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The research in this publication was partially or fully funded by Colorado Parks and Wildlife.
Dan Prenzlow, Director, Colorado Parks and Wildlife • Parks and Wildlife Commission: Marvin McDaniel, Chair • Carrie Besnette Hauser, Vice-Chair
Marie Haskett, Secretary • Taishya Adams • Betsy Blecha • Charles Garcia • Dallas May • Duke Phillips, IV • Luke B. Schafer • James Jay Tutchton • Eden Vardy
�Received: 17 June 2016
|
Accepted: 3 December 2017
DOI: 10.1111/1365-2656.12801
RESEARCH ARTICLE
Hunger mediates apex predator’s risk avoidance response in
wildland–urban interface
Kevin A. Blecha1
| Randall B. Boone2 | Mathew W. Alldredge3
1
Graduate Degree Program in
Ecology, Colorado State University, Fort
Collins, CO, USA
2
Department of Ecosystem Science and
Sustainability and the Natural Resources
Ecology Laboratory, Colorado State
University, Fort Collins, CO, USA
3
Mammals Research Section, Colorado Parks
and Wildlife, Fort Collins, CO, USA
Correspondence
Kevin A. Blecha
Email: kevin.blecha@gmail.com
Funding information
U.S. Fish and Wildlife Service, Grant/Award
Number: Federal Aid Grant #W-204-R1;
Colorado Parks and Wildlife Game Cash
Funds
Handling Editor: Jean-Michel Gaillard
Abstract
1. Conflicts between large mammalian predators and humans present a challenge to
conservation efforts, as these events drive human attitudes and policies concerning predator species. Unfortunately, generalities portrayed in many empirical carnivore landscape selection studies do not provide an explanation for a predator’s
occasional use of residential development preceding a carnivore–human conflict
event. In some cases, predators may perceive residential development as a risk–
reward trade-off.
2. We examine whether state-dependent mortality risk-sensitive foraging can explain an apex carnivore’s (Puma concolor) occasional utilization of residential areas.
We assess whether puma balance the risk and rewards in a system characterized
by a gradient of housing densities ranging from wildland to suburban. Puma GPS
location data, characterized as hunting and feeding locations, were used to assess
landscape variables governing hunting success and hunting site selection. Hunting
site selection behaviour was then analysed conditional on indicators of hunger
state.
3. Residential development provided a high energetic reward to puma based on increases in prey availability and hunting success rates associated with increased
housing density. Despite a higher energetic reward, hunting site selection analysis
indicated that pumas generally avoided residential development, a landscape type
attributed with higher puma mortality risk. However, when a puma experienced
periods of extended hunger, risk avoidance behaviour towards housing waned.
4. This study demonstrates that an apex carnivore faces a trade-off between acquiring energetic rewards and avoiding risks associated with human housing. Periods
of hunger can help explain an apex predator’s occasional use of developed landscapes and thus the rare conflicts in the wildland–urban interface. Apex carnivore
movement behaviours in relation to human conflicts are best understood as a
three-player community-level interaction incorporating wild prey distribution.
KEYWORDS
camera traps, cougar puma concolor, energetics, housing avoidance, human–predator conflict,
patch use, risk–reward trade-off, step selection function
J Anim Ecol. 2018;87:609–622.
wileyonlinelibrary.com/journal/jane�
© 2018 The Authors. Journal of Animal Ecology
© 2018 British Ecological Society
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BLECHA et al.
1 | I NTRO D U C TI O N
is hunger. Although hunger-mediated decision making was concep-
Conflicts between apex predators and humans undermine large car-
implied in theoretical models (Charnov, 1976), and demonstrated
tualized at the forefront of optimal foraging theory (Emlen, 1966),
nivore conservation efforts in a world with an ever-expanding resi-
experimentally with small and short-lived organisms (Caraco, 1981),
dential footprint. For instance, wild predator attacks on humans are
case studies in an experimental setting (penned subjects) with higher
rare but can drive human attitudes and policy-making decisions that
trophic levels of carnivores are uncommon and relatively recent
are ultimately detrimental to the predator species (Löe & Röskaft,
(small canid: Berger-Tal et al., 2009; avian predator: Embar, Raveh,
2004; Woodroffe, 2000). Despite various resource selection studies
Burns, & Kotler, 2014). Attempts to demonstrate hunger mediated
reporting carnivores’ general avoidance to anthropogenic develop-
risk aversion in large predators in a natural landscape have been
ment (Berger, 2007; Burdett et al., 2010; Carroll & Miquelle, 2006;
unfruitful (Cooper, Pettorelli, & Durant, 2007; Hilborn, Pettorelli,
Valeix, Hemson, Loveridge, Mills, & Macdonald, 2012), mass media
Orme, & Durant, 2012). Empirically demonstrating hunger-mediated
report mostly on the rare instances of conflict-involving human
decision making in apex predators with respect to human imposed
safety (Sakurai, Jacobson, & Carlton, 2013; Wolch, Gullo, & Lassiter,
risks is important for understanding three-party community-level
1997). Unfortunately, the risk avoidance generalities portrayed in re-
trophic interactions.
source selection studies of large predators do not explain the rare or
The goal of this study was to test whether hunger can influence an
occasional event when apex predators use areas near human dwell-
apex predator’s occasional use of residential development in a land-
ings. This spatio-temporal overlap between apex predators and hu-
scape characterized by a patchwork of wildland, rural, exurban and
mans is a predictor of conflict rates (Kertson, Spencer, & Grue, 2011;
suburban housing densities. Our model system is characterized by
Kertson, Spencer, Marzluff, Hepinstall-Cymerman, & Grue, 2011;
a three-player game between mobile prey (i.e. cervids), an apex wild
Nyhus & Tilson, 2004; Teichman, Cristescu, & Darimont, 2016;
predator (puma: Puma concolor) and a de facto “predator” of the wild
Torres, Mansfield, Foley, Lupo, & Brinkhaus, 1996). Understanding
predator, which in this case is represented by human activities asso-
the drivers of faunal landscape utilization patterns that underlie the
ciated with residential housing (Moss, Alldredge, & Pauli, 2016; Ordiz,
occasional conflict event is key to conserving apex predators.
