Improving and extending biodiversity indices (CASE studentship with the RSPB) (BUTLER_UBIO18EE)
Multi-species indicators are embedded in environmental management, sustainable development and biodiversity conservation policy and practice, acting as metrics against which progress towards national, regional and global targets are measured. The choice of species included in an indicator has a defining influence on how well it reflects ecosystem condition, the speed and extent to which it responds to environmental change and the confidence intervals around its metric value. Whilst multiple methods of species' selection have been adopted, these predominantly rely on expert opinion or simply data availability; generally applicable and objective methods for species' selection are frequently lacking.
There is now growing demand from policy makers and stakeholders for greater consistency and standardisation in species selection to facilitate the cross-national benchmarking of indicators and for the development of better indicators of the stocks and flows of ecosystem services.
Research plan and methods
In partnership with the RSPB, this project will deliver a widely applicable, objective process for indicator species' selection that ensures quality, functionality and transparency, and which can be used to critically review existing indicators and to develop new indicator sets. The successful applicant will use both simulated communities and analyses of long-term monitoring data, drawing on existing and new trait datasets, to:
- Quantify the impact of alternative species-selection protocols on indicator representativeness, reactivity and precision,
- Compare and contrast the temporal and spatial dynamics of indicators reflecting alternative community characteristics,
- Develop national, regional and pan-European indicators for non-avian farmland biodiversity.
The student will receive training in the construction, handling and analyses of large, long-term monitoring and trait databases, GIS and advanced spatial analyses in R; and is expected to develop a high level of competency in statistical modelling. There may also be the opportunity to develop both computer programming and website design skills.
Candidates must have a good Honours degree in a relevant subject area (Ecology, Biology or Environmental Sciences). Experience of handling large datasets and familiarity with computer packages such as R will be an advantage.
This project has been shortlisted for funding by the EnvEast NERC Doctoral Training Partnership, comprising the Universities of East Anglia, Essex and Kent, with over twenty other research partners. Undertaking a PhD with the EnvEast DTP will involve attendance at mandatory training events throughout the course of the PhD.
Shortlisted applicants will be interviewed on 12/13 February 2018.
Successful candidates who meet RCUK's eligibility criteria will be awarded a NERC studentship - in 2017/18, the stipend is £14,553. In most cases, UK and EU nationals who have been resident in the UK for 3 years are eligible for a stipend. For non-UK EU-resident applicants NERC funding can be used to cover fees, RTSG and training costs, but not any part of the stipend. Individual institutes may, however, elect to provide a stipend from their own resources.
EnvEast welcomes applicants from quantitative disciplines who may have limited background in environmental sciences. Excellent candidates will be considered for an award of an additional 3-month stipend to take appropriate advanced-level courses in the subject area.
For further information, please visit www.enveast.ac.uk/apply.
This job comes from a partnership with Science Magazine and