Foxes at your front door? Habitat selection and home range of urban red foxes (Vulpes vulpes)

The red fox (Vulpes vulpes) is one of the most adaptable carnivorans, thriving in cities across the globe. Understanding movement patterns and habitat use by urban foxes will assist with their management to address wildlife conservation and public health concerns. Here we tracked ve foxes across the suburbs of Perth, Western Australia. Three females had a core home range (50% kernel density estimate; KDE) averaging 37 ± 20 ha (range 22–60 ha) or a 95% KDE averaging 174 ± 130 ha (range 92–324 ha). One male had a core home range of 95 ha or a 95% KDE covering 352 ha. The other male covered an area of ~ 4 or ~ 6 times this: having a core home range of 371 ha or 95% KDE of 2,062 ha. All ve foxes showed statistically signicant avoidance of residential locations and signicant preference for parkland. Bushland reserves, golf courses, and water reserves were especially preferred locations. Foxes moved quickest (i.e. commuting) when they were in close proximity to roads and slowest (i.e. foraging) when they were further from roads. We compare these ndings with a review of the literature for urban fox home ranges. The movement patterns we describe are likely to be common for urban foxes, with half of the published home range estimates for urban foxes (principally based on VHF data) excluding data for ‘lost’ individuals or animals that showed ‘excursions’. It is likely that the home range estimates for these urban exploiters have therefore been grossly underestimated to date. Further application of GPS trackers that allow remote download will vastly improve our understanding of the space use of urban foxes. I.M. by hand injection. Foxes were allowed to fully inside the covered crush cage for 15–20 prior to transported and released at the site of capture. GPS collars were programmed to take 33 positions every 24 hours, with a focus of greater x frequency during nocturnal times to capture fox roaming activity in more detail: 30 min x interval between 18:00–08:00 h (28 location xes daily), 60 min x interval between 08:00–09:00 h and between 17:00–18:00 h (2 location xes daily), and 120 min x interval between 09:00–17:00 h (3 xes daily). and only once each for the other four animals. It would have been informative to have had seasonal data for our animals to determine whether there were changes in space use over the year. Such questions may be achievable with satellite technology that do not rely on base station downloads. Analytical methods will also result in a difference in home range area estimates. We found differences in home range area estimates using different statistical methods. Compared with estimates that ignore temporal autocorrelation, the use of autocorrelation-corrected analysis yields larger home range estimates for animals that move across their home range over a matter of hours or days.


