AI 中文总结
研究基于红海龟追踪数据构建随机广义朗之万方程，发现其主动运动产生无法用洋流解释的异常环路，该环路可维持觅食区域并产生超扩散，或启发机器人搜索与AI优化算法。
AI 中文摘要
从昆虫、鸟类、海洋捕食者、哺乳动物到人类，栖息于不同环境并在不同空间尺度活动的动物，常呈现看似随机的运动路径。过去十年，新型生物记录技术以更高精度记录这些模式，生成了大量实验数据。核心挑战是构建数据驱动的数学模型来理解这类复杂模式。许多动物运动偏离布朗运动，可通过相关随机游走、莱维游走或主动粒子动力学描述，但这些运动模型未纳入从实验轨迹提取的长期非马尔可夫记忆。本文基于西非海岸觅食的红海龟（Caretta caretta）卫星追踪数据，构建随机广义朗之万方程，发现这些海龟表现出由大规模环路构成的主动运动，该环路无法用洋流或手性解释，能将运动维持在特定觅食区域，且在中等时间尺度产生类似莱维游走的超扩散。我们由此识别出一种基于环路的主动异常搜索形式，与众多动物物种的觅食模式相关，或可为机器人搜索策略及基于AI的元启发式优化算法提供启发。
英文摘要
Animals inhabiting diverse environments by moving across different spatial scales, from insects to birds, marine predators, mammals and even humans, often display apparently random movement paths. Over the past decade, novel biologging technologies have recorded these patterns in increasing detail, generating a wealth of experimental data. A central challenge is to understand such complex patterns by constructing data-driven mathematical models. Many animal movements depart from Brownian motion, as described by correlated random walks, Lévy walks, or active particle dynamics. Yet, these movement models do not incorporate long-term non-Markovian memory extracted from experimental trajectories. Here, we construct a stochastic generalised Langevin equation from satellite tracking data for loggerhead sea turtles (Caretta caretta) foraging off the coast of West Africa. We find that these turtles exhibit active movement characterised by large-scale loops that are not explained by ocean currents or chirality. These loops maintain movement within a specific foraging region and, over intermediate timescales, generate superdiffusion similar to Lévy walks. We thus identify a loop-based form of active anomalous search related to foraging patterns observed across a wide range of animal species, which may inspire robotic search strategies and AI-based metaheuristic optimisation algorithms.
Comments11 pages, 4 figures (accompanied by 38 pages of supplementary information with 40 figures and 1 table)
