微生物学报

Item	Results
pH	6.14
Soil organic matter (g/kg)	32.33
Total nitrogen (g/kg)	1.13
Available phosphorus (mg/kg)	9.49
Available potassium (mg/kg)	123.16

Item	Results
pH	6.14
Soil organic matter (g/kg)	32.33
Total nitrogen (g/kg)	1.13
Available phosphorus (mg/kg)	9.49
Available potassium (mg/kg)	123.16

Treatment	Treatment number	Fertilizer application (kg/hm²)	Pig manure application (kg/hm²)
Inorganic fertilizer	P0	180	0	105	-
P1	180	75	105	-
P2	180	150	105	-
Organic amendments	70% P0+M	144	0	84	3 187
70% P1+M	144	53	84	3 187
70% P2+M	144	105	84	3 187

Treatment	Treatment number	Fertilizer application (kg/hm²)	Pig manure application (kg/hm²)
Inorganic fertilizer	P0	180	0	105	-
P1	180	75	105	-
P2	180	150	105	-
Organic amendments	70% P0+M	144	0	84	3 187
70% P1+M	144	53	84	3 187
70% P2+M	144	105	84	3 187

Fertilization treatments	ASV number	Taxa name	Indicator value index	P value
P0	ASV162	Bradyrhizobium	Bradyrhizobium erythrophlei	0.802	0.001^***
		ASV566	Pseudomonas	Pseudomonas brenneri	0.675	0.013^*
		ASV1555	Afipia	Afipia sp. GAS231	0.661	0.043^*
		ASV449	Bradyrhizobium	Bradyrhizobium icense	0.612	0.045^*
		ASV233	Streptomyces	Streptomyces sp. CdTB01	0.577	0.044^*
		ASV266	Bradyrhizobium	Bradyrhizobium sp.	0.575	0.034^*
P1	ASV90	Streptomyces	Streptomyces sp. 11-1-2	0.707	0.002^**
		ASV538	Bradyrhizobium	Bradyrhizobium japonicum	0.615	0.011^*
P2	ASV452	Herbaspirillum	Herbaspirillum hiltneri	0.766	0.001^***
		ASV219	Bradyrhizobium	Bradyrhizobium sp. CCGE-LA001	0.722	0.002^**
		ASV1214	Burkholderia	Burkholderia cepacia	0.707	0.013^*
		ASV850	Methylobacterium	Methylobacterium phyllosphaerae	0.645	0.046^*
		ASV86	Collimonas	Collimonas arenae	0.615	0.007^**
P0+M	ASV537	Pseudomonas	Pseudomonas stutzeri	0.833	0.002^**
		ASV975	Bradyrhizobium	Bradyrhizobium icense	0.783	0.003^**
		ASV696	Burkholderia	Burkholderia stagnalis	0.767	0.001^***
		ASV999	Streptomyces	Streptomyces venezuelae	0.730	0.009^**
		ASV688	Burkholderia	Burkholderia cepacia	0.720	0.007^**
		ASV505	Ralstonia	Ralstonia pickettii	0.707	0.002^**
		ASV1521	Ralstonia	Ralstonia pickettii	0.707	0.020^*
		ASV1024	Lysobacter	Lysobacter antibioticus	0.686	0.004^**
		ASV328	Bradyrhizobium	Bradyrhizobium icense	0.617	0.001^***
70% P1+M	ASV509	Paracoccus	Paracoccus contaminans	0.690	0.024^*
		ASV1245	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.655	0.016^*
		ASV20	Rhodoplanes	Rhodoplanes sp. Z2-YC6860	0.612	0.017^*
		ASV100	Janibacter	Janibacter indicus	0.596	0.036^*
70% P2+M	ASV415	Streptomyces	Streptomyces katrae	0.742	0.001^***
		ASV331	Streptomyces	Streptomyces katrae	0.730	0.001^***
		ASV217	Pseudomonas	Pseudomonas synxantha	0.632	0.025^*
		ASV285	Labrenzia	Labrenzia sp. VG12	0.598	0.036^*
		ASV380	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.592	0.050^*
		ASV21	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.518	0.012^*

Fertilization treatments	ASV number	Taxa name	Indicator value index	P value
P0	ASV162	Bradyrhizobium	Bradyrhizobium erythrophlei	0.802	0.001^***
		ASV566	Pseudomonas	Pseudomonas brenneri	0.675	0.013^*
		ASV1555	Afipia	Afipia sp. GAS231	0.661	0.043^*
		ASV449	Bradyrhizobium	Bradyrhizobium icense	0.612	0.045^*
		ASV233	Streptomyces	Streptomyces sp. CdTB01	0.577	0.044^*
		ASV266	Bradyrhizobium	Bradyrhizobium sp.	0.575	0.034^*
P1	ASV90	Streptomyces	Streptomyces sp. 11-1-2	0.707	0.002^**
		ASV538	Bradyrhizobium	Bradyrhizobium japonicum	0.615	0.011^*
P2	ASV452	Herbaspirillum	Herbaspirillum hiltneri	0.766	0.001^***
		ASV219	Bradyrhizobium	Bradyrhizobium sp. CCGE-LA001	0.722	0.002^**
		ASV1214	Burkholderia	Burkholderia cepacia	0.707	0.013^*
		ASV850	Methylobacterium	Methylobacterium phyllosphaerae	0.645	0.046^*
		ASV86	Collimonas	Collimonas arenae	0.615	0.007^**
P0+M	ASV537	Pseudomonas	Pseudomonas stutzeri	0.833	0.002^**
		ASV975	Bradyrhizobium	Bradyrhizobium icense	0.783	0.003^**
		ASV696	Burkholderia	Burkholderia stagnalis	0.767	0.001^***
		ASV999	Streptomyces	Streptomyces venezuelae	0.730	0.009^**
		ASV688	Burkholderia	Burkholderia cepacia	0.720	0.007^**
		ASV505	Ralstonia	Ralstonia pickettii	0.707	0.002^**
		ASV1521	Ralstonia	Ralstonia pickettii	0.707	0.020^*
		ASV1024	Lysobacter	Lysobacter antibioticus	0.686	0.004^**
		ASV328	Bradyrhizobium	Bradyrhizobium icense	0.617	0.001^***
70% P1+M	ASV509	Paracoccus	Paracoccus contaminans	0.690	0.024^*
		ASV1245	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.655	0.016^*
		ASV20	Rhodoplanes	Rhodoplanes sp. Z2-YC6860	0.612	0.017^*
		ASV100	Janibacter	Janibacter indicus	0.596	0.036^*
70% P2+M	ASV415	Streptomyces	Streptomyces katrae	0.742	0.001^***
		ASV331	Streptomyces	Streptomyces katrae	0.730	0.001^***
		ASV217	Pseudomonas	Pseudomonas synxantha	0.632	0.025^*
		ASV285	Labrenzia	Labrenzia sp. VG12	0.598	0.036^*
		ASV380	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.592	0.050^*
		ASV21	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.518	0.012^*

