SwimmingIndividuals: A high-performance agent-based model for marine ecosystems
4 affiliations
- 1Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, Florida, United States of America
- 2Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, United States of America
- 3NOAA Southeast Fisheries Science Center, Miami, Florida, United States of America
- 4University of California Santa Cruz, Santa Cruz, California, United States of America
Abstract
Because they are based on established theory, mechanistic models are essential for projecting ecological responses to novel conditions. Yet mechanistic agent-based model (ABM) applications have often been limited by computational constraints. This paper introduces SwimmingIndividuals, an open-source ABM framework that advances our ability to simulate physiological processes, animal movement, higher trophic level marine ecosystem dynamics, and fisheries removals at large ecological scales. The software's key contributions are a suite of mechanistic sub-models governing the full life cycle of each agent, including visual prey detection, adaptive behaviors such as diel vertical migration (DVM), a flexible bioenergetics engine with three taxon-specific metabolic architectures, and a spatial pathfinding algorithm for habitat-seeking movement. These processes are made computationally tractable through a hybrid CPU/GPU architecture written in Julia. Unlike prior ABMs that use fixed perception radii or single-equation bioenergetics, SwimmingIndividuals enables emergent ecological dynamics across populations of superindividual agents. We demonstrate the framework through three Gulf of America simulations. The model reproduced DVM, cetacean diving patterns, size-based trophic shifts, and von Bertalanffy growth curves consistent with published King Mackerel stock assessments. Long-term simulations generated realistic population dynamics and quantified impacts of fishery harvest control rules across five fleets. SwimmingIndividuals provides a powerful, flexible, open-source platform for mechanistic marine ecosystem research and ecosystem-based fisheries management. 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 developed SwimmingIndividuals, an open-source ‘virtual laboratory’ for marine ecology and fisheries science. This software creates large, realistic simulations of virtual organisms from multiple taxa simultaneously, each with unique biological traits. Modeled animals make decisions based on their internal physiological state and their perception of light, temperature, and prey. By simulating these individual actions at scale, complex ecosystem-level patterns (e.g., population dynamics, food web structure, and seasonal migrations) emerge without being explicitly programmed. SwimmingIndividuals provides the scientific community with a tool to investigate fundamental biological questions, explore ‘what-if’ scenarios for fisheries management, and forecast how marine life can respond to future environmental changes.
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BibTeX
@article{woodstock2026swimmingindividuals,
title = {{SwimmingIndividuals: A high-performance agent-based model for marine ecosystems}},
author = {Matthew S. Woodstock and Jann Paul Mattern and Zhen Wu and Gregory L. Britten},
journal = {PLOS Computational Biology},
year = {2026},
month = nov,
doi = {10.1371/journal.pcbi.1013610},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013610},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SwimmingIndividuals: A high-performance agent-based model for marine ecosystems
AU - Matthew S. Woodstock
AU - Jann Paul Mattern
AU - Zhen Wu
AU - Gregory L. Britten
PY - 2026
DA - 2026/11/06
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1013610
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013610
AB - Because they are based on established theory, mechanistic models are essential for projecting ecological responses to novel conditions. Yet mechanistic agent-based model (ABM) applications have often been limited by computational constraints. This paper introduces SwimmingIndividuals, an open-source ABM framework that advances our ability to simulate physiological processes, animal movement, higher trophic level marine ecosystem dynamics, and fisheries removals at large ecological scales. The software's key contributions are a suite of mechanistic sub-models governing the full life cycle of each agent, including visual prey detection, adaptive behaviors such as diel vertical migration (DVM), a flexible bioenergetics engine with three taxon-specific metabolic architectures, and a spatial pathfinding algorithm for habitat-seeking movement. These processes are made computationally tractable through a hybrid CPU/GPU architecture written in Julia. Unlike prior ABMs that use fixed perception radii or single-equation bioenergetics, SwimmingIndividuals enables emergent ecological dynamics across populations of superindividual agents. We demonstrate the framework through three Gulf of America simulations. The model reproduced DVM, cetacean diving patterns, size-based trophic shifts, and von Bertalanffy growth curves consistent with published King Mackerel stock assessments. Long-term simulations generated realistic population dynamics and quantified impacts of fishery harvest control rules across five fleets. SwimmingIndividuals provides a powerful, flexible, open-source platform for mechanistic marine ecosystem research and ecosystem-based fisheries management. 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 developed SwimmingIndividuals, an open-source ‘virtual laboratory’ for marine ecology and fisheries science. This software creates large, realistic simulations of virtual organisms from multiple taxa simultaneously, each with unique biological traits. Modeled animals make decisions based on their internal physiological state and their perception of light, temperature, and prey. By simulating these individual actions at scale, complex ecosystem-level patterns (e.g., population dynamics, food web structure, and seasonal migrations) emerge without being explicitly programmed. SwimmingIndividuals provides the scientific community with a tool to investigate fundamental biological questions, explore ‘what-if’ scenarios for fisheries management, and forecast how marine life can respond to future environmental changes.
SN - 1553-7358
ER -