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Stat i s t i c s & M athemat ic s impr o ving A g r ic u lt u r e,the Envir onment, Food & Health

Biomathematics &Statistics ScotlandBiennial Report2013/2015

CONTENTS

Overview Director’s IntroductionMethodologies

Statistical Genomics & BioinformaticsProcess & Systems ModellingStatistical Methodology

ApplicationsPlant ScienceAnimal Health & WelfareEcology & Environmental ScienceHuman Health & Nutrition

Knowledge ExchangeUser-friendly SoftwareTraining for ScientistsPostgraduate Research & Training

Facilities and PeopleInformation TechnologyStaffResearch StudentsManagement Group

Appendix 1 Selected Research Grants, Contracts & Subcontracts

Appendix 2 Refereed Publications

Appendix 3 Conference Presentations, Lectures & Seminars

Appendix 4 External Committees

Glossary of Organisational AcronymsContact Points

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“to improve science and society through an understanding of variation, uncertainty and risk”

METHODOLOGIESBioSS has an international reputation for its research in biomathematics and statistics. Our research is partitioned into three themes, each of which draws on the expertise and experience of staff:

statistical genomics and bioinformaticsprocess and systems modellingstatistical methodology

APPLICATIONS

KNOWLEDGE EXCHANGE

BioSS consultants add quantitative expertise to research throughout Scotland. Our staff have technical skills that are applicable to a wide range of scientific problems and the communication skills that allow them to interact effectively with scientists from other disciplines. Scientific areas in which we have particular expertise include:

plant scienceanimal health and welfareecology and environmental sciencehuman health and nutrition

BioSS bridges the gap between the development and application of biomathematics and statistics, and we are strongly committed to the dissemination of modern quantitative methods to the scientific community, government and the private sector. Key aspects of our programme of knowledge transfer include:

development of software productsdelivery of training courses for scientistssupervision of PhD students

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Dear Reader,

Welcome to the BioSS Biennial Report covering the period from April 2013 to March 2015. Our principal objective in producing this report is to demonstrate the added value that the quantitative expertise of BioSS brings to scientific endeavour. This added value accrues both from the knowledgeable application of existing methodologies, but particularly from the development of new approaches that lead to novel insights. By bringing quantitative experts from statistical, modelling and computational backgrounds into lasting and productive collaborations with researchers working in the life, environmental and socio-economic sciences, BioSS enables the potential benefits of quantitative thinking to be realised more fully. These benefits are manifest in knowledge acquisition by the research community and in the uptake of scientific knowledge by society as a whole, particularly in better informed government policies and enhanced innovation by the private sector.

Given the diverse nature of methodological expertise in BioSS and our wide range of application areas, we have necessarily had to be selective in the topics highlighted, and indeed of the descriptions of these. I very much hope that, amongst the array of highlights, you will find both topics of direct interest of which you are aware and items more tangential to your own interests of which you were previously unaware. Should you require further information about any of our activities, do visit our website (www.bioss.ac.uk) or contact us by email (enquires@bioss.ac.uk), and we shall be pleased to discuss topics of mutual interest with you.

The period covered by this report has been an important one in the development of BioSS. In terms of personnel, I would like to accord my particular thanks to Professor Chris Glasbey, who retired in August 2013 following an influential and productive career, commencing in the former A(F)RC Unit of Statistics followed by 26 years as Head of Research in BioSS from its inception in 1987. He has been replaced as Head of Research by Glenn Marion, who has since been awarded the distinction of Visiting Professor of Mathematical Biology by SRUC in recognition of his theoretical work on stochastic systems modelling and advances in related statistical inference methods, motivated by and applied to help resolve real-world problems in epidemiology and ecology.

From a scientific perspective, we have benefitted from the process of reflection on past achievements and future plans, brought about by preparation for an external scientific review commissioned by our parent body James Hutton Institute and taking place in February 2014. The review panel, chaired by Professor Sylvia Richardson, Director of the MRC Biostatistics Unit in Cambridge, commented very favourably on the quality of work undertaken across the full range of BioSS’ activities. The praise contained in the written report is to the credit of all of the personnel associated with BioSS, including our external collaborators. Many of the challenges faced by BioSS, particularly those brought about by the increasingly quantitative nature of science, are similar to those faced by comparator groups elsewhere and can best be addressed by BioSS staff operating at a high level in order to make the most strategic use of their time and expertise.

BioSS External Review Panel, 2014

“An overall characteristic of the statistical work developed in BioSS is an innovative and well considered use of modern model building approaches and associated

computational tools, successfully applied to a wide range of problems. The panel was impressed by the strong ethos of collaborations, both within BioSS and with its external

partners, which works well for mutual benefits.”

The relationship between BioSS and its partner organisations, which collectively act as Main Research Providers (MRPs) for the Scottish Government Rural and Environment Science and Analytical Services Division (RESAS), has continued to advance. The creation during early 2015 of proposals for the next 5-year portfolio of scientific activities funded by RESAS, due to commence in April 2016, has brought the family of MRPs closer together through inter-organisational as well as inter-disciplinary planning, both of research activities and of knowledge exchange activities. With their many long-term collaborations and generic quantitative skill sets, BioSS staff have a key role to play in fulfilling RESAS’ vision of greater integration in the delivery of research by the MRPs. I was pleased, therefore, to receive an invitation to join the Directors’ Executive Committee (DEC). I am grateful for the warm reception I have received from existing members of the DEC, and hope my involvement on behalf of BioSS augments the collective skill set being applied to the shape the proposals and associated interactions with RESAS at the highest level.

The funding environment in which the research community operates has not been comfortable for some time. It was encouraging, therefore, to hear supportive statements about the need for more not less research coming from both Westminster and the Director of the Confederation for British Industry, as debate intensified ahead of the Autumn Statement 2015. Those of us in the Scottish research institute sector have been fortunate that the Scottish Government has continued to recognise the discovery and application of scientific knowledge as important contributors to societal wellbeing and future wealth creation, and that RESAS staff have prioritised continuity of funding for the MRPs in the face of cuts in their total budget allocation.

Regardless of the details of future funding settlements at UK and Scottish levels, it is incumbent on all scientists in receipt of public funding to repay the confidence and investment bestowed in them. I believe that BioSS, with its broad quantitative skills and outward looking nature, is uniquely well placed to contribute to the enhancement and creative use of scientific understanding. I hope that, having read this report, you will appreciate the basis of this confidence in BioSS, the increasingly pivotal role played by quantitative specialists in scientific discovery and the impact of scientific knowledge on society in the 21st Century.

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Development of more advanced statistical and computational methodologies is required to keep pace with new experimental technologies in molecular biology and genetics. Challenges to be met include dealing with increased volumes of data, improving the robustness of inference to noise in the data, and enhancing the models to make them biologically more realistic. Here we present four examples of how we have addressed these issues in BioSS.

Finding sparse networks in gene expression dataHigh-throughput technologies for measuring gene expression, such as microarrays and next-generation sequencing, have enabled scientists to routinely collect expression data for thousands of genes across a range of experimental conditions. For example, whole genome microarrays for the human can measure RNA levels for c. 20,000 genes.

The interactions between these genes can be visualized as a network, where the genes are represented as nodes and the interactions between them as edges. Graphical models are a well-known way to construct such networks from data. They are based on the estimation of a partial correlation matrix (inverse of the correlation matrix) and link the nodes corresponding to two variables if there is sufficient evidence that they explain variation in each other after accounting for other confounding variables.

In order to use this approach in cases where the number of genes greatly exceeds the number of samples, we have to assume that the network is sparse, both for mathematical / computational reasons and in order to obtain an interpretable network. There are various ways to enforce this sparsity, and many of these use a lasso-type penalised regression approach. All such methods depend on a tuning parameter to determine how sparse the estimated network will actually be. BioSS has developed a new method to choose this parameter, applying the minimisation of risk functions calculated using network structure properties such as connectivity or clustering. We have also generalised our method to the situation where two sets of samples (e.g. RNA tumor samples and healthy ones) are analysed simultaneously to estimate both joint and differential networks.

Joint sparse gene expression network estimated from 25 paired tumor / normal human colon samples. Edges indicate interactions between genes, coloured according to whether these interactions are common to both sample types (blue), only present in tumor samples (green), or only present in samples of healthy tissue (red).

Quantitative methodologies must be constantly developed to meet the changing demands of science, take advantage of the opportunities provided by new computing, and address the challenges offered by novel data collection technologies. To best satisfy its remit while responding to these dynamics, methodological research at BioSS is structured under three themes.

Statistical Genomics & BioinformaticsDevelopments in molecular genetics technologies are generating enormous quantities of data, often of new data types, enabling deeper studies of genomes and the relationship between genetics and biological function. Simultaneously, new computing technologies are allowing easy access to rapidly increasing computer processing power and data storage capacity. BioSS aims to develop and automate methodology for analysing these data, harnessing the increased computing power to extract maximum information from the data.

Process & Systems ModellingMathematical modelling plays a major role in achieving many scientific objectives, including testing scientific understanding and generating novel hypotheses. The development of associated statistical tools enables the nature of key processes, which can be difficult, costly or impractical to measure directly, to be characterised using incomplete and noisy data. Development and analysis of models contributes significantly to the understanding of complex systems, leading to actionable insights that can play an important role in addressing societal challenges

Statistical MethodologyStatistical methodology needs constant development, firstly to keep pace with the requirements of new technologies being used in the biological and environmental sciences, and secondly to address new questions that arise as science becomes ever more quantitative. In particular, there is a pressing need for new methodology to correctly interpret large, highly-structured data sets. BioSS will develop and adapt methodology in the key areas of image analysis and spatially-, temporally- and spatio-temporally-structured data.

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Linkage map construction in blackcurrant using genotyping by sequencing dataGenetic linkage maps are a vital tool for locating genes responsible for important traits such as yield or disease resistance in plants and animals, and modern genotyping technologies are leading to increasingly dense maps. Genotyping by sequencing is a recent approach that enables large numbers of single nucleotide polymorphism sites (SNPs) to be detected and is suitable for the analysis of crops such as blackcurrant that do not have a sequenced genome. Each segregating SNP has two alleles, and the data are obtained as read counts of each allele in two parents and their F1 offspring. Graphical exploration led to a method based on functional regression to identify the genotypes for each individual. After construction of the linkage map, further SNPs with unusual segregation patterns were detected and were mapped as quantitative traits using the allele count data. This identified that some of these unusual patterns were likely to be due to the presence of a third allele (a null allele) with a zero read count at a SNP, which was allowed for in subsequent analyses. Taken as a whole, the method gave a high-quality linkage map with up to 200 SNPs on each linkage group and more approximate locations for many SNPs of lower quality. This is an important tool towards identifying candidate genes for key economic traits.

Plotting read counts for a SNP site shows that the parents (circles) have both alleles (are of genotype AB). Their offspring (crosses) show three classes AA, AB and BB in a 1:2:1 ratio.

Studying the effects of campylobacter infection in pigs by high throughput gene expression analysisCampylobacter is the most common source of food poisoning, accounting for over 250,000 cases in the UK and an estimated 9,000,000 in Europe each year. The cost of campylobacteriosis is estimated by the European Food Safety Authority (EFSA) to be €2.4 billion (£1.8 billion) annually. In order to better understand the disease mechanism, BioSS has been working with researchers at the Moredun Research Institute, to identify the genes and biological processes that are altered during infection with Campylobacter from gene expression profiles generated by microarray technology using the pig gastrointestinal system as a model. Samples are prepared from control and infected samples from the colon and ileum and subject to statistical analyses that identify genes whose expression level between conditions are significantly different. Gene set enrichment analysis (GSEA) approaches are then used to find the key biological pathways represented and to prioritise the genes themselves. In addition to confirming host genes and processes already implicated in existing Campylobacter infection studies, many novel high priority genes have been identified and are being validated experimentally. Volcano plot of genes showing at least 2-fold differential expression between control and campylobacter infected colon samples with false discovery rate (fdr) set at 5% (blue). Genes that fail to pass the fdr and fold-change thresholds (red).

Rapid typing of E. coli isolates using whole cell MALDI mass spectrometryFoodborne pathogens, including E. coli O157 and related strains (collectively called enterohaemorrhagic E. coli – EHEC), continue to cause public health problems, requiring typing of strains during outbreak investigations. Identification of bacterial strain by conventional means can be laborious and time-consuming. As an alternative, MALDI mass spectrometry (MS) proteomic profiles are quick and cost-effective to obtain. The possibility of using them for typing has been explored in collaboration with the Moredun Research Institute.

MS is a technologically advanced approach for detecting biological molecules. The complex spectra obtained from the spectrometer require a series of pre-processing steps to convert these data into formats suitable for bacterial identification and typing. High-quality pre-processing usually includes a combination of smoothing, baseline correction, peak extraction and alignment, and normalisation procedures. BioSS has produced a quick, widely applicable, semi-automatic and statistically robust MS data pre-processing pipeline to facilitate these tasks. Once processed, the data can be analysed using statistical clustering and phylogenetic methods to investigate relationships amongst strains. Relationships identified by MALDI-MS are also being used as input for a predictive model based on shrinkage linear discriminant analysis. Taken as a whole, this approach demonstrates greater than 90% accuracy and allows users to identify the most significant spectral features involved in distinguishing between bacterial strains.

Application of these methods and associated software to 92 EHEC isolates shows a good level of differentiation and reproducibility among biological samples. When interpreted using these new quantitative tools, MALDI-MS is showing promise as a typing method that could be rapidly applied during an infection outbreak, supporting better public health epidemiology.

For the 92 E. coli – EHEC isolates, the dendrogram on the left-hand side represents the similarity relationships identified by statistical clustering based on their MS fingerprints. The E. coli O157 serogroup is neatly distinguished from other EHEC O serogroups, including sorbitol-fermenting (SF) O157. A heat map of the processed MS intensities across the 4.3 – 9.8 kDa m/z range (darker shading corresponds to higher concentrations) demonstrates the key differences between the isolates clustered into the identified groups.

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Mathematical models of biological and environmental processes both encapsulate and enhance our understanding of these systems. New methodologies are required to address the complexity of the problems to which models are increasingly applied. Novel statistical methods are being used to extract information from limited data to support real world decision-making. As illustrated by the examples described below, BioSS is working closely with scientific collaborators to develop such methods in the context of specific applications.

A modelling framework to examine traceability in the Scottish sheep populationFollowing the UK foot-and-mouth disease outbreak in 2001, EU regulations led to electronic tagging of sheep so that individuals can be identified. In Scotland, electronic readers identify individual sheep as they move in batches through critical control points (mostly markets and abattoirs). The readings are then uploaded to a central database (ScotEID).

Operational aspects of electronic identification (EID) inevitably lead to uncertainty, including read errors. However, the impact of these uncertainties on traceability, defined in terms of the ability to determine possible infectious contacts of an identified sheep in the event of a disease outbreak, is far from clear. BioSS has developed a novel, simulation-based, stochastic modelling framework, which enables assessment of traceability in the presence of EID data and other relevant sources of information. Traceability is then interpreted as an emergent property of the modelled system, potentially depending on sheep demography, batch selection processes at holdings, properties of the between-holding network of sheep movements, patterns of infectious contacts between sheep and variations in read error rates.

This formulation enables both an assessment of the current system and the targeting of disease control efforts, while taking into account system uncertainty. Outcomes from various disease control strategies can be compared.

Simulated results based on 2011/2012 batch movement data. Estimated levels of contact (darker red means more contact) between a specific sheep residing in the south of Scotland and sheep in all Scottish parishes, given perfect knowledge of individual sheep movements (left) or knowing only

electronic readings (right). When only electronic reads are available, the complexity of the sheep movement network and the amount of read uncertainties can lead to many more parishes with non-zero estimated contact. However, parishes at greatest risk can still be identified due to their higher contact estimates.

Modelling methane production from ruminantsLivestock agriculture is estimated to contribute between 9% and 18% of total anthropogenic greenhouse gas (GHG) emissions. A significant element of such emissions is the production of methane in ruminants, which also represents a loss of energy that might otherwise be available for growth or milk production.

In collaboration with scientists at RINH and SRUC, we have developed a simple process-based model to simulate methane production in ruminants. The model represents two classes of microbes: the fermenters, which grow by consuming ingested feed and produce hydrogen as a by-product; and, the methanogens, which consume the hydrogen producing methane in the process. We obtain the model’s parameter values from the literature and also by using time series data of feed intake and hydrogen and methane production from a group of finishing steers on two different diets. The model captures the dynamics of the system (see figure), demonstrating a level of correlation that can act as a starting point for assessing mitigation strategies for reducing methane emissions from ruminants, e.g. through changes in the composition of feedstocks.

