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arXiv 2401.04815q-bio.PE
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嵌套性促进最大熵二分食物网的稳定性

Nestedness Promotes Stability in Maximum-Entropy Bipartite Food Webs

Zhening Li, John Harte

AI总结:

本文在最大信息熵框架下推导相邻营养层间能量流的简单公式,并引入矩阵偶极矩度量,证明高嵌套性食物网拓扑能增强种群动态稳定性,从而解释自然群落的稳定性。

摘要:

食物网拓扑结构以及食物网联结上的能量流动速率能够影响生态系统的性质,例如稳定性。当前能量流模型对稳定性的预测往往对其公式化细节敏感,且其复杂性使得阐明一般现象背后的机制变得困难。在此,我们在最大信息熵推断框架(MaxEnt)内,推导出一个简单公式,用于描述两个相邻营养层之间每条联结所承载的能量流。该模型的输入是食物网的拓扑结构以及进入或离开每个物种节点的聚合能量通量。对于以两个营养层之间的消费者-资源相互作用为主导的生态系统,我们基于MaxEnt模型的能量流预测构建了一个物种动力学模型。对该模型的数学分析和模拟表明,具有较高矩阵偶极矩的食物网拓扑结构能够增强对种群规模小扰动的稳定性,其中矩阵偶极矩是我们引入的一个简单嵌套性度量。由于嵌套二分亚网络在食物网中自然出现,我们的结果为自然群落的稳定性提供了一种解释。

英文摘要:

Food web topology and energy flow rates across food web linkages can influence ecosystem properties such as stability. Stability predictions from current models of energy flow are often sensitive to details in their formulation, and their complexity makes it difficult to elucidate underlying mechanisms of general phenomena. Here, within the maximum information entropy inference framework (MaxEnt), we derive a simple formula for the energy flow carried by each linkage between two adjacent trophic layers. Inputs to the model are the topological structure of the food web and aggregate energy fluxes entering or exiting each species node. For ecosystems with interactions dominated by consumer-resource interactions between two trophic layers, we construct a model of species dynamics based on the energy flow predictions from the MaxEnt model. Mathematical analyses and simulations of the model show that a food web topology with a higher matrix dipole moment promotes stability against small perturbations in population sizes, where the \textit{matrix dipole moment} is a simple nestedness metric that we introduce. Since nested bipartite subnetworks arise naturally in food webs, our result provides an explanation for the stability of natural communities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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