Robust Trend Estimation From Temporally Irregular Recreational Fisheries Surveys: A Panel Modeling Framework for Sparse Time Series

Abstract

Monitoring of recreational fisheries faces ongoing challenges due to irregular data collection and sampling gaps. We used a robust statistical approach combining cross-sectional panel modeling with generalized linear models to analyze discontinuous time series data. We modeled recreational boating patterns across four distinct Western Australian locations using camera monitoring data (2011–2014), and integrated weather conditions and temporal factors to improve trend estimation. Environmental conditions were strongly related to boating activity, particularly wind effects of distinct north–south patterns. Recreational activity decreased at northern locations during easterly winds and at southern locations during northerly winds. Cross-validation demonstrated how environmental factors can be leveraged to predict recreational fishing efforts across temporal discontinuities. Advanced time series testing confirmed that environmental–boating relationships were consistent when bridging significant data gaps. This methodological framework provides fisheries researchers with practical tools to reliably estimate effort from intermittent monitoring programs and to enable more informed decision-making despite the constraints of sparse sampling. The approach can be readily applied to other ecological monitoring systems where continuous data collection is impractical or cost-prohibitive.