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Winter hunting habitat of pumas Puma concolor in northwestern Utah and southern Idaho, USA


Abstract and Figures

The diet of pumas Puma concolor in North America con-sists primarily of large ungulates (Anderson 1983) that they stalk to kill (Koford 1946, Hornocker 1970, Sei-densticker, Hornocker, Wiles & Messick 1973, Wilson 1984). Researchers have observed that large stalking felids usually need to approach to within 15-20 m of their prey for a successful attack (Elliot, Cowan & Holling 1977, Van Orsdol 1984). To approach potential prey, stalking predators require sufficient 'hunting cover' (El-liott et al. 1977 , Van Orsdol 1984, Sunquist & Sunquist 1989). Pumas therefore should have rather specific habitat requirements for successful hunting (Hornocker 1970, Laing 1988, Sunquist & Sunquist 1989), and some field evidence supports this prediction. Logan & Irwin (1985) found higher use by pumas and more cache sites in mixed conifer and mountain mahogany Cercocarpus ledi-folius habitat in steep or rugged terrain. They 'inferred' Winter hunting habitat of pumas Puma concolor in northwestern Utah and southern Idaho, USA
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© WILDLIFE BIOLOGY · 9:2 (2003)
The diet of pumas Puma concolor in North America con-
sists primarily of large ungulates (Anderson 1983) that
they stalk to kill (Koford 1946, Hornocker 1970, Sei-
densticker, Hornocker, Wiles & Messick 1973,Wilson
1984). Researchers have observed that large stalking
felids usually need to approach to within 15-20 m of their
prey for a successful attack (Elliot, Cowan & Holling
1977, Van Orsdol 1984). To approach potential prey,
stalking predators require sufficient 'hunting cover' (El-
liott et al. 1977 , Van Orsdol 1984, Sunquist & Sunquist
Pumas therefore should have rather specific habitat
requirements for successful hunting (Hornocker 1970,
Laing 1988, Sunquist & Sunquist 1989), and some field
evidence supports this prediction. Logan & Irwin (1985)
found higher use by pumas and more cache sites in mixed
conifer and mountain mahogany Cercocarpus ledi-
folius habitat in steep or rugged terrain. They 'inferred'
Winter hunting habitat of pumas Puma concolor in northwestern
Utah and southern Idaho, USA
John W. Laundré & Lucina Hernández
Laundré, J.W. & Hernández, L. 2003: Winter hunting habitat of pumas Puma
concolor in northwestern Utah and southern Idaho, USA. - Wildl. Biol. 9: 123-
Pumas Puma concolor are stalking predators of large ungulates that usually cache
their prey. We hypothesize that they require specific habitats to successfully stalk
their prey and that they select cache sites based on some set of criteria. We test-
ed these predictions during a study of predation by pumas on mule deer Odo-
coileus hemionus in south-central Idaho and northwestern Utah, USA. We found
cache points of puma-killed deer in winter by locating radio-collared pumas.
We then located where pumas had killed deer (kill points) by tracks in the snow.
We classified these kill points relative to the dominant forest type and associ-
ation with open, edge or forested areas. At a subset of the kill points and asso-
ciated cache points, we also estimated tree and shrub density, tree diameter at
breast height (dbh), shrub height and slope. Pumas killed deer more often than
expected (P < 0.001) in juniper-pinyon habitat and in edge areas. Tree densi-
ties and dbh at cache points were significantly greater (P < 0.001) than at kill
points or surrounding areas. We concluded that pumas relied on specific habi-
tat characteristics to kill mule deer, and selected cache sites with older, larger
Key words: hunting habitat, Idaho, kill-sites, mule deer, pumas, Utah
John W. Laundré, Instituto de Ecología, A.C. Durango Regional Center, Km
5 carr. a Mazatlán, 34000, Durango, Durango, México, and Idaho State Uni-
versity, Pocatello, ID 83209, USA - e-mail:
Lucina Hernández, Instituto de Ecologia, A.C., Durango Regional Center, Km
5 Carr. a Mazatlán, 34000, Durango, Durango, México - e-mail: lucina@
Corresponding author: John W. Laundré
Received 18 April 2002, accepted 9 September 2002
Associate Editor: Henryk Okarma
124 © WILDLIFE BIOLOGY · 9:2 (2003)
that animals were using these areas to approach their prey.
Laing (1988) found kill/cache sites more often than
expected in pinyon-juniper/lava rock habitat and attrib-
uted that to cover and topographic features which pro-
vided good stalking cover. Koehler & Hornocker (1991)
also found that pumas preferred specific forest types and
terrain, again ascribing this to stalking cover. Jalkotzy,
Ross & Wierzchowski (2000), in a regional scale anal-
ysis, found more kills in areas with greater terrain rug-
gedness. However, apart from these general conside-
rations and larger scale analyses, few studies have
measured specific habitat characteristics of actual sites
where pumas have captured their prey. Most of the data
are actually from cache sites which can be up to 200 m
from kill sites (J.W. Laundré, unpubl. data) and may not
represent actual kill habitat. Thus, the prediction that
pumas require specific habitat characteristics to success-
fully hunt remains untested.
Pumas hunt singly and typically kill prey larger than
themselves. Consequently they often have to cache it for
later use. Caching behaviour is common among the
large solitary felids (Schaller & Vasconselos 1978, Sun-
quist 1981) and is a method to conserve food and to pro-
tect it from scavengers and competitors, including con-
specifics (Sunquist & Sunquist 1989). Pumas cache
their prey by placing it under a tree or bush and cover-
ing it with soil, leaves, sticks (Shaw 1989) and snow.
Apart from this observation, there has been little quan-
tification of cache site characteristics for pumas. They
can drag their prey up to 200 m from the kill site, often
passing up seemingly adequate cache sites (J.W. Laundré,
pers. obs.). This would indicate that some site selection
is occurring. Thus, we predict that pumas are not caching
their prey under the first available tree, but are instead
selecting some factor or factors that make one site bet-
ter than another.
