When to monitor and when to act: Value of information theory for multiple management units and limited budgets

Abstract

AbstractThe question of when to monitor and when to act is fundamental to applied ecology and notoriously difficult to answer. Value of information (VOI) theory holds great promise to help answer this question for many management problems. However,VOItheory in applied ecology has only been demonstrated in single‐decision problems and has lacked explicit links between monitoring and management costs.Here, we present an extension ofVOItheory for solving multi‐unit decisions of whether to monitor before managing, while explicitly accounting for monitoring costs. Our formulation helps to choose the optimal monitoring/management strategy among groups of management units (e.g. species, habitat patches) and can be used to examine the benefits of partial and repeat monitoring.To demonstrate our approach, we use case‐simulated studies of single‐species protection that must choose among potential habitat areas, and classification and management of multiple species threatened with extinction. We provide spreadsheets and code to illustrate the calculations and facilitate application. Our case studies demonstrate the utility of predicting the number of units with a given outcome for problems with probabilities of discrete states and the efficiency of having a flexible approach to manage according to monitoring outcomes.Synthesis and applications. The decision to act or gather more information can have serious consequences for management. No decision, including the decision to monitor, is risk‐free. Our multi‐unit expansion of Value of Information theory can reduce the risk in monitoring/acting decisions for many applied ecology problems. While our approach cannot account for the potential value of discovering previously unknown threats or ecological processes via monitoring programmes, it can provide quantitative guidance on whether to monitor before acting, and which monitoring/management actions are most likely to meet management objectives.