Biological Conservation 190 (2015) 23–33

Contents lists available at ScienceDirect

Biological Conservation
journal homepage: www.elsevier.com/locate/biocon

Mesopredator spatial and temporal responses to large predatorsand human development in the Santa Cruz Mountains of California
Yiwei Wang ⇑, Maximilian L. Allen, Christopher C. Wilmers
Center for Integrated Spatial Research, Environmental Studies Department, 1156 High Street, University of California, Santa Cruz, CA 95064, United Statesa r t i c l e

i n f oArticle history:
Received 21 August 2014
Received in revised form 10 May 2015
Accepted 17 May 2015

Keywords:
Mesopredator
Puma concolor
Occupancy
Habitat fragmentation
Mesopredator release
Trophic cascade
Canis latransRecreation
Competitiona b s t r a c t
Human-driven declines of apex predators can trigger widespread impacts throughout ecological communities. Reduced apex predator occupancy or activity can release mesopredators from intraguild competition, with unknown repercussions on the ecological community. As exurban development continues toexpand worldwide, it is important to document how mesopredators are impacted by the combined inﬂuences of apex predators and humans. We used motion-detecting camera traps to examine spatial andtemporal patterns of meso- and apex predator occupancy and activity in a fragmented landscape in
California. We hypothesized that both spatial and temporal partitioning among the carnivore guild would
be affected by varied levels of human inﬂuence. We found that higher residential development reduced
puma occupancy but was not related to the occupancy of mesopredators. Bobcats, grey foxes, andVirginia opossums were detected more often at sites occupied by pumas, whereas coyotes and raccoons
were detected less often. The detection probabilities of smaller mesopredators were related to coyotes, adominant mesopredator, but the magnitude and direction of these correlations differed depending upon
puma occupancy. We also found that species altered their activities temporally in locations with higher
human use, with pumas, bobcats and coyotes reducing diurnal activities and increasing nocturnal ones.
These activity shifts were reﬂected in reduced temporal partitioning between intraguild competitors,
with unknown effects on species interactions and repercussions to the prey community. Our results suggest that human development and activity alters predator community structure through both direct andindirect pathways. Therefore effective carnivore conservation requires an understanding of how mesopredators respond to varying levels of apex predator and anthropogenic inﬂuences.
Ó 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction
Anthropogenic-driven extirpation of apex predators fromecosystems across the globe has had led to large changes in community structure and dynamics in diverse ecosystems (Estes
et al., 2011). Such changes to the predator guild can greatly alter
ecological networks by releasing mesopredators from intraguild
predation and competition, thus initiating trophic cascades that
propagate throughout food webs (Levi and Wilmers, 2012; Nosset al., 1996; Pace et al., 1999; Polis and Holt, 1992). The combined
extirpation of apex predators and release of mesopredators has
been identiﬁed as a possible cause for the decline or extinction of
songbirds and small mammals, because they are disproportionately preyed upon by mesopredators (Crooks and Soulé, 1999;
Johnson et al., 2007; Ritchie and Johnson, 2009).

⇑ Corresponding author.
E-mail address: yiweiwang@dataone.unm.edu (Y. Wang).

Habitat fragmentation due to human development can lead tothe extirpation of top predators (Gehrt et al., 2010; Šálek et al.,2014) and irreversibly alter ecosystem stability (Hansen et al.,2005). Exurban development (0.06–2.5 houses/hectare) is the fastest growing form of land use change in the United States, andbuilding low-density developments adjacent to wildlands resultsin habitat fragmentation, increased human wildlife contact andconﬂict, and homogenization of ecological communities (Hansen
et al., 2005; McKinney, 2006). The spread of human development
or activity into nearby open space disrupts both sensitive species(e.g. apex predators) and entire communities by altering predator
interactions and their traditional ecological roles. These changes
do not always progress linearly with increasing levels of development; instead abrupt shifts in community composition may occur
with gradual increases in exurban development (Hansen et al.,2005). Because exurban development now encompass more thanﬁve times as much land as do suburban and urban development,
it is crucial that we understand how low-density human pressuresalter the composition and interactions of nearby predator

http://dx.doi.org/10.1016/j.biocon.2015.05.007
0006-3207/Ó 2015 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Y. Wang et al. / Biological Conservation 190 (2015) 23–33

