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Assessing mood changes and vulnerability to stressors in dairy cattle Lecorps, Benjamin 2020

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  ASSESSING MOOD CHANGES AND VULNERABILITY TO STRESSORS IN DAIRY CATTLE by  Benjamin Lecorps  B.Sc., University of Caen Normandy, 2010 M.Sc., University of Paris XIII, 2013  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Applied Animal Biology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2020  © Benjamin Lecorps, 2020   ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:  ASSESSING MOOD CHANGES AND VULNERABILITY TO STRESSORS IN DAIRY CATTLE  submitted by Benjamin Lecorps in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Animal Biology  Examining Committee: Dr. Marina A. G. von Keyserlingk, Professor, Applied Animal Biology, UBC Supervisor  Dr. Daniel M. Weary, Professor, Applied Animal Biology, UBC Supervisory Committee Member  Dr. Suzanne Waiblinger, Associate Professor, Institute of Animal Welfare Science, VetMedUni External Examiner Dr. Kenneth Craig, Professor, School of Psychology, UBC University Examiner Dr. James Vercammen, Professor, Strategy and Business Economics Division, UBC  University Examiner      iii Abstract Routine farm management can involve painful and stressful procedures that cause negative affective states and may have long-lasting consequences. Despite a growing interest in animal welfare and affective states, few studies have explored whether housing conditions and routine farm procedures induce long-lasting negative affective states such as negative mood. The first aim of this thesis was to develop methodologies to explore whether dairy cattle show evidence of negative mood in response to common stressful conditions. For this, I first used an adapted judgment bias test to assess changes in mood following hot-iron disbudding. My results suggested that calves experience anhedonia (i.e. the reduced ability to experience pleasure) after hot-iron disbudding. Thus, I designed tests aiming to assess whether calves display anhedonia-like responses after experiencing hot-iron disbudding, regrouping and post-partum stressors including cow-calf separation. My results showed that cattle display signs of negative mood (i.e. negative judgment bias and anhedonia) in response to stressful routine farm procedures. The second aim of this thesis was to explore why individuals show strong variation in how they cope with stressors. For instance, I explored whether individual variation in expectations would predict higher vulnerability to stressors. Negative expectations (i.e. pessimism) may lead to negative perceptions, stronger responses, poor coping strategies (avoidance-based coping strategies), and poor recovery from stressors. My results show that stable differences in pessimism exist in non-weaned dairy calves and that more pessimistic animals perceive and respond more negatively to stressors. I conclude that the study of mood-related changes and individual differences help better understand how living conditions affect farm animal welfare.    iv Lay Summary Dairy cattle are routinely subjected to stressors. These can have long-lasting consequences on animals’ affective states, especially in more vulnerable animals. My thesis aimed to explore how cattle feel after experiencing emotionally challenging situations such as hot-iron disbudding, social mixing and separation from the calf. In addition, some individuals display more pessimistic views that negatively affect their capacity to cope with challenges. I showed that more pessimistic calves perceive and respond more negatively to stressors. Understanding how routine farm procedures impact cattle’s affective states is of tremendous importance to assess their negative welfare consequences. More considerations should be taken regarding individual differences that can strongly affect the way individuals perceive and respond to emotional challenges.       v Preface A part of Chapter 1 has been submitted for publication as Lecorps, B., D. M. Weary and M. A. G. von Keyserlingk. Pessimism as a marker of vulnerability to stressors in animals. Neuroscience and Biobehavioral reviews. (in review). B. Lecorps developed the main ideas for this project and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input and editing drafts. This chapter did not require ethics approval.  A version of Chapter 2 has been published as B. Lecorps., B. Ludwig., M. A. G. von Keyserlingk and D. M. Weary. 2019. Pain-induced pessimism and anhedonia: Evidence from a novel probability-based judgment bias test. Frontiers in Behavioural Neuroscience. 13:54. B. Lecorps designed the study, collected all data with the help of the second author, and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate numbers: # A160310). A version of Chapter 3 has been published as B. Lecorps., D. M. Weary and M. A. G. von Keyserlingk (2020). Regrouping induces anhedonia-like responses in dairy heifers. Journal of Dairy Science Communications. 1:2. B. Lecorps designed the study, collected all data, and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate numbers: # A190128). A version of Chapter 4 has been submitted for publication B. Lecorps., Welk, A., D. M. Weary and M. A. G. von Keyserlingk. Evidence of post-partum anhedonia in dairy cows. PLoS ONE. (in review). B. Lecorps designed the study, collected all data with the second author, and    vi wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate number: # A150117).  A version of Chapter 5 has been published as B. Lecorps., D. M. Weary and M. A. G. von Keyserlingk. 2018. Pessimism and fearfulness in dairy calves. Scientific Reports, 8:1421. B. Lecorps designed the study, collected all data, and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate number: # A150117).  A version of Chapter 6 has been published as B. Lecorps., S. Kappel., D. M. Weary and M. A. G. von Keyserlingk. 2018. Dairy calves’ personality traits predict their response to an emotional challenge. Scientific Reports, 8:16350. B. Lecorps designed the study, collected all data with the second author, and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate number: # A150117).  A version of Chapter 7 has been published as B. Lecorps., E. Nogues., M. A. G. von Keyserlingk and D. M. Weary. 2020. Pessimistic dairy calves are more vulnerable to pain-induced anhedonia. PLoS ONE. 15(11): e0242100. B. Lecorps designed the study, collected all data with the second author, and wrote the manuscript. M.A.G. von Keyserlingk and D.M. Weary acted in the typical role of supervisors, providing input at all stages of the study, helping with statistical analysis, and editing drafts. This project received UBC Animal Care approval (certificate number: # A160310).     vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Dedication .....................................................................................................................................xv Chapter 1. Introduction.................................................................................................................1 1.1 Major stressors encountered by farm animals ................................................................ 7 1.1.1 Pain ............................................................................................................................. 8 1.1.2 Social stress ............................................................................................................... 10 1.1.3 Separation from offspring ......................................................................................... 13 1.2 Vulnerability to stressors: a matter of pessimism? ....................................................... 14 1.2.1 Sensitivity to rewards and punishments .................................................................... 15 1.2.2 Response to stressors ................................................................................................ 18 1.2.3 Vulnerability to mood-related disorders ................................................................... 20 1.3 Thesis objectives ........................................................................................................... 24 Chapter 2: Pain-induced pessimism and anhedonia following hot-iron disbudding .............26 2.1  Introduction ................................................................................................................... 26 2.2 Materials and Methods .................................................................................................. 27 2.2.1 Animals ..................................................................................................................... 27 2.2.2 Experimental setup.................................................................................................... 28 2.2.3 Training ..................................................................................................................... 29 2.2.4 Disbudding procedure ............................................................................................... 30 2.2.5 Testing....................................................................................................................... 31 2.2.6 Statistical analysis ..................................................................................................... 31 2.3  Results ........................................................................................................................... 32 2.4 Discussion ..................................................................................................................... 33 2.5 Conclusions ................................................................................................................... 36 Chapter 3: Regrouping induces anhedonia-like responses in dairy heifers ...........................37 3.1 Introduction ................................................................................................................... 37 3.2 Materials and Methods .................................................................................................. 39 3.2.1 Anhedonia testing ..................................................................................................... 39 3.2.2 Behavioural measures ............................................................................................... 40 3.2.3  Statistical analysis ..................................................................................................... 42 3.3. Results ........................................................................................................................... 42 3.4 Discussion ..................................................................................................................... 44 3.5 Conclusions ................................................................................................................... 46 Chapter 4: Evidence of post-partum anhedonia in dairy cows ...............................................48 4.1 Introduction ................................................................................................................... 48 4.2 Materials and Methods .................................................................................................. 50    viii 4.2.1 Animals and housing................................................................................................. 51 4.2.2 Separation from the calf and post-partum housing ................................................... 52 4.2.3 Anhedonia testing ..................................................................................................... 52 4.2.4 Statistical analysis ..................................................................................................... 53 4.3 Results ........................................................................................................................... 54 4.3.1  Experiment 1 ............................................................................................................. 54 4.3.2 Experiment 2 ............................................................................................................. 55 4.4  Discussion ..................................................................................................................... 57 4.5  Conclusions ................................................................................................................... 60 Chapter 5: Pessimism and fearfulness in dairy calves .............................................................62 5.1 Introduction ................................................................................................................... 62 5.2 Materials and Methods .................................................................................................. 64 5.2.1 Animals ..................................................................................................................... 64 5.2.2 General procedure ..................................................................................................... 64 5.2.3 Judgment bias test ..................................................................................................... 65 5.2.4 Personality tests ........................................................................................................ 67 5.2.5 Statistical analysis ..................................................................................................... 68 5.3 Results ........................................................................................................................... 70 5.3.1  Judgment biases: Response to ambiguous locations ................................................. 70 5.3.1 Personality traits........................................................................................................ 72 5.3.2 Relationship between personality traits and judgment bias ...................................... 74 5.4 Discussion ..................................................................................................................... 75 5.5 Conclusions ................................................................................................................... 79 Chapter 6: Dairy calves’ personality traits predict social proximity and response to an emotional challenge ......................................................................................................................80 6.1 Introduction ................................................................................................................... 80 6.2 Materials and Methods .................................................................................................. 82 6.2.1 Animals and housing................................................................................................. 82 6.2.2  Individual traits. ........................................................................................................ 83 6.2.3 Home-pen social behaviour ...................................................................................... 83 6.2.4 Transportation ........................................................................................................... 84 6.2.5 Physiological and behavioural measurements .......................................................... 84 6.2.6 Statistical analysis ..................................................................................................... 85 6.3 Results ........................................................................................................................... 86 6.3.1 Experiment 1: Home-pen social behaviour............................................................... 86 6.3.2 Experiment 2: Emotional response to transportation ................................................ 88 6.4 Discussion ..................................................................................................................... 93 6.5 Conclusions ................................................................................................................... 97 Chapter 7: Pessimistic dairy calves are more vulnerable to pain-induced anhedonia ..........98 7.1 Introduction ................................................................................................................... 98 7.2 Materials and Methods ................................................................................................ 100 7.2.1 Animals and housing............................................................................................... 100 7.2.2  Experimental procedures ....................................................................................... 101 7.2.3 Training and testing for judgment bias ................................................................... 101 7.2.4 Anhedonia ............................................................................................................... 102    ix 7.2.5 Hot-iron disbudding ................................................................................................ 103 7.2.6 Statistical analysis ................................................................................................... 103 7.3 Results ......................................................................................................................... 105 7.3.1 Response to judgment bias tests. ............................................................................ 105 7.3.2 Pain-induced anhedonia. ......................................................................................... 106 7.4 Discussion ................................................................................................................... 108 7.5 Conclusions ................................................................................................................. 112 Chapter 8: General Discussion and Conclusions ....................................................................113 8.1 Thesis findings .................................................................................................................. 113 8.2 Contributions, limitations and future directions ............................................................... 113 8.3 General conclusions .......................................................................................................... 121 References ...................................................................................................................................123 Appendices ..................................................................................................................................169 Appendix A: Sequences of cues presentation for training calves in the judgment bias test (Chapter 2). ............................................................................................................................. 169     x List of Tables Table 2.1. Training and testing procedure for the gambling bias task. ..........................................30 Table 3.1. Description of agonistic behaviours observed during regrouping and inter-observer reliability scores. ............................................................................................................................41 Table 5.1. Loadings for the Principal Component Analysis performed on averages of behaviors expressed over the two sessions. ....................................................................................................74     xi List of Figures Figure 1.1. Response to a judgment bias test at the population and individual levels.....................3 Figure 1.2. Schematic representation of the origins of stable individual variation in optimism. ....6 Figure 2.1. Experimental apparatus. ..............................................................................................28 Figure 2.2. Least-square mean latency (± SE) of calves (n=9) to reach locations associated with different probabilities of reward and punishment. .........................................................................32 Figure 3.1. Timeline of the experimental procedure......................................................................40 Figure 3.2. Change in brush use after regrouping and return to the home-pen. ............................43 Figure 4.1. Parturition induced anhedonia. ....................................................................................55 Figure 4.2. Separation from calf induced anhedonia in dairy cows. ..............................................56 Figure 5.1. Apparatus designed for training and testing judgment bias. .......................................66 Figure 5.2. Latency (mean + SEM) to reach the different locations for Session 1 (average of Day 1 and 2) and 2 (average of Day 3 and 4). .......................................................................................71 Figure 5.3. Consistency in the latency to approach ambiguous locations in Session 1 versus Session 2. .......................................................................................................................................72 Figure 5.4. Consistency over time for A) fearfulness and B) sociability dimensions. ..................73 Figure 5.5. Relationship between fearfulness score determined from the Principal Component Analysis (PCA) and judgment bias. ...............................................................................................75 Figure 6.1. Consistency in individual social proximity scores. .....................................................87 Figure 6.2. Relationship between sociability and social proximity score. .....................................88 Figure 6.3. Consistency in response to transportation. ..................................................................89 Figure 6.4. Relationship between the maximum eye temperature (after loading) and the number of vocalizations during transportation. ..........................................................................................90 Figure 6.5. Relationship between the trait pessimism and the average number of vocalizations expressed during transportation. ....................................................................................................91 Figure 6.6. Relationship between the trait fearfulness and the change in maximum eye temperature after loading into the trailer. ......................................................................................92 Figure 6.7. Relationship between traits a) fearfulness and b) pessimism and the average maximum eye temperature measured after loading into the trailer. ..............................................93 Figure 7.1. Timeline of the experimental procedure....................................................................101 Figure 7.2. Latencies (raw data; mean ± SE) to touch the different locations of the judgment bias tests. .............................................................................................................................................106 Figure 7.3. Change in sweet solution consumption after disbudding and relationship with individual levels of pessimism. ....................................................................................................107 Figure 7.4. Changes (%) in sugar solution intake over the five days following hot-iron disbudding in calves (n = 17). ......................................................................................................108     xii List of Abbreviations CI: confidence intervals CL: confidence limits HPA: Hypothalamic pituitary adrenal  HR: Human reactivity ICC: intra-class-correlation coefficient JBT: Judgment bias test LMM: Linear mixed model M: Middle NO: Novel object NP: near-punishment NR: near-reward nS+: near-positive stimulus OF: Open-Field P: punishment PCA: principal component analysis S+: positive stimulus S-: Punishment stimulus SERT: serotonin transporter SMT: Social motivation test        xiii Acknowledgements First, I would like to thank Nina, my supervisor, for her immense help during the course of this thesis. This adventure would not have started without your openness and I am forever grateful that you accepted me to become one of your PhD students. I also wish to thank Dan for acting as a supervisor over the years. You both provided me with invaluable support, advice and I will deeply miss our frequent and endless discussions. You both contributed enormously to the scientist I am today. I am also grateful to the UBC Dairy Education and Research Centre (Dairy Centre) farm staff especially Nelson, Brad, Barry, Bill and Ted from whom I learnt a lot! I am most grateful to all the students that were involved and that helped during the course of this thesis - there are many of you (Allison, Amandine, Ashley, Brent, Deborah, Emeline, Hortense, Javieira, Manon, Pierre and Sarah). It has been a real pleasure and tremendous fun to work with each of you and I hope that I have modestly contributed to your training. A special thank you to you Brent for your commitment, dedication and friendship! Training the calves would not have possible without your enthusiasm and this adventure really taught me a lot about how to be a mentor.  I also wish to thank all the colleagues that became friends over the years (Allison, Ana, Augusto, Biljana, Emeline, Heather, Jane, Laura, Michael, Nico, Thomas and Tracy) and all the students of the Dairy Centre that made this experience a very fun and enriching one! Heather, your advice has always meant a lot for me and I miss our 3h-long conversations in the car when travelling back to the farm from the Point Grey campus. Laura, you are a wonderful friend, thanks for being the best listener! A special note also for Thomas - the time spent at the farm would not have been the same without you!    xiv I am of course deeply thankful to all my family (Caroline, Eric, Grégoire, Guillaume, Guy, Myriam, Patrice and Patricia) and friends (Anthony, Boris, Camille (les 3!), Clementine, Élodie, Joffrey, Jeremy, Juliette, Lucas, Manu, Margaux, Marie, Maxime, Quentin (les 3!), Rissrousse, Thibaud, Thibaut and Vanessa) from France that were a constant source of support and motivation. Thanks to all who visited and/or sent some cheese/wine to help me feel at home in Canada – this and your constant moral support was invaluable! Lastly, I wish to thank Eugénie, my partner. It would be vain to try to explain how much your support and love mattered to me and how it made all this possible.    xv Dedication   In memory of my friends, Lucie and Jo.        1 Chapter 1. Introduction  Preventing and minimizing suffering (seen as a severe and persistent negative affective state; Weary, 2019) is one of the core principle of animal welfare science (Dawkins, 1980, Broom, 1991). This requires some assessment of the animal’s affective state (described as “emotions and other feelings that are experienced as pleasant or unpleasant rather than hedonically neutral”; Fraser, 2008). Hence, a recent surge of interest has pushed for more research on “felt emotions”, even if they may never be fully understood in non-human animals (Weary et al., 2017) and recent evidence show that animals experience prolonged positive or negative affective states (i.e. referred to as mood; Mendl et al., 2010b).  Persistent negative affective states have been explored in biomedical research aiming to model the negative effects of chronic stressors in animals. These models attempt to mimic conditions that lead to behavioral, physiological and cognitive changes typically seen in human depression or other psychopathologies. These changes are commonly referred to as “depressive-like states” in animal models of human depression and include behavioural signs of negative mood and anhedonia (i.e. the loss of interest in pleasurable activities/resources; Rygula et al., 2005; Treadway and Zald, 2011), the two main symptoms of depression (DSM-V). For instance, laboratory rats showed signs of low mood (Papciak et al., 2013) and of anhedonia (Rygula et al., 2005) when subjected to chronic stress procedures.   Other changes have been explored such as anxiety (Neumann et al., 2011), despair (Chourbaji et al., 2005), disrupted sleep patterns (Grønli et al., 2004), inactivity (Meerlo et al., 1996a; b) and effects on cognitive functions, such as learning and memory (Conrad, 2010). In many cases, these changes can be alleviated with anti-depressive drugs (Wang et al., 2017; Neville et al., 2020). Collectively, these studies provide a body of evidence that laboratory animals       2 experience long-lasting negative mood states when they are subjected to chronic exposure to stressors.  