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Use of Camera Traps to Examine the MesopredatorRelease Hypothesis in a Fragmented MidwesternLandscapeAuthor(s): Michael V. Cove, Brandon M. Jones, Aaron J. Bossert,Donald R. Clever Jr., Ryan K. Dunwoody, Bryan C. White, andVictoria L. JacksonSource: The American Midland Naturalist, 168(2):456-465. 2012.Published By: University of Notre DameURL: http://www.bioone.org/doi/full/10.1674/0003-0031-168.2.456

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Notes and Discussion

Use of Camera Traps to Examine the Mesopredator Release Hypothesis in aFragmented Midwestern Landscape

ABSTRACT.—The mesopredator release hypothesis (MRH) has been suggested as a reasonwhy many mammalian generalist mesopredators flourish and become abundant. However,the MRH has only been examined in a limited number of field studies. Some studies haveargued that coyotes (Canis latrans) act as top predators in fragmented forest systems andcoyote presence has a positive effect on song bird diversity and abundance by controllingmesopredator abundance. We integrated camera trap data and occupancy modeling todetermine the factors that affect coyote detection probability and habitat use in a fragmentedsuburban landscape in central Missouri. We then examined the influence of coyote presenceand other habitat variables on mesopredator detection probability and habitat use in thesame system. Coyote detection was negatively related to increasing forest cover, whereasred fox (Vulpes vulpes) detection was positively related to increasing urbanization. Coyoteoccurrence models suggested little habitat selection, while the mesopredator occurrencemodels suggested an affinity for urbanization. Although there was a slight negative effect ofcoyote presence on site use by other mesopredators, we suggest that the smaller species arebetter adapted to coexisting with humans and thus have increased in abundance.

INTRODUCTION

Many mammalian generalist mesopredators [medium-sized carnivores and Virginia opossums(Didelphis virginiana)] have flourished and become abundant in various North American systems(Crooks and Soule, 1999; Garrott et al., 1993; Prugh et al., 2009). This has led to increased predation riskon prey species, particularly migratory song birds (Donovan et al., 1997). The mesopredator releasehypothesis (MRH) states that in the absence of larger carnivores, mesopredators exhibit an increase inabundance due to release from predation and competition (Crooks and Soule, 1999; Prugh et al., 2009).

The MRH is commonly cited and accepted as a community dynamic but has only been examined in alimited number of field studies (Brashares et al., 2010; Crooks and Soule, 1999; Gehrt and Clark, 2003;Prugh et al., 2009). Crooks and Soule (1999) argued that coyotes (Canis latrans) act as the top predatorin fragmented forest systems in southern California and coyote presence had a positive effect onsong bird diversity and abundance indices by controlling mesopredator abundance. Although theascendance of coyote to the role of apex predator seems reasonable, there is much debate as to the rolethat this species plays in shaping the remainder of the native mesopredator community and their effectson lower trophic levels (Gehrt and Clark, 2003; Prugh et al., 2009). Within the Canidae, coyotes killsympatric fox species (Urocyon cinereoargenteus, Vulpes macrotis, V. velox, and V. vulpes) which can influencethe habitat use and distribution of the smaller canids (Donadio and Buskirk, 2006; Palomares and Caro,1999). In their Illinois study, Gosselink et al. (2003) determined that red foxes used human-alteredhabitats which were generally avoided by sympatric coyotes as a partitioning strategy.

Gehrt and Clark (2003) suggested that there is substantial support for the MRH within Canidae, butthey argued that there is limited support for coyotes as top predators influencing other mesopredators,particularly raccoons (Procyon lotor). Subsequent telemetry studies of sympatric raccoons and coyotes(Gehrt and Prange, 2007) and sympatric striped skunks (Mephitis mephitis) and coyotes (Prange andGehrt, 2007) found little to no avoidance of areas used by coyotes. These studies also revealed nomortalities of raccoons or striped skunks due to coyote predation, which would also be an assumptionfor the MRH. The generalist nature of these mesopredators and opossums allows them to use human-derived resources which may have greater influence over their distribution in forest patches withinurban/suburban landscapes (Garrott et al., 1993; Prange and Gehrt, 2004; Litvaitis and Villafuerte,1995). There is, however, substantial evidence that coyotes have a role in controlling non-native feral cat(Felis catus) populations through predation (Brashares et al., 2010; Crooks and Soule, 1999; Grubbs andKrausman, 2009).

