Environmental metagenetics unveil novel plant-pollinator interactions.

Honey bees are efficient pollinators of flowering plants, aiding in the plant reproductive cycle and acting as vehicles for evolutionary processes. Their role as agents of selection and drivers of gene flow is instrumental to the structure of plant populations, but historically, our understanding of their influence has been limited to predominantly insect-dispersed flowering species. Recent metagenetic work has provided evidence that honey bees also forage on pollen from anemophilous species, suggesting that their role as vectors for transmission of plant genetic material is not confined to groups designated as entomophilous, and leading us to ask: could honey bees act as dispersal agents for non-flowering plant taxa? Using an extensive pollen metabarcoding dataset from Canada, we discovered that honey bees may serve as dispersal agents for an array of sporophytes (Anchistea, Claytosmunda, Dryopteris, Osmunda, Osmundastrum, Equisetum) and bryophytes (Funaria, Orthotrichum, Sphagnum, Ulota). Our findings also suggest that honey bees may occasionally act as vectors for the dispersal of aquatic phototrophs, specifically Coccomyxa and Protosiphon, species of green algae. Our work has shed light on the broad resource-access patterns that guide plant-pollinator interactions and suggests that bees could act as vectors of gene flow, and potentially even agents of selection, across Plantae.

Validating a multi-locus metabarcoding approach for characterizing mixed-pollen samples.

BACKGROUND: The mutualistic interaction between entomophilous plants and pollinators is fundamental to the structure of most terrestrial ecosystems. The sensitive nature of this relationship has been disrupted by anthropogenic modifications to natural landscapes, warranting development of new methods for exploring this trophic interaction. Characterizing the composition of pollen collected by pollinators, e.g. Apis mellifera, is a common means of exploring this relationship, but traditional methods of microscopic pollen assessment are laborious and limited in their scope. The development of pollen metabarcoding as a method of rapidly characterizing the abundance and diversity of pollen within mixed samples presents a new frontier for this type of work, but metabarcoding may have limitations, and validation is warranted before any suite of primers can be confidently used in a research program. We set out to evaluate the utility of an integrative approach, using a set of established primers (ITS2 and rbcL) versus melissopalynological analysis for characterizing 27 mixed-pollen samples from agricultural sites across Canada. RESULTS: Both individual markers performed well relative to melissopalynology at the family level with decreases in the strength of correlation and linear model fits at the genus level. Integrating data from both markers together via a multi-locus approach provided the best rank-based correlation between metagenetic and melissopalynological data at both the genus (ρ = 0.659; p < 0.001) and family level (ρ = 0.830; p < 0.001). Species accumulation curves indicated that, after controlling for sampling effort, melissopalynological characterization provides similar or higher species richness estimates than either marker. The higher number of plant species discovered via the metabarcoding approach simply reflects the vastly greater sampling effort in comparison to melissopalynology. CONCLUSIONS: Pollen metabarcoding performed well at characterizing the composition of mixed pollen samples relative to a traditional melissopalynological approach. Limitations to the quantitative application of this method can be addressed by adopting a multi-locus approach that integrates information from multiple markers.

More than mesolectic: Characterizing the nutritional niche of Osmia cornifrons.

Characterizing the nutritional needs of wild bee species is an essential step to better understanding bee biology and providing suitable supplemental forage for at-risk species. Here, we aim to characterize the nutritional needs of a model solitary bee species, Osmia cornifrons (Radoszkowski), by using dietary protein-to-lipid ratio (P:L ratio) as a proxy for nutritional niche and niche breadth. We first identified the mean target P:L ratio (~3.02:1) and P:L collection range (0.75-6.26:1) from pollen provisions collected across a variety of sites and time points. We then investigated the P:L tolerance range of larvae by rearing bees in vitro on a variety of diets. Multifloral and single-source pollen diets with P:L ratios within the range of surveyed provisions did not always support larval development, indicating that other dietary components such as plant secondary compounds and micronutrients must also be considered in bee nutritional experiments. Finally, we used pollen metabarcoding to identify pollen from whole larval provisions to understand how much pollen bees used from plants outside of their host plant families to meet their nutritional needs, as well as pollen from individual forager bouts, to observe if bees maintained strict floral constancy or visited multiple plant genera per foraging bout. Whole larval provision surveys revealed a surprising range of host plant pollen use, ranging from ~5% to 70% of host plant pollen per provision. Samples from individual foraging trips contained pollen from multiple genera, suggesting that bees are using some form of foraging decision making. Overall, these results suggest that O. cornifrons have a wide nutritional niche breadth, but while pollen P:L ratio tolerance is broad, a tolerable P:L ratio alone is not enough to create a quality diet for O. cornifrons, and the plant species that make up these diets must also be carefully considered.

