Using ITS2 metabarcoding and microscopy to analyse shifts in pollen diets of honey bees and bumble bees along a mass-flowering crop gradient

Unveiling the diet of two generalist stink bugs, Halyomorpha halys and Pentatoma rufipes (Hemiptera: Pentatomidae), through metabarcoding of the ITS2 region from gut content

BACKGROUND

The use of DNA metabarcoding has become an increasingly popular technique to infer feeding relationships in polyphagous herbivores and predators. Understanding host plant preference of native and invasive herbivore insects can be helpful in establishing effective integrated pest management (IPM) strategies. The invasive Halyomorpha halys and native Pentatoma rufipes are piercing-sucking stink bug pests that are known to cause economic damage in commercial fruit orchards.

RESULTS

In this study, we performed molecular gut content analysis (MGCA) on field-collected specimens of these two herbivorous pentatomids using next-generation amplicon sequencing (NGAS) of the internal transcribed spacer 2 (ITS2) barcode region. Additionally, a laboratory experiment was set up where H. halys was switched from a mixed diet to a monotypic diet, allowing us to determine the detectability of the initial diet in a time series of ≤3 days after the diet switch. We detected 68 unique plant species from 54 genera in the diet of two stink bug species, with fewer genera found per sample and a smaller diet breadth for P. rufipes than for H. halys. Both stink bug species generally prefer deciduous trees over gymnosperms and herbaceous plants. Landscape type significantly impacted the observed genera in the diet of both stink bug species, whereas season only had a significant effect on the diet of H. halys.

CONCLUSION

This study provides further insights into the dietary composition of two polyphagous pentatomid pests and illustrates that metabarcoding can deliver a relevant species-level resolution of host plant preference. © 2024 Society of Chemical Industry.

Effects of floral resources on honey bee populations in Mexico: Using dietary metabarcoding to examine landscape quality in agroecosystems

The decline of honey bee populations significantly impacts the human food supply due to poor pollination and yield decreases of essential crop species. Given the reduction of pollinators, research into critical landscape components, such as floral resource availability and land use change, might provide valuable information about the nutritional status and health of honey bee colonies. To address this issue, we examine the effects of landscape factors like agricultural area, urban area, and climatic factors, including maximum temperature, minimum temperature, relative humidity, and precipitation, on honey bee hive populations and nutritional health of 326 honey bee colonies across varying landscapes in Mexico. DNA metabarcoding facilitated the precise identification of pollen from 267 plant species, encompassing 243 genera and 80 families, revealing a primary herb-based diet. Areas characterized by high landscape diversity exhibited greater pollen diversity within the colony. Conversely, colonies situated in regions with higher proportions of agricultural and urban landscapes demonstrated lower bee density. The maximum ambient temperature outside hives positively correlated with pollen diversity, aligning with a simultaneous decrease in bee density. Conversely, higher relative humidity positively influenced both the bee density of the colony and the diversity of foraged pollen. Our national-level study investigated pollen dietary availability and colony size in different habitat types, latitudes, climatic conditions, and varied levels and types of disturbances. This effort was taken to gain a better insight into the mechanisms driving declines in honey bee populations. This study illustrates the need for more biodiverse agricultural landscapes, the preservation of diverse habitats, and the conservation of natural and semi-natural spaces. These measures can help to improve the habitat quality of other bee species, as well as restore essential ecosystem processes, such as pollination and pest control.

Finding the perfect pairs: A matchmaking of plant markers and primers for multi-marker eDNA metabarcoding

As the scope of plant eDNA metabarcoding diversifies, so do the primers, markers and methods. A wealth of primers exists today, but their comparative evaluation is lacking behind. Similarly, multi-marker approaches are recommended but debates persist regarding barcode complementarity and optimal combinations. After a literature compilation of used primers, we compared in silico 102 primer pairs based on amplicon size, coverage and specificity, followed by an experimental evaluation of 15 primer pairs on a mock community sample covering 268 plant species and genera, and about 100 families. The analysis was done for the four most common plant metabarcoding markers, rbcL, trnL, ITS1 and ITS2 and their complementarity was assessed based on retrieved species. By focusing on existing primers, we identify common designs, promote alternatives and enhance prior-supported primers for immediate applications. The ITS2 was the best-performing marker for flowering vascular plants and was congruent to ITS1. However, the combined taxonomic breadth of ITS2 and rbcL surpassed any other combination, highlighting their high complementarity across Streptophyta. Overall, our study underscores the significance of comprehensive primer and barcode evaluations tailored to metabarcoding applications.

