RESEARCH ARTICLE
Landscape Use and Co-Occurrence Patterns
of Neotropical Spotted Cats
Mariana B. Nagy-Reis1*, James D. Nichols2, Adriano G. Chiarello3, Milton Cezar Ribeiro4,
Eleonore Z. F. Setz1
1 Department of Animal Biology, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo,
Brazil, 2 Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, Maryland, United States of
America, 3 Department of Biology, Universidade de São Paulo (USP), Ribeirão Preto, São Paulo, Brazil,
4 Spatial Ecology and Conservation lab (LEEC), Department of Ecology, Universidade Estadual de São
Paulo (UNESP), Rio Claro, São Paulo, Brazil
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OPEN ACCESS
Citation: Nagy-Reis MB, Nichols JD, Chiarello AG,
Ribeiro MC, Setz EZF (2017) Landscape Use and
Co-Occurrence Patterns of Neotropical Spotted
Cats. PLoS ONE 12(1): e0168441. doi:10.1371/
journal.pone.0168441
Editor: Emmanuel Serrano Ferron, Universidade de
Aveiro, PORTUGAL
Received: June 30, 2016
Accepted: December 1, 2016
Published: January 4, 2017
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: MBNR received funding from the
Coordination for the Improvement of Higher
Education Personnel (CAPES; www.capes.gov.br)
and Idea Wild (www.ideawild.org). EZFS received
funding from the São Paulo Research Foundation
(FAPESP; grant 2013/07162-6; www.fapesp.br).
AGC received funding from The Brazilian Science
Council (CNPq; process 305902/2014-8; www.
cnpq.br). MCR has been continuously supported
* mariana.nbreis@gmail.com
Abstract
Small felids influence ecosystem dynamics through prey and plant population changes.
Although most of these species are threatened, they are accorded one of the lowest
research efforts of all felids, and we lack basic information about them. Many felids occur in
sympatry, where intraguild competition is frequent. Therefore, assessing the role of interspecific interactions along with the relative importance of landscape characteristics is necessary to understand how these species co-occur in space. Here, we selected three
morphologically similar and closely related species of small Neotropical cats to evaluate the
roles of interspecific interactions, geomorphometry, environmental, and anthropogenic landscape characteristics on their habitat use. We collected data with camera trapping and scat
sampling in a large protected Atlantic forest remnant (35,000 ha). Throughout occupancy
modeling we investigated whether these species occur together more or less frequently
than would be expected by chance, while dealing with imperfect detection and incorporating
possible habitat preferences into the models. We used occupancy as a measure of their
habitat use. Although intraguild competition can be an important determinant of carnivore
assemblages, in our system, we did not find evidence that one species affects the habitat
use of the other. Evidence suggested that proximity to the nature reserve (a more protected
area) was a more important driver of Neotropical spotted cats’ occurrence than interspecific
interactions or geomorphometry and environmental landscape characteristics—even
though our entire study area is under some type of protection. This suggests that small felids
can be sensitive to the area protection status, emphasizing the importance of maintaining
and creating reserves and other areas with elevated protection for the proper management
and conservation of the group.
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by grants and scholarships from The Brazilian
Science Council (CNPq; process 312045/2013-1)
and São Paulo Research Foundation (FAPESP;
grant 2013/50421-2). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
Introduction
Predators play an important role in regulating ecosystem functioning and dynamics [1, 2].
They affect prey populations and, as a result, alter plant abundance, composition, succession,
dispersion, and diversity [2, 3]. Consequently, the loss of this key group can lead to regime
shifts, alternative states of ecosystems, and possible losses of ecosystem services [2]. Given the
role of carnivores on ecosystem functioning and their sensitivity to the environment, landscapes with these animals—implying a relatively intact food web—have high potential for
ecological integrity [4], which encompasses ecosystem health, biodiversity, stability, and sustainability [5]. Therefore, maintenance of carnivores can serve as a useful tool for protected
area design and conservation planning [6].
Among the carnivores, felids are extreme as obligatory flesh eaters [7]. Historically, felids
have suffered several anthropogenic impacts, which represent a major cause of felid mortality
in several regions of the globe and account for up to 70% of deaths in some populations [8, 9].
For example, larger felids are frequently involved in conflicts with humans due to preying on
domestic animals or livestock [9–13], and small felids are constantly suffering from fur trade
[8, 14]. Although several countries banned the export of wildcat skins and signed the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES) [9], illegal hunting of felids still occurs (e.g., [11]). Felids can also be exposed to diseases carried by
domestic carnivores and to poaching, which even at moderate levels over a relatively short
period of time can lead to massive population decline [9, 15, 16]. Hunting and these other
human-related pressures are most likely to occur in areas with high accessibility or low (or
inefficient) protection status, and in unrestricted areas surrounding protected areas [9, 17–19].
In highly protected populations, anthropogenic mortality is rare [9]. More recently, mammalian carnivores face local extinction due to habitat loss and fragmentation, exacerbated by their
relatively large home ranges, low densities, and direct persecution by humans [4, 20–22].
Given the current scenario, understanding how species relate spatially to the environment
and to human disturbances is critical to the assignment of areas for conservation and the
development of conservation strategies [23–26]. Besides landscape characteristics, interspecific
interactions may also regulate the occurrence, distribution or persistence of species [27]. In
carnivore assemblages, intraguild interference competition and killing are potentially important determinants of species abundance and distribution and can lead to adaptive responses in
use of space and activity patterns [7, 28, 29], which enable the coexistence of morphologically
similar species [30].
Because of relatively recent divergence and constraints imposed by foraging and diet, felids
present similar morphologies [31, 32]. Thus, they are a good model to understand how closely
related and morphologically similar species can coexist. However, though felids are frequently
sympatric, with the largest assemblages occurring in the tropical regions of the Americas, little
attention has been given to the coexistence of these species and its implications [8]. The Neotropical spotted cats are the main sympatric small felids in Neotropical rainforests: Leopardus
pardalis—ocelot, L. wiedii—margay, and L. guttulus—oncilla (formerly known as L. tigrinus
[33]). These species have discrete differences in body sizes (8–15 kg, 3-9kg, and 1.5-3kg,
respectively [34]) and share an important portion of their food sources [35, 36], therefore differential food exploitation is unlikely and intraguild competition is probably high [37]. They
have been accorded one of the lowest research efforts of all felids, and more basic information
on their biology and ecology is urgently needed [7, 8].