communities in order to balance conservation and development
needs (Bateman and Fleming, 2012).
While apex predators limit mesopredators through competitionand predation, they also affect mesopredator survival and reproduction through indirect, trait-mediated effects (Ritchie andJohnson, 2009). Natural selection favors adaptations such as
behavioral avoidance through spatiotemporal partitioning, which
reduce costly interactions between mesopredators and dominantcompetitors (Gehrt and Clark, 2003; Wang and Fisher, 2012;
Wilson et al., 2010). Within the predator guild, however, apex
predators can also beneﬁt smaller predators by suppressing
larger-bodied mesopredators (Berger and Conner, 2008;
Elmhagen et al., 2010). Levi and Wilmers (2012) demonstratedan ‘‘intraguild cascade’’ in which the apex predator, gray wolves(Canis lupus), suppressed the mesopredator, coyotes (Canis latrans),
and thereby released the smaller red foxes (Vulpes vulpes). In addition, carrion provided by apex predators can serve as an importantfood source for smaller predators (Allen et al., 2015a; Ruth andMurphy, 2009; Selva et al., 2005; Wilmers and Getz, 2005).
Therefore, the relationship between dominant and smaller predators may reﬂect a complex balance of risk-avoidance and energetic
needs, all of which may be inﬂuenced by anthropogenic subsidies
and disturbances and direct predation.
We used camera traps to simultaneously examine how the
combined inﬂuences of bottom-up exurban development andtop-down apex predator pressures affected mammalian mesopredator communities in the Santa Cruz Mountains of California.
Native carnivores in the region include pumas (Puma concolor, anapex predator), coyotes (a dominant mesopredator and an emerging apex predator in many urbanizing regions), bobcats (Lynx rufus;
a dominant mesopredator), and subordinate mammalian mesopredators (e.g., grey foxes, Urocyon cinereoargentus, striped skunks,
Mephitis mephitis, Virginia opossum, Didelphus virginianus, and raccoons, Procyon lotor). We placed motion-detecting digital cameras
along a gradient of human development to test whether spatial andtemporal niche partitioning of the local predator community
shifted across disparate levels of human inﬂuence.
Based on results from previous studies of human impacts on
predators, we expected the predator community to respond tothe combined inﬂuences of higher ordered predators and development and formulated the following hypotheses. (1) Predator
responses to anthropogenic pressures would vary by species:
Puma occupancy would decline as human development and activity increased due to their particular sensitivity to anthropogenic
inﬂuences (Crooks, 2002; Wilmers et al., 2013). Exurban development would not reduce the occupancy probabilities of bobcats,coyotes and grey foxes because we expected these versatile predators to adapt to low levels of development, especially when accessto nearby open spaces are available (Goad et al., 2014; Riley, 2006).
However, we expected detection levels of bobcats to decline withhigher development, because this species is less likely to use developed areas (Riley, 2006). In contrast, we expected occupancy probabilities for synanthropic species, such as raccoons and stripedskunks, and domestic species (e.g., domestic cats, Felis catus) toincrease with development (Bateman and Fleming, 2012;
McKinney, 2006). (2) Predator interactions would be altered withincreasing anthropogenic development: Dominant mesopredators(i.e. coyotes and bobcats), which have greater niche overlap withpumas, would be deterred by higher puma occupancy and activity
whereas subordinate mesopredators would be released by thissuppression of their competitors (Levi and Wilmers, 2012). In areas
where puma occupancies declined, we expected coyotes would act
as an emergent apex predator and negatively affect the occupancy
and detection of smaller mesopredators (Gehrt and Prange, 2007;
Levi and Wilmers, 2012; Pace et al., 1999). (3) Subordinate predators would exhibit temporal avoidance of dominant ones to reducerisk of intraguild predation and competition (Palomares and Caro,
1999; Polis and Holt, 1992). However, we expected areas withhigher human activity would reduce temporal niche partitioning
by restricting wildlife activities during diurnal hours (George andCrooks, 2006; Reed and Merenlender, 2008).
2. Methods
2.1. Study Site
Our study occurred in the southern Santa Cruz Mountains inwest-central California, an area encompassed within Santa Cruz,
Santa Clara and San Mateo counties (Fig. 1). This region has alegacy of preserving large tracts of open space, with 24% of the surrounding San Francisco Bay Area held in some form of public land
trust or conservation easement (Rissman and Merenlender, 2008).
The Santa Cruz Mountains includes diverse habitats ranging from
intact wildlands to urban regions, providing a gradient of environmental conditions to study the impacts of development on interspeciﬁc interactions. Signiﬁcant portions of public land areavailable for a wide variety of recreational activities, including biking, hiking, and dog walking. There are also many large private
landholdings, some of which are managed for resource extraction
activities, but are not open to the public for recreation. Our study
area abuts the urban municipalities of Santa Cruz and the SouthBay Area and contains several small suburban and exurban mountain communities. A major highway bisects our study region, andnumerous arterial (>35 mph), neighborhood (<35 mpg) andunpaved roads also mark the landscape.
The Santa Cruz Mountains experiences a dry season from May
to October and a wet season from November to April. Small tolarge-sized mammalian carnivores that occur in the Santa Cruz
Mountains include: pumas, bobcats, coyotes, grey foxes, red foxes,
raccoons, striped skunks, Virginia opossums (hereafter opossums),
western spotted skunks (Spilogale gracilis), American badgers(Taxidea taxus), and two domestic species, cats and dogs (Canis
lupus familiaris).
2.2. Camera trap survey
Between May 2011 and June 2013 we placed cameras (Bushnell
Trophy Cam; Bushnell Corp., Overland Park, KS, USA) at 50 locations throughout the study site (Fig. 1) to monitor animal activity
across a gradient of human development from undeveloped to
exurban neighborhoods. We measured human development frombuilding structures identiﬁed from high-resolution satellite imagery. We digitized structures manually in exurban landscapesand used address points to locate houses in suburban and urban
areas to create a development layer in ArcGIS (v. 10.0, ESRI 2010,
Redlands, CA). We used ArcGIS to randomly select 50 camera
locations across a stratiﬁed development gradient of approximately 0–2 houses per hectare. We further restricted potential
camera locations to within 100 meters from a road or trail for accesspurposes and at least 1 km away from the next closest camera.
We traveled to each randomly generated location and placed acamera along the closest trail or road to maximize native carnivore
detection (Gompper et al., 2006). Most trails we selected were usedby vehicles on an infrequent to regular basis. When recreational
trails were not available, we placed the camera on a well-deﬁned
game trail wide enough for people to walk on. We programmed
the cameras to take three photographs when triggered with a
one-minute delay between successive image sets. We excluded
data collected by cameras when they were heavily obscured by fallen or growing vegetation or when interference by humans or animals signiﬁcantly altered the camera angle or ﬁeld of visibility. We

Y. Wang et al. / Biological Conservation 190 (2015) 23–33

25

Fig. 1. Map of study area with camera locations marked with red dots and housing development areas represented by grey. Inset marks location of the Santa Cruz Mountains
in California. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

visited all cameras for maintenance either every one or twomonths, depending on the amount of activity at the site.
2.3. Spatial activity analyses
We used single-season, two-species occupancy models to
explore how apex predators and humans development inﬂuenced
mesopredator occupancy and detection. Occupancy models
improve upon traditional methods of examining spatial patternsof species occurrence by providing unbiased estimates of species
occupancy and explicitly accounting for imperfect detection(MacKenzie et al., 2004, 2002). Habitat and survey covariates canalso be incorporated when estimating both occupancy and detection through a logit link (MacKenzie et al., 2004, 2002). We used
two-species occupancy models to test whether the occupancy
and detection of subordinate species were inﬂuenced by the occupancy of more dominant species. We also tested whether the subordinate predators responded to habitat and anthropogenic factors
conditional upon occupancy by pumas. Lastly, we incorporated
coyote activity as covariates to test whether they inﬂuenced theoccupancy and detection of subordinate mesopredators.
We compiled a detection history for all carnivore species during
a truncated study period lasting from October 2011 to October
2012. We demarcated each survey period as a full week beginning
Monday at 10AM PST for a total of 53 surveys. We assumed that
our cameras were closed to occupancy changes over the one-year
period because the species we targeted for monitoring live several
years in the wild and maintain generally stable home ranges (Riley,2006). For each survey period, we recorded whether a species waspresent or absent based on whether it was captured by the camera
trap. Upon completion of our camera-trapping study, we had
enough data to model occupancy for 8 predators: pumas, coyotes,
bobcats, grey foxes, raccoons, striped skunks, opossums, anddomestic cats.
We used the program PRESENCE 5.9 (United States Geological
Survey Patuxent Wildlife Research Center, Laurel, Maryland;
MacKenzie et al. 2006), which utilizes a maximum-likelihoodestimation modeling approach to compare all models. We usedthe conditional parameterization of the two-species occupancy

model (Richmond et al., 2010) to examine interactions between
pairs of dominant and subordinate carnivores. Under this model
structure, we had a priori categorizations of dominant (A) and subordinate (B) species, and the conditional model parameterization
allowed us to explicitly test whether species B occupancy ðwB Þ anddetection ðpB ; r B Þ estimates were conditional upon those of species
A. We did not test whether the detection of species B was inﬂuenced
by the detection of species A (i.e., we set r BA ¼ rBa ) because of limited
data due to the infrequent detection of pumas. The full set of parameters and covariates utilized by the models and their descriptions islisted in Table 1.
Our occupancy covariates for native predators were humandevelopment and percent forest habitat, which we included
because many native carnivores are positively associated with natural vegetation (Šálek et al., 2014). We derived our development
covariate using the kernel density tool in ArcGIS to calculate housing density from the GIS housing layer at radii (h) of 100 m, 300 m,
and 500 m. We selected these values to reﬂect the range of scales

Table 1
List of parameters and covariates and their deﬁnitions used in occupancy models.
Parameter or
covariate

Description

wAwBA

Probability of site occupancy for species A
Probability species B occupies site given species A
occupies the site
Probability species B occupies site given species A does
not occupy the site
Probability of detection for species A, independent ofpresence of species B
Probability of detection for species B, given species A isabsentProbability of detection for species B, given species A and
B are present
Kernel housing density estimates with a radius of 500 m
Percent forested habitat in the surrounding area with
500 m radius
Average coyote activity per 100 days

wBa
pA
pB
rB
Development (D)Forest (F)Coyote activity
(C)

