Introduction

Measures taken to increase in global food production have led to the erosion of biodiversity1. The fate of bumblebees has attracted particular attention2,3,4 perhaps in part because their decline has also been linked with reduced food production5,6.

The coexistence of sympatric bumblebee species within plant communities partly relies on the extent to which they compete for available food resources. Competition theory dictates that pollinator species coexisting within the same ecological niche must partition resources7 or else the less well-adapted species will become locally extinct8. At the same time, the plant species they serve are at least partly dependent upon pollinators to ensure adequate seed set and to avoid inbreeding9. Thus, the needs of both plants and their pollinators are intertwined10.

At a practical level, the ability to study interactions from both perspectives (pollinator to plant and plant to pollinator) requires both a reliable means for accurate bee identification and an effective means of characterising the pollen being moved. This is most commonly achieved through observation of bee foraging in the field, either directly11,12,13,14 or by video recording15. However, Bombus species are also noted for convergent colour patterns and high levels of intraspecific variation12,13,14, and so reliable identification of many bee species can be challenging, particularly for cryptic species14. The type subgenus Bombus s.str. is widespread in Europe and comprises of four morphologically similar species in the UK: Bombus cryptarum; B. lucorum; B. magnus and B. terrestris. Of these, only B. terrestris can be identified in the field with reasonable confidence, despite a minority of B. terrestris workers having been described as phenotypically indistinguishable from other taxa in the subgenus16. The remaining three species are notoriously difficult to differentiate and are commonly grouped under one of the following informal epithets: B. lucorum species complex; B. lucorum s.l. or B. terrestris s.l.17,18,19,20. The group nevertheless represents some of the commonest and widespread members of the genus, and all are important pollinators21. Scriven and colleagues17 addressed this problem using Cytochrome Oxidase I (CO1) DNA amplicons from tarsal samples to identify bees captured around the UK to a high level of certainty. Information on foraging behaviour was inferred indirectly from where the bee was captured17,22.

Characterisation of pollen carriage presents a different kind of challenge. Anatomical diagnosis of pollen recovered from captured bees appears to be the most widely used means of describing pollen carriage by pollinators foraging within a plant community23,24. Although the approach is widely used, it is notoriously labour-intensive, prone to operator bias and provides highly variable taxonomic separation of plant groups25. These limitations have led to increasing interest in metabarcoding of pollen loads to profile pollen carried by pollinators26,27,28. The premise of this approach is that the number of plant-taxon-specific barcode sequences recovered on a Next Generation Sequencing platform is broadly representative of the number of pollen grains carried by the bee25,29 and therefore greatly enhances the resolution of diet reconstruction when compared to the use of anatomical phenotype to separate pollen types23.

The current study seeks to explore the value of combining CO1 barcoding for pollinator species identification with pollen metabarcoding to define the interactions between bumblebees and the flowering plants they pollinate in species-rich grassland communities. Specifically, we aim to survey for resource partitioning between co-foraging bumblebee species, seasonal shifts in pollinator service and site-specific effects on pollen carriage. For this, we used CO1 barcodes to identify bumblebee specimens and rbcL 454 pyrosequencing metabarcoding to characterise the pollen they carried during the 2008/9 summer seasons.

Results

CO1 barcoding of bumblebees

A neighbour-joining tree provided clear separation of CO1 barcode sequences from all 24 species of Bombus recorded in the UK, with samples clustering into species-specific clades (Fig. S1). There was 100% bootstrap support for each species-specific clade and each clade contained only the representative barcodes from one species. Thus, the data generated suggested that CO1 reference barcode sequence is able to consistently differentiate between UK bumblebees at species rank (Fig. S1).

We then used the reference barcoding sequence resource to determine the species identity of captured bees. The vast majority (272/288) of bees collected yielded high-quality bi-directional CO1 DNA barcode sequences. When these sequences were subject to BLASTn searches against the reference barcodes of all UK bumblebee species, the top hits were invariably strong and included several representatives of the same species. When the same sequences were used to assemble a neighbour-joining tree, all query samples from bees captured in the study sites unequivocally aligned to one of eight species-specific clades, again with 100% bootstrap support (Fig. 1). Thus, the CO1 barcode data suggested that we captured eight species of bumblebee, with no ambiguous identifications for any of the captured individuals. These identifications were congruent with the conservative morphological identifications made in the field and subsequently in the laboratory. All specimens morphologically identified as belonging to the B. lucorum groups were binned as either B. cryptarum (17 samples) or B. lucorum (16 samples), but did not include any examples of B. magnus.

