Global change threatens biodiversity across ecosystems worldwide, yet soil biodiversity remains comparatively poorly understood due to the challenges of quantifying belowground life. As a result, its main drivers and spatial patterns remain unresolved across environmental gradients. Here, we analyzed soil biodiversity across 102 sites in the north-eastern Iberian Peninsula, spanning ecosystems from arid to boreal (alpine) biomes. Using multitaxon DNA metabarcoding, we assessed richness and Shannon diversity of bacteria, fungi, nematodes, and microarthropods and related these patterns to climatic variables, soil physicochemical properties, habitat and vegetation heterogeneity, and anthropogenic context. Diversity patterns showed coordinated variation among groups. Microbial diversity was strongly associated with soil pH-dependent nutrient relationships: soluble manganese (Mn) emerged as a key predictor of microbial diversity, showing positive effects under alkaline conditions, while calcium (Ca) covaried with pH and aluminium (Al) in ways consistent with constraints on bacterial communities, i.e. Ca alleviating Al toxicity in acidic soils, favouring bacterial diversity. Fungal richness also showed nutrient-related signals, negative with higher total nitrogen or nitrates with low pH, but positive with higher available phosphorus in alkaline conditions. In contrast, soil-fauna diversity was primarily linked to habitat structure and heterogeneity: richness increased with structural vegetation heterogeneity, amplified under high shrub cover for nematodes and varied with annual temperature and tree cover for microarthropods. Overall, combining soluble soil chemistry with reproducible habitat-heterogeneity indices in a compact regional design reveals how driver sets differ across trophic groups and improves the mechanistic interpretation of multi-taxon soil biodiversity patterns.

Monitoring biodiversity patterns and their changes in Arctic coastal ecosystems is critical under ongoing climate change. However, common current approaches require high effort and expertise and this in turn limits the spatial and temporal scale of these monitoring efforts. Here, we investigated both the fish and the marine invertebrate communities across Svalbard using a multi-marker environmental DNA metabarcoding approach. We collected and analysed marine water, sediment and zooplankton filtered from marine water from sites influenced by the warm West Spitsbergen Current and the cold East Spitsbergen Current. Following metabarcoding amplification using mitochondrial COI, 12S, 16S and nuclear 18S markers and high-throughput sequencing, we retrieved an extensive overview of Svalbard marine biodiversity. Water, sediment and especially zooplankton samples collected across Svalbard revealed spatial differences in community composition, with significantly distinct assemblages in the northwest and southeast of Svalbard. We identified potential bioindicator species for use in rapid assessment of impacts of marine temperature increase and confirmed observed patterns of ongoing shifts in community structure as a response to changes in dominant water masses. Overall, our findings show that species composition depending on fine-scale climate variation of Arctic waters can be effectively studied and monitored using environmental DNA. These insights can help us understand current and evolving climate-driven changes.

Ongoing climate change is increasing global temperatures, with cities experiencing amplified warming due to the urban heat island effect. Urban trees, forbs, and grasses are widely planted to mitigate heating. However, many plant species produce allergenic pollen, causing hay fever symptoms that can discourage people from using green spaces. This avoidance may increase heat exposure and reduce the physical and mental benefits of urban greenery. Despite this, optimal urban green design that minimizes allergy risks remains unclear, partly due to limited knowledge of local airborne pollen dispersion in urban heat islands. To address this gap, we investigated whether (1) local vegetation influences urban pollen concentrations and composition, (2) urban trees, forbs, or grasses contribute most to allergenic pollen loads, and (3) public or private urban spaces are the main source of airborne allergenic pollen. We conducted spatial and temporal street-level pollen monitoring at 10 sites across two neighborhoods in Leiden, the Netherlands, over 12 months. Samples were analyzed using DNA metabarcoding and microscopy. We identified 366 plant species across 105 families. Results show that public green spaces are the primary source of allergenic pollen in cities. Grasses dominated pollen loads in summer, whereas trees contributed most in winter, spring, and autumn. Notably, allergenic tree pollen was detected well beyond peak flowering periods. These findings highlight the importance for critically selecting urban tree species and the use of more non-allergenic forbs and less grasses when designing healthy urban green infrastructure.

Macroinvertebrates are widely used in freshwater biomonitoring, but current methods are labor-intensive and require taxonomic expertise. DNA metabarcoding offers a potential solution, though results vary per applied sampling protocol. Here, we compare several protocols using macroinvertebrate samples from a Dutch peatland. Live-sorted specimens were (i) identified morphologically followed by (ii) non-destructive (soft-lysis) and (iii) destructive DNA extraction protocols (aggressive-lysis). Additionally, (iv) unsorted samples (including substrate and plant material) were homogenized, and (v) water-derived eDNA collected. Only 25