Frequently, studies on species occurrence and distribution assume that all species present at
a location are detected with certainty. However, accounting for imperfect detection is fundamental to avoid omission errors (false absences) [38, 39] and resultant bias in parameter
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Habitat Use and Co-Occurrence of Spotted Cats
estimation [40]. Such omission errors may lead to incorrect inferences about species-habitat
relationships or patterns of species co-occurrence [41]. A recent adaptation to occupancy
models [42] allows such problems to be dealt with by incorporating detection probability, as
well as possible habitat preferences, directly into the model set and evaluating co-occurrence
patterns among pairs of species [41].
We investigated the role of geomorphometry, environmental and anthropogenic landscape
attributes at multiple scales and interspecific interactions in the habitat use of Neotropical
spotted cats in a large Atlantic forest remnant. Using a likelihood-based framework, we estimated the probability of occurrence and co-occurrence while accounting explicitly for imperfect detectability and habitat preferences. We used occupancy as a measure of habitat use, and
developed specific models based on different hypotheses about effects of landscape characteristics and competition on the habitat use of Neotropical spotted cats. We have two alternative
hypotheses: 1) The habitat use of Neotropical spotted cats is mainly driven by landscape characteristics—If landscape characteristics are important factors determining how spotted cats
use the habitat, we would expect anthropogenic-related variables to be the main predictors of
occurrence, given the sensitivity of carnivores to anthropogenic impacts [8, 9]. More specifically, because human-related pressures are most likely to occur in areas with low protection
status or with high accessibility [9, 17–19], we would predict a negative association between
habitat use and road density (a measure of human accessibility) and between habitat use and
distance to a more protected area (a reserve). We would also expect that prey, hydrographic
density, and forest cover would have a positive effect on habitat use, since those variables represent important resources (food and water availability) and high-quality habitats, but they
would have a smaller influence than the human-related variables. We would also expect elevation to have a weak influence on how Neotropical spotted cats use the habitat, unless species
are segregating in altitude due to the montaneous terrain in our study area; this phenomenon
has been observed in other taxa (e.g., [41]). 2) The habitat use of Neotropical spotted cats is
mainly driven by interspecific interactions—If competition is a major determinant of the habitat use of Neotropical spotted cats, we would expect their occupancy and/or detection probability to be lower when another spotted cat is present or detected; co-occurrence should be less
than expected by chance, predicting avoidance, considering the commonness of interference
intraguild competition and killing among carnivores [7, 28, 29]. We believe that knowledge of
these potential relationships will be useful for planning management actions towards the conservation of this key group in Neotropical forests and helpful in clarifying the role of interspecific interactions on the occurrence of small felids, which could benefit our understanding of
how small felids occur in other ecosystems worldwide.
Methods
Study area
Serra do Japi (southeast Brazil, coordinates 47˚03’40"W to 46˚52’20"W and 23˚22’30"S to 23˚
11’35"S; Fig 1) is one of the few large remnants of Atlantic Forest. The Atlantic Forest represents a global hotspot for biodiversity conservation [43], and currently it is highly fragmented
(more than 80% of the remnants are < 50 ha in size), highly isolated (average distance between
fragments is 1,440 m), and under negative edge influences (73% of remnants are 250 m from
any forest edge) [44]. The study area is a Natural Heritage Area (35,000 ha) considered part of
the UNESCO’s Atlantic Forest Biosphere Reserve [45]. Located within this area is the Biological Municipal Reserve (REBIO—2,071 ha) surrounded by a Buffer Zone (11,946 ha) (Fig 1).
The REBIO presents the highest protection status in the area, where the only permitted activities are research and education. In the Natural Heritage Area and in the Buffer Zone, however,
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Fig 1. Study area. Study area and the sampling sites’ locations at Serra do Japi (Brazil) where Neotropical spotted cats
were sampled using camera trap and scat sampling.
doi:10.1371/journal.pone.0168441.g001
agriculture, horticulture and other forms of economic activities are present. The vegetation of
the area is characterized as semideciduous mesophilic forest with mountainous terrain and a
seasonal climate [46]. The mean temperature is 19.7˚C and the mean annual rainfall is 1,422
mm, with a dry and cold season occurring from April to September and a wet and warm season from October to March [46].
Data collection
From April 2013 to September 2014, we collected data on three Neotropical spotted cats (Leopardus pardalis, L. wiedii, and L. guttulus) at 45 sampling sites (spaced approx. 1.5 km apart)
distributed in a regular grid across the forest remnant. This distance between the sampling
sites provided a good coverage of our study area, and it is in agreement with the TEAM “terrestrial vertebrate (camera trap) monitoring protocol implementation manual” [47]. Furthermore, we had no evidence for spatial autocorrelation in data while using this spacing (S1
Table).
We conducted three campaigns at each sampling site to collect data: 1- April 2013 to September 2013; 2- October 2013 to March 2014; 3- April 2014 to September 2014. A different
group of 15 sampling sites was surveyed every two months within each campaign (see S1 Fig
for details). Hence, 45 sites were sampled during each campaign. We used camera trapping
(passive infrared camera traps; Bushnell Trophy Cam; N = 5,198 trap days) and scat sampling
during the first and the second campaigns, and only scat sampling during the third campaign.
All cameras were fixed about 20 cm above ground and no bait was used. Traps were installed
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Habitat Use and Co-Occurrence of Spotted Cats
with a minimum distance of approx. 50 m from roads or trails, and none of the sampling sites
was located close to roads that were highly used or open to the public. We visited each sampling site six times and collected all scats found along a 1-km segment in the dirt road closest
to each site. There was no overlap between neighboring sampled areas because each sampling
site was usually located in the center of its 1-km segment.
