Kelvin Smith PhD Scholarships 2014/15

A ‘Hybrid’ Approach to Evaluating Mobile Technologies for Health and Wellbeing (mHealth) - Claire McCallum (LKAS PhD Candidate)

An ever-expanding market and appetite for mobile technologies for health and wellbeing (e.g. physical activity and sleep trackers, diet and medication loggers, and smoking and alcohol cessation applications) has emerged over the last five years. Recognising their potential for health improvement, health providers, including NHS organisations, have begun to invest in and recommend these ‘mHealth’ technologies to patients. There is no lack of rapidly evolving mHealth applications: common characteristics include ready-availability (they are often free to download), interoperability (with other apps and with social network sites), adaptability and mutability (they are configurable by users, are regularly updated by providers, and exist within a rapidly evolving infrastructure). These applications are commonly used on smartphones, but the advent of smart-watches and smart-glasses is likely to spur further evolution and innovation over the coming years.

The characteristics of mHealth applications mean that the robust (and often time consuming), scientific paradigms that are traditionally used to measure the effectiveness of health interventions (e.g. the randomised controlled trial) are ill-suited to this rapidly-evolving field. These paradigms often assume long-term commitment to a stable artefact and are unable to capitalise on the opportunity afforded by the rich availability of real-time application data and metadata.
This project aims to develop innovative evidence-based methodologies to inform the future evaluation, development, commissioning and procurement of mHealth technologies and wider public mHealth policy. It will do so by drawing on and ‘hybridising’ best-evidence and approaches from social sciences, public health sciences and human computer interaction (HCI).

There are three main objectives:
1) review existing evidence/debate on the evaluation of mHealth technologies in social sciences, public health sciences, computing sciences and human computer interaction (HCI);
2) inform the development of novel methodological approaches to the evaluation of mHealth technologies;
3) pilot and refine the new methodologies using existing mHealth applications.

Host College: Social Sciences

Project team: 
Dr Cindy Gray (Principal investigator)
Institute of Health and Wellbeing

Dr John Rooksby & Dr Matthew Chalmers (Co-investigators)
School of Computing Science

Claire McCallum (LKAS PhD Candidate)

The role of polyploidy, hybridisation and mating system on the ability of crops to adapt to changing environments - Elizabeth Mittell (LKAS PhD Candidate)

Food security in the omics era: the role of polyploidy, hybridisation and mating system on the ability of crops to adapt to changing environments

An acknowledged threat to food security is the ability of crops and livestock to respond to increased environmental variability resulting from climate change. In plants, crops are often selected to be able to self-propagate but this comes at a cost to genetic variation, which could reduce potential for adaptation to changing environments. Hybridising different strains can increase genetic variation and combine desirable traits from different species but this is often accompanied by doubling of the genome (polyploidisation) to increase stability of the hybrid combination. Theoretically, this should increase flexibility to adapt to changing conditions. However, the relative effects of such breeding strategies on adaptability and the consequences for yield of economically important traits remain largely untested. Importantly, plants adapting to changing environments need to be able to continue to attract beneficial symbionts (pollinators and soil microbes that enable them to process nutrients) and to combat potentially new threats (pathogens and herbivores) but it is not known how the combination of mating system, ploidy and hybridisation affect such interactions. An exciting technological development is characterisation of microbial communities using deep sequencing approaches. The vast amounts of data generated and the difficulty of resolving species based on short sequences means that improved methods need to be developed for characterising microbial diversity and interpreting what this means for interacting organisms.

Using a common garden approach, the purpose of this project is to use economically important Brassica napus (oilseed rape, which is used for both biofuel and edible oil production and is a polyploid hybrid that reproduces by self-fertilising) as a model to investigate:

1) the relative ability of plants with different traits to adapt to new environments;

2) the role of the microbial community in plant adaptation;

3) the consequences of the interaction between plant fitness and microbial community diversity for oil seed yield.

