Bats as a Model for Enhancing IUCN Red List Assessments: Real‐Time Data, Contributor Networks, and Specialized Training to Address Common Challenges
ABSTRACT The IUCN Red List of Threatened Species is a critical tool in global conservation, providing essential information on species' conservation status worldwide. However, the current assessment process faces several challenges, including data gaps, standard inconsistencies across taxonomic...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Conservation Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/conl.13089 |
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| Summary: | ABSTRACT The IUCN Red List of Threatened Species is a critical tool in global conservation, providing essential information on species' conservation status worldwide. However, the current assessment process faces several challenges, including data gaps, standard inconsistencies across taxonomic groups, and a limited assessor pool. Data gaps are common for many taxa, particularly those more difficult to observe or identify with wide, fragmented ranges. We use bats as a model to highlight challenges and propose solutions relevant to many taxonomic groups. Basic presence data and population estimates are often missing, with critical information unpublished or inaccessible for assessments. Assessors are responsible for reviewing all available information, seeking advice from local or taxon‐specific experts, and compiling a comprehensive species status assessment. We propose a network of regional operators, researchers, and stakeholders who could regularly contribute updated data on populations, threats, and conservation actions, employing a dynamic real‐time repository. This approach would enable assessors to access an up‐to‐date overview, improving the Red List assessments' efficiency, accuracy, and consistency. Expanding assessors and training early‐career professionals would also standardize evaluation criteria and reduce subjectivity. By capitalizing on IUCN's training expertise, these changes aim to enhance the robustness of assessments, supporting more effective, evidence‐based conservation. |
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| ISSN: | 1755-263X |