In addition to the felids’ data, we collected information on their main prey using the records
from the camera traps. This was possible due to the height at which the cameras were positioned
and the number of records (i.e., detections: Nsmall mammals = 77; Nmedium-sized mammals = 897;
Nground-dwelling birds = 1,936). Although cameras are more commonly used to survey mediumand large-sized mammals, they can also be used to collect data on small mammals [48–50] and
ground-dwelling birds [51–53]. They may provide a new and cost-effective technique for surveying terrestrial small mammals, particularly when presence data are the main requirement of
the survey [54–55]. The use of camera traps to collect data on small mammals to assess prey
availability for carnivores was already performed [56], but here we went a step further and
included imperfect detection on prey estimates (through occupancy modeling, see “Single-species occupancy models” section) instead of using the number of captures. We considered smallsized mammals (< 1 kg; mainly small rodents and small marsupials) and small birds (< 0.2 g;
mainly passerines and doves) as the main prey for margay and oncilla, and small and mediumsized mammals (< 13 kg; mainly small rodents, small marsupials, opossums, Brazilian rabbit,
paca, armadillo, and porcupine) as well as small and medium-sized birds (< 0.5 g; passerines,
doves, and tinamous) for ocelot [35, 57–61].
Identification of felids using tricology and genetics
Because felids defecate conspicuously to signal their presence [62, 63] and groom frequently
[64], samples of scats with hairs are particularly easy to obtain. After washing the scats with
running water and drying them, we collected the guard hairs found in each sample to identify
the species to which the hair belonged. We cleaned the guard hairs with ethyl alcohol, and the
cuticular impressions were obtained by pressing the hairs against a thin layer of nail varnish
and leaving them to dry for three to five minutes on glass slides with the help of a bench vise
(adapted from [65]). We photographed the cuticular impressions at 400x magnification (S2
Fig) and compared the pattern of the cuticles with our reference collection (obtained from
hairs collected from museum specimens) and published guides [66, 67].
Hair sampling can lead to reliable detections of rare and cryptic animals [68], and the use
of mammalian hair for identification of taxa, known as tricology, is an established low-cost
method (e.g., [69–74]). This technique has also been proven to be as consistent as molecular
methods for identification of some Neotropical felids [75]. To test the accuracy of our identification through tricology, we conducted molecular analysis for 74% of the samples (N = 49).
We used mini-barcoding for molecular identification, comparing two markers from mitochondrial DNA [ATP6 (126 bp) and cytochrome oxidase I gene (COI) (187 bp)], applying the
primers developed by [76]. The obtained sequences were compared with reference sequences
from tissue samples of each species. We achieved confirmation for 100% of the samples, giving
us confidence in our identification and confirming the reliability of the method.
Landscape covariates
We mapped with Quantum Gis software [77] the vegetation cover, hydrography and roads of
the study area using high resolution satellite image interpretation at a 1:5,000 scale and cartographic maps (Secretariat of Economy and Planning—São Paulo State Government, at
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1:10,000). We validated the cover map by extensive field verification with a botanist (see
acknowledgements).
Because observed responses of the organisms to the environment may depend on the extent
at which the environment is being perceived (and thus measured; [21, 78, 79]), we adopted a
multi-spatial extent—also found in the literature as multiple scales [78]–approach while testing
the influence of the covariates on felid occupancy. The extents were defined as concentric circles (buffers) of 500 and 1,000 m radius around each sampling site, and we calculated each
site covariate for each extent. The smaller buffer (which covers 78 ha) is equivalent to the minimum known size for an ocelot home range (see [80]). This extent is likely smaller than the
average home range of the other two cats as well [80]. The area of the larger buffer (314 ha) is
closer to the home range of some ocelots, but might be also regarded as spatially conservative,
considering the huge variation in home range size known from this and the other two cats
[80]. Further, the larger buffer is the maximum we can use without overlapping with buffers of
adjacent sampling points.
We determined five landscape site covariates: mean elevation, percentage of high-quality
forest cover, hydrographic density, road density, and ‘weighted distance to reserve border’.
The percentage of high-quality forest cover was measured as the percentage of intermediate
and advanced forest succession cover within each buffer, which was calculated using Geographical Resources Analysis Support System (GRASS) [81]. Road and hydrographic densities
within each buffer were calculated with the Kernel density function in ArcGIS software [82].
We obtained the mean elevation at each buffer from digital elevation models (DEM) available
from Topodata Geomorphic database of Brazil [83]. We measured the distance from each sampling site to the nearest boundary of the Biological Municipal Reserve (REBIO), giving negative distances (in meters) to sites within the REBIO and positive distances otherwise (i.e., the
center of the reserve received the smallest value). Then the covariate ‘weighted distance to
reserve border’ was obtained by multiplying these distances by the protection status weight of
the subarea in which each sampling site was located (REBIO = 1; REBIO’s Buffer Zone = 2;
within the Natural Heritage Area but outside these two areas = 3; outside these three areas =
4). For ocelot, we used the distance to reserve border instead of ‘weighted distance to reserve
border’ (due to the lack of convergence in the models with this latter covariate). We normalized all covariates and used only covariates with low correlation (S2 Table) in the final model
sets (i.e., group of models being tested together).
Single-species occupancy models
We used occupancy modeling [42]—with a likelihood-based approach—to estimate the site
occupancy (ψ) of each spotted cat species and their main potential prey, and to evaluate the
influential factors on the felids’ occupancy while accounting for detection probability (p).
Because the size of the home ranges of ocelots, margays, and oncillas likely exceed that of our
sampling unit [84–87], we used occupancy as a measure of their habitat use instead of “true
occupancy” [38, 88]. A basic assumption of the occupancy models is that the sites are closed to
changes in occupancy during the repeated surveys [89]. This assumption can be relaxed when
occupancy is interpreted as “use” and movement in and out of the sampled area is random
[41, 89], similarly to our approach for the felids. We assessed the usage made of sites with various habitat characteristics within their home range, therefore we should interpret our result in
a third-order selection level [90]. However, because individual animals were not identified,
measures were made at the population level (i.e., it is a Design I study type; [91]).