Host College: Medical Veterinary and Life Sciences

Project team:
Dr Barbara Mable (Principal investigator)
Institute of Biodiversity, Animal Health & Comparative Medicine

Dr Christina Cobbold (Co-investigator)
School of Mathematics and Statistics

Dr Bill Mullen (Co-investigator)
Institute of Cardiovascular and Medical Sciences

Dr Chris Quince (Co-investigator)
School of Engineering

Mr. Stephen Herrington (Project Partner)
Curator, Glasgow Botanic Gardens

Elizabeth Mittell (LKAS PhD Candidate)

Investigation of the bone adaptation and fracture in the third metacarpal (MCIII) bone of thoroughbred racehorses - Karol Lewandowski (LKAS PhD Candidate)

Third metacarpal bone (MCIII) fracture has a massive welfare and economic impact on horse racing, representing 45% of all fatal lower limb fractures, which in themselves represent more than 80% of reasons for death or euthanasia on GB racecourses. Factors affecting equine bone loading including training and ground surface are associated with risk of fracture. To comprehend the relationship between these factors and fracture, we propose to develop a computational model for the investigation of bone loading, adaptation, and fracture. To achieve this complex goal, we have assembled an interdisciplinary team of researchers including an equine clinician and engineers. We propose to develop a 3-D finite element (FE) model of MCIII capable of simulating the response of bone to loading and fracture. We hypothesise that the mechanically regulated adaption of MCIII, in response to mechanical loading, can be computationally modelled, and that this model can be validated through comparison with cadaver bones. An archive of cadaver bones from horses that sustained fractures while racing is available from previous work conducted by Dr Tim Parkin.

The aims and methods of this study are as follows:

1. Analyse material properties and anisotropy of MCIII at trabecular level
We will accurately determine the material properties of MCIII through a combination of FE analysis of microscale images and mechanical testing of specific bone regions.

2. Computational modelling of mechanically-regulated bone adaptation
Using macroscale images, we will generate FE models of the entire bone of Thoroughbred horses with no history of race training. We will subsequently perform FE analysis of bone adaptation to simulate adaptation of bone density in response to loading exercise. The predictive model will be compared with FE analysis of bones for trained horses and validated against observed patterns of bone adaptation in archived bones.

3. Investigate the initiation and propagation of fracture in MCIII
We will undertake fracture analysis comparing the material response for bone density distributions with and without training obtained in Aim 2. The predicted fracture path will be compared against observed fracture patterns in archived bones. We will investigate the influence of the loading pattern and bone density distribution on the propensity for fracture.

Host College: Science & Engineering

Project team:
Dr Lukasz Kaczmarczyk (Principal investigator)

Dr John Marshall (Co-investigator)
Veterinary Medicine

Prof Chris Pearce (Co-investigator)

Dr Tim Parkin (Co-investigator)
Veterinary Medicine

Karol Lewandowski (LKAS PhD Candidate)

Measuring and Accounting for Spatial and Temporal Trends in Electoral Violence - Fatma Elsafoury (LKAS PhD Candidate)

The aim of this project is to develop the data retrieval and analysis tools to identify, code, measure and account for spatial and temporal trends in electoral violence. The project will involve collaboration between political science and computing science and will result in methodological innovation in both fields.

In recent decades, more states have begun to hold elections, but many of these events have been beset by violence. Addressing spatial and temporal patterns in electoral violence is thus an urgent social and political need in today’s world. One of the reasons why electoral violence is not better understood is that political scientists currently have limited data on the phenomenon, and data collection via traditional political science methods is difficult, due to the covert nature of electoral violence and due to the dangers associated with conventional fieldwork-based data collection techniques.

Gaining a clearer insight into the spatio-temporal dynamics of electoral violence will have multiple advantages for Scholars and practitioners alike, shedding light on an under-studied phenomenon, providing policy-makers with an early-warning device and enabling practical strategies for combating electoral violence to be improved.

