Biodiversity Ensures Plant–Pollinator Phenological Synchrony Against Climate Change

Ecology Letters, (2013) 16: 1331–1338 doi: 10.1111/ele.12170 LETTER Biodiversity ensures plant–pollinator phenological synchrony against climate change

Abstract Ignasi Bartomeus,1,2* Mia G. Climate change has the potential to alter the phenological synchrony between interacting mutualists, such Park,3 Jason Gibbs,3,4 Bryan N. as plants and their pollinators. However, high levels of biodiversity might buffer the negative effects of Danforth,3 Alan N. Lakso5 and species-specific phenological shifts and maintain synchrony at the community level, as predicted by the bio- Rachael Winfree1,6 diversity insurance hypothesis. Here, we explore how biodiversity might enhance and stabilise phenological synchrony between a valuable crop, apple and its native pollinators. We combine 46 years of data on apple flowering phenology with historical records of bee pollinators over the same period. When the key apple pollinators are considered altogether, we found extensive synchrony between bee activity and apple peak bloom due to complementarity among bee species’ activity periods, and also a stable trend over time due to differential responses to warming climate among bee species. A simulation model confirms that high biodiversity levels can ensure plant–pollinator phenological synchrony and thus pollination function.

Keywords Bees, crop pollination, ecosystem function, ecosystem services, phenology, pollination, response diversity, stabilising mechanism.

Ecology Letters (2013) 16: 1331–1338

disturbances than are the species themselves (Tylianakis et al. 2008; INTRODUCTION Veddeler et al. 2010). Interactions require that the species co-occur The idea that biodiversity can buffer ecosystem functioning against in space and time; thus, the biodiversity insurance hypothesis might the loss of individual species is referred to as the biodiversity insur- also apply to phenological synchrony among species. Higher rich- ance hypothesis (Lawton & Brown 1993; Naeem & Li 1997; Loreau ness of potentially interacting species (e.g. either plants or pollina- et al. 2001). This hypothesis predicts that biodiversity can ensure tors) could buffer the interaction against fluctuations in the ecosystem functions in two ways: (1) a performance-enhancing numbers of individual species over time. Perhaps, the main way effect (i.e. an increase in the mean level of function provided) and such buffering could occur is through species’ differential (2) a buffering effect (i.e. a reduction in the temporal variance in responses to environmental change, a mechanism known as function). There is an extensive theoretical (Yachi & Loreau 1999) response diversity (Ives et al. 1999; Walker et al. 1999; Elmqvist and experimental literature supporting the insurance hypothesis by et al. 2003). However, this phenological extension of the biodiver- showing that increased species richness leads to both higher mean sity insurance hypothesis, and the role of response diversity in driv- ecosystem function and lower variance in function over time ing it, has been explored very little (Jiang & Pu 2009). (reviewed in Balvanera et al. 2006; Cardinale et al. 2012). However, Here, we investigate phenological synchrony as a novel dimen- far fewer studies have explored this question in non-experimental sion of the biodiversity–ecosystem functioning relationship, using systems (but see Klein et al. 2003; Laliberte et al. 2010; Garibaldi plants and pollinators responding to climate warming as a model et al. 2011), and in particular, long-term studies are lacking. The few system. Human-mediated climate change has the potential to mod- studies to explicitly explore the effect of scale suggest that it is pre- ify species phenologies in a directional, long-term manner (Parme- cisely across large spatiotemporal scales that biodiversity insurance san 2006). Average global temperature has already increased by effects might be strongest (Loreau et al. 2003; Isbell et al. 2009, 0.6 °C, resulting in detectable shifts in phenology (Parmesan 2011; Reich et al. 2012). 2006), notably for species active in early spring (Fitter & Fitter A second knowledge gap in the field of biodiversity–ecosystem 2002; Bartomeus et al. 2011). Different taxa show divergent rates functioning research stems from the fact that research on the of advance (Root et al. 2003), and this makes species interactions insurance hypothesis has been dominated by experiments focusing particularly vulnerable due to potential phenological mismatch on a single trophic level, most often plants (Balvanera et al. 2006). (Visser & Both 2005; Rafferty & Ives 2011). Animal-mediated pol- Yet, in reality, interactions among species determine the outcome lination is a particularly important interaction to understand in the for many important functions, such as pollination and pest control. context of climate change, given that animal pollinators are In addition, species interactions may be more sensitive to human required by most of the world’s flowering plant species (Ollerton

1Department of Entomology, Rutgers University, New Brunswick, NJ, 08901, 5Department of Horticulture, Cornell University, Ithaca, NY, 14853, USA USA 6Department of Ecology, Evolution and Natural Resources, Rutgers University, 2Swedish University of Agricultural Sciences, SE-75007, Uppsala, Sweden New Brunswick, NJ, 08901, USA 3Department of Entomology, Cornell University, Ithaca, NY, 14853, USA *Correspondence: E-mail: [email protected] 4Department of Entomology, Michigan State University, East Lansing, MI, 48824, USA

