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1639
Landscape analysis of risk factors for white pine
blister rust in the Mixed Forest Province of
Minnesota, U.S.A.1
Mark A. White, Terry N. Brown, and George E. Host
Abstract: The abundance of eastern white pine (Pinus strobus L.) has been significantly reduced in northeastern Minnesota over the past 120 years. White pine blister rust (WPBR), a commonly lethal fungal disease of white pine, was
introduced in Minnesota in approximately 1914 and now, along with other factors such as herbivore browsing, poses a
major challenge to attempts to reestablish white pines in the region. A map delineating broad WPBR hazard zones for
the Lake States region was prepared in 1964. We created a higher resolution map that estimates the spatial variability
of WPBR hazard in the Laurentian Mixed Forest Province of Minnesota using modern geographic information system
techniques and readily available spatial databases. The new map has significantly higher resolution than the old and
demonstrates that even within areas previously classified as “high hazard”, there are significant acreages of “lowhazard” areas where white pine regeneration may be possible. Our analyses are consistent with previous work in the
Lake States region, showing that climate, topographic characteristics, and distance from water bodies and wetlands
have a strong influence on WPBR infection hazard. We also present methods for analyzing forest conditions at regional
scales using commonly available spatial data sets.
Résumé : L’abondance du pin blanc (Pinus strobus L.) a connu une baisse significative dans le Nord-Est du Minnesota
au cours des 120 dernières années. La rouille vésiculeuse du pin blanc (RVPB), une maladie cryptogamique qui entraîne généralement la mort du pin blanc, a été introduite au Minnesota vers 1914. Cette maladie, combinée à d’autres
facteurs tels que le broutage par les herbivores, constitue un obstacle majeur aux efforts déployés pour rétablir le pin
blanc dans la région. Une carte délimitant de grandes zones de susceptibilité à la RVPB a été établie en 1964 pour la
région des Grands Lacs. Nous avons créé une carte avec une plus haute résolution qui permet d’estimer la variabilité
spatiale de la susceptibilité à la RVPB dans la zone de forêt mixte laurentienne du Minnesota à l’aide de techniques
modernes utilisant un système d’information géographique et des bases de données à référence spatiale facilement accessibles. La nouvelle carte a une résolution significativement plus grande que la vieille et démontre que même à
l’intérieur des zones précédemment classées « à risque élevé », il y a d’importantes superficies « à faible risque » où il
serait possible de régénérer le pin blanc. Nos analyses corroborent les résultats de travaux antérieurs dans la région des
Grands Lacs qui montrent que le climat, la topographie et la proximité de plans d’eau ou de milieux humides ont une
forte influence sur les risques d’infection par la RVPB. Nous présentons également des méthodes pour analyser les
conditions en forêt à l’échelle régionale à l’aide de bases de données à référence spatiale généralement disponibles.
[Traduit par la Rédaction]
White et al.
1650
Introduction
Since the Euro-American settlement period, the abundance of eastern white pine (Pinus strobus L.) in the Mixed
Forest Province of Minnesota has declined dramatically because of intensive harvest (Jones 1992) and post-logging
slash fires (Ahlgren 1973). White pine restoration efforts
have been hampered by increased herbivore populations
(white-tailed deer, Odocoileus virginianus Zimm.), fire supReceived 5 June 2001. Accepted 2 May 2002. Published
on the NRC Research Press Web site at http://cjfr.nrc.ca
on 4 September 2002.
M.A. White,2 T.N. Brown, and G.E. Host. Center for Water
and the Environment, Natural Resources Research Institute,
University of Minnesota, Duluth, MN 55811, U.S.A.
1
Contribution No. 319 for the Center for Water and the
Environment, Natural Resources Research Institute,
University of Minnesota, Duluth, Minn.
2
Corresponding author (e-mail: [email protected]).
Can. J. For. Res. 32: 1639–1650 (2002)
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pression (Tester et al. 1997), and the introduction of the fungal disease white pine blister rust (WPBR) in 1914 (Hummer 2000). WPBR is caused by Cronartium ribicola J.C
Fischer, a basidiomycete fungus. The C. ribicola life cycle
alternates between pines of the subgenus Strobus, creating
perennial cankers, and Ribes L. species (gooseberries, currants), where it produces a foliar disease (Geils et al. 1999).
WPBR can limit regeneration by killing young white pine,
and larger trees can be killed as branch dieback proceeds
over time.
Environmental factors affecting WPBR infection
WPBR is a complex pathosystem. Aeciospores are released from white pine in the early spring, which infect
Ribes. Urediospores are generated on Ribes leaves and infect
Ribes. From July through September, basidiospores are released from telia and may infect white pine through stomata
on leaf undersurface or young stems (Maloy 1997). The release of basidiospores from Ribes occurs at night (Van
Arsdel 1967).
DOI: 10.1139/X02-078
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Can. J. For. Res. Vol. 32, 2002
Fig. 1. Ecological provinces of Minnesota with the original WPBR hazard zones superimposed over the Laurentian Mixed Forest Province.
Given the presence of susceptible host species and infectious pathogens, climate determines the distribution and
abundance of WPBR in the northern Lake States. The lack
of sufficient moisture is the primary factor limiting the distribution of WPBR in the Lake States. WPBR is more prevalent in areas that have either low daily maximum
temperatures or long cool periods during the day. With increasing latitude and elevation, daily maximum temperatures
decrease. With increased latitude and elevation, evaporation
is lower, enhancing infection opportunities. Therefore, at the
macroclimatic scale, WPBR tends to increase with latitude,
elevation, and proximity to cold water bodies (e.g., Lake Superior) (Van Arsdel 1972).
Meso- and micro-climatic factors are embedded within
regional-scale climatic gradients and influence WPBR distribution. At the mesoscale, hill–valley structures influence
WPBR distribution, where WPBR is more prevalent at
higher elevations and less common in broad river valleys
(Van Arsdel 1972). Van Arsdel (1965) determined that night
breezes near the Great Lakes are important as carriers of
WPBR sporidia. Cool air moves off the land surface collecting WPBR sporidia from Ribes. This cool air hits warmer air
above the lake surface creating a backflow over the land surface. The spores are deposited in an 11 km wide strip from
16 to 27 km from the lakeshore. Night air movement pat-
terns also match the distribution of WPBR in white pine
adjacent to swamp forests (Van Arsdel 1967).