This cross-College interdisciplinary project will harness the power of ‘big data’ retrieval methodologies to develop a data collection tool that can be deployed in real time to monitor electoral violence via the collection of web-based information on incidents of electoral violence that occur before, during and after an election.

The project will also involve the use of the resulting data to model spatial and temporal trends in electoral violence with a view to understanding common patterns in its initiation and development.

Host College: Social Sciences

Project team:
Prof Sarah Birch (Principal investigator)
School of Social and Political Sciences

Dr Paul Cockshott (Co-investigator)
School of Computing Science

Fatma Elsafoury (LKAS PhD Candidate)

Recovering the dynamics of talk: tracking temporal dependence in multilevel models for speech - Craig Alexander (LKAS PhD Candidate)

Describing the numerous factors that constrain and promote particular aspects of linguistic behaviour in interaction is very difficult, tracking their temporal nature even more so. The recent adoption of more advanced quantitative methods has enhanced the modelling, and so understanding, of linguistic patterns. At the same time, the availability of digital recording and storage capacity is leading to increasingly large corpora of complex linguistic data for such investigations. But amidst the wealth of such data, linguists face a serious analytical challenge. While spoken language corpora typically contain numerous instances of features which recur over time in interactions (language behaviour is dynamic), statistical analyses depend on techniques which aggregate these instances (analyses of language are essentially static). Even newer methods which assume repetition of instances, or recognize complex groupings in the data, are unable to identify and exploit the fundamental contribution of dynamic, recurring, and ordered patterns in language data.

This project begins a collaboration between statistics and linguistics to develop dynamic quantitative models of spoken language data. In linguistics, the tension between large corpora based on comparatively few speakers is eased by using statistical models which can process nested data. The research will go beyond just modelling the complex nested structure of language data to also incorporate the underlying temporal dynamics of speech.

This new form of statistical analysis will not only predict the factors constraining and promoting language patterns, taking account of the speakers who utter them and the words they occur in, but at the same time it will automatically recover crucial information about the temporal nature of language variation, shifting our analytical perspective a step closer to the dynamics of talk.

The student will develop a statistical model to analyse specific aspects of language (e.g. phonology, lexicon) using data from the electronic real-time corpus of Glaswegian vernacular, Sounds of the City. The corpus is a searchable, multilayered, database of 58 hours of 136 speakers, recorded between 1970 and 2010, with orthographic transcripts and automatically phonemically segmented waveforms, amenable to automatic acoustic analyses of durational (e.g. segment durations) and resonance characteristics of speech (e.g. formant measures from FFT spectral analysis). The corpus software, LABB-CAT, is open source and can be flexibly adjusted by the programmer, both to meet the needs of, and benefit from the results of, the project.

This project initiates a programme to develop statistical models which account for the complex nested structure, and the underlying dynamics, of language. Analysing linguistic data generally uses random-effects models to account for the correlation of data within speakers and words, and to avoid biasing the inference (overestimation of p-values, inefficient weighting of the data). Such models have two important drawbacks: they ignore temporal and contextual aspects of speech, and they only allow inference about the mean effects, failing to recover more complex aspects, such as multimodality and unobserved groupings.

The cornerstone of the statistical analysis will be the combination of a random effects model (to accurately represent nested data structures) and a hidden Markov model (to represent temporal dynamics and grouping structures). In a hidden Markov model, an unobserved ("hidden") variable is introduced to track temporal dependency and context. Previous researchers have modelled the time course of features by manually coding, but such procedures are extremely time-consuming and difficult to automate, thus not scaling well to large corpora. Some context information might however be available, so the "hidden" state is actually not fully "hidden", requiring a "semi-supervised" model. The assumption of Gaussianity of the random effects might also not be realistic, requiring non-parametric random effects.