We adopted a single-season approach [42], wherein data from all three campaigns were
combined. Data from both methods (camera trapping and scat sampling) were included, but
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in different sampling occasions. The decision to adopt a single-season modeling instead of a
multi-season approach was based on our previous analyses, which provided no evidence of
changes in occupancy across the three campaigns. Specifically, we fit both full multi-season
models and models where colonization and extinction were fixed to zero (no change), and the
latter models were ranked as the best (S3 Table). We also developed models that included an
effect of campaign/time on detection probabilities, but model selection results provided no evidence that this effect was needed (S4 Table).
We used a two-step approach while modeling the occupancy of each species of felid and
prey: 1) assess spatial scale that best represents species’ response to each covariate; and 2)
investigate sources of variation in ψ, determining the most influential covariates for occupancy, the parameter with biological meaning and in which we were most interested; as
detailed next. To determine the scale that best represents each species’ response to the habitat,
we used a general model for p (that consisted of as many potential covariates as possible) and
allowed occupancy (ψ) to vary (following [92]) by only the focal habitat covariate measured at
the extents of the two buffer sizes (S5 Table). After selecting the appropriate extent for each
covariate, we developed another model set to investigate the variation in occupancy (S6
Table). In this second step, we allowed ψ to be constant (ψ(.)) or to vary as a function of either
a single covariate or a combination of two (only additive effects). We built only models that
translated plausible biological hypotheses regarding the effects of variables on each species’
occupancy (ψ). We used each covariate at its extent of stronger response for each species (from
the previous step) and a general model for p. The potential covariates used in the general
model for p were: method used to survey each sampling occasion (camera trapping or scat
sampling), degree of soil coverage by plants or leaf litter on the roads where scats were sampled
(0- no coverage; 1- low to medium coverage; and 2- high coverage), and percentage of highquality forest cover at the 500 m buffer around each sampling site. By using a general model
for the parameters that were not investigated within a specific model set, we reduced the possibility that imposed constraints (on p, for example) would result in residual sampling variation
being attributed to a variation in occupancy.
We evaluated candidate models and estimated parameters using PRESENCE software [93]
to determine the covariates that best explain occupancy. We ranked candidate models using
the Akaike’s Information Criterion adjusted for small sample size (AICc; N = number of sites)
[94] and excluded all models that did not converge. We considered the covariate(s) from the
top-ranked model(s) (i.e., models with ΔAICc<2) as the most likely determinant(s) of the species’ occupancy. When different spatial extents were equally plausible (ΔAICc<2), we chose to
use the extent closer to the home range size of the spotted cats (1,000 m) in the final models.
Additionally, we assessed the relative importance of each covariate by summing the Akaike
weights (wi) of all the models (i) in which that covariate was present [94]. We applied model
averaging [94] to estimate the overall occupancy of each felid at our study area and to estimate
the site occupancy of each prey. The prey index was obtained by summing the site occupancy
of all potential prey for each felid species.
Co-occurrence models
We investigated whether the presence of one felid influences the occupancy and detection
probability of another felid by pair-wise comparisons for all felid species using two-species
single-season occupancy models [41]. We used the ψBa/rBa parameterization in PRESENCE
software [93], assuming that the dominant species was always the larger (i.e., greater mean
body mass) in the analyzed pair (ocelot in ocelot-margay and ocelot-oncilla pairs, and
margay in the margay-oncilla pair; [34]). The parameters estimated for occupancy were:
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ψA (occupancy of dominant species), ψBA (occupancy of subordinate species when the
dominant species is present), and ψBa (occupancy of subordinate species when the dominant species is absent). We modeled ψA, ψBA, and ψBa, incorporating the best covariate
revealed in the single-species models for each species to account for possible differences in
habitat preferences. We also incorporated the method used to survey each sampling occasion (camera trapping and scat sampling) as a covariate for p in all co-occurrence analyses.
We built models that assumed that the occupancy of the subordinate species was influenced
by the dominant species (ψBA6¼ψBa) or was independent of the dominant species (ψBA =
ψBa).
For detection probability, the parameters estimated were: rA (probability of dominant species being detected when the subordinate species is present), pA (probability of dominant species being detected when the subordinate species is absent), pB (probability of subordinate
species being detected when the dominant species is not present), rBA (probability of subordinate species being detected when the dominant species is present and detected), rBa (probability of subordinate species being detected when the dominant species is present but not
detected). We built models where the detection probability of the subordinate species was
influenced by the presence (pB6¼rBa; pB6¼rBA) or detection (rBa6¼rBA) of the dominant species or was independent of the dominant species (pB = rBa = rBA), as well as models that
assumed that the detection of the dominant species was influenced by the detection of the subordinate species (rA6¼pA) or independent (rA = pA).
We calculated the species interaction factor (SIF) for occupancy (phi; [95]) and detection
rBA
probability (delta ¼ rA ððrA rA
; adapted from the formula for phi from [95]).
rBAÞþðð1 rAÞ rBaÞÞ
The SIF is a ratio of how likely the two species are to co-occur compared to what would be
expected under a hypothesis of independence [94]. We obtained the parameter estimates by
model averaging [95] the estimates of each species-pair model set (i.e., group of all models
tested for each species-pair). If two species occur or are detected independently, SIF = 1. If
SIF<1, species co-occur or are detected less frequently than would be expected if they were
independent (i.e., avoidance). If SIF>1, species co-occur or are detected more frequently than
expected (i.e., aggregation) [95].
To rank candidate models, we adopted the same procedure as the single-species analysis. To
infer about the co-occurence patterns, we considered the estimated parameters (ψA, ψBA, ψBa,
rA, pA, pB, rBa, rBA), the relationships among them, the top-ranked model(s) (ΔAICc<2), and
the SIF calculated for each species pair.
Ethics statement
Jundiaı́ City Hall and private owners provided permission to conduct this project at Serra do
Japi. During this research, the animals were observed in their natural environment and none
of them were captured or handled. Therefore, there are no protocols to be reported to institutional or governmental agencies that regulate animal research.
Results
Spatial scale
Models with the same covariate measured at different extents (500 m vs. 1,000 m) were usually
equally supported, with the exception of high-quality forest cover for ocelot, which was better
explained by the 500 m extent than the 1,000 m extent (S5 Table).