Direct examination of MRH is difficult to quantify and it is critically important to consider samplingdesigns to measure appropriate parameters to elucidate trophic interactions (Brashares et al., 2010;

Am. Midl. Nat. (2012) 168:456–465

456

Gehrt and Clark, 2003; Prugh et al., 2009). Crooks and Soule (1999) used track surveys as an index ofabundance to suggest coyote influence over the other mesopredators, but Anderson (2001) cautionsagainst using such indices without considering detection probability. Camera traps are an alternativesampling method and are commonly used in surveys of mammalian carnivores (Gompper et al., 2006;O’Connell et al., 2006; Ordenana et al., 2010), but no studies have used this technique to examineinteractions between sympatric coyotes and smaller mesopredators in the suburban Midwest. Like tracksurveys, camera traps may fail to detect species presence at a site and MacKenzie et al. (2006) suggestincluding detection probability parameters within an occupancy modeling framework to more robustlyestimate site use and the factors that affect species occurrence.

Our primary objective was to integrate camera trap data and occupancy modeling analysis todetermine the factors that affect coyote detection probability (P) and habitat use (Y) of forest patchesin a fragmented suburban landscape in central Missouri. We hypothesized that forest cover and locationwould have positive effects on coyote occurrence but that the relationship would be the inverse fordetection probability. We then aimed to determine how coyote presence and other habitat variablesaffect mesopredator detection probability (P) and habitat use (Y) within the same forest patches. Basedon previous studies (Crooks and Soule, 1999; Gosselink et al., 2003), we hypothesized that coyotes wouldexert negative influence over the distribution and forest patch use by foxes and feral cats which wouldbe relegated to more urban areas. We also hypothesized that coyote presence would have negative butlimited influence over forest patch use by the remainder of the mesopredator guild (raccoons, stripedskunks, and opossums) which would be more positively influenced by proximity to human resources(Crooks and Soule, 1999; Gehrt and Prange, 2007; Prange and Gehrt, 2004).

METHODS

From Oct. 2009 to May 2010, we conducted camera trap surveys at two areas with high suburbanhabitat alteration: Longview Lake, Lee’s Summit, Missouri (38u549350N 294u289110W) and Warrens-burg, Missouri (38u459470N 293u44960W). Though the two locations are both within suburban/urbanlandscapes, they vary in that Longview Lake has more contiguous forest in the area, whereas the forestpatches in Warrensburg are highly interspersed among human development. Although coyotes and theother mesopredators are legally trapped in Missouri, our camera sites were on public and private landwhere trapping did not occur over the course of our study. We selected a total of 22 forest patch sites, 14at the Warrensburg area and eight at the Longview Lake area. Two sites from the original Warrensburgsites were discarded due to camera theft, and two additional camera sites were selected on privateproperty outside city limits. All sites we selected were .500 m apart with a mean nearest neighbordistance of 1622 (6814 SD) m. We selected a random location within each forest patch and used either aReconyx RM45 IR Game Camera (RECONYX, Inc., 3828 Creekside Lane, Suite 2, Holmen, Wisconsin54636, USA) or a Moultrie Game Spy 4.0 Camera (EBSCO Industries, Inc., P.O. Box 1943, Birmingham,Alabama 35201, USA). To increase our detections of mesopredators, we baited cameras with 1–2 kg ofdeer meat or butcher scraps secured to a bait tree 3–5 m from the camera trap. In an effort to minimizetheft, we only left cameras at a site for 10–18 d and we revisited cameras for rebaiting once per week andas needed. Because cameras were only left at a site for 10–18 consecutive days, we believe this small timeframe to reflect the true state of habitat use and possible avoidance of coyotes.

