微生物学报

Sampling site	Habitat type	Longitude (E)	Latitude (N)
Changling County	Suaeda land	123°27′34″	44°31′16″
Bare land	123°27′15″	44°31′24″
Heavily stressed maize land	123°27′34″	44°31′20″
Moderately stressed maize land	123°27′35″	44°31′21″
Weakly stressed maize land	123°27′35″	44°31′16″
Da’an City	Suaeda land	123°51′09″	45°36′21″
Heavily stressed maize land	123°38′01″	45°25′59″
Weakly stressed maize land	123°51′17″	45°36′37″

Sampling site	Habitat type	Longitude (E)	Latitude (N)
Changling County	Suaeda land	123°27′34″	44°31′16″
Bare land	123°27′15″	44°31′24″
Heavily stressed maize land	123°27′34″	44°31′20″
Moderately stressed maize land	123°27′35″	44°31′21″
Weakly stressed maize land	123°27′35″	44°31′16″
Da’an City	Suaeda land	123°51′09″	45°36′21″
Heavily stressed maize land	123°38′01″	45°25′59″
Weakly stressed maize land	123°51′17″	45°36′37″

Sampling site	Habitat type	Soil physicochemical properties
Changling County	Suaeda land	0.736±0.057	0.052±0.005	0.025±0.002	4.920± 0.626	34.447± 5.663	0.604± 0.597	9.884± 0.090	0.426±0.141
Bareland	0.691±0.097	0.054±0.004	0.028±0.006	6.880± 2.061	42.691± 6.665	1.290± 0.429	9.684± 0.076	0.560±0.226
Heavily stressed maize land	1.117±0.506	0.097±0.033	0.026±0.004	14.100± 4.953	73.899± 18.486	0.761± 0.338	8.980± 0.203	0.126±0.110
Moderately stressed maize land	1.086±0.132	0.097±0.018	0.035±0.014	32.500± 13.915	70.366± 9.676	0.483± 0.096	8.700± 0.116	0.123±0.064
Weakly stressed maize land	0.978±0.286	0.089±0.022	0.037±0.005	11.460± 5.619	68.600± 23.150	0.127± 0.073	8.694± 0.137	0.045±0.012
Da’an City	Suaeda land	1.313±0.086	0.052±0.008	0.031±0.003	45.840± 6.437	26.203± 3.515	10.202± 1.518	10.350± 0.096	4.115±1.120
Heavily stressed maize land	0.873±0.109	0.073±0.019	0.030±0.008	30.160± 22.632	56.529± 14.816	0.882± 0.217	9.898± 0.306	0.817±0.277
Weakly stressed maize land	1.671±0.048	0.132±0.009	0.049±0.007	4.940± 1.467	80.377± 10.441	0.078± 0.007	8.528± 0.083	0.107±0.130

Sampling site	Habitat type	Soil physicochemical properties
Changling County	Suaeda land	0.736±0.057	0.052±0.005	0.025±0.002	4.920± 0.626	34.447± 5.663	0.604± 0.597	9.884± 0.090	0.426±0.141
Bareland	0.691±0.097	0.054±0.004	0.028±0.006	6.880± 2.061	42.691± 6.665	1.290± 0.429	9.684± 0.076	0.560±0.226
Heavily stressed maize land	1.117±0.506	0.097±0.033	0.026±0.004	14.100± 4.953	73.899± 18.486	0.761± 0.338	8.980± 0.203	0.126±0.110
Moderately stressed maize land	1.086±0.132	0.097±0.018	0.035±0.014	32.500± 13.915	70.366± 9.676	0.483± 0.096	8.700± 0.116	0.123±0.064
Weakly stressed maize land	0.978±0.286	0.089±0.022	0.037±0.005	11.460± 5.619	68.600± 23.150	0.127± 0.073	8.694± 0.137	0.045±0.012
Da’an City	Suaeda land	1.313±0.086	0.052±0.008	0.031±0.003	45.840± 6.437	26.203± 3.515	10.202± 1.518	10.350± 0.096	4.115±1.120
Heavily stressed maize land	0.873±0.109	0.073±0.019	0.030±0.008	30.160± 22.632	56.529± 14.816	0.882± 0.217	9.898± 0.306	0.817±0.277
Weakly stressed maize land	1.671±0.048	0.132±0.009	0.049±0.007	4.940± 1.467	80.377± 10.441	0.078± 0.007	8.528± 0.083	0.107±0.130

苏打盐碱地丛枝菌根真菌群落结构与多样性特征

PDF下载

陈晓烨 ¹^,² , 朱欢欢 ¹^,² , 孙晨雨 ¹^,³ , 李秋实 ¹^,² , 张雅然 ¹^,² , 杨艳 ¹^,² , 曹瑞卿 ¹^,² , 王聪 ¹ , 仰剑霞 ¹ , 董强 ¹ , 李杏春 ¹ , 陈沛霖 ¹^,^* , 高程 ¹^,^*

微生物学报 | 黑土地微生物组 2025,65(8): 3413-3431

收起

微生物学报 | 黑土地微生物组 2025, 65(8): 3413-3431

苏打盐碱地丛枝菌根真菌群落结构与多样性特征

全屏

陈晓烨¹^,², 朱欢欢¹^,², 孙晨雨¹^,³, 李秋实¹^,², 张雅然¹^,², 杨艳¹^,², 曹瑞卿¹^,², 王聪¹, 仰剑霞¹, 董强¹, 李杏春¹, 陈沛霖¹^,^*, 高程¹^,^*

作者信息

^1.中国科学院微生物研究所，微生物多样性与资源创新利用全国重点实验室，北京

^2.中国科学院大学生命科学学院，北京

^3.山东农业大学生命科学学院，山东泰安

Structures and diversity of arbuscular mycorrhizal fungal communities in soda saline-alkali land

Xiaoye CHEN¹^,², Huanhuan ZHU¹^,², Chenyu SUN¹^,³, Qiushi LI¹^,², Yaran ZHANG¹^,², Yan YANG¹^,², Ruiqing CAO¹^,², Cong WANG¹, Jianxia YANG¹, Qiang DONG¹, Xingchun LI¹, Peilin CHEN¹^,^*, Cheng GAO¹^,^*

Affiliations

^1.State Key Laboratory of Microbial Diversity and Innovative Utilization, Chinese Academy of Sciences, Beijing, China

^2.College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

^3.College of Life Sciences, Shandong Agricultural University, Tai’an, Shandong, China

出版时间: 2025-08-04 doi: 10.13343/j.cnki.wsxb.20250399

文章导航

摘要

收起

盐碱地是全球范围内广泛分布的一类退化土壤，其中苏打盐碱地因其盐碱共存的特征治理难度较大，是制约土壤资源有效利用的重要障碍。丛枝菌根真菌(arbuscular mycorrhizal fungi, AMF)通过促进养分吸收和增强抗逆性改善植物的生长与存活能力，为苏打盐碱地的改造利用提供潜在途径。 【目的】 探究在苏打盐碱胁迫梯度下AMF群落结构和多样性随胁迫和其他环境因子的变化。 【方法】 采集吉林省长岭县和大安市中受到不同程度苏打盐碱胁迫的土壤样品，包括长有碱蓬(Suaeda glauca)的荒地(pH 10.0-10.5)、无植被覆盖的裸地(pH 9.5-10.0)和胁迫程度不同的玉米(Zea mays L.)农田(pH 8.5-10.0)。利用18S rRNA基因高通量测序方法检测AMF群落结构与多样性。 【结果】 检测到的AMF在属水平上以内养囊霉属(Entrophospora)、斗管囊霉属(Funneliformis)、根孢囊霉属(Rhizoglomus)和多氏囊霉属(Dominikia)为主。AMF相对多度与土壤总碳、总氮、总磷、有效氮显著正相关，与土壤pH、电导率和盐度显著负相关。AMF群落结构与土壤总碳和pH显著相关；AMF香农多样性(α多样性)与土壤全磷含量和盐度呈显著正相关；AMF群落结构离散度(β多样性)与电导率显著负相关；AMF的α多样性和β多样性显著负相关。 【结论】 盐碱胁迫对AMF群落施加了同质性选择，使其群落变小、β多样性降低、α多样性升高、种类组成发生改变。

