Validating the Use of Qualitative Behavioral

This article was downloaded by: [114.30.124.18] On: 16 April 2015, At: 16:23 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Applied Animal Welfare Science Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/haaw20

Validating the Use of Qualitative Behavioral Assessment as a Measure of the Welfare of Sheep During Transport a

a

a

Sarah L. Wickham , Teresa Collins , Anne L. Barnes , David W. a

ab

a

Miller , David T. Beatty , Catherine A. Stockman , Dominique c

d

a

Blache , Françoise Wemelsfelder & Patricia A. Fleming a

Veterinary & Life Sciences, Murdoch University, Perth, Australia

b

Dubai Airport Freezone, Dubai, United Arab Emirates

c

Click for updates

School of Animal Biology, Faculty of Natural and Agricultural Sciences, The University of Western Australia, Perth, Australia d

Animal and Veterinary Sciences Group, Scotland's Rural College, Midlothian, Scotland, United Kingdom Published online: 19 Feb 2015.

To cite this article: Sarah L. Wickham, Teresa Collins, Anne L. Barnes, David W. Miller, David T. Beatty, Catherine A. Stockman, Dominique Blache, Françoise Wemelsfelder & Patricia A. Fleming (2015): Validating the Use of Qualitative Behavioral Assessment as a Measure of the Welfare of Sheep During Transport, Journal of Applied Animal Welfare Science, DOI: 10.1080/10888705.2015.1005302 To link to this article: http://dx.doi.org/10.1080/10888705.2015.1005302

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,

Downloaded by [114.30.124.18] at 16:23 16 April 2015

systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions

JOURNAL OF APPLIED ANIMAL WELFARE SCIENCE, 1–18, 2015 Copyright q Taylor & Francis Group, LLC ISSN: 1088-8705 print/1532-7604 online DOI: 10.1080/10888705.2015.1005302

Validating the Use of Qualitative Behavioral Assessment as a Measure of the Welfare of Sheep During Transport

Downloaded by [114.30.124.18] at 16:23 16 April 2015

Sarah L. Wickham,1 Teresa Collins,1 Anne L. Barnes,1 David W. Miller,1 David T. Beatty,1,2 Catherine A. Stockman,1 Dominique Blache,3 Franc¸oise Wemelsfelder,4 and Patricia A. Fleming1 1

Veterinary & Life Sciences, Murdoch University, Perth, Australia 2 Dubai Airport Freezone, Dubai, United Arab Emirates 3 School of Animal Biology, Faculty of Natural and Agricultural Sciences, The University of Western Australia, Perth, Australia 4 Animal and Veterinary Sciences Group, Scotland’s Rural College, Midlothian, Scotland, United Kingdom

We tested the application of qualitative behavioral assessment (QBA) as a welfare assessment tool. Sheep were exposed to road transport treatments, and behavioral expressions were compared between experimental treatments and validated by correlation with physiological measures. We compared journeys differing in ventilation (closed vs. open-sided trailer), flooring (grip vs. nongrip flooring), and driving styles (stop –start vs. continuous driving). Blood samples were collected immediately before loading and after unloading; heart rate and core body temperatures were recorded continuously. Continuous video footage was edited to show individual sheep to observers for QBA using free-choice profiling (observers used their own descriptive terms). There was significant consensus in observers’ scores for the sheep in each experiment ( p , .001). Observers distinguished between sheep exposed to flooring ( p ¼ .014) or driving-style ( p ¼ .005) treatments, but not between ventilation treatments. QBA scores were compared ( p , .05) with plasma leptin, glucose, and insulin-like growth factor-1 concentrations; white blood cell profiles; red blood cell counts; hematocrit; body temperatures; and heart rate variability. Observer assessments reflected treatment differences, and correlations between behavioral expression and physiological responses were found. Keywords:

QBA, behavioral expression, flooring, ventilation, driving

Many researchers favor using a number of techniques for assessing nonhuman animal welfare (Broom, 1991; Dawkins, 2004, 2006) because each method can be restrictive in its application, measurement, or interpretation. Physiological measurements may be limited as on-farm welfare assessment tools because they can be difficult to obtain, they are often expensive and

Correspondence should be sent to Patricia A. Fleming, Veterinary & Life Sciences, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia. Email: [email protected]

1

Downloaded by [114.30.124.18] at 16:23 16 April 2015

2

WICKHAM ET AL.

invasive, and they need to be evaluated under carefully controlled conditions (Stockman et al., 2011). The act of measuring physiological variables can also affect the welfare of the animals (e.g., De Silva, Kiehm, Kaltenbach, & Dunn, 1986), resulting in difficulty establishing normal ranges. The timing of sampling is also important; for example, while glucocorticoid release occurs a few minutes after the start of a stressful event and persists for about an hour after the end of the stressor (Morme`de et al., 2007), concentration of plasma cortisol (one of the primary stress hormones) can peak within 10 min (Kent & Ewbank, 1983; Lay, Friend, Bowers, Grissom, & Jenkins, 1992). Consequently, it can be easy to miss the response of the hypothalamic-pituitaryadrenal (HPA) axis to a stressor if the measurements are not carried out at the correct time. There can also be issues with interpretation of physiological measures. For example, cortisol (reflecting elevated HPA activity) “serves a wide range of physiological, behavioral and cognitive functions and can be elevated in a number of contexts that may or may not be ‘stressful’ in the aversive sense of the term” (e.g., territoriality, attachment behavior, anticipation of food, predatory behavior, focused attention, social presentation, sustained effort, and effortful thought; reviewed by Erickson, Drevets, & Schulkin, 2003, p. 234). Consequently, these physiological responses may indicate a change in an animal’s state but not whether this change is positive or negative in terms of the animal’s welfare. Understanding the psychological state of an animal is therefore important in informing welfare assessments (Watanabe, 2007). Behavior is an expression of an animal’s emotional, biological, and physiological states, and if behavioral assessments are focused more broadly, then arguably motivation and mental state can also be assessed (Dawkins, 2004). Measures such as preference trials or behavioral demand tests may reveal important information regarding an animal’s psychological or physiological state; however, long periods of time may be required to capture this information or the measures may not be directly made in reference to a particular context, such as commercial livestock conditions. Behavioral actions may be relatively easy to record (e.g., describing and quantifying each movement or action an animal makes; Barnett & Hemsworth, 1990); however, interpretation of these measures is often equivocal, and the measures are not designed to describe behavior in terms of an individual’s perspective (Wemelsfelder, 1997). Consequently, quantitative analysis may also have limitations as an indicator of an animal’s welfare (Knowles, 1998). For example, sheep after transport often do not rest; they appear alert and active (Knowles, 1998) and may be assigned high scores for locomotion. However, it is the way in which they appear active that is most informative; for example, high locomotion scores could be attributed to a sheep who is fearful and moving away from a stressor or to a sheep who is exploring his or her surroundings (Wemelsfelder & Farish, 2004). Qualitative behavioral assessment (QBA) has been suggested as an assessment tool for use in animal welfare studies. QBA uses observer assessments to integrate many pieces of information from the animal and how he or she interacts with his or her environment to capture how the animal is behaving rather than what he or she is doing (Wemelsfelder, Hunter, Mendl, & Lawrence, 2000, 2001). By focusing on the whole animal, behavior is no longer just a physical movement but is evaluated in a larger context with expressive and psychological quality, where observers summarize all perceived details of an animal’s posture and movement into descriptions of expressive demeanor (e.g., relaxed, anxious, playful, and content). Consequently, affective states that are difficult to assess may be evident; for example, tiredness (lack of

