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Diverse self-organized patterns and complex pattern transitions in a discrete ratio-dependent predator-prey system

机译:不同的自组织模式和在离散比率依赖于捕食者 - 猎物系统中的复杂模式转换

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The spatiotemporal complexity of a discrete ratio-dependent predator-prey system is investigated via development of a coupled map lattice model. Through stability analysis and bifurcation analysis, the critical conditions for stable homogeneous stationary and oscillatory states are determined. Meanwhile, pattern formation conditions are derived by Turing instability analysis. Based on the theoretical results, numerical simulations are performed, exhibiting rich patterns of spatiotemporal dynamics of the discrete system. On the route to chaos induced by Neimark-Sacker bifurcation, dynamic variation occurs from invariant cycles, experiencing periodic window and period-doubling process, to chaotic attractors. A variety of patterns are self-organized and demonstrate diverse types in configuration, including cold spots, labyrinth, cold stripes-spots, spirals, hot stripes, circles, arcs, disk, mosaics and fractals. Complex pattern transitions occur among the diverse patterns, suggesting sensitivity of pattern formation to parameter variations. Moreover, spatiotemporal chaos is found in pattern formation process, where tiny variations in initial conditions can result to the self-organization of different patterns. This approach reveals great diversity and complexity of pattern self-organization and pattern transition in predator-prey interactions, promoting comprehending on the spatiotemporal complexity of spatially extended predator-prey system. (C) 2018 Elsevier Inc. All rights reserved.
机译:通过开发耦合的地图格子模型来研究离散比率依赖于捕食者 - 捕食系统的时空复杂性。通过稳定性分析和分岔分析,确定了稳定的均匀静止和振荡状态的临界条件。同时,通过提取不稳定分析来得出图案形成条件。基于理论结果,进行数值模拟,表现出分立系统的富时空动态的丰富模式。在Neimark-Sacker分叉诱导的混沌途径上,动态变化发生在不变的周期,经历周期性窗口和周期加倍过程中,以混沌吸引子。各种模式是自组织的,并展示配置的不同类型,包括冷点,迷宫,冷条纹,螺旋,热条纹,圆圈,弧形,磁盘,马赛克和分形。在不同的模式中发生复杂的模式转换,表明模式形成对参数变化的敏感性。此外,在图案形成过程中发现了时尚混乱,其中初始条件的微小变化可能导致不同模式的自我组织。这种方法揭示了捕食者 - 猎物交互中的模式自组织和模式过渡的巨大多样性和复杂性,促进了对空间延长的捕食者 - 猎物系统的时空复杂性的理解。 (c)2018年Elsevier Inc.保留所有权利。

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