Contrasting Exogenous and Endogenous Soil Microbial Carbon Use Efficiencies Under Global Changes

Aim Microbial carbon use efficiency ( CUE ) is one of the key indicators for the formation and release of soil carbon. CUE can be divided into exogenous CUE ( CUE ex , efficiency in using external carbon sources measured by e.g. 13 C or 14 C labeling) and endogenous CUE ( CUE en , efficiency in using internal carbon sources measured by 18 O labeling). Global changes strongly influence CUE, which response depends on the carbon source. However, the effect size and drivers of CUE ex and CUE en responses to global changes remain unclear, leading to large uncertainties when forecasting terrestrial carbon cycling. We aimed to quantify the magnitude and direction of microbial CUE ex and CUE en responses to global changes. Location Global. Time Period 2011–2024. Major Taxa Studied Soil microorganisms. Methods Database containing 213 paired microbial CUE ex and 155 paired microbial CUE en data was integrated and meta‐analysed to assess the impacts of global change factors on microbial CUE. Additional information gathered encompassed latitude, longitude, climate, plant properties, soil properties, microbial properties and experimental conditions. Results We found that CUE ex decreased with absolute latitude, while CUE en showed the opposite trend. Warming reduced CUE ex and CUE en by 3.6% and 16.5%, respectively. Drought increased CUE ex by 7.9%, but decreased CUE en by 14.3%. Nutrient inputs consistently decreased CUE ex by 5.0%–17.1%, while nitrogen and nitrogen combined with phosphorus and potassium inputs increased CUE en by 25.5% and 43.1%, respectively. Aridity index, soil pH and cation exchange capacity were the main factors influencing microbial CUE ex . In contrast, microbial respiration and growth rates, followed by microbial biomass, were the major predictors of microbial CUE en . Main Conclusions Biogeochemical models should account for the opposite spatial patterns of microbial CUE ex and CUE en , as well as their respective specific drivers under global changes, to accurately predict microbial responses to various carbon sources.