FOREXCLIM (Forests and extreme weather events: Solutions for risk resilient management in a changing climate) was a FP7 ERA-NET project, which ended on March 2021, involved partners from Technical University of Munich, Lund University, and University of Ljubljana. It can be seen as the forerunner of FORECO project.
The aim of the project was to support decision-making in the identification of silvicultural strategies that enable a high degree of stability in the provision of diverse ecosystem services despite the uncertainties related to climate change and extreme events, and timber market developments. It included:
- a large-scale information collection of expert knowledge;
- developing a methodology to simulate the economic damage caused by extreme events in forest stands;
- integrating the management component into the process-based LPJ-GUESS model in order to link the simulation results with the optimization.
With the goal of integrating the experience from forestry practice into the planning of future forest development, researchers carried out an online survey for forest practitioners in Slovenia and Germany (Chreptun et al. 2023). The aim was to investigate the optimized forest compositions to provide different Ecosystem Services (ES). This information was then incorporated into a robust, multi-criteria optimization approach, which was used to identify forest compositions that minimize trade-offs between ecosystem services.
Results showed that the German participants had a strong preference for native tree species in uneven-aged, mixed stands. This forest type occurred frequently in the optimized forest landscapes. Forest types with the structural characteristic “even-aged” or forest types that include exotic tree species, on the other hand, were integrated into the optimized forest landscape rather rarely or only in very small proportions. In contrast, even-aged forest types were more often included in the optimized forest landscapes in the Slovenian data approach. In addition, the Slovenian foresters rated the exotic forest type and the forest without forest management positively. In both countries, the higher the expected uncertainty, the more diverse the optimized forest landscapes became. In Germany, the forest compositions that were optimized for all ecosystem services simultaneously – or carbon storage alone – were most similar to the current forest landscape. In Slovenia, the forest composition optimized for avalanche protection was closest to the current forest landscape.
Economic impacts of natural disturbances on forests were assessed with a new methodology using Monte-Carlo simulations (Knoke et al. 2021). Accounting for the effect of extreme events by analyzing the lowest (worst) 5% of simulated economic returns lead to significantly higher damage than previously assumed. Furthermore, researchers showed that refraining from salvage logging after extreme disturbances did not necessarily result in major economic losses for forest owners, indicating a potential cost-effective avenue to improve forest biodiversity.
Silvicultural measures, including planting selected tree species, thinning stands and harvesting at a specific time, were integrated in the process-based ecosystem model LPJ-GUESS (Lindeskog et al. 2021). In this first application, the modeled carbon stocks and fluxes were compared with forest inventory data at continental and country level. The inclusion of timber harvesting in the simulations increased the total European carbon sink by 32% over the period 1991-2015 and improved the simulation of the observed European carbon sink, growing stock and net annual increment (NAI). The agreement of modeled values and observations for individual European countries varies, but the NAI is generally closer to observations when timber harvesting is included in the simulations.
Lastly, various ecosystem indicators and their changes in a future climate and with simplified management scenarios were calculated with LPJ-GUESS for the whole of Europe and optimal management portfolios were created (Gregor et al. 2022). For each grid cell in Europe, researchers calculated the management portfolio that ensures the best balanced provision of all ecosystem services and is valid for all four assumed representative concentration pathways (RCPs). The current proportion of deciduous trees (45% in the grid cells considered) increased to 59% according to our optimization. Furthermore, the optimization showed that additional areas (about ⅓ of the current managed forest area) with unmanaged forests can be beneficial. However, these positive effects are accompanied by a 15 percent decrease in timber production. Meeting increasing wood demands without transferring ecological impacts elsewhere will require a thorough assessment of trade-offs between Ecosystem Services when developing concrete forest management strategies.
- Rammig, A., Bahn, M., Vera, C., Knoke, T., Paul, C., Vollan, B., … & Thonicke, K. (2020). Adaptive capacity of coupled social-ecological systems to absorb climate extremes. In Climate Extremes and Their Implications for Impact and Risk Assessment (pp. 257-278). Elsevier.
- Chreptun, C., Ficko, A., Gosling, E., & Knoke, T. (2023). Optimizing forest landscape composition for multiple ecosystem services based on uncertain stakeholder preferences. Science of The Total Environment, 857, 159393.
- Knoke, T., Gosling, E., Thom, D., Chreptun, C., Rammig, A., & Seidl, R. (2021). Economic losses from natural disturbances in Norway spruce forests–A quantification using Monte-Carlo simulations. Ecological Economics, 185, 107046.
- Lindeskog, M., Lagergren, F., Smith, B., & Rammig, A. (2021). Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4. 0, r9333): Implementation and evaluation of simulations for Europe. Geoscientific Model Development Discussions, 2021, 1-42.
- Gregor, K., Knoke, T., Krause, A., Reyer, C. P., Lindeskog, M., Papastefanou, P., … & Rammig, A. (2022). Trade‐Offs for Climate‐Smart Forestry in Europe Under Uncertain Future Climate. Earth’s Future, 10(9), e2022EF002796.