Observed and modelled methane production from a finishing steer on a concentrate diet. Measurements (solid black line) are taken in a respiration chamber at 6 minute intervals. The model simulation (dashed red line) is driven by the individual steer’s feed intake, but otherwise uses parameter values derived from methane measurements excluding the steer in question.

Data driven risk assessment for disease incursionsThe ability to respond to livestock disease outbreaks, and thus control their impact by targeting premises at greatest risk, is greatly enhanced if disease dynamics can be accurately characterised and quantified. A key factor to consider in the infection process is the spatial spread of disease between farms, which in livestock epidemic models is typically characterised by transmission, or kernel density functions modulated as a function of distance between farms. In situations of re-emerging diseases, for which small but repeated outbreaks often occur, extracting maximal information about disease spread is statistically challenging.

We have created a novel toolkit for scientists and policy-makers to make better use of data from past disease incursions than is possible with current methods. Even when such outbreaks are small, simulated test cases show we can make use of the limited data available to more confidently quantify the spread of disease between farms, including choosing between different transmission functions using novel latent-residual methods devised in collaboration with Heriot-Watt University. Our tools, therefore, provide critical information for risk assessment of future disease incursions.

Risk maps of the eastern England area showing two different transmission functions. Scale ranges from pale pink (highest risk) to grey green (lowest risk). Infection risk maps of farm premises during an incursion of classical swine fever in eastern England using two models with different transmission functions for spatial spread. Our latent residual method indicates the spatial transmission function underlying the left-hand map does not fit the small historical data sets as well as the transmission function underlying the right-hand map.

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3D histogram of colours of pixels from an image of a sweet pepper plant, used to summarise the image as a basis for estimating leaf area. Volumes of spheres increase in proportion to the numbers of pixels represented. Those in an inclined cylinder from lower left to top right contain only the plants, whereas those at the back and shaded blue contain mostly the contrasting background placed behind the plants when images were taken.

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Complex responses to movement-based disease controlEndemic livestock diseases cause significant loss to UK agriculture, and some endemic pathogens, such as Escherichia coli O157, also pose significant risks to human health. Hence, controlling such diseases benefits the livestock, farmers and human populations. Livestock disease controls are often linked to herd movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of the impacts of farm-to-farm spread of infection.

Working with colleagues at the University of Glasgow, SRUC and MRI, this work focused on characterising system level vulnerability to disease in terms of the expected number of additional farms directly infected by a single infected farm, a quantity known to the epidemiological modelling community as R*, which can also be correlated to the long-term proportion of infected farms (see figure). R* is a threshold quantity analogous to the more widely used basic reproduction number R0, for which any value greater than unity implies that the disease is expected to spread and persist. However, for metapopulations, calculation of R* is both more tractable and more meaningful than R0. For example, high R0 may only imply high within farm disease transmission. To date R* calculations have focused on so called household models of diseases in stable human populations and have not taken explicit account of movements of individuals between locations, which are a key driver of the

between farm spread of endemic livestock disease. Applying algebraic methods, we have been able to calculate R* in the presence of both livestock movement and associated movement based controls, enabling us to demonstrate the use of R* as an indicator which aids the understanding of persistence and control of endemic disease.

Results show that under movement-based controls, an infection could be successfully controlled at high movement rates but persist at intermediate rates (see figure), due to the potential for undetected build-up of infections within herds. Ongoing application of this approach to a range of important livestock diseases, including E. coli O157, shows that disease control at the system level is complex, with changes in livestock movement aiding control of one disease, but exacerbating others.

Advances in science create demands for new statistical methodologies. Our research responds to the needs of our scientific collaborators, as they change the nature of the data they collect, the information they wish to extract from data, and the applications of this information. Here we illustrate some highlights from our research, demonstrating the breadth of motivating influences.

Automated estimation of leaf area developmentExperimental work in horticulture involves recording and measuring many characteristics of the plants being grown, a process which is known as phenotyping. High-throughput automated plant phenotyping, using data from digital images, has recently become popular, because it offers the potential to make this part of research faster and more efficient.

Leaf area is one important characteristic in understanding plant performance, but it is time-consuming and destructive to measure accurately. In an EU FP7 funded project, with several partners, including Biometris, BioSS investigated a method for using a histogram of image intensities to automatically measure plant leaf area of tall pepper (Capsicum annuum L.) plants in the greenhouse. During the project, our collaborators created a database of over 15,000 images. Three-dimensional histograms of the distribution of RGB colour intensities were constructed from each image, and the histogram bin counts for pooled combinations of intensities (see figure) were reduced to a small set of principal components that defined the design matrix in a regression model for predicting total leaf area of plants whose leaves were measured manually following destructive harvesting. Regression calibrations were then performed for six different developmental times, enabling predicted leaf areas to be estimated for all plants grown.

The development of leaf area was investigated by fitting linear relations between predicted leaf area and time, with special attention given to the genotype by time interaction and its genetic basis in the form of quantitative trait loci (QTLs). The experiment used several pepper genotypes produced by crossing established but dissimilar cultivars. Although this trial contained a limited number of parental genotypes, the estimation of leaf area from so many plants enabled confirmation of a previously identified QTL for leaf area growth.

Image analysis provides a powerful and efficient way to study and identify the genetic basis of growth and developmental processes in plants. Its use has the potential to make the development of new crop cultivars, as well as any agronomic experimentation to improve plant characteristics, more effective and productive. Further development in this area will benefit from inter-disciplinary work involving agricultural engineering, computer science, statistics and crop genetics.

(A) R* has a characteristic curve, starting at 0 for no movement (K=0), increasing to an intermediate peak, and then decreasing to 1 for high movement rates. Disease intervention with probability p reduces R*.

(B) The equilibrium proportion of farms infected depends on R*. When R*>1, the disease spreads sufficiently between farms to persist, otherwise it will ultimately fade out. When disease intervention probability p>0, then R*>1 only for intermediate movement rates K, and so the disease can only persist for a limited range of movement rates. Note that the lines for p=0.8 and p=1 are indistinguishable, because, for all values of K, R*<1, and so the equilibrium proportion of infected herds is zero.

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Compositional analysis of behavioural dataModern technologies measuring animal location, movement and metabolism enable objective and accurate monitoring of animal activities and health, and calculating daily activity budgets from such data can provide important insights. However, these studies are hampered by compositional constraints: times allocated for individual behaviours are non-negative and total time is pre-determined, e.g. 24 hours or other fixed duration depending on the measurement protocol. These compositional constraints induce a co-dependence structure between time allocations to different behaviours, leading to inconsistent and paradoxical results when using methods of data analysis developed for unconstrained data.

Compositional data analysis (CoDA) has been developed to provide a sound mathematical framework that directly addresses the constrained and relative nature of compositional data, motivated primarily by the analysis of concentration data occurring in geochemistry and environmental sciences. By adapting and extending CoDA methods to address the particular issues encountered in animal behaviour and physical activity data, we have been helping to improve inferences about relationships involving behavioural patterns, either considering these patterns as a multivariate response or constituting multiple explanatory variables, enabling synergistic analyses involving all behaviours as a whole rather than considering each in isolation.

Representation of the differential behaviour patterns of pigs observed in social groups (red; MIX) or in their home pens (blue; CON) according to relative amounts of three postural behaviour types (standing, lying laterally and sitting). Principal components (PC) in compositional space are curved in the ternary diagram, the distributions of scores of the first PC (red) differing between types of animals. Grey lines indicate points with constant proportions of one variable. The second PC is shown in yellow.

Relating biological responses to weatherSmart modelling of the dependencies of biological responses on weather can provide an improved understanding of the potential impacts of climate change and enable more accurate predictions in the short- and long-term, leading to better management decisions. While this may be challenging due to the high-dimensional nature of weather data, these difficulties can be managed through the application of specific mechanistic models or by placing sensible penalty constraints on the sequence of coefficients in regression models that use daily weather records as explanatory variables. One important application for these sophisticated modelling techniques is in phenology.

Phenology is the study of the timing of natural events, such as the commencement of flowering or the nesting of birds. BioSS has been investigating methods for relating phenology to weather since 2001, when it was asked to review the phenology records from the Last family. Since then we have demonstrated that penalised regression can be a powerful tool for phenology, giving easily interpretable results. More recently, in collaboration with the University of Edinburgh, we assessed the performance of various types of statistical models on a classic phenology data set. This series of records was started by Robert Marsham in 1736 and continued by his family until 1958. It consists of timings of leafing and flowering for many species, several of which play an important role in the ecology of woodlands. Understanding climate-driven pressures for change is particularly important in the context of woodlands due to the long-term impacts of management decisions.

Although the penalised regression models were much easier to fit and were able to identify the main features of the relationships between timing of flowering / leafing dates and daily temperatures throughout the year, we found that mechanistic models gave more accurate predictions. For most species, a delaying effect of warm autumn temperatures was evident, in addition to the expected advancing impact of warmer springs. By fitting

selected mechanistic models in a Bayesian framework using Markov chain Monte Carlo methods and drawing on temperature simulations from future climate projections, we were able to derive potential distributions of flowering and leafing dates in the time periods 2010-2039 and 2040-2069. In addition to alterations in the timing for individual species, we found that the order of spring events for different species in woodlands is likely to change, which will have important implications for the ecology of woodlands.

We are currently working on a study that focuses on modelling the numbers of aphids flying over a season. This poses additional methodological problems, because the biological response in this case is multivariate and the data are counts of individuals caught. If the results are successful, however, it will aid forecasting of the spread of aphid-transmitted potato viruses.

Violin plots of the distributions of spring events observed by the Marsham family (top) and predicted using model-based projected climates (middle and bottom).

Developing Markov switching autoregressive models to analyse animal movement dataAnimal movement data collected at a high temporal frequency can be very rich in information, but extracting this information poses many challenges. Certain issues must be overcome. First, the number of underlying behaviour types is unknown. Second, the correlation structure between observations depends on the types of behaviour being exhibited, with long bouts of one behaviour leading to long-range dependencies among the data, including asymmetric cycles and high autocorrelations at high time lags.

We have adapted Markov switching autoregressive models (MSARMs), developed previously to assess river conditions from water chemistry time series and to identify behavioural states of animals from locational data. These models conjecture that the time series of movements of an individual derive from a latent set of behavioural states, modelled as a Markov chain, between which individuals switch according to some probabilistic rules. Switches between states, and movements within any state, are stochastic outcomes with underlying rates, variances and correlations influenced by diel cycle (day or night), lunar cycle (binary) and lunar phase (4 categories). Fitting these models using Markov chain Monte Carlo techniques allows us to identify the number of behaviour types best supported by the data, along with the associated movement patterns and rates of interchange between behaviours.

Fitting MSARMs to data from flapper skates (Dipturus intermedia), a marine apex predator threatened with extinction, identified evidence for two behavioural states and indicated where these occur in the water column.

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Biological, environmental and social scientists benefit greatly from ready access to modern statistical and mathematical expertise to ensure that their research is carried out in the most effective way and to help them analyse and interpret the increasingly complex data

that are now routinely collected. BioSS is unique in the UK for combining in a single organisation a wide breadth both of methodological expertise and of experience in the application of quantitative methods to underpin scientific research.

BioSS staff are either permanently located at client organisations or make regular visits to their sites. Thus local contacts are readily accessible and well-known to their scientific collaborators. Local advice is supplemented by specialist expertise from BioSS staff at other sites when required.

Scientific applications, in which we have particular knowledge, can be grouped into four broad areas:

Plant science

Animal health and welfare

Ecology and environmental science

Human health and nutrition

Long-term working relationships are particularly valuable, as they allow us to develop a deeper understanding of specific areas of science and to become genuine research partners. The quality and impact of BioSS’ contributions to research programmes are reflected in our jointly authored outputs, including many papers in refereed journals and conference presentations.

The expertise developed in our interactions with the Scottish Government’s Main Research Providers is of value to a much wider community. Formal agreements with other research organisations have been established, facilitating collaborative support for their research and, by helping fund additional posts, allowing BioSS to strengthen its skill base. These relationships also extend our range of scientific and organisational contacts and are an important way of raising BioSS’ profile.

Research in plant science is essential if we are to meet the challenge of feeding the growing global population in a period of climate change. Our collaborations with plant scientists range from studies of molecular processes within cells to the interactions between crops, pathogens and their environment. Alongside the need to make optimal use of traditional types of data, we must develop and apply new approaches to make efficient use of the increasingly large volumes of molecular marker, transcriptomic, metabolomic and proteomic data that are being generated by new technologies.

Identification of factors causing ‘crumbly’ fruit in raspberryThe disorder known as ‘crumbly’ fruit, which causes fruit to develop incorrectly or to crumble readily when picked, is becoming a serious problem in the raspberry industry in Scotland and other parts of Europe. Varieties showing this tendency, even infrequently, are likely to be rejected by growers. BioSS has collaborated with staff from the James Hutton Institute to analyse genetic and environmental factors affecting raspberry crumbliness in both fields and polytunnels over six growing seasons. Crumbly fruit is usually scored as a binary (present/absent) trait, and a generalised linear model was used to relate observed scores to genetic information for a large raspberry population consisting of two diverse parents and 188 of their offspring. Two quantitative trait loci (QTLs) for crumbly fruit have been found consistently in different seasons. One of these QTLs is close to a region associated with control of fruit ripening, and we found evidence for an association of crumbliness with time to ripening, with a higher proportion of crumbly fruit tending to occur in offspring that takes longer to reach the green fruit stage. These results suggest that a test based on genetic markers close to the QTL locations for a wide range of raspberry breeding lines has potential for the selection of lines that are less prone to crumbly fruit. This is important for both Scottish raspberry breeders to eliminate unsuitable raspberry lines at an early stage, and Scottish raspberry growers to reduce losses in yield.

Diversity of Escherichia coli isolated from a barley trial with organic supplements E. coli is generally considered to be a mammal-adapted organism that only persists transiently outside the host. When detected in soil or water samples, the assumption is that this is evidence of faecal contamination. However, E. coli is adaptable and is present in a wide range of environmental conditions. In a study undertaken with pathologists at the James Hutton Institute, we investigated the diversity of E. coli from a barley field trial treated with two bulky organic soil amendments, municipal compost and bovine slurry. Soil and plant tissue samples were collected at different times of year under different irrigation regimes and tested for the presence of E. coli. Pathogenic isolates of E. coli were detected in soil samples and barley root and grain samples. Phylogenetic network analyses of alignments based on DNA sequences obtained from these samples found that E. coli isolates from the same source tended to be more closely related than isolates from different sources. This observation indicates genetic adaptation of some E. coli and supports the idea that E. coli can exist as a

Crumbly raspberries develop incorrectly.

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We support research into the health and welfare of farm livestock which is of benefit to the animals, the Scottish public and the associated commercial sectors. BioSS contributions include helping to design efficient scientific experiments and national field surveys together with the undertaking of specialist data analysis tasks to extract maximum information from new and existing data.

natural part of the soil microflora, rather than just being the transient contaminants transmitted from slurry. This work highlights the drawbacks of using E. coli as a faecal indicator species, as isolates were widespread and diverse, reinforcing the view that some are a natural part of the microflora in agricultural systems.

Epiphytic colonisation of Spinach root (magenta) by E. coli O157:H7 Sakai (green)

Molecular analysis of nematode communities as a soil health indicatorCrop yield is closely related to soil quality, so the development of swift and reliable indicators of soil health is important for agricultural management, and nematodes are key components of the soil food web. Since the development of molecular fingerprinting techniques for rapid identification of nematode species, analysis of nematode community structure for soil health monitoring has become

viable. Scientists from the James Hutton Institute sampled a grid of field plots running from shoreline to forest at Tentsmuir Point, Fife. Nematode abundances were measured and pH, salinity and vegetation composition were recorded. BioSS applied principal coordinates analysis to investigate variation in nematode community structure, and canonical analysis of principal coordinates was used to relate the community structure to environmental variation. No spatial patterns were detected in the community structure, but there were clear associations between nematode functional group and environmental variables. Bacterivore nematodes were associated with high salinity grassy plots; herbivore and carnivore nematodes with mosses, lichens and fireweed; whilst omnivore nematodes were associated with cowberry bushes. These results demonstrate the sensitivity of the nematode community to environmental variation, and, hence, their potential as excellent bio-indicators of soil health for those involved in land monitoring and management. The method has enabled the use of nematodes as an indicator of soil health in studies involving large-scale sampling and has been used to help determine the effect of land management changes on soil health.

Testing the testsEffective diagnostic tests must have the capability to accurately identify both positive and negative individuals. Where animals are to be vaccinated, then a test must Differentiate between Infected and Vaccinated Animals (i.e. be a DIVA test), since the regulatory impact and effect on population health of being unable to do so will be unacceptably severe. BioSS was tasked, in collaboration with a Contract Research Organisation (CRO) and the Universities of Cambridge and Aberystwyth, to design a field trial to establish the properties of a DIVA test for Bovine TB, recognising that both specificity and sensitivity are likely to differ between vaccinated and unvaccinated sub-populations.

