Cougar predation and population growth of

[GoatGuy]

Cougar predation and population growth of
sympatric mule deer and white-tailed deer


Hugh S. Robinson, Robert B. Wielgus, and John C. Gwilliam
Abstract: Mule deer (Odocoileus hemionus) populations throughout the west appear to be declining, whereas whitetailed
deer (Odocoileus virginianus) populations are increasing. We compared abundance, number of fetuses per female
(maternity rate), recruitment, and cause-specific adult (³1 year old) mortality rate for sympatric mule deer and whitetailed
deer in south-central British Columbia to assess population growth for each species. White-tailed deer were three
times more abundant (908 ± 152) than mule deer (336 ± 122) (mean ± 1 SE). Fetal rates of white-tailed deer (1.83)
were similar to those of mule deer (1.78). There was no statistically significant difference in recruitment of white-tailed
deer (56 fawns : 100 does) and mule deer (38 fawns : 100 does). The annual survival rate for adult white-tailed deer
(SWT = 0.81) was significantly higher than that for mule deer (SMD = 0.72). The main cause of mortality in both populations
was cougar predation. The lower mule deer survival rate could be directly linked to a higher predation rate
(0.17) than for white-tailed deer (0.09). The finite growth rate (l) was 0.88 for mule deer and 1.02 for white-tailed
deer. The disparate survival and predation rates are consistent with the apparent-competition hypothesis.

[GoatGuy]

Introduction
Mule deer (Odocoileus hemionus) populations are believed
to be decreasing, while white-tailed deer (Odocoileus
virginianus) are increasing, throughout North America (Gill
1999). In a recent survey, 45% of the jurisdictions polled reported
decreasing populations of mule deer (Crête and Daigle
1999). By contrast, only 13% reported decreasing whitetailed
deer populations, most (52%) reporting increasing whitetailed
deer populations. The reason for the concurrent
declines in mule deer populations and increases in whitetailed
deer populations remains unclear. Indirect and direct
competition for resources between the two species does not
appear to be significant. For instance, Anthony and Smith
(1977) and Swenson et al. (1983) described habitat segregation
between the species, even when populations were allopatric.
Habitat segregation, therefore, usually prevents direct competition.
When direct competition between deer species does
occur, mule deer appear to be dominant (Anthony and Smith
1977; Wood et al. 1989).
It has been suggested that the quality and availability of
mule deer habitat have declined because of timber harvest,
changes in fire regime, and direct competition with livestock
and larger ungulates, namely elk (Anthony and Smith 1977;
McNay and Voller 1995; Clements and Young 1997; Gill
1999). Conversely, the quality of white-tailed deer habitat
may have increased in response to human agricultural practices
(Swenson et al. 1983; Roseberry and Woolf 1998).
Others have suggested stochastic events (e.g., severe winter,
drought) (Unsworth et al. 1999), hunting (McCorquodale
1999), and predation (Hatter and Janz 1994; Ballard et al.
2001) as being capable of causing mule deer declines.
During the winter of 1996–1997, extreme snowfall (Fig. 1)
reduced densities of both white-tailed deer and mule deer in
southern British Columbia. Following this harsh winter, the
white-tailed population was believed to have recovered quickly,
whereas the mule deer population appeared to continue todecline (G. Woods, British Columbia Ministry of Water,
Land, and Air Protection, Nelson, personal communication).