Figure 1
figure 1

Neighbour Joining tree showing variation in Cytochrome Oxidase 1 (CO1) reference barcode sequences for eight Bombus species retrieved from the BOLD Systems V4 database and query CO1 sequences from bees captured at the study sites. Reference barcodes are shown with their BOLD Systems 4 identification code along with query bee barcodes (indicated by the diagnostic bee code number). For simplicity, query bee barcodes with identical sequences are not shown.

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The unambiguous barcode identification of all specimens to one of eight Bombus species allowed us to monitor seasonal changes in bee abundance. When samples were divided into regular collection periods from mid-June through to late August, there was some evidence of divergence between the seasonal appearance of some bee species (Table 1). B. terrestris were relatively evenly represented between the early part of the sampling period, June/July (39) and those captured in August (40) (Table 1). By comparison, captures of the two Lucorum species (B. cryptarum and B. lucorum) were significantly biased towards the early season, with 25 captures between mid-June and July, and only eight in August (Table 1; Chi squared = 6.62, P = 0.01). B. pratorum showed a more extreme bias towards early capture dates, with all 21 individuals being captured in June/July. In contrast, the vast majority of B. pascuorum specimens were captured in August (94/107) (Table 1); again a significant deviation from the even mix seen in B. terrestris (Chi squared = 31.3, P < 0.00001).

Table 1 Summary of bumblebee species captured in three UK Rhos pasture sites over the summers of 2008–2009.
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Flowering resources

In 2009, the total number of flowering heads recorded across the three study sites peaked in mid-July (6544 heads) and then declined to the lowest abundance in late August (563 heads). Across both years, 47 species were sufficiently abundant to be recorded in the transects (Table S1). Combined data revealed roughly equal numbers flowering species from June until the start of August, although the identity of species recorded changed during this time (Table S1). However, there was a notable loss of flowering species during August (Table S1). Overall, there was no significant correlation between the number of bees captured during different parts of the season and either the number of species of flowering plant (linear regression, r2 = 0.082, permutation p = 0.64, NS) or the total number of flowering heads in the study sites (linear regression, r2 = 0.22, permutation p = 0.42, NS).

RbcL barcoding

The local reference set of rbcL barcode sequences were able to diagnose all genera and all species except for the following five sister species pairs: Stellaria uliginosa/graminae; Epilobium montanum/palustre; Dactylorhiza maculate/praetermissa; Galium palustre/saxatile and Ulex europaeus /gallii.

When the set of reference rbcL sequences was expanded to include all species recorded in the surrounding 10 km2 landscape, we were able to diagnose around 70% of pollen types (Table 2). This figure included many wind-pollinated species, including the cryptic and semi-cryptic belonging to the Poaceae (grasses) and Cyperaceae/Juncaceae (sedges/rushes). However, none of the pollen barcodes recovered from any of these species/species groups exceeded 1% of the total counts recovered, and 270 of the 272 bees carried < 1% of these pollen types, with no bee carrying more than 7% of any wind-pollinated species/species group. These were therefore removed from subsequent analyses, allowing the remaining pollen barcodes to separate between 74 and 79% of the species in the surrounding 10 km square, and 100% of the genera (Table 2).

Table 2 Efficacy of rbcL pollen species diagnosis.
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Pollen carriage on captured bees

There were 191, 896 sequence reads that passed filter from the pollen samples. When these processed reads were linked to the bee from which they originated using the 454 tags, a mean of 706 pollen barcode sequences were assigned to each of the 272 bumblebees captured. When these were compared with reference barcodes, we presumed that most pollen on bees originated from local foraging. We therefore performed an initial screen matching the pollen-derived 454 sequences to the reference barcodes of plant species found within the study sites. Matches to these sequences accounted for the majority of sequences interrogated, but external (non-study site) pollen species averaged 9.6% of the recovered sequences overall (Table 3). As expected, the imposition of a 5% threshold for a pollen species to be included in the refined pollen profiles of individual bees greatly increased the proportion of local hits and reduced the mean value of external (non-study site) pollen to just 1.6% (Table 3).

Table 3 Table showing the percentage of pollen barcodes that matched to reference sequences of plants within study sites or only present in the surrounding landscape (10km2).
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Global analysis of bumblebee pollen loads

Of all sequences that passed filter, at least one of the captured bees carried > 0.99% pollen from 43 of 47 plant species recorded from study sites and from a further 11 species/OTUs from the surrounding 10 km2 landscape (Table S2A). This reduced to 37 local species and 3 from the wider landscape when a 5% threshold was imposed for inclusion of a pollen type in the profile of any one bee (Table S2B).