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Y. Wang et al. / Biological Conservation 190 (2015) 23–33

at which pumas responded to housing densities (Wilmers et al.,2013). We found that a radius of 500 m minimized model Akaike
Information Criterion (AIC; Anderson and Burnham, 2002;
Arnold, 2010) and therefore used that scale for our development
covariates. We classiﬁed forest habitat by placing a circular bufferswith radii 100 m, 300 m, and 500 m above the camera location tomatch the scales of the development covariate. We then extracted
vegetation data from the California GAP Analysis Project (Lennartz
et al., 2008) and calculated percentage forest cover by dividing thearea classiﬁed as forest or woodland by the total area of the circular buffer. We again found that a scale of 500 m minimized model
AIC and used that scale for subsequent analyses. We also usedhuman development, as deﬁned above, for our detection covariate.
For mesopredators subordinate to coyotes, we included coyote
activity as occupancy and detection covariates. Coyote activity
was deﬁned as the average number of coyote photos taken at eachsite per 100 days. We z-transformed all forest cover, development,
and coyote activity covariates.
The candidate model set for the conditional two-species parameterization is very large due to all the potential combinations ofmodel parameters. To simplify the a priori size of the candidate
model set, we carried out a multi-step modeling approach following Richmond et al. (2010). First, we identiﬁed the best occupancy
and detection model for species A by ﬁtting 8 single-species,
single-season models to the puma data with all combinations ofoccupancy, detection and site-speciﬁc covariate parameters(Appendix A (Richmond et al., 2010)). We used AIC rankings to
identify the best single species occupancy models for pumas andused the resulting top model in all subsequent two-species
co-occurrence occupancy analyses.
Using the best occupancy and detection covariates for pumas,
we then modeled co-occurrence between pumas and all subordinate species (coyotes, grey foxes, bobcats, raccoons, domestic cats,
striped skunks and opossums). For each species pair, we ﬁrst heldwB constant and compared the conditional and unconditional
detection models for species B ði:e:; pB –rB vs. pB ¼ r B Þ. For all
puma-mesopredator pairs, the conditional model was the bettermodel, so we used it to evaluate candidate covariates (i.e., development, coyote detection). We compared 25 candidate models toevaluate the inﬂuence of coyote activity and development on
mesopredator detection probabilities and to test whether covariates affected detection differently conditional upon puma site
occupancy. We discarded models from further inference if they
contained uninformative parameters as identiﬁed by Arnold(2010) and were not within 2 DAIC of the top ranked model. We
then used AIC rankings to determine the best model.
Once we identiﬁed the top detection model, we held that portion of the model constant and compared different models to test
whether species B occupancy was affected by covariates and species A occupancy. Bobcats, coyotes, raccoons and striped skunks
were prevalent across the landscape (occupancy rates >83%), andin preliminary one-species occupancy analyses, we found including occupancy covariates did not improvement models.
Therefore, we did not model the effects of puma occupancy or
any covariates on their probability of occupancy for these common
species. For domestic cats, we only compared models with andwithout human development as an occupancy covariate since we
did not expect puma occupancy or percent forest cover to affectcat occupancy. Because cats are almost always pets, we expectedthat their presence or occupancy on the landscape would be primarily associated with human development. For all other species
pairs, we compared conditional occupancy models (wBA = wBa) tounconditional ones (wBA – wBa) with all combinations of occupancy covariates. We followed the previous protocol and discarded
any models including uninformative parameters from comparison(Arnold, 2010), and we used AIC to rank all models for comparison.
2.4. Temporal activity analyses
For each photograph captured during our study period, werecorded the date, time, camera site ID, and species. To reduce
pseudoreplication, we did not record photos of animals of the same
species if they occurred within 30 min of a previous photo (Brook
et al., 2012; Lucherini et al., 2009; Monterroso et al., 2013).
Furthermore, if the camera captured two or more individuals of
the same species in one photograph we treated the event as one
time data point.
We examined whether species activity patterns differed
between sites with disparate levels of human activity by separating our data into photographs that were captured at sites withhigh human use (n = 19; 2.05–120 people/day) and those withlow human use (n = 31; 0.03–1.39 people/day). We identiﬁed
the threshold by visually examining the distribution of humanactivity data (George and Crooks, 2006) and identifying a natural
break at about 2 photos day. We therefore deﬁned high humanuse sites as all those in which the cameras captured a mean of
P14 or more humans/week and low-use sites as those with
<14 humans/week.
We used the non-parametric kernel density estimation procedure described in Ridout and Linkie (2009) and Linkie and Ridout(2011) to compare whether 10 species (pumas, bobcats, coyotes,
grey foxes, opossums, striped skunks, raccoons, domestic cats,domestic dogs, and humans) offset their temporal activities at
the two different human activity levels. We ﬁrst converted alltimes to radians and used kernel density estimation to generatea probability density distribution of each species’ activity pattern
at each human activity level (Ridout and Linkie, 2009). Next, we^ , a value ranging from 0 to 1, whichcalculated the overlap term D
is deﬁned as the area under the curve formed by taking the smaller of two density functions at each time point (Ridout and Linkie,^ value if species did not change2009). We expected a higher D
their temporal activity patterns at high human use sites and a^ value closer to 0 if they altered their activity greatly. RidoutD^ andand Linkie (2009) outlined three methods for estimating D^ 1 for small sample sizes (n < 50) and D^ 4 for larsuggested using D^ 4 at highger sample sizes, which we follow. We also compared D
versus low use sites for 15 species pairs to test whether temporal
overlap between species pairs increased in areas of higher humanuse. Finally, we obtained 95% conﬁdence intervals for these estimates from 1000 bootstrap samples. All statistics were analyzed
using the overlap package (Meredith and Ridout, 2014) in R (RDevelopment Core Team, 2013).
To compare how human activity and development might
impact temporal overlap between mesopredator species pairs on
a continuous scale, we used the same method outlined above to^ 1 between species pairs at each camera station. For
calculate Dthese analyses we focused on temporal partitioning among threemesopredators pairs for which we had the most data: coyotes, grey
foxes and bobcats. We then used multiple regression to determine
how forest cover, development, and human activity affected theamount of temporal overlap between species. Our covariates forforest cover and development are described previously, and we
calculated human activity by log-transforming the average numberof humans photographed per day at each camera location. We
started with the full model:^ 1 ¼ b þ b ðHuman Activ ityÞ þ b ðDev elopmentÞ
D0
1
2
þ b3 ðForest Cov erÞ:

Y. Wang et al. / Biological Conservation 190 (2015) 23–33

27

and used likelihood-ratio tests to compare nested models to identify the best model (Johnson and Omland, 2004).
3. Results
Our cameras operated for 21,950 cumulative trap nights at the
50 sites (mean = 439 nights; SD = 100), and we identiﬁed at least
68 species of animals from 193,815 photo sets. All carnivores,
except for domestic dogs (8.2% nocturnal activity), were primarily
active at night, with some species, such as grey foxes (94.6%), opossums (96.6%) and striped skunks (94.0%), almost exclusively so(Table 2).
3.1. Puma response to human development
The top model for pumas identiﬁed development as both aninﬂuential negative occupancy covariate (b = 1.52, SE = 0.47)
and detection covariate (b = 0.52, SE = 0.19), which aligned with
our predictions. All 8 single-species, single-season occupancy models for pumas are listed in Appendix A. The best models (DAIC < 2)
included development as an occupancy variable and all models
including development as an occupancy covariate had a cumulative AIC weight of greater than 0.99. The cumulative AIC weight
for all models including development as a detection covariate
was greater than 0.98.
3.2. Mesopredator responses to human and top predator inﬂuences
We modeled interactions between pumas (the dominant predator) and 7 subordinate predators for a one-year period, with the
top models (DAIC < 2) for each species pair summarized inAppendix B. Contrary to our expectations, no mesopredators occupancy probabilities were clearly related to puma occupancy.
However, opossum and grey fox conditional and unconditional
occupancy models had almost equal weight (DAIC < 0.2), whichmeans that it was inconclusive whether puma presence was correlated with the occupancy of either species. Opossums occupancy
was positively correlated with coyote activity (b = 1.05, SE = 0.69)
and higher forest cover (b = 0.58, SE = 0.38). Domestic cat occupancy was positively correlated with development (b = 0.60,
se = 0.32), which is in line with what we expected since they areclose human associates.
All subordinate species’ detection probabilities (as opposed tooccupancy rates) were related to the presence of pumas, although
not necessarily in the directions we predicted. In general, bobcats,
grey foxes, opossums and striped skunks were more likely to be
detected at sites with pumas whereas raccoons and coyotes wereless likely to be detected (Fig. 2). Striped skunk and raccoon detection probabilities increased with development whereas bobcat andFig. 2. A conceptual diagram detailing the average percent differences in mesopredator detection probabilities with puma occupancy and coyote activity in low(a) and high (b) development sites. Low development is represented by adevelopment z-score of 1 and high development by 1. Solid arrows originatingfrom pumas represent the differences in mesopredator detection probabilities atsites occupied by pumas compared to sites unoccupied by pumas. A positive valuemeans the species is more likely to be detected when pumas are present and anegative value means the species is less likely to be detected. Dashed arrows
originating from coyotes represent the differences in mesopredator detection
probabilities at sites occupied by pumas with low coyote activity (z-score = 0.5)
and with high coyote activity (z-score = 1). Coyote activity did not inﬂuence raccoon
detection at sites occupied by pumas. The widths of the arrows are relative to thepercent differences in detection probabilities. The size of the puma in each panel
represents how its occupancy changes from low to high development.

Table 2
Summary statistics for the top 11 species captured by the camera traps.
SpeciesNumber ofphotographs

Cameras captured(out of 50 cameras)

Percentage ofnighttime captures (%)

BobcatDomestic cat
CoyoteDeer
DogGrey fox
Human
Opossum
Puma
Raccoon
Striped skunk

4223
1429
2241
16,3549650
2464
142,616
986
217
1065
3024

49
25
47
50
44
34
50
34
33
47
49

70.4
75.4
58.3
46.6
8.2
94.6
8.8
96.6
80.7
90.7
94.0

coyote detections showed the opposite pattern (Fig. 3). Opossums,
domestic cats and grey foxes responses to development were conditional upon puma occupancy (Fig. 3). Opossum detection was
positively correlated with human development at puma-occupied
sites and negatively correlated with development at sites without
pumas. At sites occupied by pumas, grey fox detection was negatively correlated with higher development whereas domestic cat
detection increased with development at sites where pumas wereabsent. Domestic cat detection responded very strongly to increasing development, which likely explains the contrasting pattern
between puma occupancy and cat detection in areas of low versus
high development (Fig. 2).

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Y. Wang et al. / Biological Conservation 190 (2015) 23–33

With puma(A)

Without puma

1.0

1.0

0.8

0.8

0.6
Grey
P
Fox
0.4

P

0.6
0.4

0.2

0.2

0.0

0.0
0.5

0.0
-0.5

-1.0

elo
dev

0.0

0.5

0.0

-0.5

ity
tiv
ac
C.

ity
tiv
ac
C.

0.5

pm

ent

1.0

0.8

0.8

0.6

P

0.4

lo
eve

1.5

pm

2.0

ent

0.4
0.2

0.0

0.0

0.5

0.5
0.0

elo
dev

pm

0.0

ity

-0.5

ent

tiv

-1.0

0.5

ac

-0.5

C.

ity

tiv

ac

0.0

1.0

1.0

0.8

0.8

0.6
Striped
P
Skunk
0.4

P

-0.5

-0.5

0.0

0.5

1.0

1.5

elo
dev

2.0

pm

ent

0.6
0.4

0.2

0.2

0.0

0.0
0.5

de

0.0

p
velo

nt
me

1.0

0.8

0.8

0.6

P

0.4

-0.5

-0.5

0.0

0.5

de

1.0

1.5

p
velo

2.0

me

nt

0.6
0.4

0.2

0.2

0.0

0.0
0.5

d

lo
eve

pm

0.0

ity

-0.5

ent

tiv

-1.0

0.0

ac

-0.5

0.5

C.

0.0

ty
ivi

t
ac
C.

0.5

0.0

ity

-1.0

-0.5

tiv

ity

-0.5

0.5

ac

tiv

ac

0.0

C.

C.

0.5

1.0

Opossum P

0.0

1.0

0.6

0.2

C.

(B)

-0.5

d

1.0

Bobcat P

-0.5

0.5

-0.5

-0.5

0.0

0.5

d

1.0

1.5

lo
eve

2.0

pm

ent

Fig. 3. Estimated detection probabilities (P) for 6 mesopredator species as a function of puma occupancy, coyote activity (C. activity), and human development. Development
and coyote activity values have been z-transformed. Negative z values represent lower development or coyote activities and larger values represent higher development or
coyote activities.

3.3. Mesopredator responses to coyote inﬂuence
As we predicted, many subordinate species detections werelower with increased coyote activity, although some species

responded to coyotes differently depending upon puma occupancy
status (Fig. 2). Bobcats were more likely to be detected and opossums less likely to be detected at sites with higher coyote activity(Fig. 3). Grey fox detection exhibited the most negative29

Y. Wang et al. / Biological Conservation 190 (2015) 23–33

Without puma

With puma

(C)
1.0

1.0

0.8

0.8

Domestic
0.6
P
Cat
0.4

P

0.2

0.0

0.0

-0.5

-1.0

0.5

0.0

-0.5

dev

elo

p

nt
me

1.0

1.0

0.8

0.8

0.6

P

0.4

0.2
0.0

C.

C.

elo

p

0.0

dev

1.0

elo

0.0

ity

dev

nt
me

tiv

-1.0

0.0

-0.5

-0.5

0.5

1.5

p

2.0

nt
me

0.5

ac

ity

tiv

ac

-0.5

0.5

-0.5

0.4

0.0

0.0

0.0

0.6

0.2

0.5

0.5

ty
ivi
ct

0.0

a
C.

0.5

ty
ivi
ct

P

0.4

0.2

a
C.