© 2013 John Wiley & Sons Ltd/CNRS 1332 I. Bartomeus et al. Letter et al. 2011), including most crop plants (Klein et al. 2007; Garibaldi along blossoming tree rows; only bees visiting apple blossoms were et al. 2013). collected. All specimens collected were identified to species using In this study, we use long-term data sets to examine whether pol- taxonomic keys and comparison to expertly identified material, and linator biodiversity could buffer plant–pollinator interactions against subsequently deposited in the Cornell University Insect Collection, climate change, by increasing and stabilising phenological synchrony Ithaca, NY, USA (http://cuic.entomology.cornell.edu/). We col- between apple (Malus x domestica Borkh.), a valuable fruit crop and lected 2730 specimens of 82 bee species visiting apple during 2009– its wild pollinators. We use a 46-year time-series data set on the 2011 (Table S1). To select the key apple pollinators, we only further bloom phenology of commercial apple in New York State, USA, analyse the species which in combination accounted for 90% of the along with an independent data set on the phenology of wild bee total specimens collected (26 species). Removing the tail of rare spe- species that commonly visit apple flowers, which was collected over cies reduces the potential insurance that these species can provide, the same time period in a broader region centred on the focal apple but ensures that all pollinators included are, in fact, important con- orchard. First, we used contemporary data to select the key apple tributors to apple pollination. This is important because rare apple visitor species and to test if there is phenological complementarity visitors may be very abundant in historical collections having an among them. Second, we compared the empirical rate of phenologi- excessive weight in the phenology analysis. Thus, our analysis cal advance over time between apple and its diverse set of pollina- assumes that the identity of the most frequent apple visitors did not tors, in aggregate, to determine whether phenological mismatch is change dramatically during the past 50 years. This seems a reason- occurring at the community scale. Third, we asked whether different able assumption given that most bee species persisted well in our pollinator species showed different rates of phenological change study area since the 1870s (Bartomeus et al. 2013a). As a test on with respect to apple bloom over time. Such differential responses this conservative approach, we provide additional analyses including to climate change could buffer aggregate function in this case, by a wider range of species (38 species in total) found to be important stabilising phenological synchrony over time. Finally, we conducted visitors to apple flowers in other historical studies (Phillips 1933), a simulation analysis to explore the effect of pollinator species rich- and show that results of this analysis are qualitatively similar to ness on plant–pollinator phenological synchrony. We predicted that those we report in the main text (See Text S1). increasing biodiversity would: (1) increase the phenological synchrony between apple and its pollinators due to complementarity in Historical data phenology across pollinator species, and (2) stabilise changes in syn- Historical records of apple phenology (a mean of mid-season varie- chrony between apple and its pollinators through time, due to the ties typified by ‘Delicious’ and ‘Empire’ cultivars) were gathered at differential rates of phenological change over time across pollinator New York State Agricultural Experiment Station in Geneva, New species. We found that the phenologies of apple and its complete York (42.868 N, 76.978 W). In each year of the 46-year study per- community of 26 key pollinator species have largely shifted at simi- iod (1965–2011, with data missing only from 2007), an observer lar rates over 46 years of climate warming, and that asynchrony is recorded the date of mid-bloom, defined as trees having 80–100% likely prevented by the varied rates of phenological change observed of the central blossoms in the flower clusters open. Temperature among different pollinator species. The capacity of biodiversity to records for the mean April temperature (average of the maximum buffer the effects of environmental change for plant–pollinator plus the minimum daily temperatures divided by two) for each year interactions is supported by the simulation analysis, which shows were obtained from a weather station at the New York State Agri- that high levels of bee diversity increase and stabilise phenological cultural Experiment Station, within 1 km of the observed orchards. synchrony through time. Data from 1965 to 2001 in this same study were previously reported by Wolfe et al. (2005). Historical data were obtained only for the 26 bee species that col- METHODS lectively accounted for 90% of the visits to apple flowers in our 2009–2011 field study, as described above. Our data were obtained Data collection and filtering from pinned specimens housed in the Cornell University Insect Study system Collection (70% of the analysed records), and additionally from Apple is a valuable temperate-zone crop and relies on pollinators museum specimen data available through Discoverlife.org, USDA, for fruit production (Free 1993). Apple blooms in early spring for a GBIF and the Illinois Natural History Museum. The Discover- restricted period, making it a good indicator of potential phenologi- life.org data set compiles specimens from 13 collections, the major- cal mismatches resulting from climate change. Apple has an open, ity from the American Museum of National History. Overall, generalised flower morphology, which is accessible to a wide variety ~ 80% of the analysed records were reported in Bartomeus et al. of pollinators. To assess which species of pollinators visit apple (2013b). Once obtained, all records were filtered and standardised flowers and, thus, constitute the species of interest for our analysis, as in Bartomeus et al. (2013a). This includes verification by a taxo- we surveyed diversity and abundance of bees visiting apple, in nomic expert, geo-referencing of collection localities and recording Tompkins, Wayne, and Schuyler counties in upstate New York, collection dates based on information indicated on the specimen USA for three years, in the springs of 2009 - 2011. A total of 22 label, and double-checking outliers. We then transformed all collec- orchards (10 in 2009, 6 in 2010, 16 in 2011) were surveyed at least tion dates to the number of days elapsed since January 1 and refer once during the apple bloom on days with temperature > 16 °C, to this variable as ‘collection day’. with all data collection completed between 10:00 and 15:30 h. Each We used only bee specimens collected from 1965 onwards to orchard contains several varieties, the most common being match the apple phenology records. The geographical extent of the ‘McIntosh’, ‘Golden Delicious’, ‘Empire’ and ‘Jonagold’. At each bee specimens used ranges from 41.868º to 43.868º N latitude and site, multiple trials of 15-min timed, aerial netting were conducted 85º to 70º W longitude. The New York State Agricultural À À

Statistical analysis 43.5 Phenological complementarity among pollinator species in present-day data 43.0 + We used our present-day (2009–2011) survey data to measure the