When the carriage profiles of each bee species were considered in isolation, twelve plant species/species groups accounted for 88–97% of pollen carried (Table S3). Some of these twelve OTUs were above threshold (5%) for every individual bee and invariably averaged at or above 1% of pollen carried by the bee species as a whole for all plant species except Lychnis flos-cuculi (Table S3). However, at this level of data resolution, there was little evidence of pollen resource partitioning between bumblebee species, except for a modest divergence in the profile of B. pascuorum (Table S3). Note that B. jonellus was omitted from these and subsequent analyses on the basis of there being only two representatives of this species.

There was considerably more variation revealed when the profiles of individual bees were considered separately, with extensive variation being seen between members of the same bee species (Table S4). All individual bees carried several pollen species and no single pollen species was entirely diagnostic of the pollen load of any one bee species (Table S4). There were nevertheless differences in the variability of pollen profiles carried by the bee species. For example, mean number of pollen species detected on individuals varied from 4.6 pollen species per bee for B. pratorum to 9.4 species per bee for B. lapidarius. More poignantly, bees in which one pollen species dominated carriage (> 50% of counts recovered from the individual) was significantly more abundant for B. pratorum (15/21 bees) than for B. lapidarius (1/12 bees) (Chi squared with Yate’s correction = 9.8, p = 0.0018), implying a more restricted carriage for B. pratorum when compared to B. lapidarius (Table S3). Variance in pollen diversity of the other bee species, including the two cryptic Lucorum species was more modest (Tables S2, S4).

There was no clear relationship between the pollen carried by the bees and plant flowering head abundance (Tables S1, S4) and no obvious visual indication of resource allocation between bee species, except perhaps for a stronger preference shown by B. pratorum for Lychnis flos-cuculi (Table S4). Moreover, only 17 bees carried Lychnis flos-cuculi pollen above the 5% threshold, but 12 of these carried more than 80% of this pollen type. The 21 B. pratorum bees captured included ten of these twelve individuals (Table S4). Differences between pollen profiles of the other bee species was more subtle and masked by considerable variation between individuals of the same species (Table S4).

We next applied multivariate analyses to seek evidence of resource partitioning between bees. For this, we initially attempted to minimise potential skewing attributable to exponential amplification during PCR by converting the pollen carriage data into binary format. Here, each pollen type was recorded for each bee on a presence/absence basis (1, 0) provided the proportion of pollen present exceeded threshold (set between 1 and 10%). Fifteen pairwise combinations were consistently identified as significantly divergent for all threshold levels but an additional 1–5 pairings were also significant below 6% (Table S5). However, the overall F values generated by PERMANOVA fell beyond 5%. Stress levels of NMDS plots generated from the data were invariably high (> 0.2) but lowest at the 5% threshold (Table S5). The stress values improved when plots were restricted to the significantly divergent species pairings but remained above 0.21 throughout and were again lowest at the 5% threshold (data not shown).

Ternary conversion of the same data using log10 intervals starting at the optimum threshold of 5% found with the binary data (i.e. three categories of: 0 =  < 5% barcodes per bee;1 = 5–50% per bee; > 50% count-2) provided similar but only slightly improved pairwise separation of species by PERMANOVA (Table S6). NMDS plots were again suggestive of divergence but failed to provide stress test results below 0.2 (Table S6).

Finally, when count frequencies were converted into a quantitative format at the 5% threshold, a similar pattern of pairwise species separation was generated, but featured only ten significant species combinations (Table S7). Eight of the ten pairwise species comparisons generated NMDS plots with acceptable stress values of between 0.095 and 0.17. These plots revealed either modest divergence or a difference in the variability of pollen carriage between the following species combinations: B. lucorum-B. lapidarius; B. hortorum-B. lapidarius; B. hortorum-B. pratorum; B. lapidarius-B. cryptarum; B. lapidarius-B. pratorum; B. cryptarum-B. pratorum; B. pascuorum-B.pratorum; B. pratorum-B.terrestris (Fig. 2). Thus, six of the eight combinations featured B. pratorum. However, there was no evidence of divergence in the pollen carried by the two cryptic species (B. lucorum and B. cryptarum).

Figure 2
figure 2

Non-metric Multidimensional Scaling (NMDS) plots of bumblebee pollen carriage. NMDS plots comparing rbcL metabarcoding pollen carriage profiles (pollen types above 5% threshold) from Bombus species pairs with divergent profiles (Permanova, p < 0.05). (A). B. cryptarum and B. pratorum. Stress test = 0.13. (B) B. hortorum and B. lapidarius. Stress test = 0.146. (C) B. hortorum and B. pratorum. Stress test = 0.16. (D) B. lapidarius and B. cryptarum. Stress test = 0.138. (E) B. lapidarius and B. pratorum. Stress test = 0.0948. (F) B. lucorum and B. lapidarius. Stress test = 0.106 . (G) B. pascuorum and B. pratorum. Stress test = 0.199.

