ArticlePDF Available

Improving the random encounter model method to estimate carnivore densities using data generated by conventional camera-trap design

  • IBiCo - Instituto de Biología de la Conservación, Madrid, España
  • Instituto de Biología de la Conservación - IBiCo


The random encounter model, a method for estimating animal density using camera traps without the need for individual recognition, has been developed over the past decade. A key assumption of this model is that cameras are placed randomly in relation to animal movements, requiring that cameras are not set only at sites thought to have high animal traffic. The aim of this study was to define a correction factor that allows the random encounter model to be applied in photo-trapping surveys in which cameras are placed along tracks to maximize capture probability. Our hypothesis was that applying such a correction factor would compensate for the different rates at which lynxes use tracks and the surrounding area, and should thus improve the estimates obtained with the random encounter model. We tested this using data from a well-known Iberian lynx Lynx pardinus population. Firstly, we estimated Iberian lynx densities using a traditional camera-trapping design followed by spatially explicit capture-recapture analyses. We estimated the differential use rate for tracks vs the surrounding area using data from a lynx equipped with a GPS collar, and subsequently calculated the correction factor. As expected, the random encounter model overestimated densities by %. However, the application of the correction factor improved the estimate and reduced the error to %. Although there are limitations to the application of the correction factor, the corrected random encounter model shows potential for density estimation of species for which individual identification is not possible.
Improving the random encounter model method
to estimate carnivore densities using data generated
by conventional camera-trap design
Abstract The random encounter model, a method for esti-
mating animal density using camera traps without the need
for individual recognition, has been developed over the past
decade. A key assumption of this model is that cameras are
placed randomly in relation to animal movements, requir-
ing that cameras are not set only at sites thought to have
high animal traffic. The aim of this study was to define
a correction factor that allows the random encounter
model to be applied in photo-trapping surveys in which
cameras are placed along tracks to maximize capture prob-
ability. Our hypothesis was that applying such a correction
factor would compensate for the different rates at which
lynxes use tracks and the surrounding area, and should
thus improve the estimates obtained with the random
encounter model. We tested this using data from a well-
known Iberian lynx Lynx pardinus population. Firstly, we
estimated Iberian lynx densities using a traditional camera-
trapping design followed by spatially explicit capture
recapture analyses. We estimated the differential use rate
for tracks vs the surrounding area using data from a lynx
equipped with a GPS collar, and subsequently calculated
the correction factor. As expected, the random encounter
model overestimated densities by %. However, the ap-
plication of the correction factor improved the estimate and
reduced the error to %. Although there are limitations to
the application of the correction factor, the corrected
random encounter model shows potential for density
estimation of species for which individual identification is
not possible.
Keywords Camera traps, capturerecapture, carnivores,
correction factor, density estimation, Iberian lynx, Lynx
pardinus, random encounter model
Methods that accurately estimate animal abundances or
densities can greatly enhance the ability to monitor
and manage populations (Garrote et al., ; Zero et al.,
). Camera traps are highly efficient in detecting elusive
mammals such as carnivores (Cutler & Swann, ) and
over the last decade they have been used widely for estimat-
ing population sizes of secretive but individually recogniz-
able animals (Foster & Harmsen, ). Population sizes of
the tiger Panthera tigris (Karanth & Nichols, ), jaguar
Panthera onca (Boron et al., ) and Iberian lynx Lynx
pardinus (Garrote et al., ) have been estimated success-
fully using capturerecapture analyses of camera-trapping
data. However, this method is of limited use for species
that do not have markings that allow for the identification
and enumeration of individuals (Villette et al., ).
As an alternative to capturerecapture analysis, photo-
graphic capture rates (photo captures per unit time) can
be used as a density index for species that cannot be indi-
vidually identified (Carbone et al., ). Nevertheless the
use of camera-trapping rates as an index of abundance has
been debated widely (Carbone et al., ; Jennelle et al.,
; Karanth et al., ;OBrien et al., ; Harmsen
et al., ; Rovero et al., ). Although under certain cir-
cumstances camera-trap encounter indices may give accu-
rate estimates of relative abundance (OBrien et al., ),
criticisms have noted that indices derived from camera-trap
data vary between species, habitats, seasons and/or camera
placements (Larrucea et al., ; Harmsen et al., ;
Cusack, et al., a,b; Mann et al., ). However,
Rowcliffe et al. () have developed a method for estimat-
ing animal density using camera traps without individual
recognition. This method is based on modelling random
encounters between animals and cameras and takes into
account variables affecting the trapping rate. The probability
that a camera detects an animal depends on the speed of the
animal (distance travelled in a given time), the exposure
time, the detection area of the camera and the number of an-
imals present. The random encounter model method has
been tested on only a few occasions against known densities
derived by other methods (Rovero & Marshall, ; Zero
et al., ; Anile et al., ; Cusack et al., a,b). Three
studies have tested the effectiveness of the random
encounter model as a method for estimating carnivore
GERMÁN GARROTE (Corresponding author,,
Ambiente y Agua de Andalucía, c/ Johan Gutenberg s/n, Isla de la Cartuja,
41092 Seville, Spain. E-mail
RAMÓN PÉREZ DE AYALA World Wildlife FundSpain, Madrid, Spain
ANTÓN ÁLVAREZ Instituto de Biología de la Conservación, Madrid, Spain
MIGUEL A. SIMÓN Consejería de Medio Ambiente de la Junta de Andalucía,
Jaén, Spain
Received  June . Revision requested August .
Accepted  December . First published online  December .
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use,
distribution, and reproduction in any medium, provided the original work is properly cited.
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
populations (wildcat Felis silvestris, Anile et al., ; lion
Panthera leo, Cusack et al., a,b; European pine marten
Martes martes, Balestrieri et al., ). Overall, these studies
concluded that the random encounter model is a promising
method for obtaining density estimates and has the potential
to be applied widely, although further field tests for different
species and habitats are still needed.
A key assumption of the model is that cameras are placed
randomly in relation to animal movements, sampling parts of
the landscape in proportion to their coverage in the study
area, which means that they should not be placed to either
inflate or deflate encounter rates (Rowcliffe et al., ).