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Table 1. Number of records (detections) by each method (scat sampling and camera trap), number of sampling sites with detections, naïve occub from single-season single-species models, and relative increase above naïve occupancy when using
pancy, estimated occupancy probability ( C)
estimates of the three Neotropical spotted cats in a large Atlantic Forest remnant.
N detections
N sites w. detections Naïve occup.
Scats Camera traps
Detection probability (p)
b1
Scats Camera traps Occup. prob. C
Rel. increase above naïve occup.
(%)2
Ocelot
8
16
12
0.27
0.07
0.08
0.48 (±0.15)
80
Margay
27
12
17
0.38
0.11
0.05
0.64 (±0.17)
69
Oncilla
33
29
21
0.47
0.14
0.07
0.64 (±0.13)
37
1
Occupancy probability and standard deviation estimated by model averaging.
2
Percentage increase in estimated proportion of occupied sites when incorporating detection probability (p) [(estimated occupancy probability/naïve
occupancy)-1*100].
doi:10.1371/journal.pone.0168441.t001
Single-species occupancy
We had 123 detections (N = 5,198 trap days) of the three Neotropical spotted cats (Table 1; S7
Table). We had some evidence that the habitat use of the cats varied according to landscape
characteristics (Fig 2). Margay and ocelot had higher occupancies closer to a more protected
area (i.e., reserve), and weighted distance to reserve border was the main factor influencing
their habitat use, emerging as the top-ranked model with high relative importance (Fig 3; S6
Table). All other analyzed covariates had overall low relevance (Fig 3; S6 Table).
Fig 2. Neotropical spotted cats’ site occupancy. Interpolated site occupancy of the three spotted cats at an Atlantic
Forest site in Brazil: ocelot—Leopardus pardalis (top left), margay—L. wiedii (top right), oncilla—L. guttulus (bottom left).
doi:10.1371/journal.pone.0168441.g002
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Fig 3. Covariates effect on the occupancy of Neotropical spotted cats. Influence of geomorphometry,
environmental, and anthropogenic covariates on the occupancy of spotted cats in a large Atlantic Forest
remnant, showing the sum of wi (which indicates the relative importance of covariates) and the associated
beta estimates with standard error estimated from the single-season single-species models.
doi:10.1371/journal.pone.0168441.g003
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Habitat Use and Co-Occurrence of Spotted Cats
Margay and oncilla, the two smaller and more cryptic cats, had more detections from scat
sampling, a method that had a detection probability of twice the detection probability of camera traps (Table 1).
Co-occurrence patterns
There was no evidence that one species affects the occupancy of the other (Tables 2 and 3). For
all three pairs of species (ocelot-margay, ocelot-oncilla, and margay-oncilla), the models where
the pair had similar occupancies and the subordinate species had similar occupancy regardless of
the dominant being at the sampling site or not (i.e., ψA = ψBA = ψBa) were ranked as the top
models (Table 2). Furthermore, models where the covariate ‘weighted distance to reserve border’
was incorporated with ocelot and margay occupancy were always ranked better than the models
without this covariate, reinforcing the importance of the reserve for both species (Table 2).
We also found no evidence that the presence of margay or oncilla have an effect on the
detection probability of the more dominant species, the ocelot (i.e., that pA6¼rA), or that the
presence or detection of ocelot has an effect on the detection of margay (i.e., that pB6¼rBA =
rBa or pB6¼rBA6¼rBa; Tables 3 and 4). However, we found evidence that the presence of margay and the presence and detection of ocelot increases the detection probability of oncilla (i.e.,
margay-oncilla: pB6¼rBA = rBa; ocelot-oncilla: pB6¼rBA6¼rBa; Tables 3 and 4).
Table 2. Co-occurrence occupancy models used to evaluate the role of interspecific interactions on the habitat use of three sympatric Neotropical
spotted cats in a large Atlantic Forest remnant.
Model
AIC
ΔAIC
wi
K
-2LL
349.56
0
0.76
9
326.42
Ocelot-Margay
ψA(reserve dist) = ψBA(reserve dist) = ψBa(reserve dist) p(global1)
ψA(reserve dist)6¼ψBA(reserve dist) = ψBa(reserve dist) p(global)
352.25
2.69
0.20
10
325.78
ψA = ψBA = ψBa p(global)
355.87
6.31
0.03
8
335.87
ψA6¼ψBA = ψBa p(global)
358.03
8.47
0.01
9
334.89
ψA6¼ψBA6¼ψBa p(global)
361.32
11.76
0.00
10
334.85
Ocelot-Oncilla
ψA(reserve dist) = ψBA = ψBa p(global2)
386.13
0
0.34
8
366.13
ψA(reserve dist)6¼ψBA = ψBa p(global)
389.20
3.07
0.07
9
366.06
ψA(reserve dist)6¼ψBA6¼ψBa p(global)
392.53
6.40
0.01
10
366.06
Margay-Oncilla
ψA(reserve dist) = ψBA = ψBa p(global3)
443.56
0
0.60
5
432.02
ψA(reserve dist)6¼ψBA = ψBa p(global)
445.84
2.28
0.19
6
431.63
ψA(reserve dist)6¼ψBA6¼ψBa p(global)
447.44
3.88
0.09
7
430.41
ψA,ψBA = ψBa p(global)
447.61
4.05
0.08
5
436.07
ψA6¼ψBA6¼ψBa p(global)
448.84
5.28
0.04
6
434.63
ψA = ψBA = ψBa p(global)
452.62
9.06
0.01
4
443.62
p(general1) = pA6¼rA6¼pB6¼rBA6¼rBa; method;
p(general2) = pA = rA6¼pB6¼rBA6¼rBa; method;
p(general3) = pA = rA = pB = rBA = rBa; method.
