Abstract
Pollen identification and quantification are essential in ecological and evolutionary research to address plant-pollinator relationships, pollination services, and plant reproduction. Research into pollen transfer patterns has been mainly based on traditional light microscopy, however, it is time consuming, labour intensive, and requires taxonomic expertise. High-throughput methods allow automated pollen identification across large temporal and spatial scales. This study compares the accuracy of species identification and quantification of their relative pollen abundance using traditional microscopy, and two high-throughput methods, namely multispectral imaging flow cytometry (MIFC) and metabarcoding. Method performance was tested using artificial samples with known pollen species composition and pollen samples from pollinators with unknown pollen composition. After checking the agreement against the line of identity, the coefficient of determination (R2) values of linear models between the method estimates were compared. Metabarcoding performed best at identifying the taxa from artificial mixtures, while the two other methods assessed the relative abundance most accurately when there was information about species identity. Comparability between methods was overall low when assessing pollen composition on pollinators. To acquire both pollen identity and relative abundance, we recommend a metabarcoding-guided MIFC analysis that can be used as a high throughput approach for pollen research at large spatial and temporal scales.
Data availability
The datasets used in this study are accessible at https://doi.org/10.5281/zenodo.16787204.
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Acknowledgements
Our field assistants, Ana Bogdan, Milena Filip, and Cirstea Astarte Maria, assisted with sampling of the insect pollen samples. Irina Goia provided botanical expertise in the field. Sarah Herbst identified pollen samples using light microscopy. Konstantin Albrecht helped with performing MIFC measurements and image annotations. Open access funding was enabled and organised by ProjektDEAL.
Funding
Open Access funding enabled and organized by Projekt DEAL. Funding for this research was provided by the Helmholtz Association and the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig (DFG Research Centre FZT 118, iCyt– Support Unit 346001057-01). Additional funding was provided by iDiv-Flexpool funding via the project ‘PolDiv’ (34600830-13) and the Federal Ministry of Food and Agriculture on the basis of a resolution of the German Bundestag within the framework of the Federal Program for Organic Farming and Other Forms of Sustainable Agriculture via the project ‘NutriBee’ (2819NA102).
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E.M.Š., D.R., T.M.K., T.H., A.K. and S.D. planned the study design. T.H., S.D, and F.W. carried out the MIFC measurements and deep learning. A.K. carried out the bioinformatics for metabarcoding. All co-authors contributed to the data analysis. E.M.Š. curated the data and scripts. E.M.Š. led the manuscript writing, and all co-authors were involved in editing. T.M.K., D.R., and S.D. supervised the study.
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Motivans Švara, E., Rakosy, D., Knight, T.M. et al. Method comparison of microscopy, metabarcoding, and multispectral imaging flow cytometry for identification and relative abundance analysis of insect-dispersed pollen.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-47800-3
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DOI: https://doi.org/10.1038/s41598-026-47800-3
Keywords
- Pollen identification
- Relative abundance
- High-throughput
- Artificial mixtures
- Zoophilous pollen
Source: Ecology - nature.com