Downloaded by [114.30.124.18] at 16:23 16 April 2015

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

3

“engagement”) has been quantified in endurance horses at different stages of a 160-km ride (Fleming, Paisley, Barnes, & Wemelsfelder, 2013). It is also important to note that the qualitative behavioral expression of an animal is dynamic, and QBA can therefore capture changes in body language over time (Wemelsfelder, 1997). QBA studies have shown that observers can reach consensus in their assessments of behavioral expression in a number of species, including sheep (Stockman et al., 2014; Wickham et al., 2012). Of direct relevance to the present study, three recent studies were used to test the application of QBA during road transport in cattle (Stockman et al., 2011, 2013) and sheep (Wickham et al., 2012), and they demonstrated that despite limited body movement in the animals due to confinement, observers were nevertheless able to distinguish between clips of the same animals who were filmed under different transport conditions. Because most animal welfare measures have been derived from physiology (Dwyer & Bornett, 2004; Grandin, 1997), it would be useful to know how behavioral expression correlates with physiological measures. Wickham et al. (2012) found significant correlations between insulin-like growth factor-1 (IGF-1) concentrations, the neutrophil-to-lymphocyte ratio, heart rate (HR), and core body temperature (Tcore) and QBA scores that described sheep as more anxious/nervous/worried. Stockman et al. (2011, 2013) similarly found correlations between plasma glucose concentrations, neutrophil-to-lymphocyte ratio, HR, and Tcore and QBA scores for cattle. A number of studies have also demonstrated significant correlations between QBA and quantitative behavioral measures. For example, pushing and headbutting in dairy cows were also qualitatively assessed as more aggressive (Rousing & Wemelsfelder, 2006) responses to provision of feed rewards during behavioral demand testing (Stockman et al., 2014), while Napolitano et al. (2008) found that horses assessed as more calm and quiet exhibited lower frequencies of bucking and kicking. Such comparisons between behavioral expression (QBA scores) and physiology and quantitative measures provide useful validation for the relevance of behavioral expression of animals as a welfare assessment method. The overall aim of this study was to contribute to the validation of QBA for assessment of the welfare of sheep. We tested the application of QBA to sheep exposed to various road transport conditions. We did not set out to compare the welfare of animals during transport (which would require replicate transport events). Rather, we used transport as a means of challenging animals and testing for treatment differences and individual responses, because previous studies have demonstrated various physiological changes associated with transport (e.g., Knowles, 1999; Warriss et al., 1995). Such comparisons between behavioral expression (QBA scores) and physiology and quantitative measures provide useful validation for the relevance of behavioral expression of animals as a welfare assessment method. Transport is generally regarded as both a physical and psychological stressor to livestock, as it involves introducing the animal to a novel, often noisy environment, with mixing of social groups, food and water curfews, and long periods of confinement. Additionally, during transport, sheep can be exposed to different environmental conditions (e.g., different degrees of ventilation and elevated ammonium concentrations and temperature), physical conditions (e.g., flooring type, stocking density), and a range of driving conditions that can have varying effects on the animals (Smith, Grandin, Friend, Lay, & Swanson, 2004). For example, Kettlewell et al. (2001) argues that the temperature within the vehicle poses the greatest threat to the animal’s welfare and that air

4

WICKHAM ET AL.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

movement providing convective cooling is important to remove heat and moisture, while driving events (e.g., acceleration, braking, cornering, stopping, and gear changing) and vibrations (e.g., due to uneven road surfaces) affect the movement of the vehicle and in turn the ability of animals onboard to maintain their balance and posture (Cockram et al., 2004; Ruiz-de-la-Torre et al., 2001). We investigated the application of QBA to sheep exposed to three transport treatments (altered ventilation, flooring, and driving style). The aims of this study were to determine whether: 1. observers could reach consensus in their scoring of the behavioral expression of transported sheep; 2. observers could distinguish between sheep exposed to each transport treatment based on their behavioral expressions; and 3. behavioral expression was reflected in a range of physiological measures.

MATERIALS AND METHODS Animals and Transportation All experimental procedures were reviewed and approved by the Animal Ethics Committees at Murdoch University (Perth, Australia) and Curtin University (Perth, Australia). Fourteen Merino wethers (14 months of age; 46.4 ^ 0.4 kg) were randomly selected from a transport-naı¨ve flock at Muresk Institute at Curtin University in Western Australia. The animals were housed in a group pen throughout the experiment. The same individuals had been used for a previous study examining their behavior when they were first transported (transport-naı¨ve) or habituated to road transport (Wickham et al., 2012). Sheep were transported in a car-drawn, double-axle trailer with a stock cage measuring 2.03 m £ 3.55 m £ 1.55 m (Width £ Length £ Height), at a stocking rate of 0.45 m2/head (cf. commercial stocking rate of 0.25 m2/head for 50-kg sheep; Anon, 2008; the larger space per animal was used to facilitate collection of adequate video footage of individuals). The sides of the trailer had ventilation similar to commercial transport vehicles in Western Australia, with 10cm horizontal slats interspersed with 10-cm spaces. The floor was covered with a grid of 10-mm steel rods spaced 100-mm apart and placed directly upon the floor and therefore raised a maximum of 20 mm from the steel plate of the floor. The grid covered the floor area of the trailer and provided additional “grip” to the floor surface. The route taken during each transport event included a mixture of main roads (speed limit of 50– 70 km/hr) and highways (speed limit of 70 – 100 km/hr). The length of each journey was approximately 65 km and 90 min in duration. The selection of roads was similar to those selected during commercial transport to a local abattoir or shipping port, and the route included a period of continuous and smooth driving. Three challenges were applied in this study, and they comprise three distinct experiments with regard to analyses and interpretation. For each experiment, a transport event where the conditions on the trailer were manipulated was compared to a “control” transport event (C; sheep were transported seven times during an 8-day period, and the final “habituated” transport event was used). The three treatment transport events investigated the challenges of reduced

