricultural uses (Fahrig 2003; Tilman 2001; Tscharntke et al. 2005; Winfree et al. 2009; Potts et al. 2010). The unfavorable conditions within most agricultural fields have caused bees and their pollination services to be primarily associated with the scattered natural and semi-natural habitat remnants (collectively ‘semi-natural habitat’ hereafter; Kremen et al. 2002; Martin et al. 2019; Steffan- Dewenter and Tscharntke 1999). Despite the dependence of wild bees on patches of semi-natural habitat and the stark contrasts between these habitats and adjacent land uses (e.g. agriculture and forest; Figure 3; Marja et al. 2019), other land cover types may offer a range of resources to bees (Lonsdorf et al. 2009) that potentially support more diverse pollinator assemblages (Scherber et al. 2019). Novel resources of alien plants in crops or other land uses add ecological complexity

6 that may alter the bee communities in the larger landscape. Nonetheless, there is mixed support for the notion that conservation and management efforts to enhance bee diversity depend on the spatial arrangement and composition of land cover types or if it is sufficient to focus on the islands of semi-natural habitat that may differ in area, isolation, or suitability to native bees (Fahrig 2011; Hackett et al. 2019; Martin et al. 2019; Neokosmidis et al. 2018). A mosaic perspective implies that the composition and connectivity of different land cover types surrounding semi-natural habitats may influence bee diversity and abundance because land cover types differ in their seasonal resource availability (e.g. nectar, pollen, and nesting substrate; Diekötter et al. 2008; Martins et al. 2018). Such a view allows for the possibility that some land cover types benefit bees by providing complementary resources (Martins et al. 2018), while other land cover types may negatively affect bees because they lack suitable resources, expose bees to threats (e.g. pesticides), or act as habitat sinks. It further incorporates linear or network features in the landscape (i.e. riparian buffers, transmission right- of-ways, roadway edges, habitat edges), that may enhance bee diversity and pollination services by providing some foraging and nesting habitat or that act as dispersal corridors between more suitable habitats (Haddad et al. 2003; Gardiner et al 2018; Ponisio et al. 2019). In other words, a mosaic perspective predicts that bee responses are dependent upon the heterogeneity of the surrounding environmental matrix (Figure 1), including for species with lower mobility or more restricted foraging areas. Contrarily, viewing semi-natural habitat as islands puts emphasis on foraging and dispersal distance, as well as the quality of resources available within islands of semi-natural habitat (Proesmans et al. 2018; Scherber et al. 2018). From this perspective, the area and relative positioning of semi-natural habitats are the primary determinants of bee diversity and composition, while the surrounding matrix and the resources within are less important (Figure 1). It follows that bee diversity would be a function of patch size, the floral resources within semi-natural patches, and the distances among patches (Blaauw and Isaacs 2014; Bommarco et al. 2010; Steffan-Dewenter 2003). It is unclear if bees within agroecosystems respond to landscapes more as a mosaic of land cover types or a series of land-locked islands of semi-natural habitat. Habitat fragments within agricultural landscapes are not islands (With 2019), yet many studies have approached patches of semi-natural habitat within the landscape as ‘islands’ in the recent past (Didham et al. 2012; Haila 2002; Kupfer et al. 2006). I investigated how the scientific community approached

7 studies of wild bees in agricultural environments using island versus landscape approaches by conducting a literature search for research articles from 1900–2019 using Web Of Science. The search included a combination of three ‘Topic’ terms, one of which represented studies of landscape and patch features (“landscape”, “land cover”, “land use”, “spati*”, “matrix”, “habitat”, “fragmen*”, “patch”, or “mosaic”), another that specified a focus on pollinators (“bee” or “pollin*”), and a final term focused on work in agroecosystems (“agr*”). This search returned 2,309 original publications, of which 972 recorded bee responses in the field. To focus on studies of wild bees, I required that studies included non-managed bee specimens, which removed another 193 publications. Therefore, I evaluated 779 publications during the years 1985–2019 that met these requirements. Of these publications, 504 did not quantify effects of the surrounding matrix on bee diversity and abundance responses. Interestingly, 275 publications included measures of composition or diversity of land cover types of the surrounding landscape (i.e. mosaic viewpoint), but 160 of these only recorded one or two land cover types (74% included semi-natural habitat). Thus, only 115 publications included wild bee responses to landscape diversity measures of more than two land cover types, and 95% of these studies found that the surrounding matrix significantly affected bee responses. My finding that most studies focused on semi-natural habitat and potentially one other land cover within the greater landscape is particularly interesting (Figure 4). These studies still fit bee responses to a mosaic landscape framework but they more broadly suggest that there exists a mixture of perspectives among bee ecologists as to the importance of more disturbed land cover types in spatially structuring bee behaviors within agricultural landscapes. Adoption of a landscape framework for measuring bee communities appears to be increasing (Figure 4), but most approaches still rely on strong assumptions of the roles of semi-natural habitat on bee distributions which may provide inaccurate, or incomplete, conclusions when inferences are extrapolated to the rest of the landscape (Scherber et al. 2019). For instance, bees in crop fields and other land uses are generally assumed to be a result of spillover from the nearest semi-natural area, though this is rarely verified within the study design. Nevertheless, semi-natural habitat is clearly important for conserving bee diversity and pollination services in agricultural landscapes (Dauber et al. 2010; Martin et al. 2019; Tscharntke and Brandl 2004). It is well-established that bee abundance and species richness decrease with distance from semi-natural habitat, but such contributions are often attributed to the semi-natural

8 habitat without specific recognition of the characteristics of the habitat edge or resources provided by the adjacent land uses and field margins. These assumptions may not be supported, as studies with grid-sampling of the larger landscape (including agricultural fields) have provided evidence for species-rich pollinator assemblages in different land cover types (Scherber et al. 2018) comprised of several bee species with restricted foraging ranges (Gathmann and Tscharntke 2002; Zurbuchen et al. 2010). Using agricultural land cover as an example, bees nest within cultivated areas of pollinator-dependent crops (Krug et al. 2010; Sardiñas et al. 2016). The growth of conservation tillage practices in the U.S. (Claassen et al. 2018) and reduced mechanical disturbance of soils may further result in suitable nesting substrate for ground- nesting bees within production areas of intensely managed crops (Cunningham-Minnick et al. 2019), which may partially explain the positive trend of large and solitary bee abundance responses to intensive agriculture in a recent meta-analysis (Coutinho et al. 2018). Dependency on semi-natural habitat further implies that the plant species composition of semi-natural fragments will be strong predictors of the bee community along with habitat area and isolation. Studies show that the changes within plant communities of semi-natural fragments coincide with extirpations of bee species but also reveal new relationships between the bee community and plants, some involving alien plant species (Beismeijer et al. 2006; Burkle et al. 2013). Invasion by alien plant species along these habitat edges may provide dense patches of novel floral resources (Boutin and Jobin 1998; Knops et al. 1999). The responses of the bee community may involve a mixture of two ecological processes. As plant diversity decreases and the invader establishes a dominant source of floral resources, bee abundances of some species will increase while others will decrease. If bees use floral resources of the invader, then composition of the bee community will shift in favor of those species. If bees cannot use the invasive floral resources, their populations will either be reduced due to competition for dwindling native food sources, or they will relocate to another foraging area within the habitat patch or disperse to another patch. Alternatively, if bees develop a preference for alien floral resources but their populations cannot be sustained by reduced resources of the patch, population persistence may require bees to use resources in surrounding land cover types or forage among different patches of the same land cover type. By understanding how spatial patterns of the bee community change across seasons or years, we can predict how bees are using resources of native plants within patches, as well as

9 those of alien plants such as crops or invasive species. In turn, species sorting processes that favor more vagile or generalist bee species, which are likely to benefit from alien resources, can be understood in the context of the larger landscape. Insight into these processes will allow us to predict bee distributions and abundance in response to land use and invasive or crop plants. Therefore, if an island model best predicts bee responses to invasive plants, the bee community should exhibit a monotonic decline of a continuous variable that represents changes in functional composition as it pertains to foraging distance and ability (i.e. body size; Greenleaf et al. 2007). In an island-model framework, where bees respond to floral resources of invasive plants, stronger foraging bees can travel between patches and should increase their presence in the invaded semi-natural habitat only during the invader’s flowering period. Weak foragers should increase throughout the season due to supplemental floral resources from the invader early in the season. If bees are not responding to invasive floral resources under an island framework, bee abundance and diversity should decline due to competition effects of the invader on other flowering plants. In accordance with Island Biogeography Theory (MacArthur and Wilson 1963), bee diversity would exhibit a strong positive relationship with patch size and decreased distance to nearby patches whether or not bees use invasive floral resources. If bees use invasive floral resources and increase in abundance and diversity due to nesting and food resources in other land cover types, then we should view bee responses to invasive plants in a mosaic landscape framework. Establishing and validating a landscape framework in which to predict interactions of bee communities in agricultural landscapes is challenging, even without considering invasive plants. Recent advances in statistical methods and remote sensing technology in combination with long- term studies can now aide the understanding of pollinator interactions at broader spatial scales (Thomas et al. 2004). Still, it is unclear if standardized sampling methods are even representative of the bee community or their responses to the landscape (Hall 2018; Joshi et al. 2015; McCravy 2018; Rhoades et al. 2017). Different sampling methods favor different behaviors and functional attributes exhibited by bee species, and these are the same traits that determine bee responses to features of the landscape (Coutinho et al. 2018). Finally, there are still many unquantified features of agricultural landscapes that may affect bee community diversity and abundance, including vegetation structure, seasonal variation, and invasive plants.

Despite our knowledge gaps, spatially explicit simulation models of bee abundance and visitation have been conducted based on parameter estimations of habitat quality in nesting and flowering sources (Lonsdorf et al. 2009; Olsson et al. 2015; Sirami et al. 2019). Providing terms that account for maximizing species fitness (foraging distance of bees relative to the resource) improves models and provides better fit to field data (Olsson et al. 2015). However, more complex landscapes require more precisely defined parameters (Lonsdorff et al. 2009; Olsson et al. 2015), which can be tricky due to the confusion of which spatial scale bee communities respond. As stated in Olsson et al. (2015), the interaction between landscape features and the complex mixture of functional and life history attributes (Coutinho et al. 2018; Ponisio et al. 2019) or intra- and inter-annual variation (Steffan-Dewenter and Westphal 2008) in the bee community can also make it difficult to accurately predict bee responses. Currently, it appears that modifications to the landscape are filtering the bee community by suites of life history and functional attributes (Bartomeus et al. 2013; Coutinho et al. 2018), some of which may be best described in the context of bee phylogeny (Grab et al. 2019; Harrison et al. 2018). Effects of dominant vegetation of dynamic plant communities, structure contrasts of land cover types, and seasonal turnover of solitary bee populations determine composition of the bee community but are not considered in predictions provided to conservation managers in part due to an island view of the landscape. These are issues that need addressed to provide reliable and applicable spatial predictions of bees and their pollination services. Therefore, we may need to re-evaluate the underlying assumptions that treat semi-natural habitat as ‘islands’ and the primary refuge for bees. My research employed a series of manipulative and observational field experiments along the edges of forest habitat remnants that were adjacent to agriculture to test the roles of forest fragments and a dominant invasive shrub (Lonicera maackii) in structuring native bee communities and their pollination services. Furthermore, I tested the effects of forest habitat characteristics and the surrounding landscape composition on the seasonal patterns of bee diversity and abundance to assess whether native bee assemblages depend more on the mosaic of land cover types or the patchwork of islands of semi-natural habitat in the agricultural matrix. First, I measured the distance-dependent responses of the bee community to flowers of L. maackii by removing flower buds along heavily invaded forest edges that were adjacent to crop fields (Chapter 1). Using a paired-plot design along the edge of each of five isolated forest

11 habitats, I quantified pollination services of the bee community at distances up to 200 m into the adjacent crop field using sentinel cucumber plants and evaluated shifts in bee species composition between flower-removal treatments and across distances. In a second study, I measured the effects of L. maackii on the seasonal patterns of bee diversity and abundance from April to November along an edge of 12 forest patches that represented a gradient of L. maackii densities (Chapter 2). I used statistical modeling to test whether changes in bee abundance and species richness of two different functional groups were best explained by floral resources within forest habitats or by the surrounding land cover composition measured at different spatial extents using coarse and fine resolution land cover maps. In Chapter 3, I evaluated the effects of L. maackii on the vertical stratification of the bee community at 10 forest patches before, during, and after the flowering period of L. maackii. I used general linear models, structural equation models, and multivariate analyses to relate the functional composition of the bee community to tree species composition and vertical structure of the forest edge. Finally, I concluded by describing how these studies contribute insight to the long-standing and untested assumptions of bee community responses to forest fragments of agricultural landscapes, discuss the implications of how dominant invasive plants are structuring bee communities, and revisit the two dominant perspectives on using mosaic landscape or island habitat frameworks to manage and conserve bee diversity in agricultural landscapes.

4 References Aizen MA, Morales CL, Morales JM (2008) Invasive mutualists erode native pollination webs. PLoS Bio 6(2):e31. Albrecht M, Schmid B, Hautier Y, Müller CB (2012) Diverse pollinator communities enhance plant reproductive success. Proc R Soc B 279:4845-4852. Bartomeus I, Ascher JS, Gibbs J, Danforth BN, Wagner DL, Hedtke SM, Winfree R (2013) Historical changes in northeastern US bee pollinators related to shared ecological traits. PNAS 110(12):4656-4660. Baskett CA, Emery SM, Rudgers JA (2011) Pollinator visits to threatened species are restored following invasive plant removal. Int. J. Plant Sci. 172(3):411-422. Beck G, Zimmerman K, Schardt JD, Stone J, Lukens RR, Reichard S, Randall J, Cangelosi AA, Cooper D, Thompson JP (2006) Invasive species defined in a policy context: recommendations from the Federal Invasive Species Advisory Committee. Invasive Plant Science and Management 1(4):414-421. Biesmeijer JC, Roberts SPM, Reemer M, Ohlemüller R, Edwards M, Peeters T, Schaffers AP, Potts SG, Kleukers R, Thomas CD, Settele J, Kunin WE (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:351- 354. Bjerknes A, Totland Ø, Hegland SJ, Nielsen A (2007) Do alien plant invasions really affect pollination success in native plant species? Biological Conservation 138:1-12. Blaauw BR, Isaacs R (2014) Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wildflowers. Basic Appl Ecol 15:701- 711. Bommarco R, Biesmeijer JC, Meyer B, Potts SG, Pöyry J, Roberts SPM, Steffan-Dewenter I, Ӧchkinger E (2010) Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc R Soc B 277:2075-2082. Boutin C, Jobin B (1998) Intensity of agricultural practices and effects on adjacent habitats. Ecological Applications 8(2):544-557. Burkle LA, Marlin JC, Knight TM (2013) Plant-pollinator interactions over 120 years: loss of species co-occurrence, and function. Science 339:1611-1615. Calderone NW (2012) Insect pollinated crops, insect pollinators and US agriculture: trend analysis of aggregate data for the period 1992-2009. PLoS ONE 7(5):e37235. Carvalheiro LG, Biesmeijer JC, Benadi G, Fründ J, Stang M, et al. (2014) The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecol Lett 17:1389-1399. Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1:e1400253. Chrobock T, Weiner CN, Werner M, Blüthgen N, Fischer M, Kleunen M (2013) Effects of native pollinator specialization, self-compatibility and flowering duration of European plant species on their invasiveness elsewhere. Journal of Ecology 101(4):916-923. Chung YA, Burkle LA, Knight TM (2014) Minimal effects of an invasive flowering shrub on the pollinator community of native forbs. PLOS ONE 9(10):e109088. Claassen R, Bowman M, McFadden J, Smith D, Wallander S (2018) Tillage intensity and conservation cropping in the United States. EIB 197, U.S. Department of Agriculture,

Economic Research Service, September 2018. Corli A, Sheppard CS (2019) Effects of residence time, auto-fertility and pollinator dependence on reproductive output and spread of alien and native Asteraceae. Plants 8(108). Coutinho JGE, Garibaldi LA, Viana BF (2018) The influence of local and landscape scale on single response traits in bees: A meta-analysis. Agriculture, Ecosystems and Environment 256:61-73. Cunningham-Minnick MJ, Peters VE, Crist TO (2019) Nesting habitat enhancement for wild bees within soybean fields increases crop production. Apidologie 50:833-844. Dauber J, Biesmeijer JC, Gabriel D, Kunin WE, Lamborn E, Meyer B, Nielsen A, Potts SG, Roberts SPM, Sõber V, et al. (2010) Effects of patch size and density on flower visitation and seed set of wild plants: a pan-European approach. Journal of Ecology 98:188-196. Dicks LV, Viana B, Bommarco R, Brosi B, Arizmendi MC, Cunningham SA, Galetto L, Hill R, Lopes AV, Pires C, Taki H, Potts SG (2016) Ten policies for pollinators What governments can do to safeguard pollination services. Science 354:975-976. Didham RK, Ghazoul J, Stork NE, Davis AJ (1996) Insects in fragmented forests: a functional approach. TREE 11(6):255:260. Didham RK, Kapos V, Ewers RM (2012) Rethinking the conceptual foundations of habitat fragmentation research. Oikos 121:161-170. Diekötter T, Billeter R, Crist TO (2008) Effects of landscape connectivity on the spatial distribution of insect diversity in agricultural mosaic landscapes. Basic Appl Ecol 9:298- 307. Essenberg CJ (2012) Explaining variation in the effect of floral density on pollinator visitation. The American Naturalist 180(2):153-166. Ewers RM, Didham RK (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews 81:117-142. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution, and Systematics 34:487-515. Fahrig L, Baudry J, Brotons L, Burel FG, Crist TO, Fuller RJ, Sirami C, Siriwardena GM, Martin J (2011) Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol Lett 14:101-112. Feldman TS, Morris WF, Wilson WG (2004) When can two plant species facilitate each other’s pollination? OIKOS 105:197-207. Fenster CB (1991) Selection on floral morphology by hummingbirds. Biotropica 23(1):98-101. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockström J, Sheehan J, Siebert S, Tilman D, Zaks DPM (2011) Solutions for a cultivated planet. Nature 478:337-342. Forrest JRK, Thorp RW, Kremen C, Williams NW (2015) Contrasting patterns in species and functional-trait diversity of bees in an agricultural landscape. Journal of Applied Ecology 52:706-715. Fründ J, Dormann CF, Holzschuh A, Tscharntke T (2013) Bee diversity effects on pollination depend on functional complementarity and niche shifts. Ecology 94(9):2042-2054. Gardiner MM, Riley CB, Bommarco R, Ӧckinger E (2018) Rights-of-way: a potential conservation resource. Front. Ecol. Environ. 16(3):149-158.

Garibaldi LA, Steffan-Dewenter I, Winfree R, Aizen MA, Bommarco R, Cunningham SA, Kremen C, Carvalheiro LG, Harder LD, Afik O, et al. (2013) Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339:1608-1611. Gathmann, A., Tscharntke, T. (2002) Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757- 764. Gibbs J, Joshi NK, Wilson JK, Rothwell NL, Powers K, Haas M, Gut L, Biddinger DJ, Isaacs R (2017) Does passive sampling accurately reflect the bee (Apoidea: Anthophila) communities pollinating apple and sour cherry orchards? Environmental Entomology 46(3):579-588. Gill KA, O’Neal ME (2015) Survey of soybean insect pollinators: community identification and sampling method analysis 44(3):488-498. Goodell K, Parker I (2017) Invasion of a dominant floral resource: effects on the floral community and polliantion of native plants. Ecology 98(1):57-69. Graham JB, Nassauer JI (2019) Wild bee abundance in temperate agroforestry landscapes: assessing effects of alley crop composition, landscape configuration, and agroforestry area. Agroforest Syst 93:837-850. Greenleaf SS, Williams NM, Winfree R, Kremen C (2007) Bee foraging ranges and their relationship to body size. Oecologia 153:589-596. Grindeland JM, Sletvold N, Ims RA (2005) Effects of floral display size and plant density on pollinator visitation rate in a natural population of Digitalis pupurea. Functional Ecology 19(3):383-390. Hackett TD, Sauve AMC, Davies N, Montoya D, Tylianakis JM, Memmott J (2019) Reshaping our understanding of species’ roles in landscape-scale networks. Ecol Lett 22:1367-1377. Haddad NM, Bowne DR, Cunningham A, Danielson BJ, Levey DJ, Sargen S, Spria T (2003) Corridor use by diverse taxa. Ecology 84(3):609-615. Haila Y (2002) A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecol Appl 12(2):321-334. Hall M (2018) Blue and yellow vane traps differ in their sampling effectiveness for wild bees in both open and wooded habitats. Agricultural and Forest Entomology 20:487-495. Harrison T, J Gibbs, R Winfree (2018) Phylogenetic homogenization of bee communities across ecoregions. Global Ecology and Biogeography 27:1457-1466. Hass AL, Brachmann L, Batáry P, Clough Y, Behling H, Tscharntke T (2018) Maize-dominated landscapes reduce bumblebee colony growth through pollen diversity loss. J Appl Ecol 00:1-11. Hegland SJ, Boeke L (2006) Relationships between the density and diversity of floral resources and flower visitor activity in a temperate grassland community. Ecological Entomology 31:532-538. Herron-Sweet CR, Lehnhoff EA, Burkle LA, Littlefield JL, Mangold JM (2016) Temporal- and density-dependent impacts of an invasive plant on pollinators and pollination services to a native plant. Ecosphere 7(2):e01233. Hobbs PR, Sayre K, Gupta R (2008) The role of conservation agriculture in sustainable agriculture. Phil Trans R Soc B 363:543-555. Hobbs RJ, Higgs E, Hall CM, Bridgewater P, Chapin III FS, Ellis EC, Ewel JJ, Hallett LM, Harris J, Hulvey KB, et al. (2014) Managing the whole landscape: historical, hybrid, and novel ecosystems. Front. Ecol. Environ. 12(10):557-564.

Isaacs R, Williams N, Ellis J, Pitts-Singer TL, Bommarco R, Vaughan M (2017) Integrated Crop Pollination: combining strategies to ensure stable and sustainable yields of pollination- dependent crops. Basic Appl Ecol 22:44-60. Joshi NK, Leslie T, Rajotte EG, Kammerer MA, Otieno M, Biddinger DJ (2015) Comparative trapping efficiency to characterize bee abundance, diversity, and community composition in apple orchards. Ann Entomol Soc Am 108(5):785-799. Kaiser-Bunbury CN, Muff S, Memmott J, Müller CB, Caflisch A (2010) The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behavior. Ecol Lett 13:442-452. Kaiser-Bunbury CN, Valentin T, Mougal J, Matatiken D, Ghazoul J (2011) The tolerance of island plant-pollinator networks to alien plants. Journal of Ecology 99:202-213. Kleijn D, Winfree R, Bartomeus , Carvalheiro G, Henry M, Isaacs R, Klein AM, Kremen C, M’Gonigle L, Rader R, et al. (2015) Delivery of crop pollination services is an insufficient argument for wild pollinator conservation. Nat Comm 16(6):7414. Klein AM, Vaissière, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T (2006) Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274(1608). Kleunen M, Dawson W, Essl F, Pergl J, Winter M, Weber E, Kreft H, Weigelt P, Kartesz J, Nishino M, et al. (2015) Global exchange and accumulation of non-native plants. Science 525:100-104. Knapp JL, Becher MA, Rankin CC, Twiston-Davies G, Osborne JL (2019) Bombus terrestris in a mass-flowering pollinator-dependent crop: A mutualistic relationship? Ecology and Evolution 9:609-618. Knauer AC, Schiestl (2015) Bees use honest floral signals as indicators of reward when visiting flowers. Ecol Lett 18:135-143. Knops JMH, Tilman D, Haddad NM, Naeem S, Mitchell CE, Haarstad J, Ritchie ME, Howe KM, Reich PB, Siemann E, Groth J (1999) Effects of plant species richness on invasion dynamics, disease outbreaks, insect abundances and diversity. Ecol Lett 2:286-293. Kovács-Hostyánszki A, Espíndola A, Vanbergen AJ, Settele J, Kremen C, Dicks LV (2017) Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecol Lett 20:673-689. Kremen C, Williams NM, Thorp RW (2002) Crop pollination from native bees at risk from agricultural intensification. PNAS 99(26):16812-16816. Krug C, Alves-dos-Santos I, Cane J (2010) Visiting bees of Cucurbita flowers (Cucurbitaceae) with emphasis on the presence of Peponapis fervens Smith (Eucerini – Apidae) – Santa Catarina, southern Brazil. Oecologia Australis 14(1):128-139. Kunin W, Iwasa Y (1996) Pollinator foraging strategies in mixed floral arrays: density effects and floral constancy. Theoretical Population Biology 49:232-263. Kupfer JA, Malanson GP, Franklin SB (2006) Not seeing the ocean for the islands: the mediating influence of matrix-based processes on forest fragmentation effects. Global Ecol Biogeogr 15:8-20. Lonsdorf E, Kremen C, Ricketts T, Winfree R, Williams, Greenleaf S (2009) Modelling pollination services across agricultural landscapes. Annals of Botany 103:1589-1600. Lovell ST, Sullivan WC (2006) Environmental benefits of conservation buffers in the United States: Evidence, promise, and open questions. Ag Ecosys Environ 112:249-260.

MacArthur RH, Wilson EO (1963) An equilibrium theory of insular zoogeography. Evolution 17(4):373-387. Magrach A, Holzschuh A, Bartomeus I, Riedinger V, Roberts SPM, Rundlöf, Vujić A, Wickens JB, Wickens VJ, Bommarco R, et al. (2018) Plant-pollinator networks in semi-natural grasslands are resistant to the loss of pollinators during blooming of mass-flowering crops. Ecography 41(1):62-74. Makino TT, Sakai S (2007) Experience changes pollinator responses to floral display size: from size-based to reward-based foraging. Functional Ecology 21:854-863. Marja R, Kleijn D, Tscharntke T, Klein AM, Frank T, Batáry P (2019) Effectiveness of agri- environmental management on pollinators is moderated more by ecological contrast than by landscape structure or land-use intensity. Ecol Lett 22:1493-1500. Martin EA, Dainese M, Clough Y, Báldi A, Bommarco R, Gagic V, Garratt MPD, Holzschuh A, Kleijn D, Kovács-Hostyánszki A, Marini L, Potts, SG, Smith HG, Hassan DA, Albrecht M, Andersson GKS, Asís JD, Aviron S, Balzan MV, Baños-Picón L, Bartomeus I, Batáry P, Burel F, Caballero-López B, Concepción ED, Coudrain V, Dänhardt J, Diaz M, Diekötter T, Dormann CF, Duflot R, Entling MH, Farwig N, Fischer C, Frank T, Garibaldi LA, Hermann J, Herzog F, Inclán D, Jacot K, Jauker F, Jeanneret P, Kaiser M, Krauss J, Féon VL, Marshall J, Moonen A, Moreno G, Riedinger V, Rundlöf M, Rusch A, Scheper J, Schneider G, Schüepp C, Stutz S, Sutter L, Tamburini G, Thies C, Tormos J, Tscharntke T, Tschumi M, Uzman D, Wagner C, Zubair-Anjum M, Steffan-Dewenter I (2019) The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol Lett 22:1083-1094. McCravy (2018) A review of sampling and monitoring methods for beneficial arthropodsd in agroecosystems. Insects 9:170. McKinney AM, Goodell K (2010) Shading by invasive shrub reduces seed production and pollinator services in a native herb. Biol Invasions 12:2751-2763. McKinney AM, Goodell K (2011) Plant-pollinator interactions between an invasive and native plant vary between sites with different flowering phenology. Plant Ecol 212:1025-1035. Michener CD (2000) The bees of the world. The Johns Hopkins University Press (2nd Edition) Balitmore MD. 953 pp. Milfont MO, Rocha EEM, Lima AON, Freitas BM (2013) Higher soybean production using honeybee and wild pollinators, a sustainable alternative to pesticides and autopollination. Environ Chem Lett 11:335-341. Mitchell RJ (1994) Effects of floral traits, pollinator visitation, and plant size on Ipomopsis aggregata fruit production. The American Naturalist 143(5):870-889. Mitchell RJ, Karron JD, Holmquist KG, Bell JM (2004) The influence of Mimulus ringens floral display size on pollinator visitation patterns. Functional Ecology 18:116-124. Molina-Montenegro MA, Badano EI, Cavieres LA (2008) Positive interactions among plant species for pollinator service: assessing the ‘magnet species’ concept with invasive species. OIKOS 117(12):1833-1839. Montero-Castaño A, Vilà M (2012) Impact of landscape alteration and invasions on pollinators: a meta-analysis. J Ecol 100:884-893. Morales CL, Traveset A (2009) A meta-analysis of impacts of alien vs. native plants on pollinator visitation and reproductive success of co-flowering native plants. Ecol Lett 12:716-728.

Neokosmidis L, Tscheulin T, Devalez J, Petanidou T (2018) Landscape spatial configuration is a key driver of wild bee demographics. Insect Science 25:172-182. Nicholson CC, Ricketts TH, Koh I, Smith HG, Lonsdorf EV, Olsson O (2019) Flowering resources distract pollinators from crops: Model predictions from landscape simulations. Journal of Applied Ecology 56:618-628. Olsson O, Bolin A, Smith HG, Lonsdorf EV (2015) Modeling pollinating bee visitation rates in heterogeneous landscapes from foraging theory. Ecological Modelling 316:133-143. Papiorek S, Junker RR, Alves-dos-Santos I, Melo GAR, Amaral-Neto LP, Sazima M, Wolowski M, Freitas L, Lunau K (2015) Bees, birds and yellow flowers: pollinator-dependent convergent evolution of UV patterns. Plant Biology 18:46-55. Parachnowitsch SL, Kessler A (2010) Pollinators exert natural selection on flower size and floral display in Penstemon digitalis. New Phytologist 188:393-402. Perrot T, Gaba S, Roncoroni M, Gautier J, Bretagnolle V (2018) Bees increase oilseed rape yield under real field conditions. Agriculture, Ecosystems and Environment 266:39-48. Peter CI, Johnson SD (2008) Mimics and magnets: the importance of color and ecological facilitation in floral deception. Ecology 89(6):1583-1595. Phalan B, Onial M, Balmford A, Green RE (2011) Reconciling food production and biodiversity conservation: Land sharing and land sparing compared. Science 333:1289-1291. Pleasants JM (1980) Competition for bumblebee pollinators in Rocky Mountain plant communities. Ecology 61(6)1446-1459. Ponisio LC, Valpine P, M’Gonigle LK, Kremen C (2019) Proximity of restored hedgerows interacts with local floral diversity and species’ traits to shape long-term pollinator metacommunity dynamics. Ecol Lett 22:1048-1060. Potts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, Kunin WE (2010) Global pollinator declines: trends, impacts and drivers. Trends in Ecology and Evolution 25(6):345-353. Potts SG, Imperatriz-Fonseca V, Ngo HT, Aizen MA, Biesmeijer JC, Breeze TD, Dicks LV, Garibaldi LA, Hill R, Settele J, Vanbergen AJ (2016) Safeguarding pollinators and their values to human well-being. Nature 540:220-229. Powers AG (2010) Ecosystem services and agriculture: tradeoffs and synergies. Phil Trans R Soc B 365:2959-2971. Proesmans W, Bonte D, Smagghe G, Meeus I, Verheyen K (2019) Importance of forest fragments as pollinator habitat varies with season and guild. Basic Appl Ecol 34:95-107. Rader R, Bartomeus I, Tylianakis JM, Laliberté E (2014) The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Diversity Distrib 20:908-917. Ramankutty N, Mehrabi Z, Waha K, Jarvis L, Kremen C, Herrero M, Rieseberg LH (2018) Trends in global agricultural land use: Implications for environmental health and food security. Annu Rev Plant Biol 69:789-815. Reverté S, Bosch J, Arnan X, Roslin T, Stefanescu C, Calleja JA, Molowny-Horas R, Hernández-Castellano C, Rodrigo A (2019) Spatial variability in a plant-pollinator community across a continuous habitat: high heterogeneity in the face of apparent uniformity. Ecography 42:1558-1568. Rhoades P, Griswold T, Waits L, Bosque-Pérez NA, Kennedy CM, Eigenbrode SD (2017) Sampling technique affects detection of habitat factors influencing wild bee communities. J Insect Conserv 21:703-714.

Riedinger V, Mitesser O, Hovestadt T, Steffan-Dewenter I, Holzschuh A (2015) Annual dynamics of wild bee densities: attractiveness and productivity effects of oilseed rape. Ecology 96(5):1351-1360. Rosas-Guerrero V, Aguilar R, Martén-Rodríguez S, Ashworth L, Lopezaraiza-Mikel M, Bastida JM, Quesada M (2014) A quantitative review of pollination syndromes: do floral traits predict effective pollinators? Ecol Lett 17:388-400. Sánchez-Bayo F, Wyckhuys KAG (2019) Worldwide decline of the entomofauna: A review of its drivers. Biol Conserv 232:8-27. Sandring S, Agren J (2009) Pollinator-mediated selection on floral display and flowering time in the perennial herb Arabidopsis lyrata. Evolution 63(5):1292-1300. Sardiñas HS, Tom K, Ponisio LC, Rominger A, Kremen C (2016) Sunflower (Helianthus annuus) pollination in California’s Central Valley is limited by native bee nest site location. Ecol Appl 26(2):438-447. Scherber C, Beduschi T, Tscharntke T (2019) Novel approaches to sampling pollinators in whole landscapes: a lesson for landscape-wide biodiversity monitoring. Landsape Ecol 34:1057- 1067. Scherber C, Andert H, Niedringhaus R, Tscharntke T (2018) A barrier island perspective on species-area relationships. Ecology and Evolution 8:12879-12889. Schulte LA, Niemi J, Helmers MJ, Liebman M, Arbuckle GJ, James DE, Kolka RK, O’Neal ME, Tomer MD, Tyndall JC, Asbjornsen H, Drobney P, Neal J, Ryswyk GV, Witte C (2017) Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn-soybean croplands. PNAS 114(42):11247-11252. Senapathi D, Goddard MA, Kunin WE, Baldock KCR (2017) Landscape impacts on pollinator communities in temperate systems: evidence and knowledge gaps. Functional Ecology 31:26-37. Sirami C, Gross N, Baillod AB, Bertrand C, Carrié R, Hass A, Henckel L, Miguet P, Vuillot C, Alignier A, et al. (2019) Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. PNAS 116(33):16442-16447. Smith C, Weinman L, Gibbs J, Winfree R (2019) Specialist foragers in forest bee communities are small, social or emerge early. J Anim Ecol 88:1158-1167. Southwick EE, Loper GM, Sadwick SE (1981) Nectar production, composition, energetics and pollinator attractiveness in spring flowers of Western New York. American Journal of Botany 68(7):994-1002. Stanton ML, Snow AA, Handel SN (1986) Floral evolution: attractiveness to pollinators increases male fitness. Science 232(4758):1625-1627. Stebbins GL (1970) Adaptive radiation of reproductive characteristics in angiosperms Part I: pollination mechanisms. Annu. Rev. Ecol. Evol. Syst. 1:307-326. Steffan-Dewenter I, Tscharntke T (1999) Effects of habitat isolation on pollinator communities and seed set. Oecologia 121:432-440. Steffan-Dewenter I (2003) Importance of habitat area and landscape context for species richness of bees and wasps in fragmented orchard meadows. Conserv Biol 17(4):1036-1044. Steffan-Dewenter I, Westphal C (2008) The interplay of pollinator diversity, pollination services and landscape change. Journal of Applied Ecology 45:737-741. Stout JC, Tiedeken EJ (2017) Direct interactions between invasive plants and native pollinators: evidence, impacts and approaches. Funct Ecol 31:38-46.

Thomas JA, Telfer MG, Roy DB, Preston CD, Greenwood JJD, Asher J, Fox R, Clarke RT, Lawton JH (2004) Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303:1879-1881. Tilman D, Fargione J, Wolff B, D’Antonio C, Dobson A, Howarth R, Schindler D, Schlesinger WH, Simberloff D, Swackhamer D (2001) Forecasting agriculturally driven global environmental change. Science 292:281-284. Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. PNAS 108(50):20260-20264. Traveset A, Richardson DM (2006) Biological invasions as disruptors of plant reproductive mutualisms. Trends in Ecology and Evolution 21(4)208-216. Tscharntke T, Brandl R (2004) Plant-insect interactions in fragmented landscapes. Annu. Rev. Entomol. 49:405-430. Tscharntke T, Klein AM, Kruess A, Steffan-Dewenter I, Thies C (2005) Landscape perspectives on agricultural intensification and biodiversity – ecosystem service management. Ecol Lett 8:857-874. Tscharntke T, Clough Y, Wanger TC, Jackson L, Motzke I, Perfecto I, Vandermeer J, Whitbread A (2012) Global food security, biodiversity conservation and the future of agricultural intensification. Biol Conserv 151:53-59. United Nations (1999) World population prospects: The 1998 revision, vol 1., Comprehensive Tables. UN Department of Economic and Social Affairs, Population Division, New York. United Nations (2019) Probabilistic population projections Rev 1 based on the World Population Prospects 2019 Rev 1. UN Department of Economic and Social Affairs, Population Division. Valdovinos FS (2019) Mutualistic networks: moving closer to a predictive theory. Ecol Lett 22:1517-1534. Vanbergen AJ, Espíndola A, Aizen MA (2018) Risks to pollinators and pollination from invasive alien species. Nat Ecol Evol 2:16-25. Vázquez DP, Morris WF, Jordano P (2005) Interaction frequency as a surrogate for the total effect of animal mutualists on plants. Ecol Lett 8:1088-1094. Vilá M, Bartomeus I, Dietzsch AC, Petanidou T, Steffan-Dewenter I, Stout JC, Tscheulin T (2009) Invasive plant integration into native plant-pollinator networks across Europe. Proceedings of the Royal Society B 276:3887-3893. Weiss MR (1991) Floral colour changes as cues for pollinators. Nature 354:227-229. Westphal C, Steffan-Dewenter I, Tschartnke T (2003) Mass flowering crops enhance pollinator densities at a landscape scale. Ecol Lett 6:961-965. Wiens JA (1995) Habitat fragmentation: Island v landscape perspectives on bird conservation. Ibis 137:S97-S104. Williams NM, Cariveau D, Winfree R, Kremen C (2011) Bees in disturbed habitats use, but do not prefer, alien plants. Basic Appl Ecol 12:332-341. Winfree R, Aguilar R, Vázquez DP, LeBuhn G, Aizen MA (2009) A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90(8):2068-2076 With KA (2019) Landscape effects on community structure and dynamics. Pages 434-511 in Essentials of Landscape Ecology. Oxford University Press, Oxford, United Kingdom. Woodcock BA, Garratt MPD, Powney GD, Shaw RF, Osborne JL, Soroka J, Lindström SAM, Stanley D, Ouvrard P, Edwards ME , et al. (2019) Meta-analysis reveals that pollinator

functional diversity and abundance enhance crop pollination and yield. Nature Communications 10:1481. Zurbuchen A, Landert L, Klaiber J, Müller A, Hein S, Dorn S (2010) Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biological Conservation 143:669-676.

Figure 1: Island versus landscape views of bee communities and their pollination services in agricultural environments. The island model emphasizes patch area and distances between patches within an inhospitable matrix, while the landscape model views semi- natural habitat patches as part of a larger mosaic of land cover types that, together, influence biodiversity at any given location.

Figure 2: Predicted responses of the bee community to invasive plant flowers. Invasive plant inflorescences attract bees within a given distance (blue: lesser attraction; red: greater attraction) and may facilitate visitation and pollination services to nearby plants when invasive flowers are at low densities (a). A more intense floral display (depicted as greater flower density) may saturate the pollinator community and suppress visitation and pollination services to nearby plants (b). These hypotheses are shown in two dimensions but apply to the height dimension as well. As populations of invasive plants spread to provide the dominant source of local floral resources, the species composition of the bee community should shift in favor of populations that acclimate to use the invader’s floral resources (c).

Figure 3: Aerial imagery of agricultural landscapes focused on regions with intensively managed maize. Images are from 10,000 m above sea level. Relative image locations and year taken are clockwise from far left: Colombia (2017), Midwestern United States (2014), Germany (2017), SE China (2017), SE Australia (2019), Zambia (2017). 24

Figure 4: Publications per year on wild bees in agricultural landscapes (grey bars), as well as those that analyzed ≤ 2 (dark blue) or > 2 land cover types (light blue) in the surrounding matrix. 25

Chapter 1: Bee communities and pollination services in adjacent crop fields following flower removal in an invasive forest shrub (Cunningham-Minnick et al. in press)

1 Abstract The habitat boundaries between crops and semi-natural areas influence bee movements and pollination services to crops. Edges also provide favorable conditions for invasive plants, which may usurp pollinators and reduce visitation to native or crop plants. Alternatively, floral displays of alien plants may facilitate, or increase, the pollination success of adjacent plants by attracting more pollinators to the area. Therefore, pollination services of bees from semi-natural habitats to crop areas should vary with the presence of invasive floral resources and distance from habitat edges. To test the hypothesis that floral resources of invasive forest shrubs affect the bee community and pollination services in adjacent crop fields, I conducted a two-year field experiment along forest-crop edges at five isolated forest remnants. I removed flower buds from a dominant invasive shrub, Lonicera maackii (Amur honeysuckle), along forest-crop edges and paired removals with controls of intact flowers. The bee community, their pollination services, and flower visitation rates were quantified along a 200 m gradient into an adjacent crop field using pan traps and sentinel cucumber plants. Impacts to the bee community were dependent of bee functional traits. Larger bees visited fewer sentinel cucumber flowers in flower removal plots, which corresponded with decreased cucumber pollination compared to plots with honeysuckle flowers at distances > 100 m from forest edges. Small-bodied and weaker flying bees visited sentinel plants more frequently closer to the forest edge and increased pollination services to cucumber at distances < 100 m from L. maackii shrubs in flower removal plots. After two years, bee abundance and species richness increased within flower removal plots across all distances. High functional diversity of the bee community increased pollination services to sentinel plants and increased cucumber production within 200 m from forest remnants. My findings suggest that dense floral resources of invasive shrubs suppressed forest-edge bee communities and their pollination services, but also attracted large-bodied generalist bees which were effective pollinators. This study helps

26 explain how life histories and functional attributes of bees can predict either facilitation or suppression of pollination services to crop or native plants in response to invasive floral resources.

