Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine learning algorithms. Mapping performance was assessed using the confusion matrix, and the Z-test among the Kappa indexes of the matrices. In a conventional soil survey, the soils described and classified in the Brazilian System of Soil Classification [Argissolos Vermelho-Amarelos Distróficos-PVAd (Acrisols), Cambissolos Háplicos Tb Distróficos-CXbd (Cambisols), Gleissolos Háplicos Háplicos Tb Distróficos-GXbd (Gleysols), Latossolos Amarelos Distróficos-LAd (Xanthic Ferralsos), Latossolos Vermelho-Amarelos Distróficos-LVAd (Rhodic Ferralsols), and Neossolos Litólicos Distróficos-RLd (Neossols)] were grouped into composite mapping units (MU) using the conventional method. The eight algorithms showed similar performance without statistical difference (Kappa 0.42-0.48). The mapping of soils with varying slopes (LAd, LVAd, CXbd) showed lower accuracy, whereas soils on hydromorphic lowlands (GXbd) were classified more accurately. In map algebra, the result was rather satisfactory, with 63-67 % agreement between the conventional soil map and maps produced by machine learning. The areas with the largest disagreement in the DSM occurred in the LAd unit due to subtle color variation in the Latossolos mantle without a clear relation to any environmental variable, highlighting difficulties in DSM regarding hill slope landforms. Model performance was satisfactory, and good agreement with the conventional soil map demonstrates the importance of the DSM as a potential complementary tool for assisting soil mapping in mountainous areas in Brazil for the purpose of land use planning.

The behavior and feeding habits of different species of seabirds can infl uence the enrichment of trace metals in Antarctic soils. This study aimed to evaluate the infl uence of different species of seabirds on the concentrations of potentially toxic metals in Antarctic soils. For this, we collected soil samples in areas infl uenced by penguins, kelp gulls, and giant petrels. We analyzed the concentration of total organic carbon (TOC), total nitrogen (TN), available phosphorus (P) and metals by three different methods of extraction: USEPA 3051A, Mehlich-1, and distilled water. The concentrations of Cr and Hg presented positive correlations with P, TOC, and TN by the USEPA 3051A method, indicating the biotransport of these metals by seabirds. Soils infl uenced by penguins showed higher levels of P, TOC, TN, Cr, and Hg. Comparing the results from the different extractors, we found that Hg had the highest relative levels in the exchangeable fraction and the soil solution. Therefore, the soils with the infl uence of penguins present higher levels of biotransported trace metals, but this does not necessarily mean that these birds have a higher biotransport potential, since the concentration of trace metals in these soils may be related to their degree of ornithogenesis.

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