Keywords: anthropogenic, diet, red fox, stable isotope analysis, urbanisation, Vulpes vulpes

2.1. Dietary Analysis

Vibrissae samples were collected from a total of 93 fox carcasses sourced by the Animal and Plant Health Agency (APHA) as part of the annual monitoring of the cestode parasite (Echinococcus multilocularis) in the British fox population. The ethical review was deemed favourable by the Nottingham Trent University Ethical Review Committee. Georeferenced samples were collected between April 2021 and January 2023 via a network of gamekeepers and landowners. Carcasses were frozen and defrosted at 4°C before examination where sex and age (subadult: < 12 months; adult: ≥ 12 months) were determined, the latter via tooth analysis (Giraudoux, Romig, and Eckert (2001)). No cubs (< 6 months of age) were collected. Date of death was used to assign season to each carcass: spring (March–May), summer (June–August), autumn (September–November) and winter (December–February). Up to five of the longest vibrissae from each fox carcass were plucked and sent to Nottingham Trent University where they were stored at 5°C until further analysis.

Preparation of vibrissae was conducted incorporating recommendations from SUERC (Scottish Universities Environmental Research Centre). Vibrissae samples were initially cleaned using a 2:1 chloroform:methanol solution and air‐dried for 24 h. The base of each vibrissa was cut to lengths of between 3 and 5 mm to achieve individual sample weights of ~0.5 mg. The growth rates of red fox vibrissae have been estimated at 0.32 mm/day (Jacquier et al. 2020); therefore, each sample represented between 9.3 and 15.5 days of previous dietary information. Each sample was placed in a tin capsule (5 × 3.5 mm) and sent for consecutive C and N SIA at SUERC. A continuous‐flow mass spectrometer (Delta Plus XP; Thermo Scientific, Bremen, Germany) and elemental analyser (vario PYRO cube; Elementar, Langenselbold, Germany) were used to ascertain the isotope ratios of C and N from each sample. Three internal standards (GEL, ALAGEL and GLYGEL) were used after every 10 samples to maintain instrumental validity. Isotope ratios were denoted as δ values (δ¹³C/ δ¹⁵N) in permil (%) based on the equation:

where R _sample is the respective ratio of ¹³C/¹²C or ¹⁵N/¹⁴N and R _standard is the international references of Vienna PeeDee Belemnite and atmospheric N₂ for carbon and nitrogen, respectively.

To identify the proportional contribution of different food types in the fox samples, we included six broad food groups from the published fox diet literature encompassing both anthropogenic foods and natural diet items, these were: human food, pet food, mammals, birds, invertebrates and fruit (Harris 1981; Contesse et al. 2004; Soe et al. 2017). These represented only potential food sources, in which the data were not collected directly, but the information regarding the δ¹³C and δ¹⁵N of each food group was derived from the published literature or by requesting data from the authors (Table 1).

2.2. Trophic Discrimination Factors and Stable Isotope Values of Dietary Resources

To account for the physiological enrichment of C and N isotopes in consumers from dietary sources, trophic discrimination factors (TDFs) (i.e., the difference in isotopic values between food source and consumer tissue) must be applied (Phillips et al. 2014). TDFs have been calculated for various red fox tissues (Roth and Hobson 2000), but do not exist for vibrissae. However, TDF values for wolves ( Canis lupus ), a related canid species, suggest very similar values between fur and vibrissae (McLaren, Crawshaw, and Patterson 2015). The red fox TDF values for C and N (i.e., Δ¹³C and Δ¹⁵N) determined by Roth and Hobson (2000) were generated from samples taken from captive fur‐farmed foxes fed on a single pellet diet. However, Δ¹³C values can vary due to factors such as dietary source (i.e., C3 or C4 plant source), which we expect to be mixed in the context of natural and anthropogenic food (Stephens et al. 2022). We therefore used the 2.0% (±0.2 SD) and 3.3% (±0.2 SD) TDF values determined by Stephens, Shipley, and Moll (2023) for Δ¹³C and Δ¹⁵N, respectively. These values are similar to Roth and Hobson (2000), 2.6% for Δ¹³C and 3.3% for Δ¹⁵N, although the error terms were not clearly provided.

2.3. Measuring Urbanisation

Impervious surface density (ISD), which measures the percentage of sealed surface areas such as roads and buildings, is a common proxy measure for urbanisation (Wu 2014). We therefore used ISD to determine urbanisation within a 500 m radius of the location of each fox carcass following Scholz et al. (2020), which also allows for our results to be comparative to this study. Mean ISD was determined per sample using a 2018 COPERNICUS raster map with a resolution of 20 m (https://land.copernicus.eu). As defined by Scholz et al. (2020), individuals were considered ‘urban’ if ISD was ≥ 25% and ‘rural’ if < 25%. This resulted in 23 of 93 samples classified as ‘urban’ (Figure 1).

A six‐figure Ordnance Survey grid reference of each carcass location was provided by the APHA, the centre of the grid location formed the origin from which a circle with a 500 m radius was drawn. The resulting area covered 0.79 km², which acknowledged fox range variability and the tendency for smaller urban ranges. For example, fox range was 0.75 km² at the highest human population densities (Main et al. 2020). Other research has noted even smaller urban home ranges (e.g., 0.14 km²; Tolhurst et al. 2016). We assumed foxes were resident rather than dispersing or itinerant, since most dispersing individuals are sub‐adult males during autumn and winter periods (Harris and Trewhella 1988).

Lastly, since some of the foxes originated from similar locations, we grouped datapoints from foxes that were within 500m and killed within seven days of each other to control for a lack of independence between these points. We believe these criteria sufficiently account for any non‐independence both spatially, as it accords with our defined fox home range, and temporally, since a minimum of seven days should reflect an adequate difference in diet. A separate 'Location ID' category was formed in which 34 of 93 datapoints were placed into 13 groups (n = 72).

