ArticlePDF Available

Use of camera traps for wildlife studies. A review



As human threats continue to impact natural habitats, there is an increasing need to regularly monitor the trends in large vertebrate populations. Conservation efforts must be directed appropriately, but field work necessary for data collection is often limited by time and availability of people. Camera traps are used as an efficient method to insure continuous sampling and to work in difficult to access areas. In the present study, we illustrate how this instrument is serving a diverse field of studies, such as animal behavior, population monitoring and fauna-flora interaction. By looking at the material and technical aspects of various models of camera trap for implementation in different field studies in animal ecology, we highlight the need to choose appropriate camera trap models for the target species and to set up solid sampling protocols to successfully achieve study objectives.
SE Biotechnol. Agron. Soc. Environ.201418(3),446-454 Focus on:
As human threats continue to impact natural habitats, there is an increasing need to regularly monitor the trends in large
vertebratepopulations.Conservationeffortsmustbedirectedappropriately, but eld work necessary for data collection is
andtowork in difcult toaccessareas.In the present study,we illustratehowthisinstrumentis serving a diverseeldof
Utilisation des pièges photographiques pour l’étude de la faune sauvage (synthèsebibliographique). Alors que les
pressions anthropiques continuent de dégrader les habitats naturels, le besoin de suivre régulièrement les tendances des
populationsdegrands vertébrés augmente.Lesefforts de conservationdoiventêtrede plus enplusciblésmais les travaux
deterraintels que lecomportementanimal,lesuivi de populations etlesinteractionsfaune-ore. En analysant lesaspects
techniques et matériels permettant d’assurer différents types de travaux d’écologie animale, nous mettons en évidence la
nécessitédesélectionnerdumatérieletdemettreenplaceunprotocoled’échantillonnageadaptéàl’espèce etauxobjectifs
Mots-clés.Gestionde la fauneetdela ore sauvages,recensementdelapopulation, comportement animal,photographie,
among large vertebrates, throughout the world and
the degradation of natural habitats hosting their
populationsare nowadays widelyaccepted as fact.It
has therefore never been so important to understand
how animal populations respond to modern threats
and to document the functioning of ecosystems and
tobeable to implementappropriatemanagementand
conservation strategies. Regular updating of data on
animalpopulationdensity and on the degreeofinter-
temporal variations in populations and communities
(Bouché et al., 2012). Camera traps are increasingly
beingused to study wildlife behaviorand to conduct
2008;O’Connell etal.,2011;Roveroetal., 2013).In
camera trapping studies, to present some technical
We conducted a general literature review on camera
trapping using the SciVerse Scopus® database and
Useofcameratrapsforwildlifestudies.Areview 447
list of documents consulted has enabled us to gain a
over recent decades and of the main issues regarding
sampling and data analysis. To conduct our study on
thetechnicalaspectsof cameratraps, we searchedfor
as wellas those usedin recent scientic publications.
com, 2013) and Camera Traps cc®’s (Camera Traps
the different models. Those two companies distribute
together 18brands of camera traps, which, to our
knowledge, include the vast majority of camera trap
models on the market. We could get the price for
3.1. Diversity of uses of camera traps
Whileremote photographies have been used for more
years later, Carthew et al. (1991) and Kucera et al.
(1993) listed the advantages of the automated camera
trap system for anarray of different eld applications
of large mammal conservation appeared in the 1990s
andfocusedonthetiger,Panthera tigris(e.g.,Grifths,
1993; Karanth, 1995). Following the designation of
P. tigris as endangered (Chundawat et al., 2011), one
of thefew “agship”species listed on the IUCN red-
listasearlyas1986,thesestudiesaimedat estimating
homerangespanand populationsize.Inthis way, the
andmoregenerally, themonitoringofotherthreatened
was highlighted in a study on the activity patterns
of mammal communities in Indonesian rain forests
(van Schaik et al., 1996). The aforementioned early
studies oftheuseofcameratrapsclearly illustrate the
major advantages of using the technique, including
beingableto observecrypticorelusiveanimalsliving
in difcult to access habitats such as dense tropical
for studying the behavior of carnivores, as they are
difcult toobserveintheirnaturalhabitat duetotheir
ofmanyother scientic paperssincethe beginningof
the 21st century, revealing more about the ecology of
ranges.A goodexample isthestudyofMoruzziet al.
(2002), whichpromotestheuseof thistechnology for
estimating carnivore distribution over large area and
A large proportion of conservation projects aim at
theestimationofpopulationdensity (e.g.,Kalleet al.,
or simply with the presence of species in given areas
(e.g., Gil-Sanchez et al., 2011; Gray et al., 2011; Liu
or lesser extent, related to habitat use behaviors and
Some studies also deal with activity budget (e.g.,
(2011) reportedthe storingbehavior of non-ripefruits
2012) or social interaction (Lopucki, 2007; Srbek-
Camera traps are also increasingly being used to
study plant-animal interactions such as seed dispersal
et al., 2011; Campos et al., 2012; Koike et al., 2012;
Pender et al., 2013). Moreover, focal observations
capacity of a given plant species, to listthe frugivore
species interacting with the plants and to dene the
of seed dispersal. Camera traps are revolutionary in
thisregard,astheyallowthe identication of diurnal,
nocturnal, and shy species that would not be seen
using other methods such as direct observation. This
pouchedratCricetomys emini(Wroughton,1910),was
448 Biotechnol. Agron. Soc. Environ. 201418(3),446-454
3.2. Various technical aspects
the advantages and disadvantages of using different
