one: introduction 1

1.1. The effect of the visual environment on avian welfare

1.1. Summary

There is an increasing body of evidence showing that the vision of birds is significantly different from that of humans in many respects. Most behavioural research so far has concentrated on the ecological functions of these differences, in particular the role of ultraviolet (UV, referring to UVA portion of spectrum, 320-400 nm) vision in behaviours such as mate choice and foraging (see review, Cuthill et al. 2000b).

It is clear that the appearance of objects and conspecifics affects many aspects of bird behaviour. The appearance of an object depends not just on the visual system of the viewer, but on the characteristics of the illuminating light and the degree to which that object reflects or absorbs the light falling upon it. In sensory ecology, we assume that the visual systems of animals are adapted to making visual discriminations under the range of lighting conditions found in their natural habitats (Lythgoe 1979; Barth and Schmid 2001). However, we frequently keep birds indoors in farms, zoos and laboratories, under artificial light in visual environments that are quite unlike natural visual scenes. Most artificial lighting is UV deficient. Also, conventional fluorescent lights flicker, albeit at a level above that which most humans can perceive. Given that that birds can perceive UV (reviewed by Cuthill et al. 2000b) and may have better motion perception than us (D'Eath 1998; Fleishman and Endler 2000), such lighting may limit the functionality of their visual systems and impair their welfare (Sherwin et al. 1999; Maddocks 2001; Greenwood et al. 2002; Maddocks et al. 2002c). Also, experiments performed under these conditions may have poor external validity1, as may experiments in which the animal looks at computer or video monitors as stimuli (D’Eath 1998; Fleishman and Endler 2000).

1 The extent to which the results of an experiment can correctly be generalised to other circumstances one: introduction 2

There is a substantial body of research into the effects of different light sources and different lighting regimes on birds. Most of this work has been on poultry, and has either focussed on trying to maximise growth or egg production, or minimising aberrant behaviours such as feather pecking and cannibalism (see reviews, Manser 1996; Lewis and Morris 1998; Maddocks 2001). Studies assessing the effect of light environments on general welfare are rarer, and often produce conflicting or inconsistent results. This is partly because different studies use different methods, timescales and sample sizes with which to assess welfare, and partly because the interpretation of many studies is problematic as more than one experimental lighting variable has been manipulated simultaneously (Maddocks 2001).

This thesis concentrates on whether or not the absence of UV, or the flicker rate of conventional fluorescent light, adversely affects bird welfare. The aim was to run tightly controlled experiments which, unlike most previous studies, were not confounded by the simultaneous manipulation of several variables, and controlled for the sensitivity of birds to UV light as well as their sensitivity to human visible wavelengths. As the only way to accurately assess welfare is to use a range of behavioural, physiological and health measures (Dawkins 1990; Broom 1991; Appleby and Hughes 1997; Broom 2001), welfare has been assessed using a variety of welfare indicators, namely preferences, behavioural measures and blood plasma levels of the major ‘stress’ hormone in birds, corticosterone.

In this review, there are five main sections. In section 1.2., I discuss what animal ‘welfare’ means and describe how welfare may be measured. This includes a consideration of the philosophical problems underlying the assessment of animal welfare. As biologists often choose to measure stress as a potential welfare indicator, in section 1.3., I discuss the meaning and measurement of ‘stress’. I then turn to the two parameters of light environments whose effects I have investigated. In section 1.4., I describe the differences between human and avian colour vision and explain why the absence of UV in standard artificial lights may adversely affect bird welfare. In sections 1.5. and 1.6., I discuss what is known about the sensitivity of humans and birds to repetitive visual stimulation, both from the flicker from fluorescent lamps and from spatially repetitive elements in the visual surround, and the potential welfare effects of such stimuli. I have one: introduction 3

not covered the effects of light intensity, photoperiod or different types of light source on bird welfare and productivity, as these aspects of lighting are not central to the topic of this thesis, and recent reviews on these subjects already exist (see Lewis et al. 1992; Manser 1996; Lewis and Morris 1998; Maddocks 2001). Section 1.7. summarises the main points of the previous sections and explains why the research in this thesis was carried out.

1.2. Welfare assessment

1.2.1. The meaning and measurement of ‘welfare’

Animal ‘welfare’ is a term that has sprung from the increasing concern of society about the treatment of animals and their quality of life (Fraser et al. 1997). The public looks to science for guidance in their concerns. However, animal welfare is not a purely scientific concept and involves value judgements that differ between people (Duncan and Fraser 1997). This means that firstly it is hard to formulate a concrete definition of the term, and that secondly that entirely objective welfare assessment is difficult to achieve (Clark et al. 1997a; Bath 1998; Webster 1998). Our interest in animal welfare springs from the moral concern we feel for animals. This is based upon the belief that they have the capacity for subjective experience, in particular the concern that they may be able to suffer, that is to experience unpleasant subjective feelings (Broom and Johnson 1993). For example, unless we are prepared to extend our concerns to plants, it would not matter on a moral level if an animal was underweight, or injured, if it had no awareness of hunger or pain (Duncan and Fraser 1997). Whether or not an animal possesses awareness of such things will depend on its sensory and cognitive capacities. For example, pain in humans seems to have both a sensory and cognitive component, and without both components intact humans do not suffer from pain. Some humans with nerve damage cannot feel noxious stimuli that are damaging to tissue (Bateson 1991). However, humans whose sensory inputs are intact but who have had the frontal lobes of their cerebral cortex removed may report that they can feel pain but it no longer bothers them (Sweet 1971). Although it is possible that many animals do not have the requisite sensory and cognitive abilities to one: introduction 4

suffer in the sense that humans experience suffering, most people believe that animals can experience emotions and feelings, and that they can experience pleasure if their life is good, and suffer if conditions are bad. Hence the capacity of animals to experience suffering and enjoyment is a fundamental concern in the study of animal welfare (Dawkins 1988; Rollin 1992; Sambraus 1998; Dawkins 2001a), and some say that in fact it is the only issue (Duncan 1993). Welfare also includes assessing the health and state of behavioural and physiological coping mechanisms of the animal (Broom 1986, 1996, 1998, 2001; Dawkins 2001a). That said, although disease and injury are universally recognised as being signs of poor welfare, evidence from the human literature suggests that individual perception of welfare and happiness is not correlated strongly with physical health, being more reliant on the temperament, expectations and degree of social support and control over their life each individual has (Lutgendorf 2001).

Welfare therefore depends partly on subjective states that correspond to how animals feel (Duncan 1993; Dawkins 1998), and partly on their state of health (Broom 2001). Welfare can be conceptualised as a multi-dimensional state of well-being, or characteristic of an animal, that can vary along a scale from being very poor to very good (Broom and Johnson 1993; Broom 2001). Although this is a helpful concept, sometimes individual welfare indicators may not always vary along a simple continuum, as indicators of poor welfare may not be simply the opposite of indicators of good welfare (Knierim et al. 2001). Alternatively, good welfare can be conceptualised as the degree of evolutionary fitness which the animal possesses, i.e. the degree to which the animal is ‘on track’ to survive and reproduce (Dawkins 2001a). However, if fitness is defined as reproductive success, animals with high fitness do not necessarily have better welfare than those with low fitness (Jensen 2001). There are no absolute standards or definitions, as animal welfare is a multidimensional concept involving many factors for which there is no obvious combining or weighting formula (Fraser 1995). The measurement of any element of welfare is essentially a measure of quality of life, with peoples’ values determining how much importance they place on any particular measure (Duncan and Fraser 1997).

In order to suffer from any given factor, an animal needs to be aware of its experience and have unpleasant feelings about it. Suffering would almost certainly increase if the animal could mentally ‘travel through time’, and remember specific unpleasant events, one: introduction 5 and also anticipate future unpleasant events. This means that when making a welfare judgement based on an animal’s feelings one has to consider the cognitive capacities of the animal, and confront the difficult question of whether or not the animal is conscious of or aware of anything (Dawkins 1998). However, poor welfare can also be considered to occur in the absence of negative feelings or suffering, for example if the animal has a disease but is unaware of it (Broom 2001). Although investigations of welfare usually concentrate on identifying poor welfare, it is also valuable to evaluate the state of animals experiencing good welfare for comparative purposes. It is hard to directly demonstrate contentment and pleasure, as its measurement has been obscured by an emphasis on the measurement of distress, a lack of adequate terminology to describe good welfare, and the difficulty of measuring the central nervous systems that regulate feelings of pleasure (Carter 2001; Knierim et al. 2001; Lutgendorf 2001). However, good welfare may be operationally defined as an absence of problems and an ability to function normally (Broom 2001), or a state in which the animal can function well and feels well (Knierim et al. 2001).

The task of animal welfare science is to try to understand animal needs and to find ways to provide for them. This information then forms the basis for our ethical and subjective judgements about how animals should be treated. Deciding exactly what standards of animal husbandry make us feel comfortable in ourselves lies entirely in the realms of human belief, and the decision is outside of the boundaries of hard science. That is, the answer cannot be derived exclusively from biological data (Bermond 2001; Sachser 2001) as the emotional and conscious feelings of any organism are not directly observable (Sambraus 1998; Bermond 2001; Van Rooijen 2001). However, the objective measurement of ‘welfare’ is still feasible if one assumes that conscious states will have certain properties that are observable in certain aspects of physiology and behaviour (Appleby and Hughes 1997). Quantitative research into how animals respond to various features of their environment is therefore important in the field of animal welfare, as it provides primary information upon which ethical beliefs can subsequently be based. Naturally, certain assumptions about the properties that these unobservable states of mind may have will underlie the interpretation of some of my work, and I fully accept that they are questionable. After all, it is likely that not all capacities are the same in humans and animals (Bateson 1991), and argument-by-analogy certainly has its weaknesses (Sherwin one: introduction 6

2001). As the subjective experience of suffering may be completely different in other species (Bateson 1991), and since there is no firm scientific evidence that I know of that unequivocally proves that any organism, human or non-human, consciously experiences anything, I therefore ask the reader to keep in mind that although animals can behave in remarkably complex ways, they may not experience things in the way that we do and, unlike humans, may have no concept of happiness or sadness.

Consequently, I plan to retain the distinction between belief and hard evidence as much as possible throughout this thesis. My objective is to investigate the behavioural and physiological responses of birds in response to manipulations of the visual environment. This will provide useful information, as behavioural and physiological responses to particular environments can help when making ethical judgements about their effects on welfare, regardless of whether or not feelings are involved (Broom 2001). However, it will not prove conclusively whether or not birds have feelings about, or suffer in, different light environments. At our present state of knowledge, judgements of this type still have to rest on a mixture of evidence, belief, assumptions and inferences, and the influence of the cultural norms on which our society is based.

Developing a comprehensive understanding of the welfare of any animal is far from easy. It can be very difficulty to assess and compare the welfare of different groups of animals, as the welfare of any animal is reliant on such a wide range of interacting factors (Clark et al. 1997a). The response to these factors may differ between individuals according to their sex, genetic predispositions and prior experience, and even within the same individual according to age, time of year or current mental and physiological state (Broom 2001; Knierim et al. 2001; Sachser 2001).

Welfare is currently assessed using a wide range of methodologies from diverse disciplines, encompassing responses to stimuli that may be neural, physiological or behavioral (Fraser et al. 1997). Assessment of welfare may emphasise feelings, health, or a combination of both, including measures of how well the animals cope with their environment (Broom 2001), and for social animals, the quality of their social experiences and social bonds (Carter 2001). Whatever the method employed, welfare studies require particular caution in interpretation, as the criteria chosen may not always be relevant to one: introduction 7 the well-being of the animal (Clark et al. 1997b). Duncan and Fraser (1997) categorised measurement methods into three main approaches: ‘feelings-based’ (see section 1.2.2.), ‘function based’ (see section 1.2.3.) and ‘natural behaviour’ (see section 1.2.4.).

1.2.2. ‘Feelings-based’ approaches

‘Feelings based’ approaches (Duncan and Fraser 1997) centre around the view that animals have feelings, that they should not suffer and that they should not be deprived of pleasure. Welfare is defined in terms of the quality of subjective feelings or emotional experiences of the animal. Emotions are not the same thing as feelings, as the existence of an emotion does not imply conscious experience of that emotion (Rolls 1999). Emotions are literally ‘states of mind’, in that they have a signalling and co-ordinating effect on brain structures and behaviour, and are characterised by certain patterns of neurological activity, hormone release and physiological and behavioural consequences, whereas feelings are mental constructs (e.g. anxiety) of which the individual is aware (Broom, 1998; Broom, 2001; Spruijt 2001). The emotional state of an animal will determine what conscious feelings it may be able to have. For animals that have evolved emotions, good welfare means that the individual animals feel well, in that they experience positive affective states (Fraser et al. 1997; Broom, 1998; Sachser, 2001). Emotions are thought to be adaptive in that they provide a ‘common currency’ which which animals can evaluate the priority to which they should give different stimuli, and which act as reinforcers for learning (Broom 1998; Dawkins 2001a). Therefore, investigations of what factors animals find to be positively or negatively reinforcing are essential in the investigation of their emotional state and feelings (Dawkins 2001a). The aim of feelings-based approaches is to identify factors that produce negative feelings, such as pain, fear, frustration, hunger or thirst, and factors that promote positive feelings, such as comfort and contentment.

Methods involve giving animals choices to see what they prefer, and by looking at their motivation to avoid or obtain certain conditions. The assumption is that animals will choose, and work to obtain, an environment in which they get more positive feelings. one: introduction 8

Researchers may also look for behavioural, physiological and neurological indicators of emotional states in response to certain stimuli (Duncan and Fraser 1997; Carter 2001). For example, negative emotional states may be signalled by vocalising or by the performance of stereotypical behaviour. Certain physiological responses to a stimulus may also indicate negative states, for example rises in heart rate and/or corticosteroid hormones reflect general arousal.

The most common approach in the investigation of feelings is to use preference tests, which require animals to choose between two or more options or environments. They are used to determine animals’ preferences for factors such as temperature, lighting and flooring. They can also be used as a measure of how strongly aversive an animal finds something (Fraser and Matthews 1997). The principle behind preference testing is simple, but getting accurate information about what animals like can be difficult. Preferences can vary with experience, age, time and environmental conditions (Fraser and Matthews 1997) and level of arousal (Walters et al. 1982). There may also be considerable individual differences in behaviour (Manteca and Deag 1994). Also, preference for one condition over another only shows relative preference. An animal may prefer one option to another, but this does not mean that it likes either of them. It is also possible that the less preferred choice is still valued by the animal (Duncan 1993), or that the animal has only investigated one option; animals must experience all the environments they have as alternatives before they can truly be said to have made a choice (Hughes 1977). It is also important to control for previous experience and preferred group size to separate the effects of genuine preference from a preference for conditions that are either novel or familiar. A further potential complication is that animals that have been exposed to unfavourable conditions for some time may no longer make meaningful choices due to general unresponsiveness or learned helplessness (Broom and Johnson 1993).

It has been claimed that behavioural measures of animal aversion to something are easier to interpret in terms of suffering than are measures of general physiological arousal (Rushen 1996). However, choice and preference cannot be used to infer conscious experience, either (Dawkins 2001a, 2001b). It is notable that parasitic plants will reject and grow away from host plants of poor nutritional value, before having fed from the one: introduction 9

potential host (Kelly 1992). This is very similar to animal avoidance mechanisms, and yet most people would be unwilling to attribute the experience of aversion to a plant (Dawkins 2001b). It is important to appreciate that if choice mechanisms are ‘hard-wired’ there is no need for the animal to have emotional feelings about the choice, as it adds nothing to the animal’s functional response (Rolls 1999; Dawkins 2001a).

Nevertheless, preference tests are still central to welfare research, as they give a good indication as to whether there are any potential welfare or stress effects worthy of further investigation by other means (Dawkins 1999). If animals are shown to have a preference, operant conditioning can be used to assess the strength of the preference or aversion by investigating how hard an animal will work to ‘pay’ for its preferred conditions or to avoid something. By seeing how hard the animal will work if the ‘price’ of a preferred condition goes up, we can see how essential that condition is to the animal. Behaviours the animal continues irrespective of the cost (‘inelastic’ behaviours) are regarded as vital to welfare, whereas behaviours that cease as the price goes up (‘elastic’ behaviours) are seen as of lesser importance (Dawkins 1983a, 1990). Elasticity can be measured by increasing the workload necessary to gain a reward, such as the number of pecks needed to gain food, or via limiting the time available for all activities and measuring the percentage change in consumption relative to percent change in time. The percent change in behaviour as the price increases can be plotted against the cost to produce a ‘demand curve’. How such demand curves should be interpreted is debatable. Originally, it was suggested that the shape of the curve should be used to determine the relative importance of each behaviour, with inelastic behaviours being considered to be the most vital ones (Dawkins 1998, plus see Fig 1.1. overleaf).

one: introduction 10

Figure 1.1. Hypothetical inelastic and elastic demand curves. The inelastic demand remains high as the cost to the animal increases, whereas the elastic demand decreases with increased cost.