Our objective was to test the predictions that habitat
characteristics of sites where pumas killed mule deer
Odocoileus hemionus in winter and subsequently cached
them, are unique subsets of the various habitats available.
The results of testing these predictions could help in-
crease our understanding of what constitutes success-
ful winter hunting and caching habitat for pumas and po-
tentially, how habitat can affect the impact of pumas on
their prey.
Study area
Our study was performed in the counties of Cassia
(south-central Idaho) and Box Elder (northwestern
Utah), USA. The site spanned about 2,500 km2and con-
tained five small, isolated mountain ranges with eleva-
tions of 1,830-3,151 m a.s.l. Mountain ranges were
fragmented into open and forested habitat patches that
varied in size, complexity and isolation from nearby
patches. Climate was characterized by hot, dry summers
(20-35°C) and cold, windy winters (-25 to 4°C). Hu-
midity rarely exceeded 40%, and precipitation was spo-
radic with an annual mean of 30 cm.
Forested patches were divided into four major types:
1) Douglas fir, a forest type dominated by Douglas fir
Pseudotsuga menziensii but with occasional subalpine
fir Abies lasiocarpa, 2) quaking aspen Populus tremu-
loides, 3) juniper-pinyon, a woodland mix of juniper
Juniperus osteosperma and J. scopulorum and pinyon
pine Pinus edulis, and 4) curl-leaf mountain mahogany
Cercocarpus ledifolius. Dominant shrubs in open areas
included big sagebrush Artemisia tridentata, gray rab-
bitbrush Chrysothamnus nauseosus, bitterbrush Purshia
tridentata, and buffaloberry Shepherdia rotundifolia.
In the winters of 1985-2001, we located sites where
pumas cached mule deer carcasses (cache sites) by
either walking into the area of a radio-collared animal
or following tracks found crossing roads. At each cache
site, we marked the actual location of the carcass (cache
point) with flagging. When possible, we located the area
(kill site) and actual location (kill point) where the
pumas killed the deer by following tracks in the snow.
Thus, some sites located consisted only of cache
sites/points whereas for others we were able to identi-
fy cache and kill sites/points.
At identified kill points, we classified the surround-
ing site relative to macro structure in the categories
open, edge or forest. Our criteria for the open, edge or
forest designations were based on the distance from a
forest patch and/or density of trees. Sites were classi-
fied as open if they were more than 20 m outside the edge
of a forest. Edge sites were those from 20 m outside a
forest patch to 15 m into the forest patch (Altendorf,
Laundré, López-Gonzáles & Brown 2001, Holmes
2000). We also designated 'edge like' areas where the
distance among trees permitted seeing a minimum of
20 m. The 20-m limit was based on data reported for oth-
er stalking felids as the typical distance from its prey a
predator needs to approach undetected for a successful
attack (Sunquist & Sunquist 1989). Kill sites within a
forest patch and >15 m from an opening were consid-
ered forest sites. For kill sites located at edges and in
forests, we classified the forest type based on the pre-
© WILDLIFE BIOLOGY · 9:2 (2003)
dominant tree species (Juniper-pinyon, Douglas fir,
aspen and mountain mahogany) as described above. We
also classified the forest types at cache-only sites when
there were no other forest types within 200 m (maxi-
mum drag distance; J.W. Laundré, unpubl. data).
We revisited most sites the following summers and
measured tree density, tree diameter at breast height
(dbh), shrub density, shrub height and slope. Shrub
measurements were limited to shrubs 50 cm high or
more. We rationalized that in the winter when snow was
often >50 cm deep, shrubs <50 cm would likely not func-
tion as cover for a puma. We used the point quarter
method in measuring these characteristics (Brower,
Zar & von Ende 1990) at cache and kill points. We also
established a grid of 16 points, 10 m apart and centered
on the cache or kill points (Fig. 1) and took the same
measurements. We used the averages of the measure-
ments from these 16 points as estimates for cache and
kill sites and compared them to the data from the cache
and kill points.
To determine if kill points were equally distributed in
the three macro structural types (open, edge and forest)
we used a G-test design (Zar 1999). As pumas usually
drag their prey into forested areas (J.W. Laundré, pers.
obs.), we did not perform this test on cache sites. We also
used a G-test design to test for equal selection of for-
est type. This test included kill sites and cache sites where
we were able to identify the forest type. The expected
number of sites per structure and forest type were cal-
culated based on the percentage of each type in the study
area. As accurate vegetation maps were not available for
the area, we estimated the percentage of each category
by centering a transparent grid (1,000 grid cells) over
U.S. Bureau of Land Management and Forest Service
colour aerial photographs of the mountains in the study
area (Marcum & Loftsgaarden 1980). We limited the esti-
mation to the mountains because pumas rarely used the
valleys. Each photo covered an area of approximately
10 km2. We selected only those photos that covered ele-
vations 2,000 m a.s.l., because pumas in our study rarely
used areas at lower elevation (J.W. Laundré, unpubl.
data). In each photo, we randomly selected 50 of the grid
cell intersections and classified where they fell on the
photo relative to open, edge or forest and to Douglas fir,
juniper/pinyon, mountain mahogany or aspen forest
type. We then used the number of intersections in each
kill or cache point
Sample points within kill or
cache site
10 meters
Kill or cache site
Figure 1. Experimental design to measure tree density, tree diameter
at breast height (dbh), shrub density, shrub height, and slope at kill and
cache sites. Kill and cache points (x) were the center sample points of
the grid. Kill and cache sites were defined as a 50 ×50 m area surrounding
kill and cache points. All grids were oriented magnetic north-south for
Open Edge Forest
x2 = 56.0
P < 0.001
x2 = 30.1
P < 0.001
Figure 2. Observed () and expected () number of kill points found
in the three structural classifications (open, edge and forest; A) and four
forest types (Douglas fir (DF), juniper-pinyon (J/P), mountain mahogany
(MM) and aspen (AP); B). The figures above the columns gives the num-
ber of observed and expected sites in each of the three structural
classes and each of the four forest types.