Latitude 42.5 current extent of phenological complementarity among the bee species that pollinate apple. For this analysis, data from all orchards 42.0 were pooled to allow us to characterise phenology at the species –80 –78 –76 –74 –72 –70 level. Each year was analysed separately because there were strong Longitude phenological differences in weather across years (i.e. early vs. late springs), which could have obscured otherwise consistent patterns Figure 1 Map of the study area. The cross (+) indicates the location of the New among bee species. York State Agricultural Experiment Station in Geneva, New York, USA. Each bee collection used is a grey circle, with is maximum density around the city of Ithaca, New York. Grey boundaries are counties within New York State. Observed rate of phenological change for apple and its pollinators We used our historical data sets on both apple flowering and bee activity to measure the rate of phenological change over time. First, Experiment Station in Geneva is located in the central point of the we measured the rate of phenological advance for apple as the study area (42.868 N, 76.978 W; Fig. 1). These geographical limits slope of peak bloom against year (in units of days/year). Second, to (covering only two latitude degrees) were selected to minimise the measure the rate of phenological advance for the pollinator commu- extent to which underlying geographical variation might complicate nity as a whole, we performed a joint analysis for all specimens of an understanding of phenological synchrony, while simultaneously our 26 apple-visiting bee species over time, using R package nlme utilising sufficient data points for statistical analysis. Latitude was (Pinheiro et al. 2013). Overall, advance rate was measured as the additionally controlled for in the analysis as described below. To slope of bee collection date against year, while bee genus and spe- ensure independence of samples, we used only one specimen per cies nested within genus were also included as random factors. Plant species from a given collection event, defined by unique combina- and bee slopes ( SE) were compared with t-tests. Temperature Æ tions of collector, date and location. Specimens collected on apple changes over time were also assessed by regressing mean April tem- were excluded to ensure that our measures of bee phenology and perature against year. apple bloom phenology were independent (only 95 bee specimens were excluded for this reason; < 0.3% of the records). Honey bees Asynchrony analysis: is response diversity maintaining pollinator synchrony with were not included in our analysis because they are a managed spe- apple over time? cies and their phenological patterns, therefore, also reflect manage- As our measure of phenological asynchrony, we computed the dif- ment decisions. One exotic species, Osmia cornifrons, was also ference between the date each bee specimen was collected and the excluded on the grounds that it was introduced to North America date of peak apple bloom in the same year (Fig. 2). Thus, differ- in 1970s. Its numbers have increased dramatically, which could lead ences with larger absolute values indicate greater asynchrony between to sampling artefacts that would mislead our analysis of phenology. apple and its pollinators. We restricted the maximum asynchrony For bumble bees (the genus Bombus), only gynes were included in that can result in an interaction within realistic thresholds based on the analysis since this caste is predominantly active during apple apple and bee biology, by excluding in the analysis bees that could flowering. In contrast, the worker caste hatches later in the season, not have interacted with apple because their period of activity fell thus largely missing apple bloom, and in addition is less sensitive to well outside apple’s flowering period. We included specimens col- climate variables in spring (see Bartomeus et al. 2011). lected within plus or minus 25 days around the peak bloom date Because latitude has a strong effect on bee phenology (Bartomeus recorded for a given year. This conservative 25-day definition of et al. 2011), we corrected for the effect of latitude prior to conduct- complete asynchrony accounts for the extended apple bloom period ing the analysis by standardising all bee collection dates to the lati- (3–4 weeks in our study area), as well as the fact that bees were likely tude of the location where the apple bloom data were collected. To active before and after the day on which they were collected (species do this, we multiplied each collection date by a coefficient resulting are active a minimum of 4 weeks in our study area; Bartomeus et al. from the relationship between latitude and collection date found for 2013a). Specimens excluded from the analysis for being outside of a much larger data set of spring bee species throughout the north- this time window were predominantly mid to late summer specimens eastern USA (5.90 days per latitude degree; Bartomeus et al. 2011). of multivoltine species that fly until fall, such as Augochlora or Cerati- Prior to the correction, we checked that year and latitude were not na. After filtering our data set to include only specimens collected strongly correlated in our data set (year-latitude Pearson correla- within 25 days from the peak bloom on each year, we retained Æ tion = 0.05). Note, that the maximum correction we used was 1378 specimens for the analysis (Fig. 3). Sensitivity analysis showed À ~ 5 days, and that most records (~ 70%) come from the region near that our results are robust to the choice of threshold (Fig. S1). Ithaca, New York (Fig. 1); hence, those records received less than We regressed our measure of plant–pollinator asynchrony (i.e. the 2 days of correction. Furthermore, our study region is characterised temporal difference between apple bloom and pollinator activity) by small elevation shifts, and previous work indicates that within this against year (Fig. 2c), while including model terms for bee species region longitude has very little effect on bee phenology (Bartomeus and the species 9 year interaction. A significant interaction would et al. 2011). All analyses used this latitude-corrected collection day indicate that species show response diversity, or differential changes (hereafter referred to simply as collection day). After all of the data in their asynchrony with apple bloom over time. Diagnostic plots filtering steps above, we retained 2230 specimens of 26 species, col- for all analyses were examined for heteroscedasticity, as well as to lected by, at least, 162 collectors in 1044 different collection events. ensure the normality of errors.

Apple peak Apple peak (a) bloom (b)bloom (c)

Bee activity Bee activity at t0 at t0

Bee activity Bee activity 0 at t1 at t1 Phenological asynchrony (days)

t0 t0 t1 t1 t0 t0 t1 t1 Phenological advance (days) Phenological advance (days) Time (years)

Figure 2 Hypothetical scenarios of phenological advance. Bee activity (ﬁne grey distributions) and apple peak bloom (thick black/red lines) are a schematic representation of our data. (a) A stable scenario where both bees and apple change at the same pace. Change is indicated by the arrow direction between t0 and t1. Both taxa can be synchronised (solid lines) or not synchronised (dotted lines). (b) Unstable scenarios where apple peak bloom advances more slowly (solid lines) or more quickly (dotted lines) than bee activity. (c) The resulting phenological asynchrony over time, which is stable (ﬂat slope) in two scenarios (black lines), but unstable (negative or positive slope) in two others cases (red lines). Note that the asynchrony can either decrease (lines approaching 0) or increase (lines diverging from 0) depending on the relationship between the baseline synchrony observed (y-axis intercept) and the direction of the divergence between bee and apple phenology (slope).

a random sample of the 26 apple pollinators and, for each richness level, performed a regression analysis of phenological asynchrony of Augochlora all selected species against year (see Fig. S2 for an schematic view of the simulation). Xylocopa Baseline phenological asynchrony was defined as the y-intercepts of the regression of plant–pollinator asynchrony against year, which Ceratina approximates the bee-apple asynchrony at the beginning of the time series (1965). If we observe temporal differences in pollinator activ- Lasioglossum ity among bee species (i.e. different pollinator species are complementary in their phenologies), we expect baseline asynchrony (in Bombus days) to decrease as pollinator richness increases, because the proba- bility of including complementary species increases. Conversely, if Andrena different pollinator species are not complementary in their phenologies then baseline asynchrony should not change with pollinator Colletes richness (Fig. S2). While an alternative measure of asynchrony would have been the mean of the entire time series, we used a –50 0 50 100 150 baseline measure of asynchrony at the y-intercept to obtain mea- Asynchrony (days) sures of mean asynchrony not confounded with the rate of change over time (see below). Figure 3 Synchrony between common apple-visiting bee species and apple peak The stability of pollinator and apple bloom asynchrony over time bloom. Data are pooled by genus for concision. Note that some genera such as was measured as the slope of the above-mentioned regression, i.e. Andrena are centred around the peak bloom date for apple (dash-dot line), while as the rate of change in phenological synchrony over time, with other genera occur before (e.g. Colletes) or after (e.g. Bombus gynes). Finally, other genera are most active later in the growing season or have several generations slopes close to zero indicating stability. As pollinator richness active during summer and synchronise with apple bloom only at the beginning increases, we expect the slope of the difference between apple of their flight period (e.g. Lasioglossum, Xylocopa, Ceratina, Augochlora). Boxplot bloom and pollinator activity (i.e. asynchrony) against time to get widths are proportional to the square root of the sample size. The 25 days closer to zero, if pollinator species have differential phenological around the peak bloom date, which encompass all the data used in the main shifts over time. In this case some pollinator species will be advanc- analysis, are indicated with dotted lines. Negative values indicate dates before ing their phenology faster than apple, and others slower than apple, apple peak bloom, and positive values after. thus cancelling any overall effect (Fig. 2a,c, Fig. S2). Conversely, if the different pollinator species all respond similarly and are consis- Simulation analysis: does plant–pollinator phenological synchrony increase with tently shifting their phenologies either faster or slower than apple, pollinator species richness? then increasing pollinator richness should not have a stabilising We conducted a simulation analysis to explore the effect of bee spe- effect (Fig. 2b,c). cies richness on the baseline phenological asynchrony between apple Our measures of baseline and stability of phenological synchrony and its pollinators and on the stability of such asynchrony through as a function of pollinator species richness should be robust esti- time (see definition of both measures below). Briefly, the simulation mates for several reasons. First, our measures are an intercept and a created pollinator communities of different richness levels based on slope, respectively, thus they are unbiased and consistent statistical

