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Modeling Gray Wolf (Canis lupus) Habitat in the Pacific Northwest, U.S.A

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Gray wolves (Canis lupus) were once widespread throughout most of North America including the Pacific Northwest. Wolves were extirpated from the Pacific Northwest in the early 20th century and have been absent for over 60 years. The success of reintroduction efforts in Idaho and the greater Yellowstone area, however, has caused wolf populations in these regions to rise dramatically, giving way to wolf dispersal into Oregon. This study used a Geographic Information System (GIS) and wolf pack locations from the Rocky Mountain region to model gray wolf habitat. A priori models were created under the hypotheses that wolf habitat (1) will include a relatively high prey density, (2) will be limited by human influence, (3) will include favorable landscape characteristics such as forest cover and public ownership, and (4) may be influenced by some combination of these factors. Logistic regression was used to select the best model for predicting wolf habitat. The resulting model was tested in Idaho, Montana, and Wyoming and applied to Oregon to reveal approximately 68,500 km2 of potential wolf habitat that could support a population of approximately 1450 wolves. The final model, which included variables of forest cover and public lands, was applied to the greater Pacific Northwest to identify possible locations for wolf colonization throughout the area. The model may be relevant for other parts of the world where wolf reintroductions are planned or recolonizations are taking place. In addition, the methods presented in our study may be applicable to other wide-ranging large carnivores in other regions.
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17
Modeling Gray Wolf (Canis lupus) habitat in the Pacific
Northwest, U.S.A.
ABSTRACT: Gray wolves (Canis lupus) were once widespread throughout most of North America including the Pacific
Northwest. Wolves were extirpated from the Pacific Northwest in the early 20th century and have been absent for over
60 years. The success of reintroduction efforts in Idaho and the greater Yellowstone area, however, has caused wolf
populations in these regions to rise dramatically, giving way to wolf dispersal into Oregon. This study used a Geographic
Information System (GIS) and wolf pack locations from the Rocky Mountain region to model gray wolf habitat. A priori
models were created under the hypotheses that wolf habitat (1) will include a relatively high prey density, (2) will be
limited by human influence, (3) will include favorable landscape characteristics such as forest cover and public
ownership, and (4) may be influenced by some combination of these factors. Logistic regression was used to select the
best model for predicting wolf habitat. The resulting model was tested in Idaho, Montana, and Wyoming and applied to
Oregon to reveal approximately 68,500 km2 of potential wolf habitat that could support a population of approximately
1450 wolves. The final model, which included variables of forest cover and public lands, was applied to the greater
Pacific Northwest to identify possible locations for wolf colonization throughout the area. The model may be relevant for
other parts of the world where wolf reintroductions are planned or recolonizations are taking place. In addition, the
methods presented in our study may be applicable to other wide-ranging large carnivores in other regions.
Keywords: Canis lupus, geographic information systems, habitat modeling, logistic regression, Oregon, Pacific
Northwest, wolf, wolves
Tad Larsen and William J. Ripple
Tad Larsen (Corresponding Author)
Department of Forest Resources
280 Peavy Hall
Oregon State University
Corvallis, OR 97331
Phone: (541) 737-3090
Fax: (541) 737-3049
tad.larsen@oregonstate.edu
William J. Ripple
Department of Forest Resources
280 Peavy Hall
Oregon State University
Corvallis, OR 97331
Phone: (541) 737-3056
Fax: (541) 737-3049
bill.ripple@oregonstate.edu
Journal of Conservation Planning Vol 2 (2006) 17—33
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
18
INTRODUCTION
Gray wolves (Canis lupus) historically had the most
extensive range of any land mammal. Originally ranging
over much of the northern hemisphere, their range has
since been reduced significantly over the centuries by
humans (Mech and Boitani 2003). Wolves were
extirpated from the conterminous United States with the
exception of a small population in northern Minnesota in
the early 20th century (Mech et al. 1995, Mladenoff and
Sickley 1998). Since gaining protection from the
Endangered Species Act (1974) and being reintroduced
into Yellowstone and central Idaho (1995 – 1996), wolves
have begun to recolonize areas in the northern Great
Lake states and the Rocky Mountain region (Fuller 1995,
Mech et al. 1995, Mladenoff et al. 1995, Pletscher et al.
1997, USFWS et al. 2002). An increase of wolf
populations in Idaho has resulted in some wolves
dispersing into Oregon to seek out new habitat (ODFW
2003). These dispersing wolves have ignited much
controversy regarding the potential of gray wolf recovery
in Oregon. Our study focused on ecological factors to
assess the potential wolf habitat in Oregon.
Because wolves are habitat generalists, they can live in
most places in North America that have a sufficient prey
base (Fuller et al. 1992, Haight et al. 1998). Conflicts
typically occur, however, when they occupy areas close
to humans. The majority of wolf mortality is human-
caused whether accidental, intentional or indirectly
through disease (Mech and Goyal 1993, Mladenoff et al.
1995). Predicting favorable wolf habitat thus becomes a
process of locating areas that contain sufficient prey and
provide security from humans to lessen conflict
(Mladenoff et al. 1995).
Prey Availability
The single most important factor for considering wolf
habitat is the availability of prey. A review of documented
wolf studies from the various regions throughout North
America shows that approximately two-thirds of the
variation in wolf density can be explained by variation in
prey biomass (Keith 1983, Fuller 1989, Fuller et al. 2003).
Although wolves are generally not prey-specific, large
ungulates make up the majority of their diet (Fuller et al.
1992, Haight et al. 1998, Corsi et al. 1999, Fuller et al.
2003). In the eastern portion of North America, white-
tailed deer (Odocoileus virginianus) and moose (Alces
alces) in single prey systems typically constitute the
majority of a wolf’s diet (Mech 1970, Peterson 1999).
However, in the northern and western portions, many
different combinations of ungulate species including elk
(Cervus elaphus), moose, caribou (Rangifer tarandus),
muskox (Ovibos moschatus), white-tailed deer, mule deer
(Ododcoileus hemionus), black-tailed deer (O. h.
columbianus), bighorn sheep (Ovis canadensis), and
bison (Bison bison) can be available to wolves in a multi-
prey system (Ballard et al. 1987, Weaver 1994, Fuller et
al. 2003). In addition, beaver (Castor canadensis) and
hares (Lepus americanus and L. othus) are important
secondary prey in the spring and summer seasons (Fuller
1989, Weaver 1994, Jedrzejewski et al. 2002). Due to
the relatively small biomass of beavers and hares,
however, ungulates (primarily immature ungulates) still
make up the greater prey biomass during these times
(Fuller 1989, Mech and Peterson 2003).
Several studies in western North America have found that
in terms of biomass, elk are the most important prey
species for wolves (Huggard 1993, Weaver 1994, Smith
et al. 2000, Peterson and Ciucci 2003). In a review of
western North America studies, Weaver (1994) found wolf
predation on elk and deer to be roughly equal in numbers
(42%), but the elk were far more important in terms of
biomass (56% for elk compared to 20% for deer).
Human Presence
Road Density
In addition to prey availability, wolves require areas that
minimize wolf-human conflicts (Mech 1995, Mladenoff et
al. 1995). One of the most important factors in
determining suitable wolf habitat is road density (Theil
1985, Fuller et al. 1992, Mladenoff et al. 1995). In some
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
19
cases lightly traveled roads can be used as travel
corridors by wolves, but wolves often avoid roads that are
heavily traveled and easily accessible by humans
(Thurber et al. 1994, Mladenoff et al. 1995). Human
interactions with wolves are a primary source of wolf
mortality due to legal, illegal, and accidental killings or
indirectly through disease (Theil 1985, Mech 1989,
Mladenoff et al. 1995).
Theil (1985) found that wolf breeding occurred in areas
with a road density of 0.59 km/km
2
(linear kilometers of
roads per square kilometer) in 13 northern Wisconsin
counties. Other studies in Minnesota and Michigan
provided similar results and a basis for assessing wolf
habitat suitability in the Lake States (Jensen et al. 1986,
Mech et al. 1988). Dispersing wolves, however, have
been shown to travel through areas of high road densities
in order to find suitable habitat (Mech et al. 1995). Since
wolves are not necessarily deterred by the roads
themselves, but rather humans that use the roads, the
difficulty with measuring road density for habitat models
becomes an issue of human activity. And, while the level
of road usage may be a relatively accurate measure for
habitat modeling, such information is rarely available.
Human Density
In the Great Lakes region, Fuller et al. (1992) found that
most wolf packs (88%) in Minnesota were located in
areas where human density was 8 humans/km
2
.
Mladenoff et al. (1995) found that the mean human
density in wolf pack areas in the Great Lakes region was
1.5 humans/km
2
. Light and Fritts (1994) found dispersing
wolves in the Dakotas to be in areas with a mean human
density of 3.5 humans/km
2
, with 8.2 humans/km
2
being
the greatest human density. Human density can be
difficult to assess because most data are only available at
the census tract/block or county level which can vary
significantly in size between tracts/blocks or counties.
Landscape Characteristics
Several landscape characteristics have also been found
to be associated with wolf habitat. These characteristics
may not be a requirement by wolves per se, but rather
may provide additional security from human contact
(Singelton et al. 2002, Boitani 2003). Mladenoff et al.
