E. Emiel van Loon

Warming and changing litter inputs jointly influence soil organic carbon dynamics in subalpine and alpine ecosystems, yet the molecular-scale interactions in these soils remain poorly understood. We incubated subalpine pasture and forest mineral soils for 360 days at three temperatures (12.5, 16.5, 20.5 °C), with and without addition of Lolium perenne grass litter, and analysed free extractable lipids as molecular tracers of decomposition. In soils without litter addition, warming alone caused little change and revealed low temperature sensitivity in the native free extractable lipid pool. With litter addition, decomposition of litter-derived lipids was strongly temperature dependent during the early incubation phase (first weeks to months, 0–56 days), but this temperature effect weakened and largely disappeared during the remainder of the 360-day incubation. Pasture soils showed faster early decomposition of litter-derived lipids than forest soils, indicating an effect of land-cover history. A biomarker-based inverse model (VERHIB, Vegetation Reconstruction with the Help of Inverse Modeling and Biomarkers) confirmed a strong early decline of litter-derived lipids (losses of up to ∼80 %) and a partial convergence of remaining litter-derived fractions in all temperatures after one year. Our findings demonstrate that the availability and quality of fresh litter, rather than temperature alone, control molecular-level decomposition of soil organic carbon in these subalpine soils, underscoring the importance of vegetation-driven inputs for predicting soil carbon responses to climate warming.

Schistosomiasis is a parasitic disease that affects over 250 million people worldwide, with the majority living in rural areas of sub‐Saharan Africa. The parasite relies on freshwater snails of the genus Biomphalaria as intermediate hosts. Mapping snail distribution is vital for identifying disease transmission hotspots. However, expert‐led monitoring is often constrained by limited resources and restricted access to remote areas, highlighting the need for scalable and cost‐effective alternatives. This study evaluates the effectiveness of citizen science in predicting Biomphalaria spp. presence by comparing models built from expert ‐ and citizen‐collected data. We tested two scenarios: the first one assumed perfect detection and focused on environmental and geomorphological predictors, while the second accounted for imperfect detection to explore discrepancies between citizen observations and expert‐derived detection probabilities. In the perfect detection scenario, the expert and citizen models identified site type and NDVI as significant environmental predictors of snail presence. Although both models demonstrated low marginal R ² values (~16%–17%), indicating limited explanatory power of broad‐scale environmental predictors, conditional R ² values exceeded 65%, suggesting that fine‐scale, site‐specific habitat characteristics are critical determinants of Biomphalaria spp. presence. For the imperfect detection scenario, the expert model and the citizen observations showed minimal discrepancies, primarily explained by individual observer variability and differences in sampling effort. Increased sampling effort consistently reduced false negatives and led to unexpected observations of snail presence by the citizens (i.e. observed presence in sites predicted unsuitable by the expert model). Practical implication . Our findings demonstrate that citizen science data, when properly structured and statistically accounted for bias and errors, can generate ecological modelling outputs comparable to those based on expert‐led surveys. We highlight the importance of accounting for observer variability, providing calibrated training and optimizing sampling strategies to enhance data quality. This study presents a transferable and cost‐efficient framework for participatory ecological monitoring in resource‐limited and undersampled regions.

Rapid growth in bio-logging—the use of animal-borne electronic tags to document the movements, behaviour, physiology and environments of wildlife—offers opportunities to mitigate biodiversity threats and expand digital natural history archives. Here we present a vision to achieve such benefits by accounting for the heterogeneity inherent to bio-logging data and the concerns of those who collect and use them. First, we can enable data integration through standard vocabularies, transfer protocols and aggregation protocols, and drive their wide adoption. Second, we need to develop integrated data collections on standardized data platforms that support data preservation through public archiving and strategies that ensure long-term access. We outline pathways to reach these goals, highlighting the need for resources to govern community data standards and guide data mobilization efforts. We propose the launch of a community-led coordinating body and provide recommendations for how stakeholders—including government data centres, museums and those who fund, permit and publish bio-logging work—can support these efforts.

Vegetation is a major contributor of terrestrial evaporation and influences subsequent precipitation over land. Studies suggest that forests are crucial for moisture recycling, although the specific contribution of different vegetation to precipitation remains unclear. Using a moisture recycling approach, we investigate the contribution of transpiration from trees and non‐tree vegetation to precipitation over Africa. We use precipitation source regions from simulated atmospheric moisture trajectories, constrained by observation‐based evaporation and precipitation products, and fractional vegetation cover data. Our findings show that trees provide a higher flux to precipitation (∼777 mm year⁻¹) than non‐tree vegetation (∼342 mm year⁻¹). However, considering the smaller spatial extent of trees compared to non‐tree vegetation, precipitation in most watersheds effectively depends more on the latter. Overall, non‐tree vegetation appears equally important as trees in terms of volumetric contributions to precipitation, and deserves attention in further research, considering ongoing land use changes that affect the continental water cycle.

The ecosystem services and immense biodiversity of Amazon rainforests are threatened by deforestation and forest degradation. A key goal of modern archaeology and paleoecology in Amazonia is to establish the extent and duration of past forest disturbance by humans. Fossil phytoliths are an established proxy to identify the duration of disturbance in lake sedimentary and soil archives. What is not known, is the spatial scale of such forest disturbances when identified by phytoliths. Here we use phytolith assemblages to detect local-scale forest openings, provide an estimate of extent, and consider long-term forest recovery. We use modern phytolith assemblages of 50 Amazonian lakes to i) assess how phytolith assemblages vary across forest cover at 5 spatial scales (100 m, 200 m, 500 m, 1 km, 2 km), ii) model which phytolith morphotypes can accurately predict forest cover at 5 spatial scales, and iii) compare phytoliths with pollen to quantify their relative ability to detect forest cover changes. DCA results show phytolith assemblages could be used to differentiate low, intermediate, and high forest cover values, but not to distinguish between biogeographical gradients across Amazonia. Beta regression models show Poaceae phytoliths can accurately predict forest cover within 200 m of Amazonian lakes. This modern calibration dataset can be used to make quantitative reconstructions of forest cover changes in Amazonia, to generate novel insights into long-term forest recovery. Combining phytoliths and pollen provides a unique opportunity to make qualitative and quantitative reconstructions of past vegetation changes, to better understand how human activities, environmental and climatic changes have shaped modern Amazonian forests.