Carbon Use Efficiency and Its Temperature Sensitivity Covary in Soil Bacteria

Area of Science:

• Microbial ecology and soil science
• Environmental microbiology
• Genomics and physiology

Background:

• Soil microbes play a critical role in carbon cycling by allocating carbon to growth versus respiration, a process known as carbon use efficiency (CUE).
• Current ecosystem models often assume homogeneous microbial responses to environmental changes, potentially oversimplifying soil carbon dynamics.
• Understanding the variability in microbial CUE is crucial for predicting soil carbon stock responses to climate change.

Purpose of the Study:

• To quantify the environmental sensitivity of bacterial carbon use efficiency (CUE) across different temperatures and substrates.
• To investigate the relationship between genomic composition and CUE temperature sensitivity in soil bacteria.
• To determine if physiological traits or genomic markers better explain CUE variability.

Main Methods:

• Characterized CUE of 23 soil bacterial isolates from seven phyla across three temperatures and four substrates.
• Searched for gene markers correlated with CUE temperature sensitivity on glucose and other substrates.
• Analyzed the correlation between rRNA operon copy number and CUE.

Main Results:

• Temperature significantly altered CUE in isolate- and substrate-specific ways.
• No robust functional gene markers for CUE or its temperature sensitivity were identified.
• A positive correlation was found between rRNA operon copy number and CUE, contrary to expectations.
• Bacterial taxa with high CUE showed a decrease in efficiency at higher temperatures, while inefficient taxa increased their CUE.

Conclusions:

• Bacterial CUE and its temperature sensitivity are highly variable and better explained by observed physiology than genomic composition.
• The CUE-temperature response is constrained by growth rate and existing CUE levels, influencing bacterial acclimation to warming.
• Findings challenge assumptions of homogeneous microbial responses in ecosystem models, highlighting the need for more nuanced approaches.