Analyzing pathogen suppressiveness in bioassays with natural soils using integrative maximum likelihood methods in R

Area of Science:

• Soil microbiology
• Plant pathology
• Ecosystem function analysis

Background:

• Soil suppressiveness is a key ecosystem function for controlling plant pathogens.
• Traditional infection assays struggle with co-occurring pathogens, leading to inaccurate soil suppressiveness estimations.
• Accurate measurement of soil suppressiveness is crucial for understanding soil health and agricultural sustainability.

Purpose of the Study:

• To develop a novel statistical method for accurately estimating soil suppressiveness in the presence of multiple plant pathogens.
• To correct for confounding effects of co-infections in pathogen infection assays.
• To provide a reliable tool for assessing the suppressive potential of natural soils.

Main Methods:

• Development of a two-pathogen mono-molecular infection model in R.
• Integration of numerical simulation with iterative maximum likelihood fitting.
• Application of the model to correct infection parameters affected by co-occurring pathogens.

Main Results:

• Uncorrected data significantly underestimates or overestimates soil suppressiveness when multiple pathogens are present.
• The new model accurately estimates plant infection rates and plant resistance times.
• The method provides necessary corrections for infection parameters in multi-pathogen environments.

Conclusions:

• The developed R method precisely quantifies soil suppressiveness, overcoming limitations of traditional assays.
• This approach is vital for accurate soil health assessments in diverse natural soil environments.
• The model's adaptability and potential extensions enhance its utility for future ecological studies.