Comparative methods for assessing the age and of the red fox (Vulpes vulpes crucigera) in Central Romania

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A total of 70 skull specimens and 65 carcasses were examined, with morphological determinations and anatomical evaluations conducted to understand the variability within the local population. Craniometric analysis involved 32 specific measurements, and regression equations were derived to estimate the age of specimens based on measurable parameters. The study area encompassed diverse ecological conditions, spanning on an area of almost 75 000 ha, providing insights into the adaptability of red foxes. The results highlight the significance of total body weight in estimating age and suggest the potential for further research to refine population characterization. Two equations resulted from the regression model, which can help the age determination directly on the terrain. The Principal Component Analysis was applied to all specimens in the study to analyze their morphological characteristics, revealing significant variations in skull and body parameters. Extending this study to different regions of Europe can enhance our understanding of red fox variations in skull and anatomical characteristics. Animal Science Figures Figure 1 Figure 2 Figure 3 1. Introduction The red fox ( Vulpes vulpes L. ), one of the most widely distributed species of foxes, spans approximately 70 million square kilometres, inhabiting the Northern Hemisphere from the Polar Circle to Northern Africa, Central America, the Asian steppes, and occasionally venturing into the Southern Hemisphere (Castello J., 2018 ; Larivière & Pasitschniak-Arts, 1996 ). As the most widely distributed animal in the Canidae family, it boasts a medium-sized body, with approximately 45 subspecies identified worldwide (Macdonald D.W. & Sillero-Zubiri C, 2004; Wozencraft, 1993 ). For the purposes of this study, there is the subspecies Vulpes vulpes crucigera Bechstein , primarily distributed in Europe, excluding Spain, Scandinavia, and certain islands in the Mediterranean (Larivière & Pasitschniak-Arts, 1996 ; Macdonald D.W. & Sillero-Zubiri C, 2004). The studied subspecies exhibits a general appearance characterized by an elongated trunk and nose, accompanied by relatively short limbs, suggesting excellent manoeuvrability and an ability to navigate thickets (Alexander et al., 2023 ). Its head and trunk length typically range from 80–90 cm, with a height measuring between 35–40 cm (Cotta et al., 2001 ). Sexual dimorphism is challenging to assess in living animals, often confirmed posthumously through examination of sexual organs (Hartová-Nentvichová et al., 2010 ). The main objective of this study is to understand the population of red foxes in the Brasov area by analysing a large number of collected specimens, followed by identifying and presenting conclusions regarding the estimation of the age of the studied specimens through the analysis of morphological, anatomical, and craniometric characteristics. The motivation for this study is the lack of in-depth knowledge about the species, due to the absence of scientific works at the national level, with the red fox species as the subject. The primary goals of this research are to compare the results obtained from different methods of assessing the age of collected specimens and to derive theoretical regression equations for estimating age based on measurable parameters obtained in the field. 2. Methods and materials 2.1. Study area The research was conducted using red fox skulls collected from Brașov county, Central Romania, in 2020. To comprehensively analyse red fox population and anatomical variabilities, the study encompassed two distinct eco-regions: mountains and hills, covering a total area of 74,676 hectares. This area overlaps with 6 hunting management units: 17 Geamăna, 18 Timiș, 19 Sânpetru, and 20 Prejmer-Cernatu, along with 23 Gârcin and 25 Tărlung. Two research areas were selected, both within the ecological optimum of the species and at its limit (Comșia A.M., 1961), in order to conduct a comprehensive study of the local population, aiming to capture the real variability of anatomical-morphological and cranial characteristics, considering the entire diversity of conditions in which the red fox species thrives due to its ecological plasticity. The study area encompasses altitudes ranging from 500 to 850 meters, with average annual temperatures between 7.5°C and 7.6°C. The annual precipitation ranges from 595.0 to 782.8 mm. The vegetation consists primarily of three zones: meadow vegetation along the Olt River characterized by willow and black alder forest groups, eutrophic swamps, which houses relict species like Schoenetum armerietum barcensis, Cladietum marisei, Caricetum davallianae , and xerophytic vegetation dominated by oak forests. 2.2. Sample collection In the present study, morphological determinations and general examinations were conducted on a total of 70 skull specimens of Vulpes vulpes crucigera Bechstein , comprising 42 males and 28 females. Anatomical evaluations were performed on 65 fox carcasses, including 39 males and 26 females. Additionally, for the craniometric study, a total of 65 skulls were analysed and measured, consisting of 40 males and 25 females. Out of these samples, initially, 10 young specimens and 60 adult specimens were assessed, and their classification into age groups was only possible after determining their age using specific methods. Morphological and anatomical measurements For the craniometric analysis, a total of 32 specific measurements (see Appendix, Table 1 ) were performed using a professional digital calliper with a precision of 0.01mm, according to the methods described in the specialized literature (Hartová-Nentvichová et al., 2010 ; Lynch & Lynch, 1996 ). To better systematize the measurements, they were divided into three categories: elements on the dorsal face, elements on the ventral face, and elements on the lateral and occipital face. Additionally, a total of 14 specific anatomical measurements were performed for the evaluation of the carcasses (see Appendix, Table 2 ). To accurately determine the age of the specimens, a method for analysing dentin layers at the level of teeth was used, which is universally considered rigorous and precise, by highlighting the annual layers of secondary dentin cement (Andera, 2007 ; Harris, 1978 ; Manjunatha & Soni, 2014 ). 2.3. Statistical analysis A discriminatory analysis, as a multivariate technique, was used to separate the two groups of observations(Coomans et al., 1989 ) and a simple and a multiple regression was applied on the most important variables in order to obtain mathematical equation able to assess the age of the red fox. The analysis were done using the XLStat extension for Microsoft Excel. Given the challenge of separating size from form in multivariate analyses of morphological data (Claude, 2008 ), a principal component analysis (PCA) was employed to reduce the dimensionality of the dataset while preserving its key functionalities. In the first analysis, we focused on examining the relationship between various skull parameters of the red fox and the determined sex and age, as indicated by the provided variables. In the second analysis, our emphasis was on exploring the link between the morpho-anatomical variation of the red fox body and age and sex. 3. Results 3.1. Estimating the age of the collected specimens through discriminatory analysis The elements analysed for estimating the age of collected specimens, in accordance with the specialized literature, include morphological characteristics (total body length), anatomical features (total body weight), craniometric dimensions (total skull length and maximum zygomatic width), as well as the shape and dimensions of the sagittal crest (expressed as total skull height) (Hartová-Nentvichová et al., 2010 ; Lynch & Lynch, 1996 ). The study of these five characteristics was conducted for all 65 specimens with known values for these elements, using a discriminatory analysis through the stepwise method. This analysis initially resulted in a model with three characteristics: total body weight, total skull length, and total skull height, with only total body weight showing statistical significance, as presented in Table 1 . Table 1 Selection variables from the discriminatory analysis Sample number = 65 Initial data for age discriminatory analysis Steps: 3, Number of variables in the model: 3; Group: Age (3) Wilks’ Lambda: 0,20088 approx. F (6,120) = 24,623 p< ,0000 Wilks’ Partial F test p-level Toler. 