Raccoon

0.6

-0.5

-0.5

0.0

0.5

dev

1.0

elo

1.5

p

2.0

nt
me

Fig. 3 (continued)

relationship to coyote activity, but only at sites occupied by pumas(Fig. 3). At sites without pumas, grey fox detection was near zeroand showed no relationships with any covariates (Fig. 3). Stripedskunk and cat detections were also correlated with coyote activity,
but these associations were weak (Fig. 3). Finally, raccoon detections decreased with coyote activity at sites not occupied by pumas(Fig. 3).

coyotes at high human use locations. At a ﬁner spatial scale, we
found that overlap between all three mesopredator species pairs(bobcat and grey fox, coyote and grey fox, and bobcat and coyote)signiﬁcantly increased with human activity and overlap between
bobcats and coyotes signiﬁcantly increased with both humanactivity and development (Table 4).
4. Discussion

3.4. Temporal responses to human inﬂuences
In accordance with our predictions, predator species reduced
their diurnal activities in areas more regularly used by people.
Pumas and coyotes increased nighttime activity and reduced daytime activity, particularly during the morning hours, in sites withhigh human activity (Fig. 4). Bobcats exhibited similar activity
peaks and valleys at both levels of human use, but with overalllower daytime and higher nighttime activity levels. Grey fox, opossum, striped skunk and raccoon activity patterns did not change athigh human use sites, most likely because these species were
almost exclusively active nocturnally. Humans and dogs in highhuman use sites were more active in mornings and less active inafternoons, possibly reﬂecting a bias toward morning recreationin parks.
Coyotes and smaller mesopredator species (grey fox, opossum,
raccoon, and striped skunk) showed the highest temporal segregation of all species pairs in both low (45.4–54.5% overlap) and high^ 4 (overlap) values(65.3–73.9%) human use areas (Table 3). All D
between coyotes and smaller mesopredators increased by morethan 15% in higher human use sites with non-overlapping conﬁdence intervals. Coyote and domestic cat temporal overlap also
increased by 19.9% from 70.4% to 90.3%. Pumas exhibited the lowest temporal overlap with opossums and coyotes. However, overlap increased somewhat between pumas and opossums andOur study explored how the combined inﬂuences of top predators and humans affect the activity and composition of the carnivore
community. As we expected, pumas and native mesopredators
exhibited differential sensitivity to human development and activity. Exurban habitats and open space areas with high levels of humanactivity were still occupied and used by pumas, while bobcats andcoyotes were ubiquitous across the landscape. Puma presence andcoyote activity were associated with changes in mesopredator activity (Fig. 3), while human activity increased temporal overlap among
predators in areas of higher human use (Table 3). Because our results
are correlational in nature, we discuss our ﬁndings in the context ofthe broader literature and explore some alternate variables that may
have affected our observations.
4.1. Anthropogenic inﬂuences on apex predator occupancy and activity
As we predicted, puma occupancy and activity declined withincreasing exurban development and human activity. Despite thenegative association with development, pumas continued to utilize
sites with moderate to high levels of exurban development, particularly if these locations bordered open spaces. This ﬁnding supports the conclusions drawn by Wilmers et al. (2013) that pumas
regularly travel and hunt throughout human-dominated landscapes in spite of their preference for undeveloped habitats. Our

30

Y. Wang et al. / Biological Conservation 190 (2015) 23–33∆ =0.84(0.76-0.94)

Kernel Density Estimates∆ =0.88(0.85-0.91)∆ =0.95(0.93-0.99)∆ =0.82(0.76-0.84)∆ =0.92(0.91-0.97)∆ =0.94(0.90-0.99)∆ =0.91(0.87-0.97)∆ =0.88(0.83-0.93)∆ =0.84(0.81-0.88)

Time of Day∆ =0.96(0.95-0.98)∆ =0.88(0.86-0.90)

Time of Day

Time of Day

Fig. 4. Kernel density estimates of daily activity patterns of 10 animal species and humans in areas of high (blue lines) and low (black lines) human use in the Santa Cruz
^ , with D^ ¼ 1 representing no temporal shifts between low and high human use areas and D^ ¼0
Mountains. Temporal activity shifts for species are represented by D
representing complete activity shift. Light blue colors indicate a decrease in activity and yellow colors represent an increase in activity in areas of higher human use.

ﬁndings also suggest that pumas, like other predators (Carter et al.,2012; Riley et al., 2003; Schuette et al., 2013), may utilize temporal
partitioning to avoid humans by reducing their morning activity
and increasing their nighttime activity between midnight and
6AM at sites of higher human use (Fig. 4).
4.2. Puma inﬂuences on mesopredators
Our research revealed that mesopredator species displayed disparate responses to pumas (Fig. 3). Coyote and raccoon detection
probabilities were the most negatively associated with puma occupancy. Coyotes and raccoons may have reduced their activity insites occupied by pumas to avoid encountering a dominant competitor (Ritchie and Johnson, 2009) and intraguild predator(Logan and Sweanor, 2001). In contrast, bobcats, grey foxes andstriped skunks were all detected more often at sites with pumas,
suggesting that these three mesopredators can either successfully
avoid confrontations with pumas or beneﬁt from suppressed activity of coyotes and raccoons. Although we expected bobcats to avoid
pumas due to their similarities as felids and because pumas sometimes kill and consume bobcats, a previous study also found no evidence that bobcats avoided pumas (Hass, 2009). Opossum
detection probabilities were positively associated with higher
development sites occupied by pumas. While this is surprisingbecause pumas prey on opossums, opossums may be beneﬁttingfrom reduced activity of raccoons due to puma occupancy of thesesites (Ginger et al., 2003).

4.3. Development inﬂuences on mesopredator activity
Bobcats and coyotes were present throughout all levels of exurban development, although their detection probabilities declinedwith increased development. Coyote detections decreased to a lesser degree with higher development than bobcat detections, indicating that bobcats are perhaps more sensitive to development.
Our results partially support previous studies that show bobcat
detection declining with development (Goad et al., 2014;
Ordenana et al., 2010; Riley, 2006). In contrast to those studies
however, we did not observe declining bobcat occupancy acrossour development gradient, possibly because we capped our development gradient at exurban levels. We found no impact by development on coyote occupancy and only a weak negative inﬂuence
on detection. This corroborates previous research highlighting
the coyote’s ability to thrive in anthropogenic ecosystems(Bateman and Fleming, 2012; Prange and Gehrt, 2004), which provides further conﬁrmation that they may be an emergent apex
predator in developed areas.
Conﬁrming our expectations, detections of synanthropic species, such as raccoons and striped skunks, were positively correlated with higher human development, but their occupancies
were consistent across all development levels. In contrast, grey
fox detections decreased with higher development. While Bidlack(2007) and Riley (2006) concluded that grey foxes were capableof persisting in small urban patches, our results aligned more withthe meta-analysis of camera-trap studies from southern California,

31

Y. Wang et al. / Biological Conservation 190 (2015) 23–33

Table 3^ 4 Þ between species pairs in areas of low and high human use. Bolded rows indicate differences between low and high overlap are statistically signiﬁcant at
Overlap values ðD
p < 0.05.
Species pair

^ 4 (95% CI)
Low use D^ 4 (95% CI)
High use D

Difference (High–Low)

Puma, Domestic cat
Puma, Raccoon
Puma, Grey fox
Puma, Striped skunk
Puma, Bobcat
Puma, Coyote
Puma, Opossum
Coyote, Bobcat
Coyote, Domestic cat
Coyote, Raccoon
Coyote, Grey fox
Coyote, Striped skunk
Coyote, Opossum
Bobcat, Grey fox

