Decision Support Systems (DSS) are increasingly important for modern forest management, offering tools to plan, implement, and evaluate strategies that balance production, conservation, and climate adaptation. Integrative Forest Management (IFM) emphasizes multifunctionality safeguarding biodiversity, mitigating climate risks, and sustaining ecosystem services, yet the extent to which current DSS meet these demands remains unclear.

This study presents findings from Deliverable 5.4 of the TRANSFORMIT Horizon Europe project, which assessed DSS capacity to support IFM principles. We developed a Catalogue of DSS, informed by a survey of 42 DSS managers across Eurasia and North America, to evaluate functionalities against 40 IFM-related variables. These variables span forest production, protection, and conservation, including indicators for ecosystem services, disturbance regimes, and biodiversity.

Results reveal a mixed picture. DSS are robust in traditional forestry domains, like estimating timber yield, stand development metrics, and carbon accounting, yet they exhibit significant gaps in IFM-critical areas. Representation of non-wood forest products, recreational values, hydrological services, and soil carbon remains limited, constraining multifunctional forest planning. Similarly, while some DSS simulate abiotic disturbances (storms, wildfires), few address biotic threats (insects, pathogens), reducing their utility for resilience-based management under climate change. Biodiversity support is weakest: most tools rely on structural proxies (e.g., deadwood) rather than species-level indicators or habitat connectivity, limiting their capacity to inform conservation-oriented decisions. Despite these shortcomings, DSS have advanced considerably, enabling multi-objective analyses and holistic assessments that were unattainable a generation ago. They increasingly integrate ecosystem services and climate-related risks, supporting IFM aspirations at multiple spatial scales. However, usability challenges and a research-practice gap persist, as many tools remain tailored to scientific rather than operational contexts.

To fully realize DSS potential for IFM, enhancements are needed in three areas: (i) ecological complexity, i.e., better modeling of biodiversity and habitat dynamics; (ii) disturbance representation, i.e., improved simulation of climate-driven risks; and (iii) user experience, i.e., intuitive visualization and stakeholder-oriented design. Aligning DSS functionality with policy objectives and practitioner needs will be critical for fostering adaptive, multifunctional forestry.

European initiatives like the TRANSFORMIT Horizon project facilitate progress toward this goal, bridging science and practice to develop DSS that enable balanced, evidence-based decisions. By addressing current limitations, DSS can become key enablers of climate-smart, biodiversity-friendly forest management, supporting resilience and sustainability in an era of rapid environmental change.

How to cite: Mazziotta, A., Kurttila, M., and Vacik, H.: Development of Decision Support Systems for Integrative Forest Management: Insights from a Eurasian and North American survey, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1346, https://doi.org/10.5194/egusphere-egu26-1346, 2026.

Societal demands for forest biodiversity and ecosystem services (BES) are growing and diversifying, which necessitates careful decision-making in forest management. Decision support systems (DSS) are a valuable tool to compare different management strategies and model the trade-offs between BES objectives, and they are successfully applied for forest management at the resolution of forest stands and landscapes. However, there is a growing interest in developing DSS at an even finer resolution: the individual-tree level.

We present a systematic review of tree-level decision support systems in forest management, which take individual-tree data as input, apply an optimisation algorithm, and prescribe a management decision for every tree as the output. Tree-level DSS directly include relevant tree attributes in the planning process rather than relying on aggregated proxies at the stand level. This enables a greater flexibility and precision in forest management, which complements the developments in close-to-nature forestry, remote sensing and autonomous forest machines. Our review identified 47 studies that describe a tree-level DSS. These studies use diverse optimisation techniques such as heuristic algorithms, mathematical programming and machine learning to generate the decisions. Several management targets have been addressed in the studies, such as economic value, biodiversity, forest fire risk mitigation and the amenity of the landscape. Thanks to advances in remote sensing, rich information about individual trees can be derived, although the attributes typically gathered during field inventory, like species, tree height and diameter at breast height, are still the most commonly used in decision-making.

