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

Venier et al. 1403
relationship with both variables was in the direction that was
expected based on previous research. The risk rating for
Scleroderris disease was highest at colder temperatures and
with more snow. Therefore, the good fit of this model is
consistent with our knowledge of the ecology of the fungus.
The gridded estimates of climate allow us to map predictions
of the probability of occurrence of the disease anywhere
in Ontario. All sampling was conducted in red pine
stands in a wide range of locations across the province;
therefore, the climate domain sampled corresponds closely
with the Ontario climate domain of red pine. We cannot
legitimately extrapolate our model results outside of the climate
domain of our samples; therefore, we have not extended
our spatial predictions into the far north of Ontario.
A first, obvious test of a spatial prediction of the probability
of occurrence is to compare the map of the prediction
with the distribution of the disease from the raw data. Our
spatial prediction matches well with the original observations.
It is important to remember that the spatial distribution
of the raw data was not directly included in the
modelling process. Values of presence and absence were
modelled against values of climate. This original model is
not spatially explicit as would be the case when modelling
with some geostatistical techniques such as kriging or
splining. Our ability to make the spatial predictions is based
on the availability of the gridded estimates of the independent
climate variables over the area of concern, not on the
spatially explicit nature of the original data.
We have developed a consistent and highly predictive
model for the relationship between the probability of occurrence
of Scleroderris disease and climate. For a disease, this
type of model and spatial prediction can be viewed as a “risk
rating” for the probability of occurrence. In the case of a
hazard rating, the model and spatial extension tells us where
we should be most vigilant about surveying for the disease.
A lot of forest diseases, including white pine blister rust, are
also known to be restricted in distribution by climate. In this
case it is the infection period during the growing season that
is restrictive. The fungus produces spores from July to September
and requires a cool period for both spore production
and successful infection; the infection process also requiring
abundant moisture (van Arsdel et al. 1959; van Arsdel
1961). Microsite and vegetation do have a significant effect
on local climate; therefore, hazard to the disease can vary
greatly over even a small area. However, for operational
purposes, climatic data has been used to delineate hazard
zones for white pine blister rust in the Lake States (Van
Arsdel 1961), Quebec (Lavellee 1974), and more recently,
Ontario (Gross 1985). Similarly, G. abietina requires cool
wet periods of about 36 h for spore liberation and infection.
This also imposes a climatic restriction in more southern areas.
However, unlike blister rust, this process can occur at
any point from late spring through early fall when conditions
are appropriate (Skilling et al. 1986). In addition, disease
distribution of the EU race is believed to be largely
through planting of infected nursery stock (Laflamme1993),
but the disease has never been able to become established
south of its present range even though cool wet springs and
summers do occur periodically. Hence, we believe the greatest
constraint to disease establishment to be cold period duration
as suggested by Marosy et al. (1989). As with the
Table 3. Summary of classification diagnostics for 10 replicates
of sampling 50% of the original data to test classification
accuracy.
Classification
diagnostics Mean Minimum Maximum SD
Concordance 85.2 83.9 86.6 1.01
Sensitivity 77.7 76.0 79.6 1.23
Specificity 77.8 75.5 79.5 1.45
False negatives 30.5 26.6 33.6 2.15
False positives 15.7 14.3 17.4 1.19
white pine blister rust, local climate conditions based on
local topography, vegetation, and soils are likely to be important
in determining the presence of the disease. However,
the broader scaled climate provides the context for the local
scale. Appropriate mesoclimate is a prerequisite for the
disease.
Many studies have examined the relationships between
macro- or meso-climate and distribution of vegetation (e.g.,
Woodward 1987; Huntley and Webb 1989; Lindenmayer et
al. 1996) or distribution of animals (Nix 1986; Root 1988a,
1988b; Repasky 1991). Woodward (1987) documents a long
history of the recognition of these associations, although he
acknowledges that the mechanisms are poorly understood.
Other work on this scale has been used to link climate and
biological distributions to improve bird atlas maps (Osborne
and Tiger 1992) or to map wildlife distributions (Walker
1990; Buckland and Elston 1993; Skidmore and Gauld 1996;
Venier et al. in press). The spatial predictions arising from
this type of statistical modelling have important management
applications including providing information on the
probability of occurrence of a species where it has not been
sampled. For places such as Ontario that have very large areas,
much of which are inaccessible, the ability to reliably
predict the probability of occurrence is essential for good
land management.
We thank Kathy Campbell, Terry Dumond, Kevin Lawrence,
Janice McKee, Almos Mei, and Norm Szcyrek for logistic
support.
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© 1998 NRC Canada