2 Introduction Anthropogenic modifications, including habitat loss and intensive management practices, are globally decreasing bee diversity in agricultural regions through the degradation of plant- pollinator mutualisms (Tilman et al. 2001; Winfree et al. 2009; Burkle et al. 2013; Potts et al. 2016). Uncultivated lands within fragmented agricultural landscapes serve as important refugia for pollinator diversity (Tscharntke et al. 2005; Benton et al. 2003). The presence of invasive plants within these refugia further influence pollination networks by shifting the species composition of plant and pollinator communities in semi-natural habitats and adjacent crops (Richardson et al. 2000; Traveset and Richardson 2006). Potential loss of plant-pollinator relationships in agroecosystems threatens pollination services in a time of increased global demand for pollinator-dependent crops (Burkle et al. 2013; Potts et al. 2016). An estimated 80% of crop pollination is provided by just 2% of bee species, which represent the most abundant generalist foragers in the landscape (Kleijn et al. 2015). Though relatively few wild bees directly support the world food supply, these species still depend on floral resources in the surrounding landscape when crops are flowering and when they are not (Eickwort and Ginsberg 1980). Therefore, alterations to the plant or bee community may change the pollination network and availability of crop-pollinating bees. Shifts in bee species composition may alter the composition of functional traits (e.g. body size, diet breadth, nesting habitat) as well as flower visitation, and understanding these interactions may help forecast future changes in pollination services to crops and uncultivated plants (Bjerknes et al. 2007; Bartomeus et al. 2013). Functional diversity may be a better predictor of pollination services than taxonomic diversity (Fründ et al. 2013), but a loss of either can result in degradation of plant- pollinator relationships and a loss of ecosystem services, including pollination of crops (Potts et al. 2016). Since many floral visitors of alien invasive plant species are also generalists (Bartomeus et al. 2008; Williams et al. 2011), there is a growing need for studies that address the effects of alien floral resources on bee community composition and pollination services. Invasive alien plants are commonly found along forest-crop edges in agricultural landscapes (Boutin and Jobin 1998) and are generally detrimental to plant communities (Richardson et al. 2000; Traveset and Richardson 2006; Morales and Traveset 2009; Pyšek et al. 2012; Vilá et al. 2011). The ability of invasive plants to outcompete native species leads to decreased richness and abundance of the local plant community (Vilá et al. 2011). Changes in

28 floral resource availability may, in turn, structure responses of bee communities to alien flowers (Vanbergen et al. 2018). For example, oilseed rape monocultures provide supplemental food sources for generalist bumblebee populations when adjacent to forests (Westphal et al. 2003). However, it is unclear how supplemental alien floral resources change the species composition of bee communities, although most evidence suggests filtering that favors generalist foragers (Williams et al. 2011; Fiedler et al. 2012; Burkle et al. 2013). Exploitation of pollination services provides invasive plants an additional competitive advantage over native plant species. Attractive floral displays and large nectar and pollen rewards enable invasive plants to influence plant-pollinator networks by redirecting a large portion of the bee community (Lopezaraiza-Mikel et al. 2007; Bartomeus et al. 2008; Vilá et al. 2009). A reduction of fitness has been observed in native plants in close proximity to invasive plants with abundant blooms; alternatively, spillover of pollinators from the invasive to the native plant species may benefit native plant fitness (Bartomeus et al. 2008; Bezemer et al. 2014; Vanbergen et al. 2018). The effects of invasive plant species on the pollinator community may therefore influence the pollination success of other plant species. Fiedler et al. (2012) examined these interactions by removing individual invasive plants, which provided insights into changes in nesting habitat for ground-dwelling bees, sunlight availability, and other habitat factors that affect bee and plant colonization. Another approach is to isolate the effects of the additional floral resources provided by invasive plants using experimental removal of flower buds while otherwise leaving the plant intact (McKinney and Goodell 2010). Amur honeysuckle (Lonicera maackii) is an alien flowering shrub that grows in dense populations along forest-crop edges in the eastern United States and displaces native flora and fauna (McNeish and McEwan 2016; Hoven et al. 2017). In the spring, L. maackii displays dense and showy white flowers that are attractive to bee visitors (Goodell et al. 2010; Jachuła et al. 2019) and are primarily cross-pollinated (Barriball et al. 2014; Goodell and Iler 2007). Despite extensive knowledge of the invasion ecology of L. maackii, little is known about how its flowers affect bee communities and their pollination services. The few studies conducted found both evidence for decreased and increased pollination services to neighboring plants (McKinney and Goodell 2010, 2011). I conducted a two-year field experiment that involved the removal of L. maackii flowers along forest-crop edges and paired controls of shrubs with intact flowers. I also quantified

29 pollination services to sentinel plants placed along a distance gradient into recently planted soybean and corn fields that were adjacent to forest edges with and without L. maackii flower removals. I hypothesized that in response to the removal of L. maackii flowers during the first year of the study, bees would need to find alternative floral resources, either by moving into the forest or adjacent crop fields. I focused my study on the crop field by recording bee abundances, visitation, and pollination of sentinel plants in a distance-dependent manner from forest edges. Specifically, I predicted that removal of L. maackii flowers would result in bees foraging at greater distances from the forest edge and pollination success of crop sentinel plants would increase with greater bee visitation. One year following the first flower removal, I hypothesized that fewer bees would be captured in flower removal plots due to shifts in bee foraging in response to decreased food availability during the previous year, and that this would result in decreased pollination services to crops. Finally, I hypothesized that removing L. maackii flowers would affect the functional composition of the bee community because a subset of generalist bees rely heavily on floral resources provided by L. maackii. I therefore predicted that flower removals would shift the bee community from generalist to specialist species that are less dependent on L. maackii.

3 Methods 3.1 Study Design Five study sites were established in early spring 2013 on private lands in SE Indiana and SW Ohio, USA (39.422°N, -84.897°W; 39.539°N, -84.760°W; 39.572°N, -84.801°W; 39.645°N, -84.701°W; 39.666°N, -84.788°W). Each site consisted of an isolated, secondary-growth forest patch with a south-facing edge of dense L. maackii (Supplementary Figure 1) adjacent to either conventional corn or soybean monocultures. Each site contained a pair of experimental and control plots along the same forest edge. In the experimental plot of this paired-plot design, L. maackii flower buds were removed by cutting terminal branches with garden shears in 2013 and again in 2014; the control plot had no manipulation. Experimental and control plots were 100 m x 5 m, separated by 75 m. Plot size was determined by the length of the forest-crop edge and changes in vegetation composition, especially L. maackii cover, with distance into the forest patch. Paired removal and control plots were both > 200 m from the nearest hedgerow or other source of semi-natural habitat. 30

During L. maackii flowering (May – early June), corn or soybean plants in crop fields are small or have not yet emerged. Fields also lacked weedy flowering forbs due to intensive management practices prior to planting. Therefore, I used sentinel plants in crop fields to measure pollination services of foraging bee communities associated with experimental and control plots at the forest-crop edge. The use of sentinel plants is advantageous due to the ability to control for exogenous factors associated with the pollinator community that may influence pollination success, such as flower density, flower morphology, and host fidelity (Hoehn et al. 2008; McKinney and Goodell 2010, 2011; Herbertsson et al. 2017). I chose Northern Pickling Organic Cucumber (Cucumis sativus; Johnny’s Selected Seeds, Cat# 330.51) as a sentinel plant. This cultivar is a monoecious and obligate-outcrossing cucumber with an open flower morphology, which provides easily accessible nectar-rich and pollen-rich flowers to all pollinators. Cucumber seeds were planted and grown in a greenhouse in early spring. Just before moving them outside, plants were repotted into 19-L buckets that were modified into self-watering containers that held 11 L of water (Appendix A). After L. maackii buds were removed and before L. maackii flowered, sentinel cucumber plants were placed outside the greenhouse to acclimate for one week, then taken into the field. A 2.5-cm wire mesh was fastened around cucumber buckets to prevent wildlife browsing. Two caged cucumber plants were placed 5 m apart at four distances (20 m, 50 m, 100 m, 200 m) from the forest edge into the crop field. The pairs of plants at each distance were perpendicular to the center of each 100-m long control or removal plot along the forest edge. Open flowers on sentinel plants were removed prior to the study, and each time plants were moved from the field due to management activities. The number and location of developing fruit were also recorded and marked with a piece of string before plants were removed from the field. Before returning to the field, flowers and newly developing fruits (including aborted) were removed to exclude pollination effects from the temporary holding area. Otherwise, cucumber plants remained in the production area and water reservoirs were refilled weekly for the duration of L. maackii flowering (3-4 weeks). When L. maackii flowering ended, sentinel plants were kept outside and flowers were removed daily until developing cucumbers matured.

3.2 Bee Community The foraging bee community was sampled using pan traps (96 mL white soufflé cups; Solo® #p325w-0001) kept white or painted fluorescent blue or yellow (Guerra Paint & Pigment Corp) as described in Droege (2008). One trap of each color was filled 75% with detergent water to reduce surface tension and was placed on the east and west side of each sentinel plant, for a total of six pan traps per plant. In 2014, pan traps were also placed along the first row of crops within the production area (distance of 0 m). To reduce evaporation and prevent wind from displacing traps, all traps were placed in the soil such that the lip of each pan was slightly above the surrounding soil. Traps were present continuously and reset every 2-3 days except during crop management activities. Each set of traps sampled the bee community for a total of 12-15 days in 2013 and in 2014. Bees were pooled at each distance due to the proximity of the duplicate sentinel plants (5 m), for a total of 12 pan traps (2 plants per distance per plot x 6 traps per plant) per sample. Bees were identified to species in the laboratory. Intertegular span (distance between tegula), body length, front wing length, scopa position (or corbiculae), and relative body hair coverage (0%, 25%, 50%, 75%, 100%) were measured and estimated for all female bees. Intertegular span and body length are correlated with maximum foraging distance and considered representatives of bee body size (Cane 1987; Gathmann and Tscharntke 2002; Greenleaf et al. 2007). Wing length is correlated with wing area and therefore wing loading calculations, which affects flight performance (Vance and Roberts 2014). Bees exhibit a diverse set of morphological adaptations for collecting pollen (Michener 1999), though the precise pollen-handling mechanisms and pollination functions are not well understood (Portman et al. 2019). However, primary pollen collection locations on bees may affect pollination success (Parker et al. 2015); pollen collected by body hair may be in locations that bees cannot groom, which can affect pollen transport and pollination outcomes (Koch et al. 2017). Therefore, I used scopa location to represent different pollen-handling behaviors and relative percent body hair to represent incidental pollen transfer. Since body hair on some specimens may be worn off from age or the curation process (i.e. washing, drying), the average relative percentage of each species was used in analyses. Social, nesting, and foraging behaviors for each species were obtained from the literature (Mitchell 1960; Mitchell 1962; Richards et al. 2011; Ascher and Pickering 2018).

3.3 Sentinel Flower Visitation Every plant was observed for flower visitation by insects three times in 2013 at weekly intervals and once in 2014 during the flowering period of L. maackii based on weather (no observations during rain events) and farmer management of the crop fields. Every observation consisted of a 10-min period of watching flowers for insect visitation, where a visit was defined as contact with a stamen or stigma. Flowers on individual plants with 1, 2, or 3 open flowers with full visibility of stigmas or anthers were observed for each time period. During each observation, wind speed (m/s), percent sun (0%, 25%, 50%, 75%, 100%), counts of open flowers (male and female), and time-of-day were recorded. Percent sun was estimated from cloud cover immediately prior to the observation period, where overcast equated to 0% sun and no clouds translated into 100% sun. General descriptions (e.g. bee, ant, beetle) were given for each visitor with additional details (i.e. relative size, color, genus when possible) recorded for all bee visitors. Since spotted (Diabrotica undecimpunctata) and striped (Acalymma vittatum) cucumber beetles were not observed leaving the flowers they occupied, I did not include them in my analysis. To account for variable weather patterns, the order of observation was randomly assigned within plots, such that every plant within a site was observed within the same day. To control for within-day temporal variation (morning/afternoon) in bee flight times, the order of treatment and control plots was reversed in each set of observations.

3.4 Sentinel Fruit Production Only cucumbers that developed after sentinel plants were moved into the fields were sampled for analysis. Cucumber measurements were taken when fruits matured, defined here as the appearance of yellow on the outside of the skin. Each mature cucumber was taken to the lab, had center girth and length measured, aged at room temperature for two weeks, then stored at - 20°C. Once thawed, the meso- and endocarp were removed and fermented. The fermentation process allowed all organic material to be cleanly removed from seeds (Whitaker and Davis 1962). Seeds were air-dried at room temperature for one week, counted, and weighed.

3.5 Analyses Since the emergence of bees in experimental plots would be dependent of pollen and

33 nectar sources the previous year, the removal of L. maackii floral resources the first year of the experiment should have largely influenced spatial foraging patterns of bees. In contrast, bees sampled in the second year may reflect changes in nest establishment and emergence from the first year of manipulation as well as differences in bee foraging patterns during the second year. Moreover, I implemented an additional set of pan traps at the forest edge (0 m) during the second year of the study. I therefore analyzed data from each year separately except that I pooled data from 2013 and 2014 on pollinator visitation to sentinel plants because visitation was too sparse to be analyzed separately by year and there were no additional sentinel plants at the 0-m sample location during the second year. All analyses were performed in the R programming language (R Core Team 2016) using linear, generalized linear, and zero-inflated models with the lmer() and glmmadmb() functions in lme4 (Bates et al. 2015) and glmmADMB (Fournier et al. 2012) packages, respectively. The lme4 package performs linear and generalized linear models quickly and is preferred for data that is not zero-inflated, in which case the formally known glmmADMB (now glmmTMB) package performs well, especially when random effects are involved. Model selection was determined using the bias-corrected AICc (Mazzerole 2016). All models containing predictor variables with a variance inflation factor (Fox and Weisberg 2011) > 4 were dropped from the analyses using the car package, which performs well depending on the complexity of the random effects terms. I report the best-fitting models with the lowest AICc and competing models (∆AICc ≤ 2) are discussed. Due to the paired-plot design of the study, response intercepts were allowed to vary randomly by site. 3.5.1 Bee Community Species richness and abundance of bees sampled in pan traps were modeled with generalized linear mixed effects models. Fixed effects included distance from forest edge (continuous), flower removal treatment (categorical; hereafter treatment), and their interaction. Site was considered to be a random effect. Overall significance of fixed effects was determined by a likelihood ratio test with a null model with random site intercepts. The effects of treatment and distance on bee species composition were tested using distanced-based redundancy analysis (dbRDA) with the dbrda() function in the vegan package (Oksanen et al. 2016) using Bray- Curtis dissimilarities on a species matrix with singletons and doubletons removed. The vegan package includes a multitude of multivariate methods for analyzing communities. Significance of the dbRDA model and terms were determined by permutation (n = 500) of treatment and

34 distance within sites. To determine which bees shifted between treatments, I calculated 95% CI of natural log response ratios of each species abundance at each distance between control and experimental plots. 3.5.2 Functional Traits I examined bee functional trait responses using life history characteristics and morphometrics of bees from pan traps (Supplementary Table 1). I removed from the analysis 76 males, 4 females of species with unknown information, and 27 females with missing measurements due to body damage. All other individuals and species were included. Body size is related to foraging and flight ability (Greenleaf et al. 2007), while morphometric ratios are useful to understand carrying loads (Berwaerts et al. 2002). To determine which functional trait metrics to use for each year, I performed principal component analysis, using a method described in Cailliez (1983), on a transformed matrix of the mean functional trait values within each bee sample weighted by relative abundance. The metric that best explained the first principal component axis was chosen for further analyses. I also considered ecological and life history traits of each species (i.e. Mitchell 1960; Mitchell 1962; Gibbs 2010; Richards et al. 2011; Bartomeus et al. 2013). Sociality was categorized as solitary, social, or parasitic, and species known to exhibit communal and facultative sociality are classified here as solitary. A species was categorized as oligolectic if it was documented (Ascher & Pickering 2018) foraging on ≤ 10 plant families and polylectic if found on >10 families. It follows that most Nomada species were considered specialized nectar foragers here; therefore, all Nomada morphospecies females in the bidentate-group were assumed to be oligolectic for analyses since their natural histories are not described elsewhere. Functional measures provide an alternative to species composition as measures of ecological differences between communities (Diaz and Cabido 2001). Functional diversity measures, including richness (FRic), evenness (FEve), and divergence (FDiv) (Villéger et al. 2008; Laliberté and Legendre 2010), consider the differences in life history or ecological traits represented within species assemblages in relation to the overall community (Villéger et al. 2008). I derived functional diversity using the dbFD() function in the FD package, which provides many approaches and indices to evaluate the diversity and relatedness of functional and life history attributes along, for example, environmental gradients (Laliberté et al. 2014). Since bee sexual dimorphism implies functional trait differences between sexes, I chose only to include

35 females in these analyses as well. I removed species with unknown natural histories and individuals with missing body parts as previously described. Therefore, 75 and 69 species were included within the analyses of the functional bee communities for 2013 and 2014, respectively. FRic, FEve, and FDiv were assigned values of 0, 1, and 0.5, respectively, when functional diversity metrics could not be calculated from at least three unique combination of traits within a sample (Laliberté et al. 2014). Model selection using treatment and distance as predictors was then performed as described previously. I used RLQ methods, or a fourth corner analysis, to test the relationship among the foraging bee community, their functional traits, and treatment. RLQ methods provide an integrative perspective by simultaneously analyzing the links between three sets of data matrices: the site x environment R matrix, the species identity x abundance L matrix, and the species x species trait Q matrix (Dolédec et al. 1996) in the ade4 package, which uniquely offers three-table coinertia analyses (Dray and Dufour 2007). In my case, the R matrix included treatment and distance, and the P-values are based on 100,000 permutations of the SRLQ statistic described in Dray and Legendre (2008) to test for a global relationship among matrices. 3.5.3 Visitation Rates Pollinator visitation to sentinel plant flowers was analyzed using generalized linear models. Due to low numbers of observed visitors, data from both years were pooled, and responses included the presence or absence of a pollination visit by a bee, as well as by other insects, using a binomial error distribution. Continuous fixed effects included wind speed, percent sun, count of open flowers, time of day, and distance, while treatment was a categorical predictor. All continuous predictors were log transformed and then centered (subtracting the mean) to reduce covariation in models. 3.5.4 Sentinel Plants Four responses representing different measures of cucumber pollination success were analyzed using mixed effects models. Total seeds produced by a single plant provided a measure of fitness. I also measured mean seeds per fruit per plant to evaluate pollination quality and to account for variation among plants. Total number and volume of cucumbers per plant were analyzed to address differences in fruit production. Volume (V) of a cucumber was calculated as

풍 ∗ 품ퟐ V = , where 풍 is cucumber length, and 품 is the circumference (girth) at the midpoint of the ퟒ흅 cucumber. Measures from pairs of plants located in the same treatment and distance (not

36 independent) were averaged before analysis. Fixed effects included distance, treatment, and their interaction. Model selection involved the AICc criterion, and significance was based on likelihood ratio tests of models with and without predictor variables.

4 Results 4.1 Bee community & functional traits Pan traps collected a total of 1071 bees of 79 species in 2013, and 638 bees of 80 species in 2014, for a total of 1712 individuals representing 112 species, 22 genera, and 5 families (Supplementary Table 1). Many species were infrequently captured and represented 30 singletons and 11 doubletons in 2013, and 35 singletons and 10 doubletons in 2014. Bee abundance and species richness declined with distance from forest edges in best-fitting and competing models (Supplementary Table 2). Distance best explained a decrease in bee abundance (χ2(1) = 10.47; P < 0.005) and species richness in 2013 (χ2(1) = 14.56; P < 0.001) (Figures. 1a & 1b). Treatment and distance were the best predictors in 2014, with the removal of L. maackii flowers resulting in a higher bee abundance (χ2(3) = 17.13; P < 0.001) and species richness (χ2(3) = 23.19; P < 0.0001) across all distances into the crop field (Figures. 1c & 1d).

Bee species composition was significantly influenced by distance in 2013 (Fpseudo(5,34) =

2.10; P < 0.05) and 2014 (Fpseudo(5,44) = 2.95; P < 0.005; Supplementary Figure 2). Distance effects explained 7% and 14% of the variation represented by the constrained ordinations for 2013 and 2014, respectively. Treatment did not increase model fit for 2013 (χ2(1) = 0.16; P = 0.41) or 2014 (χ2(1) = 0.16; P = 0.68). Considering each species abundance separately, eight bee species exhibited distance-dependent changes in abundance to L. maackii flower removal, though not all in the same direction. Apis mellifera, Lasioglossum (Dialictus), and several species of Andrena showed significant differences in abundance between treatments at specific distances from the edge (Table 1). Intertegular span and body length were the best morphometric predictors of the functional bee community in 2013 and 2014, respectively. Functional group richness was greater with the removal of L. maackii flowers and decreased with distance into the crop field in 2013 (χ2(3) = 10.58; P < 0.01), but not in 2014 (χ2(2) = 3.69; P = 0.16). There was no relationship between functional evenness and these predictors (Supplementary Table 3). Fourth corner analysis showed a significant global relationship among the species abundances, functional traits (i.e.

37 sociality, nesting substrate, body size, degree of foraging specialization), and environmental factors (i.e. distance, flower-removal treatment) in 2013 (P < 0.01), but not 2014 (P = 0.61; Figure 2).

4.2 Bee visits to sentinel plants I observed 129 visitors, 60 of which were bees, during 43.5 hrs of 10-min observations across years. Other visitors included ladybird beetles (Coccinellidae) (n = 27), Diptera (n = 17), and other Coleoptera (n = 7). The best model for bee visits to sentinel flowers, with 19% support, included effects of treatment, wind and sentinel plant flower abundance (χ2(3) = 14.05; P < 0.005). More bee visits to sentinel plant flowers were observed in control plots, and increased with increasing numbers of sentinel plant flowers (Figure 3). All competing models for bee- flower visits also included the effect of treatment (Table 2). The best model to explain frequency of a flower visit by non-bee pollinators included only the model covariate wind speed (χ2(1) = 2.99; P = 0.08; Supplementary Figure 3). No competing models for visitation by insects other than bees included an effect of treatment (Table 2).

4.3 Sentinel plant production I harvested 309 cucumbers containing 24,712 seeds in 2013 and 371 cucumbers with 26,622 seeds in 2014. In 2013, the number of seeds produced by each plant was affected by the interaction between treatment and distance (χ2(3) = 8.47; P < 0.05; Figure 4a; see Supplementary Table 4 for competing models and coefficients). While cucumber plants in flower removal plots showed decreased seed production with increased distances from the forest edge, the opposite trend occurred within control plots. The interaction with treatment and distance was not represented in the 2014 best model (Figure 4e), which showed an overall decrease in seed production with increasing distance into the crop field (χ2(1) = 3.96; P < 0.05). In 2013, the best model suggested more seed production per fruit in control plots, with increased pollination quality with greater distances (χ2(2) = 8.60; P < 0.05; Figure 4b). However, there was no difference between treatments, and pollination quality tended to decrease with increasing distance in 2014 (χ2(1) = 2.79; P = 0.09; Figure 4f). There was also no effect of treatment or distance on fruit set in 2013 or 2014 (Figures. 4c & 4g), although competing models suggested a

38 slight decrease in number of fruit produced with increasing distance from the forest edge. Treatment and distance did not impact cucumber production volume in 2013 (Figure 4d), but plants of flower removal plots in 2014 produced larger cucumbers compared to control plots across distances (χ2(2) = 7.87; P < 0.05; Figure 4h).

5 Discussion My results suggest that floral resources provided by dense populations of invasive plants along forest edges suppress bee functional diversity and pollination services in adjacent croplands near forest edges but can facilitate crop production away from forest-crop edges. Facilitation occurred at farther distances from the forest edge with larger and more effective pollinating bees (Garibaldi et al. 2015) that were frequently observed at sentinel flowers in control plots. Competition between L. maackii and crop flowers was observed at distances near the forest edge with smaller bees collected with pan traps that decreased in abundance with distance into the crop field. A year into the study, L. maackii flowers were suppressing the bee community from foraging into the crop field, which resulted in increased production of cucumber volume in flower-removal plots. The fourth corner analysis further supported that the observed distance-dependent effects of L. maackii flowers on the bee community were mediated through bee functional traits. Thus, L. maackii flowers play an important role in modifying pollination services and a subset of the bee community up to 200 m from forest-crop edges dominated by the floral display of this invader in an agricultural landscape of the Midwestern United States.

5.1 Bee community & functional traits Contrary to my initial hypothesis, I found no difference in the overall abundance or number of bee species during the first year of L. maackii flower removal, as measured by pan traps. In addition, bee abundance and species richness were both higher in crop fields adjacent to the flower removal plots in the year following the first removal. As I predicted, bee abundance and species richness declined from the forest edge, although the decline occurred at a lower rate than expected. I observed a 45% and 42% decrease in bee abundance and species richness, respectively, between 20 m and 200 m into the crop field in 2013. The sampling of an additional distance near the forest edge (0 m) in 2014 increased the 39 observed number of bees and bee species by 8% and 5%, respectively, suggesting that a significant portion of the forest-edge bees foraged well into the large, monoculture crop fields. Despite the observed effect of flower removal on bee species richness and abundance, treatment did not explain a significant proportion of the overall compositional differences of the bee community sampled by pan trapping (Supplementary Figure 2). This result was surprising because I detected distant-dependent changes in species abundances between treatments in eight bee species, including five species in the genera Apis and Andrena. Low abundances of many species sampled within the crop field likely led to an underestimation of the species responding to L. maackii flowers, but these two genera are supported as primary visitors of L. maackii floral resources in my study region (Goodell and Iler 2007, Supplementary Figure 1). Collectively, my results suggest a usage gradient of L. maackii floral resources by the bee community. The low proportion of bee species that shifted abundances in response to Lonicera flowers may also partially explain why bee abundance of pan traps in 2013, as well as overall bee community composition, were not affected by L. maackii flower removal. Lonicera maackii is thought to provide generalists floral resources, but only 16 of the 112 species (14%) sampled by pan traps are known foragers of the genus Lonicera (Ascher and Pickering 2018). In fact, 15 of these bee species had higher occurrence in the crop field following the initial removal of L. maackii flowers in 2013, supporting my hypothesis that bees would need to find alternative floral resources, and would move into the adjacent crop field (Figure 2; Supplementary Table 1). Thus, while the majority of bee species recorded were not affected by the treatment, the 16 species that are known foragers of the genus Lonicera comprised 62% of the total bees captured in pan traps and thus represent an important component of the sampled bee fauna. Consistent with my predictions, bee species and bee functional traits differed with distance from the forest edge and between treatments, suggesting that the removal of L. maackii flowers affected the bee community based on functional traits (Figure 2). Pairwise correlations of species life history traits with the flower removal treatment did not demonstrate significant differences (data not shown) unlike other studies comparing different habitat cover types (Harrison et al. 2018). However, my results revealed a significant overall response of the functional community to L. maackii flowers across distances (Figure 2), suggesting that L. maackii flowers affect bees with a suite of functional traits (Coutinho et al. 2018). For example, larger bees were associated with greater distances into the crop field, suggesting that larger bees

40 forage at farther distances from the forest edge. Many of these species were also generalists and associated with foraging on Lonicera (Supplementary Table 1). It is also possible that the removal of L. maackii flowers enhanced the attraction of small-bodied bees to the pan traps (Morandin and Kremen 2013), as L. maackii shrubs exhibited an intense floral display and were the dominant source of available floral resources. This suggests that L. maackii flowers were distracting many small species from the adjacent crop field, which should reduce pollination of nearby crops that co-flower (Nicholson et al. 2019).

5.2 Bee visits to sentinel plants Bee visitation to cucumber flowers was negatively affected by the removal of L. maackii flowers after accounting for wind velocity and cucumber flower density (Figure 3; Table 2). Contrary to my hypothesis for 2013, visitation rates were lower to sentinel plants in flower- removal plots and this difference was consistent across all distances from the forest edge. These results may appear to contradict my findings from pan traps that overall bee abundance and species richness decreased with distance into the crop field but were greater in flower-removal plots in 2014. One explanation for this discrepancy is that the bee visitors of sentinel plant flowers and those captured in pan traps were not the same species. This is consistent with other findings that showed high flower densities of alien plants affect bees of different body sizes in a distance-dependent manner (Benjamin et al. 2014), which may be partially explained by the limitations of pan trap sampling. Pan traps are known to sample large-bodied bees less effectively than small bees (Droege 2008; Gibbs et al. 2017), and therefore large bees were likely under-represented in pan trap data. For example, 30% of bee visits to sentinel plant flowers were bumblebees (Bombus spp), a large-bodied genus known to aggregate at dense floral displays (Bruninga-Socolar et al. 2016; Kallioniemi et al. 2017) and move throughout fragmented landscapes (Kreyer et al. 2004). Yet bumblebees were infrequently captured in my pan traps (< 1% of individuals were Bombus). Bumblebees were also observed foraging on L. maackii flowers along the forest edge during my study and in Goodell and Iler (2007). Bombus foragers were recorded more frequently visiting sentinel plant flowers in plots with intact honeysuckle flowers (78% of all Bombus visits). Smaller bees may also be more likely to visit pan traps than sentinel plant flowers, especially in more open and windy agricultural environments because the pan traps have a lower vertical positioning than sentinel plant flowers (Hoehn et al. 2008).

5.3 Sentinel plant production In support of my hypothesis, pollination services varied by distance, treatment, and year and were best explained by a functionally diverse bee community that responded differently to flower removal and distance from forest edges. Treatment and distance were important predictors of seed set per plant, but did not affect fruit set or seeds per cucumber (Figure 4). Despite the observed declines in bee abundances with distance into the crop field, seed set of sentinel plants increased with distance from the forest edge in plots where L. maackii flowers were left intact in 2013. Seeds per cucumber followed a similar pattern in both treatments in 2013, suggesting that few bees were responsible for the observed increase of pollination services with distance into control plots. If large-bodied bees were underestimated by pan traps, then some large bee species affected by the honeysuckle flower removal in 2013 were also likely responsible for the increased pollination services in control plots relative to flower removal plots. Bee assemblages exhibiting greater richness of functional groups best explained the overall pollination success of sentinel plants in 2013, suggesting that a functionally diverse community provided the most effective pollination services (Fründ et al. 2013; Woodcock et al. 2019). Despite no difference in seed set between treatments in 2014, the removal of L. maackii flowers resulted in cucumber fruits of greater volume. Since cucumber volume is correlated with weight (Marcelis 1992), these findings collectively suggest that a diverse subset of the forest- edge bee community forages within adjacent crop fields and that L. maackii flowers along these edges can suppress bees that increase cucumber crop production. Overall, my study provides evidence that flowers of L. maackii may facilitate the pollination of crop plants by large-bodied bees well into the interior of the crop field. However, increases in smaller bees at distances closer to forest edges in flower-removal plots demonstrated that honeysuckle flowers may also compete with nearby crop plants for small-bodied pollinators, potentially reducing overall crop pollination and output as indicated by my findings of lower seed set and fruit volume production of cucumbers near flowering L. maackii shrubs in 2013 and 2014, respectively. Therefore, management options for invasive plants should consider the probable pollinators of the adjacent crop before management actions are implemented (Garibaldi et al. 2015). The overlap of the L. maackii flowering season (i.e. late spring when few other plant species are producing large floral displays), geographic range, and shared pollinator community

(e.g. Andrena, Bombus) with several other economically important crop species such as blackberries, blueberries, flaxseed (but see Williams et al. 1991), grapes, raspberries, and strawberries, as well as common varieties of apple, cherry, olive, and peach trees, lends further support for considering the important role of L. maackii flowers in crop pollination (Drummond 2019; Javorek et al. 2002; Martins et al. 2018; Tuell et al. 2009; Whitney 1984). However, my results also suggest that flowering crops immediately adjacent to L. maackii flowers are less adequately pollinated. Therefore, studies testing the mechanisms responsible for these distance- dependent effects are needed, though the concept of exploiting invasive floral resources to enhance production of crops could be valuable considering anticipated increases in plant invasions and the costs required to remove them (Seebens et al. 2018; Shackleton et al. 2019). Considering the increasing number of invasive flowering woody shrubs (Richardson and Rejmánek 2011) and future anticipated invasions (Seebens et al. 2018), invasive plant species management has become a key priority in all regions. My results highlight the important role of a dominant invasive plant species for the bee community, suggesting that alternative and native foraging plants need to be included in restoration programs that focus on the removal of invasive plant species with large floral displays. I recommend planting native forage with overlapping but sequential phenology to restore lost floral resources and encourage bees to remain along adjacent semi-natural habitat edges following invasive species removal (Menz et al. 2011). My study spanned two years and does not provide insight into long-term effects of large-scale flower removal without flower replacement, but other studies have shown that bees in agricultural landscapes may have limited floral resource availability during their flight season even without removing flowers (Persson and Smith 2013). Therefore, research on the effects of hedgerow restoration or field buffers may provide insight into best practices for restoring forest-crop edges following invasive plant removal, such that populations of more specialized bee species are protected as well as pollination services to nearby crops (Morandin and Kremen 2013). Agricultural landscapes and disturbed forests throughout the region are heavily invaded and dominated by L. maackii. The temporal and spatial extent of flower-removal plots was limited by feasibility of this large scale, replicated experiment. Weather differences between years may have affected bee responses one year after initial flower removals, and longer multi- year studies are needed to address bee responses to flower removals of invasive plants. The spatial extent of my study was also limited, but still involved large expanses of flowering

43 honeysuckle. Apart from flower removal areas, the 75-m buffer strip, control plots, and other edges of the forest patches were invaded with L. maackii. To estimate the extent of flowers removed compared to those retained along the forest edge, I calculated L. maackii densities at three of the five woodlots and used a recently published negative binomial hurdle model developed for invasive Lonicera to estimate flower abundance of L. maackii at these sites (Hassett and McGee 2017). I estimated that > 12.5 million L. maackii flowers were in the 75 m between removal and control plots while the 100-m treatment plots had an estimated > 16.7 million flowers removed (see Supplementary Table 5 for calculations). Thus, the results of my study should be interpreted in the relatively small extent of flower removal relative to buffer areas and control plots and other forest edges. The borders of removal and control plots were relatively close in terms of maximum foraging distances of most bee species (> 100 m; Greenleaf et al. 2007). Despite these limitations, I did observe important differences in bee and pollination responses between control and treatment plots, indicating that the removal of flowers was effective. With more effort, removal of honeysuckle flowers from the edges of entire woodlots could be compared with control woodlots that are more widely separated to reduce the confounding effects of paired removal and control plots in my study. Statistically, generalized linear models deal with the spatial dependence in paired treatment-control plots by treating woodlots as a random effect, but it does not preclude the movements of foraging bees between treatments and controls. Much work is also needed to quantify bee foraging patterns and typical foraging ranges which were shown to be much shorter than maximum foraging ranges (Gathmann and Tscharntke 2002; Zurbuchen et al. 2010), but are also largely unknown and context dependent on nesting and floral resources (Sardiñas et al. 2016). For instance, Smith et al. (2013) found that bees visiting cucumber flowers in organic farms in Indiana responded best to semi-natural habitat cover within 250 m, the shortest distance analyzed. I conclude that a functionally diverse bee community occurred in crop fields adjacent to forest edges within agricultural landscapes, and that experimental changes in floral resource availability of an invasive shrub had distance-dependent and trait-based effects on bee distributions and pollination services in crop fields. Bee communities associated with small forest patches with dense populations of invasive L. maackii shrubs likely reflect strong species sorting effects of the larger regional pool of bee species, which may partly explain why flower

44 removals showed resilient responses in bee functional diversity and pollination services. Nonetheless, the removal of large, showy flower resources of invasive shrubs did increase bee species richness and abundance along forest-crop edges, while larger generalist bee species provided greater pollination services farther into crop fields. Since the removal of invasive plants from semi-natural areas is costly and labor-intensive, my findings suggest tradeoffs in the control of invasive flowering plants compared to other management strategies that may be used to enhance pollination services and bee diversity in adjacent crops.

6 References Ascher J. S., J. Pickering. 2018. Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). Accessed November 15, 2018. http://www.discoverlife.org/mp/20q?guide=Apoidea_species. Barriball K., K. Goodell, O. J. Rocha. 2014. Mating patterns and pollinator communities of the invasive shrub Lonicera maackii: a comparison between interior plants and edge plants. International Journal of Plant Science 175(8):946-954. Bartomeus I., M. Vilá, L. Santamaría. 2008. Contrasting effects of invasive plants in plant- pollinator networks. Oecologia 155:761-770. Bartomeus I., J. S. Ascher, J. Gibbs, B. N. Danforth, D. L. Wagner, S. M. Hedtke, R. Winfree. 2013. Historical changes in northeastern US bee pollinators related to shared ecological traits. Proceedings of the National Academy of Sciences of the United States of America 110(12):4656-4660. Bates D., M. Maechler, B. Bolker, S. Walker. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67(1):1-48. Benjamin F. E., J. R. Reilly, R. Winfree. 2014. Pollinator body size mediates the scale at which land use drives crop pollination services. Journal of Applied Ecology 51:440-449. Benton T. G., J. A. Vickery, J. D. Wilson. 2003. Farmland biodiversity: is habitat heterogeneity the key? TRENDS in Ecology and Evolution 18(4):182-188. Berwaerts K., H. Van Dyck, P. Aerts. 2002. Does flight morphology relate to flight performance? An experimental test with the butterfly Pararge aegeria. Functional Ecology 16:484-491. Bezemer T. M., J. A. Harvey, J. T. Cronin. 2014. Response of native insect communities to invasive plants. Annual Review of Entomology 59:119-141. Bjerknes A., Ø. Totland, S. J. Hegland, A. Nielsen. 2007. Do alien plant invasions really affect pollination success in native plant species? Biological Conservation 138:1-12. Boutin C., B. Jobin. 1998. Intensity of agricultural practices and effects on adjacent habitats. Ecological Applications 8(2):544-557. Bruninga-Socolar B., E. E. Crone, R. Winfree. 2016. The role of floral density in determining bee foraging behavior: A natural experiment. Natural Areas Journal 36(4):392-399. Burkle L. A., J. C. Marlin, T. M. Knight. 2013. Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339:1611-1615. Cailliez F. 1983. The analytical solution of the additive constant problem. Psychometrika 48:305-310. Cane J. H. 1987. Estimation of bee size using intertegular span (Apoidea). Journal of the Kansas Entomological Society 60(1):145-147. Coutinho J. G. E., L. A. Garibaldi, B. F. Viana. 2018. The influence of local and landscape scale on single response traits in bees: A meta-analysis. Agriculture, Ecosystems and Environment 256:61-73. Cunningham-Minnick M. J., Peters V. E., Crist T. O. in press. Bee communities and pollination services in adjacent crop fields following flower removal in an invasive forest shrub. DOI: 10.1002/eap.2078. Diaz S., M. Cabido. 2001. Vive la difference: plant functional diversity matters to ecosystem processes. Trends in Ecology and Evolution 16:646-655.

Dolédec S., D. Chessel, C. J. F. Braak, S. Champely. 1996. Matching species traits to environmental variables: a new three-table ordination method. Environmental and Ecological Statistics 3(2):143-166. Dray S., A. B. Dufour. 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software. 22(4):1-20. Dray S., P. Legendre. 2008. Testing the species traits-environment relationships: The fourth- corner problem revisited. Ecology 89(1):3400-3412. Droege S. 2008. The very handy manual: how to catch and identify bees and manage a collection. USGS Native Bee Inventory Monitoring Lab. Drummond F. 2019. Reproductive biology of wild blueberry (Vaccinium augustifolium Aiton). Agriculture. 9(4):69. Eickwort G. C., H. S. Ginsberg. 1980. Foraging and mating behavior in Apoidea. Annual Review of Entomology 25:421-446. Fiedler A. K., D. A. Landis, M. Arduser. 2012. Rapid shift in pollinator communities following invasive species removal. Restoration Ecology 20(5):593-602. Fournier D. A., H. J. Skaug, J. Ancheta, J. Ianelli, A. Magnusson, M. Maunder, A. Nielson, J. Sibert. 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software 27:233-249. Fox J., S. Weisberg. 2011. An {R} companion to applied regression, Second Edition. Thousand Oaks, CA: Sage. Fründ J., C. F. Dormann, A. Holzschuh, T. Tscharntke. 2013. Bee diversity effects on pollination depend on functional complementarity and niche shifts. Ecology 94(9):2042-2054. Garibaldi L. A., I. Bartomeus, R. Bommarco, A. M. Klein, S. A. Cunningham, M. A. Aizen, V. Boreux, M. P. D. Garratt, L. G. Carvalheiro, C. Kremen, C. L. Morales, C. Schüepp, N. P. Chacoff, B. M. Freitas, V. Gagic, A. Holzschuh, B. K. Klatt, K. M. Krewenka, S. Krishnan, M. M. Mayfield, I. Motzke, M. Otieno, J. Petersen, S. G. Potts, T. H. Ricketts, M. Rundlöf, A. Sciligo, P. A. Sinu, I. Steffan-Dewenter, H. Taki, T. Tscharntke, C. H. Vergara, B. F. Viana, M. Woyciechowski. 2015. Trait matching of flower visitors and crops predicts fruit set better than trait diversity. Journal of Applied Ecology 52:1436- 1444. Gathmann A., T. Tscharntke. 2002. Foraging ranges of solitary bees. Journal of Animal Ecology 71:757-764. Gibbs J. 2010. Revision of the metallic species of Lasioglossum (Dialictus) in Canada (Hymenoptera, Halictidae, Halictini). Zootaxa 2591:1-382. Gibbs J., N. K. Joshi, J. K. Wilson, N. L. Rothwell, K. Powers, M. Haas, L. Gut, D. J. Biddinger, R. Isaacs. 2017. Does passive sampling accurately reflect the bee (Apoidea: Anthophila) communities pollinating apple and sour cherry orchards? Environmental Entomology 46(3):579-588. Goodell K., A. M. Iler. 2007. Reproduction and habitat-mediated pollinator services in the invasive shrub Lonicera maackii. Pages 47-57 in NC Cavender, ed. Ohio Invasive Plants Research Conference Proceedings. Ohio Biological Survey, Delaware, OH. Goodell K., A. M. McKinney, C. H. Lin. 2010. Pollen limitation and local habitat-dependent pollinator interactions in the invasive shrub Lonicera maackii. International Journal of Plant Sciences 171(1):63-72.

