Predicting tipping points in mutualistic networks through dimension reduction
Abstract
Complex systems in many fields, because of their intrinsic nonlinear dynamics, can exhibit a tipping point (point of no return) at which a total collapse of the system occurs. In ecosystems, environmental deterioration can lead to evolution toward a tipping point. To predict tipping point is an outstanding and extremely challenging problem. Using complex bipartite mutualistic networks, we articulate a dimension reduction strategy and establish its general applicability to predicting tipping points using a large number of empirical networks. Not only can our reduced model serve as a paradigm for understanding the tipping point dynamics in real world ecosystems for safeguarding pollinators, the principle can also be extended to other disciplines to address critical issues, such as resilience and sustainability.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 08, 2018
- Source ID
- 10.1073/pnas.1714958115
Entities
People
- Alan Hastings
- Celso Grebogi
- Junjie Jiang
- Thomas P. Seager
- Wei Lin
- Ying-Cheng Lai
- Zi-Gang Huang
Organizations
- Arizona State University
- Fudan University
- Lanzhou University
- National Science Foundation
- Office of Naval Research
- University of Aberdeen
- University of California
- Xi'an Jiaotong University