Modeling ant battles by means of a diffusion-limited Gillespie algorithm

Area of Science:

• Ecology
• Mathematical Biology
• Invasive Species Research

Background:

• Invasive species pose significant ecological threats.
• Understanding interspecies conflict dynamics is crucial for predicting invasive species impact.
• Laboratory experiments provide data on ant battle behaviors.

Purpose of the Study:

• To develop realistic models for predicting invasive species interaction dynamics.
• To simulate and analyze the fighting strategies of Lasius neglectus and Lasius paralienus.
• To compare chemical and agent-based modeling approaches for ecological interactions.

Main Methods:

• Developed a chemical model treating ant groups as chemical species.
• Derived a system of differential equations with parameters estimated from experimental data.
• Employed a standard Gillespie algorithm to model reaction fluctuations.
• Implemented a spatial agent-based model incorporating diffusion and compartment reactions.

Main Results:

• The models successfully describe observed ant battle dynamics.
• Parameter estimation allowed for quantitative comparison between model and experimental data.
• The Gillespie algorithm effectively modeled stochastic fluctuations in ant interactions.
• The spatial agent-based model improved the reproduction of observed behaviors.

Conclusions:

• Two distinct modeling approaches, chemical and agent-based, can effectively simulate ant battle dynamics.
• These models provide a framework for predicting invasive species interactions.
• Mathematical modeling is a valuable tool for understanding ecological conflicts and species invasion.