Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA.
Nat Ecol Evol. 2022 Oct;6(10):1524-1536. doi: 10.1038/s41559-022-01850-8. Epub 2022 Aug 29.
Mutualistic systems can experience abrupt and irreversible regime shifts caused by local or global stressors. Despite decades of efforts to understand ecosystem dynamics and determine whether a tipping point could occur, there are no current approaches to estimate distances (in state/parameter space) to tipping points and compare the distances across various mutualistic systems. Here we develop a general dimension-reduction approach that simultaneously compresses the natural control and state parameters of high-dimensional complex systems and introduces a scaling factor for recovery rates. Our theoretical framework places various systems with entirely different dynamical parameters, network structure and state perturbations on the same scale. More importantly, it compares distances to tipping points across different systems on the basis of data on abundance and topology. By applying the method to 54 real-world mutualistic networks, our analytical results unveil the network characteristics and system parameters that control a system's resilience. We contribute to the ongoing efforts in developing a general framework for mapping and predicting distance to tipping points of ecological and potentially other systems.
互利共生系统可能会因局部或全球压力而经历突然且不可逆转的状态转变。尽管数十年来人们一直努力了解生态系统动态并确定是否可能出现临界点，但目前还没有方法来估计临界点的距离（在状态/参数空间中），也无法比较各种互利共生系统之间的距离。在这里，我们开发了一种通用的降维方法，该方法同时压缩了高维复杂系统的自然控制和状态参数，并引入了恢复率的标度因子。我们的理论框架将具有完全不同动力学参数、网络结构和状态扰动的各种系统置于同一尺度上。更重要的是，它基于丰度和拓扑结构的数据来比较不同系统到达临界点的距离。通过将该方法应用于 54 个真实的互利共生网络，我们的分析结果揭示了控制系统弹性的网络特征和系统参数。我们为开发用于映射和预测生态系统以及潜在其他系统临界点距离的通用框架做出了贡献。
