Provider: Ingenta Connect
Database: Ingenta Connect
Content: application/x-research-info-systems
TY - ABST
AU - Augustin, Nicole H.
AU - Kublin, Edgar
AU - Metzler, Berthold
AU - Meierjohann, Elsa
AU - von Wühlisch, Georg
TI - Analyzing the Spread of Beech Canker
JO - Forest Science
PY - 2005-10-01T00:00:00///
VL - 51
IS - 5
SP - 438
EP - 448
KW - forest management
KW - generalized linear model
KW - generalized linear mixed model
KW - environmental management
KW - beech canker
KW - forestry research
KW - natural resources
KW - generalized additive model
KW - forest resources
KW - forestry
KW - spatial correlation
KW - forestry science
KW - forest
KW - binomial data
KW - natural resource management
KW - Nectria ditissima
N2 - 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.
UR - http://www.ingentaconnect.com/content/saf/fs/2005/00000051/00000005/art00006
ER -