Beech canker caused by Nectria ditissima was abundant in many plots of a large beech provenance trial in Germany. In 149 plots a total of 9015 plants were assessed. No difference in susceptibility of individual provenances was established. However, significant spatial correlation was found between canker incidence in the plantation and the distance to neighbouring diseased shelterwood. The latter evidently served as a source of inoculum. Predominant juveniles tended to be more infected presumably by being more exposed to the inoculum. Trees planted up to 20 m from diseased shelterwood were most significantly infected, up to 48% per plot. In order to ascertain the role of wind for dispersing inoculum, distance zones were combined with locally predominant wind directions. The resulting wind dispersal zones served better for modelling disease dispersal than the distance zones.

We investigate the spread of Nectria canker of beech, which is a fungal chronic disease caused by Nectria ditissima Tul. et C. Tul. Data are available from a beech provenance trial. A possible influential factor on the proportion of infected trees per plot is the wind dispersal zone(s) (wdz), a categorical variable describing the distance and wind direction from diseased shelterwood, the source of infection. We investigate the effect of wdz and whether the disease incidence in the regeneration can be explained alone by the wdz using different approaches accounting for spatial correlation in the data. One method uses generalized estimating equations (GEE) where, through specification of a general variance–covariance matrix allowing for nonindependence, spatial correlation can be accounted for in the model. The second method uses generalized additive models (GAM) and the spatial autocorrelation is dealt with by modeling it as a spatial trend. The third method uses generalized linear mixed models (GLMM) with a random effect accounting for spatial correlation and heterogeneity. We show that, in the beech data, some spatial correlation is present that is over and above that accounted for by the wdz. Therefore, methods not accounting for this correlation are inappropriate. The GLMM is the most appropriate model because it manages to model the biological process best: It explains the variation in disease incidence by the wdz and by secondary infection. Hence it yields the most precise estimates. FOR. SCI. 51(5):438–448.