Quantitative stratigraphic correlation of the Lower Triassic in South China based on conodont unitary associations

Abstract

Unitary Association Method (UAM) analyses of conodont faunas from 28 sections spanning the biggest Phanerozoic end-Permian mass extinction and significant global environmental and ecosystem perturbations during the succeeding Early Triassic are presented. Based on 72 conodont species, 26 Unitary Association Zones (UAZs) are established for the latest Permian to earliest Middle Triassic of South China. These UAZs provide quantitative high-resolution tools to correlate sequences in the Early Triassic of South China and to compare and test high-resolution conodont biostratigraphy based on interval conodont zones developed over the past three decades. Our quantitative analyses provide insights on ongoing debates relating to the First Appearance Datum (FAD) of the conodont Hindeodus parvus which is used to place and subsequently correlate the "Golden Spike" defining the base of the Triassic (Permian-Triassic Boundary) at the base of Bed 27c at the GSSP Meishan D Section. Previous proposals that suggested potential earlier occurrences of H. parvus below its FAD at Meishan section are not supported by our results. In deep water sections, the First Occurrence (FO) of H. parvus lies at the base of UAZ 5 at the Bianyang section while it lies within UAZ 6 at the Meishan section. This indicates that the earliest occurrence of H. parvus in South China is in the Bianyang section but this conclusion needs further testing due to the reliance on "spot" data by the UAM. Anomalously high occurrences of Hindeodus in the Mingtang section (close to the Bianyang section) further suggests that the Bianyang-Mingtang area may have provided a temporally extended habitable zone for anchignathodontid conodonts. Stage boundaries currently proposed or established using interval conodont zones locate within or between UAZs and are difficult to correlate with carbon isotope curves. UAZs are useful in helping to define GSSPs by recognizing which correlations are most robust and in the selection of the most appropriate species and level for GSSPs.