Biodiversity and Conservation (2022) 31:2607–2624
https://doi.org/10.1007/s10531-022-02445-2
ORIGINAL PAPER

Human presence drives bobcat interactions among the U.S.
carnivore guild
Tru Hubbard1 · Michael V. Cove2 · Austin M. Green3 · Fabiola Iannarilli4 ·Maximilian L. Allen5 · Summer H. LaRose6 · Chris Nagy7 · Justin A. Compton8 ·Diana J.R. Lafferty1
Received: 30 November 2021 / Revised: 16 May 2022 / Accepted: 30 May 2022 / Published online: 29 June 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022

Abstract
Mammalian carnivores are elusive and enigmatic species that often play keystone rolesin ecosystems through direct and indirect effects. Growing evidence shows that humanactivity can impact carnivore behavior and community structure by altering predator-preyinteractions, shifting diel activity patterns, and altering wildlife movement. Our goal was
to investigate the ecological role of bobcats (Lynx rufus) across carnivore communities
in the continental USA by quantifying variation in spatiotemporal patterns and determining what environmental and human factors influenced carnivore community interactions.
Using camera trap data from the inaugural nationwide Snapshot USA project dataset
collected from September – October 2019, we constructed diel activity density curves,
applied multispecies occupancy models, and calculated attraction-avoidance ratios. Our
results suggest that bobcats display the greatest flexibility in their diel activity among thesuite of carnivores sampled. Further, bobcats respond differentially at large spatial scales
relative to the presence of dominant or subordinate carnivores, with fluctuating impacts
mediated by human and environmental factors. Bobcats’ co-occurrence with dominantcarnivores (i.e., wolves Canis sp., pumas Puma concolor) was influenced primarily byhuman-related factors, whereas co-occurrence with subordinate carnivores (i.e., foxes) was
more influenced by environmental factors (i.e., precipitation, gross primary production[GPP]). Bobcats appear to interpret humans as the apex predator on the landscape regardless of the presence of dominant or subordinate species. Understanding the influence ofhumans as “super predators’’, as well as the importance of environmental factors that impact intraguild carnivore interactions across the USA is critical for establishing successful
management practices to promote functioning communities.
Keywords Apex predator · Bobcat · carnivore · Intraguild interactions · Occupancy
modeling · Spatiotemporal activity

Communicated by Adeline Loyau.
Extended author information available on the last page of the article

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Introduction
Large mammalian carnivores often play complex and sometimes keystone roles in structuring terrestrial ecosystems by driving trophic cascades Miller et al. 2018; Suraci et al. 2019;
Dröge et al. 2016; Clinchy et al. 2016). Yet, in most cases small to medium-sized carnivores (i.e., < 15 kg, hereafter ‘mesocarnivores’) make up most predators in local food webs
(Roemer et al. 2009). Smaller size and increased adaptability allow mesocarnivores to be far
more numerous than larger, apex predators, and to be more diverse in behavior and distributions in nearly all land cover types (Roemer et al. 2009). Consequently, mammalian carnivore guilds have emerged as exciting ecological models for examining the many ways inwhich intraguild interactions (e.g., resource competition, intraguild predation) reverberate
across trophic levels to affect and even mediate ecological processes (Lombardi et al. 2020;
Gompper et al. 2016; López-Bao et al. 2016; Green et al. 2018). Intraguild interactions canfacilitate or limit species’ coexistence by driving carnivore temporal and spatial distributions (Miller et al. 2018), mediating carnivore and prey densities, and influencing species’
behavior (Gompper et al. 2016; Grassel et al. 2015). Thus, research focusing on mesocarnivores and their interplay with prey, larger carnivores, and human activity is crucial tounderstanding drivers of spatial and temporal dynamics as well as species co-occurrence
across human-modified landscapes.
Wildlife communities can be strongly influenced by competition between interacting
carnivore species, and changes in population density of even a single carnivore species canhave significant ecological effects throughout an ecosystem Miller et al. 2018; Suraci et al.
2019; Dröge et al. 2016; Clinchy et al. 2016). In fact, large carnivores have the potential
to initiate top-down effects that influence mesocarnivores’ behavior, abundance, and distribution through both consumptive and non-consumptive pathways (Shores et al. 2019;
Dellinger et al. 2018). For example, following gray wolf (Canis lupus) reintroduction on
Isle Royale (Michigan, U.S.A.) in 1958, coyotes (Canis latrans) were driven to near local
extinction (Linnell and Strand 2000). In contrast, coyote populations have grown and range
expanded across much of North America following historical wolf extirpation in conjunction with the remarkable adaptability of coyotes to urban environments. This expansion
resulted in population changes for several smaller carnivore species such as kit fox (Vulpes
macrotis), and black-footed ferrets (Mustela nigripes) (Linnell and Strand 2000), especiallyas coyotes began to fulfill the role of a de facto apex predator.
Multiple behavioral mechanisms can enable carnivore coexistence: temporal partitioning, habitat segregation (López-Bao et al. 2016; Dröge et al. 2016), and the ability of subordinate species to adopt strategies such as active avoidance (Ruiz-Villar et al. 2021) or
prey-switching (Ghoddousi et al. 2017) to avoid or reduce competition with dominant sympatric species. However, carnivore community structure is also mediated by environmental
factors that correspond to unique ecoregion characteristics (e.g., climate, vegetation), andvarying levels of human disturbance (e.g., urbanization, recreation). Environmental factors
play a key role in carnivores’ associations with distinct land cover characteristics (Gompper
et al. 2016), wherein traits related to resource use are critical for carnivore co-occurrence
and moderating intraguild conflict (Davies et al. 2007; Green et al. 2018). Further, carnivores are highly sensitive to human disturbance due to their large home range requirements,
high metabolic demands (Ripple et al. 2014), and history of direct persecution (Smith et al.
2015; Kays et al. 2017). Thus, some carnivores perceive humans as “super predators” and13