For Bovine TB there is no perfect “gold standard” test. The standard field test lacks sensitivity, and the best test is expensive, requiring the animal to be killed and examined post mortem. Understanding what trial data could be available and how to analyse these effectively were critical in designing the study.

BioSS proposed that, for a small group of animals, both DIVA tests and post mortem results should be used to provide direct information on sensitivity and specificity. Subsequently, data from the two independent DIVA tests would be collected in parallel from larger populations of interest. Analysing the additional data by Bayesian latent class analysis, while using the initial results to define informative priors, would allow estimation of the test sensitivities and specificities in vaccinated and unvaccinated animals, along with the infection prevalence in both populations.

This hybrid method of data analysis supports identification of study designs that have sufficient numbers to quantify test characteristics to the prescribed precision, but which avoid the greater costs associated with collecting large amounts of post mortem data.

The effect of increasing second phase sample size on precision and its variability for estimates of test specificity using two independent DIVA tests, given an initial 30 positive animals and 100 negative animals tested by both DIVA tests and post mortem examination. The upper (white) boxplot denotes the potential range of values for the upper limit of the 95% confidence interval (CI) of estimated specificity, given a specified sample size. The lower (blue) boxplot is the equivalent for the lower limit of the 95% CI. As the sample size increases, the limits of the 95% CI are more likely to be close to the true value: the horizontal red line.Studies of nematode

communities have the potential to establish bio-indicators of soil health.

Sampling at Tentsmuir Point

Ecology & Environmental Science

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Climate change and species ranges of wild birdsThe extent to which climate change may diminish the efficacy of protected areas is one of today’s most pressing conservation issues. Many projections suggest that the distribution shifts of climate-driven species will leave protected areas impoverished and these species inadequately protected, while other evidence indicates that intact ecosystems within protected areas will be resilient to change.

We used data from an international database of observations of wild birds to show how recent changes in the distribution of 139 Tanzanian bird species are linked to climate change, protected area status and land degradation. The methodology employed was Bayesian species distribution modelling, accounting specifically for both spatial autocorrelation and detection probability, the latter using information on sampling effort recorded for each survey.

Our results indicated that protected area status tends to be associated with lower local extinction and higher colonisation rates. This suggests that the continued maintenance of existing protected areas is an appropriate conservation response to the challenge of climate and environmental change.

Plot showing predicted presence probability of Fischer’s sparrow-lark (Eremopterix leucopareia), as size of square, for two modelling situations: (a) where the modelling does not take account of observer effort; and, (b) where it does. Squares coloured red correspond to actual sightings; those coloured black indicate no sighting was made (possibly due to zero or low effort). The background green shades represent the proportion of savannah habitat in the square (higher for darker shades). Protected areas are also marked. It can be seen that not accounting for observer effort leads to a severe underestimation of population range in the southern areas where observer effort was lower.

BioSS’s interactions with ecologists and environmental scientists are centred on the need to develop and interpret a robust evidence base to underpin national policies. Our collaborative contributions for design and analysis range in scale from detailed experiments, which investigate particular processes under controlled conditions, through to national monitoring schemes, which provide broadly based information about our changing environment.

Measuring positive welfare in litters of pigletsPlay behaviour is of considerable interest in animal behavioural science and, given that play behaviour tends to occur only under good environmental conditions, has relevance to practical aspects of livestock management. In particular, play has been suggested as a potential indicator for high levels of animal welfare, as opposed to the focus of past research, which has generally aimed at identifying indicators of poor welfare.

BioSS, in collaboration with SRUC, the Roslin Institute and the University of Natural Resources and Life Sciences in Vienna, investigated spontaneous play behaviour in litters of domestic pigs. By fitting linear mixed models to the behaviour data, we were able to quantify the sources of variation in the number of observed occurrences of different types of play behaviour, both at individual and at litter levels, and to relate such variation in play to measures of pre- and postnatal development.

When interpreting observed behaviours, an understanding of the impact of factors treated as random effects in the mixed model, such as litter and the litter by date interaction, is just as important as recognising the influence of fixed effects such as gender or birth weight. Given that increases in the number of observed occurrences of play behaviours were associated positively with physical development in young pigs at the litter level, this work has provided quantitative support for the use of play as an indicator of positive welfare in piglets.

Better understanding of the attitudes and intentions of farmersWhen formulating government policy, or preparing publicity material to inform and influence the agricultural community, it is preferable to understand the attitudes and intentions of different types of people, rather than basing plans on unrealistic assumptions about ‘average’ farmers. In recent work with SRUC, data from a survey of Scottish farmers were used to explore current behaviours and intentions as regards uptake of innovative animal health and welfare technologies, including, for example, electronic ID (EID) tagging, wearing activity monitors and application of genomic science.

Using latent class models, we can allow for the possibility that different individuals belong to different sub-populations, each sub-group having its own distribution of opinions and responses. Based on the patterns of responses in the full data set, we found greatest support for there being three sub-populations: non-adopters (75% of the population), who were not particularly interested in any innovations; EID adopters (20%), who were largely interested in making more use of EID technologies; and, enthusiastic adopters (5%), who were interested in all technologies. Age, level of education, budget and other factors were found to influence the probabilities of an individual being assigned to each sub-population. This ability of latent class modelling to identify and better characterise sub-populations has potential to help formulate improved agricultural policies and more precisely target messages to different farmers.

The effect of age on the probability of farmers being assigned membership of the different technology adoption sub-populations.

Play behaviour by piglets can be a sign of high levels of animal welfare.

Human Health & Nutrition

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The role of a balanced diet in promoting human health is widely recognised. Research in this area must encompass a wide range of issues, from the quality of food available via the science of nutrition to the behaviour of individuals. There are many complex interactions, and the variation in observed processes can be large. Statistical and mathematical models have a central role in enabling the interpretation of experimental and observational studies.

Environmental quality and emotional well-beingParticipation in outdoor physical activities is believed to provide both physical and mental health benefits. Walking for Health is England’s largest network of health walk schemes, encouraging people across the country to lead a more active lifestyle. The positive effects of these free, short, led health walks on participant well-being are widely acknowledged. Researchers are particularly interested in the way the different environmental and emotional factors involved in these walks interrelate. In collaboration with the James Hutton Institute, the University of Salford and the University of Michigan, we analysed questionnaire data gathered after group walks within the scheme to establish the mediating and moderating effects of environmental quality on the emotional well-being of the respondents.

Our analyses were conducted in a mixed model framework, enabling valid inferences about factors of interest that could be obtained from a data set containing multiple observations on each participating individual and could be separated from the effects of physical activity. Against a backdrop of increasing interest in the relationship between nature and health, this study sheds further light on specific factors improving the experience and should prove useful when designing future walks.

Multi-level mediation and moderation analysis of reported effects and perceived environmental factors allow us to assess the interactions and relationships between environmental quality and emotional well-being.

Effectiveness of measures to tackle diffuse pollutionDiffuse pollution from agriculture is a major cause for the failure to achieve objectives set out in the EU Water Framework Directive. A Programme of measures have been introduced with the aim of achieving good ecological status in surface water bodies; evidence is required to assess the effectiveness of these measures.

In 2006-7 a series of monitoring stations was established in sub-catchments of the Lunan Water in Angus, to collect data and to allow the effect of such measures to be separated from other factors, including hydrology and changes in land use, which can also have influenced water quality. In collaboration with the James Hutton Institute, BioSS looked at trends in concentrations and loads of nitrate, soluble reactive phosphorus, suspended solids and turbidity. Nutrient concentrations were obtained from fortnightly samples, but as flow was measured continuously, we were able to estimate quarterly loads using the modelled relationship between flow and concentration obtained from those occasions when concentrations were measured.

Although evidence for downward trends in nitrate concentrations was found, evidence for corresponding data in

nitrate loads was not found. This apparent discrepancy is most likely due to the concentration data being more sensitive to change, since load estimates are greatly influenced by temporal variability in hydrology. Downward trends in concentrations might be interpreted by policy makers as demonstrating that regulations are having an impact, but a longer time frame would be needed to confirm the effectiveness of measures on loads.

Classifying tumour biopsiesColorectal cancer is the fourth most common cause of death from cancer, accounting for 8% of all cancer deaths. It is of particular interest to nutritional scientists, as it is known to be strongly influenced by diet. Scientists from the Rowett Institute of Nutrition and Health have developed a targeted in-house multiplex gene array to measure expression of 21 markers (genes) known to be involved in the development of colorectal cancer. This array has been used to study differences between normal, polyp and tumour colon biopsies (58 samples in total), which represent different stages of the disease. Analysing the differential gene expression patterns, we can find genes of diagnostic value for staging the diseases, as well as learn about the gene regulatory mechanisms involved.

BioSS applied orthogonal partial least squares discriminant analysis (OPLS-DA) as a method to visualize the main patterns in the data (see plot), as well as to identify classifiers for the three sample types. Leave-one-out cross validation showed that 100% of the normal samples, 88% of the polyps and 94% of the tumours could be classified correctly in this data set. [In particular, the study showed that epithelial genes such as NOX1 and LGR5 express at high levels in polyps, which might be helpful in predicting individuals with a propensity for tumour development.

Bi-plot obtained from OPLS-DA. The first component (horizontal axis) separates normal (white triangles) from other samples, whereas the second component (vertical axis) distinguishes between polyp (yellow triangles) and tumour (red triangles) samples. Dark circles represent the genes, highlighting the wide transition between expression of genes such as $M6DA(N) in normal samples and the expression of a different set of genes such as CCND1 in unhealthy samples.

Observed nitrate concentrations in the Lunan Water at Hatton, Angus, with fitted model incorporating seasonal variation and long-term trend.

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Knowledge Exchange

Knowledge exchange takes various forms, some examples are given below.The Forum for Quantitative Science (FQS) was created by BioSS in 2010 with support from the Scottish Government. FQS brings together staff from CAMERAS partnership organisations and BioSS’ fellow RESAS Main Research Providers, who are interested in the application of quantitative sciences. Workshops are run with themes of common interest and with the aims of sharing knowledge and improving links between participants. The workshop in March 2014 had a theme of Modelling in Space and/or Time. Prof Marian Scott OBE, from the University of Glasgow, invited as a guest speaker, gave a stimulating introductory presentation based upon her experiences in environmental science. Further presentations by representatives from most of the attending organisations demonstrated the wide range of challenging quantitative issues being addressed by diverse methodologies, with details and areas of mutual interest explored during subsequent discussions.

Within EPIC, the Scottish Government-funded Centre of Expertise in Animal Disease Outbreaks, BioSS has led a series of workshop activities with stakeholders to elicit opinions about the future of the Scottish cattle and sheep industries. Using what is known as a ‘scenario planning’ framework, farmers, veterinarians, government officials, retail specialists and representatives of other key stakeholder groups reviewed the main drivers of change in the livestock industries, then developed narratives describing plausible ‘future histories’ for these industries

over a 25-year time window. The outputs from these exercises were collated as reports for a general audience, circulated to major stakeholders as hard copies, and made available to the general public via the EPIC website. The co-construction of these scenarios has proven to be an excellent way to promote knowledge exchange between the EPIC scientists, policy customers and the wider stakeholder community.

BioSS has interacted with the general public in a variety of ways. Some staff members have visited schools to talk about their interest in quantitative science and the importance of school subjects in the real world. We have a STEM Ambassador, who has participated in “I’m a Scientist Get Me Out of Here” and initiatives of the RSE Young Academy of Scotland. These events aim to inspire young people to take an interest in science and even to consider careers in science. Our research has its widest reach through the media. A notable example during the reporting period was BioSS’ work on woodland phenology, described elsewhere, which was publicised in various newspapers and on national radio, as well as on ecological blogs. Since then, BioSS has established a social media presence, which is allowing us to maintain contact with a growing cadre of followers.

“BioSS works closely with its collaborators and end-users to ensure the relevance of its activities. We place particular emphasis on understanding their upcoming challenges and communicating in a way that enables knowledge transfer and maximises impact.”

Meeting health and environmental targets with minimum dietary changeIt is well known that most people consume diets which deviate in some ways from the nutritional recommendations issued by the Scottish and UK governments. Using data based on the diets of individuals collected by the National Diet and Nutrition Survey rolling programme, we have investigated the amount of change that is needed by different individuals in order for each to meet the nutritional recommendations, and what the impact might be of including a target on the greenhouse gas emissions (GHGE) associated with the revised diets. The mathematical technique of linear programming is able to construct diets which optimise some criterion while meeting a set of constraints. These constraints can ensure the nutritional recommendations are met, but diets based only on these recommendations are unappealing. Additional constraints are also usually included. Here these were based on limiting change to current diets. Although our objective was to alter each person’s diet as little as possible, we found that substantial change was needed for the majority of people to satisfy the nutritional recommendations, usually involving increasing or reducing the amounts of some types of foods consumed by at least 50%. Moving to diets which meet nutritional targets will produce only small reductions in GHGE, and further change is needed to achieve significantly lower values in GHGE. This research also gave insight into the targets most demanding to achieve, which were an upper limit for sodium intake and a lower limit for dietary fibre.

Effects of long-term supplementation of diets with fish oilControlling glucose concentrations in humans by dietary means offers many benefits compared with medical intervention, but it is believed that past studies have provided inconsistent outcomes due to the short duration of dietary supplementation.

Consequently, a recent study at the Rowett Institute of Nutrition and Health aimed to establish whether long-term (nine months) fish oil supplementation improves glucose metabolism and insulin sensitivity in older people with impaired glucose regulation. Whilst monitoring glucose concentrations in the blood of participating volunteers provides information on net changes, it does not tell us whether these changes are due to differences in the production rate, disposal rate, or both. Therefore, in collaboration with BioSS, a complex study protocol was designed in which, at the start of the study and after nine months of supplementation, volunteers were given an infusion of stable isotopes in conjunction with frequent blood sampling. We then developed mathematical models based on differential equations in non-steady state. We fitted these models to the data obtained from the infusions, and this allowed for quantification of the uptake rate of glucose, endogenous production of glucose, glucose disposal, and the sensitivity of these variables to insulin levels. Our analyses found no evidence for any long-term effect on these outcomes from fish oil supplementation, which was supported by differences of less than 10% in the mean values of the associated model parameters between the control and treatment (fish oil supplement) groups.

Illustration of a week’s diet constructed by linear programming to minimise greenhouse gas emissions whilst satisfying a wide range of dietary requirements.

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Training for Scientists

BioSS has a well-established suite of training courses, each of which contains a mixture of expository and practical sessions. These courses allow participants to improve their understanding of quantitative methods, to extend their ability to implement these methods using computer packages, and to develop their knowledge of non-standard situations which require expert intervention.

Training programmeOur provision of training in statistics, bioinformatics and mathematical modelling has played a central role in developing our long-term collaborations with research scientists for the past two decades. The core of our training effort has been a programme of intensive one or two day courses, which have taken place in rooms equipped with the necessary computing facilities, usually at the participants’ site or nearby. Feedback obtained from our online evaluation is largely positive, indicating that these courses are well received by participants.

BioSS also provides in-house statistics courses for external organisations in the UK and abroad, selecting and mixing material as required from our ‘off-the-shelf ’ courses, to best serve the interests of the commissioning organisations. We also arrange workshop sessions to discuss specific interests of participants to avoid the overheads of preparing full courses from scratch.

The suite of BioSS courses ranges from basic statistics for scientific applications through to more specialist topics, with course titles as follows.

Basic Statistics courses

Getting Started in R

Experimental Design and Analysis of Variance

Regression and Curve Fitting

Graphical Methods for Multivariate Data

Statistical Methods for Repeated Measures Data

Introduction to Mixed Models and REML

Introduction to Mathematical Modelling

Phylogenetic Trees from Molecular Sequences

Statistical Design and Analysis of Microarray Experiments

Linkage analysis and QTL mapping in plants

User-Friendly Software

BioSS creates user-friendly software, to enable the algorithms developed in our research to be easily deployed and applied by non-technical users. Here we describe a selection of pieces of software written during the reporting period.

StagePop – modelling stage-structured populations in RStagePop enables ecologists to model the dynamics of populations that are stage-structured (those in which individuals transition between distinct stages, e.g. eggs, juveniles, adults for most insects). Although initially developed to model the impact climate change may have on populations whose development rates are temperature dependent, such as potato cyst nematodes, its capability is much more general, including modelling interacting species in complex food webs.