Inelastic demand Quantity of

behaviour Elastic demand

Price

However, this method is difficult to apply if the elasticity of a demand is variable (i.e. if plotting the data on log-log co-ordinates results in a non linear curve, e.g. Houston 1997). Alternatively, the area under the demand curve could be calculated, with greater areas under the curve indicating greater inelasticity (Houston 1997). However, this approach is not always appropriate either. Consider the three demand curves in Fig 1.2. The curve A indicates elastic behaviour, whereas the straight lines B and C indicate inelastic behaviour. However there is a greater area under B than C. The areas under A and C are similar, and yet the tailing off of the curve of A seems to indicate that A is the more elastic behaviour (Dawkins 1997).

Figure 1.2. Hypothetical demand curve for three different behaviours (Dawkins 1997).

A Quantity of B behaviour

C

Price one: introduction 11

Further criticisms of this approach are that animals may not be able to judge the relative benefit of denying themselves an immediate gain (in terms of not working) in order to receive a payoff later (e.g. access to desired condition, Dawkins 1988; Duncan 1993; Mendl 1997). Also, it may not be possible to rank the relative importance of animal needs using demand curves, as a given work load may not be an equivalent ‘common currency’ for each need (Appleby 1997). Furthermore, certain elastic behaviours may be very valuable to animals (Hughes and Duncan 1988). Equally, it is possible that a highly motivated animal may not actually suffer when it cannot perform its desired behaviour (Townsend 1990). That said, demand curves can still be useful in assessing the importance of certain factors to animals, even if the demand elasticity does not have a simple relationship with welfare, as they provide an indication of how motivated an animal is in certain conditions (Dawkins 1990; Timberlake 1990). However, recent work has highlighted that considering price elasticity in isolation may be invalid as this method fails to consider the income the animal has available, and is only useful if one is comparing two resources whose initial demand level is equal (Kirkden et al. 2003). Furthermore, successive units of a resource are not necessarily equal in value to the animal. Therefore, Kirkden et al. (2003) suggest that it is instead better to investigate how much a given quantity of resource is worth to an animal relative to other things when the animal has a fixed income to spend. This method of investigation is called the consumer surplus technique and is commonly used in human economics to assess consumer demand.

1.2.3. ‘Function-based’ approaches

‘Function based’ approaches (Duncan and Fraser 1997) define welfare in terms of the normal biological functioning of the animal. Variables usually include measures of growth, lifespan, and physiological and behavioural functions (Deag 1996; Manteca 1998). Welfare is assumed to be decreased by disease or malnutrition, and good welfare to be indicated by high growth, good health, high reproductive rate and longevity (Duncan and Fraser 1997). Function based approaches usually concentrate on the measurement of biological ‘stress’, which is considered in more depth in section 1.3. one: introduction 12

It is certainly easier to demonstrate a change in biological functioning than it is to demonstrate a change in what an animal experiences. However, it is not always easy to relate such variables to the animal’s quality of life. For example, if an animal is challenged by restraint, pain or cold, changes occur in its pituitary and adrenal cortex resulting in increased release of glucocorticoids (Selye 1950). It is often assumed that an increase in such chemicals indicates reduced welfare. However, these secretions vary routinely in response to many changes in the animal’s environment, and a rise in their levels may not induce a negative mental state in the animal. A change in physiological function, such as corticosteroid levels, can only be interpreted in terms of welfare if the animal’s normal adaptive responses to environmental change are understood (Sapolsky 1994). Many physiological changes are indicative of the animal’s level of general excitement or arousal rather than showing whether the animal finds a given factor pleasant or unpleasant (Dawkins 2001a). For example, increased concentrations of glucocorticoids are also associated with positive states in humans such as sexual arousal as well as with negative events (Toates 1995; Reul et al. 2001). This is because rises in glucocorticoid concentrations mobilise glucose stores to provide energy, and are indicative of preparation for action, which may be either pleasurable or unpleasant. The same is true of many other autonomic responses. For example, both a fleeing prey animal and the predator which pursues it may both have elevated corticosterone levels, raised body temperature and a high heart rate, yet the psychological state of the two animals is likely to be very different (Dawkins 2001a). Also, autonomic responses in humans do not always correlate well with their reported emotions or behaviour, and furthermore particular autonomic responses can be shown to accompany very different emotions (Dawkins 2001a, 2001b).

The behaviour patterns of an animal can also be analysed in order to assess how well it is coping with or functioning in a particular environment. Behavioural mechanisms are often highly responsive, and can be seen in response to relatively mild stressors that may not provoke a measurable physiological response (Reul et al. 2001). However, behaviour patterns do not, in themselves, directly tell us about the welfare status of an animal, as analysing a stream of behaviour is not sufficient to show whether or not an animal is aware of its content. Neuropsychological evidence shows extreme disjunction between your abilities and behaviours and what you are aware of. For example, humans with one: introduction 13

‘blindsight’ make visual discriminations without awareness; agnosics who claim not to understand language may have sensitivities to certain words; agnosics who say they cannot recognise what an object is or what it is for may still pick up that object and use it correctly (Weiskrantz 2001). Indeed, many animal behavioural responses are similar to those seen in some plants, which are organisms that no one believes are conscious (Dawkins 2001a, 2001b).

1.2.4. ‘Natural behaviour’ approach

A third approach to welfare measurement compares behaviour in the wild to behaviour in captivity. The underlying idea is that the animal will not be content if it cannot perform the full behavioural repertoire exhibited by its wild counterparts. This approach has been widely criticised (Duncan and Fraser 1997). It is assumed that natural behaviour patterns are adaptive, and promote health and well-being through the fulfilment of the animal’s ‘nature’ (Rollin 1992). A greater repertoire of behaviour may correlate with a higher level of welfare (Sherwin and Nicol 1993). However, behaviours which are normally seen in the wild may not indicate good welfare in artificial environments (Sachser 2001), and indeed may not be optimal (Jensen 2001). It is also important to note that the full behavioural repertoire includes activities that are adaptations for adverse circumstances (Duncan and Fraser 1997). For example, a wild bird sitting hunched up with its feathers fluffed up is probably cold, and the absence of this natural behaviour in captivity is more likely to be an indicator of good rather than poor welfare. Animals in the wild normally experience adverse events and periods of prolonged poor welfare (Dawkins 2001a; Sachser 2001), a situation which is presumably undesirable in captive animals. Also, there is often little evidence as to what normal behaviour in any given situation really is. Furthermore, it can be hard to say whether the presence or absence of a given behaviour is an adaptation to a situation or whether it is ‘disturbed’ (Sambraus 1998). The judgement clearly depends on what we define as a normal behavioural repertoire. However, it is unlikely that activities such as stereotypies (repetitive behaviours with no obvious goal or function) and ‘vacuum behaviours’ (e.g. dust-bathing in the absence of a substrate) indicate good welfare even if they increase the range of behaviours the animal one: introduction 14 performs (Dawkins 1990). This is particularly so as such behaviours have been associated with down-regulation of opioids and dopamine in the frontal cortex in mammals (Zanella et al. 1996; Zanella et al. 1998), changes which are associated with depression in humans.

It is also important to note that the needs of domestic animals, which are likely to need and expect the conditions of artificial, human-created environments (such as reliable food availability) may be quite unlike their wild counterparts, and they may suffer severely if forced to lead a ‘natural’ life (Knierim et al. 2001; Sachser 2001). That said, domestic animals are still strongly influenced by their wild ancestry, and an understanding of the natural behaviour and sensory systems of animals is therefore valuable. It enables us to identify factors which are likely to be stressful (Dawkins 2001a; Knierim et al. 2001; Sachser 2001) and also to identify the normal social behaviour of each species, as being able to perform normal social behaviour and form social bonds is an important component of good welfare (Carter 2001).

1.2.5. ‘Synthetic’ approach

Although the approaches I have discussed frequently lead to similar conclusions, different measures of welfare do not always correlate well. For example, high growth rate can be used as a functional approach to welfare assessment. Yet sudden death syndrome in broiler chickens is associated with birds with a higher than average growth rate (Leeson et al. 1995). Also, behavioural measures of welfare may correlate poorly with physiological markers, as sometimes behaviours considered to be poor welfare indicators (e.g. stereotypies) may be found in conjunction with markers conventionally associated with good welfare (e.g. higher opioid levels, see Mormède et al. 2001). As Marian Dawkins (1983b) says, it is “dangerous to put too much weight on any one measure.”

There is another problem with relying on a single measure as evidence of reduced welfare. Scientists may try to get an ‘objective’ measurement of stress caused by a particular factor that they preconceive to be stressful. If one measure fails to give the expected answer, they simply try other sets of ‘objective’ measures until a measure is one: introduction 15 found that supports the scientists’ initial intuitions. The tendency of journal editors to be more interested in publishing significant than non-significant results worsens the situation still further. When someone finds that a factor does not appear to adversely affect welfare, the work is less likely to be published. These problems may conspire to give a biased but allegedly objective view of how stressful any factor really is, which irritates Webster (1998) into stating that “a lot of well-intended welfare research is neither very good science, nor helpful to the animals”.

Consequently, it is wise to use a range of methodologies to compile a comprehensive profile of animal physiological and behavioural responses to stimuli before drawing firm conclusions (Clark et al. 1997b; Broom 2001; Dawkins 2001a; Reul et al. 2001). Preference tests are a useful starting point, but if we wish to make welfare decisions based on the results from preference testing, we need further information. For example, it is useful to know how strongly an animal prefers a given choice and how strongly it will avoid less preferred one. It is also useful to establish whether the factors under consideration have any behavioural or physiological effects.

As these different approaches may lead to different conclusions, the relative likely importance of each measure to welfare should be assessed (Appleby and Hughes 1997; Dawkins 2001a). However, this is not easy, as there is no general agreement as to how we should do this, or on how to decide if different components are truly independent of each other, or as to how we should decide which components are the most important ones. There are no ‘universal’ indicators of welfare, as different stressors may require animals to respond in different ways, and may generate completely different physiological and conscious experiences that consequently may need to be assessed in different ways. The situation is further complicated by the fact that many animal responses are variable, context-dependent and are sometimes just as easily classed as being fitness-enhancing as being welfare-reducing. For example, an animal may be inactive because it is bored or depressed, or on the other hand, reduced activity could be interpreted as a sign that the animal has everything it needs (Dawkins 2001a).

To attempt to get the necessary ‘synthetic view’, I will be using a range of measures to assess the effects of light environments on birds. This will include elements from all three one: introduction 16

approaches: ‘feelings-based’ tests of preference, and monitoring of physiological change; ‘function-based’ measures of growth rates, stress hormones and physical development, and also comparison of behavioural repertoires performed under different light conditions. An element of the ‘natural behaviour’ approach will be applied by looking specifically for aberrant behaviours which tend to appear only in captivity (for example, stereotypic movement patterns and feather pecking). Some of these measures are classic measures of ‘stress’, which is discussed in section 1.3.

1.3. Stress assessment

1.3.1. The meaning of ‘stress’

Animal welfare is more than the presence or absence of stress (Carter 2001). However, the concept of stress is very important in the assessment of welfare (Broom 2001). When we talk colloquially about being ‘stressed’, we usually mean that we feel some subjective combination of being anxious, tired and overwhelmed. However, biologists also use the term ‘stress’ to describe the suite of physiological and behavioural responses that an animal makes in response to changes occurring in its environment, particularly in response to situations that are difficult, challenging or unpredictable. There is no generally accepted definition for the term ‘stress’. It has been used to describe both environmental change and its physiological consequences, and a wide variety of mental and physiological processes, and particularly of the activation of the hypothalamic- pituitary-adrenal (HPA) axis. Sometimes stress has been equated with the response of the body to stimulation or demands of any kind (see review, Broom 2001). However, not all stimulation is harmful; indeed, appropriate environmental stimulation and some degree of challenge can favour good welfare (Selye 1974; Broom and Johnson 1993), particularly as the experience of coping successfully with stress may lead to strong positive emotions (Sachser 2001). Therefore, defining stress as any response to an environmental effect is not helpful in the assessment of welfare (Broom 2001). It is more useful to think of stress as the effect of a factor which disturbs the animal’s homeostatic mechanisms because it is one: introduction 17

either threatening (McEwen 2001) or harmful (Broom 2001). Stress can be defined as harmful if it overtaxes the control systems of the animal and results in adverse consequences that eventually would reduce the fitness of the animal (Broom and Johnson 1993; Broom 2001). The degree of stressfulness of any factor may be variable between individuals, which may be partly genetically determined (Plotsky et al. 2001). Other relevant factors are the suitability of the individual’s coping strategies in response to the stressor (Lutgendorf 2001), the animal’s previous experiences (Plotsky et al. 2001; Reul et al. 2001) and the individual’s degree of control over, and psychological perception of that stressor. The degree of stressfulness of any factor to an animal may also differ over time, as animals’ motivations may change (Jensen 2001). Therefore, the factor should only be called a stressor if it harms the individual concerned (Broom 2001; Jensen 2001). If the environmental effect is either beneficial to the animal or does not result in harm to the animal, then that effect is better thought of as ‘stimulation’ or ‘challenge’ rather than as stress (Broom 2001).

1.3.2. Coping with stress

There are many potentially stressful environmental challenges that animals have to cope with. For example, food shortage, bad weather, drought, infections, parasites, injury, attacks from predators or conspecifics, competition for resources, excessive or insufficient stimulation, and inability to control (Broom 2001; von Holst 2001) or predict (Sachser 2001) stressful factors in the environment. Psychosocial factors can also be potent stressors (McEwen 2001). In social species, the pressures of establishing and maintaining a social position may be considerable (Mendl 2001). Social defeat appears to be a powerful stressor, and in support of this, some studies have found that social animals of low rank do not seem to cope with stressors as well as high ranking animals (von Holst 2001). For social animals, an inability to perform normal social behaviour may also be stressful (Carter 2001).

Animals respond to stressors by activating a variety of coping systems, which include some functions of the immune system, autonomic nervous system and neuroendocrine one: introduction 18

systems such as the HPA axis and behavioural systems (Broom 2001; Forkman et al. 2001). An animal can be said to be ‘coping’ with the stressors if it remains in control of its physical and mental stability, the functioning of its immune, physiological and behavioural responses, and responds to these stressors in such as way as to nullify the potentially damaging effect of the stressors (Broom and Johnson 1993; Wiepkema 1995; Broom 1991; Dawkins 2001a; Sachser 2001). In this way it appears to have adapted successfully to its current environment (Plotsky et al. 2001). That said, ability to cope may not indicate good welfare, as animals which are coping but at high cost may also have poor welfare (Jensen 2001). The coping strategy used by the animal in response to such factors may be either short or long term, and may be either rely on ‘feedforward’ control or ‘negative feedback’ control (Broom and Johnson 1993).

Feedforward control is anticipatory and involves predicting that the animal’s body state will become outside of a given acceptable range and acting to prevent this occurring. An example of a short term feedforward strategy is displayed by many small birds, which lay down fat over the course of the day to sustain them overnight. An example of a longer term feedforward strategy is migrant birds eating more than they need for their immediate requirements, over a period of many days prior to migration, to lay down sufficient fat to sustain them over the course of their journey. Other examples of behaviours that act to avoid a future reduction in fitness include avoidance of predators, grooming to reduce the incidence of disease and hoarding food in anticipation of later food shortage (Dawkins 2001a). Feedforward control can also involve the animal behaving in a particular way as a result of previous experience (Koolhaas et al. 2001). Although adaptive in the wild, these anticipatory mechanisms can actually cause poor welfare if they are continually activated in captivity (Dawkins 2001a), particularly if the animal is continuously using significant amounts of energy to respond to perceived threats (Jensen 2001). This situation can arise as animals have neural systems that detect whether or not they are safe. Hence, there need be no real threat, but if the nervous system registers an event as life- threatening on a metaphorical level and the animal cannot remove itself from this ‘threat’, then welfare may be reduced (Reul et al. 2001). Welfare may also be reduced if the threat is ill-defined or imaginary, particularly if there is no obvious behavioural response that would end the threat, leading to a state of extra vigilance which at least in humans, is accompanied by anxiety or worry (McEwen 2001). one: introduction 19

In contrast, negative feedback control is reactive, and refers to homeostatic mechanisms that detect that the animal’s body state are already outside of a given acceptable range and act to return that body state to within the acceptable range. For example, cells that sense body temperature may sense that the body temperature is too low, and trigger a nervous system response that leads to changes in behaviour and physiology that increase body temperature or minimise heat loss, such as the animal shivering and diverting blood flow from its extremities to its main internal organs. This sort of mechanism involves mechanisms that sense that homeostasis is disturbed, and ‘repair’ mechanisms that act to restore it (Dawkins 2001a).