Recent work has used similar approaches to understand the effects of living conditions typically associated with captivity on animal affective states. Changes in mood have been studied via the effects on cognitive processes such as attention, judgment and memory, commonly referred to as cognitive biases (Paul et al., 2005). These measures provide reliable proxies to detect both positive and negative affective states in various species (Mendl et al., 2009). The most popular test relies on effects of affective states on decision-making (i.e. the judgment bias test; Box 1) with the assumption that individuals in a negative state become more pessimistic about the future and display more cautious behaviours in response to ambiguous situations (Roelofs et al., 2016). Consistent with work done on humans (Anderson et al., 2012; Iigaya et al., 2016), a number of studies have shown that situations intended to negatively affect animal mood induce pessimistic interpretations in laboratory animals (i.e. negative judgment bias; (Mendl et al., 2009; Neville et al., 2020; Nguyen et al., 2020), including some conditions relevant to housing conditions (e.g. unpredictable housing; Harding et al., 2004).   Other studies have used anhedonia-like responses to study the effects of routine laboratory procedures on rodents’ affective states. For instance, tail-handling, one of the most frequent handling methods in mice (Deacon, 2006), is sufficiently aversive to trigger anxiety (Hurst and West, 2010) and anhedonia (Clarkson et al., 2018), showing that even simple procedures may have detrimental effects and trigger persistent negative affective states in captive animals. These examples from laboratory rodents indicate that routine management procedures can have serious consequences on the animals. These studies also illustrate how methodologies aiming to assess mood-related changes give a way to infer about the welfare of captive animals (Baciadonna and       3 McElligott, 2015) with the assumption that living conditions may have a number of negative consequences on affective states.  Box 1. How to measure optimism/pessimism in animals? Pessimists and optimists differ in their perception of ambiguity with the former having negative expectations (they see the glass as half-empty) and the later having positive expectations (they see the glass as half-full). Judgment bias tests create situations where animals have to make a decision in response to one or multiple ambiguous cues; the responses are then taken to reflect the animal’s (positive or negative) expectations (Roelofs et al., 2016). Despite subtle differences in experimental design, all judgement bias tests include the following characteristics: 1) animals are trained to associate a specific cue (S+) with a reward and a different cue (S-) with either the absence of a reward, a lower valued reward (qualitatively or quantitatively), or a punishment and, 2) once animals are able to reliably distinguish between the rewarded and alternative cue they are exposed to ambiguous cues (intermediate cues for which animals do not have information). Animals’ responses are measured either as latency to touch the cue or as the proportion of go responses, and these responses are typically graded (see Figure 1.1). Individuals greatly vary in responses, with pessimists responding less or slower (consistent with higher expectation of punishment) and optimists responding more or faster (consistent with higher expectation of reward).  Figure 1.1. Response to a judgment bias test at the population and individual levels.  Calves (n=22) were trained in a spatial learning task to associate one cue (S+) with a reward and another (S-) with a punishment. Responses were collected using latency to touch “trained” (S+ and S-) and ambiguous intermediate cues (nS+, M and nS-) that were never rewarded or punished (empirical data presented come from Chapter 5). The dashed curve represents the population response (average of all points) and each dot represents a single individual. Individuals above the curve (for ambiguous locations) showed more pessimistic responses than average, and animals under the curve showed more optimistic responses.       4  Farm animals can also experience challenging living conditions, but few published articles have addressed mood changes in these species. Some empirical studies have examined depressive-like behaviours in farm animals, including learned helplessness (a deficit in avoidance learning; horses: Fureix and Meagher, 2015, Hall et al., 2008), anhedonia (pigs: Figueroa et al., 2015) and cognitive biases (sheep: Destrez et al., 2013, cattle; Neave et al., 2013, pigs: Douglas et al., 2012, laying hens: Zidar et al., 2018) indicating that farm species express similar changes to those observed in laboratory rodents when subjected to stressors. However, to date, there is a lack of information on whether routine procedures have long-lasting negative effects on mood in farm animals.  Animal welfare is ultimately a matter of the individual and when subjected to a stressor, not all animals react in similar ways. There is evidence from the laboratory animal literature that stress only triggers depressive-like states in a fraction of the animals tested (Armario and Nadal, 2013; Czéh et al., 2016; Wang et al., 2017), suggesting that some animals are more vulnerable to stressors than others. These differences are likely driven by, in part, internal factors such as personality traits. In humans, cognitive vulnerabilities such as persistent pessimistic expectations are believed to contribute to (Everaert et al., 2013, 2014) and predict (Carver and Scheier, 2014) the development and maintenance of mood disorders. The natural individual variation that exists with respect to general expectations (i.e. positive: optimism or negative: pessimism) when contemplating future outcomes, is believed to affect the way individuals perceive and respond to stressors, leading to either increased resilience (defined as the “capacity to maintain healthy emotional functioning in the aftermath of stressful experiences”; Parker et al., 2006) or vulnerability (Carver and Scheier, 2014). For instance, optimism may positively contribute to       5 resilience (Carver and Scheier, 2014; Dantzer et al., 2018) and protect against psychopathologies such as anxiety and depression in humans (Carver and Scheier, 2014).  Stable individual differences in optimism/pessimism have been documented in non-human animal species. Optimistic animals have been described as having “either, a higher expectation of reward, or a lower expectation of punishment (threat), than the same animal in a different state (or a different animal)” (Bateson, 2016). This definition helps relate to the go/no-go methodology often used to measure optimistic or pessimistic judgments in animals (Mendl et al., 2009; Box 1). Studies on pigs (Sus scrofa domesticus: Murphy et al., 2013), dogs (Canis familiaris: Starling et al., 2014), chimpanzees (Pan troglodytes : Bateson and Nettle, 2015), dolphins (Ursiops truncates: Clegg et al., 2017), and rats (Rattus rattus: Rygula et al., 2013) have all shown varying degrees of consistency over time (i.e. ranging from few days to 8 weeks), an important feature of any personality trait (Carter et al., 2013). Although the mechanisms underlying the origins of stable differences in optimism in humans and non-human animals are still unclear (Box 2), some individuals consistently perceive ambiguity in a more positive way than others, suggesting that optimism/pessimism behaves as a stable trait (Roelofs et al., 2016).  Box 2. Origins of judgment bias. Negative judgment bias induced by a stressor and believed to reflect low mood are particularly well-explained by Bayes’ theorem. According to this perspective, perceptions are not only affected by bottom-up sensory inputs but also by prior expectations (Clark, 2013). The effect of expectations on perception is illustrated in the pain literature where “perception is biased toward the expected level of pain” (Hoskin et al., 2019). Consistent with this idea, responses to ambiguous cues should be based on prior expectations that are updated according to the current living conditions of an individual (Trimmer et al., 2011; Weary, 2019). If an animal has previously experienced something negative, this is expected to down-regulate expectations leading to a more negative judgment (i.e. mood-induced pessimism). How stable inter-individual differences in judgment bias emerge is currently unknown. The state-behaviour feedbacks theory postulates that “individuals differ in behaviour because they differ in state” (Sih et al., 2015). In the case of optimism, we predict that a relationship exists between mood (state) and optimistic/pessimistic behaviours. When the effect of current state on current behaviour is       6 positive and vice versa, among individual variation should increase over time (“fanning-out” pattern; (Sih et al., 2015), explaining why individuals drift apart. This is illustrated for mood and optimism/pessimism in Figure 1.2 for two individuals that differ in mood early in life. One starts in a slightly positive mood and the other is in a slightly negative mood. Because of this initial divergence and the self-sustaining relationship between mood and optimism, the optimist is hypothesized to place greater weight upon positive events and the pessimist on negative events. Exposure to a similar sequence of events is thus hypothesized to increase the difference between the two individuals over time.  Figure 1.2. Schematic representation of the origins of stable individual variation in optimism.  Two individuals are represented by coloured dash lines. Individuals were initially at similar levels of mood before the first divergence or series of divergence occurred. Due to this initial divergence we hypothesized that the tendency of optimists to weight positive events more positively and of pessimists to weight negative events more negatively would reinforce the differences between individuals over time. As illustrated in the figure, the optimistic individual showed higher increase in mood state after positive experiences (+ signs) when the pessimistic individual showed higher response to negative experiences (- signs).     In this chapter, I will first review the available evidence indicating that farm animals are subjected to stressful situations that are likely to lead to negative mood, focusing mostly upon examples from dairy cattle, pigs and chickens (Section 1.1). This literature review provides context for the empirical work described in Chapters 2, 3 and 4 that contribute to expand our understanding of persistent negative affective states triggered by routine farm procedures in dairy cattle. In a       7 second section (1.2), I explore why some individuals may be more vulnerable to stressors than others. In particular, I explore whether stable differences in pessimism favor vulnerability to stressors in non-human animals. This review provides context for the empirical work described in Chapters 5, 6 and 7 where I focus on how personality traits may drive responses to stressors in calves.   1.1 Major stressors encountered by farm animals Captive animals are subjected to many different stressors that may originate from the physical environment (e.g. excessive temperatures: Polsky and von Keyserlingk, 2017), social environment (e.g. damaging behaviors: Rodenburg et al., 2013) and routine husbandry procedures (e.g. painful procedures: Herskin and Di Giminiani, 2018).  If given the chance, farm animals avoid a variety of routine procedures thought to cause fear, pain, distress or boredom (e.g. Kirkden and Pajor, 2006; Rushen, 1996). For instance, calves associate a specific place with the experience of pain and avoid it in the future (Ede et al., 2019c). In addition, animals are frequently denied agency (Špinka, 2019) when subjected to stressful procedures and have limited ability to predict either their occurrence or the outcome, elements that are likely to exacerbate the negative effects of these stressors (Weary, 2019). Not surprisingly, studies in sheep have shown that repeated exposure to mildly aversive, unpredictable and uncontrollable events induce negative mood (Destrez et al., 2013), that can partially be mitigated by exposure to positive events (Destrez et al., 2014). In the following section I explore some farm management procedures that may act as major (i.e. enough to have long-lasting consequences on mood) stressors and that may lead to depressive-like states. Some procedures may be experienced once (e.g. some painful procedures) or       8 periodically (e.g. co-mingling with new conspecifics) or even experienced continuously (e.g. restrictive housing); all are expected to have deleterious consequences. More frequent exposure to stressors is expected to render animals more vulnerable and major stressors may act as triggers for more severe changes in mood, such as depression.  1.1.1 Pain  The experience of acute or chronic pain is associated with depressive-like states both in humans and laboratory animals (Kremer et al., 2020). Many farm animals are subjected to surgical procedures (e.g. hot-iron disbudding of dairy calves; castration and tail docking of piglets; beak trimming of chicks) that have short- and longer-term effects. These painful procedures trigger changes in behavior, physiology and motivation (pigs: Herskin and Di Giminiani, 2018, cattle: Stafford and Mellor, 2011, chickens: Gentle, 2011). For instance, pigs undergoing castration vocalize (Weary et al., 1998) and disbudded calves show wound-directed behaviors for hours after the procedure (Faulkner and Weary, 2000), behaviors that can be mitigated using pain control (Hansson et al., 2011; Winder et al., 2018).   Hot-iron disbudding of dairy calves is one of the most studied painful procedure in farm animals. In addition to acute behavioral and physiological changes, recent studies suggest that hot-iron disbudding causes calves to experience short-term depressive-like states; disbudded calves express negative judgment bias up to 22 h after the procedure (i.e. pessimism; Daros et al., 2014; Neave et al., 2013). Other work also showed that hot iron disbudding negatively affects play behaviours (Rushen and de Passillé, 2012; Mintline et al., 2013), suggesting a reduced interest towards pleasurable activities (i.e. anhedonia). In addition, dairy calves avoid the place where they       9 have been disbudded (Ede et al., 2019c), an effect that can be mitigated using pain control (Ede et al., 2019b). These latter results indicate that the procedure may induce conditioned fear.  Whether painful procedures trigger long-lasting pain has mostly been ignored in farm animals (Herskin and Di Giminiani, 2018; Herskin and Nielsen, 2018). Beak-trimmed chicks (> 10 d of age) have been shown to display persistent (up to 6 weeks) pain-related guarding behaviors (Gentle et al., 1990), suggesting long-lasting pain. Pain sensitization (allodynia and hyperalgesia) was also reported for 60 to 105 days after hot-iron disbudding in calves (Casoni et al., 2019) and for at least 90 days after tail-docking in lambs (Larrondo et al., 2019). Long-lasting pain may also be generated by the captive environment. For instance, evidence suggest that hens with keel bone fractures and lame cows and sows experience chronic pain (Nasr et al., 2013; Daros et al., 2019; Kremer et al., 2020). These pathologies have been clearly linked to management and housing factors, such as slippery concrete flooring for dairy cows (Solano et al., 2015). These painful pathologies are common in indoor systems (e.g. prevalence of keel bone fracture can reach 97% of the flock at the end of a production cycle; Rodenburg et al., 2008).   Persistent pain is a risk factor for depressive states in humans and substantial evidence indicates anxiodepressive consequences of chronic pain in laboratory animals (Kremer et al., 2020), but little information exists on this topic for farm animals. Armstrong et al. (2020) recently showed that keel-bone fractures down-regulate adult hippocampus neurogenesis, a neurobiological correlate of chronic stress and depressive-like states (Gould and Tanapat, 1999), results that are consistent with studies in a mice neuropathic pain model (Dimitrov et al., 2014). In summary, farm animals experience many forms of pain that can have long-term effects on their mood.         10 1.1.2 Social stress  Most farm animals are gregarious and group-housing is usually assumed to provide benefits (Rault, 2012). However, the social environment can be the source of social stress depending on housing conditions and the stability of the social group. Social stressors can be powerful mediators of psychopathologies, especially if experienced repeatedly (Huhman, 2006). Laboratory rodent social stress models have explored effects of social subordination, crowding, isolation and social instability (Beery and Kaufer, 2015). For example, social isolation, restraint and repeated social defeat are widely used to induce depressive-like states in laboratory animals (Wang et al., 2017). In farm animals, some social stressors happen intermittently and are likely to act as intense acute stressors (e.g. social isolation and social mixing also termed regrouping) with effects lasting for hours to days (Arey and Edwards, 1998). In contrast, other management factors (e.g. housing conditions) may act as chronic stressors subjecting animals to daily social pressure. Social isolation has adverse effects (Cacioppo and Hawkley, 2009) depending on duration, age and species ecology (Beery and Kaufer, 2015). Most farm animals are highly motivated for social contact (Holm et al., 2002; Søndergaard et al., 2011) and housing that deprives animals of social contact may be particularly detrimental. Social deprivation early in life predisposes individuals to reacting negatively to stressors (Parker and Maestripieri, 2011), increasing vulnerability to psychopathologies later in life (Beck, 2008; Daskalakis et al., 2013).  For instance, deprivation of maternal care in rodents can permanently alter gene expression in the offspring and have important downstream effects on stress physiology (high basal HPA activity, low recovery; Meaney and Szyf, 2005), neural development and cognitive functions (Ladd et al., 2005). Hence, maternal separation or poor maternal care may lead to vulnerable phenotypes in adult rodents (Lupien et al., 2009), a phenomenon that can be expressed over generations (Franklin et al., 2010).       11 Even brief periods of isolation can trigger acute stress responses (e.g. cattle: Rushen et al., 1999). In addition, social isolation seems to exacerbate the negative effects of repeated stressors. Single housed mice showed depressive-like behaviors after repeated restraint, but group-housed mice did not (Liu et al., 2013).  Dairy calves are typically housed individually. In line with results obtained in laboratory animals, individually housed calves have poorer social, cognitive and emotional development than calves reared in groups or with the dam (reviewed in Costa et al., 2016). Calves reared individually were more emotionally reactive (Duve and Jensen, 2012; Duve et al., 2012; Jensen and Larsen, 2014), played less (Duve et al., 2012; Jensen et al., 2015; Valníčková et al., 2015) and had impaired cognitive abilities (Gaillard et al., 2014; Meagher et al., 2015). Individually-housed calves also engaged in fewer social interactions when provided the opportunity (Le Neindre and Sourd, 1984; Jensen, 1999; Krohn et al., 1999; Flower and Weary, 2001; De Paula Vieira et al., 2012), and were more submissive (Veissier et al., 1994; Duve and Jensen, 2011), indicating effects that persist beyond the isolation period. Individually housed dairy calves express more pessimistic judgments compared to pair-housed calves, consistent with the idea that social deprivation, by itself, has a negative impact on mood (Bučková et al., 2020).  Other farm species may also be socially deprived during adulthood. Horses are often subjected to long periods of social isolation that trigger stereotypic behaviors (Lesimple et al., 2016) and increase fear and anxiety (Visser et al., 2008; Lesimple et al., 2011), effects that can be mitigated by pair housing (Visser et al., 2008).   Social mixing is frequent in farming systems, despite known adverse effects (Proudfoot and Habing, 2015; Hemsworth, 2018; Peden et al., 2018). Animals may be moved from one social group to another depending on age, reproductive status or performance (Verdon and Rault, 2018).       12 In addition to disrupting established social bonds, ‘mixed’ farm animals such as pigs or cattle engage in aggressive competition to re-establish a stable hierarchy, inducing social stress (Otten et al., 2002; Coutellier et al., 2007) and injuries, especially in pigs (Hemsworth, 2018; Peden et al., 2018). When associated with other stressors such as weaning, social mixing induces more intense responses including vocalizations, reduced BW gain and fighting that results in skin lesions (Hötzel et al., 2011); these effects can be mitigated if piglets are given the opportunity to socialize before weaning (Weary et al., 2002; D’Eath, 2005). Similarly, when combined with reduced space allowance or higher densities, social instability can have more profound consequences (Hemsworth et al., 2013, 2016). In rats, social instability acts as a major stressor that impairs stress physiology (Haller et al., 1999) and can lead to depressive-like states (Herzog et al., 2009). Although regrouping is considered a stressor in farm animals, studies have mostly focused on the acute behavioral (von Keyserlingk et al., 2008) and physiological responses (Huzzey et al., 2012a) with limited information on affective state.  Other routine farm procedures may also induce harmful social behaviors. Group housed farm animals are typically kept at densities that result in increased competition, agonistic (DeVries et al., 2004; Proudfoot et al., 2009; Hemsworth et al., 2013; Hemsworth, 2018) and damaging behaviors (Widowski et al., 2016). Reduced space allowance per animal can be especially detrimental to younger (Huzzey et al., 2012a) and more submissive dairy cows (Huzzey et al., 2012b). Higher stocking densities may have negative effects (e.g. increasing aggressive behaviors) even in the absence of competition for specific resources if animals perceive the lack of control on their personal space as aversive, similarly to what is observed in humans (Engelniederhammer et al., 2019). Consistent with this view, crowding induced social stress even when feed was available ad libitum in mice (Reiss et al., 2007). Crowding is likely to act as a chronic stressor, as evidenced       13 by long-term effects on damaging behaviors (Widowski et al., 2016), stress physiology (Haller et al., 1999), and time budget (Winckler et al., 2015) that lead to long-term effects on animals’ mood and coping styles (Chaby et al., 2013), and depressive-like behaviors (Herzog et al., 2009) in rodents. Irrespective of space per individual, group size may also have negative effects.   1.1.3 Separation from offspring  Although cows naturally provide maternal care that last for months until their calf is weaned, most dairy cows are separated from their newborn calf shortly after birth (von Keyserlingk and Weary, 2007). Most dams, including dairy cows, respond to separation by vocalizing and increasing their activity (see Flower and Weary, 2003). These behaviors are believed to be driven by motivation to reunite (Newberry and Swanson, 2008). Consistent with the idea that the emotional bond strengthens with time, cows display more intense responses when separated after a few days versus a few hours of contact (Weary and Chua, 2000; Stěhulová et al., 2008). Although these behaviors indicate a response to separation, they do not give much information on how cows feel after separation from their calf and for how long these effects may last. Mother rats and mice repeatedly separated from their pups show impaired memory (Sung et al., 2010; Aguggia et al., 2013), short-term and long-term behavioral deficits (Boccia et al., 2007; Maniam and Morris, 2010; Sung et al., 2010), dysregulation of the hypothalamic-pituitary-adrenal axis (Maniam and Morris, 2010; Orso et al., 2018) and neurobiological changes (Sung et al., 2010), all consistent with the experience of depressive-like states (Alves et al., 2019). Whether cow-calf separation induces prolonged negative affective states has not been explored. The separation of an offspring from its mother is considered by many as a major stressor, and separation usually happens concurrently with a myriad of additional stressors. In addition to parturition that is likely painful       14 (Mainau and Manteca, 2011), dairy cattle are subjected to diet changes, unstable social environments, and increased contact with humans (Cook and Nordlund, 2004). These stressors are likely to have additive effects and render dairy cattle vulnerable. This is well-illustrated by the increased incidence of disease during the weeks following parturition (Leblanc, 2010), perhaps facilitated by these social stressors (Proudfoot and Habing, 2015).  In conclusion, farm animals are frequently subjected to intense and repetitive stressors. The cumulative effects of these stressors are not currently understood but persistent exposure are known to lead to depressive-like states in laboratory rodents (Wang et al., 2017); similar effects are thus expected in farm animals. However, individuals may vary in their response to these stressors with some animals being more vulnerable than others.  1.2 Vulnerability to stressors: a matter of pessimism? The following section reviews the work exploring the relationship between stable individual differences in pessimism and stress vulnerability in non-human animals. I specifically explore whether stress vulnerability in pessimistic animals is driven by 1) more negative perceptions of reinforcers and, 2) more intense and less efficient coping strategies contributing to higher risks for stress-related psychopathologies. I expected that the work done in animals would yield results consistent with those from studies on humans and would improve our understanding of why some animals display higher vulnerability to stressors. Most empirical work on this topic originates from laboratory animals; these results will serve as a basis for the empirical work I conducted in dairy calves.         15 1.2.1 Sensitivity to rewards and punishments  Methodologies used in judgment bias tests rely on perceptions of ambiguity (Roelofs et al., 2016) frequently created using both positive and negative reinforcers (see Box 1). According to the expected utility theory (Loewenstein et al., 2008), when animals face an ambiguous cue they base their decisions on their subjective perception of the outcomes (how pleasant the reward and how aversive the punishment) and the anticipated probability of their occurrence (Mendl et al., 2009; Nettle and Bateson, 2012; Iigaya et al., 2016). An animal may refrain from approaching an ambiguous cue because the fear of punishment outweighs the joy of reward. Pessimistic animals may have a lowered perception of the hedonic value of the reward, a higher aversion of the negative reinforcer, or a negative appraisal of the likelihood of reward versus punishment.  In humans, pessimists and optimists are believed to pay selective attention to reinforcing cues, to differ in their persistence (i.e. the amount of effort they are willing to engage) and in their confidence in attaining goals (Hecht, 2013). Although optimistic people tend to overestimate the probability of reward when asked about an uncertain stimulus, evidence suggest they do not underestimate future losses (Stankevicius et al., 2014), suggesting that they are hypersensitive to rewards but not hyposensitive to punishments. Similarly, optimistic rats were more motivated to access a reward (i.e. willing to pay a higher price measured using number of lever presses) yet did not differ in motivation to avoid punishments (i.e. electric shocks; Rygula et al., 2015). These results suggest that optimistic rats were more persistent (i.e. willing to press the lever more often), likely because they attributed a higher intrinsic value to the reward or because of their higher confidence that their behaviour would be rewarded (as shown in humans; Carver and Scheier, 2014).       