Predicting temporal stability and resilience from resistance and recovery

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 both^1-4. Likewise, resilience (sensu proximity to unperturbed levels after a perturbation^5-10) also has components of withstanding (resistance) and recovering after perturbations^11,12. 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 theory^11,12. 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.