Genetic tagging reveals a significant impact of poison baiting on an invasive species

Management and Conservation

Genetic Tagging Reveals a Significant Impactof Poison Baiting on an Invasive Species

OLIVER BERRY,1,2 School of Animal Biology (M092) and Invasive Animals Co-operative Research Centre, The University of Western Australia,Crawley, WA 6009, Australia

DAVE ALGAR, Department of Environment and Conservation, Western Australian Wildlife Research Centre, P.O. Box 51, Wanneroo,WA 6946, Australia

JOHN ANGUS, Department of Environment and Conservation, Western Australian Wildlife Research Centre, P.O. Box 51, Wanneroo,WA 6946, Australia

NEIL HAMILTON, Department of Environment and Conservation, Western Australian Wildlife Research Centre, P.O. Box 51, Wanneroo,WA 6946, Australia

STEFFI HILMER, Department of Environment and Conservation, Western Australian Wildlife Research Centre, P.O. Box 51, Wanneroo,WA 6946, Australia

DUNCAN SUTHERLAND,3 Department of Environment and Conservation and Invasive Animals Co-operative Research Centre,Dwellingup Research Centre, Banksiadale Rd, Dwellingup, WA 6213, Australia

ABSTRACT Globally, invasive predators are major pests of agriculture and biodiversity and are the focus ofcomprehensive control programs. Because these species are typically elusive, wary of traps, and occur at lowdensities, their fundamental population dynamics are difficult to determine and quantitative evaluations ofcontrol programs are rarely conducted. Noninvasive DNA analysis has the potential to resolve this long-standing limitation to pest management. We carried out a landscape-scale experiment to quantify reductionin the abundance of a red fox (Vulpes vulpes) population when baited with sodium fluoroacetate (1080) poison(the most widely used method of fox control in Australia). We collected fox hairs with hair snares during 44-day sessions over the course of 6 months at a site in semi-arid Western Australia. The first session tookplace in late summer just prior to when juvenile foxes typically disperse, and the final session followed aerialbaiting with 1080 poison. We obtained consensus microsatellite genotypes from 196 samples, and used themto conduct both spatially explicit and open model capture–recapture analysis. Twenty-eight percent of trapnights yielded hair samples suitable for identification of individual foxes, which is more than an order ofmagnitude greater than trapping rates reported with conventional techniques. Fox density changed littleduring 3 pre-baiting sessions and averaged 0.73 foxes/km2 (�0.33 SE), which is less than most previoustrap-based estimates for Australian foxes. Density dropped significantly in response to baiting to 0.004 foxes/km2. Prior to baiting, the apparent survival of foxes remained static (0.72 � 0.14 SE), but in response tobaiting it dropped precipitously and was effectively zero. This experiment provides the first quantitativeassessment of the effectiveness of 1080 poison baiting for reducing fox density, and in this case demonstratesit to be a highly effective method for culling foxes from a region. Further, it demonstrates that noninvasiveDNA analysis will provide significantly more data than conventional trapping methods. This method is likelyto provide greater precision and accuracy than conventional methods and therefore result in more robustevaluations of management strategies for the fox in Australia, and for cryptic species elsewhere.� 2011 TheWildlife Society.

KEY WORDS Australia, density, invasive species, microsatellite DNA, pest management, red fox, survival,Vulpes vulpes.

Invasive predators, including canids, felids, mustelids,snakes, and amphibians are major threats to biodiversityand agriculture worldwide, and their control is often expen-

sive and time-consuming (Braysher 1993). Despite the enor-mous resources devoted to pest control, the elusive nature ofmany species makes it difficult to accurately determine theeffectiveness of control programs (Saunders et al. 1995),which limits the capacity to evaluate the cost-effectivenessof alternative management strategies. An alternative to di-rectly measuring the density of elusive species is to employactivity indices, including counts of scats, footprints, andother signs (Caughley 1977, Sadlier et al. 2004). Thesemethods are inexpensive to implement and are extensivelyused in wildlife management (Caughley and Sinclair 1994).The utility of activity indices, however, is limited because

Received: 23 November 2010; Accepted: 26 August 2011;Published: 5 December 2011

Additional Supporting Information may be found in the online versionof this article.1E-mail: [email protected] address: CSIRO Marine and Atmospheric Research, PMB 5Floreat, WA 6014, Australia.3Present address: Phillip Island Nature Parks, PO Box 97, Cowes, VIC3922, Australia.

The Journal of Wildlife Management 76(4):729–739; 2012; DOI: 10.1002/jwmg.295

Berry et al. � Impact of Poison Baiting on a Canid Population 729

individuals are generally indistinguishable, meaning changesin activity are readily confused with changes in density(Anderson 2001). Furthermore, analyses such as epidemio-logical and population dynamic modeling require quantita-tive estimates of density as well as key ecological parametersincluding survival and social contact rates (White et al. 1995,Pech et al. 1997, Hone 1999b), and these cannot be extractedfrom activity index data.Noninvasive analysis of DNA (genetic tagging) is increas-

ingly employed to estimate basic population parameters suchas population size and density in species that are otherwisedifficult to monitor (Piggott and Taylor 2004). A majoradvance brought by this technology is the ability to distin-guish individuals. The majority of applications havefocused on native wildlife with high conservation value(e.g., Lucchini et al. 2002). However, there exist opportu-nities to extend the method to the investigation of thepopulation dynamics of invasive species (Efford et al.2009). Furthermore, most noninvasive studies have solelyestimated population size of the species with relativelystable population sizes as they have either not been subjectedto control measures or to changes in resource availability(but see Marucco et al. 2009). Yet, the capacity to detectchanges in population density and other vital rates enablesalternative management strategies to be evaluated.The red fox (Vulpes vulpes) is a medium-sized (3–7 kg)

