Systematic mapping of monitoring and evaluation in rewilding

Rewilding is a landscape recovery approach that seeks to enhance the functioning of degraded ecosystems by reinstating natural processes, and it is increasingly being adopted by organizations and landowners. Despite its popularity, there is no accepted way to assess rewilding's impacts, which is due in part to inconsistent monitoring and evaluation. To address this gap, we developed a systematic map of a unique dataset combining academic and gray literature on monitoring and evaluation in rewilding case studies. We mapped definitions of success, study systems, methods of monitoring, and evaluation metrics for rewilding case studies to compare where the different strands of literature overlap and contrast in focus. We found that researchers and practitioners have been focused on different aspects of rewilding and that the limited recording of case study details and lack of robust experimental design make it difficult to pool and compare data. Researchers and rewilding practitioners face challenges in building a robust evidence base from which to evaluate rewilding outcomes. These challenges include resources as well as spatial and time constraints, which all limit the consistency of data gathering among projects. More comparisons across rewilding projects, better standardization of data collection and data recording methods, and increased collaboration between researchers and practitioners could help remedy these limitations. We developed recommendations for how to monitor and evaluate rewilded landscapes to improve data quality and availability. These include the broad adoption of low-cost and scalable survey designs with greater focus on comparisons across sites and continued effort toward open-source data storage.