Website visits can predict angler presence using machine learning

TL;DR

This study demonstrates that online fishing platform data, combined with machine learning, can effectively predict angler presence and effort at lakes, aiding sustainable fisheries management.

Contribution

It introduces a novel approach using online platform visits and auxiliary data to predict angler effort, improving spatial and temporal prediction capabilities.

Findings

Lake website visits predict boat presence with 78% accuracy.

Additional environmental data improve boat count predictions.

Models perform well on known lakes but poorly on unknown lakes.

Abstract

Understanding and predicting recreational angler effort is important for sustainable fisheries management. However, conventional methods of measuring angler effort, such as surveys, can be costly and limited in both time and spatial extent. Models that predict angler effort based on environmental or economic factors typically rely on historical data, which often limits their spatial and temporal generalizability due to data scarcity. In this study, high-resolution data from an online fishing platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over five years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather and angler-reported features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R2 of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R2 = 0.21). The results demonstrate the value of integrating data from online fishing platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management.