Tetraploid MapTetraploid Map is a programme for constructing linkage maps and carrying out QTL mapping for populations of auto -tetraploid species (e.g. those with chromosomes in sets of four rather than in pairs), especially potato. The original Tetraploid Map was published in 2007, but it is now being revised to handle the advances in size and information content of new genetic marker technologies, such as single nucleotide polymorphism (SNP) data from the Infinium 8300 potato SNP array.

Over-Years UniformityThe DUST environment for evaluating data from Distinctness, Uniformity and STability crop trial results has been enhanced with an improved method for analysing plant uniformity over a period of years. An implementation of this method, written in R, is currently being assessed by the International Union for the Protection of New Varieties of Plants (UPOV) as a replacement for existing, less flexible methods and, if successful, will be used internationally.

Animal Tomogram Analysis Routines: ATARBioSS has continued to develop the STAR (Sheep Tomogram Analysis Routines) environment for analysing images of living animals. During this reporting period, upgrades have focused on enhancing the stability of implementation and improving methods for semi-automated segmentation. A generalisation involving replacement of some sheep-specific elements has been released to the wider research community under the name ATAR.

ADRIÀ CABALLÉ MESTRES

Postgraduate Research & Training

KATIE EMELIANOVA

What are recent BioSS students doing now?

Chris Sutherland is a Postdoctoral Research Associate at Cornell University, USA, working on the design and analysis of wildlife surveys.

Laura Walton is statistical consultant at PHASTAR, a specialist contract research organisation focused on the pharmaceutical sector.

Siu-yin (Max) Lau is a Postdoctoral Research Fellow at Princeton University, USA, developing inference methodology for epidemic processes and applying this to the study of measles.

During the period 2011-13, BioSS supervised 11 PhD students, registered at the Universities of Aberdeen, Edinburgh, Glasgow, and York, Heriot-Watt University, and Universiti Putra Malaysia, and linked with organisations ranging from the Royal Botanical Gardens, Edinburgh, to Microsoft Research, Cambridge. Two of these students have written of their experiences below.

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BioSS’ PhD programme plays an important role in training future generations of quantitative scientists and driving research forward. The research environment we provide in partnership with our co-supervisors allows students to develop their statistical, mathematical and computational skills to handle challenging applications. Our students graduate with state-of-the-art methodological skills and experience for solving real-world problems, thus providing them with an excellent foundation for a successful career.

BioSS has a thriving PhD programme, jointly supervising students with a range of collaborating universities and research institutes (see P30). All projects combine theory

and application, developing new statistical, mathematical or bioinformatics methodology to solve important and difficult issues in biological and environmental sciences.

We recruit students with strong mathematical / statistical / computing backgrounds, good communication skills and enthusiasm for addressing real-world problems. They graduate equipped with state-of-the-art methodological expertise and experience of collaborating with scientists in other disciplines, thus laying the foundations for successful careers.

As well as being provided with excellent computing facilities, including access to high performance machines, students participate with staff in a dynamic programme of research meetings, reading groups and computing workshops, offering many opportunities for interaction.

For further details of possible projects and funding, see http://www.bioss.ac.uk/postgrad.html

Alastair Rushworth is now a Lecturer in Mathematics and Statistics at the University of Strathclyde.

Gustaf Rydevik works at the Roslin Institute as a Postdoctoral Research Fellow in EPIC, the Scottish Government’s Centre of Expertise on Animal Disease Outbreaks.

I am a PhD student currently researching the impacts that gene and genome duplication have on species generation and functional and morphological diversity. The model system I use is the tropical genus Begonia, due to the incredible diversity seen in the almost 2,000 species it comprises.

My studies involve applying Next Generation Sequencing to investigate two closely related but morphologically distinct species of Begonia, using bioinformatics techniques to assess how frequent duplication is in the two species, which functions duplicated genes perform, and how duplicate genes are diverging.

BioSS offers a fantastic environment in which to work, with a wealth of knowledge in computational and bioinformatics systems. The support I receive is invaluable in helping me to traverse the intersection of evolutionary and computational biology, where my work often lies. In a broader context, working amongst statisticians in BioSS has exposed me to mathematical and statistical methods, elements of which I have incorporated in my research to the benefit of my PhD.

I am currently doing a PhD in statistical methods to estimate sparse networks for high-dimensional genomics data. My particular interest is in the study of two measures of multivariate data dependency, the correlation matrix and its inverse, known as the partial correlation matrix, for datasets in which the dimension exceeds the sample size. I am also keen to consider joint partial correlation matrix estimation techniques that are appropriate when we have more than one data set for the same individuals.

My PhD is being undertaken jointly in BioSS and the University of Edinburgh, where I have two excellent supervisors, one in each institution. I find that this is the perfect combination for widening my knowledge in theoretical and applied statistics, as well as gaining experience in both teaching and research. BioSS staff members have a very broad range of areas of expertise, which supports my learning of statistical skills that go beyond my current research topic.

Staff as at 31 March 2015BioSS staff, students and associates meet regularly to discuss research ideas, consultancy projects and organisational matters.

INVESTOR IN PEOPLE

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Director: Prof. David Elston MSc

James Hutton Institute AberdeenPrincipal Consultant, Ecology & Environmental Science: Mark Brewer PhDBetty Duff BSc, Altea Lorenzo-Arribas MSc, Jackie Potts PhD, Luigi Spezia PhD

Rowett Institute of Nutrition & HealthPrincipal Consultant, Human Health & Nutrition: Graham Horgan PhDGrietje Holtrop PhD, Claus-Dieter Mayer PhD

SRUC AuchincruiveSarah Brocklehurst PhD

The King’s Buildings, EdinburghHead of Research & Leader of Process & Systems Modelling Research Theme: Prof. Glenn Marion PhD Leader of Statistical Bioinformatics Research Theme: Ian Simpson PhDActing Leader of Statistical Methodology Research Theme: Adam Butler PhDPrincipal Consultant, Animal Health & Welfare: Iain McKendrick PhDExternal Development Manager: Adrian Roberts MScIT Manager: David Nutter BScAdministrative Officer: Sarah Hirstwood

Adam Butler PhD, Stephen Catterall PhD, Jason Colbourne BSc , Zhou Fang PhD, Kokouvi Gamado PhD, Diane Glancy, Giles Innocent PhD, Helen Kettle PhD, Muriel Kirkwood DA, Jiayi Liu PhD, Mintu Nath PhD, Ian Nevison MA, Javier Palarea-Albaladejo PhD

Associates: Ross Davidson, Matthew Denwood, Dirk Husmeier, Naomi Fox, Andrew Harding, Jim McNicol, Helena Oakey, Chris Pooley, Jamie Prentice, Chris Theobald, Megan Towers

Staff leaving between 1 April 2013 and 31 March 2015: Chris Glasbey, Alec Mann

James Hutton Institute DundeePrincipal Consultant, Plant Science: Frank Wright PhDColin Alexander PhD, Christine Hackett PhD, Katrin MacKenzie PhD, Katharine Preedy PhD

BioSS’ scientific achievements are enabled by excellent computing facilities designed to meet staff needs, including a choice of desktop platforms for personal use and ready access to shared resources such as multi-core servers and multi-processor clusters.

Core provisionBioSS provides all scientific staff, students and visiting collaborators with personal desktop and mobile computing facilities suited to their personal circumstances. Fully supported core platforms include Windows and Linux. Individuals wishing to use alternative platforms are accommodated as much as possible but need to demonstrate greater self-reliance. A suite of productivity and scientific software is available for use on all devices, whilst software with licence restrictions on numbers of uses is obtained to meet specific needs.

Computational tasks which are beyond the capabilities of personal provision are made possible through shared multi-processor servers that enable our scientific staff to make use of the most recent advances in high-performance computing. These shared facilities include a dedicated machine for high memory operations required of large data sets and for the particular form of parallelisation implemented by NVIDIA Tesla GPGPU technology.

External servicesBioSS has access to additional, large-scale computer resources for more computationally intensive tasks, including the cluster managed by the University of Edinburgh. We have also begun to deploy special-purpose applications on a commercial cloud computing platform, providing an excellent blend of isolation from other infrastructure and transparent costs for our clients and collaborators. In addition, we are further exploring the use of cloud services for prototyping and showcasing BioSS’ work.

InfrastructureIn order to keep pace with the requirement to handle ever larger volumes of data, BioSS has planned and commenced implementation of an upgrade of the wired network installed at its Edinburgh site to 10GB line speeds. Concurrently, the analogue telecoms system is being replaced with a modern VoIP system, and improvements to the power infrastructure will enable us to host additional servers in our on-site computer room, as well as to meter the electricity consumption. Once completed, this refurbishment will significantly improve the general working environment, with enhancements to the lighting, power, network and fittings in the corridor and each of the offices.

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Management Group as at 31st March 2015Research Students April 2013 – March 2015

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Altea Lorenzo Arribas Statistical Methods for Analysis of Ordinal Response Data Mark Brewer with Dr. Antony Overstall, University of Glasgow

Chris Corbett Development and quantitative validation of improved sustainable donkey parasite control programmes Giles Innocent with Dr M. Denwood, Dr L. Matthews and Dr S. Love, University of Glasgow

Katie Emelianova The fate of duplicated genes in BegoniaIan Simpson with Dr C Kidner, Royal Botanic Garden Edinburgh

Max Lau (Siu-yin Lau)Novel Bayesian inference in epidemics model assessment and integrating epidemiological and genetic dataGlenn Marion with Prof G Gibson and Dr G Streftaris, Heriot-Watt University; PhD awarded 2015

Ewan McHenryDoing more with less; optimising control of invasive American mink in ScotlandDavid Elston with Prof X Lambin and Dr T Cornulier, University of Aberdeen

Adrià Caballé Mestres Statistical methods to estimate sparse networks for high-dimensional genomics dataClaus Mayer with Dr N. Bochkina, University of Edinburgh

Alastair RushworthSpatial regression for river networksMark Brewer with Dr S Langan and Dr S Dunn, James Hutton Institute, and Prof A Bowman, University of Glasgow; PhD awarded 2014

Gustaf RydevikStatistical aspects of emerging wildlife disease surveillanceGiles Innocent and Glenn Marion with Prof M Hutchings, SRUC, and Prof P White, University of York; PhD awarded 2015

Beatrice SellExploring and quantifying relationships between gut bacteria and their productsGrietje Holtrop with Prof I Stansfield, University of Aberdeen

Syarifah Nasrisya Syed Nor AzlanStochastic and spatial modelling of vector borne disease: parameter estimation and risk assessment of dengue fever in Peninsular MalaysiaGlenn Marion with Dr I Krishnarajah, Universiti Putra Malaysia

Laura WaltonModelling the effects of ecology on wildlife disease surveillanceGlenn Marion with Prof M Hutchings, SRUC, and Prof P White, University of York; PhD awarded 2014

Ludovica Luisa VissatFormal language support for ecological modellingGlenn Marion with Prof J Hillston, University of Edinburgh, and Dr M Smith, Microsoft Research, Cambridge

PhD students with project title and supervisors

David Elston Director

His research interests include multilevel models, with environmental and

ecological applications, andpopulation dynamics

modelling.

Graham Horgan Principal Consultant for Human Health &

Nutrition and coordinator of the BioSS programme

of training courses for scientists

His research interests lie in statistical methods in

biomedical and animal sciences, image analysis and spatial data interpretation.

Frank Wright Principal Consultant for

Plant Science.

Specialises in the analysis of genome sequence data, including estimating

phylogenetic relationships from DNA multiple

alignments.

David Nutter IT Manager

Responsible for organisationand delivery of IT supportin BioSS. He is chair of theComputer Liaison Groupwhich draws together IT

expertise in seven Scottishresearch organisations.

Sarah HirstwoodAdministrative Officer

Leads the delivery of administrative services inBioSS, including finance,

personnel and record- keeping. She is chief point

of contact on administrative matters between BioSS and

our parent organisation, James Hutton Institute.

Iain McKendrick Principal Consultant for Animal Health & Welfare

His research interests are in the development of statistical multilevel

modelling and mathematical modelling techniques to make them

more applicable to problems in veterinary epidemiology.

Adrian Roberts External Development

ManagerHe is leader of BioSS’ many inputs to the assessment of

new plant varieties, and undertakes research on the

relationship between phenology and the weather.

Ian SimpsonLeader of the Statistical

Bioinformatics and Genomics research theme His main research interests lie in developing statistical,

machine learning and computational methods to analyse and integrate the

many types of genome scale data currently being

generated.

Mark Brewer Principal Consultant for

Ecology & Environmental Science

His personal research interests lie in modelling spatially- and temporally-

correlated data in a Bayesian setting.

Glenn Marion Head of Research and leader of the Process

and Systems Modelling research theme

His primary researcharea is stochastic processmodelling, motivated by

collaborations withscientists from many

disciplines.

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Appendix 1 Selected Research Grants, Contracts & Subcontracts April 2013-March 2015

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EUROPEAN PROJECTS

FAME – future of the Atlantic marine environment, with RSPB and 5 research partners, funded by EU (A Butler).

GLAMURS – green lifestyles, alternative models and upscaling regional sustainability, with James Hutton Institute and 10 research partners, funded by EU (M J Brewer).

LEGATO – land-use intensity and ecological engineering: assessment tools for risks and opportunities in irrigated rice based production systems, with 20 research partners, funded by German Government (G R Marion, H Kettle).

MAPRA – modelling animal pathogens: review and adaptation, with Institute of Occupational Medicine, Heriot-Watt University and University of Copenhagen, funded by EFSA (I J McKendrick, S Catterall).

MiSAFE – the development and validation of microbial soil community analysis for forensics purposes, with JHI and 6 research partners, funded by EU (M J Brewer).

NeuroFAST, with RINH and 12 research partners, funded by EU (G W Horgan).

NoHoW – evidence-based ICT tools for weight loss maintenance, with 5 research partners, funded by EU (G W Horgan, D Nutter).

SATIN – satiety innovation, with RINH, funded by EU (G Holtrop, G W Horgan).

Statistical training for panel on dietetic products, nutrition and allergies, funded by EFSA (G W Horgan, C-D Mayer).

WildTech – novel technologies for surveillance of emerging and re-emerging infections of wildlife, with SRUC and 12 European partners, funded by EU (G R Marion, G T Innocent).

UK RESEARCH COUNCIL PROJECTS

Investigating how the type and quantity of food affect foraging behaviour and the neural circuits controlling feeding in broiler breeder chickens, with SRUC and Roslin Institute, funded by BBSRC (S Brocklehurst).

Macronutrient cycles programme: Conwy project, with CEH, funded by NERC (A Butler, Z Fang).

GOVERNMENT PROJECTS

Biological impacts of climate change observation-net 2, with 5 research partners, funded by a Defra-led consortium including CCW, NIEA, NE, SNH and JNCC (M J Brewer, D A Elston).

Development and testing of operational models of bovine tuberculosis in British cattle and badgers, with TB modelling initiative consortium, funded by Defra (G R Marion, S Catterall).

Development of an improved COYU procedure, UK agency and EU funded by SASA, CVPO and Fera (A M I Roberts, D Nutter).

Electronic identification of the Scottish sheep flock, with Scottish Agricultural Organisation Society, funded by Scottish Government (S Brocklehurst, S Catterall, I J McKendrick, C A Glasbey).

Further work on host parasite interactions, including the potential for the control of sheep scab using immunological approaches, and the development of diagnostic tools, with MRI, funded by Defra (G T Innocent, M Nath).

Measurements of methane emissions from livestock and their manures, with IBERS and University of Aberystwyth, funded by Defra (I J McKendrick).

Modelling the distribution of vascular plant species, with CEH, funded by Welsh Government (A Butler, M J Brewer, Z Fang).

Provision of statistical services for VCU work in Scotland, funded by Scottish Government (A M I Roberts, M A M Kirkwood, I M Nevison).

To develop a cost effective and practical method to reduce E. coli O157 infection in cattle prior to slaughter, with SRUC, funded by Defra (I J McKendrick, J Liu).

GOVERNMENT AGENCY PROJECTS

Advice, committee work and analysis for crop trials and certification, funded by Fera (A M I Roberts, E A Hunter, M A M Kirkwood, A D Mann, I M Nevison, D Nutter).

E. coli O157 super-shedding in cattle and mitigation of human risk, with MRI and Roslin Institute, funded by FSA (M Nath, J Palarea-Albaladejo).

Field trial design to test and validate the performance of the CattleBCG vaccine and associated DIVA diagnostic test in England and Wales – SE3287, with Triveritas Ltd, University of Cambridge and University of Aberystwyth, funded by Defra (G T Innocent, I J McKendrick).

Refinements of tern Sterna sp. tracking data modelling, funded by JNCC (M J Brewer, J M Potts).

SNH Statistical Support Framework, funded by SNH (M J Brewer, D A Elston, A Butler, L Spezia, J M Potts, A M I Roberts).