Important challenges for the further development of tree-level DSS are to include natural disturbance risk predisposition in the management decisions; to design generalisable approaches that accommodate diverse forest BES, rather than focusing only on specific case studies; to connect tree-level decisions with management plans at larger spatial scales; and to enable the real-world implementation of the optimised decisions. Informed by the findings of our review, we will present our ongoing work on a new tree-level DSS designed to address these challenges.

How to cite: Perry, N., Schweier, J., Bont, L. G., Kushwaha, S. K. P., Peltola, H., Eyvindson, K., Astrup, R., Chapman, M., and Blattert, C.: Tree-Level Decision Support Systems for Forest Management: a Systematic Review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7922, https://doi.org/10.5194/egusphere-egu26-7922, 2026.

Forest decision-making is becoming increasingly complex due to shifting environmental conditions, rising uncertainty, and evolving societal demands linked to climate change, stakeholder preferences, and forest multifunctionality. To support sustainable forest management effectively, Decision Support Systems (DSS) must integrate diverse information and knowledge sources, objectives, and decision contexts, which poses a number of challenges in their conceptual design.

We developed a shared knowledge base for integrated forest DSS by formalizing a domain ontology, building on a decade-long knowledge repository developed in the context of European network activities (e.g., Community of Practice ForestDSS, COST FORSYS, DSS4ES). Through an expert-driven revision and validation process, we refined concepts and definitions, improved structural coherence, and identified missing elements relevant to both current and future decision contexts.

The resulting ForestDSS ontology highlights central components and design elements of forest DSS, with particular focus on climate sensitivity, natural disturbances, ecosystem services, and landscape-scale interactions. By explicitly representing these components (e.g. data, models, methods, user interface) and their relationships, the ontology provides a structured framework to design, document, compare, and evaluate DSS for multifunctional forest management.

This ontology-based knowledge structuring supports improved characterization of existing DSS and accelerates the development of next-generation tools. It enables transparent concept reuse, more consistent integration of models, data, and stakeholder inputs, and clearer communication across disciplines. The ForestDSS ontology thus serves as a collaborative knowledge resource for research, education, and practice, supporting sustainable forest management at the landscape scale.

How to cite: Reuter, S., Griess, V. C., Mazziotta, A., Rosset, C., Vacik, H., Vinogradovs, I., and Díaz-Yáñez, O.: Proposing an Ontology for the Innovative Design of Future-Ready Forest Decision Support Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12626, https://doi.org/10.5194/egusphere-egu26-12626, 2026.

Integrated decision support systems are fundamental for addressing complex issues related to forest ecosystems and the land use sector, such as climate, biodiversity or disturbances, and their impact on industry and society. Therefore, it is important to develop and use tools that can better incorporate potential challenges to forest ecosystems, socio-economic trends and political choices, and show their consequences for multiple natural resources. SiTree is a flexible, cross-platform and open-source individual-tree simulator framework written in R. Simulations produced using SiTree are currently and actively being used to inform policy decisions and in research, from carbon uptake under different management options to the provision of different forest ecosystem services, such as timber production and biodiversity. An overview of the current state with practical examples where SiTree simulation tool is being used will be presented. Future possibilities and capabilities for the development of SiTree will be discussed, with a focus, among others, on better linking land use to social trends and policy-making, predicting large-scale disturbances in forests and estimating the provision of forest ecosystem services.  

How to cite: Sevillano, I. and Antón-Fernández, C.: SiTree - A framework to implement single-tree simulators and its potential as a decision support system for ecosystem services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21362, https://doi.org/10.5194/egusphere-egu26-21362, 2026.

How to cite: Skudra, A., Vinogradovs, I., and Sisenis, L.: Cross-scale integration of ecosystem services into forest planning: structural tensions in developing an integrated DSS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7233, https://doi.org/10.5194/egusphere-egu26-7233, 2026.

Forest management across Europe is confronted with a broad range of uncertainties, including the ecological and economic implications of silvicultural adaptation strategies. Especially in regions with limited forest accessibility, silvicultural constraints, and challenging topographic conditions, the economically viable potential for multifunctional management of forest ecosystem services is determined by its costs. In Swiss mountain forests, for example, costs for timber harvesting and extraction regularly exceed timber revenues; nevertheless, forest management is considered essential to sustain the forests’ protective function against gravitational hazards. Especially under future forest dynamics, it remains unexplored to what extent timber production alone is sufficient to cover forest management costs. Thus, for long-term assessments of management costs across various biogeographic conditions, structured frameworks that integrate both dynamic forest modelling and operational considerations are required to assess potential economic barriers for future forest management.

To close this gap, we present a comprehensive framework to assess socio-economically best suitable timber harvesting methods (BEST) and corresponding harvesting costs in response to long-term forest dynamics. Across the Swiss National Forest Inventory (NFI), we integrated (i) dynamic simulations of alternative management strategies under climate change using the forest model MASSIMO, (ii) technical assessments of state-of-the-art harvesting methods, and (iii) timber harvesting productivity models allowing the estimation of associated harvesting costs.

Our results revealed considerable temporal shifts in BEST portfolios that were mediated by an interplay of varying topographic conditions, differences in forest accessibility, as well as current forest composition and corresponding forest trajectories – for example, with higher shares of air- and cable-based harvesting methods being assigned within the Alpine regions. Further, considerable shifts in harvesting costs in response to long-term forest dynamics were observed. For example, in the Jura, the proportion of managed NFI plots with harvesting costs of < 50 CHF m^-3 decreased from approx. 80 % (year 2023) to 50 % (year 2113) under a management strategy aiming for constant growing stocks. Over the simulation period, mean timber harvesting costs remained comparatively stable in the Swiss Prealps and Alps, whereas long-term increases were modelled for both the Jura and Plateau. Notably, harvesting costs under BEST were consistently lower than those estimated under the continuation of currently applied methods (i.e. as documented within the NFI), highlighting the potential for increased cost efficiency through shifts in harvesting methods.

We conclude that climate- and management-induced shifts in forest dynamics may affect the economically viable potential for forest ecosystem service provision. Especially in regions where management costs outweigh timber revenues, economic assessments and decision-support tools need to adopt a supply-cost perspective by accounting for shifts in harvesting methods and associated costs. Further, the development of strategies aiming for forests’ adaptation to climate change needs to consider their long-term economic and technical implications to proactively identify real-world barriers to successful implementation.

How to cite: Mutterer, S., Schweier, J., Stadelmann, G., Fuchs, J. M., Flury, R., Griess, V. C., Thürig, E., and Bont, L. G.: The costs of providing tomorrow’s forest ecosystem services: A framework for assessing harvesting methods and management costs under future forest dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13424, https://doi.org/10.5194/egusphere-egu26-13424, 2026.

Forested landscape is a highly complex socio-ecological system, wherein timber production and trade is a very important factor in local economies as well as in the well-being of the region. The management of forested landscapes requires multi-stakeholder interventions and decision support tools that can address ecosystem services, risks, and uncertainties at the landscape scale. This study requires the use of a National level database to support the development of a Decision Support System (DSS) linking forest growth, timber supply, and wood quality with marketing mechanisms across nine Italian regions. Data collected were to evaluate first timber market methods across four dimensions, which comprise the following metrics for economic efficiency: prices, net revenue to forest owners, transaction costs, price variability and payment timing. Market access and demand efficiency are assessed through bidder participation, geographical distribution of buyers, timber volumes, administrative constraints, extent of market access, and the allocation of timber for energy use, industrial, functional use, and premium use according to the cascade use principle.  The indicators of operational efficiency include sale duration, harvesting start and duration, logistics of sale responsibility, quality of information, and sustainability certification. Governance and transparency are assessed through regulatory clarity, e-platforms of sales, traceability and EUTR traceability and compliance, conflict incidence, systems of control and enforcement of timber trades, and limitations of protected zones and land use. These findings are used for developing an integrated DSS that is capable of performing multi-criteria analysis, assessment of disturbance scenarios, and visualizing trade-offs among timber production, timber market and biodiversity. This study emphasizes that institutional support and market development will contribute to increasing the value and sustainability of timber and its carbon sequestration capacity, whereas organizational constraints continue to limit market development for central, southern, and island regions. In conclusion, these observations provide support for further development of DSS on Italian forest landscapes, which focuses on dealing with issues of sustainable timber production efficiency, sales, and market efficiency, as well as ecosystem services provision.

