This project will provide a step-change in our understanding of managed pollinator disease. We will use a combined modelling and molecular approach to investigate the dynamics of European Foul Brood (EFB) as an exemplar of endemic brood disease of honey bee colonies using historic data derived from long-term monitoring of apiaries in England and Wales. We will utilise a program of statistical, analytical and spatially explicit modelling to address the problem. Statistical modelling approaches will be used to identify putative covariates involved in the epidemiology of disease (e.g. land use, weather, management practices) (Newcastle); analytical modelling approaches will be used to investigate the role of transmission processes in determining the epidemiology of disease (Warwick & Bath); and spatially explicit models to investigate spatial spread of disease in the context of investigating the efficacy of different practical control measures (Warwick & Newcastle). The modelling will be parameterised using historic datasets which include the timing and reported incidence of EFB distribution in honey bee apiaries across England and Wales (Fera). Molecular approaches based on microsatellite markers and comparative genomics will be employed to characterise host and parasite diversity (Fera & Bath) for use as additional covariates in the statistical, analytical and spatially explicit models exploring the epidemiology of EFB in relation to host resistance. These data will be used for the testing and validation of the theoretical and spatially explicit models. We (Fera & Bath) have, in collaboration with the Sanger centre in Cambridge, already generated a draft genome sequence for M. plutonius. These data will greatly facilitate the identification of suitable markers for the characterisation of large and representative population samples and will also shed light on the genes responsible for virulence, and how pathogenesis proceeds in the bee host. EFB will provide a paradigm which we can test against other pollinator diseases. For example, developed models will be used to investigate the epidemiology of 14 honey bee diseases collected across 5000 apiaries as part of an ongoing Defra funded monitoring programme (Fera). Dissemination of project results is explicit within the project framework and includes, the production of a list of key end-users, stakeholder workshops, bi-annual project newsletters, reporting in industry literature, a disease management summary document and conference attendance. The modelling analytical and spatially explicit models developed within this project will act as tools to guide strategy in the face of a plethora of disease threats for managed and wild pollinators.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The Oxford-Warwick Statistics Programme will train a new cohort of at least 50 graduates in the theory, methods and applications of Statistical Science for 21st Century data-intensive environments and large-scale models. This is joint project lead by the Statistics Departments of Oxford and Warwick. These two departments, ranked first and second for world leading research in the last UK research assessment exercise, can provide a wonderful stimulating training environment for doctoral students in statistics. The Centre's pool of supervisors are known for significant international research contributions in modern computational statistics and related fields, contributions recognised by over 20 major National and International Awards since 2008. Oxford and Warwick attract students with competitively won international scholarships. The programme leaders expect to expand the cohort to 11 or 12 per year by bringing these students into the CDT, and raising their funding up to CDT-level using £188K in support from industry and £150K support from donors. The need to engage in large-scale highly structured statistical models has been recognized for some time within areas like genomics and brain-imaging technologies. However, the UK's leading industries and sciences are now also increasingly aware of the enormous potential that data-driven analysis holds. These industries include the engineering, manufacturing, pharmaceutical, financial, e-commerce, life-science and entertainment sectors. The analysis bottleneck has moved from being able to collect and record relevant data to being able to interpret and exploit vast data collections. These and other businesses are critically dependent on the availability of future leaders in Statistics, able to design and develop statistical approaches that are scalable to massive data. The UK can take a world lead in this field, being a recognized international leader in Statistics; and OxWaSP is ideally placed to realize the potential of this opportunity. The Centre is focused on a new type of training for a new type of graduate statistician in statistical methodology and computation that is scalable to big data. We will bring a new focus on training for research, by teaching directly from the scientific literature. Students will be thrown straight into reading and summarizing journal papers. Lecture-format contact is used sparingly with peer-to-peer learning central to the training approach. This is teaching and learning for research by doing research. Cohort learning will be enhanced via group visits to companies, small groups reproducing results from key papers, student-orientated paper discussions, annual workshops and a three-day off-site retreat. From the second year the students will join their chosen supervisors in Warwick and Oxford, five in each Centre coming together regularly for research group meetings that overlap Oxford and Warwick, for workshops and retreats, and teaching and mentoring of students in earlier years. The Centre is timely and ambitious, designed to attract and nurture the brightest graduate statisticians, broadening their skills to meet the new challenge and allowing them to flourish in a focused, communal, research-training environment. The strategic vision is to train the next generation of statisticians who will enable the new data-intensive sciences and industries. The Centre will offer a vehicle to bring together industrial partners from across the two departments to share ideas and provide an important perspective to our students on the research challenges and opportunities within commercial and social enterprises. Student's training will be considerably enhanced through the Centre's visits, lectures, internships and co-supervision from global partners including Amazon, Google, GlaxoSmithKline, MAN and Novartis, as well as smaller entrepreneurial start-ups Deepmind and Optimor.