lm camera trapping equipment depending on the
researchobjectives. Giventherapidadvancesinsuch
anddigital models existing on the market nowadays,
lmcameras arecompeted.Wepresentherethemost
important characteristics to take into account when
choosing digital equipment. Characteristics such as
trigger speed, detection zone, recovery time, night
detection and battery consumption can vary greatly
collected,such asthenumberof speciesdetectedand
the choice of the most appropriate equipment is an
Trigger speed. Trigger speed is the time delay
necessary for the camera to shoot a picture once an
animal has interrupted the infrared beam within the
camera’s detection zone. This delay can vary from
between 0.197seconds for the Reconyx HC500
model to 4.206seconds for the Stealth Cam Rogue
IR model. Given the relatively narrow eld of view
of most camera trap lenses (42mm), a slow trigger
study goals and the target animal species, this time
a wildlife survey (Pereira et al., 2012), fast moving
animalsarelikely to pass in frontof the camera trap
withoutstopping. In thiscase,avery reactivecamera
(with a fast trigger speed) would be necessary so it
thecamera’seldof view.Intheircomparativestudy
Hughson et al. (2010) showed that some camera
models (such as the fast Reconyx) can detect up to
86% more animal species. If the trigger speed is too
what the beam has detected). Hughson et al. (2010)
observedthat, incomparisonwithother models,Leaf
River cameras took the highest percentage of empty
pictures.In the case of acamera installed in front of
likely to stay longer (to either depredate the nest or
(Garrote et al., 2012; Trolle et al., 2003) even if the
The detection zone. The detection zone is the zone
covered by the camera’s infrared beam in which
movementcan be detected. The zone variesin width
and depth, depending on the model (Table 1). This
The eld of view.Theeldofviewisthezonecovered
The eld of view is generally 42° but there are rare
upto54°(Table 1)andtheMoultriepanoramicmodel,
thusndmodelswith adetectionzonewiderthanthe
widerthantheeldofview(Figure 1a),theadvantage
The limitation in this case is that the camera is also
likely to take empty pictures when animals enter the
andtriggering the camera)butwithoutmaking itinto
thantheeldofview(Figure 1b),thedetectionzoneis
animalsarelikelyto occupytheeldofviewwithout
crossing the detection zone. As presented in table 1,
the detection zone can be described with a given
whichit willdetectan animal.Thedetectiondistance
of a camera is an important aspect to consider when
mass.Largeranimals will bemore easily detected at
Recovery time.Recovery timeistheamountof time
necessaryforthecamerato prepare to shoot the next
the wide differences in recovery time for different
as it can be a very important aspect for some study
goals. The fastest camera can take a picture every
Useofcameratrapsforwildlifestudies.Areview 449
withina few seconds is very usefulwhen needing to
2010). Also, having different views of a species of
Nighttime pictures. Nighttime pictures are very
useful, as a wide range of taxa exhibit exclusive
nocturnalactivity.Twomethods existforcameratrap
night photography: incandescent ash and infrared
light. Incandescent ash allows color pictures to be
taken, which are generally of better resolution and
for individual animal identication with the use of
that the ash has a strong risk of scaring the animal
method is much more discrete, and is consequently
used by wildlife researchers than incandescent ash
(Meek et al., 2012). The infrared light emitted by a
thecameratotakeblack-and-whitepictures,is hardly
visible. The most discrete and best solution to avoid
scaringwildlife is to use a camera with a “no-glow”
Black 60, Reconyx Hyperre SM750, etc.). These
infrared cameras, shooting black and white pictures,
Battery consumption. Battery life can also be a
crucial point to consider when preparing eld work
= Detection zone = Field of view
Figure 1. Diagramoftheeldofviewandofthedetection
zone for two types of camera trap — Schéma des zones
de vue et de détection d’après deux types de pièges
a.detectionzonewiderthantheeldofview—zone de détection
plus large que la zone de vue;b.detectionzonenarrowerthanthe
eldofview—zone de détection plus étroite que la zone de vue.
Table 1.Maintechnicalcharacteristicsofsomecameratrapmodelsfoundonthemarketatthetimeofstudy—Principales
caractéristiques techniques de modèles de pièges photographiques disponibles sur le marché au moment de l’étude.
Brand Model Detection zone
Angle Distance Total
Field of
view (°)
speed (s)
time (s)
Cuddeback AmbushIR,V 7.6 11 8 42 5 0.25 NA 100-200
Scoutguard SG565F,V 64.7 14 116 42 8 1.31 5.1 100-200
Moultrie Panoramic150IR,V 150 NA NA 150 8 0.95 6.2 200-300
Moultrie I-35sIR 40 9 31 40 4 2.5 60 100-200
micro6IR,V 15.1 24 76 42 6 1.08 NA 100-200
Uway UM562IR,V,C 60.5 16 133 42 5 1.2 NA 300-400
Leupold RCX-1IR,V 48.2 10 42.5 54 8 0.93 2.8 100-200
Reconyx HC500IR,V 33.4 18.6 100 42 3.1 0.2 1 400-500
Spypoint Live3GIR,V,I 41.9 17 110 42 8 2.7 10 400-500
Primos TruthCamX 45.2 13.7 456 42 1.3 1.2 5 200-300
Spypoint ProX 50 21 82.5 39 12 1.76 10 400-50
Boldcharactersindicateminimaandmaximavaluesfoundforeachrespectivefeature—Les caractères en gras indiquent les valeurs
minimales et maximales trouvées pour chaque caractéristique.
450 Biotechnol. Agron. Soc. Environ. 201418(3),446-454
characteristicsneed tobetakenintoaccount, suchas
thelevelofenergy consumption inmonitoringmode
(when the camera is on and ready to take pictures
if it detects movement) and the level of energy
These variables can vary greatly depending on the
available models and will then vary in suitability
depending on the habitat, the faunal composition
presentinthehabitatandaccessibilityof the camera
for the changing of batteries. For example, in the
be better to use a camera that requires little energy
inmonitoringmode (asbatteryreplacementisnot as
frequent) and for nighttime picture taking (as only
amodel that uses as little energy as possible for the
processing of daytime pictures would be preferred.