carnivore native to Europe, North America, Asia, and Africa.Since its introduction to Australia in the 1870s, the fox hasbecome a significant burden to agriculture and a threat tobiodiversity (Rolls 1969, Saunders et al. 1995), costingan estimated US$ 238 million annually in lost productivity,lost environmental amenity, and control programs (McLeod2004). The fox is also recognized as a potentially critical anddifficult to manage vector for the rabies virus if it were toenter Australia (Saunders et al. 1995), whose capacity to carryan epidemic depends on its population densities and dispersalbehavior (Anderson 1986, White et al. 1995). The mostwidely used method for fox control in Australia is baitsinjected with the poison sodium fluoroacetate (commonlyreferred to as 1080). Baiting is conducted by hand or aerialdrops, which are both costly (McLeod 2004). Yet, quantita-tively evaluating the effectiveness of baiting on fox popula-tions is very difficult by conventional means because foxesare extremely wary of traps, and where estimates are madethey are difficult to achieve and usually inaccurate (Saunderset al. 1995).We demonstrate a genetic-tagging approach to quantita-

tively evaluate the effectiveness of a management programfor reducing the density of a fox population inhabitinga semi-arid environment. We monitored the density offoxes with 4 sampling sessions, the last of which followedan aerial delivery of 1080 poison baits. We estimated thedensity of foxes throughout the study, and quantifiedthe reduction in density and apparent survival induced bybaiting. We also investigated how the cost-effectiveness ofnoninvasive methodologies may be increased by identifyingand discarding low quality samples prior to genotypinganalysis.

STUDY AREA

This experiment formed part of a larger research programthat focused onmeasuring changes in the density and survivalof invasive red foxes, feral cats (Felis catus), and feral dogs(Canis familiaris) in response to 1080 baiting at a site in themid-west of Western Australia. We carried out the study atKarara and Lochada Pastoral Stations, Western Australia(2885800500S, 11684005100E; Fig. 1), which were adjacent de-stocked pastoral leases (approx. 1,500 km2). The landscapewas semi-arid and received on average approximately320 mm of rainfall annually (Morawa townsite; Bureau ofMeteorology 2011). Karara-Lochada lies on the interface ofthe South West Botanical Province and the EremaeanBotanical Province. The area was characterized by mixedAcacia shrublands and sparse York Gum woodlands(Eucalyptus loxophleba) on sandplains. The stations lie withinthe Yilgarn geological region and across the boundaries ofthe Murchison Plateau and the Salinaland Plateau, withfrequent granite rises and low domes. Little predator controlhad been conducted in the region prior to this study andmostly by neighboring properties in an ad hoc manner tocontrol feral dogs. Foxes in this region exhibited the typicalAustral life cycle with most births occurring in spring (Sep–Oct), juvenile dispersal occurring in autumn (Apr–May), andmating occurring in winter (Jun–Jul; Saunders et al. 1995).

METHODS

Hair CollectionWe collected hair by means of a sticky wicket hair snare,which consisted of 3 dowel rods (height approx. 50 cm),covered with packing tape then a layer of double sided tape(48 mm; Stylus 740; Stylus Tapes International, Sydney,Australia), and pressed into the ground so that the distancebetween rods was approximately 7 cm at their bottomand approximately 12 cm at their top. We positioned thesticky wickets at the opening of a corral made from locallycollected woody debris (see Fig. S1, available online atwww.onlinelibrary.wiley.com). The corral formed a narrowcorridor (approx. 30 cm wide, 100 cm long), with 3 lures: aFeline Attracting Phonic (FAP; Algar et al. 2002), Pongo(blended mixture of cat feces and urine), and a nontoxicEradicat1 bait (Algar and Burrows 2004) placed at the culde sac end. This design collects hairs from animals as theyinvestigate the lures and are forced to pass between the stickywickets to reach them. We removed collected hair dailyfrom the tape with sterilized forceps. Samples were placedin a sealed paper envelope, labeled, frozen at �208 C, thentransported to the DNA laboratory within 2 weeks ofcollection.We set 88 hair snares in 8 transects of 11 monitoring plots

with hair snares placed at 1-km intervals and transects aminimum of 5 km apart (Fig. 1). We employed a mixture oflinear and irregularly shaped transects that encompassed 908bends. By necessity, transects ran along dirt tracks andsnares were placed adjacent and running parallel to the trackswithin the shrubland vegetation. Although the tracks werepredominantly cleared of vegetation, in general the dominant

730 The Journal of Wildlife Management � 76(4)

shrubland vegetation at Karara-Lochada was open enoughthat foxes could travel unimpeded through it. We thereforeassumed that the placement of hair snares in the vicinity oftracks minimally biased our analysis. Furthermore, our anal-ysis should be representative of the study area because of theeven density of tracks throughout Karara-Lochada (Fig. 1).We smoothed the sandy substrate within the corral to permitdetection of animals entering. We also extended a 1-m widestrip of smoothed sand from the corral across the adjacenttrack. The experiment consisted of 4 sampling sessions,45 days apart (Feb, Apr, Jun, and Aug 2008). The first3 sessions were prior to baiting, and the final session was22 days after an aerial baiting program. Each hair collectionsession consisted of 4 consecutive monitoring days (occa-sions). On each day, the monitoring plots and snares werechecked for the presence of animal footprints and hair. Thesandy substrate allowed us to detect all visits to the moni-toring plots, and in most cases also identify the speciesmaking the visit based on footprints (Triggs 1996). Sandpads in front of snares were smoothed each day.