ψA = occupancy of dominant species; ψBA = occupancy of subordinate species when the dominant species is present; ψBa = occupancy of subordinate
species when the dominant species is absent. Reserve dist = Covariate ‘weighted distance to reserve border’. We used “=” to designate that two or more
parameters were set as equal (e.g., ψBA = ψBa means that the occupancy of the subordinate species is independent of that of the dominant species). We
used “6¼” to designate that two or more parameters were set as different (e.g., ψBA6¼ψBa models assumed that the occupancy of the subordinate species
was influenced by the dominant species).
doi:10.1371/journal.pone.0168441.t002
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Habitat Use and Co-Occurrence of Spotted Cats
Table 3. Occupancy (ψ), detection probability (p and r), and species interaction factor (SIF—phi and delta) estimated from co-occurrence occupancy models of three sympatric Neotropical spotted cats in a large Atlantic Forest remnant.
ψA
ψBA
ψBa
rA
pA
pB
rBA
rBa
Phi
Delta
Ocelot—Margay
0.60
0.63
0.63
0.06
0.06
0.08
0.08
0.08
1.00
1.00
Ocelot—Oncilla
0.67
0.80
0.80
0.05
0.05
0.04
0.41
0.10
1.00
3.53
Margay—Oncilla
0.61
0.68
0.63
0.09
0.08
0.08
0.13
0.12
1.03
1.11
ψA = occupancy of dominant species; ψBA = occupancy of subordinate species when the dominant species is present; ψBa = occupancy of subordinate
species when the dominant species is absent; rA = probability of dominant species being detected when the subordinate species is present; pA = probability
of dominant species being detected when the subordinate species is absent; pB = probability of subordinate species being detected when the dominant
species is not present; rBA = probability of subordinate species being detected when the dominant species is present and detected; rBa = probability of
subordinate species being detected when the dominant species is present but not detected; Phi = ratio of how much more (>1) or less (<1) likely the species
are to co-occur at a site compared to what would be expected if the species occurred independently of each other; Delta = ratio of how much more (>1) or
less (<1) likely the species are to be detected together in a survey compared to what would be expected if they were detected independently.
doi:10.1371/journal.pone.0168441.t003
Discussion
When investigating species’ distribution and use of habitat, assessing the relative importance
of landscape attributes and the role of interspecific interactions is often difficult. Here, we used
occupancy modeling [41, 42] to explicitly incorporate detection probability and habitat variables while examining co-occurrence patterns and landscape use by three sympatric and morphologically similar species of Neotropical cats. Predators can be considered a key group
because they affect prey and plant populations, influencing ecosystem dynamics [1–3].
Table 4. Co-occurrence occupancy models used to evaluate the role of interspecific interactions on the detection probability of three sympatric
Neotropical spotted cats in a large Atlantic Forest remnant.
Model
AIC
ΔAIC
wi
K
-2LL
327.60
Ocelot-Margay
ψ(top) pA = rA = pB = rBA = rBa
339.14
0
0.77
5
ψ(top) pA = rA,pB = rBA = rBa
341.81
2.67
0.20
6
327.60
ψ(top) pA6¼rA6¼pB6¼rBA = rBa
346.45
7.31
0.02
8
326.45
ψ(top) pA6¼rA6¼pB6¼rBA6¼rBa
349.56
10.42
0.00
9
326.42
366.13
Ocelot-Oncilla
ψ(top) pA = rA6¼pB6¼rBA6¼rBa
386.13
0
0.21
8
ψ(top) pA = rA = pB = rBA = rBa
391.69
5.56
0.01
5
380.15
ψ(top) pA6¼rA6¼pB = rBA = rBa
393.20
7.07
0.01
7
376.17
ψ(top) pA = rA6¼pB = rBA = rBa
394.36
8.23
0.00
6
380.15
432.02
Margay-Oncilla
ψ(top) pA = rA = pB = rBA = rBa
443.56
0
0.28
5
ψ(top) pA = rA6¼pB6¼rBA = rBa
444.05
0.49
0.22
7
427.02
ψ(top) pA = rA6¼pB = rBA = rBa
444.96
1.40
0.14
6
430.75
ψ(top) pA6¼rA6¼pB6¼rBA = rBa
445.41
1.85
0.11
8
425.41
ψ(top) pA6¼rA6¼pB = rBA = rBa
445.46
1.90
0.11
7
428.43
ψ(top) pA = rA6¼pB6¼rBA6¼rBa
445.69
2.13
0.10
8
425.69
ψ(top) pA6¼rA6¼pB6¼rBA6¼rBa
447.42
3.86
0.04
9
424.28
While modeling detection probability (p) for each pair of species, we incorporated the covariate “method” in p and kept occupancy (ψ) as it was in the topranked model from the co-occurrence occupancy models used to evaluate ψ (Table 2): Ocelot vs. Margay ψ(top) = [ψA(reserve dist) = ψBA(reserve dist) =
ψBa(reserve dist)]; Ocelot vs. Oncilla ψ(top) = [ψA(reserve dist)6¼ψBA = ψBa]; Margay vs. Oncilla ψ(top) = ψA = ψBA = ψBa.
doi:10.1371/journal.pone.0168441.t004
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However, most species of felids are threatened, or we lack basic information about them. All
three Neotropical spotted cats analyzed here are believed to be suffering from population
decrease, and two (L. wiedii and L. guttulus) are considered ‘Near Threatened’ or ‘Vulnerable’
[96]. We demonstrated that the proximity to a more protected area (nature reserve) is the
main factor influencing the habitat use of Neotropical spotted cats within their home ranges in
a large Atlantic forest remnant, and we had low support for the hypothesis that interspecific
interactions modulate how they use the landscape.
Our first prediction that among the landscape characteristics, the human-related variables
would be more important predictors of landscape use by Neotropical spotted cats was in part
corroborated. Roads and human accessibility, for instance, are important determinants of the
occupancy of sensitive species (e.g., game species—[97]); and road kills can be common
among felids and other carnivores [98, 99]. Here, we had some evidence that road density,
which is also a measure of human accessibility, may have a negative effect on occupancy of
some spotted cats (given its negative beta estimates for two out of the three species analyzed),
particularly on ocelots; however, it did not have a strong effect and was not a major factor for
them. Besides area accessibility, higher protection status is also usually associated with lower
human-related pressures [9, 17, 18]. Our results showed that more strict protection status is
important for the Neotropical spotted cats, especially for ocelots and margays, which used
more areas closer to the reserve even though our entire study area is under some type of protection. Although ‘weighted distance to reserve border’ was not a high-ranked model for
oncilla, the influence of this covariate on its occupancy was also in the predicted direction
(negative). In previous studies, only ocelots or larger felids (pumas and jaguars) were more
commom in areas with higher legal protection than in less protected areas [100, 101]. Whether
these distinct outcomes are derived from differences in the felid community structure (e.g.,
more larger felids occuring in these previous studies than in our study area) or arise from
some intrinsic local characteristic of the sites (e.g., level of anthropogenic pressures) can only
be answered with further studies.