2.4. Statistical Analysis

All statistical analyses, other than permutational multivariate analyses of variance (PERMANOVAs), were conducted in R Version 4.3.2 (R Core Team 2023). PERMANOVA tests were run in MATLAB (2023) using the Fathom Toolbox (Jones 2017). To answer our first and third objectives, we used Linear Mixed‐Effect Models (LMMs) on each of δ¹³C and δ¹⁵N separately, which included the (fixed effect) predictor variables of location (rural and urban), sex (male and female), age (< 12 and ≥ 12 months) and season (spring, summer, autumn and winter). Location ID was included as a random effect to control for the geographical non‐independence of some datapoints.

Following data exploration and model‐checking, the response variable in each case was modelled with a Gaussian distribution and an identity link function. We used the lme4 and lmerTest packages in R to conduct the LMMs (Bates et al. 2015; Kuznetsova et al. 2017). Overall, there were three missing data points, one for age and two for season. To answer the second objective, to determine the extent of anthropogenic food consumption compared to other food resources in urban and rural foxes, we estimated the proportional relative contribution of each food group in their respective diets using SIMMR, a Bayesian isotope mixing model R package (Govan et al. 2023). The mixing model included the TDFs (and their standard deviations), the isotope data from our fox samples and the δ¹³C and δ¹⁵N concentrations of each food source to account for differential elemental concentrations that may exist between food sources of omnivorous species (Phillips and Koch 2002). Since human hair was used as a proxy for human food, we used the elemental concentrations as estimated by Hopkins and Ferguson (2012) for this food group. Since TDFs can influence model outputs (Phillips et al. 2014), the TDFs of Roth and Hobson (2000) were included in an additional mixing model to compare both model outputs. Both SIMMR models ran at the default settings of 10,000 iterations and a burn‐in of 1000. The mixing models were checked for successful convergence, all food sources had a value of 1.

To determine the difference in isotopic variation between the food sources, PERMANOVA tests were conducted.

Sample sizes and the spread of isotope values for each variable under investigation can be found in the Appendix A. Table A1 and Figures A1, A2, A3, A4.

3.1. Location

For each element, location significantly predicted both δ¹³C and δ¹⁵N (Table 2), with urban foxes demonstrating higher δ¹³C and lower δ¹⁵N than rural foxes (Figure 2). The variance for Location ID (random effect) was greater for δ¹³C than δ¹⁵N (Table 3).

3.2. The Proportion of Anthropogenic Food in Rural and Urban British Fox Diet

Prior to modelling, significant differences between the δ¹³C and δ¹⁵N of each food group were revealed across each pairwise comparison (Table 4).

The Bayesian mixing model showed that human and pet food (anthropogenic food) each contributed similarly to the urban fox diet, constituting (as percentages) a combined 34.6% of their diet compared to 6.0% in rural fox diets. Birds were the largest dietary source for both urban and rural foxes, despite a lower portion in the urban fox diet. Mammals formed a similar proportion of the rural fox diet alongside birds but formed a lower amount of the urban diet compared to anthropogenic food (Table 5). Figure 3 shows the food sources with the spread of rural and urban fox isotope values, including the respective 95% credible intervals for each food source.

In contrast, the mixing model which used the TDFs from Roth and Hobson (2000) demonstrated a comparable amount of anthropogenic food in rural fox diet (4.6%); however there was a smaller proportion for urban foxes (21.4%). In this model, mammals were the largest food source for rural foxes, followed by fruit. Mammals and birds still contributed similarly to the urban fox diet, despite forming a lower proportion overall; however, fruit was the food group with the greatest proportion (Table 6). Figure 4 demonstrates the 95% confidence intervals for this model.

3.3. Sex, Age and Season

For δ¹³C, females demonstrated significantly higher values than males, however, for δ¹⁵N, there was no significant effect of sex (Table 2; Figure 5).

For each element, age (subadult and adult) was not significant for δ¹³C or δ¹⁵N (Table 2). Additionally, season was also not significant for δ¹³C or δ¹⁵N (Table 2).

4.1. Anthropogenic Food Use by Foxes in Britain

Our mixing model indicated that anthropogenic food (human and pet food) formed a larger dietary proportion in British urban foxes than rural foxes. The higher δ¹³C and lower δ¹⁵N of urban compared to rural foxes is a pattern demonstrated in previous studies that constrast between conspecifics in urban environments to those in areas classified as rural/less impacted by human activity. Examples include red foxes in Germany (Scholz et al. 2020), San Joaquin kit foxes ( Vulpes macrotis mutica ) in California, USA (Newsome et al. 2010) and caracals ( Caracal caracal ) in South Africa (Leighton et al. 2023). Values of δ¹³C in British anthropogenic food are lower compared to US anthropogenic food, due to the greater use of corn in the US food industry (Jahren and Kraft 2008; Hülsemann et al. 2015). Whilst this may impact the ability to distinguish between natural and anthropogenic food use, our results reinforce that anthropogenic use by foxes in Britain can be determined from SIA.

Furthermore, an additional mixing model that used the TDFs from the highly cited Roth and Hobson (2000) study led to a lower proportion of anthropogenic food in urban fox diets (21.4%). These TDFs differed slightly from the ones in our main mixing model for Δ¹³C (+0.6%); additionally, error terms were not clearly provided and therefore not included. Nevertheless, their use led to a reasonable difference in anthropogenic food dietary proportions. The TDFs in the main model more accurately reflect the fox diet, since they consider the impact of a mixed (C3 and C4) diet, which would be expected for urban foxes.