Plants, pollinators and their interactions under global ecological change: The role of pollen DNA metabarcoding.

Anthropogenic activities are triggering global changes in the environment, causing entire communities of plants, pollinators and their interactions to restructure, and ultimately leading to species declines. To understand the mechanisms behind community shifts and declines, as well as monitoring and managing impacts, a global effort must be made to characterize plant-pollinator communities in detail, across different habitat types, latitudes, elevations, and levels and types of disturbances. Generating data of this scale will only be feasible with rapid, high-throughput methods. Pollen DNA metabarcoding provides advantages in throughput, efficiency and taxonomic resolution over traditional methods, such as microscopic pollen identification and visual observation of plant-pollinator interactions. This makes it ideal for understanding complex ecological networks and their responses to change. Pollen DNA metabarcoding is currently being applied to assess plant-pollinator interactions, survey ecosystem change and model the spatiotemporal distribution of allergenic pollen. Where samples are available from past collections, pollen DNA metabarcoding has been used to compare contemporary and past ecosystems. New avenues of research are possible with the expansion of pollen DNA metabarcoding to intraspecific identification, analysis of DNA in ancient pollen samples, and increased use of museum and herbarium specimens. Ongoing developments in sequencing technologies can accelerate progress towards these goals. Global ecological change is happening rapidly, and we anticipate that high-throughput methods such as pollen DNA metabarcoding are critical for understanding the evolutionary and ecological processes that support biodiversity, and predicting and responding to the impacts of change.

Soybean is a Common Nectar Source for Honey Bees (Hymenoptera: Apidae) in a Midwestern Agricultural Landscape.

Large-scale soybean [Glycine max (L.) Merr.] cultivation has substantially transformed the Midwestern landscape in recent decades. Floral nectar produced by immense fields of soybeans has the potential to influence foraging ecology and resource accumulation of honey bee (Apis mellifera L.) colonies. In this study, we combined microscopic and molecular pollen analysis of honey samples with waggle dance inference of spatial foraging patterns to demonstrate that honey bees routinely forage on soybeans in Ohio. In analyzing honey samples from across the state, we found ubiquitous presence of soybean pollen in honey collected from agricultural lands during soybean bloom. The abundance of soybean pollen in honey increased with the amount of soybean fields surrounding the apiaries. Honey bee waggle dances recorded during soybean bloom revealed that honey bees preferred soybean fields for foraging over other habitat types. With these results, future research efforts aimed at enhancing mutual interactions between soybeans and honey bees may represent an unexplored pathway for increasing soybean production while supporting honey bees and other pollinators in the surrounding landscape.

Honey Bees and Neonicotinoid-Treated Corn Seed: Contamination, Exposure, and Effects.

Most corn (Zea mays) seeds planted in the United States in recent years are coated with a seed treatment containing neonicotinoid insecticides. Abrasion of the seed coating generates insecticide-laden planter dust that disperses through the landscape during corn planting and has resulted in many "bee-kill" incidents in North America and Europe. We investigated the linkage between corn planting and honey bee colony success in a region dominated by corn agriculture. Over 3 yr we consistently observed an increased presence of corn seed treatment insecticides in bee-collected pollen and elevated worker bee mortality during corn planting. Residues of seed treatment neonicotinoids, clothianidin and thiamethoxam, detected in pollen positively correlated with cornfield area surrounding the apiaries. Elevated worker mortality was also observed in experimental colonies fed field-collected pollen containing known concentrations of corn seed treatment insecticides. We monitored colony growth throughout the subsequent year in 2015 and found that colonies exposed to higher insecticide concentrations exhibited slower population growth during the month of corn planting but demonstrated more rapid growth in the month following, though this difference may be related to forage availability. Exposure to seed treatment neonicotinoids during corn planting has clear short-term detrimental effects on honey bee colonies and may affect the viability of beekeeping operations that are dependent on maximizing colony size in the springtime. Environ Toxicol Chem 2021;40:1212-1221. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Application of plant metabarcoding to identify diverse honeybee pollen forage along an urban-agricultural gradient.