Introduction
The red fox (Vulpes vulpes) is one of the most adaptable mammal carnivores, with native and non-native populations across all continents except Antarctica. Its generalist ecology, with few speci c habitat requirements and opportunistic use of a diverse range of foods (Forbes-Harper et al., 2017;Harris, 1981b;Reynolds and Tapper, 1995), has undoubtedly contributed to success of the red fox in urban landscapes (Bateman and Fleming, 2012). Around the world, red foxes have been reported in at least 114 cities (Soulsbury et al., 2010), supported by an abundance of anthropogenic resources (Bateman and Fleming, 2012). Some urban and peri-urban locations have very high fox densities. For example, in Melbourne (Victoria, Australia), up to 16 individuals·km − 2 have been recorded (Marks and Bloom eld, 1999), which is 2-5 times the densities recorded for rural areas of Victoria (Coman et al., 1991). In Bristol (UK), urban areas support fox densities of up to 20 or 37 individuals·km − 2 (Baker et al., 2001;Harris, 1981a).
Red foxes were introduced into Australia from Europe during the second half of the 19th century (Turner, 2017). Just as in parts of its native range, in Australia, the fox has actively colonised urban habitats (Soulsbury et al., 2010;Wilkinson and Smith, 2001) and has been recorded in six Australian cities (Soulsbury et al., 2010), including Perth, Western Australia (Fleming and Crawford, 2019). Urban fox management is a matter of concern across many parts of the globe, not only due to their predatory impact, but also due to risk of disease transmission and human and animal health. For example, a potentially fatal parasite of pet dogs, canine heartworm (Diro laria immitis), the larval stages of which are transmitted via mosquitoes, has been identi ed in foxes in Sydney and Melbourne (Marks and Bloom eld, 1998;Mulley and Starr, 1984). Urban foxes can also act as a de nitive host for many zoonotic intestinal helminths, including Echinococcus granulosus, Toxocara canis and hookworms (Brochier et al., 2007;Fischer et al., 2005;Hofer et al., 2000;Jenkins and Craig, 1992) as well as viral diseases such as rabies (Rosatte and Allan, 2009;Rosatte et al., 1992). Understanding movements and habitat preference of urban foxes can, therefore, inform how they are managed for wildlife conservation as well as public health reasons (Pluemer et al., 2019).
Foxes require secluded and secure daytime rest sites in urban areas, and can have a number (two to ve) of diurnal shelter sites used in each home-range, generally located in areas without human access, or where access is restricted to daylight hours (Baker et al., 2000;Marks and Bloom eld, 2006). Foxes make use of thickets of dense vegetation (Marks and Bloom eld, 2006;White et al., 2006), but novel arti cial habitat is also used, including spaces beneath buildings (Harris and Rayner, 1986), drainage culverts (Marks and Bloom eld, 2006), or debris such as a disused boiler units (Marks and Bloom eld, 2006) or abandoned car bodies (this study). Foxes also need natal den sites in which they leave young, and will generally dig earths (Harris and Rayner, 1986). Of 72 natal dens within a 20 km radius of the Melbourne GPO, burrows that had been dug by foxes were most common (92%), although rabbit warrens and arti cial structures were also used.
Red foxes have an opportunist diet and readily exploit a diversity of high-nutrition food in human-modi ed landscapes (Bateman and Fleming, 2012;Contesse et al., 2004). In addition to items that rural foxes consume (i.e. small mammals, fruit, earthworms, etc.), urban foxes often rely heavily on scavenged anthropogenic food (Baker et al., 2000;Doncaster et al., 1990;Saunders et al., 1993). For example, in Zurich, over 50% of fox stomachs examined contained anthropogenic food and this increased from suburban to city centre zones (Contesse et al., 2004). Synanthropic rodents (house mice Mus musculus and rats, e.g. black rat Rattus rattus), and chicken coops provide important resources for foxes (Contesse et al., 2004), while many invasive and garden plants also provide easily sourced fruit (e.g. olives, gs, blackberries, mulberries, grapes) (Fleming and Crawford, 2019;Forbes-Harper et al., 2017). Even in more rural landscapes human mediated food sources (orchard crops, livestock, introduced rodents) are utilised by foxes, with 97.4% of 230 scats analysed around the town of Bindoon, Western Australia containing anthropogenic sources of food (Dawson et al., 2016).
Home range analyses for red foxes have been carried out across the globe, with smaller home ranges in urban than rural locations (Main et al., 2020), presumably due to more reliable access to high quality food. But even within urban areas, there is a large degree of variability, with published home range sizes varying by orders of magnitude; between 3.6-460 ha (Table 1 and references therein). Understanding habitat selection would help understand the factors that contribute to this marked variability in space use. The aim of our study was, therefore, to investigate the home range and movement patterns of suburban red foxes in Perth, Western Australia. ¥ L signals lost (number of individuals); E 'excursions' excluded in estimating home range area. ‡ Potentially same animals (or same animals but different measure recorded).

Methods
Our study was approved by the Murdoch University Animal Ethics Committee (Permit number RW2612/13). We had approval to work under Scienti c License Number U6 2014-2016 and worked in accordance with the Department of Primary Industries and Regional Development (DPIRD) Permit to trap a Vertebrate Pest (r45).