Baits and Baiting Program

Poison baiting was carried out with Eradicat1 baits (Algarand Burrows 2004), which are formulated for feral cats but

are readily taken by foxes and feral dogs. The baits are similarin appearance to a small sausage, weighing approximately20 g wet-weight and are composed of kangaroo meat mince,chicken fat, and flavor enhancers (Australian Patent No.AU 781829). Toxic baits were dosed at 4.5 mg of sodiumfluoroacetate per bait. We delivered baits from an aircraft at adensity of 50 baits/km2 over the entire Karara-Lochadaproperty and extending 5 km outside of the propertyboundary.

Laboratory Analysis

We transported hairs to a laboratory dedicated to trace DNAsamples and immediately began DNA extraction. No con-centrated DNA samples or polymerase chain reaction (PCR)products entered this laboratory, and surfaces were routinelysubject to ultraviolet sterilization. We completed all pipet-ting with aerosol-resistant tips and carried out PCR setup ina sterile isolation chamber.We combined 1–10 hairs for extraction from each sample

and recorded the number sampled. We cut 10–20 mm of thehairs, including the follicle, into 1.5-ml tubes and extractedDNA from them with a Qiagen DNeasy tissue kit (Qiagen,Valencia, CA) incorporating a final elution of 200-mlAE buffer (Qiagen). Prior to genotyping, we conducted a

Figure 1. Karara-Lochada station in mid-west Western Australia where the investigation of the effects of poison baiting on red fox abundance took place inFebruary–August 2008. Grey lines indicate unsealed roads. Additional unsealed roads exist that are not marked.


quantitative real-time PCR incorporating melt–curve analy-sis of a species-diagnostic mitochondrial DNA (mtDNA)fragment (Berry and Sarre 2007). This step allowed us toidentify non-fox samples to ensure that only fox samplesreceived further genotyping. This analysis was confined tosessions 2–4 because species identifications were not con-ducted for session 1. In addition, we measured the mito-chondrial DNA content in each DNA extract according tostandard qPCR methodologies (Marijke et al. 2009), andrecorded the relative quantities of mtDNA in terms of Ct

value (cycle threshold, the PCR cycle when fluorescent signalincreases beyond a threshold above the background fluores-cence signal). We employed the automated threshold func-tion available in the Rotor-Gene 6.1 analysis software(Corbett Life Science, New South Wales, Australia). Weconducted PCR for microsatellite DNA genotyping withPlatinum Taq (cat. 10966-034; Invitrogen, Carlsbad, CA)in 3 PCR multiplex reactions, each consisting of 3 markers(8 microsatellites and 1 sex-marker; Table 1). Our PCRconditions were as follows: 1� PCR Buffer (Invitrogen),2.5 mM MgCl2, 0.2 mM each dNTP, 0.4 mg/ml BSA,0.5 U Taq, 0.2 mM each primer. We diluted fluorescentlylabeled forward primers equally with unlabeled forward pri-mers to ensure that peak heights were not saturated duringfragment analysis. We replicated each PCR 3 times foreach sample. Our PCR cycling conditions consisted of948 C 2 min, (948 C 30 s, Tannealing 20 s, 728 C30 s) � 35, 728 C 2 min.

Data AnalysisWe scored consensus genotypes according to the criteria ofFrantz et al. (2003). These consensus genotypes were subjectto a further round of checking for 1-allele and 2-allelemismatches (Paetkau 2004). This analysis was facilitatedby the GENECAP macro (Wilberg and Dreher 2004).We re-checked the raw data for samples with 1-allele and2-allele mismatches. Where the mismatches could not beresolved the samples were subject to an additional 3 replicatePCRs. No 1- or 2-allele mismatches remained after thisround of checking. To avoid multiple individuals beingsimultaneously sampled on a single hair-snare we checkedfor the presence of more than 2 alleles at any loci.

Identification of Samples Likely to Provide GenotypesWe used logistic regression to determine whether mtDNAquantification (Ct score) and/or number of hairs as explana-

tory variables could predict whether a consensus genotypewith 5 or more loci would be obtained from a sample. Weselected 5 loci as the criterion for a sufficiently distinctivegenotype because it provides probability of identity valuesPID(sib) (Waits et al. 2001) of approximately 0.01 (seeResults; Table 1). The probability of identity representsthe probability that 2 different individuals will bear identicalgenotypes. PID(sib) and PID(HW) represent the probabilitythat full siblings and unrelated individuals will bear identicalgenotypes, respectively (Waits et al. 2001). Considering thatfull siblings are likely to represent a small, but unknown,proportion of the population, PID(sib) is a conservative cri-terion (Waits et al. 2001). Logistic regression was imple-mented in SPSS 16.0 (SPSS Inc., Chicago, IL), and weidentified the model best supported by the data with Akaike’sinformation criterion corrected for small sample size (AICc;Burnham and Anderson 2002).

