  Global species richness is a key biodiversity metric. Despite recent efforts
to estimate global species richness, the resulting estimates have been highly
uncertain and often logically inconsistent. Estimates lower down either the
taxonomic or geographic hierarchies are often larger than those above. Further,
these estimates have been represented in a wide variety of forms, including
intervals (a, b), point estimates with no uncertainty, and point estimates with
either symmetrical or asymmetrical bounds, making it difficult to combine
information across different estimates. Here, we develop a Bayesian
hierarchical approach to estimate the global species richness from published
studies. It allows us to recover interval estimates at each level of the
hierarchy, even when data are partially or wholly unobserved, while respecting
logical constraints, and to determine the effects of estimation on the whole
hierarchy of obtaining future estimates anywhere within it
