Testing differences between pathogen compositions with small samples and sparse data

Soubeyrand Samuel, Garreta Vincent, Monteil Caroline, Suffert Frédéric, Goyeau Henriette, Berder Julie, Moinard Jacques, Fournier Elisabeth, Tharreau Didier, Morris Cindy E., Sache Ivan. 2017. Testing differences between pathogen compositions with small samples and sparse data. Phytopathology, 107 (10) : pp. 1199-1208.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Url - jeu de données :

Quartile : Q1, Sujet : PLANT SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie

Abstract : The structure of pathogen populations is an important driver of epidemics affecting crops and natural plant communities. Comparing the composition of two pathogen populations consisting of assemblages of genotypes or phenotypes is a crucial, recurrent question encountered in many studies in plant disease epidemiology. Determining whether there is a significant difference between two sets of proportions is also a generic question for numerous biological fields. When samples are small and data are sparse, it is not straightforward to provide an accurate answer to this simple question because routine statistical tests may not be exactly calibrated. To tackle this issue, we built a computationally intensive testing procedure, the generalized Monte Carlo plug-in test with calibration test, which is implemented in an R package available at A simulation study was carried out to assess the performance of the proposed methodology and to make a comparison with standard statistical tests. This study allows us to give advice on how to apply the proposed method, depending on the sample sizes. The proposed methodology was then applied to real datasets and the results of the analyses were discussed from an epidemiological perspective. The applications to real data sets deal with three topics in plant pathology: the reproduction of Magnaporthe oryzae, the spatial structure of Pseudomonas syringae, and the temporal recurrence of Puccinia triticina.

Mots-clés Agrovoc : Agent pathogène, Génétique des populations, Épidémiologie, Contrôle de maladies, Plante de culture, Échantillonnage, Modèle mathématique, Étude de cas, Oryza sativa, Magnaporthe grisea, Pseudomonas syringae, Puccinia recondita, Donnée statistique

Mots-clés géographiques Agrovoc : France, Madagascar, Chine

Classification Agris : H20 - Plant diseases
U30 - Research methods

Champ stratégique Cirad : Axe 4 (2014-2018) - Santé des animaux et des plantes

Agence(s) de financement européenne(s) : European Commission

Programme de financement européen : FP7

Projet(s) de financement européen(s) : Plant and Food Biosecurity

Auteurs et affiliations

  • Soubeyrand Samuel, INRA (FRA)
  • Garreta Vincent, INRA (FRA)
  • Monteil Caroline, INRA (FRA)
  • Suffert Frédéric, INRA (FRA)
  • Goyeau Henriette, INRA (FRA)
  • Berder Julie, INRA (FRA)
  • Moinard Jacques, AgroParisTech (FRA)
  • Fournier Elisabeth, INRA (FRA)
  • Tharreau Didier, CIRAD-BIOS-UMR BGPI (FRA) ORCID: 0000-0003-3961-6120
  • Morris Cindy E., INRA (FRA)
  • Sache Ivan, INRA (FRA)

Source : Cirad-Agritrop (

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