16  Food rewards are typically involved in judgment bias tests; thus, higher optimism may also be driven by higher food motivation (Bateson et al., 2015). However, consistent with the idea that optimism is associated with higher sensitivity to rewards in general (and not only food rewards), a study showed that optimistic rats display higher rates of 50kHz vocalizations when tickled (Rygula et al., 2012). These vocalizations are emitted in positive contexts in rats (Panksepp and Burgdorf, 2003). As these vocalizations have been associated with the hedonic value of the eliciting stimulus (Burgdorf et al., 2000, 2011), the authors concluded that optimistic rats enjoyed the tickling more than their pessimistic counterparts (Rygula et al., 2012). To the best of our knowledge, this is the only study showing that optimism is positively related to pleasure in animals. Given that pleasure is a multi-faceted construct (Treadway and Zald, 2011), ‘wanting’, ‘liking’ or other reward-related processes (e.g. memorization) should be explored to disentangle what hedonic aspects are affected by variation in general expectations. This would also give a better sense of how much pessimism and anhedonia are related.  Optimists are believed to be more persistent, which can be adaptive when persistence increases the probability of reward without additional costs. In other cases, however, persistence can be maladaptive. Optimistic humans have higher gambling expectations, maintain these expectations after negative outcomes, and rarely reduce their bet despite experiencing losses (Gibson and Sanbonmatsu, 2004), suggesting that optimistic people may be hyposensitive to negative outcomes. Rafa et al. (2016) found that optimistic rats were more prone to bet after hopeless “clear losses” (i.e. 3 OFF signals) compared to pessimistic individuals, but no differences were observed for “near-misses” (i.e. 2 ON and 1 OFF signal) and “near-losses” (i.e. 2 OFF and 1 ON). Thus, optimistic rats appeared to bet more (as much as for the “near losses” condition) in situations unlikely to lead to rewards (i.e. unrealistic optimism), suggesting that they did not       17 considered near losses and clear losses as different, maybe because of a lowered capacity for self-inhibition (as suggested in starlings Bateson et al., 2015). In contrast, pessimistic rats displayed graded responses and did not respond to clear losses. Unrealistic optimism (i.e. displaying optimism in face of almost impossible odds) is considered a ‘normal’ disposition in healthy humans (Sharot, 2011) but is absent in depressed patients (Sharot and Garrett, 2016).  In addition, when measuring sensitivity to positive and negative feedback using a probabilistic reversal learning task (Rygula and Popik, 2016; see Rygula et al., 2018 for a full description of the task), pessimistic rats were indeed more sensitive to negative feedback (but not less sensitive to positive ones) suggesting that they adapt their behaviour faster when experiencing a punishment.   Other work has shown that some rats consistently display risk-preferring choices when tested in the gambling test. Langdon et al. (2019) showed that these animals were hyposensitive to reward losses and hypersensitive to rewards, a result linked with dopamine-induced optimism in humans (Sharot et al., 2012) and higher dopaminergic activity in optimistic chicks (Zidar et al., 2018). Although these results suggest that optimism favors risky behaviours, some work on humans suggests that optimism is negatively associated with risky behaviours such as smoking and eating unhealthy food (Carver and Scheier, 2014). More research is needed to understand how variation in general expectations (i.e. optimism/pessimism) affect hedonic value attributed to rewards of different nature, sensitivity to risk and capacity to self-inhibit.  Collectively this body of evidence indicates that pessimistic rats are less motivated to access a reward, enjoy tickling less, and are more sensitive to negative feedback. These results indicate that pessimistic choices may be driven by perception of reinforcers. Pessimistic animals were less likely to pursue rewarding experiences and more likely to adapt their behaviour after       18 experiencing a punishment. In contrast, optimistic rats were focused on the positives, to the point where they bet even if there is little chance of success (i.e. unrealistic optimism).   1.2.2 Response to stressors  As argued by Carver and Segerstrom (Carver et al., 2010) “anticipating good versus anticipating bad—is linked to core processes that underlie behavior”. In humans, optimism clearly relates to efforts that are invested in the pursuit of goals; optimists are described as hopeful and persistent while pessimists as doubtful and hesitant. Efforts that optimists are willing to make seems to affect different aspects of their life (e.g. social relationships), but the main differences are observed in response to stressors. Pessimists are usually considered to be more responsive to stressors (Carver et al., 2010) and to respond to them in a different way (Solberg Nes and Segerstrom, 2006; Carver et al., 2010). In this section, I explore whether inter-individual differences in judgment bias modulates how, and how much, non-human animals respond to stressful situations.  ‘Coping’ is a broad term used to describe how individuals face adversity (Koolhaas et al., 2010). In some cases, individuals may adopt a strategy that is ineffective or counterproductive, potentially leading to more negative or long-term effects of the stressor. Repeated failed attempts to cope with a stressor can contribute to overload resilience mechanisms and increase the risk of psychopathologies (Anisman, 2015).   Individuals differ in how they cope with stressors, adopting a variety of different behavioural strategies. Koolhaas et al. (1999) described the concepts of “proactive” vs. “reactive” coping. For instance, when an electric probe was introduced into the home-pen (the defensive burying task; (Pinel et al., 1989), rats were consistent in their behavioural strategy of either       19 engaging in proactive behaviours to make the threat disappear (i.e. burying the probe) or in reactive ‘avoidance’ behaviours (i.e. keeping their distance from the threat; (Koolhaas et al., 1999)).   Analogous coping strategies have been described in humans (approach vs avoidance coping; Suls and Fletcher, 1985). Individuals scoring high in optimism generally adopt approach-based coping strategies (also referred to as engagement coping) that aim to “eliminate, reduce, or manage stressors or their emotional consequences” rather than “avoid, ignore, or withdraw from stressors or their emotional consequences” (Solberg Nes and Segerstrom, 2006). Thus, the negative interpretations that pessimistic people form seem to affect their response to stressors and drive more avoidance-based strategies. Similar results have been reported in dogs (based on owners’ responses to a questionnaire Barnard et al., 2018) and pigs (Asher et al., 2016). In the latter study, optimistic animals were reported to be more proactive, based on shorter latencies to approach, and increased time spent in close proximity, to a novel object. Although the animals in both studies were not in the presence of actual threats (unlike the electric prod; i.e. Koolhaas et al., 1999), the results provide some insights into how animals appraise and respond to a novel or unfamiliar stimuli.   In humans, engagement coping is related to higher confidence in one’s ability to deal with challenging situations, decreases in the expression of negative feelings/thoughts and better psychological adjustments (Carver and Scheier, 2014). In contrast, coping strategies based on avoidance, although efficient on the short-term, can lead to an accumulation of negative emotions and to despair in humans (Scheier and Carver, 1992). In rats exposed to shock-probes in the home pen, proactive and reactive copers are equally successful in avoiding shocks (Koolhaas et al., 1999). However, being successful in avoiding future shocks does not mean animals are perceiving the stressor in a similar manner. Rats that avoid the electrical probe may still be affected by its       20 persistent presence, while the proactive approach of burying the probe may provide more permanent relief from the stressor. Consistent with this view, there is some evidence in animals linking the expression of more reactive coping styles to higher vulnerabilities to stress-related pathologies, suggesting that these animals may be more negatively affected by chronic stress (Koolhaas and van Reenen, 2016). Negative views/expectations may also enhance the intensity of the response to a stressor over which animals have little or no control, worsening their subjective experience. For instance, pessimistic humans are known to experience higher levels of pain catastrophizing (Goodin et al., 2013; Hanssen et al., 2013, 2014).  Animal studies exploring how variation in optimism/pessimism is related to coping are scarce. Results to date suggest that pessimistic animals use avoidance-based strategies. Future studies should take into account variation in personality traits such as optimism when exploring coping response to stressors. It is still debated whether traits such as optimism drive coping strategies (top-down hypothesis: Carver and Connor-Smith, 2010) or whether personality traits are the reflection of consistent individual variation when responding to specific situations (bottom-up hypothesis: Segerstrom and Smith, 2019). Data obtained to date in animals suggest that pessimism leads to more negative appraisal of stressful situations triggering avoidance-based coping strategies (i.e. support the top-down hypothesis). That said, personality traits are often explored using the way animals cope with novel situations rendering distinction between personality traits and coping styles difficult. Future studies should aim at disentangling these aspects.  1.2.3 Vulnerability to mood-related disorders As reviewed in section (1.1), persistent exposure to stressors can alter the coping abilities of individuals and lead to mood disorders (Anisman, 2015). This is particularly true when       21 individuals display negative expectations and adopt inefficient coping strategies (Carver and Scheier, 2014). In the cognitive model of depression, pessimistic interpretations are seen as both a symptom (dysfunctional attitudes) and a risk factor for depressive disorders (Beck, 2008; Everaert et al., 2017). In contrast, optimism is seen as a source of resilience (Kleiman et al., 2017), with some evidence indicating that optimism can mitigate the effects of acute (Baumgartner et al., 2018) and chronic stressors (Riolli and Savicki, 2003), reducing post-traumatic stress (Riolli et al., 2002; Birkeland et al., 2017) and the likelihood (Carver and Gaines, 1987; Giltay et al., 2006) and intensity of depressive symptoms (Shnek et al., 2001). On this basis, I hypothesize that pessimistic judgments in animals will: 1) increase vulnerability to mood disorders (i.e. hypersensitivity to stressors) and, 2) be expressed by animals experiencing poor psychological wellbeing. There is considerable evidence in support of the second statement, including studies showing that negative experiences trigger more pessimistic judgment biases (barren housing: Brydges et al., 2010; Douglas et al., 2012; Harding et al., 2004; Richter et al., 2012), pain: (Neave et al., 2013; Daros et al., 2014); chronic stress: Destrez et al., 2013) or other symptoms of mood-related disorders, such as anhedonia (Rygula et al., 2005), inactivity (Fureix and Meagher, 2015) and learned helplessness (Chourbaji et al., 2005). In addition, a recent meta-analysis showed that animals’ judgment can be altered in expected directions using diverse pharmacological agents (Neville et al., 2020). Only a few studies have explored whether pessimistic animals are more vulnerable to chronic stress and more likely to develop depressive-like states in response to challenging situations. Work in animal models of depression showed that individuals vary in how vulnerable they are; chronic stress only triggers depressive-like states in a proportion of animals exposed (Armario and Nadal, 2013; Czéh et al., 2016; Wang et al., 2017), and there are few explanations for why these differences exists. Differences in optimism may explain why some animals develop       22 depressive like symptoms and others do not. Thus, higher level of pessimism should be linked to other predisposing factors and to higher expression of depressive-like behaviours when subjected to chronic stressors.  Variations in the gene coding for the serotonin transporter (SERT 5-HTT) have been linked to higher stress vulnerability in humans (Caspi et al., 2003). Lower expression of the transporter has also been linked to the expression of negative cognitive bias and negative beliefs (Fox et al., 2009; Pergamin-Hight et al., 2012), two predisposing factors for mood disorders (Beck and Bredemeier, 2016) and to a higher risk of depression under stressful conditions (Karg et al., 2011). Acute pharmacological manipulation of the serotonergic system resulted in changes in judgment bias in the expected direction in sheep (Doyle et al., 2011), rats (Rygula et al., 2014) and pigs (Stracke et al., 2017), suggesting that judgment bias in animals is also affected by the serotonergic system. Another study compared mice selected for a high level of expression of the serotonin transporter to wild type animals. When evaluating their responsiveness to cues predicting negative outcomes (foot-shocks 100% of the time), uncertain negative outcomes (foot-shocks 20% of the time) or no outcomes (no foot shocks), animals with higher gene expression showed lower responsiveness to the partially-punished cue (i.e. diminished fear-conditioning), indicating lower negative expectations to uncertain negative outcomes (consistent with an optimistic bias; (Mchugh et al., 2015). Similarly, Kloke et al. (2014) noted a trend for a relationship in a similar direction in 5-HTT knock-out mice. These results are consistent with research showing lower expression of anxiety-related behaviours in animals over-expressing the gene (Jennings, 2006; Krakenberg et al., 2019). Although, one recent study found no main effects of polymorphism in the serotonin transporter (5-HTT) gene on judgment bias in mice (Krakenberg et al., 2019), these findings should       23 be viewed with caution given that of the 3 ambiguous cues used, only the middle one was truly ambiguous (i.e. triggering different responses than learned cues).  Animals having lower expression of the serotonin transporter, and other genetic predisposing factors to stress vulnerability (Beck and Bredemeier, 2016), are predicted to express a negative pessimistic bias even in the absence of stressors. Thus, they may be more prone to develop depression-like symptoms in response to stressful situations. For instance, stress-induced anhedonia occurred sooner, and lasted longer, in pessimistic rats (Rygula et al., 2013), and these animals were also more vulnerable to stress-induced motivational deficits (Drozd et al., 2017). In addition, a series of experiments demonstrated that pessimism affects individuals’ sensitivity to a range of different antidepressant drugs (Golebiowska and Rygula, 2017; Drozd et al., 2019). Multiple studies show a hemisphere specialization in humans (Hecht, 2013) and non-human animals; the right hemisphere controlling responses to emotional information, especially in negative situations (Rogers, 2010). Pessimistic views have been found associated with a right hemisphere dominance (Hecht, 2013) that is also typically found in depressed patients (Nusslock et al., 2011) and recovery from depression is associated with restoration of hemispheric balance (Stewart et al., 2010; Hecht, 2013). Right hemisphere dominance may also act as a marker of vulnerability to depression. For instance, healthy people neither currently experiencing depression nor having history of the pathology, showing a right hemisphere dominance were more likely to develop a first depression episode (Nusslock et al., 2011). This effect is believed to be mediated by the association between cognitive vulnerabilities, such as increased pessimism, and right hemispheric dominance. Studies in common marmosets also showed that left-handed individuals (right hemisphere dominance) were more likely to consider ambiguous stimuli as negative compared to right-handed ones (Gordon and Rogers, 2015). Similar results were also found in       24 horses where pessimistic individuals have also been shown to favour the use of their left-forelimb when initiating movement (Marr et al., 2018). In the case of left-handed marmosets, these individuals are also more fearful and less social (Braccini and Caine, 2009; Rogers, 2009; Gordon and Rogers, 2015). This combined evidence suggest that pessimistic interpretations are associated with a behavioural phenotype characterized by right-hemisphere dominance, higher anxiety and lower social rank. Collectively, these results suggest that pessimism renders individuals more at risk of developing and maintaining depressive-like symptoms, consistent with what is known in humans (Carver and Scheier, 2014). Studies to date suggest that polymorphism in the serotonergic system may be a source of variation in optimism. Longitudinal and experimental studies in animals offer opportunities to better understand the origin of stable pessimistic expectations (e.g. genetic, early-life, prenatal) and how they affect individual development and vulnerability to stressors. Taking into account these differences has the potential to improve the validity of animal models of mood disorders and our understanding of individual differences in animal welfare.  1.3 Thesis objectives The overall aims of this thesis were to: 1) assess whether dairy cattle express persistent negative mood after experiencing routine farm procedures, and 2) investigate whether stable individual differences in pessimism may render some individuals more vulnerable to stressors. I hypothesized that dairy cattle would experience persistent negative mood when experiencing pain associated with hot-iron disbudding (Chapter 2), social stress induced via social mixing (Chapter 3) and multiple stressors associated with parturition, including separation from the calf (Chapter 4). Furthermore, I hypothesized that dairy calves would express stable individual       25 differences in various personality traits including pessimism (Chapter 5), and that these differences would be associated with response to routine stressors such as transportation (Chapter 6) and hot-iron disbudding (Chapter 7).        26 Chapter 2: Pain-induced pessimism and anhedonia following hot-iron disbudding A version of this chapter has been published:  B. Lecorps., B. Ludwig., M. A. G. von Keyserlingk and D. M. Weary. 2019. Pain-induced pessimism and anhedonia: Evidence from a novel probability-based judgment bias test. Frontiers in Behavioural Neuroscience. 13:54.  2.1  Introduction Judgment bias tests (JBTs) have been used to assess long-lasting emotional states (i.e. mood) in animals. In JBTs animals are trained to differentiate between cues that have positive and negative outcomes, and then are tested using ambiguous, intermediate cues; a decreased responsiveness to these intermediates (i.e. pessimistic judgment bias) is expected when animals are in a negative emotional state (Paul et al., 2005). However, repeated exposure to the intermediate cues can result in a loss of ambiguity as animals learn to associate these with a specific outcome (Roelofs et al., 2016). As ambiguous cues are commonly unrewarded, the loss of ambiguity may lead to decreased responsiveness (i.e. increased latencies to respond or decreased frequency of optimistic choices; (Doyle et al., 2010; Barker et al., 2018), affecting the validity of the tests. Several studies have attempted to prevent animals from learning to recognize ambiguous cues, for example, by using partial reinforcement for the training stimuli and thus rendering the lack of reinforcement for ambiguous cues less salient (Neave et al., 2013; Daros et al., 2014; Barker et al., 2016). In addition, the number of ambiguous cues presented can be minimized providing animals with fewer opportunities to learn (Hintze et al., 2018). In this study, we aimed to avoid       27 the problem of declining ambiguity by intentionally training animals to recognize the different cues (in this case different locations) and associate these with specific probabilities of reward and punishment (e.g. from left to right: Positive: 100%/0%; Near-Positive: 75%/25%; Middle: 50%/50%; Near-Negative: 25%/75%; Negative: 0%/100%). Thus, in our design, the task is not based on ambiguity but rather on reward probabilities that are known to the calves (even though the outcome for any specific trial is random within the constraints of that probability function). We predicted that calves would show higher approach latencies to cues associated with a lower probability of reward (and higher probability of punishment). To test the ability of this method to detect changes in mood we used hot-iron disbudding, a routine procedure known to cause postoperative inflammatory pain (Stafford and Mellor, 2011) and pessimistic judgement bias in calves (Neave et al., 2013). We predicted that animals would exhibit a pessimistic judgement bias (i.e., have a reduced expectation of reward and/or an increased expectation of punishment indicated by higher latencies to approach the intermediate locations) in the hours after disbudding, and that responses would return to baseline in the days following the procedure when pain had dissipated.  2.2 Materials and Methods 2.2.1 Animals Nine female Holstein calves (BW: 38.3 ± 3.6 kg) were enrolled in the experiment from 10 d to 35 d old. Within 6 h of birth, calves were separated from their dam and fed 4 L of >50 g/L IgG colostrum. Calves were housed singly (pen size 1.2 x 2.0 m) until 7 d of age after which they were moved to a double pen (2.4 x 2.0 m) and pair housed for the duration of the experiment. Calves were fed 4 L of whole pasteurized milk twice per day (at 0800 and 1600 h) using a nipple       28 bottle, and had ad libitum access to water, hay, and grain. Fresh sawdust was added daily to the pens.  2.2.2 Experimental setup The experimental setting (Figure 2.1) consisted of the same apparatus described by Destrez et al. (2013). The extreme right and left locations were designated as either the positive (S+) or negative (S-) locations (pseudo-randomly balanced across calves), and the intermediate three locations were designated as near positive: nS+, middle: M, near negative: nS-. Calves were familiarized with the apparatus in pairs for 10 min, 1 day before the training phase began.  Figure 2.1. Experimental apparatus.  All 5 locations were assigned a specific probability of reward/punishment (S+: 100%/0%; nS+: 75%/25%; M: 50%/50%; nS-: 25%/75%; S-: 0%/100%). Calves (n=9) were trained for 20 d before being tested.        29 2.2.3 Training  Animals were trained individually in a go/no-go spatial judgement bias task to discriminate between five different locations each associated with a different probability of reward/punishment (S+: 100%/0%; nS+: 75%/25%; M: 50%/50%; nS-: 25%/75%; S-: 0%/100%). When calves were rewarded, they were allowed to drink milk for 10 s. When calves were punished, they could not access milk from the bottle and instead received a puff of air to the face and had to wait 1 min before starting the next trial. For each trial, calves could choose to “go” (i.e., touch the bottle and receive the reward or punishment) or “no-go” (i.e., either wait for 30 s in the arena, or return to the start box to start a new trial). No-go responses were attributed the maximum latency. Training was divided into four phases (Table 2.1). Sequences used for training and testing as well as a video showing how calves responded to the different locations, can be found in the Appendices (Appendix A). Following training, baseline measures were recorded over two sessions each consisting of 20 consecutive trials. All probe locations were presented 4 times in pseudo-randomized sequences designed to minimize the number of consecutive rewards and punishments.       30 Table 2.1. Training and testing procedure for the gambling bias task.  2.2.4 Disbudding procedure Immediately before disbudding calves were sedated with a subcutaneous injection of xylazine (Rompun, 2%, Bayer Inc., Ontario; 0.1 mL/kg body weight; half-life 30 min), followed by a cornual nerve block on each horn bud (4 mL per side of 2% Lidocaine; Ayerst Veterinary Labs, Ontario; half-life 90 min). Five minutes later a hot-iron (Rhinehart X-30; Rhinehart Development Corp., Spencerville, IN) was applied to each horn bud for approximately 15 s.          31 2.2.5 Testing Animals were tested at 6, 22, and 70 h after disbudding. Each test consisted of 20 consecutive trials in which probe locations were presented (four trials each) in pseudo-randomized sequences (using the same criteria as in baseline sessions). 2.2.6 Statistical analysis Calves were allowed 30 s to approach the probe or return to the start box, but in 764 of 800 trials (i.e. 95.5%) they made a decision in less than 10 s (i.e. they either touched the bottle “go” or went back to the start box “no-go”). We therefore used 10 s as the maximum latency to avoid over-weighted outliers.  We used a curvilinear model including the latency to reach each location (as the response variable) and tested the effects of distance from the positive probe location. Calf was included as a random effect. Model residuals were scrutinized for outliers and normality. We then compared latencies to touch the different locations to the time calves took to go to the S+ location using non-parametric 2-sample permutation tests because model residuals were not normally distributed even after logarithmic transformations.  We compared latencies between baseline and testing sessions at 6, 22, and 70 h after disbudding using three different models including the interaction between probe location (distance in meters with respect to S+) and session (2 levels) with calf specified as a random effect. When the effect of session x location interaction was significant additional tests were performed by location.          32 2.3  Results During baseline tests the latency to touch the bottle increased with the probability of punishment (Χ2 = 525.4, df = 4, P < 0.0001; Figure 2.2a), indicating that calves were able to discriminate among the different locations. Calves showed longer latencies to approach the M (Z = 2.9, P = 0.001), nS- (Z = 6.9, P < 0.001) and S- (Z = 9.6, P < 0.001) locations compared to S+, but the latency to approach the nS+ cue did not differ from the S+. Calves tended to “go” (i.e. approach the test stimulus), regardless of location. For example, during the baseline sessions calves always showed a “go” response to the S+, nS+ and M locations, and almost always responded in this way to the nS- (91.7% “go”) but went less frequently to the S- (41.7% “go”). At 6 h after disbudding, response latencies increased (F(1,8) = 12.9, P = 0.007), with some evidence for a session x location interaction (F(4,32) = 2.3, P = 0.08; Figure 2.2b). This interaction was driven by calves taking longer to touch the S+ (F(1,8) = 25.3, P = 0.001), nS+ (F(1,8) = 52.7, P < 0.001) and M (F(1,8) = 21.9, P = 0.0016) after disbudding in comparison with the baseline. At 22 h after disbudding, no effect of testing session and no session x location interactions were observed (Figure 2.2c). At 70 h after disbudding response latencies were shorter than at baseline (F(1,8) = 5.8, P = 0.043), with again some evidence of a session x location interaction (F(4,32) = 2.0, P =0.12; Figure 2.2d), driven by session differences at the S- location (F(1,8) = 4.7, P = 0.061). Figure 2.2. Least-square mean latency (± SE) of calves (n=9) to reach locations associated with different probabilities of reward and punishment.   Latencies were collected for the 5 locations (S+: 100%/0%; nS+: 75%/25%; M: 50%/50%; nS-: 25%/75%; S-: 0%/100%) before (a), and 6 (b), 22 (c) and 70 h (d) after hot-iron disbudding. Each location was presented 4 times in a pseudo-randomized order (i.e. 20 trials). Baseline latencies to reach each location were calculated over two consecutive days of testing (40 trials; 8 measures per location).        33   2.4 Discussion Our aim was to develop a method of assessing judgement biases that eliminates the risk that initially ambiguous intermediate cues lose ambiguity with repeated testing. Calves were trained to associate each location with a different probability of reward/punishment, such that the latency to approach these intermediate locations declined in relation to reward probability (and increased in relation to the likelihood of punishment).  