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Density and population structure of the jaguar (Panthera onca) in a protected area of Los Llanos, Venezuela, from 1 year of camera trap monitoring



Density is crucial for understanding large carnivore ecology and conservation, but estimating it has proven methodologically difficult. We conducted 1 year of camera trapping to estimate jaguar (Panthera onca) density and population structure in the Los Llanos region of Venezuela on the Hato Piñero ranch, where hunting is prohibited and livestock are excluded from half of ranch lands. We identified 42 different jaguars and determined their sex, age class, and reproductive status. We estimated adult jaguar densities with spatial capture-recapture models, using sex/reproductive state and session as covariates. Models without temporal variation received more support than models that allowed variation between sessions. Males, reproductive females, and nonreproductive females differed in their density, baseline detectability, and movement. The best estimate of total adult jaguar population density was 4.44 individuals/100 km². Based on reproductive female density and mean number of offspring per female, we estimated cub density at 3.23 individuals/100 km² and an overall density of 7.67 jaguars/100 km². Estimated jaguar population structure was 21% males, 11% nonreproductive females, 26% reproductive females, and 42% cubs. We conclude that extending the sampling period to 1 year increases the detectability of females and cubs and makes density estimates more robust as compared to the more common short studies. Our results demonstrate that the Venezuelan Llanos represent important jaguar habitat, and further, they emphasize the importance of protected areas and hunting restrictions for carnivore conservation.
Density and population structure of the jaguar (Panthera onca)
in a protected area of Los Llanos, Venezuela, from 1 year
of camera trap monitoring
Włodzimierz Jędrzejewski
&Maria F. Puerto
&Joshua F. Goldberg
Mark Hebblewhite
&María Abarca
&Gertrudis Gamarra
&Luis E. Calderón
José F. Romero
&Ángel L. Viloria
&Rafael Carreño
&Hugh S. Robinson
Margarita Lampo
&Ernesto O. Boede
&Alejandro Biganzoli
&Izabela Stachowicz
Grisel Velásquez
&Krzysztof Schmidt
Received: 8 November 2016 / Accepted: 10 November 2016
#The Author(s) 2016. This article is published with open access at
Abstract Density is crucial for understanding large carnivore
ecology and conservation, but estimating it has proven meth-
odologically difficult. We conducted 1 year of camera trap-
ping to estimate jaguar (Panthera onca)densityandpopula-
tion structure in the Los Llanos region of Venezuela on the
Hato Piñero ranch, where hunting is prohibited and livestock
are excluded from half of ranch lands. We identified 42 dif-
ferent jaguars and determined their sex, age class, and repro-
ductive status. We estimated adult jaguar densities with spatial
capture-recapture models, using sex/reproductive state and
session as covariates. Models without temporal variation re-
ceived more support than models that allowed variation be-
tween sessions. Males, reproductive females, and nonrepro-
ductive females differed in their density, baseline detectability,
and movement. The best estimate of total adult jaguar popu-
lation density was 4.44 individuals/100 km
. Based on
reproductive female density and mean number of offspring
per female, we estimated cub density at 3.23 individuals/
100 km
and an overall density of 7.67 jaguars/100 km
Estimated jaguar population structure was 21% males, 11%
nonreproductive females, 26% reproductive females, and 42%
cubs. We conclude that extending the sampling period to
1 year increases the detectability of females and cubs and
makes density estimates more robust as compared to the more
common short studies. Our results demonstrate that the
Venezuelan Llanos represent important jaguar habitat, and
further, they emphasize the importance of protected areas
and hunting restrictions for carnivore conservation.
Keywords Carnivore conservation .Felid ecology .Hato
Piñero .Jaguar breeding .Populationdensity estimate .Spatial
Communicated by: Karol Zub
Electronic supplementary material The online version of this article
(doi:10.1007/s13364-016-0300-2) contains supplementary material,
which is available to authorized users.
*Krzysztof Schmidt
Centro de Ecología, Instituto Venezolano de Investigaciones
Científicas (IVIC), Carretera Panamericana km 11, Caracas 1020-A,
Evolution, Ecology and Organismal Biology Program, University of
California, Riverside, CA 92521, USA
Wildlife Biology Program, Department of Ecosystem and
Conservation Sciences, University of Montana,
Missoula, MT 59812, USA
Hato PiñeroUPSAT Piñero, El Baúl, Cojedes 2213, Venezuela
Panthera, New York, NY 10018, USA
College of Forestry and Conservation, University of Montana,
Missoula, MT 59812, USA
Fundación para el Desarrollo de las Ciencias, Físicas, Matemáticas y
NaturalesFUDECI, Caracas 1010-A, Venezuela
Departamento de Biología, Facultad de Ciencias, Universidad de Los
Andes ULA, Mérida 5101, Venezuela
Mammal Research Institute, Polish Academy of Sciences,
17-230 Białowieża, Poland
Mamm Res
DOI 10.1007/s13364-016-0300-2
Population density is central to understanding the ecology, spatial
distribution, and abundance of all organisms (Krebs 2001), yet
estimating density reliably remains a challenging problem in
applied ecology. This issue remains especially persistent for eco-
logically important large carnivores. Carnivore population den-
sity is one of the major components that determines the impacts
of predation on prey populations (Holling 1959; Messier 1994;
Jędrzejewska and Jędrzejewski 1998). Accurate density esti-
mates are also critical to evaluate population size and trends of
large carnivores, an increasingly important aim given worldwide
declines of many of these species (Gros et al. 1996; Treves and
Karanth 2003; Ripple et al. 2014).
For large carnivores, like other threatened and endangered
species, the knowledge of population structure and demogra-
phy can help predict population trends and long-term persis-
tence (Shaffer 1981; Coulson et al. 2001;Cooleyetal.2009).
Demographic parameters may depend upon species biology
but also reflect the breeding performance of a population. A
high proportion of breeding females and cubs suggests a high
reproduction rate and potentially a growing population.
Conversely, carnivore populations with few breeding individ-
uals may have a higher extinction risk. Thus, integrating
methods to estimate large carnivore population breeding struc-
ture with density could improve population trend predictions
and promote effective conservation (Woodroffe 2011;
Rosenblatt et al. 2014).
As the top predator in the Neotropics, the jaguar
(Panthera onca) may have large impacts on prey popula-
tions, and an important role in trophic cascades and eco-
system regulation (Terborgh et al. 2001; Cavalcanti and
Gese 2010; Estes et al. 2011). Like many large carnivores,
the jaguar has experienced a rapid contraction of its nat-
ural range due to anthropogenic influences, especially
habitat alteration and fragmentation (Quigley and
Crawshaw 1992; Nowell and Jackson 1996;Sanderson
et al. 2002a;Zeller2007). Moreover, the reported densi-
ties of the jaguar vary substantially across its present dis-
tribution, but the factors that shape this variation are poor-
ly understood (see Maffei et al. 2011 and Tobler and
Powell 2013 for review).
A variety of field and statistical methods have been
used to estimate large carnivore population densities
(e.g., Gros et al. 1996; Karanth and Nichols 1998;
Stander 1998; Wilson and Delahay 2001). Initial attempts
at estimating jaguar density were based on radio-tracking
(Schaller and Crawshaw 1980;C
et al. 2002). In the last decade, camera trapping combined
with capture-recapture statistical methods has become
common (Maffei et al. 2011). The recent development of
spatially explicit capture-recapture (SCR) methods has
further improved the quality of density estimates
(Borchers and Efford 2008;Nossetal.2012;Royle
et al. 2014). However, application of these methods may
still present unresolved issues, such as the large differ-
ences among estimates from consecutive seasons within
the same study area (e.g., de la Torre and Medellin 2011;
Foster and Harmsen 2012;Tobleretal.2013). This vari-
ation among estimates may result not only from study
areas of insufficient size but also from low detectability,
especially of females and juveniles. The low number of
detections may be partially attributable to short study pe-
riods (13 months), which are commonly used to address
the assumption of population closure in capture-recapture
models (Karanth and Nichols 1998;Silveretal.2004;
Maffei et al. 2011). In theory, prolonged study periods
may allow for immigration, emigration, births, and deaths
in the study area and lead to overestimates or underesti-
mates of abundance by the closed population models ap-
plied in SCR packages (White et al. 1982; Kendall et al.
However, extending study duration may bring important
benefits, such as an increased number of detections. For
example, better detectability of all sex/age groups would
allow estimating population breeding structure and would
broaden the applicability of camera trapping to an array of
other ecological and conservation questions (du Preez
et al. 2014). While the open population models are still
less established and have been rarely applied to density
estimates (e.g., Gardner et al. 2010; Whittington and
Sawaya 2015), it would be practical to find solutions for
applying the commonly used SCR methods based on
closed population models to long-term camera trapping
Here, we study a jaguar population in Los Llanos, a region
of vast plains interspersed with numerous rivers, marshes,
open grasslands, and forests, extending through large parts
of Venezuela and Colombia. This unique region constitutes
an important habitat for jaguar in northern South America
(Hoogesteijn and Mondolfi 1992; Sanderson et al. 2002b;
Rabinowitz and Zeller 2010); however, human-jaguar con-
flicts related to frequent jaguar attacks on cattle threaten pop-
ulations of this carnivore in the area (Hoogesteijn et al. 1993).
Conservation measures implemented on cattle ranches can
improve the prospects of jaguar persistence in this region
(Hoogesteijn and Chapman 1997).
In this work, we assessed the impact of study design on the
estimates of jaguar density and population structure in the
partially protected Hato Piñero ranch in the Venezuelan Los
Llanos. First, we examined the impact of sampling period on
population density estimates with SCR methods. To fulfill the
assumptions of the closed population model, we divided our
long study to shorter sessions and used these sessions as a
covariate in our SCR models. We tested whether increased
study duration leads to increased detectability, especially of
Mamm Res
females and cubs, and if it improves robustness of the density
estimates. We further estimated basic reproductive parameters
and structure of the jaguar population to assess the status of
this species in our study area and the efficacy of the protection
measures applied in Hato Piñero. Based upon our findings, we
made recommendations for conservation and future studies of
jaguars and other large carnivores.
