State‐and‐transition models as a contextual framework for leading indicators of restoration trajectories

New incentives and instruments for financing ecosystem restoration require frameworks that support planning, monitoring and reporting, including the identification and use of leading indicators. Leading indicators have the potential to predict the outcomes of restoration interventions before full recovery has occurred. State‐and‐transition models are a form of ecosystem dynamics modelling that has been widely and systematically applied to classify and describe ecosystem dynamics in North America and Australia, including ecosystem recovery efforts. State‐and‐transition models provide a framework for the organisation and selection of leading, lagging and coincident indicators for monitoring restoration outcomes. We outline a process for strengthening the application of state‐and‐transition models to restoration. This includes key considerations that are unique to restoration, such as the importance of starting states and how restoration management interventions can be incorporated into models as drivers of transitions. We demonstrate the approach through a case study and apply our restoration state‐and‐transition model to help guide the timing of restoration management actions informed by indicators. We then use insights on ecological recovery dynamics provided by the restoration state‐and‐transition model to generate hypotheses for identifying and selecting leading indicators. For example, the soil nutrient status of retired farmland is predicted to be a leading indicator of potential native forb establishment. Restoration‐targeted state‐and‐transition models play a key role in systematically translating detailed practitioner insights, ecological data and indicators into structured formats that are accessible to a broad audience. To credibly implement the framework and leading indicators in nature markets, complementary scientific tools and governance processes are needed. This includes establishing agreed benchmarks for indicators, designing robust audit mechanisms and conducting further research to develop the evidence base for leading indicators.