Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-10-31T18:16:00.844Z Has data issue: false hasContentIssue false

A network perspective on animal welfare

Published online by Cambridge University Press:  01 January 2023

T Rowland*
Affiliation:
Animal Behaviour, Cognition, and Welfare Research Group, School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK
TW Pike
Affiliation:
Animal Behaviour, Cognition, and Welfare Research Group, School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK
OHP Burman
Affiliation:
Animal Behaviour, Cognition, and Welfare Research Group, School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK
*
* Contact for correspondence: trowland@lincoln.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The scientific study of animal welfare involves measuring physiological, behavioural, and/or cognitive variables to infer the welfare state of animals. Such an approach implies these measures are indicators, or reflect, an unmeasured latent variable of welfare state. Drawing inspiration from recent developments in human psychology and psychiatry, in this paper we propose an alternative perspective in the form of a network theory of animal welfare. This theory posits that there is no latent variable; rather, welfare is a network system of causal interactions between and within behavioural, physiological, and cognitive components. We then describe a statistical network modelling approach motivated by network theory, in which welfare-related response variables are associated with each other after controlling for all other variables measured. In three examples using simulated data, we demonstrate how this approach can be used, and the sort of novel insights it can bring. These examples cover a range of species and research questions, which network analysis is well suited to address. We believe a network approach to animal welfare science holds promise for developing our understanding of the concept of animal welfare, as well as producing practical and meaningful information to improve the welfare of animals.

Type
Research Article
Copyright
© 2021 Universities Federation for Animal Welfare

References

Altenbuchinger, M, Weihs, A, Quackenbush, J, Grabe, H and Zacharias, H 2020 Gaussian and mixed graphical models as (multi-)omics data analysis tools. Biochimica et Biophysica Acta-Gene Regulatory Mechanisms 1863(6): 194418. https://doi.org/10.1016/j.bbagrm.2019.194418CrossRefGoogle ScholarPubMed
Asher, L, Collins, L, Ortiz-Pelaez, A, Drewe, J, Nicol, C and Pfeiffer, D 2009 Recent advances in the analysis of behavioural organisation and interpretation as indicators of animal welfare. Journal of the Royal Society Interface 6(41): 11031119. https://doi.org/10.1098/rsif.2009.0221CrossRefGoogle ScholarPubMed
Bailoo, J, Murphy, E, Varholick, J, Novak, J, Palme, R and Wurbel, H 2018 Evaluation of the effects of space allowance on measures of animal welfare in laboratory mice. Scientific Reports 8: 713. https://doi.org/10.1038/s41598-017-18493-6CrossRefGoogle ScholarPubMed
Blanken, T, Van der Zweerde, T, Van Straten, A, Van Someren, E, Borsboom, D and Lancee, J 2019 Introducing network inter-vention analysis to investigate sequential, symptom-specific treat-ment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics 88(1): 5254. https://doi.org/10.1159/000495045CrossRefGoogle Scholar
Borsboom, D 2017 A network theory of mental disorders. World Psychiatry 16(1): 513. https://doi.org/10.1002/wps.20375CrossRefGoogle ScholarPubMed