(1995) found that public land ownership was strongly
related to favorable wolf habitat in the Great Lakes
region. Houts (2000) also found that land ownership was
significantly different between wolf and non-wolf locations
in the northern Rocky Mountain region. Low human
density and low road density, in addition to a greater
amount of wilderness areas, make public land generally
suitable for wolf habitat. Mladenoff et al. (1995) also
found that private industrial forest ownership was strongly
related to favorable wolf habitat.
Forest cover has also been shown to be strongly related
to wolf habitat since it provides habitat for avoiding
humans (Boitani 2003). In the Great Lakes region,
Mladenoff et al. (1995) found that although most pack
areas were located within mixed or deciduous forest, over
92% of all wolf pack areas were located within some type
of forest. In the Rocky Mountain region, Houts (2000)
also found forest cover (mainly conifer dominated) to be a
significant component of wolf habitat.
Previous Models
Several Geographic Information System (GIS) based
models have proven to be effective at predicting habitat
suitability for large carnivores including wolves (Clark et
al. 1993, Mladenoff et al. 1995, Schadt et al. 2002,
Fernandez et al. 2003). Mladenoff et al. (1995) used
logistic regression to map the probability of wolf habitat in
Wisconsin. Stepwise logistic regression resulted in a
model with a road density term to be effective in
assessing wolf habitat throughout the region (Mladenoff
et al. 1995). Later studies corroborated these earlier
results (Mladenoff et al. 1999). Subsequently, other wolf
habitat models have been applied to various areas in the
northern U.S. Rocky Mountains, southern U.S. Rocky
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
20
Mountains, and Italy (Corsi et al. 1999, Houts 2000,
Carroll et al. 2003).
Only one study has modeled wolf habitat in Oregon
(Carroll et al. 2001). Ungulate density data were based
on numbers from a remote sensing “tasseled-cap
greenness” technique that were not found to be
correlated with ungulate harvest data. In addition, the
authors did not test or validate the model, however, with
any measure of wolf habitat (e.g. presence/absence
data).
The objectives of this study were to provide a more
comprehensive model for predicting wolf habitat in
Oregon and the Pacific Northwest. Logistic regression
was used to select the best approximating wolf habitat
model from a set of a priori models based on the previous
wolf research. These a priori models were grouped under
the hypotheses that wolf habitat (1) includes relatively
high densities of prey (Keith 1983, Fuller 1989, Fuller et
al. 2003), (2) is limited by human influence (Theil 1985,
Fuller et al. 1992, Mladenoff et al. 1995), (3) includes
favorable landscape characteristics such as forest cover
and public ownership (Mladenoff et al. 1995, Houts 2000),
and (4) may be influenced by some combination of these
factors.
METHODS
Study Area
The study area for this project included Oregon, Idaho,
Montana, and Wyoming. These states have many similar
characteristics including diverse ecosystems, large
amounts of public land and wilderness areas, and similar
ungulate species. Although wolves have been absent
from Oregon for over 60 years, wolves were reintroduced
into Yellowstone National Park (31 wolves) and central
Idaho (35 wolves) in 1995–1996 (USFWS et al. 2002). In
addition, wolves have dispersed from Canada into
northwestern Montana and through Glacier National Park
(Boyd et al. 1995). Currently, there are an estimated 108
wolves in northwestern Montana, 271 in the Greater
Yellowstone ecosystem, and 285 in central Idaho
(USFWS et al. 2002, USFWS 2004). Since wolves
currently reside in Idaho, Montana, and Wyoming but not
in Oregon, the models were created for Idaho, and the
best model was tested in Idaho, Montana, and Wyoming
and then applied to Oregon.
Spatial Data
Three main factors generally need to be addressed when
assessing wolf habitat: sufficient prey available, low levels
of human influence, and adequate landscape
characteristics (e.g. forest cover and land ownership). In
order to address the availability of prey in Oregon, data
sets were created illustrating ungulate range and density.
Thus, range maps were developed for elk (Cervus
elaphus) and deer (Odocoileus hemionus and O.
virginianus), the main source of prey accessible to wolves
in Oregon.
Ungulate density data were obtained by applying existing
deer and elk population estimates for 68 wildlife
management units in Oregon to the area of ungulate
range within those management units. An Ungulate
Biomass Index (UBI) was used to normalize the relative
biomass of deer and elk, in which the relative biomass of
elk were the equivalent of the relative biomass of three
deer (Keith 1983, Fuller 1989, Mladenoff et al. 1995,
Fuller et al. 2003). Therefore, all UBI values were
measured in terms of deer biomass. For example, the
UBI value for 300 elk in a management unit would be
900; the same value as a management unit containing
900 deer. These ungulate density calculations were
undertaken for elk and deer separately. All ungulate data
were converted from vector to raster data with a 1 km2
cell size for subsequent analysis.
Road density and human density were used to identify
areas with limited human presence. Road densities were
calculated from the U.S. Census Bureau 2000 TIGER
(Topologically Integrated Geographic Encoding and
Referencing) road data (line). These data are equivalent
to the solid lines on a USGS 1:100,000 quadrangle
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
21
(metadata available online at: http://www.census.gov/geo/
www/tiger/rd_2ktiger/tlrdmeta.txt). Paved roads and
improved unsurfaced roads passable year-round by 2
wheel drive automobiles were included for density
calculations, but unimproved forest roads (e.g. logging
roads) and trails were omitted. The Spatial Analyst
extension of ArcMap (ESRI, Redlands, CA) was used
with a search radius of 5 km and output cell size of 1 km2
to calculate road densities in kilometers of road per
square kilometer area (km/km2).
Because most human density data are only available at
the census block/tract or county level that can vary in size
by hundreds of square kilometers, the accuracy of these
data may be questionable for habitat modeling purposes.
The 2000 U.S. Census data at the block group level were
used as a measure for human density to provide a
comparable dataset to previous models (Mladenoff et al.
1995) in addition to a human presence variable. In order
to test a more accurate measure of human impact,
LandScan Global Population 2002 data created by the
Oak Ridge National Laboratory were used (Dobson et al.
2000). These data have a resolution of 30 arc seconds
(approximately 1 km2) and estimate the number of
humans per unit area. The dataset was created from a
population model that not only incorporates census data,
but also roads, slope, land cover, populated places, lights
visible from satellites at night, and other factors to result
in a global human density grid (Dobson et al. 2000).
Because many variables that measure human impact are
used, these data may provide a more accurate
assessment for modeling wolf habitat than census data
alone.
Landscape variables that were found to be significant in
previous models (e.g. public ownership and forest cover)
were incorporated to provide additional insight into
predicting wolf habitat (Mladenoff et al. 1995, Houts
2000). Land ownership was obtained from the U.S.
Bureau of Land Management at a 1:100,000 scale.
These data were then queried to include only public
lands. Land cover data were obtained from the U.S.
Geological Survey National Land Cover Data dataset.
These data are derived from 30 m Landsat Thematic
Mapper (TM) imagery for the conterminous U.S. The
data were queried to include only forest cover. The
percentage of forest cover and public ownership were
converted to a 1 km2 continuous layer by running the
ArcMap Spatial Analyst neighborhood analysis over a
three km radius.
Precipitation data were obtained from the Oregon Climate
Service to incorporate as a climatic variable. These data
were created from the Parameter-elevation Regressions
on Independent Slopes Model (PRISM) and represent
average annual precipitation over a 29-year period (Daly
et al. 2002). Although precipitation was not used in
previous models, we investigated its importance with
regard to ecosystem productivity. These data were
measured in millimeters of precipitation at a resolution of
approximately 4 km2.
Topographic variables such as elevation and slope were
not included in the analysis although some studies have
found them to be of note for certain wolf activities (Paquet
et al. 1996). Wolves are likely to be driven from areas of
generally low elevations and slopes, however, where
human settlements and infrastructures occur (Dobson et
al. 2000) and the relationship between topographic
features and pack presence/absence on a landscape
scale would likely be reversed due to the greater need of
wolves to avoid humans.
In order to test the models, wolf pack data were obtained
for wolf populations in the Rocky Mountains (Idaho,
Montana, and Wyoming). These data were based on
GPS and radio-collared tracking locations obtained by
National Park Service and US Fish and Wildlife in 2003
(USFWS et al. 2004). The radio-collared wolves were
tracked by aircraft a minimum of two times per month and
many were tracked more frequently from the ground
(USFWS et al. 2004). Wolf pack polygons were created
by the minimum convex polygon procedure in the “Animal
Movement” extension for ArcView. Where packs were
known to exist, but lacked radio-collared locations,
polygons of average wolf pack size were created to
represent pack locations (Steve Carson, personal
communication).
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
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Model Selection
In order to find the best overall model for wolf habitat, a
priori models were separated into four categories (Table
1). The first category was grouped under a hypothesis
that the probability of wolf occupancy will increase with
some measure of prey availability (H1). To test this
hypothesis, models were based on elk, deer, and overall
ungulate densities. The second category of models was
grouped under a hypothesis that the probability of wolf
occupancy will decrease with increasing human presence
(H2). To test this hypothesis, models were based on road
density, human density, and human impact. The third
category was grouped under a hypothesis that the
probability of wolf occupancy will increase with favorable
landscape characteristics (H3). To test this hypothesis,
models were based on percent of forest cover, percent of
public ownership, and precipitation. The fourth category
of models was grouped under the hypothesis that there
may be an additive effect of prey availability, human
presence, and/or favorable landscape characteristics
(H4). Therefore, the models with the best-fit values from
each of the first three categories were used in all
combinations (i.e., H1 + H2; H1 + H3; H2 + H3; and H1 +
H2 + H3) to measure the additive effects.
