Jorge A. Villa

The paper maps soil microbiome functions in wetlands to understand methane emissions by analyzing microbial communities and gene expression across spatial and temporal gradients.

This paper investigates microbial and geochemical factors influencing methane dynamics in three coastal wetlands with varying salinity, finding that methanogen richness and abundance predict methane concentrations, while methanotrophs vary by site.

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 integrates multi-omics data from wetlands to link microbial communities to methane fluxes, identifying conserved genera and metabolic interactions that influence emissions.