Coral reefs are iconic, high-diversity, ecosystems that currently face an unprecedented set of hazards threatening their long-term existence. While widely recognized for being highly diverse, the extent of the diversity is still very poorly known. Much of it is yet to be discovered, and there is more than an order of magnitude uncertainty around the total number of multi-cellular species they host. As part of the Census of Marine Life CReefs Project, taxonomists’ knowledge on the number of species on coral reefs within particular taxonomic groups was elicited (Fisher et al., 2011; O’Leary et al., 2011) in an attempt to provide a better estimate of global coral-reef species richness. In ecology, expert opinion can provide valuable information for statistical modelling, particularly when data are limited or unreliable (e.g. Low Choy et al., 2009). However, unlike data-driven models, expertdriven models are not calibrated to empirical data. Instead, they rely entirely on the credibility and expertise of the experts. Therefore, it is important to be able to estimate how expert an expert is. In this case, how expert were the taxonomists elicited? In this paper, we develop a conceptual model that describes criteria defining taxonomic expertise. This model was developed in group discussion with four taxonomists/ecologists about what constitutes an expert in the field of taxonomy. Criteria for evaluating what makes an expert were established first. Then a conceptual model was built, which identified relationships between these criteria. Together, these criteria and their interactions provide a conceptual model for defining taxonomic expertise, in the context of the data being elicited for the number of species on coral reefs. This conceptual model provides the basis for the next step of eliciting prior distributions of the parameters for these the relationships, to provide a full specification of a prior for a Bayesian hierarchical model (Donald et al., 2011).