1- Toler. TW 0,3682 0,5454 24,9983*** 0,0000 0,9466 0,0533 LTCr 0,2145 0,9363 2,0381 0,1391 0,6524 0,3475 HCr 0,2086 0,9629 1,1554 0,3218 0,6731 0,3268 It is observed that the first element from the generated model, Total Weight (TW), participates dominantly in discrimination (0.3682), corresponding to the value of the F test, followed in decreasing order by Total Skull Length (LTCr) with 0.2145 and Skull Height (HCr) with 0.2086, which do not have statistical significance. Subsequently, the following steps were taken generating the canonical functions that participate in discrimination (n = 2) and standardizing their coefficients (Eigen test), structuring the coefficients (by establishing correlations between the variables in the model), and determining the canonical means of the variables (their participation in discrimination). At the end of the analysis, all 65 samples were classified into the three predetermined age classes (a- adults, m - mature, j - juveniles). According to Fig. 1 , a relatively grouped arrangement of specimens into the three age classes is observed, with a more distinct separation of juveniles and a relatively overlapping distribution of adults and matures.The statistical conclusion of the discriminatory analysis of the studied characteristics regarding the accuracy of assigning the specimens "a priori" to the three age classes is presented in the final table of the analysis. Table 2 Discriminatory analysis results Group Classification matrix Rows: Observed classification (initial) Columns: Resulting classification (final) Percentage (%) adult (a) mature (m) juvenile (j) adult (a) 80,95 17 4 0 mature (m) 91,18 3 31 0 juvenile (j) 90,00 1 0 9 Total 87,69 21 35 9 According to the final data of the analysis (Table 2 ), it is observed a high degree of accuracy in assigning the studied specimens to their respective age class as an initial estimation through the analysis of predetermined elements. The maximum percentage of correct assignments is observed in the mature specimens (91%), with only three specimens showing a 90% correct assignment rate, and practically one specimen being placed in the next age class through the analysis. The adults, totalling 21 specimens, show an 81% correct assignment rate, with 4 specimens being placed in the mature category by the analysis. Comparing the estimated ages, categorized into the three classes, with the ages determined through the method of annual dentin layers, we observe that through this type of discriminatory analysis of morphological, anatomical, and cranial characteristics, the final accuracy of age estimation is 87.69%, making it a valuable form of age estimation in the absence of dental specimens. 3.2. Estimating the age of the collected specimens through regression Another possible method for determining the age of the collected specimens is using the stepwise regression equation, which considers the same morphological, anatomical, and cranial characteristics as in the previous methods. Age was considered as the dependent variable, quantified with values: 1 (juvenile class), 2 (adult class), and 3 (mature class). The independent variables considered were total body length, total body weight, total cranial length, maximum zygomatic width, and total cranial height. The stepwise analysis selected the same three variables with significant influence on age estimation as in the discriminant analysis: total body weight, total cranial length, and total cranial height, according to the data in Table 3 . Table 3 Regression summary for the dependent variables N = 65 Summary of the regression: Age (multiple regressions for the collected foxes) R = 0.8805636 R2 = 0.78864411 Adjusted R 2 = 0.77824955 F(3,61) = 79.447 p < 0.0000 Standard error of estimation: 0.32493 Beta St. Er. B Std. Er. t(61) p-level Intercept. - - -5,10548 0,820087 -6,22554 0,00000 TW 0,606301 0,089251 0,00052 0,000076 6,79319 0,0000 LTCr 0,211542 0,111478 0,01614 0,008506 1,89761 0,064248 HCr 0,142500 0,102829 0,0387 0,027929 1,38579 0,170856 The high value of the multiple regression coefficient R2 = 0.7886 is observed, which explains the phenomenon to the extent of 78.86%, corresponding to the correlation between age and the three variables in the model (body weight, cranial length, and cranial height). After establishing the significance of the participation of the three variables in age estimation, it is found that only total body weight is significant. As a result of this, a regression Eq. ( 1 ) of the following form can be generated: $$AGE=\text{0,00052}*TW-\text{5,10548}$$ 1 Where, AGE represents the estimated age and TW the total weight 3.3. Estimating the age of live specimens through multiple regression A possible method for estimating the age of live specimens was also studied using the stepwise regression equation, for which morphological and anatomical characteristics were considered, as in the case of the previous methods. Once again, age was considered as the dependent variable, quantified with values: 1 (juveniles), 2 (adults), and 3 (mature). The independent variables considered were external morphological characteristics: total body length, trunk length, tail length, head length, total body weight, chest circumference, abdomen circumference, and height at withers. The stepwise analysis selected three variables with significant influence on age estimation in the calculation model, as in the case of the regression analysis of the collected specimens: the total body weight of the specimen (p < 0.0000), height at rake (p < 0.000453), and chest circumference (p < 0.180764), according to the data in Table 4 . Table 4 Summary of multiple regression N = 65 Summary of the regression: Age (multiple regressions for live foxes) R = 0.89245934 R2 = 0.79648368 Adjusted R2 = 0.78647468 F(3,61) = 79.577 p < 0.0000 Standard error of estimation: 0.34239 Beta St. Er. B Std. Er. t(61) p-level Intercept. - - -11,4538 2,750033 -4,16496 0,0001 TW 0,516529 0,101657 0,0004 0,000087 5,08108 0,000004 HG 0,341941 0,099651 0,2029 0,059136 3,43139 0,001083 CC 0,101506 0,085775 0,0959 0,081077 1,18341 0,241239 In this case, we also observe a high value of the multiple regression coefficient R2 = 0.7964, which explains the phenomenon to a proportion of 74.64%, namely the correlation between age and the three variables chosen in the model (total body weight, height at withers, and chest circumference). After establishing the significance of the participation of the three variables in age estimation, it is found that only two characteristics are statistically significant: total body weight and height at withers. As a result, a regression Eq. ( 2 ) of the following form was generated: $$AGE=\text{0,0004}*TW+HG-\text{11,4538}$$ 2 Where, AGE represents the estimated age, TW the total weight and HG the height at rake 3.4. Analysis of principal components of craniometry The Principal Component Analysis was applied for all the specimens from all the age group and both sexes. The first and second principal component accounted for 57,8% and 6,4% of the variance, respectively (Fig. 2 ). The sexual dimorphism in shape and size of the skull is not pronounced and overlaps between red fox males and females (Fig. 2 ). Differences are mainly in size but not in shape, and the differences are dictated by the age class. Assessment centres on individual contributions to primary factors, with the distance metric (Dist), exemplified by character 1's 7.658 distance, revealing its specific location in the multivariate space. PCA outcomes unveil variable contributions to major components, where variables like 'Age. Class' with a substantial contribution (ctr) of 3.741 to Dim.1 exert a significant influence in shaping the primary additives. Examining factor contributions to prominent components unveils specific responsibilities; for example, 'HTrOc' (occipital triangle height) makes a significant contribution (ctr) of 9.236 to Dim.Three, underscoring its relevance in determining this feature. 3.5 Analysis of the principal morphological components of the body The Principal Component Analysis was applied for all the specimens from all the age group and both sexes. The first and second principal component accounted for 78,4% and 6,5% of the variance, respectively (Fig. 2 ). The sexual dimorphism accordingly to the body anatomy is pronounced and does not overlap between red fox males and females (Fig. 3 b). Differences are mainly in size, as males tend to be larger than the females. In the Principal Component Analysis (PCA), eigenvalues offer insights into variance, with the first principal component (Dim.1) dominating at 10.188, capturing 78.37% of the total variance; subsequently, cumulative variance reaches 98.35% by Dim.9, highlighting the consolidation of statistics and emphasizing the importance of key elements. In terms of variable contributions, TL, LWT, LT, HG, HC, LH, LN, LB, and NC play significant roles in shaping Dim.1, with contributions expressed as percentages, emphasizing their importance in shaping key components. Variable descriptions delineate specific contributions to various dimensions (Dim.1, Dim.2, Dim.3). Notably, TL significantly influences Dim.1 and Dim.2, demonstrating its role in reducing dataset variability. The principal components from PCA serve diverse analytical functions. Focusing on elements with substantial contributions facilitates the identification of distinct physical trends, aiding in pinpointing outliers and specific traits among red foxes, for the gender of the specimen, but also for the age. 4. Discussion This study aimed to identify unconventional methods for estimating the age of red foxes directly in the field using rudimentary utensils, without the need for laboratory analysis. Although the estimation methods are developed through regressions, they are of interest to study, especially since only classical methods are present in the specialized literature (Devenish-Nelson et al., 2013 ; Harris, 1978 ; Roulichová & Anděra, 2007 ). In the current dataset, the average age of females is very similar to males, which contradicts findings from other populations (Younes, 2023 ). Additionally, a limited number of individuals have been identified in the younger age classes, potentially indicating that younger specimens are more susceptible to hunting. The red fox seems to exhibit resilience to intensive hunting due to the abundance of young foxes in the population and its high reproductive rate, which confirms finding of (Basuony, 2015 ; Younes, 2023 ). Upon validating multiple studies, it becomes evident that both female and male red foxes undergo changes in the shape and size of their skulls as they age (Gos’kov & Korytin, 2016 ). There is a noticeable trend towards an increase in width dimensions rather than length dimensions, resulting in older individuals having skulls with greater widths compared to younger foxes (Parsons et al., 2020 ; Younes, 2023 ). The developed equations can be easily applied in the field, but for greater mathematical accuracy, the data sample should be expanded to other geographic areas and subspecies. The equations presented are theoretically applicable only in the study area and for the Vulpes vulpes crucigera Bechstein subspecies, and their use in other areas and species should be approached with great caution. These equations improve the knowledge of the ecological and behavioural characters of the red fox. To create more comprehensive equation that can incorporate all the initially considered variables and effectively characterize the entire fox population, further research efforts need to be directed towards expanding the study sample size. This represents a key focus for future investigations. The study successfully analyzed the anatomical and morphological characteristics of red fox specimens, particularly focusing on the subspecies Vulpes vulpes crucigera in the Brasov area. Various methods, including morphological examinations and discriminatory analysis, were employed to estimate the age of the collected specimens with a high degree of accuracy, showcasing the effectiveness of these techniques in the absence of dental specimens. The development of regression equations based on measurable parameters provides a valuable tool for estimating the age of red fox specimens, highlighting the potential for further research to refine and expand these equations for a more comprehensive characterization of the fox population. The study area encompassed diverse ecological conditions, allowing for a comprehensive understanding of the anatomical variabilities and cranial characteristics of the red fox species in different habitats, contributing to a more holistic view of the population dynamics. Future research efforts should focus on increasing sample sizes to enhance the accuracy and applicability of regression equations for age estimation, as well as exploring additional variables to create more comprehensive models for characterizing the red fox population in Central Romania. References Alexander, S., Hodson, A., Mitchell, D., Nicolson, D., Orrell, T., & Perez-Gelabert, D. (2023). The Integrated Taxonomic Information System. In Catalogue of Life Checklist . ITIS. https://doi.org/10.48580/dfrdl-4ky Andera, M. (2007). Simple method of age determination in red fox, Vulpes vulpes. Folia Zool , 440–444. https://www.researchgate.net/publication/242021275 Basuony, A. E. (2015). Intraspecific variations among Egyptian Populations of the Red Fox, Vulpes vulpes . Al-Azhar University. Castello J. (2018). Red Fox-Like Canids. Canids of the World: Wolves, Wild Dogs, Foxes, Jackals, Coyotes, and Their Relatives , 116 , 172. Claude, Julien. (2008). Morphometrics with R . Springer. 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S., & Soni, NishitK. (2014). Estimation of age from development and eruption of teeth. Journal of Forensic Dental Sciences , 6 (2), 73. https://doi.org/10.4103/0975-1475.132526 Parsons, K. J., Rigg, A., Conith, A. J., Kitchener, A. C., Harris, S., & Zhu, H. (2020). Skull morphology diverges between urban and rural populations of red foxes mirroring patterns of domestication and macroevolution. Proceedings of the Royal Society B: Biological Sciences , 287 (1928). https://doi.org/10.1098/rspb.2020.0763 Roulichová, J., & Anděra, M. (2007). Age determination in the Red Fox (Vulpes vulpes): a comparative study Určování věku u lišky obecné (Vulpes vulpes): srovnávací studie . 