4.2. The Influence of Sex, Age and Season on Isotopic Values

We predicted there would be no isotopic differences between males and females; however, females demonstrated higher δ¹³C than males, which suggested their diet contained more anthropogenic food than males. We hypothesise that adult females may utilise this resource more when raising offspring. During the cubbing season in spring, reproducing females are known to provide cubs with more energetically optimised food, such as larger food items, compared to their own diet (Lanszki et al. 2023). The abundant, predictable and likely energy‐dense anthropogenic food items may constitute optimal energy intake. Indeed, females have demonstrated greater use of intentional provisioning during this period (Dorning and Harris 2017; Fawcett, Fawcett, and Soulsbury 2017). In comparison, males may not utilise anthropogenic food sources as much as females, since resident males may reduce foraging from intentional provisioning patches during the winter (Dorning and Harris 2017), as they likely spend time making extra territorial excursions in search of mating opportunities (Iossa et al. 2008). Whilst the results did not demonstrate a significant influence of sex on δ¹⁵N, females generally had lower values than males which may be more notable in a larger dataset.

Age did not impact fox stable isotope values, since we found no difference in the δ¹³C and δ¹⁵N between subadults and adults, which confirmed our prediction. Age may covary with social status, as we assume that older individuals are more likely to be dominant (Baker et al. 1998). Since dominants and subordinates use food from provisioning households differently (Dorning and Harris 2017), dissimilarities in isotopic patterns for age might be expected. However, this assumes a degree of rigidity in dominance retention that may vary by sex and population density (Iossa et al. 2009). We hypothesise that age may also play a role in the consumption of anthropogenic food more strongly in females than males. Since urban female use of anthropogenic feeding sites is greater in spring (Fawcett, Fawcett, and Soulsbury 2017; Dorning and Harris 2017) and assuming these females are raising offspring, we might expect differences in anthropogenic food use between older breeding and younger non‐breeding females. Additionally, many yearling females fail to reproduce (Iossa et al. 2008). Furthermore, studies have shown that subadults of both sexes (< 12 months old) differed in both dietary item consumption and dietary breadth compared to adult females, although there was no difference between subadults and adult males (Kidawa and Kowalczyk 2011; Forbes‐Harper et al. 2017). Nevertheless, these potential age and social status effects may be obscured isotopically by the abundance of multiple sources of anthropogenic foods in an urban environment, whereby most urban fox individuals consume anthropogenic food indiscriminately. However, there appears to at least be a difference in anthropogenic food use at the level of sex.

Our prediction that winter would have significantly lower δ¹⁵N and higher δ¹³C compared to the other seasons was not confirmed. Across seasons, δ¹⁵N was the lowest and δ¹³C was the highest in the winter, however this was not statistically significant. Despite our results, urban foxes tend to increase their anthropogenic food use during the winter to supplement their diet when natural food abundance is low (e.g., Harris 1981). Indeed, the number of visiting urban foxes to households that intentionally provide food is greatest during the winter (Dorning and Harris 2017). In more natural habitats, foxes can consume more carrion in the winter when other prey species are scarce (Goszczyńsk 1986; Sidorovich, Sidorovich, and Izotova 2006). Scholz et al. (2020) did not find the time of year to influence isotopic values in red foxes, although this may also result from analytical differences. We used a categorical predictor, season, whereas they used a continuous predictor of Julian day within a linear model.

4.3. Implications of Anthropogenic Food Use

In our study, foxes in urban areas and females (more than males) consumed the most anthropogenic food and are therefore more likely to be impacted by any positive or negative consequences. For example, synanthropes may experience improved body condition (i.e., energy reserves calculated using measures of body mass and skeletal morphology; Lyons et al. 2017); however, such mass increases may also be negatively associated with measures of obesity or insulin resistance (Banks et al. 2003; Leith et al. 2020). In contrast, anthropogenic food consumption in coyotes ( Canis latrans ) has been linked to poorer body condition, lower protein intake, mange infestation, human–conflict behaviour and greater microbiome diversity (Murray et al. 2015b; Sugden et al. 2020). The body mass and skeletal size of foxes have been shown to increase across a rural‐to‐urban gradient, although their body condition remained unchanged (Stepkovitch et al. 2019). However, research has not yet explored urban fox body condition in relation to diet using SIA. We might find, as in previous research, a subset of the population to be adversely impacted by anthropogenic food use (e.g., Banks et al. 2003; Leith et al. 2020). Alternatively, even if body condition itself is unaffected by anthropogenic food use, synanthropes may experience other health impacts such as malnutrition and oxidative stress (Bernat‐Ponce et al. 2023). Lastly, any potential impact of anthropogenic food consumption by foxes may be limited to the winter season. Whilst our results did not indicate that anthropogenic food use is potentially greater during the winter, previous research has shown otherwise (Harris 1981; Doncaster, Dickman, and Macdonald 1990; Saunders et al. 1993). Whilst such consumption may be prevalent in other seasons, a potential urban population‐level impact of anthropogenic food may be most pronounced during the winter.

4.4. Fruit Consumption in Red Fox Diet

Notably, fruit formed 26.2% and 16.5% of rural and urban fox diets, respectively, despite the distance between fruit and the fox samples within isotopic space. Mixing models assume equal concentrations of carbon and nitrogen from each source, which can impact estimates if violated (Phillips and Koch 2002). The inclusion of elemental concentrations accounted for the relatively lower nitrogen of fruit compared to other sources; the exclusion of which resulted in negligible dietary fruit proportions. In previous urban fox diet studies in Britain, fruit formed between 5% and 9% of the diet (Harris 1981; Doncaster, Dickman, and Macdonald 1990; Saunders et al. 1993). Fruit (under the category of ‘plant’) formed approximately 37% (frequency of occurrence) in fox diets across Europe (Soe et al. 2017). On a continental scale, urbanisation (i.e., human footprint index) was positively associated with fruit consumption (Castañeda et al. 2022). Whilst our findings demonstrated a lower proportion of fruit in urban areas compared to rural fox diets, our mixing model results fall within a reasonable estimate. Additionally, previous British urban fox diet studies may have under‐reported fruit proportions as a consequence of sampling method biases (Castañeda et al. 2022). Notably, fruit proportions increased when using the TDFs from Roth and Hobson (2000), which again demonstrates the importance of TDFs in modelling.

