A meta-analysis reveals that the protective role of silicon in grasses against fungal pathogens depends on infection mechanism
Data files
May 20, 2026 version files 2.90 MB
-
Meta_Analysis_Files.zip
2.90 MB
-
README.md
2.29 KB
Abstract
Silicon (Si) can improve plant resistance to abiotic and biotic stresses, particularly in grasses. However, the ability of Si to mitigate infections caused by pathogens with diverse infection strategies remain unclear. Moreover, while Si accumulation in plants can be increased through the application of Si fertilisers, the type, and dosage of fertiliser used may influence the extent of the protective effect of Si against pathogens. We performed a meta-analysis of 114 studies that provided 2872 observations and on 14 grass host species and 31 of their pathogens to provide quantitative assessment of the benefits of Si fertilisation for improving disease resistance in grasses. The potential mechanisms underlying changes in disease severity were investigated by analysing the impacts of Si on pathogens with different modes of infection and colonisation. On average, Si fertilisation decreased disease severity by 43% and increased grass biomass by 35%. The Si effect varied depending on both the plant and pathogen species and experimental conditions, with the benefits particularly pronounced for rice and in controlled environment studies. The disease suppressive effect of Si was greater for pathogens that produce an appressorium, a structure involved in cell wall penetration and the release of effector proteins, and reduced for pathogens that enter through the stomata.
Dataset DOI: 10.5061/dryad.gmsbcc31c
Description of the data and file structure
This dataset contains all the data and code required to replicate the meta-analysis as published in Thorne et al 2026 Plant Cell & Environment. The complete extracted meta-data from the studies included in the analysis is contained in Si_Pathogen_Data.csv. Meta-Analysis.qmd is a quarto file that can be run in R to repeat the analysis. This dataset is provided to allow for the full replication of the findings of this study and to serve as a resource for future research.
Files and variables
Meta_Analysis_Files.zip is a zip file containing all the files needed to repeat the meta-analysis. Specifically, within this folder are:
- Meta-Analysis for Dryad PCE.Rproj: R project file to run meta-analysis in
- Si_Pathogen_Data.csv: complete meta-data extracted from studies included in meta-analysis
- Meta-Analysis.qmd: R file used to carry out meta-analysis (see Meta-Analysis.html file for the complete code output, including extra analyses not included in the published manuscript)
- Readme.txt: explanation of column headings from Si Pathogen Data.csv and Dpi_Only.csv
- Dpi_Only.csv: Subset of information included in main meta-data file that allows analysis focusing specifically on studies that measured disease severity on multiple dats
- Grass_Tree_Dec25.tre: File needed to make phylogenetic tree to include phylogeny in analysis
- Renv folder, renv.lock, and .Rprofile are files needed to specify the specific version of R packages used
Renv has been used to capture the specific versions of R packages used (renv folder, renv.lock)
Code/software
The meta-analysis was conducted in R. The script used is a quarto file. By downloading the renv package, it is possible to download the exact version of the R libraries used for this analysis, otherwise the required packages are indicated at the start of the script.
Access information
Data was derived from the following sources:
- Data extracted from published studies (all listed in reference list of published manuscript and within Si_Pathogen_Data.csv file)
