Publications

Close-to-nature forestry (CNF) is considered an effective strategy to adapt forests to climate change while sustaining ecosystem services and biodiversity (BES). However, for forest management it remains unclear whether current CNF strategies sufficiently reduce forests’ predisposition to climate-change-induced shifts in disturbance regimes. To address this increasing complexity, we introduce the integration of a climate-sensitive forest gap model with assessments of predisposition to fire, bark beetle, and windthrow disturbances, as well as BES provision. We conducted simulations for a forest enterprise in the Central Swiss Alps, covering a large elevation gradient, under three climate scenarios (historical, SSP2–4.5, and SSP5–8.5). The simulations additionally considered six management strategies, including CNF variants with different management intensities and climate-adapted approaches. Our results indicate that climate change will dynamically alter disturbance predisposition across elevation gradients. Site-related predisposition to fire and bark beetle infestation generally increased under climate change, while stand-related predisposition to all disturbances varied with climate scenario and elevation. Under moderate warming (SSP2–4.5), stand-related predisposition to fire and windthrow increased across all elevations. In contrast, under severe warming (SSP5–8.5), long-term reductions in stand-related predisposition to fire, bark beetle infestation, and windthrow occurred at lower elevations due to climate-change-induced shifts in forest dynamics, while predisposition increased at higher elevations with improved growing conditions. Our results further show that increasing management intensity generally reduces stand-related disturbance predisposition but focusing purely on disturbance mitigation can also lead to trade-offs, such as reduced BES provision. We conclude that climate-adapted forest management must account for both stand-related and site-related predisposition to prioritize disturbance-prone ‘hotspots’, especially in areas of high BES value. Proactively reducing disturbance predisposition may involve trade-offs regarding BES provision but may be crucial to avoid potential BES losses from severe disturbances. As climate change may alter trade-offs between BES and disturbance mitigation, we underscore the need for decision support systems in long-term forest planning to account for conflicting management objectives. © 2025 The Authors

The vulnerability of forests to wind damage depends to a large degree on the characteristics of the specific stand and its neighboring stands, making forest management a key action in modifying the forest's wind damage vulnerability. Thus, by strategically planning where and when different forest management activities are scheduled to happen, forest managers can influence a forest's vulnerability to wind damage. In this study, we present a long-term forest planning model that identifies optimal forest management activities accounting for this specific vulnerability. The main decision in the model concerns the management of each individual stand throughout the planning horizon when the objective is to fulfil traditional long-term forest management goals and also to reduce the vulnerability to wind damage. In the model, consideration of wind damage is included by banning management activities such as final fellings in stands adjacent to highly vulnerable stands. Furthermore, the optimization model applied is specifically structured to be solvable using exact solution techniques. The model is evaluated for a case study area of 2450 hectares in southern Sweden for a 70-year planning horizon. Results suggest that it is possible to incorporate wind damage considerations into a long-term harvest scheduling problem. The proposed model excels in its ability to offer flexibility, allowing users to freely modify the settings in the model to choose their definition of vulnerability to wind damage. In addition, the model can be included in a traditional decision support system for forest planning utilizing exact solution techniques. © 2025 The Author(s)

Augmented Reality (AR) is revolutionizing various industries by enabling the real-time, context-driven integration of data into the physical environment. In forestry, AR offers opportunities to enhance operational efficiency, improve occupational safety, and support sustainable management practices. This study explored AR's potential in forestry through qualitative interviews with a diverse range of stakeholders, including forestry professionals and AR experts from multiple countries. The interviews, analyzed using qualitative content analysis, revealed a wide range of AR applications—spanning forest inventory, silvicultural operations, disaster management, logistics, and stakeholder communication. Key findings highlighted AR's ability to optimize processes such as real-time hazard detection, automated forest inventory, and interactive data visualization. The study also underscored AR's role in improving occupational safety, reducing errors, and supporting decision-making. Technologies such as Mixed Reality smart glasses integrated with GPS and Real-Time Kinematic systems were identified as essential for achieving the precision required. However, the study acknowledged barriers to implementation, including high acquisition costs, limited device durability, and user resistance due to discomfort and device complexity. Recommendations include the development of user-friendly applications featuring voice-controlled interfaces and modular visualization modes to improve usability and prevent information overload. Despite current limitations, AR holds potential to transform forestry by streamlining workflows, reducing environmental impact, and enhancing occupational safety. This research offers insights for AR software developers, hardware manufacturers, and forestry stakeholders, providing a roadmap for innovation and implementation. By overcoming identified barriers and leveraging AR's capabilities, forestry could achieve substantial improvements in efficiency and sustainability. © 2025 The Authors