To extend battery life, some brands (e.g., Reconyx,
Picture resolution. Picture resolution, expressed
in megapixels (Mpx), can vary more than 10fold
between models. Some Primos models take pictures
of relatively low resolution (1.3Mpx), whereas the
Spypoint Pro-X takes pictures up to 12Mpx. The
advantageof lowerresolutionimagesis thattheyare
lessheavytostore so more pictures canbesavedon
agivenmemorycard but, as having lesspixels,they
would recommend to select for models with higher
resolution pictures and especially when individual
identicationis needed.Amore detailed and precise
picture can surely help being more accurate when
looking at differences in fur patterns and marks to
differentiate between individuals. However, the
number of pixels advertised by manufacturers must
be considered cautiously because it is not the only
factor affecting picture’s quality. Image sensor, the
componenthousing thepixels,isalso veryimportant
an increase in the number of pixels is automatically
associated with a decrease in pixel size. Yet smaller
pixelsarelesssensitiveto light, produce more noise
(i.e. the range of light intensities being captured)
with fewer pixels but a larger sensor can produce
pictures of higher quality than a camera with more
pixels packed into a smaller sensor. Unfortunately,
Camera cost. At the time of writting, cameras traps
cost from about USD40 to 1,200, though more than
half(54%)of themodelscomparedinthis studycost
betweenUSD100 to 200.Whilethe cheapestmodels
can have an infrared ash (Hunter GSC35-20IR;
Wildgame Innovations Red4), the most expensive
are able to transmit pictures to cell phones or email
In addition to these main characteristics, various
additional options serving specic research needs
ofthe same trigger event. Some cameras also record
3.3. Sampling methods
Individual behavior. Studies aiming to report on
2011). Those often have to be completed with data
collected using other protocols such as telemetry or
Population level studies. Studies dealing with
effort and more complex sampling design. To do so,
other more traditional methods. However, Gompper
et al. (2006) proved camera traps to be inefcient at
by scat surveys, DNA analysis and/or snowtracking.
When comparing different methodologies for the
trappingappearto be themostappropriatemethodin
difcult to access areas compared to line transect or
traps to estimating population density can involve
complexsampling designandbesubjecttonumerous
biases. Firstly,it is important to consider the bias of
disproportionally samples more easily accessible or
more attractive places for wildlife where detection
procedure to characterize an animal population in a
(cameratraps) inarandomorsystematicway(Foster
etal., 2011).Asexplained byRowcliffeetal.(2013),
Useofcameratrapsforwildlifestudies.Areview 451
cameras can be positioned in less or more attractive
places to animals as long as those are proportionally
sampledinregards to their relative occurrence in the
studied ecosystem. Thus, using a grid and a random
number generator, or following a stratied design
allow ones to select positions where to install the
etal., 2013). However,some researchers have set up
their cameras in specic places where the targeted
encounter rate (e.g., Sanderson, 2004; Weckel et al.,
2006); some have even tried to lure animals with
attractive smells or baits (e.g., Trolle et al., 2003;
to consider variations in detection probability due to
the material used. The use of incandescent ash at
inuence future visitation rates in the vicinity of the
in the case of capture-recapture sampling or studies
on habitat use of nocturnal species, it is preferable
to avoid using camera models with an incandescent
ash.Inaddition, it is important tomakesure all set
up cameras have sufcient battery life for a given
sampling period. Due to spatial variations in animal
of pictures taken can greatly vary between cameras
andsome can see their batteries getting empty much
morerapidlythan others do. Cameras runningout of
is a common issue when using camera traps data.
Sampling design with low detection probability, due
populationestimates (Foster et al., 2011).As a mean
to increase sample size, setting up two cameras at a
samestation allows obtaining pictures of both anks
Intra-community interactions. In the case of seed
visual eld includes the fruits or seeds of interest to
maximize the chances of photographing frugivores
From personal experience, two remaining limitations
when the camera is positioned close to a fruit/seed
sample so observers can easily quantify the number
of items manipulated by animals. Here, the focal
all the animals visiting the area. The camera would
then record a limited number of visiting species and
individualanimals.By contrast,thesecondlimitation
occurs when the camera is positioned to sample the
widest area possible below a fruiting tree canopy,in
orderto systematically record all visitinganimals. In
thisscenario, the focal distance might be too high to
seeds manipulated. An alternative couldbe to set up
two or more cameras at a same location to sample
boththe tree canopy’sshadow and a fruit sample on
theoor.In thelatter case, an alternative toevaluate
species-specic contribution to seed removal could
be to consider visit frequencies per species in the
assessed with an exclusion experiment (Culot et al.,
Data analysis.Theidenticationofindividualanimals
is generally made by natural fur marks, injuries, and
coloration patterns (dots, bands). This identication
is, however, always subjective and likely to vary
accordingto theobserverand thuslikelyto affectthe
identication, different computer models are able to
help matching pictures of marked individuals (Kelly,
andto be more precise inmaking population density
population estimate. The spatially explicit capture-
recapture technique is increasingly used for this
etal., 2011).This techniqueassumesthatanimalsare
independently distributed in space and that they use
considers, on the one hand, a population parameter
of individual recognition. The detection process is
probability of detecting an animal, which decreases
Camera trap data are also used to generate
size. However, the power of such indices is limited
compared to true estimates of population density
for different reasons. Firstly, variations in indices
cannotnecessarilybe attributed to true variationsin
452 Biotechnol. Agron. Soc. Environ. 201418(3),446-454
suchindices one needs to makethe assumption that
true (Sollman et al., 2012). Secondly, those indices
are rarely calibrated with the actual population and
thusonly givelittleinformationonthetruedynamic
the calculation of a condence interval (variance)
et al., 2006), though Bengsen et al. (2011) adapted
aGeneral Index Model able to account for variance
Camera traps data such as species detection/
non-detection can also be used in occupancy model
(e.g., MacKenzie et al., 2003; Long et al., 2011) to
predictspeciesoccurrenceand determine population
probability data and thus prevent the recording of
falseabsence.This has veryhelpfulimplicationsfor
species and the type of ecosystem, it is essential to
rstchoosetheappropriate equipment to collect the
useof camera trappingbyscientists,we believethat
the available technologies should and will know
resulting from larger sensor and more efcient
infraredbeam would allow a better identication of
individuals,especiallyfor marked nocturnalspecies.
spooking animals and get more unblurred pictures.
Next, the implementation of appropriate sampling
protocolsmustbeseriouslyconsidered.In a general
way, we believe that homogenization of detection
probability could improve the use of camera traps
data by diminishing biases and allowing stronger
inter-site and inter-species data comparison. This
– atthecamerascale,byusingcameramodelshaving
 similarfeatures(detectionzone,eldofview,trigger
 speed,etc.),
– attheecosystemscalebyimplementingstandardized
 samplingscheme(numberofcameras,spacing,and
 placement).