Recursive predictive modelling, such as HMM, is the basis of most speech recognition systems, which are used to automatically parse and segment language corpora. However these typically do not include random effects to model the nested structure of most datasets of this kind. Using a HMM-based model for the work of this project has another potential outcome, specifically to improve HMM speech and language parsers themselves, by combining two stages of analysis into a single step, leading to improved efficiency and correct propagation of uncertainty.

The project would suit a Mathematics or Statistics graduate with a keen interest in linguistics and phonetics, or a linguist or phonetician with a strong interest or background in quantitative methods.

Research training and student experience

The first year will entail a substantial period of research training across both disciplines. For statistics, the student will take courses offered by the Scottish Mathematical Sciences Training Centre (SMSTC) and the Academy for PhD Training in Statistics (APTS), undertake the generic skills and employability training offered by the University, and participate in postgraduate away-days which provide general research training, such as thesis writing in LaTeX and computational skills. They may also take courses offered as part of the newly-developed Masters programmes in Statistics. English Language is recognized as a provider of excellent research training in Language Sciences and Social and Applied Linguistic Investigations Pathways for the ESRC Doctoral Training Centre (DTC) for Scotland. The scholar's core training in phonetics and linguistics within English Language will be enhanced with the additional resources from the DTC.

The scholar will be based in the School of Mathematics and Statistics, and will benefit from an active and vibrant research environment which includes weekly seminars, School colloquia, visits by researchers of high international standing and opportunities for overseas research visits and participation in international workshops and conferences. They will also be a full member of the Glasgow University Laboratory of Phonetics, whose new location nearby will allow them to fully benefit from the lively research group based there. As for previous KS projects, we envisage the scholar's supervision to take place through a series of plenary supervisions, and regular subject-specific supervisions.

Host College: Science & Engineering

Project team:
Dr Ludger Evers (Principal investigator)
Mathematics & Statistics

Jane Stuart-Smith (Co-investigator)
Critical Studies

Dr Tereza Neocleous (Co-investigator)
Mathematics & Statistics

Craig Alexander (LKAS PhD Candidate)

The allocation of attention and mitigation of disease transmission: theory and evidence from Tanzania - Putthi Lim (LKAS PhD Candidate)

Ongoing research in Tanzania has generated an extensive evidence base relating to disease (rabies) transmission from animals (dogs) to humans. The preventive measures required to mitigate transmission are well understood (a threshold vaccination coverage in dogs needs to be achieved in a given locality, and sustained in successive vaccination campaigns). However, in practice, participation in vaccination campaigns varies, such that the number of dogs vaccinated often falls below the required threshold leading to ongoing transmission and human deaths that would otherwise be prevented: in Africa alone more than 25,000 people die from rabies each year. Such issues are characteristic of preventive top-down interventions aimed at mitigating disease transmission that typically require mass participation to effect population wide impacts.

A key insight from psychology is that attention is inherently selective. If a villager’s attention is focused on other competing daily tasks, this will result in less attention being paid to a village-level rabies vaccination campaign and therefore, a lower likelihood of participation. However, there are serious gaps in both the evidence base and the theoretical modelling of the endogenous allocation of attention.

Focusing on rabies, this project will:
(a) develop a new model of the endogenous allocation of attention, and
(b) using the results derived from the model, design and evaluate the impact of novel community-based prevention interventions.

These will aim to make the task of participation in a village-level vaccination campaign salient amongst the set of competing daily tasks confronting villagers residing in target villages in Tanzania and therefore, increase participation in such campaigns. The theoretical insights and the evidence generated by the project will inform a cutting-edge innovative research agenda on the allocation of attention as well as the design of community-led public health interventions in Tanzania and elsewhere in Africa and Asia.

The scholar will be enrolled in the PhD programme in Economics at the Adam Smith Business School (ASBS). Funding is available for PhD students to attend specialised training courses at Glasgow University and elsewhere. Specialised PhD level courses in econometric techniques and behavioural economics are offered at Glasgow University as is software demonstration and programming (matlab, dynare, stata, EVIEWS). In addition, PhD students can attend advanced courses organised by the Scottish Institute for Research in Economics (SIRE).