Study area
Hato Piñero ranch encompasses a total area of 800 km
in the
south-eastern part of Cojedes state of Venezuela. It includes
the hills of Macizo de El Baúl and vast plains between the
rivers Cojedes, Portuguesa, Chirgua, and Pao. We conducted
the study in the north-central portion of Hato Piñero, between
68.0334° W, 8.9827° N and 68.2148° W, 8.8562° N (Fig. A1).
The landscape is dominated by a mosaic of open lowland
savanna, partially converted to pastures, open marshes, decid-
uous and dry forests, and chaparral on the hillsides (Huber
et al. 2006). Precipitation drives the seasonal climate with
most rain occurring between June and November. Average
annual rainfall is approximately 1400 mm (Polisar et al.
2003); however, there is profound variation between years
(data of the meteorological station of El Baúl). The main rivers
are located on the borders of the ranch, but a network of small
streams, channels, and artificial ponds and lakes is well devel-
oped inside these borders. Between July and October, large
parts of the ranch are normally flooded, and in the driest peri-
odbetween February and Mayonly the largest rivers and
a small number of artificial ponds retain water.
Until 2009, Hato Piñero was a private cattle ranch with ap-
proximately 50% of its area maintained as a nature reserve that
strived to preserve the jaguar and its prey communities. Hunting
was prohibited within ranch boundaries. In 2010, the Venezuelan
government expropriated Hato Piñero as a state farm but retained
the conservation protections of the ranch. However, after expro-
priation, the number of cattle increased from about 11,000 in
2009 to approximately 13,000 in 2014. In recent years, the num-
ber of domestic buffalo has also increased, reaching approxi-
mately 2000 in 2014. In addition to livestock, common prey
species include peccaries (Pecari tajacu and Tayassu pecari),
capybaras (Hydrochoerus hydrochaeris), white-tailed deer
(Odocoileus virginianus), tapir (Tapir us t erre str is ), caimans
(Crocodilus crocodilus), and giant anteater (Myrmecophaga
tridactyla)(Polisaretal.2003; Scognamillo et al. 2003;
Jędrzejewskietal.2014). Sympatric carnivores include puma
(Puma concolor), ocelot (Leopardus pardalis), jaguarundi
(Puma yagouaroundi), and crab-eating fox (Cerdocyon thous).
Camera trapping
From July 2013 to July 2014 (376 days), we conducted a con-
tinuous camera trapping effort in the study area (Table 1,
Fig. A1). We used 2756 camera-traps at any given time, mostly
HC 500 (Reconyx Inc., Holmen, WI, USA) and TrophyCam HD
Max (Bushnell, Overland Park, MI, USA). We aimed to distrib-
ute the cameras in a regular grid of 2 km by 2 km; however, we
adjusted camera positions in response to local topography, site
accessibility, and the presence of jaguar trails indicated by track
records (Fig. A1). Normally, we placed cameras along small dirt
roads, animal trails, and water-bodies, one camera per site. The
total area of the polygon encompassing all camera stations was
168.1 km
. To improve the quality of imagery for individual
identification, we placed a small piece of carpet (10 cm × 10 cm)
soaked with a beaver castoreum/catnip oil lure in front of the
cameras (usually at a distance of 34m)(Schmidtand
Kowalczyk 2006;Schlexer2008). Placing lures does not bias
density estimates but may improve individual identifications or
detectability (Gerber et al. 2012; du Preez et al. 2014).
Table 1 Summary of camera-
trapping effort for jaguar
(Panthera onca) study conducted
in Hato Piñero, Venezuela, over
six sessions during July 2013
July 2014
Session N
N adult jaguars
Captures Recaptures
1 47 48 142.7 252 20 215 195
2 40 56 135.3 175 22 152 130
3 65 27 114.5 171 20 138 118
4 69 42 157.4 315 23 264 241
5 57 32 146.1 168 21 148 127
6 98 31 113.7 288 23 230 207
mean 62.7 39.3 134.9 228.2 21.5 191 170
Total 376 194 168.1 1369 28 1147 1018
The following parameters are presented: the numbers of trapping days, camera trap sites, and all photos with
identifiable individuals (taken with at least 10-min difference), numbers of jaguar captures and recaptures, and
minimum convex polygon of all cameras during each session. The number of captures and recaptures per session
has been defined using one capture per occasion (day)
Mamm Res
We divided our sampling period into six sessions that
corresponded with camera maintenance and data download.
Mean session duration was 63 days (range 4799 days,
Tab le 1). During each site visit, we inspected the cameras to
adjust camera settings and, if required, the spatial location of
cameras due to landscape changes (i.e., flooding, droughts,
fires, etc.), observation of jaguar tracks, or technical problems.
We also refreshed the lure when servicing each camera.
We identified individual jaguars based on unique spot patterns
(Silver et al. 2004). We distinguished four sex/age groups: males,
nonreproductive females, reproductive females, and cubs. Cubs
included obviously young and immature individuals recorded
with adult females. Sex of adult individuals was determined by
the presence/absence of testicles or nipples and other reproduc-
tive signs. We classified females as reproductive if they were
recorded with cubs at any point during the study year, and as
nonreproductive, if they were never recorded with cubs. We
treated presence of cubs as an objective criterion for evidence
of breeding. Classification of breeding or non was held constant
for the entire study period. Although simplified, we believe this
classification justified by the long reproductive cycle of female
jaguars (i.e., 3 months gestation and 17 months care of cubs) and
long (34 years) time to first reproduction (Crawshaw and
Quigley 1991; De Paula et al. 2013). We make the assumption
that reproductive females maintain their territories for long pe-
riods (i.e., years) and any short-term event (i.e., losing cubs)
would not change their territory size. Furthermore, we generally
recorded older cubs (>3 months old), which would have survived
the presumed very early peak in juvenile mortality documented
in other large carnivores (Jędrzejewska et al. 1996; Palomares
et al. 2005). The identification process was performed by two
authors independently (MFP and MA) and verified by a third
(WJ). Unidentifiable captures were excluded from subsequent
analyses. For capture-recapture models, we defined daily sam-
pling occasions such that we considered only one capture per day
per trap, i.e., binomial detection histories (Royle et al. 2009;
Goldberg et al. 2015).
Population density estimation for adult jaguars
We applied maximum likelihood SCR models within the secr
2.10.3 R package (Efford et al. 2004,2009; Borchers and
Efford 2008;Efford2016) to estimate jaguar densities. These
hierarchical models define (1) a spatial model of the distribution
of animal activity centers and (2) a spatial observation model
relating the probability of detecting an individual at a particular
trap to the distance from the animals activity center (Efford
2004). For the observation model, we used a hazard half-
normal detection function:
λdðÞ¼1exp λ0exp d2
 ð1Þ
where λ
represents the baseline detection probability at an
individuals activity center, σdefines the shape of the decline
in detection away from the activity center and can be interpreted
in terms of the animal movement distribution, and dspecifies
the distance between a detector (camera trap) and the activity
center (Efford et al. 2009;Efford2016). This detection model
implies a Binomial distribution of detections of an individual at
a particular detector (Efford and Fewster 2013; Royle et al.
2014). We used a 15-km buffer around the study area to include
the activity centers of any individuals that may have been ex-
posed to sampling. We checked the adequacy of the buffer size
by examining likelihoods and estimates from models with larg-
er buffers. We applied full likelihood models with three sex/
reproductive status groups (adult males, adult reproductive fe-
males, and adult nonreproductive females) and six shorter ses-
sions as covariates (Borchers and Efford 2008). By doing this,
we also fulfilled the assumptions of the closed population mod-
el in analyzing our long dataset. We fit models with all possible
additive combinations of sex/reproductive status groups and
sessions as covariates on density (D), λ
,andσ. For density,
we always used sex/female reproductive state as a covariate to
provide an estimate of population structure and did not consider
intercept-only models. We assessed how D,λ
across sessions and sex/reproductive status groups and how this
variation influenced the overall density estimate. We evaluated
models with AICc (corrected Akaike information criterion) and
AICc weights (Hurvich and Tsai 1989; Wagenmakers and
Farrell 2004). To test the effect of study duration on estimates
of all parameters, we compared models that included session
covariates in the parameters D,λ
,andσ(corresponding to the
situation when model parameters were estimated based on sep-
arate sessions, as in short-term studies) with the best model that
did not include any session covariates.
The spatial scale parameter, σ, implies an estimate of indi-
vidual space use and the scale of movement about an activity
center. These estimates provide another means of addressing
the reliability of our model results through comparison to
telemetry-derived home range sizes. We transformed the σ
values for each sex/reproductive state into the radius of an in-
dividual activity range, encompassing 95% of animal locations
during the observation period. We made this conversion using
the hra function in the R package SCRbook (Royle et al. 2013;
package), since an analytical solution with the hazard half-
normal detection function is not readily available. The hra func-
tion approximates the 95% activity range of an individual, giv-
en parameter values, using a discrete meshwork of points about
the activity center. We calculated 95% activity range size for our
best model and compared them to home range size estimates
from radio-tracking.
To allow comparisons with earlier studies, we also applied
nonspatial capture-recapture methods to estimate adult jaguar
density (see Appendix Bfor methods and results).
Mamm Res
Estimating cub density and population structure
We estimated densities separately for males, reproductive fe-
males, and nonreproductive females. We attempted to fit
models directly to observations of cubs, but their sparse cap-
ture histories did not provide sufficient data for a maximum
likelihood analysis. To estimate cub density, we multiplied the
reproductive female density from our best model by mean
number of cubs per reproductive female. These estimates
allowed calculation of total jaguar density and population
structure for males, nonreproductive females, reproductive fe-
males, and cubs.
