spatial-prioritization

Plan protected-area networks with integer linear programming for biodiversity targets.

2|Updated Mar 2, 2026

Search Tags:#prioritization #reproducibility #cost-analysis #conservation #ILP #planning-units #irreplaceability

Ready to run

npx skills add https://github.com/baratadiego/ecological-agent-skills --skill spatial-prioritization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spatial-prioritization
Source: https://github.com/baratadiego/ecological-agent-skills/tree/main/skills/spatial-prioritization
Command: npx skills add https://github.com/baratadiego/ecological-agent-skills --skill spatial-prioritization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic conservation planning helps AI agents identify priority protected-area networks that efficiently represent biodiversity targets while minimizing cost and maximizing spatial compactness, using ILP formulations like prioritizr.

Core Features & Use Cases

Guided ILP-based planning for 30×30 and target-driven conservation networks.
Produces outputs such as priority maps, irreplaceability surfaces, and cost-effectiveness curves for decision-makers.
Use cases include reserve design, gap analysis, and scenario comparisons in R/Python workflows.

Quick Start

Run a baseline prioritization using your cost raster and SDM/Biodiversity feature layers to generate a protected-area network.

Frequently Asked Questions about spatial-prioritization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design a protected-area network using integer linear programming?▼

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.

What data is required to run systematic conservation planning for 30x30 targets?▼

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.

Can I use Gurobi or HiGHS solvers for reserve design and gap analysis?▼

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.

How does irreplaceability analysis work in spatial prioritization?▼

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.

What is the best way to optimize biodiversity representation while minimizing costs?▼

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.

Why does my protected-area prioritization require explicit targets?▼

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.