Estimating and indexing feral cat population abundancesusing camera traps

Andrew BengsenAB John ButlerA and Pip MastersA

AKangaroo Island Natural Resources Management Board 35 Dauncey Street Kingscote SA 5223 AustraliaBCorresponding author Present address NSW Department of Primary Industries Locked Bag 6006 OrangeNSW 2800 Australia Email andrewbengsenuqconnecteduau

AbstractContextThe ability tomonitor changes in population abundance is critical to the success of pest animalmanagement and

research programs Feral cats (Felis catus) are an important pest animal but current monitoring techniques have limitedsensitivity or are limited in use to particular circumstances or habitats Recent advances in camera-trappingmethods providethe potential to identify individual feral cats and to use this information to estimate population abundances usingcapturendashmarkndashrecapture (CMR) methods

AimsHere we use amanipulative study to test whether camera-trapping andCMRmethods can be used to estimate feralcat abundances

MethodsWeestablished a grid of infrared cameras and lure stations over three pastoral properties onKangaroo IslandAustralia for 15 days We then reduced the population abundance with an intensive trapping program and repeated thecamera survey We estimated population abundances using robust design CMR models and converted abundanceestimates to densities using home-range data from GPS tracking We also calculated relative abundance indices from thesame data

Key results The CMR methods produced credible estimates of the change in population abundance with usefulconfidence intervals showinga statistically identifiablepopulationdecline fromat least 07 catskmndash2before trappingdown to04 cats kmndash2 after trapping The indexing method also showed a statistically identifiable decrease in abundance

Conclusions Camera-trapping and CMR methods can provide a useful method for monitoring changes in the absoluteabundance of feral cat populations Camera-trap data may also be used to produce indices of relative abundance when theassumptions of CMR models cannot be met

Implications These methods are widely applicable The ability to reliably estimate feral cat abundances allows for moreeffective management than is generally available

Additional keywords abundance index camera trap Felis catus feral cat markndashrecapture

Received 26 July 2011 accepted 20 October 2011 published online 9 December 2011

Introduction

Feral cats (Felis catus) are a widespread invasive species withsubstantial and diverse impacts on biodiversity and economicvalues across the globe (eg McLeod 2004 Nogales et al 2004)The amelioration of many of these impacts requires that catabundances or densities in a given area be reduced (egRisbey et al 2000 Keitt and Tershy 2003) This in turngenerally requires reliable methods to estimate populationabundances or densities so that appropriate managementstrategies can be formulated and the efficacy of controlprograms can be assessed However feral cat populations aregenerally difficult to enumerate because cats are usually solitaryand elusive resulting in low detectability (Edwards et al 2000)

Because of these difficulties most attempts to monitorchanges in feral cat populations during control programs haverelied on indirect indices of relative abundance such as spotlight

counts tracking plots and bait-take (Forsyth et al 2005) Thevalid use of these indices is predicated on the assumption thatchanges in the index value are monotonic with changes in thesubject population (Caughley 1977) However this assumptiondoes not always hold (Wilson and Delahay 2001) and is oftenuntested (Conn et al 2004) Spotlight counts for example can beunsuitable for monitoring cats because they have a low ability todetect cats that are actually present (Mahon et al 1998 Edwardset al 2000 Read and Eldridge 2010) and can therefore probablydetect only very large population changes (Forsyth et al 2005)Passive tracking plots on unsealed roads and tracks may be moreuseful than spotlight counts (Edwards et al 2000) but can stillsuffer low sensitivity because cats do not move along thesefeatures (Read and Eldridge 2010) Furthermore results maybe biased because the habitat type that is sampled is notnecessarily representative of the broader landscape (Mahon

CSIRO PUBLISHING

Wildlife Research 2011 38 732ndash739httpdxdoiorg101071WR11134

Journal compilation CSIRO 2011 wwwpublishcsiroaujournalswr

et al 1998) Indices using bait-take to determine the efficacy ofcat-control programs generally have little value because themonitoring method is not independent of the control methodand because baits may be taken by non-target species (Forsythet al 2005)

Another alternative to monitoring changes in absoluteabundance is to monitor changes in occupancy ie theproportion of sampling units occupied by the target speciesOccupancy can be a useful surrogate for abundance whensubject species are territorial and spatial sampling units areapproximately equivalent to territory sizes or it can be aninformative state variable to monitor in its own right(MacKenzie et al 2006) Occupancy models have been usedto monitor changes in the prevalence of feral cat populationsinferred from photographic observations of feral cats capturedusing animal-triggered cameras (camera-traps) (Robley et al2010) but it is unclear how these changes might relate topopulation abundances

The few published examples of attempts to estimate theabsolute abundance of feral cat populations have used ad hocapproaches that are not widely applicable or comparable toother situations (eg Van Aarde 1979 Genovesi et al 1995)Recently camera-traps have increasingly been used to collectdata for the direct estimation of population abundances usingcapturendashmarkndashrecapture (CMR) techniques particularly forlarge felids that can be individually recognised using distinctnatural markings such as pelage patterns (eg Karanth andNichols 1998) This method has also recently been trialledwith feral cats in southern Australia (Robley et al 2010) theimplications of this work warrant further exploration

We used a combination of camera-trapping CMR analysisand home-range data from GPS-tracking to estimate abundancesand densities of feral cats on Kangaroo Island Australia Weused a manipulative study to determine whether camera-trappingcould provide abundance estimates which would allow theeffects of feral cat management programs to be assessed We

also tested whether a modified version of the commonly usedgeneral index model (Engeman 2005) or occupancy modelscould be used to monitor changes in the cat population in casethe assumptions of CMR analysis were violated

Materials and methods

The study was conducted on the eastern end of Kangaroo IslandSouth Australia (35480S 137580E) in December 2010 andJanuary 2011 About 51 of the islandrsquos 4405 km2 is coveredin native vegetation predominantly low Eucalyptusspp woodlands with mixed crops and pasture comprisingmost of the remainder The study site was situated over threeadjacent pastoral properties (intensive sheep and cattle grazing)which supported substantial patches of low- and medium-heightopen eucalypt woodlands linked by corridors along fence linesGrasstrees (Xanthorrhoea tateana) were prominent throughoutand the interlocking aprons of leaves formed by dense standsof this species provided abundant shelter for cats (A Bengsenpers obs) The pervasiveness of native vegetation was typicalof pastoral properties throughout the island The site wasoverlaid with a 39 39-km sampling grid comprising 36cells (Fig 1) The 065-km cell-edge length corresponded withapproximately half the estimated home-range width for anaverage female cat on the island (14 km2 Paton 1994)

Thirteen feral cats were captured in cage traps baited withtinned fish or fresh chicken and fitted with a combination of aGPS logger and a VHF tracking collars (Sirtrack HavelockNorth New Zealand) between June and October 2010 as partof another study (A J Bengsen J A Butler and P Mastersunpubl data) Eleven collars were programmed to attempt alocation fix every 5 h and two were programmed for a 2-hinterval between fixes We marked a further six cats that weretoo small to be fitted with collars (lt25 kg) with small-numberedbrass ear tags (Hauptner DietlikonndashZuumlrich Switzerland) Welocated tags in different positions on the ear to enable cats to be

Fig 1 Kangaroo Island (inset) and the study-site grid (right) showing the distribution ofmajor land-use types and thesampling grid

Estimating feral cat abundance using cameras Wildlife Research 733

recognised on subsequent recapture on the basis of ear-tagposition and general coat pattern without the need for closeexamination All cats were released at site of capture

In November 2010 we deployed one camera station in eachgrid cell for 15 consecutive days This spacing ensured that nocamera was farther from its nearest neighbour than the diameterof an average home range estimated from cats in similarhabitat on Kangaroo Island (134 km Paton 1994) Stationswere located within tree lines or stands of native vegetationEach station comprised a fresh chicken wing secured within awire cage (26 8 8 cm) that was pegged firmly to the groundLures were changed after 6 and 12 days We augmented eachsite with a visual lure comprising three white duck feathers tiedtogether and suspended from overhanging vegetation via afishing swivel to (1) provide a long-range visual attractant toincrease catsrsquo ability to detect the lure station (2) appeal to catsrsquohunting instincts because cats are heavily reliant on visual cuesfor detecting prey (Turner and Meister 1988) and (3) provide analternative lure to overcome biases that could result from relyingon a single lure type We used attractants to draw cats to themonitoring stations rather than relying on passive observation ofcats at unbaited stations because we wanted to minimise theduration of the study so as to avoid excessive movement ofanimals onto or out of the study area

We monitored lure stations using passive infrared-triggeredcameras (Reconyx RC60 MC65 or PC85 Holmen WI USA)attached to a tree or post ~3m from the lure station with thesensor positioned ~30 cm above ground Obstructions aroundthe lure cage were trimmed to provide an open field of view forthe cameraWe programmed cameras to record three consecutiveimages each time the sensor was triggered with a minimalinterval between images in a set and no break between setsso that we could capture as many images as possible from eachevent Cameras recorded 31 megapixel colour images duringthe day under ambient light and monochromatic images at nightunder an infrared flash using the default setting for night imageswhich balances image quality flash range and shutter speed Allimages were time and date stamped

After the initial 15-day sample we conducted an intensive15-day trapping program using cage traps baited with fish orchicken to remove as many cats as possible from the siteincluding a 400-m buffer around the outer camera trapsTracking collars were recovered during this period Thecamera-sampling procedure was then repeated using the samecamera and lure-station locations to allow the sensitivity of theestimation procedures to be assessed with reference to theremoval of a known number of cats

Analysis

We constructed two sets of detection histories for each samplingperiod one lsquonaturalrsquo and one lsquoenhancedrsquo To construct thenatural detection histories we identified individual adult feralcats from photographic observations using characteristics suchas pelage patterns sex and morphology We broke each 15-daysampling period into five 3-day sessions for which each catwas recorded as observed or not observed This breakdownwas chosen before data collection to provide a balance amongthe number of sessions available to construct detection histories

(five) sufficient time within each session to achieve highdetection probabilities (three days) and a short samplingperiod to reduce the risk of population closure violation(15 days) We discarded observations that could not bedefinitively attributed to an individual cat because of poor-quality images or low information content We created thelsquoenhancedrsquo detection histories using the same process but alsoby using the presence of GPS collars and ear tags to assist theidentification process by increasing the information content ofphotos