Climate change is creating an urgent need to implement adaptation strategies in forest ecosystems. Assisted migration (AM) of species or genotypes adapted to future climate is one promising strategy, but operational implementation is challenging and likely varies across ownerships. We conducted an online survey (n = 174) of federal, state, Tribal and private industrial forest managers in Washington, Oregon, and California to understand individual and organizational practices around climate adaptation, perceived barriers to AM, and differences between private industry and public agencies. Most respondents expressed personal concern about climate change, and 77% have adjusted their reforestation practices as a result. Over half have implemented AM; species mix changes, increased vegetation control, and decreased planting densities were also common. Individual respondents often indicated multiple changes, suggesting inclination toward bet-hedging approaches to adaptation. Although > 90% of respondents indicated organizational concern about climate change, a similar number identified organizational barriers to AM, most commonly availability and knowledge of seed sources and uncertainty of outcomes, with policy and social barriers more common for public agencies. If barriers are addressed, there is greatest potential to increase AM on private industrial land given lower current use than on public land and high planting rates (89% of reforestation) at high densities (> 300 trees per acre). These findings highlight the need for research to support AM implementation across tree species, decision support tool training, and increased seed transfer among organizations. Addressing implementation barriers is critical for supporting appropriate use of AM for climate adaptation in western US forests. © The Author(s), under exclusive licence to Society of American Foresters 2025.

The goal of this study was to develop a GIS-based Decision Support Model for selecting the best timber harvesting systems on steep terrain. The model combines multiple layers, each representing an important factor in mechanized logging. These layers are used to create a final map that functions as a spatially explicit Decision Support Model that helps decide which machines are best suited for different forest areas. A key idea of this study is to consider not only operational criteria (slope, ruggedness, wetness, and road accessibility), but also a fundamental silvicultural aspect, i.e., the assessment of tree growth classes to enable the integration of silvicultural deliberations into timber harvest planning. The data used for this model come from orthophoto image and a Digital Terrain Model (DTM). The operational factors were analyzed using GIS tools, while the silvicultural aspects were assessed using the deep learning algorithm DeepForest and tree growth equations (allometric functions). The model was tested by comparing its results with field data taken in a Norway Spruce stand in South Tyrol/Italy. The findings show that the model reliably evaluates operational factors. For silvicultural aspects, it tends to underestimate the number of small trees, but provides a good representation of tree size classes within a forest stand. The innovation of this method is that it relies on low-cost, open-source tools instead of expensive 3D scanning devices. © 2025 by the authors.

Forest Management Priority (FMP) refers to the allocation of limited resources in forestry to achieve pre-established objectives. In Mediterranean forests, wildfire suppression is a primary focus yet challenges arise when forest management needs to be actively implemented. Additionally, productivity, a critical factor in FMP, is often overlooked. In response, we propose (1) a method to estimate forest productivity using remote sensing and (2) the integration of this data into a GIS-based Multiple-Criteria Decision Analysis (MCDA) framework with a participatory approach to propose a novel FMP index for Mediterranean forests. This method aims to enhance FMP by guiding resource allocation to key areas, using the island of Ibiza as a test case. Our approach to mapping forest productivity yielded a 20.4 % relative error in site index and 43.5 % in mean annual increment. Incorporating this data into the GIS MCDA allows decision-makers to evaluate multiple information layers also including wildfire risk, terrain slope, forest stress, accessibility, and landscape visibility. We tested five prioritization scenarios: high productivity, environmental protection, wildfire risk management, a multipurpose scenario, and a business-as-usual scenario. When comparing the FMP index distribution, most scenarios showed a broader prioritization of areas than current practices, highlighting opportunities for improvement. In the high productivity scenario, 0.903 M m3 of timber were categorized as high priority for management, translating to a mean annual growth of 20,539 m3. We believe this work provides a valuable framework for stakeholders to adopt better forest management practices, promoting bioeconomy and optimizing the use of limited public and private funds. © 2025

