Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL

Maximising environmental pressure-response relationship signals from diatom-based metabarcoding in rivers

DNA metabarcoding has been performed on a large number of river phytobenthos samples collected from the UK, using rbcL primers optimised for diatoms. Within this dataset the composition of non-diatom sequence reads was studied and the effect of including these in models for evaluating the nutrient gradient was assessed. Whilst many non-diatom taxonomic groups were detected, few contained the full diversity expected in riverine environments. This may be due to the performance of the current primers in characterising the wider phytobenthic community and influenced by the sampling method employed, as both were developed specifically for diatoms. Nevertheless, the study identified considerable diversity in some groups, e.g. Eustigmatophyceae and a wider distribution than previously thought for freshwater Phaeophyceae. These results offer a strong case for the benefits of metabarcoding for expanding knowledge of aquatic biodiversity in the UK and elsewhere. Many of the ASVs associated with non-diatoms showed significant pressure responses; however, models that included non-diatoms had similar predictive strength to those based on diatoms alone. Whilst limitations of the primers for assessing non-diatoms may play a role in explaining these results, the diatoms provide a strong signal along the nutrient gradient and other algae, therefore, add little unique information. We recommend that future developments should use ASVs to calculate metrics, with links to reference databases made as a final step to generate lists of taxa to support interpretation. Any further exploration of the potential of non-diatoms would benefit from access to a well-curated reference database, similar to diat.barcode. Such a database does not yet exist, and we caution against the indiscriminate use of NCBI GenBank as a taxonomic resource as many rbcL sequences deposited have not been curated.

Decrypting seasonal patterns of key pollen taxa in cool temperate Australia: A multi-barcode metabarcoding analysis

Pollen allergies pose a considerable global public health concern. Allergy risk can vary significantly within plant families, yet some key pollen allergens can only be identified to family level by current optical methods. Pollen information with greater taxonomic resolution is therefore required to best support allergy prevention and self-management. We used environmental DNA (eDNA) metabarcoding to deepen taxonomic insights into the seasonal composition of airborne pollen in cool temperate Australia, a region with high rates of allergic respiratory disease. In Hobart, Tasmania, we collected routine weekly air samples from December 2018 until October 2020 and sequenced the internal transcribed spacer 2 (ITS2) and chloroplastic tRNA-Leucine tRNA-Phenylalanine intergenic spacer (trnL-trnF) regions in order to address the following questions: a) What is the genus-level diversity of known and potential aeroallergens in Hobart, in particular, in the families Poaceae, Cupressaceae and Myrtaceae? b) How do the atmospheric concentrations of these taxa change over time, and c) Does trnL-trnF enhance resolution of biodiversity when used in addition to ITS2? Our results suggest that individuals in the region are exposed to temperate grasses including Poa and Bromus in the peak grass pollen season, however low levels of exposure to the subtropical grass Cynodon may occur in autumn and winter. Within Cupressaceae, both metabarcodes showed that exposure is predominantly to pollen from the introduced genera Cupressus and Juniperus. Only ITS2 detected the native genus, Callitris. Both metabarcodes detected Eucalyptus as the major Myrtaceae genus, with trnL-trnF exhibiting primer bias for this family. These findings help refine our understanding of allergy triggers in Tasmania and highlight the utility of multiple metabarcodes in aerobiome studies.

Uncovering floral composition of paper wasp nests (Hymenoptera: Vespidae: Polistes) through DNA metabarcoding

As the social organism, Polistes wasps build a communal nest using woody fibers with saliva for sustaining brood and adult population throughout the season. Limited information exists regarding the identification specific plant materials employed in wasp nest building. Thus, we firstly tested if the DNA metabarcoding approach utilizing rbcL and trnL molecular markers could identify the plant species quantitatively and qualitatively inform the mixed-origin woody samples. A threshold of 0.01 proportion of reads was applied for rbcL and trnL molecular markers, while this threshold for median proportion was 0.0025. In assessing taxa richness, the median proportion demonstrated superior performance, exhibiting higher taxa detection power, however, rbcL marker outperformed in quantitative analysis. Subsequently, we applied DNA metabarcoding to identify the plant materials from the nests of two Polistes species, P. mandarinus and P. rothneyi. The results showed that higher preference of Quercus and Robinia as the major nest building materials regardless of the surrounding plant communities, by two wasp species. Material diversity was higher for P. rothneyi than P. mandarinus, which may explain the abundance of this species possibly with heightened adaptive capacities in their nesting behavior. This study demonstrated that DNA metabarcoding could identify the complex nest-building plant materials of paper wasps and provide insights into their ecological interactions in the natural ecosystem.

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).