Network metrics	Inorganic fertilization		Organic amendments
Nodes	36	41	39	39	49	56
Edges	33	50	49	40	70	61
Positive rate (%)	48.48	54.00	53.06	37.50	64.00	62.30
Negative rate (%)	51.52	46.00	46.94	62.50	36.00	37.70
Average degree	1.833	2.439	2.513	2.051	3.061	2.179
Average weighted degree	1.213	3.201	1.65	1.326	2.051	1.394
Diameter	4	7	8	7	7	14
Density	0.052	0.061	0.066	0.054	0.064	0.040
Modularity	0.822	0.642	0.616	0.698	0.553	0.751
Average clustering coefficient	0.465	0.421	0.191	0.385	0.459	0.285

Network metrics	Inorganic fertilization		Organic amendments
Nodes	36	41	39	39	49	56
Edges	33	50	49	40	70	61
Positive rate (%)	48.48	54.00	53.06	37.50	64.00	62.30
Negative rate (%)	51.52	46.00	46.94	62.50	36.00	37.70
Average degree	1.833	2.439	2.513	2.051	3.061	2.179
Average weighted degree	1.213	3.201	1.65	1.326	2.051	1.394
Diameter	4	7	8	7	7	14
Density	0.052	0.061	0.066	0.054	0.064	0.040
Modularity	0.822	0.642	0.616	0.698	0.553	0.751
Average clustering coefficient	0.465	0.421	0.191	0.385	0.459	0.285

有机培肥对土壤含 phoD 基因细菌群落组成和磷素有效性的影响

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赵晓坤 ¹^,² , 方梓西 ¹^,² , 刘顺莉 ¹^,² , 潘玮涵 ¹^,² , 尹江琴 ¹^,² , 谢小雨 ¹^,² , 陈远学 ⁴ , 陈新平 ¹^,²^,³ , 郎明 ¹^,²^,³^,^*

微生物学报 | 研究报告 2025,65(12): 5482-5499

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微生物学报 | 研究报告 2025, 65(12): 5482-5499

有机培肥对土壤含 phoD 基因细菌群落组成和磷素有效性的影响

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赵晓坤¹^,², 方梓西¹^,², 刘顺莉¹^,², 潘玮涵¹^,², 尹江琴¹^,², 谢小雨¹^,², 陈远学⁴, 陈新平¹^,²^,³, 郎明¹^,²^,³^,^*

作者信息

^1.西南大学资源环境学院，西南山地绿色低碳重点实验室，重庆

^2.西南大学，长江经济带农业绿色发展研究中心，重庆

^3.西南大学，农业科学研究院，重庆

^4.四川农业大学资源学院，四川成都

Effects of organic amendments on phoD-harboring bacterial communities and phosphorus availability in soil

Xiaokun ZHAO¹^,², Zixi FANG¹^,², Shunli LIU¹^,², Weihan PAN¹^,², Jiangqin YIN¹^,², Xiaoyu XIE¹^,², Yuanxue CHEN⁴, Xinping CHEN¹^,²^,³, Ming LANG¹^,²^,³^,^*

Affiliations

^1.Key Laboratory of Low-carbon Green Agriculture in Southwestern China, College of Resources and Environment, Southwest University, Chongqing, China

^2.Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China

^3.Academy of Agricultural Sciences, Southwest University, Chongqing, China

^4.College of Resources, Sichuan Agricultural University, Chengdu, Sichuan, China

出版时间: 2025-12-04 doi: 10.13343/j.cnki.wsxb.20250413

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摘要

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目的探究有机培肥条件下土壤中含碱性磷酸酶基因(phoD)细菌的群落结构、网络复杂度及稳定性特征，揭示其对微生物介导的土壤磷素转化和有效性的影响。方法试验依托四川雅安13年长期定位试验点的玉米体系设置3个无机磷肥处理，分别为0、75、150 kg/hm² (P0、P1和P2)。2018年在此基础上开展裂区实验，设置有机培肥处理，无机肥用量减少30%同时添加猪粪(P0+M、70% P1+M、70% P2+M)。运用高通量测序技术和生物学分析方法测定含phoD基因细菌的群落结构特征，揭示不同供磷水平下有机培肥对土壤含phoD基因细菌群落的影响及其介导的土壤有效磷的调控作用。结果随着供磷水平增加，无机肥和有机培肥处理均显著提高了土壤有机质(soil organic matter, SOM)、速效磷(olsen P)和有机磷(organophosphorus, Po)含量，显著降低了土壤pH。供磷水平和有机培肥显著改变了含phoD细菌的群落组成和网络特征。其中，低磷水平(P0、P0+M)下伊卡慢生根瘤菌(Bradyrhizobium icense)为优势物种和指示物种，其相对丰度随施磷量增加显著降低。添加磷后(P1、P2、70% P1+M和70% P2+M)，有效慢生根瘤菌(Bradyrhizobium diazoefficiens)和解多聚物浅粉不完全光合杆菌(Roseateles depolymerans)为优势物种，其相对丰度随施磷量增加显著增加。同时，有机培肥处理条件下3个优势物种的相对丰度均高于对应无机磷处理。此外，有机培肥处理的网络节点和连接数量均高于对应无机磷处理。随机森林模型结果显示，优势类群中的Bradyrhizobium icens是土壤速效磷最强的预测因子。未去除优势物种时含phoD基因细菌群落网络稳定性无显著差异；然而，去除优势物种后各处理含phoD基因细菌群落网络稳定性均显著下降。结论有机培肥在不同供磷水平下提高了含phoD基因细菌群落中优势物种的相对丰度，增加了含phoD基因细菌网络的复杂度，从而增强了含phoD基因细菌群落的网络稳定性，影响了土壤中磷素的有效性。