How reliable is metabarcoding for pollen identification? An evaluation of different taxonomic assignment strategies by cross-validation

Metabarcoding is a powerful tool, increasingly used in many disciplines of environmental sciences. However, to assign a taxon to a DNA sequence, bioinformaticians need to choose between different strategies or parameter values and these choices sometimes seem rather arbitrary. In this work, we present a case study on ITS2 and rbcL databases used to identify pollen collected by bees in Belgium. We blasted a random sample of sequences from the reference database against the remainder of the database using different strategies and compared the known taxonomy with the predicted one. This in silico cross-validation (CV) approach proved to be an easy yet powerful way to (1) assess the relative accuracy of taxonomic predictions, (2) define rules to discard dubious taxonomic assignments and (3) provide a more objective basis to choose the best strategy. We obtained the best results with the best blast hit (best bit score) rather than by selecting the majority taxon from the top 10 hits. The predictions were further improved by favouring the most frequent taxon among those with tied best bit scores. We obtained better results with databases containing the full sequences available on NCBI rather than restricting the sequences to the region amplified by the primers chosen in our study. Leaked CV showed that when the true sequence is present in the database, blast might still struggle to match the right taxon at the species level, particularly with rbcL. Classical 10-fold CV-where the true sequence is removed from the database-offers a different yet more realistic view of the true error rates. Taxonomic predictions with this approach worked well up to the genus level, particularly for ITS2 (5-7% of errors). Using a database containing only the local flora of Belgium did not improve the predictions up to the genus level for local species and made them worse for foreign species. At the species level, using a database containing exclusively local species improved the predictions for local species by ∼12% but the error rate remained rather high: 25% for ITS2 and 42% for rbcL. Foreign species performed worse even when using a world database (59-79% of errors). We used classification trees and GLMs to model the % of errors vs. identity and consensus scores and determine appropriate thresholds below which the taxonomic assignment should be discarded. This resulted in a significant reduction in prediction errors, but at the cost of a much higher proportion of unassigned sequences. Despite this stringent filtering, at least 1/5 sequences deemed suitable for species-level identification ultimately proved to be misidentified. An examination of the variability in prediction accuracy between plant families showed that rbcL outperformed ITS2 for only two of the 27 families examined, and that the % correct species-level assignments were much better for some families (e.g. 95% for Sapindaceae) than for others (e.g. 35% for Salicaceae).

The value of twinned pollinator-pollen metabarcoding: bumblebee pollination service is weakly partitioned within a UK grassland community

Predicting ecological impact of declining bumblebee (Bombus) populations requires better understanding of interactions between pollinator partitioning of floral resources and plant partitioning of pollinator resources. Here, we combine Cytochrome Oxidase 1 (CO1) barcoding for bumblebee identification and rbcL metabarcoding of pollen carried by bees in three species-rich UK pastures. CO1 barcoding assigned 272 bees to eight species, with 33 individuals belonging to the cryptic Bombus lucorum complex (16 B. lucorum and 17 B. cryptarum). Seasonal bias in capture rates varied by species, with B. pratorum found exclusively in June/July and B. pascuorum more abundant in August. Pollen metabarcoding coupled with PERMANOVA and NMDS analyses revealed all bees carried several local pollen species and evidence of pollen resource partitioning between some species pairings, with Bombus pratorum carrying the most divergent pollen load. There was no evidence of resource partitioning between the two cryptic species present, but significantly divergent capture rates concorded with previous suggestions of separation on the basis of foraging behaviour being shaped by local/temporal differences in climatic conditions. Considering the bee carriage profile of pollen species revealed no significant difference between the nine most widely carried plant species. However, there was a sharp, tipping point change in community pollen carriage across all three sites that occurred during the transition between late July and early August. This transition resulted in a strong divergence in community pollen carriage between the two seasonal periods in both years. We conclude that the combined use of pollen and bee barcoding offers several benefits for further study of plant-pollinator interactions at the landscape scale.