Greenleaf S. S., N. M. Williams, R. Winfree, C. Kremen. 2007. Bee foraging ranges and their relationship to body size. Oecologia 153:589-596. Harrison T., J. Gibbs, R. Winfree. 2018. Forest bees are replaced in agricultural and urban landscapes by native species with different phenologies and life-history traits. Global Change Ecology 24:287-296. Hassett M. R., G. G. McGee. 2017. Negative binomial hurdle models to estimate flower production for native and nonnative Northeastern shrub taxa. Forest Science 63(6):577- 585. Herbertsson L., M. Rundlöf, H. G. Smith. 2017. The relation between oilseed rape and pollination of later flowering plants varies across plant species and landscape contexts. Basic and Applied Ecology 24:77-85. Hoehn P., T. Tscharntke, J. M. Tylianakis, I. Steffan-Dewenter. 2008. Functional group diversity of bee pollinators increases crop yield. Proceedings of the Royal Society B 275:2283- 2291. Hoven B. M., D. L. Gorchov, K. S. Knight, V. E. Peters. 2017. The effect of emerald ash borer- caused tree mortality on the invasive shrub Amur honeysuckle and their combined effects on tree and shrub seedlings. Biological Invasions 19:2813-2836. Jachuła J., B. Denisow, M. Strzałkowska-Abramek. 2019. Floral reward and insect visitors in six ornamental Lonicera species – Plants suitable for urban bee-friendly gardens. Urban Forestry & Urban Greening 44:126390. Javorek S. K., K. E. Mackenzie, S. P. Vaner Kloet. 2002. Comparative pollination effectiveness among bees (Hymenoptera: Apoidea) on lowbush blueberry (Ericaceae: Vaccinium augustifolium). Annals of the Entomological Society of America 95(3):345-351. Kallioniemi E., J. Ǻström, G. M. Rusch, S. Dahle, S. Ǻström, J. O. Gjershaug. 2017. Local resources, linear elements and mass-flowering crops determine bumblebee occurrences in moderately intensified farmlands. Agriculture, Ecosystems and Environment 239:90-100. Kleijn D., R. Winfree, I. Bartomeus, G. Carvalheiro, M. Henry, R. Isaacs, A. M. Klein, C. Kremen, L. M’Gonigle, R. Rader, T. H. Ricketts, N. M. Williams, N. L. Adamson, J. S. Ascher, A. Báldi, P. Batáry, F. Benjamin, J. C. Biesmeijer, E. J. Blitzer, R. Bommarco, M. R. Brand, V. Bretagnolle, L. Button, D. P. Cariveau, R. Chifflet, J. F. Colville, B. N. Danforth, E. Elle, M. P. D. Garratt, F. Herzog, A. Holzschuh, B. G. Howlett, F. Jauker, S. Jha, E. Knop, K. M. Krewenka, V. Le Féon, Y. Mandelik, E. A. May, M. G. Park, G. Pisanty, M. Reemer, V. Riedinger, O. Rollin, M. Rundlöf, H. S. Sardiñas, J. Scheper, A. R. Sciligo, H. G. Smith, I. Steffan-Dewenter, R. Thorp, T. Tscharntke, J. Verhulst, B. F. Viana, B. E. Vaissière, R. Veldtman, C. Westphal, S. G. Potts. 2015. Delivery of crop pollination services is an insufficient argument for wild pollinator conservation. Nature Communications 16(6):7414 Koch L., K. Lunau, P. Wester. 2017. To be on the safe site – Ungroomed spots on the bee’s body and their importance for pollination. PLoS ONE 12(9):e0182522. Kreyer D., A. Oed, K. Walther-Hellwig, R. Frankl. 2004. Are forests potential landscape barriers for foraging bumblebees? Landscape scale experiments with Bombus terrestris agg. and Bombus pascuourum (Hymenoptera, Apidae). Biological Conservation 116:111-118. Laliberté E., P. Legendre. 2010. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91:299-305. Laliberté E., P. Legendre, B. Shipley. 2014. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12.

Lopezaraiza-Mikel M. E., R. B. Hayes, M. R. Whalley, J. Memmott. 2007. The impact of an alien plant on a native plant-pollinator network: an experimental approach. Ecology Letters 10:539-550. Marcelis L. F. M. 1992. Non-destructive measurements and growth analysis of the cucumber fruit. Journal of Horticultural Science 67(4):457-464. Martins K. T., C. H. Albert, M. J. Lechowicz, A. Gonzalez. 2018. Complementary crops and landscape features sustain wild bee communities. Ecological Applications 28(4):1093- 1105. Mazerolle M. J. 2016. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R Package version 2.0-4. McKinney A. M., K. Goodell. 2010. Shading by invasive shrub reduces seed production and pollinator services in a native herb. Biological Invasions 12:2751-2763. McKinney A. M., K. Goodell. 2011. Plant-pollinator interactions between an invasive and native plant vary between sites with different flowering phenology. Plant Ecology 212:1025- 1035. McNeish R. E., R. W. McEwan. 2016. A review on the invasion ecology of Amur honeysuckle (Lonicera maackii, Caprifoliaceae) a case study of ecological impacts at multiple scales. Journal of the Torrey Botanical Society 143(4):367-385. Menz M. H. M, R. D. Phillips, R. Winfree, C. Kremen, M. A. Aizen, S. D. Johnson, K. W. Dixon. 2011. Reconnecting plants and pollinators: challenges in the restoration of pollination mechansims. Trends in Plant Science 16(1):1360-1385. Michener C. D. 1999. The corbiculae of bees. Apidologie 30(1):67-74. Mitchell T. B. 1960. Bees of the Eastern United States. Volume I. Technical bulletin No. 141 (North Carolina Agricultural Experiment Station). Mitchell T. B. 1962. Bees of the Eastern United States. Volume II. Technical Bulletin No. 152 (North Carolina Agricultural Experiment Station). Morales C. L., A. Traveset. 2009. A meta-analysis of impacts of alien vs. native plants on pollinator visitation and reproductive success of co-flowering native plants. Ecology Letters 12:716-728. Morandin L. A., C. Kremen. 2013. Hedgerow restoration promotes pollinator populations and exports native bees to adjacent fields. Ecological Applications 23(4):829-839. Nicholson C. C., T. H. Ricketts, I. Koh, H. G. Smith, E. V. Lonsdorf, O. Olsson. 2019. Flowering resources distract pollinators from crops: Model predictions from landscape simulations. Journal of Applied Ecology 56:618-628. Oksanen J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs, H. Wagner. 2016. Vegan: Community ecology package. R Package version 2.4-1. Parker A. J., J. L. Tran, J. L. Ison, J. D. K. Bai, A. E. Weis, J. D. Thomson. 2015. Pollen packing affects the function of pollen on corbiculate bees but not non-corbiculate bees. Arthropod-Plant Interactions 9:197-203. Persson A. S., H. G. Smith. 2013. Seasonal persistence of bumblebee populations is affected by landscape context. Agriculture, Ecosystems and Environment 165:201-209. Portman Z. M., M. C. Orr, T. Griswold. 2019. A review and updated classification of pollen gathering behavior in bees (Hymenoptera, Apoidea). Journal of Hymenoptera Research 71:171-208.

Potts S. G., V. Imperatriz-Fonseca, H. T. Ngo, M. A. Aizen, J. C. Biesmeijer, T. D. Breeze, L. V. Dicks, L. A. Garibaldi, R. Hill, J. Settele, A. J. Vanbergen. 2016. Safeguarding pollinators and their values to human well-being. Nature 540:220-229. Pyšek P., V. Jarošík, P. Hulme, J. Pergl, M. Hejda, U. Schaffner, M. Vilá. 2012. A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment. Global Change Biology 18:1725-1737. R Core Team. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Richards M. H., A. Rutgers-Kelly, J. Gibbs, J. L. Vickruck, S. M. Rehan, C. S. Sheffield. 2011. Bee diversity in naturalizing patches of Carolinian grasslands in southern Ontario, Canada. Canadian Entomology 143:279-299. Richardson D. M., N. Allsopp, C. M. D’Antonio, S. J. Milton, M. Rejmánek. 2000. Plant invasions –the role of mutualisms. Biological Reviews 75:65-93. Richardson D. M., M. Rejmánek. 2011. Trees and shrubs as invasive alien species – a global review. Diversity and Distributions 17:788-809. Sardiñas H. S., K. Tom, L. C. Ponisio, A. Rominger, C. Kremen. 2016. Sunflower (Helianthus annuus) pollination in California’s Central Valley is limited by native bee nest site location. Ecological Applications 26(2):438-447. Seebens H., T. M. Blackburn, E. E. Dyer, P. Genovesi, P. E. Hulme, J. M. Jeschke, S. Pagad. P Pyšek, M. van Kleunen, M. Winter, M. Ansong, M. Arianoutsou, S. Bacher, B. Blasius, E. G. Brokerhoff, G. Brundu, C. Capinha, C. E. Causton, L. Celesti-Grapow, W. Dawson, S. Dullinger, E. P. Economo, N. Fuentes, B. Guénard, H. Jäger, J. Kartesz, M. Kenis, I. Kühn, B. Lenzner, A. M. Liebhold, A. Mosena, D. Moser, W. Nentwig, M. Nishino, D. Pearman, J. Pergl, R. Scalera, S. Schindler, K. Štajerová, B. Tokarska-Guzik, K. Walker, D. F. Ward, T. Yamanaka, F. Essl. 2018. Global rise in emerging alien species results from increased accessibility of new source pools. Proceedings of the National Academy of Sciences 115(10):E22640-E2273. Shackleton R. T., C. M. Shackleton, C. A. Kull. 2019. The role of invasive alien species in shaping local livelihoods and human well-being: A review. Journal of Environmental Management 229:145-157. Smith A. A., M. Bentley, H. L. Reynolds. 2013. Wild bees visiting cucumber on Midwestern U.S. organic farms benefit from near-farm semi-natural areas. Journal of Economic Entomology 106(1):97-106. Tilman D., J. Fargione, B. Wolff, C. D’Antonio, A. Dobson, R. Howarth, D. Schindler, W. H. Schlesinger, D. Simberloff, D. Swackhamer. 2001. Forecasting agriculturally driven global environmental change. Science 292:281-284. Traveset A., D. M. Richardson. 2006. Biological invasions as disruptors of plant reproductive mutualisms. TRENDS in Ecology and Evolution 21(4):208-216. Tscharntke T., A. M. Klein, A. Kruess, I. Steffan-Dewenter, C. Thies. 2005. Landscape perspectives on agricultural intensification and biodiversity – ecosystem service management. Ecology Letters 8:857-874. Tuell J. K., J. A. Ascher, R. Isaacs. 2009. Wild bees (Hymenoptera: Apoidea: Anthophila) of the Michigan highbush blueberry agroecosystem. Annals of the American Entomological Society of America 102(2):275-287.

Vanbergen A. J., A. Espíndola, M. A. Aizen. 2018. Risks to pollinators and pollination from invasive alien species. Nature Ecology & Evolution 2:16-25. Vance J. T., S. P. Roberts. 2014. The effects of artificial wing wear on the flight capacity of the honey bee Apis mellifera. Journal of Insect Physiology 65:27-36. Vilá M., I. Bartomeus, A. C. Dietzsch, T. Petanidou, I. Steffan-Dewenter, J. C. Stout, T. Tscheulin. 2009. Invasive plant integration into native plant-pollinator networks across Europe. Proceedings of the Royal Society B 276:3887-3893. Vilá M., J. L. Espinar, M. Hejda, P. E. Hulme, V. Jarošík, J. L. Maron, J. Pergl, U. Schaffner, Y. Sun, P. Pyšek. 2011. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecology Letters 14:702-708. Villéger S., N. W. H. Mason, D. Mouillot. 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89:2290-2301. Westphal C., I. Steffan-Dewenter, T. Tscharntke. 2003. Mass flowering crops enhance pollinator densities at a landscape scale. Ecology Letters 6:961-965. Whitaker T. W., G. N. Davis. 1962. Cucurbits. In: Polunin, N. (Ed.), Word Crops Books. Interscience, New York, pp. 157-159. Whitney G. G. 1984. The reproductive biology of raspberries and plant-pollinator community structure. American Journal of Botany 71(7):887-894. Williams I. H., J. R. Simpkins, A. P. Martin. 1991. Effect of insect pollination on seed production in linseed (Linum-usitatissimum). Journal of Agricultural Science 117(1):75- 79. Williams N. M., D. Cariveau, R. Winfree, C. Kremen. 2011. Bees in disturbed habitats use, but do not prefer, alien plants. Basic and Applied Ecology 12:332-341. Winfree R., R. Aguilar, D. P. Vázquez, G. LeBuhn, M. A. Aizen. 2009. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90(8):2068-2076. Woodcock B. A., M. P. D. Garratt, G. D. Powney, R. F. Shaw, J. L. Osborne, J. Soroka, S. A. M. Lindström, D. Stanley, P. Ouvrard, M. E. Edwards, F. Jauker, M. E. McCracken, Y. Zou, S. G. Potts, M. Rundlöf, J. A. Noriega, A. Greenop, H. G. Smith, R. Bommarco, W. van der Werf, J. C. Stout, I. Steffan-Dewenter, L. Morandin, J. M. Bullock, R. F. Pywell. 2019. Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield. Nature Communications 10:1481. Zurbuchen A., L. Landert, J. Klaiber, A. Müller, S. Hein, S. Dorn. 2010. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biological Conservation 143:669-676.

Table 1: Log-response ratios of the probability of presence and absence for each species (with 95% CI) that demonstrated a difference in abundance between treatments within distances and years. Ratios were calculated as the natural log of the species abundance in control plots subtracted from that in flower-removal plots; a positive mean indicates increased bee abundance in flower removal plots relative to controls; a negative mean indicates a decrease.

Year Distance (m) Species Mean Lower 95% Upper 95%

2013 20 Lasioglossum cattellae 0.42 0.08 0.75 2013 20 Andrena nasonii -0.39 -0.71 -0.08 2013 20 Hoplitis producta -0.42 -0.75 -0.08 2013 20 Andrena cressonii -0.44 -0.85 -0.03 2013 20 Apis mellifera -0.94 -1.41 -0.46 2013 50 Andrena nasonii 0.64 0.12 1.17 2013 50 Lasioglossum hitchensi 0.54 0.21 0.88 2013 50 Apis mellifera -0.50 -0.92 -0.07 2013 100 Andrena vicina -0.28 -0.53 -0.02 2013 200 Andrena perplexa -0.50 -0.92 -0.07 2014 0 Andrena nasonii 0.98 0.74 1.22

Table 2: Pollinator visitation of sentinel plants in response to flower removal of honeysuckle shrubs at forest edges, distance from forest edges, and microclimate factors. Competing model coefficients (± 1 SE) of predictor variables in general linear models with pollinator visitation expressed as binomial responses. Each competing model is represented by a row for the occurrence of a pollinator visit from bees (above) and all other taxa (below). All predictors are log-transformed. Wt = cumulative weight of competing models.

Intercept Wind (m/s) Floral (n) Distance (m) Sun (%) Time (min) Treatment Floral:Wind Sun:Wind df ∆AICc Wt

-1.24 ± 0.22 -0.63 ± 0.30 0.41 ± 0.26 na na na -0.85 ± 0.35 na na 4 0.00 0.19

-1.21 ± 0.21 -0.61 ± 0.29 na na na na -0.88 ± 0.35 na na 3 0.46 0.35

-1.24 ± 0.22 -0.62 ± 0.30 0.42 ± 0.26 na -0.20 ± 0.17 na -0.88 ± 0.35 na na 5 0.81 0.47

-1.25 ± 0.22 -0.64 ± 0.30 0.49 ± 0.28 na na na -0.88 ± 0.35 0.43 ± 0.48 na 5 1.29 0.57

-1.21 ± 0.21 -0.59 ± 0.29 na na -0.20 ± 0.17 na -0.90 ± 0.35 na na 4 1.33 0.67 Bees -1.25 ± 0.22 -0.60 ± 0.30 0.40 ± 0.26 -0.15 ± 0.19 na na -0.85 ± 0.35 na na 5 1.44 0.77

-1.26 ± 0.22 -0.62 ± 0.30 0.52 ± 0.28 na -0.24 ± 0.17 na -0.91 ± 0.35 0.54 ± 0.49 na 6 1.69 0.85

-1.22 ± 0.21 -0.57 ± 0.29 na -0.17 ± 0.19 na na -0.87 ± 0.35 na na 4 1.80 0.93

-1.26 ± 0.22 -0.65 ± 0.30 0.46 ± 0.27 na -0.25 ± 0.18 na -0.86 ± 0.35 na -0.30 ± 0.30 6 1.94 1.00

-1.52 ± 0.16 -0.48 ± 0.16 na na na na na na na 2 0.00 0.31

-1.49 ± 0.16 na na na na na na na na 1 0.96 0.50

-1.52 ± 0.16 -0.46 ± 0.16 na -0.10 ± 0.19 na na na na na 3 1.75 0.63

-1.52 ± 0.16 -0.48 ± 0.28 na na -0.09 ± 0.17 na na na na 4 1.79 0.76 Not Bees -1.52 ± 0.16 -0.50 ± 0.28 na na na 0.12 ± 0.25 na na na 3 1.79 0.88

-1.52 ± 0.16 -0.49 ± 0.28 0.06 ± 0.24 na na na na na na 3 1.98 1.00

Figure 1: Fitted best models demonstrating the relationship between distance from forest edge with honeysuckle shrubs (x-axis), and honeysuckle floral-removal treatment. Bee abundance and richness are from pan trap samples in 2013 (a, b) and 2014 (c, d), respectively. Circles and solid lines represent control plots while triangles and dashed lines represent flower removal plots. Raw values are grey points and means are black points.

Figure 2: Multivariate associations between species functional traits and environmental factors weighted by the bee community on RLQ axes (co-inertia analysis) for 2013 (left) and 2014 (right). Points and lines represent relative positioning of functional traits (dashed lines; triangles) and environmental factors (solid lines; black circles). Species scores are plotted (circles). 55

Figure 3: Predicted values and 95% CI of expected occurrence of a bee visitation to cucumber flowers during an observation (y-axis) in response to the number of open flowers on sentinel plants (x-axis) between control (solid line) and honeysuckle flower removal (dashed line) plots. Shifts in responses are demonstrated under conditions of no wind (left) and maximum observed wind (right). Grey shapes are jittered. Solid circles represent plants associated with control plots and triangles plants with floral removal plots.

Figure 4: Relationships between sentinel plant pollination, flower-removal treatments, and distance from forest edge. Confidence intervals (vertical bars) are for mean (black shapes) number of seeds per plant (a & e), seeds per fruit (b & f), fruit (c & g), and production measured as fruit volume (d & h). Lines represent fitted values of the best model between treatments (circles and solid lines = control; triangles and dashed lines = flower removal) with distance from the forest edge in 2013 (a-d) and 2014 (e-h). If floral removal is not in best model, mean relationships of treatments are represented by a solid line.

Supplementary Table 1: Sampled bees with mean functional trait values considered in analyses. Total abundance of each species is provided followed by number of females in parentheses. Missing body parts excluded 1-9 females of a species in functional trait analyses (^). Males and unknown species were not included in analyses (^^), although bidentate Nomada morphospecies were assumed specialized foragers. Some traits were suggested in literature but not empirically demonstrated (†). Known to forage on Lonicera spp (*). Species inferred to heavily use L. maackii floral resources are highlighted.

Species Total (F) Sociality Nest Host f Body Body IT FW FW:Body IT:Body IT:FW

Abundance Hair (%) (mm) (mm) (mm)

gAgapostemon sericeus 5 (5) Solitary Soil Poly 25-50 9.1 1.7 6.5 0.71 0.19 0.26

gAgapostemon texanus 1 (1) Solitary Soil Poly 25-50 10.0 2.1 7.1 0.71 0.21 0.30

aAgapostemon virescens 76 (76) Solitary Soil Poly 25-50 11.3 2.4 8.2 0.73 0.22 0.30

qAndrena arabis 1 (1) Solitary Soil Poly 25-50 9.8 2.1 7.5 0.77 0.21 0.28

qAndrena carlini 2 (2) Solitary Soil Poly 75-100 12.2 3.1 9.2 0.75 0.25 0.33

qAndrena commoda 11 (11) Solitary Soil Poly 50-75 11.1 2.7 9.4 0.85 0.24 0.28

qAndrena cressonii 68 (67)^ Solitary Soil Poly 25-50 10.0 2.3 7.4 0.74 0.23 0.31

qAndrena erigeniae 1 (1) Solitary Soil Oligo 25-50 8.2 2.1 7.0 0.85 0.26 0.30

qAndrena forbesii 3 (3) Solitary Soil Poly 25-50 10.0 2.3 7.6 0.76 0.23 0.31

qAndrena geranii 1 (1) Solitary Soil Oligo 25-50 9.2 2.3 6.2 0.67 0.25 0.37

qAndrena hippotes 1 (1) Solitary Soil Poly * 25-50 8.7 2.0 7.6 0.87 0.23 0.26

qAndrena illini 1 (1) Solitary Soil Oligo 25-50 13.7 3.3 12.6 0.92 0.24 0.26

qAndrena imitatrix 9 (9) Solitary Soil Poly 25-50 9.3 2.2 6.7 0.73 0.24 0.33 qAndrena melanochroa 14 (14) Solitary Soil Oligo 25-50 6.2 1.3 4.7 0.76 0.21 0.27 qAndrena miranda 1 (1) Solitary Soil Poly 25-50 9.4 1.9 7.1 0.76 0.20 0.27 qAndrena morrisonella 11 (11) Solitary Soil Oligo 25-50 8.8 2.2 6.6 0.75 0.25 0.34 qAndrena nasonii 255 (247)^ Solitary Soil Poly* 50-75 8.1 1.9 5.9 0.74 0.23 0.33 qAndrena perplexa 32 (32)^ Solitary Soil Oligo 50-75 11.8 2.8 9.5 0.80 0.24 0.29 qAndrena personata 24 (24) Solitary Soil Oligo 0-25 6.3 1.2 4.7 0.74 0.19 0.26 qAndrena phaceliae 12 (12) Solitary Soil Oligo 25-50 8.7 1.9 6.3 0.73 0.22 0.31 qAndrena pruni 1 (1) Solitary Soil Oligo 50-75 9.4 2.5 9.0 0.96 0.27 0.28 qAndrena robertsonii 1 (1) Solitary Soil Poly 0-25 8.8 1.6 5.4 0.61 0.18 0.30 qAndrena rugosa 1 (1) Solitary Soil Poly 0-25 11.2 2.2 7.0 0.63 0.20 0.31 qAndrena vicina 56 (56)^ Solitary Soil Poly * 50-75 12.6 3.0 9.9 0.79 0.24 0.31 qAndrena violae 2 (2) Solitary Soil Oligo 25-50 9.45 2.3 6.5 0.69 0.24 0.35 qAndrena wilkella 9 (5) Solitary Soil Poly 50-75 9.9 2.5 7.6 0.79 0.26 0.32

Apis mellifera 37 (37) Social na Poly * 50-75 11.8 3.3 9.1 0.78 0.29 0.36 aaAugochlora pura 9 (9)^ Solitary Wood Poly 0-25 8.2 1.6 5.7 0.70 0.20 0.29 rAugochlorella aurata 30 (30) Social Soil Poly * 25-50 7.5 1.7 4.7 0.63 0.23 0.41 kAugochloropsis metallica 4 (4) Social Soil Poly 25-50 9.8 2.0 6.5 0.67 0.21 0.31

eBombus bimaculatus 5 (4) Social Soil Poly * 75-100 14.1 4.6 11.8 0.85 0.32 0.38 eBombus griseocolis 3 (3) Social Soil Poly * 75-100 15.5 5.4 14.8 0.96 0.36 0.37 eBombus impatiens 2 (2) Social Soil Poly 75-100 17.0 5.1 13.5 0.80 0.31 0.39 eBombus pensylvanicus 1 (1) Social Soil Poly 75-100 25.7 6 20.1 0.78 0.23 0.30 xCalliopsis andreniformis 13 (3) Solitary Soil Poly 25-50 6.3 1.5 4.1 0.65 0.24 0.38 mCeratina calcarata 25 (23)^ Solitary Wood Poly * 0-25 7.0 1.5 4.7 0.68 0.21 0.31 pCeratina dupla 19 (16)^ Solitary Wood Poly * 0-25 6.3 1.4 4.3 0.68 0.23 0.34

Ceratina mikmaqi 9 (7) Solitary† Wood Poly 0-25 6.8 1.4 4.7 0.69 0.21 0.30 mCeratina strenua 30 (25)^ Solitary Wood Poly 0-25 5.7 1.1 3.9 0.69 0.20 0.29

Eucera belfragei 5 (5) Solitary Soil Na 75-100 13.3 3.4 9.0 0.68 0.26 0.38

Eucera dubitata 21 (20) Solitary Soil Oligo 75-100 13.0 3.3 8.8 0.68 0.25 0.37 pEucera hamata 19 (7) Solitary Soil Poly l 75-100 14.4 4.0 10.9 0.76 0.28 0.36

Eucera rosae 1 (1) Solitary Soil Oligo 75-100 15.0 3.5 12.8 0.85 0.23 0.27 uHalictus confusus 7 (7) Social Soil Poly 0-25 7.2 1.5 5.0 0.69 0.21 0.30 tHalictus ligatus 5 (5) Social Soil Poly 25-50 8.4 1.9 5.8 0.70 0.23 0.33 bHalictus parallelus 4 (4) Social Soil Oligo 25-50 13.2 2.7 9.5 0.72 0.20 0.29 zHalictus rubicundis 5 (5) Solitary Soil Poly 25-50 10.0 2.1 7.6 0.77 0.21 0.28 wHolcopasites calliopsidis 1 (0) Parasite Soil Oligo na na na na na na na

oHoplitis pilosifrons 5 (2) Solitary Wood Poly 0-25 8.8 1.8 5.2 0.59 0.21 0.35 nHoplitis producta 14 (4) Solitary Wood Poly 0-25 7.4 1.4 4.9 0.66 0.19 0.29 sHoplitis simplex 1 (0) Solitary Wood Oligo na na na na na na na vHylaeus mesillae 1 (0) Solitary Wood Poly na na na na na na na vHylaeus modestus 2 (2) Solitary Wood Poly 0-25 5.5 1.0 3.7 0.70 0.19 0.28 vLasioglossum admirandum 13 (13) Social† Soil Poly 25-50 6.0 1.4 4.0 0.67 0.23 0.34

Lasioglossum albipenne 1 (1) Social† Soil† Poly 25-50 6.3 1.5 3.7 0.70 0.19 0.28 vLasioglossum atwoodi 1 (1)^ Social† Soil Oligo 25-50 na na na na na na cLasioglossum bruneri 1 (1)^ Social Soil Poly * 25-50 na na na na na na

Lasioglossum callidum 1 (1) Social† Soil† Oligo 25-50 7.4 1.5 4.3 0.58 0.20 0.35

Lasioglossum carlinvillense 1 (1) Social† Soil† Na 25-50 4.8 1.0 3.0 0.63 0.21 0.33

Lasioglossum cattellae 8 (8) Social† Soil† Oligo 25-50 5.6 1.1 4.0 0.71 0.20 0.28 j, vLasioglossum cinctipes 1 (1) Social Soil Poly 25-50 7.4 1.5 5.7 0.77 0.20 0.26 vLasioglossum coeruleum 1 (1) Social Wood Poly 0-25 6.6 1.3 4.9 0.74 0.20 0.27

Lasioglossum coreopsis 1 (1) Social† Soil† Oligo 0-25 5.4 1.0 3.2 0.59 0.19 0.31 vLasioglossum coriaceum 14 (14) Solitary Soil Poly * 25-50 9.8 2.1 7.7 0.79 0.22 0.28 vLasioglossum cressonii 10 (10) Social† Wood Poly 50-75 6.5 1.4 4.5 0.70 0.21 0.30 vLasioglossum ellisiae 1 (1) Social† Soil Oligo 25-50 5.0 1.0 3.9 0.78 0.2 0.26

vLasioglossum ephialtum 1 (1) Social† Soil Poly * 25-50 6.3 1.2 4.7 0.75 0.19 0.26 cLasioglossum fuscipenne 1 (1) Solitary Soil Oligo 25-50 9.6 2.1 7.3 0.76 0.22 0.29 vLasioglossum hitchensi 594 (591)^ Social† Soil Poly * 25-50 5.6 1.2 3.7 0.67 0.21 0.32 vLasioglossum imitatum 5 (5) Social Soil Poly 0-25 4.2 0.9 3.1 0.74 0.20 0.28 vLasioglossum laevissimum 1 (1) Social Soil Poly 25-50 5.5 1.2 4.1 0.75 0.22 0.29 vLasioglossum nigroviride 1 (1) Social† Soil Oligo 0-25 7.2 1.8 5.2 0.73 0.25 0.34

Lasioglossum nymphale 1 (1) Social† Soil† Oligo 25-50 4.6 0.9 2.5 0.54 0.20 0.36 cLasioglossum obscurum 1 (1) Social Soil Oligo 0-25 5.3 1.5 4.1 0.77 0.28 0.37

Lasioglossum paradmirandum 3 (3) Social† Soil† Oligo 25-50 5.6 1.1 3.8 0.69 0.20 0.29 jLasioglossum pectinatum 2 (2) Solitary† Soil† Oligo 0-25 8.4 1.7 5.6 0.69 0.20 0.30 jLasioglossum pectorale 1 (1) Solitary† Soil† Poly 0-25 6.0 1.6 4.3 0.72 0.27 0.37 iLasioglossum platyparium 2 (2) Parasite Soil† Oligo 0-25 5.4 1.3 3.1 0.57 0.23 0.41

Lasioglossum sp1 1 (1)^^ na na Na 25-50 5.5 1.0 3.0 0.55 0.18 0.33 cLasioglossum tegulare 8 (8) Social Soil Poly 25-50 4.9 0.9 3.8 0.69 0.19 0.28

Lasioglossum trigeminum 2 (2) Social† Soil† Oligo 25-50 5.2 1.1 3.7 0.71 0.21 0.30 jLasioglossum truncatum 3 (3) Social† Soil† Poly 25-50 9.2 1.8 6.7 0.73 0.19 0.27 vLasioglossum versatum 20 (20)^ Social Soil Poly 25-50 6.7 1.4 4.4 0.67 0.22 0.32 vLasioglossum zephyrum 2 (2) Social Soil Poly 25-50 5.3 1.3 4.3 0.80 0.25 0.31

vMegachile mendica 1 (1) Solitary Wood Poly 50-75 11.7 3.3 7.6 0.65 0.28 0.43 yNomada affabilis 1 (1) Parasitic Soil Oligo 0-25 11.4 2.3 8.2 0.72 0.20 0.28 yNomada articulata 5 (3) Parasitic Soil Poly 0-25 8.6 1.6 6.1 0.72 0.18 0.26 yNomada australis 1 (0) Parasitic Soil Oligo na na na na na na na yNomada bethunei 1 (1) Parasitic Soil Oligo 0-25 9.5 2.1 7.8 0.82 0.22 0.27 f, yNomada bidentate 1 1 (1) Parasitic Soil Na 0-25 6.7 1.4 4.6 0.69 0.21 0.30 f, yNomada bidentate 2 2 (2) Parasitic Soil Na 0-25 7.6 1.9 6.0 0.79 0.24 0.31 f, yNomada bidentate 3 64 (64)^ Parasitic Soil Na 0-25 7.8 1.6 5.4 0.70 0.21 0.30 yNomada composita 1 (1) Parasitic Soil Oligo 0-25 8.0 1.8 5.8 0.73 0.23 0.31 yNomada depressa 3 (3) Parasitic Soil Oligo 0-25 6.4 1.3 4.4 0.70 0.21 0.30 yNomada illinoensis 2 (1) Parasitic Soil Oligo 0-25 7.4 1.1 5.4 0.73 0.15 0.20 yNomada imbricate 2 (2) Parasitic Soil Oligo 0-25 9.9 2.0 7.5 0.76 0.20 0.27 yNomada obliterate 1 (1) Parasitic Soil Oligo 0-25 9.8 2.2 7.7 0.79 0.22 0.29 yNomada parva 2 (2) Parasitic Soil Oligo 0-25 5.8 1.1 4.0 0.69 0.18 0.27 d,qOsmia atriventris 1 (1) Solitary Wood Poly 25-50 7.9 2.1 5.9 0.75 0.27 0.36 d,qOsmia bucephala 5 (5) Solitary Wood Poly * 75-100 13.7 3.8 9.8 0.72 0.27 0.38 h,qOsmia collinsiae 1 (1) Solitary† Wood† Oligo 25-50 9.9 2.7 6.7 0.68 0.27 0.40 qOsmia cornifrons 1 (1) Solitary Wood Oligo 50-75 11.3 3.2 7.8 0.69 0.28 0.41

d,qOsmia georgica 4 (4) Solitary Wood Oligo 25-50 8.2 2.3 6.1 0.75 0.28 0.37

d,qOsmia pumila 21 (21) Solitary Wood Poly * 25-50 8.2 2.0 5.6 0.68 0.25 0.36

cSphecodes ranunculi 1 (1) Parasitic Soil Oligo 0-25 9.6 1.8 6.8 0.71 0.19 0.26

oStelis lateralis 1 (0) Parasitic Wood Oligo 0-25 na na na na na na

cXylocopa virginica 2 (2) Solitary Wood Poly * 50-75 21.6 6.2 18.3 0.85 0.29 0.34 a(Abrams & Eickwort 1980), b(Albert & Packer 2013), c(Bartomeus et al. 2013), d(Cane et al. 2007), e(Colla et al. 2011), f(Ascher & Pickering 2018), g(Eickwort 1981), h(Frohlich 1983), i(Gibbs 2010), j(Gibbs et al., 2013), k(Gibbs 2017), l(Krombein et al. 1979), m(Lawson et al. 2018), n(Medler 1961), o(Michener 1955), p(Miliczky 1985), q(Mitchell 1960 & 1962), r(Mueller 1996), s(Neff 2009), t(Packer & Knerer 1987), u(Richards et al. 2010), v(Richards et al., 2011), w(Rozen Jr 1967), x(Shinn 1967), y(Snelling 1986), z(Soucy 2002), aa(Stockhammer 1966)

Supplementary Table 1 References: Abrams J., G. C. Eickwort. 1981. Nest switching and guarding by the communal sweat bee Agapostemon virescens (Hymenoptera, Halictidae). Insectes Sociaux 28:105-116. Albert J. R, L. Packer. 2013. Nesting biology and phenology of a population of Halictus farinosus Smith (Hymenoptera, Halictidae) in northern Utah. Journal of Hymenoptera Research 32:55-73. Ascher J. S., J. Pickering. 2018. Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). http://www.discoverlife.org/mp/20q?guide=Apoidea_species. Accessed November 15, 2018. Bartomeus I., J. S. Ascher, J. Gibbs, B. N. Danforth, D. L. Wagner, S. M. Hedtke, R. Winfree. 2013. Historical changes in northeastern US bee pollinators related to shared ecological traits. Proceedings of the National Academy of Sciences of the United States of America 110(12):4656- 4660. Cane J. H., T. Griswold, F. D. Parker. 2007. Substrates and materials used for nesting by North American Osmia bees (Hymenoptera: Apiformes: Megachilidae). Annals of the Entomological Society of America 100(3):350-358. Colla S., L. Richardson, P. Williams. 2011. Bumble bees of the Eastern United States. United States Department of Agriculture & Pollinator Partnership. FS-972. Eickwort G. C. 1981. Aspects of the nesting biology of five Nearctic species of Agapostemon (Hymenoptera: Halictidae). Journal of the Kansas Entomological Society 54(2):337-351. Frohlich D. R. 1983. On the nesting biology of Osmia (Chenosmia) bruneri (Hymenoptera: Megachilidae). Journal of the Kansas Entomological Society 56(2):123-130. Gibbs J. 2010. Revision of the metallic species of Lasioglossum (Dialictus) in Canada (Hymenoptera, Halictidae, Halictini). Zootaxa 2591:1-382. Gibbs J., L. Packer, S. Dumesh, B. N. Danforth. 2013. Revision and reclassification of Lasioglossum (Evylaeus), L. (Hemihalictus) and L. (Sphecodogastra) in eastern North America (Hymenoptera: Apoidea: Halictidae). Zootaxa 3672:1-117. Gibbs J. 2017. Notes on the nests of Augochloropsis metallica fulgida and Megachile mucida in central Michigan (Hymenoptera: Halictidae, Megachilidae). Zootaxa 4352:1-160. Krombein K. V., P. D. Hurd Jr., D. R. Smith, B. D. Burks. 1979. Catalog of Hymenoptera in America north of Mexico. Washington, Smithsonian Institution Press. Vol. 2: p 2128. Lawson S. P., W. A. Shell, S. S. Lombard, S. M. Rehan. 2018. Climatic variation across a latitudinal gradient affect phenology and group size, but not social complexity in small carpenter bees. Insectes Sociaux 65:483-492. Medler J. T. 1961. A note on Hoplitis producta (Cress.) in Wisconsin (Hymenoptera: Megachilidae). The Canadian Entomologist 93(7):571-573. Michener C. D. 1955. Some biological observations on Hoplitis pilosifrons and Stelis lateralis (Hymenoptera, Megachilidae). Journal of the Kansas Entomological Society 28(3):81-87. Miliczky E. R. 1985. Observations on the nesting biology of Tetranolia hamata Bradley with a description of its mature larva (Hymenoptera: Anthophoridae). Journal of the Kansas Entomological Society 58(4):686-700. Mitchell T. B. 1960 & 1962. Bees of the Eastern United States. Volumes I & II. Technical bulletin (North Carolina Agricultural Experiment Station). Mueller U. G. 1996. Life history and social evolution of the primitively eusocial bee Augochlorella striata (Hymenoptera: Halictidae). Journal of the Kansas Entomological Society 69(4):116-138. Neff J. L. 2009. The biology of Hoplitis (Robertsonella) simplex (Cresson), with a synopsis of the subgenus Robertsonella Titus. Journal of Hymenoptera Research 18(2):151-166. Packer L., G. Knerer. 1987. The biology of a subtropical population of Halictus ligatus Say (Hymenoptera; Halictidae). The transition between annual and continuously brooded colony cycles. Journal of the Kansas Entomological Society 60(4):510-516.

Richards M. H., J. L. Vickruck, S. M. Rehan. 2010. Colony social organsiation of Halictus confusus in Southern Ontario, with comments on sociality in the subgenus H. (Seladonia). Journal of Hymenoptera Research 19(1):144-158. Richards M. H., A. Rutgers-Kelly, J. Gibbs, J. L. Vickruck, S. M. Rehan, C. S. Sheffield. 2011. Bee diversity in naturalizing patches of Carolinian grasslands in southern Ontario, Canada. The Canadian Entomologist 143:279-299. Rozen Jr J. G. 1967. Review of the biology of panurgine bees, with observations on North American forms (Hymenoptera, Andrenidae). American Museum Novitates. American Museum of Natural History, New York, New York. Issue 2297. Shinn A. F. 1967. A revision of the bee genus Calliopsis and the biology and ecology of C. andreniformis (Hymenoptera, Andrenidae). The University of Kansas Science Bulletin 21:753-936. Snelling R. R. 1986. Contributions toward a revision of the new world nomadine bees. A partitioning of the genus Nomada (Hymenoptera: Anthophoridae). Contributions in Science 376:1-32. Soucy S. L. 2002. Nesting biology and socially polymorphic behavior of the sweat bee Halictus rubicundus (Hymenoptera: Halictidae). Annals of the Entomological Society of America 95(1):57-65. Stockhammer K. A. 1966. Nesting habits and life cycle of a sweat bee, Augochlora pura (Hymenoptera: Halictidae). Journal of the Kansas Entomological Society 39:157-192.

Supplementary Table 2: Bee species richness and abundance in response to flower removal treatments and distance from forest edges. Coefficients (± 1 SE) of predictor variables honeysuckle flower removal treatment (Tx) and distance into the crop field (Distance) in the best-fitting and competing models in 2013 (above) and 2014 (below).

Response Intercept Tx Distance df ∆AICc Abundance 3.55 ± 0.51 Na -0.20 ± 0.06 4 0.00

3.51 ± 0.51 0.10 ± 0.10 -0.20 ± 0.06 5 1.66

2013 Richness 2.88 ± 0.34 Na -0.21 ± 0.06 3 0.00 2.84 ± 0.34 0.09 ± 0.10 -0.21 ± 0.06 4 1.63

Abundance 2.53 ± 0.36 0.25 ± 0.29 -0.12 ± 0.04 6 0.00

2014 Richness 1.97 ± 0.25 0.17 ± 0.28 -0.11 ± 0.03 6 0.00

Supplementary Table 3: Bee functional diversity in response to flower removal treatments and distance from forest edges. Coefficients (± 1 SE) of competing models explaining functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv) of the bee community in 2013 (above) and 2014 (below).

Response Intercept Tx log(Distance) Tx:log(Distance) df ∆AICc FRic 0.73 ± 0.29 0.70 ± 0.26 -0.08 ± 0.06 -0.15 ± 0.06 8 0.00 1.09 ± 0.27 na -0.15 ± 0.05 na 6 0.53

FEve 0.77 ± 0.01 na na na 3 0.00

0.79 ± 0.02 -0.03 ± 0.02 na na 4 1.36 2013 0.71 ± 0.06 na 0.01 ± 0.01 na 4 1.74 FDiv 0.80 ± 0.03 0.05 ± 0.02 na na 4 0.00 0.83 ± 0.02 na na na 3 1.00 FRic 0.36 ± 0.09 0.06 ± 0.11 -0.03 ± 0.02 na 6 0.00

0.26 ± 0.08 0.06 ± 0.11 na na 5 0.95

FEve 0.84 ± 0.03 na na na 3 0.00 2014 0.80 ± 0.04 na 0.01 ± 0.01 na 4 1.00 FDiv 0.69 ± 0.05 na 0.03 ± 0.01 na 6 0.00

Supplementary Table 4: Sentinel plant pollination success in response to flower removal treatments and distance from forest edges. Best-fitting model coefficients (± 1 SE) for each response in 2013 (above) and 2014 (below). Distance is log-transformed. Flower removal treatment = “Tx”.

