A spatial, climate-determined risk rating for Scleroderris disease of pines in Ontario

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Abstract
We used historical distribution data of Scleroderris disease (caused by the fungus Gremmeniella abietina var. abietina (Lagerb.) Morelet) in Ontario to model its probability of occurrence as a function of climate factors. A logistic regression model of the probability of occurrence as a function of the mean temperature of the coldest quarter and the precipitation of the coldest quarter was a very good fit. The concordance (index of classification accuracy) of the model was 84%. We subsampled the data repeatedly, generated new parameter estimates, and tested the predictions against data not included in the model. Classification accuracy was similar for each subsample model; therefore, we concluded that the final model is stable. Gridded estimates of the climate variables were used to spatially extend the two-variable logistic regression model and produce a probability of occurrence map for Scleroderris disease across Ontario. The predicted map of probability of occurrence fits well with the map of the observed locations of the disease. These results lend credence to previous work that suggests that distribution of Scleroderris disease is strongly influenced by climate. The classification results also suggest that this model is a useful tool for assessing the risk of Scleroderris disease throughout Ontario.

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