Having a standard sampling protocol would
also permit more solid use of statistical models and
interpretationof results.Theuseof computertoolsto
commonbutalldoesstill not agree basic assumption
requirements. Future development of computer tools
forpopulationdensity,abundance andsiteoccupancy
estimates would need to rely on empirical validated
results on individual habitat use behavior and
funded by the “Politique Scientique Fédérale Belge”
AzlanJ.M. & SharmaD.S.K., 2006. The diversity and
activitypatternsof wild felids in a secondary forest in
BabweteeraF. & BrownN., 2010. Spatial patterns of tree
their vertebrate seed dispersers. J. Trop. Ecol., 26(2),
BarrowsC.W. et al., 2005. A framework for monitoring
multiple-speciesconservationplans. J. Wildl.Manage.,
BauerJ.W., LoganK.A., SweanorL.L. & BoyceW.M.,
2005. Scavenging behaviour in puma. Southwestern
BengsenA.J., LeungL.K.-P., LapidgeS.J. & GordonI.J.,
2011. Using a general index approach to camera-trap
abundanceindices.J. Wildl. Manage.,75(5),1222-1227.
spider monkeys (Ateles belzebuth) and red howler
monkeys(Alouatta seniculus)inEasternEcuador.Int. J.
elephants in West African savannahs? Synthesis and
comparison of main gamecount methods. Biotechnol.
Agron. Soc. Environ.,16(1),77-91.
Camera Traps cc, 2013.,
CamposR.C., SteinerJ. & ZillikensA., 2012. Bird and
mammalfrugivores ofEuterpe edulisatSanta Catarina
island monitored by camera traps. Stud. Neotropical
Fauna Environ.,47(2),105-110.
with automated photography. J. Wildl.Manage., 55,
CharruauP. & HénautY., 2012. Nest attendance and
acutus) in Quintana Roo, Mexico. Anim. Biol., 62(1),
Useofcameratrapsforwildlifestudies.Areview 453
ChundawatR.S.et al., 2011.Panthera tigris. IUCN 2012,
IUCN Red List of Threatened Species. Version 2012.2,,(15/11/2012).
small primate species (Saguinus mystax and Saguinus
fuscicollis) in the Amazonian forest of Peru. J. Trop.
in wildlife ecology: a review. Wildl. Soc. Bull., 27(3),
EffordM.G., 2011. Estimation of population density by
FosterR.J. & HarmsenB.J., 2011. A critique of density
estimation from camera-trap data. J. Wildl. Manage.,
GarroteG. et al., 2012. The effect of attractant lures in
camera trapping: a case study of population estimates
fortheIberianlynx(Lynx pardinus).Eur. J. Wildl. Res.,
Gil-SánchezJ.M.et al., 2011.Theuse of camera trapping
forestimatingIberianlynx(Lynx pardinus)homeranges.
Eur. J. Wildl. Res.,57(6),1203-1211.
of noninvasive techniques to survey carnivore
communitiesinnortheasternNorthAmerica.Wildl. Soc.
GrayT.N.E. & PhanC., 2011. Habitat preferences and
activity patterns of the larger mammal community in
Phnom Prich Wildlife Sanctuary, Cambodia. Rafes
Bull. Zool.,59(2),311-318.
GrifthsM., 1993. Population density of Sumatran tigers
Tiger Species Survival Plan,6(2),17-18.
HughsonD.L., DarbyN.W. & DunganJ.D., 2010.
Comparison of motion-activated cameras for wildlife
investigations. California Fish Game, 96(2), 101-
of tiger and leopard in a tropical deciduous forest of
using photographic capture-recapture sampling. Acta
KaranthK.U., 1995. Estimating tiger Panthera tigris
populations from camera-trap data using capture–
recapturemodels.Biol. Conserv.,71,333-338.
Serengeticheetahs.J. Mammalogy,82(2),440-449.
KoikeS.et al.,2012.Seedremovaland survivalinAsiatic
blackbearUrsus thibetanusfaeces:effectofrodentsas
secondaryseeddispersers.Wildl. Biol.,18(1),24-34.
KuceraT.E. & BarrettR.H., 1993. In my experience: the
Trailmaster® camera system for detecting wildlife.
Wildl. Soc. Bull.,21(4),505-508.
by carnivores at different spatial scales in a plantation
forest landscape in Patagonia, Argentina. For. Ecol.
LiuX. et al., 2012. Monitoring wildlife abundance and
diversity with infra-red camera traps in Guanyinshan
NatureReserveofShaanxiProvince,China.Ecol. Indic.,
LongR.A., MacKayP., RayJ. & ZielinskiW., eds, 2008.
Noninvasive survey methods for carnivores.Washington:
LongR.A. et al., 2011. Predicting carnivore occurrence
with noninvasive surveys and occupancy modeling.
Landscape Ecol.,26,327-340.
ŁopuckiR., 2007. Social relationships in a bank vole
Clethrionomys glareolus (Schreber, 1780) population:
videomonitoringundereldconditions.Polish J. Ecol.,
MacKenzieD.I. et al., 2003. Estimating site occupancy,
colonization, and local extinction when a species is
MeekP.D. & PittetA., 2012. User-based design
specications for the ultimate camera trap for wildlife
research.Wildl. Res.,39(8),649-660.
MendozaE., MartineauP.R., BrennerE. & DirzoR.,
2011. A novel method to improve individual animal
identication based on camera-trapping data. J. Wildl.
camerasforsurveyingcarnivoredistribution.Wildl. Soc.
NakamuraJ.,ed.,2005.Image sensors and signal processing
for digital still cameras. Boca Raton, FL, USA: CRC
NegrõesN. et al., 2012. One or two cameras per station?
Ecol. Res.,27,638-648.
NyiramanaA., MendozaI., KaplinB.A. & ForgetP.-M.,
O’BrienT.G. & KinnairdM.F., 2011. Density estimation
recapture methods and standard trapping grid. Ecol.
O’ConnellA.F., NicholsJ.D. & KaranthK.U., 2011.
Camera traps in animal ecology: methods and analyses.
Oliveira-SantosL.G.R. et al., 2012. Abundance changes
andactivityexibilityoftheoncilla,Leopardus tigrinus
(Carnivora: Felidae), appear to reect avoidance of
2013. Large-scale rodent control reduces pre- and
post-dispersal seed predation of the endangered
Hawaiian lobeliad, Cyanea superba subsp. superba
(Campanulaceae).Biol. Invasion,15(1),213-223.