Response Intercept Tx Distance Tx:Distance df ∆AICc Total Seeds 4.30 ± 0.53 1.66 ± 0.64 0.31 ± 0.11 -0.38 ± 0.15 6 0.00 5.65 ± 0.20 na na na 3 1.63 Seeds/Fruit 3.06 ± 0.23 -0.06 ± 0.14 0.15 ± 0.05 na 7 0.00 Total Fruit 1.40 ± 0.09 na na na 2 0.00

1.57 ± 0.28 na -0.04 ± 0.06 na 3 1.80

2013 1.40 ± 0.09 na na na 3 1.95 Fruit Volume 8.92 ± 0.12 na na na 3 0.00 9.21 ± 0.27 na -0.07 ± 0.06 na 4 0.81 8.81 ± 0.36 0.81 ± 0.49 0.04 ± 0.08 -0.21 ± 0.11 6 1.38 9.00 ± 0.13 -0.08 ± 0.10 na na 4 1.56 Total Seeds 6.11 ± 0.39 na -0.11 ± 0.06 na 4 0.00 5.63 ± 0.32 na na na 3 1.74 Seeds/Fruit 3.72 ± 0.23 na -0.05 ± 0.03 na 5 0.00

3.50 ± 0.19 na na na 4 0.51

Total Fruit 1.58 ± 0.09 na na na 2 0.00 2014 1.79 ± 0.26 na -0.05 ± 0.06 na 3 1.40 Fruit Volume 9.55 ± 0.16 0.10 ± 0.05 -0.07 ± 0.03 na 5 0.00 9.60 ± 0.16 na -0.07 ± 0.03 na 4 0.94 9.46 ± 0.21 0.28 ± 0.27 -0.05 ± 0.04 -0.04 ± 0.06 6 1.90

Supplementary Table 5: Flower abundance estimations of L. maackii shrubs within five 5 x 5- m quadrats along three edges used in the study. Base circumference of every shrub > 1 cm in diameter was measured within quadrats (data unpublished). Formula (푒(훽0+훽1(푑푔푙) + 훽2(퐶푂푉))) extracted from Hassett and McGee (2017), where β0 is the intercept, β1 and β2 are coefficients for the basal diameter (dgl) of each stem and percent canopy cover (COV), respectively. Using parameters derived from stands in New York, USA, β0 = 3.49, β1 = 0.66, and β2 = not significant. Lonicera maackii on edges generally produce more flowers and berries than those in forest interiors (Barriball et al. 2014) likely due to their high sun exposure, so I also assumed β2 = 0. Mean basal diameter of all shrubs within the evaluated 125 m2 area is conservatively represented as the number of L. maackii flowers in 250 m2 of the 10 x 100-m plots in this study (1/2 the area).

Site Mean dgl (cm) Total Flowers Flowers Between Plots Flowers Removed Stems (75 m) (100 m) (5 x 25 m2)

1 9.24 127 1,853,438 5,560,314 7,413,752 2 8.59 153 1,453,959 4,361,877 5,815,836 3 10.76 233 9,273,167 27,819,501 37,092,668

Mean: 4,193,521 12,580,564 16,774,085

References Barriball K., K. Goodell, O. J. Rocha. 2014. Mating patterns and pollinator communities of the invasive shrub Lonicera maackii: a comparison between interior plants and edge plants. International Journal of Plant Science 175(8):946-954.

Hassett M. R., G. G. McGee. 2017. Negative binomial hurdle models to estimate flower production for native and nonnative Northeastern shrub taxa. Forest Science 63(6):577-585.

Supplementary Figure 1: Invasion of Amur honeysuckle (Lonicera maackii) along the forest-crop edge at one of the five study sites (left) and two common visitors of L. maackii flowers: large mining bee, Andrena sp (top right); the honeybee, Apis mellifera (bottom right).

Supplementary Figure 2: Constrained ordination using distance-based redundancy analysis (dbRDA) of bee species composition in flower-removal and control plots. Biplot arrow represents direction of increasing distance from forest edges with honeysuckle shrubs (significant predictor). Confidence ellipses (95%) are provided for honeysuckle floral removal (dashed) and control (solid) samples. Points represent samples taken at different distances (size of the point) from forest edges where honeysuckle shrubs were with (circle) or without (triangle) flowers. 72

Supplementary Figure 3: Fitted values with 95% CI of expected frequency of a visitation event by a potential non-bee pollinator to one of three cucumber flowers within a 10-minute observation period (y-axis) due to wind speed (x-axis). Jittered grey points represent raw occurrence values, were “1” represents at least one visitation event and “0” represents no visitor observed.

Chapter 2: Community responses of native bees to landscape composition depend on bee functional traits and seasonal floral resource availability

1 Abstract Context: Plant-pollinator interactions are changing due to anthropogenic modifications of natural habitats. Invasive plants along forest fragment edges in agricultural landscapes alter plant- pollinator relationships, yet their roles in the spatial dynamics of bee communities are unknown.

Objectives: Objective 1: Quantify roles of invasive flowering shrubs and the surrounding agricultural mosaic landscape in structuring seasonal dynamics of bee communities in forest fragments. Objective 2: Determine the scale-dependent and spatial-resolution responses of forest-edge bee communities to forest habitat and surrounding landscape features.

Methods: Bee and flowering plant communities were sampled along forest fragment edges in Indiana and Ohio, USA, April-November of 2015. Species richness, abundance and composition of two functionally distinct bee community components (those caught with pan versus vane traps) were analyzed over time in response to land cover composition and resource availability at different spatial scales.

Results: Bee abundance and species richness was greater at sites with more field-margin habitat and changed seasonally in response to the flowering tree community. Bees captured with pan traps responded to fine-grained land cover types within 0.5 km of sites, and bee abundance increased with greater field-margin width. Alternatively, landscape composition at coarse-grain resolution within 3.0 km of sites predicted bees captured with vane trap. Both components of the bee community responded positively to forest habitat and were seasonally affected by invasive shrub density and the native woody plant community.

Conclusions: Bee community responses to the landscape mosaic were dependent on bee body sizes and hence their foraging ability. Invasive shrubs may temporarily benefit bees, but weak

foragers were most negatively affected by isolation and degradation of high-quality habitat in this fragmented agroecosystem.

2 Introduction Modern agriculture is implicated in global pollinator declines due to intensive and expansive alterations to the landscape (Kearns et al. 1998; Montero-Castaño and Vilà 2012; Potts et al. 2016). Changes in land use remove natural habitat, resulting in specialist pollinator species isolated within fragments of high-quality habitat (Bartomeus et al. 2013; Hung et al. 2019), while generalists often persist under a wider range of land uses (Kleijn et al. 2015; Hung et al. 2019; Potts et al. 2016; Winfree et al. 2009). Frequent disturbances further lead to plant species invasions and, in turn, degraded plant-pollinator networks (Aizen et al. 2008; Burkle et al. 2013; Montero-Castaño and Vilà 2012; Vanbergen et al. 2018). These human-modified landscapes can still support or restore pollinator communities by enhancing connectivity between patches of preferred habitat (Morandin and Kremen 2013; Öckinger et al. 2019; Ponisio et al. 2019; Wagner et al. 2019) if the surrounding matrix is of adequate quality (Diekötter et al. 2007; Jauker et al. 2009; Mallinger et al. 2016; Senapathi et al. 2017; Winfree et al. 2009). The use of habitat patches by bees and other pollinators is therefore a function of available resources, patch isolation, pollinator mobility, and seasonal variation (Mallinger et al. 2016; Morandin and Kremen 2013; Öckinger et al. 2019). Thus, the response of pollinators to floral resource availability, including resources of alien origin, should depend on the matrix composition within the landscape (Crowl et al. 2008, Kallioniemi et al. 2017; Mallinger et al. 2016; Senapathi et al. 2017; Stout and Tiedeken 2017). Edge habitat increases with fragmentation of the landscape (Fahrig 2003), which can increase new plant-pollinator interactions. Weedy and invasive plant species are associated with disturbances along habitat edges, including agricultural field margins (Boutin and Jobin 1998, Simberloff et al. 1992). It follows that bees likely contact flowers of invasive species while moving through habitat edges and field margins (Kallioniemi et al. 2017). Alien floral resources are not a preferred food source for native bees, but invasive plants that grow in high densities with showy inflorescences are used by many pollinators (Kallioniemi et al. 2017; Vanbergen et al. 2018, Williams et al. 2011). Therefore, habitat edges are likely to be focal points for the incorporation of invasive plant species into plant-pollinator networks. The use of floral resources, including those of invasive plants, by bee communities in fragmented landscapes is not fully understood but is likely a product of the functional attributes of bee species and the configuration or composition of different land cover types in the landscape

(Coutinho et al 2018; Jauker et al. 2009; Ponisio et al. 2019; Stout and Tiedeken 2017). Bees are morphologically diverse and demonstrate a wide range of responses to landscape features (Coutinho et al. 2018; Ponisio et al. 2019). For instance, body size is correlated with foraging distance (Gathmann and Tscharntke 2002; Greenleaf et al. 2007), with smaller bees responding to surrounding landscape features at shorter distances from habitats than larger bees (Benjamin et al. 2014). Large-bodied bees may therefore be less restricted in their movements through areas of frequent disturbance within the landscape, such as intensively managed agricultural fields and human-made structures (Coutinho et al. 2018). Bee species richness and abundance strongly decline with distance from semi-natural habitats (Garibaldi et al. 2011; Jauker et al. 2009; Ricketts et al. 2008), so that patch isolation may further restrict bee foraging to resources within semi-natural habitats. Following the establishment of invasive plant species in pollination networks of habitat fragments (Aizen et al. 2008; Carvalheiro et al. 2014), environmental filtering favors generalist bee species with broad geographical ranges (Aizen 2008; Hung et al. 2019; Stout and Tiedeken 2017) and diets that include invasive plants phylogenetically related to natives (Carvalheiro et al. 2014). Since large bees may be less restricted to resources within the patch, it remains unclear if flowering invasive plants structure the bee community based on foraging behaviors and life history attributes of bees, or whether the compositional heterogeneity of land cover types in the surrounding landscape is a more important set of factors (Fahrig et al. 2011). I addressed this question by sampling forest-edge bee communities in forest fragments that varied in the density of a flowering shrub, Lonicera maackii (Amur honeysuckle). Lonicera maackii is a driver of changes in plant community composition and ecosystem processes in forest fragments, especially near forest edges (Haffey and Gorchov 2019; Hoven et al. 2017; McNeish and McEwan 2016). Fruits of L. maackii are consumed by avian dispersal vectors, particularly along woodlot edges (Bartuszevige and Gorchov 2006), where disturbances due to adjacent agriculture create favorable conditions for dense population growth of L. maackii. The high densities of this species within forests have further been demonstrated to alter pollination services provided by local bee communities (McKinney and Goodell 2010 & 2011), especially along forest edges (Barriball et al. 2014), and may attract bees from outside forest fragments (Cunningham-Minnick et al. in press). Therefore, L. maackii serves as an excellent study species

to understand spatial and temporal responses of the functional bee community to alien invasive floral resources. Specifically, I asked two study questions: 1) How are bee communities in forest-edge habitats influenced by the surrounding landscape characteristics, and at what spatial scales? 2) What role does an invasive flowering shrub play in structuring the seasonal responses of bee communities, and does this depend on the surrounding landscape composition? I hypothesized that bees would respond to landscape features at different spatial scales depending on their body sizes which relate to foraging ability. I predicted that bees with greater body size and foraging ability would respond to landscape features at broader scales than bees with small body size and lesser foraging ability. I further expected the abundance and species richness of all bees to respond positively to forest land cover types and negatively to agricultural land cover types of frequent use due to limited availability of nesting and floral resources. I also hypothesized that the effects of L. maackii on the abundance of weak and strong foraging bees would differ seasonally due to variation in the availability of resources within different spatial extents of the landscape mosaic. I predicted that greater L. maackii density would increase the abundance of bees with less foraging ability due to the dependence of smaller bees on resources at a more limited range of scales closer to forest edges. Since larger bees move more easily throughout the mosaic landscape and can access resources in other high-quality patches, I further expected that L. maackii would exhibit an effect of lower magnitude on strong foraging bees. To address these questions and hypotheses, I used two trapping methods that favor bees of different body sizes to represent two broad functional groups of foraging ability. I then evaluated the responses of each component of the bee community to local features of the forest edge, as well as land cover composition measured at fine and coarse resolution up to 3 km from the focal patch edge.

3 Materials and Methods 3.1 Site Selection My study sites were established on private lands in SE Indiana and SW Ohio during spring 2015. I selected 12 secondary-growth forest patches adjacent to corn or soybean monocultures (Figures 1a & 1b; Supplementary Table 1). Patches varied in size (5.6 – 27.5 ha) and ranged from 6.1 to 47.8 km apart. Within each forest patch, a 5-m wide belt transect was established that spanned 100 m of a forest-crop edge. I measured the total stem basal area of L. 78

maackii shrubs in five randomly chosen 5 x 5-m quadrats within the belt transect and extrapolated this total to the area of the transect. This allowed transects along patch edges to represent a gradient of L. maackii densities from 0.00 to 23.12 m2 ha-1 across the 12 patches (Supplementary Table 1). I verified the presence of L. maackii in all 12 forest patches to ensure that colonization had occurred in each patch.

3.2 Bee Community The bee community was sampled along the forest edge during April–November 2015. The order of site sampling was randomized and an effort was made to avoid sampling during rain events. Since pan traps and vane traps favor bees of different sizes (Gibbs et al. 2017; Hall 2018; Joshi 2015; Rhoades 2017; Richards et al. 2011), I sampled large bees using blue and yellow vane traps (SpringStar® Items #ZBVT and #ZYVT) and small bees with modified pan traps (0.59 L) that were painted fluorescent blue or yellow (Droege 2008), or left white. One blue vane trap was paired with one yellow vane trap and hung from a post 1.2 m off the ground at 25-m and 75-m distance points along each transect, for a total of four vane traps per site. Propylene glycol was placed within vane traps as a preservative. Vane traps continuously sampled bees and were reset every two weeks. Since yellow and blue vane traps also capture different bees (Hall 2018), bees in vane traps were pooled for each sampling period within a site. One pan trap was placed every 5 m along the forest edge and trap color alternated such that a total of seven pan traps of each color were used. Pan traps sampled the bee community for 24 hrs every two weeks. Samples from each interval were pooled within a site. During the flowering period of L. maackii (11th May-3rd June), sampling effort was doubled, where vane traps were collected and reset every week and pan traps were set out for 24 hrs every week at each site.

3.3 Local Floral Resource Availability To quantify available floral resources, stem counts of all flowering herbaceous forbs were conducted every 2-3 weeks in five 5 x 5-m quadrats randomly selected within each 5 x 100-m forest-edge transect, with the constraint that adjacent quadrats were not sampled during the same period. Flowering stems were recorded until the first freeze (5th October), resulting in 12 sample periods or 60 quadrat samples per site. All plants with inflorescences < 2 m off the ground were identified in the field to the lowest taxonomic level (genus, or species when possible), and later 79

pooled to represent different flower morphologies. Individual flowering shrubs (multi-stemmed) as well as the number of inflorescences on small trees and vines < 2 m off the ground were also recorded. Shrubs exhibit disproportionately large quantities of inflorescences (Hassett and McGee 2017) compared to individual herbaceous stems, but I counted individual shrubs since L. maackii was the dominant shrub species and its density was considered as a separate predictor variable. When more than 250 stems of a particular species were flowering within a quadrat, I recorded the species abundance for that quadrat as 250. Counts of all sexually mature tree species (i.e. entomophilous, anemophilous, etc.) within transects at each site were also recorded to account for potential floral resources in the higher strata of the canopy.

3.4 Landscape Features I calculated relative area of different landscape features in the agricultural matrix in 2015 to understand the resources available to the bee community during the sampling season. I used two different methods to acquire landscape composition at different resolutions (coarse and fine) and combined land cover types into categories based on their expected potential to provide nesting and floral resources to the bee community (e.g. Lonsdorf et al. 2009, Olsson et al. 2015). 3.4.1 Features of Course Resolution With ArcGIS Desktop (version 10.2.1), I calculated the relative area of land cover types within oval buffer zones defined by radii of 0.1, 0.5, 1.0, 2.0, or 3.0 km distances from the terminal points of the 100-m transect. Land cover types within the radii were based on the coarse resolution (30 x 30-m) of the Cropland Data Layer (CDL) for 2015 (USDA 2015; Figure 1). Land cover types were lumped into eight categories based on expected resource availability (Table 1): (1) corn: mostly intensively managed, (2) fallow: fallow land and crops used by pollinators, (3) flowerless: cereal crops (other than corn) and alfalfa (alfalfa is not generally grown for seed in the region and is harvested before flowering), (4) forest: secondary growth deciduous and mixed forests in varying stages of succession, (5) impervious: open water and areas with high concentrations of concrete and other impervious surfaces, (6) grassland: hay fields and cool-season grasses, (7) residential: land parcels associated with places of residence and businesses, and (8) soybean: a mass-flowering crop grown under intensive management practices.

3.4.2 Features of Fine Resolution Since the resolution of CDL data may be too coarse to detect smaller landscape features that are potentially important to bees, I also developed higher resolution land cover maps by delineating 2015 colored aerial photographs extracted from Google Earth Pro at an altitude of 305 m to reduce spatial grain to 0.15 m. Smaller buffer radii of 0.1, 0.5, and 1.0 km were used in the analysis of the high-resolution map. Strips, habitat < 10 m in width, that were located between two land cover types that did not resemble either cover type were classified as land cover types based on the land cover types they were adjacent to. Overall, cover classifications were of 11 categories: (1) cornNFC: corn and other non-flowering crops, (2) disturbed SNH: semi- natural habitat (SNH) with signs of heavy disturbance or in early succession (i.e. scrubland, shrublands), (3) drainage: vegetated agricultural drainage-ways and vegetated strips between adjacent crop fields, (4) forest: restricted to areas of dense deciduous and mixed tree growth with an unmanaged understory, (5) grassland: hay fields and cool-season grasses, (6) imperviousfine: impervious surfaces such as parking lots, roads, and structures, (7) lawn: grass associated with buildings, (8) soybean: soybean crop (9) stripF: strips of habitat between forest and any adjacent land cover type, often comprised of forbs and cool-seasoned grasses, (10) stripother: strips of habitat that were not between crop fields or adjacent to forest, and (11) water: open water (Figure 2). Since native prairie remnants and restored grasslands comprise little area in this region, grasslands in this study were differentiated from lawns by color, shape or proximity to buildings.

3.5 Analyses Since the sampling effort and protocol was different for pan and vane traps, I analyzed them separately in models with habitat and landscape variables. However, I compared the overall species composition of bees sampled by each trap type using a distance-based redundancy analysis on a Bray-Curtis dissimilarity matrix of species abundances normalized by trap type (to account for abundance differences by trap) with the dbrda() function in the vegan package (Oksanen et al., 2016) in R version 3.5.2 (R Core Team 2018). The vegan package offers a variety of methods for community analysis including dimension-reduction approaches. The sole predictor was trap type, and site was treated as a random effect; a p-value was derived from 1000 permutations of the Pseudo-F statistic. I also compared the body-size distributions of all bees

sampled with the two trapping protocols using nonmetric multidimensional scaling (NMDS) plots with the metaMDS() function in the vegan package. All other analyses were also performed in the R programming language. Generalized additive models were built using the gam() function in the mcgv (Wood 2016) package to determine how bee species richness and abundance were influenced by L. maackii densities, flower species abundance and composition, and surrounding land cover types at different spatial scales and levels of resolution. The mcgv package allows for smoothing parameter estimation of mixed effects models using poisson and negative binomial families. Total abundance, number of taxa, and evenness of flowering plants were predictors of the flowering plant community. Abundance of flowering stems provided a measure of floral resource availability, taxon richness of flowering plants represented availability of flower traits, and evenness represented the distribution of these flowering morphologies within the understory plant community. Further, to account for natural turnover of the bee community throughout the season (Figure 3), I applied penalized cubic regression splines to all predictors involving time (number of days into the study). This included tensor products (two-way interactions) which were allowed between time and any predictor except relative area of land cover classes. All continuous predictors were centered to reduce collinearity of predictor variables. After centering, any pair of predictors with a Pearson’s correlation coefficient > 0.70 were not included within the same model. Tree and flower abundance and richness predictors were natural log-transformed, along with the width of the field margin adjacent to forest edges. Since model selection using multiple regression splines is computationally costly (Wood 2017), I investigated all combinations of predictors from a global model over multiple system cores with the pdredge() function in the MuMIn package (Barton 2019). The MuMIn package provides options for evaluating models and performing model selection, but is unique in offering a user- parameterized function that systematically evaluates every combination of predictors that can be easily performed over multiple processing cores for faster output. Evaluation of AICc values (Mazzerole, 2016) was performed to find best and competing models (∆AICc ≤ 2) of a predefined subset of all possible models. For instance, only models with main effects and two- way interactions were considered, and models with more than eight terms were dropped from analyses. Interactions involving land cover types were not considered, and only one predictor describing the tree community and one predictor describing the flowering understory community

were permitted within a given model. Models with interactions, including smoothing interactions with time, were not considered without the linear main effect. Only results for best models (lowest AICc) and the top three competing models were considered. Since landscape composition within 0.5 km of a site was largely limited to agriculture and the forest patch, I evaluated the effect of landscape heterogeneity (Shannon-Weiner Index) only in models that included relative amounts of land cover types > 0.5 km of the sites.

4 Results 4.1 Communities I sampled a total of 17,900 bees (pan traps: 2,854; vane traps: 15,046) from forest edges between 21st April and 10th November that represented 191 taxonomic species and 26 morphological species (10 males; 16 females) of 29 genera from five families (Supplementary Table 2). All female morphotypes (40 individuals) and species were included within analyses, while male morphotypes (33 individuals) and individuals that could not be identified to species due to body damage, curation issues, or Andrena spp parasitized by Stylops (Jůzová et al. 2015) were omitted from all analyses. Therefore, 17,762 individuals (pan traps: 2,798; vane traps: 14,964) of 191 species and 16 morphs were analyzed. I recorded a total of 26,049 inflorescences and flowering stems and shrubs between 23rd April and 2nd October that represented 101 taxa, including four species of trees, three species of shrubs, seven genera of vines, four cane-producing species, sedges (collectively grouped), and more than 82 herbaceous species from 33 families that had different flower morphologies (Supplementary Table 3). The last set of observations prior to freezing temperatures occurred on 2nd October. In addition to floral counts, I recorded 1,369 trees of 40 species that were estimated to be of sexually mature size (Supplementary Table 4).

4.2 Bee Community Composition

Overall, the bee community composition varied by trap type (FPseudo = 11.50, df = 11, P < 0.001; Figure 4), which accounted for 30% of the variation in species composition. Differences in bee species composition across the 12 forest patches accounted for another 41% of the variation. Bees of vane traps were larger than those of pan traps (95% CIvane = 9.61 ± 0.05 (mm);

95% CIpan = 7.52 ± 0.09 (mm)). However, bees of vane traps exhibited a bimodal distribution in body length with peaks at 6–9 mm, which overlapped body sizes of bees in pan traps, as well as 12–15 mm (Figure 5). Despite being of the same size, the subset of sampled species 6–9 mm in length were often different between pan and vane traps. For instance, of the 126 male and female morphotypes (males and females are sexually dimorphic) with body lengths 6–9 mm, 34 were unique to pan traps and 29 were unique to vane traps (Supplementary Table 2). Compared to pan traps, vane traps sampled proportionally more individuals of species that use wood as a nesting substrate (vane: 47%, pan: 30%), and pan traps sampled more clepto-parasitic species than vane traps (vane: 0.5%, pan: 6.9%), all of which were ground-nesting species. Both trap types sampled bees of each family and included ground and wood-nesting species. Therefore, no obvious pattern explained why these smaller bees were sampled by vane traps or why bees 9–12 mm in body length were generally not sampled by either trap, other than vane traps did not only discriminate bees based on body size.

4.3 Habitat Patch and Landscape Composition Effects Abundance and species richness of both bee community components (pan and trap samples) were affected by changing characteristics of the forest edge as well as land cover types at different scales and resolution. Responses of bees in pan traps were best explained by landscape features ≤ 0.5 km from belt transects with fine-grain resolution, while best-fitting models explaining vane trap bee abundance and species richness were based on land cover types of coarse resolution at ≤ 3.0 km and ≤ 1.0 km, respectively (Supplementary Figure 1). 4.3.1 Responses of pan trap bees The best model predicting bee abundance of pan traps included predictors of the habitat patch and landscape composition, which together explained 51.2% of the null deviance (Supplementary Table 5). There was a negative response to species richness of trees within the belt transect (5 x 100 m) and a positive response to the width of the field margin adjacent to the transect. There was also an interaction between season and the effect of tree species richness on bee abundance where pan traps sampled more bees in sites with relatively fewer tree species early in the season. This pattern reversed later in the season and fewer bees were found in pan traps in sites with fewer tree species relative to sites with more tree species (Supplementary Figure 2a). The only effect of landscape composition in the best model for bee abundance was a

positive effect of the amount of forest within 0.5 km. Competing models were more complex but all included landscape composition within 0.5 km. The competing model with the lowest AICc showed that tree species richness had a negative effect that was mediated by the season, a positive effect of field margin width, a positive effect of the proportion of forest and a negative effect of the relative area of habitat strips adjacent to forests at 0.5 km (∆AICc = 0.22). The second and third competing models were similar, but additionally included a negative effect of open water (∆AICc = 0.61) or heavily disturbed semi-natural habitat (∆AICc = 0.69). Species richness of pan traps responded similarly as abundance to local and landscape variables within 0.5 km of the transect and explained 53.0% of the null deviance. Habitat characteristics included within the best model were a positive effect of the field margin width, a negative effect of tree abundance within the transect, and an interaction between season and tree abundance at the forest edge with changes in magnitude nearly identical to bee abundance responses to tree species richness during the season (Supplementary Table 5 and Supplementary Figures 2a & 2b). Landscape composition predictors of the best model explaining bee species richness in pan traps included positive effects of the relative amount of forest habitat as well as negative effects of lawn and drainage-ways within crop fields. Competing models only differed through substitution of the negative effects of lawn and drainage-ways in the best model. In order of lowest to highest ∆AICc, competing models included effects of the proportional area of the following land cover types in addition to a positive effect of the proportion of forest; a negative effect of grassland as well as a positive effect of strips of habitat that were not between crop fields or adjacent to forest (∆AICc = 1.08); a negative effect of lawn (∆AICc = 1.14); a negative effect of impervious surfaces as well as a positive effect of strips of habitat not between crop fields or adjacent to forest (∆AICc = 1.47). 4.3.2 Responses of vane trap bees The best model predicting bee abundance sampled with vane traps also included predictors of the habitat patch edge and landscape composition which explained 63.4% of the null deviance (Supplementary Table 5). Bee abundance responded negatively to the abundance of trees within the transect and the magnitude of this effect was mediated by the season (Supplementary Figure 2c). There was a pronounced increase in the number of bees sampled through spring and early summer at sites with fewer trees. This pattern reversed around July when sites with fewer trees exhibited a strong decrease in bees sampled, while sites with high

tree abundance had more bees after this time. Of landscape composition within 3 km of the belt transect, the proportion of forest was positively related to vane bee abundance while fallow land demonstrated negative effects in the best model. All competing models had negative effects of tree abundance that were mediated by the season, positive effects of the proportion of forest, and negative effects of fallow landscape composition. The competing model with the lowest ∆AICc showed the same habitat effects as the best model, but exhibited forest and fallow landscape composition effects at 2 km from the belt transect (∆AICc = 1.03). Also containing the same local effects as the best model, a second competing model suggested negative effects of the proportion of flowerless crops on bee abundance at 3 km in addition to negative effects of fallow land and positive effects of forest land cover types (∆AICc = 1.05). A third competing model had similar effects of forest and fallow land cover types at 3 km, but demonstrated a positive local effect of flowering stem abundance on vane trap bee abundance in addition to the effects of season and tree abundance (∆AICc = 1.21). Shannon-Weiner Index of landscape heterogeneity did not result in a better fit than proportion of forest in the best model (∆AICc = 22.32). Finally, the best model predicting bee species richness of vane traps explained 60.8% of the null deviance and included local habitat variables of season and tree abundance, as well as forest, fallow land, and impervious surface landscape composition (Supplementary Table 5). Similar to other responses, the best model predicting bee species richness of vane traps included a negative effect of tree abundance and an interaction between tree abundance and season where sites with fewer trees showed increased numbers of bee species relative to sites with more trees, and this pattern flipped during the summer months (Supplementary Figure 2d). The proportion of forest and impervious surfaces within 1 km of the transect demonstrated positive effects on bee species richness in the best model, while fallow landscape composition exhibited negative effects. The competing model with the lowest AICc had the same landscape composition effects within 1 km of the transect as the best model, but suggested that tree richness had a negative effect on bee species richness and was mediated by the season (AICc = 0.82). A second competing model for bee species richness of vane traps showed a negative response to tree abundance with an interaction of season, as well as a positive effect of flowering stem abundance within the transect as local predictors (AICc = 1.32). There were also landscape composition effects showing that the proportion of fallow land as well as soybean cropland within 3 km was negatively related to bee species richness. The final competing model included only one

landscape variable and suggested that the amount of flowerless cropland within 3 km of the transect was negatively affecting bee species richness (AICc = 1.41). However, there were many important predictor terms describing characteristics within the transect, including negative effects of tree abundance that varied in magnitude by season, as well as a negative effect of greater evenness of the flowering plant community within the transect, which also demonstrated an interaction with tree abundance such that the magnitude of the flowering evenness effect was greater when there were more trees. Shannon-Weiner Index of landscape heterogeneity did not result in a better fit than proportion of forest, fallow land, and impervious surface in explaining species richness of vane traps (∆AICc = 22.98).

4.4 Seasonal Effects and L. maackii Density The effect of seasonality was included in all competing models and was a highly 2 2 significant predictor in all best models (panabund: χ (4.8) = 93.8, P < 0.0001; panrich: χ (3.1) = 2 2 75.5, P < 0.0001; vaneabun: χ (5.2) = 174.9, P < 0.0001; vanerich: χ (6.2) = 153.2, P < 0.0001; Supplementary Table 5). The nonlinear relationships between time and bee responses accounted for many natural processes associated with life histories of populations within the bee communities (e.g. flight seasons, weather conditions, temperature thresholds), but also included variation in responses attributable to changes in resources of the surrounding environment that were not captured by other predictors within the model. It is possible that effects of L. maackii density were incorporated within the umbrella effect of season. Partial residual plots showing the effect of season in each of the best models demonstrated a strong deviation in the residuals from the fitted values during the flowering period of L. maackii (May 11–June 3) for bees of pan traps but not vane traps (Supplementary Figure 3). Partial residuals between 11 May and 3 June that were originally fit with season were extracted and refit with L. maackii density. This resulted in a significant linear fit for bee abundance, but not bee species richness, responses for samples of pan traps during the flowering period of L. maackii (abundance: F(1,37) = 7.1, P < 0.05;

Supplementary Figure 4a; richness: F(1,37) = 1.7, P = 0.20). Repeating this process for the best vane trap models showed no significance for vane trap bee abundance (F(1,21) = 0.67, P = 0.42), but a marginal relationship for bee species richness: F(1,21) = 2.6, P = 0.12; Supplementary Figure 4b).

5 Discussion This study demonstrates that bee communities in forest-crop edges were most influenced by the tree community in forest-edge habitat, seasons, amount of adjacent field margin, and the composition of surrounding land cover types. Density of L. maackii also affected the bee community during its flowering period, but these ephemeral effects were incorporated within the more extensive seasonal changes of the bee communities and differed by the functional component of the bee community. Using different trapping methods to partition the bee community by body size, I sampled two distinct components of the bee community: larger bees that are strong flyers, and smaller bees that were more likely restricted in foraging ability. Although there was overlap in species identity between bee species sampled in pan traps and vane traps (Supplementary Table 2), body sizes of samples and the species composition of each trap were significantly different (Figures 4 and 5). This was consistent with other studies that found differences in bee species composition between pan and vane trap sampling methods (Joshi et al. 2015; McCravy et al. 2019) and provided an opportunity to describe the responses of each component of the bee community to forest habitat characteristics, including effects of L. maackii, as well as their scale-dependent responses to the surrounding landscape composition.

5.1 Bee Responses to Resolution and Scale Consistent with my predictions, smaller bees responded to fine-grained measures of land cover types within a more limited range of spatial scales, while larger bees of stronger foraging ability responded to a mix of landscape features at broad scales as well as forest habitat variables. The bee abundance and species richness sampled in pan traps was best explained by forest habitat characteristics and land cover types of fine resolution within 0.5 km of the focal forest edge, suggesting that these smaller bees inhabit the forest edge and are weaker foragers that depend upon resources within or near the forest patch (Figure 1b). For instance, greater width of the field margin between the forest edge and the adjacent crop field was a strong positive predictor of both bee abundance and species richness in all models. This finding suggests that more resource availability in field margins supports weaker foragers of the forest edge and is consistent with results from restoration projects of similar margins of crop fields (Morandin and Kremen 2013). Interestingly, strips of habitat adjacent to forests within the

surrounding landscape resulted in lower abundance of small bees within the transect. I suspect that greater amounts of strip-habitat between the forest patch and surrounding land cover types represented a greater variety of, and perhaps a complementary set of, floral or nesting resources for bees of the forest patch. Small bees using complementary resources adjacent to other edges of the forest patch may be less likely sampled within the transect (Torné-Noguera et al. 2014). Strips of habitat that were not adjacent to forests or between crop fields showed positive effects on bee species richness and likely provided resources for small bees that differed from the conditions associated with the forest edge (Ponisio et al. 2019). All other important landscape composition variables in models predicted negative responses for bees of limited foraging ability and suggested that most land cover types surrounding the focal forest patch (i.e. water, disturbed semi-natural, lawn, in-field drainage, and impervious surface habitat) provide few resources for these bees. Considering the better fit of fine resolution land cover models, these lines of evidence collectively suggest that composition and heterogeneity of the landscape immediately surrounding forest habitats are the best predictors of abundances of smaller bees. Alternatively, if some species of small bees typically forage farther than 0.5 km from habitats (Winfree et al. 2009), these results may be indicative of limited area of suitable habitat or the degree of isolation among habitat patches (Krewenka et al. 2011). Nevertheless, my results emphasize the importance of characteristics of the forest patch for smaller bees along the forest edge. Bees from vane traps responded at broader scales than those from pan traps and were sensitive to different landscape features. All competing models of bee abundance from vane traps showed positive associations with land cover types, specifically forests, within the largest buffer areas and at coarse resolution, suggesting that large-bodied bees are strong flyers that potentially move among forest patches up to 3 km apart (Figure 1b). Since bees are central-place foragers, this suggest that many nests were at least 1.5 km from the transect. Some large-bodied bee species exhibit the capability to travel the entire 3 km in single foraging trip (Greenleaf et al. 2007) and are less sensitive to land-use modifications of the landscape (Coutinho et al. 2018) than smaller species. However, greater relative area of fallow land (and pollinator-friendly crops) in the surrounding landscape demonstrated a negative relationship with the abundance of bees found in forest-edge habitats. The collective area of clover, tomatoes, dry beans, and peaches that are associated with pollinators was < 5% of this land cover type, suggesting that the negative

response exhibited by these larger bees was primarily a response to greater area of fallow land. Fallow land was included in this landscape variable due to its available nesting substrate and the potential for weedy floral resources, but the results suggest that fallow land is a low-quality habitat and not beneficial to strong foragers at forest edges (Basu et al. 2016). Species richness responses of bees in vane traps generally varied by land cover type at scales ≥ 1 km from transects and may be best explained by complementary resources provided by forests and lacking in fallow land (Supplementary Table 5). However, bee species richness was higher with more area of impervious surface, defined by barren, high-density residential, and open water habitats (Table 1). Considering there are no available resources in open water, and those within barren land are minimal, I suspect that some species found at forest edges are also associated with urban areas (Collado et al. 2019). The movement of bees from habitat types of different qualities to the forest edge would suggest that compositional heterogeneity of the landscape is important for bees, particularly strong foragers. Replacing land cover type predictors in best models of vane trap responses with landscape heterogeneity (as measured by Shannon diversity of land cover types) decreased model fit. Therefore, strong foragers at forest edges may be affected by overall landscape heterogeneity, but bee diversity seems to benefit from increased amounts of forest and possibly urban areas.

5.2 Seasonal Resource Availability: Flowers, Lonicera maackii, and Trees If most small bees are restricted to nesting within or near the forest patch, then floral resource availability proximal to the forest edge likely plays an important role in structuring the bee community. However, floral resource availability along the forest edge was not included as a predictor in any best model for pan trap responses (Supplementary Table 5). Burkle et al. (2013) found that long-term changes in similar plant communities resulted in bee extirpations, which may suggest that many small or specialized bees in this study region have already been eliminated, leaving those bees that forage on a wide variety of floral resources (Smith et al. 2019). The flowering community was important for predicting responses of bees captured by vane traps. Evenness of the flower community was negatively associated with bees and deemphasizes the overall importance of flower diversity in this system. A significant fraction of the flowering understory plants were those of other invasive or weedy species (i.e. Allaria petiolata (14%), Lamium spp (8.6%), Stellaria media (8.5%), Toxicodendron radicans (1.6%),

Vitis spp (5.4%), etc) or plants that primarily rely on pollination vectors other than bees (i.e. Ambrosia trifida (1%), Asimina triloba (1.5%), Polygonaceae spp (5.6%), Urticaceae spp (4.3%), Trillium sessile (4.3%), etc). Therefore, high quality flowers for bees were limited and specialist bee-plant relationships clearly did not comprise a large portion of the overall plant-pollinator network. Since the abundance of flowering stems inside the forest edge was not important in the best models for bee responses, small bees also likely foraged on the flowering resources within the field margin between the forest edge and crops. The presence of forested habitat in every competing model suggests that most bees sampled within the transects likely used resources throughout the forest patch. It follows then, that sources of large quantities of floral resources, such as trees or the invasive L. maackii shrubs, resulted in a greater response from the bee community than the other flowering forbs and shrubs within forest edges which may have provided lower quality or smaller quantities of floral resources to bees. In support of my hypothesis, L. maackii density elicited different responses to each component (i.e. bees of vane traps versus pan traps) of the bee community (Supplementary Figure 4). Since these effects were captured by seasonal effects within the models and were only tested during the flowering period of L. maackii, it is unclear if L. maackii density affects bees throughout the season. Nonetheless, my results showed that L. maackii density likely increased the abundance of small bees, but not large bees, during its flowering period. Lonicera maackii floral resources may therefore support populations of weaker foragers that inhabit the forest edge, consistent with other studies that showed that dense L. maackii floral resources supported bees sampled with pan traps (Cunningham-Minnick et al. in press). The partial residuals of the best-fitting model with a seasonal term showed a trend of increased species richness of large bees with greater L. maackii density during flowering (Supplementary Figure 4b). Considering there were no changes in large bee abundance, stronger foragers may be visiting L. maackii floral resources that originated elsewhere (≤ 1–3 km) in the landscape. Since forest was a beneficial land cover type for species richness of large bees, it is likely that varied habitat quality among forested habitats supported different species of larger bees. This was also reflected in my ordinations using NMDS, which showed site effects on bee species composition. Therefore, L. maackii floral resources are likely used by bees of different foraging ability and may be more important for supporting weak foragers that rely on resources of forest-edge habitat.

Other studies generally support these findings. For instance, experimental removal of invasive shrub flowers or individuals of L. maackii, or other species altered the local foraging bee community (Cunningham-Minnick et al. in press, Fiedler et al. 2012; Hanula and Scott 2011). Cunningham-Minnick et al. (in press) also found that L. maackii floral resources directly affected the abundance of relatively few bee species, many of which were solitary with short flight seasons. Therefore, effects on bees with limited use of L. maackii as forage may not be detectable throughout the season using a density gradient approach across sites. This may partly explain why the effects of L. maackii were masked by seasonal effects. Alternatively, there were other large sources of flowers that also exhibited seasonal turnover and were influencing the bee community as well. Other mass-flowering sources along the forest edge may have been more important to the bee community than L. maackii. Considering the importance of trees within the forest transect (Supplementary Figure 2), L. maackii and its resources may primarily affect bees that forage higher in the tree canopy. Trees provide large sources of flowers. For instance, a single red maple tree (Acer rubrum) can produce 956,000 seeds, which is representative of at least as many showy red flowers (Abbott 1974). Despite the fact that they can be the dominant source of floral resources in the landscape, I am not aware of any study that demonstrated bee abundance or diversity shifts due to the tree communities of deciduous forests, though bees do nest and forage in upper strata of deciduous forests (Sobek et al. 2009; Ulyshen et al. 2010). Recent pollen studies also provide support for the hypothesis that trees provide a high proportion of food resources to many bees (Bertrand et al. 2019; Proesmann et al. 2019; Smith et al. 2019; Tucker et al. 2019). However, most studies that measure forest bee communities do not discuss their findings in the context of vertical trap placement. In a prairie system, Geroff et al. (2014) found that a larger fraction of the bee community was found when traps were elevated in response to structural complexity. This hypothesis is supported in part by the findings that tree abundance or species richness within the transect had negative impacts on all bee responses and the magnitude of this relationship was mediated by the change in season (Supplementary Table 5). These results clearly showed that fewer bees of fewer species were sampled earlier in the season in sites with fewer trees, and this relationship flipped after most deciduous trees were finished flowering such that more bees were sampled in sites with fewer trees later in the season (Supplementary Figure

2). These findings strongly suggest that the mass quantities of tree flowers were attracting bees and distracting them from visiting pan and vane traps (Nicholson et al. 2019). Cunningham- Minnick and Crist (in revision) found higher bee abundance and species richness across vertical strata of the forest-edge early in the season in response to a more abundant and species rich tree community followed by a negative relationship once L. maackii was in bloom. It follows that my data is most representative of the portion of the bee community that was not foraging in the higher strata of the forest-edge canopy. Alternatively, sites may have fewer mature trees due to the competitive effects of larger trees (Zhang et al. 2016). This would suggest that the greater abundance and species richness of bees in sites with low abundances of trees early in the season is reflective of more resources within the expansive crowns of larger woody individuals. Since I did not measure basal area in this study, the mechanism defining bee responses to the flowering tree community is unclear, although Cunningham-Minnick et al. (in revision) found that L. maackii density and basal area of mature shrubs and trees with showy flowers, in addition to their peak flowering time, were important predictors of species composition of the bee community. Overlap in the distributions of body sizes between bees sampled in pan and vane traps further suggests that there were functional differences other than body size targeted by each trap type which may be associated with foraging height in addition to foraging distance. For instance, bees using tree resources would be more likely to encounter suspended vane traps in their flight path than pan traps on the ground. Therefore, vane traps represented a mix of small-bodied bees that typically forage short distances (e.g. Hylaeus spp; Zurbuchen et al. 2010) and large-bodied species with greater foraging distances (e.g. Bombus spp; Greenleaf et al. 2007). It is unclear why neither component of the bee community included bees 9-12 mm in body length and this may indicate that other components of the bee community were not sampled due to restricted sampling heights or general sampling methods. For example, the data in this study represents bees sampled up to 1.2 m from the forest-edge floor, but it was recently demonstrated that the foraging bee community is vertically stratified up to 16 m from the forest floor along temperate forest edges invaded with woody shrubs (Cunningham-Minnick and Crist In Revision).

