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Species distribution modeling based on the automated identification of citizen observations

Botella C., Joly A., Bonnet P., Monestiez P., Munoz F.. 2018. Applications in Plant Sciences, 6 (2) : 11 p..

DOI: 10.1002/aps3.1029

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.

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