Unknown

Dataset Information

0

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

altmetric image

Publications

Species distribution modeling based on the automated identification of citizen observations.

Botella Christophe C   Joly Alexis A   Bonnet Pierre P   Monestiez Pascal P   Munoz François F  

Applications in plant sciences 20180201 2


<h4>Premise of the study</h4>A species distribution model computed with automatically identified plant observations was developed and evaluated to contribute to future ecological studies.<h4>Methods</h4>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.<h4>Results</h4>The trained mode  ...[more]

Similar Datasets

| S-EPMC9245884 | biostudies-literature
| S-EPMC8131797 | biostudies-literature
| S-EPMC6802020 | biostudies-literature
| S-EPMC7663073 | biostudies-literature
| S-EPMC6468778 | biostudies-literature
| S-EPMC7187145 | biostudies-literature
| S-EPMC8019030 | biostudies-literature
| S-EPMC7322891 | biostudies-literature
| S-EPMC8372924 | biostudies-literature
| S-EPMC3608019 | biostudies-literature