Borsboom, D and Cramer, A 2013 Network analysis: An inte-grative approach to the structure of psychopathology. Annual Review of Clinical Psychology 9: 91121. https://doi.org/10.1146/annurev-clinpsy-050212-185608CrossRefGoogle Scholar
Bringmann, L, Elmer, T, Epskamp, S, Krause, R, Schoch, D, Wichers, M, Wigman, JTW and Snippe, E 2019 What do cen-trality measures measure in psychological networks? Journal of Abnormal Psychology 128(8): 892903. https://doi.org/10.1037/abn0000446CrossRefGoogle ScholarPubMed
Browning, H 2019 If I could talk to the animals: Measuring subjective animal welfare. PhD Thesis, Australian National University, Australia. https://openresearch-repository.anu.edu.au/handle/1885/206204Google Scholar
Burger, J, Stroebe, M, Perrig-Chiello, P, Schut, H, Spahni, S, Eisma, M and Fried, E 2020 Bereavement or breakup: Differences in networks of depression. Journal of Affective Disorders 267: 18. https://doi.org/10.1016/j.jad.2020.01.157CrossRefGoogle ScholarPubMed
Christensen, A, Golino, H and Silvia, P 2020 A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality 6: 10951108. https://doi.org/10.1002/per.2265CrossRefGoogle Scholar
Costantini, G, Epskamp, S, Borsboom, D, Perugini, M, Mottus, R, Waldorp, L and Cramer, A 2015 State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality 54: 1329. https://doi.org/10.1016/j.jrp.2014.07.003CrossRefGoogle Scholar
Cramer, A, van Borkulo, C, Giltay, E, van der Maas, H, Kendler, K, Scheffer, M and Borsboom, D 2016 Major depression as a complex dynamic system. PLoS One 11(12): e0167490. https://doi.org/10.1371/journal.pone.0167490CrossRefGoogle ScholarPubMed
Cramer, A, Van der Sluis, S, Noordhof, A, Wichers, M, Geschwind, N, Aggen, S, Kendler, KS and Borsboom, D 2012 Dimensions of normal personality as networks in search of equi-librium: You can't like parties if you don't like people. European Journal of Personality 26(4): 414431. https://doi.org/10.1002/per.1866CrossRefGoogle Scholar
Cramer, A, Waldorp, L, van der Maas, H and Borsboom, D 2010 Comorbidity: A network perspective. Behavioral and Brain Sciences 33(2-3): 137193. https://doi.org/10.1017/S0140525X09991567CrossRefGoogle ScholarPubMed
Dalege, J, Borsboom, D, van Harreveld, F, van den Berg, H, Conner, M and van der Maas, H 2016 Toward a formalised account of attitudes: The causal attitude network (CAN) model. Psychological Review 123(1): 222. https://doi.org/10.1037/a0039802CrossRefGoogle Scholar
Dalege, J, Borsboom, D, van Harreveld, F and van der Maas, H 2017 Network analysis on attitudes: A brief tutorial. Social Psychological and Personality Science 8(5): 528537. https://doi.org/10.1177/1948550617709827CrossRefGoogle ScholarPubMed
Dalege, J, Borsboom, D, van Harreveld, F and van der Maas, H 2018 The attitudinal entropy (AE) framework as a general the-ory of individual attitudes. Psychological Inquiry 29(4): 175193. https://doi.org/10.1080/1047840X.2018.1537246CrossRefGoogle Scholar
Duncan, I 2002 Poultry welfare: science or subjectivity? British Poultry Science 43(5): 643652. https://doi.org/10.1080/0007166021000025109CrossRefGoogle ScholarPubMed
Epskamp, S 2015 IsingSampler: Sampling Methods and Distribution Functions for the Ising Model (Version R package version 0.2). https://CRAN.R-project.org/package=IsingSamplerGoogle Scholar
Epskamp, S, Borsboom, D and Fried, E 2018a Estimating psy-chological networks and their accuracy: A tutorial paper. Behavior Research Methods 50(1): 195212. https://doi.org/10.3758/s13428-017-0862-1CrossRefGoogle Scholar
