Nutritional ecology of a prototypical generalist predator, the red fox (Vulpes vulpes)

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Among mammalian predators, the red fox Vulpes vulpes is a widespread, opportunistic forager: its diet has been largely studied, outlining wide variation according to geographic and climatic factors. We aimed to check if, throughout the species’ European range, diets vary widely in macronutrient composition or foxes can combine complementary foods to gain the same nutrient intake. First, we assessed fox’s intake target in the framework of nutritional geometry. Secondly, we tried to highlight the effects of unbalanced diets on fox density, which was assumed as a proxy for Darwinian fitness, as assessed in five areas of the western Italian Alps. Unexpectedly, the target macronutrient ratio of the fox (52.4% protein-, 38.7% lipid- and 8.9% carbohydrate energy) was consistent with that of hypercarnivores, such as wolves and felids, except for carbohydrate intakes in urban and rural habitats. The inverse relation between density and the deviation of observed macronutrient ratios from the intake target suggests that fox capability of surviving in a wide range of habitats may not be exempt from fitness costs and that nutrient availability should be regarded among the biotic factors affecting animal abundance and distribution. Biological sciences/Zoology/Animal behaviour Biological sciences/Zoology/Animal physiology Biological sciences/Ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Variation in food availability is a major factor affecting breeding densities and reproduction success, with ultimate consequences on population dynamics 1 . Availability of food resources is expected to act as a limiting factor more for specialist predators, which use one or few resources, than for generalist predators, which opportunistically exploit the most abundant and easily accessible food resources available at any one time and locality 2 . Whether the two feeding tactics are equally profitable is still debated 3,4 , especially for terrestrial predators 5 . According to optimal foraging models, trophic niche breadth should depend on the diversity and abundance of available prey, and net energy gain 6 , that is the ratio between the caloric value of each potentially available prey and the amount of energy spent for finding, pursuing, killing and consuming it. Notwithstanding, in the last two decades, a growing body of evidence has outlined that foraging is not exclusively driven by energy acquisition and many species tend to regulate the macronutrient composition of their diet to a target ratio (“intake target”) 7–9 . As the macronutrient composition of the diet has been demonstrated to affect many fundamental fitness-related traits, including growth 7,10 , fecundity 11 and lifespan 12,13 , we may expect that, for any species, suitable habitats are those which offer food resources capable of satisfying the nutritional requirements of a number of individuals in all the phases of their life cycle. Specialist predators possess morphological and behavioural adaptations which are supposed to increase their foraging efficiency 14 and feed on foods relatively invariant in their nutrient composition, which coincides with the predator’s target macronutrient ratios. In contrast, generalists must be capable of shifting between resources before the opportunity occurs, which implies preexisting behavioural and physiological adaptations 15 , and need to combine several nutritionally complementary foods to achieve their intake target. The few available studies 16 suggest that, based on their tolerance towards carbohydrates, mammalian predators can be aligned along a carnivore–omnivore continuum, ranging from obligate carnivores, such as wolves ( Canis lupus ) 17 to poorly specialized ursids 18 . The ability of using fat or carbohydrates as sources of non-protein energy may be expected to be a physiological prerequisite for generalist predators, allowing them to rely on a wide variety of food resources 19 . Geographic and seasonal variation in the composition of generalist predators’ diets makes it difficult to compare the diet of populations of widespread species. However, using nutritional geometry Gazzola and Balestrieri 20 have recently demonstrated that using a wide variety of food resources does not imply as much variation in the nutritional composition of diets: although using a wide range of fruit and small mammals, widespread carnivores such as martens ( Martes martes and Martes foina ) can be considered macronutrient specialists (i.e. the macronutrient compositions of the diets of different populations are similar 21 ). Among carnivore mammals, the red fox ( Vulpes vulpes ) is considered a prototypical generalist predator: its feeding habits vary widely spatially, temporally and in response to human influence, reflecting the biogeographical patterns of distribution and abundance of food resources 22–24 . Records of local specialization, due to the disproportionate profitability of anthropogenic resources, reflect the highly opportunistic behaviour of this species 25 . This dietary flexibility allows foxes to occur in a wide variety of habitats, from sea level up to 4500 m, including several cities 26 . Its geographical range is the widest of any member of the order Carnivora (ca. 70 million km²), including most of the Northern Hemisphere, from the Arctic Circle to northern Africa, and Australia 27 , where it was introduced in the 1870s 28 . Such a wide distribution rises an interesting question, that is whether different populations persist on diets that vary widely in macronutrient composition or are capable of using complementary foods to gain the same nutrient intake throughout the species’ range. The first nutritional strategy has been reported for the wild boar ( Sus scrofa ), which is a dietary generalist and tolerates a wide range of macronutrient ratios across its whole range, particularly in terms of proportion of energy from protein 29 . In contrast, mustelids, such as martens ( Martes spp.) and the Eurasian badger ( Meles meles ), tend to keep constant the percent protein energy, while showing a gradient of tolerance towards carbohydrates 16,20 . Laboratory experiments on Drosophila melanogaster 11 and mice 12 suggest that unbalanced diets may have profound effects on life span and reproduction. While the broad fundamental macronutrient niche of wild boars has been suggested to enhance their invasion success, increasing the reproductive output of sows 29 , we still do not know whether an excess of carbohydrates may affect the individual fitness of free-ranging carnivores. To assess the macronutrient niche of the red fox, we applied right-angle mixture triangles (RMT) 21,30 , in the framework of nutritional geometry. Data were extracted from published reports following the approach proposed by Remonti et al. 19,31 . Based on the wide variety of foods used by foxes, we expected a wide degree of inter-population variation in the percent energy provided by carbohydrates, as so as the recording of clusters of unbalanced diets. Secondly, we made an attempt to highlight the effects of unbalanced diets on fox density, which was assumed as a proxy for Darwinian fitness. As diet is only one of several factors that may affect population density, samplings were carried out in five areas belonging to the same biogeographical region, in a radius of ca. 30 km (western Italian Alps). We aimed to assess both the yearly diet of each population and their correspondent density, calculated through a faecal DNA-based genetic census. We expected macronutrient ratios to affect individual fitness, and, therefore, populations showing nutritional balances close to the intake target to achieve higher densities than those with unbalanced diets. Results Estimation of the intake target Protein energy ranged between 36.8% and 71.0%, lipid energy from 25.7% to 51.4%, while carbohydrate energy made up between 0.1% and 29.9%. The target macronutrient ratio of the fox (mean ± SE) was assessed as 52.4 ± 1.7% protein energy, 38.7 ±1.0% lipid energy and 8.9 ±1.6% carbohydrate energy (Table 1). Overall diets clustered into three groups: ‘average’, mainly from mixed habitats, ‘low P‐high C’, including mostly diets of urban and cultivated areas, and ‘high P’ (> 60%) diets from mixed and forested habitats (Fig. 3). On average, carbohydrate energy tended to increase along the natural-to-urban habitat gradient, while lipid energy was the highest in mixed habitats (Table 3). Higher than average carbohydrate energies were recorded in mountainous, forest areas of northern Italy (1, 3 and 14 in table 1), where fruit accounted for 30-50% of the diet (%mV). High protein intakes were related to the consumption of lagomorphs in both cultivated (2, 8) and low-altitude forest areas (27, 30), wild deer in mixed habitats (6) and ungulates (10: NW Italian Alps, 22: Sweden) or small mammals (16: northern Belarus) in woodland. Fox diet in Alpine habitats Overall, 391 km of transects were surveyed (on average 78.2 km per area; min-max: 56.0-116.5), yielding 615 faecal samples (on average 112.6 samples per area; min-max: 115-131). The analysis of fox diet showed differences in the relative importance of the major food items in the five study areas. Mice ( Apodemus sylvaticus ) were the most frequent prey in all the three western areas, while in Piedmont voles (mostly Myodes glareolus ) prevailed. In terms of volume, the highest values were achieved by ungulates in all study areas in Aosta Valley, while in two eastern sampling sites voles dominated also in terms of volume. As expected, fruits were less frequently eaten in winter while insects were most preyed on in summer. Ungulates, eaten as carrions, were mostly used in winter-spring. The frequency of occurrence of most major food items showed significant variation among areas (Table SI1), nonetheless, small rodents formed the bulk of fox diet in all study areas (Fig. SI1). Protein energy ranged between 46.6% and 68.3%, lipid energy from 28.9% to 51.7%, while carbohydrate energy made up between 0.7% and 8.15% (Table 4). Overall, seasonal variation in the macronutrient ratios provided by diet was higher in the Aostan valleys than in Piedmont areas (Fig. SI2). The macronutrient intake of Saint-Barthélemy valley was the closest to the intake target, while the highest percent deviation of carbohydrate energy from the target were recorded for the two eastern areas (Tab. 5). Fox numbers Genotyping success ranged between 35.7% for the valley of the River Chalamy and 82.8% for Saint-Barthélemy’s (mean: 52.8%). Sixteen different genotypes were recorded in Saint-Barthélemy valley, 9 each in Elvo and Nomenon valleys, 8 in Cervo valley and 7 in Chalamy valley. The number of “captures” per individual varied between 1 and 5. Applying CAPWIRE’s TIRM model, the largest population was assessed for Saint-Barthélemy, with 30 individuals (CI: 16-30), followed by Nomenon with 28 individuals (11-30), Elvo with 23 (9-30), Cervo with 19 (8-30) and Chalamy with 12 (7-19). The lowest density was recorded for the Chalamy population, 0.7 ind/km 2 , and the highest for that of the Nomenon valley, 2.4 ind/km 2 (EL: 1.9; SB = 2.2; CV = 1.73 ind/km 2 ). Fox relative abundance (RA) ranged