We conducted a retrospective mensurative experiment (Eberhardt
and Thomas 1991) to test whether these perceived
trends were real, and if so, to test 4 hypotheses that could
help explain the observed population trends.
The factors that are thought to limit ungulate species can
be grouped into 4 categories: (1) human harvest (Caughley
and Sinclair 1994, p. 286); (2) stochastic events that are densityindependent
(Unsworth et al. 1999); (3) food and habitat
limitation, which is density-dependent if habitat quality is
stable (McCullough et al. 1990; Mackie et al. 1998); and
(4) predation, which can be density-dependent, densityindependent,
or inversely density-dependent (Messier 1994).
Predation is often the leading cause of adult mortality in
deer populations (for example, see Bleich and Taylor 1998)
and thus may have the most dramatic effect on population
growth (White and Bartmann 1997).
The primary purpose of this research was to determine the
population trend for both deer species, and if any divergence
in growth rates existed, to identify the most likely cause. We
tested 4 plausible hypotheses to assess which of the above
factors were most limiting for mule deer populations in southcentral
British Columbia: (1) overhunting; (2) environmental
stochastic effects; (3) limited resources or poor habitat; and
(4) predation. A secondary purpose was to determine if predation
on mule deer was consistent with density-dependent
or density-independent population processes.
The overhunting hypothesis predicts that any observed decline
in mule deer is due to human-caused mortality. If excessive
hunting is causing mule deer populations to decline,
most mortalities of adults should be attributed to hunter harvest.
Furthermore, adult female mule deer should show higher
rates of human-caused mortality than adult female whitetailed
deer.
The stochastic-event hypothesis predicts that both species
will be limited by random density-independent events that
reduce maternity, recruitment, and survival. Both white-tailed
deer and mule deer should be susceptible to such events and
therefore should show similar directional trends in their vital
rates over time. Mule deer mortality should be positively
correlated with white-tailed deer mortality among years. Any
differences in growth rate should be caused by proportional
(not directional) differences in the effect of the environment
on vital rates (e.g., reproduction, natural mortality). If stochastic
events affect mule deer more dramatically than whitetailed
deer, the correlation between survival and winter severity
should be greater for mule deer than for white-tailed
deer.
The limited resources/poor habitat hypothesis predicts that
forage is limiting the mule deer population by reducing maternity,
recruitment, and survival. If both species are limited
by habitat (e.g., through limitation of food resources), population
growth rates should parallel one another, with growth
slowing as the populations tend toward carrying capacity
(McCullough 1992). If mule deer are more limited by habitat
(through intraspecific competition and (or) interspecific
competition with white-tailed deer), over-winter survival rates
should be lower than for white-tailed deer, with most winter
mortality caused by malnutrition (Short 1981). Furthermore,
maternity and recruitment levels should also be lower for
mule deer because of poor female condition (Connolly 1981,
p. 247).
The predation hypothesis predicts that differences in

[GoatGuy]

population growth will be caused by differences in predation
rate. If predation is limiting, it should be the primary cause
of mortality of adult mule deer. Maternity rates should be
high, whereas recruitment should be low. Following the severe
population reductions in the winter of 1996–1997, both
species of deer should have been at low densities relative to
normal local levels. Messier (1994) showed that type III,
density-dependent predation was especially strong at lower
ranges of prey density. The density dependent predation hypothesis
predicts that as the mule deer population declines,
the predation rate will decline because of a decrease in predator
functional response (number of kills per predator per
unit time) and numerical response (number of predators per
unit area) (Solomon 1949; Messier 1994). White-tailed deer
and mule deer growth rates should parallel one another, with
predation rates decreasing with prey densities. The density
independent predation or apparent-competition hypothesis (Holt
1977) predicts that the mule deer mortality rate will remain
high (type I total predation rate) and (or) increase (type II
total predation rate) as the mule deer population declines because
of the presence of alternative prey (white-tailed deer)
(Messier 1994; Sinclair and Pech 1996).
Study area
Our study area was located in south-central British Columbia
between the towns of Creston (49°06¢N, 116°31¢W) and
Castlegar (49°18¢N, 117°38¢W), just north of the Washington–
Idaho border (Fig. 2). It encompassed approximately 4000 km2,
including the Corn Creek drainage, Pend d’Oreille and South
Salmo rivers, and headwaters of the Upper Priest River. The
physiography of the area is mountainous, with elevations
ranging from 450 to 2165 m.
The climate is Pacific Maritime / Continental, with most
annual precipitation falling in the form of snow (Environment
Canada, Vancouver, British Columbia). Environment
Canada maintains weather stations on the east (Creston) and
west (Castlegar) edges of the study area and provided the
following data. Mean (1961–1990) temperatures range from
–3.0°C (January) to 19.3°C (July) in Creston and from 3.2°C
(January) to 19.9°C (July) in Castlegar. Mean (1961–1990)
annual snowfall is 140.6 cm in Creston (elevation 597 m)
and 224.6 cm in Castlegar (elevation 494 m).
The study area lies within two biogeoclimatic zones: interior
cedar – hemlock (ICH) and Engelmann spruce – subalpine
fir (ESSF) (Meidinger and Pojar 1991). The ICH zone extends
from the lowest elevations of the study area to approximately
1200 m. Western red-cedar (Thuja plicata) and western hemlock
(Tsuga heterophylla) are the dominant tree species in
mature forests, with black cottonwood (Populus balsamifera
trichocarpa) the climax in moister areas. Open mixed stands
of Douglas-fir (Pseudotsuga menziesii) and ponderosa pine
(Pinus ponderosa) are common on more xeric south-facing
slopes (Ketcheson et al. 1991). The ESSF zone occurs from
approximately 1200 to 2100 m. White spruce (Picea glauca)
dominates the climax forest, with subalpine fir (Abies lasiocarpa)
composing the understory and lodgepole pine (Pinus
contorta) common following fire (Coupe et al. 1991).
Fire suppression in the last 50 years has reduced the main
source of natural disturbance. The last major fires in the area
occurred in the 1930s and the forests have now regenerated
to mixed coniferous stands (Woods 1984). Timber has been
harvested in the area since the turn of the 20th century, and
forestry is now the dominant form of disturbance.
The combination of climate and physiography creates seasonally
migratory deer populations (e.g., Garrott et al. 1987).
Both white-tailed deer and mule deer congregate on winter
ranges between December and April. Deer winter ranges are
generally on south- to west-facing slopes and provide a juxtaposition
of open shrub fields and timber stands with higher
canopy closure (Woods 1984; Pauley et al. 1993; Armleder
et al. 1994). Higher elevation winter ranges (900–1200 m)
are almost exclusively occupied by mule deer, whereas lower
elevation ranges (£900 m) are predominantly used by whitetailed
deer. Most winter ranges are located wholly within the
ICH zone. Both species range higher into the ESSF zone
during summer, with mule deer at an average maximum elevation
of 1800 m in early October. The elevation range of
white-tailed deer is not quite as high, but they have been observed
up to 1700 m (J. Gwilliam and H. Robinson, unpublished
data).
In addition to deer, elk (Cervus elaphus), moose (Alces
alces), bighorn sheep (Ovis canadensis), and mountain caribou
(Rangifer tarandus caribou) were found in the study area,
roughly in that order of abundance. Common predators included
coyotes (Canis latrans), black bears (Ursus americanus),
bobcats (Lynx rufus), and cougars (Puma concolor). Low
numbers of grizzly bears (Ursus arctos), lynx (Lynx canadensis),
and wolves (Canis lupus) were also present over the course
of the study.
Deer harvest was permitted throughout the study area except
as dictated by private land owners. Mule deer bucks
were hunted from 10 September to 31 October annually.
White-tailed deer bucks were hunted from 10 September to
30 November during each year of the study. A limited-entry
hunt for white-tailed deer does was conducted in 1997 from
10 October to 10 December and again in 2000 from 1 November
to 20 December.

