Lures change the detectability of feral cats on the arid landscape

doi:10.21203/rs.3.rs-3226073/v1

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Lures change the detectability of feral cats on the arid landscape

https://doi.org/10.21203/rs.3.rs-3226073/v1

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published 08 Dec, 2023

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Feral cats (Felis catus) are difficult to manage and harder to detect. For 20 years, we have been using an annual aerial baiting program to suppress feral cat numbers on the Matuwa National Park in Western Australia. The efficacy of this management is monitored via pre-bait and post-bait surveys of feral cat activity on sandy tracks, and at least 120 camera-traps. In 2021, we detected cats on only five occasions, despite using 130 camera-traps with an olfactory lure known as Catastrophic. In this study, we measured the detectability of feral cats at three camera-traps arrangements on Matuwa: passive cameras (n = 57), camera with the Catastrophic lure (n = 120), and cameras with a Magnum Scrape-Dripper ® (n = 63) dispersing cat urine. All 120 sites had two cameras, one with Catastrophic for comparison with data from prior surveys, plus a camera that was either passive or had a dripper. Camera-traps were active for an average of 57 days (range 55–60 days). Cats were detected by cameras with drippers 29 times more than passive cameras and six times more than Catastrophic cameras. Further, cats spent on average 2.6 seconds in front of passive cameras, 8.5 seconds in front of Catastrophic cameras, and 65.5 seconds in front of drippers. The ability to detect feral cats affects the magnitude and accuracy of abundance or occupancy estimates for cats. Therefore, our research highlights a severe weakness in current feral cat survey methodologies and any management decisions derived from that data.

feral cat

Felis catus

Australia

olfactory lure

survey

rangelands

Feral cats (Felis catus), cats that live in the wild and can survive without human resources, are a key threatening process to native species around the world (Medina et al., 2011; Risbey et al., 2000; Sims et al., 2008; Smith and Quin, 1996), and cause billions of dollars damage to the natural and agricultural environment (Pimentel, 2007; Stelzer et al., 2019). Predation by feral cats has been identified as one of the major obstacles to the successful reintroduction of extirpated native fauna (Hardman et al., 2016; Moseby et al., 2011; Priddel and Wheeler, 2004; Short, 2016). Therefore, the suppression of feral cat populations is a critical component to the successful conservation of small to medium-sized native fauna (Fischer and Lindenmayer, 2000; McKenzie et al., 2007).

While implementing methods of feral cat control is difficult; measuring the outcomes of feral cat control, the pre- and post-management abundance of cats, is harder. Detecting feral cats is difficult because they are a cryptic species that avoids interactions with humans (Gosling et al., 2013) and in some environments cannot be readily detected via remote sensing technologies, such as camera-traps, despite the use of lures (Moseby et al., 2015; Stokeld et al., 2016). Estimating the abundance of an animal species typically requires capturing or identifying individual animals on multiple occasions (Lancia et al., 2005). Capturing feral cats on multiple occasions is extremely difficult, requiring the use of multiple labour-intensive techniques (McGregor et al., 2016) because feral cats frequently lack the unique markings required to identify individuals remotely (Rees et al., 2019). Failure to detect a feral cat when it is present (false zeros) is detrimental to parameter estimation in abundance estimation models (MacKenzie and Royle, 2005; Moilanen, 2002). Therefore, even models that do not require information on individuals, such as occupancy models, require detection probabilities to be greater than 0.3 (MacKenzie et al., 2002).

Despite the difficulties, our need to monitor the efficacy of feral cat management continues to drive research with the aim of estimating the abundance (Bengsen et al., 2011; Elizondo and Loss, 2016) or occupancy (Comer et al., 2018; Hohnen et al., 2016; Jean-Pierre et al., 2022; Vanek et al., 2021) of feral cats on the global landscape. And unreliable estimates derived from detection probabilities below 0.3 are being published (Krauze-Gryz et al., 2012; Taggart et al., 2019; Wysong et al., 2020b). In 2021, we had a similar intent to estimate the abundance of feral cats via camera-traps, but on 130 camera-traps we only detected cats on five occasions (C. A. Lohr et al., 2021). All the referenced exemplar studies used camera-traps to detect feral cats.

Therefore, there is a need to improve our ability to detect feral cats using camera-traps and minimise the risk of false zeros. Prior studies have tested several lures for feral cats including artificial olfactory lures, food-based lures, and visual lures with insignificant results (Read et al., 2015; Stokeld et al., 2016). Spraying wooden stakes with 4 ml whole cat urine on a single occasion increased the detection rate for cats sixfold but still only generated detection rates of 6 to 22% (Hanke and Dickman, 2013). Increasing survey duration for cameras with a food lure to 30 days can generate appropriate detection probabilities (Moore et al., 2020).