© 2013 John Wiley & Sons Ltd/CNRS Letter Biodiversity and phenological synchrony 1335 estimators (Plackett 1950; Lai et al. 1978). This means that they (a) should be affected by differences in phenology among species, but 300 less so by the number of individuals in the distribution. To check for possible bias associated with larger uncertainty on the estimates 250 at low richness levels due to smaller sample size, we also present a simulation in which all models are rareﬁed to the same sample size, 200 which shows qualitatively similar patterns (Fig. S3). Second, we do not interpret the variability across iterations at a given richness level, 150 but rather only the mean value obtained across all iterations at a Collection day given richness level. We do this because, as is the case for many 100 biodiversity experiments, the similarity of species composition across the randomly created bee communities within a richness level will be higher at higher richness levels (because fewer options are 1970 1980 1990 2000 2010 possible). Therefore, the variability across iterations at a given rich- Year ness level will be lower at higher richness levels (Fukami et al. (b) 2001). Third, because pollinators shifting either earlier or later than 10 apple would be of equal interest, we base all our analyses on the 9 absolute values of each simulated community’s response (Fig. S2). Each iteration of the simulation proceeds as follows. The simula- 8 tion randomly selects a number of species (from 2 to 24 of the 26 7 available species). For each of these species pools, it runs the regression between phenological asynchrony and year, using the model 6 described above. It then calculates the two values explained above 5 for each simulated species pool: ﬁrst, the absolute value of the base- Mean april temperature 4 line phenological asynchrony, measured as the predicted value in 1965; second, the stability of phenological synchrony over time (i.e. 1970 1980 1990 2000 2010 the rate of change), calculated as the absolute value of the slope. We ran 100 iterations for each species richness level (Fig. S2). Year

Figure 4 Change in temperature and phenology of apple and its pollinators over a 46-year period. Collection day is expressed in days since 1 January. (a) Apple RESULTS peak bloom (fill circles and solid regression line) and bee specimens (empty Phenological complementarity among pollinator species in circles and dotted regression line) are shown. Some pollinator species extend into the summer making the bee intercept higher than for apple. (b) Mean daily present-day data maximum April temperature is expressed in degrees Celsius. Our recent surveys of apple orchards confirm that some bee species are collected early in the apple bloom season, while other species are collected later on. This result is consistent across years (2009: April temperature has increased in parallel by 0.92 °C in the focal F56,386 = 2.08, p < 0.001; 2010: F40,257 = 1.65, p = 0.01; 2011: study area. Although this pattern is not significant for our focal data F67,832 = 1.86, p < 0.001; Fig. S4). Thus, in the present-day data, (year estimate = 0.02 0.018 °C per year; p-value = 0.28; Fig. 4b), Æ bee species show phenological complementarity with regard to their overall, our larger study region shows a recent significant increase in pollinating activity at apple. mean April temperature (Bartomeus et al. 2011).

Observed rate of phenological change for apple and its Asynchrony analysis: is response diversity maintaining pollinator pollinators synchrony with apple over time? Both apple and associated bee pollinators are advancing their phenol- We further analysed the difference between apple peak bloom and ogy through time. The phenological advance of apple was previously bee collection day for the key apple pollinators, using only speci- reported by Wolfe et al. (2005) for the period 1965–2001 and is here mens that can potentially interact with apple (i.e. collected within confirmed with 10 more years of data. The date of apple mid-bloom 25 days of peak bloom). We find no change in the degree of asyn- has advanced by roughly 2 days per decade (year estimate = chrony over time (year estimate = 0.005 0.024 days of differ- À Æ 0.18 0.07 days per year, p = 0.01; Fig. 4a). The key bee pollina- ence per year, p = 0.72), indicating a stable level of phenological À Æ tors are, in aggregate, advancing their phenology at a similar rate synchrony (overall community falls into scenario drawn in Fig. 2a, (year estimate = 0.19 0.04 days per year, p < 0.001). A simple solid lines). Moreover, the mean phenological synchrony is very À Æ comparison of the apple and aggregate bee slopes shows that they high as denoted by the phenological synchrony being centred are not significantly different (t-test on the two slopes; t = 0.13, p- around zero (Fig. 5; predicted value in 1965 = 0.2). This analysis value = 0.89; Fig. 4a). Note that the bee intercept of this model is also revealed a significant interaction term between year and bee greater than that of apple, because this analysis includes species with species (species x year interaction term; F25,1316 = 1.76, p = 0.01; extended periods of activity (Fig. 3). Lastly, in keeping with the phe- Fig. 5), indicating that different bee species showed differential nological advances shown by apple and its pollinators, the mean shifts in their phenology with respect to apple over time (i.e. some

(a)

20 12

10 10 8

0 6

4 –10 Asynchrony (days) 2 Baseline asynchrony (days)

–20 0 24681012141618202224 Number species 1970 1980 1990 2000 2010 (b) Year 0.6

Figure 5 Response diversity among bee species in terms of their phenological 0.5 shifts over time. Asynchrony (number of days between bee specimen collection date and the peak bloom of apple) as measured over the 46-year period. Dashed 0.4 red line shows the slope of the full model including all species and is centred around zero. Coloured lines represent the slopes for the each individual bee 0.3 species with more than 50 records. See Table S2 for individual models. 0.2 particular species fall into the scenario drawn in Fig. 2b). See Table 0.1 S2 for the individual models for each species.

