ArticlePDF Available

Abstract and Figures

The composition of local mammalian carnivore communities has far-reaching effects on terrestrial ecosystems worldwide. To better understand how carnivore communities are structured, we analysed camera trap data for 108 087 trap days across 12 countries spanning five continents. We estimate local probabilities of co-occurrence among 768 species pairs from the order Carnivora and evaluate how shared ecological traits correlate with probabilities of co-occurrence. Within individual study areas, species pairs co-occurred more frequently than expected at random. Co-occurrence probabilities were greatest for species pairs that shared ecological traits including similar body size, temporal activity pattern and diet. However, co-occurrence decreased as compared to other species pairs when the pair included a large-bodied carnivore. Our results suggest that a combination of shared traits and top-down regulation by large carnivores shape local carnivore communities globally.
Content may be subject to copyright.
LETTER Ecological correlates of the spatial co-occurrence of sympatric
mammalian carnivores worldwide
Courtney L. Davis,
1,2
*
Lindsey N. Rich,
3
Zach J. Farris,
4,5
Marcella J. Kelly,
4
Mario S. Di Bitetti,
6,7,8
Yamil Di Blanco,
6,7
Sebastian Albanesi,
9
Mohammad S. Farhadinia,
10,11
Navid Gholikhani,
12
Sandra Hamel,
13
Bart J. Harmsen,
14,15
Claudia Wultsch,
4,14,16
Mamadou D. Kane,
17
Quinton Martins,
18,19
Asia J. Murphy,
1,2
Robin Steenweg,
20
Sunarto Sunarto,
21
Atieh Taktehrani,
12
Kanchan Thapa,
4,22
Jody M. Tucker,
23
Jesse Whittington,
24
Febri A. Widodo,
21
Nigel G. Yoccoz
13
and
David A.W. Miller
1
Abstract
The composition of local mammalian carnivore communities has far-reaching effects on terrestrial
ecosystems worldwide. To better understand how carnivore communities are structured, we anal-
ysed camera trap data for 108 087 trap days across 12 countries spanning five continents. We esti-
mate local probabilities of co-occurrence among 768 species pairs from the order Carnivora and
evaluate how shared ecological traits correlate with probabilities of co-occurrence. Within individ-
ual study areas, species pairs co-occurred more frequently than expected at random. Co-occur-
rence probabilities were greatest for species pairs that shared ecological traits including similar
body size, temporal activity pattern and diet. However, co-occurrence decreased as compared to
other species pairs when the pair included a large-bodied carnivore. Our results suggest that a
combination of shared traits and top-down regulation by large carnivores shape local carnivore
communities globally.
Keywords
Camera trap, ecological traits, global assessment, interspecific interactions, local community
structure, spatial co-occurrence.
Ecology Letters (2018)
INTRODUCTION
The composition of ecological communities is shaped by
interspecific interactions (Birch 1957; Hardin 1960; Rosen-
zweig 1966). Hutchinson’s (1957) theory of a realised vs. fun-
damental niche was one of the first to evaluate species
interactions and how they may cause an individual to occupy
areas smaller than the area they would reside in the absence
of competition and predation. Since then, area-specific assess-
ments of species interactions have illuminated behavioural
responses such as spatial partitioning between apex and
mesocarnivores (Ritchie & Johnson 2009; Brook et al. 2012),
temporal or spatial partitioning between predators and their
prey (Miller et al. 2012; Davis et al. 2017) or between poten-
tially competing carnivores (Di Bitetti et al. 2009, 2010) and
local extinctions resulting from native species being
1
Department of Ecosystem Science and Management, Pennsylvania State
University, University Park, PA 16802, USA
2
Intercollege Degree Program in Ecology, Pennsylvania State University,
University Park, PA 16802, USA
3
Department of Environmental Science, Policy and Management, University
of California, Berkeley, CA 94720, USA
4
Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA
24060, USA
5
Department of Health and Exercise Science, Appalachian State University,
Boone, NC 28608, USA
6
Instituto de Biolog
ıa Subtropical (IBS) nodo Iguaz
u, Universidad Nacional
de Misiones and CONICET, Bertoni 85, 3370 Puerto Iguaz
u, Misiones,
Argentina
7
Asociaci
on Civil Centro de Investigaciones del Bosque Atl
antico (CeIBA),
Bertoni 85, 3370 Puerto Iguaz
u, Misiones, Argentina
8
Facultad de Ciencias Forestales, Universidad Nacional de Misiones, Bertoni
124, 3380 Eldorado, Misiones, Argentina
9
Fundaci
on ProYungas, Per
u 1180, (4107), Yerba BuenaTucum
an, Argentina
10
Wildlife Conservation Research Unit, Department of Zoology, University of
Oxford, The Recanati-Kaplan Centre, Tubney, Abingdon OX13 5QL, UK
11
Future4Leopards Foundation, No.4, Nour 2, Mahallati, Tehran, Iran
12
Iranian Cheetah Society, PO Box 14155-8549, Tehran, Iran
13
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries
and Economics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
14
Panthera,New York, NY 10018, USA
15
University of Belize, Environmental Research Institute (ERI), Price Centre
Road, PO box 340, Belmopan, Belize
16
Sackler Institute for Comparative Genomics, American Museum of Natural
History, New York, NY 10024, USA
17
Senegalese National Parks Directorate, Dakar, Senegal
18
The Cape Leopard Trust, Cape Town, South Africa
19
Audubon Canyon Ranch, PO Box 1195,Glen Ellen, CA, USA
20
Species at Risk, Resource Management, Alberta Environment and Parks,
Grande Prairie, AB, Canada
21
World Wildlife Fund, Jakarta, Indonesia
22
World Wildlife Fund, Conservation Science Unit, Baluwatar, Nepal
23
U.S. Forest Service, Sequoia National Forest, Porterville, CA 93257, USA
24
Parks Canada, Banff National Park Resource Conservation, Banff, AB,
Canada
*Correspondence: E-mail: cld303@psu.edu
©2018 John Wiley & Sons Ltd/CNRS
Ecology Letters, (2018) doi: 10.1111/ele.13124
outcompeted by exotics (Bailey et al. 2009; Farris et al.
2015a). As such, the concept of interspecific interactions has
been, and is still, a central theme of ecological investigations
(Wisz et al. 2013).
One of the primary ways in which interspecific interactions
are evaluated is by assessing species’ patterns of co-occurrence
(i.e. species asymmetrical spatial distributions; Mackenzie
et al. 2004; Richmond et al. 2010; Waddle et al. 2010). Co-
occurring species often display niche segregation as it serves
to reduce resource competition, promoting coexistence (Brown
& Wilson 1956; Hutchinson 1959; P
eriquet et al. 2015). Niche
segregation should occur when species directly compete for
resources, and competition should be strongest in cases where
species share similar life history traits (Brown & Wilson
1956). Alternatively, if competition and niche segregation are
not the primary drivers of local species distributions, trait sim-
ilarities should lead to greater co-occurrence because of shared
environmental and resource affinities (i.e. habitat or environ-
mental filtering; Van der Valk 1981; Keddy 1992; Weiher &
Keddy 1999; Di
az et al. 1998; Weiher et al. 1998). Attempts
to explain patterns of co-occurrence tend to focus on species’
dietary and habitat preferences because the partitioning of
resources can influence the degree to which competition
occurs (Donadio & Buskirk 2006; Hayward & Kerley 2008;
Yackulic et al. 2014).
Behaviour, morphology, and phylogenetic proximity also
can play pivotal roles in influencing the strength and direction
of interspecific interactions at local scales (Kronfeld-Schor &
Dayan 2003; Donadio & Buskirk 2006; Davies et al. 2007;
Yackulic et al. 2014). Species that exhibit different temporal
activity patterns (e.g. diurnal vs. nocturnal) may be more likely
to co-occur as they have a lower probability of direct competi-
tion compared to species which are active at similar times of
the day (Kronfeld-Schor & Dayan 2003; Hayward & Slotow
2009; Bischof et al. 2014; P
eriquet et al. 2015). Alternatively, a
species’ overarching social structure (i.e. group, pair or soli-
tary) can influence their resource requirements, detectability by
other species and ability to outcompete interspecific competi-
tors (Palomares & Caro 1999; de Oliveira & Pereira 2014). In
turn, social structure could influence the likelihood that species
co-occur. Body size may also influence co-occurrence via com-
petition (e.g. Dayan et al. 1989, 1990; McDonald 2002) or
direct aggression (e.g. Sidorovich et al. 1999) of similar-sized
carnivores (Rosenzweig 1966), as well as top-down pressures
of larger carnivores (Palomares & Caro 1999; Saether 1999;
Terborgh et al. 1999; Elmhagen & Rushton 2007). Lastly, phy-
logenetic proximity may also shape patterns of co-occurrence
because closely related species (e.g. within a family) are often
similar in their resource requirements, thereby leading to
greater competition among closely related taxa (e.g. within,
rather than among, taxonomic groups; Gittleman 1985; Van
Valkenburgh 1989; Donadio & Buskirk 2006).
The influence of interspecific interactions is particularly
widespread within carnivore guilds (Rosenzweig 1966; Palo-
mares & Caro 1999). Elucidating how carnivore interactions
influence patterns of co-occurrence, and the ecological traits
driving these interactions, is key to our understanding of
niche dynamics, interspecies competition, mesopredator
release (Dayan et al. 1989; Estes et al. 1998; Berger et al.
2001) and carnivore population dynamics (Robinson et al.
2014; P
eriquet et al. 2015). Interactions between carnivore
species can also influence human perception and tolerance of
carnivores, thereby affecting human-predator coexistence (e.g.
Farhadinia et al. 2017). Despite the availability of detailed
information on intraguild interactions at the site-specific
levels, we have a poor understanding of global patterns in car-
nivore co-occurrence (Linnell & Strand 2000; Elmhagen &
Rushton 2007; P
eriquet et al. 2015). Improving this under-
standing requires local occurrence data for carnivore commu-
nities across large spatial or temporal scales. Historically,
resource constraints have limited our ability to collect such
data sets for wide-ranging and often elusive species. In the last
decade, however, the exponential increase in the use of camera
trap surveys has opened the door to studying mammalian car-
nivore species in remote areas across the world (Rich et al.
2017; Steenweg et al. 2017). Collaborative research efforts and
the aggregation of data collected across large spatial scales
and international borders allow us to draw conclusions about
patterns of spatial interactions across ecosystems rather than
solely within a single study area, thus providing new and
important insights into the underlying processes of community
structure that are consistent across global scales (Steenweg
et al. 2017).
Our goal was to investigate co-occurrence within the order
Carnivora and determine which ecological traits influence the
spatial distributions of sympatric species (i.e. the overlap or
avoidance of two species in habitat use). To accomplish this
goal, we used a pre-existing dataset (see Rich et al. 2017)
consisting of remote camera trap data from surveys in 13
study areas in 12 countries, which included observations of
86 mammalian carnivore species in four of the five major
biomes worldwide. We approached the analysis as a two-step
process. First, we analysed these data using a pair-wise co-
occurrence estimator to quantify relative co-occurrence of
sympatric species while accounting for imperfect detection
(Mackenzie et al. 2004; Richmond et al. 2010; Waddle et al.