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

5

Downloaded by [114.30.124.18] at 16:23 16 April 2015

ventilation (Experiment 1), reduced flooring grip (Experiment 2), and more erratic driving (Experiment 3) during road transport. Between each treatment transport event, sheep were transported once per day as per the habitation transport events to reinforce the predictability of transport treatments. Experiment 1: Ventilation treatment. During the closed ventilation (CV) transport event, all sides of the trailer were covered with transparent, colorless polycarbonate sheets to minimize the amount of airflow into the trailer while traveling. The sizes of the sheets matched the sizes of the sides of the trailer. Fisher, Stewart, Tacon, and Matthews (2002) found that the temperature and humidity within a sheep transport vehicle rose rapidly when there was little air movement; therefore, we predicted the greatest effect of limited ventilation would be at the end of the transport event. Experiment 2: Flooring treatment. During the nongrip flooring (NG) transport event, the metal flooring grid was removed and the truck was cleaned of accumulated fecal matter, thereby reducing the grip of the floor. Increases in physiological variables have been seen at the beginning of a journey and have stabilized further into the journey (Kent, 1997), and consequently, we predicted that the flooring challenge would have the most notable effect at the start of the journey, before the sheep could habituate themselves to the flooring type. Experiment 3: Stop –start driving. During the stop – start (SS) driving event, sheep were subjected to erratic driving, which involved starting from a stationary position, accelerating to 60 km/hr over, 30 s, continuing at that speed for , 15 s, and then decelerating to full stop over , 15 s. This was repeated 10 times. This type of driving reflects part of the route used to move sheep through built-up urban areas with traffic lights. Kent (1997) noted that sudden vehicle movements elicit acute changes in physiological variables, and therefore we predicted that the effects of the SS treatment would likely occur immediately on commencement of that challenge. From continuous recordings during each transport event, video footage for QBA, Tcore data, and HR data were selected using the final 30 min (Experiment 1), first 30 min after departure (Experiment 2), or the first 15 min of commencing the SS driving treatment (Experiment 3), as we predicted the greatest effects of each of the experimental treatments at these time points. Equivalent time points were selected from the C transport event for comparison (Figure 1).

Environmental Measures Temperature and relative humidity on the trailer were recorded during transport using two data loggers (Onset HOBO H8 Pros, #H08-032-IS, OneTemp Pty Ltd., Australia) to ensure that there were not marked differences in ambient temperature between transport events. Each logger was positioned at sheep-head height, with one at the front and one at the rear of the trailer. The loggers recorded dry-bulb temperature (8C) and relative humidity (%) every 2 s throughout each transport event. Data were averaged over the first 40 min and the last 40 min of each transport event.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

6

WICKHAM ET AL.

FIGURE 1 Diagram showing the experimental design. Sheep were monitored during four transport events, including one control and three experimental trips. Variables were measured during the key time windows (shaded regions); continuous video footage was edited to isolate clips for these time windows for qualitative behavioral assessment.

Physiological Measurements A number of physiological variables have been used previously as indicators of altered physiological states during transport (reviewed by Knowles, 1999). Transport-naı¨ve sheep show increased Tcore, HRs, plasma cortisol concentrations, white blood cell counts, and neutrophil-tolymphocyte ratios compared with transport-habituated sheep (Wickham et al., 2012). Similarly, transport- naı¨ve cattle show increased Tcore, increased cortisol and glucose concentrations, and increases in the neutrophil-to-lymphocyte ratio compared with transport-habituated cattle (Stockman et al., 2011, 2013). Many of these measures are linked with activation of the HPA axis and an immune system response due to stress. It is important to note that even if physiological measures did not show significant transport treatment effects (i.e., individual variability may exceed the differences between treatment groups), individual differences in physiological responses may still be correlated with their individual behavioral expression scores. Core body temperature. Body temperature may be used to measure stress response in animals because increases in heat production in the absence of physical activity or increased diet reflect increased activity of the sympathetic nervous system due to the presence of a stressor (Sjaastad, Hove, & Sand, 2010). Tcore was recorded using implanted temperature loggers

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

7

Downloaded by [114.30.124.18] at 16:23 16 April 2015

(iButtons, Maxim Dalla, Sunnyvale, CA). The loggers were surgically implanted into the peritoneal cavity in the right paralumbar fossa, as described by Wickham et al. (2012), 11 days before the commencement of the study. To take into account circadian patterns in Tcore, comparison of Tcore was carried out for the same time of day on nontransport days (Figure 1). For Experiment 1, the last 40 min of the C and CV transport events were compared with the same 40-min period averaged over 2 consecutive nontransport days following the C or prior to the CV events. For Experiment 2, Tcore was averaged for the first 40 min of the C and NG transport events and compared to the 3 consecutive nontransport days after the C or NG transport events. For Experiment 3, Tcore was averaged during the last 40 min of the C and SS transport events and compared to 2 consecutive nontransport days after the C and SS transport events. Blood collection and hematology. Previous studies have revealed a range of responses to transport that may be obtained through analyses of blood samples (Cole, Roussel, & Whitney, 1997; Crookshank, Elissalde, White, Clanton, & Smalley, 1979; Heiman et al., 1997; Jones & Allison, 2007; Kent & Ewbank, 1983). Blood was collected from all 14 sheep via jugular venipuncture, as described by Wickham et al. (2012), immediately before and after each experimental transport event as the animals were held in a race toward or away from the livestock trailer. Stored samples were assayed for glucose, beta-Hydroxybutyric acid (b-OH), adrenocorticotropic hormone, cortisol, IGF-1, insulin, and leptin. Hematological assays were carried out within 24 hr after collection. Hematocrit, red blood cell count, white blood cell count, and the numbers of neutrophils, lymphocytes, eosinophils, basophils, and monocytes were recorded (Wickham et al., 2012). Heart rate. HR in sheep can change during isolation and transport, while in the presence of a human or a dog, or in an environment such as a new location or flock (Baldock & Sibly, 1990; Schmidt et al., 2010). The benefit of measuring HR during a stress response is that HR can change within one or two heartbeats (von Borell et al., 2007) and shows marked individual variability (Baldock & Sibly, 1986). HR (beats per minute) was recorded for each of the 14 sheep every 5 s ¨ y, during transport events using external HR monitors (Polar Equine S625X, Polar Electro O Finland; Wickham et al., 2012) and was analyzed for average HR and HR variability (HRV) for 5min intervals 5 min to 10 min before departure and during transport (samples were collected for different time windows for each experiment). In Experiment 1, samples were collected 70 min to 75 min after departure; in Experiment 2, samples were collected 5 min to 10 min after departure; and in Experiment 3, samples were collected 45 min to 50 min after departure. Due to intermittent loss of contact with the HR monitor (lost connection of the electrodes and interference), we obtained HR data on C (n ¼ 9), CV (n ¼ 10), NG (n ¼ 13), and SS (n ¼ 13) sheep. Statistical analysis. Repeated-measures analysis of variance (ANOVA) was used to compare time points (Tcore, control day vs. transport day; blood parameters, before vs. after transport) and treatments (C vs. CV, C vs. NG, and C vs. SS). A factorial ANOVA was used to compare time points for HR and HRV due to incomplete data collection (STATISTICA 8.0 [data analysis software system, www.statsoft.com], Tulsa, OK). Qualitative Behavioral Assessment Video recording. Video footage was recorded throughout all transport events using two digital Panasonic SDR-H250 camcorders (15 frames per second) fixed to the front and back of

