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• 2013-2022close
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Oil crops are one of the most important agricultural commodities. In the U.K. (and Northern Europe and Canada) oilseed rape is the dominant oil crop and worldwide it accounts for about 12% of the total oil and fat production. There is an increasing demand for plant oils not only for human food and animal feed but also as renewable sources of chemicals and biofuels. This increased demand has shown a doubling every 8 years over the last four decades and is likely to continue at, at least, this rate in the future. With a limitation on agricultural land, the main way to increase production is to increase yields. This can be achieved by conventional breeding but, in the future, significant enhancements will need genetic manipulation. The latter technique will also allow specific modification of the oil product to be achieved. In order for informed genetic manipulation to take place, a thorough knowledge of the biosynthesis of plant oils is needed. Crucially, this would include how regulation of oil quality and quantity is controlled. The synthesis of storage oil in plant seeds is analogous to a factory production line, where the supply of raw materials, manufacture of components and final assembly can all potentially limit the rate of production. Recently, we made a first experimental study of overall regulation of storage oil accumulation in oilseed rape, which we analysed by a mathematical method called flux control analysis. This showed that it is the final assembly that is the most important limitation on the biosynthetic process. The assembly process requires several enzyme steps and we have already highlighted one of these, diacylglycerol acyltransferase (DGAT), as being a significant controlling factor. We now wish to examine enzymes, other than DGAT, involved in storage lipid assembly and in supply of component parts. This will enable us to quantify the limitations imposed by different enzymes of the pathway and, furthermore, will provide information to underpin logical steps in genetic manipulation leading to plants with increased oil synthesis and storage capabilities. We will use rape plants where the activity of individual enzymes in the biosynthetic pathway have been changed and quantify the effects on overall oil accumulation. To begin with we will use existing transgenic oilseed rape, with increased enzyme levels, where increases in oil yields have been noted; these are available from our collaborators (Canada, Germany). For enzymes where there are no current transgenic plants available, we will make these and carry out similar analyses. Although our primary focus is on enzymes that increase oil yields, we will also examine the contribution the enzyme phospholipid: diacylglycerol acyltransferase (PDAT) makes to lipid production because this enzyme controls the accumulation of unsaturated oil, which has important dietary implications. In the analogous model plant Arabidopsis, PDAT and DGAT are both important during oil production. Once we have assembled data from these transgenic plants we will have a much better idea of the control of lipid production in oilseed rape. Our quantitative measurements will provide specific targets for future crop improvements. In addition, because we will be monitoring oil yields as well as flux control we will be able to correlate these two measures. Moreover, plants manipulated with multiple genes (gene stacking) will reveal if there are synergistic effects of such strategies. Because no one has yet defined quantitatively the oil synthesis pathway in crops, data produced in the project will have a fundamental impact in basic science. By combining the expertise of three important U.K. labs. with our world-leading international collaborators, this cross-disciplinary project will ensure a significant advance in knowledge of direct application to agriculture.

All animals need to make the most of new opportunities or deal with changing environmental conditions. These changes may be short-term such as seasonal change, or long-term shifts such as climate change, and often impact the availability of food resources and, potentially, survival. Broadly, two different strategies might be used to increase access to resources in a changing environment. Animals might develop new solutions to problems (innovation), and thus find new resources themselves, or they might observe others and copy successful solutions. The latter, called social learning, is expected to be much more frequent than innovation, allows new behaviours to spread rapidly between individuals and is thought to be fundamental in forming traditions. Social learning has long fascinated biologists and anthropologists; understanding how behaviours spread and traditions are maintained in animals can shed light on the factors promoting complex culture in humans. An important determinant of social learning is the social organisation of the population in which learning occurs. It was long thought that only humans could exhibit highly developed cultural transmission due to their capacity for communication and learning that is facilitated by long-term social bonds. However recent research has found locally maintained cultural behaviour in a wide range of animals. Further, social network analysis in both human and other animal populations has allowed population structure to be accurately measured. Thus, using social networks to map the spread of new behaviours provides an exciting opportunity to understand this important learning process. In this study, we will study the spread of novel information in wild populations of a common bird, the great tit. All individuals in our large study population are tagged with microchips allowing us to track them automatically; our pilot data shows that they will learn socially. We will develop devices where one of two simple solutions provides access to food, and train an individual to solve one solution on the task in captivity before releasing it back into the woodland where we will place a number of these devices. Using this approach, we will be able to track who has learnt, from whom they learnt, and which of the two solutions they learnt. Using the social network of this population, we will track the spread of the new behaviours, and determine what characteristics made some individuals more important in spreading them. By training different individuals on the two different solutions, we will also see how local traditions develop and are maintained. Not all traditions or behaviours are advantageous. For example, in humans it has been shown that obesity can spread through friendship groups. In the second phase of this project we will alter the reward to different solutions of the task by replacing the popular solution with a low reward (peanut granules instead of a worm), maintaining the high reward on the less popular solution. This will test whether bad traditions are maintained through social reinforcement where individuals blindly copy the majority even when better solutions exist. Finally, we will develop some new technology that will predict what solution new individuals should be learning based upon the behavior of the group they belong to. By changing the behaviour at the device in response, we will then test in detail what elements of the behaviour observed in others is used when social learning. This will be the first time that anyone has used an active device to directly manipulate the behaviour of wild animals in this way. This will itself advance scientists' abilities to understand what rules individuals follow when making decisions such as who to copy and when. Such knowledge will be widely applicable across disciplines, for example in providing new opportunities for active conservation of threatened species by introducing behaviours that improve survival.