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A fine-tuned global distribution dataset of marine forests
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,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156423/ https://www.ncbi.nlm.nih.gov/pubmed/32286314 http://dx.doi.org/10.1038/s41597-020-0459-x |
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author | Assis, Jorge Fragkopoulou, Eliza Frade, Duarte Neiva, João Oliveira, André Abecasis, David Faugeron, Sylvain Serrão, Ester A. |
author_facet | Assis, Jorge Fragkopoulou, Eliza Frade, Duarte Neiva, João Oliveira, André Abecasis, David Faugeron, Sylvain Serrão, Ester A. |
author_sort | Assis, Jorge |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7156423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71564232020-04-24 A fine-tuned global distribution dataset of marine forests Assis, Jorge Fragkopoulou, Eliza Frade, Duarte Neiva, João Oliveira, André Abecasis, David Faugeron, Sylvain Serrão, Ester A. Sci Data Data Descriptor 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. Nature Publishing Group UK 2020-04-14 /pmc/articles/PMC7156423/ /pubmed/32286314 http://dx.doi.org/10.1038/s41597-020-0459-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Assis, Jorge Fragkopoulou, Eliza Frade, Duarte Neiva, João Oliveira, André Abecasis, David Faugeron, Sylvain Serrão, Ester A. A fine-tuned global distribution dataset of marine forests |
title | A fine-tuned global distribution dataset of marine forests |
title_full | A fine-tuned global distribution dataset of marine forests |
title_fullStr | A fine-tuned global distribution dataset of marine forests |
title_full_unstemmed | A fine-tuned global distribution dataset of marine forests |
title_short | A fine-tuned global distribution dataset of marine forests |
title_sort | fine-tuned global distribution dataset of marine forests |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156423/ https://www.ncbi.nlm.nih.gov/pubmed/32286314 http://dx.doi.org/10.1038/s41597-020-0459-x |
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