ArticleNature ecology & evolution2022
Estimating comparable distances to tipping points across mutualistic systems by scaled recovery rates.
Article in Nature ecology & evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Detecting and forecasting tipping points from sample variance alone.PNAS nexus · 2026Article
- Observing network dynamics through sentinel nodes.Nature communications · 2025Article
- Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A small set of critical hyper-motifs governs heterogeneous flow-weighted network resilience.Nature communications · 2025Article
- Deep learning resilience inference for complex networked systems.Nature communications · 2024Article
- Network properties determine neural network performance.Nature communications · 2024Article
- Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.Royal Society open science · 2024Article
- Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality.Research (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
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.
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