Network Models and Simulation Analytics for Multi-scale Dynamics of Biological Invasions

Area of Science:

• Ecology
• Network Science
• Mathematical Biology

Background:

• Globalization and climate change accelerate invasive species spread globally.
• Spread mechanisms are often modeled as diffusion on spatial networks.
• Existing models lack a unified graph-theoretic framework and recent network science advancements.

Purpose of the Study:

• Develop a generic multi-scale spatial network framework for biological invasion models.
• Investigate how individual and combined pathways influence invasion spread rate and patterns.
• Address analytical complexity arising from multi-scale network structures.

Main Methods:

• Developed a generic multi-scale spatial network framework.
• Derived theoretical bounds for spectral radius and diameter of multi-scale networks.
• Simulated a multi-pathway diffusion model on synthetic and real-world networks.
• Applied regression tree analysis to identify key parameters influencing invasion dynamics.

Main Results:

• Established connections between spectral radius, diameter, and diffusion processes.
• Demonstrated how network properties are influenced by model parameters.
• Identified critical network and diffusion parameters governing invasion dynamics through simulations and regression analysis.

Conclusions:

• The proposed framework unifies diverse invasion models within a network science context.
• Understanding network structure is crucial for predicting and managing invasive species spread.
• The study provides a foundation for more robust and generalizable modeling of biological invasions.