THE GEOLOGIC TIME SCALE 2020: INTEGRATING THE STRATIGRAPHIC RANGE DATA FROM CONODONTS AND OTHER CARBONATE FACIES FOSSILS

The current Ordovician time scale (GTS 2012; Cooper & Sadler, in Gradstein et al., 2012) is derived from a global CONOP9 composite of graptolite first and last appearance events with interpolated radiometric dates . Local carbonate successions are correlated with graptolite zones derived from the composite using correlation charts in a subjective manner (tie points set by expert opinion on how the different sets of zones are related). We suggest that the direct integration of the stratigraphic range data from carbonate facies fossils (e.g., conodonts and chitinozoans) into a global multi-clade CONOP9 composite Ordovician range chart will significantly increase the precision and usefulness of the Ordovician time scale. The construction of a global multi-clade composite poses a number of challenges, such as analyzing a very large data set, integrating the North American Midcontinent and North Atlantic conodont successions, and finding enough sections that contain index species from both black shale and carbonate biofacies to effective link the disparate facies. In this study we present several initial steps in constructing an integrated CONOP9 composite that may be used as the basis for the Ordovician GTS 2020. First, we constructed separate CONOP9 conodont composites from Laurentia (Midcontinent fauna) and Baltoscandia (North Atlantic fauna), evaluated range similarities and inconsistencies, and attempted to establish common zonal boundaries. Second we present an example of a fully integrated graptolite, conodont, chitinozoan, and ostracod range chart from Baltoscandia that spans the Lasnamagi to Porkuni regional stages (Mid Darriwilian to Hirnantian), and show how this range chart can be used to project graptolite zonal boundaries into carbonate sections and correlate the regional chitinozoan zonation more precisely with the standard global stages. Finally, we investigate ways in which other types of stratigraphic data can be successfully integrated into automated graphic correlation networks.