A review and conceptual framework for a new era of forest management planning

Baskent, Emin Zeki (author), Bončina, Andrej (author), Borges, José Guilherme (author)

Forests deliver a wide array of ecosystem services. They can be sustainably provided across spatial and temporal scales through ecosystem-based multi-objective forest management planning. The growing complexity of ecological dynamics—driven by climate change, natural disturbances, and evolving societal demands—necessitates a shift from static to adaptive, data-driven planning. Such an approach can simultaneously ensure the provision of desired ecosystem services while safeguarding the ecological integrity of forest ecosystems. This paper examines the theoretical foundations and proposes a conceptual framework for forest management planning, integrating emerging technologies such as Digital Twin (DT) systems and recent scientific advancements in ecosystem based forest management planning. At the core of this approach lies the dynamic coupling of real-time, high-resolution data with clearly defined management objectives and conservation targets rooted in ecological reference conditions. The proposed DT-integrated Forest Management Planning (DT-eFMP) framework facilitates continuous monitoring, scenario testing, and participatory decision-making while maintaining alignment with policy frameworks and sustainability goals. Emphasis is placed on the intelligent design of silvicultural regimes that emulate natural disturbance patterns, enhance forest resilience, and optimize the provision of ecosystem services. By incorporating advanced simulation, forecasting, and learning mechanisms, the framework provides a restructured decision support system capable of managing uncertainty and improving long-term planning outcomes. This work contributes an operationally relevant framework for forest managers and policymakers, offering both a scientific foundation and a practical guide for advancing adaptive and resilient forest ecosystem management in the digital era.

• Language: English
• Keywords: adaptive forest management, ecosystem-based planning, digital twins, learning process, modeling, ecosystem services
• Work type: Article
• Typology: 1.02 - Review Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Author Accepted Manuscript
• Year: 2026
• Number of pages: 20 str.
• Numbering: Vol. 79, art. 101851
• PID: 20.500.12556/RUL-181960
• UDC: 630*61
• ISSN on article: 2212-0416
• DOI: 10.1016/j.ecoser.2026.101851
• COBISS.SI-ID: 275735811
• Publication date in RUL: 21.04.2026

Record is a part of a journal

• Title: Ecosystem services
• Publisher: Elsevier
• ISSN: 2212-0416
• COBISS.SI-ID: 519670809

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: prilagodljivo gospodarjenje z gozdovi, ekosistemsko načrtovanje, digitalni dvojčki, proces učenja, modeliranje, ekosistemske storitve

Projects

• Funder: Other - Other funder or multiple funders
• Project number: QK21010354
• Name: Progressive methods of forest management planning for supporting sustainable forest management

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P4-0059
• Name: Gozd, gozdarstvo in obnovljivi gozdni viri