38 , 55–71. Wozencraft, W. C. (1993). Order Carniviora. In Mammal species of the world . Smithsonian Institution Press. Younes, M. (2023). The Variation of the Skull and Sexual Dimorphism of Red Fox Sample from Egypt. Egyptian Academic Journal of Biological Sciences, B. Zoology , 15 (1), 259–274. https://doi.org/10.21608/eajbsz.2023.305316 Additional Declarations The authors declare no competing interests. Supplementary Files Annex1.Cranialparametersandabbreviation.docx Annex1.Cranialparametersandabbreviation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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classes\u003c/p\u003e","description":"","filename":"Figure1.Specimensdistributioninageclasses.png","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/66fe58c53fe8a50c62180367.png"},{"id":51681893,"identity":"7ef9e6d9-242b-49a2-bdbf-d5f55b4776e1","added_by":"auto","created_at":"2024-02-27 06:46:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218850,"visible":true,"origin":"","legend":"\u003cp\u003eSpecimens distribution in age classes\u003c/p\u003e","description":"","filename":"Figure1.Specimensdistributioninageclasses.png","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/d2e8b2e9f9d4d415ce6480c7.png"},{"id":51681896,"identity":"181f1e3f-4636-46c8-ba84-42dd9415218f","added_by":"auto","created_at":"2024-02-27 06:46:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227413,"visible":true,"origin":"","legend":"\u003cp\u003eAnatomical characterises principal component analysis\u003c/p\u003e","description":"","filename":"Figure3.Anatomicalcharacterisesprincipalcomponentanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/83473d513d3390f2d6a59a93.png"},{"id":51682188,"identity":"6de651fb-74b6-439f-998f-dcec83963912","added_by":"auto","created_at":"2024-02-27 06:54:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":956601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/cda46216-46b9-47f5-a7bf-3c059cf16612.pdf"},{"id":51681892,"identity":"5e10ce98-4560-4ad4-9dce-62844c67509f","added_by":"auto","created_at":"2024-02-27 06:46:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16087,"visible":true,"origin":"","legend":"","description":"","filename":"Annex1.Cranialparametersandabbreviation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/09a2cac830b8e6ff81bb4507.docx"},{"id":51681894,"identity":"504ef067-909f-41e9-8601-40796c3afd17","added_by":"auto","created_at":"2024-02-27 06:46:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16087,"visible":true,"origin":"","legend":"","description":"","filename":"Annex1.Cranialparametersandabbreviation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3988562/v1/e49467caa9195f4416986954.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative methods for assessing the age and of the red fox (Vulpes vulpes crucigera) in Central Romania\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe red fox (\u003cem\u003eVulpes vulpes L.\u003c/em\u003e), one of the most widely distributed species of foxes, spans approximately 70\u0026nbsp;million square kilometres, inhabiting the Northern Hemisphere from the Polar Circle to Northern Africa, Central America, the Asian steppes, and occasionally venturing into the Southern Hemisphere (Castello J., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Larivi\u0026egrave;re \u0026amp; Pasitschniak-Arts, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). As the most widely distributed animal in the Canidae family, it boasts a medium-sized body, with approximately 45 subspecies identified worldwide (Macdonald D.W. \u0026amp; Sillero-Zubiri C, 2004; Wozencraft, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). For the purposes of this study, there is the subspecies \u003cem\u003eVulpes vulpes crucigera Bechstein\u003c/em\u003e, primarily distributed in Europe, excluding Spain, Scandinavia, and certain islands in the Mediterranean (Larivi\u0026egrave;re \u0026amp; Pasitschniak-Arts, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Macdonald D.W. \u0026amp; Sillero-Zubiri C, 2004).\u003c/p\u003e \u003cp\u003eThe studied subspecies exhibits a general appearance characterized by an elongated trunk and nose, accompanied by relatively short limbs, suggesting excellent manoeuvrability and an ability to navigate thickets (Alexander et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its head and trunk length typically range from 80\u0026ndash;90 cm, with a height measuring between 35\u0026ndash;40 cm (Cotta et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Sexual dimorphism is challenging to assess in living animals, often confirmed posthumously through examination of sexual organs (Hartov\u0026aacute;-Nentvichov\u0026aacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The main objective of this study is to understand the population of red foxes in the Brasov area by analysing a large number of collected specimens, followed by identifying and presenting conclusions regarding the estimation of the age of the studied specimens through the analysis of morphological, anatomical, and craniometric characteristics. The motivation for this study is the lack of in-depth knowledge about the species, due to the absence of scientific works at the national level, with the red fox species as the subject. The primary goals of this research are to compare the results obtained from different methods of assessing the age of collected specimens and to derive theoretical regression equations for estimating age based on measurable parameters obtained in the field.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe research was conducted using red fox skulls collected from Brașov county, Central Romania, in 2020. To comprehensively analyse red fox population and anatomical variabilities, the study encompassed two distinct eco-regions: mountains and hills, covering a total area of 74,676 hectares. This area overlaps with 6 hunting management units: 17 Geamăna, 18 Timiș, 19 S\u0026acirc;npetru, and 20 Prejmer-Cernatu, along with 23 G\u0026acirc;rcin and 25 Tărlung.\u003c/p\u003e \u003cp\u003e Two research areas were selected, both within the ecological optimum of the species and at its limit (Comșia A.M., 1961), in order to conduct a comprehensive study of the local population, aiming to capture the real variability of anatomical-morphological and cranial characteristics, considering the entire diversity of conditions in which the red fox species thrives due to its ecological plasticity. The study area encompasses altitudes ranging from 500 to 850 meters, with average annual temperatures between 7.5\u0026deg;C and 7.6\u0026deg;C. The annual precipitation ranges from 595.0 to 782.8 mm. The vegetation consists primarily of three zones: meadow vegetation along the Olt River characterized by willow and black alder forest groups, eutrophic swamps, which houses relict species like \u003cem\u003eSchoenetum armerietum barcensis, Cladietum marisei, Caricetum davallianae\u003c/em\u003e, and xerophytic vegetation dominated by oak forests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample collection\u003c/h2\u003e \u003cp\u003eIn the present study, morphological determinations and general examinations were conducted on a total of 70 skull specimens of \u003cem\u003eVulpes vulpes crucigera Bechstein\u003c/em\u003e, comprising 42 males and 28 females. Anatomical evaluations were performed on 65 fox carcasses, including 39 males and 26 females. Additionally, for the craniometric study, a total of 65 skulls were analysed and measured, consisting of 40 males and 25 females. Out of these samples, initially, 10 young specimens and 60 adult specimens were assessed, and their classification into age groups was only possible after determining their age using specific methods.