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DENS (r = -0.29). As expected, OCLV and ORST were almost completely correlated (Fig. 1E-F and 1K). Additionally, these variables were strongly unbalanced and included 71 missing values; hence they had to be removed. The supplementary variable GRW was clearly structured by the other variables (Fig. 1B), and thus strongly correlated to them. As we were more interested in these underlying correlates, the descriptive variable GRW was not included in the model until the ultimate step of the analysis. Finally, Fig. 1K showed that no explicative factor or variable was obviously correlated to the dependent variable Y, further highlighting the need for an explicative model. GLM. An initial GLM was built with the 5 remaining variables and their biologically meaningful interactions. AREA was then dropped because it was not significant (P = 0.24, chi-square test on 1 df). The deviance of the resulting model was much higher than twice the number of degrees of freedom, a value that is used as a rule of thumb for the detection of overdispersion (31). As the analysis of the residuals indicated no obvious problem with the model, intra-orchard dependence was the most probable cause of overdispersion. Thus the model was corrected to allow extrabinomial variation (φ = 0.042) so as not to overestimate the significance of the effects. As the remaining 4 variables and 4 interactions were significant (all P-values < 4.3×10 -3 ), the final model for pi was thus: ln(pi/(1-pi)) = a0 + a1,i (CLV) + a2,i (RST) + a3×AGEi + a4×DENSi + a5,i (CLV:AGE)×AGEi + a6,i (RST:AGE)×AGEi + a7,i (RST:DENS)×DENSi + a8×AGEi×DENSi. This model was then checked. One point appeared to be overly influential on the basis of Cook’s distance (9). As the analyses provided consistent results with or without this point, it was not discarded from the data set. The asymptotic results and parametric bootstrap distributions consistently identified only one slightly outlying value that was conserved because the associated information was accurate. A quantile-quantile plot indicated that the normal distribution roughly approximated the distribution of the standardized deviance residuals (Fig. 2A). There was no indication of inadequacy in the linear predictors because they were not correlated to the residuals. When plotted against the included variables, the residuals showed no particular trend other than a slight overestimation of ESFY incidence in young (2 to 4 year-old) orchards. The residuals were also randomly distributed with respect to the excluded variable AREA. On the contrary, the residuals significantly differed with respect to the grower (Fig. 2C) and origin of the planting material (Fig. 2D): the observed mean of the residuals was frequently outside the range expected under the null hypothesis of independence. This fact indicates that the human factors not only summarized the other variables, but also had an additional significant effect that remained even after including some of the underlying factors in the model. The weighted linear regression between observed and predicted incidence (Fig. 2B) had the equation: Observed = 0.946×Predicted + 0.003 (R 2 = 0.49, and standard error (SE) of 0.065 and 0.006, respectively). On the logit scale, a R 2 of 0.42 confirmed that the fit of the model was acceptable for an observational study: this model captured half of the variability of the data with a relatively small number of parameters (21 of the initial 224 df were used). The same procedure carried out on the external data set resulted in a higher R 2 of 0.63, but the model tended to overestimate the incidence, as shown by the equation of the regression line: Observed = 0.870×Predicted - 0.016 (SE = 0.088 and 0.009, respectively). Thus, despite the lack of information on the rootstock in the validation data set, the model still provided an acceptable prediction of ESFY incidence. The extent of the confidence intervals for some parameters of the model (Table 3) confirmed that the 4 variables and 4 interactions included in the model only explained part of the variability of the observed incidence. For these parameters, the discrepancy between bootstrap and the asymptotic confidence intervals was sometimes quite high (e.g., for the rootstocks), indicating that the assumptions of the asymptotic results were not fulfilled. Thus, in the rest of the study, we used the more reliable and more conservative bootstrap intervals. - 32 -
Influence of the Risk Factors. The mean temporal evolution of ESFY incidence in the study area (Fig. 3A) was summarized by the equation: Y=1/(1+e -0.198t+4.69 ). However, this binomial overdispersed model was not satisfactory (R 2 = 0.1), thus indicating a major role for the other variables. The analysis of deviance for the one-variable models (Table 4) showed that the grower was the best single explanatory factor, much better than the cultivar or the other variables. However, because of the observed multicorrelation, it was statistically more accurate to assess the role of each variable after adjusting for the effect of the others. The corresponding deviance analysis (Table 5) showed that tree age was the most significant variable, followed by both density and cultivar, and then the rootstock, with the area of the orchard having no influence on disease incidence. These results were robust to some alterations of the model, because congruent conclusions (except that CLV became much more significant than DENS) were drawn when the same procedure was performed after fitting a simpler model without the interaction terms (not shown). The interaction between the density and the age of the orchard was more significant than any other interaction. No conclusion could be drawn from the model concerning the effect of different levels of the factors, because the interactions were significant. The predictions for specific values were generally inconclusive as well, because of the