Boosting propagule transport models with individual‐specific data from mobile apps
Bibliographic record
Abstract
Abstract 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 analyse 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 interwaterbody 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 54.7 km. 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. Policy implications. We make two major contributions: (1) we provide a model that uses mobile app data to boost the mechanistic accuracy of classic propagule transport models, and (2) we demonstrate the importance of individual‐specific behaviour of vectors for propagule transport. Ignoring vectors' local preferences and their tendency to revisit previous destinations can lead to significant overestimates of vector traffic and biased estimates of propagule flows. This has clear implications for the management of invasive species and animal diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".