We used a suite of models representing eight possible sourcesof variation in capture probabilities in closed populations (Otiset al 1978) to estimate the abundance of feral cats before andafter trapping for both the natural and enhanced datasetsModels were implemented using the R package lsquoRcapturersquoversion 21-0 (Baillargeon and Rivest 2007) running under Rversion 2111 (R Development Core Team 2011) For eachcombination of sampling period and dataset we identified thebest-fitting model by examining data summaries deviancesPearson residuals and Akaikersquos information criterion (AIC)We then applied these models in a robust design model(Pollock 1982) implemented using Rcapture to produce finalabundance estimates and detection and survival probabilitiesThe robust model design assumes that populations are closed togains and losses during each primary sampling period (beforeand after trapping in the present study) but they may be openbetween periods (Pollock 1982) We then estimated the densityof feral cats at our study site by dividing the abundanceestimates by the effectively sampled area which we definedby adding a buffer around the outer camera traps equal inwidth to the mean home-range radius of nine male and fourfemale GPS-collared cats with at least 120 location fixes over60 days The home-range radius for each cat was calculated asthe radius of a circle equal in area to the median home range ofall cats estimated using adaptive local convex hulls(A J Bengsen J A Butler and P Masters unpubl data)

We used a resampling procedure to explore the effects ofreducing the numbers of camera stations on the accuracy andprecision of abundance estimates relative to estimates from thefull dataset We divided the 36-cell sampling grid into ninequadrats each containing four camera stations We randomlyselected one camera station per quadrat and used the recordsfor these nine stations to construct new detection histories foreach period We then calculated abundance estimates for eachperiod by using a log-linear model which accounted forheterogeneity in capture probabilities among individuals(Model Mh) which is the simplest model that is robust toheterogeneity in capture probabilities We repeated thesubsampling procedure 500 times using one two or threecamera stations per quadrat equating to 9 18 or 27 cameras intotal For each number of cameras we noted the proportionof sample pairs which provided a statistically detectablenegative difference between sample periods at the 005 a level(z-score gt 196) We regarded this value as an indication ofthe power of a survey using a particular number of cameras todetect the occurrence a population reduction when one actuallyoccurs

To evaluate the utility of relative abundance indicesconstructed from camera-trap data as a tool for monitoring

734 Wildlife Research A Bengsen et al

changes in the abundance of feral cats we calculated abundanceindices using four variants of the widely used general index(GI) model This method calculates an index and variancecomponents on the basis of the number of animals observed ateachmonitoring station on each day by using linearmixed-effectsmodels with random effects for monitoring station and day(Engeman 2005) We modelled the counts of cats at camerastations using a null GI model with no fixed effects and a GImodel with a fixed effect for sampling period The random-dayeffect was nested within the sampling periods We also usedmodified GI models with Poisson error distributions and log-linkfunctions to test whether these were more capable of identifyingand describing the effects of removing cats than were thestandard normal models The use of a Poisson error structurefor count data can provide several advantages over a normal errordistribution andmay be more generally appropriate for use in GImodels where data are counts of individuals at monitoringstations (Bengsen et al 2011) Indices were calculated as theback-transformed expected counts for each period whichdescribed the expected number of cat observationscamerandash1 dayndash1 Parameters were estimated by restrictedmaximum likelihood using the R package lsquolme4rsquo version0999375-18 (Bates and Maechler 2010) We used theindexndashmanipulatendashindex (IMI) method to derive populationestimates using the indices for each sampling period and theknown change in population from the trapping program(Caughley 1977)

Finally we tested whether the removal of cats from thepopulation resulted in detectable changes in simple occupancymodels We constructed daily cat-detection histories for eachlure station so that cats were recorded as either present orabsent for each day at each station We then broke this matrixinto six 5-day periods three before we manipulated thepopulation and three after This breakdown was chosen toallow high detection probabilities within each period (overfive 1-day occasions) whilst keeping periods short enough toprovide a fine-grained illustration of changes in occupancythroughout the survey We used Program PRESENCE (v 30USGS-PWRC Laurel MD USA) to construct a series of nestedmodels the most complex of which allowed for detectionprobability and colonisation to vary among the six periodsThe relative support for each model given the data wasassessed using AIC

Results

Twenty-six individual adult cats were recognised by theirnatural markings from 88 different records across bothsampling periods including 20 cats from 57 records during thefirst period and 12 cats from 31 records during the secondThirteen (65) of the cats identified from the first samplewere removed during the trapping operation as well as afurther five cats that could not be identified from camerarecords Therefore there were at least 25 adult cats on thesampling grid during the 30 days from the start of samplinguntil the end of trapping Most cats identified from camerarecords were black and grey tabbies either classic striped (iebearing a dark angular spiral shape on their flanks 35)spotted (31) or mackerel striped (ie bearing narrow vertical

stripes on their flanks 12) Other coat patterns included aginger classic tabby a uniformly black cat and a lightlyspotted ginger cat

The numbers of identifiable individuals did not increasewhen collars and ear tags were used to assist in identificationEar tags alone provided little benefit because they were notconsistently distinguishable in images taken at night under aninfrared flash and were absent from two of the seven ear-taggedcats that were tagged 2 and 5 months before camera-trappingEighteen observations (127) of cats were unavailable forCMR analysis during the first sampling period because theycould not be identified as individuals and 11 (131) in thesecond sampling period Results presented hereafter refer toresults from the natural dataset

The probability of any camera-trap recording a cat was highfor both sampling periods (mean se before 088 007after 097 002) and most cats were recorded at more thanone camera station during each period (mean se before295 052 stations catndash1 after 317 067 stations catndash1)Many of the camera stations recorded more than one catduring each sampling period (max = 3 cats) The averageprobability of any cat being recorded during each occasionwas also high at 050 for each period and 75 of cats werefirst recorded during the first three sampling occasions duringeach period

Examination of model fit statistics identified the Mh closed-population model as the best fit for the data from the firstsampling period and Model M0 as the best fit for the secondperiod There was some support for a time effect in models forboth sampling periods and a high degree of model-selectionuncertainty for the second period However all models in the95 confidence set for each sampling period had very similarparameter estimates and the M0 model parameters for thesecond period were the most conservative with respect tothe magnitude of change from the first period (Table 1) Theabundance estimate for the first period should be regarded asa lower bound on the actual abundance which may not beproperly estimable in the presence of heterogeneity (Rivestand Baillargeon 2007) The estimated abundance of feralcats decreased by 55 after the trapping program

Table1 Estimatedabundancesof feral cats andmodel selectioncriteriacalculated from closed population models before and after the removal

of 18 adult cats from a pastoral study site on Kangaroo IslandAIC Akaikersquos information criterion Di difference in AIC between modeliand the most supported model wi Akaike weight relative likelihood of the

model given the data

Dataset Model Abundance se 95 CI AIC Di wi

Lower Upper

Before MhA 226 25 200 295 6182 000 085

removal MthA 224 24 213 288 6532 350 015

M0 203 06 200 217 7154 972 001After M0 126 09 120 139 5355 000 035removal Mt 123 06 120 136 5360 005 034

MthA 124 07 120 149 5489 134 018

MhA 125 09 120 136 5536 181 014

AEstimates from models with a heterogeneity component (h) representestimates of a lower bound on the population size

Estimating feral cat abundance using cameras Wildlife Research 735

(Table 1) corresponding with a survival probability(mean se) of 036 011 and an estimated 42 24 newarrivals Four adult cats that were not detected during thefirst sampling period were detected during the second eachon more than one 3-day occasion The abundance estimatesgenerated by the closed population and robust models wereconsistent with the removal of 18 cats from the study site andthe appearance of four new cats creating a net populationdecrease of 14 cats which was within the 95 confidencelimits of the difference between samples (393 1647) Medianhome-range radius calculated from GPS-collar location fixeswas 129 km providing an effectively sampled area of3007 km2 and an estimated density of at least 07 cats kmndash2

before trapping and 04 cats kmndash2 after trapping The effectivelysampled area showed complete coverage of the sampling gridwith no holes

The resampling procedure indicated that a samplingprotocol using 18 cameras arranged with two cameras perfour-cell quadrat (270 camera-days) would have 96 powerto identify a statistically detectable population decrease at the005 a level on the basis of the observed data A plot of thebootstrapped 95 confidence intervals for each number ofcameras confirmed that at least 18 cameras were needed toachieve separation between samples taken before and aftertrapping (Fig 2) A sampling protocol using 27 cameras withthree cameras per quadrat (405 camera-days) would have had100 power to detect the induced population decline whereasnine cameras (135 camera-days) would have had only 65power

Counts of cat numbers per site per day were randomlydistributed and closely matched a Poisson distribution(Fisherrsquos exact test n = 1080 P= 097) Consequently themodified GI models using the Poisson error distributionprovided a much better fit to the data than did the modelsusing the standard normal distribution The normal andPoisson GI models that included a fixed effect for samplingperiod identified strong declines in the expected number ofcats per site per day However model-selection criteriaindicated that the normal GI model incorporating a fixedeffect for sampling period provided no greater explanatorypower than the normal null model (Table 2) The IMIprocedure provided population estimates of 48 cats beforetrapping and 38 after using a value of 18 cats removed fromthe site When we corrected for the four assumed immigrantsIMI estimates were 37 cats before and 23 cats after

Comparison of nested occupancy models showed thatoccupancy decreased slightly after the population wasmanipulated but the two most parsimonious models for thedata allowed only for variation in colonisation or detectionprobability among the six 5-day periods (Table 3) Variation indetection probability among periods was important in all threemodels with non-zero Akaike weights

Discussion

The combined camera-trapping and CMR methods used in thepresent study provided an effective method for monitoring

0

5

10

15

20

25

30

Number of cameras

Mea

n ab

unda

nce

plusmn 9

5 C

I

9 2718

Fig 2 Feral cat abundance estimates and 95 bootstrapped confidenceintervals for sampling periods before (solid circles) and after (open circles) theremoval of 18 cats calculated from a resampling procedure using 9 18 or27 camera stations of the possible 36 stations Broken lines representabundance estimates derived from all 36 cameras with shaded regionsshowing their 95 profile log-likelihood confidence intervals