wide confidence intervals. However, the analysis of the model without interaction terms indicated that the incidence was significantly higher on GF 305 rootstock and on the cv. Orangered. This was further confirmed by the predicted ESFY progress in cv. Orangered and cv. Hargrand with the complete model (Fig. 3B). Spatial Analysis of the Residuals. A simple map of the standardized deviance residuals clearly pointed out the spatial correlation between residuals. However, this correlation could have been at least partly explained by the spatial factor GRW (Fig. 2C), which had been discarded from the model. Thus, the mean of each level of this factor was deducted from the corresponding residuals. Then, the grower-adjusted residuals were used to compute an empirical variogram and its confidence envelopes obtained by random labeling within homogeneous subgroups (Fig. 4). The first values of γ(h) (up to a distance of 100 m) were below or near the 95% confidence envelope (P-values ranging from
Page 1 and 2: Ecole Nationale Supérieure Agronom
Page 3 and 4: Glossaire A leur première occurren
Page 5 and 6: C. Conclusions sur l’étude des v
Page 7 and 8: Introduction - 7 - « Deux grands m
Page 9 and 10: l’intensification permanente de l
Page 11 and 12: Ceci nous amène aux enjeux liés
Page 13 and 14: (b) Coût de la lutte contre l’ES
Page 15 and 16: Royaume- Uni Espagne Allemagne Belg
Page 17 and 18: 1992) ; par contre, le cerisier dou
Page 19 and 20: 6) Vection Le vecteur de l’ESFY (
Page 21 and 22: (b) Caractéristiques de la vection
Page 23 and 24: Conifères ? Rétention du phytopla
Page 25 and 26: Partie I : Identifier des facteurs
Page 27 and 28: Identifying Risk Factors from a Sur
Page 29 and 30: MATERIALS AND METHODS Data Record a
Page 31: visual inspection of their spatial
Page 35 and 36: Model Application This kind of mode
Page 37 and 38: ACKNOWLEDGEMENTS We are much indebt
Page 39 and 40: 42. Seemüller, E., and Schneider,
Page 41 and 42: TABLE 5: Analysis of deviance for e
Page 43 and 44: Fig. 2. Examination of the model fi
Page 45 and 46: II. Bilan Au-delà des effets évid
Page 47 and 48: Partie II : Identifier les cycles b
Page 49 and 50: A toolbox for the specific detectio
Page 51 and 52: Bordeaux). Plant samples were also
Page 53 and 54: cycles). The analyses were performe
Page 55 and 56: The above semi-quantification relie
Page 57 and 58: Table 1. Sequence of the primers an
Page 59 and 60: ESFY G1R (X) ESFY G2 (X) AP15R (X)
Page 61 and 62: Survival of European Stone Fruit Ye
Page 63 and 64: C. pruni Reimmigrants The percentag
Page 65 and 66: Table 1. Detection of ESFY from C.
Page 67 and 68: Si l’expérience est répétée d
Page 69 and 70: Il y a cependant deux réserves à
Page 71 and 72: The spread of European stone fruit
Page 73 and 74: inside the cage. The cages were rem
Page 75 and 76: Quantification of ‘Ca. P. prunoru
Page 77 and 78: prunorum’: the infectious reimmig
Page 79 and 80: Table 2. Occurrence of Cacopsylla p
Page 81 and 82: Number of C. pruni on P. spinos 40
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V. Bilan sur le fonctionnement de l
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Partie III : Tester des hypothèses
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émergents migrent dans des massifs
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identique à Qc(d) sauf qu’elle e
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MATERIALS AND METHODS Characteristi
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allows summarising in one graph (i)
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(A) (B) Fig. 1. Summary of the spat
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Investigating Disease Spread betwee
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when some plants may be missing for
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the graphical display by an appropr
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shows that the proportion of missin
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the approach developed by Pélissie
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APPENDIX Test 2. This section corre
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several types of points. J. R. Stat
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TABLE 4. Type I error and power of
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Fig. 2. Spatiotemporal pattern of d
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IV. Application à l’analyse de c
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Verger D1-4 Verger BH1 Verger BH2 V
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annuelle (Figure 1 de l’Article V
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consiste à disposer de vergers à
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Partie IV : Synthétiser l’inform
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Tableau 6. Propriétés biologiques
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Conclusion - 127 - « Il importe de
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fonctionnement du système épidém
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Tableau 7. Avantages et inconvénie
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malades correspondrait alors unique
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Expérimentations expérimentales S
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Références bibliographiques - 137
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30. Chabrolin C. (1924) Quelques ma
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86. Institute of Medicine (1992) Em
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138. Morvan G. (1977) Apricot chlor
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190. Torres E., Martin M. P., Paltr
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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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