Camera trapping and detection numbers for sex/age
Our total sampling effort was 12,302 camera trap-nights. We
obtained 1465 captures, including 1369 with identifiable indi-
viduals (Table 1). In total, we identified 42 jaguars, including
14 adult males, 14 adult females, and 14 cubs. Of the 14
photographed adult females, 7 were actively reproducing
and photographed with cubs (Photos D1,D2). Although we
registered equal numbers of males, females, and cubs in the
study area, the capture frequency of each group differed: 58%
of identified photos were those of males (798 captures), 33%
of identifiable captures were those of females (452), and only
9% were those of cubs (119). On average, males were cap-
tured 56 times each, females 32 times, and cubs only 9 times.
Reproductive females had slightly higher total number of cap-
tures than nonreproductive females (257 versus 195, respec-
tively) and higher average number of captures per individual
(38 versus 28). Reproductive females were more frequently
captured alone (109 times) than accompanied by cubs (69
captures) during the period of known offspring dependency.
Conversely, cubs were recorded more frequently with their
mothers (83 individual captures) than alone (36 captures).
Density estimates of adult jaguars
Of the 32 models analyzed with the secr package with sex/
reproductive status and sessions as covariates, those which
did not allow density to vary across sessions obtained the
lowest AICc values. In contrast, models assuming density
variation across sessions received less support (Tables 2,
C1). The top secr model for adult jaguars included an
effect of sex/female reproductive status on D,λ
and between session variation in λ
. All three parameters
differed significantly between sex/reproductive status
groups (Table 3). Baseline detection probability was low-
est for reproductive females (0.04 on average), whereas
nonreproductive females had the greatest baseline detect-
ability (0.13 on average) and males intermediate (0.08 on
average) to the two female reproductive classes. Males had
the largest values of estimated movement distribution
(σ= 2.97 ± 0.09 km), while reproductive and nonrepro-
ductive females obtained smaller σvalues (2.04 ± 0.11 and
2.32 ± 0.19 km, respectively). Estimated densities were
higher for reproductive females and males (1.97 ± 0.33
and 1.62 ± 0.22 individuals/ 100 km
, respectively) than
for nonreproductive females (0.85 ± 0.19 individuals/
100 km
). In total, the best model estimated 4.44 ± 1.16
adult jaguars/100 km
(Table 3).
Cub density and jaguar population structure
From observations of the seven reproductive females with
offspring during the study period, we estimated 1.64 cubs
per reproductive female on average. Additionally, one cub
was recorded alone on a single occasion and had an un-
known mother. We excluded this lone observation from
further analysis. On the basis of reproductive female den-
sity from the top model, we estimated 3.23 cubs/100 km
in the study area. Thus, we estimated a total density of
7.67 jaguars/100 km
. Jaguar population structure was
21% adult males, 11% nonreproductive females, 26% re-
productive females, and 42% cubs (Table 3).
Table 2 Model selection results
for selected models analyzed in
secr 2.10.3, with six sessions and
three sex/reproductive state
groups as covariates
Model AIC
Dsexg λ
sexg + session σsexg 11,485.0 0.0 0.68
Dsexg λ
sexg σsexg 11,487.8 2.8 0.17
Dsexg λ
sexg σsession + sexg 11,489.1 4.0 0.09
Dsexg λ
sexg + session σsexg + session 11,490.0 5.0 0.06
Dsexg + session λ
sexg + session σsexg + session 11,502.2 17.2 0.00
Selection parameters for all 32 analyzed models are presented in Table C1 (Supplementary materials)
Ddensity, λ
baseline detection probability, σmovement distribution parameter, sexg sex/reproductive state group
Mamm Res
Estimates of home range sizes
Based on the detection and movement parameter values, we
estimated 95% home range sizes. Males had the largest ranges
(167 km
), while nonreproductive females and reproductive
females moved in smaller ranges (103 and 79 km
ly, Table 3).
Study duration and density estimates
The best secr model included seasonal variation in the base-
line detection probability, but not in density or the movement
distribution parameter. The top model that excluded seasonal
variation in all parameters had the second overall rank order in
our model set (ΔAIC
=0.17,Table2) and produced
similar overall density estimates (D
= 4.47 ± 1.06 jaguars/
100 km
). In contrast, the model assuming between session
variation in all three parameters gave highly variable results:
from 3.65 to 5.62 jaguars/100 km
in different sessions
(Fig. 1, Table C2). This model received no support from our
model selection criterion (ΔAIC
Our study provides a robust estimate of jaguar density from a
large, long-term photographic capture-recapture dataset. This
scope allowed us to address the concerns of many previous
jaguar studies, including small sample sizes, low detectability
of females and cubs, and limited spatial and temporal extent to
provide a more complete description of the jaguar population
in our study area. We estimate breeding and nonbreeding fe-
male density as well as cub density and total population struc-
ture for jaguars in Hato Piñero. We record high jaguar densi-
ties in the Venezuelan Llanos, providing evidence of the im-
portance of this habitat for conservation. Moreover, our ap-
proach to spatial and temporal study design may offer useful
guidance for future capture-recapture studies of jaguars and
other large carnivores.
Jaguar population density in Hato Piñero
and implications for conservation
Our estimates of jaguar density in Hato Piñero of 4.4 adults/
100 km
and 7.6 total jaguars/100 km
(including cubs) are
among the highest documented in South and Central America.
Comparable density estimates have been reported only in the
tropical forests of Peru, Belize, and Guatemala (Moreira et al.
2008; Harmsen et al. 2010; Tobler et al. 2013; Kelly and Rowe
2014) and in the wetlands of the Brazilian Pantanal (Soisalo and
Cavalcanti 2006). This high jaguar density most likely results
from high prey availability and productivity in our study area.
Karanth et al. (2004) demonstrated a similar relationship for ti-
gers in India. Polisar et al. (2003) estimated that the biomass of
potential jaguar prey in Hato Piñero was about 750 kg/km
wild prey and about 7700 kg/km
of livestock. These estimates
place Hato Piñero in a class of biomass availability and produc-
tivity with Manu National Park, Peru (270 kg/km
of wild prey),
Table 3 Parameter estimates from the top model of jaguar density
Model/parameter Session 1 Session 2 Session 3 Session 4 Session 5 Session 6 Mean
Dsexg λ
sexg + session σsexg (ΔAICc = 0)
males (SE) 0.09 (0.01) 0.06 0.01 0.10 (0.02) 0.09 (0.01) 0.06 (0.01) 0.10 (0.01) 0.08
reproductive females (SE) 0.04 (0.01) 0.03 (0.00) 0.04 (0.01) 0.04 (0.01) 0.02 (0.00) 0.04 (0.01) 0.04
nonreproductive females (SE) 0.14 (0.03) 0.10 (0.02) 0.16 (0.05) 0.14 (0.02) 0.09 (0.02) 0.16 (0.04) 0.13
σmales (SE) 2.97 (0.09) 2.97 (0.09) 2.97 (0.09) 2.97 (0.09) 2.97 (0.09) 2.97 (0.09) 2.97
σreproductive females (SE) 2.04 (0.11) 2.04 (0.11) 2.04 (0.11) 2.04 (0.11) 2.04 (0.11) 2.04 (0.11) 2.04
σnonreproductive females (SE) 2.32 (0.21) 2.32 (0.21) 2.32 (0.21) 2.32 (0.21) 2.32 (0.21) 2.32 (0.21) 2.32
Dmales (SE) 1.62 (0.22) 1.62 (0.22) 1.62 (0.22) 1.62 (0.22) 1.62 (0.22) 1.62 (0.22) 1.62
Dreproductive females (SE) 1.97 (0.33) 1.97 (0.33) 1.97 (0.33) 1.97 (0.33) 1.97 (0.33) 1.97 (0.33) 1.97
Dnonreproductive females (SE) 0.85 (0.19) 0.85 (0.19) 0.85 (0.19) 0.85 (0.19) 0.85 (0.19) 0.85 (0.19) 0.85
Dadult jaguars total (SE) 4.44 (1.16) 4.44 (1.16) 4.44 (1.16) 4.44 (1.16) 4.44 (1.16) 4.44 (1.16) 4.44
Dcubs 3.23 3.23 3.23 3.23 3.23 3.23 3.23
95% home range males (km
) 167 167 168 167 167 168 167
95% home range reproductive females (km
)797979797979 79
95% home range nonreproductive females (km
) 103 102 103 103 102 103 103
For each sex/reproductive state group, values of λ
,σ,andDfor the top models are presented. Cub density was calculated by multiplying reproductive
female density by mean number of cubs/per female. Home range sizes were estimated from σand λ
values (see BMethods^)
Ddensity (individuals/100 km
), λ
baseline detection probability, σmovement distribution parameter, sexg sex/reproductive state group, SE standard
Mamm Res
and the Pantanal, Brazil (380 kg/km
), two famous jaguar
hotspots (Schaller 1983;Emmons1987).
The wet parts of Los Llanos, with mosaics of seasonally
flooded savannahs, marshes, dry or wet forests, and numerous
rivers and streams, may provide exceptionally good condi-
tions for jaguars. However, cattle breeding, human-jaguar
conflicts, and hunting likely limit jaguar population growth
outside the few protected areas in Los Llanos (Hoogesteijn
et al. 1993; González Fernández 1995; Hoogesteijn and
Hoogesteijn 2008). Boron et al. (2016) conducted camera-
trapping study in an unprotected part of the Colombian Los
Llanos and documented much lower adult jaguar density (2.2
jaguars/100 km
) than in Hato Piñero, despite the similarities
in primary productivity, forest cover, and human population
density between the two study areas. However, in the
Colombian study site, hunting of jaguars and its prey is com-
mon, as well as retaliatory killing of jaguars due to their at-
tacks on cattle (Boron et al. 2016). This contrast strongly
supports the efficacy of the jaguar conservation measures
adopted in Hato Piñero, including prohibition of hunting,
50% land excluded from cattle grazing, and development of
eco-tourism. Similar protections have benefitted jaguar con-
servation in the Pantanal of Brazil (Zimmermann et al. 2005b;
Greve 2014; Hoogesteijn et al. 2016).