Whether feedforward or negative feedback coping strategies are better depends on the situation. Feedforward mechanisms work best in highly predictable situations, whereas negative feedback mechanisms may work better in variable and unpredictable conditions (Koolhaas et al. 2001). Coping systems may also vary across individuals for a variety of reasons, for example due to the possession of different stable innate strategies, differences in early experiences or to variations in individual circumstances (Forkman et al. 2001).

Both feedforward and negative feedback coping strategies may involve feelings. Feelings are thought to be involved in monitoring how well the animal’s state is being regulated, being positive when regulation is successful and negative when regulation is not achieved (Wiepkema 1985), or when there is a mismatch between the individual’s expectations and its perception of its external and internal environment, for example if the environment lacks expected stimuli or resources or is generally unpredictable (Sachser 2001). Positive feelings appear to be correlated with improved immune function and may ‘buffer’ individuals to the negative effects of stressful events (Lutgendorf 2001). However, other brain processes and other aspects of physiology and body state are also involved in coping with stressors (Broom 2001; Carter 2001).

one: introduction 20

1.3.3. Indicators of stress

Stress has a wide range of multi-systemic effects (Irwin 2001), and therefore a wide variety of measures have been used to quantify it. It is such a complex concept that we are unlikely to ever get a simple scale of measurement for it (Wiepkema and Koolhaas 1993). Many researchers measure autonomic variables (e.g. heart rate, sweat gland activity, blood pressure) as an indicator of perceived stress, whereas others concentrate on systemic catecholamine secretion or behavioural changes (Plotsky et al. 2001; Porges 2001). Other effects thought to be correlated with increased stress levels are metabolic changes affecting the immune system. When the animal is under acute stress the immune system activity is up-regulated to accelerate healing should the animal get injured, and lymphocytes and natural killer cells migrate from blood into tissues, especially those near to skin (McEwen 2001). However, long term stress is associated with the depression of the immune system which, coupled with a stress-induced increased permeability of the blood-brain barrier (Fuchs et al. 2001), leads to an increased incidence of diseases and infections and an increase in cytokine production within the brain leading to sickness behaviour and fever (Dantzer 2001). The immune system is a very complicated defence system, whose activity can be assessed in a variety of ways. Immunocompetence can be assessed by both quantitative measures of adrenal weight, or cell numbers and qualitative measures of their activity. For example, immune cell activity can be assessed by measuring the number of lymphocytes in the blood, the ratio of lymphoctyes to heterophils, the activity of natural killer cells, the rate of neutrophile release, or the induction of cytokine receptors and CD4 molecules on cell surfaces (Reul et al. 2001). The consequences of immune system depression can also be measured, for example prevalence of disease or parasite infestation after application of a stressor. Knowledge of the duration of the stressor may be critical to interpreting these measures, as immune system activity is frequently enhanced by acute stress but suppressed by chronic stress (McEwen 2001).

Sick, stressed or depressed animals, including humans, usually have decreased food intake and increased metabolic rate, and the resulting negative energy balance causes weight loss and impaired growth. Hence, changes in body weight are sensitive indicators one: introduction 21

of stress and can be considered to be an objective measure of the duration and seriousness of the stressor (Dallman 2001). Since whether or not the animal is in negative energy balance depends both on its food intake and its metabolic rate, a useful measure of stress is the calorific efficiency of the animal (weight gained/calories ingested). This is worth measuring, as even an animal that is increasing its food intake may still be losing weight if it is severely stressed (Dallman 2001). Changes in body weight can also be used to predict likelihood of survival in sick animals, and knowledge of the relationship between changes in weight and the degree of success the animal is having in coping with illness or other stressors is therefore very valuable, particularly in veterinary practice (see Redgate et al. 1991). However, occasionally changes in body weight do not give an accurate indication of welfare, for example in animals which have growing tumour masses, or in individuals subject to psychological stressors with continual access to palatable food, who may ‘comfort eat’ and gain so much fat that they are at risk of conditions such as hypertension, arterial disease, stroke and diabetes (Dallman 2001).

Stress may also cause changes in neurophysiology, which may result in negative psychological effects such as anxiety, fear and depression (Broom 2001; Carter 2001). Stress is highly correlated with onset of depression and anxiety in humans, which may precipitate further self-perpetuating stress, as being depressed is in itself profoundly stressful and is associated with increased risk of ill health and mortality (Irwin 2001). Whilst the demonstration of these mental states in animals is difficult, we now have sufficient understanding of the underlying chemical imbalances and neuropathology that underlie these conditions in humans (for example, low levels of serotonin and dopamine in brain tissues) for which analogies can be sought in animals. Stress may also cause cognitive and memory deficits, seemingly by affecting neural plasticity in the hippocampal region of the brain involving retraction of the dendrites of cells in the CA3 pyramidal region, reducing neurogenesis so that a reversible reduction in hippocampal volume is seen (Fuchs et al. 2001).

Stress can also be assessed via its effects on reproductive success. Stress may decrease reproductive performance, either because the animal does not have sufficient energy to reproduce or court the opposite sex after having experienced the stressor, or because the because it has impaired the animals’ ability to produce the behavioural or physical signals one: introduction 22

of ‘quality’ upon which their conspecifics judge potential partners. Such signals may be the ability to achieve symmetry in bilaterally symmetric traits despite stress during development (i.e. possessing low ‘fluctuating asymmetry’, Møller and Swaddle 1997), the ability to reach a certain size, or to produce large or colourful display structures and maintain them in good condition, or the ability to perform territorial and courtship display. For example in the case of swallows, Hirundo rustica, the length and symmetry of a male bird’s tail is positively correlated with reproductive success and negatively correlated with developmental stress (Møller 1994).

Challenges that induce stress are intrinsic to life and are totally natural. However, if too many challenges occur within a short timescale, then welfare may be compromised. That said, it is notable that an absence of challenge may be equally undesirable as it may engender apathy and an enduring sense of boredom, and encourage animals to self- stimulate by performing stereotypical behaviours (Wemelsfelder and Birke 1997; Mormède et al. 2001). Therefore, the challenge for animal welfare science may not be to minimise stress to the lowest possible levels. Rather, we need to identify ‘optimum stress’ environments in which animals are stimulated but not overtaxed. For humans, and probably for other species, optimal levels of stimulation resemble a U-shaped function with limits that differ for different individuals, with both very high and very low levels of stimulation being aversive (Knierim et al. 2001).

There is huge range of methods which could be potentially used to assess the degree of stress an organism experiences, and discussing all of these in depth is beyond the scope of this review. However, a central tenet of stress theory is that coping with stressors requires energy. In vertebrates, a major response to stressors is the mobilisation of glucose energy via a physiological cascade involving release of adrenaline and glucocorticoids from the adrenal glands (Nelson 2001). As I have used the latter extensively as a measure of physiological arousal (see Chapters 4, 5 and 6), I discuss adrenocortical responses to stress in depth in the next section, before discussing how these hormones and their activity can be measured, and how such measurements relate to welfare.

one: introduction 23

1.3.4. Adrenocortical responses to stress

The effects of stress are registered in many structures and physiological functions within the nervous system (Porges 2001), which is heavily involved in the coping strategies which enable animals to manage stress. Rapid neuronal responses to stress are elicited via activation of excitatory (e.g. glutamate) or inhibitory (e.g. gamma-amino butyric acid, GABA) receptor and ion channel complexes that directly influence neuronal activity (Fuchs et al. 2001). However, there are three main stress systems within vertebrate nervous systems that modulate this fast transmission of information via slower-acting neurotransmitters and hormones - the immune system, the sympathetic-adrenal-medullary (SAM) axis and the hypothalamic-pituitary-adrenal (HPA) axis. All three systems cause an acute set of biochemical responses that prepare the body for action in response to challenge. The immune system releases cytokines, that increase core temperature and which have modulating effects in other neural and endocrine systems. The SAM axis controls a suite of responses known collectively as the ‘fight or flight’ response. It involves the sympathetic nervous system, and has two pathways. The first triggers the release of adrenaline from the adrenal medulla into the blood in response to signals from nerves throughout the body. The second pathway is antagonistic to the first, and comprises nerve endings in tissues throughout the body that release noradrenaline (Fuchs et al. 2001). The HPA system is a classic neuroendocrine circuit which stimulates the adrenal cortex to release glucocorticoids into the blood, via the production of corticotrophin releasing factor (CRF), adrenocorticotrophic hormone (ACTH) and corticosteroids (cortisol and/or corticosterone). These systems can be activated independently of each other, especially in chronic stress (Forkman et al. 2001). Activation of the immune system, or the HPA and SAM axes, is not in itself pathological. Harm is only likely to be sustained if the responses are prolonged and demanding (Fuchs et al. 2001; McEwen 2001).

Many hormones and neurotransmitters affect the reactivity of the immune system and the HPA and SAM axes, such as serotonin, sex steroids, opioids, glutamate and GABA. Other peptides (such as oxytocin and vasopressin in mammals, or their avian equivalents, mesotocin and vasotocin) may also regulate neurological and autonomic states and affect one: introduction 24

the probability of certain behaviours occuring (Carter 2001; Knierim et al. 2001; Nelson 2001). The manner in which these factors and the three major stress systems interact is beyond the scope of this review. Instead, I shall concentrate on the effects of adrenal gland hormones of the HPA axis, in particular cortisol and corticosterone, as these hormones are the main mediator of stress-induced changes in behavioural, immunological, neurological and neuroendocrine systems (Forkman et al. 2001). These ‘stress hormones’ are the most commonly used measure of HPA activity, as first, they are relatively easy to measure as compared to neuropeptides, as their levels can be quantified from blood, saliva or faeces (Carter 2001; Nelson 2001). Second, the traditional view that autonomic nervous system activity involving the adrenal glands was a reliable indicator of emotional state (Porges 2001) has lead to a heavy emphasis on measurement of glucocorticoids.

Glucocorticoids are hormones which form the final step in a neuroendocrine cascade beginning in the central nervous system that acts to ensure that there are adequate energy substrates and co-ordinated responses to equip the animal to cope with most categories of stressor (Plotsky et al. 2001). These hormones are released from the adrenal cortex, mainly under control of ACTH, which is produced from the pituitary gland. In turn, ACTH is regulated by CRF from the hypothalamus (Carter 2001; Reul et al. 2001). Stress is often inferred from absolute levels, or changes in levels, of ACTH, CRF or glucocorticoids (cortisol, corticosterone).

Different species produce different proportions of cortisol or corticosterone, the functional significance of which is unknown. Some species predominantly produce cortisol (e.g. primates, guinea pigs) whereas others predominately produce corticosterone (e.g. rats, most mice, poultry) and some species produce a fairly even mixture of the two (e.g. dogs, pigs; see Forkman et al. 2001). However, the most abundant stress hormone may not necessarily be the most important one, so it may prove worthwhile to measure the ‘minor’ steroids as well as the ‘major’ ones when assessing stress. New evidence suggests that cortisol entry to the primate brain may be regulated by a multiple drug resistant pump, which means that other minor adrenal steroids which are capable of crossing the blood-brain barrier may be far more important in the neurophysiology of primates than previously realised. It is not known if this holds true for other species one: introduction 25

(Forkman et al. 2001). The levels of glucocorticoid hormones fluctuate in response to a variety of stressful experiences (Sachser et al. 1998), and the typical blood plasma concentration of glucocorticoids in response to a given stressor varies both between species and within species. Individuals may respond differently to stressors according to both their genotype, ontogeny, age, sex, genetic predisposition and their previous adult individual experiences and degree of social support (Carter 2001; Sachser 2001; Koolhaas et al. 2001) and according to the context and the duration of the stressor (Reul et al. 2001). It is likely that the HPA axis of wild animals is more reactive to stressors than that of their domesticated counterparts (Carter 2001; Knierim et al. 2001).

It is thought that glucocorticoids may mediate the adoption of coping or ‘alternate’ strategies in response to stressors (Wingfield 1994). Their effects are wide ranging, and co-ordinate the actions and the subsequent sensitivity of disparate systems throughout the body (Forkman et al. 2001), serving both to alert the animal to an event or challenge and to enable it to defend its homeostasis. Like all steroid hormones, glucocorticoids activate receptor complexes inside cells, which then get translocated into the cell nucleus and induce plasticity by repressing or enhancing a range of glucocorticoid-responsive genes. As they influence gene expression and alter the electrical activity of excitable cells, glucocorticoids are potent modulators of cell physiology and behaviour (Fuchs et al. 2001).

In the short term, production of glucocorticoids is essential for the maintenance of homeostasis in the face of stressors, and is likely to be both protective and adaptive. For example, acute elevation of corticosteroids may promote escape from adverse environmental conditions, or increase foraging activity when food is scarce (Wingfield 1994). Corticosteroids also facilitate learning and the laying down of memories associated with strong emotion by inducing structural plasticity in the nervous system (McEwen 2001; Reul et al. 2001). They may also boost immune performance. Briegel et al. (1999) found that adding high concentrations of cortisol to the intravenous infusions given to patients with septic infections in an intensive care unit hastened recovery as compared to controls. That said, administration of glucocorticoids may produce different metabolic effects from similar levels of naturally elevated endogenous glucocorticoids in response to stress, probably because stress stimulates sympathetic nervous system one: introduction 26

activity and CRF and inflammatory cytokine production, whereas exogenous glucocorticoids inhibit these. For example, exogenous glucocorticoids elevate insulin promoting fat storage, whereas stress induced elevations of glucocorticoids are correlated with reductions in insulin and weight loss in both humans and rats (Gelfand et al. 1984; Dallman 2001).

When overused, systems that normally protect the animal can increase the likelihood of disease. Chronically activated glucocorticoids are usually seen as being pathological due to their harmful effects, in particular their ability to impair cognitive, reproductive and immune function (Wingfield 1994; Dantzer 2001; Fuchs et al. 2001; McEwen 2001). Chronically high levels are also correlated with protein loss and decreased growth (see review, Wingfield 1994). As glucocorticoids increase metabolic rate, chronic elevation of glucocorticoids can be very costly for animals that cannot replenish energy easily (Forkman et al. 2001). Chronically elevated glucocorticoids can impair cognitive function by decreasing gene expression in hippocampal receptors, which leads to impaired feedback mechanisms similar to that seen in human patients with depression (Fuchs et al. 2001; Reul et al. 2001). It has been suggested that cognitive function may be impaired via neuronal loss (e.g. Wingfield 1994). However, recent studies challenge this view. Although adrenalectomy and consequent removal of corticosteroids increases neurogenesis, both hyperactivity (e.g. patients with Cushing’s disease or depression) and hypoactivity of the HPA axis (e.g. patients with post-traumatic stress disorder) are correlated with shrinkage of the hippocampus (see review, Fuchs et al. 2001). This shrinkage is, however, unlikely to be due to neuronal loss, as normalisation of cortisol in patients with Cushings disease increases hippocampal volume. The reduction in volume is therefore more likely to occur as a consequence of dendritic atrophy (Luine et al. 1994) or perhaps loss of glial cells (which are the ‘support’ cells for neurons), as is seen in the frontal cortex of patients with major depressive disorder (Ongur et al. 1998).

However, it is notable that other receptor changes occur in the brain in response to stress, independently of the effects of glucocorticoids. For example, physical or social stress also suppresses expression of 5-HT1A receptors in rats and tree shrews, a condition associated with increased anxiety and depression in humans. Therefore, it seems unlikely that the one: introduction 27

brain’s plasticity in entirely explicable in terms of glucocorticoid activity (McEwen 2001).