关键词

有机培肥 / 含phoD基因细菌 / 优势物种 / 群落结构 / 网络稳定性

Abstract

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Objective To investigate the community structure, network complexity, and stability of soil bacteria harboring the alkaline phosphatase gene (phoD) under the application of organic amendments, elucidating their regulatory mechanisms in microbially mediated soil phosphorus (P) transformation and availability. Methods We conducted the experiment within a 13-year long-term maize field trial located in Ya’an, Sichuan. The experiment comprised three mineral P fertilizer treatments: 0, 75, and 150 kg/hm² (designated as P0, P1, and P2, respectively). In 2018, a split-plot design was implemented with organic amendment treatments, where mineral P application was reduced by 30% and supplemented with pig manure (P0+M, 70% P1+M, and 70% P2+M treatments). The phoD-harboring bacterial community structure was characterized by high-throughput sequencing and bioinformatic analyses, which revealed the effects of organic amendments with varying P supply levels on phoD-harboring bacterial communities and their regulation of soil available P. Results As the P supply level increased, both mineral and organic amendments significantly increased the content of soil organic matter (SOM), Olsen-P, and organic P (Po), while significantly decreasing soil pH. P levels and organic amendments markedly altered the community composition and network characteristics of phoD-harboring bacteria. Under low-P conditions (P0, P0+M), Bradyrhizobium icense emerged as both the dominant and indicator species, with its relative abundance decreasing significantly as P application increased. Under P-amended treatments (P1, P2, 70% P1+M, and 70% P2+M), Bradyrhizobium diazoefficiens and Roseateles depolymerans became the predominant species, exhibiting significant increases in relative abundance with higher P inputs. Notably, the relative abundance of all the three dominant species under the application of organic amendments was higher than that in corresponding inorganic P treatments. Furthermore, organic amendments increased the network nodes and connectivity links compared with corresponding mineral P treatments. Random forest analysis further identified B. icense as the strongest predictor of soil available P. The stability of phoD-harboring bacterial networks showed no significant difference across treatments. However, after the removal of dominant species, the network stability declined significantly in all treatments. Conclusion Organic amendments increase the relative abundance of dominant species within the phoD-harboring bacterial community across different P supply levels. They enhance the network complexity of phoD-harboring bacteria, thereby improving the network stability of these bacterial communities and ultimately influencing the availability of soil P.

Key words

organic amendments / phoD-harboring bacteria / dominant species / community structure / network stability

引用本文

赵晓坤, 方梓西, 刘顺莉, 潘玮涵, 尹江琴, 谢小雨, 陈远学, 陈新平, 郎明. 有机培肥对土壤含 phoD 基因细菌群落组成和磷素有效性的影响. 微生物学报, 2025 , 65 (12) : 5482 -5499 . DOI: 10.13343/j.cnki.wsxb.20250413

Xiaokun ZHAO, Zixi FANG, Shunli LIU, Weihan PAN, Jiangqin YIN, Xiaoyu XIE, Yuanxue CHEN, Xinping CHEN, Ming LANG. Effects of organic amendments on phoD-harboring bacterial communities and phosphorus availability in soil[J]. Acta Microbiologica Sinica, 2025 , 65 (12) : 5482 -5499 . DOI: 10.13343/j.cnki.wsxb.20250413

正文

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在集约化农田生态系统中施用磷肥是保障作物产量的关键要素^[1]。大量磷素以有机磷或固定态无机磷的形式存在，难以被植物吸收利用^[2]。农业生产中，虽以矿质肥料形式输入磷素，但磷肥施入土壤后易与铁、铝、钙发生吸附沉淀反应，或与有机磷化合物结合^[3-4]，导致其移动性差，当季利用率仅为10%-25%^[5]。因此，农业生产中通常大量施用磷肥以满足农作物生长发育对磷素的需求，但过量施用磷肥会导致磷素向环境中迁移，进而引发土壤酸化、养分失衡以及微生物群落结构恶化等问题^[1,3-4,6-7]，影响生态系统的平衡^[8-9]。因此提升农业生产中的磷利用效率对农业可持续发展意义重大。

长期试验结果表明，有机培肥模式在维持农业生态系统可持续性以及推动农业绿色健康发展等方面发挥着至关重要的作用^[7,10]。有机培肥作为农田生态系统中的一种可持续土壤管理策略，通过减少化肥施用量并增加土壤有机质的养分来源改善了土壤的物理、化学和生物学特性，有效缓解了土壤酸化等问题^[1,3,6]。例如，在中国南方速效磷缺乏的酸性黄土中，长期氮磷钾肥与有机肥配施可降低土壤中交换性酸和铝含量，在缓解土壤酸化的同时显著提高土壤有效磷含量^[4]。除了影响土壤性质和养分含量外，有机培肥还能通过刺激微生物生长、提高资源利用率以及增强微生物群落的新陈代谢与活性促进代谢过程中有机酸的分泌以溶解稳定态磷，从而提升土壤磷素有效性^[1,11]；同时，它可调节解磷细菌群落的多样性和丰度，提高碱性磷酸酶活性，优化微生物群落结构与功能，并通过增强微生物网络的复杂度、稳定性和类群关联性进一步提升土壤多功能性和磷的有效性^[3,12-13]。此外，有机培肥还能通过为微生物矿化有机磷提供丰富底物，显著影响土壤胞外酶^[14]。因此，充分理解施肥对土壤理化性质和土壤微生物群落的影响对农业施肥管理具有重要意义。