6 Conclusion Bees sampled with pan traps and vane traps represented functionally different components of the bee community and responded to landscape features at different spatial scales and resolutions. Bees from pan traps responded largely to resources associated with isolated forest patches in this fragmented system, including the forest patch and field margins and other strips of habitat near the forest patch. Therefore, I conclude that small-bees are largely dependent upon properties of the forest patch and forest-crop edges. In contrast, bees sampled by vane traps represented a different component of the bee community that responded to the composition of land cover types surrounding the focal forest patch at broader spatial scales. These were generally larger-bodied species that were also positively related to the amount of forest and urban area in the surrounding landscape but exhibited negative responses to fallow ground. Therefore, I infer that large-bodied bees interacted more with the surrounding landscape than smaller bees, and were positively or negatively associated with specific land-cover types rather than land-cover diversity per se. I conclude that the abundance and species richness of bees along the forest edge were strongly affected by seasonal changes in large sources of flowering plants within forests as well as the surrounding landscape. During the flowering period of L. maackii, small-bodied bees showed increased abundance while large-bodied foragers showed an increase in species richness, but analysis of residuals demonstrated patterns were largely captured by the seasonal variation of bee community turnover. Both components of the bee community also responded to resource availability of the tree community, an unprecedented finding. Considering the distribution of body-sizes of both components of the bee community, I hypothesizethat the observed functional differences between bees sampled in vane and pan traps are associated with foraging height as well as body size. Overall, these findings demonstrate how bees with different life histories and functional attributes have responded to anthropogenic land uses and the presence of invasive plants in agricultural landscapes.

7 References

Abbott HG (1974) Some characteristics of fruitfulness and seed germination in red maple. Tree Planters’ Notes 25(2):25-27. Aizen MA, Morales CL, Morales JM (2008) Invasive mutualists erode native pollination webs. PLoS Bio 6(2):e31. Barriball K., K. Goodell, O. J. Rocha. 2014. Mating patterns and pollinator communities of the invasive shrub Lonicera maackii: a comparison between interior plants and edge plants. Int J Plant Sci 175(8):946-954. Bartomeus I, Ascher JS, Gibbs J, Danforth BN, Wagner DL, Hedtke SM, Winfree R (2013) Historical changes in northeastern US bee pollinators related to shared ecological traits. PNAS 110(12):4656-4660. Barton K (2019) MuMIn: Multi-Model inference. R package version 1.43.6. Bartuszevige AM, Gorchov DL (2006) Avian seed dispersal of an invasive shrub. Biol Invasions 8:1013-1022. Basu P, Parui AK, Chatterjee S, Dutta A, Chakraborty P, Roberts S, Smith B (2016) Scale dependent drivers of wild bee diversity in tropical heterogeneous agricultural landscapes. Ecol Evol 6(19):6983-6992. Benjamin FE, Reilly JR, Winfree R (2014) Pollinator body size mediates the scale at which land use drives crop pollination services. J Appl Ecol 51:440-449. Bertrand C, Eckerter PW, Ammann L, Entling MH, Gobet E, Herzog F, Mestre L, Tinner W, Albrecht M (2019) Seasonal shifts and complementary use of pollen sources by two bees, a lacewing and a ladybeetle species in European agricultural landscapes. J Appl Ecol 00:1-12. Boutin C, Jobin B (1998) Intensity of agricultural practices and effects on adjacent habitats. Ecol Appl 8(2):544-557. Burkle LA, Marlin JC, Knight TM (2013) Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339:1611-1615. Carvalheiro LG, Biesmeijer JC, Benadi G, Fründ J, Stang M, et al. (2014) The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecol Lett 17:1389-1399. Collado MA, Sol D, Bartomeus I (2019) Bees use anthropogenic habitats despite strong natural habitat preferences. Divers Distrib 25:924-935. Coutinho JGE, Garibaldi LA, Viana BF (2018) The influence of local and landscape scale on single response traits in bees: A meta-analysis. Agri Ecosyst Environ 256:61-73. Crowl TA, Crist TO, Parmenter RR, Belovsky G, Lugo AE (2008) The spread of invasive species and infectious disease as drivers of ecosystem change. Front Ecol Environ 6(5):238- 246. Cunningham-Minnick MJ, Cirst TO (in revision) Floral resources of an invasive shrub alter native bee communities at different vertical strata in forest-edge habitat. Biol Invasions. Cunningham-Minnick MJ, Peters VE, Crist TO (in press) Bee communities and pollination services in adjacent crop fields following flower removal in an invasive forest shrub. DOI: 10.1002/eap.2078. Diekötter T, Haynes KJ, Mazeffa D, Crist TO (2007) Direct and indirect effects of habitat area and matrix composition on species interactions among flower-visiting insects. Oikos 116:1588-1598. Droege S (2008) The Very Handy Manual : How to Catch and Identify Bees and Manage a

Collection. USGS Native Bee Inventory Monitoring Lab. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst 34:487-515. Fahrig L, Baudry J, Brotons L, Burel FG, Crist TO, Fuller RJ, Sirami C, Siriwardena GM, Martin JL (2011) Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol Lett 14:101-112. Fiedler AK, Landis DA, Arduser M (2012) Rapid shift in pollinator communities following invasive species removal. Restor Ecol 20(5):593-602. Garibaldi LA, Steffan-Dewenter I, Kremen C, Morales JM, Bommarco R, et al. (2011) Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol Lett 14:1062–72. Gathmann A, Tscharntke T (2002) Foraging ranges of solitary bees. J Animal Ecol 71:757-764. Geroff RK, GibbsJ, McCravy KW (2014) Assessing bee (Hymenoptera: Apoidea) diversity of an Illinois restored tallgrass prairie: methodology and conservation considerations. J Insect Conserv 18:951–964. Gibbs J, Joshi NK, Wilson JK, Rothwell NL, Powers K, Haas M, Gut L, Biddinger DJ, Isaacs R (2017) Does passive sampling accurately reflect the bee (Apoidea: Anthophila) communities pollinating apple and sour cherry orchards? Environ Entomol 46(3):579- 588. Greenleaf SS, Williams NM, Winfree R, Kremen C (2007) Bee foraging ranges and their relationship to body size. Oecologia 153:589-596. Haffey CM, Gorchov DL (2019) The effects of deer and an invasive shrub, Lonicera maackii, on forest understory plant composition. EcoScience 26(3):237-247. Hall M (2018) Blue and yellow vane traps differ in their sampling effectiveness for wild bees in both open and wooded habitats. Agr Forest Entomol 20:487-495. Hanula J, Horn S (2011) Removing an invasive shrub (Chinese privet) increases native bee diversity and abundance in riparian forests of the southeastern United States. Insect Conserv Diver 4:275-283. Hassett MR, McGee GG (2017) Negative binomial hurdle models to estimate flower production for native and nonnative Northeastern shrub taxa. Forest Sci 63(6):577-585. Hoven BM, Gorchov DL, Knight KS, Peters VE (2017) The effect of emerald ash borer-caused tree mortality on the invasive shrub Amur honeysuckle and their combined effects on tree and shrub seedlings. Biol Invasions 19:2813-2836. Hung KJ, Ascher JS, Davids JA, Holway DA (2019) Ecological filtering in scrub fragments restructures the taxonomic and functional composition of native bee assemblages. Ecology 100(5): e02654. Jauker F, Diekötter T, Schwarzbach F, Wolters V (2009) Pollinator dispersal in an agricultural matrix: opposing responses of wild bees and hoverflies to landscape structure and distance from main habitat. Landscape Ecol 24:547:555. Joshi NK, Leslie T, Rajotte EG, Kammerer MA, Otienno M, Biddinger DJ (2015) Comparative trapping efficiency to characterize bee abundance, diversity, and community composition in apple orchards. Ann Entomol Soc Am 108(5):785-799. Jůzová K, Y Nakase, J Straka (2015) Host specialization and species diversity in the genus Stylops (Strepsiptera: Stylopidae), revealed by molecular phylogenetic analysis. Zool J Linn Soc 174:228-243.

Kallioniemi E, Åström J, Rusch GM, Dahle S, Åström S, Gjershaug JO (2017) Local resources, linear elements and mass-flowering crops determine bumblebee occurrences in moderately intensified farmlands. Agr Ecosyst Environ 239:90-100. Kearns CA, Inouye DW, Waser NM (1998) Endangered mutualisms: the conservation of plant- pollinator interactions. Annu Rev Ecol Syst 29:83-112. Kleijn D, Winfree R, Bartomeus , Carvalheiro G, Henry M, Isaacs R, Klein AM, Kremen C, M’Gonigle L, Rader R, et al. (2015) Delivery of crop pollination services is an insufficient argument for wild pollinator conservation. Nat Comm 16(6):7414. Krewenka KM, Holzschuh A, Tscharntke T, Dormann CF (2011) Landscape elements as potential barriers and corridors for bees, wasps, and parasitoids. Biol Conserv 144:1816- 1825. Lonsdorf E, Kremen C, Ricketts T, Winfree R, Williams, Greenleaf S (2009) Modelling pollination services across agricultural landscapes. Annals of Botany 103:1589-1600. Mallinger RE, Gibbs J, Gratton C (2016) Diverse landscapes have a higher abundance and species richness of spring wild bees by providing complementary floral resources over bees’ foraging periods. Landscape Ecol 31:1523-1535. Mazerolle MJ (2016) AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R Package version 2.0-4. McCravy KW, Geroff RK, Gibbs J (2019) Bee (Hymenoptera: Apoidea: Anthophila) functional traits in relation to sampling methodology in a restored tallgrass prairie. Fla Entomol 102(1): 134-140. McKinney A. M., K. Goodell. 2010. Shading by invasive shrub reduces seed production and pollinator services in a native herb. Biol Invasions 12:2751-2763. McKinney A. M., K. Goodell. 2011. Plant-pollinator interactions between an invasive and native plant vary between sites with different flowering phenology. Plant Ecol 212:1025-1035. McNeish RE, McEwan RW (2016) A review on the invasion ecology of Amur honeysuckle (Lonicera maackii, Caprifoliaceae) a case study of ecological impacts at multiple scales. J Torrey Bot Soc 143(4):367-385. Montero-Castaño A, Vilà M (2012) Impact of landscape alteration and invasions on pollinators: a meta-analysis. J Ecol 100:884-893. Morandin LA, Kremen C (2013) Hedgerow restoration promotes pollinator populations and exports native bees to adjacent fields. Ecol Appl 23(4):829-839. Nicholson CC, Ricketts TH, Koh I, Smith HG, Lonsdorf EV, Olsson O (2019) Flowering resources distract pollinators from crops: Model predictions from landscape simulations. J Appl Ecol 56:618-628. Öckinger E, Winsa M, Roberts SPM, Bommarco R (2019) Mobility and resource use influence the occurrence of pollinating insects in restored seminatural grassland fragments. Restor Ecol 26(5):873-881. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: community ecology package. R package version 2.5-5. https//CRAN.R- project.org/package=vegan Olsson O, Bolin A, Smith HG, Lonsdorf EV (2015) Modeling pollinating bee visitation rates in heterogeneous landscapes from foraging theory. Ecological Modelling 316:133-143.

Ponisio LC, Valpine P, M’Gonigle LK, Kremen C (2019) Proximity of restored hedgerows interacts with local floral diversity and species’traits to shape long-term pollinator metacommunity dynamics. Ecol Lett 22:1048-1060. Potts SG, Imperatriz-Fonseca V, Ngo HT, Aizen MA, Biesmeijer JC, Breeze TD, Dicks LV, Garibaldi LA, Hill R, Settele J, Vanbergen AJ (2016) Safeguarding pollinators and their values to human well-being. Nature 540:220-229. Proesmans W, Smagghe G, Meeus I, Bonte D, Verheyen K (2019) The effect of mass-flowering orchards and semi-natural habitat on bumblebee colony performance. Landscape Ecol 34:1033-1044. R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ Rhoades P (2017) Sampling technique affects detection of habitat factors influencing wild bee communities. J Insect Conserv 21:703-714. Richards MH, Rutgers-Kelly A, Gibbs J, Vickruck JL, Rehan SM, Sheffield CS (2011) Bee diversity in naturalizing patches of Carolinian grasslands in southern Ontario, Canada. Can Entomol 143:279-299. Ricketts TH, Regetz J, Steffan-Dewenter I, Cunningham SA, Kremen C, et al. (2008) Landscape effects on crop pollination services: are there general patterns? Ecol Lett 11:499–515. Senapathi D, Goddard MA, Kunin WE, Baldock KCR (2017) Landscape impacts on pollinator communities in temperate systems: evidence and knowledge gaps. Funct Ecol 31:26-37. Simberloff D, Farr JA, Cox J, Mehlman DW (1992) Movement corridors: conservation bargains or poor investments? Conserv Biol 6(4):493-504. Smith C, Weinman L, Gibbs J, Winfree R (2019) Specialist foragers in forest bee communities are small, social or emerge early. J Animal Ecol 88:1158-1167. Sobek S, Tscharntke T, Scherber C, Schiele S, Steffan-Dewenter I (2009) Canopy vs. understory: does tree diversity affect bee and wasp communities and their natural enemies across forest strata? Forest Ecol Manag 258:609-615. Stout JC, Tiedeken EJ (2017) Direct interactions between invasive plants and native pollinators: evidence, impacts and approaches. Funct Ecol 31:38-46. Torné-Noguera A, Rodrigo A, Arnan X, Osorio S, Barril-Graells H, Rocha-Filho LC, Bosch J (2014) Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9(5):e97255. Tucker SK, Ginsberg HS, Alm SR (2019) Eastern carpenter bee (Hymenoptera: Apidae): Nest structure, nest cell provisions, and trap nest acceptance in Rhode Island. Environ Entomol 48(3):702-710. Ulyshen MD, Soon V, Hanula JL (2010) On the vertical distribution of bees in a temperate deciduous forest. Insect Conserv Diver 3:222-228. USDA National Agricultural Statistics Service Cropland Data Layer (2015) Published crop- specific data layer [online]. Available at https://nassgeodata.gmu.edu/CropScape/ (Accessed May 2019). USDA-NASS, Washington, DC. Vanbergen AJ, Espíndola A, Aizen MA (2018) Risks to pollinators and pollination from invasive alien species. Nat Ecol Evol 2:16-25. Wagner DL, Metzler KJ, Frye H (2019) Importance of transmission line corridors for conservation of native bees and other wildlife. Biol Conserv 235:147-156. Williams NM, Cariveau D, Winfree R, Kremen C (2011) Bees in disturbed habitats use, but do not prefer, alien plants. Basic Appl Ecol 12:332-341.

Winfree R, Aguilar R, Vázquez DP, LeBuhn G, Aizen MA (2009) A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90(8):2068-2076 Wood SN, Pya N, Saefken B (2016) Smoothing parameter and model selection for general smooth models (with discussion) Journal of the American Statistical Association 111:1548- 1575. Wood SN (2017) Generalized additive models: an introduction with R. 2nd Edition, Chapman and Hall/CRC Press. 476 pp. Zhang Z, Papaik MJ, Wang X, Hao Z, Ye J, Lin F, Yuan Z (2016) The effect of tree size, neighborhood competition and environment on tree growth in an old-growth temperate forest. J Plant Ecol 10(6):970-980. Zurbuchen A., L. Landert, J. Klaiber, A. Müller, S. Hein, S. Dorn. 2010. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol Conserv 143:669-676.

Table 1: Unsupervised land cover classes contained within the Cropland Data Layer (CDL) database were lumped into eight categories: corn, fallow, flowerless, forest, grassland, impervious, residential, and soybean.

corn fallow flowerless forest grassland impervious residential soybean

Corn Clover W-wheat Shrubland Hay Open Water Developed (Open) Soy

W-wheat & Corn Dry Beans W-wheat & Sorghum Evergreen Forest Grassland/Pasture Developed (High) Developed (Low) W-wheat & Soy

Tomatoes Barley & Sorghum Deciduous Forest Barren Developed (Med)

Peaches Barley Woody Wetlands

Fallow Oats Herb Wetlands

Spelt

Triticale

Sod Grass

Alfalfa*

100

Figure 1: Study sites in the agricultural matrix of SE Indiana and SW Ohio, USA (a). Relative area of landcovers within ovals scaled by 0.1, 0.5, 1.0, 2.0, or 3.0 km radius from terminal ends of belt transects were used in models by classifying landcovers of the landscape (b) into a discrete set of categories using the Cropland Data Layer (CDL) from 2015 (c). 101

Figure 2: Land cover types at spatial scales of 0.1, 0.5, and 1.0 km were analyzed using the coarse (30 x 30-m) resolution (corn = corn; forest = forest, grassland = grassland, imp = impervious, res = residential) of Cropland Data Layer data from 2015 (a), as well as the fine (0.15 x 0.15-m) resolution (cornFC = cornFC, dSNH = disturbed SNH, drain = drainage, f & forest = forest, g = grassland, i = imperviousfine, la = lawn, sf = stripF, so = stripother, water = water) of delineated aerial photographs taken in 2015 (b). Star represents the center of the 100-m belt transect.

102

Figure 3: Fitted regression splines (red lines) on raw data points of abundance (a and c) and species richness (b and d) of bees sampled in pan traps (a and b) and vane traps (c and d) throughout the year. Letters on x-axis represent first letter of the month, beginning with April. 103

Figure 4: Non-metric multi-dimensional scaling representing the dissimilarities among species composition of vane trap (dark gold) and pan trap (yellow) samples of a site (large circle). Lines represent 95% confidence ellipses; black dots represent species scores. (K = 2; stress = 0.17) 104

Figure 5: Kernel density of bee body length of specimens sampled in vane traps (dark gold) superimposed onto pan traps (yellow). Small bees sampled in vane traps likely represent bees that use resources closer to vane traps (i.e. not low-lying). Each individual was assigned a body length based on sex and species (see Supplementary Table 2 for details). 105

Supplementary Table 1: Lonicera maackii density based on basal area within the 5-m forest edge, site location, aspect of each forest-edge site, size of the associated forest fragment, and width of forest-agriculture interface. Sites are listed in increasing order of L. maackii density.

Site L. maackii Latitude Longitude Aspect Patch Size Margin Width (°N) (°W) (m) (m2/ha) (N, E, S, W) (ha) 1 0.00 39.505 -84.921 N 15.34 3.1 2 0.30 39.797 -84.754 W 16.66 3.8 3 0.61 39.663 -84.793 W 5.55 3.9 4 1.29 39.637 -84.488 E 27.51 10.1 5 1.49 39.785 -84.492 W 16.94 2.3 6 3.62 39.558 -84.899 S 19.11 5.3 7 8.66 39.572 -84.801 S 14.19 6.3 8 9.32 39.485 -84.509 S 13.43 3.4 9 9.46 39.423 -84.896 N 11.92 2.9 10 10.98 39.375 -84.862 S 22.73 5.0 11 21.80 39.516 -84.661 S 21.93 2.9 12 23.12 39.645 -84.701 S 17.31 3.0

106

Supplementary Table 2: Species abundances by trap. Females (F) and males ( (M) ) of each species and morphotype (morpho-) used in analyses. Species are alphabetically ranked within taxonomic family. Total abundance across trapping methods (Total) and average size of females (males) are calculated means of three individuals. Queens of social species were not measured (i.e. Bombus spp) and were counted as workers for this estimation.

Species Pan Traps Vane Traps Total Body length (mm) F (M) F (M) F (M)

Andrenidae Andrena algida 0 (0) 1 (0) 1 9.0 Andrena brevipalpis 0 (1) 0 (2) 3 (7.0) Andrena carlini 28 (0) 23 (1) 52 12.2 (8.6) Andrena commoda 13 (7) 17 (2) 39 11.6 (9.4) Andrena cragini 1 (0) 0 (0) 1 12.1 Andrena crataegi 0 (0) 0 (1) 1 (10.0) Andrena cressonii 12 (3) 10 (4) 29 9.5 (8.1) Andrena distans 0 (1) 0 (7) 8 (7.0) Andrena erigeniae 57 (0) 8 (1) 66 8.3 (6.5) Andrena erythronii 0 (0) 1 (0) 1 9.8 Andrena forbesii 3 (0) 1 (4) 8 10.2 (7.6) Andrena fragilis 0 (0) 5 (0) 5 8.8 Andrena geranii 2 (0) 5 (1) 8 9.0 (6.1) Andrena heraclei 1 (0) 1 (0) 2 8.9 Andrena hippotes 0 (0) 1 (4) 5 9.1 (7.5) Andrena illini 3 (4) 8 (4) 19 13.2 (11.1) Andrena imitatrix 7 (24) 30 (33) 94 8.8 (7.3) Andrena integra 0 (0) 0 (2) 2 (6.2) Andrena macra 0 (1) 0 (0) 1 (8.2) Andrena mandibularis 0 (0) 0 (3) 3 (6.7) Andrena melanochroa 0 (0) 2 (0) 2 6.1 Andrena miserabilis 2 (0) 5 (10) 17 7.8 (7.1) Andrena morrisonella 3 (0) 3 (0) 6 8.8

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Andrena nasonii 96 (34) 55 (27) 212 8.2 (7.0) Andrena nuda 0 (2) 1 (0) 3 9.8 (7.1) Andrena perplexa 11 (9) 38 (54) 112 12.8 (9.8) Andrena personata 22 (16) 6 (5) 49 5.9 (5.2) Andrena phaceliae 10 (11) 3 (0) 24 8.7 (6.8) Andrena platyparia 0 (0) 1 (0) 1 8.8 Andrena polemonii 1 (0) 2 (0) 3 7.3 Andrena pruni 4 (3) 21 (9) 37 11.4 (9.3) Andrena robertsonii 2 (1) 6 (3) 12 8.3 (7.1) Andrena rugosa 2 (1) 8 (8) 19 8.5 (7.8) Andrena simplex 0 (0) 1 (0) 1 9.2 Andrena vicina 2 (0) 12 (1) 15 12.1 (9.0) Andrena violae 60 (3) 17 (0) 80 9.4 (8.4) Andrena wilkella 4 (0) 18 (12) 34 10.8 (9.6) Andrena wilmattae 0 (0) 1 (0) 1 12.0 Andrena ziziae 4 (1) 2 (3) 10 6.0 (5.5) Andrena morph-1 0 (0) 1 (0) 1 9.5 Andrena morph-2 0 (0) 1 (0) 1 10.4 Andrena morph-3 1 (0) 1 (0) 2 9.4 Andrena morph-4 0 (0) 1 (0) 1 7.1 Calliopsis andreniformis 4 (4) 2 (0) 10 6.8 (5.7)

Apidae Anthophora abrupta 0 (0) 7 (16) 23 13.8 (14.4) Anthophora bomboides 0 (0) 4 (3) 7 13.7 (14.1) Anthophora terminalis 0 (0) 2 (1) 3 10.7 (13.2) Apis mellifera 10 (0) 203 (0) 213 12.7 Bombus auricomus 0 (0) 8 (0) 8 19.5 Bombus bimaculatus 6 (4) 277 (587) 874 14.8 (15.8)

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Bombus citrinus 0 (0) 5 (4) 9 21.2 (16.8) Bombus fervidus 2 (0) 23 (0) 25 15.3 Bombus griseocolis 3 (0) 36 (6) 45 12.4 (18.6) Bombus impatiens 23 (1) 986 (58) 1068 13.9 (13.9) Bombus pensylvanicus 6 (0) 67 (16) 89 17.3 (18.8) Bombus perplexus 0 (1) 4 (5) 10 13.2 (14.4) Bombus vagans 0 (0) 5 (5) 10 13.0 (12.9) Cemolobus ipomoeae 1 (0) 5 (3) 9 14.3 (14.6) Ceratina calcarata 163 (81) 1116 (189) 1549 7.2 (6.3) Ceratina dupla 18 (9) 49 (8) 84 5.6 (5.4) Ceratina mikmaqi 44 (11) 57 (6) 118 8.6 (6.0) Ceratina strenua 84 (24) 102 (16) 226 5.6 (5.6) Eucera atriventris 1 (4) 16 (27) 47 12.3 (12.5) Eucera belfragei 0 (0) 1 (3) 4 12.9 (11.0) Eucera dubitata 1 (2) 7 (25) 36 12.8 (12.2) Eucera hamata 9 (1) 43 (63) 116 14.7 (13.7) Eucera rosae 0 (1) 2 (11) 16 13.8 (11.2) Holcopasites calliopsidis 1 (0) 0 (0) 1 5.2 Melissodes agilis 0 (0) 1 (0) 1 13.1 Melissodes bidentis 0 (0) 1 (0) 1 10.3 Melissodes bimaculata 53 (13) 1773 (159) 1998 12.9 (11.6) Melissodes communis 0 (0) 0 (1) 1 (10.7) Melissodes denticulatus 3 (0) 39 (3) 45 11.1 (8.0) Melissodes desponsus 8 (2) 155 (77) 242 13.1 (12.7) Melissodes illatus 0 (0) 0 (1) 1 (9.3) Melissodes trinodis 0 (0) 1 (0) 1 NA Melitoma taurea 0 (0) 9 (20) 29 12.5 (11.1) Nomada affabilis 0 (0) 2 (0) 2 11.1 Nomada armatella 0 (1) 0 (0) 1 (7.3)

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Nomada articulata 0 (1) 0 (0) 1 (9.3) Nomada australis 0 (0) 0 (1) 1 (7.1) Nomada bethunei 2 (0) 0 (1) 3 9.3 (8.5) Nomada bidentate-1 (F) 1 (0) 0 (0) 1 7.6 Nomada bidentate-2 (F) 0 (0) 1 (0) 1 7.2 Nomada bidentate-3 (F) 5 (0) 0 (0) 5 7.3 Nomada bidentate-4 (F) 1 (0) 0 (0) 1 7.4 Nomada bidentate-5 (F) 2 (0) 2 (0) 4 7.3 Nomada bidentate-6 (F) 11 (0) 3 (0) 14 6.9 Nomada composita 0 (0) 0 (1) 1 (8.1) Nomada cressonii 2 (2) 0 (0) 4 8.3 (6.8) Nomada denticulatus 1 (2) 1 (0) 4 6.6 (7.8) Nomada depressa 42 (5) 4 (7) 58 6.4 (7.3) Nomada florilega 4 (0) 1 (0) 5 7.2 Nomada gracilis 7 (0) 1 (0) 8 8.2 Nomada illinoensis 1 (4) 0 (0) 5 5.8 (5.7) Nomada imbricata 2 (2) 2 (2) 7 10.6 (7.6) Nomada integerrima 1 (0) 0 (0) 1 8.8 Nomada luteoloides 15 (4) 10 (12) 41 11.0 (9.4) Nomada obliterata 0 (1) 0 (0) 1 (7.4) Nomada parva 3 (42) 1 (1) 47 5.5 (5.2) Nomada pygmaea 2 (6) 0 (2) 10 6.8 (7.0) Nomada sayi 2 (6) 0 (4) 12 6.2 (6.1) Nomada sulphurata 1 (0) 0 (0) 1 10.9 Nomada valida 1 (0) 0 (0) 1 7.7 Nomada morph-1 (F) 2 (0) 0 (0) 2 7.4 Peponapis pruinosa 1 (0) 26 (6) 33 13.5 (11.6) Ptilothrix bombiformis 1 (0) 14 (1) 16 13.9 (12.4) Xylocopa virginica 0 (1) 8 (6) 15 22.3 (20.3)

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Colletidae Colletes inaequalis 1 (0) 5 (1) 7 13.4 (9.8) Hylaeus affinis 7 (0) 3 (1) 11 5.2 (6.0) Hylaeus annulatus 1 (0) 5 (0) 6 5.6 Hylaeus fedorica 0 (2) 7 (0) 9 4.9 (5.0) Hylaeus illinoisensis 0 (0) 2 (2) 4 5.1 (4.2) Hylaeus mesillae 1 (1) 13 (2) 17 4.3 (3.4) Hylaeus modestus 2 (0) 13 (5) 20 5.2 (4.8) Hylaeus saniculae 0 (0) 2 (0) 2 4.3 Hylaeus sparsus 1 (0) 4 (0) 5 5.8

Halictidae Agapostemon sericeus 0 (0) 6 (3) 9 9.9 (8.7) Agapostemon splendens 1 (0) 0 (0) 1 9.1 Agapostemon texanus 3 (0) 1 (2) 6 11.1 (9.9) Agapostemon virescens 109 (0) 252 (3) 364 10.8 (9.7) Augochlora pura 127 (35) 3573 (1606) 5341 8.3 (7.2) Augochlorella aurata 90 (13) 332 (40) 475 6.9 (6.7) Augochloropsis metallica 11 (1) 50 (15) 77 9.4 (8.6) Augochloropsis sumptuosa 1 (0) 0 (0) 1 8.2 Halictus confusus 49 (15) 38 (2) 104 6.8 (6.0) Halictus ligatus 24 (2) 17 (2) 45 9.0 (7.6) Halictus rubicundis 4 (1) 24 (3) 32 10.1 (9.7) Lasioglossum abanci 2 (0) 0 (0) 2 4.9 Lasioglossum admirandum 7 (0) 10 (0) 17 6.4 Lasioglossum apocyni 0 (0) 2 (1) 3 4.4 (5.3) Lasioglossum atwoodi 2 (0) 0 (0) 2 4.7 Lasioglossum birkmanni 8 (0) 9 (1) 18 5.8 (5.7)

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Lasioglossum bruneri 4 (0) 12 (1) 17 6.9 (5.7) Lasioglossum callidum 0 (0) 2 (0) 2 5.4 Lasioglossum cattellae 17 (0) 21 (6) 44 5.1 (5.5) Lasioglossum cinctipes 1 (0) 20 (1) 22 7.3 (7.6) Lasioglossum coeruleum 16 (0) 197 (2) 215 6.3 (6.1) Lasioglossum coriaceum 96 (3) 315 (134) 548 10.0 (9.0) Lasioglossum cressonii 13 (0) 18 (5) 36 6.6 (5.2) Lasioglossum ephialtum 17 (0) 8 (0) 25 5.8 Lasioglossum fattigi 1 (0) 1 (0) 2 4.6 Lasioglossum forbesii 0 (0) 3 (0) 3 9.4 Lasioglossum foxii 0 (0) 3 (3) 6 6.0 (4.5) Lasioglossum fuscipenne 1 (0) 54 (0) 55 9.4 Lasioglossum gotham 1 (0) 11 (0) 12 6.4 Lasioglossum hitchensi 417 (6) 646 (20) 1089 4.7 (4.5) Lasioglossum imitatum 11 (0) 15 (0) 26 4.0 Lasioglossum katherinae 0 (0) 1 (1) 2 4.7 (5.9) Lasioglossum laevissimum 3 (0) 3 (0) 6 5.5 Lasioglossum lionotum 1 (0) 0 (0) 1 4.3 Lasioglossum nigroviride 0 (0) 5 (0) 5 6.9 Lasioglossum nymphaearum 0 (0) 1 (0) 1 6.5 Lasioglossum oblongum 3 (0) 1 (0) 4 5.1 Lasioglossum obscurum 39 (0) 88 (0) 127 5.1 (5.6) Lasioglossum paradmirandum 4 (0) 3 (0) 7 4.5 Lasioglossum pectorale 1 (0) 2 (0) 3 5.9 Lasioglossum pilosum 0 (0) 1 (0) 1 6.0 Lasioglossum planatum 2 (0) 0 (0) 2 4.8 Lasioglossum platyparium 1 (0) 1 (0) 2 5.2 Lasioglossum subversans 0 (0) 0 (1) 1 (6.5) Lasioglossum subviridatum 14 (0) 5 (1) 20 5.8 (5.3)

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Lasioglossum tegulare 5 (1) 4 (0) 10 4.3 (3.4) Lasioglossum trigeminum 7 (0) 9 (0) 16 5.4 Lasioglossum truncatum 5 (0) 17 (0) 22 9.1 Lasioglossum versans 0 (0) 0 (1) 1 (4.3) Lasioglossum versatum 49 (1) 68 (0) 118 7.3 (NA) Lasioglossum viridatum 5 (0) 1 (1) 7 5.1 (4.9) Lasioglossum weemsi 20 (0) 17 (0) 37 5.4 Lasioglossum zephyrum 9 (1) 43 (1) 54 5.9 (5.3) Lasioglossum morph-1 (F) 1 (0) 0 (0) 1 10.4 Lasioglossum morph-2 (F) 0 (0) 1 (0) 1 5.7 Lasioglossum morph-3 (F) 1 (0) 0 (0) 1 5.3 Lasioglossum morph-4 (F) 2 (0) 0 (0) 2 5.2 Lasioglossum morph-5 (F) 0 (0) 1 (0) 1 4.1 Lasioglossum morph-6 (F) 0 (0) 1 (0) 1 4.8 Lasioglossum morph-1 (M) 0 (1) 0 (3) 4 (7.5) Sphecodes aroniae 1 (1) 0 (0) 2 8.8 (8.2) Sphecodes cressonii 3 (0) 0 (0) 3 5.3 Sphecodes fattigi 1 (0) 0 (0) 1 9.0 Sphecodes heraclei 1 (0) 0 (0) 1 6.9 Sphecodes johnsonii 1 (0) 0 (0) 1 6.4 Sphecodes prosphorus 0 (0) 0 (1) 1 (9.7) Sphecodes ranunculi 2 (1) 1 (0) 4 8.6 (7.8)

Megachilidae Chelostoma philadelphi 1 (3) 0 (2) 6 6.9 (5.6) Heriades leavitti 0 (3) 6 (2) 11 6.1 (5.3) Heriades variolosa 2 (1) 28 (1) 32 6.3 (4.5) Hoplitis pilosifrons 2 (3) 0 (0) 5 8.9 (7.2) Hoplitis producta 6 (11) 0 (0) 17 7.3 (6.4)

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Megachile campanulae 1 (2) 12 (4) 19 10.6 (8.8) Megachile exilis 0 (0) 1 (0) 1 10.1 Megachile mendica 0 (0) 5 (1) 6 11.8 (10.5) Megachile pugnata 0 (0) 2 (0) 2 14.4 Osmia atriventris 7 (16) 4 (0) 27 7.6 (6.2) Osmia bucephala 11 (11) 14 (15) 51 14.3 (12.1) Osmia chalybea 0 (0) 6 (0) 6 13.5 Osmia collinsiae 0 (4) 0 (2) 6 (7.0) Osmia conjuncta 1 (0) 0 (0) 1 8.3 Osmia cordata 0 (3) 1 (1) 5 9.5 (6.2) Osmia cornifrons 0 (1) 2 (2) 5 11.7 (8.6) Osmia distincta 0 (1) 0 (0) 1 (6.2) Osmia georgica 3 (5) 1 (2) 11 8.3 (6.4) Osmia lignaria 1 (2) 1 (0) 4 10.3 (9.1) Osmia pumila 63 (49) 32 (16) 160 8.0 (6.3) Osmia taurus 1 (13) 2 (4) 20 10.8 (8.6) Osmia texana 0 (1) 0 (0) 1 (10.1)

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Supplementary Table 3: Flowering plant community with open flowers < 2 m from the ground. Species, or groups of species, are alphabetically ranked under their respective taxonomic families. Flower color (Red/Pink/Orange (R), White (W), Yellow (Y), Blue/Purple (B), Green (G)), the number of sites in which flowering stems occurred (out of 12), the number of flowering stems (Herbaceous), clusters of flowers (Vines), and apparent individuals (shrubs and trees), as well as the associated growth habit (Tree (T), Woody Vine (VW), Herbaceous Vine (VH), Herb (H), Woody Shrub (SW), Cane-like Shrub (SC) and Sedge (Se)) are listed. Two colors indicate differences in flower color, with the first letter representing petals, and the second referring to the general appearance of the rest of the flower (green is not included). Here, a plant is considered a vine if it is a climbing vine. Differ in flower morphology, though the same color (*).

Species Flower Color Sites Present Abundance Plant Type

Adoxaceae Sambucus nigra W 3 29 T

Anacardiaceae

Toxicodendron radicans W 5 423 VW

Annonaceae Asimina triloba R 2 382 T

Apiaceae Aethusa cynapium W 1 3 H Conium maculatum W 1 5 H Cryptotaenia canadensis W 1 2 H Daucus carota W 4 31 H Erigenia bulbosa W 2 17 H Osmorhiza spp W 7 168 H Sanicula odorata Y 9 1215 H Torilis spp W 1 13 H

Araceae Arisaema triphyllum R 4 43 H

Asclepiadaceae

Cynanchum leave W 1 3 VH

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Asparagaceae Maianthemum racemosum W 4 224 H Polygonatum spp W 4 113 H

Asteraceae Ageratina altissima W 5 427 H Ambrosia trifida Y 7 245 H Bidens spp (Beggar-ticks) Y 3 22 H Cirsium arvense B 1 2 H Lactuca GROUP1 B 2 29 H Lactuca GROUP2 Y 1 1 H Packera spp Y 2 5 H Solidago spp Y 8 188 H Symphyotrichum cordifolium B 1 3 H Taxaracum officinale Y 6 59 H Vernonia spp B 3 4 H GROUP1 (Dense) YB 2 112 H GROUP2 (Dense) WY 11 394 H GROUP3 (Single) WY 5 12 H GROUP4 YY

Balsaminaceae Impatiens capensis R 4 16 H

Berberidaceae Podophyllum peltatum W 1 4 H

Bignoniaceae

Campsis radicans R 1 8 VW

Boraginaceae Buglossoides arvensis W 2 8 H

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Brassicaceae Alliaria petiolata W 12 3657 H Barbarea vulgaris Y 1 2 H Cardamine concatenate W 3 5 H

Campanulaceae Campanula americanum B 5 94 H Lobelia inflata W 2 8 H

Caprifoliaceae

Lonicera japonica W 1 121 VH Lonicera maackii W 11 457 S

Caryophyllaceae Stellaria media W 9 2218 H Stellaria pubera W 1 2 H

Commelinaceae Commelina communis B 2 18 H

Cornaceae Cornus spp W 2 289 T

Cyperaceae Carex spp G 3 260 Se

Fabaceae Desmodium GROUP1 R 1 3 H Medicago lupulina Y 2 3 H Trifolium repens W 1 1 H

Fumariaceae Dicentra cucullaria W 2 11 H

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Grossulariaceae Ribes uva-crispa W 1 121 S

Hippocastenaceae Aesculus glabra Y 2 6 T

Hydrophyllaceae Phacelia purshii B 1 11 H

Hypericaceae Hypericum spp Y 1 3 H

Lamiaceae Glechoma hederacea B 2 535 H Hedeoma spp R 1 25 H Lamium amplexicaule R 1 144 H Lamium purpureum R 11 2104 H Leonurus cardiaca R 1 6 H Lycopus GROUP1 W 2 117 H

Liliaceae Erythronium americanum Y 1 2 H Ornithogalum umbellatum W 1 114 H Trillium grandiflora W 2 49 H Trillium sessile R 7 1126 H

Limnanthaceae Floerkea proserpinacoides W 4 105 H

Montiaceae Claytonia spp R 10 2666 H

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Oleaceae Ligustrum sinense W 1 7 S

Onagraceae Circaea lutetiana W/R 8 704 H

Oxalidaceae Oxalis stricta Y 7 68 H

Phrymaceae Phryma leptostachya R 1 1 H

Phytolaccaceae Phytolacca americana W 4 33 H

Polygonaceae

Fallopia scandens W 3 1013 VH Persicaria virginiana* W 6 69 H Persicaria GROUP1* W 1 2 H Persicaria GROUP2 R 7 384 H

Ranunculaceae Actaea spp W 6 211 H Hydrastis canadensis W 1 15 H Ranunculus abortivus Y 5 25 H

Rosaceae Agrimonia spp Y 1 1 H Duchesnea indica Y 3 11 H Geum vernum Y 3 24 H Geum GROUP1 W 11 774 H Potentilla spp Y 2 14 H

Rosa multiflora WY 8 29 SW

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Rubus occidentalis W 4 30 SC

Rubus (Blackberry) spp W 9 144 SC

Rubiaceae Galium spp W 8 220 H

Scrophulariaceae Veronica arvensis BW 1 1 H

Smilacaceae

Smilax spp G 2 36 VW

Solanaceae Physalis spp (W/R)Y 1 3 H Solanum carolinense (multi) WY 1 1 H Solanum ptycanthum (single) WY 1 9 H

Urticaceae Pilea pumila G 4 619 H Urtica dioica W 4 513 H

Verbenaceae Verbena urticifolia W 3 6 H

Violaceae Viola GROUP1 Y 6 337 H Viola GROUP2 B 8 224 H Viola GROUP3 W 1 611 H

Vitaceae

Vitis spp W 5 1399 VW

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Supplementary Table 4: Tree species abundances within 0.5 ha forest-edge sites ranked by extrapolated L. maackii density (m2/ha). Species are listed alphabetically.

