层级May-Leonard模型中物种共存的选择规则
Selection Rules for Species Coexistence in a Hierarchical May-Leonard Model
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中文总结 AI 辅助
本研究通过平均场分析和蒙特卡洛模拟，发现层级May-Leonard模型中完全共存不稳定，共存状态受限于层级网络的独立集，建立了连接层级相互作用与生物多样性的理论框架。
中文摘要 AI 辅助
进化动力学中的一个核心挑战是理解为什么某些物种组合能够持续存在而其他组合则消失。尽管循环相互作用模型为生物多样性的维持提供了基本见解，但关于层级竞争相互作用如何塑造长期群落组织所知甚少。在此，我们研究了May-Leonard模型的一个层级扩展，其中物种通过有向捕食链相互作用，同时经历繁殖和死亡。结合平均场分析与蒙特卡洛模拟，我们表明完全共存状态通常是不稳定的，导致动力学演化为低维共存状态。模拟进一步揭示了随机灭绝主导小种群，随着系统规模增大，动力学逐渐接近平均场预测。层级相互作用结构并非允许任意物种组合，而是通过仅选择特定物种子集进行长期持续来动态约束共存。我们表明这些可允许的共存状态在层级相互作用网络中具有自然的图论解释，即独立集，从而为层级群落中的共存提供了普遍约束。总之，这些结果建立了一个连接层级相互作用、动力学选择、图拓扑和生物多样性组织的理论框架，将经典May-Leonard模型扩展到循环竞争之外。
英文摘要
One of the central challenges in evolutionary dynamics is understanding why some species combinations persist while others disappear. Although cyclic-interaction models have provided fundamental insights into biodiversity maintenance, much less is known about how hierarchical competitive interactions shape long-term community organization. Here, we investigate a hierarchical extension of the May-Leonard model, in which species interact through a directed predation chain while undergoing reproduction and mortality. Combining mean-field analysis with Monte Carlo simulations, we show that the fully coexisting state is generically unstable, causing the dynamics to evolve toward lower-dimensional coexistence states. The simulations further reveal stochastic extinctions dominating small populations with the dynamics progressively approaching the mean-field predictions as the system size increases. Rather than permitting arbitrary species combinations, the hierarchical-interaction structure dynamically constrains coexistence by selecting only specific subsets of species for long-term persistence. We show that these admissible coexistence states have a natural graph-theoretic interpretation as independent sets in the hierarchical interaction network, thereby providing general constraints on coexistence in hierarchical communities. Together, these results establish a theoretical framework linking hierarchical interactions, dynamical selection, graph topology, and biodiversity organization, extending the classical May-Leonard model beyond cyclic competition.
发表机构
- Raja Rammohun Roy Mahavidyalaya（拉贾·拉姆莫汉·罗伊大学）
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad（海德拉巴信息技术学院计算自然科学和生物信息中心）
- Sister Nibedita Govt. General Degree College for Girls（西比尔·尼贝迪塔女子政府综合学位学院）
- School of Science, Constructor University（Constructor大学理学院）
- Department of Mathematics, Nalanda University（那烂陀大学数学系）
机构由 AI 辅助整理，请以论文原文为准。