126 © WILDLIFE BIOLOGY · 9:2 (2003)
category to estimate the percentage covered by each
structure type.
For the micro structural analysis, we used a two-
way analysis of variance to test the null hypothesis
that cache or kill points did not differ in structure from
the surrounding cache and kill sites, nor between each
other. The first treatment (points/sites) was to test for
differences among kill points, cache points, kill sites and
cache sites. The second treatment was among the dif-
ferent kills that we found. We used this design to par-
tition out the inter-site variability and to better test the
main hypothesis of no differences among kill points,
cache points, kill sites and cache sites. We did these
analyses for the five characteristics measured and adjust-
ed the probabilities for multiple tests with a Bonferroni
correction factor (Neu, Byers & Peek 1974). If signif-
icant differences were found among sites, we used a mul-
tiple range test to identify those differences. All rejec-
tion levels were set at P < 0.05, and all means are pre-
sented with ± standard errors.
We sampled 71 aerial photos (3,550 points) and based
on this analysis, forest composition in our study area con-
sisted of 44.0% Douglas fir, 40.9% juniper, 5.3% moun-
tain mahogany, and 9.8% aspen. Relative to structure
types, 48.2% of the study area 2,000 m a.s.l. was open
habitat, 28.5% edge habitat, and 23.3% forest.
We located cache sites of 94 deer killed by pumas. We
identified the kill points at 52 of these sites. Of these
points, pumas killed deer significantly more often in edge
and less often in open habitats (Fig. 2A). For 91 sites,
we were able to classify the forest type associated or most
likely associated with the kill sites. The remaining three
sites were classified as open and, thus, did not have a for-
est type associated with them. Based on our analysis,
pumas killed significantly more deer in the juniper-
pinyon forest type and significantly fewer in the Douglas
fir forest type (Fig. 2B).
We took measurements of microhabitat structure at 76
areas. Of these, 38 had both kill and accompanying cache
points. There were five with kill points only because
pumas killed but did not drag the deer and 33 cache-only
sites (we could not reliably determine the kill point). We
used only the 38 sites with data from both kill and
cache sites in our statistical comparison. For these
areas, we found no differences in shrub density, shrub
height, or slope among kill points, cache points, kill sites
and cache sites (Table 1). For tree densities and dbh,
means at cache points were significantly higher than those
at kill points, kill sites and cache sites (see Table 1).
Relative to our analysis of macro structure at kill
points, our field designation of these points as open, edge
and forest was based on our visual perception of the area
and was subject to possible bias. For the 43 kill points
where we took micro structural measurements, we orig-
inally classified 31 as edges, nine as forest and three as
open. To test for possible bias, we compared the means
of tree densities, dbh, shrub density, and shrub height
of these three groups to corresponding predetermined
edge, forest and open areas we previously measured in
our study area (Table 2; Altendorf et al. 2001). We
found no significant differences in any of the compar-
isons, which indicates that this bias was minimal.
Table 1. Means (± SE) of micro structure measurements at 38 kill points, kill sites (area immediately around the kill point), cache points
and cache sites (area immediately around cache point). The results of the main treatment effects (sites) from the two-way analysis of vari-
ance comparisons are presented. Where there is a significant difference among sites, the mean that was found different by multiple range
testing is indicated with an asterisk (*).
Kill point Kill site Cache point Cache site F P
Tree density (#/100 m2) 5.2 ± 1.4 3.2 ± 0.48 9.2 ± 1.3* 4.1 ± 0.5 7.4 <0.001
Tree dbh (cm) 12.7 ± 1.4 11.0 ± 0.9 17.7 ± 1.2* 12.9 ± 0.7 8.1 <0.001
Shrub density (#/100 m2) 24.0 ± 5.1 15.8 ± 3.4 13.6 ± 2.9 13.4 ± 2.4 2.1 0.10
Shrub height (cm) 76.4 ± 3.2 77.4 ± 2.8 83.4 ± 3.4 82.6 ± 2.4 2.1 0.10
Slope (%) 13.3 ± 1.3 15.8 ± 1.3 14.9 ± 1.4 15.1 ± 0.9 0.7 0.56
Table 2. Comparison of mean micro habitat structure measurements at kill points (KP) designated as open, edge and forest to the same mea-
surements made at predetermined sites (PS) (Altendorf et al. 2001). By definition, there were no tree measurements in open areas. Sample
sizes are given in parentheses. There were no statistical differences between any of the comparisons.
Edge Forest Open
Tree density (#/100 m2) 2.9 ± 0.6 (31) 3.5 ± 0.9 (14) 14.0 ± 3.8 (9) 10.0 ± 2.8 (14)
Tree dbh (cm) 10.9 ± 2.1 (31) 10.9 ± 4.0 (14) 11.6 ± 2.1 (9) 15.9 ± 2.1 (14)
Shrub density (#/100 m2) 15.8 ± 4.2 (31) 14.3 ± 3.8 (14) 10.8 ± 3.1 (9) 11.8 ± 4.0 (14) 40.0 ± 16.6 (4) 22.9 ± 8.1 (14)
Shrub height (cm) 73.8 ± 3.7 (31) 73.3 ± 1.9 (14) 94.8 ± 8.4 (9) 78.9 ± 3.6 (14) 65.7 ± 4.3 (4) 60.1 ± 4.3 (14)
© WILDLIFE BIOLOGY · 9:2 (2003)
Other studies (Hornocker 1970, Logan & Irwin 1985,
Laing1988, Koehler & Hornocker 1991, Williams,
McCarthy & Picton1995, Jalkotzy et al. 2000) have also
documented that pumas use specific forest/terrain types
and, based primarily on cache site data, catch more
prey in these areas. Our data from actual kill points sup-
port the findings of these previous studies in that we also
found pumas killing more deer than expected in one for-
est type and less in another (see Fig. 2). However, in our
area at least, these differences were possibly more relat-
ed to winter habitat selection by deer. Juniper-pinyon
areas are usually at lower elevations and on south-
southwest facing slopes, which are used frequently by
deer in the winter (J.W. Laundré, pers. obs.). Less-
used Douglas fir areas are at higher elevations and are
used by deer early in the winter but are abandoned as
snow depths increase (J.W. Laundré, pers. obs.). Thus,
it may be more than just a forest type effect on catch-
ability of deer. Indeed, previous authors (Hornocker 1970,
Logan & Irwin 1985, Laing1988, Koehler & Hornocker
1991) have interpreted their results in terms of pre-
ferred forest/terrain types providing the right condi-
tions for pumas to successfully stalk their prey, i.e.
