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

1398
A spatial, climate-determined risk rating for
Scleroderris disease of pines in Ontario
L.A. Venier, A.A Hopkin, D.W. McKenney, and Y. Wang
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.
Résumé : Nous avons utilisé des données retraçant l’historique de la distribution du chancre scléroderrien, causé par le
champignon Gremmeniella abietina var. abietina (Lagerb.) Morelet, en Ontario pour modéliser la probabilité que la
maladie se développe en fonction des facteurs climatiques. Un modèle de régression logistique de la probabilité que la
maladie se développe en fonction de la température moyenne et de la précipitation des 3 mois les plus froids était très
adéquat. La concordance (l’indice de précision des prédictions) du modèle était de 84%. Nous avons utilisé des
sous-échantillons des données de façon répétitive pour générer de nouvelles estimations des paramètres et nous avons
testé les prédictions avec des données non incluses dans le modèle. La précision des prédictions était semblable pour
chaque modèle tiré des sous-échantillons de telle sorte que nous avons conclu que le modèle final était stable. Les
variables climatiques estimées pour chaque cellule d’un système de quadrillage ont été utilisées pour appliquer de
façon spatiale le modèle de régression logistique à deux variables et produire une carte indiquant la probabilité que le
chancre scléroderrien se développe, partout en Ontario. La carte ainsi obtenue correspond bien avec la carte qui
indique les endroits où la maladie a été observée. Ces résultats donnent de la crédibilité aux travaux antérieurs qui
suggèrent que la distribution du chancre scléroderrien est fortement influencée par les conditions climatiques. Le
résultat des prédictions suggère également que ce modèle est un outil utile pour évaluer les risques que le chancre
scléroderrien survienne, partout en Ontario.
[Traduit par la rédaction] Venier et al. 1404
Scleroderris disease, caused by the fungus Gremmeniella
abietina (Lagerb.) Morelet var. abietina, has been regarded
as a major pest of pine species in North America for more
than 30 years (Laflamme 1993). Two distinct strains of the
fungus are recognized as being damaging to pines in North
America; these are referred to as the North American (NA)
and European (EU) races. Both races are morphologically
similar and have similar ecological requirements. The two
Received December 22, 1997. Accepted June 30, 1998.
L.A. Venier, 1 A.A Hopkin, and D.W. McKenney. Canadian
Forest Service, 1219 Queen St. East, Sault Ste. Marie, ON
P6A 3V5, Canada. e-mail: lvenier@nrcan.gc.ca,
ahopkin@nrcan.gc.ca, dmckenney@nrcan.gc.ca
Y. Wang. Canadian Forest Service, Northern Forestry Centre,
5320 - 122nd Street, Edmonton, AB T6H 3S5, Canada.
e-mail: ywang@nrcan.gc.ca
1 Author to whom all correspondence should be addressed.
races can only be distinquished on the basis of biochemical
tests (Bernier et al. 1994). Either race will cause mortality to
pine trees less than 1minheight and cause cankering and
loss of merchantable volume to trees less than 3.5 m in
height (Dorworth 1976).
The NA race, believed to be native to the continent
(Laflamme 1993) occurs over the range of pines in Ontario
(Sippell et al. 1966; Hopkin and McKenney 1995), primarily
on red pine (Pinus resinosa Ait.) and jack pine (P. banksiana
Lamb.). The disease has been recorded as causing cankering
and mortality to jack pine and red pine seedlings and has
been associated with numerous planting failures in Ontario
since the 1950s (Punter 1967; Dorworth 1970). While the
NA strain can kill or damage younger trees it does not cause
significant damage to trees over 2minheight, although it
will attack lower branches. The EU race, which has been
considered a damaging disease of pines in Europe for at
least 100 years (Donaubauer 1972), was first discovered in
North America in 1975 in New York State (Skilling 1977)
where it caused mortality to several thousand hectares of
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Venier et al. 1399
semimature red and Scots pine (Pinus sylvestris L.). More
recently it has caused extensive damage to red pine plantations
in western Quebec (Laflamme and Lachance 1987).
In Ontario, the EU race was first identified in 1985
(Myren and Davis 1986) in south-central Ontario, but it has
been restricted in its range through annual detection, followed
by control efforts primarily consisting of pruning
lower branches and burning of diseased material (Hopkin
and Laflamme 1995). However, since 1991 the EU race has
expanded its range in Ontario (Hopkin and McKenney
1995). In Quebec where the disease has been present since
1983, both races co-exist over much of the area where red
pine is planted (Laflamme and Bussieres 1990; Laflamme
1993). The disease is believed to be restricted in its southernmost
distribution by climate. Marosy et al. (1989) suggested
the fungus requires an incubation period of at least
44 days of temperatures between –6 and 5°C after initial infection
before disease symptoms are apparent. Karlman et
al. (1994) also observed increasing disease incidence and severity
in Sweden with increasing latitude, snow cover, and
lower temperatures. In Ontario the disease has not been
found below 44°30′N despite repeated surveys (Hopkin and
McKenney 1995), strongly suggesting a relationship between
the disease and climate. On this basis it should be
possible to determine which areas have a climate that would
be conducive to the disease. White pine blister rust
(Cronartium ribicola J.C. Fisch.) is a disease affecting white
pine (Pinus strobus L.) and is known to have temperature
and moisture requirements for successful infection to occur
(van Arsdel et al. 1956, 1961). On the basis of the known
climatological and physiographic characters related to these
requirements, van Arsdel (1961) developed hazard zones for
the Lake States area of the United States. This work has
been used to define blister rust hazard zones for other areas
including Ontario (Gross 1985).
