Multiproxy analysis of permafrost preserved faeces provides an unprecedented insight into the diets and habitats of extinct and extant megafauna

The study of faecal samples to reconstruct the diets and habitats of extinct megafauna has traditionally relied on pollen and macrofossil analysis. DNA metabarcoding has emerged as a valuable tool to complement and refine these proxies. While published studies have compared the results of these three proxies for sediments, this comparison is currently lacking for permafrost preserved mammal faeces. Moreover, most metabarcoding studies have focused on a single plant-specific DNA marker region. In this study, we target both the commonly used chloroplast trnL P6 loop as well as nuclear ribosomal ITS (nrITS). The latter can increase taxonomic resolution of plant identifications but requires DNA to be relatively well preserved because of the target length (∼300–500 bp). We compare DNA results to pollen and macrofossil analyses from permafrost and ice-preserved faeces of Pleistocene and Holocene megafauna. Samples include woolly mammoth, horse, steppe bison as well as Holocene and extant caribou. Most plant identifications were found using DNA, likely because the studied faeces contained many vegetative remains that could not be identified using macrofossils or pollen. Several taxa were, however, identified to lower taxonomic levels uniquely with macrofossil and pollen analysis. The nrITS marker provides species level taxonomic resolution for commonly encountered plant families that are hard to distinguish using the other proxies (e.g. Asteraceae, Cyperaceae and Poaceae). Integrating the results from all proxies, we are able to accurately reconstruct known diets and habitats of the extant caribou. Applying this approach to the extinct mammals, we find that the Holocene horse and steppe bison were not strict grazers but mixed feeders living in a marshy wetland environment. The mammoths showed highly varying diets from different non-analogous habitats. This confirms the presence of a mosaic of habitats in the Pleistocene ‘mammoth steppe’ that mammoths could fully exploit due to their flexibility in food choice

Analysis of automatic image classification methods for Urticaceae pollen classification

Pollen classification is considered an important task in palynology. In the Netherlands, two genera of the Urticaceae family, named Parietaria and Urtica, have high morphological similarities but induce allergy at a very different level. Therefore, distinction between these two genera is very important. Within this group, the pollen of Urtica membranacea is the only species that can be recognized easily under the microscope. For the research presented in this study, we built a dataset from 6472 pollen images and our aim was to find the best possible classifier on this dataset by analysing different classification methods, both machine learning and deep learning-based methods. For machine learning-based methods, we measured both texture and moment features based on images from the pollen grains. Varied feature selection techniques, classifiers as well as a hierarchical strategy were implemented for pollen classification. For deep learning-based methods, we compared the performance of six popular Convolutional Neural Networks: AlexNet, VGG16, VGG19, MobileNet V1, MobileNet V2 and ResNet50. Results show that compared with flat classification models, a hierarchical strategy yielded the highest accuracy with 94.5% among machine learning-based methods. Among deep learning-based methods, ResNet50 achieved an accuracy of 99.4%, slightly outperforming the other neural networks investigated. In addition, we investigated the influence on performance by changing the size of image datasets to 1000 and 500 images, respectively. Results demonstrated that on smaller datasets, ResNet50 still achieved the best classification performance. An ablation study was implemented to help understanding why the deep learning-based methods outperformed the other models investigated. Using Urticaceae pollen as an example, our research provides a strategy of selecting a classification model for pollen datasets with highly similar pollen grains to support palynologists and could potentially be applied to other image classification tasks

Automatic Pollen Species Image Identification

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 CO2 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 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

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

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

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