  Prediction of angler behaviors, such as catch rates and angler pressure, is
essential to maintaining fish populations and ensuring angler satisfaction.
Angler behavior can partly be tracked by online platforms and mobile phone
applications that provide fishing activities reported by recreational anglers.
Moreover, angler behavior is known to be driven by local site attributes. Here,
the prediction of citizen-reported angler behavior was investigated by
machine-learning methods using auxiliary data on the environment,
socioeconomics, fisheries management objectives, and events at a freshwater
body. The goal was to determine whether auxiliary data alone could predict the
reported behavior. Different spatial and temporal extents and temporal
resolutions were considered. Accuracy scores averaged 88% for monthly
predictions at single water bodies and 86% for spatial predictions on a day in
a specific region across Canada. At other resolutions and scales, the models
only achieved low prediction accuracy of around 60%. The study represents a
first attempt at predicting angler behavior in time and space at a large scale
and establishes a foundation for potential future expansions in various
directions.