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We next subdivided the data by location. Quantitative data (5% threshold) collected from the RF site indicated a significant overall effect of bee species on pollen carriage (PERMANOVA, F = 2.035; p = 0.0099), but only yielded significant divergence between B. cryptarum and B. pascuorum (Table S8; Fig. 3A). Counts recorded from the RG site indicated a stronger divergence between species overall (PERMANOVA, F = 4.386; p = 0001) and included three divergent species pairings, all involving B. pratorum, viz: B. hortorum-B. pratorum; B. pascuorum-B. pratorum; B. pratorum-B.terrestris (Table S8). NMDS plots of these combinations had stress values in the range 0.11–0.19, with all indicative of at least modest divergence in pollen carriage (Fig. 3B). Evidence of resource partitioning also came from the RGHY site (PERMANOVA, F = 3.292; p = 0.0001), with the following five species combinations diverging significantly: B. lucorum-B. lapidarius; B. lucorum- B. pascuorum; B. lapidarius- B. pascuorum; B. lapidarius- B. pratorum and B. pascuorum- B.terrestris (Table S8). NMDS plots of these combinations all possessed stress levels below 20% and indicated at least modest divergence in carriage profiles for some combinations (Fig. 3C). The two cryptic species of the Lucorum complex (B. lucorum and B. cryptarum) failed to diverge significantly in all three sites.

Figure 3
figure 3

Non-metric Multidimensional Scaling (NMDS) plots of site-specific bumblebee pollen carriage. NMDS plots of species pairings in (A) Rhos Fullbrook, RF. B. pascuorum and B. cryptarum. Stress test = 0.115. (B) Rhos Glandenys, RG. i. B. hortorum and B. pratorum. Stress test = 0.117. ii. B. pascuorum and B. pratorum. Stress test = 0.189. iii. B. pascuorum and B. terrestris. Stress test = 0.154. (C) Rhos Glyn yr Helyg, RGYH. i. B. lucorum and B. lapidarius. Stress test = 0.083. ii. B. lucorum and B. pascuorum. Stress test = 0.143. iii. B. lapidarius and B. pascuorum. Stress test = 0.137. iv. B. lapidarius and B. pratorum. Stress test = 0.185. v. B. pascuorum and B. terrestris. Stress test = 0.153.

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We next sought more subtle evidence of niche separation between B. lucorum an B. cryptarum based on the seasonal timing of foraging. The date and location of capture of these two species revealed that co-appearance of both species on a collection date-site combination was marginally but significantly lower than expected by chance (Table S9; Fisher’s exact test, p = 0.037). This may imply conditions favouring the appearance of each species may differ. We investigated this possibility further by comparing ambient temperatures of the two species when they were captured. This revealed a modest but significant increase in capture rates of B. lucorum when temperatures fell in the range 17–19 °C and of B. cryptarum in low temperatures (12–15 °C) (Chi squared = 4.20, p = 0.041).

Exploitation of pollinator resources among plant species

We next sought to compare how pollen from different plant species were carried by the array of bumblebee pollinators present. Summary histogram plots of the nine most dominant plant species (defined as those carrying more than 4.2% of total pollen recorded) revealed some variance in the proportion of bee species carrying different pollen species (Fig. S2) but failed to indicate significant divergence between plant species or collection sites (Table S10). However, when the same profiles were divided by collection date, there was a highly significant seasonal effect and significant diversion between NMDS profiles generated in June and July when compared with those from early and late August (Table S11, Fig. 4A–D).

Figure 4
figure 4

Non-metric Multidimensional Scaling (NMDS) plots of seasonal changes to bumblebee pollen carriage. NMDS plots comparing rbcL metabarcoding pollen carriage profiles (pollen types above 5% threshold) on all Bombus species captured in (A) June (dots) and early August (filled triangles). Stress test = 0.19. (B) June (dots) and late August (open triangles) Stress test = 0.2. (C) July (open circles) and early August (filled triangles). Stress test = 0.17 (D) July (open circles) and late August (open triangles). Stress test = 0.17.

