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The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos

Biological trait analysis (BTA) is a valuable tool for evaluating changes in community diversity and its link to ecosystem processes as well as environmental and anthropogenic perturbations. Trait-based analytical techniques like BTA rely on standardised datasets of species traits. However, there ar...

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Autores principales: Meijer, Kasper J., Gusmao, Joao Bosco, Bruil, Lisa, Franken, Oscar, Grimm, Ise A., van der Heide, Tjisse, Hijner, Nadia, Holthuijsen, Sander J., Hübner, Lisa, Thieltges, David W., Olff, Han, Eriksson, Britas Klemens, Govers, Laura L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656422/
https://www.ncbi.nlm.nih.gov/pubmed/37978182
http://dx.doi.org/10.1038/s41597-023-02728-5
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author Meijer, Kasper J.
Gusmao, Joao Bosco
Bruil, Lisa
Franken, Oscar
Grimm, Ise A.
van der Heide, Tjisse
Hijner, Nadia
Holthuijsen, Sander J.
Hübner, Lisa
Thieltges, David W.
Olff, Han
Eriksson, Britas Klemens
Govers, Laura L.
author_facet Meijer, Kasper J.
Gusmao, Joao Bosco
Bruil, Lisa
Franken, Oscar
Grimm, Ise A.
van der Heide, Tjisse
Hijner, Nadia
Holthuijsen, Sander J.
Hübner, Lisa
Thieltges, David W.
Olff, Han
Eriksson, Britas Klemens
Govers, Laura L.
author_sort Meijer, Kasper J.
collection PubMed
description Biological trait analysis (BTA) is a valuable tool for evaluating changes in community diversity and its link to ecosystem processes as well as environmental and anthropogenic perturbations. Trait-based analytical techniques like BTA rely on standardised datasets of species traits. However, there are currently only a limited number of datasets available for marine macrobenthos that contain trait data across multiple taxonomic groups. Here, we present an open-access dataset of 16 traits for 235 macrozoobenthic species recorded throughout multiple sampling campaigns of the Dutch Wadden Sea; a dynamic soft bottom system where humans have long played a substantial role in shaping the coastal environment. The trait categories included in this dataset cover a variety of life history strategies that are tightly linked to ecosystem functioning and the resilience of communities to (anthropogenic) perturbations and can advance our understanding of environmental changes and human impacts on the functioning of soft bottom systems.
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spelling pubmed-106564222023-11-17 The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos Meijer, Kasper J. Gusmao, Joao Bosco Bruil, Lisa Franken, Oscar Grimm, Ise A. van der Heide, Tjisse Hijner, Nadia Holthuijsen, Sander J. Hübner, Lisa Thieltges, David W. Olff, Han Eriksson, Britas Klemens Govers, Laura L. Sci Data Data Descriptor Biological trait analysis (BTA) is a valuable tool for evaluating changes in community diversity and its link to ecosystem processes as well as environmental and anthropogenic perturbations. Trait-based analytical techniques like BTA rely on standardised datasets of species traits. However, there are currently only a limited number of datasets available for marine macrobenthos that contain trait data across multiple taxonomic groups. Here, we present an open-access dataset of 16 traits for 235 macrozoobenthic species recorded throughout multiple sampling campaigns of the Dutch Wadden Sea; a dynamic soft bottom system where humans have long played a substantial role in shaping the coastal environment. The trait categories included in this dataset cover a variety of life history strategies that are tightly linked to ecosystem functioning and the resilience of communities to (anthropogenic) perturbations and can advance our understanding of environmental changes and human impacts on the functioning of soft bottom systems. Nature Publishing Group UK 2023-11-17 /pmc/articles/PMC10656422/ /pubmed/37978182 http://dx.doi.org/10.1038/s41597-023-02728-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Meijer, Kasper J.
Gusmao, Joao Bosco
Bruil, Lisa
Franken, Oscar
Grimm, Ise A.
van der Heide, Tjisse
Hijner, Nadia
Holthuijsen, Sander J.
Hübner, Lisa
Thieltges, David W.
Olff, Han
Eriksson, Britas Klemens
Govers, Laura L.
The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title_full The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title_fullStr The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title_full_unstemmed The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title_short The seafloor from a trait perspective. A comprehensive life history dataset of soft sediment macrozoobenthos
title_sort seafloor from a trait perspective. a comprehensive life history dataset of soft sediment macrozoobenthos
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656422/
https://www.ncbi.nlm.nih.gov/pubmed/37978182
http://dx.doi.org/10.1038/s41597-023-02728-5
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