Data and Code for "Navigating Spatial Trade-offs in Restoration Planning: A Multi-Objective Optimization Framework Integrating Ecological Feasibility"

This repository contains the data and code associated with the manuscript "Navigating Spatial Trade-offs in Restoration Planning: A Multi-Objective Optimization Framework Integrating Ecological Feasibility". The study presents a large-scale spatial restoration planning framework based on Mixed-Integer Linear Programming (MILP) and Goal Programming. The framework explicitly integrates ecological feasibility as a restoration objective alongside extinction risk reduction, carbon storage, and water provision, enabling the identification of balanced restoration priorities under a fixed restoration budget and land suitability constraints. The methodology is applied to continental Chile using 812,910 planning units at 1 km² spatial resolution. The repository includes the datasets, preprocessing workflows, and optimization models required to reproduce the results presented in the manuscript. Particular emphasis is placed on evaluating how the explicit inclusion of ecological feasibility influences restoration priorities, objective trade-offs, and the spatial structure of compromise solutions. The repository provides a transferable optimization framework that can be adapted to restoration planning problems in other regions and ecological contexts.