Abstract
Aims
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.
Methods
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.
Results
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.
Conclusions
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.
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Acknowledgements
This work was financed by grant and fellowships from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/Brazil) and Instituto Nacional de Ciência e Tecnologia (INCT) da Fixação Biológica do Nitrogênio (Brazil) for Brazilian researchers; Deutscher Akademischer Austauschdienst (DAAD/Germany) for PBdC; Bielefeld Young Researchers Fund for SBC. The bioinformatics support of the BMBF-funded project “Bielefeld-Giessen Center for Microbial Bioinformatics – BiGi (Grant number031A533)” within the German Network for Bioinformatics Infra-structure (de.NBI) is gratefully acknowledged.
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Fig. S1
Relative Phyla (p_) distribution per sample. Phyla legend is in the order of the most common to the rarest. L = Londrina; P = Ponta Grossa; M = Marechal Cândido Rondon; 0 = pre-planting; 1 = non-inoculated control; 2 = Azospirilum brasilisense Ab-V5; 3 = Achromobacter sp. VC36; 4 = Pseudomonas sp. 4311; 5 = Pseudomonas sp. 4312; a = first replicate; b = second replicate (GIF 49 kb)
Fig. S2
SIMPER percentages from the treatment-control pairs, at each taxonomic level. Color gradient shows lower values in red, higher values in green. L = Londrina; P = Ponta Grossa; M = Marechal Cândido Rondon; 2 = Azospirillum brasilense Ab-V5; 3 = Achromobacter sp. VC36; 4 = Pseudomonas sp. 4311; and 5 = Pseudomonas sp. 4312 (GIF 38 kb)
Fig. S3
SIMPER dissimilarity data from Online Resource 4 treated under a NMDS instead of a PCA. (a) NMDS based on Euclidian distance, with an output similar to the SIMPER-PCA approach (b) Shepard plot showing fit and stress level for the NMDS solution based on Euclidean distance (c) NMDS based on the Bray-Curtis dissimilarity, calculating a dissimilarity metric on dissimilarity data, showing clear artifacts (d) Shepard plot showing fit and stress level for the NMDS solution based on Bray-Curtis dissimilarity. Red = Londrina (L); Blue = Ponta Grossa (P); black = Marechal Cândido Rondon (M); full circle = Azospirillum brasilense Ab-V5 (2); empty circle = Achromobacter sp. VC36 (3); empty square = Pseudomonas sp. 4311 (4); and full square = Pseudomonas sp. 4312 (5) (GIF 25 kb)
Fig. S4
Example of the SIMPER-PCA approach at OTU level without transforming the signal as suggested in the text. Different numbers represent different OTUs. (GIF 34 kb)
Fig. S5
Histograms for frequencies of the average dissimilarities calculated from SIMPER tests as given by the PAST output (a) and with the signal modification (b). These values are presented on Online Resource 4. The curved line shows the theoretical normal distribution for the dataset. The lower value for the central bin in part (b), that might resemble a dent in the normal distribution, shows that there are not many taxa that explain very little variance. This happens because of the minimum frequency OTU cut off. OTUs with less than 200 occurrences were removed from the dataset, exactly because they would explain little variance and increase background noise. Without this cut-off, the central bin in this figure would be much higher, total variance explained would be lower, but the clustering of the datapoints in the SIMPER-PCA approach would not differ drastically. (GIF 11 kb)
Online Resource 1
Full dataset of crop productivity. At location column: L = Londrina; P = Ponta Grossa; M = Marechal Cândido Rondon. At inoculant column: 1 = non-inoculated control; 2 = Azospirillum brasilense Ab-V5; 3 = Achromobacter sp. VC36; 4 = Pseudomonas sp. 4311; and 5 = Pseudomonas sp. 4312. At block column: blocks one to four, for ANOVA analysis. (XLSX 15 kb)
Online Resource 2
Full sequencing results (absolute values) for every read at all at taxa levels. At sample column: L = Londrina; P = Ponta Grossa; M = Marechal Cândido Rondon, 1 = non-inoculated control; 2 = Azospirillum brasilense Ab-V5; 3 = Achromobacter sp. VC36; 4 = Pseudomonas sp. 4311; and 5 = Pseudomonas sp. 4312; a = first replicate; b = second replicate. (XLSX 869 kb)
Online Resource 3
Some of the SIMPER percentages from the treatment-control pairs, at OTU level. The full signaled results, used for Fig. 4, are presented on Online Resource 4. Av. dissim. = taxa average dissimilarity; contrib. % = contribution of that taxa to total dissimilarity; Cumul. % = cumulative total dissimilarity; Mean abundances = normalized and transformed counts of each taxa for control and treatments; p_ = Phyla; c_ = Class; o_ = Order; f_ = Family; g_ = genera. (XLSX 17 kb)
Online Resource 4
Signal transformed SIMPER dissimilarities from the treatment-control pairs, at OTU level, used to create Fig. 4 and Fig. S3. L = Londrina; P = Ponta Grossa; M = Marechal Cândido Rondon. At inoculant column: 1 = non-inoculated control; 2 = Azospirillum brasilense Ab-V5; 3 = Achromobacter sp. VC36; 4 = Pseudomonas sp. 4311; and 5 = Pseudomonas sp. 4312.; p_ = Phyla; c_ = Class; o_ = Order; f_ = Family; g_ = genera. (XLSX 360 kb)
Online Resource 5
SIMPER values for each taxon at pre-planting conditions, using Londrina as the standard compared to Marechal Cândido Rondon (a) and Ponta Grossa (b), both at OTU level. (XLSX 339 kb)
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da Costa, P.B., de Campos, S.B., Albersmeier, A. et al. Invasion ecology applied to inoculation of plant growth promoting bacteria through a novel SIMPER-PCA approach. Plant Soil 422, 467–478 (2018). https://doi.org/10.1007/s11104-017-3492-6
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DOI: https://doi.org/10.1007/s11104-017-3492-6