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Frequent wildfires associated with emplacement of the Central Atlantic Magmatic Province (CAMP) are thought to have been important drivers of two significant changes in terrestrial plant communities and diversity during the Triassic-Jurassic Mass Extinction (TJME, ca. 201.51 Ma). However, it remains to be investigated whether these two changes are potentially related to different wildfire types. To better understand this relationship, we used a new method to reanalyze fossil pollen and spores across the Triassic-Jurassic transition in the Jiyuan Basin from the North China Plate. Results show that two peaks in wildfire frequency experienced different types of wildfires, with each linked to significant changes in plant communities and diversity losses. In the first wildfire peak, canopy fires dominated and are accompanied by significant losses of canopy forming plants, while in the second wildfire peak, ground cover fires dominated accompanied by significant losses of ground cover plants. Changes in atmospheric humidity conditions were an important control on the two different wildfire peaks. Relatively humid climatic conditions corresponded to the prevalence of canopy fires and hindered the spread and development of ground cover fires in wet surface conditions. Conversely, relatively arid climatic conditions corresponded with the prevalence of ground cover fires in dry surface environments. Our results provide a potential relationship between terrestrial plant communities and wildfire types, which is important to further understanding of terrestrial environmental and floral changes driven by Large Igneous Provinces. [ABSTRACT FROM AUTHOR]

AbstractHypothesis testing for the Weibull distribution always raises attention in the literature. It is challenging especially in small-sample scenarios. In this paper, we propose a new Kolmogorov-Smirnov type test based on the distance between two empirical functions, one from the data and another from the representative points of the underlying distribution. A bias correction technique is used to estimate unknown parameters in the Weibull distribution. We also discuss how to choose a suitable number of representative points based on the proposed loss function, which is recommended for constructing the empirical function from the Weibull distribution. To compare with tailor-made tests in the literature, a simulation study of the empirical type I error and testing power is conducted. It shows that our proposed test statistic is more powerful in most alternative scenarios, and it significantly improves the power of testing in small-sample scenarios. Finally, two real-world datasets are conducted to further demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]