Logistic regression methods were used to compare pack
locations with non-pack locations. Non-pack locations
were based on random polygons (equal in size to the
mean wolf pack size) at least 10 km away from pack
polygons in order to minimize spatial autocorrelation
(Mladenoff et al. 1995). The Information Theoretic
approach following Burnham and Anderson (2002) was
used to select the best models. Small sample size
adjusted Akaike’s Information Criterion (AICC), delta
AICC and Akaike’s weights were used to rank models
(Burnham and Anderson 2002). The best model was
selected based on lowest AICC values for each
hypothesis. Finally, the best models from each
hypothesis were compared to each other to find the best
overall wolf habitat model.
TABLE 1 Summary of Variables Used in Logistic
Regression Models
Models
*
Definition of variables
Hypothesis 1 (H1) -
Prey availability
UngD
Density of elk and deer per
square km (UBI/km
2
)
ElkD
Density of elk per square km
(UBI/km
2
)
DeerD
Density of deer per square
km (UBI/km
2
)
Hypothesis 2 (H2) -
Human presence
RdD
Linear km of road per
square km (km/km
2
)
HuD
Number of humans per
square mile from census
block
group data (humans/km
2
)
HuP
Measurement of human
presence based on
LandScan data
(humans/km
2
)
RdD + HuD
Hypothesis 3 (H3) -
Landscape
characteristics
%For Percentage of forest cover
%Pub Percentage of public land
Precip Annual precipitation (mm)
%For + %Pub
%For + Precip
Hypothesis 4 (H4) -
Additive effects
H1 + H2
H1 + H3
H2 + H3
H1 + H2 + H3
*
Models based on hypotheses that wolf habitat will be identified by
that availability of prey (H1); will be restricted by the presence of
human activity (H2); that some landscape characteristics are
favorable to wolf habitat (H3); and that there may be an additive effect
of prey availability, human presence, or favorable landscape
characteristics (H4).
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
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Model Application
The a priori model selected to be the best approximating
wolf habitat model was applied to Idaho in order to test
the accuracy of the model against the wolf pack and
random polygons. In addition, the model was applied to
Montana and Wyoming and tested against packs and
random polygons as a means of validating the model.
Success was measured by assessing the mean
probability for wolf pack occurrence calculated by the
model for observed wolf packs versus the mean
probability for wolf pack occurrence calculated by the
model for random polygons. The model would be
considered successful if the model predicted a high
probability (>50%) where wolves are present and
predicted a low probability (<50%) where wolves are not
present. Finally, the model was applied to Oregon in
order to identify potential wolf habitat in the state.
Estimating Capacity
Predicting how many wolf packs a given amount of
habitat will support can be difficult. Wolves are social
animals so pack dynamics are very complex to model. In
order to avoid predicting the social complexity of wolf
packs, estimates of wolf density can be based on the
numbers of wolves in relation to prey abundance. Fuller
et al. (2003) compiled data from previous research to
study the relationship between wolf density and prey
availability (Keith 1983, Fuller 1989). Results yielded the
following equation:
W = 3.5 + 3.27U
where W is the number of wolves/1000 km
2
and U is the
UBI/km
2
(r
2
= 0.64, 31 df, P < 0.001). This equation was
used to estimate the number of wolves that could initially
be supported in potential habitat in Oregon based on
current prey population estimates. Estimates were
grouped together into five regions for analysis: the
northeast region, the Cascade region, the Siskiyou/
Klamath (southwest) region, the central coastal region,
and the northern coastal region. Patches of wolf habitat
with a capacity less than four wolves were eliminated
from further analysis. This ensured all areas of wolf
habitat contained enough prey density to support at least
a small number of wolves since prey densities were not
included in the final model.
RESULTS
Spatial Data
Univariate statistics (Kruskal-Wallis rank sum test) show
that most variables included in the models were
significantly different (P < 0.001) between pack and non-
packs (Table 2, page 24) (Mladenoff et al. 1995,
Fernandez et al. 2003). The exceptions were deer
density and ungulate density which did not show
significant differences (P = 0.070 and 0.065 respectively).
Elk density, percent forest, percent public land, and
precipitation were all found to be higher in wolf pack
areas than in random polygons (P < 0.001). Road
density, human density, and human presence were all
found to be lower in wolf pack areas than random
polygons (P < 0.001). These results supported the initial
hypotheses. Deer density and ungulate density,
however, were found to be at similar levels between wolf
packs and random polygons.
Model Selection
The best model from the prey availability hypothesis set
included elk density (Table 3, page 25). This model was
8 AIC
C
lower than the next best model and received 98%
of the Akaike’s weight from this group of models. This elk
density variable was retained for inclusion in our final
modeling step of building additive models associated with
the three main hypotheses. The best model from the
human presence hypothesis set included human density
based on the 2000 US census data. The model was 3
AIC
C
lower than the next best model and received 57% of
the Akaike’s weight from this group of models. The next
closest model was road density and human density, but a
correlation matrix showed these variables to be highly
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
24
TABLE 2 Statistical Comparisons for Habitat Variables between Packs (n = 50) and
Random Non-pack Polygons (n = 50)
Variable Packs
*
Non-Packs
*
Χ
2
P
RdD
(km/km
2
)
0.12 ± 0.12 0.39 ± 0.30 12.82 <0.001
HuD
(hu./km
2
)
0.23 ± 0.32 3.33 ± 4.56 17.98 <0.001
HuP
(hu./km
2
)
0.11 ± 0.18 2.33 ± 3.69 15.62 <0.001
UngD
(UBI/km
2
)
3.76 ± 2.02 2.79 ± 2.35 3.40 0.065
ElkD
(UBI/km
2
)
2.87 ± 1.32 1.33 ± 1.79 11.50 <0.001
DeerD
(UBI/km
2
)
0.85 ± 1.19 1.20 ± 1.26 3.29 0.07
%For (%) 85.42 ± 18.44 19.67 ± 33.26 27.50 <0.001
%Pub (%) 93.75 ± 12.62 53.30 ± 32.25 22.34 <0.001
Precip (mm) 1012.81 ± 330.95 479.52 ± 267.18 24.58 <0.001
All variables were tested using Kruskal-Wallis rank sum test
*
Values are means ± 1 SE
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25
TABLE 3 Summary of Logistic Regression Models for Wolf Habitat vs.
Non-habitat in Idaho
Model
*
K AIC
C
i
Akaike W
i
Hypothesis 1 (H1)
ElkD 2 62.27 0 0.98
UngD 2 70.92 8.65 0.01
DeerD 2 72.42 10.15 0.01
Hypothesis 2 (H2)
HuD 2 51.20 0 0.57
RdD + HuD 3 52.68 1.48 0.27
HuP 2 54.22 3.02 0.12
RdD 2 56.49 5.29 0.04
Hypothesis 3 (H3)
%For + %Pub 3 17.87 0 >0.99
%For 2 34.07 16.20 3.03 * 10
-4
%For + Precip 3 35.41 17.54 1.55 * 10
-4
Precip 2 43.26 25.39 3.06 * 10
-6
%Pub 2 45.88 28.01 8.25 * 10
-7
Hypothesis 4 (H4)
HuD + %For + %Pub 4 20.22 0 0.44
ElkD + %For + %Pub 4 20.23 0.02 0.43
ElkD + %For + %Pub + HuD 5 22.69 2.47 0.13
ElkD + HuD 3 47.81 27.59 4.47 * 10
-7
Final models
%For + %Pub 3 17.87 0 0.77
ElkD + %For + %Pub 4 20.23 2.36 0.23
HuD 2 51.20 33.33 4.43 * 10
-8
ElkD 2 62.27 44.40 1.75 * 10
-10
*
Models based on hypotheses that wolf habitat will be identified by the availability of prey (H1); that wolf
habitat will be restricted by the presence of human activity (H2); that some landscape characteristics are
favorable to wolf habitat (H3); and that there may be an additive effect of prey availability, human presence,
or favorable landscape characteristics (H4).
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
26
correlated (r = 0.77) as were human density and human
presence (r = 0.95). Human density, therefore, was the
only model from the second category to be retained for
inclusion in the final modeling step. The best model from
the landscape characteristics hypothesis included percent
forest cover and percent public ownership. The model
was 16 AIC
C
lower than the next best model and received
99% of the Akaike’s weight from this group of models.
The percent forest cover and percent public ownership
were retained for inclusion in the final modeling step.
The best model from the additive effect hypothesis set
included human density, percent forest cover, and percent
public ownership. The model was only 0.02 AIC
C
lower
than the next best model which included elk density,
percent forest cover and percent public ownership and
received 44% of the Akaike’s weight from this group of
models as compared to 43% for the next best model. A
correlation matrix, however, showed that human density
was negatively correlated with public land (r = -0.7).
Thus, the best model that contained both parameters was
no longer considered for further analyses. The next best
model which included elk density, percent forest cover
and percent public ownership was used in further
analyses.
