Rabbit Anti-Human Met Polyclonal antibody

The restoration of forest ecosystems on former agricultural land faces numerous challenges resulting mainly from differences in soil properties between post-arable and continuously forested ground. We revisited a 34-year-old but very well-documented experiment with enrichment in organic matter of the arable sandy soil aiming to accelerate humus formation and soil biota regeneration in Scots pine plantation. The goal of our study was to determine whether the addition of a mixture of pine bark with sawdust or lupine cultivation would change the selected soil properties and, as a result, would shift soil microarthropods of post-agricultural plantations towards those occurring in pine monocultures on continuously forested land. The effect of tillage depth was also studied. The soil properties of the experimental plantation differed significantly from those of the forest soil with respect to the pH, nitrogen (N) content, C/N ratio, sum of exchangeable base cations and degree of soil saturation with base cations. Moreover, the diversity and taxonomic and functional structures of Collembola assemblages differed considerably from the assemblages of pine monoculture of the same age growing on continuously forested land. The expectation of the experiment was not met; however, a certain significant ameliorative effect on some soil properties was observed with some treatments. The total organic carbon content (TOC), C/N ratio and cation exchange capacity increased as a result of the addition of bark and sawdust. In plots where deep tillage was applied, the TOC and N contents were lower than those in plots with shallow soil cultivation. A significant increase in the number of hemiedaphic forms at the expense of euedaphic and atmobiotic forms was observed in plots with lupine, while a mixture of pine bark and sawdust caused an increase in the number of euedaphic forms. Soil pH was identified as a key parameter that determined the difference between forest and post-agricultural collembolan assemblages. The variability of the structure of post-agricultural assemblages depends on N and exchangeable Mg2+ contents and hydrolytic acidity. The enrichment of post-arable soil with organic matter and tillage had long-lasting effects on soil properties but did not stimulate restoration of forest collembolan assemblages on former farmland.

Land cover mapping in complex environments can be challenging due to their landscape heterogeneity. With the increasing availability of various open-access remotely sensed datasets, more images acquired by different sensors and on different dates tend to be used to improve land cover classification accuracy. Selecting an appropriate feature domain with the best landscape separability is therefore crucial in meeting the requirement of computational efficiency and model interpretability. Variable selection is widely used in pattern recognition to enhance model parsimony. This study focused on the variable selection process and proposed a series of methods to select the optimal feature domain to improve land cover classification in a complex urbanized coastal area. Two decision tree models (CART-Classification and Regression Tree and CIT-Conditional Inference Tree) and five variable importance measures (GINI, PVIM-Permutated Variable Importance Measure, MDMinimum Depth, IPM-Intervention of Prediction Measure, and CPVIM-Conditional Permutation Variable Importance Measure) based on random forests were considered. Variable importance measures were applied to a set of spectral, spatial and temporal features derived from medium-resolution satellite images. Backward elimination methods were used to select the optimal feature subset. It is found that compared to the traditional band-only model, the variable selection process can significantly improve the model parsimony and computational efficiency. The CPVIM based on CIT decision tree model was more reliable in selecting relevant features regardless their correlations, but CART tended to generate higher classification accuracy. Therefore, the combination of the CART model and the ranking from the CPVIM variable measure is recommended to achieve higher classification accuracy and better data interpretability. The novelty of our work is with the insight into the merits of integrating variable selection in the land cover classification process over complex environments.