Open Access

Florian Diekert, Daniel Heyen, Frikk Nesje, Soheil Shayegh

• Abrupt changes in some complex socio-ecological systems can be anticipated by observing their behaviour under increasing stress before they cross a tipping point. Despite notable progress in identifying statistical indicators that can provide early warning signals (EWS) of tipping points, they have yet to find direct application in management. Here, we develop a theoretical model of an early warning system (EWSys) that integrates EWS information into a simple decision-making process. This model consists of a tipping indicator, whose value increases as the system approaches the tipping point, and a trigger value, beyond which a binary EWS is sent. We demonstrate that although EWSys can help balance the risk of tipping by providing information to update the belief about the location of the tipping point, it may also result in more risky behaviour in the case that no EWS is received. This leads to a tension between better information about the location of the tipping point and increasedAbrupt changes in some complex socio-ecological systems can be anticipated by observing their behaviour under increasing stress before they cross a tipping point. Despite notable progress in identifying statistical indicators that can provide early warning signals (EWS) of tipping points, they have yet to find direct application in management. Here, we develop a theoretical model of an early warning system (EWSys) that integrates EWS information into a simple decision-making process. This model consists of a tipping indicator, whose value increases as the system approaches the tipping point, and a trigger value, beyond which a binary EWS is sent. We demonstrate that although EWSys can help balance the risk of tipping by providing information to update the belief about the location of the tipping point, it may also result in more risky behaviour in the case that no EWS is received. This leads to a tension between better information about the location of the tipping point and increased risk of crossing it. Our framework complements the emergence of resilience indicators of complex human–natural systems by providing a better understanding of how, when and why they can be used to improve decision making.…