stalking habitat. Additionally, Laing (1988) found over-
story cover and horizontal visibility to differ from areas
of high and low puma use, indicating the possible
importance of structural characteristics. However, it
had yet to be tested if these results can be extrapolated
to where pumas actually kill deer. In our study area we
were able to identify kill points at 52 sites. Signs in the
snow indicated that pumas usually made contact with
the deer within 10 m of the initiation of pursuit, and that
deer rarely travelled more than 10-15 m after the puma
made contact. So we considered these points to be rep-
resentative of the entire attack sequence. Data from
the macro and micro structure analyses at these iden-
tified kill points clearly indicate that structural charac-
teristics are important factors, at least in the winter, and
that these characteristics are found in edge and edge-like
areas. Thus, it is not the forest type that a puma is in,
but where it is within that forest type that is important
to its winter hunting success.
Studies of other stalking felids demonstrate that these
are more successful if they approach their prey to with-
in 10-20 m before attacking (Sunquist & Sunquist
1989). Although we found no reported data, we assumed
that pumas need to approach to similar distances. Sun-
quist & Sunquist (1989) also stressed the importance of
stalking cover to enable a predator to approach unde-
tected to within these distances. For example, grass
heights of 0.3-0.8 m increased capture success of African
lions Panthera leo (Elliott et al. 1977, Van Orsdol
This need for stalking predators to approach undetected
explains the selective use of edge areas found in our
study. We would expect a low number of kills in the open
areas where the high visibility puts the puma at a dis-
advantage (Laing 1988). The low number of kills found
in the forest is likely a result of a combination of fac-
tors. Tree densities possibly are too high and obscure
the puma’s view (Laing 1988); the average density of
trees at forest kill points (see Table 2) equates to an
approximate tree-to-tree distance of 7 m (Brower et al.
1999). Additionally, deer generally use the forest area
for resting (Collins 1983). At these times deer are sta-
tionary and usually vigilant (J.W. Laundré, pers. obs.)
and have a greater chance of seeing an approaching puma
and escaping before its arrival. Forest edges or edge-like
areas, on the other hand, are areas where deer are most
likely to be moving, e.g. from feeding in open areas to
forest bed sites. Additionally, mean tree-to-tree dis-
tances are approximately 17 m which may provide ade-
quate visibility to detect moving deer but still sufficient
cover to approach undetected to within attacking dis-
tance. We propose that it is these elements of edge and
edge-like areas that enhance a puma’s ability to detect
and approach close enough to attack deer, making these
areas successful winter hunting habitat for pumas in our
area. It was difficult to ascertain actual kill points in the
summer. Thus, we do not have comparable data for this
season to test if puma hunting patterns change in this
season. Others (Seidensticker et al. 1973, Williams et al.
1995) have reported that pumas rely more on small
mammals in summer than in winter and thus, their hunt-
ing strategies may differ at these times.
For large, solitary predators like pumas, attacking a
prey larger than themselves represents a major energy
expenditure (Ackerman, Lindzey & Hemker 1986) and,
if successful, a major energy gain for that investment.
In the framework of optimal foraging theory, meat
stolen by other animals can represent a major loss of the
benefits (energy gain) relative to the costs (energy ex-
pended) and becomes a relevant aspect of the acquisi-
tion of prey. In energetic terms, then, an important con-
sideration for a predator beyond what to kill and where
to kill it is how to save that energy for its use. As the loss
of meat to other animals, including conspecifics, can be
extensive (Wright 1960, Packer 1986, Sunquist & Sun-
quist1989, Murphy, Felzien, Hornocker & Ruth 1998),
caching should be a highly developed adaptation. Most
accounts of caching behaviour in felids are quite gen-
eral, e.g. placing their kills in dense cover (Schaller &
128 © WILDLIFE BIOLOGY · 9:2 (2003)
Vasconselos 1978, Sunquist 1981, Sunquist & Sun-
quist 1989), or in the case of leopards Panthera pardus,
placing their kills in trees (Houston 1979). For pumas,
the general observation of caching their prey under
trees (Shaw 1989) was supported by our data. Because
the cache point is at the base of one or more trees, the
average distance measurement of the four quadrants at
this point would be extremely small, resulting in our
higher tree density estimates relative to the surround-
ing area (cache site). However, what we did not predict
was the significantly larger dbh estimates at the cache
points. Pumas did not randomly place their kills under
the most convenient tree but selected trees with signif-
icantly larger dbh (= older, taller trees). This suggests
that cache site selection, at least for pumas, may be both
important and complex. Why tree size would be a selec-
tion factor can only be speculated at this time. Based on
observation of tracks around kill sites, we believe that
pumas often rest up to 100 m from the cache site (J.W.
Laundré, unpubl. data). Reports by others of dead coy-
otes Canis latrans at kill sites (Boyd & O’Gara 1985,
Koehler & Hornocker 1991, Murphy et al. 1998) indi-
cate that pumas actively defend their cached prey at
times. Perhaps the taller tree at the cache site enables
pumas to maintain visual contact with the cache site and
thus, to better defend it from scavengers. Obviously,
further, more detailed analyses of cache site character-
istics than made here are needed to define the role of this
and other possible factors in the selection of cache sites
by pumas. Other factors that could be important include
height of lowest branches or basal circumference.
