Pollen Analysis Could Be Revolutionised with DNA Techniques

Published: 20th October 2018

This article was originally published in the Bulletin of the Chartered Institute of Ecology and Environmental Management (CIEEM), inpractice, Issue 99, Genetic Techniques and Technologies, in March 2018.

Identifying the botanical composition of pollen (palynology) has important implications for environmental management, public health, and even forensics. DNA-based pollen analysis offers significant advantages in terms of cost, scale, and taxonomic resolution over the more conventional morphological methods. DNA-based technologies are now beginning to be applied to palynology and early results are very promising. These approaches could revolutionise the field to facilitate pollination research on an unprecedented scale and provide a deep understanding of plant-pollinator networks. 

Introduction

Identification of pollen origin (plant palynology) is a key aspect of pollination ecology and agro-ecological studies. It has been used to link suspects with crime scenes, investigate bee foraging behaviour, provide an early warning system for hayfever sufferers, and even to determine the floral composition of honey. Here we discuss the use of DNA techniques for detecting plant species in pollen from an environmental perspective, outlining current capabilities and limitations. 

What is possible and why is it important?  

A pollen sample is usually made up of a variety of different botanical sources, which have traditionally been characterised using microscopy. This is labour intensive and heavily reliant on a very specialised group of taxonomic experts (palynology botanists) and an extensive set of reference materials. The identification process is slow, and therefore only small samples tend to be analysed, with low abundance species often undetected. Moreover, the taxonomic resolution to which most plants can be differentiated by morphometrics is relatively broad and subject to observer biases. Notwithstanding the skill of palynology botanists, current methods of species determination stand to benefit from a DNA based approach, which has the potential to be more scalable and cost effective, and to provide more highly resolved botanical data. Indeed, a growing catalogue of literature has shown that pollen DNA metabarcoding provides typically higher taxonomic resolution (e.g. Kraaijeveld et al. 2015), higher detection rates at all taxonomic levels (e.g. Smart et al. 2017), and higher accuracy (e.g. Vamosi et al. 2016) than does morphological palynology.  

Plant-pollinator interactions  

Perhaps the most exciting application of DNA-based pollen identification is its use in characterising plant-pollinator foraging networks. These networks are important for establishing how pollinators use floral resources, and in particular whether they are pollinating important plants such as flowering crops. Direct field observations have historically been used to study networks, but these are often limited in scale, with sampling restricted to a very small number of communities, species, or individuals (Vamosi et al. 2016) because of the prohibitively high amount of effort required. 

Instead of directly observing plantpollinator interactions, indirect connections can be established by pollen analysis (Figure 2). The use of pollen data, whether identified morphologically (e.g. Bosch et al. 2009) or molecularly (e.g. Bell et al. 2017, Pornon et al. 2016, 2017), reveals more associations than do field observations because significant observer biases are avoided. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and therefore it is feasible to study these networks at the large scales required for macro-ecological research. DNA-based palynology has already helped to reveal previously unknown plant-pollinator interactions (Pornon et al. 2017). It has also provided further insights, including the fact that honeybees only use a small proportion of the plant species that they visit (de Vere et al. 2017); that the quality and diversity of honeybeecollected pollen is influenced by season rather than landscape structure (Danner et al. 2017); and that Diptera are key pollinators that should not be neglected (Galliot et al. 2017). 

The majority of research on pollen networks to date has focused on honeybees, principally due to the availability of honey and pollen for analysis. However, our own work at the Natural History Museum focuses on hoverflies (Diptera: Syrphidae). These flies are important flower visitors, and we have been exploring their feeding preferences by metabarcoding pollen sampled from their bodies and intestines. Early results appear to support the hypothesis that many hoverfly species require flat, open flowers and inflorescences such as those provided by daisies and wild carrot. Most hoverfly species have short tongues and so cannot easily access the complicated or tubular flowers that many bumblebees visit. Through our research, we hope to understand how different species of hoverfly use floral resources throughout the year in rural Britain. 

Current limitations to DNA-based palynology  

Whilst DNA analysis holds many advantages over current morphology-based methods, it is still a developing method that could benefit from further research and development, and remains subject to some limitations. First, although molecular skills are increasingly widespread and more accessible than expertise in morphological palynology, currently there is still a reliance on highly-trained individuals and specialist laboratories to conduct the analysis. Work is already underway to develop technologies that make molecular work simpler to conduct, less prone to human error and more portable, but for the moment these are some way from being operational. 