Outdoor airborne allergens: Characterization, behavior and monitoring in Europe

Aeroallergens or inhalant allergens, are proteins dispersed through the air and have the potential to induce allergic conditions such as rhinitis, conjunctivitis, and asthma. Outdoor aeroallergens are found predominantly in pollen grains and fungal spores, which are allergen carriers. Aeroallergens from pollen and fungi have seasonal emission patterns that correlate with plant pollination and fungal sporulation and are strongly associated with atmospheric weather conditions. They are released when allergen carriers come in contact with the respiratory system, e.g. the nasal mucosa. In addition, due to the rupture of allergen carriers, airborne allergen molecules may be released directly into the air in the form of micronic and submicronic particles (cytoplasmic debris, cell wall fragments, droplets etc.) or adhered onto other airborne particulate matter. Therefore, aeroallergen detection strategies must consider, in addition to the allergen carriers, the allergen molecules themselves. This review article aims to present the current knowledge on inhalant allergens in the outdoor environment, their structure, localization, and factors affecting their production, transformation, release or degradation. In addition, methods for collecting and quantifying aeroallergens are listed and thoroughly discussed. Finally, the knowledge gaps, challenges and implications associated with aeroallergen analysis are described.

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

Contrasting patterns of foraging behavior in neotropical stingless bees using pollen and honey metabarcoding

Stingless bees are major flower visitors in the tropics, but their foraging preferences and behavior are still poorly understood. Studying stingless bee interactions with angiosperms is methodologically challenging due to the high tropical plant diversity and inaccessibility of upper canopy flowers in forested habitats. Pollen DNA metabarcoding offers an opportunity of assessing floral visitation efficiently and was applied here to understand stingless bee floral resources spectra and foraging behavior. We analyzed pollen and honey from nests of three distantly related stingless bee species, with different body size and social behavior: Melipona rufiventris, Scaptotrigona postica and Tetragonisca angustula. Simultaneously, we evaluate the local floristic components through seventeen rapid botanical surveys conducted at different distances from the nests. We discovered a broad set of explored floral sources, with 46.3 plant species per bee species in honey samples and 53.67 in pollen samples. Plant families Myrtaceae, Asteraceae, Euphorbiaceae, Melastomataceae and Malpighiaceae dominated the records, indicating stingless bee preferences for abundant resources that flowers of these families provide in the region. Results also reinforce the preference of stingless bees for forest trees, even if only available at long distances. Our high-resolution results encourage future bee-plant studies using pollen and honey metabarcoding in hyper-diverse tropical environments.

Microscale pollen release and dispersal patterns in flowering grass populations

Characterizing pollen release and dispersion processes is fundamental for knowledge advancement in ecological, agricultural and public health disciplines. Understanding pollen dispersion from grass communities is especially relevant due to their high species-specific allergenicity and heterogeneously distributed source areas. Here, we aimed to address questions concerning fine level heterogeneity in grass pollen release and dispersion processes, with a focus on characterizing the taxonomic composition of airborne grass pollen over the grass flowering season using eDNA and molecular ecology methods. High resolution grass pollen concentrations were compared between three microscale sites (<300 m apart) in a rural area in Worcestershire, UK. The grass pollen was modelled with local meteorology in a MANOVA (Multivariate ANOVA) approach to investigate factors relevant to pollen release and dispersion. Simultaneously, airborne pollen was sequenced using Illumina MySeq for metabarcoding, analysed against a reference database with all UK grasses using the R packages DADA2 and phyloseq to calculate Shannon's Diversity Index (α-diversity). The flowering phenology of a local Festuca rubra population was observed. We found that grass pollen concentrations varied on a microscale level, likely attributed to local topography and the dispersion distance of pollen from flowering grasses in local source areas. Six genera (Agrostis, Alopecurus, Arrhenatherum, Holcus, Lolium and Poa) dominated the pollen season, comprising on average 77 % of the relative abundance of grass species reads. Temperature, solar radiation, relative humidity, turbulence and wind speeds were found to be relevant for grass pollen release and dispersion processes. An isolated flowering Festuca rubra population contributed almost 40 % of the relative pollen abundance adjacent to the nearby sampler, but only contributed 1 % to samplers situated 300 m away. This suggests that most emitted grass pollen has limited dispersion distance and our results show substantial variation in airborne grass species composition over short geographical scales.