8

WICKHAM ET AL.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

the transport trailer, above sheep-head height (approximately 1.6 m from the trailer floor). Ten of the 14 sheep were clearly visible in the footage from all transport events; therefore, only 10 sheep were used for QBA and for comparison with the physiological measures. One clip (20 – 60 s long) of each individual was chosen from each experimental journey (Experiment 1, during the last 15 min; Experiment 2, within the first 15 min after departure; and Experiment 3. within the first 15 min of commencing the SS driving treatment). Equivalent time points were selected from the C transport event for comparison. The clips selected were the first available clip where (a) the vehicle was in motion, and (b) the sheep’s face was visible for the entire duration. The 10 clips for each treatment were edited to mask out the background, which included the sides of the vehicle and the landscape beyond, and the individual focal sheep was highlighted by increasing the opacity of the surrounding animals in the same frame (Adobe Premiere Pro CS3 and Adobe After Effects CS3). Observers and procedures. Fifty-seven observers, including 17 men (30%) and 40 women (70%), were recruited for the study. One man withdrew from the study before Experiment 2, and another man and 3 women withdrew before Experiment 3. Each observer was required to attend four sessions. The observers were unaware of the experimental treatments or that the sheep were being transported. In Session 1 (term generation), observers were instructed in the free-choice profiling procedures for QBA (Wickham et al., 2012) and then were shown 13 video clips of sheep demonstrating a range of behaviors. Observers generated an average of 21 ^ 7 descriptive terms (range ¼ 9 – 37). In Sessions 2 to 4 (quantification), observers viewed video footage of each individual sheep during experimental and habituated transport events, and they used their own descriptive terms as quantitative rating scales. The 20 clips in each quantification session were shown to observers in randomized order. Observers quantified each sheep for every term by marking on a visual analogue scale. They were told to think of the distance between the zero point and their mark on the scale as reflecting the intensity of the animal’s expression of that term. These scores were then analyzed by generalized Procrustes analysis (GPA) in Genstat (VSN International, Hemel Hempstead, Hertfordshire, UK) for each of the three experiments separately. GPA calculates a consensus or “best fit” profile between observer assessments through complex pattern matching. The number of dimensions of the consensus profile is then reduced to several main dimensions (usually two or three) explaining the variation between animals through principle components analysis. The meaning of each GPA dimension is interpreted by analysis of the correlations between the consensus scores and each individual observer’s terms. See Wemelsfelder et al. (2000) and Wickham et al. (2012) for detailed descriptions of procedures. Validity of the QBA consensus. A Procrustes statistic, which indicates the percentage of variation between observers explained by the consensus dimensions, was calculated. Whether this consensus is a significant feature of the data set or, alternatively, an artifact of the Procrustean calculation procedures is determined through a randomization test (Dijksterhuis & Heiser, 1995), comparing the Procrustes statistic with the results of 100 randomized profiles by a one-way t test. The procedure rearranges at random each observer’s scores and produces new permutated data matrices.

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

9

Downloaded by [114.30.124.18] at 16:23 16 April 2015

Treatment effects in behavioral expression scores. Each animal received a quantitative score on each of the GPA dimensions. To compare the effect of experimental treatments, the GPA scores for each dimension were analyzed using repeated-measures ANOVA (Statistica 9.0, StatSoft Inc., Tulsa, OK) with the scores for each sheep for each transport treatment compared as the repeated measures. Correlation of physiology with behavioral expression. The physiology and QBA analyses were compared by determining how each of the 10 individual sheep’s scores on each GPA dimension (each measured twice, once for each treatment within each experiment) correlated with the measured physiological variables. A Pearson’s correlation matrix (Excel for Windows 2003, Microsoft Inc., Redmond, WA) was developed for the proportional changes for each of the physiological measurements (hormones, metabolites, and hematological parameters: after-transport values divided by before-transport values; HR: values for 5 min to 10 min after departure divided by values for 5 min to 10 min before departure; Tcore: transport-day values divided by nontransport-day values) as well as the scores for each GPA dimension. Proportional values expressed the change in each measure over the duration of transport as a single value to compare to the QBA scores. RESULTS For all three experiments, the GPA consensus profile explained a significant proportion of the variation among the observers, which differed significantly from the mean randomized profile in each case ( p , .001), indicating there was significant consensus in how the observers scored the sheep (Table 1).

TABLE 1 Terms Correlated With Generalized Procrustes Analysis (GPA) Consensus Profile Dimensions Low Values

High Values

Treatment Effects

Experiment 1: Ventilation: Consensus profile explained 48.7% of variation, t(99) ¼ 67.3, p , .001 (n ¼ 57 observers) GPA1 (36.3%) Relaxed/calm/sleepy Alert/anxious/responsive F(1, 9) ¼ 0.09, p ¼ .776 GPA2 (15.6%) Happy/alert/curious Worried/frightened/nervous F(1, 9) ¼ 0.10, p ¼ .761 Experiment 2: Flooring: Consensus profile explained 51.0% of variation, t(99) ¼ 86.64, p , .001 (n ¼ 56 observers) GPA1 (40.6%) Calm/relaxed/comfortable Anxious/agitated/worried F(1, 9) ¼ 3.95, p ¼ .078 GPA2 (11.1%) Tired/passive/terrified Alert/curious/aware F(1, 9) ¼ 9.25, p ¼ .014; sheep scored lower for nongrip flooring treatment Experiment 3: Driving style: Consensus profile explained 48.2% of variation, t(99) ¼ 61.57, p , .001 (n ¼ 52 observers) GPA1 (54.8%) Calm/relaxed/sleepy Alert/anxious/nervous F(1, 9) ¼ 13.88, p ¼ .005; sheep scored higher for stop–start treatment GPA2 (9.3%) Alert/calm/relaxed Scared/terrified/worried F(1, 9) ¼ 1.29, p ¼ .284 Note. Terms with the highest correlations with either low or high values of the GPA consensus profile dimensions are shown.