Protected areas and other refuges play a crucial role for
maintaining other large carnivore populations in landscapes
with large human impacts (Mills 1991; Thapar 1999;
Naughton-Treves et al. 2005; Carroll and Miquelle 2006).
Our data confirm the importance of protected areas for jaguar
conservation. We show that large protected areas, like Hato
Piñero, can maintain robust jaguar populations at high density
and high reproductive output. As such, they may be a source
of dispersing individuals, supporting the persistence of jaguar
populations in the surrounding areas.
Differences between sex/reproductive groups
Consistent with previous studies accounting for sex dif-
ferences, we found higher detection and movement pa-
rameter estimates for males than females (Sollmann
et al. 2011;Tobleretal.2013). Our decision to discrim-
inate between reproductive and nonreproductive females
further refined the differences among females, based upon
whether we observed them caring for cubs. The low de-
tection probability of reproductive females may result
from protective maternal behavior. Females with cubs
may select the safest areas and reduce use of exposed
movement corridors, such as roads and trails, to avoid
male jaguars and pumas that may threaten young.
Infanticide by unrelated males is common in other felids
(e.g., Packer and Pusey 1983; Balme et al. 2013). The
higher detectability, larger movement ranges, and lower
Fig. 1 Comparison of jaguar total densities in Hato Piñero, Venezuela,
during July 2013July 2014 estimated with three different models: (1) the
top model selected with AICc, (2) the model with all parameters constant
across sessions (corresponding to a long-term study), and (3) for the
model with all parameters free to vary between sessions (corresponding
to short-term studies). Error bars denote ±1 SE
Mamm Res
density of nonreproducing females may result from young
individuals using transient territories and moving large
distances in search of a territory (Beier 1995;Schmidt
Length of study duration
The capture-recapture literature frequently emphasizes the im-
portance of the closed population (no birth, immigration,
death, or emigration) assumption of capture-recapture models
(White et al. 1982; Kendall et al. 1997). To address this con-
cern, most researchers have adopted the recommendation that
study periods should not exceed 3 months (Karanth and
Nichols 1998; Silver et al. 2004). Although the closed popu-
lation assumption is important, meeting this requirement does
not require such stringent limits on study period. In the anal-
ysis of our long-term data, we introduced sessions as a covar-
iate to act as a surrogate for the shorter time frames typically
employed by camera trapping studies. The best model allowed
for seasonal variation in one of the parameters (baseline de-
tection probability), but not in the other two (density and
movement distribution). Furthermore, the model that ignored
seasonal variation provided similar parameter estimates and
received some statistical support. In contrast, the model as-
suming variation between sessions in all parameters (corre-
sponding to short-term studies) received no statistical support
and produced much less precise density estimates.
Seasonal fluctuations in activity and density estimates have
been shown in other jaguar studies (de la Torre and Medellin
2011; Harmsen et al. 2011; Kelly and Rowe 2014; Tobler et al.
2013). This variation may be attributable to poor camera site
selection, camera failures, stolen cameras, local fires, or due to
seasonal changes in jaguar spatial activity patterns. Changes
in jaguar distribution may depend upon the availability of
water sources. During the dry season, only a few artificial
ponds, lakes, and streams persisted in our study area.
Alternatively, the spatial distribution of jaguars may depend
upon jaguar reproductive cycles. Scognamillo et al. (2002)
reported that the two jaguar females they radio-tracked in
Hato Piñero drastically reduced their activity ranges for about
2 months after giving birth to cubs. Comparable patterns of
seasonal activity changes related to reproductive cycles have
been demonstrated for other felids, e.g., for lynx in Białowieża
Primeval Forest (Schmidt 1998). Thus, seasonal changes in
activity patterns and territory use may have some impact on
detection probability and population assessments, as shown
by our results. Density estimates from data with a limited
temporal extent may have a greater stochastic component
and be less precise than those obtained in long-term studies.
The increased sampling duration in our study did not lead
to any obvious overestimates of population density and pro-
duced several key benefits. Most importantly, the long-term
monitoring increased detection numbers and led to better
parameter estimates for the most elusive groups within the
jaguar population. We could estimate densities separately for
each sex, age, and reproductive group to provide a more com-
plete description of jaguar population structure. Our estimate
of population structure with reproductive females and cubs
comprising 26 and 42%, respectively, of all individuals indi-
cates a healthy, productive jaguar population. Similar high
share of reproducing females and cubs has been observed in
radio-tracking studies of other large felid populations, e.g.,
lynx in partially protected Białowieża Forest in Poland
(Jędrzejewski et al. 1996).
In sum, the advantages of long-term studies suggest that
extended camera trap monitoring in combination with spatial
capture-recapture models may offer significant insights into
the population biology of target species.
Home range size estimates
Our estimates of year-round home range sizes (79 km
reproducing females, 103 km
for nonreproducing females,
and 167 km
for males) show, in general, a similar pattern to
estimates obtained with telemetry studies and further corrob-
orate our density estimates. Previous radio-telemetry research,
conducted on two male and two female jaguars in Hato Piñero
during 19961998, estimated average seasonal home range
sizes of 65 km
for females and 100 km
for males (Polisar
et al. 2003; Scognamillo et al. 2002,2003). In a GPS telemetry
study of jaguars conducted by Cavalcanti and Gese (2009)in
Pantanal, Brazil, a habitat similar to Hato Piñero, seasonal
female home ranges varied from 34 to 101 km
63 km
) and seasonal male home ranges varied from 58 to
263 km
(average: 156 km
). Slightly higher average esti-
mates obtained with SCR methods may result from the fact
that this technique assumes circular home ranges, while telem-
etry captures actual space use, which is rarely circular (e.g.,
Cavalcanti and Gese 2009). However, the higher estimates of
home range sizes of nonreproductive females can result from
their transient character which can cause a bias in movement
estimates (Royle et al. 2016).
Our study demonstrates that protected areas in Los Llanos are
potentially important jaguar habitat and that jaguar popula-
tions in this region may reach some of the highest densities
recorded for South America. Jaguar conservation plans and
actions should pay more attention to this region and promote
increasing the number of protected areas in Los Llanos.
Although today, Hato Piñero is an unquestionable jaguar
hotspot in northern South America, it needs more international
concern to maintain its good state of conservation, especially
in the context of the political instability, growing environmen-
tal risks, and uncertain future of this region.
Mamm Res
Our study also suggests new perspectives on future re-
search. Spatial capture-recapture studies of jaguars and similar
species, with camera traps, may benefit from extended moni-
toring not limited to 3 months. Based upon our experience, we
recommend large study areas with dense trap stations to max-
imize the number of individuals captured and number of de-
tections. Although we did not quantify the effect of lures, we
found them to increase picture quality and thus detectability.
Long-term camera trapping can provide additional insights
into carnivore population biology.
Acknowledgements Collecting data for this article was possible due to
financial support from the budgets of Instituto Venezolano de
Investigaciones Científicas (IVIC) and Mammal Research Institute of
the Polish Academy of Sciences and grants from Polish Ministry of
Science and Higher Education (grant NN304336339) and Panthera
Corporation (2010 Research and Conservation Grant and the Liz
Claiborne Art Ortenberg Jaguar Research Grants 2011, 2012, 2014).
We are grateful to all the personnel of Hato Piñero who made our work
possible. We are grateful to the IVIC Transportation Center, and especial-
ly to Argenis Hurtado and all IVIC drivers who participated in our expe-
ditions to Hato Piñero, also to Dinora Sánchez and Giovanni Colmenares
from Biodiven IVIC for making their car available for us. Special thanks
we direct to coordinators of Ecology Center of IVIC: Dr. Marta Francisco,
Dr. Astolfo Mata, Dr. Ascanio Rincón, and administration workers
Yugdalia García, Robert Vargas, and all staff of the Ecology Center for
their kind support to our work. Dr. Fernando Ruette and Miguel
Fernández (IVIC) shared their computers to assist with the analysis.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
crea tiveco mmons. org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
Balme GA, Batchelor A, Woronin Britz N, Seymour G, Grover M, Hes L,
Macdonald DW, Hunter LT (2013) Reproductive success of female
leopards Panthera pardus: the importance of top-down processes.
Mammal Rev 43:221237
Beier P (1995) Dispersal of juvenile cougars in fragmented habitat. J
Wildlife Manage 59:228237
Borchers DL, Efford M (2008) Spatially explicit maximum likelihood
methods for capturerecapture studies. Biometrics 64:377385.
Boron V, Tzanopoulos J, Gallo J, Barragan J, Jaimes-Rodriguez L,
Schaller G, Payán E (2016) Jaguar densities across human-
dominated landscapes in Colombia: the contribution of unprotected
areas to long term conservation. PLoS One 11:e0153973.