Using ArcGIS 9.3 (ESRI, 380 New York Street, Redlands, California 92373, USA), we overlaid allcamera trap locations onto a digitized land use/land cover map to measure landscape characteristicsassociated with each site. We created 250-m radius buffers around each camera trap site and measuredthe total forest cover (ha) and total suburban/urban land use (ha) within each buffer. We wereinterested in the specific effects of each cover type because forest cover is the natural habitat for thesespecies, but also urban cover has been suggested to strongly impact carnivore and mesopredatordistributions in other studies (Ordenana et al., 2010; Prange and Gehrt, 2004). We also included avariable for location (i.e., Warrensburg or Longview Lake) due to differences in landscape-scale forestcover and location-specific effects. We also recorded camera trap type to examine camera-specific effectson detection. Because all mesopredators were detected by both infrared and flash cameras, we pooledall data to test for an effect of camera type on detection probability (see Results) and then discarded thiscovariate from further analysis. Although seasonal effects or extreme low temperatures could also affect

2012 NOTES AND DISCUSSION 457

detection probabilities, we considered our surveys to be conducted within one season. The species ofinterest are active year-round and our bait was sufficient to attract mesopredators even duringprecipitation events and cold temperatures, so we did not include climatic or seasonal covariates.

We compiled all trapping records to create a binary detection history (detected 5 1, not detected 5 0)for each species. For comparisons to other camera trap studies, we used raw detection histories toestimate latency to initial detection (LTD – Gompper et al., 2006), which is the mean number oftrapnights required to first detect a species, and calculated trap success as the number ofindependent detections per 100 trapnights (Kelly and Holub, 2008). A detection for a species wasonly considered independent based on the 24-h clock so multiple records on the same day werestill only considered as one detection. We partitioned the detection histories into 3-d samplingunits, leading to a maximum of six repeat surveys at each site. To examine how habitat covariatesaffected our ability to detect a species, we followed a two-step process similar to that employed byLong et al. (2011) in which we modeled detection probability and then used the covariates withthe most support as a constant set when deriving occupancy estimates. We first modeled coyotedetection as a function of intercept-only model, global model, individual models for forest, urban,location, and additive models including cover type and location, for a total of seven a priorimodels. We then modeled coyote occurrence (use) as a function of the same seven a priori modelswith the constant detection covariate set. We modeled mesopredator detection probabilities andoccurrence (use) using the same procedures as for coyotes, but we incorporated an additionalmodel covariate of coyote site use, and additive models with cover type and coyote site use for atotal of 10 a priori models. Finally, we pooled the three most common mesopredators and feralcats together for an occurrence analysis similar to the pooled analysis of Crooks and Soule (1999)using the same 10 a priori models as the individual species occurrence models. Although there maybe additional influences on detection and occurrence, we limited our model development in thecontext of our sample size and parsimony; we considered all a priori models to be biologicallyplausible explanations of species occurrence in this fragmented landscape.

For analysis, we used a single-season model in program PRESENCE 2.4 (Hines, 2009). The bestapproximating models were selected based on the Akaike Information Criterion corrected for smallsample size (AICc) and Akaike weights (wi). To evaluate model fit for each species, we performed 10,000simulated parametric bootstraps for the global model (all covariates) and estimated an overdispersionfactor (c). For models that had c . 1, we inflated standard errors by a factor of !c, and used a quasi-corrected AICc (QAICc) for model selection (Burnham and Anderson, 2002). We considered all modelscontained within 95% CI (gwi 5 0.95) to have support as our top-ranking models (Burnham andAnderson, 2002). From the subsequent confidence set, we considered covariates to have strong effectson detection and occurrence if coefficients were retained in multiple competing models and did notoverlap with 0 at the 95% CI.

All animal research was in accordance with the guidelines approved by The American Society ofMammalogists (Sikes et al., 2011). Our camera trapping protocol was approved by the University ofCentral Missouri Institutional Animal Care and Use Committee (Permit No. 10-3202).

