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Underestimated Ecosystem Resilience: Tipping Point Evasion Driven by Adaptive Plant-Pollinator Interactions

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Figshare2025-05-19 更新2026-04-08 收录
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https://figshare.com/articles/dataset/Adaptive_dispersal_in_mutualistic_interaction_trigger_tipping_point_evasion_in_a_saltmarsh_ecosystem/28375268/2
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资源简介:
Global change threatens ecosystem resilience and triggers irreversible critical transitions, especially in ecologically vulnerable coastal wetlands. Previous studies reported that biotic interactions interfered with biophysical feedback process, forcing ecosystem resilience more susceptible. However, dynamic critical threshold of ecosystem resilience lacks quantification, particularly involving adaptive mutualistic interaction. Our study integrates biophysical feedback and plant-pollinator interactions into a spatial vegetation model and generates dynamic thresholds of a saltmarsh ecosystem in the Yellow River Delta, China. Results showed that adaptive plant-pollinator interaction accelerates the biophysical feedback, enabling the ecosystem to evade the tipping point of collapsing and to enhance the tipping point of recovering. This is particularly important at the early stages of vegetation colonization. A comparison with an ecosystem without mutualistic interaction showed that adaptive mutualistic interaction was important in withstanding higher environmental pressures, despite an overall decline in ecosystem resilience under noise-type habitat loss and rate-type escalating salinization. Adaptive plant-pollinator interaction therefore resulted in a more effective and sustainable ecosystem restoration. Our study emphasizes the essential role of plant-pollinator interaction in strengthening ecosystem resilience against external disturbances. Neglecting this adaptive mechanism can underestimate ecosystem resilience. Management that collaborative conservation of plants and pollinators is a prospective approach to deal with climate change challenges and maintain ecosystem sustainability.
提供机构:
Jiao, Le; Zhang, Peng; Sun, Tao
创建时间:
2025-05-19
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