In this paper, we model the relationship between the probability
of occurrence of Scleroderris disease and elements of
climate on a broad geographic scale. There is a correlation
between occurrence and abundance for most organisms
(Hergstrom and Niall 1990; Yamamura 1990; Venier and
Fahrig 1996; Venier and Fahrig 1998); therefore, we expect
the probability of occurrence to be a good estimate of risk.
Evidence from the literature presented above suggests that
climate is an important influence on the distribution of the
disease. A model of Scleroderris disease distribution as a
function of climate with a good fit and high classification
accuracy should lend support to the idea of climatic influence
and will also provide a means of predicting areas of
high risk for Scleroderris disease. Our principle objective is
to generate a reliable spatial estimate of the occurrence of
the disease. Therefore, we are limited to using explanatory
variables for which we have spatial estimates for Ontario. A
long-term data set of the distribution of the disease in Ontario
is available because of the historic and ongoing monitoring
of the disease by the Canadian Forest Service. We
also have spatial estimates of climate for all of Ontario (with
a 1-km resolution grid) derived from long-term mean
monthly averages. Climate data in this form allows us to attach
climate estimates to the observations of presence and
absence of the disease, as well as develop spatial predictions
of the model across the province.
Table 1. Climate variables used in analysis.
Variable Description
ANNTEMP Mean annual temperature
MXRNG Maximum diurnal temperature range
XTMPHT* Mean temperature of the hottest month
XTMPCLDM Mean temperature of the coldest month
XSNLRNG* Mean seasonal temperature ranges
MXTMPHT Maximum temperature of the hottest month
MNTMPCLD Minimum temperature of the coldest month
ARNGTMP Annual range in temperature
XTMPHTQ Mean temperature of the hottest quarter
XTMPCLDQ* Mean temperature of the coldest quarter
APRP* Annual precipitation
PRPRNG Precipitation range
SSNLT* Seasonal variation in precipitation
PRPHTQ* Precipitation of the hottest quarter
PRPCLDQ Precipitation of the coldest quarter
STRTGRW* Julian day of start of the growing season
ENDGRW* Julian day of end of the growing season
DAYGRW Days in the growing season
PRPP1 Total precipitation for period 1
PRPP3 Total precipitation for period 3
DAYGRWP3* Growing degree-days for period 3
AMXTMP* Annual mean maximum temperature
AMNTMP* Annual mean minimum temperature
Note: The growing season starts on the last day of five consecutive
days when the mean daily temperature is above 5°C (beginning no sooner
than March 1 and ending by July 31). The growing season ends the first
day from August 1 with a minimum temperature of less than –2°C. Period
1 is 3 months prior to the growing season. Period 3 is the growing
season. Variables retained in the backward stepwise selection procedure of
the logistic regression analysis are marked with asterisk.
Disease distribution
Information on the presence and absence of Scleroderris disease
(both North American and European races) was compiled from
surveys conducted in Ontario by the Canadian Forest Service from
1985 to 1993. Field technicians conducted aerial reconnaissance of
pine plantations, generally by fixed-wing aircraft, to locate infected
plantations. The aerial surveys were conducted from May to
early June, when symptoms were most visible, over townships containing
pine plantations in south-central Ontario. Aerial surveys
were followed by ground surveys. In northern regions where only
the NA race is known to exist, only ground surveys were used. At
each plantation visited, 500 trees were inspected along transects
randomly distributed throughout the plantation. There were 1139
pine plantations sampled across Ontario for presence or absence of
the disease, and samples were submitted for diagnostics for confirmation.
Therefore, the disease distribution data are recorded as binary
data (presence or absence). At each plantation, the disease
was confirmed as present or absent by visual inspection followed
by a laboratory confirmation using serological techniques (Dorworth
and Krywienczyk 1975). The geographic location, to the
nearest kilometre, was recorded for each plantation. More details
of survey methodology and diagnostics are described by Hopkin
and McKenney (1995).
Climate data
The climate data were interpolated from a network of 471
weather stations in and around the province of Ontario. Mackey et
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1400 Can. J. For. Res. Vol. 28, 1998
Table 2. Diagnostics from four logistic regression analyses.