Isolating the species element in grass pollen allergy: A review

Grass pollen is a leading cause of allergy in many countries, particularly Europe. Although many elements of grass pollen production and dispersal are quite well researched, gaps still remain around the grass species that are predominant in the air and which of those are most likely to trigger allergy. In this comprehensive review we isolate the species aspect in grass pollen allergy by exploring the interdisciplinary interdependencies between plant ecology, public health, aerobiology, reproductive phenology and molecular ecology. We further identify current research gaps and provide open ended questions and recommendations for future research in an effort to focus the research community to develop novel strategies to combat grass pollen allergy. We emphasise the role of separating temperate and subtropical grasses, identified through divergence in evolutionary history, climate adaptations and flowering times. However, allergen cross-reactivity and the degree of IgE connectivity in sufferers between the two groups remains an area of active research. The importance of future research to identify allergen homology through biomolecular similarity and the connection to species taxonomy and practical implications of this to allergenicity is further emphasised. We also discuss the relevance of eDNA and molecular ecological techniques (DNA metabarcoding, qPCR and ELISA) as important tools in quantifying the connection between the biosphere with the atmosphere. By gaining more understanding of the connection between species-specific atmospheric eDNA and flowering phenology we will further elucidate the importance of species in releasing grass pollen and allergens to the atmosphere and their individual role in grass pollen allergy.

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 (&lt;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.

Towards DNA-Based Methods Analysis for Honey: An Update

Honey is a natural product widely consumed all over the world due to its relationship with healthy benefits. Additionally, environmental and ethical issues have a higher weight in the consumer's choice to buy honey as a natural product. Following the high demand of this product, several approaches have been suggested and developed aiming at the assessment of honey's quality and authenticity. Target approaches, such as pollen analysis, phenolic compounds, sugars, volatile compounds, organic acids, proteins, amino acids, minerals, and trace elements, showed an efficacy, particularly concerning the honey origin. However, a special highlight is given to DNA markers, due to their useful applicability in environmental and biodiversity studies, besides the geographical, botanical, and entomological origins. Different DNA target genes were already explored for addressing diverse sources of honey DNA, with DNA metabarcoding attaining a relevant importance. This review aims to describe the latest advances on DNA-based methods applied in honey related studies, identifying the research needs for the development of new and additional required methodologies, and to select the most adequate tools for future research projects.

Laboratory protocol is important to improve the correlation between target copies and metabarcoding read numbers of seed DNA in ground beetle regurgitates

DNA metabarcoding is increasingly important for studying feeding interactions, yet it remains unresolved whether reporting read counts or occurrences is to be preferred. To address this issue for gut content samples, basic experimental data on the relationship between read numbers and initial prey DNA amounts and how both change over digestion time is needed. Using regurgitates of the carabid Pseudoophonus rufipes the digestion of Taraxacum officinale seeds was documented for 128 h post-feeding to determine how the number of (1) seed DNA copies and (2) metabarcoding reads change over digestion time, and (3) how they correlate to each other. Additionally, we tested (4) whether PCR cycle-numbers during library preparation affect this correlation. The number of copies and reads both decreased with digestion time, but variation between samples was high. Read and copy numbers correlated when using a library preparation protocol with 35 cycles (R² = 42.0%), yet a reduction to 30 cycles might significantly improve this correlation, as indicated by additional PCR testing. Our findings show that protocol optimization is important to reduce technical distortions of read numbers in metabarcoding analysis. However, high inter-sample variation, likely due to variable digestive efficiency of individual consumers, can blur the relationship between the amount of food consumed and metabarcoding read numbers.

Assessing the lysis of diverse pollen from bulk environmental samples for DNA metabarcoding

Pollen is ubiquitous year-round in bulk environmental samples and can provide useful information on previous and current plant communities. Characterization of pollen has traditionally been completed based on morphology, requiring significant time and expertise. DNA metabarcoding is a promising approach for characterizing pollen from bulk environmental samples, but accuracy hinges on successful lysis of pollen grains to free template DNA. In this study, we assessed the lysis of morphologically and taxonomically diverse pollen from one of the most common bulk environmental sample types for DNA metabarcoding, surface soil. To achieve this, a four species artificial pollen mixture was spiked into surface soils collected from Colorado, North Carolina, and Pennsylvania, and subsequently subjected to DNA extraction using both the PowerSoil and PowerSoil Pro Kits (Qiagen) with a heated incubation (either 65 °C or 90 °C). Amplification and Illumina sequencing of the internal transcribed spacer subunit 2 (ITS2) was completed in duplicate for each sample (total n, 76), and the resulting sequencing reads taxonomically identified using GenBank. The PowerSoil Pro Kit statistically outperformed the PowerSoil Kit for total DNA yield. When using either kit, incubation temperature (65 °C or 90 °C) used had no impact on the recovery of DNA, plant amplicon sequence variants (ASVs), or total plant ITS2 reads. This study highlighted that lysis of pollen in bulk environmental samples is feasible using commercially available kits, and downstream DNA metabarcoding can be used to accurately characterize pollen DNA from such sample types.