between 0.08 and 0.24 faeces/100 m and tended to increase with density (P = 0.09, R 2 = 0.67; Fig. 4). Pre-reproductive density ranged between 0.17 and 0.37 ind/km 2 (mean ± SE: 0.21 ± 0.04 ind/km 2 ) and tended to decrease with increasing deviations of the macronutrient ratio from the target (P = 0.037, R 2 = 0.78; Fig. 5). Discussion The red fox occurs in a wide geographic range where it must cope with a diversity of environmental conditions and large variation in the availability of food resources. Its food habits have been widely studied, highlighting a great trophic diversity, which may be expected to result in an equally broad inter-population variation in the macronutrient intake. Notwithstanding, the analysis of available studies providing a volumetric or biomass estimate of the importance of the food resources used by foxes throughout its European range revealed that, on average, the protein requirements of the fox are typical of strict carnivores such as wolves (54%) 17 or domestic and feral cats (52%) 32,33 . Respect to hypercarnivores, foxes seem to tolerate some carbohydrates in their diet, although their contribution was usually lower than expected based on their opportunistic food habits. These results underpin the need for considering macronutrient ratios to draw an effective picture of generalist predators’ diets, because food diversity can conceal their actual nutritional requirements 21 . As reported for badgers 31 , in urban and rural habitats macronutrient ratios differed the most from the target, particularly for percent carbohydrate energy. Carbohydrate intake is probably affected by the availability of anthropogenic food resources, given the opportunity of searching for food in garbage cans, compost piles and orchards 47 . While shortage in animal prey, particularly in summer, has been reported to affect survival and/or fecundity in another canid, the coyote ( Canis latrans ) 48 , no information is available, to the best of our knowledge, about the detrimental effects of carbohydrate overeating. Carbohydrates are generally considered noxious to carnivores, inducing sharp changes in intestinal metabolism and interfering with the digestion of protein and absorption of minerals 49 . Nonetheless, there is no evidence that a high consumption of fruit during summer could impair the reproduction of red foxes during the following spring 50 , suggesting that carbohydrates may be well tolerated by foxes, or even partially necessary for a balanced diet. The reviewed dietary studies aimed to determine the relative importance of food items in the diet rather than the absolute amount of food consumed or their macronutrient composition. We acknowledge that assessing macronutrient ratios using such studies cannot but provide a rough estimate of the actual intake target of the fox. Nonetheless, the analysis of fox diet in the five Alpine areas, which was carried out by assessing the relative volume of each food category as carefully as possible, allowed to assess macronutrient ratios consistent with the general picture drawn through the literature review, yielding macronutrient ratios similar to those assessed for most fox populations living in forested areas throughout Europe. In our study areas, fox diet was poor in fruit (average Vm% = 8.5) respect to previous studies carried out in the western Italian Alps (Vm% = 15–32%) 39,51,52 . Based on anecdotal information, in summer 2021 rodents were very abundant, because of a mast year for beech ( Fagus sylvatica ), suggesting that the recorded shift may depend on the higher-than-average availability of this food resource, as already recorded for martens in NW Piedmont 53 . Average genotyping success (52.8%) was consistent with previous studies based on faecal DNA (e.g., 48% 54 , 58% 55 ). Densities fell within the range reported for Italian fox populations (1–2,5 foxes/km 2 ) 56 . Although the use of marking intensity as an index of relative abundance or for assessing habitat preferences has been long challenged 57,58 , the recorded relationship between density and the index of relative abundance suggests that marking intensity can be used as an effective index to compare fox abundances (see also Lanszki et al. about Lutra lutra 59 ). To investigate the effects of the macronutrient composition of fox diet on Darwinian fitness, we assessed winter densities, which were assumed to be less sensitive to variation in local conditions (e.g., number of cubs, percentage of barren females) than post-reproductive densities 60 . Mean values were consistent with those reported by Bartoń and Zalewsky by reviewing 69 studies throughout Europe and Asia 61 . While we are well aware that sample size is too low to draw sound conclusions, the inverse relation between density and the deviation of observed macronutrient ratios from the intake target suggests that the nutrient composition of available foods can drive fox abundance, affecting the chance of achieving diets able to satisfy its nutritional, i.e. physiological, requirements. Although density is only a rough proxy for fitness, our results are consistent with laboratory experiments, which demonstrated that generalists pay the cost of relying on unbalanced diets, suffering either high mortality rates and disease risk 62,63 or low reproductive outputs 12 . This result implies that although foxes can adapt to local and seasonal variations in food availability and then survive in a wide variety of habitats 24 , this capability may not be exempt from fitness costs. Nutrient availability should be considered, together with habitat productivity 64 , among the biotic factors affecting animal abundance and distribution. Conclusions Following Machovsky-Capuska et al. 21 , by analysing the nutritional niche of a well-known generalist predator we demonstrated that the characterisation of dietary niches cannot disregard the nutritional composition of food resources. The red fox, although being capable of relying on foods largely varying in their nutrient composition, showed to “defend” 8 the protein intake target typical of hypercarnivore mammals. Moreover, we provided some field-based evidence that not only food availability per se but also the macronutrient composition of foods may affect at least animals’ distribution, if not their life history traits. Materials and Methods Assessment of the intake target Following Remonti et al. 31 , we searched the available literature using the keywords: "diet," “food habits”, "trophic niche," "fox", “ Vulpes ” and "macronutrients". We found 73 papers and selected the studies based on the following criteria: (i) results had to be expressed as percent volume or biomass; (ii) the study lasted at least one year (4 seasons); (iii) the number of analysed samples had to be higher than 60. The last two criteria intended to select only those studies providing an effective picture of fox diet. Thirty studies met these criteria and were used to assess the intake target. As environmental conditions may imply different resource availability, based on the description of the study areas, the dataset was split in four main habitats: Urban, Arable, Mixed, and Forest habitats (Table 1 ). All the selected studies were conducted in Europe, ranging between 27° and 59° N in latitude and 8° W and 29° E in longitude (Fig. 1 ). The macronutrient ratio of each diet was assessed by multiplying the percent volume or biomass of every food item by the respective percentage of each macronutrient. To obtain, on a wet weight basis, the mean percentage of protein, lipids, and carbohydrates of the food items used by the red fox, we checked the available literature on the nutritional composition of food 17,31,32 (Table 2 ). Undetermined items were assigned with the mean value calculated for the foods belonging to the same major group. Macronutrient energy ratios (MER) were calculated by multiplying the overall macronutrient ratios by Atwater’s coefficients (14.64 kJ g –1 for protein, 35.56 kJ g –1 for lipids and 14.64 kJ g –1 for carbohydrates 33 ). To compare the macronutrient composition of the thirty selected diets, we used right-angled mixture triangles, which represent the three‐component nutritional compositions of diets as Cartesian points in a two‐dimensional nutrient space 30 . Percent protein energy was shown on the third axis (the ‘implicit’, or I‐axis), which varies inversely as distance from the origin increases 30 . Study area To assess the effect of unbalanced diets on fitness, five areas in the western Italian Alps (Fig. 2 ) were selected according to the following criteria: (i) altitude ranged between 1000 and 2200 m a.s.l.; (ii) areas had to be well delimited by mountain ridges; (iii) anthropic impact was low, mainly semi-nomadic livestock rearing and slow tourism (hiking, mountain-bike); (iv) hunting pressure, which can alter population density, was negligible. In general, in all areas the climate is typically Alpine continental with long and cold winters. Snow cover lasts 5–6 months a year with maximum depth during January–February (1.5–2.5 m) and mean temperatures are generally below 0°C from November to February. Notwithstanding, the two most eastern sampling areas (upper valleys of the rivers Cervo and Elvo, province of Biella, Piedmont) are rainier in May and October-November, while the south-central area (valley of the River Chalamy, Mont Avic Natural Park, Aosta Valley region), is the most xeric (Bocca et al., 2016). Between 1000 and 1500 m a.s.l. mixed deciduous woods consist of beech ( Fagus sylvatica ), chestnut ( Castanea sativa ), ash ( Fraxinus excelsior ) and green alder ( Alnus viridis ). In the valleys of the rivers Nomenon (Gran Paradiso National Park, Aosta Valley region) and Saint-Barthélemy (Aosta Valley region), above 1500 m coniferous forests predominate, with larch ( Larix decidua ), Scots pine ( Pinus sylvestris ), Norway spruce ( Picea abies ) and silver fir ( Abies alba ), which are substituted by mountain pine ( Pinus mugo ) in the River Chalamy valley. In the two eastern areas, human activities and climate contributed to prevent the growth of conifers, replaced by shrubs of green alder and hazel ( Corylus avellana ). Alpine prairies cover the slopes above 1500 m a.s.l. Variation in rainfall and vegetation cover were expected to affect food availability to foxes. Sampling methods In each study area, we identified three to five transects between 1000 and 2000 m a.s.l. The transects were chosen based on the availability of pathways and were surveyed from March 2021 to March 2022, aiming to collect a minimum of 30 scats per season (October-December: autumn; January-March: winter; April-June: spring; July-September: summer) in each area 34 . The identification of fox faeces was based on their morphology and size (diameter > 10 mm), which allow to distinguish them from those of other mesocarnivores, such as martens Martes spp. 35 . Samples were preserved into plastic bags, labelled with an identification number. Fox numbers were assessed through faecal DNA-based genetic samplings. Between September 2021 and February 2022, we collected 30 samples per area, selecting fresh-looking faeces to obtain amplifiable, non-degraded DNA. For every sample, we withdrew ca. 1 g of faecal material from the external surface, where it is more probable to find flaking