关键词

盐碱地 / 丛枝菌根真菌 / 微生物群落结构 / 微生物群落多样性

Abstract

收起

Saline-alkali land is a common type of degraded soil with wide distribution across the globe. Among all types of saline-alkali land, soda saline-alkali land, characterized by the coexistence of high salinity and alkalinity, is particularly difficult to be managed and represent a major obstacle to the effective utilization of soil resources. Arbuscular mycorrhizal fungi (AMF) enhance plant growth and survival by improving nutrient uptake and increasing stress resistance, offering promising potential for the reclamation and utilization of saline-alkali land. [Objective] To explore how the structures and diversity of the AMF community vary along a soda saline-alkaline stress gradient in response to stress and other environmental factors. [Methods] We collected soil samples subjected to varying levels of soda saline-alkaline stress from Changling County and Da’an City in Jilin Province. The sampling sites included Suaeda glauca-covered wildland (pH 10.0-10.5), unvegetated bare land (pH 9.5-10.0), and maize (Zea mays L.) farmlands under varying levels of stress (pH 8.5-10.0). The structures and diversity of AMF communities in these soil samples were analyzed by Illumina-based 18S rRNA gene sequencing. [Results] AMF communities were predominantly composed of Entrophospora, Funneliformis, Rhizoglomus, and Dominikia. The relative abundance of AMF was significantly positively correlated with soil total carbon, total nitrogen, total phosphorus, and available nitrogen, and it was significantly negatively correlated with soil pH, electrical conductivity, and salt content. The AMF community structure was significantly associated with soil total carbon and pH. The Shannon diversity (alpha diversity) of AMF showed significantly positive correlations with total phosphorus and salt content, while the AMF community structure dispersion (beta diversity) was significantly negatively correlated with electrical conductivity. Moreover, the alpha diversity and beta diversity of AMF had a significantly negative correlation. [Conclusion] Saline-alkaline stress exerted homogeneous selection on the AMF community, leading to reduced community size, decreased beta diversity, increased alpha diversity, and altered community composition.

Key words

saline-alkali land / arbuscular mycorrhizal fungi / microbial community structure / microbial community diversity

引用本文

陈晓烨, 朱欢欢, 孙晨雨, 李秋实, 张雅然, 杨艳, 曹瑞卿, 王聪, 仰剑霞, 董强, 李杏春, 陈沛霖, 高程. 苏打盐碱地丛枝菌根真菌群落结构与多样性特征. 微生物学报, 2025 , 65 (8) : 3413 -3431 . DOI: 10.13343/j.cnki.wsxb.20250399

Xiaoye CHEN, Huanhuan ZHU, Chenyu SUN, Qiushi LI, Yaran ZHANG, Yan YANG, Ruiqing CAO, Cong WANG, Jianxia YANG, Qiang DONG, Xingchun LI, Peilin CHEN, Cheng GAO. Structures and diversity of arbuscular mycorrhizal fungal communities in soda saline-alkali land[J]. Acta Microbiologica Sinica, 2025 , 65 (8) : 3413 -3431 . DOI: 10.13343/j.cnki.wsxb.20250399

正文

收起

盐碱地主要是指盐土、碱土和各种不同程度盐化和碱化土壤的总称，盐土主要指以氯化物和硫酸盐阴离子为主的土壤，电导率(electrical conductivity, EC)>4.0 dS/m，pH<8.5；碱土则主要指以碳酸盐和重碳酸盐阴离子为主的土壤，EC<4.0 dS/m，pH>8.5；当土壤的EC>4.0 dS/m，pH>8.5时，即为盐碱土^[1-3]。土地盐碱化问题是各国一直以来面临的困难与挑战，长期制约着耕地资源的可持续利用，严重威胁粮食安全和农业可持续发展^[4-5]。据联合国粮食及农业组织统计，目前全球有超过13.81亿hm²的土地受到盐碱化影响，占全球陆地面积的10.7%^[6]。全球每年因盐碱化导致退化的土地面积约1 000万hm²，已成为干旱和半干旱地区土地资源利用的主要限制因素之一^[7]。

我国盐碱地分布范围广、面积大且类型多样，面积约1亿hm²，土壤类型主要以黄河滩地和滨海盐土、西北内陆盐碱土和东北苏打盐碱土最为典型^[8-9]。其中，苏打盐碱土的盐分以碳酸盐和重碳酸盐为主，具有盐碱并存、治理难度大的特点，以其特有的理化性质区别于其他盐碱土类型，盐碱土的高盐高碱复合胁迫会加剧对生物生长和生存的抑制作用^[10-11]。松嫩平原是中国苏打盐碱地的典型分布区域，也是全球苏打盐碱地的主要分布区域之一，盐碱化土地面积达393.7 hm^{2 [12-15]}。同时，该区域又是我国重要的粮食主产区，在国家粮食安全战略中具有重要地位。我国耕地资源紧张、粮食安全压力加大，亟需在提升现有耕地产能的同时科学开发盐碱地等后备耕地资源，以夯实粮食安全底线^[16]。缓解松嫩平原苏打盐碱化程度、提升苏打盐碱地可利用性，对增加耕地产出效益、保障国家粮食安全和实现农业生产可持续具有重要的现实意义与战略价值。

盐胁迫通过对植物产生渗透和氧化胁迫导致叶片失水和体内盐分增加，破坏植物细胞结构，而碱胁迫的高pH值还会进一步对植物造成损害，抑制养分和水分吸收，产生离子毒性^[17-18]。面对盐碱胁迫，植物进化出一系列生理生化和分子机制来适应环境，例如积累渗透性物质、调节光合作用途径和增强抗氧化酶和抗氧化分子等^[19-21]。然而在更加强烈或长期的盐碱胁迫下，自身机制通常难以有效维持植物的正常生长和生理功能，因而有益微生物的帮助显得尤为重要。丛枝菌根真菌(arbuscular mycorrhizal fungi, AMF)作为连接土壤微生物和植物的重要桥梁，能与陆地上大部分维管植物形成互惠共生结构，促进植物对营养物质的吸收，增强植物对生物胁迫(如病原菌)和非生物胁迫(如干旱和盐分胁迫)的耐受性^[22-26]。已有研究表明，丛枝菌根真菌可以通过增强养分吸收、维持离子稳态、改善水分状态、提升抗氧化能力和促进光合作用等多个方面增强植物对盐碱胁迫的适应能力^[27-30]。AMF与植物形成紧密的共生关系，植物为AMF提供光合作用产物，AMF反过来为植物提供难以获取的养分，促进植物对氮、磷等无机营养物质的吸收^[31-32]。同时，AMF的菌丝可以连接植物宿主根系形成共同菌丝网络(common mycorrhizal network, CMN)，能够调节植物间的养分分配、信号传递和植物种群变化^[32-33]。

微生物作为土壤生态系统的重要组成部分，其群落组成和功能受到环境和植物的塑造，同时也通过调控养分循环、缓解非生物胁迫等过程影响土壤和植物健康^[34-36]。目前关于盐碱胁迫对细菌群落组成和功能的研究已较为丰富。研究表明盐碱胁迫会显著降低土壤细菌群落多样性，细菌对碳源的代谢活性和功能多样性也显著下降^[37-39]。此外，细菌群落与真菌群落对盐碱胁迫表现出不同的响应，细菌群落对盐碱胁迫比真菌群落更敏感，其群落结构显著改变，嗜盐碱和寡营养类群得到富集^[40-41]。与细菌不同，AMF作为与植物密切联系的专性共生体，具有较复杂的生活史和有限的扩散能力。盐度和pH能够直接影响AMF的孢子萌发、菌丝生长以及其在植物根部的定殖率^[42-43]，从而调控AMF群落的空间分布特征。与其他类型盐碱地相比，苏打盐碱地具有更强的碱性，其与盐胁迫共同作用形成更复杂和严峻的胁迫环境。尽管有研究表明AMF对盐碱胁迫具有适应能力^[44-46]，但在苏打盐碱地的盐碱复合胁迫下，顺盐碱梯度AMF群落组成和多样性如何变化仍不明确。

本研究提出以下假设：沿盐碱梯度AMF群落结构趋于简单化，物种多样性降低，群落间相似性增加。本研究选择受不同程度盐碱胁迫的苏打盐碱地土壤：有碱蓬(Suaeda glauca)的荒地(pH 10.0-10.5)、无植被覆盖的裸地(pH 9.5-10.0)和种有玉米(Zea mays L.)的农田(pH 8.5-10.0)，探究盐碱胁迫梯度下AMF群落组成和多样性如何变化。

1 材料与方法

收起

1.1 研究地点与土壤处理方法

本研究采样地点位于吉林省松原市长岭县和大安市白城市，选取了3类不同土地利用类型的样地：(1) 有碱蓬(Suaeda glauca)生长的荒地，土壤pH为10.0-10.5；(2) 无植被覆盖的裸地，pH为9.5-10.0；(3) 种植玉米(Zea mays L.)的农田，pH为8.5-10.0。在长岭县采集了5种类型土壤样品，在大安市采集了3种类型的土壤样品，每个样品采集5份，共40份土壤样品(表1)。所选玉米农田均经历了不同年限的土壤改良工作，根据土壤改良年限及玉米植株的生长状况划分为高度胁迫、中度胁迫和低度胁迫3类。其中，高度胁迫玉米地的土壤改良年限不足1年，植被覆盖度低，存活下来的玉米植株生长严重受限。中度胁迫玉米地的改良年限为1-2年，玉米植株生长状况优于高度胁迫玉米地，但株高明显低于低度胁迫玉米地，植株分布也较为稀疏。低度胁迫玉米地的改造年限超过2年，其中玉米长势良好，无明显的胁迫迹象。