Carroll C, Miquelle DG (2006) Spatial viability analysis of Amur tiger
Panthera tigris altaica in the Russian far east: the role of protected
areas and landscape matrix in population persistence. J Appl Ecol
Cavalcanti SMC, Gese EM (2009) Spatial ecology andsocial interactions
of jaguars (Panthera onca) in the southern Pantanal, Brazil. J
Mammal 90:935945. doi:10.1644/08-MAMM-A-188.1
Cavalcanti SMC, Gese EM (2010) Kill rates and predation patterns of
jaguars (Panthera onca) in the southern Pantanal, Brazil. J Mammal
91:722736. doi:10.1644/09-MAMM-A-171.1
Ceballos G, Chávez C, Rivera A, Manterola C (2002) Tamaño
poblacional y conservación del jaguar en la reserva de la biosfera
de Calakmul, Campeche, México. In: Medellín RA, Equihua CA,
Chetkiewicz CL, Crawshaw P, Rabinowitz A, Redford KH,
Robinson JG, Sanderson EW, Taber A (eds) El jaguar en el nuevo
milenio. Fondo de cultura económica FCE-Universidad nacional
autónoma de México UNAM-Wildlife Conservation Society,
México. UNAM-Wildlife Conservation Society, México, pp. 403
Cooley HS, Wielgus RB, Koehler G, Maletzke B (2009) Source popula-
tions in carnivore management: cougar demography and emigration
in a lightly hunted population. Anim Conserv 12:321328
Coulson T, Catchpole EA, Albon SD, Morgan BJT, Pemberton JM,
Clutton-Brock TH, Crawley MJ, Grenfell BT (2001) Age, sex, den-
sity, winter weather, and population crashes in Soay sheep. Science
292:15281531. doi:10.1126/science.292.5521.1528
Crawshaw PG Jr (1995) Comparative ecology of ocelot Felis pardalis
and jaguar Panthera onca in a protected subtropical forest in Brazil
and Argentina. PhD thesis, University of Florida, Gainesville, USA
Crawshaw PG, Quigley HB (1991) Jaguar spacing, activity and habitat
use in a seasonally flooded environment in Brazil. J Zool 223:357
de la Torre JA, Medellin RA (2011) Jaguars Panthera onca in the greater
Lacandona ecosystem, Chiapas, Mexico: population estimates and
future prospects. Oryx 45:546553
De Paula RC,Desbiez A, Cavalcanti SMC (2013) Plano de ação nacional
para conservação da onça-pintada. Instituto Chico Mendes de
Conservação da Biodiversidade, ICMBio, Brasilia, p 384.
du Preez BD, Loveridge AJ, Macdonald DW (2014) To bait or not to bait:
a comparison of camera-trapping methods for estimating leopard
Panthera pardus density. Biol Conserv 176:153161
Efford M (2004) Density estimation in live-trapping studies. Oikos 106:
Efford M (2016) SECR 2.10- spatially explicit capturerecapture in R
Available at
Efford MG, Fewster RM (2013) Estimating population size by spatially
explicit capturerecapture. Oikos 122:918928
Efford MG, Dawson DK, Robbins CS (2004) DENSITY: software for
analysing capture-recapture data from passive detector arrays. Anim
Biodivers Conserv 27:217228
Efford MG, Dawson DK, Borchers DL (2009) Population density esti-
mated from locations of individuals on a passive detector array.
Ecology 90:26762682
Emmons LH (1987) Comparative feeding ecology of felids in a
Neotropical rain-Forest. Behav Ecol and Sociobiol 20:271283
Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ,
Carpenter SR, Essington TE, Holt RD, Jackson JBC, Marquis RJ,
Oksanen L, Oksanen T, Paine RT, Pikitch EK, Ripple WJ, Sandin
SA, Scheffer M, Schoener TW, Shurin JB, Sinclair ARE, Soulé ME,
Virtanen R, Wardle DA (2011) Trophic downgrading of planet earth.
Science 333:301306
Foster RJ, Harmsen BJ (2012) A critique of density estimation from
camera-trap data. J Wildlife Manage 76:224236
Gardner B, Reppucci J, Lucherini M, Royle JA (2010) Spatially explicit
inference for open populations: estimating demographic parameters
from camera-trap studies. Ecology 91:33763383
Mamm Res
Gerber BD, Karpanty SM, Kelly MJ (2012) Evaluating the potential
biases in carnivore capturerecapture studies associated with the
use of lure and varying density estimation techniques using
photographic-sampling data of the Malagasy civet. Popul Ecol 54:
Goldberg JF, Tempa T, Norbu N, Hebblewhite M, Mills LS, Wangchuk
TR, Lukacs P (2015) Examining temporal sample scale and model
choice withspatial capture-recapture models in the common leopard
Panthera pardus. PLoS One 10:e0140757
González Fernández A (1995) Livestock predation in the Venezuelan
Llanos. Cat News 22:1415
Greve S (2014) Ecotourism: an opportunity for Jaguar conservation at
Fazenda Barranco Alto Lodge, In: ISCONTOUR 2014-Tourism
Research Perspectives: Proceedings of the International Student
Conference in Tourism Research, p 191. BoDBooks on Demand
Gros PM, Kelly MJ, Caro TM (1996) Estimating carnivore densities for
conservation purposes: indirect methods compared to baseline de-
mographic data. Oikos 77:197206
Harmsen BJ, Foster RJ, Silver SC, Ostro LE, Doncaster CP (2010) The
ecology of jaguars in the Cockscomb Basin wildlife sanctuary,
Belize. In: MacDonald DW, Loveridge A (eds) The biology and
conservation of wild felids. Oxford University Press, Oxford, pp.
Harmsen BJ, Foster RJ, Doncaster CP (2011) Heterogeneous capture
rates in low density populations and consequences for capture-
recapture analysis of camera-trap data. Popul Ecol 53:253259
Holling CS (1959) The components of predation as revealed by a study of
small-mammal predation of the European pine sawfly. The
Canadian Entomologist 91:293320
Hoogesteijn R, Chapman C (1997) Large ranches as conservation tools in
the Venezuelan Llanos. Oryx 31:274284
Hoogesteijn R, Hoogesteijn A (2008) Conflicts between cattle ranching
and large predators in Venezuela: could use of water buffalo facili-
tate felid conservation? Oryx 42:132138
Hoogesteijn R, Mondolfi E (1992) El jaguar: Tigre americano.Armitano,
Caracas, Venezuela, p 182
Hoogesteijn R, Hoogesteijn A, Mondolfi E (1993) Jaguar predation and
conservation: cattle mortality caused by felines on three ranches in
the Venezuelan Llanos. Symposium of the Zoological Society of
London 65:391407
Hoogesteijn R, Hoogesteijn A, Tortaro FR, Rampin LE, Vilas Boas-
Concone H, May-Junior JA, Sartorello L (2016) Conservación de
jaguares (Panthera onca) fuera de áreas protegidas: turismo de
observación de jaguares en propiedades privadas del Pantanal,
Brasil. In: Payán-Garrido E, Lasso-Alcalá C, Castaño-Uribe C
(eds) Conservación de grandes vertebrados en áreas no protegidas
de Colombia, Venezuela y Brasil. Instituto de Investigación de
Recursos Biológicos Alexander von Humboldt (IAvH), Bogota,
pp. 259274
Huber O, de Stefano RD, Aymard G, Riina R (2006) Flora and Vegetation
of the Venezuelan Llanos: a review. In: Pennington T, Lewis GP,
Ratter JA (eds) Neotropical savannas and seasonally dry forests:
plant diversity, biogeography, and conservation. Taylor & Francis,
Florida, pp. 95120
Hurvich CM, Tsai CL (1989) Regression and time series model selection
in small samples. Biometrika 76:297307
Jędrzejewska B, Jędrzejewski W (1998) Predation in vertebrate commu-
nities: the Bialowieza primeval Forest as a case study. In:Ecological
studies 135 Germany. Springer, Berlin Heidelberg, p. 443
Jędrzejewska B, Jędrzejewski W, Bunevich AN, Miłkowski L, Okarma H
(1996) Population dynamics of wolves Canis lupus in Bialowieża
primeval Forest (Poland and Belarus) in relation to hunting by
humans, 18471993. Mammal Rev 26:103126
Jędrzejewski W, Jędrzejewska B, Okarma H, Schmidt K, Bunevich A,
Miłkowski L (1996) Population dynamics (1869-1994),
demography, and home ranges of the lynx in Białowieżaprimeval
Forest (Poland and Belarus). Ecography 19:122138
Jędrzejewski W, Cerda H, Viloria A, Gamarra JG, Schmidt K (2014)
Predatory behavior and kill rate of a female jaguar (Panthera onca)
on cattle. Mammalia 78:235238
Karanth KU, Nichols JD (1998) Estimation of tiger densities in India
using photographic captures and recaptures. Ecology 79:28522862
Karanth KU, Nichols JD, Kumar NS, Link WA, Hines JE (2004) Tigers
and their prey: predicting carnivore densities from prey abundance.
P Natl Acad Sci USA 101:48544858
Kelly MJ, Rowe C (2014) Analysis of 5-years of data from Rio Bravo
Conservation and Management Area (RBCMA) and one year of
data from Gallon Jug/Yalbac Ranch, on trap rates and occupancy
for predators and prey, including jaguar density estimates in
unlogged versus sustainably logged areas. Progress Report for:
Rio Bravo Conservation and Management Area, Programme for
Belize. May 10, 2014
Kendall WL, Nichols JD, Hines JE (1997) Estimating temporary emigra-
tion using capture-recapture data with Pollock's robust design.
