COS 118-3
Assessing the quality of scientific data used to justify listing of endangered mammals on the IUCN’s Red List

Thursday, August 13, 2015: 2:10 PM
319, Baltimore Convention Center
Madeline P. Nolan, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI
Nathan D. Jacobson, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI
Teegan A. McClung, School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI
Bradley J. Cardinale, School of Natural Resources & Environment, University of Michigan, Ann Arbor, MI

Since its inception in 1963, the International Union for Conservation of Nature (IUCN) Red List has served as a valuable tool for species conservation by providing an inventory of globally threatened species.  The magnitude of its use and impact are apparent from the 4.2 million annual visits to the Red List website and the 1,759 journal articles that currently reference the Red List as a source of information.  In 1994, and later in 2001 and 2012, the Red List underwent significant revisions to transition from the use of expert opinion to justify species listings, to the use of a more transparent and quantitative process for listing species. While there have been a number of critiques to assess earlier revisions, there have been few assessments since the most recent 2012 revision.  Here we assess the type of references, as well as the quality of data presented in references, that were cited as justification for the listing of 228 randomly selected mammalian species (20 percent of all listed mammals) on the IUCN Red List.  


Despite the intention of the IUCN to generate a list that is rigorous and quantitative, our analysis revealed a lack of quantitative data in 72% of the 228 assessed species.  Surprisingly, 46 (20%) of the species listings did not have a single referenced source and 27 (15%) did not contain an accessible reference. Of the minority of listings that cited references with actual data (25%), most contained only a single time-point measurement of the species population size or geographic range.  These are relatively weak forms of inference that offer no information about population trends or future extinction risk. Unfortunately, our results suggest that expert opinion and qualitative forms of inference are still being heavily relied upon to justify species listings on the IUCN Red List. Better data quality and justification are essential for the Red List to become an accurate and reliable source of information on globally threatened species. Additionally, new measures need to be adopted that explicitly state the source and reliability of listing information. In the meantime, scientists who use the Red List for their research need to be more careful about drawing conclusions from a source of information that is largely based on expert opinion.