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Supporting analyses of the data used in the 2013 report on site condition monitoring for otters, funded by SNH (M J Brewer, L Spezia).

Sustainable Intensification Platform Research – Project 1, with NIAB and many others, funded by Defra (I J McKendrick).

 Sustainable Intensification Platform Research – Project 2, with University of Exeter and many others, funded by Defra (I J McKendrick).

GENERAL CONSULTANCY AGREEMENTS

Provision of statistical advice to Royal Society for the Protection of Birds research staff, funded by RSPB (A Butler).

Service level agreement for provision of statistical consultancy to Triveritas Ltd (I J McKendrick, G T Innocent, M Nath, J Palarea-Albaladejo).

Statistical research with scientists at Centre for Ecology and Hydrology, Edinburgh, funded by CEH (A Butler).

Statistical consultancy, advice and collaboration to Royal Horticultural Society, funded by RHS (I M Nevison).

Statistical consultancy, advice and collaboration to Science and Advice for Scottish Agriculture, funded by SASA (A M I Roberts, G W Horgan, A D Mann, I M Nevison, J Palarea-Albaladejo, Z Fang, E I Duff).

LEVY BOARD PROJECTS

Advice on the Home-Grown Cereals Authority recommended list variety trial processing system, funded by the HGCA (A M I Roberts, I M Nevison).

Advice for, and analysis of, independent variety trials, funded by the Potato Council (A M I Roberts, M A M Kirkwood, I M Nevison).

Appropriate fungicide doses on barley – production of dose response curves to advise growers on fungicide efficacy and potential resistance, with SRUC, funded by HGCA (A M I Roberts).

Free-living nematodes, with SRUC, funded by TSB and Potato Council (Z Fang).

OTHER PROJECTS

Analysis of seabird density data, with ESS Ecology, funded by Scottish and Southern Energy Renewables (D A Elston).

Design and analysis of experiments to test Agrinos products, with James Hutton Institute, funded by Agrinos (C A Hackett, E I Duff, K M MacKenzie, C J Alexander, F Wright).

Design and analysis of variety trials, funded by British Society of Plant Breeders (A M I Roberts, M A M Kirkwood, I M Nevison).

Formal language support for ecological modelling, with University of Edinburgh, funded by Microsoft Research (G R Marion).

Review of malting barley crop data, funded by Simpsons Malting Ltd (A M I Roberts, E A Hunter, J McNicol, Z Fang).

Statistical consultancy for novel vaccine against haemorrhagic septicaemia in cattle and buffalo for use by resource poor farmers, with Moredun Research Institute, funded by Wellcome Trust (M Nath).

Imaging applications in broiler chickens, funded by Aviagen (C A Glasbey, D Nutter).

Why some hosts have high parasite burdens and the implications for the design of sustainable control strategies, with University of Glasgow, funded by Wellcome Trust (G R Marion).

Appendix 2 Refereed Publications

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Martínez-Silva I., Sestelo M., Bidegain G., Lorenzo-Arribas A. & Roca-Pardinas J. 2014. Nonparametric regression applied to sea urchin growth. In sea urchins: habitat, embryonic development and importance in the environment. Ed:E. R. Banks, 1-32. Nova Science Publishers. ISBN 978-1-63321-550-4.

Pai B., Siripornmongcolchai T., Berentsen B., Pakzad A., Vieuille C., Pallesen S., Pajak M., Simpson I., Armstrong J.D., Wibrand K. & Bramham C.R. 2014. NMDA receptor-dependent regulation of miRNA expression and association with Argonaute during LTP in vivo. Frontiers in Cellular Neuroscience 7, doi: 10.3389/fncel. 2013.00285.

Palarea Albaladejo J. & Martin-Fernandez J.A. 2013. Values below detection limit in compositional chemical data. Analytica Chimica Acta 764, 32-43.

Palarea Albaladejo J., Martin-Fernandez J.A. & Buccianti A. 2014. Compositional methods for estimating elemental concentrations below the limit of detection in practice using R. Journal of Geochemical Exploration 141, 71-77.

Palarea Albaladejo J., Martin-Fernandez J.A. & Olea R.A. 2014. A bootstrap estimation scheme for chemical compositional data with nondetects. Journal of Chemometrics 28, 585-599.

Prentice J.C., Marion G., White P.C.L., Davidson R.S. & Hutchings M.R. 2014. Demographic processes drive increases in wildlife disease following population reduction. PLoS ONE 9, e86563.

Ribeiro K.M., Braga R., Horgan G.W., Ferreira D.D. & Safadi T. 2014. Principal component analysis in the spectral analysis of the dynamic laser speckle patterns. Journal of the european optical society rapid publications 9, 14009.

Ribeiro K.M., Braga R., Scalco M. & Horgan G.W. 2013. Leaf area estimation of medium size plants using optical metrology. Revista Brasileira de Engenharia Agricola e Ambiental 17, 595-601.

Rushworth A.M., Bowman A.W., Brewer M.J. & Langan S.J. 2013. Distributed lag models for hydrological data. Biometrics 69, 537-544.

Russell J.R., Hackett C.A., Hedley P.E., Liu H., Milne L., Bayer M., Marshall D., Jorgensen L., Gordon S. & Brennan R.M. 2014. The use of genotyping by sequencing in blackcurrant (Ribes nigrum): developing high-resolution linkage maps in species without reference genome sequences. Molecular Breeding 33, 835-849.

Settele J., Kühn I., Klotz S., Arida G., Bergmeier E., Burkhard B., Bustamante J.V., Dao Thanh Truong, Escalada M., Görg C., Grescho V., Chien Ho Van, Heong K.L., Hirneisen N., Hotes S., Jahn R., Klotzbücher T., Marion G., Marquez L. & others 19. 2013. Kulturlandschaftsforschung in Südostasien – das LEGATO-Projekt. Berichte. Geographie und Landeskunde 87, 315-323.

Soares R.R., Barbosa H.C., Braga R., Botega J.V.L. & Horgan G.W. 2013. Biospeckle PIV (Particle Image Velocimetry) for analysing fluid flow. Flow Measurement and Instrumentation 30, 90-98.

Song Y., Glasbey C.A., Horgan G.W., Polder G., Dieleman A. & van der Heijden G.W.A.M. 2014. Automatic fruit recognition and counting from multiple images. Biosystems Engineering 118, 203-215.

Song Y., Glasbey C.A., Polder G. & van der Heijden G.W.A.M. 2014. Non-destructive automatic leaf area measurements by combining stereo and time-of-flight images. IET Computer Vision 8, 391-403.

Spezia L., Cooksley S., Brewer M.J., Donnelly D. & Tree A. 2014. Mapping species distributions in one dimension by non-homogeneous hidden Markov models: freshwater pearl mussels in the River Dee. Environmental and Ecological Statistics 21, 487-505.

Spezia L., Cooksley S., Brewer M.J., Donnelly D. & Tree A. 2014. Modelling species abundance in a river by negative binomial hidden Markov models. Computational Statistics and Data Analysis 71, 599-614.

Sutherland C., Elston D.A. & Lambin X. 2013. Accounting for false positive detection error induced by transient individuals. Wildlife Research 40, 490-498.

Sutherland C., Elston D.A. & Lambin X. 2014. A demographic, spatially explicit patch occupancy model of metapopulation dynamics and persistence. Ecology 95, 3149-3160.

1. METHODOLOGYAlimi N.A., Bink M., Dieleman A., Nicolai M., Wubs M., Heuvelink E., Magan J.J., Voorrips R., Jansen J., Canas-Rodrigues P., van der Heijden G.W.A.M., Vercauteren A., Vuylsteke M., Song Y., Glasbey C.A., Barocsi A., Lefebvre V., Palloix A. & van Eeuwijk F. 2013. Genetic and QTL analyses of yield and a set of physiological traits in pepper. Euphytica 190, 181-201.

Beale C.M., Brewer M.J. & Lennon J.J. 2014. A new statistical framework for the quantification of covariate associations with species distributions. Methods in Ecology and Evolution 5, 421-432.

Brendel M., Janssen A., Mayer C-D. & Pauly M. 2014. Weighted logrank permutation tests for randomly right censored life science data. Scandinavian Journal of Statistics 41, 742-761.

Fazey I., Bunse L., Msika J., Pinke M., Preedy K., Evely A.C., Lambert E., Hastings E., Morris S. & Reed M.S. 2014. Evaluating knowledge exchange in interdisciplinary and multi-stakeholder research. Global Environmental Change, 25, 204-220.

Fox N.J., Marion G., Davidson R.S., White P.C.L. & Hutchings M.R. 2013. Modelling parasite transmission in a grazing system: the importance of host behaviour and immunity. PLoS ONE 8, e77996.

Gamado K.M., Streftaris G. & Zachary S. 2014. Modelling under-reporting in epidemics. Journal of Mathematical Biology 69, 737-765.

Geddes A., Elston D.A., Hodgson M.E.A. & Birnie R.V. 2013. Stochastic model-based methods for handling uncertainty in areal interpolation. International Journal of Geographic Information Science 27, 785-803.

Glasbey C.A. 2013. Semi-automatic 3D segmentation. In Farm Animal Imaging: Dublin 2012, Eds. C. Maltin, C. Craigie and L. Bunger, 67-68. QMS: Scotland. ISBN 978-0-9570709-3-6.

Hackett C.A., Bradshaw J.E. & Bryan G.J. 2014. QTL mapping in autotetraploids using SNP dosage information. Theoretical and Applied Genetics 127, 1885-1904.

Hackett C.A., McLean K. & Bryan G.J. 2013. Linkage analysis and QTL mapping using SNP dosage data in a tetraploid potato mapping population. PLoS ONE 8, e63939.

Hardstaff J.L., Bulling M.T., Marion G., Hutchings M.R. & White P.C.L. 2013. Modelling the impact of vaccination on tuberculosis in badgers. Epidemiology and Infection 141, 1417-1427.

Hardstaff J.L., Marion G., Hutchings M.R. & White P.C.L. 2014. Evaluating the tuberculosis hazard posed to cattle from wildlife across Europe. Research in Veterinary Science 97, S86-S93.

Harrison P.J., Buckland S.T., Yuan Y., Elston D.A., Brewer M.J., Johnston A. & Pearce-Higgins J.W. 2014. Assessing trends in biodiversity over space and time using the example of British breeding birds. Journal of Applied Ecology 51, 1650-1660.

Hindle M.M., Martin S.F., Noordally Z., van Ooijen G., Barrios-Llerena M.E., Simpson I., Le Bihan T. & Millar A.J. 2014. The reduced kinome of Ostreococcus tauri: core eukaryotic signalling components in a tractable model species. BMC Genomics 15, 644.

Jimenez de Cisneros J.P., Stear M.J., Mair C., Singleton D., Stefan T., Stear A., Marion G. & Matthews L. 2014. An explicit immunogenetic model of gastrointestinal nematode infection in sheep. Journal of the Royal Society Interface 11, 20140416.

Kettle H., O’Donnelly R., Marion G. & Flint H.J. 2014. pH feedback and phenotypic diversity within bacterial functional groups of the human gut. Journal of Theoretical Biology 342, 62-69.

Lau M.S.Y., Marion G., Streftaris G. & Gibson G. 2014. New model diagnostics for spatiotemporal systems in epidemiology and ecology. Journal of the Royal Society Interface 11, 20131093.

Lorenzo-Arribas A., Martínez-Silva I., Gómez-Mateu M., Perez-Alvarez N., Perpiñán Fabuel H. & Valero Coppin O. 2013. Young biostatisticians in Spain: career or race? Boletin de Estadistica e Investigación Operativa 29, 266-282.

Martinez-Silva I., Roca-Pardinas J., Lustres-Perez V., Lorenzo-Arribas A. & Cadarso-Suarez C. 2013. Flexible quantile regression models: applications to the study of the sea urchin, Paracentrotus lividus (Lamarck, 1816). Statistics and Operations Research Transactions 37, 81-94.

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Shepherd L.V.T., Hackett C.A., Alexander C.J., Sungurtas J., Pont S.D.A., Stewart D., McNicol J.W., Wilcockson S.J., Leifert C. & Davies H.V. 2014. Effect of agricultural production systems on the potato metabolome. Metabolomics 10, 212-224.

Simpson C.G., Lewandowska D., Liney M., Davidson D., Chapman S., Fuller J., McNicol J.W., Shaw P. & Brown J.W.S. 2014. Arabidopsis PTB1 and PTB2 proteins negatively regulate splicing of a mini-exon splicing reporter and affect alternative splicing of endogenous genes differentially. New Phytologist 203, 424-436.

Welch D., Scott D. & Elston D.A. 2013. Declining incidence of multi-trunking over time in a Scottish plantation of Picea sitchensis. Scandinavian Journal of Forest Research 28, 17-27.

Winder L., Alexander C.J., Woolley C., Perry J.N. & Holland J.M. 2013. The spatial distribution of canopy-resident and ground-resident cereal aphids (Sitobion avenae and Metopolophium dirhodum) in winter wheat. Arthropod-Plant Interactions 7, 21-32.

Winder L., Alexander C.J., Woolley C., Perry J.N. & Holland J.M. 2014. Cereal aphid colony turnover and persistence in winter wheat. PLoS ONE 9, e106822. Public Library of Science, San Francisco, California, USA.

Woodhead M., Williamson S., Smith K., McCallum S., Jennings S.N., Hackett C.A. & Graham J. 2013. Identification of quantitative trait loci for cane splitting in red raspberry (Rubus idaeus). Molecular Breeding 31, 111-122.

Wright K.M. & MacKenzie K. 2014. Probing protein targeting to plasmodesmata using fluorescence recovery after photo-bleaching. In The Methods in Molecular Biology Series, 259-274. Ed: M. Heinlein, Humana Press. ISBN 978-1-4939-1522-4.

3. ANIMAL HEALTH AND WELFAREBartley P.M., Katzer F., Rocchi M.S., Maley S.W., Silvan J.B., Purslow C., Nath M., Pang Y.P., Chianini F. & Innes E.A. 2013. Development of maternal and foetal immune responses in cattle following experimental challenge with Neospora caninum at day 210 of gestation. Veterinary Research 44, 91.

Bull T.J., Schock A., Sharp J.M., Greene M., McKendrick I.J., Sales J., Linedale R. & Stevenson K. 2013. Genomic variations associated with attenuation in Mycobacterium avium subsp. paratuberculosis vaccine strains. BMC Microbiology 13, 11.24.

Cantón G.J., Benavides J., Maley S.W., Katzer F., Palarea Albaladejo J., Bartley P., Rocchi M.S., Innes E.A. & Chianini F. 2013. Immune phenotyping of placentas following experimental inoculation with Neospora caninum during pregnancy. Journal of Comparative Pathology 148, 52.

Cantón G.J., Katzer F., Benavides J., Maley S.M., Palarea Albaladejo J., Pang Y.P., Bartley P., Rocchi M.S., Innes E.A. & Chianini F. 2013. Phenotypic characterisation of the cellular immune infiltrate in placentas of cattle following experimental inoculation with Neospora caninum in late gestation. Veterinary Research 44, 60.

Cantón G.J., Katzer F., Maley S.W., Bartley P.M., Benavides J., Palarea Albaladejo J., Pang Y.P., Smith S., Rocchi M.S., Buxton D.A., Inglis N.F. & Chianini F. 2014. Inflammatory infiltration into placentas of Neospora caninum challenged cattle correlates with clinical outcome of pregnancy. Veterinary Research 45, 11.

Cantón G.J., Katzer F., Maley S.W., Bartley P.M., Benavides J., Palarea Albaladejo J., Pang Y.P., Smith S., Rocchi M.S., Buxton D.A., Innes E.A. & Chianini F. 2014. Cytokine expression in the placenta of pregnant cattle after inoculation with Neospora caninum. Veterinary Immunology and Immunopathology 161, 77-89.

Cantón G.J., Konrad J.L., Moore D.P., Caspe S.G., Palarea Albaladejo J., Campero C.M. & Chianini F. 2014. Characterization of the immune cell infiltration in the placentas from water buffaloes (Bubalus bubalis) inoculated with Neospora caninum during pregnancy. Journal of Comparative Pathology 150, 463-468.

Theobald C.M. & Davie A.M. 2014. Group testing, the pooled hypergeometric distribution, and estimating the number of defectives in small populations. Communications in Statistics-Theory and Methods 43, 3019-3026.

2. PLANT SCIENCEAzevedo R.A., Lea P.J., Leather S.R., McNicol J.W., Millman CA., Haslam R.P. & Valkonen J.P.T. 2014. The Centenary of Annals of Applied Biology in 2014. Annals of Applied Biology 164, 1-7.

