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

Towards a toolkit for global insect biodiversity monitoring

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Predicting plant-pollinator interactions: concepts, methods, and challenges

Plant-pollinator interactions are ecologically and economically important, and, as a result, their prediction is a crucial theoretical and applied goal for ecologists. Although various analytical methods are available, we still have a limited ability to predict plant-pollinator interactions. The predictive ability of different plant-pollinator interaction models depends on the specific definitions used to conceptualize and quantify species attributes (e.g., morphological traits), sampling effects (e.g., detection probabilities), and data resolution and availability. Progress in the study of plant-pollinator interactions requires conceptual and methodological advances concerning the mechanisms and species attributes governing interactions as well as improved modeling approaches to predict interactions. Current methods to predict plant-pollinator interactions present ample opportunities for improvement and spark new horizons for basic and applied research.

Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding

One of the most critical steps for accurate taxonomic identification in DNA (meta)-barcoding is to have an accurate DNA reference sequence dataset for the marker of choice. Therefore, developing such a dataset has been a long-term ambition, especially in the Viridiplantae kingdom. Typically, reference datasets are constructed with sequences downloaded from general public databases, which can carry taxonomic and other relevant errors. Herein, we constructed a curated (i) global dataset, (ii) European crop dataset, and (iii) 27 datasets for the EU countries for the ITS2 barcoding marker of vascular plants. To that end, we first developed a pipeline script that entails (i) an automated curation stage comprising five filters, (ii) manual taxonomic correction for misclassified taxa, and (iii) manual addition of newly sequenced species. The pipeline allows easy updating of the curated datasets. With this approach, 13% of the sequences, corresponding to 7% of species originally imported from GenBank, were discarded. Further, 259 sequences were manually added to the curated global dataset, which now comprises 307,977 sequences of 111,382 plant species.

Using individual-based trait frequency distributions to forecast plant-pollinator network responses to environmental change

Determining how and why organisms interact is fundamental to understanding ecosystem responses to future environmental change. To assess the impact on plant-pollinator interactions, recent studies have examined how the effects of environmental change on individual interactions accumulate to generate species-level responses. Here, we review recent developments in using plant-pollinator networks of interacting individuals along with their functional traits, where individuals are nested within species nodes. We highlight how these individual-level, trait-based networks connect intraspecific trait variation (as frequency distributions of multiple traits) with dynamic responses within plant-pollinator communities. This approach can better explain interaction plasticity, and changes to interaction probabilities and network structure over spatiotemporal or other environmental gradients. We argue that only through appreciating such trait-based interaction plasticity can we accurately forecast the potential vulnerability of interactions to future environmental change. We follow this with general guidance on how future studies can collect and analyse high-resolution interaction and trait data, with the hope of improving predictions of future plant-pollinator network responses for targeted and effective conservation.

Mega-Barcoding Projects: Delivering National DNA Barcoding Initiatives for Plants

Plant DNA barcoding has a multitude of applications ranging from species detection and biomonitoring to investigating ecological networks and checking food quality. The ability to accurately identify species, using DNA barcoding, depends on the quality and comprehensiveness of the reference library that is used. This chapter describes how to create plant reference libraries using the rbcL, matK, and ITS2 DNA barcode regions. It covers the creation of species lists, the collection of specimens from the field and herbarium, DNA extraction, PCR amplification, and DNA sequencing. This methodology gives special attention to using samples from herbaria, as they represent important collections of easily accessible, taxonomically verified plant material.

Temporal change in floral availability leads to periods of resource limitation and affects diet specificity in a generalist pollinator

Generalist species are core components of ecological networks and crucial for the maintenance of biodiversity. Generalist species and networks are expected to be more resilient, and therefore understanding the dynamics of specialization and generalization in ecological networks is a key focus in a time of rapid global change. Whilst diet generalization is frequently studied, our understanding of how it changes over time is limited. Here we explore temporal variation in diet specificity in the honeybee (Apis mellifera), using pollen DNA metabarcoding of honey samples, through the foraging season, over two years. We find that, overall, honeybees are generalists that visit a wide range of plants, but there is temporal variation in the degree of specialization. Temporal specialization of honeybee colonies corresponds to periods of resource limitation, identified as a lack of honey stores. Honeybees experience a lack of preferred resources in June when switching from flowering trees in spring to shrubs and herbs in summer. Investigating temporal patterns in specialization can identify periods of resource limitation that may lead to species and network vulnerability. Diet specificity must therefore be explored at different temporal scales in order to fully understand species and network stability in the face of ecological change.

Unraveling the potential of environmental DNA for deciphering recent advances in plant-animal interactions: a systematic review

MAIN CONCLUSION

Environmental DNA-based monitoring provides critical insights for enhancing our understanding of plant-animal interactions in the context of worldwide biodiversity decrease for developing a global framework for effective plant biodiversity conservation. To understand the ecology and evolutionary patterns of plant-animal interactions (PAI) and their pivotal roles in ecosystem functioning advances in molecular ecology tools such as Environmental DNA (eDNA) provide unprecedented research avenues. These methods being non-destructive in comparison to traditional biodiversity monitoring methods, enhance the discernment of ecosystem health, integrity, and complex interactions. This review intends to offer a systematic and critical appraisal of the prospective of eDNA for investigating PAI. The review thoroughly discusses and analyzes the recent reports (2015-2022) employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) to outline the recent progression in eDNA approaches for elucidating PAI. The current review envisages that eDNA has a significant potential to monitor both plants and associated cohort of prospective pollinators (avian species and flowering plants, bees and plants, arthropods and plants, bats and plants, etc.). Furthermore, a brief description of the factors that influence the utility and interpretation of PAI eDNA is also presented. The review establishes that factors such as biotic and abiotic, primer selection and taxonomic resolution, and indeterminate spatio-temporal scales impact the availability and longevity of eDNA. The study also identified the limitations that influence PAI detection and suggested possible solutions for better execution of these molecular approaches. Overcoming these research caveats will augment the assortment of PAI analysis through eDNA that could be vital for ecosystem health and integrity. This review forms a critical guide and offers prominent insights for ecologists, environmental managers and researchers to assess and evaluate plant-animal interaction through environmental DNA.

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.

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.

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.

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.