GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 76-30
Presentation Time: 9:00 AM-5:30 PM


EDIE, Stewart M., Department of the Geophysical Sciences, The University of Chicago, 5734 South Ellis Ave, Chicago, IL 60637, SMITS, Peter D., Committee on Evolutionary Biology, University of Chicago, Chicago, IL 60615 and JABLONSKI, David, Geophysical Sciences, Univ of Chicago, 5734 S. Ellis Ave, Chicago, IL 60637,

Systematics has grown into a hypothetico-deductive science, and is often overlooked as a source of bias in macroecological and macroevolutionary studies. Paleontologists routinely account for an incomplete species record using spatial and temporal patterns of stratigraphic occurrences, but rarely consider the robustness of these occurrences in light of their taxonomic quality. We developed a probabilistic model of species discovery to facilitate the ongoing evaluation of the taxonomic record's stability and completeness. To explore the utility of our approach, we examined the influence of taxonomic biases on biogeographic patterns of species richness for an animal clade that has seen an unabated flow of new species over the last 150 years: the extant marine bivalves.

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.