0.800 (0.706–0.869)
0.793 (0.774–0.937)
0.775 (0.697–0.852)
0.749 (0.674–0.825)
0.748 (0.649–0.813)
0.708 (0.621–0.782)
0.659 (0.554–0.730)
0.851 (0.799–0.875)
0.704 (0.641–0.726)
0.545 (0.485–0.557)
0.543 (0.479–0.555)
0.52 (0.465–0.530)
0.454 (0.389–0.464)
0.675 (0.630–0.699)

0.817 (0.745–0.896)
0.844 (0.725–0.866)
0.806 (0.733–0.891)
0.836 (0.776–0.930)
0.851 (0.777–0.921)
0.837 (0.764–0.908)
0.771 (0.680–0.850)
0.915 (0.889–0.945)
0.903 (0.858–0.944)
0.739 (0.681–0.781)
0.701 (0.665–0.727)
0.732 (0.695–0.764)
0.653 (0.594–0.672)
0.771 (0.745–0.788)-0.017
0.051
0.031
0.087
0.103
0.129
0.112
0.064
0.199
0.194
0.158
0.212
0.199
0.114

Table 4Top models describing the inﬂuence of covariates on species temporal overlap between foxes and coyotes, bobcats and foxes, and bobcats and coyotes.
Species Pair

Intercept estimates (Standard errors)

VariableCoefﬁcient estimates (Standard errors)

p-value

Adj. R2

Fox, coyote
Bobcat, fox
Bobcat, coyote

0.487 (0.059)
0.689 (0.026)
0.701 (0.018)

Human activity
Human activity
Development
Human activity

0.139 (0.049)
0.065 (0.023)
0.073 (0.024)
0.074 (0.020)<0.019<0.013<0.001

0.417
0.269
0.323

which found grey foxes to be sensitive to urban development(Ordenana et al., 2010). It is also possible that grey foxes, like redfoxes (Gosselink et al., 2003), may use urban patches as refugia
from coyotes (Riley, 2006), but we did not monitor any urban site.
Taken together, this suggests the carnivore community in ourstudy area was generally adaptable to varying levels of anthropogenic disturbances, although some species were more sensitive
to humans than others.
4.4. Combined inﬂuences of coyotes and pumas on mesopredator
activity
The relationship between mesopredator detection probabilities
and coyotes did not always match our predictions (Fig. 3). Contrary
to our expectations, bobcats were more likely to be detected withincreased coyote activity. Although coyotes are intraguild predators of bobcats, previous studies have shown that the two will
coexist in close spatiotemporal proximity (Fedriani et al., 2000).
This co-occurrence may also be due to their similar adaptability
to disparate habitats and prey species and relative tolerance ofhuman disturbances. Opossum and grey fox detection probabilities
declined with higher coyote activity, as we expected based on previous research (Crooks and Soulé, 1999). Raccoon detections only
exhibited a negative relationship with coyote activity at sites without pumas. While both pumas and coyotes will kill raccoons, raccoons are a frequent prey species of pumas in California (Allenet al., 2015b; Smith et al., 2015) and may consequently respond
more strongly to pumas than coyotes.
Changes in apex predator membership (Levi and Wilmers,2012) and human development can inﬂuence foodwebs by altering
relationships between predator guild members. For example, ourresults suggest a potential among-predator cascade involving
pumas, coyotes and grey foxes. We found evidence that grey foxes
were detected less where coyote activity was high and rarely atsites not occupied by pumas, whereas coyotes were detected lessfrequently at sites occupied by pumas. Because coyotes were present at most sites, it appears that they do coexist with pumas andgrey foxes on a coarse spatial scale. However, coyotes and grey
foxes may practice more ﬁne-scale avoidance to circumvent theirmore dominant competitors. In cases where puma occupancy is
reduced in more developed areas, coyote activity may increase,with unknown impacts on grey foxes and the prey community.
Domestic cat occupancy increased with development, as we
expected, but their detection probabilities exhibited complex relationships with apex predator occupancy. Cat detection was positively correlated with development at sites without pumas(Fig. 3), and also exhibited a weak positive relationship withincreased coyote activity at sites with pumas. This result is difﬁcult
to interpret and contradictory to what we expected since both coyotes and pumas are known predators of domestic cats (Crooks andSoulé, 1999; Gehrt et al., 2013). At sites without pumas, cat owners
may be more willing to let their pets out whereas at sites withpumas, they may restrict cat activities out of fear of predation.
Alternatively, since there is relatively little selective pressure fordomestic cats to exhibit behavioral avoidance of predators, native
predator activity and occupancy may not be appropriate determinants of domestic cat detection rates. Instead, because mostdomestic cats are pets, they might not have properly modulated
fear responses to predators and their responses to wild predators
may be based on their individual experiences and thus any correlational patterns may be spurious. To tease out these complex patterns between domestic cats and wild carnivores, more targeted
research on cat activities and owner preferences is necessary.
4.5. Anthropogenic inﬂuences on temporal partitioning
Consistent with our predictions, we found that higher humanactivity was associated with increased temporal overlap between
bobcat, coyote, and grey fox species pairs on a site-by-site basis,and higher development was also signiﬁcantly correlated withgreater bobcat and coyote overlap (Table 4). While shifts toward
increased nocturnal activity in response to humans are well documented (Riley et al., 2003), previous studies have not considered
the potential ramiﬁcations of these shifts on species interactions.

32

Y. Wang et al. / Biological Conservation 190 (2015) 23–33

Temporal partitioning between predators species is a potential
evolutionary strategy employed by subordinate species to avoid
encountering and being killed by a dominant competitor (Gehrt
and Prange, 2007; Hayward and Slotow, 2009; Palomares andCaro, 1999; Schuette et al., 2013). However, higher levels of humanactivity intensiﬁed temporal overlap between grey foxes and themore dominant bobcats and coyotes, potentially augmenting interspeciﬁc conﬂict between competing predators. The consequencesof reduced temporal niche partitioning on species interactions
and exploitation of prey species are unknown and require further
exploration. For example, if coyotes increase nighttime activity,
they may disproportionately target nocturnal prey species andrelease diurnal species from predation.
5. Conclusion
Our study results add to the global body of evidence that documents both the ability of carnivores to coexist in close proximitywith humans and the diverse repercussions that such intimate
cohabitation has on the carnivore community (Goad et al., 2014;
Recio et al. in press; Schuette et al., 2013; Šálek et al., 2014).
Unlike previous studies, we simultaneously investigated how the
predator community was affected by the combined inﬂuences ofincreasing human development and activity and declining apex
predator occupancy and activity. Occupancy models can be powerful tools for studying species interactions and, combined with
non-invasive monitoring methods, can provide a useful framework
for assessing species responses to new developments or mitigation
actions.
Human-occupied landscapes that encompass or border large
areas of open space are capable of supporting diverse predator communities, but increasing development pressures may threaten thecarnivore community. This highlights the need for preserved areas,
both for human recreation and to mitigate the inﬂuence of development on wildlife. Parks, in particular, serve the dual purposes ofprotecting the land from development and creating opportunities
for nature recreation and education for people (Zaradic et al.,2009). Management regulations (e.g., by limiting human activities
temporally or spatially) should balance the beneﬁts of humanrecreation and access to nature with the negative impacts on native
wildlife communities. As anthropogenic inﬂuences increase, studies like ours can help elucidate the context-dependent spatiotemporal responses of mesopredators to the combined inﬂuences ofhumans and apex predators to better inform management options,environmental outreach needs, and future research directions. Thisis particularly relevant, as expanding exurban development will
directly impact apex predator behavior and occupancy, with cascading inﬂuences on mesopredators and the rest of the ecologicalcommunity.