In conclusion, the results of our study suggest that
pumas hunt more successfully in the winter at the edges
of forest patches and select cache sites at the base of larg-
er, older trees. Thus, the effectiveness of puma preda-
tion in the winter is limited by habitat structure (Logan
& Irwin 1985) and both pumas and mule deer in our
study area are aware of these limits (Holmes 2000,Al-
tendorf et al. 2001). Additionally, Koloski & Lindzey
(2000), in a comparison between two forested areas with
different edge densities, found that within home ranges
of pumas from both areas, edge densities were equal.
Therefore, pumas may not only be selecting successful
hunting habitat, forest edges, on a localized daily scale
but also on a larger home range scale; i.e. a minimal
amount of edge in the home range may be needed to
catch sufficient prey. Based on these observations, we
predict that the use of an area by pumas in the winter,
and puma impact on prey populations during that sea-
son will be related to the proportion of successful hunt-
ing habitat available.
Successful caching of prey by pumas can reduce their
kill frequency and, thus, reduce their potential impact on
prey populations. Inadequate caching habitat might lead
to higher losses of kills, and hence a higher kill frequency
(Hornocker 1970, Murphy et al. 1998). Based on this,
we predict that prey populations in areas with good hunt-
ing but poor caching habitat would experience higher lev-
els of predation.
The implications of these predictions are that the
effects of puma predation might be managed by manipu-
lating characteristics of successful hunting and caching
habitat. Such management of predation effects via habi-
tat manipulation could potentially help reduce some cur-
rent human conflicts related to predator-prey relation-
Acknowledgements - we thank the following organizations:
ALSAM Foundation, Boone and Crockett Club, Earthwatch,
Inc., Idaho State University, National Rifle Association, The
Eppley Foundation, U.S. Bureau of Land Management, the
Northern Rockies Conservation Cooperative, Idaho Department
of Fish and Game, Mazamas, the Merril G. and Emita E. Hast-
ing Foundation, Patagonia, Inc., SEACON of the Chicago Zoo-
logical Society, the William H. and Mattie Wattis Harris
Foundation, and Utah Division of Wildlife for financial and
logistic support. We would like to thank the many Earthwatch
volunteers without whose help this work would not have
been accomplished. We also would like to thank J. Loxterman,
B. Holmes, K. Altendorf, C. López González and S. Blum for
their help in the field. We thank Harley Shaw and Ian Ross
for their helpful comments on this manuscript. Lastly, we espe-
cially thank Kevin Allred and Ken Jafek. It is only through their
tireless enthusiasm and willing use of their tracking dogs
that this study was possible.
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... Over the last 70 years, fire suppression and forest encroachment have reduced prevalence of grasslands used as bighorn winter range by up to 50% (Demarchi et al., 2000;Stent et al., 2013). Tied to direct losses of winter range from fire suppression and forest encroachment are indirect effects of increased pressure from predators like cougar (Puma concolor) that depend on forest edges for stalking and ambushing their prey (Laundré & Hern andez, 2003;Laundré & Loxterman, 2007) and have been known to diminish small and/or isolated bighorn sheep populations (Festa-Bianchet et al., 2006;Ross et al., 1997;Wehausen, 2006). Industrial development has also impacted winter range and may continue to cause future impacts (Poole et al., 2016). ...
... While our simulation quantifies the direct benefit of a conservation action (e.g., prescribed burning) on bighorn sheep populations, we recognize that our simulation does not account for additional indirect benefits. For example, prescribed burning may increase sightability for bighorn sheep, thereby releasing some pressure from predators like cougars, which are highly dependent on forest edges for ambushing their prey (Laundré & Hern andez, 2003;Laundré & Loxterman, 2007). Finding ways to improve population outcomes directly and indirectly will be central for conservation success, especially where other activities may adversely contribute to cumulative effects or where population growth is important for populations (or subpopulations) at or over CC. ...
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Successful cumulative effects management is fundamental for conservation policy and practice. We investigated the application of a carrying capacity (CC) model as a cumulative effects management tool for bighorn sheep ( Ovis canadensis canadensis ) in British Columbia, Canada, where CC is defined as the natural limit of a sustainable population that is set by the availability of resources in the environment. We estimated winter CC using forage availability across winter ranges, weighted by relative selection by sheep and a safe use factor, and divided by overwinter forage requirements to determine how many sheep the landscape can support. We explored application of our model to decision‐making about new industrial projects or conservation activities in a cumulative effects context. Cumulative effects include both positive and negative contributions to animal populations and we simulated the potential positive outcomes of burning to increase bighorn sheep carrying capacity in our study area. Our results show that carefully planned conservation actions could generate a 5% increase in CC (i.e., from 493 to 519 sheep). Robust tools and scientific techniques that are capable of quantifying multiple impacts and conservation actions and that consider spatial processes over long temporal scales, such as the CC model presented, should be applied to help inform decisions about how to better manage cumulative habitat change and achieve conservation objectives.
... Hunting success depends on access to cover that allows the felid to get close to the prey. Pursuits rarely last for more than 50 m, but struggles can continue further before the prey is subdued (Laundré andHernández 2003, Haglund 1966). The snow leopard (Panthera uncia) is an apex predator of the alpine ecosystems of High Asia. ...
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The hunting behaviours of the snow leopard (Panthera uncia) are poorly understood. In this note, we describe the successful hunt of an adult male ibex (Capra sibirica) by a known male snow leopard. The hunt started in a mountain slope close to three large boulders and progressed downhill for 115 m until it concluded at the bottom of a drainage. By comparing the habitat where the ibex was killed to the kill sites of 158 ibex and 17 argali (Ovis ammon) that were killed by GPS-collared snow leopards, we demonstrate that the majority (62%) of these were kills occurred in drainages. We propose that in successful hunts, snow leopards commonly ambush from above, causing prey individuals to typically flee downhill. Thereby the prey maintain their momentum and it is not until they are slowed down upon reaching the bottom of the drainage that the snow leopards are able to subdue them.
