DNA metabarcoding using nrITS2 provides highly qualitative and quantitative results for airborne pollen monitoring

Abstract

Airborne pollen monitoring is of global socio-economic importance as it provides information on presence and prevalence of allergenic pollen in ambient air. Traditionally, this task has been performed by microscopic investigation, but novel techniques are being developed to automate this process. Among these, DNA metabarcoding has the highest potential of increasing the taxonomic resolution, but uncertainty exists about whether the results can be used to quantify pollen abundance. In this study, it is shown that DNA metabarcoding using trn L and nrITS2 provides highly improved taxonomic resolution for pollen from aerobiological samples from the Netherlands. A total of 168 species from 143 genera and 56 plant families were detected, while using a microscope only 23 genera and 22 plant families were identified. NrITS2 produced almost double the number of OTUs and a much higher percentage of identifications to species level (80.1%) than trn L (27.6%). Furthermore, regressing relative read abundances against the relative abundances of microscopically obtained pollen concentrations showed a better correlation for nrITS2 (R 2 = 0.821) than for trn L (R 2 = 0.620). Using three target taxa commonly encountered in early spring and fall in the Netherlands ( Alnus sp., Cupressaceae/Taxaceae and Urticaceae) the nrITS2 results showed that all three taxa were dominated by one or two species ( Alnus glutinosa/incana , Taxus baccata and Urtica dioica ). Highly allergenic as well as artificial hybrid species were found using nrITS2 that could not be identified using trn L or microscopic investigation ( Alnus × spaethii , Cupressus arizonica , Parietaria spp.). Furthermore, perMANOVA analysis indicated spatiotemporal patterns in airborne pollen trends that could be more clearly distinguished for all taxa using nrITS2 rather than trn L. All results indicate that nrITS2 should be the preferred marker of choice for molecular airborne pollen monitoring.

Environmental DNA Metabarcoding: A Novel Contrivance for Documenting Terrestrial Biodiversity

“…The lesser the eDNA in soil, the lesser its signal will be shared between two adjacent communities as the spatial distribution of soil organisms is intrinsically highly structured horizontally and vertically [ 43 ]. Horizontal observation has been observed with either traditional methods or DNA metabarcoding [ 44 , 45 ]. In the same way, there is a vertical discrepancy in the community composition of fungi, bacteria and meiofauna between organic and mineral horizons of soil [ 46 , 47 ].…”

Automatic detection of airborne pollen: an overview

“…Pollen networks need to be able not only to detect pollen from plants present in the area of interest, but also aeroallergens from long-range transport and new pollen types in an area, produced by shifting ranges, neophytes, or invasive species, all of which may only be present in small quantities. For example, a very early alder season was detected and attributed to newly planted Spaeth Alder (Alnus x spaethii; Gehrig et al, 2014;Polling et al, 2022), which flowers much earlier than native alder species, and further plantation of this species could be prevented. Another example is the spread and increase of ragweed (Ambrosia artemisiifolia; Taramarcaz et al, 2005;Buters et al, 2015), which was detected at an early stage in the mid-1990s in Switzerland, thus allowing effective control actions, an effort continued until now and effectively allowing the almost complete reduction of ragweed populations in the country.…”

Pollen DNA metabarcoding and related methods in global change ecology: prospects, challenges, and progress

“…The relationship between the presence of specific pollen types and human health responses can be investigated to identify the most harmful species (Rowney et al, 2021). Airborne pollen monitoring has been occurring for many years, and successful amplification of DNA from historical microscope slides shows the potential to monitor long-term ecological changes (Marcel Polling et al, 2022).…”