论文标题
竞争性生态网络中密度调节和稳定性之间的互连
Interconnection between density-regulation and stability in competitive ecological network
论文作者
论文摘要
在自然生态系统中,物种可以以非线性密度依赖性的生长曲线自我调节为特征。许多分类单元的物种显示出大量密度依赖性降低,以减少人口大小。然而,许多人表现出相反的趋势。小人口的密度调节最小,当人口大小接近承载能力时,密度的调节会显着增加。 Theta-Logistic生长方程可以描绘生长曲线中种内密度调节,theta是密度调节参数。在这项研究中,我们在竞争性物种相互作用的数学模型的帮助下研究了这些不同增长谱对竞争性生态界稳定性的作用。该手稿涉及随机矩阵理论,以了解竞争相互作用的经典theta-logistic模型的稳定性。我们的结果表明,具有强密度依赖性的物种更多的物种在低密度下自我调节会导致更稳定的社区。因此,稳定性也取决于生态网络的复杂性。物种网络连接(链路密度)显示出稳定性提高的一致趋势,而社区规模(物种丰富度)显示出上下文依赖的效果。我们还从两种不同的生活历史策略的方面解释了我们的结果:R和K选择。我们的结果表明,竞争网络的稳定性随社区中R选择的物种的比例而增加。我们的结果是强大的,无论不同的网络体系结构如何。
In natural ecosystems, species can be characterized by the nonlinear density-dependent self-regulation of their growth profile. Species of many taxa show a substantial density-dependent reduction for low population size. Nevertheless, many show the opposite trend; density regulation is minimal for small populations and increases significantly when the population size is near the carrying capacity. The theta-logistic growth equation can portray the intraspecific density regulation in the growth profile, theta being the density regulation parameter. In this study, we examine the role of these different growth profiles on the stability of a competitive ecological community with the help of a mathematical model of competitive species interactions. This manuscript deals with the random matrix theory to understand the stability of the classical theta-logistic models of competitive interactions. Our results suggest that having more species with strong density dependence, which self-regulate at low densities, leads to more stable communities. With this, stability also depends on the complexity of the ecological network. Species network connectance (link density) shows a consistent trend of increasing stability, whereas community size (species richness) shows a context-dependent effect. We also interpret our results from the aspect of two different life history strategies: r and K-selection. Our results show that the stability of a competitive network increases with the fraction of r-selected species in the community. Our result is robust, irrespective of different network architectures.