土壤微生物在参与土壤磷转化过程中发挥着至关重要的作用^[15]。解磷微生物自身对磷素供应水平具有响应特性，在磷供应充足时积累磷，在磷限制条件下会将难溶性的有机磷或无机磷水解为生物可利用的有效磷^[16]。基于宏基因组学的研究发现，微生物参与磷转化过程主要由四大类磷循环功能基因介导，包括无机磷溶解基因、有机磷矿化基因、磷吸收和转运基因以及磷饥饿响应调控基因。目前，利用扩增子测序手段研究土壤中微生物参与的无机磷溶解过程主要集中在编码葡萄糖酸脱氢酶辅酶因子的pqqC基因。Zhuo等^[17]研究沉积物中解磷基因的表达时发现，pqqC基因的表达促使铁铝结合态磷向钙结合态磷转化，加速了磷的释放。已有研究发现，长期施肥通过改变土壤pH、土壤有机质(soil organic matter, SOM)和总氮含量影响携带pqqC基因的细菌群落，从而影响土壤中磷素的转化和有效性^[18-19]。同时，利用该手段研究微生物参与的有机磷矿化过程主要聚焦于包含编码碱性磷酸酶基因的微生物。Wu等^[20]研究表明，减磷配施有机肥具有提高磷循环相关基因(phoD、phoC和pqqC)丰度的潜力，这可以促进土壤磷有效性，在减少磷肥用量的同时提高磷利用效率。对宏基因组数据集中3个不同碱性磷酸酶基因家族(phoA、phoD和phoX)的流行率分析发现，phoD是土壤样品中细菌最常见的调控碱性磷酸酶分泌的基因，且丰度很高^[21]。此外，Yang等^[22]研究发现在磷低效土壤中，解磷细菌可能通过优化含有phoD和pqqC基因的细菌群落组成来增强有机磷(organophosphorus, Po)的矿化和无机磷(inorganic phosphorus, Pi)的溶解，提高磷的有效性。施肥的种类和水平显著影响含phoD基因细菌的群落特性，进而影响土壤中磷素的转化和有效性。Fei等的研究表明，有机培肥能够更有效地重塑细菌群落组成，提高土壤生物磷转化效率，从而提高磷的有效性和磷肥的利用率^[23]，其中有机培肥体系中含phoD基因的细菌群落组成与不施肥和施用化肥相比存在显著差异^[13,24]，原因在于有机肥的施入能够缓解环境胁迫对根际含phoD基因细菌群落的影响^[12]。还有研究发现有机肥的施入能缓解矿物肥施用对含phoD基因细菌多样性下降的影响，维持含phoD基因细菌对有机磷矿化的潜力^[13]。在小麦-玉米轮作体系中，有机磷可用性对含phoD基因细菌多样性有显著影响，被认为是最重要的土壤特性^[15]。因此深入探究含phoD基因细菌群落的优势类群，理解磷循环微生物在有机培肥条件下的响应机制，对科学合理施肥和保持土壤养分状况具有重要意义。

本研究依托四川雅安酸性紫色土长期定位点的玉米体系，基于不同供磷水平的无机肥和有机培肥处理，探究供磷水平和有机培肥对含碱性磷酸酶基因细菌群落结构、群落组成(优势物种分析)、网络特征和稳定性的影响，以及其介导的磷素转化机制。在有机肥替代化肥的大背景下，探究含碱性磷酸酶基因细菌群落特征及其对磷素的影响，可为利用生物手段提高磷肥利用效率提供科学依据。

1 材料与方法

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1.1 试验地概况和试验设计

试验地点位于2012年开始实施的四川雅安玉米体系长期定点定位试验站(29°58′59.1″N，102°58′56.4″E)。该地区属于亚热带季风性湿润气候，降水集中在夏季，平均年降水量为1 732 mm，土壤类型为紫色湿润雏形土。2018年试验前土壤(0-20 cm)基本理化性质见表1。本研究材料为‘仲玉三号’春玉米，供试无机肥料为碳铵(N, 17%)、过磷酸钙(P₂O₅, 12%)、氯化钾(K₂O, 60%)。供试有机肥料为猪粪(manure, M)，其全碳、全氮、全磷和全钾含量分别为222.88、14.30、11.40、6.64 g/kg (烘干基)，含水量为89.9%。本研究设置3个无机肥供磷水平，3个供磷水平处理P₂O₅的施用梯度为：0 (不施磷，P0)、75 kg/hm² (适量施磷，P1)、150 kg/hm² (高量施磷，P2)，具体施肥处理见表2。于2018年采用裂区设计，即3个无机磷供磷水平为试验大区，大区间设2.0 m的间隔；大区内划分为6个小区，小区间无间隔，分别设定3个无机磷供磷水平处理和对应的有机培肥(3 187 kg/hm²)处理，重复6次。小区面积为1.4 m×9.5 m=13.3 m²。玉米施肥为条施，播种前开沟，沟深20 cm左右，将50%的氮肥和全部磷肥、钾肥、猪粪作底肥均匀施于沟内，然后覆土；另外50%的氮肥在大喇叭口期追施。

1.2 土样采集及理化特性的测定

于2019年在玉米收获期进行土壤样品采集。在每个小区行间和窝间各随机选取2点(共4点) ，采集对应的地下部0-20 cm的土壤，用土钻取样后，去除土样中的动、植物残体，分别混合均匀作为一个小区的土样。将土样带回实验室过筛，分为两部分。一部分保存于-20 ℃冰箱中用于DNA提取和扩增序列测定；另一部分风干用于土壤理化指标的测定，包括pH、速效磷(available phosphorus, AP)、有机磷(organophosphorus, Po)、全磷(total phosphorus, Pt)、SOM、速效钾(available potassium, AK)和全氮(total nitrogen, TN)。以土水比为1:2.5 (质量体积比)用pH计(上海仪电科学仪器股份有限公司)测定pH；TN使用改进克氏法和外加热法测定；SOM使用重铬酸钾容量法-外加热法滴定；土壤AP在pH 8.5时用0.5 mol/L NaHCO₃提取，然后用比色法测定^[25]；Po的测定采用灼烧法^[26]；Pt采用酸溶-钼锑抗比色法^[27]；AK采用醋酸铵-火焰光度计法。每一个土壤样品的理化性质和后续的DNA提取均测定2个平行，保证样品测定的准确性。

1.3 土壤 phoD 细菌群落DNA抽提和PCR扩增

采用FastDNA SPIN Kit (MP Biomedicals公司)从土壤中提取DNA，使用NanoDrop 2000检测DNA浓度与纯度，同时通过1%琼脂糖凝胶电泳检测DNA提取质量。使用的特异性引物为ALPs-F730 (5′-CAGTGGGACGACCACGAGGT-3′)和ALPs-1101 (5′-GAGGCCGATCGGCATGTCG-3′)扩增phoD基因^[11]，扩增片段大小为371 bp。PCR反应体系(20 μL)：2×ChamQ SYBR Color qPCR Master Mix 10 μL (南京诺唯赞生物科技股份有限公司)，上、下游引物(5 μmol/L)各0.8 μL，50×ROX Reference Dye Ⅱ 0.4 μL，DNA 2 μL，灭菌水(ddH₂O) 6 μL。PCR反应条件：95 ℃预变性3 min；95 ℃变性30 s，55 ℃退火30 s，72 ℃延伸30 s，共27个循环；72 ℃终延伸10 min，4 ℃保存。

