Functional traits data for testate amoebae of Northern Holarctic realm.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jiahui Su, Yuri A Mazei, Andrey N Tsyganov, Viktor A Chernyshov, Alexander A Komarov, Elena A Malysheva, Kirill V Babeshko, Natalia G Mazei, Damir A Saldaev, Boris Levin, Basil N Yakimov
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引用次数: 0

Abstract

The functional traits of soil protists have been employed in ecological research to enhance comprehension of the underlying mechanisms of ecological processes. Among the numerous soil protists, testate amoebae emerge as a prominent and abundant group, playing a pivotal role in soil micro-food webs. Furthermore, they are regarded as valuable bioindicators for environmental monitoring and palaeoecological studies due to their sensitivity to environmental changes. We screened 372 testate amoebae species widely distributed across Northern Holarctic realm and collected trait data, representing the morphological and feeding characteristics of testate amoebae. The dataset would provide valuable basis for investigation of the functional diversity and ecological roles of testate amoebae, thus facilitating further research on soil protist communities and ecosystem dynamics.

全北极圈北部睾丸变形虫的功能特征数据。
土壤原生生物的功能特征已被用于生态学研究,以加深对生态过程内在机制的理解。在众多土壤原生动物中,睾丸变形虫是一个突出而丰富的群体,在土壤微食物网中发挥着关键作用。此外,由于其对环境变化的敏感性,它们被认为是环境监测和古生态研究的重要生物指标。我们筛选了广泛分布于北北极地区的 372 种睾丸变形虫,并收集了代表睾丸变形虫形态和摄食特征的性状数据。该数据集将为研究睾丸变形虫的功能多样性和生态作用提供宝贵的依据,从而促进对土壤原生生物群落和生态系统动态的进一步研究。
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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.
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