Tracking
Site selection was largely based on local knowledge. A three-week period trialling cage traps to capture foxes resulted in no captures, and therefore we moved to leg hold trapping. Padded jaw leg hold traps were set in vegetated areas that were likely to be attractive fox habitat where animals would be funnelled through a small space (e.g. along fences, around gaps in fences, adjacent to road underpasses). Traps were checked and cleared within 3 hours of sunrise and on days forecast to be over 30°C, traps were re-checked mid-morning to ensure that no animals were re-trapped after they had already been cleared.
Between December 2015 and July 2016, we captured and equipped six red foxes with ATS W500 Wildlink GPS logging collars (Advanced Telemetry Solutions Australia, Queensland, Australia). All foxes were caught using Victor Softcatch® #1.5 padded jaw leg hold traps (Woodstream Corporation, USA). Traps were buried just below ground level and positioned along game trails or in areas frequented by foxes and incorporated a scent (e.g. fox anal glands, urine) or visual (e.g. bird feathers, mammal fur) lure to entice foxes into the traps. Traps were checked once every 12 hours (i.e. early in the morning and late afternoon). A 1.5 m noose pole (Ketch-All, USA) was used to physically restrain trapped foxes and transfer them to a covered crush cage (W.A. Poultry Equipment & Coast to Coast Vermin Traps, Australia) for observation and holding before processing. Captured foxes were chemically immobilised using 0.14 mg/kg Medetomidine (Troy Laboratories, Australia) administered I.M. by hand injection. Once effectively immobilised, foxes were removed from the crush cage and examined for injuries. Sex for each animal was recorded, and the weight and morphometrics assessed to support visual estimates that all animals were in good body condition. During recumbency, body temperature, respiration and heart rate were monitored. GPS collars ( Fig.  1) were only tted to animals >4 kg body weight. Once collars were tted, foxes were replaced into the covered crush cage and sedation was reversed by administering 0.4 mg/kg of Atipamezole (Troy Laboratories, Australia) I.M. by hand injection. Foxes were allowed to fully recover inside the covered crush cage for 15-20 min, prior to being transported and released at the site of capture. GPS collars were programmed to take 33 positions every 24 hours, with a focus of greater x frequency during nocturnal times to capture fox roaming activity in more detail: 30 min x interval between 18:00-08:00 h (28 location xes daily), 60 min x interval between 08:00-09:00 h and between 17:00-18:00 h (2 location xes daily), and 120 min x interval between 09:00-17:00 h (3 location xes daily).
Location data was recovered from GPS collars remotely by radio tracking collared foxes via VHF signal to within approximately 200 m of their current den or rest site, before establishing a UHF connection to a portable base station which downloaded the location data from the collar. The metropolitan landscape in which foxes were collared resulted in signi cant interference to receiving and establishing both VHF and UHF signals, and as such remote download of location data from collars in the eld was possible on only a few occasions. The majority of location data was retrieved when collared foxes were re-caught as part of ongoing City Council pest management operations.