Although the increase was clear for the nS- and S- locations, it was less clear for the most rewarded locations. Calves were statistically slower to touch the M location compared to the S+, but the time difference was small, and there was no difference in approach latency for the nS+ and S+ stimuli. Given that reward probability varied linearly across the 5 locations, one might expect       34 that the responses should have also increased in a linear manner. However, according to the Expected Utility theory, decisions are based on how the reward and punishment are valued by the animal, and by the interaction between values and outcome probabilities (Loewenstein et al., 2008). In this study, we controlled for outcome probability but not for the value of the reinforcers. Our finding of low latencies to touch the most rewarded locations may be explained by differences in the value of reinforcers, with the reward being more attractive than the punisher was aversive.  We intentionally varied rewards/punishers in relation to the generalization gradient to facilitate training, making it impossible to distinguish learned responses to intermediate reward probabilities from a stimulus generalization function. Curvilinear functions are common in judgment bias tests, probably because the inherent value attributed to the reinforcers by the animals is usually unknown (Barnard et al., 2018). The curvilinear response in the current study suggests that the extensive training with the intermediates changed the animals’ perception of the reinforcers, most likely by decreasing the aversive nature of the punisher.  Although not systematically recorded, we noted different behaviors when calves approached the different locations. For instance, calves almost always only touched the nS- bottle with their nose, but directly latched onto the nipple of the nS+ bottle. The expression of micro-behaviors provides fertile ground for predictions based upon the different expectations calves had when approaching different locations; these predictions should be made explicit and tested in future research (Weary et al., 2017). The current experiment was designed to detect changes in mood-based decision-making. After disbudding (when animals were likely in pain), calves responded more negatively (i.e. with longer approach latencies) to positive (S+) and the two closest intermediate locations (i.e. nS+ and       35 M). This response biased was only found 6 h after disbudding when the inflammatory pain is thought to be most intense (Stafford and Mellor, 2011; Mintline et al., 2013). Pessimistic responses are usually detected at intermediate locations, but we observed a bias that extended beyond the intermediate locations to include the S+ location. This response is consistent with some earlier work using traditional JBT (Harding et al., 2004; Novak et al., 2016). Reduced responding to the S+ may be driven by reduced motivation to access the reward (i.e. a decrease in the reward value), referred to as anhedonia and characterized by motivational and consummatory deficits in the consumption of specific resources (Treadway and Zald, 2011). Anhedonia is usually expressed when in a negative emotional state such as depression (Rizvi et al., 2016) or pain (Yalcin et al., 2014). Our results suggest that pain associated with disbudding induced mood changes that either triggered a loss in motivation (i.e. anhedonia) or lowered calves’ expectations of being rewarded (i.e. pessimism). Previous work in calves found a pessimistic bias to ambiguous probes after disbudding (especially to intermediate and near-negative cues; Neave et al., 2013; Daros et al., 2014), with no change in responding to the S+; this previous research used a different design (a color discrimination task) and a different response measure (go/no-go frequency) than that used in the current study. Latency measures are preferable when the number of observations is too low to accurately estimate the percentage of go responses, and some have argued that latency is a more sensitive indicator of motivation (Bateson and Nettle, 2015). Most importantly, the reduced responding to intermediate cues in previous work may have been due to calves learning that these cues were unrewarded, although this would not explain why the bias was focused at only one end of the generalization curve (Neave et al., 2013; Daros et al., 2014). It is important to note that previous work did not continue to test calves after the pain was expected to dissipate; in contrast, the current study shows that the negative bias disappeared when calves were       36 re-tested at 22 and 70 h after disbudding. Whether hot-iron disbudding induces anhedonia needs to be confirmed. An alternate hypothesis is that calves were slower because the procedure affected their locomotion. In addition, as milk is the main component of the calves’ diet, the reduced motivation to go to most rewarded locations could be due to appetite loss (i.e. anorexia), another behavioural change associated with negative mood and depressive-like states (Maes et al., 2012). At 70 h after disbudding responses again differed from baseline. At this time calves showed a reduced latency to approach the S- location relative to baseline tests; this result can be interpreted as an optimistic response bias, perhaps associated with a positive contrast effect driven by calves no longer experiencing the inflammatory pain (Boissy et al., 2007). However, a positive contrast would be expected to cause a positive response bias also at other locations, including the nS- location. Calves were not tested between 22 and 70 h; this period between tests may have increased the animal’s interest in the task.   2.5 Conclusions We developed a probability-based judgment bias task for animals that reduces the risk that responses to intermediate cues are confounded with loss of ambiguity. Animals were trained to discriminate among locations associated with different probabilities of reward and punishment. Calves showed increased latencies to touch highly rewarded locations 6 h after disbudding, suggesting that they suffered from pain-induced pessimism and/or anhedonia. These results suggest that calves experience depression-like symptoms on the hours after disbudding.         37 Chapter 3: Regrouping induces anhedonia-like responses in dairy heifers A version of this chapter has been submitted for publication: Lecorps. B, Weary. D and von Keyserlingk. MAG. Short communication: Regrouping induces anhedonia-like responses in dairy heifers. Journal of Dairy Science Communications. (in review). Graphical abstract   3.1 Introduction  Given that cattle are gregarious and motivated for social contact (Holm et al., 2002), group-housing is assumed to provide benefits (Rault, 2012). However, in some circumstances, the social environment can be the source of acute or chronic social stress (Beery and Kaufer, 2015). Some routine practices such as social mixing (also termed regrouping or co-mingling) may have negative effects lasting for hours to days (Arey and Edwards, 1998; Patt et al., 2012). When regrouped dairy cattle typically engage in more aggressive behaviors directed towards the new member of the group (von Keyserlingk et al., 2008).  Although, regrouping has been shown to cause negative physiological (Veissier et al., 2001) and behavioral effects in dairy cattle (von Keyserlingk et al., 2008; Nogues et al., 2020),       38 little is known regarding the effects of this routine practice on cattle’s affective states. Stressors originating from the social environment may trigger negative affective states (Beery and Kaufer, 2015) and are typically used as chronic stressors to induce depressive-like states in laboratory animals (Wang et al., 2017).  There has been a recent increase of interest in developing methodologies to assess affective states and mood changes in dairy cattle (see review by Ede et al., 2019e), including anhedonia that has been recently suggested following a painful procedure in calves (Lecorps et al., 2019b; Chapter 2). Anhedonia is defined as motivational and consummatory deficits towards pleasurable experiences (Treadway and Zald, 2011) and is typically associated with negative mood in humans and non-human animals (Rygula et al., 2005; Figueroa et al., 2015; Scheggi et al., 2018). In this study, our aim was to explore whether 6-month old juvenile dairy heifers would display anhedonia in the hours and days after regrouping.  Cattle are motivated to use grooming devices (mechanical brushes; (McConnachie et al., 2018), suggesting that their use is rewarding. Thus, we first explored whether heifers’ motivation to use a mechanical brush would be reduced after regrouping with unfamiliar conspecifics in an unfamiliar environment. We predicted that heifers should experience anhedonia on the day of regrouping (i.e. acute response) but would return to baseline values on the days following, when behavioral changes associated with regrouping typically wane (von Keyserlingk et al., 2008). A second objective was to explore whether the individual change in brush use was related to animals’ experiences during regrouping. Considering the negative effects of social defeat, we predicted that heifers subjected to more agonistic interactions would suffer from higher anhedonia. In addition, we also expected that hunger and behavioral fatigue may have negative effects on heifers. We predicted that animals that were less synchronized during feeding (feeding when feed was low in       39 quality and also low in quantity) and rested for shorter durations would show higher changes in brush use (i.e. higher anhedonia).   3.2 Materials and Methods  The study was approved by the University of British Columbia’s Animal Care Committee (#A19-0128). We enrolled 16 Holstein heifers in this study (mean ± SD: 183.2 ± 19.2 d of age at the time of regrouping) that had been bred and raised at the UBC Dairy Education and Research Center. Before regrouping, heifers were housed in two stable groups of 8 animals in a pen consisting of a sawdust-bedded open pack (approx. 56 m2), fitted with a feed barrier with 13 feeding spaces. The regrouping pens consisted of two sand-bedded freestall pens (approx. 65 m2) equipped with 13 stalls (1.44 m2 per stall) and 16 headlocks. Heifers were fed a total mixed ration (TMR) and provided water ad libitum.  A total of four different host groups were used for regrouping. Every week, two heifers were regrouped (one in each of two host groups) for 56 h before being brought back to their initial group. Regrouping involved a change in both the social and the physical environment; changes in the social environment involved regrouping with 12 unfamiliar heifers (mean ± SD) 259.3 days ± 28.4d old and changes in the physical environment involved moving from a bedded pack to freestalls and a change in the type of feed barrier. The host groups had been formed at least two weeks prior to the first regrouping event. Regrouping occurred at 8am before feed delivery.   3.2.1 Anhedonia testing Heifers were individually tested for anhedonia using a mechanical brush (mini swinging brush MSB, DeLaval, Sweden). Briefly, animals were first habituated to the testing arena (sawdust       40 bedded open-pack identical to the home pen described above) as a group (i.e. 3 sessions of 1 hour per day per group). Then, heifers were brought in pairs to the testing arena every 2 d for 10min until each heifer used the brush for more than 10s in each of two consecutive sessions. The animals were then brought individually to the testing arena every 2 d for 10min and the total time spent brushing was collected. Brush tests were always done at approx. 1600h. Baseline measures were taken 6, 4, and 2 d before regrouping. Animals were tested 8 h and 56 h after regrouping. After the second test, heifers were brought back to their home-pen and tested again 2 and 4 days afterwards (Figure 1). Figure 3.1. Timeline of the experimental procedure.  Calves (n = 16) were individually tested on the brush test before (3 times), during (2 times) and after regrouping (2 times). Tests were always separated by 48 h and took place at 1600 h. Regrouping took place at 0800 h with the first and second test taking place 8 h and 56 h afterwards, respectively. Baseline was calculated using the average brush use of the three pre-regrouping tests. Brush tests always occurred in the same arena and away from the other pens.    3.2.2 Behavioural measures During the 8 first and 8 last hours of regrouping, agonistic behaviors initiated or received by the focal heifer were continuously recorded (WV-CW504SP, Panasonic, Osaka, Japan). 8hTime after regrouping56h2 daysBaseline Post-regroupingHome-penRegrouping penAnhedonia test0 +2-2-4-6 +4 +6      41 Behaviors were collected according to Nogues et al. (2020). Briefly, these included displacements, replacements, avoidances and fights (Table 1). Observers were blinded for individual differences in brush use and inter-observer reliability scores were calculated using the intra-class correlation test in R (package irr) using a subset of videos watched by two observers; agreement between observers on all measures is provided in Table 1. The total number of agonistic interactions received was calculated by summing displacements, replacements and threats. Fights were not included because of the bidirectional nature of this behaviour, rendering impossible to know who initiated the interaction. We also collected the time spent resting, using instantaneous scan sampling every 5 min. To assess feeding synchronicity, we counted the number of host heifers also feeding at each 5 min scan where the focal heifer was at the feedbunk. A synchronicity score was then calculated by averaging the number of host heifers that were feeding at the same time than the focal heifer; lower scores indicate that the focal heifer went feeding when the feed bunk was not occupied by many host heifers (avoid feeding peaks).  Table 3.1. Description of agonistic behaviours observed during regrouping and inter-observer reliability scores. Behaviour Description ICC (3, f, consistency) (df) F 95% CL Displacement Push away another individual using head against another part of the body 0.99 (47,48) 138 0.975 - 0.992 Replacement The heifer initiating the displacement also replaces the individual at the feeder or at the stall 0.80 (31,32) 9.05 0.633 - 0.897 Fight Reciprocal head to head contact lasting more than 5 seconds 0.84 (7,8) 11.1 0.421 - 0.964 Threat Movement initiated presenting the forehead in direction of another heifer and resulting in the latter avoiding contact 0.80 (15,16) 11 0.597 - 0.938        42 3.2.3  Statistical analysis Each focal heifer was tested individually and thus considered the statistical unit. Power analyses were run using the function ‘pwr’ in R, other statistical analyses were performed in SAS (v. 9.4; SAS Inst. Inc., Cary, NC, USA). Sample size of 15 individuals was recommended for power set at 0.8, significance level set at 0.05 and a Cohen’s d equal to 0.8. Of the 16 calves tested, one was excluded since she failed to use the brush during the 3 baseline tests and another one was injured during regrouping. The heifer was given medication and immediately returned to her initial group. We first explored whether heifers used the brush less (compared to baseline) 8 h and 56 h after regrouping using paired two-sided t-tests. Data were checked for normality of the differences.  We then explored whether the individual variation in the change in brush use could be explained by agonistic behaviors received and behaviors expressed by heifers during the 8 h preceding the two brush tests. Two mixed linear models were built (using the PROC MIXED procedure in SAS) using the change in brush use 8 h and 56 h after regrouping as response variables. In both cases, the number of agonistic behaviors received, the time spent resting and synchronization to feed on the 8 hours preceding the brush tests (model 1: from 0 to 8h; model 2: from 48 to 56 h) were added as fixed effects. To control for the variation originating from the host groups, host-group identity was included as a random effect in both models. Models were graphically checked for the normality of residuals and the presence of outliers.  3.3. Results Heifers used the brush on average (mean ± SD) 194.8 ± 110 s on baseline days. Animals reduced their use of the brush by (mean ± SD change) 43.5 ± 26.7% (Figure 2) 8 hours after       43 regrouping was initiated (t14 = 5.44, CI = - 96.18 to - 41.8, P < 0.001). No change (with respect to baseline) was observed 56 h after regrouping (P > 0.05) indicating that overall, animals returned to pre-regrouping values. Figure 3.2. Change in brush use after regrouping and return to the home-pen.  Calves (n = 15) were individually tested on the brush test before (3 times), during (2 times) and after regrouping (2 times). Baseline was calculated using the three pre-regrouping tests when animals were housed in their home-pen. Percentage change was calculated for each of the post-regrouping time-points. Test on Day 0 was performed 8 h after regrouping and test on Day 2 was performed 56 hours after regrouping. Tests on day 4 and 6 were performed 48 and 96 h after the return to the home-pen, respectively. Tests were always performed two days apart at 1600 h. Brush tests always occurred in the same dedicated arena, away from the other pens. Boxes represent the interquartile ranges with median change. Each dot is an individual point.   We observed great variation in the frequency of agonistic interactions heifers received during the two 8 h-periods of video watching (Range: Day 1: 51 to 235; Day 3: 12 to 84). Heifers       44 also varied greatly in their behavioral response. Synchronization at the feeder ranged from (Day 1: 2.8 to 8.0; Day 3: 3.3 to 8.5) and time spent resting ranged from (Day 1: 0 to 35.7 %; Day 3: 8.3 to 45.8 %). However, the change (in percentage with respect to pre-regrouping values) in use of the brush 8 h and 56 h after regrouping was not related to any of the other behaviors collected during the 8 h preceding both tests (all P > 0.05).  3.4 Discussion These results indicate that heifers showed evidence of anhedonia 8 h after regrouping but the effect waned over time (no effect 56 h after regrouping) a result consistent with previous results obtained by our group showing a reduced interest in pleasurable resources such as milk after hot-iron disbudding (Lecorps et al., 2019b; Chapter 2). Hence, this study confirms the utility of anhedonia testing to explore long-lasting negative affective states originating from routine farm procedures in dairy cattle (Ede et al., 2019d). The use of a mechanical brush as a pleasant experience that is modulated by current mood states of dairy cattle is particularly promising and appears to be a suitable alternative to sweet solution widely used in other species (Scheggi et al., 2018) but that may not be biologically-relevant for weaned cattle. We encourage future studies to further explore this option. Pharmacological manipulations may be especially useful to confirm the sensitivity of the test to detect long-lasting negative affective states.  Our results also confirm that regrouping is a stressful experience and triggers changes in mood in dairy heifers. Social defeats induced via chronic exposure to the resident-intruder test (where rats experience social defeats when introduced to a new and occupied environment; Rygula et al., 2005) or via weeks of various social stressors (e.g. unpredictable phases of isolation and crowding; Herzog et al., 2009) were found to trigger an anhedonia-like response. However, in our       45 study the stress-induced anhedonia was no longer detectable 56 h after regrouping. This result is consistent with previous studies showing that the negative effects associated with regrouping wane with time; neither agonistic interactions nor milk production were different from pre-regrouping values 3 days after regrouping in adult dairy cows (von Keyserlingk et al., 2008). However, our results also indicate that some animals maintained a low brush use when tested 56 h after regrouping suggesting that some animals may still experience negative mood at this time. We expected that animals who 1) experienced more frequent agonistic interactions, 2) were less synchronized at the feeder or 3) spent less time resting in the new freestall environment to show higher reduction in brush use. However, none of these hypotheses were supported by our results. Although, these behaviors are most often used as outcome measures in studies evaluating the effects of regrouping, no evidence to date link changes in these behaviors with how negatively regrouping is perceived by cattle.  The absence of a link between agonistic behaviors received and anhedonia is particularly surprising. There is an abundant literature exploring the negative affective states following social defeats in laboratory rodents (Rygula et al., 2005; Herzog et al., 2009; Papciak et al., 2013). However, some evidence indicate that persistent anhedonia may be triggered by social defeats only after long-term chronic exposure (Yu et al., 2011) and not after a single episode (Razzoli et al., 2011). Arguably, heifers face many social defeat episodes when regrouped (up to 235 over the first 8 h in the current study), but frequencies may fail to capture the intensity of these agonistic interactions. The variation in mood change after regrouping may instead be related to the perceived loss in social contact with familiar conspecifics. Previous work showed that calves form preferential social interactions (Raussi et al., 2010; Lecorps et al., 2019a), especially when they are raised in a       46 stable group for a long-time (Bolt et al., 2017), which was the case in our study. To the best of our knowledge whether the negative effects associated with regrouping are due to the loss of a specific social companion have yet to be explored.  The change in physical environment may also be responsible for the change in mood. Some evidence suggest that being regrouped in a new environment is more detrimental than being regrouped in the home-pen (Schirmann et al., 2011). In addition, cattle typically need some time to get used to freestalls (von Keyserlingk et al., 2011) and the most noticeable change in behavior is a decrease in resting time. Here, we expected that low resting time would negatively affect heifers’ mood but this prediction was not supported by our results. The individual variation observed may also be affected by differences in personality traits or dominance status. Recent evidence suggest that cattle vary in sociability (Gibbons et al., 2010) and aggressiveness (Gibbons et al., 2009), two traits that may affect their response to social confrontations that arise during regrouping. In addition, dominance status modulates the response to social confrontations (e.g. pigs: Otten et al., 1999), with results suggesting that higher loss in social status may be accompanied by higher negative affective states (pigs: Otten et al., 2002). We encourage future studies to explore whether personality traits and social status interact with the affective response to regrouping in cattle.  3.5 Conclusions Heifers experiencing regrouping displayed signs of anhedonia 8 h but not 56 h after the procedure. These results suggest that routine procedures such as regrouping may lead to negative mood that are relatively short-lived. No evidence suggest that this response was affected by agonistic behaviors received during regrouping.       47        48 Chapter 4: Evidence of post-partum anhedonia in dairy cows A version of this chapter has been submitted for publication: Lecorps. B, Welk. A, Weary. D and von Keyserlingk. MAG. Evidence of post-partum anhedonia in dairy cows. Royal Society Open Science. (in review).  4.1 Introduction Exposure to severe or chronic stressors can produce long-lasting negative affective states in humans (Anisman, 2015) and other animals (Harding et al., 2004; Shoji and Mizoguchi, 2010; Destrez et al., 2013), potentially leading to mood disorders such as depression (Slattery and Cryan, 2017). For instance, chronic stressors lead to responses consistent with depression in laboratory animals (Slattery and Cryan, 2017), including anxiety (Wang et al., 2017), cognitive bias (Papciak et al., 2013), learned helplessness (Chourbaji et al., 2005; Wang et al., 2017), and anhedonia (Rygula et al., 2005; Antoniuk et al., 2019).  Anhedonia, defined as “deficits in the hedonic response to rewards (“consummatory anhedonia”) and a diminished motivation to pursue them (“motivational anhedonia”)” (Treadway and Zald, 2011), is typically associated with the experience of chronic stressors (Rizvi et al., 2016) and is one of the most studied behavioural changes associated with mood disorders in animal models (Scheggi et al., 2018; Antoniuk et al., 2019). Recent work on farm animals provide some evidence of anhedonia. For instance, pain due to hot-iron disbudding in dairy calves was associated with reduced time spent playing (Rushen and de Passillé, 2012), and decreased motivation for milk (Lecorps et al., 2019b). Similarly, pigs reduced their consumption of a sweet solution when subjected to restraint and social stress (Figueroa et al., 2015). Taken together, these results suggest       49 that exploring how motivated farm animals are for pleasurable activities may be a promising method for detecting changes in mood (Ede et al., 2019d). Most work on dairy cattle during the post-partum period has focused on health issues (Leblanc, 2010). However, parturition in cattle is also associated with stressors including pain (Mainau and Manteca, 2011), unstable social environments (Cook and Nordlund, 2004), and separation from the newborn calf (Meagher et al., 2019). All of these stressors are likely to induce negative affective states. For instance, work in other species have shown that pain (Refsgaard et al., 2016), social stress (Herzog et al., 2009), and separation from the offspring (Alves et al., 2019) are all associated with persistent negative affective states (i.e. negative mood). In laboratory rodents, anhedonia is typically measured using a drop in consumption of, or preference for, sweet solutions (Willner et al., 1992). However, dairy cows are subjected to multiple diet changes around parturition that are likely to change their motivation for food (Franchi et al., 2019), preventing the use of a food-based reward. Here we used a novel approach based on the motivation and use of a mechanical brush, a grooming device that dairy cows are as motivated to access as fresh feed (McConnachie et al., 2018). Previous studies have shown that simply being stroked can induce positive affective states in cows (Schmied et al., 2008; Proctor and Carder, 2015), in rats (Panksepp and Burgdorf, 2003) and lambs (Coulon et al., 2015), suggesting that brushing is rewarding. In some of these studies, animals were actively approaching the human stroking them (or tickling in the case of rats), an additional sign that it was perceived as pleasurable by the animals. Thus, the aim of Experiment 1 was to explore whether cows change their use of a mechanical brush after parturition. We used younger cows that were giving birth for the first time as they are believed to be more vulnerable to stressors around calving (Parker et al., 2007; Neave et al., 2017). We predicted that cows would increase their latency to use the brush (i.e. show       50 reduced motivation) and decrease their use of the brush (i.e. reduced pleasure) post-partum, consistent with anhedonia. We also expected that cows would return to their baseline (pre-partum) levels of brush use in the weeks following parturition.  On most dairy farms, the calf is removed from the cow soon after birth. This practice is likely a primary stressor for post-partum cows (for review see Meagher et al., 2019). For instance, cows and calves vocalized and were more active after separation (Lidfors, 1996; Weary and Chua, 2000; Flower and Weary, 2001; Pérez-Torres et al., 2016), and calves separated after 6 weeks of contact displayed a negative judgement bias, indicative of persistent negative mood in the hours following separation (Daros et al., 2014). Experiment 2 aimed to explore whether separation from the calf would induce anhedonia-like responses in cows, as evidenced by a reduced interest in brushing. Cows were either separated from their calf immediately after calving (early separation treatment) or allowed 29 days of contact (late separation treatment). Cows of the late separation treatment were allowed 24 h of full contact and then separated from their calf every morning starting on the 2nd day post-partum and reunited every afternoon until day 29. We hypothesized that cows in both treatment groups would decrease their use of the brush post-partum and that the late separation treatment would show more evidence of anhedonia after the first separation on day 2 (cows did not know they would be reunited at that time) and after permanent separation on day 30.   