\u003c/p\u003e \u003cp\u003eMorphological and anatomical measurements\u003c/p\u003e \u003cp\u003eFor the craniometric analysis, a total of 32 specific measurements (see Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were performed using a professional digital calliper with a precision of 0.01mm, according to the methods described in the specialized literature (Hartov\u0026aacute;-Nentvichov\u0026aacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lynch \u0026amp; Lynch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). To better systematize the measurements, they were divided into three categories: elements on the dorsal face, elements on the ventral face, and elements on the lateral and occipital face. Additionally, a total of 14 specific anatomical measurements were performed for the evaluation of the carcasses (see Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ). To accurately determine the age of the specimens, a method for analysing dentin layers at the level of teeth was used, which is universally considered rigorous and precise, by highlighting the annual layers of secondary dentin cement (Andera, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harris, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Manjunatha \u0026amp; Soni, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eA discriminatory analysis, as a multivariate technique, was used to separate the two groups of observations(Coomans et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and a simple and a multiple regression was applied on the most important variables in order to obtain mathematical equation able to assess the age of the red fox. The analysis were done using the XLStat extension for Microsoft Excel.\u003c/p\u003e \u003cp\u003eGiven the challenge of separating size from form in multivariate analyses of morphological data (Claude, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a principal component analysis (PCA) was employed to reduce the dimensionality of the dataset while preserving its key functionalities. In the first analysis, we focused on examining the relationship between various skull parameters of the red fox and the determined sex and age, as indicated by the provided variables. In the second analysis, our emphasis was on exploring the link between the morpho-anatomical variation of the red fox body and age and sex.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Estimating the age of the collected specimens through discriminatory analysis\u003c/h2\u003e \u003cp\u003eThe elements analysed for estimating the age of collected specimens, in accordance with the specialized literature, include morphological characteristics (total body length), anatomical features (total body weight), craniometric dimensions (total skull length and maximum zygomatic width), as well as the shape and dimensions of the sagittal crest (expressed as total skull height) (Hartov\u0026aacute;-Nentvichov\u0026aacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lynch \u0026amp; Lynch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The study of these five characteristics was conducted for all 65 specimens with known values for these elements, using a discriminatory analysis through the stepwise method. This analysis initially resulted in a model with three characteristics: total body weight, total skull length, and total skull height, with only total body weight showing statistical significance, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection variables from the discriminatory analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003enumber\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eInitial data for age discriminatory analysis\u003c/p\u003e \u003cp\u003eSteps: 3, Number of variables in the model: 3; Group: Age (3)\u003c/p\u003e \u003cp\u003eWilks\u0026rsquo; Lambda: 0,20088 approx. F (6,120)\u0026thinsp;=\u0026thinsp;24,623 p\u0026lt; ,0000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilks\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eToler.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1- Toler.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,3682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,5454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,9983***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,9466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,2145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,9363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,0381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,6524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,3475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,9629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,1554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,3218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,6731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,3268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt is observed that the first element from the generated model, Total Weight (TW), participates dominantly in discrimination (0.3682), corresponding to the value of the F test, followed in decreasing order by Total Skull Length (LTCr) with 0.2145 and Skull Height (HCr) with 0.2086, which do not have statistical significance.\u003c/p\u003e \u003cp\u003eSubsequently, the following steps were taken generating the canonical functions that participate in discrimination (n\u0026thinsp;=\u0026thinsp;2) and standardizing their coefficients (Eigen test), structuring the coefficients (by establishing correlations between the variables in the model), and determining the canonical means of the variables (their participation in discrimination). At the end of the analysis, all 65 samples were classified into the three predetermined age classes (a- adults, m - mature, j - juveniles).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a relatively grouped arrangement of specimens into the three age classes is observed, with a more distinct separation of juveniles and a relatively overlapping distribution of adults and matures.The statistical conclusion of the discriminatory analysis of the studied characteristics regarding the accuracy of assigning the specimens \"a priori\" to the three age classes is presented in the final table of the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminatory analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eClassification matrix\u003c/p\u003e \u003cp\u003eRows: Observed classification (initial)\u003c/p\u003e \u003cp\u003eColumns: Resulting classification (final)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadult (a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emature (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ejuvenile (j)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadult (a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emature (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejuvenile (j)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the final data of the analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it is observed a high degree of accuracy in assigning the studied specimens to their respective age class as an initial estimation through the analysis of predetermined elements. The maximum percentage of correct assignments is observed in the mature specimens (91%), with only three specimens showing a 90% correct assignment rate, and practically one specimen being placed in the next age class through the analysis. The adults, totalling 21 specimens, show an 81% correct assignment rate, with 4 specimens being placed in the mature category by the analysis.