Understanding animal foraging ecology requires large sample sizes spanning broad environmental and temporal gradients. For pollinators, this has been hampered by the laborious nature of morphologically identifying pollen. Identifying pollen from urban environments is particularly difficult due to the presence of diverse ornamental species associated with consumer horticulture. Metagenetic pollen analysis represents a potential solution to this issue. Building upon prior laboratory and bioinformatic methods, we applied quantitative multilocus metabarcoding to characterize the foraging ecology of honeybee colonies situated in urban, suburban, mixed suburban-agricultural and rural agricultural sites in central Ohio, USA. In cross-validating a subset of our metabarcoding results using microscopic palynology, we find strong concordance between the molecular and microscopic methods. Our results suggest that forage from the agricultural site exhibited decreased taxonomic diversity and temporal turnover relative to the urban and suburban sites, though the generalization of this observation will require replication across additional sites and cities. Our work demonstrates the power of honeybees as environmental samplers of floral community composition at large spatial scales, aiding in the distinction of taxa characteristically associated with urban or agricultural land use from those distributed ubiquitously across the sampled landscapes. Observed patterns of high forage diversity and compositional turnover in our more urban sites are likely reflective of the fine-grain heterogeneity and high beta diversity of urban floral landscapes at the scale of honeybee foraging. This provides guidance for future studies investigating how relationships between urbanization and measures of pollinator health are mediated by variation in floral resource dynamics across landscapes.

A screening-level assessment of the pollinator-attractiveness of ornamental nursery stock using a honey bee foraging assay.

In urban and suburban landscapes characterized by extensive designed greenspaces, the support of pollinator communities hinges significantly on floral resources provided by ornamental plants. The attractiveness of ornamental plants to pollinators, however, cannot be presumed, and some studies suggest that a majority of ornamental plant varieties receive little or no pollinator visitation. Here, we harness the sampling power of the western honey bee, a generalist pollinator whose diet breadth overlaps substantially with that of other pollinators, to survey the utilization of ornamental plants grown at three commercial nurseries in Connecticut, USA. Using a combination of DNA metabarcoding and microscopy, we identify, to genus-level, pollen samples from honey bee colonies placed within each nursery, and we compare our results with nursery plant inventories to identify the subset of cultivated genera that were visited during pollen foraging. Samples were collected weekly from May to September, encompassing the majority of the growing season. Our findings show that some plant genera known to be cultivated as ornamentals in our system, particularly ornamental trees and shrubs (e.g. Hydrangea, Rosa, Spiraea, Syringa, Viburnum), functioned as major pollen sources, but the majority of plants inventoried at our nurseries provided little or no pollen to honey bees. These results are in agreement with a growing body of literature highlighting the special importance of woody plants as resources for flower-visiting insects. We encourage further exploration of the genera highlighted in our data as potential components of pollinator-friendly ornamental greenspace.

Quantitative multi-locus metabarcoding and waggle dance interpretation reveal honey bee spring foraging patterns in Midwest agroecosystems.

We explored the pollen foraging behaviour of honey bee colonies situated in the corn and soybean dominated agroecosystems of central Ohio over a month-long period using both pollen metabarcoding and waggle dance inference of spatial foraging patterns. For molecular pollen analysis, we developed simple and cost-effective laboratory and bioinformatics methods. Targeting four plant barcode loci (ITS2, rbcL, trnL and trnH), we implemented metabarcoding library preparation and dual-indexing protocols designed to minimize amplification biases and index mistagging events. We constructed comprehensive, curated reference databases for hierarchical taxonomic classification of metabarcoding data and used these databases to train the metaxa2 DNA sequence classifier. Comparisons between morphological and molecular palynology provide strong support for the quantitative potential of multi-locus metabarcoding. Results revealed consistent foraging habits between locations and show clear trends in the phenological progression of honey bee spring foraging in these agricultural areas. Our data suggest that three key taxa, woody Rosaceae such as pome fruits and hawthorns, Salix, and Trifolium provided the majority of pollen nutrition during the study. Spatially, these foraging patterns were associated with a significant preference for forests and tree lines relative to herbaceous land cover and nonflowering crop fields.

A reference cytochrome c oxidase subunit I database curated for hierarchical classification of arthropod metabarcoding data.

