生态系统
理论(学习稳定性)
反应性(心理学)
摄动(天文学)
生态学
消光(光学矿物学)
计算机科学
生物
物理
医学
古生物学
替代医学
病理
量子力学
机器学习
作者
Yilin Yang,Katharine Z. Coyte,Kevin R. Foster,Aming Li
标识
DOI:10.1038/s41467-023-42580-0
摘要
Abstract Understanding stability—whether a community will eventually return to its original state after a perturbation—is a major focus in the study of various complex systems, particularly complex ecosystems. Here, we challenge this focus, showing that short-term dynamics can be a better predictor of outcomes for complex ecosystems. Using random matrix theory, we study how complex ecosystems behave immediately after small perturbations. Our analyses show that many communities are expected to be ‘reactive’, whereby some perturbations will be amplified initially and generate a response that is directly opposite to that predicted by typical stability measures. In particular, we find reactivity is prevalent for complex communities of mixed interactions and for structured communities, which are both expected to be common in nature. Finally, we show that reactivity can be a better predictor of extinction risk than stability, particularly when communities face frequent perturbations, as is increasingly common. Our results suggest that, alongside stability, reactivity is a fundamental measure for assessing ecosystem health.
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