10

WICKHAM ET AL.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

Details of the physiological responses are given in the supplementary material online. None of the measured physiological variables had values outside “normal” ranges. There were changes in physiological variables that were common to all three transport studies, including decreases in leptin, cortisol, and IGF-1 concentrations after transport (which was also noted in the naı¨ve- vs. habituated-transport study for these animals; Wickham et al., 2012). Additionally, Tcore was higher during transport days compared with the nontransport days, but it rose and then fell with time over the duration of each transport event. However, for the context of the present study, the important differences in physiology were those that demonstrated a significant Treatment £ Time interaction, which would reflect that the experimental treatments resulted in differing physiology. These are discussed below in respect to each experiment. Experiment 1: Ventilation Treatment (Control vs. Closed Ventilation) Qualitative behavioral assessment. High values for GPA1 were characterized by terms such as alert/anxious/responsive, and low values were associated with terms such as relaxed/ calm/sleepy (Table 1). High values for GPA2 were characterized by terms such as worried/ frightened/nervous, and low values were associated with terms such as happy/alert/curious (Table 1). There were no significant differences between sheep during the closed- and openventilation treatments for either GPA dimension (Table 1; Figure 2a). Physiology. Leptin concentration decreased by only 4% for the CV transport event and by 14% for the C transport event ( p , .001). The b-OH concentration decreased by 7.5% for the C event and increased by 12% for CV ( p , .05). Concentration of both insulin and glucose decreased for C but increased for CV ( p , .05). White blood cell numbers did not change for C, but they decreased significantly for CV ( p , .05). The same predeparture hematocrit levels were measured for both transport events, but there was a greater decrease for C ( p , .05). Neutrophil numbers increased and lymphocyte numbers decreased during both transport events, with larger changes recorded for C compared with CV ( p , .05). Our ventilation treatments had no effect on HR and HRV. Compared with the corresponding nontransport-day values, Tcore decreased by 0.198C (both average and maximum Tcore) for C, but it increased by 0.458C and 0.508C (average and maximum Tcore, respectively) for CV. These changes did not reflect differences in ambient temperatures, which increased during both transport events. Correlation of physiology with behavioral expression. Leptin concentration and HR both correlated negatively with GPA1, while red blood cell count was negatively correlated with GPA2. Therefore, sheep described as more relaxed/calm/sleepy had increased leptin concentrations and HR, while sheep described as happy/alert/curious had higher numbers of red blood cells. Experiment 2: Flooring Treatment (Control vs. Nongrip Flooring) Qualitative behavioral assessment. High values for GPA1 were characterized by terms such as anxious/agitated/worried, and low values were associated with terms such as calm/ relaxed/comfortable (Table 1). High values for GPA2 were characterized by terms such as alert/ curious/aware, and low values were associated with terms such as tired/passive/terrified (Table 1). There were no significant differences between the flooring treatments on GPA1, but NG

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

(a)

0.2 0.15

CV7

CV4 C9

CV10

0.1 GPA Dimension 1

11

C5

C7 CV5

C4

0.05

C1

C10

0

CV6

CV9 C3

–0.05

C2 CV8

–0.1

CV2

C6

CV3 C8

–0.15

CV1 0

–0.1

0.1

0.2

GPA Dimension 2 (b)

0.2

C3 C4

0.15 C10 NG8 NG5

GPA Dimension 1

0.1

C7 C6

0.05

C2 0 NG1 –0.05

NG7

C9 C1

NG2

NG4

NG3 NG6

–0.15 –0.2 –0.2

C8

C5 NG9

–0.1

NG10 –0.1

0

0.2

0.1

GPA Dimension 2 (c)

0.3

SS9

0.2 SS2

SS4 SS3 GPA Dimension 1

Downloaded by [114.30.124.18] at 16:23 16 April 2015

–0.2 –0.2

0.1

0

–0.1

C9 SS1 SS7 SS6 SS5 C10 C7 C2 C4 C1 SS8 C6 C3 C8 C5

SS10

–0.2

–0.3 –0.2

–0.15 –0.1 –0.05

0

0.05

0.1

0.15

0.2

GPA Dimension 2

FIGURE 2 Positions of individual sheep (represented by numbers) on Generalized Procrustes Analysis (GPA) Dimensions 1 and 2 for (a) control (C) versus closed ventilation (CV; Experiment 1), (b) C versus nongrip (NG) flooring (Experiment 2), and (c) C versus stop–start driving (SS; Experiment 3). Each sheep (n ¼ 10) is represented twice (numbers represent their IDs), once for each treatment; these 10 individuals of the 14 transported sheep were visible in footage across all treatments to allow qualitative behavioral analysis to be carried out.

12

WICKHAM ET AL.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

sheep scored significantly lower on GPA2 compared with C sheep: Animals were scored as more tired/passive/terrified for NG compared with C (Table 1; Figure 2b). Physiology. Leptin concentration showed a greater decrease during the NG transport event (14%) compared with the C transport event (4.5%; p , .001). The b-OH concentration increased 16% for C but decreased 7.5% for NG ( p , .05); glucose showed the opposite pattern and decreased during C but increased during NG ( p , .05). Hematocrit and red blood cell numbers both increased for C (4% and 6%) but decreased for NG (8.5% and 8.5%; p , .001). Eosinophil and lymphocyte numbers increased for C but decreased for NG, while the reverse was true for neutrophil numbers and the neutrophil-to-lymphocyte ratio ( p , .05). Transport resulted in significantly increased HR in sheep compared with before transport ( p , .01), but there were no significant Treatment £ Time interactions for HR or HRV. Transport resulted in a significant increase in Tcore, with a greater increase for NG (average Tcore, 0.488C; and maximum Tcore, 0.458C) compared with C (0.148C for both average and maximum Tcore) transport events ( p , .001). Correlation of physiology with behavioral expression. Leptin and IGF-1 concentrations, hematocrit, red blood cell count, monocyte numbers, HRV, and average and maximum Tcore were positively correlated with GPA1 (i.e., higher in sheep described as more anxious/agitated/ worried), while plasma glucose concentration was lower (Table 2). Hematocrit, red blood cell count, and average and maximum Tcore were positively correlated with GPA2 (i.e., higher for sheep described as more alert/curious/aware). Experiment 3: Driving Style Treatment (Control vs. Stop – Start Driving) Qualitative behavioral assessment. High values for GPA1 were characterized by terms such as alert/anxious/nervous, and low values were associated with terms such as calm/relaxed/ sleepy (Table 1). High values for GPA2 were characterized by terms such as scared/terrified/ worried, and low values were associated with terms such as alert/calm/relaxed (Table 1). SS sheep scored significantly higher on GPA1 compared with C: Animals were scored as more alert/anxious/nervous for SS compared with C (Table 1; Figure 2c). There were no significant differences between the driving treatments on GPA2. Physiology. Leptin concentration decreased by 14% for the C driving treatment compared with only 4% for the SS driving treatment. Red blood cell numbers decreased less for SS compared with marked changes during C. White blood cell numbers decreased for C but increased for SS. Cortisol concentration was higher before transport compared with after transport for both transport events; however, it decreased by 46% for C and only 23% for SS. HR showed an effect of transport, but both HR and HRV showed no significant Treatment £ Time interaction effects. Compared with a 1.98C decrease (both average and maximum Tcore values) for C (compared with the same 40-min period on the nontransport days), Tcore increased substantially during SS (average Tcore, 0.588C; and maximum Tcore, 0.658C; p , .001). Average and maximum Tcore were 0.238C and 0.298C, respectively, higher during SS compared with C. Average Tcore of the sheep was higher during the last 40 min of transport during SS, but it was lower during the equivalent period of C. Correlation of physiology with behavioral expression. Leptin concentration and average and maximum Tcore correlated positively with GPA1, while HRV, basophil, and lymphocyte