Density Estimation

We estimated fox density during each session with thespatially explicit capture–recapture methods (SECR) imple-mented in the SECR 1.4.0 package (Efford 2010) availablefor the R programming environment (R Version 2.11.1; RDevelopment Core Team 2010). This method uses thelocation that each animal is captured to fit a model of thedetection process and to provide an estimate of density that isunbiased by incomplete detection and edge effects (Efford2004). We used the full maximum likelihood estimator(Borchers and Efford 2008) of density (D), and a half-normaldetection function incorporating g0 (susceptibility to captureat the center of a home range), and s (a measure of the spatialextent of susceptibility to capture). We identified our hairsnares as proximity detectors (allowing multiple within-oc-casion captures per individual), and ran the analysis with abuffer of 4,000 m, which is equal to the largest movementdetected within a session. Larger buffers (up to 10,000 m),and alternative detection functions had minimal effect onparameter estimates. When estimating density, we comparedmodels that incorporated sex, a behavioral response to cap-ture (probability of recapture 6¼ probability of capture), andheterogeneity in g0 and s, as well as combinations of theseparameters. We also evaluated models that incorporatedchanges in density between sessions. We evaluated the levelof support for alternative models with AICc (Burnham andAnderson 2002), which is a measure of the relative goodness

Table 1. Microsatellite DNAmarkers and polymerase chain reaction (PCR) amplification conditions used in this study. References are 1 (Wandeler and Funk2006), 2 (Ostrander et al. 1993), 3 (Berry et al. 2007), and 4 (Guyon et al. 2003). PID(HW) and PID(SIB) refer to probability of identities among unrelated and fullsiblings respectively (Waits et al. 2001).

Locus PCR multiplex Annealing temperature (8 C) Allelic size range Number of alleles PID(HW) PID(sib) Reference

Wan374 1 58 100–112 6 0.1596 0.454 1C01.251 1 58 128–141 4 0.1340 0.426 2SRY 1 58 78 1 3Wan142 2 56 132–148 7 0.0657 0.363 1Wan402 2 56 80–93 5 0.1305 0.423 1Ren195 2 56 130–149 5 0.1265 0.421 4C213 3 58 109–111 3 0.1976 0.476 2Wan 502 3 58 76–78 3 0.2443 0.519 1Ren135 3 58 157–163 3 0.3500 0.579 4


of fit of a statistical model. Corrections for overdispersionhave not yet been developed for SECR (M. Efford,University of Otago, personal communication), hence wedid not employ QAICc. We used model averaging to calcu-late parameter estimates of competing models determined byAICc differences relative to the model with the smallestAICc value (DAICc; Burnham and Anderson 2002).

Estimates of Population Size and Apparent SurvivorshipWe assumed population closure during each 4-day sessionand applied open model capture-recapture analysis withPollock’s Robust design (Kendall et al. 1997) to estimatethe size of the trappable fox population within sessions, andapparent survival (s) between sessions. We also modeled theimpact of poison baiting on fox apparent survival by con-straining survivorship as constant prior to baiting (i.e., equalapparent survival between sessions 1 and 2, and 2 and 3). Weevaluated models incorporating a behavioral response tocapture (probability of recapture 6¼ probability of capture),variation in capture probability between sexes, heterogeneityin capture probability (2-class mixture), and combinations ofthese models. We conducted this analysis with the programMARK (White and Burnham 1999). We ranked modelsaccording to AICc corrected for low-level overdispersion(QAICc), based on an overdispersion inflation factor (c) of1.25. We model averaged parameter estimates.

RESULTS

Hair Collection and Genotypic Summary StatisticsWe collected 295 hair samples, of which 294 samples wereobtained prior to baiting within 1,056 trap nights (8 trans-ects, 11 sticky wickets/transect, 4 occasions, 3 sessions). Thisrepresents a snare success rate of 27.8%. Samples containedon average 4.65 hairs (�0.16 SE). Melt-curve analysis ofsessions 2–4 excluded 13 samples from nontarget species(4 cats, 1 dog, 8 unknown species). Of the samples identifiedas fox, 282 samples were genotyped. Ninety-three did notamplify enough loci to reliably distinguish individuals; 0(n ¼ 62), 1 (n ¼ 7), 2 (n ¼ 6), 3 (n ¼ 9), 4 (n ¼ 9), and194 samples did amplify enough loci to distinguish individ-uals; 5 (n ¼ 0), 6 (n ¼ 8), 7 (n ¼ 58), 8 (n ¼ 128). Noinstances of more than 3 alleles at a locus were recorded. Theprobability of identity (PID(HW)) for an 8-locus genotypewas 3.92 � 10�7, and for PID(sib) (Waits et al. 2001) was0.0018 (Table 1). We considered a 5-locus or greater geno-type to be sufficient to distinguish individuals. The PID(HW)

and PID(sib) for the 5 most informative markers were2.09 � 10�5, and 0.012, respectively, and 3.6 � 10�4 and0.027, respectively for the 5 least informative markers.However, among samples identified as recaptures, themean PID(sib) between any pair of samples was 0.0034(�0.0002 SE), and only 2 out of 138 pairwise comparisonsof apparent recaptures exceeded PID(sib) of 0.01(PID(sib) ¼ 0.012 and 0.015), which were recorded on iden-tical or within 3 hair snares of previous records of thatgenotype, respectively, and in both cases were of the samesex as apparent recaptures. Thus, at worst we are >98.5%assured that these 2 apparent recaptures do not represent full

sibs rather than recaptures. These PID figures are almostidentical to those calculated from 106 tissue samples takenfrom a similar environment at Yalgoo, approximately100 km distant (O. Berry, The University of WesternAustralia, unpublished data).

