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Frontiers in veterinary science2019; 6; 154; doi: 10.3389/fvets.2019.00154

Sex and Age Don’t Matter, but Breed Type Does-Factors Influencing Eye Wrinkle Expression in Horses.

Abstract: Identifying valid indicators to assess animals' emotional states is a critical objective of animal welfare science. In horses, eye wrinkles above the eyeball have been shown to be affected by pain and other emotional states. From other species we know that individual characteristics, e.g., age in humans, affect facial wrinkles, but it has not yet been investigated whether eye wrinkle expression in horses is systematically affected by such characteristics. Therefore, the aim of this study was to assess how age, sex, breed type, body condition, and coat colour affect the expression and/or the assessment of eye wrinkles in horses. To this end, we adapted the eye wrinkle assessment scale from Hintze et al. (1) and assessed eye wrinkle expression in pictures taken from the left and the right eye of 181 horses in a presumably neutral situation, using five outcome measures: a qualitative first impression reflecting how worried the horse is perceived by humans, the extent to which the brow is raised, the number of wrinkles, their markedness and the angle between a line through both corners of the eye and the topmost wrinkle. All measures could be assessed highly reliable with respect to intra- and inter-observer agreement. Breed type affected the width of the angle [ = 8.20, 0.05). In conclusion, horses' eye wrinkle expression and its assessment in neutral situations was not systematically affected by the investigated characteristics, except for "breed type", which accounted for some variation in "angle"; how much eye wrinkle expression is affected by emotion or perhaps mood needs further investigation and validation.
Publication Date: 2019-05-29 PubMed ID: 31192235PubMed Central: PMC6549476DOI: 10.3389/fvets.2019.00154Google Scholar: Lookup
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  • Journal Article

Summary

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The research study investigates various factors that might influence the expression of eye wrinkles in horses, a potential emotional state indicator, and finds that breed type is a significant contributor.

Research Objectives

  • The study aimed to identify any individual characteristics or factors that could systematically impact the expression of eye wrinkles in horses. The potential indicators considered were age, sex, breed type, body condition, and coat colour.

Methodology

  • The researchers adapted the eye wrinkle assessment scale and evaluated eye wrinkle expressions using images of 181 horses in a supposedly neutral situation.
  • They examined five outcome measures: a qualitative first impression of how worried the horse appears to humans, the degree to which the brow is lifted, the number of wrinkles, their markedness, and the angle between a line through both corners of the eye and the highest wrinkle.
  • Both intra- and inter-observer agreement were evaluated for reliability.

Results

  • Breed type significantly influenced the angle width, with thoroughbreds having the narrowest angle, followed by warmbloods and coldbloods.
  • No impact on any of the five outcome measures was found based on the other assessed characteristics.
  • No difference in eye wrinkle expression between the left and right eye area was observed.

Conclusion

  • Apart from breed type which accounted for some variation in the eye wrinkle angle, none of the investigated characteristics including age, sex, body condition, coat colour, and side of the face systematically affected eye wrinkle expression in a neutral setting.
  • The influence of emotion or mood on eye wrinkle expression in horses requires further investigation and validation.

Cite This Article

APA
Schanz L, Krueger K, Hintze S. (2019). Sex and Age Don’t Matter, but Breed Type Does-Factors Influencing Eye Wrinkle Expression in Horses. Front Vet Sci, 6, 154. https://doi.org/10.3389/fvets.2019.00154

Publication

ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 6
Pages: 154
PII: 154

Researcher Affiliations

Schanz, Lisa
  • Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Austria.
  • Department of Equine Economics, Nuertingen-Geislingen University of Applied Sciences, Nürtingen, Germany.
Krueger, Konstanze
  • Department of Equine Economics, Nuertingen-Geislingen University of Applied Sciences, Nürtingen, Germany.
  • Biology I, University of Regensburg, Regensburg, Germany.
Hintze, Sara
  • Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Austria.