At the microscale, forest canopy structure and small-scale
topographic variation can influence WPBR infection. Small
canopy openings (diameter < 0.5 canopy height) retain moisture and remain cooler longer than closed canopy or larger
openings and, thus, are more susceptible to WPBR infection.
Cold air moves downslope and accumulates in narrow valleys or depressions creating favorable conditions for WPBR
infection (Van Arsdel 1972).
Although eastern white pine is highly susceptible to
WPBR infection (Bingham 1972; Hoff et al.1980), genetic
resistance does occur in individuals and populations (French
1992). The presence of resistant individuals would mitigate
the potential effects of environmental factors on WPBR incidence and damage.
Previous hazard analysis and rating systems
The original WPBR hazard map for the Lake States region (Minnesota, Wisconsin, and Michigan) was based on
broad climatic and topographic patterns (Van Arsdel 1964).
These hazard zones reflect the likelihood of WPBR infection
and are described relative to the level of management required to maintain white pine (Fig. 1). Three hazard zones
were defined in the Mixed Forest Province in Minnesota
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White et al.
(moderate required minor management intervention; high
and very high required increasing intervention). Robbins et
al. (1988) found no statistical relationship to Van Arsdel’s
(1964) hazard zones on sites in the eastern Upper Peninsula
of Michigan. Charlton (1963) developed hazard zones based
on climate variables for the states in the northeastern United
States. In this system, hazard is a function of the frequency
of weather conditions favorable to WPBR infection. Hazard
is defined as potential stand damage. Validation work across
the region showed good correspondence with the hazard
zones. However, a study in Maine showed a higher incidence of WPBR in Charlton’s low hazard zone (Ostrofsky et
al. 1988). To date, there have been few attempts to create
landscape-level hazard maps utilizing geographic information systems (GIS) and readily available data. In New Mexico, Geils et al. (1999) produced a preliminary GIS-based
hazard map for a 60 900 ha area of the Lincoln National
Forest. They used an additive ranking system based on elevation, topographic position, and plant association to rank
sites relative to existing and (or) potential WPBR infection
on southwestern white pine (Pinus strobiformis Engelm.).
Van Arsdel et al. (1961) designed a site-level rating system for areas in southern Wisconsin. This additive index
used values assigned to topographic and vegetation cover
classes to create a composite score for each stand. Accuracy
assessment showed this index predicted WPBR infection
89% of the time. Hunt (1983), with the exception of slope,
found little correlation with site factors and WPBR canker
incidence on western white pine (Pinus monticola Dougl.) in
British Columbia. An expert system approach was used to
develop a WPBR hazard rating for western white pine in
northern Idaho (Rust 1988). This system predicted the probability of Ribes spp. occurrence on a site based on habitat
type, and then if a Ribes spp. population is present, WPBR
hazard was estimated based on site variables. In general,
hazard rating systems focus on damage potential to white
pine and the level of management required to mitigate damages. Geils et al. (1999) note that hazard rating information
can be combined with damage or impact models to predict
resource or ecological impacts.
While the original hazard map for the Mixed Forest Province has been useful, it did not show spatial variability
within broad WPBR hazard zones (Van Arsdel 1964)
(Fig. 1). Our goal was to produce a more fine-grained map
of WPBR hazard for the Mixed Forest Province in Minnesota, based on a spatial model linking spatial–environmental
data with WPBR occurrence information. This modeling approach has successfully been used to predict fire effects
(Miller and Urban 1999), plant disease hazard (Geils et al.
1999), wildlife habitat (Mladenoff et al. 1995), and insect
outbreaks (Gray et al. 2000). The results of these models can
then be incorporated into larger decision support systems for
natural resource planning (Fedra 1995; Power and
Saarenmaa 1995).
Study region
The Laurentian Mixed Forest Province of Minnesota covers approximately 94 000 km2 (Fig. 1) (Minnesota Department of Natural Resources 1999a). This is a glacial
landscape dominated by Wisonsinan-age glacial drift and
1641
landforms. Glacial lacustrine features such as glacial lakes
Agassiz, Upham, and Aitkin, and morainal features cover
large land areas in this region. Sandy outwash plains and
channels are also common (Albert 1995). The climate is
characterized by long, cool winters and short, mild summers.
Annual precipitation ranges from approximately 530 to
780 mm (McNab and Avers 1994). At the time of settlement, conifers dominated both upland and lowland forests.
On upland areas, jack pine (Pinus banksiana Lamb.) dominated on dry, fire-prone sites such as outwash plains and
thin soils over bedrock. White and red pine (Pinus resinosa
Ait.) occurred on sandy moraines. Post-disturbance aspen–
birch forests were present on a wide variety of upland soil
types. Wetland types included large areas of lowland conifer
forest dominated by black spruce (Picea mariana (Mill.)
BSP) and tamarack (Larix laricina (Du Roi) K. Koch) as
well as open bogs and patterned peatlands (Albert 1995).
Methods
Data sources and preparation
WPBR occurrence
The Minnesota Department of Natural Resources Phase II
Forest Inventory Database was the source of information for
WPBR occurrence. This is a continuous forest inventory for
state-owned and -managed land in Minnesota. Cover type
polygons are delineated from aerial photography, photogrametrically rectified, and digitized. Inventory data comes
from either field inventory or air photograph interpretation
(Minnesota Department of Natural Resources 1983). We selected polygons using the following criteria: (i) white pine
was present, (ii) cover types in which white pine normally
occurs, and (iii) polygons had been surveyed in the field.