Building on the results generated by Hanke and Dickman (2013), we compared camera-traps with the olfactory lure commonly used for feral cat research in Western Australia, Catastrophic (Outfoxed Pest Control, Victoria, Australia) against passive cameras (no lure), and Magnum-Scrape Drippers® (Wildlife Research Center, Minnesota, USA) filled with whole cat urine. We hypothesised that the slow automated application of cat urine over time would produce a greater magnitude of difference between passive cameras and lured cameras and might generate detection probabilities above 0.3.

Study site

The Rangelands Restoration program on the Matuwa National Park (2,440 km²; ex-Lorna Glen pastoral lease) in central Western Australia (26° 13′ S, 121° 33′ E; Fig. 1) aims to achieve the successful reconstruction and conservation of Australian arid zone native species diversity (C. Lohr et al., 2021a, 2021b; Treloar et al., 2021). Matuwa consists of two main land systems: Bullimore–sand plains dominated by spinifex (Triodia spp.), and stony plains dominated by acacia shrublands with mulga (Acacia aneura) being a common species (Beard et al., 2013; Mabbutt, 1963). We used land systems maps to describe six habitat types on Matuwa (Fig. 1). Being in the arid zone, Matuwa is characterised by extreme temperatures and low and erratic rainfall with the annual average of 261.7 mm (Bureau of Meteorology, records 1898–2018; weather station No. 13012 located in Wiluna, Western Australia 137 km WSW of Matuwa).

Feral cat management

Since 2003, we have been using the toxic bait known as Eradicat® to control feral cats on Matuwa (Algar et al., 2013, 2007; Algar and Burrows, 2004). Eradicat® baits contain 4.5 mg of the directly injected toxin ‘1080’ (sodium monofluoroacetate). Most years, baits are deployed from a fixed wing aircraft at a rate of 50 baits/km² during the cool, dry winter periods when the abundance and activity of all prey types is at its lowest (Algar and Burrows, 2004). Typically, we monitor the efficacy of the bait deployments via 10-day pre-bait and post-bait camera surveys or through the track activity index (Lohr and Algar, 2020).

Pilot trials

The Magnum-Scrape Dripper ® is a slow-release device powered by changes in air pressure rather than a truly automated device. It is designed for use in winter conditions in the USA, which are typically far colder than conditions in Western Australia. Therefore, a pilot study was used to measure the duration of fluid release from the dripper. Ten drippers were set up on the grounds of the Department of Biodiversity, Conservation and Attractions (DBCA) Wildlife Research Centre, Perth, Western Australia during January 2022. Drippers were filled with either water or whole cat urine marked with food dye. We used white sand beneath the drippers to record whether the units dripped during each 24-hour period.

Cat urine

Urine was collected from captive feral or domestic cats via a double-stacked litter tray filled with plastic beads. The upper tray has a fine wire mesh base that allows urine to pass into the lower tray and away from further cat activity. The beads and trays were washed daily after the urine was collected. The urine from multiple cats was filtered, combined and frozen for later use.

Camera-trap monitoring

One camera-trap array with two camera-traps (Reconyx PC900 Hyperfire Professional Covert camera; Reconyx, Wisconsin, U.S.A.), spaced 40 m apart, at each of 120 sites was used to monitor feral cat activity on Matuwa pre- and post-management. The sites were selected using a stratified-random design based on the 20 most common geological types in the Wiluna region (Farrell, 1999). Ultimately, camera-traps were installed in six habitat types, each with one of three types of olfactory lure (Supplemental Information Table S1). The cameras were placed between 30 m and 200 m away from an ungazetted track (Fig. 1). Sites were, on average, 2.80 km from their nearest neighbour (min = 0.97 km, max = 5.92 km). Spatial autocorrelation is very unlikely at this scale (Kays et al., 2010) despite the potentially large home-ranges of feral cats at this site (Wysong, 2016a).

All cameras were mounted on a 30 cm high plastic sand peg, facing south, with the aperture parallel to ground, in a space with at least 3 m of open ground in front of the camera. Approximately 2 ml of the commercial olfactory lure (Catastrophic from Outfoxed Pest Control, Victoria, Australia) was placed on the top of a 60 cm wooden stake placed 3 m from the front of one camera at each site. Catastrophic has been the lure used during camera trapping between 2018 and 2022 (C. A. Lohr et al., 2021; Lohr and Algar, 2020) and hence using this lure at all 120 sites maintains on-going time series data on Matuwa. The second camera at each site was either left as a passive camera (n = 57) with an unscented stake, or a stake was installed beneath a Magnum-Scrape Dripper ® filled with 6 fl oz of whole cat urine, which was hung from naturally occurring branches (n = 63). Herbaceous vegetation was removed if present immediately in front of the camera. Camera-traps were programmed to capture three photos per trigger, with no quiet period. Timed photos were also taken at 23:00 h to monitor the quality of photos and operation of the camera.

