Researchers at The University of Manchester in collaboration with Hebei University of Technology have identified how a new class of catalyst can break down lignin – one of the most abundant components of plant biomass – into useful chemical building blocks, offering a more sustainable route to replace fossil-based materials.

This study, published in Forest Ecosystems, examines what controls the stability of forest regeneration under climate variability using 15 years of seedling data from a temperate forest in northeastern China. It finds that the stability of dominant species is the primary driver of consistent seedling survival and recruitment, outweighing the roles of biodiversity and species asynchrony. Seasonal drought reduces regeneration stability by weakening both dominant species and compensatory dynamics among species, while deeper snow enhances stability by buffering environmental stress. Local factors further shape these patterns, with dense tree neighborhoods reducing survival stability but sometimes supporting more stable recruitment. Overall, the results show that forest regeneration is highly sensitive to climate change, and that maintaining stable dominant species is key to sustaining forest resilience.

This study analyzed 2000–2023 data from China’s arid Three-North region to assess how vegetation greening affects water use. While leaf area and transpiration increased, the sensitivity of transpiration fraction to further greening declined by 32%, mainly due to soil moisture limitations. Wetter areas showed lower sensitivity, indicating saturation, whereas drying regions experienced the steepest reductions. Even with continued leaf area index growth, water stress constrained hydrological impact. The findings highlight that vegetation restoration boosts water fluxes but is increasingly limited by water availability, informing sustainable ecosystem and water management strategies.

Researchers at the Swedish University of Agricultural Sciences (SLU) evaluated regression techniques to address "design-bias" in forest mapping, where low volumes are overestimated and high volumes underestimated. Their proposed MAVGAR method effectively reduced bias with high-quality data like laser scanning while maintaining accuracy. For weaker satellite data, classical calibration worked best for bias reduction, while ordinary least squares remained suitable for minimizing overall error. Published in Forest Ecosystems, the study offers guidance for improving forest maps used in carbon accounting and biodiversity monitoring.

Sub-headline: Zhejiang University researchers introduce K-SELinux, leveraging the K framework to automate the verification of complex security policies