Table 2 Model selection criteria for four variants of the general indexmodel applied to observations of feral cats collected at camera-traps at

a pastoral site on Kangaroo IslandIndex decline refers to the decrease in index values between two samplingperiods after the removal of 18 cats from the study site AIC Akaikersquosinformation criterionK number of parametersDi difference inAICbetweenmodeli and the most supported model wi Akaike weight relative likelihood

of the model given the data

Errordistribution

Predictor Indexdecline ()

AIC Log(L) K Di wi

Poisson Period 62 7949 ndash3934 4 0 084Null na 7982 ndash3961 3 33 016

Normal Null na 15339 ndash7630 4 0 062Period 61 15349 ndash7625 5 1 038

Table 3 Model selection criteria for thefivemost supportedmodels of aset of nested models explaining variability in feral cat detection histories

at 36 camera-traps at a pastoral site on Kangaroo IslandAIC Akaikersquos information criterion K number of parameters Di differencein AIC between modeli and the most supported model wi Akaike weight

relative likelihood of the model given the data

Time-varying parameter AIC K Di wi

Colonisation and detection probability 103486 36 000 069Detection probability 103744 32 258 019Occupancy and detection probability 103882 37 396 010Colonisation 107004 7 3518 000Occupancy 107236 8 3750 000

736 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

et al 1998) Indices using bait-take to determine the efficacy ofcat-control programs generally have little value because themonitoring method is not independent of the control methodand because baits may be taken by non-target species (Forsythet al 2005)

Another alternative to monitoring changes in absoluteabundance is to monitor changes in occupancy ie theproportion of sampling units occupied by the target speciesOccupancy can be a useful surrogate for abundance whensubject species are territorial and spatial sampling units areapproximately equivalent to territory sizes or it can be aninformative state variable to monitor in its own right(MacKenzie et al 2006) Occupancy models have been usedto monitor changes in the prevalence of feral cat populationsinferred from photographic observations of feral cats capturedusing animal-triggered cameras (camera-traps) (Robley et al2010) but it is unclear how these changes might relate topopulation abundances

The few published examples of attempts to estimate theabsolute abundance of feral cat populations have used ad hocapproaches that are not widely applicable or comparable toother situations (eg Van Aarde 1979 Genovesi et al 1995)Recently camera-traps have increasingly been used to collectdata for the direct estimation of population abundances usingcapturendashmarkndashrecapture (CMR) techniques particularly forlarge felids that can be individually recognised using distinctnatural markings such as pelage patterns (eg Karanth andNichols 1998) This method has also recently been trialledwith feral cats in southern Australia (Robley et al 2010) theimplications of this work warrant further exploration

We used a combination of camera-trapping CMR analysisand home-range data from GPS-tracking to estimate abundancesand densities of feral cats on Kangaroo Island Australia Weused a manipulative study to determine whether camera-trappingcould provide abundance estimates which would allow theeffects of feral cat management programs to be assessed We

also tested whether a modified version of the commonly usedgeneral index model (Engeman 2005) or occupancy modelscould be used to monitor changes in the cat population in casethe assumptions of CMR analysis were violated

Materials and methods

The study was conducted on the eastern end of Kangaroo IslandSouth Australia (35480S 137580E) in December 2010 andJanuary 2011 About 51 of the islandrsquos 4405 km2 is coveredin native vegetation predominantly low Eucalyptusspp woodlands with mixed crops and pasture comprisingmost of the remainder The study site was situated over threeadjacent pastoral properties (intensive sheep and cattle grazing)which supported substantial patches of low- and medium-heightopen eucalypt woodlands linked by corridors along fence linesGrasstrees (Xanthorrhoea tateana) were prominent throughoutand the interlocking aprons of leaves formed by dense standsof this species provided abundant shelter for cats (A Bengsenpers obs) The pervasiveness of native vegetation was typicalof pastoral properties throughout the island The site wasoverlaid with a 39 39-km sampling grid comprising 36cells (Fig 1) The 065-km cell-edge length corresponded withapproximately half the estimated home-range width for anaverage female cat on the island (14 km2 Paton 1994)

Thirteen feral cats were captured in cage traps baited withtinned fish or fresh chicken and fitted with a combination of aGPS logger and a VHF tracking collars (Sirtrack HavelockNorth New Zealand) between June and October 2010 as partof another study (A J Bengsen J A Butler and P Mastersunpubl data) Eleven collars were programmed to attempt alocation fix every 5 h and two were programmed for a 2-hinterval between fixes We marked a further six cats that weretoo small to be fitted with collars (lt25 kg) with small-numberedbrass ear tags (Hauptner DietlikonndashZuumlrich Switzerland) Welocated tags in different positions on the ear to enable cats to be

Fig 1 Kangaroo Island (inset) and the study-site grid (right) showing the distribution ofmajor land-use types and thesampling grid

Estimating feral cat abundance using cameras Wildlife Research 733

recognised on subsequent recapture on the basis of ear-tagposition and general coat pattern without the need for closeexamination All cats were released at site of capture

In November 2010 we deployed one camera station in eachgrid cell for 15 consecutive days This spacing ensured that nocamera was farther from its nearest neighbour than the diameterof an average home range estimated from cats in similarhabitat on Kangaroo Island (134 km Paton 1994) Stationswere located within tree lines or stands of native vegetationEach station comprised a fresh chicken wing secured within awire cage (26 8 8 cm) that was pegged firmly to the groundLures were changed after 6 and 12 days We augmented eachsite with a visual lure comprising three white duck feathers tiedtogether and suspended from overhanging vegetation via afishing swivel to (1) provide a long-range visual attractant toincrease catsrsquo ability to detect the lure station (2) appeal to catsrsquohunting instincts because cats are heavily reliant on visual cuesfor detecting prey (Turner and Meister 1988) and (3) provide analternative lure to overcome biases that could result from relyingon a single lure type We used attractants to draw cats to themonitoring stations rather than relying on passive observation ofcats at unbaited stations because we wanted to minimise theduration of the study so as to avoid excessive movement ofanimals onto or out of the study area

We monitored lure stations using passive infrared-triggeredcameras (Reconyx RC60 MC65 or PC85 Holmen WI USA)attached to a tree or post ~3m from the lure station with thesensor positioned ~30 cm above ground Obstructions aroundthe lure cage were trimmed to provide an open field of view forthe cameraWe programmed cameras to record three consecutiveimages each time the sensor was triggered with a minimalinterval between images in a set and no break between setsso that we could capture as many images as possible from eachevent Cameras recorded 31 megapixel colour images duringthe day under ambient light and monochromatic images at nightunder an infrared flash using the default setting for night imageswhich balances image quality flash range and shutter speed Allimages were time and date stamped

After the initial 15-day sample we conducted an intensive15-day trapping program using cage traps baited with fish orchicken to remove as many cats as possible from the siteincluding a 400-m buffer around the outer camera trapsTracking collars were recovered during this period Thecamera-sampling procedure was then repeated using the samecamera and lure-station locations to allow the sensitivity of theestimation procedures to be assessed with reference to theremoval of a known number of cats

Analysis

We constructed two sets of detection histories for each samplingperiod one lsquonaturalrsquo and one lsquoenhancedrsquo To construct thenatural detection histories we identified individual adult feralcats from photographic observations using characteristics suchas pelage patterns sex and morphology We broke each 15-daysampling period into five 3-day sessions for which each catwas recorded as observed or not observed This breakdownwas chosen before data collection to provide a balance amongthe number of sessions available to construct detection histories

(five) sufficient time within each session to achieve highdetection probabilities (three days) and a short samplingperiod to reduce the risk of population closure violation(15 days) We discarded observations that could not bedefinitively attributed to an individual cat because of poor-quality images or low information content We created thelsquoenhancedrsquo detection histories using the same process but alsoby using the presence of GPS collars and ear tags to assist theidentification process by increasing the information content ofphotos

We used a suite of models representing eight possible sourcesof variation in capture probabilities in closed populations (Otiset al 1978) to estimate the abundance of feral cats before andafter trapping for both the natural and enhanced datasetsModels were implemented using the R package lsquoRcapturersquoversion 21-0 (Baillargeon and Rivest 2007) running under Rversion 2111 (R Development Core Team 2011) For eachcombination of sampling period and dataset we identified thebest-fitting model by examining data summaries deviancesPearson residuals and Akaikersquos information criterion (AIC)We then applied these models in a robust design model(Pollock 1982) implemented using Rcapture to produce finalabundance estimates and detection and survival probabilitiesThe robust model design assumes that populations are closed togains and losses during each primary sampling period (beforeand after trapping in the present study) but they may be openbetween periods (Pollock 1982) We then estimated the densityof feral cats at our study site by dividing the abundanceestimates by the effectively sampled area which we definedby adding a buffer around the outer camera traps equal inwidth to the mean home-range radius of nine male and fourfemale GPS-collared cats with at least 120 location fixes over60 days The home-range radius for each cat was calculated asthe radius of a circle equal in area to the median home range ofall cats estimated using adaptive local convex hulls(A J Bengsen J A Butler and P Masters unpubl data)

We used a resampling procedure to explore the effects ofreducing the numbers of camera stations on the accuracy andprecision of abundance estimates relative to estimates from thefull dataset We divided the 36-cell sampling grid into ninequadrats each containing four camera stations We randomlyselected one camera station per quadrat and used the recordsfor these nine stations to construct new detection histories foreach period We then calculated abundance estimates for eachperiod by using a log-linear model which accounted forheterogeneity in capture probabilities among individuals(Model Mh) which is the simplest model that is robust toheterogeneity in capture probabilities We repeated thesubsampling procedure 500 times using one two or threecamera stations per quadrat equating to 9 18 or 27 cameras intotal For each number of cameras we noted the proportionof sample pairs which provided a statistically detectablenegative difference between sample periods at the 005 a level(z-score gt 196) We regarded this value as an indication ofthe power of a survey using a particular number of cameras todetect the occurrence a population reduction when one actuallyoccurs

To evaluate the utility of relative abundance indicesconstructed from camera-trap data as a tool for monitoring