This resulted in a data set of 9741 polygons distributed
across the Laurentian Mixed Forest Province (Fig. 1). Insect
and disease occurrence are recorded for up to nine tree species in a polygon. Inventory instructions state that recording
of insect and disease damage is only necessary when greater
than 10% of trees are affected (Minnesota Department of
Natural Resources 1983). Inventory workers receive classroom and field training in identifying insect and disease
damage. There are no accuracy assessments of the insect or
disease damage inventory. From this we infer that polygons
coded for WPBR presence indicate that WPBR is conspicuous in the stand and represents more than a light infestation.
Some polygons coded for WPBR absence may have low levels of infestation and were recorded as such for this reason.
The mean and median polygon size was 8.4 and 5.2 ha, respectively.
Ribes occurrence
Since Ribes is the alternate host in the complex
pathosystem of WPBR, the presence of Ribes is a necessary
factor in the development of WPBR on eastern white pine.
Depending on topography, the presence of wetlands and water bodies, and night wind movements, spores dispersed
from Ribes can travel as far as 27 km (Van Arsdel 1967). We
did not directly include Ribes occurrence in the WPBR hazard analysis, because there is no synoptic database showing
their distribution. However, plant community data for the
study region indicate that Ribes spp. occur within or adja© 2002 NRC Canada
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Can. J. For. Res. Vol. 32, 2002
Table 1. Topographic aspect for classes
1 and 2.
Aspect (°)
Class 1
338–22
23–67
68–112
113–157
158–202
203–247
248–292
292–337
Flat
Class 2
0–90
91–180
181–270
270–360
Flat
Code
Direction
1
2
3
4
5
6
7
8
9
N
NE
E
SE
SW
W
SW
NW
Flat
1
2
3
4
5
NE
SE
SW
NW
Flat
cent to most of the major forest habitat types in which eastern white pine occurs (Kotar and Burger 2000). Therefore,
our model assumes Ribes presence either within or adjacent
to sample forest inventory polygons.
Climate
We used a 1-km grid cell climate database to derive 12
climatic variables. Temperature and precipitation data were
derived from 30-year (1961–1990) climatological summaries
published by the National Climatic Data Center. Climate station data were interpolated at a 1-km grid cell resolution using a multiple regression model based on latitude, longitude,
and elevation (ZedX Inc. 1995). Since spore dispersal from
Ribes occurs in July, August, and September in this region,
we chose the following monthly climate variables for this
time period: mean minimum temperature, mean maximum
temperature, total precipitation, and potential evapotranspiration (PET). PET was calculated using the
Thornthwaite water balance method (Thornthwaite 1948).
Topographic data
United States Geological Survey 30-m resolution digital
elevation models were used estimate elevation, slope, and
aspect class. We used two different aspect classes, class 1,
with nine categories, and class 2, with five classes (Table 1),
which was identical to that used by Van Arsdel (1972). We
added the nine-class aspect data to discriminate aspect
classes at a finer level than the original five-class system.
Distance from water and swamp forest
Open water data were extracted from a Landsat-derived
land-cover classification (Minnesota DNR 1999b). We calculated six different classes of Euclidean distance from water for sample polygons based on water body size, with the
following divisions: (i) <100 ha, (ii) ≥100 and <1000 ha,
(iii) ≥1000 and < 10 000, (iv) ≥ 10 000 ha, (v) Lake Superior, and (vi) all water bodies including Lake Superior. We
used the National Wetlands Inventory (NWI) (Minnesota
Department of Natural Resources 1999c) data to locate forested wetland patches in the study area. We then calculated
Euclidean distance from sample polygons to the nearest forested wetland patch for the study region.
Data analysis
Spatial data integration
For each sample polygon we calculated the mean value
for continuous spatial data variables and the majority value
for categorical variables using Arc-Info’s ZonalMean and
ZonalMajority functions, respectively (Environmental Systems Research Institute Inc. 1999). Although sample polygons vary somewhat in size and topographic characteristics,
the mean or majority value should capture the central tendency of each polygon. The final database included 22 spatial data variables. The values for each polygon integrate
data from different scales; climate data reflect mesoscale
conditions, while slope and aspect indicate more local conditions; elevation varies at both local and regional scales. Creating predictive models or classifications of biological
processes at regional scales necessarily requires integration
of biophysical data quantified at different resolutions. Ecological classification systems classify the landscape based on
repeating patterns of soil, landform, climate, and vegetation
(Host et al. 1995; Banner et al. 1996). For a given point on
the landscape, the natural vegetation is influenced by a suite
of variables including soil, landform, climate, and disturbance that function at different scales. We assume that the
same is true for plant disease hazard modeling, in that
WPBR hazard for a given location on the landscape is a
function of climate, elevation, topography, and Ribes presence. Geils et al. (1999) integrated multiscale data on elevation, topography, and plant association to create a WPBR
hazard classification for the Sacramento Ranger district in
southern New Mexico. For this analysis, we used a 30-m cell
resolution purely as a GIS implementation detail to preserve
topographic information (slope, aspect, elevation). The effective resolution of the map is not 30 m but is defined by
the mean forest polygon size (8.4 ha) used to estimate
WPBR–environment relationships.
Electivity analysis
To test the null hypothesis that WPBR is randomly distributed across the landscape with respect to topography, climate, and proximity to water, we used the electivity index
described by Jacobs (1974) and utilized by Jenkins (1979).
The electivity index was initially used to determine whether
herbivores discriminated for or against particular plant food
given the food’s overall abundance in a community. We used
the index to determine whether WPBR selectively occurred
on any slope class, aspect class, climate variable class, or
distance from water or wetland class. The formula to compute electivity indices for WPBR incidence and each environmental variable class was
[1]
Eij = ln
(rij )(1 − p j )
( p j )(1 − rij )
where Eij is the electivity for WPBR type i on spatial variable class j (topography, climate, distance from water). rij is
the proportion of WPBR class i on variable class j, and pj is
the proportion of the variable class that occurs in class j.
