Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach

Microbial nitrogen metabolism is a complicated and key
process
in mediating environmental pollution and greenhouse gas emissions
in rivers. However, the interactive drivers of microbial nitrogen
metabolism in rivers have not been identified. Here, we analyze the
microbial nitrogen metabolism patterns in 105 rivers in China driven
by 26 environmental and socioeconomic factors using an interpretable
causal machine learning (ICML) framework. ICML better recognizes the
complex relationships between factors and microbial nitrogen metabolism
than traditional linear regression models. Furthermore, tipping points
and concentration windows were proposed to precisely regulate microbial
nitrogen metabolism. For example, concentrations of dissolved organic
carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce
bacterial denitrification and nitrification, respectively. The concentration
windows for NO3–-N (15.9–18.0
mg/L) and DOC (9.1–10.8 mg/L) enabled the highest abundance
of denitrifying bacteria on a national scale. The integration of ICML
models and field data clarifies the important drivers of microbial
nitrogen metabolism, supporting the precise regulation of nitrogen
pollution and river ecological management.