Støen, Delibes, & Swenson, 2011). Conflicts between pumas and hu-
All organisms face trade-offs between acquiring energy and
mans in this system (Torres et al., 1996) exemplify the challenges facing
avoiding mortality (Brown, 1992; Lima & Dill, 1990; Mangel & Clark,
the conservation of large carnivores in regions experiencing a growing
1986) or injury (Berger-Tal, Mukherjee, Kotler, & Brown, 2009; Brown
human footprint. Empirical analysis of fauna’s patch-use behaviours
& Kotler, 2004) risks. A key to conceptualizing predator space use in
have focused on puma energy maximization behaviours (Laundré,
relation to human–carnivore conflicts is to understand how trade-
2010; Pierce, Bleich, & Bowyer, 2000), prey avoidance towards puma
offs influence animal space use in a three-party community-level
(Donadio & Buskirk, 2016; Laundré, 2010) or puma aversion to human-
interaction involving mobile herbivore prey, a mobile apex predator
mediated risks (Burdett et al., 2010; Kertson et al., 2011; Kertson,
and humans (Berger, 2007; Hebblewhite et al., 2005; Magle, Simoni,
Spencer, & Grue, 2011; Wilmers et al., 2013) providing a wealth of
Lehrer, & Brown, 2014). Wild predators tend to show attraction to
system-specific knowledge for our case study to build upon. Besides a
areas of higher prey availability (Keim, DeWitt, & Lele, 2011). On
few empirical studies providing demonstrations within the context of
the other hand, wild predators’ risk avoidance behaviour towards an-
a three-player system (Berger, 2007; Hebblewhite et al., 2005), little
thropogenic development (Burdett et al., 2010; Carroll & Miquelle,
to no direct evidence has been provided for hunger mediating a large
2006; Valeix et al., 2012) may lead to prey-seeking refuge near de-
carnivore’s risk avoidance decisions, a behaviour that could explain
veloped features (Berger, 2007; Hebblewhite et al., 2005; Houston,
the occasional utilization of residential development and thus the oc-
McNamara, & Hutchinson, 1993). Thus, for the wild predator, risk
casional human–carnivore conflict.
and reward would be positively correlated; the predator would face
We first examine whether pumas are facing a risk–reward trade-
conflicting demands of patch selection that a simple attraction or
off with respect to housing density by assessing patch selection and
avoidance relationship between two parties (predator–prey or pred-
energetic reward. We measure puma’s patch selection in relation to
ator–human) cannot predict.
housing density, a landscape attribute associated with higher puma
An explanation of occasional risky patch use is state-dependent
mortality risk in this specific study area (Moss et al., 2016), by re-
risk-sensitive foraging, which indicates that organisms facing higher
examining the well-established prediction that puma generally avoid
energetic demands use riskier patch selection strategies (Caraco,
higher housing densities (Burdett et al., 2010; Kertson et al., 2011;
1980; Mangel & Clark, 1986; McNamara & Houston, 1987). Empirical
Kertson, Spencer, & Grue, 2011; Wilmers et al., 2013). We then ex-
studies speculate that human–predator conflicts in the urban–wild-
amine if housing density can be associated with energetic reward
land interface are more likely to involve predators that are very young
by testing whether housing influences puma hunting success. If
or old (Loveridge, Valeix, Elliot, & Macdonald, 2017), are in poor
puma’s hunting success increases in the relatively higher housing
physical condition (Beier, 1991), or injured (Goodrich, Seryodkin,
density, despite puma’s general avoidance to houses, a risk–reward
Miquelle, & Bereznuk, 2011), all of which may cause those animals
trade-off is likely present. However, if housing has a negative or
to have difficulties meeting energetic demands. A more direct, uni-
non-significant influence on hunting success, then pumas may be
versal and temporally sensitive proxy for a predator’s energetic state
avoiding houses based on a higher energetic reward elsewhere, in
�BLECHA et al.
addition to an increased mortality risk; housing presents no risk–
Journal of Animal Ecology
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611
fitting (Birk & White, 2014; Fraker & Luttbeg, 2012) to the close-
reward trade-off to puma. Next, by expanding on the patch selec-
range attacks used by puma (i.e. <25 m; Beier, Choate, & Barrett,
tion model, we test whether the degree of risk avoidance response is
1995; Holmes & Laundré, 2006), given the camera trap’s relatively
dependent on the current hunger state (days since last feeding). We
small field of view (<30 m). Second, determining absolute prey den-
predict puma’s risk avoidance behaviour towards housing will wane
sities at the small spatial scales housing sites occur (i.e. <1 ha) is not
as hunger level increases past the expected hunger level at which
only difficult, but imprudent given the spatio-temporal scale of pred-
feeding events normally occur. Finally, in a complementary analysis,
ator and prey movements. Third, PER is fitting to how puma may per-
we test whether other state-dependent factors (sex, age and season)
ceive prey value of a site; camera trap encounter rates are not only
that influence a puma’s energetic constraints will also mediate avoid-
a function of large-scale animal abundance, but the smaller-scale
ance response to humans.
habitat utilization patterns of the prey individuals (Blecha, 2015;
Gurarie & Ovaskainen, 2012; Rowcliffe, Field, Turvey, & Carbone,
2 | M ATE R I A L S A N D M E TH O DS
2.1 | Study system description
2008). While predators have imperfect knowledge of numerical prey
availabilities (Lima & Zollner, 1996), they likely respond to habitat
cues associated with prey (Williams & Flaxman, 2012).
The Blecha (2015) camera trap companion study revealed that
This study took place in a foothill–montane system of Colorado’s
mule deer are more likely encountered as housing density increased
Front Range in an elevation gradient of 1,595–3,173 m. The
from rural to low suburban (0–2.7 houses/ha), but thereafter show-
2
2,700 km study area is situated between the continental divide
ing a declining response to housing density. At least 97% of the
and the Denver, CO, USA metropolitan area (39°51′ N, 105°20′ W).
study area is characterized by this rural to low-suburban density
Housing density spanned wildlands and rural (0–0.068 houses/ha), to
(Appendix S1) in which mule deer and housing density were posi-
exurban (0.068–1.47 houses/ha), suburban (1.47–10 houses/ha) and
tively associated. Mule deer were also more likely to be encountered
urban (>10 houses/ha) classes (Theobald, 2005). A continuous vari-
on ridge tops (higher topographic position index), in lower eleva-
able for housing density was developed under a dasymetric model-
tions, southerly latitudes, increasingly southeast aspects and shrub
ling approach combining information on the locations of man-made
lands. Raccoon and house cat, two common small-bodied prey spe-
roofed structures and U.S. census bureau block group housing den-
cies, were associated with increased housing density and declining
sity estimates (Appendix S1). Suburban and urban densities are con-
distance to nearest structure (man-made roofed). The PER response
strained mostly to cells of small towns and the fringes of larger cities
to housing density for primary prey species (mule deer) and syan-
(Boulder and Golden, CO) comprising 3% of the study area (Figure S1).