Stability in synchrony (days/year) synchrony in Stability 0.0 Simulation analysis: does plant–pollinator phenological synchrony 24681012141618202224 increase with pollinator species richness? Number species The simulation indicates that increasing pollinator species richness Figure 6 Simulation of biodiversity effects on phenological asynchrony. We increases the baseline phenological synchrony with apple bloom and randomly selected species to create bee communities of 2–24 species, then also stabilises synchrony over time (i.e. makes the slope closer to calculated the baseline, and stability of, phenological synchrony for each zero in Fig. 2c). Baseline synchrony increases monotonically from randomly assembled community using linear models as described for the almost 4 days of mean difference between apple and its pollinators historical data in the main text (see Fig. S2 for a graphical portrayal of this at richness levels of two species, to 0.3 days of mean difference at analysis). Results shown here represent 100 iterations for each richness level. 24 species (Fig. 6a). Similarly, the stability of the phenological syn- Black dots are the mean values reported in the text, and the boxplots reflects the distribution of the 100 iterations. (a) Baseline phenological asynchrony between chrony increases with increasing pollinator richness. The rate of apple and bees (in days) reported as the absolute value of the predicted phenological dissociation would be 0.11 days per year for a species difference between apple and bee activity dates in 1965. Larger values indicate richness level of 2 (i.e. leading to almost 5 days’ mismatch over the greater asynchrony. (b) Stability of phenological synchrony between bees and 45 years period of the study), but the rate of asynchrony decreases apple as the absolute value of the slope of the model (in days of difference per quickly with increasing species richness, such that at the highest year). Greater stability is indicated by slopes closer to zero. richness level it is only 0.02 days per year (Fig. 6b). Overall, as we increase richness, the scenario changes from being qualitatively simi- mentarity among bee species in phenological activity (Fig. 2, Fig. lar to Fig. 2b to being qualitatively similar to Fig. 2a. S4). Because the frequency of pollinator visits to flowers is a reliable predictor of pollination function, this phenological synchrony is likely to result in more effective apple crop pollination (Vazquez DISCUSSION et al. 2005; Garibaldi et al. 2013). The phenologies of a spring blooming plant, apple and its pollinator Previous work has shown that rates of phenological advance bee community have advanced at similar rates during the last related to global warming seem broadly consistent between general- 46 years. A high diversity of floral visitors appears to be stabilising ist plants and bees at large spatial scales (Bartomeus et al. 2011), plant–pollinator phenological synchrony against climate change via and that bees and the plants they pollinate appear to use similar the mechanism of response diversity. Different bee species show environmental cues to time their spring emergence (Forrest & changes in phenology that are either faster or slower than apple, Thomson 2011). Results from the present study, based on bees thus leading to stable phenological synchrony between apple peak associated with a single mass-blooming crop over a 46-year time bloom and the summed activity of all members of the apple bee period show the same pattern: the rate of advance of apple peak community over a 46-year time period. Moreover, we show that at bloom in central New York State and the rate of advance of early any one time, high bee diversity increases the mean phenological spring apple pollinators are similar because we found significant synchrony between pollinators and apple bloom, due to comple- phenological shifts in both partners (a mean of 8 days since 1965).

Even though our time series data on apple bloom were limited to a functional complementarity among pollinator species (Albrecht et al. single focal orchard, there is evidence that peak bloom in other 2012; Fr€und et al. 2013) and also lead to more stable pollination across apple orchards across a wider geographical region are advancing time or space (Klein et al. 2003; Winfree & Kremen 2009), e.g. when their phenology at similar rates (Wolfe et al. 2005; slope of three pollinators have diverse responses to land use change (Cariveau et al. orchards = 0.20 days/year, compared with 0.18 days/year in 2013), or to weather conditions (Brittain et al. 2013). But long-term À À our study), suggesting that our results have generality for the larger temporal effects, such as responses to climate warming, that likely region across which our bee data were collected. play a critical role in maintaining real-world ecosystem functions have In addition to the stabilising effect exerted by the diverse pollina- rarely been explored (but see Rader et al. 2013 for a modelling tor community, there are several other reasons why we should not approach), despite the fact that biological insurance may be particu- expect climate change, at least at its current level, to cause pheno- larly important at large spatial and temporal scales (Loreau et al. logical mismatch between plants and pollinators. First, the present- 2003). Our study adds to several recent lines of evidence indicating day year-to-year variability in the onset of apple flowering (12 days that high levels of biodiversity are needed to sustain ecosystem func- across a 3-year interval; Fig. S3) exceeds the mean change experi- tion in real-world ecosystems (Isbell et al. 2011; Reich et al. 2012). enced over the nearly 50-year time series (8 days). Second, we show Historical specimen used for analysis are accessible as a supple- that even at low diversity levels, we expect only moderate asyn- mentary data set hosted on Dryad doi:10.5061/dryad.9g7d8. chrony (less than 5 days) between bees and apple over the 46-year period. In addition to the insurance provided by biodiversity, evolu- ACKNOWLEDGEMENTS tion may also play an important role in synchronising plant–pollinator interactions (Anderson et al. 2012; Gilman et al. 2012). For We thank all those who collected the bees used in our analyses, and example, bee or plant individuals that become asynchronised might the museums for access to their collections and John Ascher for be selected against, in favour of individuals that maintain synchrony. providing taxonomic expertise. We thank S. Droege and collabora- Overall, our results suggest that pollination systems, especially for tors for contributions to the data set, DiscoverLife and GBIF for generalised species such as apple, may be buffered against climate making the data publicly available and rOpenSci.org for Rgbif pack- change, but caution is needed when extrapolating these results to age. Data capture at Cornell was supported by a National Science more specialised plant–pollinator systems or to predicting future Foundation Division of Biological Infrastructure (DBI) grant [Grant trends under continued climate warming. 0956388; J.S. Ascher, PI]. Apple bloom data collection and field The general synchrony observed at the community level in our surveys of bee diversity in central New York apple orchards were study system supports the biodiversity insurance hypothesis. In fact, supported by Smith Lever and Hatch Funds administered by the our data support its two main predictions (Yachi & Loreau 1999). Cornell University Agricultural Experiment Station and a USDA- First, we demonstrate that high levels of biodiversity increase the NIFA grant [USDA 2010-03689] to BND. IB was funded by post- mean phenological synchrony between bee pollinators and apple doctoral Fellowship EX2009- 1017 from the Spanish Education flowering, as some species tend to fly earlier than the apple peak Ministry. We thank three anonymous reviewers for insightful com- bloom, while others fly later; thus, phenological complementarity ments that improved the manuscript. among bee species increases the degree of phenological synchrony with apple as species richness increases (Fig. 6 and Fig. S5). Second, AUTHOR CONTRIBUTIONS high biodiversity levels stabilise the phenological synchrony over time. Species-rich bee communities are more likely to include species I.B. and R.W. designed research; J.G., M. P., A.N.L. and B.N.D. with differential responses to climate change, and thus a buffering collected and provided data; I.B. analysed data; I.B. and RW lead effect occurs which minimises directional change in phenological the manuscript writing, and B.N.D., M. P. and J.G. contributed sub- synchrony at the community level (Fig. 4). 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2 Biodiversity ensures plant-pollinator phenological synchrony against climate

3 change

5 Authors: Ignasi Bartomeus, Mia G. Park, Jason Gibbs, Bryan N. Danforth, Alan N.

6 Lakso, Rachael Winfree

1 7 Table S1: List of species collected in apple orchards during 2009-2011. The 26

8 main visitor species, in bold, constitute ~90% of the visits recorded and were used

9 in the analyses. Ceratina calcarata and C. dupla records were combined due to

10 recent alterations to the taxonomy of these species which requires re-examination

11 of all historical material. The following identification keys were used in conjunction

12 with examination of expertly identified material in the Cornell University Insect

