Using Participatory Workshops to Integrate State-and-Transition Models Created With Local Knowledge and Ecological Data

Abstract

State-and-transition models (STMs) depict current understanding of vegetation dynamics and are being created for most ecological sites in the United States. Model creation is challenging due to inadequate long-term data, and most STMs rely on expert knowledge. There has been little systematic documentation of how different types of knowledge have been integrated in STMs, or what these distinct knowledge sources offer. We report on a series of participatory workshops where stakeholders helped to integrate STMs developed for the same region using local knowledge and ecological field data. With this exploratory project, we seek to understand what kinds of information local knowledge and ecological field data can provide to STMs, assess workshops as a method of integrating knowledge and evaluate how different stakeholders perceive models created with different types of knowledge. Our analysis is based on meeting notes, comments on draft models, and workshop evaluation questionnaires. We conclude that local knowledge and ecological data can complement one another, providing different types of information at different spatial and temporal scales. Participants reported that the workshop increased their knowledge of STMs and vegetation dynamics, suggesting that engaging potential model users in developing STMs is an effective outreach and education approach. Agency representatives and ranchers expressed the value of both the local knowledge and data-driven models. Agency participants were likely to critique or add components based on monitoring data or prior research, and ranchers were more likely to add states and transitions based on personal experience. As STM development continues, it is critical that range professionals think systematically about what different forms of data might contribute to model development, how we can best integrate existing knowledge and data to create credible and useful models, and how to validate the resulting STMs.