4 years ago

Invasion ecology applied to inoculation of plant growth promoting bacteria through a novel SIMPER-PCA approach

Volker F. Wendisch, Carolina Weigert Galvão, Alexander Sczyrba, André Gustavo Battistus, Pedro Beschoren da Costa, Samanta Bolzan de Campos, Rafael Mazer Etto, Andreas Albersmeier, Odair José Andrade Pais dos Santos, Paul Dirksen, Luciane Maria Pereira Passaglia, Vandeir Francisco Guimarães, André Luiz Martinez de Oliveira, Karina Maria Lima Milani, André Luis Pereira Dresseno, Andréia Cristina Peres Rodrigues da Costa



Plant growth promoting bacteria (PGPB) have been used on crops for years, but inoculants that are efficient in some locations may not be efficient in others. Here, we applied classical invasion ecology theory to PGPB inoculation in order to identify patterns that can be used to predict plant growth promoting (PGP) efficiency. The hypotheses that the inoculant that causes most impact will be the most efficient PGPB, and that the most invasible locations would have higher PGP efficiency, were tested. We also aim to present our statistical approach to analyze SIMPER results.


Using next generation sequencing targeting the 16S rDNA gene in metagenomics samples, we analyzed samples of pre-planting bulk soil and rhizosphere of inoculated maize plants. Bacterial communities of inoculated plants were compared to the non-inoculated controls, in order to estimate the inoculant invasion impact. Crop yield was compared to different indexes, and a novel data exploration approach was employed.


The most efficient inoculant was not the most invasive, and a nutrient per diversity ratio was unable to predict inoculant efficiency or invasion impact. However, the efficient inoculation treatment presented an enrichment of specific pre-planting taxa.


Invasion ecology frameworks could not anticipate field results of inoculated plants. Nonetheless, our data exploration approach, which is explained in detail, can be useful to raise new hypothesis and improve the visualization of dissimilarity data.

Publisher URL: https://link.springer.com/article/10.1007/s11104-017-3492-6

DOI: 10.1007/s11104-017-3492-6

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