采集过程中避开植物根际，收集表层(0-10 cm)的非根际土壤，用孔径为2 mm的土壤筛进行筛分，去除土壤中较大颗粒、碎石和植物残体等。取适量筛分后的土壤于自封袋中，采集得到的土壤一部分使用冰袋运输至实验室后-80 ℃冰箱中储存，另一部分在常温下风干，用于土壤理化性质测试。

1.2 土壤理化性质测试

本研究中测定的土壤理化性质包括总碳(total carbon, TC)、总氮(total nitrogen, TN)、全磷(total phosphorus, TP)、有效磷(available phosphorus, AP)、有效氮(available nitrogen, AN)、pH、电导率(electrical conductivity, EC)和盐度(表2)，由内蒙古鼎铖检测服务有限公司测定。总碳、总氮使用元素分析仪(Elementar公司)测定^[47-48]，全磷通过碱熔-钼锑抗比色法利用紫外分光光度计(PerkinElmer公司)测量^[49]，有效磷通过碳酸氢钠浸提-钼锑抗比色法利用紫外分光光度计(PerkinElmer公司)测定^[50]，有效氮使用碱解蒸馏法通过凯氏定氮仪(FOSS公司)测定^[51-52]。土壤pH通过pH计(Sartorius公司)测定，测定所用土样和水的质量体积比为1:2.5^[53]。土壤电导率使用电导率仪(上海仪电科学仪器股份有限公司)对土样与水质量体积比为1:5的土壤浸提液测定得到，盐度通过质量法测定得到^[54-55]。

1.3 DNA提取和PCR扩增

1.3.1 DNA提取

使用DNeasy PowerSoil DNA Pro Kit，(Qiagen公司)对土壤微生物DNA进行提取，参照说明书并进行适当优化：样品加入CD1缓冲液后于65 ℃水浴加热10 min，随后使用FastPrep-24 5G均质仪(MP公司)以6 m/s的速度处理10 s。提取的DNA经NanoDrop 2000分光光度计(ThermoFisher Scientific公司)测定浓度和纯度，并在-20 ℃保存备用。

1.3.2 PCR扩增和DNA测序

PCR反应体系(20 μL)：2×Phanta Flash Master Mix酶10 μL，上、下游引物(10 μmol/L)各2.5 μL，DNA模板(5 ng/μL) 2.5 μL，ddH₂O 0.5 μL，0.3%牛血清蛋白2 μL。对真菌ITS区域进行PCR扩增使用的引物对为fITS7 (5′-GT GARTCATCGAATCTTTG-3′)和ITS4-Fun (5′-A GCCTCCGCTTATTGATATGCTTAART-3′)^[56-57]。PCR反应条件：98 ℃预变性30 s；98 ℃变性10 s，58 ℃退火5 s，72 ℃延伸40 s，35个循环；72 ℃终延伸1 min。采用巢式PCR对AMF的ITS区域进行扩增，第1轮使用的引物对为GeoA2 (5′-CCAGTAGTCATATGCTTGTCTC-3′)和AML2 (5′-GAACCCAAACACTTTGGTTTC C-3′)^[58-59]。PCR反应条件：98 ℃预变性30 s；98 ℃变性10 s，58 ℃退火30 s，72 ℃延伸40 s，20个循环；72 ℃终延伸1 min。第2轮使用的引物对为NS31 (5′-TTGGAGGGCAAGT CTGGTGCC-3′)和AMDGR (5′-CCCAACTATC CCTATTAATCAT-3′)^[60-61]。PCR反应条件：98 ℃预变性30 s；98 ℃变性10 s，58 ℃退火30 s，72 ℃延伸40 s，30个循环；72 ℃终延伸1 min。

将每个样本的PCR产物等体积混合，通过胶回收试剂盒(Axygen公司)进行纯化，并用Qubit 2.0荧光剂(Life Technologies公司)测定纯化产物浓度。最终利用Illumina NovaSeq 6000平台(上海派森诺生物科技有限公司)进行测序。

1.4 生物信息分析

利用QIIME 2 (v2023.7)进行测序数据处理和质量控制^[62]。利用demultiplex (去混合)操作，根据条码序列(barcode)将样品的序列数据分配到对应的样品中。使用USEARCH (v11.0.667)和cutadapt合并配对序列，对测序数据进行引物修剪，丢弃长度小于100 bp的短序列^[63-64]。对测序数据进行质量控制，去除期望错误过多、最大质量评分小于42的低质量序列，再将得到的序列进行去重复和去除低频序列。利用USEARCH根据97%的相似性对序列进行聚类，划分为不同的可操作分类单元(operational taxonomic unit, OTU)^[63]。将真菌和丛枝菌根真菌OTU代表序列利用BLAST (v2.16.0+)^[65]分别与EUKARYOME数据库(v1.9.4)^[66]进行比对，并按照比对一致性(percent identity)阈值大于90%的标准进行筛选。将筛选后的丛枝菌根真菌OTU序列在Galaxy v22.05平台(https://galaxy.pasteur.fr/)上构建系统发育树。具体步骤为：使用MAFFT工具进行比对，采用BMGE (block mapping and gathering with entropy)进行序列清理，最后使用MrBayes进行系统发育树的构建，生成树文件用于确定比对后的OTU是否属于目标分类群，并验证其系统发育关系在发育树中的一致性和可信度。树文件的可视化在iTOL平台(https://itol.embl.de/)完成。

1.5 数据分析和可视化

数据分析和可视化均在R v4.4.1中完成^[67]。对于多组样本之间的差异，使用agricolae包(v1.3.7)^[68]中的kruskal命令进行Kruskal-Wallis非参数检验。通过vegan包(v2.6.8)中的adonis2函数进行置换多元方差分析(permutational multivariate analysis of variance, PERMANOVA)，检验不同样本间群落结构的差异。所有数据可视化均使用ggplot2包(v3.5.1)^[69]实现。α多样性计算通过vegan包(v2.6.8)^[70]中的diversity实现，β多样性计算中的Bray-Curtis距离和Jaccard距离通过vegan包(v2.6.8)中的vegdist功能计算。Simpson成对相异性指数通过betapart包(v1.6)^[71]中的beta.pair命令计算得到。冗余分析(redundancy analysis, RDA)通过vegan包(v2.6.8)实现，利用envfit函数来检验环境变量与微生物群落分布之间的关系。基于构建的系统发育树和群落组成数据，利用picante包(v1.8.2)^[72]中的ses.mpd函数，通过平均成对系统发育距离(mean pairwise distance, MPD)乘以-1计算净种间亲缘关系指数(net relatedness index, NRI)。使用“taxa.labels”零模型构建方法，对系统发育树的物种标签进行随机置换，重复999次，通过置换检验评估MPD是否显著偏离随机期望。NRI指数为正表明系统发育结构聚集，NRI指数为负则表明系统发育结构发散。利用vegan包(v2.6.8)的betadisper函数对AMF群落的Bray-Curtis距离矩阵进行主坐标分析(principal coordinates analysis, PCoA)。

2 结果与分析

收起

2.1 土壤理化性质

本研究比较了不同土壤样品的盐碱理化特征，包括pH值、电导率和盐度(图1)。各样品在pH、电导率和盐度这3种理化性质表现出显著的梯度变化。其中，大安市碱蓬地的3项指标显著高于其他样地，表明其处于最强的盐碱胁迫状态；玉米地中pH和电导率呈现出与作物长势相对应的由高至低的梯度分布。土壤样品的盐碱理化因子构成了一个连续的、可用于AMF群落分析的自然胁迫梯度。

2.2 AMF的群落组成

通过Illumina高通量测序将处理后的AMF序列进行划分和比对，得到58个符合条件的OTUs，共523 919条AMF的相关序列。为减少样本之间因测序深度不同导致的后续分析差异，选取最小序列数189对AMF的OTUs数据进行抽平处理，移除在所有样本中丰度为0的OTUs (OTU488和OTU696)，最终得到56个OTUs (图2A)。

用抽平后的OTU表格进行群落组成分析，得到各个土壤样品中AMF在不同分类水平的群落组成(图2C)。检测得到的AMF均属于球囊菌门(Glomeromycota)，在属水平上主要由内养囊霉属(Entrophospora, 22.0%)、斗管囊霉属(Funneliformis, 19.0%)和根孢囊霉属(Rhizoglomus, 14.1%)组成，其次是多氏囊霉属(Dominikia, 12.4%) (图2C)。值得注意的是，长岭县高胁迫玉米地内养囊霉属(Entrophospora)的占比和中度胁迫玉米地根孢囊霉属(Rhizoglomus)显著高于其他生境，分别达到50.7%和45.2%。冗余分析(RDA)进一步探究环境因子对AMF群落结构的影响(图2D)。结果表明，前两轴共解释了56.59%的群落结构变异(RDA1为44.73%，RDA2为11.86%)。其中，总碳和pH对AMF群落结构具有较强的解释力，可能是驱动AMF群落组成差异的重要因子。净种间亲缘关系指数(NRI)与pH的回归分析表明，样本NRI值与pH无显著关系。置换检验显示，每个样本的NRI值均不显著，说明群落中物种之间的系统发育距离分布无显著聚集或发散趋势，表现为随机的AMF群落系统发育结构(图2B)。