4.5. Limitations and Future Research

There are multiple methods for defining urbanisation; we followed the same impervious surface density method as Scholz et al. (2020) to produce comparative results. Future research could consider the relationship between additional urban metrics and fox isotopes. Metrics might include developed open spaces (such as parks and gardens), since δ¹³C can be positively correlated with open space (Handler, Lonsdorf, and Ardia 2020), as range size can be influenced by residential garden size and connectivity (Baker et al. 2000; Tolhurst et al. 2020). Furthermore, more data are required to sufficiently examine interactions between location and sex. Practically, more rural foxes were collected than urban foxes due to the biased nature of fox control, the majority of which was conducted by rural gamekeepers. The low urban to high rural fox ratio in Scholz et al. (2020) likely reflects the difficulty in attaining urban fox samples.

The potential interaction between sex and age in anthropogenic food use could be further elucidated by treating age as a continuous variable rather than a binary variable (subadult and adult), since dominants have been shown to live around 4.5 and subordinates 2.1 years on average (Baker et al. 1998). Future work that treats age in years may notice potential age effects in anthropogenic food consumption in foxes at least older than 2 years, due to an increased likelihood of dominant status.

Some of our isotopic data points were plotted outside the convex hull defined by the food sources, where rural data points had higher δ¹⁵N. This could suggest the existence of an unconsidered food group utilised by the foxes or that the food group isotopic range was not fully representative. Alternatively, these data points may result from factors such as fertilisers used in arable farming, which inflate nitrogen values (Schmidt and Ostle 1999); these are location‐dependent and difficult to consider at the national scale. Therefore, future work could also explore the impact of location on isotopic variation in wildlife food sources across Britain.

Lastly, it should be clearly stated that anthropogenic food use in foxes is isotopically defined by the presence of C₄ plants (i.e., corn/maize) in these resources (Jahren and Kraft 2008; Newsome et al. 2010), which does not cover the full range of potential anthropogenic foods (e.g., garden or allotment produce; Bateman and Fleming 2012). Whilst C₄‐heavy anthropogenic food is most likely associated with poor nutrition and negative health outcomes (Cordain et al. 2005; Banks et al. 2003; Leith et al. 2020), SIA does not cover the full range of possible anthropogenic resources in fox diets.

TABLE A1.

The number of data points for each variable overall and within the main variable of location (rural/urban).