Comparing the best models from the four hypotheses
revealed that the overall best model included percent
forest cover and percent public ownership. This model
was more than 2 AIC
C
lower than the next best model and
received 77% of the Akaike weight from this group of
models. This final model was considered to be the best
approximating model for predicting wolf habitat. The
equation for this model is as follows:
logit(P) = -21.10(± 10.67) + (0.10 (± 0.05) * %For)
+ (0.19 (± 0.11) * %Pub)
Model Application
The probability of wolf habitat was calculated using the
equation: P = e
logit(P)
/ 1 + e
logit(P)
This calculation revealed that there was a significant
difference between the mean percent probability for packs
(89.9% ± 17.9) and the mean percent probability for
random polygons (11.9% ± 17.2) in Idaho (Χ
2
= 35.37; P <
0.001 from Kruskal-Wallis rank sum test).
Testing the model against the packs and random
polygons in Montana and Wyoming showed that the
model also worked well in those states (Figure 1, page
27). There was a significant difference between the mean
percent probability for packs (79.2% ± 11.3) and the mean
percent probability for random polygons (5.1% ± 11.3; Χ
2
= 66.40; P < 0.001 from Kruskal-Wallis rank sum test). In
addition, there was no evidence of a difference between
pack results in Idaho versus Montana or Wyoming (P =
0.05 from two-sample t-test) or between results of random
polygons (P = 0.08 from two-sample t-test).
A wolf pack probability greater than 50% (Mladenoff et al.
1995, Fernandez et al. 2003) was used in estimating wolf
habitat in Oregon. Based on this approach, there is
approximately 68,500 km
2
of wolf habitat in Oregon. The
Cascade region has the greatest amount of wolf habitat
(approximately 33,500 km
2
) in Oregon (Figure 2, page
27). The northeast region has the next largest portion of
wolf habitat (approximately 22,800 km
2
) followed by the
Siskiyou/Klamath (approximately 6500 km
2
), the central
coastal (approximately 3200 km
2
), and the northern
coastal (approximately 2500 km
2
) regions.
Estimating Capacity
Applying the equation developed by Fuller et al (2003) to
the estimated available habitat, Oregon would be able to
support approximately 1450 wolves with an average
density of 21 wolves/1000 km
2
. The Cascade region
would be able to support approximately 600 (18
wolves/1000 km
2
) wolves, the northeast region
approximately 460 (20 wolves/1000 km
2
) wolves, the
Siskiyou/Klamath region approximately 120 (18
wolves/1000 km
2
) wolves, the central coastal region
approximately 144 (45 wolves/1000 km
2
) wolves, and the
north coastal region approximately 129 wolves (52
wolves/1000 km
2
).
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
27
FIGURE 2 Modeled wolf habitat >50% in the Rocky Mountain region.
FIGURE 1 Modeled wolf habitat >50% in the Rocky Mountain region.
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
28
DISCUSSION
Spatial Data
Mladenoff et al. (1995) found deer density not to be
related to wolf distribution (8.58 deer/km
2
in pack
territories versus 8.38 deer/km
2
in non-pack territories)
and suggested that the ability of deer to live in close
proximity to humans may influence this relationship. Our
results corroborate the Great Lakes study and found deer
to be at similar densities in random polygons and wolf
pack locations (p = 0.07, Table 2). Houts (2000) found
elk density to be higher in wolf areas than non-wolf areas
(p <0.004) in the Rocky Mountain region. Our results
were similar; elk density was approximately 2 times
higher in wolf pack areas than non-pack areas (p <0.001,
Table 2).
Previous studies (Thiel 1985, Fuller et al. 1992, Mladenoff
et al. 1995) in the upper-Midwest found road density to be
significantly lower in areas where wolves were present
(<0.6 km/km
2
) than in areas that wolves did not inhabit.
Overall, we found road density to be relatively low in our
study area compared to the Great Lakes, but road density
was still found to be significantly lower in wolf pack
locations (0.1 km/km
2
) than random polygons (0.4 km/
km
2
). We also found that road density did not perform as
well as human density, which differs from the research in
Wisconsin (Mladenoff et al. 1995, Mladenoff et al. 1999).
This may be due to the relatively low road density overall
in western states compared to the Great Lake states.
We found human density to be much lower in wolf pack
areas (0.2 humans/km
2
) than non-pack areas (3.3
humans/km
2
) in our study area which is consistent with
other studies from the Great Lakes region (Fuller et al.
1992, Mladenoff et al. 1995) and the Rocky Mountain
region (Houts 2000). Mladenoff et al. (1995) found mean
human density in Wisconsin to be 1.5 humans/km
2
in wolf
pack areas and 5.2 humans/km
2
in non-pack areas. Our
results were likely due to the low relative human
population in the northern Rocky Mountain region and the
large amount of wilderness areas that wolves inhabit.
Although the LandScan human presence data did not
perform as well as census blocks in our habitat models,
our results suggest that human impact models may be a
valuable tool for assessing wolf or other large carnivore
habitat. Since the LandScan data are available for the
entire globe and consistent at relatively fine resolution,
they may represent an efficient and relatively accurate
database for assessing potential wolf habitat in other
regions of the world.
Houts (2000) found that wolf habitat in the Rocky
Mountain region was characterized by forest cover and
public land. In addition, Mladenoff et al. (1995) found wolf
packs to include higher percentages of forest cover and
public ownership in Wisconsin. Our results tend to
corroborate these previous studies and our final model
included percent forest cover and percent public land.
Since wolves are still expanding in the study area, wolf
absence does not necessarily mean an uninhabited area
will not provide wolf habitat. As their populations increase
in a given region, wolves will likely inhabit areas with
higher road and human densities and possibly less public
forested land. However, this study does reflect the
habitat characteristics that the expanding population has
utilized in the Rocky Mountains and will likely use when
dispersing to surrounding areas.
Model Selection
While Mladenoff et al. (1995) successfully used stepwise
logistic regression to select a wolf habitat model, most
natural resource modeling has since shifted to using a
priori hypotheses as a means of model creation and
selection (Burnham and Anderson 2002). Our study
followed the guidelines of Burnham and Anderson (2002)
in order to create a robust wolf habitat model for the
northwest United States. The selected prediction model
used public land and forest cover to identify potential wolf
habitat in our study area. Although no human presence
data were used in the final model, public lands generally
have low road and human densities (r = -0.85 and r = -
0.70 respectively). These variables are therefore
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
29
indirectly taken into account when determining wolf
habitat with the final model.
Although no prey densities were used in the final model,
most forested areas in our study area contain adequate
levels of prey. In fact, applying the second best model
that included elk density, percent forest cover, and
percent public ownership (ElkD + %For + %Pub) in
Oregon showed only a 0.5% difference from the first
model with regard to predicting wolf habitat probability
>50%.
Model Application
When applied to Oregon, the final model predicted over
68,500 km
2
of probable wolf habitat (P 0.5). Most of the
contiguous land available for wolves is in the Cascade
mountain region, while smaller blocks of land are
available in the northeast region, the Klamath region, and
the central and northern coastal regions. The relatively
small amount of wolf habitat in western Oregon may be
underestimated due to the large amount of private
industrial forest available that may be considered habitat.
Mladenoff et al. (1995) found that wolf pack areas
contained more private industrial forest land than non-
pack areas. Due to the relatively small amount of private
industrial forest and lack of data in the study area outside
of the Oregon coastal region, private industrial forest
lands were not included in the analysis. However,
including western Oregon private industrial forests in post
hoc analysis (in conjunction with the public land variable)
shows that there is a much greater amount of wolf habitat
in western Oregon (Figure 3). In fact, including private
industrial forests raises the amount of wolf habitat in
western Oregon by more than 23,000 km
2
(from 45,700
km
2
to 68,700 km
2
). Further research is warranted,
however, to study wolves in relation to these land types.
FIGURE 3 Modeled wolf habitat >50% in Oregon including private industrial forests in Western Oregon.
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
30
Since the wolf data used in our analyses were collected
throughout the year, we used year-round ranges for
ungulates instead of winter/summer. Because ungulates
in the study area migrate primarily by elevation over
relatively short distances versus long distance migrations,
the final predicted wolf habitat in our analyses will likely
incorporate year-round wolf habitat. At various times of
the year wolves may migrate relatively short distances to
follow ungulate migration, but we feel these movements
will likely be from the center to the perimeter of the
predicted wolf habitat areas. Modeling winter versus
summer habitat, however, would be beneficial research in
the future.
The spatial pattern of available land in Oregon differs
from that of Idaho, Montana, and Wyoming. Oregon has
less contiguous predicted habitat and more patches
spread out over the state which would require wolves to
cross areas of unsuitable habitat in order to reach higher
quality habitat. Many studies have shown, however, that
wolves are able to cross large distances through
unsuitable areas while dispersing (Mech 1995, Mech and
Boitani 2003). “Pioneering” wolves have been known to
disperse over large distances, with mates or in order to
find mates, and settle in new habitats far from the nearest
source population (Wabakken et al. 2001, Mech and
Boitani 2003).
Estimating Capacity
We estimate that Oregon is capable of supporting
approximately 1450 wolves based on current elk
population estimates. This estimate is based on previous
studies in relatively stable predator-prey ecosystems
throughout North America (Keith 1983, Fuller 1989, Fuller
et al. 2003). Since Oregon does not currently have
wolves, the predicted capacity could be overestimated
depending on the affect the wolf population has on the
ungulate population. Wolves will likely cause a decrease
in ungulate numbers which, in turn, would lower the
capacity of wolves until some equilibrium is reached.
