Fungi, bacteria and soil pH: the oxalate-carbonate pathway as a model for metabolic interaction

The oxalatecarbonate pathway involves the oxidation of calcium oxalate to low-magnesium calcite and represents a potential long-term terrestrial sink for atmospheric CO2. In this pathway, bacterial oxalate degradation is associated with a strong local alkalinization and subsequent carbonate precipitation. In order to test whether this process occurs in soil, the role of bacteria, fungi and calcium oxalate amendments was studied using microcosms. In a model system with sterile soil amended with laboratory cultures of oxalotrophic bacteria and fungi, the addition of calcium oxalate induced a distinct pH shift and led to the final precipitation of calcite. However, the simultaneous presence of bacteria and fungi was essential to drive this pH shift. Growth of both oxalotrophic bacteria and fungi was confirmed by qPCR on the frc (oxalotrophic bacteria) and 16S rRNA genes, and the quantification of ergosterol (active fungal biomass) respectively. The experiment was replicated in microcosms with non-sterilized soil. In this case, the bacterial and fungal contribution to oxalate degradation was evaluated by treatments with specific biocides (cycloheximide and bronopol). Results showed that the autochthonous microflora oxidized calcium oxalate and induced a significant soil alkalinization. Moreover, data confirmed the results from the model soil showing that bacteria are essentially responsible for the pH shift, but require the presence of fungi for their oxalotrophic activity. The combined results highlight that the interaction between bacteria and fungi is essential to drive metabolic processes in complex environments such as soil.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.