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The shift from hunting and gathering to an agricultural way of life was one of the most profound events in the history of our species and one which continues to impact our existence today. Understanding this process is key to understanding the origins and rise of human civilization. Despite decades of study, however, fundamental questions regarding why, where and how it occurred remain largely unanswered. Such a fundamental change in human existence could not have been possible without the domestication of selected animals and plants. The dog is crucial in this story since it was not only the first ever domestic animal, but also the only animal to be domesticated by hunter-gatherers several thousand years before the appearance of farmers. The bones and teeth of early domestic dogs and their wild wolf ancestors hold important clues to our understanding of how, where and when humans and wild animals began the relationship we still depend upon today. These remains have been recovered from as early as 15,000 years ago in numerous archaeological sites across Eurasia suggesting that dogs were either domesticated independently on several occasions across the Old World, or that dogs were domesticated just once and subsequently spreading with late Stone Age hunter gatherers across the Eurasian continent and into North America. There are also those who suggest that wolves were involved in an earlier, failed domestication experiment by Ice Age Palaeolithic hunters about 32,000 years ago. Despite the fact that we generally know the timing and locations of the domestication of all the other farmyard animals, we still know very little for certain about the origins of our most iconic domestic animal. New scientific techniques that include the combination of genetics and statistical analyses of the shapes of ancient bones and teeth are beginning to provide unique insights into the biology of the domestication process itself, as well as new ways of tracking the spread of humans and their domestic animals around the globe. By employing these techniques we will be able to observe the variation that existed in early wolf populations at different levels of biological organization, identify diagnostic signatures that pinpoint which ancestral wolf populations were involved in early dog domestication, reveal the shape (and possibly the genetic) signatures specifically linked to the domestication process and track those signatures through time and space. We have used this combined approach successfully in our previous research enabling us to definitively unravel the complex story of pig domestication in both Europe and the Far East. We have shown that pigs were domesticated multiple times and in multiple places across Eurasia, and the fine-scale resolution of the data we have generated has also allowed us to reveal the migration routes pigs took with early farmers across Europe and into the Pacific. By applying this successful research model to ancient dogs and wolves, we will gain much deeper insight into the fundamental questions that still surround the story of dog domestication.

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.

Worldwide, seaweed aquaculture has been developing at an unabated exponential pace over the last six decades. China, Japan, and Korea lead the world in terms of quantities produced. Other Asiatic countries, South America and East Africa have an increasingly significant contribution to the sector. On the other hand, Europe and North America have a long tradition of excellent research in phycology, yet hardly any experience in industrial seaweed cultivation. The Blue Growth economy agenda creates a strong driver to introduce seaweed aquaculture in the UK. GlobalSeaweed: - furthers NERC-funded research via novel collaborations with world-leading scientists; - imports know-how on seaweed cultivation and breeding into the UK; - develops training programs to fill a widening UK knowledge gap; - structures the seaweed sector to streamline the transfer of research results to the seaweed industry and policy makers at a global scale; - creates feedback mechanisms for identifying emergent issues in seaweed cultivation. This ambitious project will work towards three strands of deliverables: Knowledge creation, Knowledge Exchange and Training. Each of these strands will have specific impact on key beneficiary groups, each of which are required to empower the development of a strong UK seaweed cultivation industry. A multi-pronged research, training and financial sustainability roadmap is presented to achieve long-term global impact thanks to NERC's pump-priming contribution. The overarching legacy will be the creation of a well-connected global seaweed network which, through close collaboration with the United Nations University, will underpin the creation of a Seaweed International Project Office (post-completion of the IOF award).