Acknowledgments
We thank the numerous public and private landowners for giving us access to their land and for their willingness to supportscience and conservation. Many undergraduate interns contributed
to this project by maintaining cameras in the ﬁeld and sorting camera photographs. In particular, we thank Lee Hibbeler, who helped
coordinate many of these activities. We thank Barry Nickel andAaron Cole for help obtaining GIS data, Yasaman Shakeri andPaul Houghtaling for providing ﬁeld assistance, and Jim Estes for
insightful comments on the manuscript. Funding was provided
by the American Mammalogy Association, Sigma Xi Grant
G2009101017, NSF Grant #0963022, the Gordon and Betty
Moore Foundation, and the Department of Environmental Studies
at University of California Santa Cruz.

Appendix A. Supplementary material
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.biocon.2015.05.
007.

References
Allen, M.L., Wilmers, C.C., Elbroch, M., Wittmer, H.U., 2015a. The comparative
effects of large carnivores on the acquisition of carrion by scavengers. Am. Nat.
185, 822–833. http://dx.doi.org/10.1086/681004.
Allen, M.L., Elbroch, L.M., Casady, D.S., Wittmer, H.U., 2015b. The feeding and spatial
ecology of mountain lions (Puma concolor) in Mendocino National Forest,
California. California Fish Game J. 101, 51–65.
Anderson, D.R., Burnham, K.P., 2002. Avoiding pitfalls when using informationtheoretic methods. J. Wildlife Manage. 66, 912–918.
Arnold, T.W., 2010. Uninformative parameters and model selection using Akaike’s
Information Criterion. J. Wildlife Manage. 74, 1175–1178.
Bateman, P.W., Fleming, P.A., 2012. Big city life: carnivores in urban environments.
J. Zool. 287, 1–23.
Berger, K.M., Conner, M.M., 2008. Recolonizing wolves and mesopredator
suppression of coyotes: impacts on pronghorn population dynamics. Ecol.
Appl. 18, 599–612.
Bidlack, A.L., 2007. Mesocarnivore responses to changes in habitat and resource
availability in California, In Environmental Science, Policy and Management.
University of California, Berkeley, Berkeley, CA, p. 201.
Brook, L.A., Johnson, C.N., Ritchie, E.G., 2012. Effects of predator control on
behaviour of an apex predator and indirect consequences for mesopredator
suppression. J. Appl. Ecol. 49, 1278–1286.
Carter, N.H., Shrestha, B.K., Karki, J.B., Pradhan, N.M.B., Liu, J.G., 2012. Coexistence
between wildlife and humans at ﬁne spatial scales. Proc. National Acad. Sci.
United States of America 109, 15360–15365.
Crooks, K.R., 2002. Relative sensitivities of mammalian carnivores to habitat
fragmentation. Conserv. Biol. 16, 488–502.
Crooks, K.R., Soulé, M.E., 1999. Mesopredator release and avifaunal extinctions in a
fragmented system. Nature 400, 563–566.
R Development Core Team, 2013. R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna, Austria.
Elmhagen, B., Ludwig, G., Rushton, S.P., Helle, P., Linden, H., 2010. Top predators,
mesopredators and their prey: interference ecosystems along bioclimatic
productivity gradients. J. Anim. Ecol. 79, 785–794.
Estes, J.A., Terborgh, J., Brashares, J.S., Power, M.E., Berger, J., Bond, W.J., Carpenter,
S.R., Essington, T.E., Holt, R.D., Jackson, J.B.C., Marquis, R.J., Oksanen, L., Oksanen,
T., Paine, R.T., Pikitch, E.K., Ripple, W.J., Sandin, S.A., Scheffer, M., Schoener, T.W.,
Shurin, J.B., Sinclair, A.R.E., Soulé, M.E., Virtanen, R., Wardle, D.A., 2011. Trophic
downgrading of planet earth. Science 333, 301–306.
Fedriani, J.M., Fuller, T.K., Sauvajot, R.M., York, E.C., 2000. Competition andintraguild predation among three sympatric carnivores. Oecologia 125, 258–
270.
Gehrt, S.D., Clark, W.R., 2003. Raccoons, coyotes, and reﬂections on themesopredator release hypothesis. Wildlife Soc. Bull. 31, 836–842.
Gehrt, S.D., Prange, S., 2007. Interference competition between coyotes andraccoons: a test of the mesopredator release hypothesis. Behav. Ecol. 18, 204–
214.
Gehrt, S.D., Riley, S.P.D., Cypher, B.L., 2010. Urban carnivores: ecology, conﬂict andconservation.
Gehrt, S.D., Wilson, E.C., Brown, J.L., Anchor, C., 2013. Population ecology of freeroaming cats and interference competition by coyotes in urban parks. Plos One
8 (9), e75718.
George, S., Crooks, K., 2006. Recreation and large mammal activity in an urban
nature reserve. Biol. Conserv. 133, 107–117.
Ginger, S.M., Hellgren, E.C., Kasparian, M.A., Levesque, L.P., Engle, D.M., Leslie, D.M.,
2003. Niche shift by Virginia opossum following reduction of a putativecompetitor, the raccoon. J. Mammal. 84, 1279–1291.
Goad, E.H., Pejchar, L., Reed, S.E., Knight, R.L., 2014. Habitat use by mammals varies
along an exurban development gradient in northern Colorado. Biol. Conserv.
176, 172–182.
Gompper, M.E., Kays, R.W., Ray, J.C., Lapoint, S.D., Bogan, D.A., Cryan, J.R., 2006. A
comparison of noninvasive techniques to survey carnivore communities innortheastern North America. Wildlife Soc. Bull. 34, 1142–1151.
Gosselink, T.E., Van Deelen, T.R., Warner, R.E., Joselyn, M.G., 2003. Temporal habitat
partitioning and spatial use of coyotes and red foxes in east-central Illinois. J.
Wildlife Manage. 67, 90–103.
Hansen, A.J., Knight, R.L., Marzluff, J.M., Powell, S., Brown, K., Gude, P.H., Jones, A.,
2005. Effects of exurban development on biodiversity: patterns, mechanisms,
and research needs. Ecol. Appl. 15, 1893–1905.
Hass, C.C., 2009. Competition and coexistence in sympatric bobcats and pumas. J.
Zool. 278, 174–180.
Hayward, M.W., Slotow, R., 2009. Temporal partitioning of activity in large African
carnivores: tests of multiple hypotheses. South Afr. J. Wildlife Res. 39, 109–125.
Johnson, J.B., Omland, K.S., 2004. Model selection in ecology and evolution. Trends
Ecol. Evol. 19, 101–108.