Foito A., Byrne S., Hackett C.A., Hancock R.D., Stewart D. & Barth S. 2013. Short-term response in leaf metabolism of perennial ryegrass (Lolium perenne) to alterations in nitrogen supply. Metabolomics 9, 145-156.

Graham J., Hackett C.A., Smith K., Karley A., Mitchell C., Roberts H. & O’Neill T. 2014. Genetic and environmental regulation of plant architectural traits and opportunities for pest control in raspberry. Annals of Applied Biology 165, 318-328.

Holden N., Wright F., MacKenzie K., Marshall J., Mitchell S., Mahajan A., Wheatley R. & Daniell T. 2014. Prevalence and diversity of Escherichia coli isolated from a barley trial supplemented with bulky organic soil amendments: green compost and bovine slurry. Letters in Applied Microbiology 58, 205-212.

Johnson S.N., Mitchell C., McNicol J.W., Thompson J. & Karley A. 2013. Downstairs drivers-root herbivores shape communities of above-ground herbivores and natural enemies via changes in plant nutrients. Journal of Animal Ecology 82, 1021-1030.

Kaczmarek A., MacKenzie K., Kettle H. & Blok V. 2014. Influence of temperature on the plant parasitic nematodes Globodera Rostochiensis and G. Pallida: assessing the impact of soil temperature. Phytopathologia Mediterranea 53, 396-405.

Liu H., Bayer M., Druka A., Russell J.R., Hackett C.A., Poland J., Ramsay L., Hedley P.E. & Waugh R. 2014. An evaluation of genotyping by sequencing to map the Breviaristatum-e (ari-e) locus in cultivated barley. BMC Genomics 15, 104.

Morris W., Hancock R.D., Ducreux L.J.M., Morris J.A., Usman M., Verrall S.R., Sharma S.K., Bryan G.J., McNicol J.W. & Hedley P.E. 2014. Day length dependent restructuring of the leaf transcriptome and metabolome in potato genotypes with contrasting tuberization phenotypes. Plant Cell and Environment 37, 1351-1363.

Phillips D., Wnetrzak J., Nibau C., Barakate A., Ramsay L., Wright F., Higgins J.D., Perry R.M. & Jenkins G. 2013. Quantitative high resolution mapping of HvMLH3 foci in barley pachytene nuclei reveals a strong distal bias and weak interference. Journal of Experimental Biology 64, 2139-2154.

Prashar A., Yildiz J., McNicol J.W., Bryan G.J. & Jones H.G. 2013. Infra-red thermography for high throughput field phenotyping in Solanum tuberosum. PLoS ONE 8, e0065816.

Raczynska K.D., Stepien A., Kierzkowski D., Kalak M., Bajczyk M., McNicol J.W., Simpson C.G., Szweykowska-Kulinska Z., Brown J.W.S. & Jarmolowski A. 2014. The SERRATE protein is involved in alternative splicing in Arabidopsis thaliana. Nucleic Acids Research 42, 1224-1244.

Schreiber M., Wright F., MacKenzie K., Schwerdt J.G., Burton R.A., Fincher G.B., Marshall D., Waugh R. & Halpin C. 2014. The barley genome sequence assembly reveals three additional members of the CslF(1,3;1,4)-β-glucan synthase gene family. PLoS ONE 9, e90888.

Sharma S.K., Bolser D., de Boer J., Sonderkaer M., Amoros W., Carboni M.F., D’Ambrosio J.M., de la Cruz G., DiGenova A., Douches D.S., Eguiluz M., Guo X., Guzman F., Hackett C.A., Hamilton J.P., Li G., Li Y., Lozano R., Maass A., Marshall D., Martinez D., McLean K., Mejia N., Milne L., Munive S., Nagy I., Ponce O., Ramirez M., Simon R., Thomson S.J., Torres Y., Waugh R., Zhang Z., Huang S., Visser R.G.F., Bachem C.W.B., Sagredo B., Feingold S.E., Orjeda G., Veilleux R.E., Bonierbale M., Jacobs J.M.E., Milbourne D., Martin D. & Bryan G.J. 2013. Construction of reference chromosome-scale pseudomolecules for potato: Integrating the potato genome with genetic and physical maps. G3: Genes, Genomes, Genetics 3, 2031-2047.

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Turner S., Nath M., Horgan G.W. & Edwards S.A. 2013. Measuring chronic social tension in groups of growing pigs using inter-individual distances. Applied Animal Behaviour Science 146, 26-36.

Umstatter C., Brocklehurst S., Ross D. & Haskell M.J. 2013. Can the location of cattle be managed using broadcast audio cues? Applied Animal Behaviour Science 147, 34-42.

Vali L., Hoyle D., Innocent G.T., McKendrick I.J., Pearce M.C., Mellor D.J., Porphyre T., Locking M., Allison L.J., Hanson M.F., Matthews L., Gunn G.J., Woolhouse M.E.J. & Chase-Topping M.E. 2014. E. coli O157 on Scottish cattle farms: evidence of local spread and persistence using repeat cross-sectional data. BMC Veterinary Research 10.

Wells B., Innocent G.T., Eckersall P.D., Nisbet A.J. & Burgess S.T.G. 2013. Two major ruminant acute phase proteins, haptoglobin and serum amyloid A, as serum biomarkers during active sheep scab infestation. BMC Veterinary Research 44.

4. ECOLOGY AND ENVIRONMENTAL SCIENCEAnderson H.B., Evans P.G., Potts J.M., Harris M.P. & Wanless S. 2014. The diet of common guillemot (Uria aalge) chicks provides evidence of changing prey communities in the North Sea. Ibis 156, 23-34.

Ball B.C., Griffiths B., Wheatley R., Walker R.L., Rees R., Watson C.A., Gordon H., Hallett P., McKenzie B., Nevison I.M. & Topp K. 2014. Seasonal nitrous oxide emissions in field soils under soil management of reduced tillage, compost application or organic farming. Agriculture Ecosystems & Environment 189, 171-180.

Beale C.M., Baker N.E., Brewer M.J. & Lennon J.J. 2013. Protected area networks and savannah bird biodiversity in the face of climate change and land degradation. Ecology Letters 16, 1061-1068.

Bogdanova M., Wanless S., Harris M.P., Lindstrom J., Butler A., Newell M., Sato K., Watakuni Y., Parsons M. & Daunt F. 2014. Among-year and within-population variation in foraging distribution of European shags Phalacrocorax aristotelis over a twenty-year period: implications for marine spatial planning. Biological Conservation 170, 292-299.

Britton A.J., Mitchell R.J., Potts J.M. & Genney D.R. 2014. Developing monitoring protocols for rapid surveillance of priority lichen species in Scotland. The Lichenologist 46, 471-482.

Buccianti A., Nisi B., Martin-Fernandez J.A. & Palarea Albaladejo J. 2014. Methods to investigate the chemistry of groundwaters with values for nitrogen compounds below the detection limit. Journal of Geochemical Exploration 141, 78-88.

Burthe S., Newell M., Goodman G., Butler A., Bregnballe T., Harris E., Wanless S., Cunningham E.J.A. & Daunt F. 2013. Endoscopy as a novel method for assessing endoparasite burdens in free-ranging European shags (Phalacrocorax aristotelis). Methods in Ecology and Evolution 4, 207-216.

Burthe S., Wanless S., Newell M., Butler A. & Daunt F. 2014. Assessing the vulnerability of the marine bird community in the western North Sea to climate change and other anthropogenic impacts. Marine Ecology Progress Series 507, 277-295.

Chapman S.J., Bell J.S., Campbell C.D., Hudson G., Lilly A., Nolan A.J., Robertson J., Potts J.M. & Towers W. 2013. Comparison of soil carbon stocks in Scottish soils between 1978 and 2009. European Journal of Soil Science 64, 455-465.

Chapman S., Stevens L.J., Boevink P.C., Engelhardt S., Alexander C.J., Harrower B., Champouret N., McGeachy K., Van Weymers P.S.M., Chen X., Birch P.R.J. & Hein I. 2014. Detection of the virulent form of AVR3a from Phytophthora infestans following artificial evolution of potato resistance gene R3a. PLoS ONE 9, e110158. Public Library of Science, San Francisco, California, USA.

Cornulier T., Yoccoz N.G., Bretagnolle V., Brommer J.E., Butet A., Ecke F., Elston D.A., Framstad E., Henttonen H., Hornfeldt B., Huitu O., Imholt C., Ims R.A., Jacbos J., Jedrzejewska B., Millon A., Petty S.J., Pietiainen H., Tkadlec E., Zub K. & Lambin X. 2013. Europe-wide dampening of population cycles in keystone herbivores. Science 340, 63-66.

Chianini F., Siso S., Ricci E., Eaton S.L., Finlayson J., Pang Y.P., Hamilton C.M., Steele P.J., Reid H.W., Cantile C., Sales J., Jeffrey M., Dagleish M.P. & Gonzalez L. 2013. Pathogenesis of scrapie in ARQ/ARQ sheep after subcutaneous infection: effect of lymphadenectomy and immune cell subset changes in relation to prion protein accumulation. Veterinary Immunology and Immunopathology 152, 348-358.

Dixon L., Brocklehurst S., Sandilands V., Bateson M., Tolkamp B.J. & D’Eath R. 2014. Measuring motivation for appetitive behaviour: food-restricted broiler breeder chickens cross a water barrier to forage in an area of wood shavings without food. PLoS ONE 9.

Dixon L., Sandilands V., Bateson M., Brocklehurst S., Tolkamp B.J. & D’Eath R. 2013. Conditioned place preference or aversion as animal welfare assessment tools: limitations in their application. Applied Animal Behaviour Science 148, 164-176.

Getachew A.M., Innocent G.T., Proudman C.J., Trawford, A., Feseha G., Reid S.W.J., Faith B. & Love S. 2013. Field efficacy of praziquantel oral paste against naturally acquired equine cestodes in Ethiopia. Parasitology Research 112, 141-146. Blackwell and Hall, Almería, Spain.

Hodgson J.C., Dalgleish M., Gibbard L., Bayne C., Finlayson J., Moon G.M. & Nath M. 2013. Seven strains of mice as potential models of bovine pasteurellosis following intranasal challenge with a bovine pneumonic strain of Pasteurella multocida A:3 – comparisons of disease and pathological outcomes. Research in Veterinary Science 94, 634-340.

Hughes V.M., Denham S., Bannantine J.P., Chianini F., Kerr K., May L., McLuckie J., Nath M. & Stevenson K. 2013. Interferon gamma responses to proteome-determined specific recombinant proteins: potential as diagnostic markers for ovine Johne’s disease. Veterinary Immunology and Immunopathology 155, 197-204.

Katzer F., Cantón G.J., Burrells A., Palarea Albaladejo J., Horton B., Bartley P.M., Pang Y.P., Chianini F., Innes E.A. & Benavides J. 2014. Immunization of lambs with the S48 strain of Toxoplasma gondii reduces tissue cyst burden following oral challenge with a complete strain of the parasite. Veterinary Parasitology 206, 46-56.

Kenyon F., McBean D., Greer A.W., Burgess C.G.S., Morrison A., Bartley D.J., Bartley Y., Devin L., Nath M. & Jackson F. 2013. A comparative study of the effects of four treatment regimens on ivermectin efficacy, body weight and pasture contamination in lambs naturally infected with gastrointestinal nematodes in Scotland. International Journal for Parasitology: Drugs and Drug Resistance 3, 77-84.

Longbottom D., Livingstone M., Maley S.M., van der Zon A., Rocchi M.S., Wilson K., Wheelhouse N., Dagleish M.P., Aitchison K., Wattegedera S., Nath M., Entrican G. & Buxton D.A. 2013. Intranasal infection with Chlamydia abortus induces dose-dependent latency and abortion in sheep. PLoS ONE 8, e57950.

Nickbakhsh S., Matthews L., Dent J.E., Innocent G.T., Arnold M.E., Reid S.W.J. & Kao R.R. 2013. Implications of within-farm transmission for network dynamics: consequences for the spread of avian influenza. Epidemics 5, 67-76.

Nisbet A.J., McNeilly T., Wildblood L.A., Morrison A., Bartley D.J., Bartley Y., Longhi C., McKendrick I.J., Palarea Albaladejo J. & Matthews J.B. 2013. Successful immunization against a parasitic nematode by vaccination with recombinant proteins. Vaccine 31, 4017-4023.

Ricci P., Rooke J.A., Nevison I.M. & Waterhouse A. 2013. Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics. Journal of Animal Science 91, 5379-5389.

Shaughnessy L.J., Smith L.A., Evans J., Anderson D., Caldow G., Marion G., Low J.C. & Hutchings M.R. 2013. High prevalence of paratuberculosis in rabbits is associated with difficulties in controlling the disease in cattle. The Veterinary Journal 198, 267-270.

Strachan N.J.C., Rotariu O., Smith-Palmer A., Cowden J., Sheppard S.K., O’Brien S., Maiden M.C.J., Macrae M., Bessell P.R., Matthews L., Reid S.W.J., Innocent G.T., Ogden I.D. & Forbes K.J. 2013. Identifying the seasonal origins of human campylobacteriosis. Epidemiology and Infection 141, 1267-1275. Blackwell and Hall, Almería, Spain.

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Young J.C., Jordan A., Searle K., Butler A., Simmons P. & Watt A.D. 2013. Framing scale in participatory biodiversity management may contribute to more sustainable solutions. Conservation Letters 6, 333-340.

5. HUMAN HEALTH AND NUTRITIONDrew J.E., Farquharson A.J., Horgan G.W., Duthie S.J. & Duthie G.G. 2014. Postprandial cell defense system responses to meal formulations: stratification through gene expression profiling. Molecular Nutrition & Food Research 58, 2066-2079.

Drew J.E., Farquharson A.J., Mayer C-D., Vase H., Coates P.J., Steele R.J. & Carey F.A. 2014. Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma. PLoS ONE 9, e113071. Public Library of Science, San Francisco, California, USA.

Elliot R.M., de Roos B., Duthie S.J., Bouwman F.G., Rubio-Aliga I., Crosley K., Polley A.C., Heim C., Mayer C-D., Coort S.J., Evelo C.T., Mulholland F., Daniel H., Mariman E. & Johnson I.T. 2014. Transcriptome analysis of peripheral blood mononuclear cells in human subjects following a 36 hr fast provides evidence of effects on genes regulating inflammation, apoptosis and energy metabolism. Genes and Nutrition 9, 1-11. Springer-Verlag Berlin Heidleberg.

Gratz S.W., Richardson A.J., Duncan G. & Holtrop G. 2014. Annual variation of dietary deoxynivalenol exposure during years of different Fusarium prevalence: a pilot biomonitoring study. Food Additives and Contaminants Part A: Chemistry Analysis Control Exposure and Risk Assessment 31, 1579-1585. Taylor and Francis / CRC Press.

Haggarty P., Campbell D.M., Knox S., Horgan G.W., Hoad G., Boulton E., McNeill G. & Wallace A.M. 2013. Vitamin D in pregnancy at high latitude in Scotland. British Journal of Nutrition 109, 898-905.

Haggarty P., Hoad G., Campbell D.M., Horgan G.W., Piyathilake C. & McNeill G. 2013. Folate in pregnancy and imprinted gene and repeat element methylation in the offspring. American Journal of Clinical Nutrition 97, 94-99.

Haggarty P., Hoad G., Horgan G.W. & Campbell D.M. 2013. DNA methyltransferase candidate polymorphisms, imprinting methylation, and birth outcome. PLoS ONE 8, e68896.

Herwig A., Campbell G., Mayer C-D., Boelen A., Anderson R.A., Ross A.W., Mercer J. & Barrett P. 2014. A thyroid hormone challenge in hypothyroid rats identifies T3 regulated genes in the hypothalamus and in models with altered energy balance and glucose homeostasis. Thyroid 24, 1575-1593.

Hoggard N., Cruickshank M., Moar K.M., Bestwick C., Holst J.J., Russell W. & Horgan G.W. 2013. A single supplement of a standardised bilberry (Vaccinium myrtillus L.) extract (36% wet weight anthocyanins) modifies glycaemic response in individuals with type 2 diabetes controlled by diet and lifestyle. Journal of Nutritional Science 5, 1241-1252.

Hold G., Berry S., Saunders K.A., Drew J.E., Mayer C-D., Brooks H., Gay N.J., El-Omar E. & Bryant C.E. 2014. The TLR4 D299G and T399I SNPs are constitutively active to up-regulate expression of TRIF-dependent genes. PLoS ONE 9, e111460. Public Library of Science, San Francisco, California, USA.

Lobley G.E., Holtrop G., Bremner D.M., Calder A.G., Milne E. & Johnstone A.M. 2013. Impact of short-term consumption of diets high in either non-starch polysaccharides or resistant starch in comparison with moderate weight loss on indices of insulin sensitivity in subjects with metabolic syndrome. Nutrients 5, 2144-2172.

