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.

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.  ​

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 butterflies^1,2 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 models^5,2 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.