Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions:accuracy of local predictions(agreement of modelled and observed annual data),realism of environmental responses(agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) andgeneral applicability(proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Unfortunately, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a state-of-the-science biogeochemical, biophysical, validated process-based model on historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions (i.e., forests are left to develop undisturbed). Such a model, forced by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario to disentangle the effect of climate change, spanned several age classes as representative of the current European forests' context, for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with longstanding ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO₂ and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age class diversity -lacking in most, if not all, the current Global Vegetation Models - for reliable and robust assessments of the impacts of climate change on future forests' stability and resilience capacity. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies.

Purpose of Review: Forests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time. This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals. Recent Findings: Forest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management. Summary: Amid the ongoing’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.