thropic small species (raccoon and housecat) is supported by other
Interstitial to the urban cells is a patchwork of exurban housing de-
camera trap studies in nearby areas and similar systems (Goad,
velopment and rural/wildland tracts (Figure S1) that comprised most
Pejchar, Reed, & Knight, 2014; Lewis et al., 2015; Smith, Wang, &
of the study area (exurban: 27.3%, rural: 69.7%). The spatial hetero-
Wilmers, 2016). Predictions from the mule deer PER model were de-
geneity in housing density created a natural experimental landscape
veloped for the study area extent to be used as a covariate in subse-
in which a variety of patch types were available for puma to choose
quent models of hunting success and patch selection.
from. Spatial increases in housing density were correlated with an in-
Mule deer in this system appear to be relatively non-migratory
crease in mortality risk for pumas examined in this study; non-hunting
(Kufeld, Bowden, & Schrupp, 1989) to partially migratory (sum-
human-associated mortality (i.e. pet and livestock depredation retali-
mer and winter ranges separated > 6 km) (Conner & Miller, 2004).
ations) was the primary mortality agent (Moss et al., 2016). Puma pri-
Examination of the puma movement data did not reveal distinct
marily preyed upon mule deer (Odocoileus hemionus), followed by elk
home ranges between summer and winter. Thus, most of the puma
(Cervus elaphus), raccoon (Procyon lotor), housecat (Felis catus), along
and prey movements fell within the defined study area after remov-
with small proportions (<2%) of domestic dog (Canis familiaris), red fox
ing locations representing forays and emigrations.
(Vulpes Vulpes) and coyote (Canis latrans) (K. Blecha unpublished data).
Spatial availability of primary ungulate prey (mule deer) and synanthropic prey species (i.e. raccoon, housecat) was obtained for the
2.2 | Defining puma hunting and feeding locations
study area with a companion study (Blecha, 2015). The companion
A sample of pumas (n = 54: 35 females and 19 males) were captured
study monitored a sample of 131 camera trap sites for nearly an annual cycle (x− = 318 nights per site) to model expected animal encoun-
(IACUC: 16-2008 – Colorado Parks and Wildlife USDA Registration
# 84-R0045) and fit with GPS collars (Vectronic Aerospace GmbH,
ter rates (number of animals captured within field of view per trigger
Berlin, DEU) programmed to record locations every 3 hours at night
per day monitored) as a function of landscape covariates (i.e. housing
and 3 or 4 hr during the day. Pumas were aged to the nearest 12-
density, distance to man-made roofed structure, vegetation and to-
month interval. Only pumas greater than 18 months of age were fit
pography). We refer to this expected measure as potential encounter
with GPS collars. Puma movements were monitored for a mean of
rate (hereafter, referred to as PER), as it does not consider the move-
488 days (range of subjects: 51–1679 days) between January 2008
ment behaviours of puma. Employing this PER metric is justified by
and December 2012. The long duration of prey handling activities
the following three premises. First, the PER utilizes a spatial-scale
(small prey: 3–24 hr, large prey: 24 hr to several weeks) allowed
�612
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Journal of Animal Ecology
feeding and non-feeding events to be predicted from the spatio-
BLECHA et al.
the centroids of identified clusters (feeding and non-feeding) were
temporal characteristics of GPS collar location data (Anderson &
input as “use” locations (Figure 1). For each ending node of a move-
Lindzey, 2003); utilizing a basic cluster unit defined by two consecu-
ment step, a set of 10 matched available locations were created by
tive GPS locations occurring within 200 m and 4 days. We build upon
projecting a set of new possible step distances and turning angles
established feeding site prediction techniques (Anderson & Lindzey,
from the initial node of the step, using standard trigonometric func-
2003), by incorporating accelerometer and detection probability
tions (Figure 1). Our SSF incorporates the observed distributions of
data with basic cluster attributes in a companion study (Blecha &
step distances (log normal) and turning angles (wrapped normal) to
Alldredge, 2015). Development of our customized model required
inform the generation of matched available locations (Appendix S2).
a stratified (by puma and month) sample of ground-truthed clusters
Available locations generated outside the extent of the study area
(total visits = 1,718) to inform the creation of a GLM (binomial family)
were truncated (Appendix S2). Given the temporal interval of the
to predict which clusters represented feeding sites and which did not.
steps, these available locations were ones that the puma could
Feeding events carried out on all prey types and sizes (lagomorphs–
have chosen at the end of the step, but did not for whatever reason
cervids) were included in the model. Cross-validation indicated that
(Thurfjell et al., 2014). Landscape covariates (Appendix S1) of used
the model predicted feeding events well (88.6%—receiver Operator
(response variable designated as ‘1’) and available locations (response
Characteristics: Area Under Curve measure). Details of the GPS col-
variable designated as ‘0’) were compared in a case–control design
lar settings, customized clustering techniques and feeding prediction
(Figure 1), using a mixed-effect conditional logistic regression model
model can be found in Blecha and Alldredge (2015). Each cluster
(Therneau, 2012; R Development Core Team 2013). Each set of the
(feeding and non-feeding events) was simplified to a single location
used and available locations were assigned a unique identifying num-
represented by the centroid (geometric mean of constituent GPS lo-
ber (Figure 1), which served in the analysis as a stratum (a “case”). A
cations), with a time stamp equal to that of the first recorded GPS
detailed description of the statistical model is found in Appendix S2.
location.
We identified energetic reward value of landscape attributes by
Hunting locations were defined as any location that potentially
modelling relative hunting success rates. Success is defined by the
represented prey search (encounter), stalking and attack launching
likelihood of a movement into a patch manifesting into a successful
steps of a predation sequence (Hebblewhite et al., 2005; Hilborn
kill as a function of housing density or other landscape covariates
et al., 2012). Puma are primarily stalking, rather than pure ambush,
(Appendix S1). For simplicity, we did not consider all components
predators that actively search for prey (Banfield, 2012). Once a
involved in the foraging sequence such as prey encounter, stalking
search effort results in a targeted prey item, a stalk will follow to
(Banfield, 2012) and attack launch probabilities (Hilborn et al.,
position itself for a short ambush or for directly launching an attack
2012); only the overall result (kill or no kill) of a movement event
(Williams et al., 2014). Little is known about puma’s prey search be-
into a certain patch type (e.g. inter-patch movements) is of concern.