Statistical analyses
Tracking We used the 'rhr' package (Signer and Balkenhol, 2015) in R to estimate site delity for each individual. Site delity was determined based on suggested methods by Spencer et al. (1990), where site delity exists (and therefore the animal possesses a home range) if the observed area an animal uses is smaller than the area used if an individual's movement was random (Munger 1984). We then used three packages to calculate home range estimates. We used the 'rhr' (Signer and Balkenhol, 2015) and 'adehabitatHR' (Calenge, 2019) packages in R to calculate 50% and 95% kernel density home range estimates (KDE), and the 'ctmm' package in R to calculate the Autocorrelationcorrected Kernel Density Estimator AKDE C (Calabrese et al., 2016;Fleming et al., 2015) for 50% and 95% kernels using the model of best t to each individual's data and accounting for error using HDOP (horizontal dilution of precision) values (Fleming et al., 2020). Fox #1.FJ displayed a signi cant range shift and to accommodate AKDEc analysis we subset the data to northern and southern home ranges using the 'segclust2d' package (Patin et al., 2018) using the home-range mode.
For habitat selection analyses, all GPS locations were compared with available geospatial databases, from which we extracted data for land use category (residential, industrial, parkland, transport corridors, water bodies, commercial/education facilities) (Geoscience Australia, 2018). We extracted the Euclidean distance to the nearest road ( exploited a thin coastal reserve between his southern and northern home ranges, we divided his data into four sections and calculated potential land use categories for each of these sections for comparison with the observed data. We estimated minimal speed of movement (m/h) from consecutive xes (i.e. every 30 minutes overnight, every 2 h during daylight) by trigonometry and converted for differences in the x schedules or missed data by calculating an hourly displacement as a surrogate of animal speed. Using the glm function in 'lme4' package in R (Bates et al., 2012), we carried out generalised linear regression analyses with speed as dependent variable and time of day (day 06:00 h-18:00 h or night 19:00 h-05:00 h), temperature (records from the collar), land use, distance to nearest road, whether the site was on a road reserve, grass presence, vegetation presence, and vegetation height as predictor variables. To identify which factors made the strongest contribution to model t for each individual, a set of best-t models was generated using combinations of predictor variables using the dredge function in the 'MuMIn' package (Barton and Barton, 2015) in R. We compared models using Akaike Information Criterion (AIC) (Burnham & Anderson, 2002) and calculated model weights (w i ) for each of the top models (DAIC<2). Each predictor variable was mean-standardised, allowing calculation of standardised β values, but raw values were also calculated to evaluate effects for each individual variable (where the other variables were held constant) using ggemmeans in the 'ggeffects' package (Lüdecke, 2018) in R. Model assumptions were checked and nal models were validated by examining the residuals using the 'DHARMa' package (Hartig, 2020) in R. Values are presented as means ± 1SD.

Results
The ve foxes were tracked for an average of 102 ± 81 (range 25-226) days (Table 2). Four individuals had stationary home ranges and showed site delity using both the linearity index and the mean squared distance from the centre of activity. Two individuals did not show site delity (#1.FJ, #3.ES). Female #3.ES moved her core activity area mid-way through the monitoring period and was found dead after 1 month, away from the centre of her previous home range. Male #1.FJ was tracked for the longest and across the greatest distance and showed a distinct range shift from a southern to a northern home range, making extensive use of a narrow coastal reserve to move northsouth (Fig. 2) for at least part of the tracking period.