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When rij is high relative to pj, electivity scores are positive,
indicating a positive association with that variable. For example, if WPBR was found on 60% of sites in the elevation
class 500–550 m, and this elevation range accounted for
20% of the sites, the electivity score would be 1.79, indicating a positive association of WPBR with this elevation
range. Likewise, When rij is low relative to pj, electivity
scores are negative, indicating a selection against that variable for WPBR. This index has proven useful in assessing
the relationship between vegetation classes and soil types
and topography (Pastor and Broschart 1989), and soil–
landform based ecological classification systems (Host et al.
1995; White and Host 2000). We calculated confidence intervals for the electivity plots using resampling methods.
One thousand random subsamples of 50% of the data set
were taken for each class of each variable, and the mean and
standard deviation were calculated. These were used to generate the error bars corresponding to two standard deviations
or approximately a 95% confidence interval (Fig. 2).
WPBR hazard classification
Based on the electivity analysis of WPBR occurrence and
spatial variables, a 30-m grid of electivity values was calculated across the study region for each variable. We created a
map showing the continuous distribution of WPBR hazard
by summing electivity scores for all 22 spatial variables for
each 30-m cell in the study region. From this map, three
WPBR hazard classes were derived from the statistical distribution of electivity scores for the polygon sample data. A
threshold, S–, for BR– (low hazard) classification is calculated as the mean of the sum of electivity scores for BR+
(high hazard) sites minus k1 times the standard deviation of
electivity scores for BR+ sites. Sites with total electivity
scores below S– are classified as BR–. The threshold S? for
BR? (WPBR indeterminate) sites is calculated as the mean
of the sum of electivity scores for BR+ sites minus k2 times
the standard deviation of electivity scores for BR+ sites.
Sites whose total electivity score is below S? but above S–
are classified as BR?. The remaining sites, whose sums of
electivity scores are above S?, are classified as BR+ sites.
The k1 and k2 values are arbitrary. However, they serve a
very important and useful purpose; they allow the user to
control the risk level of the classifications.
The use of the mean of the sum of BR+ sites and the values for k1 and k2 are based on decision risk assessment protocols (Eastman 1999). Since the map is designed to aid in
white pine management decision-making, we chose parameters that limit the worst type of classification error, where
BR+ sites are classified as BR–. By using the mean sum of
electivity scores for BR+ sites, we ensure that the classification is based on scores from the positive side of the distribution of WPBR electivity scores. s+, the standard deviation of
electivity scores for BR+ sites accounts for the variability of
BR+ electivity scores. The constants K1 = 1 and K2 = 0 allow us to separate the strongly BR– sites from the strongly
BR+ sites.
Our classification of WPBR infection hazard is defined as
follows: (i) low, WPBR may occur sporadically but little
management intervention is required; (ii) indeterminate,
given no strong weight of evidence for or against low or
high hazard, we assume WPBR can occur and moderate
1643
management intervention may be required; and (iii) high,
highest probability of WPBR infection hazard, significant
management intervention required to grow and maintain
eastern white pine.
Classification accuracy assessment
To test the accuracy of the WPBR hazard classification we
withheld 30% of the samples from the original from training
data set (n = 2841). We applied the algorithms described
above to the 30% test data set using the mean and standard
deviation from the 70% training data.
Results
Electivity analysis
Topographic variables
Elevation showed the strongest positive and negative relationships with WPBR occurrence; elevations below 375 m
were strongly negative, while elevations above 450 m
showed a strong positive association with WPBR occurrence
(Fig. 2a). Analysis of two aspect classifications showed that
flat areas tended to have negative associations with WPBR
(Fig. 2b). Slope degree also indicated a relatively strong negative relationship with flat topography (Fig. 2c) and increasing positive association with WPBR with steeper slopes
(Fig. 2c).
Climate variables
July, August, and September showed similar patterns for
mean minimum temperature; BR+ associations occurred at
the lowest minimum temperatures, and BR– values were associated with higher minimum temperatures (Fig. 2d). All 3
months have BR– values at the highest temperature values;
however, sample sizes are small and confidence intervals are
large (Fig. 2d).
September and July had similar patterns for monthly maximum temperatures. Relatively strong BR+ associations are
indicated in the lower maximum temperature range, while
BR– or neutral associations occur at higher temperatures
(Fig. 2e). Potential evapotranspiration (PET) values show a
similar pattern to mean maximum temperature; BR+ values
are related to the lowest PET values, while BR– and neutral
values are associated with higher PET (greater moisture deficit) (Fig. 2f).
Electivity scores for mean monthly precipitation showed
similar trends for all 3 months. Weak negative or neutral associations occurred at low to medium values, BR+ values
were associated with higher precipitation (Fig. 2g).
Distance to water
Water body size electivity scores for classes 1, 2, 3, and 6
(<100 ha, ≥100 and <1000 ha, ≥1000 and < 10 000 ha, and
all water bodies including Lake Superior, respectively) indicated weak BR– or neutral associations. Distance from Lake
Superior, however, showed strong BR+ values from approximately 16 to 28 km (Fig. 2h). Similarly, electivity scores for
the ≥ 10 000 ha class showed BR+ values in the 15- to 20km range. Although these scores were relatively low, they
may indicate a related effect with other large water bodies.
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Fig. 2. Electivity plots indicating the relationship between WPBR presence or absence and nine representative environmental variables.
Error bars show 95% confidence limits for each electivity estimate.
Distance to forested wetland
Electivity for distance to forested wetland showed a weak
BR– association for distances less than 200 m. Distances
from 200 to 600 m indicated a weak BR+ relationship, although confidence intervals show that they are above 0. Dis-
tance from 600 to 1000 m showed somewhat stronger BR+
association (Fig. 2i).
