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This study addresses the need for a cohesive pan-European forest monitoring system by developing a methodological framework to generate and provide spatially explicit and complementary indicators of forest dynamics. Utilizing Copernicus High-Resolution Layer Tree Cover Density data, we operationalize two key indicators-forest extent and condition-essential for robust forest monitoring across Europe. Our multi-step data processing methodology enhances data interoperability and usability, mitigating biases. By integrating both, changes in forest area and canopy density between 2012 and 2018, our approach provides nuanced insights into forest dynamics. These indicators offer robust monitoring supporting the assessment of forest resilience amidst climate change impacts and other stressors. This paper contributes a ready-to-use dataset on European forest dynamics, leveraging advanced technologies and big data availability to support sustainable forest management and the evaluation of Agenda 2030 goals. • Development of spatially explicit indicators for forest extent and condition. • Integration of Copernicus HRL TCD data using a standardized processing framework. • Application of multi-step data processing to ensure data quality and reliability.
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http://dx.doi.org/10.1016/j.mex.2025.103303 | DOI Listing |
Plant Cell Environ
September 2025
Department of Landscape Architecture, Zhejiang Sci-Tech University, Hangzhou, China.
Sugar metabolism is commonly implicated as crucial in the transition between growth and cessation during winter; however, its exact role remains elusive. The evergreen iris (Iris japonica) ceases growth in winter without entering endodormancy, yet it continues to sustain sugar metabolism and transport throughout the season. Here, we elucidate the mechanisms underlying the sugar-mediated growth transition-the shift between growth and cessation-in I.
View Article and Find Full Text PDFAmbio
September 2025
Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm, Sweden.
This study investigates how the seven core resilience principles are integrated into assessments of forest system resilience to natural or human-induced disturbances across engineering, ecological, and social-ecological resilience concepts. Following PRISMA guidelines, a literature search in the Web of Science database using the keywords "resilience", "forest" and "ecosystem services" yielded 1828 studies, of which 330 met the selection criteria. The most commonly used criterion was diversity, a sub-criterion of "diversity and redundancy", appearing in 50% of studies.
View Article and Find Full Text PDFSci Rep
September 2025
Grupo de investigación en Biología Matemática y Computacional (BIOMAC), Departamento de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia.
Snakebite envenoming is a neglected tropical disease that affects mainly rural populations, where antivenom is scarce. Understanding environmental drivers of snakebite incidence is critical for public health preparedness. This study employs causal inference to assess the impact of rainfall on snakebite surges in Colombia, with broader implications for tropical regions.
View Article and Find Full Text PDFMath Biosci
September 2025
Department of Mathematics, Western University, London, Ontario, N6A 5B7, Canada. Electronic address:
Pine wilt disease (PWD) is mainly spread by Monochamus alternatus (in short, M. alternatus). Woodpecker, as the natural predator of M.
View Article and Find Full Text PDFBioresour Technol
September 2025
State Key Laboratory of Bioreactor Engineering, Qingdao Innovation Institute of East China University of Science and Technology, East China University of Science and Technology, Shanghai 200237, China; National Center of Bio-Engineering & Technology (Shanghai), East China University of Science and T
α-Amylase is a high-value enzyme widely applied in food, feed, textile, and bioenergy industries, yet achieving stable high-level production in Aspergillus niger remains difficult due to nonlinear fermentation dynamics and limited real-time control. To this end, an AI-driven fermentation optimization framework was established by combining multivariate machine learning, Raman spectroscopy-based glucose monitoring, and time-series transcriptomics. Twelve algorithms were benchmarked, with Random Forest showing the best predictive power, while SHAP interpretation highlighted glucose as the dominant regulatory factor.
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