Understanding why females stop reproduction prior to the end of their lives is a key objective in the biological, medical and social sciences. In traditional human societies for example, women typically have their last child at 38 but may live for a further 20 years or so. This phenomenon is by no means restricted to humans and across many species of mammals, birds and fish, females may have a lifespan that extends far beyond their last birth. Why is this? Three possible reasons have been suggested: i) It could simply be a byproduct of females living for a long time; ii) it may benefit post-reproductive females by increasing the survival of their offspring and/or grand offspring or iii) old females may lose out to young females when competing for the food needed to support pregnancy and producing milk. In humans it seems that a combination of ii and iii have driven the evolution of menopause. Currently however, almost nothing is known about the forces that have shaped the post-reproductive lifespan in non-human animals that live in close-knit family groups. In this project we will test for the first time the current evolutionary theory for the post-reproductive lifespan in a non-human animal. Our study will focus on two populations of killer whales Orcinus orca that live off the coast of North America. Killer whales have the longest post-reproductive lifespan of all non-human animals; females stop reproducing in their 30s-40s but can survive into their 90s. We will use data collected over the last three decades during which time more than 600 whales have been recorded. We will use information about births and deaths to examine how social factors shape fertility and survival. In particular we will ask the following questions: (1) How do post-reproductive females benefit from a post-reproductive lifespan? (2) In what ways do older females provide support to their offspring / grand offspring? (3) Do older females lose out when competing with younger females for the food needed to reproduce? (4) Can the observed benefits (question 1) and the consequences of reproductive competition (question 3) explain the evolution of the long post-reproductive lifespan in killer whales? We will address questions 1 and 3 by using the long term data documenting births and deaths in both populations. We will use analysis techniques similar to those used by insurance companies to calculate life expectancy when deciding what premiums to charge people on their life insurance. In our analysis we will examine how survival is affected by the presence and behavior of post-reproductive females. We will address question 2 by using video and photographic records to examine social interactions between mothers and their offspring / grand offspring. We will test how important this relationship is for survival. Finally we will address question 4 by building a simulation model of the populations. We will use our observations from the whales to set the parameters in the model [e.g. the amount by which post-reproductive females increase the survival of their offspring]. The model will then simulate evolution, allowing us to examine if the effects we are seeing in the populations are sufficient to have driven the evolution of the long post-reproductive lifespan in killer whales. This programme of research promises to advance our understanding of how natural selection has shaped life history evolution in species that live in close-knit family groups. Our work will provide the first test of the current evolutionary theory for the evolution of menopause in non-human animals and the outputs of this work will provide an informative comparison for the evolution of human life history. More generally, our work will advance our understanding of the ageing process in social species and the interplay between an individual's social relationships and its life expectancy.

This project is concerned with measuring changes in global rainfall and ensuring that computer models of the climate can predict how rainfall will change in the future. As carbon dioxide and other greenhouse gases are continually added to the atmosphere, it is understood that the temperature of the surface of the earth will rise. Warmer air can hold more moisture, so as the Earth warms the rate at which the atmosphere extracts water from the surface of the earth and dumps it back as rain will also increase. Knowing precisely how much global rates of rainfall will change into the future is important to many people including farmers wanting to know which crops to plant and nations wanting to build domestic water and hydroelectric infrastructure. Measuring the total rainfall around the world is no mean feat. On land, measurements are made directly (by catching the rain) or by reliable 'indirect' methods based on river flow and how wet the soil is. However, two-thirds of the globe is covered by ocean. It is hard to catch rain in the middle of the ocean without actually being there to do it. Although many 'indirect' methods exist for measuring rainfall over the ocean there is great uncertainty about how much rainfall has changed over the ocean in the last 50 years or so. Thankfully there is a solution. The ocean itself acts as a giant rain catcher. Water that falls as rain is fresh water, like the water we drink. Most of the ocean however, is very salty. So the more rain that falls, the fresher the ocean water gets and the more evaporation that occurs the saltier the ocean water gets. Oceanographers can measure just how salty the water in the ocean is and have been doing so regularly for more than 50 years now. The question remains however, how do you turn measurements of the salinity of the ocean into measurement of how much rain has fallen? Well, by looking all around the globe and counting up how much more salty water there is and how much fresh water there is, researchers can estimate how much water has been evaporated in one place and fallen as rain in another. The researchers involved in this project will do this using all the observations of salinity in the ocean taken over the last 50 years. They will estimate just how much rainfall has changed. They will compare this with computer models which are commonly used to predict what will happen in the future to see how accurate they are and how they can be improved.

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.