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Article Dans Une Revue Applications in Plant Sciences Année : 2018

Species distribution modeling based on the automated identification of citizen observations

Résumé

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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Dates et versions

hal-01739481 , version 1 (22-03-2018)

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Paternité - Pas d'utilisation commerciale

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Christophe Botella, Alexis Joly, Pierre Bonnet, Pascal P. Monestiez, François Munoz. Species distribution modeling based on the automated identification of citizen observations. Applications in Plant Sciences, 2018, Green Digitization: Online Botanical Collections Data Answering Real‐World Questions, 6 (2), pp.1-11. ⟨10.1002/aps3.1029⟩. ⟨hal-01739481⟩
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