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The IUCN Red List is the most widely used tool to measure extinction risk and report biodiversity trends. Accurate and standardised conservation status assessments for the IUCN Red List are limited by a lack of adequate information; and need consistent and unbiased interpretation of that information. Variable interpretation stems from a lack of quantified thresholds in certain areas of the Red List guidelines. Thus, even in situations with sufficient information to make a Red List assessment, inconsistency can occur when experts, especially from different regions, interpret the guidelines differently, thereby undermining the goals and credibility of the process. In such an information vacuum, assessors make assumptions depending on their level of Red List experience (subconscious bias) and their personal values or agendas (conscious bias). We highlight two major issues where such bias influences assessments: relating to fenced subpopulations that require intensive management; and defining benchmark geographic distributions and thus the inclusion/exclusion of introduced subpopulations. We suggest assessor bias can be reduced by refining the Red List guidelines to include quantified thresholds for when to include fenced/intensively managed subpopulations or subpopulations outside the benchmark distribution; publishing case studies of difficult assessments to enhance cohesion between Specialist Groups; developing an online accreditation course on applying Red List criteria as a prerequisite for assessors; and ensuring that assessments of species subject to trade and utilisation are represented by all dissenting views (for example, both utilitarian and preservationist) and reviewed by relevant Specialist Groups. We believe these interventions would ensure consistent, reliable assessments of threatened species between regions and across assessors with divergent views, and will thus improve comparisons between taxa and counteract the use of Red List assessments as a tool

Ambiguity in guideline definitions introduces assessor bias and influences consistency in IUCN Red List status assessments