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Identifying the mechanisms that contribute to the variability of suspended particulate matter concentrations in coastal areas is important but difficult, especially due to the complexity of physical and biogeochemical interactions involved. Our study addresses this complexity and investigates changes in the horizontal spread and composition of particles, focusing on cross-coastal gradients in the southern North Sea and the English Channel. A semi-empirical model is applied on in situ data of SPM and its organic fraction to resolve the relationship between organic and inorganic suspended particles. The derived equations are applied onto remote sensing products of SPM concentration, which provide monthly synoptic maps of particulate organic matter concentrations (here, particulate organic nitrogen) at the surface together with their labile and less reactive fractions. Comparing these fractions of particulate organic matter reveals their characteristic features along the coastal-offshore gradient, with an area of increased settling rate for particles generally observed between 5 and 30 km from the coast. We identify this area as the transition zone between coastal and offshore waters with respect to particle dynamics. Presumably, in that area, the turbulence range and particle composition favor particle settling, while hydrodynamic processes tend to transport particles of the seabed back towards the coast. Bathymetry plays an important role in controlling the range of turbulent dissipation energy values in the water column, and we observe that the transition zone in the southern North Sea is generally confined to water depths below 20 m. Seasonal variations in suspended particle dynamics are linked to biological processes enhancing particle flocculation, which do not affect the location of the transition zone. We identify the criteria that allow a transition zone and discuss the cases where it is not observed in the domain. The impact of these particle dynamics on coastal carbon storage and export is discussed.

ABSTRACT: Numerous fish populations show strong year-to-year variations in recruitment. The early life stages play a crucial role in determining recruitment and dispersal patterns. A helpful tool to understand recruitment and dispersal involves simulations with a Lagrangian transport model, which results from the coupling between a hydrodynamic model and an individual-based model. Larval transport models require sound knowledge of the biological processes governing larval dispersal, and they may be highly sensitive to the parameters selected. Various assumptions about larval traits, behaviour and other model parameters can be tested by comparing simulation results with field data to identify the most sensitive parameters and to improve model calibration. This study shows that biological parameterization is more important than inter-annual variability in explaining the year-to-year differences in larval recruitment of common sole in the North Sea and the eastern English Channel. In contrast, year-to-year variability of connectivity leads to higher variability than changes in the biological parameters. The most influential parameters are pelagic larval duration, spawning period and mortality. Calibration over a 12 yr recruitment survey shows that a scenario with low mortality associated with a long larval duration and behaviour involving nycthemeral and tidal migration best reproduces the observations. This research provides insights into factors influencing fish dispersal and recruitment, suggesting a strategy for enhancing the accuracy of models in upcoming studies. The study supports the improvement of larval dispersal modelling by incorporating an easily applicable sensitivity analysis for both calibration and validation. Incorporating sensitivity analyses enhances larval dispersal models, providing performing tools that can contribute to informed fisheries management and understanding of recruitment variability.

The transferability of single or joint species distribution models ((j)SDMs) depends on their ability to predict beyond the observed environmental range and to remain consistent despite shifts in biotic interactions. Transfer accuracy may be improved by recent advances in the application of deep learning that provide greater flexibility and potentially superior predictive accu- racy than traditional approaches. We implemented jSDMs with deep and machine learning algorithms and measured the transfer accuracy from continental to regional areas in communities with different species composition. We ran jSDMs with deep neural networks (DNN), elastic net (EN), and stacked SDMs (sSDM) with random forests (RF). We used 134 689 occurrence records representing 1776 species of six taxonomic groups (beetles, birds, bryophytes, fungi, lichens and plants) from 2387 forest plots in Europe. We employed an agnostic modelling approach that covered most of the environmental con- ditions by including more than 100 satellite-derived variables and 98 climatic variables. The predictive power of the models within the training continental area was evaluated using AUC, whereas the transfer accuracy in the regional area was evalu- ated with the Boyce index calculated with independent presence records. We found that the DNN–jSDMs outperformed other models at continental scale, but model transfer from continental to regional extent was less accurate. We found that the accuracy of regional predictions was higher for taxonomic groups with better representation in the continental data, such as birds, bryophytes and plants. Depending on the algorithm and the taxonomic group, we achieved acceptable (Boyce > 0) to accurate (Boyce > 0.5) transferability for 32–78% of the species. Our findings underscored the need of considering trade-offs among hyperparameter tuning, spatial scales and model complexity. Our findings also suggest that the varying biotic interac- tion structures and, particularly, the different species compositions of the transfer areas, may affect model transferability more than previously considered.