RESULTS

Over 308 trapnights we detected seven mesopredators and two domestic carnivores (Table 1).Raccoons and opossums were the most commonly detected mesopredators (each detected at 19 sites),followed by red foxes and coyotes (detected at 10 sites each). Camera trap success was high and LTD waslow for these species (Table 1). Although feral cats were detected on 21 independent occasions at sixsites, 13 of those detections were from a single site without coyotes in the Warrensburg area, and nonewere detected in the Longview Lake area. Due to the overdispersion of these data cats were excludedfrom further single species model analyses; however, it is notable that only four of the independent catdetections occurred at two sites with coyotes present. Gray fox (one site) and striped skunk (two sites)were also of interest for further analyses but were not included due to sparse data at multiple sites andtheir inclusion would lead to non-convergence in the models and inappropriate inference. Bobcat(Lynx rufus—four sites) and domestic dog (Canis familiaris—eight sites) were also excluded fromanalyses because of low detection rates and no a priori hypotheses regarding their occurrence. In the

458 THE AMERICAN MIDLAND NATURALIST 168(2)

preliminary analysis, camera trap type had little effect on detection probability for mesopredators withdetection probabilities of 0.56 (60.04 SE) and 0.58 (60.04 SE) for flash and infrared cameras,respectively.

Forest cover was contained in three of the top competing models and had a significant negative effecton detection for coyotes (gwi 5 0.709—Table 2) thus we included this detection covariate in all coyoteoccurrence models. Urban cover was contained in three of the top competing models and had asignificant positive effect on red fox detection (gwi 5 0.524—Table 2) and we included it as thedetection covariate in the red fox occurrence models. The location covariate effect also agreed indirection (2) with a priori hypothesis for detection, however coyote presence had an inverse (+) yetvariable relationship to fox detection than our a priori hypothesis. For raccoons and opossums, the topmodels indicated a constant detection probability due to such high rates of detection at all sites ofoccurrence, so no detection covariates were used in the subsequent occurrence models.

Coyote occurrence was not strongly influenced by any of the covariates that we measured, butcoefficients for forest cover and location did agree in direction (+) with the a priori hypotheses(Table 3). Fox occurrence was negatively related to the Longview Lake location. Urban, forest, andcoyote presence all had positive but weakly supported relationships with red fox occurrence. Urbancover was weakly related (+) to raccoon and opossum occurrence in the individual species models andboth species presented with negative but variable influence from coyote presence. Urban cover had themost support (gwi 5 0.732—Table 3) as a strong predictor and was contained as a positive covariate inthe top three occurrence models when all mesopredators (four species) were pooled together. Coyotepresence and location both had negative but highly variable effects with limited support for allmesopredators. Additionally, we did obtain a sequence of six photos in which a coyote interacted withan opossum for .5 min and subsequently ignored the smaller species which appeared to feign dead.

DISCUSSION

We detected all of the mesopredators that we expected to occur at our study areas within a shortperiod of time. Our capture successes were much higher than those found in a recent camera trap studyin Virginia (Kelly and Holub, 2008). Raccoons were the most commonly detected species in our studywith a trap success of 38.96 captures/100 trapnights as opposed to 2.81/100 trapnights in the Virginiastudy (Kelly and Holub, 2008). Coyotes were also more commonly detected in our study with a trapsuccess of 7.79/100 trapnights compared to 1.01/100 trapnights with nearly three times the surveyeffort (891 trapnight—Kelly and Holub, 2008) of our study. We used food bait as an attractant whereasKelly and Holub (2008) used no attractants and we suggest that this may account for the higher capturesuccess of mesopredators in our study. Our study also occurred from late fall to early spring whenresources are limited and the food bait is particularly attractive. This also may account for our low LTDs

TABLE 1.—Selected estimates of latency to initial detection (LTD) with associated standard errors inparentheses, trap success, and total number of detections from mesopredator camera trap surveys in thesuburban Midwest, conducted Oct. 2009-May 2010