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consequently respond by modifying their habitat use and behavior (Clinchy et al. 2016;
Smith et al. 2017), driving widespread community and ecosystem-level effects that canincrease species spatiotemporal overlap (Murphy et al. 2021). Human disturbance and subsequent landscape transformation can also affect carnivores indirectly by impeding species
movement, shifting when and where encounters occur (Murphy et al. 2021), and further
diverting time and energy to risk avoidance behaviors (e.g., vigilance, fleeing [Breck et al.
2019]).
The bobcat (Lynx rufus) is a widespread mesocarnivore that serves as a de facto top predator in many ecosystems across North America. Thus, the bobcat can mediate wildlife community structure, and influence ecosystem functions (Roberts and Crimmins 2010). Like
many mesocarnivores, bobcat populations across much of the continental United States suffered from landscape transformation associated with human development (Rose et al. 2020)and overexploitation for pelts (Johnson et al. 2010). However, since the early 1990s, many
bobcat populations have recovered and are increasing (Roberts and Crimmins 2010). Still,bobcats, and many mesocarnivores, are viewed as harvestable resources or pests that require
active management (Roemer et al. 2009). Despite exploitation, bobcat populations haveshown remarkable resilience to anthropogenic pressures, as indicated by an expanding geographic range and exploitation of peri-urban spaces (Young et al. 2019; Johnson et al. 2010).
Thus, bobcats serve as an excellent ecological model to investigate predators that operate at
two functional levels (e.g., apex and subordinate) in the carnivore hierarchy depending on
local community structure and habitat characteristics (Roemer et al. 2009). Further, understanding how bobcats coexist with potentially dominant (e.g., puma [Puma concolor], graywolf, red wolf [Canis rufus], coyote) and subordinate (e.g., red fox [Vulpes vulpes], gray fox[Urocyon cinereoargenteus]) carnivores can provide new insights for understanding carnivore guild dynamics and implementing effective carnivore conservation and management
strategies (Robert and Crimmins 2010).
To investigate species co-occurrence and drivers of bobcat spatiotemporal activity within
the carnivore guild of the United States (U.S.A.), we used 2019 Snapshot USA camera trap
dataset (Cove et al. 2021), which is a large collaborative effort to sample mammal populations throughout the 50 US states using standardized methods across stratified habitats andurbanization zones. We explored variation in bobcat spatiotemporal activity and assessed
carnivore co-occurrence by constructing diel activity density curves (Lashley et al. 2018),
applying multispecies occupancy models (Rota et al. 2016), and calculating attractionavoidance ratios (Parsons et al. 2016). We tested three hypotheses: (1) bobcat spatial andtemporal activity varies based on the presence or absence of dominant carnivores among
the communities represented across the U.S.A., (2) bobcats, as a de facto apex predator in
the absence of wolves and pumas, would influence subordinate mesocarnivores, and (3)environmental variability and human disturbance (i.e., human intensity) would influence
bobcat spatiotemporal activity differently depending on which species are present in thecarnivore community. We predicted that bobcats would reduce their temporal overlap withdominant carnivores (i.e., pumas, gray and red wolves) and avoid sites previously visited byboth dominant and subordinate carnivores. Our research findings provide novel insights into
carnivore co-occurrence and community structure across a diverse range of ecosystems that
can aid future management of expanding bobcat populations across the U.S.A.

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Materials and methods
Study area
This study was conducted across the contiguous lower 48 states of the U.S.A. (Fig. 1),
which extend across a vast latitudinal range (25.17° to 46.07°) and consists of varying elevation and climates resulting in a diverse land cover types and vegetation communities
that comprise 10 unique ecoregions that highlight the major ecological areas of the U.S.A.
(EPA 2016; Cove et al. 2021). The myriad lands of the U.S.A. support abundant medium
to large-sized predators and prey (e.g., > 500 g) commonly captured on camera traps (Coveet al. 2021). Members of the carnivore guild make up a substantial portion of these species
including coyote, bobcat, puma, and several fox species; some exhibit extensive ranges
reaching coast to coast and experience considerable variation in environmental conditions,hunting practices, and management strategies.