454 Biotechnol. Agron. Soc. Environ. 201418(3),446-454
PereiraP. et al., 2012. Coexistence of carnivores in
a heterogeneous landscape: habitat selection and
ecologicalniches.Ecol. Res.,27(4),745-753.
RoveroF., ZimmermannF., BerziD. & MeekP., 2013.
“Which camera trap type and how many do I need?”
A review of camera features and study designs for a
range of wildlife research applications. HystrixItal. J.
RowcliffeJ.M. et al., 2011. Quantifying the sensitivity of
camera traps: an adapted distance sampling approach.
Methods Ecol. Evol.,2,464-476.
RowcliffeJ.M.,KaysR.,CarboneC.&JansenP.A., 2013.
density from camera trap rates. J. Wildl. Manage., 77,
SandersonJ.G., 2004. Camera phototraping monitoring
protocol. The tropical ecology, assessment and
monitoring (team) initiative, http://www.teamnetwork.
SavidgeJ.A.& SeibertT.F., 1988. An infrared trigger and
cameratoidentify predators atarticialnests.J. Wildl.
ScheibeK.M. et al., 2008. Long-term automatic video
recording as a tool for analysing the time patterns of
J. Wildl. Res.,54(1),53-59.
SequinE.S., JaegerM.M., BrussardP.F. & BarrettR.H.,
2003. Wariness of coyotes to camera traps relative to
social status and territory boundaries. Lincoln, NE,
SeufertV., LindenB. & FischerF., 2010. Revealing
Afr. J. Ecol.,48(4),914-922.
SilveiraL., JácomoA.T.A. & Diniz-FilhoJ.A.F., 2003.
Camera trap, line transect census and track surveys: a
comparativeevaluation.Biol. Conserv.,114(3),351-355.
amustelid?Eira barbara(Carnivora)cacheunripefruits
to consume them once ripened. Naturwissenschaften,
SollmannR., MohamedA., SamejimaH. & WilltingA.,
2012. Risky business or simple solution – Relative
abundanceindicesfromcamera-trapping.Biol. Conserv.,
Srbek-AraujoA.C., SilveiraL.F. & ChiarelloA.G., 2012.
The red-billed curassow (Craxblumenbachii): social
organization, and daily activity patterns. Wilson J.
Ornithol.,124(2),321-327., 2013.,
trappingdata.J. Mammalogy,84(2),607-614.
van SchaikC.P. & GrifthsM., 1996. Activity periods of
throughtimeandspace.J. Zool.,270,25-30.
WeggeP.,PokheralC.Pd.&JnawaliS.R., 2004. Effectsof
trapping effort and trap shyness on estimates of tiger
abundance from camera trap studies. Anim. Conserv.,
... Camera traps are an innovative way to study animal behaviour (Steen 2009;Trolliet et al. 2014;Caravaggi et al. 2017). Their use, and other new sources of images and data, such as internet forums and social media (Lourenço 2019), are yielding new data on wild species. ...
Full-text available
Citation: Duncan James R. and Kerbrat Riki. Long-eared Owl Asio otus behaviour, prey provisioning and diet during the nestling period using a camera trap in 2015 in Manitoba, Canada. Abstract A camera trap was set up at a Long-eared Owl Asio otus nest in Manitoba, Canada in 2015. This was the rst time this nocturnal species has been studied in this manner. An analysis of 128,694 images collected over 15 d during the nestling period revealed new information on Long-eared Owl behaviour and diet. Initially, the male delivered prey to the female brooding nestlings and the female rarely left the nest. The female often performed a raised wing display with erect body plumage when receiving prey from the male reminiscent, in part, of a male precopulatory display. Two unsuccessful nestling predation attempts were recorded. When the oldest nestling was 16-17 d old the female spent less time on the nest which coincided with the inferred onset of thermoregulation and observed ability of nestlings to feed themselves. Prey deliveries increased up to edging in response to increased nestling energy requirements and to expedite edging and maximizing nestling survival. Prey provisioning at the nest then decreased as successive nestlings edged and were fed directly outside the view of the camera. The majority (92.5%) of 106 prey deliveries were small mammals, especially voles (Cricetidae), which was consistent with other diet studies derived from pellet analysis. Only 48.1% of delivered prey were identi ed to species. Nestling diet did not signi cantly change with time. Results from this study demonstrate the potential and limitations of camera traps for future research on the behaviour and diet of nesting Long-eared Owls and other nocturnal species.
... Moreover, the use of camera trapping has recently gained an additional boost from the increased availability and sophistication of software to process images and videos as well as the emergence of novel quantitative approaches to analysing the derived data (Niedballa, Sollmann, Courtiol, & Wilting, 2016;Norouzzadeh et al., 2018;Young, Rode-Margono, & Amin, 2018;Willi et al., 2019;Windell, Lewis, Gramza, & Crooks, 2019). Some review studies have focused on describing temporal trends in the publication of camera-trap studies and summarising their most commonly addressed topics (Burton et al., 2015;McCallum, 2013;Meek, Ballard, & Fleming, 2015;Rovero, Zimmermann, Berzi, & Meek, 2013;Trolliet, Huynen, Vermeulen, & Hambuckers, 2014), including one in our focal country (Mandujano, 2019). An overall increase in the availability of camera trapping data is highly positive, but there would be a more direct impact if they were to concentrate on areas with greater urgency for information due to their levels of biotic richness, threat, or both. ...
The magnitude of human impact on biodiversity makes producing information on the conservation status of wildlife an urgent matter. Despite the increasingly widespread use of camera trapping for mammal monitoring, there are no assessments on how this tool helps fill specific knowledge gaps. We reviewed studies published between 2000 and 2018 in Mexico, a country with very high mammalian diversity, and analysed their spatial distribution. Specifically, we looked at how the number of studies at the level of the country’s states related to a) each state’s medium/large mammalian species richness and b) each state’s proportion of mammalian species classified as threatened at the national and global level. Moreover, we assessed the occurrence of studies within protected areas, terrestrial ecoregions, and mammal geographic provinces. Finally, we recorded the proportion of studies focused on estimating mammal population density and community richness that incorporated measures of variability and completeness, respectively. Based on a compilation of 191 papers published in 48 journals, we found a weak relationship between the number of studies and mammalian species richness and no clear evidence of a relationship between the number of studies and the proportion of threatened species. The studies concentrated on a few mammalian species, protected areas, forested ecoregions, and mammal geographic provinces in the country’s southern region. More than half of the studies that conducted population density estimations included measures of variability, but only one-third of the studies estimating species richness included completeness assessments. There is a need for more coordinated efforts to take full advantage of camera traps in order to produce more comprehensive and standardised surveys of the status of mammalian fauna at the country level.