Lobley G.E., Johnstone J.A., Fyfe C., Horgan G.W., Holtrop G., Bremner D.M., Broom J.I., Schweiger L. & Welch A. 2014. Glucose uptake by the brain on chronic high-protein weight-loss diets with either moderate or low amounts of carbohydrate. British Journal of Nutrition 11, 586-597.

Dunn S.M., Sample J., Potts J.M., Abel C., Cook Y., Taylor C., Napier F. & Vinten A.J.A. 2014. Recent trends in catchment water quality in Eastern Scotland: elucidating the roles of hydrology and land use. Environmental Science: Processes & Impacts 16, 1659-1675.

Elston D.A., Spezia L., Baines D. & Redpath S. 2014. Working with stakeholders to reduce conflict – modelling the impact of varying hen harrier Circus cyaneus densities on red grouse Lagopus lagopus populations. Journal of Applied Ecology 51, 1236-1245.

Heath M.R., Culling M.A., Crozier W.W., Fox C.J., Gurney W.S.C., Hutchinson W.F., Nielsen E.E., O’Sullivan M., Preedy K., Righton D.A., Speirs D.C., Taylor M.I., Wright P.J. & Carvalho G.R. 2014. Combination of genetics and spatial modelling highlights the sensitivity of cod (Gadus morhua) population diversity in the North Sea to distributions of fishing. ICES Journal of Marine Science 71, 794-807. Oxford University Press, Oxford, UK.

Helliwell R., Wright R.F., Jackson-Blake L., Ferrier R.C., Aherne J., Cosby B.J., Evans C.D., Forsius M., Hruska J., Jenkins A., Kram P., Kopáček J., Majer V., Moldan F., Posch M., Potts J.M., Rogora M. & Schöpp W. 2014. Assessing recovery from acidification of European surface waters in the year 2010: an evaluation of projections made with the MAGIC model in 1995. Environmental Science and Technology 48, 13280-13288. American Chemical Society (ACS), Washington, DC, USA.

Mitchell R.J., Beaton J.K., Bellamy P.E., Broome A., Chetcuti J., Eaton S., Ellis C., Gimona A., Harmer R., Hester A.J., Hewison R.L., Hodgetts N.G., Iason G.R., Kerr G., Littlewood N.A., Newey S., Potts J.M., Pozsgai G., Ray D., Sim D.A., Stockan J.A., Taylor A.F.S., Taylor P. & Woodward S. 2014. Ash dieback in the UK: a review of the ecological and conservation implications. Biological Conservation 175, 95-109.

O’Reilly-Wapstra J.M., Moore B.D., Brewer M.J., Beaton J.K., Sim D.A., Wiggins N. & Iason G.R. 2014. Pinus sylvestris sapling growth and recovery from mammalian browsing. Forest Ecology and Management 325, 18-25.

Owen E., Daunt F., Moffat C., Elston D.A., Wanless S. & Thompson P. 2013. Analysis of fatty acids and fatty alcohols reveals seasonal and sex-specific changes in the diets of seabirds. Marine Biology 160, 987-999.

Pérez-Barbería F.J., Duff E.I., Brewer M.J. & Guinness F.E. 2014. Evaluation of methods to age Scottish red deer: the balance between accuracy and practicality. Journal of Zoology 294, 180-189.

Poggio L., Gimona A. & Brewer M.J. 2013. Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates. Geoderma 209-210, 1-14.

Rhind S.M., Kyle C.E., Kerr C., Osprey M., Zhang Z., Duff E.I., Lilly A., Nolan A.J., Hudson G., Towers W., Bell J.S., Coull M. & McKenzie C. 2013. Concentrations and geographic distribution of selected organic pollutants in Scottish surface soils. Environmental Pollution 182, 15-27.

Roe J., Ward Thompson C., Aspinall P., Brewer M.J., Duff E.I., Miller D.R., Mitchell R. & Clow A. 2013. Green space and stress: evidence from cortisol measures in deprived urban communities. International Journal of Environmental Research and Public Health 10, 4086-4103.

Searle K., Barber J., Stubbins F., Labuschagne K., Carpenter S., Butler A., Denison E., Sanders C., Mellor P.S., Wilson A., Nelson N., Gubbins S. & Purse B.V. 2014. Environmental drivers of Culicoides phenology: how important is species-specific variation when determining disease policy? PLoS ONE 9, e111876.

Searle K., Blackwell A., Falconer D., Sullivan M., Butler A. & Purse B.V. 2013. Identifying environmental drivers of insect phenology across space and time: Culicoides in Scotland as a case study. Bulletin of Entomological Research 103, 155-170.

Van der Jagt A.P.N., Craig T., Anable J., Brewer M.J. & Pearson D.G. 2014. Unearthing the picturesque: the validity of the preference matrix as a measure of landscape aesthetics. Landscape and Urban Planning 124, 1-13.

Young J.C., Jordan A., Chapman D., Searle K., Butler A., Simmons P. & Watt A.D. 2013. Does stakeholder involvement really benefit biodiversity conservation? Biological Conservation 158, 359-370.

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McConnon A., Horgan G.W., Lawton C., Stubbs J., Shepherd R., Astrup A., Darlenska T.H., Kunova V., Larson T.M., Lindroos A.K., Martinez A., Papadaki A., Pfeiffer A., Van Baak M.A. & Raats M. 2013. Experience and acceptability of diets of varying protein content and glycemic index in an obese cohort: results from the Diogenes trial. European Journal of Clinical Nutrition 67, 990-995.

McGeoch S.C., Johnstone A.M., Lobley G.E., Adamson J., Hickson K., Holtrop G., Clark L.F., Pearson D.W., Abraham P., Megson I.L. & MacRury S.M. 2013. A randomised crossover study to assess the effect of an oat-rich diet on glycaemic control, plasma lipids and postprandial glycaemia, inflammation and oxidative stress in Type 2 diabetes. Diabetic Medicine 30, 1314-1323.

Neacsu M., Fyfe C., Horgan G.W. & Johnstone A.M. 2014. Appetite control and biomarkers of satiety with vegetarian (soya) and meat based high-protein diets for weight loss in obese men. American Journal of Clinical Nutrition 100, 548-558.

Ostertag L.M., Kroon P.A., Wood S., Horgan G.W., Cienfuegos-Jovellanos E., Saha S., Duthie G.G. & de Roos B. 2013. Flavan-3-ol-enriched dark chocolate and white chocolate improve acute measures of platelet function in a gender-specific way: a randomized controlled human intervention trial. Molecular Nutrition & Food Research 57, 191-202.

Ou O., Allen-Redpath K., Urgast D., Gordon M-J., Campbell G., Feldmann J., Nixon G.F., Mayer C-D., Kwun I-S. & Beattie J.H. 2013. Plasma zinc’s alter ego is a low molecular weight humoral factor. FASEB Journal 27, 3672-3682.

Salonen A., Lahti L., Saloharvi J., Holtrop G., Korpela K., Duncan S.H., Date P., Farquharson F., Johnstone A.M., Lobley G.E., Louis P., Flint H.J. & de Vos W.M. 2014. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. International Society for Microbial Ecology Journal 8, 2218-2230. Nature Publishing Group.

Stubbs J., O’Reilly L., Whybrow S., Fuller Z., Johnstone A.M., Livingstone B., Ritz P. & Horgan G.W. 2014. Measuring the difference between actual and reported food intakes in the context of energy balance under laboratory conditions. British Journal of Nutrition 111, 2032-2043.

Tighe P., Duthie G.G., Brittenden J., Vaughan N., Mutch W., Simpson W., Duthie S.J., Horgan G.W. & Thies F. 2013. Effects of wheat and oat-based whole grain foods on serum lipoprotein size and distribution in overweight middle aged people: a randomised controlled trial. PLoS ONE 8, e70436.

Wallace J.M., Bhattacharya S., Campbell D.M. & Horgan G.W. 2014. Inter-pregnancy weight change impacts placental weight and is associated with the risk of adverse pregnancy outcomes in the second pregnancy. BMC Pregnancy and Childbirth 14, 40.

Wallace J.M., Bhattacharya S. & Horgan G.W. 2013. Gestational age, gender and parity specific centile charts for placental weight for singleton deliveries in Aberdeen, UK. Placenta 34, 269-274.

Whitelaw N., Bhattacharya S., Hoad G., Horgan G.W., Hamilton M. & Haggarty P. 2014. Epigenetic status in the offspring of spontaneous and assisted conception. Human Reproduction 29, 1452-1458.

Whybrow S., Ritz P., Horgan G.W. & Stubbs J. 2013. An evaluation of the IDEEAtm activity monitor for estimating energy expenditure. British Journal of Nutrition 109, 173-183.

Zhang X., McGeoch S.C., Johnstone A.M., Holtrop G., Sneddon A., MacRury S.M., Megson I.L., Pearson D.W., Abraham P., de Roos B., Lobley G.E. & O’Kennedy N. 2014. Platelet-derived microparticle count and surface molecule expression differ between subjects with and without type 2 diabetes, independently of obesity status. Journal of Thrombosis and Thrombolysis 37, 455-463. Springer-Verlag Berlin Heidelberg.

Zhang X., McGeoch S.C., Megson I.L., MacRury S.M., Johnstone A.M., Abraham P., Pearson D.W., de Roos B., Holtrop G., O’Kennedy N. & Lobley G.E. 2014. Oat-enriched diet reduces inflammatory status assessed by circulating cell-derived microparticle concentrations in type 2 diabetes. Molecular Nutrition & Food Research 58, 1322-1332.

Alexander C.J. 2014. Statistical analysis of metabolomic data. Seminar. Moredun Research Institute, Edinburgh.

Brewer M.J. 2013. Bayesian modelling. Lecture. Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.

Brewer M.J. 2013. Bayesian temporal compositional analysis in water quality monitoring. Contributed talk. 29th European Meeting of Statisticians, Budapest, Hungary.

Brewer M.J. 2013. Best practice workshop for species distribution models – models and algorithms. Invited talk. Species Distribution Modelling, Charles Darwin House, London.

Brewer M.J. 2013. Climate envelopes for species distribution models. Invited talk. Species Distribution Modelling, Charles Darwin House, London.

Brewer M.J. 2013. Random effects modelling. Lecture. Statistics for Environmental Evaluation: Quantifying the Environment Workshop, University of Glasgow.

Brewer M.J. 2013. Source apportionment in hydrology – Bayesian modelling of uncertainty. Invited talk. Bayes in the Environment, Royal Statistical Society, London.

Brewer M.J. 2013. Source distribution modelling for compositional analysis in hydrology. Seminar. School of Mathematics, Statistics and Actuarial Science, University of Kent.

Brewer M.J. 2014. Bayesian modelling. Lecture. Statistics for Environmental Evaluation: Quantifying the Environment Workshops (January and September), University of Glasgow.

Brewer M.J. 2014. Random effects modelling. Lecture. Statistics for Environmental Evaluation: Quantifying the Environment Workshops (January and September), University of Glasgow.

Brewer M.J. 2014. Statistical ecology: everything we know isn’t wrong. Contributed talk. International Statistical Ecology Conference, Montpellier, France.

Brewer M.J. 2014. Statistical ecology: everything we know isn’t wrong. Seminar. School of Mathematics and Statistics, University of St Andrews.

Butler A., Searle K.R., Mobbs D., Owen E., Daunt F., Bogdanova M. & Bolton M. 2014. Quantifying the impact of human activity on seabird behaviour: a statistical perspective. Contributed talk. International Statistical Ecology Conference, Montpellier, France.

Butler A., Spezia L., Owen E., Bolton M. & Pinto C. 2014. Work with BioSS on modelling animal movement using HMMs. Invited talk. Symposium on Hidden Markov Models, University of Glasgow.

Catterall S. & Marion G. 2014. Emergence of diversity-stability-productivity relationships in a generic multi-species resource competition framework. Contributed talk. 9th European Conference on Mathematical and Theoretical Biology, Gothenburg, Sweden.

Fang Z. 2013. Sparse penalised methods in phenology. Contributed talk. International Workshop on Statistical Modelling, Palermo, Italy.

Fang Z. 2013. Sparse penalised methods in phenology. Poster presentation. International Workshop on Statistical Modelling, Palermo, Italy.

Fang Z. 2014. Predicting the Results of the Scottish Referendum. Invited talk. Royal Statistical Society International Conference, Sheffield.

Glasbey C.A. 2013. Dynamic programming versus graph cut algorithms for phenotyping by image analysis. Invited talk. 49th Australian and New Zealand Industrial and Applied Mathematics Conference, Newcastle, Australia.

Appendix 3Conference Presentations, Lectures & Seminars 2013 - 2014

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Lorenzo-Arribas A. & Brewer M.J. 2014. Cumulative link mixed models in practice. Contributed talk. Young Statisticians Meeting, Bristol.

Lorenzo-Arribas A. & Brewer M.J. 2014. Cumulative link mixed models and the partial proportional odds assumption. Seminar. School of Mathematical Sciences, Queen Mary University of London.

Lorenzo-Arribas A. & Brewer M.J. 2014. Questionnaire design and analysis toolkit. Poster presentation. Young Statisticians Meeting, Bristol.

Lorenzo-Arribas A., Brewer M.J. & Fischer A. 2013. Analysing ordinal questionnaire data. Contributed talk. Young Statisticians Meeting, Imperial College London.

Lorenzo-Arribas A., Brewer M.J., Fischer A., Conniff A. & Craig T. 2014. Practical issues in ordinal response modelling. Poster presentation. Royal Statistical Society International Conference, Sheffield.

Mann A.D., Glasbey C.A., McLean K.A. & Bunger L. 2013. ATAR: Animal Tomogram Analysis Routines. Poster presentation and software demonstration. FAIM II: Second Annual Conference on Carcass Evaluation, Meat Quality, Software and Traceability, Kaposvár University, Hungary.

Marion G. 2013. Inferring the spatial dynamics of invasion. Invited talk. Modelling Tools in Invasion Biology Workshop, Uppsala, Sweden.

Marion G. 2013. Understanding impacts of animal behaviour in wildlife-disease systems. Seminar. Mathematical Biology, School of Mathematics, University of Edinburgh.

Marion, G. & Catterall, S. 2014. Emergent relationships between diversity, stability and productivity: a multi-species resource competition framework. Seminar. Mathematics and Statistics Group, University of Stirling.

Marion, G. & Catterall, S. 2014. Emergent relationships between diversity, stability and productivity: A multi-species resource competition framework. Colloquium. Mathematics and Statistics Department, University of Strathclyde.

Marion, G. 2014. Stochastic modelling of wildlife-disease systems. Invited talk. PEPA Club, School of Informatics, University of Edinburgh.

Marion, G., Walton, L., Davidson, R.S., White, P.C.L., Gavier-Widen, D., Yon, L. & Hutchings, M.R. 2014. The ecology of wildlife disease surveillance. Invited talk. European Wildlife Disease Association Conference, Edinburgh.

Marion, G., Walton, L.., Davidson, R.S., White, P.C.L., Gavier-Widen, D., Yon, L. & Hutchings, M.R. 2014. The ecology of wildlife disease surveillance. Contributed talk. 9th European Conference on Mathematical and Theoretical Biology, Gothenburg, Sweden.

Mayer C-D. 2013. Quantifying overall correlation in high-dimensional omics data. Contributed talk. International Biometric Society 4th Channel Network Conference, St. Andrews.

Mayer C-D. 2014. Exploratory analysis of (multiple) omics data sets. Contributed talk. Network Analysis for Systems Nutrition Research, The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.

McKendrick I.J. 2013. Statistics in support of government policy: an EPIC experience. Invited talk. Strathclyde University Population Modelling and Epidemiology Research Day, Glasgow.

Nath M., Pooley C., Bishop S.C. & Marion G. 2014. A Bayesian modelling framework to integrate genetics and epidemiology in field disease data. Poster presentation. 10th World Congress on Genetics Applied to Livestock Production, Vancouver, British Columbia, Canada.

Nath M., Pooley C., Bishop S.C. & Marion G. 2014. Bayesian modelling to estimate genetic and epidemiological parameters from field disease data. Contributed talk. Bayes Lectures 2014, University of Edinburgh.

Glasbey C.A. 2013. Dynamic programming versus graph cut algorithms for phenotyping by image analysis. Seminar. Institute for Mathematical Research, Universiti Putra Malaysia, Kuala Lumpur, Malaysia.

Glasbey C.A. 2013. Dynamic programming versus graph cut algorithms for phenotyping by image analysis. Seminar. CSIRO Mathematical and Information Sciences Division, Sydney, Australia.