Therefore, to avoid inflated encounter rates and exaggerated
estimates, the random encounter model requires that cameras
are not set only at sites thought to have high animal traffic
(e.g. tracks, underpasses or water sources; Rowcliffe et al.,
). Because of this requirement, rare species such as carni-
vores may be detected too infrequently for density estimates
to be calculated (Rowcliffe et al., ). Many carnivore spe-
cies are more likely to be detected along tracks than with
random camera placements (Larrucea et al., ; Harmsen
et al., ;Blake&Mosquera,; Di Bitetti et al., ;
Cusack et al., a,b). Even with camera traps set along
roads, carnivore species have low detection rates because of
their naturally low densities, large home ranges and secretive
habits (Trolle & Kéry, ;OConnell & Nichols, ). The
random arrangement of cameras in relation to animal move-
ments thus requiresa much greater sampling effort to achieve
detection rates that can be used to generate reliable estimates.
The aim of this study was to define a correction factor
that enables the random encounter model method to be ap-
plied to traditional photo-trapping surveys of carnivores,
with cameras placed on tracks to maximize capture prob-
ability. Our hypothesis was that if the differential use rate
between tracks and the rest of the area is known, then it is
possible to calculate a correction factor for expected devia-
tions from random encounter model density estimates that
are caused by the violation of the methods key assumption.
We tested this hypothesis using data from a well-known
Iberian lynx population, a species with fur patterns that
allow identification of individual animals (Garrote et al.,
). Firstly, we estimated Iberian lynx densities using
spatially explicit capturerecapture. We then estimated the
differential use rate for tracks vs surrounding area using
GPS collar data, and calculated a correction factor. Finally,
we compared the density estimates from the original ran-
dom encounter model with those derived from the adjusted
model, to evaluate the usefulness of the correction factor.
Study area
We conducted our study on a private estate in the Sierra de
Andújar Natural Park, which lies within the known range of
the Sierra Morena Iberian lynx population in south-eastern
Spain (Simón et al., ;Fig. ).Theareaismanagedforbig
game hunting and has high densities of red deer Cervus
elaphus andwildboarSus scrofa. Altitudes range between
 and , m, and the vegetation consists primarily of well-
preserved Mediterranean forests dominated by oaks Quercus
ilex,Quercus faginea and Quercus suber, and scrublands with
Quercus coccifera,Pistacia lentiscus,Arbutus unedo,Phillyrea
angustifolia and Myrtus communis.
The estate collaborates with the long-term (. years)
Iberian lynx conservation programme, which uses intensive
camera trapping throughout the year to closely monitor
individuals within the population (Gil-Sánchez et al.,
). Data from continuously operating camera traps in
the estate showed that  Iberian lynxes occupied the
study area during the study period.
The camera-trap survey was carried out during Sep-
temberOctober  and followed traditional protocols
used in Iberian lynx monitoring (Gil-Sánchez et al., )
that are designed to generate population estimates using
capturerecapture analysis (Garrote et al., ). We placed
the cameras on m wide tracks that are built for use by
cars moving around the hunting estates. Cameras were po-
sitioned in trees or on poles c.  cm above ground level and
.m from the opposite side of the track. We discarded
records of animals detected beyond this .-m limit to fix
the detection radius required for the random encounter
model analysis. We used nine Moultrie M- camera traps
(PRADCO, Birmingham, USA) with a mean spacing of
. ±SE . km. Cameras operated continuously and the
delay period between photographs was set at  s. Every 
days we checked all cameras to ensure they were working
correctly and to download any photographs. No cameras
malfunctioned and therefore there were no gaps in the data.
We considered the density derived from spatially explicit
capturerecapture analysis as the baseline for comparison
with the density estimates obtained using the random
encounter model. Spatially explicit capturerecapture uses
two distinct submodels within its workflow to compute
densities (D). One submodel simulates an animals distribu-
tion from the capture history to give the individuals activity
centre as an output, whereas the second simulates the cap-
ture process on the basis of the radial distance between the
estimated centre of activity and the traps (Efford et al.,
). Input data is in two files, one containing the name
and geographical coordinates of the detectors (cameras)
and another containing the capture histories (i.e. season,
animal identification, the occasion and the detector). We
set the trap detector type for the camera-trapping analysis
as proximity (allowing for multiple detections of the same
individual during the same event; Efford et al., ), the
distribution model as Poisson (assuming homogeneous
100 G. Garrote et al.
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
distribution of home range centres in the study; Borchers &
Efford, ), and the detection function as half-normal
(the capture probabilities decrease linearly with the distance
of the camera station from the centre of the individuals
home range; Efford et al., ).
Following Jůnek et al. () we employed two models:
the null model, in which detection is affected only by the
use of space, and the -class finite mixture model, which al-
lows for the modelling of variation in detection probability
amongst individuals. We ranked candidate models using
the Akaike information criterion corrected for small samples
sizes (AICc). This analysis was performed using Density
.(Efford & Fewster, ).
Random encounter model parameterization
To convert camera-trapping rates into lynx density, we used
the equation:
following Rowcliffe et al. (), where yis the number of
independent photographic events, tthe total camera survey
effort, vthe average speed of animal movement, and rand
θthe radius and angle of the camera trap detection zone,
We measured the arc θof the cameras through trials in
which we placed a scale with -cm divisions that could be
read in photographs at a distance of .m from the camera.
We activated the camera by walking in front of it and then
measured the width of the photograph with the scale. We
used trigonometry to infer the angle of the width of the
photograph and the distance from the scale. We found
that the camera radius was .m, i.e. the other side of the
track, thus reducing the possibility of an error associated
with variation in the radius. We assumed the mean speed
of movement of the Iberian lynx to be ±SE . km/day,
following Palomares et al. ().
We computed the overall variance in the random
encounter model density estimates using bootstrapping,
which incorporated the variance associated with the en-
counter rate (estimated by re-sampling camera locations
with , replicas; Rowcliffe et al., ; Cusack et al.,
a,b) and the standard errors associated with the esti-
mation of the speed parameter. Camera-related parameters
(Rowcliffe et al., ) were assumed to have no variance
(Zero et al., ).
Correction factor
The correction factor represents the lynxespreference for
or avoidance of roads and is calculated as the ratio of road
availability to road use in the study area (Fig. ). The propor-
tional availability of roads was defined by the surface area
occupied by roads divided by the total size of the study
area. To estimate the surface area occupied by roads we
added a buffer of . m either side of the lines represent-
ing roads, resulting in a total width of .m (equal to the
camera-trap detection radius). The total size of the study
area, or effective trapping area, was defined as the minimum
convex polygon around camera-trap stations with an added
buffer strip of the mean maximum distance moved by
recaptured lynx (Parmenter et al., ). To estimate the
lynxesuse of roads, we used data from the only lynx in
the study area equipped with a GPS collar, which gives a
position signal every hours and has an activity sensor.
