Microplastics, particularly microfibres (< 5 mm), are a significant environmental pollutant. Detecting and quantifying them in complex matrices is challenging and time-consuming. This study presents two open-source visual recognition models, YOLOv7 and Mask R-CNN, trained on extensive datasets for efficient microfibre identification in environmental samples. The YOLOv7 model is a new introduction to the microplastic quantification research, while Mask R-CNN has been previously used in similar studies. YOLOv7, with 71.4 % accuracy, and Mask R-CNN, with 49.9 % accuracy, demonstrate effective detection capabilities. Tested on aquatic samples from Seyðisfjörður, Iceland, YOLOv7 rapidly identifies microfibres, outperforming manual methods in speed. These models are user-friendly and widely accessible, making them valuable tools for microplastic contamination assessment. Their rapid processing offers results in seconds, enhancing research efficiency in microplastic pollution studies. By providing these models openly, we aim to support and advance microplastic quantification research. The integration of these advanced technologies with environmental science represents a significant step forward in addressing the global issue of microplastic pollution and its ecological and health impacts.

Current climate and environmental changes have brought unprecedented rates of change to mountain ecosystems. These changes are impacting the provisioning of ecosystem services. Despite the increase in academic publications on ecosystem services, research on cultural ecosystem services (CES) and their availability in mountain regions has largely been neglected. Here we analyse how important different CES are for inhabitants and visitors in the Lower Engadine region (Switzerland). We use questionnaires and maps to identify the most important CES for individual and collective wellbeing as well as their geographical location in the region. We had 48 participants in this study of which 28 grew up in the Lower Engadine. Our results show that the most important (i.e., ‘Highly important’) CES are: ‘The view of mountains, rivers or glaciers’; the presence of plants typical for the region, for example Fire Lily and Edelweiss (i.e., Lilium bulbiliferum subsp. croceum, Edelweiss-Leontopodium alpinum); ‘Hiking’; ‘Local customs’; ‘Watching large mammals’; and the importance of ‘Terraces for traditional Agriculture activities’. Results from the spatial analysis show that identical geographical locations in the Lower Engadine provide multiple CES and bring health benefits to the users.

Saltmarshes have emerged as important sinks for microplastic (MP) pollution, yet little is known about the long-term accumulation and retention mechanisms of MPs in these environments. This study presents the first chronological record of MPs in Mediterranean saltmarsh sediments, using sediment cores dated via a combination of AMS radiocarbon (¹⁴C) and radionuclide (²¹⁰Pb, ¹³⁷Cs, ²⁴¹Am) from two saltmarshes located on the Adriatic Sea coast of Croatia (Blace and Jadrtovac). MPs were extracted and analysed across core depths and assessed in relation to geochemical parameters (organic matter (OM), carbonates, organic carbon (C-org), total nitrogen (TN), phosphorus (P) forms’ content, and grain size distribution). Results show that MPs first appear in sediments dated to 1950 in Jadrtovac and post-1960 in Blace, with concentrations increasing markedly in more recent surface layers. Jadrtovac exhibited higher MP concentrations (up to 0.5 MPs g⁻¹), dominated by fibres (86%) associated with urban and maritime sources, while Blace showed lower concentrations, dominated by fragments (60%), likely from localised sources such as agriculture or single-use packaging. Polymer analysis confirmed contrasting source profiles, with rayon and cellophane dominating in Jadrtovac, and polypropylene and olefin in Blace. MPs positively correlated with OM, C-org, P, TN and sand content, and negatively with clay and carbonate content. Principal component analysis (PCA) confirmed that MPs were associated with organic-rich, sandy sediments. These findings demonstrate that OM composition and sediment texture significantly influence MP retention and highlight the role of saltmarshes as long-term archives of plastic pollution in low-energy coastal settings.

Accurate and spatially explicit habitat maps are essential for monitoring ecological change, supporting nature‐based solutions and informing policy. Despite advances in habitat mapping, classification accuracy varies across landscapes and thematic resolutions, highlighting the need for systematic evaluation of classification workflows. This study evaluates EcoservR, an open‐source rule‐based toolkit for habitat mapping, using field survey data from two contrasting landscapes in northern England: Merseyside (MER) and the North York Moors (NYM). Habitat classifications were generated by integrating multiple national spatial datasets and were evaluated against ecological survey records and high‐resolution aerial imagery across three levels of the Phase 1 habitat classification hierarchy developed by the Joint Nature Conservation Committee (JNCC). Overall accuracy ranged from 0.612 ± 0.015 (Level 3, NYM) to 0.804 ± 0.012 (Level 1, NYM). Broad habitat classes such as woodland and standing water achieved high precision, while grasslands, heathlands and mires showed lower precision. Accuracy declined with increasing thematic detail, reflecting ecological heterogeneity and limitations in the resolution and currency of input datasets. Observed land‐cover patterns corresponded with the known ecological structure of each landscape, although classification biases indicate that fine‐scale habitat assessments and quantitative ecosystem‐service estimates require supplementary data. Practical implications . EcoservR provides a reproducible workflow for integrating multiple spatial datasets to generate spatially consistent habitat maps. This approach supports regional planning and nature‐recovery initiatives where broad habitat categories are sufficient, and its rule‐based architecture allows adaptation to other landscapes where comparable datasets are available.