4.2 Materials and Methods This experiment was conducted at The University of British Columbia’s (UBC) Dairy Education and Research Centre. All procedures were carried out in accordance with relevant guidelines and regulations and were approved by the UBC Animal Care Committee (AUP A15-      51 0117). No animals were subjected to an avoidable stressful procedure although the intensity of separation distress may have been greater in the late separation cows. Separations had to be carried out at the end of the experiment. To minimize the distressful effects of separation, all calves were nutritionally independent (i.e. they knew how to drink from a milk feeder). 4.2.1 Animals and housing In Experiment 1, 30 female nulliparous Holstein dairy cows (mean ± SD) 2.0 ± 0.1 years old were enrolled 42 days before parturition and followed until 42 days after parturition. In Experiment 2, 24 Holstein female dairy cows (4.3 ± 1.9 years old; parity: 3.1 ± 1.6) were enrolled (pseudo-randomly allocated in two treatment groups: late separation: n = 11, early separation: n = 13) 24 d before parturition until 30 d after parturition. Animals were always given ad libitum food and water and returned to the routine farming schedule once experiments ended. Animals’ health was continuously checked by the farm staff and the herd veterinarian. Starting about 6 weeks before calving, animals in both experiments were kept in a pen with 12 sand-bedded lying stalls and 16 feeding spaces. Animals were provided with a minimum of 1 lying stall and 60 cm of feeding space per individual. Three weeks before calving, animals were moved into a similar pen. Animals had ad libitum access to water and a diet formulated according to (Washington DC, 2001) containing 25.1% rye straw, 34% grass hay, 40.5% grass silage, and 0.5% minerals for the first 21 days of the study. Due to the increased nutritional demands in the last stages of pregnancy the diet was reformulated 3 weeks before parturition as follows; 27.3% rye straw, 47% corn silage, and 26% mash. Post-partum cows were fed 4.6% alfalfa hay, 11.25% grass hay, 45.3% corn silage, and 30% mash.         52 4.2.2 Separation from the calf and post-partum housing In Experiment 1, when imminent signs of calving were present (i.e. udder enlargement, milk letdown, relaxation of tail ligaments), cows were individually moved to a straw-bedded maternity pen. After parturition, animals were separated from their calf within (mean ± SD) 1.7 ± 1.8 hours of birth. Cows were taken to the milking parlor approximately 6 ± 3.7 h after calving. After milking they were moved to a new pen of lactating animals (48 lying stalls and 48 feed-bunk spaces). In Experiment 2, when signs of calving were present (as described above), cows were moved to a maternity pen that consisted of an alley and a sawdust-bedded pack. At calving, animals were separated as described above (early separation treatment) or allowed extended contact, including suckling (late separation treatment). The latter group had full contact with the calf for approximately 24 h after birth (except during milking). Cow and calf were then separated during the day and allowed contact during the night. Cows were kept in the maternity pen for approximately 24 h after calving, regardless of treatment, and then moved to the post-partum pen including 12 lying stall and 6 feed bins. Every day, at 1830 h, when cows returned from milking, early separated cows returned directly to this pen and late separated cows entered an adjacent pen where they were reunited with their calves until 0630 h the following day. This schedule continued until day 29 when late separated cows were permanently separated from their calves.  4.2.3 Anhedonia testing In Experiment 1, all animals were naïve to mechanical brushes at the outset of the study. Cows were given opportunities to access a mechanical brush located in a familiar alleyway (15m2 x 3m2) at regular intervals. The alleyway was chosen so that it was not associated with potential       53 meaning such as going to the milking parlor. Cows had no access to a brush in their home-pen. At testing times, cows were gently handled in direction of the familiar alley. Habituation to the mechanical brush began 6 weeks before parturition and consisted of familiarizing animals with the brush and the alleyway on a daily basis. Individual testing started on week 4 before parturition and continued every 7 days until calving, the last test being done on average 6 ± 3.2 days before parturition (day varied due to unpredictable calving dates). Cows were then tested at 2, 7, 14, 21, and 42 days after parturition for 10 min. Latency to use the brush and duration of brushing were recorded. Brush tests were always performed between 1300 and 1500 h. In Experiment 2, habituation to the brush was as described for Experiment 1, but cows were tested for 10 min every 6 days beginning 24 days before calving and on day 2, 6, 12, 18, 24, and 30 after calving. Latency to access the mechanical brush and duration of brushing were measured during each test. Brush tests were always performed between 1300 and 1500 h.  4.2.4 Statistical analysis In all analyses, individual cows were considered the statistical unit. Power analyses were run using the function ‘pwr’ in R. Sample size of 24 individuals was recommended for power set at 0.8, significance level set at 0.05 and a Cohen’s d equal to 0.6. Statistical analyses were all made according to a priori predictions and significant interactions were explored by stratification. Experiment 1 One animal became ill and three animals failed to learn how to use the brush and thus were excluded from analyses. We explored if cows were slower to approach the brush and used the brush less 2 days after calving compared to baseline, using paired (two-sided) t-tests. Normality of the distribution of differences between measures collected on day 2 versus the baseline was       54 verified graphically. Latencies were log transformed. Cohen's d were calculated as measures of effect size. Inter-observer reliability scores were obtained for brush use. For this, a subset of 16 videos were watched by a blind observer. Results showed very good reliability (ICC (3, f, consistency) = 0.98, Cl95 = 0.95 -- 0.99). Experiment 2 Power analyses were run using the function ‘pwr’ in R. Sample size of 24 individuals was recommended for power set at 0.8, significance level set at 0.05 and a Cohen’s f equal to 0.6. In Experiment 2, one animal was removed from analyses due to health issues. On the 2nd day post-partum, we expected that cows from both groups would show an increase in latency to use the brush and a decrease in brush use, but we expected this effect to be stronger in the late separation cows. For this, we used two linear mixed models (one for each response variables: latency and brush use), exploring the effect of Day and the interaction between Day and Treatment; cow identity was added as a random effect. Latency residuals were normalized using a log10 transformation. On the 30th day (the day following permanent separation), we compared latency and brush use with respect to the previous week independently for both treatments using paired-sample Fisher-Pitman permutation tests. Cohen's d were calculated as measures of effect size.  4.3 Results 4.3.1  Experiment 1 Consistent with our predictions, cows increased their latency to use the brush on day 2 postpartum (by 39 ± 15.7 %, t1,24 = 3.12, CL95: 0.04 – 0.17, P = 0.005, d = 0.69; Fig 1a) and reduced brush use (a decline of 47 ± 5.5 %, t1,25 = - 7.43, CL95: (-)257.2 – (-)145.6, P < 0.0001, d = 1.55;       55 Fig 1b). Latency and use progressively returned to baseline over subsequent testing (on days 7, 14, 21 and 42; Fig 1).  Figure 4.1. Parturition induced anhedonia.  A) Latency to use the brush on days after calving (n = 26). B) Duration of brush use on days after calving in primiparous cows (n = 26). The baseline measures were obtained during the last brush test before calving.  4.3.2 Experiment 2 No differences were found regarding the latency to use the brush (P > 0.05; Fig 2a). Similarly, we found no effect of treatment (F(1,21) = 0.10, P = 0.76) and no interaction between       56 treatment and test day on the latency to use the brush (F(1,19) = 0.68, P = 0.42). However, and consistently with the results from Experiment 1, cows from both treatments decreased their use of the brush on day 2 (Estimate = (-) 104.5, SE = 17.5, CL95: (-)141.1 – (-)68.0, F(1,20) = 35.63, P < 0.0001, d = 3.70) and we found that cows in the late separation treatment showed a more pronounced decline in brush use (late separation: - 40.52 ± 8.51 % vs early separation: - 11.35 ± 7.77 %; day*treatment interaction: F(1,20) = 8.91, P = 0.007, d = 0.90; Fig 2b). The late separation cows were then reunited with their calf following the afternoon milking and spent the night with their calf before being again temporarily separated the following morning. This pattern of contact at night and separation during the day was continued for 29 days. Cows of the early-separation group were provided no further contact with their calf. All cows were subjected to the brush test on postpartum days 6, 12, 18, and 24, with no treatment differences detected (all Ps > 0.05; Fig 2b). On day 29, late separation cows were permanently separated (not allowed to reunite with their calf in the afternoon) and all cows were tested the following afternoon (day 30). We found no evidence of increased latency to use the brush (Z = 1.25, n = 11, P = 0.38), but we found a decline in brush use on day 30 compared to day 24 (by 29.40 ± 12.40 %; Z = 2.38, n = 11, P = 0.008, d = 1.31). In contrast, no changes were detected between day 24 and day 30 for cows in the early separation treatment (all Ps > 0.05).  Figure 4.2. Separation from calf induced anhedonia in dairy cows.  A) Latency (mean ± SE) to use the brush and B) brush use (mean ± SE) for early separated cows (i.e. permanently separated from their offspring within 2 h of birth; n = 12) and late separated cows (i.e. allowed 24 h of continuous contact after birth and then 12 h/d of contact for 29 days before permanent separation; n = 11). The last measure preceding calving was used for comparisons regarding the first separation (day 2 postpartum). Similarly, we used data from day 24 as a baseline when testing the effect of separation on day 29 (and tested on day 30). Boxes represent the period for which statistical comparisons were made to assess effects of separation.       57   4.4  Discussion Cows showed a reduced interest and use of the mechanical brush in the days after parturition, and this effect took almost 3 weeks to return to baseline (prepartum) levels after       58 calving. To our knowledge this is the first evidence of long-lasting anhedonia-like response in cows. Our results show that cows experiencing parturition for the first time were affected by the diverse stressors associated with this period. The drop in interest and use of the brush after parturition was most likely due to the cumulative effects of these stressors, similar to what is observed in chronically stressed animal models of depression and their reduced consumption and preference for sweet solutions (Rygula et al., 2005). This experiment did not aim at disentangling which of these stressors was mainly affecting cows, but depressive-like states were found in other species such as rats when subjected to pain (Thompson et al., 2018), social stress (Herzog et al., 2009), and separation from the pups (Alves et al., 2019). Reducing the myriad of stressors that cows are subjected to after calving may improve their psychological well-being and may lead to higher resilience to common infectious diseases that occur in the weeks after calving. Indeed, there is a growing body of evidence linking negative affective states with compromised immune function (Dantzer et al., 2018) and the succession of stressors happening after calving might explain why dairy cows are vulnerable to disease following parturition (Proudfoot and Habing, 2015). The reduced interest to use the brush may be explained by a drop in motivation to perform self-maintenance behaviours. Grooming behaviours change in response to stressors in laboratory animals (Rojas-Carvajal and Brenes, 2020), but not always in the direction of a reduced performance (van Erp et al., 1994). Future studies should explore other measures of self-maintenance behaviour in cattle.  In Experiment 2, most cows reduced their use of the brush after calving but our results failed to show an increase in latency similar to what was found in Experiment 1. Although       59 comparisons are difficult because of differences in methodology between the two experiments (i.e. intervals of testing, familiarity with the brush before training and alleyway location), the difference may be due to the use of primiparous cows in Experiment 1 and mostly multiparous animals in Experiment 2; multiparous cows may have been less sensitive to postpartum stressors given that they already experienced these challenges. Changes in latencies (Experiment 2) may have been undetectable in multiparous dairy cows with our current sample size; a larger sample size would have allowed us to detect more subtle differences in this and other measures. Nevertheless, motivational and consummatory deficits are both believed to be indicators of anhedonia (Treadway and Zald, 2011) and future studies should explore both aspects to broaden our understanding of the effects of stressors on reward valuation. Results from Experiment 2 indicate that separation from the calf can trigger anhedonia-like responses in the dam, and the extent of anhedonia observed is dependent, in part, on the nature of contact between the dam and her calf before separation. The magnitude of the decline was more pronounced in cows that had full contact for 24 hours than those that were separated within 2 hours. These results indicate that cows that spent more time with their calf after birth showed a stronger response to separation. Moreover, cows that were able to have contact with their calf during the nighttime hours showed a second decline in brush use after permanent separation, when tested on day 30 after calving.  Collectively these findings provide the first evidence that a cow experiences low mood after separation from her calf, an effect that was not dependent on when separation occurs within the first month. Previous work in our laboratory has documented 6-week-old calves that had nighttime contact with their mother also experience negative mood after separation (using a judgment bias test; Daros et al., 2014). This later work with earlier research on non-human       60 primates (Harlow, 1959; Parker and Maestripieri, 2011) show the negative effects of parental separation. Few studies have focused on early separation effects on the mothers. Previous work on cow response to separation have shown acute behavioural (Lidfors, 1996; Weary and Chua, 2000; Flower and Weary, 2001) and physiological responses to separation (Hopster et al., 1995; Sandem and Braastad, 2005). Mother rats repeatedly separated from their pups showed impaired memory (Sung et al., 2010; Aguggia et al., 2013), short-term and long-term behavioural deficits (Boccia et al., 2007; Maniam and Morris, 2010; Sung et al., 2010), dysregulation of the hypothalamic-pituitary-adrenal axis (Maniam and Morris, 2010), and neurobiological changes (Sung et al., 2010), consistent with a depressive phenotype (Alves et al., 2019).  To the best of our knowledge, our study is the first to provide preliminary evidence that cows experience negative mood after parturition and separation from the calf. One limitation of our study is the absence of a second measure of affect. This would have allowed for stronger inferences regarding anhedonia based upon changes in brush use. Other methods of assessing affective states in dairy cattle are available (Ede et al., 2019d), including judgment bias testing (Neave et al., 2013) and changes in cortisol secretions (Burnett et al., 2015), but these are not without limitations. In addition, pharmacological manipulations could be used to mitigate the negative affective states associated with parturition and separation from the calf.    4.5  Conclusions Dairy cattle expressed a reduced interest and use of a mechanical brush postpartum suggesting an anhedonia-like response. Separation from the calf also induced a reduced use of the       61 brush in cows that could spend time with their calf. These results are the first to suggest that dairy cows may experience negative mood postpartum.        62 Chapter 5: Pessimism and fearfulness in dairy calves A version of this chapter has been published: Lecorps., D. M. Weary and M. A. G. von Keyserlingk. 2018. Pessimism and fearfulness in dairy calves. Scientific Reports, 8:1421.  5.1 Introduction  It is now well recognized that emotional states can induce attention, memory and judgment biases (Paul et al., 2005). These affect-induced cognitive changes have been observed in many species (Mendl et al., 2009). Much interest has focused on the use of judgment biases, assessed by exposing animals to ambiguous situations. Response to ambiguity is believed to reflect pessimism or optimism, defined as an increased propensity to form negative or positive expectations, respectively (Salmeto et al., 2011).   Cognitive biases have been extensively studied in relation to animal welfare (Baciadonna and McElligott, 2015). For instance, following an experience predicted to elicit negative emotions such as fear (Destrez et al., 2012), pain (Neave et al., 2013) or chronic stress (Destrez et al., 2013), farm animals showed a negative bias associated with pessimism. In contrast, subjecting farm animals to environmental enrichment reduced pessimistic judgments, and in some cases caused positive judgment biases (Burman et al., 2011; Destrez et al., 2014).   Judgment bias is most often considered as a state that is subjected to change according to the mood of the animal, a consequence of the accumulation of shorter-term emotional responses (Mendl et al., 2010a), but there is a growing body of evidence suggesting that animals express stable individual differences in this response (Faustino et al., 2015). Pessimism and optimism are considered traits in humans (Kam and Meyer, 2012), but to our knowledge the consistency of individual differences in pessimism has received little attention in non-human animals other than       63 rats (Rygula et al., 2013). More pessimistic rats were found to express higher vulnerability to stress-induced anhedonia (Rygula et al., 2013), to be more sensitive to negative feedbacks (Rygula and Popik, 2016), and to express an inflammatory immune profile compared to optimistic animals (Curzytek et al., 2017), suggesting that trait pessimism might favor the development of depressive states (see Chapter 1). Recently, a study on dolphins has shown that individuals differ consistently over time in pessimism and that more pessimistic animals show fewer affiliative behaviors (Clegg et al., 2017). Although pessimism and optimism in humans are related to some of the ‘big five’ personality traits (Nettle and Penke, 2010) such as neuroticism and extraversion (Marshall et al., 1992), to date only one study has linked stable individual characteristics to differences in judgment bias in non-human animals, in this case work on pigs (Asher et al., 2016).   Different terms are used to describe individual variation in animals, including personality traits (Gosling and John, 1999; Bell et al., 2009), temperament (Réale et al., 2007), behavioral syndrome (Sih et al., 2004) and coping styles (Koolhaas et al., 1999). In the current paper, we use the term trait, but regardless, the underlying idea is that behaviors show consistency over time and across contexts. In the case of farm animals, much of the initial work focused on differences in fearfulness or emotional reactivity (van Reenen et al., 2002, 2004; Lansade et al., 2008a) defined as a “general susceptibility to react to a variety of potentially threatening situations” (Boissy and Bouissou, 1995). This work often addressed how fearfulness affects animal welfare (Koolhaas and van Reenen, 2016) and productivity (Hemsworth et al., 2002; Müller and von Keyserlingk, 2006). Recent studies have found consistent differences in other traits such as sociability (Lansade et al., 2008b; Gibbons et al., 2010) and aggressiveness (Gibbons et al., 2009), but few studies have examined how individual characteristics may explain the variation in cognitive processes such as judgment biases.       64   The aims of this study were to explore whether dairy calves: express consistent differences in the way they perceive and react to ambiguous situations (i.e. judgment bias) and whether these differences are associated with more conventionally assessed personality traits. We predicted that calves would consistently differ in the way they reacted to ambiguous situations and that more fearful animals would make more pessimistic judgments.  5.2 Materials and Methods  The study was approved by the University of British Columbia’s Animal Care Committee (# A15-0117) and cared for according to the guidelines outlined by the Canadian Council of Animal Care (Council and Care, 2009).   5.2.1 Animals   Twenty-two females Holstein calves were enrolled. Within 6 h of birth calves (BW 37.8 ± 4.4 kg) were separated from their dam and fed at least 4L of > 50 g/L of IgG colostrum. Calves were kept in individual pens until 5 d of age. On d 6 calves were moved to a 35m2 group pen (n = 9 ± 1 calves/group). Calves had access to 12 L/d of whole pasteurized milk and ad libitum access to water, hay and grain. Fresh sawdust was added weekly to the group pen. Animals were kept with the same pen-mates for the entire experiment. 5.2.2 General procedure  The spatial learning task required for the Judgment Bias Test was completed first (described below). Training required approximately 12 d. Calves were tested for two consecutive days starting at d 25 and again at d 50.   Personality traits were characterized using four tests applied once per day starting at d 30 (Session 1) and then repeated on d 53 (Session 2). The order of the tests was: Open field (OF),       65 Novel Object Test (NOT), Human’s Reactivity test (HRT), and lastly, the Social Motivation test (SMT) which was divided into two parts: 1) a social isolation and, 2) a runway test. 5.2.3 Judgment bias test  We used a spatial learning task adapted from Destrez et al. (2012). Groups of 2 to 3 calves were familiarized with the apparatus (see Figure 5.1) for 10 min 1 d before the training phase began but all animals were trained alone. Five consecutive training trials took place each day. During this first 3 d of training, animals were only trained to associate one side of the apparatus (alternately assigned to calves) with a reward (i.e. milk). All animals successfully found the milk holder in less than 30 s by the third day of training. Training then introduced negative events: an unrewarded (empty bottle) placed on the opposite side from where the calves had previously received a reward. To facilitate training of the negative event animals that touched the empty bottle received an air-puff in the face as a mild punishment (Destrez et al., 2012, 2013). Positive and negative events occurred alternatively during the 5 daily training trials. The last trial consisted of only rewarded events to avoid any negative association with the testing area. On each entry into the testing area, calves were allowed 30 s to approach and touch the milk holder before the response was deemed a ‘no-go’. Latencies to touch the bottles were measured and no-go responses were allocated the maximum latency (i.e. 30 s) For calves to be considered as trained they had to approach all positive milk holders (‘go’ response) and never approach the negative milk holder (‘no-go’ response) 10 consecutive times (i.e. 5 trials per day for 2 consecutive days). All animals were trained for 10 d, regardless of when they reached the learning criteria.         66 Figure 5.1. Apparatus designed for training and testing judgment bias.  In this example, the rewarded (milk) side was on the left and the punisher (absence of milk + blower) on the right, but training locations were assigned alternately to calves. Once calves met the learning criteria for distinguishing between the rewarded and punished locations, they were presented with ambiguous locations (from left to right: NR: Near Reward, M: Middle, NP: Near Punishment) that were neither rewarded nor punished.     Once calves were trained they were tested by placing an empty milk bottle at one of three intermediate (ambiguous) locations: near positive (0.75m from the positive location), middle (located directly half way (1.5m) from the positive and negative locations) and near negative (0.75m from the negative location); similarly to Chapter 2. On day 1 of testing, animals were exposed first to the positive location (rewarded with milk) and then to the negative one (punished with the blower). Animals were then exposed to ambiguous locations one at a time, beginning with the near positive, followed by the near negative and finishing with the middle location. On day 2 of testing the order was changed to Negative, Reward, Middle, Near negative and Near positive.       67 To ensure that calves remembered the task, we provided two additional days of training on the days before the second test session.  5.2.4 Personality tests  Except the social motivation test, all tests took place in the same experimental area (a 25m2 test pen) and in all cases animals were tested individually. Animals were gently directed inside the test pen. The 10-min test began once the gate was closed. Behaviors were recorded using a camera (WV-CW504SP, Panasonic, Osaka, Japan) positioned 7 m above the pen. Ethovision XT 10 (Noldus, Netherlands) and BORIS (Friard et al., 2016), version 2.05 (www. were used for the behavioral analysis.  For the open field test animals entered the experimental pen that was empty and unfamiliar (Forkman et al., 2007). Distance covered within the pen, time spent exploring and number of vocalizations were recorded. The novel object test involved placing a novel object (a black empty 50 L plastic bucket) in the center of the experimental pen. Latencies to contact the object, duration of contacts, time spent in proximity of the object, total distance covered and number of vocalizations were recorded. The human reactivity test involved an unfamiliar human standing immobile in the middle of the experimental pen. We recorded the latency to contact the human, duration of contacts, time spent at proximity, distance covered and number of vocalizations (Waiblinger et al., 2006; Forkman et al., 2007). The social motivation test is a variant of the runway test used to assess social motivation of dairy cows (Gibbons et al., 2010). This test was done in a familiar alley (25 m x 5 m) that was divided into two distinct spaces (i.e. a holding area and the alleyway) separated by a fence that did not prevent visual contact. For this test, the entire group of calves was moved to the holding pen and given 10 minutes to habituate. Then one calf was randomly selected and gently moved to the other end of the alleyway where she was placed in a       68 start box (150 cm x 50 cm) for 5 min. Visual contact with the herd was maintained for the duration of the test. The number of vocalizations was recorded for 5 min, after which the calf was allowed to leave the start box. The time taken to return to within 5 m of the gate separating the rest of the group was measured. 