2010). We then used estimates of co-occurrence (i.e. species
interaction factor) to determine how shared ecological traits,
including diet, body size, temporal activity patterns, social
structure and phylogenetic proximity, correlated with co-
occurrence probabilities. We predicted that species pairs with
shared ecological traits (e.g. similar in body size or dietary
preferences) would be more likely to compete for resources,
and hence more likely to display spatial avoidance. Our anal-
ysis provides the first global assessment of carnivore spatial
co-occurrence patterns, exemplifying a framework for other
collaborative, global-scale studies on species interactions.
MATERIAL AND METHODS
Study areas
We used a pre-existing data set consisting of camera trap sur-
vey data (raw data previously published in Rich et al. 2017)
from 13 study areas spanning 12 countries and 5 continents
(Fig. 1). Study area size ranged from 42 to 18 714 km
2
, and
within each study area, between 22 and 319 (
x=143;
SD =85.5) camera stations (Table 1) were deployed. We only
©2018 John Wiley & Sons Ltd/CNRS
2C. L. Davis et al. Letter
included study areas with >1000 trap days, with realised
effort ranging from 1170 to 35 441 (
x=9007; SD =8891) trap
days (Table 1; Appendix S1).
North and Central American study areas included five
national parks in western Canada (Steenweg et al. 2016), the
Sierra Nevada Mountains of California, USA (Tucker et al.
(a) (b) (c) (d) (e) (f)
(g) (h) (i) (j) (k) (l)
Figure 1 Locations of the 13 study areas, which include remote camera trap surveys conducted in 12 countries, spanning 5 continents and 4 of the 5 major
biomes worldwide. Examples of co-occurring species pairs include: (a) Nasua nasua (South American coati) and Eira barabara (tayra) in Argentina, ©M.
Di Bitetti; (b) Puma concolor (puma) and Panthera onca (jaguar) in Belize, ©Belize Jaguar Project/Virginia Tech; (c) Urocyon cinereoargenteus (grey fox)
and Martes pennanti (fisher) in United States, ©J. Tucker/US Forest Service; (d) Ursus arctos (grizzly bear) and Canis lupus (grey wolf) in Canada, ©Park
Canada; (e) Gulo gulo (wolverine) and Vulpes vulpes (red fox) in Norway, ©S. Killengreen; (f) Acinonyx jubatus venaticus (Asiatic cheetah) and Canis lupus
(grey wolf) in Iran, ©Iranian Cheetah Society/CACP/DoE/Panthera; (g) Leptailurus serval (Serval) and Panthera leo (lion) in Senegal, ©M. Kane; (h)
Panthera pardus (leopard) and Vulpes chama (cape fox) in South Africa, ©Q. Martins/Cape Leopard Trust; (i) Proteles cristata (aardwolf) and Otocyon
megalotis (bat-eared fox) in Botswana, ©L. Rich/Panthera; (j) Eupleres goudotii (falanouc) and Cryptoprocta ferox (fosa) in Madagascar, ©Z. Farris; (k)
Hemigalus derbyanus (banded palm civet) and Paradoxurus hermaphroditus (common palm civet) in Sumatra, ©F. Widodo/WWF; (l) Panthera tigris tigris
(Bengal tiger) and Pantherus pardus fusca (Indian leopard) in Nepal, ©K. Thapa/WWF.
©2018 John Wiley & Sons Ltd/CNRS
Letter Global patterns in Carnivora co-occurrence 3
2014) and the Mayan Forest in Belize (Wultsch et al. 2016).
We included two study areas in South America; the first from
northeastern Argentina, in the Atlantic Forest of Misiones
Province (Di Bitetti et al. 2006), and the second in the Yungas
ecoregion in northwestern Argentina (Di Bitetti et al. 2011).
In Africa, we included studies conducted in the Ngamiland
District of northern Botswana (Rich et al. 2016), Niokolo
Koba National Park in Senegal (Kane et al. 2015), Cederberg
mountains of South Africa (Martins 2010) and Madagascar’s
Masoala-Makira protected area (Farris et al. 2015b). In Asia,
we included the southern Riau landscape of central Sumatra
in Indonesia (Sunarto et al. 2015), the Chitwan National Park
in Nepal (Thapa & Kelly 2017) and several reserves across
central Iran (Farhadinia et al. 2014). We also included a sin-
gle European study area, located in northern Norway (Hamel
et al. 2013; Henden et al. 2014).
Quantifying co-occurrence
We estimated co-occurrence probabilities for 768 species
pairs. The number of species pairs ranged from 3 in Norway
to 210 in Botswana (Table 1; see Appendix S2 for species
list). In estimating patterns of co-occurrence, we were inter-
ested in determining whether species occurred at a site more
or less often than expected under a hypothesis of indepen-
dence (Mackenzie et al. 2004). Deviations from independence
occur when distributions are non-random with respect to
each other. To quantify these deviations, we used two-species
occupancy models (Mackenzie et al. 2004; Richmond et al.
2010) to understand pair-wise carnivore co-occurrence pat-
terns within each study area. This method allowed us to
account for imperfect detection at a camera station (i.e.
when a species is present but not photographed) by treating
each trap day (i.e. 24-h. period) as a repeat survey (Dorazio
& Royle 2005; Rich et al. 2016). These detection/non-detec-
tion data allowed us to estimate occupancy and detection
probabilities for every combination of co-occurring species.
Species detected on <3 occasions were not included in our
analyses.
We fit models using a Bayesian formulation of the single-
season two-species model parameterisation presented by
Richmond et al. (2010). This parameterisation estimates con-
ditional probabilities for both occupancy and detection (e.g.
the probability species B is present given species A is present
or absent and vice versa) and improves model convergence
(Richmond et al. 2010). We were able to derive unconditional
probabilities of species occupancy and detection from the con-
ditional probabilities. We did not investigate covariate rela-
tionships on occupancy or detection, which allowed us to
avoid classifying dominant/subordinate relationships between
co-occurring species (Richmond et al. 2010).
We modelled latent occurrence of species A and B at
camera station jas Bernoulli random variables,
zA;jBernoulliðWAÞand
zB;jBernoulliðWBa ð1WAÞþWBA WAÞ;
where w
A
was the probability species A occurred in the study
area, w
BA
was the occupancy probability of species B given
species A was present, and w
Ba
was the occupancy probability
of species B given species A was absent.w
B
(occupancy of
species B) was a derived quantity, given by w
A
9w
BA
+(1 -
w
A
)9w
Ba
. We estimated the probability of observing species
A and species B at camera station jas:
yA;jBernoulliðzA;jpAÞand
yB;jBernoulliðzB;jpBÞ;respectively:
The probability of detecting either species A or B at camera
station jduring a trapping session was a function of the prob-
ability of detecting the species (p
A
and p
B
) given it occurs at
site j(z
A,j
and z
B,j
). Our study focuses on the spatial rather
than temporal co-occurrence between species pairs. In other
words, y
A,j
and y
B,j
are the number of 24-h. time periods dur-
ing which the respective species was photographed at site j
and were modelled using a Binomial (rather than a Bernoulli)
Table 1 List of camera trap surveys included in our analysis and corresponding reference, detailing the number of camera stations, number of trap days,
study area size, number of carnivore species detected and number of detected species pairs in each study area location
Study area
No. Camera
stations
No. Trap
days
Footprint
(km
2
)
No. Species
detected
1
No. Species
pairs References
Argentina
Misiones 103 5104 1547 10 45 Di Bitetti et al. (2006)
Yungas 46 1258 376 8 28 Di Bitetti et al. (2011)
Belize 213 12 437 1030 11 55 Wultsch et al. (2016)
Botswana 179 5345 1154 21 210 Rich et al. (2016)
Canada 167 35 441 14 628 12 66 Steenweg et al. (2016)
Iran 220 12 768 4749 10 45 Farhadinia et al. (2014)
Madagascar 151 8795 42 6 15 Farris et al. (2015b)
Nepal 78 1170 509 7 21 Thapa & Kelly (2017)
Norway 66 1832 18 714 3 3 Hamel et al. (2013), Henden et al.
(2014)
Senegal 58 3721 525 13 78 Kane et al. (2015)
South Africa 22 5077 2476 14 91 Martins (2010)
Sumatra 92 8009 524 12 66 Sunarto et al. (2015)
United
States
319 7130 8453 10 45 Tucker et al. (2014)
1
The number of species detected per study area that were included in our analysis, which does not include species with <3 detections.
©2018 John Wiley & Sons Ltd/CNRS
4C. L. Davis et al. Letter
distribution to speed up computation (K
ery 2010). This means
we evaluated whether species occurred at the same site, but
not necessarily at the same time. Co-occurrence here can be
defined as overlap in the use of sites between species.
We then derived the species interaction factor (SIF; Rich-
mond et al. 2010), or probability of co-occurrence, for each
species pair as
SIF ¼WAWBA=ðWAðWAWBA þð1WAÞWBaÞÞ
When the occurrence of one species is independent of the
other, SIF =1.0. When two species co-occur more frequently
than would be expected under a hypothesis of independence,
SIF and its credible interval will be >1.0. When species occur
less often than expected, SIF and its credible interval will be
<1.0. Note that SIF values <1.0 indicate potential spatial
avoidance, which may result from a habitat-mediated relation-
ship or changes in the behaviour of one or both species. We
were not able to disentangle these two mechanisms using our
sampling framework. Instead, we use the term ‘spatial avoid-
ance’ in situations where SIF and 95% credible intervals are
<1.0 to indicate that two species simply do not co-occur at
the spatial scale we examine.
We estimated posterior distributions in R (R Core Develop-
ment Team 2016) using the package R2Jags (Plummer 2011)
to call JAGS (version 4.2.0). Estimates were generated from 3
chains of 50 000 iterations after a burn-in of 10 000 iterations.
We drew uninformative priors from a uniform distribution of
0 to 1 for all parameters. We assumed model convergence
when values of the Gelman-Rubin statistic were <1.1 (Gelman
et al. 2004). R code is provided in Appendix S3.
Drivers of global co-occurrence
Using our pair-wise SIF estimates, we investigated global car-
nivore co-occurrence patterns to determine which ecological
traits explain the spatial distributions of sympatric carnivores
worldwide. We log-transformed mean SIF estimates for all
species pairs. The log-transformed values provided a more
symmetrical distribution and standardised deviations for cases
where interactions were more and less likely (SIF is con-
strained between 0 and , while ln[SIF] can range from
to ). We then fit regression models to ln[SIF] that tested
hypotheses about the drivers of mammalian carnivore co-
occurrence.
