Emergent collective dynamics from motile photokinetic organisms
J. Morales, P. Munoz, D. Noto, H.N Ulloa, F. Guzman-Lastra

TL;DR
This paper develops a three-dimensional agent-based model to understand how individual photokinetic behaviors of aquatic organisms lead to diverse vertical migration patterns, revealing key regimes influenced by motility and light bias.
Contribution
It introduces a novel mechanistic framework linking individual swimming behaviors and environmental light cues to population-scale diel vertical migration patterns.
Findings
Four distinct migration regimes identified: Surface Accumulation, Shallow DVM, Deep DVM, Sinking.
Regimes governed by Peclet number and vertical swimming bias W.
Upward bias promotes surface aggregation; downward bias causes sinking.
Abstract
The day-night cycle drives the largest biomass migration on Earth: the diel vertical migration (DVM) of aquatic organisms. Here, we present a three-dimensional agent-based model that incorporates photokinesis, gyrotaxis, and stochastic reorientation to explore how individual-level swimming behaviors give rise to population-scale DVM patterns. By solving Langevin equations for swarms of swimmers, we identify four distinct regimes -- Surface Accumulation, Shallow DVM, Deep DVM, and Sinking -- governed by two key dimensionless parameters: the Peclet number (Pe), representing motility persistence, and the vertical swimming asymmetry ratio (W=wdown/wup), encoding photokinetic bias. These regimes emerge from nonlinear interactions between light-driven navigation and active noise, diagnosed through topological and statistical features of vertical distributions. A critical feedback is…
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Taxonomy
TopicsMicro and Nano Robotics