PhD students in Economics are encouraged to present their work outside Glasgow. All PhD students are given financial support (up to £2000) to present their work to international conferences.

Host College: Social Sciences

Project team:
Prof Sayantan Ghosal (Principal investigator)
Adam Smith Business School

Dr Katie Hampson & Dr Tiziana Lembo (Co-investigators)
The Boyd Orr Centre for Population and Ecosystem Health, IBAHCM

TBC (LKAS PhD Candidate)

The Dynamics of Plasma Membrane Transporters: A New Drug Target for Type 2 Diabetes - Silke Machauer (LKAS PhD Candidate)

The dynamics of movement of transporters, receptors and other cell surface molecules is highly regulated and functionally important. These movements underpin many physiologically relevant processes. The disposition and dynamics of proteins within the plasma membrane can be effectively studied using total internal reflection fluorescence microscopy (TIRFM) which can image and study a ‘thin layer’ of molecules, such as those located within the plasma membrane. The Cooper lab has designed/built such a system. This project will use TIRFM to study glucose transporter dynamics in fat cells as a paradigm for the behaviour of physiologically relevant molecules on the cell surface.

A major action of insulin is to increase the rate of glucose transport into fat and muscle and is achieved by the insulin-stimulated delivery of glucose transporter proteins to the cell surface. There are two populations of glucose transporters in the plasma membrane: stationary clusters and dispersed, mobile monomers. Insulin increases both the delivery of transporters to the cell surface and their subsequent dispersal within the membrane; dispersal is stimulated ~60-fold by insulin. This latter step represents a novel therapeutic target if the mechanism behind dispersal can be defined.

This studentship will systematically examine glucose transporter dispersal, and will study how two novel genes, identified by the Gould lab, impact on this. Transporter dispersal could be employed as a drug-discovery platform as a means to identify lead compounds in the treatment of insulin resistance and/or Type 2 diabetes. A combination of access to TIRFM and new gene targets uniquely positions us to rapidly advance this area.

These studies will be of mechanistic/practical interest to many groups across the University who study any process occurring within the plasma membrane, including signalling in health and disease, virus entry etc., and will offer the potential to step-change our understanding of many biological processes.

Host College: Medical Veterinary and Life Sciences

Project team:
Prof Gwyn Gould (Principal investigator)
Institute of Molecular, Cell and Systems Biology

Prof Jon Cooper (Co-investigator)

Silke Machauer (LKAS PhD Candidate)

Transport Network Analytics – Building a new generation of traffic models using heterogeneous big data - Ashwini Venkatasubramaniam (LKAS PhD Candidate)

The availability of large volumes of data from user-generated content and emerging infrastructure-based sensors allow for a step change in how traffic flow is modelled and understood. The vast amount of data available as part of the Glasgow Future City Demonstrator (FCD) project funded by the UK Technology Strategy Board (£24m) provides a unique opportunity to develop more powerful and realistic data-enriched transport models.

In the traditional transport modelling literature, cities have been theorised to be static systems being in equilibrium. These idealised mathematical models fail to consider adequately the spatiotemporal propagation of congestion, which overlook dynamic aspects of road network such as urban mobility patterns and user behaviour. The availability of urban big data on transport will enable the development of a new generation of models overcoming these shortcomings and allowing better understanding of these issues and their complex interactions and for the testing and evaluation of long-held traveller behaviour assumptions relating to the equilibrium and steady-state nature of road networks. Such empirically-grounded understanding will allow the modelling of dynamic flow problems in evolving networks and appropriately will support the development of innovative transport products and services in future cities.