Data Acquisition
We used the published Snapshot USA 2019 dataset, generated through a collaborative
nation-wide camera-trapping initiative that resulted in the beginning of a systematic effort
to document annual trends and distributions of mammal communities across the UnitedStates (Cove et al. 2021). Snapshot USA used the eMammal platform, a data management
system and archive for camera trap research projects (McShea 2016 - https://emammal.
si.edu/snapshot-usa). A full description of Snapshot USA 2019 methods and full dataset areavailable in Cove et al. (2021).

Fig. 1 Map of study area consisting of the 48 contiguous United States with the color of each state representing relative bobcat abundance (RAB = detectionsi/total trap nights X 100) inferred from camera trap detections across arrays with dots signifying whether bobcats were detected (solid) or not detected (open) at an
array

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We queried Snapshot USA 2019 data by selecting all detections of nine carnivore species(American badger [Taxidea taxus], bobcat, coyote, fisher [Pekania pennanti], gray fox, graywolf, red fox, red wolf, and puma) from the contiguous USA that exhibit primarily carnivorous diets and for which there were at least 30 detections. Previous research demonstrates
that 30–50 detections can provide acceptable diel pattern estimates when investigating rare
species (Lashley et al. 2018). Puma was included with only 29 detections; this exception
was made because pumas are expected to elicit a strong response on bobcat activity (Wanget al. 2015) and thus were of particular interest. Despite the well-documented interactions
between bobcat and Canada lynx (Lynx canadensis), we had to exclude this species due tothe extremely limited number of detections that consisted of only 3 total in the lower 48
states, thus not allowing for viable comparisons. Although lynx share many similar features with the congener bobcat leading to occasional misidentification Gooliaf and Hodges(2018), Thornton et al. (2019) and Kays et al. (2022) provided evidence that this misidentification was not a limitation in our analyses due to the restricted range of lynx and themultiple photo burst requirements of the Snapshot dataset. We defined a detection as a single
eMammal aggregated sequence, grouped as (a) consecutive images with individuals of thesame or different species at the same site, and (b) images that were taken within one minuteof the previous image. Our query resulted in 5,076 detections from 108 camera trap arrays
across all 48 contiguous states. We calculated the relative abundance (RAB) of each carnivore species i across ecoregions at the individual camera level using the following formula:

RABi = detectionsi /total trap nights X 100.
Due to the difficulty of estimating true abundance of species at a national scale and for species which individuals are not uniquely identifiable, we used RAB that offers a simple andaccurate index of abundance (Parsons et at. 2017), but does not account for potential bias
arising from imperfect detection (Palmer et al. 2018).

Statistical analyses
We implemented statistical tests and models for all temporal and spatial analyses in RStudioversion 1.3.1073 (R Core Team 2021). Given the amount of data recorded for each species,
they were included in different parts of our analyses (Table 1).

Table 1 Breakdown of species included in each portion of the analyses
Analysis
BobcatAmerican Coyote
FisherBadger
X
X
X
X
Temporal Activity
X
Single-Species
Occupancy
X
X*
Multispecies
Occupancy
X
X
Spatiotemporal
Avoidance*Included in analysis as a covariate

Gray Gray Red Red Puma
Fox Wolf Fox Wolf
X
X
X
X
X

X
X

X

X
X

X

X
X

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Temporal activity
We determined diel activity patterns using the start of detection times for nine carnivore
species across four different U.S.A. time zones. Detection times were anchored to sunrise
and sunset events using NOAA calculations in a Microsoft Excel VBA translation (https://peltiertech.com/2021). We converted sunrise and sunset to radians via the ‘transtime’ function in the package ‘activity’ (Rowcliffe 2021) to express detection times relative to the twosolar events. We constructed 95% kernel density estimation curves and estimated the mean
temporal overlap coefficient (Δ; scaled from 0 to 1) using the function overlapEst in package ‘overlap’ (Meredith and Ridout 2020). The estimator Δ1 was applied when the species
with the smaller sample was less than 50 observations and the estimator Δ4 was used when
the species with the smaller sample was greater than 75 observations, with no species having a value between 50 and 75 (Meredith and Ridout 2020). We then calculated confidence
intervals using a bias-corrected logit-scale bootstrap of 10,000 resampled estimates for eachspecies-species pair to determine if the two species have exclusionary (Δ = 0) or complete
overlap (Δ = 1; Ridout and Linkie 2009; Schmid and Schmidt 2006; Miller et al. 2018). We
then performed a Watson’s Two-Sample Test of Homogeneity (Rao and SenGupta 2001)
using function ‘watson.two.test’ in the package ‘circular’ (Agostinelli and Lund 2017) todetermine if times of detection for each species differed significantly from each other, anda Wald Test bootstrapped 1,000 times to compare activity level estimates calculated using
the package ‘activity’ (Rowcliffe 2021) among species. Activity level (act) is an ecological metric that refers to the proportion of time an animal spends active, providing an index
for energy expended, foraging effort, and even vulnerability to risk (Rowcliffe et al. 2014).
Finally, we repeated this process using only bobcat detections to determine whether bobcat
activity patterns showed significant differences across ecoregions, areas of various harvest
management (Elbroch 2017) or population status (e.g., furbearer, threatened) (Roberts andCrimmins 2010).

