First ceratosaurian dinosaur from Australia

The basal theropod dinosaur clade Ceratosauria, and its subclade Abelisauroidea, is characteristic of late Mesozoic terrestrial vertebrate faunas in western Gondwana (South America, Africa, Madagascar, and India) and Europe. Yet unambiguous records of ceratosaurs have hitherto been absent from Australia, where the theropod assemblage appears to include several typically Laurasian clades. Here, we report the first evidence of ceratosaurs (and potentially abelisauroids) from eastern Gondwana––a diagnostic astragalocalcaneum from the Aptian (121–125?Ma) of Victoria, Australia. Ceratosauria thus occurred in both western and eastern Gondwana during the Early Cretaceous. This fossil adds to the poorly known dinosaur fauna of Australia, a major clade of basal theropods, emphasising that its mid-Cretaceous theropod diversity was surprisingly cosmopolitan despite relative geographic isolation, including clades that have been thought to be typical of both Gondwana and Laurasia––Ceratosauria, Spinosauridae, Carcharodontosauria, Tyrannosauroidea, and Deinonychosauria. Such a contemporaneous association of theropod clades is unknown from other Gondwanan continents and questions the views that the late Mesozoic dinosaur fauna of Australia was dominated by Gondwanan or Laurasian elements, extreme isolation, relictualism, and/or novelty as a ‘centre of origin’. The cosmopolitan theropod fauna of Australia probably reflects the global distribution of these clades early in their history, prior to significant continental breakup.     

A fast Bayesian method for updating and forecasting hourly ozone levels

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.