GPA2 GPA1

3: Driving style

anxious/agitated/ worried alert/curious/aware alert/anxious/ nervous scared/terrified/ worried

Descriptive Terms "

"

"

IGF-1

Leptin

"

#

Glucose " "

" "

Hematocrit "

Monocytes

#

Basophils

#

Lymphocytes

HR

#

"

HRV

"

" "

"

Average and Maximum Tcore

Note. GPA ¼ generalized Procrustes analysis; IGF-1 ¼ insulin-like growth factor-1; HR ¼ heart rate; HRV ¼ heart rate variability; Tcore ¼ core temperature. We have only shown data for Experiments 2 and 3, where we recorded significant treatment effects.

GPA2

GPA1

2: Flooring

Experiment

Red Blood Cell Count

Physiological Measure

TABLE 2 Correlation of Physiological Measures With GPA Scores

Downloaded by [114.30.124.18] at 16:23 16 April 2015

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

13

14

WICKHAM ET AL.

numbers were negatively correlated (Table 2). Therefore, sheep described as more alert/anxious/ nervous had increased leptin concentrations, higher Tcore, lower HRV, and decreased numbers of basophils and lymphocytes. Plasma glucose concentration and average and maximum Tcore were positively correlated with GPA2 (i.e., higher in sheep described as more scared/terrified/ worried).

Downloaded by [114.30.124.18] at 16:23 16 April 2015

DISCUSSION We have demonstrated that people can reach consensus in their assessments of the behavioral expression of individual sheep and can distinguish between sheep exposed to altered transport conditions (altered flooring and driving behavior). This supports a previous study (Wickham et al., 2012) where observers distinguished between sheep who were either naı¨ve or habituated to transport or cattle exposed to various transport treatments (Stockman et al., 2011, 2013). The GPA consensus profiles explained half the variation between the observer scores for all three experiments, showing that there was consensus between observers with regard to how they scored the behavioral expression of sheep. From these GPA consensus profiles, it was possible to identify distinct clusters of words with similar meanings. For example, calm, relaxed, and comfortable formed one cluster of words, and anxious, agitated, and worried formed another cluster on GPA1 for the flooring study (Experiment 2). Similar clusters were found for Experiments 1 and 3. It is important to note that we found treatment differences in GPA scores for Experiments 2 and 3. For Experiment 2, sheep were scored as more alert/curious/aware for NG flooring compared with C (where they were scored as more tired/passive/terrified). For Experiment 3, sheep were scored as more alert/anxious/nervous during SS driving compared with C (where sheep were scored as more calm/relaxed/sleepy). We did not find a treatment difference in the ventilation treatment (Experiment 1), which may reflect that the magnitude of stress during the CV transport event was not sufficient to alter the behavior of the sheep (Smith et al., 2004); the duration of the journey may have to be longer, or the ambient temperature higher, before any behavioral differences would be observed. Previous studies have shown that sheep were more stressed during a journey that contained frequent stops and accelerations (Bradshaw, Hall, & Broom, 1996; Cockram et al., 2004; Ruizde-la-Torre et al., 2001). For example, Cockram et al. (2004) found that more than 80% of losses of balance of sheep during experimental transport trips could have been caused by a driving event. Sheep had fewer losses of balance, increased lying behavior, more rumination, and fewer disturbances on a motorway journey compared with single carriageway driving, which was most likely due to fewer driving events occurring on the motorway, and the chances of sheep losing their balance increased with two or more events occurring simultaneously (Cockram et al., 2004). Ruiz-de-la-Torre et al. (2001) found that sheep had greater concentrations of cortisol and increased HRs when they were transported with frequent changes in acceleration compared with smooth roads with few changes in acceleration. Similarly, in the present study, increased HR was recorded during SS compared with C, suggesting that there was a greater stress response during the SS event. Although we have found previous studies comparing the effects of ventilation and driving style on animals during transport, there appears to have been little attention paid regarding the

