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.

SUBMITTER: Botella C 

PROVIDER: S-EPMC5851560 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature