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New paper – Trends and indicators for quantifying moth abundance and occupancy in Scotland

Byron Morgan and Emily Dennis have had a paper published in Journal of Insect Conservation.

The full paper can be accessed here.

Trends and indicators for quantifying moth abundance and occupancy in Scotland

E. B. Dennis, T. M. Brereton, B. J. T. Morgan, R. Fox, C. R. Shortall, T. Prescott, S. Foster

Moths form an important part of Scotland’s biodiversity and an up-to-date assessment of their status is needed given their value as a diverse and species-rich taxon, with various ecosystem roles, and the known decline of moths within Britain. We use long-term citizen-science data to produce species-level trends and multi-species indicators for moths in Scotland, to assess population (abundance) and distribution (occupancy) changes. Abundance trends for moths in Scotland are produced using Rothamsted Insect Survey count data, and, for the first time, occupancy models are used to estimate occupancy trends for moths in Scotland, using opportunistic records from the National Moth Recording Scheme. Species-level trends are combined to produce abundance and occupancy indicators. The associated uncertainty is estimated using a parametric bootstrap approach, and comparisons are made with alternative published approaches. Overall moth abundance (based on 176 species) in Scotland decreased by 20% for 1975–2014 and by 46% for 1990–2014. The occupancy indicator (based on 230 species) showed a 16% increase for 1990–2014. Alternative methods produced similar indicators and conclusions, suggesting robustness of the results, although rare species may be under-represented in our analyses. Species abundance and occupancy trends were not clearly correlated; in particular species with negative population trends showed varied occupancy responses. Further research into the drivers of moth population changes is required, but increasing occupancy is likely to be driven by a warming summer climate facilitating range expansion, whereas population declines may be driven by reductions in habitat quality, changes in land management practices and warmer, wetter winters.

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New paper – Exact inference for integrated population modelling

Takis Besbeas and Byron Morgan have recently published a paper in Biometrics developing an approach for exact inference for integrated modelling.

The paper can be accessed here.

Exact inference for integrated population modelling

Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time‐series likelihood component can be conveniently approximated using Kalman filter methodology. However, the natural way to model systems which have a discrete state space is to use hidden Markov models (HMMs). The proposed method avoids the Kalman filter approximations and Monte Carlo simulations. Subject to possible numerical sensitivity analysis, it is exact, flexible, and allows the use of standard techniques of classical inference. We apply the approach to data on Little owls, where the model is shown to require a one‐dimensional state space, and Northern lapwings, with a two‐dimensional state space. In the former example the method identifies a parameter redundancy which changes the perception of the data needed to estimate immigration in integrated population modelling. The latter example may be analysed using either first‐ or second‐order HMMs, describing numbers of one‐year olds and adults or adults only, respectively. The use of first‐order chains is found to be more efficient, mainly due to the smaller number of one‐year olds than adults in this application. For the lapwing modelling it is necessary to group the states in order to reduce the large dimension of the state space. Results check with Bayesian and Kalman filter analyses, and avenues for future research are identified

 

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New paper – Functional data analysis of multi-species abundance and occupancy data sets

Emily Dennis (Butterfly Conservation) and Byron Morgan (SE@K) have recently published a paper in Ecological Indicators exploring multi-species abundance and occupancy indices using Functional Data Analysis tools.

The full paper can be accessed here.

Functional data analysis of multi-species abundance and occupancy data sets

Emily B.Dennis, Byron J.T.Morgan, RichardFox, David B.Roy and Tom M.Brereton

Multi-species indicators are widely used to condense large, complex amounts of information on multiple separate species by forming a single index to inform research, policy and management. Much detail is typically lost when such indices are constructed. Here we investigate the potential of Functional Data Analysis, focussing upon Functional Principal Component Analysis (FPCA), which can be easily carried out using standard R programs, as a tool for displaying features of the underlying information. Illustrations are provided using data from the UK Butterflies for the New Millennium and UK Butterfly Monitoring Scheme databases. The FPCAs conducted result in a huge simplification in terms of dimensional reduction, allowing species occupancy and abundance to be reduced to two and three dimensions, respectively. We show that a functional principal component arises for both occupancy and abundance analyses that distinguishes between species that increase or decrease over time, and that it differs from percentage trend, which is a simplification of complex temporal changes. We find differences in species patterns of occupancy and abundance, providing a warning against routinely combining both types of index within multi-species indicators, for example when using occupancy as a proxy for abundance when insufficient abundance data are available. By identifying the differences between species, figures displaying functional principal component scores are much more informative than the simple bar plots of percentages of significant trends that often accompany multi-species indicators. Informed by the outcomes of the FPCA, we make recommendations for accompanying visualisations for multi-species indicators, and discuss how these are likely to be context and audience specific. We show that, in the absence of FPCA, using mean species occupancy and total abundance can provide additional, accessible information to complement species-level trends. At the simplest level, we suggest using jitter plots to display variation in species-level trends. We encourage further application to other taxa, and recommend the routine augmentation of multi-species indicators in the future with additional statistical procedures and figures, to serve as an aid to improve communication and understanding of biodiversity metrics, as well as reveal potentially hidden patterns of behaviour and guide additional directions for investigation.

 

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National centre leadership role for Dr Rachel McCrea

Dr Rachel McCrea of the Statistical Ecology @ Kent group from the School of Mathematics, Statistics and Actuarial Science is taking on the role of Director of the National Centre for Statistical Ecology (NCSE).

 

The NCSE is a joint research centre which aims to “develop, apply and communicate innovative statistical methods for collecting and analysing ecological data, thereby improving the understanding and management of wild populations and their environment”.  It was established in 2005 and the current consortium unites research excellence across the Universities of Kent, St Andrews, Bath, Bristol, Edinburgh, Essex, Exeter, Glasgow and Sheffield, together with the Centre for Ecology and Hydrology.  A number of project partners are actively involved with the centre, including Biomathematics and Statistics Scotland, the Centre for Environment, Fisheries and Aquaculture Science, the Game and Wildlife Conservation Trust and Marine Scotland. The centre also has a considerable number of international members spanning the globe and initiated the world-leading biennial International Statistical Ecology Conference, the next of which will be hosted in Sydney in June 2020.

 

Rachel commenced this exciting role on 1st April, and together with the new Deputy Director, Professor Ruth King from the University of Edinburgh, looks forward to driving new NCSE initiatives. They have secured funding, jointly with the previous co-directors of the NCSE Professors Steve Buckland and Byron Morgan, to host an International Centre for Mathematical Sciences meeting in Edinburgh in June 2019 on the topic of Addressing Statistical Challenges of Modern Technological Advances.  This meeting will be used to identify future directions and associated thematic areas of the NCSE to ensure that the centre remains at the forefront of internationally leading statistical ecology research.  ​

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Paper – Estimating age‐dependent survival from age‐aggregated ringing data

The paper Estimating age‐dependent survival from age‐aggregated ringing data—extending the use of historical records, by Marina, Diana, Stephen Freeman (Centre for Ecology and Hydrology), Rob Robinson (BTO), Stephen Baillie (BTO) and Eleni, has been published in Ecology and Evolution.

Open access paper

Abstract:

Bird ring‐recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid‐20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring‐recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age‐dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651–795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring‐recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651–795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring‐recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.

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PhD project on Network models for capture-recapture data

Click here to submit your application.

Project description: Capture-recapture data are commonly collected and modelled for the study of wildlife populations (McCrea and Morgan, 2014). Existing capture-recapture models typically assume independence between individuals, with no network structure. Hence, the probability of capture, survival, or movement of particular individuals is assumed to be unaffected by events occurring to other individuals, or actions of these individuals, in the same population. The assumption of independence between individuals gives rise to likelihood functions that are simple products of probabilities of individual capture histories.

However, animal populations are structured by interaction networks among individuals and many species tend to live in social groups that result in these interaction networks differing from random (Wey et al., 2008; Pinter-Wollman et al., 2014; Krause et al., 2015;). In these cases, probabilities of certain events involving particular individuals might be related to their position in the social network. For example, individuals that are strongly connected in a network may be more likely to be captured at the same event. However, network relationships may also result in covariation in survival, or associated traits, between individuals. For example, the spread of directly transmitted infectious diseases will be linked to network structure and this could result in social covariance in survival if infection elevates mortality. Therefore, the survival probability and capture histories of individuals in the same network are expected to change according to the histories of all individuals in that network.

Moreover, there may an additional level of clustering, with certain networks, which are in close proximity for example, affecting each other, maybe to a less, but still not negligible extent. Additionally, it is possible that network structure changes over time, with network membership, and as a result network behaviour, also changing over time. Capture-recapture models offer a possibility of modelling network structure, while accounting for changing membership over time.

A wide range of statistical social network models have been developed, especially within sociology, and are increasingly applied across a range of fields (Cranmer et al., 2017; Silk et al. 2017). However, no such modelling approach exists for capture-recapture data. Ignoring a potential network structure, and hence ignoring dependence between individuals in the same network or dependence between linked networks, may lead to biased estimates of demographic parameters of interest, such as survival probability. Further, incorporating network structure within existing capture-recapture models offers a statistical framework to understand longer-term network dynamics that is more robust to turnover in membership than many existing statistical models of networks.

The project will develop network models for capture-recapture data, motivated by a long-term data set on badgers. The European badger Meles meles is a medium-sized carnivorous mammal widely distributed across the Western Palearctic. In high density populations badgers live in territorial social groups, and individuals interact frequently with other group members and much more rarely with individuals from neighbouring groups (Weber et al. 2013 Current Biology). One such high density population in South West England has been studied using mark-recapture methods for over 40 years providing a unique long-term demographic dataset ideally suited to investigate the questions outline above (McDonald et al., 2018). Capture-recapture models (e.g. McDonald et al., 2016) and social network approaches (e.g. Weber et al., 2013, Silk et al. 2018) have been used to provide valuable insights into this population, but have yet to be combined within a single approach. Therefore, this project offers an important opportunity to gain a new understanding as to how social interactions relate to key demographic processes.

Although the project is motivated by a capture-recapture data set, the methods developed and applied will be more generally applicable to the study of network data. Given the size and complexity of the available data set, the developed methods will need to be scalable to large data sets, making them even more appealing and relevant in the study of network data.

 

References

Cranmer, S. J., Leifeld, P., McClurg, S. D., & Rolfe, M. (2017). Navigating the range of statistical tools for inferential network analysis. American Journal of Political Science, 61(1), 237-251

Krause, J., James, R., Franks, D. W., & Croft, D. P. (Eds.). (2015). Animal social networks. Oxford University Press, USA.

McCrea, R. S., & Morgan, B. J. (2014). Analysis of capture-recapture data. CRC Press.

McDonald, J. L., Robertson, A., & Silk, M. J. (2018). Wildlife disease ecology from the individual to the population: Insights from a long‐term study of a naturally infected European badger population. Journal of Animal Ecology, 87(1), 101-112.

Pinter-Wollman, N., Hobson, E. A., Smith, J. E., Edelman, A. J., Shizuka, D., De Silva, S., … & Fewell, J. (2013). The dynamics of animal social networks: analytical, conceptual, and theoretical advances. Behavioral Ecology, 25(2), 242-255.

Silk, M. J., Croft, D. P., Delahay, R. J., Hodgson, D. J., Weber, N., Boots, M., & McDonald, R. A. (2017). The application of statistical network models in disease research. Methods in Ecology and Evolution.

Silk, M. J., Weber, N. L., Steward, L. C., Hodgson, D. J., Boots, M., Croft, D. P., … & McDonald, R. A. (2018). Contact networks structured by sex underpin sex‐specific epidemiology of infection. Ecology letters, 21(2), 309-318.

Weber, N., Carter, S. P., Dall, S. R., Delahay, R. J., McDonald, J. L., Bearhop, S., & McDonald, R. A. (2013). Badger social networks correlate with tuberculosis infection. Current Biology, 23(20), R915-R916.

Wey, T., Blumstein, D. T., Shen, W., & Jordan, F. (2008). Social network analysis of animal behaviour: a promising tool for the study of sociality. Animal behaviour, 75(2), 333-344.

 

Applicants should have a good degree in statistics, mathematics, computer science, or related subjects with a strong numerical component. They should be comfortable working with data and learning new methods, determined, and interested in engaging with the practical applications of their research.

Supervisors

The supervisory team brings together multidisciplinary expertise covering statistics and ecology.

Dr Eleni Matechou and Dr Xue Wang, University of Kent
Dr Matthew Silk, University of Exeter

 

 

 

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Fully-funded ARIES PhD studentship – Evidence based decision making for reintroduction success: translocating threatened prey reptiles into an endemic predator-packed system in Mauritius

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This studentship will be based in the Institute of Zoology, London and will be affiliated with the School of Mathematics, Statistics and Actuarial Science, University of Kent.

Click here to submit your application by the 8th of January.

ARIES PhD studentship with Dr John Ewen of the Institute of Zoology (ZSL) and partners

Supervisors

Dr John Ewen (Institute of Zoology)

Dr Rachel McCrea (University of Kent)

Dr Nik Cole and Dr Richard Young (Durrell Wildlife Conservation Trust)

Dr Stefano Canessa (Institute of Zoology/University of Ghent)

Project Description

Reintroduction biology assists in providing the science support for improved reintroduction outcomes. Both the frequency of reintroductions and publication of reintroduction science are increasing, yet their integration remains limited1. Embedding science within reintroduction decisions faced by practitioners offers a strategic use of evidence to make the best management decisions. The skills required by scientists need developing including group facilitation, elicitation of expert knowledge, quantitative modelling of predicted and observed outcomes of management alternatives, risk analysis and optimisation. Our project offers a package where these aspects will be developed to produce a trained professional able to engage with multi-stakeholder groups undertaking species recovery. The student will develop and apply these skills to a lesser night gecko (LNG, Nactus coindemirensis) reintroduction in Mauritius.

 

Reintroductions of Mauritian reptiles to rebuild island communities have largely been successful, but have relied on translocating species in trophic order from prey to predator. However, bottom-up community reintroductions are not always possible. Round Island supports the last semi-intact natural reptile community, dominated by intraguild predators. To restore Round Island’s reptile community requires reintroducing threatened reptile prey species, such as the LNG. This project will work with a team including Mauritius government, NGO (Mauritian Wildlife Foundation), UK based partners (Durrell Wildlife Conservation Trust) and ARIES DTP hosts (ZSL and University of Kent) to plan and test a range of alternative reintroduction approaches to establish LNG on Round Island. Methods developed for LNG reintroduction will be globally relevant as practitioners grapple with how to establish prey species where predators remain.

 

Methodology

  1. Global review of reptile reintroductions with a focus on releases of prey species into areas where predators remain.
  2. Group based and facilitated development of the reintroduction problem.
  3. Quantitative population modelling using a mix of ongoing monitoring data and expert elicited judgements across various translocation strategies.
  4. A LNG translocation to Round Island that will test alternative release strategies.

 

Funding Notes

The project has been shortlisted for funding by the ARIES NERC Doctoral Training Partnership (https://www.aries-dtp.ac.uk), with a stipend of £14,777 per annum and a generous training and travel budget.

 

ARIES is committed to equality & diversity, and inclusion of students of any and all backgrounds. All ARIES Universities have Athena Swan Bronze status as a minimum.

 

Students with high level numerical skills will be eligible for 3 months of additional stipend after the end of the 3.5 years to take advanced-level courses in branches of environmental sciences related to the project in the first 3-6 months of study.

 

Shortlisted applicants will be interviewed by ARIES on 26th/27th February 2019, with shortlisting taking place at the University of Kent on the 31st January 2019.

 

Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship – in 2018/19 the stipend is £14,777. In most cases, UK and EU nationals who have been resident in the UK for 3 years are eligible for a full award.

 

We seek a people person, passionate about wildlife conservation, with an interest in reptiles and a strong quantitative background in demographic modelling. The candidate should enjoy periods of fieldwork on a remote tropical island with basic communal living arrangements.

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Fully-funded ARIES PhD project: Modelling butterfly abundance at varying spatial scales to inform conservation delivery

Click here to submit your application by the 8th of January.

If you have any queries please email Rachel McCrea: R.S.McCrea@kent.ac.uk

Supervisors

Dr Rachel McCrea, University of Kent and Dr Emily Dennis, Butterfly Conservation

 

Other Supervisory Team Members

Professor Byron Morgan, University of Kent

Professor Tom Brereton, Butterfly Conservation

Dr David Roy, Centre for Ecology and Hydrology

Project Summary

Three-quarters of UK butterfly species have declined over the past four decades. Butterflies respond quickly to habitat and climatic change, hence their population status is a valuable biodiversity indicator. Analysis of long-term butterfly monitoring datasets has provided some of the world’s best evidence of the biological impacts of climate change, including major phenological and distribution shifts, evolutionary responses and the impacts of extreme events.

Population trends are primarily assessed at national scales. This project will undertake more detailed analysis across spatial scales (e.g across regions, specific habitats or individual sites) to identify butterfly population responses to major drivers of change.  As well as delivering high impact scientific insight, this will underpin more effective conservation, from local land management to strategic planning across regions, including the production of new biodiversity indicators and site level alerts.

National-scale butterfly monitoring will be enhanced by refining survey guidance for threatened species, improving knowledge of butterfly lifespans and furthering methods for assessing species threatened status.

Background

A key feature of statistical models applied to butterflies1,2 involves accounting for seasonal variation in counts, as butterflies emerge throughout the year via one or more broods. Flight period patterns vary geographically, for example emergence can be later further north in the UK3. Seasonal patterns are typically assumed to be fixed across space. Counts from the UK Butterfly Monitoring Scheme (UKBMS) are made under standardised conditions4 to minimise bias due to variation in the probability of detecting individuals.

 

Project Aim

To better explain butterfly population dynamics during a period of rapid environmental change, to project future changes under scenarios of climate change and to create direct benefits for the conservation of butterflies.

 

Objectives

  1. Determine whether accounting for spatial variation in phenology influences population trend estimates, particularly at varying scales, where data are likely to be sparser and more susceptible to variation.
  2. Assess the influence of external factors such as weather and time of day on counts and population trends. Identify optimal times of day for detecting target species and testing for evidence of lower counts on hot days as butterfly activity may drop in extreme temperatures. Fine-tune UKBMS sampling procedures and guidance.
  3. Extend knowledge of butterfly lifespans. Further verification of recently developed models5,2 via simulation-based testing and comparison with estimates from capture-recapture data. Assess the influence of lifespans on butterfly population trends and determine their relevance for measuring species conservation status e.g. classifying Red Lists6.
  4. Account for lifespan and variation in detection to produce more robust population trend estimates. Trends for under-utilised local scales or habitat types will provide new scientific insights and will allow Butterfly Conservation to better assess and refine conservation and policy measures to inform where to direct management efforts. This has particular relevance for Priority Species for conservation action and for some more common butterfly species for which the drivers of recent population declines are not well understood.

 

Person Specification and Training Opportunities

 

Applicants should have a good degree in a subject such as statistics, mathematics, or another scientific discipline with a substantial quantitative component. A keen interest in ecology is advantageous.

 

The student will benefit from being immersed in an established Statistical Ecology @ Kent (SE@K) research group (and its wider collaborators), with training opportunities through National Centre for Statistical Ecology (NCSE) meetings, Academy for PhD Training in Statistics courses and the ability to contribute to the running of specialist quantitative training events lead by SE@K.  The student will develop practical skills through field-work and data collection with Butterfly Conservation and will attend ARIES DTP training events to develop essential environmental science skills.  The student will have extensive opportunity to present their work to varied communities (wider membership of NCSE, statistical and ecological conferences and organisations currently working with members of SE@K).  By spending part of the project with Butterfly Conservation, the student will gain experience of working within a conservation organisation and gain new skills through attending field surveys and QGIS training. The supervisory team will ensure ample opportunity for independent development.  On graduating, the student will possess a transferable knowledge of modern methods of data science and statistics which will be particularly applicable for careers in conservation and ecology as well as other applied fields.