... However, it is important to recognize that predator hunting behaviour can influence the overall strength of antipredator responses, and may have driven strong spatial antipredator responses by deer. This is because prey species may have more reliable cues regarding predation risk from ambush predators that rely on cover to facilitate hunting and killing (Hopcraft et al., 2005;Laundré & Hernández, 2003) given the strong positive correlation between thick vegetative cover and mortality. The tight association between habitat structure and risk predicts stronger spatial antipredator responses by prey towards ambushing predators in comparison to predators that visually identify and chase down prey (i.e. ...
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Predation risk and prey responses exhibit fluctuations in space and time. Seasonal ecological disturbances can alter landscape structure and permeability to influence predator activity and efficacy, creating predictable patterns of risk for prey (seasonal risk landscapes). This may create corresponding seasonal shifts in antipredator behaviour, mediated by species ecology and trade‐offs between risk and resources. Yet, how human recreation interacts with seasonal risk landscapes and antipredator behaviour remains understudied. In South Florida, we investigated the impact of a seasonal ecological disturbance, specifically flooding, which is inversely related to human activity, on interactions between Florida panthers ( Puma concolor coryi ) and white‐tailed deer ( Odocoileus virginianus ). We hypothesized that human activity and ecological disturbances would interact with panther‐deer ecology, resulting in the emergence of two distinct seasonal landscapes of predation risk and the corresponding antipredator responses. We conducted camera trap surveys across southwestern Florida to collect detection data on humans, panthers and deer. We analysed the influence of human site use and flooding on deer and panther detection probability, co‐occurrence and diel activity during the flooded and dry seasons. Flooding led to decreased panther detections and increased deer detections, resulting in reduced deer‐panther co‐occurrence during the flooded season. Panthers exhibited increased nocturnality and reduced diel activity overlap with deer in areas with higher human activity. Supporting our hypothesis, panthers' avoidance of human recreation and flooding created distinct risk schedules for deer, driving their antipredator behaviour. Deer utilized flooded areas to spatially offset predation risk during the flooded season while increasing diurnal activity in response to human recreation during the dry season. We highlight the importance of understanding how competing risks and ecological disturbances influence predator and prey behaviour, leading to the generation of seasonal risk landscapes and antipredator responses. We emphasize the role of cyclical ecological disturbances in shaping dynamic predator–prey interactions. Furthermore, we highlight how human recreation may function as a ‘temporal human shield,’ altering seasonal risk landscapes and antipredator responses to reduce encounter rates between predators and prey.
... Consequently, we cannot entirely rule out human disturbance as the mechanism causing elk to avoid open, logged areas during the day, although we consider it unlikely, given the low levels of background human activity. Also, because cougars (Puma concolor), and not wolves (Canis lupus), are the primary predators of elk in this study area, it is unlikely that retreating into dense canopy would yield any reduction in predation risk, given that cougars typically use structurally complex cover to approach prey (Atwood et al., 2009;Laundré & Hern andez, 2003). Given the lack of coursing predators in open areas and the low levels of human disturbance (including hunting) in this study area, the most likely explanation why elk selected higher canopy cover during the day may have been to reduce thermal stress. ...
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There is an increasing need to understand how animals respond to modifications of their habitat following landscape-scale disturbances such as wildfire or timber harvest. Such disturbances can promote increased use by herbivores due to changes in plant community structure that improve forage conditions, but can also cause avoidance if other habitat functions provided by cover are substantially reduced or eliminated. Quantifying the total effects of these disturbances, however, is challenging because they may not fully be apparent unless observed at successional timescales. Further, the effects of disturbances that improve habitat quality may be density dependent such that the benefits are 1) less valuable to high-density populations because the per-capita benefits are reduced when shared among more users, or alternatively, 2) more valuable to animals living in high densities because resources may be more depleted from greater intraspecific competition. We used 30 years of telemetry data on elk occurring at two distinct population densities to quantify changes in space use at diel, monthly, and successional timescales following timber harvest. Elk selected for logged areas at night only, with selection strongest during mid-summer, and peak selection occurring 14 years post-harvest but persisting for 26-33 years. This pattern of increased selection at night following a reduction in overhead canopy cover is consistent with elk exploiting improved nutritional conditions for foraging. The magnitude of selection for logged areas was 73% higher for elk at low population density, consistent with predictions from the ideal free distribution. Yet elk avoided these same areas during daytime up to 28 years post-logging and instead selected for untreated forest, suggesting a role for cover to meet other life history requirements. Our results demonstrate that while landscape-scale disturbances can lead to increased selection by large herbivores suggesting the improvement in foraging conditions can persist over short-term successional timescales, the magnitude of the benefits may not be equal across population densities. Further, the enduring avoidance of logging treatments during daytime indicates a need for structurally-intact forest and suggests that a mosaic of forest patches of varying successional stages and structural completeness will likely be most beneficial to large herbivores. This article is protected by copyright. All rights reserved.
... Vigilance is a behavioural response to the changing landscape of fear, and prey animals may be more vigilant in response to different odour cues, or in some habitat types compared with others (Gigliotti et al., 2021;Laundré & Hernández, 2003;Marino & Baldi, 2008). There are different types of vigilance. ...
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Vigilance is an important anti-predator behaviour that can be an indicator of the predation risk faced by potential prey animals. Here, we assess the collective vigilance, or the vigilance level of an entire group, of corvids (Family: Corvidae) at experimentally placed carcasses in a desert environment in Australia. Specifically, we explore the relationship between collective vigilance levels and the habitat in which the carcass was placed, the time since a potential predator (dingo Canis dingo, wedge-tailed eagle Aquila audax or red fox Vulpes vulpes) was present at a carcass, and the group size of corvids around the carcass. We found that corvids are more vigilant in open habitat, but that group size and the recent presence of a potential predator does not affect the ollective vigilance behaviour of corvids. The results demonstrate the important link between habitat and vigilance, and that animals may adopt anti-predator behaviours irrespective of the size of the group in which they occur or the recent presence of a potential predator.
