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Species distribution modeling based on the automated identification of citizen observations

Abstract : 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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https://hal.umontpellier.fr/hal-01739481
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Soumis le : jeudi 22 mars 2018 - 16:35:31
Dernière modification le : mardi 6 octobre 2020 - 16:17:02
Archivage à long terme le : : jeudi 13 septembre 2018 - 07:15:37

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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

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Christophe Botella, Alexis Joly, Pierre Bonnet, Pascal Monestiez, François Munoz. Species distribution modeling based on the automated identification of citizen observations. Applications in Plant Sciences, Wiley, 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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