运动光动力生物体的涌现集体动力学
Emergent collective dynamics from motile photokinetic organisms
AI总结：
本研究通过三维个体模型揭示光动力与趋重力性如何产生四种昼夜垂直迁移模式，并发现游泳不对称比和佩克莱数决定状态，强调光梯度在生态自组织中的核心作用。
摘要：
昼夜循环驱动着地球上最大规模的生物迁移：水生生物昼夜垂直迁移（DVM）。本文提出了一个三维基于个体的模型，该模型整合了光动力效应、趋重力性和随机重定向，以探究个体水平的游泳行为如何产生种群尺度的DVM模式。通过求解游泳者群体的朗之万方程，我们识别出四种不同的状态——表面聚集、浅层DVM、深层DVM和下沉——这些状态由两个关键的无量纲参数控制：佩克莱数（Pe），代表运动持续性；以及垂直游泳不对称比（W=w_down/w_up），编码光动力偏差。这些状态源于光驱动导航与主动噪声之间的非线性相互作用，并通过垂直分布的拓扑和统计特征进行诊断。我们揭示了一个关键反馈：向上偏置的游泳（W<1）促进表面聚集，而过度向下偏置（W>1）导致不可逆的下沉。解析估计将状态边界与趋重力性对齐和速度反转联系起来。总之，我们的结果提供了一个机制框架来解释DVM的多样性，并强调了光梯度（而非绝对强度）在塑造生态自组织中的核心作用。
英文摘要：
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 uncovered: upward-biased swimming (W<1) promotes surface aggregation, while excessive downward bias (W>1) leads to irreversible sinking. Analytical estimates link regime boundaries to gyrotactic alignment and velocity reversals. Together, our results provide a mechanistic framework to interpret DVM diversity and emphasize the central role of light gradients-beyond absolute intensity-in shaping ecological self-organization.
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