A decision making approach for assignment of ecosystem services to forest management units: A case study in northwest Turkey

In general, studies are carried out to determine the location and to resolve trade-offs in the management of forests according to ecosystem services. The government of Turkey also has been advancing this type of analysis in the practical management of forests. The creation of ecosystem services (ES) suitability map for forests through a participatory approach can provide forest managers valuable information that informs ecosystem-based management plans. The objective of this paper is to formulate and solve the problem of assigning management actions to forest stands in order to optimally provide ecosystem services that are consistent with, and based on Sustainable Development Goals (SDG). We outline a model for a participatory process that has seven steps: (1) determine the possible ES that can be supported on the landscape, (2) create a suitability map for each management unit (stand of trees), (3) define SDG for each ES, (4) create the stakeholder groups, (5) determine the importance of each SDG, (6) determine the contribution of ES to each SDG, and (7) assign ES to each management unit in an optimal manner. We used pairwise comparisons and the Delphi method to determine stakeholder preferences. This information guided the assignment of management actions to management units when optimizing ES outcomes. This approach was applied to a case study centered on the Belgrad Forest, located in northwest Turkey. The percent of land assigned to ES was respectively, water regulation (71%), cultural heritage (17%), education (9%), water supply (2%), and carbon (1%). Through this process, for this case study area, there were no assignments for recreation and aesthetic quality services. As a result, the Belgrade Forest was zoned into management classes unlike the current situation. The pairwise comparisons and the Delphi method can help formalize public participation in the decision-making process. Results from these can inform a resource allocation optimization model through the development of coefficients and constraints.