利用AMF序列占全部真菌序列的相对比例来计算AMF的总丰度，AMF序列相对占比在两地不同生境中的变化趋势相似，并且不同生境类型中AMF占比存在显著区别(图3A)。在低胁迫玉米地中AMF的平均相对多度(长岭县2.48%和大安市6.88%)显著高于其他类型生境，而碱蓬地(长岭县0.27%和大安市0.63%)和裸地(0.09%)中AMF比例最低。AMF序列相对占比与土壤理化性质的相关性分析进一步表明，除土壤有效磷(图3F)外，其他环境因子均与AMF占比呈显著线性关系：总碳、总氮、全磷和碱解氮与其呈正相关(图3B-3E)，而pH、电导率和盐度与其则呈负相关(图3G-3I)。这说明土壤养分状况和环境胁迫对AMF的分布具有显著影响。

2.3 AMF群落的α多样性

在长岭县玉米地中α多样性指数呈现随胁迫强度增加而减小的趋势，低度胁迫玉米地AMF群落的香农多样性指数高于其他玉米地，大安市低度胁迫玉米地也表现出相似的趋势(图4A)。值得注意的是，长岭县的裸地和大安市的碱蓬地显示出较高的α多样性水平。AMF群落香农多样性指数关于不同土壤理化性质的相关性分析表明，AMF的α多样性与全磷(R=0.335，P=0.035)和盐度(R=0.323，P=0.042)呈显著正相关(图4B、4C)，与其他土壤理化性质无显著相关性。

2.4 AMF群落的β多样性

为了进一步比较不同样地AMF群落结构的差异和变化模式，基于Bray-Curtis距离进行了主坐标分析(PCoA)和β多样性分析(图5A-5C)。PERMANOVA检验(F=2.78，P=0.001)和Kruskal-Wallis检验(χ²=25.3，P=6.778×10^-4)说明不同生境下土壤AMF群落结构存在明显差异，且AMF群落在各个样点内的离散度差异显著(图5A)。为进一步评估不同生境中AMF群落结构和组内离散度差异，进行了Bray-Curtis相异性分析和β离散度分析(图5B、5C)。不同样地Bray-Curtis相异性分析结果表明，相比碱蓬地和裸地，玉米地AMF群落间组成差异较大(图5B)。大安市碱蓬地和长岭县裸地中AMF群落结构的离散度均低于玉米农田，表明在玉米农田中AMF群落的内部变异性更大，群落结构更为分散(图5C)。

为进一步探究土壤理化性质对AMF群落结构的影响，分析了基于Bray-Curtis距离计算的群落结构相异性与不同土壤理化性质欧式距离矩阵之间的相关性，结果显示两者之间的相关性并不显著，表明单一土壤理化因子对AMF群落结构变异的解释力有限。AMF群落组成可能受到多个环境因子共同作用，且其响应关系可能较为复杂，难以用单一理化性质的线性关系加以解释。接着对AMF群落结构离散度与土壤理化性质的关系进行探究，发现AMF群落的结构离散度与pH (R=-0.224，P=0.165) (图5I)和盐度(R=-0.312，P=0.050) (图5K)无显著线性关系，而与电导率呈显著负相关(R=-0.380，P=0.016) (图5J)。

用不同β多样性度量方式，包括AMF群落的Bray-Curtis相异性、组内离散度、Jaccard相异性和Simpson距离与AMF群落α多样性进行相关性分析(图6)，发现AMF的α多样性与β多样性指标均呈负相关关系。这说明随AMF群落内部的离散度和群落结构差异增加，群落的α多样性减小。

3 讨论与结论

收起

3.1 AMF群落组成和多样性受到土壤养分和盐碱胁迫共同驱动

本研究中总碳和pH是影响AMF群落组成最显著的因子(图2D)。Yang等^[73]研究表明，地上植物生物量变化可能引起土壤总碳含量的变化，进而调控AMF群落结构和多样性。虽然本研究未直接测定地上植物的生物量，但在玉米地等人类活动干扰强、生物量高的生境中土壤总碳含量较高，能够在一定程度上反映碳分配对AMF群落的调控。此外，土壤pH值对AMF群落构建和生存也十分关键。pH可能直接影响AMF孢子萌发、菌丝生长及定殖过程^[43,74]，并在不同生态系统中对AMF群落组成和多样性产生重要影响^[45,75-76]。与前人研究结果一致，在受到盐胁迫的生态系统中，尽管pH显著影响AMF的群落组成，但对多样性的影响并不显著^[77]。

顺盐碱胁迫梯度AMF群落物种多样性增加，而群落结构变得更加趋同(图4C、图5J)。这说明盐度对AMF群落多样性的影响并非简单的抑制作用，高胁迫环境可能筛选出适应高盐碱环境的AMF种类，并且这些物种能够在高盐碱环境下稳定共存。此外，土壤全磷含量也表现出与AMF的α多样性正相关关系(图4B)。目前已有许多研究证明土壤养分含量尤其是氮和磷与AMF多样性存在密切联系^[78-80]。土壤中磷含量会影响AMF的α多样性，通常有效磷和AMF的α多样性呈负相关，环境中磷含量充足时植物分配给AMF的碳减少，导致AMF的多度减小^[81-82]。然而，本研究中除全磷外其他养分元素并未与AMF多样性呈现直接的线性关系，可能在高度盐碱的局域尺度下AMF群落组成和多样性随胁迫梯度的变化并非单一因子驱动，而是受到多种因素的交互作用与微环境异质性的影响。

3.2 植被特征与人类活动共同影响AMF群落分布格局

AMF群落的结构变化不仅能够反映其对土壤理化因子的直接响应，也可能体现其与植物共生网络和遗留效应之间的复杂关系。尽管本研究未直接收集地上植物相关数据，但已有研究表明植物种类也显著影响AMF群落。例如，在碱蓬生境中AMF的根系定殖率通常低于10%^[46,83]，而玉米中定殖率通常较高^[84-85]。裸地AMF序列数的相对占比最低，且玉米地AMF序列数相对占比随胁迫程度的减小而增加(图3A、3G-3I)，推测在植被覆盖度较高、生物量较大的地点中，即使植物死亡土壤中残留的AMF也可能通过孢子和菌丝网络维持较强的生存能力。

α多样性分析表明裸地AMF群落的多样性水平较高，同时裸地的β多样性却显著低于其他生境(图4A、5B、5C)，表明尽管裸地中AMF物种丰富度较高，但样本间群落结构较为一致，空间异质性较低。这可能与植被覆盖缺失导致AMF与植物的互作关系缺乏有关。裸地缺少与植物的相互作用，受到的人类活动扰动也较小，可能为多种适应高盐碱环境的AMF提供了生存空间，体现为更高的α多样性。相比之下，碱蓬作为典型的盐生植物，可能对AMF起到特定的招募作用^[46]，而玉米农田也可能因为长期的农业活动筛选出特定的AMF物种^[86-87]。然而，β多样性显著降低，反映盐碱胁迫施加的同质选择效应。在该选择压力下，AMF群落结构收敛、空间异质性降低，而物种丰富度可能维持在较高水平。

AMF群落在各种生境下的α多样性和不同β多样性度量方式均表现出显著的负相关性(图6)。这与前人研究结果一致，Zhu等^[88]对盐碱荒地和非盐碱农田中AMF群落的研究发现，农田和荒地中AMF的α多样性和β多样性也呈相反的变化趋势，在非盐胁迫的农田中AMF的α多样性更低、β多样性更高，而在盐胁迫荒地中AMF群落拥有更高的α多样性和更低的β多样性。耕作活动可能会干扰和破坏AMF通过菌丝形成的菌丝网络^[89-90]，使得AMF群落被分割成多个小而孤立的区域，这些分散的区域内群落的个体数量较少，稀有种易随机灭绝，更容易受到随机漂变的影响，导致α多样性减小和β多样性增加。群落个体数量少更易受到随机漂变的这种现象已在许多研究中被提出^[91-94]，并且有研究显示随机漂变增强与较低的α多样性和较高的β多样性存在关联^[95-97]。这些变化趋势反映了垦殖活动对土壤AMF群落的影响，说明在苏打盐碱地土壤改良过程中土壤生态系统中的AMF群落向适应农业生产方向改变。该结果有助于更好地了解盐碱地在改良过程中AMF群落变化规律，为苏打盐碱地科学开发和改良提供了理论支持，对推动苏打盐碱地的有效利用和保障粮食安全具有积极意义。

基金

收起

中国科学院战略性先导科技专项(XDA28030401)
国家自然科学基金(32322053)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

赵耕毛, 杨梦圆, 陈硕, 苏纪康, 吕慧琳, 贾慧昕, 刘兆普. 我国盐碱地治理: 现状、问题与展望[J]. 南京农业大学学报, 2025, 48(1): 14-26.

