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 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.
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Subjects: | F40 - Écologie végétale, F70 - Taxonomie végétale et phytogéographie, C30 - Documentation et information, C20 - Vulgarisation, |
Online Access: | http://agritrop.cirad.fr/587696/ http://agritrop.cirad.fr/587696/1/aps3.1029.pdf |
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dig-cirad-fr-5876962021-11-09T09:38:24Z http://agritrop.cirad.fr/587696/ http://agritrop.cirad.fr/587696/ Species distribution modeling based on the automated identification of citizen observations. Botella Christophe, Joly Alexis, Bonnet Pierre, Monestiez Pascal, Munoz François. 2018. Applications in Plant Sciences, 6 (2), n.spéc. Green Digitization: Online Botanical Collections Data Answering Real-World Questions:e1029, 11 p.https://doi.org/10.1002/aps3.1029 <https://doi.org/10.1002/aps3.1029> Researchers Species distribution modeling based on the automated identification of citizen observations Botella, Christophe Joly, Alexis Bonnet, Pierre Monestiez, Pascal Munoz, François eng 2018 Applications in Plant Sciences F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/587696/1/aps3.1029.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1002/aps3.1029 10.1002/aps3.1029 info:eu-repo/semantics/altIdentifier/doi/10.1002/aps3.1029 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1002/aps3.1029 |
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F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation |
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F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation Botella, Christophe Joly, Alexis Bonnet, Pierre Monestiez, Pascal Munoz, François Species distribution modeling based on the automated identification of citizen observations |
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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. |
format |
article |
topic_facet |
F40 - Écologie végétale F70 - Taxonomie végétale et phytogéographie C30 - Documentation et information C20 - Vulgarisation |
author |
Botella, Christophe Joly, Alexis Bonnet, Pierre Monestiez, Pascal Munoz, François |
author_facet |
Botella, Christophe Joly, Alexis Bonnet, Pierre Monestiez, Pascal Munoz, François |
author_sort |
Botella, Christophe |
title |
Species distribution modeling based on the automated identification of citizen observations |
title_short |
Species distribution modeling based on the automated identification of citizen observations |
title_full |
Species distribution modeling based on the automated identification of citizen observations |
title_fullStr |
Species distribution modeling based on the automated identification of citizen observations |
title_full_unstemmed |
Species distribution modeling based on the automated identification of citizen observations |
title_sort |
species distribution modeling based on the automated identification of citizen observations |
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http://agritrop.cirad.fr/587696/ http://agritrop.cirad.fr/587696/1/aps3.1029.pdf |
work_keys_str_mv |
AT botellachristophe speciesdistributionmodelingbasedontheautomatedidentificationofcitizenobservations AT jolyalexis speciesdistributionmodelingbasedontheautomatedidentificationofcitizenobservations AT bonnetpierre speciesdistributionmodelingbasedontheautomatedidentificationofcitizenobservations AT monestiezpascal speciesdistributionmodelingbasedontheautomatedidentificationofcitizenobservations AT munozfrancois speciesdistributionmodelingbasedontheautomatedidentificationofcitizenobservations |
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1758025717840347136 |