cells of the intestinal wall, using disposable sticks (the remaining material was stored for diet analysis). The test-tubes, containing 95% ethanol, were frozen until DNA extraction 36 . Moreover, fox relative abundance (RA) was expressed as number of scats / 100 m of transect 37 . Sampling was totally non-invasive and did not need the approval of any institutional or licensing committee. Diet analysis We first separated the remains of each prey/food contained in each faecal sample. The minimum number of individuals of each prey type was estimated by the number and position (left/right) of diagnostic hard parts (e.g.: jaw bones for mammals, radio-ulnae for amphibians). When no diagnostic part was found, the remains of a prey item were considered to belong to a single individual. The relative volume (%V) of each food item “as ingested” was assessed following Kruuk and Parish’s method 38 , which has been widely used for assessing carnivore diets and provides volume estimates as accurate as those obtained by the analysis of stomach contents 39 . The percent frequency (%F) was calculated as the ratio between the number of times (samples) a food item occurs and the total number of analysed scats × 100. The percent mean volume (%Vm = total estimated volume of each food item as ingested / total number of faecal samples = %F × %V / 100) reflects the proportional contribution of each food item to the overall diet 38 . Percent energy ratios were then assessed as so as for literature data and compared using right-angled mixture triangles. The Chi-squared test (χ 2 ) was used to compare the raw frequency data of the major food categories: fruit, insects, birds, mice, dormice, voles, insectivores and ungulates. To account for multiple tests on related data, the level of significance was corrected using Holm-Bonferroni's sequential technique 40 . Genetic analysis The QIAamp Fast DNA Stool Mini Kit was used to extract the DNA from faecal samples. We followed the manufacturer instructions, except for final phase, when the ATE buffer was added in three steps of 60 µl each to improve the effectiveness of DNA extraction. Genotyping was carried out using a multiplex PCR of 20 autosomal microsatellite loci (RF 21, 59, 125, 127, 131, 143, 155, 156, 162, 165, 199, 200 41 ; VVM 219, 85, 838, 529, 189, 844, 828 42 ), explicitly developed for the red fox. The quality of DNA was initially screened by four replicated PCRs of two microsatellites. Only those samples showing more than 50% positive PCRs were further amplified four times at each of the remaining 18 microsatellites. Four multiplex PCRs were conducted, splitting microsatellites based on fragment size and labelling by fluorescent dyes, and using the QIAGEN Multiplex PCR Kit protocol (15 min at 95°C; 35 cycles of three steps: 30 s at 94°C, 90 s at 57–63°C, and 60 s at 72°C; 30 min at 62°C; the final volume was reduced to 25 ul). To lower the probability of retaining false homozygotes or false allele errors, a multitube-approach of 4 independent replicates was used 43 . To construct consensus genotypes heterozygotes were accepted only when the two alleles were recorded in ≥ 2 replicates, while a single allele had to be recorded in ≥ 3 replicates to confirm homozygosity 44,45 . PCR products were analysed in an automated sequencer ABI 3130XL (Foster City, CA), and visualized using Genemapper (Thermo Fisher Scientific). Assessment of population density To assess the size of the five populations we used CAPWIRE (“CApture WIth REplacement”) estimators 46 , applying the two available models: the Equal Capture Model (ECM), which assumes equal-capture probabilities among individuals; and the Two-Innate Rates Model (TIRM), which assumes that the population includes two groups of individuals, some easy to capture and some that are difficult to capture. The best model was chosen by a likelihood ratio test (LRT) and confidence intervals were estimated through parametric bootstrap. Population density was calculated as the ratio between population size and the correspondent surveyed area (km 2 ). We assumed that mountain ridges coincided with the boundaries of fox home ranges and excluded the steep and rocky areas above 2200 m a.s.l., which were assumed to be not suitable or scarcely used by foxes. To assess fox pre-reproductive density, assumed as a rough indicator for fitness, the individuals sampled only once or in autumn, where filed as itinerants or young of the previous year and discarded. To assess the relationship between fitness and nutrition, mean deviations of observed ratios from the intake target ( \(\left(\frac{\left|\left(obs-target\right)\right|}{target}\right)\times 100)\) were plotted against pre-reproductive density values for each of the five fox populations. The relationships between RA and density and mean deviation of observed macronutrient ratios and pre-reproductive density were tested using linear regression models. Declarations Author contributions A.B and A.G.: conceptualization of the study; A.B. and S.G.: field surveys; S.G. and E.D.: diet analysis; N.M., E.V., R.C. and F.Z.: genetic analysis; A.B., P.T.: data analysis; all authors wrote the manuscript, contributed critically to the drafts and approved its publication. Competing interests The authors declare no competing interests. Data availability Data are included in the paper or in the online version as supplementary information All reviewed datasets are cited in the reference list. For any other reasonable request, contact the corresponding author. References Lack, D. The natural regulation of animal numbers (Oxford University Press, 1954). Newton, I. The role of food in limiting bird numbers. Ardea 68 , 11–30 (1980). Recher, H. F. Specialist or generalist: avian response to spatial and temporal changes in resources in Avian foraging: theory, methodology and applications. Studies in avian biology 13 (ed. 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Zool. 37 , 157-162 (2013). Fedriani, J.M., Palomares, F., Delibes, M. Niche relations among three sympatric Mediterranean carnivores. Oecologia 121 , 138-148 (1999). Doncaster, C.P., Dickman, C.R., Macdonald, D.W. Feeding ecology of red foxes ( Vulpes vulpes ) in the city of Oxford, England. J. Mamm. 71 , 188-194 (1990). Lanszki, J. et al . Diet composition of the golden jackal and the sympatric red fox in an agricultural area (Hungary). Folia Zool. 65 , 310-322 (2016). Castañeda, I., Zarzoso-Lacoste, D., Bonnaud, E. Feeding behaviour of red fox and domestic cat populations in suburban areas in the south of Paris. Urban Ecosyst. 23 , 731-743 (2020). Tables Table 1. Fox macronutrient (Protein, Lipids, Carbohydrates) intakes as assessed by the analysis of the 30 selected diet studies. Ref Sample size P L C Habitat Lat Long 1 121 43.84 39.59 16.56 forest 45.44 7.19 117 36.82 37.61 25.57 urban 2 115 66.57 33.06 0.37 arable 45.10 8.38 3 133 41.58 29.57 28.85 forest 46.26 11.45 4 78 37.97 35.65 26.37 mixed 45.25 8.53 114 49.67 44.16 6.17 mixed 45.16 9.37 5 1139 59.45 38.49 2.05 mixed 51.48 19.53 6 340 61.60 35.44 2.96 mixed 43.31 11.08 7 663 40.07 44.55 15.38 arable 42.76 11.33 8 393 68.32 31.55 0.13 arable 50.45 -2.26 9 749 37.46 32.58 29.95 arable 51.27 -2.35 10 922 62.23 30.68 7.09 forest 45.59 7.08 11 234 57.21 41.60 1.19 forest 55.02 29.02 12 264 44.10 49.76 6.15 forest 41.41 13.50 13 223 51.11 43.05 5.84 mixed 44.51 8.75 14 189 41.69 39.57 18.74 forest 44.30 8.92 15 178 53.33 41.36 5.31 forest 37.08 3.23 678 61.37 33.15 5.48 forest 41.03 4.53 16 933 55.66 42.72 1.62 forest 53.00 27.50 593 63.42 33.20 3.37 forest 17 767 54.17 44.53 1.30 mixed 55.51 22.50 18 1010 44.49 51.43 4.08 forest 46.41 17.45 19 256 43.03 31.19 25.77 urban 51.00 13.00 20 190 51.11 42.71 6.17 mixed 39.00 22.00 21 144 56.15 43.12 0.73 forest 49.26 20.00 22 570 70.96 25.74 3.30 forest 59.40 15.30 23 148 42.35 45.76 11.89 mixed 55.16 25.33 24 102 57.55 40.37 2.08 mixed 27.25 -8.17 25 433 50.88 45.00 4.12 arable 51.34 17.40 26 1022 59.02 36.55 4.43 arable 51.34 17.40 27 320 69.19 29.05 1.75 forest 37.16 6.43 28 1939 37.64 38.71 23.66 urban 51.45 -1.15 29 268 44.07 46.06 9.87 arable 45.51 17.56 30 159 57.59 41.38 1.03 arable 48.46 2.17 169 61.75 32.46 5.78 forest 220 56.30 42.10 1.59 urban Mean 52.35 38.75 8.89 Table 2. Percent macronutrient composition of the major food items in the diet of the red fox. Food items Protein Lipids Carbohydrates Wild fruit Rubus sp. 1.3 0.0 5.7 Prunus sp. 1.0 0.2 11.4 Sambucus nigra 0.66 0.5 18.4 Cultivated fruit Plums 0.7 0.28 11.4 Pears 0.3 0.4 8.9 Vitis vinifera 0.5 0.1 13.5 Ficus carica 0.75 0.3 19.2 Undetermined fruit 0.56 0.18 9.6 Insects Larvae 17.8 13.3 0.0 Adults 20.3 8.6 0.0 Birds Anseriformes 18.3 5.95 0.94 Galliformes 25.8 1.9 0.2 Passeriformes 21.7 5.4 0.1 Columbiformes 18.47 23.8 0.0 Undetermined birds 21.06 9.3 0.3 Mammals Small mammals 19.6 9.8 0.0 Lagomorphs 21.8 2.32 0.0 Ungulates 21.0 0.8 0.0 Martes sp. 20.3 6.9 0.0 Table 3. Inter-habitat variation of fox macronutrient intake along the natural-to-urban gradient. Habitat % protein % lipids % carbohydrates Forest 55.0 37.5 7.5 Mixed 51.7 41.1 7.2 Arable land 53.0 38.8 8.2 Urban 43.4 37.4 19.2 Table 4. Macronutrient ratios in the diet of the red fox in the five study areas. Study area % protein % lipids % carbohydrates Nomenon 68.27 28.86 2.87 S. Barthelemy 47.71 44.14 8.15 Chalamy 56.29 40.70 3.02 Cervo 50.57 48.72 0.71 Elvo 46.56 51.70 1.74 Table 5. Percent deviation from the intake target assessed for the five fox diets in Alpine habitats. Study area % protein % lipids % carbohydrates Mean Nomenon 30.4 25.5 67.7 41.2 S. Barthelemy 8.9 13.9 8.3 10.4 Chalamy 7.5 5.0 66.1 26.2 Cervo 3.4 25.7 92.0 40.4 Elvo 11.1 33.4 80.4 41.6 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3891530","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270005782,"identity":"55b8cf3e-25d4-4d33-a1f6-59606f401033","order_by":0,"name":"Alessandro Balestrieri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACdgY2hgcMNjJsEmCuBBFamIFaEhjSeGBaiNAD0XKYB2Y+YS38zczHHiT8Oc/DJ9187MMHBos6glokDrOlGyS23eZhkzmWPHMGUQ47zGMmkdgA1CKRY8zMQ4wW+cP83yQS/pwDasn/TJwWg8NAxQlsB0C2MBOnxfAwmznQL8kgvxgzzjCQkGwgpEXuePOzBx/+2MnJz25+zPChoo6foC3o7iRVwygYBaNgFIwCrAAAySYucH+M0H8AAAAASUVORK5CYII=","orcid":"","institution":"Università di Milano","correspondingAuthor":true,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Balestrieri","suffix":""},{"id":270005783,"identity":"4b5d1cd6-6e9a-4b76-9db6-982849eea27a","order_by":1,"name":"Sofia Gigliotti","email":"","orcid":"","institution":"Università di Padova","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Gigliotti","suffix":""},{"id":270005784,"identity":"b6dbb966-70e1-43bb-91bc-f703db373e3f","order_by":2,"name":"Romolo Caniglia","email":"","orcid":"","institution":"Area per la Genetica della Conservazione, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)","correspondingAuthor":false,"prefix":"","firstName":"Romolo","middleName":"","lastName":"Caniglia","suffix":""},{"id":270005785,"identity":"d8e344cd-01b1-4b13-b08c-47ca231dd945","order_by":3,"name":"Edoardo Velli","email":"","orcid":"","institution":"Area per la Genetica della Conservazione, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)","correspondingAuthor":false,"prefix":"","firstName":"Edoardo","middleName":"","lastName":"Velli","suffix":""},{"id":270005786,"identity":"e899c017-9da7-47ec-ad2e-23fb646dda3b","order_by":4,"name":"Francesco