An integrative environmental pollen diversity assessment and its importance for the Sustainable Development Goals

Hornick, Thomas; Richter, Anett; Bastl, Maximilian; Bohlmann, Stephanie; Bonn, Aletta; Bumberger, Jan; Dietrich, P.; Gemeinholzer, Birgit; Grote, Rüdiger; Heinold, Bernd; Keller, Alexander; Luttkus, Marie; Mäder, Patrick; Švara, Elena Motivans; Passonneau, Sarah; Punyasena, Surangi W.; Rákosy, Demetra; Richter, Ronny; Sickel, Wiebke; Steffan‐Dewenter, Ingolf; Theodorou, Panagiotis; Treudler, Regina; Werchan, Barbora; Werchan, Matthias; Wolke, Ralf; Dunker, Susanne

doi:10.1002/ppp3.10234

Cited by 18 publications

(13 citation statements)

References 72 publications

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“…Slide imaging can be largely automated, and pollen detection and classification of each grain take seconds. The diverse range of fields that use palynology—from pollination researchers to forensic analysts to palaeoclimatologists—will benefit from automation (Hornick et al, 2022). Automation promises a future where data collection effort is minimized, and analysts focus their efforts on the interpretation of palynological results.…”

Section: Discussionmentioning

confidence: 99%

Automated identification of diverse Neotropical pollen samples using convolutional neural networks

et al. 2022

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1. Pollen is used to investigate a diverse range of ecological problems, from identifying plant-pollinator relationships to tracking flowering phenology. Pollen types are identified according to a set of distinctive morphological characters which are understood to capture taxonomic differences and phylogenetic relationships among taxa. However, categorizing morphological variation among hyperdiverse pollen samples represents a challenge even for an expert analyst.2. We present an automated workflow for pollen analysis, from the automated scanning of pollen sample slides to the automated detection and identification of pollen taxa using convolutional neural networks (CNNs). We analysed aerial pollen samples from lowland Panama and used a microscope slide scanner to capture three-dimensional representations of 150 sample slides. These pollen sample images were annotated by an expert using a virtual microscope.Metadata were digitally recorded for ~100 pollen grains per slide, including location, identification and the analyst's confidence of the given identification. We used these annotated images to train and test our detection and classification CNN models. Our approach is two-part. We first compared three methods for training CNN models to detect pollen grains on a palynological slide. We next investigated approaches to training CNN models for pollen identification.3. Because the diversity of pollen taxa in environmental and palaeontological samples follows a long-tailed distribution, we experimented with methods for addressing imbalanced representation using our most abundant 46 taxa. We found that properly weighting pollen taxa in our training objective functions yielded improved accuracy for individual taxa. Our average accuracy for the 46-way classification problem was 82.3%. We achieved 89.5% accuracy for our 25 most abundant taxa. 4. Pollen represents a challenging visual classification problem that can serve as a model for other areas of biology that rely on visual identification. Our results add to the body of research demonstrating the potential for a fully automated [Corrections added on 05 July, 2022, after first online publication: The affiliation of the author "Shu Kong" has been corrected.] | 2051 Methods in Ecology and Evoluঞon PUNYASENA Et Al.

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Section: Discussionmentioning

confidence: 99%

Automated identification of diverse Neotropical pollen samples using convolutional neural networks

et al. 2022

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“…An overview of recent developments of using flow cytometry in pollen analyses is provided by Kron et al, [125]. Automation of pollen analyses has been a research focus since the 1990s [126,127] mainly for its multiple facets in different research areas [128] for example as application in fossil pollen analyses (1.1.4) but also for plant-pollinator interactions (1.1.3/1.1.6) and aerial pollen monitoring cytometry and (on-chip) sorting have been already applied to collect fossil pollen grains from sediments for radiocarbon dating [66,130].…”

Section: Pollenmentioning

confidence: 99%

The potential of multispectral imaging flow cytometry for environmental monitoring

et al. 2022

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Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time‐intensive and not well‐suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant‐pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep‐learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA‐ or enzyme‐activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

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“…Airborne pollen is involved in many different aspects covering health, environment and climate (Hornick et al, 2022). For decades mature networks have collected airborne pollen in many countries, most often with the focus on improving life for hay fever sufferers (Ziello et al, 2012).…”

Section: Abstract Edna • Cyclones • Air • Microbiology • Biostatistic...mentioning

confidence: 99%

Summer pollen flora in rural and urban central England dominated by nettle, ryegrass and other pollen missed by the national aerobiological network

et al. 2022

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Abundance and diversity of airborne pollen are important to human health and biodiversity. The UK operational network collects airborne pollen from 8 flowering trees, grasses and three weeds using Hirst traps and microscopic identification from urban areas. Knowledge of total pollen diversity and differences between rural and urban zones is limited. We collect environmental DNA (eDNA) from air during summer and autumn over 3 years with mini cyclones from one urban and one rural site. Data are analysed using next generation sequencing and metabarcoding. We find the most common genus, Urtica (57%), is also identified by the national network. The grasses Lolium (10%), Agrostis (2%) and Holcus (1%) are in the national network grouped at family level, while Brassica (2%), Chenopodium (1%), Impatiens (2%), Plantago (4%) and Tilia (7%) are not part of the UK operational network. DNA from 138 genera was identified, where 2% of the sample could not be associated with specific genera. 40% of the sample was classified better using eDNA methods at the genus level, than by optical methods. We calculate Bray–Curtis dissimilarity for the rural and urban zones and find a systematic difference in biodiversity. Overall, this shows airborne DNA reveals more information than methods based on morphological differences. The results also suggest data from sites located in large urban areas will be less representative for less populated rural areas. This presents a dilemma in balancing a network and the associated costs delivering health relevant information to the most populated areas vs. a nation-wide approach.

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An integrative environmental pollen diversity assessment and its importance for the Sustainable Development Goals

Cited by 18 publications

References 72 publications

Automated identification of diverse Neotropical pollen samples using convolutional neural networks

Automated identification of diverse Neotropical pollen samples using convolutional neural networks

The potential of multispectral imaging flow cytometry for environmental monitoring

Summer pollen flora in rural and urban central England dominated by nettle, ryegrass and other pollen missed by the national aerobiological network

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