Our primary prediction was that similarity of species pairs
would affect the amount of spatial overlap. To assess similar-
ity, we included information on species’ temporal activity pat-
terns, dietary habits, social structure, body size (both
categorically and as a ratio), taxonomy at the family level (as
a proxy for relatedness) and information on the study areas’
species diversity and climate. Categorisation of species’ tempo-
ral activity pattern, dietary habits, social structure and body
size was based on a review of peer-reviewed literature, IUCN
red-list species accounts (IUCN 2016, Appendix S2), and
when necessary, expert knowledge of principal investigators
from individual study areas. We assigned the temporal activity
pattern for a species to be diurnal, nocturnal, crepuscular (i.e.
active primarily at twilight) or cathemeral (i.e. irregularly
active at any time of day or night). We designated the dietary
habit for each species as strict carnivore, omnivore or insecti-
vore. We used a species’ tendency to exhibit grouping (i.e. >2
individuals of the same species) vs. pairing and solitary beha-
viour to assign social structure. To account for body size, we
characterised the mean weight ratio (heavier:lighter species)
between two species, including it as a log-transformed contin-
uous variable. We used the mean weight to accommodate for
body size differences between sexes of the same species. We
also assigned species to a body size group, with species cate-
gorised as small (<2 kg), smallmedium (25 kg), medium
large (515 kg) or large (>15 kg) body size. Our categorical
and continuous variables characterising body size were never
included in the same model. Finally, to account for related-
ness, we categorised all Carnivora species included in this
study based on their taxonomy at the family level. Our data
included representative species of the families Canidae, Feli-
dae, Herpestidae, Mustelidae, Procyonidae and Viverridae.
Families represented by few species (Ursidae, Mephitidae,
Eupleridae and Hyaenidae) were grouped together into a sin-
gle category and classified as ‘Other’.
We summarised the categorical variables of diet, temporal
activity pattern, social structure, body size and taxonomic
similarity in two ways. For the first coarse comparison
method, we compared species with differing trait values (e.g.
when species A |B are strict carnivore |omnivore) to those
where pairs shared the trait value (e.g. strict carnivore |strict
carnivore). In other words, species pairs were either labelled
the ‘same’ or ‘different’ for all categorical variables of interest.
For the fine-scale trait comparison, species pairs were categor-
ically valued for all combinations of a trait (e.g. strict carni-
vore |strict carnivore =1, strict carnivore |omnivore =2,
strict carnivore |insectivore =3, etc.). By allowing for both
types of comparisons, we were able to explore whether co-
occurrence was driven primarily by coarse- (same vs. differ-
ent) or fine-scale trait combinations. Lastly, to account for
differences among study areas, we included covariates repre-
senting species diversity and climate. Specifically, we included
fixed effects for the observed number of carnivore species in
each study area and the study area’s climate as determined by
the K
oppen-Geiger climate classification system (Kottek et al.
2006). Study areas were categorised into equatorial (n=4),
arid (n=3), warm temperature (n=4), snow (n=1) or
polar (n=1) regions.
We had no a priori hypothesis on the combined influence of
these variables, so we explored 864 model combinations. We
estimated posterior distributions using R2Jags to call JAGS in
R. Estimates were generated from 3 chains of 20 000 itera-
tions after a burn-in of 5000 iterations (Appendix S3). We
drew uninformative priors from a uniform distribution of 0 to
1 for all parameters. We used Deviance Information Criterion
(DIC; Speigelhalter et al. 2002) to select among our compet-
ing models and present the subset of competitive models (i.e.
DDIC <5) in Appendix S4. Models were considered equivalent
with DDIC <2 and according to the parsimony principle, we
chose the best model as the model with the lowest number of
parameters (Burnham & Anderson 2002; Speigelhalter et al.
2002). We report coefficient estimates from this model in
Appendix S5 and used these estimates to predict SIF (
d
SIF)
and 95% credible intervals (CI) for all species pairs.
©2018 John Wiley & Sons Ltd/CNRS
Letter Global patterns in Carnivora co-occurrence 5
We determined how sensitive our results were to changes in
how the data were analysed and which data were included in
our analysis. First, we compared our results to a Bayesian
weighted regression approach to determine how parameter
estimates for the three variables included in all competing
models (i.e. body size, temporal activity pattern and diet cate-
gory) were affected by the estimated uncertainty in mean SIF
for each species pair. In addition, we assessed whether data-
rich study areas, Botswana and South Africa, may have been
driving the observed relationships for our global analysis. A
full description of the methods for these sensitivity analyses
can be found in Appendix S7.
RESULTS
Quantifying co-occurrence
The mean SIF value across all 768 species pairs was ^
x[95%
CI] =1.24[1.19, 1.28], indicating that on average, species were
more likely to co-occur than expected under a hypothesis of
independence. Nonetheless, SIF variation was large within
study areas, with some species pairs showing strong overlap in
site use and others strong avoidance (Fig. 2). The largest SIF
(4.84[2.25, 7.97]) was estimated for Galerella sanguinea
R
uppell (slender mongoose) and Cynictis penicillata (G.
[Baron] Cuvier) (yellow mongoose) in Botswana. Other species
pairs exhibiting large spatial overlap included: Vulpes ruepellii
Schinz (R
uppel’s fox) and Vulpes cana Blanford (Blanford’s
fox) in Iran (4.25[2.22, 6.77]), and Paradoxurus hermaphroditus
(Pallas) (common palm civet) and Hemigalus derbyanus (Gray)
(banded civet) in Sumatra (3.60[1.53, 6.63]). Strong overlap in
site use (mean SIF and 95% CI >1) was found for 91 species
pairs (Fig. 2).
Spatial avoidance (mean SIF and 95% CI <1) was found
for only 13 of our 768 species pairs (Fig. 2). Examples
include: Panthera pardus saxicolor Pocock (Persian leopard)
and Hyaena hyaena (L.) (striped hyena) in Iran (0.24[0.01;
0.83]), Panthera leo (L.) (lion) and Ictonyx striatus (Perry)
(striped polecat) in Botswana (0.39[0.05, 0.94]) and Urocyon
cinereoargenteus (Schreber) (grey fox) and Conepatus semis-
triatus (Boddaert) (striped hog-nosed skunk) in Belize (0.67
[0.39, 0.97]). The smallest SIF of 0.16[0.00, 0.53] was esti-
mated for Lycalopex gymnocercus G. Fischer (pampas fox)
and Eira Barbara (L.) (tayra) from the Yungas study area in
Argentina. We provide species-specific estimates of mean
occupancy and detection probabilities in Appendix S6.
Drivers of global co-occurrence
The predominant drivers of species’ co-occurrence were body
size, temporal activity pattern and diet category, being the three
variables included in all competing models (Appendix S4). With
the exception of large species, species pairs with similar body
sizes occurred at the same sites more often than expected under
independence (Fig. 3a). For species pairs categorised as differ-
ent in size, small-sized species occurred more often than
expected at the same sites as small-medium species (Fig. 3a).
Species pairs that included a large-bodied carnivore exhibited
depressed SIF values relative to all other pairs and to the global
mean, yet still indicated an overall independence (SIF =1.0) in
co-occurrence because credible intervals overlapped 1.0
(Fig. 3a). This pattern is particularly evident in large |large spe-
cies pairs, where the mean predicted value was the only body
size grouping with SIF <1.0, indicating that species of large
body size tend to spatially avoid one another, though the 95%
CI for this estimate slightly overlapped 1.0.
With respect to diet, same diet species pairs co-occurred at
the same sites more frequently than expected for strict carni-
vores and particularly for insectivores, but not for omnivores
(Fig. 3b). Species pairs with differing diets showed indepen-
dent occurrence in general, with the exception of strict carni-
vore |omnivore species pairs that showed overlap in site use
(Fig. 3b). Contrary to expectations, carnivores with similar
temporal activity patterns co-occurred disproportionately
more often than pairs that differed in temporal activity pat-
tern (Fig. 3c). Within species pairs with similar temporal
activity patterns, cathemeral, crepuscular and diurnal species
showed the greatest overlap in site use. Species pairs with dif-
ferent temporal activity patterns showed an overall indepen-
dence in spatial site occurrence (SIF =1.0), with the exception
of crepuscular |cathemeral species pairs showing a slight spa-
tial overlap (Fig. 3c).
We found that parameter estimates were comparable (i.e.
overlapping 95% CI) between the unweighted and weighted
regression approaches, suggesting that our results were not sen-
sitive to uncertainties in mean SIF (Appendix S7). Our results
were also robust to whether data-rich study areas, Botswana
and South Africa, were included in the analysis (Appendix S7).
DISCUSSION
The composition and structure of mammalian carnivore com-
munities have far-reaching effects on the structure and func-
tion of terrestrial ecosystems (Roemer et al. 2009; Estes et al.
2011; Ripple et al. 2014). Local community structure depends
on a combination of species-specific environmental affinities
(i.e. habitat preferences or selection) and interactions among
species (Leibold et al. 2004). Our study provides important
insights into the role of each in shaping local carnivore com-
munities across ecosystems. Overall, mammalian carnivores
tended to overlap spatially, but there was wide heterogeneity
across species pairs with some showing large spatial overlap
and others showing large spatial avoidance. Specifically, we
found that body size, temporal activity patterns and dietary
habits were related to co-occurrence patterns, where species
that shared similar ecological traits generally had greater over-
lap in site use. These results suggest that at the spatial scale of
our study, shared ecological traits are not leading to competi-
tive exclusion, but are rather causing species to select sites
where resource availability is likely similar, and thus tend to
co-occur. The overall trend of spatial overlap may be attribu-
ted to our coarse-scale analyses as we were unable to account
for fine-scale differences in species’ spatial and temporal activ-
ity patterns. Regardless, our study provides an important first
step in understanding the drivers of carnivore co-occurrence
at a global scale, and a foundation from which future studies
interested in more fine-scale assessments of species-pair rela-
tionships can build.
©2018 John Wiley & Sons Ltd/CNRS
6C. L. Davis et al. Letter
Large carnivores reduce the abundance of co-occurring spe-
cies through direct predation (Hakkarainen & Korpimaki
1996; Salo et al. 2008; Krauze-Gryz et al. 2012) and incite
changes in behaviour (Creel et al. 2001; Ritchie & Johnson
2009) and resource use (P
eriquet et al. 2015), thus shifting the
role that smaller carnivores play in ecological communities
(Bischof et al. 2014; de Oliveira & Pereira 2014). Large
carnivores can also promote the abundance of small species
by reducing the abundance of mesopredators (Estes et al.
1998; Ripple et al. 2014). The cascading effects of mesopreda-
tor release have been documented in a variety of systems and
taxa worldwide (Terborgh et al. 1999, 2001; Brashares et al.
2010), often resulting in the widespread loss of biodiversity
and ecosystem collapse (Estes et al. 1998; Berger et al. 2001;
Figure 2 Estimated carnivore co-occurrence (SIF) for all 768 sympatric species pairs across our 13 study areas (and 95% CI). The red line indicates
independently occurring species (SIF =1) and the blue dotted line represents the estimated mean for each study area, with the light blue zone representing
the 95% CI. SIF values >1 indicate co-occurrence between two species, while SIF values <1 indicate lack of co-occurrence.
©2018 John Wiley & Sons Ltd/CNRS
Letter Global patterns in Carnivora co-occurrence 7
Ripple & Beschta 2006; Prugh et al. 2009). While our results
do not suggest spatial avoidance among species of any body
size, depressed SIF values in pairs that include a large or med-
ium-large species supports the notion that large-bodied carni-
vores influence local community structure, particularly
through the effects of large-bodied species on one another
(Palomares & Caro 1999; Saether 1999; Terborgh et al. 1999,
2001; Elmhagen & Rushton 2007; Ripple et al. 2014; Swanson
et al. 2016).