13 Collection: Mitchell 1960, 1962; LaBerge 1973, 1980; Bouseman & LaBerge 1978;

14 Gibbs 2011.

Genus species Number of visits Andrena crataegi Robertson 421 Andrena regularis Malloch 381 Andrena vicina Smith 307 Bombus impatiens Cresson 189 Andrena nasonii Robertson 163 Andrena miserabilis Cresson 126 Xylocopa virginica (Linnaeus) 115 Andrena hippotes Robertson 84 Andrena w-scripta Viereck 73 Andrena carlini Cockerell 69 Colletes inaequalis Say 68 Andrena rugosa Robertson 67 Augochlora pura (Say) 37 Andrena forbesii Robertson 35 Lasioglossum hitchensi Gibbs 35 Andrena perplexa Smith 31 Bombus bimaculatus Cresson 30 Ceratina calcarata/dupla Robertson/Say 29 Osmia cornifrons (Radoszkowski)* 28 Andrena cressonii Robertson 25 Andrena commoda Smith 21 Andrena mandibularis Robertson 21 Lasioglossum cinctipes (Provancher) 21 Lasioglossum quebecense (Crawford) 21 Andrena milwaukeensis Graenicher 20 Bombus perplexus Cresson 19

2 Lasioglossum foxii (Robertson) 19 Andrena morrisonella Viereck 16 Lasioglossum versatum (Robertson)** 16 Halictus rubicundus (Christ)** 15 Lasioglossum paradmirandum (Knerer & Atwood) 15 Andrena dunningi Cockerell 14 Andrena imitatrix Cresson 14 Lasioglossum obscurum (Robertson)** 14 Bombus griseocollis (DeGeer) 13 Andrena pruni Robertson 12 Halictus confusus Smith** 12 Lasioglossum coeruleum (Robertson) 12 Lasioglossum lineatulum (Crawford)** 10 Lasioglossum laevissimum (Smith) 8 Lasioglossum zonulum (Smith) 8 Nomada cressonii Robertson 8 Lasioglossum weemsi (Mitchell) 7 Osmia pumila Cresson 6 Andrena barbilabris (Kirby) 5 Andrena erythronii Robertson 5 Augochlorella aurata (Smith) 5 Bombus sandersoni Franklin 4 Lasioglossum cressonii (Robertson) 4 Nomada luteoloides Robertson 4 Agapostemon sericeus (Forster) 3 Andrena geranii Robertson 3 Andrena nivalis Smith** 3 Lasioglossum planatum (Lovell) 3 Lasioglossum versans (Lovell) 3 Andrena algida Smith 2 Lasioglossum coriaceum (Smith)** 2 Lasioglossum perpunctatum (Ellis)** 2 Lasioglossum truncatum (Robertson) 2 Nomada pygmaea Cresson 2 Osmia conjuncta Cresson 2 Osmia lignaria Say 2 Andrena bisalicis Vierecki 1 Andrena nuda Robertson 1 Andrena wilkella (Kirby)** 1 Bombus fervidus (Fabricius)** 1 Bombus terricola Kirby** 1 Lasioglossum ephialtum Gibbs 1 Lasioglossum imitatum (Smith)** 1

3 Lasioglossum nigroviride (Graenicher) 1 Lasioglossum nymphaearum (Robertson) 1 Lasioglossum subviridatum (Cockerell) 1 Lasioglossum zephyrum (Smith) 1 Osmia atriventris Cresson 1 Osmia bucephala Cresson 1 Osmia taurus Smith 1 Sphecodes confertus Say 1 Sphecodes cressonii (Robertson) 1 Andrena arabis Robertson 1 Nomada ceanothi Cockerell 1 Nomada composita Mitchell 1 Nomada imbricata Smith 1 15 * non-native species excluded from analyses (Batra 1979) 16 ** The 12 extra species included in the joint analysis of our data and Phillips (1933) 17 data.

4 18 Table S2: Models for the key apple pollinators. The degree of synchrony is

19 reported as the predicted value of the model for 1965, and is given in number of

20 days before (-) or after (+) peak bloom. The model estimate indicates whether the

21 slope is increasing (+) or decreasing (-) with time. Sample sizes (number of

22 individual specimens) are also reported. Models including several species (in bold)

23 include genus and species as random factors.

Sample Species Synchrony Estimate SE p-value size Activity after apple bloom – all species 5.733 -0.093 0.034 0.007** 646 Synchrony decreasing – all species 2.953 0.101 0.095 0.272 89 Augochlora pura 4.686 0.319 0.523 0.555 12 Andrena perplexa 2.918 0.07 0.121 0.566 35 Andrena w-scripta 2.496 0.119 0.282 0.683 12 Bombus impatiens 1.095 0.157 0.173 0.373 23 Bombus bimaculatus 0.18 0.489 0.95 0.629 7 Synchrony increasing – all species 6.303 -0.126 0.037 >0.001*** 557 Andrena commoda 18.886 -0.064 0.092 0.502 13 Andrena crataegi 12.188 -0.15 0.053 0.005 * 99 Andrena milwaukeensis 5.903 -0.035 0.147 0.814 32 Andrena cressonii 4.456 -0.211 0.126 0.099 · 59 Bombus perplexus 4.283 -0.084 0.484 0.873 5 Lasioglossum foxii 2.848 -0.184 0.126 0.148 88 Andrena nasonii 1.539 -0.089 0.072 0.219 186 Andrena hippotes 1.325 -0.102 0.101 0.314 75

Activity before apple bloom – all species -4.231 0.093 0.033 0.004 ** 732 Synchrony increasing – all species -4.704 0.109 0.035 >0.001*** 646 Andrena vicina -1.219 0.032 0.086 0.715 78 Ceratina calcarata/dupla -2.525 0.132 0.182 0.477 24 Andrena rugosa -2.55 0.129 0.129 0.317 78 Andrena regularis -3.577 0.068 0.154 0.663 41 Andrena carlini -4.000 0.039 0.084 0.642 138 Lasioglossum quebecense -4.613 0.316 0.145 0.034 * 61 Andrena miserabilis -5.829 0.041 0.077 0.590 85

5 Lasioglossum cinctipes -6.469 0.014 0.189 0.941 29 Xylocopa virginica -10.088 0.392 0.150 0.017* 22 Colletes inaequalis -11.332 0.174 0.105 0.101 59 Lasioglossum hitchensi -13.67 0.687 0.26 0.018* 18 Andrena mandibularis -20.436 0.688 0.297 0.041* 13 Synchrony decreasing – all species -0.649 -0.158 0.104 0.133 86 Andrena forbesii -0.649 -0.158 0.104 0.133 86 24 25

6 26 Text S1: Analysis including historical apple visitors for the selection of the key

27 species.

