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C. Bilan Cet article indique que l’hypothèse la plus simple mentionnée en introduction (A1i) n’est pas recevable : les motifs observés ne sont pas compatibles avec une seule transmission pour chaque vecteur arrivant de façon aléatoire et indépendante des plantes malades, même si on tient compte de l’hétérogénéité des variétés dans ce “méta-verger” ou si on ne regarde que les contaminations finales (pour éluder la question du matériel initial potentiellement infecté). Ce qui apparaît encore plus clairement, c’est que la combinaison porte-greffe/cultivar a un impact crucial sur le développement temporel de la maladie, et plus particulièrement sur la durée avant l’observation des premiers arbres malades (et non sur l’incidence cumulée finale, puisque dans 3 des 4 vergers, l’incidence cumulée 17 ans après plantation est sensiblement la même). Les explications les plus cohérentes avec ces caractéristiques sont l’effet de la combinaison porte-greffe/cultivar sur la durée d’incubation, et/ou la plantation de matériel contaminé dans 2 des 4 vergers. Pour aller plus loin dans l’analyse, on a besoin de tester si la localisation des plantes malades dépend des plantes symptomatiques aux dates précédentes. Il s’agit donc cette fois de tester l’indépendance spatiale entre deux ensembles de points qui sont eux-mêmes potentiellement structurés spatialement. Ce test existe déjà en milieu continu, mais pas sur des grilles régulières comme les vergers, où il faut corriger la statistique de test pour qu’elle prenne en compte la censure générée par les arbres manquants et par les arbres malades à la première date (qui ne guérissent pas). L’article suivant présente la méthode qui a été développée pour surmonter les difficultés propres à ce type de données. III. Article VI : “Investigating Disease Spread Between Two Dates with Permutation Tests on a Lattice” Gaël Thébaud, Nathalie Peyrard, Sylvie Dallot, Agnès Calonnec et Gérard Labonne (Phytopathology, sous presse) - 96 -
Investigating Disease Spread between Two Assessment Dates with Permutation Tests on a Lattice Gaël Thébaud, Nathalie Peyrard, Sylvie Dallot, Agnès Calonnec, and Gérard Labonne First, third, and fifth authors: Institut national de la recherche agronomique (INRA), UMR BGPI, CIRAD TA 41/K, Campus international de Baillarguet, 34398 Montpellier Cedex 5, France; first and second authors: INRA, Unité de Biométrie, Domaine Saint-Paul, Site Agroparc, 84914 Avignon Cedex 9, France; and fourth author: INRA, UMR Santé Végétale, 71 avenue Edouard Bourlaux, BP 81, 33883 Villenave-d’Ornon Cedex, France. Corresponding author: Gaël Thébaud; E-mail address: thebaud@ensam.inra.fr ABSTRACT Thébaud, G., Peyrard, N., Dallot, S., Calonnec, A., and Labonne, G. xxxx. Investigating disease spread between two assessment dates with permutation tests on a lattice. Phytopathology #:xxx-xxx. Mapping and analyzing the disease status of individual plants within a study area at successive dates can give insight into the processes involved in the spread of a disease. We propose a permutation method to analyze such spatiotemporal maps of binary data (healthy or diseased plants) in regularly spaced plantings. It requires little prior information on the causes of disease spread and handles missing plants and censored data. A Monte Carlo test is used to assess whether the location of newly diseased plants is independent of the location of previously diseased plants. The test takes account of the significant spatial structures at each date in order to separate nonrandomness caused by the structure at one date from nonrandomness caused by the dependence between newly diseased plants and previously diseased plants. If there is a nonrandom structure at both dates, independent patterns are simulated by randomly shifting the entire pattern observed at the second date. Otherwise, independent patterns are simulated by randomly reallocating the positions of one group of diseased plants. Simulated and observed patterns of disease are then compared through distance-based statistics. The performance of the method and its robustness are evaluated by its ability to accurately identify simulated independent and dependent bivariate point patterns. Additionally, two real-world spatiotemporal maps with contrasting disease progress illustrate how the tests can provide valuable clues about the processes of disease spread. This method can supplement biological investigations and be used as an exploratory step before developing a specific mechanistic model. Additional keywords: censoring, distance class, European stone fruit yellows, nonparametric, Plum pox virus, toroidal shift. One of the main goals of plant disease epidemiologists is to understand the underlying processes of epidemics. When the epidemiology of a disease is poorly known, for example in the case of an emerging disease (2,37), the initial questions often include the following: How to summarize the progress of the disease in time and space? Is there secondary spread at the scale considered, i.e., do previously diseased plants provide the inoculum that infects the newly diseased plants? What are the biological processes responsible for disease spread? Answering these questions can help identify control strategies, design sampling procedures (21,23), or build mechanistic models (15). Understanding plant disease epidemiology was initially based on the analysis of the temporal progress of diseases (41), with the introduction of correction factors (basically, a reduction of the rate of disease progress) to take into account the effect of disease clustering - 97 -
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Ecole Nationale Supérieure Agronom
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Glossaire A leur première occurren
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C. Conclusions sur l’étude des v
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Introduction - 7 - « Deux grands m
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l’intensification permanente de l
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Ceci nous amène aux enjeux liés
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(b) Coût de la lutte contre l’ES
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Royaume- Uni Espagne Allemagne Belg
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1992) ; par contre, le cerisier dou
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6) Vection Le vecteur de l’ESFY (
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(b) Caractéristiques de la vection
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Conifères ? Rétention du phytopla
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Partie I : Identifier des facteurs
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Identifying Risk Factors from a Sur
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MATERIALS AND METHODS Data Record a
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visual inspection of their spatial
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Influence of the Risk Factors. The
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Model Application This kind of mode
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ACKNOWLEDGEMENTS We are much indebt
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42. Seemüller, E., and Schneider,
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TABLE 5: Analysis of deviance for e
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Fig. 2. Examination of the model fi
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Page 137 and 138: Références bibliographiques - 137
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Annexes - 147 -
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if (estim!=1) W2.99) text(B2,0,past
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if ((sum(Do[1:4])!=0)&(sum(do1D)==0
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III. Annexe 3 : Programme R pour si
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IV. Annexe 4 : “Testing Boolean A
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Testing boolean assumption in the n
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ehavior, which is difficult to obse
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distributed. So, the length distrib
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ule which transforms the tangent po
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large void segments will clearly ap
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together with local measurements at
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Kamphorst E.C. , Chadøeuf J. , Jet
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c.d.f 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2
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0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4
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c.d.f. c.d.f. c.d.f. 0.0 0.2 0.4 0.
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RESUME Les maladies (ré-)émergent
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