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Assessment of site characteristics as predictors of the vulnerability of Norway spruce ( Picea abies Karst.) stands to attack by Ips typographus L. (Col., Scolytidae)
Author(s) -
Dutilleul P.,
Nef L.,
Frigon D.
Publication year - 2000
Publication title -
journal of applied entomology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.795
H-Index - 60
eISSN - 1439-0418
pISSN - 0931-2048
DOI - 10.1046/j.1439-0418.2000.00440.x
Subject(s) - picea abies , karst , bark beetle , forestry , vulnerability (computing) , ecology , bark (sound) , forester , national park , biology , regression analysis , altitude (triangle) , physical geography , statistics , geography , mathematics , paleontology , computer security , computer science , geometry
The intensity of bark beetle Ips typographus L. (Col., Scolytidae) attack on Norway spruce ( Picea abies Karst.) is known to vary greatly among stands. In a control strategy approach, previous studies investigated the relationships between the variability in intensity of I. typographus attack and site characteristics such as stand age and altitude, mean tree circumference, growth rate and nearest‐neighbour distance, soil moisture, pH in H 2 O and KCl, and soil contents of C, N, K, P, Mg, Ca, Fe, Cu, Zn and Mn. The data analysis method used in these studies was mainly the multiple linear regression, with the mean number of attacks per spruce tree in a stand as variable to explain. Previous results showed that the expected vulnerability of a Norway spruce stand to attack by I. typographus can be estimated on the basis of simple information of easy access to the forester, when the data on the stand in question is used with others for fitting the regression model. Prediction of the vulnerability of a stand, without including its data in the fitting of the model, was shown to be more approximate. Therefore, the objectives of this study were: (1) to improve the performance of models predicting the vulnerability of Norway spruce stands to attack by I. typographus , based on site characteristics; (2) to assess the stability of such predictive models when these are built using a moderate number of stands; and (3) to incorporate the resulting information in a global approach to control and prevention. Published data were re‐analysed for these purposes. A jackknifed multiple linear regression procedure, in which each stand in turn is discarded when fitting the model (jackknife replication), is presented. A great variability in the models fitted, depending on the stand discarded, is observed. For instance, the number of explanatory variables retained ranges from one (i.e. soil P content, for five jackknife replications) to 10 (for one jackknife replication), for R 2 ‐values ranging from 0.5 to 1.0 and for one influential stand (i.e. the same stand characterized by an atypically low number of insect attacks compared to other stands with similar soil P content) against many influential stands. Differences between the model finally selected here using the revisited data and the models proposed earlier are discussed. A path analysis diagram is proposed for a more comprehensive modelling of Norway spruce stand vulnerability to I. typographus attack, based on site characteristics.