Single-species and multispecies occupancy models
The purpose of using occupancy modeling was to test two independent but not mutuallyexclusive hypotheses: (1) The Dominant Hypothesis, where both wolves and pumas will
have top-down effects on bobcat spatiotemporal behavior, and (2) The Subordinate Hypothesis, where bobcats will elicit top-down effects on red and gray fox spatiotemporal behavior.
If the dominant hypothesis is supported, we would expect bobcat occupancy to be negatively correlated with wolf and puma presence, and if the subordinate hypothesis holds thenfox occupancy would be negatively correlated with bobcat presence.
We created binary detection history matrices (1 = detected, 0 = not detected) using thestart and end date for each camera array using the function detectionHistory in the package‘camtrapR’ version 2.0.3 (Niedballa et al. 2016), for six of the nine carnivore species. We
excluded badgers and fishers from occupancy analyses because these species occupy distinct ranges, as well as coyotes due to the species occurring at > 90% of sites. Further, we
combined red wolves and gray wolves into a single category, ‘wolf’, given that we expect
similar responses from bobcats to both species. For our analysis, we summarized detection events at the camera array level by aggregating data across all deployments withineach array. We accounted for imperfect detection by using weekly sampling occasions that

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included data from only the first 8 weeks of collection, which provided adequate repeat samples at each camera array while minimizing the amount of “missing” sampling occasions as
the number of operational camera sites declined towards the end of the study period (Naidoo
and Burton 2020). Although this excluded data from analysis, adding additional weeks had
little to no effect on improving our estimates given that few sites collected data longer thaneight weeks, and no additional array captured a bobcat that had not been detected in theprevious eight weeks.
Before fitting single and multi-species models we centered and scaled all numericalcovariates (Table 2) and checked for correlations between all covariate pairs using the function ‘cor’ available in base R and package corrplot (Wei and Simko 2017) to visualize thevalues, with a threshold of 0.7 (Dormann et al. 2013) to remove any correlated covariates.
The only covariates that were highly correlated were minimum and maximum temperature(Pearson’s r = 0.87), thus we excluded them from our final models. We first explored theeffects of remaining covariates on probability of occupancy and detection of bobcat using
single-species occupancy models (Mackenzie et al. 2003). We started by evaluating a set
Table 2 Descriptive statistics of environmental, human, and sampling covariates included in General Linear
Mixed Models and Occupancy models for the detections of 9 carnivore species across 108 camera trap arrays.
AICc was calculated by running single-species bobcat occupancy models; models in bold ranked above thenull model (AICc = 643.96)
Model/Covariate
Description
Mean
Max
Min
AICc
Value
Human Impact Model*Human intensity# of individual human 169.00
6142.00
0639.39detections*Human population size
human pop. density
326.608084.00
0641.86(GPW) at 1km sq.
Nearest building
average distance to830.71
5289.1928.32
644.32nearest building (m)Bobcat hunting status
protected, legal, noNANANA
645.14limits
Cultivated land
average cultivated
0.04
0.49
0
645.41land at 1km2.
Environmental Impact Model*Gross Primary Productivity
Ave. cumulative GPP 12534.97
25318.50
1567.99644.55(GPP)a
Max Temperature
max temp. (C)
20.41
31.16
8.41
644.64a
Min Temperature
min temp. (C)
8.07
25.05
-4.47
645.89*Precipitation
3-hour accumulation
0.19
0.61
0
645.73of total precipitation
Ecoregion
ecoregion based on
NANA
NA
656.15
EPA Level I
Miscellaneous Covariates*Coyote Detection Rate
coyote detections per 0.05
0.44
0627.82array/survey days
Survey Days
total # of survey days 488.00
1674.00
94.00
645.13
Bobcat Pop. Status
decreasing, stable,
NANA
NA
649.36
increasing, unknown*Inclusion in the final multi-covariate models.
a

From nearest NOAA station.

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of covariates on detection while holding occupancy (ψ) as a constant intercept. Once thebest supported parameterization for detection was found, we held it constant and ran several
univariate single-species occupancy models to evaluate the effects of various environmental and anthropogenic factors on bobcat occupancy based on AICc values (Burnham andAnderson 2002). After investigating each covariate’s effect on bobcat occupancy, we created two additive impact models based on a priori hypotheses to determine which factors
have greater influence on dominant and subordinate carnivores’ relationships with the bobcat: (1) Human Impact Model (i.e., human intensity, human population density [Table 2])and (2) Environmental Impact Model (i.e., precipitation, gross primary production [GPP][Table  2]).
Finally, we implemented multispecies occupancy models to investigate our previously
stated hypotheses in which dominant carnivores may have top-down effects on bobcat, andbobcat may have top-down effects on subordinate carnivores (Rota et al. 2016). Each model
identified the probability that two or more species would occupy the same site as a functionof our selected model covariates (Rota et al. 2016). Additionally, we fit our Human Impact
and Environmental Impact models with and without a covariate for coyote detection rate
for the dominant carnivores due to the strong association between coyote and bobcat occupancy. We ran all occupancy models using the R-package ‘unmarked’ (Fiske 2011).