haviours or probability a search event results in a prey encounter;
Each kill location and the preceding hunting locations since the
search effort is likely highly variable and potentially nonexclusive
conclusion of prior prey handling activities were assigned (grouped)
to other behaviours (i.e. social). While any single GPS location, or
to a unique hunting sequence identifier. We used a mixed-effect
non-feeding cluster centroid, recorded between kills have been al-
conditional logistic regression model (Therneau, 2012) in a case–
located to search time when calculating hunting success in other
control design to describe the probability of a movement event
studies (Merrill et al., 2010; McPhee, Webb & Merrill, 2012), we
manifesting as a kill event, conditional on landscape attributes of
constrained the potential hunting locations with two process-
the given hunting sequence identifier. This model tests for differ-
ing steps: (1) Considering a very low probability (i.e. <1%) of day-
ences in landscape attributes between the kill site and the hunting
time clusters occurring as kills (Blecha & Alldredge, 2015; Elbroch,
locations (collected since the last feeding event) preceding the kill
Lendrum, Newby, Quigley, & Craighead, 2013), all single locations
(Figure 1). Each kill location was coded as ‘1’, each hunting location
and non-feeding clusters occurring entirely within day-light hours
was coded as a ‘0’, and hunting sequence identifier served as the
were removed; these were likely resting events. (2) We removed any
stratum (a “case”) term (Figure 1). Any kill event not preceded by at
GPS locations that were spatially separate but temporally coincid-
least one hunting location was removed. A detailed description of
ing within the time span of an ongoing prey handling activity; these
the statistical model is found in Appendix S2. The input datasets
were assumed to be non-hunting activities. In summary, a potential
are available from the Dryad Digital Repository (Blecha, Boone, &
hunting location is any night-time GPS location or cluster that is not
Alldredge, 2018).
already associated with an ongoing feeding event.
For both the hunting success and SSF analyses, a candidate
set of models, parameterized with landscape covariates and prey
2.3 | Patch selection and hunting success models
availability (PER predictions), was examined using an information theoretic approach to find the most parsimonious model
Puma patch selection was inferred using a step selection func-
(Burnham & Anderson, 2002). All natural (topographic and veg-
tion (SSF) movement analysis comparing landscape attributes
etation) and housing density covariates have been previously
(Appendix S1) of used locations to that of generated available loca-
demonstrated as important variables in past puma studies (Table
tions (Thurfjell, Ciuti, & Boyce, 2014). All single GPS locations and
S2.1 in Appendix S2). A range of spatial scales was tested for
�BLECHA et al.
Journal of Animal Ecology
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613
each landscape covariate (Wilmers et al., 2013; Appendix S1).
success increase or patch attraction (when considering a hunt-
Inter-individuality in patch selection (Sih, Bell, & Johnson, 2004;
ing success model or SSF model, respectively), while a negative
Wolf & Weissing, 2012) was accounted for by incorporating ran-
β indicated a hunting success decrease or patch avoidance. β es-
dom slope terms allowing the landscape covariates to vary by the
timate confidence intervals overlapping zero indicate no change
individual animal identifier (b animal_ID). Fixed-effect coefficient
in hunting success or a lack of selection (neutral response) for
estimates (βs) were standardized (covariate coefficient values
the covariate. Mixed-effect candidate models were assessed by
centred and scaled by standard deviation) and reported by their
model parsimony and the overall variation contributed (σ 2) for
linearized scale along with confidence intervals (95% Walds). For
each random slope effect specified. A σ 2 near zero would indi-
an increasing value of a covariate, a positive β indicated a hunting
cate that the random slope term varied little with respect to the
F I G U R E 1 Conceptual diagram of puma hunting success model and patch selection model input. A hypothetical travelling path, derived
with GPS collars and cluster analysis (direction of movement indicated with thin black arrows), is overlaid on elevation and housing density
covariates. An example hunting sequence is shown with enlarged points to demonstrate the comparisons made during the SSF (step
selection function) and hunting success models. The SSF compares the patch attributes of hunting locations (blue and red points) to that of
a set of matched generated hunting locations (yellow) based on the movement step characteristics (i.e. location 1 is compared to 1’, location
2 is compared to 2’). The hunting success model compares (black arrows) the kill site attributes to that of the preceding hunting locations
composing the respective hunting sequence (locations 1–4) [Colour figure can be viewed at wileyonlinelibrary.com]
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Journal of Animal Ecology
BLECHA et al.
associated fixed term. Given the complexity in this model selec-
confidence intervals for kill events were calculated for each month
tion process, a concise set of final candidate models were created
of the annual cycle.
using the final covariates selected for each of the natural, anthro-
To complement the hunger level analysis with other intrinsic
pogenic and prey availability covariate types. A detailed descrip-
variables related to hunger state, we examined the SSF response to
tion of this model parameterization and model selection strategy
housing density by puma age (yearly classes), sex and month fac-
is found in Appendix S2.
tor levels. Extrinsically, prey availability or accessibility may change
seasonally. In North American winters (Jan–May), ungulate prey
2.4 | Hunger and complementary state-dependent tests
The most parsimonious SSF model was rerun on subsets of the
populations decline; no population inputs are being made. Thus,
puma avoidance to humans may wane as the winter progresses. In
late spring and early summer (June–July), an ungulate birth pulse
SSF input data queried for each state. To examine whether the
provides a new cohort of available prey; puma should avoid humans
response to housing density was dependent on hunger state, a
more. For each factor level, the βs and 95% confidence limits of the
hunger measure was attributed to each hunting and kill location,
SSF analysis were assessed and compared across levels. Mean and
based on the amount of time that had passed since conclusion
95% confidence intervals were calculated for observed hunger level
of the prior feeding activity (Figure 1). The hunger measure was
and observed housing density for each month.
binned into 1 of 11 intervals (0–1 days…, 9–10 days, 10 + days) in
which coefficient values (βs) and 95% confidence limits were assessed and compared.
Pertaining to the influence of hunger level on foraging strategy,
it is important to know at which hunger interval a kill event was
3 | R E S U LT S
3.1 | Patch selection model
expected to occur. To develop a baseline for foraging behaviours
For each puma, 121 to 3,087 locations (kill site and hunting com-
under expected conditions for the population, the distribution mean
bined) were obtained for “use” locations (44,994 total use locations).
and median of the hunger level for all kill events was calculated.
