Chevalier Séverine, Beauchard Olivier, Teacă Adrian, Begun Tatiana, Todorova Valentina, Vandenbulcke Luc, Soetaert Karline, Grégoire Marilaure
MAST-FOCUS, Department of Astrophysics, Geophysics and Oceanography, University of Liège, Allée du 6 Aoû t 19, 4000, Liège, Belgium.
Marine Biology, Department of Biology, Ghent University, Krijgslaan 281/S8 B-9000, Ghent, Belgium.
Sci Data. 2025 Jun 7;12(1):957. doi: 10.1038/s41597-025-05311-2.
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.
底栖生态数据对于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识别栖息地提供了补充信息。另一方面,描述分类单元特征的性状数据对于预测人为对海洋生态系统的影响是必需的。事实上,物种性状现在被广泛用于理解群落中的自然选择或突出生态系统功能。尽管对性状数据的需求不断增加,但收集这些数据具有挑战性、耗时,而且对于主要海洋生态系统没有适当确立的程序。在此,我们分享一个数据集,该数据集包含1995年至2017年间黑海西北大陆架上215个分类单元的大型底栖动物出现情况,以及记录的127个分类单元的27个性状,这些性状与生命周期和生态系统功能相关。此外,我们提供了一个由模型生成或从现场数据汇编而来的物理和化学变量的非生物数据集。这个数据集旨在填补黑海的功能知识空白,并为未来涵盖生态系统功能、生物多样性保护和管理的研究提供研究机会。