Species that overlap in space may reduce competition by
exhibiting different activity patterns (e.g. diurnal vs. noctur-
nal; Kronfeld-Schor & Dayan 2003; Hayward & Slotow 2009;
Di Bitetti et al. 2009, 2010; Bischof et al. 2014; P
eriquet et al.
2015). In our study, however, species sharing similar temporal
activity patterns showed the strongest overlap in site use. For
example, Puma yagouaroundi (
E. Geoffroy Saint-Hilaire)
(jaguarundi) and Nasua nasua (L.) (South American coati) in
Argentina are both medium-large, diurnal species that show
strong co-occurrence but diverge in dietary habits (Appendix S2).
Spatial coexistence also may be maintained through differentia-
tion on another niche axis (e.g. vertical habitat partitioning,
resource partitioning in prey size) or through fine-scale parti-
tioning of temporal activity (i.e. differences in the time of peak
activity; Farris et al. 2015c; Hayward & Slotow 2009; Sunarto
et al. 2015). Again, there is wide heterogeneity in spatial over-
lap between species pairs within study areas, and similar species
that show strong spatial overlap might also display temporal
avoidance.
Differences in dietary niche breadths among species also
influence the degree to which competition occurs (e.g. Hayward
& Kerley 2008). We report high spatial overlap between species
categorised as insectivores. In this case, heterogeneity in the
Figure 3 Predicted SIF and 95% CI) for each species trait combination of (a) body size, (b) diet, and (c) temporal activity pattern across the 13 study
areas.
©2018 John Wiley & Sons Ltd/CNRS
8C. L. Davis et al. Letter
spatial and temporal availability of insect prey (e.g. termites)
likely induces co-occurrence despite the strong overlap in diet-
ary preferences (Pringle et al. 2010). In addition, species that
both scavenge and actively hunt can exploit an ephemeral but
consistent resource, thus reducing the reliance on a particular
prey source during times of low prey abundance, unfavourable
environmental conditions, or high competition (Devault et al.
2003; Selva & Fortuna 2007). Scavenging carnivores, such as
Crocuta crocuta (Erxleben) (spotted hyena), Panthera leo (lion)
and Panthera pardus (L.) (common leopard) in Botswana and
Senegal occurred independently of one another, despite a high
degree of overlap in dietary habit, temporal activity pattern and
body size (Appendix S2). Previous studies have indicated that
prey-switching (H
oner et al. 2002) and changes in the scaveng-
ing behaviour (e.g. increased consumption of unfavourable ele-
ments, such as bones) of C. crocuta may alleviate strong
competition with P. leo during times of low prey abundance
(Kruuk 1972; P
eriquet et al. 2015). Coexistence can also be
facilitated between species of similar dietary preferences
through partitioned selection (e.g. by prey age or size) and use
of food resources. For example, Panthera onca (L.) (jaguar) and
Puma concolor (L.) (puma) in Belize partition their diet by prey-
ing on different species according to body plan. Panthera onca,
as a stronger predator, prefers the slower but armoured Dasy-
pus novemcinctus L. (nine banded armadillo), while the faster
and more agile P. concolor preys on the more vulnerable but
fast Cuniculus paca (L.) (paca; Foster et al. 2010).
Spatial avoidance can occur at various scales, but it can be
challenging to differentiate microhabitat vs. macrohabitat
resource partitioning (Bischof et al. 2014). When using data
from remote camera trap surveys, assessments of co-occur-
rence patterns are often limited to the macrohabitat scale.
Carnivore movements and responses to other carnivore spe-
cies may occur at a finer spatio-temporal scale than we
assessed in our analysis (Swanson et al. 2016; Dr
oge et al.
2017). Furthermore, factors such as ecosystem productivity,
topographic features (e.g. mountains vs. open terrains), habi-
tat patch size and quality (e.g. protected vs. degraded or frag-
mented), vegetation structure (e.g. grassland vs. rainforest),
carnivore densities and resource availability (e.g. prey densi-
ties, water), or type of camera trap site (e.g. random vs. trails,
baited vs. not baited), which we did not account for, also play
an important role in determining carnivore co-occurrence at
the landscape level (Elmhagen & Rushton 2007; Hoeinghaus
et al. 2007; Bischof et al. 2014; Peoples & Frimpong 2015;
P
eriquet et al. 2015; Hernandez-Santin et al. 2016).
Our analyses examined one key axis of co-occurrence, spatial
overlap, but did not allow us to differentiate between a spe-
cies’ presence and the explicit use of resources. Additionally,
while we assigned species according to their general temporal
activity patterns, dietary habits and social structure, this cate-
gorisation may not adequately capture a species’ behaviour at
a particular study area or trap location. Temporal activity pat-
terns, for example, are fluid and can be altered according to
resource availability (Loveridge & Macdonald 2002; Hernan-
dez-Santin et al. 2016), changes in the abundance or behaviour
of co-occurring species (Creel et al. 2001; Ritchie & Johnson
2009) and human activity (e.g. McVittie 1979; Kitchen et al.
2000). Similarly, diet is closely tied to the availability of
resources at the local level (H
oner et al. 2002; Ramesh et al.
2012). Co-occurrence at higher order interactions (i.e. among
3+species) may also differ from the pair-wise interactions we
examined, but computing SIF values based on multi-species
occupancy modelling remains challenging. Nevertheless, our
examination of pair-wise interactions allowed us to explicitly
test, at the course spatial scale we examined, whether shared
ecological traits affected co-occurrence by increasing competi-
tion due to niche overlap or by similar species sharing similar
environmental and resource affinities.
Our study is the first global-scale assessment of species co-
occurrence patterns and provides novel insights into the
macro-ecological processes that influence the spatial distribu-
tions of sympatric mammalian carnivores worldwide. We
demonstrated that species with similar ecological traits were
often more likely to overlap spatially, suggesting that shared
habitat affinities may influence occurrence patterns at coarse
spatial scales. Therefore, competition and niche segregation
were not the primary drivers of local species occurrence,
though these patterns may change when considering the fine-
scale differences in species’ spatial and temporal activity
patterns that we were unable to account for. We found a dif-
ferent pattern with respect to body size, with species tending
to have a lower co-occurrence when paired with a larger car-
nivore. These results suggest that top-down processes may
also be important in structuring carnivore communities.
Moreover, the methods we employed highlight the utility of
remote camera trap survey data to non-invasively study inter-
actions among elusive species and offer a starting point for
other collaborative, global-scale assessments (Butchart et al.
2010; Rich et al. 2017; Steenweg et al. 2017).
ACKNOWLEDGEMENTS
We thank the Ministry of the Environment, Wildlife and Tour-
ism, the Department of Wildlife and National Parks, and the
Botswana Predator Conservation Trust for permission to con-
duct the study in Botswana; the Ministry of Environment,
Water, Forest and Tourism and Wildlife Conservation Society
in Madagascar; the Department of National Parks and United
States Agency for International Development/Wula Nafaa
Project in Senegal; and The Cederberg Conservancy and Cape-
Nature in South Africa for permission and/or supporting the
research in Africa. We thank Parks Canada staff and volun-
teers for collecting data in Canada, the US Forest Service for
financing and collecting data in the USA along with volunteers
from the Student Conservation Association, and the Belize
Forest Department, Belize Audubon Society, Programme for
Belize, Las Cuevas Research Station, Bull Run Farm, Gallon
Jug Estate, and Yalbac Ranch and Cattle Company for per-
mission and support in conducting research in Belize. Funding
for camera trap surveys in Canada was provided in part by
NSF LTREB Grant 1556248. We thank the Ministry of Ecol-
ogy and Natural Resources of Misiones, the National Park
Administration of Argentina, Ledesma S.A. and Arauco SA
for permissions and support to conduct camera trap surveys.
We thank the Iran Department of Environment for permission
to work within the reserves in Iran, Department of National
Parks and Wildlife Conservation in Nepal for permission to
©2018 John Wiley & Sons Ltd/CNRS
Letter Global patterns in Carnivora co-occurrence 9
conduct surveys in Chitwan National Park, and in Indonesia,
WWF Networks, US Fish & Wildlife Service and the Hurvis
Family for financially supporting the research, the Indonesian
Ministry of Forestry for permission to conduct the study, and
the WWF Team for their support. We also thank the Direc-
torate for Nature Management and The Norwegian Research
Council for financing camera trap surveys in Norway.
DATA AVAILABILITY
Data associated with this paper have been deposited in
Dryad, https://doi.org/10.5061/dryad.7f9j57n
AUTHORSHIP
CLD and DAWM analysed the data and prepared the manu-
script; LNR and ZJF initiated this global effort and coordi-
nated the consolidation and management of data from all
study areas; study design and data collection was performed
by: MSD, YD and SA in Argentina; MJK, CW and BJH in
Belize; JMT in United States; JW and RS in Canada; SH and
NGY in Norway; MSF, NG and AT in Iran; KT and MJK in
Nepal; SS, FAW and MJK in Sumatra; ZJF, AJM and MJK
in Madagascar; QM in South Africa; LNR, DAWM and MJK
in Botswana, and MDK and MJK in Senegal. All authors con-
tributed input into the design and interpretation of the analysis
and contributed to writing the final manuscript.
DATA ACCESSIBILITY STATEMENT
Data available from the Dryad Digital Repository: http://doi.
org/10.5061/dryad.7f9j57n
REFERENCES
Bailey, L., Reid, J.A., Forsman, E.D. & Nichols, J.D. (2009).
Modeling co-occurrence of northern spotted and barred owls:
accounting for detection probability differences. Biol. Cons., 142,
29832989.
Berger, J., Stacey, P.B., Bellis, L. & Johnson, M. (2001). A mammalian
predator-prey imbalance: grizzly bear and wolf extinction affect avian
Neotropical migrants. Ecol. Appl., 11, 947960.
Birch, L.C. (1957). The meanings of competition. Am. Nat., 91, 518.
Bischof, R., Ali, H., Kabir, M., Hameed, S. & Nawaz, M.A. (2014).
Being the underdog; an elusive small carnivore uses space with prey
and time without enemies. J. Zool., 293, 4048.
Brashares, J.S., Epps, C.W. & Stoner, C.J. (2010). Ecological and
Conservation Implications of Mesopredator Release. Island Press, In J.
Terborgh & J. Estes. Trophic Cascades.
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, 12781286.
Brown, W.L. & Wilson, E.O. (1956). Character displacement. Syst. Zool.,
5, 4964.
Burnham, K.P. & Anderson, D.R. (2002). Model Selection and
Multimodel Inference: A Practical Information-Theoretic Approach.
Springer Science & Business Media, New York, NY.
Butchart, S.H.M., Walpole, M., Collen, B., van Strien, A., Scharlemann,
J.P.W., Rosamunde, E.A.A. et al. (2010). Global biodiversity:
indicators of recent declines. Science, 328, 11641168.
Creel, S., Spong, G. & Creel, N. (2001). Interspecific competition and the
population biology of extinction-prone carnivores. In Carnivore
Conservation (eds Gittleman, J.L., Funk, S.M., Macdonald, D.W.,
Wayne, R.K.). Cambridge University Press, Cambridge, pp. 3560.