All GPS location data used in analyses had a horizontal dilution of precision (HDOP) of < 3, and were therefore considered to be reliable estimates of location. Using these data to calculate autocorrelated-corrected home ranges, the three females had a core home range (50% kernel) averaging 37 ± 20 ha (range 22-60ha) or a 95% kernel averaging 174 ± 130 ha (range 92-324 ha) ( Table 2). Male #3.HX had a core home range of 95 ha or a 95% kernel covering 352 ha and largely remained within a bushland reserve, only occasionally moving along road reserves or exploring adjacent industrial properties. By contrast, male #1.FJ covered an area ~ 4 or ~ 6 times this: having a core home range of 371 ha or 95% kernel of 2,062 ha (the average of area estimates for the northern and southern parts of his home range estimated separately). He frequently moved between locations, remaining around core patches for days or weeks before moving again. This animal travelled a minimum of 2,500 km in the 7.5 months it was tracked, covering a total area (95%AKDE c ) of 7,257 ha (CI 6,011-8,618 ha).
These movements were not dispersal movements, because he regularly retraced his tracks. Instead, they conform to the de nition of shifts (sensu Cavallini, 1996), where the animal moved between activity centres. He regularly travelled many kilometres overnight to return to a previously-used diurnal rest site. Sighting of this animal on camera trap (Fig. 3c) around a wetland where 13 swans had been found beheaded showed that he was in excellent condition. He was eventually trapped as part of pest animal control (after the collar battery life had expired) and euthanised.
There was a strong correlation between the three methods of home range assessment used, although we found that AKDE c yielded values that were 5-104% greater than adehabitatHR or rhr for the two animals that moved during the study period (#1.FJ and #3.ES) compared with values that were 17% smaller than adehabitatHR or rhr for the other three animals (Table 3).
Compared with the land use categories present in a 1 km and 3 km radius buffers centred about the geometric median point of the animal's home range, all ve foxes showed statistically signi cant avoidance of residential locations and signi cant preference for parkland (Table 4). Bushland reserves, golf courses, and water reserves were especially preferred locations. Four golf courses were used by two individuals (Fig. 4), where their activities were focussed around the native vegetation between the greens. Waterbodies were also preferred sites (Fig. 3) where foxes were recorded consuming waterbirds during the course of this study (Jo Taylor, City of Stirling).
A summary of the modelling of describing speed of movements (minimum displacement distance between consecutive locations) for each fox is shown in Fig. 5. All ve foxes showed a signi cant effect of time of day (day/night) on speed, remaining reasonably immobile during daylight hours. The inclusion of collar temperature improved model t, reinforcing that foxes only moved overnight, when temperatures were lowest (shown for one individual in Fig. 6a). The two males (#1.FJ and #3.HX) showed a time of day x distance to road interaction term, moving fastest (i.e. commuting) when they were in close proximity to the roads and slowest (i.e. foraging) when they were further from the road (Fig. 6b). Fox #4.CC showed overall faster movement when closer to the roads. Foxes #1.FJ and #5.DB were signi cantly slower when moving through vegetated areas, but showed a positive relationship with vegetation height, suggesting that they were more likely foraging amongst lower vegetation than tall canopy. Foxes #2.ES and #3.HX moved signi cantly slower when located on grassed areas. There were few data for land use that was not parkland, and therefore the effect of land use on speed of movement was not signi cant for any of the foxes. Fox #2.ES showed faster movements around water bodies (but there were very few datapoints for this female. Fox #5.DB showed fastest speed around residential areas and slower around parkland (compared with the baseline of education land use). Fox #3.HX showed faster movement around road reserves and slowest movements across parkland.

