  Management of invasive species and pathogens requires information about the
traffic of potential vectors. Such information is often taken from vector
traffic models fitted to survey data. Here, user-specific data collected via
mobile apps offer new opportunities to obtain more accurate estimates and to
analyze how vectors' individual preferences affect propagule flows. However,
data voluntarily reported via apps may lack some trip records, adding a
significant layer of uncertainty. We show how the benefits of app-based data
can be exploited despite this drawback.
  Based on data collected via an angler app, we built a stochastic model for
angler traffic in the Canadian province Alberta. There, anglers facilitate the
spread of whirling disease, a parasite-induced fish disease. The model is
temporally and spatially explicit and accounts for individual preferences and
repeating behaviour of anglers, helping to address the problem of missing trip
records.
  We obtained estimates of angler traffic between all subbasins in Alberta. The
model's accuracy exceeds that of direct empirical estimates even when fewer
data were used to fit the model. The results indicate that anglers' local
preferences and their tendency to revisit previous destinations reduce the
number of long inter-waterbody trips potentially dispersing whirling disease.
According to our model, anglers revisit their previous destination in 64% of
their trips, making these trips irrelevant for the spread of whirling disease.
Furthermore, 54% of fishing trips end in individual-specific spatially
contained areas with mean radius of 54.7km. Finally, although the fraction of
trips that anglers report was unknown, we were able to estimate the total
yearly number of fishing trips in Alberta, matching an independent empirical
estimate.
