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

IF 5.8 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

{"title":"A spatial inventory of freshwater macroinvertebrate occurrences in the Guineo-Congolian biodiversity hotspot.","authors":"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","doi":"10.1038/s41597-025-04471-5","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"227"},"PeriodicalIF":5.8000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802732/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04471-5","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

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.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

求助全文

约1分钟内获得全文去求助

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.

期刊最新文献

A chromosome-level genome assembly of the mud carp (Cirrhinus molitorella). A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology. Author Correction: Global Crop-Specific Fertilization Dataset from 1961-2019. Data on the diet and nutrition of urban and rural bumblebees. SpiDa-MRI: behavioral and (f)MRI data of adults with fear of spiders.