\u003c/p\u003e \u003cp\u003eComparing the estimated ages, categorized into the three classes, with the ages determined through the method of annual dentin layers, we observe that through this type of discriminatory analysis of morphological, anatomical, and cranial characteristics, the final accuracy of age estimation is 87.69%, making it a valuable form of age estimation in the absence of dental specimens.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Estimating the age of the collected specimens through regression\u003c/h2\u003e \u003cp\u003eAnother possible method for determining the age of the collected specimens is using the stepwise regression equation, which considers the same morphological, anatomical, and cranial characteristics as in the previous methods. Age was considered as the dependent variable, quantified with values: 1 (juvenile class), 2 (adult class), and 3 (mature class). The independent variables considered were total body length, total body weight, total cranial length, maximum zygomatic width, and total cranial height. The stepwise analysis selected the same three variables with significant influence on age estimation as in the discriminant analysis: total body weight, total cranial length, and total cranial height, according to the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression summary for the dependent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSummary of the regression: Age (multiple regressions for the collected foxes) R\u0026thinsp;=\u0026thinsp;0.8805636 R2\u0026thinsp;=\u0026thinsp;0.78864411 Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.77824955 F(3,61)\u0026thinsp;=\u0026thinsp;79.447 p\u0026thinsp;\u0026lt;\u0026thinsp;0.0000 Standard error of estimation: 0.32493\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt. Er.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Er.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et(61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5,10548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,820087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6,22554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,606301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,089251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,00052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,000076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,79319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,211542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,111478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,01614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,008506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,89761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,064248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,142500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,102829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,027929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,38579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,170856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe high value of the multiple regression coefficient R2\u0026thinsp;=\u0026thinsp;0.7886 is observed, which explains the phenomenon to the extent of 78.86%, corresponding to the correlation between age and the three variables in the model (body weight, cranial length, and cranial height).\u003c/p\u003e \u003cp\u003eAfter establishing the significance of the participation of the three variables in age estimation, it is found that only total body weight is significant. As a result of this, a regression Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of the following form can be generated:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$AGE=\\text{0,00052}*TW-\\text{5,10548}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, AGE represents the estimated age and TW the total weight\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Estimating the age of live specimens through multiple regression\u003c/h2\u003e \u003cp\u003eA possible method for estimating the age of live specimens was also studied using the stepwise regression equation, for which morphological and anatomical characteristics were considered, as in the case of the previous methods. Once again, age was considered as the dependent variable, quantified with values: 1 (juveniles), 2 (adults), and 3 (mature). The independent variables considered were external morphological characteristics: total body length, trunk length, tail length, head length, total body weight, chest circumference, abdomen circumference, and height at withers.\u003c/p\u003e \u003cp\u003eThe stepwise analysis selected three variables with significant influence on age estimation in the calculation model, as in the case of the regression analysis of the collected specimens: the total body weight of the specimen (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0000), height at rake (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000453), and chest circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.180764), according to the data in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of multiple regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSummary of the regression: Age (multiple regressions for live foxes)\u003c/p\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.89245934 R2\u0026thinsp;=\u0026thinsp;0.79648368 Adjusted R2\u0026thinsp;=\u0026thinsp;0.78647468 F(3,61)\u0026thinsp;=\u0026thinsp;79.577 p\u0026thinsp;\u0026lt;\u0026thinsp;0.0000 Standard error of estimation: 0.34239\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt. Er.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Er.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et(61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11,4538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,750033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,16496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,516529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,101657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,000087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,08108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,000004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,341941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,099651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,059136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,43139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,001083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,101506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,085775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,081077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,18341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,241239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this case, we also observe a high value of the multiple regression coefficient R2\u0026thinsp;=\u0026thinsp;0.7964, which explains the phenomenon to a proportion of 74.64%, namely the correlation between age and the three variables chosen in the model (total