arXiv subjects

Julia S. Schmid

Publications and source records attributed to Julia S. Schmid.

4 recordsLinked to original sources

arXiv2025

Website visits can predict angler presence using machine learning

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.

physics.soc-ph↗

arXiv2025

Analyzing recreational fishing effort -- Gender differences and the impact of Covid-19

Recreational fishing is an important economic driver and provides multiple social benefits. To predict fishing activity, identifying variables related to variation, such as gender or Covid-19, is helpful. We conducted a Canada-wide email survey of users of an online fishing platform and analyzed responses focusing on gender, the impact of Covid-19, and variables directly related to fishing effort. Genders (90% men and 10% women) significantly differed in demographics, socioeconomic status, and fishing skills but showed similar fishing preferences, fishing effort in terms of trip frequency, and travel distance. Covid-19 altered trip frequency for almost half of fishers, with changes varying by gender and activity level. A Bayesian network revealed travel distance as the main determinant of trip frequency, negatively impacting fishing activity for 61% of fishers, with fishing expertise also playing a role. The results suggest that among active fishers, socio-economic differences between genders do not drive fishing effort, but responses to Covid-19 were gender-specific. Recognizing these patterns is critical for equitable policy-making and accurate socio-ecological models, thereby improving resource management and sustainability.

physics.soc-ph↗

arXiv2025

Webpage Views as a Proxy for Angler Pressure and Effort: Insights from Bayesian Networks

Reliable angler activity data inform fisheries management. Traditionally, such data are gathered through surveys, but an innovative cost-effective approach involves utilizing online platforms and smartphone applications. These citizen-sourced data were reported to correlate with conventional survey information. However, the nature of this correlation--whether direct or mediated by intermediate variables--remains unclear. We applied BNs to data from conventional surveys, the Angler's Atlas website, the MyCatch smartphone application, and environmental data across Alberta and Ontario, Canada, to detect probabilistic dependencies. Using Bayesian model averaging, we quantified the strength of connections between variables. Waterbody webpage views were directly related to daily and weekly-aggregated boat counts in Ontario (51\% and 100\% probability) and to weekly-aggregated creel survey-reported fishing duration in Alberta (100\%). This highlights the value of citizen-sourced data in providing unique insights beyond meteorological factors, with online interest serving as a potentially reliable proxy for angler pressure and effort.

physics.soc-ph↗

arXiv2024

Can machine learning predict citizen-reported angler behavior?

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.

physics.soc-ph↗