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Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera ( Urtica and Parietaria ) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

Ongoing pressures on global biodiversity require conservation action that is not possible without effective biomonitoring. Terrestrial vertebrate surveys are commonly performed using camera traps, a time‐intensive method known to miss many small or arboreal species and birds. Recent advances have shown airborne eDNA to be a potentially suitable technique to more effectively monitor vertebrate communities in a time‐ and cost‐effective manner. Here, we test whether commercially available air samplers that collect air particles 24/7 during a 1‐week period can be used to detect the presence of vertebrates through airborne eDNA. The results are compared to camera trap records at three locations with differing habitats in the Netherlands. Simultaneous sampling with three different air samplers for 3 weeks resulted in detection of 154 vertebrate taxa, of which the majority were birds or mammals (113 and 33 species, respectively), along with four fish and four amphibian species. All species observed using camera traps were also retrieved via airborne eDNA, although not on every day of sampling. The Burkard spore trap, used routinely for pollen monitoring, showed the highest number of vertebrate species, and only in three samples when a mammal species was detected using a camera trap it remained undetected via eDNA. We also detected unique species at the three locations using airborne eDNA, indicative of the habitat in which they were living. However, we also detected species that we could not account for. The multitude of species found using airborne eDNA compared to camera traps indicate the sensitivity of the method; however, subsequent studies should prioritize validation of these findings through alternative biomonitoring approaches. In this manuscript, we report on our study using airborne eDNA to monitor terrestrial vertebrate biodiversity in nature and compare the results with detections made using camera traps. Airborne eDNA detected all species identified using camera traps and many additional species. This indicates the sensitivity of the method, however, subsequent studies should prioritize validation of these findings through alternative biomonitoring approaches in order to gain better understanding of dispersal and fate of airborne eDNA.

Recent data shows increasing numbers of hay fever patients, with approximately 10-30% of the population affected worldwide (Pawankar et al. 2011). This increase is most likely caused by prolonged and intensified pollen seasons which in turn have been linked to increased CO 2 concentrations (Ziska et al. 2003, D'Amato et al. 2007, Albertine et al. 2014). Apart from this, especially in cities, the so-called ‘heat island effect’ enables exotic plant species to establish themselves there. In the Netherlands alone, six new species settle in cities on a yearly basis and some of these are severely allergenic (Denters 2004). Pollen concentrations in the air are currently monitored using pollen samplers that collect pollen on sticky traps. These are checked manually under the microscope, a process that requires highly trained specialists. Moreover, microscopic pollen identification rarely allows discrimination of pollen types at species or even genus level even though the allergenicity may be very different. While there has been progress in automating the microscope using machine learning, automatic microscopes have not been able to systematically identify pollen to the species level. We designed an automated approach identify a predefined set of pollen on microscopic pollen samples. We use 2D light microscope images and a confocal fluorescence microscope for 3D images to create a reference dataset of highly similar pollen species to train automated image recognition software, and compare the results. The most accurate method will be used to apply to a pollen sample time series (1970-present) to find trends in allergenic pollen species over time. Here I present the first results of this research and the challenges to overcome.

DNA metabarcoding has become a cost-effective method to assess species composition of mixed samples. Developments such as advances in sequencing technology and increased species coverage of reference databases can be leveraged to gain more insights from metabarcoding experiments, given suitable tools. To this end, we introduce PIMENTA, a new pipeline that streamlines the analysis of Nanopore DNA metabarcoding sequencing data. PIMENTA consists of four phases: pre-processing, clustering per sample, reclustering of all samples, and taxonomic identification. PIMENTA expands a workflow created by Voorhuijzen-Harink et al. Multiple updates have been made, including parallelization of the analysis of multiple samples with the use of high-performance computing (HPC), implementation of a local taxonomy database, and expansion of the taxonomic results summary. Settings have been optimized to process higher quality nanopore reads, for an increased accuracy of taxonomic identification. We evaluated the pipeline with mock samples of zooplankton species, incorporating COI, 18SV4, and 18SV9 marker sequences. The performance and runtime have been benchmarked against two other existing pipelines. PIMENTA was able to quickly identify species with a high resolution and minimal misidentifications.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/WFSRDataScience/PIMENTA

Infantile esotropia, a common form of strabismus, is treated either by bilateral recession (BR) or unilateral recession-resection (RR). Differences in degree of alignment achieved by these two procedures have never been examined in a randomised controlled trial.