Fish databases for improving their conservation in Colombia.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-13 DOI:10.1038/s41597-024-04352-3
Luz F Jiménez-Segura, Daniel Restrepo-Santamaria, Juan G Ospina-Pabón, María C Castellanos-Mejía, Daniel Valencia-Rodríguez, Andrés F Galeano-Moreno, José L Londoño-López, Juliana Herrera-Pérez, Víctor M Medina-Ríos, Jonathan Álvarez-Bustamante, Manuela Mejía-Estrada, Marcela Hernández-Zapata, Luis J García-Melo, Omer Campo-Nieto, Iván D Soto-Calderón, Carlos DoNascimiento
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Abstract

Progress in the acquisition of massive sets of molecular data and in the bioinformatic capabilities for their processing have revolutionised species identification, filling gaps in crucial areas such as taxonomy, phylogenetic inference, biogeography, and even biodiversity conservation. Advanced DNA sequencing and metabarcoding have uncovered previously hidden diversity, although their effectiveness is highly dependent on the accuracy of reference DNA databases at local and regional scales. The compilation of information on freshwater fishes from the Magdalena River basin is an important milestone in improving our knowledge of the genetic and taxonomic diversity of a highly endemic region in the Neotropical context. Here, we share DNA data from 1,270 specimens representing 183 species, cross-referenced with complete collecting and catalogue information, along with high resolution photographs of voucher specimens when alive. This collection of multiple sources of information based on fish specimen records not only contributes to future research on Neotropical fish systematics and ecology, but also to conservation decisions in one of the South American rivers with a highest level of endemism.

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改善哥伦比亚鱼类保护的鱼类数据库。
在获取大量分子数据和处理这些数据的生物信息学能力方面取得的进展彻底改变了物种鉴定,填补了分类学、系统发育推断、生物地理学甚至生物多样性保护等关键领域的空白。先进的DNA测序和元条形码已经发现了以前隐藏的多样性,尽管它们的有效性高度依赖于参考DNA数据库在局部和区域尺度上的准确性。马格达莱纳河流域淡水鱼信息的汇编是提高我们对新热带环境下高度特有种地区遗传和分类多样性认识的一个重要里程碑。在这里,我们分享了来自183个物种的1,270个标本的DNA数据,并与完整的收集和目录信息进行了交叉参考,同时还提供了凭证标本活着时的高分辨率照片。这种基于鱼类标本记录的多种信息来源的收集不仅有助于未来对新热带鱼类系统分类学和生态学的研究,而且有助于对具有最高地方性的南美河流之一的保护决策。
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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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