The most parsimonious SSF candidate model included a suite of nat-
To graphically examine whether hunger level may change because
ural covariates, housing density and mule deer PER (Table 1). Puma
of seasonal availability of prey, the mean hunger level and 95%
response to natural topographic covariates included a negative
TA B L E 1
Model selection results for SSF (step selection function) and hunting success analysis, ranked by model parsimony
Analysis
SSF
Candidate model
df
Log likelihood
a
144
−104255.7
0
a
Natural1 + housing density
143
−104323.3
133.3
0.0
Natural1a + mule deer PER
134
−104656.1
781.9
0.0
Natural1 + housing density + mule deer PER
1.0
96
−104851.1
1095.8
0.0
Housing density + mule deer PER
82
−106002.6
3370.7
0.0
Housing density
46
−106167.3
3627.7
0.0
Mule deer PER
37
−106568.1
4410.2
0.0
0
−106739.3
4678.5
0.0
53
−7930.5
Natural2b + housing density
Natural2
b
Natural3c + housing density + mule deer PER
c
0
1.0
56
−7953.7
52.7
0.0
76
−8082.1
349.5
0.0
Natural3 + mule deer PER
59
−8123.4
398.2
0.0
Natural3c + housing density
35
−8185.5
473.7
0.0
Natural3c
34
−8204.5
510.0
0.0
Housing density + mule deer PER
46
−8217.4
559.3
0.0
Housing density
Mule deer PER
Null
a
AICc weight
Natural1a
Null
Hunting Success
ΔAICc
7
−8311.2
668.8
0.0
26
−8298.0
680.6
0.0
0
−8371.6
774.9
0.0
Natural1 includes covariates for topographic aspect (ASP180), elevation (ELEV), Euclidean distance to perennial water (NHD), topographic slope
(SLOPE), average canopy cover (CC_AVG90), Euclidean distance to forest edge (FOREDGE) and the interaction between CC_AVG90 and FOREDGE.
b
Natural2 includes covariates for topographic aspect (ASP45), elevation (ELEV), topographic position index (TPI_100), forest presence (FOREST),
Euclidean distance to forest edge (FOREDGE) and interaction between FOREST and FOREDGE.
c
Natural3 includes all Natural2 covariates except for TPI_100.
�Journal of Animal Ecology
BLECHA et al.
response (avoidance) to increasing southern aspects (βASP180 = −0.156),
|
615
(Natural2) the topographic position index, a variable collinear with
lower elevations (βELEV = −0.273) and a small attraction to loca-
mule deer PER (Table 1). Hunting success was positively associated
tions farther from perennial water sources (βNHD = 0.067) (Table 2).
with natural covariates including increasing south-western aspects
Relationship with forest edge (βFOREDGE = −0.207) depended on de-
(βASP45 = −0.072), decreasing elevation (βELEV = −0.271), decreas-
gree of canopy cover (βCC_avg90*FOREDGE = 0.048); forest edge attrac-
ing topographic position (locations closer to drainage bottoms:
tion was more important when considering locations outside forest
βTPI_100 = −0.379), increasing forest presence (βFOREST = 0.009) and
patches (Table 2). Puma showed strong avoidance (βHDM150 = −0.812)
decreasing Euclidean distance to forest edge (βFOREDGE = −0.002)
to locations of relatively higher housing density, along with a slight
(Table 2). An interaction between forest presence and forest edge
attraction (βMULEDEER = 0.080) to locations with a higher mule deer
indicated a stronger Euclidean distance effect when outside forest
PER (Table 2). Inter-individuality in patch selection was found with
patches (Table 2). Hunting success improved with increasing hous-
respect to elevation, slope and housing density (banimal_ID: σ2 = 0.086,
ing density (βHDM400 = 0.127) (Table 2). Inter-individual heterogene-
0.023 and 1.015 respectively) (Table 2).
ity was found with respect to elevation and topographic position
(banimal_ID: σ2 = 0.114 and 0.014 respectively).
3.2 | Hunting success test
A total of 4,334 hunting sequences, containing an equivalent sam-
3.3 | State-dependent tests
ple of kill locations and 38,164 hunting locations, were used for
Hunting behaviours, resulting in a kill, occurred at a median hunger
comparing each kill location to its respective preceding hunting lo-
level of 1.38 days (M = 2.12 days; Figure 2). Although uncommon,
cations. The most parsimonious candidate hunting success model
puma hunger levels could extend for a period greater than 10 days
(which held all AICc weight) included a suite of natural covariates
before a kill was made (Figure 2). As hunger increased, the magnitude
and housing density (Table 1). The grouping of “natural” covari-
of the effect (absolute βHDM) of puma avoidance to higher housing
ates represented a set of covariates with (Natural1) and without
densities declined (Figure 2). The strongest avoidance of houses was
TA B L E 2
Coefficient estimates for most parsimonious SSF (step selection function) and hunting success models
Fixed Effects
Model
Covariate
Β est.
SE
LCL
UCL
Random Slope
Effects σ2
SSF
ASP180a
−0.156
0.006
−0.167
−0.145
—
b
−0.273
0.045
−0.360
−0.186
NHDc
0.067
0.007
0.053
0.082
SLOPEd
0.092
0.022
0.048
0.135
0.023
CC_avg90 e
0.252
0.008
0.237
0.267
—
FOREDGEf
−0.207
0.007
−0.222
−0.193
—
0.048
0.007
0.035
0.061
—
−0.812
0.148
−1.101
−0.522
0.080
0.007
0.067
0.094
—
ASP45a
−0.072
0.038
−0.220
−0.071
—
ELEVb
−0.271
0.0421
−0.350
−0.184
0.114
ELEV
CC_avg90 x FOREDGE
HDM150
g
MULEDEERh
Hunting success
i
−0.379
0.027
−0.431
−0.327
FORESTj
0.009
0.021
−0.031
0.050
TPI_100
0.014
—
−0.002
0.021
−0.043
0.039
—
HDM400 g
0.127
0.016
0.095
0.159
—
−0.110
0.023
−0.156
−0.064
—
Corresponds to decreasing northeast (45°) or south (180°) solar aspect.