5.2.5 Statistical analysis  All statistical analyses were made using R version 3.2.1. Inter-observer reliability was evaluated using the intra-class-correlation coefficient (ICC (3, f) consistency, package ICC) applied to a random selection of 20 videos assessed by two observers. Only duration of exploration of the test environment and duration of contact with the object or the human were measured from video. Observers showed good agreement (duration of exploration: ICC = 0.78, CI: 0.58 – 0.98; duration of contact: ICC = 0,79, CI: 0.55 – 1,03). One animal was excluded from the study due to an extended period of illness. Judgment biases   A linear mixed-effects model (LMM) was calculated with the R package lme4 (Bates et al., 2014) and P values were extracted by Wald Chi-square tests (type III). The model included the latency to reach the bottle (as the response variable), and tested the effects of distance (in meters) from the rewarded side and session as fixed effects, as well as the interaction between distance and session. Calf was included as a random effect. Model residuals were scrutinized for outliers and normality.  To assess consistency over time, latency to reach each ambiguous location was averaged over the two days. To correct for activity differences, we subtracted the time needed to reach the rewarded bottle from the average latency to reach all ambiguous locations. Measures of latency from all three ambiguous locations were then averaged to get one overall measure of pessimism       69 by session; this was then used to perform linear regression analyses by permutation to test consistency over time (package lmPerm (Wheeler, 2010)). Personality tests.  We used averages of the same behaviors expressed in the four different personality tests for each session (e.g. vocalizations in the OF, NO, HRT and SMT were averaged to give only one value per calf per session: Average vocalizations). Only the latency to reach the herd was not averaged with other latencies (latency to contact the object and the human) as the former was used as a proxy for social motivation; the other latencies were used as proxies for fearfulness. A principal component analysis (PCA: package FactoMineR; Lê et al., 2008) that summarized correlated variables into principal components, was undertaken using the averages of behaviors expressed in Session 1. Then, to calculate individual PC coordinates for the second session, we used the function predict to perform another PCA on the same space created for Session 1. This allowed us to obtain comparable PCs for both sessions. Relationship between pessimism and personality traits.  Measures from Session 1 and 2 were averaged to give an average value for each behavior and a new PCA was performed. Measures included in the PCA were: average number of vocalizations, average distance covered, average time spent in exploration, average latency to contact the object and the human, average time spent at proximity of the object and the human, average time spent in contact with the object and the human and the average latency to reach the herd. Individual coordinates on each of the two principal components were used for further analyses.  Similarly, we averaged the measures of judgment biases arising from Session 1 and 2 (average of latencies to reach all ambiguous locations) to get one individual measure of judgment       70 bias per calf.   The relationship between personality dimensions (individual coordinates in the two components of the PCA) and individual levels of pessimism (mean value of both sessions) was assessed using permutation tests for regression.  5.3 Results 5.3.1  Judgment biases: Response to ambiguous locations  As expected, the latency to reach the ambiguous bottles was affected by the distance from the reward (Linear regression: Chisq = 192.6, P < 0.001), with calves taking longer to approach bottles positioned further from the positive training stimuli (Figure 5.2). We found no difference in latency between sessions (Chisq = 0.78, P = 0.38). We also found no evidence of an interaction between session and distance (Chisq = 1.7, P = 0.19), suggesting that repetition did not affect the way calves responded to the different locations.  To assess consistency in judgment bias over time, we averaged the latencies to reach all ambiguous locations for each session. This average latency to reach the three ambiguous locations was consistent over time (Linear regression by permutation: R2 = 0.32 β = 0.50, P = 0.008, see Figure 5.3).         71 Figure 5.2. Latency (mean + SEM) to reach the different locations for Session 1 (average of Day 1 and 2) and 2 (average of Day 3 and 4).  Calves were trained to associate one side with a reward and the other side with a punishment. Once trained three ambiguous locations were presented between the two previously learnt locations (Near reward, Middle and Near Punishment) and latencies to reach these ambiguous locations were used to assess pessimism.            72 Figure 5.3. Consistency in the latency to approach ambiguous locations in Session 1 versus Session 2. Each point shows the average for a calf based on the latency to approach all three ambiguous locations on both test days within a session (n = 21). The measures were corrected for activity by subtracting the time taken to reach the ambiguous bottles from the time taken to reach the rewarded one.  5.3.1 Personality traits  Averages of similar behaviors expressed in the four different tests (Open Field, Novel Object, Human Reactivity, Social Motivation) were calculated and then used for further analysis. The Principal Component Analysis (PCA) performed for Session 1 revealed two main dimensions with eigenvalues ≥ 1. The first dimension explained 42.6% of the variation and the second component explained 25.6%. The first principal component was primarily explained by the average latency to contact (regression coefficient: r = 0.91), the average time spent at proximity (r = -0.94) and the average time spent in contact with the human and the novel object (r = -0.86). The second component was mainly explained by the average number of vocalizations (r = 0.82) and       73 by the latency to reach the herd during the social motivation test (r = -0.66). According to the variable loadings, we labeled the first dimension “Fearfulness” and the second “Sociability”. PCA coordinates have been calculated using the same space for Session 2 and individual PCA coordinates were used to assess the relationship across the two test sessions: fearfulness (Linear regression by permutation: R2 = 0.70, β = 0.80, P < 0.0001; Figure 5.4a) and sociability (R2 = 0. 74, β = 0.88, P < 0.0001; Figure 5.4b) were both consistent across time.  Figure 5.4. Consistency over time for A) fearfulness and B) sociability dimensions. Principal component analyses were performed using averages of similar behaviors expressed in the four tests. Individual coordinates for Session 2 were calculated using the function predict.PCA that keeps the same space created for Session 1. Individual coordinates on each component of each PCA were used (n = 21 calves). More positive values mean that calves were either more fearful (slower to make contact with the novel object and the unfamiliar human) or more sociable (high number of vocalizations and short latency to reach the herd).           74 Table 5.1. Loadings for the Principal Component Analysis performed on averages of behaviors expressed over the two sessions.  Variables Dimension 1 Dimension 2    Average_Vocalizations -0.05 0.79 Average_Latency (NO + HR) 0.87 0.18 Average_Latency to reach the herd (SMT) -0.12 -0.76 Average_Exploration 0.62 -0.63 Average_Contact -0.90 -0.22 Average_Distance -0.49 0.19 Average_Proximity -0.97 -0.08     Eigenvalue 3.14 1.72 Variance explained (%) 44.8 24.6 Cumulative variance explained (%) 44.8 69.4  5.3.2 Relationship between personality traits and judgment bias A new PCA was performed using averages of the behaviors performed during the personality tests across Session 1 and 2. Individual coordinates on the two first dimensions (with eigenvalues > 1) were used to assess the relationship with judgment biases. According to the variable loadings, we again termed these “Fearfulness” and “Sociability” (see Table 5.1). Fearfulness showed a positive relationship with the average latency to reach the ambiguous locations (Linear regression by permutation: R2 = 0.30, β = 1.27, P = 0.01; Figure 5.5), indicating that animals that made more pessimistic judgments were also the most fearful (i.e. more reluctant to make contact with the novel object and the human). There was no relationship between sociability and judgment bias (R2 = 0.05, P = 0.34).         75 Figure 5.5. Relationship between fearfulness score determined from the Principal Component Analysis (PCA) and judgment bias.  Animals considered more fearful were more reluctant to contact the novel object and the unfamiliar human and had higher positive values on the fearfulness score. The average of judgment biases was obtained by averaging latencies to reach all three ambiguous locations during Session 1 and 2 (n = 21 calves).    5.4 Discussion  The results of this study demonstrate that dairy calves are individually consistent in judgments when confronted with ambiguous situations, and that these individual differences are related to fearfulness as assessed using more conventional personality tests. Two main personality traits emerged from our analysis: fearfulness and sociability. Both were consistent over time, but we found no evidence that judgment bias was related to sociability.       76 Spatial learning tasks have been used to assess judgment bias in animals, but to the best of our knowledge this is the first study to use spatial learning to explore judgment bias in calves. Like many recent studies (Mendl et al., 2010a; Destrez et al., 2012, 2013), our results show that the latency to explore the probes increase when the distance between the probe and the rewarded side increase. Contrary to some studies (Doyle et al., 2010; Destrez et al., 2013) we found no evidences of habituation across multiple tests, but in the current study calves were exposed to each probe just twice within each session.  Our results indicate that calves differ in their baseline levels of pessimism and do so in a consistent way over a period of 3 weeks. To our knowledge, this is the first time that consistency in judgment bias over time has been demonstrated in farm animals. As recently discussed by Faustino et al. (2015), pessimism could be considered as both a state (i.e. changes according to the emotional context) and as a trait with some animals persistently judging ambiguous situations as potentially “negative” or “positive”. Judgment biases occur when humans and animals are subjected to mood changes (Paul et al., 2005). Stability in individual levels of judgment bias (referred to here as the trait Pessimism) could be explained by relative time stability in mood state.  These findings have implications for animal welfare, as more pessimistic animals may fail to seize opportunities that are commonly assumed to provide good welfare (e.g. environmental enrichment). For instance, work on rodents has shown that pessimistic rats were less motivated to gain access to a reward (Rygula and Popik, 2016). Most importantly, animals that are inherently pessimistic may struggle when faced with emotional challenges. As demonstrated in rats (Rygula et al., 2013) and as well established in humans (Scheier and Carver, 1992), trait pessimism increases vulnerability to stressful events and is thought to contribute to the development of depression. On the other hand, animals that are more optimist may suffer when situations do not       77 lead to reward. For instance, optimistic animals might be more sensitive to discrepancies in expectations and thus may be easily frustrated. This argument has been used to explain the link between optimism and loss of immunity when individuals were subjected to persistent uncontrollable situations (Segerstrom, 2005). Moreover, this variation may explain the inconsistent results found in some cognitive bias studies in animals (Wichman et al., 2012), and suggests that individual variation should be taken into account when possible.   According to Roelofs et al. (2016), an important validation of judgment bias tests in animals is the existence of correlations between judgment bias scores and personality traits. In the current study, calves that made more pessimistic judgments were also more fearful (i.e. more reluctant to contact the novel object and the unfamiliar human). Pessimistic people tend to engage in avoidance coping strategies when faced with challenging situations (Scheier and Carver, 1992) and similar results were recently obtained in pigs (Asher et al., 2016); animals showing more pessimistic judgments after environmental change were the less proactive ones (i.e. they made fewer contacts with novel objects and were more distressed by social isolation). In addition, rats that were less anxious in unconditioned fear tests (i.e. open-field and elevated plus maze tests) made more optimistic judgments (Parker, 2008), suggesting a link between emotional reactivity and judgment bias. Collectively these results suggest that individual differences in fearfulness modulate mood-related cognition in animals as is the case in humans where trait pessimism is positively associated with neuroticism and negatively to extraversion (Kam and Meyer, 2012).   To our knowledge, this is the first study to investigate individual variation in animals using averages of similar behaviors expressed in four different tests rather than a single measure arising from one test. Recent work in the human personality literature makes use of aggregated measures across context to better represent personality traits and reports high consistency over time (Fleeson,       78 2001, 2004, 2007). Animal studies investigating personality are often based on behaviors expressed in different situations but with similar causes. For instance, the different tests used in this study have been used to elicit fear responses in farm animals (Forkman et al., 2007), and tests exposed animals to different forms of social deprivation. Therefore, we expected fearfulness and sociability to be the two main traits explaining individual differences in these tests. Please note that we only aggregated behaviors that we had predicted were similarly motivated. For example, given that different underlying motivations are described with regards to the latency to reach the herd (reflecting sociability; see Gibbons et al., 2010) and the latency to contact the novel object and the human (reflecting fearfulness; see Forkman et al., 2007), we did not average these variables. Although, many previous studies have used PCA (van Reenen et al., 2013; Nawroth et al., 2017) and found consistent traits, the use of behavioral averages appears to increase the ability to capture individual differences across different contexts as suggested in the human psychology literature (Nettle and Penke, 2010). We encourage future studies to use averaged behaviors in conjunction with PCA.  Our results provide support for the idea that animal personality is multidimensional. Koolhaas and van Reenen (2016) recently argued that personality traits in animals could be described using two (fearfulness and sociability) or three (fearfulness, coping styles and sociability) dimensions. Our study confirms the existence of at least two personality dimensions in dairy calves. Our results did not show clear binomial responses generally associated with coping styles (Koolhaas et al., 1999), hence we used the term fearfulness and sociability to describe the individual traits in this study. As expected from previous studies (van Reenen et al., 2005; van der Staay et al., 2009), latency to contact the human and the novel object and time spent in proximity or in contact accounted for the greatest proportion of variation. The novel object and human       79 reactivity test have been frequently used to assess fearfulness of farm animals (Romeyer and Bouissou, 1992; Visser et al., 2001; Waiblinger et al., 2006) and usually trigger consistent behavioral and physiological response associated with fear (Visser et al., 2002; van Reenen et al., 2005). These tests have long been considered reliable measures of fearfulness in farm animals (Boissy and Bouissou, 1995; Rushen et al., 1999; Waiblinger et al., 2006; Forkman et al., 2007).   Vocalizations have been used as a measure of distress and fear in farm animals (Geverink et al., 2002; Manteuffel et al., 2004). However, our results illustrate the social influence on vocal behaviors, given that the number of vocalizations loaded on the second dimension of the PCA, along with the latency to reach the herd after isolation (considered the gold standard to assess social motivation in farm animals Gibbons et al., 2010). This result agrees with previous work indicating that number of vocal responses of dairy cattle are independent of fearfulness when observed during standardized tests (van Reenen et al., 2005). Similar results have also been reported in sheep (Ligout et al., 2011) and horses (Lansade et al., 2008b) where vocalizations were related to the expression of social behaviors. These results add to the growing evidence showing stable individual differences in sociability in farm animals (Boissy and Le Neindre, 1997; Sibbald et al., 2005; Gibbons et al., 2010).  5.5 Conclusions  Dairy calves show persistent differences in pessimism and in the more traditional personality traits of fearfulness and sociability. All three traits are consistent over time, and individual differences in pessimism are related to fearfulness.       80 Chapter 6: Dairy calves’ personality traits predict social proximity and response to an emotional challenge A version of this chapter has been published: B. Lecorps., S. Kappel., D. M. Weary and M. A. G. von Keyserlingk. 2018. Dairy calves’ personality traits predict their response to an emotional challenge. Scientific Reports, 8:16350.   6.1 Introduction Individual traits, often referred to as personality traits, are defined as behaviours or suites of behaviour that consistently vary among individuals. One recent framework describes five main traits: shyness-boldness, exploration-avoidance, activity, sociability and aggressiveness (Réale et al., 2007). Among these, the most commonly studied are based on how animals react to emotionally challenging situations or to potential threats (e.g. shyness/boldness, or in case of farm animals, fearfulness) and the way animals behave with their social partners (e.g. sociability, aggressiveness). Fearfulness can be seen as a “general susceptibility to react to a variety of potentially threatening situations” (Boissy, 1995), with bolder individuals more prone to take risks while shyer ones are considered more careful (Stankowich and Blumstein, 2005), and Sociability refers to the propensity of an individual to keep close contact with conspecifics (Réale et al., 2007). Recent work in farm animals showed stable individual differences for both traits (Gibbons et al., 2010; Koolhaas and van Reenen, 2016; Lecorps et al., 2018b). Personality traits are typically identified using standardized behavioural tests (Forkman et al., 2007), although difficulties remain in understanding behaviours expressed during these tests (Carter et al., 2013). In farm animals, assessment of personality traits has mainly relied on open       81 field, novel object, and human reactivity tests (Waiblinger et al., 2006; Forkman et al., 2007). However, few studies have addressed whether these tests predict responses in more naturalistic situations (i.e. their validity; Forkman et al., 2007; Carter et al., 2013). As argued by Perals et al. (2017), personality traits labelled through the use of standardized tests need to be validated using behaviours expressed in related contexts. Ideally, a trait predicts individual responses in related situations (i.e. shows convergent validity) but not in unrelated ones (i.e. shows discriminant validity). For instance, fearfulness should be able to predict behaviours that are expressed in fearful situations but not in others.  The aim of the current study was to assess the validity of the dairy calf personality traits described in the previous Chapter (i.e. pessimism, fearfulness and sociability). These traits were identified using responses to standardized tests (pessimism as assessed using repeated judgment bias tests; fearfulness and sociability as assessed using open field, novel object, human reactivity and social motivation tests). Similar behaviours measured in the different tests were aggregated as this generates more reliable information than single-context measures (Fleeson, 2001, 2004; Fleeson and Law, 2015). Calves were characterized at approximately 25 d and 50 d old and all three traits were individually consistent across two test sessions (Lecorps et al., 2018b; Chapter 5). Furthermore, pessimism and fearfulness were positively related, suggestive of a behavioural syndrome (Bell et al., 2009). To assess both convergent and discriminant validity, we measured individual responses in two different situations. In Experiment 1, individual proximity scores were calculated in the home-pen. We expected that more sociable animals (but not more fearful or pessimistic animals) would have higher social contact with the others. In Experiment 2, we measured the behavioural (i.e. vocalizations) and physiological (maximum eye temperature) response to transportation, a known stressor for farm animals (Grandin and Shivley, 2015). We       82 expected that fearfulness and pessimism (but not sociability) would relate to the intensity of the stress response.   6.2 Materials and Methods These experiments were conducted at the University of British Columbia’s (UBC) Dairy Education and Research Centre, located in Agassiz, British Colombia, Canada (49°N, 121°W). The animals were cared for according to the guidelines outlined by the Canadian Council of Animal Care (2009). All procedures carried out in this study were approved by the UBC Animal Ethics Committee (A15-0117). All methods were performed in accordance with the relevant guidelines and regulations.  6.2.1 Animals and housing We used 19 of the 21 animals tested in Chapter 5. Two animals, belonging to the same groups but that were not phenotyped in the previous study, were added. Four animals were not included as they were housed in another group that could not be subjected to the transportation component of the current study. Holstein calves were reared according to standard practice at the UBC Dairy Education and Research Centre. Calves were group-housed from 5 d of age. Before weaning, animals had access to 12 L of pasteurized milk as well as ad libitum access to grain, hay and water (for more details see Lecorps et al., 2018b; Chapter 5) for additional details. Calves were weaned gradually by decreasing the amount of milk calves had access to from 12 to 0 L a day over a 2-week period ending at approximately 100 d of age. At the time of testing for the current study (when calves were approximately 4 months old), animals were group housed (n = 9 ± 1 heifers per group) on an open, sawdust-bedded pack measuring 7 x 5 m. Calves had ad libitum access of grain       83 (Hi-Pro Medicated Calf Starter, Chilliwack, BC, Canada with an overall DM of 89.5%; chemical composition shown as % of DM, 90% DM; CP 21%, NDF 19%, ADF 11%), hay and water.  6.2.2  Individual traits. A full description of the assessment of pessimism, fearfulness and sociability can be found in Chapter 5. Briefly, animals were tested for judgment bias using a spatial learning task similar to that used on sheep (Destrez et al., 2012). Testing was conducted at 25 and 50 d of age. In both sessions, calves were presented with an empty bottle placed at three ambiguous locations located between the previously rewarded and punished training locations. We used the average latency to reach ambiguous locations as a measure of pessimism. Fearfulness and sociability were assessed using four standardized tests (Open field, Novel object, Human reactivity and Social motivation test), commonly used in farm animals (Forkman et al., 2007; Gibbons et al., 2010). One test was performed per day.  6.2.3 Home-pen social behaviour Social behaviours were assessed when the animals were 113 ± 8.3 and 118 ± 8.3 d of age, approximately 30 d after weaning off milk. Behaviours were recorded using two digital cameras (WV-CW504SP, Panasonic, Osaka, Japan) placed 5 m above the calves for two periods of 48 h (T1 and T2).  Calf behaviour (lying or standing) and position relative to any neighbouring calves were recorded using 5-min scan sampling for two sessions of 48 h separated by 3 d. Scans in which animals were feeding were excluded to avoid confounding proximity with competition for food.       84 To be considered neighbours, animals were required to be less than one head length apart (Duve and Jensen, 2012). Individual proximity scores were calculated as follows: Individual proximity score =  𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠 𝑤ℎ𝑖𝑙𝑒 𝑟𝑒𝑠𝑡𝑖𝑛𝑔𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑐𝑎𝑛𝑠 𝑟𝑒𝑠𝑡𝑖𝑛𝑔 +𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑠 𝑤ℎ𝑖𝑙𝑒 𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑐𝑎𝑛𝑠 𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔  6.2.4 Transportation Each calf was individually transported on two 10-min journeys undertaken on consecutive days when they were approximately 120 ± 8.3 d of age. Calves were tested in random order and one at the time. The calf was gently moved out of the group by a familiar handler and loaded onto an open trailer (140 x 120 x 110 cm). After loading, the trailer remained stationary for 1 min, was then towed by a vehicle for 5 min, was again stationary for 1 min and then again towed for 5 min. The trailer then remained stationary for an additional 1 min before unloading. Total distance was 2 km of paved road including 12 turns at an average speed of 12 km/h and never exceeding 20 km/h. Although, the configuration of the trailer prevented animals from jumping out, the vehicle was immobilized in the rare cases where animals fell inside the trailer (2 times over 38 transportations), giving them sufficient time to stand up before transportation continued.  6.2.5 Physiological and behavioural measurements Maximum eye temperature was measured with an infrared camera (T650sc, FLIR Systems USA, Boston, MA). Distance between the camera and the eye was kept constant at approximately 1 m. Measurements were taken at three time points: 1) 30 min before any kind of perturbation (Baseline), 2) just after loading in the trailer (Post-loading), and 3) at the end of transportation (Post-transportation). Video files were analysed with the FlirResearchIR software (Flir Systems USA, Boston, MA). For each picture, the ambient temperature at the time of transportation was       85 set in the software during the analysis. A minimum of 1 and a maximum of 5 images were taken for each time point. Images were analysed only if they provided a clear and focused view of the eyes. Baseline values were used to calculate changes in maximum eye temperature after loading and after transportation. In addition, as maximum eye temperature measured at the beginning of unconditioned fear tests were recently shown to predict behavioural phenotype (Lecorps et al., 2016), we also used maximum eye temperature after loading for further analyses. In addition, we counted the number of vocalizations from the moment the calf entered the trailer until it was unloaded.   6.2.6 Statistical analysis All analyses were done with R (version 3.4.2). Linear regressions were calculated using the lm function and the associated p-value was calculated via permutation (Package pgirmess; Anderson and Robinson, 2001). From the 19 animals used in the study 17 had been phenotyped for personality traits earlier in life. Consequently, when referring to personality traits, the results of this study are based on these 17 animals only. Personality traits Principle component analysis was used to define the personality traits, as described in (Lecorps et al., 2018b; Chapter 5). Social proximity score We calculated an individual proximity score for each of the two 48 h sessions. Consistency over time was assessed using linear regression. We then averaged the proximity scores over the two time-periods to get one measure per calf and assessed the relationship between this measure and each of the personality traits.       