28 Selecting apple pollinating bee species to include in our analyses was a critical step

29 taken prior to conducting the analyses with the historical data. For the analysis

30 reported in the main text, we chose key pollinator species based on their abundance

31 (i.e. those making up to 90% of samples) in a 3-year survey of bee visiting flowers in

32 apple orchards conducted 2009-2011. Here, we repeated our analysis using a

33 different bee species list, based on both our own data from the recent period, and a

34 historical data set collected in 1931. The only historical survey data available that

35 provide the information we need (i.e. bees collected from apple flowers, with

36 species-level identification and abundance of each species) dates back to 1931,

37 which was 34 years before our time series began (Phillips 1933). Thus this list is not

38 optimal for our purposes because it would include bee species that were present on

39 apple in the 1930s, but potentially absent by the time our study began in 1965. In

40 particular, modernized chemical agriculture including widespread insecticide use

41 began in the late 1940’s and may have caused large shifts in pollinator communities

42 in agricultural habitats. Consequently, the analysis presented here is likely

43 conservative, in that it likely reflects greater differences from the main analysis than

44 would be the case if we had a species list generated in 1965. In any case, in

45 comparing our bee species list from 2009-2011 with that of Phillips from 1931, we

46 found that 50% of the dominant species (i.e. those making up to 90% of samples)

47 were the same in both datasets. Twelve dominant species present in the bee

48 communities collected by Phillips (1933) were found to be rare in our surveys.

7 49 Therefore, we generated a new list of key apple pollinators by adding these 12 extra

50 species, for a total 38 species (instead of the 26 reported in the main text).

52 The results of analyses as performed with this new species list were broadly similar

53 to those reported in the main text. First, the rate of phenological advance of the

54 aggregate bee community (n = 3254; estimate =-0.11 ± 0.04 days/year; p < 0.003)

55 was still similar to the rate of advance for apple (estimate = -0.18 ± 0.07). Second,

56 we still found response diversity in rate of phenological change among bee species,

57 i.e., we found a significant interaction between species and year (n = 1595;

58 year*species: F37,1519 = 1.68, p = 0.006) and a non-significant trend for the overall

59 community (year: slope = 0.039 ± 0.07, F1,1519 = 0.37, p = 0.55). However, the

60 baseline asynchrony was higher (the intercept was 0.9 days compared with the 0.2

61 days presented in the main text). Finally, the simulation (Fig S5) showed a similar

62 trend until reaching the level of 24 species reported in the main text, and

63 furthermore adding more species (up to 36) did not further decrease temporal or

64 baseline asynchrony in a pronounced way (mean baseline asynchrony at richness

65 level 2 = 5.53, richness level of 24 = 1.22, richness level of 36 = 1.08; mean slope at

66 richness level of 2 = 0.15, richness level of 24 = 0.027, richness level of 36 = 0.019).

67 68

8 69 Fig S1: Sensitivity analysis on the number of days included around apple peak

70 bloom. For 25 (black thin dotted lines; as presented in main text), 30 (red thin

71 dotted line) and 20 (blue thin dotted line) days around the peak bloom, we show

72 that collection of all bee species in aggregate was centered around zero, and with

73 non significant slopes over time (25 days model, black line n = 1378, slope = 0.04 ±

74 0.07, F1,1326 = 1.13, p-value = 0.72; 30 days model, dotted red line, n = 1123, slope =

75 0.08 ± 0.08, F1,1099 = 1.37, p-value = 0.24; 30 days model, blue line, n = 1542, slope =

76 0.06 ± 0.06, F1,1511 = 0.011, p-value = 0.93). Likewise, for all three choices of

77 threshold we found similar results for response diversity, measured as the

78 interaction between bee species and rate of phenological change over time, although

79 the interaction fell slightly below significance for the 20-day threshold, possibly due

80 to smaller sample sizes (25 days model F25,1326= 1.76, p-value = 0.01, 20 days model,

81 F25,1099 = 1.48, p-value = 0.06; 30 days model, F25,1511 = 1.76, p-value = 0.01).

9 30

-10 Asynchrony (days) Asynchrony -20

-30

1970 1980 1990 2000 2010

82 Year 83

10 84 Fig S2: Schematic representation of the simulation. A) Example for a richness

85 level of 2 species. In each iteration (10 in the example shown here, 100 in in the

86 actual analysis), all specimens of 2 randomly selected species were analyzed

87 together and the y-intercept (distance; red dot) and the slope were calculated for

88 iteration i. Then baseline asynchrony and stability were calculated as the average of

89 absolute values of those measures, respectively. B) Same example for richness levels

90 of 24 species. Note that in this case we expect y-intercepts to be lower and slopes to

91 be flatter, because within the pool of 24 species, opposing directionalities are often

92 cancelled.

A Example for richness = 2 bee species; B Example for richness = 24 bee species; 10 iterations 10 iterations

slope i

distance i slope i distance i { {0 specimens together mean days from apple peak bloom for all

time time

'baseline asynchrony' = (|distance 1| + |distance 2| + ... + |distance i|) / 10

'stability' = (|slope 1| + |slope 2| + ... + |slope i|) / 10 94

11 95 Fig S3: Simulation of biodiversity effects on phenological asynchrony with

96 rarefied models. In order to check for possible bias associated with sample size

97 differences across richness levels, we repeated the simulation rarefying all models

98 to a constant 400 specimens. First, we randomly selected species to create bee

99 communities of 6 to 26 species, composed of all specimens of each species. Second-

100 and this is the key step that differs from the main text- we subsampled each of these

101 randomly created communities such that each contained only 400 specimens. (In

102 the case that the randomly created community contained less than 400 specimens, it

103 was discarded). Richness values < 6 were not used to avoid rarefaction to very small

104 sample sizes. We then calculated the baseline and stability of phenological

105 synchrony for each randomly assembled community using linear models, as in the

106 main text. As for the main text, results represent 100 iterations for each richness

107 level. Black dots are mean values, and the boxplots reflect the distribution of the 100

108 iterations. A) Baseline phenological asynchrony between apple and bees (in days)

109 reported as the absolute value of the predicted difference at 1965. B) Stability of

110 phenological synchrony between bees and apple as the absolute value of the slope of

111 the model (in days of difference per year). If specimen abundance were driving the

112 result in the main text the pattern would no longer be observed in this simulation. In

113 contrast, we see that both baseline asynchrony and stability decrease with richness,

114 although the trend is not as strong as when the complete data set is used, possibly

115 due to smaller sample size.

12 A B 1.0

15 0.8

0.6 10

0.4

Baseline asynchrony (days) asynchrony Baseline 0.2 Stability in synchrony (days/year) synchrony in Stability

0 0.0

6 8 10 12 14 16 18 20 22 24 6 8 10 12 14 16 18 20 22 24

116 number species number species 117

13 118 Fig S4: Phenological complementarity among apple bee visitors. For each

119 species and year (y-axes) a boxplot of the collection days (days from 1 January; x-

120 axes) for all specimens of that species is shown. Some species were collected only at

121 the beginning of apple flowering, while others only at the end. Note that there is

122 important variability in apple flowering among years, due to early versus late

123 springs. See main text for statistical details.