Epskamp, S, Cramer, A, Waldorp, L, Schmittmann, V and Borsboom, D 2012 qgraph: network visualizations of relation-ships in psychometric data. Journal of Statistical Software 48(4): 118. https://doi.org/10.18637/jss.v048.i04CrossRefGoogle Scholar
Epskamp, S and Fried, E 2018 A tutorial on regularised partial correlation networks. Psychological Methods 23(4): 617634. https://doi.org/10.1037/met0000167CrossRefGoogle Scholar
Epskamp, S, Waldorp, L, Mottus, R and Borsboom, D 2018b The Gaussian Graphical Model in cross-sectional and time-series data. Multivariate Behavioral Research 53(4): 453480. https://doi.org/10.1080/00273171.2018.1454823CrossRefGoogle ScholarPubMed
Fried, E 2020 Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry 31(3): 271288. https://doi.org/10.1080/1047840X.2020.1853461CrossRefGoogle Scholar
Fried, E, Bockting, C, Arjadi, R, Borsboom, D, Amshoff, M, Cramer, A, Epskamp, S, Tuerlinckx, F, Carr, D and Stroebe, M 2015 From loss to loneliness: The relationship between bereavement and depressive symptoms. Journal of Abnormal Psychology 124(2): 256265. https://doi.org/10.1037/abn0000028CrossRefGoogle ScholarPubMed
Fried, E and Cramer, A 2017 Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science 12(6): 9991020. https://doi.org/10.1177/1745691617705892CrossRefGoogle ScholarPubMed
Fried, E, van Borkulo, C, Cramer, A, Boschloo, L, Schoevers, R and Borsboom, D 2017 Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology 52(1): 110. https://doi.org/10.1007/s00127-016-1319-zCrossRefGoogle ScholarPubMed
Fried, E, van Borkulo, C and Epskamp, S 2020 On the importance of estimating parameter uncertainty in network psychomet-rics: A response to Forbes et al (2019). Multivariate Behavioural Research 1-6. https://doi.org/10.1080/00273171.2020.1746903CrossRefGoogle Scholar
Fruchterman, T and Reingold, E 1991 Graph drawing by force-directed placement. Software-Practice & Experience 21(11): 11291164. https://doi.org/10.1002/spe.4380211102CrossRefGoogle Scholar
Goold, C, Vas, J, Olsen, C and Newberry, RC 2016 Using net-work analysis to study behavioural phenotypes: an example using domestic dogs. Royal Society Open Science 3: 160268. http://dx.doi.org/10.1098/rsos.160268CrossRefGoogle Scholar
Hallquist, M, Wright, A and Molenaar, P 2019 Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1640103CrossRefGoogle Scholar
Haslbeck, J, Borsboom, D and Waldorp, L 2019a Moderated network models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1677207CrossRefGoogle Scholar
Haslbeck, J, Ryan, O, Robinaugh, D, Waldorp, L and Borsboom, D 2019b Modelling psychopathology: From data models to formal theories. PsyArXiv. https://doi.org/10.31234/osf.io/jgm7fCrossRefGoogle Scholar
Haslbeck, J and Waldorp, L 2020 mgm: Estimating time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software 93(8): 146. https://doi.org/10.18637/jss.v093.i08CrossRefGoogle Scholar
Isvoranu, AM, Guloksuz, S, Epskamp, S, van Os, J, Borsboom, D and Investigators, G 2020 Toward incorporating genetic risk scores into symptom networks of psychosis. Psychological Medicine 50(4): 636643. https://doi.org/10.1017/S003329171900045XCrossRefGoogle ScholarPubMed
Kiddie, J and Collins, L 2014 Development and validation of a quality of life assessment tool for use in kennelled dogs (Canis familiaris). Applied Animal Behaviour Science 158: 5768. https://doi.org/10.1016/j.applanim.2014.05.008CrossRefGoogle Scholar
Kleinhappel, T, John, E, Pike, T, Wilkinson, A and Burman, O 2016 Animal welfare: a social networks perspective. Science Progress 99(1): 6882. https://doi.org/10.3184/003685016X14495640902331CrossRefGoogle ScholarPubMed