Development and Testing of the A1 Volumetric Air Sampler, an Automatic Pollen Trap Suitable for Long-Term Monitoring of eDNA Pollen Diversity

Airborne pollen surveys provide information on various aspects of biodiversity and human health monitoring. Such surveys are typically conducted using the Burkard Multi-Vial Cyclone Sampler, but have to be technically optimized for eDNA barcoding. We here developed and tested a new airborne pollen trap, especially suitable for autonomous eDNA-metabarcoding analyses, called the A1 volumetric air sampler. The trap can sample pollen in 24 different tubes with flexible intervals, allowing it to operate independently in the field for a certain amount of time. We compared the efficiency of the new A1 volumetric air sampler with another automated volumetric spore trap, the Burkard Multi-Vial Cyclone Sampler, which features shorter and fewer sampling intervals to evaluate the comparability of ambient pollen concentrations. In a sterile laboratory environment, we compared trap performances between the automated volumetric air samplers by using pure dry pollen of three species-Fagus sylvatica, Helianthus annuus and Zea mays-which differ both by exine ornamentation and pollen size. The traps had a standard suction flow rate of 16.5 L/min, and we counted the inhaled pollen microscopically after a predefined time interval. Our results showed that though we put three different pollen types in the same container, both the traps inhaled all the pollens in a statistically significant manner irrespective of their size. We found that, on average, both traps inhaled equal an number of pollens for each species. We did not detect any cross-contamination between tubes. We concluded that the A1 volumetric air sampler has the potential to be used for longer and more flexible sampling intervals in the wild, suitable for autonomous monitoring of eDNA pollen diversity.

Honey DNA metabarcoding revealed foraging resource partitioning between Korean native and introduced honey bees (Hymenoptera: Apidae)

Honey DNA metabarcoding provides information of floral sources of honey and foraging plant preferences of honey bees. We evaluated the floral composition of honey from two different species of honey bees, Apis cerana honey (ACH) and A. mellifera honey (AMH) in a mixed apiary located in a semi-forest environment to understand the floral preference and level of interspecific competition on floral resource. Three honey samples were collected from different hives of each species in mid-August. In total, 56 plant taxa were identified across the honey samples and among them, 38 taxonomic units were found in ACH compared with a total of 33 in AMH. The number of major plants (> 1% of reads) in honey samples was 9 and 11 in ACH and AMH respectively indicating the higher diversity of plant taxa in AMH. 23 taxonomic units were found exclusively in ACH, 18 taxonomic units were found only in AMH and 15 taxonomic units were shared between ACH and AMH indicating that 73% of the taxonomic units were present only in honey originated from one of the honeybee species. Qualitative and quantitative analyses of the shared major plants revealed the division of floral resource between these co-existing honey bee species pointing to a low level of interspecific competition between these two important pollinators.

The potential of metabarcoding plant components of Malaise trap samples to enhance knowledge of plant-insect interactions

The worldwide rapid declines in insect and plant abundance and diversity that have occurred in the past decades have gained public attention and demand for political actions to counteract these declines are growing. Rapid large-scale biomonitoring can aid in observing these changes and provide information for decisions for land management and species protection. Malaise traps have long been used for insect sampling and when insects are captured in these traps, they carry traces of plants they have visited on the body surface or as digested food material in the gut contents. Metabarcoding offers a promising method for identifying these plant traces, providing insight into the plants with which insects are directly interacting at a given time. To test the efficacy of DNA metabarcoding with these sample types, 79 samples from 21 sites across Germany were analysed with the ITS2 barcode. This study, to our knowledge, is the first examination of metabarcoding plant DNA traces from Malaise trap samples. Here, we report on the feasibility of sequencing these sample types, analysis of the resulting taxa, the usage of cultivated plants by insects near nature conservancy areas and the detection of rare and neophyte species. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. Metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen and is the only possible identification method for the other plant traces from Malaise traps and could provide a broad utility for future studies of plant-insect interactions.

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.

Using DNA Metabarcoding to Identify Floral Visitation by Pollinators

The identification of floral visitation by pollinators provides an opportunity to improve our understanding of the fine-scale ecological interactions between plants and pollinators, contributing to biodiversity conservation and promoting ecosystem health. In this review, we outline the various methods which can be used to identify floral visitation, including plant-focused and insect-focused methods. We reviewed the literature covering the ways in which DNA metabarcoding has been used to answer ecological questions relating to plant use by pollinators and discuss the findings of this research. We present detailed methodological considerations for each step of the metabarcoding workflow, from sampling through to amplification, and finally bioinformatic analysis. Detailed guidance is provided to researchers for utilisation of these techniques, emphasising the importance of standardisation of methods and improving the reliability of results. Future opportunities and directions of using molecular methods to analyse plant–pollinator interactions are then discussed.

