Equine obesity: linking predictions from evolutionary biology with the ingestive behaviour and subsequent body condition of uk horses and ponies
Equine obesity: linking predictions from evolutionary biology with the ingestive behaviour and subsequent body condition of uk horses and ponies
批准号:
BB/H01568X/1
负责人:
金额:
$9.59万
依托单位:
依托单位国家:
英国
项目类别:
Training Grant
财政年份:
2010
资助国家:
英国
项目状态:
已结题
起止时间:
2010 至 --
中文摘要
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英文摘要
Over the past three decades, models of foraging have yielded many insights into the functions and mechanisms governing how animals should respond to changing food availability and environmental conditions, but including social environment (where living in a group could alter an individual's chances of securing food) has presented some radically new predictions. Models [1,2] considered animals foraging in pairs that were able to assess each others' energetic reserves, and where foraging together was advantageous to individuals. The models suggested that over time, the pair's level of energy reserves should become greatly separated - where one individual has low reserves, and its colleague has high. Furthermore, predictions suggest that behaviours should become highly synchronised between pair members. Surprisingly, these robust individual differences could arise without there being any inherent difference between the pair's members, who had the same energetic requirements, and were following the same behavioural rule set (dependent upon the reserve levels of both a focal animal and its partner). Predictions were also generated about how groups of animals following these rules should behave [3], and a survey of field studies suggest that these patterns are seen in a wide variety of species [2]. These results have obvious implications for theoretical and ecological work on group living in animals, but also have relevance to the welfare and management of domesticated, socially-living animals, such as horses, cattle, and sheep. This studentship will focus on equine behaviour: unlike other species, privately-owned horses are most likely to be kept long-term in small, stable social groups, and therefore present an ideal study system for exploring the interplay between model predictions, group behaviour, and the potential welfare implications of group-housing. This studentship will explore this problem by creating and testing predictions of feeding behaviour and energy reserve allocation, based upon a solid theoretical foundation. Using these models could suggest how individual horse's fat reserves might vary across groups of different sizes, and how variation might occur between herd-mates sharing identical external conditions (food, exercise, etc.). In Bristol, the student will be trained to use these techniques to generate predictions about how horses should behave in response to novel environmental and social conditions that they would not have experienced in their evolutionary history (meaning that the rules that they would normally follow had evolved for a completely different set of environmental and social parameters). At the same time, the student will be trained by WALTHAM and Bristol in behavioural and body-condition scoring techniques, and in the use of non-invasive ultrasound techniques. The student will interact with owners to record data from horses and ponies housed under a variety of housing/grazing conditions and herd sizes, after training in experimental technique, design and data analysis. As well as ongoing modelling, the first year will involve collecting data exploring individual and group behaviour and decision-making in relation to the body condition of individuals, at differing time-scales. Building on these data, the models would be refined to explore how the differences in regimens faced by the study animals affected their energy reserves and behaviour. Furthermore, the effects of differences in energetic requirements on behaviour and condition could be explored by comparing insulin-resistant with 'normal' individuals. Throughout, the data collected would be integrated with the modelling work (which would be refined on an iterative basis to accommodate any set-backs or unforeseen effects arising from the dataset). 1. Rands et al. (2003) Nature 423:432 2. Rands et al. (2008) BMC Evol Biol 8:51 3. Rands et al. (2004) Proc R Soc B 271:2613, (2006) Behav Ecol Sociobiol 60:572
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