1st December 2017 to 23rd March 2018 – Online
This course focusses on integrating diverse data for effective environmental management – with examples focussing on Marine Protected Areas – but also suitable for any kind of environmental management problems. No knowledge of statistics (Bayesian or otherwise) is required – this is not an in-depth statistical course, but a guide to synthesising data and making robust decisions based on data and using simple and intuitive models embedded in Microsoft Excel worksheets.
Successful marine protected areas (MPAs) need to fulfil a wide range of functions, from protecting ecological indicators such as fish stocks or biodiversity through to maintaining stakeholder engagement and ensuring sufficient economic benefit can be obtained from the MPA and surrounding area. These functions are multidisciplinary, and in many cases, can be antagonistic in nature.
Data to support scientific and management decisions comes in a wide variety of formats, from conclusive outcomes of rigorous meta-analysis at one end of the scale, to anecdotal stories from local fishers at the other. Integrating multiple data sources to provide interdisciplinary information in a manner transparent manner to stakeholders may therefore seem an impossible challenge, yet through this online course you will be guided through how this can be done in a simple and easy to understand manner.
The training programme is run online by Bournemouth University with contributions from JNCC, the Marine Management Authority and Natural England. Places for 25 students will be offered FREE OF CHARGE due to funding of the course by NERC. This will include access to the full set of resources, and comprehensive online support and help throughout. Places will be prioritised for NERC funded PhD students (including those recently completed), however, other applications will be considered form other applicants, including those with other PhD funding or working professionally. The course is accredited for delivery at postgraduate level, and successful completion will result in 20 level 7 credits.
For further information, or to apply, please contact the course leader – Dr Rick Stafford at email@example.com. To apply, please include the following information:
Current status (e.g. PhD student, recently completed PhD, Post-doc, Work in industry)
Current funder for work (e.g. NERC, ESRC, University funded, or other)
When did you complete your PhD (if relevant)
Country of residence (e.g. UK)
A brief (max 100 words) description of your academic or professional interest in the course