WPBR hazard map
We summed electivity scores for all spatial variables for
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each 30-m pixel in the study region to create a continuous
surface of the sum of electivity scores (Fig. 3a). While this
map shows relative differences in WPBR hazard, it may not
be useful for selecting areas for white pine regeneration, because pixels are not identified by hazard classes based on
the statistical distribution of electivity scores. To provide a
more useful map, we classified WPBR hazard based on the
following parameters:
x+ is the mean sum of electivity scores for BR+ sites = 181
s+ is the standard deviation of electivity scores for BR+
sites = 632
Si is the sum of electivity scores for site i
k1 is an arbitrary constant = 1
k2 is an arbitrary constant = 0
Ci is the classification for site i
[2]
S– = x+ – k1 × s+
[3]
S? = x+ – k2 × s+
[4]
Ci = (BR–, Si < S–) or (BR?, Si < S?) or (BR+,
Si ≥ S?)
where S– = BR– sites, S? = BR?, and k1 and k2 are arbitrary
constants. Using values of k1 = 1 and k2 = 0, pixels with an
electivity sum of less than – 451 are classified as BR– (low
hazard), sums greater than – 451 and less than 181 are classified as BR? (indeterminate), and sums greater than or equal
to 181 are classified as BR+ (high hazard).
Figure 3b shows the distribution of WPBR hazard classes
in the Mixed Forest Province of Minnesota. BR– (low hazard) covers 43% of the land area, BR? (indeterminate) =
45%, and BR+ (high hazard) = 12% (Table 2). Areas of high
hazard are concentrated in the northeastern portion of the
study region, and in the west-central portion. Indeterminate
hazard occurs in a wide band across the centre of the study
region. Low hazard areas are most common in the southern
third and north-central regions of the map.
Classification accuracy assessment
We used the above equations and parameters to classify
the 30% test WPBR occurrence data and compare predicted
values to actual WPBR presence or absence (Table 3). The
accuracy values for the testing set are almost identical to the
full data set (Table 3). The classification predicted BR– in
92% of cases, while BR+ was correctly predicted in 47% of
cases. Eighty-three percent of sites classified as BR? were
actually BR– (Table 3).
Discussion
For a given location on the landscape, the hazard class is
based on the cumulative electivity scores for 22 variables
that relate to WPBR hazard at the macro- and meso-scales.
While individual variables may have a greater or lesser influence on WPBR hazard, it is the sum of electivity for these
variables that yields the overall estimate of hazard class.
Variables are weighted by the strength of their relationship
to WPBR occurrence. Elevation (Fig. 2a), for example,
shows the greatest range in electivity values (–2.0 to 1.4)
and, therefore, has a greater proportional influence than distance to forested wetland where the range is –0.5 to 0.5
(Fig. 2i). Other WPBR hazard classifications have used ad-
ditive methods based on subjective ranking systems of environmental variables (Van Arsdel et al. 1961; Geils et al.
1999). In these systems, expert knowledge is used to weight
criteria. Each site is assigned a score based on the cumulative total for environmental variables. Our system is similar,
however, the weighting of a variable (e.g., elevation range)
is based on the strength of the electivity relationship. This
additive approach is widely used in hazard and decision support modeling (Eastman 1999).
Elevation and topographic factors
In Wisconsin, WPBR becomes more common as latitude
increases regardless of topographic variation, as the cooler
climate favors WPBR infection (Van Arsdel et al. 1961).
The majority of our study region is north of the most northern sites studied by Van Arsdel et al. (1961). Our analysis of
aspect shows flat, north and northeasterly aspects tended toward negative associations (Fig. 2b). Flat sites have less exposure to night breezes and may have lower infection hazard
than sites on steeper slopes (Hunt 1983). Van Arsdel et al.
(1961) showed high WPBR percentages on all aspects with
the exception of southeast. Negative associations on northerly aspects in our study region could indicate that a shorter
growing season inhibits WPBR infection on these sites.
Campbell and Antos (2000) report high-elevation stands in
British Columbia with low frequency of frost-free days had
lower infection levels. However, Van Arsdel (1972) stated
that moisture was the limiting factor in WPBR distribution
in the northern Lake States.
Electivity scores for slope show a general increase from
negative scores with 0° slopes to BR+ scores for the steepest
slopes. In northern Wisconsin, slope had little influence on
WPBR infection (Van Arsdel et al. 1961). However, in British Columbia, Hunt (1983) found that stands on steeper
slopes, because of their greater exposure to evening breezes,
had a greater infection hazard than those on flat topography.
Electivity analysis shows that elevation has a strong influence on WPBR distribution as elevation has the greatest
range in electivity values. Values less than 400 m are
strongly negative, while values greater than 450 m are
strongly BR+. With increasing elevation, daily maximum
temperatures and evaporation decrease, creating more favorable conditions for WPBR infection. The large, contiguous
areas of high hazard (Fig. 3b) in the northeastern and westcentral portions of the study region occur on higher elevation areas. The electivity map for elevation (Fig. 4) closely
resembles the classified WPBR hazard map (Fig. 3b).
Climate
In general, electivity analysis of climate variables was
consistent with previous work in the northern Lake States
and eastern United States (Charlton 1963; Van Arsdel 1972).
Lower monthly minimum and maximum temperatures were
associated with WPBR presence, while higher temperatures
were associated with neutral or negative WPBR. August and
September electivity values for precipitation have similar
patterns, with low and medium values showing negative or
neutral values and higher values showing stronger positive
associations. However, August BR+ values occur at 360,
while in September, BR+ scores occur at lower precipitation
values (340), which may indicate the effect of higher August
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Fig. 3. (a) The sum of electivity scores for WPBR presence or absence for 22 environmental variables. Color gradation indicates relative WPBR hazard, light colors indicate a negative relationship or low hazard, orange to red hues indicate a positive relationship or
high hazard. (b)WPBR hazard classification based on the statistical distribution of the sum electivity scores for WPBR presence or absence and 22 environmental variables. The original WPBR hazard zones are superimposed over Figs. 3a and 3b.
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Table 2. Percent area for two WPBR hazard
classifications within the Mixed Forest Province of Minnesota: Van Arsdel (1964) and
higher resolution WPBR hazard classification.
Hazard class
Van Arsdel (1964)
Moderate
High
Very high
Higher resolution classification
Low
Indeterminate
High
Table 3. Accuracy assessment for WPBR hazard prediction
based on 30% testing data set using the standard deviation and
mean from the 70% training set and the full data set using the
standard deviation and mean from the full data set.