ZHAO

, YANG

, CHEN

, SU

, LÜ

, JIA

, LIU

. Saline-alkali land management in China: current situation, problems and prospects[J]. Journal of Nanjing Agricultural University, 2025, 48(1): 14-26 (in Chinese).

[2]

王遵亲. 中国盐渍土[M]. 北京: 科学出版社, 1993.

WANG

. Saline Soils of China[M]. Beijing: Science Press, 1993 (in Chinese).

[3]

SHAINBERG

, LETEY

. Response of soils to sodic and saline conditions[J]. Hilgardia, 1984, 52(2): 1-57.

[4]

HASSANI

, AZAPAGIC

, SHOKRI

. Global predictions of primary soil salinization under changing climate in the 21st century[J]. Nature Communications, 2021, 12: 6663.

[5]

SHOKRI

, HASSANI

, SAHIMI

. Multi-scale soil salinization dynamics from global to pore scale: a review[J]. Reviews of Geophysics, 2024, 62(4): e2023RG000804.

[6]

FAO. Global status of salt-affected soils-main report[EB/OL]. [2025-01-13]. https://doi.org/10.4060/cd3044en

[7]

WANG

, LIU

, MA

, WANG

, ZHANG

, BAHADUR

, CHEN

, LIU

, ZHANG

, ZHAO

. Research progress regarding the role of halophilic and halotolerant microorganisms in the eco-environmental sustainability and conservation[J]. Journal of Cleaner Production, 2023, 418: 138054.

[8]

冯起, 尹鑫卫, 朱猛, 张举涛, 刘蔚, 席海洋, 鱼腾飞, 杨林山, 刘文, 陆志翔. 统筹推进西北地区盐碱地综合治理利用: 现状、挑战与对策建议[J]. 中国科学院院刊, 2024, 39(12): 2060-2073.

FENG

, YIN

, ZHU

, ZHANG

, LIU

, XI

, YU

, YANG

, LIU

, LU

. Overall promotion of integrated management and utilization of saline-alkali land in Northwest China: conditions, challenges, and recommendations[J]. Bulletin of Chinese Academy of Sciences, 2024, 39(12): 2060-2073 (in Chinese).

[9]

曹晓风, 孙波, 陈化榜, 周俭民, 宋显伟, 刘小京, 邓向东, 李秀军, 赵玉国, 张佳宝, 李家洋. 我国边际土地产能扩增和生态效益提升的途径与研究进展[J]. 中国科学院院刊, 2021, 36(3): 336-348.

CAO

, SUN

, CHEN

, ZHOU

, SONG

, LIU

, DENG

, LI

, ZHAO

, ZHANG

, LI

. Approaches and research progresses of marginal land productivity expansion and ecological benefit improvement in China[J]. Bulletin of Chinese Academy of Sciences, 2021, 36(3): 336-348 (in Chinese).

[10]

SAQIB

, AKHTAR

, QURESHI

. Sodicity intensifies the effect of salinity on grain yield and yield components of wheat[J]. Journal of Plant Nutrition, 2008, 31(4): 689-701.

[11]

JAVID

, FORD

, NICOLAS

. Tolerance responses of Brassica juncea to salinity, alkalinity and alkaline salinity[J]. Functional Plant Biology, 2012, 39(8): 699-707.

[12]

WANG

, SEKI

, MIYAZAKI

, ISHIHAMA

. The causes of soil alkalinization in the Songnen Plain of Northeast China[J]. Paddy and Water Environment, 2009, 7(3): 259-270.

[13]

张巍, 冯玉杰. 松嫩平原盐碱土理化性质与生态恢复[J]. 土壤学报, 2009, 46(1): 169-172.

ZHANG

, FENG

. Physico-chemical properties and ecological recovery of saline-alkaline soil in Songnen plain[J]. Acta Pedologica Sinica, 2009, 46(1): 169-172 (in Chinese).

[14]

孙广友, 王海霞. 松嫩平原盐碱地大规模开发的前期研究、灌区格局与风险控制[J]. 资源科学, 2016, 38(3): 407-413.

SUN

, WANG

. Large scale development to saline-alkali soil and risk control for the Songnen Plain[J]. Resources Science, 2016, 38(3): 407-413 (in Chinese).

[15]

徐子棋, 许晓鸿. 松嫩平原苏打盐碱地成因、特点及治理措施研究进展[J]. 中国水土保持, 2018(2): 54-59, 69.

, XU

. The cause of formation and characteristics of soda saline-alkaline land of the Songnen Plain and the study progress of control measures[J]. Soil and Water Conservation in China, 2018(2): 54-59, 69 (in Chinese).

[16]

沈仁芳, 王超, 孙波. “藏粮于地、藏粮于技”战略实施中的土壤科学与技术问题[J]. 中国科学院院刊, 2018, 33(2): 135-144.

SHEN

, WANG

, SUN

. Soil related scientific and technological problems in implementing strategy of “storing grain in land and technology”[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(2): 135-144 (in Chinese).

[17]

YANG

, XU

, TANG

, JIN

, YANG

. The impact of alkaline stress on plant growth and its alkaline resistance mechanisms[J]. International Journal of Molecular Sciences, 2024, 25(24): 13719.

[18]

MUCHATE

, NIKALJE

, RAJURKAR

, SUPRASANNA

, NIKAM

. Plant salt stress: adaptive responses, tolerance mechanism and bioengineering for salt tolerance[J]. The Botanical Review, 2016, 82(4): 371-406.

[19]

ZAHRA

, HINAI MS

, HAFEEZ

, REHMAN

, WAHID

, SIDDIQUE

KHM

, FAROOQ

. Regulation of photosynthesis under salt stress and associated tolerance mechanisms[J]. Plant Physiology and Biochemistry, 2022, 178: 55-69.

[20]

FANG

, HOU

, LIANG

. Response mechanisms of plants under saline-alkali stress[J]. Frontiers in Plant Science, 2021, 12: 667458.

[21]

CHOUDHARY

, WANI

, NAEEM

, KHAN

MMA

, AFTAB

. Cellular responses, osmotic adjustments, and role of osmolytes in providing salt stress resilience in higher plants: polyamines and nitric oxide crosstalk[J]. Journal of Plant Growth Regulation, 2023, 42(2): 539-553.

[22]

BEGUM

, QIN

, AHANGER

, RAZA

, KHAN

, ASHRAF

, AHMED

, ZHANG

. Role of arbuscular mycorrhizal fungi in plant growth regulation: implications in abiotic stress tolerance[J]. Frontiers in Plant Science, 2019, 10: 1068.

[23]

CAVAGNARO

, BENDER

, ASGHARI

, van der HEIJDEN

MGA

. The role of arbuscular mycorrhizas in reducing soil nutrient loss[J]. Trends in Plant Science, 2015, 20(5): 283-290.

[24]

GAO

, COURTY

, VAROQUAUX

, COLE

, MONTOYA

, XU

, PURDOM

, VOGEL

, HUTMACHER

, DAHLBERG

, COLEMAN-DERR

, LEMAUX

, TAYLOR

. Successional adaptive strategies revealed by correlating arbuscular mycorrhizal fungal abundance with host plant gene expression[J]. Molecular Ecology, 2023, 32(10): 2674-2687.

[25]

LENOIR

, FONTAINE

, LOUNÈS-HADJ SAHRAOUI

. Arbuscular mycorrhizal fungal responses to abiotic stresses: a review[J]. Phytochemistry, 2016, 123: 4-15.

[26]

Van der HEIJDEN

MGA

, MARTIN

, SELOSSE

, SANDERS

. Mycorrhizal ecology and evolution: the past, the present, and the future[J]. New Phytologist, 2015, 205(4): 1406-1423.

[27]

EVELIN

, DEVI

, GUPTA

, KAPOOR

. Mitigation of salinity stress in plants by arbuscular mycorrhizal symbiosis: current understanding and new challenges[J]. Frontiers in Plant Science, 2019, 10: 470.

[28]

FARGHALY

, NAFADY

, ABDEL-WAHAB

. The efficiency of arbuscular mycorrhiza in increasing tolerance of Triticum aestivum L. to alkaline stress[J]. BMC Plant Biology, 2022, 22(1): 490.