FIG. 1 (a) Location of the study
area in southern Spain, design
of the camera-trap grid, and
locations obtained from GPS
collar data for one Iberian lynx
Lynx pardinus. (b) Detail of
on-road and off-road Iberian
lynx localities.
Improving the random encounter model 101
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
We used GPS data from the same months as the camera
survey, but from the year following the camera-trapping
surveys (SeptemberOctober ). We only used locations
recorded while the animal was active because the animal
has to be moving to be photo-trapped. With these data,
we determined and quantified the on-road and off-road
localizations. The proportional use of roads by lynxes
was defined as the number of on-road localizations out of
all localizations. We conducted all spatial analyses with
the geographical information system QGIS . (QGIS
Development Team, ). We calculated the correction
factor CF using the following equation:
CF =road availability
road use =
road surface
total surface
on-road localizations
total localizations
This factor corrects the random encounter model results
(REM) and gives the corrected random encounter model
result (REMc) as:
We used bootstrapping to assign measures of accuracy to
the correction factor (re-sampling of the on-road and off-
road locations with replacements) and then calculated the
overall standard deviation and % confidence intervals for
corrected random encounter model estimates, as described
above. We carried out all analyses in R..(R Development
Core Team, ). As with the random encounter model
estimates, we generated two corrected random encounter
model estimates.
Camera traps accumulated  trap days over a period of
 days and generated  Iberian lynx detections, resulting
in a total trapping rate of . captures/ trap days. We
identified all  lynxes previously known to inhabit the
area;  trap days ( days) were needed to capture all of
them. The mean maximum distance moved was , ±SE
 m, which implies a total size of the study area of , ha.
Spatially explicit capturerecapture surveys The null model
(AICc = .;ΔAIC = ;parameters) performed better
than the -class finite mixture model (AICc = .;
ΔAIC = .;parameters) and was used for the spatially
explicit capturerecapture analysis. The population density
estimates calculated using this model were . ±SE .
individuals/ ha (Table ). Movement parameter σwas
. ±SE . m.
Random encounter model The mean trapping rate in the
dataset for the camera traps was . ±SE . detections/
 trap days, resulting in a population density estimate of
. ±SE . individuals/ ha based on the random
encounter model.
Random encounter model with correction factor The total
road surface area was .ha out of a total area of
,.ha, which corresponds to a road availability of .%.
We recorded  GPS localizations for lynx on the move, of
which  were along roads; lynx road use was thus .%.
Consequently the correction factor value was . ±SE .
and the estimated % confidence interval was ...
After applying the correction factor to the random encounter
model estimates, we obtained a density of . ±SE .
individuals/ ha (Table ). The random encounter model
without the correction factor overestimates the lynx density
by %. The corrected random encounter model greatly
improves the estimate as it only differs by % from the estimate
calculated using the spatially explicit capturerecapture null
Density estimates calculated with a random encounter
model, using data from conventional camera-trapping sur-
veys in which cameras are placed along tracks, resulted in
an overestimate of lynx density in our study area. This was
expected because the sampling design did not fulfil the key
assumption that cameras should be placed randomly in rela-
tion to animal movements (Rowcliffe et al., ). Our results
suggest that the application of a correction factor to the
random encounter model based on lynx road use greatly im-
proves the estimates. In addition, it allows us to use data from
conventional survey designs in which cameras are positioned
specifically to increase the capture probability; thus, the re-
quired sampling effort is less than in the standard random
encounter model methodology with random camera place-
ment. The random encounter model requires that cameras
should be deployed for as long as is necessary to obtain a
minimum of  photographs but preferably at least 
(Rowcliffe et al., ). In Rovero et al. () there is a
personal communication from Rowcliffe that suggests the
minimum number of photographs should be at least .
There are numerous studies of on-road vs off-road capture
rates that provide an indication of the sampling effort
needed to reach this number of captures. For instance, Di
Bitetti et al. () and Blake & Mosquera ()have
determined the on-road and off-road encounter rates for
TABLE 1 Density estimates (D) for a population of  Iberian lynxes
Lynx pardinus, obtained using different methodologies.
Method D±SE (95% CI)
Spatially explicit capturerecapture 0.31 ±0.13 (0.130.68)
Random encounter model 1.43 ±0.34 (0.423.74)
Corrected random encounter model 0.36 ±0.21 (0.100.94)
102 G. Garrote et al.
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
several mammal species. For the ocelot Leopardus pardalis
(similar in size to the Iberian lynx), capture rates are .
. captures/ trap days off-road vs .. captures/
 trap days on-road. These capture rates imply that
,, trapping days off-road and  trapping
days on-road are required to obtain at least  captures.
The effort required off-road is thus  times greater than
on-road to achieve the same number of captures.
However, there are variations between carnivore species
in the detection probability along tracks vs random camera
placements. Although detection probability is greater along
roads and main tracks for medium-sized and large carni-
vores (e.g. jaguars and ocelots), detection probabilities for
small carnivores (e.g. Cape grey mongoose Galerella pulver-
ulenta and common genet Genetta genetta) tend to be the
same or greater with random camera placements (Wearn
et al., ; Di Bitetti et al., ; Mann et al., ).
Photo capture rates are influenced by habitat structure
and type, as well as by season (Larrucea et al., ;
Harmsen et al., ; Cusack et al., a,b; Mann et al.,
). For example, tracks in dense vegetation possess a
greater capacity for funnelling animals past a camera than
tracks in open areas where animals have more room to
wander off-track (Harmsen et al., ). Reproduction sta-
tus (in those species where it is seasonal) may also influence
the capture rate as it determines the movement range and
thus the capture probability (Nichols et al., ). Male
and female road use could also be different (Conde et al.,
) and individual differences may also occur. In our case
the correction factor was calculated using data from a single
male Iberian lynx. The inclusion of more individuals of both
sexes will improve the correction factor and thus the estimates
obtained. However, the use of GPS collars on a sufficient
number of individuals as a means of establishing a correction
factor for different species will often be logistically and eco-
nomically unviable. Thus, it would be advisable to examine
the possibility of establishing the correction factor for un-
marked species using on-road/off-road camera trap surveys.
Additionally, the identification of correction factors for differ-
ent species could make it possible to retrospectively estimate
abundance using previously collected camera-trap data.