Sustainable forest management (FOMA) requires explicit knowledge of the ecosystem services (ES) provided by forests and how they can be improved by different FOMA alternatives, especially when threatened by climate change and the productivity/profitability is low. Decision Support Systems (DSS) have evolved as powerful tools that facilitate decision-making in multi-objective FOMA. However, their use is often limited, as they are mono-objective, are based on empirical relationships, consider limited ES or do not define which silvicultural actions can be deployed, where and when. Thus, decision-makers and forest planners cannot adjust them to local conditions or specific criteria. CAFE (Carbon, Aqua, Fire & Eco-resilience) is a multi-objective DSS for FOMA that quantifies and optimizes different ES that stem from forest management. Its main contribution is coupling eco-hydrological process-based models and multi-objective optimization with genetic evolutionary algorithms. The output of the DSS, shown in a user-friendly interface, is a selection of the best-performing solutions (Pareto Optimal Front) of FOMA that optimize the user-selected ES. This tool allows designing and planning silvicultural operations such as thinning or planting required for meeting multiple objectives in FOMA by answering four fundamental questions: How much (thinning intensity or plantation density), where (spatial allocation), when (year of next intervention) and how (target forest strata in which the stand is vertically divided) thinning. This paper presents the design and components of CAFE, and a practical demonstration in contrasted forests. CAFE might contribute to addressing current challenges of meeting global environmental policy goals by stating baselines and additionality in FOMA. © 2025 Elsevier Ltd

Plants serve as the basis for ecosystems and provide a wide range of essential ecological, environmental, and economic benefits. However, forest plants and other forest systems are constantly threatened by degradation and extinction, mainly due to misuse and exhaustion. Therefore, sustainable forest management (SFM) is paramount, especially in the wake of global climate change and other challenges. SFM ensures the continued provision of plants and forests to both the present and future generations. In practice, SFM faces challenges in balancing the use and conservation of forests. This review discusses the transformative potential of artificial intelligence (AI), machine learning, and deep learning (DL) technologies in sustainable forest management. It summarizes current research and technological improvements implemented in sustainable forest management using AI, discussing their applications, such as predictive analytics and modeling techniques that enable accurate forecasting of forest dynamics in carbon sequestration, species distribution, and ecosystem conditions. Additionally, it explores how AI-powered decision support systems facilitate forest adaptive management strategies by integrating real-time data in the form of images or videos. The review manuscript also highlights limitations incurred by AI, ML, and DL in combating challenges in sustainable forest management, providing acceptable solutions to these problems. It concludes by providing future perspectives and the immense potential of AI, ML, and DL in modernizing SFM. Nonetheless, a great deal of research has already shed much light on this topic, this review bridges the knowledge gap. © 2025 by the authors.

Forest plantations hold substantial promise for effective management of biomass through forestation of understocked forests to achieve optimal carbon management. This study investigates active forest management to explore trade-offs between timber and carbon sequestration by analyzing four management scenarios with ETÇAP model in a forest area in Türkiye, adhering to national management guidelines. The results highlight the significance of selecting appropriate tree species and plantation levels to harmonize ecosystem services. Plantations with higher amounts offer greater opportunities for harvested volume and carbon stock. Black pine appeared as a superior performer of carbon stock (204.98 Mg ha−1) compared to other tree species, while hardwood species enhance soil carbon and habitat. The living and litter carbon showed substantial increases, surpassing 100 Mg ha−1 across all strategies. Cumulative carbon and balance variations stem mainly from forest growth, with softwood plantations achieving the highest increment of 1.9–5.6 m³ ha⁻1 year⁻1 by 2110. The study highlighted the critical role of forest soil and living carbon in driving carbon dynamics. It is essential to formulate appropriate management strategies when choosing tree species and their planting rates for climate-smart forestry, pinpointing a notable limitation that the carbon stock is calculated regardless of variations in tree sizes. © 2025 Elsevier Ltd

Technological progress in the last decades has driven great advances in many fields of knowledge. A wide range of tools and services are now available and constantly evolving to handle vast amounts of available data as well as the increased complexity of real-world case studies and analytical alternatives. Most sectors have embraced new methodologies to provide solutions to their problems, and the forestry sector is no exception. Important steps have been taken to update the forestry sector and introduce new large-scale experimental designs, digital tools and more extensive forestry databases. However, assimilation of this progress by forest managers remains largely pending. The more specialized technical knowledge and computing skills required to use this new generation of tools constitutes a known barrier to uptake. In this work, we present the SIMANFOR cloud-based Decision Support System service for simulating forest management alternatives. Its evolution, internal structure and potential applications are described. A case study was developed to demonstrate simulator performance under diverse management scenarios and highlight the benefits of this tool for forest managers. SIMANFOR cloud services are free and can be accessed at www.simanfor.es. © 2024