Resilience of socially valued properties of natural systems to repeated disturbance: A framework to support value‐laden management decisions

As global change delivers new disturbance patterns to agricultural and ecological systems, there is profound concern that many of the natural systems on which humans rely are exhibiting gradual declines or sudden regime shifts. We describe a “flow‐kick” paradigm for modeling populations subject to regular patterns of discrete shocks, and use it to develop a framework that both identifies management strategies to achieve socially valued system properties and characterizes resilience of these properties to changing disturbance patterns. We apply the framework to four examples of logistically growing species where the socially valued properties are assumed to be (i) high abundance, (ii) low abundance, (iii) fishery yield, and (iv) fishery profit. In Example 3, we describe how the flow‐kick approach generalizes the classical maximum sustainable yield. In Example 4, the flow‐kick approach surprises us by identifying management strategies that yield higher profits than the maximum admitted by the corresponding Gordon–Schaeffer model. Recommendations for Resource Managers Consider using flow‐kick modeling to: –explicitly represent the dynamics of a natural resource that is shaped by discrete, recurrent disturbances (e.g., harvests of a biological population). –quantify the resilience of a natural resource, and its socially valued properties, to discrete, recurrent disturbances. –quantify the resilience of a managed resource to additional disturbance or change in management strategy. Instead of using management to optimize according to a single objective, consider what functional properties of a system create value for stakeholders, and identify disturbance patterns (management strategies) that achieve acceptable levels of stakeholder value.When multiple stakeholders derive different values from a resource, search for mutually acceptable management strategies by first identifying acceptable value levels, and then identifying the disturbance patterns that achieve acceptable levels for each stakeholder. Do different stakeholders' sets of acceptable disturbance patterns overlap? If not, how close are they?