Percent area
9
50
42
43
45
12
Note: Because of rounding error, sum of percent
area for (Van Arsdel 1964) adds up to 101%.
temperatures and PET. This suggests that WPBR hazard varies in space and time because of the interaction of seasonal
climate differences and topographic variation. Regional climate conditions are favorable for WPBR infection (Van
Arsdel 1972); thus, some of the variability we observe in
electivity values relates to mesoscale effects of elevation and
topography. It is important to view climate relationships to
WPBR infection hazard in the context of macroscale climate
gradients. In the Lake States region and in northwestern
North America, WPBR infection rates tend to decrease on a
southward latitudinal gradient. In the Lake States region this
is due to increasing growing season temperatures that create
unfavorable conditions for the completion of the C. ribicola
life cycle across an increasing range of topographic and site
conditions (Van Arsdel 1961). However, for whitebark pine
(Pinus albicaulis Engelm.) in northwestern North America,
the decrease in infection rates is due to the decrease in precipitation (Hoff and Hagel 1990). The Mixed Forest Province in Minnesota may not encompass a large enough
geographic area to detect strong macroscale climate effects
on WPBR infection.
Distance from water and forested wetlands
Van Arsdel (1965) demonstrated that large water bodies
such as Lake Superior influence the distribution of WPBR
through their relationship to night breezes and spore dispersal. Electivity analysis of WPBR occurrence for the
North Shore of Lake Superior in Minnesota shows a similar
pattern (Fig. 2h). A related pattern is evident with distance
from water bodies greater than 10 000 ha with positive
electivity scores occurring in a band from approximately 15
to 20 km. Van Arsdel (1965) describes a similar pattern
around smaller lakes, and stated that the distance to heavily
infected areas varied with topography and was usually associated with the first high ridge away from the lake. Our analysis of distance from forested wetland shows a relatively
weak relationship (Fig. 2i); however, the results parallel
those of Van Arsdel (1967).
Distribution of WPBR hazard and comparison of
classifications
A comparison of the original WPBR hazard map (Van
Arsdel 1964) with the higher resolution map shows a strong
Testing data set
Classified BR– (low)
Classified BR?
(indeterminate)
Classified BR+ (high)
Full data set
Classified BR– (low)
Classified BR?
(indeterminate)
Classified BR+ (high)
Actually
BR–
Actually
BR+
92
83
8
17
53
47
92
82
8
18
54
46
general correspondence (Fig. 3b, Table 4). The three classes
mapped by Van Arsdel (1964) in the Mixed Forest Province
are defined similarly in terms of management intervention
level to the higher resolution map classes. These maps differ
most in the indeterminate class (BR?). Van Arsdel’s map
classifies 56% of the indeterminate area as very high hazard
(Table 4), while our analysis shows that 82% of the samples
classified as indeterminate were low hazard (Table 3). The
original map was extrapolated based on analyses in northern
Wisconsin and broad-scale environmental conditions in
Minnesota. The higher resolution WPBR hazard map suggests that a greater proportion of the land area (43%) has a
lower infection hazard when compared with the original
map (9%) (Table 2). The new, higher resolution map builds
on previous work (Van Arsdel 1972) and indicates significant variability of WPBR hazard within the Mixed Forest
Province of Minnesota.
Model limitations
The electivity analysis methods we have applied describe
correlations of WPBR incidence with environmental variables but do not imply cause and effect. However, we chose
our suite of variables based on previous, more intensive
work that developed the relationships and causal factors related to WPBR infection hazard (Van Arsdel et al. 1961; Van
Arsdel 1964, 1967, 1972; Hunt 1983).
The model assumes that Ribes occurs with sufficient frequency in the study region for spore dispersal to occur with
favorable environmental conditions. Forest-management
agencies attempted to control WPBR through Ribes eradication programs beginning in 1909 and ending in 1967 (Maloy
1997). Research indicates that Ribes eradication programs
have led to reduced WPBR infection levels in Wisconsin and
Maine (Van Arsdel 1972; Ostrofsky et al. 1988). Ribes control programs were implemented in the Mixed Forest Province of Minnesota, but detailed information on the extent
and effectiveness is unknown. More accurate data on the
present distribution of Ribes would likely improve the
model.
Although there is no accuracy assessment of the inventory
data we used to derive WPBR occurrence, we used a large
sample of polygons (n = 9741), and our analysis showed
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Fig. 4. Map of electivity scores for elevation and WPBR occurrence.
Table 4. Percentage of high-resolution WPBR hazard classes within original WPBR hazard zones
(Van Arsdel 1964).
Original WPBR
hazard map
Moderate
High
Very high
Total
High resolution WPBR hazard
classes
Low
20
65
16
101
Indeterminate
1
42
56
99
High
0
19
81
100
Note: Because of rounding error, sum values are above
or below 100%.
strong similarities to previous work in the region (Van
Arsdel 1972). More complete data on WPBR occurrence including numbers of infected trees and numbers of cankers
would add important information to the model and allow for
more specific estimates of damage potential.
Map accuracy and utility of hazard map for white pine
management
The k1 = 1 and k2 = 0 values from eqs. 1 and 2 produce a
conservative classification. This approach limits the worst
type of classification error where a BR+ site (high hazard) is
classified as BR– (low hazard) to 8%. There is a tradeoff;
while false negatives are minimized, false positives (classified BR+, actually BR–) are high at 53%. True low-hazard
sites are predicted at 92%, while true high hazard are predicted at 47% (Table 3). However, these values should be interpreted in the context of the hazard-rating system. Within
the low-hazard area, WPBR may occur, but conditions favorable to the spread of the disease occur infrequently. Given
the 8% misclassification, managing for white pine in these
areas is relatively low risk. Within the high-hazard area,
conditions favorable to WPBR infection occur much more
frequently as reflected by the higher risk level (47%). The
indeterminate area is classified as such, because there is not
sufficient weight of evidence to classify as either low or
high hazard; however, the risk level is still low at 17%. Because of uncertainty on the accuracy of the input data on
WPBR incidence, a conservative classification approach is
warranted (Eastman 1999).