Prey availability can usually influence the abundance, density, occupancy and habitat use of
carnivores [56, 102–105]. Although the relationship between the prey index and occupancy
was in the predicted direction (positive), we did not have enough evidence that this variable,
hydrographic density, high-quality forest cover, and elevation influence the spotted cats’ habitat use. The possible opportunistic feeding behavior of Neotropical small cats [36] could
explain the lack of effect of prey on their habitat use. However, we also note that prey availability, measured as the sum of prey occupancy at each sampling site, varied only slightly across
sites (mean 1.31±0.12) and did not account for differences in prey density. Therefore, we cannot yet discard a possible effect of prey on spotted cats’ habitat use.
Intraguild competition can be an important determinant of carnivore abundance and distribution, as it can lead to spatial or temporal segregation among species [7, 28, 106]. However,
when examining species co-occurrence patterns, it is often difficult to distinguish the difference between habitat preferences and competitive exclusions. Furthermore, since species present at a location are not always detected with certainty, incorporating detection probability
along with habitat preferences directly into the model set may avoid incorrect inferences about
co-occurrence patterns [41]. By adopting this approach, we found no evidence that the presence of either ocelot, margay, or oncilla have a negative influence on how the other species use
the habitat. Thus competitive exclusion among them is unlikely, at least within the analyzed
scale, and at a relatively conserved and protected area such as ours. The lack of spatial partitioning based on interspecific interaction was also found for North American carnivores, and
similarly to our study, habitat preferences were more important in structuring the community
[107, 108]. Nonetheless, because occupancy does not account for variations in density, it is still
PLOS ONE | DOI:10.1371/journal.pone.0168441 January 4, 2017
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Habitat Use and Co-Occurrence of Spotted Cats
possible that the presence of one species affects the density of the other, as previously suggested
[109]. We also encourage further research to analyze the effects of larger felids (e.g., pumas
and jaguars) on the habitat use of smaller cats, which unfortunately, we did not have data to
investigate (only six detections of pumas and none of jaguar). In addition, since land use and
human activity can alter occupancy patterns [110] and behavior [111] of carnivores, it would
be of interest to explore whether interspecific relations and co-occurrence patterns among
small felids are affected by different degrees of human disturbances, which unfortunately we
did not have enough data to investigate. Finally, although the models used here can yield
strong inferences about species co-occurrence patterns, this does not imply strong inference
about the processes that generated the observed patterns, and it would be useful to observe the
system dynamics over time [41].
We found evidence that one species affects the behavior of the other in at least one respect,
as oncilla is more likely to be detected if margay is present or if ocelot is either present or
detected. Felids are highly territorial and use scent marks such as urine and feces to mark their
territories [62]. Two things should be noted from the fact that most of the felids’ detections
came from scat sampling, and therefore, their territorial scent mark: first, since we collected
the feces as they were detected, sites with scat detections became unmarked after each sampling occasion. Second, scent marks are frequently over-marked either by the same animal or
other individuals [62]. Therefore, we suggest that the presence of a spotted cat (and consequently, its feces) increased the detection of another spotted cat as it was attracted to either
over-mark the feces (before it was removed) or use an area rendered unmarked (through collection of the scats). The small felids may be able to avoid direct confrontations by marking
recently used areas within their home range and using different parts of their home range
according to where the other is currently marking (i.e., using the area). However, to clarify the
mechanisms underlying our findings, more detailed studies on the behavior of the Neotropical
spotted cats are necessary. What we can suggest so far is that territorial demarcation may
potentially help to regulate how these small felids share their habitats, consequently alleviating
or reducing the competition among them.
In conclusion, our results suggest that human-related factors, such as distance to a reserve,
are more important drivers of Neotropical spotted cats’ habitat use than are interspecific interactions, environmental landscape attributes, geomorphometry or, potentially, prey availability.
We suggest that the dietary overlap of the three species might be small enough to allow co-existence [57–61], or that another behavioral mechanism in addition to differences in habitat preference, such as time partitioning, may allow them to co-exist [100, 112, 113].
Management recommendations
Given the importance and vulnerability of spotted cats in Neotropical forests, several actions
are required for their conservation, including enhancing forest connectivity and gathering
basic information on their ecology and behavior [4, 114]. Here we underline the main management recommendations resulting from our study:
1. We demonstrated that the most important factor for managing small felids can be the
maintenance and establishment of nature reserves and other areas that are managed to
ensure minimal disturbance and human presence (i.e., IUCN Protected Area Category Ia—
Strict Nature Reserve [115]). Protected areas can decrease habitat loss [116] and anthropogenic pressures [17, 18] as well as improve the occupancy of key groups (Nagy-Reis et al.
present study). However, because poaching may still occur illegally, effective law enforcement and other management actions such as environmental education are also important
to ensure the conservation of small felids.
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Habitat Use and Co-Occurrence of Spotted Cats
2. The use of methods that incorporate detection probability is critical to the understanding of
species’ responses to habitat and interspecific interactions. We showed how dealing with
imperfect detection results in a substantial relative increase above naïve occupancy for rare,
cryptic, and elusive animals such as small felids, reinforcing the importance of accounting for
detection probability in ecological and behavioral studies on such species. Our research also
emphasizes the feasibility of alternative methods for surveying felids, such as scat sampling
combined with tricology, which has a higher detection probability than camera traps and supplies non-invasive material for studying diet and performing molecular analysis, providing
essential information on population parameters and ecology [63, 117]. We also confirmed the
reliability of tricology as a low cost alternative to molecular methods for identification of Neotropical felids, as long as meticulous procedure is adopted by a trained researcher.