Krause, J, Lusseau, D and James, R 2009 Animal social networks: an introduction. Behavioral Ecology and Sociobiology 63(7): 967973. https://doi.org/10.1007/s00265-009-0747-0CrossRefGoogle Scholar
Lafit, G, Adolf, J, Dejonckheere, E, Myin-Germeys, I, Viechtbauer, W and Ceulemans, E 2021 Selection of the num-ber of participants in intensive longitudinal studies: A user-friendly shiny app and tutorial for performing power analysis in Multilevel Regression Models that account for temporal dependencies. Advances in Methods and Practices in Psychological Science 4(1): 14. https://doi.org/10.1177/2515245920978738CrossRefGoogle Scholar
Lange, J, Dalege, J, Borsboom, D, van Kleef, G and Fischer, A 2020 Toward an integrative psychometric model of emotions. Perspectives on Psychological Science 15(2): 444468. https://doi.org/10.1177/1745691619895057CrossRefGoogle ScholarPubMed
Marsman, M, Borsboom, D, Kruis, J, Epskamp, S, van Bork, R, Waldorp, J, van der Maas, LG and Maris, G 2018 An introduction to network psychometrics: Relating iSing network models to item response theory models. Multivariate Behavioral Research 53(1): 1535. https://doi.org/10.1080/00273171.2017.1379379CrossRefGoogle ScholarPubMed
Mason, G and Mendl, M 1993 Why is there no simple way of meas-uring animal welfare. Animal Welfare 2(4): 301319CrossRefGoogle Scholar
Mellor, D 2012 Animal emotions, behaviour and the promotion of positive welfare states. New Zealand Veterinary Journal 60(1): 18. https://doi.org/10.1080/00480169.2011.619047CrossRefGoogle ScholarPubMed
Mellor, D 2016 Updating animal welfare thinking: Moving beyond the ‘Five Freedoms’ towards ‘A Life Worth Living’. Animals 6(3): 21. https://doi.org/10.3390/ani6030021CrossRefGoogle ScholarPubMed
Neethirajan, S 2020 The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research 29: 100367. https://doi.org/https://doi.org/10.1016/j.sbsr.2020.100367CrossRefGoogle Scholar
R Core Team 2019 R: A Language and Environment for Statistical Computing (Version version 3.6.1). R Foundation for Statistical Computing: Vienna, Austria. https://www.R-project.orgGoogle Scholar
Robinaugh, D, Hoekstra, R, Toner, E and Borsboom, D 2020 The network approach to psychopathology: a review of the liter-ature 2008-2018 and an agenda for future research. Psychological Medicine 50(3): 353366. https://doi.org/10.1017/S0033291719003404CrossRefGoogle Scholar
Rowland, T, Pike, T and Burman, O 2020 A network perspective on animal welfare. osf.io/u5bfjGoogle Scholar
Sandøe, P and Simonsen, H 1992 Assessing animal welfare: Where does science end and philosophy begin? Animal Welfare 1(4): 257267. https://doi.org/10.7120/09627286.1.3.257CrossRefGoogle Scholar
Scheffer, M, Bolhuis, J, Borsboom, D, Buchman, T, Gijzel, S, Goulson, D, Kammenga, JE, Kemp, B, van de Leemput, IA, Levin, S, Martin, CM, Melis, RJF, van Nes, EH, Romero, LM and Rikkert, M 2018 Quantifying resilience of humans and other animals. Proceedings of the National Academy of Sciences of the United States of America 115(47): 1188311890. https://doi.org/10.1073/pnas.1810630115CrossRefGoogle ScholarPubMed
Schmittmann, V, Cramer, A, Waldorp, L, Epskamp, S, Kievit, R and Borsboom, D 2013 Deconstructing the construct: A net-work perspective on psychological phenomena. New Ideas in Psychology 31(1): 4353. https://doi.org/10.1016/j.newideapsych.2011.02.007CrossRefGoogle Scholar
van Bork, R, Rhemtulla, M, Waldorp, L, Kruis, J, Rezvanifar, S and Borsboom, D 2019 Latent variable models and networks: Statistical equivalence and testability. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1672515CrossRefGoogle Scholar