Carroll et al. (2001) estimated the wolf capacity for
Oregon to be approximately 790 animals based on a
model of deer abundance. Our results, however, were
based on current elk and deer estimates for each wildlife
management unit. In addition, these results increase to
approximately 2200 wolves if private industrial lands are
included in the analysis, with about three quarters of the
estimated wolves located in western Oregon (Figure 3,
page 29).
The estimated capacities of wolves in the coastal areas
are relatively high due to the high densities of black-tailed
deer in the coastal range where primary productivity is
also relatively high. Although deer density was not found
to be related to wolf habitat, deer will inevitably make up a
significant portion of the prey biomass, particularly in the
western portion of the state. It is unusual for wolf density
to be greater than 40 wolves/1000 km
2
, but there are
some exceptions including a study on Isle Royale where
wolf densities reached as high as 92 wolves/1000 km
2
(Peterson and Page 1988, Fuller et al. 2003). Fuller
(1989) also recorded wolf densities in Minnesota to be as
high as 69 wolves/1000 km
2
within the past 25 years.
It is difficult for wildlife biologists to estimate ungulate
populations, especially in western Oregon due to the
large amounts of forested land cover. Therefore,
confidence intervals on ungulate estimates used are fairly
large. In addition, the latest black-tailed deer estimates
used in our analysis do not reflect the current population
losses of deer due to hair-loss syndrome (ODFW 2001).
The lower densities of deer would also limit the wolf
estimates. These are the best wolf population estimates
that can be provided, however, until more accurate
assessments of ungulate populations are available. It is
also important to note that these analyses are a
“snapshot” of wolf habitat and populations under current
policies. Any changes in these policies (e.g. lowering
protection) would likely affect numbers of wolves.
Our final model of forest cover and public land could likely
be applied to the entire western United States as an initial
means of analyzing wolf habitat for conservation
management. The data used in the final model are
consistent across states and easily obtainable. Applying
the model to the Pacific Northwest (Figure 4, page 31)
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
31
shows that there is significant habitat available for wolves
with sufficient connectivity between large areas of habitat.
Outside of the habitat utilized by the current Rocky
Mountain population, most of the available habitat is in
the Cascade Range in Washington and Oregon. In
addition, there are smaller patches of habitat in northern
Washington and northeast to central Oregon that may act
as corridors for relatively safe dispersal or small
populations linking the larger core habitat areas. Once
established in the Cascade Range, dispersal into western
Washington and Oregon would be likely.
From our wolf habitat analysis it appears that there is a
large amount of wolf habitat in the Northwest region of the
U.S. The future of wolves in the Pacific Northwest will
ultimately depend, however, on the level of human
tolerance for dispersing wolves and the policies set forth
by governmental agencies.
We envision that our approach to modeling wolf habitat
will be of use to biologists and policy makers in
developing wolf management plans in other areas of
North America. The methods presented in our study may
be applicable to other wide-ranging large carnivores. In
addition, our model may also be relevant for other parts of
the world where wolf reintroductions are planned or wolf
recolonizations are taking place.
ACKNOWLEDGEMENTS
We would like to thank the Oregon Department of Fish
and Wildlife, the Idaho Department of Fish and Game, the
Montana Department of Fish, Wildlife, and Parks, and
Todd Black from Utah State University for providing the
necessary data for this project. We would also like to
thank Robert Beschta, Robert Anthony, Betsy Glenn, Ben
Miller, and Katie Dugger for reviewing an earlier draft of
this paper.
FIGURE 4 Modeled wolf habitat >50% in the Pacific Northwest.
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
32
LITERATURE CITED
Ballard, W.B., J.S. Whitman, and C.L. Gardner, 1987. Ecology
of an exploited wolf population in south-central Alaska. Wildlife
Monographs, 98:1–54.
Boitani, L., 2003. Wolf conservation and recovery. In: Mech,
L.D., and L. Boitani (eds.), Wolves: Behavior, Ecology, and
Conservation, (Chicago: University of Chicago Press), Pp. 317-
340.
Boyd, D.K., P.C. Paquet, S. Donelon, R.R. Ream, D. H.
Pletscher, and C.C. White, 1995. Transboundary movements of
a recolonizing wolf population in the Rocky Mountains. In:
Carbyn, L.N., S. H. Fritts, and D.R. Seip (eds.), Ecology and
Conservation of Wolves in a Changing World. Canadian
Circumpolar Institute, (Edmonton: University of Alberta), Pp.
135-140.
Burnham, K.P., and D.R. Anderson, 2002. Model Selection and
Multimodel Inference: A Practical Information-Theoretic
Approach. 2
nd
edition. (New York: Springer).
Carroll, C., R.F. Noss, N.H. Schumaker, and P.C. Paquet, 2001.
Is the return of the wolf, wolverine, and grizzly bear to Oregon
and California biologically feasible? In: Maehr, D.S., R.F. Noss,
and J.L. Larkin (eds.). Large Mammal Restoration. (Washington:
Island Press), Pp. 25-46.
Carroll, C., M.K. Phillips, N.H. Schumaker, and D.W. Smith,
2003. Impacts of landscape change on wolf restoration success:
Planning a reintroduction program based on static and dynamic
spatial models. Conservation Biology, 17:536-548.
Clark, J.D., J.E. Dunn, and K.G. Smith, 1993. A multivariate
model of female black bear habitat use for a geographic
information system. Journal of Wildlife Management, 57:519-
526.
Corsi, F., E. Dupre, and L. Boitani, 1999. A large-scale model of
wolf distribution in Italy for conservation planning. Conservation
Biology, 13:150-159.
Daly, C., W. Gibson, and G. Taylor, 2002. 103 - Year High
Resolution Precipitation Climate Data Set for the Conterminous
United States. Spatial Climate Analysis Service, Corvallis,
Oregon.
Dobson, J.E., E.A. Bright, P.R. Coleman, R.C. Durfee, and B.A.
Worley, 2000. LandScan: A global population database for
estimating populations at risk. Photogrammetric Engineering
and Remote Sensing, 66:849-857.
Fernandez, N., M. Delibes, F. Palomares, and D. Mladenoff,
2003. Identifying breeding habitat for the Iberian lynx;
Inferences from a fine-scale spatial analysis. Ecological
Applications, 13:1310-1324.
Fuller, T.K., 1989. Population dynamics of wolves in north-
central Minnesota. Wildlife Monographs, 105:1-41.
Fuller, T.K., 1995. Comparative population dynamics of North
American wolves and African wild dogs. In: Carbyn, L.N., S.H.
Fritts, and D.R. Seip (eds.). Ecology and Conservation of
Wolves in a Changing World. (Edmonton: Canadian
Circumpolar Institute, University of Alberta), Pp. 325-328.
Fuller, T.K., W.E. Berg, G.L. Radde, M.S. Lenarz, and G.B.
Joselyn, 1992. A history and current estimate of wolf distribution
and numbers in Minnesota. Wildlife Society Bulletin, 20:42-55.
Fuller, T.K., L.D. Mech, and J.F. Cochrane, 2003. Wolf
Population Dynamics. In: Mech, L.D., and L. Boitani (eds.).
Wolves: Behavior, Ecology, and Conservation. (Chicago:
University of Chicago Press), Pp. 161-191.
Haight, R.G., D.J. Mladenoff, and A.P. Wydeven, 1998.
Modeling disjunct gray wolf populations in semi-wild landscapes.
Conservation Biology, 12:879-888.
Houts, M.E., 2000. Modeling gray wolf habitat in the Northern
Rocky Mountains. M.S. thesis, University of Kansas, Lawrence.
Huggard, D.J., 1993. Prey selectivity of wolves in Banff National
Park. I. Prey species. Canadian Journal of Zoology, 71:130–39.
Jedrzejewski, W., K. Schmidt, J. Theuerkauf, B. Jedrzejewska,
N. Selva, K. Zub, and L. Szymura, 2002. Kill rates and predation
by wolves on ungulate populations in Bialowieza Primeval
Forest (Poland). Ecology, 83:1341–1356.
Jensen, W.F., T.K. Fuller, and W.L. Robinson, 1986. Wolf
(Canis lupus) distribution on the Ontario-Michigan border near
Sault Ste. Marie. Canadian Field-Naturalist, 100:363-366.
Keith, L.B., 1983. Population dynamics of wolves. In: L.N.
Carbyn (ed.). Wolves in Canada and Alaska: Their status,
biology, and management. Canadian Wildlife Service Report
Series, No. 45. Edmonton, Alberta. Pp. 66-77.
Light, D.S., and S.H. Fritts, 1994. Gray wolf (Canis lupus)
occurrences in the Dakotas. American Midland Naturalist,
121:387-389.
Mech, L.D., 1970. The Wolf: The Ecology and Behavior of an
Endangered Species. (New York: Natural History Press).
Mech, L.D., 1989. Wolf population survival in an area of high
road density. American Midland Naturalist, 121:387-389.
Mech, L.D., 1995. The challenge and opportunity of recovering
wolf populations. Conservation Biology, 9:270-278.
Mech, L.D., and L. Boitani, 2003. Wolf social ecology. In: Mech,
L.D., and L. Boitani (eds.). Wolves: Behavior, Ecology, and
Conservation. (Chicago: University of Chicago Press), Pp. 1-34.