Zambuto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Zambuto","suffix":""},{"id":270005787,"identity":"59771b1b-cc35-48f8-bc75-aca5c7a78999","order_by":5,"name":"Erika De Giorgi","email":"","orcid":"","institution":"Università di Milano","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"De Giorgi","suffix":""},{"id":270005788,"identity":"d45325e7-be10-4fb2-81ff-7ad188ff5367","order_by":6,"name":"Nadia Mucci","email":"","orcid":"","institution":"Area per la Genetica della Conservazione, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Mucci","suffix":""},{"id":270005789,"identity":"172aa4c6-b89d-43be-a107-803e4902c750","order_by":7,"name":"Paolo Tremolada","email":"","orcid":"","institution":"Università di Milano","correspondingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Tremolada","suffix":""},{"id":270005790,"identity":"c4de3367-639b-40d5-91d5-f795b6d62ca7","order_by":8,"name":"Andrea Gazzola","email":"","orcid":"","institution":"Università di Pavia","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Gazzola","suffix":""}],"badges":[],"createdAt":"2024-01-23 15:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3891530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3891530/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-58711-6","type":"published","date":"2024-04-04T15:01:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50405662,"identity":"277b7e53-ec63-4637-aa44-3e3dbc49aa79","added_by":"auto","created_at":"2024-01-31 04:58:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6412099,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the 30 selected studies (1\u003csup\u003e39\u003c/sup\u003e; 2\u003csup\u003e25\u003c/sup\u003e; 3\u003csup\u003e65\u003c/sup\u003e; 4\u003csup\u003e66\u003c/sup\u003e; 5\u003csup\u003e67\u003c/sup\u003e; 6\u003csup\u003e68\u003c/sup\u003e; 7\u003csup\u003e69\u003c/sup\u003e; 8\u003csup\u003e70\u003c/sup\u003e; 9\u003csup\u003e71\u003c/sup\u003e; 10\u003csup\u003e51\u003c/sup\u003e; 11\u003csup\u003e72\u003c/sup\u003e; 12\u003csup\u003e73\u003c/sup\u003e; 13\u003csup\u003e74\u003c/sup\u003e; 14\u003csup\u003e75\u003c/sup\u003e; 15\u003csup\u003e76\u003c/sup\u003e; 16\u003csup\u003e77\u003c/sup\u003e; 17\u003csup\u003e78\u003c/sup\u003e; 18\u003csup\u003e35\u003c/sup\u003e; 19\u003csup\u003e79\u003c/sup\u003e; 20\u003csup\u003e80\u003c/sup\u003e: 21\u003csup\u003e81\u003c/sup\u003e; 22\u003csup\u003e82\u003c/sup\u003e; 23\u003csup\u003e83\u003c/sup\u003e; 24\u003csup\u003e84\u003c/sup\u003e; 25\u003csup\u003e85\u003c/sup\u003e; 26\u003csup\u003e86\u003c/sup\u003e; 27\u003csup\u003e87\u003c/sup\u003e; 28\u003csup\u003e88\u003c/sup\u003e; 29\u003csup\u003e89\u003c/sup\u003e; 30\u003csup\u003e90\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/d83577eddf73a8c448d30663.jpg"},{"id":50405661,"identity":"a48b458d-5d4f-4b77-9727-6c036ce0a9ec","added_by":"auto","created_at":"2024-01-31 04:58:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10664333,"visible":true,"origin":"","legend":"\u003cp\u003eThe five study areas (red polygons) in the western Italian Alps where fox diets, macronutrient ratios and densities were assessed in 2021-2022.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/84bbcb6a4bf045c7dd8bdb2c.jpg"},{"id":50405943,"identity":"9b526ecf-5c0e-4e56-9305-1e99099dfbc0","added_by":"auto","created_at":"2024-01-31 05:06:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97777,"visible":true,"origin":"","legend":"\u003cp\u003eRight-angled mixture triangle showing the macronutrient ratios of the 30 selected diet studies (squares: arable land, dots: urban habitats, triangles: mixed habitats, diamonds: forests). The yellow square marks the intake target (mean macronutrient ratio). The coordinate for the implicit variable is read as the difference between 100% and the value at which the diagonal with a slope of −1 that passes through the point identified by the primary coordinates intersects the I‐axis.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/14db90537fd0b4579377bf5c.jpg"},{"id":50405942,"identity":"83ade3e3-4982-4f9d-8f15-9d85a4483adb","added_by":"auto","created_at":"2024-01-31 05:06:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63078,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between fox relative abundance (RA = N of scats/100 m) and density in the five study areas (valleys of the rivers CH-Chalamy, CV-Cervo, EL-Elvo, SB-San Barthelemy and NO-Nomenon).\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/de73e23c018094eec006bffa.jpg"},{"id":50405658,"identity":"8e2a4a3e-6eb8-4385-b486-a5b1cdd753f9","added_by":"auto","created_at":"2024-01-31 04:58:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68384,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between pre-reproductive density and mean deviation of the macronutrient ratio from the intake target in the five study areas (valleys of the rivers CH-Chalamy, CV-Cervo, EL-Elvo, SB-San Barthelemy and NO-Nomenon).\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/fa6d935ec84d5231cb89cce7.jpg"},{"id":54303759,"identity":"061e224e-6576-4a95-a0ab-ece247858108","added_by":"auto","created_at":"2024-04-08 15:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/5a5ee3e1-d8c8-4f1e-b4f5-1498126cef28.pdf"},{"id":50405663,"identity":"8b9e782f-c6e0-4090-8013-d151f9e1e9eb","added_by":"auto","created_at":"2024-01-31 04:58:23","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":458679,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3891530/v1/e25822190239dd4d715e2131.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional ecology of a prototypical generalist predator, the red fox (Vulpes vulpes)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVariation in food availability is a major factor affecting breeding densities and reproduction success, with ultimate consequences on population dynamics\u003csup\u003e1\u003c/sup\u003e. Availability of food resources is expected to act as a limiting factor more for specialist predators, which use one or few resources, than for generalist predators, which opportunistically exploit the most abundant and easily accessible food resources available at any one time and locality\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhether the two feeding tactics are equally profitable is still debated\u003csup\u003e3,4\u003c/sup\u003e, especially for terrestrial predators\u003csup\u003e5\u003c/sup\u003e. According to optimal foraging models, trophic niche breadth should depend on the diversity and abundance of available prey, and net energy gain\u003csup\u003e6\u003c/sup\u003e, that is the ratio between the caloric value of each potentially available prey and the amount of energy spent for finding, pursuing, killing and consuming it.\u003c/p\u003e \u003cp\u003eNotwithstanding, in the last two decades, a growing body of evidence has outlined that foraging is not exclusively driven by energy acquisition and many species tend to regulate the macronutrient composition of their diet to a target ratio (\u0026ldquo;intake target\u0026rdquo;)\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. As the macronutrient composition of the diet has been demonstrated to affect many fundamental fitness-related traits, including growth\u003csup\u003e7,10\u003c/sup\u003e, fecundity\u003csup\u003e11\u003c/sup\u003e and lifespan\u003csup\u003e12,13\u003c/sup\u003e, we may expect that, for any species, suitable habitats are those which offer food resources capable of satisfying the nutritional requirements of a number of individuals in all the phases of their life cycle.\u003c/p\u003e \u003cp\u003eSpecialist predators possess morphological and behavioural adaptations which are supposed to increase their foraging efficiency\u003csup\u003e14\u003c/sup\u003e and feed on foods relatively invariant in their nutrient composition, which coincides with the predator\u0026rsquo;s target macronutrient ratios. In contrast, generalists must be capable of shifting between resources before the opportunity occurs, which implies preexisting behavioural and physiological adaptations\u003csup\u003e15\u003c/sup\u003e, and need to combine several nutritionally complementary foods to achieve their intake target.\u003c/p\u003e \u003cp\u003eThe few available studies\u003csup\u003e16\u003c/sup\u003e suggest that, based on their tolerance towards carbohydrates, mammalian predators can be aligned along a carnivore\u0026ndash;omnivore continuum, ranging from obligate carnivores, such as wolves (\u003cem\u003eCanis lupus\u003c/em\u003e)\u003csup\u003e17\u003c/sup\u003e to poorly specialized ursids\u003csup\u003e18\u003c/sup\u003e. The ability of using fat or carbohydrates as sources of non-protein energy may be expected to be a physiological prerequisite for generalist predators, allowing them to rely on a wide variety of food resources\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeographic and seasonal variation in the composition of generalist predators\u0026rsquo; diets makes it difficult to compare the diet of populations of widespread species. However, using nutritional geometry Gazzola and Balestrieri\u003csup\u003e20\u003c/sup\u003e have recently demonstrated that using a wide variety of food resources does not imply as much variation in the nutritional composition of diets: although using a wide range of fruit and small mammals, widespread carnivores such as martens (\u003cem\u003eMartes martes\u003c/em\u003e and \u003cem\u003eMartes foina\u003c/em\u003e) can be considered macronutrient specialists (i.e. the macronutrient compositions of the diets of different populations are similar\u003csup\u003e21\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eAmong carnivore mammals, the red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) is considered a prototypical generalist predator: its feeding habits vary widely spatially, temporally and in response to human influence, reflecting the biogeographical patterns of distribution and abundance of food resources\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Records of local specialization, due to the disproportionate profitability of anthropogenic resources, reflect the highly opportunistic behaviour of this species\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis dietary flexibility allows foxes to occur in a wide variety of habitats, from sea level up to 4500 m, including several cities\u003csup\u003e26\u003c/sup\u003e. Its geographical range is the widest of any member of the order Carnivora (ca. 70\u0026nbsp;million km\u0026sup2;), including most of the Northern Hemisphere, from the Arctic Circle to northern Africa, and Australia\u003csup\u003e27\u003c/sup\u003e, where it was introduced in the 1870s\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSuch a wide distribution rises an interesting question, that is whether different populations persist on diets that vary widely in macronutrient composition or are capable of using complementary foods to gain the same nutrient intake throughout the species\u0026rsquo; range.\u003c/p\u003e \u003cp\u003eThe first nutritional strategy has been reported for the wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e), which is a dietary generalist and tolerates a wide range of macronutrient ratios across its whole range, particularly in terms of proportion of energy from protein\u003csup\u003e29\u003c/sup\u003e. In contrast, mustelids, such as martens (\u003cem\u003eMartes\u003c/em\u003e spp.) and the Eurasian badger (\u003cem\u003eMeles meles\u003c/em\u003e), tend to keep constant the percent protein energy, while showing a gradient of tolerance towards carbohydrates\u003csup\u003e16,20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLaboratory experiments on \u003cem\u003eDrosophila melanogaster\u003c/em\u003e\u003csup\u003e11\u003c/sup\u003e and mice\u003csup\u003e12\u003c/sup\u003e suggest that unbalanced diets may have profound effects on life span and reproduction. While the broad fundamental macronutrient niche of wild boars has been suggested to enhance their invasion success, increasing the reproductive output of sows\u003csup\u003e29\u003c/sup\u003e, we still do not know whether an excess of carbohydrates may affect the individual fitness of free-ranging carnivores.\u003c/p\u003e \u003cp\u003eTo assess the macronutrient niche of the red fox, we applied right-angle mixture triangles (RMT)\u003csup\u003e21,30\u003c/sup\u003e, in the framework of nutritional geometry. Data were extracted from published reports following the approach proposed by Remonti et al.\u003csup\u003e19,31\u003c/sup\u003e. Based on the wide variety of foods used by foxes, we expected a wide degree of inter-population variation in the percent energy provided by carbohydrates, as so as the recording of clusters of unbalanced diets.\u003c/p\u003e \u003cp\u003eSecondly, we made an attempt to highlight the effects of unbalanced diets on fox density, which was assumed as a proxy for Darwinian fitness. As diet is only one of several factors that may affect population density, samplings were carried out in five areas belonging to the same biogeographical region, in a radius of ca. 30 km (western Italian Alps). We aimed to assess both the yearly diet of each population and their correspondent density, calculated through a faecal DNA-based genetic census.\u003c/p\u003e \u003cp\u003eWe expected macronutrient ratios to affect individual fitness, and, therefore, populations showing nutritional balances close to the intake target to achieve higher densities than those with unbalanced diets.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEstimation of the intake target\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein energy ranged between 36.8% and 71.0%, lipid energy from 25.7% to 51.4%, while carbohydrate energy made up between 0.1% and 29.9%. The target macronutrient ratio of the fox (mean \u0026plusmn; SE) was assessed as 52.4 \u0026plusmn; 1.7% protein energy, 38.7 \u0026plusmn;1.0% lipid energy and 8.9 \u0026plusmn;1.6% carbohydrate energy (Table 1). Overall diets clustered into three groups: \u0026lsquo;average\u0026rsquo;, mainly from mixed habitats, \u0026lsquo;low P‐high C\u0026rsquo;, including mostly diets of urban and cultivated areas, and \u0026lsquo;high P\u0026rsquo; (\u0026gt; 60%) diets from mixed and forested habitats (Fig. 3). On average, carbohydrate energy tended to increase along the natural-to-urban habitat gradient, while lipid energy was the highest in mixed habitats (Table 3). Higher than average carbohydrate energies were recorded in mountainous, forest areas of northern Italy (1, 3 and 14 in table 1), where fruit accounted for 30-50% of the diet (%mV). High protein intakes were related to the consumption of lagomorphs in both cultivated (2, 8) and low-altitude forest areas (27, 30), wild deer in mixed habitats (6) and ungulates (10: NW Italian Alps, 22: Sweden) or small mammals (16: northern Belarus) in woodland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFox diet in Alpine habitats\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 391 km of transects were surveyed (on average 78.2 km per area; min-max: 56.0-116.5), yielding 615 faecal samples (on average 112.6 samples per area; min-max: 115-131).\u003c/p\u003e\n\u003cp\u003eThe analysis of fox diet showed differences in the relative importance of the major food items in the five study areas. Mice (\u003cem\u003eApodemus sylvaticus\u003c/em\u003e) were the most frequent prey in all the three western areas, while in Piedmont voles (mostly \u003cem\u003eMyodes glareolus\u003c/em\u003e) prevailed. In terms of volume, the highest values were achieved by ungulates in all study areas in Aosta Valley, while in two eastern sampling sites voles dominated also in terms of volume. As expected, fruits were less frequently eaten in winter while insects were most preyed on in summer. Ungulates, eaten as carrions, were mostly used in winter-spring. The frequency of occurrence of most major food items showed significant variation among areas (Table SI1), nonetheless, small rodents formed the bulk of fox diet in all study areas (Fig. SI1).\u003c/p\u003e\n\u003cp\u003eProtein energy ranged between 46.6% and 68.3%, lipid energy from 28.9% to 51.7%, while carbohydrate energy made up between 0.7% and 8.15% (Table 4).\u003c/p\u003e\n\u003cp\u003eOverall, seasonal variation in the macronutrient ratios provided by diet was higher in the Aostan valleys than in Piedmont areas (Fig. SI2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe macronutrient intake of Saint-Barth\u0026eacute;lemy valley was the closest to the intake target, while the highest percent deviation of carbohydrate energy from the target were recorded for the two eastern areas (Tab. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFox\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003enumbers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotyping success ranged between 35.7% for the valley of the River Chalamy and 82.8% for Saint-Barth\u0026eacute;lemy\u0026rsquo;s (mean: 52.8%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSixteen different genotypes were recorded in Saint-Barth\u0026eacute;lemy valley, 9 each in Elvo and Nomenon valleys, 8 in Cervo valley and 7 in Chalamy valley. The number of \u0026ldquo;captures\u0026rdquo; per individual varied between 1 and 5.\u003c/p\u003e\n\u003cp\u003eApplying CAPWIRE\u0026rsquo;s TIRM model, the largest population was assessed for Saint-Barth\u0026eacute;lemy, with 30 individuals (CI: 16-30), followed by Nomenon with 28 individuals (11-30), Elvo with 23 (9-30), Cervo with 19 (8-30) and Chalamy with 12 (7-19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe lowest density was recorded for the Chalamy population, 0.7 ind/km\u003csup\u003e2\u003c/sup\u003e, and the highest for that of the Nomenon valley, 2.4 ind/km\u003csup\u003e2\u003c/sup\u003e (EL: 1.9; SB = 2.2; CV = 1.73 ind/km\u003csup\u003e2\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFox relative abundance (RA) ranged between 0.08 and 0.24 faeces/100 m and tended to increase with density (P = 0.09, R\u003csup\u003e2\u003c/sup\u003e = 0.67; Fig. 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePre-reproductive density ranged between 0.17 and 0.37 ind/km\u003csup\u003e2\u003c/sup\u003e (mean \u0026plusmn; SE: 0.21 \u0026plusmn; 0.04 ind/km\u003csup\u003e2\u003c/sup\u003e) and tended to decrease with increasing deviations of the macronutrient ratio from the target (P = 0.037,\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e = 0.78; Fig. 5).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe red fox occurs in a wide geographic range where it must cope with a diversity of environmental conditions and large variation in the availability of food resources. Its food habits have been widely studied, highlighting a great trophic diversity, which may be expected to result in an equally broad inter-population variation in the macronutrient intake.\u003c/p\u003e \u003cp\u003eNotwithstanding, the analysis of available studies providing a volumetric or biomass estimate of the importance of the food resources used by foxes throughout its European range revealed that, on average, the protein requirements of the fox are typical of strict carnivores such as wolves (54%)\u003csup\u003e17\u003c/sup\u003e or domestic and feral cats (52%)\u003csup\u003e32,33\u003c/sup\u003e. Respect to hypercarnivores, foxes seem to tolerate some carbohydrates in their diet, although their contribution was usually lower than expected based on their opportunistic food habits.\u003c/p\u003e \u003cp\u003eThese results underpin the need for considering macronutrient ratios to draw an effective picture of generalist predators\u0026rsquo; diets, because food diversity can conceal their actual nutritional requirements\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs reported for badgers\u003csup\u003e31\u003c/sup\u003e, in urban and rural habitats macronutrient ratios differed the most from the target, particularly for percent carbohydrate energy. Carbohydrate intake is probably affected by the availability of anthropogenic food resources, given the opportunity of searching for food in garbage cans, compost piles and orchards\u003csup\u003e47\u003c/sup\u003e. While shortage in animal prey, particularly in summer, has been reported to affect survival and/or fecundity in another canid, the coyote (\u003cem\u003eCanis latrans\u003c/em\u003e)\u003csup\u003e48\u003c/sup\u003e, no information is available, to the best of our knowledge, about the detrimental effects of carbohydrate overeating. Carbohydrates are generally considered noxious to carnivores, inducing sharp changes in intestinal metabolism and interfering with the digestion of protein and absorption of minerals\u003csup\u003e49\u003c/sup\u003e. Nonetheless, there is no evidence that a high consumption of fruit during summer could impair the reproduction of red foxes during the following spring\u003csup\u003e50\u003c/sup\u003e, suggesting that carbohydrates may be well tolerated by foxes, or even partially necessary for a balanced diet.\u003c/p\u003e \u003cp\u003e The reviewed dietary studies aimed to determine the relative importance of food items in the diet rather than the absolute amount of food consumed or their macronutrient composition. We acknowledge that assessing macronutrient ratios using such studies cannot but provide a rough estimate of the actual intake target of the fox. Nonetheless, the analysis of fox diet in the five Alpine areas, which was carried out by assessing the relative volume of each food category as carefully as possible, allowed to assess macronutrient ratios consistent with the general picture drawn through the literature review, yielding macronutrient ratios similar to those assessed for most fox populations living in forested areas throughout Europe.