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

1402 Can. J. For. Res. Vol. 28, 1998
Fig. 2. Plot of the observations (small vertical bars) and modeled relationship (solid line) between probability of Scleroderris
occurrence (a) versus temperature in the coldest quarter with precipitation in the coldest quarter held constant at the mean and
(b) versus precipitation in the coldest quarter with temperature in the coldest quarter held constant at the mean.
can explain much of the distribution of Scleroderris disease
using mesoclimatic variables. However, with this type of
correlative study it is impossible to directly interpret mechanisms.
Fortunately, some more local work has been conducted
on the possible climate mechanisms controlling
disease distribution. Marosy et al. (1989) suggested that the
disease required a cold weather period of –6 to 5°C and that
snow cover provided the optimum situation of a consistent
temperature near 0°C. Other workers such as Laflamme
(1991) have noted the effect of snow accumulation in depressed
areas on creating a conducive environment for the
disease. This relationship between snow accumulation and
the incidence of Scleroderris disease has been observed
previously in both Ontario (Martin 1964) and in northern
Europe (Roll-Hansen et al. 1992; Karlman et al. 1994).
In a more parsimonious model we included mean temperature
of the coldest quarter as a proxy for number of conducive
days. While this mean usually exceeds the lower limit
of –6°C, sufficiently low temperatures ensure a lasting snow
cover near the base of the tree where disease development
begins. We also included the mean precipitation in the coldest
quarter (usually snow), which would also relate to the
availability of snow cover. This two-variable model was also
a very good fit with high classification accuracy. The
© 1998 NRC Canada