carbon use efficiency

We hypothesized that CUEST and microbial metabolic limitations varied significantly across grassland types owing to habitat heterogeneity, with EEA and soil dynamics exerting differential effects on CUE.The primary objectives of this global survey were to (1) investigate CUEST variation characteristics across different grassland types, (2) elucidate the linkages between CUEST, soil stoichiometry, and enzyme-driven nutrient thresholds, and (3) examine the environmental factors influencing extracellular enzymes across various grassland ecosystems.
These data from peer-reviewed publications accessible through Web of Science (http://apps.webofknowledge.com) and Google Scholar (http://scholar.google.com/) prior to December 2023, using "extracellular enzymes", "soil enzymes", or “EEA” as keywords.To mitigate selection bias in publication choice, we adhered to specific criteria in selecting and organizing articles, aiming to secure high-quality datasets for meta-analysis: (1) inclusion of field studies, (2) provision of pertinent soil chemical indicators such as SOC, total N (TN), and total P (TP), along with microbial indicators such as microbial biomass carbon (MBC), microbial biomass N and P contents (MBN and MBP), and (3) reporting of extracellular enzyme activities linked to C-acquiring enzymes (e.g., β-1,4-glucosidase, BG), N-acquiring enzymes (e.g., β-1,4-N-acetylglucosaminidase, NAG; L-leucine aminopeptidase, LAP), and P-acquiring enzymes (Acid phosphatase, AP) (see Table S1). Additionally, we recorded a broad array of related environmental variables and information such as author details, publication sources, publication year, latitude, longitude, elevation, mean annual temperature (MAT), mean annual precipitation (MAP), soil texture (http://www.fao.org/about/en/), and soil depth. The extracted values primarily represent averages derived from multiple samples, carefully collected across diverse geographical locations, experimental treatments, observational perspectives, and temporal intervals—thereby capturing a comprehensive and representative dataset. In cases where raw data were not explicitly provided in the original articles, we adopted a rigorous two-pronged approach: (1) directly contacting corresponding authors to obtain missing datasets, and (2) cross-referencing supplementary literature from the same sampling points, ensuring data reliability through methodological consistency. Our systematic screening process identified 59 high-quality published papers that met our stringent inclusion criteria, forming a robust foundation for our meta-analysis.