... We list the variables in the model, the reason behind each model, and which of the hypotheses the model is associated with. The four main hypotheses include the food perishability hypothesis (Mattson et al. 2007;Bischoff-Mattson and Mattson 2009;Barry et al. 2019), resource pulse hypothesis (Samelius et al. 2007), consumption time hypothesis (Careau et al. 2007;Mattson et al. 2007;Cristescu et al. 2014), and the kleptoparasitism deterrence hypothesis (Laundré and Hernández 2003;Balme et al. 2017;Elbroch et al. 2017a;Allen et al. 2021a). Terms with a superscript " 2 " (e.g., adjusted mass 2 ) indicate a quadratic relationship between probability of caching and the predictor covariate. ...
Pumas (Puma concolor) are solitary large carnivores that exhibit high energetic investments while hunting prey that often take multiple days to consume. Therefore, pumas should behave in a way to maximize their energetic gains, including using caching, which is a behavior used by many mammal species to preserve and store food or to conceal it from conspecifics and scavengers to limit their losses. Yet pumas do not always cache their kills. In order to understand caching behavior, we used variables associated with the kills such as prey mass, search time, climate, and habitat to test 20 ecological models (representing four a priori hypotheses: food perishability, resource pulse, consumption time, and kleptoparasitism deterrence) in an information-theoretic approach of model selection to explore factors related to the caching behavior. Models were run with information from tracked radio-collared pumas in California over a 2.5-year period and identified a total of 352 kills. Overall, we documented pumas caching 61.5% of their kills, including 71.6% of Black-tailed Deer (Odocoileus hemionus columbianus), their primary prey in the study area. The model with a quadratic effect of adjusted mass of prey on puma caching probability had all of the empirical support (w = 1.00). Specifically, pumas were most likely to cache intermediate-sized prey, such as yearling and adult female deer, and also fed from cached kills for longer periods of time. Larger prey may be too large to easily cache, making it less energetically efficient—while small prey can often be consumed quickly enough to not require caching. This suggests that intermediate-sized prey may be the optimal size for caching, allowing a puma to feed for multiple days while not greatly increasing energetic output. The hypotheses we tested were not mutually exclusive and pumas caching their prey may occur for several reasons; nevertheless, our study demonstrated that pumas use caching to extend their foraging time and maximize energetic gains when preying on intermediate-sized prey.
... Often with ecological studies involving telemetry and cam trapping, abundance but not density is reported because the spatial extent of site sampled is not generally provided. Density can also provide insight into trophic interactions (Nilsson 2001, Laundré and Hernández 2003, McPeek 2019. The density of both plant and animal species directly relates to density-dependent associations which have been shown to regulate population growth rates (Ray andHastings 1996, Jenkins et al. 1999). ...
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Facilitative interactions between shrub and animal species influence the structure and composition of communities. The benefits associated with woody shrub species can critically influence local animal populations, in particular. Here, we tested the relative importance of the density of shrub species on the local abundance of animal populations using a meta‐analysis. Full‐text review for shrub density, animal abundance or density and sampling effort, resulted in a total of 113 independent observations that reported both shrub density and animal abundance. A meta‐regression of shrub density on animal density with feeding functional group of the animal species as a moderator was used to test the predictive capacity of this simple vegetation measure on animal populations. Shrub density positively predicted animal abundance in these studies – particularly in deserts and grasslands. Shrub and woody plant density can thus be used as a potential rapid proxy for habitat in predicting local animal abundances. This method can support restoration and conservation of resident animal species in impacted ecosystems structured by woody shrubs globally.
... For example, pumas are ambush predators that require vegetative structure for cover, and therefore they select against open habitats (Gray et al. 2016). Pumas also select against areas of deep snow, likely because it hinders movement and reduces ungulate prey abundance (CDFW 2015;Laundré and Hernández 2003;Poole and Mowat 2005). ...
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Pumas ( Puma concolor ) were eliminated from most of the eastern USA a century ago. In the past couple of decades, their recovery in the West has increased puma dispersal into the Midwest, with some individuals even traveling to the East Coast. We combined published expert opinion data and a habitat suitability index in an analysis that identified 17 areas in the Upper Midwest, Ozarks, Appalachia, and New England that could potentially host puma populations in the future. Thirteen of these were larger than 10,000 km ² and so likely to ensure a puma population’s long-term genetic health. Further, we quantified patch size, human density, livestock density, percent public land, and a sociocultural index reflecting wildlife values for comparing patches, as well as present a summary of current legislation relevant to puma management in the East. Our work may be useful in identifying suitable areas to restore pumas based not only on the quality of their biophysical habitat, but also on social values conducive to puma-human coexistence.
... Avoiding predation risk may therefore take the form of temporal avoidance whereby elk change their patterns of resource use only when wolves are present Cusack et al., 2020). In contrast, ambush or stalking predators (e.g., mountain lions [Puma concolor]) that rely on finescale landscape features (e.g., stalking cover) to approach prey should generate spatially predictable cues, with the net result that habitat signals of predation should be much stronger (Laundré & Hernández, 2003;Podgórski et al., 2008). ...