Metabarcoding is a popular application which warrants continued methods optimization. To maximize barcoding inferences, hierarchy-based sequence classification methods are increasingly common. We present methods for the construction and curation of a database designed for hierarchical classification of a 157 bp barcoding region of the arthropod cytochrome c oxidase subunit I (COI) locus. We produced a comprehensive arthropod COI amplicon dataset including annotated arthropod COI sequences and COI sequences extracted from arthropod whole mitochondrion genomes, the latter of which provided the only source of representation for Zoraptera, Callipodida and Holothyrida. The database contains extracted sequences of the target amplicon from all major arthropod clades, including all insect orders, all arthropod classes and Onychophora, Tardigrada and Mollusca outgroups. During curation, we extracted the COI region of interest from approximately 81 percent of the input sequences, corresponding to 73 percent of the genus-level diversity found in the input data. Further, our analysis revealed a high degree of sequence redundancy within the NCBI nucleotide database, with a mean of approximately 11 sequence entries per species in the input data. The curated, low-redundancy database is included in the Metaxa2 sequence classification software (http://microbiology.se/software/metaxa2/). Using this database with the Metaxa2 classifier, we performed a cross-validation analysis to characterize the relationship between the Metaxa2 reliability score, an estimate of classification confidence, and classification error probability. We used this analysis to select a reliability score threshold which minimized error. We then estimated classification sensitivity, false discovery rate and overclassification, the propensity to classify sequences from taxa not represented in the reference database. Our work will help researchers design and evaluate classification databases and conduct metabarcoding on arthropods and alternate taxa.

Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker.

Motivation: Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data. Results: We evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality. Availability and implementation: Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/. Supplementary information: Supplementary data are available at Bioinformatics online.

Evaluating and optimizing the performance of software commonly used for the taxonomic classification of DNA metabarcoding sequence data.

The taxonomic classification of DNA sequences has become a critical component of numerous ecological research applications; however, few studies have evaluated the strengths and weaknesses of commonly used sequence classification approaches. Further, the methods and software available for sequence classification are diverse, creating an environment in which it may be difficult to determine the best course of action and the trade-offs made using different classification approaches. Here, we provide an in silico evaluation of three DNA sequence classifiers, the rdp Naïve Bayesian Classifier, rtax and utax. Further, we discuss the results, merits and limitations of both the classifiers and our method of classifier evaluation. Our methods of comparison are simple, yet robust, and will provide researchers a methodological and conceptual foundation for making such evaluations in a variety of research situations. Generally, we found a considerable trade-off between accuracy and sensitivity for the classifiers tested, indicating a need for further improvement of sequence classification tools.

Pollen DNA barcoding: current applications and future prospects.

Identification of the species origin of pollen has many applications, including assessment of plant-pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and has few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.

Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach.

PREMISE OF THE STUDY: Difficulties inherent in microscopic pollen identification have resulted in limited implementation for large-scale studies. Metabarcoding, a relatively novel approach, could make pollen analysis less onerous; however, improved understanding of the quantitative capacity of various plant metabarcode regions and primer sets is needed to ensure that such applications are accurate and precise. METHODS AND RESULTS: We applied metabarcoding, targeting the ITS2, matK, and rbcL loci, to characterize six samples of pollen collected by honey bees, Apis mellifera. Additionally, samples were analyzed by light microscopy. We found significant rank-based associations between the relative abundance of pollen types within our samples as inferred by the two methods. CONCLUSIONS: Our findings suggest metabarcoding data from plastid loci, as opposed to the ribosomal locus, are more reliable for quantitative characterization of pollen assemblages. Furthermore, multilocus metabarcoding of pollen may be more reliable than single-locus analyses, underscoring the need for discovering novel barcodes and barcode combinations optimized for molecular palynology.

Application of ITS2 metabarcoding to determine the provenance of pollen collected by honey bees in an agroecosystem.

PREMISE OF THE STUDY: Melissopalynology, the identification of bee-collected pollen, provides insight into the flowers exploited by foraging bees. Information provided by melissopalynology could guide floral enrichment efforts aimed at supporting pollinators, but it has rarely been used because traditional methods of pollen identification are laborious and require expert knowledge. We approach melissopalynology in a novel way, employing a molecular method to study the pollen foraging of honey bees (Apis mellifera) in a landscape dominated by field crops, and compare these results to those obtained by microscopic melissopalynology. • METHODS: Pollen was collected from honey bee colonies in Madison County, Ohio, USA, during a two-week period in midspring and identified using microscopic methods and ITS2 metabarcoding. • RESULTS: Metabarcoding identified 19 plant families and exhibited sensitivity for identifying the taxa present in large and diverse pollen samples relative to microscopy, which identified eight families. The bulk of pollen collected by honey bees was from trees (Sapindaceae, Oleaceae, and Rosaceae), although dandelion (Taraxacum officinale) and mustard (Brassicaceae) pollen were also abundant. • DISCUSSION: For quantitative analysis of pollen, using both metabarcoding and microscopic identification is superior to either individual method. For qualitative analysis, ITS2 metabarcoding is superior, providing heightened sensitivity and genus-level resolution.