Setting lures at 120 pairs of cameras spaced over an area approximately 2,440 km² takes one team of personnel approximately five days. Hence, the 10-day survey periods were staggered from the date that each camera was set with a lure. Similarly, the aerial bait deployment required 2.5 days, and hence photos captured were staggered from the date the nearest batch of baits was deployed.

Photos were stored in the Colorado Parks and Wildlife Photo Warehouse database (CPW) (Ivan and Newkirk, 2016). All photos of feral cats were viewed by at least two observers to confirm species identification. To minimise temporal autocorrelation, we grouped consecutive photos if they were < 5 min apart and used these sequences as independent detections for subsequent analysis (Kays et al., 2015). We also analysed data from all photos captured to assess the amount of time cats spends in front of each camera.

Analysis of monitoring data

Camera-trap data were analysed via mixed-effects models in the package glmmTMB (Brooks et al., 2017), with lure type, habitat, and time since lure set, the solstice, or bait application as fixed effects, while site was used as a random effect. Zero-inflated negative binomial models were used for statistical tests involving all cameras, 86% of which did not detect any cats. Models were compared via Akaike’s Information Criterion (AICc) in the package AICcmodavg 2.3-1 (Mazerolle, 2020).

Identifying individuals

Individual feral cats were identified manually by three observers and through the software Wild ID version 1.0 (Bolger et al., 2012). Wild ID uses the Scale Invariant Feature Transform (SIFT) operator to extract distinctive features invariant to picture scale and rotation. For the algorithm to identify two potentially matching individuals it must first identify at least three potentially matching features within two photos, and those three features must form a triangle with proportional but consistent angles and edge lengths. Following analysis by the SIFT algorithm, observers are required to compare each focal image with images of the top 20 matching individuals. The false rejection rate of matching images and the false acceptance rate of non-matching images for Wild ID is 0.67% and 0.00% respectively (Bolger et al., 2012). We report the individual identifications for each method.

Manufacturer instructions for the Magnum-Scrape Dripper ® state that the bottle should be filled with 4 fl oz of liquid. We confirmed that completely filling the bottle stops the unit from functioning. Over-filling the bottle with 6.5 fl oz (190 ml) generates an average operational time of 11.5 days when the unit is exposed to summer conditions (mean temperatures min = 18.1°C, max = 31.4°C). Volume of liquid in the bottle was significantly correlated with duration (ln Duration = 0.81 + 0.24; F_1,15 = 31.42, p = 5.02^− 5, r² = 0.68). Three pilot units were installed in a location shaded from morning sun and remained operational for 15, 16, and 17 days respectively. Other trials suggested that lowering the viscosity of the liquid in the bottles may extend the duration of liquid dispersal, but we persisted with trials of whole cat urine as the lure rather than fully investigate this question.

In total, the passive cameras collected eight photos of cats (3 detections), the Catastrophic cameras collected 84 photos (15 detections), and cameras with urine drippers collected 270 photos (25 detections) in 50 days. On average a cat remained within the field of view of a passive camera for 2.66 seconds (SE = 0.33 s), a Catastrophic camera for 8.53 seconds (SE = 2.78 s), and a camera with a urine dripper for 65.48 seconds (SE = 17.63 s). Hence, photos taken by cameras with drippers were typically much clearer, providing good opportunity for individual identification, whereas photos from passive cameras could not be used to identify individuals.

Analysis of this count data revealed that cameras with urine drippers were significantly more effective (p = 4.62^− 3) and captured up to 55 times more photos than cameras with the Catastrophic lure, when placed in calcrete habitat (Fig. 2). Similarly, drippers were at least two-fold more effective at attracting cats on stony habitats than cameras with Catastrophic. Zero-inflated negative binomial models found that both Catastrophic (p = 7.34^− 3) and urine drippers (p = 2.41^− 5) resulted in significantly more photos than passive cameras, but that only urine drippers resulted in significantly more independent cat detections (p = 1.55^− 3). Site had minimal effect on the variance at 3.28^− 9 for photos and 1.55 for cat detections.

