China's massive reforestation effort has transformed more than 77 million hectares of land, but new research shows that planted forests face different drought risks from natural forests. Using satellite observations from 1990 to 2024 and machine learning analysis, researchers found that planted forests are more vulnerable to rapid-onset flash droughts but less prone to prolonged slow droughts. The differences are linked to forest structure: natural forests have greater species diversity, more complex canopies, and deeper root systems that help buffer sudden water shortages, while planted forests are typically younger and less diverse. The findings highlight a trade-off in drought resilience and suggest that future forest management should prioritize mixed-species, structurally diverse forests to better withstand increasing climate extremes.

A new commentary in Forest Ecosystems warns that peer review in forestry journals is under mounting pressure: published articles have quadrupled since 2000 while the reviewer workforce has shrunk. The authors examine how AI tools could help fill the gap, finding they are useful for catching technical errors but cannot replace the deeper judgement of human experts. They call on journals to experiment with new review models, consider compensating reviewers, and develop clear policies for responsible AI use.

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.