A spatial inventory of freshwater macroinvertebrate occurrences in the Guineo-Congolian biodiversity hotspot.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-06 DOI:10.1038/s41597-025-04471-5

Emmanuel O Akindele, Abiodun M Adedapo, Oluwaseun T Akinpelu, Esther D Kowobari, Oluwatosin C Folorunso, Ibrahim R Fagbohun, Tolulope A Oladeji, Olanrewaju O Aliu, Oluwatobiloba S Adenola, Babasola W Adu, Francis O Arimoro, Sylvester S Ogbogu, Sami Domisch

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Abstract

The Guineo-Congolian region, extending from Guinea in West Africa to the central part of Africa, is considered an important biodiversity hotspot in the Afrotropics. Aside from the underreporting and underestimation of freshwater ecosystems, the challenges regarding incorrect coordinates and taxonomical inaccuracies in freshwater species occurrence data pose another major hurdle that may hinder freshwater conservation efforts in the hotspot. Hence, for any biogeographic analysis, species distribution modelling or conservation initiative, it is crucial to use datasets that are, to the largest possible extent, free of spatial and taxonomic errors. We present the final output of 8,809 occurrences consisting of 4 phyla, eight classes, 32 orders, and 1,104 species. We also added the Hydrography90m stream network attributes to the macroinvertebrate occurrence records, such that the data spans across 2,890 sub-catchments and Strahler stream orders 1-12. These records are considered valid and can be used for biogeographic analysis of freshwater macroinvertebrates in this important yet understudied freshwater biodiversity hotspot.

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几内亚-刚果生物多样性热点地区淡水大型无脊椎动物发生的空间清查。

几内亚-刚果地区从西非的几内亚延伸到非洲中部，被认为是非洲热带地区重要的生物多样性热点地区。除了对淡水生态系统的低报和低估外，淡水物种发生数据中不正确的坐标和分类不准确的挑战构成了另一个可能阻碍热点地区淡水保护工作的主要障碍。因此，对于任何生物地理分析、物种分布建模或保护计划来说，使用尽可能不存在空间和分类错误的数据集是至关重要的。我们提出了8809个事件的最终输出，包括4门，8纲，32目，1104种。我们还将Hydrography90m流网络属性添加到大型无脊椎动物发生记录中，使数据跨越2,890个子集水区和Strahler流1-12级。这些记录被认为是有效的，可以用于淡水大型无脊椎动物的生物地理分析，这是一个重要的淡水生物多样性研究不足的热点。

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来源期刊

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.