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Methods
Trapping and monitoring
The Columbia Basin Fish and Wildlife Compensation Program
began radio-collaring and monitoring deer in February
1997. Both mule deer and white-tailed deer were added to
the sample each winter in an attempt to achieve a sample
size of 40–50 animals of each species (Pollock et al. 1989).
Deer were captured on winter ranges (Fig. 2) using helicopter
net-gunning and Clover traps from December to March.
Additional animals were chemically immobilized opportunistically
over the summer months. Animals were injected
with a mixture of 4 mg/kg Telazol™ (tiletamine/zolazepam)
and 2 mg/kg Rompun™ (xylazine hydrochloride) using radiotelemetry
darts (Pneu-Dart Inc. Williamsport, Pennsylvania)
(Kilpatrick et al. 1997). Each animal was fitted with a radio
transmitter equipped with mortality switch on a 6-h delay
(Lotek Inc., Newmarket, Ontario). Through a combination of
aerial and ground telemetry, deer were checked for mortality
signals daily during spring and summer. The frequency of
monitoring was reduced in winter to every 2 or 3 days because
of the reduced risk of carcasses being scavenged in the
absence of bears. Any deer that died within 7 days of handling was censored from the database. All animals were
handled in accordance with Washington State University
Animal Care Permit No. 2843.
Adult mortality
Mortality signals were normally investigated within 24 h.
Cause of death was established on the basis of carcass condition.
For example, McNay and Voller (1995) classified
fresh kills with neck and head injuries, clean incisions at the
gut, and partially buried remains as predation by cougars
(see also O’Gara 1978; Roffe et al. 1996). Femur marrow
consistency was visually assessed as an indication of health
at time of death (Cheatum 1949).
The program MICROMORT (Heisey and Fuller 1985) was
used to calculate survival and cause-specific mortality rates,
both within years and across the study period. To obtain sufficient
data to analyse seasonal survival, we pooled seasonal
data across all 4 years of the study. Seasons were divided
into summer (1 July to 20 September), fall migration/rut
(1 October to 31 December), winter (1 January to 30 April),
and spring migration (1 May to 30 June) based on the movement
patterns of radio-collared mule deer (e.g., Garrott et al.
1987). One-tailed binomial z tests were used to test the null
hypothesis that mule deer mortality rates were less than or
equal to (£) those of white-tailed deer (Zar 1984, p. 101;
Nelson and Mech 1986). Simple regression was used to test
for a correlation between annual survival rates of adults of
the two species. A higher human-caused mortality rate for
mule deer would support the overhunting hypothesis. A higher
natural mortality rate for mule deer would support the

