Microbial successional pattern along a glacier retreat gradient from Byers Peninsula, Maritime Antarctica.

The retreat of glaciers in Antarctica has increased in the last decades due to global climate change, influencing vegetation expansion, and soil physico-chemical and biological attributes. However, little is known about soil microbiology diversity in these periglacial landscapes. This study characterized and compared bacterial and fungal diversity using metabarcoding of soil samples from the Byers Peninsula, Maritime Antarctica. We identified bacterial and fungal communities by amplification of bacterial 16 S rRNA region V3-V4 and fungal internal transcribed spacer 1 (ITS1). We also applied 14C dating on soil organic matter (SOM) from six profiles. Physico-chemical analyses and attributes associated with SOM were evaluated. A total of 14,048 bacterial ASVs were obtained, and almost all samples had 50% of their sequences assigned to Actinobacteriota and Proteobacteria. Regarding the fungal community, Mortierellomycota, Ascomycota and Basidiomycota were the main phyla from 1619 ASVs. We found that soil age was more relevant than the distance from the glacier, with the oldest soil profile (late Holocene soil profile) hosting the highest bacterial and fungal diversity. The microbial indices of the fungal community were correlated with nutrient availability, soil reactivity and SOM composition, whereas the bacterial community was not correlated with any soil attribute. The bacterial diversity, richness, and evenness varied according to presence of permafrost and moisture regime. The fungal community richness in the surface horizon was not related to altitude, permafrost, or moisture regime. The soil moisture regime was crucial for the structure, high diversity and richness of the microbial community, specially to the bacterial community. Further studies should examine the relationship between microbial communities and environmental factors to better predict changes in this terrestrial ecosystem.

Potential of plant species adapted to semi-arid conditions for phytoremediation of contaminated soils.

Physical and chemical remediation techniques used in contaminated areas are expensive and damaging to the soil structure. Biological alternatives, such as phytoremediation, are economical and applicable to large areas. The main limitation of phytoremediation is identifying plants that are both capable of stabilizing and/or absorbing metals from soil and adapted to edaphoclimatic conditions of the contaminated areas. The objective of this study is to evaluate the ability of plant species adapted to Brazilian semi-arid conditions to grow in soils contaminated with Pb. A greenhouse experiment was carried out in a 4 × 5 factorial: four plant species (M. oleifera, P. juliflora, A. peregrina, and U. ruziziensis) and five Pb concentrations in soil (0.0; 0.52; 1.05; 2.10, and 4.20 g kg-1). All species grew at all Pb levels, but only P. juliflora and A. peregrina did not exhibit significant reductions in most growth variables. U. ruziziensis, despite showing reductions in growth variables, was the species with the highest dry matter accumulation in both shoots and roots, in addition to accumulating higher amounts of Pb. We conclude that the species P. juliflora, A. peregrina and U. ruziziensis are more suitable for cultivation in soils containing high levels of Pb.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence.

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.