Detecting Airborne Pollen Using an Automatic, Real-Time Monitoring System: Evidence from Two Sites

Airborne pollen monitoring has been an arduous task, making ecological applications and allergy management virtually disconnected from everyday practice. Over the last decade, intensive research has been conducted worldwide to automate this task and to obtain real-time measurements. The aim of this study was to evaluate such an automated biomonitoring system vs. the conventional ‘gold-standard’ Hirst-type technique, attempting to assess which may more accurately provide the genuine exposure to airborne pollen. Airborne pollen was monitored in Augsburg since 2015 with two different methods, a novel automatic Bio-Aerosol Analyser, and with the conventional 7-day recording Hirst-type volumetric trap, in two different sites. The reliability, performance, accuracy, and comparability of the BAA500 Pollen Monitor (PoMo) vs. the conventional device were investigated, by use of approximately 2.5 million particles sampled during the study period. The observations made by the automated PoMo showed an average accuracy of approximately 85%. However, it also exhibited reliability problems, with information gaps within the main pollen season of between 17 to 19 days. The PoMo automated algorithm had identification issues, mainly confusing the taxa of Populus, Salix and Tilia. Hirst-type measurements consistently exhibited lower pollen abundances (median of annual pollen integral: 2080), however, seasonal traits were more comparable, with the PoMo pollen season starting slightly later (median: 3 days), peaking later (median: 5 days) but also ending later (median: 14 days). Daily pollen concentrations reported by Hirst-type traps vs. PoMo were significantly, but not closely, correlated (r = 0.53–0.55), even after manual classification. Automatic pollen monitoring has already shown signs of efficiency and accuracy, despite its young age; here it is suggested that automatic pollen monitoring systems may be more effective in capturing a larger proportion of the airborne pollen diversity. Even though reliability issues still exist, we expect that this new generation of automated bioaerosol monitoring will eventually change the aerobiological era, as known for almost 70 years now.

Imaging Flow Cytometry as a Quick and Effective Identification Technique of Pollen Grains from Betulaceae, Oleaceae, Urticaceae and Asteraceae

Despite the continuous and intensive development of laboratory techniques, a light microscope is still the most common tool used in pollen grains differentiation. However, microscopy is time-consuming and needs well-educated and experienced researchers. Other currently used techniques can be categorised as images and non-images analysis, but each has certain limitations. We propose a new approach to differentiate pollen grains using the Imaging Flow Cytometry (IFC) technique. It allows for high-throughput fluorescence data recording, which, in contrast to the standard FC, also enables real-time control of the results thanks to the possibility of digital image recording of cells flowing through the measuring capillary. The developed method allows us to determine the characteristics of the pollen grains population based on the obtained fluorescence data, using various combinations of parameters available in the IDEAS software, which can be analysed on different fluorescence channels. On this basis, we distinguished pollen grains both between and within different genera belonging to the Betulaceae, Oleaceae, Urticaceae and Asteraceae families. Thereby, we prove that the proposed methodology is sufficient for accurate, fast, and cost-effective identification and potentially can be used in the routine analysis of allergenic pollen grains.

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.

Plant biodiversity assessment through pollen DNA metabarcoding in Natura 2000 habitats (Italian Alps)

Monitoring biodiversity is of increasing importance in natural ecosystems. Metabarcoding can be used as a powerful molecular tool to complement traditional biodiversity monitoring, as total environmental DNA can be analyzed from complex samples containing DNA of different origin. The aim of this research was to demonstrate the potential of pollen DNA metabarcoding using the chloroplast trnL partial gene sequencing to characterize plant biodiversity. Collecting airborne biological particles with gravimetric Tauber traps in four Natura 2000 habitats within the Natural Park of Paneveggio Pale di San Martino (Italian Alps), at three-time intervals in 1 year, metabarcoding identified 68 taxa belonging to 32 local plant families. Metabarcoding could identify with finer taxonomic resolution almost all non-rare families found by conventional light microscopy concurrently applied. However, compared to microscopy quantitative results, Poaceae, Betulaceae, and Oleaceae were found to contribute to a lesser extent to the plant biodiversity and Pinaceae were more represented. Temporal changes detected by metabarcoding matched the features of each pollen season, as defined by aerobiological studies running in parallel, and spatial heterogeneity was revealed between sites. Our results showcase that pollen metabarcoding is a promising approach in detecting plant species composition which could provide support to continuous monitoring required in Natura 2000 habitats for biodiversity conservation.

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.