Mech, L.D., S.H. Fritts, G. Radde, and W.J. Paul, 1988. Wolf
distribution and road density in Minnesota. Wildlife Society
Bulletin, 16:85-87.
Larson, Ripple / Journal of Conservation Planning Vol 2 (2006) 17—33
33
Mech, L.D., S.H. Fritts, and D. Wagner, 1995. Minnesota wolf
dispersal to Wisconsin and Michigan. American Midland
Naturalist, 133:368-370.
Mech, L.D., and S.M. Goyal, 1993. Canine parvovirus effect on
wolf population change and pup survival. Journal of Wildlife
Management, 62:1-10.
Mech, L.D., and R.O. Peterson, 2003. Wolf-prey relations. In:
Mech, L.D., and L. Boitani (eds.). Wolves: Behavior, Ecology,
and Conservation. (Chicago: University of Chicago Press), Pp.
131-160.
Mladenoff, D.J., and T.A. Sickley, 1998. Assessing potential
gray wolf restoration in the northeastern United States: A spatial
prediction of favorable habitat and potential population levels.
Journal of Wildlife Management, 62:1-10.
Mladenoff, D.J., T.A. Sickley, R.G. Haight, and A.P. Wydeven,
1995. A regional landscape analysis and prediction of favorable
gray wolf habitat in northern Great Lakes region. Conservation
Biology, 9:279-294.
Mladenoff, D.J., T.A. Sickley, and A.P. Wydeven, 1999.
Predicting gray wolf landscape recolonization: Logistic
regression models vs. new field data. Ecological Applications,
9:37-44.
Oregon Department of Fish and Wildlife (ODFW), 2001. Black-
tailed deer hair loss syndrome. Oregon Department of Fish and
Wildlife [online]. Available: http://www.dfw.state.or.us/
ODFWhtml/InfoCntrWild/btd_hairloss.htm. Accessed February
2004.
Oregon Department of Fish and Wildlife (ODFW), 2003. An
introduction to Oregon wolf issues. Available: http://
www.dfw.state.or.us/ODFWhtml/InfoCntrWild/gray_wolf/
wolf_main.htm.
Paquet, P.C., J. Wierzchowski & C. Callaghan, 1996. Effects of
human activity on gray wolves in the Bow River Valley, Banff
National Park, Alberta. In: Green, J., C. Pacas, S. Bayley, and L.
Cornwell (eds.). A Cumulative Effects Assessment and Futures
Outlook for the Banff Bow Valley. Prepared for the Banff Bow
Valley Study. (Ottawa: Department of Canadian Heritage), 7.
Peterson, R.O., 1999. Wolf-moose interaction on Isle Royale:
The end of natural regulation? Ecological Applications, 9:10–16.
Peterson, R.O., and P. Ciucci, 2003. The wolf as a carnivore. In:
Mech, L.D., and L. Boitani (eds.). Wolves: Behavior, Ecology,
and Conservation. (Chicago: University of Chicago Press), Pp.
105-130.
Peterson, R.O., and R.E. Page, 1988. The rise and fall of the
Isle Royale wolves. Journal of Mammology, 69:89–99.
Pletscher, D.H., R.R. Ream, D.K. Boyd, M.W. Fairchild, and
K.E. Kunkel, 1997. Population dynamics of a recolonizing wolf
population. Journal of Wildlife Management, 61:459–465.
Schadt, S., E. Revilla, T. Wiegand, F. Knauer, P. Kaczensky, U.
Breitenmoser, I. Bufka, J. Cerveny, P. Koubek, T. Huber, C.
Stanisa, and L. Trepl, 2002. Assessing the suitability of central
European landscapes for the reintroduction of Eurasian lynx.
Journal of Applied Ecology, 39:189-203.
Singleton, P.H., W.L. Gaines, and J.F. Lehmkuhl, 2002.
Landscape permeability for large carnivores in Washington: A
Geographic Information System weighted-distance and least-
cost corridor assessment. USDA For. Serv. Res. Pap. PNW-RP-
549. Pacific Northwest Research Station, Portland, OR.
Smith, D.W., L.D. Mech, M. Meagher, W.E. Clark, R. Jaffe, M.K.
Phillips, and J.A. Mack, 2000. Wolf-bison interactions in
Yellowstone National Park. Journal of Mammalogy, 81:1128-
1135.
Thiel, R.P., 1985. Relationship between road densities and wolf
habitat suitability in Wisconsin. American Midland Naturalist,
113:404-407.
Thurber, J.M., R.O. Peterson, T.R. Drummer, and S.A.
Thomasma, 1994. Gray wolf response to refuge boundaries and
roads in Alaska. Wildlife Society Bulletin, 22:61-68.
U.S. Fish and Wildlife Service (USFWS), Nez Perce Tribe,
National Park Service, and USDA Wildlife Services, 2002.
Rocky Mountain Wolf Recovery 2001 Annual Report. Meier, T.
(ed.). USFWS, Ecological Services, Helena, Montana.
U.S. Fish and Wildlife Service (USFWS), Nez Perce Tribe,
National Park Service, and USDA Wildlife Services, 2004.
Rocky Mountain Wolf Recovery 2003 Annual Report. Meier, T.
(ed.). USFWS, Ecological Services, Helena, Montana.
Wabakken, P., H. Sand, O. Liberg, and A. Bjarvall, 2001. The
recovery, distribution, and population dynamics of wolves on the
Scandinavian peninsula, 1978–1998. Canadian Journal of
Zoology, 79:710–725.
Weaver, J.L., 1994. Ecology of wolf predation amidst high
ungulate diversity in Jasper National Park, Alberta. Ph.D.
dissertation, University of Montana, Missoula.
... Habitat models allow researchers to assess habitat suitability for a particular species based on that species' use of habitat in another location. Such models have been utilized in management frameworks and have proven effective at predicting wolf habitat use in different regions of the United States (Mladenoff et al. 1995;Carroll et al. 2003;Larsen and Ripple 2006;Oakleaf et al. 2006). For purposes of this study, we adopted the definition of habitat presented by Hall et al. (1997): "The resources and conditions present in an area that produce occupancy-including survival and reproduction-by a given organism." ...
... Therefore, wolves require habitat that minimizes wolf-human conflicts, which typically occur when wolves occupy areas near humans and livestock (Mech 1995;Mladenoff et al. 1995;Fritts et al. 2003;Oakleaf et al. 2006). Wolves generally select areas remote from human influence, with high human population densities precluding the presence of wolf packs (Fuller et al. 1992;Mladenoff et al. 1995;Larsen and Ripple 2006;Oakleaf et al. 2006). Similarly, several studies (Thiel 1985;Fuller et al. 1992;Mladenoff et al. 1995) have found road density to be one of the most important factors in determining suitable wolf habitat. ...
... Other variables found to be associated with wolf habitat use include public land ownership and vegetation cover. However, as noted by Larsen and Ripple (2006), "these characteristics may not be a requirement by wolves per se, but rather may provide Associate editor: Nathan R. DeJager additional security from human contact." Characterized by lower human and road densities, public lands were positively correlated to wolf habitat use in the Great Lakes and Rocky Mountain regions (Mladenoff et al. 1995;Larsen and Ripple 2006). ...
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After almost a century of absence, gray wolves (Canis lupus) are beginning to recolonize California. Based on current knowledge of wolf habitat use, we developed an expert opinion model to explore the prospects for wolf recovery in Northern California. In our model, we consider the following variables: ungulate prey availability, forest canopy cover, human population density, road density, and livestock distribution. The resulting maps predict favorable wolf habitat and identify areas with high potential for wolf–human conflict in Northern California. Validation and refinement of our model will be possible once California-specific wolf distribution data becomes available. Until then, the preliminary findings from this study can inform management of this endangered species.
... When implemented in our model, the density-threshold represents an arbitrary biological threshold where wolves begin to self-regulate through intraspecific strife or are limited by available prey. Larsen and Ripple (2006) created a habitat suitability map for wolves in Oregon and found that a maximum of 1,450 wolves could occupy Oregon. This value increased to 2,200 wolves if industrial timberland in western Oregon was classified as suitable wolf habitat. ...
... Both the Fuller et al. (2003) and Carbone and Gittleman (2002) equations produce similar estimates of wolf population size and fall within the range reported by Larsen and Ripple (2006). However, these estimates were calculated under the assumption wolves will not cause reductions in prey populations. ...
... Spatially-explicit models could provide a more biologically realistic representation of wolf population dynamics; however, spatially-explicit models require substantial amounts of data that is currently not available in Oregon to effectively parameterize the model. Habitat suitability maps have been developed for Oregon (e.g., Larsen and Ripple 2006), but these maps have not been validated and use of these maps would introduce another unknown source of error in population models. Furthermore, the effects of habitat on survival, reproduction, and dispersal of wolves in Oregon are unknown and it would be impossible to accurately model these effects without unwarranted speculation. ...
Research
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Technical report to the Oregon Fish and Wildlife Commission describing results from an individual-based population model used to assess population viability of wolves in Oregon.