Lonicera maackii density (m2/ha) Species 0.00 0.30 0.61 1.29 1.49 3.62 8.66 9.32 9.46 10.98 21.80 23.12 Total

Acer negundo 2 0 11 0 0 0 1 7 2 0 0 0 24 Acer saccharinum 0 27 0 0 0 0 0 0 0 0 0 0 27 Acer saccharum 106 1 0 40 13 13 4 0 0 24 0 0 201 Aesculus glabra 0 0 0 0 3 0 4 0 0 14 5 0 26 Ailanthus altissima 0 0 0 0 0 0 0 3 0 0 0 0 3 Asimina triloba 31 0 9 10 0 0 25 0 0 0 0 0 75 Carya cordiformis 6 7 5 19 2 2 6 0 0 0 0 27 74 Carya laciniosa 0 0 0 0 0 4 6 0 0 0 2 0 12 Carya ovata 0 1 5 10 38 0 0 0 0 0 0 0 54 Celtis occidentalis 4 8 25 23 8 27 48 0 6 4 7 3 163 Cornus florida 0 12 1 0 0 0 0 0 10 0 0 0 23 Cornus racemosa 0 0 0 0 1 0 0 0 2 0 0 0 3 Crataegus spp 0 1 0 4 4 0 0 0 0 0 5 0 14 Fagus grandifolia 1 0 13 10 0 0 5 0 0 0 0 0 29 Fraxinus quadrangulata* 0 0 0 0 0 0 0 0 0 0 2 0 2 Fraxinus spp* 3 27 13 16 20 16 5 1 3 9 15 8 136 Gleditsia triacanthos 0 0 0 7 0 0 0 0 5 0 3 0 15 Gymnocladus dioicus 0 0 0 0 0 0 2 0 0 0 0 0 2

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Juglans nigra 17 0 0 9 2 5 1 0 2 1 0 4 41 Juniperus virginiana 0 0 0 3 0 0 0 0 0 0 0 5 8 Liriodendron tulipifera 1 0 0 0 0 0 0 0 0 0 0 0 1 Maclura pomifera 1 0 0 0 0 0 0 1 0 0 0 0 2 Morus alba 0 15 0 0 0 0 2 8 0 0 0 0 25 Ostrya virginiana 1 17 12 9 0 0 3 0 0 0 0 0 42 Planatus occidentalis 0 0 0 1 0 0 0 0 0 0 0 0 1 Populus deltoides 0 0 0 0 0 1 0 0 0 0 0 0 1 Prunus serotina 0 1 12 0 0 2 13 0 21 0 0 5 54 Quercus alba 0 0 0 0 2 0 0 0 0 0 0 0 2 Quercus bicolor 0 0 3 0 0 0 0 0 0 0 0 0 3 Quercus macrocarpa 0 1 0 0 0 0 0 0 0 0 2 0 3 Quercus muehlenbergii 0 0 0 7 0 0 1 0 1 0 8 0 17 Quercus palustris 0 0 0 0 14 0 0 0 0 0 0 0 14 Quercus rubra 1 5 0 1 27 0 0 0 0 1 2 0 37 Robinia pseudoacacia 0 0 0 0 2 0 0 2 0 0 0 0 4 Sambucus nigra 0 4 0 0 0 2 0 0 0 0 0 0 6 Tilia americana 5 8 8 0 0 0 0 0 0 0 0 0 21 Ulmus americana 0 35 0 6 0 0 0 0 0 0 0 0 41 Ulmus rubra 0 0 4 33 19 15 3 0 7 3 11 10 105 Vibernum prunifolium 0 0 3 9 0 0 0 0 0 0 0 1 13 Zanthoxylum americanum 0 4 0 0 41 0 0 0 0 0 0 0 45

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TOTALS 180 174 124 217 196 87 129 22 59 56 62 63 1369 * Fraxinus trees of the region had recently been infested with Agrilus planipennis, the Emerald ash borer (Wildman 2008), and many were dying. Therefore, most of the Fraxinus individuals in these study locations were likely in various stages of decline in 2015 (Hoven et al. 2017). In this study, individuals were counted if standing and with the crown intact.

References Wildman RH (2008) Ohio’s forest resources, 2006. Note. NRS-22 U.S. Department of Agriculture, Forest Service, Northern Research Station. Newton Square, PA. Hoven HM, Gorchov DL, Knight KS, Peters VE (2017) The effect of emerald ash borer-caused tree mortality on the invasive shrub Amur honeysuckle and their combined effects on tree and shrub seedlings. Biol Invasions 19:2813-2836.

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Supplementary Table 5: Top four generalized additive models of abundance (not shaded) and species richness (shaded) for bees of pan traps at fine resolution (0.15 m x 0.15 m) and vane traps at coarse resolution (30 m x 30 m). Models are ranked by AICc. Degrees of freedom include penalized smoothed terms. Important predictors of characteristics of the focal forest patch included the linear variables of abundance, species richness, or evenness of the flowering herbaceous community (flowerabun, flowerrich, flowereven) or abundance or species richness of the tree community (treeabun, treerich) along the transect of forest edge, margin width between the belt transect and adjacent crop field (marginT), and the non-linear effect of the day of the study ( s(time) ). Patch-scale interactions included nonlinear effects of tree abundance or species richness with the day of the study ( ti(time, tree) ) and the linear effects of tree abundance with evenness of the flowering plant community (treeabun:flowereven). Important landscape predictors included the proportion of land cover types, including semi-natural habitat (SNH) that is frequently disturbed (disturbed SNH), margins between soybean/corn fields as well as drainage ways within crop (drainage), fallow fields and crops that are bee- pollinated (fallow), crops that are not bee-pollinated or do not produce a showy flower (flowerless crop), forested SNH (forest), high densities of buildings, roads, parking lots and other impenetrable surfaces (impervious surface), grass lawns (lawn), soybean crop fields (soybean), strips (<10 m wide) of habitat between forest and any other land cover type (stripF), strips of habitat that were not between two crop fields nor adjacent to forest land cover type (stripother), and open water (water). Direction of the relationship between response and predictor is either positive (+) or negative (-) and depicted as superscripts following each linear predictor. Smoother terms are not provided a linear direction. Scale is 2.0 km (*).

Model df ∆AICc (-) (+) (+) s(time) + ti(time, treerich) + treerich + marginT + forest 14.4 0.00 (-) (+) (+) (-) s(time) + ti(time, treerich) + treerich + marginT + forest + stripF 15.5 0.22 (-) (+) (+) (-) (-) Pan s(time) + ti(time, treerich) + treerich + marginT + forest + stripF + water 16.6 0.61 Abund. (-) (+) (+) (-) (-) s(time) + ti(time, treerich) + treerich + marginT + forest + stripF + disturbed SNH 16.6 0.69 (-) (+) (+) (-) (-) s(time) + ti(time, treeabun) + treeabun + marginT + forest + lawn + drainage 14.2 0.00

(-) (+) (+) (-) (+) s(time) + ti(time, treeabun) + treeabun + marginT + forest + grassland + stripother 14.2 1.08

(-) (+) (+) (-) Pan

Rich. s(time) + ti(time, treeabun) + treeabun + marginT + forest + lawn 13.2 1.14 (-) (+) (+) (-) (+) s(time) + ti(time, treeabun) + treeabun + marginT + forest + impervfine + stripother 14.2 1.47 (-) (+) (-) s(time) + ti(time, treeabun) + treeabun + forest + fallow 17.3 0.00 (-) (+) (-) s(time) + ti(time, treeabun) + treeabun + forest + fallow * 17.5 1.03 (-) (+) (-) (-)

Vane s(time) + ti(time, treeabun) + treeabun + forest + fallow + flowerless crop 18.1 1.05 Abund. (-) (+) (+) (-) s(time) + ti(time, treeabun) + treeabun + flowerabun + forest + fallow 18.3 1.21 (-) (+) (-) (+) s(time) + ti(time, treeabun) + treeabun + forest + fallow + impervious surface 14.2 0.00

(-) (+) (-) (+) s(time) + ti(time, treerich) + treerich + forest + fallow + impervious surface 14.1 0.82

(-) (+) (-) (-) Vane Vane Rich. s(time) + ti(time, treeabun) + treeabun + flowerabun + fallow + soybean 14.2 1.32 (-) (-) (-) (+) s(time) + ti(time, treeabun) + treeabun + flowereven + treeabun:flowereven + flowerless crop 14.2 1.41

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Supplementary Figure 1: Differences in AICc scores of best models for bee abundance (a and c) and species richness (b and d) responses of samples from pan traps (a and b) and vane traps (c and d) across spatial scales. Competing models were ∆AICc < 2. Coarse resolution models were performed at all spatial scales, while fine resolution models only included 0.1, 0.5, and 1 km scales. 125

Supplementary Figure 2: Changes in effects of either tree species richness (a) or abundance (b-d) on bee abundance (a & c) or species richness (b & d) of pan traps (a & b) or vane traps (c & d) as the season progressed (April, July, and November shown). Magnitude of the response by bees is represented by a blue-yellow color gradient, where a negative response is indicated by blue and a positive response is represented by yellow. Pixels of gray represent uncertainty due to a lack of observations. Contour lines were overlaid for ease of interpretation. The y-axis was natural-log transformed, and both axes were centered within the analysis.

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Supplementary Figure 3: Partial residual plots of the smoothing effect of season in best models for pan trap bee abundance (a), pan trap bee species richness (b), vane trap bee abundance (c), and vane trap bee species richness (d). Season is a continuous predictor and marked by the first letter of the month, where the first “A” indicates April and “N” indicates November. Intervals (between dashed lines) represent two standard errors from the fitted value (black line). Partial residuals are plotted in the background. Red shaded region represents the time period in which L. maackii flowered (May 11- June 3).

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Supplementary Figure 4: Partial residuals from the best model explaining bee abundance of pan traps (a) and species richness of vane traps (b) that were used to fit the season predictor refit to L. maackii density. Only partial residuals from 11 May – June 3 were used to represent the time period when L. maackii was flowering. 128

Chapter 3: Floral resources of an invasive shrub alter native bee communities at different vertical strata in forest-edge habitat (Cunningham-Minnick and Crist, in revision)

1 Abstract Disturbances associated with intensive agriculture facilitate the spread of invasive plants that become dominant along habitat edges in fragmented landscapes. Many invasive woody plants offer large quantities of floral resources to bees, but little is known about how invasive plants affect the use of flowering resources in the forest canopy and understory by native bee communities. I sampled the bee community at vertical strata along forest-agriculture edges that varied in density of a dominant invasive shrub Lonicera maackii before, during, and after the flowering period. I also recorded diameters and species identities of woody stems. Bee and woody plant abundance, diversity, and life-history traits were then spatially and temporally compared in response to L. maackii density. Overall, I found that L. maackii structured bee communities through its floral resources by altering bee species composition and supporting greater abundances and species richness of bees during and after its flowering period. I also demonstrated a diverse and abundant bee community up to 16 m high in the forest canopy that is supported by floral resources of native shrubs and trees but sensitive to woody shrub invasion. These findings collectively suggest that this invasive shrub structures the bee community in favor of species that use its own flowers and competes with co-flowering woody species at different vertical strata. My study emphasizes the importance of forest trees and shrubs for bees in agricultural landscapes and demonstrates potential risks for early-season bees as well as those that cannot use floral resources of this invasive shrub.

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2 Introduction Historic pollinator-plant mutualisms are lost within habitat fragments of agricultural landscapes (Burkle et al. 2013) due to invasion of alien plant species that provide floral resources (Montero-Castaño and Vilá 2012; Vanbergen et al. 2017). Invasive flowering plants tend to be supergeneralists (Vilà et al. 2009) and can outcompete native species through the usurpation of local pollinators by offering large quantities of nectar and pollen (Williams et al. 2011). This can lead to novel pollinator-plant mutualisms and structure local bee communities in favor of species that exploit these additional floral resources (Aizen et al. 2008; Bartomeus et al. 2013; Burkle et al. 2013; Kleijn et al. 2015; Vanbergen et al. 2017). Competition for pollinators between invasive and native herbaceous flowering species is well studied (Carvalheiro et al. 2014; Morales and Traveset 2009; Stout and Tiedeken 2017) and may focus around the quantity (Carvalheiro et al. 2014) and physical properties of flowers (Morales and Traveset 2009). However, interactions among alien invasive and native woody plants and pollinators are not understood and will likely remain unknown until relationships between pollinators and native woody species are described (Stout and Tiedeken 2017). Despite the common acceptance that native and entomophilous woody species with showy flowers are beneficial to wild bees, there is a general lack of literature addressing bee community responses to woody plant floral resources with or without showy flowers. Native woody plants in temperate forests can provide large quantities of floral resources to pollinators (Somme et al. 2016), yet the few studies that quantified flower abundance of woody vegetation used small sample sizes of individual stems (Feret et al. 1982, McCarthy and Quinn 1989) or coarse estimation of flower density (Grisez 1975). Flower quantities among the vertical strata of the forest canopy are largely unknown, but based on seed data can range from 2,000 in the small insect-pollinated (entomophilous) understory tree Viburnum prunifolium (Blackhaw; Laska and Stiles 1994) to over 956,000 in the entomophilous (in-part) canopy species Acer rubrum (Red Maple; Abbott 1974; Batra 1985). Each stem of invasive shrubs can add hundreds to thousands of additional flowers to the understory (Hassett and McGee 2017), but the relative use of these floral resources is unknown compared with other flowers throughout the canopy. There is evidence that tropical bees partition the use of canopy strata based on nesting or food resource availability (Roubik 1993; Nuttman et al. 2011; Stangler et al. 2016), emphasizing that partitioning of vertical space may be due to different functional and life-history attributes of

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bees and woody vegetation. Direct observations among temperate strata have been incidental (Gabriel and Garrett 1984) or derived from studies that focus on the more accessible understory and subcanopy species (Batra 1985; Macior 1978; Rhoades 2010; Robertson 1929; Willson and Schemske 1980), which produce a small percentage of the total available floral resources. In general, associations between native bees and temperate woody species were only recently discovered and rely largely on body pollen (Ascher and Pickering 2018; Bertrand et al. 2019; Proesmans et al. 2019; Smith et al. 2019; Wood et al. 2018). Other studies reported bee occurrences at higher canopy strata of temperate forests, particularly of species that use wood as a nesting substrate (Sobek et al. 2009; Ulyshen et al. 2010). Vertical differences in bee communities were also demonstrated among the shorter canopies of grassland and cropland systems (Geroff et al. 2014; Hoehn et al. 2008; Klecka et al. 2018; Tuell and Issacs 2009). Therefore, bees can partition vertical space in different systems, but the distribution of bees and the effects of tree and shrub species composition and invasive woody plants on bee communities are largely unexplored for habitat edges in temperate forests. To test the responses of bee communities to woody vegetation and their floral resources, I evaluated bee species composition and functional composition at different vertical strata along the edges of forest fragments in an agricultural landscape. I also tested the effects of alien floral resources on bee species composition among vertical strata by quantifying changes in the bee community before, during, and after the flowering period of the entomophilous (Barriball et al. 2014; Goodell et al. 2010) and invasive woody shrub, Lonicera maackii (Amur honeysuckle), in 10 forest patches representing a gradient of invasive shrub density. Lonicera maackii is patchily distributed throughout forest fragments and early successional habitat of the eastern United States (McNeish and McEwan 2016). It competes with neighboring vegetation by altering soil properties and biota, blocking light, and affecting foraging patterns of pollinators (Collier et al. 2002; Cunningham-Minnick et al. In Press; Goodell et al. 2010; McKinney and Goodell 2010; McNeish and McEwan 2016). During 3–4 weeks in late spring, even small stems (< 2 cm basal diameter) of shrubs can each produce >1,000 flowers in the understory (Hassett and McGee 2017). Flowers are primarily outcrossed, rich in nectar and pollen (Barriball et al. 2014; Jachuła et al. 2019; Southwick et al. 1981), attract a variety of pollinators including bees (Goodell et al. 2010; Jachuła et al. 2019), and form red berries that are spread along the edges of forest patches by birds (Bartuszevige and Gorchov 2006). Lonicera maackii is, therefore, an excellent model

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species to study plant-pollinator interactions between bees and woody vegetation communities along invaded forest edges. Specifically, I hypothesized that the bee community would change over time in response to the phenology of woody species. Since trees and shrubs produce large quantities of floral resources, I predicted greater bee abundance with larger quantities of flowering woody plants. Due to the different flowering morphologies and pollen dispersal mechanisms (e.g. insect- mediated, wind) of woody species, I anticipated entomophilous species to be the best predictors of bee abundance, species richness, and species composition. I also hypothesized that the abundant and dense floral resources of L. maackii would affect the occurrence of bee species within the forest understory. Considering previous work suggesting that L. maackii floral resources provide supplemental food to the forest-edge bee community (Cunningham-Minnick et al. in press), I predicted that greater L. maackii density would result in higher bee abundance in the understory, and not affect bee species richness across vertical strata.

3 Materials and Methods 3.1 Site Selection I selected a total of 10 isolated patches of temperate forest (area 5.6-27.5 ha) in the early spring of 2016 on private lands in SE Indiana and SW Ohio in the USA (Supplementary Table 1). Forests comprised primarily deciduous trees that represented a range of early to late- successional woody species that grow well in the moist clayey soils typical of the flat and uncultivated land in the region. Dominant canopy species included Prunus serotina (black cherry), Juglans nigra (black walnut), Celtis occidentalis (hackberry), Carya spp (hickory), and Acer spp (maple) while the frequent subcanopy species were Aesculus glabra (buckeye), Asimina triloba (pawpaw), and Carpinus caroliniana (American hornbeam). Few native shrubs consistently grew within these forest patches, but there were often dense clusters of ash (Fraxinus spp) seedlings, pawpaw, and spicebush (Lindera benzoin) within the understory and at the forest edge (personal observation). Each patch neighbored agricultural fields of conventional corn or soybean monocultures (Supplementary Figure 1). Forest habitats represented a gradient of L. maackii densities along the forest edges (Supplementary Table 1) and were 6.1 to 47.8 km apart. Along the edge of each forest patch (site), I established a 100 x 10-m belt transect and extrapolated L. maackii density within each transect by measuring total stem basal area in five

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randomly chosen 5 x 5-m quadrats adjacent to the crop field in 2015. To ensure all sites were susceptible to L. maackii invasion, I verified the presence of an L. maackii individual along the perimeter of every patch.

3.2 Bee Community I sampled the foraging bee community four times using UV-reflective blue vane traps (SpringStar® Item #ZBVT): once before (late April), twice during (mid and late May), and once after (late June) the flowering period of L. maackii. Blue vane traps favorably catch bees that are larger and stronger flyers compared to the more often used pan traps (Gibbs et al. 2017; Joshi et al. 2015; Rhoades et al. 2017) but are also suspected to better sample small and large species that nest in wood (Chapter 2). I sampled the bee community a second time during May following poor sampling conditions due to frequent rain events. Otherwise, I set traps during favorable conditions and did not experience issues with unfavorable weather. Vertical arrays of traps placed along the forest edge sampled the bee community (similar to that of Nuttman et al. (2011); Supplementary Figure 2). Each vertical array consisted of six traps attached to a rope, with traps evenly spaced every 3 m and the first trap 1 m from the ground (therefore, at heights of 1, 4, 7, 10, 13, and 16 m). I established one array of traps in the first 50 m of the belt transect, and another in the second 50 m stretch, for a total of two vertical arrays (hereafter “plots”) and 12 traps at each site. I secured traps at the ground using a large stake at the edge of the site and in the canopy by looping black paracord over a tree limb >16 m above the forest floor. Therefore, overall canopy height was >16 m and traps did not reach crowns of the tallest trees across sites. Traps contained propylene glycol as a preservative and were active for 48 hours each sampling period. I washed all bee specimens and identified bees to species.

3.3 Woody Plant Community I recorded species identity and stem diameter at breast height (1.2 m) of every tree and native shrub taller than 1.2 m, herein referred to as woody vegetation, within the 100 x 10-m belt transect at each site. I then calculated basal area for each individual stem and considered stems with diameter at breast height < 3 cm as 3 cm in diameter. These small individuals contribute negligibly to total basal area estimates and do not provide floral resources for bees (with the exception of native shrubs; see Supplementary Table 2), yet the presence of other resources (i.e.

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leaves and twigs for nesting material) may be important in predicting the occurrence of cavity- nesting bees (Cane et al. 2007) and understanding interactions with L. maackii. I did not quantify infrequent lianas and invasive shrubs other than L. maackii, though I observed Vitis spp., Toxicodendron radicans, Rosa multiflora, Campsis radicans, Lonicera japonica, and other vining species. As density of L. maackii was used as a predictor variable, I did not include this species in measures of woody plant composition. I extracted life history traits of woody vegetation from the literature, including flowering period in relation to observed L. maackii bloom (aided by field observations), floral display properties, known use of insect pollinators (entomophily), and typical size at maturity (Supplementary Table 2). To account for tree and shrub species with flowering times spanning multiple sampling periods, I derived two metrics to represent flowering time of woody vegetation at a site. For the first method, I assigned a value of 1–5 to woody plant species within one of five categories representing unique combinations of bee sampling periods relative to L. maackii flowering: 1=before, 2=before/during, 3=during, 4=during/after, 5=after. Weighted by the abundance of woody plants within each category, I then calculated the arithmetic mean for each site and used the value as an ordinal metric to represent the flowering time of woody vegetation relative to that of L. maackii. For the second method, I scored the flowering of each woody plant species before, during, and after L. maackii flowering such that each species of woody plants flowered in one, two or all three categories. I extracted a composite variable of flowering times by performing a principal component analysis using the rda function in the vegan package (Oksanen et al. 2019). The species scores on the first Principal Component (Supplementary Table 2; Supplementary Figure 3) applied to the species abundances and then averaged provided a composite metric that also represented mean flowering time of the woody vegetation community at a site. For each method, I used mean values at each site in analyses. The ordinal metric for flowering time of all woody vegetation was similar to the composite metric with a Pearson’s correlation coefficient of 0.74 while the correlation between the two metrics for mature woody vegetation was different (r = -0.13). For maturity designations, I considered a stem as mature if it was of the age or size at which that species typically flowers. I derived maturity estimates from diameter at breast height measurements of individuals of each species, and used size estimates at maturity (diameter at breast height) from measured and predicted values of other studies (i.e. Burns and Honkala 1990;

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Parker et al. 1985; USDA 2008). When literature sources used plant height to represent maturity, I estimated individual heights using parameter estimates derived in the Weiskittel et al. (2016) tree growth model which represented most of these same species in New York (Supplementary Table 2).

3.4 Analyses 3.4.1 Bee Abundance and Species Richness I built generalized linear mixed models using the glmmTMB function in the glmmTMB package to determine the roles of woody vegetation and L. maackii in explaining bee abundance and species richness using negative binomial and Poisson error distributions, respectively. The glmmTMB package works well for modeling ecological count data and accommodates models with complex random effects and autoregressive terms and can incorporate zero-inflation extensions (Brooks et al. 2017). Visuals of model products were made with the ggplot2 package, which provides a variety of user-friendly commands to create precise graphics of model output (Wickham 2016). Within mixed effects models, I included the time of bee sampling relative to L. maackii flowering (i.e. before, during, after), hereafter “sampling period,” as a predictor. Samples in unfavorable conditions during L. maackii flowering in mid-May resulted in much fewer bees (337 individuals of 54 species) compared to the sample taken in favorable conditions in late May (752 individuals of 63 species). Therefore, I dropped the sample taken under poor conditions from all analyses and results. For all analyses, a sample consisted of two traps pooled at sequential heights to represent a vertical stratum (understory [1 m and 4 m], subcanopy [7 m and 10 m], canopy [13 m and 16 m]) in each plot of each site. The random effect of vertical stratum nested within the unique plot (n = 20) was used in all models to account for the spatial correlation among samples within a plot. I also included an autoregressive term to account for autocorrelation among bee samples of each plot across sampling periods using the ar1 function. Other model predictors included vertical stratum, sampling period, mature woody vegetation abundance, woody vegetation species richness, ordinal metric of peak flowering time of woody vegetation, composite metric of peak flowering time of woody vegetation, and basal area of entomophilous or showy-flowered woody vegetation species within the transect (extracted from literature). I centered all continuous predictors to reduce collinearity and identified correlated variables (corr ≥ 0.7) prior to analyses and did not include them within the same model. I log-

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transformed L. maackii density, woody vegetation abundance and species richness, and mature woody vegetation abundance and species richness before centering. I checked for multicollinearity using the check_collinearity function in the performance package, which is currently one of few that evaluates glmmTMB objects (Lüdecke et al. 2019) and dropped models with any term that had a variance inflation factor > 4.0. All analyses were performed in the R programming language (R Core Team, 2018). To find the best predictors of bee abundance and species richness responses, I investigated all main effects and two-way interactions. Model creation included flowering time for woody vegetation (i.e. composite and ordinal metrics for mature and all woody stems) as well as density and diversity (i.e. basal area of all, entomophilous, and showy-flowered woody species, as well as abundance and species richness for mature and all stems) predictors such that only one flowering time and one density or diversity predictor was permitted in any given model. I performed model selection using AICc values (Mazzerole, 2016) to find competing models (∆AICc ≤ 2). Models with more than six terms were dropped from analyses. I determined model significance by a likelihood ratio test of the intercept model with and without fixed effects. 3.4.2 Bee Community Composition Distance-based redundancy analysis (dbRDA) analyzed bee species composition as implemented by the dbrda function in the vegan package, which contains a variety of ordination methods for community analyses (Oksanen et al. 2018). I removed singletons (n = 30) and doubletons (n = 13) to analyze 5,719 bees of 60 species. Sampling period, L. maackii density, vertical stratum, either all or mature woody vegetation abundance, woody vegetation species richness, basal area of all woody species, woody species with showy flowers, and entomophilous woody species, as well as either ordinal metric of peak flowering time of all or mature woody vegetation, or composite metric of peak flowering time of all or mature woody vegetation served as predictors for full models. To reduce the sparsity of the species matrix, I combined samples of the two plots within a site (per vertical stratum) for dbRDA and RLQ analyses. I did not explore interactive effects among predictors. A step-up and step-down approach with the ordistep function selected best models, whereby the terms were sequentially added to a model or removed, respectively, to acquire a model with maximum terms that when permutated, had a p- value < 0.05.

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I also used RLQ methods from the ade4 package (Dray and Dufour 2007) to examine the relationships among the bee community, bee functional traits, and environmental predictors. The ade4 package is also useful for other multivariate analyses including two-tabled coinertia analysis, but is to my knowledge the only package that provides a three-table analysis, or RLQ analysis, which simultaneously analyzes the links among three data matrices, including the site x environment R matrix, the species x abundance L matrix, and the species x trait Q matrix (Dolédec et al. 1996). Due to the behavioral and morphological differences between male and female bees, I chose bee species traits similar between sexes, including body size (mm), nesting substrate (soil/wood), sociality (solitary/social), and foraging specialization (oligolectic/polylectic) for the Q matrix to provide one value for each trait per species (Supplementary Table 3). The average body length of three males and three females based on availability determined the body size value for each species. For Bombus spp, I also included three queens within the calculation. I obtained nesting substrate, sociality, and foraging specialization from the literature (Ascher and Pickering 2018; Gibbs 2010; Gibbs et al. 2013, Michener 2000, Richards et al. 2011). I investigated foraging specialization and found that 41% of the bee species were known to forage on < 10 plant families, but only accounted for 4% of the total abundance. Given the infrequency of specialist behaviors in these bee communities, I removed foraging specialization from the RLQ analysis. Since Apis mellifera (honeybee) is a managed species and unlikely to represent viable wild populations in this area, they were also excluded from the RLQ analysis. To visualize associations among environmental predictors that explained the most variation among the three matrices, I included species richness of all woody vegetation, total basal area of individuals with showy flowers, and the composite metric of mean flowering time for all woody vegetation in the RLQ analysis, as well as sampling period, vertical strata, L. maackii density, and two derived environmental predictors: percentage of woody vegetation estimated to not be of flowering age (immature) and percentage of woody vegetation estimated to be < 7 m in height. The total variation (inertia) of the RLQ analysis represented by the SRLQ statistic (Dray and Legendre 2008) was permuted 100,000 times with a bias correction to derive the reported p-value, though correlations and general trends are discussed.

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4 Results 4.1 Bee Abundance and Species Richness I analyzed 5,775 bees representing 103 species of 22 genera and five families across the 10 forest patches (Supplementary Table 3). Additionally, I measured 2,703 shrubs and trees of 50 species, 34 genera, and 23 families (Supplementary Table 2). Generalized linear mixed effects models demonstrated that bee abundance was best predicted by L. maackii density and two-way interactions of total abundance of woody vegetation with the sampling period as well as vertical 2 2 stratum (χ (10) = 45.4; P < 0.0001; R adj = 0.22). Overall, L. maackii density exhibited a weak positive effect on bee abundance (Figure 1; 0.09 ± 0.06, Z = 1.83, P = 0.07). There were generally fewer bees sampled in each array of traps during L. maackii flowering (95% CI: 27 – 49 individuals) than after (94 – 244 individuals) but not before (29 – 135 individuals). However, a significant interaction between the sampling period and abundance of woody vegetation (χ2(2) = 15.27; P < 0.001) demonstrated there were 1.94 ± 0.79 (log units) more bee occurrences earlier in the season with more stems of woody vegetation (interaction coefficient: 1.81 ± 0.45; Z = 4.0; P < 0.0001) relative to the sampling period during the L. maackii flowering. This interaction did not exhibit a significant effect on bees sampled after the L. maackii flower pulse in May (Figure 2A; interaction coefficient: 0.43 ± 0.44; Z = 0.99; P = 0.32). On average, the distribution of total bee abundance across the vertical strata were similar (understory: 33.7%; subcanopy: 32.6%; canopy: 33.7%). There was a weak interaction between vertical strata and tree abundance (χ2(2) = 5.07; P = 0.08) that showed greater bee abundance in the canopy stratum at sites with a greater number of trees (Figure 2B; interaction coefficient: 0.44 ± 0.20; Z = 2.19; P < 0.05). Otherwise, more trees resulted in a negative effect on bee abundance (-0.79 ± 0.36; Z = -2.20; P < 0.05). Two competing models were similar to the best model but did not include either the interaction between vertical strata and woody vegetation abundance (∆AICc = 0.28) or L. maackii density (∆AICc = 0.62). A third competing model included the sampling period, vertical strata, a positive effect of L. maackii density, a negative effect of total basal area of all woody vegetation and its interaction with the sampling period where there was greater bee abundance with more basal area before the flowering period of L. maackii (Table 1). Lonicera maackii density was not included in the best generalized linear mixed model explaining bee species richness. Instead, this model included vertical stratum, an interaction

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between species richness of all woody vegetation and sampling period, and the composite metric 2 2 of flowering time of mature woody vegetation (χ (8) = 49.31; P < 0.0001; R adj = 0.24). There were more bee species in the understory than the subcanopy (0.23 ± 0.08; Z = 3.1; P < 0.005) and canopy (0.42 ± 0.08; Z = 5.3; P < 0.001; Figure 3). Relative to the flowering period of L. maackii, more species of bee were sampled after (0.27 ± 0.13; Z = 2.2; P < 0.05) but not before (-0.13 ± 0.11; Z = -1.2; P = 0.25). Fewer bee species were found at sites with more species of woody vegetation (-0.67 ± 0.24; Z = -2.8; P < 0.05), but bee species richness was greater with more species of woody vegetation prior to the flowering period of L. maackii (interaction coefficient: 1.42 ± 0.33; Z=4.3; P < 0.001; Figure 4). Finally, sites with more mature woody vegetation that flowered later in the season were negatively associated with bee species richness (-0.06 ± 0.03; Z = -2.3, P = 0.17). Within the best model explaining bee species richness, the composite metric of mature woody vegetation performed better than the ordinal metric (∆AICc = 4.65). A competing model shared predictors of the best model but included a positive effect of the abundance of mature woody vegetation instead of a negative effect of woody vegetation species richness, as well as an interaction with the sampling period that showed less bee species with more mature woody stems early in the season (∆AICc = 0.21). Another competing model was similar to the previous, but replaced the negative effect of a later flowering time of mature woody vegetation with a positive effect of L. maackii density and its interaction with sampling period such that there were the most bee species during the flowering period of L. maackii (∆AICc = 1.63). Finally, a third competing model included a positive effect of L. maackii, a positive effect of total basal area of mature woody vegetation, and effects of vertical strata and sampling period. There was an interaction between sampling period and total basal area of mature woody vegetation that reflected the higher bee species richness with greater basal area during and after the flowering period of L. maackii, compared to the bee richness before the flowering period of L. maackii. Another interaction demonstrated similar temporal effects on bee species richness at sites with greater L. maackii density (∆AICc = 1.72; Table 1).

4.2 Bee Community Composition The distance-based redundancy analysis significantly explained bee species composition

(Pseudo-F = 2.93; df = 3; P < 0.005), though environmental variables accounted for only 9% of the variation. Lonicera maackii density was a predictor in every top model (Table 2).

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Constrained variation of the best model was explained by the composite metric of flowering time for all woody vegetation (Pseudo-F = 2.08; df = 1; P < 0.05), L. maackii density (Pseudo-F = 1.89; df = 1; P < 0.05), and total basal area of all woody plant species with showy flowers

(Pseudo-F = 1.73; df = 1; P = 0.07). Sampling period (Pseudo-F = 0.59; df = 2; P = 0.95) and vertical strata (Pseudo-F = 0.53; df = 2; P = 0.97) were not important predictors of bee species composition. Predictors of the full model in the RLQ analysis captured 63% of the total inertia, but did not demonstrate a global relationship among environmental predictors, structure of the bee community, and bee species life history traits (P = 0.23; Figure 5; Supplementary Figure 4). However, there was a high correlation between the first species trait axis and RLQ Axis 1 (corr = 0.98) as well as the first environmental axis and RLQ Axis 1 (corr = 0.87).

5 Discussion This study demonstrated for the first time that bee communities shift temporally and spatially due to the phenology of woody species, including native and invasive plants. Lonicera maackii may play multiple roles in structuring the bee community along forest edges. Consistent with other studies, L. maackii floral resources supported a component of the forest-edge bee community with flight times during and after its flowering period (Cunningham-Minnick et al. in press; Goodell et al. 2010). My findings also suggest that a more abundant and diverse woody plant community supports early-season bees prior to L. maackii flowering. However, L. maackii density altered the species composition of the bee community across sampling periods. My results collectively suggest that L. maackii modifies the bee community in favor of species that use its floral resources and may interfere with plant-pollinator relationships between woody species and bees.

5.1 Lonicera maackii Affects Vertical Use of Forest Edges Bees responded to L. maackii density through multiple mechanisms. Lonicera maackii density affected the bee community through its floral resources, but its overall effects on bees were mediated by the woody plant community (Figures 1, 2, and 4). Therefore, L. maackii structures the bee community along forest-agriculture edges in this fragmented landscape, as the

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relationships between bees and resources among vertical strata were influenced by the flowers of L. maackii. Our prediction that L. maackii density would positively affect bee abundance was supported. The best model explaining bee species richness did not include a term for L. maackii density, though competing models showed an overall positive, as well as seasonally dependent, effects of L. maackii density on bee species richness (Table 1). More bee species along forest edges with greater densities of L. maackii during and after its bloom suggested that L. maackii flowers attracted species to the forest edge from outside the forest patch. Greater L. maackii density also resulted in higher bee abundances, further suggesting that its flowers provided supplemental food resources to many bee species and that these effects extended into the summer. Since L. maackii was also an important predictor of bee species composition (Table 1), L. maackii resources are collectively driving changes in the bee community. These findings are remarkably consistent with those of Cunningham-Minnick et al. (in press) who inferred through a flower-removal experiment that L. maackii flowers provided supplemental food to large-bodied bees along heavily invaded forest edges. The temporal component within my study design revealed an interference role of L. maackii on the relationships between bees and woody vegetation. Considering that later flowering times of the mature woody plants in a site were positively correlated with L. maackii density (Supplementary Figure 5), shrubs and trees that co-flowered with L. maackii were more prevalent at sites with more invasive shrubs. However, the bee community was less abundant and species rich in a species-rich and abundant woody plant community when L. maackii was in bloom (Table 1; Figures 2A and 4), suggesting that the bee community was not responding positively a greater amount woody vegetation co-flowering with L. maackii. One possible explanation supported by the interaction between canopy stratum and woody plant abundance suggests that bees in the subcanopy were drawn to L. maackii flowers in the understory (Figure 2B). Considering that L. maackii density played an important role in altering bee species composition, these findings suggest that bee assemblages at heavily invaded sites during L. maackii flowering were comprised of species that use and benefit from L. maackii floral resources despite the abundant and diverse entomophilous woody plants that were co-flowering throughout the vertical strata (Supplemental Table 2). Since these bee species were generally large-bodied and generalist foragers that were social and nested in the soil (Figure 5; Chapter 2),

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it is unclear what proportion of them originated from the forest patch or relied on floral resources of woody species that co-flower with L. maackii. Nevertheless, these results suggest that bee species that use L. maackii floral resources were more dominant in sites with more flowering L. maackii and that this changed the distribution of the bee community across vertical strata. Differences in woody vegetation communities with greater L. maackii density are noteworthy, though my study was not designed to test long-term effects of L. maackii on woody plants. My findings showed a negative correlation between L. maackii density and immature woody vegetation, particularly of species that flowered in the early spring (Figure 5). Additionally, woody species with showy flowers (often entomophilous) were correlated with sites of greater L. maackii density (Figure 5, Supplementary Figure 5). Considering these relationships and other studies that directly demonstrated reductions in woody vegetation due to L. maackii (Collier et al. 2002; Hoven et al. 2018), one explanation for my findings includes L. maackii suppression of woody vegetation in combination with pollination of co-flowering trees and shrubs by the generalist pollinators benefitting from L. maackii floral resources. Under this scenario, the loss of woody species with an early-season flower phenology could detrimentally affect early-season bees in years to come and is one explanation for why I found fewer bee species before the flowering period of L. maackii at sites with a greater density of this invasive shrub (Table 1). Alternatively, forest edges comprised primarily of woody vegetation that co- flowers with L. maackii are more likely to have bee species within the local pollinator pool that use L. maackii floral resources, which may facilitate the pollination and spread of this invader. Provided that a diverse and abundant woody plant community is important to preserve an abundant and diverse bee community, especially early-season bees (Figures 2A and 4), long-term woody-mediated effects on the bee community need to be further investigated. Overall, L. maackii had a strong influence on bee community composition both seasonally and spatially. Lonicera maackii stands of greater density supported more large-bodied and social generalist bee species during and after its flowering period, which coincided with a shift towards late-flowering woody species (Figure 5; Supplementary Figure 5). Since the diverse assemblage of early-season bees relies on early-flowering species of woody plants, the mechanistic role of L. maackii on early-season bees and flowering trees and shrubs needs clarified. These findings suggest that dense floral resources of L. maackii stands compete with those of co-flowering native woody vegetation in different vertical strata and support a subset of

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bee species. Thus, my study suggests that L. maackii is structuring the bee community in favor of social or late-season generalists that use its floral resources.

5.2 Bees Across Vertical Strata The bee community shifted temporally and spatially in response to floral resources of native and invasive woody plants. Considering vertical stratum was not a predictor of bee species composition, bee abundances were evenly distributed across the vertical strata. Since there were more bees in the canopy stratum early in the season with greater abundances of trees, it is likely that the bee community is seasonally dependent on resources of woody species at heights > 16 m from the ground. This may be representative of concentrated floral resources in crowns of the tallest species where my traps did not reach, especially in early spring when most trees flower (Gabriel and Garrett 1984). Bee species richness was highest in the understory (Figure 3), yet 66% of species were found in the subcanopy and canopy strata and 18 species were not sampled in the understory. Evidence that bees seasonally partition vertical space within the forest-edge habitat emphasizes the importance of including woody species in the plant-pollinator networks of eastern temperate forests. Overall, abundant and species-rich bee assemblages were distributed throughout the vertical strata, suggesting that native shrubs and trees provide important resources to forest bee communities. The mechanism for this expansive distribution of the bee community was in part the size and floral phenology of mature woody plants. Bees generally exhibited greater abundance and species richness in response to species richness and abundance of woody vegetation, especially prior to L. maackii flowering. This may be due in part to large woody plants with showy flowers, as reflected by the bee community changes with cumulative basal area of these species. Contrary to my predictions, the density of shrubs and trees with showy flowers (often associated with insect pollination) was not predictive of either bee abundance or species richness nor associated with high species richness of woody plants (Figure 5). Inclusion of all flowering woody vegetation early in season produced the best-fitting models and positively affected bee abundance and species richness responses early in the season when most non- entomophilous species flower, suggesting that bees use resources from woody species with a variety of pollination syndromes, including anemophilic (wind-pollinated) species.