Although this WPBR infection hazard map has much
higher resolution than the original, it is not intended as a
site-specific tool for white pine management. The spatial
resolution of input environmental data ranges from 30-m
grid cells for the elevation and topographic data to 1 km for
the climate data and, thus, is not capable of detecting sitelevel conditions that influence WPBR infection risk (Van
Arsdel 1972). The effective resolution of this map is that of
the forest polygons used to define WPBR relationships with
environmental variables. The mean polygon size for this data
set was 8.4 ± 10.6 ha (mean ± SD). We suggest that the minimum useful resolution is in the range of 5–15 ha. As this is
a modeled probability map, a given point on the ground may
not reflect the classification; however, the surrounding
neighborhood of 5 ha to 1 km2 should more accurately show
the predicted hazard condition.
This map is a decision-support tool that can be used for
local or regional planning work as a first step in determining
areas where eastern white pine regeneration success is more
likely. Once appropriate low-hazard areas are identified, sitespecific decision support tools can be applied (Van Arsdel et
al. 1961). Computer-based decision-support systems that
provide useful, scientifically based analyses can be valuable
tools for natural resource management decisions (Fedra
1995). The high-resolution WPBR hazard map is an exam© 2002 NRC Canada
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White et al.
ple of a component that could be integrated into a white pine
management decision-support system including herbivory
risk, soil and landform conditions, and other factors that influence white pine establishment and development (Tester et
al. 1997).
Conclusions
(1) Our landscape-scale analysis of infection hazard for
WPBR is consistent with earlier work in the Lake States
region in demonstrating the relationship of infection
hazard to climate, elevation, topography, and distance to
water and wetlands.
(2) Using readily available spatial databases, we were able
to classify WPBR infection hazard over a large landscape (94 000 km2) at a scale that can be useful to land
managers.
(3) We present useful methods for integrating and analyzing
readily available spatial data on forest conditions and
physical variables. These methods are relatively easy to
implement and can be applied to other forest landscape
analyses such as ecosystem classification (White and
Host 2000). Confidence intervals generated from
resampling methods indicate the strength and variability
of electivity relationships, while classification accuracy
assessment shows the overall precision of the WPBR
hazard classification. In addition, the user can control
the risk level of the classification.
(4) Decision-support systems could play an important role
in managing for eastern white pine in the Mixed Forest
Province. Eastern white pine has declined precipitously
over the last 120 years because of the post-settlement
logging and subsequent slash fires. Current management
practices including fire suppression tend to favor hardwood over conifer species and also contribute to high
white-tailed deer populations. These factors, along with
WPBR infection hazard, present problems for managers
in maintaining and restoring eastern white pine ecosystems in this region. An integrated decision-support system which included information on WPBR and
herbivory risk, soil, landform, and topography and other
variables could help land managers focus on appropriate
areas for the restoration and maintenance of eastern
white pine.
Acknowledgements
This study was funded by the Minnesota Department of
Natural Resources, Division of Forestry through the University of Minnesota, College of Natural Resources. The manuscript was greatly improved by the comments of two
anonymous reviewers.
References
Ahlgren, C.E. 1973. Regeneration of red pine and white pine following wildfire and logging in northeastern Minnesota. J. For.
74: 135–140.
Albert, D.A. 1995. Regional landscape ecosystems of Michigan,
Minnesota, and Wisconsin: a working map and classification.
USDA For. Serv. Gen. Tech. Rep. NC-178.
1649
Banner, A., Meidinger, D.V., Lea, E.C., Maxwell, R.E., and Von
Sacken, B.C. 1996. Ecosystem mapping methods for British Columbia. Environ. Monit. Assess. 39: 97–117.
Bingham, R.T. 1972. Taxonomy, crossability, and relative blister
rust resistance of 5-needled white pine. In Biology of rust resistance in forest trees. U.S. Dep. Agric. Misc. Publ. 12221.
pp. 271–278.
Campbell, E.A., and Antos, J.A. 2000. Distribution and severity of
white pine blister rust and mountain pine beetle on whitebark
pine in British Columbia. Can. J. For. Res. 30: 1051–1059.
Charlton, J.W. 1963. Relating climate to eastern white pine blister
rust infection and hazard. USDA Forest Service, Eastern Region, Upper Darby, Pa.
Eastman, J.R. 1999. IDRISI 32 Guide to GIS and image processing. Vol. 2. Clark Laboratoriess, Clark University, Worcester,
Mass.
Environmental Systems Research Institute. 1999. Arc-Info, version
8.2 ed. Environmental Systems Research Institute, Redlands,
Calif.
Fedra, K. 1995. Decision support for natural resources management: models, GIS and expert systems. AI Appl. 9: 3–19.
French, D.W. 1992. White pine blister rust can be controlled. In
Proceedings of the White Pine Symposium: History, Ecology,
Policy and Management. Edited by R.A. Stine and M.L.
Baughman. College of Natural Resources and Minnesota Extension Service, University of Minnesota, St. Paul, Minn. Nat.
Resour. Bull. 6044-S. pp. 123–125.
Geils, B.W., Conklin, D.A., and Van Arsdel, E.P. 1999. A preliminary hazard model of white pine blister rust for the Sacramento
Ranger District, Lincoln National Forest. USDA For. Serv. Res.
Note RMRS-RN-6.
Gray, D.R., Regniere, J., and Boulet, B. 2000. Analysis and use of
historical patterns of spruce budworm defoliation to forecast
outbreak patterns in Quebec. For. Ecol. Manage. 127: 217–231.
Hoff, R., Bingham, R.T., and McDonald, G.I. 1980. Relative blister
rust resistance of white pines. Eur. J. For. Pathol. 10: 307–316.
Hoff, R., and Hagel, S. 1990. Diseases of whitebark pine with special emphasis on white pine blister rust. In Proceedings of a
Symposium on Whitebark Pine Ecosystems: Ecology and Management of a High Mountain Resource. USDA For. Serv. Gen.
