Species distribution modeling based on the automated identification of citizen observations. (14th March 2018)

• Record Type: Journal Article
• Authors: Botella, Christophe; Joly, Alexis; Bonnet, Pierre; Monestiez, Pascal; Munoz, François
• Is Part Of: Applications in plant sciences. Volume 6:Number 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-03-14
• Subjects: automated species identification -- citizen science -- crowdsourcing -- deep learning -- invasive alien species -- species distribution modeling; Plants -- Periodicals; Plant physiology -- Periodicals; Plant Physiological Phenomena; Plant physiology; Plants; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 580
• DOI: 10.1002/aps3.1029
• Languages: English
• ISSNs: 2168-0450

Abstract

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.