1. Baker, P. J. , Funk S. M., Harris S., and White P. C. L.. 2000. “Flexible Spatial Organization of Urban Foxes, Vulpes vulpes , Before and During an Outbreak of Sarcoptic Mange.” Animal Behaviour 59, no. 1: 127–146. 10.1006/anbe.1999.1285. [DOI] [PubMed] [Google Scholar]
2. Baker, P. J. , Robertson C. P. J., Funk S. M., and Harris S.. 1998. “Potential Fitness Benefits of Group Living in the Red Fox.” Animal Behaviour 56, no. 6: 1411–1424. 10.1006/anbe.1998.0950. [DOI] [PubMed] [Google Scholar]
3. Balestrieri, A. , Remonti L., and Prigioni C.. 2011. “Assessing Carnivore Diet by Faecal Samples and Stomach Contents: A Case Study With Alpine Red Foxes.” Open Life Sciences 6, no. 2: 283–292. 10.2478/s11535-010-0106-1. [DOI] [Google Scholar]
4. Banks, W. A. , Altmann J., Sapolsky R. M., Phillips‐Conroy J. E., and Morley J. E.. 2003. “Serum Leptin Levels as a Marker for a Syndrome X‐Like Condition in Wild Baboons.” Journal of Clinical Endocrinology and Metabolism 88, no. 3: 1234–1240. 10.1210/jc.2002-021695. [DOI] [PubMed] [Google Scholar]
5. Bateman, P. W. , and Fleming P. A.. 2012. “Big City Life: Carnivores in Urban Environments.” Journal of Zoology 287, no. 1: 1–23. 10.1111/j.1469-7998.2011.00887.x. [DOI] [Google Scholar]
6. Bates, D. , Mächler M., Bolker B., and Walker S.. 2015. “Fitting Linear Mixed‐Effects Models Using lme4.” Journal of Statistical Software 67, no. 1: 1–48. 10.18637/jss.v067.i01. [DOI] [Google Scholar]
7. Bernat‐Ponce, E. , Gil‐Delgado J. A., Guardiola J. V., and López‐Iborra G. M.. 2023. “Eating in the City: Experimental Effect of Anthropogenic Food Resources on the Body Condition, Nutritional Status, and Oxidative Stress of an Urban Bioindicator Passerine.” Journal of Experimental Zoology Part A: Ecological and Integrative Physiology 339, no. 9: 803–815. 10.1002/jez.2730. [DOI] [PubMed] [Google Scholar]
8. Bol, R. , Marsh J., and Heaton T. H. E.. 2007. “Multiple Stable Isotope (¹⁸O, ¹³C, ¹⁵N and ³⁴S) Analysis of Human Hair to Identify the Recent Migrants in a Rural Community in SW England.” Rapid Communications in Mass Spectrometry 21, no. 18: 2951–2954. 10.1002/rcm.3168. [DOI] [PubMed] [Google Scholar]
9. Carpenter, M. , and Savage A. M.. 2021. “Nutrient Availability in Urban Food Waste: Carbohydrate Bias in the Philadelphia–Camden Urban Matrix.” Journal of Urban Ecology 7, no. 1: juab012. 10.1093/jue/juab012. [DOI] [Google Scholar]
10. Castañeda, I. , Doherty T. S., Fleming P. A., Stobo‐Wilson A. M., Woinarski J. C., and Newsome T. M.. 2022. “Variation in Red Fox Vulpes vulpes Diet in Five Continents.” Mammal Review 52, no. 3: 328–342. 10.1111/mam.12292. [DOI] [Google Scholar]
11. Cecchetti, M. , Crowley S. L., Goodwin C. E. D., et al. 2021. “Contributions of Wild and Provisioned Foods to the Diets of Domestic Cats That Depredate Wild Animals.” Ecosphere 12, no. 9: e03737. 10.1002/ecs2.3737. [DOI] [Google Scholar]
12. Contesse, P. , Hegglin D., Gloor S., Bontadina F., and Deplazes P.. 2004. “The Diet of Urban Foxes ( Vulpes vulpes ) and the Availability of Anthropogenic Food in the City of Zurich, Switzerland.” Mammalian Biology 69, no. 2: 81–95. 10.1078/1616-5047-00123. [DOI] [Google Scholar]
13. Cordain, L. , Eaton S. B., Sebastian A., et al. 2005. “Origins and Evolution of the Western Diet: Health Implications for the 21st Century.” American Journal of Clinical Nutrition 81, no. 2: 341–354. 10.1093/ajcn.81.2.341. [DOI] [PubMed] [Google Scholar]
14. Darimont, C. T. , and Reimchen T. E.. 2002. “Intra‐Hair Stable Isotope Analysis Implies Seasonal Shift to Salmon in Gray Wolf Diet.” Canadian Journal of Zoology 80, no. 9: 1638–1642. 10.1139/z02-149. [DOI] [Google Scholar]
15. DeNiro, M. J. , and Epstein S.. 1978. “Influence of Diet on the Distribution of Carbon Isotopes in Animals.” Geochimica et Cosmochimica Acta 42, no. 5: 495–506. 10.1016/0016-7037(78)90199-0. [DOI] [Google Scholar]
16. DeNiro, M. J. , and Epstein S.. 1981. “Influence of Diet on the Distribution of Nitrogen Isotopes in Animals.” Geochimica et Cosmochimica Acta 45, no. 3: 341–351. 10.1016/0016-7037(81)90244-1. [DOI] [Google Scholar]
17. Doncaster, C. P. , Dickman C. R., and Macdonald D. W.. 1990. “Feeding Ecology of Red Foxes ( Vulpes vulpes ) in the City of Oxford, England.” Journal of Mammalogy 71, no. 2: 188–194. 10.2307/1382166. [DOI] [Google Scholar]
18. Dorning, J. , and Harris S.. 2017. “Dominance, Gender, and Season Influence Food Patch Use in a Group‐Living, Solitary Foraging Canid.” Behavioural Ecology 28, no. 5: 1302–1313. 10.1093/beheco/arx092. [DOI] [Google Scholar]
19. Fawcett, J. K. , Fawcett J. M., and Soulsbury C. D.. 2017. “Seasonal and Sex‐Specific Differences in Feeding Site Attendance by Red Foxes Vulpes vulpes .” Mammal Study 42, no. 2: 117–120. 10.3106/041.042.0207. [DOI] [Google Scholar]
20. Forbes‐Harper, J. L. , Crawford H. M., Dundas S. J., et al. 2017. “Diet and Bite Force in Red Foxes: Ontogenetic and Sex Differences in an Invasive Carnivore.” Journal of Zoology 303, no. 1: 54–63. 10.1111/jzo.12463. [DOI] [Google Scholar]
21. Gimmel, A. , Eulenberger U., and Liesegang A.. 2021. “Feeding the European Hedgehog ( Erinaceus europaeus L.)—Risks of Commercial Diets for Wildlife.” Journal of Animal Physiology and Animal Nutrition 105: 91–96. 10.1111/jpn.13561. [DOI] [PubMed] [Google Scholar]
22. Giraudoux, P. , Romig T., and Eckert J.. 2001. Age Determination of Foxes. WHO/OIE Manual on Echinococcosis in Humans and Animals: A Public Health Problem of Global Concern, 191–194. Paris: World Organisation for Animal Health. [Google Scholar]