Although the use of roads is not as pronounced in herbi-
vores as it is in carnivores, it has been observed in species such
as the South American tapir Tapirus terrestris (Di Bitetti et al.,
), Kirks dik-dik Madoqua kirkii and the hippopotamus
Hippopotamus amphibius (Cusack et al., a,b). The esti-
mates of herbivore densities established using a random en-
counter model (Zero et al., ; Caravaggi et al., )donot
take into account the possibility that species prefer to move
along roads, which could affect results. It is thus important
to carry out detailed studies of on-road vs off-road use by
species for which individuals cannot be identified, to establish
whether or not a correction factor needs to be applied when
estimating population densities.
The application of the proposed correction factor to
the random encounter model using conventional capture
recapture camera-trap methodology improves the accuracy
and precision of lynx density estimates. The use of con-
ventional camera-trap design data vs random-design data
allows population estimates to be performed over shorter
study periods, thereby reducing project costs (White, ;
Garrote et al., ). Even though further studies (e.g. on
habitat influence and intersexual variation) are needed to
allow the correction factor to be used in other areas or spe-
cies, the corrected random encounter model shows potential
for estimating the density of species for which individual
identification is not possible.
Acknowledgements The study was supported by LIFE Project
10NAT/ES/570 (Recovery of the historical distribution of the
Iberian lynx (Lynx pardinus) in Spain and Portugal). We thank
Pedro Monterroso and Sonia Illanas for their collaboration.
Author contributions Study conception and design: GG; data
collection: GG, JMS, MR, SdL; data analysis: GG, RPdA, AA, MAS;
writing: all authors.
Conflict of interest None.
Ethical standards This research abided by the Oryx guide-
lines on ethical standards. All necessary permits were obtained
from Consejería de Medio Ambiente y Ordenación del Territorio
(Andalusian government).
Wildcat population density on the Etna volcano, Italy: a comparison
of density estimation methods. Journal of Zoology,,.
CAPELLI, E., TIROZZI, P., ALFINO, S. et al. () Pine marten
density in lowland riparian woods: a test of the random
encounter model based on genetic data. Mammalian Biology,
BLAKE, J.G. & MOSQUERA,D.() Camera trapping on and off
trails in lowland forest of eastern Ecuador: does location matter?
Mastozoología Neotropical,,.
BORCHERS, D.L. & EFFORD, M.G. () Spatially explicit maximum
likelihood methods for capturerecapture studies. Biometrics,
RODRIGUEZ, L., SCHALLER,G.&PAYÁN,E.() Jaguar densities
across human-dominated landscapes in Colombia: the contribution
of unprotected areas to long term conservation. PLOS ONE,
DICK, J.T., MONTGOMERY, W.I. & REID,N.() An invasive
native mammalian species replacement process captured by camera
trap survey random encounter models. Remote Sensing in Ecology
and Conservation,,.
N., GINSBERG, J.R. et al. () The use of photographic rates to
estimate densities of tigers and other cryptic mammals. Animal
Conservation Forum,,.
Improving the random encounter model 103
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
SEXTON, J.O., MANTEROLA, C. et al. () Sex matters: modeling
male and female habitat differences for jaguar conservation.
Biological Conservation,,.
MACDONAL D, D.W. & COULSON,T.(a) Random versus game
trail-based camera trap placement strategy for monitoring terrestrial
mammal communities. PLOS ONE,,e.
DICKMAN, A.J. et al. (b) Applying a random encounter model to
estimate lion density from camera traps in Serengeti National Park,
Tanzania: density estimation of Serengeti lions. The Journal of
Wildlife Management,,.
CUTLER, T.L. & SWANN, D.E. () Using remote photography in
wildlife ecology: a review. Wildlife Society Bulletin (),
DIBITETTI, M.S., PAVI OLO,A.&DEANGELO,C.() Camera trap
photographic rates on roads vs. off roads: location does matter.
Mastozoología Neotropical,,.
EFFORD, M.G. & FEWSTER, R.M. () Estimating population size by
spatially explicit capturerecapture. Oikos,,.
EFFORD, M.G., DAW SON, D.K. & BORCHERS, D.L. () Population
density estimated from locations of individuals on a passive detector
array. Ecology,,.
FOSTER, R.J. & HARMSEN, B.J. () A critique of density estimation
from camera-trap data. The Journal of Wildlife Management,
GUZMAN, N., GARCÍA, F.J. et al. () Estimation of the Iberian
lynx (Lynx pardinus) population in the Doñana area, SW Spain,
using capturerecapture analysis of camera-trapping data. European
Journal of Wildlife Research,,.
TELLERÍA, J.L. & SIMÓN, M.Á. () The effect of attractant lures in
camera trapping: a case study of population estimates for the Iberian
lynx (Lynx pardinus). European Journal of Wildlife Research,
LILLO, S., PÉREZ, J. et al. () The use of camera trapping for
estimating Iberian lynx (Lynx pardinus) home ranges. European
Journal of Wildlife Research,,.
DONCASTER, C.P. () Spatial and temporal interactions of
sympatric jaguars (Panthera onca) and pumas (Puma concolor)
in a neotropical forest. Journal of Mammalogy,,.
JENNELLE, C.S., RUNGE, M.C. & M ACKENZIE, D.I. () The use of
photographic rates to estimate densities of tigers and other cryptic
mammals: a comment on misleading conclusions. Animal
Conservation Forum,,.
K. & HEJCMANOVÁ,P.() Application of spatial and closed
capturerecapture models on known population of the western
derby eland (Taurotragus derbianus derbianus) in Senegal.
PLOS ONE ,e.
KARANTH, K.U. & NICHOLS, J.D. () Estimation of tiger densities
in India using photographic captures and recaptures. Ecology,
SMITH, J.L.D., MCDOUGAL, C. et al. () Science deficiency in
conservation practice: the monitoring of tiger populations in India.
Animal Conservation,,.
() Cameras, coyotes, and the assumption of equal detectability.
Journal of Wildlife Management,,.
MANN, G.K.H., ORIAIN, M.J. & PARKER, D.M. () The road less
travelled: assessing variation in mammal detection probabilities
with camera traps in a semi-arid biodiversity hotspot. Biodiversity
and Conservation,,.
() Estimating breeding proportions and testing hypotheses
about costs of reproduction with capturerecapture data. Ecology,
OBRIEN, T.G., KINNAIRD, M.F. & WIBISONO, H.T. () Crouching
tigers, hidden prey: sumatran tiger and prey populations in a
tropical forest landscape. Animal Conservation,,.