[29]

CAO

, WU

, ZHUKOVA

, TANG

, WENG

, LI

, YANG

. Arbuscular mycorrhizal fungi (AMF) species and abundance exhibit different effects on saline-alkaline tolerance in Leymus chinensis[J]. Journal of Plant Interactions, 2020, 15(1): 266-279.

[30]

WANG

, LIN

, YANG

, TAO

, YAN

, ZHOU

, ZHANG

. Arbuscular mycorrhizal fungi improve the growth and performance in the seedlings of Leymus chinensis under alkali and drought stresses[J]. PeerJ, 2022, 10: e12890.

[31]

SHI

, WANG

. Mycorrhizal symbiosis in plant growth and stress adaptation: from genes to ecosystems[J]. Annual Review of Plant Biology, 2023, 74: 569-607.

[32]

WIPF

, KRAJINSKI

, van TUINEN

, RECORBET

, COURTY

. Trading on the arbuscular mycorrhiza market: from arbuscules to common mycorrhizal networks[J]. New Phytologist, 2019, 223(3): 1127-1142.

[33]

TEDERSOO

, BAHRAM

, ZOBEL

. How mycorrhizal associations drive plant population and community biology[J]. Science, 2020, 367(6480): eaba1223.

[34]

TRIVEDI

, LEACH

, TRINGE

, SA TM, SINGH

. Plant-microbiome interactions: from community assembly to plant health[J]. Nature Reviews Microbiology, 2020, 18(11): 607-621.

[35]

PHILIPPOT

, CHENU

, KAPPLER

, RILLIG

, FIERER

. The interplay between microbial communities and soil properties[J]. Nature Reviews Microbiology, 2023, 22(4): 226-239.

[36]

FIERER

. Embracing the unknown: disentangling the complexities of the soil microbiome[J]. Nature Reviews Microbiology, 2017, 15(10): 579-590.

[37]

YANG

, TANG

, CUI

, CHEN

, LIU

, GUO

. Saline-alkali stress reduces soil bacterial community diversity and soil enzyme activities[J]. Ecotoxicology, 2022, 31(9): 1356-1368.

[38]

ZHAO

, BAI

, GAO

, ZHAO

, ZHANG

, CUI

. Shifts in the soil bacterial community along a salinity gradient in the Yellow River Delta[J]. Land Degradation & Development, 2020, 31(16): 2255-2267.

[39]

ZHANG

, SHI

, CUI

, YUE

, LI

, LIU

, TRIPATHI

, CHU

. Salinity is a key determinant for soil microbial communities in a desert ecosystem[J]. mSystems, 2019, 4(1): e00225-18.

[40]

WANG

, CHEN

, WANG

. Responses of soil microbial communities and their network interactions to saline-alkaline stress in Cd-contaminated soils[J]. Environmental Pollution, 2019, 252: 1609-1621.

[41]

LIU

, HU

, WANG

, XUE

, LI

. Effects of saline-alkali stress on bacterial and fungal community diversity in Leymus chinensis rhizosphere soil[J]. Environmental Science and Pollution Research, 2022, 29(46): 70000-70013.

[42]

JUNIPER

, ABBOTT

. Soil salinity delays germination and limits growth of hyphae from propagules of arbuscular mycorrhizal fungi[J]. Mycorrhiza, 2006, 16(5): 371-379.

[43]

COUGHLAN

, DALPÉ

, LAPOINTE

, PICHÉ

. Soil pH-induced changes in root colonization, diversity, and reproduction of symbiotic arbuscular mycorrhizal fungi from healthy and declining maple forests[J]. Canadian Journal of Forest Research, 2000, 30(10): 1543-1554.

[44]

KRISHNAMOORTHY

, KIM

, SA TM. Changes of arbuscular mycorrhizal traits and community structure with respect to soil salinity in a coastal reclamation land[J]. Soil Biology and Biochemistry, 2014, 72: 1-10.

[45]

FANG

, LIU

, WANG

, LU

, LI

, YANG

. Electrical conductivity and pH are two of the main factors influencing the composition of arbuscular mycorrhizal fungal communities in the vegetation succession series of Songnen saline-alkali grassland[J]. Journal of Fungi, 2023, 9(9): 870.

[46]

WANG

, LIU

, LIN

, ZHOU

. Arbuscular mycorrhizal status of wild plants in saline-alkaline soils of the Yellow River Delta[J]. Mycorrhiza, 2004, 14(2): 133-137.

[47]

NELSON

, SOMMERS

.Methods of Soil Analysis[M]. 2nd Edition. Madison: American Society of Agronomy, 1982: 539-579.

[48]

JIMENEZ

, LADHA

. Automated elemental analysis: a rapid and reliable but expensive measurement of total carbon and nitrogen in plant and soil samples[J]. Communications in Soil Science and Plant Analysis, 1993, 24(15/16): 1897-1924.

[49]

中华人民共和国生态环境部. 土壤总磷的测定碱熔-钼锑抗分光光度法: HJ 632—2011 [S]. 北京: 中国环境科学出版社, 2012.

Ministry of Ecology and Environment of the People’s Republic of China. Soil-determination of total phosphorus by alkali fusion-Mo-Sb anti spectrophotometric method: HJ 632—2011 [S]. Beijing: China Environmental Science Press, 2012 (in Chinese).

[50]

中华人民共和国生态环境部. 土壤有效磷的测定碳酸氢钠浸提-钼锑抗分光光度法: HJ 704—2014 [S]. 北京: 中国环境科学出版社, 2014.

Ministry of Ecology and Environment of the People’s Republic of China. Soil quality-determination of available phosphorus-sodium hydrogen: HJ 704—2014 [S]. Beijing: China Environmental Science Press, 2014 (in Chinese).

[51]

鲍士旦. 土壤农化分析[M]. 3版. 北京: 中国农业出版社, 2000.

BAO

. Soil and Agricultural Chemistry Analysis[M]. 3rd edition. Beijing: China Agriculture Press, 2000 (in Chinese).

[52]

莎娜, 张三粉, 骆洪, 狄彩霞, 聂晶, 李秀萍, 连海飞. 两种土壤碱解氮测定方法的比较[J]. 内蒙古农业科技, 2014, 42(6): 25-26, 33.

SHA

, ZHANG

, LUO

, DI

, NIE

, LI

, LIAN

. Comparison two kinds of determination method of soil alkaline hydrolysis nitrogen[J]. Inner Mongolia Agricultural Science and Technology, 2014, 42(6): 25-26, 33 (in Chinese).

[53]

中华人民共和国生态环境部. 土壤 pH值的测定电位法: HJ 962—2018 [S]. 北京: 中国环境科学出版社, 2019.

Ministry of Ecology and Environment of the People’s Republic of China. Soil-determination of pH-potentiometry: HJ 962—2018 [S]. Beijing: China Environmental Science Press, 2019 (in Chinese).

[54]

王艳, 王正祥, 廉晓娟, 肖辉, 王立艳, 贺宏达. 天津滨海地区土壤电导率的测定及其与含盐量的关系[J]. 天津农业科学, 2011, 17(2): 18-21

WANG

, WANG

, LIAN

, XIAO

, WANG

, HE

. Measurement of soil electric conductivity and relationship between soluble salt content and electrical conductivity in Tianjin coastal area[J]. Tianjin Agricultural Sciences, 2011, 17(2): 18-21 (in Chinese).

[55]

中华人民共和国生态环境部. 土壤电导率的测定电极法: HJ 802—2016 [S]. 北京: 中国环境科学出版社, 2016.

Ministry of Ecology and Environment of the People’s Republic of China. Soil quality-determination of conductivity-electrode method: HJ 802—2016 [S]. Beijing: China Environmental Science Press, 2016 (in Chinese).

[56]

TAYLOR

, WALTERS

, LENNON

, BOCHICCHIO

, KROHN

, CAPORASO

, PENNANEN

. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for illumina amplicon sequencing[J]. Applied and Environmental Microbiology, 2016, 82(24): 7217-7226.

[57]

IHRMARK

, BÖDEKER

ITM

, CRUZ-MARTINEZ

, FRIBERG

, KUBARTOVA

, SCHENCK

, STRID

, STENLID

, BRANDSTRÖM-DURLING

, CLEMMENSEN

, LINDAHL

. New primers to amplify the fungal ITS2 region-evaluation by 454-sequencing of artificial and natural communities[J]. FEMS Microbiology Ecology, 2012, 82(3): 666-677.

[58]

SCHWARZOTT

, SCHÜßLER

. A simple and reliable method for SSU rRNA gene DNA extraction, amplification, and cloning from single AM fungal spores[J]. Mycorrhiza, 2001, 10(4): 203-207.

[59]

LEE

, LEE

, YOUNG

JPW

. Improved PCR primers for the detection and identification of arbuscular mycorrhizal fungi[J]. FEMS Microbiology Ecology, 2008, 65(2): 339-349.

