A spatial, climate-determined risk rating for Scleroderris disease of ...

Venier et al. 1401
Fig. 1. (a) The spatial distribution of observations (presence and absence) of Scleroderris disease in Ontario (n = 1139). (b) Spatial
prediction of the probability of Scleroderris occurrence based on a logistic regression model with precipitation in the coldest quarter
and temperature of the coldest quarter as explanatory variables.
contribute much to increased classification accuracy. There
was a large loss in concordance for models with only one of
either mean temperature in the coldest quarter or precipitation
in the coldest quarter (Table 2).
We chose the two-variable model as our final model because
of its relatively high concordance and its relative simplicity.
According to the model, Scleroderris disease is more
likely to be found at lower winter temperatures (Fig. 2a) and
in places with more winter precipitation (Fig. 2b). The classification
accuracy of the test data based on models built
from half of the original data was the same as the original
classification accuracy of the model. This result suggests
that the model based on these two explanatory variables is
very stable. Also, the standard deviation of the classification
diagnostics for the 10 test runs was very low (Table 3) indicating
that the results were not dependent on any single set
of observations in the data.
Based on the results of this study, we conclude that there
are strong associations between the distribution of Scleroderris
disease in Ontario and mesoscaled climate. The classification
accuracy of the 11 variable model suggests that we
© 1998 NRC Canada