The pre-bait and post-bait survey periods were each 10 days long, staggered from the date lure was set in front of a camera, and made up 33% of the complete 60-day survey period. Despite this, only 24% of the photos were taken during the 10-day survey periods. Only 11 photos were taken in the pre-bait period, all at cameras with Catastrophic (Fig. 3). Whereas 82 photos were taken during the post-bait period with six at passive cameras, four at Catastrophic cameras, and 72 at cameras with urine drippers.

On average, passive, Catastrophic, and urine cameras detected cats 32, 19, and 31 days after the solstice, respectively (Fig. 4). Only Catastrophic cameras detected feral cats prior to the solstice (Fig. 3). Pairwise results from a linear mixed effects model (variance for site = 171.77, SD = 13.11) suggest that cameras lured with urine were significantly different from both passive (p = 0.03) and Catastrophic cameras (p < 1.00^–3), whereas passive cameras did not differ from Catastrophic cameras (p = 0.55). Catastrophic cameras detected feral cats at a more consistent rate over time (Fig. 4).

From 45 independent cat detections observers classed 73% of detections (n = 33) as suitable for use in identifying the individual cat. Photos from four events were too blurry, and eight events only captured a small portion of the cat (i.e., tail or head only). The three manual observers identified 28, 17, or 26 individual cats in the 33 remaining detections, with 3, 4, and 4, individual cats being observed at multiple locations or times. Three of these cats were detected at the same location and camera on a different day, and one cat travelled 4,477 m between site 50 and site 51.

Unfortunately, the largest score provided by the Wild ID software between any two independent detections was 0.0195. These two photos were taken two minutes apart by a pair of cameras spaced 40 m apart, were taken in daylight with full colour, and manually identified as containing the same individual (Fig. 5). By comparison, 291 out of 357 (81.5%) cat photos obtained scores above 0.5 for a sequential photo taken within a detection event.

Our primary hypothesis was confirmed: the slow release of cat urine generated significantly more feral cat detections by camera traps than the commercial Catastrophic lure or passive camera-traps. Our secondary hypothesis, that the use of cat urine as a lure would increase our detection probability to 30% was nearly supported with feral cats being detected at 27% of our urine lured sites, 9% of Catastrophic lured sites, and 5% of passive sites. The use of cat urine as a lure will allow for more accurate estimates of feral cat occupancy and abundance on the landscape.

With detection rates of 5 and 9%, the use of passive cameras or cameras lured with Catastrophic are likely to result in a considerable number of false zeros and hence generate biased estimates of occupancy (MacKenzie and Royle, 2005; Moilanen, 2002). Consideration of the detection rate is paramount when interpreting the results of occupancy analyses for feral cats.

During this experiment the camera-traps were set on average, 2.80 km from their nearest neighbouring pair of cameras, which is a camera-trap layout designed to minimise spatial autocorrelation and is suitable for use with occupancy analysis. In occupancy, a survey must be a period during which it is reasonable to assume occupancy is static or changes in occupancy occur completely at random (MacKenzie et al., 2017). Past studies have shown that the average feral cats’ home-range on Matuwa has a diameter of 2.93 km for females and 3.70 km for males (Wysong, 2016b), and hence spacing cameras 2.80 km apart should create independent and static survey sites for occupancy analysis. Our data demonstrate that the use of the cat urine lure increases the detection rate for feral cats without violating the assumption of static occupancy survey sites. We did not use our data to generate estimates of feral cat occupancy on Matuwa due to the variation in detection created by the three lures.

Unfortunately, our data are not sufficient for estimating the abundance or density of feral cats on Matuwa. Only four individual cats were recorded on more than one occasion over 60 days and calculating abundance or density estimates requires recapturing individuals.

Many animal species selectively use some habitat types more than others. Habitat selection is defined as the use of habitat relative to the availability of habitat (Mayor et al., 2009). Habitat selection may also occur on a temporal-scale with particular habitat types being used during specific seasons or periods of the day. The results derived from passive cameras suggest that feral cats selectively use sandplain and stony habitats, whereas cameras lured with whole cat urine detected feral cats on all available habitat types (Fig. 2). The biased data generated by passive cameras may contribute to the hypothesis that wild dogs or dingoes (Canis familiaris) suppress the activity or abundance of feral cats (Brook et al., 2012; Wysong et al., 2020a).

Our results verify the results of prior studies that a camera survey period of 10 days is not sufficient for feral cats in the arid zone. Research from the Pilbara region found that the probability of detecting a feral cat with a camera trap and a food lure was less than 40% after 10 days whereas the probability rose to above 75% if the camera remained in place for 30 days (Moore et al., 2020). Before-after-control-impact (BACI) experimental designs should carefully consider the implications of survey duration and any aversive stimuli that might be created by cameras (Glen et al., 2022) on potential results (Comer et al., 2018). We might have detected more feral cats after baiting on Matuwa because any aversive stimuli that had been created with the establishment of the cameras declined over time and hence the second application of lures to the cameras was more attractive to any cats remaining in the vicinity.