23. Goszczyńsk, J. 1986. “Diet of Foxes and Martens in Central Poland.” Acta Theriologica 31, no. 36: 491–506. [Google Scholar]
24. Govan, E. , Jackson A. L., Inger R., Bearhop S., and Parnell A. C.. 2023. “Simmr: A Package for Fitting Stable Isotope Mixing Models in R (arXiv:2306.07817).” ArXiv. 10.48550/arXiv.2306.07817. [DOI] [Google Scholar]
25. Handler, A. M. , Lonsdorf E. V., and Ardia D. R.. 2020. “Evidence for Red Fox ( Vulpes vulpes ) Exploitation of Anthropogenic Food Sources Along an Urbanization Gradient Using Stable Isotope Analysis.” Canadian Journal of Zoology 98, no. 2: 79–87. 10.1139/cjz-2019-0004. [DOI] [Google Scholar]
26. Harris, S. 1981. “The Food of Suburban Foxes ( Vulpes vulpes ), With Special Reference to London.” Mammal Review 11, no. 4: 151–168. 10.1111/j.1365-2907.1981.tb00003.x. [DOI] [Google Scholar]
27. Harris, S. , and Trewhella W. J.. 1988. “An Analysis of Some of the Factors Affecting Dispersal in an Urban Fox ( Vulpes vulpes ) Population.” Journal of Applied Ecology 25, no. 2: 409–422. 10.2307/2403833. [DOI] [Google Scholar]
28. Hoffmann, M. , and Sillero‐Zubiri C.. 2016. Vulpes vulpes . The IUCN Red List of Threatened Species 2016, e.T23062A46190249.
29. Hopkins, J. B. , and Ferguson J. M.. 2012. “Estimating the Diets of Animals Using Stable Isotopes and a Comprehensive Bayesian Mixing Model.” PLoS One 7, no. 1: e28478. 10.1371/journal.pone.0028478. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Huelsemann, F. , Flenker U., Koehler K., and Schaenzer W.. 2009. “Effect of a Controlled Dietary Change on Carbon and Nitrogen Stable Isotope Ratios of Human Hair.” Rapid Communications in Mass Spectrometry 23, no. 16: 2448–2454. 10.1002/rcm.4039. [DOI] [PubMed] [Google Scholar]
31. Hülsemann, F. , Lehn C., Schneiders S., et al. 2015. “Global Spatial Distributions of Nitrogen and Carbon Stable Isotope Ratios of Modern Human Hair: Global Human Carbon and Nitrogen Isotope Ratios.” Rapid Communications in Mass Spectrometry 29, no. 22: 2111–2121. 10.1002/rcm.7370. [DOI] [PubMed] [Google Scholar]
32. Iossa, G. , Soulsbury C. D., Baker P. J., Edwards K. J., and Harris S.. 2009. “Behavioural Changes Associated With a Population Density Decline in the Facultatively Social Red Fox.” Behavioural Ecology 20, no. 2: 385–395. 10.1093/beheco/arn149. [DOI] [Google Scholar]
33. Iossa, G. , Soulsbury C. D., Baker P. J., and Harris S.. 2008. “Body Mass, Territory Size, and Life‐History Tactics in a Socially Monogamous Canid, the Red Fox Vulpes vulpes .” Journal of Mammalogy 89, no. 6: 1481–1490. 10.1644/07-MAMM-A-405.1. [DOI] [Google Scholar]
34. Jacquier, M. , Simon L., Ruette S., Vandel J.‐M., Hemery A., and Devillard S.. 2020. “Isotopic Evidence of Individual Specialization Toward Free‐Ranging Chickens in a Rural Population of Red Foxes.” European Journal of Wildlife Research 66, no. 1: 15. 10.1007/s10344-019-1352-9. [DOI] [Google Scholar]
35. Jahren, A. H. , and Kraft R. A.. 2008. “Carbon and Nitrogen Stable Isotopes in Fast Food: Signatures of Corn and Confinement.” Proceedings of the National Academy of Sciences 105, no. 46: 17855–17860. 10.1073/pnas.0809870105. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Johnston, R. F. 2001. “Synanthropic Birds of North America.” In Avian Ecology and Conservation in an Urbanizing World, edited by Marzluff J. M., Bowman R., and Donnelly R.. Boston, MA: Springer. 10.1007/978-1-4615-1531-9_3. [DOI] [Google Scholar]
37. Jones, D. L. 2017. Fathom Toolbox for MATLAB: Software for Multivariate Ecological and Oceanographic Data Analysis. St. Petersburg, FL, USA: College of Marine Science, University of South Florida. https://www.usf.edu/marine‐science/research/matlab‐resources/index.aspx/. [Google Scholar]
38. Kelly, J. F. 2000. “Stable Isotopes of Carbon and Nitrogen in the Study of Avian and Mammalian Trophic Ecology.” Canadian Journal of Zoology 78, no. 1: 1–27. 10.1139/z99-165. [DOI] [Google Scholar]
39. Kidawa, D. , and Kowalczyk R.. 2011. “The Effects of Sex, Age, Season and Habitat on Diet of the Red Fox Vulpes vulpes in Northeastern Poland.” Acta Theriologica 56: 209–218. 10.1007/s13364-011-0031-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Kuznetsova, A. , Brockhoff P. B., and Christensen R. H. B.. 2017. “lmerTest Package: Tests in Linear Mixed Effects Models.” Journal of Statistical Software 82, no. 13: 1–26. [Google Scholar]
41. Lanszki, J. , Bende Z., Nagyapáti N., Lanszki Z., and Pongrácz P.. 2023. “Optimal Prey for Red Fox Cubs—An Example of Dual Optimizing Foraging Strategy in Foxes From a Dynamic Wetland Habitat.” Ecology and Evolution 13, no. 4: e10033. 10.1002/ece3.10033. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Leckie, F. M. , Thirgood S. J., May R., and Redpath S. M.. 1998. “Variation in the Diet of Red Foxes on Scottish Moorland in Relation to Prey Abundance.” Ecography 21, no. 6: 599–604. 10.1111/j.1600-0587.1998.tb00552.x. [DOI] [Google Scholar]
43. Leighton, G. R. , Froneman W., Serieys L. E., and Bishop J. M.. 2023. “Trophic Downgrading of an Adaptable Carnivore in an Urbanising Landscape.” Scientific Reports 13, no. 1: 21582. 10.1038/s41598-023-48868-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Leith, D. A. , Mpofu B. S., van Velden J. L., et al. 2020. “Are Cape Peninsula Baboons Raiding Their Way to Obesity and Type II Diabetes?‐A Comparative Study.” Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 250: 110794. 10.1016/j.cbpa.2020.110794. [DOI] [PubMed] [Google Scholar]