Ground-mounted solar parks in Europe are often implemented in agricultural landscapes on fields with agricultural history. The subsequent decrease in management intensity presents opportunities for biodiversity development, which might especially be interesting for arthropods and their resources, given their ongoing biodiversity losses in agricultural landscapes. While several studies suggest that solar parks have the potential to enhance pollinator biodiversity, impacts on other arthropods remain unclear and there is limited knowledge on how these shifts compare to pre-conversion biodiversity and the potential of solar parks to contribute to biodiversity conservation. In this study, we used field surveys and insect trapping to compare flower resources, vascular plant diversity, pollinator diversity and soil-emergent arthropod diversity in Dutch solar parks to previous land use (i.e. intensive agricultural grasslands) and semi-natural conditions (i.e. extensively managed grasslands). Flower and nectar plant availability, wild bee and hoverfly abundance, but not butterfly abundance, were significantly higher in solar parks than in intensive grasslands. Vascular plant diversity was similar in solar parks and extensive grasslands, and higher than in intensive grasslands. Overall soil-emergent arthropod richness was similar across all habitat types, yet biomass and abundance were lower in solar parks than in the other habitats. Community composition differed between habitat types across all studied arthropod groups. Synthesis and applications. Our results suggest that while biodiversity can increase in solar parks post-conversion compared to previous land use, not all taxonomic groups benefit from the land use change. Potential benefits for certain groups (particularly for butterflies and soil-emergent arthropods) are likely lost due to disturbance during land conversion and solar panel presence. As a result, our study indicates that solar parks have the potential to combine biodiversity conservation on the surfaces not covered by solar panels, but that more insight is needed in the solar park design (e.g. conversion process and row width) and management, particularly to improve butterfly and soil-emergent arthropod biodiversity.

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 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.

Abstract 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.

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.

The Province of Zeeland asked Wageningen Environmental Research to study the composition of the diet offeral cats living on the island Neeltje Jans, located in the Oosterscheldekering, one of the largerst Deltaworks along the Dutch coast. This island harbours valuable breeding colonies of various sea birds. We studied the cat’s diet by collecting scats during the bird breeding season of 2021 and identifying the prey composition based on two complementary methods: morphological identification and genetic identification by means of eDNA metabarcoding. The results showed that the diet mainly consists of voles and rabbits,although evidence of a total of 8 bird species was observed in the scats. In only one scat we observed DNA from one of the sea birds breeding on the island (a European herring gull or a Lesser black-backed gull), suggesting that the impact of the feral cats on Neeltje Jans on the local breeding colonies of these species is likely to be limited. A study of the population dynamics of these birds, including the role of cats in the totalpredation mortality, would help to further assess the risks.

Biological particles suspended in the atmosphere have a crucial role in the dynamics of the biosphere underneath. Although much attention is paid for the chemical and physical properties of these particles, their biological taxonomic identity, which is relevant for ecological research, remains little studied. We took air samples at 300 meters above the forest in central Amazonia, in seven periods of 7 days, and used high-throughput DNA sequencing techniques to taxonomically identify airborne fungal and plant material. The use of a molecular identification technique improved taxonomic resolution when compared to morphological identification. This first appraisal of airborne diversity showed that fungal composition was strikingly different from what has been recorded in anthropogenic regions. For instance, basidiospores reached 30% of the OTUs instead of 3–5% as found in the literature; and the orders Capnodiales and Eurotiales—to which many allergenic fungi and crop pathogens belong—were much less frequently recorded than Pleosporales, Polyporales, and Agaricales. Plant OTUs corresponded mainly to Amazonian taxa frequently present in pollen records such as the genera Helicostilys and Cecropia and/or very abundant in the region such as Pourouma and Pouteria. The origin of extra-Amazonian plant material is unknown, but they belong to genera of predominantly wind-pollinated angiosperm families such as Poaceae and Betulaceae. Finally, the detection of two bryophyte genera feeds the debate about the role of long distance dispersal in the distribution of these plants.

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.

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.

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.

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 TEX 86 . 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.

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.

Abstract. Recent studies have shown that the Early Eocene Climatic Optimum (EECO) was preceded by a series of short-lived global warming events, known as hyperthermals. Here we present high-resolution benthic stable carbon and oxygen isotope records from ODP Sites 1262 and 1263 (Walvis Ridge, SE Atlantic) between ~ 54 and ~ 52 million years ago, tightly constraining the character, timing, and magnitude of six prominent hyperthermal events. These events, which include Eocene Thermal Maximum (ETM) 2 and 3, are studied in relation to orbital forcing and long-term trends. Our findings reveal an almost linear relationship between δ13C and δ18O for all these hyperthermals, indicating that the eccentricity-paced covariance between deep-sea temperature changes and extreme perturbations in the exogenic carbon pool persisted during these events towards the onset of the EECO, in accordance with previous observations for the Paleocene Eocene Thermal Maximum (PETM) and ETM2. The covariance of δ13C and δ18O during H2 and I2, which are the second pulses of the "paired" hyperthermal events ETM2-H2 and I1-I2, deviates with respect to the other events. We hypothesize that this could relate to a relatively higher contribution of an isotopically heavier source of carbon, such as peat or permafrost, and/or to climate feedbacks/local changes in circulation. Finally, the δ18O records of the two sites show a systematic offset with on average 0.2 ‰ heavier values for the shallower Site 1263, which we link to a slightly heavier isotopic composition of the intermediate water mass reaching the northeastern flank of the Walvis Ridge compared to that of the deeper northwestern water mass at Site 1262.