OCONNELL, A.F. & NICHOLS, J.D. (eds) ()Camera Traps in
Animal Ecology: Methods and Analyses. Springer, Tokyo, Japan.
J.M. () Spatial ecology of Iberian lynx and abundance of
European rabbits in Southwestern Spain. Wildlife Monographs,
DUNNUM, J.L., FRANKLIN, A.B. et al. () Small-mammal density
estimation: a field comparison of grid-based vs. web-based density
estimators. Ecological Monographs,,.
QGIS DEVELOPMENT TEAM ()QGIS Geographic Information
System. Open Source Geospatial Foundation Project.
en/site [accessed  June ].
EVELOPMENT CORE TEAM ()The R Project for Statistical
Computing. [accessed  December ].
ROVERO,F.&MARSHALL, A.R. () Camera trapping photographic
rate as an index of density in forest ungulates. Journal of Applied
camera trap type and how many do I need?A review of camera
features and study designs for a range of wildlife research
applications. Hystrix,,.
Estimating animal density using camera traps without the
need for individual recognition. Journal of Applied Ecology,
Clarifying assumptions behind the estimation of animal density
from camera trap rates: density estimates from camera trap rates.
The Journal of Wildlife Management,,.
MCCAIN, E., FERNÁNDEZ, L. et al. () Reverse of the
decline of the endangered Iberian Lynx. Conservation Biology,,
TROLLE,M.&KÉRY,M.() Camera-trap study of ocelot and
other secretive mammals in the northern Pantanal. Mammalia,
camera trapping provide accurate estimates of small mammal
(Myodes rutilus and Peromyscus maniculatus) density in the
boreal forest? Journal of Mammalogy,,.
EWERS, R.M. () Assessing the status of wild felids in a
highly-disturbed commercial forest reserve in Borneo and
the implications for camera trap survey design. PLOS ONE,
WHITE, G.C. ()CaptureRecapture and Removal Methods for
Sampling Closed Populations. Los Alamos National Laboratory,
Los Alamos, USA.
() Monitoring an Endangered savannah ungulate, Grevys zebra
Equus grevyi: choosing a method for estimating population
densities. Oryx,,.
104 G. Garrote et al.
, 2021, 55(1), 99104 ©The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605318001618 Published online by Cambridge University Press
... whether or not they accounted for variance in all parameters) or reported explicitly that they had not considered precision in some of the measured variables (e.g. Balestrieri et al., 2016;Garrote et al., 2021;Pfeffer et al., 2018). ...
... Looking into the bibliography (Appendix S1), we observed that most of the deficient procedures to estimate day range are those in which tagged animals with GPS collars were used to estimate day range without accounting for tortuosity (e.g. Caravaggi et al., 2016;Garrote et al., 2021;Massei et al., 2018;Rovero & Marshall, 2009;Zero et al., 2013). It is well described that estimate day range assuming straight-line distances between consecutive fixes notably underestimate day range, and some studies concluded that more than 5 fixesÁmin 1 would be required to get tolerably accurate estimates (Marcus Sennhenn-Reulen et al., 2017). ...
... Looking into literature (Appendix S1), we observed that habitual practice is to determine the dimensions of the detection zone by a series of trials in which the camera was approached by a person from varying directions (e.g. Cusack et al., 2015;Garrote et al., 2021;Loonam et al., 2021;Massei et al., 2018;Rowcliffe et al., 2008). In this respect, some studies have evidenced that detection zone is determined by different factors such as environmental conditions and camera trap settings Rowcliffe et al., 2011). ...
Full-text available
Population density estimates are important for wildlife conservation and management. Several camera trapping‐based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for species‐specific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REM‐reference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation‐based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals. Several camera trapping‐based methods for estimating wildlife population densities have been developed, one of which, the random encounter model (REM), has been widely applied because of its practical advantages such as no need for species‐specific study design. Here we used a combination of literature review and empirical study to compare the performance of REM with “gold standard” reference methods. We reviewed 77 REM‐reference density comparisons, and we also sampled 13 mammal populations. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate led to overestimation of density, while deficient estimates of detection zone resulted in underestimations. In conclusion, the REM is a reliable method for estimating wildlife population densities when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way for wider application of REM for monitoring terrestrial mammals.
... Random encounter model is a reliable method of estimating population density of multiple species using camera traps 228 not they accounted for variance in all parameters) or reported explicitly that they had not considered precision as one of the measured variables (e.g. Balestrieri et al., 2016Garrote et al., 2021). ...
... Many studies did not estimate REM parameters for the target population but considered values from published studies, which could lead to over-or underestimation of parameters and thus densities (e.g. Anile et al., 2014;Caravaggi et al., 2016;Garrote et al., 2021). Similarly, studies that estimated REM parameters based on incomplete data or estimated detection zones by testing on humans would probably result in biased density estimates (e.g. ...