Species LTD Trap success Total detections

Raccoon (Procyon lotor) 2.42 (0.39) 38.96 120Opossum (Didelphis virginiana) 3.84 (0.95) 37.34 115Red fox (Vulpes vulpes) 3.11 (0.98) 8.77 27Coyote (Canis latrans) 4.90 (1.15) 7.79 24Domestic cat (Felis catus) 2.83 (1.25) 6.82 21Domestic dog (Canis familiaris) 5.25 (1.51) 4.55 14Gray fox (Urocyon cinereoargenteus) 2.00a 2.92 9Bobcat (Lynx rufus) 5.25 (1.80) 1.95 6Striped skunk (Mephitis mephitis) 6.50 (1.50) 1.62 5

a No standard error reported because gray fox was only detected at one siteOnly bolded species were used for further analysis

2012 NOTES AND DISCUSSION 459

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460 THE AMERICAN MIDLAND NATURALIST 168(2)

as compared to studies in upstate New York with extensive trap effort (4728 trapnights—Gompper et al.,2006). The LTD for coyote in our study was 4.90 (61.15 SE) trapnights whereas Gompper et al. (2006)did not record coyotes with camera traps until approximately 40 trapnights of survey effort. Althoughfood bait was used in that study it occurred during the summer months when natural resources arehighly available and bait may go unnoticed. Furthermore, mesopredators are most likely accustomed tohuman presence in our suburban study sites and are less wary of human disturbance (camera traps andbait). For these reasons we believe our sampling effort was sufficient and similar to other camera trapstudies of carnivores in southern California (Ordenana et al., 2010) and the occupancy analyses ofmesopredators in New England (O’Connell et al., 2006).

We determined that coyote detection probability was negatively affected by increasing amounts offorest cover. This suggests that in the highly altered landscape of our study areas the activities of coyotesare concentrated when occurring in small forest patches, which makes them more detectable with lesssurvey effort as compared to large protected forests and wilderness areas such as the study sites ofGompper et al. (2006) and Kelly and Holub (2008). Although not significant, the location covariate wasin agreement with our a priori hypothesis for detection and suggested a negative influence of theLongview Lake location, which corresponds with the more contiguous forest habitat of that area ascompared to the smaller patches within the Warrensburg location. For these reasons, we suggest that inlarge forest patches coyote occurrence may go unnoticed, and indirect indices (track counts—Crooksand Soule, 1999) in small forest patches may reflect an artifact that coyote abundance is high. Thishighlights the importance of incorporating detection probability parameters (Anderson, 2001;MacKenzie et al., 2006) when examining trophic interactions among the mesopredator guild.

Red fox detection probability was positively affected by increasing urbanization, suggesting that foxesmay concentrate their activities around human development and hence make them more easilydetectable in these areas. This also corresponds with the findings of Gosselink et al. (2003) in which redfoxes used urban areas and human structures because they were typically avoided by coyotes. We notethat the coyote interaction covariate had an inverse relationship to our a priori hypothesis of a negativeeffect on red fox detection. Although it seems unlikely that coyote occurrence has a positive effect onfox occurrence/detection, forest cover is limited in our study areas such that the two species co-occurand resource partitioning may be temporal rather than spatial. The examination of camera trap data fortemporal partitioning may be useful in future studies of co-occurrence and apparent effects onoccurrence and detection probability between sympatric coyotes and foxes.

The other mesopredators (raccoons and opossums) in our analysis were commonly detected at themajority of the camera locations and we observed no external influence on detection probability.Although ubiquitous members of many suburban/forest interface communities, we note that there maybe external influences on detection for these species at a larger landscape scale, especiallycorresponding with lower abundance (Cove et al., in press).

The occurrence models for coyote suggested limited external factors influencing their use of habitatin our fragmented system. However, increasing forest cover and more contiguous forest habitat at theLongview Lake location did agree with a priori hypotheses and suggested positive influences over coyotedistribution in these urban/suburban landscapes. These results correspond with previous research withcoyotes observed in multiple urban studies, but they often require natural cover and tend to avoid thehighly developed interfaces (Crooks and Soule, 1999; Gosselink et al., 2003). This is the inverserelationship as suggested by Ordenana et al. (2010) that coyotes more commonly occur in areas withhigh urban cover in southern California. Ordenana et al. (2010) did not consider detection probabilityin their analyses and with coyote occurrence high for the entire study area; the increasing occurrencemay be an artifact of sampling. However, these studies do highlight the generalist nature of this speciesand its potential for high patch overlap with other generalist mesopredators across their distribution.