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Bees may utilize large woody individuals for nesting locations or to supplement pollen provision for brood. For example, Augochlora pura is a generalist forager and solitary sweat bee that was found at every site in April, May, and June. Consistent with Ulyshen et al. (2010), I found that A. pura is a dominant bee of the forest canopy, comprising 36% and 44% of the 3,692 individuals recorded in the subcanopy and canopy strata, respectively. As A. pura uses decaying wood as a nesting substrate, death of Fraxinus trees following the recent invasion of the Emerald ash borer beetle, Agrilus planipennis (Wildman 2008), may have augmented populations of A. pura through the provision of nesting substrate. Other wood-nesting genera (i.e. Hylaeus, Ceratina, Osmia) were also associated with higher vertical strata and greater woody species richness (Figure 5; Supplementary Figure 4). I also found many soil-nesting species in the canopy and subcanopy, suggesting that a subset of the understory bee community is foraging on floral resources in the tree canopy. For instance, Andrena imitatrix, Andrena nasonii, Andrena perplexa, Augochlorella aurata, Bombus impatiens, Halictus rubicundis, and many Lasioglossum spp. were commonly sampled in strata above the understory (Supplementary Table 3). Although some of these bee species are strong flyers with non-foraging behaviors that may bring them ≥7 m above the forest floor, it seems unlikely they incidentally entered UV reflective traps. These species are all common generalist foragers and have been associated with tree pollen, most often of entomophilous species (Ascher and Pickering 2018). While it is not surprising that bees may also exploit the large sources of pollen released by most anemophilic species, this particular relationship is sparsely found within the literature (but see Chambers 1945, Smith et al. 2019, and Tucker et al. 2019) and especially rare in publications pertaining to agroecosystems (Saunders 2018). In support of my hypothesis that the bee community would shift with the phenology of woody species, I also found that the bee community was responsive to the mean peak flowering time of woody vegetation. Relative flowering time was an important predictor for all models explaining variation in bee species composition (Table 2). Sites composed of trees and shrubs with generally late flowering times had fewer bee species, suggesting that some bee species exhibited foraging preferences, or requirements, for resources of earlier flowering species of woody vegetation. This inference was further supported by the finding that higher richness of woody species resulted in higher species richness of bees, but only during the first sampling period (Figure 4). Temporally, this suggests that woody species which flower early in the season

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may support solitary bee species with short flight seasons (e.g. Nomada spp, Andrena spp) or supplement colony foundresses and early members of social genera (e.g. Bombus, Augochlorella, Halictus, Lasioglossum; Supplementary Table 3) that will forage throughout the season. Sites with high basal area of woody vegetation with showy flowers were more associated with both later flower times and social bees of the understory stratum than the early-season woody vegetation, richness of woody species, and the canopy stratum (Figure 5). This is likely due to strong effects of a few woody species on the bee community later in the season. For example, Prunus serotina (Black cherry) is an entomophilous species with showy flowers during the L. maackii flowering period and had the third most basal area of any entomophilous species across sites. Other species that flower after L. maackii, such as Gleditsia triacanthos, Sambucus nigra, and Cornus racemosa, may also attract many bees in the summer, but these woody plants had fewer individuals, or were smaller in size than P. serotina. Tilia americana (Basswood), however, had more basal area than P. serotina across sites and was scored as the last species to flower (Supplementary Figure 3). Interestingly, I found low abundances of bees in the last sampling period in sites with more T. americana (Supplementary Figure 6). Some Tilia spp are associated with mass bee deaths (Fossen et al. 2019), but this relationship is debated (Koch and Stevenson 2017; Jacquemart et al. 2018) in part because no mechanism has been confirmed (Jacquemart et al. 2018; Fossen et al. 2019). Still, my study suggests a link between T. americana and decreased bee occurrences that should be further investigated. Overall, the rich floral resources of shrubs and trees provided food to social species of bees throughout the season (Bertrand et al. 2019; Proesmans et al. 2019; Smith et al. 2019). This suggests that the bee community exploits the seasonal turnover in flowering phenology of woody species along forest edges. Early-flowering woody species are comprised of wind- and insect- pollinated plants that may provide food and nesting resources to solitary bees, colony foundresses, and young colonies, while those with a later phenology (predominantly entomophilous species with showy flowers here) may provide floral resources to growing colonies of social species to supplement pollen from herbaceous sources (Bertrand et al. 2019; Proesmans et al. 2019; Smith et al. 2019). Since I did not investigate small woody or herbaceous flowering plants or larger woody plants outside the belt transect, my results do not reflect the contribution of flowering herbaceous plants to the bee community. I recognize that most, if not all, of the bee species I sampled are

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likely to use herbaceous flowering plants (Chapter 2) or trees and shrubs within the forest interior (Ascher and Pickering 2018; Burkle et al. 2013), some of which may do so exclusively. For example, Andrena violae is a solitary miner bee and a specialist forager on small herbaceous flowering species in the genus Viola. I sampled A. violae from five sites and all were found in traps 1 m from the forest floor in April. Therefore, it is unlikely that this specialist uses resources of woody vegetation, even though it was still recorded in my study. The inclusion of bees such as A. violae that rely on low-lying herbaceous resources in my results may distort the interpreted use of woody vegetation by bees within the understory, especially later in the season when some bees shift their use of woody vegetation resources to those of the herbaceous species (Bertrand et al. 2019). Additionally, other studies have found competitive effects between L. maackii density and the herbaceous community (e.g. shading, pollinator competition) which may also reduce flowering plant abundance (Christopher et al. 2014; Miller and Gorchov 2004) and the abundance of flowers on plants (Miller and Gorchov 2004) that are available to bees. Therefore, it will be important in future studies to measure floral resource availability of woody and herbaceous species to bees to fully understand how plant resources along the forest edge structure bee communities.

6 Conclusion My study shows for the first time that native trees and shrubs along forest edges provide important floral resources to the bee community during the temperate growing season and that bee communities respond differentially to woody species in understory and canopy strata. I conclude that woody species with early-season flower phenologies support bee populations, and the establishment of L. maackii on forest-agriculture edges structure the bee community in all vertical strata by benefitting late-season bee species through the provision of floral resources and by altering usage of floral resources of native species in other vertical strata. This study demonstrates that flowering invasive shrubs can alter the spatial distribution of the bee community and emphasizes that not considering trap height placement can distort the true responses of the bee community to local features of forest-edge habitat.

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7 References Abbott HG (1974) Some characteristics of fruitfulness and seed germination in red maple. Tree Planters’ Notes 25(2):25-27. Aizen MA, Morales CL, Morales JM (2008) Invasive mutualists erode native pollination webs. PLoS Biol 6:e31. Ascher JS, Pickering J (2018) Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). Accessed February 10, 2019. http://www.discoverlife.org/mp/20q?guide=Apoidea_species. Barriball K, Goodell K, Rocha OJ (2014) Mating patterns and pollinator communities of the invasive shrub Lonicera maackii: a comparison between interior plants and edge plants. Int J Plant Sci 175(8):946-954. Bartomeus I, Ascher JS, Gibbs J, Danforth BN, Wagner DL, Hedtke SM, Winfree R (2013) Historical changes in northeastern US bee pollinators related to shared ecological traits. PNAS 110(12):4656-4660. Bartuszevige AM, Gorchov DL (2006) Avian seed dispersal of an invasive shrub. Biol Invasions 8:1013-1022. Batra SWT (1985) Red Maple (Acer rubrum L.), an important early spring food resource for honey bees and other insects. J Kansas Entomol Soc 58(1):169-172. Bertrand C, Eckerter PW, Ammann L, Entling MH, Gobet E, Herzog F, Mestre L, Tinner W, Albrecht M (2019) Seasonal shifts and complementary use of pollen sources by two bees, a lacewing and a ladybeetle species in European agricultural landscapes. J Appl Ecol 00:1-12. Brooks ME, Kristensen K, Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug JH, Maechler M, Bolker BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9(2):378- 400. Burkle LA, Marlin JC, Knight TM (2013) Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611. Burns RM, Honkala BH. [Technical coordinators] (1990) Silvics of North America: Volume 2. Hardwoods. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 654. 1711 p. Cane JH, Griswold T, Parker F (2007) Substrates and materials used for nesting by North American Osmia bbes (Hymenoptera: Apiformes: Megachilidae). Ann Entomol Soc Am 100(3):350-358. Carvalheiro LG, Biesmeijer JC, Benadi G, Fründ J, Stang M, Bartomeus I, Kaiser-Bunbury CN, Baude M, Gomes SIF, Merckx V, Baldock KCR, Bennett ATD, Boada R, Bommarco R, Cartar R, Chacoff N, Dänhardt J, Dicks LV, Dormann CF, Ekroos J, Henson KSE, Holzschuh A, Junker RR, Lopezaraiza-Mikel M, Memmott J, Montero-Castaño A, Nelson IL, Petanidou T, Power EF, Rundlöf M, Smith HG, Stout JC, Temitope K, Tscharntke T, Tscheulin T, Vilá M, Kunin WE (2014) The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecol Lett 17:1389-1399. Chambers VH (1945) British bees and wind-borne pollen. Nature 155:145. Christopher CC, Matter SF, Cameron GN (2014) Individual and interactive effects of Amur honeysuckle (Lonicera maackii) and white-tailed deer (Odocoileus virginianus) on herb

147

communities in deciduous forests in the eastern United States. Bio Invasions 16(11):2247-2261. Collier MH, Vankat JL, Hughes MR (2002) Diminished plant richness and abundance below Lonicera maackii, an invasive shrub. Am Midl Nat 147:60-71. Cunningham-Minnick MJ, Crist TO (In Press) Bee communities and pollination services in adjacent crop fields following flower removals in an invasive forest shrub. Ecol Appl. DOI: 10.1002/eap.2078. Dolédec S, Chessel D, Braak CJF, Champely S (1996) Matching species traits to environmental variables: a new three-table ordination method. Environ Ecol Stat 3(2):143-166. Dray S, Dufour AB (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22(4):1-20. Dray S, Legendre P (2008) Testing the species traits-environment relationships: The fourth- corner problem revisited. Ecology 89(1):3400-3412. Feret PP, Kreh RE, Merkle SA, Oderwald RG (1982) Flower abundance, premature acorn abscission, and acorn production in Quercus alba L. Bot Gaz 143(2):216-218. Fossen T, Holmelid B, Ovstedal DO (2019) Bumblebee death associated with Tilia x europaea L. Biochem Syst Ecol 82:16-23. Gabriel WJ, Garrett PW (1984) Pollen vectors in sugar maple (Acer saccharum). Can J Bot 62:2889-2990. Geroff RK, Gibbs J, McCravy KW (2014) Assessing bee (Hymenoptera: Apoidea) diversity of an Illinois restored tallgrass prairie: methodology and conservation considerations. J Insect Conserv 18:951:964. Gibbs J (2010) Revision of the metallic species of Lasioglossum (Dialictus) in Canada (Hymenoptera, Halictidae, Halictini). Zootaxa 2591:1-382. Gibbs J, Packer L, Dumesh S, Danforth BN (2013) Revision and reclassification of Lasioglossum (Evylaeus), L. (Hemihalictus) and L. (Sphecodogastra) in eastern North America (Hymenoptera: Apoidea: Halictidae). Zootaxa 3672:1-117. Gibbs J, Joshi NK, Wilson JK, Rothwell NL, Powers K, Haas M, Gut L, Biddinger DJ, Isaacs R (2017) Does passive sampling accurately reflect the bee (Apoidea: Anthophila) communities pollinating apple and sour cherry orchards? Environ Entomol 46(3):579- 588. Goodell K, McKinney AM, Lin CH (2010) Pollen limitation and local habitat-dependent pollinator interactions in the invasive shrub Lonicera maackii. Int J Plant Sci 171(1):63- 72. Grisez TJ (1975) Flowering and seed production in seven hardwood species. United States Department of Agriculture Forest Service Research Paper, Northeastern Forest Experiment Station, NE-315. Hassett MR, McGee GG (2017) Negative binomial hurdle models to estimate flower production for native and nonnative Northeastern shrub taxa. Forest Sci 63(6):577-585. Hoehn P, Tscharntke T, Tylianakis JM, Steffan-Dewenter I (2008) Functional group diversity of bee pollinators increases crop yield. P R Soc B 275:2283-2291. Hoven BM, Gorchov DL, Knight KS, Peters VE (2017) The effect of emerald ash borer-caused tree mortality on the invasive shrub Amur honeysuckle and their combined effects on tree and shrub seedlings. Biol Invasions 19:2813-2836. Jacquemart A, Moquet L, Ouvrard P, Quetin-Leclercq J, Herent MF, Quinet M (2018) Tilia trees: toxic or valuable resources for pollinators? Apidologie 49(5):538-550.

148

Jachuła J, Denisow B, Strzałkowska-Abramek M (2019) Floral reward and insect visitors in six ornamental Lonicera species – Plant suitable for urban bee-friendly gardens. Urban For Urban Gree 44:126390. Joshi NK, Leslie T, Rajotte EG, Kammerer MA, Otienno M, Biddinger DJ (2015) Comparative trapping efficiency to characterize bee abundance, diversity, and community composition in apple orchards. Ann Entomol Soc Am 108(5):785-799. Klecka J, Hadrava J, Koloušková P (2018) Vertical stratification of plant-pollinator interactions in a temperate grassland. PeerJ 6:e4998. Kleijn D, Winfree R, Bartomeus I, Carvalheiro LG, Henry M, Isaacs R, Klein AM, Kremen C, M’Gonigle LK, Rader R, Ricketts TH, Williams NM, Adamson NL, Ascher JS, Báldi A, Batáry P, Benjamin F, Biesmeijer JC, Blitzer EJ, Bommarco R, Brand MR, Bretagnolle V, Button L, Cariveau DP, Chifflet R, Colville JF, Danforth BN, Elle E, Garratt MPD, Herzog F, Holzschuh A, Howlett BG, Jauker F, Jha S, Knop E, Krewenka KM, Féon VL, Mandelik Y, May EA, Park MG, Pisanty G, Reemer M, Riedinger V, Rollin O, Rundlöf M, Sardiñas HS, Scheper J, Sciligo AR, Smith HG, Steffan-Dewenter I, Thorp R, Tscharntke T, Verhulst J, Viana BF, Vaissiére BE, Veldtman R, Westphal C, Potts SG (2015) Delivery of crop pollination services is an insufficient argument for wild pollinator conservation. Nat Comm 6:7414. Koch H, Stevenson PC (2017) Do linden trees kill bees? Reviewing the causes of bee deaths on silver linden (Tilia tomentosa). Biol Letters 13:20170484. Laska MS, Stiles EW (1994) Effects of fruit crop size on intensity of fruit removal in Viburnum prunifolium (Caprifoliaceae). Oikos 69(2):199-202. Lefcheck, JS (2016) piecewiseSEM: Piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol Evol. 7(5):573-579. Lüdecke D, Makowski D, Waggoner P (2019) performance: Assessment of regression models performance. R package version 0.4.0. https://CRAN.R- project.org/package=performance. Macior LW (1978) Pollination ecology of vernal angiosperms. Oikos 30(3):452-460. McCarthy BC, Quinn JA (1989) Within- and among-tree variation in flower and fruit production in two species of Carya (Juglandaceae). Am J Bot 76(7):1015-1023. McKinney AM, Goodell K (2010) Shading by invasive shrub reduces seed production and pollinator services in a native herb. Biol Invasions 12:2751-2763. McNeish RE, McEwan RW (2016) A review on the invasion ecology of Amur honeysuckle (Lonicera maackii, Caprifoliaceae) a case study of ecological impacts at multiple scales. J Torrey Bot Soc 143(4):367-385. Michener CD (2000) The bees of the world. The Johns Hopkins University Press (2nd Edition). Baltimore, MD. pp 26, 708-709. Miller KE, Gorchov DL (2004) The invasive shrub, Lonicera maackii, reduces growth and fecundity of perennial forest herbs. Oecologia 139:359-375. Montero-Castaño A, Vilá M (2012) Impact of landscape alteration and invasions on pollinators: a meta-analysis. J Ecol 100:884-893. Morales CL, Traveset A (2009) A meta-analysis of impacts of alien vs native plants on pollinator visitation and reproductive success of co-flowering native plants. Ecol Lett 12:716-728. Nuttman CV, Otieno M, Kwapong PK, Combey R, Willmer P, Potts SG (2011) The utility of aerial pan-trapping for assessing insect pollinators across vertical strata. J Kansas Entomol Soc 84(4):260-270.

149

Okansen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: Community Ecology Package. R package version 2.5-5. Parker GR, Leopold DJ, Eichenberger JK (1985) Tree dynamics in an old-growth, deciduous forest. Forest Ecol Manag 11:31-57. Proesmans W, Smagghe G, Meeus I, Bonte D, Verheyen K (2019) The effect of mass-flowering orchards and semi-natural habitat on bumblebee colony performance. Landscape Ecol 34:1033-1044. R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Rhoades PR (2010) Four aspects of dogwood pollination: insect visitation, a novel approach to identify pollen, floral volatile emission, and tracking parentage. (Masters Thesis) Retrieved from The University of Tennessee TRACE. Rhoades P, Griswold T, Waits L, Bosque-Pérez NA, Kennedy CM, Eigenbrode SD (2017) Sampling technique affects detection of habitat factors influencing wild bee communities. J Insect Conserv 21:703-714. Richards MH, Rutgers-Kelly A, Gibbs J, Vickruck JL, Rehan SM, Sheffield CS (2011) Bee diversity in naturalizing patches of Carolinian grasslands in southern Ontario, Canada. Can Entomol 143:279-299. Robertson C (1929) Flowers and insects; lists of visitors of four hundred and fifty-three flowers. Carlinville, Ill. 221 p. Roubik DW (1993) Tropical pollinators in the canopy and understory: field data and theory for stratum “preferences”. J Insect Behav 6(6):659-673. Saunders ME (2018) Insect pollinators collect pollen from wind-pollinated plants: implications for pollination ecology and sustainable agriculture. Insect Conserv Diver 11:13-31. Smith C, Weinman L, Gibbs J, Winfree R (2019) Specialist foragers in forest bee communities are small, social or emerge early. J Anim Ecol 88:1158-1167. Sobek S, Tscharntke T, Scherber C, Schiele S, Steffan-Dewenter I (2009) Canopy vs. understory: does tree diversity affect bee and wasp communities and their natural enemies across forest strata? Forest Ecol Manag 258:609-615. Somme L, Moquet L, Quinet M, Vanderplanck M, Michez D, Lognay G, Jacquemart A (2016) Food in a row: urban trees offer valuable floral resources to pollinating insects. Urban Ecosyst 19:1149-1161. Southwick EE, Loper GM, Sadwick SE (1981) Nectar production, composition, energetics and pollinator attractiveness in spring flowers of western New York. Amer J Bot 68(7):994- 1002. Stangler ES, Hanson PE, Steffan-Dewenter I (2016) Vertical diversity patterns and biotic interactions of trap-nesting bees along a fragmentation gradient of small secondary rainforest remnants. Apidologie 47:527-538. Stout JC, Tiedeken EJ (2017) Direct interactions between invasive plants and native pollinators: evidence, impacts and approaches. Funct Ecol 31:38-46. Tucker SK, Ginsberg HS, Alm SR (2019) Eastern carpenter bee (Hymenoptera: Apidae): Nest structure, nest cell provisions, and trap nest acceptance in Rhode Island. Environ Entomol 48(3):702-710. Tuell JK, Isaacs R (2009) Elevated pan traps to monitor bees in flowering crop canopies. Entomol Exl Appl 131:93-98.

150

Ulyshen MD, Soon V, Hanula JL (2010) On the vertical distribution of bees in a temperate deciduous forest. Insect Conserv Diver 3:222-228. U.S. Department of Agriculture, Forest Service (2008) The Woody Plant Seed Manual. U.S. Department of Agriculture, Agriculture Handbook 727, Washington D.C. 1228 p. Vanbergen AJ, Espíndola A, Aizen MA (2017) Risks to pollinators and pollination from invasive alien species. Nat Ecol Evol 2:16-25. Vilà M, Bartomeus I, Dietzsch AC, Petanidou T, Steffan-Dewenter I, Stout JC, Tscheulin T (2009) Invasive plant integration into native plant-pollinator networks across Europe. P R Soc B 276:3887-3893. Weiskittel A, Kuehne C, McTague JP, Oppenheimer M (2016) Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA. For Ecosyst 3:26. Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag New York, 2016. Wildman RH (2008) Ohio’s forest resources, 2006. Note. NRS-22 U.S. Department of Agriculture, Forest Service, Northern Research Station. Newton Square, PA. Williams NM, Cariveau D, Winfree R, Kremen C (2011) Bees in disturbed habitats use, but do not prefer, alien plants. Basic Appl Ecol 12:332-341. Willson MF, Schemske DW (1980) Pollinator limitation, fruit production, and floral display in pawpaw (Asimina triloba). B Torrey Bot Club 107(3):401-408. Wood TJ, Kaplan I, Szendrei Z (2018) Wild bee pollen diets reveal patterns of seasonal foraging resources for honey bees. Front Ecol Evol 6:210.

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Table 1: Top four generalized linear mixed models of abundance and species richness for bees. Models are ranked by AICc. All models include a random intercept of vertical stratum (n = 3) nested within trap arrays (n = 20), as well as an autoregressive term for temporal autocorrelation for each array among sampling periods. Predictors included L. maackii density (L. maackii), composite flowering time of mature woody vegetation (Flower timeM), abundance of all woody vegetation (WVA Abundance) and mature woody vegetation (WVM Abundance), basal area of all woody vegetation (WVA BA) and mature woody vegetation (WVM BA), sampling period (Period), and vertical stratum (stratum). Direction of the relationship between response and continuous predictor is either positive (+) or negative (-) and depicted as superscripts following each predictor. Directions of the relationships between levels of categorical predictors and responses are described in the text. Two-way interactions are depicted with an asterisk between the two terms “ * ”.

Response Model df ∆AICc

(-) (+) Period + WVA Abundance + Period*WVA Abundance + stratum + stratum*WVA Abundance + L. maackii 16 0.00

(-) (+) Period + WVA Abundance + Period*WVA Abundance + stratum + L. maackii 14 0.28 Abundance (-) Period + WVA Abundance + Period*WVA Abundance + stratum + stratum*WVA Abundance 15 0.62

(-) (+) Period + WVA BA + Period*WVA BA+ stratum + L. maackii 14 0.90

(-) (-) Period + WVA Richness + Period*WVA Richness + stratum + Flower timeM 13 0.00

(+) (-) Species Period + WVM Abundance + Period*WVM Abundance + stratum + Flower timeM 13 0.21 Richness (+) (+) Period + WVM Abundance + Period* WVM Abundance + stratum + Period*L. maackii + L. maackii 15 1.63

(+) (+) Period + WVM BA + Period*WVM BA + stratum + Period*L. maackii + L. maackii 15 1.72

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Table 2: Top five models predicting changes in bee composition. Models are listed in order of decreasing variance explained by the constraining variables. Predictors include L. maackii density (L. maackii), basal area (m2/ha) of mature individuals of woody species with showy flowers (WVM, sho BA), species richness of all woody vegetation (WVA Richness), and composite mean peak flowering time of woody vegetation during the season for all (Flower timeA) and only mature (Flower timeM) stems.

Model Variance Explained (%) Pseudo-F (df) P-value

Density + WVM,sho BA + Flower timeA 9.3 2.93 (3) < 0.005

Density + WVA BA + Flower timeM 8.7 2.74 (3) < 0.005

Density + WVA Abundance + Flower timeM 7.5 2.33 (3) < 0.005

Density + WVA Richness + Flower timeA 7.3 2.26 (3) < 0.005

Density + WVA Richness + Flower timeM 7.3 2.25 (3) < 0.005

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Figure 1: Best model predictions of bee abundance as a function of L. maackii density. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis.

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Figure 2: Best model predictions of bee abundance as a function of woody vegetation abundance for each of the three sampling periods (A): before, during, and after the flowering period of L. maackii, as well as each of the three vertical strata (B): understory, subcanopy, and canopy. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis.

155

Figure 3: Bee species richness at each canopy layer. Averages and 95% CI of best model predictions. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis. Not significant (NS), *** P < 0.005, **** P < 0.001. 156

Figure 4: Best model predictions of bee species richness as a function of the species richness of woody vegetation for each of the three sampling periods: before, during, and after the flowering period of L. maackii. Observed values (hollow circles) are jittered ±0.15 log units along the y-axis. 157

Figure 5: Bee composition from RLQ analysis. Visualization of maximized correlations of a co-inertia analysis between species trait (Q: nesting substrate (Soil Nesting; Wood Nesting), sociality (Social; Solitary), Body Length; dashed lines and triangles) and environment (R: sampling period relative to L. maackii flowering (Before; During; After), vertical strata from which bees were sampled (Understory; Subcanopy; Canopy), species richness of woody vegetation (WV Richness), percentage of woody vegetation estimated < 7 m tall (<7 m tall), basal area of woody vegetation with showy flowers (WVsho BA), L. maackii density (L. maackii), and mean peak flowering time of woody vegetation derived from PCA (See methods; Flower time); solid lines and circles) matrices weighted by species abundances (L: species-site matrix) superimposed on species scores (grey circles).

158

Supplementary Table 1: Lonicera maackii basal area, site locations, aspect of each forest-edge site, and the size of the associated forest fragment.

Site L. maackii Latitude Longitude Aspect Patch Size (°N) (°W) (m2/ha) (N, E, S, W) (ha)

1 0.00 39.505 -84.921 N 15.34 2 0.03 39.797 -84.754 W 16.66 3 0.61 39.663 -84.793 W 5.55

4 1.29 39.637 -84.488 E 27.51 5 3.62 39.558 -84.899 S 19.11 6 8.66 39.572 -84.801 S 14.19 7 9.32 39.485 -84.509 S 13.43 8 9.46 39.423 -84.896 N 11.92 9 10.98 39.375 -84.862 S 22.73 10 23.12 39.645 -84.701 S 17.31

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Supplementary Table 2: Summary statistics and life history attributes of woody species found in the ten forest-edge transects. Sexual maturity, or life stage at which an individual flowers, was assigned for each species based on stem DBH (see Methods for details). Individual tree heights were estimated to determine if a stem was short (< 7 m) or tall (> 7 m), as well as maturity when provided information was height-specific. Heights were estimated using a modified form of Weiskittel et al.’s (2016) equation for “American beech” (AB), “ash” (AS), “other hardwood” (OH), “other softwood” (OS), “red maple” (RM), and “sugar maple” (SM) species classes. Since all individuals were on forest-agriculture edges, I assumed canopy closure did not affect tree growth, and assigned a value of 0 to the associated parameters. Predicted values from fitted lines were used when sources offered variable relationships between size (DBH or height) and maturity status within the region (^). All measured understory shrubs were assumed to be mature; maturity was assumed at DBH > 3 cm from personal observation (#), or at DBH > 10 cm (##). Total abundance of each species; estimated number of immature individuals, mean DBH (cm) ± 1 SE of estimated mature and immature individuals, estimated abundance of short (< 7 m) and tall (> 7 m) individuals, estimated flowering time in relation to L. maackii bloom (before/during/after), 1st Principal Component axis represents flowering time relative to other species from PCA (see Methods), and insect relationship with flowers, including showy (S) and entomophilous (E), are listed for each species.

Species Abundance DBH (cm) Estimated Ht Flowering PCA Insect (Immature) Score Mature Immature short tall Time Relation d,eAcer negundo(SM) 46 (35) 19.5±1.5 5.7±0.5 31 15 Before -1.37 E e,fAcer rubrum(RM) 3 (0) 11.7±3.0 NA 2 1 Before -0.09 E e,fAcer saccharinum(SM) 52 (28) 25.0±2.9 4.2±0.4 29 23 Before -1.55 E e,g,ggAcer saccharum(SM) 320 (213) 17.7±0.8 5.1±0.1 221 99 Before -9.54 E #Aesculus glabra(OH) 52 (29) 6.1±0.7 3.0±0.0 47 5 Before -1.55 S,E hAsiminia triloba(OH) 231 (189) 4.5±0.3 3.0±0.0 230 1 Before -6.89 S,E ##Betula lenta(AB) 1 (0) 17.8 NA 0 1 Before -0.03 - iCarpinus caroliniana(OH) 146 (122) 9.1±0.5 3.6±0.1 138 8 Before/During -4.24 -

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aCarya cordiformis^ 136 (100) 28.3±1.9 5.4±0.3 91 45 Before -4.05 - aCarya laciniosa^ 11 (4) 26.7±4.7 6.2±1.1 4 7 Before/During -0.32 - a,ggCarya ovata^ 31 (25) 26.5±4.6 4.6±0.5 23 8 Before/During -0.90 - aCarya tomentosa^ 34 (20) 27.5±2.6 6.3±0.8 15 19 Before/During -0.99 - Catalpa speciosa(OH) 1 (1) NA 4.9 1 0 After 0.03 S,E jCeltis occidentalis(OH) 435 (338) 23.2±1.1 5.5±0.2 291 144 Before/During -12.63 * k,lCornus florida(OH) 33 (0) 4.1±0.2 NA 33 0 Before/During -0.96 S,E lCornus racemosa(OH) 36 (0) 3.3±0.1 NA 36 0 After 1.05 S,E mCrataegus sp1(OH) 2 (0) 8.1±0.2 NA 2 0 Before/During -0.06 S,E mCrataegus sp2(OH) 5 (0) 7.8±0.7 NA 4 1 Before/During -0.15 S,E mCrataegus sp3(OH) 3 (2) 4.2±0.0 3.0±0.0 3 0 Before/During -0.09 S,E mCrataegus sp4(OH) 4 (1) 6.7±0.4 3.0±0.0 4 0 Before/During -0.12 S,E mCrataegus sp5(OH) 2 (2) NA 3.0±0.0 2 0 Before/During -0.06 S,E nEuonymus alatus(OH) 7 (0) 3.0±0.0 NA 7 0 During 0.01 S,E oFagus grandifolia(AB) 84 (49) 11.9±1.0 3.8±0.1 72 12 Before -2.50 - p,ggFraxinus americana**(AS) 129 (112) 27.5±5.1 3.4±0.1 112 17 Before -3.85 - Fraxinus quadrangulata(AS) 1 (1) NA 3.0 1 0 Before -0.03 - qGleditsia triacanthos(OH) 19 (6) 27.2±4.1 3.2±0.1 6 13 After 0.55 S,E bGymnocladus dioicus^ 7 (6) 22.9 9.2±1.2 6 1 During 0.01 S,E r,ggJuglans nigra(OH) 100 (15) 34.0±1.2 6.8±0.8 11 89 Before/During -2.90 -

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#Juniperus virginiana(OS) 8 (2) 8.4±1.7 3.0±0.0 8 0 Before -0.24 - Lindera benzoin 175 (0) 3.0 ± 0.0 NA 175 0 Before -5.22 E ##Liriodendron tulipifera(OH) 1 (0) 41.1±0.0 NA 0 1 During/After 0.03 S,E ##Maclura pomifera(OH) 13 (3) 24.1±4.3 7.5±0.9 3 10 Before/During -0.38 - Malus sp1(OH) 1 (1) NA 3.0±0.0 1 0 Before/During -0.03 S,E Malus sp2(OH) 1 (1) NA 3.0±0.0 1 0 Before/During -0.03 S,E sMorus alba(OH) 70 (19) 11.2±1.2 3.0±0.0 49 21 During 0.05 - tOstrya virginiana(OH) 13 (4) 11.7±2.6 3.0±0.0 9 4 During 0.01 - uPlatanus occidentalis(OH) 1 (0) 59.7 NA 0 1 Before -0.03 - vPopulus deltoides(OH) 4 (0) 45.3±16.6 NA 0 4 Before -0.12 - c,wPrunus serotina(OH) 91 (19) 17.9±1.0 4.0±0.2 32 59 During 0.07 S,E a,x,y,ggQuercus bicolor^ 6 (0) 32.5±5.4 NA 0 6 Before -0.18 - a,x,ggQuercus macrocarpa^ 4 (4) NA 5.9±1.7 3 1 Before -0.12 - a,x,zQuercus muehlenbergii^ 25 (14) 15.3±3.1 3.6±0.2 19 6 Before -0.75 - a,x,ggQuercus rubra^ 19 (3) 35.3±8.4 4.8±0.6 12 7 Before -0.57 - cRobinia pseudoacacia^ 6 (1) 21.6±6.2 3.5 1 5 During 0.00 S,E aaSambucus nigra 29 (0) 3.0±0.0 NA 29 0 After 0.84 S,E bbTilia americana(OH) 92 (41) 20.3±1.9 3.4±0.1 52 40 After 2.67 S,E cc,dd,ggUlmus americana(OH) 67 (15) 11.2±0.8 3.7±0.2 40 27 Before -2.00 E ccUlmus rubra(OH) 86 (28) 11.0±1.2 3.4±0.1 62 24 Before -2.56 -

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eeVibrunum prunifolium 40 (0) 3.3±0.1 NA 40 0 Before/During -1.16 S,E ffZanthoxylum americanum 20 (0) 3.0±0.0 NA 20 0 Before/During -0.58 E TOTALS 2703 (1463) 4.5 14.6 1240 868 - - -

* No literature was not found to suggest either entomophily or anemophily. ** Fraxinus americana and F. pennsylvanica are collectively referred to as F. americana a(Nelson 1965),b(Pinchot 1907), c(Janick & Moore 1996), d(Overton 1990). e(Zasada & Strong 2008), f(Abbott 1974), g(Godman et al. 1990), h(Willson & Schemske1980), i(Metzger 1990a), j(Bonner 2008a), k(Tirmenstein 1991), l(Brinkman & Vankus 2008), m(Lasseigne & Blazich 2008), n(Dirr 1998), o(Bonner & Leak 2008), p(Schlesinger 1990), q(Sullivan 1994), r(Williams 2008), s(Barbour et al. 2008), t(Metzger 1990b), u(Bonner 2008b), v(Cooper 1990), w(Grisez et al. 2008), x(Cho & Boerner 1991), y(Rogers 1990), z(Sanders 1990), aa(Brinkman & Johnson 2008),bb(Crow 1990), cc(Barbour & Brinkman 1990), dd(Bey 2008), ee(Bonner et al. 2008), ff(Bonner 2008c), gg(Parker et al. 1985)

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Supplementary Table 2 References: Abbott HG (1974) Some characteristics of fruitfulness and seed germination in red maple. Tree Planters’ Notes 25(2):25-27. Barbour JR, Brinkman KA (1990) Ulmaceae-Elm family Ulmus L. elm. In: The woody plant seed manual. p 1143-1149. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Barbour JR, Read RA, Barnes RL (2008) Moraceae-Mulberry family Morus l. mulberry. In: The woody plant seed manual. p 728-732. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Bey CF (2008) Ulmus americana L. In: Silvics of North America: Volume 2, Hardwoods. p 1534-1545. Burns RM, BH Honkala, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Bonner FT (2008a) Ulmaceae-Elm family Celtis L. hackberry. In The woody plant seed manual. p 366-368. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Bonner FT (2008b) Platanaceae-Planetree family Planatus L. sycamore. In: The woody plant seed manual. p 850-853. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Bonner FT (2008c) Rutaceae-Rue family Zanthoxylum L. prickly-ash. In: The woody plant seed manual. p 1180-1182. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Bonner FT, Leak WB (2008) Fagaceae-Beech family Fagus L. beech. In: The woody plant seed manual. p 520-524. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Bonner FT, Gill JD, Pogge FL (2008) Caprifoliaceae-Honeysuckle family Viburnum L. viburnum. In: The woody plant seed manual. p 1162-1167. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Brinkman KA, Johnson WG (2008) Caprifoliaceae-Honeysuckle family Sambucus L. elder. In: The woody plant seed manual. p 1014-1018. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Brinkman KA, Vankus V (2008) Cornaceae-Dogwood family. Cornus L. dogwood. In: The woody plant seed manual. p 428-433. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Cho DS, Boerner REJ (1991) Canopy disturbance patterns and regeneration of Quercus species in two Ohio old-growth forests. Vegetatio 93:9-18. Cooper DT (1990) Populus deltoides Bartr. Ex Marsh. In: Silvics of North America: Volume 2, Hardwoods. p 1044-1052. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Crow TR (1990) Tilia americana L. In: Silvics of North America: Volume 2, Hardwoods. p 784- 791. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Dirr MA (1998) Manual of Woody Landscape Plants: Their identification, ornamental characteristics, culture, propagation and uses, 5th ed. Stipes Publishing LLC. Champaign, IL. Godman RM, Yawney HW, Tubbs CH (1990) Acer saccharum Marsh. Sugar Maple. In: Silvics of North America: Volume 2, Hardwoods. P 194-215. Burns RM, Honkala BH,

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[Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Grisez TJ, Barbour JR, Karrfalt RP (2008) Rosaceae-Rose family Prunus L. cherry, peach, and plum. In: The woody plant seed manual. p 875-890. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Janick J, Moore JN (1996) Fruit breeding. Volume 1, Tree and tropical fruits. New York: John Wiley & Sons. 616 pp. Lasseigne FT, Blazich FA (2008) Rosaceae-Rose family Crataegus L. hawthorn, haw, thorn, thorn-apple. In The woody plant seed manual. p 447-456. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727. Metzger FT (1990a) Carpinus caroliniana Walt. In: Silvics of North America: Volume 2, Hardwoods. p 368-381. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Metzger FT (1990b) Ostrya virginiana (Mill.) K. Koch. In: Silvics of North America: Volume 2, Hardwoods. p 962-977. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Nelson TC (1965) Bitternut hickory (Carya cordiformis (Wangenh.) K. Koch). In: Silvics of forest trees of the United States. p 111-114. Fowells HA. U.S. Department of Agriculture, Agriculture Handbook 271, Washington DC. Overton RP (1990) Acer negundo L. Boxelder. In Silvics of North America: Volume 2, Hardwoods. p 132-141. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Parker GR, Leopold DJ, Eichenberger JK (1985) Tree dynamics in an old-growth, deciduous forest. For Ecol Man 11:31-57. Pinchot G (1907) Coffeetree (Gymnocladus dioicus). United States Department of Agriculture, Forest Service – Circular 91. Rogers R (1990) Quercus bicolor Willd. In Silvics of North America: Volume 2, Hardwoods. p 1199-1205. Burns RM, BH Honkala, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Sander IL (1990) Quercus muehlenbergii Engelm. In Silvics of North America: Volume 2, Hardwoods. p 1346-1352. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Schlesinger RC (1990) Fraxinus americana L. In Silvics of North America: Volume 2, Hardwoods. p 668-679. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Sullivan J (1994) Gleditsia triacanthos. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: http//www.fs.fed.us/database/feis/plants/tree/gletri/all.html. Tirmenstein DA (1991) Cornus florida. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: https://www.fs.fed.us/database/feis/plants/shrub/corflo/all.html. Weiskittel A, Kuehne C, McTague JP, Oppenheimer M (2016) Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA. For Ecosyst 3:26.

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Williams RD (1990) Juglans nigra L. Black Walnut. In Silvics of North America: Volume 2, Hardwoods. p 771-789. Burns RM, Honkala BH, [Technical coordinators]. U.S. Department of Agriculture, Agriculture Handbook 654, Washington DC. Willson MF, Schemske DW (1980) Pollinator limitation, fruit production, and floral display in Pawpaw (Asimina triloba). B Torrey Bot Club 107(3):401-408. Zasada JC, Strong TF (2008) Aceraceae-Maple family, Acer L. maple. In The woody plant seed manual. p 204-216. U.S. Department of Agriculture, Forest Service, Agriculture Handbook 727.

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Supplementary Table 3: Life history attributes of bee species, including mean body length of three males, three females, and up to three queens based on availability (each * corresponds to one queen). Sampled bee abundance in the understory (Und), subcanopy (Sub), and canopy (Can) canopy layers, as well as the total abundance of bees (Abundance) and only males (parentheses). Nomada bidentate were assumed oligolects (#). Traits suggested but not empirically demonstrated in the literature (†).

