Calibrating Remote Camera Network Retrievals with Boat-Based Recreational Fishing Effort from Traditional Surveys Using a Bayesian Hierarchical Approach

Effective monitoring of recreational fisheries is increasingly challenged by respondent fatigue and increasing operational resource constraints. While traditional surveys are fundamental to understanding catch and effort, they are commonly spatially and temporally limited. This study evaluates three complementary survey designs, periodic Phone Diary Surveys (PDS) and Boat Ramp Surveys (BRS), and continuous monitoring from a Remote Camera Network (RCN), to generate continuous, spatially resolved estimates of boat-based recreational fishing effort. Long-term datasets from Western Australia were used to compare vessel retrievals and quantify the variable proportion of fishing and general boating traffic. A hierarchical Bayesian model calibrated continuous RCN counts using PDS estimates to impute an uninterrupted time series of vessel retrievals, even where PDS data were unavailable. We highlight variability in spatial coverage of the RCN, and the implications for model uncertainty. The study presents a framework for leveraging existing datasets to maximise information value, without expanding costly monitoring programs, and improve estimation of fishing effort to support more robust stock assessments and responsive fisheries management.