86 Emotional response to transportation When multiple pictures were available and fit the criterion (range: 1 - 5), we averaged eye temperature obtained to provide one measure of maximum eye temperature for the baseline, the post-loading and the post-transportation time-points. Changes in maximum eye temperature in relation to baseline values and maximum eye temperatures were used for the statistical analysis. The total number of vocalizations during the 10 min of transport were live-recorded. As two cohorts of calves were transported on different days with different weather conditions, we included group as an interactive term in all models to control for ambient temperature differences. We assessed consistency over time for all measures (i.e. over the two transportation challenges). For the rest of the analysis we used the averages for each measure taken during both transportations. Consistency over time, the relationship between behavioural and physiological measures, and the relationships with the different individual traits were all assessed using a linear model with P values extracted using a non-parametric permutation approach as described above.  6.3 Results 6.3.1 Experiment 1: Home-pen social behaviour Calves showed consistency in their proximity score across the two-observational periods (R2 = 0.33, P = 0.009; Figure 6.1); animals that had more neighbours in the first period also had more neighbours in the second.         87 Figure 6.1. Consistency in individual social proximity scores.  Holstein heifers (n = 19) were tested twice within 1 week at approximately 115 days of age. Proximity scores were calculated using the total number of neighbours each calf had during each 48-h session, divided by the number of scans in which the calf was either lying or standing.   The average social proximity score was positively related to the sociability trait derived from the personality assessment (R2 = 0.51, P = 0.002, Figure 6.2), with animals scoring higher on the sociability trait having higher proximity scores in their home pen.          88 Figure 6.2. Relationship between sociability and social proximity score.  Sociability was assessed using personality tests done when the animals were approximately 1 and 2 months old and social proximity scores were obtained from home-pen observations (n = 17) and calculated by averaging measures over the 4 d of observations (at approximately 115 d old). The sociability trait was derived from principal component analysis using responses to four standardized tests; higher values correspond to higher levels of sociability.    6.3.2 Experiment 2: Emotional response to transportation Calves were consistent over the two transportations in their vocal responses (R2 = 0.76, P < 0.0001; Figure 6.3a) and maximum eye temperature measured after loading (R2 = 0.76, P < 0.0001; Figure 6.3b) and after transportation (R2 = 0.64, β = 0.032, P = 0.0013). Changes in maximum eye temperature from baseline to loading and transportation were not found consistent (P > 0.05).       89 The average number of vocalizations during transport was positively related to the average maximum eye temperature after loading (R2 = 0.38, P = 0.028; Figure 6.4) but not to the maximum eye temperature after transport (P > 0.05). Similarly, vocalizations were not related to the change in maximum eye temperature for either period (P > 0.1).  Figure 6.3. Consistency in response to transportation.  Calves (n = 19) were tested across two consecutive days for a) the number of vocalizations expressed by dairy heifers during 10 min transportation events, and b) the maximum eye temperature of each animal measured after loading. Each point represents a calf.          90 Figure 6.4. Relationship between the maximum eye temperature (after loading) and the number of vocalizations during transportation. Measures were averaged over the two transport sessions completed on consecutive days. Each point represents a calf (n = 19).  The average number of vocalizations during transport was not related to fearfulness or sociability (Ps > 0.05) but was positively related to pessimism (R2 = 0.46, P = 0.014; Figure 6.5). The average post-loading change in maximum eye temperature was positively related to fearfulness (R2 = 0.56, P = 0.0022; Figure 6.6) but not to pessimism or sociability (P > 0.05).          91 Figure 6.5. Relationship between the trait pessimism and the average number of vocalizations expressed during transportation.  Each point represents a calf (n= 17).          92 Figure 6.6. Relationship between the trait fearfulness and the change in maximum eye temperature after loading into the trailer. Each point represents a calf (n = 17).   The average post-transportation changes in maximum eye temperature were not related to any of the three personality traits (P > 0.05), but average post-loading maximum eye temperature was positively related to fearfulness (R2 = 0.72, P < 0.001; Figure 6.7a) and pessimism (R2 = 0.75, P < 0.001; Figure 6.7b), but not sociability (P > 0.05).         93 Figure 6.7. Relationship between traits a) fearfulness and b) pessimism and the average maximum eye temperature measured after loading into the trailer. Each point represents a calf (n = 17).    6.4 Discussion  Poor validity is a major risk in personality studies and is associated with mislabeling and misinterpretation of behaviours expressed in personality tests (Carter et al., 2013). Personality traits should both be consistent over time (i.e. reliability), and also show good external validity. The aim of this study was to investigate whether personality traits assessed during the two first month of life were valid predictors of social behaviour in the home-pen and emotional responses to transportation, tested when the animals were about 4 months old. All three personality traits showed good convergent and discriminant validity. Sociability was positively related to social proximity scores in the home-pen but was not related to the way animals responded to transportation. Fearfulness and pessimism were predictors of the response to transportation (with       94 more fearful and pessimistic animals showing elevated signs of distress), but not of social behaviours expressed in the home-pen.  The trait sociability has been described in many species (Réale et al., 2007) and can be assessed in different ways. More sociable individuals are expected to seek the presence of conspecifics, while less sociable individuals should avoid them. Thus, sociability tests have focused on how much animals aggregate with others (Sibbald et al., 2005), react to separation (van Reenen et al., 2004; Müller and Schrader, 2005; van Reenen et al., 2005; Ligout et al., 2011) or are motivated to maintain close proximity (Gibbons et al., 2010). Some have also assessed how animals behave during a forced interaction, with more sociable animals expected to display more pro-social and less aggressive behaviours. All of these different tests give opportunities to measure convergent validity of sociability. In our previous study, sociability was defined by how animals reacted to social isolation (i.e. number of vocalizations emitted) and by their motivation to return to the herd in the runway test; responses that were found highly stable over time (Lecorps et al., 2018b; Chapter 5). In the current study, proximity scores were also found to be consistent over time, with some animals spending more time, and others less time, at close proximity with other calves. We hypothesized that more sociable animals, as defined in the personality tests, would also seek close contact with their conspecifics in the home-pen. Our results showed that more sociable animals had higher proximity scores indicating good convergent validity of the sociability trait. Similar results have been found in felids (Gartner and Weiss, 2013), sheep (Ovis aries; Ligout et al., 2011) and dogs (Canis familiaris; Svartberg, 2005) as well as in adult dairy cattle (where motivation to join the herd after isolation was positively related to the number of social neighbors and synchronicity with the group; Gibbons et al., 2010). Our results indicate both convergent and       95 discriminant validity, as sociability was related to social proximity but not to the way animals reacted to transportation.  Fearfulness and its counterpart boldness are usually measured during tests where animals are exposed to different forms of novelty (e.g. novel environment or object). Some previous reports have found fearfulness – measured using standardized behavioural tests – to be inconsistent over time and to have poor convergent or discriminant validity (Japanese Quail; Miller et al., 2006, Damselfish; Beckmann and Biro, 2013, African striped mouse; Yuen et al., 2017), but others have reported high degrees of consistency over time and also good external validity (Dogs; Svartberg et al., 2005, Vervet Monkeys; Blaszczyk, 2017, African striped mouse; Yuen et al., 2016). Our results are consistent with the latter studies as calves responded consistently when subjected to two transportation events done on consecutive days and fearfulness (assessed 2 months before the transportation event) reliably predicted the way animals responded to transportations. Indeed, fearfulness predicted the change in maximum eye temperature taken before and after loading into the trailer with more fearful animals having higher increases indicating a more intense stress response. Work on horses (Equus ferus) showed that changes in maximum eye temperature were related to behaviours expressed in a novel object test; more fearful animals showing greater changes in maximum eye temperature (Dai et al., 2015). In addition, the trait fearfulness predicted maximum eye temperature measured immediately after loading into the trailer. In mice (Mus musculus), maximum eye temperature taken at the beginning of an open-field and elevated plus maze tests predicted the amount of anxiety-related behaviours expressed during the tests (Lecorps et al., 2016). Collectively these results suggest that fearful animals react more strongly to pre-experimental handling causing higher eye temperatures at the beginning of the tests compared to their non-fearful counterparts. Contrary to our prediction, fearfulness failed to predict the number       96 of vocalizations emitted during transportation, a validated measure of distress (Manteuffel et al., 2004) and high arousal (Briefer et al., 2015). Although judgment biases are now extensively used to assess emotional states of animals, some recent studies have shown animals to be consistent in the way they judge ambiguous situations, defining pessimism as a trait (Rygula et al., 2015; Lecorps et al., 2018b; Chapter 5). In this study, the trait pessimism was positively related to maximum eye temperature after loading in the trailer and to the number of vocalizations expressed during transport. These results indicate that pessimism can predict both behavioural and physiological responses to an emotional challenge such as transportation. To our knowledge, our study is the first to investigate whether individual differences in judgment bias are related to an emotional response elicited by a challenging situation later in life (in our case more than 70 days after the last judgment bias test). These results suggest that more pessimistic animals react more intensely to the emotional challenge, maybe because they had worse expectations than other calves. These results are consistent with the human literature showing that pessimistic people react more strongly to emotional challenges (Costa and McCrae, 1980; Scheier and Carver, 1992). Collectively, our results indicate that the personality traits showed a good degree of validity and measured the targeted traits. Given that personality assessment is usually restricted to behavioural testing in animals, it is critical that traits are subjected to further validation, including testing of animals’ responses to more natural situations with clear predictions about how they should affect animal’s responses. Future studies should confirm our findings and if possible, make use of other methodologies. For instance, studies exploring how affect manipulations (using pharmacological agents) modulate behaviours expressed in fear tests are still lacking in farm animals. In addition, the validity of pessimism as a trait is still in its infancy in non-human animal       97 species and future studies should aim at understanding how it modulates an individual’s life.  We see much room for work that is based on predictions arising from the wealth of information available from the human literature. As suggested by Nettle & Penke (2010), specific personality types are likely to react to sets of situations in specific ways (Nettle and Penke, 2010). For instance, neurotic persons show a greater physiological stress response when subjected to challenges (Carver and Connor-Smith, 2010). We expect that pessimistic and fearful animals might react strongly to all emotional challenges and be more negatively affected by routine farm procedures. Recent results on laboratory rats (Rattus norvegicus) showed that more pessimistic animals are more sensitive to stress-induced anhedonia (Rygula et al., 2013), experience higher motivational loss after chronic stress (Rygula et al., 2015) and have a more compromised immune system (Curzytek et al., 2017). Taken together this evidence suggests that these animals are at higher risk of developing depressive-like states or poor psychological welfare. Future studies should evaluate whether fearfulness and pessimism can predict animal’s changes in mood after routine stressors and how these traits affect the welfare of the animals in the longer term.  6.5 Conclusions Three personality traits (pessimism, fearfulness and sociability) determined using standardized behavioural tests during the first two months of life in dairy calves showed good convergent and discriminant validity. More sociable animals had higher home-pen individual proximity scores. In contrast, more fearful and pessimistic animals showed a stronger emotional response to transportation.        98 Chapter 7: Pessimistic dairy calves are more vulnerable to pain-induced anhedonia A version of this chapter has been submitted for publication: Lecorps. B, Nogues. E, von Keyserlingk. MAG and Weary. D. Pessimistic dairy calves are more vulnerable to pain-induced anhedonia. PloS ONE. (in review).  7.1 Introduction Pain is defined as “a distressing experience associated with actual or potential tissue damage with sensory, emotional, cognitive, and social components” (Williams and Craig, 2016) and, in non-human animals, is often assessed using basic behavioral (e.g. wound-directed behaviors) and physiological responses (e.g. changes in glucocorticoid levels; Sneddon et al., 2014). These measures can be useful to assess the intensity and location of the pain, but do not allow strong inferences regarding the affective component. Pain has effects on cognition in humans, including information processing and decision-making (Simons et al., 2014). Cognitive changes include cognitive biases, defined as alterations in the perception and interpretation of situations (Paul et al., 2005), and anhedonia, defined as “deficits in the hedonic response to rewards” (Treadway and Zald, 2011). Anhedonia is one of the most studied behavioral changes associated with depression in humans and may also provide insight into pain-induced affective experiences in animals. Given its subjective nature, the experience of pain varies from one individual to another (Nielsen et al., 2009). Pain is not only a matter of afferent inputs, but rather a complex and integrated response (Coghill, 2010; Wiech and Shriver, 2018), so differences in pain sensitivity       99 may originate from any stage of pain processing, including psychological and cognitive processes (Nielsen et al., 2009). For instance in humans, higher levels of optimism are associated with increased pain tolerance (Coghill, 2010; Hanssen et al., 2014) and susceptibility to placebo (Geers et al., 2010), indicating a link between general expectations and pain perception. No studies to date have explored this relationship in animals. In fact, individual differences in response to pain have been largely ignored in non-human animals, even though a better understanding of variation in pain responses may improve the validity of animal models (Barrot, 2012) and the ability to mitigate the negative effects of painful procedures. Hot-iron disbudding is routinely performed on dairy calves, and despite recent efforts to promote the use of pain control (e.g. Canadian dairy code of practice), the procedure is mostly performed with limited or no pain control (Cozzi et al., 2015; Winder et al., 2016). Thus, the procedure provides the opportunity for researchers to study pain without imposing new harms. Hot-iron disbudding leads to the expression of wound-directed behaviors and to increased cortisol levels, responses that can be mitigated using intra-operative (i.e. general and local anesthesia) and post-operative (e.g. non-steroidal anti-inflammatory drugs) pain control (Stafford and Mellor, 2011). Previous studies showed that hot-iron disbudding is aversive (Ede et al., 2019c) and induces negative mood (i.e. calves became more pessimistic towards ambiguous cues; Neave et al., 2013). Some recent evidence suggests that the latter response may be due to a lowered motivation/pleasure associated with accessing the milk reward (i.e. an anhedonia-like response; Lecorps et al., 2019b; Chapter 2) but, no study to date specifically aimed to explore whether hot-iron disbudding affects the perception of hedonic experiences. The consumption of a sweet solution is commonly used to assess anhedonia in laboratory rodents (Anisman and Matheson, 2005). Evidence suggests that sucrose is rewarding for cattle       100 (Ginane et al., 2011), especially in calves (Hellekant et al., 1994), a phenomenon that seems well-conserved across species (invertebrates (Zhang et al., 2013), pigs (Figueroa et al., 2015), horses (Fureix et al., 2015) and rodents (Ågmo et al., 1995)). In this study we used changes in the consumption of a sweet solution (5% sucrose) to infer pain-induced anhedonia after hot-iron disbudding. We expected that calves would decrease their consumption of the sweet solution following hot-iron disbudding, indicating pain-induced anhedonia. Interest in animal personality is increasing, mostly because it provides an understanding of why individuals vary in response to a similar situation (Réale et al., 2007; Carter et al., 2013). Stable inter-individual differences in optimism have recently been described in non-human animals (Rygula et al., 2013; Clegg et al., 2017), including dairy calves (Lecorps et al., 2018b; Chapter 5). Optimism seems to modulate responses to stressors in both humans (Carver and Scheier, 2014) and non-human animals (Rygula et al., 2013; Lecorps et al., 2018a; Chapter 6). Considering that no work to date has focused on the individual response to disbudding, we tested whether more pessimistic calves are more vulnerable to pain (i.e. as evidenced by greater pain-induced anhedonia).  7.2 Materials and Methods The study was approved by The University of British Columbia’s Animal Care Committee (#A16-0310-A002). All animals were housed and disbudded as part of standard farm and industry practices. 7.2.1 Animals and housing Twenty healthy Holstein female calves (mean ± SD birth weight: 39.0 ± 4.6 kg) were housed in two groups of 10 animals (mean age range: 16.5 d) in 25 m2 pens bedded with sawdust       101 where they had access to 12 L/d of pasteurized whole milk via an automated milk feeder (one teat; CF 1000 CS Combi; DeLaval Inc., Sweden), and ad libitum access to water, hay and grain.  7.2.2  Experimental procedures Calves were tested at specific ages for judgment bias and anhedonia (see Figure 7.1). Figure 7.1. Timeline of the experimental procedure.  Calves (n = 20) were trained for the judgment bias tests and then tested over 2 days starting at 25 d of age. The latency to touch each of the 3 ambiguous locations was averaged. Calves were offered ad libitum access to a sweet solution for 6 h/d (from 1600 h to 2200 h) in their home-pen and daily intakes were measured from 40 d to 49 d of age. Disbudding occurred on d 45 at 1000 h.  7.2.3 Training and testing for judgment bias Calves were individually brought from their home pen and placed in an experimental arena composed of a start box connected to a 16m2 sawdust bedded area, similar to that described in (Lecorps et al., 2018b; Chapter 5). During training, calves learned to associate one side of the apparatus with a reward (i.e. milk) and the other side with a mild punishment (empty bottle + air       102 puff). Calves were first trained (starting at 10 d old) to associate one side with the reward over 5 trials on each of 3 consecutive days. The second step of training consisted of pseudo-random presentations of bottles on the rewarded and punished sides. Training required approximately 12 ± 2 d until calves met the learning criterion (2 consecutive days without errors). Once trained, calves were tested using three ambiguous locations (i.e. bottles placed between the rewarded and punished locations); these locations were labelled “Near positive (nS+)”, “Middle (M)” and “Near negative (nS-)”, and were positioned 0.75 m, 1.5 m and 2.25 m away from the rewarded location, respectively. Testing was carried over two days when calves were approx. 25 days old; each location was presented once per day in a pseudo-random order, always starting with one of the reinforced locations (i.e. rewarded or punished). In each trial, calves were allowed up to 30 s to touch the bottles and the latency to touch was recorded. If a calf failed to approach within 30 s it was returned to the start box and 30 s was recorded as the approach latency. We used latencies because we believe these to provide a better estimate of calves’ individual responses compared to binary go/no-go responses. Ambiguous locations were never rewarded or punished. We chose to limit calves’ exposure to ambiguous cues to prevent habituation (Roelofs et al., 2016); a previous study using the same design over 4 days did not find evidence of habituation (Lecorps et al., 2018b; Chapter 5). To ensure calves were motivated to participate to the task, access to milk ended at 22:00 on days before training and testing sessions. 7.2.4 Anhedonia Starting at least 10 d before hot-iron disbudding, calves were given ad libitum access to an unflavored sweet solution (concentration: 50 g/L sucrose providing 200 kcal/L; this concentration was found to be effective in pilot work preceding this study) for 6 h/d (between 16:00 and 22:00) in their home-pen using a second automatic feeder (RIC, Insentec B. V., Marknesse, the       103 Netherlands), allowing one calf to drink at a time. Calves had no previous experience with this feeder. Calf’s identity and intake (in kg) were automatically recorded at each visit. Daily intakes were collected 5 d before and after hot-iron disbudding (see Figure 7.1). To encourage calves to drink the sweet solution, milk allowance was reduced by 25% when calves were 40 d old based on the volume consumed during the 3 preceding days. 7.2.5 Hot-iron disbudding Calves were disbudded in their home-pen at 45 ± 0.7 d old at 10:00 h. Calves were provided 0.2 mg/kg of xylazine (SC, right rump, Rompun 20 mg/mL, Bayer, Leverkusen, Germany) as a sedative, followed by a cornual nerve block on each horn (5mL per side of 2% Lidocaine; Ayerst Veterinary Labs, Ontario) as a local anesthetic. 10 min later, calves were tested for pain responses with a needle-prick (none responded) and then disbudded using a hot-iron (X30, 1.3 cm tip, Rhinehart, Spencerville, IN, USA) positioned over the horn bud for multiple short periods (total contact time of approximately 15 s). The calf was then positioned in sternal recumbency and allowed to recover.  7.2.6 Statistical analysis A previous study showed that calves were consistent in their response to judgment bias tests using a sample size of 22 animals (Lecorps et al., 2018b; Chapter 5). Here, we used 20 animals considering that a sample size of 15 individuals was recommended for power set at 0.8, significance level set at 0.05 and a Cohen’s d equal to 0.8. Calves were considered the statistical unit. Model residuals were scrutinized for outliers and normality. In cases where the normality assumption was not met, transformations were applied as described below. Responses to ambiguous cues typically follow a generalization gradient (Roelofs et al., 2016). Thus, calves were expected to increase their latency to touch locations with increased       104 distance from the rewarded cue. We used a linear mixed model to explore the fixed effect of location on response latency, with day specified as a fixed effect and calf specified as a random effect. We used latency to touch ambiguous locations to calculate the pessimism score. Latencies to touch ambiguous locations were corrected for activity by subtracting the time taken to reach each ambiguous location from the time taken to reach the rewarded one. The pessimism score was obtained by averaging the time taken to touch each location on the two days of testing. We did not have any a priori predictions on whether any specific location would be of particular interest. Therefore, we averaged response to all three ambiguous locations to provide a reliable estimate of how calves respond to ambiguity overall. We considered calves to be more pessimistic when they displayed greater overall latencies to touch the ambiguous cues, similarly to previous studies on calves (Lecorps et al., 2018b; Chapter 5) and other species (Destrez et al., 2013). Of the 20 calves enrolled, two animals did not drink the sweet solution and one animal was an extreme outlier (increasing sweet solution consumption by 225% to 1600% compared to baseline values on the days following disbudding; Dixon test Q = 0.65, P < 0.001); these animals were removed from the analyses, leaving a total of 17 calves. Baseline consumption of the sweet solution was calculated by averaging intakes from day 42 to 44 (i.e. the 3 last days before disbudding; Figure 7.1).  We first explored whether body weight affected baseline consumption of the sweet solution using a linear regression. Then, to assess whether pain associated with disbudding would reduce the consumption of the sweet solution (i.e. anhedonia), we compared intakes before (baseline) and after disbudding (day 45) using a linear mixed model including group pen as an additional fixed factor. Data were log-transformed to normalize differences.       105 To explore whether pessimism affected the change in sweet solution intake on the day of disbudding, we first calculated the percentage change relative to individual baseline consumption and explored whether this was explained by variation in Pessimism using linear regression. Log transformation improved the distribution of residuals and was thus applied. We expected that some calves would return to their baseline intake in the days following the procedure and that pessimism would affect this recovery. To test this idea, we ran a linear mixed model using the sweet solution consumption as response variable, and baseline consumption, day (45 to 49), group pen, and pessimism as fixed effects, and calf as random effect. To normalize the distribution of residuals, sweet solution consumption and baseline consumption were log-transformed.  7.3 Results 7.3.1 Response to judgment bias tests.  Location had a strong effect on latency to touch the bottles (F4,76 = 45.12, P < 0.0001, Figure 7.2), indicating that calves successfully generalized their response from the reinforced locations, with no effect of test day (P > 0.05).         106 Figure 7.2. Latencies (raw data; mean ± SE) to touch the different locations of the judgment bias tests.  Calves (n = 20) were trained to associate one side with a reward and the other side with a mild punishment. Once trained, calves were tested by presenting them with three ambiguous locations (nS+, M and nS-) between the two conditioned locations. Each point represents the averaged measure collected over the two days of testing for each calf.   7.3.2 Pain-induced anhedonia.  Calves consumed (mean ± SD) 2.01 ± 2.19 Kg/d of sweet solution before disbudding. Body weight did not relate to consumption of the sweet solution before disbudding (P > 0.05). All but 3 of the 17 calves reduced their sugar solution intake on the day of disbudding. Intake was reduced on average by 48.4 ± 44.3% (F1,16 = 18.17, P < 0.001; Figure 7.3A), and more pessimistic animals showed greater declines in intake of the sweet solution on the day of disbudding (R2 = 0.28, P = 0.029; Figure 7.3B).         107 Figure 7.3. Change in sweet solution consumption after disbudding and relationship with individual levels of pessimism.  Panel A) shows the change (%) in sugar solution intake in calves (n = 17) on the day of hot-iron disbudding relative to their baseline intake (calculated as the average of the 3 d preceding disbudding). Panel B) shows the relationship between pessimism score and the change in sweet solution consumption on the day of disbudding (dashed curves represent the Cl95% bands).   