SwimmingIndividuals: A high-performance agent-based model for marine ecosystems

Abstract

Abstract Mechanistic models are essential for projecting ecosystem responses to novel conditions, yet the application and biological realism of agent-based models (ABMs) has often been limited by computational constraints. This paper introduces SwimmingIndividuals , an open-source agent-based model (ABM) framework that advances our ability to simulate complex biological processes at large ecological scales. The software’s main contribution is its suite of mechanistic sub-models that govern the full life cycle of each agent, including physics-based visual predation, adaptive behaviors such as diel vertical migration, and a flexible bioenergetics engine with multiple taxa-specific equations. These detailed biological processes are made computationally tractable for large populations through a hybrid CPU/GPU architecture written in the Julia language. We demonstrate the framework’s capabilities through three targeted simulations. The model successfully reproduced complex ecological phenomena as emergent properties, including diel vertical migration, cetacean diving patterns, size-based trophic dynamics, population-level growth curves, and stock-recruitment relationships. Furthermore, long-term simulations generated realistic population dynamics and quantified the impacts of different fishery harvest control rules. By coupling high-resolution biological realism with technical scalability, SwimmingIndividuals provides a powerful and flexible tool for a wide range of ecological inquiries. It can be used to conduct in-silico experiments into the ecosystem-scale impacts of environmental disturbances, test fundamental ecological theory, and evaluate the efficacy of complex, ecosystem-based management strategies. This framework advances our ability to build a more mechanistic understanding of marine ecosystem resilience in a changing world. Author Summary To understand and predict how marine ecosystems function, we need to account for the decisions and life histories of the individual animals within them. We have developed a new open-source software tool, SwimmingIndividuals , that acts as a “virtual laboratory” for marine ecology and fisheries science. This software allows us to create large, realistic simulations with millions of virtual organisms, from multiple taxa and species, each with its own unique set of biological traits. These modeled animals make decisions based on their internal state (e.g., hunger) and their perception of the surrounding environment, such as light and temperature. By simulating these individual actions at scale, our software allows us to see emergent, complex, ecosystem-level patterns, like population dynamics and food web structure. SwimmingIndividuals provides the scientific community with a powerful tool to investigate fundamental biological questions, explore “what-if” scenarios for fisheries management, and forecast how marine life might respond to future environmental changes.

The authors' abstract, as published at the source. PLoS Computational Biology, 2026 · DOI ↗