1.4 Illumina MiSeq测序

使用1%琼脂糖凝胶回收PCR产物，采用DNA凝胶提取试剂盒(Axygen Biosciences公司)进行纯化，纯化后使用Tris-HCl洗脱，进行2%琼脂糖电泳检测。使用NEXTflex Rapid DNA-Seq Kit (MP Biomedicals公司)对纯化后的PCR产物进行建库，通过PCR将Illumina官方接头序列添加至目标区域外端，然后使用凝胶回收试剂盒切胶回收PCR产物，再使用Tris-HCl缓冲液冲洗，产生单链DNA片段，得到最终的文库。扩增子测序使用的平台为Illumina MiSeq。为了获得质控拼接之后的优化数据，需要在Illumina测序得到的PE reads进行样本拆分后，根据测序质量对双端reads进行质控和过滤，同时根据双端reads之间的重叠(overlap)关系进行拼接。然后使用序列降噪方法(DADA2)处理优化数据，获得扩增子序列变体(amplicon sequence variant, ASV)代表序列和丰度信息，并根据物种注释数据库maarjam081/AM进行分类注释。基于ASV代表序列及丰度信息进行生物信息学分析。原始数据提交到NCBI数据库，序列号为PRJNA1221096。

1.5 数据分析

采用双因素方差分析检验不同供磷水平之间土壤理化性质、微生物群落组成的α多样性数据差异的显著性，在5%水平上利用Duncan检验进行比较。所有统计分析均基于SPSS分析软件(v6)和R软件(v4.2.2)。α多样性的估计基于ASV丰度矩阵，包括由R软件中“vegan”计算phoD细菌的Shannon指数和Sobs指数，在ASV水平上进行非度量多维尺度分析(non-metric multidimensional scaling, NMDS)；使用“pheatmap”绘制优势类群与土壤理化性质的相关性热图；使用“indicspecies”选择指示物种(indicator species)。将各处理相对丰度前10的含phoD基因细菌类群作为优势类群(dominant taxa)，基于ASV丰度矩阵构建网络，并通过成分数据的稀疏相关性(sparse correlations for compositional data, SparCC)相关矩阵计算相关系数；对相关数据进行过滤，将相关阈值为0.6且P<0.05的数据进行网络构建。共现网络通过Gephi软件可视化，进而获得网络拓扑性质，包括节点数、连接数、网络密度、聚类系数、网络集中度和相邻节点平均个数等。其中网络中节点的大小以节点度数表示，将各处理节点度数排序前5的phoD细菌类群作为关键类群(keystone taxa)。采用Amos软件(IBM SPSS Amos 28.0.0)构建结构方程模型(model of structural equations, SEM)以表明环境因子、phoD细菌群落多样性和优势类群与网络复杂度、稳定性之间的关系。以网络分析的cohesion指数表示网络的稳定性，平均度(average degree)表示网络的复杂度，以Shannon指数表示phoD细菌群落α多样性。Cohesion指数通过R软件计算，首先构建相关性矩阵，计算优势细菌类群相对丰度矩阵的相关系数矩阵，得到观察相关性矩阵，通过控制变量零模型对优势细菌类群间的相关性进行估计，得到预期相关矩阵，计算观察与期望相关性差值矩阵，并通过对角线元素设为0以排除物种自身相关性得到修正后的相关性矩阵。对每个物种与其他物种之间的正相关和负相关系数分别求和后取平均从而得到每个物种的正/负连通性，将样本的相对丰度向量分别与正向、负向连通性向量相乘并求和，得到最终cohesion结果。模型使用偏最小二乘程模型(partial least squares regression, PLS)；PLS的数值介于0-1之间，其数值愈接近１，表示模型的适配度愈佳。

2 结果与分析

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2.1 无机和有机培肥对土壤理化性质的影响

在无机肥处理和有机培肥处理中，随着施磷水平的增加pH均显著降低(图1A)。在无机肥处理和有机培肥处理中，土壤Olsen P和SOM随施磷水平的增加而增加(图1B、1C)。同一施磷梯度下有机培肥处理的土壤pH、Olsen P和SOM均高于无机肥处理(图1)。

2.2 无机和有机培肥对含 phoD 基因细菌群落特征的影响

将高通量测序后经质量控制得到的序列与数据库对比注释，采用Shannon指数表征含phoD基因细菌群落多样性。在无机肥处理和有机培肥处理中Shannon指数均无显著变化；在相同施磷水平下Shannon指数也无显著变化(图2A)。基于Bray-Curtis距离进行主坐标分析表明，有机培肥显著影响含phoD基因细菌的群落结构(图2B)。在无机肥处理和有机培肥处理中，伊卡慢生根瘤菌(Bradyrhizobium icense)和吉氏贪铜菌(Cuprisvidus gilardii)的相对丰度随着施磷水平的增加而减少，且与pH呈正相关，与SOM、Olsen P、Pi和Po呈负相关关系；有效慢生根瘤菌(Bradyrhizobium diazoefficiens)、解多聚物浅粉不完全光合杆菌(Roseateles depolymerans)和Rhodoplanes sp. Z2-YC6860与pH呈负相关，与SOM、Olsen P、Pi和Po成正相关关系(图2C、2D)。在无机肥处理和有机培肥处理中，施磷水平(P1和70% P1+M)和高施磷水平(P2和70% P2+M)的指示物种数量相较于低施磷水平(P0和P0+M)均减少(表3)。

2.3 无机和有机培肥对含 phoD 基因细菌群落网络拓扑特征的影响

2.3.1 含 phoD 基因细菌共现网络分析

供磷水平和有机无机配施显著影响了含phoD基因细菌群落的共现网络特征。网络拓扑性质分析表明，在无机肥及有机培肥处理中连接线的数目及平均加权度随着供磷量的增加先增加后下降，均在适量施磷时达到最高值，且在同一施磷梯度下无机肥处理的网络节点数、连接线的数目均小于有机培肥处理。负相关连接线的比例在无机肥处理与有机培肥处理中均呈现先降低再升高的趋势(图3、表4)。

2.3.2 不同施肥措施下含 phoD 基因细菌网络稳定性分析

群落的负凝聚力(negative cohesion)代表群落中物种间的竞争强度。含phoD基因细菌群落的负凝聚力在无机肥处理和有机培肥处理间无显著变化。去除优势物种处理后，有机培肥处理下各水平间的负凝聚力均有显著变化，且在P0和P1处理下变化极显著(图4)。

