Bringing Uncertain Ecosystem Services into Forest Optimization

• Applicant: Professor Dr. Thomas Knoke
• Subject Area: Forestry
• Term: from 2018 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 418938102

Forest ecosystems provide numerous services for human welfare, but it is less clear how ecosystem services (ES) can be considered in forest optimization, particularly, if the ES are uncertain. Various methods of supporting forest decision-making under multiple objectives may provide valuable opportunities to improve forest optimization. ES may be described quite well with appropriate indicators, which are already partly available. Among the continuous 8optimization methods for considering multiple criteria, are promising approaches which minimize undesirable outcomes (compromise programming). These approaches can be combined well with robust optimization. Robust methods are often based on non-stochastic optimization using pre-defined uncertainty spaces. An alternative to multi-criteria decision methods for acknowledging the importance of ES in forest management would be to consider economic value coefficients in forest optimization. These could be integrated into modern portfolio approaches to support decisions about the optimal future forest composition and management. A consistent consideration of all ES based on their economic value has appeal, but also bears risks. If a certain ES is valued very highly, it could displace other essential ES which may still be important in the future. This research proposal would like to develop and analyse an optimization approach to test the consequences of integrating ES, and their uncertainties based on various information sources, into forest optimization. This approach must be capable of considering either multiple criteria to characterize ES or aggregated economic value coefficients for the optimization process. Such an approach would allow the influence of the integration of ES to be analysed, based on different information sources for comparable ES, on the resulting optimal forest composition and management.

• DFG Programme: Research Grants