Supporting Information
S1 Fig. Location of the sampling sites at the study area (Serra do Japi, Brazil) where Neotropical spotted cats were sampled using camera trap and scat sampling. Sampling sites
represented with a same color were sampled simultaneously within each campaign. Campaign
1 (April 2013 to September 2013)–Group A (black): Apr-Mai; Group B (blue): Jun-Jul; Group
C (green): Ago-Sep. Campaign 2 (October 2013 to March 2014)–Group A: Oct-Nov; Group B:
Dec-Jan; Group C: Feb-Mar. Campaign 3 (April 2014 to September 2014)–Group A: Apr-Mai;
Group B: Jun-Jul; Group C: Ago-Sep.
(PDF)
S2 Fig. Photos of the cuticular impressions of each Neotropical spotted cat at 400x magnification. From the left to the right: ocelot (Leopardus pardalis), margay (L. wiedii), and oncilla
(L. guttulus).
(PDF)
S1 Table. Spatial independence of detections for three Neotropical spotted cats sampled
with camera trap and scat sampling (1.5 km between sampling sites) at a large Atlantic
Forest remnant in Brazil. Results of Moran’s I autocorrelation tests [1]—based on the number
of detection records of each species and the geographic position of each site. We used R 2.13.0
software [2] and the ape package [3]. 1. Legendre P & Legendre L. Numerical Ecology. New
York: Elsevier; 1998. 2. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2014. Available: <www.R-project.
org>. Accessed 2 Oct 2014. 3. Paradis E, Blomberg S, Boljer B, Claude J, Cuong HS. et al. Analyses of phylogenetics and evolution: package “ape”. Available: <ape-package.ird.fr>. Accessed
10 Jul 2015.
(PDF)
S2 Table. Spearman’s correlation matrix for the site covariates measured at a large Atlantic
Forest remnant in Brazil. The four landscape site covariates (elevation, hydrographic density,
road density and percentage of high-quality forest cover) were measured at 500 and 1,000 m
spatial extents (buffer sizes). Highly correlated (rs<0.50) outcomes are in bold. S prey = prey
index for margay and oncilla; O prey = prey index for ocelot; = excluded covariates.
(PDF)
S3 Table. Multi-season single-species occupancy models used to evaluate the effect of time
(“campaign”) on the habitat use of sympatric Neotropical spotted cats at a large Atlantic
Forest remnant in Brazil. p(general) = campaign + method + soil coverage + percentage
of high-quality forest cover at 500 m buffer size; “gamma” = colonization; “eps” = extinction;
PLOS ONE | DOI:10.1371/journal.pone.0168441 January 4, 2017
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Habitat Use and Co-Occurrence of Spotted Cats
“.” = no covariate included; “0” = parameter was fixed to 0.
(PDF)
S4 Table. Model selection analysis for detection probability (p) covariates for three Neotropical spotted cats at a large Atlantic Forest remnant in Brazil. “Campaign” = campaign
in which data was collected (1- April 2013 to September 2013; 2- October 2013 to March 2014;
3- April 2014 to September 2014); “Groups” = order within the campaigns that sampling sites
were surveyed; “Method” = method used in each sampling occasion (camera trapping or scat
sampling); “.” = no covariate included (i.e., null model).
(PDF)
S5 Table. Model selection analysis for occupancy (ψ) covariates (elevation, percentage of
high-quality forest cover, hydrographic density and road density) measured at different
spatial extents (buffer sizes) for three Neotropical spotted cats at a large Atlantic Forest
remnant in Brazil. p(general) = method + soil coverage + percentage of high-quality forest
cover at 500 m buffer size.
(PDF)
S6 Table. Single-season single-species occupancy models (cumulative wi >0.80) used to
evaluate the effect of weighted distance to reserve border, geomorphometry, environmental, and anthropogenic landscape attributes on the habitat use of sympatric Neotropical
spotted cats at a large Atlantic Forest remnant in Brazil. p(general) = method + soil coverage + percentage of high-quality forest cover at 500 m buffer size.
(PDF)
S7 Table. Spearman’s correlation matrix for the three Neotropical spotted cats found at a
large Atlantic Forest remnant in Brazil (based on the number of detection records at each
sampling site).
(PDF)
Acknowledgments
We are thankful to Jundiaı́ City Hall and all private owners for permission to conduct this
project at Serra do Japi, and to Coordination for the Improvement of Higher Education Personnel (CAPES), São Paulo Research Foundation (FAPESP; grant 2013/07162-6), and Idea
Wild for their financial and equipment support. We are also grateful to J. E. Hines for assisting
in our data analysis; M. F. Rossi for land cover mapping; J. W. Ribeiro for all the landscape
metrics and relief information computing; V. H. Iwakami, M. F. Rossi and M. C. S. Canhoto
for helping with scat sampling and tricology; C. A. Estevo for aiding with prey data; B. H. Saranholi and Molecular Biodiversity and Conservation Lab (BMC/Ufscar) for molecular analysis; J. Godinho for identifying vegetation cover in the field; M. J. F. Penteado for confirming
species photo identification; and the Traldi family and Benê for assisting during data collection. The Brazilian Science Council (CNPq) provided a scholarship to AGC (process 305902/
2014-8). MCR has been continuously supported by grants and scholarships from CNPq (process 312045/2013-1) and FAPESP (grant 2013/50421-2). Megan King, a native English speaker
from British Columbia, CA did the proof reading of the manuscript.
Author Contributions
Conceptualization: MBNR AGC MCR.
Formal analysis: MBNR JDN.
PLOS ONE | DOI:10.1371/journal.pone.0168441 January 4, 2017
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Habitat Use and Co-Occurrence of Spotted Cats
Funding acquisition: MBNR EZFS.
Investigation: MBNR EZFS.
Methodology: MBNR JDN AGC MCR.
Project administration: MBNR.
Resources: EZFS JDN.
Supervision: MBNR.
Validation: MBNR JDN AGC MCR.
Visualization: MBNR MCR.
Writing – original draft: MBNR.
Writing – review & editing: MBNR JDN AGC MCR EZFS.
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