\u003c/p\u003e \u003cp\u003eIn our study areas, fox diet was poor in fruit (average Vm% = 8.5) respect to previous studies carried out in the western Italian Alps (Vm% = 15\u0026ndash;32%)\u003csup\u003e39,51,52\u003c/sup\u003e. Based on anecdotal information, in summer 2021 rodents were very abundant, because of a mast year for beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e), suggesting that the recorded shift may depend on the higher-than-average availability of this food resource, as already recorded for martens in NW Piedmont\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAverage genotyping success (52.8%) was consistent with previous studies based on faecal DNA (e.g., 48%\u003csup\u003e54\u003c/sup\u003e, 58%\u003csup\u003e55\u003c/sup\u003e). Densities fell within the range reported for Italian fox populations (1\u0026ndash;2,5 foxes/km\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e56\u003c/sup\u003e. Although the use of marking intensity as an index of relative abundance or for assessing habitat preferences has been long challenged\u003csup\u003e57,58\u003c/sup\u003e, the recorded relationship between density and the index of relative abundance suggests that marking intensity can be used as an effective index to compare fox abundances (see also Lanszki et al. about \u003cem\u003eLutra lutra\u003c/em\u003e\u003csup\u003e59\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eTo investigate the effects of the macronutrient composition of fox diet on Darwinian fitness, we assessed winter densities, which were assumed to be less sensitive to variation in local conditions (e.g., number of cubs, percentage of barren females) than post-reproductive densities\u003csup\u003e60\u003c/sup\u003e. Mean values were consistent with those reported by Bartoń and Zalewsky by reviewing 69 studies throughout Europe and Asia\u003csup\u003e61\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile we are well aware that sample size is too low to draw sound conclusions, the inverse relation between density and the deviation of observed macronutrient ratios from the intake target suggests that the nutrient composition of available foods can drive fox abundance, affecting the chance of achieving diets able to satisfy its nutritional, i.e. physiological, requirements. Although density is only a rough proxy for fitness, our results are consistent with laboratory experiments, which demonstrated that generalists pay the cost of relying on unbalanced diets, suffering either high mortality rates and disease risk\u003csup\u003e62,63\u003c/sup\u003e or low reproductive outputs\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis result implies that although foxes can adapt to local and seasonal variations in food availability and then survive in a wide variety of habitats\u003csup\u003e24\u003c/sup\u003e, this capability may not be exempt from fitness costs. Nutrient availability should be considered, together with habitat productivity\u003csup\u003e64\u003c/sup\u003e, among the biotic factors affecting animal abundance and distribution.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFollowing Machovsky-Capuska et al.\u003csup\u003e21\u003c/sup\u003e, by analysing the nutritional niche of a well-known generalist predator we demonstrated that the characterisation of dietary niches cannot disregard the nutritional composition of food resources. The red fox, although being capable of relying on foods largely varying in their nutrient composition, showed to \u0026ldquo;defend\u0026rdquo;\u003csup\u003e8\u003c/sup\u003e the protein intake target typical of hypercarnivore mammals. Moreover, we provided some field-based evidence that not only food availability \u003cem\u003eper se\u003c/em\u003e but also the macronutrient composition of foods may affect at least animals\u0026rsquo; distribution, if not their life history traits.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of the intake target\u003c/h2\u003e\n \u003cp\u003eFollowing Remonti et al.\u003csup\u003e31\u003c/sup\u003e, we searched the available literature using the keywords: \u0026quot;diet,\u0026quot; \u0026ldquo;food habits\u0026rdquo;, \u0026quot;trophic niche,\u0026quot; \u0026quot;fox\u0026quot;, \u0026ldquo;\u003cem\u003eVulpes\u003c/em\u003e\u0026rdquo; and \u0026quot;macronutrients\u0026quot;. We found 73 papers and selected the studies based on the following criteria: (i) results had to be expressed as percent volume or biomass; (ii) the study lasted at least one year (4 seasons); (iii) the number of analysed samples had to be higher than 60. The last two criteria intended to select only those studies providing an effective picture of fox diet. Thirty studies met these criteria and were used to assess the intake target. As environmental conditions may imply different resource availability, based on the description of the study areas, the dataset was split in four main habitats: Urban, Arable, Mixed, and Forest habitats (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAll the selected studies were conducted in Europe, ranging between 27\u0026deg; and 59\u0026deg; N in latitude and 8\u0026deg; W and 29\u0026deg; E in longitude (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The macronutrient ratio of each diet was assessed by multiplying the percent volume or biomass of every food item by the respective percentage of each macronutrient. To obtain, on a wet weight basis, the mean percentage of protein, lipids, and carbohydrates of the food items used by the red fox, we checked the available literature on the nutritional composition of food\u003csup\u003e17,31,32\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Undetermined items were assigned with the mean value calculated for the foods belonging to the same major group. Macronutrient energy ratios (MER) were calculated by multiplying the overall macronutrient ratios by Atwater\u0026rsquo;s coefficients (14.64 kJ g\u003csup\u003e\u0026ndash;1\u003c/sup\u003e for protein, 35.56 kJ g\u003csup\u003e\u0026ndash;1\u003c/sup\u003e for lipids and 14.64 kJ g\u003csup\u003e\u0026ndash;1\u003c/sup\u003e for carbohydrates\u003csup\u003e33\u003c/sup\u003e).\u003c/p\u003e\n \u003cp\u003eTo compare the macronutrient composition of the thirty selected diets, we used right-angled mixture triangles, which represent the three‐component nutritional compositions of diets as Cartesian points in a two‐dimensional nutrient space\u003csup\u003e30\u003c/sup\u003e. Percent protein energy was shown on the third axis (the \u0026lsquo;implicit\u0026rsquo;, or I‐axis), which varies inversely as distance from the origin increases\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy area\u003c/h2\u003e\n \u003cp\u003eTo assess the effect of unbalanced diets on fitness, five areas in the western Italian Alps (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) were selected according to the following criteria: (i) altitude ranged between 1000 and 2200 m a.s.l.; (ii) areas had to be well delimited by mountain ridges; (iii) anthropic impact was low, mainly semi-nomadic livestock rearing and slow tourism (hiking, mountain-bike); (iv) hunting pressure, which can alter population density, was negligible.\u003c/p\u003e\n \u003cp\u003eIn general, in all areas the climate is typically Alpine continental with long and cold winters. Snow cover lasts 5\u0026ndash;6 months a year with maximum depth during January\u0026ndash;February (1.5\u0026ndash;2.5 m) and mean temperatures are generally below 0\u0026deg;C from November to February. Notwithstanding, the two most eastern sampling areas (upper valleys of the rivers Cervo and Elvo, province of Biella, Piedmont) are rainier in May and October-November, while the south-central area (valley of the River Chalamy, Mont Avic Natural Park, Aosta Valley region), is the most xeric (Bocca et al., 2016).\u003c/p\u003e\n \u003cp\u003eBetween 1000 and 1500 m a.s.l. mixed deciduous woods consist of beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e), chestnut (\u003cem\u003eCastanea sativa\u003c/em\u003e), ash (\u003cem\u003eFraxinus excelsior\u003c/em\u003e) and green alder (\u003cem\u003eAlnus viridis\u003c/em\u003e). In the valleys of the rivers Nomenon (Gran Paradiso National Park, Aosta Valley region) and Saint-Barth\u0026eacute;lemy (Aosta Valley region), above 1500 m coniferous forests predominate, with larch (\u003cem\u003eLarix decidua\u003c/em\u003e), Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e), Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e) and silver fir (\u003cem\u003eAbies alba\u003c/em\u003e), which are substituted by mountain pine (\u003cem\u003ePinus mugo\u003c/em\u003e) in the River Chalamy valley. In the two eastern areas, human activities and climate contributed to prevent the growth of conifers, replaced by shrubs of green alder and hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e). Alpine prairies cover the slopes above 1500 m a.s.l.\u003c/p\u003e\n \u003cp\u003eVariation in rainfall and vegetation cover were expected to affect food availability to foxes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling methods\u003c/h2\u003e\n \u003cp\u003eIn each study area, we identified three to five transects between 1000 and 2000 m a.s.l. The transects were chosen based on the availability of pathways and were surveyed from March 2021 to March 2022, aiming to collect a minimum of 30 scats per season (October-December: autumn; January-March: winter; April-June: spring; July-September: summer) in each area\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe identification of fox faeces was based on their morphology and size (diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 mm), which allow to distinguish them from those of other mesocarnivores, such as martens \u003cem\u003eMartes\u003c/em\u003e spp.\u003csup\u003e35\u003c/sup\u003e. Samples were preserved into plastic bags, labelled with an identification number.\u003c/p\u003e\n \u003cp\u003eFox numbers were assessed through faecal DNA-based genetic samplings. Between September 2021 and February 2022, we collected 30 samples per area, selecting fresh-looking faeces to obtain amplifiable, non-degraded DNA. For every sample, we withdrew ca. 1 g of faecal material from the external surface, where it is more probable to find flaking cells of the intestinal wall, using disposable sticks (the remaining material was stored for diet analysis). The test-tubes, containing 95% ethanol, were frozen until DNA extraction\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eMoreover, fox relative abundance (RA) was expressed as number of scats / 100 m of transect\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eSampling was totally non-invasive and did not need the approval of any institutional or licensing committee.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDiet analysis\u003c/h2\u003e\n \u003cp\u003eWe first separated the remains of each prey/food contained in each faecal sample. The minimum number of individuals of each prey type was estimated by the number and position (left/right) of diagnostic hard parts (e.g.: jaw bones for mammals, radio-ulnae for amphibians). When no diagnostic part was found, the remains of a prey item were considered to belong to a single individual. The relative volume (%V) of each food item \u0026ldquo;as ingested\u0026rdquo; was assessed following Kruuk and Parish\u0026rsquo;s method\u003csup\u003e38\u003c/sup\u003e, which has been widely used for assessing carnivore diets and provides volume estimates as accurate as those obtained by the analysis of stomach contents\u003csup\u003e39\u003c/sup\u003e. The percent frequency (%F) was calculated as the ratio between the number of times (samples) a food item occurs and the total number of analysed scats \u0026times; 100. The percent mean volume (%Vm\u0026thinsp;=\u0026thinsp;total estimated volume of each food item as ingested / total number of faecal samples = %F \u0026times; %V / 100) reflects the proportional contribution of each food item to the overall diet\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003ePercent energy ratios were then assessed as so as for literature data and compared using right-angled mixture triangles.