Assessing plant pathogen infection rates in natural soils using R
- Published
- Accepted
- Subject Areas
- Agricultural Science, Biodiversity, Microbiology, Plant Science, Statistics
- Keywords
- infected control treatments, ordinary differential equation, biodiversity, maximum likelihood estimation, numerical simulation, soil resistance, bioassay, R, bbmle, deSolve, Manual
- Copyright
- © 2016 Rall et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Assessing plant pathogen infection rates in natural soils using R. PeerJ Preprints 4:e2156v1 https://doi.org/10.7287/peerj.preprints.2156v1
Abstract
The potential of soils to naturally suppress inherent plant pathogens is an important ecosystem function. Usually, pathogen infection assays are used for estimating the suppressive potential of soils. In natural soils, however, co-occurring pathogens might simultaneously infect plants complicating the estimation of a focal pathogen's infection rate as a measure of soil suppressiveness. Here, we present a method in R correcting for these unwanted effects by developing a two pathogen mono-molecular infection model. We fit the two pathogen mono-molecular infection model to data by using an integrative approach combining a numerical simulation of the model with an iterative maximum likelihood fit. We show that in presence of co-occurring pathogens uncorrected data critically under- respectively overestimate soil suppressiveness measures. In contrast, our new approach enables to precisely estimate soil suppressiveness measures such as plant infection rate and plant resistance time. Our method allows a correction of measured infection parameters that is necessary in case different pathogens are present. We propose our method to be particularly useful for exploring soil suppressiveness of natural soils from different sites (e.g., in biodiversity experiments).
Author Comment
This is a submission to PeerJ for review.