Abstract
This thesis presents a compilation of mixed methods approaches to address the complex relationship between recreation on rangelands and the conservation of these important ecosystems. Rangelands, covering over half of the earth’s land area, face increasing recreational use, yet research on the ecological impacts of such activities remains limited. A systematic literature review synthesized existing research on the effects of recreation (i.e. hiking and biking) on rangeland ecosystems, with a focus on primary production, livestock production, and ecological disturbances. Findings indicate that common recreational activities can lead to issues like soil erosion, but further research is needed to better understand these impacts, particularly as recreational use continues to rise. In response to these challenges, interpretive signage and environmental education offer promising solutions for promoting sustainable recreation practices. However, budget and time constraints often prevent small agencies from implementing such measures. To address this gap, this thesis introduces the AI Framework for Environmental and Cultural Interpretation, designed to help small agencies efficiently create interpretive content, including temporary or permanent signage during resource-limited periods. This framework was tested with local Palouse Prairie-affiliated agencies to explore the potential of AI in interpretive content creation. Given the skepticism surrounding AI, participants were interviewed before and after engaging with the framework, and their responses were coded to identify trust types. Results demonstrated a significant reduction in the lack of trust emitted by participants following the second interview, suggesting that increased transparency can enhance trust in AI tools. This project highlights the potential of artificial intelligence as an effective, time-saving tool for conservation agencies to create impactful interpretive content, ultimately supporting sustainable recreation and rangeland stewardship.