Food forests are an emerging agroecosystem in the temperate zone, aimed at providing food while supporting high levels of biodiversity. How food forestry impacts belowground biodiversity is, however, largely unknown. We compared communities of 12 taxonomic groups of soil organisms between 15 food forests and nearby grasslands, croplands and forests in Northwest Europe. Food forest soil communities appeared to differ from communities in grass- and croplands and more closely resembled forest communities in terms of total biomass or number of individuals of most taxonomic groups, with especially higher numbers of most macroarthropods. In terms of composition, food forest communities of most groups were overall intermediate between those in grass- and croplands and those in forests. For microorganismal and microfaunal groups, food forest communities bore a greater resemblance to grass- and cropland communities than to forest communities. Besides a higher alpha-diversity for non-arbuscular mycorrhizal fungi and certain macroarthropod groups in food forests, differences in alpha- and beta-diversity were overall limited. As food forests appear to support different soil communities than grass- and croplands, planting food forests could increase soil biodiversity in agricultural landscapes.

Many bacterial taxa evolved facultative symbiotic associations with insects and spread through host populations by horizontal and maternal transmission. Co-infection at the individual host level may facilitate or constrain the spread of facultative symbi- onts. Due to insufficiently detailed genotyping, co-infections of maternally transmitted symbionts often remain hidden, limit- ing our understanding of (co-)infection dynamics. Spiroplasma bacteria exhibit multiple independent origins of symbiosis with insects and have poorly understood patterns of transmission and co-infection. Here, we examined these traits of Spiroplasma symbiosis using Myrmica ants, a system known for high frequencies of single Spiroplasma infections. Through exhaustive genotyping of 75 colonies across seven Myrmica species, we uncovered multiple cryptic co-infections involving two distinct Spiroplasma clades that display significantly different infection frequencies in workers. Within Myrmica ruginodis, infection heterogeneity was contingent on ant caste and was lower in workers. Remarkably, the sMyr Spiroplasma variant infected four Myrmica species and was widespread in queens and workers. We provide phylogenomic and functional genomic support for an exceptionally stable symbiosis with maternally acquired sMyr, with a predicted infection persistence of seven million years in the Myrmica scabrinodis species group. Our findings reveal that Spiroplasma can display complex infection heterogeneity and evolve an evolutionary stable maternally acquired infection within insect hosts.

Darwin Core (DwC) is an essential standard for sharing biodiversity data. However, the term dwc:habitat suffers from an inherent ambiguity due to its free-text format. This ambiguity severely compromises the interoperability and reusability of habitat data, hindering large-scale comparative analyses and impeding the formulation of effective conservation policies. As a solution to this problem, we propose adopting controlled vocabularies and ontologies. The NATURA2000 and EUNIS habitat classifications emerge as ideal candidates to standardize dwc:habitat. NATURA2000 offers a consolidated regulatory framework and habitat type definitions with direct implications for European conservation, while EUNIS provides a more comprehensive, hierarchical, and scientifically grounded system with the ability to cross-map with other standards. The implementation of such vocabularies would significantly improve the quality, consistency, interoperability, and reusability of habitat data, more robustly supporting scientific research and conservation policies.

Litter ant communities constitute an important component of biodiversity in tropical regions. They are currently used in several ecosystem management programmes to as- sess forest health. The aim of this study was to uncover the ant diversity across forest successional stages (fallow land, secondary forest and primary forest) in the Yangambi Biosphere Reserve, the Democratic Republic of the Congo. These habitats were sam- pled at six localities using pitfall traps and Winkler extractions. In total, 190 ant species belonging to 50 genera and eight subfamilies were recorded in the Yangambi Biosphere Reserve. Ant diversity increased significantly along the successional gradient, being lowest in fallow land, intermediate in secondary forest, and highest in primary forest. Sixty ant species were shared across all three habitats, while each habitat supported a distinct assemblage of species. Primary forests contained the greatest number of exclu- sive species, followed by secondary forests and fallow land. Winkler extractors captured substantially more ant species than pitfall traps, recording nearly 50% greater species richness. However, a significant portion of the ant fauna in the Yangambi Biosphere Re- serve likely remains unrecorded, and additional sampling methods (like arboreal traps, net sweeping and baiting) could provide a more complete picture of its biodiversity.