Downloaded by [114.30.124.18] at 16:23 16 April 2015

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

15

importance of different flooring styles for transport welfare. Flooring may be an important determinant of the comfort and welfare of animals during transport, and it is an easily modified aspect of truck design that could be changed to improve welfare outcomes. Similarly, driving style affects the comfort of transported animals and can therefore be modified to improve welfare outcomes. For example, in Europe, drivers receiving bonuses in their pay for reducing fuel usage drive more slowly and with more gentle accelerations, which also has the benefit of improving animal welfare in terms of decreased bruising and bone breakages (Greger, 2007). Sheep described as generally more “active” and “alert” in Experiments 2 (flooring) and 3 (driving style; see other descriptive terms of their behavioral expression listed in Table 2) showed increases in leptin concentration, IGF-1, hematocrit, red blood cell count, monocytes, and average and maximum Tcore, or reduced basophils and lymphocytes. Plasma glucose concentration and HRV responses differed between these two experiments. These findings reflect the generally acknowledged findings regarding sheep responding to transport. For example, in response to transport, previous studies have shown increased Tcore temperature (Ingram, Cook, & Harris, 2002; Parrott, Lloyd, & Brown, 1999), increased plasma glucose concentrations (Kent & Ewbank, 1983), and increases in neutrophils and eosinophils but decreases in lymphocyte numbers (Kent & Ewbank, 1986). Leptin inhibits activity of the HPA axis as a response to stress (Heiman et al., 1997), although it has not been previously tested in a transport environment. Our study therefore indicates that the behavioral expression of animals reflects physiological responses that have been established for transportchallenged animals. We note that cortisol, insulin, and b-OH levels did not correlate to any of the GPA dimensions for any of the experiments. Transportation and sampling times are important considerations, because cortisol concentrations increase in response to loading and unloading and during the first part of a journey (Knowles, 1999; Warriss et al., 1995), and they decrease in animals once they become habituated to the process (Lay et al., 1996). Cortisol concentration measures might not have been directly relevant to the time point that we analyzed for QBA, while insulin and b-OH may similarly be too labile. These results highlight the importance of being able to measure indicators of welfare at the relevant points. Ideally, we would have sampled cortisol regularly during transport; however, showing observers footage of the animals for QBA required minimally invasive sampling methods during transport, and therefore, we had to rely on comparisons before and after transport.

CONCLUSION In conclusion, there was consensus on the ability of observers to interpret behavioral expressions of sheep during various transport treatments. We also found that our naı¨ve observers were able to distinguish between the flooring and driving-style transport treatments. We have made no assumptions about the interpretation of the observers’ scores. Although the descriptive terms can capture aspects of the affective states of these animals, we have deliberately avoided trying to interpret the findings of our study in the light of “emotional capacity of sheep” because we are adhering to the objectivity of the quantification method and not trying to superimpose qualitative interpretation. As an analogous example, even a naı¨ve observer could count the number of cattle showing a degree of lameness in a herd; however, only the opinion of an experienced animal

Downloaded by [114.30.124.18] at 16:23 16 April 2015

16

WICKHAM ET AL.

welfare scientist/farmer/livestock handler is likely to be relevant in interpretation of the importance of this value. As an important aspect of validating the use of QBA as a welfare assessment tool, the behavioral expression scores demonstrated significant correlations with numerous physiological parameters in a manner that was consistent with the interpretation that the behavioral expressions of sheep reflected their current physiological states. This makes interpretation of behavioral expression through the process of QBA a powerful method of assessing the individual animal’s experience of his or her environment. Although this experiment was carried out in a reasonably restrictive environment—that is, for sheep transported during a period of 90 min— the fact that there were significant experimental treatment differences suggests that the interpretation of behavioral expression can be extremely sensitive to even subtle changes in an animal’s environment. This method therefore holds great promise for the development of objective measures of animal welfare. ACKNOWLEDGMENTS Thanks to Murdoch University farm staff, Kim Thomas (manager), and Don Hook. Thanks also to Murdoch University Veterinary Clinical Pathology and M. Blackberry at The University of Western Australia, School of Animal Biology, for blood analyses. FUNDING This study was funded by Meat & Livestock Australia. SUPPLEMENTAL MATERIAL Supplemental data for this article can be accessed on the publisher’s website. REFERENCES Anon. (2008). Australian animal welfare standards and guidelines for the land transport of livestock. Retrieved from http://www.animalwelfarestandards.net.au/land-transport Baldock, N. M., & Sibly, R. M. (1986). Effects of management procedures on heart-rate in sheep. Applied Animal Behaviour Science, 15, 191. Baldock, N. M., & Sibly, R. M. (1990). Effects of handling and transportation on the heart rate and behaviour of sheep. Applied Animal Behaviour Science, 28, 15–39. Barnett, J. L., & Hemsworth, P. H. (1990). The validity of physiological and behavioural measures of animal welfare. Applied Animal Behaviour Science, 25, 177–187. Bradshaw, R. H., Hall, S. J. G., & Broom, D. M. (1996). Behavioural and cortisol responses of pigs and sheep during transport. Veterinary Record, 138, 223 –234. Broom, D. M. (1991). Animal welfare: Concepts and measurement. Journal of Animal Science, 69, 4167–4175. Cockram, M. S., Baxter, E. M., Smith, L. A., Bell, S., Howard, C. M., Prescott, R. J., & Mitchell, M. A. (2004). Effect of driver behaviour, driving events and road type on the stability and resting behaviour of sheep in transit. Animal Science, 79, 165–176. Cole, D. J., Roussel, A. J., & Whitney, M. S. (1997). Interpreting a bovine CBC: Collecting a sample and evaluating the erythron. Veterinary Medicine, 92, 460– 468.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