Ecology 78:563578
Krebs CJ (2001) Ecology. In: The experimental analysis of distribution
and abundance. Benjamin Cummings-Addison Wesley Longman
Maffei L, Noss AJ, Silver SC, Kelly MJ (2011) Abundance/density case
study: jaguars in the Americas. In: OConnell AF, Nichols JD,
Karanth KU (eds) Camera traps in animal ecology: methods and
analyses. Springer, Tokyo, pp. 119144
Messier F (1994) Ungulate population models with predation: a case
study with the north American moose. Ecology 75:478488
Mills MGL (1991) Conservation management of large carnivores in
Africa. Koedoe 34:8190
Moreira J, McNab R, García R, Méndez V, Ponce-Santizo G, Córdova M,
Tun S, Caal T, Corado J (2008) Densidad de jaguares en el Biotopo
Protegido Dos Lagunas, Parque Nacional Mirador Río Azul, Petén,
Guatemala. Informe Interno WCS-Programa para Guatemala, p 21
Naughton-Treves L, Holland MB, Brandon K (2005) The role of
protected areas in conserving biodiversity and sustaining local live-
lihoods. Annu Rev Environ Resour 30:219252
Noss AJ, Gardner B, Maffei L, Cuéllar E, MontañoR, Romero-Muñoz A,
Sollman R, O'Connell AF (2012) Comparison of density estimation
methods for mammal populations with camera traps in the Kaa-Iya
del gran Chaco landscape. Anim Conserv 15:527535
Nowell K, Jackson P (1996) Status survey and conservation action plan wild
cats. IUCN/SSC Cat Specialist Group, Burlington, Cambridge, p 421
Packer C, Pusey AE (1983) Adaptations of female lions to infanticide by
incoming males. Am Nat 121:716728
Palomares F, Revilla E, Calzada J, Fernández N, Delibes M (2005)
Reproduction and pre-dispersal survival of Iberian lynx in a subpop-
ulation of the Doñana National Park. Biol Conserv 122:5359
Polisar J, Maxit I, Scognamillo D, Farrell L, Sunquist ME, Eisenberg JF
(2003) Jaguars, pumas, their prey base, and cattle ranching: ecolog-
ical interpretations of a management problem. Biol Conserv 109:
Quigley HB, Crawshaw PG Jr (1992) A conservation plan for the jaguar
Panthera onca in the Pantanal region of Brazil. Biol Conserv 61:
Rabinowitz A, Zeller KA (2010) A range-wide model of landscape con-
nectivity and conservation for the jaguar, Panthera onca.Biol
Conserv 143:939945
Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite
M, Berger J, Elmhagen B, Letnic M, Nelson MP (2014) Status and
ecological effects of the worlds largest carnivores. Science 343:
1241484. doi:10.1126/science.1241484
Rosenblatt E, Becker MS, Creel S, Droge E, Mweetwa T, Schuette PA,
Watson F, Merkle J, Mwape H (2014) Detecting declines of apex
Mamm Res
carnivores and evaluating their causes: an example with Zambian
lions. Biol Conserv 180:176186
Royle JA, Chandler RB, Sollmann R, Gardner B (2014) Spatial capture-
recapture. Academic Press, Elsevier, New York, p. 569
Royle JA, Chandler RB, Sun CC, Fuller AK (2013) Integrating resource
selection information with spatial capturerecapture. Methods Ecol
Evol 4:520530
Royle JA, Fuller AK, Sutherland C (2016) Spatial capturerecapture
models allowing Markovian transience or dispersal. Popul Ecol
Royle JA, Karanth KU, Gopalaswamy AM, Kumar NS (2009) Bayesian
inference in camera trapping studies for a class of spatial capture-
recapture models. Ecology 90:32333244
Sanderson EW, Redford KH, Chetkiewicz CLB, Medellin RA,
Rabinowitz AR, Robinson JG, Taber AB (2002a) Planning to save
a species: the jaguar as a model. Conserv Biol 16:5872
Sanderson E, Chetkiewicz CL, Medellín R, Rabinowitz A, Redford K,
Robinson J, Taber A (2002b) Prioridades geográficas para la
conservación del jaguar. In: Medellín RA, Equihua CA,
Chetkiewicz CL, Crawshaw P, Rabinowitz A, Redford KH,
Robinson JG, Sanderson EW, Taber A (eds) El jaguar en el nuevo
milenio, Fondo de cultura económica FCE-Universidad nacional
autónoma de México UNAM-Wildlife Conservation Society,
México, pp. 629640
Schaller GB (1983) Mammals and their biomass on a Brazilian ranch.
Arquivos de Zoologia 31:136
Schaller GB, Crawshaw PG Jr (1980) Movement patterns of jaguar.
Biotropica 12:161168
Schlexer FV (2008) Attracting animals to detection devices. In: Long RA,
Mackay P, Zielinski WJ, Ray JC (eds) Noninvasive survey methods
for carnivores. Island Press, Washington, pp. 263292
Schmidt K (1998) Maternal behaviour and juvenile dispersal in the
Eurasian lynx. Acta Theriol 43:391408
Schmidt K, Kowalczyk R (2006) Using scent-marking stations to collect
hair samples to monitor Eurasian lynx populations. Wildlife Soc B
Scognamillo D, Maxit I, Sunquist M, Farrell L (2002) Ecología del jaguar
y el problema de la depredación de ganado en un hato de los Llanos
Venezolanos. In: Medellín RA, Equihua CA, Chetkiewicz CL,
Crawshaw P, Rabinowitz A, Redford KH, Robinson JG,
Sanderson EW, Taber A (eds) El jaguar en el nuevo milenio,
Fondo de cultura económica FCE-Universidad nacional autónoma
de México UNAM- Wildlife Conservation Society, México, pp.
Scognamillo D, Maxit IE, Sunquist M, Polisar J (2003) Coexistence of
jaguar (Panthera onca) and puma (Puma concolor) in a mosaic
landscape in the Venezuelan llanos. J Zool 259:269279
Shaffer ML (1981) Minimum population sizes for species conservation.
Bioscience 31:131134
Silver SC, Ostro LET, Marsh LK, Maffei L, Noss AJ, Kelly MJ, Wallace
RB, Gomez H, Ayala G (2004) The use of camera traps for
estimating jaguar Panthera onca abundance and density using
capture/recapture analysis. Oryx 38:148154
Soisalo MK, Cavalcanti SMC (2006) Estimating the density of a jaguar
population in the Brazilian Pantanal using camera-traps and capture-
recapture sampling in combination with GPS radio-telemetry. Biol
Conserv 129:487496
Sollmann R, Furtado MM, Gardner B, Hofer H, Jácomo ATA, Tôrres
NM, Silveira L (2011) Improving density estimates for elusive car-
nivores: accounting for sex-specific detection and movements using
spatial capturerecapture models for jaguars in Central Brazil. Biol
Conserv 144:10171024
Stander PE (1998) Spoor counts as indices of large carnivore populations:
the relationship between spoor frequency, sampling effort and true
density. J Appl Ecol 35:378385
Terborgh J, Lopez L, Nunez P, Rao M, Shahabuddin G, Orihuela G,
Riveros M, Ascanio R, Adler GH, Lambert TD (2001) Ecological
meltdown in predator-free forest fragments. Science 294:19231926
Thapar V (1999) The tragedy of the Indian tiger: starting from scratch. In:
Seidensticker J, Christie S, Jackson P (eds) Riding the tiger: tiger
conservation in human-dominated landscapes. Cambridge
University Press, Cambridge, pp. 286306
Tobler MW, Powell GVN (2013) Estimating jaguar densities with camera
traps: problems with current designs and recommendations for fu-
ture studies. Biol Conserv 159:109118
Tobler MW, Carrillo-Percastegui SE, Zúñiga Hartley A, Powell GVN
(2013) High jaguar densities and large population sizes in the core
habitat of the southwestern Amazon. Biol Conserv 159:375381
Treves A, Karanth KU (2003) Human-carnivore conflict and perspectives
on carnivore management worldwide. Conserv Biol 17:14911499
Wagenmakers EJ, Farrell S (2004) AIC model selection using Akaike
weights. Psychon B Rev 11:192196
White GC, Anderson DR, Burnham KP, Otis DL (1982) Capture-
recapture and removal methods for sampling closed populations.
Los Alamos National Laboratory, p 235
Whittington J, Sawaya MA (2015) A comparison of grizzly bear demo-
graphic parameters estimated from non-spatial and spatial open pop-
ulation capture-recapture models. PLoS One 10:e0134446
Wilson GJ, Delahay RJ (2001) A review of methods to estimate the
abundance of terrestrial carnivores using field signs and observation.
Wild life Res 28:151164
Woodroffe R (2011) Demography of a recovering African wild dog
(Lycaon pictus) population. J Mammal 92:305315
Zeller K (2007) Jaguars in the new millennium data set update: the state of the
jaguar in 2006. Wildlife Conservation Society, New York, p. 77
Zimmermann A, Walpole MJ, Leader-Williams N (2005b) Cattle
ranchers' attitudes to conflicts with jaguar Panthera onca in the
Pantanal of Brazil. Oryx 39:406412
Zimmermann F, Breitenmoser-Würsten C, Breitenmoser U (2005a) Natal
dispersal of Eurasian lynx (Lynx lynx) in Switzerland. J Zool 267:
Mamm Res
... Often, camera traps are recommended to study elusive mammals like tigers (Panthera tigris) and jaguars [3,6,17,18]. Currently camera traps have become a standard method commonly used to elucidate jaguar abundance and demographic parameters [3,6,8] using their distinctive and unique rosette patterns [6,7] with capturerecapture methods [8,19,20]. Although simultaneous comparison and adjustments of jaguar population estimates with satellite telemetry are limited [21,22], evidence has shown sex ratio biases and density overestimates derived from camera trap data [23]. ...
... We also classified jaguar sex (male, female, unknown), age (cub, young, adult), and whether individuals were collared or not collared. Adults were sexed by presence/absence of testicles and nipples [20] and aged by their size and physical appearance to categories of cubs (<12 m), young (12-24 m), and adults (>24 months; [20]). ...
... We also classified jaguar sex (male, female, unknown), age (cub, young, adult), and whether individuals were collared or not collared. Adults were sexed by presence/absence of testicles and nipples [20] and aged by their size and physical appearance to categories of cubs (<12 m), young (12-24 m), and adults (>24 months; [20]). ...
