James Stegen

This paper models microbial regulatory feedback in organic matter decomposition, finding copiotrophic traits drive positive priming effects.

The paper compiles and harmonizes a global dataset of dissolved organic matter molecular traits from 3,999 samples across diverse environments to enable cross-system comparisons and global chemogeography analysis.

The paper evaluates a process-based model for predicting stream ecosystem respiration (ERtot) in the Yakima River basin and finds it fails to explain spatial variation, highlighting the importance of sediment-associated respiration and other factors.

The paper uses AI to iteratively improve environmental predictions by combining field sampling with machine learning, significantly enhancing sediment oxygen consumption forecasts across the U.S.

The paper analyzes hyporheic zone microbiomes across North America to show conserved stress responses and distinct metabolic activity in sediments versus surface water.

The paper links geochemistry and microbial functional potential to organic matter development across seven rivers using DOM characterization, geochemical analyses, and shotgun metagenomics.

ORT links genome-scale metabolic models with reactive transport codes to improve ecosystem models using metagenomic data.

This paper introduces a new biogeochemical modeling approach that explicitly represents organic matter thermodynamics to improve predictions of microbial degradation processes and their environmental impacts.

The paper compares multi-omics data to show that metabolome biochemistry, not microbiome composition or gene expression, correlates with elevated biogeochemical function in hyporheic zones.