1.3.5. Measurement of glucocorticoids

There are three main approaches that are used to measure glucocorticoid levels and the reactivity of the HPA axis. The first approach is to measure the baseline level of glucocorticoids in the animal by sampling blood plasma, saliva, faeces or brain tissue and running a hormone assay to quantify the levels of hormone in these samples. This is a useful measure of how generally aroused the animal is, with major deviations from the species-typical baseline levels providing evidence of on-going dysregulation.

The second approach measures the reactivity of the HPA axis in response to a stressor, the idea being that more stressed animals will have responses that are faster and of a greater magnitude than animals that are less stressed. Whenever a stressful stimulus is applied, corticosteroid production rises slowly over the course of several minutes. This can be investigated by quantifying the rate of rise of corticosterone in the blood following a standardised challenge test, such as by measuring plasma corticosteroids at sequential intervals during a period of capture and restraint. This method also enables the baseline levels to be obtained provided that the first sample is taken within a minute or so of the challenge test being applied. The subsequent rate and extent of rise of hormone allows investigation of both the magnitude of the ‘stress’ response and the degree to which responses are shortened or prolonged. Normally, the maximum level of the hormone attained and the rate of rise of the hormone (maximum at a given timepoint less the baseline measurement) are analysed. This technique has been widely used to measure the reactivity of the HPA axis in birds (Wingfield 1994; Washburn et al. 2002). It is worth analysing both the baseline glucocorticoid levels and their rate of rise, as stressed individuals may not display changes in both parameters. For example, some chronically stressed humans have normal baseline cortisol levels yet show exaggerated elevations of cortisol in response to specific challenges (see review, Mormède et al. 2001). one: introduction 28

The third approach investigates the efficacy of the glucocorticoid feedback mechanism using a suppression test. Levels of plasma glucocorticoids are regulated via a negative feedback mechanism, controlled by mineralocorticoid receptors in hippocampal regions and glucocorticoid receptors scattered throughout the nervous system (Plotsky et al. 2001; Reul et al. 2001). Failure of this negative feedback mechanism may be responsible for elevation of corticosteroids in the blood. The efficacy of this mechanism can be tested by intravenously injecting dexamethasone, a synthetic hormone which does not bind to serum corticosteroid binding globulin and which mimics the suppressive effects of high dose glucocorticoids. If the negative feedback mechanism is working correctly, the levels of endogenous glucocorticoids should become lower. This technique is known as a ‘dexamethasone suppression test’ (DST) and has been widely used in the investigation of clinical depression in humans. For example, both baseline and post-dexamethasone cortisol levels are higher in patients with previous suicide attempts (Plocka- Lewandowska et al. 2001). Furthermore, abnormal DST results appear to accurately predict the likelihood of eventual suicide. For example, in a cohort of 78 clinically depressed patients, 32 had abnormal DST results. Survival analyses showed that the estimated risk for eventual suicide in the ‘abnormal’ group was 26.8%, compared with only 2.9% amongst patients with normal DST results. This ‘at risk’ group was unable to be identified from any historical or demographic risk factors (Coryell and Schlesser 2001). Non-suppression on the DST is also associated with other psychological disorders such as post-traumatic stress disorder (Atmaca et al. 2002), chronic fatigue (Gabb et al. 2002) and bulimia nervosa (Neudeck et al. 2001). However, there is some evidence that there may be sex differences in response to the DST, with the post dexamethasone cortisol being more reliably correlated with severity of depression in women than in men (Osuch et al. 2001). However, given that in general, severe psychological distress seems to co-occur with non-suppression on the DST in humans, the DST is likely to become more widely used in the investigation of animal welfare. Indeed, the response to dexamethasone suppression has recently been characterised in birds (the European starling, Sturnus vulgaris, see Rich and Romero 2001) and in future the DST could prove to be a useful welfare indicator in this group. An alternative way of testing the negative feedback mechanism would be to investigate the status of CRF receptors with a CRF challenge test, in which CRF is given intravenously and the plasma ACTH and one: introduction 29

gluocorticoids monitored. Depressed humans have been shown to have a lower hormone response to CRF than controls (Reul et al. 2001).

1.3.6. Glucocorticoids as a welfare measure

It should be noted that a rise in glucocorticoids is not necessarily an indicator of poor welfare (Jensen 2001). The HPA axis not a pure stress response system; rather, it has a variety of functions such as metabolic regulation, glucose mobilisation, growth regulation and homeostasis (Porges 2001). Elevations in glucocorticoids occur whenever energetic demands are higher than the level of energy that is immediately available (Nelson 2001), as glucocorticoids are involved in the physiological cascade that converts lipids and proteins to sugars to replenish immediately available energy stores. Therefore, we see elevations of glucocorticoids in response to situations of metabolic demand that we could consider to be positive, such as anticipation of food or sexual arousal (Toates 1995; Reul et al. 2001). It is clear that fluctuations in corticosteroids are normal events, and changes in levels can result from many causes, including circadian variability (Reul et al. 2001) and do not directly measure single variables relevant to welfare (Bath 1998).

It is therefore highly desirable to combine the measurement of corticosterone with other indices of welfare to try and establish whether the overall effects are negative or positive (Dallman 2001; Mormède et al. 2001). However, there is no guarantee that evaluating welfare using a combined approach will clearly indicate the welfare status of the animal. There is a surprisingly inconsistent relationship between elevated glucocorticoids and other physiological factors considered to be indicators of chronic stress, such as atrophy of the thymus or adrenal glands, or loss of body mass (McEwen 2001). This is probably because there is more than one ‘stress system’ within the body, not all of which are necessarily activated in response to a particular stressor. Sometimes little elevation in glucocorticoids is seen despite the application of stressors that could be considered quite severe (Porges 2001). A further complication in interpretation is that elevation of glucocorticoids may be beneficial in some aspects whilst being detrimental in others. For example, chickens experiencing high levels of social interaction, a factor which typically one: introduction 30

elevates their plasma corticosterone, were more resistant to parasites and bacterial infections, but more susceptible to viruses (Gross, 1972, 1976 cited in Dantzer 2001).

It is difficult to determine whether low levels of glucocorticoids are a sign of good or poor welfare without having an understanding of the normal baseline levels for the species in question, and the normal physiological response of that species to stressors (Sapolsky 1994). In some species, the glucocorticoid response to stress is bi-phasic, with hormone levels increasing above normal levels in response to an acute stressor, but falling below normal levels with continued exposure to the same stressor (Carter 2001).

Even if one feels certain that the elevation of corticosterone in a particular scenario is likely to be deleterious, the difficulty of deciding at what cut-off point a rise in corticosterone becomes a welfare problem remains. It has been suggested that it is reasonable to suggest a risk to welfare if there is a sustained elevation of more than 40% above normal corticosterone basal levels (Barnett and Hemsworth 1990). However, this figure is purely arbitrary, and is not clear how elevated levels of corticosterone impact on the psychological well-being of the animal, if at all. Consequently, although it is useful to measure glucocorticoids to gain information about the metabolic status of the animal, one needs to be very careful as to how one interprets the levels obtained in terms of welfare.

1.4. Avian colour vision

1.4.1. Human versus avian colour vision

There are many similarities between human and avian vision. As in all vertebrates, in both the retina is a two dimensional photoreceptor array that responds to images projected upon it by the dioptric apparatus of the eye (Martin 1985). Both birds and humans have visual photoreceptors that can be divided into two subtypes: rods and cones. Rods subserve scotopic vision, operating only at low light levels and are not thought to be involved in the perception of colour. Cones are less sensitive than rods, and become active at higher light intensities, mediate photopic vision in daylight and are involved in one: introduction 31

the perception of both colour and brightness (Walls 1963). The sensation we typically think of as ‘colour’, i.e. the sensation of blue-ness, green-ness or red-ness, arises via the neural comparison of the relative activity of two or more receptor types that have visual pigments tuned to different parts of the spectrum (Wyzecki and Stiles 1982). Hence, an animal’s perception of the colour of an object therefore cannot be measured simply by measuring the physical properties of that object; rather, colour perception is determined by the effect of these properties on the visual system viewing them. Consequently, species with different photoreceptors and/or different ways of neurally integrating their output may perceive the same object to be very different colours (Jacobs 1981; Thompson et al. 1992). Indeed, it seems likely that there will be subtle differences across bird species in colour perception, as the proportion of different types of photoreceptor in the retina differs across species (Muntz 1972; Partridge 1989), which will affect the number of natural spectra that are discriminable by each species (Vorobyev et al. 1998; Hart 2001). The differences between human and avian vision extends beyond differences in colour perception (Zeigler and Bischof 1993), but for the present only colour perception will be discussed.

Both humans and birds possess rods that are all tuned to the same wavelength and which therefore do not contribute to colour vision. Like most vertebrates, bird rods contain a medium-wavelength sensitive rhodopsin pigment that is most sensitive (i.e. has maximum ‘spectral sensitivity’, known as λmax) between 500 and 510 nm (Bowmaker et al. 1997; Das et al. 1999; Hart et al. 1998, 1999). However, the retinae of diurnal birds are dominated by cones.

Avian cone pigments differ considerably from those found in humans in several ways - notably in their number, their different spectral sensitivities and the relative proportion of different cone types found in the retina. Humans have three types of cone photopigment, each with different spectral sensitivities (See Fig. 1.3.), whereas birds have four types of visual pigment in their cones (Hart et al. 2000; Wright and Bowmaker 2001). Human cone receptors can be classified as long-wave sensitive (LWS, λmax 570 nm), medium-

wave sensitive (MWS, λmax 520 nm) or short-wave sensitive (SWS, λmax 420 nm; see

Dartnall et al. 1983). Most diurnal birds also have LWS (λmax 543-571 nm), MWS (λmax

497-509 nm) and SWS (λmax430-463 nm) pigments (see review, Cuthill et al. 2000b). In one: introduction 32

addition, birds have a fourth pigment that in the case of poultry and ducks is maximally

sensitive to violet with considerable sensitivity in the near- ultraviolet (λmax 402-426 nm, VS), and in the case of songbirds and parrots a fourth pigment that is maximally sensitive

to UV (λmax 355-376 nm, UVS; see Cuthill et al. 2000b), although the SWS and MWS are not evolutionarily homologous (Wilkie et al. 1998). It seems likely that this VS/UVS dichotomy is phylogenetic, as amino acid sequence analysis of vertebrate VS and UVS pigments hints that the ancestral pigment was UVS, with loss of UVS pigments occurring separately in mammals, amphibia and birds, with certain bird species subsequently regaining UVS pigments via a single amino acid substitution (Hunt et al. 2001a). Further opsins from different species must be sequenced to clarify the phylogenetic pattern and furthermore, there is as yet no evidence that this difference in sensitivity between the UVS and VS cone types has any great ecological relevance.

Note that although from Figure 1.3.C. it is clear that the human SWS pigment has some UV sensitivity, in practice adult humans with intact lenses are unable to utilise this UV sensitivity as the lens at the front of the eye is UV blocking, and only an insignificant amount of UV ever reaches the pigment (Goldsmith 1991; Griswold and Stark 1992). However, with the apparent exception of the mallard, Anas platyrhynchos (Jane and Bowmaker 1998), the ocular media of all bird species tested to date are transparent to the near UV (Govardovskii and Zeuva 1977; Emmerton et al. 1980; Hart 1998), although shorter UVB2 wavelengths are absorbed by proteins and uric acid in the aqueous humour (peak aborbance at 292 nm in the chicken, Gallus gallus domesticus, and turkey, Meleagris gallopavo, see Ringvold et al. 2000). Therefore, although no bird is likely to see wavelengths much shorter than 310 nm (Douglas and Marshall 1999), the transparency of the lens and ocular media to the UVA2 region enables birds to utilise the UV sensitivity of their cones, and there is positive behavioural or physiological evidence for UV sensitivity in at least 35 species of diurnal birds to date, including species of poultry, passerines, psittacines and raptors (Cuthill et al. 2000b; Honkavaara et al. 2002). Nocturnal birds do not appear to have a VS/UVS cone class (Bowmaker and Martin 1978; Koivula et al. 1997), perhaps because they do not rely on colour vision when

2 UVA: Longest wavelength portion of the UV waveband, 315-400 nm UVB: Shortest wavelength portion of the UV waveband, 280-315 nm one: introduction 33

hunting at low light intensities (Martin 1990). That said, this may simply be a measurement artefact as there is a much lower abundance of cone cells in the retinas of nocturnal birds as compared to diurnal species (Bowmaker and Martin 1978) and it is difficult to sample the whole retina using microspectrophotometry (Hart 2001).

The effective sensitivity of all the avian photopigments is modulated by the presence of different types of intraocular spectral filters that lie in front of the cone photopigments; cone oil droplets, which humans do not possess (Hart 2001). Many of these oil droplets contain carotenoid pigments (Goldsmith et al. 1984), which filter the light entering the cones (Bowmaker 1980; Goldsmith et al. 1984; Partridge 1989; Vorobyev et al. 1998), thus narrowing the effective spectral sensitivity of each pigment. They also prevent UV wavelengths reaching the longer wavelength sensitive pigments, thus preventing birds from seeing UV from the secondary, UV-sensitive peak of their longer wavelength sensitive pigments. This would otherwise allow UV sensitivity without a VS/UVS cone type (Stark et al. 1994). The presence of oil droplets may reduce the overlap between the spectral sensitivities of the different types of photoreceptors, possibly resulting in the perception of more saturated colours than that which a human might perceive and improving colour constancy (Vorobyev et al. 1998).

Colour vision is achieved by comparing the output of two or more receptor types that have different spectral sensitivities, a process which is known as ‘opponent coding’ (Wyzecki and Stiles 1982). For humans with normal colour vision, three primary colours of light can be mixed to match any perceived colour, and hence humans are said to be trichromatic (Wyzecki and Stiles 1982). It seems probable that since birds have four types of single cone, they would require the mixing of four primary colours to match any avian-visible colour, and hence are tetrachromatic (i.e. have all four single cones opponently coded, see Burkhardt 1989; Palacios et al. 1990; Palacios and Varela 1992; Bennett et al. 1994; Vorobyev et al. 1998; Osorio et al. 1999b; Cuthill et al. 2000b). If so, they will use UV sensitivity as part of their colour vision, as do goldfish (Neumeyer and Arnold 1989; Neumeyer 1992). That said, the investigation of which single cones are opponently coded in birds is very difficult, as to demonstrate this, one would have to separate the actions of single cones from that of the avian ‘double’ cones, a fifth cone class, with broad spectral sensitivity, in which the unequally sized members of the pair of one: introduction 34

cones are always closely associated. Double cones are abundant in the retinas of diurnal birds, forming up to 50% of the photoreceptor population, and have a broad peak of sensitivity around 570 nm (Jane and Bowmaker 1988; Hart 2001). It is not yet known what visual function these cones perform (Cuthill et al. 2000b), although they are thought not to be involved in colour vision (Maier and Bowmaker 1993; Vorobyev et al. 1998). They may instead be an adaptation for movement detection (Campenhausen and Kirschfeld 1998) or the detection of polarised light (Young and Martin 1984) but, that said, it is still not possible to rule out a role for double cones in avian colour vision (Palacios and Varela 1992).

The presence of UV sensitive cones in the retina does not tell us how birds perceive UV wavelengths. Whether or not UV is perceived as a chromatic signal (i.e. what we typically think of ‘colour’, corresponding to the human sensations of hue and saturation), or simply as brightness, therefore depends on how the output of the VS/UVS cone is integrated with the output of the other receptor types. If the output of the VS/UVS cone is used purely in an achromatic, ‘colour blind’ mechanism, the output of the cone could be used in isolation or simply be summed with that of the other receptors, giving the perceptual sensation of brightness. If, however, the output of the VS/UVS cone is involved in a chromatic, ‘colour vision’ mechanism, then UV wavelengths can be discriminated from the rest of the spectrum by comparing the relative output of the VS/UVS cone with that of the other cone types. This would generate the percept of UV as a chromatic aspect of ‘colour’ independent from the perception of brightness. However, even if UV is perceived chromatically by all species of birds, the precise perceptual experience of UV wavelengths may differ across species, as the percept of the ‘colour’ of an object is a psychological abstraction, rather than a spectroscopic measure of the reflectance of an object (Thompson et al. 1992).