... En particulier, la disponibilité alimentaire semble peu explicative (cf. Larsen & Ripple 2006), probablement parce que la plasticité écologique de l'espèce l'amène à pouvoir se nourrir à partir d'une très grande diversité de proies sauvages et domestiques (cf II 1 b Régime alimentaire). . Il s'agit néanmoins, le plus souvent et majoritairement, d'ongulés sauvages qui se trouvent par ailleurs être en abondance suffisante de manière assez générale en Europe (de l'ordre de 18 millions d'animaux, Apollonio et al. 2010) pour subvenir, quasiment partout, aux besoins énergétiques du prédateur. ...
... Dans ce but, la dépendance des paramètres démographiques à la densité doit aussi être prise en considération selon la typologie et la disponibilité de l'habitat. La distribution/densité de loups peut, en effet, être en partie habitat-dépendante (Falcucci et al. 2013, voire parfois être en lien avec la densité de proies (Messier 1985), même si dans ce domaine, les résultats de la littérature sont hétérogènes (voir par exemple Larsen & Ripple 2006). En outre, la variation temporelle de la qualité environnementale doit être prise en compte, de même que la façon dont cette variation s'opère dans l'espace (notion d'autocorrélation spatiale*). ...
Technical Report
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UNE EXPERTISE COLLECTIVE SUR LES ASPECTS ÉCOLOGIQUES ET BIOLOGIQUES En avril 2016, le Ministère chargé de l'Environnement a demandé à l'ONCFS et au MNHN de lancer une démarche prospective d’évaluation écologique de la situation du loup en France à l’horizon 2025/2030 en se fondant sur une expertise collective scientifique indépendante. Le lancement officiel de cette expertise a eu lieu au MNHN le 7 juillet 2016 en présence de Mme Barbara Pompili. Le Ministère chargé de l'Environnement a demandé à l'ONCFS et au MNHN de coordonner des expertises collectives scientifiques indépendantes sur différents aspects de la présence du loup en France. The French Ministry of Environment has asked to ONCFS and MNHN to coordinate independent scientific expertise on different aspects of the wolf's presence in France.
... En particulier, la disponibilité alimentaire semble peu explicative (cf. Larsen & Ripple 2006), probablement parce que la plasticité écologique de l'espèce l'amène à pouvoir se nourrir à partir d'une très grande diversité de proies sauvages et domestiques (cf II 1 b Régime alimentaire). . Il s'agit néanmoins, le plus souvent et majoritairement, d'ongulés sauvages qui se trouvent par ailleurs être en abondance suffisante de manière assez générale en Europe (de l'ordre de 18 millions d'animaux, Apollonio et al. 2010) pour subvenir, quasiment partout, aux besoins énergétiques du prédateur. ...
... Dans ce but, la dépendance des paramètres démographiques à la densité doit aussi être prise en considération selon la typologie et la disponibilité de l'habitat. La distribution/densité de loups peut, en effet, être en partie habitat-dépendante (Falcucci et al. 2013, voire parfois être en lien avec la densité de proies (Messier 1985), même si dans ce domaine, les résultats de la littérature sont hétérogènes (voir par exemple Larsen & Ripple 2006). En outre, la variation temporelle de la qualité environnementale doit être prise en compte, de même que la façon dont cette variation s'opère dans l'espace (notion d'autocorrélation spatiale*). ...
... However, according to the scenario RCP 4.5 2070 (HADGEM2-ES), this distribution is concentrated towards the western part of the area, whereas the current habitat suitability map shows a distribution in the eastern part of the area. It is thought that the rise in temperature and the decrease in precipitation will cause these changes (Larsen and Ripple, 2006). At the same time, prey species, which are preferred by target species in the field, should be taken into account as to how they will react to possible changes in the future climate. ...
Article
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The increase of human activities in recent years has led to many adverse effects and accelerated climate change at an important level. Therefore, it is very important to know how species will be affected by climate change. This study aimed to determine how the distribution of Gray wolf (Canis lupus), one of the most important predator species in the ecosystem, will be affected by climate change. The study was carried out using presence data of this species in the Lake District and the changes in the habitats and distribution of Gray wolf according to the RCP 4.5 and RCP 8.5 (HADGEM2-ES) climate scenarios. We conducted mapping and modelling of the current habitat preferences of Gray wolf using MaxEnt method based on the bioclimatic features derived from the Worldclim. 4 bioclimatic variables (BIO12, BIO13, BIO16 and BIO19), and 3 environmental variables (slope, ruggedness, topographic position index) were used in the study. The model and maps of the Gray wolf were compared with the future climate scenarios. In conclusion, it is projected that habitats which are determined as suitable for Gray Wolf will decline significantly due to climate change.
... The reintroduction of gray wolves into the Yellowstone National Park increased the dispersion of wolf packs into livestock grazing areas within the northwestern US (Larsen and Ripple, 2006), escalat-ing the incidence of cattle-wolf interactions and cattle predation by wolves in the area (Idaho Department of Fish and Game, 2016). Although the economic implications of predators on livestock systems are mainly associated with animal injury or death (Oakleaf et al., 2003;Breck and Meier, 2004), these parameters are not the only negative impacts that wolf predation causes to beef cattle systems (Laporte et al., 2010). ...
Article
This experiment compared mRNA expression of brain-blood biomarkers associated with stress-related psychological disorders, including post-traumatic stress disorder (PTSD), in beef cows from wolf-naïve and wolf-experienced origins that were subjected to a simulated wolf encounter. Multiparous, non-pregnant, non-lactating Angus-crossbred cows from the Eastern Oregon Agricultural Research Center (Burns, OR; CON; n = 10) and from a commercial operation near Council, ID (WLF; n = 10) were used. To date, gray wolves are not present around Burns, OR, and thus CON were naïve to wolves. Conversely, wolves are present around Council, ID, and WLF cows were selected from a herd that had experienced multiple wolf-predation episodes from 2008 to 2015. After a 60-d commingling and adaptation period, CON and WLF cows were allocated to groups A or B (d -1; 5 CON and 5 WLF cows in each group). On d 0, cows from group A were sampled for blood and immediately slaughtered, and samples were analyzed to evaluate inherent differences between CON and WLF cows. On d 1, cows from group B were exposed in pairs (1 CON and 1 WLF cow) to experimental procedures. Cows were sampled for blood, moved to 2 adjacent drylot pens (1 WLF and 1 CON cow/pen) and subjected to a simulated wolf encounter event for 20 min. The encounter consisted of (1) cotton plugs saturated with wolf urine attached to the drylot fence, (2) reproduction of wolf howls, and (3) three leashed dogs that were walked along the fence perimeter. Thereafter, another blood sample was collected and cows were slaughtered. Upon slaughter, the brain was removed and dissected for collection of the hypothalamus, and one longitudinal slice of the medial pre-frontal cortex, amygdala, and Cornu Ammonis (1 region of the hippocampus from both hemispheres). Within cows from group A, expression of c-Fos proto-oncogene in hippocampus and amygdala were greater (P < 0.01) in WLF vs. CON cows. Within cows from group B, expression of hippocampal brain-derived neurotrophic factor mRNA and expression of c-Fos proto-oncogene mRNA in hippocampus and amygdala were less (P ≤ 0.04) in WLF vs. CON cows. These are key biological markers known to be downregulated during stress-related psychological disorders elicited by fear, particularly PTSD. Hence, cows originated from a wolf-experienced herd presented biological evidence suggesting a psychological disorder, such as PTSD, after the simulated wolf encounter when compared with cows originated from a wolf-naïve herd. © 2017 American Society of Animal Science. All rights reserved.
... 1. In addition to the gray wolf's reduction in federal protection, suitable locations for further reintroduction have been identified by Larsen and Ripple [2006] in the Pacific Northwest, Carroll et al. [2003] in the Southern Rocky Mountains, Mladenoff and Sickley [1998] in the Northeastern United States, Glenz et al. [2001] in Valais, Switzerland and for the timber wolf in the Adirondack Park, Paquet et al. [2001]. ...
Article
Competition effects are incorporated into a model of wolf-population dynamics. A classic single-state model is augmented into a dual-state mapping of the evolution of the size of wolf packs and the number of wolf packs. This dual-state model, unlike the single-state density dependent model, is amenable to analyzing intraspecific competition. The single-state, dual-state and dual-state with competition models are estimated using Yellowstone National Park (YNP) data on wolf populations and pack structures from 1996 to 2011. The dynamic properties of each model are examined under an array of harvesting policies. Results suggest that intraspecific competition matters when projecting wolf populations. Wolf pack removal has competition-reducing effects from added territory availability, making populations more sensitive to pack size reduction than reduction in the number of packs. This research suggests that wildlife managers may consider monitoring the composition of wolf kills throughout a harvesting season, adaptively adjusting harvesting quotas and delineating harvesting zones over a few pack territories rather than spreading these effects evenly across all packs.
... We chose this three-km buffer size based on literature review (e.g. Larsen and Ripple, 2006;Belongie, 2008), which well-describes landscape characterization over a wide geographic range. ...