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Discussion

CO1 DNA barcoding has potential to improve the species identification of bees for field-based studies of foraging behaviour30, resource partitioning31and ecology32, but first requires that species assignment is not compromised by excessive intraspecific variation within the study location. Phylogenetic trees presented in the present study included sufficient reference barcodes of the 24 UK bumblebee species to provide context for both intra- and inter-specific variation of the CO1 locus in the study area. The presence of 100% bootstrap support for all species-specific clades in the tree that combined reference and query barcodes was suggestive that the bees captured did not feature any individuals with ambiguous identity. This premise was supported by strong congruence with the field and laboratory morphological diagnoses, and is concordant with similar studies based on matching high quality, good-length Sanger sequences to a filtered set of full-length reference barcodes held on the BOLD V4 database33. These features enabled unambiguous diagnosis of the two cryptic species present (B. cryptarum and B. lucorum), but concurred with the reported absence of B. magnum from mid-Wales17.

The barcode-assisted assignment of species identity enabled fluctuations in the visitation frequencies of the different bee species to be monitored in the three sites throughout the study period. Again, the seasonal patterns of bee capture were broadly in accordance with previous reports in the UK, with B. pratorum peaking early in the season and B. pascuorum being strongly biased towards late season captures in August34. Similarly, those species previously identified as peaking mid-season34, namely the lucorum group (B. cryptarum and B. lucorum), B. lapidarius and B. terrestris all spanned the entire collecting period. The one minor exception was B. hortorum, which is recognised as an early season species34, but was absent in June and peaked in August.

There are other factors that could also account for the interspecific differences in capture rates found in our study sites. For example, several works have reported differences between bumblebee species in the criteria used to select foraging sites35,36. No clear correlation was found in the present study between the abundance of bees and either floral abundance or the number of flowering plant species present, but it is nevertheless plausible that another aspect of fluctuating floral resources such as pollen or nectar yields could be at least partly responsible for the variable bee capture rates observed across the season. Such changes would occur regardless of the broader population dynamics of the species at a regional level. Thus, local capture rates are not a simple reflection of the relative abundance of each species. Other factors could similarly affect site capture rates, including known differences between bee species in nest density and foraging range37; in the size of bees38; predator threat39 or variable learning performance40. Differential foraging distances has particularly emerged as a potentially factor in shaping niche differentiation between co-foraging bumblebee species41,42. Westphal and colleagues42 noted that B. pratorum typically foraged over the shortest distances (250 m) whereas B. terrestris and B. lapidarius travel the furthest (3000 m), which could potentially lead to different patterns of community visitation as found here. Clearly, there are numerous elements that would need to be considered before gaining a predictive understanding of the patterns observed here and elsewhere. In this context it is perhaps interesting to note that we found no indication of any seasonal separation between the cryptic species B. cryptarum and B. lucorum. This finding concurs with previous works on the species17,22 and implies the driver of any ecological separation between these species probably resides elsewhere.

Resource partitioning provides an important driver of niche differentiation between sympatric bumblebee species43,44. The application of pollen metabarcoding to characterise pollen carriage has attracted interest45, but is predicated on the presumption of at least a semi-quantitative relationship between abundance of a particular pollen species and frequency of barcode counts recovered from that species. There is surprisingly little empirical evidence of a direct relationship between PCR amplicon abundance and number of pollen grains used as a template. In a rare exception, Ronca and colleagues46 found significant linear correlation between qPCR Ct values and the quantity of pollen grains in a standard dilution series. Nevertheless, there remains a realistic prospect for confounding variability when comparisons are made between more distantly related taxa. In the present study we initially attempted to address this issue by removing the quantitative element of the data set by compressing the barcode frequency information into a simple binary format (present above threshold or absent). This strategy indicated a significant divergence between pollen profiles carried by different bee species, but lacked sufficient information content to produce NMDS plots with stress test values below 0.29. The nature of interspecific carriage differences was therefore uncharacterised. Consistency in the identity of significantly divergent species pairings when the binary compression of data was relaxed to three categories implied only modest skewing attributable to PCR amplification. Likewise, there was similar concordance when sequence count data was allowed to become quantitative above 5% threshold, albeit featuring fewer species pairings. This move also increased information content sufficiently to allow interrogation using NMDS plots of eight species combinations identified as divergent by PERMANOVA. B. pratorum dominated these divergent combinations. This species was also notable in being present only during the early season captures and in possessing the least varied carriage of different pollen types. Divergent bumble bee species pairings that could be characterised by NMDS was restricted to the RGHY site and only included the following combinations: B. lucorum-B. lapidarius; B. lucorum- B. pascuorum; B. lapidarius- B. pascuorum; and B. pascuorum- B.terrestris. Thus, overall evidence of divergence in pollen carriage appeared to be at least partly site-dependent. In agreement with several previous studies on resource partitioning in Bombus43,47 although short-tongued species (B. terrestris; B. lapidarius; B. pratorum; B. lucorum) and their long-tongued counterparts (B. pascuorum; B. hortorum) did feature in the divergent species pairings, they only accounted for around half of those observed in the study. It was therefore not the only factor of importance. We also failed to find evidence of pollen carriage partitioning between the two cryptic species in any site. However, we did find marginal differences in capture rates of these two species, with more B. lucorum captured at higher temperatures and B. cryptarum dominating when temperatures were cooler. This finding is congruent with an earlier study which revealed that mixed populations of the three cryptic lucorum species (B. lucorum, B. cryptarum and B. magnus) are largely restricted to the north and West of the UK22, and that “B. lucorum is adapted for activity in warmer sunnier conditions, whereas B. magnus and B. cryptarum are adapted to forage in cooler cloudier conditions”. The same study noted that B. magnus has a strong preference for heathland, perhaps providing one possible explanation for the absence of this species from the current study. One intriguing possibility from these findings is that niche separation of these species could be based on foraging according to local ambient temperature conditions, and so could feasibly separate along altitudinal/temporal basis. Testing this possibility would clearly require further work.