Increasing elevation.
c
increasing Euclidean distance to perennial stream.
d
Increasing topographic slope.
e
Average canopy cover percentage within 90 m radius.
f
Increasing Euclidean distance to forest edge.
g
Housing density within 150 or 400 m radius respectively.
h
Potential encounter rate of mule deer (via camera trap encounter models).
i
Increasing topographic position index within 100 m radius.
j
Forest presence (30 m).
b
1.015
FOREDGEf
FOREST × FOREDGE
a
0.086
—
�616
|
Journal of Animal Ecology
BLECHA et al.
observed at 0–1 days post-feeding (βHDM = −0.909). The estimate
(β HDM increased) throughout the remainder of the summer. In the
showed a general increase in βHDM the following 3 days, until level-
fall (Sep–Nov), risk avoidance was relatively steady (Figure 4).
ling off near −0.113. Puma showed the least avoidance to houses
Mean hunger level at time of kill varied seasonally, with hunger
(βHDM = −0.045) after 4–5 days post-feeding. Confidence intervals
level increasing through the winter months (Dec–May), and then
overlapped zero greater than 9 days post-feeding (βHDM = −0.344 to
held at a low level in the summer (Jun–Sep) (Figure 4). Mean hous-
0.039), indicating that avoidance behaviour towards higher housing
ing density characterizing the kill site varied by month following a
densities was neutral (Figure 2).
similar pattern to the monthly avoidance and hunger level effect
Grouping pumas by demographic classes, the SSF model by
(Figure 4).
sex, revealed that males showed stronger avoidance to housing
(βHDM = −1.425) than shown by females (βHDM = −0.499) (Figure 3).
Pumas >60 months of age appeared to show a stronger hous-
4 | D I S CU S S I O N
ing avoidance behaviour (βHDM = −1.346 to −1.436) than those
<60 months of age (βHDM = −0.365 to −0.628). However, confidence
Puma face a trade-off between maximizing energy acquisition and
intervals of these older age classes overlapped the means of their
avoiding mortality risks when making decisions on how to use resi-
younger counterparts in some cases (Figure 3).
dential development. Residential development in the wildland to
Puma avoidance response to housing density varied by
low-suburban housing density range had a higher energetic reward
month. Over the course of the winter, effect size of avoidance
value to our model apex predator. Energetic reward was based on
behaviour declined from December (β HDM = −0.835) through May
our demonstration of hunting success increasing with increased
(β HDM = −0.211); puma avoided houses less. In June, avoidance be-
housing density and increased availability of primary and second-
haviour effect size sharpened (β HDM = −1.612), and then declined
ary small prey (Blecha, 2015). Despite a greater energetic reward,
(a)
2,500
2,250
2,000
Kill Count
1,750
1,500
1,250
1,000
750
500
250
0
(b)
0.25
SSF coefficient estimate
0.00
−0.25
−0.50
−0.75
−1.00
−1.25
−1.50
−1.75
0−1
1−2
2−3
3−4
4−5
5−6
6−7
Hunger level (Days)
7−8
8−9
9−10
10+
F I G U R E 2 Puma risk avoidance
behaviour and distribution of kill events in
relation to hunger level. (a) Histogram of
when, days since last feeding event, kills
were observed. Kills normally occurred
at a median hunger level of c. 1.3 days
(dashed line) and a mean hunger level of
2.2 days (dotted line). (b) SSF coefficient
estimates (with lower and upper 95%
confidence intervals) by hunger level (days
since last feeding event), with respect to
housing density
�Journal of Animal Ecology
BLECHA et al.
0.25
0.00
0.00
−0.25
−0.25
−0.50
−0.50
−0.75
−0.75
−1.00
−1.00
−1.25
−1.25
−1.50
−1.50
−1.75
−1.75
−2.00
−2.00
−2.25
−2.25
SSF coefficient estimate
(b)
4.5
Hunger level (Days)
0.50
0.25
0.00
−0.25
−0.50
−0.75
−1.00
−1.25
−1.50
−1.75
−2.00
−2.25
+
72
2
−7
0
60
8
−6
48
−4
36
12
Gender
−3
4
M
−2
F
6
−2.50
−2.50
24
SSF coefficient estimate
0.25
(a)
617
(b)
(a)
F I G U R E 3 Effect of demographic
class on puma risk avoidance behaviour.
SSF coefficient estimates (with lower and
upper 95% Walds confidence intervals)
with respect to housing density by gender
(a) and age class (b)
|
Age (Months)
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
(c)
Housing density
F I G U R E 4 Puma risk avoidance
behaviour, hunger and housing density by
month. (a) Coefficient estimates of puma
hunting paths via SSF (step selection
function) model with respect to housing
density. (b) Mean hunger level at time
of kill. (c) Mean housing density (houses
per ha: HDM150) at time of kill. Error
bars represent lower and upper 95%
confidence intervals on estimates
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Jan
Feb
Mar
Apr
May
Jun
Jul
Month
Aug
Sep
Oct
Nov
Dec
�618
|
Journal of Animal Ecology
pumas generally avoid these risky human-dominated habitats. This
BLECHA et al.
from black bear (Ursus americanus), coyote (Canis latrans) and human
risk aversion is likely due to puma’s mortality risk increasing in higher
hunter harvest in the wildlands (Pojar & Bowden, 2004); additional
housing density (Moss et al., 2016).
pressures that may drive mule deer to utilize exurban cover.
The degree of mortality risk-sensitive foraging is dependent
One alternative interpretation of the increased hunting success
on an individual’s hunger state. Puma generally kill at an expected
in higher housing densities is that the forager will require a higher
1–2 days post-feeding while demonstrating housing avoidance
feeding rate (Smith, Wang, & Wilmers, 2015), and thus higher hunt-
during this period. Risk avoidance behaviour wanes as hunger state
ing success rates (McPhee et al., 2012; Merrill et al., 2010) from risky
increases, until reaching a threshold of 4–10 days post-feeding in
over the safe patches (Brown & Kotler, 2004). Thus, our interpreta-
which puma movements are ambivalent to housing density. Other
tion of increased housing density conveying a higher reward value
state-dependent factors influencing the degree of risk aversion
would potentially be inaccurate. However, our camera trap data,
included sex, age, individual differences and seasonal differences.
which indicated increased availability (PER) of nearly all prey sources,
Although these ancillary factors may be specific to our system, the
serve as a second independently collected data source that high-
risk avoidance patterns exhibited agree with what one may predict
lights our original interpretation. Even without this interpretation,
for a hungry predator.
residential housing still serves as a risk–reward trade-off given the
relationship of landscape choice depending on hunger level.