Sample Quality Control by DNA QuantificationThe initial quantitative PCR step of a mtDNA fragment wasa strong predictor of the probability of achieving a successful5 or more locus genotype according to the followingrelationship: p(x) ¼ (e(33.079�1.392x))/(1 þ e(33.079�1.392x),(logistic regression of quantitative Ct value and achievementof 5 locus genotype r2 ¼ 0.840, �2 log likelihoodCt ¼ 47.149, Df ¼ 2, DAICc 0.00; see Fig. S2, availableonline at www.onlinelibrary.wiley.com). This model enabledus to retrospectively correctly identify 92.6% of hair samplesthat provided consensus genotypes and to identify 88.4% thatdid not. The number of hairs sampled predicted genotypingsuccess comparatively poorly (logistic regression of numberof hairs in sample against achievement of a 5 locus genotyper2 ¼ 0.435, �2 log likelihood hairs ¼ 124.990, Df ¼ 2,DAICc 77.84), and the combination of Ct score and numberof hairs was a worse predictor of success than Ct scorealone (r2 ¼ 0.849, �2 log likelihood Ct þ hairs ¼ 55.167,Df ¼ 3, DAICc 9.01).

Fox Density and the Effect of BaitingFour of the 34 models tested received substantial supportin the spatially explicit capture–recapture analysis (DAICc

< 2.0; Burnham and Anderson 2002), and an additional 4models received moderately strong support (DAICc < 4.0;Table 2). All of these well-supported models incorporated abehavioral response to capture and heterogeneity in responseto capture for g0 (see Fig. S3A, available online at www.on-linelibrary.wiley.com). These models also all incorporatedheterogeneity in the spatial extent of susceptibility to capture(s), and 4 of the 8 models included a sex effect for s (seeFig. S3B, available online at www. onlinelibrary.wiley.com).There was little consensus among the top models in whether

Table 2. Models fitted for spatially explicit capture-recapture analysis of redfox density at Karara-Lochada Station,Western Australia during 3 samplingsessions in 2008. Models are defined in terms of whether density (D) wasconstant (.) or varied between sessions (session), and the effects of behavior(b) and individual heterogeneity (h), on g0 (probability of capture at centerof home range) and s (spatial extent of susceptibility of capture). Model-specific configurations of b and h were assumed to be constant betweensessions. Akaike’s information criterion corrected for small sample size(AICc) and AICc differences relative to the model with the smallest AICc

value (DAICc) follow Burnham and Anderson (2002). Parameter estimatesfor models are provided in Table S1 available online at www.onlinelibrary.wiley.com. Only models with DAICc < 10 are shown.

Model Parameters AICc DAICc

D(.), g0(b þ h), s(h þ sex) 8 1499.12 0D(session), g0(b þ h), s(h þ sex) 10 1499.81 0.69D(sex), g0(b þ h), s(h) 8 1500.50 1.37D(.), g0(b þ h), s(h) 7 1500.88 1.77D(session þ sex), g0(b þ h), s(h) 10 1501.17 2.06D(session), g0(b þ h), s(h) 9 1501.45 2.33D(sex), g0(b þ h), s(sex þ h) 9 1501.48 2.36D(session þ sex), g0(b þ h), s(h þ sex) 11 1502.31 3.20


density was constant or varied among sessions 1–3 (i.e., priorto baiting), or whether density differed between males andfemales during these sessions. The estimated mean density offoxes calculated from model averaged estimates for sessions1–3 was 0.73 foxes/km2 (�0.33 SE; Fig. 2). Parameter esti-mates among these models were similar (see Table S1,available online at www. onlinelibrary.wiley.com). Post-baiting density was significantly less than pre-baiting density(Fig. 2), but could not be estimated directly with SECRmodeling because hair from only a single animal was cap-tured. However, applying the average effective samplingarea (ESA) estimated via maximum likelihood from thebest model for the previous 3 sessions and the estimate ofpopulation size from the open population analysis providedan estimate of 1.20/293.3 km2, or 0.004 foxes/km2.

Probability of Capture, Population Size,and Apparent Survival

Two models were strongly supported in the open populationanalysis (DQAICc < 2.0; Table 3). The best supportedmodel incorporated effects of baiting on apparent survival,heterogeneity in probability of capture, and a behavioralresponse to capture. The other well-supported model wasidentical except apparent survival varied between all sessionsrather than just pre- and post-baiting. All models withDQAICc < 10.0 included an effect of baiting or time on

survival, and a model identical to the best supported modelexcept with constant apparent survival was very poorly sup-ported DQAICc ¼ 19.65. After baiting, apparent survivalfell to effectively zero from an initial value between sessionsof approximately 0.69 (Fig. 3). The similar support observedfor the models ranked 1 and 2 indicated that apparentsurvival was effectively constant prior to baiting. Therewas little evidence that male and female foxes differed intheir apparent survival since all models with this effect hadDQAICc values >9.0.The trappable population was considerably larger during

the 3 pre-baiting sessions than post baiting (Fig. 3), butshowed some evidence for a decline during the pre-baitingsessions, and for a larger trappable population of males thanfemales. A behavioral response to hair capture (probability ofcapture 6¼ probability of recapture) was included in the 3best supported models and heterogeneity in probability ofcapture was a parameter in the 2 best supported models(Table 3). The estimates of probability of capture (P) andrecapture (c) for the snare-happy (c > P) and snare-shy(c < P) groups were P ¼ 0.16 (�0.06 SE), c ¼ 0.42(�0.06 SE) and P ¼ 0.13 (�0.05 SE), c ¼ 0 (�0.00 SE),respectively. Models that incorporated a sex effect in hetero-geneity of capture probability were poorly supported(DQAICc > 10). Temporary emigration (G) was not in-cluded in well-supported models.

