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Effective biodiversity conservation requires responding to threats in a timely fashion. This requires understanding the impacts of threats on biodiversity and when management needs to be implemented. However, most ecological systems face multiple threats, so monitoring to assess their impacts on biodiversity is a complex task. Indicators help simplify the challenge of monitoring but choosing the best indicator(s) to inform management is not straightforward. We provide a decision framework that can help identify optimal indicators to trigger management in a system faced with multiple threats. The approach evaluates indicators based on criteria spanning monitoring efficiency, management outcomes and the economic constraints for decision-making. Critical decision factors (or parameters) are identified and detailed in a six-step process to estimate the cost-effectiveness of alternate indicators, including threat impacts, sensitivity of indicators to detect change, and the benefits, costs and feasibility of alternative indicators and management actions. Using the Kimberley as a case study, we evaluate 18 indicators for informing management of three key threats in the region: fire and grazing, feral cat predation, and weeds. We show that indicator selection based on our approach can help improve the expected outcome of management decisions under limited resources. By accounting for multiple factors in estimating benefit and costs of monitoring, our approach improves on common approaches that select indicators based only on whether they are sensitive to change and/or cheap to monitor. We also identify how uncertainty in decision factors influences indicator selection. Although cost-effectiveness analyses are gaining popularity, ours is the first study to integrate multiple selection criteria using a return on investment framework to compare indicators for monitoring multiple threats and triggering management.

Making effective management decisions is challenging in multi-species, multi-threat systems because of uncertainty about the effects of different threats on different species. To inform management decisions, we often monitor species to detect spatial or temporal trends that can help us learn about threatening processes. However, which species to monitor and how to monitor to inform the management of threats can be difficult to determine. Value of information (VOI) analysis is an approach for quantifying the value of monitoring to inform management decisions. We developed a novel method that applies VOI analysis to quantify the benefits of different species monitoring strategies in multi-threat, multi-species systems. We applied the approach to compare the effectiveness of surveillance monitoring (monitoring species without experimentation) to targeted monitoring (monitoring species with experimentation to learn about a specific threat), and how prior information drives the benefits of these two different strategies and the species to monitor. We also illustrate the approach by applying it to two contrasting case studies for monitoring and managing declining mammals in Western Australia. Our approach shows that surveillance monitoring generally provides far lower benefits than targeted monitoring for managing threats in multi-species, multi-threat systems under economic constraints. Our approach also informs the choice of species to monitor and which threats to manage experimentally to most improve threat management outcomes. We show that the key parameters driving these choices include: the budget available for management, prior understanding of which threats cause declines in which species, the relative cost of managing these threats, and the background probability of decline. Our new VOI approach allows the evaluation of monitoring decisions in multi-species, multi-threat systems in the face of uncertainty, while explicitly accounting for the improvement in man