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landscape-connectivity

Quantify landscape connectivity with graph-theoretic indices and Circuitscape.

2|Updated Mar 2, 2026
Search Tags:#graph-theory#landscape-connectivity#Circuitscape#resistance-surface#patch-metrics#least-cost-path#connectivity-analysis
One-click install
npx skills add https://github.com/baratadiego/ecological-agent-skills --skill landscape-connectivity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: landscape-connectivity
Source: https://github.com/baratadiego/ecological-agent-skills/tree/main/skills/landscape-connectivity
Command: npx skills add https://github.com/baratadiego/ecological-agent-skills --skill landscape-connectivity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, networkx, and includes scripts (resource) components.

What problem does it solve?

Analyzes landscape connectivity by combining graph-theoretic indices with resistance-based models to identify important habitat patches and corridors for conservation planning.

Core Features & Use Cases

  • Graph-based connectivity metrics: IIC, PC, dIIC, and dPC to quantify patch and landscape importance.
  • Resistance surface integration: Build and apply resistance rasters to model functional connectivity and Circuitscape workflows.
  • Corridor identification and planning: Delineate least-cost paths and pinch points; rank patches for restoration or protection.
  • Use Case: In a landscape with multiple patches, identify top patches that contribute most to connectivity and propose corridors for conservation prioritization.

Quick Start

Provide patch centroid data and run the connectivity workflow using the included scripts to generate patch metrics and the landscape connectivity summary.

Frequently Asked Questions about landscape-connectivity

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

FAQPage Schema
How do I quantify landscape connectivity for conservation planning?▼

You can quantify landscape connectivity by combining graph-theoretic indices like IIC and PC with resistance-based models to identify important habitat patches and corridors for conservation prioritization.

How do I calculate least-cost paths and patch metrics using Circuitscape?▼

Calculate least-cost paths and patch metrics by integrating resistance rasters into Circuitscape workflows, using Python and R scripts to model functional connectivity and delineate corridor pinch points.

What data do I need to model functional connectivity and evaluate habitat fragmentation?▼

To model functional connectivity and evaluate fragmentation, you need discretized patch centroid data, a defined study area, and a resistance raster surface to apply the connectivity workflow.

How do I identify and rank the most important habitat patches for restoration?▼

Identify and rank important habitat patches for restoration by computing graph-theoretic dIIC and dPC indices, which quantify individual patch contributions to overall landscape connectivity.

Can I use NetworkX for graph-theoretic connectivity analysis with resistance surfaces?▼

Yes, you can use NetworkX alongside NumPy to build graph-theoretic models and apply resistance surfaces, enabling the computation of connectivity metrics and least-cost paths for ecological landscapes.

What is the difference between graph-theoretic indices and resistance-based models for connectivity analysis?▼

Graph-theoretic indices like IIC and PC measure structural patch importance, while resistance-based models like Circuitscape use raster surfaces to compute functional connectivity and least-cost paths.

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