There are also barriers to accurately assessing the quantitative component of pollen species diversity, and a propensity to miss low abundance species. The latter is not unique to DNA methods, but abundance metrics are often considered an important aspect of monitoring, so improving the quantitative capabilities of the approach is a key area for research if the potential of the DNA-based approach is to be maximised.

Conclusion  

DNA-based palynology is proving to be a fruitful avenue of research that provides more reliable, sensitive and resolved identifications than conventional morphological methods. Moreover, DNA metabarcoding is not prohibitively slow or costly and so more extensive tests can be performed in a timely, standardised way. While there are limitations to this newly emerging technology, these do not outweigh the high promise afforded by DNA-based palynology. Palynology is an important field with a range of practical applications, including pollinator plant networks, forensic analysis and allergen monitoring, and DNA methods will transform research, monitoring and management in these areas, enabling fast, detailed and reliable decision making. 

References

Bell, K.L., Fowler, J., Burgess, K.S., Dobbs, E.K., Gruenewald, D., Lawley, B., Morozumi, C. and Brosi, B.J. (2017). Applying pollen DNA metabarcoding to the study of plant–pollinator interactions. Applications in Plant Sciences, 5(6): 1600124 

Bosch, J., González, A.M.M., Rodrigo, A. and Navarro, D. (2009). Plant–pollinator networks: adding the pollinator’s perspective. Ecology Letters, 12(5): 409-419. 

Danner, N., Keller, A., Härtel, S. and SteffanDewenter, I. (2017). Honey bee foraging ecology: Season but not landscape diversity shapes the amount and diversity of collected pollen. PLoS ONE, 12(8): e0183716. 

de Vere, N., Jones, L.E., Gilmore, T., Moscrop, J., Lowe, A., Smith, D., Hegarty, M.J., Creer, S. and Ford., C.R. (2017). Using DNA metabarcoding to investigate honey bee foraging reveals limited flower use despite high floral availability. Scientific Reports, 7: 42838. 

Galliot, J.N., Brunel, D., Bérard, A., Chauveau, A., Blanchetête, A., Lanore, L. and Farruggia, A. (2017). Investigating a flower-insect forager network in a mountain grassland community using pollen DNA barcoding. Journal of Insect Conservation, 21(5-6): 827-837. 

Kraaijeveld, K., de Weger, L.A., Ventayol García, M.V., Buermans, H., Frank, J., Hiemstra, P.S. and Dunnen, J.T. (2015). Efficient and sensitive identification and quantification of airborne pollen using next-generation DNA sequencing. Molecular Ecology Resources, 15(1): 8-16. 

Pornon, A., Escaravage, N., Burrus, M., Holota, H., Khimoun, A., Mariette, J., Pellizzari, C., Iribar, A., Etienne, R., Taberlet, P., Vidal, M., Winterton, P., Zinger, L. and Andalo, C. (2016). Using metabarcoding to reveal and quantify plant-pollinator interactions. Scientific Reports, 6: 27282. 

Pornon, A., Andalo, C., Burrus, M. and Escaravage, N. (2017). DNA metabarcoding data unveils invisible pollination networks. Scientific Reports, 7: 16828.  

Smart, M.D., Cornman, R.S., Iwanowicz, D.D., McDermott-Kubeczko, M., Pettis, J.S., Spivak, M.S. and Otto, C.R.V. (2017). A comparison of honey bee-collected pollen from working agricultural lands using light microscopy and ITS metabarcoding. Environmental Entomology, 46(1): 38-49. 

Vamosi, J.C., Gong, Y.B., Adamowicz, S.J. and Packer, L. (2016). Forecasting pollination declines through DNA barcoding: the potential contributions of macroecological and macroevolutionary scales of inquiry. New Phytologist, 214(1): 11-18. 

About the Authors
CT - NatureMetrics

CT PhD is a senior scientist at NatureMetrics and specialises in DNAbased approaches to characterising biodiversity. He’s developed molecular techniques to identify fish communities from water, plants from soil, arthropods from traps, and even bat, bird, and otter diets from their faeces 

Contact CT at: ct@naturemetrics.co.uk 

Hannah Norman is a PhD student based at the Natural History Museum, funded by the NERC Science Solutions and a Changing Planet DTP at Imperial College London. Her research is focused on pollinators, where she has used DNA to identify insects and to characterise pollen diets of hoverflies. Contact  

Hannah at: hannah.norman14@imperial.ac.uk

Authors: Dr Cuong Q. Tang and Dr Hannah Norman
By Published On: 20th October 2018Categories: Uncategorised

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