Profiles of the bumblebee pollinators that carried the nine most numerous plant species did not provide clear statistical evidence of differentiation between plants. However, these pollinator representation profiles changed sharply during the transition between early (June-July) and late season (August) for all plant species, with a scale of change suggestive of a tipping-point restructuring of the guild composition at this time point across all three sites. The drivers for this transition are not obvious and require further investigation.

Our study found no obvious relationship between global floral abundance and diversity in the source community and the pollen profile carried by the bumblebees. This accords with the relatively few previous works that address this issue28,48. Closer examination of the food resources available to the bees in our study revealed a gradual phenological progression in the identity and abundance of flowering plant species throughout June and July, but with a slow decline in flowering abundance and diversity during August. This decline does not fit with a sharp tipping point observed in pollinator use at the start of August but did broadly coincide with the shift in the profiles of bumblebee species captured in these sites. Most bee species were more abundant in the early season, when flowering resources were slightly more abundant and diverse. Moreover, the sharp turnover in foraging bee species occurred at the July–August boundary, and appeared to coincide with the strong divergence in pollinator profiles exhibited by the commonest flowering plant species present. Given the lack of divergence between profiles of plant species, these findings appear to be most consistent with flowering time being the strongest determinant of bumblebee pollen carriage service available for these plant species. Again, this possible explanation requires further investigation.

As with preceding works on Bombus45,48,49, we found considerable variation in the diversity of pollen carried by individuals of every bee species. Here, as with previous works45,48,49 divergence between the pollen carried by different bee species appears to be subtle and prone to both temporal and spatial variance. There is similarly no clear divide between wind- and insect-pollinated plant species, with the former commonly featuring in the diet of bees, as noted at low frequency here, but reported to be more substantial in studies of early season bee foraging17,50. Gaining deeper understanding of the many drivers that shape the patterns of pollen carriage and delivery requires the ability to monitor multiple variables across several spatial and temporal scales. Our contention is that combined use of pollen and pollinator metabarcoding provides an additional tool that may help realise the growing appetite for gaining a systems level understanding of pollinator service provision for plant communities51, particularly when viewed in the context of changing land use52 and climate change.

At the most basic level, insect-mediated pollination facilitates more efficient transfer of pollen between member of a plant species than is possible through wind. Pollen delivery by pollinators reduces inbreeding and for self-incompatible species, ensures reproductive success. However, this role is to some extent compromised when a pollinator visits multiple plant species in the same community, as this increases the likelihood of interspecific pollen delivery (delivery failure) and reduces the proportion of pollen that is carried by each pollinator (reduced pollen acquisition). In this context, our finding that all individual bees of all Bombus species carried multiple pollen species on their bodies implies that all plant species within these communities, even specialists, must experience significant instances of interspecific pollen delivery and competition for pollen acquisition. These observations accord with previous reports based on foraging observations10, and also by the characterisation of pollen loads by anatomical examination/metabarcoding53,54 It therefore seems plausible that inefficiencies imposed by non-target delivery may be ubiquitous except for extreme specialist associations, but are perhaps outweighed by a net improvement in delivery over passive dispersal systems. However, the relationship between efficiency of intended pollen delivery on a per visit basis, and the overall efficacy of some pollen being delivered to target is not simple55. Indeed, a positive relationship exists between overall effectiveness of pollination and the overall frequency of visitations and is predicted to improve as variance in visitation frequency increases relative to conspecific delivery rates55. Viewed in this way, floral fidelity may be less important than visitation frequency in determining effective pollen delivery. This then, highlights a key limitation of the present work since fuller understanding would require information of visitation rates (foraging behaviour), pollen carriage and pollen disposition (delivery) rates.