This will be achieved by an interdisciplinary but closely integrated effort bringing together expertise from network management and control, optimisation, social modelling, social and travel behaviour, big data urban Informatics, data mining, and statistics. The ultimate goal of this project is to take advantage of the opportunities brought by emerging information and communication technologies, including the opportunities provided by urban big data initiatives such as the one-of-a-kind FCD as well as the Scottish Administrative Data Centre recently funded by Economic and Social Research Council (ESRC). These data initiatives will be invaluable in collecting heterogeneous data on urban mobility and user behaviour, and the recent advances in complex systems science to develop new urban network equilibrium models allowing city governments to design products and services that may influence user behaviour through information provision and transport system management.

Eligibility requirements

The successful candidate will have an excellent MSc or undergraduate degree in Engineering, Statistics, Mathematics, Computer Science, or Applied Mathematics/Sciences. Applicants with a sound knowledge, interest, and background in Operations Research, Statistics/Mathematics, Traffic Flow Theory, Physics of Transport and Traffic, Big Data Analysis, are welcome to apply for the PhD Scholarship. The ability to learn software programming (C/C++ or Python) and simulation tools (AIMSUN or VISSIM) is essential and prior experience is an advantage.

Host College: Science & Engineering

Project team:
Dr Konstantinos Ampountolas (Principal investigator)

Prof Vonu Thakuriah (Co-investigator)
Social & Political Sciences

Dr Ludger Evers (Co-investigator)
Mathematics and Statistics

Dr Jinhyun Hong (Co-investigator)
Social & Political Sciences

Ashwini Kolumam Venkatasubramaniam (LKAS PhD Candidate)

Using remote sensing to define associations between environmental parameters & vector-borne disease - Christopher Uzzell (LKAS PhD Candidate)

Understanding the impact of climate change on human health: using remote sensing to define associations between environmental parameters & vector-borne disease

Climate change has been identified as one of the biggest challenges facing humankind. It is predicted to impact not only on temperature but also atmospheric moisture, precipitation and atmospheric circulation. These in turn will impact the hydrological cycle, especially the character of precipitation (amount, frequency, intensity, duration), and extreme events (floods and droughts) directly impacting human society.

Climate change is also likely to impact human and animal health indirectly, as the geography of infectious diseases adapt, including changes in spatial and temporal distribution and seasonal activity of vector- and water-borne diseases, such as malaria, leptospirosis, dengue fever and rift valley fever, changing the risk. Many of these diseases are zoonotic (transmitted from animals to humans) and disease burden has massive impacts, not just for individual health but also economic and social impacts on families and communities, in terms of health care costs, loss of income, and huge pressure on health care facilities during epidemics. This burden is likely to increase.

Of paramount importance is the need to better understand the impact of climate change on human health. However, many knowledge gaps exist. Specifically there is a need to understand the relationship between particular disease patterns and their associated environmental parameters. Relevant environmental data can be obtained from satellite images, which provides a cost-effective rapid means of gathering data.

This inter-disciplinary project will combine fluvial geomorphology, spatial epidemiology, remote sensing and GIS technologies to develop a tool to predict disease outbreaks in Tanzania, and provide a unique training opportunity, and exciting, vital research. In turn this will provide timely warnings of epidemics due to an improved understanding of possible causal factors, and an increased ability to identify high risk populations, enabling the concentration of limited resources at hotspot locations and assisting in hazard reduction by raising community awareness of risks involved.

Please contact the Principal investigator directly to enquire about applying for this scholarship.

Host College: Science & Engineering

Project Team:

Dr Rhian Thomas (Principal investigator)
Geographical & Earth Sciences

Professor Sarah Cleaveland
Institute of Biodiversity, Animal Health and Comparative Medicine

Dr Seamus Coveney
Geographical & Earth Sciences

Dr Jo Halliday
Institute of Biodiversity, Animal Health and Comparative Medicine

Christopher Uzzell (LKAS PhD Candidate)

Who Influences Whom?: Examining Opinion Leadership and the Dissemination of Information through Social Media - Anjie Fang (LKAS PhD Candidate)

A long-standing research topic in political science and communications has focused on opinion leadership among producers of the news. Scholars have suggested that information spread by key news companies or media elites (journalists, bloggers) can impact or bias the news agenda of others. However, the evidence for such “opinion leadership” to-date has been hampered by a lack of systematic, empirical study. Due to the advent of new social media technologies and the emergence of software tools and skills to analyse them, it is now possible to study information flows systematically.

