Department of Sustainability Sciences, El Colegio de la Frontera Sur Unidad Campeche, Av. Rancho Polígono 2-A Col. Ciudad Industrial, Lerma, CP 24500, Campeche, Campeche, México.
Agrobiologia School, Universidad Michoacana de San Nicolás de Hidalgo, CP 6017, Uruapan, Michoacán, México.
Curr Microbiol. 2021 Sep;78(9):3417-3429. doi: 10.1007/s00284-021-02603-9. Epub 2021 Jul 10.
Bacterial communities have been identified as functional key members in soil ecology. A deep relation with these communities maintains forest coverture. Trees harbor particular bacteriomes in the rhizosphere, endosphere, or phyllosphere, different from bulk-soil representatives. Moreover, the plant microbiome appears to be specific for the plant-hosting species, varies through season, and responsive to several environmental factors. This work reports the changes in bacterial communities associated with dominant pioneer trees [Tabebuia rosea and Handroanthus chrysanthus [(Bignoniaceae)] during tropical forest recovery chronosequence in the Mayan forest in Campeche, Mexico. Massive 16S sequencing approach leads to identifying phylotypes associated with rhizosphere, bulk-soil, or recovery stage. Lotka-Volterra interactome modeling suggests the presence of putative regulatory roles of some phylotypes over the rest of the community. Our results may indicate that bacterial communities associated with pioneer trees may establish more complex regulatory networks than those found in bulk-soil. Moreover, modeled regulatory networks predicted from rhizosphere samples resulted in a higher number of nodes and interactions than those found in the analysis of bulk-soil samples.
细菌群落已被确定为土壤生态系统中的功能关键成员。它们与这些群落的深度关系维持着森林的覆盖。树木在根际、内共生或叶际中拥有特殊的细菌组,与普通土壤中的代表不同。此外,植物微生物组似乎对植物宿主物种具有特异性,随季节变化而变化,并对多种环境因素有响应。本研究报告了与主导先锋树种(Tabebuia rosea 和 Handroanthus chrysanthus [(Bignoniaceae)])相关的细菌群落随时间变化的情况,这些树种在墨西哥坎佩切的玛雅森林中处于热带森林恢复的时间序列中。大规模的 16S 测序方法可识别与根际、普通土壤或恢复阶段相关的分类群。Lotka-Volterra 相互作用网络模型表明,一些分类群可能对群落的其余部分具有潜在的调节作用。我们的研究结果表明,与先锋树种相关的细菌群落可能比普通土壤中发现的群落建立更复杂的调控网络。此外,从根际样本预测的模型化调控网络的节点和相互作用数量比从普通土壤样本分析中发现的要多。