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 thr...

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Bibliographic Details
Published in:Applications in plant sciences 2018-02, Vol.6 (2), p.e1029-n/a
Main Authors: Botella, Christophe, Joly, Alexis, Bonnet, Pierre, Monestiez, Pascal, Munoz, François
Format: Article
Language:English
Subjects:
Application
Artificial intelligence
automated species identification
Automation
Biodiversity
Biodiversity and Ecology
Botanics
citizen science
Computer Science
crowdsourcing
deep learning
Environmental Sciences
Identification
invasive alien species
Life Sciences
Machine learning
Plant sciences
Roads & highways
Species
species distribution modeling
Taxonomy
Typology
Vegetal Biology
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Summary: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.
ISSN:2168-0450
2168-0450
DOI:10.1002/aps3.1029