Community structure - Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological...

Area of Science:

• Microbial ecology
• Biogeochemistry
• Soil science

Background:

• Belowground ecosystem processes are highly variable and difficult to predict from microbial community data.
• Challenges include complex sample covariance, multiple microbial control mechanisms, and broad ecosystem function assessments.

Purpose of the Study:

• To investigate the predictability of methane cycling processes from microbial community data.
• To determine if the physiological scale of ecosystem function influences predictability.
• To identify biotic drivers of methane cycling in Congo Basin soils.

Main Methods:

• Soil samples collected along a wetland-to-upland gradient in the Congo Basin.
• Methane cycling processes measured under controlled laboratory conditions.
• Microbial community attributes (abundance, activity, composition, diversity) analyzed.
• Environmental covariates statistically controlled.

Main Results:

• Finer-scale methane cycling processes (e.g., hydrogenotrophic methanogenesis, high-affinity CH4 oxidation) were more predictable from microbial community structure than broader processes (e.g., gross methanogenesis, methanotrophy).
• Broad processes were primarily limited by microbial abundance.
• Fine-scale processes were associated with microbial diversity and composition.

Conclusions:

• The physiological scale at which ecosystem function is measured is critical for predictability.
• Microbial community measurements should encompass a range of controls to accurately predict ecosystem processes.
• Understanding microbial drivers requires careful consideration of process scale and community attributes.