References

This article includes 55 references
  1. Hintze S, Smith S, Patt A, Bachmann I, Würbel H. Are Eyes a Mirror of the Soul? What Eye Wrinkles Reveal about a Horse's Emotional State.. PLoS One 2016;11(10):e0164017.
  2. Wemelsfelder F. The scientific validity of subjective concepts in models of animal welfare. Anim Stud Repos (1997) 53:75–88.
  3. Paul ES, Harding EJ, Mendl M. Measuring emotional processes in animals: the utility of a cognitive approach.. Neurosci Biobehav Rev 2005 May;29(3):469-91.
  4. Ekman P, Friesen WV. Measuring facial movement. Environ Psychol Nonverbal Behav (1976) 1:56–75.
    doi: 10.1007/BF01115465google scholar: lookup
  5. Caeiro CC, Waller BM, Zimmermann E, Burrows AM, Davila-Ross M. OrangFACS: a muscle-based facial movement coding system for orangutans (Pongo spp.). Int J Primatol (2013) 34:115–29.
    doi: 10.1007/s10764-012-9652-xgoogle scholar: lookup
  6. Parr LA, Waller BM, Burrows AM, Gothard KM, Vick SJ. Brief communication: MaqFACS: A muscle-based facial movement coding system for the rhesus macaque.. Am J Phys Anthropol 2010 Dec;143(4):625-30.
    doi: 10.1002/ajpa.21401pmc: PMC2988871pubmed: 20872742google scholar: lookup
  7. Parr LA, Waller BM, Vick SJ. New Developments in Understanding Emotional Facial Signals in Chimpanzees.. Curr Dir Psychol Sci 2007 Jun 15;16(3):117-122.
  8. Parr LA, Waller BM, Vick SJ, Bard KA. Classifying chimpanzee facial expressions using muscle action.. Emotion 2007 Feb;7(1):172-81.
    doi: 10.1037/1528-3542.7.1.172pmc: PMC2826116pubmed: 17352572google scholar: lookup
  9. Waller BM, Lembeck M, Kuchenbuch P, Burrows AM, Liebal K. GibbonFACS: a muscle-based facial movement coding system for hylobatids. Int J Primatol (2012) 33:809–21.
    doi: 10.1007/s10764-012-9611-6google scholar: lookup
  10. Waller BM, Peirce K, Caeiro CC, Scheider L, Burrows AM, McCune S, Kaminski J. Paedomorphic facial expressions give dogs a selective advantage.. PLoS One 2013;8(12):e82686.
  11. Caeiro CC, Burrows AM, Waller BM. Development and application of CatFACS: are human cat adopters influenced by cat facial expressions?. Appl Anim Behav Sci (2017) 189:66–78.
  12. Wathan J, Burrows AM, Waller BM, McComb K. Correction: EquiFACS: The Equine Facial Action Coding System.. PLoS One 2015;10(9):e0137818.
  13. Vick SJ, Waller BM, Parr LA, Smith Pasqualini MC, Bard KA. A Cross-species Comparison of Facial Morphology and Movement in Humans and Chimpanzees Using the Facial Action Coding System (FACS).. J Nonverbal Behav 2007 Mar;31(1):1-20.
    doi: 10.1007/s10919-006-0017-zpmc: PMC3008553pubmed: 21188285google scholar: lookup
  14. Parr L, Waller B. The evolution of human emotion. In: Kass J. Editor. Evolution of Nervous Systems: A Comprehensive Reference. London: Academic Press Inc; (2006) 447–72.
  15. Langford DJ, Tuttle AH, Brown K, Deschenes S, Fischer DB, Mutso A, Root KC, Sotocinal SG, Stern MA, Mogil JS, Sternberg WF. Social approach to pain in laboratory mice.. Soc Neurosci 2010;5(2):163-70.
    doi: 10.1080/17470910903216609pubmed: 19844845google scholar: lookup
  16. Sotocinal SG, Sorge RE, Zaloum A, Tuttle AH, Martin LJ, Wieskopf JS, Mapplebeck JC, Wei P, Zhan S, Zhang S, McDougall JJ, King OD, Mogil JS. The Rat Grimace Scale: a partially automated method for quantifying pain in the laboratory rat via facial expressions.. Mol Pain 2011 Jul 29;7:55.
    doi: 10.1186/1744-8069-7-55pmc: PMC3163602pubmed: 21801409google scholar: lookup
  17. Keating SC, Thomas AA, Flecknell PA, Leach MC. Evaluation of EMLA cream for preventing pain during tattooing of rabbits: changes in physiological, behavioural and facial expression responses.. PLoS One 2012;7(9):e44437.
  18. McLennan KM, Rebelo CJB, Corke MJ, Holmes MA, Leach MC, Constantino-Casas F. Development of a facial expression scale using footrot and mastitis as models of pain in sheep. Appl Anim Behav Sci (2016) 176:19–26.