734 Wildlife Research A Bengsen et al

changes in the abundance of feral cats we calculated abundanceindices using four variants of the widely used general index(GI) model This method calculates an index and variancecomponents on the basis of the number of animals observed ateachmonitoring station on each day by using linearmixed-effectsmodels with random effects for monitoring station and day(Engeman 2005) We modelled the counts of cats at camerastations using a null GI model with no fixed effects and a GImodel with a fixed effect for sampling period The random-dayeffect was nested within the sampling periods We also usedmodified GI models with Poisson error distributions and log-linkfunctions to test whether these were more capable of identifyingand describing the effects of removing cats than were thestandard normal models The use of a Poisson error structurefor count data can provide several advantages over a normal errordistribution andmay be more generally appropriate for use in GImodels where data are counts of individuals at monitoringstations (Bengsen et al 2011) Indices were calculated as theback-transformed expected counts for each period whichdescribed the expected number of cat observationscamerandash1 dayndash1 Parameters were estimated by restrictedmaximum likelihood using the R package lsquolme4rsquo version0999375-18 (Bates and Maechler 2010) We used theindexndashmanipulatendashindex (IMI) method to derive populationestimates using the indices for each sampling period and theknown change in population from the trapping program(Caughley 1977)

Finally we tested whether the removal of cats from thepopulation resulted in detectable changes in simple occupancymodels We constructed daily cat-detection histories for eachlure station so that cats were recorded as either present orabsent for each day at each station We then broke this matrixinto six 5-day periods three before we manipulated thepopulation and three after This breakdown was chosen toallow high detection probabilities within each period (overfive 1-day occasions) whilst keeping periods short enough toprovide a fine-grained illustration of changes in occupancythroughout the survey We used Program PRESENCE (v 30USGS-PWRC Laurel MD USA) to construct a series of nestedmodels the most complex of which allowed for detectionprobability and colonisation to vary among the six periodsThe relative support for each model given the data wasassessed using AIC

Results

Twenty-six individual adult cats were recognised by theirnatural markings from 88 different records across bothsampling periods including 20 cats from 57 records during thefirst period and 12 cats from 31 records during the secondThirteen (65) of the cats identified from the first samplewere removed during the trapping operation as well as afurther five cats that could not be identified from camerarecords Therefore there were at least 25 adult cats on thesampling grid during the 30 days from the start of samplinguntil the end of trapping Most cats identified from camerarecords were black and grey tabbies either classic striped (iebearing a dark angular spiral shape on their flanks 35)spotted (31) or mackerel striped (ie bearing narrow vertical

stripes on their flanks 12) Other coat patterns included aginger classic tabby a uniformly black cat and a lightlyspotted ginger cat

The numbers of identifiable individuals did not increasewhen collars and ear tags were used to assist in identificationEar tags alone provided little benefit because they were notconsistently distinguishable in images taken at night under aninfrared flash and were absent from two of the seven ear-taggedcats that were tagged 2 and 5 months before camera-trappingEighteen observations (127) of cats were unavailable forCMR analysis during the first sampling period because theycould not be identified as individuals and 11 (131) in thesecond sampling period Results presented hereafter refer toresults from the natural dataset

The probability of any camera-trap recording a cat was highfor both sampling periods (mean se before 088 007after 097 002) and most cats were recorded at more thanone camera station during each period (mean se before295 052 stations catndash1 after 317 067 stations catndash1)Many of the camera stations recorded more than one catduring each sampling period (max = 3 cats) The averageprobability of any cat being recorded during each occasionwas also high at 050 for each period and 75 of cats werefirst recorded during the first three sampling occasions duringeach period

Examination of model fit statistics identified the Mh closed-population model as the best fit for the data from the firstsampling period and Model M0 as the best fit for the secondperiod There was some support for a time effect in models forboth sampling periods and a high degree of model-selectionuncertainty for the second period However all models in the95 confidence set for each sampling period had very similarparameter estimates and the M0 model parameters for thesecond period were the most conservative with respect tothe magnitude of change from the first period (Table 1) Theabundance estimate for the first period should be regarded asa lower bound on the actual abundance which may not beproperly estimable in the presence of heterogeneity (Rivestand Baillargeon 2007) The estimated abundance of feralcats decreased by 55 after the trapping program

Table1 Estimatedabundancesof feral cats andmodel selectioncriteriacalculated from closed population models before and after the removal

of 18 adult cats from a pastoral study site on Kangaroo IslandAIC Akaikersquos information criterion Di difference in AIC between modeliand the most supported model wi Akaike weight relative likelihood of the

model given the data

Dataset Model Abundance se 95 CI AIC Di wi

Lower Upper

Before MhA 226 25 200 295 6182 000 085

removal MthA 224 24 213 288 6532 350 015

M0 203 06 200 217 7154 972 001After M0 126 09 120 139 5355 000 035removal Mt 123 06 120 136 5360 005 034

MthA 124 07 120 149 5489 134 018

MhA 125 09 120 136 5536 181 014

AEstimates from models with a heterogeneity component (h) representestimates of a lower bound on the population size

Estimating feral cat abundance using cameras Wildlife Research 735

(Table 1) corresponding with a survival probability(mean se) of 036 011 and an estimated 42 24 newarrivals Four adult cats that were not detected during thefirst sampling period were detected during the second eachon more than one 3-day occasion The abundance estimatesgenerated by the closed population and robust models wereconsistent with the removal of 18 cats from the study site andthe appearance of four new cats creating a net populationdecrease of 14 cats which was within the 95 confidencelimits of the difference between samples (393 1647) Medianhome-range radius calculated from GPS-collar location fixeswas 129 km providing an effectively sampled area of3007 km2 and an estimated density of at least 07 cats kmndash2

before trapping and 04 cats kmndash2 after trapping The effectivelysampled area showed complete coverage of the sampling gridwith no holes

The resampling procedure indicated that a samplingprotocol using 18 cameras arranged with two cameras perfour-cell quadrat (270 camera-days) would have 96 powerto identify a statistically detectable population decrease at the005 a level on the basis of the observed data A plot of thebootstrapped 95 confidence intervals for each number ofcameras confirmed that at least 18 cameras were needed toachieve separation between samples taken before and aftertrapping (Fig 2) A sampling protocol using 27 cameras withthree cameras per quadrat (405 camera-days) would have had100 power to detect the induced population decline whereasnine cameras (135 camera-days) would have had only 65power

Counts of cat numbers per site per day were randomlydistributed and closely matched a Poisson distribution(Fisherrsquos exact test n = 1080 P= 097) Consequently themodified GI models using the Poisson error distributionprovided a much better fit to the data than did the modelsusing the standard normal distribution The normal andPoisson GI models that included a fixed effect for samplingperiod identified strong declines in the expected number ofcats per site per day However model-selection criteriaindicated that the normal GI model incorporating a fixedeffect for sampling period provided no greater explanatorypower than the normal null model (Table 2) The IMIprocedure provided population estimates of 48 cats beforetrapping and 38 after using a value of 18 cats removed fromthe site When we corrected for the four assumed immigrantsIMI estimates were 37 cats before and 23 cats after

Comparison of nested occupancy models showed thatoccupancy decreased slightly after the population wasmanipulated but the two most parsimonious models for thedata allowed only for variation in colonisation or detectionprobability among the six 5-day periods (Table 3) Variation indetection probability among periods was important in all threemodels with non-zero Akaike weights

Discussion

The combined camera-trapping and CMR methods used in thepresent study provided an effective method for monitoring

0

5

10

15

20

25

30

Number of cameras

Mea

n ab

unda

nce

plusmn 9

5 C

I

9 2718

Fig 2 Feral cat abundance estimates and 95 bootstrapped confidenceintervals for sampling periods before (solid circles) and after (open circles) theremoval of 18 cats calculated from a resampling procedure using 9 18 or27 camera stations of the possible 36 stations Broken lines representabundance estimates derived from all 36 cameras with shaded regionsshowing their 95 profile log-likelihood confidence intervals

Table 2 Model selection criteria for four variants of the general indexmodel applied to observations of feral cats collected at camera-traps at

a pastoral site on Kangaroo IslandIndex decline refers to the decrease in index values between two samplingperiods after the removal of 18 cats from the study site AIC Akaikersquosinformation criterionK number of parametersDi difference inAICbetweenmodeli and the most supported model wi Akaike weight relative likelihood

of the model given the data

Errordistribution

Predictor Indexdecline ()

AIC Log(L) K Di wi

Poisson Period 62 7949 ndash3934 4 0 084Null na 7982 ndash3961 3 33 016

Normal Null na 15339 ndash7630 4 0 062Period 61 15349 ndash7625 5 1 038

Table 3 Model selection criteria for thefivemost supportedmodels of aset of nested models explaining variability in feral cat detection histories

at 36 camera-traps at a pastoral site on Kangaroo IslandAIC Akaikersquos information criterion K number of parameters Di differencein AIC between modeli and the most supported model wi Akaike weight

relative likelihood of the model given the data

Time-varying parameter AIC K Di wi

Colonisation and detection probability 103486 36 000 069Detection probability 103744 32 258 019Occupancy and detection probability 103882 37 396 010Colonisation 107004 7 3518 000Occupancy 107236 8 3750 000

736 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

recognised on subsequent recapture on the basis of ear-tagposition and general coat pattern without the need for closeexamination All cats were released at site of capture

In November 2010 we deployed one camera station in eachgrid cell for 15 consecutive days This spacing ensured that nocamera was farther from its nearest neighbour than the diameterof an average home range estimated from cats in similarhabitat on Kangaroo Island (134 km Paton 1994) Stationswere located within tree lines or stands of native vegetationEach station comprised a fresh chicken wing secured within awire cage (26 8 8 cm) that was pegged firmly to the groundLures were changed after 6 and 12 days We augmented eachsite with a visual lure comprising three white duck feathers tiedtogether and suspended from overhanging vegetation via afishing swivel to (1) provide a long-range visual attractant toincrease catsrsquo ability to detect the lure station (2) appeal to catsrsquohunting instincts because cats are heavily reliant on visual cuesfor detecting prey (Turner and Meister 1988) and (3) provide analternative lure to overcome biases that could result from relyingon a single lure type We used attractants to draw cats to themonitoring stations rather than relying on passive observation ofcats at unbaited stations because we wanted to minimise theduration of the study so as to avoid excessive movement ofanimals onto or out of the study area

