罗布斯塔咖啡果表与土壤微生物群落构建机制及跨生态位网络关联研究

doi:10.13560/j.cnki.biotech.bull.1985.2025-1455

摘要/Abstract

摘要：

目的探究罗布斯塔咖啡（Coffea canephora）果表与土壤间微生物群落构建机制及跨生态位网络关联特征。方法采用qPCR和高通量测序技术分析咖啡果（CF）、非根际土（CRBS）和根际土（CRS）的微生物群落特征，并进行溯源、群落构建和跨生态位关联网络分析。结果微生物群落丰度和多样性从土壤到咖啡果表显著递减，咖啡果与土壤微生物群落结构差异显著（P<0.05）。土壤对果表群落溯源贡献率低于6%。群落构建表现出显著差异，土壤原核生物群落构建主要受确定性过程主导，而果表则为随机性过程主导；真菌普遍受扩散限制主导。跨生态位关联网络呈高模块化特征，螺状菌属（Spirosoma）和粪盘菌属（Ascobolus）是土壤与咖啡果表潜在功能协同与环境响应的核心关键枢纽。结论海南罗布斯塔咖啡果表微生物群落具有显著区别于土壤的群落结构构建特征，但两者通过特定的关键物种呈现潜在功能协同。在未来的咖啡农业管理中，可以关注并调控如螺状菌属和粪盘菌属等关键跨生态位关联网络节点。

关键词: 罗布斯塔咖啡, 生态位分化, 微生物溯源, 群落构建, 跨生态位网络

Abstract:

Objective The microbiome associated with Robusta coffee (Coffea canephora) plays a critical role in plant health and quality. However, the community assembly mechanisms and the connectivity between the coffee cherry surface and soil niches remain poorly understood. This study aimed to elucidate the microbial community construction mechanisms and characterize the cross-niche correlation network between the coffee cherry surface and the underlying soil environment. Method Quantitative PCR (qPCR) and high-throughput sequencing technologies were employed to analyze the microbial community characteristics across three distinct niches: Coffee cherry surfaces (CF), coffee bulk soil (CRBS), and coffee rhizosphere soil (CRS). Subsequently, comprehensive bioinformatic analyses were performed, including fast expectation-maximization for microbial source tracking, community assembly process modeling, and cross-niche correlation network construction. Result The microbial community abundance and diversity exhibited a significant decreasing gradient from soil to coffee cherry. Significant differences in community structure were observed between the coffee cherry and soil compartments (P<0.05). Source tracking analysis revealed that the soil contribution to the coffee cherry surface community was less than 6%, suggesting a distinct origin for the coffee cherry surface microbiome. Furthermore, the community assembly processes differed significantly across niches: soil prokaryotic community assembly was primarily governed by deterministic processes, whereas the coffee cherry surface community was dominated by stochastic processes. Fungal communities, conversely, were generally driven by dispersal limitation across all niches. The cross-niche correlation network displayed a highly modular architecture, identifying Spirosoma and Ascobolus as core keystone taxa. These taxa acted as critical hubs, facilitating potential functional synergy and environmental responses between the soil and coffee cherry surface. Conclusion This study demonstrates that the microbial community on Hainan C. canephora cherry surfaces possesses a distinct stochastic assembly pattern that differs significantly from soil communities. Despite these structural differences, potential functional synergy exists between these niches, mediated by specific keystone taxa. We conclude that future coffee agricultural management should prioritize the regulation of key cross-niche network nodes, such as Spirosoma and Ascobolus.

Key words: Robusta coffee, niche differentiation, microbial source tracking, community assembly, cross-niche network

邳文治, 王群, 刘鑫媛, 王朱珺. 罗布斯塔咖啡果表与土壤微生物群落构建机制及跨生态位网络关联研究[J]. 生物技术通报, 2026, 42(5): 147-157.

PI Wen-zhi, WANG Qun, LIU Xin-yuan, WANG Zhu-jun. Community Assembly Mechanisms and Cross-niche Network Correlation between Robusta Coffee Cherry Surface and Soil Microbiomes[J]. Biotechnology Bulletin, 2026, 42(5): 147-157.

