Automation Department, Shanghai Jiao Tong University, Shanghai 200240, Shanghai, People's Republic of China.
Key Laboratory of Imaging Processing and Intelligence Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.
J R Soc Interface. 2020 Jul;17(168):20200236. doi: 10.1098/rsif.2020.0236. Epub 2020 Jul 22.
Mutualistic networks, which describe the ecological interactions between multiple types of species such as plants and pollinators, play a paramount role in the generation of Earth's biodiversity. The resilience of a mutualistic network denotes its ability to retain basic functionality when errors and failures threaten the persistence of the community. Under the disturbances of mass extinctions and human-induced disasters, it is crucial to understand how mutualistic networks respond to changes, which enables the system to increase resilience and tolerate further damages. Despite recent advances in the modelling of the structure-based adaptation, we lack mathematical and computational models to describe and capture the co-adaptation between the structure and dynamics of mutualistic networks. In this paper, we incorporate dynamic features into the adaptation of structure and propose a co-adaptation model that drastically enhances the resilience of non-adaptive and structure-based adaptation models. Surprisingly, the reason for the enhancement is that the co-adaptation mechanism simultaneously increases the heterogeneity of the mutualistic network significantly without changing its connectance. Owing to the broad applications of mutualistic networks, our findings offer new ways to design mechanisms that enhance the resilience of many other systems, such as smart infrastructures and social-economical systems.
互利共生网络描述了植物和传粉者等多种物种之间的生态相互作用，对地球生物多样性的产生起着至关重要的作用。互利共生网络的弹性表示其在错误和故障威胁到群落的持久性时保持基本功能的能力。在大规模灭绝和人为灾害的干扰下，了解互利共生网络如何对变化做出反应至关重要，这使系统能够提高弹性并耐受进一步的破坏。尽管在基于结构的适应建模方面取得了最近的进展，但我们缺乏数学和计算模型来描述和捕捉互利共生网络的结构和动态之间的共同适应。在本文中，我们将动态特征纳入结构的适应中，并提出了一种共同适应模型，该模型极大地提高了非适应和基于结构的适应模型的弹性。令人惊讶的是，增强的原因是共同适应机制在不改变连接度的情况下，同时显著增加了互利共生网络的异质性。由于互利共生网络的广泛应用，我们的发现为设计增强许多其他系统（如智能基础设施和社会经济系统）弹性的机制提供了新途径。