Full-text available
A better understanding of population density (i.e. the number of individuals per unit area) is essential for wildlife conservation and management. Despite the fact that a wide variety of methods with which to estimate population density have already been described and broadly used, there are still relevant gaps. In the last few decades, the use of remotely activated cameras (camera traps) has been established as an effective sampling tool when compared with alternative methods. Camera trapping could, therefore, be considered a reliable tool with which to monitor those situations in which classical methods have relevant limitations. It could, for example, be used with species whose behaviour is elusive and which have low detectability (as is the case of most mammals), or populations in which the animals can be identified individually by the spot patterns on their bodies. However, there is lack of information regarding those species for which it is not possible to identify individual animals (i.e. unmarked species). Some authors that have applied camera trapping originally considered relative abundance indexes in order to monitor unmarked populations. These indices were based on encounter rates (i.e. the number of animals detected per sampling unit) observed in camera trapping studies. Methods with which to estimate the population density of unmarked populations were later described, the first of which was the random encounter model (REM). The REM models the random encounters between moving animals and static cameras in order to estimate population density. The REM does this by employing three basic parameters: i) encounter rate, ii) detection zone (area in which the cameras effectively detect animals), and iii) day range (average daily distance travelled by each individual in the population). When this thesis was first started, it was broadly discussed that the application of the REM was limited by the difficulties involved in estimating the parameters required, especially the day range. In this context, the aim of this thesis was to develop and harmonise camera trapping methodologies so as to estimate the population density and movement parameters of unmarked populations, working principally in the REM framework. The first research carried out for this thesis comprised a review of published studies concerning REM, which found that i) wrong practices in the estimation of REM parameters were frequent, and ii) the REM has rarely been compared with reference densities in empirical studies. We, therefore, then went on to evaluate the main factors that affect the probability of detection and the trigger speed of camera traps, which are relevant for encounter rate and detection zone estimation. This is shown in Chapter 1. We subsequently evaluated and described new methodologies that use camera traps to estimate the movement parameters of unmarked populations. We also evaluated the seasonal and spatial variation in these parameters. The information regarding this is provided in Chapter 2. Finally, we assessed the performance of the REM in a wide range of scenarios, and we compared it with other recently described camera trapping methods used to estimate the population density of unmarked species, as detailed in Chapter 3. The results reported in Chapter 1 show that camera trap performance as regards trigger speed and detection probability are highly influenced by different factors, such as the period of the day, the camera trap model, deployment height or sensitivity, among others. We monitored the community of birds and mammals in the study area, and we discovered that a relevant proportion of the animals that entered the theoretical detection zone were not usually recorded. These missed detections introduce bias into the encounter rate, and consequently into density. However, several camera trapping methods with which to estimate effective detection zone have been described, and they should be applied to all the populations monitored. With regard to the day range, we considered the wild boar as a model species and showed that assuming straight-line distances between consecutive locations obtained by telemetry devices underestimates this parameter, while movement behaviours should be accounted when using camera traps to estimate day range, as shown in Chapter 2.1. We then explored the use of camera traps to monitor movement parameters in greater depth, and showed that they are a reliable method. We described a new procedure with which to estimate the day range that accounts for movement behaviour, and for the ratio between fast and slow speeds. The new procedure performed well in the wide range of scenarios that we simulated, and was also tested with populations of mammals around the world. In this respect, we also described a machine learning protocol with which to identify movement behaviour obtained from camera trap records. All of this is described in Chapter 2.2. We subsequently showed that geographical (e.g. altitude), environmental (e.g. habitat fragmentation), biological (e.g. species) and management (e.g. hunting) factors affect the day range, and we reported variable day ranges in ungulates and carnivores across Europe, as shown in Chapter 2.3. We use the combination of a literature review and an empirical study to compare REM densities with those obtained using reference methods. The results showed a strong correspondence between the REM and reference densities, especially when REM parameters are estimated accurately for the target population. We also showed that the precision of the REM is lower than that of the reference methods, and provided further insights into the survey design in order to increase precision. This information is provided in Chapter 3.1. Finally, and as shown in Chapter 3.2, we used ungulates and carnivores as a target in order to compare the REM, random encounter and staying time (REST), and camera trap distance sampling (CT-DS). The REST and CTDS are two recently described methods with which to estimate the population density of unmarked species using camera traps. The results showed that the performance of the three methods is similar in terms of accuracy and precision. We recommend a survey design that will make it possible to apply all the methods, as the final selection of one of them will be mediated by the number of animals recorded and the camera trap performance. In conclusion, the results of this thesis show the usefulness of camera trapping to monitor the movement parameters and population density of wildlife and contributes with a methodological practical step forwards. In summary, the REM approach, which was tuned in this thesis, proved to be a reliable method in a wide range of environmental scenarios. The REM can be firmly established as a reference method to be implemented in multispecies monitoring programmes in the coming years, considering that it does not need to identify individual animals or spatial autocorrelation in captures. However, future developments of the REM in particular, and camera trapping unmarked methods in general, should be focused on optimising surveys designs in order to increase precision. Before this thesis was begun, the main limitations of applying the REM were the estimation of REM parameters, along with its reliability. This has, however, already been dealt with, and the main gap now concerns the low precisions obtained.
... The size of the territory varies depending on the abundance of its main prey, the wild rabbit, with the territories of the males being greater than those of the females (Female: 300-800 ha; male: 600-1200ha) The Iberian lynx plays the role of apex predator of the terrestrial vertebrate community in the Mediterranean ecosystem. The presence of the species n lynx affects the spatial distribution of other mesocarnivores as red fox (Vulpes vulpes), Egyptian mongoose (Herpestes ichneumon), beech marten (Martes foina), wildcat (Felis sylvestris), and common genet (Genetta genetta) (Garrote et al. 2019) Unverifiable observations, a type of anecdotal occurrence data, or tracks and scats detection and species assignment based on morphology are often used to assess the ranges or abundances of carnivores (Al-Johany 2007; Din & Nawaz 2010). However, the use of such data has been widely criticized since misidentification is likely to occur (Garrote & Ayala 2015, Monterroso et al. 2013). ...
... Therefore, REM method could not be applied to study design, commonly used in carnivores, as Iberian lynx, where cameras are placed on tracks to maximize capture probability. Nevertheless, Garrote et al. (2019) developed a correction factor (CF) for expected deviations from REM density estimates using data generated by conventional camera-trap design. The correction factor corrects for the differential use-rate between tracks and the rest of the area made by lynx: ...
Full-text available
This guidance reviews the methods for estimating relative abundance and density in nine large European wild carnivore species, somerepresenting relevant health concerns andprovides insights on how to obtain reliable estimations by using those methods. On a local scale, the appropriate method should take into accountthe characteristics of the study area, the estimated survey efforts, the expected results (i.e. a measure of true density or just an index of abundance to monitor the trend in space and time) the level of accuracy and precision, and a proper design so to obtain a correct interpretation of the data. Among all methods, the camera trapping (CT) methods, especially those recently developed, are the most promising for the collection of robust data and can be conducted in a wide range of species, habitats, seasons and densities with minimal adjustments. Some recently developed CT methods do not require individual recognition of the animals and are a good compromise of cost, effort and accuracy. Linear transects,particularly Kilometric Abundance Index (KAI) is applicable for monitoring large regions.A large challenge is compiling and validating abundance data at different spatial scales. Based on ENETWILD initiative, we recommend developing a permanent network and a data platform to collect and share local density estimates, so as abundance in the EU, which would enable to validate predictions for larger areas by modelling. It would allow to identify gaps in the data on wild carnivores (including the species not assessed in the present report) and to focus on these areas for improving predictions. This platform must facilitate the reporting by wildlife policy makers and relevant stakeholders, but also citizen science initiatives.Also, there is need to improve the reliability of local density estimations by developing practical research on methods able to derive densities in untested species and situations, making the application of methods easier for local teams.