QUALITATIVE BEHAVIORAL ASSESSMENT IN SHEEP WELFARE

17

Crookshank, H. R., Elissalde, M. H., White, R. G., Clanton, D. C., & Smalley, H. E. (1979). Effect of transportation and handling of calves upon blood serum composition. Journal of Animal Science, 48, 430 –435. Dawkins, M. S. (2004). Using behaviour to assess animal welfare. Animal Welfare, 13, S3–S7. Dawkins, M. S. (2006). A user’s guide to animal welfare science. Trends in Ecology & Evolution, 21, 77 –82. De Silva, M., Kiehm, D. J., Kaltenbach, C. C., & Dunn, T. G. (1986). Comparison of serum cortisol and prolactin in sheep blood sampled by two methods. Domestic Animal Endocrinology, 3, 11–16. Dijksterhuis, G. B., & Heiser, W. J. (1995). The role of permutation tests in exploratory multivariate data analysis. Food Quality and Preference, 6, 263–270. Dwyer, C. M., & Bornett, H. L. I. (2004). Chronic stress in sheep: Assessment tools and their use in different management conditions. Animal Welfare, 13, 293–304. Erickson, K., Drevets, W., & Schulkin, J. (2003). Glucocorticoid regulation of diverse cognitive functions in normal and pathological emotional states. Neuroscience & Biobehavioral Reviews, 27, 233–246. Fisher, A. D., Stewart, M., Tacon, J., & Matthews, L. R. (2002). The effects of stock crate design and stocking density on environmental conditions for lambs on road transport vehicles. New Zealand Veterinary Journal, 50, 148 –153. Fleming, P. A., Paisley, C., Barnes, A. L., & Wemelsfelder, F. (2013). Application of qualitative behavioural assessment to horses during an endurance ride. Applied Animal Behaviour Science, 144, 80–88. Grandin, T. (1997). Assessment of stress during handling and transport. Journal of Animal Science, 75, 249 –257. Greger, M. (2007). The long haul: Risks associated with livestock transport. Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, 5, 301 –312. Heiman, M. L., Ahima, R. S., Craft, L. S., Schoner, B., Stephens, T. W., & Flier, J. S. (1997). Leptin inhibition of the hypothalamic-pituitary-adrenal axis in response to stress. Endocrinology, 138, 3859–3863. Ingram, J. R., Cook, C. J., & Harris, P. J. (2002). The effect of transport on core and peripheral body temperatures and heart rate of sheep. Animal Welfare, 11, 103–112. Jones, M., & Allison, R. (2007). The ruminant complete blood count—evaluation and interpretation. Paper presented at the CanWest Veterinary Conference, Banff, Alberta, Canada. Kent, J. (1997). Stress in transported sheep. Comparative Haematology International, 7, 163–166. Kent, J. E., & Ewbank, R. (1983). The effect of road transportation on the blood constituents and behaviour of calves. I. Six months old. British Veterinary Journal, 139, 228– 235. Kent, J. E., & Ewbank, R. (1986). The effect of road transportation on the blood constituents and behaviour of calves. II. One to three weeks old. British Veterinary Journal, 142, 131–140. Kettlewell, P. J., Hoxey, R. P., Hampson, C. J., Green, N. R., Veale, B. M., & Mitchell, M. A. (2001). Design and operation of a prototype mechanical ventilation system for livestock transport vehicles. Journal of Agricultural Engineering Research, 79, 429–439. Knowles, T. G. (1998). A review of the road transport of slaughter sheep. Veterinary Record, 143, 212–219. Knowles, T. G. (1999). A review of the road transport of cattle. Veterinary Record, 144, 197–201. Lay, D. C., Friend, T. H., Bowers, C. L., Grissom, K. K., & Jenkins, O. C. (1992). A comparative physiological and behavioral study of freeze and hot-iron branding using dairy cows. Journal of Animal Science, 70, 1121–1125. Lay, D. C., Friend, T. H., Randel, R. D., Jenkins, O. C., Neuendorff, D. A., & Kapp, G. M. (1996). Adrenocorticotropic hormone dose response and some physiological effects of transportation on pregnant Brahman cattle. Journal of Animal Science, 74, 1806–1811. Morme`de, P., Andanson, S., Aupe´rin, B., Beerda, B., Gue´mene´, D., & Malmkvist, J. (2007). Exploration of the hypothalamic-pituitary-adrenal function as a tool to evaluate animal welfare. Physiology & Behavior, 92, 317–339. Napolitano, F., De Rosa, G., Braghieri, A., Grasso, F., Bordi, A., & Wemelsfelder, F. (2008). The qualitative assessment of responsiveness to environmental challenge in horses and ponies. Applied Animal Behaviour Science, 109, 342–354. Parrott, R. F., Lloyd, D. M., & Brown, D. (1999). Transport stress and exercise hyperthermia recorded in sheep by radiotelemetry. Animal Welfare, 8, 27 –34. Rousing, T., & Wemelsfelder, F. (2006). Qualitative assessment of social behaviour of dairy cows housed in loose housing systems. Applied Animal Behaviour Science, 101, 40–53. Ruiz-de-la-Torre, J. L., Velarde, A., Diestre, A., Gispert, M., Hall, S. J. G., Broom, D. M., & Manteca, X. (2001). Effects of vehicle movements during transport on the stress responses and meat quality of sheep. Veterinary Record, 148, 227–229. Schmidt, A., Biau, S., Mo¨stl, E., Becker-Birck, M., Morillon, B., Aurich, J., . . . Aurich, C. (2010). Changes in cortisol release and heart rate variability in sport horses during long-distance road transport. Domestic Animal Endocrinology, 38, 179 –189.

Downloaded by [114.30.124.18] at 16:23 16 April 2015

18

WICKHAM ET AL.

Sjaastad, O. V., Hove, K., & Sand, O. (2010). Physiology of domestic animals. Oslo, Norway: Scandinavian Veterinary Press. Smith, G. C., Grandin, T., Friend, T. H., Lay, D. J., & Swanson, J. C. (2004, December). Effect of transport on meat quality and animal welfare of cattle, pigs, sheep, horses, deer, and poultry. Retrieved from http://www.grandin.com/ behaviour/effect.of.transport.html Stockman, C. A., Collins, T., Barnes, A. L., Miller, D., Wickham, S. L., Beatty, D. T., . . . Fleming, P. A. (2011). Qualitative behavioural assessment of cattle naı¨ve and habituated to road transport. Animal Production Science, 51, 240 –249. Stockman, C. A., Collins, T., Barnes, A. L., Miller, D., Wickham, S. L., Beatty, D. T., . . . Fleming, P. A. (2013). Flooring and driving conditions during road transport influence the behavioural expression of cattle. Applied Animal Behaviour Science, 143, 18 –30. Stockman, C. A., Collins, T., Barnes, A. L., Miller, D., Wickham, S. L., Verbeek, E., . . . Fleming, P. A. (2014). Qualitative behavioural assessment of the motivation for feed in sheep in response to altered body condition score. Animal Production Science, 54, 922– 929. von Borell, E., Langbein, J., Despre´s, G., Hansen, S., Leterrier, C., Marchant-Forde, J., . . . Veissier, I. (2007). Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals: A review. Physiology & Behavior, 92, 293–316. Warriss, P. D., Brown, S. N., Knowles, T. G., Kestin, S. C., Edwards, J. E., Dolan, S. K., & Phillips, A. J. (1995). Effects on cattle of transport by road for up to 15 hours. Veterinary Record, 136, 319–323. Watanabe, S. (2007). How animal psychology contributes to animal welfare. Applied Animal Behaviour Science, 106, 193 –202. Wemelsfelder, F. (1997). The scientific validity of subjective concepts in models of animal welfare. Applied Animal Behaviour Science, 53, 75 –88. Wemelsfelder, F., & Farish, M. (2004). Qualitative categories for the interpretation of sheep welfare: A review. Animal Welfare, 13, 261– 268. Wemelsfelder, F., Hunter, E. A., Mendl, M. T., & Lawrence, A. B. (2000). The spontaneous qualitative assessment of behavioural expressions in pigs: First explorations of a novel methodology for integrative animal welfare measurement. Applied Animal Behaviour Science, 67, 193 –215. Wemelsfelder, F., Hunter, T. E. A., Mendl, M. T., & Lawrence, A. B. (2001). Assessing the ‘whole animal’: A free choice profiling approach. Animal Behaviour, 62, 209–220. Wickham, S. L., Collins, T., Barnes, A. L., Miller, D. W., Beatty, D. T., Stockman, C., . . . Fleming, P. A. (2012). Qualitative behavioral assessment of transport-naı¨ve and transport-habituated sheep. Journal of Animal Science, 90, 4523–4535.