Spatiotemporal avoidance
We used detection data from 5 species with large enough sample sizes (n > 4) across the
108 arrays to test the relative attraction and/or avoidance of a site by bobcat after previous
visitation by another carnivore, and relative attraction and/or avoidance of a site by anothercarnivore after a visitation by a bobcat (Parsons et al. 2016). This approach estimates spatiotemporal avoidance, or to what extent a site visited by species A is influenced by visitationsof species B (Niedballa et al. 2019). We carried over our top-down effect hypotheses fromour occupancy models, thus we continued to use dominant and subordinate groups of carnivores for our analyses. We used Avoidance Attraction Ratios (Parsons et al. 2016) where
odds ratios (i.e., odds of detecting species B in the absence of species A relative to the odds
of detecting species B directly after an observation of species A) were calculated holding
bobcat as both our species A and species B against the other four species (Niedballa et al.
2019). We created AARs by converting detection times to Julian hours, then comparing thetime interval after/before a bobcat and another carnivore visited a site, referred to as the T2/
T1 ratio, and then the time interval with/without the visitation of a bobcat or another carnivore, referred to as the T4/T3 ratio (Parsons et al. 2016). The T2/T1 ratio measures the net
effects of both attraction and avoidance between bobcat and other carnivores, where a logtransformed value > 1 can indicate either avoidance of the dominant species by the subordinate species or attraction to the subordinate by the dominant species. A high T4/T3 indicates
avoidance by the subordinate species, while a low value (log-transformed < 1) indicates thedominant attraction or no relationship. Thus, the two ratios can be used to discern the direction and nature of potential species interactions.
Following AAR calculations, we performed three analyses using log-transformed ratios:(1) We used T4/T3 ratios to run two-tailed t-tests to determine whether bobcat avoided orwere attracted to a site after visitation from another species, and vice versa. A mean ratio
greater than zero indicates species avoidance, whereas a mean less than 0 indicates species

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attraction (Parsons et al. 2016). (2) We also used T4/T3 ratios to run an analysis of variance(ANOVA) to determine (a) if bobcat avoided any carnivore species more than another, and(b) if any species avoided bobcat more than another. If we found significant effects basedon an α = 0.05, we performed a post-hoc Tukey-Pairwise Comparison to determine whichpair of carnivores displayed a significant relationship, and the magnitude difference between
that pair. And (3) using General Linear Mixed Models (GLMMs) we investigated whether
human activity or environmental variables (Table 2) altered how bobcats responded to dominant and subordinate carnivores based on the T2/T1 ratios. We ran the Human Impact andEnvironmental Impact models and a null model for the dominant and subordinate groups
and used the R package ‘AICcmodavg’ (Mazerolle 2020) to compare the strength of themodel effects on bobcat avoidance and attraction.

Results
With data from 108 independent camera arrays across the contiguous U.S.A., we recorded
5,076 detections of nine target carnivore species: American badger, bobcat, coyote, fisher,gray fox, gray wolf, red fox, red wolf, puma (Table 3). Bobcats were captured 417 times
and occurred at 52 camera arrays (Fig. 1). Seven other carnivore species were captured withgreater than 30 detections, with coyotes having the largest sample size (2,405 detections)and making up almost half of the total detections (Table 3).
Coyote had the highest nationwide RAB (4.61 ± 0.58SE detections/100TN; TN = trap
night) and were the only carnivore detected in the tropical wet forest ecoregion, followed by
red foxes that were also highly prevalent nationwide (3.68 ± 0.58SE detections/100TN). At
the ecoregion level, bobcats were most prevalent in the southern semi-arid highlands, but
this ecoregion consisted of only one site. Further, the Mediterranean ecoregion consistedof multiple sites and hosted the highest RAB for both coyote and gray fox, along with theTable 3 Number of aggregated eMammal camera trap image sequences (Sample size), number of arrays outof 108 in which a species was detected, estimates of overlap with bobcat activity (and related 95% confidence
intervals), and activity level estimates within the 24-hour cycle for nine carnivore species at the national
scale. Percent nocturnal includes all detections occurring outside the approximate bounds of sunrise andsunset throughout the study period
Species
SampleNumber ofOverlap Est.
Overlap CI
Activity%
Size
ArraysLevel Est. Nocturnal
Bobcat417
52-----------------------------0.640
65%
Coyote
2405
98
0.905
0.852–0.934
0.632
68%
Fisher75
18
0.767
0.634–0.850
0.444
48%
Gray Fox
480
33
0.738
0.664–0.774
0.464
85%
Gray Wolf
32
8
0.806
0.683–0.910
0.475
50%
Red Fox
1570
45
0.810
0.693–0.917
0.463
81%
American
37
15
0.839
0.735–0.923
0.508
74%
Badger
Red Wolf
31
1
0.694
0.585–0.778
0.340
93%
Puma
29
9
0.758
0.225–0.996
0.431
85%

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second highest for bobcat. In addition, we observed a pattern of increasing coyote RAB withincreasing bobcat RAB across all ecoregions.

