Robust management strategies promoting ecological resilience and economic efficiency of a mixed conifer-broadleaf forest in Southwest Germany under the risk of severe drought

Robust decision-making in forestry seeks solutions that reduce the risk of environmental damage and economic losses, which matters for designing forest adaptation measures. We propose a state-of-the-art methodology to identify robust drought adaptive strategies. First, we used a process-based model with an ensemble of climate change scenarios to simulate managed forest dynamics. Second, we quantified the ecological resilience and financial return as net present value (NPV), applying business-as-usual (BAU) and alternative adaptation strategies to regenerate the overstorey layer (trees >= 30 cm dbh) of the forest (e.g., active, reactive, and donothing). Afterward, we analyzed robustness by searching for the strategy with minimum worst-case losses of ecological and economic signposts. Our analysis found a reduced forest drought resilience under BAU and donothing strategies, determining a high probability of economic failure (- -49% and - -67% loss in NPV respectively). While, an early active strategy would increase the NPV (- +10%), and a highly-intense reactive strategy would have minimal losses (- -2%). Finally, we found that local conditions influence the signpost performance magnitude but do not vary the robust solution. We conclude that our methodology facilitates applying a robustness analysis in forestry, and drought adaptation should occur rather sooner than later, considering constant revision.

Automated summary

Robust decision-making in forestry is important for reducing the risk of environmental damage and economic losses. This study proposes a methodology to identify robust drought adaptive strategies. The analysis found that an early active strategy would increase the net present value (NPV) while a highly-intense reactive strategy would have minimal losses. The study concludes that drought adaptation in forestry should occur sooner rather than later, considering constant revision.