... Still, the use of camera traps is promising as an index for species abundance because, logically, when density increases, the chances of encounters between individuals and cameras would be expected to increase [22][23][24][48][49][50]. Such surveys are also useful when individual recognition, and, thus, capture-recapture density analysis, is not possible (though using encounter rates from camera trap data as an index of density may be applicable [24,[50][51][52][53]). Therefore, although the accuracy of this method should be further evaluated, the index provided by camera trapping is an important survey method for estimating the occurrence and conservation status of many wildlife species, especially for those identified as threatened or endangered. ...
Full-text available
The Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), in the core area of the Western Forest Complex of Thailand, harbors a diverse assemblage of wildlife, and the region has become globally significant for mammal conservation. From April 2010 to January 2012, 106 camera traps were set, and, in 1817 trap-nights, registered 1821 independent records of 32 mammal species. Of the 17 IUCN-listed (from Near Threatened to Critically Endangered) mammal species recorded, 5 species listed as endangered or critically endangered included the Asiatic elephant (Elephas maximus), tiger (Panthera tigris), Malayan tapir (Tapirus indicus), dhole (Cuon alpinus), and Sunda pangolin (Manis javanica). The northern red muntjac (Muntiacus vaginalis), large Indian civet (Viverra zibetha), Malayan porcupine (Hystrix brachyuran), and sambar deer (Cervus unicolor) were the most frequently recorded species (10–22 photos/100 trap-nights), representing 62% of all independent records, while the golden jackal (Canis aureus), clouded leopard (Neofelis nebulosa), marbled cat (Pardofelis marmorata), and Sunda pangolin were the least photographed (<0.1/100 trap-nights). Species accumulation curves indicated that the number of camera trap locations needed to record 90% of taxa recorded varied from 26 sites for herbivores to 67 sites for all mammals. TYNE holds a rich community of mammals, but some differences in photo-rates from an adjacent sanctuary and comparisons with other research on local mammals suggest that some species are rare and some are missed because of the limitations of our technique. We also conclude that the management and conservation plan, which involves the exclusion of human activities from some protected areas and strict protection efforts in the sanctuaries, is still suitable for providing key habitats for endangered wildlife populations, and that augmented and regular survey efforts will help in this endeavor.
... This is because practitioners are required to devote more time and effort to reliably identify individuals which lack distinct markings (Mattioli et al., 2018) or by choosing a survey method that can detect individuals based on other features. Failure to adopt appropriate survey methods increases the chances of individual misidentification (Soller et al., 2020), may inflate or deflate capture rates Garrote et al., 2021), bias sex/age-class structure of the population (Balme et al., 2012), and adversely impact population management and conservation decisions (Balme et al., 2010). ...
Full-text available
Large felids represent some of the most threatened large mammals on Earth, critical for both tourism economies and ecosystem function. Most populations are in a state of decline, and their monitoring and enumeration is therefore critical for conservation. This typically rests on the accurate identification of individuals within their populations. We review the most common and current survey methods used in individual identification studies of large felid ecology (body mass > 25 kg). Remote camera trap photography is the most extensively used method to identify leopards, snow leopards, jaguars, tigers, and cheetahs which feature conspicuous and easily identifiable coat patterning. Direct photographic surveys and genetic sampling are commonly used for species that do not feature easily identifiable coat patterning such as lions. We also discuss the accompanying challenges encountered in several field studies, best practices that can help increase the precision and accuracy of identification and provide generalised ratings for the common survey methods used for individual identification.
Full-text available
Abstract Reported strike events between wildlife and aircraft are hazardous to aircraft and airfield operations and are increasing globally. To develop effective mitigation strategies, the relative hazard a species poses to aircraft, as well as information relating to its life history, are key to the development of effective mitigation strategies in Wildlife Hazard Management Plans. However, given the complex nature of airfield environments with access restrictions and the presence of sensitive equipment, the collection of high‐quality ecological data can be difficult. Here we use motion‐activated camera traps to collect activity data on a population of Irish hares (Lepus timidus hibernicus) inhabiting the airfield at Dublin International Airport, to investigate the link between hare activity and aircraft activity in relation to hare strikes. Camera traps revealed that the hare population at the airfield largely displayed a bimodal crepuscular activity pattern, with activity peaking at sunrise and at sunset. Recorded hare strike times at the airfield were closely associated with hare activity times with a high temporal overlap between these datasets. In comparison, hare activity and aircraft movement activity had a moderate overlap across all seasons, with strikes peaking at times with low aircraft movements. We demonstrate the importance of understanding the circadian and seasonal activity patterns of hazardous species at airfields for targeted strike mitigation.
Full-text available
The use of camera traps to estimate population size when animals are not individually recognizable is gaining traction in the ecological literature, because of its applicability in population conservation and management. We estimated population size of synthetic animals with four camera trap sampling-based statistical models that do not rely on individual recognition. Using a realistic model of animal movement to generate synthetic data, we compared the random encounter model, the random encounter and staying time model, the association model and the time-to-event-model and we investigated the impact of violation of assumptions on the population size estimates. While under ideal conditions none of the models provided reliable population estimates, when synthetic animal movements were characterised by differences in speed (due to diverse behaviours such as locomotion, grazing and resting) none of the model provided both unbiased and precise density estimates. The random encounter model and the time-to-event-model provided precise results but tended to overestimate population size, while the random encounter and staying time model was less precise and tended to underestimate population size. Lastly, the association model was unable to provide precise results. We found that each tested model was very sensitive to the method used to estimate the range of the field-of-view of camera traps. Density estimates from both random encounter model and time-to-event-model were also very sensitive to biases in the estimate of animals’ speed. We provide guidelines on how to use these statistical models to get population size estimates that could be useful to wildlife managers and practitioners.
1.Camera trapping plays an important role in wildlife surveys, and provides valuable information for estimation of population density. While mark-recapture techniques can estimate population density for species that can be individually recognized or marked, there are no robust methods to estimate density of species that cannot be individually identified. 2.We developed a new approach to estimate population density based on the simulation of individual movement within the camera grid. Simulated animals followed a correlated random walk with the movement parameters of segment length, angular deflection, movement distance and home-range size derived from empirical movement paths. Movement was simulated under a series of population densities. We used the Random Forest algorithm to determine the population density with the highest likelihood of matching the camera trap data. We developed an R package, cameratrapR, to conduct simulations and estimate population density. 3.Compared with line transect surveys and the random encounter model, cameratrapR provides more reliable estimates of wildlife density with narrower confidence intervals. Functions are provided to visualize movement paths, derive movement parameters, and plot camera trapping results. 4.The package allows researchers to estimate population sizes/densities of animals that cannot be individually identified and cameras are deployed in a grid pattern.