无机肥处理下优势物种显著影响了土壤含phoD细菌群落网络稳定性。结果表明各无机肥处理与有机培肥处理间，含phoD基因细菌群落的cohesion指数无显著差异。然而，在无机肥处理的P1和P2中去除优势物种后群落的cohesion指数显著降低。

2.4 含 phoD 基因细菌的群落组成与群落稳定性的关系

土壤速效磷含量与含phoD基因细菌群落组成有一定相关性。随机森林分析结果表明，在无机肥处理和有机培肥处理下，B. icense细菌是影响土壤有效磷含量的最强预测因子，分别解释了超过30%和60%的土壤速效磷变化(图5A、5B)。此外，在无机肥处理中Rhodoplanes sp. Z2-YC6860、布氏假单胞菌(Pseudomonas brenneri)和B. diazoefficiens属种的细菌对土壤有效磷也具有较高影响力(图5A)；而在有机培肥处理中R. depolymerans属种的细菌对土壤有效磷具有较高影响力(图5B)。

2.5 无机和有机培肥对含 phoD 基因细菌群落介导的磷素有效性调控的综合效应

结构方程模型阐明了施用无机肥及有机培肥对土壤理化性质、含phoD基因的优势物种、含phoD基因细菌群落结构和含phoD基因微生物网络复杂度的综合效应(图6)。施肥条件下pH对SOM产生极显著正效应(路径系数为0.88)，SOM对土壤有效磷产生显著负效应(路径系数为-0.66)，含phoD基因优势物种对含phoD基因细菌群落结构产生极显著正效应，对含phoD基因微生物网络复杂度产生显著正效应，进而影响土壤有效磷含量(图6)。

在玉米体系下设置无机肥处理和有机培肥处理，探究有机培肥对土壤磷素有效性的影响(图7)。随着施磷水平的升高，土壤pH下降，土壤有机质显著增加；含phoD细菌群落网络复杂度降低，稳定性升高；B. icense为低磷水平(CK)含碱性磷酸酶基因细菌的优势物种，B. diazoefficiens和R. depolymerans为高磷水平(High)含碱性磷酸酶基因细菌的优势物种，不同供磷水平下不同的优势物种影响土壤有机磷的矿化以及土壤有效磷的含量。

3 讨论

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3.1 无机和有机培肥体系对含 phoD 基因细菌的α多样性和群落结构的影响

本研究发现施肥显著提高了土壤速效磷含量，这与Liu等^[28]的研究结果一致。然而，在无机肥和有机培肥处理下含phoD基因细菌群落的α多样性无显著变化(图2A)，该结果与Luo等^[29]在江西省红壤研究所长期定位试验中的发现相同。细菌群落的α多样性对不同施肥处理的响应表现出相对较低的敏感性^[30]，这可能与本研究中有机肥施用周期较短有关。值得注意的是，长期过量施用无机磷肥会显著降低土壤细菌生物多样性^[31-32]。Wang等^[33]在紫色土轮作体系中观察到有机培肥显著提高了含phoD基因细菌群落的α多样性，这种差异可能源于养分输入类型与不同土壤类型对含phoD基因细菌群落多样性的协同调控作用。本研究发现，有机培肥显著改变了含phoD基因细菌的群落结构(图2B)。研究证实不同养分类型的肥料施用可显著改变土壤中细菌的群落结构^[34]。例如，Shen等^[35]发现有机培肥能重塑土壤解磷细菌的群落结构；Huang等^[36]证实外源碳源的添加重塑了土壤中含phoD基因细菌的群落结构，说明有机培肥能够显著影响土壤细菌群落结构。本研究还发现，在有机培肥条件下土壤中含phoD基因的细菌群落组成也存在显著差异(图2A、2C)，这与Li等^[37]在潮土中的发现一致。即使是短期减磷配施有机肥仍会导致细菌群落组成出现差异^[38-39]。这是因为施用有机肥会向土壤中输入大量外源微生物及有机质，它们通过改变土壤理化性质(如pH、SOM等)直接或间接地刺激特定功能微生物的生长，同时抑制部分土著微生物，从而影响微生物群落的组成。

3.2 无机和有机培肥体系对含 phoD 基因细菌网络互作和稳定性的影响

共现网络分析通过解析微生物分类群间的相关性能够揭示复杂的微生物群落互作关系，评估微生物网络的复杂性^[40-42]。研究表明相较于未施肥和施用无机肥的土壤，有机培肥处理下的土壤细菌群落表现出更复杂的共生网络^[43-44]。本研究结果进一步显示有机培肥处理增加了微生物网络节点和连接线的数量，提高了网络复杂度，同时增强了微生物群落的稳定性(图4)。多项研究支持了本研究关于有机培肥增强微生物网络复杂度的发现。例如，Tang等^[30]在2年蔬菜轮作试验中发现，部分有机替代处理增加了土壤微生物群落的多样性和网络复杂度。Hu等^[45]在短期试验中同样证实施用生物有机肥比施用无机肥形成了更复杂的细菌网络结构。在长期定位试验中，Duan等^[46](38年)和Ma等^[47](10年)均观察到，相比单施无机肥，有机无机配施处理提高了微生物的网络复杂度。以上现象可能源于有机肥输入提供了更多的外源碳源，为更多细菌的生存提供了所需营养，从而促进了细菌群落的生长和互作，进而增强了微生物网络的连接性和稳定性^[44,48]。值得注意的是，在去除优势物种后网络稳定性显著降低(图4)。在结构方程模型分析中优势物种对网络结构具有显著的积极影响(图6)，表明含phoD基因的优势物种对细菌网络结构及其稳定性发挥着重要作用。