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The recovery of carnivore populations in North American has consequences for trophic interactions and population dynamics of prey. In addition to direct effects on prey populations through killing, predators can influence prey behavior by imposing the risk of predation. The mechanisms through which patterns of space use by predators are linked to behavioral response by prey and nonconsumptive effects on prey population dynamics are poorly understood. Our goal was to characterize population‐ and individual‐level patterns of resource selection by elk (Cervus canadensis) in response to risk of wolves (Canis lupus) and mountain lions (Puma concolor) and evaluate potential nonconsumptive effects of these behavioral patterns. We tested the hypothesis that individual elk risk‐avoidance behavior during summer would result in exposure to lower‐quality forage and reduced body fat and pregnancy rates. First, we evaluated individuals' second‐order and third‐order resource selection with a used‐available sampling design. At the population level, we found evidence for a positive relationship between second‐ and third‐order selection and forage, and an interaction between forage quality and mountain lion risk such that the relative probability of use at low mountain lion risk increased with forage quality but decreased at high risk at both orders of selection. We found no evidence of a population‐level trade‐off between forage quality and wolf risk. However, we found substantial among‐individual heterogeneity in resource selection patterns such that population‐level patterns were potentially misleading. We found no evidence that the diversity of individual resource selection patterns varied predictably with available resources, or that patterns of individual risk‐related resource selection translated into biologically meaningful changes in body fat or pregnancy rates. Our work highlights the importance of evaluating individual responses to predation risk and predator hunting technique when assessing responses to predators and suggests nonconsumptive effects are not operating at a population scale in this system. Example of predicted relative probabilities of selection (3rd order) for a small sample of individual female elk (n = 9), based on the underlying landscapes of risk and forage quality. The lower panel illustrates the variety of predicted relative probabilities of selection (3rd order) for elk in response to the underlying landscape of forage quality and risk. To aid illustration, all values were binned into 10 quantiles and coded from low (blue) to high (red).
Predator populations are imperiled globally, due in part to changing habitat and trophic interactions. Theoretical and laboratory studies suggest that heterogeneous landscapes containing prey refuges acting as source habitats can benefit both predator and prey populations, although the importance of heterogeneity in natural systems is uncertain. Here, we tested the hypothesis that landscape heterogeneity mediates predator-prey interactions between the California spotted owl (Strix occidentalis occidentalis) - a mature forest species - and one of its principal prey, the dusky-footed woodrat (Neotoma fuscipes) - a younger forest species - to the benefit of both. We did so by combining estimates of woodrat density and survival from live-trapping and VHF tracking with direct observations of prey deliveries to dependent young by owls in both heterogeneous and homogeneous home ranges. Woodrat abundance was approximately 2.5x higher in owl home ranges (1412 hectares) featuring greater heterogeneity in vegetation types (1805.0 ± 50.2 SE) compared to those dominated by mature forest (727.3 ± 51.9 SE), in large part because of high densities in young forests appearing to act as sources promoting woodrat densities in nearby mature forests. Woodrat mortality rates were low across vegetation types and did not differ between heterogeneous and homogeneous home ranges, yet all observed predation by owls occurred within mature forests, suggesting young forests may act as woodrat refuges. Owls exhibited a type 1 functional response, consuming approximately 2.5x more woodrats in heterogeneous (31.1/month ±5.2 SE) versus homogeneous (12.7/month ±3.7 SE) home ranges. While consumption of smaller-bodied alternative prey partially compensated for lower woodrat consumption in homogeneous home ranges, owls nevertheless consumed 30% more biomass in heterogeneous home ranges - approximately equivalent to the energetic needs of producing one additional offspring. Thus, a mosaic of vegetation types including young forest patches increased woodrat abundance and availability that, in turn, provided energetic and potentially reproductive benefits to mature forest-associated spotted owls. More broadly, our findings provide strong empirical evidence that heterogeneous landscapes containing prey refuges can benefit both predator and prey populations. As anthropogenic activities continue to homogenize landscapes globally, promoting heterogeneous systems with prey refuges may benefit imperiled predators. This article is protected by copyright. All rights reserved.
Black bears (Ursus americanus) or grizzly bears (Ursus arctos) visited 8 of 55 cougar-killed (Felis concolor) ungulates in Glacier National Park (GNP), Montana, from 1992 to 1995, and 19 of 58 cougar kills in Yellowstone National Park (YNP), Wyoming, from 1990 to 1995. Bears displaced cougars from 4 of 8 carcasses they visited in GNP and 7 of 19 in YNP. Cougar predation provided an average of 1.9 kg/day (range = 0-6.8 kg/day) of biomass to bears that fed on cougar-killed ungulates. This biomass was an important percent (up to 113%) of the daily energy needs of bears when compared to their caloric requirements reported in the literature. We suggest that ungulate carrion resulting from cougar predation is important nutritionally to bears in some regions and seasons. Cougars that were displaced from their kills by bears lost an average of 0.64 kg/day of ungulate biomass, or 17-26% of their daily energy requirements. Biologists modelling or measuring cougar predation rates should be aware that losses to scavengers may be significant.
Quantifies Felis concolor habitat use patterns by testing the hypothesis that lions use vegetation and terrain features in proportion to availability.-from Authors
The evolutionary fitness of any predator, whether it is a spider catching insects or a lion hunting buffalo, depends largely on the quality and quantity of its diet. Predatory strategies are shaped and refined by natural selection to maximize nutrient intake within the bounds of a wide range of ecological constraints (e.g., prey density, habitat) that may differ dramatically for the same species at the extremes of its geographical distribution. The basic task of finding and gathering food under these constraints fundamentally affects a species’ spacing patterns and the structure of its social systems.
A statistical technique evaluating preference or avoidance of a given habitat or forage species is presented, using moose (Alces alces) distribution patterns in an area including the Little Sioux Burn of northeastern Minnesota as an example. The technique is used in conjunction with a chi-square analysis, after the chi-square has led to the rejection of the null hypothesis that a set of observations follows an "expected" occurrence pattern. The technique involves the use of a Bonferroni z statistic which may be used in estimating whether a specific observation occurs more or less frequently than expected. The technique provides a refinement of quantitative methods which heretofore have not been used to test a multinomial distribution applicable to the example.
Use of prey, and topographic and habitat features by mountain lions (Felis concolor), bobcats (Lynx rufus), and coyotes (Canis latrans) in central Idaho was investigated to determine how syntopic carnivores coexist where resource use may overlap. There were significant differences in use of elevation, forest types, terrain, overstory density, and exposure by these predators during summer. Despite morphological and behavioral differences permitting these predators to partition resources, resource use overlapped during winter when snow confined prey and predators to lower elevations. Overlap in their diets was significant during winter resulting in mountain lions killing bobcats and coyotes while defending or usurping food caches.