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stochastic-event and (or) resource limitation/poor habitat
hypothesis. A higher predator-caused mortality rate for mule
deer would support the predation hypothesis.
Reproduction and maternity
Radio-collared mortalities and road-killed does were examined
for fetuses prior to parturition (1 June) to determine
maternity rate. Fawn mortality rates were calculated using the
difference between maternity rates and spring fawn/doe ratios
acquired from aerial survey (Kunkel and Mech 1994). A
t test assuming equal variance was used to test for differences
in maternity rates. Maternity is considered an indication
of doe health and therefore of habitat quality. Lower
maternity rates in mule deer would suggest that they were
disadvantaged because of poor habitat quality, thus supporting
the limited resources/poor habitat hypothesis.
Abundance and recruitment
We determined sex and age ratios as well as population
and recruitment estimates using aerial surveys of designated
winter ranges containing radio-collared deer (Bowden et al.
1984; Samuel et al. 1992; Unsworth et al. 1994) (Fig. 2).
Fawns were considered recruits at 9 months of age (only after
they were exposed to mortality factors similar to those affecting
adults) (Bergerud and Elliot 1986). Over-winter survival
rates for deer are often low, and in several studies fawn mortality
has been shown to be as great as 90% before November
(Kunkel and Mech 1994). Therefore, the aerial survey
was conducted in late winter to ensure an accurate measure
of recruitment.
Winter ranges were stratified into three qualitative categories
(high, medium, low) based on an existing inventory
(Heaven et al. 1998), a priori knowledge, and expected deer
densities. A random sample of each stratum was surveyed
from 12 to 14 February 2000 (Unsworth et al. 1994). Deer
were classified as recruits/yearlings, adult does, and adult
bucks based on body size and presence or absence of antlers.
Count data, group size, habitat type, and activity of the deer
when first spotted (resting, standing, running) were then analysed
using the program AERIAL SURVEY (Unsworth et al.
1994) to calculate fawn:doe ratios and abundance of each
species. AERIAL SURVEY uses sightability models (Samuel
et al. 1987) developed for elk, mule deer, bighorn sheep,
and moose. For our analysis of sex and age ratios as well as
relative abundance of both species, we used the mule deer
Hiller 12-E Idaho (winter) sightability model contained in
the program. Sightability models are designed to account for
unseen animals in aerial surveys. An estimate of the number
of animals present during the survey but missed by the observer
is based on amount of vegetative cover, vegetation
class (i.e., shrub, conifer, etc.), snow cover, group size, and
activity of the animal (running, standing, or walking) during
each observation. Unfortunately we were unable to obtain a
sightability model for white-tailed deer in our area, and lacked
the resources to develop one of our own. We believe, however,
that because of similarities in vegetation, survey timing,
and consistency of observers, our survey is equally
applicable to the two species of deer. If differences do exist,
white-tailed deer may be more secretive and possibly harder