van Borkulo, C, Borsboom, D, Epskamp, S, Blanken, T, Boschloo, L, Schoevers, R and Waldorp, L 2014 A new method for constructing networks from binary data. Scientific Reports 4. 5918. https://doi.org/10.1038/srep05918CrossRefGoogle Scholar
van Borkulo, C, Waldorp, L, Boschloo, L, Kossakowski, J, Tio, PL, Schoevers, R and Borsboom, D 2016 Comparing network structures on three aspects: A permutation test. https://www.research-gate.net/publication/314750838_Comparing_network_structures_on_three_aspects_A_permutation_test. https://doi.org/10.13140/RG.2.2.29455.38569CrossRefGoogle Scholar
van de Leemput, IA, Wichers, M, Cramer, AO, Borsboom, D, Tuerlinckx, F, Kuppens, P, van Nes, EH, ViechtBauer, W, Giltay, EJ, Aggen, SH, Derom, C, Jacobs, N, Kendler, KS, van der Maas, HLJ, Neale, MC, Peeters, F, Thiery, E, Zachar, P and Scheffer, M 2014 Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences of the United States of America 111(1): 8792. https://doi.org/10.1073/pnas.1312114110CrossRefGoogle ScholarPubMed
van der Maas, H, Dolan, C, Grasman, R, Wicherts, J, Huizenga, H and Raijmakers, M 2006 A dynamical model of general intelligence: The positive manifold of intelligence by mutu-alism. Psychological Review 113(4): 842861. https://doi.org/10.1037/0033-295X.113.4.842CrossRefGoogle Scholar
Wasserman, S and Faust, K 1994 Social Network Analysis Methods and Applications. Cambridge University Press: Cambridge, UK. https://doi.org/10.1017/CBO9780511815478CrossRefGoogle Scholar
Webster, A 1998 What use is science to animal welfare? Naturwissenschaften 85(6): 262269. https://doi.org/10.1007/s001140050496CrossRefGoogle ScholarPubMed
Whittaker, A, Howarth, G and Hickman, D 2012 Effects of space allocation and housing density on measures of wellbeing in laboratory mice: a review. Laboratory Animals 46(1): 313. https://doi.org/10.1258/la.2011.011049CrossRefGoogle ScholarPubMed
Wichers M, Groot PC and Psychosystems Group 2016 Critical slowing down as a personalised early warning signal for depression. Psychotherapy and Psychosomatics 85(2): 114116. https://doi.org/10.1159/000441458CrossRefGoogle Scholar
Wichers, M, Smit, A and Snippe, E 2020 Early warning signals based on momentary affect dynamics can expose nearby transitions in depression: A confirmatory single-subject time-series study. Journal for Person-Oriented Research 6(1): 115. https://doi.org/10.17505/jpor.2020.22042CrossRefGoogle ScholarPubMed
Williams, D and Rast, P 2020a Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical & Statistical Psychology 73(2): 187212. https://doi.org/10.1111/bmsp.12173CrossRefGoogle Scholar
Williams, D, Rast, P, Pericchi, L and Mulder, J 2020b Comparing gaussian graphical models with the posterior predictive distribution and Bayesian model selection. Psychological Methods 25(5): 653672. https://doi.org/10.1037/met0000254CrossRefGoogle ScholarPubMed
Williams, D, Rhemtulla, M, Wysocki, A and Rast, P 2019 On non-regularised estimation of psychological networks. Multivariate Behavioral Research 54(5): 719750. https://doi.org/10.1080/00273171.2019.1575716CrossRefGoogle Scholar
Zulch, H and Mills, D 2012 Life Skills for Puppies. Veloce Publishing Ltd: Dorchester, UKGoogle Scholar
Zwicker, M, Nohlen, H, Dalege, J, Gruter, G and van Harreveld, F 2020 Applying an attitude network approach to consumer behaviour towards plastic. Journal of Environmental Psychology 69. 101433. https://doi.org/10.1016/j.jenvp.2020.101433CrossRefGoogle Scholar
Supplementary material: File

Rowland et al. supplementary material
Download undefined(File)
File 154.4 KB