[60]

SATO

, SUYAMA

, SAITO

, SUGAWARA

. A new primer for discrimination of arbuscular mycorrhizal fungi with polymerase chain reaction-denature gradient gel electrophoresis[J]. Grassland Science, 2005, 51(2): 179-181.

[61]

SIMON

, LALONDE

, BRUNS

. Specific amplification of 18S fungal ribosomal genes from vesicular-arbuscular endomycorrhizal fungi colonizing roots[J]. Applied and Environmental Microbiology, 1992, 58(1): 291-295.

[62]

BOLYEN

, RIDEOUT

, DILLON

, BOKULICH

, ABNET

, AL-GHALITH

, ALEXANDER

, ALM EJ, ARUMUGAM

, ASNICAR

, BAI

, BISANZ

, BITTINGER

, BREJNROD

, BRISLAWN

, BROWN

, CALLAHAN

, CARABALLO-RODRÍGUEZ

, CHASE

, COPE

, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2[J]. Nature Biotechnology, 2019, 37(8): 852-857.

[63]

EDGAR

. Search and clustering orders of magnitude faster than BLAST[J]. Bioinformatics, 2010, 26(19): 2460-2461.

[64]

MARTIN

. Cutadapt removes adapter sequences from high-throughput sequencing reads[J]. EMBnet Journal, 2011, 17(1): 10.

[65]

CAMACHO

, COULOURIS

, AVAGYAN

, MA

, PAPADOPOULOS

, BEALER

, MADDEN

. BLAST+: architecture and applications[J]. BMC Bioinformatics, 2009, 10: 421.

[66]

TEDERSOO

, HOSSEYNI MOGHADDAM

, MIKRYUKOV

, HAKIMZADEH

, BAHRAM

, NILSSON

, YATSIUK

, GEISEN

, SCHWELM

, PIWOSZ

, PROUS

, SILDEVER

, CHMOLOWSKA

, RUECKERT

, SKALOUD

, LAAS

, TINES

, JUNG

, CHOI

, ALKAHTANI

, et al. EUKARYOME the rRNA gene reference database for identification of all eukaryotes[J/OL]. Database: the Journal of Biological Databases and Curation, 2024. https://doi.org/10.1093/database/baae043

[67]

CORE TEAM

. R: a language and environment for statistical computing[EB/OL]. [2024-06-14]. https://www.R-project.org/

[68]

COX DF, GOMEZ

, GOMEZ

. Statistical procedures for agricultural research[J]. Journal of the American Statistical Association, 1985, 80(390): 486.

[69]

WICKHAM

, SIEVERT

. Ggplot2: Elegant Graphics for Data Analysis[M]. New York: Springer, 2009.

[70]

OKSANEN

, KINDT

, LEGENDRE

, O’HARA

, STEVENS

MHH

, OKSANEN

, SUGGESTS

, MARTINEZ-SANTALLA

, MARTIN-DEVASA

, GOMEZ-RODRIGUEZ

, CRUJEIRAS

. The vegan package[J]. Community Ecology Package, 2007, 10(631-637): 719.

[71]

BASELGA

, ORME

, VILLEGER

, de BORTOLI

, LEPRIEUR

, LOGEZ

. Betapart: partitioning beta diversity into turnover and nestedness components[EB/OL]. [2023-03-13]. https://doi.org/10.32614/CRAN.package.betapart

[72]

KEMBEL

, COWAN

, HELMUS

, CORNWELL

, MORLON

, ACKERLY

, BLOMBERG

, WEBB

. Picante: R tools for integrating phylogenies and ecology[J]. Bioinformatics, 2010, 26(11): 1463-1464.

[73]

YANG

, YUAN

, ZHANG

, TANG

, LIU

, CHEN

. Changes in soil organic carbon, total nitrogen, and abundance of arbuscular mycorrhizal fungi along a large-scale aridity gradient[J]. Catena, 2011, 87(1): 70-77.

[74]

LEIFHEIT

, VERESOGLOU

, LEHMANN

, MORRIS

, RILLIG

. Multiple factors influence the role of arbuscular mycorrhizal fungi in soil aggregation: a meta-analysis[J]. Plant and Soil, 2014, 374(1): 523-537.

[75]

, QI

, YU

, XU

, LIU

, DU

, YANG

. Soil pH drives the phylogenetic clustering of the arbuscular mycorrhizal fungal community across subtropical and tropical pepper fields of China[J]. Applied Soil Ecology, 2021, 165: 103978.

[76]

LUO

, WANG

, HE

, LI

, LV

, ZHUANG

. High-throughput sequencing analysis of the rhizosphere arbuscular mycorrhizal fungi (AMF) community composition associated with Ferula sinkiangensis[J]. BMC Microbiology, 2020, 20(1): 335.

[77]

GUO

, GONG

. Differential effects of abiotic factors and host plant traits on diversity and community composition of root-colonizing arbuscular mycorrhizal fungi in a salt-stressed ecosystem[J]. Mycorrhiza, 2014, 24(2): 79-94.

[78]

QIN

, ZHANG

, FENG

, CHRISTIE

, ZHANG

, LI

, GAI

. Soil phosphorus availability modifies the relationship between AM fungal diversity and mycorrhizal benefits to maize in an agricultural soil[J]. Soil Biology and Biochemistry, 2020, 144: 107790.

[79]

LEKBERG

, ARNILLAS

, BORER

, BULLINGTON

, FIERER

, KENNEDY

, LEFF

, LUIS

, SEABLOOM

, HENNING

. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils[J]. Nature Communications, 2021, 12: 3484.

[80]

CAMENZIND

, HEMPEL

, HOMEIER

, HORN

, VELESCU

, WILCKE

, RILLIG

. Nitrogen and phosphorus additions impact arbuscular mycorrhizal abundance and molecular diversity in a tropical montane forest[J]. Global Change Biology, 2014, 20(12): 3646-3659.

[81]

, XU

, GENG

, LUO

, JU

, LI

, ZHOU

. Global arbuscular mycorrhizal fungal diversity and abundance decreases with soil available phosphorus[J]. Global Ecology and Biogeography, 2023, 32(8): 1423-1434.

[82]

SMITH

, READ

. Mycorrhizal Symbiosis[M]. 3rd edition. London: Academic Press, 2010: 11-188.

[83]

, SHI

, DIAO

, HAO

, ZHANG

, XU

, WANG

, DANG

, GUO

. Effects of arbuscular mycorrhizal fungi on growth and Na⁺ accumulation of Suaeda glauca (Bunge) grown in salinized wetland soils[J]. Applied Soil Ecology, 2021, 166: 104065.

[84]

WANG

, WHITE

, LI

. Colonization and community structure of arbuscular mycorrhizal fungi in maize roots at different depths in the soil profile respond differently to phosphorus inputs on a long-term experimental site[J]. Mycorrhiza, 2017, 27(4): 369-381.

[85]

LONDOÑO

DMM

, MEYER

, da SILVA

, HERNÁNDEZ

, de ARMAS

, SOARES

, STÜRMER

, NODARI

, SOARES

CRFS

, LOVATO

. Root colonization and arbuscular mycorrhizal fungal community composition in a genetically modified maize, its non-modified isoline, and a Landrace[J]. Mycorrhiza, 2020, 30(5): 611-621.

[86]

ALGUACIL

, LUMINI

, ROLDÁN

, SALINAS-GARCÍA

, BONFANTE

, BIANCIOTTO

. The impact of tillage practices on arbuscular mycorrhizal fungal diversity in subtropical crops[J]. Ecological Applications, 2008, 18(2): 527-536.

[87]

JANSA

, MOZAFAR

, KUHN

, ANKEN

, RUH R, SANDERS

, FROSSARD

. Soil tillage affects the community structure of mycorrhizal fungi in maize roots[J]. Ecological Applications, 2003, 13(4): 1164-1176.

[88]

ZHU

, FAZLIDDIN

, LI

, WANG

, CHEN

, YANG

, DONG

, LI

, KAKHRAMON

, TOSHKHON

, YU

, XIANG

, GAO

. Contrasting adaptations of soil prokaryotes and arbuscular mycorrhizal fungi in saline wildland and non-saline farmland[J/OL]. Fundamental Research, 2025. https://doi.org/10.1016/j.fmre.2025.02.009

[89]

SCHALAMUK

, CABELLO

. Arbuscular mycorrhizal fungal propagules from tillage and no-tillage systems: possible effects on Glomeromycota diversity[J]. Mycologia, 2010, 102(2): 261-268.

[90]

KABIR

. Tillage or no-tillage: impact on mycorrhizae[J]. Canadian Journal of Plant Science, 2005, 85(1): 23-29.

[91]

NEMERGUT

, SCHMIDT

, FUKAMI

, O’NEILL

, BILINSKI

, STANISH

, KNELMAN

, DARCY

, LYNCH

, WICKEY

, FERRENBERG

. Patterns and processes of microbial community assembly[J]. Microbiology and Molecular Biology Reviews, 2013, 77(3): 342-356.

