PROBABILISTIC MODELS OF SPECIES DISCOVERY AND BIODIVERSITY COMPARISONS
We use a Bayesian time-series model to estimate the long-term trend in the rate of species description. We find a distinct spatial pattern in the description rates of new species, which suggests a geographic instability in the taxonomic record. Generally, species description appears to be relatively more saturated in North Temperate coastlines than in Tropical and South Temperate coastlines. However, despite the regional heterogeneity of description rates, the short-term forecast of added species (15 year) preserves the currently observed regional rank order. Thus, continued species description at the estimated rates should not alter the first-order biogeographic patterning of extant bivalve species richness in the immediate future.
Our model is readily extended to other analytical groupings (e.g., stratigraphic, and taxonomic–illustrated here for families of Pectinoidea), and it can be used to avoid misleading interpretations of biodiversity patterns derived from currently observed species richness. Modeling the long-term species description rate provides a direct comparison of taxonomic knowledge among analytical groups, and short-term forecasts of species richness can determine credible shifts in the relative rank order of species richness. Together, these approaches characterize taxonomic uncertainty and improve our interpretations of macroecological and macroevolutionary patterns.