How to cite: Jabre, J. and Carbone, F.: Timber Production and Landscape-Scale Decision Support: Evidence from a Nationwide Assessment of Italian Wood Markets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6691, https://doi.org/10.5194/egusphere-egu26-6691, 2026.

We present NEXSOL, the TRANS-SAHARA WEFE Nexus Agroforestry Intervention Design Tool for Climate Resilience, developed within TRANS-SAHARA, an EU-funded Horizon project. NEXSOL is a decision support system (DSS) that translates model outputs into actionable guidance for agroforestry planning in Living Labs in Tunisia, Ghana, and Ethiopia. Positioned within the WEFE (Water–Energy–Food–Ecosystems) Nexus, it aims to support researchers, policymakers, and local authorities in exploring intervention options and assessing trade-offs and synergies that affect ecosystem services, water security, and community resilience across the Greater Northern African Region.

This contribution reports work in progress. NEXSOL is being developed to integrate three complementary modelling strands produced in TRANS-SAHARA: (i) a WEFE Nexus framework that computes cross-sectoral KPIs and applies multi-objective optimization to identify “best” solutions across water, energy, food supply, emissions, and economic dimensions; (ii) an optimization-based agroforestry/land-use allocation model that evaluates socio-economic and environmental costs and benefits under plausible future market, productivity, and policy scenarios; and (iii) climate and species-distribution projections that indicate current and future land-use suitability and related ecosystem-service implications.

Specifically, the methodology comprises: (1) the classification of model families, associated data requirements, and remaining gaps; (2) the compilation and harmonization of multi-source datasets and key performance indicators (KPIs); (3) the implementation of a WEFE modelling environment tailored to Living Lab contexts; (4) the development of optimization-based land-use allocation methods for baseline assessment and scenario exploration; (5) the integration of climate projections and species distribution modelling outputs; (6) the establishment of a reproducible data pipeline encompassing ingestion, quality assurance/quality control (QA/QC), metadata management, and version control; (7) the design of a decision-support system (DSS) user interface featuring dashboards, maps, time series, heatmaps, and structured scenario workflows; and (8) calibration and validation using spatially explicit observations. By coupling WEFE modelling with data-driven prediction and visual analytics, the tool may provide climate-robust, actionable guidance on where and how agroforestry interventions are most effective, thereby advancing multi-objective and multi-risk optimization (e.g., profitability, biodiversity, equity) and incorporating carbon-market and payments-for-ecosystem-services mechanisms. The resulting modular system is co-designed with stakeholders and validated against real-world datasets and decision processes.

This research is funded by the framework of the TRANS-SAHARA project, funded by European Union under the Horizon Europe Framework Program Grant Agreement Nº: 101182176.

How to cite: Chuprikova, E., Berlanda, M., Fröhlich, N., Gardner, E., Yeboah Asamoah, K., Monsorno, R., Bouguerra, S., and Keilmann-Gondhalekar, D.: NEXSOL: A WEFE Nexus Decision Support System for Climate-Resilient Agroforestry Planning in TRANS-SAHARA Living Labs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20611, https://doi.org/10.5194/egusphere-egu26-20611, 2026.

Natural Resource Managers (NRMs) rely on sectoral modelling approaches that are system specific such as agriculture, forests or rivers. Though the tools provide insights for individual natural systems, they are limited by lack of a holistic evaluation approach, that understands the nexus (trade-offs) between natural ecosystems. As a result, NRMs are limited by lack of integrated evidence on how and why adaptation strategies fail to deliver the intended outcomes.