Discussion
Red foxes in urban Perth showed signi cant preference for urban parkland and signi cant avoidance of residential areas. Home range estimates for four individuals tracked were reasonably consistent with some of the larger published values for red foxes (Table 1), but one of the animals we tracked covered an enormous distance, far greater than previous estimates of home range for urban foxes. We also noted that individuals with larger home ranges moved faster when they were in proximity to roads.
Comparison with published urban fox home range studies Our largest home range estimate (7,257 [6,011-8,618] ha or 72 km 2 ) stands out from the remaining data. This is partly due to the shape of the home range, with this fox using a narrow 25 km-long north-south coastal reserve. We applied a range of home range estimators, but all methods yielded similarly large values for this animal. Even segmenting the home range to a southern and northern distribution provided estimates up to an order of magnitude larger than the other foxes tracked. The average home range across 16 urban fox studies we reviewed was 86 ha, with a maximum of 718 ha recorded for a fox in urban Toronto, Canada (Rosatte and Allan, 2009) (Table 1). However, foxes are capable of using larger home ranges. In a study tracking 52 foxes across Sweden and Norway using GPS collars, Walton et al. (2017) reported three fox home ranges located in their three southern study areas -where there was greater incidence of agriculture and anthropogenic disturbance -were larger than 10 km 2 (90% LoCoH-k, n = 44), whilst in their northern study area all, bar one, fox home ranges were larger than 10 km 2 (90% LoCoH-k, average 1,950 ± 1,180 ha, n = 8). Similarly in Toronto, Canada, Rosatte and Allan (2009) reported a juvenile male fox that dispersed twice and did not settle down, resulting in an annual range of 18,285 ha (182.9 km 2 ) (data excluded from their calculation of home range analysis).
'Excursions' beyond the normal home range may represent exploration preceding natal dispersal. Of nine foxes in Bristol, UK, six were recorded making exploratory trips prior to dispersal (Woollard and Harris, 1990). These trips, lasting one night to one week, were made to areas that were never revisited, and exploratory trips were also carried out by foxes that did not eventually disperse. Disappearances and excursions were also recorded for non-dispersing animals, for example, by males searching for a mate, or directed towards speci c food sources by lactating vixens (Niewold, 1980). In a review of fox home ranges, Cavallini (1996) recorded a fraction of the red fox population acting as 'transient', 'nomadic' or ' oating', and recognised that few authors report information about these animals because they were di cult to follow and study (Lovari et al., 1994;Zabel and Taggart, 1989;Zimen, 1984).
Published home range and space use calculations derived from VHF tracking are highly likely to have underestimated fox home ranges. Six (of 15) published urban fox studies using VHF collars reported issues with lost signals (Table 1). While some are due to transmitter or receiver malfunction, missing data is likely to re ect times when the animal moved beyond the receiver range. For example, Kolb (1984) noted that 7 of 15 foxes tracked with VHF 'disappeared' and long distance unpredictable 'expeditions' away from their home ranges were reported on numerous occasions. Also, Marks and Bloom eld (2006) tted 20 foxes with VHF collars, but insu cient data were collected from nine foxes to permit reliable home-range estimates; only their daytime positions and diurnal shelter locations could be analysed for home range estimates. Half the published home range estimates have similarly ignored data for animals that moved beyond receiver range or excluded 'excursions' in estimating average ranges (Table 1). The description of methods for the remaining studies do not indicate how excursions or lost signals were handled.
A study comparing the home ranges of 10 VHF-and 3 GPS-tracked red foxes indicated that the GPS data revealed home ranges around 10 times larger than those derived from VHF technology (Towerton et al., 2016). Using GPS trackers, Hradsy et al. (2017) recorded home range estimates between 33 to >2,500 ha in a predominately forested landscape and this technology enabled tracking of foxes that 'commuted' more than 5 km between daytime refuge and farmland or towns at night. GPS tracking has also revealed substantially longer dispersal distances for red foxes than had been reported previously, with distances of 132-1,036 km recorded and cumulative distances moved ve times longer than straight-line distances (recorded using fewer data points, such as collected through VHF) (Walton et al., 2018). These ndings reinforce the value of ne-scale temporal position data in revealing aspects of the biology of tracked animals.
The GPS devices we used required proximity to download data remotely but we were successful only three times for #1.FJ and only once each for the other four animals. It would have been informative to have had seasonal data for our animals to determine whether there were changes in space use over the year. Such questions may be achievable with satellite technology that do not rely on base station downloads. Analytical methods will also result in a difference in home range area estimates. We found differences in home range area estimates using different statistical methods. Compared with estimates that ignore temporal autocorrelation, the use of autocorrelationcorrected analysis yields larger home range estimates for animals that move across their home range over a matter of hours or days.