[92]

CHASE

, MYERS

. Disentangling the importance of ecological niches from stochastic processes across scales[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2011, 366(1576): 2351-2363.

[93]

VELLEND

, SRIVASTAVA

, ANDERSON

, BROWN

, JANKOWSKI

, KLEYNHANS

, KRAFT

NJB

, LETAW

, MACDONALD

AAM

, MACLEAN

, MYERS-SMITH

, NORRIS

, XUE

. Assessing the relative importance of neutral stochasticity in ecological communities[J]. Oikos, 2014, 123(12): 1420-1430.

[94]

GAO

, MONTOYA

, XU

, MADERA

, HOLLINGSWORTH

, PURDOM

, SINGAN

, VOGEL

, HUTMACHER

, DAHLBERG

, COLEMAN-DERR

, LEMAUX

, TAYLOR

. Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics[J]. Nature Communications, 2020, 11: 34.

[95]

FODELIANAKIS

, VALENZUELA-CUEVAS

, BAROZZI

, DAFFONCHIO

. Direct quantification of ecological drift at the population level in synthetic bacterial communities[J]. The ISME Journal, 2020, 15(1): 55-66.

[96]

VACHER

, HAMPE

, PORTÉ

, SAUER

, COMPANT

, MORRIS

. The phyllosphere: microbial jungle at the plant-climate interface[J]. Annual Review of Ecology, Evolution, and Systematics, 2016, 47: 1-24.

[97]

TEDERSOO

. Biogeography of Mycorrhizal Symbiosis[M]. Cham: Springer Nature, 2017.

2025年第65卷第8期

PDF下载

142

引用本文

BibTeX

文章信息

doi: 10.13343/j.cnki.wsxb.20250399

接收时间：2025-05-20
首发时间：2026-02-06
出版时间：2025-08-04

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2025-05-20
录用日期：2025-07-16

基金

Strategic Priority Research Program of Chinese Academy of Sciences(XDA28030401)

中国科学院战略性先导科技专项(XDA28030401)

National Natural Science Foundation of China(32322053)

国家自然科学基金(32322053)

作者信息

^1.中国科学院微生物研究所，微生物多样性与资源创新利用全国重点实验室，北京

^2.中国科学院大学生命科学学院，北京

^3.山东农业大学生命科学学院，山东泰安

通讯作者:

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/wswxb/CN/10.13343/j.cnki.wsxb.20250399

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

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

关闭全屏

BibTeX
EndNote
RefWorks
TxT

Sampling site	Habitat type	Longitude (E)	Latitude (N)
Changling County	Suaeda land	123°27′34″	44°31′16″
Bare land	123°27′15″	44°31′24″
Heavily stressed maize land	123°27′34″	44°31′20″
Moderately stressed maize land	123°27′35″	44°31′21″
Weakly stressed maize land	123°27′35″	44°31′16″
Da’an City	Suaeda land	123°51′09″	45°36′21″
Heavily stressed maize land	123°38′01″	45°25′59″
Weakly stressed maize land	123°51′17″	45°36′37″

Sampling site

Habitat type

Longitude (E)

Latitude (N)

Changling County

Suaeda land

123°27′34″

44°31′16″

Bare land

123°27′15″

44°31′24″

Heavily stressed maize land

123°27′34″

44°31′20″

Moderately stressed maize land

123°27′35″

44°31′21″

Weakly stressed maize land

123°27′35″

44°31′16″

Da’an City

Suaeda land

123°51′09″

45°36′21″

Heavily stressed maize land

123°38′01″

45°25′59″

Weakly stressed maize land

123°51′17″

45°36′37″

Sampling site	Habitat type	Soil physicochemical properties
Changling County	Suaeda land	0.736±0.057	0.052±0.005	0.025±0.002	4.920± 0.626	34.447± 5.663	0.604± 0.597	9.884± 0.090	0.426±0.141
Bareland	0.691±0.097	0.054±0.004	0.028±0.006	6.880± 2.061	42.691± 6.665	1.290± 0.429	9.684± 0.076	0.560±0.226
Heavily stressed maize land	1.117±0.506	0.097±0.033	0.026±0.004	14.100± 4.953	73.899± 18.486	0.761± 0.338	8.980± 0.203	0.126±0.110
Moderately stressed maize land	1.086±0.132	0.097±0.018	0.035±0.014	32.500± 13.915	70.366± 9.676	0.483± 0.096	8.700± 0.116	0.123±0.064
Weakly stressed maize land	0.978±0.286	0.089±0.022	0.037±0.005	11.460± 5.619	68.600± 23.150	0.127± 0.073	8.694± 0.137	0.045±0.012
Da’an City	Suaeda land	1.313±0.086	0.052±0.008	0.031±0.003	45.840± 6.437	26.203± 3.515	10.202± 1.518	10.350± 0.096	4.115±1.120
Heavily stressed maize land	0.873±0.109	0.073±0.019	0.030±0.008	30.160± 22.632	56.529± 14.816	0.882± 0.217	9.898± 0.306	0.817±0.277
Weakly stressed maize land	1.671±0.048	0.132±0.009	0.049±0.007	4.940± 1.467	80.377± 10.441	0.078± 0.007	8.528± 0.083	0.107±0.130

Sampling site

Habitat type

Soil physicochemical properties

(g/100 g)

(mg/kg)

(dS/m)

Salt content

(g/100 g)

Changling County

Suaeda land

0.736±0.057

0.052±0.005

0.025±0.002

4.920±

0.626

34.447±

5.663

0.604±

0.597

9.884±

0.090

0.426±0.141

Bareland

0.691±0.097

0.054±0.004

0.028±0.006

6.880±

2.061

42.691±

6.665

1.290±

0.429

9.684±

0.076

0.560±0.226

Heavily stressed maize land

1.117±0.506

0.097±0.033

0.026±0.004

14.100±

4.953

73.899±

18.486

0.761±

0.338

8.980±

0.203

0.126±0.110

Moderately stressed maize land

1.086±0.132

0.097±0.018

0.035±0.014

32.500±

13.915

70.366±

9.676

0.483±

0.096

8.700±

0.116

0.123±0.064

Weakly stressed maize land

0.978±0.286

0.089±0.022

0.037±0.005

11.460±

5.619

68.600±

23.150

0.127±

0.073

8.694±

0.137

0.045±0.012

Da’an City

Suaeda land

1.313±0.086

0.052±0.008

0.031±0.003

45.840±

6.437

26.203±

3.515

10.202±

1.518

10.350±

0.096

4.115±1.120

Heavily stressed maize land

0.873±0.109

0.073±0.019

0.030±0.008

30.160±

22.632

56.529±

14.816

0.882±

0.217

9.898±

0.306

0.817±0.277

Weakly stressed maize land

1.671±0.048

0.132±0.009

0.049±0.007

4.940±

1.467

80.377±

10.441

0.078±

0.007

8.528±

0.083

0.107±0.130

Sampling site	Habitat type	Soil physicochemical properties
Sampling site	Habitat type	TC (g/100 g)	TN (g/100 g)	TP (g/100 g)	AP (mg/kg)	AN (mg/kg)	EC (dS/m)	pH	Salt content (g/100 g)
Changling County	Suaeda land	0.736±0.057	0.052±0.005	0.025±0.002	4.920± 0.626	34.447± 5.663	0.604± 0.597	9.884± 0.090	0.426±0.141
	Bareland	0.691±0.097	0.054±0.004	0.028±0.006	6.880± 2.061	42.691± 6.665	1.290± 0.429	9.684± 0.076	0.560±0.226
	Heavily stressed maize land	1.117±0.506	0.097±0.033	0.026±0.004	14.100± 4.953	73.899± 18.486	0.761± 0.338	8.980± 0.203	0.126±0.110
	Moderately stressed maize land	1.086±0.132	0.097±0.018	0.035±0.014	32.500± 13.915	70.366± 9.676	0.483± 0.096	8.700± 0.116	0.123±0.064
	Weakly stressed maize land	0.978±0.286	0.089±0.022	0.037±0.005	11.460± 5.619	68.600± 23.150	0.127± 0.073	8.694± 0.137	0.045±0.012
Da’an City	Suaeda land	1.313±0.086	0.052±0.008	0.031±0.003	45.840± 6.437	26.203± 3.515	10.202± 1.518	10.350± 0.096	4.115±1.120
	Heavily stressed maize land	0.873±0.109	0.073±0.019	0.030±0.008	30.160± 22.632	56.529± 14.816	0.882± 0.217	9.898± 0.306	0.817±0.277
	Weakly stressed maize land	1.671±0.048	0.132±0.009	0.049±0.007	4.940± 1.467	80.377± 10.441	0.078± 0.007	8.528± 0.083	0.107±0.130