Y. Wang et al. / Biological Conservation 190 (2015) 23–33
Johnson, C.N., Isaac, J.L., Fisher, D.O., 2007. Rarity of a top predator triggers
continent-wide collapse of mammal prey: dingoes and marsupials in Australia.
Proc. Royal Soc. Lond. Ser. B-Biol. Sci. 274, 341–346.
Lennartz, S., Bax, T., Aycrigg, J., Davidson, A., Reid, M., Congalton, R., 2008. Finalreport on land cover mapping methods for California map zones 3, 4, 5, 6, 12,
and 13.
Levi, T., Wilmers, C.C., 2012. Wolves-coyotes-foxes: a cascade among carnivores.
Ecology 93, 921–929.
Linkie, M., Ridout, M.S., 2011. Assessing tiger-prey interactions in Sumatran
rainforests. J. Zool. 284, 224–229.
Logan, K.A., Sweanor, L.L., 2001. Desert puma: evolutionary ecology andconservation of an enduring carnivore. Island Press, Washington D.C.
Lucherini, M., Reppucci, J.I., Walker, R.S., Villalba, M.L., Wurstten, A., Gallardo, G.,
Iriarte, A., Villalobos, R., Perovic, P., 2009. Activity pattern segregation ofcarnivores in the High Andes. J. Mammal. 90, 1404–1409.
MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A., Langtimm, C.A.,
2002. Estimating site occupancy rates when detection probabilities are lessthan one. Ecology 83, 2248–2255.
MacKenzie, D.I., Bailey, L.L., Nichols, J.D., 2004. Investigating species co-occurrence
patterns when species are detected imperfectly. J. Anim. Ecol. 73, 546–555.
McKinney, M.L., 2006. Urbanization as a major cause of biotic homogenization. Biol.
Conserv. 127, 247–260.
Meredith, M., Ridout, M., 2014. overlap: Estimates of coefﬁcient of overlapping foranimal activity patterns R package version 0.2.3. <http://CRAN.R-project.
org/package=overlap, http://CRAN.R-project.org/package=overlap>.
Monterroso, P., Alves, P.C., Ferreras, P., 2013. Catch me if you can: diel activity
patterns of mammalian prey and predators. Ethology 119, 1044–1056.
Noss, R.F., Quigley, H.B., Hornocker, M.G., Merrill, T., Paquet, P.C., 1996.
Conservation biology and carnivore conservation in the Rocky Mountains.
Conserv. Biol. 10, 949–963.
Ordenana, M.A., Crooks, K.R., Boydston, E.E., Fisher, R.N., Lyren, L.M., Siudyla, S.,
Haas, C.D., Harris, S., Hathaway, S.A., Turschak, G.M., Miles, A.K., Van Vuren,
D.H., 2010. Effects of urbanization on carnivore species distribution andrichness. J. Mammal. 91, 1322–1331.
Pace, M.L., Cole, J.J., Carpenter, S.R., Kitchell, J.F., 1999. Trophic cascades revealed indiverse ecosystems. Trends Ecol. Evol. 14, 483–488.
Palomares, F., Caro, T.M., 1999. Interspeciﬁc killing among mammalian carnivores.
Am. Nat. 153, 492–508.
Polis, G.A., Holt, R.D., 1992. Intraguild predation – the dynamics of complex trophic
interactions. Trends Ecol. Evol. 7, 151–154.
Prange, S., Gehrt, S.D., 2004. Changes in mesopredator-community structure in
response to urbanization. Cana. J. Zool. -Revue Canadienne De Zoologie 82,
1804–1817.
Recio, M.R., Arija, C.M., Cabezas-Díaz, S., Virgós, E. in press. Changes inMediterranean mesocarnivore communities along urban and ex-urban
gradients.
Current
Zoology.
<http://www.actazool.org/site_media/
onlineﬁrst/downloadable_ﬁle/2014/11/14/Recio1.pdf>.

33

Reed, S.E., Merenlender, A.M., 2008. Quiet, nonconsumptive recreation reduces
protected area effectiveness. Conserv. Lett. 1, 146–154.
Richmond, O.M.W., Hines, J.E., Beissinger, S.R., 2010. Two-species occupancy
models: a new parameterization applied to co-occurrence of secretive rails.
Ecol. Appl. 20, 2036–2046.
Ridout, M.S., Linkie, M., 2009. Estimating overlap of daily activity patterns fromcamera trap data. J. Agric. Biol. Environ. Statist. 14, 322–337.
Riley, S.P.D., 2006. Spatial ecology of bobcats and gray foxes in urban and ruralzones of a national park. J. Wildlife Manage. 70, 1425–1435.
Riley, S.P.D., Sauvajot, R.M., Fuller, T.K., York, E.C., Kamradt, D.A., Bromley, C.,
Wayne, R.K.Tk., 2003. Effects of urbanization and habitat fragmentation on
bobcats and coyotes in southern California. Conserv. Biol. 17, 566–576.
Rissman, A.R., Merenlender, A.M., 2008. The conservation contributions ofconservation easements: analysis of the San Francisco Bay Area protected
lands spatial database. Ecol. Soc. 13, 25.
Ritchie, E.G., Johnson, C.N., 2009. Predator interactions, mesopredator release andbiodiversity conservation. Ecol. Lett. 12, 982–998.
Ruth, T.K., Murphy, K., 2009. Competition with other carnivores for prey. In:
Hornocker, M., Negri, S. (Eds.), Cougars: Ecology and Conservation. University of
Chicago Press, Chicago, IL, pp. 163–173.
Šálek, M., Drahníková, L., Tkadlec, E., 2014. Changes in home range sizes andpopulation densities of carnivore species along the natural to urban habitat
gradient. Mammal Rev. http://dx.doi.org/10.1111/mam.12027.
Schuette, P., Wagner, A.P., Wagner, M.E., Creel, S., 2013. Occupancy patterns andniche partitioning within a diverse carnivore community exposed toanthropogenic pressures. Biol. Conserv. 158, 301–312.
Selva, N., Jedrzejewska, B., Jedrzejewski, W., Wajrak, A., 2005. Factors affecting
carcass use by a guild of scavengers in European temperate woodland. Canad. J.
Zool. -Revue Canadienne De Zoologie 83, 1590–1601.
Smith, J.A., Wang, Y., Wilmers, C.C., 2015. Top carnivores increase their kill rates on
prey as a response to human-induced fear. Proc. Royal Soc. B 282, 20142711.
Wang, Y.W., Fisher, D.O., 2012. Dingoes affect activity of feral cats, but do not
exclude them from the habitat of an endangered macropod. Wildlife Res. 39,
611–620.
Wilmers, C.C., Getz, W.M., 2005. Gray wolves as climate change buffers in
Yellowstone. PLoS Biol 3, e92. http://dx.doi.org/10.1371/journal.pbio.0030092.
Wilmers, C.C., Wang, Y., Nickel, B., Houghtaling, P., Shakeri, Y., Allen, M.L., KermishWells, J., Yovovich, V., Williams, T., 2013. Scale dependent behavioral responses
to human development by a large predator, the puma. PLOS One 8, e60590.
Wilson, R.R., Blankenship, T.L., Hooten, M.B., Shivik, J.A., 2010. Prey-mediatedavoidance of an intraguild predator by its intraguild prey. Oecologia 164, 921–
929.
Zaradic, P.A., Pergams, O.R.W., Kareiva, P., 2009. The impact of nature experience on
willingness to support conservation. Plos One 4, 5.