Issues of under-representation in quantitative DNA metabarcoding weaken the inference about diet of the tundra vole Microtus oeconomus

During the last decade, methods based on high-throughput sequencing such as DNA metabarcoding have opened up for a range of new questions in animal dietary studies. One of the major advantages of dietary metabarcoding resides in the potential to infer a quantitative relationship between sequence read proportions and biomass of ingested food. However, this relationship's robustness is highly dependent on the system under study, calling for case-specific assessments. Herbivorous small rodents often play important roles in the ecosystem, and the use of DNA metabarcoding for analyses of rodent diets is increasing. However, there has been no direct validation of the quantitative reliability of DNA metabarcoding for small rodents. Therefore, we used an experimental approach to assess the relationship between input plant biomass and sequence reads proportions from DNA metabarcoding in the tundra vole Microtus oeconomus. We found a weakly positive relationship between the number of high-throughput DNA sequences and the expected biomass proportions of food plants. The weak relationship was possibly caused by a systematic under-amplification of one of the three plant taxa fed. Generally, our results add to the growing evidence that case-specific validation studies are required to reliably make use of sequence read abundance as a proxy of relative food proportions in the diet.

Geographic source estimation using airborne plant environmental DNA in dust

Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis of dust components, we developed a pipeline that utilizes the airborne plant environmental DNA (eDNA) in settled dust to estimate geographic origin. Metabarcoding of settled airborne eDNA was used to identify plant species whose geographic distributions were then derived from occurrence records in the USGS Biodiversity in Service of Our Nation (BISON) database. The distributions for all plant species identified in a sample were used to generate a probabilistic estimate of the sample source. With settled dust collected at four U.S. sites over a 15-month period, we demonstrated positive regional geolocation (within 600 km² of the collection point) with 47.6% (20 of 42) of the samples analyzed. Attribution accuracy and resolution was dependent on the number of plant species identified in a dust sample, which was greatly affected by the season of collection. In dust samples that yielded a minimum of 20 identified plant species, positive regional attribution was achieved with 66.7% (16 of 24 samples). For broader demonstration, citizen-collected dust samples collected from 31 diverse U.S. sites were analyzed, and trace plant eDNA provided relevant regional attribution information on provenance in 32.2% of samples. This showed that analysis of airborne plant eDNA in settled dust can provide an accurate estimate regional provenance within the U.S., and relevant forensic information, for a substantial fraction of samples analyzed.

Molecular assays of pollen use consistently reflect pollinator visitation patterns in a system of flowering plants

Determining how pollinators visit plants vs. how they carry and transfer pollen is an ongoing project in pollination ecology. The current tools for identifying the pollens that bees carry have different strengths and weaknesses when used for ecological inference. In this study we use three methods to better understand a system of congeneric, coflowering plants in the genus Clarkia and their bee pollinators: observations of plant-pollinator contact in the field, and two different molecular methods to estimate the relative abundance of each Clarkia pollen in samples collected from pollinators. We use these methods to investigate if observations of plant-pollinator contact in the field correspond to the pollen bees carry; if individual bees carry Clarkia pollens in predictable ways, based on previous knowledge of their foraging behaviors; and how the three approaches differ for understanding plant-pollinator interactions. We find that observations of plant-pollinator contact are generally predictive of the pollens that bees carry while foraging, and network topologies using the three different methods are statistically indistinguishable from each other. Results from molecular pollen analysis also show that while bees can carry multiple species of Clarkia at the same time, they often carry one species of pollen. Our work contributes to the growing body of literature aimed at resolving how pollinators use floral resources. We suggest our novel relative amplicon quantification method as another tool in the developing molecular ecology and pollination biology toolbox.

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.

Choosing an Effective PCR-Based Approach for Diet Analysis of Insect Herbivores: A Systematic Review

Identification of ingested plant species using polymerase chain reaction (PCR)-based methods is an increasingly useful yet challenging approach to accurately determine the diet composition of insect herbivores and thus their trophic interactions. A typical process of detection of DNA of ingested plants involves the choice of a DNA extraction method, a genomic target region, and/or the best approach for an accurate plant species identification. The wide range of available techniques makes the choice of the most appropriate method for an accurately and timely identification of ingested plants from insect guts difficult. In our study, we reviewed the commonly used PCR-based approaches in studies published from 1977 to 2019, to provide researchers with the information on the tools which have been shown to be effective for obtaining and identifying ingested plants. Our results showed that among five insect orders used in the retrieved studies Coleoptera and Hemiptera were prevalent (33 and 28% of all the records, respectively). In 79% of the studies a DNA barcoding approach was employed. In a substantial number of studies Qiagen DNA extraction kits and CTAB protocol were used (43 and 23%, respectively). Of all records, 65% used a single locus as a targeted plant DNA fragment; trnL, rbcL, and ITS regions were the most frequently used loci. Sequencing was the dominant type of among DNA verification approaches (70% of all records). This review provides important information on the availability of successfully used PCR-based approaches to identify ingested plant DNA in insect guts, and suggests potential directions for future studies on plant-insect trophic interactions.