It is widely known that the subsurface will play a crucial role in the transition towards a carbon-neutral society, with the aid of technologies like geothermal energy, CO2-storage, .... Nevertheless, still a lot of aspects concerning the subsurface, its structure and characteristics remain to be investigated to facilitate the use of underground space in an efficient and safe way. In-depth investigation of the subsurface with conventional techniques such as seismic campaigns or drillings requires high investments, and it is not always straightforward to determine the success-rate upfront. This leads to geodata collections typically displaying a large variety and scatter, both concerning data (type) availability and in spatial distribution. Additionally, incorporating subsurface knowledge from neighboring countries often is challenging, but at the same time indispensable to increase understanding of the own subsurface, not least because some projects may display cross-border influences. It is clear that subsurface exploration benefits from a cross-border and cross-thematic data collection and interpretation approach. One way to organize such data centralization was explored in the framework of the European Horizon2020-project GeoConnect³d, by means of constructing a Structural Framework (SF) and a database of Geomanifestations (GM) for several pilot study areas. The Structural Framework defines geological units by its limits (e.g., faults, terrane boundaries, ...). All known limits and associated parameters are structured in a uniform and inter-connected way. Furthermore, the SF is designed on multiple zoom-levels, hence it can serve as a real backbone to integrate multiple other subsurface models of various scale and resolution together. Geomanifestations are anomalous observations covering a wide range of geo-disciplines, including —but not limited to— temperature, geochemistry, mineralogy and even geophysics data. Such irregularities are too often excluded or ignored in view of the larger cloud of ‘normal’ datapoints. Nevertheless, precisely these anomalies can be of great value for identifying subsurface processes and serve as an excellent pathway for communication to non-experts, and also as guideline for further research. In addition to GIS- and attribute-information, Factsheets summarize the relations between individual geomanifestations, and, if applicable, their connection to the Structural Framework. Especially the latter, the combination of the (independent) elements SF and GM, gives a powerful tool that allows exploring the subsurface in an original and cost-efficient way. The newly gained insights can be directly linked and are extremely relevant to the use of the subsurface, either as storage space or as renewable/green energy-source. But it goes further than that. The overall usability of the SF and GM database is far more fundamental, as it gives innovative clues about characteristics and processes at play in the subsurface, such as fault permeability and connectivity, the presence of advection cells in the upper crust, or gas origin and migration pathways. To quote just one example; in the area of Spa, Belgium, elevated 3He/4He-ratios were analyzed (Griesshaber et al., 1992), a parameter that can highlight mantle gas contribution in gas seeps (White, 2013). This observation was unexpected given the far distance from any volcanic activity, but suggests the presence of deep-seated, transcrustal faults and/or a large-distance connectivity till the Eifel area where mantle-derived magma was involved in recent volcanism. When indirect indications like this are not considered further, such valuable subsurface knowledge is easily overlooked and not at all taken into account for investigating in more detail in the future. Even when limited resources or funding is available, the above-illustrated SF+GM approach can shed new light on properties and processes of the subsurface, given its novel and multidisciplinary approach. An inherent drawback, however, is that such a database is never complete and includes information from a variety of sources. Not only does this demands careful consideration on which data is included (or not), it also has to be taken into account for future database expansion as well as for data interpretation. Simple visualizations on a map without further (geological) background, e.g., combining both surface and at depth data as is the case for Wiesbaden, Germany (Mittelbach & Siebert, 2014), may lead to false conclusions. However, the provided Factsheets and metadata can help in this. Furthermore, at this moment, a large proportion of the entries depends on the availability of literature data, which implies some data source bias is unavoidable. For example, CO2-data typically is measured for springs and streams, while dry CO2-seeps easier remain unnoticed and therefore are reported less consistently. New data collection campaigns, possibly including bio-indicators like plants or ants (e.g., Berberich & Schreiber, 2013), can provide a good starting point for this. The uniform and well-designed structure of the database allows very easy expansion, be it for newly discovered faults, additional geomanifestation types, or parameter updates of either part. In addition, as demonstrated in the GeoConnect³d project, the SF+GM approach is fully transferable to other study areas. This clears the way for a cost-efficient cross-border exploration of the subsurface with wins for both the academic world and common public (geoheritage, education, ...), and significantly contributes to a more data-supported outline for subsurface management. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731166. References Berberich, G., & Schreiber, U., 2013. GeoBioScience: Red Wood Ants as Bioindicators for Active Tectonic Fault Systems in the West Eifel (Germany). Animals, 3, 475-498. Griesshaber, E., O'Nions, R.K. & Oxburg, E.R., 1992. Helium and carbon isotope systematics in crustal fluids from the Eifel, the Rhine Graben and Black Forest, F.R.G. Chemical Geology, 99, 213-235. Mittelbach, G. & Siebert, S., 2014. Gutachten zur Festsetzung eines Heilquellenschutzgebietes für die Heilquellen (Große und Kleine Adlerquelle, Schützenhofquelle, Kochbrunnen, Salmquelle und Faulbrunnen) von Wiesbaden, Stadt Wiesbaden (WSG-ID 414-005), Wiesbaden, pp. 1-52. White, W.M., 2013. Chapter 12: Noble Gas Isotope Geochemistry, Isotope Geochemistry course notes. Cornell University.