  Reliable angler behavior data is important for effective fisheries
management. Traditionally, such data is gathered through surveys, but an
innovative cost-effective approach involves utilizing online platforms and
smartphone applications. Previous studies have identified correlations between
citizen-reported data from these applications and conventional survey
information. However, it remains unclear whether conventional survey data is
directly related to citizen-reported data or mainly derived from "intermediate"
variables. We applied Bayesian networks 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 measured the strength of connections between
variables. In Ontario, aerial boat counts were directly related to waterbody
webpage views, with a 51% probability. In Alberta, creel survey-reported catch
rate was directly related to citizen-reported catch rate and fishing duration,
though with low probabilities (12% and 6%, respectively). Daily fishing
durations were indirectly related, with air temperature and solar radiation as
intermediates. These findings suggest that citizen reports can complement
traditional methods for evaluating angler behavior.