body weight, height at withers, and chest circumference). After establishing the significance of the participation of the three variables in age estimation, it is found that only two characteristics are statistically significant: total body weight and height at withers. As a result, a regression Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) of the following form was generated:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$AGE=\\text{0,0004}*TW+HG-\\text{11,4538}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, AGE represents the estimated age, TW the total weight and HG the height at rake\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Analysis of principal components of craniometry\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Principal Component Analysis was applied for all the specimens from all the age group and both sexes. The first and second principal component accounted for 57,8% and 6,4% of the variance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sexual dimorphism in shape and size of the skull is not pronounced and overlaps between red fox males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Differences are mainly in size but not in shape, and the differences are dictated by the age class. Assessment centres on individual contributions to primary factors, with the distance metric (Dist), exemplified by character 1's 7.658 distance, revealing its specific location in the multivariate space. PCA outcomes unveil variable contributions to major components, where variables like 'Age. Class' with a substantial contribution (ctr) of 3.741 to Dim.1 exert a significant influence in shaping the primary additives. Examining factor contributions to prominent components unveils specific responsibilities; for example, 'HTrOc' (occipital triangle height) makes a significant contribution (ctr) of 9.236 to Dim.Three, underscoring its relevance in determining this feature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of the principal morphological components of the body\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Principal Component Analysis was applied for all the specimens from all the age group and both sexes. The first and second principal component accounted for 78,4% and 6,5% of the variance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sexual dimorphism accordingly to the body anatomy is pronounced and does not overlap between red fox males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Differences are mainly in size, as males tend to be larger than the females. In the Principal Component Analysis (PCA), eigenvalues offer insights into variance, with the first principal component (Dim.1) dominating at 10.188, capturing 78.37% of the total variance; subsequently, cumulative variance reaches 98.35% by Dim.9, highlighting the consolidation of statistics and emphasizing the importance of key elements. In terms of variable contributions, TL, LWT, LT, HG, HC, LH, LN, LB, and NC play significant roles in shaping Dim.1, with contributions expressed as percentages, emphasizing their importance in shaping key components. Variable descriptions delineate specific contributions to various dimensions (Dim.1, Dim.2, Dim.3). Notably, TL significantly influences Dim.1 and Dim.2, demonstrating its role in reducing dataset variability. The principal components from PCA serve diverse analytical functions. Focusing on elements with substantial contributions facilitates the identification of distinct physical trends, aiding in pinpointing outliers and specific traits among red foxes, for the gender of the specimen, but also for the age.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to identify unconventional methods for estimating the age of red foxes directly in the field using rudimentary utensils, without the need for laboratory analysis. Although the estimation methods are developed through regressions, they are of interest to study, especially since only classical methods are present in the specialized literature (Devenish-Nelson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Harris, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Roulichov\u0026aacute; \u0026amp; Anděra, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current dataset, the average age of females is very similar to males, which contradicts findings from other populations (Younes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, a limited number of individuals have been identified in the younger age classes, potentially indicating that younger specimens are more susceptible to hunting. The red fox seems to exhibit resilience to intensive hunting due to the abundance of young foxes in the population and its high reproductive rate, which confirms finding of (Basuony, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Younes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUpon validating multiple studies, it becomes evident that both female and male red foxes undergo changes in the shape and size of their skulls as they age (Gos\u0026rsquo;kov \u0026amp; Korytin, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). There is a noticeable trend towards an increase in width dimensions rather than length dimensions, resulting in older individuals having skulls with greater widths compared to younger foxes (Parsons et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Younes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe developed equations can be easily applied in the field, but for greater mathematical accuracy, the data sample should be expanded to other geographic areas and subspecies. The equations presented are theoretically applicable only in the study area and for the \u003cem\u003eVulpes vulpes crucigera Bechstein\u003c/em\u003e subspecies, and their use in other areas and species should be approached with great caution. These equations improve the knowledge of the ecological and behavioural characters of the red fox.\u003c/p\u003e \u003cp\u003eTo create more comprehensive equation that can incorporate all the initially considered variables and effectively characterize the entire fox population, further research efforts need to be directed towards expanding the study sample size. This represents a key focus for future investigations.\u003c/p\u003e \u003cp\u003eThe study successfully analyzed the anatomical and morphological characteristics of red fox specimens, particularly focusing on the subspecies \u003cem\u003eVulpes vulpes crucigera\u003c/em\u003e in the Brasov area. Various methods, including morphological examinations and discriminatory analysis, were employed to estimate the age of the collected specimens with a high degree of accuracy, showcasing the effectiveness of these techniques in the absence of dental specimens. The development of regression equations based on measurable parameters provides a valuable tool for estimating the age of red fox specimens, highlighting the potential for further research to refine and expand these equations for a more comprehensive characterization of the fox population. The study area encompassed diverse ecological conditions, allowing for a comprehensive understanding of the anatomical variabilities and cranial characteristics of the red fox species in different habitats, contributing to a more holistic view of the population dynamics. Future research efforts should focus on increasing sample sizes to enhance the accuracy and applicability of regression equations for age estimation, as well as exploring additional variables to create more comprehensive models for characterizing the red fox population in Central Romania.