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to spot than mule deer, leading to underestimation of their
actual numbers relative to those of mule deer.
Difference in recruitment was tested using the c2 test of
homogeneity (Sauer and Williams 1989; Unsworth et al.
1994). Low recruitment in both species would support the
density dependent predation hypothesis. Significantly lower
mule deer recruitment (with similar maternity rates) would
support the density independent predation hypothesis.
Population growth
Population growth rates (l) were estimated with a Leslie
matrix (Leslie 1945), using a female prebreeding model in
RAMAS GIS (Akcakaya et al. 1999). In a prebreeding model,
the earliest age class in the matrix is almost 1 year old
(Akcakaya et al. 1999:138). Fecundity rates were one-half
(as this was a female-only model) the total recruitment rate.
This assumes that an equal number of male and female
fawns were born in the previous spring. Fecundity of 1-yearolds
was assumed to be one-half that observed in adults, as
this would be their first breeding season (Carpenter 1997;
Mackie et al. 1998, p. 95). Because of limitations on agespecific
maternity and survival rates, we assumed that all females
2+ years old had the same fecundity and survival
rates. Adult female survival rates were calculated using
MICROMORT by censoring all males from the dataset. For
both white-tailed deer and mule deer, the maximum age was
set at 13 years. A sensitivity analysis was conducted on each
age-class model to determine which vital rate, fecundity or
survival, had the greatest effect on the population’s finite
rate of growth (Akcakaya et al. 1999, p. 56). Mean geometric
(span) and annual growth rates were calculated for each
year, based on the above method.
White-tailed deer drop their antlers earlier than mule deer.
During an aerial survey this may cause observers to unwittingly
classify some white-tailed deer bucks as does. This
would lead to overestimation of the actual number of does
and corresponding underestimation of the fawn:doe ratio or recruitment
rate. As a result our population model may underestimate
the white-tailed deer growth rate.
Density
We estimated density, or relative number (N), for each
species in each year of the study (1997–2000) by solving for
Nt–1 using the equation Rt = Nt/Nt–1, where R is the annual
growth rate and Nt is the population estimated from the 2000
aerial survey.
We tested our population-trend estimates by comparing
our model with harvest records collected by British Columbia
Ministry of Water, Land and Air Protection (Cranbrook).
This measure of relative abundance of both species was constructed
by dividing the number of animals harvested by the
number of hunter-days (effort) accumulated in management
units 4-07 and 4-08 from 1987 to 1999.
Density-dependent versus density-independent predation
We tested for density-dependent and density-independent
predation by plotting predation rate against estimated prey
density or relative abundance. The density dependent predation
hypothesis predicts that the predation rate will increase
with prey density. The density independent, or inversely den-sity dependent, predation hypothesis predicts that the predation
rate will remain stable or increase with decreasing prey
density (Messier 1994).
Results
Trapping success and sample size
From March 1997 to March 2000, 43 mule deer and 27
white-tailed deer were radio-collared and monitored. The total
numbers of animals and radio-days for each year of the
study are given in Table 1.
Adult mortality
Twenty-one mule deer mortalities were investigated. Causes
of mortality were divided into 5 categories for this analysis
(Heisey and Fuller 1985): cougar predation, other predation
(including one bobcat and one unknown predator), natural
(two mortalities, caused by malnutrition/poor condition), vehicle,
and unknown (Table 2). No mortalities were attributed
to hunting.
Thirteen white-tailed deer mortalities were investigated.
As with mule deer, causes of mortality were grouped into 5
categories for this analysis (Heisey and Fuller 1985): cougar
predation, other predation, natural (one accidental injury to a
hind leg that we believe led to poor condition and eventually
death), vehicle, and unknown (Table 3). No white-tailed deer
mortalities were attributed to poor condition/malnutrition.
As with mule deer, no radio-collared white-tailed deer were
harvested during the study.
The annual survival rate for mule deer (S1997 = 0.62 and
S1998 = 0.68) was lower than that for white-tailed deer (S1997 =
0.89 and S1998 = 0.94) during the first 2 years of the study
(z1997 = 1.88, df = 1, P1997 = 0.03 and z1998 = 2.02, df = 1,
P1998 = 0.02). In the last 2 years of the study, the whitetailed
deer survival rate decreased (S1999 = 0.77 and S2000 =
0.63), whereas that for mule deer steadily increased (S1999 =
0.72 and S2000 = 0.83), thus there was no difference in mortality
in 1999 (z1999 = 0.35, df = 1, P1999 = 0.36), and the
mule deer survival rate was actually higher in 2000 (z2000 =
1.30, df = 1, P2000 = 0.09) (Fig. 3). Over the course of the

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study, the annual adult mule deer survival rate (SSpan = 0.72)
was significantly lower than that for white-tailed deer (Sspan =
0.81) (zspan = 1.32, df = 1, Pspan = 0.09) (Fig. 3), and there
was a negative correlation (R = –0.89, R2 = 0.79, P = 0.11)
between the two.
The seasonal mule deer survival rate was lowest during
winter (S = 0.89), whereas the white-tailed deer survival rate
was lowest in spring (S = 0.93) (Fig. 4). Cougar predation
was the predominant cause of mule deer mortality in all seasons
except fall, when only unknown mortalities were recorded.
Cougar predation was also the predominant cause of
white-tailed deer mortality in all seasons except summer,
when all mortalities recorded were classified as either vehiclecaused
or unknown.
Determining cause-specific mortality rates relies on gaining
quick access to the carcass, before scavengers have removed
all evidence that may help determine the cause of
death. Unfortunately, this was not always possible, thus 4
mule deer and 2 white-tailed deer had to be classified as unknown
mortalities. We analysed these data in two ways. First
we censored unknown mortalities completely from the dataset.
This resulted in higher overall survival rates and slightly
lower variances (Table 4) and showed that mule deer suffered
from significantly higher “other” predation (z = 1.42,
df = 1, P = 0.08) than white-tailed deer. Based on these data,
we divided all mortalities into just 2 categories: predation
(including cougar) and other (unknown mortalities were simply
grouped with all other mortalities). This more conservative
analysis showed that mule deer suffered significantly
higher predation rates (z = 1.57, df = 1, P = 0.06) (Table 5).
Maternity
Mule deer does were checked for fetuses from 1997 to
spring 2000. White-tailed deer does were examined only
during spring 2000. All adult deer contained fetuses. There
was no difference in fetal rates between mule deer (m = 1.78,
SE = 0.22, n = 9) and white-tailed deer (m = 1.83, SE = 0.31,
n = 6) (t = 0.15, df = 13, P = 0.44).
Abundance and recruitment
An aerial survey conducted in February 2000 showed that
white-tailed deer were almost three times more abundant
(908 ± 152; mean ± SE) than mule deer (336 ± 122) within
the study area. White-tailed deer recruitment (56 fawns : 100
does) was higher than mule deer recruitment (38 fawns : 100
does), though not significantly so (c2 = 0.9050, df = 1, P =
0.34).
We observed fawn:doe ratios of 38:100 for mule deer and
56:100 for white-tailed deer. Based on these recruitment rates
and the prepartum maternity rates discussed above, we estimated
a fawn survival rate of 0.21 for mule deer and 0.31
for white-tailed deer.
Population growth
Using fawn recruitment rates from 2000 and annual adult
survival rates from each year, we estimated annual growth
rates (R) and mean annual geometric growth rate (l) for
each species over the course of the study. Assuming that the
fawn survival rate did not differ significantly from that in
2000, white-tailed deer showed a high growth rate following
the hard winter of 1996–1997, then growth slowed significantly
in 1999 and the population declined in 2000 (Fig. 5).
Mule deer showed a lower initial growth rate that steadily
increased to a positive growth level in 2000 (Fig. 5). However,
based on this model over the course of the study, the
mule deer population decreased annually by 12% (l = 0.88),
while the white-tailed deer population increased annually by
2% (l = 1.02), with growth of the white-tailed deer popula-