124

14 Sphecodes_confertus Lasioglossum_ephialtum Andrena_barbilabris Lasioglossum_nigroviride Andrena_nivalis Agapostemon_sericus Lasioglossum_zonulum Lasioglossum_planatum Lasioglossum_cressonii Andrena_milwaukeensis Andrena_carlini Andrena_mandibularis Lasioglossum_versans Lasioglossum_nymphaearum Bombus_perplexus Andrena_wilkella Andrena_nuda Andrena_commoda Lasioglossum_obscurum Andrena_w.scripta Augochlora_pura Andrena_regularis Nomada_cressonii Augochlorella_aurata Andrena_morrisonella Lasioglossum_quebecense Nomada_pygmaea 2009 Lasioglossum_perpunctatum Andrena_vicina Halictus_confusus Andrena_crataegi Halictus_rubicundus Lasioglossum_coeruleum Andrena_rugosa Lasioglossum_lineatulum Apis_mellifera Bombus_griseocollis Lasioglossum_versatum Lasioglossum_mitchelli

Species Colletes_inaequalis Bombus_fervidus Andrena_forbesii Bombus_impatiens Andrena_perplexa Ceratina_calcarata Andrena_hippotes Andrena_pruni Lasioglossum_weemsi Andrena_miserabilis Xylocopa_virginica Lasioglossum_paradmirandum Andrena_dunningi Andrena_nasonii Lasioglossum_coriaceum Andrena_cressonii Osmia_cornifrons Lasioglossum_laevissimum Bombus_bimaculatus Lasioglossum_foxii Andrena_erythronii Osmia_pumila Lasioglossum_truncatum Lasioglossum_imitatum Ceratina_dupla Bombus_terricola Lasioglossum_cinctipes Osmia_conjuncta Nomada_luteoloides Osmia_lignaria Andrena_perplexa Andrena_erythronii 115 120 125 130 135 140 Andrena_algida Xylocopa_virginica Bombus_griseocollis Julian day Andrena_milwaukeensis Osmia_pumila Lasioglossum_quebecense Lasioglossum_paradmirandum Lasioglossum_coeruleum Augochlora_pura Andrena_imitatrix Andrena_miserabilis Andrena_regularis

2010 Andrena_mandibularis Andrena_commoda Andrena_vicina Bombus_impatiens Bombus_perplexus Colletes_inaequalis Apis_mellifera Andrena_cressonii Andrena_carlini Species Bombus_bimaculatus Andrena_barbilabris Bombus_sandersoni Andrena_nasonii Andrena_forbesii Andrena_rugosa Andrena_crataegi Andrena_w.scripta Andrena_hippotes Lasioglossum_weemsi Lasioglossum_foxii Andrena_geranii Andrena_dunningi Andrena_bisalicis Osmia_cornifrons Lasioglossum_truncatum Lasioglossum_laevissimum Sphecodes_confertus Lasioglossum_ephialtum Andrena_barbilabris Lasioglossum_nigroviride Andrena_nivalis Agapostemon_sericus 115 120 125 130 135 140 Lasioglossum_zonulum Lasioglossum_planatum Lasioglossum_cressonii Andrena_milwaukeensis Andrena_carlini Andrena_mandibularis Julian day Lasioglossum_versans Lasioglossum_nymphaearum Bombus_perplexus Andrena_wilkella Andrena_nuda Andrena_commoda Lasioglossum_obscurum Andrena_w.scripta Augochlora_pura Andrena_regularis Nomada_cressonii Augochlorella_aurata Andrena_morrisonella Lasioglossum_quebecense Nomada_pygmaea 2011 Lasioglossum_perpunctatum Andrena_vicina Halictus_confusus Andrena_crataegi Halictus_rubicundus Lasioglossum_coeruleum Andrena_rugosa Lasioglossum_lineatulum Apis_mellifera Bombus_griseocollis Lasioglossum_versatum Lasioglossum_mitchelli

115 120 125 130 135 140 CollectionJulian day 125

15 126 Fig S5: Simulation of biodiversity effects on phenological asynchrony for 38

127 species, including the species in the historical survey by Phillips (1933). We

128 randomly selected species to create bee communities of 2 to 36 species, then

129 calculated the baseline, and stability of, phenological synchrony for each randomly

130 assembled community using linear models. Results represent 100 iterations for

131 each richness level. Black dots are mean values, and the boxplots reflect the

132 distribution of the 100 iterations. A) Baseline phenological asynchrony between

133 apple and bees (in days) reported as the absolute value of the predicted difference

134 between apple and bee activity dates in 1965. Larger values indicate greater

135 asynchrony. B) Stability of phenological synchrony between bees and apple as the

136 absolute value of the slope of the model (in days of difference per year). Greater

137 stability is indicated by slopes closer to zero.

A B 0.5

15 0.4

0.3 10

0.2

Baseline asynchrony (days) asynchrony Baseline 0.1 Stability in synchrony (days/year) synchrony in Stability

0 0.0

2 6 10 14 18 22 26 30 34 2 6 10 14 18 22 26 30 34

number species number species 138

139

16 140 References: 141 142 Batra, S. W. T. 1979. Osmia cornifrons and Pithitis smaragula, two asian bees 143 introduced into the United States for crop pollination. Maryland Agricultural 144 Experiment Station Special Miscellaneous Publications 1:307-312. 145 146 Bouseman, J.K. & LaBerge, W.E. (1978). A revision of the bees of the genus Andrena 147 of the Western Hemisphere. Part IX. Subgenus Melandrena. Trans. Amer. Ent. Soc., 148 104, 275–389. 149 150 Gibbs, J. (2011). Revision of the metallic Lasioglossum (Dialictus) of eastern North 151 America (Hymenoptera: Halictidae: Halictini). Zootaxa, 3073, 1–216. 152 153 LaBerge, W.E. (1973). A revision of the bees of the genus Andrena of the Western 154 Hemisphere. Part VI. Subgenus Trachandrena. Trans. Amer. Ent. Soc., 99, 235–371. 155 156 LaBerge, W.E. (1980). A revision of the bees of the genus Andrena of the Western 157 Hemisphere. Part X. Subgenus Andrena. Trans. Amer. Ent. Soc., 106, 395–525. 158 159 Mitchell, T.B. (1960). Bees of the Eastern United States: volume I. N.C. Ag. Exp. Sta. 160 Tech. Bull., 141, 1–538. 161 162 Mitchell, T.B. (1962). Bees of the Eastern United States: volume II. N.C. Ag. Exp. Sta. 163 Tech. Bull., 152, 1–557. 164 165 Phillips, E. F. (1933). Insects collected on apple blossoms in western New York. 166 Journal of Agricultural Research 46, 851-862. 167 168