Glasbey C.A., Allcroft D.J. & Butler A. 2013. Tobit models for multivariate, spatio-temporal and compositional data. Invited talk. 5th International Workshop on Compositional Data Analysis, Vorau, Austria.

Glasbey C.A., Song Y., Horgan G.W., van der Heijden G.W.A.M. & Polder G. 2013. Automatic phenotyping: estimating fruit numbers by clustering repeated, incomplete observations. Contributed talk. International Biometric Society 4th Channel Network Conference, St Andrews.

Glasbey, C.A. 2014. Dynamic programming versus graph cut algorithms for phenotyping by image analysis. Invited talk. Scottish and Northumbrian Academic Statisticians Meeting, Edinburgh.

Hackett C. A. 2014. Linkage analysis and QTL mapping using SNP dosage data. Series of invited presentations. SolCAP Potato Tetraploid Mapping and SNP Analysis Training Workshop, Michigan State University, East Lansing, Michigan, USA.

Hackett C.A. 2013. The importance of simple graphical explorations as part of linkage analysis and QTL mapping in plant species. Seminar. Teagasc, Carlow, Ireland.

Holtrop G., Fouret N., Kennedy C. & McArdle H.J. 2013. Modelling of iron requirements by mother and foetus during pregnancy. Contributed talk. Systems Biology Symposium, Aberdeen.

Horgan G.W., Macdiarmid J., Kyle J., Perrin A. & McNeill G. 2013. Which nutrients limit changes to more sustainable diets? Poster presentation. International Conference on Nutrition, Grenada. Published in Annals of Nutrition and Metabolism 63, 909.

Kettle H. 2014. Modelling stage-structured populations of crop pathogens: 1) under environmental change, and 2) as part of a food web, using delay differential equations. Contributed talk. European Conference on Mathematical and Theoretical Biology, Gothenburg, Sweden.

Kettle H., Blok V. & Kaczmarek A. 2013. Effect of climate change on the population dynamics of potato cyst nematodes. Poster presentation. Models in Population Dynamics and Ecology, Osnabrück University, Germany.

Kettle H., Blok V. & Kaczmarek A. 2014. Modelling stage-structured populations of crop pathogens: 1) under environmental change, and 2) as part of a food web, using delay differential equations. Invited talk. Structured Integro-Differential Models in Mathematical Biology Workshop, Wolfgang Pauli Institute, Vienna, Austria.

Kettle H., Holtrop G., Flint H.J., Louis P., Donnelly R. & Marion G. 2013. Modelling microbial diversity in the human colon. Contributed talk. Mathematical Models in Ecology and Evolution, University of York.

Kettle H., Louis P., Holtrop G. & Flint H.J. 2014. Modelling the emergent dynamics of human colonic microbiota. Invited talk. Interdisciplinary Approaches to Understanding Microbial Communities Workshop, Isaac Newton Institute for Mathematical Sciences, University of Cambridge.

Kettle H., Louis P., Holtrop G., Flint H.J., Donnelly R. & Marion G. 2014. Modelling ecosystems with parameter uncertainty and adaptation: applications to gut bacteria and stage-structured crop pathogens. Seminar. Department of Biology, University of York.

Lau, S.Y., Streftaris, G., Marion, G. & Gibson, G.J. 2014. Novel model assessment for spatio-temporal models in epidemiology and ecology. Contributed talk. Epidemics 4 – 4th International Conference on Infectious Disease Dynamics, Amsterdam, Netherlands.

Lorenzo-Arribas A. & Brewer M.J. 2013. Challenges in ordinal response data analysis: proportional and partial proportional odds logistic models. Seminar. Econometrics and Statistics Department, Universidad del País Vasco, Bilbao, Spain.

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Nevison I.M., Ricci P., Waterhouse A. & Rooke J.A. 2013. Predicting methane emissions from cattle – where meta-analysis and random coefficients modelling meet. Poster presentation. International Biometric Society 4th Channel Network Conference, St Andrews.

Palarea Albaladejo J. & Martin-Fernandez J.A. 2013. Left-censoring problems in compositional data sets. Invited talk. 6th International Conference of the European Research Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics, London.

Palarea Albaladejo J. 2013. Handling values below detection limit in compositional data sets. Seminar. School of Mathematical Sciences, University of Edinburgh.

Palarea Albaladejo J. 2013. On compositional data and values below the limit of detection. Seminar. James Hutton Institute, Aberdeen.

Palarea Albaladejo J. 2014. Compositional approach to some data analysis problems in the earth and life sciences. Seminar. Faculty of Science, Palacký University, Olomouc, Czech Republic.

Palarea Albaladejo J. 2014. What about a compositional approach to animal science data? Seminar. Scotland’s Rural College, Edinburgh.

Palarea Albaladejo J., McLean K., Wright F. & Smith D.G.E. 2013. On the use of CODA methods for bacterial identification from proteomic mass spectra. Contributed talk. Fifth Compositional Data Workshop, Vorau, Austria.

Potts J.M. & Brewer M.J. 2014. Modelling the foraging behaviour of terns. Poster presentation. Royal Statistical Society International Conference, Sheffield.

Potts J.M., Glenk K. & Colombo S. 2013. Using Bayesian Methods for benefit transfer from choice experiments. Contributed talk. Royal Statistical Society Annual Conference, Newcastle.

Preedy K., Hackett C.A. & Vogogias T. 2014. High density genetic linkage mapping with multidimensional scaling. Poster presentation. Royal Statistical Society Conference, Sheffield.

Roberts A.M.I. & Kristensen K. 2013. A proposal for a revised version of COYU. Contributed talk. UPOV Technical Working Party for Automation and Computer Programs, Seoul, Republic of Korea.

Roberts A.M.I. & Kristensen K. 2014. Development of the Combined-Over-Year Uniformity Criterion. Contributed talk. Tenth Working Seminar on Statistical Methods in Variety Testing, Poznan, Poland.

Roberts A.M.I. & Nevison I.M. 2014. Predicting distinctness decisions under heterogeneity. Contributed talk. Tenth Working Seminar on Statistical Methods in Variety Testing, Będlewo, Poland.

Roberts A.M.I. 2014. Proposed improvements to COYU. Invited talk. UPOV Technical Committee, Geneva, Switzerland.

Roberts A.M.I. 2014. Relating phenology to weather. Contributed talk. RBGE Phenology Workshop, RBGE, Edinburgh.

Roberts A.M.I. 2014. Development of the Combined-Over-Year Uniformity Criterion. Contributed talk. UPOV Technical Working Party for Automation and Computer Programs, Helsinki, Finland.

Rydevik, G., Hutchings, M.R., White, P.C.L., Davidson, R.S., Marion, G. & Innocent, G. 2013. Using multiple diagnostic tests to recover incidence trends. Poster presentation. International Biometric Society 4th Channel Network Conference, St Andrews. (Best student poster)

Sell B., Stansfield I. & Holtrop G. 2013. Modelling substrate preferences in bacteria. Poster presentation. Research Students’ Conference in Probability, Statistics and Social Statistic Research, Lancaster.

Sell B., Stansfield I. & Holtrop G. 2014. A theoretical approach to modelling enzyme activity in polysaccharide breakdown in the human large intestine. Poster presentation. Rowett/INRA 2014 Gut Microbiology: From Sequence to Function, Aberdeen.

Simpson I., Bramham CR. & Pajak M. 2014. Computational approaches to improving miRNA – mRNA interaction predictions. Poster presentation. European Conference of Computational Biology, Strasbourg, France.

Simpson I., Kind P. & Dando O. 2014. Piquant: a pipeline for assessing the performance of transcriptome quantification tools. Poster presentation. European Conference of Computational Biology, Strasbourg, France.

Simpson I., Snowden J., Page M. & Wysocka E. 2014. Towards a semi-automated framework of rule-based model creation for neuropsychiatric disease. Poster presentation. European Conference of Computational Biology, Strasbourg, France.

Simpson I., Walzel E., Armstrong D. & He X. 2014. DisEnT, a unified ontology based gene set enrichment analysis (GSEA) framework for gene disease and gene phenotype studies. Poster presentation. European Conference of Computational Biology, Strasbourg, France.

Simpson I., Watson E., Dagleish M., Benavides-Silvan J., Mason E., Inglis N., Everest P. & Smith D.G. 2014. Multi-omics analysis of Campylobacter jejuni infection in the piglet disease model. Poster presentation. Campylobacter UK, Liverpool.

Spezia L. & Pinto C. 2013. Markov switching models for high-frequency time series: flapper skate’s depth profile as a case study. Contributed talk. 9th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Modena, Italy.

Spezia L. & Pinto C. 2014. Markov switching models for high-frequency time series from automatic monitoring of animals. Contributed talk. Statistical Methods for Dynamical Stochastic Models Network Workshop, Warwick.

Spezia L. 2013. Finite and Markov-dependent mixture models. Invited talk. 23rd Simposio Internacional de Estadistica, Bogota, Colombia.

Spezia L. 2013. Generalized spatial hidden Markov models and remote sensing. Contributed talk. SCo (Sistemi Complessi) 2013, Milan, Italy.

Spezia L. 2013. Modelling data from automatic monitoring of organisms and environment. Invited talk. Sensors & Sensor Systems Workshop, University of Glasgow.

Spezia L. 2013. Spatial hidden Markov models and species distributions. Invited talk. 23rd Simposio Internacional de Estadistica, Bogota, Colombia.

Spezia L. 2014. Bayesian analysis of hidden Markov models. Seminar. Departamento de Estadistica, Universidad Nacional de Colombia, Bogota, Colombia.

Spezia L. 2014. Markov switching autoregressive models and stream isotope dynamics. Invited talk. Royal Statistical Society Environmental Statistics Section Meeting, University of Greenwich, London.

Spezia L. 2014. Non-linear models for data from automated monitoring of organisms and environment. Seminar. Departamento de Estadistica, Universidad Nacional de Colombia, Bogota, Colombia.

Vogogias T. & Hackett C.A. 2014. An interactive visualisation tool for the hierarchical clustering of large data sets. Poster presentation. 22nd Annual International Conference on Intelligent Systems for Molecular Biology, Hynes Convention Center, Boston, MA, USA.

Appendix 4External Committees April 2013-March 2015

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G. R. Marion Management Board, Strategic Partnership in Animal Science Excellence (SPASE) Editorial Board, BioRisk Associate Editor, Wildlife Research Associate Editor, Journal of Agricultural Science

C-D. Mayer RINH Scientific Review Committee RSS Highlands Local Group Committee Editorial Board, British Journal of Nutrition International Life Sciences Institute Expert Group on Establishment of the Efficacy of Intervention in those with the Metabolic Syndrome

I. J. McKendrick Deputy Director, EPIC Centre of Expertise on Animal Disease Outbreaks MRI Experiments and Ethical Review Committee Project Management Committee, DEFRA Methane Inventory Project ACO115

M. Nath MRI Experiments and Ethical Review Committee

I. M. Nevison SRUC Edinburgh Ethical Review Committee Scientific Committee, Polish Biometric Society’s Colloquium Biometricum Journal

J. M. Potts RSS Highlands Local Group Committee GenStat Procedure Library Editorial Committee Statistics Advisory Panel, European Journal of Soil Science

A. M. I. Roberts Chair, Technical Working Party on Automation and Computer Programs, International Union for the Protection of Plant Varieties Technical Committee, International Union for the Protection of Plant Varieties Enlarged Editorial Committee, International Union for the Protection of Plant Varieties Inter-Departmental Statisticians’ Group, advising UK National List and Seeds Committee Potato Experts Group, advising UK National List and Seeds Committee Vegetable DUS Group, advising UK National List and Seeds Committee SASA Ethical Review Committee

T. I. Simpson Editorial Board, American Journal of Bioinformatics Royal Society of Edinburgh, Young Academy of Scotland Patrick Wild Centre for Autism Research University of Edinburgh MSc. Bioinformatics Exam Board Member

M. J. Brewer Associate Editor, Biometrics

Associate Editor, Journal of Agricultural, Biological and Environmental Statistics

Treasurer, International Biometric Society (IBS), British and Irish Region

International Program Committee, International Biometric Conference 2016

Local Organising Committee, IBS Channel Network Meeting 2013

RSS Highlands Local Group Committee

A. Butler RSS Edinburgh Local Group Committee

RSS Environmental Statistics Section Committee

S. Brocklehurst SRUC Auchincruive Ethical Review Committee

D. A. Elston RSS Honours Committee

RSS Panel on Statistics for Ecosystem Change

RSS Highlands Local Group Committee

Academy for PhD Training in Statistics Advisory Committee

UK Environmental Change Network Scientific and Technical Advisory Group

Scottish Consortium for Rural Research Board

MRP Directors’ Executive Committee

INRA Applied Mathematics and Informatics Division Review Panel

C. A. Glasbey Program Committee, International Biometric Conference 2014

EPSRC Peer Review College

Fisher Memorial Committee

Royal Society of Edinburgh Sectional Committee B4

C. A. Hackett Associate Editor, Theoretical and Applied Genetics

G. Holtrop Editorial Board, Public Health Nutrition

Editorial Board, British Journal of Nutrition

G. W. Horgan RINH Scientific Review Committee

RSS Highlands Local Group Committee

Editorial Board, Public Health Nutrition

J. Liu Secretary, RSS Edinburgh Local Group Committee

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Glossary of Organisational Acronyms and Abbreviations

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Biomathematics and Statistics ScotlandJames Clerk Maxwell BuildingPeter Guthrie Tait RoadKing’s BuildingsEdinburgh EH9 3FD

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Acknowledgements

Design: John McNeill, Media Unlimited

Production Co-ordination: Elwood Vogt, Muriel Kirkwood & David Elston

Photography & Images: We thank, collectively, our own staff and collaborators specifically: P9 Thibaud Porphyre, University of Edinburgh. P15 James Hutton Institute P13 Cecilia Pinto, University of Aberdeen P16 James Hutton Institute P18 SRUC P19 Wiki Commons P20 James Hutton Institute P22 RINH This page, each institution photographed

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Additional pictures purchased from www.istockphoto.com

James Hutton InstituteCraigiebucklerAberdeen, AB15 8QHwww.hutton.ac.uk

James Hutton InstituteInvergowrieDundee, DD2 5DAwww.hutton.ac.uk

Moredun Research InstitutePentlands Science ParkBush LoanPenicuik, EH26 0PZwww.moredun.org.uk

Rowett Institute of Nutrition and HealthGreenburn RoadBucksburnAberdeen AB21 9SBwww.abdn.ac.uk/rowett

Royal Botanic Garden Edinburgh20A Inverleith RowEdinburgh, EH3 5LRwww.rbge.org.uk

Science and Advice for Scottish Agriculture Roddinglaw RoadEdinburgh, EH12 9FJwww.sasa.gov.uk

Scotland’s Rural College Nicholas Kemmer RoadThe King’s BuildingsEdinburgh EH9 3FHwww.sruc.ac.uk

ARC The former Agricultural Research Council

AFRC The former Agricultural and Food Research Council

AHDB Agriculture and Horticulture Development Board

BBSRC Biotechnology and Biological Sciences Research Council

BioSS Biomathematics and Statistics Scotland

CAMERAS Coordinated Agenda for Marine, Environment and Rural Affairs Science (partnership of public bodies in Scotland)

CEH Centre for Ecology and Hydrology

Defra Department of Environment, Food and Rural Affairs

EPIC Scottish Government Centre of Expertise on Animal Disease Outbreaks

EFSA European Food Safety Authority

EPSRC Engineering and Physical Sciences Research Council

EU European Union

HGCA Home-Grown Cereals Authority (now AHDB Cereals and Oilseeds)

Hutton James Hutton Institute

IBERS University of Aberystwyth Institute of Biological, Environmental and Rural Sciences

IBS International Biometric Society

INRA France’s National Institute of Agricultural Research

JNCC Joint Nature Conservation Committee

MRC Medical Research Council

MRI Moredun Research Institute

MRP Main Research Provider (of RESAS)

NIAB National Institute of Agricultural Botany

RBGE Royal Botanic Garden Edinburgh

RESAS Scottish Government Rural and Environment Science and Analytical Services Division

RHS Royal Horticultural Society

RINH Rowett Institute of Nutrition and Health

RSPB Royal Society for the Protection of Birds

RSE Royal Society of Edinburgh

RSS Royal Statistical Society

SASA Science and Advice for Scottish Agriculture

SEPA Scottish Environment Protection Agency

SNH Scottish Natural Heritage

SRUC Scotland’s Rural College

TSB Technology Strategy Board (now Innovate UK)

CONTACT POINTS FOR BioSS and its principal collaborators

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Stat i s t i c s & M athemat ic s impr o ving A g r ic u lt u r e,the Envir onment, Food & Health

Biomathematics &Statistics ScotlandBiennial Report2013/2015