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.我们提出如下假说：受生境异质性影响，不同草原类型间的CUEST与微生物代谢限制存在显著差异，且胞外酶（extracellular enzymes, EEA）和土壤动态过程对碳利用效率（CUE）具有差异化调控作用。本全球调研的核心目标包括：（1）探明不同草原类型间CUEST的变异特征；（2）阐明CUEST、土壤化学计量学与酶驱动养分阈值之间的关联机制；（3）解析不同草原生态系统中影响胞外酶活性的环境因子。 本研究的数据来源于2023年12月之前，通过科学网（Web of Science）和谷歌学术（Google Scholar）检索到的同行评议文献，检索关键词为“extracellular enzymes”、“soil enzymes”或“EEA”。为降低文献筛选过程中的选择偏倚，本研究在文献遴选与整理阶段遵循了严格的标准，以获取高质量的元分析数据集：（1）仅纳入野外原位研究；（2）需提供相关土壤化学指标，如土壤有机碳（Soil Organic Carbon, SOC）、全氮（Total Nitrogen, TN）、全磷（Total Phosphorus, TP），以及微生物指标，如微生物生物量碳（Microbial Biomass Carbon, MBC）、微生物生物量氮（MBN）与微生物生物量磷（MBP）；（3）需报道与碳获取酶（如β-1,4-葡萄糖苷酶（BG））、氮获取酶（如β-1,4-N-乙酰葡糖胺糖苷酶（NAG）、L-亮氨酸氨肽酶（LAP））及磷获取酶（酸性磷酸酶（AP））相关的胞外酶活性（详见附表S1）。 此外，本研究还记录了大量相关环境变量与基础信息，包括作者信息、文献来源、发表年份、经纬度、海拔、年平均气温（MAT）、年平均降水量（MAP）、土壤质地（数据来源：联合国粮食及农业组织官网http://www.fao.org/about/en/）以及土壤深度。所提取的数据均为多份样本的平均值，这些样本采集自不同地理位置、实验处理、观测视角与时间跨度，由此构建了覆盖全面且具有代表性的数据集。 若原始文献未明确提供原始数据，本研究采用了严谨的双路径解决方案：（1）直接联系通讯作者以获取缺失数据集；（2）交叉引用同一采样点的补充文献，并通过方法学一致性验证确保数据可靠性。经过系统筛选，本研究最终纳入59篇符合严格纳入标准的高质量文献，为本次元分析提供了坚实的数据基础。