Landscape quantifies the intermediate state and transition dynamics in ecological networks
Jinchao Lv1,2, Jin Wang3, Chunhe Li1,2,4
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Plos Computational Biology
|January 5, 2024
Summary
Ecological systems can collapse, but understanding their energy landscapes reveals intermediate states crucial for transitions. A new barrier height metric offers earlier warnings of ecosystem breakdown, aiding sustainability efforts.
Area of Science:
- Ecology
- Complex Systems Science
- Conservation Biology
Background:
- Ecological collapse and restoration mechanisms are vital for human-nature coexistence.
- Elucidating stochastic dynamical transitions in ecological systems remains challenging.
- Plant-pollinator networks serve as a model for studying ecosystem dynamics.
Purpose of the Study:
- To quantify the energy landscape of an ecological system using a plant-pollinator network example.
- To identify and analyze intermediate stable states within the ecological energy landscape.
- To develop a novel early-warning signal for ecological transitions based on landscape properties.
Main Methods:
- Quantification of the ecological energy landscape.
- Analysis of attractors representing stable states (high, low, intermediate abundance).
- Definition and application of barrier height (BH) as a transition feasibility metric.
Main Results:
- The energy landscape revealed multiple attractors, including high, low, and intermediate abundance stable states.
- Intermediate states were detected, particularly under conditions of pollinator decline.
- The indispensable role of intermediate states in facilitating system transitions was demonstrated.
- Barrier height (BH) was defined as a global quantity to evaluate transition feasibility.
Conclusions:
- The barrier height (BH) can serve as an effective early-warning signal (EWS) for impending catastrophic ecological breakdowns.
- BH provides earlier and more accurate warnings compared to traditional time-series metrics.
- These findings support the development of improved management strategies for environmental sustainability.
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