It is unclear from the literature as to how birds perceive UV wavelengths, although some previous studies suggest that birds may be able to discriminate spectral stimuli according to the signal of the UV cones relative to the other cone types (Derim-Oglu and Maximov 1994; Bennett et al. 1996; Osorio et al. 1999b). Knowledge of what type of percept UV vision gives a bird will affect the type of tasks for which UV is useful, and from a welfare perspective would enable us to predict the likelihood of harm from an absence of UV in one: introduction 35

the visual environment. If UV is perceived only through its contribution to brightness, then it may be of less welfare importance than if UV contributes to the bird’s colour vision, as the provision of UV as a specific wavelength is unlikely to be beneficial if birds are unable to discriminate UV from the rest of the spectrum. This will only apply if there are no welfare relevant UV-mediated responses that are not part of avian colour vision. It is also possible that birds reared without experience of UV will be unable to perceive UV wavelengths as adults. Many aspects of visual development require the visual system to be appropriately stimulated during development (e.g. Hubel and Wiesel 1962). Therefore, there may be a critical period in development in which the UV sensitive cones in the retina need to be stimulated in order to develop or function properly. These issues are discussed and explored in more depth in Chapter 2. one: introduction 36

Figure 1.3. Sensitivity to different wavelengths of the different types of retinal cone cells in European starlings (Fig. 1.3.A), turkeys (Fig. 1.3.B) and humans (Fig. 1.3.C). Starling photoreceptors (Fig. 1.3.A) have absorption spectra typical of passerines (Bowmaker et al. 1997; Hart 1998) whereas those of the turkey (Fig. 1.3.B) are typical of all galliformes studied to date (Bowmaker et al. 1997; Hart 1998). The major difference between the two species is the spectral location of the λmax, the starling UVS cone having a λmax of 362 nm as compared to λmax 420 nm for the VS cone of the turkey. For clarity, the mean absorptance spectrum of each pigment has been modelled by fitting raw data from spectrophotometric measurements to visual pigment templates (following methods of Stavenga et al. 1993). Reproduced with permission from Cuthill et al. (2000b). © Academic Press.

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1.4.2. Functions of UV vision in birds

1.4.2.1. Possible functions of UV vision

Since the possession of a UV sensitive cone class and tetrachromacy seem to be the ancestral state for tetrapods (Bowmaker 1991; Jacobs 1992; Yokoyama and Yokoyama 1996; Yokoyama et al. 1998; Yokoyama and Shi 2000), it is unlikely that the selective pressures for the evolution of UV sensitivity are specific to avian ecology (Cuthill et al. 2000b). UV vision is unlikely to have greater ecological significance to birds than to any other taxonomic group that can perceive UV, as UV vision has been demonstrated in many vertebrate taxa including amphibians, reptiles, fish and rodents (Jacobs 1992; Honkavaara et al. 2002). Also, there is little reason to believe that the perception of ultraviolet has any greater ecological significance to birds than the perception of any other part of the avian visible spectrum. It may even be of lesser importance in some instances. Although zebra finches, Taeniopygia guttata, use UV cues in mate choice (Bennett et al. 1996), the response of females to the removal of male UV reflection is less than that to the removal of other wavebands (Hunt et al. 2001b). The removal of long- wavelength information also seems to have a greater effect than the removal of shorter wavelengths when foraging (Maddocks et al. 2001a). This suggests that the UV waveband should be considered in conjunction with the rest of the bird-visible spectrum. However, it is appropriate to consider the visually-guided tasks for which birds are known to utilise UV, so that it is possible to make sensible predictions as to how the absence of UV in the ambient light may affect birds.

Many objects that are of interest to birds (e.g. fruit, insects, plumage) reflect UV whilst many common backgrounds do not (e.g. leaves, earth, bark), which suggests a role for UV vision in foraging and social signalling (Cuthill et al. 2000b). Also, UV light is more prone to scattering than longer wavelengths. UV wavelengths may therefore be useful in signalling to nearby receivers whilst remaining indistinct at a distance (Bennett and Cuthill 1994). This may be particularly useful at dawn and dusk (Cuthill et al. 2000b), when a high proportion of the available light consists of short wavelengths (Lythgoe 1979; Endler 1993). one: introduction 38

Scattering also induces plane polarisation, the degree of which is inversely proportional to the fourth power of the wavelength (Rayleigh’s Law, see Lythgoe 1979). UV wavelengths therefore polarise more strongly than longer wavelengths, and insects exploit this by using their UV receptors to detect UV polarisation (Brines and Gould 1982; Wehner and Bernard 1993). An animal that can detect patterns of polarised UV light in the sky could potentially use this information to aid in navigation, as this would enable the position of the sun to be located, even if it was obscured by clouds (Bennett and Cuthill 1994). However, polarisation vision is possible without UV and the issue of whether or not birds possess polarisation sensitivity is controversial and by no means settled. As most research in this area has concentrated on whether or not birds can detect polarisation at all, rather than investigating the role of specific wavelengths, I shall concentrate on the two possible functions of UV vision that have been more thoroughly researched: signalling and foraging.

1.4.2.2. UV vision in signalling

Many species have been shown to have UV reflecting plumage via UV photography or reflectance spectrophotometry (Burkhardt 1989; Burkhardt and Finger 1991; Finger and Burkhardt 1994; Andersson 1996; Andersson and Amundsen 1997; Andersson et al. 1998; Hunt et al. 1998; Cuthill et al. 1999). This phenomenon is probably created largely from the properties of the physical structure of the feathers rather than from a UV reflecting pigment (Finger et al. 1992; Finger 1995; Prum et al. 1998), although both integuments and pigments may absorb or reflect UV (Andersson 1996). It is impossible to predict which parts of the plumage will be UV reflecting from the human-visible appearance of the feathers, as a whole range of human-visible colours have been found both with and without additional UV reflection (Burkhardt 1989; Finger and Burkhardt 1994).

The presence of UV reflectance from feathers does not in itself show that this UV reflectance has any particular signalling function. For example, keratin, a protein which forms part of feathers, is widely used throughout the animal kingdom as a strengthening compound and so the fact that it happens to be UV reflective may be purely incidental, one: introduction 39

with any signalling function depending purely on the longer wavelengths that the UV reflectance happens to be associated with (Cuthill et al. 2000b). Alternatively, UV cues may have some adaptive signalling function, for example in species recognition, or in the establishment of social hierarchies (Lewis et al. 2000). Also, some species are sexually dichromatic in the UV (e.g. blue tit, Parus caeruleus, Andersson et al. 1998; Hunt et al. 1998; European starling, Sturnus vulgaris, Cuthill et al. 1999), which hints strongly that UV cues may be used in mate choice.

Behavioural experiments that directly manipulate the UV reflectance of plumage, either via placing UV blocking filters between test and stimulus birds (e.g. Maier 1993; Bennett et al. 1996; Hunt et al. 1997) or by applying UV blocking chemicals to the feathers (e.g. Andersson and Amundsen 1997; Johnsen et al. 1998), clearly show that many birds use UV reflectance from plumage to assess potential mates. The filter method does not affect the behaviour of the stimulus bird, and its effects are stable and easily quantified, but has the disadvantage that it manipulates the UV reflectance not only of the stimulus bird, but of its background. The ‘sunblock’ method does not manipulate the background, and allows selective removal of UV reflectance from specific areas of plumage, but may otherwise alter either the appearance of the bird in other ways, or alter its behaviour, for example by inducing preening (Cuthill et al. 2000b). However, both methods have thus far given similar results, in showing that a reduction in the UV reflectance of a stimulus bird makes that bird less attractive than control birds whose UV reflectance is left intact.

Pekin robins, Leothrix lutea, prefer to view conspecifics through UV transmitting (UV+) than through UV blocking (UV-) filters (Maier 1993). Also, blue tits have been shown to use UV signals in intersexual selection (Andersson et al. 1998; Hunt et al. 1999), as have pied flycatchers, Ficedula hypoleuca, (Sittari et al. 2002), budgerigars, Melopsittacus undulatus, (Pearn et al. 2001) and poultry. In the latter, supplementary UVA lighting has been shown to increase the number of attempted matings in broiler chickens (Jones et al. 2001).

Removing UV reflection from the stimulus bird reduces the overall amount of light reflected from its plumage, and therefore the preference of the birds for UV reflecting conspecifics may be based upon either the brightness or the chromatic appearance of the one: introduction 40

UV reflecting patches. The evidence suggests that females may be making choices based upon UV ‘colour’ of the patches rather than the overall intensity of their UV reflection. Smearing a mixture of preen gland fat and UV absorbing sun-block on the UV reflecting throats of male bluethroats, Luscinia svecia svecia, made males less attractive to females than males who had had a mix of preen gland fat and iron oxide (a black pigment) smeared on the throat to reduce the overall intensity of reflection of the throat by a comparable amount (Andersson and Amundsen 1997; Johnsen et al. 1998). That said, different wavelengths may not have equal importance or weighting in birds’ perception of brightness (Burkhardt and Maier 1989). Also, Bennett et al. (1996) showed that female zebra finches prefer to view males who reflect UV over males whose UV reflectance was removed by placing UV blocking filters between the male and the female. This preference was robust to variations in the apparent brightness of the birds created by placing neutral density filters between the males and the females, which implies that the reduction in preference for UV- birds is due a change in the perceived colour of the stimulus. Indeed, as it is the relative amounts of different wavelengths that visual systems interpret as chromatic signals, the removal of UV wavelengths may alter the colour appearance of the whole plumage, or the contrast of the bird against UV reflecting backgrounds (Cuthill et al. 2000b).

It is possible that the reduction in preference for UV- birds resulted from a failure of species recognition, rather than indicating that UV reflectance has any signalling function in mate choice. However, female zebra finches have preferences for UV ornaments that are not part of the birds’ plumage, as they prefer males wearing UV reflecting leg rings arranged symmetrically to males wearing such leg rings in an asymmetrical arrangement (Bennett et al. 1996). Furthermore, field experiments using the ‘sun-block’ approach have shown that reduction of UV reflectance of bluethroats had a negative effect on potential reproductive success, as UV- males were less likely to succeed in gaining extra-pair copulations (Johnsen et al. 1998).

However, none of these studies show whether or not females use natural variation in UV to make choices. Bennett et al. (1997) compared patterns of mate choice in the European starling in the presence or absence of UV cues. Individual females made similar choices in their ranking of males under both UV+ and UV- conditions. However, their pattern of one: introduction 41

rankings was consistently different in each condition. In the UV+ condition, the ranking was reliably correlated with the reflectance of the iridescent covert and throat feathers, whereas in the UV- condition it was not, although the rankings the females made were consistent.

It is perhaps unsurprising that removal of UV cues from blue tits and bluethroats reduces attractiveness (Cuthill et al. 2000b), as the maximum reflectance from these species is within the UV waveband (Andersson and Amundsen 1997; Andersson et al. 1998; Hunt et al. 1998). However, UV cues also seem to by used by species, such as starlings and zebra finches, where the UV reflectance differences are correlated with sexually dichromatic differences in the human-visible spectrum (Bennett et al. 1996, 1997; Cuthill et al. 1999). Clearly, even if a species does not have distinctive ‘pure UV’ reflectance, UV plumage cues may still play a role in its visual signalling (Cuthill et al. 2000b). There is some evidence that UV cues may act as indicators of ‘quality’ in birds, as male pied flycatchers who arrive the earliest at breeding sites have the highest degree of UV reflectance from the crown and mantle, and UV reflectance also increases with age (Siitari and Huhta 2002). However, it is not certain how important a cue the UV content of plumage ornaments is in mate choice, relative to other indicators of quality, such as male behaviour or song. For example, in blue tits, that reflect UV cues strongly, females are likely to rely less on plumage cues in mate assessment than are male blue tits, because males also produce song, which is an alternative indicator of quality (Hunt et al. 1999). However, it is clear that UV cues have some importance, and that the absence of UV from the ambient light can affect choice, and alter the criteria upon which those choices are made. Thus, UV deficient light can produce patterns of behaviour that are profoundly different from those which would be observed under natural daylight.

1.4.2.3. UV vision in foraging

Many species of fruit reflect UV (Burkhardt 1982), as do many moths and butterflies (Silberglied 1979). Some flowers also reflect UV, often in patterns that may guide insects or nectar feeding birds to the centre (Sasaki and Takahashi 2002), although UV patterns are more common amongst insect-pollinated than bird-pollinated flowers (Silberglied one: introduction 42

1979). The role of UV cues in attracting nectar-feeding birds has yet to be evaluated, although some of the earliest demonstrations of UV vision were in hummingbirds (Huth and Burkhardt 1972; Goldsmith 1980). However, recent behavioural experiments show that birds use UV sensitivity when foraging for insects, berries, seeds and mammals, and that UV vision may help locate and assess the quality of these food items.

Many fruits are dispersed by birds, and these typically turn from green to either red or black as they ripen (Snow and Snow 1988). Some fruits, such as sloes, Prunus spinosa, develop a waxy bloom as they ripen that heightens UV reflectance, and which may make them more detectable to birds (Burkhardt 1982). However, these blooms are not a UV rich ‘colour’, as they tend to enhance reflectance at all wavelengths, thus making the fruit appear brighter overall and reducing the saturation of the overall colour of the fruit (Willson and Whelan 1989). Although UV reflectance is commonest in fruits with blooms (Burkhardt 1982), yew berries, Taxus baccata, reflect UV despite lacking a bloom, and this UV reflectance decreases as the fruit ripens from green to red (Cuthill et al. 2000b), although it is more common for fruits to increase in UV reflectance as they ripen (Altshuler 2001).

Behavioural experiments on the importance of the waxy bloom have had mixed results. Some studies have found that the bloom has no effect on birds’ detection of fruit, or their preferences for it (Willson and Whelan 1989; Allen and Lee 1992). However, the black grouse, Tetrao tetrix, can detect the difference between a UV reflecting and a non-UV reflecting colour morph of bilberry berries, Vaccinium myrtillus, and displays a preference for the UV reflecting berries. This preference disappeared in the absence of UV containing illumination, strongly suggesting the birds were choosing on the basis of UV cues from the berries (Siitari and Viitala 2002). Adult redwings, Turdus iliacus, also have preferences for UV reflecting over UV reduced fruit, a preference which again only occurred under UV containing illumination. This preference may be a learnt indicator of quality, as young birds have no preference (Siitari et al. 1999). Field experiments, in which the UV content of the ambient light over fruit of the shrub Psychotria emetica was manipulated, showed that rates of fruit removal by birds and rodents were lowered when the ambient UV was filtered out (Altshuler 2001). So, along with human-visible cues, UV reflectance may help fruit-eating birds detect fruit, and provide them with indications of one: introduction 43

fruit ripeness or quality. There has been little work on the role of UV vision in the detection of seeds, although the frequency-dependent seed preferences of zebra finches are affected by the presence or absence of UV (Church et al. 2001). That said, in one study removal of longer wavelengths affected choice of seeds when foraging more than removal of short wavelengths, including the UV, did (Maddocks et al. 2001a).

Many insects, especially moths and butterflies, are UV reflective (Eisener et al. 1969; Silberglied 1979; Eguchi and Meyer-Rochow 1983; Meyer-Rochow 1991; Meyer- Rochow and Jarvilheto 1997), and birds may use these UV markings to help detect or recognise their invertebrate prey. Also, the ‘green’ caterpillars of some species of lepidoptera, such as the grey shoulder knot, Lithophane ornitopus, are highly UV reflective, and may be using UV signals to advertise their distastefulness to birds (Church et al. 1998a). There is little research in this area. Church at al. (1998a) found that blue tits foraging for either winter moths, Operophtera brumata, or green cabbage moths, Mamestra brassicae, initially find their prey items faster when the illuminant contains UV wavelengths than when it does not, and that the greater the UV contrast there is between the prey item and the background, the faster the prey item was found. This effect was transient, and as the UV appearance of the background was also altered in this experiment, it is not certain that the birds were responding to UV cues on the prey items. The removal of UV cues from the background may simply have made the task more difficult (Cuthill et al. 2000b), especially if the removal of UV lowered perceived brightness (Church et al. 1998b). However, since short wavelengths form a high proportion of the spectral composition of the ambient light at dawn and dusk (Endler 1993), UV sensitivity may allow diurnal birds to extend the period in which they can forage for prey (Cuthill et al. 2000b) and increase foraging success at these times of day.

