Integration of artificial intelligence for sustainable freshwater fishery governance: an Okavango River ecosystem perspective

Abstract

This qualitative study examined the integration of Artificial Intelligence (AI) in sustainable freshwater fishery management within the Okavango River ecosystem, combining primary field research with a comprehensive document review. The investigation explored how AI technologies, including machine learning and predictive analytics, can enhance fish stock assessment, habitat monitoring, and resource administration to achieve ecological and socio-economic sustainability. The study emphasizes the Okavango River's unique biodiversity and its critical importance to local communities while assessing AI's potential to transform traditional fishery management approaches. The research employs a dual-method approach, utilizing both face-to-face semi-structured interviews with key stakeholders (fishers, vendors, and officials) and a systematic review of relevant policy documents and documentary reviews. Thematic analysis of interview data and document content reveals key insights about AI adoption challenges, implementation opportunities, and practical applications in freshwater fisheries. Findings demonstrate AI's transformative potential in enabling real-time data collection, predictive population modeling, and overfishing prevention. However, significant barriers emerge, including technological infrastructure gaps, institutional resistance, and capacity-building needs among local stakeholders. By synthesizing field data with existing literature, this study makes a novel contribution to sustainable fishery management discourse, offering context-specific, AI-integrated strategies for the Okavango River ecosystem. The research proposes policy recommendations that address both technical implementation challenges and ethical considerations, grounded in empirical evidence from multiple data sources. Ultimately, this study highlights the critical role of AI in balancing ecosystem conservation with socio-economic development, while demonstrating how mixed-method approaches can strengthen research outcomes in environmental technology studies.