... Ecologists worldwide have widely developed imaging identification by camera traps (O'Connell et al., 2011), demonstrating the relevance of using this technique for fauna identification and monitoring. This technology allows revealing more about the ecology of wild animal feeding behaviors, especially those with nocturnal habits or highly elusive to the human presence (Trolliet et al., 2014). ...
Full-text available
We report an episode of predation on the passerine bird Rhamphocelus carbo by the bat Vampyrum spectrum. This event occurred in LN Guerra Group camp, located in the Uberlândia Forest Management Unit, private property in an area of the Amazon rainforest, near Portel on Marajó Island, State of Pará, eastern Brazilian Amazon. At 5:00 a.m. on August 29 th , 2020, a specimen of V. spectrum preyed on a specimen of R. carbo next to the visitors' dormitory. The bat captured the bird by its head, leaving only its wings and feet after feeding. This predator behavior report is of interest as it demonstrates the feeding habits and natural history of a bat species considered by the IUCN Red List of Threatened Resumo: Neste trabalho, relatamos um episódio de predação sobre a espécie de ave, a pipira-vermelha Rhamphocelus carbo, pelo morcego Vampyrum spectrum, na floresta amazônica, em uma área onde ocorrem atividades de exploração madeireira, em Portel, ilha de Marajó, Pará, Brasil. O fato ocorreu no acampamento da empresa responsável pelo manejo florestal, o grupo LN Guerra. Às cinco horas do dia 29 de agosto de 2020, um V. spectrum predou um indivíduo de R. carbo ao lado do dormitório dos visitantes. O morcego capturou a ave pela cabeça, deixando no final da predação apenas asas e patas. Esse relato de comportamento é de interesse, pois demonstra o hábito de alimentação e a história natural de uma espécie de morcego considerada pela IUCN na categoria Near Threatened. Além disso, é importante destacar que o registro foi gravado por uma pessoa não formada em biologia, mas que, com a nossa presença, começa a despertar interesse pela fauna silvestre, o que consideramos importante como incentivo à preservação da diversidade por parte dos operadores dos planos de manejo. Palavras-chave: História natural. Ave passeriforme. Chiroptera. Bacia do rio Tocantins.
... Animals could now easily be recorded non-invasively anywhere in the world (Kucera and Barrett 2011;Meek et al. 2015). Cameras have since been widely deployed to answer management and scientific questions alike, applicable to an immense spectrum of inquiry that ranges from simple species detection to animal-environment interactions and ultimately global biodiversity changes (Swann and Perkins 2014;Trolliet et al. 2014;Steenweg et al. 2017). ...
Full-text available
Camera traps are a popular tool in terrestrial wildlife research due to their low costs, easy operability, and usefulness for studying a wide array of species and research questions. The vast numbers of images they generate often require multiple human data extractors, yet accuracy and inter-observer variance are rarely considered. We compared results from 10 observers who processed the same set of multi-species camera trap images ( n = 11,560) from seven sites. We quantified inter-observer agreement and variance for (1) the number of mammals identified, (2) the number of images saved, (3) species identification accuracy and the types of mistakes made, and (4) counts of herbivore groups and individuals. We analysed the influence of observer experience, species distinctiveness and camera location. Observers varied significantly regarding image processing rates, the number of mammals found and images saved, and species misidentifications. Only one observer detected all 22 mammals (range: 18–22, n = 10). Experienced observers processed images up to 4.5 times faster and made less mistakes regarding species detection and identification. Missed species were mostly small mammals (56.5%) while misidentifications were most common among species with low phenotypic distinctiveness. Herbivore counts had high to very high variances with mainly moderate agreement across observers. Observers differed in how they processed images and what they recorded. Our results raise important questions about the reliability of data extracted by multiple observers. Inter-observer bias, observer-related variables, species distinctiveness and camera location are important considerations if camera trapping results are to be used for population estimates or biodiversity assessments.
... Therefore, clear vision of the research question and hence the adequate sampling design must precede the choice of camera features . A number of practical and local environmental factors will also affect the choice of the best camera model, notably target species, site accessibility, climate, target site, and habitat (Rovero et al. 2013, Trolliet et al. 2014, Rovero and Zimmermann 2016, Findlay et al. 2020. In this regard, the performance of the Reconyx ® cameras was more than satisfactory compared to the needs of our study. ...
Full-text available
Wildlife conservation and management need accurate methods for population survey and monitoring. Absolute counts of roe deer populations (Capreolus capreolus) are not possible, but the rapid advancement of motion‐sensitive camera technologies and new analytical approaches might potentially lead to more precise estimates at lower costs compared to traditional survey methods. We applied spatially explicit photographic capture–recapture models (SCR) in the Lake Geneva basin, Switzerland, from 25 April to 20 September 2018 to estimate roe deer densities in a pilot survey. We investigated the effect of survey duration and camera density on male roe deer density estimates to select the sampling design that produced density estimates with sufficient accuracy and precision at lower costs (i.e., material, fieldwork, data processing, and analyses). Males could be identified based on their antlers, which allowed us to apply SCR to estimate their density. Because females could not be identified individually, we inferred the overall roe deer density (adult and sub‐adult roe deer) based on the sex ratio estimated from motion‐sensitive camera photos. According to the results of sub‐sampling simulations and by taking into account the financial costs associated with fieldwork and analyses, we conclude that 20 motion‐sensitive cameras set over 20 nights (i.e., the 20/20 method) is a good compromise to provide reliable estimates of male roe deer density. Furthermore, studies estimating overall roe deer density using SCR and sex ratio estimates should be conducted from mid‐August to the end of October just after rutting season and the peak of yearling dispersal, when the movement rates of males and females, and hence their detection probabilities, are similar and when males are still carrying their antlers. This approach was successfully applied in 4 selected study areas with contrasting roe deer management regimes, resulting in overall roe deer density estimates ranging from 3.9 ± 1.3 (SE) deer/km2 forest to 22.5 ± 6.1 deer/km2 forest. Our study provides a valuable and cost‐effective approach using photographic SCR methodology and sex ratio information to calculate roe deer density estimates that can be used in management measures such as defining hunting quotas.