Alternatively, the winter solstice may have influenced feral cat behaviour and their desire to investigate foreign odours. We know from extensive studies that the domestic cat is seasonally polyoestrous under natural photoperiods. In general, ovarian cyclic activity is reduced under decreasing photoperiod and resumes after exposure to increasing light (i.e., a long-day breeder) (Bristol-Gould and Woodruff, 2006; Brown and Comizzoli, 2018). Melatonin appears to regulate photoperiod-induced seasonality in the cat, with concentrations being highest during the dark phase (Leyva et al., 1989). Leyva et al. (1989) demonstrated a return to cyclic ovarian activity in a very short time following change to a stimulatory photoperiod. At the latitude of Davis, California (36⁰ N) their experience was that cats under natural photoperiod can show sexual receptivity 25 days after the change to a favourable photoperiod. This suggests that a change in photoperiod as occurs in the first 15 days after the winter solstice is sufficient to initiate follicular activity at least in some animals.

An increase in reproductive behaviours may result in increased activity around sites of feral cat urine (Brown, 2020) and hence explain the increased number of photos captured after the solstice, confounding our ability to assess the efficacy of the baiting. In the future, we recommend that in the arid zone, both pre- and post-bait camera surveys for feral cats continue for a minimum of 20 days (Moore et al., 2020) if a suitable lure dispenser can be obtained. Both pre- and post-management surveys should occur either before, or a minimum of 15 days after the solstice to minimise the risk of results being confounded by changes in behaviour. Bait deployment may need to occur by mid-June for post-bait surveys to occur before the solstice effects reproductive behaviour, or after July 10 for pre-bait surveys to occur after the solstice. Post-bait surveys that occur in September should acknowledge the risk of collecting biased data as the proportion of females limited to their birthing dens increases substantially in September and October (Jones and Coman, 1982).

The feature matching algorithm (SIFT) used by the Wild ID software may be too strict for use with feral cats. The SIFT algorithm requires that three points on each of the two cats being compared remain in proportionally the same position for the photos to be considered a match; a condition that cannot be met if the animal changes the relative position of its markings when it changes its posture. Cats are extremely flexible (Brown, 2020) with many body postures used for communication (Deputte et al., 2021; Michael, 1961). In comparison, the giraffes (Giraffa spp.) used as a study species by Bolger et al. (2012) to test the SIFT algorithm have fewer changes in body posture, and shorter hair that is unlikely to make markings appear to move.

In conclusion, feral cat urine appears to be a more effective lure for attracting feral cats to camera traps. The duration and timing of camera-trap surveys must; however, continue to consider peaks in breeding behaviour that may increase or decrease the apparent occupancy of the arid zone by feral cats and confound the results of BACI surveys. We recommend that future experiments using urine lures for feral cats consider separating male and female urine, and or the urine of oestrus versus anoestrus females to determine if the presence of reproductive hormones in lures influences our ability to measure the density or occupancy of feral cats on the landscape.

Funding

This work was supported by the Chevron Gorgon Barrow Island Threatened and Priority Species Translocation Program, and the Western Australian Department of Biodiversity, Conservation and Attractions.

Acknowledgements

We express gratitude to all the people who contributed to the management of Matuwa: Tarlka Matuwa Piarku Aboriginal Corporation, the Matuwa and Kurrara Kurrara Rangers, the Matuwa Homestead Caretakers, the DBCA Goldfields Regional Staff, and numerous volunteers. A Department of Biodiversity, Conservation and Attractions Animal Ethics Permits (2021-55) was granted for this project.

Author Contributions

Conceptualization, C.A.L and D.A; Methodology, D.A., N.H., M.O., K.N., A.J.; Formal Analysis, C.A.L.; Resources, M.O., N.H.; Data Curation, K.N., AJ; Writing – Original Draft Preparation, C.A.L., D.A.; Writing – Review & Editing, C.A.L., D.A.; Visualization, C.A.L.; Project Administration, C.A.L.; Funding Acquisition, C.A.L.

Conflicts of Interest

The authors declare no conflict of interest.

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Lures change the detectability of feral cats on the arid landscape

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Abstract

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Introduction

Materials and Methods

Study site

Feral cat management

Pilot trials

Cat urine

Camera-trap monitoring

Analysis of monitoring data

Identifying individuals

Results

Discussion

Conclusions

Declarations

References

Supplementary Files

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