Habitat preference
Foxes show preference for parkland over built-up urban spaces Perth foxes preferentially used parkland and water reserves. Marks and Bloom eld (2006) report that 61% of natal dens used by 20 tracked foxes in Melbourne were in secluded places where public access was restricted, including schools, cemeteries, industrial and commercial lands, fenced parks, sporting clubs and lands maintained by the National Trust, councils, port authorities and areas maintained by the Commonwealth Government where public entry was prohibited. To this list of preferred locations, we add coastal reserves and golf courses. O'Connor et al., (2021) similarly report extensive use of coastal reserves at the Sunshine Coast, Queensland, Australia. These reserves provide extensive linear connectivity and there is likely to be minimal disturbance from people over night. Similarly, golf courses are usually quiet at night, with little disturbance, while some golf courses also support potential prey, such as introduced European rabbit (Oryctolagus cuniculus) or waterbird populations. Similarly, Rosatte and Allan (2009) reported that half the foxes for which they had su cient data (14/28 foxes) made extensive use of golf courses in Toronto, and Kolb (1984) noted that 75% of locations for one male fox in Edinburgh, UK were on a golf course.

Foxes avoid residential areas
Red foxes have an opportunist diet and exploit many anthropogenic food sources, and are regarded as well-adapted to urban areas. We were therefore surprised to observe that the tracked foxes showed strong avoidance of residential areas, and when they did pass through such areas, it was often when travelling rapidly between foraging areas. Similar avoidance of high human densities has been found for foxes in Toronto, Canada (Adkins and Stott, 1998) and in Virginia, USA (Rountree, 2004). By contrast, regular exploitation of gardens has been recorded in Bristol, UK, where foxes visited gardens where they were most frequently provided with food by residents (Dorning and Harris, 2017).
Foxes are likely to be avoiding people, but they may also be showing an aversion to pet dogs (Canis familiaris). Marks and Bloom eld (2006) noted that natal dens around Melbourne were more likely to be associated with properties that did not contain domestic dogs, while O'Connor et al. (2021) noted antagonistic behaviour by domestic dogs towards foxes on camera traps positioned around dens. We found foxes using sites with more cover, but in North America where they co-exist with coyotes, which prefer natural vegetation, foxes avoid these locations and select for more open areas (Mueller et al., 2018), or show no speci c habitat preference (Pluemer et al., 2019). The habitat preference of foxes could therefore re ect the probability of encountering other canids and is likely to vary between sites accordingly.

Foxes avoid urban roads
The two male foxes we tracked, which moved beyond the boundaries of a single urban reserve, avoided roads or sped up when they were in proximity to roads. We also noted that home ranges appeared to be bounded by roads. Some of these roads were busy highways, which could be acting as barriers to movements; however, many roads were small residential streets that would have minimal tra c. Roads may therefore form natural boundaries to territorial foxes.
Roadstrike is a leading cause of mortality for red foxes in the UK and USA (Gosselink et al., 2007;Harris, 1981a;Harris and Smith, 1987;Lewis et al., 1993;Soulsbury et al., 2007). In Bristol, UK, there is evidence that foxes change their activity patterns, avoiding roads prior to midnight when tra c volume is greater . Road death is likely to be biased towards individuals that disperse further, e.g. males and juveniles , and resident animals occupying high quality sites may therefore avoid using roads where possible.
Rather than using roads, quieter corridors such as railway and road reserves (Lewis et al., 1993;Rosatte and Allan, 2009), ravines associated with waterways (Rosatte and Allan, 2009) or coastal reserves (this study) provide important access and are therefore important focal points to target for management.

Management implications of this study
As is likely for cities across the world, Perth fox populations are controlled on an ad hoc basis by pest management services contracted to Shire or City Councils. Implementation of control measures is costly, limiting the scope of control and therefore its success. Furthermore, control measures necessarily require live trapping to avoid risk to stray and domestic pets, and this requires local knowledge of the home ranges and dispersal patterns. Sound understanding of how foxes use resources and habitat across the urban landscape should assist with targeting management to improve success in controlling populations of this cryptic predator.

Conclusions
The di culties of tracking animals across private property in urban landscapes has been particularly challenging. Improvements in tracking technology have a great deal of value in revealing how urban exploiters adapt to these landscapes. Robust data for locations and regular time sampling through GPS tracking are likely to con rm that we have been vastly underestimating the movements of urban foxes. Understanding the movement patterns of these cryptic carnivores will bene t management for wildlife conservation and disease transmission.

Figure 1
Attaching a collar to a red fox (Vulpes vulpes) in Perth, Western Australia.

Figure 2
Map of movements of ve foxes.    Beta estimates ± 95% con dence intervals for predictor variables that were retained in the top modelset (AICc/t-AIC <2) describing their speed of movements (minimum displacement distance for half-hour intervals at night, and 1-or 2-hour intervals during day) for each of ve red fox (Vulpes vulpes) (a-e). Variables with con dence intervals that do not overlap with zero are considered in uential; those with signi cant p-values are indicated with bold lines. Collar temperature was linked with time of day (day=1/night=0) for all ve individuals; these values are shown in black. There was a single top model describing speed of movements (minimum displacement distance between consecutive locations) for Fox #1.FJ which likely accounts for the small con dence intervals on estimates for this fox (a); the other four foxes required model averaging amongst the top model set.