\u003c/p\u003e\n \u003cp\u003eThe Chi-squared test (\u0026chi;\u003csup\u003e2\u003c/sup\u003e) was used to compare the raw frequency data of the major food categories: fruit, insects, birds, mice, dormice, voles, insectivores and ungulates.\u003c/p\u003e\n \u003cp\u003eTo account for multiple tests on related data, the level of significance was corrected using Holm-Bonferroni\u0026apos;s sequential technique\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic analysis\u003c/h2\u003e\n \u003cp\u003eThe QIAamp Fast DNA Stool Mini Kit was used to extract the DNA from faecal samples.\u003c/p\u003e\n \u003cp\u003eWe followed the manufacturer instructions, except for final phase, when the ATE buffer was added in three steps of 60 \u0026micro;l each to improve the effectiveness of DNA extraction.\u003c/p\u003e\n \u003cp\u003eGenotyping was carried out using a multiplex PCR of 20 autosomal microsatellite loci (RF 21, 59, 125, 127, 131, 143, 155, 156, 162, 165, 199, 200\u003csup\u003e41\u003c/sup\u003e; VVM 219, 85, 838, 529, 189, 844, 828\u003csup\u003e42\u003c/sup\u003e), explicitly developed for the red fox.\u003c/p\u003e\n \u003cp\u003eThe quality of DNA was initially screened by four replicated PCRs of two microsatellites. Only those samples showing more than 50% positive PCRs were further amplified four times at each of the remaining 18 microsatellites.\u003c/p\u003e\n \u003cp\u003eFour multiplex PCRs were conducted, splitting microsatellites based on fragment size and labelling by fluorescent dyes, and using the QIAGEN Multiplex PCR Kit protocol (15 min at 95\u0026deg;C; 35 cycles of three steps: 30 s at 94\u0026deg;C, 90 s at 57\u0026ndash;63\u0026deg;C, and 60 s at 72\u0026deg;C; 30 min at 62\u0026deg;C; the final volume was reduced to 25 ul).\u003c/p\u003e\n \u003cp\u003eTo lower the probability of retaining false homozygotes or false allele errors, a multitube-approach of 4 independent replicates was used\u003csup\u003e43\u003c/sup\u003e. To construct consensus genotypes heterozygotes were accepted only when the two alleles were recorded in \u0026ge;\u0026thinsp;2 replicates, while a single allele had to be recorded in \u0026ge;\u0026thinsp;3 replicates to confirm homozygosity\u003csup\u003e44,45\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003ePCR products were analysed in an automated sequencer ABI 3130XL (Foster City, CA), and visualized using Genemapper (Thermo Fisher Scientific).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of population density\u003c/h2\u003e\n \u003cp\u003eTo assess the size of the five populations we used CAPWIRE (\u0026ldquo;CApture WIth REplacement\u0026rdquo;) estimators\u003csup\u003e46\u003c/sup\u003e, applying the two available models: the Equal Capture Model (ECM), which assumes equal-capture probabilities among individuals; and the Two-Innate Rates Model (TIRM), which assumes that the population includes two groups of individuals, some easy to capture and some that are difficult to capture. The best model was chosen by a likelihood ratio test (LRT) and confidence intervals were estimated through parametric bootstrap.\u003c/p\u003e\n \u003cp\u003ePopulation density was calculated as the ratio between population size and the correspondent surveyed area (km\u003csup\u003e2\u003c/sup\u003e). We assumed that mountain ridges coincided with the boundaries of fox home ranges and excluded the steep and rocky areas above 2200 m a.s.l., which were assumed to be not suitable or scarcely used by foxes.\u003c/p\u003e\n \u003cp\u003eTo assess fox pre-reproductive density, assumed as a rough indicator for fitness, the individuals sampled only once or in autumn, where filed as itinerants or young of the previous year and discarded.\u003c/p\u003e\n \u003cp\u003eTo assess the relationship between fitness and nutrition, mean deviations of observed ratios from the intake target (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{\\left|\\left(obs-target\\right)\\right|}{target}\\right)\\times 100)\\)\u003c/span\u003e\u003c/span\u003ewere plotted against pre-reproductive density values for each of the five fox populations. The relationships between RA and density and mean deviation of observed macronutrient ratios and pre-reproductive density were tested using linear regression models.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.B and A.G.: conceptualization of the study; A.B. and S.G.: field surveys; S.G. and E.D.: diet analysis; N.M., E.V., R.C. and F.Z.: genetic analysis; A.B., P.T.: data analysis; all authors wrote the manuscript, contributed critically to the drafts and approved its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are included in the paper or in the online version as \u003cstrong\u003esupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll reviewed datasets are cited in the reference list. For any other reasonable request, contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLack, D. The natural regulation of animal numbers (Oxford University Press, 1954).\u003c/li\u003e\n\u003cli\u003eNewton, I. The role of food in limiting bird numbers. Ardea \u003cstrong\u003e68\u003c/strong\u003e, 11\u0026ndash;30 (1980).\u003c/li\u003e\n\u003cli\u003eRecher, H. F. Specialist or generalist: avian response to spatial and temporal changes in resources in \u003cem\u003eAvian foraging: theory, methodology and applications. Studies in avian biology 13\u003c/em\u003e (ed. Morrison, M.L., Ralph, C.J., Verner, J., Jehl, JR.) 333\u0026ndash;336 (Allen Press, 1990).\u003c/li\u003e\n\u003cli\u003eLoxdale H.D., Harvey J.A. The \u0026lsquo;generalism\u0026rsquo; debate: misinterpreting the term in the empirical literature focusing on dietary breadth in insects, Biol. J. Linn. 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Ecologija \u003cstrong\u003e2\u003c/strong\u003e, 27-31 (2001).\u003c/li\u003e\n\u003cli\u003eCarvalho, J.C., Gomes, P. Food habits and trophic niche overlap of the red fox, European wild cat and common genet in the Peneda-Ger\u0026ecirc;s National Park. Galemys, \u003cstrong\u003e13\u003c/strong\u003e, 39-48 (2001).\u003c/li\u003e\n\u003cli\u003eJankowiak, L., Antczak, M., Tryjanowski, P. Habitat use, food and the importance of poultry in the diet of the red fox \u003cem\u003eVulpes vulpes\u003c/em\u003e in extensive farmland in Poland. World Appl. Sci. J. \u003cstrong\u003e4\u003c/strong\u003e, 886-890 (2008).\u003c/li\u003e\n\u003cli\u003eJankowiak, L., Tryjanowski, P. Co-occurrence and food niche overlap of two common predators (red fox \u003cem\u003eVulpes vulpes\u003c/em\u003e and common buzzard \u003cem\u003eButeo buteo\u003c/em\u003e) in an agricultural landscape. Turk. J. Zool. \u003cstrong\u003e37\u003c/strong\u003e, 157-162 (2013).\u003c/li\u003e\n\u003cli\u003eFedriani, J.M., Palomares, F., Delibes, M. Niche relations among three sympatric Mediterranean carnivores. Oecologia \u003cstrong\u003e121\u003c/strong\u003e, 138-148 (1999).\u003c/li\u003e\n\u003cli\u003eDoncaster, C.P., Dickman, C.R., Macdonald, D.W. Feeding ecology of red foxes (\u003cem\u003eVulpes vulpes\u003c/em\u003e) in the city of Oxford, England. J. Mamm. \u003cstrong\u003e71\u003c/strong\u003e, 188-194 (1990).\u003c/li\u003e\n\u003cli\u003eLanszki, J. \u003cem\u003eet al\u003c/em\u003e. Diet composition of the golden jackal and the sympatric red fox in an agricultural area (Hungary). Folia Zool. \u003cstrong\u003e65\u003c/strong\u003e, 310-322 (2016).\u003c/li\u003e\n\u003cli\u003eCasta\u0026ntilde;eda, I., Zarzoso-Lacoste, D., Bonnaud, E. Feeding behaviour of red fox and domestic cat populations in suburban areas in the south of Paris. Urban Ecosyst. \u003cstrong\u003e23\u003c/strong\u003e, 731-743 (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable\u0026nbsp;1. Fox macronutrient (Protein, Lipids, Carbohydrates) intakes as assessed by the analysis of the 30 selected diet studies.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"473\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHabitat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" rowspan=\"2\"\u003e\n \u003cp\u003e45.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" rowspan=\"2\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.952380952380953%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.19047619047619%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"bottom\"\u003e\n \u003cp\u003eurban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" rowspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\"\u003e\n \u003cp\u003e37.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\"\u003e\n \u003cp\u003e35.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\"\u003e\n \u003cp\u003e26.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\"\u003e\n \u003cp\u003e45.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.08411214953271%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.317757009345794%\"\u003e\n \u003cp\u003e49.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.42056074766355%\"\u003e\n \u003cp\u003e44.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91588785046729%\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.85981308411215%\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.981308411214954%\"\u003e\n \u003cp\u003e45.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.42056074766355%\"\u003e\n \u003cp\u003e9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e1139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e49.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e53.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.08411214953271%\" valign=\"top\"\u003e\n \u003cp\u003e678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.317757009345794%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.42056074766355%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91588785046729%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.85981308411215%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.981308411214954%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.42056074766355%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" rowspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" rowspan=\"2\"\u003e\n \u003cp\u003e53.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" rowspan=\"2\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.952380952380953%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.19047619047619%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eurban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e56.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e49.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e-8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e1939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003eurban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" rowspan=\"3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\n \u003cp\u003earable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" rowspan=\"3\"\u003e\n \u003cp\u003e48.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" rowspan=\"3\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.952380952380953%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.19047619047619%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"bottom\"\u003e\n \u003cp\u003eforest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"bottom\"\u003e\n \u003cp\u003e56.