Principles and methods for automated palynology

Holt, Katherine; Bennett, K. D.

doi:10.1111/nph.12848

Cited by 80 publications

(75 citation statements)

References 37 publications

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“…To date, similar success rates have been obtained by different automatic identification methods also based on image analysis, most of them exceeding 80% [39,40,41]. However, these automatic identification methods need the presence of digital images and have not been fully automated to date.…”

Section: Discussionmentioning

confidence: 69%

Automatic and Online Pollen Monitoring

Oteros¹,

Pusch²,

Weichenmeier³

et al. 2015

Int Arch Allergy Immunol

137

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Background: Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500. Methods: The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m³/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously. Results: Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98). Conclusions: The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting.

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Section: Discussionmentioning

confidence: 69%

Automatic and Online Pollen Monitoring

Oteros¹,

Pusch²,

Weichenmeier³

et al. 2015

Int Arch Allergy Immunol

137

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show abstract

“…However, there are many efforts to create a semiautomatic and automatic systems for counting airborne pollen (Boucher et al. 2002; Holt and Bennett 2014). As a result of these studies, it should be possible to obtain information about the pollen data from the day before quickly enough to be used by a forecast system.…”

Section: Discussionmentioning

confidence: 99%

Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count

et al. 2015

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The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

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“…Traditional techniques of pollen identification rely on a high level of expertise and timeconsuming examination of morphological characters on the pollen exine, typically with low taxonomic resolution (Rahl 2008). Although some of these issues could potentially be overcome with automated analysis of digital micrographs of pollen (Holt and Bennett 2014), the lack of morphological characters for species-level taxonomic resolution in many plant groups will remain a limitation (Rahl 2008;Salmaki et al 2008;Khansari et al 2012). These issues could be resolved using new DNA barcoding technologies, making palynological information available to a broader range of studies.…”

Section: Background and Potential Of Pollen Dna Barcodingmentioning

confidence: 99%

Pollen DNA barcoding: current applications and future prospects

Bell

Vere

Keller

et al. 2016

Genome

201

170

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Identification of the species origin of pollen has many applications, including assessment of plant-pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and has few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.Key words: DNA metabarcoding, metagenomics, pollen, palynology, high-throughput sequencing, next-generation sequencing.Résumé : L'identification de l'espèce à l'origine d'un pollen se prête à de nombreuses applications dont la description des réseaux plante-pollinisateur, la reconstruction de communautés de plantes anciennes, l'authentification de produits, la surveillance des allergènes et les enquêtes médicolégales. Cependant, ces applications ont précédemment été limitées à l'identification du pollen par examen microscopique, un processus lent, à faible résolution taxonomique et qui compte peu de praticiens experts. Une alternative est l'identification du pollen par le recours aux codes à barres de l'ADN, une avenue qui permettrait de surmonter plusieurs de ces limitations. De récentes études ont montré qu'il était possible d'amplifier les marqueurs de codage tant chloroplastiques que nucléaires à partir du pollen. Ces récentes validations du métacodage à barres chez le pollen indiquent qu'il est maintenant opportun pour les chercheurs dans divers domaines de considérer l'emploi de ces méthodes dans leurs programmes de recherche. Dans cet article, les auteurs passent en revue le domaine naissant du codage à barres du pollen et discutent des nouvelles applications potentielles de cette technologie en mettant en lumière les limitations existantes ainsi que de futurs développements qui pourraient accroître son utilité dans un grand nombre d'applications. [Traduit par la Rédaction] Mots-clés : métacodage à barres, métagénomique, pollen, palynologie, séquençage à haut débit, séquençage de nouvelle génération.

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Principles and methods for automated palynology

Cited by 80 publications

References 37 publications

Automatic and Online Pollen Monitoring

Automatic and Online Pollen Monitoring

Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count

Pollen DNA barcoding: current applications and future prospects

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