UK nature-based solutions, such as tree planting, must engage with the agricultural sector, given that agriculture uses more than 70 per cent of the land in the UK and is a major emitter of greenhouse gases (GHGs). Meeting the UK's tree planting targets and reducing agricultural GHG emissions may require converting current agricultural land to alternative land-uses. Agroforestry, where trees are deliberately combined with agriculture on the same piece of land, is one alternative land-use that maintains food production, but which can also drive down GHG emissions, deliver key ecosystem services, and create and improve (rural) livelihoods. Agroforestry supports several goals not only relevant to Net Zero, but for the UK government's 25 Year Environment Plan and Clean Growth Strategy. However, the environmental and societal benefits of agroforestry can only be realized through widespread adoption by key stakeholders, including farmers and land managers. The overall objective of the AF Futures project is to co-develop strategies to overcome barriers to, identify facilitators of, and increase opportunities for agroforestry practices in different UK contexts. Research focused on understanding the similarities in preferences and perceived challenges identified by different stakeholder groups, as well as how these might be addressed in local and national contexts will be conducted with AF futures, using a multidisciplinary approach. Integration of the natural, social and economic, sciences and arts and humanities is central to activities within AF Futures. Research addressing how regulatory structures, economic incentives, socio-economic drivers and impacts, and agronomic intervention shape agroforestry practices will be integrated through different disciplinary lenses. The arts and humanities will be used to create visual transitions from past representations of agroforestry to agroforestry futures, which integrate socio- economic outcomes and future biodiversity and ecosystem services, if adoption of different particular agroforestry approaches occurs.

Seed quality traits have been identified as a driver for making changes to agricultural systems, such that sustainability might be increased substantially through a greater use of pulse crops in rotations. Currently, both home and export premium markets exist for pulses of high quality, which can impact on choices in rotations. This project aims to identify the scientific basis for seed quality parameters in the three main pea crops for human food use - vining, canning and dried pulses. The project will identify compounds that are positively or negatively associated with quality and provide information on how these change in seeds as they mature and under different growing conditions. The work will link genetical and metabolomic scientific studies with industrial assessments of quality, through studies of materials grown and harvested according to current industrial standards by the industrial partners. Genetically marked lines will be used for metabolite profiling and for industrial sensory analysis. In this way, genes and markers linked to quality will be identified. The role of candidate genes will be explored by industrial analysis of variant lines, including some near-isogenic lines that are already available as laboratory stocks. Linking quality characters to biochemical and genetic information will facilitate the more robust and rapid identification of superior lines, by enabling the deployment of genetic markers in marker assisted selection. This will lead to greater efficiency in breeding programmes aimed at quality food markets. It will also provide opportunity to develop more robust methods for assessing maturity rapidly in the field. The identity of genes and markers associated with quality will provide opportunities to manipulate the genes in the biochemical pathways via mutagenesis and to identify novel sources of natural variation within germplasm. The impact of improvements to systems for quality assessment in pulses, and the reality of meeting current and increased market demands on UK sustainable agriculture, will be explored. Predictive modelling of the consequences of changes to rotations, land maps and land-use efficiency data will be used in a scoping study, building on socio-economic data and models currently under development in relation to climate change.