Temporal overlap
Most carnivores showed primarily nocturnal activity patterns (Fig. 2), with the percent oftime active between sunset and sunrise being over 50% for eight of the nine species, except
for fisher, which exhibited 48% nocturnal activity (Table 3). Bobcat had the third lowest
percentage of nocturnal activity at 65%, while red wolf exhibited the highest percentage of
nocturnal activity at 93%. Daily activity peaks occurred before sunrise and after sunset for
all species except fisher and gray wolf, each with a large peak following sunrise. Temporal
overlap among bobcat and each carnivore species varied from 0.69 to 0.90 with coyote
exhibiting the highest overlap and red wolf exhibiting the lowest (Table 3).
Activity level estimates revealed that bobcats spent the greatest proportion of the 24-hourcycle active (act = 0.64), followed by coyotes (act = 0.63), both spending > 10% more timeof the 24-hours cycle active than the other seven carnivore species. Additionally, bobcats
had a significantly higher activity level than fisher, gray fox, red fox, red wolf, and puma
(Fig. 3; Table 3). Distribution of bobcat temporal activity was significantly different fromthe other eight carnivores, with the greatest variation between bobcat and gray fox (U2test = 1.717 – Fig. 3). No significant differences for activity distribution were found between
harvest status and population status (Table S4), but activity level estimates were significantly different for regions where bobcat populations were increasing (act = 0.616) versus
decreasing (act = 0.375; Fig. S1). Decreasing populations’ activity plot reveal low activity
in the afternoon and strong peaks nearing midnight, while increasing populations’ exhibits
steadier activity patterns throughout the day (Fig. S1). Two pairs of ecoregions returned

Fig. 2 Kernel density graphs of temporal activity patterns for nine carnivore species anchored on sunrise andsunset using nationwide U.S. detection data

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Fig. 3 Results for carnivore activity distributions (i.e., Watson U2 tests) and activity level estimates (i.e.,
Wald tests comparing bobcat diel activity to eight other carnivores with stars indicating significancesignificant differences in activity distributions (Northern Forests-Eastern Temperate Forests[U2 test = 0.194]; Southern Semiarid Highlands-Eastern Temperate Forests [U2 test = 0.229];
Table S3 and Table S4). Several ecoregions also showed a significantly different activity
level when paired with the Temperate Sierras ecoregion (Fig. S2 and Table S7).

Single-species occupancy modelingTop-ranked single-species models for bobcat occupancy included human-related covariates
and coyote detection rate associated with Ψ, but no environmental covariates. We excluded
latitude from single species models because there was no clear biological reason for inclusion and bobcats were detected across the entire north-south range of the study area.

Multispecies occupancy modeling
Dominant Hypothesis| The ‘Human Impact’ model with the addition of coyote detection rate
as a covariate was the top-ranked model for assessing top-down effects of wolves and pumas
on bobcats (Table S5). Bobcat occupancy was negatively associated with wolf and puma
presence but neither relationship was significant. Further, bobcat occupancy was affected
negatively by human intensity (β = -3.60 ± 1.89SE), but positively associated with higher
coyote detection rates (β = 1.93 ± 0.52SE; Table S6).
Subordinate Hypothesis| The ‘Environmental Impact’ model was the top-ranked modelfor top-down effects between bobcats and red and gray foxes (Table S7). Bobcat occupancy
was negatively associated with red fox presence (β = -1.32 ± 0.45SE) and positively but nonsignificantly associated with gray fox (β = 0.39 ± 0.52SE). The only significant relationshipamong compared covariates was precipitation, which had a significant negative effect (β =-1.35 ± 0.47SE) on bobcat and gray fox interaction (Table S8).

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Spatiotemporal avoidance
T-Tests| Due to low sample sizes (n = 4) for multiple carnivores, AARs were only calculated for coyote and fox (i.e., gray and red fox) when performing t-tests. When holding
bobcat as species A, bobcats displayed avoidance of coyotes (T-test: meanT4/T3 = 0.82,
t = 6.20, p < 0.0001) and avoidance of foxes (T-test: meanT4/T3 = 0.68, t = 3.48, p = 0.006).
When holding bobcat as species B, coyotes (T-test: meanT4/T3 = -0.18, t = -2.81, p = 0.007)
displayed attraction to bobcats while foxes did not change their behavior when bobcat were
present (Table S9).
Analysis of Variance (ANOVA)| In our one-way ANOVAs we included all species that
had multiple AAR values, even with low sample sizes (i.e., red fox [n = 4], puma [n = 4]).
Bobcats avoided species differently (F = 2.84, df = 3, p = 0.05; Table S13); specifically, bobcats avoided pumas more than coyotes (Tukey Pairwise Comparison, mean diff = -1.06, CI[-2.03, -0.08]). No carnivore species were found to avoid bobcat significantly more thanother species (F = 0.69, df = 4, p = 0.60; Table S10).