... Despite these difficulties, a large number of grounddwelling mammal species can be reliably detected and documented using camera trapping (Rowcliffe and Carbone, 2008;Sunarto et al., 2013;Trolliet et al., 2014), with the method being widely applied to study various wildlife species in Sumatra (Shaik and Griffiths, 1996;Holden, 2006;Linkie et al., 2008;Holden and Meijaard, 2012;McCarthy and Fuller, 2014;Pusparini and Sibarani, 2014;Jennings et al., 2015;Widodo et al., 2017). Here we provide crucial baseline information on the presence and diversity of mammalian species inhabiting the ERC PT ABT in Central Sumatra, Jambi province, by using camera traps. ...
Full-text available
An inventory of resident mammal species was conducted in an ecosystem restoration concession located roughly in the geographical center of the Indonesian island of Sumatra. To assess the diversity of the resident mammalian fauna in major habitat types and to establish baselines for future monitoring programs, two camera trap surveys were implemented. The result of both surveys was similar, revealing the presence of a large variety of native mammals with 32 species of 15 different families, 56% of which are listed as either Vulnerable, Near Threatened, Endangered, or Critically Endangered on the IUCN Red List of 2019. Overall, the encountered diversity of mammal species indicates that the surveyed concession is still comparatively intact and suitable as a habitat for native animals and confirms the importance of the area for mammal conservation in the region.
... Cameras were programmed to trigger at their highest sensitivity, capturing three pictures per trigger. To maximise the likelihood of capturing images of all vertebrate fauna passing past cameras, particularly for species which may be difficult to detect such as small-bodied or fast-moving animals, we set cameras to record with no time delay between triggers (Meek et al. 2014;Trolliet et al. 2014). Cameras were tied to trees or metal stakes and set roughly 50 cm above the ground in an effort to capture images of small-bodied mammals and reptiles in addition to larger-bodied species (Meek et al. 2012). ...
Rabies, a multi‐host pathogen responsible for the loss of roughly 59,000 human lives each year worldwide, continues to impose a significant burden of disease despite control efforts, especially in Ethiopia. However, how species other than dogs contribute to rabies transmission throughout Ethiopia remains largely unknown. In this study, we quantified interactions among wildlife species in Ethiopia with the greatest potential for contributing to rabies maintenance. We observed wildlife at supplemental scavenging sites across multiple landscape types and quantified transmission potential. More specifically, we used camera trap data to quantify species abundance, species distribution, and intra‐ and inter‐species contacts per trapping night over time and by location. We derived a mathematical expression for the basic reproductive number (R0) based on within‐ and between‐species contract rates by applying the next generation method to the susceptible, exposed, infectious, removed (SEIR) model. We calculated R0 for transmission within each species and between each pair of species using camera trap data in order to identify pairwise interactions that contributed the most to transmission in an ecological community. We estimated which species, or species pairs, could maintain transmission (R0>1${R}_0 > 1$) and which species, or species pairs, had contact rates too low for maintenance (R0<1${R}_0 < 1$). Our results identified multiple urban carnivores as candidate species for rabies maintenance throughout Ethiopia, with hyenas exhibiting the greatest risk for rabies maintenance through intra‐species transmission. Hyenas and cats had the greatest risk for rabies maintenance through inter‐species transmission. Urban and peri‐urban sites posed the greatest risk for rabies transmission. The nighttime hours presented the greatest risk for a contact event that could result in rabies transmission. Overall, both intra‐species and inter‐species contacts posed risk for rabies maintenance. Our results can be used to target future studies and inform population management decisions. This article is protected by copyright. All rights reserved
Full-text available
As the capacity to collect and store large amounts of data expands, identifying and evaluating strategies to efficiently convert raw data into meaningful information is increasingly necessary. Across disciplines, this data processing task has become a significant challenge, delaying progress and actionable insights. In ecology, the growing use of camera traps (i.e., remotely triggered cameras) to collect information on wildlife has led to an enormous volume of raw data (i.e., images) in need of review and annotation. To expedite camera trap image processing, many have turned to the field of artificial intelligence (AI) and use machine learning models to automate tasks such as detecting and classifying wildlife in images. To contribute understanding of the utility of AI tools for processing wildlife camera trap images, we evaluated the performance of a state-of-the-art computer vision model developed by Microsoft AI for Earth named MegaDetector using data from an ongoing camera trap study in Arctic Alaska, USA. Compared to image labels determined by manual human review, we found MegaDetector reliably determined the presence or absence of wildlife in images generated by motion detection camera settings (≥94.6% accuracy), however, performance was substantially poorer for images collected with time-lapse camera settings (≤61.6% accuracy). By examining time-lapse images where MegaDetector failed to detect wildlife, we gained practical insights into animal size and distance detection limits and discuss how those may impact the performance of MegaDetector in other systems. We anticipate our findings will stimulate critical thinking about the tradeoffs of using automated AI tools or manual human review to process camera trap images and help to inform effective implementation of study designs.
Full-text available
Carnivores are difficult to survey due, in large part, to their relative rarity across the landscape and wariness toward humans. Several noninvasive methods may aid in overcoming these difficulties, but there has been little discussion of the relative merits and biases of these techniques. We assess the value of 5 noninvasive techniques based on results from 2 multiyear studies of carnivores (including members of Carnivora and Didelphidae) in New York forests. Two metrics were particularly valuable in assessing the species-specific value of any particular survey technique: latency to initial detection (LTD) and probability of detection (POD). We found differences in the value of techniques in detecting different species. For midsized species (raccoon [Procyon lotor], fisher [Martes pennanti], opossum [Didelphis virginiana], and domestic cat [Felis catus]), camera traps and track-plates were approximately equivalent in detection efficiency, but the potential for wariness toward the survey apparatus resulted in higher LTD for track-plates than for cameras. On the other hand, track-plates detected small carnivores (marten [M. americana] and weasels [Mustela spp.]) more often than cameras and had higher PODs for small and midsized species than did cameras. Cameras were efficient mechanisms for surveying bears (Ursus americanus; low LTD, high POD) but functioned poorly for discerning presence of coyotes (Canis latrans; high LTD, low POD). Scat surveys and snowtracking were the best methods for coyotes, which avoided camera traps and artificial tracking surfaces. Our analysis of fecal DNA revealed that trail-based fecal surveys were inefficient at detecting species other than coyotes, with the possible exception of red foxes (Vulpes vulpes). Genetic analyses of feces and snowtracking revealed the presence of foxes at sites where other techniques failed to discern these species, suggesting that cameras and track-plates are inefficient for surveying small canids in this region. The LTD of coyotes by camera traps was not correlated with their abundance as indexed by scat counts, but for other species this metric may offer an opportunity to assess relative abundance across sites. Snowtracking surveys were particularly robust (high POD) for detecting species active in winter and may be more effective than both cameras and track-plates where conditions are suitable. We recommend that survey efforts targeting multiple members of the carnivore community use multiple independent techniques and incorporate mechanisms to truth their relative value.
