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Testing Differences Between Pathogen Compositions with Small Samples and Sparse Data.

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  • 1First and second authors: BioSP, INRA, 84914, Avignon, France; second, fourth, fifth, and sixth authors: INRA, UMR1290 Bioger, AgroParisTech, Université Paris-Saclay 78850 Thiverval-Grignon, France; third and tenth authors: INRA, UR0407 Plant Pathology, 84143 Montfavet, France; seventh author: DRAAF Midi-Pyrénées, 31074 Toulouse Cedex, France; eighth author: INRA, UMR BGPI, 34398 Montpellier, France; ninth author: CIRAD, UMR BGPI, 34398 Montpellier, France; and eleventh author: AgroParisTech, UMR1290 Bioger, 78850 Thiverval-Grignon, France.

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Summary

A new generalized Monte Carlo plug-in test with calibration accurately compares pathogen population structures, even with small samples. This method aids plant disease epidemiology and other biological fields facing sparse data challenges.

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Area of Science:

  • Plant Pathology
  • Epidemiology
  • Population Genetics

Background:

  • Pathogen population structure significantly influences crop and natural plant epidemics.
  • Comparing pathogen genotype or phenotype assemblages is a common challenge in plant disease epidemiology.
  • Standard statistical tests may lack precision for small, sparse datasets.

Purpose of the Study:

  • To develop a robust statistical method for comparing two pathogen population structures.
  • To address the limitations of conventional tests with small sample sizes and sparse data.
  • To provide a reliable tool for epidemiological studies.

Main Methods:

  • Development of a generalized Monte Carlo plug-in test with calibration.
  • Implementation of the test in an R package for accessibility.
  • Conducting simulation studies to evaluate performance against standard tests.

Main Results:

  • The proposed Monte Carlo method demonstrates accurate calibration, especially with small sample sizes.
  • Simulation studies confirmed the methodology's effectiveness and provided guidance on application based on sample size.
  • The method was successfully applied to real-world plant pathology datasets.

Conclusions:

  • The generalized Monte Carlo plug-in test offers a reliable solution for comparing pathogen population structures with limited data.
  • The R package provides a practical tool for researchers in plant pathology and related fields.
  • The study offers insights into pathogen reproduction, spatial structure, and recurrence using real case studies.