Phylogenetic Relationships, Speciation, and Origin of Armillaria in the Northern Hemisphere: A Lesson Based on rRNA and Elongation Factor 1-Alpha

Armillaria species have a global distribution and play various roles in the natural ecosystems, e.g., pathogens, decomposers, and mycorrhizal associates. However, their taxonomic boundaries, speciation processes, and origin are poorly understood. Here, we used a phylogenetic approach with 358 samplings from Europe, East Asia, and North America to delimit the species boundaries and to discern the evolutionary forces underpinning divergence and evolution. Three species delimitation methods indicated multiple unrecognized phylogenetic species, and biological species recognition did not reflect the natural evolutionary relationships within Armillaria; for instance, biological species of A. mellea and D. tabescens are divergent and cryptic species/lineages exist associated with their geographic distributions in Europe, North America, and East Asia. While the species-rich and divergent Gallica superclade might represent three phylogenetic species (PS I, PS II, and A. nabsnona) that undergo speciation. The PS II contained four lineages with cryptic diversity associated with the geographic distribution. The genus Armillaria likely originated from East Asia around 21.8 Mya in early Miocene when Boreotropical flora (56–33.9 Mya) and the Bering land bridge might have facilitated transcontinental dispersal of Armillaria species. The Gallica superclade arose at 9.1 Mya and the concurrent vicariance events of Bering Strait opening and the uplift of the northern Tibetan plateau might be important factors in driving the lineage divergence.

Devonian to Triassic tectonic evolution and basin transition in the East Kunlun−Qaidam area, northern Tibetan Plateau: Constraints from stratigraphy and detrital zircon U−Pb geochronology

The late Paleozoic to Triassic was an important interval for the East Kunlun−Qaidam area, northern Tibet, as it witnessed prolonged subduction of the South Kunlun Ocean, a major branch of the Paleo-Tethys Ocean whose closure led to the formation of Pangea. However, the geologic history of this stage is poorly constrained due to the paucity of tectonothermal signatures preserved during a magmatic lull. This article presents a set of new provenance data incorporating stratigraphic correlation, sandstone petrology, and zircon U−Pb dating to depict changes in provenance that record multiple stages of topographic and tectonic transition in the East Kunlun−Qaidam area over time in response to the evolution of the South Kunlun Ocean. Devonian intra-arc rifting is recorded by bimodal volcanism and rapid alluvial-lacustrine sedimentation in the North Qaidam Ultra High/High Pressure Belt, whose sources include the Olongbuluke Terrane and southern North Qaidam Ultra High/High Pressure Belt. Southward transgression submerged the East Kunlun−Qaidam area during the Carboniferous prior to the rapid uplift of the Kunlun arc, which changed the provenance during the Early Permian. This shift in provenance for the western Olongbuluke Terrane and thick carbonate deposition throughout the North Qaidam Ultra High/High Pressure Belt in the late Early Carboniferous indicate that the North Qaidam Ultra High/High Pressure Belt should have been inundated, terminating an ∼95 m.y. erosion history. The closure of the South Kunlun Ocean in the late Triassic generated a retroarc foreland along the Zongwulong Tectonic Belt, which is represented by the development of a deep-water, northward-tapering flysch deposystem that was supplied by the widely elevated Kunlun−Qaidam−Olongbuluke Terrane highland. This new scenario allows us to evaluate current models concerning the assembly of northern Tibet and the tectonic evolution of the Paleo-Tethys Ocean.

An Automated Snow Mapper Powered by Machine Learning

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.