长腿蚁A. gracilipes实验室实验中的随机运动建模
Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments
AI总结：
本研究通过随机建模方法揭示了长腿蚁A. gracilipes的运动机制，结合实验数据验证了主动布朗运动与跑- tumble模型的有效性，为理解蚂蚁运动生态和感觉系统提供了新视角。
摘要：
随机运动行为建模提供了一种有价值的方法，用于理解复杂运动如何从相对简单的构建块生成。蚂蚁展示出从觅食到巢穴迁移的复杂社会行为；尽管通常强调用于同步个体的交流方法，但这些个体的运动范式同样具有同等重要性。在此，我们应用随机建模方法，以更好地理解孤立的长腿蚁（A. gracilipes）样本的运动情况，该方法基于广泛的实验室跟踪实验。我们发现，主动布朗运动和跑- tumble模型的结合能够再现实验中观察到的轨迹统计信息，无论是定性还是定量。我们识别出在不同样本中可重复的概率分布，包括转向角度、运行时间和等待时间，并发现分析预测与从轨迹中经验测量的量之间有良好的一致性。拥有这样的模型使我们能够从模拟和分析中更好地理解和预测运动生态，并甚至能提供关于运动生成机制和蚂蚁感觉系统的基本见解。
英文摘要：
Stochastic modeling of movement behavior provides a valuable way to understand how complex motion can be generated from relatively simple building blocks. Ants demonstrate sophisticated social behavior ranging from foraging to nest relocation; while emphasis is often placed on the communication methods used to synchronize individuals, the movement paradigms of those individuals are of tantamount importance. Here, we apply a stochastic modeling approach to better understand the movement of isolated long-legged ant (A. gracilipes) specimens, informed by extensive laboratory tracking experiments. We find that a combination of active Brownian and run-and-tumble models reproduces the trajectory statistics observed in experiments, both qualitatively and quantitatively. We identify reproducible probability distributions for the turn angles, run times, and waiting times across specimens, and find good agreement between analytical predictions and quantities empirically measured from the trajectories. Having such a model allows for a better understanding and predictions of movement ecology from both simulations and analytics, and even can give insight into the underlying generative mechanisms of motion and the ants' sensory systems.
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