The increase of free and open online biodiversity databases is of paramount importance for current research in ecology and evolution. However, little attention is paid to using updated taxonomy in these “biodiversity big data” repositories and the quality of their taxonomic information is often questioned. Here we assess how reliable is the current use of nomenclatural classification in the distributional information available from two biodiversity information networks: GBIF and the Brazilian SpeciesLink. We use as a study case the records of Auchenipteridae, a Neotropical fish family that has been subject to recent taxonomical reviews. A data filtering procedure was applied to identify and quantify the inaccuracies in the taxonomical status of the records in three steps: assessment of identification accuracy at the family, genus or species level; current validity of species name; and assignation of inaccurate species records to different categories of classification quality. Synonyms, nonexistent combinations, and outdated combinations were reassigned to currently valid species. A total of 9148 records of Auchenipteridae fishes were analyzed, of which 4165 were from GBIF and 4983 from SpeciesLink, deriving from 46 and 31 sources, respectively. After correcting all possible records following the taxonomic data filtering steps, 6988 records (76.4% of the original) were adequate for describing species distributions, while 2160 remained inaccurate. The most inaccurate records at the species level were due to the use of outdated nomenclatures, resulting in non-valid combinations of species and genus, and synonymy. Our results evidence a large taxonomic inconsistency among records, and, most importantly, that taxonomic information obtained from repositories should be used with caution. Many inaccuracy issues may be embedded in the biodiversity databases’ records, which could lead researchers to provide an incomplete or even mistaken perspective of the variations in the natural world.

中文翻译：

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生物多样性大数据中物种识别的可靠性如何？评估在线存储库中新热带鱼类科的记录

免费和开放的在线生物多样性数据库的增加对于当前的生态学和进化研究至关重要。然而，很少有人关注在这些“生物多样性大数据”存储库中使用更新的分类法，而且它们的分类学信息的质量经常受到质疑。在这里，我们评估了当前在来自两个生物多样性信息网络的分布信息中使用命名分类的可靠性：GBIF 和巴西物种链接。我们使用金翅目鱼科的记录作为研究案例，这是一种新热带鱼科，最近接受了分类学审查。应用数据过滤程序在三个步骤中识别和量化记录分类状态的不准确性：评估科、属或种级别的识别准确性；物种名称的当前有效性；并将不准确的物种记录分配到不同的分类质量类别。同义词、不存在的组合和过时的组合被重新分配给当前有效的物种。共分析了 9148 条金翅雀科鱼类的记录，其中 4165 条来自 GBIF，4983 条来自 SpeciesLink，分别来自 46 个和 31 个来源。在按照分类数据过滤步骤纠正所有可能的记录后，6988 条记录（原始记录的 76.4%）足以描述物种分布，而 2160 条记录仍然不准确。物种层面最不准确的记录是由于使用了过时的命名法，导致物种和属的无效组合以及同义词。我们的结果证明记录之间存在很大的分类不一致，并且，最重要的是，应谨慎使用从存储库中获得的分类信息。许多不准确的问题可能包含在生物多样性数据库的记录中，这可能导致研究人员对自然界的变化提供不完整甚至错误的观点。