Davies, T.J., Meiri, S., Barraclough, T.G. & Gittleman, J.L. (2007).
Species co-existence and character divergence across carnivores. Ecol.
Lett., 10, 146152.
Davis, C.L., Miller, D.A.W., Walls, S.C., Barichivich, W.J., Riley, J.W. &
Brown, M.E. (2017). Species interactions and the effects of climate
variability on a wetland amphibian metacommunity. Ecol. Appl., 27,
285296.
Dayan, T., Simberloff, D., Tchernov, E. & Yom-Tov, Y. (1989). Inter-
and intraspecific character displacement in mustelids. Ecology, 70,
15261539.
Dayan, T., Simberloff, D., Tchernov, E. & Yom-Tov, Y. (1990). Feline
canines: community-wide character displacement in the small cats of
Israel. Am. Nat., 136, 3960.
Devault, T.L., Rhodes, O.E. Jr & Shivik, J.A. (2003). Scavenging by
vertebrates: behavioral, ecological, and evolutionary persepectives on
an important energy transfer pathway in terrestrial ecosystems. Oikos,
102, 225234.
Di Bitetti, M.S., Paviolo, A. & De Angelo, C. (2006). Density, habitat use
and activity patterns of ocelots (Leopardus pardalis) in the Atlantic
Forest of Misiones. Argentina. J. Zool., 270, 153163.
Di Bitetti, M.S., Di Blanco, Y.E., Pereira, J.A., Paviolo, A. & Jim
enez
P
erez, I. (2009). Time partitioning favors the coexistence of sympatric
crab-eating foxes (Cerdocyon thous) and pampas foxes (Lycalopex
gymnocercus). J. Mammal., 90, 479490.
Di Bitetti, M.S., De Angelo, C.D., Di Blanco, Y.E. & Paviolo, A. (2010).
Niche partitioning and species coexistence in a Neotropical felid
assemblage. Acta Oecol., 36, 403412.
Di Bitetti, M.S., Albanesi, S., Foguet, M.J., Cuyckens, G.A.E. & Brown,
A. (2011). The Yungas biosphere reserve of argentina: a hot spot of
South American wild cats. CAT News, 54, 2529.
Di
az, S., Cabido, M. & Casanoves, F. (1998). Plant functional traits and
environmental filters at a regional scale. J. Veg. Sci., 9, 113122.
Donadio, E. & Buskirk, S.W. (2006). Diet, morphology, and interspecific
killing in Carnivora. Am. Nat., 167, 524536.
Dorazio, R.M. & Royle, J.A. (2005). Estimating size and composition of
biological communities by modeling the occurrence of species. J. Am.
Stat. Assoc., 100, 389398.
Dr
oge, E., Creel, S., Becker, M.S. & M’Soka, J. (2017). Spatial and
temporal avoidance of risk within a large carnivore guild. Eco. Evol.,7,
189199.
Elmhagen, B. & Rushton, S.P. (2007). Trophic control of mesopredators in
terrestrial ecosystems: top-down or bottom-up? Ecol. Lett., 10, 197206.
Estes, J.A., Tinker, M.T., Williams, T.M. & Doak, D.F. (1998). Killer
whale predation on sea otters linking oceanic and nearshore
ecosystems. Science, 282, 473476.
Estes, J.A., Terborgh, J., Brashares, J.S., Power, M.E., Berger, J., Bond, W.J.
et al. (2011). Trophic downgrading of planet Earth. Science,333,301306.
Farhadinia, M.S., Eslami, M., Hobeali, K., Hosseini-Zavarei, F.,
Gholikhani, N. & Taktehrani, A. (2014). Status of Asiatic cheetah in
Iran: a country-scale assessment. Project Final Report, Iranian Cheetah
Society (ICS), Tehran.
Farhadinia, M.S., Johnson, P.J., Hunter, L.T. & Macdonald, D.W.
(2017). Wolves can suppress goodwill for leopards: patterns of human-
predator coexistence in northeastern Iran. Biol. Cons., 213, 210217.
Farris, Z.J., Kelly, M.J., Karpanty, S. & Ratelolahy, F. (2015a). Patterns
of spatial co-occurrence among native and exotic carnivores in north-
eastern Madagascar. Anim. Cons., 19, 189198.
Farris, Z.J., Golden, C.D., Karpanty, S., Murphy, A., Stauffer, D.,
Ratelolahy, F. et al. (2015b). Hunting, exotic carnivores, and habitat
loss: anthropogenic effects on a native carnivore community,
Madagascar. PLoS ONE, 10, e0136456.
Farris, Z.J., Gerner, B.D., Karpanty, S., Murphy, A., Andrianjakarivelo,
V., Ratelolahy, F. et al. (2015c). When carnivores roam: temporal
patterns and overlap among Madagascar’s native and exotic carnivores.
J. Zool., 296, 4557.
©2018 John Wiley & Sons Ltd/CNRS
10 C. L. Davis et al. Letter
Foster, R.J., Harmsen, B.J., Valdes, B., Pomilla, C. & Doncaster, C.P.
(2010). Food habits of sympatric jaguars and pumas across a gradient
of human disturbance. J. Zool., 280, 309318.
Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2004). Bayesian
Data Analysis. Chapman and Hall, Boca Raton, FL.
Gittleman, J.L. (1985). Carnivore body size: ecological and taxonomic
correlates. Oecologia, 67, 540554.
Hakkarainen, H. & Korpimaki, E. (1996). Competitive and predatory
interactions among raptors: an observational and experimental study.
Ecology, 77, 11341142.
Hamel, S., Killengreen, S.T., Henden, J.A., Yoccoz, N.G. & Ims, R.A.
(2013). Disentangling the importance of interspecific competition, food
availability, and habitat in species occupancy: recolonization of the
endangered Fennoscandian arctic fox. Biol. Cons., 160, 114120.
Hardin, G. (1960). The competitive exclusion principle. Science, 29, 1292
1297.
Hayward, M.W. & Kerley, G.I.H. (2008). Prey preferences and dietary
overlap amongst Africa’s large predators. South African J. Wild. Res.,
38, 93108.
Hayward, M.W. & Slotow, R. (2009). Temporal partitioning of activity in
large African carnivores: tests of multiple hypotheses. South African J.
Wild. Res., 39, 109125.
Henden, J.A., Stien, A., B
ardsen, B.J., Yoccoz, N.G. & Ims, R.A. (2014).
Community-wide mesocarnivore response to partial ungulate migration.
J. Appl. Ecol., 51, 15251533.
Hernandez-Santin, L., Goldizen, A.W. & Fisher, D.O. (2016). Introduced
predators and habitat structure influence range contraction of an
endangered native predator, the northern quoll. Biol. Cons., 203, 160
167.
Hoeinghaus, D.J., Winemiller, K.O. & Birnbaum, J.S. (2007). Local and
regional determinants of stream fish assemblage structure: inferences
based on taxonomic vs. functional groups. J. Biogeogr., 34, 324338.
H
oner, O.P., Wachter, B., East, M.L. & Hofer, H. (2002). The response of
spotted hyaenas to long-term changes in prey populations: functional
response and interspecific kleptoparasitism. J. Anim. Ecol., 71, 236246.
Hutchinson, G.E. (1957). Concluding remarks. Cold Spring Harbor
Symp., 22, 415417.
Hutchinson, G.E. (1959). Homage to Santa Rosalia or why are there so
many kinds of animals?. Am. Nat., 92, 145159.
IUCN (2016). The IUCN Red List of Threatened Species. Version 2016-3.
Available at: http://www.iucnredlist.org. Last accessed August 31, 2017.
Kane, M.D., Morin, D.J. & Kelly, M.J. (2015). Potential for camera-
traps and spatial mark-resight models to improve monitoring of the
critically endangered West African lion (Panthera leo). Biodivers.
Conserv., 24, 35273541.
Keddy, P.A. (1992). Assembly and response rules2 goals for predictive
community ecology. J. Veg. Sci., 3, 157164.
K
ery, M. (2010). Introduction to WinBUGS for Ecologists: Bayesian
approach to regression, ANOVA, mixed models and related analyses.
Academic Press, Burlington, MA.
Kitchen, A.M., Gese, E.M. & Schauster, E.R. (2000). Changes in coyote
activity patterns due to reduced exposure to human persecution. USDA
National Wildlife Research Center Staff Publications. Paper 658.
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. (2006). World
Map of the K
oppen-Geiger climate classification updated. Meteorol. Z.,
15, 259263. https://doi.org/10.1127/0941-2948/2006/0130.
Krauze-Gryz, D., Gryz, J.B., Goszczy
nski, J., Chylarecki, P. &
_
Zmihorski, M. (2012). The good, the bad, and the ugly: space use and
intraguild interactions among three opportunistic predators cat (Felis
catus), dog (Canis lupis familiaris), and red fox (Vulpes vulpes)under
human pressure. Can. J. Zool., 90, 14021413.
Kronfeld-Schor, N. & Dayan, R. (2003). Partitioning of time as an
ecological resource. Annu. Rev. Ecol. Evol. Syst., 34, 153181.
Kruuk, H. (1972). The Spotted Hyena: A Study of Predation and Social
Behavior. University of Chicago Press, Chicago, IL.
Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase,
J.M., Hoopes, M.F. et al. (2004). The metacommunity concept: a
framework for multi-scale community ecology. Ecol. Lett., 7, 601613.
Linnell, J.D. & Strand, O. (2000). Interference interactions, co-existence
and conservation of mammalian carnivores. Divers. Distrib., 6, 169176.
Loveridge, A.J. & Macdonald, D.W. (2002). Habitat ecology of two
sympatric species of jackals in Zimbabwe. J. Mammal., 83, 599607.
Mackenzie, D.I., Bailey, L.L. & Nichols, J.D. (2004). Investigating species
co-occurrence patterns when species are detected imperfectly. J. Appl.
Ecol., 73, 546555.
Martins, Q.E. (2010). The ecology of the leopard Panthera pardus in the
Cederberg Mountains. Dissertation, University of Bristol, Bristol, UK.
McDonald, R.A. (2002). Resource partitioning among British and Irish
mustelids. J. Anim. Ecol., 71, 185200.
McVittie, R. (1979). Changes in the social behaviour of South West
African cheetah. Modoqua, 2, 171184.
Miller, D.A.W., Brehme, C.S., Hines, J.E., Nichols, J.D. & Fisher, R.N.
(2012). Joint estimation of habitat dynamics and species interactions:
disturbance reduces co-occurrence of non-native predators with an
endangered toad. J. Anim. Ecol., 81, 12881297.
de Oliveira, T.G. & Pereira, J.A. (2014). Intraguild predation and
interspecific killing as structuring forces of carnivoran communities in
South America. J. Mammal Evol., 21, 427436.
Palomares, F. & Caro, T.M. (1999). Interspecific killing among
mammalian carnivores. Am. Nat., 153, 492508.
Peoples B.K., Frimpong E.A (2015). Biotic interactions and habitat drives
positive co-occurrence between facilitating and beneficiary stream
fishes. J. Biogeogr., 43, 923931.
P
eriquet, S., Fritz, H. & Revilla, E. (2015). The Lion King and the
Hyaena Queen: large carnivore interactions and coexistence. Biol. Rev.,
90, 11971214.
