Mattern, J. P. | Search bioRxiv

bioRxiv2025

SwimmingIndividuals: A High-Performance Agent-Based Model for Marine Ecosystems

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 softwares 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 frameworks 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 SummaryTo 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.

ecology↗

bioRxiv2021

A flexible Bayesian approach to estimating size-structured matrix population models

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating rate parameters of microbial populations by mechanistically describing changes in microbial cell size distributions over time. And yet, the construction, analysis, and biological interpretation of these models are underdeveloped, as current implementations do not adequately constrain or assess the biological feasibility of parameter values, leading to inference which may provide a good fit to observed size distributions but does not necessarily reflect realistic physiological dynamics. Here we present a flexible Bayesian extension of size-structured MPMs for testing underlying assumptions describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework takes prior scientific knowledge into account and generates biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we herein demonstrate the performance improvements of our approach over current models and isolate previously ignored biological processes, such as respiratory and exudative carbon losses, as critical parameters for the modeling of microbial population dynamics. The results demonstrate that this modeling framework can provide deeper insights into microbial population dynamics provided by flow-cytometry time-series data. Author summaryIdentifying the growth and population dynamics of marine microorganisms in their natural habitat is crucial to understanding the flow of carbon in the oceans but remains a grand challenge due to the invasive nature of current measurement methods. As time-series observations of population size structure have become more commonplace in aquatic environments, matrix population models (MPMs), which aim to mechanistically describe the change in size structure of these populations over time, have gained in popularity over the last decade. However, the underlying assumptions and behavior of MPMs have not been adequately scrutinized, and parameter values are difficult to interpret biologically, leading to inference that may not reflect plausible physiological dynamics. Here, we develop a Bayesian extension of the MPM framework to examine biological assumptions, improve interpretability of model output, and account for additional biological processes. We evaluated the performance of our models on a publicly available dataset of laboratory experiment time-series measurements of the cyanobacterium Prochlorococcus, Earths most abundant photosynthetic organisms, demonstrated the performance improvements of our approach over current models, and isolated previously ignored respiratory and exudative carbon losses as critical parameters for the modeling of microbial population dynamics.

microbiology↗