STRATEGIES TO IMPROVE AGE MODELS FOR THE PROTEROZOIC-PHANEROZOIC TRANSITION

The Proterozoic-Phanerozoic transition encompasses many profound changes in biological, biogeochemical, and palaeoceanographic systems, for which the timing of inception and duration are linked to environmental, geological, and geophysical forcings with a degree of confidence ranging from firm to speculative. Geochronology can enhance this confidence through its ability to assess spatial and temporal correlations, affirm or refute hypotheses of cause and effect, and calibrate the rates of change in the interacting components of the Earth system. Radical changes in our understanding of the distribution of time in the rock record have resulted from the progressive application of geochronometers across the Proterozoic-Phanerozoic transition, including U-Pb zircon geochronology on volcanic tuffs, Re-Os black shale geochronology, carbon isotope chemostratigraphy, and astrochronology. Each of these techniques has complementary strengths related to ordination, accumulation, depositional motif, precision, and accuracy. Yet most studies have applied these techniques in isolation, and often struggled to contextualize those results with other valuable chronostratigraphic information.

Geochronologists will continue to hone their particular expertise on the intrinsic challenges and rewards of telling time in this intriguing geological era. However I posit that we have entered into a new geochronological paradigm in which integrative probabilistic age modeling within a Bayesian framework provides an effective means to organize and leverage the proliferation of age and stratigraphic studies. I will review examples of the types of Bayesian age models being developed for deep time geochronology, and how these models leverage both chronostratigraphic and geochronologic components of time scales. Case studies from the Tonian to the Cambrian will highlight the old and new types of information that can be woven together within this statistical framework, setting the stage for the next generation of dynamic, probabilistic timescales across the Proterozoic-Phanerozoic transition.