Balancing ecological benefits and feasibility in large-scale restoration planning: A multi-objective optimization decision-support framework

Ecosystem restoration requires decision-support tools capable of balancing ecological benefits under limited resources while explicitly accounting for the long-term likelihood of restoration success. Despite its recognized importance, ecological feasibility has rarely been formulated as an optimization objective in spatial planning, typically being treated only as a constraint or biophysical filter. Here, we present a multi-objective optimization framework for large-scale restoration planning that explicitly incorporates ecological feasibility alongside biodiversity and ecosystem service objectives. The framework is grounded in Systematic Conservation Planning and formulated as a Mixed-Integer Linear Programming model. We jointly optimize four criteria—extinction risk reduction, carbon storage, water provision, and ecological feasibility—under a fixed restoration area budget and land suitability constraints. Optimization proceeds in two steps: single-objective benchmark solutions are first obtained to define aspiration levels, which are then integrated using Goal Programming to generate balanced compromise solutions and characterize trade-offs. Ecological feasibility is modeled as a socio-ecological property linked to recurrent disturbance regimes, operationalized through spatial proxies of wildfire frequency, anthropogenic pressure, and invasive species richness. Applied to continental Chile using 812,910 units at 1 km² resolution, incorporating feasibility leads to significant spatial reconfiguration: approximately 16.6% of selected units (under a 30% restoration target aligned with Target 2 of the Kunming–Montreal Global Biodiversity Framework) are replaced relative to solutions that ignore feasibility. This shift preserves total restored area while increasing feasibility performance by 11.1 percentage points, with minimal reductions (below 5%) in the remaining objectives (−4.1 for carbon storage and −2.4 for water provision). Spatial diagnostics based on selection frequency reveal that compromise solutions are anchored in a "backbone" of multipurpose planning units (˃ 80% of selected units), complemented by specialized units that resolve localized trade-offs. Overall, the proposed framework provides a transparent and transferable approach for integrating ecological feasibility into spatial restoration planning, enabling more robust and policy-relevant decision-making.