JOURNAL ARTICLE
Removal of historical taxonomic bias and its impact on biogeographic analyses: a case study of Neotropical tardigrade fauna.
Published In: Zoological Journal of the Linnean Society, 2024, v. 201, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ugarte, Pedro Danel de Souza; Garraffoni, André Rinaldo Senna 3 of 3
Abstract
This article examines how biased and unreliable distribution records, particularly those of "false cosmopolitan species," affect the understanding of non-marine tardigrade (water bear) diversity and biogeography in Central and South America's Neotropical and Andean regions. Using two datasets—one including all records and another excluding dubious records of species erroneously considered cosmopolitan—the study finds that most tardigrade records in these regions are geographically and taxonomically biased, with unreliable records inflating species richness estimates and obscuring true biogeographic patterns. Excluding these dubious records reduces data volume by over 65%, revealing that sampling remains uneven and insufficient across biogeographic provinces, which limits accurate macroecological inference. The authors conclude that removing unreliable records is a necessary first step toward more realistic macroecological studies of tardigrades, but emphasize that increased, systematic sampling and integrative taxonomic methods, including DNA barcoding, are essential to improve knowledge of tardigrade biodiversity and distribution in under-sampled Neotropical areas.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2024/07, Vol. 201, Issue 3, p1
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2024
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlae091
- Accession Number:178738881
- Copyright Statement:Copyright of Zoological Journal of the Linnean Society is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.