Diagnostics
Backward
stepwise selection
(11 variables)
al. (1996) generated mathematical climate surfaces for the province
using the thin-plate smoothing-spline techniques of Hutchinson
(1991). These monthly surfaces were created as a function of
latitude, longitude, and elevation to capture both spatial and temporal
variations in lapse rates. Errors associated with the surfaces
are approximately ±0.5°C for temperature related variables, and
10–20 mm for precipitation.
These surfaces enable us to append climate variables to
georeferenced (latitude, longitude, elevation) historical field survey
data. Also, the climate variables can be mapped by coupling the
mathematical surfaces to a digital elevation model (a regular grid
of latitude, longitude, and elevation representing the topography on
an area; see Moore et al. 1991). A digital elevation model for Ontario
has been developed based on the National Topographic Series
1 : 250 000 digital topographic data resolved at a 1-km grid. Mathematical
surfaces were developed for long-term mean monthly averages
of minimum temperature, maximum temperature, and
precipitation. Secondary climate variables, that are likely to reflect
processes determining distributions of organisms, were derived
from these primary surfaces (see Mackey et al. 1996 for further details
on climate modelling).
Analysis
We used logistic regression analysis to examine the probability
of occurrence of Scleroderris disease as a function of aspects of
temperature and precipitation. For each sample location where
Scleroderris disease was confirmed as present or absent, we attached
an estimate of each of the climate variables listed in
Table 1. The severity of the disease was not considered as all plantations
are at risk after the disease becomes established. We ran
several different models. First, we used all climate variables in a
backward stepwise selection procedure to identify the best possible
predictability that could be derived from these explanatory variables
using a criterion of α = 0.05 to remove a variable. Then we
selected two variables (the mean temperature of the coldest quarter
and the precipitation of the coldest quarter) that we anticipated
would explain variance in the probability of occurrence based on
our experience and on previously published work on the ecology
of the disease. We compared these two models using concordance
(an index of classification accuracy). We were attempting to identify
the most parsimonious model without incurring a large loss in
our ability to predict (based on classification). We then ran models
for each of the two selected climate variables separately and compared
the concordance with the two-variable model to evaluate the
usefulness of these variables on their own. Once we identified our
XTMPCLDQ
PRPCLDQ
XTMPCLDQ
(mean temperature
in coldest quarter)
–2log L (intercept only) 1534.2 1534.2 1534.2 1534.2
–2log L (intercept + covariates) 821.0 1089.9 1294.0 1520.9
Chi-square for covariates 713.2 444.3 240.3 13.3
df (model) 11 2 1 1
P (model) 0.0001 0.0001 0.0001 0.0003
Concordance (%) 91.4 84.4 74.0 57.6
Best probability 0.46 0.40 0.34 0.40
Sensitivity 85.6 76.8 60.8 56.7
Specificity 83.6 77.9 66.6 64.1
False negatives 22.3 30.1 45.1 48.6
False positives 10.4 16.6 28.3 31.2
PRPCLDQ
(precipitation in
coldest quarter)
Note: There were 1139 observations in the data set: 457 observations of presence and 682 observations of absence. See Table 1
for a description of the climate variables.
final model we examined the model in more detail by randomly
splitting the original data in half, running the model with half of
the data and generating a classification table from the other half of
the data. We repeated this procedure 10 times and summarized the
results. This procedure examines the ability of the model to predict
the occurrence of the species for locations that are not included in
the model and is therefore a more rigorous test of classification accuracy.
This test also indicates how dependent these results are on
the individual observations that are included in the model. Lastly,
we used the regression equation from the final model to predict the
probability of occurrence over the entire area of Ontario using the
gridded estimates of climate. We compared this map of probability
of occurrence to the original map of the observed locations of the
disease.
The results show a good match between the observed distribution
of Scleroderris disease (Fig. 1a) and the map of
probability of occurrence (Fig. 1b). We have not made predictions
for the most northern parts of Ontario because of
the relative scarcity of appropriate hosts in that area and because
we have no data that samples that environmental
space. Disease distribution was found to be closely related to
certain climatic variables. Using a backward stepwise procedure,
11 of the 23 climate variables were retained in the logistic
regression model (Table 1). The overall concordance
was 91.4%, an extremely high value, with a sensitivity (the
occurrence of an event (presence) when it was predicted) of
85.6% and specificity (the absence of an event when it was
predicted to be absent) of 83.6% (Table 2). This model has a
very good fit but is not very interpretable because there are
11 explanatory variables in the model. Also, more explanatory
variables result in a model that is increasingly dependent
on a particular set of observations.
On the basis of our knowledge of the ecology of the fungus,
two variables, mean temperature of the coldest quarter
and precipitation in the coldest quarter, were tested separately.
This model with only two variables was also a good
fit with a concordance of 84.4%, sensitivity of 76.8%, and
specificity of 77.9% (Table 2). This indicates that most of
the 11 explanatory variables in the first model do not
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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
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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
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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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