Though coyotes kill the other mesopredators (Donadio and Buskirk, 2006; Palomares and Caro,1999), other urban studies have observed no predation (Gehrt and Prange, 2007; Prange and Gehrt,2004). Our results for coyote occurrence influencing forest patch use by mesopredators contained lowmodel support for this covariate with confidence intervals of the coefficient that overlap zero. Thesefindings correspond with previous telemetry studies in urban/suburban areas (Gehrt and Prange, 2007;Prange and Gehrt, 2007) that coyotes do not appear to influence the site use by other non-canid native

2012 NOTES AND DISCUSSION 461

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(2.4

63)

2

Red

fox

Y(u

rban

+lo

c)0.

000.

756

52.

404

(1.6

35)

22.

366

(1.6

88)

25.1

93

(1.7

37)

2

Y(.

)4.

270.

089

30.

960

(1.0

17)

22

22

Y(u

rban

)5.

940.

039

40.

289

(0.7

13)

20.

905

(0.7

06)

22

Y(g

lob

al)

6.05

0.03

77

2.30

4(1

.972

)1.

405

(1.0

69)

3.34

1(2

.290

)2

5.57

8(4

.288

)0.

839

(2.6

25)

Y(f

ore

st)

6.60

0.02

84

0.93

3(0

.988

)0.

520

(0.6

42)

22

2

Rac

coo

n

Y(u

rban

)0.

000.

572

33.

839

(2.4

52)

23.

039

(2.4

49)

22

Y(.

)2.

490.

165

21.

897

(0.6

50)

22

22

Y(u

rban

+co

yote

)2.

920.

133

44.

061

(2.4

96)

23.

133

(2.3

72)

22

0.48

4(1

.596

)Y

(fo

rest

)4.

990.

047

31.

925

(0.6

65)

20.

300

(0.6

75)

22

2

Y(c

oyo

te)

5.00

0.04

73

1.65

9(0

.808

)2

22

0.57

9(1

.360

)

Op

oss

um

Y(.

)0.

000.

347

21.

607

(0.6

02)

22

22

Y(u

rban

)0.

980.

212

31.

819

(0.7

24)

20.

916

(0.7

89)

22

Y(l

oc)

2.49

0.10

03

1.40

8(0

.711

)2

20.

587

(1.3

23)

2

Y(c

oyo

te)

2.59

0.09

53

1.81

6(0

.925

)2

22

20.

398

(1.2

28)

Y(f

ore

st)

2.67

0.09

13

1.60

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)0.

106

(0.6

13)

22

2

Y(u

rban

+co

yote

)3.

510.

060

42.

377

(1.2

04)

21.

139

(0.9

36)

22

0.94

6(1

.394

)Y

(urb

an+

loc)

3.90

0.04

94

1.66

5(0

.849

)2

0.87

1(0

.785

)0.

424

(1.3

83)

2

462 THE AMERICAN MIDLAND NATURALIST 168(2)

Spec

ies

mo

del

Di

wi

K

Un

tran

sfo

rmed

coef

fici

ents

of

cova

riat

es( S

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rcep

tF

ore

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rban

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cati

on

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yote

All

mes

op

red

ato

rs1

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rban

)0.

000.

366

31.

153

(0.3

39)

20.9

71

(0.3

82)

22

Y(u

rban

+lo

c)1.

580.

166

41.

474

(0.4

69)

21.0

89

(0.4

23)

20.

734

(0.6

55)

2

Y(u

rban

+co

yote

)1.

590.

165

41.

578

(0.5

64)

21.1

66

(0.4

77)

22

0.75

6(0

.700

)Y

(.)

2.30

0.11

62

0.96

8(0

.285

)2

22

2

Y(l

oc)

4.24

0.04

43

1.12

5(0

.376

)2

22

0.40

0(0

.580

)2

Y(f

ore

st)

4.39

0.04

13

0.96

9(0

.285

)0.

125

(0.2

89)

22

2

Y(c

oyo

te)

4.45

0.04

03

1.04

4(0

.397

)2

22

20.