Baseline intakes strongly affected intakes following disbudding (F1,14 = 43.45, ß = 0.54, P < 0.001) but no changes were detected over the 5 days (P > 0.05), indicating that calves did not recover from the initial drop in sweet solution intake over this period (Figure 7.4). More pessimistic animals tended to drink less (F1,15 = 3.91, ß = - 0.007, P = 0.07) during the post-operative period.           108 Figure 7.4. Changes (%) in sugar solution intake over the five days following hot-iron disbudding in calves (n = 17). Changes were calculated relative to their baseline intake (average of the 3 d preceding disbudding). Calves were disbudded on day 45. Boxes indicate the interquartile ranges with the median, whiskers indicate the 10th and 90th percentiles and outliers are represented by dots. Raw data are presented.  7.4 Discussion Calves showed evidence of reduced consumption of a sweet solution after disbudding, indicating that the procedure may have induced anhedonia for days. More pessimistic animals showed more evidence of pain-induced anhedonia, suggesting that these animals were more affected. Given the lack of self-reports in non-human animals (and in some humans), the affective consequences of pain must be explored using other methodologies. Anhedonia may be especially useful for making inferences about the affective component of pain and other negative affective states (Ede et al., 2019d). Earlier studies have reported pain-induced anhedonia in rats (inflammatory pain: (Refsgaard et al., 2016), chronic pain: (Thompson et al., 2018)), and in       109 humans (Borsook et al., 2016; Navratilova et al., 2016). In Chapter 2, I found that calves were slower to access a milk reward 6 h after disbudding when tested in a judgment bias test, suggesting a motivational deficit consistent with anhedonia. The current study specifically examined changes in consumption of a sweet solution to confirm this hypothesis. Taken together, the results of both studies are consistent with calves attributing lower value to a reward after hot-iron disbudding (i.e. anhedonia), although anorexia cannot be ruled out as calves may have considered the sweet solution as part of their diet. Little is known regarding the hedonic value associated with the consumption of milk compared to other sweet solutions in calves. Motivation for resources other than sweet solutions (e.g. non-food based rewards) may allow stronger inferences. Due to technical issues, we were not able to collect milk and concentrate intakes for this study. These intakes would have allowed us to compare intake of the sweet solution with that from other feed sources; future studies should explore these relations to better understand the nature of calves’ responses. In addition, as the sweet solution was offered using only one teat per pen, social facilitation and competition may have affected our results. No effects of group pen were noted in the statistical analyses and no obvious signs of competition were observed on videos, but future studies should account for this or allow multiple calves to drink at once. The current study used a within-subject design where each calf was its own control. We expected that calves would decrease their consumption after hot-iron disbudding before returning to baseline levels in the subsequent days. As expected, calves reduced their consumption after disbudding, but on the days following the procedure some calves appeared to recover while others did not. The calves that failed to return to baseline consumption may have experienced more persistent pain. Future studies should explore calves’ consumption over a longer period of time.       110 Whether hot-iron disbudding induces long-lasting pain has received little attention (Herskin and Nielsen, 2018). Most studies using wound-directed behaviors and cortisol plasma levels did not explore evidence of pain beyond 24 h (Stafford and Mellor, 2011; Herskin and Nielsen, 2018). Some calves experienced reduced consumption of the sweet solution for as long as 5 days, a result consistent with other recent reports showing long-lasting pain after hot-iron disbudding (Mirra et al., 2018; Casoni et al., 2019; Adcock and Tucker, 2020). These results, along with the negative judgment bias observed after disbudding in previous studies (Neave et al., 2013; Daros et al., 2014), suggest that the procedure may induce persistent pain potentially leading to depressive-like mood in dairy calves. These results indicate that calves should be provided effective post-operative medication (e.g. NSAIDs) to mitigate the aversiveness of the procedure (Ede et al., 2019b) and restore appetite (Todd et al., 2010).  The current study does not allow strong inferences specific to pain; it is possible that other affective states associated with the procedure may also have contributed to the calves’ responses. To allow for stronger inferences specific to pain, future research could consider the addition of a sham group to better control for the non-pain related aspects of the procedure. This would have also allowed to control for any effects of age and stress associated with the procedure (e.g. sedation) on sweet solution consumption. The reduced consumption following disbudding might also have been associated with the drug used to sedate the calves. However, the behavioral and physiological effects of xylazine are known to wane after 1 h (Ede et al., 2019a), so we consider it unlikely that this affected response to sucrose 6 h after the procedure (i.e. when calves were allowed access to the sweet solution), especially given that the reduced intakes persisted on the following days.       111 Little work has explored individual differences in pain responses originating from psychological or cognitive processes in animals. In the current study, calves varied in their responses (i.e. changes in sweet solution intake) following hot-iron disbudding. The greater decline in sweet solution in pessimistic animals suggests that they were more affected by the procedure. This result is consistent with studies in humans in which pessimistic people reported worse expectations about future pain (Finan et al., 2008), more pain in a cold pressor task (Hanssen et al., 2014) and after surgery (Scheier and Carver, 1992). Furthermore, artificially induced optimism lowered pain intensity ratings suggesting a causal relationship (Hanssen et al., 2013). Pessimism may negatively interact with pain-specific expectations that are known to affect the pain response (Hoskin et al., 2019) or may have deleterious effects on how people cope with pain, notably by increasing catastrophizing. Studies to date have not found an interaction between general expectations and pain-specific expectations (Hanssen et al., 2013, 2014) but found an effect on pain catastrophizing (Bargiel-Matusiewicz and Krzyszkowska, 2009; Hanssen et al., 2014). Calves may have had different pain experiences before our study (e.g. at birth, painful gastro-intestinal diseases), and these experiences may have affected their responses to future pain. Alternatively, pessimistic calves might be more vulnerable to pain because of poorer coping abilities. This interpretation is consistent with previous studies showing that pessimistic animals were more vulnerable to stressors (Rygula et al., 2013; Chapter 6). For instance, stress-induced anhedonia was stronger and lasted longer in pessimistic rats (Rygula et al., 2013) that were also found more sensitive to negative feedback (Rygula et al., 2018). The negative expectations of pessimistic individuals are likely to contribute to the experience of negative feelings after painful or stressful experiences and to play a role in the development and maintenance of depressive symptoms such as anhedonia (Carver and Scheier, 2014).        112 7.5 Conclusions Calves display signs of anhedonia for days after hot-iron disbudding, and this response is most pronounced in pessimistic animals. Prolonged anhedonic states are consistent with the long-lasting affective effects of pain and stress associated with this procedure, and highlights the vulnerability of more pessimistic animals. Hot-iron disbudding may thus have persistent negative consequences on the welfare of dairy calves.       113 Chapter 8: General Discussion and Conclusions  8.1 Thesis findings The overall objective of my thesis was to better understand affective states of dairy cattle, and how these vary in response to routine farm procedures. My work encompassed the design of new methodologies allowing for an understanding of the consequences of hot-iron disbudding (Chapter 2), regrouping (Chapter 3) and cow-calf separation (Chapter 4). My thesis also provides a contribution to the understanding of individual differences in response to some of these stressors. This work focused on the description of a novel trait (i.e. not yet explored in farm species), pessimism (Chapter 5), as a marker of vulnerability to stressors such as transportation (Chapter 6) and hot-iron disbudding (Chapter 7) in dairy calves.  8.2 Contributions, limitations and future directions The work summarized in this thesis describes new methodologies to understand the subjective experiences of cattle. In chapter 2, I contributed to the refinement of a method used to assess judgment bias. The method proposed is based on the perception of probabilities (of being rewarded or punished) which offers an alternative to the tests based on ambiguous cues and should in theory allow for repeated testing, one limitation of many current judgment bias designs (Roelofs et al., 2016). My findings show that calves shifted their response towards the three most rewarded locations in the expected direction (more negative perception when in pain) and also highlight that this response wanes 22h and 70h after hot-iron disbudding. Given that calves returned to baseline values (pre-disbudding), the methodology seems resilient to habituation and could be used to assess long-lasting negative affective states in dairy calves and other species. However, this result       114 seems to contrast with recent evidence suggesting that calves may express some pain-related behavioral changes (e.g. hyperalgesia) for months following painful procedures (Mirra et al., 2018; Casoni et al., 2019). This discrepancy may be explained, at least in part, by the possibility that calves display negative mood after hot-iron disbudding for days but the method we used may not have been sensitive enough to detect these changes. Calves’ motivation for milk may have prevented to detect changes in mood 22 and 70 h after hot-iron disbudding. The results obtained in Chapter 2 – i.e. that calves were slower to touch the 3 most rewarded locations – are consistent with a shift in motivation to access the rewards (i.e. anhedonia) rather than a shift in perception of likelihood of being rewarded (i.e. pessimism). This contrasts with previous results obtained in calves (Neave et al., 2013; Daros et al., 2014). The differences between these studies and findings of Chapter 2 may mainly originate from the experimental design. In Chapter 2, the use of latencies may have allowed the detection of a shift in motivation to drink milk that go-no-go responses may have missed. For instance, if only frequency of go responses were used in Chapter 2, no changes would have been detected with respect to the S+ location (i.e. calves went almost 100% of the time). This result highlights that studies should provide response latencies and perhaps other measures of the animals’ responses in judgment bias tests. For instance, it appeared obvious when running tests described in Chapter 2 that calves reacted differently to the various cues. Despite that calves touched the cues most of the time, they did so in different ways – i.e. they just touched and waited ready to withdraw when approaching the most negative ones but suckled right away the most rewarded ones. These behaviours were not recorded in a systematic way preventing us to from making meaningful conclusions; however, future studies should explore the expression of such micro-behaviours that may provide a promising method for drawing inferences about subjective states (Weary et al., 2017).       115 The possibility that hot-iron disbudding triggers anhedonia is an important contribution of this work. The reduced motivation to drink milk (potentially reflecting an anhedonia-like response), observed in Chapter 2, using a modified judgment bias test was confirmed in Chapter 7, where I used a more traditional approach based on motivation to drink a sweet solution; an extensively used methodology in laboratory rodents (Scheggi et al., 2018) and used successfully in cattle for the first time during my Ph.D. work. Interestingly, the findings arising from the work described in Chapter 7 showed that hot-iron disbudding triggered a drop in sweet solution intake that failed to return to baseline values, at least at the population level even 5 days after disbudding, suggesting that calves were still experiencing anhedonia at that time. Assessing calves’ interest and motivation for a reward that is less potent than milk may be a more sensitive way to assess changes in mood. For example, it would be interesting to use a sweet solution as a reward instead of milk in the judgment bias test used in Chapter 2; I predict a similar but more persistent pattern (i.e. lasting several days) of response with calves showing long-lasting negative effects associated with the experience of pain. Although sweet solutions, including milk, may be especially relevant to assess changes in mood in young dairy cattle, these rewards may be less relevant for older cattle and when motivation for a food reward confounds the results. Thus, I used a non-food-based reward (i.e. the possibility to brush) to assess anhedonia-like responses in Chapters 3 and 4. In these chapters, I assumed that regrouping and the transition period would affect motivation for food, as access to fresh feed is often impaired due to competition when regrouped (von Keyserlingk et al., 2008; Nogues et al., 2020) and because the transition period is associated with numerous changes in diet (Grant and Albright, 2000). I used a mechanical brush, a resource cows are strongly motivated to access (McConnachie et al., 2018), with the assumption that negative affective states should decrease       116 brush use (as a proxy for anhedonia). This assumption was confirmed both in Chapters 3 and 4, suggesting that the use of a rewarding resource such as a mechanical brush can be used to draw inferences about affective states, especially in situations where sweet solutions are not appropriate. A limitation to Chapters 2, 3, 4 and 7 is the absence of a control group using a drug treatment to specifically mitigate the negative affective states induced. For instance, Chapter 2 and 7 could have included an additional group receiving a post-operative pain mitigation treatment (e.g. non-steroidal anti-inflammatory drug) known to reduce pain after disbudding (Winder et al., 2018; Ede et al., 2019b). I expect this would have led to reduced changes in latencies to touch cues in the judgment bias test (Chapter 2) and to a less severe drop in consumption of the sweet solution (Chapter 7). Collectively these results would have provided stronger evidence that pain was responsible for these responses. Similarly, anxiolytic or anti-depressant treatments could have been applied in Chapters 3 and 4, mitigating the negative effects of regrouping and parturition.  Challenges related to the use of these drugs in cattle (e.g. lack of validation, costs associated with this type of work) limited our ability to perform such studies. In lieu of a drug treatment, non-pharmacological methods of mitigating the negative effects of stressors can also be employed to validate the sensitivity of new methodologies to assess affective states and give practical ways to refine stressful farm procedures (i.e. pharmacological treatments are unlikely to be used in commercial farms to reduce stress and anxiety). For instance, some evidence suggests that regrouping cattle in pairs reduces the frequency of agonistic aggressions at the individual level (Neisen et al., 2009) and fecal cortisol metabolites (Mazer et al., 2020), suggesting lower levels of stress. Thus, calves regrouped in pairs may experience less negative affective states than calves regrouped alone in part because they are not completely isolated from familiar conspecifics. Similarly, the time around parturition (e.g. transition period)       117 involves many stressors (i.e. pre-partum social stress, diet changes, parturition, pain, post-partum social stress and separation from the calf). Future studies should try to disentangle these stressors to determine which ones have effects on mood. For instance, one would expect that cows experiencing all postpartum stressors except separation from the calf would feel better than cows experiencing all stressors. This evidence would provide additional weight to the inferences presented regarding affective states.  The work described in this thesis provides some of the first evidence indicating that dairy cattle’s mood is negatively affected when they are subjected to some routine farm procedures. This is an important novel contribution of my work given that negative mood, especially if persistent, is one of the main symptoms of depression in humans (DSV-M). Whether routine farm procedures trigger depressive-like states in dairy cattle remains unknown but the work described in this thesis provides a starting point for addressing this question. Previous work has shown that farm animals can experience depressive-like states in response to chronic stressors. For instance, sheep subjected to a chronic stress procedure became more pessimistic (Destrez et al., 2013) and pigs subjected to repeated restrain stress developed anhedonia-like responses (Figueroa et al., 2015). However, these studies did not address conditions typical of what farm animals experience in current husbandry systems.   Most studies to date described the effect of a single stressor. However, animals typically experience multiple stressors, sometimes simultaneously. In Chapters 3 and 4, I explored routine stressors as they would occur on dairy farms. For instance, regrouping (Chapter 3) typically involves a change in social and physical environment (von Keyserlingk et al., 2011) and this combination is known to have stronger effects on cattle (Schirmann et al., 2011). Similarly, in Chapter 4, I elected to first explore how heifers would react to all stressors typically involve around       118 parturition. This choice can be both considered a strength and a limitation as it provides a better understanding of what most dairy cows would feel in these situations but does not allow to identify the contribution of each stressor. For instance, is parturition challenging because of the experience of all stressors at once or simply because cows are separated from their calf? The results described in the second experiment of Chapter 4 provide some evidence that separation from the calf, on its own, induces an anhedonia-like response. Another important contribution of my work is the focus on individual responses. In most chapters, I set out to also assess individual variation using either what animals experienced (Chapter 3) or individual characteristics (Chapter 5 to 7). Results originating from Chapters 2 to 4 highlight the individual variation in response to stressors in dairy cattle but does not provide much insight on what may explain these differences. For example, I predicted that heifers receiving more agonistic interactions during regrouping would be in a worse mood than animals challenged to a lesser degree, but our results failed to support this prediction. Indeed, our results challenge the assumption that agonistic behaviours are the reason why regrouping induce negative affective states. An alternative explanation may be that the loss of contact with socially bonded individuals or the loss of social status is perceived as especially negative. Previous work has mainly focused on preventing agonistic interactions, but this may be ineffective in reducing the impact of the procedure on affective states. For instance, although regrouping at lower densities reduces negative interactions (Talebi et al., 2014), our results suggest that this will not necessarily reduce the impact of regrouping on affective states. However, it is impossible to exclude that the reduced brush use does not reflect on cattle’s affective states at this stage. Future research is needed to confirm our results and should also work to disentangle aspects of regrouping that negatively affect dairy cattle;       119 this new information will provide guidance on how best to regroup animals to avoid negative affective states.  Other aspects of my work provide some insights as to why individuals showed differences in response to stressful procedures. For instance, personality traits are increasingly explored as a way to predict how individuals react to various situations (Réale et al., 2007). This interest in personality has recently been extended to the welfare of farm animals (Neave et al., 2019; Richter and Hintze, 2019). The work undertaken as part of my thesis contributes to the description of a new trait, pessimism (Chapter 5), that explores how individuals perceive and respond to ambiguous situations. Pessimism is believed to play a role in predicting humans’ response to stressors and some aspects related to perceived quality of life (Carver et al., 2010; Carver and Scheier, 2014). This trait may therefore be especially useful to understand how non-human animals react to stressors and whether some animals (i.e. the ones that display negative expectations) may be more vulnerable to stressors. My results highlight that dairy calves display stable individual differences in pessimism, as has been shown in other species such as laboratory rats (Rygula et al., 2013). Pessimistic dairy calves were also found to be more fearful (Chapter 5), react more intensely to an acute stressor (i.e. transportation; Chapter 6) and to be more vulnerable to pain-induced anhedonia (Chapter 7). Collectively, these results indicate that pessimistic calves perceive, respond and recover more poorly from stressors than their optimistic counterparts. To my knowledge, these results are the first showing a relationship between individual differences in pessimism and response to stressors in farm animals, and extends previous work mainly done in rats (see Chapter 1.2.3).        120 Overall, the results presented in this dissertation indicate that pessimistic calves are more vulnerable to stressors. These animals may be less well equipped to cope with the stressors associated with captivity. Negative expectations may lead to negative perceptions and have wide-spread and long-lasting effects. For instance, calves’ response to an unfamiliar human can vary widely, including the following two strategies: 1) the calf makes contact quickly, assess the situation as not threatening and may play with the human, or 2) the calf avoids and stays at distance from the human. What the calf gets from this experience is arguably affected by the strategy it used. According to the results described in Chapter 5, pessimistic calves adopted the second strategy. Negative expectations may therefore prevent animals from gaining knowledge about an ambiguous situation, and if the situation was positive from enjoying this positive experience. Moreover, this may reinforce negative beliefs about ambiguous situations (i.e. in this case about unfamiliar humans), and thus increase the risk that they consider future encounters as negative (i.e. generalization). This does not mean that pessimistic animals are unable to form positive associations, but rather that their pessimistic view favors the formation and maintenance of negative ones. Future studies should explore the role of expectations as modulators of welfare. Given the current emphasis on providing farm animals with positive experiences (Boissy et al., 2007; Webb et al., 2018), we will need to consider that some animals may fail to enjoy these opportunities, simply because of their tendency to form negative expectations. Overall, there is a need for research on how animals form expectations (also referred to as priors in the human literature; Sharot and Garrett, 2016), and specifically how the animal’s response is mediated by previous experiences and individual characteristics. For instance, response to transportation (Chapter 6) may be more dependent on the animal’s perception of handling and novelty rather than the contextual details involved in transportation per se. Calves that consider       121 human contact as positive, and that have not associated novelty with negative experiences, have no reasons to panic when transported (at least in the conditions used in Chapter 6). This is consistent with the results of Chapter 6 showing that optimistic animals were the least affected by transportation. There are limitations associated with this work. Stability of the trait “pessimism” was only assessed when calves were still young (from 25 to 50 d old) and future studies should explore whether this trait stays stable or whether individuals change over time, as previously shown for other traits in cattle (Neave et al., 2020). That said, in Chapter 6, calf responses to transportation was assessed more than 70 d after the last judgment bias test. The relationship observed indicates that individual differences at young ages may keep their predictive value for long periods. Exploring why some calves started to form negative expectations when 25 d old is also worth considering; there is a gap in our understanding of how stable individual differences emerge in young animals (see Chapter 1 Box 2).  8.3 General conclusions In this thesis, I explored the impact of routine farm procedures on dairy calves and cattle’s affective states. More specifically I focused on whether hot-iron disbudding, regrouping and postpartum stressors such as cow-calf separation, would trigger long-lasting changes in mood. I showed that these stressors were all associated with changes in mood detectable for variable durations (Chapter 2, 3, 4 and 7). In addition, I explored whether some individuals would be more vulnerable to stressors, and I expected that individuals displaying more negative expectations (i.e. more pessimistic) would be more susceptible to poorly respond to stressors. Chapters 5, 6 and 7 showed that calves express stable individual differences in pessimism and that more pessimistic       122 animals perceived and responded to stressors more negatively. These results provide a better understanding of the impact of some routine farm procedures that may act as major stressors and thereby may have long-lasting consequences. The results also highlight that strong inter-individual differences exist and have to be considered to understand the negative effects of a stressor on an individual. Some individuals may be particularly vulnerable because of the negative expectations they hold.       123 References Adcock, S.J.J., and C.B. Tucker. 2020. Conditioned place preference reveals ongoing pain in calves 3 weeks after disbudding. Sci. Rep. 10:1–9. doi:10.1038/s41598-020-60260-7. Ågmo, A., A. Galvan, and B. Talamantes. 1995. Reward and reinforcement produced by drinking sucrose: Two processes that may depend on different neurotransmitters. Pharmacol. 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Rep. 8:1–14. doi:10.1038/s41598-018-23545-6.        169 Appendices Appendix A: Sequences of cues presentation for training calves in the judgment bias test (Chapter 2). Sequences used for each of the four phases and the baseline/tests. During Phase 1, calves were trained to associate the positive probe location (S+) with the reward. During Phase 2, calves were trained to associate the negative probe location (S-) with the punishment. During Phase 3, calves were trained to associate each of the intermediate probe location (nS+, M, nS-) with a different probability of being rewarded/punished. During Phase 4, all probe locations except the Middle were presented to maximize the contrast between the positive and negative sides of the arena. All probe locations were presented during baseline measurements and judgment bias testing. The dot denotes punished trials for intermediate locations.    


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