  19. Guesgen MJ, Beausoleil NJ, Leach M, Minot EO, Stewart M, Stafford KJ. Coding and quantification of a facial expression for pain in lambs.. Behav Processes 2016 Nov;132:49-56.
    doi: 10.1016/j.beproc.2016.09.010pubmed: 27693533google scholar: lookup
  20. Di Giminiani P, Brierley VL, Scollo A, Gottardo F, Malcolm EM, Edwards SA, Leach MC. The Assessment of Facial Expressions in Piglets Undergoing Tail Docking and Castration: Toward the Development of the Piglet Grimace Scale.. Front Vet Sci 2016;3:100.
    doi: 10.3389/fvets.2016.00100pmc: PMC5107875pubmed: 27896270google scholar: lookup
  21. Dalla Costa E, Minero M, Lebelt D, Stucke D, Canali E, Leach MC. Development of the Horse Grimace Scale (HGS) as a pain assessment tool in horses undergoing routine castration.. PLoS One 2014;9(3):e92281.
  22. Gleerup KB, Forkman B, Lindegaard C, Andersen PH. An equine pain face.. Vet Anaesth Analg 2015 Jan;42(1):103-14.
    doi: 10.1111/vaa.12212pmc: PMC4312484pubmed: 25082060google scholar: lookup
  23. Patton FJ, Campbell PE. Using Eye and Profile Wrinkles to Identify Individual White Rhinos. Pachyderm J. African Elephant, African Rhino Asian Rhino Spec. Groups (2011) p. 84–86.
  24. Akazaki S, Nakagawa H, Kazama H, Osanai O, Kawai M, Takema Y, Imokawa G. Age-related changes in skin wrinkles assessed by a novel three-dimensional morphometric analysis.. Br J Dermatol 2002 Oct;147(4):689-95.
  25. Kwon YH, da Vitoria Lobo N. Age classification from facial images. Zhurnal Eksp i Teor Fiz (1999) 74:1–21.
    doi: 10.1006/cviu.1997.0549google scholar: lookup
  26. Mydlová M, Dupej J, Koudelová J, Velemínská J. Sexual dimorphism of facial appearance in ageing human adults: A cross-sectional study.. Forensic Sci Int 2015 Dec;257:519.e1-519.e9.
  27. Schmidt KL, Cohn JF. Human facial expressions as adaptations: Evolutionary questions in facial expression research.. Am J Phys Anthropol 2001;Suppl 33:3-24.
    doi: 10.1002/ajpa.20001pmc: PMC2238342pubmed: 11786989google scholar: lookup
  28. Troscianko T, Montagnon R, Le Clerc J, Malbert E, Chanteau PL. The role of colour as a monocular depth cue.. Vision Res 1991;31(11):1923-9.
    doi: 10.1016/0042-6989(91)90187-Apubmed: 1771776google scholar: lookup
  29. Moscovitch M, Olds J. Asymmetries in spontaneous facial expressions and their possible relation to hemispheric specialization.. Neuropsychologia 1982;20(1):71-81.
    doi: 10.1016/0028-3932(82)90088-4pubmed: 7070653google scholar: lookup
  30. Ekman P. Asymmetry in facial expression.. Science 1980 Aug 15;209(4458):833-4.
    doi: 10.1126/science.7403851pubmed: 7403851google scholar: lookup
  31. Lansade L, Foury A, Reigner F, Vidament M, Guettier E, Bouvet G, Soulet D, Parias C, Ruet A, Mach N, Lévy F, Moisan MP. Progressive habituation to separation alleviates the negative effects of weaning in the mother and foal.. Psychoneuroendocrinology 2018 Nov;97:59-68.
  32. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares.. Equine Vet J 1983 Oct;15(4):371-2.
  33. Tuyttens FAM, Sprenger M, Van Nuffel A, Maertens W, Van Dongen S. Reliability of categorical versus continuous scoring of welfare indicators: lameness in cows as a case study. In: J Animal Welfare (2009). 18:399–405.
  34. Wewers ME, Lowe NK. A critical review of visual analogue scales in the measurement of clinical phenomena.. Res Nurs Health 1990 Aug;13(4):227-36.
    doi: 10.1002/nur.4770130405pubmed: 2197679google scholar: lookup
  35. Marsh-Richard DM, Hatzis ES, Mathias CW, Venditti N, Dougherty DM. Adaptive Visual Analog Scales (AVAS): a modifiable software program for the creation, administration, and scoring of visual analog scales.. Behav Res Methods 2009 Feb;41(1):99-106.
    doi: 10.3758/BRM.41.1.99pmc: PMC2635491pubmed: 19182128google scholar: lookup
