英国生态和水文学研究中心

Nitrous oxide (N2O) emissions from fertilised oil palm (OP) systems remain poorly quantified despite their importance for regional greenhouse gas inventories and life-cycle assessments. This study measured N2O emissions following 18 fertiliser application events across 12 smallholder OP farms in Indonesia and Malaysia between 2022 and 2025. Cumulative fertiliser-induced emissions were estimated over a 21-day measurement period and used to derive N2O emission factors (EFs) for individual fertilisation events. Individual EFs ranged from 0.01% to 1.43% of applied nitrogen fertiliser (mineral and organic). To place these measurements in a regional context, results were combined with published EFs from OP systems on mineral soils in Southeast Asia (SEA). Using log-normal statistical treatment to account for the highly skewed distribution of EFs, a regional mean EF of 0.82% of applied nitrogen was derived (95% confidence interval: 0.44–1.78%). Sensitivity analyses indicated that regional EF estimates varied according to study inclusion and analytical approach, with mean values ranging from 0.51% to 1.12% of applied nitrogen. While uncertainties remain and the observational study design does not allow the independent effects of site, year, fertiliser type and management to be disentangled, the derived EF is broadly consistent with the IPCC Tier 1 default value of 1% and represents a regional estimate based on the currently available field evidence. These results contribute to a limited empirical dataset for OP systems in SEA and provide a transparent evidence base for greenhouse gas inventories, life-cycle assessments, and future evaluations of mitigation opportunities within the sector.

European grasslands are biodiversity hotspots but face ongoing threats from intensification and abandonment. Monitoring is essential for evaluating policies that promote grassland biodiversity, but conventional expert-led surveys are costly and difficult to scale. Farmer-led biodiversity recording offers a potential solution, but its feasibility and reliability remain uncertain. This study aimed to assess whether farmer-recorded indicators can provide reliable and practical measures of grassland plant diversity that could support broader biodiversity monitoring and assessment of public goods delivery. We firstly reviewed 14 European monitoring case study schemes, supplemented by stakeholder interviews in eight of these schemes. Secondly, we compared simple farmer-recorded indicators at the field and farm system levels with expert botanical assessments across 26 low-intensity farms in the UK, Sweden, and Romania. Analysis of case study monitoring schemes revealed that limited time and technical expertise were major barriers to farmer participation, while adequate incentives, scheme simplicity, and supportive technology acted as key enablers alongside the well-documented need for training and support. Comparisons with botanical assessments showed that simple farmer-recorded measures, particularly forb species counts, were strong proxies for plant species richness and diversity, while some abundance-based measures such as vegetation cover estimates also showed significant relationships with botanist assessments. In contrast, farmer-recorded indicator species performed poorly, likely due to short indicator species lists and the lack of farmer training. Farm system metrics were also weak predictors of species richness and diversity, although they may be informative across broader gradients of management intensity. Overall, our findings demonstrate that farmer-led biodiversity recording is feasible when based on straightforward, observable indicators, combined with adequate motivation. These approaches could complement expert assessments in supporting biodiversity goals and results-based agri-environment schemes.

Urbanisation is a globally increasing phenomenon with diverse impacts on biodiversity. Interest in how urbanisation affects raptors is growing because, as top predators, they can be used as bioindicators for ecosystem functioning and sentinels for environmental change. However, a comprehensive synthesis detailing the global-scale impact of urban-related sensory and risk factors on nocturnal raptors (i.e. owls) is lacking. In this review, we examined the literature to identify such factors and to outline their association with behavioural and ecological traits of owls living in urban environments. Overall, we show that several urban-related sensory and risk factors affect owls, with vehicle collisions on roads being the most widely documented across species. Conversely, sensory pollution remains poorly investigated, which is surprising given that nocturnal and acoustic hunters, such as owls, might be severely impacted by artificial light at night (ALAN) and anthropogenic noise. We also highlight a research gap on this topic from the global south, where urbanisation is rapidly increasing. Importantly, we show that roads and sensory pollutants are associated in contrasting ways with many owl behavioural and ecological traits, such as hunting and habitat use. We argue that the interplay among roads, noise and ALAN influences how owls’ prey species use roads, which may turn areas near roads into ecological traps. The severity of their impacts may depend on the intensity and type of anthropogenic noise and artificial lights along roads. Further research in this direction will have important implications for the conservation of owls in urban environments.

Accurately quantifying and characterizing human internal exposure to micro- and nanoplastics are critical for assessing potential health risks. However, the detection of these particles in human tissues, fluids, cell systems, and relevant models remains a major analytical challenge. There is an urgent need for robust, selective, sensitive, and high-throughput methods capable of generating reliable quantitative data. Equally essential is the transparent reporting of methodological limitations and uncertainties, supported by rigorous data collection and standardized practices. These challenges are compounded by the ubiquity of plastic particles, and therefore the risk of sample contamination and their diverse properties (e.g., size, shape, composition), all adding to the complexity of identifying and quantifying them in biological matrices. To address these issues, we propose a framework that integrates orthogonal analytical techniques to enhance the data reliability. Commonly used analytical techniques for the analysis of micro- and nanoplastics are assigned a category based on their specificity when identifying plastic particles. The framework proposes minimum data requirements from orthogonal techniques for the identification of plastic particles at various confidence levels. Clear communication of analytical confidence is vital, and we present a structured approach to support this. We emphasize the importance of scientific integrity, rigorous study design, and transparent reporting in health research. Finally, we call for the universal adoption of harmonized confidence criteria for reporting the presence of plastics in humans, an essential step toward informed decision-making.

Achieving the goals of the Kunming-Montreal Global Biodiversity Framework (GBF) requires monitoring systems that can transform heterogeneous observations into consistent, decision-relevant knowledge. Yet current biodiversity data are fragmented, uneven in quality, and seldom comparable across space or time. Existing standards such as Darwin Core, Findable, Accessible, Interoperable, and Reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles provide important foundations, but they do not connect the full chain from field observation to policy reporting. We introduce the Biodiversity Monitoring Standards Framework (BMSF)-a unifying architecture that links ethical principles, standardized data collection, accredited analytical workflows, and transparent reporting into a single auditable "chain of evidence." The framework's novelty lies in its tiered and federated design, enabling national agencies, Indigenous knowledge holders, local communities, and private-sector actors to operate under shared principles while maintaining data sovereignty. By integrating Essential Variables, accredited analytical methods, and open-source implementation pathways, the BMSF allows locally generated data to be aggregated into credible, comparable indicators aligned with GBF targets. Concrete application, such as a national forest-connectivity assessment, demonstrates how the BMSF improves reproducibility, transparency, and policy relevance relative to existing approaches. Implemented generally, this framework would convert fragmented monitoring efforts into a coordinated, scalable system capable of tracking and guiding collective progress toward halting and reversing biodiversity loss.