A primer on pollen assignment by nanopore-based DNA sequencing

The possibility to identify plants based on the taxonomic information coming from their pollen grains offers many applications within various biological disciplines. In the past and depending on the application or research in question, pollen origin was analyzed by microscopy, usually preceded by chemical treatment methods. This procedure for identification of pollen grains is both time-consuming and requires expert knowledge of morphological features. Additionally, these microscopically recognizable features usually have a low resolution at species-level. Since a few decades, DNA has been used for the identification of pollen taxa, as sequencing technologies evolved both in their handling and affordability. We discuss advantages and challenges of pollen DNA analyses compared to traditional methods. With readers with little experience in this field in mind, we present a hands-on primer for genetic pollen analysis by nanopore sequencing. As our lab mainly works with pollen collected within agroecological research projects, we focus on pollen collected by pollinating insects. We briefly consider sample collection, storage and processing in the laboratory as well as bioinformatic aspects. Currently, pollen metabarcoding is mostly conducted with next-generation sequencing methods that generate short sequence reads (<1 kb). Increasingly, however, pollen DNA analysis is carried out using the long-read generating (several kb), low-budget and mobile MinION nanopore sequencing platform by Oxford Nanopore Technologies. Therefore, we are focusing on aspects for palynology with the MinION DNA sequencing device.

A detailed workflow to develop QIIME2-formatted reference databases for taxonomic analysis of DNA metabarcoding data

Background

The DNA metabarcoding approach has become one of the most used techniques to study the taxa composition of various sample types. To deal with the high amount of data generated by the high-throughput sequencing process, a bioinformatics workflow is required and the QIIME2 platform has emerged as one of the most reliable and commonly used. However, only some pre-formatted reference databases dedicated to a few barcode sequences are available to assign taxonomy. If users want to develop a new custom reference database, several bottlenecks still need to be addressed and a detailed procedure explaining how to develop and format such a database is currently missing. In consequence, this work is aimed at presenting a detailed workflow explaining from start to finish how to develop such a curated reference database for any barcode sequence.

Results

We developed DB4Q2, a detailed workflow that allowed development of plant reference databases dedicated to ITS2 and rbcL, two commonly used barcode sequences in plant metabarcoding studies. This workflow addresses several of the main bottlenecks connected with the development of a curated reference database. The detailed and commented structure of DB4Q2 offers the possibility of developing reference databases even without extensive bioinformatics skills, and avoids ‘black box’ systems that are sometimes encountered. Some filtering steps have been included to discard presumably fungal and misidentified sequences. The flexible character of DB4Q2 allows several key sequence processing steps to be included or not, and downloading issues can be avoided. Benchmarking the databases developed using DB4Q2 revealed that they performed well compared to previously published reference datasets.

Conclusion

This study presents DB4Q2, a detailed procedure to develop custom reference databases in order to carry out taxonomic analyses with QIIME2, but also with other bioinformatics platforms if desired. This work also provides ready-to-use plant ITS2 and rbcL databases for which the prediction accuracy has been assessed and compared to that of other published databases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01067-5.

Plant genetic diversity by DNA barcoding to investigate propolis origin

Identify the botanical origins of a certain type of propolis may be challenging and time demanding, since it involves bee's behavior observation, plant resins collection and chemical analysis. Thus, this study aimed to determine the plant genetic materials in propolis from southern Brazil using the DNA barcoding to investigate their botanical origins, as well as to compare it with the phytochemical composition determined by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) and with the pollinic profile. As principal results, non-native Populus carolinensis Moench (Salicaceae) was almost the only DNA source in some propolis samples, which coincided with the presence of flavonoids typical from poplar exudates. Conversely, other propolis samples had DNA material coming mainly from native plant species, most of them characterized to the species level, although no specific chemical markers from those plants could be identified by UHPLC-HRMS. However, pollen from several plants identified by the DNA barcoding were extracted from some propolis samples. Despite the identification of typical diterpenes, DNA material from Araucaria angustifolia (Bertol.) Kuntze (Araucariaceae), which have been indicated as a major resin source for propolis from preservation areas in southern Brazil, was found in very small abundancies, likely because bees do not drag tissue material containing DNA when collecting resin from this native species. In conclusion, DNA barcoding analysis successfully provided information about the provenance of propolis, although, depending on the plant resin sources, this information is likely to come from pollen.