Future efforts to quantify such effects clearly present a significant challenge. However, the relationship between foraging and pollen acquisition is neither direct nor simple, with the most frequent floral visitors not necessarily being most effective pollinators24. The combination of pollen metabarcoding, floral resource assessment and Bombus barcoding as used in this study represents only part of the way towards addressing this issue. We contend that there is need for further integration of the various aspects of the process, including the study of pollen delivery onto the stigmatic surface, if we ultimately aim to characterise efficacy of pollen movement at the landscape scale.

Methods

Survey sites

Three study sites were surveyed in Ceredigion (Wales) from mid-June to the end of August 2009, and in August 2008. All three sites were wet, unimproved, relatively species-rich, grazed pastures, owned and managed by the Royal Society for the Protection of Birds (RSPB) (Table 4).

Table 4 Summary of study sites used.
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Phenological progression

The abundance of flowering heads was estimated at 2–4 weekly intervals throughout August 2008, and from mid-June to the end of August 2009 (Table S12). We used the strategy described by Lunt56 to characterise changes in floral phenology. Briefly, all open flowers were recorded along a crenellated transect comprising of 10 m sections between two fixed points in at each site (100 m total transect length). Plant species were identified according to Stace57.

Assembly and comparison of CO1 reference barcodes for UK Bombus species

We assembled a set of reference CO1 barcodes for all species of Bombus reported to occur in the UK58 from the BOLD Systems V4 database (https://v4.boldsystems.org/, last accessed Sept 12th 2022). All barcodes retrieved were aligned and trimmed to a common length (497 bp) using and duplicated sequences removed. The remainder were then used to compile an unrooted neighbour-joining tree using Geneious software v 8.1.8 (www.geneious.com).

Bumblebee sampling

We followed the method of Memmott59 to estimate bumblebee species abundance on each site visitation. Bumblebees were captured along three linear transects per site, totally approximately 100 m (one central transect and two parallel transect 5-10 m from the site margins) for 30 min between 12.00 and 13.00 h on each collection day. Bee sampling coincided with plant survey on dates listed in Table S12. During the survey, individuals matching Bombus phenotypes from species known to occur in the location were captured directly from flowers into a plastic container. One individual was captured per container to avoid cross-contamination of pollen loads and particular attention being made to prevent pollen removal from the flower. After collection, samples were snap-cooled on dry ice to immobilise the insect and reduce the chance of pollen becoming dislodged by excessive movement. Samples stored at −80 °C until required.

Isolation of pollen from stored bees

Individual bees were placed into a 2 ml eppendorf tube containing 1 ml Nuclease-Free water, vortexed and spun at 5000 rpm for 10 min. Supernatant and the bees (and any detached limbs) were removed. The pollen precipitate was then resuspended in Proteinase K solution (275 μl digestion buffer [2% SDS, 100 mM TRIS, 20 mM EDTA, adjusted using 1 M HCl to a final a pH = 8,] and 25 μl (500 u) Proteinase K (Sigma, UK) and used immediately for DNA extraction (see below). No attempt was made to separate corbicular pollen balls from the bees or from detached bee limbs upon removal.

CO1 DNA barcoding of bumble bees

DNA was extracted in 2009 from both forelimbs of each bumble bee using the Wizard SV 96 Genomic DNA purification System (Promega, UK) according to manufacturers’ instructions. PCR amplification was performed within a month of extraction using the following universal primers for CO160:

LEP(F1), 5´-ATTCAACCAATCATAAAGATATTGG-3´:

LEP(R1), 5´-TAAACTTCTGGATGTCCAAAAAATCA-0.3´.

PCR conditions comprised: 5 cycles of 94 °C for 120 s; 45 °C for 40 s and 72 °C for 60 s, followed by 35 cycles of: 94 °C for 40 s; 51 °C for 40 s and 72 °C for 60 s, with a final 5 min extension step at 72 °C.

Strong amplicons of the appropriate size were subjected to DNA sequencing using the CO1 barcoding sequencing protocol as described by Hebert et al.60. The resultant clean sequences were trimmed to 497 bp (the maximum conserved length across all samples) using Clustal software.