Raptors also seem to use UV cues when foraging. Both kestrels, Falco tinnunculus, and rough legged buzzards, Buteo lagopus, seem to be attracted to the UV reflecting properties of the urine of their small mammal prey (Viitala et al. 1995; Koivula and Viitala 1999). Reflectance spectrophotometry showed that vole urine trails contrast strongly with the background within the UV waveband, but not in the human-visible spectrum. These raptors preferred to hunt in areas containing artificial vole trails, consisting of straw soaked in vole urine, than in areas with control trails, consisting of one: introduction 44

straw soaked in water, or in which there were no trails. This implies that the UV reflectance of vole urine may be used by raptors to identify areas of high prey abundance. In support of this, a laboratory experiment showed that kestrels spend more time in areas with a high abundance of vole scent marks under lighting containing UV wavelengths, but that this preference disappears when there is no UV in the illuminant (Viitala et al. 1995). Tengmalm’s owls, Aegolius funereus, however, do not seem to use UV cues in this way (Koivula et al. 1997), a difference which is likely to be related to their ecology, as nocturnal species probably do not rely on colour vision when hunting in dim light (Martin 1990), and indeed, may not possess UV sensitive cones (Bowmaker and Martin 1978).

1.4.3. Effect of UV on bird welfare

1.4.3.1. Possible effects of UV deficient lighting

It is clear that the perception of UV wavelengths influences avian behaviour, and is involved in ecologically relevant tasks such as mate choice and foraging. Artificial lighting is usually designed to human specifications, and is rich in long wavelengths with very little UV emission (see review, Lewis and Morris 1998). As the perception of UV may still be functional in captive birds, it is possible that the absence of UV in artificial lighting may affect the behaviour and physiology of captive birds in certain contexts, perhaps to their detriment. They may also experience chronic stress in UV deficient environments (Moinard and Sherwin 1999; Sherwin and Devereux 1999; Maddocks 2001; Maddocks et al. 2001a, 2001b, 2002c). Many features of poultry houses potentially could reflect UV wavelengths, including many substrates such as cereal seed and straw. Therefore, UV cues could be used for foraging and exploratory behaviours (Prescott and Wathes 1999b; Lewis et al. 2000). UV sensitivity is probably part of normal avian colour vision (Osorio et al. 1999b), and therefore captive birds housed under UV deficient lighting may experience a reduction in their visual capability. Parts of the plumage of many captive breeds reflect UV (Prescott and Wathes 1999b) and certain parts absorb UV and fluoresce (Sherwin and Devereux 1999), perhaps producing UV contrast. This one: introduction 45

information may be used in social signalling and mate choice (Jones and Prescott 2000; Jones et al. 2001).

The social environment may be one of the most important factors in welfare (Mendl 2001). If UV cues are used to signal social status or for individual recognition, a lack of UV may lead to increased aggression (Sherwin et al. 1999; Maddocks et al. 2001b), an effect which may affect birds of differing social rank differently. In captive environments, unfamiliar individuals are often abruptly mixed with limited opportunity to assess each other, and are likely to be mixed with animals of similar sex and size. This lack of size asymmetry may lead to an increased likelihood of fights (Mendl 2001). If the presence of UV cues facilities birds’ ability to assess status and cope with social mixing, then it may improve their welfare. Yet there has been very little research into avian wavelength-specific preferences for lighting, or the behavioural and physiological effects of the presence of absence of UV. I will consider the evidence from the literature on the effects of UV deficiency on species with violet-sensitive (VS) cones (non-passerines including all major livestock species) and species with UVS cones (passerines).

1.4.3.2. Effect of UV on poultry

UVA lighting has been shown to increase locomotor activity and mating attempts in broiler chickens (Jones et al. 2001), although this may not adversely affect productivity. Newly hatched broilers constantly exposed to supplemental UV light from insect traps for 42 days showed no significant differences in mass gain, feed consumption or mortality as compared with controls (Hogsette and Wilson 1999). Lewis et al. (2000) also found UV supplementation had no effect on the weight gain, feed intake or feed conversion efficiency of turkeys. However, there is some tentative evidence that poultry may prefer full spectrum light, and that an absence of UV may subtly affect their behaviour and physiology.

Lighting with supplemental UV is preferred by turkeys, Meleagris gallopavo (Moinard and Sherwin 1999), and a lack of environmental enrichment has been associated with an increase in feather pecking in this species (Sherwin and Devereux 1999). One of the one: introduction 46

environmental enrichments in the latter study was the provision of supplementary UV light, but as other variables were manipulated simultaneously it is not possible to determine whether UV deficient conditions are a risk factor for the establishment of feather-pecking. Lewis et al. (2000) also found that provision of UV supplementation together with provision of straw added to the litter reduced feather pecking, but again more than one variable was manipulated at once, making interpretation of the importance of UV cues difficult. Certainly, a lack of cues on the substrate may induce feather pecking (Huber Eicher and Wechsler 1997), although feather-pecking animals seem to be simply very active peckers in general, rather than displaying a redirection of their attentions from the substrate to other birds (Keeling and Wilhelmson 1997; Savory and Mann 1997). However, an absence of UV cues may reduce birds’ ability to discriminate details of plumage, as poultry have UV reflecting plumage (Prescott and Wathes 1999b), and are known to prefer to preen under daylight (Nuboer 1993).

In all of these studies, there is a need for caution in interpretation of any effects of UV wavelengths. As addition of supplemental UV probably increased the perceived brightness of the illuminant as well as altering its spectral composition, any apparent effects of UV may result simply from a preference for brighter lighting. For example, turkeys display preferences for higher light intensities (Sherwin 1998), so the preference they show for supplemental UV (Moinard and Sherwin 1999) may merely indicate they like brighter light, rather than indicating that they have any preference for UV as a specific wavelength.

However, there is some evidence for some mild stress-related effects when the light intensity (quantal flux) is balanced between UV+ and UV- conditions. Maddocks et al. (2001b) found that domestic chicks, Gallus gallus domesticus, kept under UV- conditions showed a non-significant trend to be less exploratory, and significantly higher basal plasma corticosterone concentrations than their counterparts kept under full spectrum lighting. Both groups attained similar maximal corticosterone levels in response to capture and restraint, so although birds in the UV+ condition had a higher rate of rise of corticosterone, this was largely because their baseline corticosterone levels were significantly lower. As high levels of corticosterone may reduce commercial productivity (Beuving et al. 1989), there may be some benefit in the provision of UV light, although one: introduction 47 this is not supported by the apparent absence of any effect of the presence or absence of UV emitting insect traps on productivity in chicks (Hogsette and Wilson 1999). However, the Maddocks et al. (2001b) study is the only study to have investigated the effects of the presence or absence of UV light when quantal flux has been balanced, and further work on other poultry species, using similar methods, is required before drawing firm conclusions on any general potential welfare benefit of providing UV light as part of routine husbandry. That said, it is still difficult to separate preferences for perceived brightness from any preference for spectral composition, even if the quantal flux between treatments is equal. Balancing quantal flux may not balance perceived intensity, as the avian visual system may weight information from the different cone types unequally, as in humans (Wyzecki and Stiles 1982). Certainly, people have tried to equate apparent brightness to birds by taking into account the spectral sensitivity of the species (e.g. D’Eath and Stone 1999), but this is likely to be unreliable, as Praynito and Philips (1997) found that this method does not predict successfully what birds experimentally match as two colours. Possibly, this is because double cones, rather than the single cone types, might mediate brightness perception in birds (see Maier and Bowmaker 1993; Vorobyev and Osorio 1998).

1.4.3.3. Effect of UV on passerines

Passerines clearly use UV in mate choice, but very little work has investigated how the presence or absence of full spectrum light environments affects their welfare. However, there is some evidence that they may not have a general (i.e. a non resource-based) preference for the full avian-visible spectrum, and that their preference for UV may be context specific. Both European starlings and blue tits have been shown to use UV in mate choice (Bennett et al. 1997; Andersson et al. 1998; Hunt et al. 1999), yet neither species shows a general preference for UV containing full-spectrum environments when the environments contain no resources, and furthermore show no UV preference when the cages contain simply food, water and perches (starlings, Maddocks et al. 2002b, blue tits, Hunt et al. 1999). This indicates that preferences for UV containing spectral compositions vary with behavioural context (Maddocks et al. 2002b), and suggests that UV wavelengths may therefore only be important to captive birds when making mate one: introduction 48

choice decisions. However, Maddocks et al. (2002b) tested the preferences of isolated individuals, and an alternative explanation is that isolated individuals of social species such as starlings may be too disturbed by isolation to express normal preferences. In group-living animals, the presence of conspecifics may be a prerequisite for normal behaviour. The importance of social buffering as a stress modulator is clear in many species, with members of gregarious species reacting differently to challenges according to whether they are in a social group or facing the challenge alone (Plotsky et al. 2001; Reul et al. 2001; Sachser 2001). For example, social animals kept alone have higher HPA axis activation (Knierim et al. 2001), and develop higher frequencies of abnormal behaviour (e.g. Broom and Johnson 1993) and infections (Lewis et al. 2000, cited in Forkman et al. 2001). It may therefore be more prudent to investigate the preferences of groups of social species for light environments, rather than testing the preferences of isolated individuals.

However, there is some suggestive evidence that the absence of UV from artificial lighting may influence the physiology and behaviour of passerines. Maddocks et al. (2002c) found that juvenile European starlings kept in UV deficient environments had significantly higher basal plasma corticosterone concentrations, and showed more behaviours indicative of escape, than those kept under full spectrum lighting, in the second of two experimental blocks. In the other block there was no significant difference between treatments. This difference between blocks may be due to the overriding initial stress of being in captivity swamping any treatment effects. The birds in the first experimental block had only been in captivity for 48 hours before the experiment started, and showed significantly higher basal and maximum plasma corticosterone levels than those in the second block, in which the birds entered the experiment after 7-14 days in captivity. Maddocks et al. (2002c) concluded that any stress effects of UV deficient lighting seem small relative to the general stress of adjusting to captivity, but that ‘hidden’ stress effects of such lighting might become apparent after the initial effects of capture subside. Further experiments of this nature using passerines that are either domesticated, or which have had more time to adjust to captivity, would therefore be of interest.

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1.5. Effect of ‘flicker’ from fluorescent lamps

1.5.1. Characteristics of fluorescent lights

It is becoming increasingly common practice to house captive birds, and other animals, under fluorescent lighting, as this form of lighting is longer lasting and more energy efficient than incandescent lamps (Nuboer 1993; Manser 1996). Incandescent bulbs emit light almost continuously. However, conventional fluorescent lamps pulsate in brightness.

Conventional fluorescent lamps work by the excitation of phosphors coating the inside of the lamp, and contain an inert gas at low pressure in a mercury vapour. Tungsten electrodes inside the lamps alternate rapidly between being the anode and the cathode, causing electrons from the electricity supply to discharge in bolts along the tube much like lightning. This produces energy in ultraviolet wavelengths, some of which is absorbed and held by phosphors coating the inside of the lamp. The phosphors lose energy between discharges by emitting light energy in the visible spectrum (for details, see Coaton and Marsden 1997). The discharges and subsequent emissions of light by the phosphors are rhythmic, which causes a flickering effect. In conventional lamps, this flicker occurs twice within each cycle of the alternating current electricity supply, giving a flicker rate of 100 Hz in the U.K or 120 Hz in the U.S.A. (Wilkins and Clark 1990), and sometimes at half this rate in lamps that have been under continuous operation for some time, as if the electrodes burn unevenly an asymmetrical discharge can occur (Wilkins et al. 1989). Throughout this thesis, when I refer to ‘conventional fluorescent lighting’ I mean correctly operating light sources of the type I have just described that pulsate at around 100 Hz.

Although recent literature on avian vision typically blithely refers to the effects of ‘flicker’ from lamps on birds, it is important to appreciate that the phenomenon of ‘flicker’ is not a single variable. Normally, when people talk of flicker, they are referring to the refresh rate of the lamp, that is, how many times it pulsates (or ‘cycles’) per second. However, there are two other parameters that characterise the type of flicker one: introduction 50

present. Apart from varying in refresh rate, lamps can also vary in their modulation in intensity and modulation in chromacity throughout each flicker cycle. The modulation in intensity refers to the difference between the highest and lowest light intensities emitted by the lamp throughout each cycle, and is normally expressed as a percentage. For example, a light that has the maximum possible modulation (i.e. which does not emit any light whatsoever at the dimmest point in each cycle) is said to have a modulation of 100%. Chromacity is a term that describes the appearance of the colours of neutral surfaces illuminated by the lamp, and depends on the range of wavelengths emitted by the lamp. Fluorescent lamps change in their balance of spectral output throughout the light cycle, and generally emit only light of either a brownish or greenish hue in the dimmest part of the light cycle, depending on the sort of phosphors that are contained within the lamp. Most common fluorescent lamps are halophosphate lamps, which pulsate with greater amplitude at the blue/green end of the spectrum than at the red (Wilkins and Clark 1990). Ballasts invariably run at the frequency specified by the manufacturer (Prof. Arnold Wilkins, pers. comm.), but modulation in intensity and chromacity tends to vary between different makes of lamp due to the different combination of phosphors each manufacturer uses. Hence, if the effect of the refresh rate of fluorescent light is to be investigated without the results being confounded by the simultaneous manipulation of other variables, then the time-averaged light intensity, overall spectral emission and modulation depth of the lamps under comparison need to be held constant, by using identical lamps running off different speed ballasts. That said, light sources possessing identical phosphors, but operating at different refresh rates, may differ significantly in their spectral output over time if the modulation of the light cycle is less than 100%.

1.5.2. Critical flicker fusion frequency

Whether or not a lamp appears to ‘flicker’ to any animal depends on how fast its visual system can summate incoming information from its retinal receptor responses. If the animal can do this faster than the refresh rate, or frequency, of the lamp, then the light will not appear continuous to it (D’Eath 1998). The precise threshold at which the one: introduction 51

frequency of a stimulus appears continuous instead of flickering to the observer is known as the critical flicker fusion frequency (CFF), and this threshold is reported to vary across species (for review, see D’Eath 1998). CFF is determined either by making electrophysiological measurements from the retina, or via behaviour by using discrimination experiments, which aim to see whether an animal can perceive the difference between a steady light and a flickering light of a certain frequency. However, the perceptual CFF cannot be taken as a limit above which intermittent light has the same effect as a continuous light (Wilkins et al. 1989), as cells in the central nervous system may respond to rates of flicker faster than that the animal is able to consciously perceive (Brundrett 1974; Greenhouse et al. 1988). First, this means that the two methods may yield different results. Second, it means that behavioural studies tell you only about perception, and that we need electrophysiological measurements as well to truly determine whether or not a given rate of flicker will affect the animal’s central nervous system.

There is some evidence from the human literature that the perceptual and neurophysiological limits of flicker resolution are different. Brindley (1992) used a clever psychophysical technique to demonstrate that the human nervous system resolves intermittent light at frequencies above the human CFF up to at least 125 Hz. The retina was electrically stimulated to produce the appearance of flashes of light. If the frequency of electrical stimulation was sufficiently increased the light appeared continuous. This electrical stimulation was then combined with stimulation from flickering light at frequencies above the CFF for each stimulus when presented alone. When the combined electrical and visual stimuli had slightly different frequencies, participants said they saw a beat between the two. This beat was perceptible in stimuli running at 125 Hz indicating that at some level, the visual system was resolving them. Consequently, as it appears that behavioural and electrophysiological methods of investigation are almost certainly measuring different things, continuing to label limits obtained by both methods as ‘CFF’ is potentially confusing. It would be more useful to call only the perceptual limit ‘CFF’ and perhaps call the neural limits of resolution the ‘maximum resolvable frequency’ (MRF) instead. It is likely that this confusion may explain the apparent discrepancy in the literature as to the maximum CFF of the pigeon, Columbia livia. Hendricks (1966), using an operant conditioning technique, found the pigeon to have a maximum CFF of 73 Hz, one: introduction 52

whereas Dodt and Wirth (1953) found a maximum electroretinographic response of 140 Hz in this species. Consequently, it is plausible that the maximum rate at which a pigeon can consciously perceive flicker is 73 Hz, whereas the maximum rate that can be encoded by the pigeon’s nervous system is 140 Hz.

There is no absolute CFF value. Rather, there is a range of frequencies to which the animal is differentially sensitive. This can be described by measuring the temporal modulation contrast sensitivity function, in which sensitivity is plotted against frequency (see Wandell 1995). It is normal to see an inverted ‘U’ shaped function, in which sensitivity is highest somewhere in the middle of the frequency range to which the animal is sensitive.