Pollinator nutrition and its role in merging the dual objectives of pollinator health and optimal crop production

Bee and non-bee insect pollinators play an integral role in the quantity and quality of production for many food crops, yet there is growing evidence that nutritional challenges to pollinators in agricultural landscapes are an important factor in the reduction of pollinator populations worldwide. Schemes to enhance crop pollinator health have historically focused on floral resource plantings aimed at increasing pollinator abundance and diversity by providing more foraging opportunities for bees. These efforts have demonstrated that improvements in bee diversity and abundance are achievable; however, goals of increasing crop pollination outcomes via these interventions are not consistently met. To support pollinator health and crop pollination outcomes in tandem, habitat enhancements must be tailored to meet the life-history needs of specific crop pollinators, including non-bees. This will require greater understanding of the nutritional demands of these taxa together with the supply of floral and non-floral food resources and how these interact in cropping environments. Understanding the mechanisms underlying crop pollination and pollinator health in unison across a range of taxa is clearly a win–win for industry and conservation, yet achievement of these goals will require new knowledge and novel, targeted methods.

This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.

Biomonitoring via DNA metabarcoding and light microscopy of bee pollen in rainforest transformation landscapes of Sumatra

BACKGROUND

Intense conversion of tropical forests into agricultural systems contributes to habitat loss and the decline of ecosystem functions. Plant-pollinator interactions buffer the process of forest fragmentation, ensuring gene flow across isolated patches of forests by pollen transfer. In this study, we identified the composition of pollen grains stored in pot-pollen of stingless bees, Tetragonula laeviceps, via dual-locus DNA metabarcoding (ITS2 and rbcL) and light microscopy, and compared the taxonomic coverage of pollen sampled in distinct land-use systems categorized in four levels of management intensity (forest, shrub, rubber, and oil palm) for landscape characterization.

RESULTS

Plant composition differed significantly between DNA metabarcoding and light microscopy. The overlap in the plant families identified via light microscopy and DNA metabarcoding techniques was low and ranged from 22.6 to 27.8%. Taxonomic assignments showed a dominance of pollen from bee-pollinated plants, including oil-bearing crops such as the introduced species Elaeis guineensis (Arecaceae) as one of the predominant taxa in the pollen samples across all four land-use types. Native plant families Moraceae, Euphorbiaceae, and Cannabaceae appeared in high proportion in the analyzed pollen material. One-way ANOVA (p > 0.05), PERMANOVA (R² values range from 0.14003 to 0.17684, for all tests p-value > 0.5), and NMDS (stress values ranging from 0.1515 to 0.1859) indicated a lack of differentiation between the species composition and diversity of pollen type in the four distinct land-use types, supporting the influx of pollen from adjacent areas.

CONCLUSIONS

Stingless bees collected pollen from a variety of agricultural crops, weeds, and wild plants. Plant composition detected at the family level from the pollen samples likely reflects the plant composition at the landscape level rather than the plot level. In our study, the plant diversity in pollen from colonies installed in land-use systems with distinct levels of forest transformation was highly homogeneous, reflecting a large influx of pollen transported by stingless bees through distinct land-use types. Dual-locus approach applied in metabarcoding studies and visual pollen identification showed great differences in the detection of the plant community, therefore a combination of both methods is recommended for performing biodiversity assessments via pollen identification.

Using Metabarcoding to Investigate the Strength of Plant-Pollinator Interactions From Surveys of Visits to DNA Sequences

The ongoing decline in pollinators and increasing concerns about pollination services require a better understanding of complex pollination networks, particularly their response to global climate change. While metabarcoding is increasingly used for the identification of taxa in DNA mixtures, its reliability in providing quantitative information on plant-pollinator interactions is still the subject of debate. Combining metabarcoding and microscopy, we investigated the relationships between the number and composition of sequences and the abundance and composition of pollen in insect pollen loads (IPL) and how the two are linked to insect visits. Our findings confirm that metabarcoding is more effective than microscopy in identifying plant species in IPL. For a given species, we found a strong positive relationship between the amount of pollen in IPL and the number of sequences. The relationship was stable across species even if the abundance of co-occurring species in IPL (hereafter “co-occurring pollen”) tended to reduce the sequence yield (number of sequences obtained from one pollen grain) of a given species. We also found a positive relationship between the sequence count and the frequency of visits, and between the frequency and the amounts of pollen in IPL. Our results demonstrate the reliability of metabarcoding in assessing the strength of plant-pollinator interactions and in providing a broader perspective for the analyses of plant-pollinator interactions and pollination networks.

DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring

Airborne pollen monitoring is of global socio-economic importance as it provides information on presence and prevalence of allergenic pollen in ambient air. Traditionally, this task has been performed by microscopic investigation, but novel techniques are being developed to automate this process. Among these, DNA metabarcoding has the highest potential of increasing the taxonomic resolution, but uncertainty exists about whether the results can be used to quantify pollen abundance. In this study, it is shown that DNA metabarcoding using trnL and nrITS2 provides highly improved taxonomic resolution for pollen from aerobiological samples from the Netherlands. A total of 168 species from 143 genera and 56 plant families were detected, while using a microscope only 23 genera and 22 plant families were identified. NrITS2 produced almost double the number of OTUs and a much higher percentage of identifications to species level (80.1%) than trnL (27.6%). Furthermore, regressing relative read abundances against the relative abundances of microscopically obtained pollen concentrations showed a better correlation for nrITS2 (R2 = 0.821) than for trnL (R2 = 0.620). Using three target taxa commonly encountered in early spring and fall in the Netherlands (Alnus sp., Cupressaceae/Taxaceae and Urticaceae) the nrITS2 results showed that all three taxa were dominated by one or two species (Alnus glutinosa/incana, Taxus baccata and Urtica dioica). Highly allergenic as well as artificial hybrid species were found using nrITS2 that could not be identified using trnL or microscopic investigation (Alnus × spaethii, Cupressus arizonica, Parietaria spp.). Furthermore, perMANOVA analysis indicated spatiotemporal patterns in airborne pollen trends that could be more clearly distinguished for all taxa using nrITS2 rather than trnL. All results indicate that nrITS2 should be the preferred marker of choice for molecular airborne pollen monitoring.

A high-throughput amplicon sequencing approach for population-wide species diversity and composition survey

Management of agricultural pests requires an understanding of pest species diversity, their interactions with beneficial insects and spatial-temporal patterns of pest abundance. Invasive and agriculturally important insect pests can build up very high populations, especially in cropping landscapes. Traditionally, sampling effort for species identification involves small sample sizes and is labour intensive. Here, we describe a multi-primer high throughput sequencing (HTS) metabarcoding method and associated analytical workflow for a rapid, intensive, high-volume survey of pest species compositions. We demonstrate our method using the taxonomically challenging Bemisia pest cryptic species complex as examples. The whiteflies Bemisia including the 'tabaci' species are agriculturally important capable of vectoring diverse plant viruses that cause diseases and crop losses. Our multi-primer metabarcoding HTS amplicon approach simultaneously process high volumes of whitefly individuals, with efficiency to detect rare (i.e., 1%) test-species, while our improved whitefly primers for metabarcoding also detected beneficial hymenopteran parasitoid species from whitefly nymphs. Field-testing our redesigned Bemisia metabarcoding primer sets across the Tanzania, Uganda and Malawi cassava cultivation landscapes, we identified the sub-Saharan Africa 1 Bemisia putative species as the dominant pest species, with other cryptic Bemisia species being detected at various abundances. We also provide evidence that Bemisia species compositions can be affected by host crops and sampling techniques that target either nymphs or adults. Our multi-primer HTS metabarcoding method incorporated two over-lapping amplicons of 472bp and 518bp that spanned the entire 657bp 3' barcoding region for Bemisia, and is particularly suitable to molecular diagnostic surveys of this highly cryptic insect pest species complex that also typically exhibited high population densities in heavy crop infestation episodes. Our approach can be adopted to understand species biodiversity across landscapes, with broad implications for improving trans-boundary biosecurity preparedness, thus contributing to molecular ecological knowledge and the development of control strategies for high-density, cryptic, pest-species complexes.

Bumble Bee Foraged Pollen Analyses in Spring Time in Southern Estonia Shows Abundant Food Sources

Simple Summary

Pollinators make a strong contribution to ecosystem stability. However, nowadays, they also need protection and sustainable habitat to live and develop. Not all regions can provide suitable habitats due to agricultural intensification, urbanization, climate changes and corresponding impacts. Our study was conducted in the late spring in south Estonia where arable lands were surrounded by forest patches and rural areas. For better performance, we used both light microscopy and DNA metabarcoding methods for pollen identification. We found that bumble bees foraged on the diverse food sources showing preferences for several main plant families. Additionally, in our case, land-use types did not show important effects on bumble bee food choices and foraging decisions. Various landscape features can provide diverse food sources at the early development stages and support nest longevity. Here, we can say that a better understanding of pollinators’ food preferences can help in the application of more suitable measures for their conservation.