This study developed an integrated decision support framework, that explicitly links agricultural, forests and rivers systems, to support regional NRMs in Southwest Victoria. This multi-framework foundation ensures that outputs align with real planning processes used by NRMs. The framework evaluates agricultural productivity, habitat distribution and water availability under changing climatic conditions. Using geospatial tools, AI-augmented climate modelling and integrating a multi-framework approach – the tool provides a robust streamlined analysis. The pilot workflow integrates an ensemble of machine learning models to map the impacts. Downscaled climate projections (ACCESS-CM2 SSP585, 2020–2100) were combined with biophysical and land-use data to model land suitability for canola, habitat probability for Kangaroo Grass, and stream yield for the Moorabool River. A rigorous preprocessing pipeline of normalisation, correlation check (IrI>0.7), Variance Inflation Factor (VIF<10), and ML ensemble-based feature selection, improved predictive accuracy. System-specific outputs were combined using an index-based overlay approach using Shapely packages. The analytical workflow progresses from Vulnerability zones for each ecosystem > Trade-offs/Synergies > and arriving at Adaptation Tipping Points. The decision support system (DSS) translates fragmented systems into a comprehensive model to support evidence-based decision making.

The DSS is conceptually anchored in an integrated decision-making framework, that adopts key aspects of decision-making frameworks from Integrated Catchment Management, scenario testing from Resilience Thinking framework, and Adaptation Tipping points from Dynamic Adaptive Policy Pathways, enabling outputs to align with decision processes used by regional authorities. It identifies vulnerable zones, trade-off zones, and possible adaptation tipping points under changing climatic and development pressures.

By translating complex model outputs into accessible spatial layers and scenario-based decision products, the DSS lowers technical barriers for NRMs and strengthens evidence-based planning. The framework is scalable and transferable, providing a replicable pathway for integrating ecosystem service assessments into climate adaptation policy and land-use planning across diverse regions.

How to cite: Gampa, R.: AI-Augmented Decision Support System for Evidence-Based Climate Adaptation in Regional Victoria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4144, https://doi.org/10.5194/egusphere-egu26-4144, 2026.

The expansion of global forest cover is a critical component of preserving biodiversity, promoting climate resilience, and improving community well-being worldwide. Yet, the benefits that forests provide vary significantly depending on factors such as structural complexity, species composition, and geographic context. Forest restoration and tree-planting programs therefore present a unique opportunity to intentionally shape the forest's future conditions to achieve target management objectives and deliver specific ecosystem services. However, implementing such programs at the landscape-scale becomes more complicated, and requires balancing the needs of diverse stakeholders and competing management goals. Urbanizing landscapes introduce additional layers of complexity in the form of high population densities and heterogenous land use mosaics, which intensify trade-offs between ecological and socioeconomic priorities. Furthermore, the inherent variability in each landscape's spatial configuration may present unique challenges or opportunities to balance these trade-offs, which may affect the benefits generated by planted forests. The difficulty in balancing this multitude of context-specific factors emphasizes the need for a systematic, data-driven approach which identifies strategic locations for increasing forest cover.

In this study, we present a spatial decision support system (SDSS) designed to locate optimal planting sites within urbanizing landscapes for strategically increasing tree cover and ecosystem service provisioning. The SDSS analyzes high-resolution geospatial data to identify and rank available planting locations, simulate potential implementation strategies, and integrate external models to quantify potential outcomes. Upon completion, a detailed inventory of identified sites is generated, which provides actionable information including the size and geographic coordinates of each site. The inventory also provides a concrete foundation for quantifying specific ecosystem services, such as carbon sequestration and storage potential or pollution removal. These estimates can then be evaluated alongside stakeholder priorities and management goals to identify areas where forest expansion will yield the greatest benefits. Overall, the SDSS's scalable nature aids decision-making by considering services generated locally by individual trees as well as collectively by entire forests—thus offering comprehensive, actionable insights for sustainable and effective landscape management.

This presentation will highlight a case study from the United States that explores the impacts of different forest expansion scenarios, and the SDSS's capacity to strategically inform forest restoration and expansion efforts and enhance ecosystem service provisioning worldwide.

How to cite: Smolensky, A. and Halsey, S.: Context-Driven Optimization of Ecological and Socioeconomic Benefits through Urban Forest Expansion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14268, https://doi.org/10.5194/egusphere-egu26-14268, 2026.