Measured CFF/MRF at any particular frequency depends on a whole host of variables. The degree of modulation of the stimulus, degree of retinal illumination, size and position of the target on the retina and whether viewing is monocular or binocular are all important factors. Age, body temperature, blood oxygen levels and psychological states of mood and personality also have effects (for review, see Brundrett 1974). CFF is also reported to diminish with continued exposure to the stimulus (Brundrett 1974; Mundy- Castle 1953b). In addition, sensitivity to visual stimuli may change with ill health, for example viral infection (Smith et al. 1992). In fact, CFF/MRF is so sensitive that it is now routinely used as a measure of change in cognitive function in humans (e.g. Patat et al. 2000). As a consequence of this sensitivity, it is very difficult to compare CFF/MRF between different species unless the same test conditions and stimuli have been used, and a large number of subjects have been tested.

1.5.3. Human versus avian perception of flicker

Typical human maximum CFF is reported to be around 60 Hz (D'Eath 1998) although many humans can consciously perceive higher rates of flicker than this. For example, a survey of office workers showed that nearly 25% can see some degree of flicker from 100 Hz lamps, with 10% (mostly young people) seeing surfaces illuminated by the light as flickering (Brundrett 1974). In fact, obtained CFF is variable within and between one: introduction 53

individuals, and depends greatly on the measurement method. If the flickering area is bright and stimulates a large retinal area, the CFF may be much higher, with recorded values being as high as 90 Hz (Van de Grind et al. 1973) and 107 Hz (Roehrig 1989). Greenhouse et al. (1988) also recorded human ERG responses to intermittent light at frequencies greater than 100 Hz, and Berman et al. (1991) recorded responses up to 145 Hz in response to a bright fluorescent lamp, and up to 162 Hz in response to a slide projector.

It is generally assumed that bird CFF is higher than 100 Hz and that they will see 100 Hz lights as discontinuous (Nuboer 1993; D’Eath 1998). However, measured avian CFF/MRF is variable (see review, D'Eath 1998), with values cited between 55.3 (zebra finch) to 140 Hz (pigeon), and none of these values are higher than the maximum recorded for humans. Unfortunately, with the exception of the domestic chicken and the pigeon, most species have only been tested once, by different laboratories using differing stimuli and methodologies. Consequently, the apparent species variability may simply reflect the variable techniques used to obtain CFF/MRF. Certainly, different methods have yielded differing maximum CFFs from the domestic chicken, ranging between 71.5 Hz (Jarvis et al. 2002) to 105 and 120 Hz (Nuboer et al. 1992; Nuboer 1993). Also, some of the methodologies in the older papers leave much to be desired; for example, the low value for the zebra finch (55.3 Hz) should not be taken as this species’ genuine CFF. The method used was to put a bird in a circular drum, the walls of which were covered in vertical gratings, and which could be rotated. As the stripes on the walls rotated, the birds would make head movements, first following the direction of rotation, and then making a quick return motion, in an attempt to keep the retinal image stable. The experimenters increased the speed of the rotating drum until the birds ceased making head motions, at which point they observed ‘crouching and the opening of the beak’, and stated the speed of the drum’s rotation at this point as being the limit of flicker perception (see Crozier and Wolf 1941). Hence, it is likely that what they really measured was either the maximum rate at which a bird is capable of moving its head, or the point at which the bird became too frightened or dizzy to respond, rather than the maximum perceptible flicker rate.

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There is some evidence to suggest that birds are more sensitive than humans to the frequency of light, as tests with similar stimuli have found chicken CFF to be 71.5 Hz cf 57.4 Hz in humans (Jarvis et al. 2002). However, there is currently insufficient evidence in the literature to firmly conclude whether or not birds in general can see higher rates of flicker than humans can, whether there are truly differences between bird species, or whether birds can see 100 Hz lamps as flickering.

There may be ecologically relevant species differences in CFF/MRF, as the higher the CFF of an animal, the quicker it can update information in the visual scene, and the better its motion perception will be (D’Eath 1998). It is likely that some species will have been under strong selective pressure to evolve a higher CFF than others, if their fitness depends on ability to fly fast, or to detect and capture fast moving prey. For example, poultry do not fly particularly fast, and therefore they may not need as high a CFF as some of the faster flying diurnal passerines, particularly those that fly in dense flocks, a task which requires them to constantly observe and alter their position relative to other birds at speed. It is therefore possible that many passerines have a higher CFF than poultry, and therefore may be at a potentially greater risk of experiencing adverse effects than poultry from 100 Hz light, as it is humans with the highest CFFs who are most likely to suffer adverse effects from flickering light (Wilkins 1995).

1.5.4. Effects of 100 Hz flicker on humans

1.5.4.1. Neurological basis of visual discomfort

The psychological and neurophysiological effects of flickering light have been studied most with respect to humans, as there has been much concern as to whether flickering light sources, television and visual display monitors are either stressful or harmful to us.

In humans, the most serious effects from flickering lights tend to occur at perceptible flicker rates below 50 Hz, where it can trigger a wide variety of adverse reactions, ranging from unpleasant emotions to eyestrain, headaches and seizures (Mundy-Castle one: introduction 55

1953a; Wilkins et al. 1984). However, humans frequently dislike 100 Hz fluorescent lighting, even though this flicker rate is usually above their CFF. Although people cannot usually see the flicker, and rarely complain about their ability to see or read under fluorescent light, it seems to trigger a range of adverse effects in susceptible individuals, including headache and eyestrain (see 1.5.4.2.), migraine and epilepsy (see 1.5.4.3.) and anxiety (see 1.5.4.4.), presumably because although 100 Hz exceeds the CRF of most humans, it does not exceed the MRF.

Wilkins et al. (1984) proposed that a common neurological mechanism may produce non- epileptic headaches and eyestrain, epileptic electroencephalogram (EEG) abnormalities and seizures, and other adverse effects from visual stimulation. Abnormal patterns of repetitive visual stimulation (both temporal, e.g. flickering light, and spatial, e.g. illusory patterns, see section 1.6.) may in susceptible individuals trigger abnormal patterns of neuronal excitation in the retina (or in the case of patterns, excess activation in the orientation-specific receptive fields in the visual cortex) which then spread along the visual pathway, modulating the impulse pattern (intervals) and impulse rate of neuronal firing in other brain regions. Since neuronal patterns and rate of firing are the major critical parameters underlying the transmission of information in the nervous system (Eckhorn et al. 1976), anything disrupting normal information transmission within the body clearly has the potential to have adverse effects on normal physiological and psychological function.

In epileptics, it seems that a paroxysmal discharge is triggered when normal physiological excitation in the visual cortex exceeds a critical level (Meldrum and Wilkins 1984). Wilkins et al. (1984) note that visual patterns that are epileptogenic have the same spatial and temporal characteristics as patterns that cause visual illusions in normal people without epilepsy. They therefore propose that sensory stimulation, particularly rhythmic stimulation, can cause intense cortical excitation that over a critical level may breakdown cortical inhibitory mechanisms in both epileptics and normals. If the spread of excess excitation remains local to the visual cortex, then the person will just see an illusion. If the excess excitation spreads further, the person may experience headaches, EEG phenomena and possibly clinical seizures. Whilst it is possible that the visual cortex may act as a relay station rather than the source of such EEG abnormalities, one: introduction 56

Wilkins et al. (1981) found that excitation in the cortex from repetitive visual stimuli is likely to be sufficient to cause an epileptic discharge in some people.

Reduction of cortical inhibition in response to flickering light or certain patterns might also account for the production of headaches. Nulty et al. (1987) found that the number of illusions in visually provocative patterns seen by normal subjects correlated with the number of headaches they subsequently experienced. In support of this, people who experienced consistently unilateral headaches saw consistently asymmetric illusions in the visual patterns. Also, people with the strongest EEG response to photic stimulation tend to be those who suffer most from headaches (Golla and Winter 1959).

It is most likely that 100 Hz fluctuation will affect predominantly subcortical structures, as cortical neurons do not usually resolve frequencies above 20 Hz, although that said, migraines often start with visual warnings suggestive of cortical rather than subcortical activity. In support of Wilkins et al.’s (1984) theory, there is some evidence that 100 Hz fluorescent light does cause abnormal patterns of neuronal activity in the retina, which are propagated further throughout the visual pathway. Eysel and Burandt (1984) compared the effect of daylight, incandescent light and different frequencies of fluorescent light on the visual pathway of the cat. Retinal ganglion cells responded to fluorescent light running at 100 Hz by firing action potentials in time with each pulse output from the light. Some cells responded at the same frequency as the light modulation, others more slowly, but all cells fired at the same point in the light cycle, about 9 ms after the peak. This response was not abolished until around 160 Hz and was preserved in cells higher in the visual pathway at the level of the lateral geniculate nucleus (LGN). Neurons showed a much lower average response to incandescent light or daylight and did not discharge with similar rhythmicity. The higher the overall luminance, the stronger the effect of fluorescent light on rate and pattern of neural firing was. However, not all cell types in the cat retina are sensitive to such high rates of flicker (Foerster et al. 1977).

Eysel and Burandt (1984) therefore predicted that a similar phase locked response triggered by 100 Hz fluorescent light would be found in structures closely linked to the retina and LGN. The superior colliculus is one such closely linked structure, which is one: introduction 57

thought to be involved in the control of eye movements. People make slightly larger high velocity eye movements (‘saccades’) under conventional fluorescent lighting than relatively steady lighting (Kennedy and Murray 1991). This effect is weak, but may be a sufficient perceptual ‘nuisance’ to account for human performance on complex visual search tasks being slightly poorer under conventional than higher frequency fluorescent light (Kuller and Laike 1998). However, as Wilkins et al. (1984) point out, this ‘excess excitation’ theory is purely speculative and the precise mechanism by which repetitive visual stimulation causes adverse reactions is as yet unknown.

1.5.4.2. Headaches and eyestrain

The excess neuronal excitation that exposure to 100 Hz flicker may cause in susceptible individuals may be responsible for the high incidence of headache and eyestrain that occurs under standard fluorescent lighting. For example, Wilkins et al. (1989) cite a survey commissioned by Reed employment in 1987 in which 41% of office workers reported that conventional fluorescent lighting gave them headaches or affected their eyes. In support of these complaints, Wilkins et al. (1989) used a double blind crossover design to compare incidence of headache and eyestrain in office workers under conventional fluorescent, high frequency fluorescent light and incandescent light. People chose to use conventional fluorescent lights least as measured by the duration for which they chose to turn the lights on, and the incidence of headache and eyestrain was twice as much under this than in the other two lighting conditions. The incidence of headache was also negatively correlated with the amount of daylight lighting the office. Most people were unaffected with only a small proportion of workers having regular headaches. Interestingly, people seemed unaware of changes in the lighting and of its effects, and invariably attributed their headaches to other causes. The high incidence of eyestrain in the 100 Hz fluorescent condition may be attributable to the greater number of saccadic eye movements made under 100 Hz fluorescent light (Wilkins 1986; Kennedy and Murray 1991).

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1.5.4.3. Migraine and epilepsy

Adverse effects on neuronal activity from flickering light are at their most obvious in people who suffer from migraine or epilepsy triggered by flickering light (Golla and Winter 1959; Wilkins et al. 1984; Wilkins 1995). Sensitivity is maximal at around 16 Hz, but is frequently as high as 50 Hz (Wilkins et al. 1979). Whilst light-sensitive migraine from 100 Hz lighting is relatively common, the induction of epileptic seizures from 100 Hz light is unlikely (Binnie et al. 1979; Wilkins 1995).

That said, most people do not get migraines from 100 Hz lighting. It seems that such lighting may only be detrimental to a small proportion of people, perhaps because they experience more physiological arousal than most in response to the lights. People who experience frequent headaches or migraines tend to have more eye discomfort in response to light than people who do not (Wilkins et al. 1984), and people prone to migraine have abnormally high steady state evoked potentials (EVP) for flicker frequencies above 20 Hz (Golla and Winter 1959; Jonkman and Lelieveld 1981). This threshold of response to such excitation is likely to be partly genetic, as people with migraines and epilepsy tend to have a family history of the condition (Wilkins et al. 1980). However, environment and behaviour seem to interact with genetics to determine the current response threshold, and therefore determine whether or not an individual will be adversely affected by the flicker at any given time.

1.5.4.4. Anxiety, panic and other forms of arousal

Flickering light has been associated with unpleasant feelings of anxiety, fear and panic attacks in susceptible individuals (Ulett et al. 1953; Watts and Wilkins 1989; Hazell and Wilkins 1990). However, there is also evidence that conventional fluorescent light may affect physiology in ‘normal’ controls (Hazell and Wilkins 1990) and that flicker may provoke paroxysmal neural activity in people with no history of fits and with no family history of epilepsy (Ulett et al. 1953). Normal human controls with no conscious dislike of conventional fluorescent lighting report more bodily symptoms under it than under incandescent or high frequency fluorescent light, even after only 10 minutes exposure (Hazell and Wilkins 1990). These symptoms are likely to be worsened in those who are one: introduction 59 stressed or anxious. For example, some people with no prior history of epilepsy or migraine suffer severe reactions including odd sensations, muscular jerks and blackouts when placed in stressful experimental situations with flickering light (see Ulett et al. 1953).

There also seems to be a link between fluorescent lighting and the development of agoraphobia. A double blind study of people in their own homes showed that heart rate elevates under fluorescent lighting in those who suffer from anxiety states such as agoraphobia but not in normal controls (Hazell and Wilkins 1990). The link appeared to be specific as agoraphobics did not report more headaches than controls. However, the agoraphobic group may have previously learnt a negative association between exposure to bright fluorescent light and being in a public place which might account for the anxiety. Also, any effect on heart rate is likely to be indirect. Although anxious persons seem to find flickering light particularly aversive (Ulett et al. 1953), people prone to panic attacks are more likely to interpret odd sensations in a catastrophic way (see cognitive model of panic, Clark 1986). If fluorescent light triggers any new bodily symptom as it does in controls, agoraphobics are likely to worry about the symptoms than controls, and thereby increase their own heart rate more.

1.5.4.5. Conclusions

In summary, results from the human literature indicate that conventional pulsating fluorescent lighting produces a response in the nervous system that is not registered as a sensation of flicker but which is ultimately responsible for a variety of bodily symptoms. The most serious adverse reactions occur in people who suffer from conditions such as headache, migraine and photosensitive epilepsy (Mundy-Castle 1953b; Golla and Winter 1959; Wilkins et al. 1979, 1984; Wilkins 1995). However, it is clear that many ‘normal’ people without these conditions also experience some adverse effects from repetitive visual stimulation, which may be worsened in those who are anxious (Ulett et al. 1953; Hazell and Wilkins 1990), depressed (Nulty et al. 1987), tired (Wilkins et al. 1984), or suffering from physical illness (Smith et al. 1992). Distance from the source of flicker may also have an effect; Wilkins et al. (1979) found that the closer photosensitive people sat to flickering television screens, the more likely there were to demonstrate unusual one: introduction 60

EEG activity, probably because firstly this may increase the proportion of the retina stimulated by flicker and secondly due to the increasing resolution of the flickering lines from which the picture is composed. However, it should be noted that these relationships only appear when a large group of subjects are tested and there are considerable individual differences. Still, as there is great similarity between the neurophysiology between humans and other vertebrates, it seems likely that non-human animals could also be adversely affected by 100 Hz lighting.

1.5.5. Effects of 100 Hz flicker on bird welfare

1.5.5.1. Possible effects of 100 Hz flicker on birds

Given that 100 Hz light causes a wide range of adverse physiological and psychological effects in humans and other vertebrates (Wilkins et al. 1984), and that birds may be more sensitive than humans to flicker (D’Eath 1998, but see section 1.5.3.), there is a risk that housing birds under this type of lighting may impair their welfare (Manser 1996; Sherwin 1999; Maddocks et al. 2001c). It is not clear whether or not 100 Hz lighting is perceived as flickering by birds, although there is some evidence they may be slightly more sensitive to flicker than humans (see Jarvis et al. 2002). That said, 100 Hz is very unlikely to be above the avian MRF. As the neurophysiology of birds is similar to that of other vertebrates, such lighting may still be a perceptual nuisance and perhaps stressful, as 100 Hz flicker clearly has adverse effects in some humans who cannot consciously perceive the flicker (see section 1.5.4.). Therefore, it is worthwhile to investigate the effects of 100 Hz flicker on bird welfare, regardless of whether or not the birds perceive the lights as stroboscopic. It is possible that some passerines may be at greater risk of experiencing adverse effects than poultry, as they are likely to have a higher CFF (see section 1.5.3.), although until several species have been tested using the same stimuli and methods, we cannot be certain that this is the case.

