Modeling Posidonia oceanica shoot density and rhizome primary production.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 10 2020
12 10 2020
Historique:
received:
21
01
2020
accepted:
09
09
2020
entrez:
13
10
2020
pubmed:
14
10
2020
medline:
14
10
2020
Statut:
epublish
Résumé
Posidonia oceanica meadows rank among the most important and most productive ecosystems in the Mediterranean basin, due to their ecological role and to the goods and services they provide. Estimations of crucial ecological process such as meadows productivity could play a major role in an environmental management perspective and in the assessment of P. oceanica ecosystem services. In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica shoot density and rhizome primary production using as predictive variables only environmental factors retrieved from indirect measurements, such as maps. Our predictive models showed a good level of accuracy in modeling both shoot density and rhizome productivity (R
Identifiants
pubmed: 33046821
doi: 10.1038/s41598-020-73722-9
pii: 10.1038/s41598-020-73722-9
pmc: PMC7550612
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16978Références
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