Plummer, M. (2011). JAGS: a program for the statistical analysis of
Bayesian hierarchical models by Markov Chain Monte Carlo.
Pringle, R.M., Doak, D.F., Brody, A.L., Jocqu
e, R. & Palmer, T.M.
(2010). Spatial pattern enhances ecosystem functioning in an African
savannah. PLoS Biol., 8, e1000377.
Prugh, L.R., Stoner, C.J., Epps, C.W., Bean, W.T., Ripple, W.J.,
Laliberte, A.S. et al. (2009). The rise of the mesopredator. Bioscience,
59, 779791.
R Core Development Team (2016). R: A language and environment for
statistical computing. R Foundation for Statistical Computing, Vienna,
Austria. Available at: http://www.R-project.org/. Last accessed February
23, 2018.
Ramesh, R., Kalle, R., Sankar, K. & Qureshi, Q. (2012). Dietary
partitioning in sympatric large carnivores in a tropical forest of
Western Ghats, India. Mammal Study, 37, 313321.
Rich, L.N., Miller, D.A.W., Robinson, H.S., McNutt, J.W. & Kelly, M.J.
(2016). Using camera trapping and hierarchical occupancy modeling to
evaluate the spatial ecology of an African mammal and bird
community. J. Appl. Ecol., 53, 12251235.
Rich, L.N., Davis, C.L., Farris, Z.J., Miller, D.A.W., Tucker, J.M.,
Hamel, S. et al. (2017). Assessing global patterns in mammalian
carnivore occupancy and richness by integrating local camera trap
surveys. Global Ecol. Biogeogr., 26, 918929.
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, 20362046.
Ripple, W.J. & Beschta, R.L. (2006). Linking a cougar decline, trophic
cascade, and catastrophic regime shift in Zion National Park. Biol.
Cons., 133, 397408.
Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Richie, E.G.,
Hebblewhite, M. et al. (2014). Status and ecological effects of the
world’s largest carnivores. Science, 343, 151162.
Ritchie, E.G. & Johnson, C.N. (2009). Predator interactions, mesopredator
release and biodiversity conservation. Ecol. Lett., 12, 118.
©2018 John Wiley & Sons Ltd/CNRS
Letter Global patterns in Carnivora co-occurrence 11
Robinson, Q.H., Bustos, D. & Roemer, G.W. (2014). The application of
occupancy modeling to evaluate intraguild predation in a model
carnivore system. Ecology, 95, 31123123.
Roemer, G.W., Gompper, M.E. & Van Valkenburgh, B. (2009). The
ecological role of the mammalian mesocarnivore. Bioscience, 59, 165
173.
Rosenzweig, M.L. (1966). Community structure in sympatric Carnivora.
J. Mammal., 47, 602612.
Saether, B.E. (1999). Top dogs maintain diversity. Nature, 400, 510511.
Salo, P., Nordstrom, M., Thomson, R.L. & Korpimaki, E. (2008). Risk
induced by a native top predator reduces alien mink movements.
J. Anim. Ecol., 77, 10921098.
Selva, N. & Fortuna, M.A. (2007). The nested structure of a scavenger
community. Proc. Biol. Sci., 274, 11011108.
Sidorovich, V., Kruuk, H. & MacDonald, D.W. (1999). Body size and
interactions between European and American mink (Mustel lutreola
and M. vison) in Eastern Europe. J. Zool., 248, 521527.
Speigelhalter, D.J., Best, N.G., Carlin, B.P. & Van Der Linde, A. (2002).
Bayesian measures of model complexity and fit. J. R. Statist. Soc. B,
64, 583639.
Steenweg, R., Whittington, J., Hebblewhite, M., Forshner, A., Johnston,
B., Petersen, D. et al. (2016). Remote-camera-based occupancy
monitoring at large scales: power to detect trends in grizzly bears
across the Canadian Rockies. Biol. Conserv., 201, 192200.
Steenweg, R., Hebblewhite, M., Kays, R., Ahumada, J., Fisher, J.T.,
Burton, A.C. et al. (2017). Scaling up camera traps monitoring the
planet’s biodiversity with networks of remote sensors. Front. in Ecol.
Environ., 15, 2634.
Sunarto, S., Kelly, M.J., Parakkasi, K. & Hutajulu, M.B. (2015). Cat
coexistence in central Sumatra: ecological characteristics, spatial and
temporal overlap, and implications for management. J. Zool.,296,104115.
Swanson, A., Arnold, T., Kosmala, M., Forester, J. & Packer, C. (2016).
In the absence of a “landscape of fear”: how lions, hyenas, and
cheetahs coexist. Ecol. Evol., 6, 85348545.
Terborgh, J., Estes, J.A., Paquet, P., Ralls, K., Boyd-Heger, D., Miller,
B.J. et al. (1999). The role of top carnivores in regulating terrestrial
ecosystems. In: Continental conservation: design and management
principles for long-term, regional conservation networks (eds Soul
e, M. &
Terborgh, J.). Island Press, Covelo, CA; Washington DC. pp.3964.
Terborgh, J., Lopex, L., Nu~
nez, P.V., Rao, M., Shahhabuddin, G.,
Orihuela, G. et al. (2001). Ecological meltdown in predator-free forest
fragments. Science, 294, 19231926.
Thapa, K. & Kelly, M.J. (2017). Density and carrying capacity in the
forgotten tigerland: tigers in understudied Nepalese Churia. Integrative
Zoology, 12, 211227.
Tucker, J.M., Schwartz, M.K., Truex, R.L., Wisely, S.M. & Allendorf,
F.W. (2014). Sampling affects the detection of genetic subdivision and
conservation implications for fisher in the Sierra Nevada. Conserv.
Genet., 15, 123136.
Van der Valk, A.G. (1981). Succession in wetlandsa Gleasonian
approach. Ecology, 62, 688696.
Van Valkenburgh, B. (1989). Carnivore dental adaptations and diet: a
study of trophic diversity within guilds. In: Carnivore Behavior,
Ecology, and Evolution (ed Gittleman, J.L.). Vol 1. Cornell University
Press, Ithaca, NY.
Waddle, J.H., Dorazio, R.M., Walls, S.C., Rice, K.G., Beauchamp, J.,
Schuman, M.J. et al. (2010). A new parameterization for estimating co-
occurrence of interacting species. Ecol. Appl., 20, 14671475.
Weiher, E. & Keddy, P.A. (1999). Ecological Assembly Rules: Perspectives,
Advances, Retreats. Cambridge University Press, Cambridge, UK.
Weiher, E., Clarke, G.D.P. & Keddy, P.A. (1998). Community assembly
rules, morphological dispersion, and the coexistence of plant species.
Oikos, 81, 309322.
Wisz, M.S., Pottier, J., Kissling, W.D., Pellissier, L., Lenoir, J.,
Damgaard, C.F. et al. (2013). The role of biotic interactions in shaping
distributions and realised assemblages of species: implications for
species distribution modelling. Biol. Rev., 88, 1530.
Wultsch, C., Waits, L.P. & Kelly, M.J. (2016). A comparative analysis of
genetic diversity and structure in jaguars (Panthera onca), pumas (Puma
concolor), and ocelots (Leopardus pardalis) in fragmented landscapes of
a critical Mesoamerican linkage zone. PLoS ONE, 11, e0151043.
Yackulic, C.B., Reid, J., Nichols, J.D., Hines, J.E., Davis, R. & Forsman,
E. (2014). The roles of competition and habitat in the dynamics of
populations and species distributions. Ecology, 95, 265279.
SUPPORTING INFORMATION
Additional supporting information may be found online in
the Supporting Information section at the end of the article.
Editor, Jonathan Davies
Manuscript received 28 November 2017
First decision made 18 January 2018
Second decision made 9 April 2018
Third decision made 28 May 2018
Manuscript accepted 6 June 2018
©2018 John Wiley & Sons Ltd/CNRS
12 C. L. Davis et al. Letter
... Thus, multiple sympatric species can partition their niche according to major factors: food resources, space, and time 2 . Elucidating the mechanisms of species coexistence based on their niche partitioning is important for understanding community diversity and assemblage, and for implementing effective ecosystem conservation and management strategies [3][4][5][6] . ...
Article
Full-text available
Temporal and spatio-temporal niche partitioning is an important strategy for carnivore coexistence. Camera-trap data has been analyzed through several methods to assess the temporal and spatio-temporal niche partitioning. However, different analytical approaches used to may evaluate niche partitioning detect different results. In this study, we evaluated the temporal or spatio-temporal partitioning among sympatric medium-sized carnivores, red foxes, raccoon dogs, and Japanese martens, based on three analytical methods—the temporal overlap, temporal co-occurrence, and time-to-encounter analysis—to evaluate. From May to October 2019 and 2020, we obtained the activity of the target species using camera-traps in northeastern Japan. We analyzed the data with the coefficient of temporal overlap, probabilistic co-occurrence analysis, checkerboard score, and multi-response permutation procedures. The results of the assessment of the niche partitioning differed depending on the analytical methods based on temporal and spatio-temporal partitioning. Therefore, we conclude that the choice of analytical approach is important for evaluating the temporal and spatio-temporal niche partitioning.
... Spatial analysis-Occupancy modelling are the most commonly used approaches to address interspecific competition [49]. These models allowed us to estimate occupancy (ψ) and detection (Pr) probability for every combination of coexistence ungulates. ...
Article
Full-text available
Dramatic increases in populations of wild ungulates have brought a new ecological issue in the Qinling mountains. Information on species’ niche differentiation will contribute to a greater understanding of the mechanisms of coexistence, so as to ultimately benefit the conservation and management of ecological communities. In this study, camera trapping was used to investigate spatial and temporal activity patterns of sympatric wild ungulates in the Qinling Mountains of China, where top predators were virtually absent. We obtained 15,584 independent detections of seven wild ungulate species during 93,606 camera-trap days from April 2014 to October 2017. Results showed that (i) the capture rate differed significantly across species, with the capture rate of reeve muntjac being significantly higher than that of other species; (ii) the wild boar had a higher occupancy rates (ψ = 0.888) than other six ungulates, and distance to settlements had a negative relationship with wild boar (β = −0.24 ± 0.17); (iii) the forest musk deer and mainland serow had low spatial overlaps with other five wild ungulates, while spatial overlap indices of any two given pairs of wild ungulates were relatively high; (iv) all wild ungulates species (expect wild boar) were mainly active during crepuscular and diurnal periods, and showed bimodal activity peaks at around 05:00–07:00 and 17:00–19:00; and finally, (v) all wild ungulates showed moderate to high temporal overlaps. The results provided detailed information of the spatial and temporal ecology of wild ungulate communities in forest ecosystems of China, which also would be a guide to establish conservation priorities as well as efficient management programs.
... Furthermore, the landscape of fear or chasing by single apex predator may linked to subdominant herbivores prey behavioral resource depression or competition for sharing habitat resources and prey may decrease the risk of aggressive contact or other systems of interference by predator in diverse areas, using different habitat strata, or through temporal separation of movement [41] . Moreover to temporal activity patterns and interactions between species, animals can alter their spatial territory under increased competitive interactions with other conspecifics or heterospecific species, resulting in positive or negative co-occurrence or struggle for habitat resources preferences or avoidance [29,105] , hence spatial separation is another strategy for the coexistence of sympatric species [29,65,106] . ...
