黑海西北陆架大型底栖动物数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Séverine Chevalier, Olivier Beauchard, Adrian Teacă, Tatiana Begun, Valentina Todorova, Luc Vandenbulcke, Karline Soetaert, Marilaure Grégoire
{"title":"黑海西北陆架大型底栖动物数据集。","authors":"Séverine Chevalier, Olivier Beauchard, Adrian Teacă, Tatiana Begun, Valentina Todorova, Luc Vandenbulcke, Karline Soetaert, Marilaure Grégoire","doi":"10.1038/s41597-025-05311-2","DOIUrl":null,"url":null,"abstract":"<p><p>Benthic ecological data are crucial to study and manage ecosystems. On the one hand, abiotic and species data provide complementary information to identify habitats. On the other hand, trait data, describing taxon characteristics, are required to predict anthropogenic impacts on marine ecosystems. Indeed, species traits are now widely used to understand natural selection in communities or to highlight ecosystem functions. While trait data are in growing demand, compiling them is challenging, time-consuming and there are no properly established procedures for major marine ecosystems. Here, we share a data set comprising macrozoobenthic occurrences for 215 taxa over the Black Sea northwestern shelf, between 1995 and 2017, and 27 traits documented for 127 taxa that related to life cycle and ecosystem function. In addition, we provide an abiotic data set of physical and chemical variables generated by a model or compiled from in-situ data. This data set aims to fill the functional knowledge gap in the Black Sea and offers research opportunities to future studies covering ecosystem functions, biodiversity conservation, and management.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"957"},"PeriodicalIF":6.9000,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145434/pdf/","citationCount":"0","resultStr":"{\"title\":\"A macrozoobenthic data set of the Black Sea northwestern shelf.\",\"authors\":\"Séverine Chevalier, Olivier Beauchard, Adrian Teacă, Tatiana Begun, Valentina Todorova, Luc Vandenbulcke, Karline Soetaert, Marilaure Grégoire\",\"doi\":\"10.1038/s41597-025-05311-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Benthic ecological data are crucial to study and manage ecosystems. On the one hand, abiotic and species data provide complementary information to identify habitats. On the other hand, trait data, describing taxon characteristics, are required to predict anthropogenic impacts on marine ecosystems. Indeed, species traits are now widely used to understand natural selection in communities or to highlight ecosystem functions. While trait data are in growing demand, compiling them is challenging, time-consuming and there are no properly established procedures for major marine ecosystems. Here, we share a data set comprising macrozoobenthic occurrences for 215 taxa over the Black Sea northwestern shelf, between 1995 and 2017, and 27 traits documented for 127 taxa that related to life cycle and ecosystem function. In addition, we provide an abiotic data set of physical and chemical variables generated by a model or compiled from in-situ data. This data set aims to fill the functional knowledge gap in the Black Sea and offers research opportunities to future studies covering ecosystem functions, biodiversity conservation, and management.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"957\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145434/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05311-2\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05311-2","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

底栖生物生态数据对研究和管理生态系统至关重要。一方面,非生物和物种数据为确定生境提供了补充信息。另一方面,描述分类群特征的性状数据是预测人类活动对海洋生态系统影响的必要条件。事实上,物种特征现在被广泛用于理解群落的自然选择或突出生态系统功能。虽然对性状数据的需求日益增长,但汇编这些数据既具有挑战性又耗时,而且目前还没有针对主要海洋生态系统的适当程序。在这里,我们共享了一个数据集,包括1995年至2017年黑海西北大陆架215个分类群的大型底栖动物发生情况,以及127个分类群记录的与生命周期和生态系统功能相关的27个特征。此外,我们还提供了由模型生成或从原位数据编译的物理和化学变量的非生物数据集。该数据集旨在填补黑海生态系统功能、生物多样性保护和管理等方面的知识空白,为今后的研究提供研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A macrozoobenthic data set of the Black Sea northwestern shelf.

A macrozoobenthic data set of the Black Sea northwestern shelf.

A macrozoobenthic data set of the Black Sea northwestern shelf.

A macrozoobenthic data set of the Black Sea northwestern shelf.

Benthic ecological data are crucial to study and manage ecosystems. On the one hand, abiotic and species data provide complementary information to identify habitats. On the other hand, trait data, describing taxon characteristics, are required to predict anthropogenic impacts on marine ecosystems. Indeed, species traits are now widely used to understand natural selection in communities or to highlight ecosystem functions. While trait data are in growing demand, compiling them is challenging, time-consuming and there are no properly established procedures for major marine ecosystems. Here, we share a data set comprising macrozoobenthic occurrences for 215 taxa over the Black Sea northwestern shelf, between 1995 and 2017, and 27 traits documented for 127 taxa that related to life cycle and ecosystem function. In addition, we provide an abiotic data set of physical and chemical variables generated by a model or compiled from in-situ data. This data set aims to fill the functional knowledge gap in the Black Sea and offers research opportunities to future studies covering ecosystem functions, biodiversity conservation, and management.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信