DISCUSSION

The red fox in Australia exemplifies the difficulty of manag-ing invasive carnivores. Despite causing significant economicand ecological damage worth hundreds of millions of dollarsannually, key biological attributes that are relevant to itscontrol, such as density and survival, are difficult to obtain(Saunders et al. 1995). We have demonstrated that thisfundamental data can be readily derived with noninvasiveDNA analysis for this widespread species, which permitsquantitative assessment of the effectiveness of landscape-scale control. In this case we revealed a near 100% mortalityin a target fox population and a coincident reduction indensity to effectively zero.

Table 3. Open population models used to estimate apparent survival (S), population size (N), heterogeneity mixture (pi), probability of capture (P), andprobability of recapture (c) in red foxes during 4 sessions at Karara-Lochada Station,Western Australia, 2008. A session effect is indicated by (session), whereas(.) indicates no session effect, (sex) indicates a difference between males and females, (h) indicates 2 mixture individual heterogeneity in probability of capture orrecapture, and (bait) indicates a time effect only between the period before aerial baiting compared to after baiting. G represents temporary immigration. Onlymodels with relative differences in quasi-Akaike’s information criterion corrected for small sample size (DQAICc) < 10 are shown. Parameter estimates fromthe top 4 models are provided in Table S3 available online at www.onlinelibrary.wiley.com.

Model Parameters QAICc DQAICc

S(bait), G(.), pi(.), P(h), c(h), N(sex � session) 16 326.68 0S(session), G(.), pi(.), P(h), c(h), N(sex � session) 17 327.84 1.18S(bait), G(.), P(.), c(.), N(sex � session) 13 332.15 5.48S(bait), G(.), pi(.), P(h) ¼ c(h), N(sex � session) 14 332.31 5.64S(session), G(.), P(.), c(.), N(sex � session) 14 333.23 6.56S(session), G(.), pi(.), P(h) ¼ c(h), N(sex � session) 15 333.42 6.75S(bait), G(.), P(.) ¼ c(.), N(sex � session) 12 334.34 7.68S(session), G(.), P(.) ¼ c(.), N(sex � session) 13 335.10 8.43S (sex � session), G(.), pi(.), P(h), c(h), N(sex � session) 20 335.70 9.02

Figure 2. Model-averaged estimates of red fox density at Karara-LochadaStation, Australia, in 2008 (foxes/km2 � SE). Aerial baiting with 1080poison occurred between sessions 3 and 4.


Fox DensityWe employed direct estimators of fox density in this studybecause, as is commonly the case, the sampling area could notbe easily defined from geographic features (Efford 2004,Efford et al. 2009). Based on density estimates from similarenvironments (e.g., Saunders et al. 1995), we expected foxdensity to be low in this semi-arid region, and our estimatesof density, which averaged 0.73 foxes/km2 prior to baiting,are consistent with this expectation. This estimate is less thanmost previous reports from Australia (e.g., 1.2–3.9 foxes/km2; Coman et al. 1991, 1.8–3.6 foxes/km2; Bubela et al.1998), low by worldwide standards (Sillero-Zubiri et al.2004), but comparable to the only other density estimatemade in a semi-arid environment in Australia (approx.0.5 foxes/km2; Marlow et al. 2000). From this initial lowdensity, we expected fox density to decline during the 3 pre-baiting sessions which extended across late summer to midwinter (Feb–Jun), consistent with observations of fox pop-ulations elsewhere where young of the year have high levelsof mortality as resources become scarce and territories areestablished (Pech et al. 1997, Marlow et al. 2000, Saunderset al. 2002). Although model averaged density estimatessuggest a minor trend in this direction between sessions 2and 3 (Fig. 2), overall there was greatest support for a staticand low population density prior to baiting. This consistentlow density is significant because a major motivation forresearch into fox densities in Australia has been to determinewhether populations have the capacity to sustain an epidemicof the rabies virus or other infective organisms (Coman et al.1991, White et al. 1995). Epidemiological models predictdensities of greater than 0.3–0.5 foxes/km2 are required for arabies epidemic to be sustained (Anderson 1986), suggestingthat if the densities recorded here are indicative of arid zonefox populations, the vast arid and semi-arid regions ofAustralia will not present effective barriers to the spread

of rabies and diseases with similar epidemiologicalproperties.A second motivation for directly estimating fox densities in

Australia is for the effectiveness of fox control programs to beevaluated, ideally quantitatively (Saunders et al. 1995, Markset al. 2009). We present strong evidence for a substantialdecline of approximately 95% in fox density in response toaerially delivered 1080 poison. The post-baiting density issufficiently low to prevent the spread of rabies, but is alsolikely to be low enough to significantly reduce predation rateson wildlife and stock (Banks et al. 2000). This advance overindirect and index-based methods of evaluating fox controleliminates ambiguity regarding the dynamics of populations(Saunders et al. 1995, Anderson 2001). For example, becausemeasuring change in fox abundance is difficult, in many casesit has been more practical to measure the response in wildlifeor stock to fox control (Braysher 1993). This indirect ap-proach has been central to establishing the fox as a majorthreat for native wildlife in Australia (Kinnear et al. 1988).However, recent large and widespread declines of nativewildlife populations have been reported in Australia, despiteongoing fox control (Wayne 2008). Attempts to understandthe cause of these declines have been hampered by a lack ofdirect estimates of fox abundance, and the agents responsibleremain unclear (Wayne 2008). This scenario provides strongargument for the wider deployment of direct methods toestimate the abundance of widespread invasive carnivoressuch as the fox (see also Hone 1999a), and our resultsdemonstrate that, unlike conventional trapping techniques,noninvasive DNA analysis make obtaining these direct esti-mates achievable.