We monitored lure stations using passive infrared-triggeredcameras (Reconyx RC60 MC65 or PC85 Holmen WI USA)attached to a tree or post ~3m from the lure station with thesensor positioned ~30 cm above ground Obstructions aroundthe lure cage were trimmed to provide an open field of view forthe cameraWe programmed cameras to record three consecutiveimages each time the sensor was triggered with a minimalinterval between images in a set and no break between setsso that we could capture as many images as possible from eachevent Cameras recorded 31 megapixel colour images duringthe day under ambient light and monochromatic images at nightunder an infrared flash using the default setting for night imageswhich balances image quality flash range and shutter speed Allimages were time and date stamped

After the initial 15-day sample we conducted an intensive15-day trapping program using cage traps baited with fish orchicken to remove as many cats as possible from the siteincluding a 400-m buffer around the outer camera trapsTracking collars were recovered during this period Thecamera-sampling procedure was then repeated using the samecamera and lure-station locations to allow the sensitivity of theestimation procedures to be assessed with reference to theremoval of a known number of cats

Analysis

We constructed two sets of detection histories for each samplingperiod one lsquonaturalrsquo and one lsquoenhancedrsquo To construct thenatural detection histories we identified individual adult feralcats from photographic observations using characteristics suchas pelage patterns sex and morphology We broke each 15-daysampling period into five 3-day sessions for which each catwas recorded as observed or not observed This breakdownwas chosen before data collection to provide a balance amongthe number of sessions available to construct detection histories

(five) sufficient time within each session to achieve highdetection probabilities (three days) and a short samplingperiod to reduce the risk of population closure violation(15 days) We discarded observations that could not bedefinitively attributed to an individual cat because of poor-quality images or low information content We created thelsquoenhancedrsquo detection histories using the same process but alsoby using the presence of GPS collars and ear tags to assist theidentification process by increasing the information content ofphotos

We used a suite of models representing eight possible sourcesof variation in capture probabilities in closed populations (Otiset al 1978) to estimate the abundance of feral cats before andafter trapping for both the natural and enhanced datasetsModels were implemented using the R package lsquoRcapturersquoversion 21-0 (Baillargeon and Rivest 2007) running under Rversion 2111 (R Development Core Team 2011) For eachcombination of sampling period and dataset we identified thebest-fitting model by examining data summaries deviancesPearson residuals and Akaikersquos information criterion (AIC)We then applied these models in a robust design model(Pollock 1982) implemented using Rcapture to produce finalabundance estimates and detection and survival probabilitiesThe robust model design assumes that populations are closed togains and losses during each primary sampling period (beforeand after trapping in the present study) but they may be openbetween periods (Pollock 1982) We then estimated the densityof feral cats at our study site by dividing the abundanceestimates by the effectively sampled area which we definedby adding a buffer around the outer camera traps equal inwidth to the mean home-range radius of nine male and fourfemale GPS-collared cats with at least 120 location fixes over60 days The home-range radius for each cat was calculated asthe radius of a circle equal in area to the median home range ofall cats estimated using adaptive local convex hulls(A J Bengsen J A Butler and P Masters unpubl data)

We used a resampling procedure to explore the effects ofreducing the numbers of camera stations on the accuracy andprecision of abundance estimates relative to estimates from thefull dataset We divided the 36-cell sampling grid into ninequadrats each containing four camera stations We randomlyselected one camera station per quadrat and used the recordsfor these nine stations to construct new detection histories foreach period We then calculated abundance estimates for eachperiod by using a log-linear model which accounted forheterogeneity in capture probabilities among individuals(Model Mh) which is the simplest model that is robust toheterogeneity in capture probabilities We repeated thesubsampling procedure 500 times using one two or threecamera stations per quadrat equating to 9 18 or 27 cameras intotal For each number of cameras we noted the proportionof sample pairs which provided a statistically detectablenegative difference between sample periods at the 005 a level(z-score gt 196) We regarded this value as an indication ofthe power of a survey using a particular number of cameras todetect the occurrence a population reduction when one actuallyoccurs

To evaluate the utility of relative abundance indicesconstructed from camera-trap data as a tool for monitoring

734 Wildlife Research A Bengsen et al

changes in the abundance of feral cats we calculated abundanceindices using four variants of the widely used general index(GI) model This method calculates an index and variancecomponents on the basis of the number of animals observed ateachmonitoring station on each day by using linearmixed-effectsmodels with random effects for monitoring station and day(Engeman 2005) We modelled the counts of cats at camerastations using a null GI model with no fixed effects and a GImodel with a fixed effect for sampling period The random-dayeffect was nested within the sampling periods We also usedmodified GI models with Poisson error distributions and log-linkfunctions to test whether these were more capable of identifyingand describing the effects of removing cats than were thestandard normal models The use of a Poisson error structurefor count data can provide several advantages over a normal errordistribution andmay be more generally appropriate for use in GImodels where data are counts of individuals at monitoringstations (Bengsen et al 2011) Indices were calculated as theback-transformed expected counts for each period whichdescribed the expected number of cat observationscamerandash1 dayndash1 Parameters were estimated by restrictedmaximum likelihood using the R package lsquolme4rsquo version0999375-18 (Bates and Maechler 2010) We used theindexndashmanipulatendashindex (IMI) method to derive populationestimates using the indices for each sampling period and theknown change in population from the trapping program(Caughley 1977)

Finally we tested whether the removal of cats from thepopulation resulted in detectable changes in simple occupancymodels We constructed daily cat-detection histories for eachlure station so that cats were recorded as either present orabsent for each day at each station We then broke this matrixinto six 5-day periods three before we manipulated thepopulation and three after This breakdown was chosen toallow high detection probabilities within each period (overfive 1-day occasions) whilst keeping periods short enough toprovide a fine-grained illustration of changes in occupancythroughout the survey We used Program PRESENCE (v 30USGS-PWRC Laurel MD USA) to construct a series of nestedmodels the most complex of which allowed for detectionprobability and colonisation to vary among the six periodsThe relative support for each model given the data wasassessed using AIC

Results

Twenty-six individual adult cats were recognised by theirnatural markings from 88 different records across bothsampling periods including 20 cats from 57 records during thefirst period and 12 cats from 31 records during the secondThirteen (65) of the cats identified from the first samplewere removed during the trapping operation as well as afurther five cats that could not be identified from camerarecords Therefore there were at least 25 adult cats on thesampling grid during the 30 days from the start of samplinguntil the end of trapping Most cats identified from camerarecords were black and grey tabbies either classic striped (iebearing a dark angular spiral shape on their flanks 35)spotted (31) or mackerel striped (ie bearing narrow vertical

stripes on their flanks 12) Other coat patterns included aginger classic tabby a uniformly black cat and a lightlyspotted ginger cat

The numbers of identifiable individuals did not increasewhen collars and ear tags were used to assist in identificationEar tags alone provided little benefit because they were notconsistently distinguishable in images taken at night under aninfrared flash and were absent from two of the seven ear-taggedcats that were tagged 2 and 5 months before camera-trappingEighteen observations (127) of cats were unavailable forCMR analysis during the first sampling period because theycould not be identified as individuals and 11 (131) in thesecond sampling period Results presented hereafter refer toresults from the natural dataset

The probability of any camera-trap recording a cat was highfor both sampling periods (mean se before 088 007after 097 002) and most cats were recorded at more thanone camera station during each period (mean se before295 052 stations catndash1 after 317 067 stations catndash1)Many of the camera stations recorded more than one catduring each sampling period (max = 3 cats) The averageprobability of any cat being recorded during each occasionwas also high at 050 for each period and 75 of cats werefirst recorded during the first three sampling occasions duringeach period

Examination of model fit statistics identified the Mh closed-population model as the best fit for the data from the firstsampling period and Model M0 as the best fit for the secondperiod There was some support for a time effect in models forboth sampling periods and a high degree of model-selectionuncertainty for the second period However all models in the95 confidence set for each sampling period had very similarparameter estimates and the M0 model parameters for thesecond period were the most conservative with respect tothe magnitude of change from the first period (Table 1) Theabundance estimate for the first period should be regarded asa lower bound on the actual abundance which may not beproperly estimable in the presence of heterogeneity (Rivestand Baillargeon 2007) The estimated abundance of feralcats decreased by 55 after the trapping program

Table1 Estimatedabundancesof feral cats andmodel selectioncriteriacalculated from closed population models before and after the removal

of 18 adult cats from a pastoral study site on Kangaroo IslandAIC Akaikersquos information criterion Di difference in AIC between modeliand the most supported model wi Akaike weight relative likelihood of the

model given the data

Dataset Model Abundance se 95 CI AIC Di wi

Lower Upper

Before MhA 226 25 200 295 6182 000 085

removal MthA 224 24 213 288 6532 350 015

M0 203 06 200 217 7154 972 001After M0 126 09 120 139 5355 000 035removal Mt 123 06 120 136 5360 005 034

MthA 124 07 120 149 5489 134 018

MhA 125 09 120 136 5536 181 014

AEstimates from models with a heterogeneity component (h) representestimates of a lower bound on the population size

Estimating feral cat abundance using cameras Wildlife Research 735

(Table 1) corresponding with a survival probability(mean se) of 036 011 and an estimated 42 24 newarrivals Four adult cats that were not detected during thefirst sampling period were detected during the second eachon more than one 3-day occasion The abundance estimatesgenerated by the closed population and robust models wereconsistent with the removal of 18 cats from the study site andthe appearance of four new cats creating a net populationdecrease of 14 cats which was within the 95 confidencelimits of the difference between samples (393 1647) Medianhome-range radius calculated from GPS-collar location fixeswas 129 km providing an effectively sampled area of3007 km2 and an estimated density of at least 07 cats kmndash2

before trapping and 04 cats kmndash2 after trapping The effectivelysampled area showed complete coverage of the sampling gridwith no holes

The resampling procedure indicated that a samplingprotocol using 18 cameras arranged with two cameras perfour-cell quadrat (270 camera-days) would have 96 powerto identify a statistically detectable population decrease at the005 a level on the basis of the observed data A plot of thebootstrapped 95 confidence intervals for each number ofcameras confirmed that at least 18 cameras were needed toachieve separation between samples taken before and aftertrapping (Fig 2) A sampling protocol using 27 cameras withthree cameras per quadrat (405 camera-days) would have had100 power to detect the induced population decline whereasnine cameras (135 camera-days) would have had only 65power

