TY - JOUR
T1 - Optimizing forest landscape composition for multiple ecosystem services based on uncertain stakeholder preferences
AU - Chreptun, Claudia
AU - Ficko, Andrej
AU - Gosling, Elizabeth
AU - Knoke, Thomas
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1/20
Y1 - 2023/1/20
N2 - Determining the desirable composition of a forested landscape and its associated ecosystem services (ES) is challenging because the solutions must reconcile the preferences of various forest stakeholders and account for uncertain data. By combining multi-objective robust optimization with an online survey of forest professionals in Slovenia (n = 130) and forest professionals, forest scientists, nature conservationists and forest owners in Germany (n = 649) about optimal forest landscape composition, we derived compromise portfolios of forest types. These portfolios minimize the trade-offs between five ES (stopping avalanches, carbon storage, recreation, timber production and regulating flows of water), and account for the varying capacity of eight forest types to supply ES. The resulting optimized forest landscape compositions always comprised at least two forest types. In both countries, uneven-aged native deciduous and conifer mixed stands were prominent in the optimized portfolios. In Germany, however, the optimized portfolio also contained exotic species in mixtures, whereas forest stands without active management were notable for several ES in Slovenia. Unmanaged forest stands were also selected in the forest composition optimized for nature conservationists in Germany: the nature conservationists' portfolio diverged strongly from those of the other stakeholders. Our results illustrate that diversified forested landscapes provide multiple ES, but also secure the provision of a single ES when accounting for uncertainty. The optimal forest compositions obtained by multi-objective robust optimization are a starting point for participatory planning approaches to identify the most socially acceptable strategies for adapting forest management to an uncertain future.
AB - Determining the desirable composition of a forested landscape and its associated ecosystem services (ES) is challenging because the solutions must reconcile the preferences of various forest stakeholders and account for uncertain data. By combining multi-objective robust optimization with an online survey of forest professionals in Slovenia (n = 130) and forest professionals, forest scientists, nature conservationists and forest owners in Germany (n = 649) about optimal forest landscape composition, we derived compromise portfolios of forest types. These portfolios minimize the trade-offs between five ES (stopping avalanches, carbon storage, recreation, timber production and regulating flows of water), and account for the varying capacity of eight forest types to supply ES. The resulting optimized forest landscape compositions always comprised at least two forest types. In both countries, uneven-aged native deciduous and conifer mixed stands were prominent in the optimized portfolios. In Germany, however, the optimized portfolio also contained exotic species in mixtures, whereas forest stands without active management were notable for several ES in Slovenia. Unmanaged forest stands were also selected in the forest composition optimized for nature conservationists in Germany: the nature conservationists' portfolio diverged strongly from those of the other stakeholders. Our results illustrate that diversified forested landscapes provide multiple ES, but also secure the provision of a single ES when accounting for uncertainty. The optimal forest compositions obtained by multi-objective robust optimization are a starting point for participatory planning approaches to identify the most socially acceptable strategies for adapting forest management to an uncertain future.
KW - Forest stand types
KW - Long-term forest composition
KW - Multi-objective decision-making
KW - Robust optimization
UR - http://www.scopus.com/inward/record.url?scp=85140434860&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.159393
DO - 10.1016/j.scitotenv.2022.159393
M3 - Article
C2 - 36265632
AN - SCOPUS:85140434860
SN - 0048-9697
VL - 857
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 159393
ER -