Tech. Rep. INT-270. pp. 179–190.
Host, G.E., Polzer, P.P., Mladenoff, D.J., White, M.A., and Crow,
T.R. 1995. A quantitative approach to developing regional ecosystem classifications. Ecol. Appl. 6: 608–618.
Hummer, K.E. 2000. History of the origin and dispersal of white
pine blister rust. Hortechnology, 10: 515–517.
Hunt, R.S. 1983. White pine blister rust in British Columbia II.
Can stands be hazard rated? For. Chron. 59: 30–33.
Jacobs, J. 1974. Quantitative measurement of food selection: a
modification of the forage ratio and Ivlev’s electivity index.
Oecologia, 14: 413–417.
Jenkins, S.H. 1979. Seasonal and year to year differences in food
selection by beaver. Oecologia, 44: 413–417.
Jones, A.C. 1992. The problem with white pine. In Proceedings of
the White Pine Symposium, History, Ecology, Policy and Management. Edited by R.A. Stine and M.L. Baughman. College of
Natural Resources and Minnesota Extension Service, University
of Minnesota, St. Paul, Minn. Nat. Resour. Bull. 6044-S.
pp. 64–72.
Kotar, J., and Burger, T.L. 2000. Field guide to habitat type classification for North Central Minnesota. Terra Silva Consultants,
Madison, Wis.
Maloy, O. 1997. White pine blister rust in North America: a case
history. Annu. Rev. Phytopathol. 35: 87–109.
© 2002 NRC Canada
I:\cjfr\cjfr3209\X02-078.vp
Thursday, August 29, 2002 9:50:15 AM
Color profile: Generic CMYK printer profile
Composite Default screen
1650
McNab, W.H., and Avers, P.E. 1994. Ecological subregions of the
United States. USDA For. Serv. WO-WSA-5.
Miller, C., and Urban, D.L. 1999. Interactions between forest heterogeneity and surface fire regimes in the southern Sierra Nevada. Can. J. For. Res. 29: 202–212.
Minnesota Department of Natural Resources. 1983. Phase-II. forest
survey manual, Phase II, intensive inventory. 1983. Resource
Assessment, Grand Rapids, Minn.
Minnesota Department of Natural Resources (DNR). 1999a.
Ecologicial subsections of Minnesota (Map). Minnesota Department of Natural Resources, St. Paul, Minn.
Minnesota Department of Natural Resources (DNR). 1999b. Minnesota land use and land cover: 1990’s census of the land. Minnesota Department of Natural Resources, St. Paul, Minn.
Minnesota Department of Natural Resources (DNR). 1999c. National Wetlands Inventory Polygons. Minnesota Department of
Natural Resources, Division of Waters, St. Paul, Minn.
Mladenoff, D.J., Sickley, T.A., Haight, R.G., and Wydeven, A.P.
1995. A regional landscape analysis and prediction of favorable
gray wolf habitat in the northern Great Lakes region. Conserv.
Biol. 9: 279–294.
Ostrofsky, W.D., Rumpf, T., Struble, D., and Bradbury, R. 1988.
Incidence of white pine blister rust in Maine after 70 years of a
Ribes eradication program. Plant Dis. 72: 967–970.
Pastor, J., and Broschart, M. 1989. The spatial pattern of a northern
hardwood–conifer landscape. Landsc. Ecol. 4: 55–68.
Power, J.M., and Saarenmaa, H. 1995. Object-oriented modeling
and GIS integration in a decision support system for the management of the eastern hemlock looper in Newfoundland.
Comput. Electron. Agric. 12: 1–18.
Rust, M. 1988. White pine blister rust hazard rating: an expert systems approach. AI Appl. 2: 47–50.
Can. J. For. Res. Vol. 32, 2002
Tester, J.R., Starfield A.M., and Frelich L.E. 1997. Modeling for
ecosystem management in Minnesota pine forests. Biol.
Conserv. 80: 313–324.
Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geogr. Rev. 38: 55–94.
Van Arsdel, E.P. 1961. Growing white pine in the Lake States to
avoid blister rust. USDA For. Serv. Lake States For. Exp. Stn.
Tech. Rep. 92.
Van Arsdel, E.P. 1964. Growing white pine in the Lake States to
avoid blister rust — new information for 1964. USDA For. Serv.
Lake States For. Exp. Stn. Res. Note LS-42
Van Arsdel, E.P. 1965. Relationships between night breezes and
blister rust spread on Lake States white pines. USDA For. Serv.
Lake States For. Exp. Stn. Tech. Rep. Res. Note LS-60.
Van Arsdel, E.P. 1967. The nocturnal diffusion and transport of
spores. Phytopathology, 57: 1221–1229.
Van Arsdel, E.P. 1972. Environment in relation to white pine blister rust infection. USDA For. Serv. Tech. Rep. Misc. Publ. 1221.
Van Arsdel, E.P., Riker, A.J., and Patton, R. 1956. The effects of
temperature and moisture on the spread of white pine blister
rust. Phytopathology, 46: 307–318.
Van Arsdel, E.P., Riker, A.J., Kouba, T.F., Suomi, V.E., and
Bryson, R.A. 1961. The climatic distribution of blister rust on
white pine in Wisconsin. USDA For. Serv. Lake States For. Exp.
Stn. Tech. Rep. Stn. Pap. 87.
White, M.A., and Host, G.E. 2000. Mapping range of natural variation ecosystem classes for the Northern Superior Uplands: draft
map and analytical methods. Natural Resources Research Institute, University of Minnesota, Duluth, Minn. Nat. Resour. Res.
Inst. Tech. Rep. NRRI/TR-2000/39.
ZedX. 1995. Database description: Minnesota, Michigan, and Wisconsin. Hi Rez Data Climatological series, ZedX, Boalsburg Pa.
© 2002 NRC Canada
I:\cjfr\cjfr3209\X02-078.vp
Thursday, August 29, 2002 9:50:15 AM