从抵抗和恢复的角度预测时间稳定性和韧性

1. 信息

2. 摘要

Stability can be desirable for many natural and social systems. Temporal stability, the invariability of a system over time, can be enhanced by resisting displacement during perturbations, accelerating recovery after them, or both1–4. Likewise, resilience (sensu proximity to unperturbed levels after a perturbation5–10) also has components of withstanding (resistance) and recovering after perturbations11,12. Here we develop and test new predictions for how temporal stability and resilience depend on their resistance and recovery components. We find that temporal stability could often be predicted from resistance, even without information about how quickly the system recovers. By contrast, resilience is predicted to depend at least as much on recovery as on resistance, as in earlier theory11,12. Using plant productivity data from the world’s longest-running biodiversity experiment, we find that long-term temporal stability, quantified over a quarter century at the ecosystem or species level, is predicted with moderate accuracy from single-year estimates of resistance alone, with only slight improvement by also considering recovery. Resilience was predicted with moderate accuracy by a combination of resistance and recovery at the ecosystem level. We also find that ecosystem drought resistance can be forecasted by monitoring temporal stability before the drought. Our results reveal that long-term temporal stability and short-term resistance may often be predicted from one another and clarify how resistance and recovery can be leveraged to enhance the stability of both natural and managed systems. New predictions for how temporal stability and resilience depend on their resistance and recovery components are explored.

3. 图表

4. 引用

@article{isbell2026,
  author = {Forest Isbell and Akira S. Mori and Michel Loreau and Peter B. Reich and David Tilman and Maggie I. Anderson and Caroline Brophy and Karen Castillioni and Qingqing Chen and Amber C. Churchill and Adam T. Clark and Dylan Craven and Nico Eisenhauer and Hanan C. Farah and Lau A. Gherardi and Yann Hautier and Miao He and Jin-Sheng He and Andy Hector and Sydney Hedberg and Sarah E. Hobbie and Pubin Hong and Guopeng Liang and Maowei Liang and Shan Luo and Neha Mohanbabu and Shahid Naeem and Pascal A. Niklaus and Xiaobin Pan and Cristy Portales-Reyes and Bernhard Schmid and Harry E. R. Shepherd and Steph Varghese and Michiel P. Veldhuis and Shaopeng Wang and Carmen R. E. Watkins and Qianna Xu and Liting Zheng and Chad R. Zirbel},
  title = {Predicting temporal stability and resilience from resistance and recovery},
  journal = {Nature},
  year = {2026},
  volume = {655},
  number = {8122},
  pages = {394-400},
  doi = {10.1038/s41586-026-10498-4},
  publisher = {Springer Science and Business Media LLC},
}