CO1 barcode identification of captured bees

Trimmed CO1 sequences data from captured bees were then used for bumblebee species identification. Provisional identification was assigned by BLASTn searches of each query bee sequence against the trimmed reference Bombus sequences from the BOLD Systems V4 database (http://www.boldsystems.org/ last accessed 4/2/2022). Identification was verified when query sequences and reference barcode sequences for all 24 UK Bombus species were used to compile a neighbour-joining tree on the Geneious software package. Query sequences appearing in monospecific clades containing 100% bootstrap support in the resultant tree were deemed to belong to that species.

Generation of a reference set of local rbcL plant barcodes

We retrieved reference barcodes of all plant species present in the study sites from UK plants listed in the BOLD Systems V4 database (http://www.boldsystems.org/ last accessed 4/8/2021). This set constituted the local reference barcodes.

In addition, we also identified all flowering plant species recorded within the 10km2 UK national grid square containing each of the three collection sites using the online atlas of the UK and Irish flora (https://www.brc.ac.uk/plantatlas/ last accessed 4/8/2021). This represented the regional reference barcodes.

DNA extraction from pollen load samples

Pollen samples were resuspended in aliquots containing 275 μl digestion buffer (2% SDS, 100 mM TRIS + HCl to give a pH = 8, 20 mM EDTA) and 25 μl (500u dissolved in nuclease-free water) Proteinase K (Sigma, UK). Samples were incubated at 65 °C for 15 min under constant agitation. After incubation, DNA was isolated from pollen using the Qiagen DNeasy Plant Kit (Qiagen UK) according to manufacturers’ instructions.

The following universal rbcL primers were used to generate amplicons:

F 5´ATGTCACCACAAACAGAGACTAAAGC 3´ (Kress and Erickson61).

R 5´AGTCCACCGCGTAGACATTCAT 3´

The following PCR thermocycling regime was used: 94 °C for 2 min, followed by 40 cycles of 94 °C for 30 s, 52 °C for 50 s and 72 °C for 40 s, with a final elongation cycle of 72 °C for 10 min. Strong amplicons were subjected to the DNA barcoding sequencing protocol described by (Hebert et al.60). The resultant clean bi-directional sequences were trimmed to 497 bp (the maximum conserved length all samples).

RbcL DNA barcoding of pollen loads by 454 pyrosequencing

Tagged rbcL primers were used to differentiate 454 sequences of pollen loads originating from different source bees as described previously by Meyer et al.62. Here, initial PCR primers are 5´-tagged with short nucleotide sequences (tags) in such a way that a unique tagged primer combination can be applied to each specific DNA template source coming from individual insect.

PCR amplicons from pollen loads of each bee were bulked by mixture (10 µl from each product) and purified using the PCR purification Qiagen Kit (Qiagen UK) according to manufacturers’ instructions. The resultant 454 sequencing templates comprised 14.5 µg of cleaned amplification product in total in volumes of approximately 100 µl (at 145 ng/µl as determined by nanodrop) (Thermo scientific-NanoDrop 1000). These samples were sent to Inqaba Biotechnical Industries (Pty) Ltd (South Africa) for 454 sequencing. The protocol used for 454 GS FLX titanium-based sequencing as described by Hawkins et al.63.

Assembly of rbcL pollen profiles

The rbcL sequences were searched within the BOLD Systems V4 database using a Python script (available as freeware at: https://github.com/se-srl/bumblebee). For each RbcL sequence, the script used a curl command to retrieve responses from the BOLD Systems web page. This output was then parsed and plant identifiers with the maximum similarity are considered a match. The genus and species of each result was saved. When two or more query sequences uniquely matched to different reference barcodes of the same species, counts of both variants were amalgamated. Number of matches to each species were assembled onto an Excel spreadsheet. The analytical approach taken sought to emphasise comparison of profiles between individual bees. For this reason, counts representing matches to each pollen species were converted into percentages for that bee. In this way we sought to avoid skewing in favour of individuals with high count yields. Similarly, any bees with less than 500 total counts were removed from analysis to reduce skewing due to sample size. The resulting raw profiles contained percentage matches for all pollen species to all reference plant species. Two arbitrary thresholds were applied. The first (raw profiles), removed sequence counts comprising less than 1% of those present as these were considered as potential sources of contamination. For most analyses, a much higher threshold of 5% was imposed on individual bee profiles. This was intended to reduce the presence of externally foraged pollen and ensure that all pollen types present represented only the main preferences exhibited by individual bees. By definition, this action greatly reduced diversity of pollen types recovered from individual bees and meant that profiles would bias against incidental, occasional or external foraging events.

All statistical analyses and all NMDS analyses were performed using PAST software package64.