3.3 无机和有机培肥体系对含 phoD 基因细菌群落介导的土壤磷素转化的影响

在本研究中，有机培肥处理下优势属慢生根瘤菌属(Bradyrhizobium)的相对丰度显著高于无机肥处理，表明有机肥可通过调控优势菌群丰度影响土壤有效磷含量。Bradyrhizobium作为一种共生固氮菌，可通过提高磷酸酶活性提升磷转运速率及分泌有机酸等途径促进土壤中有机磷的矿化与磷素的活化；作为典型的寡营养型phoD载体，Bradyrhizobium对碱性磷酸酶和酸性磷酸酶活性以及非活性磷溶解具有关键调控作用^[49]。外源碳源添加能够促进玉米根际解磷细菌Bradyrhizobium的增殖^[35]，其丰度的增加有助于促进磷的矿化、缓解植物生长过程中的氮磷限制^[50]。本研究还发现B. icense在低施磷水平(P0和P0+M)下作为优势物种和指示物种存在，其相对丰度随施磷量增加而显著降低；B. diazoefficiens和R. depolymerans作为优势物种，其相对丰度随施磷量增加而显著增加(图2C)。值得注意的是，B. icense和B. diazoefficiens都属于Bradyrhizobium，这一发现与Luo等^[24]在安徽省濉溪县玉米-小麦轮作长期定位试验中的发现一致。微生物可以通过产生有机酸、释放磷酸酶水解有机物中的正磷酸盐基团来提高土壤的磷素有效性^[51-52]。其中，细菌是土壤碱性磷酸酶的主要分泌者^[53]。He等^[54]对旱地粉壤土的研究发现，长期施氮降低了土壤pH，通过调控编码ALP的微生物群落缓解了土壤磷限制。这一发现与Li等^[55]在日光温室长期试验中观察到的现象形成重要互补，后者证实长期有机肥施用通过塑造phoD细菌的关键生态集群(Module #1)缓解了化肥对ALP活性的抑制、促进有机磷向有效磷转化。此外，微生物在有机肥分解过程中释放有机酸能够螯合土壤中的铁铝氧化物，减少磷素的固定位点，并与正磷酸盐竞争结合位点，促进活性磷的释放^[56]。有机肥输入增加了土壤不稳定碳库，调控细菌代谢来影响磷素转化效率^[57-58]。有机培肥可通过影响含phoD基因细菌群落，进而影响土壤磷循环和玉米生长。

4 结论

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在酸性紫色土壤中供磷水平和有机培肥显著影响含phoD基因细菌的群落组成和网络特征；其中，B. icense是低磷水平下的优势物种，B. diazoefficiens和R. depolymerans是中、高施磷水平下的优势物种。然而，在有机培肥处理下这3个优势物种的相对丰度相较于对应无机磷处理均显著增加，优势类群中的B. icens是有机培肥体系中土壤速效磷最强的预测因子。同时，有机培肥提高了含phoD基因细菌群落的网络节点和连接数。在未去除优势物种前，含phoD基因细菌群落网络稳定性无显著差异；然而，去除优势物种后各处理中含phoD基因细菌群落网络稳定性均显著下降。这表明优势物种在维持含phoD基因细菌群落网络的稳定性过程中发挥重要作用。本研究解析了有机培肥对含phoD基因细菌群落的影响，并初步评估其在土壤磷素转化中的作用，为探索提高磷肥有效性的微生物途径提供了参考。

作者贡献声明

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赵晓坤：数据统计分析，撰写文章初稿的前言和结果；方梓西：数据分析的呈图，撰写文章初稿的材料方法和讨论；刘顺莉：提供数据统计分析的方法指导，模式图制作，初稿文章的修改；潘玮涵：文章讨论部分引用文献的检索；尹江琴：文章讨论部分内容的修改；谢小雨：数据统计分析提供指导；陈远学：长期定位点试验管理；陈新平：整体试验设计，基金支持；郎明：整体试验设计，样品的采集，整体文章写作中的写作框架和内容的确定，初稿文章修改，基金支持。

作者利益冲突公开声明

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作者声明不存在任何可能会影响本文所报告工作的已知经济利益或个人关系。

基金

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国家自然科学基金(32272800)
国家重点研发计划(2023YFD1900600)
重庆市自然科学基金(CSTB2023NSCQ-MSX0507)
国家玉米产业体系(CARS-02)

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2025年第65卷第12期

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doi: 10.13343/j.cnki.wsxb.20250413

接收时间：2025-05-26
首发时间：2025-12-08
出版时间：2025-12-04

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出版历史

收稿日期：2025-05-26
录用日期：2025-08-27

基金

National Natural Science Foundation of China(32272800)

国家自然科学基金(32272800)

National Key Research and Development Program of China(2023YFD1900600)

国家重点研发计划(2023YFD1900600)

Natural Science Foundation of Chongqing(CSTB2023NSCQ-MSX0507)

重庆市自然科学基金(CSTB2023NSCQ-MSX0507)

National Maize Production System in China(CARS-02)

国家玉米产业体系(CARS-02)

作者信息

^1.西南大学资源环境学院，西南山地绿色低碳重点实验室，重庆

^2.西南大学，长江经济带农业绿色发展研究中心，重庆

^3.西南大学，农业科学研究院，重庆

^4.四川农业大学资源学院，四川成都

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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Item	Results
pH	6.14
Soil organic matter (g/kg)	32.33
Total nitrogen (g/kg)	1.13
Available phosphorus (mg/kg)	9.49
Available potassium (mg/kg)	123.16

Item

Results

6.14

Soil organic matter (g/kg)

32.33

Total nitrogen (g/kg)

1.13

Available phosphorus (mg/kg)

9.49

Available potassium (mg/kg)

123.16

Treatment	Treatment number	Fertilizer application (kg/hm²)	Pig manure application (kg/hm²)
Inorganic fertilizer	P0	180	0	105	-
P1	180	75	105	-
P2	180	150	105	-
Organic amendments	70% P0+M	144	0	84	3 187
70% P1+M	144	53	84	3 187
70% P2+M	144	105	84	3 187

Treatment

Treatment number

Fertilizer application (kg/hm²)

Pig manure application (kg/hm²)

