spatial-prioritization

To design a protected-area network using integer linear programming, you need a cost surface, biodiversity feature layers, planning unit grid, and solver settings to optimize species targets while minimizing costs. This ILP approach ensures spatial compactness and reproducibility.

Systematic conservation planning for 30x30 targets requires a cost surface, species or biodiversity feature layers, a planning unit grid, locked-in protected areas, and explicit targets. Solver settings for HiGHS or Gurobi backends are also needed.

Yes, you can use Gurobi or HiGHS solvers for reserve design and gap analysis. The workflow supports both backends to solve integer linear programming formulations, optimizing biodiversity representation and spatial compactness across R or Python environments.

Irreplaceability analysis in spatial prioritization works by evaluating planning units against biodiversity targets and cost surfaces to identify areas critical for representation. It produces irreplaceability surfaces and cost-effectiveness curves for decision-makers.

The best way to optimize biodiversity representation while minimizing costs is applying integer linear programming to a planning unit grid with feature layers. This maximizes spatial compactness and generates priority maps for scenario testing.

Protected-area prioritization requires explicit targets because the integer linear programming solver needs defined thresholds to optimize biodiversity representation. Without explicit targets, the solver cannot minimize costs or maximize spatial compactness effectively.