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.952380952380953%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.19047619047619%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"bottom\"\u003e\n \u003cp\u003eurban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.322033898305085%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.864406779661017%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.076271186440678%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805084745762711%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.957627118644067%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2. Percent macronutrient composition of the major food items in the diet of the red fox.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"507\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.29585798816568%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.173570019723865%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.11023622047244%\" rowspan=\"3\"\u003e\n \u003cp\u003eWild fruit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.28346456692913%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eRubus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.141732283464567%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47244094488189%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePrunus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSambucus nigra\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.14595660749507%\" rowspan=\"4\"\u003e\n \u003cp\u003eCultivated fruit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34319526627219%\" valign=\"bottom\"\u003e\n \u003cp\u003ePlums\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.806706114398422%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.173570019723865%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.017751479289942%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.512820512820515%\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.40963855421687%\" valign=\"bottom\"\u003e\n \u003cp\u003ePears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.867469879518072%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.759036144578314%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.903614457831326%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.06024096385542%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eVitis vinifera\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFicus carica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.11023622047244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.28346456692913%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eUndetermined fruit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.141732283464567%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47244094488189%\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.11023622047244%\" rowspan=\"2\"\u003e\n \u003cp\u003eInsects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.28346456692913%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eLarvae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.141732283464567%\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47244094488189%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.11023622047244%\" rowspan=\"5\"\u003e\n \u003cp\u003eBirds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.28346456692913%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAnseriformes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.141732283464567%\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47244094488189%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eGalliformes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePasseriformes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eColumbiformes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e18.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eUndetermined birds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e21.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.11023622047244%\" rowspan=\"4\"\u003e\n \u003cp\u003eMammals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.28346456692913%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSmall mammals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.141732283464567%\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.47244094488189%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eLagomorphs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eUngulates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.42307692307692%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMartes\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Inter-habitat variation of fox macronutrient intake along the natural-to-urban gradient.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHabitat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% lipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.29807692307692%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% carbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.29807692307692%\" valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.29807692307692%\" valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eArable land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.29807692307692%\" valign=\"top\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.29807692307692%\" valign=\"top\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Macronutrient ratios in the diet of the red fox in the five study areas.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% lipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% carbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003eNomenon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e68.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e28.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003eS. Barthelemy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e47.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e44.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003eChalamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e56.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e40.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003eCervo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e50.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e48.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003eElvo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.57676348547718%\" valign=\"top\"\u003e\n \u003cp\u003e46.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.502074688796682%\" valign=\"top\"\u003e\n \u003cp\u003e51.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.46058091286307%\" valign=\"top\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Percent deviation from the intake target assessed for the five fox diets in Alpine habitats.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% lipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% carbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003eNomenon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003eS. Barthelemy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003eChalamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e66.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003eCervo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e92.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.14487632508834%\" valign=\"top\"\u003e\n \u003cp\u003eElvo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.96113074204947%\" valign=\"top\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.19434628975265%\" valign=\"top\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.02826855123675%\" valign=\"top\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.671378091872793%\" valign=\"top\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3891530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3891530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeneralist species, which exploit a wide range of food resources, are expected to be able to combine available resources as to attain their specific macronutrient balance (intake target). Among mammalian predators, the red fox \u003cem\u003eVulpes vulpes\u003c/em\u003e is a widespread, opportunistic forager: its diet has been largely studied, outlining wide variation according to geographic and climatic factors. We aimed to check if, throughout the species\u0026rsquo; European range, diets vary widely in macronutrient composition or foxes can combine complementary foods to gain the same nutrient intake. First, we assessed fox\u0026rsquo;s intake target in the framework of nutritional geometry. Secondly, we tried to highlight the effects of unbalanced diets on fox density, which was assumed as a proxy for Darwinian fitness, as assessed in five areas of the western Italian Alps.\u003c/p\u003e \u003cp\u003eUnexpectedly, the target macronutrient ratio of the fox (52.4% protein-, 38.7% lipid- and 8.9% carbohydrate energy) was consistent with that of hypercarnivores, such as wolves and felids, except for carbohydrate intakes in urban and rural habitats.\u003c/p\u003e \u003cp\u003eThe inverse relation between density and the deviation of observed macronutrient ratios from the intake target suggests that fox capability of surviving in a wide range of habitats may not be exempt from fitness costs and that nutrient availability should be regarded among the biotic factors affecting animal abundance and distribution.\u003c/p\u003e","manuscriptTitle":"Nutritional ecology of a prototypical generalist predator, the red fox (Vulpes vulpes)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 04:58:18","doi":"10.21203/rs.3.rs-3891530/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2024-02-29T10:38:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-19T16:19:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4528b4ce-0445-4fe3-b769-d372da6b6cb2","date":"2024-02-08T16:06:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-29T16:36:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-29T16:34:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-29T05:22:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-29T05:19:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-23T15:56:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b2e1103-c53a-4b0e-8f69-cd55fa2d5068","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28453401,"name":"Biological sciences/Zoology/Animal behaviour"},{"id":28453402,"name":"Biological sciences/Zoology/Animal physiology"},{"id":28453403,"name":"Biological sciences/Ecology"}],"tags":[],"updatedAt":"2024-04-08T15:03:33+00:00","versionOfRecord":{"articleIdentity":"rs-3891530","link":"https://doi.org/10.1038/s41598-024-58711-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-04-04 15:01:02","publishedOnDateReadable":"April 4th, 2024"},"versionCreatedAt":"2024-01-31 04:58:18","video":"","vorDoi":"10.1038/s41598-024-58711-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-58711-6","workflowStages":[]},"version":"v1","identity":"rs-3891530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891530","identity":"rs-3891530","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}