Fox Survival

Largely because of the logistical difficulty of directly moni-toring wild canids, there have been few published estimatesof short-term survival rates in red foxes, and even fewer inexperimental settings involving control. We determined thatapparent survival in the Karara-Lochada fox populationduring a 4-month pre-baiting period covering late summerto early winter was high and effectively constant (Fig. 3).Although it does not measure change in survival directly, thereduction in fox density observed post-baiting implies thatintensive aerial baiting with 1080 delivered high mortality inthis population. More directly, however, we established thatapparent survival dropped to virtually zero in response tobaiting and remained at that level for at least 22 days. Byimplication, re-invasion from the surrounding unbaitedregion was slow, which is a pattern observed elsewhere inarid-zone fox populations (Thomson et al. 2000). It stands instrong contrast, however, to Australian fox populations inmore mesic regions, where mortality has been assumed orobserved to be high, but population size has been unchanged(e.g., Rushton et al. 2006, Gentle et al. 2007, Piggott et al.2008). Regrettably, in many cases monitoring has not per-mitted a distinction between rapid reinvasion or controldelivering insufficient mortality. Indeed, great variabilityexists in the reported effectiveness of baiting in reducingfox abundance in Australia (Saunders et al. 2010), and our

Figure 3. Model averaged estimates of the trappable population size of maleand female red foxes (left axis � SE), and probability of apparent survivalfor both sexes combined (right axis � SE) at Karara-Lochada Station,Australia, 2008.


results illustrate how the simultaneous and direct monitoringof both survival and density has the potential to reduceambiguities that commonly arise in evaluations of controlprograms.

Efficiency of Noninvasive DNA SamplingAn enduring difficulty of monitoring fox populations is theirextreme wariness of traps (reviewed in Saunders et al. 1995).Considering this, the snare success rate recorded during thefirst 3 sessions (1,056 trap nights) of 27.8% is high, andgreater than reported for foxes and other canids in Australiaand elsewhere with conventional traps or cameras (2.8%;Meek et al. 1995, <2%; Baker et al. 2001, <1%; Ruetteet al. 2003, 1.5–6%; Martin et al. 2007, 4%; Sarmento et al.2009), and comparable to or greater than the high ratesreported in mesocarnivore species known to be readilytrapped (e.g., Glen 2008). It is also comparable or greaterthan hair-snaring rates for other mesocarnivores (Bremner-Harrison et al. 2006, Pauli et al. 2008, Garcia-Alaniz et al.2010), and significantly greater than rates reported for analternative snare design deployed to capture fox hairs inAustralia (Vine et al. 2009). The high success is particularlysignificant considering the low density of foxes at Karara-Lochada. It also represents an underestimate of the appeal ofsticky wickets to foxes considering a further 31% of sampleswere left by foxes but did not provide suitable quality DNAfor their genotypes to be incorporated into the analysis. Inaddition, some foxes did not enter sticky wickets when theywere encountered, suggesting that further improvements indesign should be investigated, possibly also including designsthat snare only single samples (Bremner-Harrison et al.2006). Additional power to detect more subtle changes indensity will also be achieved by deploying larger numbers ofhair snares.

Fox Capture Probability and Response to CaptureAlthough achieving high raw trap success is vital to achievingrobust population estimates (Krebs 1999), identifying andaccounting for trap responses and heterogeneity in probabil-ity of capture are also fundamental requirements in capture–recapture analyses (Otis et al. 1978). The difficulty of col-lecting trapping data for red foxes has meant that few studieshave assessed these details. At Karara-Lochada, the proba-bility that individual foxes were initially ‘‘trapped’’ by thesticky wickets hair snares was relatively high (P � 0.15), andsimilar to that reported for live trapping more trap-proneanimals such as mice and stoats (Krebs et al. 1994, King andWhite 2004). Yet, we found strong evidence that not all foxesresponded in the same way to hair capture. A model incor-porating capture heterogeneity received support in both ouropen model and spatially explicit capture–recapture analysis(Tables 2 and 3). This heterogeneity reflects that althoughthe probability of initial capture (P) varied little amongindividuals, the probability of recapture (c) varied consider-ably; 1 group of foxes remained highly catchable, whereas theother became virtually uncatchable.An explanation for this observation exists in the spatio-