Counts of cat numbers per site per day were randomlydistributed and closely matched a Poisson distribution(Fisherrsquos exact test n = 1080 P= 097) Consequently themodified GI models using the Poisson error distributionprovided a much better fit to the data than did the modelsusing the standard normal distribution The normal andPoisson GI models that included a fixed effect for samplingperiod identified strong declines in the expected number ofcats per site per day However model-selection criteriaindicated that the normal GI model incorporating a fixedeffect for sampling period provided no greater explanatorypower than the normal null model (Table 2) The IMIprocedure provided population estimates of 48 cats beforetrapping and 38 after using a value of 18 cats removed fromthe site When we corrected for the four assumed immigrantsIMI estimates were 37 cats before and 23 cats after

Comparison of nested occupancy models showed thatoccupancy decreased slightly after the population wasmanipulated but the two most parsimonious models for thedata allowed only for variation in colonisation or detectionprobability among the six 5-day periods (Table 3) Variation indetection probability among periods was important in all threemodels with non-zero Akaike weights

Discussion

The combined camera-trapping and CMR methods used in thepresent study provided an effective method for monitoring

0

5

10

15

20

25

30

Number of cameras

Mea

n ab

unda

nce

plusmn 9

5 C

I

9 2718

Fig 2 Feral cat abundance estimates and 95 bootstrapped confidenceintervals for sampling periods before (solid circles) and after (open circles) theremoval of 18 cats calculated from a resampling procedure using 9 18 or27 camera stations of the possible 36 stations Broken lines representabundance estimates derived from all 36 cameras with shaded regionsshowing their 95 profile log-likelihood confidence intervals

Table 2 Model selection criteria for four variants of the general indexmodel applied to observations of feral cats collected at camera-traps at

a pastoral site on Kangaroo IslandIndex decline refers to the decrease in index values between two samplingperiods after the removal of 18 cats from the study site AIC Akaikersquosinformation criterionK number of parametersDi difference inAICbetweenmodeli and the most supported model wi Akaike weight relative likelihood

of the model given the data

Errordistribution

Predictor Indexdecline ()

AIC Log(L) K Di wi

Poisson Period 62 7949 ndash3934 4 0 084Null na 7982 ndash3961 3 33 016

Normal Null na 15339 ndash7630 4 0 062Period 61 15349 ndash7625 5 1 038

Table 3 Model selection criteria for thefivemost supportedmodels of aset of nested models explaining variability in feral cat detection histories

at 36 camera-traps at a pastoral site on Kangaroo IslandAIC Akaikersquos information criterion K number of parameters Di differencein AIC between modeli and the most supported model wi Akaike weight

relative likelihood of the model given the data

Time-varying parameter AIC K Di wi

Colonisation and detection probability 103486 36 000 069Detection probability 103744 32 258 019Occupancy and detection probability 103882 37 396 010Colonisation 107004 7 3518 000Occupancy 107236 8 3750 000

736 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

changes in the abundance of feral cats we calculated abundanceindices using four variants of the widely used general index(GI) model This method calculates an index and variancecomponents on the basis of the number of animals observed ateachmonitoring station on each day by using linearmixed-effectsmodels with random effects for monitoring station and day(Engeman 2005) We modelled the counts of cats at camerastations using a null GI model with no fixed effects and a GImodel with a fixed effect for sampling period The random-dayeffect was nested within the sampling periods We also usedmodified GI models with Poisson error distributions and log-linkfunctions to test whether these were more capable of identifyingand describing the effects of removing cats than were thestandard normal models The use of a Poisson error structurefor count data can provide several advantages over a normal errordistribution andmay be more generally appropriate for use in GImodels where data are counts of individuals at monitoringstations (Bengsen et al 2011) Indices were calculated as theback-transformed expected counts for each period whichdescribed the expected number of cat observationscamerandash1 dayndash1 Parameters were estimated by restrictedmaximum likelihood using the R package lsquolme4rsquo version0999375-18 (Bates and Maechler 2010) We used theindexndashmanipulatendashindex (IMI) method to derive populationestimates using the indices for each sampling period and theknown change in population from the trapping program(Caughley 1977)

Finally we tested whether the removal of cats from thepopulation resulted in detectable changes in simple occupancymodels We constructed daily cat-detection histories for eachlure station so that cats were recorded as either present orabsent for each day at each station We then broke this matrixinto six 5-day periods three before we manipulated thepopulation and three after This breakdown was chosen toallow high detection probabilities within each period (overfive 1-day occasions) whilst keeping periods short enough toprovide a fine-grained illustration of changes in occupancythroughout the survey We used Program PRESENCE (v 30USGS-PWRC Laurel MD USA) to construct a series of nestedmodels the most complex of which allowed for detectionprobability and colonisation to vary among the six periodsThe relative support for each model given the data wasassessed using AIC

Results

Twenty-six individual adult cats were recognised by theirnatural markings from 88 different records across bothsampling periods including 20 cats from 57 records during thefirst period and 12 cats from 31 records during the secondThirteen (65) of the cats identified from the first samplewere removed during the trapping operation as well as afurther five cats that could not be identified from camerarecords Therefore there were at least 25 adult cats on thesampling grid during the 30 days from the start of samplinguntil the end of trapping Most cats identified from camerarecords were black and grey tabbies either classic striped (iebearing a dark angular spiral shape on their flanks 35)spotted (31) or mackerel striped (ie bearing narrow vertical

stripes on their flanks 12) Other coat patterns included aginger classic tabby a uniformly black cat and a lightlyspotted ginger cat

The numbers of identifiable individuals did not increasewhen collars and ear tags were used to assist in identificationEar tags alone provided little benefit because they were notconsistently distinguishable in images taken at night under aninfrared flash and were absent from two of the seven ear-taggedcats that were tagged 2 and 5 months before camera-trappingEighteen observations (127) of cats were unavailable forCMR analysis during the first sampling period because theycould not be identified as individuals and 11 (131) in thesecond sampling period Results presented hereafter refer toresults from the natural dataset

The probability of any camera-trap recording a cat was highfor both sampling periods (mean se before 088 007after 097 002) and most cats were recorded at more thanone camera station during each period (mean se before295 052 stations catndash1 after 317 067 stations catndash1)Many of the camera stations recorded more than one catduring each sampling period (max = 3 cats) The averageprobability of any cat being recorded during each occasionwas also high at 050 for each period and 75 of cats werefirst recorded during the first three sampling occasions duringeach period

Examination of model fit statistics identified the Mh closed-population model as the best fit for the data from the firstsampling period and Model M0 as the best fit for the secondperiod There was some support for a time effect in models forboth sampling periods and a high degree of model-selectionuncertainty for the second period However all models in the95 confidence set for each sampling period had very similarparameter estimates and the M0 model parameters for thesecond period were the most conservative with respect tothe magnitude of change from the first period (Table 1) Theabundance estimate for the first period should be regarded asa lower bound on the actual abundance which may not beproperly estimable in the presence of heterogeneity (Rivestand Baillargeon 2007) The estimated abundance of feralcats decreased by 55 after the trapping program

Table1 Estimatedabundancesof feral cats andmodel selectioncriteriacalculated from closed population models before and after the removal

of 18 adult cats from a pastoral study site on Kangaroo IslandAIC Akaikersquos information criterion Di difference in AIC between modeliand the most supported model wi Akaike weight relative likelihood of the

model given the data

Dataset Model Abundance se 95 CI AIC Di wi

Lower Upper

Before MhA 226 25 200 295 6182 000 085

removal MthA 224 24 213 288 6532 350 015

M0 203 06 200 217 7154 972 001After M0 126 09 120 139 5355 000 035removal Mt 123 06 120 136 5360 005 034

MthA 124 07 120 149 5489 134 018

MhA 125 09 120 136 5536 181 014

AEstimates from models with a heterogeneity component (h) representestimates of a lower bound on the population size

Estimating feral cat abundance using cameras Wildlife Research 735

(Table 1) corresponding with a survival probability(mean se) of 036 011 and an estimated 42 24 newarrivals Four adult cats that were not detected during thefirst sampling period were detected during the second eachon more than one 3-day occasion The abundance estimatesgenerated by the closed population and robust models wereconsistent with the removal of 18 cats from the study site andthe appearance of four new cats creating a net populationdecrease of 14 cats which was within the 95 confidencelimits of the difference between samples (393 1647) Medianhome-range radius calculated from GPS-collar location fixeswas 129 km providing an effectively sampled area of3007 km2 and an estimated density of at least 07 cats kmndash2

before trapping and 04 cats kmndash2 after trapping The effectivelysampled area showed complete coverage of the sampling gridwith no holes

The resampling procedure indicated that a samplingprotocol using 18 cameras arranged with two cameras perfour-cell quadrat (270 camera-days) would have 96 powerto identify a statistically detectable population decrease at the005 a level on the basis of the observed data A plot of thebootstrapped 95 confidence intervals for each number ofcameras confirmed that at least 18 cameras were needed toachieve separation between samples taken before and aftertrapping (Fig 2) A sampling protocol using 27 cameras withthree cameras per quadrat (405 camera-days) would have had100 power to detect the induced population decline whereasnine cameras (135 camera-days) would have had only 65power

Counts of cat numbers per site per day were randomlydistributed and closely matched a Poisson distribution(Fisherrsquos exact test n = 1080 P= 097) Consequently themodified GI models using the Poisson error distributionprovided a much better fit to the data than did the modelsusing the standard normal distribution The normal andPoisson GI models that included a fixed effect for samplingperiod identified strong declines in the expected number ofcats per site per day However model-selection criteriaindicated that the normal GI model incorporating a fixedeffect for sampling period provided no greater explanatorypower than the normal null model (Table 2) The IMIprocedure provided population estimates of 48 cats beforetrapping and 38 after using a value of 18 cats removed fromthe site When we corrected for the four assumed immigrantsIMI estimates were 37 cats before and 23 cats after

Comparison of nested occupancy models showed thatoccupancy decreased slightly after the population wasmanipulated but the two most parsimonious models for thedata allowed only for variation in colonisation or detectionprobability among the six 5-day periods (Table 3) Variation indetection probability among periods was important in all threemodels with non-zero Akaike weights

Discussion

The combined camera-trapping and CMR methods used in thepresent study provided an effective method for monitoring

