Back

Divergent microbial traits influence the transformation of living versus dead root inputs to soil carbon

Sokol, N.; Foley, M.; Blazewicz, S. J.; Battacharyya, A.; Estera-Molina, K.; Firestone, M. K.; Greenlon, A.; Hungate, B. A.; Kimbrel, J.; Liquet, J.; Lafler, M.; Marple, M.; Nico, P.; Slessarev, E.; Pett-Ridge, J.

2022-09-03 ecology
10.1101/2022.09.02.506384 bioRxiv
Show abstract

Soil microorganisms influence the global carbon cycle by transforming plant inputs into soil organic carbon (SOC), but the microbial traits that facilitate this process are unresolved. While current theory and biogeochemical models suggest microbial carbon-use efficiency and growth rate are positive predictors of SOC, recent observations demonstrate these relationships can be positive, negative, or neutral. To parse these contradictory effects, we used a 13C-labeling experiment to test whether different microbial traits influenced the transformation of plant C into SOC within the microbial habitats surrounding living root inputs (rhizosphere) versus decaying root litter (detritusphere), under both normal soil moisture and droughted conditions. In the rhizosphere, bacterial-dominated communities with fast growth, high carbon-use efficiency, and high production of extracellular polymeric substances formed microbial-derived SOC under normal moisture conditions. However, in the detritusphere - and the rhizosphere under drought - more fungal-dominated communities with slower growth but higher exoenzyme activity formed plant-derived SOC. These findings emphasize that microbial traits linked with SOC accrual are not universal, but contingent on how microorganisms allocate carbon under different resource conditions and environmental stressors.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.