temporal nature of recaptures, which indicate that the dif-

ference could reflect the behaviors of territorial adults, andnonterritorial juveniles. First, only animals captured duringsession 1 (Feb) were recaptured in subsequent sessions.February (late Austral summer) is prior to the dispersal phasefor juvenile foxes (Saunders et al. 1995, Baker et al. 2001), soit is likely that these foxes were adult. Remarkably, foxes thatwere first captured in sessions 2 or 3, were never recapturedeither within or between sessions and this is reflected in thezero estimate of recapture probability identified in modelsincluding heterogeneity. These single-capture animals werenot genotyping artifacts (shadow effects; Mills et al. 2000)because their genotypes had significant genotype mismatches(�2 alleles) with existing individuals. However, sessions 2and 3 coincided with the expected period of juvenile dispersal(Baker et al. 2001), where young of the year leave their densin search of new territories (Trewhella et al. 1988). Theapparent trap-shyness of animals first captured in sessions2 and 3 may reflect the limits placed on the movements ofsubordinate foxes by territorial adults.The extent of movements between recaptures also suggests

that juvenile foxes were well represented among the trap-shygroup. Red foxes have great capacity for dispersal, particu-larly at low population densities like those observed atKarara-Lochada (Trewhella et al. 1988, Coman et al.1991, Allen and Sargeant 1993). Yet, the movement dis-tances implied by our estimates of s (approx. 0.6–1.5 km)were low, and recaptures were highly clustered, with noindividuals recaptured at distances greater than 4 km froma previous capture, and no individuals moving between trans-ects (mean movement 1,178 m � 248 SE; see Table S2,available online at www.onlinelibrary.wiley.com). Based onan established relationship between dispersal and density innorthern hemisphere foxes, the densities at Karara-Lochadaindicate that juvenile and male reproductive movements,which should have been evident in our sessions 2 and 3,should be close to 10 km (Trewhella et al. 1988, Iossa et al.2008). The observation of limited movement, together withthe long residency of many individuals first encounteredduring session 1 (i.e., adults) suggests that the movementsdetected were largely intra-territorial and therefore mostlikely by adults. This is further supported by the observationthat a sex-bias was not evident in the movement distances(see Fig. S3B and Table S1, available online at www.onlinelibrary.wiley.com), whereas a male-bias in dispersaldistance is characteristic of the dispersal phase of foxes(Storm and Montgomery 1975, Woollard and Harris1990, but see Allen and Sargeant 1993, Englund 1980).Whether the heterogeneity in capture probability repre-

sents a difference between adult and juvenile foxes cannot bedetermined with certainty from noninvasive DNA analysisbecause the age of foxes cannot be determined by thismethodology. Heterogenity in capture probability has thepotential to introduce bias in estimates of population abun-dance, so its identification is necessary if that bias is to beminimized (Otis et al. 1978). In the present case, however,estimates ofN and s for the best models that did not include atrap response or heterogeneity were little different to equiv-alent models with a trap response or heterogeneity (see


Table S3, available online at www.onlinelibrary.wiley.com).Nevertheless, our results caution against ignoring trapresponses in foxes when conducting capture–recaptureanalysis with either noninvasive or conventional trappingmethodologies.

Identification of Samples Likely to Produce DataConsiderable time and expense are involved in the analysis ofnoninvasive DNA samples. The process would be more cost-effective if the samples likely to yield data could be identifiedprior to analysis (Morin et al. 2004, Ball et al. 2007).Surprisingly, few studies employ such a screening process.The real-time PCR assay that we employed for speciesidentification has the useful property of also accurately pre-dicting the likely success of obtaining a 5 or more locusgenotype from a sample. Of 148 samples collected duringsessions 2–4, 105 provided genotypes and 43 did not. Byapplying the logistic regression as a means of identifying highquality samples to be genotyped, 5 of the 105 samples thatprovided 5 or more locus genotypes would not have beengenotyped; a loss of data of 4.8%. Conversely, 5 samples thatultimately failed to provide genotypes would have beenanalyzed. This results in an additional cost of 4.8%. Thiscost would be significantly offset by not analyzing 43 samples(38 of which would have failed to provide genotypes), andresult in a savings of 29% on the expense of wholesale samplegenotyping. In sum, assuming that the calibration data werealready available, the total cost savings of applying the logis-tic equation to identify high quality samples was 24.2%, andthe cost in terms of loss of data would be 4.8%. Furtherrefinement of the criteria for selecting high quality samplesmay prove useful. For example, the criterion employed forthe above cost analysis was that a sample must have a >50%probability of providing a genotype. This threshold might bevaried according to the relative values of data and costs ofgenotyping for a particular project.

MANAGEMENT IMPLICATIONS

Obtaining quantitative estimates of density and survival arefundamental to many ecological analyses relevant to thecontrol of invasive species, but these parameters are usuallydifficult to obtain. Noninvasive analysis of hair samplesprovided significantly more individual ‘‘captures’’ than aretypically retrieved from conventional trapping methods forthe red fox, and therefore have the potential to provide moreaccurate and precise evaluations of the effectiveness of con-trol. With this data, our experiment has provided the firstquantitative assessment of the effectiveness of 1080 poisonbaiting for reducing the density of Australia’s most costlyvertebrate predator, and has demonstrated it to be a highlyeffective method for reducing regional fox densities to levelswhere impact on wildlife and stock are likely to be greatlyreduced.

ACKNOWLEDGMENTS

We gratefully acknowledge the support of the InvasiveAnimals Cooperative Research Centre. We thank M.

Efford, J. Tatler, Y. Hitchen, A. Glen, N. Marlow, P.deTores, S. Lapidge, G. Saunders, and K. Pollock for helpfuldiscussions, and N. Marlow and anonymous reviewers forconstructive comments on the manuscript.

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Genetic tagging reveals a significant impact of poison baiting on an invasive species