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1.5.5.2. Effect of flicker on poultry

Most previous research on this topic has concentrated on chickens, as they are the species most commonly kept under fluorescent light. Both chickens and domestic turkeys do not find 100 Hz fluorescent lighting aversive as compared to incandescent lighting (Denbow et al. 1990; Widowski et al. 1992; Lewis and Morris 1998; Sherwin 1999). As some studies have shown that the birds may even prefer it to incandescent lighting (Widowski et al. 1992; Sherwin 1999), it is therefore unsurprising that when Lewis and Morris (1998) reviewed the literature, they concluded that 100 Hz flicker did not seem to harm the welfare of birds.

However, although it is clear that 100 Hz flicker is not extremely aversive to poultry (Widowski et al. 1992; Sherwin 1999), it is not easy to interpret the results of these experiments. In any comparison of the effects of fluorescent and incandescent light sources, the manipulation of refresh rate (that is, flicker rate) is confounded by the simultaneous manipulation of other aspects of the light sources. The overall intensity and chromacity of the light sources would also have differed between treatments, by virtue of their different emittance spectra. As there were no controls for treatment differences in intensity or spectral output, birds may have preferred fluorescent to incandescent light for its higher overall light intensity. Also, fluorescent lamps are richer in short wavelengths than incandescent light, and have a spectral distribution that is more similar to daylight. They also emit a small amount of UV, although the relative proportion of UV wavelengths present is much lower than that in sunlight (Moinard and Sherwin 1999). Therefore, fluorescent light may also have been preferred for its more natural colour rendering than for its flicker rate (Lewis and Morris 1998).

Preference tests alone are not sufficient to determine whether or not 100 Hz light has any adverse effects on birds. Animals experiencing adverse feelings or physiological effects caused by a stimulus will only move away from the triggering stimulus if they correctly ascribe these negative effects to the trigger stimulus. It is notable that although Wilkins et al. (1989) found that office workers were twice as likely to have headaches under 100 Hz than under high frequency fluorescent light, the headache sufferers did not attribute any of their headaches to the light. Instead, they were likely to assume that they were just one: introduction 62

having a stressful day, or had a virus, or similar. Consequently, a bird might well become unwell from being under 100 Hz light, but not realising the cause of the symptoms, continue to stay under the pulsating light, even if it has the choice of moving away to a non-flickering environment. Studies of the effects of 100 Hz light on the behaviour and physiology of poultry would therefore be very valuable.

1.5.5.3. Effect of flicker on passerines

Diurnal passerines might be expected to have a higher CFF than poultry, and may therefore be at greater risk of suffering adverse effects from 100 Hz flicker (see section 1.5.3.), yet there has been hardly any research into the effects of such lighting on the welfare of songbirds.

Maddocks et al. (2001c) exposed wild caught European starlings, which had no prior experience of artificial light, to either 100 Hz or high frequency (>30 kHz) fluorescent light for either one or 24 hours. The experiment was run in four identical experimental blocks. There were no treatment differences in the first two blocks, but there was a non- significant trend for the birds in the latter two blocks to have higher basal plasma corticosterone levels under 100 Hz lighting. The birds in the first two blocks were recently caught from the wild, and had had little time to adjust to the general stress of captivity. Therefore, any treatment effects may have been swamped in the first two blocks by the overriding stress of bringing wild animals into laboratory housing for the first time. Whilst any effects of 100 Hz flicker is clearly small relative to the initial stress of adjusting to captivity, the trend for 100 Hz lighting to be associated with higher basal corticosterone suggests that such lighting may be a source of arousal, and perhaps stress, once the birds have been in captivity for some time.

There is therefore a need to test a range of both domesticated species, and wild species that have had longer to adjust to captivity, using similar techniques employed by Maddocks et al. (2001c), before concluding that 100 Hz lighting does not adversely affect the welfare of captive passerines. The results of preference tests in which the one: introduction 63

manipulation of refresh rate is not confounded with the simultaneous manipulation of chromaticity and intensity would also aid in drawing conclusions as to how 100 Hz lighting affects passerine welfare.

1.6. Effect of repetitive spatial patterns

1.6.1. Effect of repetitive spatial patterns on humans

Artificial light may not be the only aspect of the visual environment that is potentially aversive. Repetitive spatial patterns in the visual scene may be equally stressful under certain circumstances, and seem to affect the physiology of humans in a similar way to flickering light. Visual patterns of particular spatial characteristics may cause unpleasant perceptual effects from illusions of colour, shape and motion (Wilkins et al. 1980, 1984; Watts and Wilkins 1989; Wilkins 1995). The unpleasantness of the pattern depends upon its shape, spatial frequency, contrast, size and position in the visual field (Wilkins et al. 1979, 1980; Wilkins 1995). For example, a black and white striped stimulus at certain frequencies causes normal human subjects to see illusions within the figure such as colours, shimmering or blurring (see Watts and Wilkins 1989; and try it for yourself, see Figure 1.4. overleaf). Certain visual patterns may also trigger migraines (Marcus and Soso 1989), anxiety (Watts and Wilkins 1989; Hazell and Wilkins 1990) or seizures (Wilkins et al. 1980) in susceptible people. These effects, like those of flickering lights, are thought to result from unnatural levels of repetition in the visual field leading to excess excitation within the nervous system, which may break down cortical inhibition mechanisms, thus causing a wide range of unpleasant physiological and psychological symptoms (Wilkins et al. 1984; Wilkins 1995, plus see section 1.5.4.1. for more details of this theory).

The precise effect any given grating has on the observer will depend on the viewing distance; if one sits close to a grating, the bars appear wider than if one sits further away (try this yourself with Fig 1.4.). This is because each bar of the grating falls across a larger number of adjacent cells in the retina the closer you sit to it. It is conventional, one: introduction 64

therefore, when talking of the effect of a grating, to describe the proportion of the visual field each cycle of the grating occupies at a given viewing distance. This is known as the ‘spatial frequency’ of the grating, and is measured in cycles per degree of visual angle. A ‘cycle’ is a single pattern element within the stimulus, so in a black and white grating such as Fig 1.4. one cycle is equivalent to the width of one white plus one black bar. Therefore, the narrower the bars in the grating look, the higher the spatial frequency of the stimulus is.

Wilkins et al. (1984) found that 35% of normal subjects get eye-ache, headache, tiredness or dizziness when looking at gratings of between 1 and 16 cycle deg-1, with the problem being worst at 4 cycle deg-1. The most aversive stimuli appear to be elongate or striped, with high modulation in luminance and a 50% duty cycle (Wilkins 1995). High modulation in luminance simply means that the intensity difference within each pattern element is high. For example, in a black and white grating with high modulation in luminance, one bar in each cycle is very dark whilst the other is very light. Duty cycle refers to the proportion of the width of each cycle a bar occupies, so if a black and white grating is said to have a 50% duty cycle, it means that the black and white bar are of equal width. Wilkins (1995) also reports that square-wave gratings (i.e. those in which the bars have sharp, well defined edges in luminance) are more aversive than sinusoidal gratings (where each bar in each cycle blurs smoothly into each other). This may be because square wave gratings do not consist of a single, pure frequency; rather, they present a fundamental frequency in combination with other higher frequencies.

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Figure 1.4. Black and white horizontal grating at 2.5 cm–1. The grating is square-wave with 100% modulation, and has a 50% duty cycle. Most people see visual illusions at this rate of repetition, which usually consist of seeing colours, shimmering and blurring and bending of the lines. The precise spatial frequency depends on viewing distance. At a viewing distance of 30 cm this is approximately 1.5 cycle deg-1, and at a viewing distance of 50 cm it is approximately 2.5 cycle deg-1. ______PLEASE DO NOT LOOK AT THIS PATTERN IF YOU ARE A PHOTOSENSITIVE MIGRAINE OR EPILEPSY SUFFERER. If unsure, cover one eye, and do not stare at it for longer than 30 seconds. ______

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1.6.2. Interaction between spatial and temporal repetition

It is clear that it is not just the flicker of lighting that is a potential welfare issue; the characteristics of surfaces illuminated by the lighting may also matter. If Wilkins et al.’s (1984) theory is correct in that adverse effects from both spatial and temporal visual stimulation are caused by a common mechanism in which certain spatial and temporal frequencies cause excess cortical excitation (see section 1.5.4.1.), then there could potentially be an interaction between the effect of visually provocative patterns and any effect of the type of light source they are viewed under. If both patterns and flickering light of frequencies that cause physiological arousal are both present in the visual scene at once, then the total neurological excitation produced may exceed that which would have been produced by either stimulus alone.

This has never been experimentally tested, although there is some anecdotal evidence that stimulation from spatial and temporal frequencies within the visual scene may have some kind of additive effect. Moving repetitive patterns, such as the motion of stairs moving up an escalator, windscreen wipers moving across a car windscreen or the repeated flash of oncoming car headlights in heavy traffic are known to be potent migraine triggers in some humans (pers. comm., various members of the Migraine Action Society). Also, people who suffer from television-induced epilepsy tend to only experience epileptiform EEG abnormalities when they are sitting sufficiently close to the screen for the 25 Hz line interlace pattern to have a sufficiently low spatial frequency (Wilkins 1995), although that said, the light intensity and volume also increase the closer one sits to the screen.

It is possible that much of the eyestrain reported to occur in office workers under 100 Hz fluorescent light (Wilkins et al. 1989) may only occur because in an office situation, people are often reading, a task in which the light is illuminating text, which forms a striped pattern. If a person is reading under 100 Hz light, the motion of the eyes scanning the repetitive pattern of the text, coupled with the flicker of the light, may cause difficulties in ocular motor control leading to eyestrain and headaches (Wilkins et al. 1986). one: introduction 67

1.6.3. Effect of repetitive spatial patterns on birds

The psychological and physiological effects of repetitive spatial patterns have not, to my knowledge, been tested on any animal other than a human. Therefore, the effects of such stimuli on birds are unknown. However, as the neurophysiology of humans is similar to that of other vertebrates, it seems likely that certain spatial frequencies may be aversive or stressful to non-human animals. Whether or not this is the case seems to be a topic worthy of investigation, as many captive animals are kept in cages, the bars of which form a repetitive spatial pattern. It would also be of interest to investigate whether the range of spatial frequencies that animals find aversive, if any, is different from that which humans find aversive. Also, if birds find both 100 Hz flicker and the frequency of their cage bars aversive, then there may be an additive interaction effect, with the gratings of the cage bars being more aversive or arousing when viewed under 100 Hz light than under high frequency (> 30 kHz) lighting.

1.7. Prospects

The aim of this thesis is to determine whether the presence or absence of UV light, the flicker rate of fluorescent lighting and spatial frequency of the visual surround affect the stress physiology and behaviour of birds, perhaps to the detriment of their welfare.

Chapter 2 investigates whether my study species, Japanese quail, Coturnix coturnix japonica, and European starlings, Sturnus vulgaris, are able to perceive UV, and whether their UV sensitivity is used in a chromatic or achromatic visual mechanism (i.e. whether UV is perceived as ‘colour’ or brightness). This research was essential prior to investigating the effects of UV on welfare, as if birds only use UV in a chromatic mechanism (i.e. cannot discriminate UV from any other wavelength) then it is highly unlikely that they would suffer from its absence. Quail and starlings were chosen as model species, as they form exemplars of two main classes of colour vision system in birds. Quail have a VS photoreceptor typical of non-passerines, and starlings have a UVS photoreceptor typical of passerines (see section 1.4.1). Many aspects of visual one: introduction 68

development require the visual system to be appropriately stimulated during certain ‘critical windows’ in development. It is plausible that birds reared without UV would not develop their UV sensitive cones and relevant neural pathways properly, and thus be permanently blind to UV as adults. If so, then supplementary UV would be pointless as an environmental enrichment for birds that had been previously reared without it. Hence, the perceptual abilities of quail reared both with and without UV stimulation were tested.

Having established that both starlings and quail can perceive UV, and appear to see UV as a chromatic signal, it was then worth continuing to investigate the effect of presence or absence of UV on poultry and passerine welfare. Chapter 3 investigates the issues raised in Section 1.4.4.3., and investigates whether the preferences of starlings for UV containing spectral distributions is a phenomenon exclusive to mate choice, or a general preference, by comparing the UV preferences of groups of different social compositions. Although Maddocks et al. (2002a) found the isolated starlings have no preference for UV containing light, this may be because isolated birds are too stressed to express normal preferences. Therefore, the preferences of groups of starlings versus the preferences of individuals are also compared.

Chapter 4 addresses some of the issues raised in Section 1.4.4.4. Most of the previous studies on the effects of UV on poultry are confounded by the simultaneous manipulation of spectral composition and overall light intensity. There is only one study in the literature on the effect of UV on poultry in which the light intensity has been balanced between the UV+ and UV- conditions (Maddocks et al. 2001b), in which domestic chicks showed a non-significant trend to be less exploratory, and had significantly higher basal corticosterone levels, when reared under UV- light. The result of a single study on one species is insufficient to draw conclusions about the effects of UV light on poultry welfare in general. Hence, an experiment of similar design was carried out using Japanese quail.

Most previous research on the effect of flicker from fluorescent lights has concentrated on its effects on poultry. As passerines may be at greater risk of suffering adverse effects from repetitive visual stimulation than poultry, (see section 1.5.3.), and the effects of flickering light on passerines is very little researched, Chapters 4 and 5 concentrate on the one: introduction 69

effect of flicker on a passerine species. Chapter 4 investigates the preferences of European starlings for conventional (100 Hz) versus high (>30 kHz) fluorescent light, and the effect of these lighting conditions on their behaviour and plasma corticosterone levels. Starlings were chosen as a model species as they are a fast-flying diurnal passerine that frequently travels in dense flocks. Therefore, they have a need for good motion perception and they are therefore likely to have a relatively high CFF, which would put them at higher risk of suffering adverse reactions to 100 Hz light.

Evidence from the human literature shows that repetitive spatial patterns of particular frequencies can elicit very similar adverse effects as does flickering light. Wilkins et al. (1984) proposed that these adverse effects are caused by a similar mechanism, in which repetitive visual stimulation (whether temporal or spatial) can cause excess excitation within the brain. Wilkins et al. (1986) therefore proposed that there is likely to be an interaction effect between particular rates of flicker and the spatial frequency of surfaces illuminated by the fluctuating light, as if temporal and spatial stimuli within the critical ranges are in the visual field simultaneously, their combined effects may cause more neuronal excitation (and therefore worse adverse reactions) than either the spatial or temporal stimulus in isolation. This issue seems particularly pertinent to caged laboratory animals. The most aversive patterns to humans are striped grating patterns, with plaid or ‘criss-cross’ patterns having similar effects of a lesser magnitude (Wilkins 1995). As most laboratory animals are housed in cages, they are kept in an environment in which the majority of the visual surround is striped. If this has any adverse effects on the animals, the effects may be worse if the cage bars are viewed under 100 Hz fluorescent light, than under high frequency (>30 kHz) light (see section 1.6.3.). Chapter 5 therefore concentrates on whether the flicker frequency of the lighting can interact with the spatial frequency of the surrounding visual environment to affect the preferences, behaviour and plasma corticosterone of starlings. The effect of spatial frequency on bird welfare is completely unresearched, and the ranges of spatial frequencies to which birds are most sensitive is unknown. I therefore took a comparative approach and investigated the effects of spatial frequencies within the range that are known to cause adverse reactions in humans.

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1.8. Thesis layout

The data chapters (chapters 2-6) are written as a series of co-authored papers. The existence of co-authors reflects the highly cross-disciplinary nature of this research, which has required the collaboration of different people with different technical skills. Also, more than one experimenter was often needed to run an experiment, first because some of the procedures were complex and required more than one pair of hands, and second so that the design of the experiments could be improved. For example, having two experimenters allows animals from two different treatments to be sampled simultaneously.

This thesis is presented as a series of papers, some of which are already published. Therefore, some of the information in this introductory review also appears in the introductions to the data chapters. There may also be small differences in figure and reference formatting, as different chapters were prepared for different target journals. There is a concluding chapter that places my work in the context of the existing literature, and makes recommendations for future work. Finally, there is an appendix that describes the procedure for blood sampling and radioimmunoassay that I used to quantify corticosterone levels in Chapters 4, 5 and 6.