Towards evidence-based management: integrating institutional analysis and machine learning for enhanced recreational fisheries monitoring and decision-making

This thesis consists of two separate studies, the first investigates the institutional framework and challenges in managing public recreational fisheries. It contrasts the existing system with an idealized agency structure model, highlighting monitoring, decision-making, and accountability deficiencies. Semi-structured interviews inform the analysis, revealing a disconnect between monitoring practices and actionable triggers, leading to instability in decision-making. Inconsistent information flow and limited public involvement exacerbate accountability issues. The study proposes a framework rooted in evidence-based management, featuring clear objectives, triggers, and bidirectional information flow. The second study employs machine learning techniques to identify key variables influencing angler presence, revealing reservoir area as the most influential factor. Despite greater access to information and shifting social norms, recreational anglers have maintained their historical habits and current creel survey designs capture this. These insights contribute to understanding angler behaviour and emphasize the importance of evidence-based management in fisheries monitoring and decision-making processes.