Monitoring techniques for pollen allergy risk assessment

Understanding airborne pollen allergens trends is of great importance for the high prevalence and the socio-economic impact that pollen-related respiratory diseases have on a global scale. Pursuing this aim, aeropalynology evolved as a broad and complex field, that requires multidisciplinary knowledge covering the molecular identity of pollen allergens, the nature of allergen-bearing particles (pollen grains, pollen sub-particles, and small airborne particles), and the distribution of their sources. To estimate the health hazard that urban vegetation and atmospheric pollen concentrations pose to allergic subjects, it is pivotal to develop efficient and rapid monitoring systems and reliable allergic risk indices. Here, we review different pollen allergens monitoring approaches, classifying them into I) vegetation-based, II) pollen-based, and III) allergen-based, and underlining their advantages and limits. Finally, we discuss the outstanding issues and directions for future research that will further clarify our understanding of pollen aeroallergens dynamics and allergen avoidance strategies.

Natural contaminants in bee pollen: DNA metabarcoding as a tool to identify floral sources of pyrrolizidine alkaloids and fungal diversity

The use of bee pollen as a food supplement has increased in recent years as it contains several nutrients and phytochemicals. However, depending on floral composition, bee pollen can be contaminated by pyrrolizidine alkaloids (PAs), PA N-oxides (PANOs) and toxigenic fungi found in plants, which may pose a potential health risk for consumers. Thus, a DNA metabarcoding approach based on internal transcribed spacer 2 (ITS2) region was used to identify the plant sources of 17 PAs/PANOs detected by a validated method in liquid chromatography coupled to mass spectrometry (LC-MS/MS), as well as floral and fungal diversity in 61 bee pollen samples. According to LC-MS/MS analysis, 67% of the samples contained PAs/PANOs with mean concentration of 339 µg/kg. The contamination pattern was characterised by lycopsamine- and senecionine-type PAs/PANOs. PA/PANO-producing plants were identified in 54% of the PA/PANO-contaminated samples analysed by DNA metabarcoding, which also allowed identifying the overall floral and fungal composition of 56 samples. To evaluate the performance of the molecular approach, a subset of 25 samples was analysed by classical palynology. Palynological analysis partially confirmed the results of DNA metabarcoding, which had a better performance in distinguishing pollens of different genera from Asteraceae (76%) and Brassicaceae (88%). However, the molecular analysis did not identify pollens from Castanea, Eucalyptus, Hedera and Salix, which were abundant in 11 samples according to palynology. On the other hand, the molecular analysis allowed identifying several fungal genera in 33 samples, including the toxigenic fungi Alternaria and Aspergillus, which were positively correlated to the plant genus Hypericum. Despite limitations in identifying some pollen types, these preliminary results suggest that the DNA metabarcoding could be applied in a multidisciplinary approach to give a picture of floral and fungal diversity, which can be sources of natural contaminants in bee pollen and would help to control its safety.

Using RNA-seq to characterize pollen-stigma interactions for pollination studies

Insects are essential for the reproduction of pollinator-dependent crops and contribute to the pollination of 87% of wild plants and 75% of the world’s food crops. Understanding pollen flow dynamics between plants and pollinators is thus essential to manage and conserve wild plants and ensure yields are maximized in food crops. However, the determination of pollen transfer in the field is complex and laborious. We developed a field experiment in a pollinator-dependent crop and used high throughput RNA sequencing (RNA-seq) to quantify pollen flow by measuring changes in gene expression between pollination treatments across different apple (Malus domestica Borkh.) cultivars. We tested three potential molecular indicators of successful pollination and validated these results with field data by observing single and multiple visits by honey bees (Apis mellifera) to apple flowers and measured fruit set in a commercial apple orchard. The first indicator of successful outcrossing was revealed via differential gene expression in the cross-pollination treatments after 6 h. The second indicator of successful outcrossing was revealed by the expression of specific genes related to pollen tube formation and defense response at three different time intervals in the stigma and the style following cross-pollination (i.e. after 6, 24, and 48 h). Finally, genotyping variants specific to donor pollen could be detected in cross-pollination treatments, providing a third indicator of successful outcrossing. Field data indicated that one or five flower visits by honey bees were insufficient and at least 10 honey bee flower visits were required to achieve a 25% probability of fruit set under orchard conditions. By combining the genotyping data, the differential expression analysis, and the traditional fruit set field experiments, it was possible to evaluate the pollination effectiveness of honey bee visits under orchards conditions. This is the first time that pollen-stigma-style mRNA expression analysis has been conducted after a pollinator visit (honey bee) to a plant (in vivo apple flowers). This study provides evidence that mRNA sequencing can be used to address complex questions related to stigma–pollen interactions over time in pollination ecology.

