Dataset for substrate stoichiometry drive the divergent accumulation of plant and microbial necromass carbon

To optimize the conversion of exogenous C into soil organic C, we manipulated the substrate stoichiometry (SS) match the requirements ranging from oligotrophs to copiotrophs. We assumed the stoichiometric ratios of fungi (C:N:P:S=10,000:1034:110:94) and bacteria (C:N:P:S=10,000:2004:494: 264) reflected substrate requirements of oligotrophs and copiotrophs. This study provided different SS levels by applying varying amounts of straw and N, P, and S. A total of five treatment groups were established: straw‑amended soil with no nutrient addition (NPS0), NPS0 with nutrient additions to meet the metabolic requirements from fungi (NPS1) to bacteria (NPS3), and a control soil (CK). The straw, cut into 2–5 mm pieces, was mixed with soil at a rate of 2 g per 100 g dry soil. The SS was regulated by adding or not adding the nutrient solutions (NS1, NS2 or NS3) containing ammonium nitrate, potassium dihydrogen phosphate, and ammonium sulfate (pH = 7). The concentrations of N, P, and S in NS1 were 6.42, 1.79, and 0.95 g L−1, in NS2 were 19.50, 3.52, and 1.90 g L−1, and in NS3 were 30.72, 9.98, and 4.23 g L−1, respectively. Topsoil (0–20 cm) and subsoil (20–40 cm) samples (8 kg each) were collected, sieved at 2 mm, and air-dry. The SS was regulated by adding 1 ml of NS1, NS2, or NS3 to 100 g of dry topsoil on a clean and smooth plastic sheet. The soil moisture was then adjusted to 60% field capacity with distilled water, followed by the addition and mixing of 2 g straw fragments. The mixture was then transferred to nylon mesh bags and sealed (aperture: 0.048 mm, length: 20 cm, and width: 15 cm). The subsoil was treated in the same manner. Nine replicates were maintained for each treatment for both topsoil or subsoil. Three soil pits (length: 1.5 m, width: 0.5 m, depth: 0.4 m) spaced at 0.6 m apart were dug in the field, and the topsoil and subsoil were stored separately. Three replicates of each treatment for subsoil were arranged in two rows (spaced approximately 20 cm) and vertically placed at 20–40 cm in each pit. Each pit was backfilled with the original subsoil. The same procedure was followed to fill the pits with the replicates of the five treatments for topsoil. All replicates from one pit were collected at 30, 90, and 150 days post-sowing, then brought back to the laboratory with dry ice and stored at –80℃. This study primarily investigated the following parameters: including (1) amino sugars, lignin phenols, soil water content, soil organic C (SOC), available N (AN), and available P (SAP); (2) C, N, and P cycling enzyme activities (cellobiohydrolase (CBH), β-glucosidase (BG), L-leucine aminopeptidase (LAP), β-N-acetylglucosaminidase (NAG), and acid phosphatase (AP)); and (3) bacterial and fungal diversity and community composition at the phylum level. The main statistical analyses employed included one-way ANOVA, principal coordinates analysis (PCoA), and random forest models.为优化外源碳向土壤有机碳的转化过程，我们调控底物化学计量比（substrate stoichiometry, SS）以匹配从贫营养微生物到富营养微生物的代谢需求。我们假定真菌的化学计量比（碳:氮:磷:硫=10000:1034:110:94）与细菌的化学计量比（碳:氮:磷:硫=10000:2004:494:264）分别对应贫营养微生物和富营养微生物的底物需求。本研究通过调控秸秆与氮、磷、硫的施用量，设置了不同的底物化学计量比水平。共设置5组处理：仅添加秸秆无养分补充的秸秆改良土壤组（NPS0）、从满足真菌代谢需求（NPS1）到满足细菌代谢需求（NPS3）的养分添加组（均基于NPS0），以及空白对照土壤组（CK）。将秸秆切割为2~5 mm的小段，按每100 g干土添加2 g秸秆的比例与土壤混合。底物化学计量比通过添加或不添加含有硝酸铵、磷酸二氢钾和硫酸铵的营养液（pH=7，分别记为NS1、NS2、NS3）来调控。NS1中氮、磷、硫的浓度分别为6.42、1.79和0.95 g·L⁻¹，NS2中为19.50、3.52和1.90 g·L⁻¹，NS3中为30.72、9.98和4.23 g·L⁻¹。 采集表层土壤（0~20 cm）与下层土壤（20~40 cm）样品，每份重量为8 kg，过2 mm筛后风干。在洁净平整的塑料板上，向100 g干表层土中添加1 mL NS1、NS2或NS3营养液，以调控底物化学计量比。随后用蒸馏水将土壤含水量调节至田间持水量的60%，再加入2 g秸秆片段并混匀。将混匀后的混合物装入尼龙网袋并密封（网孔直径0.048 mm，袋长20 cm，袋宽15 cm）。下层土壤的处理流程同上。表层土与下层土的每组处理均设置9个重复。在田间开挖3个土壤坑（长1.5 m、宽0.5 m、深0.4 m，坑间距0.6 m），分别储存采集到的表层土与下层土。将下层土的每组处理的3个重复分为两排（排间距约20 cm），垂直放置于每个土壤坑的20~40 cm深度处。每个坑回填原下层土壤。表层土的5组处理重复样品按相同流程放置于对应坑中。分别于播种后30 d、90 d和150 d采集每个坑内的所有重复样品，用干冰转运至实验室后保存于-80℃冰箱中。 本研究主要测定以下参数：（1）氨基糖、木质素酚类物质、土壤含水量、土壤有机碳（soil organic C, SOC）、有效氮（available N, AN）与有效磷（available P, SAP）；（2）碳、氮、磷循环相关酶活性，包括纤维二糖水解酶（cellobiohydrolase, CBH）、β-葡萄糖苷酶（β-glucosidase, BG）、L-亮氨酸氨基肽酶（L-leucine aminopeptidase, LAP）、β-N-乙酰氨基葡萄糖苷酶（β-N-acetylglucosaminidase, NAG）以及酸性磷酸酶（acid phosphatase, AP）；（3）细菌与真菌的多样性及门水平群落组成。本研究采用的主要统计分析方法包括单因素方差分析（one-way ANOVA）、主坐标分析（principal coordinates analysis, PCoA）与随机森林模型。