Technical Report
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The report analyzes 27 gray wolf habitat modeling studies and identifies 359,000 square miles of additional habitat for gray wolves in 19 of the lower 48 states that could significantly boost the nation's 40-year wolf recovery efforts. The gray wolf population could be doubled to around 10,000 by expanding recovery into areas researchers have identified as excellent habitat that is yet unoccupied by wolves in the Northeast, West Coast, Western Great Lakes, southern Rocky Mountains, the Grand Canyon, Southwest and Texas. According to the studies, these areas are capable of supporting a minimum of 5,000 wolves, which would nearly double the existing wolf population. The report documents 56 instances over 30 years where wolves have dispersed from existing core recovery areas to states where they have yet to reestablish. These events, which frequently have ended in the dispersing wolves being shot, highlight the need for continued federal protections and recovery planning to increase the odds for dispersing wolves to survive and recolonize former terrain. Recovering wolves to these additional areas is necessary to ensure the long-term survival of gray wolves in the lower 48 states and enrich the diversity of U.S. ecosystems that have lacked the gray wolf as a top predator for decades. At last count the three existing wolf populations combined include only roughly 5,400 wolves, which is below what scientists have identified as the minimum viable population size necessary to avoid extinction. In wolf recovery areas where federal protections have been removed, under state management aggressive wolf hunting and trapping seasons have been set, resulting in declines in wolf populations. With gray wolf populations isolated from one another and experiencing declines, doubling the population by facilitating wolf recovery in additional areas is needed to secure the future of gray wolves in the U.S. Studies following reintroduction of wolves to Yellowstone National Park have documented that wolves as top predators play pivotal roles in shaping the structure and function of ecosystems, benefitting a wide range of species, including beavers, songbirds, grizzly bears, foxes, bison, pronghorn and more. Gray wolves are also a substantial draw for people from around the world. Millions of people have traveled to Yellowstone from around the world to see the gray wolves reintroduced in 1995 and 1996, and polls consistently show that a broad majority of the American public supports the recovery of gray wolves, including to new areas where they don't currently occur. Instead of stripping gray wolves of federal endangered species act protections in most of the lower 48, as is currently proposed by the Obama administration, a national recovery plan should be developed to restore wolves to suitable habitat the species once called home.
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A LARGE, DARK WOLF poked his nose out of the pines in Yellowstone National Park as he thrust a broad foot deep into the snow and plowed ahead. Soon a second animal appeared, then another, and a fourth. A few minutes later, a pack of thirteen lanky wolves had filed out of the pines and onto the open hillside. Wolf packs are the main social units of a wolf population. As numbers of wolves in packs change, so too, then, does the wolf population (Rausch 1967). Trying to understand the factors and mechanisms that affect these changes is what the field of wolf population dynamics is all about. In this chapter, we will explore this topic using two main approaches: (1) meta-analysis using data from studies from many areas and periods, and (2) case histories of key long-term studies. The combination presents a good picture-a picture, however, that is still incomplete. We also caution that the data sets summarized in the analyses represent snapshots of wolf population dynamics under widely varying conditions and population trends, and that the figures used are usually composites or averages. Nevertheless, they should allow generalizations that provide important insight into wolf population dynamics.
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We conducted a regional-scale evaluation of landscape permeability for large carnivores in Washington and adjacent portions of British Columbia and Idaho. We developed geographic information system based landscape permeability models for wolves (Canis lupus), wolverine (Gulo gulo), lynx (Lynx canadensis), and grizzly bear (Ursus arctos). We also developed a general large carnivore model to provide a single generalization of the predominant landscape patterns for the four focal species. The models evaluated land cover type, road density, human population density, elevation, and slope to provide an estimate of landscape permeability. We identified five concentrations of large carnivore habitat between which we evaluated landscape permeability. The habitat concentration areas were the southern Cascade Range, the north-central Cascade Range, the Coast Range, the Kettle-Monashee Ranges, and the Selkirk-Columbia Mountains. We evaluated landscape permeability in fracture zones between these areas, including the 1-90 Snoqualmie Pass area, the Fraser-Coquihalla area, the Okanogan Valley, and the upper Columbia and Pend Oreille River valleys. We identified the portions of the Washington state highway system that passed through habitat linkages between the habitat concentration areas and areas accessible to the focal species. This analysis provides a consistent measure of estimated landscape permeability across the analysis area, which can be used to develop conservation strategies, contribute to future field survey efforts, and help identify management priorities for the focal species.
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During September 1980-December 1986, 81 radio-collared wolves (Canis lupus) were monitored in and near the 839-km2 Bearville Study Area )BSA) in north-central Minnesota. Each year winter-territory size averaged 78-153 km2; no territories had road densities >0.72 km/km2. From zero to 30% of radiomarked pup, yearling, or adult wolves left their territories each month. Pups left natal packs during January-March and older wolves left frequently during September-April. Wolves temporarily leaving territories moved 5-105 km away and were absent 3-118 days; up to 6 exploratory moves were made prior to dispersal. Dispersing wolves traveled 5-100 km away during periods of 1-265 days. One disperser joined and established pack, but 16 others formed new packs. Annual dispersal rates were about 0.17 for adults, 0.49 for yearlings, and 0.10 for pups. Each year mean pack size ranged from 5-9 in November/December to 4-6 in March. Annual wolf density (including 16% lone wolves) ranged from 39-59 wolves/1,000 km2 in November-December and 29-40 wolves/1,000 km2 in March. Annual immigration was 7%. The observed mean annual finite rate of increase was 1.02, and annual rates of increase were correlated with mean number of pups per pack in November. Litters averaged 6.6 pups at birth and 3.2 pups by mid-November, at which time pups made up 46% of pack members. Annual survival of radio-marked wolves >5 months old was 0.64. Despite legal protection, 80% of identified wolf mortality was human caused (30% shot, 12% snared, 11% hit by vehicles, 6% killed by government trappers, and 21% kill by humans in some undetermined manner); 10% of wolves that died were killed by other wolves. During sample periods in 2 winters, wolves were located twice daily to estimate predation rates on white-tailed deer (Odocoileus virginianus). Estimated minimum kill rates during January-February (x = 21 days/kill/wolf) did not differ between winters with differing snow depths. Winter consumption averaged 2.0 kg deer/wolf/day (6% body wt/day). Scat analyses indicated deer were the primary prey in winter and spring, but beaver (Castor canadensis) were an important secondary prey (20-47% of items in scats) during April-May. Neonatal deer fawns occurred in 25-60% of scats during June-July whereas the occurrence of beaver declined markedly. Overall, deer provided 79-98% of biomass consumed each month. Adult wolves consumed an estimated 19/year, of which 11 were fawns. A review of North American studies indicates that wolf numbers are directly related to ungulate biomass. Where deer are primary prey, territory size is related to deer density. Per capita biomass availability likely affects pup survival, the major factor in wolf population growth. Annual rates of increase of exploited populations vary directly with mortality rates, and harvest exceeding 28% of the winter population often result in declines. Management decisions concerning wolf and ungulate density and ungulate harvest by humans can be made using equations that incorporate estimate of wolf density, annual ungulated kill per wolf, ungulate densities, potential rate of increase for ungulates, and harvest.
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Gray wolves (Canis lupus) were extirpated in North and South Dakota in the 1920-1930s and rarely reported from the mid-1940s to late 1970s. From 1981 to 1992, 10 wolves were killed in the Dakotas, five of them in 1991—1992. Mortality sites were 46-561 km from the nearest known wolf population, and four were within a single 1175 km2 area. Eight of the 10 animals were £2 years old, suggesting dispersing individuals. Mortality occurred in agrarian prairie areas with mean road densities of 0.71 km/km'2 and human densities of 3.5/km'2. Habitat at mortality sites was radically different from where these wolves apparently originated, demonstrating extreme flexibility in dispersal behavior of wolves. Further increase in wolf occurrences in the Dakotas is likely, related to wolf population increases and range expansion in adjacent states and provinces, especially Minnesota.
Article
THE FIRST REAL BEGINNING to our understanding of wolf social ecology came from wolf 2204 on 23 May 1972. State depredation control trapper Lawrence Waino, of Duluth, Minnesota, had caught this female wolf 112 km ( 67 mi) south of where L. D. Mech had radio-collared her in the Superior National Forest 2 years earlier. A young lone wolf, nomadic over 100 km2 (40 mi2) during the 9 months Mech had been able to keep track of her, she had then disappeared until Waino caught her. From her nipples it was apparent that she had just been nursing pups. "This was the puzzle piece I needed," stated Mech. "I had already radio-tracked lone wolves long distances, and I had observed pack members splitting off and dispersing. My hunch was that the next step was for loners to find a new area and a mate, settle down, produce pups, and start their own pack. Wolf 2204 had done just that."
Article
AS 1 (L. o. MECH) watched from a small ski plane while fifteen wolves surrounded a moose on snowy Isle Royale, I had no idea this encounter would typify observations I would make during 40 more years of studying wolf-prey interactions. My usual routine while observing wolves hunting was to have my pilot keep circling broadly over the scene so I could watch the wolves' attacks without disturbing any of the animals. Only this time there was no attack. The moose held the wolves at bay for about 5 minutes (fig. p), and then the pack left. From this observation and many others of wolves hunting moose, deer, caribou, muskoxen, bison, elk, and even arctic hares, we have come to view the wolf as a highly discerning hunter, a predator that can quickly judge the cost/benefit ratio of attacking its prey. A successful attack, and the wolf can feed for days. One miscalculation, however, and the animal could be badly injured or killed. Thus wolves generally kill prey that, while not always on their last legs, tend to be less fit than their conspecifics and thus closer to death. The moose that the fifteen wolves surrounded had not been in this category, so when the wolves realized it, they gave up. That is most often the case when wolves hunt.