P₂O₅

K₂O

Inorganic fertilizer

180

105

180

105

180

150

105

Organic amendments

70% P0+M

144

3 187

70% P1+M

144

3 187

70% P2+M

144

105

3 187

Fertilization treatments	ASV number	Taxa name	Indicator value index	P value
P0	ASV162	Bradyrhizobium	Bradyrhizobium erythrophlei	0.802	0.001^***
		ASV566	Pseudomonas	Pseudomonas brenneri	0.675	0.013^*
		ASV1555	Afipia	Afipia sp. GAS231	0.661	0.043^*
		ASV449	Bradyrhizobium	Bradyrhizobium icense	0.612	0.045^*
		ASV233	Streptomyces	Streptomyces sp. CdTB01	0.577	0.044^*
		ASV266	Bradyrhizobium	Bradyrhizobium sp.	0.575	0.034^*
P1	ASV90	Streptomyces	Streptomyces sp. 11-1-2	0.707	0.002^**
		ASV538	Bradyrhizobium	Bradyrhizobium japonicum	0.615	0.011^*
P2	ASV452	Herbaspirillum	Herbaspirillum hiltneri	0.766	0.001^***
		ASV219	Bradyrhizobium	Bradyrhizobium sp. CCGE-LA001	0.722	0.002^**
		ASV1214	Burkholderia	Burkholderia cepacia	0.707	0.013^*
		ASV850	Methylobacterium	Methylobacterium phyllosphaerae	0.645	0.046^*
		ASV86	Collimonas	Collimonas arenae	0.615	0.007^**
P0+M	ASV537	Pseudomonas	Pseudomonas stutzeri	0.833	0.002^**
		ASV975	Bradyrhizobium	Bradyrhizobium icense	0.783	0.003^**
		ASV696	Burkholderia	Burkholderia stagnalis	0.767	0.001^***
		ASV999	Streptomyces	Streptomyces venezuelae	0.730	0.009^**
		ASV688	Burkholderia	Burkholderia cepacia	0.720	0.007^**
		ASV505	Ralstonia	Ralstonia pickettii	0.707	0.002^**
		ASV1521	Ralstonia	Ralstonia pickettii	0.707	0.020^*
		ASV1024	Lysobacter	Lysobacter antibioticus	0.686	0.004^**
		ASV328	Bradyrhizobium	Bradyrhizobium icense	0.617	0.001^***
70% P1+M	ASV509	Paracoccus	Paracoccus contaminans	0.690	0.024^*
		ASV1245	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.655	0.016^*
		ASV20	Rhodoplanes	Rhodoplanes sp. Z2-YC6860	0.612	0.017^*
		ASV100	Janibacter	Janibacter indicus	0.596	0.036^*
70% P2+M	ASV415	Streptomyces	Streptomyces katrae	0.742	0.001^***
		ASV331	Streptomyces	Streptomyces katrae	0.730	0.001^***
		ASV217	Pseudomonas	Pseudomonas synxantha	0.632	0.025^*
		ASV285	Labrenzia	Labrenzia sp. VG12	0.598	0.036^*
		ASV380	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.592	0.050^*
		ASV21	Bradyrhizobium	Bradyrhizobium diazoefficiens	0.518	0.012^*

Fertilization treatments

ASV number

Taxa name

Indicator value index

P value

Genus

Species

ASV162

Bradyrhizobium

Bradyrhizobium erythrophlei

0.802

0.001^***

ASV566

Pseudomonas

Pseudomonas brenneri

0.675

0.013^*

ASV1555

Afipia

Afipia sp. GAS231

0.661

0.043^*

ASV449

Bradyrhizobium

Bradyrhizobium icense

0.612

0.045^*

ASV233

Streptomyces

Streptomyces sp. CdTB01

0.577

0.044^*

ASV266

Bradyrhizobium

Bradyrhizobium sp.

0.575

0.034^*

ASV90

Streptomyces

Streptomyces sp. 11-1-2

0.707

0.002^**

ASV538

Bradyrhizobium

Bradyrhizobium japonicum

0.615

0.011^*

ASV452

Herbaspirillum

Herbaspirillum hiltneri

0.766

0.001^***

ASV219

Bradyrhizobium

Bradyrhizobium sp. CCGE-LA001

0.722

0.002^**

ASV1214

Burkholderia

Burkholderia cepacia

0.707

0.013^*

ASV850

Methylobacterium

Methylobacterium phyllosphaerae

0.645

0.046^*

ASV86

Collimonas

Collimonas arenae

0.615

0.007^**

P0+M

ASV537

Pseudomonas

Pseudomonas stutzeri

0.833

0.002^**

ASV975

Bradyrhizobium

Bradyrhizobium icense

0.783

0.003^**

ASV696

Burkholderia

Burkholderia stagnalis

0.767

0.001^***

ASV999

Streptomyces

Streptomyces venezuelae

0.730

0.009^**

ASV688

Burkholderia

Burkholderia cepacia

0.720

0.007^**

ASV505

Ralstonia

Ralstonia pickettii

0.707

0.002^**

ASV1521

Ralstonia

Ralstonia pickettii

0.707

0.020^*

ASV1024

Lysobacter

Lysobacter antibioticus

0.686

0.004^**

ASV328

Bradyrhizobium

Bradyrhizobium icense

0.617

0.001^***

70% P1+M

ASV509

Paracoccus

Paracoccus contaminans

0.690

0.024^*

ASV1245

Bradyrhizobium

Bradyrhizobium diazoefficiens

0.655

0.016^*

ASV20

Rhodoplanes

Rhodoplanes sp. Z2-YC6860

0.612

0.017^*

ASV100

Janibacter

Janibacter indicus

0.596

0.036^*

70% P2+M

ASV415

Streptomyces

Streptomyces katrae

0.742

0.001^***

ASV331

Streptomyces

Streptomyces katrae

0.730

0.001^***

ASV217

Pseudomonas

Pseudomonas synxantha

0.632

0.025^*

ASV285

Labrenzia

Labrenzia sp. VG12

0.598

0.036^*

ASV380

Bradyrhizobium

Bradyrhizobium diazoefficiens

0.592

0.050^*

ASV21

Bradyrhizobium

Bradyrhizobium diazoefficiens

0.518

0.012^*

Network metrics	Inorganic fertilization		Organic amendments
Nodes	36	41	39	39	49	56
Edges	33	50	49	40	70	61
Positive rate (%)	48.48	54.00	53.06	37.50	64.00	62.30
Negative rate (%)	51.52	46.00	46.94	62.50	36.00	37.70
Average degree	1.833	2.439	2.513	2.051	3.061	2.179
Average weighted degree	1.213	3.201	1.65	1.326	2.051	1.394
Diameter	4	7	8	7	7	14
Density	0.052	0.061	0.066	0.054	0.064	0.040
Modularity	0.822	0.642	0.616	0.698	0.553	0.751
Average clustering coefficient	0.465	0.421	0.191	0.385	0.459	0.285

Network metrics

Inorganic fertilization

Organic amendments

P0+M

70% P1+M

70% P2+M

Nodes

Edges

Positive rate (%)

48.48

54.00

53.06

37.50

64.00

62.30

Negative rate (%)

51.52

46.00

46.94

62.50

36.00

37.70

Average degree

1.833

2.439

2.513

2.051

3.061

2.179

Average weighted degree

1.213

3.201

1.65

1.326

2.051

1.394

Diameter

Density

0.052

0.061

0.066

0.054

0.064

0.040

Modularity

0.822

0.642

0.616

0.698

0.553

0.751

Average clustering coefficient

0.465

0.421

0.191

0.385

0.459

0.285