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Regular evaluation of jaguar (Panthera onca) population status is an important part of conservation decision-making. Currently, camera trapping has become the standard method used to estimate jaguar abundance and demographic parameters, though evidence has shown the potential for sex ratio biases and density overestimates. In this study, we used camera trap data combined with satellite telemetry data from one female jaguar to estimate jaguar population density in the dry forest of Santa Rosa National Park in the Guanacaste Conservation Area of northwestern Costa Rica. We analyzed camera trap data collected from June 2016 to June 2017 using spatial capture- recapture methods to estimate jaguar density. In total, 19 individual jaguars were detected (11 males; 8 females) with a resulting estimated population density of 2.6 females (95% [CI] 1.7–4.0) and 5.0 male (95% [CI] 3.4–7.4) per 100 km2. Based on telemetry and camera trap data, camera placement might bias individual detections by sex and thus overall density estimates. We recommend population assessments be made at several consecutive 3-month intervals, that larger areas be covered so as not to restrict surveys to one or two individual home ranges, as in our case, and to carry out long-term camera monitoring programs instead of short-term studies to better understand the local population, using auxiliary telemetry data to adjust field designs and density estimations to improve support for jaguar conservation strategies.
... Information on population density has become an important tool in large carnivore conservation as it can be an indication of population status and viability (Jędrzejewski et al. 2017). Density estimates that note a declining population can highlight the need to prevent local extinction, which can lead to deleterious trophic cascade events (Schmitz et al. 2000;Ripple et al. 2014). ...
... This assumption aims to minimise the chance that immigration, emigration or deaths bias density estimates. Some researchers have suggested that shorter time periods ensure that the population is closed (Tobler and Powell 2013), while others suggest that for some species, such as wide-ranging large carnivores, longer sampling surveys provide more accurate density estimates (Jędrzejewski et al. 2017;Devens et al. 2019;Dupont et al. 2019;Harmsen et al. 2020). Due to the large area of the reserve and the limited number of camera traps, we decided to run a longer survey period (6 months) to increase the likelihood of capturing all adult leopards present on the reserve. ...
The African leopard (Panthera pardus pardus) has lost much of its historical range within South Africa. The remaining suitable habitat for the species includes both protected and unprotected areas in a fragmented landscape mosaic, bringing the species into close contact with human settlements. In order to make successful management decisions for the conservation of the species, more information is needed on leopard populations that exist in these highly fragmented habitats. The aim of our study was to determine the density of a population of leopards on Loskop Dam Nature Reserve (LDNR), Mpumalanga, South Africa. LDNR is located in a highly fragmented landscape and is surrounded by a variety of human settlements including game farms, livestock farms and rural towns. There are several smaller reserves 20–45 km away from LDNR, which may allow leopard movement and connectivity within the region. We determined population density by running a 164-day camera trap survey that covered a total area of 148.77 km2 within the reserve. Leopard density was estimated using Spatially Explicit Capture–Recapture models implemented in the program ‘secr’ in R using four different models. The most supported model was a sex-based model that allowed for differences in detection probabilities between males and females. The population density estimated with this model was 7.7 ± 2.0 (range 4.7–12.6) leopards per 100 km2. This density estimate in LDNR is comparable to other leopard populations in protected areas with similar habitat types and fragmented landscapes within South Africa. This study highlights that isolated, protected natural areas have the potential to harbour significant populations of leopards, which is important for the management and conservation of the species.
... We analysed empirical data from five jaguar studies ( Fig. 1) that included (1) long-term camera trap survey conducted in Hato Piñero, the Llanos, Cojedes, Venezuela (Jędrzejewski et al. 2014(Jędrzejewski et al. , 2017(Jędrzejewski et al. , 2021 (Morato et al. 2016(Morato et al. , 2018Kanda et al. 2019;Kantek et al. 2021;Thompson et al. 2021). In all these studies, individual jaguars were identified based on the unique spot patterns. ...
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Most large felids are classified as solitary species, with only lions ( Panthera leo ) and cheetahs ( Acinonyx jubatus ) exhibiting social, collaborative behaviours. Herein, we present evidence of the formation of male coalitions by jaguars ( Panthera onca ), based on data from five studies conducted with camera trapping, GPS telemetry, and direct observations in the Venezuelan Llanos and Brazilian Pantanal. Out of 7062 male records obtained with camera traps or visual observations, we detected 105 cases of male-male interactions, of which we classified 18 as aggression, nine as tolerance, 70 as cooperation/coalition, and eight as unidentified. In two studies, two male jaguars formed stable coalitions lasting over 7 years each. In the Llanos, each coalition male paired and mated with several females. For male jaguar coalitions, we documented similar behaviours as recorded earlier in lions or cheetahs, which included patrolling and marking territory together, invading territories of other males, collaborative chasing and killing other jaguars, and sharing prey. However, different from lions or cheetahs, associated male jaguars spent less time together, did not cooperate with females, and did not hunt cooperatively together. Our analysis of literature suggested that male jaguar coalitions were more likely to form when females had small home range size, a proxy of females’ concentration, while in lions, the male group size was directly correlated with the female group size. Similarly, locally concentrated access to females may drive formation of male coalitions in cheetahs. We conclude that high biomass and aggregation of prey are likely drivers of sociality in felids. Significance statement The division into social and solitary species in large felids has so far seemed unambiguous, with only lions and cheetahs classified as social species, in which male coalitions also occurred. Our data show that, under certain conditions, male coalitions may also form in jaguar populations. Factors that drive formation of male coalitions in lions and cheetahs, but not in other species of large cats, have not been clear until now. Our analyses indicate that in jaguars, lions, and cheetahs, the concentration of females likely plays the most important role. In jaguars, the probability of male coalition occurrence is highest in populations with the smallest mean female home range size (and thus likely high local density of females), while in lions, male group size is most strongly correlated with female group size.
... Relatively high densities of jaguars in proximity to human habitations, economic activities, and transportation routes are possible, but are best accomplished through attention to conflict and threat-reducing measures that harmonize economic and environmental priorities. High jaguar densities were found in a study area in Venezuela on a ranch managing about 10,000 head of cattle [45], with possible solutions, including forest blocks for natural prey (open area to forest ratio of 50:50), no hunting of natural prey, and tight livestock management (controlled reproduction, good nutritional status, and moving cattle among pastures and savannas on a seasonal basis and in response to conflicts). Although the management measures available to large ranches may be difficult to replicate in the small operations of the Muskitia Hondureña, in a section of the same bi-national JCU in the Muskitia Nicaragüense, improved livestock management, tighter herd control, better nutrition through silvopastoral systems and improved pastures, conservation agreements, and moderation of hunting, resulted in higher livestock productivity, the recovery of 800 km 2 of forests, increased bird diversity, no decreases in mammal diversity or abundance, and a drastic decline in human-jaguar conflict [46][47][48]. ...
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Livestock predation is a global problem and constitutes the main source of conflict between large carnivores and human interests. In Latin America, both jaguar and puma are known to prey on livestock, yet studies in Mesoamerica have been scattered and few have been carried out in Honduras. We interviewed ranchers in a biosphere reserve where jaguars and pumas are present. Local indigenous communities reported livestock predation (average annual loss of 7% from 2010–2019), with preventive and retaliatory killing as their main actions against predation by the jaguar and puma. Other sources of cattle loss included diseases and theft. The extensive management system (free grazing) lets cattle access forests where predators are more common. We found that livestock predation is not random, but rather, related to landscape variables and human influence. Sites farther from human influence and closer to forest cover were more susceptible to predation. Jaguar and puma persistence in the biosphere reserve will require measures that facilitate human–carnivore coexistence and comply with Sustainable Development Goals (SDG) 2 and 15 (zero hunger and biodiversity conservation). We propose management practices to mitigate livestock predation in the presence of large carnivores based on examples of proven human–carnivore coexistence in Venezuela, Brazil, Paraguay, and Nicaragua, such as improving the spatial arrangement of livestock (maintaining a distance from forest areas) and the incorporation of confinement pens for young calves (at least the first three months of life) and their mothers. If the pens are built close to the property’s house and have constant surveillance and/or dogs, the results are likely to be more effective. Deploying these proven tools may help change the current negative perception of ranchers towards large carnivores that is essential to conservation under the aims of SDG 15. We recommend government policies and support aimed to strengthen livestock health to increase productivity and to reduce their vulnerability to predation. Finally, this study represents a baseline to understand the magnitude of the human–carnivore conflict over cattle in one of the largest biosphere reserves in Mesoamerica.
... The consequences of anthropogenic and climatic disturbances on animal communities and their environment have been the focus of conservation research in recent decades (Cardinale et al., 2012;Dirzo et al., 2014). Suitable habitat for animals unable to adapt to anthropogenic change is decreasing due to habitat loss and fragmentation (Fischer and Lindenmayer, 2007;Gehring and Swihart, 2003), harvesting or poaching (Gangaas et al., 2013;Martin and Caro, 2013;Carter, 2017), pollution of rivers and water bodies (Jepson et al., 2016;Desforges et al., 2016), mortality linked to humananimal conflict (Jędrzejewski et al., 2017), climate change (Brodie and Pearson, 2016;Descamps et al., 2017) and emerging diseases caused by fungi and parasites (Weinstein et al., 2017). Between 1970 and2005, it is estimated that the global population of African fauna within protected areas fell by around 60% on a continental scale (Gandiwa et al., 2016;Murn et al., 2016). ...
... Due to the jaguars' secretive nature, little is known about this big cat's reproductive and rearing behavior (Eizirik et al. 2002). Limited to few field observations and data from captive animals, the jaguar's reproductive parameters are significant yet poorly known aspects determining the species population dynamics (Jędrzejewski et al. 2017). Little is known about the causes of jaguar mortality in the wild (Tortato et al. 2017), nevertheless mortality rates are assumed to be higher during the first year of life. ...