0

5

10

15

20

25

30

Number of cameras

Mea

n ab

unda

nce

plusmn 9

5 C

I

9 2718

Fig 2 Feral cat abundance estimates and 95 bootstrapped confidenceintervals for sampling periods before (solid circles) and after (open circles) theremoval of 18 cats calculated from a resampling procedure using 9 18 or27 camera stations of the possible 36 stations Broken lines representabundance estimates derived from all 36 cameras with shaded regionsshowing their 95 profile log-likelihood confidence intervals

Table 2 Model selection criteria for four variants of the general indexmodel applied to observations of feral cats collected at camera-traps at

a pastoral site on Kangaroo IslandIndex decline refers to the decrease in index values between two samplingperiods after the removal of 18 cats from the study site AIC Akaikersquosinformation criterionK number of parametersDi difference inAICbetweenmodeli and the most supported model wi Akaike weight relative likelihood

of the model given the data

Errordistribution

Predictor Indexdecline ()

AIC Log(L) K Di wi

Poisson Period 62 7949 ndash3934 4 0 084Null na 7982 ndash3961 3 33 016

Normal Null na 15339 ndash7630 4 0 062Period 61 15349 ndash7625 5 1 038

Table 3 Model selection criteria for thefivemost supportedmodels of aset of nested models explaining variability in feral cat detection histories

at 36 camera-traps at a pastoral site on Kangaroo IslandAIC Akaikersquos information criterion K number of parameters Di differencein AIC between modeli and the most supported model wi Akaike weight

relative likelihood of the model given the data

Time-varying parameter AIC K Di wi

Colonisation and detection probability 103486 36 000 069Detection probability 103744 32 258 019Occupancy and detection probability 103882 37 396 010Colonisation 107004 7 3518 000Occupancy 107236 8 3750 000

736 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

(Table 1) corresponding with a survival probability(mean se) of 036 011 and an estimated 42 24 newarrivals Four adult cats that were not detected during thefirst sampling period were detected during the second eachon more than one 3-day occasion The abundance estimatesgenerated by the closed population and robust models wereconsistent with the removal of 18 cats from the study site andthe appearance of four new cats creating a net populationdecrease of 14 cats which was within the 95 confidencelimits of the difference between samples (393 1647) Medianhome-range radius calculated from GPS-collar location fixeswas 129 km providing an effectively sampled area of3007 km2 and an estimated density of at least 07 cats kmndash2

before trapping and 04 cats kmndash2 after trapping The effectivelysampled area showed complete coverage of the sampling gridwith no holes

The resampling procedure indicated that a samplingprotocol using 18 cameras arranged with two cameras perfour-cell quadrat (270 camera-days) would have 96 powerto identify a statistically detectable population decrease at the005 a level on the basis of the observed data A plot of thebootstrapped 95 confidence intervals for each number ofcameras confirmed that at least 18 cameras were needed toachieve separation between samples taken before and aftertrapping (Fig 2) A sampling protocol using 27 cameras withthree cameras per quadrat (405 camera-days) would have had100 power to detect the induced population decline whereasnine cameras (135 camera-days) would have had only 65power

Counts of cat numbers per site per day were randomlydistributed and closely matched a Poisson distribution(Fisherrsquos exact test n = 1080 P= 097) Consequently themodified GI models using the Poisson error distributionprovided a much better fit to the data than did the modelsusing the standard normal distribution The normal andPoisson GI models that included a fixed effect for samplingperiod identified strong declines in the expected number ofcats per site per day However model-selection criteriaindicated that the normal GI model incorporating a fixedeffect for sampling period provided no greater explanatorypower than the normal null model (Table 2) The IMIprocedure provided population estimates of 48 cats beforetrapping and 38 after using a value of 18 cats removed fromthe site When we corrected for the four assumed immigrantsIMI estimates were 37 cats before and 23 cats after

Comparison of nested occupancy models showed thatoccupancy decreased slightly after the population wasmanipulated but the two most parsimonious models for thedata allowed only for variation in colonisation or detectionprobability among the six 5-day periods (Table 3) Variation indetection probability among periods was important in all threemodels with non-zero Akaike weights

Discussion

The combined camera-trapping and CMR methods used in thepresent study provided an effective method for monitoring

0

5

10

15

20

25

30

Number of cameras

Mea

n ab

unda

nce

plusmn 9

5 C

I

9 2718

Fig 2 Feral cat abundance estimates and 95 bootstrapped confidenceintervals for sampling periods before (solid circles) and after (open circles) theremoval of 18 cats calculated from a resampling procedure using 9 18 or27 camera stations of the possible 36 stations Broken lines representabundance estimates derived from all 36 cameras with shaded regionsshowing their 95 profile log-likelihood confidence intervals

Table 2 Model selection criteria for four variants of the general indexmodel applied to observations of feral cats collected at camera-traps at

a pastoral site on Kangaroo IslandIndex decline refers to the decrease in index values between two samplingperiods after the removal of 18 cats from the study site AIC Akaikersquosinformation criterionK number of parametersDi difference inAICbetweenmodeli and the most supported model wi Akaike weight relative likelihood

of the model given the data

Errordistribution

Predictor Indexdecline ()

AIC Log(L) K Di wi

Poisson Period 62 7949 ndash3934 4 0 084Null na 7982 ndash3961 3 33 016

Normal Null na 15339 ndash7630 4 0 062Period 61 15349 ndash7625 5 1 038

Table 3 Model selection criteria for thefivemost supportedmodels of aset of nested models explaining variability in feral cat detection histories

at 36 camera-traps at a pastoral site on Kangaroo IslandAIC Akaikersquos information criterion K number of parameters Di differencein AIC between modeli and the most supported model wi Akaike weight

relative likelihood of the model given the data

Time-varying parameter AIC K Di wi

Colonisation and detection probability 103486 36 000 069Detection probability 103744 32 258 019Occupancy and detection probability 103882 37 396 010Colonisation 107004 7 3518 000Occupancy 107236 8 3750 000

736 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

changes in feral cat abundance at our study site The estimateddecrease in population size after we removed cats from the sitewas consistent with the actual number of cats removedaccounting for the movement of new cats onto the samplinggrid This has not previously been demonstrated for feral catsOur estimate of feral cat density before we manipulated thepopulation was similar to a previous estimate of 07 cats kmndash2

obtained using radio-telemetry on a mixed farm and bushlandsite on the island (Paton 1994) although both studies useddifferent ad-hoc methods for density estimation

An important consideration in the design of camera-trapCMR studies is to ensure that every animal in the subjectpopulation has a non-zero probability of encountering acamera station during the sampling period (Karanth andNichols 1998) The density of stations in the present studyrelative to the home-range sizes estimated from GPS-trackingdata should have ensured that any adult cat on the samplinggrid had access to at least one camera station This is supportedby the high capture probabilities for each sampling period thehigh numbers of different camera stations at which individualcats were detected and the absence of gaps in the effectivelysampled area estimated from GPS-tracking data Consequentlythe four cats that were recorded for the first time during thesecond sampling period were most probably new immigrantsfrom outside the sampling grid rather than cats previouslyundetected within the grid Furthermore the mean home-rangeradius we used to determine our camera spacing (067 kmcalculated from Paton 1994) was almost half the mean home-range radius estimated at our site (129 km) indicating that wecould have covered a greater sampling area using the samenumber of cameras spread over a larger sampling grid Thiswas confirmed by the resampling procedure which showedthat a 50 decrease in camera-station density would have hadnegligible effect on the power of the study to detect a populationdecline However home ranges of feral cats can vary widelyamong different regions (Moseby et al 2009) and the spatialscale of camera-trapping surveys should be determined by thebest estimates of local cat home ranges

Sampling designs for CMR studies should also aim tomaximise capture probabilities because samples with lowcapture probabilities require much larger numbers of markedindividuals to produceuseful parameter estimates (Burnham et al1987) Capture probabilities were high in the present studybut could have been improved by reducing the proportion ofcat observations that were unable to be attributed to anidentifiable individual Most of these instances were due tolow information content in the images because parts of catsrsquobodies were obscured by vegetation or because insufficientproportions of catsrsquo bodies were visible to the camera Thefirst of these limitations could be reduced by removing orheavily trimming low vegetation which could obscuredistinctive stripe patterns on the legs The second limitationcould be reduced by using paired cameras at each site(Karanth and Nichols 1998) or increasing the amount of timethat cats spent in front of the camera and thereby the numberof images recorded of cats in different postures

The other form of low information content that could havebeen a problem was the presence of multiple uniformlycoloured cats We recorded one black cat on one occasion

during our surveys but removed two during the trappingoperation Both of the black cats captured in the present studyhad white patches in the groin area and one had white pointson the rear feet These markings would have been difficult todetect using our lure-station arrangement but may have beenmore visible if cats were required to stretch up to access a raisedlure (eg Robley et al 2010)

In addition to low information content low informationquality sometimes prevented the identification of cats Thiswas mainly due to insufficient contrast to enable the definitiveidentification of markings at night under the infrared flash orblurred images of cats in motion These limitations could bereduced by increasing the time that cats spend in front ofcameras and hence the number of images available toscrutinise for distinctive features It may also have beenpreferable to have used the lsquohigh qualityrsquo setting in thecameras for night photography at the expense of flash rangeLow contrast was particularly apparent on ginger-colouredcats Use of a white flash to provide colour images at nightmight also have improved image contrast and informationquality It is not known whether feral cats would avoidcamera-traps with a white flash (eg Wegge et al 2004) butexamination of photo series showed that some cats turnedtheir attention directly towards cameras within two seconds ofthe first photograph being taken and occasionally approachedcameras to investigate them more closely These observationssuggest that some cats may have been able to detect theinfrared flash (eg Newbold and King 2009) or perhaps asound when the camera was activated

The correspondence between estimated changes in absoluteabundance modelled using CMR procedures and relativeabundance modelled using the GI approach suggests that GImodels applied to camera-trap data should be useful formonitoring changes in feral cat populations if the requirementsof CMR models such as consistent identification of individualscannot be met However none of the IMI population estimateswas within the confidence intervals of the CMR estimates sothe IMI method may not be generally useful for convertingindices to abundance estimates Reliable estimates of absoluteabundance are generally likely to be more useful formanagement purposes than estimates of change in relativeabundance because they contain more information Howeverthe question of whether a pest management programeffectively reduces the abundance of pests at a site isessentially relative in nature and may be well served by anabundance index that places a premium on repeatability andprecision rather than the accurate estimation of populationparameters (Engeman 2005) An indexing approach such asthe GI models used in the present study might be most usefulin situations where low information content or quality inimages preclude the attribution of a large proportion ofobservations to individual cats for example in areas whereuniformly coloured cats are common The differences in indexresults between GI models with Poisson and those withnormal error distributions highlight the importance of choosingan error distribution that is appropriate for the data

