Posted by Design Studio
16 September 2024
Introduction
Earth System Models (ESMs) are essential for predicting climate change impacts but often lack accurate representations of soil organic carbon (SOC) dynamics due to the omission of microbial processes. Microbial carbon use efficiency (CUE) is a key factor influencing SOC, representing the proportion of carbon converted into microbial biomass versus that lost through respiration. However, most models treat CUE as a constant, overlooking its variability under different environmental conditions. In this study, we synthesize knowledge on CUE controlling factors and databases to dig into complexities, address controversies, and offer a comprehensive approach to quantify CUE variations in carbon cycle models and their impact on simulated soil carbon stocks.
Methods for Measuring Microbial CUE
Microbial CUE is measured through methods like isotopic labelling, stoichiometric modelling, and other techniques. Each method has its strengths and limitations, with significant variability in results due to differences in experimental conditions. We emphasize the importance of distinguishing between different measurement methods and meticulously considering experimental details to ensure the accuracy and comparability of data across studies.
Regulatory Factors Governing Microbial CUE
Microbial CUE is influenced by both biological (e.g., microbial community composition, physiological state) and abiotic factors (e.g., temperature, soil moisture, nutrient availability). These factors interact in complex ways, making it essential to consider them when modelling SOC dynamics. We highlight the need for models that capture this complexity to accurately predict the effects of environmental changes on microbial processes and SOC.
Data Availability and Challenges
Field measurements of microbial CUE are limited, with most data coming from lab incubations. This scarcity of in-situ data, coupled with inconsistencies in measurement methods, hampers global-scale modelling of CUE. We emphasize the importance of filling data gaps, particularly in critical ecosystems like tropical forests and peatlands.
SOC-CUE Relationship
The relationship between CUE and SOC is complex and varies depending on environmental and biological factors. While higher CUE generally correlates with increased SOC, this relationship is not consistent across all conditions.
Integrating Genomic Data with CUE and Carbon Models
Advances in genomic data can enhance predictions of microbial CUE by integrating microbial traits into carbon models. Genome-scale metabolic models (GEMs) and trait-based models offer new ways to simulate microbial interactions and predict CUE.
Constraining CUE Using Model-Data Fusion
Data assimilation techniques like Bayesian inference can refine biogeochemical models by integrating observational data, offering CUE predictions. We discuss innovative approaches that combine data assimilation with deep learning to create global maps of microbial CUE.
Diagnosing CUE from Existing Models or Simulation Archives
We propose a method to estimate microbial CUE at the ecosystem level by calculating the ratio between soil heterotrophic respiration and gross decomposition within models. This “model-diagnosed CUE” offers a simplified yet effective metric for assessing microbial CUE without direct measurements. Analyzing diagnosed CUE across different models can help identify structural differences and refine SOC predictions.
Conclusion
We underscore the need for a multifaceted approach to improve the understanding and modelling of microbial CUE in the global carbon cycle. By integrating diverse data sources, refining measurement techniques, and developing sophisticated models, researchers can enhance predictions of SOC dynamics, ultimately improving climate models and informing more effective mitigation strategies.