Amplicon Sequencing of Variable 16S rRNA from Bacteria and ITS2 Regions from Fungi and Plants, Reveals Honeybee Susceptibility to Diseases Results from Their Forage Availability under Anthropogenic Landscapes

European Apis mellifera and Asian Apis cerana honeybees are essential crop pollinators. Microbiome studies can provide complex information on health and fitness of these insects in relation to environmental changes, and plant availability. Amplicon sequencing of variable regions of the 16S rRNA from bacteria and the internally transcribed spacer (ITS) regions from fungi and plants allow identification of the metabiome. These methods provide a tool for monitoring otherwise uncultured microbes isolated from the gut of the honeybees. They also help monitor the composition of the gut fungi and, intriguingly, pollen collected by the insect. Here, we present data from amplicon sequencing of the 16S rRNA from bacteria and ITS2 regions from fungi and plants derived from honeybees collected at various time points from anthropogenic landscapes such as urban areas in Poland, UK, Spain, Greece, and Thailand. We have analysed microbial content of honeybee intestine as well as fungi and pollens. Furthermore, isolated DNA was used as the template for screening pathogens: Nosema apis, N. ceranae, N. bombi, tracheal mite (Acarapis woodi), any organism in the parasitic order Trypanosomatida, including Crithidia spp. (i.e., Crithidia mellificae), neogregarines including Mattesia and Apicystis spp. (i.e., Apicistis bombi). We conclude that differences between samples were mainly influenced by the bacteria, plant pollen and fungi, respectively. Moreover, honeybees feeding on a sugar based diet were more prone to fungal pathogens (Nosema ceranae) and neogregarines. In most samples Nosema sp. and neogregarines parasitized the host bee at the same time. A higher load of fungi, and bacteria groups such as Firmicutes (Lactobacillus); γ-proteobacteria, Neisseriaceae, and other unidentified bacteria was observed for Nosema ceranae and neogregarine infected honeybees. Healthy honeybees had a higher load of plant pollen, and bacteria groups such as: Orbales, Gilliamella, Snodgrassella, and Enterobacteriaceae. Finally, the period when honeybees switch to the winter generation (longer-lived forager honeybees) is the most sensitive to diet perturbations, and hence pathogen attack, for the whole beekeeping season. It is possible that evolutionary adaptation of bees fails to benefit them in the modern anthropomorphised environment.

A pilot study of eDNA metabarcoding to estimate plant biodiversity by an alpine glacier core (Adamello glacier, North Italy)

Current biodiversity loss is a major concern and thus biodiversity assessment of modern ecosystems is compelling and needs to be contextualized on a longer timescale. High Throughput Sequencing (HTS) is progressively becoming a major source of data on biodiversity time series. In this multi proxy study, we tested, for the first time, the potential of HTS to estimate plant biodiversity archived in the surface layers of a temperate alpine glacier, amplifying the trnL barcode for vascular plants from eDNA of firn samples. A 573 cm long core was drilled by the Adamello glacier and cut into sections; produced samples were analyzed for physical properties, stable isotope ratio, and plant biodiversity by eDNA metabarcoding and conventional light microscopy analysis. Results highlighted the presence of pollen and plant remains within the distinct layers of snow, firn and ice. While stable isotope ratio showed a scarcely informative pattern, DNA metabarcoding described distinct plant species composition among the different samples, with a broad taxonomic representation of the biodiversity of the catchment area and a high-ranking resolution. New knowledge on climate and plant biodiversity changes of large catchment areas can be obtained by this novel approach, relevant for future estimates of climate change effects.

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

Worldwide pollinator declines lead to pollination deficits in crops and wild plants, and managed bees are frequently used to meet the increasing demand for pollination. However, their foraging can be affected by flower availability and colony size. We investigated how mass-flowering oilseed rape (OSR) can influence the pollen resource use of small and large honey bee (Apis mellifera L.) and bumble bee (Bombus terrestris L.) colonies. Colonies were placed adjacent to strawberry fields along a gradient of OSR availability in the landscapes. We used ITS2 metabarcoding to identify the pollen richness based on ITS2 amplicon sequencing and microscopy for quantification of target pollen. Bumble bees collected pollen from more different plant genera than honey bees. In both species, strawberry pollen collection decreased with high OSR availability but was facilitated by increasing strawberry flower cover. Colony size had no effect. The relationship between next-generation sequencing-generated ITS2 amplicon reads and microscopic pollen counts was positive but pollen type-specific. Bumble bees and, to a lesser degree, honey bees collected pollen from a wide variety of plants. Therefore, in order to support pollinators and associated pollination services, future conservation schemes should sustain and promote pollen plant richness in agricultural landscapes. Both bee species responded to the availability of flower resources in the landscape. Although honey bees collected slightly more strawberry pollen than bumble bees, both can be considered as crop pollinators. Metabarcoding could provide similar quantitative information to microscopy, taking into account the pollen types, but there remains high potential to improve the methodological weaknesses.