From conflict management to collaborative adaptation: Designing participatory governance frameworks for Human–Wildlife relations in Sanjiangyuan National Park, China

Human–wildlife relations in alpine ecosystems reflect the combined effects of climate change, land-use transitions, and governance capacity. Rather than focusing solely on local conflict dynamics, this study investigates how adaptive governance can be institutionally embedded to anticipate and respond to ecological and social uncertainties. Drawing on multiple data sources—including official and historical records of eco-ranger co-management policy, village-level focus group discussions. We also conducted a three-round Delphi process with 30 multidisciplinary experts (conservation scientists, rangeland managers, and policy practitioners)—we developed collaboratively a governance framework linking climate risk assessment to decision-making processes in Sanjiangyuan National Park (SNP). The evidence from multiple sources identified four core adaptation domains: (i) climate-informed spatial planning, (ii) flexible grazing regulation, (iii) participatory monitoring of wildlife mobility, and (iv) cross-sector coordination mechanisms. Expert consensus emphasized the need for adaptive feedback systems to integrate real-time ecological data (including opportunities to link with quantitative ecological indicators) into local decision cycles. By synthesizing insights across multiple perspectives, this study proposes an governance framework for operationalizing adaptive governance in climate-sensitive protected areas. Unlike previous work focusing on community-level perceptions or household adaptation strategies, this paper advances a participatory governance approach centered on institutional design and social learning. It contributes to broader debates on how conservation governance can evolve toward resilience-oriented, co-managed systems under environmental change. The proposed framework provides concrete steps for integrating climate information into routine protected area management. Future work should validate the framework with more representative data and a more diverse expert pool and cross-regional application. • Multi-source data were integrated to support adaptive governance in climate-sensitive parks. • Four key adaptation domains were identified to guide institutional transformation in SNP. • The study proposes a learning-based governance framework for collaborative conservation.