Species distribution modeling based on the automated identification of citizen observations

Botella Christophe, Joly Alexis, Bonnet Pierre, Monestiez Pascal, Munoz François. 2018. Species distribution modeling based on the automated identification of citizen observations. Applications in Plant Sciences, 6 (2), n.spéc. Green Digitization: Online Botanical Collections Data Answering Real-World Questions:e1029, 11 p.

Journal article ; Article de revue à facteur d'impact Revue en libre accès total
Published version - Anglais
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Quartile : Q3, Sujet : PLANT SCIENCES

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.

Classification Agris : F40 - Plant ecology
F70 - Plant taxonomy and geography
C30 - Documentation and information
C20 - Extension

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

  • Botella Christophe, INRIA (FRA)
  • Joly Alexis, INRIA (FRA)
  • Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389 - auteur correspondant
  • Monestiez Pascal, INRA (FRA)
  • Munoz François, Université Grenoble Alpes (FRA)

Source : Cirad-Agritrop (

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