{"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}
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.
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.