General Linear mixed models
Dominant | Dominant models held bobcats as species A and the dominant carnivores as
species B. Neither impact model had a greater model weight than the null model (wi = 0.70;
Table S15 and Table S16), indicating the overall patterns observed in previous T-tests – bobcats avoid coyotes and coyotes are attracted to bobcats – did not appear to vary with humanimpact or environmental factors (Fig. 4).
Subordinate | Subordinate models held subordinate carnivores as species A and bobcat as species B. The Human Impact model ranked highest with the greatest model
weight (wi = 0.80, R2 = 0.40). Human intensity showed a near-significant positive effectFig. 4 (a) Effect of human intensity, human population density, precipitation, and gross primary production(GPP) for dominant and subordinate carnivores. (b) Relationship between each model covariate (i.e., humanintensity, human population density, precipitation, and GPP) and logT2/T1 ratio for dominant (blue) andsubordinate (red) carnivores. Bands represent 95% confidence intervals

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2619(β = 0.004 ± 0.002SE), whereas human population density had a significant negative effect(β = -0.0003 ± 0.0001SE; Fig. 4, Table S13).

Discussion
Many researchers have investigated the spatiotemporal behavior of carnivores, yet few studies have encompassed a study area as large and diverse in both ecosystem structure and carnivore diversity as the contiguous United States and that covers most of the total distributionof a species. Our large-scale survey of carnivore communities encompassed nine species
including our focal species, the bobcat. Bobcats exhibited consistent distribution of temporal activity across the variable U.S.A. landscape. However, the small variation observed inbobcat temporal activity among ecoregions provides evidence that bobcats can persist andlocally modify their behaviors to survive across ecosystems. Flexibility in traits such as
foraging strategy can allow carnivores to coexist successfully in different communities withvarying environmental factors (Monterroso et al. 2014). Indeed, bobcat diel activity curves
were significantly different from all eight carnivores in that bobcats were generally more
diurnal and utilized a greater proportion of the 24-hour cycle than other carnivores. Furthermore, we provide evidence that bobcats respond differently on a large spatial scale to dominant carnivores (i.e., wolf, puma) and subordinate carnivores (i.e., red and gray fox), withfluctuating impacts mediated by human and environmental factors. Based on our Human
Impact model, heightened human intensity resulted in bobcats displaying weaker avoidance responses to dominant carnivores, but stronger avoidance responses to subordinate
carnivores (Fig. 4). These data support the concept of the human “super predator” (Clinchy
et al. 2016), which suggests that bobcats perceive humans as the greatest threat. Therefore,
even when dominant carnivores such as pumas and wolves are present, bobcats tend to prioritize avoiding contact with people. Dominant carnivores were negatively associated with
bobcat occupancy and observed to be strongly impacted by human-related factors, while cooccurrence with subordinate carnivores was affected more by environmental factors. Given
the Human Impact model (i.e., human intensity, human population density) best explained
dominant carnivores’ and bobcat landscape use, as well as negatively affecting the probability of the two species occupying the same space, our results suggest that human impacts
surpass environmental impacts in structuring carnivore communities.
Throughout this study, we addressed differing effects that dominant and subordinate carnivores had on temporal and spatial activity of bobcats. Our findings indicate that bobcats
used the greatest proportion of the day when compared to other species evaluated, suggesting bobcats may mediate competition through behavioral plasticity allowing for flexible
temporal activity patterns (Frey et al. 2017). Greater plasticity in temporal behavior allows
bobcats to adjust their activity in the presence or absence of potential competitors andavoid prey access constraints by using time periods when common prey species (i.e., diurnal squirrels, chipmunks) are most active (Monterroso et al. 2014). Spatiotemporally, bobcats showed avoidance behavior towards all species tested (Table S9), while no carnivore
avoided bobcats more than another. Further, our Human Impact model was the only model
to elicit significant effects on either carnivore group. Both human intensity and human population influenced bobcat spatiotemporal behavior, but the strong effect of human population
was primarily driven by two outliers (Fig. 4). We chose not to exclude these data from anal-

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ysis as they reflected real-world data that could reveal an important relationship. Future surveys would benefit from targeting camera trap efforts in highly urban environments (sensu
Herrera et al. 2021) to help us better capture the gradient of urbanization and understandcarnivore-human interactions in an urbanizing planet.
Our multispecies occupancy models revealed differing trends for which factors impacted
relationships among carnivore community members. Dominant carnivores and their relationship with bobcats were highly associated with human factors (i.e., human population
density, human intensity), but effects varied between wolves and pumas. Human factors
negatively affected the probability of bobcat-wolf interactions, which might result fromwolves commonly using man-made features such as low-use roads/trails to traverse thelandscape (Lesmerises et al. 2013), while bobcats generally select home range sites withlow road densities (Poessel et al. 2014). On the other hand, human intensity resulted in apositive effect on the probability of bobcat-puma interactions, as did coyote detection rate.
Although these positive interactions were weak, these results provide an example of how
humans may act as a ‘super predator’’ on the landscape (Table S6; Clinchy et al. 2016), andhow coyotes may positively mediate the interactions between the two species (Breck et al.
2019; Cove et al. 2012). Interactions between bobcat and gray fox were strongly influenced
by precipitation, with heightened average 3-hour precipitation throughout the study period
negatively corresponding with their interactions. This negative effect was not observed for
red fox; thus, precipitation likely affects the two fox species differently. The differences in
how red and gray foxes respond to environmental variables may allow for spatial partitioning to avoid direct competition, but likely requires high resolution tracking data to further
elucidate.
The relationship between bobcats and coyotes was unique among carnivores investigated. Specifically, coyotes were recorded at nearly every site where bobcats were captured,
coyotes were the only species to show an attraction to bobcats, and coyotes had ~ 90% temporal overlap with bobcats (Table 3). Given the high spatiotemporal similarities between
these two species, we expect to find differences across multiple niche dimensions such as
diet and landcover preference that allow coyotes and bobcats to coexist at such a high levelacross multiple niche axes (Hutchinson 1957). For example, bobcats are obligate carnivores, whereas coyotes are highly adaptable and omnivorous allowing them to be ubiquitous across the U.S.A. (Breck et al. 2019; Duncan et al. 2020). Differences in diet may
allow these two species to spatiotemporally coexist closely, but coyote attraction to bobcats
could potentially be a result of coyote seizing opportunities to usurp bobcat kills (Allen etal. 2015). Coyotes and bobcats are two of the most ubiquitous mesocarnivores in the U.S.A.
and our results showcase that these species overlap strongly in space and time. As such,high resolution tracking data for both red foxes and coyotes could enable ecologists to test
fundamental ecological theories about the partitioning of multiple niche axes to advance ourunderstanding of species’ competitive coexistence.
Snapshot USA data used for these analyses were generated by a collaborative standardized effort among researchers across the country, resulting in a nationwide camera trapping
dataset (Cove et al. 2021). Yet for many rare carnivores, subsequent data from ongoing
Snapshot USA surveys will be required to produce robust results that would allow us to fully
understand the complex carnivore community across the U.S.A. For example, bobcats andlynx share many common morphological and behavioral traits that could result in intensecompetition. However, the lynx’s range has contracted ~ 40%, shifting north into Canada,