45. Lyons, J. , Mastromonaco G., Edwards D. B., and Schulte‐Hostedde A. I.. 2017. “Fat and Happy in the City: Eastern Chipmunks in Urban Environments.” Behavioural Ecology 28, no. 6: 1464–1471. 10.1093/beheco/arx109. [DOI] [Google Scholar]
46. Main, M. T. , Davis R. A., Blake D., Mills H., and Doherty T. S.. 2020. “Human Impact Overrides Bioclimatic Drivers of Red Fox Home Range Size Globally.” Diversity and Distributions 26, no. 9: 1083–1092. 10.1111/ddi.13115. [DOI] [Google Scholar]
47. McKinney, M. L. 2002. “Urbanization, Biodiversity, and Conservation: The Impacts of Urbanization on Native Species Are Poorly Studied, but Educating a Highly Urbanized Human Population About These Impacts Can Greatly Improve Species Conservation in all Ecosystems.” Bioscience 52, no. 10: 883–890. 10.1641/0006-3568(2002)052[0883:UBAC]2.0.CO;2. [DOI] [Google Scholar]
48. McKinney, M. L. 2006. “Urbanization as a Major Cause of Biotic Homogenization.” Biological Conservation 127, no. 3: 247–260. 10.1016/j.biocon.2005.09.005. [DOI] [Google Scholar]
49. McLaren, A. A. D. , Crawshaw G. J., and Patterson B. R.. 2015. “Carbon and Nitrogen Discrimination Factors of Wolves and Accuracy of Diet Inferences Using Stable Isotope Analysis: Carbon and Nitrogen Discrimination Factors.” Wildlife Society Bulletin 39, no. 4: 788–796. 10.1002/wsb.599. [DOI] [Google Scholar]
50. Murray, M. , Cembrowski A., Latham A. D. M., Lukasik V. M., Pruss S., and St Clair C. C.. 2015b. “Greater Consumption of Protein‐Poor Anthropogenic Food by Urban Relative to Rural Coyotes Increases Diet Breadth and Potential for Human–Wildlife Conflict.” Ecography 38, no. 12: 1235–1242. 10.1111/ecog.01128. [DOI] [Google Scholar]
51. Murray, M. , Edwards M. A., Abercrombie B., and St. Clair C. C.. 2015a. “Poor Health Is Associated With Use of Anthropogenic Resources in an Urban Carnivore.” Proceedings of the Royal Society B: Biological Sciences 282, no. 1806: 20150009. 10.1098/rspb.2015.0009. [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Newsome, S. D. , Ralls K., Job C. V. H., Fogel M. L., and Cypher B. L.. 2010. “Stable Isotopes Evaluate Exploitation of Anthropogenic Foods by the Endangered San Joaquin Kit Fox ( Vulpes macrotis mutica ).” Journal of Mammalogy 91, no. 6: 1313–1321. 10.1644/09-MAMM-A-362.1. [DOI] [Google Scholar]
53. Newton, J. 2010. “Stable Isotope Ecology.” In eLS, edited by Wiley J. and Sons L.. 1st ed. Chichester: Wiley. 10.1002/9780470015902.a0021231. [DOI] [Google Scholar]
54. Nielsen, J. M. , Clare E. L., Hayden B., Brett M. T., and Kratina P.. 2018. “Diet Tracing in Ecology: Method Comparison and Selection.” Methods in Ecology and Evolution 9, no. 2: 278–291. 10.1111/2041-210X.12869. [DOI] [Google Scholar]
55. O'Connell, T. C. , and Hedges R. E. M.. 1999. “Investigations Into the Effect of Diet on Modern Human Hair Isotopic Values.” American Journal of Physical Anthropology 108, no. 4: 409–425. 10.1002/(SICI)1096-8644(199904)108:4<409::AID-AJPA3>3.0.CO;2-E. [DOI] [PubMed] [Google Scholar]
56. O'Connell, T. C. , Hedges R. E. M., Healey M. A., and Simpson A. H. R. W.. 2001. “Isotopic Comparison of Hair, Nail and Bone: Modern Analyses.” Journal of Archaeological Science 28, no. 11: 1247–1255. 10.1006/jasc.2001.0698. [DOI] [Google Scholar]
57. O'Connell, T. C. , Kneale C. J., Tasevska N., and Kuhnle G. G. C.. 2012. “The Diet‐Body Offset in Human Nitrogen Isotopic Values: A Controlled Dietary Study.” American Journal of Physical Anthropology 149, no. 3: 426–434. 10.1002/ajpa.22140. [DOI] [PMC free article] [PubMed] [Google Scholar]
58. O'Leary, M. H. 1988. “Carbon Isotopes in Photosynthesis.” Bioscience 38, no. 5: 328–336. 10.2307/1310735. [DOI] [Google Scholar]
59. Ouellette, R. , and Schulte‐Hostedde A.. 2024. “Effects of Urbanization on Diet in Eastern Chipmunks ( Tamias striatus ) as Determined by Stable Isotopes.” Canadian Journal of Zoology 103: 1–9. 10.1139/cjz-2024-0032. [DOI] [Google Scholar]
60. Phillips, D. L. , Inger R., Bearhop S., et al. 2014. “Best Practices for Use of Stable Isotope Mixing Models in Food‐Web Studies.” Canadian Journal of Zoology 92, no. 10: 823–835. 10.1139/cjz-2014-0127. [DOI] [Google Scholar]
61. Phillips, D. L. , and Koch P. L.. 2002. “Incorporating Concentration Dependence in Stable Isotope Mixing Models.” Oecologia 130, no. 1: 114–125. 10.1007/s004420100786. [DOI] [PubMed] [Google Scholar]
62. R Core Team . 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R‐project.org/. [Google Scholar]
63. Rauber, F. , Louzada M. L. D. C., Steele E. M., Millett C., Monteiro C. A., and Levy R. B.. 2018. “Ultra‐Processed Food Consumption and Chronic Non‐Communicable Diseases‐Related Dietary Nutrient Profile in the UK (2008–2014).” Nutrients 10, no. 5: 587. 10.3390/nu10050587. [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Rauber, F. , Steele E. M., Louzada M. L. D. C., Millett C., Monteiro C. A., and Levy R. B.. 2020. “Ultra‐Processed Food Consumption and Indicators of Obesity in the United Kingdom Population (2008‐2016).” PLoS One 15, no. 5: e0232676. 10.1371/journal.pone.0232676. [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Reynolds, J. C. , and Aebischer N. J.. 1991. “Comparison and Quantification of Carnivore Diet by Faecal Analysis: A Critique, With Recommendations, Based on a Study of the Fox Vulpes vulpes .” Mammal Review 21, no. 3: 97–122. 10.1111/j.1365-2907.1991.tb00113.x. [DOI] [Google Scholar]
