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

Boosting propagule transport models with individual-specific data from mobile apps