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tion in 1996 and 1997 countered by declines in 1999 and
2000. Both populations were most sensitive to changes in
adult survival rates, the elasticity in the adult survival rate
(0.83) being much higher than the elasticity in fecundity
(0.17).
Density
Using harvest and hunter-effort records for both species
we compared population trends over the previous 13 years
(Fig. 6). The population trends for mule deer and whitetailed
deer are positively correlated (R = 0.62, R2 = 0.38, P =
0.02); however, while the white-tailed deer population showed
a significant correlation between population trend and winter
snow accumulation (R = 0.58, R2 = 0.34, P = 0.04), the mule
deer population did not (R = 0.34, R2 = 0.12, P = 0.26).
© 2002 NRC Canada
Robinson et al. 563
Fig. 4. Seasonal cause-specific mortality rates for mule and white-tailed deer in south-central British Columbia, 1997–2000 (survival =
1 – total mortality).
Cause of
mortality
Mule deer
mortality rate
White-tailed deer
mortality rate z score* P
Predation 0.17 (0.00186) 0.09 (0.00111) 1.57 0.06
Other 0.11 (0.00124) 0.10 (0.00128) 0.11 0.46
Survival rate 0.72 (0.00261) 0.81 (0.00215) 1.32 0.09
Note: Values in parentheses show the variance.
*One-tailed binomial z score.
Table 5. Cause-specific mortality rates for mule deer and whitetailed
deer in south-central British Columbia, 1997–2000, using
only two mortality classes, predation and other (unknowns are
grouped with other).