66. Robertson, A. 2012. Investigating the Causes and Consequences of Individual Niche Variation in Group Living Badgers. PhD Thesis. University of Exeter (United Kingdom). [Google Scholar]
67. Roth, J. D. , and Hobson K. A.. 2000. “Stable Carbon and Nitrogen Isotopic Fractionation Between Diet and Tissue of Captive Red Fox: Implications for Dietary Reconstruction.” Canadian Journal of Zoology 78, no. 5: 848–852. 10.1139/z00-008. [DOI] [Google Scholar]
68. Saunders, G. , White P., Rayner J., and Harris S.. 1993. “Urban Foxes ( Vulpes vulpes ): Food Acquisition, Time and Energy Budgeting of a Generalised Predator.” Symposia of the Zoological Society of London 65: 215–234. [Google Scholar]
69. Schmidt, O. , and Ostle N. J.. 1999. “Tracing Nitrogen Derived From Slurry in Earthworms Using 15N/14N Stable Isotope Ratios at Natural Abundances.” Applied Soil Ecology 12, no. 1: 7–13. 10.1016/S0929-1393(98)00160-7. [DOI] [Google Scholar]
70. Scholz, C. , Firozpoor J., Kramer‐Schadt S., et al. 2020. “Individual Dietary Specialization in a Generalist Predator: A Stable Isotope Analysis of Urban and Rural Red Foxes.” Ecology and Evolution 10, no. 16: 8855–8870. 10.1002/ece3.6584. [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Schulte‐Hostedde, A. I. , Mazal Z., Jardine C. M., and Gagnon J.. 2018. “Enhanced Access to Anthropogenic Food Waste Is Related to Hyperglycemia in Raccoons ( Procyon lotor ). Conservation.” Physiology 6, no. 1: coy026. 10.1093/conphys/coy026. [DOI] [PMC free article] [PubMed] [Google Scholar]
72. Scott, D. M. , Berg M. J., Tolhurst B. A., et al. 2014. “Changes in the Distribution of Red Foxes ( Vulpes vulpes ) in Urban Areas in Great Britain: Findings and Limitations of a Media‐Driven Nationwide Survey.” PLoS One 9, no. 6: e99059. 10.1371/journal.pone.0099059. [DOI] [PMC free article] [PubMed] [Google Scholar]
73. Sidorovich, V. E. , Sidorovich A. A., and Izotova I. V.. 2006. “Variations in the Diet and Population Density of the Red Fox Vulpes vulpes in the Mixed Woodlands of Northern Belarus.” Mammalian Biology 71, no. 2: 74–89. 10.1016/j.mambio.2005.12.001. [DOI] [Google Scholar]
74. Smith, B. N. , and Epstein S.. 1971. “Two Categories of 13C/12C Ratios for Higher Plants.” Plant Physiology 47, no. 3: 380–384. 10.1104/pp.47.3.380. [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Soe, E. , Davison J., Süld K., Valdmann H., Laurimaa L., and Saarma U.. 2017. “Europe‐Wide Biogeographical Patterns in the Diet of an Ecologically and Epidemiologically Important Mesopredator, the Red Fox Vulpes vulpes : A Quantitative Review.” Mammal Review 47, no. 3: 198–211. 10.1111/mam.12092. [DOI] [Google Scholar]
76. Steele, K. W. , and Daniel R. M.. 1978. “Fractionation of Nitrogen Isotopes by Animals: A Further Complication to the Use of Variations in the Natural Abundance of 15N for Tracer Studies.” Journal of Agricultural Science 90, no. 1: 7–9. 10.1017/S002185960004853X. [DOI] [Google Scholar]
77. Stephens, R. B. , Ouimette A. P., Hobbie E. A., and Rowe R. J.. 2022. “Reevaluating Trophic Discrimination Factors (Δδ13C and Δδ15N) for Diet Reconstruction.” Ecological Monographs 92, no. 3: e1525. 10.1002/ecm.1525. [DOI] [Google Scholar]
78. Stephens, R. B. , Shipley O. N., and Moll R. J.. 2023. “Meta‐Analysis and Critical Review of Trophic Discrimination Factors (Δ13C and Δ15N): Importance of Tissue, Trophic Level and Diet Source.” Functional Ecology 37, no. 9: 2535–2548. 10.1111/1365-2435.14403. [DOI] [Google Scholar]
79. Stepkovitch, B. , Martin J. M., Dickman C. R., and Welbergen J. A.. 2019. “Urban Lifestyle Supports Larger Red Foxes in Australia: An Investigation Into the Morphology of an Invasive Predator.” Journal of Zoology 309, no. 4: 287–294. 10.1111/jzo.12723. [DOI] [Google Scholar]
80. Sugden, S. , Sanderson D., Ford K., Stein L. Y., and St. Clair C. C.. 2020. “An Altered Microbiome in Urban Coyotes Mediates Relationships Between Anthropogenic Diet and Poor Health.” Scientific Reports 10, no. 1: 22207. 10.1038/s41598-020-78891-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
81. The Mathworks Inc . 2023. MATLAB Version: 23.2.0 (R2023b). Natick, Massachusetts: Mathworks Inc. https://www.mathworks.com. [Google Scholar]
82. Tieszen, L. L. , Boutton T. W., Tesdahl K. G., and Slade N. A.. 1983. “Fractionation and Turnover of Stable Carbon Isotopes in Animal Tissues: Implications for δ13C Analysis of Diet.” Oecologia 57, no. 1: 32–37. 10.1007/BF00379558. [DOI] [PubMed] [Google Scholar]
83. Tolhurst, B. , Grogan A., Hughes H., and Scott D.. 2016. “Effects of Temporary Captivity on Ranging Behaviour in Urban Red Foxes ( Vulpes vulpes ).” Applied Animal Behaviour Science 181: 182–190. 10.1016/j.applanim.2016.05.004. [DOI] [Google Scholar]
84. Tolhurst, B. A. , Baker R. J., Cagnacci F., and Scott D. M.. 2020. “Spatial Aspects of Gardens Drive Ranging in Urban Foxes ( Vulpes vulpes ): The Resource Dispersion Hypothesis Revisited.” Animals 10, no. 7: 1167. 10.3390/ani10071167. [DOI] [PMC free article] [PubMed] [Google Scholar]
85. Wu, J. 2014. “Urban Ecology and Sustainability: The State‐Of‐The‐Science and Future Directions.” Landscape and Urban Planning 125: 209–221. 10.1016/j.landurbplan.2014.01.018. [DOI] [Google Scholar]

Data Availability Statement

Data will be made available to reviewers and editors. Public access will be granted upon publication (DOI: https://figshare.com/s/56f3eb0fd8e62e489b0e).