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander, S., Hodson, A., Mitchell, D., Nicolson, D., Orrell, T., \u0026amp; Perez-Gelabert, D. (2023). The Integrated Taxonomic Information System. In \u003cem\u003eCatalogue of Life Checklist\u003c/em\u003e. ITIS. https://doi.org/10.48580/dfrdl-4ky\u003c/li\u003e\n\u003cli\u003eAndera, M. (2007). Simple method of age determination in red fox, Vulpes vulpes. \u003cem\u003eFolia Zool\u003c/em\u003e, 440\u0026ndash;444. https://www.researchgate.net/publication/242021275\u003c/li\u003e\n\u003cli\u003eBasuony, A. E. (2015). \u003cem\u003eIntraspecific variations among Egyptian Populations of the Red Fox, Vulpes vulpes\u003c/em\u003e. Al-Azhar University.\u003c/li\u003e\n\u003cli\u003eCastello J. (2018). Red Fox-Like Canids. \u003cem\u003eCanids of the World: Wolves, Wild Dogs, Foxes, Jackals, Coyotes, and Their Relatives\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 172.\u003c/li\u003e\n\u003cli\u003eClaude, Julien. (2008). \u003cem\u003eMorphometrics with R\u003c/em\u003e. Springer.\u003c/li\u003e\n\u003cli\u003eComșia A.M. (1961). \u003cem\u003eBiologia și principiile culturii v\u0026acirc;natului\u003c/em\u003e. Editura Academiei RPR.\u003c/li\u003e\n\u003cli\u003eCoomans, D., Broeckaert, I., Das Gupta, S., Fisher, R. A., Fix, E., Hodges, J. L., \u0026amp; Peterson, R. P. (1989). Discriminatory analysis. Nonparametric discrimination: small sample performance. In \u003cem\u003eUSAF School of Aviation Medicine\u003c/em\u003e (Vol. 57, Issue 3). Academic Press.\u003c/li\u003e\n\u003cli\u003eCotta, V., Bodea, M., \u0026amp; Micu, I. (2001). \u003cem\u003eV\u0026acirc;natul şi v\u0026acirc;nătoarea \u0026icirc;n Rom\u0026acirc;nia\u003c/em\u003e. Editura Ceres.\u003c/li\u003e\n\u003cli\u003eDevenish-Nelson, E. S., Harris, S., Soulsbury, C. D., Richards, S. A., \u0026amp; Stephens, P. A. (2013). Demography of a carnivore, the red fox, Vulpes vulpes: What have we learnt from 70 years of published studies? \u003cem\u003eOikos\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e(5), 705\u0026ndash;716. https://doi.org/10.1111/j.1600-0706.2012.20706.x\u003c/li\u003e\n\u003cli\u003eGos\u0026rsquo;kov, A. M., \u0026amp; Korytin, N. S. (2016). Changes of skull size in the red fox (Vulpes vulpes) during the second half of the 20th century in the Middle Urals and neighboring regions. \u003cem\u003eRussian Journal of Ecology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(6), 568\u0026ndash;571. https://doi.org/10.1134/S1067413616060060\u003c/li\u003e\n\u003cli\u003eHarris, S. (1978). Age determination in the Red fox (Vulpes vulpes)-an evaluation of technique efficiency as applied to a sample of suburban foxes. In \u003cem\u003eJ. Zool\u003c/em\u003e (Vol. 184).\u003c/li\u003e\n\u003cli\u003eHartov\u0026aacute;-Nentvichov\u0026aacute;, M., Anděra, M., \u0026amp; Hart, V. (2010). Sexual dimorphism of cranial measurements in the red fox vulpes vulpes (Canidae, Carnivora) from the Czech Republic. \u003cem\u003eFolia Zoologica\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(4), 285\u0026ndash;294. https://doi.org/10.25225/fozo.v59.i4.a3.2010\u003c/li\u003e\n\u003cli\u003eLarivi\u0026egrave;re, S., \u0026amp; Pasitschniak-Arts, M. (1996). Vulpes vulpes. \u003cem\u003eMammalian Species\u003c/em\u003e, \u003cem\u003e537\u003c/em\u003e, 1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eLynch, J. M., \u0026amp; Lynch, J. M. (1996). \u003cem\u003eSexual Dimorphism in Cranial Size and Shape among Red Foxes Vulpes vulpes from North-East SEXUAL DIMORPHISM IN CRANIAL SIZE AND SHAPE AMONG RED FOXES VULPES VULPES FROM NORTH-EAST IRELAND\u003c/em\u003e (Vol. 96, Issue 1).\u003c/li\u003e\n\u003cli\u003eMacdonald D.W., \u0026amp; Sillero-Zubiri C. (2004). \u003cem\u003eThe Biology and Conservation of Wild Canids\u003c/em\u003e. Oxford University Press.\u003c/li\u003e\n\u003cli\u003eManjunatha, B. S., \u0026amp; Soni, NishitK. (2014). Estimation of age from development and eruption of teeth. \u003cem\u003eJournal of Forensic Dental Sciences\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 73. https://doi.org/10.4103/0975-1475.132526\u003c/li\u003e\n\u003cli\u003eParsons, K. J., Rigg, A., Conith, A. J., Kitchener, A. C., Harris, S., \u0026amp; Zhu, H. (2020). Skull morphology diverges between urban and rural populations of red foxes mirroring patterns of domestication and macroevolution. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e, \u003cem\u003e287\u003c/em\u003e(1928). https://doi.org/10.1098/rspb.2020.0763\u003c/li\u003e\n\u003cli\u003eRoulichov\u0026aacute;, J., \u0026amp; Anděra, M. (2007). \u003cem\u003eAge determination in the Red Fox (Vulpes vulpes): a comparative study Určov\u0026aacute;n\u0026iacute; věku u li\u0026scaron;ky obecn\u0026eacute; (Vulpes vulpes): srovn\u0026aacute;vac\u0026iacute; studie\u003c/em\u003e. \u003cem\u003e38\u003c/em\u003e, 55\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eWozencraft, W. C. (1993). Order Carniviora. In \u003cem\u003eMammal species of the world\u003c/em\u003e. Smithsonian Institution Press.\u003c/li\u003e\n\u003cli\u003eYounes, M. (2023). The Variation of the Skull and Sexual Dimorphism of Red Fox Sample from Egypt. \u003cem\u003eEgyptian Academic Journal of Biological Sciences, B. Zoology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 259\u0026ndash;274. https://doi.org/10.21608/eajbsz.2023.305316\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Transylvania University of Brașov","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3988562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on the anatomical-morphological and cranial characteristics of the red fox subspecies \u003cem\u003eVulpes vulpes crucigera Bechstein\u003c/em\u003e in Central Romania. A total of 70 skull specimens and 65 carcasses were examined, with morphological determinations and anatomical evaluations conducted to understand the variability within the local population. Craniometric analysis involved 32 specific measurements, and regression equations were derived to estimate the age of specimens based on measurable parameters. The study area encompassed diverse ecological conditions, spanning on an area of almost 75 000 ha, providing insights into the adaptability of red foxes. The results highlight the significance of total body weight in estimating age and suggest the potential for further research to refine population characterization. Two equations resulted from the regression model, which can help the age determination directly on the terrain. The Principal Component Analysis was applied to all specimens in the study to analyze their morphological characteristics, revealing significant variations in skull and body parameters. Extending this study to different regions of Europe can enhance our understanding of red fox variations in skull and anatomical characteristics.\u003c/p\u003e","manuscriptTitle":"Comparative methods for assessing the age and of the red fox (Vulpes vulpes crucigera) in Central Romania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 06:46:34","doi":"10.21203/rs.3.rs-3988562/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67de2622-bd50-40ba-b9c9-c944150220d3","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28958438,"name":"Animal Science"}],"tags":[],"updatedAt":"2024-02-27T06:46:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 06:46:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3988562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3988562","identity":"rs-3988562","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}