Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Nutrient remobilization in tree foliage as affected by soil nutrients and leaf life span

Nutrient remobilization is a key process in nutrient conservation in plants and in nutrient cycling in ecosystems. To predict the productivity of terrestrial ecosystems, we thus need to improve our understanding of the factors that control remobilization. We studied the remobilization rates of several major nutrients (N, P, S, K, Ca, and Mg) in 102 forest ecosystems representing large environmental gradients at the country scale (France). Total amounts or availability of nutrients in soils were correlated with nutrient remobilization: the larger the soil nutrient pool, the lower the remobilization rate (e.g., P remobilization decreased with increasing total or extractable inorganic P in soils). Soil type and soil parent material influenced nutrient remobilization indirectly through their effect on soil nutrients. Nutrient remobilization was also affected by the quality of soil organic matter (C:N and C:P ratios) and K‐Ca‐Mg antagonisms. In addition to soil properties, plant‐related parameters (nutrient concentrations in foliage and leaf life span) and climate variables (e.g., precipitation and actual evapotranspiration) were also correlated with nutrient remobilization. Using multivariate analysis, we found that soil nutrient richness and the life span of the leaf were generally the two most important factors controlling nutrient remobilization. As a whole, the nutrient remobilization rate is regulated by soil nutrients through negative feedback. This general ecological pattern is modulated by ecophysiological constraints of plants, mainly leaf life span or the capability of plants to move Ca through the phloem sap