This project will address the question of who influences whom among national and international news producers by analysing the nature of the ties among media companies and media elites on Twitter, and the flow of information across these networks. The project will provide social network analysis and related diagnostics, mapping who follows whom among media companies and elites, and why.

Drawing from theory in political communication, and relying upon the ‘Big Data’ processing/analytics skills of computer science to handle the extraordinary large social media datasets, this project will break new ground in our understanding of the agenda of the mass media and the possibilities for media influence.

The LKAS PhD scholarship is for a period of 48 months, which includes a tuition waiver, an annual grant at the UK Research Council recommended rates (£13,863 in 2014/15 ), and up to £5300 annually to support research costs and conference travel. Students from both EU and non EU countries are encouraged to apply. The candidate is expected to be in residence in the fall of 2014. A 10 week summer 2014 paid internship may also be awarded.

Download this information here: KS project full info: Who Influences Whom?

Host College: Social Sciences

Project team:
Dr Philip Habel (Principal investigator)
School of Social and Political Sciences

Dr Iadh Ounis and Dr Craig Macdonald (Co-investigators)
School of Computing Science

Anjie Fang (LKAS PhD Candidate)

Developing an inter-disciplinary method for characterising & mapping the environmental legacies of past human actions - Thomas Muller (LKAS PhD Candidate)

Woodland ecosystems: developing an inter-disciplinary method for characterising & mapping the environmental legacies of past human actions

Conservationists and policy makers value ancient ‘primary’ forest over other naturally-regenerated or actively-restored ‘secondary’ woodlands. Due to their temporal continuity in the landscape, ancient woodlands are considered to support higher biodiversity and a greater range of ‘ecosystem services’ (i.e. the benefits people derive from ecosystems, such as carbon sequestration, drainage regulation, health benefits).

Internationally, significant effort is directed towards protecting ancient woodlands, yet our understanding of the connections between their history and current character is founded more on assumption than evidence-based research. People have shaped the distribution and character of ‘natural’ woodlands, but such human influence is under-acknowledged. Traditional ecological field methods alone cannot provide the depth of information needed to understand when, where and how diverse woodlands develop.

This is important because economic and policy change are encouraging woodland expansion across Europe. In Scotland, government policy is to increase woodland cover from 17% to 25% of land area by 2050. However, we have little idea how or where new woodlands should be developed in order to provide the biodiversity and ecosystem services we desire.

Interdisciplinary research is the key to understanding the links between the historic development and present-day values of woodland ecosystems. This PhD will combine archaeological, historical and cartographic research with ecological survey, taking advantage of the rich but under-utilised evidence for the development of woodlands through time (e.g. historic maps, documents, aerial photographs, archaeological field remains). Geographical Information Systems (GIS) software will facilitate collation, management and analysis of diverse scientific and historical data.

The objectives are to:
1) Develop methods for characterising and quantifying changing woodland cover;
2) Model changing landscape context and distribution of woodlands in Nithsdale (south-west Scotland);
3) Quantify variation in the character of woodlands in relation to their landscape location and management history;
4) Identify indicators of historic woodland management and biodiversity.

Host College: Arts

Project team:
Dr Chris Dalglish (Principal investigator)

Dr David Forrest (Co-investigator)
Geographical and Earth Sciences

Dr G. Matt Davies (Co-investigator)
Interdisciplinary Studies

Prof. Paul Bishop (Co-investigator)
Geographical and Earth Sciences

Thomas Muller (LKAS PhD Candidate)

For more information, please see Woodland Ecosystems - full details.