Further, the same pattern in population growth following the
heavy snowfall of 1996–1997 documented during this study
seemed to be repeated in 1992–1993 and 1988–1989.
Density-dependent versus density-independent predation
We plotted total predation rates against population densities
estimated through the method discussed above (Fig. 7).
The predation rate seemed to increase with prey density
(density dependent) for white-tailed deer. By contrast, the
predation rate seemed to increase (inversely density dependent)
with decreasing prey density of mule deer.
Discussion
Overhunting hypothesis
If human harvest was responsible for the decline of mule
deer in the study area, female mule deer should have suffered
higher harvest rates than white-tailed deer. During the
course of this study, humans killed no radio-collared deer of
either species. However, from 1997 to 1999, 96 mule deer
and 312 white-tailed deer were harvested from management
units 4-07 and 4-08, which make up our study area (British
Columbia Ministry of Water, Lands and Air Protection,
Cranbrook, unpublished data). This apparent discrepancy in
our results may be due to the small number of males radiocollared
(Table 1). Given that white-tailed deer were found
to be three times as abundant as mule deer, harvest rates
seem to be in proportion to each species’ availability. Human
harvest does not seem to account for the difference in
growth rates or population sizes of these two species.
Stochastic-event hypothesis
Several researchers have documented high mortality rates
resulting from particularly harsh winters (Loison and Langvatn
1998; McCorquodale 1999). If both species were affected by
stochastic events, there should be no significant directional
difference in the trends of their annual survival rates over
time. Unsworth et al. (1999) saw a strong correlation in survival
rates of mule deer across three states and suggested
that stochastic events may regulate deer populations by having
a strong influence on fawn recruitment. In our study
area, however, there were significant differences in adult
survival rates in the first 2 years and last year of the study,
and across the entire study period when all mortalities were
pooled (Fig. 3). A comparison of the survival rates of both
species (Fig. 3) also shows a negative correlation between
the two (R = –0.89). Finally, the differences in survival rate
were not caused by differences in mortalities attributable to
malnutrition or starvation, which suggests that environmental
stochastic events were not responsible for long-term declines
in this mule deer population.
Limited resources/poor habitat hypothesis
Although our sample of maternity rates was small, the
numbers found are very similar to rates reported in earlier
studies with larger sample sizes in the same region. Just
south of our study area, Zender (1987) found 1.74 fetuses
per road-killed pregnant white-tailed deer (n = 106) compared
with 1.83 in this study. Zeigler (1978) sampled 61
mule deer in central Washington State and counted 1.73 fetuses
per doe compared with 1.78 in our area. Large variations
are often seen in both species and are likely tied to
differences in predominant habitat type (Mackie et al. 1998,
p. 94). The habitats studied by Zender (1987) and Zeigler
(1978) are similar to our own and are likely typical of animals
inhabiting this environment, suggesting that the maternity
rates we found were representative of the both populations.
Both species show relatively high maternity rates compared
with those obtained in other studies. Mackie et al.
(1998, p. 94) reported values ranging from 1.25 to 1.90 fawns
per doe for mule deer and from 1.50 to 2.0 for white-tailed
deer. All females of reproductive age examined in this study
were pregnant. Given the relatively high maternity rates observed,
neither species seems to be limited by habitat.
Further evidence that neither species is nutritionally stressed
is provided by the low occurrence of mortalities attributed to
malnutrition. Only 2 of 21 mule deer mortalities were the result
of poor animal condition as indicated by femur marrow
consistency. Only 1 of 13 white-tailed deer mortalities was
classified as natural. The mule deer survival rate was lowest
in winter, but cougar predation and not malnutrition posed
the highest risk to mule deer during that season (Fig. 4). It is
possible that mule deer are weakened during winter and
therefore more susceptible to cougar predation; however, no
animals classified as cougar mortalities displayed poor condition
as evidenced by poor femur marrow consistency. When combined,
these factors (relatively high maternity rate and low
mortality rate attributable to poor condition) suggest that this
mule deer population’s decline was not caused by limited
food or habitat.

[GoatGuy]

