This publication provides a comprehensive review of the use of GDGTs (Glycerol Dialkyl Glycerol Tetraethers) for reconstructing past temperatures, with a particular focus on marine applications. It covers the full research workflow — from sample collection and processing to analytical methodologies, data interpretation, and data archiving. Importantly, the authors highlight best practices and propose recommendations to standardize approaches across the field. This review therefore serves as a valuable reference for both the paleoclimatology and organic geochemistry communities.

Rapid changes to the climate of the boreal zone have resulted in large-scale 'greening' and range shifts. Feng and others explain these changes at a global scale and with unprecedented spatial and temporal resolution, demonstrating a change in boreal forest demography toward younger stands, an increase in areal extent of boreal forests, and a northward gain that outpaced losses on it southern fringe. These ongoing changes further emphasize the importance of boreal forests to earth system processes and the work in the present manuscript presents a path forward for detailed studies of large-scale land cover changes.

The study analyzed lake sediments from the southwest Maya lowlands to track past land use and population size. The authors found that over 3,300 years, land use was highly variable, with more fire and maize agriculture earlier (3500-2000 BP) with a denser population but less burning in a later period (1600-1000 BP). Surprisingly, their hydrogen isotope data showed no strong signature of drought locally, suggesting that climate in that area remained relatively stable. Therefore, the authors argue that collapse or population decline in the southwest Maya lowlands was likely driven more by social, political, or regional factors than by local drought.

Mercury emissions from artisanal and small-scale gold mining (ASGM) are one of the main sources of mercury (Hg) release into the environment, yet they are by far the least understood. This, together with the fact that ASGM emissions (besides their global implications) also directly/locally affect the poorest communities makes this a critical research topic. The authors demonstrate that bioaccumulation pathways in globally important crops are often overlooked, with implications for interventions, international collaborations, and emerging technologies.

In aquatic ecosystems, the production of nitrous oxide (N2O), an important greenhouse gas, was until recently mostly ascribed to the activity of nitrifiers and denitrifiers. Recent research has revealed that a range of other organisms are also capable of producing N2O, but quantifying their contribution has remained challenging. Plouviez et al. present a novel approach based on the stable nitrogen and oxygen isotope composition of N2O, combined with the intramolecular distribution of 15N ("site preference"). They found that various microalgae grown in darkness present distinct stable isotope patterns, which paves the way for improving our ability to distinguish the contribution of different microbial groups to N2O production in aquatic ecosystems.

This study presents a novel view on water vapour dynamics and the role it plays for atmospheric composition and transport mechanisms.

This study reveals rapid and severe soil degradation following deforestation in the Eastern African Rift, with cropland soils on mafic parent material losing up to 69% of their soil organic carbon. At the same time, reactive metal phases were found to stabilize soil organic carbon in highly degraded mafic soils despite strongly reduced fertility indicators. These findings suggest that soil organic carbon content may not always reflect soil fertility in degraded tropical soils. These results provide important insights for sustainable land management and food security in tropical Africa.

Soltani et al. present an automated workflow for transforming weakly annotated citizen science plant photographs into robust training data for drone-based remote sensing of vegetation. Their approach demonstrates that plant images collected by non-specialists can be effectively leveraged in machine learning models to improve the accuracy of species mapping using drones.