The use of attractants at monitoring stations can be usefulwhen bait preferences of the target species are known and thestudy aims to maximise detections while minimising study

Estimating feral cat abundance using cameras Wildlife Research 737

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

duration (Kays and Slauson 2008) These conditions willgenerally apply to programs aiming to evaluate the efficacy ofpest-management activities as in the present study whereasprograms aiming to monitor changes in abundance overdifferent seasons or locations should consider using unbaitedmonitoring stations to avoid unknown biases or confoundingeffects Any such monitoring program should also includemethods for estimating detectability because feral cats orother species may exhibit different movement patterns underdifferent conditions (eg due to seasonal breeding behaviour)This might preclude the use of the GI models used in this studyunless consistency of detectability could be established by othermeans (eg Bengsen et al 2011)

Occupancy was not by itself useful for monitoring changesin the feral cat population in the present study because therewere no detectable changes in occupancy despite the removal ofa large number of cats from the site Occupancy could not beexpected to provide a useful surrogate for abundance because catswere not territorial as indicated by overlapping home ranges(A J Bengsen J A Butler and P Masters unpubl data) andmultiple records of different cats at the same lure station on thesame day Occupancy could be useful for monitoring changes inthe pervasiveness of cats at a site over time and this could beused to investigate recolonisation after control programsHowever the relatively low influence of occupancy as adeterminant of site-specific detection histories in the presentstudy indicates that cats were present throughout most of thesite for the duration of the study despite the removal of a largeproportion of the local population

Several authors have offered general suggestions forimproving the efficacy of camera-trap surveys to monitoranimal populations (eg Swann et al 2004 Kays and Slauson2008 Kays et al 2009) To these we add the following specificsuggestions for monitoring feral cats

(1) to maximise detectability monitoring stations should use avariety of lures and should not rely on food-based lureswhich may only appeal to a subset of the population

(2) to maximise the information content of image sets lurestations should be arranged to keep cats in front of thecamera for as long as possible and make as much of theirbody visible to the camera as possible Cameras should beprogrammed to record the highest-quality images and

(3) sampling designs should allow for alternative means ofdrawing inferences from the data in case CMRassumptions such as the ability to identify individuals arenot valid at a particular site Occupancy should notautomatically be regarded as a proxy for abundance butmay be informative in its own right

Conclusions

We conclude that camera-trapping can be used to identifyindividual feral cats and that when combined with anappropriate sampling regime camera-trapping data can beused to estimate population abundances using a CMRapproach Application and adaptation of these methods couldallow for more strategic and effective management of feral catpopulations than currently occurs However these methods

require replication in different contexts before their generalapplicability can be fully assessed

Acknowledgements

The project was funded by the State Government of South Australia and theInvasive Animals Cooperative Research Centre We thank P AtkinsonR Gale S Clark J Desbiolles D Ball and T Reeves for access to theirproperties The Kangaroo Island Veterinary Clinic sedated and performedhealth checks on GPS-collared cats Comments from P Fleming P MeekJ Read and an anonymous reviewer improved earlier drafts The project waspermitted by the South Australian Department for Environment and HeritageWildlife Ethics Committee (Project 522009)

References

Baillargeon S and Rivest L P (2007) Rcapture loglinear models forcapturendashrecapture in R Journal of Statistical Software 19 1ndash31

Bates D andMaechlerM (2009) lme4 Linearmixed-effects models usingS4 classes R package version 0999375-18 Available at httpCRANR-projectorgpackage=lme4 [verified November 2011]

Bengsen A J Leung L K P Lapidge S J and Gordon I J (2011)Usingageneral indexapproach to analyzecamera-trapabundance indicesThe Journal of Wildlife Management 75(5) 1222ndash1227 doi101002jwmg132

Burnham K P Anderson D R White G C Brownie C and PollockK H (1987) Design and analysis methods for fish survival experimentsbased on releasendashrecapture Monograph 5 American Fisheries SocietyBethesda MD

Caughley G (1977) lsquoAnalysis of Vertebrate Populationsrsquo (WileyNew York)

Conn P Bailey L and Sauer J (2004) Indexes as surrogates to abundancefor low-abundance species In lsquoSampling Rare or Elusive SpeciesConcepts Designs and Techniques for Estimating PopulationParametersrsquo (Ed W L Thompson) pp 59ndash74 (Island PressWashington DC)

Edwards G P De Preu N D Shakeshaft B J and Crealy I V (2000) Anevaluation of two methods of assessing feral cat and dingo abundance incentral AustraliaWildlife Research 27 143ndash149 doi101071WR98067

EngemanRM (2005) Indexingprinciples andawidelyapplicableparadigmfor indexing animal populations Wildlife Research 32 203ndash210doi101071WR03120

Forsyth D M Robley A J and Reddiex B (2005) lsquoReview of MethodsUsed toEstimate theAbundance of FeralCatsrsquo (ArthurRylah Institute forEnvironmental Research Melbourne)

Genovesi P BesaM and Toso S (1995) Ecology of a feral catFelis catuspopulation in an agricultural area of northern Italy Wildlife Biology 1233ndash237

KaranthKU andNichols J D (1998) Estimation of tiger densities in Indiausing photographic captures and recaptures Ecology 79 2852ndash2862doi1018900012-9658(1998)079[2852EOTDII]20CO2

Kays R W and Slauson K M (2008) Remote cameras In lsquoNoninvasiveSurveyMethods for Carnivoresrsquo (EdsR A Long PMacKay J Ray andW Zielinski) pp 110ndash140 (Island Press Washington DC)

Kays R Kranstauber B Jansen P Carbone C Rowcliffe M FountainT and Tilak S (2009) Camera traps as sensor networks for monitoringanimal communities In lsquoThe 34th IEEE Conference on Local ComputerNetworksrsquo pp 811ndash818 (IEEE Computer Society Zurich)

Keitt B S and Tershy B R (2003) Cat eradication significantly reducesshearwater mortality Animal Conservation 6 307ndash308 doi101017S1367943003003378

MacKenzie D Nichols J Royle J Pollock K Bailey L and HinesJ (2006) lsquoOccupancy Estimation and Modeling Inferring Patterns andDynamics of Species Occurrencersquo (Academic Press Burlington MA)

738 Wildlife Research A Bengsen et al

Mahon P S Banks P B and Dickman C R (1998) Population indicesfor wild carnivores a critical study in sand-dune habitat south-westernQueensland Wildlife Research 25 11ndash22 doi101071WR97007

McLeod R (2004) lsquoCounting the Cost Impact of Invasive Animals inAustralia 2004rsquo (Cooperative Research Centre for Pest AnimalControl Canberra)

Moseby K Stott J and Crisp H (2009) Movement patterns of feralpredators in an arid environment ndash implications for control throughpoison baiting Wildlife Research 36 422ndash435 doi101071WR08098

Newbold H and King C (2009) Can a predator see lsquoinvisiblersquo lightInfrared vision in ferrets (Mustelo furo)Wildlife Research 36 309ndash318doi101071WR08083

Nogales M Martiacuten A Tershy B Donlan C Veitch D Puerta NWood B and Alonso J (2004) A review of feral cat eradication onislands Conservation Biology 18 310ndash319 doi101111j1523-1739200400442x

Otis D Burnham K White G and Anderson D (1978) Statisticalinference from capture data on closed animal populations WildlifeMonographs 62 3ndash135

Paton D (1994) Ecology of cats in South Australia and testing possiblemethods of control annual progress report University of Adelaide

Pollock K (1982) A capture-recapture design robust to unequal probabilityof capture The Journal of Wildlife Management 46 752ndash757doi1023073808568

R Development Core Team (2011) R A Language and Environment forStatistical Computing Available at httpwwwR-projectorg (RFoundation for Statistical Computing Vienna)

Read J and Eldridge S (2010) An optimised rapid detection technique forsimultaneously monitoring activity of rabbits cats foxes and dingoes inthe rangelands The Rangeland Journal 32 389ndash394 doi101071RJ09018

RisbeyDACalverMC Short JBradley J S andWright IW (2000)The impact of cats and foxes on the small vertebrate fauna of HeirissonProng Western Australia II A field experiment Wildlife Research 27223ndash235 doi101071WR98092

Rivest L and Baillargeon S (2007) Applications and extensions ofChaorsquos moment estimator for the size of a closed populationBiometrics 63 999ndash1006 doi101111j1541-0420200700779x

Robley A Gormley A Woodford L Lindeman M Whitehead BAlbert R Bowd M and Smith A (2010) lsquoEvaluation of CameraTrap Sampling Designs Used to Determine Change in Occupancy Rateand Abundance of Feral Catsrsquo (Arthur Rylah Institute for EnvironmentalResearch Melbourne)

Swann D E Hass C C Dalton D C and Wolf S A (2004) Infrared-triggered cameras for detecting wildlife an evaluation and reviewWildlife Society Bulletin 32 357ndash365 doi1021930091-7648(2004)32[357ICFDWA]20CO2

Turner D C andMeister O (1988) Hunting behaviour of the domestic catIn lsquoTheDomesticCat theBiologyof itsBehaviourrsquo (EdsDCTurner andPBateson) pp 111ndash121 (CambridgeUniversityPressCambridgeUK)

Van Aarde R (1979) Distribution and density of the feral house cat Feliscatus on Marion Island South African Journal of Antarctic Research 914ndash19

Wegge P Pokheral C and Jnawali S (2004) Effects of trapping effort andtrap shyness on estimates of tiger abundance from camera trap studiesAnimal Conservation 7 251ndash256 doi101017S1367943004001441

Wilson G J and Delahay R J (2001) A review of methods to estimate theabundance of terrestrial carnivores using field signs and observationWildlife Research 28 151ndash164 doi101071WR00033

Estimating feral cat abundance using cameras Wildlife Research 739

wwwpublishcsiroaujournalswr