Data from: Hysteresis in an experimental phytoplankton population

The road to recovery of a deteriorated system is often different, and fraught with more barriers, than the path to the system's deterioration. This phenomenon is called hysteresis, and is inherent to systems presenting alternative stable states. In such systems, the stability of a given state is the product of positive feedback loops. A broad range of natural systems have been predicted to show hysteretic behaviour, but hysteresis has so far only been unambiguously demonstrated at cellular or metabolic levels, not yet at the population or ecosystem level. To extend our understanding of hysteresis at the population level, we performed an experiment on light-stressed cyanobacteria and found hysteresis between alternative stable states. Furthermore, during the experiment, the cyanobacteria adapted physiologically to high light levels, and deviated from their theoretically predicted pathway of hysteresis, therewith also avoiding extinction. Our experiment confirmed that a population that loses resilience due to deteriorating external conditions can show a delayed – hysteretic – recovery-response when conditions are improved. This population-level study also indicates that the slowness of these systems may obscure the true state they are in, which is important to factor into ecosystem monitoring. Additionally, we show that adaptation can drastically alter the systems’ predicted behaviour to ecosystem management. Flexibility of species and slowness should, therefore, be included in the monitoring and prediction of ecosystem responses to environmental changes.退化系统的恢复路径往往与系统退化路径截然不同，且面临更多阻碍。这一现象被称为迟滞现象（hysteresis），是存在多稳态（alternative stable states）的系统的固有特性。已有研究预测，大量自然系统均会表现出迟滞行为，但截至目前，迟滞现象仅在细胞或代谢层面得到明确证实，尚未在种群或生态系统层面观测到。为拓展我们对种群层面迟滞现象的认知，我们以光胁迫下的蓝藻（cyanobacteria）为实验对象开展研究，在其多稳态之间观测到了迟滞现象。此外，实验过程中，蓝藻在生理层面适应了高光环境，偏离了理论预测的迟滞路径，同时也避免了种群灭绝。本实验证实，因外部环境恶化而丧失恢复力的种群，在环境改善时会表现出延迟的迟滞性恢复响应。这项种群层面的研究还表明，系统响应的迟缓性可能会掩盖其真实状态，这一点在生态系统监测中需予以重点考量。此外，我们的研究显示，生理适应可大幅改变生态系统管理中对系统行为的预测模式。因此，在监测和预测生态系统对环境变化的响应时，应纳入物种适应性与系统响应迟缓性这两个关键因素。