Common across various taxa, infanticide is a highly variable phenomenon present from insects to birds to mammals. In felids, antagonistic sexual coevolution led to the development of female counterstrategies to infanticide spanning particular sexual behavior, physiology, and social strategies. Numerous protective behaviors are well documented for large felids such as lions, cheetahs, and pumas that rely on cooperative defenses and polyandrous mating to protect their cubs from infanticide. Nevertheless, little is known about other wildcat species adopting such behaviors. Solitary and enigmatic, jaguars (Panthera onca) are the largest cat existing in the Americas. Little is known about this big cats’ reproductive and rearing behavior, mainly due to its secretive nature. Here, field observations in two major wetland ecosystems of South America show new and unique findings on female jaguar counterstrategies towards male infanticide. Our findings suggest that, like their big cat relatives in Africa, jaguars have evolved behavioral counterstrategies to protect their young in response to antagonistic sexual coevolution.
... Even just the 13 telemetered individuals that were all present in 2015 with on average 96% of GPS locations contained within the study area (236.7 km²) would suggest a density of approximately 5.4 jaguars/100 km² without considering the additional 56 individuals detected with cameras. Just this density estimate from telemetered individuals is comparable or exceeds other high jaguar density estimates such as 4.5 jaguars/100 km² from the Peruvian Amazon (Tobler et al. 2018), 4.4 jaguars/100 km² in the Venezuelan Llanos (Jędrzejewski 2017), and 6.6-6.7 jaguars/100 km² in the southern Pantanal (Soisalo and Cavalcanti 2006). We observed the highest density during the wet season, 14.3 jaguars/100 km², but, with only one season of monitoring, it is not yet clear whether this is a biological effect or a single anomalous year. ...
Energetic subsidies between terrestrial and aquatic ecosystems can strongly influence food webs and population dynamics. Our objective was to study how aquatic subsidies affected jaguar (Panthera onca) diet, sociality, and population density in a seasonally flooded protected area in the Brazilian Pantanal. The diet (n = 138 scats) was dominated by fish (46%) and aquatic reptiles (55%), representing the first jaguar population known to feed extensively on fish and to minimally consume mammals (11%). These aquatic subsidies supported the highest jaguar population density estimate to date (12.4 per 100 km²) derived from camera traps (8,065 trap nights) and GPS collars (n = 13). Contrary to their mostly solitary behavior elsewhere, we documented social interactions previously unobserved between same‐sex adults including cooperative fishing, co‐traveling, and play. Our study demonstrates that aquatic subsidies, frequently described in omnivores, can also transform the ecology and behavior of obligate carnivores.
... Detection was considered as one independent event of a species per camera and day (24 h) [69]. Therefore, we counted photos with multiple individuals of the same species in the frame as single detection for that species [70,71] to minimize bias in estimates of relative abundance [72]. ...
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This study is the first systematic assessment of large herbivore (LH) communities in Limpopo National Park (LNP) in Mozambique, an area where most LH species were extinct until the early 2000s. We investigate whether LH community parameters are linked with the availability of habitat types or the distance between sampling sites and the origin of LH resettlement. We placed camera traps in five habitat types in resettled and not-resettled areas to compare species richness, relative abundance index, grazers–browsers–mixed feeder and naïve occupancy of 15 LH species. While the richness decreased along the distance gradient of LH resettlement, relative abundance index strongly responded to habitat features. The grazer–browser–mixed feeder ratio oscillated, while from resettled to not-resettled areas, the ratio increased. Most species show a wide distribution range. The associations of most LH community parameters with habitat types rather than distance to initial release, together with the species-specific and guild-specific response patterns of LH, suggest LNP to already be in an intermediate stage of restoration. Our results highlight the importance of post-release monitoring of reintroduced wildlife as a tool to assess the success of ecological restoration initiatives in transboundary conservation areas.
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En este libro se presentan los primeros seis protocolos donde se discuten metodologías concretas, como el uso de cámaras trampa, un sistema participativo de colecta biológica, el uso de drones para el monitoreo de fauna silvestre y metodologías para el estudio fisicoquímico y organoléptico de carne y subproductos de reptiles. Además, se incluyen dos trabajos con un enfoque más integral que describen un conjunto de metodologías dirigidas a mejorar el monitoreo de aves silvestres y garantizar el uso sostenible de carne de origen silvestre. Con este libro COMFAUNA pretende mejorar la producción de informaciones útiles para el uso de la biodiversidad y que permitan fortalecer argumentos políticos que mejoren el uso sostenible de la misma.
While population density is a basic demographic parameter, it is rarely available for the elusive European wildcat, despite its wide distribution. Italy hosts at least five different wildcat populations, and little information is available for the wildcats inhabiting the northeast of the Italian peninsula. With the aim to provide the first report on European wildcat population density, we used spatially explicit capture-recapture models applied to camera trapping data in a pre-alpine area in NE Italy. The survey was carried out from May 18th to September 14th, 2015, using 31 camera traps distributed within a 1 × 1 km grid, placing a single camera per km2. We collected 32 videos of wildcats, corresponding to a total of eleven individuals. Density ± SE estimate was 0.35 ± 0.12 individuals per km2, with the encounter probability (g0) equal to 0.10 ± 0.03, and the spatial scale (σ) equal to 461 ± 62 m, corresponding to a mean home range size of 3.36 km2. In addition, to evaluate our sampling design and the robustness of our estimates, we simulated data generation and fitted SECR models under several realistic combinations of number and spacing of detectors, and sampling efforts. Considering the relative standard errors and relative bias, our sampling design produced robust estimates, whereas in scenarios with short sampling periods or greater spacing of detectors, the estimates were inadequate. Our study provides previously unavailable data on the biology of the European wildcat from NE Italy and some important considerations concerning sampling design to plan future research.
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Predation, one of the most dramatic interactions in animals' lives, has long fascinated ecologists. This volume presents carnivores, raptors and their prey in the complicated net of interrelationships, and shows them against the background of their biotic and abiotic settings. It is based on long-term research conducted in the best preserved woodland of Europe's temperate zone. The role of predation, whether limiting or regulating prey (ungulate, rodent, shrew, bird, and amphibian) populations, is quantified and compared to parts played by other factors: climate, food resources for prey, and availability of other potential resources for predators.
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Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park's population of grizzly bears requires continued conservation-oriented management actions.
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Large carnivores such as jaguars (Panthera onca) are species of conservation concern because they are suffering population declines and are keystone species in their ecosystems. Their large area requirements imply that unprotected and ever-increasing agricultural regions can be important habitats as they allow connectivity and dispersal among core protected areas. Yet information on jaguar densities across unprotected landscapes it is still scarce and crucially needed to assist management and range-wide conservation strategies. Our study provides the first jaguar density estimates of Colombia in agricultural regions which included cattle ranching, the main land use in the country, and oil palm cultivation, an increasing land use across the Neotropics. We used camera trapping across two agricultural landscapes located in the Magdalena River valley and in the Colombian llanos (47–53 stations respectively; >2000 trap nights at both sites) and classic and spatially explicit capture-recapture models with the sex of individuals as a covariate. Density estimates were 2.52±0.46–3.15±1.08 adults/100 km2 in the Magdalena valley, whereas 1.12±0.13–2.19±0.99 adults/100 km2 in the Colombian llanos, depending on analysis used. We suggest that jaguars are able to live across unprotected human-use areas and co-exist with agricultural landscapes including oil-palm plantations if natural areas and riparian habitats persist in the landscape and hunting of both jaguar and prey is limited. In the face of an expanding agriculture across the tropics we recommend land-use planning, adequate incentives, regulations, and good agricultural practices for range-wide jaguar connectivity and survival.
The tiger (Panthera tigris) is an endangered, large felid whose demographic status is poorly known across its distributional range in Asia. Previously applied methods for estimating tiger abundance, using total counts based on tracks, have proved unreliable. Lack of reliable data on tiger densities not only has constrained our ability to understand the ecological factors shaping communities of large, solitary felids, but also has undermined the effective conservation of these animals. In this paper, we describe the use of a field method proposed by Karanth (1995), which combines camera-trap photography, to identify individual tigers, with theoretically well-founded capture–recapture models. We developed a sampling design for camera-trapping and used the approach to estimate tiger population size and density in four representative tiger habitats in different parts of India. The field method worked well and provided data suitable for analysis using closed capture–recapture models. The results suggest the potential for applying this methodology to rigorously estimate abundances, survival rates, and other population parameters for tigers and other low-density, secretive animal species in which individuals can be identified based on natural markings. Estimated probabilities of photo-capturing tigers present in the study sites ranged from 0.75 to 1.00. Estimated densities of tigers >1 yr old ranged from 4.1 ± 1.31 to 16.8 ± 2.96 tigers/100 km2 (mean ± 1 se). Simultaneously, we used line-transect sampling to determine that mean densities of principal tiger prey at these sites ranged from 56.1 to 63.8 ungulates/km2. Tiger densities appear to be positively associated with prey densities, except at one site influenced by tiger poaching. Our results generally support the prediction that relative abundances of large felid species may be governed primarily by the abundance and structure of their prey communities.
Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors approach is practical - it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.
African wild dogs (Lycaon pictus) are endangered, having disappeared from many areas where other large carnivore species have persisted. The relative vulnerability of this species has been attributed variously to its disproportionate exposure to anthropogenic threats, limitation by larger competing predators, and Allee effects caused by obligate cooperative breeding. The natural recovery of a wild dog population living on private and community land in northern Kenya provided an opportunity to investigate these potential constraints on population growth. Within a decade the population increased from near-extinction to become the 6th largest in the world. Rates and causes of mortality, and reproductive rates, were similar on community lands, where people and livestock were abundant but competing predators suppressed, and on commercial ranches, where human and livestock densities were lower but competitors more abundant. Larger packs produced larger litters, indicating a component Allee effect. However, because pack size was unrelated to population size, growth of the population was not impeded at low densities; that is, no demographic Allee effect was detectable. These results show that, despite earlier concerns, wild dogs can achieve rapid population recovery, even in a human-dominated landscape. This recovery was probably facilitated by local pastoralist traditions, which combine vigilant herding of livestock with little or no hunting of wild prey. This success might be replicated in other areas where traditional pastoralism is still practiced.