Abstract

Agricultural landscapes usually provide higher quantities of single-source food, which are noticeably lacking in diversity and might thus have low nutrient value for bumble bee colony development. Here, in this study, we analysed the pollen foraging preferences over a large territory of a heterogeneous agricultural landscape: southern Estonia. We aimed to assess the botanical diversity of bumble bee food plants in the spring time there. We looked for preferences for some food plants or signs of food shortage that could be associated with any particular landscape features. For this purpose, we took Bombus terrestris commercial hives to the landscape, performed microscopy analyses and improved the results with the innovative DNA metabarcoding technique to determine the botanical origin of bumble bee-collected pollen. We found high variability of forage plants with no strong relationship with any particular landscape features. Based on the low number of plant species in single flights, we deduce that the availability of main forage plants is sufficient indicating rich forage availabilities. Despite specific limitations, we saw strong correlations between microscopy and DNA metabarcoding data usable for quantification analyses. As a conclusion, we saw that the spring-time vegetation in southern Estonia can support bumble bee colony development regardless of the detailed landscape structure. The absence of clearly dominating food preference by the tested generalist bumble bee species B. terrestris makes us suggest that other bumble bee species, at least food generalists, should also find plenty of forage in their early development phase.

Harnessing the Power of Metabarcoding in the Ecological Interpretation of Plant-Pollinator DNA Data: Strategies and Consequences of Filtering Approaches

Although DNA metabarcoding of pollen mixtures has been increasingly used in the field of pollination biology, methodological and interpretation issues arise due to its high sensitivity. Filtering or maintaining false positives, contaminants, and rare taxa or molecular features could lead to different ecological results. Here, we reviewed how this choice has been addressed in 43 studies featuring pollen DNA metabarcoding, which highlighted a very high heterogeneity of filtering methods. We assessed how these strategies shaped pollen assemblage composition, species richness, and interaction networks. To do so, we compared four processing methods: unfiltering, filtering with a proportional 1% of sample reads, a fixed threshold of 100 reads, and the ROC approach (Receiver Operator Characteristic). The results indicated that filtering impacted species composition and reduced species richness, with ROC emerging as a conservative approach. Moreover, in contrast to unfiltered networks, filtering decreased network Connectance and Entropy, and it increased Modularity and Connectivity, indicating that using cut-off thresholds better describes interactions. Overall, unfiltering might compromise reliable ecological interpretations, unless a study targets rare species. We discuss the suitability of each filtering type, plead for justifying filtering strategies on biological or methodological bases and for developing shared approaches to make future studies more comparable.

Pollen DNA metabarcoding identifies regional provenance and high plant diversity in Australian honey

Accurate identification of the botanical components of honey can be used to establish its geographical provenance, while also providing insights into honeybee (Apis mellifera L.) diet and foraging preferences. DNA metabarcoding has been demonstrated as a robust method to identify plant species from pollen and pollen-based products, including honey. We investigated the use of pollen metabarcoding to identify the floral sources and local foraging preferences of honeybees using 15 honey samples from six bioregions from eastern and western Australia. We used two plant metabarcoding markers, ITS2 and the trnL P6 loop. Both markers combined identified a total of 55 plant families, 67 genera, and 43 species. The trnL P6 loop marker provided significantly higher detection of taxa, detecting an average of 15.6 taxa per sample, compared to 4.6 with ITS2. Most honeys were dominated by Eucalyptus and other Myrtaceae species, with a few honeys dominated by Macadamia (Proteaceae) and Fabaceae. Metabarcoding detected the nominal primary source provided by beekeepers among the top five most abundant taxa for 85% of samples. We found that eastern and western honeys could be clearly differentiated by their floral composition, and clustered into bioregions with the trnL marker. Comparison with previous results obtained from melissopalynology shows that metabarcoding can detect similar numbers of plant families and genera, but provides significantly higher resolution at species level. Our results show that pollen DNA metabarcoding is a powerful and robust method for detecting honey provenance and examining the diet of honeybees. This is particularly relevant for hives foraging on the unique and diverse flora of the Australian continent, with the potential to be used as a novel monitoring tool for honeybee floral resources.