161

(0.5

69)

Y(g

lob

al)

4.67

0.03

56

1.79

6(0

.632

)0.

525

(0.3

62)

1.4

21

(0.5

16)

20.

711

(0.6

95)

20.

511

(0.7

10)

Mo

del

sp

rese

nte

dm

ake

up

the

95%

con

fid

ence

set,

wh

ereD

iis

AIC

cd

iffe

ren

ce(D

iQA

ICc

wh

ere

ind

icat

edb

y1),

wiis

the

Aka

ike

wei

ght,

and

Kis

the

nu

mb

ero

fm

od

elp

aram

eter

sC

ova

riat

es:

fore

stan

du

rban

are

the

stan

dar

diz

edva

lues

for

the

tota

lco

vera

ge(h

a)o

ffo

rest

and

sub

urb

an/

urb

anh

abit

ats

wit

hin

site

bu

ffer

s;lo

cati

on

(lo

c)is

the

bin

om

ialt

erm

tod

iffe

ren

tiat

eb

etw

een

War

ren

sbu

rgan

dL

on

gvie

wL

ake,

Mis

sou

rist

ud

yar

eas;

coyo

teis

the

tro

ph

icin

tera

ctio

nte

rmfo

rco

yote

site

use

TA

BL

E3.

—C

on

tin

ued

2012 NOTES AND DISCUSSION 463

mesopredators. Alternatively, urbanization had the highest model support as a positive influence on theoccurrence of the smaller mesopredators and this likely reflects their ability to capitalize on human-derived resources to increase in abundance. Litvaitis and Villafuerte (1995) noted that mesopredatorsin other regions have exhibited increased abundance in recent years, despite the loss of the toppredators many years prior, due to human resource utilization. The photos of coyote-opossuminteractions also suggested limited influence over the smaller mesopredator by the coyote, whichproceeded to the bait tree. It is unfortunate that we were unable to directly examine coyote-catinteractions more thoroughly since other studies have shown evidence of coyotes limiting catdistributions through predation (Grubbs and Krausman, 2009). However, we do suggest that coyotesmay have a role in reducing cat detections as suggested by the limited four cat photos from sites withco-occurring coyotes in our surveys.

Coyotes are the top predator in many Midwestern systems due to the eradication of larger predators,but our study findings are in agreement with other studies (Gehrt and Prange, 2007; Prange and Gehrt,2007; Prugh et al., 2009) that suggest that the coyote does not act as a keystone species and theirpresence does not strongly influence the remaining native mesopredator community. Thus, theheightened abundance of raccoons, opossums, and possibly foxes in urban/suburban habitats is mostlikely a result of resource exploitation rather than MRH. Coyotes may still benefit the native communityby limiting feral cat populations (Crooks and Soule, 1999; Grubbs and Krausman, 2009) and coyotepresence may limit pet owners from allowing their cats to free range. Future studies of carnivore/mesopredator community dynamics will benefit from using a similar occupancy modeling approach andwith larger sample sizes may explore multi-state, multi-season, and species co-occurrence models toexplore habitat and trophic relationships.

Acknowledgments.—We would like to thank the U.S. Army Corps of Engineers at Longview Lake andprivate landowners in Warrensburg for their assistance in planning and permission to conduct thisresearch on their lands. Partial funding for this research came from the University of Central Missouriin the form of a Willard North Graduate Research Grant, Honors College Undergraduate ResearchGrants, and the Biology Department Alumni Fund. Our manuscript was strengthened with thesuggestions from two anonymous reviewers.

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MICHAEL V. COVE,1 BRANDON M. JONES, AARON J. BOSSERT, DONALD R. CLEVER JR., RYAN K.DUNWOODY, BRYAN C. WHITE, AND VICTORIA L. JACKSON, Department of Biology and EarthScience, University of Central Missouri, Warrensburg 64093. Submitted 31 October 2011; Accepted 30 April2012.

1 Corresponding author: Telephone: (203) 417-8244; e-mail: m.cove3@gmail.com

2012 NOTES AND DISCUSSION 465