Earliest Triassic microbialites in the South China Block and other areas; controls on their growth and distribution

Earliest Triassic microbialites (ETMs) and inorganic carbonate crystal fans formed after the end-Permian mass extinction (ca. 251.4 Ma) within the basal Triassic Hindeodus parvus conodont zone. ETMs are distinguished from rarer, and more regional, subsequent Triassic microbialites. Large differences in ETMs between northern and southern areas of the South China block suggest geographic provinces, and ETMs are most abundant throughout the equatorial Tethys Ocean with further geographic variation. ETMs occur in shallow-marine shelves in a superanoxic stratified ocean and form the only widespread Phanerozoic microbialites with structures similar to those of the Cambro-Ordovician, and briefly after the latest Ordovician, Late Silurian and Late Devonian extinctions. ETMs disappeared long before the mid-Triassic biotic recovery, but it is not clear why, if they are interpreted as disaster taxa. In general, ETM occurrence suggests that microbially mediated calcification occurred where upwelled carbonate-rich anoxic waters mixed with warm aerated surface waters, forming regional dysoxia, so that extreme carbonate supersaturation and dysoxic conditions were both required for their growth. Long-term oceanic and atmospheric changes may have contributed to a trigger for ETM formation. In equatorial western Pangea, the earliest microbialites are late Early Triassic, but it is possible that ETMs could exist in western Pangea, if well-preserved earliest Triassic facies are discovered in future work

Massive dolomitization of a Messinian reef in the Great Bahama Bank: a numerical modelling evaluation of Kohout geothermal convection

The hypothesis that Kohout thermal convection may have induced the massive dolomitization of the 60 m thick lowest more reefal unit in well Unda [top of Great Bahama Bank (GBB)] is evaluated through numerical modelling. A two‐dimensional (2‐D) section, including lithological and petrophysical data, together with datings for the sediments of the GBB, was used in the basin model TEMISPACK to reconstruct the history of the whole platform, with a focus on the reef unit. Simulations showed that during high sea‐level periods, Kohout convection is a valid mechanism in the settings of the GBB, although the convection cell remains flat in most cases because of high permeability anisotropy. This mechanism induces rapid fluid flow in the superficial as well as in the deeper parts of the platform, with velocities of at least two orders of magnitude higher than with compaction alone. Lithology appears as a strong control of fluid circulations at the margin scale through the permeability anisotropy, for which a critical value lies between values of 10 and 100. The reefal unit in Unda is part of a larger area determined by the lithologic distribution, in which flow velocities are significantly higher than in the rest of the platform. These velocities are high enough to bring the magnesium necessary to precipitate the observed amounts of dolomite, within durations in agreement with the available time of post‐reef deposition high sea level(s). However, neither fluid flow pattern nor flow velocities are able to explain the preferential massive dolomitization of the lower reef unit and the complete absence of dolomite in the upper one

Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times

In item response theory modeling of responses and response times, it is commonly assumed that the item responses have the same characteristics across the response times. However, heterogeneity might arise in the data if subjects resort to different response processes when solving the test items. These differences may be within-subject effects, that is, a subject might use a certain process on some of the items and a different process with different item characteristics on the other items. If the probability of using one process over the other process depends on the subject’s response time, within-subject heterogeneity of the item characteristics across the response times arises. In this paper, the method of response mixture modeling is presented to account for such heterogeneity. Contrary to traditional mixture modeling where the full response vectors are classified, response mixture modeling involves classification of the individual elements in the response vector. In a simulation study, the response mixture model is shown to be viable in terms of parameter recovery. In addition, the response mixture model is applied to a real dataset to illustrate its use in investigating within-subject heterogeneity in the item characteristics across response times