  36. Corel Cooperation. CorelDRAW Version 17.1.0.572. (2014).
  37. R Core Team. A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; (2014) Available online at: http://www.R-project.org/.
  38. RStudio Team. Integrated Development for R. Boston, MA: RStudio, Inc; (2016) Available online at: http://www.rstudio.com/.
  39. Gamer M, Lemon J, Fellows I, Singh P. Various Coefficients of Interrater Reliability And Agreement. R package version 0.84 (2012). Available online at: https://CRAN.R-project.org/package=irr.
  40. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.. J Chiropr Med 2016 Jun;15(2):155-63.
    doi: 10.1016/j.jcm.2016.02.012pmc: PMC4913118pubmed: 27330520google scholar: lookup
  41. Cicchetti DV. Guidlines, criteria, and rules of thumb for evalauting normed and standardized assessment instruments in psychology. Psychol Assess (1994) 6:284–90.
    doi: 10.1037/1040-3590.6.4.284google scholar: lookup
  42. Landis JR, Koch GG. The measurement of observer agreement for categorical data.. Biometrics 1977 Mar;33(1):159-74.
    doi: 10.2307/2529310pubmed: 843571google scholar: lookup
  43. Tu YK, Kellett M, Clerehugh V, Gilthorpe MS. Problems of correlations between explanatory variables in multiple regression analyses in the dental literature.. Br Dent J 2005 Oct 8;199(7):457-61.
    doi: 10.1038/sj.bdj.4812743pubmed: 16215581google scholar: lookup
  44. Martin P, Bateson PPG. Measuring Behaviour: an Introductory Guide. Cambridge: Cambridge University Press; (2007).
  45. Cramér H. Mathematical Methods of Statistics (PMS-9). Princeton, NJ: Princeton University Press; (2016).
  46. Lumley T, Alan M. Regression Subset Selection. R package version 2.9 (2009). Available online at: https://CRAN.R-project.org/package=leaps.
  47. Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A. Classification and Regression Training. R package version 6.0-80 (2018). Available online at: https://CRAN.R-project.org/package=caret.
  48. Venables WN, Ripley BD. Modern Applied Statistics With S, 4th ed. New York, NY: Springer; (2002).
    doi: 10.1007/978-0-387-21706-2google scholar: lookup
  49. Broadhurst D, Goodacre R, Jones A, Rowland JJ, Douglas BK. Genetic algorithms as a method for variable selectionin multiple linear regression and partial least squares regression, with applications to pyrolsis mass spectrometry. Anal Chim Acta (1997) 348:71–86.
  50. André CD, Narula SC, Elian SN, Tavares RA. An overview of the variables selection methods for the minimum sum of absolute errors regression.. Stat Med 2003 Jul 15;22(13):2101-11.
    doi: 10.1002/sim.1437pubmed: 12820276google scholar: lookup
  51. Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team. Linear and Nonlinear Mixed Effects Models_. R package version 3.1-137 (2018). Available online at: https://CRAN.R-project.org/package=nlme>.
  52. Lenth R. Least-squares means: the R package lsmeans. J Stat Softw (2016) 69:1–33.
    doi: 10.18637/jss.v069.i01google scholar: lookup
  53. Giles SL, Rands SA, Nicol CJ, Harris PA. Obesity prevalence and associated risk factors in outdoor living domestic horses and ponies.. PeerJ 2014;2:e299.
    doi: 10.7717/peerj.299pmc: PMC3970797pubmed: 24711963google scholar: lookup
  54. Visser EK, Neijenhuis F, de Graaf-Roelfsema E, Wesselink HG, de Boer J, van Wijhe-Kiezebrink MC, Engel B, van Reenen CG. Risk factors associated with health disorders in sport and leisure horses in the Netherlands.. J Anim Sci 2014 Feb;92(2):844-55.
    doi: 10.2527/jas.2013-6692pubmed: 24352963google scholar: lookup
  55. Schanz L, Krueger K, Hintze S. Sex and age don't matter but breed type does-Factors influencing eye wrinkle expression in horses. BioRxiv (2019) 2019:567149.
    doi: 10.1101/567149google scholar: lookup

Citations

This article has been cited 11 times.