Thesis
Full-text available
The nocturnal activities of animals are influenced by the brightness of the moon in different moon phases. Further, behaviour of prey animals, and also density, may fluctuate in response to predators through both lethal effects and non-lethal (fear) effects. As we understand, wildlife may experience fear from a range of predators, including large carnivores, mesopredators, domestic dogs and humans, the latter being regarded as a super predator. In such landscapes with the occurrence of predators, the prey is likely to be more alert in order to lower the danger of being killed. Further, flight response is an appropriate, recognised and measurable indicator (as flight initiation distance, FID) of fear effects in terrestrial animals. In this research, our specific aims were: 1) to investigate the effects of moonlight on activity patterns and the interactions between a large carnivore (North China leopard Panthera pardus japonensis) and their prey; 2) to analyse the den-site selection by the mesopredator, red fox (Vulpes vulpes montana) at multiple scales in a patchy human-dominated landscape; 3) to describe the habitat factors and predator density effects on the spatial abundance of cape hare (Lepus capensis) distribution; 4) to explore the increased FID in golden marmots (Marmota caudata aurea) in response to domestic dogs, and; 5) to understand how the occurrence of conspecifics in the neighboring space may influence FID in cape hare under the effect of human disturbance. These collective works contributed to the understanding of fear ecology and their implications for predator-prey interactions in China and Pakistan. We used camera-traps to investigate the first aim; for the remaining four objectives, we laid out transect lines in different habitats to explore how the fear effects stimulated by humans and predators influence other mammals. A total of 102 camera locations operated between March 2017-May 2019 and circadian activities of each species was analyzed by using temporalniche overlap model, as well as Generalized Linear Mixed Effects Model (GLMM) to link habitat structures with leopards and prey species. We derived Resource Selection Functions (RSFs) to predict the potential distribution of red fox dens at three spatial scales. We used the standard line transect distance sampling method to calculate the seasonal density of hare and comparative density of red fox. A traditional live-trapping protocol was used to capture a sample of golden marmots at the four colonies. Lastly, we used human stimuli at the start of each sampling period for the cape hare investigation to link with disturbances and flight response. The main results of this study are the following: (1) North China leopard exhibited an irregular activity pattern, wild boar (Sus scrofa) indicated lunar phobic behaviour and avoided leopard, and roe deer (Capreolus pygargus) were lunar philic. Tolai hare (Lepus tolai) showed lunar phobic behavior. The nocturnal activities of leopards, roe deer and tolai hare were positively related. The occurrence of leopard day vs. night activity during four different lunar phases were exhibited a preference with distance to deciduous forest and secondary roads, while avoided to lower elevations. Roe deer showed a preference to secondary roads. Wild boar displayed avoidance of intermediate elevation. Tolai hare indicated preference to grassland. Further, cloud cover, moonlight risk index (MRI), humans and season had diverse effects on leopard and prey interactions. (2) We found that for red fox den occurrence, elevation was the most significant covariate at landscapes scale, and distance to forest had negative effect; at patch scale, distance to forests were negatively correlated with number of dens and positively linked to shrubs. Furthermore, at microhabitat scale, den occurrence was negatively linked with hiding cover and positively associated with tree density and anthropogenic features – den occurrence was negatively related with distance to roads and positively correlated with Indian pika (Ochotona roylei)burrow existence. We found that den entrance dimensions were larger for natal dens than resting dens. (3) We identified that, the population density of hare was highest in bare areas and the lowest in mixed plantations. In summer, we found a positive correlation between hare and red fox density in a bare area, and in winter, in shrubs land. The relative density of red fox was lowest in subalpine habitat. We found that hare pellet indices were positively connected with indices of herbs in plantation forest, shrubs in mixed forest, trees in two selected habitat sites, and negatively linked to cultivated land, roads, and rivers in mixed and streams in bare areas. (4) We measured FID in 72 Golden marmots from four colonies in the Karakoram Range, Pakistan. We found that the domestic dog (Canis familiaris) caused greater FID than pedestrian alone, and adult marmots nearer to roads showed greater FID. However, marmot age and colony substrate had more marked influences on FID, which was also greater at lower elevations where there were clusters of human settlements and livestock pastures. (5) Our results showed that foraging hares have smaller FIDs than vigilant ones. Social animals reduced FID of the focal hare due to a mutual vigilance, while a solitary animal had greater FID due to less cooperative defense for predator detection. This research has demonstrated that fear effects exist in human-dominated landscapes, and that environmental factors can drive temporal activities of predator-prey interactions which are linked with lunar phase. It also showed that human disturbances, such as domestic dogs, influenced the core activity zones of burrowing herbivores. The studies also show the scale of fear and provide a superior chance to recognize the biological significance of fear ecology and its application for future wildlife conservation in human-dominated landscapes.
... Furthermore, the landscape of fear created by predators may be linked to the spatial distribution of prey and changes in temporal foraging movements (Bischof et al., 2014). Further to temporal activity patterns, animals may also alter habitat resource selection in different seasons (Ramesh et al., 2012;; hence, spatial separation is another strategy for the coexistence of sympatric species (Davis et al., 2018;Zhao et al., 2020). ...
Article
Full-text available
The nocturnal activities of predators and prey are influenced by several factors, including physiological adaptations, habitat quality and, we suspect, corresponds to changes in brightness of moonlight according to moon phase. In this study, we used a dataset from 102 camera traps to explore which factors are related to the activity pattern of North China leopards (Panthera pardus japonensis) in Shanxi Tieqiaoshan Provincial Nature Reserve (TPNR), China. We found that nocturnal activities of leopards were irregular during four different lunar phases, and while not strictly lunar philic or lunar phobic, their temporal activity was highest during the brighter moon phases (especially the last quarter) and lower during the new moon phase. On the contrary, roe deer (Capreolus pygargus) exhibited lunar philic activity, while wild boar (Sus scrofa) and tolai hare (Lepus tolai) were evidently lunar phobic, with high and low temporal activity during the full moon, respectively. In terms of temporal overlap, there was positive overlap between leopards and their prey species, including roe deer and tolai hare, while leopard activity did not dip to the same low level of wild boar during the full moon phase. Human activities also more influenced the temporal activity of leopards and wild boar than other species investigated. Generally, our results suggested that besides moonlight risk index (MRI), cloud cover and season have diverse effects on leopard and prey nocturnal activity. Finally, distinct daytime and nighttime habitats were identified, with leopards, wild boar, and tolai hare all using lower elevations at night and higher elevations during the day, while leopards and roe deer were closer to secondary roads during the day than at night
... Species are not distributed randomly across space; rather, they form communities whose structure and composition are molded by biotic interactions (Davis et al., 2018;Wisz et al., 2013). With the dramatic increase of the human footprint on the planet (Venter et al., 2016), however, anthropogenic disturbance, particularly land-use change, has become a prominent factor altering interspecific interactions and even community composition (e.g., Kiffner et al., 2015;Rovero et al., 2020;Suraci et al., 2021). ...
Article
Full-text available
Mammalian communities inhabiting temperate grasslands are of conservation concern globally, and especially in Central Asia, where livestock numbers have dramatically increased in recent decades, leading to overgrazing and land‐use change. Yet, how this pervasive presence of livestock herds affects the community of wild mammals remains largely unstudied. We used systematic camera trapping at 216 sites across remote, mountainous areas of the Mongolian Altai to assess the spatial and temporal patterns of occurrence and the inter‐specific relationships within a mammalian community that includes different categories of livestock. By adopting a recently proposed multi‐species occupancy model that incorporates inter‐specific correlation in occupancy, we found several statistically strong correlations in occupancy among species pairs, with the majority that involved livestock. The sign of such associations was markedly species‐dependent, with larger wild species of conservation concern, namely snow leopard and Siberian ibex, avoiding livestock presence. As predicted, we found evidence of a positive correlation in occupancy between predators and their respective main prey. Contrary to our expectations, a number of intra‐guild species pairs also showed positive co‐occurrence, with no evidence of spatio‐temporal niche partitioning. Overall, our study suggests that livestock encroaching into protected areas influences the whole local community of wild mammals. Though pastoralism has co‐existed with wildlife for millennia in central Asian grasslands, our findings suggest that policies and practices to decrease the pressure of livestock husbandry on wildlife are needed, with special attention on large‐sized species such as the snow leopard and its wild prey, which seem to be particularly sensitive to this pervasive livestock presence.
... Despite the rapidly growing number of camera trap studies (Burton et al., 2015;Steenweg et al., 2017), relatively few have pooled data across a large number of sites to collaboratively address conservation questions at regional and global scales. Notable exceptions include regional evaluation of trends in occupancy (Beaudrot et al., 2016) and functional composition of mammal communities (Rovero et al., 2020), and similar large-scale assessments of carnivore assemblages and threatened species (Davis et al., 2018). These studies highlight the potential to further scale up camera trap data to inform global-scale analyses (Steenweg et al., 2017). ...
Chapter
Although African rainforests harbour a high diversity of small carnivores, few studies have been conducted on these species' ecology and interspecific relations. We carried out a camera‐trapping survey to examine habitat use and activity patterns of small carnivores in the Moukalaba–Doudou National Park, Gabon. The study area ( ~ 500 km 2 ) consists of various types of vegetation, including forest on dry soils, swamp forest, montane forest and savannah. We detected nine species of small carnivores in the study area. The seven most common carnivores were broadly classified into forest‐interior species ( n = 3), savannah/forest‐edge species ( n = 3) and aquatic‐habitat species ( n = 3), in agreement with observations by other researchers. Occupancy analysis suggested further habitat separation within the small carnivore assemblage: among the savannah/forest‐edge species, African civets, Civettictis civetta , more often used the forest edge and less frequently entered the savannah interior compared with Egyptian mongooses, Herpestes ichneumon , and rusty‐spotted genets, Genetta maculata . Among the forest‐interior species, black‐legged mongooses, Bdeogale nigripes , were more closely associated with mature secondary dry forest than were long‐nosed mongooses, Xenogale naso , and servaline genets, Genetta servalina . These two forest mongoose species, with similar body size and diet, exhibited different activity patterns. However, their habitat use and activity patterns were not affected by one another's presence, indicating that they had different preferences. Our results show that most pairs of small carnivores in the Moukalaba differ in either habitat use or time of activity, which may promote their coexistence across this region. This suggests that maintenance of habitat heterogeneity may be important for the conservation of these species. The relative proportion of small carnivores over space and time may reflect the degree of degradation of the forest; therefore, long‐term monitoring by using camera‐traps is highly recommended.
