From DNA to diagnostics: A case study using macroinvertebrate metabarcoding to assess the effectiveness of restoration measures in a Dutch stream.

Stream ecosystems are under pressure due to multiple stressors. Restoration measures can halt further degradation and improve their ecological status. However, assessment of the effectiveness of the implemented measures is often insufficient because of logistic and financial constraints. DNA-metabarcoding has been proposed to scale up sample processing, although its application as a diagnostic tool has received less attention. The aim of our study was to evaluate if DNA-metabarcoding of stream macroinvertebrates can be used to compute a stressor-specific index to assess the effectiveness of a stream restoration project. For this purpose, we sampled the upstream, restored, and downstream section of a recently restored lowland stream in the Netherlands. At each site, we applied three different methods of macroinvertebrate identification: morphological identification of bulk samples (morphology), DNA-metabarcoding of the same bulk samples (DNA) and metabarcoding of eDNA extracted from the water (eDNA). First, we compared the community composition identified by each method. The communities identified by morphology and DNA were highly similar, whereas the communities generated by the eDNA differed. Second, we analysed whether the identification methods could be used to assess the effectiveness of the restoration project, focussing on a stressor-specific index for flow as the restoration measures aimed at improving flow conditions. Both the morphology and bulk DNA samples indicated improved flow conditions in the restored section of the stream (i.e., less stress from the reduction or absence of flow than in the unrestored sections). Contrary, the eDNA-water samples did not differentiate the amount of stress throughout the catchment, although applying recent developments in eDNA sampling could lead to more robust results. In conclusion, this study forms proof of concept that DNA from bulk samples can be utilized to assess the effectiveness of restoration measures, showing the added value of this approach for water managers.

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

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 trnL 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 trnL (27.6%). Furthermore, regressing relative read abundances against the relative abundances of microscopically obtained pollen concentrations showed a better correlation for nrITS2 (R2 = 0.821) than for trnL (R2 = 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 trnL 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 trnL. All results indicate that nrITS2 should be the preferred marker of choice for molecular airborne pollen monitoring.

Neural networks for increased accuracy of allergenic pollen monitoring.

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.

A Warm, Stratified, and Restricted Labrador Sea Across the Middle Eocene and Its Climatic Optimum.

Several studies indicate that North Atlantic Deep Water (NADW) formation might have initiated during the globally warm Eocene (56-34 Ma). However, constraints on Eocene surface ocean conditions in source regions presently conducive to deep water formation are sparse. Here we test whether ocean conditions of the middle Eocene Labrador Sea might have allowed for deep water formation by applying (organic) geochemical and palynological techniques, on sediments from Ocean Drilling Program (ODP) Site 647. We reconstruct a long-term sea surface temperature (SST) drop from ~30°C to ~27°C between 41.5 to 38.5 Ma, based on TEX86. Superimposed on this trend, we record ~2°C warming in SST associated with the Middle Eocene Climatic Optimum (MECO; ~40 Ma), which is the northernmost MECO record as yet, and another, likely regional, warming phase at ~41.1 Ma, associated with low-latitude planktic foraminifera and dinoflagellate cyst incursions. Dinoflagellate cyst assemblages together with planktonic foraminiferal stable oxygen isotope ratios overall indicate low surface water salinities and strong stratification. Benthic foraminifer stable carbon and oxygen isotope ratios differ from global deep ocean values by 1-2‰ and 2-4‰, respectively, indicating geographic basin isolation. Our multiproxy reconstructions depict a consistent picture of relatively warm and fresh but also highly variable surface ocean conditions in the middle Eocene Labrador Sea. These conditions were unlikely conducive to deep water formation. This implies either NADW did not yet form during the middle Eocene or it formed in a different source region and subsequently bypassed the southern Labrador Sea.