Full-text available
In marginal habitats, populations should reach lower densities, as a consequence of both lower overall abundance and increasing home range size. To test if recently colonized riparian woods crossing intensively cultivated lowlands of NW Italy represent marginal habitats for the forest-specialist pine marten Martes martes, we assessed its population density by a recently developed, camera-trap-based non-invasive method, the Random Encounter Model (REM). As the central assumption of the REM is that animals move randomly with respect to cameras, we suspected that this method may be unsuitable for species with a strong tendency to use linear elements of the territory as usual paths and select habitats, such as woodland, which are likely to be under-represented in fragmented landscapes. To test for the efficacy of the REM, we also applied a faecal DNA-based genetic census to obtain an independent estimate of the minimum number of individuals occurring in the study area.Camera-trapping used 10 camera-traps, deployed for 10 days within a 2 km2 large unit, for a total of 6 units and 12 km2. Pine marten density was estimated at 0.48 (0.36-0.60) ind/km2. All the faecal samples identified by a mDNA-based PCR-RFLP method as pine marten were genotyped at 15 microsatellite loci using a multiplex protocol. We identified 15 different individuals, corresponding to a density ranging between 0.8 and 2.0 in/km2. Using the most conservative genetic estimate, the REM underestimated population density of about 60% proving to be unreliable for estimating pine marten population size. We suggest that this may be the case for elusive species for which the assessment of average daily movements cannot be achieved without the use of invasive methods.
Full-text available
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.
Full-text available
Camera traps are used to estimate densities or abundances using capture-recap- ture and, more recently, random encounter models (REMs). We deploy REMs to describe an invasive-native species replacement process, and to demonstrate their wider application beyond abundance estimation. The Irish hare Lepus timidus hibernicus is a high priority endemic of conservation concern. It is threatened by an expanding population of nonnative, European hares L. euro- paeus, an invasive species of global importance. Camera traps were deployed in thirteen 1 km squares, wherein the ratio of invader to native densities were cor- roborated by night-driven line transect distance sampling throughout the study area of 1652 km2. Spatial patterns of invasive and native densities between the invader’s core and peripheral ranges, and native allopatry, were comparable between methods. Native densities in the peripheral range were comparable to those in native allopatry using REM, or marginally depressed using Distance Sampling. Numbers of the invader were substantially higher than the native in the core range, irrespective of method, with a 5:1 invader-to-native ratio indi- cating species replacement. We also describe a post hoc optimization protocol for REM which will inform subsequent (re-)surveys, allowing survey effort (camera hours) to be reduced by up to 57% without compromising the width of confidence intervals associated with density estimates. This approach will form the basis of a more cost-effective means of surveillance and monitoring for both the endemic and invasive species. The European hare undoubtedly represents a significant threat to the endemic Irish hare.
Full-text available
Camera traps are increasingly important in studies of mammals throughout the world. Typically, cameras are placed along trails or other travel routes. Yet, species composition and photographic rate may differ between cameras set along trails and those set off trails. We tested this idea in eastern Ecuador. Pairs of cameras were placed at 10 locations along narrow (< 1 m wide) trails in lowland forest and approximately 50 m away from trail sites in adjacent forest. Excluding people, there was little difference in total number of records between trail (333 photographs, 506 trap days) and off-trail sites (306 photographs, 509 trap days). Capture rates varied among locations (11 to 148/100 trap days on trails; 19 to 217/100 trap days off trails) and were not correlated between pairs of cameras on and off trails (r = 0.37, P = 0.29). People were only photographed along trails but capture rates of other species on trails were not correlated with numbers of people photographed at the same site (r = -0.10, P > 0.75). Twenty-three species were photographed, including 21 on trails and 22 off trails; Panthera onca was only photographed along trails whereas Tinamus major and Priodontes maximus were only photographed off trails. Species accumulation curves were similar for both sets of cameras; in both cases, curves approached an asymptote after about 200 records. Latency to first detection (LTD) varied from < 1 day (e.g., Mazama americana in trail and off-trail cameras) to 294 days (Procyon cancrivorus in on-trail cameras). Overall, LTD values were correlated between pairs of cameras on and off trails (rs = 0.66, P < 0.01); means did not differ between cameras on and off trails. Species composition varied among trap locations but trail and off-trail cameras did not form distinct groups based on species composition.
Full-text available
Estimating population densities of small mammals (< 100g) has typically been carried out by intensive livetrapping, but this technique may be stressful to animals and the effort required is considerable. Here, we used camera traps to detect small mammal presence and assessed if this provided a feasible alternative to livetrapping for density estimation. During 2010–2012, we used camera trapping in conjunction with mark–recapture livetrapping to estimate the density of northern red-backed voles (Myodes rutilus) and deer mice (Peromyscus maniculatus) in the boreal forest of Yukon, Canada. Densities for these 2 species ranged from 0.29 to 9.21 animals/ha and 0 to 5.90 animals/ha, respectively, over the course of this investigation. We determined if hit window—the length of time used to group consecutive videos together as single detections or “hits”—has an effect on the correlation between hit rate and population density. The relationship between hit rate and density was sensitive to hit window duration for Myodes with R 2 values ranging from 0.45 to 0.59, with a 90-min hit window generating the highest value. This relationship was not sensitive to hit window duration for Peromyscus, with R 2 values for the tested hit windows ranging from 0.81 to 0.84. Our results indicate that camera trapping may be a robust method for estimating density of small rodents in the boreal forest when the appropriate hit window duration is selected and that camera traps may be a useful tool for the study of small mammals in boreal forest habitat.
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.
Remote photography and infrared sensors are widely used in the sampling of wildlife populations worldwide, especially for cryptic or elusive species. Guiding the practitioner through the entire process of using camera traps, this book is the first to compile state-of-the-art sampling techniques for the purpose of conducting high-quality science or effective management. Chapters on the evaluation of equipment, field sampling designs, and data analysis methods provide a coherent framework for making inferences about the abundance, species richness, and occupancy of sampled animals. The volume introduces new models that will revolutionize use of camera data to estimate population density, such as the newly developed spatial capture-recapture models. It also includes richly detailed case studies of camera trap work on some of the world's most charismatic, elusive, and endangered wildlife species. Indispensible to wildlife conservationists, ecologists, biologists, and conservation agencies around the world, the text provides a thorough review of the subject as well as a forecast for the use of remote photography in natural resource conservation over the next few decades.