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2621

while the bobcat is reclaiming much of their former habitat throughout North America (Laliberte and Ripple 2004; Marrotte et al. 2020). Further, where these two species co-occur,
researchers have found they exhibited habitat partitioning across different land cover types,
prey, and snow conditions, with bobcats preferentially using areas that lynx avoided (Marrotte et al. 2020). Additional future camera trap surveys in targeted areas of sympatry forthese felids will be required for further inference into the drivers of their niche partitioning.
Our findings revealed that bobcats are adaptable predators that coexist with several dominant and subordinate carnivores with variation in activity and occupancy based on the community structure, and environmental and anthropogenic factors. Bobcats have the potential
to function in different capacities within a carnivore guild, driven by the presence of dominant carnivores such as wolves and pumas, and are highly influenced by human factors thatcan outweigh the environmental effects of their habitat. Furthermore, our results provide
evidence that bobcat may help facilitate behavioral cascades among dominant carnivores(i.e., wolf, puma) and subordinate carnivores (i.e., foxes), but more research must be conducted to assess whether these findings hold across their entire range (Shores et al. 2019).
Knowledge about intra-guild relationships within diverse carnivore communities such as
across the U.S.A. is essential for understanding the role of the human “super predator” andhow anthropogenic changes will continue to influence wildlife communities.
Acknowledgements We acknowledge Snapshot 2019, which was a collaborative effort among scientists
across the U.S.A. who contributed to the inaugural wildlife camera-trap survey producing a database accessible to the public. This project was financially supported by the Biology Department at Northern Michigan
University. TH was supported by the NMU Biology Development Fund, the Spooner Student Research Fund,an Excellence in Education Grant, and a Student Technology Innovation Award.
Author contribution TH and DJRL conceived the idea and secured funding; All contributors collected data
for Snapshot USA; TH led manuscript writing with technical assistance from MVC and DJRL. MLA and
CN assisted with analysis of temporal activity. AMJ assisted with analysis of spatiotemporal avoidance. FI
assisted with analysis of single and multispecies occupancy modeling. All authors reviewed and provided
constructive feedback on the manuscript.
Data Availability All data from this manuscript has been archived as part of Snapshot USA 2019 available athttps://doi.org/10.1002/ecy.3353.

Declarations
Conflict of interest The authors declare they have no conflicts of interest/no competing interests.

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Authors and Affiliations
Tru Hubbard1 · Michael V. Cove2 · Austin M. Green3 · Fabiola Iannarilli4 ·Maximilian L. Allen5 · Summer H. LaRose6 · Chris Nagy7 · Justin A. Compton8 ·Diana J.R. Lafferty1
Tru Hubbard
truhubba@nmu.edu
1

Wildlife Ecology and Conservation Science Lab, Department of Biology, Northern Michigan
University, 49855 Marquette, MI, USA2

Mammalogy Unit, North Carolina Museum of Natural Sciences, 27601 Raleigh, NC, USA

3

School of Biological Sciences, University of Utah, 84112 Salt Lake City, UT, USA

4

Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect St,
06511 New Haven, CT, USA

5Illinois Natural History Survey, University of Illinois, 1816 S. Oak Street, 61820 Champaign,
IL, USA

6

School of Natural Resources, University of Missouri, 302 Anheuser Busch Natural Resources
Building, 65211 Columbia, MO, USA

7

Mianus River Gorge, 167 Mianus River Rd, 10506 Bedford, NY, USA

8

Biology and Chemistry Department, Springfield College, 01109 Springfield, MA, USA

13