Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Determinants for the subcellular localization of the inner and outer spore coat hubs in Bacillus subtilis

Endospores, or spores for simplicity, are a highly resistant cell type produced by some bacterial species under adverse conditions. Two main protective layers contribute to the resilience of spores: the cortex, composed of peptidoglycan, and the outermost proteinaceous coat. In Bacillus subtilis, the coat comprises up to 80 different proteins, organized into four sublayers: the basement layer, the inner coat, the outer coat and the crust. These proteins are synthesized at different times during sporulation and deposited at the spore surface in multiple coordinated waves. Central to coat formation is a group of morphogenetic proteins that guide the assembly of the coat components. Targeting of the coat proteins to the surface of the developing spore is mainly controlled by the SpoIVA morphogenetic ATPase. In a second stage, the coat proteins fully encircle the spore, a process termed encasement that requires the morphogenetic protein SpoVID. Assembly of the inner coat requires SafA, whereas formation of the outer coat and the crust requires CotE. SafA interacts directly with the N terminus of SpoVID. (...)