Full-text available
Motion-triggered cameras are useful in wildlife investigations but quantitative metrics derived from photographs potentially include substantial error. We compared six models of cameras placed sideby-side at a small spring in Mojave National Preserve, California, for 63 days in the spring of 2006, and for 40 days in the fall of 2007. Total number of different species detected varied by camera from 2 to 14 in the first trial and from 1 to 6 in the second. Total number of wildlife photographs taken by each camera ranged from 18 to 348 in the first trial and from 0 to 95 in the second. Photographic rates of a single species, mule deer (Odocoileus hemionus), differed by as much as 100% between two units of the same camera model. We did find, however, that the distribution of time intervals between photographs of mule deer was similar for different cameras. These results indicate that photographic rates and number of species detected by motion-triggered cameras can vary significantly even for identical models placed side by side, and have important implications regarding the interpretation of such data across areas.
Social behaviour of the bank vole was video recorded during direct encounters between individuals under natural conditions. The apparatus consisted of miniature video cameras, a system of image processing and recording, and infrared emitters. This device enabled continuous 24-h observations at several sites simultaneously. The study was conducted in an alder swamp Ribo nigri-Alnetum located in the Kampinos National Park, central Poland (52 degrees 20'N, 20 degrees 25'E). Observations were made in the late summers of 2002 and 2003 at six independent baited sites for 10 days and nights per each site. Rodents visiting the sites were individually marked by fur clipping. In sum, 13 053 visits to the sites and 1868 encounters between two marked individuals of C. glareolus were video recorded during 1440 hours of observation. It has been found that under natural conditions, bank voles most often avoided each other (55% of the encounters). In the case of close contacts they were aggressive (30%), rarely tolerant (7%), and during the remaining encounters they showed a mixed behaviour. The voles met mainly in the night (94% of the encounters) despite of 25% of their daily activity ran during the day. The frequency and character of encounters depended on the sex, age, and the origin of individuals. Encounters between males were more aggressive than between females (P <0.01). In encounters between opposite sexes, males were dominants (P <0.00 1). Individuals with a larger body mass were dominant in access to food (P <0.000). Cases of the dominance of juveniles over adults were interpreted as a result of the site of their origin. Social relations between individuals were characterised by persistence and repeatability in time. The results are compared with the literature describing experiments with animals kept in the laboratory or in enclosures, and field observations based on trapping techniques and telemetry.
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.
With the increasing popularity of remote photography in wildlife research, a large variety of equipment and methods is available to researchers. To evaluate advantages and disadvantages of using various types of equipment for different study objectives, we reviewed 107 papers that used either time-lapse or animal-triggered photography to study vertebrates in the field. Remote photography was used primarily to study avian nest predation, feeding ecology, and nesting behavior; additional applications included determining activity patterns, presence-absence monitoring, and estimating population parameters. Using time-lapse equipment is most appropriate when animals occur frequently in the photographic frame, the activity of interest occurs repeatedly, or no distinct event occurs to trigger a camera. In contrast, animal-triggered (light or mechanically triggered) systems are appropriate when events occur infrequently or unpredictably and there is a great likelihood that a trigger will be activated. Remote photography can be less time consuming, costly, and invasive than traditional research methods for many applications. However, researchers should be prepared to invest time and money troubleshooting problems with remote camera equipment, be aware of potential effects of equipment on animal behavior, and recognize the limitations of data collected with remote photography equipment.
We investigated the fate of seeds of five tree species hill cherry Prunus jamasakura, Korean hill cherry P. verecunda, Japanese bird cherry P. grayana, giant dogwood Swida controversa and crimson glory vine Vitis coignetiae in the faeces of the Asiatic black bear Ursus thibetanus in a temperate forest in central Japan. Clarifying the fate of seeds dispersed by endozoochorous seed dispersers will enhance assessments of their roles as primary seed dispersers. We established several experimental treatments in the field. Each faeces sample was covered by cages with different mesh sizes which limited accessibility by animals (NM: no mesh, SM: 1 mm mesh and MM: 10 mm mesh). We examined whether seed removal varied among tree species and between mesh-size treatments from 2004 to 2007 (N=625 samples). We set up an automatic camera trap 1.5 m above the ground at all NM treatments. In the NM treatments, the number of seeds of all tree species decreased immediately after the faeces were set. In June of the following year, < 1% of the seeds from any species remained in the vicinity of the faeces. However, we found 3.0-13.2% intact seeds of all species in the soil below the faeces, as well as within a 10-m radius around the faeces. In the NM treatments, most seed removals were observed within four days after the faeces were set. For all tree species in the MM treatment, most of the seeds were present on the surface of the soil, and 1-2% of the seeds germinated at the location where faeces were set. In the SM treatment, none of the seeds from any of the tree species disappeared and germinated. We took a total of 415 photographs at the NM sites, 97.8% of which were of rodents either holding or eating seeds. Many of the seeds contained in the bear faeces were removed and eaten by rodents. However, 2.1-5.1% of the seeds survived and germinated, which implies that rodents may also act as secondary seed dispersers.
We describe an electronic trigger coupled to an automatic camera for identifying predators at artificial nests. The system is portable, correctly exposes the predator during day or night for clear photographs, has close-up capabilities, automatic advance, and an external power supply for extended field use.