Chapter
Mammalian species composition might change in relation to biotic or abiotic factors depending on the scale of investigation. Ecomorphology is one of the tools that can be employed to understand how species composition changes through space and time. Here, the morphological diversity of small carnivore guilds (defined as a pool of carnivoran species whose body mass is < 7 kg) is explored using 2D geometric morphometrics of mandibles belonging to 61 species. A strong taxonomic signal emerges by looking at mandibular morphospace so that separation of carnivoran families is apparent. Mustelids are the most distinct, being characterized by short and curved corpus mandibulae, while felids exhibit a typical hypercarnivore mandible with no crushing molar area. Overlap occurs between canids, viverrids, and herpestids possibly in relation to their generalized feeding habits and killing behaviours. When species are grouped according to their presence/absence into six carnivoran species‐rich ecosystems, an ecogeographical pattern occurs. Guilds from higher latitudes such as Yellowstone (USA) and Krokonose (Europe) together with the Kruger (South Africa) assemblage are highly depleted of mandibular morphotypes. In contrast, guilds from tropical areas (Gunung Lensung, Indonesia; Yasuni, Ecuador; and La Amistad, Panama) exhibit high diversity of mandibular shapes corresponding to higher values of morphological disparity. This latter parameter correlates positively with precipitation variables, supporting a strong influence of climate on the historical community assembly of small carnivore guilds. Clearly, small carnivores can play a key role in ecosystem functioning and more theoretical work is needed to better identify this at multiple spatial and temporal scales.
Chapter
Carnivore species are believed to exert strong competitive pressure on each other, resulting in adaptations to allow for niche separation through resource partitioning. However, factors that promote ecological separation among species in tropical forests are difficult to explain and are poorly understood because robust field studies are lacking. We examined spatial, temporal and morphological segregation between tropical carnivores in a protected forest in north‐central Thailand. Sympatric spatial overlap was calculated from radio‐telemetry data of 38 individuals from six species (5 yellow‐throated martens, Martes flavigula , 20 leopard cats, Prionailurus bengalensis , 2 Asiatic golden cats, Catopuma temminckii , 4 clouded leopards, Neofelis nebulosa , 5 binturongs, Arctictis binturong , and 2 dholes, Cuon alpinus ) in the same study area. Spatial overlap was then correlated with 14 independent variables (i.e. skull and dental morphology, body mass, habitat use and activity patterns) compared among the six species. We predicted that carnivores with differing morphology and activity patterns would exhibit more spatial overlap because these species would compete less for prey resources. Our statistical analyses indicated that lower mean carnassial length and activity patterns in closed habitat cover were significantly correlated ( p < 0.05) with species spatial overlap. Binturongs appeared to have the greatest amount of spatial overlap with other species of carnivores, whereas dholes had the least spatial overlap; also, dholes and yellow‐throated martens tended to be more active in open habitats and during diurnal time periods, whereas clouded leopards and Asiatic golden cats were more active in closed cover and were more arrhythmic in activity. Although these results provide useful information on carnivore coexistence, we recommend that future studies monitor larger sample sizes of carnivore species over the same time period to provide more robust statistical analyses. In addition, we suggest that future research on carnivore coexistence evaluates the impacts of anthropogenic activity on study results.
Article
Mesocarnivores play important roles in shaping the ecosystems they inhabit, but are often overlooked in comparison to larger-bodied apex predators. Sympatric mesocarnivore species can minimise interspecific competition by spatial avoidance or by altering temporal activity to reduce encounter rates. Here, we used camera traps to investigate the spatial and temporal co-occurrence of mesocarnivores in the Qinling Mountains of China. We obtained 1,312 independent detections of target mesocarnivore species with an effort of 93,606 camera-trap days from April 2014 to October 2017. Our results showed that: (1) the relative activity indices (RAI) differed among mesocarnivores species, with the RAI of hog badger being significantly higher than that of other species; (2) the probability of occupancy varied among species, with yellow-throated marten (ᴪ =0.338) having the highest occupancy estimates; overall, occupancy by yellow-throated marten correlated positively with vegetation type (β=0.31±0.13); (3) Asiatic golden cat, Siberian weasel and ferret badger tended to segregate themselves spatially, while spatial overlap indices of any two given pairs of leopard cat, masked palm civet, hog badger and yellow-throated marten were relatively high; (4) leopard cat was nocturnal and crepuscular, while masked palm civet and ferret badger were primarily nocturnal, and yellow-throated marten was diurnal and crepuscular. Hog badger had no clear daily pattern; (5) all species except the yellow-throated marten showed moderate to high temporal overlap. Our results show that spatial and temporal segregation of mesocarnivores may serve as the mechanism to reduce competition and facilitate coexistence.
Article
Full-text available
Biodiversity loss is a major driver of ecosystem change, yet the ecological data required to detect and mitigate losses are often lacking. Recently, camera trap surveys have been suggested as a method for sampling local wildlife communities, because these observations can be collated into a global monitoring network. To demonstrate the potential of camera traps for global monitoring, we assembled data from multiple local camera trap surveys to evaluate the interchange between fine- and broad-scale processes impacting mammalian carnivore communities.
Article
Full-text available
Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote-camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real-time biodiversity data but also serves to connect people with nature.
Article
Full-text available
Within a large carnivore guild, subordinate competitors (African wild dog, Lycaon pictus, and cheetah, Acinonyx jubatus) might reduce the limiting effects of dominant competitors (lion, Panthera leo, and spotted hyena, Crocuta crocuta) by avoiding them in space, in time, or through patterns of prey selection. Understanding how these competitors cope with one other can inform strategies for their conservation. We tested how mechanisms of niche partitioning promote coexistence by quantifying patterns of prey selection and the use of space and time by all members of the large carnivore guild within Liuwa Plain National Park in western Zambia. Lions and hyenas specialized on wildebeest, whereas wild dogs and cheetahs selected broader diets including smaller and less abundant prey. Spatially, cheetahs showed no detectable avoidance of areas heavily used by dominant competitors, but wild dogs avoided areas heavily used by lions. Temporally, the proportion of kills by lions and hyenas did not detectably differ across four time periods (day, crepuscular, early night, and late night), but wild dogs and especially cheetahs concentrated on time windows that avoided nighttime hunting by lions and hyenas. Our results provide new insight into the conditions under which partitioning may not allow for coexistence for one subordinate species, the African wild dog, while it does for cheetah. Because of differences in responses to dominant competitors, African wild dogs may be more prone to competitive exclusion (local extirpation), particularly in open, uniform ecosystems with simple (often wildebeest dominated) prey communities, where spatial avoidance is difficult.
Article
Full-text available
Aggression by top predators can create a “landscape of fear” in which subordinate predators restrict their activity to low-risk areas or times of day. At large spatial or temporal scales, this can result in the costly loss of access to resources. However, fine-scale reactive avoidance may minimize the risk of aggressive encounters for subordinate predators while maintaining access to resources, thereby providing a mechanism for coexistence. We investigated fine-scale spatiotemporal avoidance in a guild of African predators characterized by intense interference competition. Vulnerable to food stealing and direct killing, cheetahs are expected to avoid both larger predators; hyenas are expected to avoid lions. We deployed a grid of 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate concurrent patterns of habitat use by lions, hyenas, cheetahs, and their primary prey. We used hurdle models to evaluate whether smaller species avoided areas preferred by larger species, and we used time-to-event models to evaluate fine-scale temporal avoidance in the hours immediately surrounding top predator activity. We found no evidence of long-term displacement of subordinate species, even at fine spatial scales. Instead, hyenas and cheetahs were positively associated with lions except in areas with exceptionally high lion use. Hyenas and lions appeared to actively track each, while cheetahs appear to maintain long-term access to sites with high lion use by actively avoiding those areas just in the hours immediately following lion activity. Our results suggest that cheetahs are able to use patches of preferred habitat by avoiding lions on a moment-to-moment basis. Such fine-scale temporal avoidance is likely to be less costly than long-term avoidance of preferred areas: This may help explain why cheetahs are able to coexist with lions despite high rates of lion-inflicted mortality, and highlights reactive avoidance as a general mechanism for predator coexistence.
Article
Full-text available
Disentangling the role that multiple interacting factors have on species responses to shifting climate poses a significant challenge. However, our ability to do so is of utmost importance to predict the effects of climate change on species distributions. We examined how populations of three species of wetland breeding amphibians, which varied in life history requirements, responded to a six-year period of extremely variable in precipitation. This interval was punctuated by both extensive drought and heavy precipitation and flooding, providing a natural experiment to measure community responses to environmental perturbations. We estimated occurrence dynamics using a discrete hidden Markov modeling approach that incorporated information regarding habitat state and predator-prey interactions. This approach allowed us to measure how metapopulation dynamics of each amphibian species was affected by interactions among weather, wetland hydroperiod, and co-occurrence with fish predators. The pig frog, a generalist, proved most resistant to perturbations, with both colonization and persistence being unaffected by seasonal variation in precipitation or co-occurrence with fishes. The ornate chorus frog, an ephemeral wetland specialist, responded positively to periods of drought owing to increased persistence and colonization rates during periods of low-rainfall. Low probabilities of occurrence of the ornate chorus frog in long-duration wetlands were driven by interactions with predators due to low colonization rates when fishes were present. The mole salamander was most sensitive to shifts in water availability. In our study area, this species never occurred in short-duration wetlands and persistence probabilities decreased during periods of drought. At the same time, negative effects occurred with extreme precipitation because flooding facilitated colonization of fishes to isolated wetlands and mole salamanders did not colonize wetlands once fishes were present. We demonstrate that the effects of changes in water availability depend on interactions with predators and wetland type and are influenced by the life history of each of our species. The dynamic species occurrence modeling approach we used offers promise for other systems when the goal is to disentangle the complex interactions that determine species responses to environmental variability. This article is protected by copyright. All rights reserved.
Article
* Apex predators can benefit ecosystems through toptextendashdown control of mesopredators and herbivores. However, apex predators are often subject to lethal control aimed at minimizing attacks on livestock. Lethal control can affect both the abundance and behaviour of apex predators. These changes could in turn influence the abundance and behaviour of mesopredators. * We used remote camera surveys at nine pairs of large Australian rangeland properties, comparing properties that controlled dingoes Canis lupus dingo with properties that did not, to test the effects of predator control on dingo activity and to evaluate the responses of a mesopredator, the feral cat Felis catus. * Indices of dingo abundance were generally reduced on properties that practiced dingo control, in comparison with paired properties that did not, although the effect size of control was variable. Dingoes in uncontrolled populations were crepuscular, similar to major prey. In populations subject to control, dingoes became less active around dusk, and activity was concentrated in the period shortly before dawn. * Shifts in feral cat abundance indices between properties with and without dingo control were inversely related to corresponding shifts in indices of dingo abundance. There was also a negative relationship between predator visitation rates at individual camera stations, suggesting cats avoided areas where dingoes were locally common. Reduced activity by dingoes at dusk was associated with higher activity of cats at dusk. * Our results suggest that effective dingo control not only leads to higher abundance of feral cats, but allows them to optimize hunting behaviour when dingoes are less active. This double effect could amplify the impacts of dingo control on prey species selected by cats. In areas managed for conservation, stable dingo populations may thus contribute to management objectives by restricting feral cat access to prey populations. * ~Synthesis and applications. Predator control not only reduces indices of apex predator abundance but can also modify their behaviour. Hence, indicators other than abundance, such as behavioural patterns, should be considered when estimating a predator's capacity to effectively interact with lower trophic guilds. Changes to apex predator behaviour may relax limitations on the behaviour of mesopredators, providing enhanced access to resources and prey.