Predation hypothesis
The adult survival rate is the most important variable in
the growth of any deer population (White and Bartmann
Fig. 7. Total predation rates and associated linear trend lines
plotted against density of mule deer (solid line) and white-tailed
deer (broken line) in south-central British Columbia, 1997–2000.
1997; sensitivity analysis in this study (see Results: population
growth)). In several mule deer populations, predation
has been found to be the major source of adult mortality
(e.g., Bleich and Taylor 1998). Cougar predation was the
primary cause of adult mortality in both species in our study
area, accounting for 19 of 28 (68%) deaths for which a
cause could be determined. As such, predation had the most
direct effect on population growth for both species. The predation
rate was significantly higher for mule deer (0.17) than
for white-tailed deer (0.09) (P = 0.06), suggesting that predation
was the cause of this mule deer population’s decline.
Density-dependent and density-independent predation
If predation was density-dependent for both mule deer and
white-tailed deer, the two species should have paralleled one
another in population growth, with growth slowing as the
populations increased, because of increased predation pressure.
In the last 2 years of our study, population-growth rates
deviated dramatically, with decreases in mule deer growth
rates mirrored by increases in white-tailed deer growth rates
(Fig. 5). The rate of predation on mule deer remained high
throughout the study, regardless of mule deer density, but increased
on white-tailed deer as that population rebounded
from the severe winter of 1996–1997 (Fig. 7). Thus, predation
appears to be density-independent or inversely densitydependent
for mule deer and density-dependent for whitetailed
deer.
Messier (1994) suggested that in multiprey systems, the
functional and numerical responses of predators are independent
of the density of any one prey species, and therefore
predators are capable of decreasing prey densities to low
equilibrium points. In our study area, the total number of
predators is likely set by the size of the total prey population.
However, the total predation rate on mule deer seems to
be more strongly tied to the abundance of white-tailed deer
than to that of mule deer. A difference in predation such as
this is consistent with the apparent-competition (alternative
prey) hypothesis first described by Holt (1977).
Apparent competition
Apparent competition can occur at three spatial/temporal
scales (Holt 1977; Holt and Lawton 1994). Predators may be
supported by a single prey species, but during periods of low
availability may switch to a secondary prey species (Hamlin
et al. 1984; Sweitzer et al. 1997). Secondly, predators may
move between habitats and encounter different prey (Seip
1992). Thirdly, invading prey may artificially inflate predator
numbers, resulting in increased predation on resident alternative
prey (Holt 1977; Pech et al. 1995; Namba et al.
1999; Sinclair et al. 1998, p. 569).
White-tailed deer populations are thought to be increasing
across the West (Crête and Daigle 1999), possibly in response
to anthropogenic habitat modifications (Roseberry and
Woolf 1998). This increase may have placed mule deer in
the position of secondary prey described by Holt (1977).
Mule deer may therefore be at risk of depensatory predation,
especially following perturbations in white-tailed deer densities
(Sweitzer et al. 1997).
Traditional theory states that cougars are territorial predators
which show a strong ability to regulate their own population
(Seidensticker et al. 1973; Lindzey et al. 1994). Recent research
has suggested, however, that this may not hold and that
cougars more strongly resemble a migratory generalist that responds
to prey/deer densities (Pierce et al. 2000). Mobile predators,
with short generation times, will respond most quickly
to fluctuations in prey densities and therefore have a strong
numerical response (Körpimaki and Norrdahl 1991). As generalist
predators, cougars may show only a short time lag
while adjusting their number to that of their primary prey.
This time lag may be long enough to cause disproportionate
predation on secondary prey (mule deer) but quick enough
to allow primary prey (white-tailed deer) to quickly rebound
to a high equilibrium.
The harvest data also seem to support the apparentcompetition
hypothesis (Fig. 7). On three occasions over the
past 12 years, mule deer and white-tailed deer populations
have experienced vastly divergent trends in abundance following
major perturbations (1988–1989, 1992–1993, 1996–
1997). We suggest that the number of cougars in the system
is set by the number of primary prey (white-tailed deer). Following
perturbation, mule deer (secondary prey) come under
depensatory predation pressure, causing a further decline in
the mule deer population until cougar densities adjust to the
new white-tailed deer density. As predation on white-tailed
deer is density-dependent, their numbers quickly rebound
under reduced predation pressure.
Management implications
When deer populations are at their lowest, managers often
turn their attention to the effects of predators (Carpenter
1997). Despite high hunting pressure on cougars in our study
area (52 cougars were removed from the study area through
legal harvest from 1996 to 1999; British Columbia Ministry
of Water, Land and Air Protection, Cranbrook, unpublished
data), predation mortality of mule deer seemingly remained
constant (13–19%) (Table 2). Although cougars are the proximate
cause of mule deer decline, the ultimate cause may be
the presence of an abundant invading primary prey (whitetailed
deer). As long as prey numbers are sufficient, immigrating
cougars and other generalist predators may quickly
replace harvested animals, thus maintaining high predation
pressure on mule deer. Increased cougar harvest may be appropriate
following harsh winters to assuage predation pressure
on secondary prey (mule deer). Actively adjusting the
cougar population to a lower equilibrium may eliminate the
lag time in the numerical response of cougars, thus lessening
the period of time that mule deer would be under increased
predation pressure. However, gradual reductions of whitetailed
deer (to prevent prey switching) may provide a more
long-term solution to declining mule deer populations within
our study area, and across the west, by directly reducing
numbers of generalist predators and indirectly reducing predation
pressure on mule deer.
The results and recommendations presented in this paper
are not unequivocal because of the small sample sizes, short
time series, and lack of experimental replication. We were
unable to attain our sample goal of 40–50 animals of each
species for each year. This reduction in radio-collared animals
increases the variance of the rates associated with specific
mortality causes and reduces the chance of detecting
real differences between species (Type II error). We urge
other researchers to test for apparent competition in systems

[GoatGuy]

where mule deer and white-tailed deer are sympatric and
mule deer are thought to be declining.
Acknowledgements
Funding was provided by the Columbia Basin Fish and
Wildlife Compensation Program and Washington State University.
We thank E.O. Garton, P.I. Ross, L. Shipley, and two
anonymous reviewers for valuable comments on the manuscript.
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[GoatGuy]

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[SHAKER]

My time is limited.....could you para-phrase this quickly for me?

[Elkaholic]

Or post a link to a .pdf or .doc, but a paraphrase would be good.

[horshur]

My time is limited.....could you para-phrase this quickly for me?

I'll try........

The whitetail deer in traditional mule deer range allows a higher cougar population in a smaller area and favors the whitetail in the long run over the mulie. More mulies get ate over time.....more so than if the whitetail did not supplement and boost the population of cougar.

Please correct me if I am wrong.

[blackbart]

GoatGuy. Thank-you for your post. It is nice to read something a little different and more informative than the typical "what's your biggest buck bubba?" thread. Keep the biological studies / information coming.

[SHAKER]

I see! Thanx that makes sence.