4.1 | Risk–reward trade-off
We recognize that prey–habitat associations alone may not fully
validate a patch’s energetic reward value; any predator engaged in a
Given that patches characterized by a relatively higher housing den-
simple correlated random walk inherently encounters more prey in
sity have a greater energetic reward value to puma, the avoidance
prey-rich patches (Avgar, Kuefler, & Fryxell, 2011). Our hunting suc-
behaviour towards housing is likely due to puma’s increased mortality
cess model cannot fully identify the underlying components driving
risk (see companion study: Moss et al., 2016). The direct association
success (i.e. prey availability vs. prey accessibility), as it does not
with increased mortality found in this system, along with human con-
decompose the predation sequence into prey search (encounter),
flicts being a common mortality source for puma in other populations
stalking and attack launching probability (Hebblewhite et al., 2005;
(Burdett et al., 2010; Torres et al., 1996), supports postulates that puma
Hilborn et al., 2012). Regardless, these caveats pertaining to attack
avoidance behaviour towards residential development is fear based.
launch and kill probabilities are less interesting to our primary ques-
The hunting success model indicates higher housing densities
tion regarding drivers of puma’s initial utilization of residential devel-
as more rewarding to puma, despite puma’s general avoidance to-
opment. Predators may also place reward value on habitats with good
wards housing development. This is likely attributable to the positive
stalking cover (Balme, Hunter, & Slotow, 2007; Hopcraft, Sinclair, &
association between encounter rates of prey (primary and alterna-
Packer, 2005), which was demonstrated by a higher hunting success in
tive) and housing densities found in this system (Blecha, 2015; Goad
lower topographic positions (drainage bottoms) despite primary prey
et al., 2014; Lewis et al., 2015; Smith et al., 2016). In addition, PER of
having a higher PER closer to ridge tops (Blecha, 2015). Alternatively,
mule deer prey was a positive predictor in candidates of the hunt-
aggregations of prey may be an expression of heightened prey vigi-
ing success model. However, collinearity with topographic position
lance to predators (Roberts, 1996) thus reducing puma’s accessibility.
precluded mule deer PER from the most parsimonious model. The
However, this is unsupported given evidence that localized ungulate
effect found is unlikely to be attributable to unmeasured temporal
aggregations are positively correlated with higher ungulate densities
heterogeneity. Long-term heterogeneity (i.e. seasonal) of PER was
(Borkowski, 2000). The employed camera trap methods of the com-
inherently controlled for in the conditional logistic regression tech-
panion study (Blecha, 2015) incorporated group size into encounter
nique; events within the kill sequence strata occurred within rela-
rate estimates (Rowcliffe et al., 2008), thus there is support for larger
tively short intervals. If an intra-diel effect was present in PER, we
prey aggregations occurring in higher housing densities where hunt-
would predict that deer utilization near human development would
ing success was higher.
be greatest at night (Polfus & Krausman, 2012) coinciding with puma
One may ask if puma dietary shifts from larger wild prey sources
hunting activities, a pattern that would further bolster inferences
towards smaller synanthropic prey sources when near houses
of the energetic reward value being based in prey availability rather
(Kertson, Spencer, & Grue, 2011; Smith et al., 2016) may result in
than accessibility (i.e. ability to stalk prey).
an ecological trap in terms of energetic reward. Such might be the
Generalized predictions of animal space use in this predator–prey
case when the caloric reward value decreases per prey item, despite
game must consider residentially developed areas, which are per-
the overall increase in kill success (as we demonstrate) or kill rate.
ceived as a stationary “predator” to the wild predator, but a poten-
However, small prey items may be captured easier than a more dan-
tial refuge to the wild prey. Patch risk and reward perceived by the
gerous and unwieldy large prey item (i.e. deer), thus requiring lower
mobile predator appear to be positively correlated, an association
energetic expenditures. In addition, a “dine and dash” hypothesis
that may appear when prey alter their distribution on the landscape
is supported by small prey items having relatively shorter handling
to avoid the predator (Laundré, 2010) by seeking habitats perceived
times than large prey items; small prey items are less likely to be
as risky to the predator (Berger, 2007; Hebblewhite et al., 2005). In
abandoned due to human disturbances (Smith et al., 2015) or scav-
this system, mule deer prey are also faced with predation pressures
engers (Allen, Elbroch, Wilmers, & Wittmer, 2015).
�Journal of Animal Ecology
BLECHA et al.
4.2 | Hunger as a state-dependent risk
avoidance factor
|
619
hunger state governing the movement decisions underlying the foraging behaviours in a large mobile predator. Only a few experimental examples exist of hunger-mediated phenomena in higher trophic
Landscape utilization models are often placed within the context
levels (Berger-Tal et al., 2009; Embar et al., 2014). Hunger measures
of animals balancing risk avoidance or reproduction behaviours
in large carnivore studies have been inferred from belly size esti-
with maximizing energetic reserves (Mangel & Clark, 1986); hunger
mates (Packer, Swanson, Ikanda, & Kushnir, 2011) or time since last
is rarely focused on directly. Organisms experience hunger sensa-
kill (McPhee et al., 2012), but were employed as response variables
tions as an endocrine response to an empty gastrointestinal tract
rather than predictor variables. Although predicting a hunger level
(Nakazato et al., 2001), thus increased hunger sensation is a proxy
response with specific intrinsic and extrinsic covariates are inter-
for the depletion of energetic reserves. In our study of pumas, the
esting, these predictor variables are less universally applied across
median hunger level (1.38 days) is marked by a housing avoidance
systems and taxa; employing hunger (or energetic state directly)
coefficient that approximates the general SSF model. Risk avoid-
as a predictor variable puts a universal physiological mechanism at
ance towards housing reaches a threshold between 4 and 10 days,
the forefront for understanding animal space use. Technological ad-
in which case puma movements appear to be ambivalent towards
vances in biotelemetry techniques aimed at quantifying hunger level
housing. Based on the expected hunger level of 1.3 days, the case in
or energetic state (Williams et al., 2014) will likely expand upon our
which a puma has not eaten in 4—10 days is a relatively uncommon
simple proxy for hunger state in future studies.
event. While a higher expected hunger level appears to accompany
longer-term (seasonal) energetic deficits, an extended hunger bout
can occur at any time. We observed some hunger bouts extending
longer than digesta passage time of large carnivores (Childs-Sanford
& Angel, 2006; Warner, 1981) (i.e. >5–10 days). Prolonged bouts of
4.3 | Placing human–large carnivore conflicts within
state-dependent risk-sensitive foraging
Placing space use overlap between large carnivores and humans
hunger would likely translate to a depletion of energetic reserves
within the context of a three-way community-level interaction (hu-
that influences pumas foraging strategy in relation to risky habitats.