Species Body Length Nesting Sociality Hosta Canopy Layer Abundance (mm) Substrate Und Sub Can (males) bAgapostemon splendens 10.0 Soil Solitary Poly 1 0 0 1 (1) bAgapostemon virescens 11.6 Soil Solitary Poly 13 5 2 20 (0) cAndrena arabis 8.4 Soil Solitary Poly 2 0 0 2 (0) cAndrena bisalics 12.0 Soil Solitary Oligo 0 1 0 1 (0) cAndrena carlini 12.5 Soil Solitary Poly 1 1 1 3 (0) cAndrena ceanothi 7.0 Soil Solitary Oligo 1 0 0 1 (1) cAndrena commoda 12.8 Soil Solitary Poly 2 1 0 3 (0) cAndrena cressonii 9.3 Soil Solitary Poly 1 0 1 2 (1) cAndrena erigeniae 8.5 Soil Solitary Oligo 3 0 0 3 (0) cAndrena erythronii 9.5 Soil Solitary Oligo 1 1 3 5 (0) cAndrena forbesii 10.4 Soil Solitary Poly 1 0 1 3 (1) cAndrena frigida 8.0 Soil Solitary Oligo 1 0 0 1 (1) cAndrena heraclei 7.1 Soil Solitary Oligo 2 1 0 3 (3) cAndrena hippotes 7.2 Soil Solitary Poly 2 0 1 3 (3)

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cAndrena illini 12.1 Soil Solitary Oligo 4 4 4 12 (6) cAndrena imitatrix 8.1 Soil Solitary Poly 13 18 15 46 (30) cAndrena macoupinensis 8.0 Soil Solitary Oligo 0 0 2 2 (2) cAndrena miranda 9.1 Soil Solitary Poly 0 0 1 1 (0) cAndrena miserabilis 7.8 Soil Solitary Poly 0 2 1 3 (1) cAndrena nasonii 8.2 Soil Solitary Poly 9 9 9 27 (5) cAndrena perplexa 11.6 Soil Solitary Oligo 4 11 3 18 (7) cAndrena personata 6.1 Soil Solitary Oligo 1 0 0 1 (0) cAndrena platyparia 7.2 Soil Solitary Oligo 1 0 0 1 (1) cAndrena pruni 10.5 Soil Solitary Oligo 8 3 0 11 (5) cAndrena robertsonii 7.3 Soil Solitary Poly 1 0 0 1 (1) cAndrena rugosa 8.6 Soil Solitary Poly 2 1 0 3 (2) cAndrena sayi 8.2 Soil Solitary Oligo 0 1 0 1 (1) cAndrena vicina 11.0 Soil Solitary Poly 1 0 0 1 (0) cAndrena violae 9.5 Soil Solitary Oligo 11 0 0 11 (0) cAndrena w-scripta 8.8 Soil Solitary Poly 0 1 0 1 (0) cAnthophora abrupta 13.5 Soil Solitary Oligo 1 1 0 2 (1) Apis mellifera 13.0 Hive Social Poly 32 19 14 65 (0) eAugochlora pura 7.9 Wood Solitary Poly 1028 1204 1460 3692 (339) fAugochlorella aurata 7.5 Soil Social Poly 31 48 47 126 (12)

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gAugochloropsis metallica 9.8 Soil Social Poly 4 1 1 6 (1) dBombus auricomis 28.5* Soil Social Oligo 0 1 0 1 (0) dBombus bimaculatus 17.0*** Soil Social Poly 124 75 17 216 (124) dBombus fervidus 19.0** Soil Social Poly 2 0 1 3 (0) dBombus griseocolis 17.6* Soil Social Poly 0 6 1 7 (0) dBombus impatiens 18.5*** Soil Social Poly 98 115 113 326 (0) dBombus pensylvanicus 26.3*** Soil Social Poly 1 1 1 3 (0) dBombus perplexus 13.9 Soil Social Poly 1 2 0 3 (2) hCemolobus ipomoeae 14.7 Soil Solitary Oligo 3 0 1 4 (2) hCeratina calcarata 6.9 Wood Solitary Poly 111 48 16 175 (19) hCeratina dupla 6.4 Wood Solitary Poly 3 2 0 5 (2) heCeratina mikmaqi 7.6 Wood Solitary Poly 1 0 0 1 (0) hCeratina strenua 5.7 Wood Solitary Poly 6 2 2 10 (0) iColletes inaequalis 13.8 Soil Solitary Poly 0 0 2 2 (0) hEucera atriventris 14.0 Soil Solitary Oligo 12 4 1 17 (13) hEucera dubitata 12.3 Soil Solitary Oligo 10 2 2 14 (10) hEucera hamata 15.1 Soil Solitary Poly 36 14 4 54 (14) hHalictus confusus 7.0 Soil Social Poly 0 3 0 3 (1) hHalictus rubicundis 10.3 Soil Social Poly 1 2 2 5 (0) hHylaeus fedorica 5.2 Wood Solitary Oligo 7 8 7 22 (14)

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hHylaeus mesillae 4.1 Wood Solitary Poly 1 0 0 1 (0) hHylaeus modestus 5.9 Wood Solitary Poly 0 4 5 9 (6) hHylaeus saniculae 3.9 Wood Solitary Oligo 1 0 1 2 (1) hHylaues sparsus 5.5 Wood Solitary Oligo 1 0 0 1 (1) jLasioglossum admirandum 7.4 Soil Social† Poly 0 0 1 1 (0) jLasioglossum atwoodi 4.7 Soil Social† Oligo 2 0 0 2 (0) Lasioglossum cattellae 5.1 Soil Social Oligo 5 3 9 17 (0) jLasioglossum cinctipes 8.4 Soil Social Poly 1 1 3 5 (1) jLasioglossum coeruleum 6.8 Wood Social Poly 35 17 16 68 (0) jLasioglossum coriaceum 9.9 Soil Solitary Poly 53 50 48 151 (0) jLasioglossum cressonii 6.9 Wood Social† Poly 2 5 2 9 (1) jLasioglossum ephialtum 5.2 Soil Social† Poly 3 1 2 6 (1) jLasioglossum foxii 6.5 Soil Solitary Poly 1 0 1 2 (0) jLasioglossum fuscipenne 9.0 Soil Solitary Oligo 1 2 4 7 (0) lLasioglossum gotham 6.3 Soil Social Oligo 0 0 1 1 (0) jLasioglossum hitchensi 4.9 Soil Social Poly 49 43 47 139 (2) Lasioglossum illinoense 5.0 Soil Social Oligo 1 0 0 1 (0) j,mLasioglossum imitatum 4.4 Soil Social Poly 0 1 2 3 (0) jLasioglossum lineatulum 6.1 Soil Social Poly 1 0 0 1 (0) jLasioglossum nigroviride 6.4 Soil Social† Oligo 0 0 1 1 (0)

170

nLasioglossum oblongum (nr) 5.6 Wood Social Poly 1 0 0 1 (1) mLasioglossum obscurum 5.0 Soil Social Oligo 3 2 6 11 (1) jLasioglossum paradmirandum 5.1 Soil Social† Oligo 1 0 0 1 (0) Lasioglossum pilosum 6.9 Soil Social Poly 2 0 0 2 (0) kLasioglossum quebecense 7.8 Soil Solitary Poly 0 1 0 1 (0) Lasioglossum smilacinae 6.6 Soil Social Oligo 0 1 1 2 (0) oLasioglossum subviridatum 5.6 Wood Social Oligo 4 1 0 5 (0) kLasioglossum truncatum 8.0 Soil† Social† Poly 11 14 11 36 (3) j,mLasioglossum versatum 6.1 Soil Social Poly 10 16 20 46 (1) Lasioglossum weemsi 5.4 Soil Social Oligo 3 0 2 5 (0) j,mLasioglossum zephyrum 5.8 Soil Social Poly 1 4 0 5 (0) jMegachile campanulae 9.2 Wood Solitary Oligo 4 0 1 5 (1) h,jMegachile mendica 12.6 Wood Solitary Poly 1 0 2 3 (1) hMelissodes bimaculata 12.5 Soil Solitary Poly 107 68 10 185 (40) h,jMelissodes desponsa 13.3 Soil Solitary Oligo 2 0 0 2 (0) hMelissodes trinodis 12.0 Soil Solitary Oligo 1 0 0 1 (0) hMelitoma taurea 11.6 Soil Solitary Oligo 6 3 0 9 (4) pNomada annulatus 9.8 Soil Parasitic Oligo 1 0 0 1 (1) a,pNomada bidentate 7.0 Soil Parasitic Oligo# 0 1 0 1 (0) (Female Morph-1)

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a,pNomada bidentate 7.5 Soil Parasitic Oligo# 1 0 0 1 (1) (Male Morph-1) a,pNomada bidentate 6.0 Soil Parasitic Oligo# 1 0 0 1 (1) (Male Morph-2) pNomada sulphurata 11.3 Soil Parasitic Oligo 1 0 0 1 (1) rOsmia albiventris 8.3 Wood Solitary Oligo 1 0 0 1 (0) rOsmia atriventris 7.9 Wood Solitary Poly 1 1 0 2 (0) rOsmia bucephala 14.1 Wood Solitary Poly 2 2 3 7 (1) tOsmia cornifrons 10.6 Wood Solitary Oligo 1 2 0 3 (0) rOsmia pumila 7.1 Wood Solitary Poly 18 15 11 44 (2) sPeponapis pruinosa 12.7 Soil Solitary Poly 6 2 0 8 (1) qXylocopa virginica 21.9 Wood Solitary Poly 2 2 2 6 (3) TOTALS - - - - 1947 1880 1948 5775 (702) a(Ascher and Pickering 2018), b(Eickwort 1981), c(Mitchell 1960), d(Colla et al. 2011), e(Stockhammer 1966), f(Mueller 1996), g(Gibbs 2017), h(Michener 2000), i(López-Uribe et al. 2015), j(Richards et al. 2011), k(Gibbs 2013), l(Batra 1987), m(Michener 1966), n(Sakagami & Michener 1962; Discovered by KV Krombein & PJS Moure in 1959), o(Gibbs 2011), p(Snelling 1986), q(Gerling & Hermann 1978; Gerling & Hermann (1978) describe cohabitation of X. virginica with sister- mates that may or may not have developed ovaries, and Richards et al. (2011) categorize this species as social. Considering X. virginica is not an obligate social bee, it is considered solitary here.), r(Cane et al. 2007), s(Julier & Roulston 2009), t(McKinney & Park 2012)

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Supplementary Table 3 References: Ascher JS, J Pickering (2018) Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). http://www.discoverlife.org/mp/20q?guide=Apoidea_species. Accessed November 15, 2018. Batra SWT (1987) Ethology of the vernal eusocial bee, Dialictus laevissimus (Hymenoptera: Halictidae). J Kansas Entomol Soc 60(1):100-108. Cane JM, Griswold T, Parker FD (2007) Substrates and materials used for nesting by North American Osmia bees (Hymenoptera: Apiformes: Megachilidae). Ann Entomol Soc Am 100(3):350-358. Colla S, Richardson L, Williams P (2011) Bumble bees of the Eastern United States. United States Department of Agriculture & Pollinator Partnership. FS-972. Eickwort GC (1981) Aspects of the nesting biology of five Nearctic species of Agapostemon (Hymenoptera: Halictidae). J Kansas Entomol Soc 54(2):337-351. Gerling D, Hermann HR (1978) Biology and mating behavior of Xylocopa virginica L. (Hymenoptera, Anthophoridae). Behav Ecol Sociobiol 3:99-111. Gibbs J (2011) Revision of the metallic Lasioglossum (Dialictus) of eastern North America (Hymenoptera: Halictidae: Halictini). Zootaxa 3073:1-216. Gibbs J (2017) Notes on the nests of Augochloropsis metallica fulgida and Megachile mucida in central Michigan (Hymenoptera: Halictidae, Megachilidae). Zootaxa 4352:1-160. Julier HS, Roulston TH (2009) Wild bee abundance and pollination service in cultivated pumpkins: farm management, nesting behavior and landscape effects. J Econ Entomol 102(2):563-573. López-Uribe MM, Morreale SJ, Santiago CK, Danforth BN (2015) Nest suitability, fine-scale population structure and male-mediated dispersal of a solitary ground nesting bee in an urban landscape. PLOS one 10(5):e0125719. McKinney MI, Park YL (2012) Nesting activity and behavior of Osmia cornifrons (Hymenoptera: Megachilidae) elucidated using videography. Psyche 814097:1-7. Michener CD (1966) The bionomics of a primitively social bee, Lasioglossum versatum (Hymenoptera: Halictidae). J Kansas Entomol Soc 39(2):193-217. Michener CD (2000) The bees of the world. The Johns Hopkins University Press (2nd Edition). Baltimore, MD. pp 26, 708-709. Mitchell TB (1960) Bees of the Eastern United States. Volume I. Technical bulletin (North Carolina Agricultural Experiment Station). Mueller UG (1996) Life history and social evolution of the primitively eusocial bee Augochlorella striata (Hymenoptera: Halictidae). J Kansas Entomol Soc 69(4):116-138. Richards MH, Rutgers-Kelly A, Gibbs J, Vickruck JL, Rehan SM, Sheffield CS (2011) Bee diversity in naturalizing patches of Carolinian grasslands in southern Ontario, Canada. Can Entomol 143:279-299. Sakagami SF, Michener CD (1962) The nest architecture of the sweat bees (Halictinae), a comparative study of behavior. The University of Kansas Press. Lawrence, KS. 135 pp. Snelling RR (1986) Contributions toward a revision of the new world nomadine bees. A partitioning of the genus Nomada (Hymenoptera: Anthophoridae). Contributions in Science 376:1-32. Stockhammer KA (1966) Nesting habits and life cycle of a sweat bee, Augochlora pura (Hymenoptera: Halictidae). J Kansas Entomol Soc 39:157-192.

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Supplementary Figure 1: Map depicting region in which study took place. All sites were located within the featured area, with one site at 39.558ºN, -84.899ºW (see Supplementary Table 1 for site details) magnified to show an example of an isolated forest patch in the landscape (inset).

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Supplementary Figure 2: An array of six traps at heights of 1, 4, 7, 10, 13, and 16 m from the ground. Traps are connected with black parachord (300 lb test) at the top of each blue vane (holes provided by manufacturer). Knots in rope used to secure positioning of each trap. The parachord is anchored into the ground with a stake (not visible). Propylene gycol is in bottom of traps. Inset: close-up of trap at 16 m and the location where parachord is looped around branch (red circle). Looped parachord returns to ground and is secured around the trunk of a tree (not shown).

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Supplementary Figure 3: PCA of the woody vegetation community that flowered before, during, and after (black dots) the L. maackii bloom weighted by total species abundance. Species scores (red dots) on the first Principal Component were used for analyses to represent the mean relative peak flowering time (Supplementary Table 2). Magnification shows species scores near origin (inset).

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Supplementary Figure 4: Species scores of RLQ axes. See Figure 4 for details. Six species of Bombus were removed and are located in the direction of the arrow in the order of B. impatiens, B. pensylvanicus, B. fervidus, B. bimaculatus, and B. auricomis.

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Supplementary Figure 5: Predictions of mature tree abundance as a function of densities of L. maackii at sites with early (solid), mid (dashed), and late (dotted) peak flowering times. 178

Supplementary Figure 6: Mean (± SE) bee abundances after the flowering period of L. maackii between forest edges with and without T. americana trees. Observed abundances (hollow circles) are jittered 0.1 log unit. Models included data from after L. maackii bloom. Likelihood ratio test was used to evaluate significance of T. americana presence. Y-axis is natural-log transformed. 179

General Conclusion

1 Introduction

There is an urgent need to improve our predictions of bee community responses to land- use changes and species invasions within agricultural landscapes. Global loss of high-quality habitat, increased invasions of alien plants, and intensification of agricultural land all lead to losses of bee diversity and pollination services. These changes in land-use and their impacts on bee communities are expected to continue in the coming decades as demands for food production increase to accommodate the needs of our growing population. Bees are both important to food production through their pollination services and at the same time sensitive to land-use change and agricultural intensification. Perhaps not surprisingly, a great deal of research effort and resources have been invested into these issues over the last 20 years, which have resulted in an explosion of research into field studies of bee communities in agricultural landscapes. However, it has been difficult to generalize bee responses to changes in the landscape as well as floral and nesting resources of bees due to the complex and evolving relationships between bees and flowering plants, invasion of plant species, and the different scales at which bees interact with the environment. Thus, we lack a unified framework in which to predict bee responses to the environment, and the underlying assumptions of different study approaches can result in unclear or overly-simplified recommendations for bee conservation. It follows that to conserve bees and their pollination services within agricultural landscapes of the future, we first need a common framework in which to analyze bee responses to human- dominated landscapes, and to forecast future changes in bee communities and their pollination services in the face of ongoing changes in agricultural land uses. Here, I briefly return to the literature trends described in the General Introduction, where studies have approached bee community responses within either the context of islands of suitable habitat surrounded by a matrix of inhospitable habitat, or as a landscape mosaic of different land cover types, each with its own resources and different levels of habitat suitability for bees. I provide evidence that a synthetic view of bee responses to the landscape may contribute towards a more unified framework for understanding bee diversity and distribution responses to land-use 180

change. I then describe findings of my studies that addressed responses of bee communities to a dominant invasive shrub along forest fragment edges. Finally, I discuss how flowering invasive plants may shift support for a simple island-matrix view or mosaic landscape view, and what this implies for bee conservation and management in agroecosystems.

2 Shifting perception of bee responses to the landscape My review of the literature addressed wild bee studies conducted in agricultural landscapes and showed clear trends of a transitioning viewpoint on bee community responses to features of the landscape over the last two decades (Figure 1). It also highlighted gaps in our understanding of the scale at which bees respond (Figure 2). This body of literature is rapidly growing, yet the importance of the surrounding landscape is still unclear as a large portion of studies continue to only include properties of the patch in explaining bee responses. Here, I provide a summary of the broad range of approaches used to relate features of the landscape to wild bee communities as research in the discipline has progressed. Ultimately, I find there is a need for a synthetic framework. With a synthetic framework, we may understand the ‘how’ and ‘why’ bees are predicted to respond to islands of semi-natural habitat, a mosaic of land cover types, or perhaps both. Nearly all studies I evaluated (Introduction Chapter for approach) had considered or focused on natural or semi-natural habitat (hereafter ‘SNH’) fragments that were typically described as remnant or restored grasslands or forest. Studies that framed bee responses to characteristics of the patch, including patch size or length of patch edge, discussed their findings in the context of the broader landscape and comprised 60% of wild bee publications, (species lists excluded). Generally, these studies compared bee responses among different features of the same habitat type or the same features present among different land cover types. Other studies made conclusions regarding surrounding landscape effects on bee responses, yet their methods did not demonstrate that these statistical analyses were performed. Such studies used one of two approaches. The more common approach included measures of patch isolation, or distance from the nearest SNH patch as predictor of bee responses. Patch isolation was interpreted as a feature of the landscape by considering the distance outside the extent of the patch. Since the habitat within this space was not considered, measures of patch isolation are only extended properties of the patch and conform to an island view. The second and less common approach analyzed

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surrounding land cover types separately from models that evaluated bee responses. Such an analysis is informative of landscape diversity within the study region, but caution should be exercised in making conclusions regarding bee responses to the surrounding landscape when these variables are excluded from bee analyses. The proportion of studies that considered the surrounding landscape composition in their analyses increased over the years from 0% (1985–1999) to 51% in 2019 (Figure 1) and represented a wide range of classifications of land cover types by investigators. For instance, of the 275 publications that treated the spatial configuration of the surrounding landscape as a mosaic, 160 (58%) simplified land cover types into one or two aggregate categories (usually SNH and agriculture) and often excluded other land cover types from analyses with the justification that their relative area was nominal or not important for bee responses. Simplifying the heterogeneity of the landscape into so few land cover types is problematic for two reasons. First, this effectively results in the analysis of one land cover (usually SNH) as the categories could be relabeled as ‘SNH’ or ‘not SNH’. Second, many of these studies found strong negative correlations between the two land cover type variables used, which forced models to only use one land cover type variable. It is not surprising that this approach most often led to the conclusion that ‘SNH’ was more beneficial to bees than ‘not SNH’. Nevertheless, such approaches account for variation in bee community responses and generally provide a more accurate analysis than not including the surrounding landscape at all. Overall, I found that only 15% of publications on wild bee responses in agricultural landscapes considered more than two land cover types of the surrounding matrix in their analysis, though this was most common in 2019 at 32% (data not shown). Landscape diversity, as measured by Shannon’s Diversity of habitat types, was generally a weak predictor of the bee community and suggests that some land cover types have a greater importance to bees. Results of these studies generally agreed that different types of SNH (i.e. forest and grassland) are supportive of bees but were not always the strongest predictors of bee responses. Therefore, the inclusion of multiple land cover types in analyses of bee responses provided additional insight into roles of other land uses in providing resources to bees. However, my review also revealed a range of negative to positive responses of bees to the same land cover types and appears sensitive to the bee species and extent of the landscape analyzed.

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Few studies considered different components of the bee community at different scales of landscape composition in their analysis, though many attempted to fit the overall bee community to different spatial scales. Body size was the most common functional attribute used to partition bee community responses to the landscape (e.g. Adhikari et al. 2019; Bartomeus 2015; Benjamin et al. 2014). Small bees typically responded to landscape features at smaller scales than larger bees and this was expected to be associated with foraging ability of the bee species (Benjamin et al. 2014). Other functional and life history attributes of bee species also differentiated responses to scale of landscape composition (e.g. Basu et al. 2016; Knapp et al. 2019; Sivakoff et al. 2018). For example, bee species with large bodies generally responded to the landscape at a larger spatial scale when social than when solitary. Alternatively, small bee species that are solitary and specialist foragers may need to fly longer distances to acquire preferred pollen and may respond to features at larger scales when resources are not available near the nest (Zurbuchen et al. 2010). Thus, responses of bee communities to landscape structure and composition cannot be captured by a single spatial scale nor by a single functional attribute and are instead a consequence of the interaction between the functional composition of the bee community and spatial configuration of different land cover types, each of which potentially provides a different availability of nesting and floral resources to different bee species.

3 Effects of Lonicera maackii on bees Resources for bees within agricultural landscapes are dynamic, especially when considering crop rotations including corn/soybean rotations) and future perturbations to the flowering community are expected as the number of emerging invasive plants increases (Seebens et al. 2018). Changes in seasonal weather patterns and resources characteristic of each land cover type affect the spatial and seasonal distribution of flowering plants. Since bees are central-place foragers, accessible floral resources should be limited to those that bees typically encounter within their foraging ranges from nests. Therefore, it is not surprising that the spatial and seasonal variability in available floral resources combined with the wide range of foraging abilities of different bee species have resulted in various spatial and temporal scales at which researchers have tested bee responses to the surrounding landscape composition (Figure 2). The three field studies comprising my dissertation research collectively demonstrated a species- sorting effect of a dominant invasive shrub on the functional and life-history composition of bee

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communities along forest patch edges. The results of these studies provided insight into scale- dependent responses of the bee community to Lonicera maackii and other features of the landscape. However, different standardized sampling methods were found to sample different components of the bee community many of which exhibited different spatial and temporal responses. Finally, analysis of floral resource availability in the woody vegetation community within forest-patch edges additionally demonstrated novel roles of canopy strata in structuring the bee community and its responses to L. maackii. In Chapter 1, I described a two-year field experiment where I removed flower buds from L. maackii shrubs along heavily invaded forest edges and measured shifts in bee species composition and pollination services to sentinel plants up to 200 m into the adjacent crop field. I found that flowers of L. maackii modified the functional composition of the bee community, as well as their visitation and pollination services to sentinel cucumber flowers. Specifically, I concluded that during its flowering period in the late spring, L. maackii and its attractive flowers suppressed a component of the forest-edge bee community comprised largely of ground-dwelling and clepto-parasitic or solitary species with a narrower diet breadth, which affected their pollination services. Removing the flowers released this component of the bee community and increased pollination services relative to plots with intact L. maackii flowers at distances < 100 m in the first year and at all distances after two years. These findings showed that most of the forest-edge bee community sampled with pan traps forage < 200 m from the edge of the forest fragment after a dominant source of floral resources was removed. However, there were many bees within the adjacent crop field that did not respond to L. maackii flowers, and I also observed increased visitation rates and subsequent pollination services to sentinel cucumber plants ≥ 100 m into the crop field by some large-bodied species (e.g. Bombus spp) when adjacent to intact L. maackii flowers. This suggested that L. maackii flowers attracted some strong foragers to the forest patch and increased usage of the adjacent crop field. Here, I did not consider the surrounding landscape composition and can only infer that a component of the bee community of heavily invaded edges of forest patches uses L. maackii floral resources. Flowering plant communities within the forest interior and field margins between the forest edge and the adjacent crop field exhibited turnover during the study and suggested a role of other available floral resources on bee responses to L. maackii. Therefore, in Chapter 2, I evaluated the seasonal patterns of bee diversity and abundance compared to local and landscape

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variables associated with different available resources for bees at different densities of L. maackii. Since my observations of sentinel flower visitations in Chapter 1 yielded a different subset of bees than my primary trapping method (pan traps), I included the complementary method of using vane traps.

My results from Chapter 2 demonstrated that different components of the bee community respond to landscape composition as well as resources of the forest edge. My study supported the common claim that resources of the SNH patch are important to bee communities. Components of the bee community accessed different seasonal sources of flowers which highlights the importance of flowering plant communities on SNH patches throughout the season. For example, the abundance of pan trap bees responded positively to the resources within the field margin between forest edges and crops, while bees of vane traps showed positive responses to available floral resources in the understory of the forest edge. Interestingly, both components of the bee community exhibited similar seasonal responses to the flowering tree community and suggested that resources at higher strata in the canopy were distracting bees from traps during the flowering period of trees in the spring. Since I only considered trees that were of reproductive size and did not measure basal area, the actual density of trees within each transect was unknown. Therefore, I cannot identify the mechanism of this negative relationship in early spring between the tree community and the bee community in the herbaceous stratum, as there is a well-known inverse relationship between tree size and tree density that may be playing a role. For instance, I selected the depth of my sites into the forest interior based in part on differences in L. maackii density and vegetation structure. Therefore, tree composition and size structure may have also differed between the forest edges and interiors. Nevertheless, I found some evidence that seasonal changes in the overall flowering tree community may have masked effects on the bee community due to L. maackii. My results showed that L. maackii effects elicited different responses between pan trap bees and vane trap bees. The abundance of the former tended to increase while there was a trend of increased species richness of bees in the latter during the flowering period of L. maackii.

Comparing the body sizes of bees sampled in pan traps against those in vane traps uncovered the possibility that these sampling methods may favor bees of particular foraging behaviors instead of body size. Specifically, I found that bees of vane traps generally represented

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more bees that responded to the surrounding landscape composition and used wood as a nesting substrate, were not parasitic, or were large and social. These results supported the finding that a large proportion of observed visitors to sentinel plants in Chapter 1 were Bombus, a large social genus which was under-represented in pan traps. Therefore, it appears that L. maackii may attract additional species of strong foragers in the landscape to the forest edge while supporting some species of weaker foragers that inhabit the forest patch.

The seasonal effects of the tree community on both components of the bee community suggested two important hypotheses that could provide further insight into L. maackii effects on the bee community. First, functional composition differences of the bee community represented by each trap type were more consistent with a vertical stratification of the bee community along the forest edge than selection based only on body size. Secondly, stronger negative effects of more trees on bees early in the season when most deciduous trees were flowering suggested that more bees were attracted into the upper layers of the canopy and away from traps, which would have diluted the effect of L. maackii shrubs. Therefore, I conducted an empirical study whereby bees were sampled at different strata of the canopy before, during, and after L. maackii flowered (Chapter 3).

By evaluating the vertical stratification of bee communities in response to L. maackii density, I found that bee community responses to vegetation structure are affected by L. maackii floral resources and the nearby woody community. In support of my first hypothesis, bees sampled in the understory stratum (≤ 4 m) represented only 34% of the abundance sampled in the study. Further, wood-nesting bees were most frequently sampled in the canopy stratum. Collectively, these lines of evidence suggest that vertical placement of traps is important in forest habitats due to a vertical stratification of the bee community. We did not find an important role of vertical strata on bee composition, likely due to the simultaneous use of heights up to 16 m by most species of the bee community. However, the important roles of vertical strata were demonstrated through interactions with temporal and woody vegetation predictors which explained bee abundance and bee species richness and showed that bees use resources at different vertical strata at different times of the year. Therefore, not considering trap height placement is likely to distort the true responses of the bee community to local patch features. Comparing bee abundance and species richness across habitat types is also likely to result in

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altered conclusions regarding bee responses to features of the landscape. For instance, only considering the subset of the bee community sampled in the herbaceous strata may give the false impression that bee communities along forest edges are less abundant and may alter the predicted responses of bees to landscape composition. In support of my second hypothesis, the flowering times of the tree communities at each site affected bee community composition. Variation in densities of L. maackii among forest patches also affected bee species composition and increased the abundance of large soil-dwelling bees that were social and generalist foragers after L. maackii flowered.

This study also demonstrated strong correlations between L. maackii and the woody plant community, and these differences in woody species composition resulted in different bee assemblages particularly in the early spring. Lonicera maackii stands of greater density had fewer saplings, fewer trees and shrubs with an early-season flower phenology, and more trees and shrubs that co-flowered with L. maackii. Since I did not conduct long-term studies, I cannot conclude, for instance, if L. maackii is structuring the woody plant community in favor of co- flowering species that may share pollinators, or if L. maackii reaches greater densities along forest edges with a greater proportion of species that co-flower with L. maackii. However, these differences in species composition of the woody plant community resulted in fewer early-season bees at sites with more L. maackii due to fewer trees and shrubs that flower early in the season. Many of these early-season bees are wood-nesting or solitary species that forage in the canopy early in the season (i.e. Hylaeus, Ceratina, Augochlora, Osmia, and Andrena). I did find evidence that flowering L. maackii reduces bee visitation to other vertical strata with co- flowering trees, particularly in the subcanopy strata. This altered the species composition of the bee community throughout the vertical strata and shows that L. maackii flowers filter the bee community in favor of species that use its own floral resources, whether or not it is the cause or effect of a simplified assemblage of early-season woody vegetation.

Overall, my studies demonstrated that L. maackii changed the taxonomic and functional composition of bee communities along edges of forest patches in fragmented agricultural landscapes. I showed that L. maackii may affect bee communities through a variety of mechanisms, including its highly attractive floral resources to bees as well as competitive effects on surrounding flora that result in a loss of alternative food and nesting sources for bees. There

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was evidence that some bee populations were facilitated by L. maackii, but I also found that L. maackii was negatively affecting other bee species. Taken together, the findings from all my studies agreed that more L. maackii shrubs resulted in more bees at the forest edge while L. maackii was in bloom and were associated with more social species with larger bodies later in the summer. The combined studies also showed that L. maackii can exhibit negative effects on bee abundance and diversity when mediated through its competitive effects with the nearby flora. Therefore, its impact on bees of forest patch edges is context-dependent, but ecologically important. My studies collectively suggest that L. maackii alters the species composition of the bee community along forest edges by becoming the dominant source of available floral resources and favors generalist and often social bee species that use them.

4 Invasive plant effects on a synthetic framework Lonicera maackii exhibited effects on the bee community at different times during the flowering season and led to altered bee species composition. Other researchers have searched for, and found, relationships between changes in the landscape and bee morphology, behavior, ecological associations, phylogeny, and physiology. Throughout this work, I tested and discussed my findings in terms of life history and functional attributes of species within the bee communities examined. However, it was clear that L. maackii affected the bee community by a different suite of functional traits as each study represented a slightly different functional component of the bee community. This suggested that L. maackii was already linked to a large proportion of forest-edge bee communities but affected species differently due to suites of functional and life history attributes. Using multivariate approaches on functional attributes to predict bee responses supported this conclusion, but my findings on the surrounding landscape composition also suggested that combinations of traits varied in importance seasonally and spatially.

The effects of Lonicera maackii on bee communities of forest fragment edges were mediated by seasonality and the surrounding landscape composition at different scales of analysis. My findings agree with other recent publications on bee responses to the landscape composition and suggest that the spatial scale of these responses is predicted by composition of functional and life history attributes of the bee community as opposed to single traits (Coutinho

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et al. 2018; Ponisio et al. 2019). Based on my findings, some bees rely on resources of the focal forest patch more than others and their responses may fit an island framework better than a mosaic landscape framework. Functional attributes exhibited by bees that primarily responded to the patch should differ from the functional composition of bees that rely more on the surrounding landscape at a larger scale, though there was not a clear monotonic response of functional composition by the bee community. Therefore, creation of a synthetic model derived from components of the bee community that respond best to either the habitat patch or the surrounding landscape mosaic and representing suites of known and unknown traits along a functional gradient can be used to evaluate the species-sorting effects of L. maackii on the functional bee community. This would provide insight into our idiosyncratic views of bee community responses in agricultural landscapes and lead towards a unified framework for determining how the composition of bee communities changes along an island-mosaic gradient in response to both characteristics of highly suitable habitat islands and the surrounding landscape composition and configuration (Introduction Figure 1).

4.1 Development of ALRIB To test the hypothesis that L. maackii shifted the relative roles of forest habitats and surrounding landscape mosaic in predicting forest-edge bee communities, I developed a simple index that represents the relative likelihood of a bee forager to respond to the landscape more as a mosaic of different land cover types versus a series of islands of SNH. Results from Chapter 2 showed that the component of the bee community sampled in vane traps was different in species composition than the component sampled by pan traps. Bees of vane traps responded best to landscape composition up to 3 km from the forest edge while bees of pan traps were best predicted by characteristics of the focal patch (0.5 km from forest edge). Therefore, I used the species composition of each of these components of the bee community to derive the Agricultural Landscape Response Index for Bees (ALRIB).

From the data of Chapter 2, I omitted species with < 5 individuals so that the ALRIB was based on a data set of 17,596 individuals of 120 species. The index value for each species was chosen to range from 0, which represented species that rely the most on resources of the SNH patch, to 1, which represented species that are most affected by resources in the surrounding

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landscape. To calculate the ALRIB, I first standardized species abundances between trap types. The relative abundance of each species in both pan and vane traps was calculated. I then derived the index value of each species by calculating the proportion of the relative abundance sampled in vane traps as 푥푖=푣푎푛푒푖⁄(푣푎푛푒푖 + 푝푎푛푖), where 푣푎푛푒푖 is the relative abundance of species i in vane traps and 푝푎푛푖 is the relative abundance of species i in pan traps. Therefore, if a species was only sampled in pan traps, it would be assigned a value of 0, while a species only sampled in vane traps would be assigned a value of 1. When applied to another data set, the ALRIB score is calculated as a weighted mean representative of species composition of a bee community or sample. Therefore, each ALRIB score is a species-specific index that is weighted by the abundance of each species to provide a weighted mean ALRIB score per sample or site. Using the bee community at the site level as an example, it follows that the ALRIB score of site j is 푛 퐴퐿푅퐼퐵푗 = ∑푖=1 푦푖 where n is the number of species considered in the data set at site j and 푦푖 =

푥푖 ∗ 푎푖, where 푎푖 is the overall relative abundance of species i at site j.

4.2 Application of ALRIB Using one value per site, I tested changes in ALRIB responses to L. maackii predictors used in each of my studies. Therefore, ALRIB scores were fit against L. maackii flower removal treatment (Chapter 1), L. maackii density throughout the season (Chapter 2), and L. maackii density as it related to the bee communities of different vertical strata (Chapter 3). When applied to the full data sets of my studies, the ALRIB results demonstrated trends that spatial use of the landscape by bee communities was altered by L. maackii and collectively suggested that forest- edge bee communities invaded by L. maackii are shifting bee species composition in favor of species that are more likely to use the surrounding landscape. In support of my findings that L. maackii suppressed the bee community while attracting some species that are large-bodied and generalist foragers of the landscape (Chapter 1), there was a trend for species composition of bee communities within the adjacent crop field to increase their ALRIB scores when L. maackii flowers were removed (Figure 3). The release of bees with a higher ALRIB score following the removal of a dominant source of flowers suggests that L. maackii is changing the composition of the bee community at forest edges from species that rely on the forest patch to species that more readily use resources in the surrounding landscape. In further support of this line of thought, I

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also found a trend that the composition of bee communities at forest edges throughout the season increased their ALRIB scores in response to increased densities of L. maackii (Figure 4; Chapter 2). This trend also suggests that L. maackii modified the bee community composition at the forest edge in favor of species that are not dependent on the resources of the forest patch and can make use of resources in other land cover types within the matrix. ALRIB scores of bee communities across a vertical gradient of the forest edge did not change in response to L. maackii density (Figure 5; Chapter 3). Considering my findings that the tree community played important roles in determining bee responses at the forest edge in Chapters 2 and 3, this result most likely suggests a high degree of vertical movement of bees across canopy strata. However, this relationship may change over time if tree seedling recruitment are declining with increasingly dense populations of L. maackii (Hoven et al. 2017). Without trees in earlier life stages, dense stands of L. maackii shrubs may reduce canopy height following the death of mature trees and become the overall dominant source of floral resources. Alternatively, the effects of changes in species abundances in bee communities at different sites may have been diluted by the prevalence of one solitary species that nests in dead wood, Augochlora pura, which comprised 64% of species abundances of the data set. Finally, I applied the ALRIB to species composition at each site as a demonstration, but this greatly reduced statistical power. Applying it to sample-level species composition would likely result in a clearer interpretation.

The index presented here is a very simple metric to demonstrate an approach in which bee ecologists, conservation managers, and applied scientists can evaluate the relative roles of habitat patches and the surrounding landscape in structuring bee communities. Creation of the ALRIB provided a unique opportunity to parse bees based on interactions with the landscape instead of categorized functional attributes that may misrepresent ecological relationships and are infinitely numerous. Therefore, the ALRIB represents responses of all species of the bee community but does not exclusively depend on species identity or traits. Here, the ALRIB provided spatial information on functional composition changes of bee communities associated with SNH fragments in an agricultural landscape in response to an invasive shrub. The index incorporated bee responses to the surrounding landscape composition and was applied to other data sets without re-quantifying the surrounding landscape.

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5 Conclusion I suggest that future studies consider building models based on the individualistic responses of bee species within the community to predict changes in bee community composition in response to invasive plants and other land-use changes that alter available resources to bees. I showed that bee responses to the scale of the landscape were not highly dependent on body size, although there was a relationship. This is most likely due to spatial configuration of resources in the surrounding landscape interacting with the functional attributes and life histories of species within the bee communities and is too complex to be summarized by a single behavioral or morphological feature. Further, the incorporation of vegetation structure into models showed that the bee community was clearly vertically stratified at the edges of forest patches. This suggests that our understanding of bee foraging patterns within agricultural landscapes needs further work, as few studies account for components of the bee community > 2 m from the ground.

If future studies incorporate the fact that bee composition changes in time and three dimensions of space, the species-specific differences in scale responses to landscape features should not impede the ability to make accurate predictions of bee community responses. My work suggests that there are fewer forest-edge bees that rely heavily on resources associated with the forest patch where there is more L. maackii and that this relationship represents a shift in the species composition of forest-edge bee communities towards species that use resources in the surrounding landscape. With inclusion of the relationship between L. maackii and forest patches of degraded quality, I conclude that the invasion of L. maackii into forest fragments has a strong filtering effect on an otherwise diverse and variable bee community that, like much of the surrounding land uses with frequent disturbance, has a homogenizing effect on the bee species diversity that is present in intensively managed agricultural landscapes.

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6 References Adhikari S, Burkle LA, O’Neill K, Weaver DK, Delphia CM, Menalled F (2019) Dryland organic farming partially offsets negative effects of highly simplified agricultural landscapes on forbs, bees, and bee-flower networks. Environ Entomol 48(4):826-835. Bartomeus I, Gagic V, Bommarco R (2015) Pollinators, pests and soil properties interactively shape oilseed rape yield. Basic Appl Ecol 16:737-745. Basu P, Parui AK, Chatterjee S, Dutta A, Chakraborty P, Roberts S, Smith B (2016) Scale dependent drivers of wild bee diversity in tropical heterogeneous agricultural landscapes. Ecol Evol 6(19):6983-6992. Benjamin FE, Reilly JR, Winfree R (2014) Pollinator body size mediates the scale at which land use drives crop pollination services. J Appl Ecol 51:440-449. Coutinho JGE, Garibaldi LA, Viana BF (2018) The influence of local and landscape scale on single response traits in bees: A meta-analysis. Ag Ecosyst Ecol 256:61-73. Hoven BM, Gorcho DL, Knight KS, Peters VE (2017) The effect of emerald ash borer-caused tree mortality on the invasive shrub Amur honeysuckle and their combined effects on tree and shrub seedlings. Biol Invasions 19:2813-2836. Knapp JL, Shaw RF, Osborne JL (2019) Pollinator visitation to mass-flowering courgetti and co- flowering wild flowers: Implications for pollination and bee conservation on farms. Basic Appl Ecol 34:85-94. Ponisio LC, Valpine P, M’Gonigle LK, Kremen C (2019) Proximity of restored hedgerows interacts with local floral diversity and species’ traits to shape long-term pollinator metacommunity dynamics. Ecol Lett 22:1048-1060. Seebens H, Blackburn TM, Dyer EE, Genovesi P, Hulme PE, Jeschke JM, Pagad S, Pyšek P, Kleunen M, Winter M, Ansong M, Arianoutsou M, Bacher S, Blasius B, Brockerhoff EG, Brundu G, Capinha C, Causton CE, Celesti=Grapow L, Dawson W, Dullinger S, Economo EP, Fuentes N, Guénard B, Jäger H, Kartesz J, Kenis M, Kühn I, Lenzner B, Liebhold AM, Mosena A, Moser D, Nentwig W, Nishino M, Pearman D, Pergl J, Rabitsch W, Rojas-Sandoval J, Roques A, Rorke S, Rossinelli S, Roy HE, Scalera R, Schindler S, Štajerová K, Tokarska-Guzik B, Walker K, Ward DF, Yamanaka T, Essl F (2018) Global rise in emerging alien species results from increased accessibility of new source pools. PNAS 115(10):E2264-E2273. Sivakoff FS, Prajzner SP, Gardiner MM (2018) Unique bee communities within vacant lots and urban farms result from variation in surrounding urbanization intensity. Sustainability 10:1926. Zurbuchen A, Landert L, Klaiber J, Müller A, Hein S, Dorn S (2010) Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol Conserv 143:669-676.

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Figure 1: Generalized linear model predictions of the proportion of wild bee publications that analyzed the landscape as a function of year published. Black circles represent raw data. Shaded region indicates 95% CI of predicted values.

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Figure 2: Proportion of studies that analyzed the surrounding matrix that used different maximum distances (radii) from sampling location to evaluate bee abundance and diversity responses to area of land covers. Bars represent increments of 0.1 km except for three bars representing 3.1–4.0 km, 4.1–5.0 km and 5.1–15.0 km. 195

Figure 3: ALRIB values of bee communities in adjacent crop fields in response to flower removals of L. maackii shrubs at five sites over two years. Statistics are based on a linear model fit of the removal treatment to ALRIB responses.

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Figure 4: ALRIB of bee communities (April-November) at forest edges fitted to different densities of L. maackii. Each point represents the bee community of a site. Shaded area indicates the 95% CI. 197

Figure 5: ALRIB of bee communities throughout the canopy strata along forest edges before, during, and after the flowering period of L. maackii fitted to different densities of L. maackii. Each point represents the bee community of a site. Shaded area indicates the 95% CI. 198

Appendix A Plans for a self-watering container that uses a wicking (evaporation) mechanism Note: Plans use the standard system E Materials PLANT A) 5 gallon bucket B) Perforated cup for water absorption C) Plate D A D) 1” OD PVC pipe E) 1” x 1” mesh chicken wire SOIL SOIL C 2” x 4” wire fencing for cheap trellis (not shown) 3 gal L B H2O Construction & Justification reservoir 1) Drill an overflow hole (1/4”) just below line 3-gal self-watering sentinel plant design of plate attachment on both sides of bucket a. When refilling reservoir, water coming out of these holes will serve as your “full” line and does not risk rotting the plant’s root system 2) Soil/water reservoir divider a. Trim plate to shape of bucket opening (plastic or aluminum); leave tabs for attachment to bucket if using aluminum b. Cut hole in plate center width of cup c. Cut a 1” diameter hole 1” from plate perimeter towards plate center i. This is where the watering tube will pass from above the soil to the reservoir d. Drill 5/8” – 1” holes throughout cup i. Location of water absorption into the soil. Note: too small of holes will be plugged with terminal roots and wicking mechanism will not work. e. Hot glue (plastic) or solder (aluminum) cup to plate f. Attach divider using screws on divider tabs for aluminum, or screws (to hold up) and hot glue/plastic epoxy for plastic, to inside of bucket just above fill line 3) Water refill mechanism. a. Cut 1” OD pvc pipe to a length that sticks above bucket rim 1” when resting at bottom. b. Drill a ½” hole 1/2” from one end of pipe on both sides i. This will allow water input even if pipe is evenly on bucket bottom surface c. Use silicone (needs to be flexible) to secure pipe to divider (in 1” hole) 4) Fill with soil and sentinel plant; place trellis if needed; attach 1” chicken wire along outside rim of bucket for wildlife deterrence 5) Fill reservoir with water using an elongated funnel (it should reach the water tube; the chicken wire provides some give if needed) 6) Carry to location; try not to get too wet. Estimated carrying weight when full with a 3 gallon reservoir: 25-30 lbs

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