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Contributed Talk

Session: : Fisheries Management And Models 1

COS 285-4 - Predicting angler behavior using mobile app data and Bayesian networks

Thursday, August 10, 2023
2:15 PM – 2:30 PM PDT
Location: E141
Abstract: Recreational angler play an important role in providing researchers and managers with valuable data about the state of fisheries. Angler behavior can be tracked by mobile phone applications and on online platforms at a low cost and in a high temporal and spatial resolution. However, in contrast to data gathered by conventional methods, the citizen-reported data is biased toward certain groups of anglers. To predict “ground-truth” angler behavior (e.g., catch rates and angler pressure) in freshwater bodies across Canada we applied advanced machine-learning models, including Bayesian networks. The models were trained and compared on their prediction performance using reported data from the Angler’s Atlas website and the MyCatch app, auxiliary variables on the environment as well as different conventional angler surveys. We show how Bayesian networks are best suited for making predictions due to their ability to easily deal with missing values in the input variables. We illustrate how our methods can be used to support fisheries management and conservation in a rapidly changing environment.