mans, predator and prey) is important for understanding human–
The hunger level effect on risk avoidance is complemented by
wildlife conflicts. Our case study demonstrates that inter-t axa
other associations between risk avoidance and intrinsic (age class,
relationships should not be generalized as piecemeal two-player
sex) or extrinsic factors (season). Female puma’s lower risk avoidance
attraction–avoidance games between carnivores and herbivores
response is likely related to higher energetic requirements when in
(Laundré, 2010), herbivores and humans (Polfus & Krausman, 2012)
maternal states (Knopff, Knopff, Kortello, & Boyce, 2010). While
or carnivores and humans (Burdett et al., 2010; Kertson et al., 2011;
maternal status is unavailable in this analysis, observations of our
Kertson, Spencer, & Grue, 2011; Wilmers et al., 2013). Empirical
marked females with a range of dependent young (<12 months of
examples rooted in energy maximization theory alone (Charnov,
age) were common (unpublished data). Despite uncertainty in the age
1976), or risk avoidance theory alone, provide contradictory predic-
effect, it is supported by younger pumas exhibiting higher usage of
tions when the herbivores are using anthropogenic development as
residential areas than their older counterparts (Kertson, Spencer, &
refuge. Generalization from past predator–human empirical studies
Grue, 2013). Seasonal differences (analysis by month) in risk avoid-
(Burdett et al., 2010; Kertson et al., 2011; Kertson, Spencer, & Grue,
ance behaviour are potentially driven by extrinsic changes in wild
2011; Wilmers et al., 2013) do not explain a predator’s occasional
prey availability (Valeix et al., 2012). PER measures should be lower
use of these inherently risky landscapes. Public trust in the ecologi-
during periods of low numerical prey availability. Periods coinciding
cal sciences is eroded when generalizations of wildlife space use fail
with the lowest numerical ungulate availability (late winter through
to explain the occasional overlap between humans and “fearsome”
the ungulate pre-birthing pulse: April–May) correlate with the least
carnivores.
avoidance of human dwellings and highest hunger levels. Following
State-dependent risk-sensitive foraging, as a function of physio-
this depressed availability, the ungulate birth pulse results in a peak
logical state, provides a unified explanation to the occasional carni-
in numerical ungulate prey availability (June–July); puma avoidance
vore–human overlap and conflict. Under this hypothesis, any factor
towards housing increases and the expected hunger level decreases.
causing an increased level of hunger or depressed energetic reserves
This effect could be driven by deer avoiding humans during the
may be linked to increased conflicts. These factors may include in-
fawning period. Alternative explanations for the seasonal effect on
trinsic states suggestive of decreased hunting efficiency, such
risk avoidance are predator maternal state, as puma energetic intake
as younger age, sick, injured or maternally active females (Linnell,
is influenced by offspring rearing behaviours (Knopff et al., 2010).
Odden, Smith, Aanes, & Swenson, 1999) or a sudden extrinsic reduc-
However, a peak in puma birthing activities in August (CMGWG,
tion in primary prey species (Inskip & Zimmermann, 2009). Currently,
2005) did not appear to influence risk avoidance behaviour.
no quantitative information is compiled on human–carnivore con-
Hunger-mediated behavioural choices are likely made in all or-
flicts in our specific study area. However, the state-dependent in-
ganisms. Despite hunger potentially being one of the most primeval
trinsic and extrinsic factors influencing risk avoidance in our subjects
fitness adaptations, it has received very little empirical attention.
are associated with higher human–carnivore conflicts in similar sys-
Our case study provides one of the first direct demonstrations of
tems (predator gender: Torres et al., 1996; predator age: Teichman
�620
|
Journal of Animal Ecology
et al., 2016; seasonality: Patterson, Kasiki, Selempo, & Kays, 2004;
Kolowski & Holekamp, 2006). This study benefits conservation
efforts aimed at large carnivores, afflicted by human–carnivore
conflicts, by providing a better understanding of carnivore space
utilization.
AC K N OW L E D G E M E N T S
We are grateful to Lisa Angeloni, Mevin Hooten and Jake Ivan for
providing comments on an earlier draft of the manuscript. We thank
Colorado Parks and Wildlife and the Jefferson County Open Space
of Colorado for funding. We were fortunate for the efforts in the
field put forth by Tasha Blecha (Eyk), Joe Halseth, Darlene Kilpatrick,
Gabrielle Coulombe, Rebecca Mowry, Ryan Platte, Eric Newkirk,
Elizabeth Joyce, Duggins Wroe, Matt Strauser, Laura Nold, Wynne
Moss, Fred Quarterone and Chris Woodward for subject capture,
kill-site ground-truthing and database management. Data collection would not have been possible without the land access granted
by hundreds of private landowners. This work was supported by
Colorado Parks and Wildlife Game Cash Funds and U.S. Fish and
Wildlife Service Federal Aid Grant #W-204-R1.
AU T H O R S ’ C O N T R I B U T I O N S
K.A.B. designed the project, conducted data processing and analysis, led data collection and writing of the manuscript. R.B.B. assisted
with development of housing density map and writing of manuscript.
M.W.A. designed kill-site identification process, assisted with analysis and writing of manuscript.
DATA AC C E S S I B I L I T Y
Data available from the Dryad Digital Repository: https://doi.
org/10.5061/dryad.67sf3 (Blecha et al., 2018).
ORCID
Kevin A. Blecha
http://orcid.org/0000-0002-9284-0685
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Hunger mediates apex predator’s risk avoidance response in
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Hunger mediates apex predator's risk avoidance response in wildland–urban interface
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Camera traps
Cougar (<em>Puma concolor</em>)
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Housing avoidance
Human–predator conflict
Patch use
Risk–reward trade-off
Step selection function
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<span>Puma (</span><i>Puma concolor</i><span>), an apex predator, can live at the edge of cities where pockets of low-density human dwellings form residential patches in the wildland–urban interface. Blecha, Boone, and Alldredge (</span><span><a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.12815#jane12815-bib-0003" class="bibLink tab-link">2018</a></span><span>) tracked puma via global positioning system (GPS) telemetry collars to determine when and where they hunted and made kills. Well-fed puma (1–2 days between kills) strongly avoided residential patches despite these areas having higher mule deer (</span><i>Odocoileus hemionus</i><span>) densities and higher kill success for puma. However, the strong avoidance of residential patches completely disappeared as puma became hungrier (4–10 days since last kill) making it more likely that hungry individuals hunted in residential areas and ultimately increasing the likelihood of puma–human conflict.</span>
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Blecha, Kevin A.
Boone, Randall B.
Alldredge, Mathew W.
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<p>Blecha, K. A., R. B. Boone, and M. W. Alldredge. 2018. Hunger mediates apex predator's risk avoidance response in wildland-urban interface. Journal of Animal Ecology 87:609–622. <a href="https://doi.org/10.1111/1365-2656.12801" target="_blank" rel="noreferrer noopener"> https://doi.org/10.1111/1365-2656.12801</a></p>
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