Assessing plant pathogen infection rates in natural soils using R

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1 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
2 Institute of Ecology, Friedrich-Schiller Universität Jena, Jena, Germany
3 Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
4 Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
5 Department of Animal Ecology, J.F. Blumenbach Institute of Zoology and Anthropology, Georg-August Universität Göttingen, Germany
DOI
10.7287/peerj.preprints.2156v1
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
Rall BC, Latz E. 2016. Assessing plant pathogen infection rates in natural soils using R. PeerJ Preprints 4:e2156v1

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.

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