Species distribution modeling based on the automated identification of citizen observations

PREMISE OF THE STUDY: A species distribution model computed with automatically identified plant observations was developed and evaluated to contribute to future ecological studies. METHODS: We used deep learning techniques to automatically identify opportunistic plant observations made by citizens through a popular mobile application. We compared species distribution modeling of invasive alien plants based on these data to inventories made by experts. RESULTS: The trained models have a reasonable predictive effectiveness for some species, but they are biased by the massive presence of cultivated specimens. DISCUSSION: The method proposed here allows for fine-grained and regular monitoring of some species of interest based on opportunistic observations. More in-depth investigation of the typology of the observations and the sampling bias should help improve the approach in the future.