A fine-tuned global distribution dataset of marine forests - Sorbonne Université Accéder directement au contenu
Article Dans Une Revue Scientific Data Année : 2020

A fine-tuned global distribution dataset of marine forests

Résumé

Species distribution records are a prerequisite to follow climate-induced range shifts across space and time. However, synthesizing information from various sources such as peer-reviewed literature, herbaria, digital repositories and citizen science initiatives is not only costly and time consuming, but also challenging, as data may contain thematic and taxonomic errors and generally lack standardized formats. We address this gap for important marine ecosystem-structuring species of large brown algae and seagrasses. We gathered distribution records from various sources and provide a fine-tuned dataset with ~2.8 million dereplicated records, taxonomically standardized for 682 species, and considering important physiological and biogeographical traits. Specifically, a flagging system was implemented to signal potentially incorrect records reported on land, in regions with limiting light conditions for photosynthesis, and outside the known distribution of species, as inferred from the most recent published literature. We document the procedure and provide a dataset in tabular format based on Darwin Core Standard (DwC), alongside with a set of functions in R language for data management and visualization.
Fichier principal
Vignette du fichier
Assis et al. - 2020 - A fine-tuned global distribution dataset of marine.pdf (1.54 Mo) Télécharger le fichier
Origine : Publication financée par une institution
Loading...

Dates et versions

hal-02550647 , version 1 (22-04-2020)

Identifiants

Citer

Jorge Assis, Eliza Fragkopoulou, Duarte Frade, João Neiva, André Oliveira, et al.. A fine-tuned global distribution dataset of marine forests. Scientific Data , 2020, 7, pp.119. ⟨10.1038/s41597-020-0459-x⟩. ⟨hal-02550647⟩
58 Consultations
45 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More