Website visits can predict angler presence using machine learning
<|reference_start|>Website visits can predict angler presence using machine learning: Understanding and predicting recreational fishing activity is important for sustainable fisheries management. However, traditional methods of measuring fishing pressure, such as surveys, can be costly and limited in both time and spatial extent. Predictive models that relate fishing activity to environmental or economic factors typically rely on historical data, which often restricts their spatial applicability due to data scarcity. In this study, high-resolution angler-generated data from an online 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-generated 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 angler-generated data from online platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management.<|reference_end|>
@article{schmid2024website,
    title={Website visits can predict angler presence using machine learning},
    author={Julia S. Schmid (1) and Sean Simmons (2) and Mark A. Lewis (1 and 3
  and 4 and 5) and Mark S. Poesch (5) and Pouria Ramazi (6) ((1) Department of
  Mathematical and Statistical Sciences, University of Alberta, Edmonton,
  Alberta, Canada, (2) Anglers Atlas, Goldstream Publishing, Prince George,
  British Columbia, Canada, (3) Department of Mathematics and Statistics,
  University of Victoria, Victoria, British Columbia, Canada, (4) Department of
  Biology, University of Victoria, Victoria, British Columbia, Canada, (5)
  Department of Biological Sciences, University of Alberta, Edmonton, Alberta,
  Canada, (6) Department of Mathematics and Statistics, Brock University, St.
  Catharines, Ontario, Canada)},
    journal={arXiv preprint arXiv:2409.17425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17425},
    primaryClass={physics.soc-ph cs.LG}
}