图/表 5

图1 咖啡果与土壤微生物群落多样性和结构差异原核生物（A）和真菌（B）丰度（qPCR基因拷贝数）。误差棒代表标准差（SD），不同小写字母表示基于ANOVA在P<0.05水平的差异，下同。原核生物（C）和真菌（D）在门水平微生物群落组成。LEfSe分析原核生物（E）和真菌（F）特征生物标记物（LDA>4.0，P<0.05）。原核生物（G）和真菌（H）基于Bray-Curtis距离的主坐标分析（PCoA）。图中数值为基于PERMANOVA检验的F值与P值（*** P<0.001）。CF：咖啡果（n=18）；CRBS：非根际土（n=6）；CRS：根际土（n=6），下同

Fig. 1 Diversity and structural differences of coffee cherry and soil microbial communitiesAbundance of prokaryotes (A) and fungi (B) based on qPCR gene copy numbers. Error bars indicate the standard deviation (SD). Different lowercase letters indicate significant differences based on ANOVA (P<0.05). The same below. Microbial community composition of prokaryotes (C) and fungi (D) at the phylum level. LEfSe analysis identifies characteristic biomarkers for prokaryotes (E) and fungi (F) (LDA score>4.0, P<0.05). Principal Coordinate Analysis (PCoA) of prokaryotic (G) and fungal (H) communities based on Bray-Curtis distances. The values displayed in the plots represent F-values and P-values derived from PERMANOVA tests (*** P<0.001). CF, Coffee cherry (n = 18); CRBS, bulk soil (n = 6); CRS, rhizosphere soil (n = 6), the same below

表1 跨生态位关联网络属性

Table 1 Properties of the cross-niche correlation networks

组别 Group		网络属性 Network property
组别 Group		节点 Nodes	边 Edges	正相关边 Positive edges	平均度 Average degree	平均路径长度 Average path length	网络直径 Network diameter	连通度 Connectivity	模块化 Modularity
原核生物	CF-CRBS	679	1009	625	2.97	1.65	4	0.0044	0.97
	CF-CRS	661	1107	652	3.35	1.94	4	0.0032	0.95
	CRBS-CRS	795	2380	1310	5.99	1.90	7	0.0075	0.92
真菌	CF-CRBS	602	752	370	2.50	1.47	2	0.0042	0.98
	CF-CRS	707	1120	559	3.17	2.73	8	0.0045	0.95
	CRBS-CRS	765	1568	911	4.10	2.94	8	0.0054	0.94

图2 咖啡果与土壤微生物溯源及基于系统发育的零模型群落构建差异原核生物（A）和真菌（B）基于FEAST模型的溯源分析。原核生物（C，E）和真菌（D，F）的βNTI和群落构建过程比例

Fig. 2 Microbial source tracking and differences in community assembly based on phylogeny-based null models in coffee cherry and soilFast expectation-maximization for microbial source tracking of prokaryotic (A) and fungal (B) communities. The βNTI values and proportions of community assembly processes for prokaryotes (C, E) and fungi (D, F)

图3 咖啡果-土壤微生物跨生态位关联网络原核生物（A-C）和真菌（D-F）跨生态位关联网络

Fig. 3 Cross-niche correlation networks of coffee cherry- soil microbiomesCross-niche correlation networks of prokaryotic (A-C) and fungal (D-F) communities

图4 咖啡果-土壤微生物跨生态位关联网络关键节点及其子网络原核生物（A-C）和真菌（D-F）咖啡果与土壤跨生态位关键节点，跨域网络中关键物种的判定基于节点在模块内的连通度（Zi ）和模块间的连通度（Pi ），模块中心点（Module hubs，Zi >2.5）。原核生物（G-I）和真菌（J-K）咖啡果与土壤跨生态位关键节点子网络

Fig. 4 Keystone nodes and their subnetworks in the coffee cherry-soil microbial cross-niche correlation networkCross-niche keystone nodes for prokaryotes (A-C) and fungi (D-F) in coffee cherry and soil environments. In the cross-domain networks, keystone nodes were identified based on their within-module connectivity (Zi ) and among-module connectivity (Pi ). Module hubs were defined as nodes with Zi >2.5. Subnetworks of these cross-niche keystone nodes are shown for prokaryotes (G-I) and fungi (J-K)

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