Mapping ecosystem services trade-offs as a decision tool for comprehensive ecological land-use planning

Comprehensive ecological land-use (LU) planning is inherently multi-objective, as it requires the assessment of spatial development suitability across three core objectives: food security, ecological security, and living space. This process involves a complex many-to-many matching problem between two interacting components: multi-suitable land covers (LCs) and competing LU demands. To address this challenge, this study introduces the Mapping Ecosystem Services Trade-offs (MEST) framework, a novel spatial approach designed to resolve such many-to-many relationships. The framework is grounded in nonlinear scientific thinking, which emphasizes relational understanding of complex phenomena, and integrates this perspective with spatial mapping techniques. Specifically, MEST leverages ecosystem services (ES) trade-offs and cascade relationships through a divergent reasoning approach tailored to complex spatial interactions. Eight essential ES are quantified and spatially mapped, both individually and interactively, at a socially relevant scale using a monetary valuation method. Autocorrelation, correlation, and clustering analyses are then employed to identify six distinct socio-ecological subsystems: the Food Provisioning Zone, Eco-Protection Zone, Eco-Transition Zone, Urban Tourism Zone, Ecologically Vulnerable Zone, and Ecological Harmony Zone. Each subsystem represents an area in which ecosystem functions do not produce counterbalancing or mutually constraining effects. Based on these results, three strategic principles are proposed to restructure production, living, and ecological spaces in Khorasan, guiding the spatial delineation of Agricultural Production Areas, Urban Construction Areas, and Ecological Protection Areas. This work addresses a persistent challenge in sustainable development—reconciling competing LUs without diminishing the supply or resilience of vital ES. Overall, the MEST framework demonstrates strong potential for resolving LU conflicts through a simple yet effective algorithm, offering a practical and relationship-oriented approach to sustainable LU management.