Funding

The project has been shortlisted for up to 4 years, with 3.5 years minimum, of funding by the ARIES NERC Doctoral Training Partnership (https://www.aries-dtp.ac.uk) with a stipend of £14,777 per annum and a generous training and travel budget for attending UK-based and international conferences, as well as for time spent visiting Butterfly Conservation.

 

ARIES is committed to equality & diversity, and inclusion of students of any and all backgrounds. All ARIES Universities have Athena Swan Bronze status as a minimum.

 

Students with high level numerical skills will be eligible for 3 months of additional stipend after the end of the 3.5 years to take advanced-level courses in branches of environmental sciences related to the project in the first 3-6 months of study.

 

Shortlisted applicants will be interviewed on 26th/27th February 2019.

 

Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship – in 2018/19 the stipend is £14,777. In most cases, UK and EU nationals who have been resident in the UK for 3 years are eligible for a full award.

 

References

1Dennis, E.B., Morgan, B.J.T., Freeman, S.N., Brereton, T.M. and Roy, D.B. (2016). A generalized abundance index for seasonal invertebrates. Biometrics, 72, 1305-1314.

2Dennis, E.B., Morgan, B.J.T., Brereton, T., Freeman, S.N. and Roy, D.B. (2016). Dynamic models for longitudinal butterfly data. Journal of Agricultural, Biological, and Environmental Statistics, 21, 1-21.

3Roy, D.B. and Asher, J. (2003). Spatial trends in the sighting dates of British butterflies. International Journal of Biometeorology, 47, 188-192.

4Pollard, E. and Yates, T.J. (1993). Monitoring Butterflies for Ecology and Conservation: the British Butterfly Monitoring Scheme. Chapman & Hall, London.

5Matechou, E., Dennis, E.B., Freeman, S.N. and Brereton, T. (2014). Monitoring abundance and phenology in (multivoltine) butterfly species: a novel mixture model. Journal of Applied Ecology, 51, 766-775.

6Bubová, T., Kulma, M., Vrabec, V. and Nowicki, P. (2016). Adult longevity and its relationship with conservation status in European butterflies. Journal of Insect Conservation, 20, 1021-1032.

 

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NERC Advanced Training Course

Statistical models for wildlife population assessment and conservation

 

Dates and Location: 7-11 January 2019, University of Kent, Canterbury Campus

Deadline for Applications: (Extended) 5pm on Wednesday 31st October 2018, by emailing advancedtraining@kent.ac.uk with a completed application form

Specifications: We have 30 fully-funded places (inc. travel within the UK and accommodation); priority will be given to NERC-funded PhD students but if spaces remain we are able to offer the funded places to other UK-based PhD students and early-career researchers.

Description:

Within the environmental sector there is currently a shortage of practitioners equipped with the statistical modelling skills to carry out reliable population assessments. Consequently, environmental impact assessments (EIAs) and development mitigation projects often use population assessment protocols that are not fit-for-purpose. The skills shortage arises because (1) recent advances in statistical models for population assessment are largely confined to the academic sector with little penetration to the end-users; and (2) although many postgraduate programmes have a statistical modelling training component, this often fails to expose PhD students to new models in the area and the potential applications these have for conservation practice. This training programme will provide a cohort of PhD students and early career researchers/practitioners with the relevant modelling skills required for a career that involves wildlife population assessment for conservation.

The workshop will focus on ecological questions that arise in conservation practice and use real case study data. Training will include individual-based models, such as capture-recapture, but will also embrace scenarios more frequently used in EIA, such as batch-marked, presence/absence, site occupancy and counts. Applications will include newts, butterflies, birds, bees, beetles, ibex and bats. Each module will be accompanied by a practical computer session using R and each module builds on the last so that delegates build a portfolio of statistical skills.

More detailed information about the workshop timetable and training outcomes is available here.

Trainers: Dr Rachel McCrea, Dr Eleni Matechou, Dr Diana Cole, Richard Griffiths.

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